diff --git a/.clang-tidy b/.clang-tidy index 952c0cca82580..310c3d182c8f2 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -17,8 +17,10 @@ Checks: > -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, performance-*, portability-*, + -portability-simd-intrinsics, misc-*, -misc-const-correctness, -misc-non-private-member-variables-in-classes, -misc-no-recursion, + -misc-use-anonymous-namespace, FormatStyle: none diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile new file mode 100644 index 0000000000000..522ee8147d63a --- /dev/null +++ b/.devops/cpu.Dockerfile @@ -0,0 +1,92 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +ARG TARGETARCH + +ARG GGML_CPU_ARM_ARCH=armv8-a + +RUN apt-get update && \ + apt-get install -y build-essential git cmake libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "$TARGETARCH" = "amd64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ + elif [ "$TARGETARCH" = "arm64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ + else \ + echo "Unsupported architecture"; \ + exit 1; \ + fi && \ + cmake --build build -j $(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile new file mode 100644 index 0000000000000..0bc4e7ee13f66 --- /dev/null +++ b/.devops/cuda.Dockerfile @@ -0,0 +1,94 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=12.4.0 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} AS build + +# CUDA architecture to build for (defaults to all supported archs) +ARG CUDA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 + +WORKDIR /app + +COPY . . + +RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_CUDA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile deleted file mode 100644 index 05bff1bdf6a22..0000000000000 --- a/.devops/full-cuda.Dockerfile +++ /dev/null @@ -1,33 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release -j$(nproc) && \ - cp build/bin/* . - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-musa.Dockerfile b/.devops/full-musa.Dockerfile deleted file mode 100644 index 575e81b486d13..0000000000000 --- a/.devops/full-musa.Dockerfile +++ /dev/null @@ -1,26 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -RUN apt-get update && \ - apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release -j$(nproc) && \ - cp build/bin/* . - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile deleted file mode 100644 index df496bcd2b7ee..0000000000000 --- a/.devops/full-rocm.Dockerfile +++ /dev/null @@ -1,50 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -# Enable cURL -ENV LLAMA_CURL=1 -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev - -RUN make -j$(nproc) - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile deleted file mode 100644 index 2a06f82b738ae..0000000000000 --- a/.devops/full.Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -ENV LLAMA_CURL=1 - - -RUN make -j$(nproc) - -ENV LC_ALL=C.utf8 - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile new file mode 100644 index 0000000000000..af783f5e998eb --- /dev/null +++ b/.devops/intel.Dockerfile @@ -0,0 +1,91 @@ +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 + +## Build Image + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build + +ARG GGML_SYCL_F16=OFF +RUN apt-get update && \ + apt-get install -y git libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ + echo "GGML_SYCL_F16 is set" \ + && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ + fi && \ + echo "Building with dynamic libs" && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +### Full +FROM base AS full + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] + diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile deleted file mode 100644 index 7796891d5b53c..0000000000000 --- a/.devops/llama-cli-cuda.Dockerfile +++ /dev/null @@ -1,38 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libgomp1 - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-cli / - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile deleted file mode 100644 index 0706f732a98f0..0000000000000 --- a/.devops/llama-cli-intel.Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build - -ARG GGML_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git - -WORKDIR /app - -COPY . . - -RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ - echo "GGML_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ - fi && \ - echo "Building with static libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ - ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \ - cmake --build build --config Release --target llama-cli - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime - -COPY --from=build /app/build/bin/llama-cli /llama-cli - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-musa.Dockerfile b/.devops/llama-cli-musa.Dockerfile deleted file mode 100644 index 3372749bee70e..0000000000000 --- a/.devops/llama-cli-musa.Dockerfile +++ /dev/null @@ -1,31 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the MUSA runtime image -ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -RUN apt-get update && \ - apt-get install -y build-essential git cmake - -WORKDIR /app - -COPY . . - -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libgomp1 - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-cli /llama-cli - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile deleted file mode 100644 index e60c747bdbf11..0000000000000 --- a/.devops/llama-cli-rocm.Dockerfile +++ /dev/null @@ -1,45 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -RUN make -j$(nproc) llama-cli - -ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile deleted file mode 100644 index 92a6e04793491..0000000000000 --- a/.devops/llama-cli-vulkan.Dockerfile +++ /dev/null @@ -1,27 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION AS build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget libgomp1 - -# Install Vulkan SDK -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk - -# Build it -WORKDIR /app -COPY . . -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \ - cmake --build build --config Release --target llama-cli - -# Clean up -WORKDIR / -RUN cp /app/build/bin/llama-cli /llama-cli && \ - rm -rf /app - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile deleted file mode 100644 index 7f741aa46ecf0..0000000000000 --- a/.devops/llama-cli.Dockerfile +++ /dev/null @@ -1,23 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential git - -WORKDIR /app - -COPY . . - -RUN make -j$(nproc) llama-cli - -FROM ubuntu:$UBUNTU_VERSION AS runtime - -RUN apt-get update && \ - apt-get install -y libgomp1 - -COPY --from=build /app/llama-cli /llama-cli - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cpp-cuda.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec index 7425d3a9d7a40..3bbf4a4def2a5 100644 --- a/.devops/llama-cpp-cuda.srpm.spec +++ b/.devops/llama-cpp-cuda.srpm.spec @@ -17,10 +17,10 @@ Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) License: MIT -Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz +Source0: https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz BuildRequires: coreutils make gcc-c++ git cuda-toolkit Requires: cuda-toolkit -URL: https://github.com/ggerganov/llama.cpp +URL: https://github.com/ggml-org/llama.cpp %define debug_package %{nil} %define source_date_epoch_from_changelog 0 diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec index 4d5560089816c..45902dcf896e0 100644 --- a/.devops/llama-cpp.srpm.spec +++ b/.devops/llama-cpp.srpm.spec @@ -18,10 +18,10 @@ Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) License: MIT -Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz +Source0: https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz BuildRequires: coreutils make gcc-c++ git libstdc++-devel Requires: libstdc++ -URL: https://github.com/ggerganov/llama.cpp +URL: https://github.com/ggml-org/llama.cpp %define debug_package %{nil} %define source_date_epoch_from_changelog 0 diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile deleted file mode 100644 index bf8a198f99f73..0000000000000 --- a/.devops/llama-server-cuda.Dockerfile +++ /dev/null @@ -1,43 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=12.6.0 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} AS build - -# CUDA architecture to build for (defaults to all supported archs) -ARG CUDA_DOCKER_ARCH=default - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -# Use the default CUDA archs if not specified -RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ - export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ - fi && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-server /llama-server - -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile deleted file mode 100644 index b503b8cfe1084..0000000000000 --- a/.devops/llama-server-intel.Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build - -ARG GGML_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ - echo "GGML_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ - fi && \ - echo "Building with dynamic libs" && \ - cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ - cmake --build build --config Release --target llama-server - -FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev curl - -COPY --from=build /app/build/bin/llama-server /llama-server - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-musa.Dockerfile b/.devops/llama-server-musa.Dockerfile deleted file mode 100644 index eb67201c18775..0000000000000 --- a/.devops/llama-server-musa.Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG MUSA_VERSION=rc3.1.0 -# Target the MUSA build image -ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the MUSA runtime image -ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_MUSA_DEV_CONTAINER} AS build - -RUN apt-get update && \ - apt-get install -y build-essential git cmake libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) && \ - mkdir -p /app/lib && \ - find build -name "*.so" -exec cp {} /app/lib \; - -FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/lib/ / -COPY --from=build /app/build/bin/llama-server /llama-server - -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile deleted file mode 100644 index 8553af75b61fc..0000000000000 --- a/.devops/llama-server-rocm.Dockerfile +++ /dev/null @@ -1,54 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} AS build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH="\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102" - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV GGML_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -# Enable cURL -ENV LLAMA_CURL=1 -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev curl - -RUN make -j$(nproc) llama-server - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile deleted file mode 100644 index 6aa7867791a38..0000000000000 --- a/.devops/llama-server-vulkan.Dockerfile +++ /dev/null @@ -1,31 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION AS build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget - -# Install Vulkan SDK and cURL -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk libcurl4-openssl-dev curl - -# Build it -WORKDIR /app -COPY . . -RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ - cmake --build build --config Release --target llama-server - -# Clean up -WORKDIR / -RUN cp /app/build/bin/llama-server /llama-server && \ - rm -rf /app - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile deleted file mode 100644 index 02accc85e1368..0000000000000 --- a/.devops/llama-server.Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION AS build - -RUN apt-get update && \ - apt-get install -y build-essential git libcurl4-openssl-dev - -WORKDIR /app - -COPY . . - -ENV LLAMA_CURL=1 - -RUN make -j$(nproc) llama-server - -FROM ubuntu:$UBUNTU_VERSION AS runtime - -RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev libgomp1 curl - -COPY --from=build /app/llama-server /llama-server - -ENV LC_ALL=C.utf8 -# Must be set to 0.0.0.0 so it can listen to requests from host machine -ENV LLAMA_ARG_HOST=0.0.0.0 - -HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] - -ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile new file mode 100644 index 0000000000000..1e87737abfb71 --- /dev/null +++ b/.devops/musa.Dockerfile @@ -0,0 +1,108 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG MUSA_VERSION=rc3.1.1 +# Target the MUSA build image +ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_MUSA_DEV_CONTAINER} AS build + +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y \ + build-essential \ + cmake \ + python3 \ + python3-pip \ + git \ + libcurl4-openssl-dev \ + libgomp1 + +COPY requirements.txt requirements.txt +COPY requirements requirements + +RUN pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt + +WORKDIR /app + +COPY . . + +# Use the default MUSA archs if not specified +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_MUSA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index b88e6ca809f1f..6e8050a499635 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -31,6 +31,7 @@ # Increases the runtime closure size by ~700M useMpi ? false, useRocm ? config.rocmSupport, + rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets, enableCurl ? true, useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake @@ -132,12 +133,12 @@ effectiveStdenv.mkDerivation (finalAttrs: { --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";" ''; - # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015, + # With PR#6015 https://github.com/ggml-org/llama.cpp/pull/6015, # `default.metallib` may be compiled with Metal compiler from XCode # and we need to escape sandbox on MacOS to access Metal compiler. # `xcrun` is used find the path of the Metal compiler, which is varible # and not on $PATH - # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion + # see https://github.com/ggml-org/llama.cpp/pull/6118 for discussion __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders; nativeBuildInputs = @@ -188,7 +189,7 @@ effectiveStdenv.mkDerivation (finalAttrs: { ] ++ optionals useRocm [ (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang") - (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets)) + (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets) ] ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") @@ -219,7 +220,7 @@ effectiveStdenv.mkDerivation (finalAttrs: { broken = (useMetalKit && !effectiveStdenv.isDarwin); description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; - homepage = "https://github.com/ggerganov/llama.cpp/"; + homepage = "https://github.com/ggml-org/llama.cpp/"; license = lib.licenses.mit; # Accommodates `nix run` and `lib.getExe` diff --git a/.devops/nix/python-scripts.nix b/.devops/nix/python-scripts.nix index 392e9ffe41bf5..56ea182788764 100644 --- a/.devops/nix/python-scripts.nix +++ b/.devops/nix/python-scripts.nix @@ -34,7 +34,7 @@ let # server tests openai - behave + pytest prometheus-client ]; in diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile new file mode 100644 index 0000000000000..48e7e6aaa5b77 --- /dev/null +++ b/.devops/rocm.Dockerfile @@ -0,0 +1,113 @@ +ARG UBUNTU_VERSION=24.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=6.3 +ARG AMDGPU_VERSION=6.3 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +### Build image +FROM ${BASE_ROCM_DEV_CONTAINER} AS build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggml-org/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported +# gfx906 is deprecated +#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html + +#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102' +ARG ROCM_DOCKER_ARCH=gfx1100 + +# Set nvcc architectured +ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +# ENV CC=/opt/rocm/llvm/bin/clang +# ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN apt-get update \ + && apt-get install -y \ + build-essential \ + cmake \ + git \ + libcurl4-openssl-dev \ + curl \ + libgomp1 + +WORKDIR /app + +COPY . . + +RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \ + && cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib \ + && find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_ROCM_DEV_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3-pip \ + python3 \ + python3-wheel\ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/tools.sh b/.devops/tools.sh index 24dcfd35079cb..41a6b1e55c7d2 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -8,28 +8,36 @@ arg1="$1" shift if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert_hf_to_gguf.py "$@" + exec python3 ./convert_hf_to_gguf.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then - ./llama-quantize "$@" + exec ./llama-quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then - ./llama-cli "$@" + exec ./llama-cli "$@" +elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then + exec ./llama-bench "$@" +elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then + exec ./llama-perplexity "$@" elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Converting PTH to GGML..." - for i in `ls $1/$2/ggml-model-f16.bin*`; do + for i in $(ls $1/$2/ggml-model-f16.bin*); do if [ -f "${i/f16/q4_0}" ]; then echo "Skip model quantization, it already exists: ${i/f16/q4_0}" else echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..." - ./llama-quantize "$i" "${i/f16/q4_0}" q4_0 + exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0 fi done elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then - ./llama-server "$@" + exec ./llama-server "$@" else echo "Unknown command: $arg1" echo "Available commands: " echo " --run (-r): Run a model previously converted into ggml" echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512" + echo " --bench (-b): Benchmark the performance of the inference for various parameters." + echo " ex: -m model.gguf" + echo " --perplexity (-p): Measure the perplexity of a model over a given text." + echo " ex: -m model.gguf -f file.txt" echo " --convert (-c): Convert a llama model into ggml" echo " ex: --outtype f16 \"/models/7B/\" " echo " --quantize (-q): Optimize with quantization process ggml" diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile new file mode 100644 index 0000000000000..9064f383858fa --- /dev/null +++ b/.devops/vulkan.Dockerfile @@ -0,0 +1,89 @@ +ARG UBUNTU_VERSION=24.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK and cURL +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk libcurl4-openssl-dev curl + +# Build it +WORKDIR /app + +COPY . . + +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl libvulkan-dev \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + python3-wheel \ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.editorconfig b/.editorconfig index eac38a15f1a67..5d63d0a51e466 100644 --- a/.editorconfig +++ b/.editorconfig @@ -40,3 +40,11 @@ indent_style = tab [examples/cvector-generator/*.txt] trim_trailing_whitespace = unset insert_final_newline = unset + +[models/templates/*.jinja] +indent_style = unset +indent_size = unset +end_of_line = unset +charset = unset +trim_trailing_whitespace = unset +insert_final_newline = unset diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml index 550ee1b498ab9..b85bf5741e5a3 100644 --- a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml +++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml @@ -24,7 +24,8 @@ body: - type: dropdown id: operating-system attributes: - label: Which operating systems do you know to be affected? + label: Operating systems + description: Which operating systems do you know to be affected? multiple: true options: - Linux @@ -41,14 +42,17 @@ body: description: Which GGML backends do you know to be affected? options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] multiple: true + validations: + required: true - type: textarea - id: steps_to_reproduce + id: info attributes: - label: Steps to Reproduce + label: Problem description & steps to reproduce description: > - Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + Please give us a summary of the problem and tell us how to reproduce it. If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us. placeholder: > + I'm trying to compile llama.cpp with CUDA support on a fresh install of Ubuntu and get error XY. Here are the exact commands that I used: ... validations: required: true @@ -61,12 +65,22 @@ body: If possible, please do a git bisect and identify the exact commit that introduced the bug. validations: required: false + - type: textarea + id: command + attributes: + label: Compile command + description: > + Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true - type: textarea id: logs attributes: label: Relevant log output description: > - Please copy and paste any relevant log output, including the command that you entered and any generated text. + Please copy and paste any relevant log output, including any generated text. This will be automatically formatted into code, so no need for backticks. render: shell validations: diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml index 1adb162b79a77..1ccef0793d45e 100644 --- a/.github/ISSUE_TEMPLATE/011-bug-results.yml +++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -26,7 +26,8 @@ body: - type: dropdown id: operating-system attributes: - label: Which operating systems do you know to be affected? + label: Operating systems + description: Which operating systems do you know to be affected? multiple: true options: - Linux @@ -43,6 +44,8 @@ body: description: Which GGML backends do you know to be affected? options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] multiple: true + validations: + required: true - type: textarea id: hardware attributes: @@ -55,20 +58,20 @@ body: - type: textarea id: model attributes: - label: Model + label: Models description: > - Which model at which quantization were you using when encountering the bug? + Which model(s) at which quantization were you using when encountering the bug? If you downloaded a GGUF file off of Huggingface, please provide a link. placeholder: > e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M validations: required: false - type: textarea - id: steps_to_reproduce + id: info attributes: - label: Steps to Reproduce + label: Problem description & steps to reproduce description: > - Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + Please give us a summary of the problem and tell us how to reproduce it. If you can narrow down the bug to specific hardware, compile flags, or command line arguments, that information would be very much appreciated by us. placeholder: > diff --git a/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/.github/ISSUE_TEMPLATE/019-bug-misc.yml index 124cdee919089..1904e31fdc436 100644 --- a/.github/ISSUE_TEMPLATE/019-bug-misc.yml +++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml @@ -14,7 +14,7 @@ body: id: version attributes: label: Name and Version - description: Which version of our software are you running? (use `--version` to get a version string) + description: Which version of our software is affected? (You can use `--version` to get a version string.) placeholder: | $./llama-cli --version version: 2999 (42b4109e) @@ -24,7 +24,8 @@ body: - type: dropdown id: operating-system attributes: - label: Which operating systems do you know to be affected? + label: Operating systems + description: Which operating systems do you know to be affected? multiple: true options: - Linux @@ -33,28 +34,40 @@ body: - BSD - Other? (Please let us know in description) validations: - required: true + required: false - type: dropdown id: module attributes: label: Which llama.cpp modules do you know to be affected? multiple: true options: + - Documentation/Github - libllama (core library) - llama-cli - llama-server - llama-bench - llama-quantize - Python/Bash scripts + - Test code - Other (Please specify in the next section) validations: - required: true + required: false + - type: textarea + id: command + attributes: + label: Command line + description: > + Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: false - type: textarea - id: steps_to_reproduce + id: info attributes: - label: Steps to Reproduce + label: Problem description & steps to reproduce description: > - Please tell us how to reproduce the bug and any additional information that you think could be useful for fixing it. + Please give us a summary of the problem and tell us how to reproduce it (if applicable). validations: required: true - type: textarea @@ -62,7 +75,7 @@ body: attributes: label: First Bad Commit description: > - If the bug was not present on an earlier version: when did it start appearing? + If the bug was not present on an earlier version and it's not trivial to track down: when did it start appearing? If possible, please do a git bisect and identify the exact commit that introduced the bug. validations: required: false @@ -71,8 +84,8 @@ body: attributes: label: Relevant log output description: > - Please copy and paste any relevant log output, including the command that you entered and any generated text. + If applicable, please copy and paste any relevant log output, including any generated text. This will be automatically formatted into code, so no need for backticks. render: shell validations: - required: true + required: false diff --git a/.github/ISSUE_TEMPLATE/020-enhancement.yml b/.github/ISSUE_TEMPLATE/020-enhancement.yml index 02dd4f575a686..cee1446f5a097 100644 --- a/.github/ISSUE_TEMPLATE/020-enhancement.yml +++ b/.github/ISSUE_TEMPLATE/020-enhancement.yml @@ -6,7 +6,7 @@ body: - type: markdown attributes: value: | - [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas) + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggml-org/llama.cpp/discussions/categories/ideas) - type: checkboxes id: prerequisites @@ -16,11 +16,11 @@ body: options: - label: I am running the latest code. Mention the version if possible as well. required: true - - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). + - label: I carefully followed the [README.md](https://github.com/ggml-org/llama.cpp/blob/master/README.md). required: true - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). required: true - - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share. + - label: I reviewed the [Discussions](https://github.com/ggml-org/llama.cpp/discussions), and have a new and useful enhancement to share. required: true - type: textarea diff --git a/.github/ISSUE_TEMPLATE/030-research.yml b/.github/ISSUE_TEMPLATE/030-research.yml index 18975dbbfd0fe..e774550d5908c 100644 --- a/.github/ISSUE_TEMPLATE/030-research.yml +++ b/.github/ISSUE_TEMPLATE/030-research.yml @@ -6,7 +6,7 @@ body: - type: markdown attributes: value: | - Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) - type: checkboxes id: research-stage diff --git a/.github/ISSUE_TEMPLATE/040-refactor.yml b/.github/ISSUE_TEMPLATE/040-refactor.yml index b6e6ab36defd6..2fe94e26c6988 100644 --- a/.github/ISSUE_TEMPLATE/040-refactor.yml +++ b/.github/ISSUE_TEMPLATE/040-refactor.yml @@ -6,8 +6,8 @@ body: - type: markdown attributes: value: | - Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. - Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + Don't forget to [check for existing refactor issue tickets](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. - type: textarea id: background-description diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index eb8c4b472df4c..0d246533c9515 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,11 +1,11 @@ blank_issues_enabled: true contact_links: - name: Got an idea? - url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas + url: https://github.com/ggml-org/llama.cpp/discussions/categories/ideas about: Pop it there. It may then become an enhancement ticket. - name: Got a question? - url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a + url: https://github.com/ggml-org/llama.cpp/discussions/categories/q-a about: Ask a question there! - name: Want to contribute? - url: https://github.com/ggerganov/llama.cpp/wiki/contribute + url: https://github.com/ggml-org/llama.cpp/wiki/contribute about: Head to the contribution guide page of the wiki for areas you can help with diff --git a/.github/labeler.yml b/.github/labeler.yml index 89436740d1ffb..1b47bc96885c4 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -3,19 +3,18 @@ Kompute: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-kompute.h - - ggml/src/ggml-kompute.cpp + - ggml/src/ggml-kompute/** - README-kompute.md Apple Metal: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-metal.h - - ggml/src/ggml-metal.cpp + - ggml/src/ggml-metal/** - README-metal.md SYCL: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-sycl.h - - ggml/src/ggml-sycl.cpp - ggml/src/ggml-sycl/** - docs/backend/SYCL.md - examples/sycl/** @@ -27,8 +26,8 @@ Nvidia GPU: Vulkan: - changed-files: - any-glob-to-any-file: - - ggml/ggml_vk_generate_shaders.py - - ggml/src/ggml-vulkan* + - ggml/include/ggml-vulkan.h + - ggml/src/ggml-vulkan/** documentation: - changed-files: - any-glob-to-any-file: @@ -75,11 +74,7 @@ server: ggml: - changed-files: - any-glob-to-any-file: - - ggml/include/ggml*.h - - ggml/src/ggml*.c - - ggml/src/ggml*.cpp - - ggml/src/ggml*.h - - ggml-cuda/** + - ggml/** nix: - changed-files: - any-glob-to-any-file: diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 997c6d9d05397..d0bdd73c4439c 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1 @@ - - -- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) -- Self-reported review complexity: - - [ ] Low - - [ ] Medium - - [ ] High +*Make sure to read the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR* diff --git a/.github/workflows/bench.yml.disabled b/.github/workflows/bench.yml.disabled index 1c8787ef78f7e..0370c8943fa0e 100644 --- a/.github/workflows/bench.yml.disabled +++ b/.github/workflows/bench.yml.disabled @@ -1,5 +1,5 @@ # TODO: there have been some issues with the workflow, so disabling for now -# https://github.com/ggerganov/llama.cpp/issues/7893 +# https://github.com/ggml-org/llama.cpp/issues/7893 # # Benchmark name: Benchmark @@ -57,17 +57,7 @@ jobs: if: | inputs.gpu-series == 'Standard_NC4as_T4_v3' - || ( - github.event_name == 'schedule' - && github.ref_name == 'master' - && github.repository_owner == 'ggerganov' - ) || github.event_name == 'pull_request_target' - || ( - github.event_name == 'push' - && github.event.ref == 'refs/heads/master' - && github.repository_owner == 'ggerganov' - ) steps: - name: Clone id: checkout diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index abaf2c504c8f4..b96e1f50acc9e 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -10,10 +10,10 @@ on: push: branches: - master - paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal'] + paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal'] + paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} @@ -43,6 +43,12 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-arm64 + evict-old-files: 1d + - name: Dependencies id: depends continue-on-error: true @@ -53,16 +59,14 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build - cmake .. \ + cmake -B build \ + -DCMAKE_BUILD_RPATH="@loader_path" \ -DLLAMA_FATAL_WARNINGS=ON \ -DLLAMA_CURL=ON \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ - -DGGML_RPC=ON \ - -DBUILD_SHARED_LIBS=OFF - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test id: cmake_test @@ -88,6 +92,7 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/* - name: Upload artifacts @@ -107,6 +112,12 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-x64 + evict-old-files: 1d + - name: Dependencies id: depends continue-on-error: true @@ -118,13 +129,13 @@ jobs: run: | sysctl -a # Metal is disabled due to intermittent failures with Github runners not having a GPU: - # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 + # https://github.com/ggml-org/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 cmake -B build \ + -DCMAKE_BUILD_RPATH="@loader_path" \ -DLLAMA_FATAL_WARNINGS=ON \ -DLLAMA_CURL=ON \ -DGGML_METAL=OFF \ - -DGGML_RPC=ON \ - -DBUILD_SHARED_LIBS=OFF + -DGGML_RPC=ON cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -151,6 +162,7 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* - name: Upload artifacts @@ -160,68 +172,16 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip name: llama-bin-macos-x64.zip - ubuntu-focal-make: - runs-on: ubuntu-20.04 - env: - LLAMA_NODE_AVAILABLE: true - LLAMA_PYTHON_AVAILABLE: true - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 - - - uses: actions/setup-node@v4 - with: - node-version: "20" - - - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - run: | - CC=gcc-8 make -j $(nproc) - - - name: Test - id: make_test - run: | - CC=gcc-8 make tests -j $(nproc) - make test -j $(nproc) - - ubuntu-focal-make-curl: - runs-on: ubuntu-20.04 - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - LLAMA_CURL: 1 - run: | - CC=gcc-8 make -j $(nproc) + ubuntu-cpu-cmake: + strategy: + matrix: + include: + - build: 'x64' + os: ubuntu-22.04 + - build: 'arm64' + os: ubuntu-22.04-arm - ubuntu-latest-cmake: - runs-on: ubuntu-latest + runs-on: ${{ matrix.os }} steps: - name: Clone @@ -230,6 +190,12 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-cpu-cmake + evict-old-files: 1d + - name: Dependencies id: depends run: | @@ -239,10 +205,11 @@ jobs: - name: Build id: cmake_build run: | - mkdir build - cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF - cmake --build . --config Release -j $(nproc) + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) - name: Test id: cmake_test @@ -279,14 +246,15 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | cp LICENSE ./build/bin/ - zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/* + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/* - name: Upload artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} uses: actions/upload-artifact@v4 with: - path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip - name: llama-bin-ubuntu-x64.zip + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip + name: llama-bin-ubuntu-${{ matrix.build }}.zip ubuntu-latest-cmake-sanitizer: runs-on: ubuntu-latest @@ -296,13 +264,19 @@ jobs: strategy: matrix: sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug, Release] + build_type: [Debug] steps: - name: Clone id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }} + evict-old-files: 1d + - name: Dependencies id: depends run: | @@ -313,19 +287,52 @@ jobs: id: cmake_build if: ${{ matrix.sanitizer != 'THREAD' }} run: | - mkdir build - cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) - name: Build (no OpenMP) id: cmake_build_no_openmp if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=OFF + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-latest-llguidance: + runs-on: ubuntu-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential + + - name: Build + id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DGGML_OPENMP=OFF - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + cmake .. \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_LLGUIDANCE=ON + cmake --build . --config Release -j $(nproc) - name: Test id: cmake_test @@ -343,6 +350,12 @@ jobs: id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-rpc + evict-old-files: 1d + - name: Dependencies id: depends run: | @@ -352,10 +365,9 @@ jobs: - name: Build id: cmake_build run: | - mkdir build - cd build - cmake -DGGML_RPC=ON .. - cmake --build . --config Release -j $(nproc) + cmake -B build \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) - name: Test id: cmake_test @@ -370,6 +382,14 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-vulkan + evict-old-files: 1d - name: Dependencies id: depends @@ -377,15 +397,49 @@ jobs: wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list sudo apt-get update -y - sudo apt-get install -y build-essential vulkan-sdk + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk - name: Build id: cmake_build run: | - mkdir build + cmake -B build \ + -DGGML_VULKAN=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | cd build - cmake -DGGML_VULKAN=ON .. - cmake --build . --config Release -j $(nproc) + # This is using llvmpipe and runs slower than other backends + ctest -L main --verbose --timeout 2700 + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip + name: llama-bin-ubuntu-vulkan-x64.zip ubuntu-22-cmake-hip: runs-on: ubuntu-22.04 @@ -402,21 +456,32 @@ jobs: sudo apt-get update sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-hip + evict-old-files: 1d + - name: Build with native CMake HIP support id: cmake_build run: | - cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON + cmake -B build -S . \ + -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \ + -DGGML_HIP=ON cmake --build build --config Release -j $(nproc) - name: Build with legacy HIP support id: cmake_build_legacy_hip run: | - cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON + cmake -B build2 -S . \ + -DCMAKE_C_COMPILER=hipcc \ + -DCMAKE_CXX_COMPILER=hipcc \ + -DGGML_HIP=ON cmake --build build2 --config Release -j $(nproc) ubuntu-22-cmake-musa: runs-on: ubuntu-22.04 - container: mthreads/musa:rc3.1.0-devel-ubuntu22.04 + container: mthreads/musa:rc3.1.1-devel-ubuntu22.04 steps: - name: Clone @@ -429,10 +494,17 @@ jobs: apt-get update apt-get install -y build-essential git cmake libcurl4-openssl-dev + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-musa + evict-old-files: 1d + - name: Build with native CMake MUSA support id: cmake_build run: | - cmake -B build -S . -DGGML_MUSA=ON + cmake -B build -S . \ + -DGGML_MUSA=ON cmake --build build --config Release -j $(nproc) ubuntu-22-cmake-sycl: @@ -467,14 +539,21 @@ jobs: id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl + evict-old-files: 1d + - name: Build id: cmake_build run: | source /opt/intel/oneapi/setvars.sh - mkdir build - cd build - cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx .. - cmake --build . --config Release -j $(nproc) + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx + cmake --build build --config Release -j $(nproc) ubuntu-22-cmake-sycl-fp16: runs-on: ubuntu-22.04 @@ -508,77 +587,22 @@ jobs: id: checkout uses: actions/checkout@v4 - - name: Build - id: cmake_build - run: | - source /opt/intel/oneapi/setvars.sh - mkdir build - cd build - cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON .. - cmake --build . --config Release -j $(nproc) - - # TODO: build with GGML_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know - # how to debug it. - # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124 - macOS-latest-make: - runs-on: macos-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - run: | - GGML_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu) - - - name: Test - id: make_test - run: | - GGML_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu) - GGML_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu) - - # TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know - # how to debug it. - # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584 - # would be great if we fix these - macOS-latest-cmake: - runs-on: macos-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl-fp16 + evict-old-files: 1d - name: Build id: cmake_build run: | - sysctl -a - mkdir build - cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF .. - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 + source /opt/intel/oneapi/setvars.sh + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx \ + -DGGML_SYCL_F16=ON + cmake --build build --config Release -j $(nproc) macOS-latest-cmake-ios: runs-on: macos-latest @@ -588,6 +612,12 @@ jobs: id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-ios + evict-old-files: 1d + - name: Dependencies id: depends continue-on-error: true @@ -598,9 +628,7 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build - cmake -G Xcode .. \ + cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ @@ -609,7 +637,7 @@ jobs: -DCMAKE_SYSTEM_NAME=iOS \ -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO macOS-latest-cmake-tvos: runs-on: macos-latest @@ -619,6 +647,12 @@ jobs: id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-tvos + evict-old-files: 1d + - name: Dependencies id: depends continue-on-error: true @@ -629,9 +663,7 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build - cmake -G Xcode .. \ + cmake -B build -G Xcode \ -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ @@ -640,7 +672,7 @@ jobs: -DCMAKE_SYSTEM_NAME=tvOS \ -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO macOS-latest-swift: runs-on: macos-latest @@ -654,21 +686,36 @@ jobs: id: checkout uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-swift + evict-old-files: 1d + - name: Dependencies id: depends continue-on-error: true run: | brew update - - name: xcodebuild for swift package - id: xcodebuild + - name: Build llama.cpp with CMake + id: cmake_build run: | - xcodebuild -scheme llama -destination "${{ matrix.destination }}" + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + sudo cmake --install build --config Release - - name: Build Swift Example - id: make_build_swift_example + - name: xcodebuild for swift package + id: xcodebuild run: | - make swift + xcodebuild -scheme llama-Package -destination "${{ matrix.destination }}" windows-msys2: runs-on: windows-latest @@ -684,6 +731,13 @@ jobs: - name: Clone uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-msys2 + variant: sccache + evict-old-files: 1d + - name: Setup ${{ matrix.sys }} uses: msys2/setup-msys2@v2 with: @@ -691,25 +745,11 @@ jobs: msystem: ${{matrix.sys}} install: >- base-devel + git mingw-w64-${{matrix.env}}-toolchain mingw-w64-${{matrix.env}}-cmake mingw-w64-${{matrix.env}}-openblas - - name: Build using make - shell: msys2 {0} - run: | - make -j $(nproc) - - - name: Clean after building using make - shell: msys2 {0} - run: | - make clean - - - name: Build using make w/ OpenBLAS - shell: msys2 {0} - run: | - make GGML_OPENBLAS=1 -j $(nproc) - - name: Build using CMake shell: msys2 {0} run: | @@ -728,7 +768,7 @@ jobs: cmake --build build --config ${{ matrix.build }} -j $(nproc) windows-latest-cmake: - runs-on: windows-2019 + runs-on: windows-latest env: OPENBLAS_VERSION: 0.3.23 @@ -739,23 +779,25 @@ jobs: matrix: include: - build: 'noavx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF' - build: 'avx2-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON' - build: 'avx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF' - build: 'avx512-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON' - build: 'openblas-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' - build: 'vulkan-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON' - build: 'llvm-arm64' - defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' - build: 'msvc-arm64' - defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' + - build: 'llvm-arm64-opencl-adreno' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON' steps: - name: Clone @@ -764,6 +806,13 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-${{ matrix.build }} + variant: sccache + evict-old-files: 1d + - name: Clone Kompute submodule id: clone_kompute if: ${{ matrix.build == 'kompute-x64' }} @@ -797,6 +846,26 @@ jobs: run: | choco install ninja + - name: Install OpenCL Headers and Libs + id: install_opencl + if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }} + run: | + git clone https://github.com/KhronosGroup/OpenCL-Headers + cd OpenCL-Headers + cmake -B build ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build --target install + git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader + cd OpenCL-ICD-Loader + cmake -B build-arm64-release ` + -A arm64 ` + -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build-arm64-release --target install --config release + - name: Build id: cmake_build run: | @@ -826,7 +895,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 @@ -862,6 +931,7 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt + Copy-Item .\examples\run\linenoise.cpp\LICENSE .\build\bin\Release\linenoise.cpp.txt 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\* - name: Upload artifacts @@ -871,12 +941,47 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip name: llama-bin-win-${{ matrix.build }}.zip - windows-latest-cmake-cuda: + ubuntu-latest-cmake-cuda: + runs-on: ubuntu-latest + container: nvidia/cuda:12.6.2-devel-ubuntu24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Install dependencies + env: + DEBIAN_FRONTEND: noninteractive + run: | + apt update + apt install -y cmake build-essential ninja-build libgomp1 git + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-cuda + evict-old-files: 1d + + - name: Build with CMake + run: | + cmake -S . -B build -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CUDA_ARCHITECTURES=89-real \ + -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_NATIVE=OFF \ + -DGGML_CUDA=ON + cmake --build build + + windows-2019-cmake-cuda: runs-on: windows-2019 strategy: matrix: - cuda: ['12.2.0', '11.7.1'] + cuda: ['12.4', '11.7'] build: ['cuda'] steps: @@ -884,24 +989,89 @@ jobs: id: checkout uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 0 - - name: Install CUDA toolkit - id: cuda-toolkit - uses: Jimver/cuda-toolkit@v0.2.15 + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 with: - cuda: ${{ matrix.cuda }} - method: 'network' - sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]' + key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }} + variant: sccache + evict-old-files: 1d + + - name: Install Cuda Toolkit 11.7 + if: ${{ matrix.cuda == '11.7' }} + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-11.7.4.6-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-11.7.101-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-11.7.91-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cudart-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvcc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvrtc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libcublas-windows-x86_64-11.7.4.6-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvtx-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\visual_studio_integration-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvprof-windows-x86_64-11.7.101-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cccl-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V11_7=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + + - name: Install Cuda Toolkit 12.4 + if: ${{ matrix.cuda == '12.4' }} + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-12.4.131-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-12.4.5.8-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-12.4.127-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cudart-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvcc-windows-x86_64-12.4.131-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvrtc-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libcublas-windows-x86_64-12.4.5.8-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvtx-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_profiler_api-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\visual_studio_integration-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvprof-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cccl-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja - name: Build id: cmake_build - run: | - mkdir build - cd build - cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON - cmake --build . --config Release -j $((${env:NUMBER_OF_PROCESSORS} - 1)) -t ggml - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} + shell: cmd + run: | + call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat" + cmake -S . -B build -G "Ninja Multi-Config" ^ + -DLLAMA_BUILD_SERVER=ON ^ + -DGGML_NATIVE=OFF ^ + -DGGML_CUDA=ON ^ + -DGGML_RPC=ON + set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1 + cmake --build build --config Release -j %NINJA_JOBS% -t ggml + cmake --build build --config Release - name: Determine tag name id: tag @@ -930,10 +1100,12 @@ jobs: name: llama-bin-win-cu${{ matrix.cuda }}-x64.zip - name: Copy and pack Cuda runtime + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} run: | - echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}" + echo "Cuda install location: ${{ env.CUDA_PATH }}" $dst='.\build\bin\cudart\' - robocopy "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip $dst\* - name: Upload Cuda runtime @@ -961,6 +1133,13 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-sycl + variant: sccache + evict-old-files: 1d + - name: Install run: | scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL @@ -984,7 +1163,7 @@ jobs: - name: Build the release package id: pack_artifacts - if: ${{ ( github.event_name == 'pull_request' && github.base_ref == 'master' ) }} + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin" @@ -1009,7 +1188,7 @@ jobs: 7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/* - name: Upload the release package - if: ${{ ( github.event_name == 'pull_request' && github.base_ref == 'master' ) }} + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} uses: actions/upload-artifact@v4 with: path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip @@ -1039,12 +1218,23 @@ jobs: run: | & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ${{ github.job }} + evict-old-files: 1d + - name: Build id: cmake_build run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . ` + -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` + -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` + -DCMAKE_BUILD_TYPE=Release ` + -DGGML_HIP=ON ` + -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} windows-latest-cmake-hip-release: @@ -1059,6 +1249,14 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-hip-release + evict-old-files: 1d - name: Install id: depends @@ -1080,7 +1278,13 @@ jobs: run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . ` + -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" ` + -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" ` + -DCMAKE_BUILD_TYPE=Release ` + -DAMDGPU_TARGETS=${{ matrix.gpu_target }} ` + -DGGML_HIP=ON ` + -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} md "build\bin\rocblas\library\" cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" @@ -1118,6 +1322,27 @@ jobs: - name: Checkout code uses: actions/checkout@v4 + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + sudo cmake --install build --config Release + + - name: xcodebuild for swift package + id: xcodebuild + run: | + xcodebuild -scheme llama-Package -destination 'generic/platform=iOS' + - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build @@ -1128,6 +1353,12 @@ jobs: - name: Clone uses: actions/checkout@v4 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: android-build + evict-old-files: 1d + - name: Set up JDK uses: actions/setup-java@v3 with: @@ -1145,35 +1376,17 @@ jobs: ./gradlew build --no-daemon -# freeBSD-latest: -# runs-on: macos-12 -# steps: -# - name: Clone -# uses: actions/checkout@v4 -# -# - name: Build -# uses: cross-platform-actions/action@v0.19.0 -# with: -# operating_system: freebsd -# version: '13.2' -# hypervisor: 'qemu' -# run: | -# sudo pkg update -# sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas -# gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu` - release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} runs-on: ubuntu-latest needs: - - ubuntu-focal-make - - ubuntu-latest-cmake - - macOS-latest-make - - macOS-latest-cmake + - ubuntu-cpu-cmake + - ubuntu-22-cmake-vulkan - windows-latest-cmake - - windows-latest-cmake-cuda + - windows-2019-cmake-cuda + - windows-latest-cmake-sycl - windows-latest-cmake-hip-release - macOS-latest-cmake-arm64 - macOS-latest-cmake-x64 @@ -1185,6 +1398,12 @@ jobs: with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: release + evict-old-files: 1d + - name: Determine tag name id: tag shell: bash @@ -1210,7 +1429,7 @@ jobs: - name: Create release id: create_release - uses: anzz1/action-create-release@v1 + uses: ggml-org/action-create-release@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: @@ -1430,3 +1649,37 @@ jobs: # popd # emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }} # make + + openEuler-latest-cmake-cann: + if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }} + defaults: + run: + shell: bash -el {0} + runs-on: ubuntu-24.04-arm + strategy: + matrix: + cann: + - '8.0.rc3.beta1-910b-openeuler22.03-py3.10' + device: + - 'ascend910b3' + build: + - 'Release' + container: ascendai/cann:${{ matrix.cann }} + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Dependencies + run: | + yum update -y + yum install -y git gcc gcc-c++ make cmake + + - name: Build + run: | + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${{ matrix.build }} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${{ matrix.device }} + cmake --build build -j $(nproc) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index f63860d14147f..276a217d45005 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -17,7 +17,7 @@ jobs: steps: - uses: actions/stale@v5 with: - exempt-issue-labels: "refactor,help wanted,good first issue,research,bug" + exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap" days-before-issue-stale: 30 days-before-issue-close: 14 stale-issue-label: "stale" diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index a953cdac907ae..c81d21fcd7e67 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -10,12 +10,10 @@ name: Publish Docker image on: - #pull_request: - push: - branches: - - master - paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal'] - workflow_dispatch: # allows manual triggering, useful for debugging + workflow_dispatch: # allows manual triggering + schedule: + # Rebuild daily rather than on every push because it is expensive + - cron: '12 4 * * *' concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} @@ -29,29 +27,22 @@ permissions: jobs: push_to_registry: name: Push Docker image to Docker Hub - #if: github.event.pull_request.draft == false - runs-on: ubuntu-latest + runs-on: ubuntu-22.04 env: COMMIT_SHA: ${{ github.sha }} strategy: + fail-fast: false matrix: config: - - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" } - - { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" } + # Multi-stage build + - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false} + - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete - #- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - #- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" } + #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true } steps: - name: Check out the repo uses: actions/checkout@v4 @@ -59,10 +50,12 @@ jobs: fetch-depth: 0 # preserve git history, so we can determine the build number - name: Set up QEMU - uses: docker/setup-qemu-action@v2 + uses: docker/setup-qemu-action@v3 + with: + image: tonistiigi/binfmt:qemu-v7.0.0-28 - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v2 + uses: docker/setup-buildx-action@v3 - name: Log in to Docker Hub uses: docker/login-action@v2 @@ -82,26 +75,34 @@ jobs: # determine tag name postfix (build number, commit hash) if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then - TAG_POSTFIX="b${BUILD_NUMBER}" + TAG_POSTFIX="-b${BUILD_NUMBER}" else SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-') - TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}" + TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}" fi - # list all tags possible - TAGS="" - TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}," - TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}" - - echo "output_tags=$TAGS" >> $GITHUB_OUTPUT - echo "output_tags=$TAGS" # print out for debugging + if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then + TYPE="" + else + TYPE="-${{ matrix.config.tag }}" + fi + PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:" + FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}" + LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}" + SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}" + echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT + echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT + echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT + echo "full_output_tags=$FULLTAGS" # print out for debugging + echo "light_output_tags=$LIGHTTAGS" # print out for debugging + echo "server_output_tags=$SERVERTAGS" # print out for debugging env: GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' - # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example - name: Free Disk Space (Ubuntu) - uses: jlumbroso/free-disk-space@main + if: ${{ matrix.config.free_disk_space == true }} + uses: ggml-org/free-disk-space@v1.3.1 with: # this might remove tools that are actually needed, # if set to "true" but frees about 6 GB @@ -116,13 +117,59 @@ jobs: docker-images: true swap-storage: true - - name: Build and push Docker image (tagged + versioned) - if: github.event_name == 'push' + - name: Build and push Full Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.full_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: full + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Light Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.light_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: light + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Server Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }} uses: docker/build-push-action@v6 with: context: . push: true platforms: ${{ matrix.config.platforms }} # tag list is generated from step above - tags: ${{ steps.tag.outputs.output_tags }} + tags: ${{ steps.tag.outputs.server_output_tags }} file: ${{ matrix.config.dockerfile }} + target: server + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index ae86e99275265..f02b7c2194bcf 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -23,5 +23,7 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: editorconfig-checker/action-editorconfig-checker@main + - uses: editorconfig-checker/action-editorconfig-checker@v2 + with: + version: v3.0.3 - run: editorconfig-checker diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml index 368dbdbe5dccc..0b0f300aa402a 100644 --- a/.github/workflows/labeler.yml +++ b/.github/workflows/labeler.yml @@ -11,7 +11,7 @@ jobs: steps: - uses: actions/checkout@v4 with: - repository: "ggerganov/llama.cpp" + repository: "ggml-org/llama.cpp" - uses: actions/labeler@v5 with: configuration-path: '.github/labeler.yml' diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml deleted file mode 100644 index 0da6acdf1c81e..0000000000000 --- a/.github/workflows/nix-ci-aarch64.yml +++ /dev/null @@ -1,72 +0,0 @@ -name: Nix aarch64 builds - -on: - workflow_dispatch: # allows manual triggering - schedule: - # Rebuild daily rather than on every push because QEMU is expensive (e.g. - # 1.5h instead of minutes with the cold cache). - # - # randint(0, 59), randint(0, 23) - - cron: '26 12 * * *' - # But also rebuild if we touched any of the Nix expressions: - push: - branches: - - master - paths: ['**/*.nix', 'flake.lock'] - pull_request: - types: [opened, synchronize, reopened] - paths: ['**/*.nix', 'flake.lock'] - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -# Fine-grant permission -# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token -permissions: - # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub - id-token: write - contents: read - -jobs: - nix-build-aarch64: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install QEMU - # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 - run: | - sudo apt-get update - sudo apt-get install -y qemu-user-static qemu-system-aarch64 - sudo usermod -a -G kvm $USER - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-platforms = aarch64-linux - extra-system-features = nixos-test kvm - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: llama-cpp - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.aarch64-linux" - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --systems aarch64-linux - --flake - ".#checks.aarch64-linux" diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml deleted file mode 100644 index 8ecbbe53b4ed1..0000000000000 --- a/.github/workflows/nix-ci.yml +++ /dev/null @@ -1,79 +0,0 @@ -name: Nix CI - -on: - workflow_dispatch: # allows manual triggering - push: - branches: - - master - pull_request: - types: [opened, synchronize, reopened] - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -# Fine-grant permission -# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token -permissions: - # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub - id-token: write - contents: read - -jobs: - nix-eval: - strategy: - fail-fast: false - matrix: - os: [ ubuntu-latest, macos-latest ] - runs-on: ${{ matrix.os }} - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: List all flake outputs - run: nix flake show --all-systems - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)" - nix-build: - strategy: - fail-fast: false - matrix: - os: [ ubuntu-latest, macos-latest ] - runs-on: ${{ matrix.os }} - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: llama-cpp - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --flake - ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml deleted file mode 100644 index 3a6a96e263e59..0000000000000 --- a/.github/workflows/nix-flake-update.yml +++ /dev/null @@ -1,22 +0,0 @@ -name: update-flake-lock -on: - workflow_dispatch: - schedule: - - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00 - -jobs: - lockfile: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@main - - name: Update flake.lock - uses: DeterminateSystems/update-flake-lock@main - with: - pr-title: "nix: update flake.lock" - pr-labels: | - nix - pr-reviewers: philiptaron,SomeoneSerge - token: ${{ secrets.FLAKE_TOKEN }} diff --git a/.github/workflows/nix-publish-flake.yml b/.github/workflows/nix-publish-flake.yml deleted file mode 100644 index 2c3c1ebdaeff1..0000000000000 --- a/.github/workflows/nix-publish-flake.yml +++ /dev/null @@ -1,36 +0,0 @@ -# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes -name: "Publish a flake to flakestry & flakehub" -on: - push: - tags: - - "*" - workflow_dispatch: - inputs: - tag: - description: "The existing tag to publish" - type: "string" - required: true -jobs: - flakestry-publish: - runs-on: ubuntu-latest - permissions: - id-token: "write" - contents: "read" - steps: - - uses: flakestry/flakestry-publish@main - with: - version: "${{ inputs.tag || github.ref_name }}" - flakehub-publish: - runs-on: "ubuntu-latest" - permissions: - id-token: "write" - contents: "read" - steps: - - uses: "actions/checkout@v4" - with: - ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}" - - uses: "DeterminateSystems/nix-installer-action@main" - - uses: "DeterminateSystems/flakehub-push@main" - with: - visibility: "public" - tag: "${{ inputs.tag }}" diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index a8d46f31dd4f5..ddfdf73b8fce2 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -1,6 +1,13 @@ name: flake8 Lint -on: [push, pull_request] +on: + push: + branches: + - master + paths: ['.github/workflows/python-lint.yml', '**/*.py'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['.github/workflows/python-lint.yml', '**/*.py'] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 699ac095d6c83..3a29107d0ea5a 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -76,20 +76,49 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt - - name: Verify server deps - id: verify_server_deps + # Setup nodejs (to be used for verifying bundled index.html) + - uses: actions/setup-node@v4 + with: + node-version: '22.11.0' + + - name: WebUI - Install dependencies + id: webui_lint + run: | + cd examples/server/webui + npm ci + + - name: WebUI - Check code format + id: webui_format run: | git config --global --add safe.directory $(realpath .) - cd examples/server - git ls-files --others --modified + cd examples/server/webui git status - ./deps.sh + + npm run format + git status + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Files do not follow coding style. To fix: npm run format" + echo "${modified_files}" + exit 1 + fi + + - name: Verify bundled index.html + id: verify_server_index_html + run: | + git config --global --add safe.directory $(realpath .) + cd examples/server/webui + git status + + npm run build git status - not_ignored_files="$(git ls-files --others --modified)" - echo "Modified files: ${not_ignored_files}" - if [ -n "${not_ignored_files}" ]; then - echo "Repository is dirty or server deps are not built as expected" - echo "${not_ignored_files}" + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Repository is dirty or server/webui is not built as expected" + echo "Hint: You may need to follow Web UI build guide in server/README.md" + echo "${modified_files}" exit 1 fi @@ -106,9 +135,9 @@ jobs: -DGGML_OPENMP=OFF ; cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server - - name: Build - id: cmake_build - if: ${{ matrix.sanitizer != 'THREAD' }} + - name: Build (sanitizers) + id: cmake_build_sanitizers + if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }} run: | cmake -B build \ -DGGML_NATIVE=OFF \ @@ -118,18 +147,37 @@ jobs: -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + - name: Build (sanitizers) + id: cmake_build + if: ${{ matrix.sanitizer == '' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + - name: Tests id: server_integration_tests + if: ${{ matrix.sanitizer == '' }} + run: | + cd examples/server/tests + ./tests.sh + + - name: Tests (sanitizers) + id: server_integration_tests_sanitizers + if: ${{ matrix.sanitizer != '' }} run: | cd examples/server/tests - PORT=8888 ./tests.sh + LLAMA_SANITIZE=1 ./tests.sh - name: Slow tests id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests - PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow + SLOW_TESTS=1 ./tests.sh server-windows: @@ -180,11 +228,12 @@ jobs: run: | cd examples/server/tests $env:PYTHONIOENCODING = ":replace" - behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp + pytest -v -x -m "not slow" - name: Slow tests id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests - behave.exe --stop --no-skipped --no-capture --tags slow + $env:SLOW_TESTS = "1" + pytest -v -x diff --git a/.gitignore b/.gitignore index 307c065f793e6..56b5ac2c18cfe 100644 --- a/.gitignore +++ b/.gitignore @@ -18,6 +18,7 @@ *.metallib *.o *.so +*.swp *.tmp # IDE / OS @@ -97,6 +98,7 @@ examples/server/*.css.hpp examples/server/*.html.hpp examples/server/*.js.hpp examples/server/*.mjs.hpp +examples/server/*.gz.hpp !build_64.sh !examples/*.bat !examples/*/*.kts @@ -104,6 +106,10 @@ examples/server/*.mjs.hpp !examples/sycl/*.bat !examples/sycl/*.sh +# Server Web UI temporary files +node_modules +examples/server/webui/dist + # Python /.venv diff --git a/AUTHORS b/AUTHORS index 1bd36158a72f4..6796b29413595 100644 --- a/AUTHORS +++ b/AUTHORS @@ -1,4 +1,4 @@ -# date: Wed Jun 26 19:36:34 EEST 2024 +# date: Tue Feb 4 13:04:05 EET 2025 # this file is auto-generated by scripts/gen-authors.sh 0cc4m @@ -7,6 +7,7 @@ 2f38b454 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com> 44670 <44670@users.noreply.github.com> +65a <10104049+65a@users.noreply.github.com> AN Long AT Aarni Koskela @@ -19,20 +20,30 @@ Adithya Balaji AdithyanI Adrian Adrian Hesketh +Adrien Gallouët +Adrien Gallouët +Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com> Ahmet Zeer AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> +AidanBeltonS Aisuko +Akarshan Biswas Akarshan Biswas +Al Mochkin <14274697+amochkin@users.noreply.github.com> Albert Jin Alberto <57916483+albbus-stack@users.noreply.github.com> +Alberto Cabrera Pérez +Alberto Cabrera Pérez Alex Alex Azarov Alex Azarov Alex Klinkhamer Alex Klinkhamer Alex Nguyen +Alex O'Connell <35843486+acon96@users.noreply.github.com> Alex Petenchea Alex Renda +Alex Tuddenham <61622354+AlexsCode@users.noreply.github.com> Alex von Gluck IV Alexey Parfenov Ali Chraghi <63465728+alichraghi@users.noreply.github.com> @@ -45,18 +56,26 @@ AmirAli Mirian <37371367+amiralimi@users.noreply.github.com> Ananta Bastola Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> András Salamon +Andreas (Andi) Kunar +Andreas Kieslinger <47689530+aendk@users.noreply.github.com> Andrei Andrew Canis Andrew Downing Andrew Duffy Andrew Godfrey +Andrew Minh Nguyen <40281306+amqdn@users.noreply.github.com> +Andy Salerno Andy Tai +Anthony Van de Gejuchte +Antonis Makropoulos Arik Poznanski +Armen Kaleshian Artem Artem Zinnatullin Artyom Lebedev Asbjørn Olling Ásgeir Bjarni Ingvarsson +Asghar Ghorbani Ashish <1856117+ashishdatta@users.noreply.github.com> Ashok Gelal <401055+ashokgelal@users.noreply.github.com> Ashraful Islam @@ -75,13 +94,21 @@ Ben Siraphob Ben Williams Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com> Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com> +Benson Wong Bernat Vadell +Bernhard M. Wiedemann +Bert Wagner +Billel Mokeddem Bingan <70050083+binganao@users.noreply.github.com> +Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com> Bodo Graumann Bono Lv Borislav Stanimirov +Borislav Stanimirov Branden Butler +Brandon Squizzato <35474886+bsquizz@users.noreply.github.com> Brian +Brian Cunnie Bruce MacDonald Bryan Honof CJ Pais @@ -90,32 +117,51 @@ Calvin Laurenson Cameron Cameron Kaiser Carolinabanana <140120812+Carolinabanana@users.noreply.github.com> +CarryFun <76023481+CarryFun@users.noreply.github.com> +Carsten Kragelund Jørgensen +CarterLi999 <664681047@qq.com> Casey Primozic Casey Primozic CausalLM <148736309+CausalLM@users.noreply.github.com> Cebtenzzre +CentricStorm Chad Brewbaker +Changyeon Kim Chao Jiang +Charles Xu <63788048+chaxu01@users.noreply.github.com> +Charles Xu +Chen Xi +Chen Xi Cheng Shao +Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Chris Elrod Chris Kuehl Christian Demsar Christian Demsar Christian Falch <875252+chrfalch@users.noreply.github.com> +Christian Kastner Christian Kögler +Christian Köhnenkamp Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com> +Christopher Nielsen <62156882+mascguy@users.noreply.github.com> Clark Saben <76020733+csaben@users.noreply.github.com> Clint Herron +Conrad Kramer +Corentin REGAL CrispStrobe <154636388+CrispStrobe@users.noreply.github.com> +Csaba Kecskemeti Cuong Trinh Manh DAN™ Damian Stewart +Dan Johansson <164997844+eddnjjn@users.noreply.github.com> +Dan Johansson Dane Madsen DaniAndTheWeb <57776841+DaniAndTheWeb@users.noreply.github.com> Daniel Bevenius Daniel Drake Daniel Hiltgen Daniel Illescas Romero +Daniel Kleine <53251018+d-kleine@users.noreply.github.com> Daniele <57776841+daniandtheweb@users.noreply.github.com> DannyDaemonic Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com> @@ -129,19 +175,29 @@ David Pflug David Renshaw David Sommers <12738+databyte@users.noreply.github.com> David Yang +DavidKorczynski Dawid Potocki Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com> Dean Deins +Denis Spasyuk <34203011+dspasyuk@users.noreply.github.com> +Derrick T. Woolworth Deven Mistry <31466137+deven367@users.noreply.github.com> +Dibakar Gope Didzis Gosko +Diego Devesa +Diogo Teles Sant'Anna +Djip007 <3705339+Djip007@users.noreply.github.com> Djip007 Don Mahurin DooWoong Lee (David) Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com> +Dou Xinpeng <15529241576@163.com> +Dou Xinpeng <81913537+Dou-Git@users.noreply.github.com> Douglas Hanley Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com> Ebey Abraham +Echo Nolan Ed Lee Ed Lepedus Eddie-Wang @@ -149,12 +205,16 @@ Edward Taylor Elaine Elbios <141279586+Elbios@users.noreply.github.com> Elton Kola +Emreerdog <34742675+Emreerdog@users.noreply.github.com> Engininja2 <139037756+Engininja2@users.noreply.github.com> Equim +Eric Curtin +Eric Curtin Eric Sommerlade Eric Zhang <34133756+EZForever@users.noreply.github.com> Erik Garrison Erik Scholz +Esko Toivonen Ettore Di Giacinto Evan Jones Evan Miller @@ -166,19 +226,27 @@ FK Fabian Fabio R. Sluzala Faez Shakil +Faisal Zaghloul +Faisal Zaghloul +Fan Shupei FantasyGmm <16450052+FantasyGmm@users.noreply.github.com> +Farbod Bijary <110523279+farbodbj@users.noreply.github.com> Fattire <528174+fat-tire@users.noreply.github.com> Felix Finn Voorhees Firat +FirstTimeEZ <179362031+FirstTimeEZ@users.noreply.github.com> Folko-Ven <71110216+Folko-Ven@users.noreply.github.com> Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com> Francisco Melo <43780565+francis2tm@users.noreply.github.com> Frank Mai FrankHB +Frankie Robertson Fred Douglas <43351173+fredlas@users.noreply.github.com> Frederik Vogel Gabe Goodhart +Gabe Goodhart +Gaetan Bisson GainLee Galunid Gary Linscott @@ -187,12 +255,15 @@ Gavin Zhao Genkagaku.GPT Georgi Gerganov Gilad S +Gilad S. <7817232+giladgd@users.noreply.github.com> Giuseppe Scrivano GiviMAD Govlzkoy Guillaume "Vermeille" Sanchez Guillaume Wenzek +Guoliang Hua <32868157+nbcsm@users.noreply.github.com> Guoteng <32697156+SolenoidWGT@users.noreply.github.com> +Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com> Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com> Haggai Nuchi Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com> @@ -203,35 +274,47 @@ Haoxiang Fei Harald Fernengel Hatsune Miku <129688334+at8u@users.noreply.github.com> HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com> +Haus1 Henk Poley Henri Vasserman Henrik Forstén Herman Semenov Hesen Peng +HimariO Hoang Nguyen Hong Bo PENG Hongyu Ouyang <96765450+casavaca@users.noreply.github.com> Howard Su Hua Jiang +Huang Qi Huawei Lin Hugo Roussel +Huifeng Ou <79071290+ho2103@users.noreply.github.com> Ian Bull Ian Bull Ian Scrivener +Icecream95 Ido S IgnacioFDM Igor Okulist +Ihar Hrachyshka Ikko Eltociear Ashimine Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com> Ionoclast Laboratories Isaac McFadyen IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com> +Ivan +Ivan Filipov <159561759+vanaka11@users.noreply.github.com> Ivan Komarov Ivan Stepanov +JFLFY2255 JH23X <165871467+JH23X@users.noreply.github.com> +Jack Mousseau Jack Mousseau JackJollimore <130917767+JackJollimore@users.noreply.github.com> +Jaeden Amero Jaemin Son +Jafar Uruç Jag Chadha Jakub N James A Capozzoli <157492257+jac-jim@users.noreply.github.com> @@ -243,11 +326,16 @@ Jannis Schönleber Jared Van Bortel Jared Van Bortel Jason McCartney +Jason Stillerman Jean-Christophe Hoelt Jean-Michaël Celerier Jed Fox +Jeff Bolz +Jeffrey Morgan Jeffrey Quesnelle +Jeroen Mostert Jesse Jojo Johnson +Jett Janiak Jeximo Jhen-Jie Hong Jiahao Li @@ -258,6 +346,9 @@ Jiří Podivín <66251151+jpodivin@users.noreply.github.com> Jiří Sejkora Joan Fontanals Joan Fontanals +João Dinis Ferreira +Joe Eli McIlvain +Joe Todd Johan Johannes Gäßler Johannes Rudolph @@ -273,8 +364,11 @@ Josh Ramer Joyce Juan Calderon-Perez <835733+gaby@users.noreply.github.com> Judd +Juk Armstrong <69222624+jukofyork@users.noreply.github.com> Julius Arkenberg +Jun Hee Yoo Jun Jie <71215065+junnjiee16@users.noreply.github.com> +Junil Kim Junyang Lin Juraj Bednar Justin Parker @@ -285,6 +379,7 @@ Justine Tunney Juuso Alasuutari KASR Kamil Tomšík +Karol Kontny <82021046+kkontny@users.noreply.github.com> Karsten Weiss Karthick Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com> @@ -292,16 +387,19 @@ Karthik Sethuraman Kasumi <90275229+kasumi-1@users.noreply.github.com> Kawrakow <48489457+ikawrakow@users.noreply.github.com> Keiichi Tabata +Keke Han Kenvix ⭐ Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Kevin Gibbons Kevin Ji <1146876+kevinji@users.noreply.github.com> Kevin Kwok Kevin Lo +Kevin Wang Kolen Cheung Konstantin Herud Konstantin Zhuravlyov Kunshang Ji +Kyle Bruene Kyle Liang Kyle Mistele Kylin <56434533+KyL0N@users.noreply.github.com> @@ -315,22 +413,30 @@ LeonEricsson <70749762+LeonEricsson@users.noreply.github.com> Leonardo Neumann Li Tan Linwei Wang +Liu Jia <109258120+Septa2112@users.noreply.github.com> +Liu Jia LoganDark +Loïc Carrère LostRuins <39025047+LostRuins@users.noreply.github.com> +LostRuins Concedo <39025047+LostRuins@users.noreply.github.com> Luciano Luo Tian Lyle Dean +M-A M. Yusuf Sarıgöz +Ma Mingfei Maarten ter Huurne Mack Straight Maël Kerbiriou MaggotHATE +Mahesh Madhav <67384846+heshpdx@users.noreply.github.com> Manuel <44313466+makuche@users.noreply.github.com> Marc Köhlbrugge Marco Matthies <71844+marcom@users.noreply.github.com> Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Marian Cepok Mark Fairbairn +Mark Zhuang Marko Tasic Markus Tavenrath Martin Delille @@ -342,11 +448,16 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com> Mateusz Charytoniuk Matheus C. França Matheus Gabriel Alves Silva +Mathieu Baudier +Mathieu Geli Mathieu Nayrolles +Mathijs Henquet Mathijs de Bruin Matt Clayton <156335168+mattjcly@users.noreply.github.com> Matt Pulver +Matt Stephenson Matteo Boschini <12133566+mbosc@users.noreply.github.com> +Matteo Mortari Mattheus Chediak Matthew Tejo Matvey Soloviev @@ -356,8 +467,11 @@ Maxime <672982+maximegmd@users.noreply.github.com> Maximilian Winter Meng Zhang Meng, Hengyu +Mengqing Cao Merrick Christensen Michael Coppola +Michael Engel +Michael Francis Michael Hueschen Michael Kesper Michael Klimenko @@ -365,52 +479,81 @@ Michael Podvitskiy Michael Potter Michael de Gans Michaël de Vries +Michał Moskal +Michał Tuszyński +Michelle Tan <41475767+MichelleTanPY@users.noreply.github.com> Mihai Mike Mikko Juola Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> +Minsoo Cheong Mirko185 Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> +MistApproach <98988043+MistApproach@users.noreply.github.com> Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Mohammadreza Hendiani Mohammadreza Hendiani +Molly Sophia +MorganRO8 <47795945+MorganRO8@users.noreply.github.com> Murilo Santana Musab Gultekin Nam D. Tran <42194884+namtranase@users.noreply.github.com> Nathan Epstein +Natsu NawafAlansari <72708095+NawafAlansari@users.noreply.github.com> Nebula Neo Zhang <14088817+arthw@users.noreply.github.com> Neo Zhang Neo Zhang Jianyu Neuman Vong +NeverLucky <92274250+nvrxq@users.noreply.github.com> +Nexes the Old <124105151+Nexesenex@users.noreply.github.com> Nexesenex <124105151+Nexesenex@users.noreply.github.com> Niall Coates <1349685+Niall-@users.noreply.github.com> +Nicholai Tukanov +Nico Bosshard Nicolai Weitkemper Nicolás Pérez +Nicolò Scipione Nigel Bosch +Nikita Sarychev <42014488+sARY77@users.noreply.github.com> Niklas Korz +NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com> +Nikolaos Pothitos Nikolas <127742645+nneubacher@users.noreply.github.com> Nindaleth +Nuno +OSecret <135510162+OLSecret@users.noreply.github.com> Oleksandr Nikitin Oleksii Maryshchenko Olivier Chafik Ondřej Čertík Ouadie EL FAROUKI +PAB +Pablo Duboue +Pascal Patry Patrice Ferlet Paul Tsochantaris +Pavel Zloi Pavol Rusnak +Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com> Pedro Cuenca +Peter Peter Sugihara Phil H <5756783+phiharri@users.noreply.github.com> Philip Taron Phillip Kravtsov Pierre Alexandre SCHEMBRI Pierrick Hymbert +Pieter Ouwerkerk +Plamen Minev +Prashant Vithule <119530321+Vithulep@users.noreply.github.com> Przemysław Pawełczyk Qin Yue Chen <71813199+chenqiny@users.noreply.github.com> Qingyou Meng Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com> +R0CKSTAR +R0CKSTAR RJ Adriaansen Radoslav Gerganov Radosław Gryta @@ -419,11 +562,16 @@ Raj Hammeer Singh Hada Ralph Soika Rand Xie Randall Fitzgerald +Random Fly Reinforce-II +Rémy Oudompheng Ren Xuancheng Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> +Reza Kakhki RhinoDevel +Riccardo Orlando Riceball LEE +Rich Dougherty Richard Kiss Richard Roberson Rick G <26732651+TheFlipbook@users.noreply.github.com> @@ -434,26 +582,39 @@ Riley Stewart Rinne Rinne Robert Brisita <986796+rbrisita@users.noreply.github.com> +Robert Collins +Robert Ormandi <52251610+ormandi@users.noreply.github.com> Robert Sung-wook Shin Robey Holderith Robyn Roger Meier Roland <14355895+rbur0425@users.noreply.github.com> +Romain Biessy Romain D <90720+Artefact2@users.noreply.github.com> Romain Neutron Roman Parykin Ron Evans Ron Jailall +Roni Ronny Brendel Ronsor Rowan Hart +Ruan <47767371+ruanych@users.noreply.github.com> +Ruchira Hasaranga +Rudi Servo +Ruixin Huang <18860020911@163.com> Rune <43761327+Rune-AI@users.noreply.github.com> +RunningLeon +RunningLeon Ryan Landay Ryder Wishart Ryuei Rőczey Barnabás <31726601+An0nie@users.noreply.github.com> +SRHMorris <69468379+SRHMorris@users.noreply.github.com> +SXX SakuraUmi Salvador E. Tropea +Salvatore Mesoraca Sam Spilsbury Sami Farin <3876865+Safari77@users.noreply.github.com> Samuel Maynard @@ -463,23 +624,29 @@ Sebastián A SebastianApel <13675545+SebastianApel@users.noreply.github.com> Senemu <10880819+Senemu@users.noreply.github.com> Sergey Alirzaev +Sergio López Sergio López Sertaç Özercan <852750+sozercan@users.noreply.github.com> SeungWon Jeong <65549245+redlion0929@users.noreply.github.com> ShadovvBeast Shakhar Dasgupta +Shane A Shangning Xu <32517059+xushangning@users.noreply.github.com> +Shankar +Shanshan Shen <467638484@qq.com> Shijie <821898965@qq.com> Shintarou Okada Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com> Shouzheng Liu Shuichi Tsutsumi +Shupei Fan Sigbjørn Skjæret Simon Willison Siwen Yu Sky Yan Slaren <2141330+slaren@users.noreply.github.com> Slava Primenko +Small Grass Forest SoftwareRenderer <138734813+SoftwareRenderer@users.noreply.github.com> Someone Someone Serge @@ -491,25 +658,33 @@ Stefan Sydow Steffen Röcker Stephan Walter Stephen Nichols +Steve Bonds Steve Grubb Steven Prichard Steven Roussey Steward Garcia <57494570+FSSRepo@users.noreply.github.com> +StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com> Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com> +Sukriti Sharma SuperUserNameMan +Sutou Kouhei Tai Duc Nguyen Taikono-Himazin Tameem <113388789+AhmadTameem@users.noreply.github.com> Tamotsu Takahashi +Tei Home Thái Hoàng Tâm <75922889+RoyalHeart@users.noreply.github.com> Thatcher Chamberlin Theia Vogel Thérence <13496987+Royalphax@users.noreply.github.com> Thibault Terrasson Thomas Klausner +Thorsten Sommer Tim Miller +Tim Wang Timmy Knight Timothy Cronin <40186632+4imothy@users.noreply.github.com> +Ting Lou Ting Lou Ting Sun Tobias Lütke @@ -517,32 +692,44 @@ Tom C Tom Jobbins <784313+TheBloke@users.noreply.github.com> Tomas Tomáš Pazdiora +Tony Wasserka <4840017+neobrain@users.noreply.github.com> Tristan Druyen Tristan Ross +Trivikram Kamat <16024985+trivikr@users.noreply.github.com> Tungsten842 <886724vf@anonaddy.me> Tungsten842 Tushar UEXTM.com <84163508+uextm@users.noreply.github.com> +Ujjawal Panchal <31011628+Ujjawal-K-Panchal@users.noreply.github.com> Ulrich Drepper Uzo Nweke Vaibhav Srivastav Val Kharitonov Valentin Konovalov +Valentin Mamedov <45292985+Inf1delis@users.noreply.github.com> Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com> +Vali Malinoiu <0x4139@gmail.com> Victor Nogueira Victor Z. Peng +Viet-Anh NGUYEN (Andrew) +Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com> Vlad Vladimir Vladimir Malyutin Vladimir Zorin +VoidIsVoid <343750470@qq.com> Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com> +Wang Qin <37098874+wangqin0@users.noreply.github.com> +Wang Ran (汪然) WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com> Weird Constructor Welby Seely Wentai Zhang WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com> William Tambellini +William Tambellini Willy Tarreau +Woof Dog <197125663+woof-dog@users.noreply.github.com> Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com> Wu Jian Ping Wu Jian Ping @@ -551,15 +738,25 @@ Xiang (Kevin) Li Xiao-Yong Jin XiaotaoChen Xiaoyi Chen +Xie Yanbo Xingchen Song(宋星辰) +Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com> Xuan Son Nguyen +Xuan-Son Nguyen +Yaiko Yann Follet <131855179+YannFollet@users.noreply.github.com> Yaroslav Yazan Agha-Schrader Yiming Cui Yishuo Wang +Yoshi Suhara +Yoshi Suhara +Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com> +Yüg Yui +Yun Dou +Yuri Khrustalev Yusuf Kağan Hanoğlu Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com> ZHAOKAI WANG @@ -568,19 +765,27 @@ Zay <95888118+isaiahbjork@users.noreply.github.com> Zenix Zhang Peiyuan Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com> +Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> +Zhiyuan Li +Zhiyuan Li ZhouYuChen Ziad Ben Hadj-Alouane Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> Zsapi a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com> +a3sh <38979186+A3shTnT@users.noreply.github.com> adel boussaken afrideva <95653597+afrideva@users.noreply.github.com> +ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com> agray3 akawrykow <142945436+akawrykow@users.noreply.github.com> +alek3y <44779186+alek3y@users.noreply.github.com> alexpinel <93524949+alexpinel@users.noreply.github.com> alonfaraj alwqx +amd-dwang amd-lalithnc +amritahs-ibm andrijdavid anon998 <131767832+anon998@users.noreply.github.com> anzz1 @@ -588,24 +793,31 @@ apaz apcameron <37645737+apcameron@users.noreply.github.com> arch-btw <57669023+arch-btw@users.noreply.github.com> arcrank +ardfork <134447697+ardfork@users.noreply.github.com> arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com> +aryantandon01 <80969509+aryantandon01@users.noreply.github.com> at8u <129688334+at8u@users.noreply.github.com> automaticcat +awatuna <23447591+awatuna@users.noreply.github.com> +b4b4o bandoti <141645996+bandoti@users.noreply.github.com> beiller bhubbb <79117352+bhubbb@users.noreply.github.com> bmwl bobqianic <129547291+bobqianic@users.noreply.github.com> +brucepro bryanSwk <93190252+bryanSwk@users.noreply.github.com> bsilvereagle bssrdf byte-6174 <88070277+byte-6174@users.noreply.github.com> +cduk <19917266+cduk@users.noreply.github.com> cebtenzzre chaihahaha chiranko <96988916+chiranko@users.noreply.github.com> clibdev <52199778+clibdev@users.noreply.github.com> clyang cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com> +codezjx coezbek comex compilade <113953597+compilade@users.noreply.github.com> @@ -614,10 +826,14 @@ cpumaxx <163466046+cpumaxx@users.noreply.github.com> crasm crasm daboe01 +daghanerdonmez <44506702+daghanerdonmez@users.noreply.github.com> +daminho <37615795+daminho@users.noreply.github.com> david raistrick ddh0 ddpasa <112642920+ddpasa@users.noreply.github.com> deepdiffuser <112834445+deepdiffuser@users.noreply.github.com> +devojony <61173062+devojony@users.noreply.github.com> +ditsuke divinity76 dm4 dotpy314 <33351922+dotpy314@users.noreply.github.com> @@ -625,18 +841,25 @@ drbh ds5t5 <145942675+ds5t5@users.noreply.github.com> dylan eastriver +ebraminio ebraminio eiery <19350831+eiery@users.noreply.github.com> eric8607242 fairydreaming <166155368+fairydreaming@users.noreply.github.com> +fengerhu1 <2748250768@qq.com> +fj-y-saito <85871716+fj-y-saito@users.noreply.github.com> fraxy-v <65565042+fraxy-v@users.noreply.github.com> github-actions[bot] gliptic +gn64 goerch grahameth <96447521+grahameth@users.noreply.github.com> +gtygo gwjr <502526+gwjr@users.noreply.github.com> h-h-h-h <13482553+h-h-h-h@users.noreply.github.com> hankcs +haopeng <657407891@qq.com> +hipudding hoangmit hongbo.mo <352280764@qq.com> hopkins385 <98618192+hopkins385@users.noreply.github.com> @@ -649,12 +872,16 @@ hxer7963 hydai iSma iacore <74560659+iacore@users.noreply.github.com> +icppWorld <124377669+icppWorld@users.noreply.github.com> igarnier intelmatt <61025942+intelmatt@users.noreply.github.com> iohub +issixx <46835150+issixx@users.noreply.github.com> jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com> jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> jameswu2014 <545426914@qq.com> +jdomke <28772296+jdomke@users.noreply.github.com> +jiahao su jiez <373447296@qq.com> jneem joecryptotoo <80373433+joecryptotoo@users.noreply.github.com> @@ -667,6 +894,7 @@ junchao-loongson <68935141+junchao-loongson@users.noreply.github.com> jwj7140 <32943891+jwj7140@users.noreply.github.com> k.h.lai kaizau +kallewoof kalomaze <66376113+kalomaze@users.noreply.github.com> kang katsu560 <118887472+katsu560@users.noreply.github.com> @@ -674,32 +902,46 @@ kchro3 <62481661+kchro3@users.noreply.github.com> khimaros kiltyj klosax <131523366+klosax@users.noreply.github.com> +krystiancha kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> kunnis kuronekosaiko +kustaaya <58045274+kustaaya@users.noreply.github.com> kuvaus <22169537+kuvaus@users.noreply.github.com> kwin1412 <42286931+kwin1412@users.noreply.github.com> l3utterfly +laik ldwang le.chang leejet +leo-pony +lexasub +lhez limitedAtonement liuwei-git <14815172+liuwei-git@users.noreply.github.com> lon <114724657+longregen@users.noreply.github.com> loonerin <132926317+loonerin@users.noreply.github.com> +ltoniazzi <61414566+ltoniazzi@users.noreply.github.com> luoyu-intel m3ndax maddes8cht <55592906+maddes8cht@users.noreply.github.com> +mahorozte <41834471+mahorozte@users.noreply.github.com> makomk manikbhandari maor-ps <154728172+maor-ps@users.noreply.github.com> +mashdragon <122402293+mashdragon@users.noreply.github.com> +matiaslin <45382001+matiaslin@users.noreply.github.com> +matt23654 +matteo mdrokz mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com> minarchist mj-shifu <77107165+mj-shifu@users.noreply.github.com> mmyjona momonga <115213907+mmnga@users.noreply.github.com> +momonga <146910567+mmngays@users.noreply.github.com> moritzbrantner <31051084+moritzbrantner@users.noreply.github.com> +musoles <135031143+musoles@users.noreply.github.com> mzcu nanahi <130121847+na-na-hi@users.noreply.github.com> ngc92 <7938269+ngc92@users.noreply.github.com> @@ -716,16 +958,21 @@ omahs <73983677+omahs@users.noreply.github.com> oobabooga <112222186+oobabooga@users.noreply.github.com> opparco ostix360 <55257054+ostix360@users.noreply.github.com> +pculliton +peidaqi pengxin99 perserk +piDack <104877312+piDack@users.noreply.github.com> pmysl postmasters pudepiedj qingfengfenga <41416092+qingfengfenga@users.noreply.github.com> +qingy1337 qouoq qunash rabidcopy rankaiyx +redbeard rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com> rhuddleston rimoliga <53384203+rimoliga@users.noreply.github.com> @@ -733,6 +980,7 @@ runfuture sandyiscool sasha0552 semidark +serhii-nakon <57632032+serhii-nakon@users.noreply.github.com> sharpHL <132747147+sharpHL@users.noreply.github.com> shibe2 singularity <12184989+singularity-s0@users.noreply.github.com> @@ -741,42 +989,59 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com> slaren <2141330+slaren@users.noreply.github.com> slaren snadampal <87143774+snadampal@users.noreply.github.com> +someone13574 <81528246+someone13574@users.noreply.github.com> +standby24x7 staviq stduhpf strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com> swittk takov751 <40316768+takov751@users.noreply.github.com> tarcey +tc-mb <157115220+tc-mb@users.noreply.github.com> texmex76 <40733439+texmex76@users.noreply.github.com> thement <40525767+thement@users.noreply.github.com> +thewh1teagle <61390950+thewh1teagle@users.noreply.github.com> tjohnman +toyer <2042519524@qq.com> tslmy ubik2 uint256_t uint256_t unbounded +uvos +uvos valiray <133289098+valiray@users.noreply.github.com> +vb vik viric vodkaslime <646329483@qq.com> vvhg1 <94630311+vvhg1@users.noreply.github.com> vxiiduu <73044267+vxiiduu@users.noreply.github.com> +wangshuai09 <391746016@qq.com> wbpxre150 <100937007+wbpxre150@users.noreply.github.com> whoreson <139810751+whoreson@users.noreply.github.com> woachk <24752637+woachk@users.noreply.github.com> wonjun Jang woodx <124784234+woodx9@users.noreply.github.com> +wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> wzy <32936898+Freed-Wu@users.noreply.github.com> xaedes xaedes +xctan xloem <0xloem@gmail.com> yangli2 +ymcki <84055651+ymcki@users.noreply.github.com> yuiseki +yuri@FreeBSD zakkor zhangkaihuo +zhentaoyu zhouwg <6889919+zhouwg@users.noreply.github.com> zhouwg zrm Ștefan-Gabriel Muscalu +杨朱 · Kiki 源文雨 <41315874+fumiama@users.noreply.github.com> +蕭澧邦 <45505768+shou692199@users.noreply.github.com> +谢乃闻 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> diff --git a/CMakeLists.txt b/CMakeLists.txt index 994e61e45fedd..7b2a1845e5c7c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -16,6 +16,7 @@ endif() list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/") set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) set(LLAMA_STANDALONE ON) @@ -46,11 +47,11 @@ if (WIN32) add_compile_definitions(_CRT_SECURE_NO_WARNINGS) endif() -if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC") - add_compile_options("$<$:/source-charset:utf-8>") - add_compile_options("$<$:/source-charset:utf-8>") - add_compile_options("$<$:/execution-charset:utf-8>") - add_compile_options("$<$:/execution-charset:utf-8>") +if (MSVC) + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/bigobj>") + add_compile_options("$<$:/bigobj>") endif() # @@ -79,26 +80,21 @@ option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE}) # 3rd party libs option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) +option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) # Required for relocatable CMake package include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake) # override ggml options -set(GGML_SANITIZE_THREAD ${LLAMA_SANITIZE_THREAD}) -set(GGML_SANITIZE_ADDRESS ${LLAMA_SANITIZE_ADDRESS}) -set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED}) -set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS}) -set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS}) +set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS}) +set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS}) # change the default for these ggml options if (NOT DEFINED GGML_LLAMAFILE) set(GGML_LLAMAFILE_DEFAULT ON) endif() -if (NOT DEFINED GGML_AMX) - set(GGML_AMX ON) -endif() - if (NOT DEFINED GGML_CUDA_GRAPHS) set(GGML_CUDA_GRAPHS_DEFAULT ON) endif() @@ -122,16 +118,62 @@ llama_option_depr(WARNING LLAMA_SYCL GGML_SYCL) llama_option_depr(WARNING LLAMA_SYCL_F16 GGML_SYCL_F16) llama_option_depr(WARNING LLAMA_CANN GGML_CANN) +if (NOT MSVC) + if (LLAMA_SANITIZE_THREAD) + message(STATUS "Using -fsanitize=thread") + + add_compile_options(-fsanitize=thread) + link_libraries (-fsanitize=thread) + endif() + + if (LLAMA_SANITIZE_ADDRESS) + message(STATUS "Using -fsanitize=address") + + add_compile_options(-fsanitize=address -fno-omit-frame-pointer) + link_libraries (-fsanitize=address) + endif() + + if (LLAMA_SANITIZE_UNDEFINED) + message(STATUS "Using -fsanitize=undefined") + + add_compile_options(-fsanitize=undefined) + link_libraries (-fsanitize=undefined) + endif() +endif() + # -# build the library +# 3rd-party # if (NOT TARGET ggml) add_subdirectory(ggml) # ... otherwise assume ggml is added by a parent CMakeLists.txt endif() + +# +# build the library +# + add_subdirectory(src) +# +# utils, programs, examples and tests +# + +if (LLAMA_BUILD_COMMON) + add_subdirectory(common) +endif() + +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION) + include(CTest) + add_subdirectory(tests) +endif() + +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES) + add_subdirectory(examples) + add_subdirectory(pocs) +endif() + # # install # @@ -147,24 +189,14 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files") set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files") -# At the moment some compile definitions are placed within the ggml/src -# directory but not exported on the `ggml` target. This could be improved by -# determining _precisely_ which defines are necessary for the llama-config -# package. -# -set(GGML_TRANSIENT_DEFINES) -get_target_property(GGML_DIRECTORY ggml SOURCE_DIR) -get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS) -if (GGML_DIR_DEFINES) - list(APPEND GGML_TRANSIENT_DEFINES ${GGML_DIR_DEFINES}) -endif() -get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS) -if (GGML_TARGET_DEFINES) - list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES}) -endif() -get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES) +set(LLAMA_PUBLIC_HEADERS + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h) + +set_target_properties(llama + PROPERTIES + PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}") -set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h) install(TARGETS llama LIBRARY PUBLIC_HEADER) configure_package_config_file( @@ -201,22 +233,4 @@ configure_file(cmake/llama.pc.in @ONLY) install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" - DESTINATION lib/pkgconfig) - -# -# utils, programs, examples and tests -# - -if (LLAMA_BUILD_COMMON) - add_subdirectory(common) -endif() - -if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION) - include(CTest) - add_subdirectory(tests) -endif() - -if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES) - add_subdirectory(examples) - add_subdirectory(pocs) -endif() + DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig) diff --git a/CMakePresets.json b/CMakePresets.json index 436448967c9af..13bdd7907ab40 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -31,6 +31,13 @@ { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, { "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } }, + { + "name": "x64-windows-llvm", "hidden": true, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake" + } + }, + { "name": "arm64-windows-msvc", "hidden": true, "architecture": { "value": "arm64", "strategy": "external" }, @@ -70,6 +77,11 @@ { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] }, { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] }, + { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] }, + { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] }, + { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] }, + { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] }, + { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] }, { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] }, { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] }, diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 0000000000000..72d594b46e911 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,11 @@ +# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs + +/ci/ @ggerganov +/.devops/*.Dockerfile @ngxson +/examples/server/ @ngxson +/ggml/src/ggml-cuda/fattn* @JohannesGaessler +/ggml/src/ggml-cuda/mmq.* @JohannesGaessler +/ggml/src/ggml-cuda/mmv.* @JohannesGaessler +/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler +/ggml/src/ggml-opt.cpp @JohannesGaessler +/ggml/src/gguf.cpp @JohannesGaessler diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4c882c254cac5..8fab0de6f14db 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,9 +1,12 @@ # Pull requests (for contributors) +- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier - Test your changes: - - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the `ggml` library - - Execute [the full CI locally on your machine](ci/README.md) before publishing -- Optionally rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs + - Execute [the full CI locally on your machine](ci/README.md) before publishing + - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`) + - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) + - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` +- Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly - If your PR becomes stale, don't hesitate to ping the maintainers in the comments @@ -11,23 +14,114 @@ - Squash-merge PRs - Use the following format for the squashed commit title: ` : (#)`. For example: `utils : fix typo in utils.py (#1234)` -- Optionally pick a `` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules +- Optionally pick a `` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules +- Consider adding yourself to [CODEOWNERS](CODEOWNERS) # Coding guidelines - Avoid adding third-party dependencies, extra files, extra headers, etc. - Always consider cross-compatibility with other operating systems and architectures - Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple -- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit +- Vertical alignment makes things more readable and easier to batch edit - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` -- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963) +- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets +- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo` + - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary + ```cpp + // OK + llama_context * ctx; + const llama_rope_type rope_type; + + // not OK + struct llama_context * ctx; + const enum llama_rope_type rope_type; + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_ + +- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code +- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines) - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices -- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ +- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggml-org/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ ![matmul](media/matmul.png) +# Naming guidelines + +- Use `snake_case` for function, variable and type names +- Naming usually optimizes for longest common prefix (see https://github.com/ggml-org/ggml/pull/302#discussion_r1243240963) + + ```cpp + // not OK + int small_number; + int big_number; + + // OK + int number_small; + int number_big; + ``` + +- Enum values are always in upper case and prefixed with the enum name + + ```cpp + enum llama_vocab_type { + LLAMA_VOCAB_TYPE_NONE = 0, + LLAMA_VOCAB_TYPE_SPM = 1, + LLAMA_VOCAB_TYPE_BPE = 2, + LLAMA_VOCAB_TYPE_WPM = 3, + LLAMA_VOCAB_TYPE_UGM = 4, + LLAMA_VOCAB_TYPE_RWKV = 5, + }; + ``` + +- The general naming pattern is `_`, with `` being `_` + + ```cpp + llama_model_init(); // class: "llama_model", method: "init" + llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove" + llama_sampler_get_seed(); // class: "llama_sampler", method: "get_seed" + llama_set_embeddings(); // class: "llama_context", method: "set_embeddings" + llama_n_threads(); // class: "llama_context", method: "n_threads" + llama_adapter_lora_free(); // class: "llama_adapter_lora", method: "free" + ``` + + - The `get` `` can be omitted + - The `` can be omitted if not necessary + - The `_context` suffix of the `` is optional. Use it to disambiguate symbols when needed + - Use `init`/`free` for constructor/destructor `` + +- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else + + ```cpp + typedef struct llama_context * llama_context_t; + + enum llama_pooling_type llama_pooling_type(const llama_context_t ctx); + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_ + +- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension +- Python filenames are all lowercase with underscores + +- _(TODO: abbreviations usage)_ + +# Preprocessor directives + +- _(TODO: add guidelines with examples and apply them to the codebase)_ + + ```cpp + #ifdef FOO + #endif // FOO + ``` + +# Documentation + +- Documentation is a community effort +- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference +- When you notice incorrect or outdated documentation, please update it + # Resources The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects: -https://github.com/ggerganov/llama.cpp/projects +https://github.com/ggml-org/llama.cpp/projects diff --git a/Makefile b/Makefile index 14c05e93e7535..5339d490b4e68 100644 --- a/Makefile +++ b/Makefile @@ -1,3 +1,7 @@ +ifndef LLAMA_MAKEFILE +$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) +endif + # Define the default target now so that it is always the first target BUILD_TARGETS = \ libllava.a \ @@ -18,6 +22,7 @@ BUILD_TARGETS = \ llama-infill \ llama-llava-cli \ llama-minicpmv-cli\ + llama-qwen2vl-cli\ llama-lookahead \ llama-lookup \ llama-lookup-create \ @@ -34,6 +39,7 @@ BUILD_TARGETS = \ llama-server \ llama-simple \ llama-simple-chat \ + llama-run \ llama-speculative \ llama-tokenize \ llama-vdot \ @@ -46,6 +52,7 @@ TEST_TARGETS = \ tests/test-arg-parser \ tests/test-autorelease \ tests/test-backend-ops \ + tests/test-chat \ tests/test-chat-template \ tests/test-double-float \ tests/test-grammar-integration \ @@ -250,11 +257,11 @@ endif # Compile flags # -# keep standard at C11 and C++11 +# keep standard at C11 and C++17 MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU MK_CFLAGS = -std=c11 -fPIC -MK_CXXFLAGS = -std=c++11 -fPIC -MK_NVCCFLAGS = -std=c++11 +MK_CXXFLAGS = -std=c++17 -fPIC +MK_NVCCFLAGS = -std=c++17 ifdef LLAMA_NO_CCACHE GGML_NO_CCACHE := 1 @@ -440,6 +447,10 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) MK_CFLAGS += -march=native -mtune=native HOST_CXXFLAGS += -march=native -mtune=native + # Usage AMX build test + #MK_CFLAGS += -march=graniterapids -mtune=graniterapids + #HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids + # Usage AVX-only #MK_CFLAGS += -mfma -mf16c -mavx #MK_CXXFLAGS += -mfma -mf16c -mavx @@ -452,7 +463,7 @@ endif ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 - # https://github.com/ggerganov/llama.cpp/issues/2922 + # https://github.com/ggml-org/llama.cpp/issues/2922 MK_CFLAGS += -Xassembler -muse-unaligned-vector-move MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move @@ -574,15 +585,18 @@ endif ifndef GGML_NO_AMX MK_CPPFLAGS += -DGGML_USE_AMX - OBJ_GGML_EXT += ggml/src/ggml-amx/ggml-amx.o ggml/src/ggml-amx/mmq.o + OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o endif +# only necessary for the CPU backend files +MK_CPPFLAGS += -Iggml/src/ggml-cpu + ifdef GGML_RPC MK_CPPFLAGS += -DGGML_USE_RPC OBJ_GGML_EXT += ggml/src/ggml-rpc.o endif # GGML_RPC -OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu)) +OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu)) OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu)) ifdef GGML_CUDA_FA_ALL_QUANTS @@ -666,6 +680,10 @@ ifdef GGML_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(GGML_CUDA_CCBIN) endif # GGML_CUDA_CCBIN +ifdef GGML_CUDA_NO_FA + MK_NVCCFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + ifdef GGML_CUDA_FA_ALL_QUANTS MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS endif # GGML_CUDA_FA_ALL_QUANTS @@ -751,7 +769,7 @@ vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp endif # GGML_VULKAN -ifdef GGML_HIPBLAS +ifdef GGML_HIP ifeq ($(wildcard /opt/rocm),) ROCM_PATH ?= /usr AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) @@ -786,6 +804,10 @@ ifdef GGML_CUDA_NO_PEER_COPY HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY endif # GGML_CUDA_NO_PEER_COPY +ifdef GGML_CUDA_NO_FA + HIPFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) @@ -806,7 +828,7 @@ ggml/src/ggml-cuda/%.o: \ ggml/src/ggml-common.h \ ggml/src/ggml-cuda/common.cuh $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -endif # GGML_HIPBLAS +endif # GGML_HIP ifdef GGML_MUSA ifeq ($(wildcard /opt/musa),) @@ -814,7 +836,7 @@ ifdef GGML_MUSA else MUSA_PATH ?= /opt/musa endif - MTGPU_TARGETS ?= mp_21 mp_22 + MUSA_ARCHITECTURES ?= 21;22 MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib @@ -833,7 +855,8 @@ ifdef GGML_MUSA CXX := $(MUSA_PATH)/bin/clang++ MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc - MUSAFLAGS += $(addprefix --cuda-gpu-arch=, $(MTGPU_TARGETS)) + MUSAFLAGS = -fsigned-char -x musa -mtgpu + MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch)) ifdef GGML_CUDA_FORCE_MMQ MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ @@ -861,6 +884,10 @@ ifdef GGML_CUDA_NO_PEER_COPY MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY endif # GGML_CUDA_NO_PEER_COPY +ifdef GGML_CUDA_NO_FA + MUSAFLAGS += -DGGML_CUDA_NO_FA +endif # GGML_CUDA_NO_FA + ifdef GGML_CUDA_FA_ALL_QUANTS MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS endif # GGML_CUDA_FA_ALL_QUANTS @@ -877,14 +904,14 @@ ggml/src/ggml-cuda/ggml-cuda.o: \ ggml/src/ggml-backend-impl.h \ ggml/src/ggml-common.h \ $(wildcard ggml/src/ggml-cuda/*.cuh) - $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $< + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< ggml/src/ggml-cuda/%.o: \ ggml/src/ggml-cuda/%.cu \ ggml/include/ggml.h \ ggml/src/ggml-common.h \ ggml/src/ggml-cuda/common.cuh - $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $< + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< endif # GGML_MUSA ifdef GGML_METAL @@ -939,7 +966,6 @@ DIR_COMMON = common OBJ_GGML = \ $(DIR_GGML)/src/ggml.o \ - $(DIR_GGML)/src/ggml-aarch64.o \ $(DIR_GGML)/src/ggml-alloc.o \ $(DIR_GGML)/src/ggml-backend.o \ $(DIR_GGML)/src/ggml-backend-reg.o \ @@ -947,9 +973,11 @@ OBJ_GGML = \ $(DIR_GGML)/src/ggml-quants.o \ $(DIR_GGML)/src/ggml-threading.o \ $(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \ - $(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \ $(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \ $(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \ $(OBJ_GGML_EXT) OBJ_LLAMA = \ @@ -968,6 +996,7 @@ OBJ_COMMON = \ $(DIR_COMMON)/ngram-cache.o \ $(DIR_COMMON)/sampling.o \ $(DIR_COMMON)/speculative.o \ + $(DIR_COMMON)/chat.o \ $(DIR_COMMON)/build-info.o \ $(DIR_COMMON)/json-schema-to-grammar.o @@ -1061,8 +1090,8 @@ endif ifdef REMOVE_WARNING $(info !!! REMOVAL WARNING !!!) $(info The following LLAMA_ options have been removed and are no longer supported) -$(info - LLAMA_DISABLE_LOGS (https://github.com/ggerganov/llama.cpp/pull/9418)) -$(info - LLAMA_SERVER_VERBOSE (https://github.com/ggerganov/llama.cpp/pull/9418)) +$(info - LLAMA_DISABLE_LOGS (https://github.com/ggml-org/llama.cpp/pull/9418)) +$(info - LLAMA_SERVER_VERBOSE (https://github.com/ggml-org/llama.cpp/pull/9418)) $(info ) endif @@ -1089,17 +1118,10 @@ DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d) # Default target all: $(BUILD_TARGETS) +# force c++ build for source file that have same name as c file # Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files -# g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml -$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \ - ggml/src/ggml-cpu/ggml-cpu.cpp \ - ggml/include/ggml-backend.h \ - ggml/include/ggml.h \ - ggml/include/ggml-alloc.h \ - ggml/src/ggml-backend-impl.h \ - ggml/include/ggml-cpu.h \ - ggml/src/ggml-impl.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ # Rules for building object files $(DIR_GGML)/%.o: $(DIR_GGML)/%.c @@ -1136,8 +1158,15 @@ $(LIB_COMMON_S): $(OBJ_COMMON) # Include dependency files -include $(DEP_FILES) +# Clean generated server assets +clean-server-assets: + find examples/server -type f -name "*.js.hpp" -delete + find examples/server -type f -name "*.mjs.hpp" -delete + find examples/server -type f -name "*.css.hpp" -delete + find examples/server -type f -name "*.html.hpp" -delete + # Clean rule -clean: +clean: clean-server-assets rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS) rm -rvf *.a *.dll *.so *.dot find ggml src common tests examples pocs -type f -name "*.o" -delete @@ -1167,6 +1196,11 @@ llama-infill: examples/infill/infill.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-run: examples/run/run.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-simple: examples/simple/simple.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1340,20 +1374,18 @@ llama-server: \ examples/server/utils.hpp \ examples/server/httplib.h \ examples/server/index.html.hpp \ - examples/server/completion.js.hpp \ examples/server/loading.html.hpp \ - examples/server/deps_daisyui.min.css.hpp \ - examples/server/deps_markdown-it.js.hpp \ - examples/server/deps_tailwindcss.js.hpp \ - examples/server/deps_vue.esm-browser.js.hpp \ + common/chat.cpp \ + common/chat.h \ + common/chat-template.hpp \ common/json.hpp \ - common/stb_image.h \ + common/minja.hpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) # Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: -examples/server/%.hpp: examples/server/public/% Makefile +examples/server/%.hpp: examples/server/public/% FORCE Makefile @( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \ echo "unsigned char $${NAME}[] = {" && \ cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \ @@ -1391,6 +1423,14 @@ llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual +llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \ + examples/llava/llava.cpp \ + examples/llava/llava.h \ + examples/llava/clip.cpp \ + examples/llava/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift (cd examples/batched.swift; make build) @@ -1447,6 +1487,11 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \ $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +tests/test-chat: tests/test-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + tests/test-opt: tests/test-opt.cpp \ $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1528,7 +1573,7 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \ # Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed. # # Mark legacy binary targets as .PHONY so that they are always checked. -.PHONY: main quantize perplexity embedding server +.PHONY: FORCE main quantize perplexity embedding server # Define the object file target examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp diff --git a/Package.swift b/Package.swift index d9e8a4e2d21d7..01c996d242037 100644 --- a/Package.swift +++ b/Package.swift @@ -2,57 +2,6 @@ import PackageDescription -var sources = [ - "src/llama.cpp", - "src/llama-vocab.cpp", - "src/llama-grammar.cpp", - "src/llama-sampling.cpp", - "src/unicode.cpp", - "src/unicode-data.cpp", - "ggml/src/ggml.c", - "ggml/src/ggml-aarch64.c", - "ggml/src/ggml-alloc.c", - "ggml/src/ggml-backend.cpp", - "ggml/src/ggml-backend-reg.cpp", - "ggml/src/ggml-cpu/ggml-cpu.c", - "ggml/src/ggml-cpu/ggml-cpu.cpp", - "ggml/src/ggml-cpu/ggml-cpu-aarch64.c", - "ggml/src/ggml-cpu/ggml-cpu-quants.c", - "ggml/src/ggml-threading.cpp", - "ggml/src/ggml-quants.c", -] - -var resources: [Resource] = [] -var linkerSettings: [LinkerSetting] = [] -var cSettings: [CSetting] = [ - .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), - .unsafeFlags(["-fno-objc-arc"]), - .headerSearchPath("ggml/src"), - // NOTE: NEW_LAPACK will required iOS version 16.4+ - // We should consider add this in the future when we drop support for iOS 14 - // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) - // .define("ACCELERATE_NEW_LAPACK"), - // .define("ACCELERATE_LAPACK_ILP64") -] - -#if canImport(Darwin) -sources.append("ggml/src/ggml-common.h") -sources.append("ggml/src/ggml-metal/ggml-metal.m") -resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal")) -linkerSettings.append(.linkedFramework("Accelerate")) -cSettings.append( - contentsOf: [ - .define("GGML_USE_ACCELERATE"), - .define("GGML_USE_METAL"), - .define("GGML_USE_CPU") - ] -) -#endif - -#if os(Linux) - cSettings.append(.define("_GNU_SOURCE")) -#endif - let package = Package( name: "llama", platforms: [ @@ -65,26 +14,6 @@ let package = Package( .library(name: "llama", targets: ["llama"]), ], targets: [ - .target( - name: "llama", - path: ".", - exclude: [ - "build", - "cmake", - "examples", - "scripts", - "models", - "tests", - "CMakeLists.txt", - "Makefile", - "ggml/src/ggml-metal-embed.metal" - ], - sources: sources, - resources: resources, - publicHeadersPath: "spm-headers", - cSettings: cSettings, - linkerSettings: linkerSettings - ) - ], - cxxLanguageStandard: .cxx11 + .systemLibrary(name: "llama", pkgConfig: "llama"), + ] ) diff --git a/README.md b/README.md index 5f7933c132dc3..f7f1a521e40a8 100644 --- a/README.md +++ b/README.md @@ -3,30 +3,40 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) -[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) -[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) +[![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml) -[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) +[Roadmap](https://github.com/users/ggml-org/projects/7) / [Project status](https://github.com/ggml-org/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +> [!IMPORTANT] +> New `llama.cpp` package location: [ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp/pkgs/container/llama.cpp) +> +> Update your container URLs to: `ghcr.io/ggml-org/llama.cpp` +> +> More info: https://github.com/ggml-org/llama.cpp/discussions/11801 + ## Recent API changes -- [Changelog for `libllama` API](https://github.com/ggerganov/llama.cpp/issues/9289) -- [Changelog for `llama-server` REST API](https://github.com/ggerganov/llama.cpp/issues/9291) +- [Changelog for `libllama` API](https://github.com/ggml-org/llama.cpp/issues/9289) +- [Changelog for `llama-server` REST API](https://github.com/ggml-org/llama.cpp/issues/9291) ## Hot topics -- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123 -- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669 -- Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) +- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggml-org/llama.cpp/pull/11427 +- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode +- Universal tool call support in `llama-server`: https://github.com/ggml-org/llama.cpp/pull/9639 +- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim +- Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123 +- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669 +- Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) ---- ## Description The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide -variety of hardware - locally and in the cloud. +range of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks @@ -36,14 +46,17 @@ variety of hardware - locally and in the cloud. - Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has -improved significantly thanks to many contributions. It is the main playground for developing new features for the -[ggml](https://github.com/ggerganov/ggml) library. +The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggml-org/ggml) library. -**Supported models:** +
+Models Typically finetunes of the base models below are supported as well. +Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md) + +#### Text-only + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 - [x] LLaMA 3 🦙🦙🦙 @@ -53,22 +66,23 @@ Typically finetunes of the base models below are supported as well. - [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) -- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423) +- [X] [BERT](https://github.com/ggml-org/llama.cpp/pull/5423) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) -- [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) +- [X] [Starcoder models](https://github.com/ggml-org/llama.cpp/pull/3187) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) -- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) -- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) +- [X] [MPT](https://github.com/ggml-org/llama.cpp/pull/3417) +- [X] [Bloom](https://github.com/ggml-org/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) - [X] [StableLM models](https://huggingface.co/stabilityai) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) -- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [PLaMo-13B](https://github.com/ggml-org/llama.cpp/pull/3557) - [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) +- [x] [PhiMoE](https://github.com/ggml-org/llama.cpp/pull/11003) - [x] [GPT-2](https://huggingface.co/gpt2) -- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) +- [x] [Orion 14B](https://github.com/ggml-org/llama.cpp/pull/5118) - [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) @@ -79,6 +93,7 @@ Typically finetunes of the base models below are supported as well. - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) - [x] [OLMo](https://allenai.org/olmo) +- [x] [OLMo 2](https://allenai.org/olmo) - [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924) - [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330) - [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia) @@ -88,17 +103,17 @@ Typically finetunes of the base models below are supported as well. - [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM) - [x] [Flan T5](https://huggingface.co/models?search=flan-t5) - [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca) -- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) +- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat) - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966) - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) +- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1) +- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct) -(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md)) - -**Multimodal models:** +#### Multimodal - [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) @@ -109,8 +124,13 @@ Typically finetunes of the base models below are supported as well. - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) +- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge) +- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) + +
-**Bindings:** +
+Bindings - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) @@ -123,6 +143,7 @@ Typically finetunes of the base models below are supported as well. - Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs) - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) +- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) - C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html) - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) @@ -132,321 +153,341 @@ Typically finetunes of the base models below are supported as well. - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) - Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama) -- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) +- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggml-org/llama.cpp/pull/6326) - Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) -**UI:** +
-Unless otherwise noted these projects are open-source with permissive licensing: +
+UIs -- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) -- [iohub/collama](https://github.com/iohub/coLLaMA) +*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* + +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) +- [Dot](https://github.com/alexpinel/Dot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0) - [janhq/jan](https://github.com/janhq/jan) (AGPL) -- [nat/openplayground](https://github.com/nat/openplayground) -- [Faraday](https://faraday.dev/) (proprietary) +- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0) +- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT) +- [LARS](https://github.com/abgulati/LARS) (AGPL) +- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) +- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) - [LMStudio](https://lmstudio.ai/) (proprietary) -- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary) -- [ramalama](https://github.com/containers/ramalama) (MIT) - [LocalAI](https://github.com/mudler/LocalAI) (MIT) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) -- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) -- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) -- [ollama/ollama](https://github.com/ollama/ollama) +- [MindMac](https://mindmac.app) (proprietary) +- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) +- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0) +- [nat/openplayground](https://github.com/nat/openplayground) (MIT) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT) +- [ollama/ollama](https://github.com/ollama/ollama) (MIT) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) -- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) -- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) -- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) +- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) -- [RAGNA Desktop](https://ragna.app/) (proprietary) -- [RecurseChat](https://recurse.chat/) (proprietary) -- [semperai/amica](https://github.com/semperai/amica) -- [withcatai/catai](https://github.com/withcatai/catai) -- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) -- [Msty](https://msty.app) (proprietary) -- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) -- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later) -- [Dot](https://github.com/alexpinel/Dot) (GPL) -- [MindMac](https://mindmac.app) (proprietary) -- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) -- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) -- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) -- [AIKit](https://github.com/sozercan/aikit) (MIT) -- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) -- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) -- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) -- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [ramalama](https://github.com/containers/ramalama) (MIT) +- [semperai/amica](https://github.com/semperai/amica) (MIT) +- [withcatai/catai](https://github.com/withcatai/catai) (MIT) +- [Autopen](https://github.com/blackhole89/autopen) (GPL) -*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* +
-**Tools:** +
+Tools - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML - [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage -- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example) +- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example) -**Infrastructure:** +
+ +
+Infrastructure - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly +- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server +- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale +- [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes. +
+ +
+Games -**Games:** - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. -## Demo +
-
-Typical run using LLaMA v2 13B on M2 Ultra +## Supported backends -``` -$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -I llama.cpp build info: -I UNAME_S: Darwin -I UNAME_P: arm -I UNAME_M: arm64 -I CFLAGS: -I. -O3 -std=c11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE -I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS -I LDFLAGS: -framework Accelerate -I CC: Apple clang version 14.0.3 (clang-1403.0.22.14.1) -I CXX: Apple clang version 14.0.3 (clang-1403.0.22.14.1) - -make: Nothing to be done for `default'. -main: build = 1041 (cf658ad) -main: seed = 1692823051 -llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest)) -llama_model_loader: - type f32: 81 tensors -llama_model_loader: - type q4_0: 281 tensors -llama_model_loader: - type q6_K: 1 tensors -llm_load_print_meta: format = GGUF V1 (latest) -llm_load_print_meta: arch = llama -llm_load_print_meta: vocab type = SPM -llm_load_print_meta: n_vocab = 32000 -llm_load_print_meta: n_merges = 0 -llm_load_print_meta: n_ctx_train = 4096 -llm_load_print_meta: n_ctx = 512 -llm_load_print_meta: n_embd = 5120 -llm_load_print_meta: n_head = 40 -llm_load_print_meta: n_head_kv = 40 -llm_load_print_meta: n_layer = 40 -llm_load_print_meta: n_rot = 128 -llm_load_print_meta: n_gqa = 1 -llm_load_print_meta: f_norm_eps = 1.0e-05 -llm_load_print_meta: f_norm_rms_eps = 1.0e-05 -llm_load_print_meta: n_ff = 13824 -llm_load_print_meta: freq_base = 10000.0 -llm_load_print_meta: freq_scale = 1 -llm_load_print_meta: model type = 13B -llm_load_print_meta: model ftype = mostly Q4_0 -llm_load_print_meta: model size = 13.02 B -llm_load_print_meta: general.name = LLaMA v2 -llm_load_print_meta: BOS token = 1 '' -llm_load_print_meta: EOS token = 2 '' -llm_load_print_meta: UNK token = 0 '' -llm_load_print_meta: LF token = 13 '<0x0A>' -llm_load_tensors: ggml ctx size = 0.11 MB -llm_load_tensors: mem required = 7024.01 MB (+ 400.00 MB per state) -................................................................................................... -llama_new_context_with_model: kv self size = 400.00 MB -llama_new_context_with_model: compute buffer total size = 75.41 MB - -system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | -sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000 -generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0 - - - Building a website can be done in 10 simple steps: -Step 1: Find the right website platform. -Step 2: Choose your domain name and hosting plan. -Step 3: Design your website layout. -Step 4: Write your website content and add images. -Step 5: Install security features to protect your site from hackers or spammers -Step 6: Test your website on multiple browsers, mobile devices, operating systems etc… -Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine! -Step 8: Start marketing and promoting the website via social media channels or paid ads -Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc… -Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further! -How does a Website Work? -A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable! -The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking. -How to -llama_print_timings: load time = 576.45 ms -llama_print_timings: sample time = 283.10 ms / 400 runs ( 0.71 ms per token, 1412.91 tokens per second) -llama_print_timings: prompt eval time = 599.83 ms / 19 tokens ( 31.57 ms per token, 31.68 tokens per second) -llama_print_timings: eval time = 24513.59 ms / 399 runs ( 61.44 ms per token, 16.28 tokens per second) -llama_print_timings: total time = 25431.49 ms -``` +| Backend | Target devices | +| --- | --- | +| [Metal](docs/build.md#metal-build) | Apple Silicon | +| [BLAS](docs/build.md#blas-build) | All | +| [BLIS](docs/backend/BLIS.md) | All | +| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU | +| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU | +| [CUDA](docs/build.md#cuda) | Nvidia GPU | +| [HIP](docs/build.md#hip) | AMD GPU | +| [Vulkan](docs/build.md#vulkan) | GPU | +| [CANN](docs/build.md#cann) | Ascend NPU | +| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU | -
+## Building the project -
-Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h). +The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries: -And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook: +- Clone this repository and build locally, see [how to build](docs/build.md) +- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md) +- Use a Docker image, see [documentation for Docker](docs/docker.md) +- Download pre-built binaries from [releases](https://github.com/ggml-org/llama.cpp/releases) -https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 +## Obtaining and quantizing models -
+The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: -## Usage +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) +- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf) -Here are the end-to-end binary build and model conversion steps for most supported models. +You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf /[:quant]` -### Basic usage +After downloading a model, use the CLI tools to run it locally - see below. -Firstly, you need to get the binary. There are different methods that you can follow: -- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md) -- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md) -- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md) -- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases) +`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo. -You can run a basic completion using this command: +The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`: -```bash -llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 +- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes +- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123) +- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268) +- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669) -# Output: -# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. -``` +To learn more about model quantization, [read this documentation](examples/quantize/README.md) -See [this page](./examples/main/README.md) for a full list of parameters. +## [`llama-cli`](examples/main) -### Conversation mode +#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. -If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter: +-
+ Run in conversation mode -```bash -llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv - -# Output: -# > hi, who are you? -# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? -# -# > what is 1+1? -# Easy peasy! The answer to 1+1 is... 2! -``` + Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME` -By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) + ```bash + llama-cli -m model.gguf -```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml -``` + # > hi, who are you? + # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? + # + # > what is 1+1? + # Easy peasy! The answer to 1+1 is... 2! + ``` -You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters: +
-```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' -``` +-
+ Run in conversation mode with custom chat template -### Web server + ```bash + # use the "chatml" template (use -h to see the list of supported templates) + llama-cli -m model.gguf -cnv --chat-template chatml -[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. + # use a custom template + llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:' + ``` -Example usage: +
-```bash -./llama-server -m your_model.gguf --port 8080 +-
+ Run simple text completion -# Basic web UI can be accessed via browser: http://localhost:8080 -# Chat completion endpoint: http://localhost:8080/v1/chat/completions -``` + To disable conversation mode explicitly, use `-no-cnv` -### Interactive mode + ```bash + llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv -> [!NOTE] -> If you prefer basic usage, please consider using conversation mode instead of interactive mode + # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. + ``` -In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. +
-Here is an example of a few-shot interaction, invoked with the command +-
+ Constrain the output with a custom grammar -```bash -# default arguments using a 7B model -./examples/chat.sh + ```bash + llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' -# advanced chat with a 13B model -./examples/chat-13B.sh + # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"} + ``` -# custom arguments using a 13B model -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt -``` + The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md). -Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program. + For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ -![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) +
-### Persistent Interaction -The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file. +## [`llama-server`](examples/server) -```bash -# Start a new chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh +#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs. -# Resume that chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh +-
+ Start a local HTTP server with default configuration on port 8080 -# Start a different chat with the same prompt/model -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh + ```bash + llama-server -m model.gguf --port 8080 -# Different prompt cache for different prompt/model -PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \ - CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh -``` + # Basic web UI can be accessed via browser: http://localhost:8080 + # Chat completion endpoint: http://localhost:8080/v1/chat/completions + ``` -### Constrained output with grammars +
-`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only: +-
+ Support multiple-users and parallel decoding -```bash -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' -``` + ```bash + # up to 4 concurrent requests, each with 4096 max context + llama-server -m model.gguf -c 16384 -np 4 + ``` -The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md). +
-For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. +-
+ Enable speculative decoding -## Build + ```bash + # the draft.gguf model should be a small variant of the target model.gguf + llama-server -m model.gguf -md draft.gguf + ``` -Please refer to [Build llama.cpp locally](./docs/build.md) +
-## Supported backends +-
+ Serve an embedding model -| Backend | Target devices | -| --- | --- | -| [Metal](./docs/build.md#metal-build) | Apple Silicon | -| [BLAS](./docs/build.md#blas-build) | All | -| [BLIS](./docs/backend/BLIS.md) | All | -| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU | -| [MUSA](./docs/build.md#musa) | Moore Threads MTT GPU | -| [CUDA](./docs/build.md#cuda) | Nvidia GPU | -| [hipBLAS](./docs/build.md#hipblas) | AMD GPU | -| [Vulkan](./docs/build.md#vulkan) | GPU | -| [CANN](./docs/build.md#cann) | Ascend NPU | + ```bash + # use the /embedding endpoint + llama-server -m model.gguf --embedding --pooling cls -ub 8192 + ``` + +
+ +-
+ Serve a reranking model + + ```bash + # use the /reranking endpoint + llama-server -m model.gguf --reranking + ``` + +
+ +-
+ Constrain all outputs with a grammar + + ```bash + # custom grammar + llama-server -m model.gguf --grammar-file grammar.gbnf + + # JSON + llama-server -m model.gguf --grammar-file grammars/json.gbnf + ``` + +
-## Tools -### Prepare and Quantize +## [`llama-perplexity`](examples/perplexity) -> [!NOTE] -> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. +#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text. -To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. +-
+ Measure the perplexity over a text file -Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. -It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face. + ```bash + llama-perplexity -m model.gguf -f file.txt -To learn more about quantizing model, [read this documentation](./examples/quantize/README.md) + # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ... + # Final estimate: PPL = 5.4007 +/- 0.67339 + ``` -### Perplexity (measuring model quality) +
-You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). -For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). +-
+ Measure KL divergence + + ```bash + # TODO + ``` + +
+ +[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md) +[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity) + +## [`llama-bench`](examples/llama-bench) + +#### Benchmark the performance of the inference for various parameters. + +-
+ Run default benchmark + + ```bash + llama-bench -m model.gguf + + # Output: + # | model | size | params | backend | threads | test | t/s | + # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | pp512 | 5765.41 ± 20.55 | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | tg128 | 197.71 ± 0.81 | + # + # build: 3e0ba0e60 (4229) + ``` + +
+ +## [`llama-run`](examples/run) + +#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3]. + +-
+ Run a model with a specific prompt (by default it's pulled from Ollama registry) + + ```bash + llama-run granite-code + ``` + +
+ +[^3]: [RamaLama](https://github.com/containers/ramalama) + +## [`llama-simple`](examples/simple) + +#### A minimal example for implementing apps with `llama.cpp`. Useful for developers. + +-
+ Basic text completion + + ```bash + llama-simple -m model.gguf + + # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of + ``` + +
-To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md) ## Contributing @@ -454,27 +495,26 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio - Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch - Collaborators will be invited based on contributions - Any help with managing issues, PRs and projects is very appreciated! -- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions +- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions - Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information -- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205) +- Make sure to read this: [Inference at the edge](https://github.com/ggml-org/llama.cpp/discussions/205) - A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) ## Other documentation -- [main (cli)](./examples/main/README.md) -- [server](./examples/server/README.md) -- [jeopardy](./examples/jeopardy/README.md) -- [GBNF grammars](./grammars/README.md) +- [main (cli)](examples/main/README.md) +- [server](examples/server/README.md) +- [GBNF grammars](grammars/README.md) -**Development documentation** +#### Development documentation -- [How to build](./docs/build.md) -- [Running on Docker](./docs/docker.md) -- [Build on Android](./docs/android.md) -- [Performance troubleshooting](./docs/development/token_generation_performance_tips.md) -- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks) +- [How to build](docs/build.md) +- [Running on Docker](docs/docker.md) +- [Build on Android](docs/android.md) +- [Performance troubleshooting](docs/development/token_generation_performance_tips.md) +- [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks) -**Seminal papers and background on the models** +#### Seminal papers and background on the models If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: - LLaMA: @@ -485,3 +525,19 @@ If your issue is with model generation quality, then please at least scan the fo - GPT-3.5 / InstructGPT / ChatGPT: - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) + +## Completions +Command-line completion is available for some environments. + +#### Bash Completion +```bash +$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash +$ source ~/.llama-completion.bash +``` +Optionally this can be added to your `.bashrc` or `.bash_profile` to load it +automatically. For example: +```console +$ echo "source ~/.llama-completion.bash" >> ~/.bashrc +``` + +## References diff --git a/SECURITY.md b/SECURITY.md index f4322c6ee4d18..6a1bb6c32cd8e 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -62,6 +62,6 @@ Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp- However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released. -Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new). +Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new). A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure. diff --git a/Sources/llama/llama.h b/Sources/llama/llama.h new file mode 100644 index 0000000000000..41725880ed8c0 --- /dev/null +++ b/Sources/llama/llama.h @@ -0,0 +1,4 @@ +#pragma once + +#include + diff --git a/Sources/llama/module.modulemap b/Sources/llama/module.modulemap new file mode 100644 index 0000000000000..d010555b1cb65 --- /dev/null +++ b/Sources/llama/module.modulemap @@ -0,0 +1,5 @@ +module llama [system] { + header "llama.h" + link "llama" + export * +} diff --git a/ci/README.md b/ci/README.md index 4064705190697..8245c9df65db8 100644 --- a/ci/README.md +++ b/ci/README.md @@ -1,11 +1,11 @@ # CI -In addition to [Github Actions](https://github.com/ggerganov/llama.cpp/actions) `llama.cpp` uses a custom CI framework: +In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework: https://github.com/ggml-org/ci It monitors the `master` branch for new commits and runs the -[ci/run.sh](https://github.com/ggerganov/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us +[ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled to cover various hardware architectures, including GPU and Apple Silicon instances. diff --git a/ci/run.sh b/ci/run.sh index 20610e56009be..77c32ce0050aa 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -326,17 +326,17 @@ function gg_run_open_llama_7b_v2 { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -460,17 +460,17 @@ function gg_run_pythia_1_4b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -591,17 +591,17 @@ function gg_run_pythia_2_8b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -815,7 +815,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then ln -sfn ${mnt_models} ${SRC}/models-mnt # Create a fresh python3 venv and enter it - python3 -m venv "$MNT/venv" + if ! python3 -m venv "$MNT/venv"; then + echo "Error: Failed to create Python virtual environment at $MNT/venv." + exit 1 + fi source "$MNT/venv/bin/activate" pip install -r ${SRC}/requirements.txt --disable-pip-version-check diff --git a/cmake/build-info.cmake b/cmake/build-info.cmake index ea3dc55c83439..c1a456e179872 100644 --- a/cmake/build-info.cmake +++ b/cmake/build-info.cmake @@ -44,7 +44,7 @@ if(MSVC) set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) else() execute_process( - COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER} + COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER} OUTPUT_VARIABLE OUT OUTPUT_STRIP_TRAILING_WHITESPACE ) diff --git a/cmake/common.cmake b/cmake/common.cmake new file mode 100644 index 0000000000000..0f54871e4143d --- /dev/null +++ b/cmake/common.cmake @@ -0,0 +1,33 @@ +function(llama_add_compile_flags) + if (LLAMA_FATAL_WARNINGS) + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + list(APPEND C_FLAGS -Werror) + list(APPEND CXX_FLAGS -Werror) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + add_compile_options(/WX) + endif() + endif() + + if (LLAMA_ALL_WARNINGS) + if (NOT MSVC) + list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes + -Werror=implicit-int -Werror=implicit-function-declaration) + + list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn) + + list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function) + + list(APPEND C_FLAGS ${WARNING_FLAGS}) + list(APPEND CXX_FLAGS ${WARNING_FLAGS}) + + ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) + + add_compile_options("$<$:${C_FLAGS};${GF_C_FLAGS}>" + "$<$:${CXX_FLAGS};${GF_CXX_FLAGS}>") + else() + # todo : msvc + set(C_FLAGS "" PARENT_SCOPE) + set(CXX_FLAGS "" PARENT_SCOPE) + endif() + endif() +endfunction() diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in index 5c55bc6b822a6..90cbec5b6f133 100644 --- a/cmake/llama-config.cmake.in +++ b/cmake/llama-config.cmake.in @@ -3,159 +3,13 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) -set(GGML_STATIC @GGML_STATIC@) -set(GGML_NATIVE @GGML_NATIVE@) -set(GGML_LTO @GGML_LTO@) -set(GGML_CCACHE @GGML_CCACHE@) -set(GGML_AVX @GGML_AVX@) -set(GGML_AVX2 @GGML_AVX2@) -set(GGML_AVX512 @GGML_AVX512@) -set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@) -set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@) -set(GGML_AVX512_BF16 @GGML_AVX512_BF16@) -set(GGML_AMX_TILE @GGML_AMX_TILE@) -set(GGML_AMX_INT8 @GGML_AMX_INT8@) -set(GGML_AMX_BF16 @GGML_AMX_BF16@) -set(GGML_FMA @GGML_FMA@) -set(GGML_LASX @GGML_LASX@) -set(GGML_LSX @GGML_LSX@) -set(GGML_RVV @GGML_RVV@) -set(GGML_SVE @GGML_SVE@) - -set(GGML_ACCELERATE @GGML_ACCELERATE@) -set(GGML_OPENMP @GGML_OPENMP@) -set(GGML_CPU_HBM @GGML_CPU_HBM@) -set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@) - -set(GGML_CUDA_FORCE_MMQ @GGML_CUDA_FORCE_MMQ@) -set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@) -set(GGML_CUDA_F16 @GGML_CUDA_F16@) -set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@) -set(GGML_CUDA_NO_PEER_COPY @GGML_CUDA_NO_PEER_COPY@) -set(GGML_CUDA_NO_VMM @GGML_CUDA_NO_VMM@) -set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@) -set(GGML_CUDA_GRAPHS @GGML_CUDA_GRAPHS@) - -set(GGML_HIP_UMA @GGML_HIP_UMA@) - -set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@) -set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@) -set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@) -set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@) -set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@) -set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@) -set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@) - -set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@) -set(GGML_METAL_NDEBUG @GGML_METAL_NDEBUG@) -set(GGML_METAL_SHADER_DEBUG @GGML_METAL_SHADER_DEBUG@) -set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@) -set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@) -set(GGML_METAL_STD @GGML_METAL_STD@) - -set(GGML_SYCL_F16 @GGML_SYCL_F16@) -set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@) -set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@) - - @PACKAGE_INIT@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@") set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@") set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@") -find_package(Threads REQUIRED) - -set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@") -set(_llama_link_deps "") -set(_llama_link_opts "") -foreach(_ggml_lib ggml ggml-base) - string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") - find_library(${_ggml_lib_var} ${_ggml_lib} - REQUIRED - HINTS ${LLAMA_LIB_DIR} - NO_CMAKE_FIND_ROOT_PATH - ) - list(APPEND _llama_link_deps "${${_ggml_lib_var}}") - message(STATUS "Found ${${_ggml_lib_var}}") -endforeach() - -foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan) - string(TOUPPER "GGML_${backend}" backend_id) - set(_ggml_lib "ggml-${backend}") - string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") - - find_library(${_ggml_lib_var} ${_ggml_lib} - HINTS ${LLAMA_LIB_DIR} - NO_CMAKE_FIND_ROOT_PATH - ) - if(${_ggml_lib_var}) - list(APPEND _llama_link_deps "${${_ggml_lib_var}}") - set(${backend_id} ON) - message(STATUS "Found backend ${${_ggml_lib_var}}") - else() - set(${backend_id} OFF) - endif() -endforeach() - -if (NOT LLAMA_SHARED_LIB) - if (APPLE AND GGML_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED) - list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK}) - endif() - - if (GGML_OPENMP) - find_package(OpenMP REQUIRED) - list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX) - endif() - - if (GGML_CPU_HBM) - find_library(memkind memkind REQUIRED) - list(APPEND _llama_link_deps memkind) - endif() - - if (GGML_BLAS) - find_package(BLAS REQUIRED) - list(APPEND _llama_link_deps ${BLAS_LIBRARIES}) - list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS}) - endif() - - if (GGML_CUDA) - find_package(CUDAToolkit REQUIRED) - endif() - - if (GGML_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK}) - endif() - - if (GGML_VULKAN) - find_package(Vulkan REQUIRED) - list(APPEND _llama_link_deps Vulkan::Vulkan) - endif() - - if (GGML_HIP) - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) - list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas) - endif() - - if (GGML_SYCL) - find_package(DNNL) - if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") - list(APPEND _llama_link_deps DNNL::dnnl) - endif() - if (WIN32) - find_package(IntelSYCL REQUIRED) - find_package(MKL REQUIRED) - list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) - endif() - endif() -endif() +find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake) find_library(llama_LIBRARY llama REQUIRED @@ -167,12 +21,10 @@ add_library(llama UNKNOWN IMPORTED) set_target_properties(llama PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}" - INTERFACE_LINK_LIBRARIES "${_llama_link_deps}" - INTERFACE_LINK_OPTIONS "${_llama_link_opts}" - INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}" + INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;" IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" IMPORTED_LOCATION "${llama_LIBRARY}" - INTERFACE_COMPILE_FEATURES cxx_std_11 - POSITION_INDEPENDENT_CODE ON ) + INTERFACE_COMPILE_FEATURES c_std_90 + POSITION_INDEPENDENT_CODE ON) check_required_components(Llama) diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in index 326acbb6108fd..6fb58b5f6881b 100644 --- a/cmake/llama.pc.in +++ b/cmake/llama.pc.in @@ -1,10 +1,10 @@ prefix=@CMAKE_INSTALL_PREFIX@ -exec_prefix=${prefix} -libdir=${exec_prefix}/lib -includedir=${prefix}/include +exec_prefix=@CMAKE_INSTALL_PREFIX@ +libdir=@CMAKE_INSTALL_FULL_LIBDIR@ +includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@ Name: llama Description: Port of Facebook's LLaMA model in C/C++ -Version: @PROJECT_VERSION@ -Libs: -L${libdir} -lllama +Version: @LLAMA_INSTALL_VERSION@ +Libs: -L${libdir} -lggml -lggml-base -lllama Cflags: -I${includedir} diff --git a/cmake/x64-windows-llvm.cmake b/cmake/x64-windows-llvm.cmake new file mode 100644 index 0000000000000..0603d738fbef0 --- /dev/null +++ b/cmake/x64-windows-llvm.cmake @@ -0,0 +1,11 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR x86_64 ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( arch_c_flags "-march=native" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" ) + diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 62a8a7db5652f..17146fffc1168 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -2,6 +2,8 @@ find_package(Threads REQUIRED) +llama_add_compile_flags() + # Build info header # @@ -54,14 +56,19 @@ add_library(${TARGET} STATIC arg.cpp arg.h base64.hpp + chat.cpp + chat.h common.cpp common.h console.cpp console.h json-schema-to-grammar.cpp json.hpp + llguidance.cpp log.cpp log.h + minja/chat-template.hpp + minja/minja.hpp ngram-cache.cpp ngram-cache.h sampling.cpp @@ -79,12 +86,56 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info) # Use curl to download model url if (LLAMA_CURL) find_package(CURL REQUIRED) - add_definitions(-DLLAMA_USE_CURL) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL) include_directories(${CURL_INCLUDE_DIRS}) find_library(CURL_LIBRARY curl REQUIRED) set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY}) endif () +if (LLAMA_LLGUIDANCE) + include(ExternalProject) + set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source) + set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release) + + # Set the correct library file extension based on platform + if (WIN32) + set(LLGUIDANCE_LIB_NAME "llguidance.lib") + # Add Windows-specific libraries + set(LLGUIDANCE_PLATFORM_LIBS + ws2_32 # Windows Sockets API + userenv # For GetUserProfileDirectoryW + ntdll # For NT functions + bcrypt # For BCryptGenRandom + ) + else() + set(LLGUIDANCE_LIB_NAME "libllguidance.a") + set(LLGUIDANCE_PLATFORM_LIBS "") + endif() + + ExternalProject_Add(llguidance_ext + GIT_REPOSITORY https://github.com/guidance-ai/llguidance + # v0.6.12: + GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09 + PREFIX ${CMAKE_BINARY_DIR}/llguidance + SOURCE_DIR ${LLGUIDANCE_SRC} + BUILD_IN_SOURCE TRUE + CONFIGURE_COMMAND "" + BUILD_COMMAND cargo build --release + INSTALL_COMMAND "" + BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME} ${LLGUIDANCE_PATH}/llguidance.h + UPDATE_COMMAND "" + ) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE) + + add_library(llguidance STATIC IMPORTED) + set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME}) + add_dependencies(llguidance llguidance_ext) + + target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH}) + # Add platform libraries to the main target + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS}) +endif () + target_include_directories(${TARGET} PUBLIC .) -target_compile_features (${TARGET} PUBLIC cxx_std_11) +target_compile_features (${TARGET} PUBLIC cxx_std_17) target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads) diff --git a/common/arg.cpp b/common/arg.cpp index 32240f21f2469..3c169b5b5f48e 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2,6 +2,7 @@ #include "log.h" #include "sampling.h" +#include "chat.h" #include #include @@ -22,6 +23,11 @@ common_arg & common_arg::set_examples(std::initializer_list return *this; } +common_arg & common_arg::set_excludes(std::initializer_list excludes) { + this->excludes = std::move(excludes); + return *this; +} + common_arg & common_arg::set_env(const char * env) { help = help + "\n(env: " + env + ")"; this->env = env; @@ -37,6 +43,10 @@ bool common_arg::in_example(enum llama_example ex) { return examples.find(ex) != examples.end(); } +bool common_arg::is_exclude(enum llama_example ex) { + return excludes.find(ex) != excludes.end(); +} + bool common_arg::get_value_from_env(std::string & output) { if (env == nullptr) return false; char * value = std::getenv(env); @@ -119,28 +129,75 @@ std::string common_arg::to_string() { // utils // -static void common_params_handle_model_default(common_params & params) { - if (!params.hf_repo.empty()) { +static void common_params_handle_model_default( + std::string & model, + const std::string & model_url, + std::string & hf_repo, + std::string & hf_file, + const std::string & hf_token, + const std::string & model_default) { + if (!hf_repo.empty()) { // short-hand to avoid specifying --hf-file -> default it to --model - if (params.hf_file.empty()) { - if (params.model.empty()) { - throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); + if (hf_file.empty()) { + if (model.empty()) { + auto auto_detected = common_get_hf_file(hf_repo, hf_token); + if (auto_detected.first.empty() || auto_detected.second.empty()) { + exit(1); // built without CURL, error message already printed + } + hf_repo = auto_detected.first; + hf_file = auto_detected.second; + } else { + hf_file = model; } - params.hf_file = params.model; - } else if (params.model.empty()) { - params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); } - } else if (!params.model_url.empty()) { - if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); + // make sure model path is present (for caching purposes) + if (model.empty()) { + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = hf_repo + "_" + hf_file; + // to make sure we don't have any slashes in the filename + string_replace_all(filename, "/", "_"); + model = fs_get_cache_file(filename); + } + } else if (!model_url.empty()) { + if (model.empty()) { + auto f = string_split(model_url, '#').front(); f = string_split(f, '?').front(); - params.model = fs_get_cache_file(string_split(f, '/').back()); + model = fs_get_cache_file(string_split(f, '/').back()); } - } else if (params.model.empty()) { - params.model = DEFAULT_MODEL_PATH; + } else if (model.empty()) { + model = model_default; } } +const std::vector kv_cache_types = { + GGML_TYPE_F32, + GGML_TYPE_F16, + GGML_TYPE_BF16, + GGML_TYPE_Q8_0, + GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, + GGML_TYPE_IQ4_NL, + GGML_TYPE_Q5_0, + GGML_TYPE_Q5_1, +}; + +static ggml_type kv_cache_type_from_str(const std::string & s) { + for (const auto & type : kv_cache_types) { + if (ggml_type_name(type) == s) { + return type; + } + } + throw std::runtime_error("Unsupported cache type: " + s); +} + +static std::string get_all_kv_cache_types() { + std::ostringstream msg; + for (const auto & type : kv_cache_types) { + msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", "); + } + return msg.str(); +} + // // CLI argument parsing functions // @@ -243,7 +300,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } - common_params_handle_model_default(params); + // TODO: refactor model params in a common struct + common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file, params.hf_token, DEFAULT_MODEL_PATH); + common_params_handle_model_default(params.speculative.model, params.speculative.model_url, params.speculative.hf_repo, params.speculative.hf_file, params.hf_token, ""); + common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file, params.hf_token, ""); if (params.escape) { string_process_escapes(params.prompt); @@ -266,6 +326,14 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both"); } + if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) { + throw std::runtime_error(string_format( + "error: the supplied chat template is not supported: %s%s\n", + params.chat_template.c_str(), + params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates" + )); + } + return true; } @@ -298,6 +366,157 @@ static void common_params_print_usage(common_params_context & ctx_arg) { print_options(specific_options); } +static void common_params_print_completion(common_params_context & ctx_arg) { + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + + for (auto & opt : ctx_arg.options) { + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + + printf("_llama_completions() {\n"); + printf(" local cur prev opts\n"); + printf(" COMPREPLY=()\n"); + printf(" cur=\"${COMP_WORDS[COMP_CWORD]}\"\n"); + printf(" prev=\"${COMP_WORDS[COMP_CWORD-1]}\"\n\n"); + + printf(" opts=\""); + auto print_options = [](const std::vector & options) { + for (const common_arg * opt : options) { + for (const char * arg : opt->args) { + printf("%s ", arg); + } + } + }; + + print_options(common_options); + print_options(sparam_options); + print_options(specific_options); + printf("\"\n\n"); + + printf(" case \"$prev\" in\n"); + printf(" --model)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --grammar-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gbnf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --chat-template-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.jinja' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" *)\n"); + printf(" COMPREPLY=( $(compgen -W \"${opts}\" -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" esac\n"); + printf("}\n\n"); + + std::set executables = { + "llama-batched", + "llama-batched-bench", + "llama-bench", + "llama-cli", + "llama-convert-llama2c-to-ggml", + "llama-cvector-generator", + "llama-embedding", + "llama-eval-callback", + "llama-export-lora", + "llama-gbnf-validator", + "llama-gen-docs", + "llama-gguf", + "llama-gguf-hash", + "llama-gguf-split", + "llama-gritlm", + "llama-imatrix", + "llama-infill", + "llama-llava-cli", + "llama-llava-clip-quantize-cli", + "llama-lookahead", + "llama-lookup", + "llama-lookup-create", + "llama-lookup-merge", + "llama-lookup-stats", + "llama-minicpmv-cli", + "llama-parallel", + "llama-passkey", + "llama-perplexity", + "llama-q8dot", + "llama-quantize", + "llama-quantize-stats", + "llama-qwen2vl-cli", + "llama-retrieval", + "llama-run", + "llama-save-load-state", + "llama-server", + "llama-simple", + "llama-simple-chat", + "llama-speculative", + "llama-speculative-simple", + "llama-tokenize", + "llama-tts", + "llama-vdot" + }; + + for (const auto& exe : executables) { + printf("complete -F _llama_completions %s\n", exe.c_str()); + } +} + +static std::vector parse_device_list(const std::string & value) { + std::vector devices; + auto dev_names = string_split(value, ','); + if (dev_names.empty()) { + throw std::invalid_argument("no devices specified"); + } + if (dev_names.size() == 1 && dev_names[0] == "none") { + devices.push_back(nullptr); + } else { + for (const auto & device : dev_names) { + auto * dev = ggml_backend_dev_by_name(device.c_str()); + if (!dev || ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) { + throw std::invalid_argument(string_format("invalid device: %s", device.c_str())); + } + devices.push_back(dev); + } + devices.push_back(nullptr); + } + return devices; +} + +static void add_rpc_devices(std::string servers) { + auto rpc_servers = string_split(servers, ','); + if (rpc_servers.empty()) { + throw std::invalid_argument("no RPC servers specified"); + } + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + throw std::invalid_argument("failed to find RPC backend"); + } + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); + ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); + if (!ggml_backend_rpc_add_device_fn) { + throw std::invalid_argument("failed to find RPC device add function"); + } + for (const auto & server : rpc_servers) { + ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str()); + if (dev) { + ggml_backend_device_register(dev); + } else { + throw std::invalid_argument("failed to register RPC device"); + } + } +} + bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) { auto ctx_arg = common_params_parser_init(params, ex, print_usage); const common_params params_org = ctx_arg.params; // the example can modify the default params @@ -314,6 +533,10 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e } exit(0); } + if (ctx_arg.params.completion) { + common_params_print_completion(ctx_arg); + exit(0); + } } catch (const std::invalid_argument & ex) { fprintf(stderr, "%s\n", ex.what()); ctx_arg.params = params_org; @@ -323,7 +546,22 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e return true; } +static std::string list_builtin_chat_templates() { + std::vector supported_tmpl; + int32_t res = llama_chat_builtin_templates(nullptr, 0); + supported_tmpl.resize(res); + res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); + std::ostringstream msg; + for (auto & tmpl : supported_tmpl) { + msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", "); + } + return msg.str(); +} + common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + // load dynamic backends + ggml_backend_load_all(); + common_params_context ctx_arg(params); ctx_arg.print_usage = print_usage; ctx_arg.ex = ex; @@ -345,7 +583,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example */ auto add_opt = [&](common_arg arg) { - if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) { + if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) { ctx_arg.options.push_back(std::move(arg)); } }; @@ -367,6 +605,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex exit(0); } )); + add_opt(common_arg( + {"--completion-bash"}, + "print source-able bash completion script for llama.cpp", + [](common_params & params) { + params.completion = true; + } + )); add_opt(common_arg( {"--verbose-prompt"}, string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"), @@ -547,11 +792,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex )); add_opt(common_arg( {"--no-context-shift"}, - string_format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), + string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), [](common_params & params) { params.ctx_shift = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(common_arg( {"--chunks"}, "N", string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), @@ -574,7 +819,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.prompt = value; } - )); + ).set_excludes({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"--no-perf"}, string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), @@ -598,7 +843,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.prompt.pop_back(); } } - )); + ).set_excludes({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"--in-file"}, "FNAME", "an input file (repeat to specify multiple files)", @@ -625,7 +870,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.prompt = ss.str(); fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str()); } - )); + ).set_excludes({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-e", "--escape"}, string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), @@ -684,15 +929,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-cnv", "--conversation"}, - string_format( - "run in conversation mode:\n" - "- does not print special tokens and suffix/prefix\n" - "- interactive mode is also enabled\n" - "(default: %s)", - params.conversation ? "true" : "false" - ), + "run in conversation mode:\n" + "- does not print special tokens and suffix/prefix\n" + "- interactive mode is also enabled\n" + "(default: auto enabled if chat template is available)", + [](common_params & params) { + params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-no-cnv", "--no-conversation"}, + "force disable conversation mode (default: false)", [](common_params & params) { - params.conversation = true; + params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; } ).set_examples({LLAMA_EXAMPLE_MAIN})); add_opt(common_arg( @@ -746,7 +995,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.warmup = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN})); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING})); add_opt(common_arg( {"--spm-infill"}, string_format( @@ -773,7 +1022,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--sampling-seq"}, "SEQUENCE", + {"--sampling-seq", "--sampler-seq"}, "SEQUENCE", string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), [](common_params & params, const std::string & value) { params.sampling.samplers = common_sampler_types_from_chars(value); @@ -786,13 +1035,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sampling.ignore_eos = true; } ).set_sparam()); - add_opt(common_arg( - {"--penalize-nl"}, - string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"), - [](common_params & params) { - params.sampling.penalize_nl = true; - } - ).set_sparam()); add_opt(common_arg( {"--temp"}, "N", string_format("temperature (default: %.1f)", (double)params.sampling.temp), @@ -822,6 +1064,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sampling.min_p = std::stof(value); } ).set_sparam()); + add_opt(common_arg( + {"--top-nsigma"}, "N", + string_format("top-n-sigma sampling (default: %.1f, -1.0 = disabled)", params.sampling.top_n_sigma), + [](common_params & params, const std::string & value) { + params.sampling.top_n_sigma = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_MAIN}).set_sparam()); add_opt(common_arg( {"--xtc-probability"}, "N", string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability), @@ -847,6 +1096,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--repeat-last-n"}, "N", string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n), [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value)); + } params.sampling.penalty_last_n = value; params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n); } @@ -901,6 +1153,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--dry-penalty-last-n"}, "N", string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n), [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value)); + } params.sampling.dry_penalty_last_n = value; } ).set_sparam()); @@ -1134,18 +1389,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); add_opt(common_arg( {"-ctk", "--cache-type-k"}, "TYPE", - string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()), + string_format( + "KV cache data type for K\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_k) + ), [](common_params & params, const std::string & value) { - // TODO: get the type right here - params.cache_type_k = value; + params.cache_type_k = kv_cache_type_from_str(value); } ).set_env("LLAMA_ARG_CACHE_TYPE_K")); add_opt(common_arg( {"-ctv", "--cache-type-v"}, "TYPE", - string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()), + string_format( + "KV cache data type for V\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_v) + ), [](common_params & params, const std::string & value) { - // TODO: get the type right here - params.cache_type_v = value; + params.cache_type_v = kv_cache_type_from_str(value); } ).set_env("LLAMA_ARG_CACHE_TYPE_V")); add_opt(common_arg( @@ -1279,7 +1544,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--rpc"}, "SERVERS", "comma separated list of RPC servers", [](common_params & params, const std::string & value) { - params.rpc_servers = value; + add_rpc_devices(value); + GGML_UNUSED(params); } ).set_env("LLAMA_ARG_RPC")); } @@ -1304,7 +1570,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "- isolate: only spawn threads on CPUs on the node that execution started on\n" "- numactl: use the CPU map provided by numactl\n" "if run without this previously, it is recommended to drop the system page cache before using this\n" - "see https://github.com/ggerganov/llama.cpp/issues/1437", + "see https://github.com/ggml-org/llama.cpp/issues/1437", [](common_params & params, const std::string & value) { /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } @@ -1312,14 +1578,52 @@ common_params_context common_params_parser_init(common_params & params, llama_ex else { throw std::invalid_argument("invalid value"); } } ).set_env("LLAMA_ARG_NUMA")); + add_opt(common_arg( + {"-dev", "--device"}, "", + "comma-separated list of devices to use for offloading (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.devices = parse_device_list(value); + } + ).set_env("LLAMA_ARG_DEVICE")); + add_opt(common_arg( + {"--list-devices"}, + "print list of available devices and exit", + [](common_params &) { + std::vector rpc_devices; + std::vector all_devices; + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); + if (ggml_backend_reg_name(reg) == std::string("RPC")) { + rpc_devices.push_back(dev); + } else { + all_devices.push_back(dev); + } + } + } + // insert RPC devices in front + all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end()); + printf("Available devices:\n"); + for (size_t i = 0; i < all_devices.size(); ++i) { + auto * dev = all_devices[i]; + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); + } + exit(0); + } + )); add_opt(common_arg( {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", "number of layers to store in VRAM", [](common_params & params, int value) { params.n_gpu_layers = value; if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } ).set_env("LLAMA_ARG_N_GPU_LAYERS")); @@ -1336,10 +1640,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { -#ifdef GGML_USE_SYCL - fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); - exit(1); -#endif // GGML_USE_SYCL params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { throw std::invalid_argument("invalid value"); @@ -1407,7 +1707,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--lora"}, "FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)", [](common_params & params, const std::string & value) { - params.lora_adapters.push_back({ std::string(value), 1.0 }); + params.lora_adapters.push_back({ std::string(value), 1.0, nullptr }); } // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); @@ -1415,7 +1715,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--lora-scaled"}, "FNAME", "SCALE", "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)", [](common_params & params, const std::string & fname, const std::string & scale) { - params.lora_adapters.push_back({ fname, std::stof(scale) }); + params.lora_adapters.push_back({ fname, std::stof(scale), nullptr }); } // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); @@ -1469,19 +1769,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_ARG_MODEL_URL")); add_opt(common_arg( - {"-hfr", "--hf-repo"}, "REPO", - "Hugging Face model repository (default: unused)", + {"-hf", "-hfr", "--hf-repo"}, "/[:quant]", + "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n" + "example: unsloth/phi-4-GGUF:q4_k_m\n" + "(default: unused)", [](common_params & params, const std::string & value) { params.hf_repo = value; } ).set_env("LLAMA_ARG_HF_REPO")); + add_opt(common_arg( + {"-hfd", "-hfrd", "--hf-repo-draft"}, "/[:quant]", + "Same as --hf-repo, but for the draft model (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.hf_repo = value; + } + ).set_env("LLAMA_ARG_HFD_REPO")); add_opt(common_arg( {"-hff", "--hf-file"}, "FILE", - "Hugging Face model file (default: unused)", + "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)", [](common_params & params, const std::string & value) { params.hf_file = value; } ).set_env("LLAMA_ARG_HF_FILE")); + add_opt(common_arg( + {"-hfv", "-hfrv", "--hf-repo-v"}, "/[:quant]", + "Hugging Face model repository for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO_V")); + add_opt(common_arg( + {"-hffv", "--hf-file-v"}, "FILE", + "Hugging Face model file for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE_V")); add_opt(common_arg( {"-hft", "--hf-token"}, "TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)", @@ -1650,6 +1973,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.public_path = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); + add_opt(common_arg( + {"--no-webui"}, + string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params) { + params.webui = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI")); add_opt(common_arg( {"--embedding", "--embeddings"}, string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), @@ -1763,22 +2093,55 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--jinja"}, + "use jinja template for chat (default: disabled)", + [](common_params & params) { + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA")); + add_opt(common_arg( + {"--reasoning-format"}, "FORMAT", + "reasoning format (default: deepseek; allowed values: deepseek, none)\n" + "controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).\n" + "only supported for non-streamed responses", + [](common_params & params, const std::string & value) { + /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; } + else if (value == "none") { params.reasoning_format = COMMON_REASONING_FORMAT_NONE; } + else { std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK")); add_opt(common_arg( {"--chat-template"}, "JINJA_TEMPLATE", - "set custom jinja chat template (default: template taken from model's metadata)\n" - "if suffix/prefix are specified, template will be disabled\n" - "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template", - [](common_params & params, const std::string & value) { - if (!common_chat_verify_template(value)) { - throw std::runtime_error(string_format( - "error: the supplied chat template is not supported: %s\n" - "note: llama.cpp does not use jinja parser, we only support commonly used templates\n", - value.c_str() - )); - } + string_format( + "set custom jinja chat template (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { params.chat_template = value; } ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); + add_opt(common_arg( + {"--chat-template-file"}, "JINJA_TEMPLATE_FILE", + string_format( + "set custom jinja chat template file (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(params.chat_template)); + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), @@ -1885,7 +2248,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_env("LLAMA_LOG_VERBOSITY")); add_opt(common_arg( {"--log-prefix"}, - "Enable prefx in log messages", + "Enable prefix in log messages", [](common_params &) { common_log_set_prefix(common_log_main(), true); } @@ -2013,34 +2376,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.speculative.n_max = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX")); add_opt(common_arg( {"--draft-min", "--draft-n-min"}, "N", string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min), [](common_params & params, int value) { params.speculative.n_min = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN")); add_opt(common_arg( {"--draft-p-split"}, "P", string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split), [](common_params & params, const std::string & value) { params.speculative.p_split = std::stof(value); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT")); add_opt(common_arg( {"--draft-p-min"}, "P", string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min), [](common_params & params, const std::string & value) { params.speculative.p_min = std::stof(value); } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN")); add_opt(common_arg( {"-cd", "--ctx-size-draft"}, "N", string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx), [](common_params & params, int value) { params.speculative.n_ctx = value; } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); + add_opt(common_arg( + {"-devd", "--device-draft"}, "", + "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.speculative.devices = parse_device_list(value); + } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", @@ -2048,18 +2419,136 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.speculative.n_gpu_layers = value; if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); add_opt(common_arg( {"-md", "--model-draft"}, "FNAME", "draft model for speculative decoding (default: unused)", [](common_params & params, const std::string & value) { params.speculative.model = value; } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT")); + + add_opt(common_arg( + {"-mv", "--model-vocoder"}, "FNAME", + "vocoder model for audio generation (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-use-guide-tokens"}, + "Use guide tokens to improve TTS word recall", + [](common_params & params) { + params.vocoder.use_guide_tokens = true; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + + // model-specific + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + add_opt(common_arg( + {"--embd-bge-small-en-default"}, + string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF"; + params.hf_file = "bge-small-en-v1.5-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-e5-small-en-default"}, + string_format("use default e5-small-v2 model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF"; + params.hf_file = "e5-small-v2-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-gte-small-default"}, + string_format("use default gte-small model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF"; + params.hf_file = "gte-small-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-1.5b-default"}, + string_format("use default Qwen 2.5 Coder 1.5B (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF"; + params.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-3b-default"}, + string_format("use default Qwen 2.5 Coder 3B (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF"; + params.hf_file = "qwen2.5-coder-3b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-7b-default"}, + string_format("use default Qwen 2.5 Coder 7B (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"; + params.hf_file = "qwen2.5-coder-7b-q8_0.gguf"; + params.port = 8012; + params.n_gpu_layers = 99; + params.flash_attn = true; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); return ctx_arg; } diff --git a/common/arg.h b/common/arg.h index a6700d323cc14..49ab8667b1052 100644 --- a/common/arg.h +++ b/common/arg.h @@ -12,6 +12,7 @@ struct common_arg { std::set examples = {LLAMA_EXAMPLE_COMMON}; + std::set excludes = {}; std::vector args; const char * value_hint = nullptr; // help text or example for arg value const char * value_hint_2 = nullptr; // for second arg value @@ -53,9 +54,11 @@ struct common_arg { ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {} common_arg & set_examples(std::initializer_list examples); + common_arg & set_excludes(std::initializer_list excludes); common_arg & set_env(const char * env); common_arg & set_sparam(); bool in_example(enum llama_example ex); + bool is_exclude(enum llama_example ex); bool get_value_from_env(std::string & output); bool has_value_from_env(); std::string to_string(); diff --git a/common/chat.cpp b/common/chat.cpp new file mode 100644 index 0000000000000..9ebe4c5784cbc --- /dev/null +++ b/common/chat.cpp @@ -0,0 +1,1601 @@ +#include "chat.h" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "minja/chat-template.hpp" +#include "minja/minja.hpp" + +#include + +typedef minja::chat_template common_chat_template; + +struct common_chat_templates { + bool has_explicit_template; // Model had builtin template or template overridde was specified. + std::unique_ptr template_default; // always set (defaults to chatml) + std::unique_ptr template_tool_use; +}; + +struct templates_params { + json messages; + json tools; + common_chat_tool_choice tool_choice; + json json_schema; + bool parallel_tool_calls; + bool stream; + std::string grammar; + bool add_generation_prompt = true; + bool extract_reasoning = true; +}; + +common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) { + if (tool_choice == "auto") { + return COMMON_CHAT_TOOL_CHOICE_AUTO; + } + if (tool_choice == "none") { + return COMMON_CHAT_TOOL_CHOICE_NONE; + } + if (tool_choice == "required") { + return COMMON_CHAT_TOOL_CHOICE_REQUIRED; + } + throw std::runtime_error("Invalid tool_choice: " + tool_choice); +} + +template <> +std::vector common_chat_msgs_parse_oaicompat(const json & messages) { + std::vector msgs; + + try { + + if (!messages.is_array()) { + throw std::runtime_error("Expected 'messages' to be an array, got " + messages.dump()); + } + + for (const auto & message : messages) { + if (!message.is_object()) { + throw std::runtime_error("Expected 'message' to be an object, got " + message.dump()); + } + + common_chat_msg msg; + if (!message.contains("role")) { + throw std::runtime_error("Missing 'role' in message: " + message.dump()); + } + msg.role = message.at("role"); + + if (message.contains("content")) { + const auto & content = message.at("content"); + if (content.is_string()) { + msg.content = content; + } else if (content.is_array()) { + for (const auto & part : content) { + if (!part.contains("type")) { + throw std::runtime_error("Missing content part type: " + part.dump()); + } + const auto & type = part.at("type"); + if (type != "text") { + throw std::runtime_error("Unsupported content part type: " + type.dump()); + } + common_chat_msg_content_part msg_part; + msg_part.type = type; + msg_part.text = part.at("text"); + msg.content_parts.push_back(msg_part); + } + } else if (!content.is_null()) { + throw std::runtime_error("Invalid 'content' type: expected string or array, got " + content.dump() + " (ref: https://github.com/ggml-org/llama.cpp/issues/8367)"); + } + } else { + throw std::runtime_error("Expected 'content' (ref: https://github.com/ggml-org/llama.cpp/issues/8367)"); + } + if (message.contains("reasoning_content")) { + msg.reasoning_content = message.at("reasoning_content"); + } + if (message.contains("name")) { + msg.tool_name = message.at("name"); + } + if (message.contains("tool_call_id")) { + msg.tool_call_id = message.at("tool_call_id"); + } + if (message.contains("tool_calls")) { + for (const auto & tool_call : message.at("tool_calls")) { + common_chat_tool_call tc; + if (!tool_call.contains("type")) { + throw std::runtime_error("Missing tool call type: " + tool_call.dump()); + } + const auto & type = tool_call.at("type"); + if (type != "function") { + throw std::runtime_error("Unsupported tool call type: " + tool_call.dump()); + } + if (!tool_call.contains("function")) { + throw std::runtime_error("Missing tool call function: " + tool_call.dump()); + } + const auto & fc = tool_call.at("function"); + if (!fc.contains("name")) { + throw std::runtime_error("Missing tool call name: " + tool_call.dump()); + } + tc.name = fc.at("name"); + tc.arguments = fc.at("arguments"); + if (tool_call.contains("id")) { + tc.id = tool_call.at("id"); + } + msg.tool_calls.push_back(tc); + } + } + + msgs.push_back(msg); + } + } catch (const std::exception & e) { + throw std::runtime_error("Failed to parse messages: " + std::string(e.what()) + "; messages = " + messages.dump(2)); + } + + return msgs; +} + +template <> +json common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text) { + json messages = json::array(); + for (const auto & msg : msgs) { + if (!msg.content.empty() && !msg.content_parts.empty()) { + throw std::runtime_error("Cannot specify both content and content_parts"); + } + json jmsg { + {"role", msg.role}, + }; + if (!msg.content.empty()) { + jmsg["content"] = msg.content; + } else if (!msg.content_parts.empty()) { + if (concat_typed_text) { + std::string text; + for (const auto & part : msg.content_parts) { + if (part.type != "text") { + LOG_WRN("Ignoring content part type: %s\n", part.type.c_str()); + continue; + } + if (!text.empty()) { + text += '\n'; + } + text += part.text; + } + jmsg["content"] = text; + } else { + auto & parts = jmsg["content"] = json::array(); + for (const auto & part : msg.content_parts) { + parts.push_back({ + {"type", part.type}, + {"text", part.text}, + }); + } + } + } else { + jmsg["content"] = json(); // null + } + if (!msg.reasoning_content.empty()) { + jmsg["reasoning_content"] = msg.reasoning_content; + } + if (!msg.tool_name.empty()) { + jmsg["name"] = msg.tool_name; + } + if (!msg.tool_call_id.empty()) { + jmsg["tool_call_id"] = msg.tool_call_id; + } + if (!msg.tool_calls.empty()) { + auto & tool_calls = jmsg["tool_calls"] = json::array(); + for (const auto & tool_call : msg.tool_calls) { + json tc { + {"type", "function"}, + {"function", { + {"name", tool_call.name}, + {"arguments", tool_call.arguments}, + }}, + }; + if (!tool_call.id.empty()) { + tc["id"] = tool_call.id; + } + tool_calls.push_back(tc); + } + } + messages.push_back(jmsg); + } + return messages; +} + +template <> +std::vector common_chat_msgs_parse_oaicompat(const std::string & messages) { + return common_chat_msgs_parse_oaicompat(json::parse(messages)); +} + +template <> +std::vector common_chat_tools_parse_oaicompat(const json & tools) { + std::vector result; + + try { + if (!tools.is_null()) { + if (!tools.is_array()) { + throw std::runtime_error("Expected 'tools' to be an array, got " + tools.dump()); + } + for (const auto & tool : tools) { + if (!tool.contains("type")) { + throw std::runtime_error("Missing tool type: " + tool.dump()); + } + const auto & type = tool.at("type"); + if (!type.is_string() || type != "function") { + throw std::runtime_error("Unsupported tool type: " + tool.dump()); + } + if (!tool.contains("function")) { + throw std::runtime_error("Missing tool function: " + tool.dump()); + } + + const auto & function = tool.at("function"); + result.push_back({ + /* .name = */ function.at("name"), + /* .description = */ function.at("description"), + /* .parameters = */ function.at("parameters").dump(), + }); + } + } + } catch (const std::exception & e) { + throw std::runtime_error("Failed to parse tools: " + std::string(e.what()) + "; tools = " + tools.dump(2)); + } + + return result; +} + +template <> +std::vector common_chat_tools_parse_oaicompat(const std::string & tools) { + return common_chat_tools_parse_oaicompat(json::parse(tools)); +} + +template <> +json common_chat_tools_to_json_oaicompat(const std::vector & tools) { + if (tools.empty()) { + return json(); + } + + auto result = json::array(); + for (const auto & tool : tools) { + result.push_back({ + {"type", "function"}, + {"function", { + {"name", tool.name}, + {"description", tool.description}, + {"parameters", json::parse(tool.parameters)}, + }}, + }); + } + return result; +} + +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) { + if (use_jinja) { + try { + common_chat_msg msg; + msg.role = "user"; + msg.content = "test"; + + auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl); + + common_chat_templates_inputs inputs; + inputs.messages = {msg}; + + common_chat_templates_apply(tmpls.get(), inputs); + return true; + } catch (const std::exception & e) { + LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what()); + return false; + } + } + llama_chat_message chat[] = {{"user", "test"}}; + const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0); + return res >= 0; +} + +std::string common_chat_format_single( + const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja) { + + common_chat_templates_inputs inputs; + inputs.use_jinja = use_jinja; + + std::string fmt_past_msg; + if (!past_msg.empty()) { + inputs.messages = past_msg; + inputs.add_generation_prompt = false; + fmt_past_msg = common_chat_templates_apply(tmpls, inputs).prompt; + } + std::ostringstream ss; + // if the past_msg ends with a newline, we must preserve it in the formatted version + if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { + ss << "\n"; + }; + // format chat with new_msg + inputs.messages.push_back(new_msg); + inputs.add_generation_prompt = add_ass; + auto fmt_new_msg = common_chat_templates_apply(tmpls, inputs).prompt; + // get the diff part + ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); + return ss.str(); +} + +std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) { + common_chat_templates_inputs inputs; + inputs.use_jinja = use_jinja; + auto add_simple_msg = [&](auto role, auto content) { + common_chat_msg msg; + msg.role = role; + msg.content = content; + inputs.messages.push_back(msg); + }; + add_simple_msg("system", "You are a helpful assistant"); + add_simple_msg("user", "Hello"); + add_simple_msg("assistant", "Hi there"); + add_simple_msg("user", "How are you?"); + return common_chat_templates_apply(tmpls, inputs).prompt; +} + +#define CHATML_TEMPLATE_SRC \ + "{%- for message in messages -%}\n" \ + " {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \ + "{%- endfor -%}\n" \ + "{%- if add_generation_prompt -%}\n" \ + " {{- '<|im_start|>assistant\n' -}}\n" \ + "{%- endif -%}" + +void common_chat_templates_free(struct common_chat_templates * tmpls) { + delete tmpls; +} + +bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls) { + return tmpls->has_explicit_template; +} + +const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant) { + if (variant != nullptr) { + if (strcmp(variant, "tool_use") == 0) { + if (tmpls->template_tool_use) { + return tmpls->template_tool_use->source().c_str(); + } + return nullptr; + } else { + LOG_DBG("%s: unknown template variant: %s\n", __func__, variant); + } + } + return tmpls->template_default->source().c_str(); +} + +common_chat_templates_ptr common_chat_templates_init( + const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override, + const std::string & eos_token_override) +{ + std::string default_template_src; + std::string template_tool_use_src; + + bool has_explicit_template = !chat_template_override.empty(); + if (chat_template_override.empty()) { + GGML_ASSERT(model != nullptr); + const auto * str = llama_model_chat_template(model, /* name */ nullptr); + if (str) { + default_template_src = str; + has_explicit_template = true; + } + str = llama_model_chat_template(model, /* name */ "tool_use"); + if (str) { + template_tool_use_src = str; + has_explicit_template = true; + } + } else { + default_template_src = chat_template_override; + } + if (default_template_src.empty() || default_template_src == "chatml") { + if (!template_tool_use_src.empty()) { + default_template_src = template_tool_use_src; + } else { + default_template_src = CHATML_TEMPLATE_SRC; + } + } + std::string token_bos = bos_token_override; + std::string token_eos = eos_token_override; + if (model) { + const auto * vocab = llama_model_get_vocab(model); + const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) { + if (token == LLAMA_TOKEN_NULL) { + if (default_template_src.find(jinja_variable_name) != std::string::npos + || template_tool_use_src.find(jinja_variable_name) != std::string::npos) { + LOG_WRN("common_chat_templates_init: warning: vocab does not have a %s token, jinja template won't work as intended.\n", name); + } + return std::string(); + } + return common_token_to_piece(vocab, token, true); + }; + token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token"); + token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token"); + } + common_chat_templates_ptr tmpls(new common_chat_templates()); + tmpls->has_explicit_template = has_explicit_template; + try { + tmpls->template_default = std::make_unique(default_template_src, token_bos, token_eos); + } catch (const std::exception & e) { + LOG_ERR("%s: failed to parse chat template (defaulting to chatml): %s \n", __func__, e.what()); + tmpls->template_default = std::make_unique(CHATML_TEMPLATE_SRC, token_bos, token_eos); + } + if (!template_tool_use_src.empty()) { + try { + tmpls->template_tool_use = std::make_unique(template_tool_use_src, token_bos, token_eos); + } catch (const std::exception & e) { + LOG_ERR("%s: failed to parse tool use chat template (ignoring it): %s\n", __func__, e.what()); + } + } + return tmpls; +} + +std::string common_chat_format_name(common_chat_format format) { + switch (format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only"; + case COMMON_CHAT_FORMAT_GENERIC: return "Generic"; + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo"; + case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x"; + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools"; + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1"; + case COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING: return "DeepSeek R1 (extract reasoning)"; + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1"; + case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro"; + case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B"; + case COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING: return "Command R7B (extract reasoning)"; + default: + throw std::runtime_error("Unknown chat format"); + } +} + +const common_grammar_options grammar_options { + /* .dotall = */ false, + /* .compact_spaces = */ false, + // /* .compact_spaces = */ true, +}; + +static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) { + // // https://json.nlohmann.me/features/parsing/sax_interface/ + struct json_error_locator : public nlohmann::json_sax { + std::size_t position; + bool found_error; + + json_error_locator() : position(0), found_error(false) {} + + bool parse_error(std::size_t position, const std::string &, const json::exception &) override { // NOLINT + this->position = position - 1; + this->found_error = true; + return false; + } + bool null() override { return true; } // NOLINT + bool boolean(bool) override { return true; } // NOLINT + bool number_integer(number_integer_t) override { return true; } // NOLINT + bool number_unsigned(number_unsigned_t) override { return true; } // NOLINT + bool number_float(number_float_t, const string_t &) override { return true; } // NOLINT + bool string(string_t &) override { return true; } // NOLINT + bool binary(binary_t &) override { return true; } // NOLINT + bool start_object(std::size_t) override { return true; } // NOLINT + bool key(string_t &) override { return true; } // NOLINT + bool end_object() override { return true; } + bool start_array(std::size_t) override { return true; } // NOLINT + bool end_array() override { return true; } + }; + json_error_locator err_loc; + json::sax_parse(it, end, &err_loc); + + std::string::const_iterator temptative_end; + if (err_loc.found_error) { + temptative_end = it + err_loc.position; + } else { + temptative_end = end; + } + std::string json_sub {it, temptative_end}; + try { + out = json::parse(json_sub); + it = temptative_end; + return true; + } catch (const std::exception &) { + return false; + } +} + + +/** + * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between. + * Aggregates the prefix, suffix and in-between text into the content. + */ +static common_chat_msg parse_json_tool_calls( + const std::string& input, + const std::optional & trigger_opt, + const std::regex & function_regex, + const std::regex & close_regex) { + std::smatch match; + + common_chat_msg result; + result.role = "assistant"; + + + auto end = input.end(); + auto it = input.begin(); + + if (trigger_opt) { + if (!std::regex_search(it, end, match, *trigger_opt)) { + result.content = input; + return result; + } + result.content = match.prefix().str(); + it = match.suffix().first; + } + + while (it != end) { + std::sregex_iterator rend; + std::sregex_iterator rit(it, end, function_regex); + if (rit == rend) { + result.content += std::string(it, end); + break; + } + auto name = rit->str(1); + result.content += std::string(it, rit->prefix().second); + it = rit->suffix().first; + + json arguments; + if (!parse_json(it, end, arguments)) { + throw std::runtime_error("Failed to parse json tool call arguments: " + input); + } + if (!std::regex_search(it, end, match, close_regex)) { + throw std::runtime_error("Malformed input, missing closing pattern: " + input); + } + it = match.suffix().first; + result.tool_calls.push_back({name, arguments.is_string() ? arguments.get() : arguments.dump(), /* id= */ ""}); + } + + if (!result.tool_calls.empty()) { + if (!string_strip(result.content).empty()) { + LOG_WRN("Content found with tool calls: %s\n", result.content.c_str()); + } + result.content = ""; + } + return result; +} + +static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) { + auto content_end = input.find(prefix); + size_t tc_start = std::string::npos; + + common_chat_msg result; + result.role = "assistant"; + const auto process_tool_calls = [&](const json & tool_calls) { + for (const auto & tool_call : tool_calls) { + const auto & arguments = tool_call.at("arguments"); + result.tool_calls.push_back({ + tool_call.at("name"), + arguments.is_string() ? arguments.get() : arguments.dump(), + tool_call.contains("id") ? tool_call.at("id") : "", + }); + } + }; + if (content_end == std::string::npos) { + result.content = input; + } else { + tc_start = content_end + prefix.size() - rstrip_prefix; + result.content = input.substr(0, content_end); + auto tool_calls = json::parse(input.substr(tc_start)); + process_tool_calls(tool_calls); + } + return result; +} + +static void foreach_function(const json & tools, const std::function & fn) { + for (const auto & tool : tools) { + if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) { + LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str()); + continue; + } + fn(tool); + } +} + +static std::string apply( + const common_chat_template & tmpl, + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) +{ + minja::chat_template_inputs tmpl_inputs; + tmpl_inputs.messages = messages; + tmpl_inputs.tools = tools; + tmpl_inputs.add_generation_prompt = add_generation_prompt; + tmpl_inputs.extra_context = extra_context; + // TODO: add flag to control date/time, if only for testing purposes. + // tmpl_inputs.now = std::chrono::system_clock::now(); + + minja::chat_template_options tmpl_opts; + // To avoid double BOS / EOS tokens, we're manually removing begining / trailing tokens + // instead of using `chat_template_options.use_bos_token = false`, since these tokens + // may be needed inside the template / between messages too. + auto result = tmpl.apply(tmpl_inputs, tmpl_opts); + if (string_starts_with(result, tmpl.bos_token())) { + result = result.substr(tmpl.bos_token().size()); + } + if (string_ends_with(result, tmpl.eos_token())) { + result = result.substr(0, result.size() - tmpl.eos_token().size()); + } + return result; +} + +static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + + auto tool_call_schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto tool_schema = json { + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + }}, + {"required", json::array({"name", "arguments"})}, + }; + if (function.contains("description")) { + tool_schema["description"] = function.at("description"); + } + if (inputs.parallel_tool_calls) { + tool_schema.at("properties")["id"] = { + {"type", "string"}, + {"minLength", 4}, + }; + tool_schema.at("required").push_back("id"); + } + tool_call_schemas.emplace_back(tool_schema); + }); + const auto tool_call = + inputs.parallel_tool_calls + ? json { + {"type", "object"}, + {"properties", { + {"tool_calls", { + {"type", "array"}, + {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + {"minItems", 1}, + }}, + }}, + {"required", json::array({"tool_calls"})}, + } + : json { + {"type", "object"}, + {"properties", { + {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + }}, + {"required", json::array({"tool_call"})}, + }; + const auto schema = + inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED + ? json { + {"anyOf", json::array({ + tool_call, + { + {"type", "object"}, + {"properties", { + {"response", inputs.json_schema.is_null() + ? json {{"type", "string"}} + : inputs.json_schema + }, + }}, + {"required", json::array({"response"})}, + }, + })} + } + : tool_call; + + data.grammar_lazy = false; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + builder.add_schema("root", schema); + }, grammar_options); + + auto tweaked_messages = common_chat_template::add_system( + inputs.messages, + "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request"); + + data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_GENERIC; + return data; +} +static common_chat_msg common_chat_parse_generic(const std::string & input) { + json data = json::parse(input); + common_chat_msg result; + result.role = "assistant"; + if (data.contains("tool_calls")) { + for (const auto & tool_call : data.at("tool_calls")) { + result.tool_calls.push_back({ + tool_call.at("name"), + tool_call.at("arguments").dump(), + tool_call.contains("id") ? tool_call.at("id") : "", + }); + } + } else if (data.contains("tool_call")) { + result.tool_calls.push_back({ + data.at("tool_call").at("name"), + data.at("tool_call").at("arguments").dump(), + /* id= */ "", + }); + } else if (data.contains("response")) { + const auto & response = data.at("response"); + result.content = response.is_string() ? response.get() : response.dump(2); + } + return result; +} + +static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + // Important note: the model is probably trained to take a JSON stringified arguments value. + // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object. + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + {"id", { + {"type", "string"}, + // Nemo's template expects a 9-character alphanumeric ID. + {"pattern", "^[a-zA-Z0-9]{9}$"}, + }}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema)); + }, grammar_options); + data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true}); + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO; + return data; +} +static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) { + return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]"); +} + +static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"tool_call_id", { + {"type", "string"}, + // Command-R's template expects an integer string. + {"pattern", "^[0-9]{1,10}$"}, + }}, + {"tool_name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"parameters", function.at("parameters")}, + }}, + {"required", json::array({"tool_call_id", "tool_name", "parameters"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\""); + }, grammar_options); + data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false}); + data.preserved_tokens = { + "<|START_RESPONSE|>", + "<|END_RESPONSE|>", + "<|START_THINKING|>", + "<|END_THINKING|>", + "<|END_ACTION|>", + }; + auto adjusted_messages = json::array(); + for (const auto & msg : inputs.messages) { + auto has_reasoning_content = msg.contains("reasoning_content") && msg.at("reasoning_content").is_string(); + auto has_tool_calls = msg.contains("tool_calls") && msg.at("tool_calls").is_array(); + if (has_reasoning_content && has_tool_calls) { + auto adjusted_message = msg; + adjusted_message["tool_plan"] = msg.at("reasoning_content"); + adjusted_message.erase("reasoning_content"); + adjusted_messages.push_back(adjusted_message); + } else { + adjusted_messages.push_back(msg); + } + } + data.prompt = apply(tmpl, adjusted_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {}); + data.format = inputs.extract_reasoning ? COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING : COMMON_CHAT_FORMAT_COMMAND_R7B; + return data; +} +static common_chat_msg common_chat_parse_command_r7b(const std::string & input, bool extract_reasoning) { + static std::regex thought_regex("(<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|>)([\\s\\S\\n\\r]*)"); + static std::regex action_regex("<\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>"); + static std::regex response_regex("(?:<\\|START_RESPONSE\\|>)?([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>"); + + std::smatch match; + + common_chat_msg result; + result.role = "assistant"; + + std::string rest = input; + + if (std::regex_match(rest, match, thought_regex)) { + if (extract_reasoning) { + result.reasoning_content = match[2].str(); + } else if (!match[2].str().empty()) { + // Let the unparsed thinking tags through in content only if their insides aren't empty. + result.content = match[1].str(); + } + rest = match[3].str(); + } + if (std::regex_match(rest, match, action_regex)) { + auto actions_str = match[1].str(); + auto actions = json::parse(actions_str); + for (const auto & action : actions) { + result.tool_calls.push_back({ + /* .name = */ action.at("tool_name"), + /* .arguments = */ action.at("parameters").dump(), + /* .id = */ action.at("tool_call_id"), + }); + } + } else if (std::regex_match(rest, match, response_regex)) { + auto response = match[1].str(); + result.content += response; + } else { + result.content += rest; + } + return result; +} + +static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector & expected_properties) { + if (!parameters.is_object() || !parameters.contains("type") || parameters.at("type") != "object" || !parameters.contains("properties") || !parameters.contains("required")) { + throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties"); + } + const auto & parameters_properties = parameters.at("properties"); + const auto & parameters_required = parameters.at("required"); + for (const auto & prop : expected_properties) { + if (!parameters_properties.contains(prop)) { + throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop); // NOLINT + } + if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) { + throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop); // NOLINT + } + } + if (parameters_properties.size() != expected_properties.size()) { + throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", ")); + } +} + +static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct templates_params & inputs, bool allow_python_tag_builtin_tools) { + auto builtin_tools = json::array(); + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + + auto handle_builtin_tool = [&](const std::string & name, const json & parameters) { + if (name == "wolfram_alpha" || name == "web_search" || name == "brave_search") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py + expect_tool_parameters(name, parameters, {"query"}); + } else if (name == "python" || name == "code_interpreter") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py + expect_tool_parameters(name, parameters, {"code"}); + } else { + return false; + } + + std::vector kvs; + for (const auto & [key, value] : parameters.at("properties").items()) { + kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); // NOLINT + } + + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\"")); + builtin_tools.push_back(name); + + return true; + }; + + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + + // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime + if (allow_python_tag_builtin_tools) { + handle_builtin_tool(name, parameters); + } + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"{\" space " + "( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? " + "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " + + builder.add_schema(name + "-args", parameters) + + " \"}\"")); + data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true}); + }); + data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true}); + if (!builtin_tools.empty()) { + data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false}); + } + builder.add_rule("root", string_join(tool_rules, " | ")); + }, grammar_options); + data.additional_stops.push_back("<|eom_id|>"); + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, { + {"tools_in_user_message", false}, + {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools}, + }); + data.format = allow_python_tag_builtin_tools && !builtin_tools.empty() + ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS + : COMMON_CHAT_FORMAT_LLAMA_3_X; + return data; +} +static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) { + // TODO: tighten & simplify the parser, don't accept leading text context. + static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": "); + static std::regex close_regex("\\}"); + static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)"); + + if (with_builtin_tools) { + std::smatch match; + if (std::regex_match(input, match, builtin_call_regex)) { + auto name = match[1].str(); + auto raw_args = match[2].str(); + + // TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing. + auto it_eq = raw_args.find('='); + auto arg_name = raw_args.substr(0, it_eq); + auto arg_value_str = raw_args.substr(it_eq + 1); + auto arg_value = json::parse(arg_value_str); + + common_chat_msg msg; + msg.role = "assistant"; + msg.content = match.prefix().str(); + msg.tool_calls.push_back({ + /* .name = */ name, + /* .arguments = */ (json { + {arg_name, arg_value}, + }).dump(), + /* .id = */ "", + }); + return msg; + } + } + return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex); +} + +static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED && inputs.json_schema.is_null(); + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + auto args_rule = builder.add_schema(name + "-args", parameters); + tool_rules.push_back(builder.add_rule(name + "-call", + "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n" + "```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\"")); + }); + // Distill Qwen 7B & 32B models seem confused re/ syntax of their tool call opening tag, + // so we accept common variants (then it's all constrained) + builder.add_rule("root", + "( \"<｜tool▁calls▁begin｜>\" | \"<｜tool_calls_begin｜>\" | \"<｜tool calls begin｜>\" | \"<｜tool\\\\_calls\\\\_begin｜>\" ) " + "(" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " " + "\"<｜tool▁calls▁end｜>\"" + " space"); + data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false}); + data.grammar_triggers.push_back({"<｜tool_calls_begin｜>", /* .at_start = */ false}); + data.grammar_triggers.push_back({"<｜tool calls begin｜>", /* .at_start = */ false}); + data.grammar_triggers.push_back({"<｜tool\\_calls\\_begin｜>", /* .at_start = */ false}); + data.preserved_tokens = { + "", + "", + "<｜tool▁sep｜>", + "<｜tool▁calls▁end｜", + "<｜tool▁call▁end｜>", + }; + }, grammar_options); + } + auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + + // Hacks to fix the official (broken) prompt. + // It is advisable to use --chat-template-file models/templates/llama-cpp-deepseek-r1.jinja instead, + // until the official template is fixed. + if (tmpl.source().find("{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}") != std::string::npos) { + // Don't leave the chat dangling after tool results + if (string_ends_with(prompt, "<｜tool▁outputs▁end｜>")) { + prompt += "<｜end▁of▁sentence｜>"; + if (inputs.add_generation_prompt) { + prompt += "<｜Assistant｜>"; + } + } + // Fix up tool call delta example added by Minja + prompt = std::regex_replace( + prompt, + std::regex("(<｜tool▁call▁end｜>)[\\s\\r\\n]*(<｜tool▁outputs▁begin｜>|<｜User｜>)"), + "$1<｜tool▁calls▁end｜><｜end▁of▁sentence｜>$2"); + } + data.prompt = prompt; + data.format = inputs.extract_reasoning ? COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING : COMMON_CHAT_FORMAT_DEEPSEEK_R1; + return data; +} +static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input, bool extract_reasoning) { + static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n"); + static std::regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>"); + static std::regex reasoning_content_regex("((?:)?([\\s\\S\\r\\n]*?))?([\\s\\S\\r\\n]*)"); + static std::regex tool_calls_regex("[\\s\\r\\n]*(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>)([\\s\\S\\r\\n]*?)<｜tool▁calls▁end｜>"); + common_chat_msg msg; + msg.role = "assistant"; + std::smatch match; + if (std::regex_match(input, match, reasoning_content_regex)) { + std::string rest; + if (extract_reasoning) { + msg.reasoning_content = string_strip(match[2].str()); + } else { + msg.content = match[1].str(); + } + rest = match[3].str(); + + if (std::regex_search(rest, match, tool_calls_regex)) { + auto tool_calls = match[1].str(); + auto msg2 = parse_json_tool_calls(tool_calls, std::nullopt, function_regex, close_regex); + msg.tool_calls = std::move(msg2.tool_calls); + } else { + msg.content += std::string(rest.begin() + rest.find_first_not_of(" \r\n"), rest.end()); + } + } else { + msg.content = input; + } + return msg; +} + +static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) { + LOG_DBG("%s\n", __func__); + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, { + {"datetime", "Jan 29 2025 13:00:00 GMT"}, + {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))}, + }); + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function.at("name")}, + }}, + {"arguments", function.at("parameters")}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema)); + }, grammar_options); + data.grammar_triggers.push_back({" functools[", /* .at_start = */ false}); + data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2; + } else { + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + } + return data; +} +static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) { + return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1); +} + +static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct templates_params & inputs) { + // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}... + // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2; + if (inputs.tools.is_array() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector first_tool_rules; + std::vector subsequent_tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + auto args_rule = builder.add_schema(name + "-args", parameters); + first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule)); + subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule)); + data.grammar_triggers.push_back({name, /* .at_start = */ true}); + data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false}); + }); + auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space"; + if (inputs.parallel_tool_calls) { + auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space"; + builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*"); + } else { + builder.add_rule("root", first_rule); + } + + }, grammar_options); + } + return data; +} + +static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) { + auto expected_it = expected.begin(); + auto tmp_it = it; + while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) { + ++tmp_it; + ++expected_it; + } + if (expected_it == expected.end()) { + it = tmp_it; + return true; + } + return false; +} + +static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) { + static std::regex function_regex(R"((?:>>>)?(\w+)\n)"); + static std::regex close_regex(R"($|(?=>>>))"); + + std::string content; + auto it = input.begin(); + const auto end = input.end(); + + if (consume(it, end, "all\n")) { + std::smatch match; + if (std::regex_search(it, end, match, function_regex)) { + auto fun_it = match.prefix().second; + content = std::string(it, fun_it); + it = fun_it; + } else { + common_chat_msg res; + res.role = "assistant"; + res.content = std::string(it, end); + return res; + } + } + // TODO: tighten & simplify. + try { + auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex); + res.content = content + res.content; + return res; + } catch (const std::exception & e) { + LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what()); + common_chat_msg res; + res.role = "assistant"; + res.content = input; + return res; + } +} + +static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct templates_params & inputs) { + // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt + common_chat_params data; + json tools = inputs.tools.is_null() ? inputs.tools : json::array(); + std::string python_code_argument_name; + auto has_raw_python = false; + + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + const auto & parameters = function.at("parameters"); + std::string name = function.at("name"); + if (name == "python" || name == "ipython") { + if (!parameters.contains("type")) { + throw std::runtime_error("Missing type in python tool"); + } + has_raw_python = true; + const auto & type = parameters.at("type"); + if (type == "object") { + auto properties = parameters.at("properties"); + for (auto it = properties.begin(); it != properties.end(); ++it) { + if (it.value().at("type") == "string") { + if (!python_code_argument_name.empty()) { + throw std::runtime_error("Multiple string arguments found in python tool"); + } + python_code_argument_name = it.key(); + } + } + if (python_code_argument_name.empty()) { + throw std::runtime_error("No string argument found in python tool"); + } + } else if (type != "string") { + throw std::runtime_error("Invalid type in python tool: " + type.dump()); + } + } + tool_rules.push_back(builder.add_rule(name + "-call", "\"\" " + builder.add_schema(name + "-args", parameters) + " \"\" space")); + }); + if (has_raw_python) { + tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*")); + data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false}); + } + auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space"; + builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); + data.grammar_triggers.push_back({"([\s\S\n]*)$)"); + std::smatch match; + if (std::regex_search(input, match, python_tag_regex)) { + auto code = match[1].str(); + common_chat_msg msg; + msg.role = "assistant"; + msg.content = match.prefix().str(); + msg.tool_calls.push_back({ + /* .name = */ "python", + /* .arguments = */ (json {{"code", code}}).dump(), + /* .id = */ "", + }); + return msg; + } + static std::regex function_regex(R"()"); + static std::regex close_regex(R"()"); + // TODO: tighten & simplify. + return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex); +} + +static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + // (content)?({"name": "foo", "arguments": {"a": 1}})* + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.at("parameters"); + builder.resolve_refs(parameters); + tool_rules.push_back(builder.add_schema(name + "-call", { + {"type", "object"}, + {"properties", json { + {"name", json {{"const", name}}}, + {"arguments", parameters}, + }}, + {"required", json::array({"name", "arguments"})}, + })); + }); + auto tool_call = "\"\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"\" space"; + builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); + data.grammar_triggers.push_back({"", /* .at_start = */ false}); + data.preserved_tokens = { "" }; + }, grammar_options); + + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO; + return data; +} +static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) { + try { + std::regex start_pattern(R"([\n\s]*)"); + std::regex middle_pattern(R"([\n\s]*[\n\s]*)"); + std::regex end_pattern(R"([\n\s]*[\n\s]*$)"); + + common_chat_msg msg; + msg.role = "assistant"; + + auto end = input.end(); + std::sregex_iterator rend; + std::sregex_iterator rit(input.begin(), end, start_pattern); + if (rit == rend) { + msg.content = input; + return msg; + } + + msg.content = rit->prefix(); + + auto it = rit->suffix().first; + while (it != end) { + json call; + if (!parse_json(it, end, call)) { + throw std::runtime_error("Failed to parse json tool call"); + } + const auto & arguments = call.at("arguments"); + msg.tool_calls.push_back({ + call.at("name"), + arguments.dump(), + // arguments.is_string() ? arguments.get() : arguments.dump(), + /* id= */ "", + }); + rit = {it, end, middle_pattern}; + if (rit != rend) { + it = rit->suffix().first; + } else { + rit = {it, end, end_pattern}; + if (rit == rend) { + throw std::runtime_error("Malformed input, missing "); + } + break; + } + } + return msg; + } catch (const std::exception & e) { + LOG_ERR("Failed to parse hermes 2 pro input: %s\n", e.what()); + common_chat_msg msg; + msg.role = "assistant"; + msg.content = input; + return msg; + } +} + +static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) { + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + data.grammar_lazy = false; + if (!inputs.json_schema.is_null()) { + if (!inputs.grammar.empty()) { + throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); + } + data.grammar = json_schema_to_grammar(inputs.json_schema); + } else { + data.grammar = inputs.grammar; + } + return data; +} + +static common_chat_params common_chat_templates_apply_jinja( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + templates_params params; + params.tools = common_chat_tools_to_json_oaicompat(inputs.tools); + const auto & tmpl = params.tools.is_array() && tmpls->template_tool_use + ? *tmpls->template_tool_use + : *tmpls->template_default; + const auto & src = tmpl.source(); + const auto & caps = tmpl.original_caps(); + params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content); + params.add_generation_prompt = inputs.add_generation_prompt; + params.extract_reasoning = inputs.extract_reasoning; + params.tool_choice = inputs.tool_choice; + params.grammar = inputs.grammar; + if (!inputs.json_schema.empty()) { + params.json_schema = json::parse(inputs.json_schema); + } + + if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) { + LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n"); + params.parallel_tool_calls = false; + } else { + params.parallel_tool_calls = inputs.parallel_tool_calls; + } + + if (params.tools.is_array()) { + if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) { + throw std::runtime_error("Cannot specify grammar with tools"); + } + if (caps.supports_tool_calls && !caps.supports_tools) { + LOG_WRN("Template supports tool calls but does not natively describe tools. The fallback behaviour used may produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n"); + } + } + + // DeepSeek R1: use handler in all cases except json schema (thinking / tools). + if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos && params.json_schema.is_null()) { + return common_chat_params_init_deepseek_r1(tmpl, params); + } + + // Command R7B: : use handler in all cases except json schema (thinking / tools). + if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos && params.json_schema.is_null()) { + return common_chat_params_init_command_r7b(tmpl, params); + } + + // Use generic handler when mixing tools + JSON schema. + // TODO: support that mix in handlers below. + if ((params.tools.is_array() && params.json_schema.is_object())) { + return common_chat_params_init_generic(tmpl, params); + } + + // Functionary prepends "all\n" to plain content outputs, so we use its handler in all cases. + if (src.find(">>>all") != std::string::npos) { + return common_chat_params_init_functionary_v3_2(tmpl, params); + } + + // Firefunction v2 requires datetime and functions in the context even w/o tools, so we also use its handler in all cases. + if (src.find(" functools[") != std::string::npos) { + return common_chat_params_init_firefunction_v2(tmpl, params); + } + + // Plain handler (no tools) + if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return common_chat_params_init_without_tools(tmpl, params); + } + + // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools) + if (src.find("") != std::string::npos) { + return common_chat_params_init_hermes_2_pro(tmpl, params); + } + + // Functionary v3.1 (w/ tools) + if (src.find("<|start_header_id|>") != std::string::npos + && src.find("ipython<|end_header_id|>") != std::string::npos) { + auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos; + return common_chat_params_init_llama_3_1_tool_calls(tmpl, params, allow_python_tag_builtin_tools); + } + + // Mistral Nemo (w/ tools) + if (src.find("[TOOL_CALLS]") != std::string::npos) { + return common_chat_params_init_mistral_nemo(tmpl, params); + } + + // Generic fallback + return common_chat_params_init_generic(tmpl, params); +} + +// Legacy template route (adhoc C++ implementation of known templates), forward to llama_chat_apply_template. +static common_chat_params common_chat_templates_apply_legacy( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + int alloc_size = 0; + std::vector chat; + std::vector contents; + for (const auto & msg : inputs.messages) { + auto content = msg.content; + for (const auto & part : msg.content_parts) { + if (part.type != "text") { + LOG_WRN("Ignoring non-text content part: %s\n", part.type.c_str()); + continue; + } + if (!content.empty()) { + content += "\n";; + } + content += part.text; + } + contents.emplace_back(std::move(content)); + } + for (size_t i = 0; i < contents.size(); ++i) { + const auto & msg = inputs.messages[i]; + const auto & content = contents[i]; + chat.push_back({msg.role.c_str(), content.c_str()}); + alloc_size += (msg.role.size() + content.size()) * 1.25; + } + + std::vector buf(alloc_size); + + // run the first time to get the total output length + const auto & src = tmpls->template_default->source(); + int32_t res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + + // error: chat template is not supported + if (res < 0) { + // if the custom "tmpl" is not supported, we throw an error + // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template() + throw std::runtime_error("this custom template is not supported"); + } + + // if it turns out that our buffer is too small, we resize it + if ((size_t) res > buf.size()) { + buf.resize(res); + res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(), buf.size()); + } + + common_chat_params params; + params.prompt = std::string(buf.data(), res); + if (!inputs.json_schema.empty()) { + params.grammar = json_schema_to_grammar(json::parse(inputs.json_schema)); + } else { + params.grammar = inputs.grammar; + } + return params; +} + +common_chat_params common_chat_templates_apply( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs) +{ + GGML_ASSERT(tmpls != nullptr); + return inputs.use_jinja + ? common_chat_templates_apply_jinja(tmpls, inputs) + : common_chat_templates_apply_legacy(tmpls, inputs); +} + +static common_chat_msg common_chat_parse_content_only(const std::string & input) { + common_chat_msg msg; + msg.role = "assistant"; + msg.content = input; + return msg; +} + +common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) { + switch (format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: + return common_chat_parse_content_only(input); + case COMMON_CHAT_FORMAT_GENERIC: + return common_chat_parse_generic(input); + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: + return common_chat_parse_mistral_nemo(input); + case COMMON_CHAT_FORMAT_LLAMA_3_X: + return common_chat_parse_llama_3_1(input); + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: + return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true); + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: + return common_chat_parse_deepseek_r1(input, /* extract_reasoning= */ false); + case COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING: + return common_chat_parse_deepseek_r1(input, /* extract_reasoning= */ true); + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: + return common_chat_parse_functionary_v3_2(input); + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: + return common_chat_parse_functionary_v3_1_llama_3_1(input); + case COMMON_CHAT_FORMAT_HERMES_2_PRO: + return common_chat_parse_hermes_2_pro(input); + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: + return common_chat_parse_firefunction_v2(input); + case COMMON_CHAT_FORMAT_COMMAND_R7B: + return common_chat_parse_command_r7b(input, /* extract_reasoning= */ false); + case COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING: + return common_chat_parse_command_r7b(input, /* extract_reasoning= */ true); + default: + throw std::runtime_error("Unsupported format: " + common_chat_format_name(format)); + } +} diff --git a/common/chat.h b/common/chat.h new file mode 100644 index 0000000000000..e77bef82b9edd --- /dev/null +++ b/common/chat.h @@ -0,0 +1,134 @@ +// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers. + +#pragma once + +#include "common.h" +#include +#include + +struct common_chat_templates; + +struct common_chat_tool_call { + std::string name; + std::string arguments; + std::string id; +}; + +struct common_chat_msg_content_part { + std::string type; + std::string text; +}; + +struct common_chat_msg { + std::string role; + std::string content; + std::vector content_parts = {}; + std::vector tool_calls = {}; + std::string reasoning_content; + std::string tool_name; + std::string tool_call_id; +}; + +struct common_chat_tool { + std::string name; + std::string description; + std::string parameters; +}; + +enum common_chat_tool_choice { + COMMON_CHAT_TOOL_CHOICE_AUTO, + COMMON_CHAT_TOOL_CHOICE_REQUIRED, + COMMON_CHAT_TOOL_CHOICE_NONE, +}; + +enum common_chat_format { + COMMON_CHAT_FORMAT_CONTENT_ONLY, + COMMON_CHAT_FORMAT_GENERIC, + COMMON_CHAT_FORMAT_MISTRAL_NEMO, + COMMON_CHAT_FORMAT_LLAMA_3_X, + COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, + COMMON_CHAT_FORMAT_DEEPSEEK_R1, + COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING, + COMMON_CHAT_FORMAT_FIREFUNCTION_V2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, + COMMON_CHAT_FORMAT_HERMES_2_PRO, + COMMON_CHAT_FORMAT_COMMAND_R7B, + COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING, + + COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats +}; + +struct common_chat_templates_inputs { + std::vector messages; + std::string grammar; + std::string json_schema; + bool add_generation_prompt = true; + bool use_jinja = true; + // Parameters below only supported when use_jinja is true + std::vector tools; + common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO; + bool parallel_tool_calls = false; + bool extract_reasoning = true; +}; + +struct common_chat_params { + common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + std::string prompt; + std::string grammar; + bool grammar_lazy = false; + std::vector grammar_triggers; + std::vector preserved_tokens; + std::vector additional_stops; +}; + +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja); + +void common_chat_templates_free(struct common_chat_templates * tmpls); + +struct common_chat_templates_deleter { void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } }; + +typedef std::unique_ptr common_chat_templates_ptr; + +common_chat_templates_ptr common_chat_templates_init( + const struct llama_model * model, + const std::string & chat_template_override, + const std::string & bos_token_override = "", + const std::string & eos_token_override = ""); + +bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls); +const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant = nullptr); + + +struct common_chat_params common_chat_templates_apply( + const struct common_chat_templates * tmpls, + const struct common_chat_templates_inputs & inputs); + +// Format single message, while taking into account the position of that message in chat history +std::string common_chat_format_single( + const struct common_chat_templates * tmpls, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja); + +// Returns an example of formatted chat +std::string common_chat_format_example( + const struct common_chat_templates * tmpls, + bool use_jinja); + +std::string common_chat_format_name(common_chat_format format); +common_chat_msg common_chat_parse( const std::string & input, common_chat_format format); + +common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice); + +// Parses a JSON array of messages in OpenAI's chat completion API format. +// T can be std::string containing JSON or nlohmann::ordered_json +template std::vector common_chat_msgs_parse_oaicompat(const T & messages); +template T common_chat_msgs_to_json_oaicompat(const std::vector & msgs, bool concat_typed_text = false); + +// Parses a JSON array of tools in OpenAI's chat completion tool call API format. +// T can be std::string containing JSON or nlohmann::ordered_json +template std::vector common_chat_tools_parse_oaicompat(const T & tools); +template T common_chat_tools_to_json_oaicompat(const std::vector & tools); diff --git a/common/common.cpp b/common/common.cpp index 98524f7467ab4..d2b0d50e3ee39 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2,6 +2,9 @@ #define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING #endif +#include "ggml.h" +#include "gguf.h" + #include "common.h" #include "log.h" // Change JSON_ASSERT from assert() to GGML_ASSERT: @@ -18,6 +21,7 @@ #include #include #include +#include #include #include #include @@ -62,11 +66,29 @@ #ifdef __linux__ #include #elif defined(_WIN32) -#define PATH_MAX MAX_PATH +# if !defined(PATH_MAX) +# define PATH_MAX MAX_PATH +# endif #else #include #endif #define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 + +// +// CURL utils +// + +using curl_ptr = std::unique_ptr; + +// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one +struct curl_slist_ptr { + struct curl_slist * ptr = nullptr; + ~curl_slist_ptr() { + if (ptr) { + curl_slist_free_all(ptr); + } + } +}; #endif // LLAMA_USE_CURL using json = nlohmann::ordered_json; @@ -377,9 +399,6 @@ void common_init() { #endif LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type); - - // load dynamic backends - ggml_backend_load_all(); } std::string common_params_get_system_info(const common_params & params) { @@ -464,6 +483,48 @@ void string_replace_all(std::string & s, const std::string & search, const std:: s = std::move(builder); } +std::string string_join(const std::vector & values, const std::string & separator) { + std::ostringstream result; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + result << separator; + } + result << values[i]; + } + return result.str(); +} + +std::vector string_split(const std::string & str, const std::string & delimiter) { + std::vector parts; + size_t start = 0; + size_t end = str.find(delimiter); + + while (end != std::string::npos) { + parts.push_back(str.substr(start, end - start)); + start = end + delimiter.length(); + end = str.find(delimiter, start); + } + + parts.push_back(str.substr(start)); + + return parts; +} + +std::string string_repeat(const std::string & str, size_t n) { + if (n == 0) { + return ""; + } + + std::string result; + result.reserve(str.length() * n); + + for (size_t i = 0; i < n; ++i) { + result += str; + } + + return result; +} + std::string string_from(bool value) { return value ? "true" : "false"; } @@ -655,7 +716,17 @@ bool fs_validate_filename(const std::string & filename) { std::u32string filename_utf32; try { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif std::wstring_convert, char32_t> converter; + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + filename_utf32 = converter.from_bytes(filename); // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, @@ -832,11 +903,11 @@ struct common_init_result common_init_from_params(common_params & params) { llama_model * model = nullptr; if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_hf(params.hf_repo, params.hf_file, params.model, params.hf_token, mparams); } else if (!params.model_url.empty()) { - model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams); } else { - model = llama_load_model_from_file(params.model.c_str(), mparams); + model = llama_model_load_from_file(params.model.c_str(), mparams); } if (model == NULL) { @@ -844,26 +915,28 @@ struct common_init_result common_init_from_params(common_params & params) { return iparams; } + const llama_vocab * vocab = llama_model_get_vocab(model); + if (params.reranking) { bool ok = true; - if (llama_token_bos(model) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: model does not have a BOS token, reranking will not work\n", __func__); + if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__); ok = false; } - if (llama_token_eos(model) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__); + if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__); ok = false; } - if (llama_token_sep(model) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: model does not have a SEP token, reranking will not work\n", __func__); + if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__); ok = false; } if (!ok) { - llama_free_model(model); + llama_model_free(model); return iparams; } @@ -871,40 +944,40 @@ struct common_init_result common_init_from_params(common_params & params) { auto cparams = common_context_params_to_llama(params); - llama_context * lctx = llama_new_context_with_model(model, cparams); + llama_context * lctx = llama_init_from_model(model, cparams); if (lctx == NULL) { LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); + llama_model_free(model); return iparams; } if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) { - LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__); - llama_free_model(model); - return iparams; + LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__); + params.ctx_shift = false; } if (!params.control_vectors.empty()) { if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; - if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_model_n_layer(model); const auto cvec = common_control_vector_load(params.control_vectors); if (cvec.n_embd == -1) { llama_free(lctx); - llama_free_model(model); + llama_model_free(model); return iparams; } - int err = llama_control_vector_apply(lctx, - cvec.data.data(), - cvec.data.size(), - cvec.n_embd, - params.control_vector_layer_start, - params.control_vector_layer_end); + int err = llama_apply_adapter_cvec( + lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); if (err) { llama_free(lctx); - llama_free_model(model); + llama_model_free(model); return iparams; } @@ -912,33 +985,54 @@ struct common_init_result common_init_from_params(common_params & params) { // load and optionally apply lora adapters for (auto & la : params.lora_adapters) { - common_lora_adapter_container loaded_la; - loaded_la.path = la.path; - loaded_la.scale = la.scale; - loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str()); - if (loaded_la.adapter == nullptr) { + llama_adapter_lora_ptr lora; + lora.reset(llama_adapter_lora_init(model, la.path.c_str())); + if (lora == nullptr) { LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); llama_free(lctx); - llama_free_model(model); + llama_model_free(model); return iparams; } - iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters + + la.ptr = lora.get(); + iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters } + if (!params.lora_init_without_apply) { - common_lora_adapters_apply(lctx, iparams.lora_adapters); + common_set_adapter_lora(lctx, params.lora_adapters); } - if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__); + if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); params.sampling.ignore_eos = false; } + if (params.sampling.ignore_eos) { + for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { + if (llama_vocab_is_eog(vocab, i)) { + LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY); + params.sampling.logit_bias.push_back({i, -INFINITY}); + } + } + } + + if (params.sampling.penalty_last_n == -1) { + LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.penalty_last_n = llama_n_ctx(lctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); + } + if (params.warmup) { LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__); std::vector tmp; - llama_token bos = llama_token_bos(model); - llama_token eos = llama_token_eos(model); + llama_token bos = llama_vocab_bos(vocab); + llama_token eos = llama_vocab_eos(vocab); + // some models (e.g. T5) don't have a BOS token if (bos != LLAMA_TOKEN_NULL) { tmp.push_back(bos); @@ -953,7 +1047,7 @@ struct common_init_result common_init_from_params(common_params & params) { if (llama_model_has_encoder(model)) { llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); llama_token decoder_start_token_id = llama_model_decoder_start_token(model); - if (decoder_start_token_id == -1) { + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { decoder_start_token_id = bos; } tmp.clear(); @@ -967,28 +1061,30 @@ struct common_init_result common_init_from_params(common_params & params) { llama_perf_context_reset(lctx); } - iparams.model = model; - iparams.context = lctx; + iparams.model.reset(model); + iparams.context.reset(lctx); return iparams; } -void common_lora_adapters_apply(struct llama_context * ctx, std::vector & lora_adapters) { - llama_lora_adapter_clear(ctx); - for (auto & la : lora_adapters) { +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora) { + llama_clear_adapter_lora(ctx); + for (auto & la : lora) { if (la.scale != 0.0f) { - llama_lora_adapter_set(ctx, la.adapter, la.scale); + llama_set_adapter_lora(ctx, la.ptr, la.scale); } } } -struct llama_model_params common_model_params_to_llama(const common_params & params) { +struct llama_model_params common_model_params_to_llama(common_params & params) { auto mparams = llama_model_default_params(); + if (!params.devices.empty()) { + mparams.devices = params.devices.data(); + } if (params.n_gpu_layers != -1) { mparams.n_gpu_layers = params.n_gpu_layers; } - mparams.rpc_servers = params.rpc_servers.c_str(); mparams.main_gpu = params.main_gpu; mparams.split_mode = params.split_mode; mparams.tensor_split = params.tensor_split; @@ -1005,38 +1101,6 @@ struct llama_model_params common_model_params_to_llama(const common_params & par return mparams; } -static ggml_type kv_cache_type_from_str(const std::string & s) { - if (s == "f32") { - return GGML_TYPE_F32; - } - if (s == "f16") { - return GGML_TYPE_F16; - } - if (s == "bf16") { - return GGML_TYPE_BF16; - } - if (s == "q8_0") { - return GGML_TYPE_Q8_0; - } - if (s == "q4_0") { - return GGML_TYPE_Q4_0; - } - if (s == "q4_1") { - return GGML_TYPE_Q4_1; - } - if (s == "iq4_nl") { - return GGML_TYPE_IQ4_NL; - } - if (s == "q5_0") { - return GGML_TYPE_Q5_0; - } - if (s == "q5_1") { - return GGML_TYPE_Q5_1; - } - - throw std::runtime_error("Unsupported cache type: " + s); -} - struct llama_context_params common_context_params_to_llama(const common_params & params) { auto cparams = llama_context_default_params(); @@ -1071,8 +1135,8 @@ struct llama_context_params common_context_params_to_llama(const common_params & cparams.pooling_type = LLAMA_POOLING_TYPE_RANK; } - cparams.type_k = kv_cache_type_from_str(params.cache_type_k); - cparams.type_v = kv_cache_type_from_str(params.cache_type_v); + cparams.type_k = params.cache_type_k; + cparams.type_v = params.cache_type_v; return cparams; } @@ -1098,13 +1162,7 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p #define CURL_MAX_RETRY 3 #define CURL_RETRY_DELAY_SECONDS 2 - -static bool starts_with(const std::string & str, const std::string & prefix) { - // While we wait for C++20's std::string::starts_with... - return str.rfind(prefix, 0) == 0; -} - -static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) { +static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) { int remaining_attempts = max_attempts; while (remaining_attempts > 0) { @@ -1128,9 +1186,9 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_ } static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) { - // Initialize libcurl - std::unique_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; if (!curl) { LOG_ERR("%s: error initializing libcurl\n", __func__); return false; @@ -1144,11 +1202,9 @@ static bool common_download_file(const std::string & url, const std::string & pa // Check if hf-token or bearer-token was specified if (!hf_token.empty()) { - std::string auth_header = "Authorization: Bearer "; - auth_header += hf_token.c_str(); - struct curl_slist *http_headers = NULL; - http_headers = curl_slist_append(http_headers, auth_header.c_str()); - curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers); + std::string auth_header = "Authorization: Bearer " + hf_token; + http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); } #if defined(_WIN32) @@ -1158,8 +1214,7 @@ static bool common_download_file(const std::string & url, const std::string & pa #endif // Check if the file already exists locally - struct stat model_file_info; - auto file_exists = (stat(path.c_str(), &model_file_info) == 0); + auto file_exists = std::filesystem::exists(path); // If the file exists, check its JSON metadata companion file. std::string metadata_path = path + ".json"; @@ -1201,11 +1256,13 @@ static bool common_download_file(const std::string & url, const std::string & pa std::string etag; std::string last_modified; }; + common_load_model_from_url_headers headers; + { typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { - common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata; + common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata; static std::regex header_regex("([^:]+): (.*)\r\n"); static std::regex etag_regex("ETag", std::regex_constants::icase); @@ -1342,17 +1399,17 @@ static bool common_download_file(const std::string & url, const std::string & pa } struct llama_model * common_load_model_from_url( - const char * model_url, - const char * path_model, - const char * hf_token, + const std::string & model_url, + const std::string & local_path, + const std::string & hf_token, const struct llama_model_params & params) { // Basic validation of the model_url - if (!model_url || strlen(model_url) == 0) { + if (model_url.empty()) { LOG_ERR("%s: invalid model_url\n", __func__); return NULL; } - if (!common_download_file(model_url, path_model, hf_token)) { + if (!common_download_file(model_url, local_path, hf_token)) { return NULL; } @@ -1363,9 +1420,9 @@ struct llama_model * common_load_model_from_url( /*.no_alloc = */ true, /*.ctx = */ NULL, }; - auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params); + auto * ctx_gguf = gguf_init_from_file(local_path.c_str(), gguf_params); if (!ctx_gguf) { - LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, path_model); + LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, local_path.c_str()); return NULL; } @@ -1384,13 +1441,13 @@ struct llama_model * common_load_model_from_url( // Verify the first split file format // and extract split URL and PATH prefixes { - if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) { - LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, path_model, n_split); + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), local_path.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, local_path.c_str(), n_split); return NULL; } - if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) { - LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url, n_split); + if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url.c_str(), n_split); return NULL; } } @@ -1417,14 +1474,14 @@ struct llama_model * common_load_model_from_url( } } - return llama_load_model_from_file(path_model, params); + return llama_model_load_from_file(local_path.c_str(), params); } struct llama_model * common_load_model_from_hf( - const char * repo, - const char * model, - const char * path_model, - const char * hf_token, + const std::string & repo, + const std::string & remote_path, + const std::string & local_path, + const std::string & hf_token, const struct llama_model_params & params) { // construct hugging face model url: // @@ -1438,32 +1495,111 @@ struct llama_model * common_load_model_from_hf( std::string model_url = "https://huggingface.co/"; model_url += repo; model_url += "/resolve/main/"; - model_url += model; + model_url += remote_path; + + return common_load_model_from_url(model_url, local_path, hf_token, params); +} + +/** + * Allow getting the HF file from the HF repo with tag (like ollama), for example: + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4 + * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s + * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo) + * + * Return pair of (with "repo" already having tag removed) + * + * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files. + */ +std::pair common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) { + auto parts = string_split(hf_repo_with_tag, ':'); + std::string tag = parts.size() > 1 ? parts.back() : "latest"; + std::string hf_repo = parts[0]; + if (string_split(hf_repo, '/').size() != 2) { + throw std::invalid_argument("error: invalid HF repo format, expected /[:quant]\n"); + } + + // fetch model info from Hugging Face Hub API + json model_info; + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + std::string res_str; + std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag; + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data); + auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t { + static_cast(data)->append((char * ) ptr, size * nmemb); + return size * nmemb; + }; + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str); +#if defined(_WIN32) + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + if (!hf_token.empty()) { + std::string auth_header = "Authorization: Bearer " + hf_token; + http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + } + // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response + http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); + http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json"); + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + + CURLcode res = curl_easy_perform(curl.get()); + + if (res != CURLE_OK) { + throw std::runtime_error("error: cannot make GET request to HF API"); + } + + long res_code; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code); + if (res_code == 200) { + model_info = json::parse(res_str); + } else if (res_code == 401) { + throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token"); + } else { + throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str())); + } + + // check response + if (!model_info.contains("ggufFile")) { + throw std::runtime_error("error: model does not have ggufFile"); + } + json & gguf_file = model_info.at("ggufFile"); + if (!gguf_file.contains("rfilename")) { + throw std::runtime_error("error: ggufFile does not have rfilename"); + } - return common_load_model_from_url(model_url.c_str(), path_model, hf_token, params); + return std::make_pair(hf_repo, gguf_file.at("rfilename")); } #else struct llama_model * common_load_model_from_url( - const char * /*model_url*/, - const char * /*path_model*/, - const char * /*hf_token*/, + const std::string & /*model_url*/, + const std::string & /*local_path*/, + const std::string & /*hf_token*/, const struct llama_model_params & /*params*/) { LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); return nullptr; } struct llama_model * common_load_model_from_hf( - const char * /*repo*/, - const char * /*model*/, - const char * /*path_model*/, - const char * /*hf_token*/, + const std::string & /*repo*/, + const std::string & /*remote_path*/, + const std::string & /*local_path*/, + const std::string & /*hf_token*/, const struct llama_model_params & /*params*/) { LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); return nullptr; } +std::pair common_get_hf_file(const std::string &, const std::string &) { + LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); + return std::make_pair("", ""); +} + #endif // LLAMA_USE_CURL // @@ -1562,21 +1698,23 @@ std::vector common_tokenize( const std::string & text, bool add_special, bool parse_special) { - return common_tokenize(llama_get_model(ctx), text, add_special, parse_special); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_tokenize(vocab, text, add_special, parse_special); } std::vector common_tokenize( - const struct llama_model * model, + const struct llama_vocab * vocab, const std::string & text, bool add_special, bool parse_special) { // upper limit for the number of tokens int n_tokens = text.length() + 2 * add_special; std::vector result(n_tokens); - n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); @@ -1585,12 +1723,18 @@ std::vector common_tokenize( } std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_token_to_piece(vocab, token, special); +} + +std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) { std::string piece; piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' - const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special); + const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); if (n_chars < 0) { piece.resize(-n_chars); - int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special); + int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); GGML_ASSERT(check == -n_chars); } else { @@ -1600,13 +1744,19 @@ std::string common_token_to_piece(const struct llama_context * ctx, llama_token return piece; } -std::string common_detokenize(llama_context * ctx, const std::vector & tokens, bool special) { +std::string common_detokenize(const struct llama_context * ctx, const std::vector & tokens, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_detokenize(vocab, tokens, special); +} + +std::string common_detokenize(const struct llama_vocab * vocab, const std::vector & tokens, bool special) { std::string text; text.resize(std::max(text.capacity(), tokens.size())); - int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); if (n_chars < 0) { text.resize(-n_chars); - n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization } @@ -1616,91 +1766,6 @@ std::string common_detokenize(llama_context * ctx, const std::vector= 0; -} - -std::string common_chat_apply_template(const struct llama_model * model, - const std::string & tmpl, - const std::vector & msgs, - bool add_ass) { - int alloc_size = 0; - bool fallback = false; // indicate if we must fallback to default chatml - std::vector chat; - for (auto & msg : msgs) { - chat.push_back({msg.role.c_str(), msg.content.c_str()}); - alloc_size += (msg.role.size() + msg.content.size()) * 1.25; - } - - const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str(); - std::vector buf(alloc_size); - - // run the first time to get the total output length - int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size()); - - // error: chat template is not supported - if (res < 0) { - if (ptr_tmpl != nullptr) { - // if the custom "tmpl" is not supported, we throw an error - // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template() - throw std::runtime_error("this custom template is not supported"); - } else { - // If the built-in template is not supported, we default to chatml - res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size()); - fallback = true; - } - } - - // if it turns out that our buffer is too small, we resize it - if ((size_t) res > buf.size()) { - buf.resize(res); - res = llama_chat_apply_template( - fallback ? nullptr : model, - fallback ? "chatml" : ptr_tmpl, - chat.data(), chat.size(), add_ass, buf.data(), buf.size()); - } - - std::string formatted_chat(buf.data(), res); - return formatted_chat; -} - -std::string common_chat_format_single(const struct llama_model * model, - const std::string & tmpl, - const std::vector & past_msg, - const common_chat_msg & new_msg, - bool add_ass) { - std::ostringstream ss; - auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false); - std::vector chat_new(past_msg); - // if the past_msg ends with a newline, we must preserve it in the formatted version - if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { - ss << "\n"; - }; - // format chat with new_msg - chat_new.push_back(new_msg); - auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass); - // get the diff part - ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); - return ss.str(); -} - -std::string common_chat_format_example(const struct llama_model * model, - const std::string & tmpl) { - std::vector msgs = { - {"system", "You are a helpful assistant"}, - {"user", "Hello"}, - {"assistant", "Hi there"}, - {"user", "How are you?"}, - }; - return common_chat_apply_template(model, tmpl, msgs, true); -} - // // KV cache utils // @@ -1789,7 +1854,9 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) break; case 0: // max absolute for (int i = 0; i < n; i++) { - if (sum < std::abs(inp[i])) sum = std::abs(inp[i]); + if (sum < std::abs(inp[i])) { + sum = std::abs(inp[i]); + } } sum /= 32760.0; // make an int16 range break; diff --git a/common/common.h b/common/common.h index 5c579b5abfe03..efe8e7f796521 100644 --- a/common/common.h +++ b/common/common.h @@ -2,8 +2,9 @@ #pragma once -#include "llama.h" +#include "llama-cpp.h" +#include #include #include #include @@ -24,22 +25,20 @@ #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" -struct common_lora_adapter_info { +struct common_adapter_lora_info { std::string path; float scale; -}; -struct common_lora_adapter_container : common_lora_adapter_info { - struct llama_lora_adapter * adapter; + struct llama_adapter_lora * ptr; }; using llama_tokens = std::vector; // build info extern int LLAMA_BUILD_NUMBER; -extern char const * LLAMA_COMMIT; -extern char const * LLAMA_COMPILER; -extern char const * LLAMA_BUILD_TARGET; +extern const char * LLAMA_COMMIT; +extern const char * LLAMA_COMPILER; +extern const char * LLAMA_BUILD_TARGET; struct common_control_vector_load_info; @@ -80,6 +79,7 @@ enum llama_example { LLAMA_EXAMPLE_LLAVA, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_PARALLEL, + LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_COUNT, }; @@ -95,6 +95,7 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_TEMPERATURE = 7, COMMON_SAMPLER_TYPE_XTC = 8, COMMON_SAMPLER_TYPE_INFILL = 9, + COMMON_SAMPLER_TYPE_PENALTIES = 10, }; // dimensionality reduction methods, used by cvector-generator @@ -103,6 +104,17 @@ enum dimre_method { DIMRE_METHOD_MEAN, }; +enum common_conversation_mode { + COMMON_CONVERSATION_MODE_DISABLED = 0, + COMMON_CONVERSATION_MODE_ENABLED = 1, + COMMON_CONVERSATION_MODE_AUTO = 2, +}; + +struct common_grammar_trigger { + std::string word; + bool at_start; +}; + // sampling parameters struct common_params_sampling { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler @@ -128,16 +140,18 @@ struct common_params_sampling { int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float top_n_sigma = -1.00f;// -1.0 = disabled float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = false; // consider newlines as a repeatable token bool ignore_eos = false; bool no_perf = false; // disable performance metrics + bool timing_per_token = false; std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY std::vector samplers = { + COMMON_SAMPLER_TYPE_PENALTIES, COMMON_SAMPLER_TYPE_DRY, COMMON_SAMPLER_TYPE_TOP_K, COMMON_SAMPLER_TYPE_TYPICAL_P, @@ -147,7 +161,11 @@ struct common_params_sampling { COMMON_SAMPLER_TYPE_TEMPERATURE, }; - std::string grammar; // optional BNF-like grammar to constrain sampling + std::string grammar; // optional BNF-like grammar to constrain sampling + bool grammar_lazy = false; + std::vector grammar_trigger_words; // optional trigger words to trigger lazy grammar + std::vector grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens. + std::set preserved_tokens; std::vector logit_bias; // logit biases to apply @@ -156,17 +174,38 @@ struct common_params_sampling { }; struct common_params_speculative { + std::vector devices; // devices to use for offloading + int32_t n_ctx = 0; // draft context size int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding - int32_t n_min = 5; // minimum number of draft tokens to use for speculative decoding + int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) float p_split = 0.1f; // speculative decoding split probability - float p_min = 0.9f; // minimum speculative decoding probability (greedy) + float p_min = 0.75f; // minimum speculative decoding probability (greedy) struct cpu_params cpuparams; struct cpu_params cpuparams_batch; - std::string model = ""; // draft model for speculative decoding // NOLINT + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + + std::string model = ""; // draft model for speculative decoding // NOLINT + std::string model_url = ""; // model url to download // NOLINT +}; + +struct common_params_vocoder { + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + + std::string model = ""; // model path // NOLINT + std::string model_url = ""; // model url to download // NOLINT + + bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT +}; + +enum common_reasoning_format { + COMMON_REASONING_FORMAT_NONE, + COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content` }; struct common_params { @@ -178,9 +217,6 @@ struct common_params { int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width int32_t n_print = -1; // print token count every n tokens (-1 = disabled) @@ -193,6 +229,15 @@ struct common_params { int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = 0.1f; // KV cache defragmentation threshold + // offload params + std::vector devices; // devices to use for offloading + + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + + enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs + struct cpu_params cpuparams; struct cpu_params cpuparams_batch; @@ -201,16 +246,16 @@ struct common_params { ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; - enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings - struct common_params_sampling sampling; + struct common_params_sampling sampling; struct common_params_speculative speculative; + struct common_params_vocoder vocoder; std::string model = ""; // model path // NOLINT - std::string model_alias = "unknown"; // model alias // NOLINT + std::string model_alias = ""; // model alias // NOLINT std::string model_url = ""; // model url to download // NOLINT std::string hf_token = ""; // HF token // NOLINT std::string hf_repo = ""; // HF repo // NOLINT @@ -223,14 +268,13 @@ struct common_params { std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT std::string logits_file = ""; // file for saving *all* logits // NOLINT - std::string rpc_servers = ""; // comma separated list of RPC servers // NOLINT std::vector in_files; // all input files std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_overrides; - bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply) - std::vector lora_adapters; // lora adapter path with user defined scale + bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply) + std::vector lora_adapters; // lora adapter path with user defined scale std::vector control_vectors; // control vector with user defined scale @@ -254,11 +298,11 @@ struct common_params { bool kl_divergence = false; // compute KL divergence bool usage = false; // print usage + bool completion = false; // print source-able completion script bool use_color = false; // use color to distinguish generations and inputs bool special = false; // enable special token output bool interactive = false; // interactive mode bool interactive_first = false; // wait for user input immediately - bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool prompt_cache_all = false; // save user input and generations to prompt cache bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it @@ -281,8 +325,10 @@ struct common_params { bool warmup = true; // warmup run bool check_tensors = false; // validate tensor data - std::string cache_type_k = "f16"; // KV cache data type for the K - std::string cache_type_v = "f16"; // KV cache data type for the V + ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K + ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V + + common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; // multimodal models (see examples/llava) std::string mmproj = ""; // path to multimodal projector // NOLINT @@ -305,7 +351,9 @@ struct common_params { std::string hostname = "127.0.0.1"; std::string public_path = ""; // NOLINT std::string chat_template = ""; // NOLINT + bool use_jinja = false; // NOLINT bool enable_chat_template = true; + common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; std::vector api_keys; @@ -384,13 +432,13 @@ bool set_process_priority(enum ggml_sched_priority prio); // #ifdef __GNUC__ -#ifdef __MINGW32__ -#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +# if defined(__MINGW32__) && !defined(__clang__) +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +# else +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +# endif #else -#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) -#endif -#else -#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) +# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) #endif LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) @@ -399,6 +447,10 @@ std::string string_format(const char * fmt, ...); std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); +std::string string_join(const std::vector & values, const std::string & separator); +std::vector string_split(const std::string & str, const std::string & delimiter); +std::string string_repeat(const std::string & str, size_t n); + void string_replace_all(std::string & s, const std::string & search, const std::string & replace); template @@ -432,6 +484,16 @@ std::vector string_split(const std::string & input, ch return parts; } +static bool string_starts_with(const std::string & str, + const std::string & prefix) { // While we wait for C++20's std::string::starts_with... + return str.rfind(prefix, 0) == 0; +} + +static bool string_ends_with(const std::string & str, + const std::string & suffix) { // While we wait for C++20's std::string::ends_with... + return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0; +} + bool string_parse_kv_override(const char * data, std::vector & overrides); void string_process_escapes(std::string & input); @@ -454,23 +516,39 @@ std::string fs_get_cache_file(const std::string & filename); // Model utils // +// note: defines object's lifetime struct common_init_result { - struct llama_model * model = nullptr; - struct llama_context * context = nullptr; - std::vector lora_adapters; + llama_model_ptr model; + llama_context_ptr context; + + std::vector lora; }; struct common_init_result common_init_from_params(common_params & params); -struct llama_model_params common_model_params_to_llama (const common_params & params); +struct llama_model_params common_model_params_to_llama ( common_params & params); struct llama_context_params common_context_params_to_llama(const common_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); -struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); -struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); +struct llama_model * common_load_model_from_url( + const std::string & model_url, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); + +struct llama_model * common_load_model_from_hf( + const std::string & repo, + const std::string & remote_path, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); + +std::pair common_get_hf_file( + const std::string & hf_repo_with_tag, + const std::string & hf_token); // clear LoRA adapters from context, then apply new list of adapters -void common_lora_adapters_apply(struct llama_context * ctx, std::vector & lora_adapters); +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora); // // Batch utils @@ -508,7 +586,7 @@ std::vector common_tokenize( bool parse_special = false); std::vector common_tokenize( - const struct llama_model * model, + const struct llama_vocab * vocab, const std::string & text, bool add_special, bool parse_special = false); @@ -520,45 +598,23 @@ std::string common_token_to_piece( llama_token token, bool special = true); +std::string common_token_to_piece( + const struct llama_vocab * vocab, + llama_token token, + bool special = true); + // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` // optionally renders special/control tokens std::string common_detokenize( - llama_context * ctx, + const struct llama_context * ctx, const std::vector & tokens, bool special = true); -// -// Chat template utils -// - -// same with llama_chat_message, but uses std::string -struct common_chat_msg { - std::string role; - std::string content; -}; - -// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid -bool common_chat_verify_template(const std::string & tmpl); - -// CPP wrapper for llama_chat_apply_template -// If the built-in template is not supported, we default to chatml -// If the custom "tmpl" is not supported, we throw an error -std::string common_chat_apply_template(const struct llama_model * model, - const std::string & tmpl, - const std::vector & chat, - bool add_ass); - -// Format single message, while taking into account the position of that message in chat history -std::string common_chat_format_single(const struct llama_model * model, - const std::string & tmpl, - const std::vector & past_msg, - const common_chat_msg & new_msg, - bool add_ass); - -// Returns an example of formatted chat -std::string common_chat_format_example(const struct llama_model * model, - const std::string & tmpl); +std::string common_detokenize( + const struct llama_vocab * vocab, + const std::vector & tokens, + bool special = true); // // KV cache utils @@ -574,7 +630,8 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si // Embedding utils // -void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2); +// TODO: repace embd_norm with an enum +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); @@ -603,6 +660,10 @@ common_control_vector_data common_control_vector_load(const std::vector #include #include @@ -11,11 +13,6 @@ using json = nlohmann::ordered_json; -template -static std::string join(Iterator begin, Iterator end, const std::string & separator); - -static std::string repeat(const std::string & str, size_t n); - static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") { auto has_max = max_items != std::numeric_limits::max(); @@ -128,8 +125,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & if (sub_len > 0) { auto from_sub = from.substr(i + 1); auto to_sub = to.substr(i + 1); - auto sub_zeros = repeat("0", sub_len); - auto sub_nines = repeat("9", sub_len); + auto sub_zeros = string_repeat("0", sub_len); + auto sub_nines = string_repeat("9", sub_len); auto to_reached = false; out << "("; @@ -188,8 +185,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & auto max_digits = max_s.length(); for (auto digits = min_digits; digits < max_digits; digits++) { - uniform_range(min_s, repeat("9", digits)); - min_s = "1" + repeat("0", digits); + uniform_range(min_s, string_repeat("9", digits)); + min_s = "1" + string_repeat("0", digits); out << " | "; } uniform_range(min_s, max_s); @@ -318,49 +315,6 @@ std::unordered_map GRAMMAR_LITERAL_ESCAPES = { std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; -template -std::string join(Iterator begin, Iterator end, const std::string & separator) { - std::ostringstream result; - if (begin != end) { - result << *begin; - for (Iterator it = begin + 1; it != end; ++it) { - result << separator << *it; - } - } - return result.str(); -} - -static std::vector split(const std::string & str, const std::string & delimiter) { - std::vector tokens; - size_t start = 0; - size_t end = str.find(delimiter); - - while (end != std::string::npos) { - tokens.push_back(str.substr(start, end - start)); - start = end + delimiter.length(); - end = str.find(delimiter, start); - } - - tokens.push_back(str.substr(start)); - - return tokens; -} - -static std::string repeat(const std::string & str, size_t n) { - if (n == 0) { - return ""; - } - - std::string result; - result.reserve(str.length() * n); - - for (size_t i = 0; i < n; ++i) { - result += str; - } - - return result; -} - static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function & replacement) { std::smatch match; std::string result; @@ -389,6 +343,7 @@ static std::string format_literal(const std::string & literal) { class SchemaConverter { private: + friend std::string build_grammar(const std::function & cb, const common_grammar_options & options); std::function _fetch_json; bool _dotall; std::map _rules; @@ -418,7 +373,7 @@ class SchemaConverter { for (size_t i = 0; i < alt_schemas.size(); i++) { rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i))); } - return join(rules.begin(), rules.end(), " | "); + return string_join(rules, " | "); } std::string _visit_pattern(const std::string & pattern, const std::string & name) { @@ -481,7 +436,7 @@ class SchemaConverter { for (const auto & item : ret) { results.push_back(to_rule(item)); } - return std::make_pair(join(results.begin(), results.end(), " "), false); + return std::make_pair(string_join(results, " "), false); }; while (i < length) { @@ -539,7 +494,7 @@ class SchemaConverter { } curly_brackets += '}'; i++; - auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ","); + auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ","); int min_times = 0; int max_times = std::numeric_limits::max(); try { @@ -809,10 +764,11 @@ class SchemaConverter { public: SchemaConverter( const std::function & fetch_json, - bool dotall) + bool dotall, + bool compact_spaces) : _fetch_json(fetch_json), _dotall(dotall) { - _rules["space"] = SPACE_RULE; + _rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE; } void resolve_refs(json & schema, const std::string & url) { @@ -854,7 +810,7 @@ class SchemaConverter { return; } std::string pointer = ref.substr(ref.find('#') + 1); - std::vector tokens = split(pointer, "/"); + std::vector tokens = string_split(pointer, "/"); for (size_t i = 1; i < tokens.size(); ++i) { std::string sel = tokens[i]; if (target.is_null() || !target.contains(sel)) { @@ -905,7 +861,7 @@ class SchemaConverter { for (const auto & v : schema["enum"]) { enum_values.push_back(_generate_constant_rule(v)); } - return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space"); + return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space"); } else if ((schema_type.is_null() || schema_type == "object") && (schema.contains("properties") || (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) { @@ -1019,10 +975,10 @@ class SchemaConverter { void check_errors() { if (!_errors.empty()) { - throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n")); + throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n")); } if (!_warnings.empty()) { - fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str()); + fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str()); } } @@ -1035,11 +991,35 @@ class SchemaConverter { } }; -std::string json_schema_to_grammar(const json & schema) { - SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false); - auto copy = schema; - converter.resolve_refs(copy, "input"); - converter.visit(copy, ""); +std::string json_schema_to_grammar(const json & schema, bool force_gbnf) { +#ifdef LLAMA_USE_LLGUIDANCE + if (!force_gbnf) { + return "%llguidance {}\nstart: %json " + schema.dump(); + } +#else + (void)force_gbnf; +#endif // LLAMA_USE_LLGUIDANCE + return build_grammar([&](const common_grammar_builder & callbacks) { + auto copy = schema; + callbacks.resolve_refs(copy); + callbacks.add_schema("", copy); + }); +} + +std::string build_grammar(const std::function & cb, const common_grammar_options & options) { + SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces); + common_grammar_builder builder { + /* .add_rule = */ [&](const std::string & name, const std::string & rule) { + return converter._add_rule(name, rule); + }, + /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) { + return converter.visit(schema, name == "root" ? "" : name); + }, + /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) { + converter.resolve_refs(schema, ""); + } + }; + cb(builder); converter.check_errors(); return converter.format_grammar(); } diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h index 41623b3464528..62a3b0a4477cc 100644 --- a/common/json-schema-to-grammar.h +++ b/common/json-schema-to-grammar.h @@ -5,4 +5,18 @@ #define JSON_ASSERT GGML_ASSERT #include "json.hpp" -std::string json_schema_to_grammar(const nlohmann::ordered_json& schema); +std::string json_schema_to_grammar(const nlohmann::ordered_json & schema, + bool force_gbnf = false); + +struct common_grammar_builder { + std::function add_rule; + std::function add_schema; + std::function resolve_refs; +}; + +struct common_grammar_options { + bool dotall = false; + bool compact_spaces = false; +}; + +std::string build_grammar(const std::function & cb, const common_grammar_options & options = {}); diff --git a/common/llguidance.cpp b/common/llguidance.cpp new file mode 100644 index 0000000000000..2feeb93c87e30 --- /dev/null +++ b/common/llguidance.cpp @@ -0,0 +1,270 @@ +#include "sampling.h" +#include "log.h" + +#ifdef LLAMA_USE_LLGUIDANCE + +# include "llguidance.h" +# include + +struct llama_sampler_llg { + const llama_vocab * vocab; + std::string grammar_kind; + std::string grammar_data; + LlgTokenizer * tokenizer; + LlgConstraint * grammar; + LlgMaskResult llg_res; + bool has_llg_res; +}; + +static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind, + const char * grammar_data) { + LlgConstraintInit cinit; + llg_constraint_init_set_defaults(&cinit, tokenizer); + const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL"); + if (log_level && *log_level) { + cinit.log_stderr_level = atoi(log_level); + } + auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data); + if (llg_get_error(c)) { + LOG_ERR("llg error: %s\n", llg_get_error(c)); + llg_free_constraint(c); + return nullptr; + } + return c; +} + +static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) { + return "llguidance"; +} + +static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + LlgCommitResult res; + llg_commit_token(ctx->grammar, token, &res); + ctx->has_llg_res = false; + } +} + +static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + if (!ctx->has_llg_res) { + if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) { + ctx->has_llg_res = true; + } else { + LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar)); + llg_free_constraint(ctx->grammar); + ctx->grammar = nullptr; + } + } + if (ctx->has_llg_res) { + if (ctx->llg_res.is_stop) { + for (size_t i = 0; i < cur_p->size; ++i) { + if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) { + cur_p->data[i].logit = -INFINITY; + } + } + } else { + const uint32_t * mask = ctx->llg_res.sample_mask; + for (size_t i = 0; i < cur_p->size; ++i) { + auto token = cur_p->data[i].id; + if ((mask[token / 32] & (1 << (token % 32))) == 0) { + cur_p->data[i].logit = -INFINITY; + } + } + } + } + } +} + +static void llama_sampler_llg_reset(llama_sampler * smpl) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (!ctx->grammar) { + return; + } + + auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str()); + llg_free_constraint(ctx->grammar); + ctx->grammar = grammar_new; + ctx->has_llg_res = false; +} + +static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_llg *) smpl->ctx; + + auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr); + + // copy the state + { + auto * result_ctx = (llama_sampler_llg *) result->ctx; + + if (ctx->grammar) { + result_ctx->grammar_kind = ctx->grammar_kind; + result_ctx->grammar_data = ctx->grammar_data; + result_ctx->grammar = llg_clone_constraint(ctx->grammar); + result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer); + } + } + + return result; +} + +static void llama_sampler_llg_free(llama_sampler * smpl) { + const auto * ctx = (llama_sampler_llg *) smpl->ctx; + + if (ctx->grammar) { + llg_free_constraint(ctx->grammar); + llg_free_tokenizer(ctx->tokenizer); + } + + delete ctx; +} + +static llama_sampler_i llama_sampler_llg_i = { + /* .name = */ llama_sampler_llg_name, + /* .accept = */ llama_sampler_llg_accept_impl, + /* .apply = */ llama_sampler_llg_apply, + /* .reset = */ llama_sampler_llg_reset, + /* .clone = */ llama_sampler_llg_clone, + /* .free = */ llama_sampler_llg_free, +}; + +static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len, + uint32_t * output_tokens, size_t output_tokens_len) { + const llama_vocab * vocab = (const llama_vocab *) user_data; + int r = 0; + try { + r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false, + true); + } catch (const std::exception & e) { + GGML_ABORT("llama_tokenize failed: %s\n", e.what()); + } + if (r < 0) { + return -r; + } + return r; +} + +static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) { + // TODO store the tokenizer in the vocab somehow + static const llama_vocab * vocab_cache; + static LlgTokenizer * tokenizer_cache; + + if (vocab_cache == vocab) { + return llg_clone_tokenizer(tokenizer_cache); + } + + auto tok_eos = llama_vocab_eot(vocab); + if (tok_eos == LLAMA_TOKEN_NULL) { + tok_eos = llama_vocab_eos(vocab); + } + + size_t vocab_size = llama_vocab_n_tokens(vocab); + + auto token_lens = new uint32_t[vocab_size]; + // we typically have ~7 bytes per token; let's go on the safe side here + auto token_bytes_size = vocab_size * 16 + 1024 * 1024; + auto token_bytes = new uint8_t[token_bytes_size]; + + size_t offset = 0; + for (size_t i = 0; i < vocab_size; i++) { + size_t max_token = 1024; + if (token_bytes_size - offset < max_token) { + GGML_ABORT("token_bytes buffer too small\n"); + } + + llama_token token = i; + auto dp = (char *) token_bytes + offset; + auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size == 0) { + size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size != 0) { + *dp = '\xff'; // special token prefix marker + size += 1; + } + } + + token_lens[i] = size; + offset += size; + } + + LlgTokenizerInit tinit = { + /* .vocab_size = */ (uint32_t) vocab_size, + /* .tok_eos = */ (uint32_t) tok_eos, + /* .token_lens = */ token_lens, + /* .token_bytes = */ token_bytes, + /* .tokenizer_json = */ nullptr, + /* .tokenize_assumes_string = */ true, + /* .tokenize_fn = */ llama_sampler_llg_tokenize_fn, + /* .use_approximate_greedy_tokenize_fn = */ false, + /* .tokenize_user_data = */ vocab, + }; + + char error_buffer[1024]; + LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer)); + + delete[] token_bytes; + delete[] token_lens; + + if (tokenizer == nullptr) { + LOG_ERR("llg tokenizer error: %s\n", error_buffer); + return tokenizer; + } + + if (tokenizer_cache) { + llg_free_tokenizer(tokenizer_cache); + } + vocab_cache = vocab; + tokenizer_cache = tokenizer; + + return llg_clone_tokenizer(tokenizer_cache); +} + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, + const char * grammar_data) { + auto * ctx = new llama_sampler_llg; + + if (grammar_kind != nullptr && grammar_kind[0] != '\0') { + auto tokenizer = llama_sampler_llg_new_tokenizer(vocab); + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ grammar_kind, + /* .grammar_data = */ grammar_data, + /* .tokenizer = */ tokenizer, + /* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data), + /* .llg_res = */ {}, + /* .has_llg_res = */ false, + }; + } else { + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ {}, + /* .grammar_data = */ {}, + /* .tokenizer = */ nullptr, + /* .grammar = */ nullptr, + /* .llg_res = */ {}, + /* .has_llg_res = */ false, + }; + } + + return llama_sampler_init( + /* .iface = */ &llama_sampler_llg_i, + /* .ctx = */ ctx + ); +} + +#else + +llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) { + LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); + return nullptr; +} + +#endif // LLAMA_USE_LLGUIDANCE diff --git a/common/log.cpp b/common/log.cpp index 04c7c0ed10595..52b31470c46bd 100644 --- a/common/log.cpp +++ b/common/log.cpp @@ -1,5 +1,6 @@ #include "log.h" +#include #include #include #include @@ -14,16 +15,6 @@ void common_log_set_verbosity_thold(int verbosity) { common_log_verbosity_thold = verbosity; } -#define LOG_COL_DEFAULT "\033[0m" -#define LOG_COL_BOLD "\033[1m" -#define LOG_COL_RED "\033[31m" -#define LOG_COL_GREEN "\033[32m" -#define LOG_COL_YELLOW "\033[33m" -#define LOG_COL_BLUE "\033[34m" -#define LOG_COL_MAGENTA "\033[35m" -#define LOG_COL_CYAN "\033[36m" -#define LOG_COL_WHITE "\033[37m" - static int64_t t_us() { return std::chrono::duration_cast(std::chrono::system_clock::now().time_since_epoch()).count(); } @@ -206,6 +197,7 @@ struct common_log { vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy); } #endif + va_end(args_copy); } entry.level = level; diff --git a/common/log.h b/common/log.h index 66605cc69a314..c56bb50d95db0 100644 --- a/common/log.h +++ b/common/log.h @@ -2,9 +2,20 @@ #include "ggml.h" // for ggml_log_level +#define LOG_CLR_TO_EOL "\033[K\r" +#define LOG_COL_DEFAULT "\033[0m" +#define LOG_COL_BOLD "\033[1m" +#define LOG_COL_RED "\033[31m" +#define LOG_COL_GREEN "\033[32m" +#define LOG_COL_YELLOW "\033[33m" +#define LOG_COL_BLUE "\033[34m" +#define LOG_COL_MAGENTA "\033[35m" +#define LOG_COL_CYAN "\033[36m" +#define LOG_COL_WHITE "\033[37m" + #ifndef __GNUC__ # define LOG_ATTRIBUTE_FORMAT(...) -#elif defined(__MINGW32__) +#elif defined(__MINGW32__) && !defined(__clang__) # define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) #else # define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) diff --git a/common/minja/chat-template.hpp b/common/minja/chat-template.hpp new file mode 100644 index 0000000000000..882ba41bd1bb0 --- /dev/null +++ b/common/minja/chat-template.hpp @@ -0,0 +1,529 @@ +/* + Copyright 2024 Google LLC + + Use of this source code is governed by an MIT-style + license that can be found in the LICENSE file or at + https://opensource.org/licenses/MIT. +*/ +// SPDX-License-Identifier: MIT +#pragma once + +#include "minja.hpp" +#include +#include +#include + +using json = nlohmann::ordered_json; + +namespace minja { + +struct chat_template_caps { + bool supports_tools = false; + bool supports_tool_calls = false; + bool supports_tool_responses = false; + bool supports_system_role = false; + bool supports_parallel_tool_calls = false; + bool supports_tool_call_id = false; + // meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object. + // Most other templates (and OpenAI's API) expect the arguments object to be stringified. + bool requires_object_arguments = false; + // CohereForAI/c4ai-command-r-plus simple variant + bool requires_non_null_content = false; + // MiniMaxAI/MiniMax-Text-01 special + bool requires_typed_content = false; +}; + +struct chat_template_inputs { + nlohmann::ordered_json messages; + nlohmann::ordered_json tools; + bool add_generation_prompt = true; + nlohmann::ordered_json extra_context; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); +}; + +struct chat_template_options { + bool apply_polyfills = true; + bool use_bos_token = true; + bool use_eos_token = true; + bool define_strftime_now = true; + + bool polyfill_tools = true; + bool polyfill_tool_call_examples = true; + bool polyfill_tool_calls = true; + bool polyfill_tool_responses = true; + bool polyfill_system_role = true; + bool polyfill_object_arguments = true; + bool polyfill_typed_content = true; +}; + +class chat_template { + + private: + chat_template_caps caps_; + std::string source_; + std::string bos_token_; + std::string eos_token_; + std::shared_ptr template_root_; + std::string tool_call_example_; + + std::string try_raw_render( + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const + { + try { + chat_template_inputs inputs; + inputs.messages = messages; + inputs.tools = tools; + inputs.add_generation_prompt = add_generation_prompt; + inputs.extra_context = extra_context; + // Use fixed date for tests + inputs.now = std::chrono::system_clock::from_time_t(0); + + chat_template_options opts; + opts.apply_polyfills = false; + + auto prompt = apply(inputs, opts); + // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str()); + return prompt; + } catch (const std::exception & e) { + // fprintf(stderr, "try_raw_render error: %s\n", e.what()); + return ""; + } + } + + public: + + chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token) + : source_(source), bos_token_(bos_token), eos_token_(eos_token) + { + template_root_ = minja::Parser::parse(source_, { + /* .trim_blocks = */ true, + /* .lstrip_blocks = */ true, + /* .keep_trailing_newline = */ false, + }); + + auto contains = [](const std::string & haystack, const std::string & needle) { + return haystack.find(needle) != std::string::npos; + }; + + const std::string user_needle = ""; + const std::string sys_needle = ""; + const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}}; + const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}}; + + caps_.requires_typed_content = + !contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle) + && contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle); + + const auto dummy_user_msg = caps_.requires_typed_content + ? dummy_typed_user_msg + : dummy_str_user_msg; + const json needle_system_msg = { + {"role", "system"}, + {"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)}, + }; + + caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle); + + auto out = try_raw_render(json::array({ + dummy_user_msg + }), json::array({ + { + {"name", "some_tool"}, + {"type", "function"}, + {"function", { + {"name", "some_tool"}, + {"description", "Some tool."}, + {"parameters", { + {"type", "object"}, + {"properties", { + {"arg", { + {"type", "string"}, + {"description", "Some argument."}, + }}, + }}, + {"required", json::array({ "arg" })}, + }}, + }}, + }, + }), false); + caps_.supports_tools = contains(out, "some_tool"); + + auto make_tool_calls_msg = [&](const json & tool_calls) { + return json { + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", tool_calls}, + }; + }; + auto make_tool_call = [](const std::string & tool_name, const json & arguments) { + return json { + {"id", "call_1___"}, + {"type", "function"}, + {"function", { + {"arguments", arguments}, + {"name", tool_name}, + }}, + }; + }; + const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}}; + + // Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want. + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})), + }), {}, false); + auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':"); + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})), + }), {}, false); + auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':"); + + caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments; + caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments; + auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false); + auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false); + caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle); + + if (caps_.supports_tool_calls) { + auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump()); + auto tc1 = make_tool_call("test_tool1", dummy_args); + auto tc2 = make_tool_call("test_tool2", dummy_args); + auto out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({tc1, tc2})), + }), {}, false); + caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2"); + + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({tc1})), + { + {"role", "tool"}, + {"name", "test_tool1"}, + {"content", "Some response!"}, + {"tool_call_id", "call_911_"}, + } + }), {}, false); + caps_.supports_tool_responses = contains(out, "Some response!"); + caps_.supports_tool_call_id = contains(out, "call_911_"); + } + + try { + if (!caps_.supports_tools) { + const json user_msg { + {"role", "user"}, + {"content", "Hey"}, + }; + const json args { + {"arg1", "some_value"}, + }; + const json tool_call_msg { + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + { + // TODO: detect if requires numerical id or fixed length == 6 like Nemo + {"id", "call_1___"}, + {"type", "function"}, + {"function", { + {"name", "tool_name"}, + {"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))}, + }}, + }, + })}, + }; + std::string prefix, full; + { + chat_template_inputs inputs; + inputs.messages = json::array({user_msg}); + inputs.add_generation_prompt = true; + prefix = apply(inputs); + } + { + chat_template_inputs inputs; + inputs.messages = json::array({user_msg, tool_call_msg}); + inputs.add_generation_prompt = false; + full = apply(inputs); + } + auto eos_pos_last = full.rfind(eos_token_); + if (eos_pos_last == prefix.size() - eos_token_.size() || + (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) { + full = full.substr(0, eos_pos_last); + } + size_t common_prefix_length = 0; + for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) { + if (prefix[i] != full[i]) { + break; + } + if (prefix[i] == '<') { + // DeepSeek R1's template (as of 20250209) adds a trailing if add_generation_prompt, + // but it removes thinking tags for past messages. + // The prefix and full strings diverge at vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <. + continue; + } + common_prefix_length = i + 1; + } + auto example = full.substr(common_prefix_length); + if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) { + fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n"); + } else { + tool_call_example_ = example; + } + } + } catch (const std::exception & e) { + fprintf(stderr, "Failed to generate tool call example: %s\n", e.what()); + } + } + + const std::string & source() const { return source_; } + const std::string & bos_token() const { return bos_token_; } + const std::string & eos_token() const { return eos_token_; } + const chat_template_caps & original_caps() const { return caps_; } + + // Deprecated, please use the form with chat_template_inputs and chat_template_options + std::string apply( + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(), + bool apply_polyfills = true) + { + fprintf(stderr, "[%s] Deprecated!\n", __func__); + chat_template_inputs inputs; + inputs.messages = messages; + inputs.tools = tools; + inputs.add_generation_prompt = add_generation_prompt; + inputs.extra_context = extra_context; + inputs.now = std::chrono::system_clock::now(); + + chat_template_options opts; + opts.apply_polyfills = apply_polyfills; + + return apply(inputs, opts); + } + + std::string apply( + const chat_template_inputs & inputs, + const chat_template_options & opts = chat_template_options()) const + { + json actual_messages; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_tool_calls = false; + auto has_tool_responses = false; + auto has_string_content = false; + for (const auto & message : inputs.messages) { + if (message.contains("tool_calls") && !message["tool_calls"].is_null()) { + has_tool_calls = true; + } + if (message.contains("role") && message["role"] == "tool") { + has_tool_responses = true; + } + if (message.contains("content") && message["content"].is_string()) { + has_string_content = true; + } + } + + auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role; + auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools; + auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples; + auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls; + auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses; + auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments; + auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content; + + auto needs_polyfills = opts.apply_polyfills && (false + || polyfill_system_role + || polyfill_tools + || polyfill_tool_calls + || polyfill_tool_responses + || polyfill_object_arguments + || polyfill_typed_content + ); + + if (needs_polyfills) { + actual_messages = json::array(); + + auto add_message = [&](const json & msg) { + if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) { + actual_messages.push_back({ + {"role", msg.at("role")}, + {"content", {{ + {"type", "text"}, + {"text", msg.at("content")}, + }}}, + }); + } else { + actual_messages.push_back(msg); + } + }; + + std::string pending_system; + auto flush_sys = [&]() { + if (!pending_system.empty()) { + add_message({ + {"role", "user"}, + {"content", pending_system}, + }); + pending_system.clear(); + } + }; + + json adjusted_messages; + if (polyfill_tools) { + adjusted_messages = add_system(inputs.messages, + "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) + + (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n")); + } else { + adjusted_messages = inputs.messages; + } + + for (const auto & message_ : adjusted_messages) { + auto message = message_; + if (!message.contains("role") || !message.contains("content")) { + throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump()); + } + std::string role = message.at("role"); + + if (message.contains("tool_calls")) { + if (polyfill_object_arguments || polyfill_tool_calls) { + for (auto & tool_call : message.at("tool_calls")) { + if (tool_call["type"] == "function") { + auto & function = tool_call.at("function"); + auto & arguments = function.at("arguments"); + if (arguments.is_string()) { + try { + arguments = json::parse(arguments.get()); + } catch (const std::exception & ecvt) { + fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what()); + } + } + } + } + } + if (polyfill_tool_calls) { + auto content = message.at("content"); + auto tool_calls = json::array(); + for (const auto & tool_call : message.at("tool_calls")) { + if (tool_call.at("type") != "function") { + continue; + } + const auto & function = tool_call.at("function"); + auto tc = json { + {"name", function.at("name")}, + {"arguments", function.at("arguments")}, + }; + if (tool_call.contains("id")) { + tc["id"] = tool_call["id"]; + } + tool_calls.push_back(tc); + } + auto obj = json { + {"tool_calls", tool_calls}, + }; + if (!content.is_null() && content != "") { + obj["content"] = content; + } + message["content"] = obj.dump(2); + message.erase("tool_calls"); + } + } + if (polyfill_tool_responses && role == "tool") { + message["role"] = "user"; + auto obj = json { + {"tool_response", { + {"content", message.at("content")}, + }}, + }; + if (message.contains("name")) { + obj["tool_response"]["name"] = message.at("name"); + } + if (message.contains("tool_call_id")) { + obj["tool_response"]["tool_call_id"] = message.at("tool_call_id"); + } + message["content"] = obj.dump(2); + message.erase("name"); + } + + if (!message["content"].is_null() && polyfill_system_role) { + std::string content = message.at("content"); + if (role == "system") { + if (!pending_system.empty()) pending_system += "\n"; + pending_system += content; + continue; + } else { + if (role == "user") { + if (!pending_system.empty()) { + message["content"] = pending_system + (content.empty() ? "" : "\n" + content); + pending_system.clear(); + } + } else { + flush_sys(); + } + } + } + add_message(message); + } + flush_sys(); + } else { + actual_messages = inputs.messages; + } + + auto context = minja::Context::make(json({ + {"messages", actual_messages}, + {"add_generation_prompt", inputs.add_generation_prompt}, + })); + context->set("bos_token", opts.use_bos_token ? bos_token_ : ""); + context->set("eos_token", opts.use_eos_token ? eos_token_ : ""); + if (opts.define_strftime_now) { + auto now = inputs.now; + context->set("strftime_now", Value::callable([now](const std::shared_ptr &, minja::ArgumentsValue & args) { + args.expectArgs("strftime_now", {1, 1}, {0, 0}); + auto format = args.args[0].get(); + + auto time = std::chrono::system_clock::to_time_t(now); + auto local_time = *std::localtime(&time); + std::ostringstream ss; + ss << std::put_time(&local_time, format.c_str()); + return ss.str(); + })); + } + if (!inputs.tools.is_null()) { + context->set("tools", minja::Value(inputs.tools)); + } + if (!inputs.extra_context.is_null()) { + for (auto & kv : inputs.extra_context.items()) { + context->set(kv.key(), minja::Value(kv.value())); + } + } + + auto ret = template_root_->render(context); + // fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str()); + // fprintf(stderr, "apply: %s\n\n", ret.c_str()); + return ret; + } + + static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) { + json messages_with_system = messages; + + if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") { + std::string existing_system = messages_with_system.at(0).at("content"); + messages_with_system[0] = json { + {"role", "system"}, + {"content", existing_system + "\n\n" + system_prompt}, + }; + } else { + messages_with_system.insert(messages_with_system.begin(), json { + {"role", "system"}, + {"content", system_prompt}, + }); + } + return messages_with_system; + } +}; + +} // namespace minja diff --git a/common/minja/minja.hpp b/common/minja/minja.hpp new file mode 100644 index 0000000000000..c58dd66e067b1 --- /dev/null +++ b/common/minja/minja.hpp @@ -0,0 +1,2883 @@ +/* + Copyright 2024 Google LLC + + Use of this source code is governed by an MIT-style + license that can be found in the LICENSE file or at + https://opensource.org/licenses/MIT. +*/ +// SPDX-License-Identifier: MIT +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +namespace minja { + +class Context; + +struct Options { + bool trim_blocks; // removes the first newline after a block + bool lstrip_blocks; // removes leading whitespace on the line of the block + bool keep_trailing_newline; // don't remove last newline +}; + +struct ArgumentsValue; + +inline std::string normalize_newlines(const std::string & s) { +#ifdef _WIN32 + static const std::regex nl_regex("\r\n"); + return std::regex_replace(s, nl_regex, "\n"); +#else + return s; +#endif +} + +/* Values that behave roughly like in Python. */ +class Value : public std::enable_shared_from_this { +public: + using CallableType = std::function &, ArgumentsValue &)>; + using FilterType = std::function &, ArgumentsValue &)>; + +private: + using ObjectType = nlohmann::ordered_map; // Only contains primitive keys + using ArrayType = std::vector; + + std::shared_ptr array_; + std::shared_ptr object_; + std::shared_ptr callable_; + json primitive_; + + Value(const std::shared_ptr & array) : array_(array) {} + Value(const std::shared_ptr & object) : object_(object) {} + Value(const std::shared_ptr & callable) : object_(std::make_shared()), callable_(callable) {} + + /* Python-style string repr */ + static void dump_string(const json & primitive, std::ostringstream & out, char string_quote = '\'') { + if (!primitive.is_string()) throw std::runtime_error("Value is not a string: " + primitive.dump()); + auto s = primitive.dump(); + if (string_quote == '"' || s.find('\'') != std::string::npos) { + out << s; + return; + } + // Reuse json dump, just changing string quotes + out << string_quote; + for (size_t i = 1, n = s.size() - 1; i < n; ++i) { + if (s[i] == '\\' && s[i + 1] == '"') { + out << '"'; + i++; + } else if (s[i] == string_quote) { + out << '\\' << string_quote; + } else { + out << s[i]; + } + } + out << string_quote; + } + void dump(std::ostringstream & out, int indent = -1, int level = 0, bool to_json = false) const { + auto print_indent = [&](int level) { + if (indent > 0) { + out << "\n"; + for (int i = 0, n = level * indent; i < n; ++i) out << ' '; + } + }; + auto print_sub_sep = [&]() { + out << ','; + if (indent < 0) out << ' '; + else print_indent(level + 1); + }; + + auto string_quote = to_json ? '"' : '\''; + + if (is_null()) out << "null"; + else if (array_) { + out << "["; + print_indent(level + 1); + for (size_t i = 0; i < array_->size(); ++i) { + if (i) print_sub_sep(); + (*array_)[i].dump(out, indent, level + 1, to_json); + } + print_indent(level); + out << "]"; + } else if (object_) { + out << "{"; + print_indent(level + 1); + for (auto begin = object_->begin(), it = begin; it != object_->end(); ++it) { + if (it != begin) print_sub_sep(); + if (it->first.is_string()) { + dump_string(it->first, out, string_quote); + } else { + out << string_quote << it->first.dump() << string_quote; + } + out << ": "; + it->second.dump(out, indent, level + 1, to_json); + } + print_indent(level); + out << "}"; + } else if (callable_) { + throw std::runtime_error("Cannot dump callable to JSON"); + } else if (is_boolean() && !to_json) { + out << (this->to_bool() ? "True" : "False"); + } else if (is_string() && !to_json) { + dump_string(primitive_, out, string_quote); + } else { + out << primitive_.dump(); + } + } + +public: + Value() {} + Value(const bool& v) : primitive_(v) {} + Value(const int64_t & v) : primitive_(v) {} + Value(const double& v) : primitive_(v) {} + Value(const std::nullptr_t &) {} + Value(const std::string & v) : primitive_(v) {} + Value(const char * v) : primitive_(std::string(v)) {} + + Value(const json & v) { + if (v.is_object()) { + auto object = std::make_shared(); + for (auto it = v.begin(); it != v.end(); ++it) { + (*object)[it.key()] = it.value(); + } + object_ = std::move(object); + } else if (v.is_array()) { + auto array = std::make_shared(); + for (const auto& item : v) { + array->push_back(Value(item)); + } + array_ = array; + } else { + primitive_ = v; + } + } + + std::vector keys() { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + std::vector res; + for (const auto& item : *object_) { + res.push_back(item.first); + } + return res; + } + + size_t size() const { + if (is_object()) return object_->size(); + if (is_array()) return array_->size(); + if (is_string()) return primitive_.get().length(); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + + static Value array(const std::vector values = {}) { + auto array = std::make_shared(); + for (const auto& item : values) { + array->push_back(item); + } + return Value(array); + } + static Value object(const std::shared_ptr object = std::make_shared()) { + return Value(object); + } + static Value callable(const CallableType & callable) { + return Value(std::make_shared(callable)); + } + + void insert(size_t index, const Value& v) { + if (!array_) + throw std::runtime_error("Value is not an array: " + dump()); + array_->insert(array_->begin() + index, v); + } + void push_back(const Value& v) { + if (!array_) + throw std::runtime_error("Value is not an array: " + dump()); + array_->push_back(v); + } + Value pop(const Value& index) { + if (is_array()) { + if (array_->empty()) + throw std::runtime_error("pop from empty list"); + if (index.is_null()) { + auto ret = array_->back(); + array_->pop_back(); + return ret; + } else if (!index.is_number_integer()) { + throw std::runtime_error("pop index must be an integer: " + index.dump()); + } else { + auto i = index.get(); + if (i < 0 || i >= static_cast(array_->size())) + throw std::runtime_error("pop index out of range: " + index.dump()); + auto it = array_->begin() + (i < 0 ? array_->size() + i : i); + auto ret = *it; + array_->erase(it); + return ret; + } + } else if (is_object()) { + if (!index.is_hashable()) + throw std::runtime_error("Unashable type: " + index.dump()); + auto it = object_->find(index.primitive_); + if (it == object_->end()) + throw std::runtime_error("Key not found: " + index.dump()); + auto ret = it->second; + object_->erase(it); + return ret; + } else { + throw std::runtime_error("Value is not an array or object: " + dump()); + } + } + Value get(const Value& key) { + if (array_) { + if (!key.is_number_integer()) { + return Value(); + } + auto index = key.get(); + return array_->at(index < 0 ? array_->size() + index : index); + } else if (object_) { + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + auto it = object_->find(key.primitive_); + if (it == object_->end()) return Value(); + return it->second; + } + return Value(); + } + void set(const Value& key, const Value& value) { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + (*object_)[key.primitive_] = value; + } + Value call(const std::shared_ptr & context, ArgumentsValue & args) const { + if (!callable_) throw std::runtime_error("Value is not callable: " + dump()); + return (*callable_)(context, args); + } + + bool is_object() const { return !!object_; } + bool is_array() const { return !!array_; } + bool is_callable() const { return !!callable_; } + bool is_null() const { return !object_ && !array_ && primitive_.is_null() && !callable_; } + bool is_boolean() const { return primitive_.is_boolean(); } + bool is_number_integer() const { return primitive_.is_number_integer(); } + bool is_number_float() const { return primitive_.is_number_float(); } + bool is_number() const { return primitive_.is_number(); } + bool is_string() const { return primitive_.is_string(); } + bool is_iterable() const { return is_array() || is_object() || is_string(); } + + bool is_primitive() const { return !array_ && !object_ && !callable_; } + bool is_hashable() const { return is_primitive(); } + + bool empty() const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_string()) return primitive_.empty(); + if (is_array()) return array_->empty(); + if (is_object()) return object_->empty(); + return false; + } + + void for_each(const std::function & callback) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (array_) { + for (auto& item : *array_) { + callback(item); + } + } else if (object_) { + for (auto & item : *object_) { + Value key(item.first); + callback(key); + } + } else if (is_string()) { + for (char c : primitive_.get()) { + auto val = Value(std::string(1, c)); + callback(val); + } + } else { + throw std::runtime_error("Value is not iterable: " + dump()); + } + } + + bool to_bool() const { + if (is_null()) return false; + if (is_boolean()) return get(); + if (is_number()) return get() != 0; + if (is_string()) return !get().empty(); + if (is_array()) return !empty(); + return true; + } + + int64_t to_int() const { + if (is_null()) return 0; + if (is_boolean()) return get() ? 1 : 0; + if (is_number()) return static_cast(get()); + if (is_string()) { + try { + return std::stol(get()); + } catch (const std::exception &) { + return 0; + } + } + return 0; + } + + bool operator<(const Value & other) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_number() && other.is_number()) return get() < other.get(); + if (is_string() && other.is_string()) return get() < other.get(); + throw std::runtime_error("Cannot compare values: " + dump() + " < " + other.dump()); + } + bool operator>=(const Value & other) const { return !(*this < other); } + + bool operator>(const Value & other) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_number() && other.is_number()) return get() > other.get(); + if (is_string() && other.is_string()) return get() > other.get(); + throw std::runtime_error("Cannot compare values: " + dump() + " > " + other.dump()); + } + bool operator<=(const Value & other) const { return !(*this > other); } + + bool operator==(const Value & other) const { + if (callable_ || other.callable_) { + if (callable_.get() != other.callable_.get()) return false; + } + if (array_) { + if (!other.array_) return false; + if (array_->size() != other.array_->size()) return false; + for (size_t i = 0; i < array_->size(); ++i) { + if (!(*array_)[i].to_bool() || !(*other.array_)[i].to_bool() || (*array_)[i] != (*other.array_)[i]) return false; + } + return true; + } else if (object_) { + if (!other.object_) return false; + if (object_->size() != other.object_->size()) return false; + for (const auto& item : *object_) { + if (!item.second.to_bool() || !other.object_->count(item.first) || item.second != other.object_->at(item.first)) return false; + } + return true; + } else { + return primitive_ == other.primitive_; + } + } + bool operator!=(const Value & other) const { return !(*this == other); } + + bool contains(const char * key) const { return contains(std::string(key)); } + bool contains(const std::string & key) const { + if (array_) { + return false; + } else if (object_) { + return object_->find(key) != object_->end(); + } else { + throw std::runtime_error("contains can only be called on arrays and objects: " + dump()); + } + } + bool contains(const Value & value) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (array_) { + for (const auto& item : *array_) { + if (item.to_bool() && item == value) return true; + } + return false; + } else if (object_) { + if (!value.is_hashable()) throw std::runtime_error("Unashable type: " + value.dump()); + return object_->find(value.primitive_) != object_->end(); + } else { + throw std::runtime_error("contains can only be called on arrays and objects: " + dump()); + } + } + void erase(size_t index) { + if (!array_) throw std::runtime_error("Value is not an array: " + dump()); + array_->erase(array_->begin() + index); + } + void erase(const std::string & key) { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + object_->erase(key); + } + const Value& at(const Value & index) const { + return const_cast(this)->at(index); + } + Value& at(const Value & index) { + if (!index.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + if (is_array()) return array_->at(index.get()); + if (is_object()) return object_->at(index.primitive_); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + const Value& at(size_t index) const { + return const_cast(this)->at(index); + } + Value& at(size_t index) { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_array()) return array_->at(index); + if (is_object()) return object_->at(index); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + + template + T get(const std::string & key, T default_value) const { + if (!contains(key)) return default_value; + return at(key).get(); + } + + template + T get() const { + if (is_primitive()) return primitive_.get(); + throw std::runtime_error("get not defined for this value type: " + dump()); + } + + std::string dump(int indent=-1, bool to_json=false) const { + std::ostringstream out; + dump(out, indent, 0, to_json); + return out.str(); + } + + Value operator-() const { + if (is_number_integer()) + return -get(); + else + return -get(); + } + std::string to_str() const { + if (is_string()) return get(); + if (is_number_integer()) return std::to_string(get()); + if (is_number_float()) return std::to_string(get()); + if (is_boolean()) return get() ? "True" : "False"; + if (is_null()) return "None"; + return dump(); + } + Value operator+(const Value& rhs) const { + if (is_string() || rhs.is_string()) { + return to_str() + rhs.to_str(); + } else if (is_number_integer() && rhs.is_number_integer()) { + return get() + rhs.get(); + } else if (is_array() && rhs.is_array()) { + auto res = Value::array(); + for (const auto& item : *array_) res.push_back(item); + for (const auto& item : *rhs.array_) res.push_back(item); + return res; + } else { + return get() + rhs.get(); + } + } + Value operator-(const Value& rhs) const { + if (is_number_integer() && rhs.is_number_integer()) + return get() - rhs.get(); + else + return get() - rhs.get(); + } + Value operator*(const Value& rhs) const { + if (is_string() && rhs.is_number_integer()) { + std::ostringstream out; + for (int64_t i = 0, n = rhs.get(); i < n; ++i) { + out << to_str(); + } + return out.str(); + } + else if (is_number_integer() && rhs.is_number_integer()) + return get() * rhs.get(); + else + return get() * rhs.get(); + } + Value operator/(const Value& rhs) const { + if (is_number_integer() && rhs.is_number_integer()) + return get() / rhs.get(); + else + return get() / rhs.get(); + } + Value operator%(const Value& rhs) const { + return get() % rhs.get(); + } +}; + +struct ArgumentsValue { + std::vector args; + std::vector> kwargs; + + bool has_named(const std::string & name) { + for (const auto & p : kwargs) { + if (p.first == name) return true; + } + return false; + } + + Value get_named(const std::string & name) { + for (const auto & [key, value] : kwargs) { + if (key == name) return value; + } + return Value(); + } + + bool empty() { + return args.empty() && kwargs.empty(); + } + + void expectArgs(const std::string & method_name, const std::pair & pos_count, const std::pair & kw_count) { + if (args.size() < pos_count.first || args.size() > pos_count.second || kwargs.size() < kw_count.first || kwargs.size() > kw_count.second) { + std::ostringstream out; + out << method_name << " must have between " << pos_count.first << " and " << pos_count.second << " positional arguments and between " << kw_count.first << " and " << kw_count.second << " keyword arguments"; + throw std::runtime_error(out.str()); + } + } +}; + +template <> +inline json Value::get() const { + if (is_primitive()) return primitive_; + if (is_null()) return json(); + if (array_) { + std::vector res; + for (const auto& item : *array_) { + res.push_back(item.get()); + } + return res; + } + if (object_) { + json res = json::object(); + for (const auto& [key, value] : *object_) { + if (key.is_string()) { + res[key.get()] = value.get(); + } else if (key.is_primitive()) { + res[key.dump()] = value.get(); + } else { + throw std::runtime_error("Invalid key type for conversion to JSON: " + key.dump()); + } + } + if (is_callable()) { + res["__callable__"] = true; + } + return res; + } + throw std::runtime_error("get not defined for this value type: " + dump()); +} + +} // namespace minja + +namespace std { + template <> + struct hash { + size_t operator()(const minja::Value & v) const { + if (!v.is_hashable()) + throw std::runtime_error("Unsupported type for hashing: " + v.dump()); + return std::hash()(v.get()); + } + }; +} // namespace std + +namespace minja { + +static std::string error_location_suffix(const std::string & source, size_t pos) { + auto get_line = [&](size_t line) { + auto start = source.begin(); + for (size_t i = 1; i < line; ++i) { + start = std::find(start, source.end(), '\n') + 1; + } + auto end = std::find(start, source.end(), '\n'); + return std::string(start, end); + }; + auto start = source.begin(); + auto end = source.end(); + auto it = start + pos; + auto line = std::count(start, it, '\n') + 1; + auto max_line = std::count(start, end, '\n') + 1; + auto col = pos - std::string(start, it).rfind('\n'); + std::ostringstream out; + out << " at row " << line << ", column " << col << ":\n"; + if (line > 1) out << get_line(line - 1) << "\n"; + out << get_line(line) << "\n"; + out << std::string(col - 1, ' ') << "^\n"; + if (line < max_line) out << get_line(line + 1) << "\n"; + + return out.str(); +} + +class Context : public std::enable_shared_from_this { + protected: + Value values_; + std::shared_ptr parent_; + public: + Context(Value && values, const std::shared_ptr & parent = nullptr) : values_(std::move(values)), parent_(parent) { + if (!values_.is_object()) throw std::runtime_error("Context values must be an object: " + values_.dump()); + } + virtual ~Context() {} + + static std::shared_ptr builtins(); + static std::shared_ptr make(Value && values, const std::shared_ptr & parent = builtins()); + + std::vector keys() { + return values_.keys(); + } + virtual Value get(const Value & key) { + if (values_.contains(key)) return values_.at(key); + if (parent_) return parent_->get(key); + return Value(); + } + virtual Value & at(const Value & key) { + if (values_.contains(key)) return values_.at(key); + if (parent_) return parent_->at(key); + throw std::runtime_error("Undefined variable: " + key.dump()); + } + virtual bool contains(const Value & key) { + if (values_.contains(key)) return true; + if (parent_) return parent_->contains(key); + return false; + } + virtual void set(const Value & key, const Value & value) { + values_.set(key, value); + } +}; + +struct Location { + std::shared_ptr source; + size_t pos; +}; + +class Expression { +protected: + virtual Value do_evaluate(const std::shared_ptr & context) const = 0; +public: + using Parameters = std::vector>>; + + Location location; + + Expression(const Location & location) : location(location) {} + virtual ~Expression() = default; + + Value evaluate(const std::shared_ptr & context) const { + try { + return do_evaluate(context); + } catch (const std::exception & e) { + std::ostringstream out; + out << e.what(); + if (location.source) out << error_location_suffix(*location.source, location.pos); + throw std::runtime_error(out.str()); + } + } +}; + +class VariableExpr : public Expression { + std::string name; +public: + VariableExpr(const Location & location, const std::string& n) + : Expression(location), name(n) {} + std::string get_name() const { return name; } + Value do_evaluate(const std::shared_ptr & context) const override { + if (!context->contains(name)) { + return Value(); + } + return context->at(name); + } +}; + +static void destructuring_assign(const std::vector & var_names, const std::shared_ptr & context, Value& item) { + if (var_names.size() == 1) { + Value name(var_names[0]); + context->set(name, item); + } else { + if (!item.is_array() || item.size() != var_names.size()) { + throw std::runtime_error("Mismatched number of variables and items in destructuring assignment"); + } + for (size_t i = 0; i < var_names.size(); ++i) { + context->set(var_names[i], item.at(i)); + } + } +} + +enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline }; + +class TemplateToken { +public: + enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter, Break, Continue }; + + static std::string typeToString(Type t) { + switch (t) { + case Type::Text: return "text"; + case Type::Expression: return "expression"; + case Type::If: return "if"; + case Type::Else: return "else"; + case Type::Elif: return "elif"; + case Type::EndIf: return "endif"; + case Type::For: return "for"; + case Type::EndFor: return "endfor"; + case Type::Set: return "set"; + case Type::EndSet: return "endset"; + case Type::Comment: return "comment"; + case Type::Macro: return "macro"; + case Type::EndMacro: return "endmacro"; + case Type::Filter: return "filter"; + case Type::EndFilter: return "endfilter"; + case Type::Generation: return "generation"; + case Type::EndGeneration: return "endgeneration"; + case Type::Break: return "break"; + case Type::Continue: return "continue"; + } + return "Unknown"; + } + + TemplateToken(Type type, const Location & location, SpaceHandling pre, SpaceHandling post) : type(type), location(location), pre_space(pre), post_space(post) {} + virtual ~TemplateToken() = default; + + Type type; + Location location; + SpaceHandling pre_space = SpaceHandling::Keep; + SpaceHandling post_space = SpaceHandling::Keep; +}; + +struct TextTemplateToken : public TemplateToken { + std::string text; + TextTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Text, location, pre, post), text(t) {} +}; + +struct ExpressionTemplateToken : public TemplateToken { + std::shared_ptr expr; + ExpressionTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && e) : TemplateToken(Type::Expression, location, pre, post), expr(std::move(e)) {} +}; + +struct IfTemplateToken : public TemplateToken { + std::shared_ptr condition; + IfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::If, location, pre, post), condition(std::move(c)) {} +}; + +struct ElifTemplateToken : public TemplateToken { + std::shared_ptr condition; + ElifTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::Elif, location, pre, post), condition(std::move(c)) {} +}; + +struct ElseTemplateToken : public TemplateToken { + ElseTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Else, location, pre, post) {} +}; + +struct EndIfTemplateToken : public TemplateToken { + EndIfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndIf, location, pre, post) {} +}; + +struct MacroTemplateToken : public TemplateToken { + std::shared_ptr name; + Expression::Parameters params; + MacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && n, Expression::Parameters && p) + : TemplateToken(Type::Macro, location, pre, post), name(std::move(n)), params(std::move(p)) {} +}; + +struct EndMacroTemplateToken : public TemplateToken { + EndMacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndMacro, location, pre, post) {} +}; + +struct FilterTemplateToken : public TemplateToken { + std::shared_ptr filter; + FilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && filter) + : TemplateToken(Type::Filter, location, pre, post), filter(std::move(filter)) {} +}; + +struct EndFilterTemplateToken : public TemplateToken { + EndFilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFilter, location, pre, post) {} +}; + +struct ForTemplateToken : public TemplateToken { + std::vector var_names; + std::shared_ptr iterable; + std::shared_ptr condition; + bool recursive; + ForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::vector & vns, std::shared_ptr && iter, + std::shared_ptr && c, bool r) + : TemplateToken(Type::For, location, pre, post), var_names(vns), iterable(std::move(iter)), condition(std::move(c)), recursive(r) {} +}; + +struct EndForTemplateToken : public TemplateToken { + EndForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, location, pre, post) {} +}; + +struct GenerationTemplateToken : public TemplateToken { + GenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Generation, location, pre, post) {} +}; + +struct EndGenerationTemplateToken : public TemplateToken { + EndGenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndGeneration, location, pre, post) {} +}; + +struct SetTemplateToken : public TemplateToken { + std::string ns; + std::vector var_names; + std::shared_ptr value; + SetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateToken(Type::Set, location, pre, post), ns(ns), var_names(vns), value(std::move(v)) {} +}; + +struct EndSetTemplateToken : public TemplateToken { + EndSetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndSet, location, pre, post) {} +}; + +struct CommentTemplateToken : public TemplateToken { + std::string text; + CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {} +}; + +enum class LoopControlType { Break, Continue }; + +class LoopControlException : public std::runtime_error { +public: + LoopControlType control_type; + LoopControlException(const std::string & message, LoopControlType control_type) : std::runtime_error(message), control_type(control_type) {} + LoopControlException(LoopControlType control_type) + : std::runtime_error((control_type == LoopControlType::Continue ? "continue" : "break") + std::string(" outside of a loop")), + control_type(control_type) {} +}; + +struct LoopControlTemplateToken : public TemplateToken { + LoopControlType control_type; + LoopControlTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, location, pre, post), control_type(control_type) {} +}; + +class TemplateNode { + Location location_; +protected: + virtual void do_render(std::ostringstream & out, const std::shared_ptr & context) const = 0; + +public: + TemplateNode(const Location & location) : location_(location) {} + void render(std::ostringstream & out, const std::shared_ptr & context) const { + try { + do_render(out, context); + } catch (const LoopControlException & e) { + // TODO: make stack creation lazy. Only needed if it was thrown outside of a loop. + std::ostringstream err; + err << e.what(); + if (location_.source) err << error_location_suffix(*location_.source, location_.pos); + throw LoopControlException(err.str(), e.control_type); + } catch (const std::exception & e) { + std::ostringstream err; + err << e.what(); + if (location_.source) err << error_location_suffix(*location_.source, location_.pos); + throw std::runtime_error(err.str()); + } + } + const Location & location() const { return location_; } + virtual ~TemplateNode() = default; + std::string render(const std::shared_ptr & context) const { + std::ostringstream out; + render(out, context); + return out.str(); + } +}; + +class SequenceNode : public TemplateNode { + std::vector> children; +public: + SequenceNode(const Location & location, std::vector> && c) + : TemplateNode(location), children(std::move(c)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + for (const auto& child : children) child->render(out, context); + } +}; + +class TextNode : public TemplateNode { + std::string text; +public: + TextNode(const Location & location, const std::string& t) : TemplateNode(location), text(t) {} + void do_render(std::ostringstream & out, const std::shared_ptr &) const override { + out << text; + } +}; + +class ExpressionNode : public TemplateNode { + std::shared_ptr expr; +public: + ExpressionNode(const Location & location, std::shared_ptr && e) : TemplateNode(location), expr(std::move(e)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + if (!expr) throw std::runtime_error("ExpressionNode.expr is null"); + auto result = expr->evaluate(context); + if (result.is_string()) { + out << result.get(); + } else if (result.is_boolean()) { + out << (result.get() ? "True" : "False"); + } else if (!result.is_null()) { + out << result.dump(); + } + } +}; + +class IfNode : public TemplateNode { + std::vector, std::shared_ptr>> cascade; +public: + IfNode(const Location & location, std::vector, std::shared_ptr>> && c) + : TemplateNode(location), cascade(std::move(c)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + for (const auto& branch : cascade) { + auto enter_branch = true; + if (branch.first) { + enter_branch = branch.first->evaluate(context).to_bool(); + } + if (enter_branch) { + if (!branch.second) throw std::runtime_error("IfNode.cascade.second is null"); + branch.second->render(out, context); + return; + } + } + } +}; + +class LoopControlNode : public TemplateNode { + LoopControlType control_type_; + public: + LoopControlNode(const Location & location, LoopControlType control_type) : TemplateNode(location), control_type_(control_type) {} + void do_render(std::ostringstream &, const std::shared_ptr &) const override { + throw LoopControlException(control_type_); + } +}; + +class ForNode : public TemplateNode { + std::vector var_names; + std::shared_ptr iterable; + std::shared_ptr condition; + std::shared_ptr body; + bool recursive; + std::shared_ptr else_body; +public: + ForNode(const Location & location, std::vector && var_names, std::shared_ptr && iterable, + std::shared_ptr && condition, std::shared_ptr && body, bool recursive, std::shared_ptr && else_body) + : TemplateNode(location), var_names(var_names), iterable(std::move(iterable)), condition(std::move(condition)), body(std::move(body)), recursive(recursive), else_body(std::move(else_body)) {} + + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + // https://jinja.palletsprojects.com/en/3.0.x/templates/#for + if (!iterable) throw std::runtime_error("ForNode.iterable is null"); + if (!body) throw std::runtime_error("ForNode.body is null"); + + auto iterable_value = iterable->evaluate(context); + Value::CallableType loop_function; + + std::function visit = [&](Value& iter) { + auto filtered_items = Value::array(); + if (!iter.is_null()) { + if (!iterable_value.is_iterable()) { + throw std::runtime_error("For loop iterable must be iterable: " + iterable_value.dump()); + } + iterable_value.for_each([&](Value & item) { + destructuring_assign(var_names, context, item); + if (!condition || condition->evaluate(context).to_bool()) { + filtered_items.push_back(item); + } + }); + } + if (filtered_items.empty()) { + if (else_body) { + else_body->render(out, context); + } + } else { + auto loop = recursive ? Value::callable(loop_function) : Value::object(); + loop.set("length", (int64_t) filtered_items.size()); + + size_t cycle_index = 0; + loop.set("cycle", Value::callable([&](const std::shared_ptr &, ArgumentsValue & args) { + if (args.args.empty() || !args.kwargs.empty()) { + throw std::runtime_error("cycle() expects at least 1 positional argument and no named arg"); + } + auto item = args.args[cycle_index]; + cycle_index = (cycle_index + 1) % args.args.size(); + return item; + })); + auto loop_context = Context::make(Value::object(), context); + loop_context->set("loop", loop); + for (size_t i = 0, n = filtered_items.size(); i < n; ++i) { + auto & item = filtered_items.at(i); + destructuring_assign(var_names, loop_context, item); + loop.set("index", (int64_t) i + 1); + loop.set("index0", (int64_t) i); + loop.set("revindex", (int64_t) (n - i)); + loop.set("revindex0", (int64_t) (n - i - 1)); + loop.set("length", (int64_t) n); + loop.set("first", i == 0); + loop.set("last", i == (n - 1)); + loop.set("previtem", i > 0 ? filtered_items.at(i - 1) : Value()); + loop.set("nextitem", i < n - 1 ? filtered_items.at(i + 1) : Value()); + try { + body->render(out, loop_context); + } catch (const LoopControlException & e) { + if (e.control_type == LoopControlType::Break) break; + if (e.control_type == LoopControlType::Continue) continue; + } + } + } + }; + + if (recursive) { + loop_function = [&](const std::shared_ptr &, ArgumentsValue & args) { + if (args.args.size() != 1 || !args.kwargs.empty() || !args.args[0].is_array()) { + throw std::runtime_error("loop() expects exactly 1 positional iterable argument"); + } + auto & items = args.args[0]; + visit(items); + return Value(); + }; + } + + visit(iterable_value); + } +}; + +class MacroNode : public TemplateNode { + std::shared_ptr name; + Expression::Parameters params; + std::shared_ptr body; + std::unordered_map named_param_positions; +public: + MacroNode(const Location & location, std::shared_ptr && n, Expression::Parameters && p, std::shared_ptr && b) + : TemplateNode(location), name(std::move(n)), params(std::move(p)), body(std::move(b)) { + for (size_t i = 0; i < params.size(); ++i) { + const auto & name = params[i].first; + if (!name.empty()) { + named_param_positions[name] = i; + } + } + } + void do_render(std::ostringstream &, const std::shared_ptr & macro_context) const override { + if (!name) throw std::runtime_error("MacroNode.name is null"); + if (!body) throw std::runtime_error("MacroNode.body is null"); + auto callable = Value::callable([&](const std::shared_ptr & context, ArgumentsValue & args) { + auto call_context = macro_context; + std::vector param_set(params.size(), false); + for (size_t i = 0, n = args.args.size(); i < n; i++) { + auto & arg = args.args[i]; + if (i >= params.size()) throw std::runtime_error("Too many positional arguments for macro " + name->get_name()); + param_set[i] = true; + auto & param_name = params[i].first; + call_context->set(param_name, arg); + } + for (auto & [arg_name, value] : args.kwargs) { + auto it = named_param_positions.find(arg_name); + if (it == named_param_positions.end()) throw std::runtime_error("Unknown parameter name for macro " + name->get_name() + ": " + arg_name); + + call_context->set(arg_name, value); + param_set[it->second] = true; + } + // Set default values for parameters that were not passed + for (size_t i = 0, n = params.size(); i < n; i++) { + if (!param_set[i] && params[i].second != nullptr) { + auto val = params[i].second->evaluate(context); + call_context->set(params[i].first, val); + } + } + return body->render(call_context); + }); + macro_context->set(name->get_name(), callable); + } +}; + +class FilterNode : public TemplateNode { + std::shared_ptr filter; + std::shared_ptr body; + +public: + FilterNode(const Location & location, std::shared_ptr && f, std::shared_ptr && b) + : TemplateNode(location), filter(std::move(f)), body(std::move(b)) {} + + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + if (!filter) throw std::runtime_error("FilterNode.filter is null"); + if (!body) throw std::runtime_error("FilterNode.body is null"); + auto filter_value = filter->evaluate(context); + if (!filter_value.is_callable()) { + throw std::runtime_error("Filter must be a callable: " + filter_value.dump()); + } + std::string rendered_body = body->render(context); + + ArgumentsValue filter_args = {{Value(rendered_body)}, {}}; + auto result = filter_value.call(context, filter_args); + out << result.to_str(); + } +}; + +class SetNode : public TemplateNode { + std::string ns; + std::vector var_names; + std::shared_ptr value; +public: + SetNode(const Location & location, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateNode(location), ns(ns), var_names(vns), value(std::move(v)) {} + void do_render(std::ostringstream &, const std::shared_ptr & context) const override { + if (!value) throw std::runtime_error("SetNode.value is null"); + if (!ns.empty()) { + if (var_names.size() != 1) { + throw std::runtime_error("Namespaced set only supports a single variable name"); + } + auto & name = var_names[0]; + auto ns_value = context->get(ns); + if (!ns_value.is_object()) throw std::runtime_error("Namespace '" + ns + "' is not an object"); + ns_value.set(name, this->value->evaluate(context)); + } else { + auto val = value->evaluate(context); + destructuring_assign(var_names, context, val); + } + } +}; + +class SetTemplateNode : public TemplateNode { + std::string name; + std::shared_ptr template_value; +public: + SetTemplateNode(const Location & location, const std::string & name, std::shared_ptr && tv) + : TemplateNode(location), name(name), template_value(std::move(tv)) {} + void do_render(std::ostringstream &, const std::shared_ptr & context) const override { + if (!template_value) throw std::runtime_error("SetTemplateNode.template_value is null"); + Value value { template_value->render(context) }; + context->set(name, value); + } +}; + +class IfExpr : public Expression { + std::shared_ptr condition; + std::shared_ptr then_expr; + std::shared_ptr else_expr; +public: + IfExpr(const Location & location, std::shared_ptr && c, std::shared_ptr && t, std::shared_ptr && e) + : Expression(location), condition(std::move(c)), then_expr(std::move(t)), else_expr(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!condition) throw std::runtime_error("IfExpr.condition is null"); + if (!then_expr) throw std::runtime_error("IfExpr.then_expr is null"); + if (condition->evaluate(context).to_bool()) { + return then_expr->evaluate(context); + } + if (else_expr) { + return else_expr->evaluate(context); + } + return nullptr; + } +}; + +class LiteralExpr : public Expression { + Value value; +public: + LiteralExpr(const Location & location, const Value& v) + : Expression(location), value(v) {} + Value do_evaluate(const std::shared_ptr &) const override { return value; } +}; + +class ArrayExpr : public Expression { + std::vector> elements; +public: + ArrayExpr(const Location & location, std::vector> && e) + : Expression(location), elements(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + auto result = Value::array(); + for (const auto& e : elements) { + if (!e) throw std::runtime_error("Array element is null"); + result.push_back(e->evaluate(context)); + } + return result; + } +}; + +class DictExpr : public Expression { + std::vector, std::shared_ptr>> elements; +public: + DictExpr(const Location & location, std::vector, std::shared_ptr>> && e) + : Expression(location), elements(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + auto result = Value::object(); + for (const auto& [key, value] : elements) { + if (!key) throw std::runtime_error("Dict key is null"); + if (!value) throw std::runtime_error("Dict value is null"); + result.set(key->evaluate(context), value->evaluate(context)); + } + return result; + } +}; + +class SliceExpr : public Expression { +public: + std::shared_ptr start, end; + SliceExpr(const Location & location, std::shared_ptr && s, std::shared_ptr && e) + : Expression(location), start(std::move(s)), end(std::move(e)) {} + Value do_evaluate(const std::shared_ptr &) const override { + throw std::runtime_error("SliceExpr not implemented"); + } +}; + +class SubscriptExpr : public Expression { + std::shared_ptr base; + std::shared_ptr index; +public: + SubscriptExpr(const Location & location, std::shared_ptr && b, std::shared_ptr && i) + : Expression(location), base(std::move(b)), index(std::move(i)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!base) throw std::runtime_error("SubscriptExpr.base is null"); + if (!index) throw std::runtime_error("SubscriptExpr.index is null"); + auto target_value = base->evaluate(context); + if (auto slice = dynamic_cast(index.get())) { + auto start = slice->start ? slice->start->evaluate(context).get() : 0; + auto end = slice->end ? slice->end->evaluate(context).get() : (int64_t) target_value.size(); + if (target_value.is_string()) { + std::string s = target_value.get(); + if (start < 0) start = s.size() + start; + if (end < 0) end = s.size() + end; + return s.substr(start, end - start); + } else if (target_value.is_array()) { + if (start < 0) start = target_value.size() + start; + if (end < 0) end = target_value.size() + end; + auto result = Value::array(); + for (auto i = start; i < end; ++i) { + result.push_back(target_value.at(i)); + } + return result; + } else { + throw std::runtime_error(target_value.is_null() ? "Cannot subscript null" : "Subscripting only supported on arrays and strings"); + } + } else { + auto index_value = index->evaluate(context); + if (target_value.is_null()) { + if (auto t = dynamic_cast(base.get())) { + throw std::runtime_error("'" + t->get_name() + "' is " + (context->contains(t->get_name()) ? "null" : "not defined")); + } + throw std::runtime_error("Trying to access property '" + index_value.dump() + "' on null!"); + } + return target_value.get(index_value); + } + } +}; + +class UnaryOpExpr : public Expression { +public: + enum class Op { Plus, Minus, LogicalNot, Expansion, ExpansionDict }; + std::shared_ptr expr; + Op op; + UnaryOpExpr(const Location & location, std::shared_ptr && e, Op o) + : Expression(location), expr(std::move(e)), op(o) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!expr) throw std::runtime_error("UnaryOpExpr.expr is null"); + auto e = expr->evaluate(context); + switch (op) { + case Op::Plus: return e; + case Op::Minus: return -e; + case Op::LogicalNot: return !e.to_bool(); + case Op::Expansion: + case Op::ExpansionDict: + throw std::runtime_error("Expansion operator is only supported in function calls and collections"); + + } + throw std::runtime_error("Unknown unary operator"); + } +}; + +class BinaryOpExpr : public Expression { +public: + enum class Op { StrConcat, Add, Sub, Mul, MulMul, Div, DivDiv, Mod, Eq, Ne, Lt, Gt, Le, Ge, And, Or, In, NotIn, Is, IsNot }; +private: + std::shared_ptr left; + std::shared_ptr right; + Op op; +public: + BinaryOpExpr(const Location & location, std::shared_ptr && l, std::shared_ptr && r, Op o) + : Expression(location), left(std::move(l)), right(std::move(r)), op(o) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!left) throw std::runtime_error("BinaryOpExpr.left is null"); + if (!right) throw std::runtime_error("BinaryOpExpr.right is null"); + auto l = left->evaluate(context); + + auto do_eval = [&](const Value & l) -> Value { + if (op == Op::Is || op == Op::IsNot) { + auto t = dynamic_cast(right.get()); + if (!t) throw std::runtime_error("Right side of 'is' operator must be a variable"); + + auto eval = [&]() { + const auto & name = t->get_name(); + if (name == "none") return l.is_null(); + if (name == "boolean") return l.is_boolean(); + if (name == "integer") return l.is_number_integer(); + if (name == "float") return l.is_number_float(); + if (name == "number") return l.is_number(); + if (name == "string") return l.is_string(); + if (name == "mapping") return l.is_object(); + if (name == "iterable") return l.is_iterable(); + if (name == "sequence") return l.is_array(); + if (name == "defined") return !l.is_null(); + throw std::runtime_error("Unknown type for 'is' operator: " + name); + }; + auto value = eval(); + return Value(op == Op::Is ? value : !value); + } + + if (op == Op::And) { + if (!l.to_bool()) return Value(false); + return right->evaluate(context).to_bool(); + } else if (op == Op::Or) { + if (l.to_bool()) return l; + return right->evaluate(context); + } + + auto r = right->evaluate(context); + switch (op) { + case Op::StrConcat: return l.to_str() + r.to_str(); + case Op::Add: return l + r; + case Op::Sub: return l - r; + case Op::Mul: return l * r; + case Op::Div: return l / r; + case Op::MulMul: return std::pow(l.get(), r.get()); + case Op::DivDiv: return l.get() / r.get(); + case Op::Mod: return l.get() % r.get(); + case Op::Eq: return l == r; + case Op::Ne: return l != r; + case Op::Lt: return l < r; + case Op::Gt: return l > r; + case Op::Le: return l <= r; + case Op::Ge: return l >= r; + case Op::In: return (r.is_array() || r.is_object()) && r.contains(l); + case Op::NotIn: return !(r.is_array() && r.contains(l)); + default: break; + } + throw std::runtime_error("Unknown binary operator"); + }; + + if (l.is_callable()) { + return Value::callable([l, do_eval](const std::shared_ptr & context, ArgumentsValue & args) { + auto ll = l.call(context, args); + return do_eval(ll); //args[0].second); + }); + } else { + return do_eval(l); + } + } +}; + +struct ArgumentsExpression { + std::vector> args; + std::vector>> kwargs; + + ArgumentsValue evaluate(const std::shared_ptr & context) const { + ArgumentsValue vargs; + for (const auto& arg : this->args) { + if (auto un_expr = std::dynamic_pointer_cast(arg)) { + if (un_expr->op == UnaryOpExpr::Op::Expansion) { + auto array = un_expr->expr->evaluate(context); + if (!array.is_array()) { + throw std::runtime_error("Expansion operator only supported on arrays"); + } + array.for_each([&](Value & value) { + vargs.args.push_back(value); + }); + continue; + } else if (un_expr->op == UnaryOpExpr::Op::ExpansionDict) { + auto dict = un_expr->expr->evaluate(context); + if (!dict.is_object()) { + throw std::runtime_error("ExpansionDict operator only supported on objects"); + } + dict.for_each([&](const Value & key) { + vargs.kwargs.push_back({key.get(), dict.at(key)}); + }); + continue; + } + } + vargs.args.push_back(arg->evaluate(context)); + } + for (const auto& [name, value] : this->kwargs) { + vargs.kwargs.push_back({name, value->evaluate(context)}); + } + return vargs; + } +}; + +static std::string strip(const std::string & s) { + auto start = s.find_first_not_of(" \t\n\r"); + if (start == std::string::npos) return ""; + auto end = s.find_last_not_of(" \t\n\r"); + return s.substr(start, end - start + 1); +} + +static std::string capitalize(const std::string & s) { + if (s.empty()) return s; + auto result = s; + result[0] = std::toupper(result[0]); + return result; +} + +static std::string html_escape(const std::string & s) { + std::string result; + result.reserve(s.size()); + for (const auto & c : s) { + switch (c) { + case '&': result += "&"; break; + case '<': result += "<"; break; + case '>': result += ">"; break; + case '"': result += """; break; + case '\'': result += "'"; break; + default: result += c; break; + } + } + return result; +} + +class MethodCallExpr : public Expression { + std::shared_ptr object; + std::shared_ptr method; + ArgumentsExpression args; +public: + MethodCallExpr(const Location & location, std::shared_ptr && obj, std::shared_ptr && m, ArgumentsExpression && a) + : Expression(location), object(std::move(obj)), method(std::move(m)), args(std::move(a)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!object) throw std::runtime_error("MethodCallExpr.object is null"); + if (!method) throw std::runtime_error("MethodCallExpr.method is null"); + auto obj = object->evaluate(context); + auto vargs = args.evaluate(context); + if (obj.is_null()) { + throw std::runtime_error("Trying to call method '" + method->get_name() + "' on null"); + } + if (obj.is_array()) { + if (method->get_name() == "append") { + vargs.expectArgs("append method", {1, 1}, {0, 0}); + obj.push_back(vargs.args[0]); + return Value(); + } else if (method->get_name() == "pop") { + vargs.expectArgs("pop method", {0, 1}, {0, 0}); + return obj.pop(vargs.args.empty() ? Value() : vargs.args[0]); + } else if (method->get_name() == "insert") { + vargs.expectArgs("insert method", {2, 2}, {0, 0}); + auto index = vargs.args[0].get(); + if (index < 0 || index > (int64_t) obj.size()) throw std::runtime_error("Index out of range for insert method"); + obj.insert(index, vargs.args[1]); + return Value(); + } + } else if (obj.is_object()) { + if (method->get_name() == "items") { + vargs.expectArgs("items method", {0, 0}, {0, 0}); + auto result = Value::array(); + for (const auto& key : obj.keys()) { + result.push_back(Value::array({key, obj.at(key)})); + } + return result; + } else if (method->get_name() == "pop") { + vargs.expectArgs("pop method", {1, 1}, {0, 0}); + return obj.pop(vargs.args[0]); + } else if (method->get_name() == "get") { + vargs.expectArgs("get method", {1, 2}, {0, 0}); + auto key = vargs.args[0]; + if (vargs.args.size() == 1) { + return obj.contains(key) ? obj.at(key) : Value(); + } else { + return obj.contains(key) ? obj.at(key) : vargs.args[1]; + } + } else if (obj.contains(method->get_name())) { + auto callable = obj.at(method->get_name()); + if (!callable.is_callable()) { + throw std::runtime_error("Property '" + method->get_name() + "' is not callable"); + } + return callable.call(context, vargs); + } + } else if (obj.is_string()) { + auto str = obj.get(); + if (method->get_name() == "strip") { + vargs.expectArgs("strip method", {0, 0}, {0, 0}); + return Value(strip(str)); + } else if (method->get_name() == "capitalize") { + vargs.expectArgs("capitalize method", {0, 0}, {0, 0}); + return Value(capitalize(str)); + } else if (method->get_name() == "endswith") { + vargs.expectArgs("endswith method", {1, 1}, {0, 0}); + auto suffix = vargs.args[0].get(); + return suffix.length() <= str.length() && std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); + } else if (method->get_name() == "title") { + vargs.expectArgs("title method", {0, 0}, {0, 0}); + auto res = str; + for (size_t i = 0, n = res.size(); i < n; ++i) { + if (i == 0 || std::isspace(res[i - 1])) res[i] = std::toupper(res[i]); + else res[i] = std::tolower(res[i]); + } + return res; + } + } + throw std::runtime_error("Unknown method: " + method->get_name()); + } +}; + +class CallExpr : public Expression { +public: + std::shared_ptr object; + ArgumentsExpression args; + CallExpr(const Location & location, std::shared_ptr && obj, ArgumentsExpression && a) + : Expression(location), object(std::move(obj)), args(std::move(a)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!object) throw std::runtime_error("CallExpr.object is null"); + auto obj = object->evaluate(context); + if (!obj.is_callable()) { + throw std::runtime_error("Object is not callable: " + obj.dump(2)); + } + auto vargs = args.evaluate(context); + return obj.call(context, vargs); + } +}; + +class FilterExpr : public Expression { + std::vector> parts; +public: + FilterExpr(const Location & location, std::vector> && p) + : Expression(location), parts(std::move(p)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + Value result; + bool first = true; + for (const auto& part : parts) { + if (!part) throw std::runtime_error("FilterExpr.part is null"); + if (first) { + first = false; + result = part->evaluate(context); + } else { + if (auto ce = dynamic_cast(part.get())) { + auto target = ce->object->evaluate(context); + ArgumentsValue args = ce->args.evaluate(context); + args.args.insert(args.args.begin(), result); + result = target.call(context, args); + } else { + auto callable = part->evaluate(context); + ArgumentsValue args; + args.args.insert(args.args.begin(), result); + result = callable.call(context, args); + } + } + } + return result; + } + + void prepend(std::shared_ptr && e) { + parts.insert(parts.begin(), std::move(e)); + } +}; + +class Parser { +private: + using CharIterator = std::string::const_iterator; + + std::shared_ptr template_str; + CharIterator start, end, it; + Options options; + + Parser(const std::shared_ptr& template_str, const Options & options) : template_str(template_str), options(options) { + if (!template_str) throw std::runtime_error("Template string is null"); + start = it = this->template_str->begin(); + end = this->template_str->end(); + } + + bool consumeSpaces(SpaceHandling space_handling = SpaceHandling::Strip) { + if (space_handling == SpaceHandling::Strip) { + while (it != end && std::isspace(*it)) ++it; + } + return true; + } + + std::unique_ptr parseString() { + auto doParse = [&](char quote) -> std::unique_ptr { + if (it == end || *it != quote) return nullptr; + std::string result; + bool escape = false; + for (++it; it != end; ++it) { + if (escape) { + escape = false; + switch (*it) { + case 'n': result += '\n'; break; + case 'r': result += '\r'; break; + case 't': result += '\t'; break; + case 'b': result += '\b'; break; + case 'f': result += '\f'; break; + case '\\': result += '\\'; break; + default: + if (*it == quote) { + result += quote; + } else { + result += *it; + } + break; + } + } else if (*it == '\\') { + escape = true; + } else if (*it == quote) { + ++it; + return std::make_unique(std::move(result)); + } else { + result += *it; + } + } + return nullptr; + }; + + consumeSpaces(); + if (it == end) return nullptr; + if (*it == '"') return doParse('"'); + if (*it == '\'') return doParse('\''); + return nullptr; + } + + json parseNumber(CharIterator& it, const CharIterator& end) { + auto before = it; + consumeSpaces(); + auto start = it; + bool hasDecimal = false; + bool hasExponent = false; + + if (it != end && (*it == '-' || *it == '+')) ++it; + + while (it != end) { + if (std::isdigit(*it)) { + ++it; + } else if (*it == '.') { + if (hasDecimal) throw std::runtime_error("Multiple decimal points"); + hasDecimal = true; + ++it; + } else if (it != start && (*it == 'e' || *it == 'E')) { + if (hasExponent) throw std::runtime_error("Multiple exponents"); + hasExponent = true; + ++it; + } else { + break; + } + } + if (start == it) { + it = before; + return json(); // No valid characters found + } + + std::string str(start, it); + try { + return json::parse(str); + } catch (json::parse_error& e) { + throw std::runtime_error("Failed to parse number: '" + str + "' (" + std::string(e.what()) + ")"); + return json(); + } + } + + /** integer, float, bool, string */ + std::shared_ptr parseConstant() { + auto start = it; + consumeSpaces(); + if (it == end) return nullptr; + if (*it == '"' || *it == '\'') { + auto str = parseString(); + if (str) return std::make_shared(*str); + } + static std::regex prim_tok(R"(true\b|True\b|false\b|False\b|None\b)"); + auto token = consumeToken(prim_tok); + if (!token.empty()) { + if (token == "true" || token == "True") return std::make_shared(true); + if (token == "false" || token == "False") return std::make_shared(false); + if (token == "None") return std::make_shared(nullptr); + throw std::runtime_error("Unknown constant token: " + token); + } + + auto number = parseNumber(it, end); + if (!number.is_null()) return std::make_shared(number); + + it = start; + return nullptr; + } + + class expression_parsing_error : public std::runtime_error { + const CharIterator it; + public: + expression_parsing_error(const std::string & message, const CharIterator & it) + : std::runtime_error(message), it(it) {} + size_t get_pos(const CharIterator & begin) const { + return std::distance(begin, it); + } + }; + + bool peekSymbols(const std::vector & symbols) const { + for (const auto & symbol : symbols) { + if (std::distance(it, end) >= (int64_t) symbol.size() && std::string(it, it + symbol.size()) == symbol) { + return true; + } + } + return false; + } + + std::vector consumeTokenGroups(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + std::smatch match; + if (std::regex_search(it, end, match, regex) && match.position() == 0) { + it += match[0].length(); + std::vector ret; + for (size_t i = 0, n = match.size(); i < n; ++i) { + ret.push_back(match[i].str()); + } + return ret; + } + it = start; + return {}; + } + std::string consumeToken(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + std::smatch match; + if (std::regex_search(it, end, match, regex) && match.position() == 0) { + it += match[0].length(); + return match[0].str(); + } + it = start; + return ""; + } + + std::string consumeToken(const std::string & token, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + if (std::distance(it, end) >= (int64_t) token.size() && std::string(it, it + token.size()) == token) { + it += token.size(); + return token; + } + it = start; + return ""; + } + + std::shared_ptr parseExpression(bool allow_if_expr = true) { + auto left = parseLogicalOr(); + if (it == end) return left; + + if (!allow_if_expr) return left; + + static std::regex if_tok(R"(if\b)"); + if (consumeToken(if_tok).empty()) { + return left; + } + + auto location = get_location(); + auto [condition, else_expr] = parseIfExpression(); + return std::make_shared(location, std::move(condition), std::move(left), std::move(else_expr)); + } + + Location get_location() const { + return {template_str, (size_t) std::distance(start, it)}; + } + + std::pair, std::shared_ptr> parseIfExpression() { + auto condition = parseLogicalOr(); + if (!condition) throw std::runtime_error("Expected condition expression"); + + static std::regex else_tok(R"(else\b)"); + std::shared_ptr else_expr; + if (!consumeToken(else_tok).empty()) { + else_expr = parseExpression(); + if (!else_expr) throw std::runtime_error("Expected 'else' expression"); + } + return std::pair(std::move(condition), std::move(else_expr)); + } + + std::shared_ptr parseLogicalOr() { + auto left = parseLogicalAnd(); + if (!left) throw std::runtime_error("Expected left side of 'logical or' expression"); + + static std::regex or_tok(R"(or\b)"); + auto location = get_location(); + while (!consumeToken(or_tok).empty()) { + auto right = parseLogicalAnd(); + if (!right) throw std::runtime_error("Expected right side of 'or' expression"); + left = std::make_shared(location, std::move(left), std::move(right), BinaryOpExpr::Op::Or); + } + return left; + } + + std::shared_ptr parseLogicalNot() { + static std::regex not_tok(R"(not\b)"); + auto location = get_location(); + + if (!consumeToken(not_tok).empty()) { + auto sub = parseLogicalNot(); + if (!sub) throw std::runtime_error("Expected expression after 'not' keyword"); + return std::make_shared(location, std::move(sub), UnaryOpExpr::Op::LogicalNot); + } + return parseLogicalCompare(); + } + + std::shared_ptr parseLogicalAnd() { + auto left = parseLogicalNot(); + if (!left) throw std::runtime_error("Expected left side of 'logical and' expression"); + + static std::regex and_tok(R"(and\b)"); + auto location = get_location(); + while (!consumeToken(and_tok).empty()) { + auto right = parseLogicalNot(); + if (!right) throw std::runtime_error("Expected right side of 'and' expression"); + left = std::make_shared(location, std::move(left), std::move(right), BinaryOpExpr::Op::And); + } + return left; + } + + std::shared_ptr parseLogicalCompare() { + auto left = parseStringConcat(); + if (!left) throw std::runtime_error("Expected left side of 'logical compare' expression"); + + static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not\s+in\b)"); + static std::regex not_tok(R"(not\b)"); + std::string op_str; + while (!(op_str = consumeToken(compare_tok)).empty()) { + auto location = get_location(); + if (op_str == "is") { + auto negated = !consumeToken(not_tok).empty(); + + auto identifier = parseIdentifier(); + if (!identifier) throw std::runtime_error("Expected identifier after 'is' keyword"); + + return std::make_shared( + left->location, + std::move(left), std::move(identifier), + negated ? BinaryOpExpr::Op::IsNot : BinaryOpExpr::Op::Is); + } + auto right = parseStringConcat(); + if (!right) throw std::runtime_error("Expected right side of 'logical compare' expression"); + BinaryOpExpr::Op op; + if (op_str == "==") op = BinaryOpExpr::Op::Eq; + else if (op_str == "!=") op = BinaryOpExpr::Op::Ne; + else if (op_str == "<") op = BinaryOpExpr::Op::Lt; + else if (op_str == ">") op = BinaryOpExpr::Op::Gt; + else if (op_str == "<=") op = BinaryOpExpr::Op::Le; + else if (op_str == ">=") op = BinaryOpExpr::Op::Ge; + else if (op_str == "in") op = BinaryOpExpr::Op::In; + else if (op_str.substr(0, 3) == "not") op = BinaryOpExpr::Op::NotIn; + else throw std::runtime_error("Unknown comparison operator: " + op_str); + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + return left; + } + + Expression::Parameters parseParameters() { + consumeSpaces(); + if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in param list"); + + Expression::Parameters result; + + while (it != end) { + if (!consumeToken(")").empty()) { + return result; + } + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in call args"); + + if (auto ident = dynamic_cast(expr.get())) { + if (!consumeToken("=").empty()) { + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected expression in for named arg"); + result.emplace_back(ident->get_name(), std::move(value)); + } else { + result.emplace_back(ident->get_name(), nullptr); + } + } else { + result.emplace_back(std::string(), std::move(expr)); + } + if (consumeToken(",").empty()) { + if (consumeToken(")").empty()) { + throw std::runtime_error("Expected closing parenthesis in call args"); + } + return result; + } + } + throw std::runtime_error("Expected closing parenthesis in call args"); + } + + ArgumentsExpression parseCallArgs() { + consumeSpaces(); + if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in call args"); + + ArgumentsExpression result; + + while (it != end) { + if (!consumeToken(")").empty()) { + return result; + } + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in call args"); + + if (auto ident = dynamic_cast(expr.get())) { + if (!consumeToken("=").empty()) { + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected expression in for named arg"); + result.kwargs.emplace_back(ident->get_name(), std::move(value)); + } else { + result.args.emplace_back(std::move(expr)); + } + } else { + result.args.emplace_back(std::move(expr)); + } + if (consumeToken(",").empty()) { + if (consumeToken(")").empty()) { + throw std::runtime_error("Expected closing parenthesis in call args"); + } + return result; + } + } + throw std::runtime_error("Expected closing parenthesis in call args"); + } + + std::shared_ptr parseIdentifier() { + static std::regex ident_regex(R"((?!(?:not|is|and|or|del)\b)[a-zA-Z_]\w*)"); + auto location = get_location(); + auto ident = consumeToken(ident_regex); + if (ident.empty()) + return nullptr; + return std::make_shared(location, ident); + } + + std::shared_ptr parseStringConcat() { + auto left = parseMathPow(); + if (!left) throw std::runtime_error("Expected left side of 'string concat' expression"); + + static std::regex concat_tok(R"(~(?!\}))"); + if (!consumeToken(concat_tok).empty()) { + auto right = parseLogicalAnd(); + if (!right) throw std::runtime_error("Expected right side of 'string concat' expression"); + left = std::make_shared(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::StrConcat); + } + return left; + } + + std::shared_ptr parseMathPow() { + auto left = parseMathPlusMinus(); + if (!left) throw std::runtime_error("Expected left side of 'math pow' expression"); + + while (!consumeToken("**").empty()) { + auto right = parseMathPlusMinus(); + if (!right) throw std::runtime_error("Expected right side of 'math pow' expression"); + left = std::make_shared(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::MulMul); + } + return left; + } + + std::shared_ptr parseMathPlusMinus() { + static std::regex plus_minus_tok(R"(\+|-(?![}%#]\}))"); + + auto left = parseMathMulDiv(); + if (!left) throw std::runtime_error("Expected left side of 'math plus/minus' expression"); + std::string op_str; + while (!(op_str = consumeToken(plus_minus_tok)).empty()) { + auto right = parseMathMulDiv(); + if (!right) throw std::runtime_error("Expected right side of 'math plus/minus' expression"); + auto op = op_str == "+" ? BinaryOpExpr::Op::Add : BinaryOpExpr::Op::Sub; + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + return left; + } + + std::shared_ptr parseMathMulDiv() { + auto left = parseMathUnaryPlusMinus(); + if (!left) throw std::runtime_error("Expected left side of 'math mul/div' expression"); + + static std::regex mul_div_tok(R"(\*\*?|//?|%(?!\}))"); + std::string op_str; + while (!(op_str = consumeToken(mul_div_tok)).empty()) { + auto right = parseMathUnaryPlusMinus(); + if (!right) throw std::runtime_error("Expected right side of 'math mul/div' expression"); + auto op = op_str == "*" ? BinaryOpExpr::Op::Mul + : op_str == "**" ? BinaryOpExpr::Op::MulMul + : op_str == "/" ? BinaryOpExpr::Op::Div + : op_str == "//" ? BinaryOpExpr::Op::DivDiv + : BinaryOpExpr::Op::Mod; + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + + if (!consumeToken("|").empty()) { + auto expr = parseMathMulDiv(); + if (auto filter = dynamic_cast(expr.get())) { + filter->prepend(std::move(left)); + return expr; + } else { + std::vector> parts; + parts.emplace_back(std::move(left)); + parts.emplace_back(std::move(expr)); + return std::make_shared(get_location(), std::move(parts)); + } + } + return left; + } + + std::shared_ptr call_func(const std::string & name, ArgumentsExpression && args) const { + return std::make_shared(get_location(), std::make_shared(get_location(), name), std::move(args)); + } + + std::shared_ptr parseMathUnaryPlusMinus() { + static std::regex unary_plus_minus_tok(R"(\+|-(?![}%#]\}))"); + auto op_str = consumeToken(unary_plus_minus_tok); + auto expr = parseExpansion(); + if (!expr) throw std::runtime_error("Expected expr of 'unary plus/minus/expansion' expression"); + + if (!op_str.empty()) { + auto op = op_str == "+" ? UnaryOpExpr::Op::Plus : UnaryOpExpr::Op::Minus; + return std::make_shared(get_location(), std::move(expr), op); + } + return expr; + } + + std::shared_ptr parseExpansion() { + static std::regex expansion_tok(R"(\*\*?)"); + auto op_str = consumeToken(expansion_tok); + auto expr = parseValueExpression(); + if (op_str.empty()) return expr; + if (!expr) throw std::runtime_error("Expected expr of 'expansion' expression"); + return std::make_shared(get_location(), std::move(expr), op_str == "*" ? UnaryOpExpr::Op::Expansion : UnaryOpExpr::Op::ExpansionDict); + } + + std::shared_ptr parseValueExpression() { + auto parseValue = [&]() -> std::shared_ptr { + auto location = get_location(); + auto constant = parseConstant(); + if (constant) return std::make_shared(location, *constant); + + static std::regex null_regex(R"(null\b)"); + if (!consumeToken(null_regex).empty()) return std::make_shared(location, Value()); + + auto identifier = parseIdentifier(); + if (identifier) return identifier; + + auto braced = parseBracedExpressionOrArray(); + if (braced) return braced; + + auto array = parseArray(); + if (array) return array; + + auto dictionary = parseDictionary(); + if (dictionary) return dictionary; + + throw std::runtime_error("Expected value expression"); + }; + + auto value = parseValue(); + + while (it != end && consumeSpaces() && peekSymbols({ "[", "." })) { + if (!consumeToken("[").empty()) { + std::shared_ptr index; + if (!consumeToken(":").empty()) { + auto slice_end = parseExpression(); + index = std::make_shared(slice_end->location, nullptr, std::move(slice_end)); + } else { + auto slice_start = parseExpression(); + if (!consumeToken(":").empty()) { + consumeSpaces(); + if (peekSymbols({ "]" })) { + index = std::make_shared(slice_start->location, std::move(slice_start), nullptr); + } else { + auto slice_end = parseExpression(); + index = std::make_shared(slice_start->location, std::move(slice_start), std::move(slice_end)); + } + } else { + index = std::move(slice_start); + } + } + if (!index) throw std::runtime_error("Empty index in subscript"); + if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript"); + + value = std::make_shared(value->location, std::move(value), std::move(index)); + } else if (!consumeToken(".").empty()) { + auto identifier = parseIdentifier(); + if (!identifier) throw std::runtime_error("Expected identifier in subscript"); + + consumeSpaces(); + if (peekSymbols({ "(" })) { + auto callParams = parseCallArgs(); + value = std::make_shared(identifier->location, std::move(value), std::move(identifier), std::move(callParams)); + } else { + auto key = std::make_shared(identifier->location, Value(identifier->get_name())); + value = std::make_shared(identifier->location, std::move(value), std::move(key)); + } + } + consumeSpaces(); + } + + if (peekSymbols({ "(" })) { + auto location = get_location(); + auto callParams = parseCallArgs(); + value = std::make_shared(location, std::move(value), std::move(callParams)); + } + return value; + } + + std::shared_ptr parseBracedExpressionOrArray() { + if (consumeToken("(").empty()) return nullptr; + + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in braced expression"); + + if (!consumeToken(")").empty()) { + return expr; // Drop the parentheses + } + + std::vector> tuple; + tuple.emplace_back(std::move(expr)); + + while (it != end) { + if (consumeToken(",").empty()) throw std::runtime_error("Expected comma in tuple"); + auto next = parseExpression(); + if (!next) throw std::runtime_error("Expected expression in tuple"); + tuple.push_back(std::move(next)); + + if (!consumeToken(")").empty()) { + return std::make_shared(get_location(), std::move(tuple)); + } + } + throw std::runtime_error("Expected closing parenthesis"); + } + + std::shared_ptr parseArray() { + if (consumeToken("[").empty()) return nullptr; + + std::vector> elements; + if (!consumeToken("]").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } + auto first_expr = parseExpression(); + if (!first_expr) throw std::runtime_error("Expected first expression in array"); + elements.push_back(std::move(first_expr)); + + while (it != end) { + if (!consumeToken(",").empty()) { + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in array"); + elements.push_back(std::move(expr)); + } else if (!consumeToken("]").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } else { + throw std::runtime_error("Expected comma or closing bracket in array"); + } + } + throw std::runtime_error("Expected closing bracket"); + } + + std::shared_ptr parseDictionary() { + if (consumeToken("{").empty()) return nullptr; + + std::vector, std::shared_ptr>> elements; + if (!consumeToken("}").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } + + auto parseKeyValuePair = [&]() { + auto key = parseExpression(); + if (!key) throw std::runtime_error("Expected key in dictionary"); + if (consumeToken(":").empty()) throw std::runtime_error("Expected colon betweek key & value in dictionary"); + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in dictionary"); + elements.emplace_back(std::pair(std::move(key), std::move(value))); + }; + + parseKeyValuePair(); + + while (it != end) { + if (!consumeToken(",").empty()) { + parseKeyValuePair(); + } else if (!consumeToken("}").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } else { + throw std::runtime_error("Expected comma or closing brace in dictionary"); + } + } + throw std::runtime_error("Expected closing brace"); + } + + SpaceHandling parsePreSpace(const std::string& s) const { + if (s == "-") + return SpaceHandling::Strip; + return SpaceHandling::Keep; + } + + SpaceHandling parsePostSpace(const std::string& s) const { + if (s == "-") return SpaceHandling::Strip; + return SpaceHandling::Keep; + } + + using TemplateTokenVector = std::vector>; + using TemplateTokenIterator = TemplateTokenVector::const_iterator; + + std::vector parseVarNames() { + static std::regex varnames_regex(R"(((?:\w+)(?:\s*,\s*(?:\w+))*)\s*)"); + + std::vector group; + if ((group = consumeTokenGroups(varnames_regex)).empty()) throw std::runtime_error("Expected variable names"); + std::vector varnames; + std::istringstream iss(group[1]); + std::string varname; + while (std::getline(iss, varname, ',')) { + varnames.push_back(strip(varname)); + } + return varnames; + } + + std::runtime_error unexpected(const TemplateToken & token) const { + return std::runtime_error("Unexpected " + TemplateToken::typeToString(token.type) + + error_location_suffix(*template_str, token.location.pos)); + } + std::runtime_error unterminated(const TemplateToken & token) const { + return std::runtime_error("Unterminated " + TemplateToken::typeToString(token.type) + + error_location_suffix(*template_str, token.location.pos)); + } + + TemplateTokenVector tokenize() { + static std::regex comment_tok(R"(\{#([-~]?)([\s\S]*?)([-~]?)#\})"); + static std::regex expr_open_regex(R"(\{\{([-~])?)"); + static std::regex block_open_regex(R"(^\{%([-~])?\s*)"); + static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)"); + static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)"); + static std::regex expr_close_regex(R"(\s*([-~])?\}\})"); + static std::regex block_close_regex(R"(\s*([-~])?%\})"); + + TemplateTokenVector tokens; + std::vector group; + std::string text; + std::smatch match; + + try { + while (it != end) { + auto location = get_location(); + + if (!(group = consumeTokenGroups(comment_tok, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + auto content = group[2]; + auto post_space = parsePostSpace(group[3]); + tokens.push_back(std::make_unique(location, pre_space, post_space, content)); + } else if (!(group = consumeTokenGroups(expr_open_regex, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + auto expr = parseExpression(); + + if ((group = consumeTokenGroups(expr_close_regex)).empty()) { + throw std::runtime_error("Expected closing expression tag"); + } + + auto post_space = parsePostSpace(group[1]); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(expr))); + } else if (!(group = consumeTokenGroups(block_open_regex, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + + std::string keyword; + + auto parseBlockClose = [&]() -> SpaceHandling { + if ((group = consumeTokenGroups(block_close_regex)).empty()) throw std::runtime_error("Expected closing block tag"); + return parsePostSpace(group[1]); + }; + + if ((keyword = consumeToken(block_keyword_tok)).empty()) throw std::runtime_error("Expected block keyword"); + + if (keyword == "if") { + auto condition = parseExpression(); + if (!condition) throw std::runtime_error("Expected condition in if block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(condition))); + } else if (keyword == "elif") { + auto condition = parseExpression(); + if (!condition) throw std::runtime_error("Expected condition in elif block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(condition))); + } else if (keyword == "else") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "endif") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "for") { + static std::regex recursive_tok(R"(recursive\b)"); + static std::regex if_tok(R"(if\b)"); + + auto varnames = parseVarNames(); + static std::regex in_tok(R"(in\b)"); + if (consumeToken(in_tok).empty()) throw std::runtime_error("Expected 'in' keyword in for block"); + auto iterable = parseExpression(/* allow_if_expr = */ false); + if (!iterable) throw std::runtime_error("Expected iterable in for block"); + + std::shared_ptr condition; + if (!consumeToken(if_tok).empty()) { + condition = parseExpression(); + } + auto recursive = !consumeToken(recursive_tok).empty(); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(varnames), std::move(iterable), std::move(condition), recursive)); + } else if (keyword == "endfor") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "generation") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "endgeneration") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "set") { + static std::regex namespaced_var_regex(R"((\w+)\s*\.\s*(\w+))"); + + std::string ns; + std::vector var_names; + std::shared_ptr value; + if (!(group = consumeTokenGroups(namespaced_var_regex)).empty()) { + ns = group[1]; + var_names.push_back(group[2]); + + if (consumeToken("=").empty()) throw std::runtime_error("Expected equals sign in set block"); + + value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in set block"); + } else { + var_names = parseVarNames(); + + if (!consumeToken("=").empty()) { + value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in set block"); + } + } + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, ns, var_names, std::move(value))); + } else if (keyword == "endset") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "macro") { + auto macroname = parseIdentifier(); + if (!macroname) throw std::runtime_error("Expected macro name in macro block"); + auto params = parseParameters(); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(macroname), std::move(params))); + } else if (keyword == "endmacro") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "filter") { + auto filter = parseExpression(); + if (!filter) throw std::runtime_error("Expected expression in filter block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(filter))); + } else if (keyword == "endfilter") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "break" || keyword == "continue") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, keyword == "break" ? LoopControlType::Break : LoopControlType::Continue)); + } else { + throw std::runtime_error("Unexpected block: " + keyword); + } + } else if (std::regex_search(it, end, match, non_text_open_regex)) { + if (!match.position()) { + if (match[0] != "{#") + throw std::runtime_error("Internal error: Expected a comment"); + throw std::runtime_error("Missing end of comment tag"); + } + auto text_end = it + match.position(); + text = std::string(it, text_end); + it = text_end; + tokens.push_back(std::make_unique(location, SpaceHandling::Keep, SpaceHandling::Keep, text)); + } else { + text = std::string(it, end); + it = end; + tokens.push_back(std::make_unique(location, SpaceHandling::Keep, SpaceHandling::Keep, text)); + } + } + return tokens; + } catch (const std::exception & e) { + throw std::runtime_error(e.what() + error_location_suffix(*template_str, std::distance(start, it))); + } + } + + std::shared_ptr parseTemplate( + const TemplateTokenIterator & begin, + TemplateTokenIterator & it, + const TemplateTokenIterator & end, + bool fully = false) const { + std::vector> children; + while (it != end) { + const auto start = it; + const auto & token = *(it++); + if (auto if_token = dynamic_cast(token.get())) { + std::vector, std::shared_ptr>> cascade; + cascade.emplace_back(std::move(if_token->condition), parseTemplate(begin, it, end)); + + while (it != end && (*it)->type == TemplateToken::Type::Elif) { + auto elif_token = dynamic_cast((*(it++)).get()); + cascade.emplace_back(std::move(elif_token->condition), parseTemplate(begin, it, end)); + } + + if (it != end && (*it)->type == TemplateToken::Type::Else) { + cascade.emplace_back(nullptr, parseTemplate(begin, ++it, end)); + } + if (it == end || (*(it++))->type != TemplateToken::Type::EndIf) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(cascade))); + } else if (auto for_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + auto else_body = std::shared_ptr(); + if (it != end && (*it)->type == TemplateToken::Type::Else) { + else_body = parseTemplate(begin, ++it, end); + } + if (it == end || (*(it++))->type != TemplateToken::Type::EndFor) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(for_token->var_names), std::move(for_token->iterable), std::move(for_token->condition), std::move(body), for_token->recursive, std::move(else_body))); + } else if (dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndGeneration) { + throw unterminated(**start); + } + // Treat as a no-op, as our scope is templates for inference, not training (`{% generation %}` wraps generated tokens for masking). + children.emplace_back(std::move(body)); + } else if (auto text_token = dynamic_cast(token.get())) { + SpaceHandling pre_space = (it - 1) != begin ? (*(it - 2))->post_space : SpaceHandling::Keep; + SpaceHandling post_space = it != end ? (*it)->pre_space : SpaceHandling::Keep; + + auto text = text_token->text; + if (post_space == SpaceHandling::Strip) { + static std::regex trailing_space_regex(R"(\s+$)"); + text = std::regex_replace(text, trailing_space_regex, ""); + } else if (options.lstrip_blocks && it != end) { + auto i = text.size(); + while (i > 0 && (text[i - 1] == ' ' || text[i - 1] == '\t')) i--; + if ((i == 0 && (it - 1) == begin) || (i > 0 && text[i - 1] == '\n')) { + text.resize(i); + } + } + if (pre_space == SpaceHandling::Strip) { + static std::regex leading_space_regex(R"(^\s+)"); + text = std::regex_replace(text, leading_space_regex, ""); + } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast((*(it - 2)).get())) { + if (text.length() > 0 && text[0] == '\n') { + text.erase(0, 1); + } + } + if (it == end && !options.keep_trailing_newline) { + auto i = text.size(); + if (i > 0 && text[i - 1] == '\n') { + i--; + if (i > 0 && text[i - 1] == '\r') i--; + text.resize(i); + } + } + children.emplace_back(std::make_shared(token->location, text)); + } else if (auto expr_token = dynamic_cast(token.get())) { + children.emplace_back(std::make_shared(token->location, std::move(expr_token->expr))); + } else if (auto set_token = dynamic_cast(token.get())) { + if (set_token->value) { + children.emplace_back(std::make_shared(token->location, set_token->ns, set_token->var_names, std::move(set_token->value))); + } else { + auto value_template = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndSet) { + throw unterminated(**start); + } + if (!set_token->ns.empty()) throw std::runtime_error("Namespaced set not supported in set with template value"); + if (set_token->var_names.size() != 1) throw std::runtime_error("Structural assignment not supported in set with template value"); + auto & name = set_token->var_names[0]; + children.emplace_back(std::make_shared(token->location, name, std::move(value_template))); + } + } else if (auto macro_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndMacro) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(macro_token->name), std::move(macro_token->params), std::move(body))); + } else if (auto filter_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndFilter) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(filter_token->filter), std::move(body))); + } else if (dynamic_cast(token.get())) { + // Ignore comments + } else if (auto ctrl_token = dynamic_cast(token.get())) { + children.emplace_back(std::make_shared(token->location, ctrl_token->control_type)); + } else if (dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get())) { + it--; // unconsume the token + break; // exit the loop + } else { + throw unexpected(**(it-1)); + } + } + if (fully && it != end) { + throw unexpected(**it); + } + if (children.empty()) { + return std::make_shared(Location { template_str, 0 }, std::string()); + } else if (children.size() == 1) { + return std::move(children[0]); + } else { + return std::make_shared(children[0]->location(), std::move(children)); + } + } + +public: + + static std::shared_ptr parse(const std::string& template_str, const Options & options) { + Parser parser(std::make_shared(normalize_newlines(template_str)), options); + auto tokens = parser.tokenize(); + TemplateTokenIterator begin = tokens.begin(); + auto it = begin; + TemplateTokenIterator end = tokens.end(); + return parser.parseTemplate(begin, it, end, /* full= */ true); + } +}; + +static Value simple_function(const std::string & fn_name, const std::vector & params, const std::function &, Value & args)> & fn) { + std::map named_positions; + for (size_t i = 0, n = params.size(); i < n; i++) named_positions[params[i]] = i; + + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) -> Value { + auto args_obj = Value::object(); + std::vector provided_args(params.size()); + for (size_t i = 0, n = args.args.size(); i < n; i++) { + auto & arg = args.args[i]; + if (i < params.size()) { + args_obj.set(params[i], arg); + provided_args[i] = true; + } else { + throw std::runtime_error("Too many positional params for " + fn_name); + } + } + for (auto & [name, value] : args.kwargs) { + auto named_pos_it = named_positions.find(name); + if (named_pos_it == named_positions.end()) { + throw std::runtime_error("Unknown argument " + name + " for function " + fn_name); + } + provided_args[named_pos_it->second] = true; + args_obj.set(name, value); + } + return fn(context, args_obj); + }); +} + +inline std::shared_ptr Context::builtins() { + auto globals = Value::object(); + + globals.set("raise_exception", simple_function("raise_exception", { "message" }, [](const std::shared_ptr &, Value & args) -> Value { + throw std::runtime_error(args.at("message").get()); + })); + globals.set("tojson", simple_function("tojson", { "value", "indent" }, [](const std::shared_ptr &, Value & args) { + return Value(args.at("value").dump(args.get("indent", -1), /* tojson= */ true)); + })); + globals.set("items", simple_function("items", { "object" }, [](const std::shared_ptr &, Value & args) { + auto items = Value::array(); + if (args.contains("object")) { + auto & obj = args.at("object"); + if (obj.is_string()) { + auto json_obj = json::parse(obj.get()); + for (const auto & kv : json_obj.items()) { + items.push_back(Value::array({kv.key(), kv.value()})); + } + } else if (!obj.is_null()) { + for (auto & key : obj.keys()) { + items.push_back(Value::array({key, obj.at(key)})); + } + } + } + return items; + })); + globals.set("last", simple_function("last", { "items" }, [](const std::shared_ptr &, Value & args) { + auto items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not a list"); + if (items.size() == 0) return Value(); + return items.at(items.size() - 1); + })); + globals.set("trim", simple_function("trim", { "text" }, [](const std::shared_ptr &, Value & args) { + auto & text = args.at("text"); + return text.is_null() ? text : Value(strip(text.get())); + })); + globals.set("lower", simple_function("lower", { "text" }, [](const std::shared_ptr &, Value & args) { + auto text = args.at("text"); + if (text.is_null()) return text; + std::string res; + auto str = text.get(); + std::transform(str.begin(), str.end(), std::back_inserter(res), ::tolower); + return Value(res); + })); + globals.set("default", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + args.expectArgs("default", {2, 3}, {0, 1}); + auto & value = args.args[0]; + auto & default_value = args.args[1]; + bool boolean = false; + if (args.args.size() == 3) { + boolean = args.args[2].get(); + } else { + Value bv = args.get_named("boolean"); + if (!bv.is_null()) { + boolean = bv.get(); + } + } + return boolean ? (value.to_bool() ? value : default_value) : value.is_null() ? default_value : value; + })); + auto escape = simple_function("escape", { "text" }, [](const std::shared_ptr &, Value & args) { + return Value(html_escape(args.at("text").get())); + }); + globals.set("e", escape); + globals.set("escape", escape); + globals.set("joiner", simple_function("joiner", { "sep" }, [](const std::shared_ptr &, Value & args) { + auto sep = args.get("sep", ""); + auto first = std::make_shared(true); + return simple_function("", {}, [sep, first](const std::shared_ptr &, const Value &) -> Value { + if (*first) { + *first = false; + return ""; + } + return sep; + }); + return Value(html_escape(args.at("text").get())); + })); + globals.set("count", simple_function("count", { "items" }, [](const std::shared_ptr &, Value & args) { + return Value((int64_t) args.at("items").size()); + })); + globals.set("dictsort", simple_function("dictsort", { "value" }, [](const std::shared_ptr &, Value & args) { + if (args.size() != 1) throw std::runtime_error("dictsort expects exactly 1 argument (TODO: fix implementation)"); + auto & value = args.at("value"); + auto keys = value.keys(); + std::sort(keys.begin(), keys.end()); + auto res = Value::array(); + for (auto & key : keys) { + res.push_back(Value::array({key, value.at(key)})); + } + return res; + })); + globals.set("join", simple_function("join", { "items", "d" }, [](const std::shared_ptr &, Value & args) { + auto do_join = [](Value & items, const std::string & sep) { + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + std::ostringstream oss; + auto first = true; + for (size_t i = 0, n = items.size(); i < n; ++i) { + if (first) first = false; + else oss << sep; + oss << items.at(i).to_str(); + } + return Value(oss.str()); + }; + auto sep = args.get("d", ""); + if (args.contains("items")) { + auto & items = args.at("items"); + return do_join(items, sep); + } else { + return simple_function("", {"items"}, [sep, do_join](const std::shared_ptr &, Value & args) { + auto & items = args.at("items"); + if (!items.to_bool() || !items.is_array()) throw std::runtime_error("join expects an array for items, got: " + items.dump()); + return do_join(items, sep); + }); + } + })); + globals.set("namespace", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + auto ns = Value::object(); + args.expectArgs("namespace", {0, 0}, {0, (std::numeric_limits::max)()}); + for (auto & [name, value] : args.kwargs) { + ns.set(name, value); + } + return ns; + })); + auto equalto = simple_function("equalto", { "expected", "actual" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("actual") == args.at("expected"); + }); + globals.set("equalto", equalto); + globals.set("==", equalto); + globals.set("length", simple_function("length", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + return (int64_t) items.size(); + })); + globals.set("safe", simple_function("safe", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_str(); + })); + globals.set("string", simple_function("string", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_str(); + })); + globals.set("int", simple_function("int", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_int(); + })); + globals.set("list", simple_function("list", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not iterable"); + return items; + })); + globals.set("unique", simple_function("unique", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not iterable"); + std::unordered_set seen; + auto result = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto pair = seen.insert(items.at(i)); + if (pair.second) { + result.push_back(items.at(i)); + } + } + return result; + })); + auto make_filter = [](const Value & filter, Value & extra_args) -> Value { + return simple_function("", { "value" }, [=](const std::shared_ptr & context, Value & args) { + auto & value = args.at("value"); + ArgumentsValue actual_args; + actual_args.args.emplace_back(value); + for (size_t i = 0, n = extra_args.size(); i < n; i++) { + actual_args.args.emplace_back(extra_args.at(i)); + } + return filter.call(context, actual_args); + }); + }; + auto select_or_reject = [make_filter](bool is_select) { + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits::max)()}, {0, 0}); + auto & items = args.args[0]; + if (items.is_null()) + return Value::array(); + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + + auto filter_fn = context->get(args.args[1]); + if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + + auto filter_args = Value::array(); + for (size_t i = 2, n = args.args.size(); i < n; i++) { + filter_args.push_back(args.args[i]); + } + auto filter = make_filter(filter_fn, filter_args); + + auto res = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + ArgumentsValue filter_args; + filter_args.args.emplace_back(item); + auto pred_res = filter.call(context, filter_args); + if (pred_res.to_bool() == (is_select ? true : false)) { + res.push_back(item); + } + } + return res; + }); + }; + globals.set("select", select_or_reject(/* is_select= */ true)); + globals.set("reject", select_or_reject(/* is_select= */ false)); + globals.set("map", Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + auto res = Value::array(); + if (args.args.size() == 1 && + ((args.has_named("attribute") && args.kwargs.size() == 1) || (args.has_named("default") && args.kwargs.size() == 2))) { + auto & items = args.args[0]; + auto attr_name = args.get_named("attribute"); + auto default_value = args.get_named("default"); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + auto attr = item.get(attr_name); + res.push_back(attr.is_null() ? default_value : attr); + } + } else if (args.kwargs.empty() && args.args.size() >= 2) { + auto fn = context->get(args.args[1]); + if (fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + ArgumentsValue filter_args { {Value()}, {} }; + for (size_t i = 2, n = args.args.size(); i < n; i++) { + filter_args.args.emplace_back(args.args[i]); + } + for (size_t i = 0, n = args.args[0].size(); i < n; i++) { + auto & item = args.args[0].at(i); + filter_args.args[0] = item; + res.push_back(fn.call(context, filter_args)); + } + } else { + throw std::runtime_error("Invalid or unsupported arguments for map"); + } + return res; + })); + globals.set("indent", simple_function("indent", { "text", "indent", "first" }, [](const std::shared_ptr &, Value & args) { + auto text = args.at("text").get(); + auto first = args.get("first", false); + std::string out; + std::string indent(args.get("indent", 0), ' '); + std::istringstream iss(text); + std::string line; + auto is_first = true; + while (std::getline(iss, line, '\n')) { + auto needs_indent = !is_first || first; + if (is_first) is_first = false; + else out += "\n"; + if (needs_indent) out += indent; + out += line; + } + if (!text.empty() && text.back() == '\n') out += "\n"; + return out; + })); + auto select_or_reject_attr = [](bool is_select) { + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + args.expectArgs(is_select ? "selectattr" : "rejectattr", {2, (std::numeric_limits::max)()}, {0, 0}); + auto & items = args.args[0]; + if (items.is_null()) + return Value::array(); + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + auto attr_name = args.args[1].get(); + + bool has_test = false; + Value test_fn; + ArgumentsValue test_args {{Value()}, {}}; + if (args.args.size() >= 3) { + has_test = true; + test_fn = context->get(args.args[2]); + if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump()); + for (size_t i = 3, n = args.args.size(); i < n; i++) { + test_args.args.emplace_back(args.args[i]); + } + test_args.kwargs = args.kwargs; + } + + auto res = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + auto attr = item.get(attr_name); + if (has_test) { + test_args.args[0] = attr; + if (test_fn.call(context, test_args).to_bool() == (is_select ? true : false)) { + res.push_back(item); + } + } else { + res.push_back(attr); + } + } + return res; + }); + }; + globals.set("selectattr", select_or_reject_attr(/* is_select= */ true)); + globals.set("rejectattr", select_or_reject_attr(/* is_select= */ false)); + globals.set("range", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + std::vector startEndStep(3); + std::vector param_set(3); + if (args.args.size() == 1) { + startEndStep[1] = args.args[0].get(); + param_set[1] = true; + } else { + for (size_t i = 0; i < args.args.size(); i++) { + auto & arg = args.args[i]; + auto v = arg.get(); + startEndStep[i] = v; + param_set[i] = true; + } + } + for (auto & [name, value] : args.kwargs) { + size_t i; + if (name == "start") i = 0; + else if (name == "end") i = 1; + else if (name == "step") i = 2; + else throw std::runtime_error("Unknown argument " + name + " for function range"); + + if (param_set[i]) { + throw std::runtime_error("Duplicate argument " + name + " for function range"); + } + startEndStep[i] = value.get(); + param_set[i] = true; + } + if (!param_set[1]) { + throw std::runtime_error("Missing required argument 'end' for function range"); + } + int64_t start = param_set[0] ? startEndStep[0] : 0; + int64_t end = startEndStep[1]; + int64_t step = param_set[2] ? startEndStep[2] : 1; + + auto res = Value::array(); + if (step > 0) { + for (int64_t i = start; i < end; i += step) { + res.push_back(Value(i)); + } + } else { + for (int64_t i = start; i > end; i += step) { + res.push_back(Value(i)); + } + } + return res; + })); + + return std::make_shared(std::move(globals)); +} + +inline std::shared_ptr Context::make(Value && values, const std::shared_ptr & parent) { + return std::make_shared(values.is_null() ? Value::object() : std::move(values), parent); +} + +} // namespace minja diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp index a9dfb67142528..a057ae45f520f 100644 --- a/common/ngram-cache.cpp +++ b/common/ngram-cache.cpp @@ -65,13 +65,13 @@ constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) { common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); if (part_static_it == nc_static.end()) { - return -1; + return LLAMA_TOKEN_NULL; } const common_ngram_cache_part part_static = part_static_it->second; int max_count_static = 0; int sum_count_static = 0; - llama_token max_token = -1; + llama_token max_token = LLAMA_TOKEN_NULL; for (std::pair token_count_static : part_static) { const llama_token token = token_count_static.first; @@ -85,10 +85,10 @@ static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram } if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { - return -1; + return LLAMA_TOKEN_NULL; } if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { - return -1; + return LLAMA_TOKEN_NULL; } return max_token; } @@ -98,9 +98,9 @@ static llama_token try_draft( common_ngram_cache & nc_primary, const std::vector & ngrams_primary, common_ngram_cache_part & part_static, const int * min_sample_size, const int * min_percent) { - llama_token drafted_token = -1; + llama_token drafted_token = LLAMA_TOKEN_NULL; - for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) { + for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) { const common_ngram ngram_primary = ngrams_primary[i]; common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); @@ -112,7 +112,7 @@ static llama_token try_draft( int max_count_primary = 0; int max_count_static = 0; int sum_count_primary = 0; - llama_token max_token = -1; + llama_token max_token = LLAMA_TOKEN_NULL; for (std::pair token_count_primary : part_primary) { const llama_token token = token_count_primary.first; @@ -154,7 +154,7 @@ void common_ngram_cache_draft( } while ((int) draft.size()-1 < n_draft) { - llama_token drafted_token = -1; + llama_token drafted_token = LLAMA_TOKEN_NULL; const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; common_ngram ngram_static; @@ -177,17 +177,17 @@ void common_ngram_cache_draft( } ngrams_cd.push_back(ngram_cd); } - if (drafted_token == -1) { + if (drafted_token == LLAMA_TOKEN_NULL) { drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); } - if (drafted_token == -1) { + if (drafted_token == LLAMA_TOKEN_NULL) { drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); } - if (drafted_token == -1) { + if (drafted_token == LLAMA_TOKEN_NULL) { drafted_token = try_draft(nc_static, ngram_static); } - if (drafted_token == -1) { + if (drafted_token == LLAMA_TOKEN_NULL) { break; } diff --git a/common/ngram-cache.h b/common/ngram-cache.h index 09c2b0319f2c0..dfe012abe493d 100644 --- a/common/ngram-cache.h +++ b/common/ngram-cache.h @@ -17,13 +17,13 @@ struct common_ngram { common_ngram() { for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { - tokens[i] = -1; + tokens[i] = LLAMA_TOKEN_NULL; } } common_ngram(const llama_token * input, const int ngram_size) { for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { - tokens[i] = i < ngram_size ? input[i] : -1; + tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL; } } diff --git a/common/sampling.cpp b/common/sampling.cpp index 0c4699a89c8b2..37a0d9c85ae30 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -113,7 +113,10 @@ struct common_sampler { void set_logits(struct llama_context * ctx, int idx) { const auto * logits = llama_get_logits_ith(ctx, idx); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_vocab = llama_vocab_n_tokens(vocab); cur.resize(n_vocab); @@ -131,24 +134,47 @@ std::string common_params_sampling::print() const { snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" - "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" + "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, - top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, + top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp, mirostat, mirostat_eta, mirostat_tau); return std::string(result); } struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) { + const llama_vocab * vocab = llama_model_get_vocab(model); + llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); lparams.no_perf = params.no_perf; + struct llama_sampler * grmr; + if (params.grammar.compare(0, 11, "%llguidance") == 0) { +#ifdef LLAMA_USE_LLGUIDANCE + grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()); +#else + GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); +#endif // LLAMA_USE_LLGUIDANCE + } else { + std::vector trigger_words; + trigger_words.reserve(params.grammar_trigger_words.size()); + for (const auto & str : params.grammar_trigger_words) { + trigger_words.push_back(str.word.c_str()); + } + + grmr = params.grammar_lazy + ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root", + trigger_words.data(), trigger_words.size(), + params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size()) + : llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"); + } + auto * result = new common_sampler { /* .params = */ params, - /* .grmr = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"), + /* .grmr = */ grmr, /* .chain = */ llama_sampler_chain_init(lparams), /* .prev = */ ring_buffer(std::max(32, params.n_prev)), /* .cur = */ {}, @@ -157,65 +183,62 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co llama_sampler_chain_add(result->chain, llama_sampler_init_logit_bias( - llama_n_vocab(model), + llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data())); - llama_sampler_chain_add(result->chain, - llama_sampler_init_penalties( - llama_n_vocab (model), - llama_token_eos(model), - llama_token_nl (model), - params.penalty_last_n, - params.penalty_repeat, - params.penalty_freq, - params.penalty_present, - params.penalize_nl, - params.ignore_eos)); - if (params.mirostat == 0) { - for (const auto & cnstr : params.samplers) { - switch (cnstr) { + if (params.top_n_sigma >= 0) { + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + llama_sampler_chain_add(result->chain, llama_sampler_init_temp (params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); + } else { + for (const auto & cnstr : params.samplers) { + switch (cnstr) { case COMMON_SAMPLER_TYPE_DRY: - { - std::vector c_breakers; - c_breakers.reserve(params.dry_sequence_breakers.size()); - for (const auto& str : params.dry_sequence_breakers) { - c_breakers.push_back(str.c_str()); + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto & str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); + } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); } - - llama_sampler_chain_add(result->chain, llama_sampler_init_dry (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); - } break; - case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); - break; - case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_XTC: - llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); - break; - case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); - break; - case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); - break; - case COMMON_SAMPLER_TYPE_INFILL: - llama_sampler_chain_add(result->chain, llama_sampler_init_infill (model)); - break; - default: - GGML_ASSERT(false && "unknown sampler type"); + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + break; + case COMMON_SAMPLER_TYPE_PENALTIES: + llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); + } } } llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); } else if (params.mirostat == 1) { llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); } else if (params.mirostat == 2) { llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); @@ -415,6 +438,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; case COMMON_SAMPLER_TYPE_XTC: return 'x'; case COMMON_SAMPLER_TYPE_INFILL: return 'i'; + case COMMON_SAMPLER_TYPE_PENALTIES: return 'e'; default : return '?'; } } @@ -429,6 +453,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; case COMMON_SAMPLER_TYPE_XTC: return "xtc"; case COMMON_SAMPLER_TYPE_INFILL: return "infill"; + case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties"; default : return ""; } } @@ -443,6 +468,7 @@ std::vector common_sampler_types_from_names(const std::vect { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, { "xtc", COMMON_SAMPLER_TYPE_XTC }, { "infill", COMMON_SAMPLER_TYPE_INFILL }, + { "penalties", COMMON_SAMPLER_TYPE_PENALTIES }, }; // since samplers names are written multiple ways @@ -489,6 +515,7 @@ std::vector common_sampler_types_from_chars(const std::stri { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES }, }; std::vector samplers; diff --git a/common/sampling.h b/common/sampling.h index 348911b18888b..2064421db4e80 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -102,3 +102,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr); std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names); std::vector common_sampler_types_from_chars(const std::string & chars); + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, + const char * grammar_kind, const char * grammar_data); diff --git a/common/speculative.cpp b/common/speculative.cpp index fe315a2703e9c..b1fff27a55f91 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -62,6 +62,10 @@ struct common_speculative * common_speculative_init( } void common_speculative_free(struct common_speculative * spec) { + if (spec == nullptr) { + return; + } + common_sampler_free(spec->smpl); llama_batch_free(spec->batch); @@ -75,10 +79,13 @@ bool common_speculative_are_compatible( const struct llama_model * model_tgt = llama_get_model(ctx_tgt); const struct llama_model * model_dft = llama_get_model(ctx_dft); - const bool vocab_type_tgt = llama_vocab_type(model_tgt); + const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); + const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); + + const bool vocab_type_tgt = llama_vocab_type(vocab_tgt); LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt); - const bool vocab_type_dft = llama_vocab_type(model_dft); + const bool vocab_type_dft = llama_vocab_type(vocab_dft); LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft); if (vocab_type_tgt != vocab_type_dft) { @@ -87,33 +94,34 @@ bool common_speculative_are_compatible( return false; } - if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) || - llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) || - llama_token_bos(model_tgt) != llama_token_bos(model_dft) || - llama_token_eos(model_tgt) != llama_token_eos(model_dft) - ) { - LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__); + if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) || + llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) || + llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) || + llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) { + LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__); + LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt)); + LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft)); return false; } { - const int n_vocab_tgt = llama_n_vocab(model_tgt); - const int n_vocab_dft = llama_n_vocab(model_dft); + const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt); + const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft); const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft); if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) { LOG_ERR("%s: draft model vocab must closely match target model to use speculation but " "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n", - __func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE); + __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE); return false; } for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) { - const char * token_text_tgt = llama_token_get_text(model_tgt, i); - const char * token_text_dft = llama_token_get_text(model_dft, i); + const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i); + const char * token_text_dft = llama_vocab_get_text(vocab_dft, i); if (std::strcmp(token_text_tgt, token_text_dft) != 0) { - LOG_ERR("%s: draft model vocab must match target model to use speculation but " + LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but " "token %d content differs - target '%s', draft '%s'\n", __func__, i, common_token_to_piece(ctx_tgt, i).c_str(), common_token_to_piece(ctx_dft, i).c_str()); @@ -244,11 +252,6 @@ llama_tokens common_speculative_gen_draft( // add drafted token for each sequence const llama_token id = cur_p->data[0].id; - // only collect very high-confidence draft tokens - if (cur_p->data[0].p < params.p_min) { - break; - } - common_sampler_accept(smpl, id, true); result.push_back(id); @@ -257,6 +260,11 @@ llama_tokens common_speculative_gen_draft( break; } + // only collect very high-confidence draft tokens + if (cur_p->data[0].p < params.p_min) { + break; + } + common_batch_add(batch, id, n_past + i + 1, { 0 }, true); // evaluate the drafted tokens on the draft model diff --git a/common/speculative.h b/common/speculative.h index 50ec0344618aa..2b51a70ca1f72 100644 --- a/common/speculative.h +++ b/common/speculative.h @@ -9,7 +9,7 @@ struct common_speculative_params { int n_draft = 16; // max drafted tokens int n_reuse = 256; - float p_min = 0.9f; // min probabiliy required to accept a token in the draft + float p_min = 0.75f; // min probability required to accept a token in the draft }; struct common_speculative * common_speculative_init(struct llama_context * ctx_dft); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index e14ad9b01c659..525ac5d0a8643 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -221,17 +221,17 @@ def set_gguf_parameters(self): self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") - n_embd = self.find_hparam(["hidden_size", "n_embd"]) - self.gguf_writer.add_embedding_length(n_embd) - logger.info(f"gguf: embedding length = {n_embd}") + if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None: + self.gguf_writer.add_embedding_length(n_embd) + logger.info(f"gguf: embedding length = {n_embd}") if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) logger.info(f"gguf: feed forward length = {n_ff}") - n_head = self.find_hparam(["num_attention_heads", "n_head"]) - self.gguf_writer.add_head_count(n_head) - logger.info(f"gguf: head count = {n_head}") + if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None: + self.gguf_writer.add_head_count(n_head) + logger.info(f"gguf: head count = {n_head}") if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) @@ -265,12 +265,6 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] - # TODO: merge into modify_tensors? (need to check tensor shapes for all arches before doing that) - def reshape_tensors(self, data_torch: Tensor, new_name: str, bid: int | None) -> Tensor: - del new_name, bid # unused - - return data_torch.squeeze() - def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: del name, new_name, bid, n_dims # unused @@ -302,7 +296,9 @@ def prepare_tensors(self): break for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)): - data = self.reshape_tensors(data_torch, new_name, bid).numpy() + # TODO: why do we squeeze here? + # data = data_torch.squeeze().numpy() + data = data_torch.numpy() # if data ends up empty, it means data_torch was a scalar tensor -> restore if len(data.shape) == 0: @@ -330,6 +326,9 @@ def prepare_tensors(self): gguf.MODEL_TENSOR.TIME_MIX_W2, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2, + gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED, + gguf.MODEL_TENSOR.POSNET_NORM1, + gguf.MODEL_TENSOR.POSNET_NORM2, ) ) or not new_name.endswith(".weight") @@ -479,6 +478,11 @@ def func(modelcls: AnyModel) -> AnyModel: return modelcls return func + @classmethod + def print_registered_models(cls): + for name in sorted(cls._model_classes.keys()): + logger.error(f"- {name}") + @classmethod def from_model_architecture(cls, arch: str) -> type[Model]: try: @@ -531,9 +535,19 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]: else: token: str = reverse_vocab[i] if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not tokenizer.added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): toktypes.append(gguf.TokenType.CONTROL) else: + # NOTE: this was added for Gemma. + # Encoding and decoding the tokens above isn't sufficient for this case. token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces toktypes.append(gguf.TokenType.USER_DEFINED) else: @@ -544,7 +558,7 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]: # NOTE: this function is generated by convert_hf_to_gguf_update.py # do not modify it manually! - # ref: https://github.com/ggerganov/llama.cpp/pull/6920 + # ref: https://github.com/ggml-org/llama.cpp/pull/6920 # Marker: Start get_vocab_base_pre def get_vocab_base_pre(self, tokenizer) -> str: # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that @@ -577,6 +591,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": # ref: https://huggingface.co/tiiuae/falcon-7b res = "falcon" + if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": + # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base + res = "falcon3" if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" @@ -631,7 +648,7 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code res = "jina-v2-code" - if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b": + if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516": # ref: https://huggingface.co/THUDM/glm-4-9b-chat res = "chatglm-bpe" if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee": @@ -664,6 +681,24 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450": # ref: https://huggingface.co/facebook/chameleon-7b res = "chameleon" + if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35": + # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0 + res = "minerva-7b" + if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65": + # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base + res = "roberta-bpe" + if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb": + # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct + res = "gigachat" + if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1": + # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct + res = "megrez" + if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3 + res = "deepseek-v3" + if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B + res = "deepseek-r1-qwen" if res is None: logger.warning("\n") @@ -673,7 +708,7 @@ def get_vocab_base_pre(self, tokenizer) -> str: logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") logger.warning("** - the pre-tokenization config has changed upstream") logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") - logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("** ref: https://github.com/ggml-org/llama.cpp/pull/6920") logger.warning("**") logger.warning(f"** chkhsh: {chkhsh}") logger.warning("**************************************************************************************") @@ -686,6 +721,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: return res # Marker: End get_vocab_base_pre + def _set_vocab_none(self) -> None: + self.gguf_writer.add_tokenizer_model("none") + def _set_vocab_gpt2(self) -> None: tokens, toktypes, tokpre = self.get_vocab_base() self.gguf_writer.add_tokenizer_model("gpt2") @@ -1669,6 +1707,178 @@ def prepare_tensors(self): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("DeciLMForCausalLM") +class DeciModel(Model): + model_arch = gguf.MODEL_ARCH.DECI + + @staticmethod + def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int: + # DeciLM-specific code + intermediate_size = int(2 * ffn_mult * n_embd / 3) + return DeciModel._find_multiple(intermediate_size, 256) + + @staticmethod + def _find_multiple(n: int, k: int) -> int: + # DeciLM-specific code + if n % k == 0: + return n + return n + k - (n % k) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + _block_configs: list[dict[str,Any]] = self.hparams["block_configs"] + assert self.block_count == len(_block_configs) + self._num_kv_heads = list() + self._num_heads = list() + _ffn_multipliers = list() + # ***linear attention layer*** + # if n_heads_in_group is None and replace_with_linear is True + # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads + # ***attention-free layer*** + # if n_heads_in_group is None and replace_with_linear is False + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 + # ***normal attention-layer*** + # if n_heads_in_group is not None, then + # _num_kv_heads[il] is num_attention_head // n_heads_in_group and + # _num_heads[il] is num_attention_head + for il in range(len(_block_configs)): + if _block_configs[il]["attention"]["n_heads_in_group"] is None: + if _block_configs[il]["attention"]["replace_with_linear"] is True: + self._num_kv_heads.append(0) + self._num_heads.append(self.hparams["num_attention_heads"]) + else: + self._num_kv_heads.append(0) + self._num_heads.append(0) + else: + self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"]) + self._num_heads.append(self.hparams["num_attention_heads"]) + _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(_ffn_multipliers) + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int) + assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float) + self._ffn_dims: list[int] = [ + DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"]) + for multiplier in _ffn_multipliers + ] + + def set_vocab(self): + # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's + # eos_token from '|eot_id|' to '|end_of_text|' + if self.hparams.get("vocab_size", 128256) == 128256: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + else: + # DeciLM-7B + self._set_vocab_llama_hf() + + def set_gguf_parameters(self): + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(self._ffn_dims) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_head_count(self._num_heads) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_file_type(self.ftype) + else: # DeciLM-7B + super().set_gguf_parameters() + if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B + self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"] + assert self.block_count == len(self._num_kv_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + if bid is not None: + if "num_key_value_heads_per_layer" in self.hparams: + n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid] + elif "block_configs" in self.hparams: + n_kv_head = self._num_kv_heads[bid] + n_head = self._num_heads[bid] + else: + n_kv_head = self.hparams.get("num_key_value_heads") + else: + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + @Model.register("BitnetForCausalLM") class BitnetModel(Model): model_arch = gguf.MODEL_ARCH.BITNET @@ -1837,29 +2047,40 @@ class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) + super().set_gguf_parameters() + embedding_scale = float(self.hparams["scale_emb"]) + self.gguf_writer.add_embedding_scale(embedding_scale) + logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}") + residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5 + self.gguf_writer.add_residual_scale(residual_scale) + logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}") + logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"] + self.gguf_writer.add_logit_scale(logit_scale) + logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}") + if self.hparams.get("rope_scaling") is not None: + if self.hparams["rope_scaling"].get("type") == "longrope": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE) + logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}") - def set_vocab(self): - self._set_vocab_llama_hf() + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] - def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: - if n_kv_head is not None and n_head != n_kv_head: - n_head //= n_kv_head + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is not None: + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) - return ( - weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape) - ) + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) + + def set_vocab(self): + self._set_vocab_sentencepiece() def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -1869,9 +2090,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter # HF models permute some of the tensors, so we need to undo that if name.endswith(("q_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) + data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) return [(self.map_tensor_name(name), data_torch)] @@ -1981,6 +2202,75 @@ def set_vocab(self): except FileNotFoundError: self._set_vocab_gpt2() + def set_gguf_parameters(self): + super().set_gguf_parameters() + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + + +@Model.register("Qwen2VLForConditionalGeneration") +class Qwen2VLModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN2VL + + def set_gguf_parameters(self): + super().set_gguf_parameters() + mrope_section = self.hparams["rope_scaling"]["mrope_section"] + mrope_section += [0] * max(0, 4 - len(mrope_section)) + self.gguf_writer.add_rope_dimension_sections(mrope_section) + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + for name, data in super().get_tensors(): + if name.startswith("visual."): + continue + yield name, data + + +@Model.register("WavTokenizerDec") +class WavTokenizerDecModel(Model): + model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if \ + name.endswith("codebook.cluster_size") or \ + name.endswith("codebook.embed_avg") or \ + name.endswith("codebook.inited"): + logger.debug(f"Skipping {name!r}") + return [] + + logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}") + + return [(self.map_tensor_name(name), data_torch)] + + def set_vocab(self): + self._set_vocab_none() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_vocab_size (self.hparams["vocab_size"]) + self.gguf_writer.add_features_length (self.hparams["n_embd_features"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"]) + self.gguf_writer.add_group_norm_eps (self.hparams["group_norm_epsilon"]) + self.gguf_writer.add_group_norm_groups (self.hparams["group_norm_groups"]) + + self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"]) + self.gguf_writer.add_posnet_block_count (self.hparams["posnet"]["n_layer"]) + + self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"]) + self.gguf_writer.add_convnext_block_count (self.hparams["convnext"]["n_layer"]) + + self.gguf_writer.add_causal_attention(False) + @Model.register("Qwen2MoeForCausalLM") class Qwen2MoeModel(Model): @@ -2110,6 +2400,15 @@ class Phi3MiniModel(Model): model_arch = gguf.MODEL_ARCH.PHI3 def set_vocab(self): + # Phi-4 model uses GPT2Tokenizer + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + tokenizer_class = tokenizer_config_json['tokenizer_class'] + if tokenizer_class == 'GPT2Tokenizer': + return self._set_vocab_gpt2() + from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' @@ -2226,7 +2525,11 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_count(rope_dims) self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) + sliding_window = self.hparams.get("sliding_window") + # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models + if sliding_window is None: + sliding_window = 0 + self.gguf_writer.add_sliding_window(sliding_window) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: n_embd = self.find_hparam(["hidden_size", "n_embd"]) @@ -2268,6 +2571,63 @@ def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) +@Model.register("PhiMoEForCausalLM") +class PhiMoeModel(Phi3MiniModel): + model_arch = gguf.MODEL_ARCH.PHIMOE + + _experts: list[dict[str, Tensor]] | None = None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"]) + self.gguf_writer.add_expert_count(self.hparams["num_local_experts"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + @Model.register("PlamoForCausalLM") class PlamoModel(Model): model_arch = gguf.MODEL_ARCH.PLAMO @@ -2475,7 +2835,7 @@ def set_vocab(self): if chat_eos_token_id is not None: # For the chat model, we replace the eos with '<|im_end|>'. # TODO: this is a hack, should be fixed - # https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048 + # https://github.com/ggml-org/llama.cpp/pull/6745#issuecomment-2067687048 special_vocab.special_token_ids["eos"] = chat_eos_token_id logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}" " in chat mode so that the conversation can end normally.") @@ -2525,7 +2885,67 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] -@Model.register("BertModel", "CamembertModel") +@Model.register("InternLM3ForCausalLM") +class InternLM3Model(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + + def set_vocab(self): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + if "added_tokens_decoder" in tokenizer_config_json: + for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items(): + if token_data.get("special"): + token_id = int(token_id) + token = token_data["content"] + special_vocab._set_special_token(token, token_id) + # update eos token + if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids: + special_vocab.special_token_ids["eos"] = token_id + + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear" or self.hparams["rope_scaling"].get("rope_type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("BertModel", "BertForMaskedLM", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT @@ -2566,7 +2986,8 @@ def set_vocab(self): # we need this to validate the size of the token_type embeddings # though currently we are passing all zeros to the token_type embeddings - self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B" + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) # convert to phantom space vocab def phantom(tok): @@ -2590,13 +3011,73 @@ def phantom(tok): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused + if name.startswith("bert."): + name = name[5:] + + if name.endswith(".gamma"): + name = name[:-6] + ".weight" + + if name.endswith(".beta"): + name = name[:-5] + ".bias" + # we are only using BERT for embeddings so we don't need the pooling layer if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): return [] # we don't need these + if name.startswith("cls.predictions"): + return [] + + if name.startswith("cls.seq_relationship"): + return [] + return [(self.map_tensor_name(name), data_torch)] +@Model.register("RobertaModel") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + @Model.register("NomicBertModel") class NomicBertModel(BertModel): model_arch = gguf.MODEL_ARCH.NOMIC_BERT @@ -2904,6 +3385,8 @@ def set_gguf_parameters(self): # required by llama.cpp, unused self.gguf_writer.add_head_count(0) + lerp_weights: dict[int, dict[str, Tensor]] = {} + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: new_name = self.map_tensor_name(name) @@ -2916,14 +3399,87 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter if new_name.endswith("time_mix_w2.weight"): data_torch = data_torch.permute(0, 2, 1) - rescale_every_n_layers = self.hparams["rescale_every"] - if rescale_every_n_layers > 0: - if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): - data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers)) + if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name: + data_torch = data_torch.squeeze() + + try: + rescale_every_n_layers = self.hparams["rescale_every"] + if rescale_every_n_layers > 0: + if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): + data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers)) + except KeyError: + pass + + # concat time_mix_lerp weights to reduce some cpu overhead + # also reduces the number of tensors in the model + if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name: + try: + self.lerp_weights[bid][new_name] = data_torch + except KeyError: + self.lerp_weights[bid] = {new_name: data_torch} + if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]): + new_name = f"blk.{bid}.time_mix_lerp_fused.weight" + data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1) + yield (new_name, data) + return yield (new_name, data_torch) +@Model.register("RWKV6Qwen2ForCausalLM") +class RWKV6Qwen2Model(Rwkv6Model): + model_arch = gguf.MODEL_ARCH.RWKV6QWEN2 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + num_attention_heads = self.hparams["num_attention_heads"] + num_key_value_heads = self.hparams["num_key_value_heads"] + hidden_size = self.hparams["hidden_size"] + head_size = hidden_size // num_attention_heads + rms_norm_eps = self.hparams["rms_norm_eps"] + intermediate_size = self.hparams["intermediate_size"] + time_mix_extra_dim = 64 if hidden_size >= 4096 else 32 + time_decay_extra_dim = 128 if hidden_size >= 4096 else 64 + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) + self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # special parameters for time_mixing in RWKV6QWEN2 + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_token_shift_count(1) + # RWKV6QWEN2 use grouped key/value like GQA + self.gguf_writer.add_head_count_kv(num_key_value_heads) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + for new_name, data in super().modify_tensors(data_torch, name, bid): + if "time_mix_w1" in new_name or "time_mix_w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg + # permute them here to avoid code changes + data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1]) + if "w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + yield (new_name, data) + continue + yield (new_name, data) + + @Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM") class MambaModel(Model): model_arch = gguf.MODEL_ARCH.MAMBA @@ -2978,7 +3534,6 @@ def set_gguf_parameters(self): _tok_embd = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - del bid # unused output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) @@ -2989,6 +3544,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter logger.debug("A_log --> A ==> " + new_name) data_torch = -torch.exp(data_torch) + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid): + data_torch = data_torch.squeeze() + # assuming token_embd.weight is seen before output.weight if self._tok_embd is not None and new_name == output_name: if torch.equal(self._tok_embd, data_torch): @@ -3053,7 +3611,6 @@ def set_gguf_parameters(self): self.gguf_writer.add_file_type(self.ftype) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: - del bid # unused if name.startswith("model.backbone") or name.startswith("model.lm_head"): # map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2 @@ -3064,28 +3621,26 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter new_name = self.map_tensor_name(name) - if name.endswith(".A_log"): - logger.debug("A_log --> A ==> " + new_name) - data_torch = -torch.exp(data_torch) - - yield (new_name, data_torch) - - def reshape_tensors(self, data_torch: Tensor, new_name: str, bid: int | None) -> Tensor: - if any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [ + if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid): + data_torch = data_torch.squeeze() + elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [ gguf.MODEL_TENSOR.SSM_A, gguf.MODEL_TENSOR.SSM_D, ]): # unsqueeze A to use similar shape semantics as Mamba-1 # (D is also unsqueezed, but for more straightforward broadcast internally) - return data_torch.reshape((*data_torch.shape, 1)) - + data_torch = data_torch.reshape((*data_torch.shape, 1)) elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid): d_model = self.find_hparam(["hidden_size", "d_model", "dim"]) d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model n_group = self.hparams.get("n_groups", 1) - return data_torch.reshape((n_group, d_inner // n_group)) + data_torch = data_torch.reshape((n_group, d_inner // n_group)) - return data_torch.squeeze() + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + yield (new_name, data_torch) @Model.register("CohereForCausalLM") @@ -3106,6 +3661,24 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) +@Model.register("Cohere2ForCausalLM") +class Cohere2Model(Model): + model_arch = gguf.MODEL_ARCH.COHERE2 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + + rotary_pct = self.hparams["rotary_pct"] + hidden_size = self.hparams["hidden_size"] + num_attention_heads = self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads))) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + @Model.register("OlmoForCausalLM") @Model.register("OLMoForCausalLM") class OlmoModel(Model): @@ -3134,9 +3707,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] -@Model.register("Olmo1124ForCausalLM") -class Olmo1124Model(Model): - model_arch = gguf.MODEL_ARCH.OLMO_1124 +@Model.register("Olmo2ForCausalLM") +class Olmo2Model(Model): + model_arch = gguf.MODEL_ARCH.OLMO2 @Model.register("OlmoeForCausalLM") @@ -3472,7 +4045,99 @@ def prepare_tensors(self): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("DeepseekForCausalLM") +class DeepseekModel(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + + self.gguf_writer.add_rope_dimension_count(rope_dim) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_weights_scale(1.0) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + + _experts: list[dict[str, Tensor]] | None = None + + @staticmethod + def permute(weights: Tensor, n_head: int, n_head_kv: int | None): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + @Model.register("DeepseekV2ForCausalLM") +@Model.register("DeepseekV3ForCausalLM") class DeepseekV2Model(Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK2 @@ -3494,6 +4159,15 @@ def set_gguf_parameters(self): self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"]) + + if hparams["scoring_func"] == "sigmoid": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + elif hparams["scoring_func"] == "softmax": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) + else: + raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}") + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: @@ -3506,6 +4180,16 @@ def set_gguf_parameters(self): _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # rename e_score_correction_bias tensors + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + # skip Multi-Token Prediction (MTP) layers + block_count = self.hparams["num_hidden_layers"] + match = re.match(r"model.layers.(\d+)", name) + if match and int(match.group(1)) >= block_count: + return [] + # process the experts separately if name.find("mlp.experts") != -1: n_experts = self.hparams["n_routed_experts"] @@ -3916,7 +4600,7 @@ def prepare_tensors(self): self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias) -@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration") +@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration") class ChatGLMModel(Model): model_arch = gguf.MODEL_ARCH.CHATGLM @@ -4022,47 +4706,15 @@ def set_vocab(self): from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) - vocab_size = hparams["padded_vocab_size"] + vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"]) assert max(tokenizer.get_vocab().values()) < vocab_size - tokpre = self.get_vocab_base_pre(tokenizer) - - merges = [] - vocab = {} - mergeable_ranks = tokenizer.mergeable_ranks - for token, rank in mergeable_ranks.items(): - vocab[ChatGLMModel.token_bytes_to_string(token)] = rank - if len(token) == 1: - continue - merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank) - assert len(merged) >= 2 and len(merged) <= 7 - merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged))) - - # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined - added_vocab = tokenizer.get_added_vocab() - reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} - - for i in range(vocab_size): - if i not in reverse_vocab: - tokens.append(f"[PAD{i}]") - toktypes.append(gguf.TokenType.UNUSED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - + tokens, toktypes, tokpre = self.get_vocab_base() self.gguf_writer.add_tokenizer_model("gpt2") self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) - special_vocab.merges = merges + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) # only add special tokens when they were not already loaded from config.json special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) @@ -4073,16 +4725,20 @@ def set_vocab(self): def set_gguf_parameters(self): n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) - n_head_kv = self.hparams.get("multi_query_group_num", n_head) + n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head)) self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) self.gguf_writer.add_embedding_length(n_embed) - self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed)) - self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed))) + self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"])) self.gguf_writer.add_head_count(n_head) self.gguf_writer.add_head_count_kv(n_head_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5)) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_dimension_count(64) + if "attention_dim" in self.hparams: + rope_dim = self.hparams["attention_dim"] + else: + rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) self.gguf_writer.add_add_bos_token(False) rope_freq = 10000 if "rope_ratio" in self.hparams: @@ -4092,7 +4748,7 @@ def set_gguf_parameters(self): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused - if name.endswith(".rotary_pos_emb.inv_freq"): + if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."): return [] name = name.removeprefix("transformer.") @@ -4400,6 +5056,7 @@ def parse_args() -> argparse.Namespace: parser.add_argument( "model", type=Path, help="directory containing model file", + nargs="?", ) parser.add_argument( "--use-temp-file", action="store_true", @@ -4437,8 +5094,15 @@ def parse_args() -> argparse.Namespace: "--metadata", type=Path, help="Specify the path for an authorship metadata override file" ) + parser.add_argument( + "--print-supported-models", action="store_true", + help="Print the supported models" + ) - return parser.parse_args() + args = parser.parse_args() + if not args.print_supported_models and args.model is None: + parser.error("the following arguments are required: model") + return args def split_str_to_n_bytes(split_str: str) -> int: @@ -4462,6 +5126,11 @@ def split_str_to_n_bytes(split_str: str) -> int: def main() -> None: args = parse_args() + if args.print_supported_models: + logger.error("Supported models:") + Model.print_registered_models() + sys.exit(0) + if args.verbose: logging.basicConfig(level=logging.DEBUG) else: diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 28cd02e5a7f66..fa4989a80c544 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -8,7 +8,7 @@ # provide the necessary information to llama.cpp via the GGUF header in order to implement # the same pre-tokenizer. # -# ref: https://github.com/ggerganov/llama.cpp/pull/6920 +# ref: https://github.com/ggml-org/llama.cpp/pull/6920 # # Instructions: # @@ -17,7 +17,7 @@ # # python3 convert_hf_to_gguf_update.py # -# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py +# - The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp @@ -65,43 +65,50 @@ class TOKENIZER_TYPE(IntEnum): # TODO: add models here, base models preferred models = [ - {"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, - {"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, - {"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, - {"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, - {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, - {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, - {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, - {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, - {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, - {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, - {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, - {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, - {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, - {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, - {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, - {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, - {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, - {"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", }, - {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! - {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, - {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, - {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, - {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, - {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, - {"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B - {"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", }, - {"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", }, - {"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", }, - {"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", }, - {"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", }, - {"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", }, - {"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", }, - {'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", }, - {'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, - {"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", }, - {"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", }, - {"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", }, + {"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, + {"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, + {"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, + {"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, + {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, + {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, + {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, + {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, + {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, + {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, + {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, + {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, + {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, + {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, + {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, + {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, + {"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", }, + {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! + {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, + {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, + {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, + {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, + {"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B + {"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", }, + {"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", }, + {"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", }, + {"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", }, + {"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", }, + {"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", }, + {"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", }, + {'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", }, + {'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, + {"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", }, + {"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", }, + {"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", }, + {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", }, + {"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"}, + {"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"}, + {"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"}, + {"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"}, + {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"}, ] @@ -239,7 +246,7 @@ def get_vocab_base_pre(self, tokenizer) -> str: logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") logger.warning("** - the pre-tokenization config has changed upstream") logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") - logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("** ref: https://github.com/ggml-org/llama.cpp/pull/6920") logger.warning("**") logger.warning(f"** chkhsh: {{chkhsh}}") logger.warning("**************************************************************************************") diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index ed1014cae075a..bdc991533b4e0 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -226,6 +226,9 @@ def get_base_tensor_name(lora_tensor_name: str) -> str: base_name = lora_tensor_name.replace("base_model.model.", "") base_name = base_name.replace(".lora_A.weight", ".weight") base_name = base_name.replace(".lora_B.weight", ".weight") + # models produced by mergekit-extract-lora have token embeddings in the adapter + base_name = base_name.replace(".lora_embedding_A", ".weight") + base_name = base_name.replace(".lora_embedding_B", ".weight") return base_name @@ -260,6 +263,10 @@ def parse_args() -> argparse.Namespace: "--base", type=Path, help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config", ) + parser.add_argument( + "--base-model-id", type=str, + help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')", + ) parser.add_argument( "lora_path", type=Path, help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)", @@ -290,6 +297,7 @@ def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]: dir_base_model: Path | None = args.base dir_lora: Path = args.lora_path + base_model_id: str | None = args.base_model_id lora_config = dir_lora / "adapter_config.json" input_model = dir_lora / "adapter_model.safetensors" @@ -313,7 +321,10 @@ def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]: lparams: dict[str, Any] = json.load(f) # load base model - if dir_base_model is None: + if base_model_id is not None: + logger.info(f"Loading base model from Hugging Face: {base_model_id}") + hparams = load_hparams_from_hf(base_model_id) + elif dir_base_model is None: if "base_model_name_or_path" in lparams: model_id = lparams["base_model_name_or_path"] logger.info(f"Loading base model from Hugging Face: {model_id}") @@ -371,15 +382,20 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]: if self.lazy: tensor = LazyTorchTensor.from_eager(tensor) base_name = get_base_tensor_name(name) - is_lora_a = ".lora_A.weight" in name - is_lora_b = ".lora_B.weight" in name + # note: mergekit-extract-lora also adds token embeddings to the adapter + is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name + is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name if not is_lora_a and not is_lora_b: if ".base_layer.weight" in name: continue + # mergekit-extract-lora add these layernorm to the adapter, we need to keep them + if "_layernorm" in name or ".norm" in name: + yield (base_name, tensor) + continue logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") if ".embed_tokens.weight" in name or ".lm_head.weight" in name: logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") - logger.error("Please refer to https://github.com/ggerganov/llama.cpp/pull/9948") + logger.error("Please refer to https://github.com/ggml-org/llama.cpp/pull/9948") sys.exit(1) if base_name in tensor_map: @@ -403,13 +419,25 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter # some archs may have the same tensor for lm_head and output (tie word embeddings) # in this case, adapters targeting lm_head will fail when using llama-export-lora # therefore, we ignore them for now - # see: https://github.com/ggerganov/llama.cpp/issues/9065 + # see: https://github.com/ggml-org/llama.cpp/issues/9065 if name == "lm_head.weight" and len(dest) == 0: raise ValueError("lm_head is present in adapter, but is ignored in base model") for dest_name, dest_data in dest: + # mergekit-extract-lora add these layernorm to the adapter + if "_norm" in dest_name: + assert dest_data.dim() == 1 + yield (dest_name, dest_data) + continue + + # otherwise, we must get the lora_A and lora_B tensors assert isinstance(dest_data, LoraTorchTensor) lora_a, lora_b = dest_data.get_lora_A_B() + # note: mergekit-extract-lora flip and transpose A and B + # here we only need to transpose token_embd.lora_a, see llm_build_inp_embd() + if "token_embd.weight" in dest_name: + lora_a = lora_a.T + yield (dest_name + ".lora_a", lora_a) yield (dest_name + ".lora_b", lora_b) diff --git a/docs/android.md b/docs/android.md index 320b62240382f..d2a835653fe5d 100644 --- a/docs/android.md +++ b/docs/android.md @@ -12,7 +12,7 @@ $ apt update && apt upgrade -y $ apt install git cmake ``` -Then, follow the [build instructions](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md), specifically for CMake. +Then, follow the [build instructions](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md), specifically for CMake. Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance: @@ -23,10 +23,10 @@ $ curl -L {model-url} -o ~/{model}.gguf Then, if you are not already in the repo directory, `cd` into `llama.cpp` and: ``` -$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" +$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" ``` -Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. +Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone: diff --git a/docs/backend/BLIS.md b/docs/backend/BLIS.md index 35d06bd0f303d..9045485771ea6 100644 --- a/docs/backend/BLIS.md +++ b/docs/backend/BLIS.md @@ -27,13 +27,6 @@ We recommend using openmp since it's easier to modify the cores being used. ### llama.cpp compilation -Makefile: - -```bash -make GGML_BLIS=1 -j -# make GGML_BLIS=1 llama-benchmark-matmult -``` - CMake: ```bash diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md index 6bdd9d2daab90..23f10175a6b2d 100644 --- a/docs/backend/CANN.md +++ b/docs/backend/CANN.md @@ -23,6 +23,8 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ## News +- 2024.11 + - Support F16 and F32 data type model for Ascend 310P NPU. - 2024.8 - Support `Q4_0` and `Q8_0` data type for Ascend NPU. - 2024.7 @@ -40,9 +42,11 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ### Ascend NPU **Verified devices** + | Ascend NPU | Status | |:-----------------------------:|:-------:| | Atlas 300T A2 | Support | +| Atlas 300I Duo | Support | *Notes:* diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md new file mode 100644 index 0000000000000..2a946dc8df0ff --- /dev/null +++ b/docs/backend/OPENCL.md @@ -0,0 +1,205 @@ +# llama.cpp for OpenCL + +- [Background](#background) +- [OS](#os) +- [Hardware](#hardware) +- [DataType Supports](#datatype-supports) +- [Model Preparation](#model-preparation) +- [CMake Options](#cmake-options) +- [Android](#android) +- [Windows 11 Arm64](#windows-11-arm64) +- [Known Issue](#known-issues) +- [TODO](#todo) + +## Background + +OpenCL (Open Computing Language) is an open, royalty-free standard for cross-platform, parallel programming of diverse accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. Similar to CUDA, OpenCL has been widely used to program GPUs and is supported by most GPU vendors. + +### Llama.cpp + OpenCL + +The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adreno GPU** firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs although the performance is not optimal. + +## OS + +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Android | Support | Snapdragon 8 Gen 3, Snapdragon 8 Elite | +| Windows | Support | Windows 11 Arm64 with Snapdragon X Elite | +| Linux | Support | Ubuntu 22.04 WSL2 with Intel 12700H | + +## Hardware + +### Adreno GPU + +**Verified devices** + +| Adreno GPU | Status | +|:------------------------------------:|:-------:| +| Adreno 750 (Snapdragon 8 Gen 3) | Support | +| Adreno 830 (Snapdragon 8 Elite) | Support | +| Adreno X85 (Snapdragon X Elite) | Support | + +## DataType Supports + +| DataType | Status | +|:----------------------:|:--------------------------:| +| Q4_0 | Support | +| Q6_K | Support, but not optimized | + +## Model Preparation + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration. + +Currently we support `Q4_0` quantization and have optimize for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize`. For example, + +```sh +./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0 +``` + +Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization. + +## CMake Options + +The OpenCL backend has the following CMake options that control the behavior of the backend. + +| CMake options | Default value | Description | +|:---------------------------------:|:--------------:|:------------------------------------------| +| `GGML_OPENCL_EMBED_KERNELS` | `ON` | Embed OpenCL kernels into the executable. | +| `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | + +## Android + +Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line, + +* Git +* CMake 3.29 +* Ninja +* Python3 + +### I. Setup Environment + +1. **Install NDK** + +```sh +cd ~ +wget https://dl.google.com/android/repository/commandlinetools-linux-8512546_latest.zip && \ +unzip commandlinetools-linux-8512546_latest.zip && \ +mkdir -p ~/android-sdk/cmdline-tools && \ +mv cmdline-tools latest && \ +mv latest ~/android-sdk/cmdline-tools/ && \ +rm -rf commandlinetools-linux-8512546_latest.zip + +yes | ~/android-sdk/cmdline-tools/latest/bin/sdkmanager "ndk;26.3.11579264" +``` + +2. **Install OpenCL Headers and Library** + +```sh +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-Headers && \ +cd OpenCL-Headers && \ +cp -r CL ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include + +cd ~/dev/llm + +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \ +cd OpenCL-ICD-Loader && \ +mkdir build_ndk26 && cd build_ndk26 && \ +cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \ + -DOPENCL_ICD_LOADER_HEADERS_DIR=$HOME/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=24 \ + -DANDROID_STL=c++_shared && \ +ninja && \ +cp libOpenCL.so ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android +``` + +### II. Build llama.cpp + +```sh +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && \ +cd llama.cpp && \ +mkdir build-android && cd build-android + +cmake .. -G Ninja \ + -DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DBUILD_SHARED_LIBS=OFF \ + -DGGML_OPENCL=ON + +ninja +``` + +## Windows 11 Arm64 + +A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the following tools are accessible from command line, + +* Git +* CMake 3.29 +* Clang 19 +* Ninja +* Visual Studio 2022 + +Powershell is used for the following instructions. + +### I. Setup Environment + +1. **Install OpenCL Headers and Library** + +```powershell +mkdir -p ~/dev/llm + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers +mkdir build && cd build +cmake .. -G Ninja ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install + +cd ~/dev/llm +git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader +mkdir build && cd build +cmake .. -G Ninja ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl" +cmake --build . --target install +``` + +### II. Build llama.cpp + +```powershell + +mkdir -p ~/dev/llm +cd ~/dev/llm + +git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp +mkdir build && cd build + +cmake .. -G Ninja ` + -DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" ` + -DCMAKE_BUILD_TYPE=Release ` + -DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" ` + -DBUILD_SHARED_LIBS=OFF ` + -DGGML_OPENCL=ON +ninja +``` + +## Known Issues + +- Qwen2.5 0.5B model produces gibberish output with Adreno kernels. + +## TODO + +- Fix Qwen2.5 0.5B +- Optimization for Q6_K +- Support and optimization for Q4_K diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index 8d8312e9158f1..5da439e94e092 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -36,12 +36,22 @@ The following release is verified with good quality: |Commit ID|Tag|Release|Verified Platform| Update date| |-|-|-|-|-| -|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| -|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| +|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| ## News +- 2025.2 + - Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC). + |GPU|Base tokens/s|Increased tokens/s|Percent| + |-|-|-|-| + |PVC 1550|39|73|+87%| + |Flex 170|39|50|+28%| + |Arc770|42|55|+30%| + |MTL|13|16|+23%| + |ARL-H|14|17|+21%| + - 2024.11 - Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer. @@ -58,7 +68,7 @@ The following release is verified with good quality: - 2024.3 - Release binary files of Windows. - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd). - - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437). + - New base line is ready: [tag b2437](https://github.com/ggml-org/llama.cpp/tree/b2437). - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. - Support detecting all GPUs with level-zero and same top **Max compute units**. @@ -97,8 +107,8 @@ SYCL backend supports Intel GPU Family: | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | | Intel Arc Series | Support | Arc 770, 730M, Arc A750 | -| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | -| Intel iGPU | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 | +| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake | +| Intel iGPU | Support | iGPU in 13700k,iGPU in 13400, i5-1250P, i7-1260P, i7-1165G7 | *Notes:* @@ -133,7 +143,7 @@ The docker build option is currently limited to *intel GPU* targets. ### Build image ```sh # Using FP16 -docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile . +docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile . ``` *Notes*: @@ -660,8 +670,10 @@ use 1 SYCL GPUs: [0] with Max compute units:512 | Name | Value | Function | |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| | GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | +| GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features based on Intel GPU type, to compare the performance increase | | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | + ## Known Issues - `Split-mode:[row]` is not supported. diff --git a/docs/build.md b/docs/build.md index 359952b30ff93..b3ecf043d7e48 100644 --- a/docs/build.md +++ b/docs/build.md @@ -3,128 +3,79 @@ **To get the Code:** ```bash -git clone https://github.com/ggerganov/llama.cpp +git clone https://github.com/ggml-org/llama.cpp cd llama.cpp ``` -In order to build llama.cpp you have four different options. +The following sections describe how to build with different backends and options. -- Using `make`: - - On Linux or MacOS: +## CPU Build - ```bash - make - ``` +Build llama.cpp using `CMake`: - - On Windows (x86/x64 only, arm64 requires cmake): - - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Extract `w64devkit` on your pc. - 3. Run `w64devkit.exe`. - 4. Use the `cd` command to reach the `llama.cpp` folder. - 5. From here you can run: - ```bash - make - ``` +```bash +cmake -B build +cmake --build build --config Release +``` - - Notes: - - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`. - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, run `make LLAMA_DEBUG=1` +**Notes**: -- Using `CMake`: +- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. +- For faster repeated compilation, install [ccache](https://ccache.dev/) +- For debug builds, there are two cases: - ```bash - cmake -B build - cmake --build build --config Release - ``` + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): - **Notes**: + ```bash + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cmake --build build + ``` - - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`. - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, there are two cases: + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): - 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + ```bash + cmake -B build -G "Xcode" + cmake --build build --config Debug + ``` - ```bash - cmake -B build -DCMAKE_BUILD_TYPE=Debug - cmake --build build - ``` + For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html). +- For static builds, add `-DBUILD_SHARED_LIBS=OFF`: + ``` + cmake -B build -DBUILD_SHARED_LIBS=OFF + cmake --build build --config Release + ``` - 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): +- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers: + - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...): + - Tab Workload: Desktop-development with C++ + - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang) + - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test + - For Windows on ARM (arm64, WoA) build with: + ```bash + cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF + cmake --build build-arm64-windows-llvm-release + ``` + Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels. + For building with ninja generator and clang compiler as default: + -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64 ```bash - cmake -B build -G "Xcode" - cmake --build build --config Debug + cmake --preset x64-windows-llvm-release + cmake --build build-x64-windows-llvm-release ``` - - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers: - - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...): - - Tab Workload: Desktop-development with C++ - - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang) - - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test - - For Windows on ARM (arm64, WoA) build with: - ```bash - cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF - cmake --build build-arm64-windows-llvm-release - ``` - Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels. - -- Using `gmake` (FreeBSD): - - 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) - 2. Add your user to **video** group - 3. Install compilation dependencies. - - ```bash - sudo pkg install gmake automake autoconf pkgconf llvm15 openblas - - gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 - ``` - -## Metal Build - -On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. -To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option. - -When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line -argument. ## BLAS Build -Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use: -### Accelerate Framework: +### Accelerate Framework This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. -### OpenBLAS: +### OpenBLAS This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. -- Using `make`: - - On Linux: - ```bash - make GGML_OPENBLAS=1 - ``` - - - On Windows: - - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases). - 3. Extract `w64devkit` on your pc. - 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`. - 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`. - 6. Run `w64devkit.exe`. - 7. Use the `cd` command to reach the `llama.cpp` folder. - 8. From here you can run: - - ```bash - make GGML_OPENBLAS=1 - ``` - - Using `CMake` on Linux: ```bash @@ -136,14 +87,6 @@ This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS i Check [BLIS.md](./backend/BLIS.md) for more information. -### SYCL - -SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. - -llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - -For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). - ### Intel oneMKL Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md). @@ -161,27 +104,87 @@ Building through oneAPI compilers will make avx_vnni instruction set available f Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. -### CUDA +### Other BLAS libraries -This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). +Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors. -For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. +## Metal Build -- Using `make`: - ```bash - make GGML_CUDA=1 - ``` -- Using `CMake`: +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option. - ```bash - cmake -B build -DGGML_CUDA=ON - cmake --build build --config Release - ``` +When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument. + +## SYCL + +SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + +llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + +For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +## CUDA + +This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed. + +#### Download directly from NVIDIA +You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads). -The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. + +#### Compile and run inside a Fedora Toolbox Container +We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/). + +**Recommended for:** + +- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/). +- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde). +- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download) + + +### Compilation +```bash +cmake -B build -DGGML_CUDA=ON +cmake --build build --config Release +``` + +### Override Compute Capability Specifications + +If `nvcc` cannot detect your gpu, you may get compile-warnings such as: + ```text +nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used +``` + +To override the `native` GPU detection: + +#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus). + +```text +GeForce RTX 4090 8.9 +GeForce RTX 3080 Ti 8.6 +GeForce RTX 3070 8.6 +``` + +#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list. + +```bash +cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89" +``` + +### Runtime CUDA environmental variables + +You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime. + +```bash +# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device. +CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf +``` + +### Unified Memory The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`. +### Performance Tuning + The following compilation options are also available to tweak performance: | Option | Legal values | Default | Description | @@ -192,14 +195,10 @@ The following compilation options are also available to tweak performance: | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | -### MUSA +## MUSA This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa). -- Using `make`: - ```bash - make GGML_MUSA=1 - ``` - Using `CMake`: ```bash @@ -207,22 +206,26 @@ This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GP cmake --build build --config Release ``` + For static build: + + ```bash + cmake -B build -DGGML_MUSA=ON \ + -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON + cmake --build build --config Release + ``` + The environment variable [`MUSA_VISIBLE_DEVICES`](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) can be used to specify which GPU(s) will be used. The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet. -### hipBLAS +## HIP -This provides BLAS acceleration on HIP-supported AMD GPUs. +This provides GPU acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). -- Using `make`: - ```bash - make GGML_HIPBLAS=1 - ``` - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ @@ -247,11 +250,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm && cmake --build build -- -j 16 ``` -- Using `make` (example for target gfx1030, build with 16 CPU threads): - ```bash - make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 - ``` - - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% @@ -265,11 +263,11 @@ You can download it from your Linux distro's package manager or from here: [ROCm The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. -### Vulkan +## Vulkan **Windows** -#### w64devkit +### w64devkit Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases). @@ -289,9 +287,14 @@ Libs: -lvulkan-1 EOF ``` -Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`. -#### Git Bash MINGW64 +Switch into the `llama.cpp` directory and build using CMake. +```sh +cmake -B build -DGGML_VULKAN=ON +cmake --build build --config Release +``` + +### Git Bash MINGW64 Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings @@ -310,20 +313,21 @@ cmake --build build --config Release Now you can load the model in conversation mode using `Vulkan` -``` -build/bin/release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv +```sh +build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv ``` -#### MSYS2 +### MSYS2 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies. - ```sh - pacman -S git \ - mingw-w64-ucrt-x86_64-gcc \ - mingw-w64-ucrt-x86_64-cmake \ - mingw-w64-ucrt-x86_64-vulkan-devel \ - mingw-w64-ucrt-x86_64-shaderc - ``` -Switch into `llama.cpp` directory and build using CMake. +```sh +pacman -S git \ + mingw-w64-ucrt-x86_64-gcc \ + mingw-w64-ucrt-x86_64-cmake \ + mingw-w64-ucrt-x86_64-vulkan-devel \ + mingw-w64-ucrt-x86_64-shaderc +``` + +Switch into the `llama.cpp` directory and build using CMake. ```sh cmake -B build -DGGML_VULKAN=ON cmake --build build --config Release @@ -335,7 +339,7 @@ You don't need to install Vulkan SDK. It will be installed inside the container. ```sh # Build the image -docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile . +docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile . # Then, use it: docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 @@ -372,7 +376,7 @@ cmake --build build --config Release # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` -### CANN +## CANN This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU. For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/). @@ -387,22 +391,26 @@ cmake --build build --config release You can test with: -`./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32` +```bash +./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32 +``` -If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`: +If the following info is output on screen, you are using `llama.cpp` with the CANN backend: ```bash -llm_load_tensors: CANN buffer size = 13313.00 MiB +llm_load_tensors: CANN model buffer size = 13313.00 MiB llama_new_context_with_model: CANN compute buffer size = 1260.81 MiB ``` For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md). -### Android +## Android To read documentation for how to build on Android, [click here](./android.md) -### Arm CPU optimized mulmat kernels +## Notes about GPU-accelerated backends + +The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`. -Llama.cpp includes a set of optimized mulmat kernels for the Arm architecture, leveraging Arm® Neon™, int8mm and SVE instructions. These kernels are enabled at build time through the appropriate compiler cpu-type flags, such as `-DCMAKE_C_FLAGS=-march=armv8.2a+i8mm+sve`. Note that these optimized kernels require the model to be quantized into one of the formats: `Q4_0_4_4` (Arm Neon), `Q4_0_4_8` (int8mm) or `Q4_0_8_8` (SVE). The SVE mulmat kernel specifically requires a vector width of 256 bits. When running on devices with a different vector width, it is recommended to use the `Q4_0_4_8` (int8mm) or `Q4_0_4_4` (Arm Neon) formats for better performance. Refer to [examples/quantize/README.md](../examples/quantize/README.md) for more information on the quantization formats. +In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option. -To support `Q4_0_4_4`, you must build with `GGML_NO_LLAMAFILE=1` (`make`) or `-DGGML_LLAMAFILE=OFF` (`cmake`). +Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building. diff --git a/docs/cuda-fedora.md b/docs/cuda-fedora.md new file mode 100644 index 0000000000000..75cd2b499d086 --- /dev/null +++ b/docs/cuda-fedora.md @@ -0,0 +1,270 @@ +# Setting Up CUDA on Fedora + +In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for: + +- [Fedora Workstation](https://fedoraproject.org/workstation/) +- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/) +- [Fedora Spins](https://fedoraproject.org/spins) +- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`. + +## Table of Contents + +- [Prerequisites](#prerequisites) +- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository) +- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment) +- [Installing Essential Development Tools](#installing-essential-development-tools) +- [Adding the CUDA Repository](#adding-the-cuda-repository) +- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs) +- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts) +- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs) +- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package) +- [Configuring the Environment](#configuring-the-environment) +- [Verifying the Installation](#verifying-the-installation) +- [Conclusion](#conclusion) +- [Troubleshooting](#troubleshooting) +- [Additional Notes](#additional-notes) +- [References](#references) + +## Prerequisites + +- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/). +- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA). +- **Internet connectivity** to download packages. + +### Using the Fedora 41 CUDA Repository + +The latest release is 41. + +- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/) + +**Note:** We recommend using a toolbox environment to prevent system conflicts. + +## Creating a Fedora Toolbox Environment + +This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system. + +**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker. + +1. **Create a Fedora 41 Toolbox:** + + ```bash + toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda + ``` + +2. **Enter the Toolbox:** + + ```bash + toolbox enter --container fedora-toolbox-41-cuda + ``` + + Inside the toolbox, you have root privileges and can install packages without affecting the host system. + +## Installing Essential Development Tools + +1. **Synchronize the DNF Package Manager:** + + ```bash + sudo dnf distro-sync + ``` + +2. **Install the Default Text Editor (Optional):** + + ```bash + sudo dnf install vim-default-editor --allowerasing + ``` + + The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package. + +3. **Install Development Tools and Libraries:** + + ```bash + sudo dnf install @c-development @development-tools cmake + ``` + + This installs essential packages for compiling software, including `gcc`, `make`, and other development headers. + +## Adding the CUDA Repository + +Add the NVIDIA CUDA repository to your DNF configuration: + +```bash +sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo +``` + +After adding the repository, synchronize the package manager again: + +```bash +sudo dnf distro-sync +``` + +## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs` + +We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go). + +```bash +ls -la /usr/lib64/libcuda.so.1 +``` + +**Explanation:** + +- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA, + on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries. + +### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found). + +```bash +sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs +``` + +### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found). + +If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files. + +#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies) + +```bash +sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs +``` + +#### 2. Update the RPM database to assume the installation of these packages. + +```bash +sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/* +``` + +**Note:** + +- The `--justdb` option only updates the RPM database, without touching the filesystem. + +#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs` + +After manually installing the dependencies, run: + +```bash +sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs +``` + +You should receive a message indicating the package is already installed: + +``` +Updating and loading repositories: +Repositories loaded. +Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed. +Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed. + +Nothing to do. +``` + +## Installing the CUDA Meta-Package + +Now that the driver libraries are installed, proceed to install CUDA: + +```bash +sudo dnf install cuda +``` + +This installs the CUDA toolkit and associated packages. + +## Configuring the Environment + +To use CUDA, add its binary directory to your system's `PATH`. + +1. **Create a Profile Script:** + + ```bash + sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh' + ``` + + **Explanation:** + + - We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system. + - The backslash `\` before `$PATH` ensures the variable is correctly written into the script. + +2. **Make the Script Executable:** + + ```bash + sudo chmod +x /etc/profile.d/cuda.sh + ``` + +3. **Source the Script to Update Your Environment:** + + ```bash + source /etc/profile.d/cuda.sh + ``` + + **Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions. + +## Verifying the Installation + +To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`): + +```bash +nvcc --version +``` + +You should see output similar to: + +``` +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2025 NVIDIA Corporation +Built on Wed_Jan_15_19:20:09_PST_2025 +Cuda compilation tools, release 12.8, V12.8.61 +Build cuda_12.8.r12.8/compiler.35404655_0 +``` + +This output confirms that the CUDA compiler is accessible and indicates the installed version. + +## Conclusion + +You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system. + +## Troubleshooting + +- **Installation Failures:** + + - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies. + - You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations. + +- **Rebooting the Container:** + + - Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue: + + ```bash + # on the host system + podman container restart --all + ``` + +- **Environment Variables Not Set:** + - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`. + - Run `echo $PATH` to check if the path is included. + - Re-source the profile script or open a new terminal session. + +## Additional Notes + +- **Updating CUDA in the Future:** + + - Keep an eye on the official NVIDIA repositories for updates to your Fedora version. + - When an updated repository becomes available, adjust your `dnf` configuration accordingly. + +- **Building `llama.cpp`:** + + - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support. + - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration. + +- **Using the Toolbox Environment:** + - The toolbox environment is isolated from your host system, which helps prevent conflicts. + - Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox. + +--- + +**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox. + +**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide. + +## References + +- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/) +- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) +- [Podman Documentation](https://podman.io/get-started) + +--- diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index 04c5ccbbe60c3..78c6f76077a2b 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -28,7 +28,7 @@ The required steps to implement for an HF model are: ```python @Model.register("MyModelForCausalLM") class MyModel(Model): - model_arch = gguf.MODEL_ARCH.GROK + model_arch = gguf.MODEL_ARCH.MYMODEL ``` 2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py) @@ -79,14 +79,14 @@ Depending on the model configuration, tokenizer, code and tensors layout, you wi - `Model#set_vocab` - `Model#write_tensors` -NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights. +NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights. ### 2. Define the model architecture in `llama.cpp` The model params and tensors layout must be defined in `llama.cpp`: 1. Define a new `llm_arch` 2. Define the tensors layout in `LLM_TENSOR_NAMES` -3. Add any non standard metadata in `llm_load_hparams` +3. Add any non-standard metadata in `llm_load_hparams` 4. Create the tensors for inference in `llm_load_tensors` 5. If the model has a RoPE operation, add the rope type in `llama_rope_type` @@ -96,24 +96,24 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`. -Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`. +Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`. -When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR. +Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR. Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/). ## GGUF specification -https://github.com/ggerganov/ggml/blob/master/docs/gguf.md +https://github.com/ggml-org/ggml/blob/master/docs/gguf.md ## Resources -- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268 -- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009 -- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283 -- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406 -- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423 -- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204 -- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491 -- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515 -- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948 +- YaRN RoPE scaling https://github.com/ggml-org/llama.cpp/pull/2268 +- support Baichuan serial models https://github.com/ggml-org/llama.cpp/pull/3009 +- support attention bias https://github.com/ggml-org/llama.cpp/pull/4283 +- Mixtral support https://github.com/ggml-org/llama.cpp/pull/4406 +- BERT embeddings https://github.com/ggml-org/llama.cpp/pull/5423 +- Grok-1 support https://github.com/ggml-org/llama.cpp/pull/6204 +- Command R Plus support https://github.com/ggml-org/llama.cpp/pull/6491 +- support arch DBRX https://github.com/ggml-org/llama.cpp/pull/6515 +- How to convert HuggingFace model to GGUF format https://github.com/ggml-org/llama.cpp/discussions/2948 diff --git a/docs/docker.md b/docs/docker.md index 8d90e6ded5738..343146dbd214f 100644 --- a/docs/docker.md +++ b/docs/docker.md @@ -7,21 +7,21 @@ ## Images We have three Docker images available for this project: -1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) -2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) -3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) +1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) Additionally, there the following images, similar to the above: -- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`) The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now). @@ -32,25 +32,25 @@ The easiest way to download the models, convert them to ggml and optimize them i Replace `/path/to/models` below with the actual path where you downloaded the models. ```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --all-in-one "/models/" 7B ``` On completion, you are ready to play! ```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 ``` or with a light image: ```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 ``` or with a server image: ```bash -docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 ``` ## Docker With CUDA @@ -60,16 +60,16 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia ## Building Docker locally ```bash -docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . -docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile . -docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile . +docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile . ``` You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. The defaults are: -- `CUDA_VERSION` set to `12.6.0` +- `CUDA_VERSION` set to `12.4.0` - `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures The resulting images, are essentially the same as the non-CUDA images: @@ -95,16 +95,16 @@ Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/ ## Building Docker locally ```bash -docker build -t local/llama.cpp:full-musa -f .devops/full-musa.Dockerfile . -docker build -t local/llama.cpp:light-musa -f .devops/llama-cli-musa.Dockerfile . -docker build -t local/llama.cpp:server-musa -f .devops/llama-server-musa.Dockerfile . +docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile . ``` You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture. The defaults are: -- `MUSA_VERSION` set to `rc3.1.0` +- `MUSA_VERSION` set to `rc3.1.1` The resulting images, are essentially the same as the non-MUSA images: diff --git a/docs/function-calling.md b/docs/function-calling.md new file mode 100644 index 0000000000000..92cb6531ab34a --- /dev/null +++ b/docs/function-calling.md @@ -0,0 +1,390 @@ +# Function Calling + +[chat.h](../common/chat.h) (https://github.com/ggml-org/llama.cpp/pull/9639) adds support for [OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) and is used in: +- `llama-server` when started w/ `--jinja` flag +- `llama-cli` (WIP: https://github.com/ggml-org/llama.cpp/pull/11556) + +## Universal support w/ Native & Generic handlers + +Function calling is supported for all models (see https://github.com/ggml-org/llama.cpp/pull/9639): + +- Native tool call formats supported: + - Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2 + - Functionary v3.1 / v3.2 + - Hermes 2/3, Qwen 2.5 + - Qwen 2.5 Coder (WIP: https://github.com/ggml-org/llama.cpp/pull/12034) + - Mistral Nemo + - Firefunction v2 + - Command R7B + - DeepSeek R1 (WIP / seems reluctant to call any tools?) + +- Generic tool call is supported when the template isn't recognized by native format handlers (you'll see `Chat format: Generic` in the logs). + - Use `--chat-template-file` to override the template when appropriate (see examples below) + - Generic support may consume more tokens and be less efficient than a model's native format. + +
+Show some common templates and which format handler they use + +| Template | Format | +|----------|--------| +| Almawave-Velvet-14B.jinja | Hermes 2 Pro | +| AtlaAI-Selene-1-Mini-Llama-3.1-8B.jinja | Llama 3.x | +| CohereForAI-aya-expanse-8b.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-default.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-rag.jinja | Generic | +| CohereForAI-c4ai-command-r-plus-tool_use.jinja | Generic | +| CohereForAI-c4ai-command-r7b-12-2024-default.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024-rag.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja | Command R7B (extract reasoning) | +| CohereForAI-c4ai-command-r7b-12-2024.jinja | Generic | +| DavieLion-Llama-3.2-1B-SPIN-iter3.jinja | Generic | +| Delta-Vector-Rei-12B.jinja | Mistral Nemo | +| EpistemeAI-Mistral-Nemo-Instruct-12B-Philosophy-Math.jinja | Mistral Nemo | +| FlofloB-83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit.jinja | Hermes 2 Pro | +| FlofloB-test_continued_pretraining_Phi-3-mini-4k-instruct_Unsloth_merged_16bit.jinja | Generic | +| HelpingAI-HAI-SER.jinja | Generic | +| HuggingFaceTB-SmolLM2-1.7B-Instruct.jinja | Generic | +| HuggingFaceTB-SmolLM2-135M-Instruct.jinja | Generic | +| HuggingFaceTB-SmolLM2-360M-Instruct.jinja | Generic | +| INSAIT-Institute-BgGPT-Gemma-2-27B-IT-v1.0.jinja | Generic | +| Ihor-Text2Graph-R1-Qwen2.5-0.5b.jinja | Hermes 2 Pro | +| Infinigence-Megrez-3B-Instruct.jinja | Generic | +| Josephgflowers-TinyLlama_v1.1_math_code-world-test-1.jinja | Generic | +| LGAI-EXAONE-EXAONE-3.5-2.4B-Instruct.jinja | Generic | +| LGAI-EXAONE-EXAONE-3.5-7.8B-Instruct.jinja | Generic | +| LatitudeGames-Wayfarer-12B.jinja | Generic | +| Magpie-Align-Llama-3-8B-Magpie-Align-v0.1.jinja | Generic | +| Magpie-Align-Llama-3.1-8B-Magpie-Align-v0.1.jinja | Generic | +| MaziyarPanahi-calme-3.2-instruct-78b.jinja | Generic | +| MiniMaxAI-MiniMax-Text-01.jinja | Generic | +| MiniMaxAI-MiniMax-VL-01.jinja | Generic | +| NaniDAO-deepseek-r1-qwen-2.5-32B-ablated.jinja | DeepSeek R1 (extract reasoning) | +| NexaAIDev-Octopus-v2.jinja | Generic | +| NousResearch-Hermes-2-Pro-Llama-3-8B-default.jinja | Generic | +| NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | Hermes 2 Pro | +| NousResearch-Hermes-2-Pro-Mistral-7B-default.jinja | Generic | +| NousResearch-Hermes-2-Pro-Mistral-7B-tool_use.jinja | Hermes 2 Pro | +| NousResearch-Hermes-3-Llama-3.1-70B-default.jinja | Generic | +| NousResearch-Hermes-3-Llama-3.1-70B-tool_use.jinja | Hermes 2 Pro | +| NovaSky-AI-Sky-T1-32B-Flash.jinja | Hermes 2 Pro | +| NovaSky-AI-Sky-T1-32B-Preview.jinja | Hermes 2 Pro | +| OnlyCheeini-greesychat-turbo.jinja | Generic | +| Orenguteng-Llama-3.1-8B-Lexi-Uncensored-V2.jinja | Llama 3.x | +| OrionStarAI-Orion-14B-Chat.jinja | Generic | +| PowerInfer-SmallThinker-3B-Preview.jinja | Generic | +| PrimeIntellect-INTELLECT-1-Instruct.jinja | Generic | +| Qwen-QVQ-72B-Preview.jinja | Generic | +| Qwen-QwQ-32B-Preview.jinja | Hermes 2 Pro | +| Qwen-Qwen1.5-7B-Chat.jinja | Generic | +| Qwen-Qwen2-7B-Instruct.jinja | Generic | +| Qwen-Qwen2-VL-72B-Instruct.jinja | Generic | +| Qwen-Qwen2-VL-7B-Instruct.jinja | Generic | +| Qwen-Qwen2.5-0.5B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-1.5B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-14B-Instruct-1M.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-14B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-32B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-32B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-3B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-72B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B-Instruct-1M.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-7B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Coder-32B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Coder-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Math-1.5B.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-Math-7B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-3B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-72B-Instruct.jinja | Hermes 2 Pro | +| Qwen-Qwen2.5-VL-7B-Instruct.jinja | Hermes 2 Pro | +| RWKV-Red-Team-ARWKV-7B-Preview-0.1.jinja | Hermes 2 Pro | +| SakanaAI-TinySwallow-1.5B-Instruct.jinja | Hermes 2 Pro | +| SakanaAI-TinySwallow-1.5B.jinja | Hermes 2 Pro | +| Sao10K-70B-L3.3-Cirrus-x1.jinja | Llama 3.x | +| SentientAGI-Dobby-Mini-Leashed-Llama-3.1-8B.jinja | Llama 3.x | +| SentientAGI-Dobby-Mini-Unhinged-Llama-3.1-8B.jinja | Llama 3.x | +| Steelskull-L3.3-Damascus-R1.jinja | Llama 3.x | +| Steelskull-L3.3-MS-Nevoria-70b.jinja | Llama 3.x | +| Steelskull-L3.3-Nevoria-R1-70b.jinja | Llama 3.x | +| THUDM-glm-4-9b-chat.jinja | Generic | +| THUDM-glm-edge-1.5b-chat.jinja | Generic | +| Tarek07-Progenitor-V1.1-LLaMa-70B.jinja | Llama 3.x | +| TheBloke-FusionNet_34Bx2_MoE-AWQ.jinja | Generic | +| TinyLlama-TinyLlama-1.1B-Chat-v1.0.jinja | Generic | +| UCLA-AGI-Mistral7B-PairRM-SPPO-Iter3.jinja | Generic | +| ValiantLabs-Llama3.1-8B-Enigma.jinja | Llama 3.x | +| abacusai-Fewshot-Metamath-OrcaVicuna-Mistral.jinja | Generic | +| ai21labs-AI21-Jamba-1.5-Large.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-405B-SFT.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-405B.jinja | Generic | +| allenai-Llama-3.1-Tulu-3-8B.jinja | Generic | +| arcee-ai-Virtuoso-Lite.jinja | Hermes 2 Pro | +| arcee-ai-Virtuoso-Medium-v2.jinja | Hermes 2 Pro | +| arcee-ai-Virtuoso-Small-v2.jinja | Hermes 2 Pro | +| avemio-GRAG-NEMO-12B-ORPO-HESSIAN-AI.jinja | Generic | +| bespokelabs-Bespoke-Stratos-7B.jinja | Hermes 2 Pro | +| bfuzzy1-acheron-m1a-llama.jinja | Generic | +| bofenghuang-vigogne-2-70b-chat.jinja | Generic | +| bytedance-research-UI-TARS-72B-DPO.jinja | Generic | +| bytedance-research-UI-TARS-7B-DPO.jinja | Generic | +| bytedance-research-UI-TARS-7B-SFT.jinja | Generic | +| carsenk-phi3.5_mini_exp_825_uncensored.jinja | Generic | +| cyberagent-DeepSeek-R1-Distill-Qwen-14B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| databricks-dbrx-instruct.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Instruct.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Lite-Base.jinja | Generic | +| deepseek-ai-DeepSeek-Coder-V2-Lite-Instruct.jinja | Generic | +| deepseek-ai-DeepSeek-R1-Distill-Llama-70B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-1.5B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-14B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Distill-Qwen-7B.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1-Zero.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-R1.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-V2-Lite.jinja | Generic | +| deepseek-ai-DeepSeek-V2.5.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-DeepSeek-V3.jinja | DeepSeek R1 (extract reasoning) | +| deepseek-ai-deepseek-coder-33b-instruct.jinja | Generic | +| deepseek-ai-deepseek-coder-6.7b-instruct.jinja | Generic | +| deepseek-ai-deepseek-coder-7b-instruct-v1.5.jinja | Generic | +| deepseek-ai-deepseek-llm-67b-chat.jinja | Generic | +| deepseek-ai-deepseek-llm-7b-chat.jinja | Generic | +| dicta-il-dictalm2.0-instruct.jinja | Generic | +| ehristoforu-Falcon3-8B-Franken-Basestruct.jinja | Hermes 2 Pro | +| fireworks-ai-llama-3-firefunction-v2.jinja | FireFunction v2 | +| godlikehhd-alpaca_data_sampled_ifd_new_5200.jinja | Hermes 2 Pro | +| godlikehhd-alpaca_data_score_max_0.7_2600.jinja | Hermes 2 Pro | +| google-gemma-2-27b-it.jinja | Generic | +| google-gemma-2-2b-it.jinja | Generic | +| google-gemma-2-2b-jpn-it.jinja | Generic | +| google-gemma-7b-it.jinja | Generic | +| huihui-ai-DeepSeek-R1-Distill-Llama-70B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Llama-8B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-14B-abliterated-v2.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-32B-abliterated.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-DeepSeek-R1-Distill-Qwen-7B-abliterated-v2.jinja | DeepSeek R1 (extract reasoning) | +| huihui-ai-Qwen2.5-14B-Instruct-1M-abliterated.jinja | Hermes 2 Pro | +| ibm-granite-granite-3.1-8b-instruct.jinja | Generic | +| indischepartij-MiniCPM-3B-OpenHermes-2.5-v2.jinja | Generic | +| inflatebot-MN-12B-Mag-Mell-R1.jinja | Generic | +| jinaai-ReaderLM-v2.jinja | Generic | +| kms7530-chemeng_qwen-math-7b_24_1_100_1_nonmath.jinja | Hermes 2 Pro | +| knifeayumu-Cydonia-v1.3-Magnum-v4-22B.jinja | Mistral Nemo | +| langgptai-qwen1.5-7b-chat-sa-v0.1.jinja | Generic | +| lightblue-DeepSeek-R1-Distill-Qwen-7B-Japanese.jinja | DeepSeek R1 (extract reasoning) | +| mattshumer-Reflection-Llama-3.1-70B.jinja | Generic | +| meetkai-functionary-medium-v3.1.jinja | Functionary v3.1 Llama 3.1 | +| meetkai-functionary-medium-v3.2.jinja | Functionary v3.2 | +| meta-llama-Llama-2-7b-chat-hf.jinja | Generic | +| meta-llama-Llama-3.1-8B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-11B-Vision-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-1B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.2-3B-Instruct.jinja | Llama 3.x | +| meta-llama-Llama-3.3-70B-Instruct.jinja | Llama 3.x | +| meta-llama-Meta-Llama-3-8B-Instruct.jinja | Generic | +| meta-llama-Meta-Llama-3.1-8B-Instruct.jinja | Llama 3.x | +| microsoft-Phi-3-medium-4k-instruct.jinja | Generic | +| microsoft-Phi-3-mini-4k-instruct.jinja | Generic | +| microsoft-Phi-3-small-8k-instruct.jinja | Generic | +| microsoft-Phi-3.5-mini-instruct.jinja | Generic | +| microsoft-Phi-3.5-vision-instruct.jinja | Generic | +| microsoft-phi-4.jinja | Generic | +| migtissera-Tess-3-Mistral-Nemo-12B.jinja | Generic | +| ministral-Ministral-3b-instruct.jinja | Generic | +| mistralai-Codestral-22B-v0.1.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.1.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.2.jinja | Generic | +| mistralai-Mistral-7B-Instruct-v0.3.jinja | Mistral Nemo | +| mistralai-Mistral-Large-Instruct-2407.jinja | Mistral Nemo | +| mistralai-Mistral-Large-Instruct-2411.jinja | Generic | +| mistralai-Mistral-Nemo-Instruct-2407.jinja | Mistral Nemo | +| mistralai-Mistral-Small-24B-Instruct-2501.jinja | Generic | +| mistralai-Mixtral-8x7B-Instruct-v0.1.jinja | Generic | +| mkurman-Qwen2.5-14B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| mlabonne-AlphaMonarch-7B.jinja | Generic | +| mlx-community-Josiefied-Qwen2.5-0.5B-Instruct-abliterated-v1-float32.jinja | Hermes 2 Pro | +| mlx-community-Qwen2.5-VL-7B-Instruct-8bit.jinja | Hermes 2 Pro | +| mobiuslabsgmbh-DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1.jinja | DeepSeek R1 (extract reasoning) | +| netcat420-MFANNv0.20.jinja | Generic | +| netcat420-MFANNv0.24.jinja | Generic | +| netease-youdao-Confucius-o1-14B.jinja | Hermes 2 Pro | +| nvidia-AceMath-7B-RM.jinja | Hermes 2 Pro | +| nvidia-Eagle2-1B.jinja | Hermes 2 Pro | +| nvidia-Eagle2-9B.jinja | Hermes 2 Pro | +| nvidia-Llama-3.1-Nemotron-70B-Instruct-HF.jinja | Llama 3.x | +| onnx-community-DeepSeek-R1-Distill-Qwen-1.5B-ONNX.jinja | DeepSeek R1 (extract reasoning) | +| open-thoughts-OpenThinker-7B.jinja | Hermes 2 Pro | +| openchat-openchat-3.5-0106.jinja | Generic | +| pankajmathur-orca_mini_v6_8b.jinja | Generic | +| princeton-nlp-Mistral-7B-Base-SFT-RDPO.jinja | Generic | +| princeton-nlp-Mistral-7B-Instruct-DPO.jinja | Generic | +| princeton-nlp-Mistral-7B-Instruct-RDPO.jinja | Generic | +| prithivMLmods-Bellatrix-Tiny-1.5B-R1.jinja | Hermes 2 Pro | +| prithivMLmods-Bellatrix-Tiny-1B-R1.jinja | Llama 3.x | +| prithivMLmods-Bellatrix-Tiny-1B-v3.jinja | Generic | +| prithivMLmods-Bellatrix-Tiny-3B-R1.jinja | Llama 3.x | +| prithivMLmods-Blaze-14B-xElite.jinja | Generic | +| prithivMLmods-Calcium-Opus-14B-Elite2-R1.jinja | Hermes 2 Pro | +| prithivMLmods-Calme-Ties-78B.jinja | Generic | +| prithivMLmods-Calme-Ties2-78B.jinja | Generic | +| prithivMLmods-Calme-Ties3-78B.jinja | Generic | +| prithivMLmods-ChemQwen2-vL.jinja | Generic | +| prithivMLmods-GWQ2b.jinja | Generic | +| prithivMLmods-LatexMind-2B-Codec.jinja | Generic | +| prithivMLmods-Llama-3.2-6B-AlgoCode.jinja | Llama 3.x | +| prithivMLmods-Megatron-Opus-14B-Exp.jinja | Hermes 2 Pro | +| prithivMLmods-Megatron-Opus-14B-Stock.jinja | Hermes 2 Pro | +| prithivMLmods-Megatron-Opus-7B-Exp.jinja | Hermes 2 Pro | +| prithivMLmods-Omni-Reasoner-Merged.jinja | Hermes 2 Pro | +| prithivMLmods-Omni-Reasoner4-Merged.jinja | Hermes 2 Pro | +| prithivMLmods-Primal-Opus-14B-Optimus-v1.jinja | Hermes 2 Pro | +| prithivMLmods-QwQ-Math-IO-500M.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen-7B-Distill-Reasoner.jinja | DeepSeek R1 (extract reasoning) | +| prithivMLmods-Qwen2.5-1.5B-DeepSeek-R1-Instruct.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-14B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-32B-DeepSeek-R1-Instruct.jinja | Hermes 2 Pro | +| prithivMLmods-Qwen2.5-7B-DeepSeek-R1-1M.jinja | Hermes 2 Pro | +| prithivMLmods-Triangulum-v2-10B.jinja | Hermes 2 Pro | +| qingy2024-Falcon3-2x10B-MoE-Instruct.jinja | Hermes 2 Pro | +| rubenroy-Zurich-14B-GCv2-5m.jinja | Hermes 2 Pro | +| rubenroy-Zurich-7B-GCv2-5m.jinja | Hermes 2 Pro | +| silma-ai-SILMA-Kashif-2B-Instruct-v1.0.jinja | Generic | +| simplescaling-s1-32B.jinja | Hermes 2 Pro | +| sometimesanotion-Lamarck-14B-v0.7.jinja | Hermes 2 Pro | +| sonthenguyen-zephyr-sft-bnb-4bit-DPO-mtbr-180steps.jinja | Generic | +| sthenno-tempesthenno-icy-0130.jinja | Generic | +| sumink-qwft.jinja | Hermes 2 Pro | +| teknium-OpenHermes-2.5-Mistral-7B.jinja | Generic | +| thirdeyeai-elevate360m.jinja | Generic | +| tiiuae-Falcon3-10B-Instruct.jinja | Hermes 2 Pro | +| unsloth-DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-DeepSeek-R1-Distill-Llama-8B.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-DeepSeek-R1.jinja | DeepSeek R1 (extract reasoning) | +| unsloth-Mistral-Small-24B-Instruct-2501-unsloth-bnb-4bit.jinja | Generic | +| upstage-solar-pro-preview-instruct.jinja | Generic | +| whyhow-ai-PatientSeek.jinja | Generic | +| xwen-team-Xwen-72B-Chat.jinja | Hermes 2 Pro | +| xwen-team-Xwen-7B-Chat.jinja | Hermes 2 Pro | + +This table can be generated with: + +```bash +./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null +``` + +
+ +# Usage - need tool-aware Jinja template + +First, start a server with any model, but make sure it has a tools-enabled template: you can verify this by inspecting the `chat_template` or `chat_template_tool_use` properties in `http://localhost:8080/props`). + +Here are some models known to work (w/ chat template override when needed): + +```shell +# Native support: + +llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M +llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L +llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M +llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M + +# Native support for DeepSeek R1 works best w/ our own template (official template buggy) + +llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q6_K_L \ +--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja + +llama-server --jinja -fa -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M \ +--chat-template-file models/templates/llama-cpp-deepseek-r1.jinja + +# Native support requires the right template for these GGUFs: + +llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \ +--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use ) + +llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \ +--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use ) + +llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \ +--chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use ) + +llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \ +--chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use ) + +# Generic format support +llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0 +llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0 +llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K +``` + +> [!TIP] +> If there is no official `tool_use` Jinja template, you may want to set `--chat-template chatml` to use a default that works with many models (YMMV!), or write your own (e.g. we provide a custom [llama-cpp-deepseek-r1.jinja](../models/templates/llama-cpp-deepseek-r1.jinja) for DeepSeek R1 distills) + +Test in CLI (or with any library / software that can use OpenAI-compatible API backends): + +```bash +curl http://localhost:8080/v1/chat/completions -d '{ +"model": "gpt-3.5-turbo", +"tools": [ + { + "type":"function", + "function":{ + "name":"python", + "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.", + "parameters":{ + "type":"object", + "properties":{ + "code":{ + "type":"string", + "description":"The code to run in the ipython interpreter." + } + }, + "required":["code"] + } + } + } +], +"messages": [ + { + "role": "user", + "content": "Print a hello world message with python." + } +] +}' +``` + +
+Show output + +```json +{ +"choices": [ + { + "finish_reason": "tool", + "index": 0, + "message": { + "content": null, + "tool_calls": [ + { + "name": "python", + "arguments": "{\"code\":\" \\nprint(\\\"Hello, World!\\\")\"}" + } + ], + "role": "assistant" + } + } +], +"created": 1727287211, +"model": "gpt-3.5-turbo", +"object": "chat.completion", +"usage": { + "completion_tokens": 16, + "prompt_tokens": 44, + "total_tokens": 60 +}, +"id": "chatcmpl-Htbgh9feMmGM0LEH2hmQvwsCxq3c6Ni8" +} +``` + +
diff --git a/docs/install.md b/docs/install.md index 10a568506835b..0e23a2c9e7ae1 100644 --- a/docs/install.md +++ b/docs/install.md @@ -7,7 +7,7 @@ On Mac and Linux, the homebrew package manager can be used via ```sh brew install llama.cpp ``` -The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668 ## Nix diff --git a/docs/llguidance.md b/docs/llguidance.md new file mode 100644 index 0000000000000..cda787b14de04 --- /dev/null +++ b/docs/llguidance.md @@ -0,0 +1,53 @@ +# LLGuidance Support in llama.cpp + +[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently. + +LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process. + +## Building + +To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option: + +```sh +cmake -B build -DLLAMA_LLGUIDANCE=ON +make -C build -j +``` + +For Windows use `cmake --build build --config Release` instead of `make`. + +This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install). + +## Interface + +There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance. + +For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/python/llguidance/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format. + +## Performance + +Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md). + +## JSON Schema + +LLGuidance adheres closely to the JSON Schema specification. For example: + +- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed. +- any whitespace is allowed. +- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first). + +Unsupported schemas result in an error message—no keywords are silently ignored. + +## Why Not Reuse GBNF Format? + +GBNF lacks the concept of a lexer. + +Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes. +LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest. + +However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase. +The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically. +See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details. + +## Error Handling + +Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future. diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 632409d5591b9..66cfab2c3b796 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -6,6 +6,10 @@ find_package(Threads REQUIRED) # ... +# flags + +llama_add_compile_flags() + # examples include_directories(${CMAKE_CURRENT_SOURCE_DIR}) @@ -16,7 +20,12 @@ else() add_subdirectory(batched) add_subdirectory(embedding) add_subdirectory(eval-callback) - add_subdirectory(gbnf-validator) + + if (NOT WIN32) + # disabled on Windows because it uses internal functions not exported with LLAMA_API + add_subdirectory(gbnf-validator) + endif() + add_subdirectory(gguf-hash) add_subdirectory(gguf-split) add_subdirectory(gguf) @@ -36,17 +45,23 @@ else() add_subdirectory(server) endif() add_subdirectory(save-load-state) + add_subdirectory(run) add_subdirectory(simple) add_subdirectory(simple-chat) add_subdirectory(speculative) add_subdirectory(speculative-simple) add_subdirectory(tokenize) + add_subdirectory(tts) + add_subdirectory(gen-docs) if (NOT GGML_BACKEND_DL) # these examples use the backends directly and cannot be built with dynamic loading add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(cvector-generator) add_subdirectory(export-lora) - add_subdirectory(quantize-stats) + if (NOT WIN32) + # disabled on Windows because it uses internal functions not exported with LLAMA_API + add_subdirectory(quantize-stats) + endif() add_subdirectory(llava) if (GGML_RPC) add_subdirectory(rpc) diff --git a/examples/base-translate.sh b/examples/base-translate.sh deleted file mode 100755 index 103a52f55e6db..0000000000000 --- a/examples/base-translate.sh +++ /dev/null @@ -1,61 +0,0 @@ -#!/bin/bash -# -# Few-shot translation example. -# Requires a base model (i.e. no fine-tuned or instruct models). -# -# Usage: -# -# cd llama.cpp -# make -j -# -# ./examples/base-translate.sh "" [extra-main-args] -# - -if [ $# -lt 2 ]; then - echo "Usage: ./base-translate.sh \"\" [extra-main-args]" - exit 1 -fi - -eargs="" -if [ $# -gt 2 ]; then - eargs="${@:3}" -fi - -ftmp="__llama.cpp_example_tmp__.txt" -trap "rm -f $ftmp" EXIT - -echo "Translate from English to French: - -=== - -sea otter, peppermint, plush girafe: - -sea otter => loutre de mer -peppermint => menthe poivrée -plush girafe => girafe peluche - -=== - -violin - -violin => violon - -=== - -phone, computer, mouse, keyboard: - -phone => téléphone -computer => ordinateur -mouse => souris -keyboard => clavier - -=== -" > $ftmp - -echo "$2 -" >> $ftmp - -model=$1 - -# generate the most likely continuation until the string "===" is found -./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt index 959acaeeebc38..68ad707f32c98 100644 --- a/examples/batched-bench/CMakeLists.txt +++ b/examples/batched-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched-bench) add_executable(${TARGET} batched-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index a3b21ad6bce44..0659ab6f119a7 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -38,7 +38,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = common_model_params_to_llama(params); - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { fprintf(stderr , "%s: error: unable to load model\n" , __func__); @@ -50,7 +50,7 @@ int main(int argc, char ** argv) { // ensure enough sequences are available ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end()); - llama_context * ctx = llama_new_context_with_model(model, ctx_params); + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); @@ -194,7 +194,7 @@ int main(int argc, char ** argv) { llama_batch_free(batch); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index 10f2e7fd117a1..55c31166ca278 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -23,12 +23,17 @@ defer { } let model_params = llama_model_default_params() -guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else { +guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else { print("Failed to load model") exit(1) } defer { - llama_free_model(model) + llama_model_free(model) +} + +guard let vocab = llama_model_get_vocab(model) else { + print("Failed to get vocab") + exit(1) } var tokens = tokenize(text: prompt, add_bos: true) @@ -41,7 +46,7 @@ context_params.n_batch = UInt32(max(n_len, n_parallel)) context_params.n_threads = 8 context_params.n_threads_batch = 8 -let context = llama_new_context_with_model(model, context_params) +let context = llama_init_from_model(model, context_params) guard context != nil else { print("Failed to initialize context") exit(1) @@ -141,7 +146,7 @@ while n_cur <= n_len { let new_token_id = llama_sampler_sample(smpl, context, i_batch[i]) // is it an end of stream? -> mark the stream as finished - if llama_token_is_eog(model, new_token_id) || n_cur == n_len { + if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len { i_batch[i] = -1 // print("") if n_parallel > 1 { @@ -207,7 +212,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] { let utf8Count = text.utf8.count let n_tokens = utf8Count + (add_bos ? 1 : 0) let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens) - let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) + let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) var swiftTokens: [llama_token] = [] for i in 0 ..< tokenCount { swiftTokens.append(tokens[Int(i)]) @@ -218,12 +223,12 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] { private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? { var result = [CChar](repeating: 0, count: 8) - let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false) + let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false) if nTokens < 0 { let actualTokensCount = -Int(nTokens) result = .init(repeating: 0, count: actualTokensCount) let check = llama_token_to_piece( - model, + vocab, token, &result, Int32(result.count), diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt index 77e33343b6673..0d439f49842b5 100644 --- a/examples/batched/CMakeLists.txt +++ b/examples/batched/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched) add_executable(${TARGET} batched.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index ba219cd4b32ae..21b95ef5e4e83 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -41,17 +41,19 @@ int main(int argc, char ** argv) { llama_model_params model_params = common_model_params_to_llama(params); - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { LOG_ERR("%s: error: unable to load model\n" , __func__); return 1; } + const llama_vocab * vocab = llama_model_get_vocab(model); + // tokenize the prompt std::vector tokens_list; - tokens_list = common_tokenize(model, params.prompt, true); + tokens_list = common_tokenize(vocab, params.prompt, true); const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; @@ -62,9 +64,10 @@ int main(int argc, char ** argv) { ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_predict, n_parallel); - llama_context * ctx = llama_new_context_with_model(model, ctx_params); + llama_context * ctx = llama_init_from_model(model, ctx_params); auto sparams = llama_sampler_chain_default_params(); + sparams.no_perf = false; llama_sampler * smpl = llama_sampler_chain_init(sparams); @@ -119,8 +122,8 @@ int main(int argc, char ** argv) { } llama_token decoder_start_token_id = llama_model_decoder_start_token(model); - if (decoder_start_token_id == -1) { - decoder_start_token_id = llama_token_bos(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = llama_vocab_bos(vocab); } common_batch_clear(batch); @@ -173,7 +176,7 @@ int main(int argc, char ** argv) { const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]); // is it an end of generation? -> mark the stream as finished - if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) { i_batch[i] = -1; LOG("\n"); if (n_parallel > 1) { @@ -235,7 +238,7 @@ int main(int argc, char ** argv) { llama_sampler_free(smpl); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt index a6790e617217e..44e5f722a9739 100644 --- a/examples/convert-llama2c-to-ggml/CMakeLists.txt +++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-convert-llama2c-to-ggml) add_executable(${TARGET} convert-llama2c-to-ggml.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 5774ac83c32c8..46a42da691830 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -2,11 +2,8 @@ This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default. -To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository: +To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository. -`$ make -j` - -After successful compilation, following usage options are available: ``` usage: ./llama-convert-llama2c-to-ggml [options] diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 988a584c99a25..bdf0eed2a9cd3 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -1,4 +1,6 @@ #include "ggml.h" +#include "gguf.h" + #include "llama.h" #include "common.h" #include "log.h" @@ -434,12 +436,12 @@ static void print_matrix(struct ggml_tensor * probs) { } } -struct llama_file { +struct my_llama_file { // use FILE * so we don't have to re-open the file to mmap FILE * fp; size_t size; - llama_file(const char * fname, const char * mode) { + my_llama_file(const char * fname, const char * mode) { fp = std::fopen(fname, mode); if (fp == NULL) { size = 0; @@ -500,7 +502,7 @@ struct llama_file { return std::string(chars.data(), len); } - ~llama_file() { + ~my_llama_file() { if (fp) { std::fclose(fp); } @@ -508,7 +510,7 @@ struct llama_file { }; static bool is_ggml_file(const char * filename) { - llama_file file(filename, "rb"); + my_llama_file file(filename, "rb"); if (file.size < 4) { return false; } @@ -576,7 +578,7 @@ static void load_vocab(const char * filename, const Config * config, struct my_l } else { // assume llama2.c vocabulary LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename); - llama_file file(filename, "rb"); + my_llama_file file(filename, "rb"); if (!file.fp) { die_fmt("%s: %s", strerror(errno), filename); } @@ -689,8 +691,8 @@ static void save_as_llama_model( gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID); gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID); gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID); - gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1); - gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1); + gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL); + gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL); gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx); gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd); @@ -909,7 +911,7 @@ int main(int argc, char ** argv) { load_vocab(params.fn_vocab_model, &config, &vocab); struct my_llama_model model; - model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); + model.hparams.n_vocab = config.vocab_size; //llama_vocab_n_vocab(lctx); model.hparams.n_ctx = params.n_ctx; model.hparams.n_embd = config.dim; //params.n_embd; model.hparams.n_ff = config.hidden_dim; diff --git a/examples/cvector-generator/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt index 0a559d60c2a6d..49ad9561c82ea 100644 --- a/examples/cvector-generator/CMakeLists.txt +++ b/examples/cvector-generator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cvector-generator) add_executable(${TARGET} cvector-generator.cpp pca.hpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/cvector-generator/README.md b/examples/cvector-generator/README.md index be4dd5250f15f..6d5fd74ad8ca0 100644 --- a/examples/cvector-generator/README.md +++ b/examples/cvector-generator/README.md @@ -3,9 +3,9 @@ This example demonstrates how to generate a control vector using gguf models. Related PRs: -- [Add support for control vectors](https://github.com/ggerganov/llama.cpp/pull/5970) -- (Issue) [Generate control vector using llama.cpp](https://github.com/ggerganov/llama.cpp/issues/6880) -- [Add cvector-generator example](https://github.com/ggerganov/llama.cpp/pull/7514) +- [Add support for control vectors](https://github.com/ggml-org/llama.cpp/pull/5970) +- (Issue) [Generate control vector using llama.cpp](https://github.com/ggml-org/llama.cpp/issues/6880) +- [Add cvector-generator example](https://github.com/ggml-org/llama.cpp/pull/7514) ## Examples diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index d1731bba64e1b..413b71d34c52b 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -1,7 +1,9 @@ +#include "ggml.h" +#include "gguf.h" + #include "arg.h" #include "common.h" #include "llama.h" -#include "ggml.h" #include "pca.hpp" #include "mean.hpp" @@ -271,7 +273,9 @@ struct tokenized_prompt { size_t max_seq_len; tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) { - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + const bool add_bos = llama_vocab_get_add_bos(vocab); tokens_pos = common_tokenize(ctx, pos, add_bos, true); tokens_neg = common_tokenize(ctx, neg, add_bos, true); max_seq_len = std::max(tokens_pos.size(), tokens_neg.size()); @@ -415,12 +419,13 @@ int main(int argc, char ** argv) { // load the model to get hparams common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); // int n_ctx = llama_n_ctx(ctx); - int n_layers = llama_n_layer(model); - int n_embd = llama_n_embd(model); + int n_layers = llama_model_n_layer(model); + int n_embd = llama_model_n_embd(model); + // get model hint param (a.k.a model arch name) char model_hint[128]; llama_model_meta_val_str(model, "general.architecture", model_hint, 128); @@ -474,8 +479,6 @@ int main(int argc, char ** argv) { // done with the model, we can now free it to make gain some memory printf("Done evaluate prompts, unload model...\n"); - llama_free(ctx); - llama_free_model(model); bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA; diff --git a/examples/cvector-generator/mean.hpp b/examples/cvector-generator/mean.hpp index 16be5ce3eecf1..4eeac1eeb7a18 100644 --- a/examples/cvector-generator/mean.hpp +++ b/examples/cvector-generator/mean.hpp @@ -15,7 +15,7 @@ static void run( for (size_t il = 0; il < v_input.size(); ++il) { // prepare output vector struct ggml_tensor * ctrl_out = v_output[il]; - ggml_format_name(ctrl_out, "direction.%ld", il+1); + ggml_format_name(ctrl_out, "direction.%zu", il+1); // calculate mean vector struct ggml_tensor * t_layer = v_input[il]; diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp index f6e307fbc4970..e88bbdde93fde 100644 --- a/examples/cvector-generator/pca.hpp +++ b/examples/cvector-generator/pca.hpp @@ -302,7 +302,7 @@ static void run_pca( // prepare output vector struct ggml_tensor * ctrl_out = v_output[il]; - ggml_format_name(ctrl_out, "direction.%ld", il+1); + ggml_format_name(ctrl_out, "direction.%zu", il+1); // run power_iteration params.i_layer = il; diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp index 11b35d2c22500..c2958ea12d92d 100644 --- a/examples/deprecation-warning/deprecation-warning.cpp +++ b/examples/deprecation-warning/deprecation-warning.cpp @@ -12,7 +12,7 @@ int main(int argc, char** argv) { } // Get only the program name from the full path - auto pos = filename.find_last_of('/'); + auto pos = filename.find_last_of("/\\"); if (pos != std::string::npos) { filename = filename.substr(pos+1); } diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 8256e789ad33a..809040307d2c9 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-embedding) add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 3f18fc6a70878..38d22c90f82bb 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -97,14 +97,17 @@ int main(int argc, char ** argv) { // load the model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); @@ -147,7 +150,7 @@ int main(int argc, char ** argv) { // check if the last token is SEP // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' for (auto & inp : inputs) { - if (inp.empty() || inp.back() != llama_token_sep(model)) { + if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) { LOG_WRN("%s: last token in the prompt is not SEP\n", __func__); LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); } @@ -180,7 +183,7 @@ int main(int argc, char ** argv) { } // allocate output - const int n_embd = llama_n_embd(model); + const int n_embd = llama_model_n_embd(model); std::vector embeddings(n_embd_count * n_embd, 0); float * emb = embeddings.data(); @@ -316,8 +319,6 @@ int main(int argc, char ** argv) { // clean up llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); llama_backend_free(); return 0; diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt index 5d1048aad74b6..95915ed91c099 100644 --- a/examples/eval-callback/CMakeLists.txt +++ b/examples/eval-callback/CMakeLists.txt @@ -2,7 +2,7 @@ set(TARGET llama-eval-callback) add_executable(${TARGET} eval-callback.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TEST_TARGET test-eval-callback) add_test(NAME ${TEST_TARGET} diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index c08e3e5f675ed..fb188f5a9e132 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -127,7 +127,10 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { } static bool run(llama_context * ctx, const common_params & params) { - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); @@ -162,8 +165,9 @@ int main(int argc, char ** argv) { // init common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); return 1; @@ -184,9 +188,6 @@ int main(int argc, char ** argv) { LOG("\n"); llama_perf_context_print(ctx); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); return 0; diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt index 1cef6e71694e2..310455787a7ef 100644 --- a/examples/export-lora/CMakeLists.txt +++ b/examples/export-lora/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-export-lora) add_executable(${TARGET} export-lora.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 67662313d075c..91238e4beb26c 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -1,12 +1,13 @@ -#include "arg.h" -#include "common.h" #include "ggml.h" #include "ggml-alloc.h" +#include "gguf.h" + +#include "arg.h" +#include "common.h" #include #include #include -#include #include static bool g_verbose = false; @@ -128,7 +129,7 @@ struct lora_merge_ctx { lora_merge_ctx( std::string & base_fname, - std::vector & lora_files, + std::vector & lora_files, std::string & outfile, int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) { fout.exceptions(std::ofstream::failbit); // fail fast on write errors @@ -265,8 +266,8 @@ struct lora_merge_ctx { fout.write((const char *)data.data(), data.size()); } - printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged); - printf("%s : wrote %ld tensors to output file\n", __func__, trans.size()); + printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged); + printf("%s : wrote %zu tensors to output file\n", __func__, trans.size()); } void copy_tensor(struct ggml_tensor * base) { @@ -344,15 +345,25 @@ struct lora_merge_ctx { gf = ggml_new_graph(ctx0); struct ggml_tensor * cur = inp_base; for (size_t i = 0; i < adapters.size(); ++i) { - struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))); - struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32)); + struct ggml_tensor * delta; + bool is_tok_embd = string_starts_with(name_base, "token_embd"); + if (is_tok_embd) { + printf("%s : detected token embeddings tensor\n", __func__); + delta = ggml_mul_mat(ctx0, + ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32), + ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)); + } else { + delta = ggml_mul_mat(ctx0, + ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))), + ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32)); + } // scale const float alpha = adapters[i]->alpha; const float rank = (float) inp_b[i]->ne[0]; const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale; delta = ggml_scale(ctx0, delta, scale); cur = ggml_add(ctx0, delta, cur); - printf("%s : + merging from adapter[%ld] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type)); + printf("%s : + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type)); printf("%s : input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]); } cur = ggml_cast(ctx0, cur, out->type); diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt index 4edd6ec7394c5..d2cb524c0a7f7 100644 --- a/examples/gbnf-validator/CMakeLists.txt +++ b/examples/gbnf-validator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gbnf-validator) add_executable(${TARGET} gbnf-validator.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index 7493af9d3aec3..a610e6a0b19d7 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -11,19 +11,15 @@ static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) { const auto cpts = unicode_cpts_from_utf8(input_str); - const llama_grammar_rules & rules = llama_grammar_get_rules (grammar); - llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); + auto & stacks_cur = llama_grammar_get_stacks(grammar); size_t pos = 0; for (const auto & cpt : cpts) { - const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy - - llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur); + llama_grammar_accept(grammar, cpt); if (stacks_cur.empty()) { error_pos = pos; error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'"; - stacks_cur = stacks_prev; return false; } ++pos; @@ -80,9 +76,10 @@ int main(int argc, char** argv) { grammar_str = buffer.str(); } - llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root"); + llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0); if (grammar == nullptr) { - throw std::runtime_error("Failed to initialize llama_grammar"); + fprintf(stdout, "Failed to initialize llama_grammar\n"); + return 1; } // Read the input file std::string input_str; diff --git a/examples/gen-docs/CMakeLists.txt b/examples/gen-docs/CMakeLists.txt index c94cda7764341..25de0af35df60 100644 --- a/examples/gen-docs/CMakeLists.txt +++ b/examples/gen-docs/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gen-docs) add_executable(${TARGET} gen-docs.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt index 633f4553594bb..15c5c68c6f402 100644 --- a/examples/gguf-hash/CMakeLists.txt +++ b/examples/gguf-hash/CMakeLists.txt @@ -4,12 +4,19 @@ install(TARGETS ${TARGET} RUNTIME) # clibs dependencies include_directories(deps/) + add_library(xxhash OBJECT deps/xxhash/xxhash.c deps/xxhash/xxhash.h) target_link_libraries(${TARGET} PRIVATE xxhash) + add_library(sha1 OBJECT deps/sha1/sha1.c deps/sha1/sha1.h) target_link_libraries(${TARGET} PRIVATE sha1) +if (NOT MSVC) + # disable warnings in 3rd party code + target_compile_options(sha1 PRIVATE -w) +endif() + add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h) target_link_libraries(${TARGET} PRIVATE sha256) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-hash/gguf-hash.cpp b/examples/gguf-hash/gguf-hash.cpp index e96c75117f533..9523ec122f573 100644 --- a/examples/gguf-hash/gguf-hash.cpp +++ b/examples/gguf-hash/gguf-hash.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "gguf.h" #include /* abort() */ #include diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt index f63887da7dfca..c407e2f0af44a 100644 --- a/examples/gguf-split/CMakeLists.txt +++ b/examples/gguf-split/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf-split) add_executable(${TARGET} gguf-split.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 7e62657e118a4..ef3ceb686f697 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -1,18 +1,19 @@ +#include "ggml.h" +#include "gguf.h" #include "llama.h" #include "common.h" #include -#include +#include +#include +#include #include +#include +#include #include #include #include -#include -#include -#include -#include - #if defined(_WIN32) #include #ifndef PATH_MAX @@ -287,7 +288,7 @@ struct split_strategy { } void print_info() { - printf("n_split: %ld\n", ctx_outs.size()); + printf("n_split: %zu\n", ctx_outs.size()); int i_split = 0; for (auto & ctx_out : ctx_outs) { // re-calculate the real gguf size for each split (= metadata size + total size of all tensors) @@ -297,7 +298,7 @@ struct split_strategy { total_size += ggml_nbytes(t); } total_size = total_size / 1000 / 1000; // convert to megabytes - printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size); + printf("split %05d: n_tensors = %" PRIi64 ", total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size); i_split++; } } diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh index d5a92d6051063..05a93222711d8 100755 --- a/examples/gguf-split/tests.sh +++ b/examples/gguf-split/tests.sh @@ -41,7 +41,7 @@ echo PASS echo # 2b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -51,7 +51,7 @@ echo PASS echo # 3b. Test the merged model is loading properly -$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32 echo PASS echo @@ -61,7 +61,7 @@ echo PASS echo # 4b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32 echo PASS echo @@ -71,7 +71,7 @@ echo #echo # 5b. Test the merged model is loading properly -#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32 +#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32 #echo PASS #echo @@ -81,7 +81,7 @@ echo PASS echo # 6b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32 echo PASS echo diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index a9569b411956b..fb04eb83f34ce 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf) add_executable(${TARGET} gguf.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index 7498f85efc4f9..f31989c8c55c6 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -1,10 +1,9 @@ #include "ggml.h" +#include "gguf.h" #include -#include #include #include -#include #include #undef MIN @@ -135,9 +134,10 @@ static bool gguf_ex_read_0(const std::string & fname) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); } } @@ -182,9 +182,10 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); } } @@ -199,7 +200,8 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) { struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); - printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data); + printf("%s: tensor[%d]: n_dims = %d, ne = (%d, %d, %d, %d), name = %s, data = %p\n", + __func__, i, ggml_n_dims(cur), int(cur->ne[0]), int(cur->ne[1]), int(cur->ne[2]), int(cur->ne[3]), cur->name, cur->data); // print first 10 elements const float * data = (const float *) cur->data; @@ -215,7 +217,7 @@ static bool gguf_ex_read_1(const std::string & fname, bool check_data) { const float * data = (const float *) cur->data; for (int j = 0; j < ggml_nelements(cur); ++j) { if (data[j] != 100 + i) { - fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]); + fprintf(stderr, "%s: tensor[%d], data[%d]: found %f, expected %f\n", __func__, i, j, data[j], float(100 + i)); gguf_free(ctx); return false; } @@ -245,6 +247,8 @@ int main(int argc, char ** argv) { check_data = false; } + srand(123456); + const std::string fname(argv[1]); const std::string mode (argv[2]); diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt index 86dfddca346fe..fa1b4dc70c2f6 100644 --- a/examples/gritlm/CMakeLists.txt +++ b/examples/gritlm/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gritlm) add_executable(${TARGET} gritlm.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp index 6e42fa0734ecb..72eb46257429e 100644 --- a/examples/gritlm/gritlm.cpp +++ b/examples/gritlm/gritlm.cpp @@ -11,6 +11,7 @@ static std::vector> encode(llama_context * ctx, const std::ve std::vector> result; const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1); @@ -19,16 +20,16 @@ static std::vector> encode(llama_context * ctx, const std::ve const std::string input_string = instruction + sentences[i]; - std::vector inputs = common_tokenize(model, input_string, true, false); + std::vector inputs = common_tokenize(vocab, input_string, true, false); const int32_t n_toks = inputs.size(); // GritLM seems to have EOS = "" // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18 - // inputs.push_back(llama_token_eos(model)); + // inputs.push_back(llama_vocab_eos(vocab)); // we want to ignore instruction tokens for mean pooling - const int32_t n_inst = common_tokenize(model, instruction, true, false).size(); + const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size(); #ifdef GRIT_DEBUG // debug tokens - should be matching as referenced in the GritLM sample @@ -52,7 +53,7 @@ static std::vector> encode(llama_context * ctx, const std::ve llama_decode(ctx, batch); // get embedding dimensions - uint64_t n_embd = llama_n_embd(model); + uint64_t n_embd = llama_model_n_embd(model); // allocate embedding output std::vector emb_unorm(n_embd, 0.0f); @@ -75,7 +76,7 @@ static std::vector> encode(llama_context * ctx, const std::ve } std::vector emb_norm(emb_unorm.size()); - common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd); + common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2); result.push_back(emb_norm); #ifdef GRIT_DEBUG @@ -97,7 +98,9 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std std::string result; const llama_model * model = llama_get_model(ctx); - llama_token eos_token = llama_token_eos(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_token eos_token = llama_vocab_eos(vocab); llama_kv_cache_clear(ctx); llama_set_embeddings(ctx, false); @@ -105,7 +108,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1); - std::vector inputs = common_tokenize(model, prompt, false, true); + std::vector inputs = common_tokenize(vocab, prompt, false, true); int32_t i_current_token = 0; while (true) { @@ -165,10 +168,10 @@ int main(int argc, char * argv[]) { llama_backend_init(); - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); + llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams); // create generation context - llama_context * ctx = llama_new_context_with_model(model, cparams); + llama_context * ctx = llama_init_from_model(model, cparams); auto sparams = llama_sampler_chain_default_params(); @@ -197,7 +200,7 @@ int main(int argc, char * argv[]) { const std::vector> d_rep = encode(ctx, documents, gritlm_instruction("")); const std::vector> q_rep = encode(ctx, queries, gritlm_instruction(instruction)); - const int n_embd = llama_n_embd(model); + const int n_embd = llama_model_n_embd(model); const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd); const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd); @@ -219,7 +222,7 @@ int main(int argc, char * argv[]) { llama_sampler_free(smpl); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); return 0; diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt index d4c8265bdb9d2..412696c47c31c 100644 --- a/examples/imatrix/CMakeLists.txt +++ b/examples/imatrix/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-imatrix) add_executable(${TARGET} imatrix.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md index bb5faec94c20a..9aa2b20347927 100644 --- a/examples/imatrix/README.md +++ b/examples/imatrix/README.md @@ -1,7 +1,7 @@ # llama.cpp/examples/imatrix -Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantized models. -More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861 +Compute an importance matrix for a model and given text dataset. Can be used during quantization to enhance the quality of the quantized models. +More information is available here: https://github.com/ggml-org/llama.cpp/pull/4861 ## Usage @@ -25,8 +25,6 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument ## Example ```bash -GGML_CUDA=1 make -j - # generate importance matrix (imatrix.dat) ./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 70ff47768c02b..4edc0bfacf125 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -3,11 +3,11 @@ #include "log.h" #include "llama.h" +#include #include #include #include #include -#include #include #include #include @@ -40,7 +40,7 @@ class IMatrixCollector { void set_params(common_params params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); void save_imatrix(int ncall = -1) const; - bool load_imatrix(const char * file_name); + bool load_imatrix(const char * fname); private: std::unordered_map m_stats; common_params m_params; @@ -100,7 +100,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const float * data = is_host ? (const float *) src1->data : m_src1_data.data(); // this has been adapted to the new format of storing merged experts in a single 3d tensor - // ref: https://github.com/ggerganov/llama.cpp/pull/6387 + // ref: https://github.com/ggml-org/llama.cpp/pull/6387 if (t->op == GGML_OP_MUL_MAT_ID) { // ids -> [n_experts_used, n_tokens] // src1 -> [cols, n_expert_used, n_tokens] @@ -429,10 +429,14 @@ static void process_logits( } static bool compute_imatrix(llama_context * ctx, const common_params & params) { - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); const int n_ctx = llama_n_ctx(ctx); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + auto tim1 = std::chrono::high_resolution_clock::now(); LOG_INF("%s: tokenizing the input ..\n", __func__); @@ -467,7 +471,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { const int n_chunk_max = tokens.size() / n_ctx; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); const int n_batch = params.n_batch; int count = 0; @@ -507,7 +511,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } common_batch_clear(batch); @@ -618,14 +622,15 @@ int main(int argc, char ** argv) { // init common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx_train = llama_model_n_ctx_train(model); if (params.n_ctx > n_ctx_train) { LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, params.n_ctx); @@ -637,18 +642,24 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - if (!compute_imatrix(ctx, params)) { - return 1; + if (params.prompt.empty()) { + if (params.in_files.empty()) { + LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n"); + return 1; + } + LOG_INF("No prompt provided; combining precomputed matrices only.\n"); + } else { + if (!compute_imatrix(ctx, params)) { + return 1; + } } + g_collector.save_imatrix(); LOG("\n"); llama_perf_context_print(ctx); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); return 0; diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 9b1aa3b63c920..fb26628d82bf9 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-infill) add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/infill/README.md b/examples/infill/README.md index 810a0c5e76697..df4d976f2bb4f 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -14,7 +14,7 @@ In this section, we cover the most commonly used options for running the `infill - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. -- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference. - `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. ## Input Prompts diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index ef700895720ff..489a208b66b34 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -131,15 +131,17 @@ int main(int argc, char ** argv) { LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); common_init_result llama_init = common_init_from_params(params); - model = llama_init.model; - ctx = llama_init.context; + model = llama_init.model.get(); + ctx = llama_init.context.get(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); LOG_DBG("n_ctx: %d\n", n_ctx); @@ -152,28 +154,28 @@ int main(int argc, char ** argv) { LOG_INF("\n"); LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - const bool add_bos = llama_add_bos_token(model); - GGML_ASSERT(!llama_add_eos_token(model)); + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); std::vector embd_inp; std::vector embd_end; std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); - GGML_ASSERT(llama_token_fim_pre(model) >= 0); - GGML_ASSERT(llama_token_fim_suf(model) >= 0); + GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0); + GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0); - inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model)); - inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model)); + inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); + inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); embd_inp = params.spm_infill ? inp_sfx : inp_pfx; embd_end = params.spm_infill ? inp_pfx : inp_sfx; if (add_bos) { - embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); } embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - const llama_token middle_token = llama_token_fim_mid(model); + const llama_token middle_token = llama_vocab_fim_mid(vocab); if (middle_token >= 0) { embd_inp.push_back(middle_token); } @@ -185,7 +187,7 @@ int main(int argc, char ** argv) { // Should not run without any tokens if (embd_inp.empty()) { - embd_inp.push_back(llama_token_bos(model)); + embd_inp.push_back(llama_vocab_bos(vocab)); LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); } @@ -420,10 +422,10 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { // deal with eot token in infill mode - if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){ + if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){ if (is_interacting && !params.interactive_first) { // print an eot token - LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str()); + LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); } LOG("\n"); console::set_display(console::user_input); @@ -463,13 +465,13 @@ int main(int argc, char ** argv) { std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); - inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model)); - inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model)); + inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); + inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); embd_inp = params.spm_infill ? inp_sfx : inp_pfx; embd_end = params.spm_infill ? inp_pfx : inp_sfx; if (add_bos) { - embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); } embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); @@ -484,7 +486,7 @@ int main(int argc, char ** argv) { is_interacting = false; } // deal with end of generation tokens in interactive mode - else if (llama_token_is_eog(model, common_sampler_last(smpl))) { + else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) { LOG_DBG("found EOS token\n"); if (params.interactive) { @@ -500,7 +502,7 @@ int main(int argc, char ** argv) { if (params.input_prefix_bos) { LOG_DBG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(model)); + embd_inp.push_back(llama_vocab_bos(vocab)); } std::string buffer; @@ -563,7 +565,7 @@ int main(int argc, char ** argv) { } // end of generation - if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) { + if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) { break; } @@ -575,15 +577,12 @@ int main(int argc, char ** argv) { } } if (!params.interactive && n_remain <= 0) { - LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str()); + LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); } LOG("\n"); common_perf_print(ctx, smpl); - llama_free(ctx); - llama_free_model(model); - common_sampler_free(smpl); llama_backend_free(); diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt index 5bdbea4e28187..17e3b9b87bae4 100644 --- a/examples/llama-bench/CMakeLists.txt +++ b/examples/llama-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-bench) add_executable(${TARGET} llama-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index bac606f471639..f518d02d38689 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -683,7 +683,7 @@ struct cmd_params_instance { bool cpu_strict; int poll; int n_gpu_layers; - std::string rpc_servers; + std::string rpc_servers_str; llama_split_mode split_mode; int main_gpu; bool no_kv_offload; @@ -696,8 +696,37 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; - if (!rpc_servers.empty()) { - mparams.rpc_servers = rpc_servers.c_str(); + if (!rpc_servers_str.empty()) { + auto rpc_servers = string_split(rpc_servers_str, ','); + + // add RPC devices + if (!rpc_servers.empty()) { + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + fprintf(stderr, "%s: failed to find RPC backend\n", __func__); + exit(1); + } + + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); + ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); + if (!ggml_backend_rpc_add_device_fn) { + fprintf(stderr, "%s: failed to find RPC device add function\n", __func__); + exit(1); + } + static std::vector devices; + devices.clear(); + for (const std::string & server : rpc_servers) { + ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str()); + if (dev) { + devices.push_back(dev); + } else { + fprintf(stderr, "%s: failed to add RPC device for server '%s'\n", __func__, server.c_str()); + exit(1); + } + } + devices.push_back(nullptr); + mparams.devices = devices.data(); + } } mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; @@ -708,7 +737,7 @@ struct cmd_params_instance { } bool equal_mparams(const cmd_params_instance & other) const { - return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers && + return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers_str == other.rpc_servers_str && split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && tensor_split == other.tensor_split; } @@ -847,8 +876,8 @@ static std::vector get_cmd_params_instances(const cmd_param struct test { static const std::string build_commit; static const int build_number; - static const std::string cpu_info; - static const std::string gpu_info; + const std::string cpu_info; + const std::string gpu_info; std::string model_filename; std::string model_type; uint64_t model_size; @@ -874,7 +903,10 @@ struct test { std::string test_time; std::vector samples_ns; - test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) { + test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) : + cpu_info(get_cpu_info()), + gpu_info(get_gpu_info()) { + model_filename = inst.model; char buf[128]; llama_model_desc(lmodel, buf, sizeof(buf)); @@ -1029,8 +1061,6 @@ struct test { const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; -const std::string test::cpu_info = get_cpu_info(); -const std::string test::gpu_info = get_gpu_info(); struct printer { virtual ~printer() {} @@ -1401,7 +1431,8 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + const int32_t n_vocab = llama_vocab_n_tokens(vocab); std::vector tokens(n_batch); @@ -1409,7 +1440,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); - tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; + tokens[0] = n_processed == 0 && llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab; for (int i = 1; i < n_tokens; i++) { tokens[i] = std::rand() % n_vocab; } @@ -1424,9 +1455,10 @@ static void test_gen(llama_context * ctx, int n_gen, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + const int32_t n_vocab = llama_vocab_n_tokens(vocab); - llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; + llama_token token = llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab; for (int i = 0; i < n_gen; i++) { llama_decode(ctx, llama_batch_get_one(&token, 1)); @@ -1521,15 +1553,15 @@ int main(int argc, char ** argv) { for (const auto & inst : params_instances) { params_idx++; if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count); + fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count); } // keep the same model between tests when possible if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) { if (lmodel) { - llama_free_model(lmodel); + llama_model_free(lmodel); } - lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams()); + lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams()); if (lmodel == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str()); return 1; @@ -1537,10 +1569,10 @@ int main(int argc, char ** argv) { prev_inst = &inst; } - llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams()); + llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams()); if (ctx == NULL) { fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str()); - llama_free_model(lmodel); + llama_model_free(lmodel); return 1; } @@ -1573,14 +1605,14 @@ int main(int argc, char ** argv) { // warmup run if (t.n_prompt > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count); + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count); } //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count); + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count); } test_gen(ctx, 1, t.n_threads); } @@ -1592,14 +1624,14 @@ int main(int argc, char ** argv) { if (t.n_prompt > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, + fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps); } test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, + fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps); } test_gen(ctx, t.n_gen, t.n_threads); @@ -1626,7 +1658,7 @@ int main(int argc, char ** argv) { ggml_threadpool_free_fn(threadpool); } - llama_free_model(lmodel); + llama_model_free(lmodel); if (p) { p->print_footer(); diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts index 2d1dfba2040da..28dbc1904888b 100644 --- a/examples/llama.android/llama/build.gradle.kts +++ b/examples/llama.android/llama/build.gradle.kts @@ -19,6 +19,7 @@ android { externalNativeBuild { cmake { arguments += "-DLLAMA_BUILD_COMMON=ON" + arguments += "-DGGML_LLAMAFILE=OFF" arguments += "-DCMAKE_BUILD_TYPE=Release" cppFlags += listOf() arguments += listOf() diff --git a/examples/llama.android/llama/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt index 2de496574f54a..6119fe09b0cb6 100644 --- a/examples/llama.android/llama/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt @@ -14,7 +14,7 @@ project("llama-android") #include(FetchContent) #FetchContent_Declare( # llama -# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp +# GIT_REPOSITORY https://github.com/ggml-org/llama.cpp # GIT_TAG master #) diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp index b3858ddfb6168..2a73983a9832f 100644 --- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -87,7 +87,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi auto path_to_model = env->GetStringUTFChars(filename, 0); LOGi("Loading model from %s", path_to_model); - auto model = llama_load_model_from_file(path_to_model, model_params); + auto model = llama_model_load_from_file(path_to_model, model_params); env->ReleaseStringUTFChars(filename, path_to_model); if (!model) { @@ -102,7 +102,7 @@ Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring fi extern "C" JNIEXPORT void JNICALL Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) { - llama_free_model(reinterpret_cast(model)); + llama_model_free(reinterpret_cast(model)); } extern "C" @@ -305,7 +305,9 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, extern "C" JNIEXPORT void JNICALL Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { - llama_batch_free(*reinterpret_cast(batch_pointer)); + //llama_batch_free(*reinterpret_cast(batch_pointer)); + const auto batch = reinterpret_cast(batch_pointer); + delete batch; } extern "C" @@ -345,6 +347,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init( jlong context_pointer, jlong batch_pointer, jstring jtext, + jboolean format_chat, jint n_len ) { @@ -354,7 +357,8 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init( const auto context = reinterpret_cast(context_pointer); const auto batch = reinterpret_cast(batch_pointer); - const auto tokens_list = common_tokenize(context, text, 1); + bool parse_special = (format_chat == JNI_TRUE); + const auto tokens_list = common_tokenize(context, text, true, parse_special); auto n_ctx = llama_n_ctx(context); auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); @@ -366,7 +370,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1init( } for (auto id : tokens_list) { - LOGi("%s", common_token_to_piece(context, id).c_str()); + LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id); } common_batch_clear(*batch); @@ -403,6 +407,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop( const auto batch = reinterpret_cast(batch_pointer); const auto sampler = reinterpret_cast(sampler_pointer); const auto model = llama_get_model(context); + const auto vocab = llama_model_get_vocab(model); if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur); if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I"); @@ -412,7 +417,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop( const auto new_token_id = llama_sampler_sample(sampler, context, -1); const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value); - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) { return nullptr; } diff --git a/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt index cf520e4594004..b964d93e37819 100644 --- a/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt +++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt @@ -65,6 +65,7 @@ class LLamaAndroid { context: Long, batch: Long, text: String, + formatChat: Boolean, nLen: Int ): Int @@ -115,10 +116,10 @@ class LLamaAndroid { } } - fun send(message: String): Flow = flow { + fun send(message: String, formatChat: Boolean = false): Flow = flow { when (val state = threadLocalState.get()) { is State.Loaded -> { - val ncur = IntVar(completion_init(state.context, state.batch, message, nlen)) + val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen)) while (ncur.value <= nlen) { val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur) if (str == null) { diff --git a/examples/llama.swiftui/README.md b/examples/llama.swiftui/README.md index 96cf743d48202..f717886d661ce 100644 --- a/examples/llama.swiftui/README.md +++ b/examples/llama.swiftui/README.md @@ -3,9 +3,9 @@ Local inference of llama.cpp on an iPhone. This is a sample app that can be used as a starting point for more advanced projects. -For usage instructions and performance stats, check the following discussion: https://github.com/ggerganov/llama.cpp/discussions/4508 +For usage instructions and performance stats, check the following discussion: https://github.com/ggml-org/llama.cpp/discussions/4508 -![image](https://github.com/ggerganov/llama.cpp/assets/1991296/2b40284f-8421-47a2-b634-74eece09a299) +![image](https://github.com/ggml-org/llama.cpp/assets/1991296/2b40284f-8421-47a2-b634-74eece09a299) Video demonstration: diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 65cd4eb515c7f..ee7141a663224 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -24,6 +24,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama actor LlamaContext { private var model: OpaquePointer private var context: OpaquePointer + private var vocab: OpaquePointer private var sampling: UnsafeMutablePointer private var batch: llama_batch private var tokens_list: [llama_token] @@ -47,13 +48,14 @@ actor LlamaContext { self.sampling = llama_sampler_chain_init(sparams) llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4)) llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234)) + vocab = llama_model_get_vocab(model) } deinit { llama_sampler_free(sampling) llama_batch_free(batch) + llama_model_free(model) llama_free(context) - llama_free_model(model) llama_backend_free() } @@ -65,7 +67,7 @@ actor LlamaContext { model_params.n_gpu_layers = 0 print("Running on simulator, force use n_gpu_layers = 0") #endif - let model = llama_load_model_from_file(path, model_params) + let model = llama_model_load_from_file(path, model_params) guard let model else { print("Could not load model at \(path)") throw LlamaError.couldNotInitializeContext @@ -79,7 +81,7 @@ actor LlamaContext { ctx_params.n_threads = Int32(n_threads) ctx_params.n_threads_batch = Int32(n_threads) - let context = llama_new_context_with_model(model, ctx_params) + let context = llama_init_from_model(model, ctx_params) guard let context else { print("Could not load context!") throw LlamaError.couldNotInitializeContext @@ -151,7 +153,7 @@ actor LlamaContext { new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1) - if llama_token_is_eog(model, new_token_id) || n_cur == n_len { + if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len { print("\n") is_done = true let new_token_str = String(cString: temporary_invalid_cchars + [0]) @@ -210,20 +212,20 @@ actor LlamaContext { llama_kv_cache_clear(context) - let t_pp_start = ggml_time_us() + let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000; if llama_decode(context, batch) != 0 { print("llama_decode() failed during prompt") } llama_synchronize(context) - let t_pp_end = ggml_time_us() + let t_pp_end = DispatchTime.now().uptimeNanoseconds / 1000; // bench text generation llama_kv_cache_clear(context) - let t_tg_start = ggml_time_us() + let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000; for i in 0...allocate(capacity: n_tokens) - let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) + let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) var swiftTokens: [llama_token] = [] for i in 0...allocate(capacity: Int(-nTokens)) @@ -324,7 +326,7 @@ actor LlamaContext { defer { newResult.deallocate() } - let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false) + let nNewTokens = llama_token_to_piece(vocab, token, newResult, -nTokens, 0, false) let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens)) return Array(bufferPointer) } else { diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 3950b9e9df843..ff3d108b2a18c 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -7,6 +7,7 @@ objects = { /* Begin PBXBuildFile section */ + 1809696D2D05A39F00400EE8 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = 1809696C2D05A39F00400EE8 /* llama */; }; 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; }; 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; }; 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; }; @@ -17,7 +18,6 @@ 8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; }; 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; }; 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; }; - DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; }; F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; }; /* End PBXBuildFile section */ @@ -42,7 +42,7 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( - DF810E132B4A5BA200301144 /* llama in Frameworks */, + 1809696D2D05A39F00400EE8 /* llama in Frameworks */, 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */, 8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */, ); @@ -151,7 +151,7 @@ ); name = llama.swiftui; packageProductDependencies = ( - DF810E122B4A5BA200301144 /* llama */, + 1809696C2D05A39F00400EE8 /* llama */, ); productName = llama.swiftui; productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */; @@ -429,7 +429,7 @@ /* End XCConfigurationList section */ /* Begin XCSwiftPackageProductDependency section */ - DF810E122B4A5BA200301144 /* llama */ = { + 1809696C2D05A39F00400EE8 /* llama */ = { isa = XCSwiftPackageProductDependency; productName = llama; }; diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift index 30c2dc4310210..1c3cd9d2efc73 100644 --- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift +++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift @@ -124,15 +124,26 @@ struct ContentView: View { } } }.sheet(isPresented: $showingHelp) { // Sheet for help modal - VStack(alignment: .leading) { + NavigationView { VStack(alignment: .leading) { - Text("1. Make sure the model is in GGUF Format") - .padding() - Text("2. Copy the download link of the quantized model") - .padding() + VStack(alignment: .leading) { + Text("1. Make sure the model is in GGUF Format") + .padding() + Text("2. Copy the download link of the quantized model") + .padding() + } + Spacer() + } + .navigationTitle("Help") + .navigationBarTitleDisplayMode(.inline) + .toolbar { + ToolbarItem(placement: .navigationBarTrailing) { + Button("Done") { + showingHelp = false + } + } } - Spacer() - } + } } } } diff --git a/examples/llama.vim b/examples/llama.vim index 57eb2a9772d51..af3fd3935d765 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -39,7 +39,7 @@ " " :call llama#init() " -" more info: https://github.com/ggerganov/llama.cpp/pull/9787 +" more info: https://github.com/ggml-org/llama.cpp/pull/9787 " " colors (adjust to your liking) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index bbf5fec586feb..319effd199aa4 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -11,7 +11,7 @@ target_include_directories(llava PUBLIC .) target_include_directories(llava PUBLIC ../..) target_include_directories(llava PUBLIC ../../common) -target_compile_features(llava PRIVATE cxx_std_11) +target_compile_features(llava PRIVATE cxx_std_17) add_library(llava_static STATIC $) if (BUILD_SHARED_LIBS) @@ -35,11 +35,25 @@ add_executable(${TARGET} llava-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-minicpmv-cli) add_executable(${TARGET} minicpmv-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +set(TARGET llama-qwen2vl-cli) +add_executable(${TARGET} qwen2vl-cli.cpp) +set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +set(TARGET llama-llava-clip-quantize-cli) +add_executable(${TARGET} clip-quantize-cli.cpp) +set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llava/README-glmedge.md b/examples/llava/README-glmedge.md new file mode 100644 index 0000000000000..603d01474513f --- /dev/null +++ b/examples/llava/README-glmedge.md @@ -0,0 +1,43 @@ +# GLMV-EDGE + +Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b). + +## Usage +Build with cmake or run `make llama-llava-cli` to build it. + +After building, run: `./llama-llava-cli` to see the usage. For example: + +```sh +./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <|user|>\n prompt <|assistant|>\n" +``` + +**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. +**note**: For GPU offloading ensure to use the `-ngl` flag just like usual + +## GGUF conversion + +1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example: + +```sh +git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b +``` + +2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents: + +```sh +python ./examples/llava/glmedge-surgery.py -m ../model_path +``` + +4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF: + +```sh +python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path +``` + +5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF: + +```sh +python convert_hf_to_gguf.py ../model_path +``` + +Now both the LLM part and the image encoder are in the `model_path` directory. diff --git a/examples/llava/README-granitevision.md b/examples/llava/README-granitevision.md new file mode 100644 index 0000000000000..d2426dc69577e --- /dev/null +++ b/examples/llava/README-granitevision.md @@ -0,0 +1,183 @@ +# Granite Vision + +Download the model and point your `GRANITE_MODEL` environment variable to the path. + +```bash +$ git clone https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview +$ export GRANITE_MODEL=./granite-vision-3.1-2b-preview +``` + + +### 1. Running llava surgery v2. +First, we need to run the llava surgery script as shown below: + +`python llava_surgery_v2.py -C -m $GRANITE_MODEL` + +You should see two new files (`llava.clip` and `llava.projector`) written into your model's directory, as shown below. + +```bash +$ ls $GRANITE_MODEL | grep -i llava +llava.clip +llava.projector +``` + +We should see that the projector and visual encoder get split out into the llava files. Quick check to make sure they aren't empty: +```python +import os +import torch + +MODEL_PATH = os.getenv("GRANITE_MODEL") +if not MODEL_PATH: + raise ValueError("env var GRANITE_MODEL is unset!") + +encoder_tensors = torch.load(os.path.join(MODEL_PATH, "llava.clip")) +projector_tensors = torch.load(os.path.join(MODEL_PATH, "llava.projector")) + +assert len(encoder_tensors) > 0 +assert len(projector_tensors) > 0 +``` + +If you actually inspect the `.keys()` of the loaded tensors, you should see a lot of `vision_model` tensors in the `encoder_tensors`, and 5 tensors (`'multi_modal_projector.linear_1.bias'`, `'multi_modal_projector.linear_1.weight'`, `'multi_modal_projector.linear_2.bias'`, `'multi_modal_projector.linear_2.weight'`, `'image_newline'`) in the multimodal `projector_tensors`. + + +### 2. Creating the Visual Component GGUF +To create the GGUF for the visual components, we need to write a config for the visual encoder; make sure the config contains the correct `image_grid_pinpoints` + + +Note: we refer to this file as `$VISION_CONFIG` later on. +```json +{ + "_name_or_path": "siglip-model", + "architectures": [ + "SiglipVisionModel" + ], + "image_grid_pinpoints": [ + [384,768], + [384,1152], + [384,1536], + [384,1920], + [384,2304], + [384,2688], + [384,3072], + [384,3456], + [384,3840], + [768,384], + [768,768], + [768,1152], + [768,1536], + [768,1920], + [1152,384], + [1152,768], + [1152,1152], + [1536,384], + [1536,768], + [1920,384], + [1920,768], + [2304,384], + [2688,384], + [3072,384], + [3456,384], + [3840,384] + ], + "mm_patch_merge_type": "spatial_unpad", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + "layer_norm_eps": 1e-6, + "hidden_act": "gelu_pytorch_tanh", + "projection_dim": 0, + "vision_feature_layer": [-24, -20, -12, -1] +} +``` + +Create a new directory to hold the visual components, and copy the llava.clip/projector files, as well as the vision config into it. + +```bash +$ ENCODER_PATH=$PWD/visual_encoder +$ mkdir $ENCODER_PATH + +$ cp $GRANITE_MODEL/llava.clip $ENCODER_PATH/pytorch_model.bin +$ cp $GRANITE_MODEL/llava.projector $ENCODER_PATH/ +$ cp $VISION_CONFIG $ENCODER_PATH/config.json +``` + +At which point you should have something like this: +```bash +$ ls $ENCODER_PATH +config.json llava.projector pytorch_model.bin +``` + +Now convert the components to GGUF; Note that we also override the image mean/std dev to `[.5,.5,.5]` since we use the siglip visual encoder - in the transformers model, you can find these numbers in the [preprocessor_config.json](https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview/blob/main/preprocessor_config.json). +```bash +$ python convert_image_encoder_to_gguf.py \ + -m $ENCODER_PATH \ + --llava-projector $ENCODER_PATH/llava.projector \ + --output-dir $ENCODER_PATH \ + --clip-model-is-vision \ + --clip-model-is-siglip \ + --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 +``` + +this will create the first GGUF file at `$ENCODER_PATH/mmproj-model-f16.gguf`; we will refer to the abs path of this file as the `$VISUAL_GGUF_PATH.` + + +### 3. Creating the LLM GGUF. +The granite vision model contains a granite LLM as its language model. For now, the easiest way to get the GGUF for LLM is by loading the composite model in `transformers` and exporting the LLM so that it can be directly converted with the normal conversion path. + +First, set the `LLM_EXPORT_PATH` to the path to export the `transformers` LLM to. +``` +$ export LLM_EXPORT_PATH=$PWD/granite_vision_llm +``` + +```python +import os +import transformers + +MODEL_PATH = os.getenv("GRANITE_MODEL") +if not MODEL_PATH: + raise ValueError("env var GRANITE_MODEL is unset!") + +LLM_EXPORT_PATH = os.getenv("LLM_EXPORT_PATH") +if not MODEL_PATH: + raise ValueError("env var LLM_EXPORT_PATH is unset!") + +tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_PATH) + +# NOTE: granite vision support was added to transformers very recently (4.49); +# if you get size mismatches, your version is too old. +# If you are running with an older version, set `ignore_mismatched_sizes=True` +# as shown below; it won't be loaded correctly, but the LLM part of the model that +# we are exporting will be loaded correctly. +model = transformers.AutoModelForImageTextToText.from_pretrained(MODEL_PATH, ignore_mismatched_sizes=True) + +tokenizer.save_pretrained(LLM_EXPORT_PATH) +model.language_model.save_pretrained(LLM_EXPORT_PATH) +``` + +Now you can convert the exported LLM to GGUF with the normal converter in the root of the llama cpp project. +```bash +$ LLM_GGUF_PATH=$LLM_EXPORT_PATH/granite_llm.gguf +... +$ python convert_hf_to_gguf.py --outfile $LLM_GGUF_PATH $LLM_EXPORT_PATH +``` + + +### 4. Running the Model in Llama cpp +Build llama cpp normally; you should have a target binary named `llama-llava-cli`, which you can pass two binaries to. Sample usage: + +Note - the test image shown below can be found [here](https://github-production-user-asset-6210df.s3.amazonaws.com/10740300/415512792-d90d5562-8844-4f34-a0a5-77f62d5a58b5.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20250221%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250221T054145Z&X-Amz-Expires=300&X-Amz-Signature=86c60be490aa49ef7d53f25d6c973580a8273904fed11ed2453d0a38240ee40a&X-Amz-SignedHeaders=host). + +```bash +$ ./build/bin/llama-llava-cli -m $LLM_GGUF_PATH \ + --mmproj $VISUAL_GGUF_PATH \ + --image cherry_blossom.jpg \ + -c 16384 \ + -p "<|system|>\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n<|user|>\n\\nWhat type of flowers are in this picture?\n<|assistant|>\n" \ + --temp 0 +``` + +Sample response: `The flowers in the picture are cherry blossoms, which are known for their delicate pink petals and are often associated with the beauty of spring.` diff --git a/examples/llava/README-minicpmo2.6.md b/examples/llava/README-minicpmo2.6.md new file mode 100644 index 0000000000000..8f591506dbbb0 --- /dev/null +++ b/examples/llava/README-minicpmo2.6.md @@ -0,0 +1,46 @@ +## MiniCPM-o 2.6 +Currently, this readme only supports minicpm-omni's image capabilities, and we will update the full-mode support as soon as possible. + +### Prepare models and code + +Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder. + +Clone llama.cpp: +```bash +git clone git@github.com:OpenBMB/llama.cpp.git +cd llama.cpp +git checkout minicpm-omni +``` + +### Usage of MiniCPM-o 2.6 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us) + +```bash +python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6 +python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 +python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model + +# quantize int4 version +./llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + +Build llama.cpp using `CMake`: +https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md + +```bash +cmake -B build +cmake --build build --config Release +``` + +Inference on Linux or Mac +``` +# run f16 version +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run quantized int4 version +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# or run in interactive mode +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i +``` diff --git a/examples/llava/README-minicpmv2.5.md b/examples/llava/README-minicpmv2.5.md index 1c8498ff9e151..b0e72a0fa7a78 100644 --- a/examples/llava/README-minicpmv2.5.md +++ b/examples/llava/README-minicpmv2.5.md @@ -6,7 +6,7 @@ Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V- Clone llama.cpp: ```bash -git clone https://github.com/ggerganov/llama.cpp +git clone https://github.com/ggml-org/llama.cpp cd llama.cpp ``` diff --git a/examples/llava/README-quantize.md b/examples/llava/README-quantize.md new file mode 100644 index 0000000000000..b931513ab7203 --- /dev/null +++ b/examples/llava/README-quantize.md @@ -0,0 +1,44 @@ +# Quantizing CLIP Visual Projector + +This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance. + +## Usage + +To quantize a CLIP visual projector model, use the following command: + +```sh +./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf +``` + +After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc). + +### Arguments + +- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format. +- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved. +- ``: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`. + +### Quantization Types + +The following quantization types are supported, based on the `enum ggml_type` definition: + +- `2` - `q4_0`: 4-bit quantization with a single scale value. +- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block. +- `6` - `q5_0`: 5-bit quantization with a single scale value. +- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block. +- `8` - `q8_0`: 8-bit quantization with a single scale value. + +### Example + +To quantize a model using the `q4_0` quantization type, you would run: + +```sh +./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2 +``` + +This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method. + +## Notes + +- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements. +- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments. diff --git a/examples/llava/README.md b/examples/llava/README.md index 012451361763c..0e3c32032055b 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -101,8 +101,27 @@ python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknow ``` **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) + **note** llava-1.6 greatly benefits from batched prompt processing (defaults work) +**note** if the language model in step `6)` is incompatible with the legacy conversion script, the easiest way handle the LLM model conversion is to load the model in transformers, and export only the LLM from the llava next model. + +```python +import os +import transformers + +model_path = ... +llm_export_path = ... + +tokenizer = transformers.AutoTokenizer.from_pretrained(model_path) +model = transformers.AutoModelForImageTextToText.from_pretrained(model_path) + +tokenizer.save_pretrained(llm_export_path) +model.language_model.save_pretrained(llm_export_path) +``` + +Then, you can convert the LLM using the `convert_hf_to_gguf.py` script, which handles more LLM architectures. + ## llava-cli templating and llava-1.6 prompting llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."` diff --git a/examples/llava/clip-quantize-cli.cpp b/examples/llava/clip-quantize-cli.cpp new file mode 100644 index 0000000000000..566506954edeb --- /dev/null +++ b/examples/llava/clip-quantize-cli.cpp @@ -0,0 +1,59 @@ +#include "arg.h" +#include "base64.hpp" +#include "log.h" +#include "common.h" +#include "sampling.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" +#include "ggml.h" + +static void print_usage(int argc, char ** argv) { + (void) argc; + + fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]); + fprintf(stderr, " type = 2 - q4_0\n"); + fprintf(stderr, " type = 3 - q4_1\n"); + fprintf(stderr, " type = 6 - q5_0\n"); + fprintf(stderr, " type = 7 - q5_1\n"); + fprintf(stderr, " type = 8 - q8_0\n"); +} + +int main(int argc, char ** argv) { + if (argc != 4) { + print_usage(argc, argv); + return 1; + } + + const std::string fname_inp = argv[1]; + const std::string fname_out = argv[2]; + + const int itype = atoi(argv[3]); + + const int64_t t_main_start_us = ggml_time_us(); + + int64_t t_quantize_us = 0; + + // load the model + { + const int64_t t_start_us = ggml_time_us(); + + if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) { + fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str()); + return 1; + } + + t_quantize_us = ggml_time_us() - t_start_us; + } + + // report timing + { + const int64_t t_main_end_us = ggml_time_us(); + + printf("\n"); + printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f); + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f); + } + + return 0; +} diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index aae49c965e905..76d4a78520575 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -7,22 +7,27 @@ #include "ggml-cpu.h" #include "ggml-alloc.h" #include "ggml-backend.h" +#include "gguf.h" -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif +//#ifdef GGML_USE_CUDA +//#include "ggml-cuda.h" +//#endif +// +//#ifdef GGML_USE_SYCL +//#include "ggml-sycl.h" +//#endif +// +//#ifdef GGML_USE_METAL +//#include "ggml-metal.h" +//#endif +// +//#ifdef GGML_USE_CANN +//#include "ggml-cann.h" +//#endif +// +//#ifdef GGML_USE_VULKAN +//#include "ggml-vulkan.h" +//#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" @@ -35,15 +40,23 @@ #include #include #include +#include #include #include #include #include -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) //#define CLIP_DEBUG_FUNCTIONS @@ -90,8 +103,11 @@ static std::string format(const char * fmt, ...) { #define KEY_HAS_VIS_ENC "clip.has_vision_encoder" #define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" #define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector" +#define KEY_HAS_GLM_PROJ "clip.has_glm_projector" #define KEY_MINICPMV_VERSION "clip.minicpmv_version" +#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" #define KEY_USE_GELU "clip.use_gelu" +#define KEY_USE_SILU "clip.use_silu" #define KEY_N_EMBD "clip.%s.embedding_length" #define KEY_N_FF "clip.%s.feed_forward_length" #define KEY_N_BLOCK "clip.%s.block_count" @@ -105,6 +121,7 @@ static std::string format(const char * fmt, ...) { #define KEY_IMAGE_MEAN "clip.vision.image_mean" #define KEY_IMAGE_STD "clip.vision.image_std" #define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_FEATURE_LAYER "clip.vision.feature_layer" #define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" #define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" @@ -118,7 +135,8 @@ static std::string format(const char * fmt, ...) { #define TN_TOKEN_EMBD "%s.token_embd.weight" #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" -#define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat +#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1" #define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" @@ -145,6 +163,15 @@ static std::string format(const char * fmt, ...) { #define TN_MINICPMV_ATTN "resampler.attn.%s.%s" #define TN_MINICPMV_LN "resampler.ln_%s.%s" +#define TN_GLM_ADAPER_CONV "adapter.conv.%s" +#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s" +#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s" +#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s" +#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" +#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" +#define TN_GLM_BOI_W "adapter.boi" +#define TN_GLM_EOI_W "adapter.eoi" + enum projector_type { PROJECTOR_TYPE_MLP, @@ -152,6 +179,8 @@ enum projector_type { PROJECTOR_TYPE_LDP, PROJECTOR_TYPE_LDPV2, PROJECTOR_TYPE_RESAMPLER, + PROJECTOR_TYPE_GLM_EDGE, + PROJECTOR_TYPE_MERGER, PROJECTOR_TYPE_UNKNOWN, }; @@ -160,6 +189,8 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_LDP, "ldp" }, { PROJECTOR_TYPE_LDPV2, "ldpv2"}, { PROJECTOR_TYPE_RESAMPLER, "resampler"}, + { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, + { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"}, }; @@ -246,7 +277,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { { const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); int arr_n = gguf_get_arr_n(ctx_gguf, i); - const void * data = gguf_get_arr_data(ctx_gguf, i); + const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); std::stringstream ss; ss << "["; for (int j = 0; j < arr_n; j++) { @@ -415,8 +446,9 @@ struct clip_hparams { char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default) - int32_t image_grid_pinpoints[32]; + std::vector image_grid_pinpoints; int32_t image_crop_resolution; + std::unordered_set vision_feature_layer; }; struct clip_layer { @@ -452,7 +484,8 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; - struct ggml_tensor * patch_embeddings; + struct ggml_tensor * patch_embeddings_0; + struct ggml_tensor * patch_embeddings_1; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; @@ -482,6 +515,12 @@ struct clip_vision_model { struct ggml_tensor * mm_4_w = NULL; struct ggml_tensor * mm_4_b = NULL; + //GLMV-Edge projection + struct ggml_tensor * mm_model_adapter_conv_w; + struct ggml_tensor * mm_model_adapter_conv_b; + struct ggml_tensor * boi_w; + struct ggml_tensor * eoi_w; + // MobileVLM projection struct ggml_tensor * mm_model_mlp_1_w; struct ggml_tensor * mm_model_mlp_1_b; @@ -542,14 +581,18 @@ struct clip_ctx { bool has_vision_encoder = false; bool has_llava_projector = false; bool has_minicpmv_projector = false; + bool has_glm_projector = false; + bool has_qwen2vl_merger = false; int minicpmv_version = 2; struct clip_vision_model vision_model; projector_type proj_type = PROJECTOR_TYPE_MLP; + int32_t max_feature_layer; float image_mean[3]; float image_std[3]; bool use_gelu = false; + bool use_silu = false; int32_t ftype = 1; bool has_class_embedding = true; @@ -595,18 +638,29 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 image_size_height = imgs->data->ny; } } + else if (ctx->has_qwen2vl_merger) { + // use the image's native resolution when image is avaible + if (is_inf) { + // if (imgs->data->nx && imgs->data->ny) { + image_size_width = imgs->data->nx; + image_size_height = imgs->data->ny; + } + } const int patch_size = hparams.patch_size; const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); + const int patches_w = image_size_width / patch_size; + const int patches_h = image_size_height / patch_size; const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + const int num_position_ids = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions; const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; - int n_layer = hparams.n_layer; const float eps = hparams.eps; + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; const int batch_size = imgs->size; - if (ctx->has_llava_projector || ctx->has_minicpmv_projector) { + if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) { GGML_ASSERT(batch_size == 1); } @@ -623,10 +677,30 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_set_name(inp_raw, "inp_raw"); ggml_set_input(inp_raw); - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + if (ctx->has_qwen2vl_merger) { + GGML_ASSERT(image_size_width % (patch_size * 2) == 0); + GGML_ASSERT(image_size_height % (patch_size * 2) == 0); + + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, patches_h, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); + inp = ggml_reshape_3d( + ctx0, inp, + hidden_size, patches_w * patches_h, batch_size); + } + else { + inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + } if (ctx->has_patch_bias) { // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); @@ -648,12 +722,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } } - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); ggml_set_name(positions, "positions"); ggml_set_input(positions); - embeddings = - ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); + if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding + embeddings = + ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); + } if (ctx->has_minicpmv_projector) { int pos_w = image_size_width/patch_size; @@ -664,6 +740,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 else if (ctx->minicpmv_version == 3) { pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); } + else if (ctx->minicpmv_version == 4) { + pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); + } ggml_set_name(pos_embed, "pos_embed"); ggml_set_input(pos_embed); } @@ -676,13 +755,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); } + std::vector embedding_stack; + const auto & vision_feature_layer = hparams.vision_feature_layer; + // loop over layers - if (ctx->has_minicpmv_projector) { - n_layer += 1; - } - for (int il = 0; il < n_layer - 1; il++) { + for (int il = 0; il < ctx->max_feature_layer; il++) { struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states + // If this is an embedding feature layer, save the output. + // NOTE: 0 index here refers to the input to the encoder. + if (vision_feature_layer.find(il) != vision_feature_layer.end()) { + embedding_stack.push_back(embeddings); + } + //const size_t nb_q_w = model.layers[il].q_w->nb[0]; // layernorm1 @@ -699,8 +784,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); - Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); + if (ctx->has_qwen2vl_merger) { + Q = ggml_rope_multi( + ctx0, Q, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + } + Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); @@ -708,6 +798,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); + if (ctx->has_qwen2vl_merger) { + K = ggml_rope_multi( + ctx0, K, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + } K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); @@ -747,6 +842,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 if (ctx->use_gelu) { cur = ggml_gelu_inplace(ctx0, cur); + } else if (ctx->use_silu) { + cur = ggml_silu_inplace(ctx0, cur); } else { cur = ggml_gelu_quick_inplace(ctx0, cur); } @@ -768,6 +865,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); } + // final layer is a vision feature layer + if (vision_feature_layer.find(ctx->max_feature_layer) != vision_feature_layer.end()) { + embedding_stack.push_back(embeddings); + } + + // If feature layers are explicitly set, stack them (if we have multiple) + if (!embedding_stack.empty()) { + embeddings = embedding_stack[0]; + for (size_t i = 1; i < embedding_stack.size(); i++) { + embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); + } + } + // llava projector if (ctx->has_llava_projector) { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); @@ -829,7 +939,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); // layer norm // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] @@ -877,7 +987,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // block_2 { // stride = 2 - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] // layer norm @@ -938,7 +1048,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // mlp_2 ne [24, 24, 2048, 1] mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); // weight ne = [3, 3, 2048, 1] - struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); @@ -985,6 +1095,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 n_head = hidden_size/d_head; num_query = 64; } + else if (ctx->minicpmv_version == 4) { + hidden_size = 3584; + n_head = hidden_size/d_head; + num_query = 64; + } struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); @@ -1019,6 +1134,45 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 GGML_ASSERT(false); } } + // glm projector + else if (ctx->has_glm_projector) { + if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); + embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + //GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + struct ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_silu_inplace(ctx0, embeddings); + embeddings = ggml_mul(ctx0, embeddings,x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); + } + } else { + GGML_ABORT("fatel error"); + } + } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); + + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + } // build the graph ggml_build_forward_expand(gf, embeddings); @@ -1142,25 +1296,30 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } -#ifdef GGML_USE_CUDA - new_clip->backend = ggml_backend_cuda_init(0); - LOG_INF("%s: CLIP using CUDA backend\n", __func__); -#endif - -#ifdef GGML_USE_METAL - new_clip->backend = ggml_backend_metal_init(); - LOG_INF("%s: CLIP using Metal backend\n", __func__); -#endif - -#ifdef GGML_USE_CANN - new_clip->backend = ggml_backend_cann_init(0); - LOG_INF("%s: CLIP using CANN backend\n", __func__); -#endif - -#ifdef GGML_USE_VULKAN - new_clip->backend = ggml_backend_vk_init(0); - LOG_INF("%s: CLIP using Vulkan backend\n", __func__); -#endif +//#ifdef GGML_USE_CUDA +// new_clip->backend = ggml_backend_cuda_init(0); +// LOG_INF("%s: CLIP using CUDA backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_METAL +// new_clip->backend = ggml_backend_metal_init(); +// LOG_INF("%s: CLIP using Metal backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_CANN +// new_clip->backend = ggml_backend_cann_init(0); +// LOG_INF("%s: CLIP using CANN backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_VULKAN +// new_clip->backend = ggml_backend_vk_init(0); +// LOG_INF("%s: CLIP using Vulkan backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_SYCL +// new_clip->backend = ggml_backend_sycl_init(0); +// LOG_INF("%s: CLIP using SYCL backend\n", __func__); +//#endif if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); @@ -1190,6 +1349,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx); } + idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ); + if (idx != -1) { + new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx); + } + + idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER); + if (idx != -1) { + new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx); + } // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search GGML_ASSERT(new_clip->has_vision_encoder); @@ -1198,11 +1366,19 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { idx = get_key_idx(ctx, KEY_USE_GELU); new_clip->use_gelu = gguf_get_val_bool(ctx, idx); + try { + idx = get_key_idx(ctx, KEY_USE_SILU); + new_clip->use_silu = gguf_get_val_bool(ctx, idx); + } catch (std::runtime_error & /*e*/) { + new_clip->use_silu = false; + } + if (verbosity >= 1) { LOG_INF("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector); + LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector); LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } @@ -1288,14 +1464,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS); int n = gguf_get_arr_n(ctx, idx); const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx); - for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) { - hparams.image_grid_pinpoints[i] = pinpoints[i]; + for (int i = 0; i < n; ++i) { + hparams.image_grid_pinpoints.push_back(pinpoints[i]); } - if (n < 32) - hparams.image_grid_pinpoints[n] = 0; - } catch (std::runtime_error & /*e*/) { - hparams.image_grid_pinpoints[0]=0; - } + } catch (std::runtime_error & /*e*/) { } + + // Load the vision feature layer indices if they are explicitly provided; + // if multiple vision feature layers are present, the values will be concatenated + // to form the final visual features. + // NOTE: gguf conversions should standardize the values of the vision feature layer to + // be non-negative, since we use -1 to mark values as unset here. + try { + int idx = get_key_idx(ctx, KEY_FEATURE_LAYER); + int n = gguf_get_arr_n(ctx, idx); + + const int32_t * vision_feature_layer = (const int32_t *)gguf_get_arr_data(ctx, idx); + + for (int i = 0; i < n; ++i) { + hparams.vision_feature_layer.insert(vision_feature_layer[i]); + } + } catch (std::runtime_error & /*e*/) { } try { int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE); @@ -1321,6 +1509,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->image_std[i] = std_data[i]; } + // Calculate the deepest feature layer based on hparams and projector type + new_clip->max_feature_layer = get_deepest_feature_layer(new_clip); + if (verbosity >= 2) { LOG_INF("\n%s: vision model hparams\n", __func__); LOG_INF("image_size %d\n", hparams.image_size); @@ -1334,8 +1525,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { LOG_INF("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); LOG_INF("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); LOG_INF("v_image_grid_pinpoints: "); - for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) { - LOG_INF("%d ", hparams.image_grid_pinpoints[i]); + for (const auto & pp : hparams.image_grid_pinpoints) { + LOG_INF("%d ", pp); + } + LOG_INF("\n"); + LOG_INF("v_vision_feature_layer: "); + for (const auto & feature_layer: hparams.vision_feature_layer) { + LOG_INF("%d ", feature_layer); } LOG_INF("\n"); LOG_INF("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); @@ -1373,11 +1569,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } try { - vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); + vision_model.patch_embeddings_0 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); } catch(const std::exception& /*e*/) { LOG_ERR("%s: failed to load vision model tensors\n", __func__); } + try { + vision_model.patch_embeddings_1 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1); + } catch(const std::exception& /*e*/) { + new_clip->has_qwen2vl_merger = false; + } // LLaVA projection if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) { @@ -1465,6 +1666,24 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight")); vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias")); } + else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight")); + vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias")); + vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight")); + vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight")); + vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias")); + vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); + vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight")); + vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); + vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W); + vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W); + } + else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) { + vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); + vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); + vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); + } else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); @@ -1503,6 +1722,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend)); clip_image_f32_batch batch; batch.size = 1; + batch.data = nullptr; ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false); ggml_gallocr_reserve(new_clip->compute_alloc, gf); size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0); @@ -1516,6 +1736,10 @@ void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size ctx_clip->load_image_size = load_image_size; } +struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) { + return ctx_clip->load_image_size; +} + struct clip_image_size * clip_image_size_init() { struct clip_image_size * load_image_size = new struct clip_image_size(); load_image_size->width = 448; @@ -1546,11 +1770,11 @@ void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { } } -static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) { +void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, clip_image_u8 * img) { img->nx = nx; img->ny = ny; img->buf.resize(3 * nx * ny); - memcpy(img->buf.data(), data, img->buf.size()); + memcpy(img->buf.data(), rgb_pixels, img->buf.size()); } bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { @@ -1560,7 +1784,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { LOG_ERR("%s: failed to load image '%s'\n", __func__, fname); return false; } - build_clip_img_from_data(data, nx, ny, img); + clip_build_img_from_pixels(data, nx, ny, img); stbi_image_free(data); return true; } @@ -1572,7 +1796,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length LOG_ERR("%s: failed to decode image bytes\n", __func__); return false; } - build_clip_img_from_data(data, nx, ny, img); + clip_build_img_from_pixels(data, nx, ny, img); stbi_image_free(data); return true; } @@ -1928,6 +2152,7 @@ static std::vector> uhd_slice_image(const clip_imag images[images.size()-1].push_back(patch); } } + clip_image_u8_free(refine_image); } return images; } @@ -1966,6 +2191,44 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli clip_image_f32_free(res); } } + for (size_t i = 0; i < imgs.size(); ++i) { + for (size_t j = 0; j < imgs[i].size(); ++j) { + if (imgs[i][j] != nullptr) { + clip_image_u8_free(imgs[i][j]); + } + } + } + return true; + } + else if (ctx->has_qwen2vl_merger) { + clip_image_u8 * resized = clip_image_u8_init(); + auto patch_size = clip_patch_size(ctx) * 2; + int nx = ceil((float)img->nx / patch_size) * patch_size; + int ny = ceil((float)img->ny / patch_size) * patch_size; + bicubic_resize(*img, *resized, nx, ny); + + res_imgs->data = new clip_image_f32[1]; + // clip_image_f32 * res = clip_image_f32_init(); + normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std); + // res_imgs->data[0] = *res; + res_imgs->size = 1; + + // clip_image_f32_free(res); + clip_image_u8_free(resized); + return true; + } + + if (ctx->has_glm_projector) { + res_imgs->size = 1; + res_imgs->data = new clip_image_f32[res_imgs->size]; + clip_image_u8 resized_image; + int32_t sz=ctx->vision_model.hparams.image_size; + bicubic_resize(*img, resized_image,sz,sz); + clip_image_f32 * res = clip_image_f32_init(); + //clip_image_save_to_bmp(resized_image, "resized.bmp"); + normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std); + res_imgs->data[0] = *res; + clip_image_f32_free(res); return true; } @@ -2013,10 +2276,10 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli } } } else { - if (params.image_grid_pinpoints[0] != 0) { + if (!params.image_grid_pinpoints.empty()) { // "spatial_unpad" with "anyres" processing for llava-1.6 std::vector> possible_resolutions; - for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) { + for (size_t i = 0; i < params.image_grid_pinpoints.size(); i+=2) { possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]}); } std::pair best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions); @@ -2154,7 +2417,15 @@ void clip_free(clip_ctx * ctx) { } size_t clip_embd_nbytes(const struct clip_ctx * ctx) { - return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); + int extra_tokens = ctx->has_glm_projector ? 2 : 0; + return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float); +} + +size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) { + clip_image_f32 img; + img.nx = img_w; + img.ny = img_h; + return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float); } int32_t clip_image_size(const struct clip_ctx * ctx) { @@ -2174,15 +2445,29 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) { } const int32_t * clip_image_grid(const struct clip_ctx * ctx) { - return ctx->vision_model.hparams.image_grid_pinpoints; + if (ctx->vision_model.hparams.image_grid_pinpoints.size()) { + return &ctx->vision_model.hparams.image_grid_pinpoints.front(); + } + return nullptr; +} + +size_t get_clip_image_grid_size(const struct clip_ctx * ctx) { + return ctx->vision_model.hparams.image_grid_pinpoints.size(); } int clip_n_patches(const struct clip_ctx * ctx) { + clip_image_f32 img; + img.nx = ctx->vision_model.hparams.image_size; + img.ny = ctx->vision_model.hparams.image_size; + return clip_n_patches_by_img(ctx, &img); +} + +int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) { const auto & params = ctx->vision_model.hparams; int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); - if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) { + if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { n_patches /= 4; } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { if (ctx->minicpmv_version == 2) { @@ -2191,6 +2476,14 @@ int clip_n_patches(const struct clip_ctx * ctx) { else if (ctx->minicpmv_version == 3) { n_patches = 64; } + else if (ctx->minicpmv_version == 4) { + n_patches = 64; + } + } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + int patch_size = params.patch_size * 2; + int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0); + int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0); + n_patches = x_patch * y_patch; } return n_patches; @@ -2307,6 +2600,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima if (ctx->has_minicpmv_projector) { GGML_ASSERT(batch_size == 1); } + if (ctx->has_glm_projector) { + GGML_ASSERT(batch_size == 1); + ggml_tensor * boi = ctx->vision_model.boi_w; + ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi)); + vec = (float*)(vec+ggml_nelements(boi)); //offset for boi + } // build the inference graph ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true); @@ -2319,7 +2618,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int image_size = hparams.image_size; int image_size_width = image_size; int image_size_height = image_size; - if (ctx->has_minicpmv_projector) { + if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) { image_size_width = imgs->data[0].nx; image_size_height = imgs->data[0].ny; } @@ -2339,7 +2638,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima for (size_t i = 0; i < imgs->size; i++) { const int nx = imgs->data[i].nx; const int ny = imgs->data[i].ny; - if (!ctx->has_minicpmv_projector) { + if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) { GGML_ASSERT(nx == image_size && ny == image_size); } @@ -2365,8 +2664,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316 struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); int* positions_data = (int*)malloc(ggml_nbytes(positions)); - int bucket_coords_h[70]; - int bucket_coords_w[70]; + int bucket_coords_h[1024]; + int bucket_coords_w[1024]; for (int i = 0; i < pos_h; i++){ bucket_coords_h[i] = std::floor(70.0*i/pos_h); } @@ -2394,12 +2693,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima else if (ctx->minicpmv_version == 3) { embed_dim = 3584; } + else if (ctx->minicpmv_version == 4) { + embed_dim = 3584; + } auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h)); float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed)); - for(int i=0;ihas_qwen2vl_merger) { + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + + const int pw = image_size_width / patch_size; + const int ph = image_size_height / patch_size; + int* positions_data = (int*)malloc(ggml_nbytes(positions)); + + int ptr = 0; + for (int y = 0; y < ph; y+=2) + { + for (int x = 0; x < pw; x+=2) + { + for (int dy = 0; dy < 2; dy++) { + for (int dx = 0; dx < 2; dx++) { + positions_data[ptr] = y + dy; + positions_data[num_patches + ptr] = x + dx; + positions_data[num_patches * 2 + ptr] = y + dy; + positions_data[num_patches * 3 + ptr] = x + dx; + ptr++; + } + } + } + } + + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); + } + else { struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); int* positions_data = (int*)malloc(ggml_nbytes(positions)); @@ -2428,16 +2757,20 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); free(positions_data); - } - { - struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); - int* patches_data = (int*)malloc(ggml_nbytes(patches)); - for (int i = 0; i < num_patches; i++) { - patches_data[i] = i + 1; + if (!ctx->has_glm_projector) { + struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); + // The patches vector is used to get rows to index into the embeds with; + // we should skip dim 0 only if we have CLS to avoid going out of bounds + // when retrieving the rows. + int patch_offset = ctx->has_class_embedding ? 1 : 0; + int* patches_data = (int*)malloc(ggml_nbytes(patches)); + for (int i = 0; i < num_patches; i++) { + patches_data[i] = i + patch_offset; + } + ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); + free(patches_data); } - ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); - free(patches_data); } } @@ -2453,14 +2786,19 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); + if (ctx->has_glm_projector) { + //eoi + ggml_tensor * eoi = ctx->vision_model.eoi_w; + int offset = ggml_nelements(embeddings); + ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi)); + } + return true; } bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) { - ggml_type type = GGML_TYPE_Q4_1; - assert(itype < GGML_TYPE_COUNT); - type = static_cast(itype); + ggml_type type = static_cast(itype); auto * ctx_clip = clip_model_load(fname_inp, 2); @@ -2513,8 +2851,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i } } - // quantize only 2D tensors - quantize &= (ggml_n_dims(cur) == 2); + // quantize only 2D tensors and bigger than block size + quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type); if (quantize) { new_type = type; @@ -2559,7 +2897,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i total_size_org += orig_size; total_size_new += new_size; gguf_set_tensor_type(ctx_out, name.c_str(), new_type); - gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size); + GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size); + gguf_set_tensor_data(ctx_out, name.c_str(), new_data); fout.write((const char *)new_data, new_size); size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size; for (size_t j = 0; j < pad; ++j) { @@ -2609,6 +2948,15 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { else if (ctx->minicpmv_version == 3) { return 3584; } + else if (ctx->minicpmv_version == 4) { + return 3584; + } + } + if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){ + return ctx->vision_model.mm_model_mlp_3_w->ne[1]; + } + if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + return ctx->vision_model.mm_1_b->ne[0]; } std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; @@ -2621,3 +2969,46 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) { } return 0; } + +bool clip_is_glm(const struct clip_ctx * ctx) { + return ctx->has_glm_projector; +} +bool clip_is_qwen2vl(const struct clip_ctx * ctx) { + return ctx->has_qwen2vl_merger; +} + +// Determine the number of encoder layers to iterate over +int get_deepest_feature_layer(const struct clip_ctx * ctx) { + // Get the index of the second to last layer; this is the + // default for models that have a llava projector + const auto & hparams = ctx->vision_model.hparams; + int n_layer = hparams.n_layer - 1; + int deepest_feature_layer = -1; + + // Handle other projectors; incrementing here indicates that we + // should use the last encoder layer for the vision features. + if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) { + n_layer += 1; + } + + // If we set explicit vision feature layers, only go up to the deepest one + for (const auto & feature_layer : hparams.vision_feature_layer) { + if (feature_layer > deepest_feature_layer) { + deepest_feature_layer = feature_layer; + } + } + return deepest_feature_layer < 0 ? n_layer : deepest_feature_layer; +} + +bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) { + clip_image_f32 clip_img; + clip_img.buf.resize(h * w * 3); + for (int i = 0; i < h*w*3; i++) + { + clip_img.buf[i] = img[i]; + } + clip_img.nx = w; + clip_img.ny = h; + clip_image_encode(ctx, n_threads, &clip_img, vec); + return true; +} diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 78588bdf1745c..002c419653a01 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -45,6 +45,7 @@ CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity CLIP_API void clip_free(struct clip_ctx * ctx); CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); +CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w); CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx); CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx); @@ -54,12 +55,15 @@ CLIP_API int32_t clip_hidden_size(const struct clip_ctx * ctx); CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx); CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx); +CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx); -CLIP_API int clip_n_patches (const struct clip_ctx * ctx); -CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); +CLIP_API int clip_n_patches (const struct clip_ctx * ctx); +CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img); +CLIP_API int clip_n_mmproj_embd (const struct clip_ctx * ctx); CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); +CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip); CLIP_API struct clip_image_size * clip_image_size_init(); CLIP_API struct clip_image_u8 * clip_image_u8_init (); @@ -70,6 +74,12 @@ CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch); CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch); +/** + * Build image from pixels decoded by other libraries instead of stb_image.h for better performance. + * The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes + */ +CLIP_API void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img); + CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ @@ -86,6 +96,13 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype); CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx); +CLIP_API bool clip_is_glm(const struct clip_ctx * ctx); +CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx); + +CLIP_API int get_deepest_feature_layer(const struct clip_ctx * ctx); + +CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec); + #ifdef __cplusplus } diff --git a/examples/llava/convert_image_encoder_to_gguf.py b/examples/llava/convert_image_encoder_to_gguf.py index 4fa1d6ceae1bb..de29687ec9236 100644 --- a/examples/llava/convert_image_encoder_to_gguf.py +++ b/examples/llava/convert_image_encoder_to_gguf.py @@ -6,7 +6,7 @@ import torch import numpy as np from gguf import * -from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel +from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel, SiglipVisionModel TEXT = "clip.text" VISION = "clip.vision" @@ -37,6 +37,18 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b def get_tensor_name(name: str) -> str: + # Standardize the transformers llava next keys for + # image newline / mm projector with the classes in haotian-liu LLaVA + if name == "image_newline": + return "model.image_newline" + if name.startswith("multi_modal_projector"): + name = name.replace("multi_modal_projector", "mm") + if "linear_1" in name: + name = name.replace("linear_1", "0") + if "linear_2" in name: + name = name.replace("linear_2", "2") + return name + if "projection" in name: return name if "mm_projector" in name: @@ -83,8 +95,14 @@ def bytes_to_unicode(): help="Save a vision-only model. It can't be used to encode texts") ap.add_argument("--clip-model-is-vision", action="store_true", required=False, help="The clip model is a pure vision model (ShareGPT4V vision extract for example)") -ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, + +# Selectable visual encoders that are compatible with this script +encoder_group = ap.add_mutually_exclusive_group() +encoder_group.add_argument("--clip-model-is-openclip", action="store_true", required=False, help="The clip model is from openclip (for ViT-SO400M type))") +encoder_group.add_argument("--clip-model-is-siglip", action="store_true", required=False, + help="the visual encoder is Siglip.") + ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) @@ -109,7 +127,12 @@ def bytes_to_unicode(): # output in the same directory as the model if output_dir is None dir_model = args.model_dir -if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip: +if ( + args.clip_model_is_vision or + not os.path.exists(dir_model + "/vocab.json") or + args.clip_model_is_openclip or + args.clip_model_is_siglip +): vocab = None tokens = None else: @@ -137,7 +160,10 @@ def bytes_to_unicode(): if args.use_f32: ftype = 0 -if args.clip_model_is_vision or args.clip_model_is_openclip: +if args.clip_model_is_siglip: + model = SiglipVisionModel.from_pretrained(dir_model) + processor = None +elif args.clip_model_is_vision or args.clip_model_is_openclip: model = CLIPVisionModel.from_pretrained(dir_model) processor = None else: @@ -187,26 +213,71 @@ def bytes_to_unicode(): if has_text_encoder: assert t_hparams is not None assert tokens is not None + if args.clip_model_is_siglip: + text_projection_dim = 0 + else: + text_projection_dim = t_hparams.get("projection_dim", config["projection_dim"]) # text_model hparams fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"]) fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"]) fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"]) - fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"])) + fout.add_uint32("clip.text.projection_dim", text_projection_dim) fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"]) fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"]) fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"]) fout.add_token_list(tokens) + + +def get_non_negative_vision_feature_layers(v_hparams): + """ + Determine the vision feature layer(s) for the llava model, which are indices into the + hidden states of the visual encoder. Note that the hidden states array generally takes the + form: + + [, , ... ] + + so feature indices should be offset as n+1 to get the output of encoder block n. + We convert all vision feature layers to non-negative so that -1 can be used in + the model as an unset value. If no vision feature layer is found, we leave it unset. + """ + num_hidden_layers = v_hparams["num_hidden_layers"] + to_non_negative = lambda layer_idx: layer_idx if layer_idx >= 0 else num_hidden_layers + layer_idx + 1 + feature_layers_key = None + # Key used for llava models in transformers + if "vision_feature_layer" in config: + feature_layers_key = "vision_feature_layer" + # Key used for llava models in the original format + elif "mm_vision_select_layer" in config: + feature_layers_key = "mm_vision_select_layer" + if feature_layers_key is not None: + feature_layers = config[feature_layers_key] + if isinstance(feature_layers, int): + feature_layers = [feature_layers] + return [to_non_negative(feature_layer) for feature_layer in feature_layers] + +# Determine if we have explicitly specified vision feature layers in our config +feature_layers = get_non_negative_vision_feature_layers(v_hparams) + if has_vision_encoder: - # vision_model hparams + # Siglip does not have a visual projector; set projection dim to 0 + if args.clip_model_is_siglip: + visual_projection_dim = 0 + else: + visual_projection_dim = v_hparams.get("projection_dim", config["projection_dim"]) + + # set vision_model hparams fout.add_uint32("clip.vision.image_size", v_hparams["image_size"]) fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"]) fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"]) fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"]) - fout.add_uint32("clip.vision.projection_dim", v_hparams.get("projection_dim", config["projection_dim"])) + fout.add_uint32("clip.vision.projection_dim", visual_projection_dim) fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"]) fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"]) - block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"] + if feature_layers: + block_count = max(feature_layers) + else: + block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"] fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count) # /** # "image_grid_pinpoints": [ @@ -258,7 +329,8 @@ def bytes_to_unicode(): fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"]) if "mm_projector_type" in v_hparams: fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"]) - + if feature_layers: + fout.add_array("clip.vision.feature_layer", feature_layers) if processor is not None: image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean # pyright: ignore[reportAttributeAccessIssue] @@ -274,7 +346,13 @@ def bytes_to_unicode(): if has_llava_projector: - model.vision_model.encoder.layers.pop(-1) + # By default, we drop the last layer for llava projector + # models unless we have explicitly set vision feature layers + if feature_layers is None: + model.vision_model.encoder.layers.pop(-1) + else: + model.vision_model.encoder.layers = model.vision_model.encoder.layers[:max(feature_layers)] + projector = torch.load(args.llava_projector) for name, data in projector.items(): name = get_tensor_name(name) diff --git a/examples/llava/glmedge-convert-image-encoder-to-gguf.py b/examples/llava/glmedge-convert-image-encoder-to-gguf.py new file mode 100644 index 0000000000000..848ef1cf3f542 --- /dev/null +++ b/examples/llava/glmedge-convert-image-encoder-to-gguf.py @@ -0,0 +1,280 @@ +import argparse +import os +import json +import re + +import torch +import numpy as np +from gguf import * + +TEXT = "clip.text" +VISION = "clip.vision" +from transformers import SiglipVisionModel, SiglipVisionConfig + +def k(raw_key: str, arch: str) -> str: + return raw_key.format(arch=arch) + + +def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool: + if name in ( + "logit_scale", + "text_model.embeddings.position_ids", + "vision_model.embeddings.position_ids", + ): + return True + + if name in ( + "vision_model.head.probe", + "vision_model.head.attention.in_proj_weight", + "vision_model.head.attention.in_proj_bias", + "vision_model.head.attention.out_proj.weight", + "vision_model.head.attention.out_proj.bias", + "vision_model.head.layernorm.weight", + "vision_model.head.layernorm.bias", + "vision_model.head.mlp.fc1.weight", + "vision_model.head.mlp.fc1.bias", + "vision_model.head.mlp.fc2.weight", + "vision_model.head.mlp.fc2.bias" + ): + return True + + if name.startswith("v") and not has_vision: + return True + + if name.startswith("t") and not has_text: + return True + + return False + + +def get_tensor_name(name: str) -> str: + if "projection" in name: + return name + if "mm_projector" in name: + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name + + return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") + + +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True) +ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16") +ap.add_argument("--text-only", action="store_true", required=False, + help="Save a text-only model. It can't be used to encode images") +ap.add_argument("--vision-only", action="store_true", required=False, + help="Save a vision-only model. It can't be used to encode texts") +ap.add_argument("--clip-model-is-vision", action="store_true", required=False, + help="The clip model is a pure vision model (ShareGPT4V vision extract for example)") +ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, + help="The clip model is from openclip (for ViT-SO400M type))") +ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter") +ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) +# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 +# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 +default_image_mean = [0.5, 0.5, 0.5] +default_image_std = [0.5, 0.5, 0.5] +ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) +ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) + +# with proper +args = ap.parse_args() + + +if args.text_only and args.vision_only: + print("--text-only and --image-only arguments cannot be specified at the same time.") + exit(1) + +if args.use_f32: + print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.") + +# output in the same directory as the model if output_dir is None +dir_model = args.model_dir + +if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip: + vocab = None + tokens = None +else: + with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f: + vocab = json.load(f) + tokens = [key for key in vocab] + +with open(dir_model + "/config.json", "r", encoding="utf-8") as f: + config = json.load(f) + if args.clip_model_is_vision: + v_hparams = config + t_hparams = None + else: + v_hparams = config["vision_config"] + t_hparams = None + +# possible data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 +# +# map from ftype to string +ftype_str = ["f32", "f16"] + +ftype = 1 +if args.use_f32: + ftype = 0 + +vision_config = SiglipVisionConfig(**v_hparams) +model = SiglipVisionModel(vision_config) +model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip"))) + +fname_middle = None +has_text_encoder = False +has_vision_encoder = True +has_glm_projector = True +if args.text_only: + fname_middle = "text-" + has_vision_encoder = False +elif args.llava_projector is not None: + fname_middle = "mmproj-" + has_text_encoder = False + has_glm_projector = True +elif args.vision_only: + fname_middle = "vision-" + has_text_encoder = False +else: + fname_middle = "" + +output_dir = args.output_dir if args.output_dir is not None else dir_model +os.makedirs(output_dir, exist_ok=True) +output_prefix = os.path.basename(output_dir).replace("ggml_", "") +fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf") +fout = GGUFWriter(path=fname_out, arch="clip") + +fout.add_bool("clip.has_text_encoder", has_text_encoder) +fout.add_bool("clip.has_vision_encoder", has_vision_encoder) +fout.add_bool("clip.has_glm_projector", has_glm_projector) +fout.add_file_type(ftype) +model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model) +fout.add_name(model_name) +if has_glm_projector: + fout.add_description("image encoder for glm4v") + fout.add_string("clip.projector_type", "adapter") +else: + fout.add_description("two-tower CLIP model") + +if has_text_encoder: + assert t_hparams is not None + assert tokens is not None + # text_model hparams + fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"]) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"]) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"]) + fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"])) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"]) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"]) + fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"]) + fout.add_token_list(tokens) + +if has_vision_encoder: + # vision_model hparams + fout.add_uint32("clip.vision.image_size", v_hparams["image_size"]) + fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"]) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"]) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"]) + fout.add_uint32("clip.vision.projection_dim", 0) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"]) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) + fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"]) + + image_mean = args.image_mean if args.image_mean is not None else default_image_mean + image_std = args.image_std if args.image_std is not None else default_image_std + fout.add_array("clip.vision.image_mean", image_mean) + fout.add_array("clip.vision.image_std", image_std) + +fout.add_bool("clip.use_gelu", True) + + +if has_glm_projector: + # model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue] + projector = torch.load(args.llava_projector) + for name, data in projector.items(): + name = get_tensor_name(name) + # pw and dw conv ndim==4 + if data.ndim == 2 or data.ndim == 4: + data = data.squeeze().numpy().astype(np.float16) + else: + data = data.squeeze().numpy().astype(np.float32) + if name.startswith("vision."): + name=name.replace("vision.","") + fout.add_tensor(name, data) + print(f"Projector {name} - {data.dtype} - shape = {data.shape}") + # print(f"Projector {name} tensors added\n") + +state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue] +for name, data in state_dict.items(): + if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector): + # we don't need this + print(f"skipping parameter: {name}") + continue + + name = get_tensor_name(name) + data = data.squeeze().numpy() + + n_dims = len(data.shape) + + # ftype == 0 -> float32, ftype == 1 -> float16 + ftype_cur = 0 + if n_dims == 4: + print(f"tensor {name} is always saved in f16") + data = data.astype(np.float16) + ftype_cur = 1 + elif ftype == 1: + if name[-7:] == ".weight" and n_dims == 2: + # print(" Converting to float16") + data = data.astype(np.float16) + ftype_cur = 1 + else: + # print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + else: + if data.dtype != np.float32: + # print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + print(f"siglip {name} - {data.dtype} - shape = {data.shape}") + # print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}") + fout.add_tensor(name, data) + + +fout.write_header_to_file() +fout.write_kv_data_to_file() +fout.write_tensors_to_file() +fout.close() + +print("Done. Output file: " + fname_out) diff --git a/examples/llava/glmedge-surgery.py b/examples/llava/glmedge-surgery.py new file mode 100644 index 0000000000000..16bb915d043cf --- /dev/null +++ b/examples/llava/glmedge-surgery.py @@ -0,0 +1,33 @@ +import argparse +import os +import torch +from transformers import AutoModel + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model", help="Path to GLM model") +args = ap.parse_args() + +# find the model part that includes the the multimodal projector weights +model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True) +checkpoint = model.state_dict() + +# get a list of mm tensor names +mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")] + +# store these tensors in a new dictionary and torch.save them +projector = {name: checkpoint[name].float() for name in mm_tensors} +torch.save(projector, f"{args.model}/glm.projector") + +clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")] +if len(clip_tensors) > 0: + clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors} + torch.save(clip, f"{args.model}/glm.clip") + + # added tokens should be removed to be able to convert Mistral models + if os.path.exists(f"{args.model}/added_tokens.json"): + with open(f"{args.model}/added_tokens.json", "w") as f: + f.write("{}\n") + +print("Done!") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") +print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.") diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 2691c6e6b2dd2..40aa0876f24a7 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -47,8 +47,12 @@ static const char * sample(struct common_sampler * smpl, int * n_past) { const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + static std::string ret; - if (llama_token_is_eog(llama_get_model(ctx_llama), id)) { + if (llama_vocab_is_eog(vocab, id)) { ret = ""; } else { ret = common_token_to_piece(ctx_llama, id); @@ -221,7 +225,7 @@ static struct llama_model * llava_init(common_params * params) { llama_model_params model_params = common_model_params_to_llama(*params); - llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); if (model == NULL) { LOG_ERR("%s: unable to load model\n" , __func__); return NULL; @@ -239,11 +243,10 @@ static struct llava_context * llava_init_context(common_params * params, llama_m auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - llama_context_params ctx_params = common_context_params_to_llama(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings - llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); if (ctx_llama == NULL) { LOG_ERR("%s: failed to create the llama_context\n" , __func__); @@ -265,7 +268,7 @@ static void llava_free(struct llava_context * ctx_llava) { } llama_free(ctx_llava->ctx_llama); - llama_free_model(ctx_llava->model); + llama_model_free(ctx_llava->model); llama_backend_free(); } @@ -323,7 +326,7 @@ int main(int argc, char ** argv) { } } - llama_free_model(model); + llama_model_free(model); return 0; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index be69885408433..518aad3f1f70b 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -11,13 +11,17 @@ #include #include -#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) -#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) - -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) // RGB uint8 image struct clip_image_u8 { @@ -212,7 +216,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector return true; } -static clip_image_f32 * only_v2_5_reshape_by_patch(clip_image_f32 * image, int patch_size) { +static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) { int width = image->nx; int height = image->ny; int num_patches = (height / patch_size) * (width / patch_size); @@ -255,25 +259,27 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip); - if (clip_is_minicpmv(ctx_clip)) { + if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) { std::vector image_embd_v; image_embd_v.resize(img_res_v.size); struct clip_image_size * load_image_size = clip_image_size_init(); + for (size_t i = 0; i < img_res_v.size; i++) { const int64_t t_img_enc_step_start_us = ggml_time_us(); - image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); + image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny)); int patch_size=14; load_image_size->width = img_res_v.data[i].nx; load_image_size->height = img_res_v.data[i].ny; clip_add_load_image_size(ctx_clip, load_image_size); + bool encoded = false; - int has_minicpmv_projector = clip_is_minicpmv(ctx_clip); - if (has_minicpmv_projector == 2) { - encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]); - } - else if (has_minicpmv_projector == 3) { + if (clip_is_qwen2vl(ctx_clip)) { encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); } + else { + encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]); + } + if (!encoded) { LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); return false; @@ -286,8 +292,11 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli int n_img_pos_out = 0; for (size_t i = 0; i < image_embd_v.size(); i++) { - std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip)); - n_img_pos_out += clip_n_patches(ctx_clip); + std::memcpy( + image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), + image_embd_v[i], + clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny)); + n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]); } *n_img_pos = n_img_pos_out; for (size_t i = 0; i < image_embd_v.size(); i++) { @@ -298,6 +307,23 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli load_image_size->height = img->ny; clip_add_load_image_size(ctx_clip, load_image_size); LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height); + delete[] img_res_v.data; + img_res_v.size = 0; + img_res_v.data = nullptr; + } + else if (clip_is_glm(ctx_clip)){ + struct clip_image_size * load_image_size = clip_image_size_init(); + load_image_size->width = img_res_v.data[0].nx; + load_image_size->height = img_res_v.data[0].ny; + clip_add_load_image_size(ctx_clip, load_image_size); + + bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); + int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2); + *n_img_pos = (pos * pos + 2); + if (!encoded){ + LOG_ERR("Unable to encode image \n"); + return false; + } } else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { // flat / default llava-1.5 type embedding @@ -327,9 +353,10 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); const int32_t * image_grid = clip_image_grid(ctx_clip); + const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip); std::vector> grid_pinpoints; - for (int i = 0; i < 32 && image_grid[i] != 0; i += 2) { + for (size_t i = 0; i < num_gridpoints; i += 2) { grid_pinpoints.push_back({image_grid[i], image_grid[i+1]}); } @@ -369,7 +396,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) { // make sure that the correct mmproj was used, i.e., compare apples to apples - int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); + int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama)); auto n_image_embd = clip_n_mmproj_embd(ctx_clip); if (n_image_embd != n_llama_embd) { LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); @@ -379,11 +406,21 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * } bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { - int num_max_patches = 6; + // Granite vision uses up to 10 patches + base patch + int num_max_patches = 11; if (clip_is_minicpmv(ctx_clip)) { num_max_patches = 10; } - float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model + if (clip_is_glm(ctx_clip)) { + num_max_patches = 1; + } + float * image_embd; + if (clip_is_qwen2vl(ctx_clip)) { + // qwen2vl don't split image into chunks, so `num_max_patches` is not needed. + image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny)); + } else { + image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model + } if (!image_embd) { LOG_ERR("Unable to allocate memory for image embeddings\n"); return false; @@ -435,7 +472,7 @@ struct llava_embd_batch { }; bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { - int n_embd = llama_n_embd(llama_get_model(ctx_llama)); + int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); for (int i = 0; i < image_embed->n_image_pos; i += n_batch) { int n_eval = image_embed->n_image_pos - i; @@ -498,10 +535,16 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long errno = 0; size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer if (ferror(file)) { - die_fmt("read error: %s", strerror(errno)); + LOG_ERR("read error: %s", strerror(errno)); + free(buffer); + fclose(file); + return false; } if (ret != (size_t) fileSize) { - die("unexpectedly reached end of file"); + LOG_ERR("unexpectedly reached end of file"); + free(buffer); + fclose(file); + return false; } fclose(file); // Close the file diff --git a/examples/llava/llava_surgery_v2.py b/examples/llava/llava_surgery_v2.py index 2d5b32fe6b236..b07c3e323c4c6 100644 --- a/examples/llava/llava_surgery_v2.py +++ b/examples/llava/llava_surgery_v2.py @@ -33,6 +33,33 @@ def save_model(model, file_path, file_type): else: torch.save(model, file_path) +# Helpers to match weight names from specific components or +# determine if a saved shard contains that component +def is_vision_tower(weight_name): + return ( + weight_name.startswith("model.vision_tower") or + weight_name.startswith("vit.") or + weight_name.startswith("vision_tower") + ) + +def is_newline(weight_name): + return ( + weight_name.startswith("model.image_newline") or + weight_name.startswith("image_newline") + ) + +def is_mm_projector(weight_name): + return ( + weight_name.startswith("model.mm_projector") or + weight_name.startswith("vision_proj.") or + weight_name.startswith("multi_modal_projector") + ) + +def newline_criteria(checkpoint): + return any(is_newline(k) for k in checkpoint.keys()) + +def proj_criteria(checkpoint): + return any(is_mm_projector(k) for k in checkpoint.keys()) # Adapted function to clean vision tower from checkpoint def clean_vision_tower_from_checkpoint(checkpoint_path): @@ -40,7 +67,7 @@ def clean_vision_tower_from_checkpoint(checkpoint_path): # file_type = 'pytorch' model_path = os.path.dirname(checkpoint_path) print(f"Searching for vision tower tensors in {checkpoint_path}") - clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))] + clip_tensors = [k for k, v in checkpoint.items() if is_vision_tower(k)] if len(clip_tensors) > 0: print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}") @@ -84,12 +111,6 @@ def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector): return newline_checkpoint_path, projector_checkpoint_path -def newline_criteria(checkpoint): - return any(k.startswith("model.image_newline") for k in checkpoint.keys()) - -def proj_criteria(checkpoint): - return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys()) - # Command-line interface setup ap = argparse.ArgumentParser() @@ -123,14 +144,14 @@ def proj_criteria(checkpoint): if newline_checkpoint_path is not None: print(f"Taking newline from {newline_checkpoint_path}") first_checkpoint, file_type = load_model(newline_checkpoint_path) - first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")] + first_mm_tensors = [k for k, v in first_checkpoint.items() if is_newline(k)] # Load the checkpoint mm_tensors = [] last_checkpoint = None if projector_checkpoint_path is not None: last_checkpoint, file_type = load_model(projector_checkpoint_path) - mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")] + mm_tensors = [k for k, v in last_checkpoint.items() if is_mm_projector(k)] if len(mm_tensors) == 0: if last_checkpoint is not None: @@ -155,5 +176,5 @@ def proj_criteria(checkpoint): save_model(projector, f"{args.model}/llava.projector", 'pytorch') print("Done!") -print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.") diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index e9cbb51ed90ab..53d902d616e85 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -31,7 +31,7 @@ static struct llama_model * llava_init(common_params * params) { llama_model_params model_params = common_model_params_to_llama(*params); - llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); if (model == NULL) { LOG_ERR("%s: unable to load model\n" , __func__); return NULL; @@ -54,7 +54,7 @@ static struct llava_context * llava_init_context(common_params * params, llama_m ctx_params.n_ctx = params->n_ctx; } - llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); if (ctx_llama == NULL) { LOG_ERR("%s: failed to create the llama_context\n" , __func__); @@ -75,7 +75,7 @@ static void llava_free(struct llava_context * ctx_llava) { } llama_free(ctx_llava->ctx_llama); - llama_free_model(ctx_llava->model); + llama_model_free(ctx_llava->model); llama_backend_free(); } @@ -140,6 +140,9 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e else if (has_minicpmv_projector == 3) { system_prompt = "<|im_start|>user\n"; } + else if (has_minicpmv_projector == 4) { + system_prompt = "<|im_start|>user\n"; + } LOG_INF("%s: image token past: %d\n", __func__, n_past); eval_string(ctx_llava->ctx_llama, (system_prompt+"").c_str(), params->n_batch, &n_past, false); process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++); @@ -167,8 +170,12 @@ static const char * sample(struct common_sampler * smpl, int * n_past) { const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + static std::string ret; - if (llama_token_is_eog(llama_get_model(ctx_llama), id)) { + if (llama_vocab_is_eog(vocab, id)) { ret = ""; } else { ret = common_token_to_piece(ctx_llama, id); @@ -223,6 +230,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm else if (has_minicpmv_projector == 3) { user_prompt = "<|im_start|>user\n" + prompt; } + else if (has_minicpmv_projector == 4) { + user_prompt = "<|im_start|>user\n" + prompt; + } } eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false); @@ -232,6 +242,9 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm else if (has_minicpmv_projector == 3) { eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false); } + else if (has_minicpmv_projector == 4) { + eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false); + } // generate the response @@ -304,7 +317,6 @@ int main(int argc, char ** argv) { const auto * tmp = llama_loop(ctx_llava, smpl, n_past); response += tmp; if (strcmp(tmp, "") == 0) break; - if (strstr(tmp, "###")) break; // Yi-VL behavior printf("%s", tmp);// mistral llava-1.6 if (strstr(response.c_str(), "")) break; // minicpm-v fflush(stdout); diff --git a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py index ea773742a832b..9b196757f07c9 100644 --- a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py +++ b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py @@ -501,7 +501,7 @@ def bytes_to_unicode(): default_image_std = [0.26862954, 0.26130258, 0.27577711] ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) -ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3', default=2) +ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2) # with proper args = ap.parse_args() @@ -545,12 +545,19 @@ def bytes_to_unicode(): minicpmv_version = args.minicpmv_version emb_dim = 4096 +block_count = 26 if minicpmv_version == 1: emb_dim = 2304 + block_count = 26 elif minicpmv_version == 2: emb_dim = 4096 + block_count = 27 elif minicpmv_version == 3: emb_dim = 3584 + block_count = 27 +elif minicpmv_version == 4: + emb_dim = 3584 + block_count = 27 default_vision_config = { "hidden_size": 1152, @@ -567,6 +574,9 @@ def bytes_to_unicode(): if minicpmv_version == 3: vision_config = SiglipVisionConfig(**default_vision_config) model = SiglipVisionTransformer(vision_config) +elif minicpmv_version == 4: + vision_config = SiglipVisionConfig(**default_vision_config) + model = SiglipVisionTransformer(vision_config) processor = None # if model.attn_pool is not None: @@ -587,7 +597,7 @@ def bytes_to_unicode(): fname_middle = "mmproj-" has_text_encoder = False has_minicpmv_projector = True - minicpmv_version = 3 + minicpmv_version = 4 elif args.vision_only: fname_middle = "vision-" has_text_encoder = False @@ -625,7 +635,6 @@ def bytes_to_unicode(): fout.add_uint32("clip.vision.projection_dim", 0) fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16) fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) - block_count = 26 fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count) if processor is not None: diff --git a/examples/llava/minicpmv-surgery.py b/examples/llava/minicpmv-surgery.py index 748ff5c57824e..ba82116582b1f 100644 --- a/examples/llava/minicpmv-surgery.py +++ b/examples/llava/minicpmv-surgery.py @@ -8,7 +8,7 @@ args = ap.parse_args() # find the model part that includes the the multimodal projector weights -model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True) +model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True, torch_dtype=torch.bfloat16) checkpoint = model.state_dict() # get a list of mm tensor names diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py new file mode 100644 index 0000000000000..c87606b4fdf4f --- /dev/null +++ b/examples/llava/qwen2_vl_surgery.py @@ -0,0 +1,165 @@ +import argparse +from typing import Dict + +import torch +import numpy as np +from gguf import * +from transformers import ( + Qwen2VLForConditionalGeneration, + Qwen2VLProcessor, + AutoProcessor, + Qwen2VLConfig +) + + +VISION = "clip.vision" + + +def k(raw_key: str, arch: str) -> str: + return raw_key.format(arch=arch) + + +def to_gguf_name(name: str) -> str: + og = name + name = name.replace("text_model", "t").replace("vision_model", "v") + name = name.replace("blocks", "blk").replace("embeddings.", "") + name = name.replace("attn.", "attn_") + name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") + # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") + name = name.replace("norm1", "ln1").replace("norm2", "ln2") + name = name.replace("merger.mlp", 'mm') + print(f"[to_gguf_name] {og} --> {name}") + return name + + +def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]: + vision_model = qwen2vl.visual + tensor_map = {} + for name, ten in vision_model.state_dict().items(): + ten = ten.numpy() + if 'qkv' in name: + if ten.ndim == 2: # weight + c3, _ = ten.shape + else: # bias + c3 = ten.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = ten[:c] + wk = ten[c: c * 2] + wv = ten[c * 2:] + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv + elif 'merger' in name: + if name.endswith("ln_q.weight"): + tensor_map['v.post_ln.weight'] = ten + elif name.endswith("ln_q.bias"): + tensor_map['v.post_ln.bias'] = ten + else: + # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" + tensor_map[to_gguf_name(name)] = ten + elif 'patch_embed.proj.weight' in name: + # NOTE: split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = ten.shape + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] + tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] + else: + tensor_map[to_gguf_name(f"vision_model.{name}")] = ten + + for new_name, ten in tensor_map.items(): + if ten.ndim <= 1 or new_name.endswith("_norm.weight"): + tensor_map[new_name] = ten.astype(np.float32) + else: + tensor_map[new_name] = ten.astype(dtype) + tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder + return tensor_map + + +def main(args): + if args.data_type == 'fp32': + dtype = torch.float32 + np_dtype = np.float32 + ftype = 0 + elif args.data_type == 'fp16': + dtype = torch.float32 + np_dtype = np.float16 + ftype = 1 + else: + raise ValueError() + + local_model = False + model_path = "" + model_name = args.model_name + print("model_name: ", model_name) + qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=dtype, device_map="cpu" + ) + cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] + vcfg = cfg.vision_config + + if os.path.isdir(model_name): + local_model = True + if model_name.endswith(os.sep): + model_name = model_name[:-1] + model_path = model_name + model_name = os.path.basename(model_name) + fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf" + + fout = GGUFWriter(path=fname_out, arch="clip") + fout.add_description("image encoder for Qwen2VL") + + fout.add_file_type(ftype) + fout.add_bool("clip.has_text_encoder", False) + fout.add_bool("clip.has_vision_encoder", True) + fout.add_bool("clip.has_qwen2vl_merger", True) + fout.add_string("clip.projector_type", "qwen2vl_merger") + + print(cfg.vision_config) + if 'silu' in cfg.vision_config.hidden_act.lower(): + fout.add_bool("clip.use_silu", True) + fout.add_bool("clip.use_gelu", False) + elif 'gelu' in cfg.vision_config.hidden_act.lower(): + fout.add_bool("clip.use_silu", False) + fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower()) + else: + raise ValueError() + + tensor_map = find_vision_tensors(qwen2vl, np_dtype) + for name, data in tensor_map.items(): + fout.add_tensor(name, data) + + fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) + fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) + fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) + fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0) # not sure what this does, put 0 here as a placeholder + fout.add_name(model_name) + """ + HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig, + it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`. + """ + + if local_model: + processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path) + else: + processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name) + fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue] + fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue] + + fout.write_header_to_file() + fout.write_kv_data_to_file() + fout.write_tensors_to_file() + fout.close() + print("save model as: ", fname_out) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") + parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") + args = parser.parse_args() + main(args) diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp new file mode 100644 index 0000000000000..132a7da543c2a --- /dev/null +++ b/examples/llava/qwen2vl-cli.cpp @@ -0,0 +1,584 @@ +#include "arg.h" +#include "base64.hpp" +#include "log.h" +#include "common.h" +#include "sampling.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" +#include "ggml.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif +#ifdef NDEBUG +#include "ggml-alloc.h" +#include "ggml-backend.h" +#endif + +#include +#include +#include +#include +#include +#include +#include + + +static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, + int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) { + int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); + const int patch_size = 14 * 2; + const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0); + const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0); + auto img_tokens = image_embed->n_image_pos; + // llama_pos mrope_pos[img_tokens * 4]; + std::vector mrope_pos; + mrope_pos.resize(img_tokens * 4); + + for (int y = 0; y < ph; y++) + { + for (int x = 0; x < pw; x++) + { + int i = y * pw + x; + mrope_pos[i] = *st_pos_id; + mrope_pos[i + img_tokens] = *st_pos_id + y; + mrope_pos[i + img_tokens * 2] = *st_pos_id + x; + mrope_pos[i + img_tokens * 3] = 0; + } + } + *st_pos_id += std::max(pw, ph); + + int processed = 0; + std::vector batch_mrope_pos; + batch_mrope_pos.resize(img_tokens * 4); + + for (int i = 0; i < img_tokens; i += n_batch) { + int n_eval = img_tokens - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + + // llama_pos batch_mrope_pos[n_eval * 4]; + std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0); + memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos)); + + llama_batch batch = { + int32_t(n_eval), // n_tokens + nullptr, // token + (image_embed->embed+i*n_embd), // embed + batch_mrope_pos.data(), // pos + nullptr, // n_seq_id + nullptr, // seq_id + nullptr, // logits + }; + + if (llama_decode(ctx_llama, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + return false; + } + *n_past += n_eval; + processed += n_eval; + } + return true; +} + + +static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past, int * st_pos_id) { + int N = (int) tokens.size(); + std::vector pos; + for (int i = 0; i < N; i += n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + auto batch = llama_batch_get_one(&tokens[i], n_eval); + // TODO: add mrope pos ids somewhere else + pos.resize(batch.n_tokens * 4); + std::fill(pos.begin(), pos.end(), 0); + for (int j = 0; j < batch.n_tokens * 3; j ++) { + pos[j] = *st_pos_id + (j % batch.n_tokens); + } + batch.pos = pos.data(); + + if (llama_decode(ctx_llama, batch)) { + LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + return false; + } + *n_past += n_eval; + *st_pos_id += n_eval; + } + return true; +} + +static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id); +} + +static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){ + std::string str2 = str; + std::vector embd_inp = common_tokenize(ctx_llama, str2, add_bos, true); + eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id); + return true; +} + +static const char * sample(struct common_sampler * smpl, + struct llama_context * ctx_llama, + int * n_past, int * st_pos_id) { + const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); + common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + + static std::string ret; + if (llama_vocab_is_eog(vocab, id)) { + ret = ""; + } else { + ret = common_token_to_piece(ctx_llama, id); + } + eval_id(ctx_llama, id, n_past, st_pos_id); + return ret.c_str(); +} + +static const char* IMG_BASE64_TAG_BEGIN = ""; + +static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) { + begin_out = prompt.find(IMG_BASE64_TAG_BEGIN); + end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out); +} + +static bool prompt_contains_image(const std::string& prompt) { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + return (begin != std::string::npos); +} + +// replaces the base64 image tag in the prompt with `replacement` +static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) { + size_t img_base64_str_start, img_base64_str_end; + find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); + if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { + LOG_ERR("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + return NULL; + } + + auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN); + auto base64_bytes_count = img_base64_str_end - base64_bytes_start; + auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count ); + + auto required_bytes = base64::required_encode_size(base64_str.size()); + auto img_bytes = std::vector(required_bytes); + base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin()); + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); + if (!embed) { + LOG_ERR("%s: could not load image from base64 string.\n", __func__); + return NULL; + } + + return embed; +} + +static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + if (begin == std::string::npos || end == std::string::npos) { + return prompt; + } + auto pre = prompt.substr(0, begin); + auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END)); + return pre + replacement + post; +} + +struct llava_context { + struct clip_ctx * ctx_clip = NULL; + struct llama_context * ctx_llama = NULL; + struct llama_model * model = NULL; +}; + +static void print_usage(int, char ** argv) { + LOG("\n example usage:\n"); + LOG("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); +} + +static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) { + + // load and preprocess the image + llava_image_embed * embed = NULL; + auto prompt = params->prompt; + if (prompt_contains_image(prompt)) { + if (!params->image.empty()) { + LOG_INF("using base64 encoded image instead of command line image path\n"); + } + embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt); + if (!embed) { + LOG_ERR("%s: can't load image from prompt\n", __func__); + return NULL; + } + params->prompt = remove_image_from_prompt(prompt); + } else { + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str()); + if (!embed) { + fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str()); + return NULL; + } + } + + return embed; +} + +static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) { + int n_past = 0; + int cur_pos_id = 0; + + const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; + + std::string system_prompt, user_prompt; + size_t image_pos = prompt.find("<|vision_start|>"); + if (image_pos != std::string::npos) { + // new templating mode: Provide the full prompt including system message and use as a placeholder for the image + system_prompt = prompt.substr(0, image_pos); + user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length()); + LOG_INF("system_prompt: %s\n", system_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + LOG_INF("user_prompt: %s\n", user_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + } else { + // llava-1.5 native mode + system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>"; + user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n"; + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + } + + eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true); + if (image_embed != nullptr) { + auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip); + qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size); + } + eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false); + + // generate the response + + LOG("\n"); + + struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling); + if (!smpl) { + LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } + + std::string response = ""; + for (int i = 0; i < max_tgt_len; i++) { + const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id); + response += tmp; + if (strcmp(tmp, "") == 0) break; + if (strstr(tmp, "###")) break; // Yi-VL behavior + LOG("%s", tmp); + if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works) + if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6 + if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6 + + fflush(stdout); + } + + common_sampler_free(smpl); + LOG("\n"); +} + +static struct llama_model * llava_init(common_params * params) { + llama_backend_init(); + llama_numa_init(params->numa); + + llama_model_params model_params = common_model_params_to_llama(*params); + + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); + if (model == NULL) { + LOG_ERR("%s: unable to load model\n" , __func__); + return NULL; + } + return model; +} + +static struct llava_context * llava_init_context(common_params * params, llama_model * model) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + + llama_context_params ctx_params = common_context_params_to_llama(*params); + ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings + + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); + + if (ctx_llama == NULL) { + LOG_ERR("%s: failed to create the llama_context\n" , __func__); + return NULL; + } + + auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + + ctx_llava->ctx_llama = ctx_llama; + ctx_llava->ctx_clip = ctx_clip; + ctx_llava->model = model; + return ctx_llava; +} + +static void llava_free(struct llava_context * ctx_llava) { + if (ctx_llava->ctx_clip) { + clip_free(ctx_llava->ctx_clip); + ctx_llava->ctx_clip = NULL; + } + + llama_free(ctx_llava->ctx_llama); + llama_model_free(ctx_llava->model); + llama_backend_free(); +} + +#ifndef NDEBUG + +static void debug_test_mrope_2d() { + // 1. Initialize backend + ggml_backend_t backend = NULL; + std::string backend_name = ""; +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + backend_name = "cuda"; + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#endif + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + backend = ggml_backend_cpu_init(); + backend_name = "cpu"; + } + + // Calculate the size needed to allocate + size_t ctx_size = 0; + ctx_size += 2 * ggml_tensor_overhead(); // tensors + // no need to allocate anything else! + + // 2. Allocate `ggml_context` to store tensor data + struct ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors() + }; + struct ggml_context * ctx = ggml_init(params); + + struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + + struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4); + ggml_set_name(pos, "pos"); + ggml_set_input(pos); + + std::vector dummy_q; + dummy_q.resize(128 * 12 * 30); + std::fill(dummy_q.begin(), dummy_q.end(), 0.1); + // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw)); + + std::vector pos_id; + pos_id.resize(30 * 4); + for (int i = 0; i < 30; i ++) { + pos_id[i] = i; + pos_id[i + 30] = i + 10; + pos_id[i + 60] = i + 20; + pos_id[i + 90] = i + 30; + } + int sections[4] = {32, 32, 0, 0}; + + // 4. Allocate a `ggml_backend_buffer` to store all tensors + ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + + // 5. Copy tensor data from main memory (RAM) to backend buffer + ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw)); + ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos)); + + // 6. Create a `ggml_cgraph` for mul_mat operation + struct ggml_cgraph * gf = NULL; + struct ggml_context * ctx_cgraph = NULL; + + // create a temporally context to build the graph + struct ggml_init_params params0 = { + /*.mem_size =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph() + }; + ctx_cgraph = ggml_init(params0); + gf = ggml_new_graph(ctx_cgraph); + + struct ggml_tensor * result0 = ggml_rope_multi( + ctx_cgraph, inp_raw, pos, nullptr, + 128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1, + 0, 1, 32, 1); + + // Add "result" tensor and all of its dependencies to the cgraph + ggml_build_forward_expand(gf, result0); + + // 7. Create a `ggml_gallocr` for cgraph computation + ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend)); + ggml_gallocr_alloc_graph(allocr, gf); + + // 9. Run the computation + int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading + if (ggml_backend_is_cpu(backend)) { + ggml_backend_cpu_set_n_threads(backend, n_threads); + } + ggml_backend_graph_compute(backend, gf); + + // 10. Retrieve results (output tensors) + // in this example, output tensor is always the last tensor in the graph + struct ggml_tensor * result = result0; + // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1]; + float * result_data = (float *)malloc(ggml_nbytes(result)); + // because the tensor data is stored in device buffer, we need to copy it back to RAM + ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result)); + const std::string bin_file = "mrope_2d_" + backend_name +".bin"; + std::ofstream outFile(bin_file, std::ios::binary); + + if (outFile.is_open()) { + outFile.write(reinterpret_cast(result_data), ggml_nbytes(result)); + outFile.close(); + std::cout << "Data successfully written to " + bin_file << std::endl; + } else { + std::cerr << "Error opening file!" << std::endl; + } + + free(result_data); + // 11. Free memory and exit + ggml_free(ctx_cgraph); + ggml_gallocr_free(allocr); + ggml_free(ctx); + ggml_backend_buffer_free(buffer); + ggml_backend_free(backend); +} + +static void debug_dump_img_embed(struct llava_context * ctx_llava) { + int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama)); + int ne = n_embd * 4; + float vals[56 * 56 * 3]; + // float embd[ne]; + std::vector embd; + embd.resize(ne); + + for (int i = 0; i < 56*56; i++) + { + for (int c = 0; c < 3; c++) + vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56); + } + + clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data()); + + std::ofstream outFile("img_embed.bin", std::ios::binary); + if (outFile.is_open()) { + outFile.write(reinterpret_cast(embd.data()), ne * sizeof(float)); + + outFile.close(); + std::cout << "Data successfully written to mrope.bin" << std::endl; + } else { + std::cerr << "Error opening file!" << std::endl; + } +} + +#endif + + +int main(int argc, char ** argv) { + ggml_time_init(); + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) { + return 1; + } + + common_init(); + + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { + print_usage(argc, argv); + return 1; + } + + auto * model = llava_init(¶ms); + if (model == NULL) { + fprintf(stderr, "%s: error: failed to init llava model\n", __func__); + return 1; + } + + if (prompt_contains_image(params.prompt)) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, ""); + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); +#ifndef NDEBUG + } else if (params.image[0].empty()) { + auto ctx_llava = llava_init_context(¶ms, model); + + debug_test_mrope_2d(); + debug_dump_img_embed(ctx_llava); + + llama_perf_context_print(ctx_llava->ctx_llama); + ctx_llava->model = NULL; + llava_free(ctx_llava); +#endif + } else { + for (auto & image : params.image) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, image); + if (!image_embed) { + LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str()); + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } + } + + llama_model_free(model); + + return 0; +} diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt index f0ae5cd89244c..3468613142de0 100644 --- a/examples/lookahead/CMakeLists.txt +++ b/examples/lookahead/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-lookahead) add_executable(${TARGET} lookahead.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookahead/README.md b/examples/lookahead/README.md index a69a471b47d39..aab3cd0ca49b9 100644 --- a/examples/lookahead/README.md +++ b/examples/lookahead/README.md @@ -4,4 +4,4 @@ Demonstration of lookahead decoding technique: https://lmsys.org/blog/2023-11-21-lookahead-decoding/ -More info: https://github.com/ggerganov/llama.cpp/pull/4207 +More info: https://github.com/ggml-org/llama.cpp/pull/4207 diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 8d0ef8b3d75e6..2f0898e6284a0 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -58,8 +58,10 @@ int main(int argc, char ** argv) { // load the target model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // Tokenize the prompt std::vector inp; @@ -147,7 +149,7 @@ int main(int argc, char ** argv) { } // here we keep adding new n-grams as we go - ngram_container ngrams_observed(llama_n_vocab(model), N, G); + ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G); // debug struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1); @@ -297,7 +299,7 @@ int main(int argc, char ** argv) { } fflush(stdout); - if (llama_token_is_eog(model, id)) { + if (llama_vocab_is_eog(vocab, id)) { has_eos = true; } @@ -474,9 +476,6 @@ int main(int argc, char ** argv) { llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); LOG("\n\n"); diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index ef19fe25e31a3..fba78ceda6fd7 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -2,22 +2,22 @@ set(TARGET llama-lookup) add_executable(${TARGET} lookup.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-create) add_executable(${TARGET} lookup-create.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-merge) add_executable(${TARGET} lookup-merge.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-stats) add_executable(${TARGET} lookup-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookup/README.md b/examples/lookup/README.md index 71c345c037a2f..07d73849b0601 100644 --- a/examples/lookup/README.md +++ b/examples/lookup/README.md @@ -8,5 +8,5 @@ The key parameters for lookup decoding are `ngram_min`, `ngram_max` and `n_draft More info: -https://github.com/ggerganov/llama.cpp/pull/4484 -https://github.com/ggerganov/llama.cpp/issues/4226 +https://github.com/ggml-org/llama.cpp/pull/4484 +https://github.com/ggml-org/llama.cpp/issues/4226 diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 7ced0aa971805..3da45ed9e0350 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -1,14 +1,9 @@ #include "arg.h" #include "common.h" #include "ngram-cache.h" -#include "ggml.h" #include "llama.h" -#include -#include -#include #include -#include #include int main(int argc, char ** argv){ @@ -25,16 +20,16 @@ int main(int argc, char ** argv){ // load the model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model_ptr & model = llama_init.model; + llama_context_ptr & ctx = llama_init.context; + GGML_ASSERT(model != nullptr); // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx, params.prompt, true, true); + inp = common_tokenize(ctx.get(), params.prompt, true, true); fprintf(stderr, "%s: tokenization done\n", __func__); - common_ngram_cache ngram_cache; common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str()); diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index dff07c075c47f..fcb289abe0e47 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -30,12 +30,11 @@ int main(int argc, char ** argv){ // load the model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_context_ptr & ctx = llama_init.context; // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx, params.prompt, true, true); + inp = common_tokenize(ctx.get(), params.prompt, true, true); common_ngram_cache ngram_cache_context; common_ngram_cache ngram_cache_dynamic; @@ -66,7 +65,7 @@ int main(int argc, char ** argv){ } const int n_input = inp.size(); - const int n_ctx = llama_n_ctx(ctx); + const int n_ctx = llama_n_ctx(ctx.get()); int n_drafted = 0; int n_accept = 0; @@ -150,9 +149,6 @@ int main(int argc, char ** argv){ LOG_INF("n_accept = %d\n", n_accept); LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); LOG("\n\n"); diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 4d92bb2385358..dbd0444ec8742 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -33,8 +33,10 @@ int main(int argc, char ** argv){ // load the model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // tokenize the prompt std::vector inp; @@ -136,7 +138,7 @@ int main(int argc, char ** argv){ LOG("%s", token_str.c_str()); } - if (llama_token_is_eog(model, id)) { + if (llama_vocab_is_eog(vocab, id)) { has_eos = true; } @@ -243,9 +245,6 @@ int main(int argc, char ** argv){ llama_batch_free(batch_tgt); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); LOG("\n\n"); diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt deleted file mode 100644 index 3b38db292320f..0000000000000 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ /dev/null @@ -1,32 +0,0 @@ -cmake_minimum_required(VERSION 3.12) -project("llama-cli-cmake-pkg" C CXX) -set(TARGET llama-cli-cmake-pkg) - -find_package(Llama 0.0.1 REQUIRED) - -# Bake common functionality in with target. Because applications -# using the relocatable Llama package should be outside of the -# source tree, llama-cli-cmake-pkg pretends the dependencies are built-in. -set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") -add_library(common OBJECT) -file(GLOB _common_files - "${_common_path}/*.h" - "${_common_path}/*.cpp" -) -target_sources(common PRIVATE ${_common_files}) - -# If the common project was part of "llama-cli-cmake-pkg" the transient -# defines would automatically be attached. Because the common func- -# tionality is separate, but dependent upon the defines, it must be -# explicitly extracted from the "llama" target. -# -get_target_property(_llama_transient_defines llama - INTERFACE_COMPILE_DEFINITIONS) - -target_compile_definitions(common PRIVATE "${_llama_transient_defines}") - -add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp) -target_include_directories(${TARGET} PRIVATE ${_common_path}) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md deleted file mode 100644 index 08d83dd08636a..0000000000000 --- a/examples/main-cmake-pkg/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# llama.cpp/example/main-cmake-pkg - -This program builds [llama-cli](../main) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree. - -## Building - -Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions. - -### Considerations - -When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package. - -### Build llama.cpp and install to C:\LlamaCPP directory - -```cmd -git clone https://github.com/ggerganov/llama.cpp -cd llama.cpp -cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64 -cmake --build build --config Release -cmake --install build --prefix C:/LlamaCPP -``` - -### Build llama-cli-cmake-pkg - - -```cmd -cd ..\examples\main-cmake-pkg -cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 -cmake --build build --config Release -cmake --install build --prefix C:/MyLlamaApp -``` diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index 5f6efaa9aa94b..af3d9150f8640 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cli) add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/main/README.md b/examples/main/README.md index 145216938fdb7..f7c2497294ab5 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -1,6 +1,6 @@ # llama.cpp/examples/main -This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. +This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. ## Table of Contents @@ -37,7 +37,7 @@ Once downloaded, place your model in the models folder in llama.cpp. ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```bash -./llama-cli -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 +./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ### Windows: @@ -66,7 +66,7 @@ In this section, we cover the most commonly used options for running the `llama- - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. -- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference. - `-mli, --multiline-input`: Allows you to write or paste multiple lines without ending each in '\' - `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has. - `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance. @@ -121,7 +121,7 @@ When --in-prefix or --in-suffix options are enabled the chat template ( --chat-t ### Chat templates - `--chat-template JINJA_TEMPLATE`: This option sets a custom jinja chat template. It accepts a string, not a file name. Default: template taken from model's metadata. Llama.cpp only supports [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template). These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled. + `--chat-template JINJA_TEMPLATE`: This option sets a custom jinja chat template. It accepts a string, not a file name. Default: template taken from model's metadata. Llama.cpp only supports [some pre-defined templates](https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template). These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled. Example usage: `--chat-template gemma` @@ -131,7 +131,7 @@ During text generation, LLaMA models have a limited context size, which means th ### Context Size -- `-c N, --ctx-size N`: Set the size of the prompt context (default: 0, 0 = loaded from model). The LLaMA models were built with a context of 2048-8192, which will yield the best results on longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context (default: 4096, 0 = loaded from model). If a LLaMA model was built with a longer context, increasing this value will yield the best results on longer input/inference. ### Extended Context Size @@ -177,16 +177,11 @@ Example usage: `--temp 0` - `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled). - `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size). -- `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty. The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1. The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`). -Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases. - -Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl` - ### DRY Repetition Penalty DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)). @@ -270,6 +265,14 @@ Being experimental and unique, XTC is disabled by default. The recommended combi Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1` +### Top-nσ Sampling + +- `--top-nsigma N`: Limit the next token selection to a subset of tokens with pre-softmax logits that are within n * σ less than the max logit (default: -1, -1 = disabled). + +Top-nσ sampling is a text generation method that selects tokens based on a statistical threshold in pre-softmax logits. It works by only sampling from tokens with logits that are within n * σ of the maximum logit. This method helps maintain a stable sampling space regardless of temperature scaling, allowing it to perform well on reasoning tasks even in high temperatures. Without complex probability manipulation, it efficiently filters tokens directly on the pre-softmax logits. A higher value for top-nsigma (e.g., 5) will take more noisy tokens into consideration, while a lower value (e.g., 1) will focous on the more informative region of the sampling space. + +Example usage: `--top-nsigma 1` + ### Logit Bias - `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion. @@ -315,9 +318,9 @@ These options help improve the performance and memory usage of the LLaMA models. ### Batch Size -- `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations. +- `-ub N`, `--ubatch-size N`: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: `512`. -- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`. +- `-b N`, `--batch-size N`: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: `2048`. ### Prompt Caching @@ -348,6 +351,7 @@ These options provide extra functionality and customization when running the LLa - `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated. - `--verbose-prompt`: Print the prompt before generating text. +- `--no-display-prompt`: Don't print prompt at generation. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. - `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index d0c28f317b8c5..cf8659b037ee3 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -4,8 +4,8 @@ #include "log.h" #include "sampling.h" #include "llama.h" +#include "chat.h" -#include #include #include #include @@ -31,6 +31,8 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static const char * DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant"; + static llama_context ** g_ctx; static llama_model ** g_model; static common_sampler ** g_smpl; @@ -83,14 +85,6 @@ static void sigint_handler(int signo) { } #endif -static std::string chat_add_and_format(struct llama_model * model, std::vector & chat_msgs, const std::string & role, const std::string & content) { - common_chat_msg new_msg{role, content}; - auto formatted = common_chat_format_single(model, g_params->chat_template, chat_msgs, new_msg, role == "user"); - chat_msgs.push_back({role, content}); - LOG_DBG("formatted: '%s'\n", formatted.c_str()); - return formatted; -} - int main(int argc, char ** argv) { common_params params; g_params = ¶ms; @@ -145,24 +139,27 @@ int main(int argc, char ** argv) { llama_context * ctx = nullptr; common_sampler * smpl = nullptr; - std::vector chat_msgs; - g_model = &model; g_ctx = &ctx; g_smpl = &smpl; + std::vector chat_msgs; + // load the model and apply lora adapter, if any LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); common_init_result llama_init = common_init_from_params(params); - model = llama_init.model; - ctx = llama_init.context; + model = llama_init.model.get(); + ctx = llama_init.context.get(); if (model == NULL) { LOG_ERR("%s: error: unable to load model\n", __func__); return 1; } + const llama_vocab * vocab = llama_model_get_vocab(model); + auto chat_templates = common_chat_templates_init(model, params.chat_template); + LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads); auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU)); @@ -196,17 +193,33 @@ int main(int argc, char ** argv) { llama_attach_threadpool(ctx, threadpool, threadpool_batch); - const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); if (n_ctx > n_ctx_train) { LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); } + // auto enable conversation mode if chat template is available + const bool has_chat_template = common_chat_templates_was_explicit(chat_templates.get()); + if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) { + if (has_chat_template) { + LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__); + params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + } else { + params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; + } + } + + // in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning + if (params.conversation_mode && !has_chat_template) { + LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__); + } + // print chat template example in conversation mode - if (params.conversation) { + if (params.conversation_mode) { if (params.enable_chat_template) { - LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(model, params.chat_template).c_str()); + LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja).c_str()); } else { LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__); } @@ -241,18 +254,30 @@ int main(int argc, char ** argv) { } } - const bool add_bos = llama_add_bos_token(model); + const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja; if (!llama_model_has_encoder(model)) { - GGML_ASSERT(!llama_add_eos_token(model)); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); } LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos); std::vector embd_inp; + auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) { + common_chat_msg new_msg; + new_msg.role = role; + new_msg.content = content; + auto formatted = common_chat_format_single(chat_templates.get(), chat_msgs, new_msg, role == "user", g_params->use_jinja); + chat_msgs.push_back(new_msg); + LOG_DBG("formatted: '%s'\n", formatted.c_str()); + return formatted; + }; + { - auto prompt = (params.conversation && params.enable_chat_template && !params.prompt.empty()) - ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode + auto prompt = (params.conversation_mode && params.enable_chat_template) + // format the system prompt in conversation mode (fallback to default if empty) + ? chat_add_and_format("system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt) + // otherwise use the prompt as is : params.prompt; if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { LOG_DBG("tokenize the prompt\n"); @@ -269,7 +294,7 @@ int main(int argc, char ** argv) { // Should not run without any tokens if (embd_inp.empty()) { if (add_bos) { - embd_inp.push_back(llama_token_bos(model)); + embd_inp.push_back(llama_vocab_bos(vocab)); LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); } else { LOG_ERR("input is empty\n"); @@ -326,7 +351,7 @@ int main(int argc, char ** argv) { params.n_keep += add_bos; // always keep the BOS token } - if (params.conversation) { + if (params.conversation_mode) { params.interactive_first = true; } @@ -450,7 +475,11 @@ int main(int argc, char ** argv) { #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) LOG_INF( " - Press Ctrl+C to interject at any time.\n"); #endif - LOG_INF( "%s\n", control_message); + LOG_INF( "%s", control_message); + if (params.conversation_mode && params.enable_chat_template && params.prompt.empty()) { + LOG_INF( " - Using default system message. To change it, set a different value via -p PROMPT or -f FILE argument.\n"); + } + LOG_INF("\n"); is_interacting = params.interactive_first; } @@ -476,12 +505,14 @@ int main(int argc, char ** argv) { std::vector embd; - // tokenized antiprompts - std::vector> antiprompt_ids; + // single-token antiprompts + std::vector antiprompt_token; - antiprompt_ids.reserve(params.antiprompt.size()); for (const std::string & antiprompt : params.antiprompt) { - antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true)); + auto ids = ::common_tokenize(ctx, antiprompt, false, true); + if (ids.size() == 1) { + antiprompt_token.push_back(ids[0]); + } } if (llama_model_has_encoder(model)) { @@ -494,8 +525,8 @@ int main(int argc, char ** argv) { } llama_token decoder_start_token_id = llama_model_decoder_start_token(model); - if (decoder_start_token_id == -1) { - decoder_start_token_id = llama_token_bos(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = llama_vocab_bos(vocab); } embd_inp.clear(); @@ -726,8 +757,8 @@ int main(int argc, char ** argv) { // check for reverse prompt using special tokens llama_token last_token = common_sampler_last(smpl); - for (std::vector ids : antiprompt_ids) { - if (ids.size() == 1 && last_token == ids[0]) { + for (auto token : antiprompt_token) { + if (token == last_token) { if (params.interactive) { is_interacting = true; } @@ -742,7 +773,7 @@ int main(int argc, char ** argv) { } // deal with end of generation tokens in interactive mode - if (llama_token_is_eog(model, common_sampler_last(smpl))) { + if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) { LOG_DBG("found an EOG token\n"); if (params.interactive) { @@ -754,7 +785,7 @@ int main(int argc, char ** argv) { } if (params.enable_chat_template) { - chat_add_and_format(model, chat_msgs, "assistant", assistant_ss.str()); + chat_add_and_format("assistant", assistant_ss.str()); } is_interacting = true; LOG("\n"); @@ -762,7 +793,7 @@ int main(int argc, char ** argv) { } // if current token is not EOG, we add it to current assistant message - if (params.conversation) { + if (params.conversation_mode) { const auto id = common_sampler_last(smpl); assistant_ss << common_token_to_piece(ctx, id, false); } @@ -770,17 +801,17 @@ int main(int argc, char ** argv) { if (n_past > 0 && is_interacting) { LOG_DBG("waiting for user input\n"); - if (params.conversation) { + if (params.conversation_mode) { LOG("\n> "); } if (params.input_prefix_bos) { LOG_DBG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(model)); + embd_inp.push_back(llama_vocab_bos(vocab)); } std::string buffer; - if (!params.input_prefix.empty() && !params.conversation) { + if (!params.input_prefix.empty() && !params.conversation_mode) { LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str()); LOG("%s", params.input_prefix.c_str()); } @@ -804,7 +835,7 @@ int main(int argc, char ** argv) { // Entering a empty line lets the user pass control back if (buffer.length() > 1) { // append input suffix if any - if (!params.input_suffix.empty() && !params.conversation) { + if (!params.input_suffix.empty() && !params.conversation_mode) { LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str()); LOG("%s", params.input_suffix.c_str()); } @@ -817,9 +848,9 @@ int main(int argc, char ** argv) { string_process_escapes(buffer); } - bool format_chat = params.conversation && params.enable_chat_template; + bool format_chat = params.conversation_mode && params.enable_chat_template; std::string user_inp = format_chat - ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer)) + ? chat_add_and_format("user", std::move(buffer)) : std::move(buffer); // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix) const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true); @@ -830,8 +861,8 @@ int main(int argc, char ** argv) { // if user stop generation mid-way, we must add EOT to finish model's last response if (need_insert_eot && format_chat) { - llama_token eot = llama_token_eot(model); - embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot); + llama_token eot = llama_vocab_eot(vocab); + embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot); need_insert_eot = false; } @@ -866,7 +897,7 @@ int main(int argc, char ** argv) { } // end of generation - if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) { + if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) { LOG(" [end of text]\n"); break; } @@ -889,9 +920,6 @@ int main(int argc, char ** argv) { common_sampler_free(smpl); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); ggml_threadpool_free_fn(threadpool); diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index c13557bac2bac..847e916de6ed8 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-parallel) add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index fd2b1c0112838..7ef43d5e12876 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -132,8 +132,10 @@ int main(int argc, char ** argv) { // load the target model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // load the prompts from an external file if there are any if (params.prompt.empty()) { @@ -358,7 +360,7 @@ int main(int argc, char ** argv) { // client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str()); if (client.n_decoded > 2 && - (llama_token_is_eog(model, id) || + (llama_vocab_is_eog(vocab, id) || (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) || client.response.find("User:") != std::string::npos || client.response.find('\n') != std::string::npos)) { @@ -416,9 +418,6 @@ int main(int argc, char ** argv) { llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); LOG("\n\n"); diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt index dc467a5d3e411..9bc5110c29309 100644 --- a/examples/passkey/CMakeLists.txt +++ b/examples/passkey/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-passkey) add_executable(${TARGET} passkey.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/passkey/README.md b/examples/passkey/README.md index 2b8e910f9658d..2f19597c48d7f 100644 --- a/examples/passkey/README.md +++ b/examples/passkey/README.md @@ -5,8 +5,8 @@ models ability to recall information from long contexts. See the following PRs for more info: -- https://github.com/ggerganov/llama.cpp/pull/3856 -- https://github.com/ggerganov/llama.cpp/pull/4810 +- https://github.com/ggml-org/llama.cpp/pull/3856 +- https://github.com/ggml-org/llama.cpp/pull/4810 ### Usage diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 09bba708f6f91..5953928d47d33 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -63,22 +63,24 @@ int main(int argc, char ** argv) { llama_model_params model_params = common_model_params_to_llama(params); - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { LOG_ERR("%s: unable to load model\n" , __func__); return 1; } + const llama_vocab * vocab = llama_model_get_vocab(model); + // initialize the context llama_context_params ctx_params = common_context_params_to_llama(params); - ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; + ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep; GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp"); - llama_context * ctx = llama_new_context_with_model(model, ctx_params); + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { LOG_ERR("%s: failed to create the llama_context\n" , __func__); return 1; @@ -223,7 +225,7 @@ int main(int argc, char ** argv) { const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1); // is it an end of generation? - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) { LOG("\n"); break; @@ -266,7 +268,7 @@ int main(int argc, char ** argv) { llama_batch_free(batch); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index be0f2fd029e67..3e68640933afb 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-perplexity) add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 64a84607c22d8..5d07421e827d1 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -3,6 +3,7 @@ #include "log.h" #include "llama.h" +#include #include #include #include @@ -296,8 +297,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); LOG_INF("%s: tokenizing the input ..\n", __func__); @@ -338,7 +342,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); const int n_batch = params.n_batch; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); int count = 0; double nll = 0.0; @@ -382,7 +386,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } const auto * batch_logits = llama_get_logits(ctx); @@ -444,8 +448,11 @@ static results_perplexity perplexity(llama_context * ctx, const common_params & // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); std::ofstream logits_stream; if (!params.logits_file.empty()) { @@ -485,7 +492,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params & const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); const int n_batch = params.n_batch; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); int count = 0; double nll = 0.0; @@ -557,7 +564,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params & // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[seq_start] = llama_token_bos(llama_get_model(ctx)); + tokens[seq_start] = llama_vocab_bos(vocab); } for (int k = 0; k < batch_size; ++k) { @@ -732,6 +739,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto } static void hellaswag_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + // Calculates hellaswag score (acc_norm) from prompt // // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl @@ -765,7 +775,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) { size_t hs_task_count = prompt_lines.size()/6; LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); - const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM; + const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM; LOG_INF("================================= is_spm = %d\n", is_spm); // The tasks should be randomized so the score stabilizes quickly. @@ -848,7 +858,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) { const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); const int max_tasks_per_batch = 32; const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); @@ -1072,6 +1082,8 @@ static std::vector load_winogrande_from_csv(const std::string * */ static void winogrande_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); constexpr int k_min_trailing_ctx = 3; @@ -1130,7 +1142,7 @@ static void winogrande_score(llama_context * ctx, const common_params & params) const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); const int max_tasks_per_batch = 128; const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); @@ -1374,6 +1386,8 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic // https://huggingface.co/datasets/truthful_qa // static void multiple_choice_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); std::istringstream strstream(params.prompt); uint32_t n_task; @@ -1482,7 +1496,7 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); const int max_tasks_per_batch = 32; const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); @@ -1655,6 +1669,9 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par } static void kl_divergence(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + if (params.logits_file.empty()) { LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__); return; @@ -1688,8 +1705,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); return; } - if (n_vocab != llama_n_vocab(llama_get_model(ctx))) { - LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx))); + if (n_vocab != llama_vocab_n_tokens(vocab)) { + LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab)); } std::vector tokens(size_t(n_ctx) * n_chunk); @@ -1701,8 +1718,8 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { const int n_batch = params.n_batch; const int num_batches = (n_ctx + n_batch - 1)/n_batch; const int nv = 2*((n_vocab + 1)/2) + 4; - const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); @@ -1761,7 +1778,7 @@ static void kl_divergence(llama_context * ctx, const common_params & params) { // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } common_batch_clear(batch); @@ -1987,14 +2004,15 @@ int main(int argc, char ** argv) { // load the model and apply lora adapter, if any common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx_train = llama_model_n_ctx_train(model); if (params.n_ctx > n_ctx_train) { LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", @@ -2023,9 +2041,6 @@ int main(int argc, char ** argv) { LOG("\n"); llama_perf_context_print(ctx); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); return 0; diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py index eb000d5ccba24..f94b82ca47570 100755 --- a/examples/pydantic_models_to_grammar_examples.py +++ b/examples/pydantic_models_to_grammar_examples.py @@ -23,7 +23,7 @@ def create_completion(host, prompt, gbnf_grammar): """Calls the /completion API on llama-server. See - https://github.com/ggerganov/llama.cpp/tree/HEAD/examples/server#api-endpoints + https://github.com/ggml-org/llama.cpp/tree/HEAD/examples/server#api-endpoints """ print(f" Request:\n Grammar:\n{textwrap.indent(gbnf_grammar, ' ')}\n Prompt:\n{textwrap.indent(prompt.rstrip(), ' ')}") headers = {"Content-Type": "application/json"} diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index bb986a716883d..9a3a0d3cd2dee 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 912caf346e75e..bd2f734670de8 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -1,7 +1,7 @@ -#include "common.h" #include "ggml.h" #include "llama.h" -#include "llama-impl.h" +#include "llama-context.h" +#include "common.h" #include #include @@ -9,11 +9,9 @@ #include #include #include -#include #include #include #include -#include #include #include #include @@ -311,7 +309,7 @@ int main(int argc, char ** argv) { auto mparams = llama_model_default_params(); mparams.use_mlock = false; - model = llama_load_model_from_file(params.model.c_str(), mparams); + model = llama_model_load_from_file(params.model.c_str(), mparams); if (model == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); @@ -321,22 +319,22 @@ int main(int argc, char ** argv) { auto cparams = llama_context_default_params(); cparams.n_ctx = 256; - ctx = llama_new_context_with_model(model, cparams); + ctx = llama_init_from_model(model, cparams); if (ctx == NULL) { fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); + llama_model_free(model); return 1; } } - const auto &tensors = llama_internal_get_tensor_map(ctx); + const auto & tensors = llama_internal_get_tensor_map(ctx); // check layer tensors int included_layers = 0; int64_t max_nelements = 0; bool is_f16 = false; - for (const auto& kv_tensor : tensors) { + for (const auto & kv_tensor : tensors) { if (!layer_included(params, kv_tensor.first)) { continue; } @@ -349,7 +347,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: error: Quantization should be tested with a float model, " "this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); return 1; } included_layers++; @@ -371,8 +369,8 @@ int main(int argc, char ** argv) { if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) { continue; } - const auto * qfns = ggml_get_type_traits(type); - const auto * qfns_cpu = ggml_get_type_traits_cpu(type); + const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); if (qfns_cpu->from_float && qfns->to_float) { if (params.verbose) { printf("testing %s ...\n", ggml_type_name(type)); @@ -382,7 +380,7 @@ int main(int argc, char ** argv) { error_stats global_stats {}; - for (const auto& kv_tensor : tensors) { + for (const auto & kv_tensor : tensors) { if (!layer_included(params, kv_tensor.first)) { continue; } @@ -411,7 +409,7 @@ int main(int argc, char ** argv) { llama_free(ctx); - llama_free_model(model); + llama_model_free(model); // report timing { const int64_t t_main_end_us = ggml_time_us(); diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 62680cda4455f..47e5cbe30cfe3 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/quantize/README.md b/examples/quantize/README.md index 704f0d56bea72..992d00e21b4fe 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -54,8 +54,6 @@ As the models are currently fully loaded into memory, you will need adequate dis Several quantization methods are supported. They differ in the resulting model disk size and inference speed. -The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interleaved variants of the `Q4_0` format, providing a data layout that is better suited for specific implementations of optimized mulmat kernels. Since these formats differ only in data layout, they have the same quantized size as the `Q4_0` format. - *(outdated)* | Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | @@ -71,22 +69,22 @@ The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interle | 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 | | 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | -- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) +- [k-quants](https://github.com/ggml-org/llama.cpp/pull/1684) - recent k-quants improvements and new i-quants - - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) - - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) - - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) - - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) - - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) - - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) - - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) - - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) - - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) - - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) - - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) - - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) - - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) + - [#2707](https://github.com/ggml-org/llama.cpp/pull/2707) + - [#2807](https://github.com/ggml-org/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggml-org/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggml-org/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggml-org/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggml-org/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggml-org/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggml-org/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggml-org/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggml-org/llama.cpp/pull/4969) + - [#4996 - k-quants tuning](https://github.com/ggml-org/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggml-org/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggml-org/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggml-org/llama.cpp/pull/5320), [another one](https://github.com/ggml-org/llama.cpp/pull/5334), and [another one](https://github.com/ggml-org/llama.cpp/pull/5361) **Llama 2 7B** diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index b989932107dba..8d47b17b6bce7 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -48,9 +48,6 @@ static const std::vector QUANT_OPTIONS = { { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", }, { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", }, { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", }, - { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, - { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, - { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, { "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", }, { "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", }, { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh index 24bc970e8632b..70f7610f9877f 100644 --- a/examples/quantize/tests.sh +++ b/examples/quantize/tests.sh @@ -47,7 +47,7 @@ echo PASS echo # 3a. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -57,7 +57,7 @@ echo PASS echo # 4b. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 +$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 echo PASS echo diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt index 66610f3111405..512a602ec045c 100644 --- a/examples/retrieval/CMakeLists.txt +++ b/examples/retrieval/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-retrieval) add_executable(${TARGET} retrieval.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md index bc5f22e2ff156..6938a1e96ee35 100644 --- a/examples/retrieval/README.md +++ b/examples/retrieval/README.md @@ -3,7 +3,7 @@ Demonstration of simple retrieval technique based on cosine similarity More info: -https://github.com/ggerganov/llama.cpp/pull/6193 +https://github.com/ggml-org/llama.cpp/pull/6193 ### How to use diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index e78a8596d8cfe..2439022a229b7 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; - common_embd_normalize(embd, out, n_embd); + common_embd_normalize(embd, out, n_embd, 2); } } @@ -143,7 +143,7 @@ int main(int argc, char ** argv) { std::vector file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator); chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end()); } - LOG_INF("Number of chunks: %ld\n", chunks.size()); + LOG_INF("Number of chunks: %zu\n", chunks.size()); llama_backend_init(); llama_numa_init(params.numa); @@ -151,15 +151,17 @@ int main(int argc, char ** argv) { // load the model common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); @@ -192,8 +194,8 @@ int main(int argc, char ** argv) { return 1; } // add eos if not present - if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) { - inp.push_back(llama_token_eos(model)); + if (llama_vocab_eos(vocab) >= 0 && (inp.empty() || inp.back() != llama_vocab_eos(vocab))) { + inp.push_back(llama_vocab_eos(vocab)); } chunk.tokens = inp; } @@ -215,7 +217,7 @@ int main(int argc, char ** argv) { struct llama_batch batch = llama_batch_init(n_batch, 0, 1); // allocate output - const int n_embd = llama_n_embd(model); + const int n_embd = llama_model_n_embd(model); std::vector embeddings(n_chunks * n_embd, 0); float * emb = embeddings.data(); @@ -298,7 +300,5 @@ int main(int argc, char ** argv) { // clean up llama_batch_free(query_batch); - llama_free(ctx); - llama_free_model(model); llama_backend_free(); } diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 5fe70dac7f193..8b1b23edad174 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -12,6 +12,10 @@ #include "ggml-vulkan.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #include "ggml-rpc.h" #ifdef _WIN32 # include @@ -91,6 +95,12 @@ static ggml_backend_t create_backend() { if (!backend) { fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__); } +#elif GGML_USE_SYCL + fprintf(stderr, "%s: using SYCL backend\n", __func__); + backend = ggml_backend_sycl_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); + } #endif // if there aren't GPU Backends fallback to CPU backend @@ -106,6 +116,8 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) { ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); #elif GGML_USE_VULKAN ggml_backend_vk_get_device_memory(0, free_mem, total_mem); +#elif GGML_USE_SYCL + ggml_backend_sycl_get_device_memory(0, free_mem, total_mem); #else #ifdef _WIN32 MEMORYSTATUSEX status; diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt new file mode 100644 index 0000000000000..cd6b0520e08bb --- /dev/null +++ b/examples/run/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-run) +add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/run/README.md b/examples/run/README.md new file mode 100644 index 0000000000000..89a5520798661 --- /dev/null +++ b/examples/run/README.md @@ -0,0 +1,50 @@ +# llama.cpp/example/run + +The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models. + +```bash +llama-run granite3-moe +``` + +```bash +Description: + Runs a llm + +Usage: + llama-run [options] model [prompt] + +Options: + -c, --context-size + Context size (default: 2048) + -n, -ngl, --ngl + Number of GPU layers (default: 0) + --temp + Temperature (default: 0.8) + -v, --verbose, --log-verbose + Set verbosity level to infinity (i.e. log all messages, useful for debugging) + -h, --help + Show help message + +Commands: + model + Model is a string with an optional prefix of + huggingface:// (hf://), ollama://, https:// or file://. + If no protocol is specified and a file exists in the specified + path, file:// is assumed, otherwise if a file does not exist in + the specified path, ollama:// is assumed. Models that are being + pulled are downloaded with .partial extension while being + downloaded and then renamed as the file without the .partial + extension when complete. + +Examples: + llama-run llama3 + llama-run ollama://granite-code + llama-run ollama://smollm:135m + llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf + llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf + llama-run https://example.com/some-file1.gguf + llama-run some-file2.gguf + llama-run file://some-file3.gguf + llama-run --ngl 999 some-file4.gguf + llama-run --ngl 999 some-file5.gguf Hello World +``` diff --git a/examples/run/linenoise.cpp/LICENSE b/examples/run/linenoise.cpp/LICENSE new file mode 100644 index 0000000000000..b006b3b24dcf7 --- /dev/null +++ b/examples/run/linenoise.cpp/LICENSE @@ -0,0 +1,26 @@ +Copyright (c) 2010-2014, Salvatore Sanfilippo +Copyright (c) 2010-2013, Pieter Noordhuis +Copyright (c) 2025, Eric Curtin + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/examples/run/linenoise.cpp/linenoise.cpp b/examples/run/linenoise.cpp/linenoise.cpp new file mode 100644 index 0000000000000..a68f12a1a0733 --- /dev/null +++ b/examples/run/linenoise.cpp/linenoise.cpp @@ -0,0 +1,1350 @@ +#ifndef _WIN32 +/* + * You can find the latest source code at: + * + * http://github.com/ericcurtin/linenoise.cpp + * + * Does a number of crazy assumptions that happen to be true in 99.9999% of + * the 2010 UNIX computers around. + * + * ------------------------------------------------------------------------ + * + * Copyright (c) 2010-2023, Salvatore Sanfilippo + * Copyright (c) 2010-2013, Pieter Noordhuis + * Copyright (c) 2025, Eric Curtin + * + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * ------------------------------------------------------------------------ + * + * References: + * - http://invisible-island.net/xterm/ctlseqs/ctlseqs.html + * - http://www.3waylabs.com/nw/WWW/products/wizcon/vt220.html + * + * Todo list: + * - Filter bogus Ctrl+ combinations. + * - Win32 support + * + * Bloat: + * - History search like Ctrl+r in readline? + * + * List of escape sequences used by this program, we do everything just + * with three sequences. In order to be so cheap we may have some + * flickering effect with some slow terminal, but the lesser sequences + * the more compatible. + * + * EL (Erase Line) + * Sequence: ESC [ n K + * Effect: if n is 0 or missing, clear from cursor to end of line + * Effect: if n is 1, clear from beginning of line to cursor + * Effect: if n is 2, clear entire line + * + * CUF (CUrsor Forward) + * Sequence: ESC [ n C + * Effect: moves cursor forward n chars + * + * CUB (CUrsor Backward) + * Sequence: ESC [ n D + * Effect: moves cursor backward n chars + * + * The following is used to get the terminal width if getting + * the width with the TIOCGWINSZ ioctl fails + * + * DSR (Device Status Report) + * Sequence: ESC [ 6 n + * Effect: reports the current cusor position as ESC [ n ; m R + * where n is the row and m is the column + * + * When multi line mode is enabled, we also use an additional escape + * sequence. However multi line editing is disabled by default. + * + * CUU (Cursor Up) + * Sequence: ESC [ n A + * Effect: moves cursor up of n chars. + * + * CUD (Cursor Down) + * Sequence: ESC [ n B + * Effect: moves cursor down of n chars. + * + * When linenoiseClearScreen() is called, two additional escape sequences + * are used in order to clear the screen and position the cursor at home + * position. + * + * CUP (Cursor position) + * Sequence: ESC [ H + * Effect: moves the cursor to upper left corner + * + * ED (Erase display) + * Sequence: ESC [ 2 J + * Effect: clear the whole screen + * + */ + +# include "linenoise.h" + +# include +# include +# include +# include +# include +# include +# include +# include +# include +# include + +# include +# include +# include + +# define LINENOISE_DEFAULT_HISTORY_MAX_LEN 100 +# define LINENOISE_MAX_LINE 4096 +static std::vector unsupported_term = { "dumb", "cons25", "emacs" }; +static linenoiseCompletionCallback *completionCallback = NULL; +static linenoiseHintsCallback *hintsCallback = NULL; +static linenoiseFreeHintsCallback *freeHintsCallback = NULL; +static char *linenoiseNoTTY(void); +static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags); +static void refreshLineWithFlags(struct linenoiseState *l, int flags); + +static struct termios orig_termios; /* In order to restore at exit.*/ +static int maskmode = 0; /* Show "***" instead of input. For passwords. */ +static int rawmode = 0; /* For atexit() function to check if restore is needed*/ +static int mlmode = 0; /* Multi line mode. Default is single line. */ +static int atexit_registered = 0; /* Register atexit just 1 time. */ +static int history_max_len = LINENOISE_DEFAULT_HISTORY_MAX_LEN; +static int history_len = 0; +static char **history = NULL; + +enum KEY_ACTION{ + KEY_NULL = 0, /* NULL */ + CTRL_A = 1, /* Ctrl+a */ + CTRL_B = 2, /* Ctrl-b */ + CTRL_C = 3, /* Ctrl-c */ + CTRL_D = 4, /* Ctrl-d */ + CTRL_E = 5, /* Ctrl-e */ + CTRL_F = 6, /* Ctrl-f */ + CTRL_H = 8, /* Ctrl-h */ + TAB = 9, /* Tab */ + CTRL_K = 11, /* Ctrl+k */ + CTRL_L = 12, /* Ctrl+l */ + ENTER = 13, /* Enter */ + CTRL_N = 14, /* Ctrl-n */ + CTRL_P = 16, /* Ctrl-p */ + CTRL_T = 20, /* Ctrl-t */ + CTRL_U = 21, /* Ctrl+u */ + CTRL_W = 23, /* Ctrl+w */ + ESC = 27, /* Escape */ + BACKSPACE = 127 /* Backspace */ +}; + +static void linenoiseAtExit(void); +int linenoiseHistoryAdd(const char *line); +#define REFRESH_CLEAN (1<<0) // Clean the old prompt from the screen +#define REFRESH_WRITE (1<<1) // Rewrite the prompt on the screen. +#define REFRESH_ALL (REFRESH_CLEAN|REFRESH_WRITE) // Do both. +static void refreshLine(struct linenoiseState *l); + +class File { + public: + FILE * file = nullptr; + + FILE * open(const std::string & filename, const char * mode) { + file = fopen(filename.c_str(), mode); + + return file; + } + + int lock() { + if (file) { + fd = fileno(file); + if (flock(fd, LOCK_EX | LOCK_NB) != 0) { + fd = -1; + + return 1; + } + } + + return 0; + } + + ~File() { + if (fd >= 0) { + flock(fd, LOCK_UN); + } + + if (file) { + fclose(file); + } + } + + private: + int fd = -1; +}; + +__attribute__((format(printf, 1, 2))) +/* Debugging function. */ +#if 0 +static void lndebug(const char *fmt, ...) { + static File file; + if (file.file == nullptr) { + file.open("/tmp/lndebug.txt", "a"); + } + + if (file.file != nullptr) { + va_list args; + va_start(args, fmt); + vfprintf(file.file, fmt, args); + va_end(args); + fflush(file.file); + } +} +#else +static void lndebug(const char *, ...) { +} +#endif + +/* ======================= Low level terminal handling ====================== */ + +/* Enable "mask mode". When it is enabled, instead of the input that + * the user is typing, the terminal will just display a corresponding + * number of asterisks, like "****". This is useful for passwords and other + * secrets that should not be displayed. */ +void linenoiseMaskModeEnable(void) { + maskmode = 1; +} + +/* Disable mask mode. */ +void linenoiseMaskModeDisable(void) { + maskmode = 0; +} + +/* Set if to use or not the multi line mode. */ +void linenoiseSetMultiLine(int ml) { + mlmode = ml; +} + +/* Return true if the terminal name is in the list of terminals we know are + * not able to understand basic escape sequences. */ +static int isUnsupportedTerm(void) { + char *term = getenv("TERM"); + if (term == NULL) return 0; + for (size_t j = 0; j < unsupported_term.size(); ++j) { + if (!strcasecmp(term, unsupported_term[j])) { + return 1; + } + } + return 0; +} + +/* Raw mode: 1960 magic shit. */ +static int enableRawMode(int fd) { + struct termios raw; + + if (!isatty(STDIN_FILENO)) goto fatal; + if (!atexit_registered) { + atexit(linenoiseAtExit); + atexit_registered = 1; + } + if (tcgetattr(fd,&orig_termios) == -1) goto fatal; + + raw = orig_termios; /* modify the original mode */ + /* input modes: no break, no CR to NL, no parity check, no strip char, + * no start/stop output control. */ + raw.c_iflag &= ~(BRKINT | ICRNL | INPCK | ISTRIP | IXON); + /* output modes - disable post processing */ + raw.c_oflag &= ~(OPOST); + /* control modes - set 8 bit chars */ + raw.c_cflag |= (CS8); + /* local modes - choing off, canonical off, no extended functions, + * no signal chars (^Z,^C) */ + raw.c_lflag &= ~(ECHO | ICANON | IEXTEN | ISIG); + /* control chars - set return condition: min number of bytes and timer. + * We want read to return every single byte, without timeout. */ + raw.c_cc[VMIN] = 1; raw.c_cc[VTIME] = 0; /* 1 byte, no timer */ + + /* put terminal in raw mode after flushing */ + if (tcsetattr(fd,TCSAFLUSH,&raw) < 0) goto fatal; + rawmode = 1; + return 0; + +fatal: + errno = ENOTTY; + return -1; +} + +static void disableRawMode(int fd) { + /* Don't even check the return value as it's too late. */ + if (rawmode && tcsetattr(fd,TCSAFLUSH,&orig_termios) != -1) + rawmode = 0; +} + +/* Use the ESC [6n escape sequence to query the horizontal cursor position + * and return it. On error -1 is returned, on success the position of the + * cursor. */ +static int getCursorPosition(int ifd, int ofd) { + char buf[32]; + int cols, rows; + unsigned int i = 0; + + /* Report cursor location */ + if (write(ofd, "\x1b[6n", 4) != 4) return -1; + + /* Read the response: ESC [ rows ; cols R */ + while (i < sizeof(buf)-1) { + if (read(ifd,buf+i,1) != 1) break; + if (buf[i] == 'R') break; + i++; + } + buf[i] = '\0'; + + /* Parse it. */ + if (buf[0] != ESC || buf[1] != '[') return -1; + if (sscanf(buf+2,"%d;%d",&rows,&cols) != 2) return -1; + return cols; +} + +/* Try to get the number of columns in the current terminal, or assume 80 + * if it fails. */ +static int getColumns(int ifd, int ofd) { + struct winsize ws; + + if (ioctl(1, TIOCGWINSZ, &ws) == -1 || ws.ws_col == 0) { + /* ioctl() failed. Try to query the terminal itself. */ + int start, cols; + + /* Get the initial position so we can restore it later. */ + start = getCursorPosition(ifd,ofd); + if (start == -1) goto failed; + + /* Go to right margin and get position. */ + if (write(ofd,"\x1b[999C",6) != 6) goto failed; + cols = getCursorPosition(ifd,ofd); + if (cols == -1) goto failed; + + /* Restore position. */ + if (cols > start) { + char seq[32]; + snprintf(seq,32,"\x1b[%dD",cols-start); + if (write(ofd,seq,strlen(seq)) == -1) { + /* Can't recover... */ + } + } + return cols; + } else { + return ws.ws_col; + } + +failed: + return 80; +} + +/* Clear the screen. Used to handle ctrl+l */ +void linenoiseClearScreen(void) { + if (write(STDOUT_FILENO,"\x1b[H\x1b[2J",7) <= 0) { + /* nothing to do, just to avoid warning. */ + } +} + +/* Beep, used for completion when there is nothing to complete or when all + * the choices were already shown. */ +static void linenoiseBeep(void) { + fprintf(stderr, "\x7"); + fflush(stderr); +} + +/* Called by completeLine() and linenoiseShow() to render the current + * edited line with the proposed completion. If the current completion table + * is already available, it is passed as second argument, otherwise the + * function will use the callback to obtain it. + * + * Flags are the same as refreshLine*(), that is REFRESH_* macros. */ +static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags) { + /* Obtain the table of completions if the caller didn't provide one. */ + linenoiseCompletions ctable; + if (lc == NULL) { + completionCallback(ls->buf, &ctable); + lc = &ctable; + } + + /* Show the edited line with completion if possible, or just refresh. */ + if (ls->completion_idx < lc->len) { + struct linenoiseState saved = *ls; + ls->len = ls->pos = strlen(lc->cvec[ls->completion_idx]); + ls->buf = lc->cvec[ls->completion_idx]; + refreshLineWithFlags(ls, flags); + ls->len = saved.len; + ls->pos = saved.pos; + ls->buf = saved.buf; + } else { + refreshLineWithFlags(ls, flags); + } + + if (lc == &ctable) { + ctable.to_free = false; + } +} + +/* This is an helper function for linenoiseEdit*() and is called when the + * user types the key in order to complete the string currently in the + * input. + * + * The state of the editing is encapsulated into the pointed linenoiseState + * structure as described in the structure definition. + * + * If the function returns non-zero, the caller should handle the + * returned value as a byte read from the standard input, and process + * it as usually: this basically means that the function may return a byte + * read from the termianl but not processed. Otherwise, if zero is returned, + * the input was consumed by the completeLine() function to navigate the + * possible completions, and the caller should read for the next characters + * from stdin. */ +static int completeLine(struct linenoiseState *ls, int keypressed) { + linenoiseCompletions lc; + int nwritten; + char c = keypressed; + + completionCallback(ls->buf, &lc); + if (lc.len == 0) { + linenoiseBeep(); + ls->in_completion = 0; + } else { + switch(c) { + case 9: /* tab */ + if (ls->in_completion == 0) { + ls->in_completion = 1; + ls->completion_idx = 0; + } else { + ls->completion_idx = (ls->completion_idx + 1) % (lc.len + 1); + if (ls->completion_idx == lc.len) linenoiseBeep(); + } + c = 0; + break; + case 27: /* escape */ + /* Re-show original buffer */ + if (ls->completion_idx < lc.len) refreshLine(ls); + ls->in_completion = 0; + c = 0; + break; + default: + /* Update buffer and return */ + if (ls->completion_idx < lc.len) { + nwritten = snprintf(ls->buf, ls->buflen, "%s", lc.cvec[ls->completion_idx]); + ls->len = ls->pos = nwritten; + } + ls->in_completion = 0; + break; + } + + /* Show completion or original buffer */ + if (ls->in_completion && ls->completion_idx < lc.len) { + refreshLineWithCompletion(ls, &lc, REFRESH_ALL); + } else { + refreshLine(ls); + } + } + + return c; /* Return last read character */ +} + +/* Register a callback function to be called for tab-completion. */ +void linenoiseSetCompletionCallback(linenoiseCompletionCallback *fn) { + completionCallback = fn; +} + +/* Register a hits function to be called to show hits to the user at the + * right of the prompt. */ +void linenoiseSetHintsCallback(linenoiseHintsCallback *fn) { + hintsCallback = fn; +} + +/* Register a function to free the hints returned by the hints callback + * registered with linenoiseSetHintsCallback(). */ +void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *fn) { + freeHintsCallback = fn; +} + +/* This function is used by the callback function registered by the user + * in order to add completion options given the input string when the + * user typed . See the example.c source code for a very easy to + * understand example. */ +void linenoiseAddCompletion(linenoiseCompletions *lc, const char *str) { + const size_t len = strlen(str); + auto copy = std::make_unique(len + 1); + if (!copy) { + return; + } + + memcpy(copy.get(), str, len + 1); + char ** cvec = static_cast(std::realloc(lc->cvec, sizeof(char *) * (lc->len + 1))); + if (cvec == nullptr) { + return; + } + + lc->cvec = cvec; + lc->cvec[lc->len++] = copy.release(); +} + +/* Helper of refreshSingleLine() and refreshMultiLine() to show hints + * to the right of the prompt. */ +static void refreshShowHints(std::string & ab, struct linenoiseState * l, int plen) { + char seq[64]; + if (hintsCallback && plen+l->len < l->cols) { + int color = -1, bold = 0; + const char *hint = hintsCallback(l->buf,&color,&bold); + if (hint) { + int hintlen = strlen(hint); + int hintmaxlen = l->cols-(plen+l->len); + if (hintlen > hintmaxlen) hintlen = hintmaxlen; + if (bold == 1 && color == -1) color = 37; + if (color != -1 || bold != 0) + snprintf(seq,64,"\033[%d;%d;49m",bold,color); + else + seq[0] = '\0'; + ab.append(seq); + ab.append(hint, hintlen); + if (color != -1 || bold != 0) + ab.append("\033[0m"); + + /* Call the function to free the hint returned. */ + if (freeHintsCallback) freeHintsCallback(hint); + } + } +} + +/* Single line low level line refresh. + * + * Rewrite the currently edited line accordingly to the buffer content, + * cursor position, and number of columns of the terminal. + * + * Flags is REFRESH_* macros. The function can just remove the old + * prompt, just write it, or both. */ +static void refreshSingleLine(struct linenoiseState *l, int flags) { + char seq[64]; + size_t plen = strlen(l->prompt); + int fd = l->ofd; + char *buf = l->buf; + size_t len = l->len; + size_t pos = l->pos; + std::string ab; + while((plen+pos) >= l->cols) { + buf++; + len--; + pos--; + } + while (plen+len > l->cols) { + len--; + } + + /* Cursor to left edge */ + snprintf(seq,sizeof(seq),"\r"); + ab.append(seq); + + if (flags & REFRESH_WRITE) { + /* Write the prompt and the current buffer content */ + ab.append(l->prompt); + if (maskmode == 1) { + while (len--) { + ab.append("*"); + } + } else { + ab.append(buf, len); + } + /* Show hits if any. */ + refreshShowHints(ab, l, plen); + } + + /* Erase to right */ + snprintf(seq,sizeof(seq),"\x1b[0K"); + ab.append(seq); + if (flags & REFRESH_WRITE) { + /* Move cursor to original position. */ + snprintf(seq,sizeof(seq),"\r\x1b[%dC", (int)(pos+plen)); + ab.append(seq); + } + + (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */ +} + +/* Multi line low level line refresh. + * + * Rewrite the currently edited line accordingly to the buffer content, + * cursor position, and number of columns of the terminal. + * + * Flags is REFRESH_* macros. The function can just remove the old + * prompt, just write it, or both. */ +static void refreshMultiLine(struct linenoiseState *l, int flags) { + char seq[64]; + int plen = strlen(l->prompt); + int rows = (plen+l->len+l->cols-1)/l->cols; /* rows used by current buf. */ + int rpos = (plen+l->oldpos+l->cols)/l->cols; /* cursor relative row. */ + int rpos2; /* rpos after refresh. */ + int col; /* colum position, zero-based. */ + int old_rows = l->oldrows; + int fd = l->ofd, j; + std::string ab; + l->oldrows = rows; + + /* First step: clear all the lines used before. To do so start by + * going to the last row. */ + if (flags & REFRESH_CLEAN) { + if (old_rows-rpos > 0) { + lndebug("go down %d", old_rows-rpos); + snprintf(seq,64,"\x1b[%dB", old_rows-rpos); + ab.append(seq); + } + + /* Now for every row clear it, go up. */ + for (j = 0; j < old_rows-1; j++) { + lndebug("clear+up"); + snprintf(seq,64,"\r\x1b[0K\x1b[1A"); + ab.append(seq); + } + } + + if (flags & REFRESH_ALL) { + /* Clean the top line. */ + lndebug("clear"); + snprintf(seq,64,"\r\x1b[0K"); + ab.append(seq); + } + + if (flags & REFRESH_WRITE) { + /* Write the prompt and the current buffer content */ + ab.append(l->prompt); + if (maskmode == 1) { + for (unsigned int i = 0; i < l->len; ++i) { + ab.append("*"); + } + } else { + ab.append(l->buf, l->len); + } + + /* Show hits if any. */ + refreshShowHints(ab, l, plen); + + /* If we are at the very end of the screen with our prompt, we need to + * emit a newline and move the prompt to the first column. */ + if (l->pos && + l->pos == l->len && + (l->pos+plen) % l->cols == 0) + { + lndebug(""); + ab.append("\n"); + snprintf(seq,64,"\r"); + ab.append(seq); + rows++; + if (rows > (int)l->oldrows) l->oldrows = rows; + } + + /* Move cursor to right position. */ + rpos2 = (plen+l->pos+l->cols)/l->cols; /* Current cursor relative row */ + lndebug("rpos2 %d", rpos2); + + /* Go up till we reach the expected positon. */ + if (rows-rpos2 > 0) { + lndebug("go-up %d", rows-rpos2); + snprintf(seq,64,"\x1b[%dA", rows-rpos2); + ab.append(seq); + } + + /* Set column. */ + col = (plen+(int)l->pos) % (int)l->cols; + lndebug("set col %d", 1+col); + if (col) + snprintf(seq,64,"\r\x1b[%dC", col); + else + snprintf(seq,64,"\r"); + ab.append(seq); + } + + lndebug("\n"); + l->oldpos = l->pos; + (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */ +} + +/* Calls the two low level functions refreshSingleLine() or + * refreshMultiLine() according to the selected mode. */ +static void refreshLineWithFlags(struct linenoiseState *l, int flags) { + if (mlmode) + refreshMultiLine(l,flags); + else + refreshSingleLine(l,flags); +} + +/* Utility function to avoid specifying REFRESH_ALL all the times. */ +static void refreshLine(struct linenoiseState *l) { + refreshLineWithFlags(l,REFRESH_ALL); +} + +/* Hide the current line, when using the multiplexing API. */ +void linenoiseHide(struct linenoiseState *l) { + if (mlmode) + refreshMultiLine(l,REFRESH_CLEAN); + else + refreshSingleLine(l,REFRESH_CLEAN); +} + +/* Show the current line, when using the multiplexing API. */ +void linenoiseShow(struct linenoiseState *l) { + if (l->in_completion) { + refreshLineWithCompletion(l,NULL,REFRESH_WRITE); + } else { + refreshLineWithFlags(l,REFRESH_WRITE); + } +} + +/* Insert the character 'c' at cursor current position. + * + * On error writing to the terminal -1 is returned, otherwise 0. */ +static int linenoiseEditInsert(struct linenoiseState * l, char c) { + if (l->len < l->buflen) { + if (l->len == l->pos) { + l->buf[l->pos] = c; + l->pos++; + l->len++; + l->buf[l->len] = '\0'; + if ((!mlmode && l->plen+l->len < l->cols && !hintsCallback)) { + /* Avoid a full update of the line in the + * trivial case. */ + char d = (maskmode==1) ? '*' : c; + if (write(l->ofd,&d,1) == -1) return -1; + } else { + refreshLine(l); + } + } else { + memmove(l->buf+l->pos+1,l->buf+l->pos,l->len-l->pos); + l->buf[l->pos] = c; + l->len++; + l->pos++; + l->buf[l->len] = '\0'; + refreshLine(l); + } + } + return 0; +} + +/* Move cursor on the left. */ +static void linenoiseEditMoveLeft(struct linenoiseState * l) { + if (l->pos > 0) { + l->pos--; + refreshLine(l); + } +} + +/* Move cursor on the right. */ +static void linenoiseEditMoveRight(struct linenoiseState * l) { + if (l->pos != l->len) { + l->pos++; + refreshLine(l); + } +} + +/* Move cursor to the start of the line. */ +static void linenoiseEditMoveHome(struct linenoiseState * l) { + if (l->pos != 0) { + l->pos = 0; + refreshLine(l); + } +} + +/* Move cursor to the end of the line. */ +static void linenoiseEditMoveEnd(struct linenoiseState * l) { + if (l->pos != l->len) { + l->pos = l->len; + refreshLine(l); + } +} + +/* Substitute the currently edited line with the next or previous history + * entry as specified by 'dir'. */ +#define LINENOISE_HISTORY_NEXT 0 +#define LINENOISE_HISTORY_PREV 1 + +static void linenoiseEditHistoryNext(struct linenoiseState * l, int dir) { + if (history_len > 1) { + /* Update the current history entry before to + * overwrite it with the next one. */ + free(history[history_len - 1 - l->history_index]); + history[history_len - 1 - l->history_index] = strdup(l->buf); + /* Show the new entry */ + l->history_index += (dir == LINENOISE_HISTORY_PREV) ? 1 : -1; + if (l->history_index < 0) { + l->history_index = 0; + return; + } else if (l->history_index >= history_len) { + l->history_index = history_len-1; + return; + } + strncpy(l->buf,history[history_len - 1 - l->history_index],l->buflen); + l->buf[l->buflen-1] = '\0'; + l->len = l->pos = strlen(l->buf); + refreshLine(l); + } +} + +/* Delete the character at the right of the cursor without altering the cursor + * position. Basically this is what happens with the "Delete" keyboard key. */ +static void linenoiseEditDelete(struct linenoiseState * l) { + if (l->len > 0 && l->pos < l->len) { + memmove(l->buf+l->pos,l->buf+l->pos+1,l->len-l->pos-1); + l->len--; + l->buf[l->len] = '\0'; + refreshLine(l); + } +} + +/* Backspace implementation. */ +static void linenoiseEditBackspace(struct linenoiseState * l) { + if (l->pos > 0 && l->len > 0) { + memmove(l->buf+l->pos-1,l->buf+l->pos,l->len-l->pos); + l->pos--; + l->len--; + l->buf[l->len] = '\0'; + refreshLine(l); + } +} + +/* Delete the previosu word, maintaining the cursor at the start of the + * current word. */ +static void linenoiseEditDeletePrevWord(struct linenoiseState * l) { + size_t old_pos = l->pos; + size_t diff; + + while (l->pos > 0 && l->buf[l->pos-1] == ' ') + l->pos--; + while (l->pos > 0 && l->buf[l->pos-1] != ' ') + l->pos--; + diff = old_pos - l->pos; + memmove(l->buf+l->pos,l->buf+old_pos,l->len-old_pos+1); + l->len -= diff; + refreshLine(l); +} + +/* This function is part of the multiplexed API of Linenoise, that is used + * in order to implement the blocking variant of the API but can also be + * called by the user directly in an event driven program. It will: + * + * 1. Initialize the linenoise state passed by the user. + * 2. Put the terminal in RAW mode. + * 3. Show the prompt. + * 4. Return control to the user, that will have to call linenoiseEditFeed() + * each time there is some data arriving in the standard input. + * + * The user can also call linenoiseEditHide() and linenoiseEditShow() if it + * is required to show some input arriving asyncronously, without mixing + * it with the currently edited line. + * + * When linenoiseEditFeed() returns non-NULL, the user finished with the + * line editing session (pressed enter CTRL-D/C): in this case the caller + * needs to call linenoiseEditStop() to put back the terminal in normal + * mode. This will not destroy the buffer, as long as the linenoiseState + * is still valid in the context of the caller. + * + * The function returns 0 on success, or -1 if writing to standard output + * fails. If stdin_fd or stdout_fd are set to -1, the default is to use + * STDIN_FILENO and STDOUT_FILENO. + */ +int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt) { + /* Populate the linenoise state that we pass to functions implementing + * specific editing functionalities. */ + l->in_completion = 0; + l->ifd = stdin_fd != -1 ? stdin_fd : STDIN_FILENO; + l->ofd = stdout_fd != -1 ? stdout_fd : STDOUT_FILENO; + l->buf = buf; + l->buflen = buflen; + l->prompt = prompt; + l->plen = strlen(prompt); + l->oldpos = l->pos = 0; + l->len = 0; + + /* Enter raw mode. */ + if (enableRawMode(l->ifd) == -1) return -1; + + l->cols = getColumns(stdin_fd, stdout_fd); + l->oldrows = 0; + l->history_index = 0; + + /* Buffer starts empty. */ + l->buf[0] = '\0'; + l->buflen--; /* Make sure there is always space for the nulterm */ + + /* If stdin is not a tty, stop here with the initialization. We + * will actually just read a line from standard input in blocking + * mode later, in linenoiseEditFeed(). */ + if (!isatty(l->ifd)) return 0; + + /* The latest history entry is always our current buffer, that + * initially is just an empty string. */ + linenoiseHistoryAdd(""); + + if (write(l->ofd,prompt,l->plen) == -1) return -1; + return 0; +} + +const char* linenoiseEditMore = "If you see this, you are misusing the API: when linenoiseEditFeed() is called, if it returns linenoiseEditMore the user is yet editing the line. See the README file for more information."; + +/* This function is part of the multiplexed API of linenoise, see the top + * comment on linenoiseEditStart() for more information. Call this function + * each time there is some data to read from the standard input file + * descriptor. In the case of blocking operations, this function can just be + * called in a loop, and block. + * + * The function returns linenoiseEditMore to signal that line editing is still + * in progress, that is, the user didn't yet pressed enter / CTRL-D. Otherwise + * the function returns the pointer to the heap-allocated buffer with the + * edited line, that the user should free with linenoiseFree(). + * + * On special conditions, NULL is returned and errno is populated: + * + * EAGAIN if the user pressed Ctrl-C + * ENOENT if the user pressed Ctrl-D + * + * Some other errno: I/O error. + */ +const char *linenoiseEditFeed(struct linenoiseState *l) { + /* Not a TTY, pass control to line reading without character + * count limits. */ + if (!isatty(l->ifd)) return linenoiseNoTTY(); + + char c; + int nread; + char seq[3]; + + nread = read(l->ifd,&c,1); + if (nread <= 0) return NULL; + + /* Only autocomplete when the callback is set. It returns < 0 when + * there was an error reading from fd. Otherwise it will return the + * character that should be handled next. */ + if ((l->in_completion || c == 9) && completionCallback != NULL) { + c = completeLine(l,c); + /* Read next character when 0 */ + if (c == 0) return linenoiseEditMore; + } + + switch(c) { + case ENTER: /* enter */ + history_len--; + free(history[history_len]); + if (mlmode) linenoiseEditMoveEnd(l); + if (hintsCallback) { + /* Force a refresh without hints to leave the previous + * line as the user typed it after a newline. */ + linenoiseHintsCallback *hc = hintsCallback; + hintsCallback = NULL; + refreshLine(l); + hintsCallback = hc; + } + return strdup(l->buf); + case CTRL_C: /* ctrl-c */ + errno = EAGAIN; + return NULL; + case BACKSPACE: /* backspace */ + case 8: /* ctrl-h */ + linenoiseEditBackspace(l); + break; + case CTRL_D: /* ctrl-d, remove char at right of cursor, or if the + line is empty, act as end-of-file. */ + if (l->len > 0) { + linenoiseEditDelete(l); + } else { + history_len--; + free(history[history_len]); + errno = ENOENT; + return NULL; + } + break; + case CTRL_T: /* ctrl-t, swaps current character with previous. */ + if (l->pos > 0 && l->pos < l->len) { + int aux = l->buf[l->pos-1]; + l->buf[l->pos-1] = l->buf[l->pos]; + l->buf[l->pos] = aux; + if (l->pos != l->len-1) l->pos++; + refreshLine(l); + } + break; + case CTRL_B: /* ctrl-b */ + linenoiseEditMoveLeft(l); + break; + case CTRL_F: /* ctrl-f */ + linenoiseEditMoveRight(l); + break; + case CTRL_P: /* ctrl-p */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV); + break; + case CTRL_N: /* ctrl-n */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT); + break; + case ESC: /* escape sequence */ + /* Read the next two bytes representing the escape sequence. + * Use two calls to handle slow terminals returning the two + * chars at different times. */ + if (read(l->ifd,seq,1) == -1) break; + if (read(l->ifd,seq+1,1) == -1) break; + + /* ESC [ sequences. */ + if (seq[0] == '[') { + if (seq[1] >= '0' && seq[1] <= '9') { + /* Extended escape, read additional byte. */ + if (read(l->ifd,seq+2,1) == -1) break; + if (seq[2] == '~') { + switch(seq[1]) { + case '3': /* Delete key. */ + linenoiseEditDelete(l); + break; + } + } + } else { + switch(seq[1]) { + case 'A': /* Up */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV); + break; + case 'B': /* Down */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT); + break; + case 'C': /* Right */ + linenoiseEditMoveRight(l); + break; + case 'D': /* Left */ + linenoiseEditMoveLeft(l); + break; + case 'H': /* Home */ + linenoiseEditMoveHome(l); + break; + case 'F': /* End*/ + linenoiseEditMoveEnd(l); + break; + } + } + } + + /* ESC O sequences. */ + else if (seq[0] == 'O') { + switch(seq[1]) { + case 'H': /* Home */ + linenoiseEditMoveHome(l); + break; + case 'F': /* End*/ + linenoiseEditMoveEnd(l); + break; + } + } + break; + default: + if (linenoiseEditInsert(l,c)) return NULL; + break; + case CTRL_U: /* Ctrl+u, delete the whole line. */ + l->buf[0] = '\0'; + l->pos = l->len = 0; + refreshLine(l); + break; + case CTRL_K: /* Ctrl+k, delete from current to end of line. */ + l->buf[l->pos] = '\0'; + l->len = l->pos; + refreshLine(l); + break; + case CTRL_A: /* Ctrl+a, go to the start of the line */ + linenoiseEditMoveHome(l); + break; + case CTRL_E: /* ctrl+e, go to the end of the line */ + linenoiseEditMoveEnd(l); + break; + case CTRL_L: /* ctrl+l, clear screen */ + linenoiseClearScreen(); + refreshLine(l); + break; + case CTRL_W: /* ctrl+w, delete previous word */ + linenoiseEditDeletePrevWord(l); + break; + } + return linenoiseEditMore; +} + +/* This is part of the multiplexed linenoise API. See linenoiseEditStart() + * for more information. This function is called when linenoiseEditFeed() + * returns something different than NULL. At this point the user input + * is in the buffer, and we can restore the terminal in normal mode. */ +void linenoiseEditStop(struct linenoiseState *l) { + if (!isatty(l->ifd)) return; + disableRawMode(l->ifd); + printf("\n"); +} + +/* This just implements a blocking loop for the multiplexed API. + * In many applications that are not event-drivern, we can just call + * the blocking linenoise API, wait for the user to complete the editing + * and return the buffer. */ +static const char *linenoiseBlockingEdit(int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt) +{ + struct linenoiseState l; + + /* Editing without a buffer is invalid. */ + if (buflen == 0) { + errno = EINVAL; + return NULL; + } + + linenoiseEditStart(&l,stdin_fd,stdout_fd,buf,buflen,prompt); + const char *res; + while((res = linenoiseEditFeed(&l)) == linenoiseEditMore); + linenoiseEditStop(&l); + return res; +} + +/* This special mode is used by linenoise in order to print scan codes + * on screen for debugging / development purposes. It is implemented + * by the linenoise_example program using the --keycodes option. */ +void linenoisePrintKeyCodes(void) { + char quit[4]; + + printf("Linenoise key codes debugging mode.\n" + "Press keys to see scan codes. Type 'quit' at any time to exit.\n"); + if (enableRawMode(STDIN_FILENO) == -1) return; + memset(quit,' ',4); + while(1) { + char c; + int nread; + + nread = read(STDIN_FILENO,&c,1); + if (nread <= 0) continue; + memmove(quit,quit+1,sizeof(quit)-1); /* shift string to left. */ + quit[sizeof(quit)-1] = c; /* Insert current char on the right. */ + if (memcmp(quit,"quit",sizeof(quit)) == 0) break; + + printf("'%c' %02x (%d) (type quit to exit)\n", + isprint(c) ? c : '?', (int)c, (int)c); + printf("\r"); /* Go left edge manually, we are in raw mode. */ + fflush(stdout); + } + disableRawMode(STDIN_FILENO); +} + +/* This function is called when linenoise() is called with the standard + * input file descriptor not attached to a TTY. So for example when the + * program using linenoise is called in pipe or with a file redirected + * to its standard input. In this case, we want to be able to return the + * line regardless of its length (by default we are limited to 4k). */ +static char *linenoiseNoTTY(void) { + char *line = NULL; + size_t len = 0, maxlen = 0; + + while(1) { + if (len == maxlen) { + if (maxlen == 0) maxlen = 16; + maxlen *= 2; + char *oldval = line; + line = (char*) realloc(line,maxlen); + if (line == NULL) { + if (oldval) free(oldval); + return NULL; + } + } + int c = fgetc(stdin); + if (c == EOF || c == '\n') { + if (c == EOF && len == 0) { + free(line); + return NULL; + } else { + line[len] = '\0'; + return line; + } + } else { + line[len] = c; + len++; + } + } +} + +/* The high level function that is the main API of the linenoise library. + * This function checks if the terminal has basic capabilities, just checking + * for a blacklist of stupid terminals, and later either calls the line + * editing function or uses dummy fgets() so that you will be able to type + * something even in the most desperate of the conditions. */ +const char *linenoise(const char *prompt) { + char buf[LINENOISE_MAX_LINE]; + + if (!isatty(STDIN_FILENO)) { + /* Not a tty: read from file / pipe. In this mode we don't want any + * limit to the line size, so we call a function to handle that. */ + return linenoiseNoTTY(); + } else if (isUnsupportedTerm()) { + size_t len; + + printf("%s",prompt); + fflush(stdout); + if (fgets(buf,LINENOISE_MAX_LINE,stdin) == NULL) return NULL; + len = strlen(buf); + while(len && (buf[len-1] == '\n' || buf[len-1] == '\r')) { + len--; + buf[len] = '\0'; + } + return strdup(buf); + } else { + const char *retval = linenoiseBlockingEdit(STDIN_FILENO,STDOUT_FILENO,buf,LINENOISE_MAX_LINE,prompt); + return retval; + } +} + +/* This is just a wrapper the user may want to call in order to make sure + * the linenoise returned buffer is freed with the same allocator it was + * created with. Useful when the main program is using an alternative + * allocator. */ +void linenoiseFree(void *ptr) { + if (ptr == linenoiseEditMore) return; // Protect from API misuse. + free(ptr); +} + +/* ================================ History ================================= */ + +/* Free the history, but does not reset it. Only used when we have to + * exit() to avoid memory leaks are reported by valgrind & co. */ +static void freeHistory(void) { + if (history) { + int j; + + for (j = 0; j < history_len; j++) + free(history[j]); + free(history); + } +} + +/* At exit we'll try to fix the terminal to the initial conditions. */ +static void linenoiseAtExit(void) { + disableRawMode(STDIN_FILENO); + freeHistory(); +} + +/* This is the API call to add a new entry in the linenoise history. + * It uses a fixed array of char pointers that are shifted (memmoved) + * when the history max length is reached in order to remove the older + * entry and make room for the new one, so it is not exactly suitable for huge + * histories, but will work well for a few hundred of entries. + * + * Using a circular buffer is smarter, but a bit more complex to handle. */ +int linenoiseHistoryAdd(const char *line) { + char *linecopy; + + if (history_max_len == 0) return 0; + + /* Initialization on first call. */ + if (history == NULL) { + history = (char**) malloc(sizeof(char*)*history_max_len); + if (history == NULL) return 0; + memset(history,0,(sizeof(char*)*history_max_len)); + } + + /* Don't add duplicated lines. */ + if (history_len && !strcmp(history[history_len-1], line)) return 0; + + /* Add an heap allocated copy of the line in the history. + * If we reached the max length, remove the older line. */ + linecopy = strdup(line); + if (!linecopy) return 0; + if (history_len == history_max_len) { + free(history[0]); + memmove(history,history+1,sizeof(char*)*(history_max_len-1)); + history_len--; + } + history[history_len] = linecopy; + history_len++; + return 1; +} + +/* Set the maximum length for the history. This function can be called even + * if there is already some history, the function will make sure to retain + * just the latest 'len' elements if the new history length value is smaller + * than the amount of items already inside the history. */ +int linenoiseHistorySetMaxLen(int len) { + char **new_ptr; + + if (len < 1) return 0; + if (history) { + int tocopy = history_len; + + new_ptr = (char**) malloc(sizeof(char*)*len); + if (new_ptr == NULL) return 0; + + /* If we can't copy everything, free the elements we'll not use. */ + if (len < tocopy) { + int j; + + for (j = 0; j < tocopy-len; j++) free(history[j]); + tocopy = len; + } + memset(new_ptr,0,sizeof(char*)*len); + memcpy(new_ptr,history+(history_len-tocopy), sizeof(char*)*tocopy); + free(history); + history = new_ptr; + } + history_max_len = len; + if (history_len > history_max_len) + history_len = history_max_len; + return 1; +} + +/* Save the history in the specified file. On success 0 is returned + * otherwise -1 is returned. */ +int linenoiseHistorySave(const char *filename) { + mode_t old_umask = umask(S_IXUSR|S_IRWXG|S_IRWXO); + File file; + file.open(filename, "w"); + umask(old_umask); + if (file.file == NULL) { + return -1; + } + chmod(filename,S_IRUSR|S_IWUSR); + for (int j = 0; j < history_len; ++j) { + fprintf(file.file, "%s\n", history[j]); + } + + return 0; +} + +/* Load the history from the specified file. If the file does not exist + * zero is returned and no operation is performed. + * + * If the file exists and the operation succeeded 0 is returned, otherwise + * on error -1 is returned. */ +int linenoiseHistoryLoad(const char *filename) { + File file; + file.open(filename, "r"); + char buf[LINENOISE_MAX_LINE]; + if (file.file == NULL) { + return -1; + } + + while (fgets(buf, LINENOISE_MAX_LINE, file.file) != NULL) { + char *p; + + p = strchr(buf,'\r'); + if (!p) p = strchr(buf,'\n'); + if (p) *p = '\0'; + linenoiseHistoryAdd(buf); + } + return 0; +} +#endif diff --git a/examples/run/linenoise.cpp/linenoise.h b/examples/run/linenoise.cpp/linenoise.h new file mode 100644 index 0000000000000..a14ec6c74062b --- /dev/null +++ b/examples/run/linenoise.cpp/linenoise.h @@ -0,0 +1,128 @@ +/* linenoise.h -- VERSION 1.0 + * + * Guerrilla line editing library against the idea that a line editing lib + * needs to be 20,000 lines of C++ code. + * + * See linenoise.cpp for more information. + * + * ------------------------------------------------------------------------ + * + * Copyright (c) 2010-2023, Salvatore Sanfilippo + * Copyright (c) 2010-2013, Pieter Noordhuis + * Copyright (c) 2025, Eric Curtin + * + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef __LINENOISE_H +#define __LINENOISE_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include /* For size_t. */ +#include + +extern const char *linenoiseEditMore; + +/* The linenoiseState structure represents the state during line editing. + * We pass this state to functions implementing specific editing + * functionalities. */ +struct linenoiseState { + int in_completion; /* The user pressed TAB and we are now in completion + * mode, so input is handled by completeLine(). */ + size_t completion_idx; /* Index of next completion to propose. */ + int ifd; /* Terminal stdin file descriptor. */ + int ofd; /* Terminal stdout file descriptor. */ + char *buf; /* Edited line buffer. */ + size_t buflen; /* Edited line buffer size. */ + const char *prompt; /* Prompt to display. */ + size_t plen; /* Prompt length. */ + size_t pos; /* Current cursor position. */ + size_t oldpos; /* Previous refresh cursor position. */ + size_t len; /* Current edited line length. */ + size_t cols; /* Number of columns in terminal. */ + size_t oldrows; /* Rows used by last refrehsed line (multiline mode) */ + int history_index; /* The history index we are currently editing. */ +}; + +struct linenoiseCompletions { + size_t len = 0; + char ** cvec = nullptr; + bool to_free = true; + + ~linenoiseCompletions() { + if (!to_free) { + return; + } + + for (size_t i = 0; i < len; ++i) { + free(cvec[i]); + } + + free(cvec); + } +}; + +/* Non blocking API. */ +int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt); +const char *linenoiseEditFeed(struct linenoiseState *l); +void linenoiseEditStop(struct linenoiseState *l); +void linenoiseHide(struct linenoiseState *l); +void linenoiseShow(struct linenoiseState *l); + +/* Blocking API. */ +const char *linenoise(const char *prompt); +void linenoiseFree(void *ptr); + +/* Completion API. */ +typedef void(linenoiseCompletionCallback)(const char *, linenoiseCompletions *); +typedef const char*(linenoiseHintsCallback)(const char *, int *color, int *bold); +typedef void(linenoiseFreeHintsCallback)(const char *); +void linenoiseSetCompletionCallback(linenoiseCompletionCallback *); +void linenoiseSetHintsCallback(linenoiseHintsCallback *); +void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *); +void linenoiseAddCompletion(linenoiseCompletions *, const char *); + +/* History API. */ +int linenoiseHistoryAdd(const char *line); +int linenoiseHistorySetMaxLen(int len); +int linenoiseHistorySave(const char *filename); +int linenoiseHistoryLoad(const char *filename); + +/* Other utilities. */ +void linenoiseClearScreen(void); +void linenoiseSetMultiLine(int ml); +void linenoisePrintKeyCodes(void); +void linenoiseMaskModeEnable(void); +void linenoiseMaskModeDisable(void); + +#ifdef __cplusplus +} +#endif + +#endif /* __LINENOISE_H */ diff --git a/examples/run/run.cpp b/examples/run/run.cpp new file mode 100644 index 0000000000000..38407d5190923 --- /dev/null +++ b/examples/run/run.cpp @@ -0,0 +1,1216 @@ +#if defined(_WIN32) +# include +# include +#else +# include +# include +# include +#endif + +#if defined(LLAMA_USE_CURL) +# include +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "chat.h" +#include "common.h" +#include "json.hpp" +#include "linenoise.cpp/linenoise.h" +#include "llama-cpp.h" +#include "log.h" + +#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32) +[[noreturn]] static void sigint_handler(int) { + printf("\n" LOG_COL_DEFAULT); + exit(0); // not ideal, but it's the only way to guarantee exit in all cases +} +#endif + +GGML_ATTRIBUTE_FORMAT(1, 2) +static std::string fmt(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + const int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::string buf; + buf.resize(size); + const int size2 = vsnprintf(const_cast(buf.data()), buf.size() + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + + return buf; +} + +GGML_ATTRIBUTE_FORMAT(1, 2) +static int printe(const char * fmt, ...) { + va_list args; + va_start(args, fmt); + const int ret = vfprintf(stderr, fmt, args); + va_end(args); + + return ret; +} + +static std::string strftime_fmt(const char * fmt, const std::tm & tm) { + std::ostringstream oss; + oss << std::put_time(&tm, fmt); + + return oss.str(); +} + +class Opt { + public: + int init(int argc, const char ** argv) { + ctx_params = llama_context_default_params(); + model_params = llama_model_default_params(); + context_size_default = ctx_params.n_batch; + ngl_default = model_params.n_gpu_layers; + common_params_sampling sampling; + temperature_default = sampling.temp; + + if (argc < 2) { + printe("Error: No arguments provided.\n"); + print_help(); + return 1; + } + + // Parse arguments + if (parse(argc, argv)) { + printe("Error: Failed to parse arguments.\n"); + print_help(); + return 1; + } + + // If help is requested, show help and exit + if (help) { + print_help(); + return 2; + } + + ctx_params.n_batch = context_size >= 0 ? context_size : context_size_default; + ctx_params.n_ctx = ctx_params.n_batch; + model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default; + temperature = temperature >= 0 ? temperature : temperature_default; + + return 0; // Success + } + + llama_context_params ctx_params; + llama_model_params model_params; + std::string model_; + std::string chat_template_file; + std::string user; + bool use_jinja = false; + int context_size = -1, ngl = -1; + float temperature = -1; + bool verbose = false; + + private: + int context_size_default = -1, ngl_default = -1; + float temperature_default = -1; + bool help = false; + + bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) { + return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, int & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atoi(argv[++i]); + + return 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atof(argv[++i]); + + return 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, std::string & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = argv[++i]; + + return 0; + } + + int parse(int argc, const char ** argv) { + bool options_parsing = true; + for (int i = 1, positional_args_i = 0; i < argc; ++i) { + if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) { + if (handle_option_with_value(argc, argv, i, context_size) == 1) { + return 1; + } + } else if (options_parsing && + (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) { + if (handle_option_with_value(argc, argv, i, ngl) == 1) { + return 1; + } + } else if (options_parsing && strcmp(argv[i], "--temp") == 0) { + if (handle_option_with_value(argc, argv, i, temperature) == 1) { + return 1; + } + } else if (options_parsing && + (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) { + verbose = true; + } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) { + use_jinja = true; + } else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0){ + if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) { + return 1; + } + use_jinja = true; + } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) { + help = true; + return 0; + } else if (options_parsing && strcmp(argv[i], "--") == 0) { + options_parsing = false; + } else if (positional_args_i == 0) { + if (!argv[i][0] || argv[i][0] == '-') { + return 1; + } + + ++positional_args_i; + model_ = argv[i]; + } else if (positional_args_i == 1) { + ++positional_args_i; + user = argv[i]; + } else { + user += " " + std::string(argv[i]); + } + } + + if (model_.empty()){ + return 1; + } + + return 0; + } + + void print_help() const { + printf( + "Description:\n" + " Runs a llm\n" + "\n" + "Usage:\n" + " llama-run [options] model [prompt]\n" + "\n" + "Options:\n" + " -c, --context-size \n" + " Context size (default: %d)\n" + " --chat-template-file \n" + " Path to the file containing the chat template to use with the model.\n" + " Only supports jinja templates and implicitly sets the --jinja flag.\n" + " --jinja\n" + " Use jinja templating for the chat template of the model\n" + " -n, -ngl, --ngl \n" + " Number of GPU layers (default: %d)\n" + " --temp \n" + " Temperature (default: %.1f)\n" + " -v, --verbose, --log-verbose\n" + " Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n" + " -h, --help\n" + " Show help message\n" + "\n" + "Commands:\n" + " model\n" + " Model is a string with an optional prefix of \n" + " huggingface:// (hf://), ollama://, https:// or file://.\n" + " If no protocol is specified and a file exists in the specified\n" + " path, file:// is assumed, otherwise if a file does not exist in\n" + " the specified path, ollama:// is assumed. Models that are being\n" + " pulled are downloaded with .partial extension while being\n" + " downloaded and then renamed as the file without the .partial\n" + " extension when complete.\n" + "\n" + "Examples:\n" + " llama-run llama3\n" + " llama-run ollama://granite-code\n" + " llama-run ollama://smollm:135m\n" + " llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n" + " llama-run " + "huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n" + " llama-run https://example.com/some-file1.gguf\n" + " llama-run some-file2.gguf\n" + " llama-run file://some-file3.gguf\n" + " llama-run --ngl 999 some-file4.gguf\n" + " llama-run --ngl 999 some-file5.gguf Hello World\n", + context_size_default, ngl_default, temperature_default); + } +}; + +struct progress_data { + size_t file_size = 0; + std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now(); + bool printed = false; +}; + +static int get_terminal_width() { +#if defined(_WIN32) + CONSOLE_SCREEN_BUFFER_INFO csbi; + GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi); + return csbi.srWindow.Right - csbi.srWindow.Left + 1; +#else + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + return w.ws_col; +#endif +} + +class File { + public: + FILE * file = nullptr; + + FILE * open(const std::string & filename, const char * mode) { + file = ggml_fopen(filename.c_str(), mode); + + return file; + } + + int lock() { + if (file) { +# ifdef _WIN32 + fd = _fileno(file); + hFile = (HANDLE) _get_osfhandle(fd); + if (hFile == INVALID_HANDLE_VALUE) { + fd = -1; + + return 1; + } + + OVERLAPPED overlapped = {}; + if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD, + &overlapped)) { + fd = -1; + + return 1; + } +# else + fd = fileno(file); + if (flock(fd, LOCK_EX | LOCK_NB) != 0) { + fd = -1; + + return 1; + } +# endif + } + + return 0; + } + + std::string to_string() { + fseek(file, 0, SEEK_END); + const size_t size = ftell(file); + fseek(file, 0, SEEK_SET); + std::string out; + out.resize(size); + const size_t read_size = fread(&out[0], 1, size, file); + if (read_size != size) { + printe("Error reading file: %s", strerror(errno)); + } + + return out; + } + + ~File() { + if (fd >= 0) { +# ifdef _WIN32 + if (hFile != INVALID_HANDLE_VALUE) { + OVERLAPPED overlapped = {}; + UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped); + } +# else + flock(fd, LOCK_UN); +# endif + } + + if (file) { + fclose(file); + } + } + + private: + int fd = -1; +# ifdef _WIN32 + HANDLE hFile = nullptr; +# endif +}; + +#ifdef LLAMA_USE_CURL +class HttpClient { + public: + int init(const std::string & url, const std::vector & headers, const std::string & output_file, + const bool progress, std::string * response_str = nullptr) { + if (std::filesystem::exists(output_file)) { + return 0; + } + + std::string output_file_partial; + curl = curl_easy_init(); + if (!curl) { + return 1; + } + + progress_data data; + File out; + if (!output_file.empty()) { + output_file_partial = output_file + ".partial"; + if (!out.open(output_file_partial, "ab")) { + printe("Failed to open file for writing\n"); + + return 1; + } + + if (out.lock()) { + printe("Failed to exclusively lock file\n"); + + return 1; + } + } + + set_write_options(response_str, out); + data.file_size = set_resume_point(output_file_partial); + set_progress_options(progress, data); + set_headers(headers); + CURLcode res = perform(url); + if (res != CURLE_OK){ + printe("Fetching resource '%s' failed: %s\n", url.c_str(), curl_easy_strerror(res)); + return 1; + } + if (!output_file.empty()) { + std::filesystem::rename(output_file_partial, output_file); + } + + return 0; + } + + ~HttpClient() { + if (chunk) { + curl_slist_free_all(chunk); + } + + if (curl) { + curl_easy_cleanup(curl); + } + } + + private: + CURL * curl = nullptr; + struct curl_slist * chunk = nullptr; + + void set_write_options(std::string * response_str, const File & out) { + if (response_str) { + curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str); + } else { + curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.file); + } + } + + size_t set_resume_point(const std::string & output_file) { + size_t file_size = 0; + if (std::filesystem::exists(output_file)) { + file_size = std::filesystem::file_size(output_file); + curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast(file_size)); + } + + return file_size; + } + + void set_progress_options(bool progress, progress_data & data) { + if (progress) { + curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); + curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data); + curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, update_progress); + } + } + + void set_headers(const std::vector & headers) { + if (!headers.empty()) { + if (chunk) { + curl_slist_free_all(chunk); + chunk = 0; + } + + for (const auto & header : headers) { + chunk = curl_slist_append(chunk, header.c_str()); + } + + curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk); + } + } + + CURLcode perform(const std::string & url) { + curl_easy_setopt(curl, CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); + curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https"); + curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L); + return curl_easy_perform(curl); + } + + static std::string human_readable_time(double seconds) { + int hrs = static_cast(seconds) / 3600; + int mins = (static_cast(seconds) % 3600) / 60; + int secs = static_cast(seconds) % 60; + + if (hrs > 0) { + return fmt("%dh %02dm %02ds", hrs, mins, secs); + } else if (mins > 0) { + return fmt("%dm %02ds", mins, secs); + } else { + return fmt("%ds", secs); + } + } + + static std::string human_readable_size(curl_off_t size) { + static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" }; + char length = sizeof(suffix) / sizeof(suffix[0]); + int i = 0; + double dbl_size = size; + if (size > 1024) { + for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) { + dbl_size = size / 1024.0; + } + } + + return fmt("%.2f %s", dbl_size, suffix[i]); + } + + static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t, + curl_off_t) { + progress_data * data = static_cast(ptr); + if (total_to_download <= 0) { + return 0; + } + + total_to_download += data->file_size; + const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size; + const curl_off_t percentage = calculate_percentage(now_downloaded_plus_file_size, total_to_download); + std::string progress_prefix = generate_progress_prefix(percentage); + + const double speed = calculate_speed(now_downloaded, data->start_time); + const double tim = (total_to_download - now_downloaded) / speed; + std::string progress_suffix = + generate_progress_suffix(now_downloaded_plus_file_size, total_to_download, speed, tim); + + int progress_bar_width = calculate_progress_bar_width(progress_prefix, progress_suffix); + std::string progress_bar; + generate_progress_bar(progress_bar_width, percentage, progress_bar); + + print_progress(progress_prefix, progress_bar, progress_suffix); + data->printed = true; + + return 0; + } + + static curl_off_t calculate_percentage(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download) { + return (now_downloaded_plus_file_size * 100) / total_to_download; + } + + static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast(percentage)); } + + static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) { + const auto now = std::chrono::steady_clock::now(); + const std::chrono::duration elapsed_seconds = now - start_time; + return now_downloaded / elapsed_seconds.count(); + } + + static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download, + double speed, double estimated_time) { + const int width = 10; + return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width, + human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width, + human_readable_time(estimated_time).c_str()); + } + + static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) { + int progress_bar_width = get_terminal_width() - progress_prefix.size() - progress_suffix.size() - 3; + if (progress_bar_width < 1) { + progress_bar_width = 1; + } + + return progress_bar_width; + } + + static std::string generate_progress_bar(int progress_bar_width, curl_off_t percentage, + std::string & progress_bar) { + const curl_off_t pos = (percentage * progress_bar_width) / 100; + for (int i = 0; i < progress_bar_width; ++i) { + progress_bar.append((i < pos) ? "█" : " "); + } + + return progress_bar; + } + + static void print_progress(const std::string & progress_prefix, const std::string & progress_bar, + const std::string & progress_suffix) { + printe("\r" LOG_CLR_TO_EOL "%s%s| %s", progress_prefix.c_str(), progress_bar.c_str(), progress_suffix.c_str()); + } + // Function to write data to a file + static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) { + FILE * out = static_cast(stream); + return fwrite(ptr, size, nmemb, out); + } + + // Function to capture data into a string + static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) { + std::string * str = static_cast(stream); + str->append(static_cast(ptr), size * nmemb); + return size * nmemb; + } +}; +#endif + +class LlamaData { + public: + llama_model_ptr model; + llama_sampler_ptr sampler; + llama_context_ptr context; + std::vector messages; // TODO: switch to common_chat_msg + std::list msg_strs; + std::vector fmtted; + + int init(Opt & opt) { + model = initialize_model(opt); + if (!model) { + return 1; + } + + context = initialize_context(model, opt); + if (!context) { + return 1; + } + + sampler = initialize_sampler(opt); + + return 0; + } + + private: +#ifdef LLAMA_USE_CURL + int download(const std::string & url, const std::string & output_file, const bool progress, + const std::vector & headers = {}, std::string * response_str = nullptr) { + HttpClient http; + if (http.init(url, headers, output_file, progress, response_str)) { + return 1; + } + + return 0; + } +#else + int download(const std::string &, const std::string &, const bool, const std::vector & = {}, + std::string * = nullptr) { + printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); + + return 1; + } +#endif + + // Helper function to handle model tag extraction and URL construction + std::pair extract_model_and_tag(std::string & model, const std::string & base_url) { + std::string model_tag = "latest"; + const size_t colon_pos = model.find(':'); + if (colon_pos != std::string::npos) { + model_tag = model.substr(colon_pos + 1); + model = model.substr(0, colon_pos); + } + + std::string url = base_url + model + "/manifests/" + model_tag; + + return { model, url }; + } + + // Helper function to download and parse the manifest + int download_and_parse_manifest(const std::string & url, const std::vector & headers, + nlohmann::json & manifest) { + std::string manifest_str; + int ret = download(url, "", false, headers, &manifest_str); + if (ret) { + return ret; + } + + manifest = nlohmann::json::parse(manifest_str); + + return 0; + } + + int huggingface_dl(std::string & model, const std::string & bn) { + // Find the second occurrence of '/' after protocol string + size_t pos = model.find('/'); + pos = model.find('/', pos + 1); + std::string hfr, hff; + std::vector headers = { "User-Agent: llama-cpp", "Accept: application/json" }; + std::string url; + + if (pos == std::string::npos) { + auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/"); + hfr = model_name; + + nlohmann::json manifest; + int ret = download_and_parse_manifest(manifest_url, headers, manifest); + if (ret) { + return ret; + } + + hff = manifest["ggufFile"]["rfilename"]; + } else { + hfr = model.substr(0, pos); + hff = model.substr(pos + 1); + } + + url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff; + + return download(url, bn, true, headers); + } + + int ollama_dl(std::string & model, const std::string & bn) { + const std::vector headers = { "Accept: application/vnd.docker.distribution.manifest.v2+json" }; + if (model.find('/') == std::string::npos) { + model = "library/" + model; + } + + auto [model_name, manifest_url] = extract_model_and_tag(model, "https://registry.ollama.ai/v2/"); + nlohmann::json manifest; + int ret = download_and_parse_manifest(manifest_url, {}, manifest); + if (ret) { + return ret; + } + + std::string layer; + for (const auto & l : manifest["layers"]) { + if (l["mediaType"] == "application/vnd.ollama.image.model") { + layer = l["digest"]; + break; + } + } + + std::string blob_url = "https://registry.ollama.ai/v2/" + model_name + "/blobs/" + layer; + + return download(blob_url, bn, true, headers); + } + + int github_dl(const std::string & model, const std::string & bn) { + std::string repository = model; + std::string branch = "main"; + const size_t at_pos = model.find('@'); + if (at_pos != std::string::npos) { + repository = model.substr(0, at_pos); + branch = model.substr(at_pos + 1); + } + + const std::vector repo_parts = string_split(repository, "/"); + if (repo_parts.size() < 3) { + printe("Invalid GitHub repository format\n"); + return 1; + } + + const std::string & org = repo_parts[0]; + const std::string & project = repo_parts[1]; + std::string url = "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch; + for (size_t i = 2; i < repo_parts.size(); ++i) { + url += "/" + repo_parts[i]; + } + + return download(url, bn, true); + } + + int s3_dl(const std::string & model, const std::string & bn) { + const size_t slash_pos = model.find('/'); + if (slash_pos == std::string::npos) { + return 1; + } + + const std::string bucket = model.substr(0, slash_pos); + const std::string key = model.substr(slash_pos + 1); + const char * access_key = std::getenv("AWS_ACCESS_KEY_ID"); + const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY"); + if (!access_key || !secret_key) { + printe("AWS credentials not found in environment\n"); + return 1; + } + + // Generate AWS Signature Version 4 headers + // (Implementation requires HMAC-SHA256 and date handling) + // Get current timestamp + const time_t now = time(nullptr); + const tm tm = *gmtime(&now); + const std::string date = strftime_fmt("%Y%m%d", tm); + const std::string datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm); + const std::vector headers = { + "Authorization: AWS4-HMAC-SHA256 Credential=" + std::string(access_key) + "/" + date + + "/us-east-1/s3/aws4_request", + "x-amz-content-sha256: UNSIGNED-PAYLOAD", "x-amz-date: " + datetime + }; + + const std::string url = "https://" + bucket + ".s3.amazonaws.com/" + key; + + return download(url, bn, true, headers); + } + + std::string basename(const std::string & path) { + const size_t pos = path.find_last_of("/\\"); + if (pos == std::string::npos) { + return path; + } + + return path.substr(pos + 1); + } + + int rm_until_substring(std::string & model_, const std::string & substring) { + const std::string::size_type pos = model_.find(substring); + if (pos == std::string::npos) { + return 1; + } + + model_ = model_.substr(pos + substring.size()); // Skip past the substring + return 0; + } + + int resolve_model(std::string & model_) { + int ret = 0; + if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) { + rm_until_substring(model_, "://"); + + return ret; + } + + const std::string bn = basename(model_); + if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://") || + string_starts_with(model_, "hf.co/")) { + rm_until_substring(model_, "hf.co/"); + rm_until_substring(model_, "://"); + ret = huggingface_dl(model_, bn); + } else if ((string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) && + !string_starts_with(model_, "https://ollama.com/library/")) { + ret = download(model_, bn, true); + } else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) { + rm_until_substring(model_, "github:"); + rm_until_substring(model_, "://"); + ret = github_dl(model_, bn); + } else if (string_starts_with(model_, "s3://")) { + rm_until_substring(model_, "://"); + ret = s3_dl(model_, bn); + } else { // ollama:// or nothing + rm_until_substring(model_, "ollama.com/library/"); + rm_until_substring(model_, "://"); + ret = ollama_dl(model_, bn); + } + + model_ = bn; + + return ret; + } + + // Initializes the model and returns a unique pointer to it + llama_model_ptr initialize_model(Opt & opt) { + ggml_backend_load_all(); + resolve_model(opt.model_); + printe("\r" LOG_CLR_TO_EOL "Loading model"); + llama_model_ptr model(llama_model_load_from_file(opt.model_.c_str(), opt.model_params)); + if (!model) { + printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str()); + } + + printe("\r" LOG_CLR_TO_EOL); + return model; + } + + // Initializes the context with the specified parameters + llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) { + llama_context_ptr context(llama_init_from_model(model.get(), opt.ctx_params)); + if (!context) { + printe("%s: error: failed to create the llama_context\n", __func__); + } + + return context; + } + + // Initializes and configures the sampler + llama_sampler_ptr initialize_sampler(const Opt & opt) { + llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params())); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1)); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(opt.temperature)); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED)); + + return sampler; + } +}; + +// Add a message to `messages` and store its content in `msg_strs` +static void add_message(const char * role, const std::string & text, LlamaData & llama_data) { + llama_data.msg_strs.push_back(std::move(text)); + llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() }); +} + +// Function to apply the chat template and resize `formatted` if needed +static int apply_chat_template(const struct common_chat_templates * tmpls, LlamaData & llama_data, const bool append, bool use_jinja) { + common_chat_templates_inputs inputs; + for (const auto & msg : llama_data.messages) { + common_chat_msg cmsg; + cmsg.role = msg.role; + cmsg.content = msg.content; + inputs.messages.push_back(cmsg); + } + inputs.add_generation_prompt = append; + inputs.use_jinja = use_jinja; + + auto chat_params = common_chat_templates_apply(tmpls, inputs); + // TODO: use other params for tool calls. + auto result = chat_params.prompt; + llama_data.fmtted.resize(result.size() + 1); + memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1); + return result.size(); +} + +// Function to tokenize the prompt +static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt, + std::vector & prompt_tokens, const LlamaData & llama_data) { + const bool is_first = llama_get_kv_cache_used_cells(llama_data.context.get()) == 0; + + const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true); + prompt_tokens.resize(n_prompt_tokens); + if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first, + true) < 0) { + printe("failed to tokenize the prompt\n"); + return -1; + } + + return n_prompt_tokens; +} + +// Check if we have enough space in the context to evaluate this batch +static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) { + const int n_ctx = llama_n_ctx(ctx.get()); + const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get()); + if (n_ctx_used + batch.n_tokens > n_ctx) { + printf(LOG_COL_DEFAULT "\n"); + printe("context size exceeded\n"); + return 1; + } + + return 0; +} + +// convert the token to a string +static int convert_token_to_string(const llama_vocab * vocab, const llama_token token_id, std::string & piece) { + char buf[256]; + int n = llama_token_to_piece(vocab, token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + printe("failed to convert token to piece\n"); + return 1; + } + + piece = std::string(buf, n); + return 0; +} + +static void print_word_and_concatenate_to_response(const std::string & piece, std::string & response) { + printf("%s", piece.c_str()); + fflush(stdout); + response += piece; +} + +// helper function to evaluate a prompt and generate a response +static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) { + const llama_vocab * vocab = llama_model_get_vocab(llama_data.model.get()); + + std::vector tokens; + if (tokenize_prompt(vocab, prompt, tokens, llama_data) < 0) { + return 1; + } + + // prepare a batch for the prompt + llama_batch batch = llama_batch_get_one(tokens.data(), tokens.size()); + llama_token new_token_id; + while (true) { + check_context_size(llama_data.context, batch); + if (llama_decode(llama_data.context.get(), batch)) { + printe("failed to decode\n"); + return 1; + } + + // sample the next token, check is it an end of generation? + new_token_id = llama_sampler_sample(llama_data.sampler.get(), llama_data.context.get(), -1); + if (llama_vocab_is_eog(vocab, new_token_id)) { + break; + } + + std::string piece; + if (convert_token_to_string(vocab, new_token_id, piece)) { + return 1; + } + + print_word_and_concatenate_to_response(piece, response); + + // prepare the next batch with the sampled token + batch = llama_batch_get_one(&new_token_id, 1); + } + + printf(LOG_COL_DEFAULT); + return 0; +} + +static int read_user_input(std::string & user_input) { + static const char * prompt_prefix_env = std::getenv("LLAMA_PROMPT_PREFIX"); + static const char * prompt_prefix = prompt_prefix_env ? prompt_prefix_env : "> "; +#ifdef WIN32 + printf("\r" LOG_CLR_TO_EOL LOG_COL_DEFAULT "%s", prompt_prefix); + + std::getline(std::cin, user_input); + if (std::cin.eof()) { + printf("\n"); + return 1; + } +#else + std::unique_ptr line(const_cast(linenoise(prompt_prefix)), free); + if (!line) { + return 1; + } + + user_input = line.get(); +#endif + + if (user_input == "/bye") { + return 1; + } + + if (user_input.empty()) { + return 2; + } + +#ifndef WIN32 + linenoiseHistoryAdd(line.get()); +#endif + + return 0; // Should have data in happy path +} + +// Function to generate a response based on the prompt +static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response, + const bool stdout_a_terminal) { + // Set response color + if (stdout_a_terminal) { + printf(LOG_COL_YELLOW); + } + + if (generate(llama_data, prompt, response)) { + printe("failed to generate response\n"); + return 1; + } + + // End response with color reset and newline + printf("\n%s", stdout_a_terminal ? LOG_COL_DEFAULT : ""); + return 0; +} + +// Helper function to apply the chat template and handle errors +static int apply_chat_template_with_error_handling(const common_chat_templates * tmpls, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) { + const int new_len = apply_chat_template(tmpls, llama_data, append, use_jinja); + if (new_len < 0) { + printe("failed to apply the chat template\n"); + return -1; + } + + output_length = new_len; + return 0; +} + +// Helper function to handle user input +static int handle_user_input(std::string & user_input, const std::string & user) { + if (!user.empty()) { + user_input = user; + return 0; // No need for interactive input + } + + return read_user_input(user_input); // Returns true if input ends the loop +} + +static bool is_stdin_a_terminal() { +#if defined(_WIN32) + HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdin, &mode); +#else + return isatty(STDIN_FILENO); +#endif +} + +static bool is_stdout_a_terminal() { +#if defined(_WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdout, &mode); +#else + return isatty(STDOUT_FILENO); +#endif +} + +// Function to handle user input +static int get_user_input(std::string & user_input, const std::string & user) { + while (true) { + const int ret = handle_user_input(user_input, user); + if (ret == 1) { + return 1; + } + + if (ret == 2) { + continue; + } + + break; + } + + return 0; +} + +// Reads a chat template file to be used +static std::string read_chat_template_file(const std::string & chat_template_file) { + File file; + if (!file.open(chat_template_file, "r")) { + printe("Error opening chat template file '%s': %s", chat_template_file.c_str(), strerror(errno)); + return ""; + } + + return file.to_string(); +} + +static int process_user_message(const Opt & opt, const std::string & user_input, LlamaData & llama_data, + const common_chat_templates_ptr & chat_templates, int & prev_len, + const bool stdout_a_terminal) { + add_message("user", opt.user.empty() ? user_input : opt.user, llama_data); + int new_len; + if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, true, new_len, opt.use_jinja) < 0) { + return 1; + } + + std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len); + std::string response; + if (generate_response(llama_data, prompt, response, stdout_a_terminal)) { + return 1; + } + + if (!opt.user.empty()) { + return 2; + } + + add_message("assistant", response, llama_data); + if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, false, prev_len, opt.use_jinja) < 0) { + return 1; + } + + return 0; +} + +// Main chat loop function +static int chat_loop(LlamaData & llama_data, const Opt & opt) { + int prev_len = 0; + llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get())); + std::string chat_template; + if (!opt.chat_template_file.empty()) { + chat_template = read_chat_template_file(opt.chat_template_file); + } + + common_chat_templates_ptr chat_templates = common_chat_templates_init(llama_data.model.get(), chat_template); + static const bool stdout_a_terminal = is_stdout_a_terminal(); + while (true) { + // Get user input + std::string user_input; + if (get_user_input(user_input, opt.user) == 1) { + return 0; + } + + const int ret = process_user_message(opt, user_input, llama_data, chat_templates, prev_len, stdout_a_terminal); + if (ret == 1) { + return 1; + } else if (ret == 2) { + break; + } + } + + return 0; +} + +static void log_callback(const enum ggml_log_level level, const char * text, void * p) { + const Opt * opt = static_cast(p); + if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) { + printe("%s", text); + } +} + +static std::string read_pipe_data() { + std::ostringstream result; + result << std::cin.rdbuf(); // Read all data from std::cin + return result.str(); +} + +static void ctrl_c_handling() { +#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) + struct sigaction sigint_action; + sigint_action.sa_handler = sigint_handler; + sigemptyset(&sigint_action.sa_mask); + sigint_action.sa_flags = 0; + sigaction(SIGINT, &sigint_action, NULL); +#elif defined(_WIN32) + auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { + return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; + }; + SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); +#endif +} + +int main(int argc, const char ** argv) { + ctrl_c_handling(); + Opt opt; + const int ret = opt.init(argc, argv); + if (ret == 2) { + return 0; + } else if (ret) { + return 1; + } + + if (!is_stdin_a_terminal()) { + if (!opt.user.empty()) { + opt.user += "\n\n"; + } + + opt.user += read_pipe_data(); + } + + llama_log_set(log_callback, &opt); + LlamaData llama_data; + if (llama_data.init(opt)) { + return 1; + } + + if (chat_loop(llama_data, opt)) { + return 1; + } + + return 0; +} diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index 0fb5e359bc9ad..0f50e50deecd7 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-save-load-state) add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 2f0cf9baa32b7..cf7cbd8159cf8 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -30,8 +30,8 @@ int main(int argc, char ** argv) { // init common_init_result llama_init = common_init_from_params(params); - llama_model * model = llama_init.model; - llama_context * ctx = llama_init.context; + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); if (model == nullptr || ctx == nullptr) { fprintf(stderr, "%s : failed to init\n", __func__); @@ -89,8 +89,6 @@ int main(int argc, char ** argv) { if (llama_decode(ctx, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); return 1; } n_past += 1; @@ -98,11 +96,8 @@ int main(int argc, char ** argv) { printf("\n\n"); - // free old context - llama_free(ctx); - // make new context - auto * ctx2 = llama_new_context_with_model(model, common_context_params_to_llama(params)); + llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params)); llama_sampler * smpl2 = llama_sampler_chain_init(sparams); @@ -123,8 +118,6 @@ int main(int argc, char ** argv) { if (read != llama_state_set_data(ctx2, state_mem.data(), state_mem.size())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); - llama_free(ctx2); - llama_free_model(model); return 1; } @@ -148,8 +141,6 @@ int main(int argc, char ** argv) { if (llama_decode(ctx2, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); llama_batch_free(batch); - llama_free(ctx2); - llama_free_model(model); return 1; } n_past += 1; @@ -157,15 +148,13 @@ int main(int argc, char ** argv) { printf("\n\n"); - llama_free(ctx2); - if (result0 != result1) { fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__); return 1; } // make new context - auto * ctx3 = llama_new_context_with_model(model, common_context_params_to_llama(params)); + llama_context * ctx3 = llama_init_from_model(model, common_context_params_to_llama(params)); llama_sampler * smpl3 = llama_sampler_chain_init(sparams); @@ -186,8 +175,6 @@ int main(int argc, char ** argv) { if (read != llama_state_set_data(ctx3, state_mem.data(), state_mem.size())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); - llama_free(ctx3); - llama_free_model(model); return 1; } @@ -204,8 +191,6 @@ int main(int argc, char ** argv) { const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0); if (ncopy != seq_store.size()) { fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size()); - llama_free(ctx3); - llama_free_model(model); return 1; } fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy); @@ -218,8 +203,6 @@ int main(int argc, char ** argv) { const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1); if (nset != seq_store.size()) { fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size()); - llama_free(ctx3); - llama_free_model(model); return 1; } fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset); @@ -239,8 +222,6 @@ int main(int argc, char ** argv) { if (llama_decode(ctx3, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); llama_batch_free(batch); - llama_free(ctx3); - llama_free_model(model); return 1; } n_past += 1; @@ -253,8 +234,6 @@ int main(int argc, char ** argv) { llama_sampler_free(smpl3); llama_batch_free(batch); - llama_free(ctx3); - llama_free_model(model); if (result0 != result2) { fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__); diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 93e876f5a5524..aee90388e4fb3 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -5,7 +5,7 @@ option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR}) if (MINGW) - # fix: https://github.com/ggerganov/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006 + # fix: https://github.com/ggml-org/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006 add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER}) endif() @@ -15,13 +15,8 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS - index.html - completion.js + index.html.gz loading.html - deps_daisyui.min.css - deps_markdown-it.js - deps_tailwindcss.js - deps_vue.esm-browser.js ) foreach(asset ${PUBLIC_ASSETS}) @@ -33,11 +28,13 @@ foreach(asset ${PUBLIC_ASSETS}) OUTPUT "${output}" COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake" ) + set_source_files_properties(${output} PROPERTIES GENERATED TRUE) endforeach() add_executable(${TARGET} ${TARGET_SRCS}) install(TARGETS ${TARGET} RUNTIME) +target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}) target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) @@ -50,4 +47,4 @@ if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/server/README.md b/examples/server/README.md index 0936e0b7ba13a..a2a0903261e31 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -7,14 +7,15 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp. **Features:** * LLM inference of F16 and quantized models on GPU and CPU * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes - * Reranking endoint (WIP: https://github.com/ggerganov/llama.cpp/pull/9510) + * Reranking endoint (WIP: https://github.com/ggml-org/llama.cpp/pull/9510) * Parallel decoding with multi-user support * Continuous batching * Multimodal (wip) * Monitoring endpoints * Schema-constrained JSON response format + * [Function calling](../../docs/function-calling.md) / tool use for ~any model -The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). +The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggml-org/llama.cpp/issues/4216). ## Usage @@ -45,10 +46,7 @@ The project is under active development, and we are [looking for feedback and co | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | | `-fa, --flash-attn` | enable Flash Attention (default: disabled)
(env: LLAMA_ARG_FLASH_ATTN) | -| `-p, --prompt PROMPT` | prompt to start generation with | | `--no-perf` | disable internal libllama performance timings (default: false)
(env: LLAMA_ARG_NO_PERF) | -| `-f, --file FNAME` | a file containing the prompt (default: none) | -| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) | | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | | `--no-escape` | do not process escape sequences | | `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | @@ -62,13 +60,15 @@ The project is under active development, and we are [looking for feedback and co | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | | `-dkvc, --dump-kv-cache` | verbose print of the KV cache | | `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | -| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | -| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | +| `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | +| `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | -| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | +| `--list-devices` | print list of available devices and exit | | `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM
(env: LLAMA_ARG_N_GPU_LAYERS) | | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE) | | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
(env: LLAMA_ARG_TENSOR_SPLIT) | @@ -102,7 +102,6 @@ The project is under active development, and we are [looking for feedback and co | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | -| `--penalize-nl` | penalize newline tokens (default: false) | | `--temp N` | temperature (default: 0.8) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled) | | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | @@ -128,7 +127,8 @@ The project is under active development, and we are [looking for feedback and co | `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | | `--grammar-file FNAME` | file to read grammar from | | `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | - +| `--jinja` | Enable experimental Jinja templating engine (required for tool use) | +| `--reasoning-format FORMAT` | Controls extraction of model thinking traces and the format / field in which they are returned (default: `deepseek`; allowed values: `deepseek`, `none`; requires `--jinja`). `none` will leave thinking traces inline in `message.content` in a model-specific format, while `deepseek` will return them separately under `message.reasoning_content` | **Example-specific params** @@ -136,6 +136,7 @@ The project is under active development, and we are [looking for feedback and co | -------- | ----------- | | `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | | `-sp, --special` | special tokens output enabled (default: false) | +| `--no-warmup` | skip warming up the model with an empty run | | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | @@ -144,6 +145,7 @@ The project is under active development, and we are [looking for feedback and co | `--host HOST` | ip address to listen (default: 127.0.0.1)
(env: LLAMA_ARG_HOST) | | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | +| `--no-webui` | Disable the Web UI (default: enabled)
(env: LLAMA_ARG_NO_WEBUI) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | | `--api-key KEY` | API key to use for authentication (default: none)
(env: LLAMA_API_KEY) | @@ -158,9 +160,16 @@ The project is under active development, and we are [looking for feedback and co | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | | `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted:
https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
list of built-in templates:
chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | +| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | +| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)
(env: LLAMA_ARG_DRAFT_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | +| `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | +| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | +| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT) | Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. @@ -170,7 +179,7 @@ Example usage of docker compose with environment variables: ```yml services: llamacpp-server: - image: ghcr.io/ggerganov/llama.cpp:server + image: ghcr.io/ggml-org/llama.cpp:server ports: - 8080:8080 volumes: @@ -188,12 +197,6 @@ services: `llama-server` is built alongside everything else from the root of the project -- Using `make`: - - ```bash - make llama-server - ``` - - Using `CMake`: ```bash @@ -207,15 +210,6 @@ services: `llama-server` can also be built with SSL support using OpenSSL 3 -- Using `make`: - - ```bash - # NOTE: For non-system openssl, use the following: - # CXXFLAGS="-I /path/to/openssl/include" - # LDFLAGS="-L /path/to/openssl/lib" - make LLAMA_SERVER_SSL=true llama-server - ``` - - Using `CMake`: ```bash @@ -223,6 +217,41 @@ services: cmake --build build --config Release -t llama-server ``` +## Web UI + +The project includes a web-based user interface that enables interaction with the model through the `/chat/completions` endpoint. + +The web UI is developed using: +- `react` framework for frontend development +- `tailwindcss` and `daisyui` for styling +- `vite` for build tooling + +A pre-built version is available as a single HTML file under `/public` directory. + +To build or to run the dev server (with hot reload): + +```sh +# make sure you have nodejs installed +cd examples/server/webui +npm i + +# to run the dev server +npm run dev + +# to build the public/index.html.gz +npm run build +``` +After `public/index.html.gz` has been generated we need to generate the c++ +headers (like build/examples/server/index.html.gz.hpp) that will be included +by server.cpp. This is done by building `llama-server` as described in the +[build](#build) section above. + +NOTE: if you are using the vite dev server, you can change the API base URL to llama.cpp. To do that, run this code snippet in browser's console: + +```js +localStorage.setItem('base', 'http://localhost:8080') +``` + ## Quick Start To get started right away, run the following command, making sure to use the correct path for the model you have: @@ -245,10 +274,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can ### Docker ```bash -docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 +docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 # or, with CUDA: -docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 +docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggml-org/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 ``` ## Testing with CURL @@ -277,23 +306,23 @@ mkdir llama-client cd llama-client ``` -Create a index.js file and put this inside: +Create an index.js file and put this inside: ```javascript -const prompt = `Building a website can be done in 10 simple steps:`; +const prompt = "Building a website can be done in 10 simple steps:" -async function Test() { +async function test() { let response = await fetch("http://127.0.0.1:8080/completion", { - method: 'POST', + method: "POST", body: JSON.stringify({ prompt, - n_predict: 512, + n_predict: 64, }) }) console.log((await response.json()).content) } -Test() +test() ``` And run it: @@ -317,153 +346,198 @@ node index.js ### POST `/completion`: Given a `prompt`, it returns the predicted completion. - *Options:* +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/completions` instead. - `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: +*Options:* - - The prompt is a string or an array with the first element given as a string - - The model's `tokenizer.ggml.add_bos_token` metadata is `true` +`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: - These input shapes and data type are allowed for `prompt`: + - The prompt is a string or an array with the first element given as a string + - The model's `tokenizer.ggml.add_bos_token` metadata is `true` - - Single string: `"string"` - - Single sequence of tokens: `[12, 34, 56]` - - Mixed tokens and strings: `[12, 34, "string", 56, 78]` +These input shapes and data type are allowed for `prompt`: - Multiple prompts are also supported. In this case, the completion result will be an array. + - Single string: `"string"` + - Single sequence of tokens: `[12, 34, 56]` + - Mixed tokens and strings: `[12, 34, "string", 56, 78]` - - Only strings: `["string1", "string2"]` - - Strings and sequences of tokens: `["string1", [12, 34, 56]]` - - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` +Multiple prompts are also supported. In this case, the completion result will be an array. - `temperature`: Adjust the randomness of the generated text. Default: `0.8` + - Only strings: `["string1", "string2"]` + - Strings and sequences of tokens: `["string1", [12, 34, 56]]` + - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` - `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. +`temperature`: Adjust the randomness of the generated text. Default: `0.8` - `dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0` +`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. - `top_k`: Limit the next token selection to the K most probable tokens. Default: `40` +`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0` - `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95` +`top_k`: Limit the next token selection to the K most probable tokens. Default: `40` - `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05` +`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95` - `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. +`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05` - `n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` +`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. - `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. - By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. +`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` - `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. +`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. +By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. - `stop`: Specify a JSON array of stopping strings. - These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` +`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`. - `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. +`stop`: Specify a JSON array of stopping strings. +These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` - `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` +`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. - `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size. +`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` - `penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true` +`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size. - `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled. +`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled. - `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. +`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. - `dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. +`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. - `dry_base`: Set the DRY repetition penalty base value. Default: `1.75` +`dry_base`: Set the DRY repetition penalty base value. Default: `1.75` - `dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` +`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` - `dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. +`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. - `dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` +`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` - `xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled. +`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled. - `xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC) +`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC) - `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. +`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. - `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` +`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` - `mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1` +`mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1` - `grammar`: Set grammar for grammar-based sampling. Default: no grammar +`grammar`: Set grammar for grammar-based sampling. Default: no grammar - `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. +`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. - `seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. +`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. - `ignore_eos`: Ignore end of stream token and continue generating. Default: `false` +`ignore_eos`: Ignore end of stream token and continue generating. Default: `false` - `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]` +`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]` - `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0` +`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0` - `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` +`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` - `t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled. +`t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled. - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. +`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. - `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1` +`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1` - `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false` +`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true` - `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values. +`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false` -**Response format** +`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values. -- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. +`timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false` -- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: +`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. -```json -{ - "content": "", - "probs": [ - { - "prob": float, - "tok_str": "" - }, - { - "prob": float, - "tok_str": "" - }, - ... - ] -}, -``` +`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name. + +`lora`: A list of LoRA adapters to be applied to this specific request. Each object in the list must contain `id` and `scale` fields. For example: `[{"id": 0, "scale": 0.5}, {"id": 1, "scale": 1.1}]`. If a LoRA adapter is not specified in the list, its scale will default to `0.0`. Please note that requests with different LoRA configurations will not be batched together, which may result in performance degradation. + +**Response format** -Notice that each `probs` is an array of length `n_probs`. +- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. + +- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements: + ``` + { + "content": "", + "tokens": [ generated token ids if requested ], + ... + "probs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + ... + ] + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + ... + ] + }, + ... + ] + }, + ``` + Please note that if `post_sampling_probs` is set to `true`: + - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0 + - `top_logprobs` will be replaced with `top_probs`. Each element contains: + - `id`: token ID + - `token`: token in string + - `bytes`: token in bytes + - `prob`: token probability, with the value between 0.0 and 1.0 + - Number of elements in `top_probs` may be less than `n_probs` - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. +- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). -- `model`: The path to the model loaded with `-m` -- `prompt`: The provided `prompt` -- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token -- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered -- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided +- `model`: The model alias (for model path, please use `/props` endpoint) +- `prompt`: The processed `prompt` (special tokens may be added) +- `stop_type`: Indicating whether the completion has stopped. Possible values are: + - `none`: Generating (not stopped) + - `eos`: Stopped because it encountered the EOS token + - `limit`: Stopped because `n_predict` tokens were generated before stop words or EOS was encountered + - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` - `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) - `tokens_evaluated`: Number of tokens evaluated in total from the prompt - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) + ### POST `/tokenize`: Tokenize a given text - *Options:* +*Options:* - `content`: (Required) The text to tokenize. +`content`: (Required) The text to tokenize. - `add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` +`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` - `with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false` +`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false` **Response:** @@ -489,7 +563,7 @@ If `with_pieces` is `true`: ``` With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k -```json +``` { "tokens": [ {"id": 198, "piece": [195]}, // hex C3 @@ -500,52 +574,68 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k ### POST `/detokenize`: Convert tokens to text - *Options:* +*Options:* + +`tokens`: Set the tokens to detokenize. - `tokens`: Set the tokens to detokenize. +### POST `/apply-template`: Apply chat template to a conversation + +Uses the server's prompt template formatting functionality to convert chat messages to a single string expected by a chat model as input, but does not perform inference. Instead, the prompt string is returned in the `prompt` field of the JSON response. The prompt can then be modified as desired (for example, to insert "Sure!" at the beginning of the model's response) before sending to `/completion` to generate the chat response. + +*Options:* + +`messages`: (Required) Chat turns in the same format as `/v1/chat/completions`. + +**Response format** + +Returns a JSON object with a field `prompt` containing a string of the input messages formatted according to the model's chat template format. ### POST `/embedding`: Generate embedding of a given text +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/embeddings` instead. + The same as [the embedding example](../embedding) does. - *Options:* +*Options:* - `content`: Set the text to process. +`content`: Set the text to process. - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. +`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. ### POST `/reranking`: Rerank documents according to a given query Similar to https://jina.ai/reranker/ but might change in the future. Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)) and the `--embedding --pooling rank` options. - *Options:* +*Options:* - `query`: The query against which the documents will be ranked. +`query`: The query against which the documents will be ranked. - `documents`: An array strings representing the documents to be ranked. +`documents`: An array strings representing the documents to be ranked. - *Aliases:* - - `/rerank` - - `/v1/rerank` - - `/v1/reranking` +*Aliases:* + - `/rerank` + - `/v1/rerank` + - `/v1/reranking` - *Examples:* +*Examples:* - ```shell - curl http://127.0.0.1:8012/v1/rerank \ - -H "Content-Type: application/json" \ - -d '{ - "model": "some-model", - "query": "What is panda?", - "top_n": 3, - "documents": [ - "hi", - "it is a bear", - "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." - ] - }' | jq - ``` +```shell +curl http://127.0.0.1:8012/v1/rerank \ + -H "Content-Type: application/json" \ + -d '{ + "model": "some-model", + "query": "What is panda?", + "top_n": 3, + "documents": [ + "hi", + "it is a bear", + "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." + ] + }' | jq +``` ### POST `/infill`: For code infilling. @@ -589,14 +679,83 @@ This endpoint is public (no API key check). By default, it is read-only. To make ```json { - "default_generation_settings": { ... }, + "default_generation_settings": { + "id": 0, + "id_task": -1, + "n_ctx": 1024, + "speculative": false, + "is_processing": false, + "params": { + "n_predict": -1, + "seed": 4294967295, + "temperature": 0.800000011920929, + "dynatemp_range": 0.0, + "dynatemp_exponent": 1.0, + "top_k": 40, + "top_p": 0.949999988079071, + "min_p": 0.05000000074505806, + "xtc_probability": 0.0, + "xtc_threshold": 0.10000000149011612, + "typical_p": 1.0, + "repeat_last_n": 64, + "repeat_penalty": 1.0, + "presence_penalty": 0.0, + "frequency_penalty": 0.0, + "dry_multiplier": 0.0, + "dry_base": 1.75, + "dry_allowed_length": 2, + "dry_penalty_last_n": -1, + "dry_sequence_breakers": [ + "\n", + ":", + "\"", + "*" + ], + "mirostat": 0, + "mirostat_tau": 5.0, + "mirostat_eta": 0.10000000149011612, + "stop": [], + "max_tokens": -1, + "n_keep": 0, + "n_discard": 0, + "ignore_eos": false, + "stream": true, + "n_probs": 0, + "min_keep": 0, + "grammar": "", + "samplers": [ + "dry", + "top_k", + "typ_p", + "top_p", + "min_p", + "xtc", + "temperature" + ], + "speculative.n_max": 16, + "speculative.n_min": 5, + "speculative.p_min": 0.8999999761581421, + "timings_per_token": false + }, + "prompt": "", + "next_token": { + "has_next_token": true, + "has_new_line": false, + "n_remain": -1, + "n_decoded": 0, + "stopping_word": "" + } + }, "total_slots": 1, - "chat_template": "" + "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", + "chat_template": "...", + "build_info": "b(build number)-(build commit hash)" } ``` - `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) +- `model_path` - the path to model file (same with `-m` argument) - `chat_template` - the model's original Jinja2 prompt template ### POST `/props`: Change server global properties. @@ -607,93 +766,45 @@ To use this endpoint with POST method, you need to start server with `--props` - None yet -### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API - -Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. +### POST `/embeddings`: non-OpenAI-compatible embeddings API - *Options:* +This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm. - See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. +Note that the response format of this endpoint is different from `/v1/embeddings`. - The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. +*Options:* - *Examples:* +Same as the `/v1/embeddings` endpoint. - You can use either Python `openai` library with appropriate checkpoints: +*Examples:* - ```python - import openai +Same as the `/v1/embeddings` endpoint. - client = openai.OpenAI( - base_url="http://localhost:8080/v1", # "http://:port" - api_key = "sk-no-key-required" - ) +**Response format** - completion = client.chat.completions.create( - model="gpt-3.5-turbo", - messages=[ - {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, - {"role": "user", "content": "Write a limerick about python exceptions"} +``` +[ + { + "index": 0, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], ] - ) - - print(completion.choices[0].message) - ``` - - ... or raw HTTP requests: - - ```shell - curl http://localhost:8080/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." - }, - { - "role": "user", - "content": "Write a limerick about python exceptions" - } + }, + ... + { + "index": P, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], ] - }' - ``` - -### POST `/v1/embeddings`: OpenAI-compatible embeddings API - - *Options:* - - See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). - - *Examples:* - - - input as string - - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": "hello", - "model":"GPT-4", - "encoding_format": "float" - }' - ``` - - - `input` as string array - - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": ["hello", "world"], - "model":"GPT-4", - "encoding_format": "float" - }' - ``` + } +] +``` ### GET `/slots`: Returns the current slots processing state @@ -710,56 +821,73 @@ Example: ```json [ - { - "dynatemp_exponent": 1.0, - "dynatemp_range": 0.0, - "frequency_penalty": 0.0, - "grammar": "", - "id": 0, - "ignore_eos": false, - "is_processing": false, - "logit_bias": [], - "min_p": 0.05000000074505806, - "mirostat": 0, - "mirostat_eta": 0.10000000149011612, - "mirostat_tau": 5.0, - "model": "llama-2-7b-32k-instruct.Q2_K.gguf", - "n_ctx": 2048, - "n_keep": 0, - "n_predict": 100000, - "n_probs": 0, - "next_token": { - "has_next_token": true, - "n_remain": -1, - "n_decoded": 0, - "stopped_eos": false, - "stopped_limit": false, - "stopped_word": false, - "stopping_word": "" - }, - "penalize_nl": true, - "presence_penalty": 0.0, - "prompt": "Say hello to llama.cpp", - "repeat_last_n": 64, - "repeat_penalty": 1.100000023841858, - "samplers": [ - "top_k", - "typical_p", - "top_p", - "min_p", - "temperature" - ], - "seed": 42, - "stop": [ - "\n" - ], - "stream": false, - "task_id": 0, - "temperature": 0.0, - "top_k": 40, - "top_p": 0.949999988079071, - "typical_p": 1.0 + { + "id": 0, + "id_task": -1, + "n_ctx": 1024, + "speculative": false, + "is_processing": false, + "params": { + "n_predict": -1, + "seed": 4294967295, + "temperature": 0.800000011920929, + "dynatemp_range": 0.0, + "dynatemp_exponent": 1.0, + "top_k": 40, + "top_p": 0.949999988079071, + "min_p": 0.05000000074505806, + "xtc_probability": 0.0, + "xtc_threshold": 0.10000000149011612, + "typical_p": 1.0, + "repeat_last_n": 64, + "repeat_penalty": 1.0, + "presence_penalty": 0.0, + "frequency_penalty": 0.0, + "dry_multiplier": 0.0, + "dry_base": 1.75, + "dry_allowed_length": 2, + "dry_penalty_last_n": -1, + "dry_sequence_breakers": [ + "\n", + ":", + "\"", + "*" + ], + "mirostat": 0, + "mirostat_tau": 5.0, + "mirostat_eta": 0.10000000149011612, + "stop": [], + "max_tokens": -1, + "n_keep": 0, + "n_discard": 0, + "ignore_eos": false, + "stream": true, + "n_probs": 0, + "min_keep": 0, + "grammar": "", + "samplers": [ + "dry", + "top_k", + "typ_p", + "top_p", + "min_p", + "xtc", + "temperature" + ], + "speculative.n_max": 16, + "speculative.n_min": 5, + "speculative.p_min": 0.8999999761581421, + "timings_per_token": false + }, + "prompt": "", + "next_token": { + "has_next_token": true, + "has_new_line": false, + "n_remain": -1, + "n_decoded": 0, + "stopping_word": "" } + } ] ``` @@ -779,9 +907,9 @@ Available metrics: ### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file. - *Options:* +*Options:* - `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter. +`filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter. **Response format** @@ -799,9 +927,9 @@ Available metrics: ### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file. - *Options:* +*Options:* - `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter. +`filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter. **Response format** @@ -834,6 +962,8 @@ This endpoint returns the loaded LoRA adapters. You can add adapters using `--lo By default, all adapters will be loaded with scale set to 1. To initialize all adapters scale to 0, add `--lora-init-without-apply` +Please note that this value will be overwritten by the `lora` field for each request. + If an adapter is disabled, the scale will be set to 0. **Response format** @@ -855,6 +985,8 @@ If an adapter is disabled, the scale will be set to 0. ### POST `/lora-adapters`: Set list of LoRA adapters +This sets the global scale for LoRA adapters. Please note that this value will be overwritten by the `lora` field for each request. + To disable an adapter, either remove it from the list below, or set scale to 0. **Request format** @@ -868,6 +1000,167 @@ To know the `id` of the adapter, use GET `/lora-adapters` ] ``` +## OpenAI-compatible API Endpoints + +### GET `/v1/models`: OpenAI-compatible Model Info API + +Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models). + +The returned list always has one single element. + +By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`. + +Example: + +```json +{ + "object": "list", + "data": [ + { + "id": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", + "object": "model", + "created": 1735142223, + "owned_by": "llamacpp", + "meta": { + "vocab_type": 2, + "n_vocab": 128256, + "n_ctx_train": 131072, + "n_embd": 4096, + "n_params": 8030261312, + "size": 4912898304 + } + } + ] +} +``` + +### POST `/v1/completions`: OpenAI-compatible Completions API + +Given an input `prompt`, it returns the predicted completion. Streaming mode is also supported. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. + +*Options:* + +See [OpenAI Completions API documentation](https://platform.openai.com/docs/api-reference/completions). + +llama.cpp `/completion`-specific features such as `mirostat` are supported. + +*Examples:* + +Example usage with `openai` python library: + +```python +import openai + +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) + +completion = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8 +) + +print(completion.choices[0].text) +``` + +### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API + +Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. + +*Options:* + +See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also supported. + +The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. + +*Examples:* + +You can use either Python `openai` library with appropriate checkpoints: + +```python +import openai + +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) + +completion = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, + {"role": "user", "content": "Write a limerick about python exceptions"} + ] +) + +print(completion.choices[0].message) +``` + +... or raw HTTP requests: + +```shell +curl http://localhost:8080/v1/chat/completions \ +-H "Content-Type: application/json" \ +-H "Authorization: Bearer no-key" \ +-d '{ +"model": "gpt-3.5-turbo", +"messages": [ +{ + "role": "system", + "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." +}, +{ + "role": "user", + "content": "Write a limerick about python exceptions" +} +] +}' +``` + +*Tool call support* + +[OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) is supported with the `--jinja` flag (and may require a `--chat-template-file` override to get the right tool-use compatible Jinja template; worst case, `--chat-template chatml` may also work). + +**See our [Function calling](../../docs/function-calling.md) docs** for more details, supported native tool call styles (generic tool call style is used as fallback) / examples of use. + +### POST `/v1/embeddings`: OpenAI-compatible embeddings API + +This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm. + +*Options:* + +See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). + +*Examples:* + +- input as string + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": "hello", + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + +- `input` as string array + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": ["hello", "world"], + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + ## More examples ### Interactive mode @@ -935,7 +1228,7 @@ Apart from error types supported by OAI, we also have custom types that are spec ### Legacy completion web UI -A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggerganov/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy` +A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggml-org/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy` For example: diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md index 353368e13b0c8..9549795ec29f9 100644 --- a/examples/server/bench/README.md +++ b/examples/server/bench/README.md @@ -6,10 +6,10 @@ Benchmark is using [k6](https://k6.io/). SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension. -Example: +Example (assuming golang >= 1.21 is installed): ```shell go install go.k6.io/xk6/cmd/xk6@latest -xk6 build master \ +$GOPATH/bin/xk6 build master \ --with github.com/phymbert/xk6-sse ``` @@ -33,7 +33,7 @@ The server must answer OAI Chat completion requests on `http://localhost:8080/v1 Example: ```shell -server --host localhost --port 8080 \ +llama-server --host localhost --port 8080 \ --model ggml-model-q4_0.gguf \ --cont-batching \ --metrics \ diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py index a9ed747f51db5..5cc6f92ab6c53 100644 --- a/examples/server/bench/bench.py +++ b/examples/server/bench/bench.py @@ -189,12 +189,12 @@ def main(args_in: list[str] | None = None) -> None: "pp": { "p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2), "avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2), - "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2), + "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2) if 'prompt_tokens_seconds' in prometheus_metrics else 0, }, "tg": { "p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2), "avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2), - "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2), + "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2) if 'predicted_tokens_seconds' in prometheus_metrics else 0, }, } with open("results.github.env", 'a') as github_env: @@ -214,11 +214,14 @@ def start_benchmark(args): k6_args = [ 'run', args.scenario, '--no-color', + '--no-connection-reuse', + '--no-vu-connection-reuse', ] k6_args.extend(['--duration', args.duration]) k6_args.extend(['--iterations', args.n_prompts]) k6_args.extend(['--vus', args.parallel]) k6_args.extend(['--summary-export', 'k6-results.json']) + k6_args.extend(['--out', 'csv=k6-results.csv']) args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} " args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]]) print(f"bench: starting k6 with: {args}") @@ -231,7 +234,7 @@ def start_server(args): server_process = start_server_background(args) attempts = 0 - max_attempts = 20 + max_attempts = 600 if 'GITHUB_ACTIONS' in os.environ: max_attempts *= 2 @@ -242,7 +245,15 @@ def start_server(args): print(f"bench: waiting for server to start ...") time.sleep(0.5) - print("bench: server started.") + attempts = 0 + while not is_server_ready(args.host, args.port): + attempts += 1 + if attempts > max_attempts: + assert False, "server not ready" + print(f"bench: waiting for server to be ready ...") + time.sleep(0.5) + + print("bench: server started and ready.") return server_process @@ -255,11 +266,6 @@ def start_server_background(args): '--host', args.host, '--port', args.port, ] - model_file = args.model_path_prefix + os.path.sep + args.hf_file - model_dir = os.path.dirname(model_file) - if not os.path.exists(model_dir): - os.makedirs(model_dir) - server_args.extend(['--model', model_file]) server_args.extend(['--hf-repo', args.hf_repo]) server_args.extend(['--hf-file', args.hf_file]) server_args.extend(['--n-gpu-layers', args.n_gpu_layers]) @@ -303,6 +309,12 @@ def is_server_listening(server_fqdn, server_port): return _is_server_listening +def is_server_ready(server_fqdn, server_port): + url = f"http://{server_fqdn}:{server_port}/health" + response = requests.get(url) + return response.status_code == 200 + + def escape_metric_name(metric_name): return re.sub('[^A-Z0-9]', '_', metric_name.upper()) diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js index bdf4f5abc87f7..2772bee5e5f38 100644 --- a/examples/server/bench/script.js +++ b/examples/server/bench/script.js @@ -56,6 +56,7 @@ const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens') const llamacpp_tokens_second = new Trend('llamacpp_tokens_second') const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second') +const llamacpp_emit_first_token_second = new Trend('llamacpp_emit_first_token_second') const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter') const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter') @@ -89,6 +90,9 @@ export default function () { ], "model": model, "stream": true, + "stream_options": { + "include_usage": true, // False to be supported in llama.cpp server + }, "seed": 42, "max_tokens": max_tokens, "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS @@ -105,12 +109,20 @@ export default function () { client.on('event', function (event) { if (promptEvalEndTime == null) { promptEvalEndTime = new Date() + llamacpp_emit_first_token_second.add((promptEvalEndTime - startTime) / 1.e3) + } + + if (event.data === '[DONE]' || event.data === '') { + return } let chunk = JSON.parse(event.data) - let choice = chunk.choices[0] - if (choice.finish_reason) { - finish_reason = choice.finish_reason + + if (chunk.choices && chunk.choices.length > 0) { + let choice = chunk.choices[0] + if (choice.finish_reason) { + finish_reason = choice.finish_reason + } } if (chunk.usage) { diff --git a/examples/server/deps.sh b/examples/server/deps.sh deleted file mode 100755 index 1ff80d0569987..0000000000000 --- a/examples/server/deps.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash -# Download and update deps for binary - -# get the directory of this script file -DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" -PUBLIC=$DIR/public - -echo "download js bundle files" - -# Note for contributors: Always pin to a specific version "maj.min.patch" to avoid breaking the CI - -curl -L https://cdn.tailwindcss.com/3.4.14 > $PUBLIC/deps_tailwindcss.js -echo >> $PUBLIC/deps_tailwindcss.js # add newline - -curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/styled.min.css > $PUBLIC/deps_daisyui.min.css -curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/themes.min.css >> $PUBLIC/deps_daisyui.min.css -echo >> $PUBLIC/deps_daisyui.min.css # add newline - -curl -L https://unpkg.com/vue@3.5.12/dist/vue.esm-browser.js > $PUBLIC/deps_vue.esm-browser.js -echo >> $PUBLIC/deps_vue.esm-browser.js # add newline - -curl -L https://cdnjs.cloudflare.com/ajax/libs/markdown-it/13.0.2/markdown-it.js > $PUBLIC/deps_markdown-it.js -echo >> $PUBLIC/deps_markdown-it.js # add newline - -ls -lah $PUBLIC diff --git a/examples/server/httplib.h b/examples/server/httplib.h index f360bd93ea098..593beb50150b1 100644 --- a/examples/server/httplib.h +++ b/examples/server/httplib.h @@ -1,14 +1,14 @@ // // httplib.h // -// Copyright (c) 2024 Yuji Hirose. All rights reserved. +// Copyright (c) 2025 Yuji Hirose. All rights reserved. // MIT License // #ifndef CPPHTTPLIB_HTTPLIB_H #define CPPHTTPLIB_HTTPLIB_H -#define CPPHTTPLIB_VERSION "0.15.3" +#define CPPHTTPLIB_VERSION "0.19.0" /* * Configuration @@ -18,8 +18,12 @@ #define CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND 5 #endif +#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND +#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND 10000 +#endif + #ifndef CPPHTTPLIB_KEEPALIVE_MAX_COUNT -#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 5 +#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 100 #endif #ifndef CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND @@ -30,20 +34,40 @@ #define CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND 0 #endif -#ifndef CPPHTTPLIB_READ_TIMEOUT_SECOND -#define CPPHTTPLIB_READ_TIMEOUT_SECOND 5 +#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND +#define CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND 5 +#endif + +#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND +#define CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND +#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND 5 +#endif + +#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND +#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND +#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND 300 +#endif + +#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND +#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND 0 #endif -#ifndef CPPHTTPLIB_READ_TIMEOUT_USECOND -#define CPPHTTPLIB_READ_TIMEOUT_USECOND 0 +#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND +#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND 5 #endif -#ifndef CPPHTTPLIB_WRITE_TIMEOUT_SECOND -#define CPPHTTPLIB_WRITE_TIMEOUT_SECOND 5 +#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND +#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND 0 #endif -#ifndef CPPHTTPLIB_WRITE_TIMEOUT_USECOND -#define CPPHTTPLIB_WRITE_TIMEOUT_USECOND 0 +#ifndef CPPHTTPLIB_CLIENT_MAX_TIMEOUT_MSECOND +#define CPPHTTPLIB_CLIENT_MAX_TIMEOUT_MSECOND 0 #endif #ifndef CPPHTTPLIB_IDLE_INTERVAL_SECOND @@ -90,8 +114,12 @@ #define CPPHTTPLIB_TCP_NODELAY false #endif +#ifndef CPPHTTPLIB_IPV6_V6ONLY +#define CPPHTTPLIB_IPV6_V6ONLY false +#endif + #ifndef CPPHTTPLIB_RECV_BUFSIZ -#define CPPHTTPLIB_RECV_BUFSIZ size_t(4096u) +#define CPPHTTPLIB_RECV_BUFSIZ size_t(16384u) #endif #ifndef CPPHTTPLIB_COMPRESSION_BUFSIZ @@ -145,11 +173,11 @@ using ssize_t = long; #endif // _MSC_VER #ifndef S_ISREG -#define S_ISREG(m) (((m)&S_IFREG) == S_IFREG) +#define S_ISREG(m) (((m) & S_IFREG) == S_IFREG) #endif // S_ISREG #ifndef S_ISDIR -#define S_ISDIR(m) (((m)&S_IFDIR) == S_IFDIR) +#define S_ISDIR(m) (((m) & S_IFDIR) == S_IFDIR) #endif // S_ISDIR #ifndef NOMINMAX @@ -165,6 +193,7 @@ using ssize_t = long; #endif using socket_t = SOCKET; +using socklen_t = int; #ifdef CPPHTTPLIB_USE_POLL #define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout) #endif @@ -194,7 +223,9 @@ using socket_t = SOCKET; #include #include #include +#ifndef __VMS #include +#endif #include #include #include @@ -269,7 +300,12 @@ using socket_t = int; #include #include -#if OPENSSL_VERSION_NUMBER < 0x30000000L +#if defined(OPENSSL_IS_BORINGSSL) || defined(LIBRESSL_VERSION_NUMBER) +#if OPENSSL_VERSION_NUMBER < 0x1010107f +#error Please use OpenSSL or a current version of BoringSSL +#endif +#define SSL_get1_peer_certificate SSL_get_peer_certificate +#elif OPENSSL_VERSION_NUMBER < 0x30000000L #error Sorry, OpenSSL versions prior to 3.0.0 are not supported #endif @@ -312,16 +348,63 @@ make_unique(std::size_t n) { return std::unique_ptr(new RT[n]); } -struct ci { - bool operator()(const std::string &s1, const std::string &s2) const { - return std::lexicographical_compare(s1.begin(), s1.end(), s2.begin(), - s2.end(), - [](unsigned char c1, unsigned char c2) { - return ::tolower(c1) < ::tolower(c2); - }); +namespace case_ignore { + +inline unsigned char to_lower(int c) { + const static unsigned char table[256] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, + 122, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, + 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, + 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, + 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, + 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 224, 225, 226, + 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, + 242, 243, 244, 245, 246, 215, 248, 249, 250, 251, 252, 253, 254, 223, 224, + 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, + 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, + 255, + }; + return table[(unsigned char)(char)c]; +} + +inline bool equal(const std::string &a, const std::string &b) { + return a.size() == b.size() && + std::equal(a.begin(), a.end(), b.begin(), [](char ca, char cb) { + return to_lower(ca) == to_lower(cb); + }); +} + +struct equal_to { + bool operator()(const std::string &a, const std::string &b) const { + return equal(a, b); } }; +struct hash { + size_t operator()(const std::string &key) const { + return hash_core(key.data(), key.size(), 0); + } + + size_t hash_core(const char *s, size_t l, size_t h) const { + return (l == 0) ? h + : hash_core(s + 1, l - 1, + // Unsets the 6 high bits of h, therefore no + // overflow happens + (((std::numeric_limits::max)() >> 6) & + h * 33) ^ + static_cast(to_lower(*s))); + } +}; + +} // namespace case_ignore + // This is based on // "http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2014/n4189". @@ -427,7 +510,9 @@ enum StatusCode { NetworkAuthenticationRequired_511 = 511, }; -using Headers = std::multimap; +using Headers = + std::unordered_multimap; using Params = std::multimap; using Match = std::smatch; @@ -534,6 +619,7 @@ using Ranges = std::vector; struct Request { std::string method; std::string path; + Params params; Headers headers; std::string body; @@ -545,11 +631,11 @@ struct Request { // for server std::string version; std::string target; - Params params; MultipartFormDataMap files; Ranges ranges; Match matches; std::unordered_map path_params; + std::function is_connection_closed = []() { return true; }; // for client ResponseHandler response_handler; @@ -560,8 +646,10 @@ struct Request { #endif bool has_header(const std::string &key) const; - std::string get_header_value(const std::string &key, size_t id = 0) const; - uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const; + std::string get_header_value(const std::string &key, const char *def = "", + size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0, + size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); @@ -581,6 +669,8 @@ struct Request { ContentProvider content_provider_; bool is_chunked_content_provider_ = false; size_t authorization_count_ = 0; + std::chrono::time_point start_time_ = + std::chrono::steady_clock::time_point::min(); }; struct Response { @@ -592,8 +682,10 @@ struct Response { std::string location; // Redirect location bool has_header(const std::string &key) const; - std::string get_header_value(const std::string &key, size_t id = 0) const; - uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const; + std::string get_header_value(const std::string &key, const char *def = "", + size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0, + size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); @@ -614,6 +706,10 @@ struct Response { const std::string &content_type, ContentProviderWithoutLength provider, ContentProviderResourceReleaser resource_releaser = nullptr); + void set_file_content(const std::string &path, + const std::string &content_type); + void set_file_content(const std::string &path); + Response() = default; Response(const Response &) = default; Response &operator=(const Response &) = default; @@ -631,6 +727,8 @@ struct Response { ContentProviderResourceReleaser content_provider_resource_releaser_; bool is_chunked_content_provider_ = false; bool content_provider_success_ = false; + std::string file_content_path_; + std::string file_content_content_type_; }; class Stream { @@ -646,8 +744,8 @@ class Stream { virtual void get_local_ip_and_port(std::string &ip, int &port) const = 0; virtual socket_t socket() const = 0; - template - ssize_t write_format(const char *fmt, const Args &...args); + virtual time_t duration() const = 0; + ssize_t write(const char *ptr); ssize_t write(const std::string &s); }; @@ -719,13 +817,18 @@ class ThreadPool final : public TaskQueue { if (pool_.shutdown_ && pool_.jobs_.empty()) { break; } - fn = std::move(pool_.jobs_.front()); + fn = pool_.jobs_.front(); pool_.jobs_.pop_front(); } assert(true == static_cast(fn)); fn(); } + +#if defined(CPPHTTPLIB_OPENSSL_SUPPORT) && !defined(OPENSSL_IS_BORINGSSL) && \ + !defined(LIBRESSL_VERSION_NUMBER) + OPENSSL_thread_stop(); +#endif } ThreadPool &pool_; @@ -746,6 +849,16 @@ using Logger = std::function; using SocketOptions = std::function; +namespace detail { + +bool set_socket_opt_impl(socket_t sock, int level, int optname, + const void *optval, socklen_t optlen); +bool set_socket_opt(socket_t sock, int level, int optname, int opt); +bool set_socket_opt_time(socket_t sock, int level, int optname, time_t sec, + time_t usec); + +} // namespace detail + void default_socket_options(socket_t sock); const char *status_message(int status); @@ -787,7 +900,6 @@ class PathParamsMatcher final : public MatcherBase { bool match(Request &request) const override; private: - static constexpr char marker = ':'; // Treat segment separators as the end of path parameter capture // Does not need to handle query parameters as they are parsed before path // matching @@ -871,8 +983,13 @@ class Server { Server &set_default_file_mimetype(const std::string &mime); Server &set_file_request_handler(Handler handler); - Server &set_error_handler(HandlerWithResponse handler); - Server &set_error_handler(Handler handler); + template + Server &set_error_handler(ErrorHandlerFunc &&handler) { + return set_error_handler_core( + std::forward(handler), + std::is_convertible{}); + } + Server &set_exception_handler(ExceptionHandler handler); Server &set_pre_routing_handler(HandlerWithResponse handler); Server &set_post_routing_handler(Handler handler); @@ -882,6 +999,7 @@ class Server { Server &set_address_family(int family); Server &set_tcp_nodelay(bool on); + Server &set_ipv6_v6only(bool on); Server &set_socket_options(SocketOptions socket_options); Server &set_default_headers(Headers headers); @@ -914,21 +1032,24 @@ class Server { bool is_running() const; void wait_until_ready() const; void stop(); + void decommission(); std::function new_task_queue; protected: - bool process_request(Stream &strm, bool close_connection, + bool process_request(Stream &strm, const std::string &remote_addr, + int remote_port, const std::string &local_addr, + int local_port, bool close_connection, bool &connection_closed, const std::function &setup_request); std::atomic svr_sock_{INVALID_SOCKET}; size_t keep_alive_max_count_ = CPPHTTPLIB_KEEPALIVE_MAX_COUNT; time_t keep_alive_timeout_sec_ = CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND; - time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND; - time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND; - time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND; - time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND; + time_t read_timeout_sec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND; + time_t read_timeout_usec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND; + time_t write_timeout_sec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND; + time_t write_timeout_usec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND; time_t idle_interval_sec_ = CPPHTTPLIB_IDLE_INTERVAL_SECOND; time_t idle_interval_usec_ = CPPHTTPLIB_IDLE_INTERVAL_USECOND; size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH; @@ -943,6 +1064,9 @@ class Server { static std::unique_ptr make_matcher(const std::string &pattern); + Server &set_error_handler_core(HandlerWithResponse handler, std::true_type); + Server &set_error_handler_core(Handler handler, std::false_type); + socket_t create_server_socket(const std::string &host, int port, int socket_flags, SocketOptions socket_options) const; @@ -985,7 +1109,7 @@ class Server { virtual bool process_and_close_socket(socket_t sock); std::atomic is_running_{false}; - std::atomic done_{false}; + std::atomic is_decommisioned{false}; struct MountPointEntry { std::string mount_point; @@ -1018,6 +1142,7 @@ class Server { int address_family_ = AF_UNSPEC; bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY; + bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY; SocketOptions socket_options_ = default_socket_options; Headers default_headers_; @@ -1037,6 +1162,7 @@ enum class Error { SSLConnection, SSLLoadingCerts, SSLServerVerification, + SSLServerHostnameVerification, UnsupportedMultipartBoundaryChars, Compression, ConnectionTimeout, @@ -1074,9 +1200,10 @@ class Result { // Request Headers bool has_request_header(const std::string &key) const; std::string get_request_header_value(const std::string &key, + const char *def = "", size_t id = 0) const; uint64_t get_request_header_value_u64(const std::string &key, - size_t id = 0) const; + uint64_t def = 0, size_t id = 0) const; size_t get_request_header_value_count(const std::string &key) const; private: @@ -1140,10 +1267,18 @@ class ClientImpl { const std::string &content_type); Result Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Post(const std::string &path, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1159,6 +1294,8 @@ class ClientImpl { Result Post(const std::string &path, const Params ¶ms); Result Post(const std::string &path, const Headers &headers, const Params ¶ms); + Result Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Post(const std::string &path, const MultipartFormDataItems &items); Result Post(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1173,10 +1310,18 @@ class ClientImpl { const std::string &content_type); Result Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Put(const std::string &path, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); Result Put(const std::string &path, @@ -1191,6 +1336,8 @@ class ClientImpl { Result Put(const std::string &path, const Params ¶ms); Result Put(const std::string &path, const Headers &headers, const Params ¶ms); + Result Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Put(const std::string &path, const MultipartFormDataItems &items); Result Put(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1203,13 +1350,23 @@ class ClientImpl { Result Patch(const std::string &path); Result Patch(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1227,13 +1384,24 @@ class ClientImpl { Result Delete(const std::string &path, const Headers &headers); Result Delete(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Options(const std::string &path); Result Options(const std::string &path, const Headers &headers); @@ -1258,6 +1426,7 @@ class ClientImpl { void set_address_family(int family); void set_tcp_nodelay(bool on); + void set_ipv6_v6only(bool on); void set_socket_options(SocketOptions socket_options); void set_connection_timeout(time_t sec, time_t usec = 0); @@ -1273,6 +1442,10 @@ class ClientImpl { template void set_write_timeout(const std::chrono::duration &duration); + void set_max_timeout(time_t msec); + template + void set_max_timeout(const std::chrono::duration &duration); + void set_basic_auth(const std::string &username, const std::string &password); void set_bearer_token_auth(const std::string &token); #ifdef CPPHTTPLIB_OPENSSL_SUPPORT @@ -1309,6 +1482,8 @@ class ClientImpl { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT void enable_server_certificate_verification(bool enabled); + void enable_server_hostname_verification(bool enabled); + void set_server_certificate_verifier(std::function verifier); #endif void set_logger(Logger logger); @@ -1375,10 +1550,11 @@ class ClientImpl { time_t connection_timeout_sec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND; time_t connection_timeout_usec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND; - time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND; - time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND; - time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND; - time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND; + time_t read_timeout_sec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND; + time_t read_timeout_usec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND; + time_t write_timeout_sec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND; + time_t write_timeout_usec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND; + time_t max_timeout_msec_ = CPPHTTPLIB_CLIENT_MAX_TIMEOUT_MSECOND; std::string basic_auth_username_; std::string basic_auth_password_; @@ -1395,6 +1571,7 @@ class ClientImpl { int address_family_ = AF_UNSPEC; bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY; + bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY; SocketOptions socket_options_ = nullptr; bool compress_ = false; @@ -1422,6 +1599,8 @@ class ClientImpl { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT bool server_certificate_verification_ = true; + bool server_hostname_verification_ = true; + std::function server_certificate_verifier_; #endif Logger logger_; @@ -1448,15 +1627,17 @@ class ClientImpl { const Headers &headers, const char *body, size_t content_length, ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, - const std::string &content_type); + const std::string &content_type, Progress progress); ContentProviderWithoutLength get_multipart_content_provider( const std::string &boundary, const MultipartFormDataItems &items, const MultipartFormDataProviderItems &provider_items) const; std::string adjust_host_string(const std::string &host) const; - virtual bool process_socket(const Socket &socket, - std::function callback); + virtual bool + process_socket(const Socket &socket, + std::chrono::time_point start_time, + std::function callback); virtual bool is_ssl() const; }; @@ -1477,6 +1658,7 @@ class Client { const std::string &client_key_path); Client(Client &&) = default; + Client &operator=(Client &&) = default; ~Client(); @@ -1523,10 +1705,18 @@ class Client { const std::string &content_type); Result Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Post(const std::string &path, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1542,6 +1732,8 @@ class Client { Result Post(const std::string &path, const Params ¶ms); Result Post(const std::string &path, const Headers &headers, const Params ¶ms); + Result Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Post(const std::string &path, const MultipartFormDataItems &items); Result Post(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1556,10 +1748,18 @@ class Client { const std::string &content_type); Result Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Put(const std::string &path, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); Result Put(const std::string &path, @@ -1574,6 +1774,8 @@ class Client { Result Put(const std::string &path, const Params ¶ms); Result Put(const std::string &path, const Headers &headers, const Params ¶ms); + Result Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Put(const std::string &path, const MultipartFormDataItems &items); Result Put(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1586,13 +1788,23 @@ class Client { Result Patch(const std::string &path); Result Patch(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1610,13 +1822,24 @@ class Client { Result Delete(const std::string &path, const Headers &headers); Result Delete(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Options(const std::string &path); Result Options(const std::string &path, const Headers &headers); @@ -1656,6 +1879,10 @@ class Client { template void set_write_timeout(const std::chrono::duration &duration); + void set_max_timeout(time_t msec); + template + void set_max_timeout(const std::chrono::duration &duration); + void set_basic_auth(const std::string &username, const std::string &password); void set_bearer_token_auth(const std::string &token); #ifdef CPPHTTPLIB_OPENSSL_SUPPORT @@ -1685,6 +1912,8 @@ class Client { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT void enable_server_certificate_verification(bool enabled); + void enable_server_hostname_verification(bool enabled); + void set_server_certificate_verifier(std::function verifier); #endif void set_logger(Logger logger); @@ -1730,6 +1959,9 @@ class SSLServer : public Server { SSL_CTX *ssl_context() const; + void update_certs(X509 *cert, EVP_PKEY *private_key, + X509_STORE *client_ca_cert_store = nullptr); + private: bool process_and_close_socket(socket_t sock) override; @@ -1768,12 +2000,16 @@ class SSLClient final : public ClientImpl { void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override; void shutdown_ssl_impl(Socket &socket, bool shutdown_gracefully); - bool process_socket(const Socket &socket, - std::function callback) override; + bool + process_socket(const Socket &socket, + std::chrono::time_point start_time, + std::function callback) override; bool is_ssl() const override; - bool connect_with_proxy(Socket &sock, Response &res, bool &success, - Error &error); + bool connect_with_proxy( + Socket &sock, + std::chrono::time_point start_time, + Response &res, bool &success, Error &error); bool initialize_ssl(Socket &socket, Error &error); bool load_certs(); @@ -1810,70 +2046,85 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) { callback(static_cast(sec), static_cast(usec)); } +inline bool is_numeric(const std::string &str) { + return !str.empty() && std::all_of(str.begin(), str.end(), ::isdigit); +} + inline uint64_t get_header_value_u64(const Headers &headers, - const std::string &key, size_t id, - uint64_t def) { + const std::string &key, uint64_t def, + size_t id, bool &is_invalid_value) { + is_invalid_value = false; auto rng = headers.equal_range(key); auto it = rng.first; std::advance(it, static_cast(id)); if (it != rng.second) { - return std::strtoull(it->second.data(), nullptr, 10); + if (is_numeric(it->second)) { + return std::strtoull(it->second.data(), nullptr, 10); + } else { + is_invalid_value = true; + } } return def; } +inline uint64_t get_header_value_u64(const Headers &headers, + const std::string &key, uint64_t def, + size_t id) { + bool dummy = false; + return get_header_value_u64(headers, key, def, id, dummy); +} + } // namespace detail inline uint64_t Request::get_header_value_u64(const std::string &key, - size_t id) const { - return detail::get_header_value_u64(headers, key, id, 0); + uint64_t def, size_t id) const { + return detail::get_header_value_u64(headers, key, def, id); } inline uint64_t Response::get_header_value_u64(const std::string &key, - size_t id) const { - return detail::get_header_value_u64(headers, key, id, 0); + uint64_t def, size_t id) const { + return detail::get_header_value_u64(headers, key, def, id); } -template -inline ssize_t Stream::write_format(const char *fmt, const Args &...args) { - const auto bufsiz = 2048; - std::array buf{}; - - auto sn = snprintf(buf.data(), buf.size() - 1, fmt, args...); - if (sn <= 0) { return sn; } - - auto n = static_cast(sn); +namespace detail { - if (n >= buf.size() - 1) { - std::vector glowable_buf(buf.size()); +inline bool set_socket_opt_impl(socket_t sock, int level, int optname, + const void *optval, socklen_t optlen) { + return setsockopt(sock, level, optname, +#ifdef _WIN32 + reinterpret_cast(optval), +#else + optval, +#endif + optlen) == 0; +} - while (n >= glowable_buf.size() - 1) { - glowable_buf.resize(glowable_buf.size() * 2); - n = static_cast( - snprintf(&glowable_buf[0], glowable_buf.size() - 1, fmt, args...)); - } - return write(&glowable_buf[0], n); - } else { - return write(buf.data(), n); - } +inline bool set_socket_opt(socket_t sock, int level, int optname, int optval) { + return set_socket_opt_impl(sock, level, optname, &optval, sizeof(optval)); } -inline void default_socket_options(socket_t sock) { - int yes = 1; +inline bool set_socket_opt_time(socket_t sock, int level, int optname, + time_t sec, time_t usec) { #ifdef _WIN32 - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, - reinterpret_cast(&yes), sizeof(yes)); - setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE, - reinterpret_cast(&yes), sizeof(yes)); + auto timeout = static_cast(sec * 1000 + usec / 1000); #else + timeval timeout; + timeout.tv_sec = static_cast(sec); + timeout.tv_usec = static_cast(usec); +#endif + return set_socket_opt_impl(sock, level, optname, &timeout, sizeof(timeout)); +} + +} // namespace detail + +inline void default_socket_options(socket_t sock) { + detail::set_socket_opt(sock, SOL_SOCKET, #ifdef SO_REUSEPORT - setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, - reinterpret_cast(&yes), sizeof(yes)); + SO_REUSEPORT, #else - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, - reinterpret_cast(&yes), sizeof(yes)); -#endif + SO_REUSEADDR, #endif + 1); } inline const char *status_message(int status) { @@ -1997,6 +2248,8 @@ inline std::string to_string(const Error error) { case Error::SSLConnection: return "SSL connection failed"; case Error::SSLLoadingCerts: return "SSL certificate loading failed"; case Error::SSLServerVerification: return "SSL server verification failed"; + case Error::SSLServerHostnameVerification: + return "SSL server hostname verification failed"; case Error::UnsupportedMultipartBoundaryChars: return "Unsupported HTTP multipart boundary characters"; case Error::Compression: return "Compression failed"; @@ -2016,8 +2269,9 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) { } inline uint64_t Result::get_request_header_value_u64(const std::string &key, + uint64_t def, size_t id) const { - return detail::get_header_value_u64(request_headers_, key, id, 0); + return detail::get_header_value_u64(request_headers_, key, def, id); } template @@ -2042,6 +2296,14 @@ inline void ClientImpl::set_write_timeout( duration, [&](time_t sec, time_t usec) { set_write_timeout(sec, usec); }); } +template +inline void ClientImpl::set_max_timeout( + const std::chrono::duration &duration) { + auto msec = + std::chrono::duration_cast(duration).count(); + set_max_timeout(msec); +} + template inline void Client::set_connection_timeout( const std::chrono::duration &duration) { @@ -2060,6 +2322,12 @@ Client::set_write_timeout(const std::chrono::duration &duration) { cli_->set_write_timeout(duration); } +template +inline void +Client::set_max_timeout(const std::chrono::duration &duration) { + cli_->set_max_timeout(duration); +} + /* * Forward declarations and types that will be part of the .h file if split into * .h + .cc. @@ -2080,6 +2348,36 @@ make_basic_authentication_header(const std::string &username, namespace detail { +#if defined(_WIN32) +inline std::wstring u8string_to_wstring(const char *s) { + std::wstring ws; + auto len = static_cast(strlen(s)); + auto wlen = ::MultiByteToWideChar(CP_UTF8, 0, s, len, nullptr, 0); + if (wlen > 0) { + ws.resize(wlen); + wlen = ::MultiByteToWideChar( + CP_UTF8, 0, s, len, + const_cast(reinterpret_cast(ws.data())), wlen); + if (wlen != static_cast(ws.size())) { ws.clear(); } + } + return ws; +} +#endif + +struct FileStat { + FileStat(const std::string &path); + bool is_file() const; + bool is_dir() const; + +private: +#if defined(_WIN32) + struct _stat st_; +#else + struct stat st_; +#endif + int ret_ = -1; +}; + std::string encode_query_param(const std::string &value); std::string decode_url(const std::string &s, bool convert_plus_to_space); @@ -2088,29 +2386,46 @@ void read_file(const std::string &path, std::string &out); std::string trim_copy(const std::string &s); +void divide( + const char *data, std::size_t size, char d, + std::function + fn); + +void divide( + const std::string &str, char d, + std::function + fn); + void split(const char *b, const char *e, char d, std::function fn); void split(const char *b, const char *e, char d, size_t m, std::function fn); -bool process_client_socket(socket_t sock, time_t read_timeout_sec, - time_t read_timeout_usec, time_t write_timeout_sec, - time_t write_timeout_usec, - std::function callback); - -socket_t create_client_socket( - const std::string &host, const std::string &ip, int port, - int address_family, bool tcp_nodelay, SocketOptions socket_options, - time_t connection_timeout_sec, time_t connection_timeout_usec, - time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, - time_t write_timeout_usec, const std::string &intf, Error &error); +bool process_client_socket( + socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec, + std::chrono::time_point start_time, + std::function callback); + +socket_t create_client_socket(const std::string &host, const std::string &ip, + int port, int address_family, bool tcp_nodelay, + bool ipv6_v6only, SocketOptions socket_options, + time_t connection_timeout_sec, + time_t connection_timeout_usec, + time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, + time_t write_timeout_usec, + const std::string &intf, Error &error); const char *get_header_value(const Headers &headers, const std::string &key, - size_t id = 0, const char *def = nullptr); + const char *def, size_t id); std::string params_to_query_str(const Params ¶ms); +void parse_query_text(const char *data, std::size_t size, Params ¶ms); + void parse_query_text(const std::string &s, Params ¶ms); bool parse_multipart_boundary(const std::string &content_type, @@ -2140,6 +2455,7 @@ class BufferStream final : public Stream { void get_remote_ip_and_port(std::string &ip, int &port) const override; void get_local_ip_and_port(std::string &ip, int &port) const override; socket_t socket() const override; + time_t duration() const override; const std::string &get_buffer() const; @@ -2270,15 +2586,70 @@ class mmap { private: #if defined(_WIN32) - HANDLE hFile_; - HANDLE hMapping_; + HANDLE hFile_ = NULL; + HANDLE hMapping_ = NULL; #else - int fd_; + int fd_ = -1; #endif - size_t size_; - void *addr_; + size_t size_ = 0; + void *addr_ = nullptr; + bool is_open_empty_file = false; }; +// NOTE: https://www.rfc-editor.org/rfc/rfc9110#section-5 +namespace fields { + +inline bool is_token_char(char c) { + return std::isalnum(c) || c == '!' || c == '#' || c == '$' || c == '%' || + c == '&' || c == '\'' || c == '*' || c == '+' || c == '-' || + c == '.' || c == '^' || c == '_' || c == '`' || c == '|' || c == '~'; +} + +inline bool is_token(const std::string &s) { + if (s.empty()) { return false; } + for (auto c : s) { + if (!is_token_char(c)) { return false; } + } + return true; +} + +inline bool is_field_name(const std::string &s) { return is_token(s); } + +inline bool is_vchar(char c) { return c >= 33 && c <= 126; } + +inline bool is_obs_text(char c) { return 128 <= static_cast(c); } + +inline bool is_field_vchar(char c) { return is_vchar(c) || is_obs_text(c); } + +inline bool is_field_content(const std::string &s) { + if (s.empty()) { return true; } + + if (s.size() == 1) { + return is_field_vchar(s[0]); + } else if (s.size() == 2) { + return is_field_vchar(s[0]) && is_field_vchar(s[1]); + } else { + size_t i = 0; + + if (!is_field_vchar(s[i])) { return false; } + i++; + + while (i < s.size() - 1) { + auto c = s[i++]; + if (c == ' ' || c == '\t' || is_field_vchar(c)) { + } else { + return false; + } + } + + return is_field_vchar(s[i]); + } +} + +inline bool is_field_value(const std::string &s) { return is_field_content(s); } + +} // namespace fields + } // namespace detail // ---------------------------------------------------------------------------- @@ -2392,20 +2763,6 @@ inline std::string base64_encode(const std::string &in) { return out; } -inline bool is_file(const std::string &path) { -#ifdef _WIN32 - return _access_s(path.c_str(), 0) == 0; -#else - struct stat st; - return stat(path.c_str(), &st) >= 0 && S_ISREG(st.st_mode); -#endif -} - -inline bool is_dir(const std::string &path) { - struct stat st; - return stat(path.c_str(), &st) >= 0 && S_ISDIR(st.st_mode); -} - inline bool is_valid_path(const std::string &path) { size_t level = 0; size_t i = 0; @@ -2448,6 +2805,21 @@ inline bool is_valid_path(const std::string &path) { return true; } +inline FileStat::FileStat(const std::string &path) { +#if defined(_WIN32) + auto wpath = u8string_to_wstring(path.c_str()); + ret_ = _wstat(wpath.c_str(), &st_); +#else + ret_ = stat(path.c_str(), &st_); +#endif +} +inline bool FileStat::is_file() const { + return ret_ >= 0 && S_ISREG(st_.st_mode); +} +inline bool FileStat::is_dir() const { + return ret_ >= 0 && S_ISDIR(st_.st_mode); +} + inline std::string encode_query_param(const std::string &value) { std::ostringstream escaped; escaped.fill('0'); @@ -2579,6 +2951,27 @@ inline std::string trim_double_quotes_copy(const std::string &s) { return s; } +inline void +divide(const char *data, std::size_t size, char d, + std::function + fn) { + const auto it = std::find(data, data + size, d); + const auto found = static_cast(it != data + size); + const auto lhs_data = data; + const auto lhs_size = static_cast(it - data); + const auto rhs_data = it + found; + const auto rhs_size = size - lhs_size - found; + + fn(lhs_data, lhs_size, rhs_data, rhs_size); +} + +inline void +divide(const std::string &str, char d, + std::function + fn) { + divide(str.data(), str.size(), d, std::move(fn)); +} + inline void split(const char *b, const char *e, char d, std::function fn) { return split(b, e, d, (std::numeric_limits::max)(), std::move(fn)); @@ -2636,6 +3029,10 @@ inline bool stream_line_reader::getline() { fixed_buffer_used_size_ = 0; glowable_buffer_.clear(); +#ifndef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR + char prev_byte = 0; +#endif + for (size_t i = 0;; i++) { char byte; auto n = strm_.read(&byte, 1); @@ -2652,7 +3049,12 @@ inline bool stream_line_reader::getline() { append(byte); +#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR if (byte == '\n') { break; } +#else + if (prev_byte == '\r' && byte == '\n') { break; } + prev_byte = byte; +#endif } return true; @@ -2671,16 +3073,7 @@ inline void stream_line_reader::append(char c) { } } -inline mmap::mmap(const char *path) -#if defined(_WIN32) - : hFile_(NULL), hMapping_(NULL) -#else - : fd_(-1) -#endif - , - size_(0), addr_(nullptr) { - open(path); -} +inline mmap::mmap(const char *path) { open(path); } inline mmap::~mmap() { close(); } @@ -2688,29 +3081,60 @@ inline bool mmap::open(const char *path) { close(); #if defined(_WIN32) - std::wstring wpath; - for (size_t i = 0; i < strlen(path); i++) { - wpath += path[i]; - } + auto wpath = u8string_to_wstring(path); + if (wpath.empty()) { return false; } +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, OPEN_EXISTING, NULL); +#else + hFile_ = ::CreateFileW(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, NULL, + OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); +#endif if (hFile_ == INVALID_HANDLE_VALUE) { return false; } LARGE_INTEGER size{}; if (!::GetFileSizeEx(hFile_, &size)) { return false; } + // If the following line doesn't compile due to QuadPart, update Windows SDK. + // See: + // https://github.com/yhirose/cpp-httplib/issues/1903#issuecomment-2316520721 + if (static_cast(size.QuadPart) > + (std::numeric_limits::max)()) { + // `size_t` might be 32-bits, on 32-bits Windows. + return false; + } size_ = static_cast(size.QuadPart); +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 hMapping_ = ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL); +#else + hMapping_ = ::CreateFileMappingW(hFile_, NULL, PAGE_READONLY, 0, 0, NULL); +#endif + + // Special treatment for an empty file... + if (hMapping_ == NULL && size_ == 0) { + close(); + is_open_empty_file = true; + return true; + } if (hMapping_ == NULL) { close(); return false; } +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0); +#else + addr_ = ::MapViewOfFile(hMapping_, FILE_MAP_READ, 0, 0, 0); +#endif + + if (addr_ == nullptr) { + close(); + return false; + } #else fd_ = ::open(path, O_RDONLY); if (fd_ == -1) { return false; } @@ -2723,22 +3147,26 @@ inline bool mmap::open(const char *path) { size_ = static_cast(sb.st_size); addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0); -#endif - if (addr_ == nullptr) { + // Special treatment for an empty file... + if (addr_ == MAP_FAILED && size_ == 0) { close(); + is_open_empty_file = true; return false; } +#endif return true; } -inline bool mmap::is_open() const { return addr_ != nullptr; } +inline bool mmap::is_open() const { + return is_open_empty_file ? true : addr_ != nullptr; +} inline size_t mmap::size() const { return size_; } inline const char *mmap::data() const { - return static_cast(addr_); + return is_open_empty_file ? "" : static_cast(addr_); } inline void mmap::close() { @@ -2757,6 +3185,8 @@ inline void mmap::close() { ::CloseHandle(hFile_); hFile_ = INVALID_HANDLE_VALUE; } + + is_open_empty_file = false; #else if (addr_ != nullptr) { munmap(addr_, size_); @@ -2782,7 +3212,10 @@ template inline ssize_t handle_EINTR(T fn) { ssize_t res = 0; while (true) { res = fn(); - if (res < 0 && errno == EINTR) { continue; } + if (res < 0 && errno == EINTR) { + std::this_thread::sleep_for(std::chrono::microseconds{1}); + continue; + } break; } return res; @@ -2813,60 +3246,43 @@ inline ssize_t send_socket(socket_t sock, const void *ptr, size_t size, }); } -inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) { +template +inline ssize_t select_impl(socket_t sock, time_t sec, time_t usec) { #ifdef CPPHTTPLIB_USE_POLL - struct pollfd pfd_read; - pfd_read.fd = sock; - pfd_read.events = POLLIN; + struct pollfd pfd; + pfd.fd = sock; + pfd.events = (Read ? POLLIN : POLLOUT); auto timeout = static_cast(sec * 1000 + usec / 1000); - return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); + return handle_EINTR([&]() { return poll(&pfd, 1, timeout); }); #else #ifndef _WIN32 if (sock >= FD_SETSIZE) { return -1; } #endif - fd_set fds; + fd_set fds, *rfds, *wfds; FD_ZERO(&fds); FD_SET(sock, &fds); + rfds = (Read ? &fds : nullptr); + wfds = (Read ? nullptr : &fds); timeval tv; tv.tv_sec = static_cast(sec); tv.tv_usec = static_cast(usec); return handle_EINTR([&]() { - return select(static_cast(sock + 1), &fds, nullptr, nullptr, &tv); + return select(static_cast(sock + 1), rfds, wfds, nullptr, &tv); }); #endif } -inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) { -#ifdef CPPHTTPLIB_USE_POLL - struct pollfd pfd_read; - pfd_read.fd = sock; - pfd_read.events = POLLOUT; - - auto timeout = static_cast(sec * 1000 + usec / 1000); - - return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); -#else -#ifndef _WIN32 - if (sock >= FD_SETSIZE) { return -1; } -#endif - - fd_set fds; - FD_ZERO(&fds); - FD_SET(sock, &fds); - - timeval tv; - tv.tv_sec = static_cast(sec); - tv.tv_usec = static_cast(usec); +inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) { + return select_impl(sock, sec, usec); +} - return handle_EINTR([&]() { - return select(static_cast(sock + 1), nullptr, &fds, nullptr, &tv); - }); -#endif +inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) { + return select_impl(sock, sec, usec); } inline Error wait_until_socket_is_ready(socket_t sock, time_t sec, @@ -2940,7 +3356,10 @@ inline bool is_socket_alive(socket_t sock) { class SocketStream final : public Stream { public: SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, - time_t write_timeout_sec, time_t write_timeout_usec); + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec = 0, + std::chrono::time_point start_time = + std::chrono::steady_clock::time_point::min()); ~SocketStream() override; bool is_readable() const override; @@ -2950,6 +3369,7 @@ class SocketStream final : public Stream { void get_remote_ip_and_port(std::string &ip, int &port) const override; void get_local_ip_and_port(std::string &ip, int &port) const override; socket_t socket() const override; + time_t duration() const override; private: socket_t sock_; @@ -2957,6 +3377,8 @@ class SocketStream final : public Stream { time_t read_timeout_usec_; time_t write_timeout_sec_; time_t write_timeout_usec_; + time_t max_timeout_msec_; + const std::chrono::time_point start_time; std::vector read_buff_; size_t read_buff_off_ = 0; @@ -2968,9 +3390,12 @@ class SocketStream final : public Stream { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT class SSLSocketStream final : public Stream { public: - SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec, - time_t read_timeout_usec, time_t write_timeout_sec, - time_t write_timeout_usec); + SSLSocketStream( + socket_t sock, SSL *ssl, time_t read_timeout_sec, + time_t read_timeout_usec, time_t write_timeout_sec, + time_t write_timeout_usec, time_t max_timeout_msec = 0, + std::chrono::time_point start_time = + std::chrono::steady_clock::time_point::min()); ~SSLSocketStream() override; bool is_readable() const override; @@ -2980,6 +3405,7 @@ class SSLSocketStream final : public Stream { void get_remote_ip_and_port(std::string &ip, int &port) const override; void get_local_ip_and_port(std::string &ip, int &port) const override; socket_t socket() const override; + time_t duration() const override; private: socket_t sock_; @@ -2988,26 +3414,42 @@ class SSLSocketStream final : public Stream { time_t read_timeout_usec_; time_t write_timeout_sec_; time_t write_timeout_usec_; + time_t max_timeout_msec_; + const std::chrono::time_point start_time; }; #endif -inline bool keep_alive(socket_t sock, time_t keep_alive_timeout_sec) { +inline bool keep_alive(const std::atomic &svr_sock, socket_t sock, + time_t keep_alive_timeout_sec) { using namespace std::chrono; - auto start = steady_clock::now(); + + const auto interval_usec = + CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND; + + // Avoid expensive `steady_clock::now()` call for the first time + if (select_read(sock, 0, interval_usec) > 0) { return true; } + + const auto start = steady_clock::now() - microseconds{interval_usec}; + const auto timeout = seconds{keep_alive_timeout_sec}; + while (true) { - auto val = select_read(sock, 0, 10000); + if (svr_sock == INVALID_SOCKET) { + break; // Server socket is closed + } + + auto val = select_read(sock, 0, interval_usec); if (val < 0) { - return false; + break; // Ssocket error } else if (val == 0) { - auto current = steady_clock::now(); - auto duration = duration_cast(current - start); - auto timeout = keep_alive_timeout_sec * 1000; - if (duration.count() > timeout) { return false; } - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + if (steady_clock::now() - start > timeout) { + break; // Timeout + } } else { - return true; + return true; // Ready for read } } + + return false; } template @@ -3018,8 +3460,7 @@ process_server_socket_core(const std::atomic &svr_sock, socket_t sock, assert(keep_alive_max_count > 0); auto ret = false; auto count = keep_alive_max_count; - while (svr_sock != INVALID_SOCKET && count > 0 && - keep_alive(sock, keep_alive_timeout_sec)) { + while (count > 0 && keep_alive(svr_sock, sock, keep_alive_timeout_sec)) { auto close_connection = count == 1; auto connection_closed = false; ret = callback(close_connection, connection_closed); @@ -3045,13 +3486,15 @@ process_server_socket(const std::atomic &svr_sock, socket_t sock, }); } -inline bool process_client_socket(socket_t sock, time_t read_timeout_sec, - time_t read_timeout_usec, - time_t write_timeout_sec, - time_t write_timeout_usec, - std::function callback) { +inline bool process_client_socket( + socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec, + std::chrono::time_point start_time, + std::function callback) { SocketStream strm(sock, read_timeout_sec, read_timeout_usec, - write_timeout_sec, write_timeout_usec); + write_timeout_sec, write_timeout_usec, max_timeout_msec, + start_time); return callback(strm); } @@ -3063,10 +3506,29 @@ inline int shutdown_socket(socket_t sock) { #endif } +inline std::string escape_abstract_namespace_unix_domain(const std::string &s) { + if (s.size() > 1 && s[0] == '\0') { + auto ret = s; + ret[0] = '@'; + return ret; + } + return s; +} + +inline std::string +unescape_abstract_namespace_unix_domain(const std::string &s) { + if (s.size() > 1 && s[0] == '@') { + auto ret = s; + ret[0] = '\0'; + return ret; + } + return s; +} + template socket_t create_socket(const std::string &host, const std::string &ip, int port, int address_family, int socket_flags, bool tcp_nodelay, - SocketOptions socket_options, + bool ipv6_v6only, SocketOptions socket_options, BindOrConnect bind_or_connect) { // Get address info const char *node = nullptr; @@ -3075,7 +3537,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, memset(&hints, 0, sizeof(struct addrinfo)); hints.ai_socktype = SOCK_STREAM; - hints.ai_protocol = 0; + hints.ai_protocol = IPPROTO_IP; if (!ip.empty()) { node = ip.c_str(); @@ -3093,20 +3555,32 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, const auto addrlen = host.length(); if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; } +#ifdef SOCK_CLOEXEC + auto sock = socket(hints.ai_family, hints.ai_socktype | SOCK_CLOEXEC, + hints.ai_protocol); +#else auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol); +#endif + if (sock != INVALID_SOCKET) { sockaddr_un addr{}; addr.sun_family = AF_UNIX; - std::copy(host.begin(), host.end(), addr.sun_path); + + auto unescaped_host = unescape_abstract_namespace_unix_domain(host); + std::copy(unescaped_host.begin(), unescaped_host.end(), addr.sun_path); hints.ai_addr = reinterpret_cast(&addr); hints.ai_addrlen = static_cast( sizeof(addr) - sizeof(addr.sun_path) + addrlen); +#ifndef SOCK_CLOEXEC fcntl(sock, F_SETFD, FD_CLOEXEC); +#endif + if (socket_options) { socket_options(sock); } - if (!bind_or_connect(sock, hints)) { + bool dummy; + if (!bind_or_connect(sock, hints, dummy)) { close_socket(sock); sock = INVALID_SOCKET; } @@ -3123,6 +3597,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #endif return INVALID_SOCKET; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); for (auto rp = result; rp; rp = rp->ai_next) { // Create a socket @@ -3148,51 +3623,41 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol); } #else + +#ifdef SOCK_CLOEXEC + auto sock = + socket(rp->ai_family, rp->ai_socktype | SOCK_CLOEXEC, rp->ai_protocol); +#else auto sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol); +#endif + #endif if (sock == INVALID_SOCKET) { continue; } -#ifndef _WIN32 +#if !defined _WIN32 && !defined SOCK_CLOEXEC if (fcntl(sock, F_SETFD, FD_CLOEXEC) == -1) { close_socket(sock); continue; } #endif - if (tcp_nodelay) { - auto yes = 1; -#ifdef _WIN32 - setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, - reinterpret_cast(&yes), sizeof(yes)); -#else - setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, - reinterpret_cast(&yes), sizeof(yes)); -#endif - } - - if (socket_options) { socket_options(sock); } + if (tcp_nodelay) { set_socket_opt(sock, IPPROTO_TCP, TCP_NODELAY, 1); } if (rp->ai_family == AF_INET6) { - auto no = 0; -#ifdef _WIN32 - setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, - reinterpret_cast(&no), sizeof(no)); -#else - setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, - reinterpret_cast(&no), sizeof(no)); -#endif + set_socket_opt(sock, IPPROTO_IPV6, IPV6_V6ONLY, ipv6_v6only ? 1 : 0); } + if (socket_options) { socket_options(sock); } + // bind or connect - if (bind_or_connect(sock, *rp)) { - freeaddrinfo(result); - return sock; - } + auto quit = false; + if (bind_or_connect(sock, *rp, quit)) { return sock; } close_socket(sock); + + if (quit) { break; } } - freeaddrinfo(result); return INVALID_SOCKET; } @@ -3225,6 +3690,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { hints.ai_protocol = 0; if (getaddrinfo(host.c_str(), "0", &hints, &result)) { return false; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); auto ret = false; for (auto rp = result; rp; rp = rp->ai_next) { @@ -3235,7 +3701,6 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { } } - freeaddrinfo(result); return ret; } @@ -3247,6 +3712,8 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { inline std::string if2ip(int address_family, const std::string &ifn) { struct ifaddrs *ifap; getifaddrs(&ifap); + auto se = detail::scope_exit([&] { freeifaddrs(ifap); }); + std::string addr_candidate; for (auto ifa = ifap; ifa; ifa = ifa->ifa_next) { if (ifa->ifa_addr && ifn == ifa->ifa_name && @@ -3256,7 +3723,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) { auto sa = reinterpret_cast(ifa->ifa_addr); char buf[INET_ADDRSTRLEN]; if (inet_ntop(AF_INET, &sa->sin_addr, buf, INET_ADDRSTRLEN)) { - freeifaddrs(ifap); return std::string(buf, INET_ADDRSTRLEN); } } else if (ifa->ifa_addr->sa_family == AF_INET6) { @@ -3269,7 +3735,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) { if (s6_addr_head == 0xfc || s6_addr_head == 0xfd) { addr_candidate = std::string(buf, INET6_ADDRSTRLEN); } else { - freeifaddrs(ifap); return std::string(buf, INET6_ADDRSTRLEN); } } @@ -3277,20 +3742,21 @@ inline std::string if2ip(int address_family, const std::string &ifn) { } } } - freeifaddrs(ifap); return addr_candidate; } #endif inline socket_t create_client_socket( const std::string &host, const std::string &ip, int port, - int address_family, bool tcp_nodelay, SocketOptions socket_options, - time_t connection_timeout_sec, time_t connection_timeout_usec, - time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, + int address_family, bool tcp_nodelay, bool ipv6_v6only, + SocketOptions socket_options, time_t connection_timeout_sec, + time_t connection_timeout_usec, time_t read_timeout_sec, + time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec, const std::string &intf, Error &error) { auto sock = create_socket( - host, ip, port, address_family, 0, tcp_nodelay, std::move(socket_options), - [&](socket_t sock2, struct addrinfo &ai) -> bool { + host, ip, port, address_family, 0, tcp_nodelay, ipv6_v6only, + std::move(socket_options), + [&](socket_t sock2, struct addrinfo &ai, bool &quit) -> bool { if (!intf.empty()) { #ifdef USE_IF2IP auto ip_from_if = if2ip(address_family, intf); @@ -3314,40 +3780,17 @@ inline socket_t create_client_socket( } error = wait_until_socket_is_ready(sock2, connection_timeout_sec, connection_timeout_usec); - if (error != Error::Success) { return false; } + if (error != Error::Success) { + if (error == Error::ConnectionTimeout) { quit = true; } + return false; + } } set_nonblocking(sock2, false); - - { -#ifdef _WIN32 - auto timeout = static_cast(read_timeout_sec * 1000 + - read_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, - reinterpret_cast(&timeout), sizeof(timeout)); -#else - timeval tv; - tv.tv_sec = static_cast(read_timeout_sec); - tv.tv_usec = static_cast(read_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, - reinterpret_cast(&tv), sizeof(tv)); -#endif - } - { - -#ifdef _WIN32 - auto timeout = static_cast(write_timeout_sec * 1000 + - write_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, - reinterpret_cast(&timeout), sizeof(timeout)); -#else - timeval tv; - tv.tv_sec = static_cast(write_timeout_sec); - tv.tv_usec = static_cast(write_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, - reinterpret_cast(&tv), sizeof(tv)); -#endif - } + set_socket_opt_time(sock2, SOL_SOCKET, SO_RCVTIMEO, read_timeout_sec, + read_timeout_usec); + set_socket_opt_time(sock2, SOL_SOCKET, SO_SNDTIMEO, write_timeout_sec, + write_timeout_usec); error = Error::Success; return true; @@ -3439,7 +3882,7 @@ inline unsigned int str2tag(const std::string &s) { namespace udl { -inline constexpr unsigned int operator"" _t(const char *s, size_t l) { +inline constexpr unsigned int operator""_t(const char *s, size_t l) { return str2tag_core(s, l, 0); } @@ -3524,8 +3967,9 @@ inline bool can_compress_content_type(const std::string &content_type) { case "application/protobuf"_t: case "application/xhtml+xml"_t: return true; - default: - return !content_type.rfind("text/", 0) && tag != "text/event-stream"_t; + case "text/event-stream"_t: return false; + + default: return !content_type.rfind("text/", 0); } } @@ -3762,8 +4206,8 @@ inline bool has_header(const Headers &headers, const std::string &key) { } inline const char *get_header_value(const Headers &headers, - const std::string &key, size_t id, - const char *def) { + const std::string &key, const char *def, + size_t id) { auto rng = headers.equal_range(key); auto it = rng.first; std::advance(it, static_cast(id)); @@ -3771,14 +4215,6 @@ inline const char *get_header_value(const Headers &headers, return def; } -inline bool compare_case_ignore(const std::string &a, const std::string &b) { - if (a.size() != b.size()) { return false; } - for (size_t i = 0; i < b.size(); i++) { - if (::tolower(a[i]) != ::tolower(b[i])) { return false; } - } - return true; -} - template inline bool parse_header(const char *beg, const char *end, T fn) { // Skip trailing spaces and tabs. @@ -3801,15 +4237,26 @@ inline bool parse_header(const char *beg, const char *end, T fn) { p++; } - if (p < end) { + if (p <= end) { auto key_len = key_end - beg; if (!key_len) { return false; } auto key = std::string(beg, key_end); - auto val = compare_case_ignore(key, "Location") - ? std::string(p, end) - : decode_url(std::string(p, end), false); - fn(std::move(key), std::move(val)); + // auto val = (case_ignore::equal(key, "Location") || + // case_ignore::equal(key, "Referer")) + // ? std::string(p, end) + // : decode_url(std::string(p, end), false); + auto val = std::string(p, end); + + if (!detail::fields::is_field_value(val)) { return false; } + + if (case_ignore::equal(key, "Location") || + case_ignore::equal(key, "Referer")) { + fn(key, val); + } else { + fn(key, decode_url(val, false)); + } + return true; } @@ -3829,27 +4276,27 @@ inline bool read_headers(Stream &strm, Headers &headers) { if (line_reader.end_with_crlf()) { // Blank line indicates end of headers. if (line_reader.size() == 2) { break; } -#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR } else { +#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR // Blank line indicates end of headers. if (line_reader.size() == 1) { break; } line_terminator_len = 1; - } #else - } else { continue; // Skip invalid line. - } #endif + } if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; } // Exclude line terminator auto end = line_reader.ptr() + line_reader.size() - line_terminator_len; - parse_header(line_reader.ptr(), end, - [&](std::string &&key, std::string &&val) { - headers.emplace(std::move(key), std::move(val)); - }); + if (!parse_header(line_reader.ptr(), end, + [&](const std::string &key, const std::string &val) { + headers.emplace(key, val); + })) { + return false; + } } return true; @@ -3894,7 +4341,7 @@ inline bool read_content_without_length(Stream &strm, uint64_t r = 0; for (;;) { auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ); - if (n <= 0) { return true; } + if (n <= 0) { return false; } if (!out(buf, static_cast(n), r, 0)) { return false; } r += static_cast(n); @@ -3937,8 +4384,19 @@ inline bool read_content_chunked(Stream &strm, T &x, assert(chunk_len == 0); - // Trailer - if (!line_reader.getline()) { return false; } + // NOTE: In RFC 9112, '7.1 Chunked Transfer Coding' mentiones "The chunked + // transfer coding is complete when a chunk with a chunk-size of zero is + // received, possibly followed by a trailer section, and finally terminated by + // an empty line". https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1 + // + // In '7.1.3. Decoding Chunked', however, the pseudo-code in the section + // does't care for the existence of the final CRLF. In other words, it seems + // to be ok whether the final CRLF exists or not in the chunked data. + // https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1.3 + // + // According to the reference code in RFC 9112, cpp-htpplib now allows + // chuncked transfer coding data without the final CRLF. + if (!line_reader.getline()) { return true; } while (strcmp(line_reader.ptr(), "\r\n") != 0) { if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; } @@ -3948,8 +4406,8 @@ inline bool read_content_chunked(Stream &strm, T &x, auto end = line_reader.ptr() + line_reader.size() - line_terminator_len; parse_header(line_reader.ptr(), end, - [&](std::string &&key, std::string &&val) { - x.headers.emplace(std::move(key), std::move(val)); + [&](const std::string &key, const std::string &val) { + x.headers.emplace(key, val); }); if (!line_reader.getline()) { return false; } @@ -3959,8 +4417,8 @@ inline bool read_content_chunked(Stream &strm, T &x, } inline bool is_chunked_transfer_encoding(const Headers &headers) { - return compare_case_ignore( - get_header_value(headers, "Transfer-Encoding", 0, ""), "chunked"); + return case_ignore::equal( + get_header_value(headers, "Transfer-Encoding", "", 0), "chunked"); } template @@ -4026,8 +4484,14 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } else if (!has_header(x.headers, "Content-Length")) { ret = read_content_without_length(strm, out); } else { - auto len = get_header_value_u64(x.headers, "Content-Length", 0, 0); - if (len > payload_max_length) { + auto is_invalid_value = false; + auto len = get_header_value_u64( + x.headers, "Content-Length", + (std::numeric_limits::max)(), 0, is_invalid_value); + + if (is_invalid_value) { + ret = false; + } else if (len > payload_max_length) { exceed_payload_max_length = true; skip_content_with_length(strm, len); ret = false; @@ -4042,13 +4506,36 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } return ret; }); -} // namespace detail +} + +inline ssize_t write_request_line(Stream &strm, const std::string &method, + const std::string &path) { + std::string s = method; + s += " "; + s += path; + s += " HTTP/1.1\r\n"; + return strm.write(s.data(), s.size()); +} + +inline ssize_t write_response_line(Stream &strm, int status) { + std::string s = "HTTP/1.1 "; + s += std::to_string(status); + s += " "; + s += httplib::status_message(status); + s += "\r\n"; + return strm.write(s.data(), s.size()); +} inline ssize_t write_headers(Stream &strm, const Headers &headers) { ssize_t write_len = 0; for (const auto &x : headers) { - auto len = - strm.write_format("%s: %s\r\n", x.first.c_str(), x.second.c_str()); + std::string s; + s = x.first; + s += ": "; + s += x.second; + s += "\r\n"; + + auto len = strm.write(s.data(), s.size()); if (len < 0) { return len; } write_len += len; } @@ -4302,22 +4789,22 @@ inline std::string params_to_query_str(const Params ¶ms) { return query; } -inline void parse_query_text(const std::string &s, Params ¶ms) { +inline void parse_query_text(const char *data, std::size_t size, + Params ¶ms) { std::set cache; - split(s.data(), s.data() + s.size(), '&', [&](const char *b, const char *e) { + split(data, data + size, '&', [&](const char *b, const char *e) { std::string kv(b, e); if (cache.find(kv) != cache.end()) { return; } - cache.insert(kv); + cache.insert(std::move(kv)); std::string key; std::string val; - split(b, e, '=', [&](const char *b2, const char *e2) { - if (key.empty()) { - key.assign(b2, e2); - } else { - val.assign(b2, e2); - } - }); + divide(b, static_cast(e - b), '=', + [&](const char *lhs_data, std::size_t lhs_size, const char *rhs_data, + std::size_t rhs_size) { + key.assign(lhs_data, lhs_size); + val.assign(rhs_data, rhs_size); + }); if (!key.empty()) { params.emplace(decode_url(key, true), decode_url(val, true)); @@ -4325,6 +4812,10 @@ inline void parse_query_text(const std::string &s, Params ¶ms) { }); } +inline void parse_query_text(const std::string &s, Params ¶ms) { + parse_query_text(s.data(), s.size(), params); +} + inline bool parse_multipart_boundary(const std::string &content_type, std::string &boundary) { auto boundary_keyword = "boundary="; @@ -4365,35 +4856,44 @@ inline bool parse_range_header(const std::string &s, Ranges &ranges) { #else inline bool parse_range_header(const std::string &s, Ranges &ranges) try { #endif - static auto re_first_range = std::regex(R"(bytes=(\d*-\d*(?:,\s*\d*-\d*)*))"); - std::smatch m; - if (std::regex_match(s, m, re_first_range)) { - auto pos = static_cast(m.position(1)); - auto len = static_cast(m.length(1)); + auto is_valid = [](const std::string &str) { + return std::all_of(str.cbegin(), str.cend(), + [](unsigned char c) { return std::isdigit(c); }); + }; + + if (s.size() > 7 && s.compare(0, 6, "bytes=") == 0) { + const auto pos = static_cast(6); + const auto len = static_cast(s.size() - 6); auto all_valid_ranges = true; split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) { if (!all_valid_ranges) { return; } - static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))"); - std::cmatch cm; - if (std::regex_match(b, e, cm, re_another_range)) { - ssize_t first = -1; - if (!cm.str(1).empty()) { - first = static_cast(std::stoll(cm.str(1))); - } - ssize_t last = -1; - if (!cm.str(2).empty()) { - last = static_cast(std::stoll(cm.str(2))); - } + const auto it = std::find(b, e, '-'); + if (it == e) { + all_valid_ranges = false; + return; + } - if (first != -1 && last != -1 && first > last) { - all_valid_ranges = false; - return; - } - ranges.emplace_back(std::make_pair(first, last)); + const auto lhs = std::string(b, it); + const auto rhs = std::string(it + 1, e); + if (!is_valid(lhs) || !is_valid(rhs)) { + all_valid_ranges = false; + return; + } + + const auto first = + static_cast(lhs.empty() ? -1 : std::stoll(lhs)); + const auto last = + static_cast(rhs.empty() ? -1 : std::stoll(rhs)); + if ((first == -1 && last == -1) || + (first != -1 && last != -1 && first > last)) { + all_valid_ranges = false; + return; } + + ranges.emplace_back(first, last); }); - return all_valid_ranges; + return all_valid_ranges && !ranges.empty(); } return false; #ifdef CPPHTTPLIB_NO_EXCEPTIONS @@ -4452,7 +4952,7 @@ class MultipartFormDataParser { const auto header = buf_head(pos); if (!parse_header(header.data(), header.data() + header.size(), - [&](std::string &&, std::string &&) {})) { + [&](const std::string &, const std::string &) {})) { is_valid_ = false; return false; } @@ -4562,7 +5062,9 @@ class MultipartFormDataParser { const std::string &b) const { if (a.size() < b.size()) { return false; } for (size_t i = 0; i < b.size(); i++) { - if (::tolower(a[i]) != ::tolower(b[i])) { return false; } + if (case_ignore::to_lower(a[i]) != case_ignore::to_lower(b[i])) { + return false; + } } return true; } @@ -4645,16 +5147,6 @@ class MultipartFormDataParser { size_t buf_epos_ = 0; }; -inline std::string to_lower(const char *beg, const char *end) { - std::string out; - auto it = beg; - while (it != end) { - out += static_cast(::tolower(*it)); - it++; - } - return out; -} - inline std::string random_string(size_t length) { static const char data[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; @@ -4768,7 +5260,18 @@ inline bool range_error(Request &req, Response &res) { last_pos = contant_len - 1; } - if (last_pos == -1) { last_pos = contant_len - 1; } + // NOTE: RFC-9110 '14.1.2. Byte Ranges': + // A client can limit the number of bytes requested without knowing the + // size of the selected representation. If the last-pos value is absent, + // or if the value is greater than or equal to the current length of the + // representation data, the byte range is interpreted as the remainder of + // the representation (i.e., the server replaces the value of last-pos + // with a value that is one less than the current length of the selected + // representation). + // https://www.rfc-editor.org/rfc/rfc9110.html#section-14.1.2-6 + if (last_pos == -1 || last_pos >= contant_len) { + last_pos = contant_len - 1; + } // Range must be within content length if (!(0 <= first_pos && first_pos <= last_pos && @@ -4795,12 +5298,11 @@ inline bool range_error(Request &req, Response &res) { inline std::pair get_range_offset_and_length(Range r, size_t content_length) { - (void)(content_length); // patch to get rid of "unused parameter" on release build assert(r.first != -1 && r.second != -1); assert(0 <= r.first && r.first < static_cast(content_length)); assert(r.first <= r.second && r.second < static_cast(content_length)); - + (void)(content_length); return std::make_pair(r.first, static_cast(r.second - r.first) + 1); } @@ -4907,10 +5409,14 @@ write_multipart_ranges_data(Stream &strm, const Request &req, Response &res, inline bool expect_content(const Request &req) { if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" || - req.method == "PRI" || req.method == "DELETE") { + req.method == "DELETE") { return true; } - // TODO: check if Content-Length is set + if (req.has_header("Content-Length") && + req.get_header_value_u64("Content-Length") > 0) { + return true; + } + if (is_chunked_transfer_encoding(req.headers)) { return true; } return false; } @@ -4955,9 +5461,76 @@ inline std::string SHA_256(const std::string &s) { inline std::string SHA_512(const std::string &s) { return message_digest(s, EVP_sha512()); } -#endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +inline std::pair make_digest_authentication_header( + const Request &req, const std::map &auth, + size_t cnonce_count, const std::string &cnonce, const std::string &username, + const std::string &password, bool is_proxy = false) { + std::string nc; + { + std::stringstream ss; + ss << std::setfill('0') << std::setw(8) << std::hex << cnonce_count; + nc = ss.str(); + } + + std::string qop; + if (auth.find("qop") != auth.end()) { + qop = auth.at("qop"); + if (qop.find("auth-int") != std::string::npos) { + qop = "auth-int"; + } else if (qop.find("auth") != std::string::npos) { + qop = "auth"; + } else { + qop.clear(); + } + } + + std::string algo = "MD5"; + if (auth.find("algorithm") != auth.end()) { algo = auth.at("algorithm"); } + + std::string response; + { + auto H = algo == "SHA-256" ? detail::SHA_256 + : algo == "SHA-512" ? detail::SHA_512 + : detail::MD5; + + auto A1 = username + ":" + auth.at("realm") + ":" + password; + + auto A2 = req.method + ":" + req.path; + if (qop == "auth-int") { A2 += ":" + H(req.body); } + + if (qop.empty()) { + response = H(H(A1) + ":" + auth.at("nonce") + ":" + H(A2)); + } else { + response = H(H(A1) + ":" + auth.at("nonce") + ":" + nc + ":" + cnonce + + ":" + qop + ":" + H(A2)); + } + } + + auto opaque = (auth.find("opaque") != auth.end()) ? auth.at("opaque") : ""; + + auto field = "Digest username=\"" + username + "\", realm=\"" + + auth.at("realm") + "\", nonce=\"" + auth.at("nonce") + + "\", uri=\"" + req.path + "\", algorithm=" + algo + + (qop.empty() ? ", response=\"" + : ", qop=" + qop + ", nc=" + nc + ", cnonce=\"" + + cnonce + "\", response=\"") + + response + "\"" + + (opaque.empty() ? "" : ", opaque=\"" + opaque + "\""); + + auto key = is_proxy ? "Proxy-Authorization" : "Authorization"; + return std::make_pair(key, field); +} + +inline bool is_ssl_peer_could_be_closed(SSL *ssl, socket_t sock) { + detail::set_nonblocking(sock, true); + auto se = detail::scope_exit([&]() { detail::set_nonblocking(sock, false); }); + + char buf[1]; + return !SSL_peek(ssl, buf, 1) && + SSL_get_error(ssl, 0) == SSL_ERROR_ZERO_RETURN; +} + #ifdef _WIN32 // NOTE: This code came up with the following stackoverflow post: // https://stackoverflow.com/questions/9507184/can-openssl-on-windows-use-the-system-certificate-store @@ -5096,68 +5669,6 @@ class WSInit { static WSInit wsinit_; #endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT -inline std::pair make_digest_authentication_header( - const Request &req, const std::map &auth, - size_t cnonce_count, const std::string &cnonce, const std::string &username, - const std::string &password, bool is_proxy = false) { - std::string nc; - { - std::stringstream ss; - ss << std::setfill('0') << std::setw(8) << std::hex << cnonce_count; - nc = ss.str(); - } - - std::string qop; - if (auth.find("qop") != auth.end()) { - qop = auth.at("qop"); - if (qop.find("auth-int") != std::string::npos) { - qop = "auth-int"; - } else if (qop.find("auth") != std::string::npos) { - qop = "auth"; - } else { - qop.clear(); - } - } - - std::string algo = "MD5"; - if (auth.find("algorithm") != auth.end()) { algo = auth.at("algorithm"); } - - std::string response; - { - auto H = algo == "SHA-256" ? detail::SHA_256 - : algo == "SHA-512" ? detail::SHA_512 - : detail::MD5; - - auto A1 = username + ":" + auth.at("realm") + ":" + password; - - auto A2 = req.method + ":" + req.path; - if (qop == "auth-int") { A2 += ":" + H(req.body); } - - if (qop.empty()) { - response = H(H(A1) + ":" + auth.at("nonce") + ":" + H(A2)); - } else { - response = H(H(A1) + ":" + auth.at("nonce") + ":" + nc + ":" + cnonce + - ":" + qop + ":" + H(A2)); - } - } - - auto opaque = (auth.find("opaque") != auth.end()) ? auth.at("opaque") : ""; - - auto field = "Digest username=\"" + username + "\", realm=\"" + - auth.at("realm") + "\", nonce=\"" + auth.at("nonce") + - "\", uri=\"" + req.path + "\", algorithm=" + algo + - (qop.empty() ? ", response=\"" - : ", qop=" + qop + ", nc=" + nc + ", cnonce=\"" + - cnonce + "\", response=\"") + - response + "\"" + - (opaque.empty() ? "" : ", opaque=\"" + opaque + "\""); - - auto key = is_proxy ? "Proxy-Authorization" : "Authorization"; - return std::make_pair(key, field); -} -#endif - inline bool parse_www_authenticate(const Response &res, std::map &auth, bool is_proxy) { @@ -5230,6 +5741,7 @@ inline void hosted_at(const std::string &hostname, #endif return; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); for (auto rp = result; rp; rp = rp->ai_next) { const auto &addr = @@ -5241,8 +5753,6 @@ inline void hosted_at(const std::string &hostname, addrs.push_back(ip); } } - - freeaddrinfo(result); } inline std::string append_query_params(const std::string &path, @@ -5291,8 +5801,8 @@ inline bool Request::has_header(const std::string &key) const { } inline std::string Request::get_header_value(const std::string &key, - size_t id) const { - return detail::get_header_value(headers, key, id, ""); + const char *def, size_t id) const { + return detail::get_header_value(headers, key, def, id); } inline size_t Request::get_header_value_count(const std::string &key) const { @@ -5302,7 +5812,8 @@ inline size_t Request::get_header_value_count(const std::string &key) const { inline void Request::set_header(const std::string &key, const std::string &val) { - if (!detail::has_crlf(key) && !detail::has_crlf(val)) { + if (detail::fields::is_field_name(key) && + detail::fields::is_field_value(val)) { headers.emplace(key, val); } } @@ -5356,8 +5867,9 @@ inline bool Response::has_header(const std::string &key) const { } inline std::string Response::get_header_value(const std::string &key, + const char *def, size_t id) const { - return detail::get_header_value(headers, key, id, ""); + return detail::get_header_value(headers, key, def, id); } inline size_t Response::get_header_value_count(const std::string &key) const { @@ -5367,13 +5879,14 @@ inline size_t Response::get_header_value_count(const std::string &key) const { inline void Response::set_header(const std::string &key, const std::string &val) { - if (!detail::has_crlf(key) && !detail::has_crlf(val)) { + if (detail::fields::is_field_name(key) && + detail::fields::is_field_value(val)) { headers.emplace(key, val); } } inline void Response::set_redirect(const std::string &url, int stat) { - if (!detail::has_crlf(url)) { + if (detail::fields::is_field_value(url)) { set_header("Location", url); if (300 <= stat && stat < 400) { this->status = stat; @@ -5436,14 +5949,25 @@ inline void Response::set_chunked_content_provider( is_chunked_content_provider_ = true; } +inline void Response::set_file_content(const std::string &path, + const std::string &content_type) { + file_content_path_ = path; + file_content_content_type_ = content_type; +} + +inline void Response::set_file_content(const std::string &path) { + file_content_path_ = path; +} + // Result implementation inline bool Result::has_request_header(const std::string &key) const { return request_headers_.find(key) != request_headers_.end(); } inline std::string Result::get_request_header_value(const std::string &key, + const char *def, size_t id) const { - return detail::get_header_value(request_headers_, key, id, ""); + return detail::get_header_value(request_headers_, key, def, id); } inline size_t @@ -5463,20 +5987,45 @@ inline ssize_t Stream::write(const std::string &s) { namespace detail { +inline void calc_actual_timeout(time_t max_timeout_msec, + time_t duration_msec, time_t timeout_sec, + time_t timeout_usec, time_t &actual_timeout_sec, + time_t &actual_timeout_usec) { + auto timeout_msec = (timeout_sec * 1000) + (timeout_usec / 1000); + + auto actual_timeout_msec = + std::min(max_timeout_msec - duration_msec, timeout_msec); + + actual_timeout_sec = actual_timeout_msec / 1000; + actual_timeout_usec = (actual_timeout_msec % 1000) * 1000; +} + // Socket stream implementation -inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec, - time_t read_timeout_usec, - time_t write_timeout_sec, - time_t write_timeout_usec) +inline SocketStream::SocketStream( + socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec, + std::chrono::time_point start_time) : sock_(sock), read_timeout_sec_(read_timeout_sec), read_timeout_usec_(read_timeout_usec), write_timeout_sec_(write_timeout_sec), - write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {} + write_timeout_usec_(write_timeout_usec), + max_timeout_msec_(max_timeout_msec), start_time(start_time), + read_buff_(read_buff_size_, 0) {} inline SocketStream::~SocketStream() = default; inline bool SocketStream::is_readable() const { - return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; + if (max_timeout_msec_ <= 0) { + return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; + } + + time_t read_timeout_sec; + time_t read_timeout_usec; + calc_actual_timeout(max_timeout_msec_, duration(), read_timeout_sec_, + read_timeout_usec_, read_timeout_sec, read_timeout_usec); + + return select_read(sock_, read_timeout_sec, read_timeout_usec) > 0; } inline bool SocketStream::is_writable() const { @@ -5553,6 +6102,12 @@ inline void SocketStream::get_local_ip_and_port(std::string &ip, inline socket_t SocketStream::socket() const { return sock_; } +inline time_t SocketStream::duration() const { + return std::chrono::duration_cast( + std::chrono::steady_clock::now() - start_time) + .count(); +} + // Buffer stream implementation inline bool BufferStream::is_readable() const { return true; } @@ -5581,9 +6136,13 @@ inline void BufferStream::get_local_ip_and_port(std::string & /*ip*/, inline socket_t BufferStream::socket() const { return 0; } +inline time_t BufferStream::duration() const { return 0; } + inline const std::string &BufferStream::get_buffer() const { return buffer; } inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) { + static constexpr char marker[] = "/:"; + // One past the last ending position of a path param substring std::size_t last_param_end = 0; @@ -5596,13 +6155,14 @@ inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) { #endif while (true) { - const auto marker_pos = pattern.find(marker, last_param_end); + const auto marker_pos = pattern.find( + marker, last_param_end == 0 ? last_param_end : last_param_end - 1); if (marker_pos == std::string::npos) { break; } static_fragments_.push_back( - pattern.substr(last_param_end, marker_pos - last_param_end)); + pattern.substr(last_param_end, marker_pos - last_param_end + 1)); - const auto param_name_start = marker_pos + 1; + const auto param_name_start = marker_pos + 2; auto sep_pos = pattern.find(separator, param_name_start); if (sep_pos == std::string::npos) { sep_pos = pattern.length(); } @@ -5664,7 +6224,7 @@ inline bool PathParamsMatcher::match(Request &request) const { request.path_params.emplace( param_name, request.path.substr(starting_pos, sep_pos - starting_pos)); - // Mark everythin up to '/' as matched + // Mark everything up to '/' as matched starting_pos = sep_pos + 1; } // Returns false if the path is longer than the pattern @@ -5763,7 +6323,8 @@ inline bool Server::set_base_dir(const std::string &dir, inline bool Server::set_mount_point(const std::string &mount_point, const std::string &dir, Headers headers) { - if (detail::is_dir(dir)) { + detail::FileStat stat(dir); + if (stat.is_dir()) { std::string mnt = !mount_point.empty() ? mount_point : "/"; if (!mnt.empty() && mnt[0] == '/') { base_dirs_.push_back({mnt, dir, std::move(headers)}); @@ -5800,12 +6361,14 @@ inline Server &Server::set_file_request_handler(Handler handler) { return *this; } -inline Server &Server::set_error_handler(HandlerWithResponse handler) { +inline Server &Server::set_error_handler_core(HandlerWithResponse handler, + std::true_type) { error_handler_ = std::move(handler); return *this; } -inline Server &Server::set_error_handler(Handler handler) { +inline Server &Server::set_error_handler_core(Handler handler, + std::false_type) { error_handler_ = [handler](const Request &req, Response &res) { handler(req, res); return HandlerResponse::Handled; @@ -5849,6 +6412,11 @@ inline Server &Server::set_tcp_nodelay(bool on) { return *this; } +inline Server &Server::set_ipv6_v6only(bool on) { + ipv6_v6only_ = on; + return *this; +} + inline Server &Server::set_socket_options(SocketOptions socket_options) { socket_options_ = std::move(socket_options); return *this; @@ -5900,27 +6468,27 @@ inline Server &Server::set_payload_max_length(size_t length) { inline bool Server::bind_to_port(const std::string &host, int port, int socket_flags) { - return bind_internal(host, port, socket_flags) >= 0; + auto ret = bind_internal(host, port, socket_flags); + if (ret == -1) { is_decommisioned = true; } + return ret >= 0; } inline int Server::bind_to_any_port(const std::string &host, int socket_flags) { - return bind_internal(host, 0, socket_flags); + auto ret = bind_internal(host, 0, socket_flags); + if (ret == -1) { is_decommisioned = true; } + return ret; } -inline bool Server::listen_after_bind() { - auto se = detail::scope_exit([&]() { done_ = true; }); - return listen_internal(); -} +inline bool Server::listen_after_bind() { return listen_internal(); } inline bool Server::listen(const std::string &host, int port, int socket_flags) { - auto se = detail::scope_exit([&]() { done_ = true; }); return bind_to_port(host, port, socket_flags) && listen_internal(); } inline bool Server::is_running() const { return is_running_; } inline void Server::wait_until_ready() const { - while (!is_running() && !done_) { + while (!is_running_ && !is_decommisioned) { std::this_thread::sleep_for(std::chrono::milliseconds{1}); } } @@ -5932,8 +6500,11 @@ inline void Server::stop() { detail::shutdown_socket(sock); detail::close_socket(sock); } + is_decommisioned = false; } +inline void Server::decommission() { is_decommisioned = true; } + inline bool Server::parse_request_line(const char *s, Request &req) const { auto len = strlen(s); if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; } @@ -5972,26 +6543,13 @@ inline bool Server::parse_request_line(const char *s, Request &req) const { } } - size_t count = 0; - - detail::split(req.target.data(), req.target.data() + req.target.size(), '?', - 2, [&](const char *b, const char *e) { - switch (count) { - case 0: - req.path = detail::decode_url(std::string(b, e), false); - break; - case 1: { - if (e - b > 0) { - detail::parse_query_text(std::string(b, e), req.params); - } - break; - } - default: break; - } - count++; - }); - - if (count > 2) { return false; } + detail::divide(req.target, '?', + [&](const char *lhs_data, std::size_t lhs_size, + const char *rhs_data, std::size_t rhs_size) { + req.path = detail::decode_url( + std::string(lhs_data, lhs_size), false); + detail::parse_query_text(rhs_data, rhs_size, req.params); + }); } return true; @@ -6030,23 +6588,24 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, if (close_connection || req.get_header_value("Connection") == "close") { res.set_header("Connection", "close"); } else { - std::stringstream ss; - ss << "timeout=" << keep_alive_timeout_sec_ - << ", max=" << keep_alive_max_count_; - res.set_header("Keep-Alive", ss.str()); + std::string s = "timeout="; + s += std::to_string(keep_alive_timeout_sec_); + s += ", max="; + s += std::to_string(keep_alive_max_count_); + res.set_header("Keep-Alive", s); } - if (!res.has_header("Content-Type") && - (!res.body.empty() || res.content_length_ > 0 || res.content_provider_)) { + if ((!res.body.empty() || res.content_length_ > 0 || res.content_provider_) && + !res.has_header("Content-Type")) { res.set_header("Content-Type", "text/plain"); } - if (!res.has_header("Content-Length") && res.body.empty() && - !res.content_length_ && !res.content_provider_) { + if (res.body.empty() && !res.content_length_ && !res.content_provider_ && + !res.has_header("Content-Length")) { res.set_header("Content-Length", "0"); } - if (!res.has_header("Accept-Ranges") && req.method == "HEAD") { + if (req.method == "HEAD" && !res.has_header("Accept-Ranges")) { res.set_header("Accept-Ranges", "bytes"); } @@ -6055,12 +6614,7 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, // Response line and headers { detail::BufferStream bstrm; - - if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status, - status_message(res.status))) { - return false; - } - + if (!detail::write_response_line(bstrm, res.status)) { return false; } if (!header_writer_(bstrm, res.headers)) { return false; } // Flush buffer @@ -6254,7 +6808,14 @@ inline bool Server::handle_file_request(const Request &req, Response &res, auto path = entry.base_dir + sub_path; if (path.back() == '/') { path += "index.html"; } - if (detail::is_file(path)) { + detail::FileStat stat(path); + + if (stat.is_dir()) { + res.set_redirect(sub_path + "/", StatusCode::MovedPermanently_301); + return true; + } + + if (stat.is_file()) { for (const auto &kv : entry.headers) { res.set_header(kv.first, kv.second); } @@ -6289,8 +6850,8 @@ Server::create_server_socket(const std::string &host, int port, SocketOptions socket_options) const { return detail::create_socket( host, std::string(), port, address_family_, socket_flags, tcp_nodelay_, - std::move(socket_options), - [](socket_t sock, struct addrinfo &ai) -> bool { + ipv6_v6only_, std::move(socket_options), + [](socket_t sock, struct addrinfo &ai, bool & /*quit*/) -> bool { if (::bind(sock, ai.ai_addr, static_cast(ai.ai_addrlen))) { return false; } @@ -6301,6 +6862,8 @@ Server::create_server_socket(const std::string &host, int port, inline int Server::bind_internal(const std::string &host, int port, int socket_flags) { + if (is_decommisioned) { return -1; } + if (!is_valid()) { return -1; } svr_sock_ = create_server_socket(host, port, socket_flags, socket_options_); @@ -6326,6 +6889,8 @@ inline int Server::bind_internal(const std::string &host, int port, } inline bool Server::listen_internal() { + if (is_decommisioned) { return false; } + auto ret = true; is_running_ = true; auto se = detail::scope_exit([&]() { is_running_ = false; }); @@ -6346,13 +6911,22 @@ inline bool Server::listen_internal() { #ifndef _WIN32 } #endif + +#if defined _WIN32 + // sockets conneced via WASAccept inherit flags NO_HANDLE_INHERIT, + // OVERLAPPED + socket_t sock = WSAAccept(svr_sock_, nullptr, nullptr, nullptr, 0); +#elif defined SOCK_CLOEXEC + socket_t sock = accept4(svr_sock_, nullptr, nullptr, SOCK_CLOEXEC); +#else socket_t sock = accept(svr_sock_, nullptr, nullptr); +#endif if (sock == INVALID_SOCKET) { if (errno == EMFILE) { // The per-process limit of open file descriptors has been reached. // Try to accept new connections after a short sleep. - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{1}); continue; } else if (errno == EINTR || errno == EAGAIN) { continue; @@ -6366,35 +6940,10 @@ inline bool Server::listen_internal() { break; } - { -#ifdef _WIN32 - auto timeout = static_cast(read_timeout_sec_ * 1000 + - read_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, - reinterpret_cast(&timeout), sizeof(timeout)); -#else - timeval tv; - tv.tv_sec = static_cast(read_timeout_sec_); - tv.tv_usec = static_cast(read_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, - reinterpret_cast(&tv), sizeof(tv)); -#endif - } - { - -#ifdef _WIN32 - auto timeout = static_cast(write_timeout_sec_ * 1000 + - write_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, - reinterpret_cast(&timeout), sizeof(timeout)); -#else - timeval tv; - tv.tv_sec = static_cast(write_timeout_sec_); - tv.tv_usec = static_cast(write_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, - reinterpret_cast(&tv), sizeof(tv)); -#endif - } + detail::set_socket_opt_time(sock, SOL_SOCKET, SO_RCVTIMEO, + read_timeout_sec_, read_timeout_usec_); + detail::set_socket_opt_time(sock, SOL_SOCKET, SO_SNDTIMEO, + write_timeout_sec_, write_timeout_usec_); if (!task_queue->enqueue( [this, sock]() { process_and_close_socket(sock); })) { @@ -6406,6 +6955,7 @@ inline bool Server::listen_internal() { task_queue->shutdown(); } + is_decommisioned = !ret; return ret; } @@ -6503,7 +7053,7 @@ inline bool Server::dispatch_request(Request &req, Response &res, inline void Server::apply_ranges(const Request &req, Response &res, std::string &content_type, std::string &boundary) const { - if (req.ranges.size() > 1) { + if (req.ranges.size() > 1 && res.status == StatusCode::PartialContent_206) { auto it = res.headers.find("Content-Type"); if (it != res.headers.end()) { content_type = it->second; @@ -6521,7 +7071,7 @@ inline void Server::apply_ranges(const Request &req, Response &res, if (res.body.empty()) { if (res.content_length_ > 0) { size_t length = 0; - if (req.ranges.empty()) { + if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) { length = res.content_length_; } else if (req.ranges.size() == 1) { auto offset_and_length = detail::get_range_offset_and_length( @@ -6550,7 +7100,7 @@ inline void Server::apply_ranges(const Request &req, Response &res, } } } else { - if (req.ranges.empty()) { + if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) { ; } else if (req.ranges.size() == 1) { auto offset_and_length = @@ -6621,7 +7171,9 @@ inline bool Server::dispatch_request_for_content_reader( } inline bool -Server::process_request(Stream &strm, bool close_connection, +Server::process_request(Stream &strm, const std::string &remote_addr, + int remote_port, const std::string &local_addr, + int local_port, bool close_connection, bool &connection_closed, const std::function &setup_request) { std::array buf{}; @@ -6651,14 +7203,6 @@ Server::process_request(Stream &strm, bool close_connection, #endif #endif - // Check if the request URI doesn't exceed the limit - if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) { - Headers dummy; - detail::read_headers(strm, dummy); - res.status = StatusCode::UriTooLong_414; - return write_response(strm, close_connection, req, res); - } - // Request line and headers if (!parse_request_line(line_reader.ptr(), req) || !detail::read_headers(strm, req.headers)) { @@ -6666,6 +7210,14 @@ Server::process_request(Stream &strm, bool close_connection, return write_response(strm, close_connection, req, res); } + // Check if the request URI doesn't exceed the limit + if (req.target.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) { + Headers dummy; + detail::read_headers(strm, dummy); + res.status = StatusCode::UriTooLong_414; + return write_response(strm, close_connection, req, res); + } + if (req.get_header_value("Connection") == "close") { connection_closed = true; } @@ -6675,11 +7227,13 @@ Server::process_request(Stream &strm, bool close_connection, connection_closed = true; } - strm.get_remote_ip_and_port(req.remote_addr, req.remote_port); + req.remote_addr = remote_addr; + req.remote_port = remote_port; req.set_header("REMOTE_ADDR", req.remote_addr); req.set_header("REMOTE_PORT", std::to_string(req.remote_port)); - strm.get_local_ip_and_port(req.local_addr, req.local_port); + req.local_addr = local_addr; + req.local_port = local_port; req.set_header("LOCAL_ADDR", req.local_addr); req.set_header("LOCAL_PORT", std::to_string(req.local_port)); @@ -6701,13 +7255,20 @@ Server::process_request(Stream &strm, bool close_connection, switch (status) { case StatusCode::Continue_100: case StatusCode::ExpectationFailed_417: - strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status, - status_message(status)); + detail::write_response_line(strm, status); + strm.write("\r\n"); break; - default: return write_response(strm, close_connection, req, res); + default: + connection_closed = true; + return write_response(strm, true, req, res); } } + // Setup `is_connection_closed` method + req.is_connection_closed = [&]() { + return !detail::is_socket_alive(strm.socket()); + }; + // Routing auto routed = false; #ifdef CPPHTTPLIB_NO_EXCEPTIONS @@ -6750,6 +7311,32 @@ Server::process_request(Stream &strm, bool close_connection, : StatusCode::PartialContent_206; } + // Serve file content by using a content provider + if (!res.file_content_path_.empty()) { + const auto &path = res.file_content_path_; + auto mm = std::make_shared(path.c_str()); + if (!mm->is_open()) { + res.body.clear(); + res.content_length_ = 0; + res.content_provider_ = nullptr; + res.status = StatusCode::NotFound_404; + return write_response(strm, close_connection, req, res); + } + + auto content_type = res.file_content_content_type_; + if (content_type.empty()) { + content_type = detail::find_content_type( + path, file_extension_and_mimetype_map_, default_file_mimetype_); + } + + res.set_content_provider( + mm->size(), content_type, + [mm](size_t offset, size_t length, DataSink &sink) -> bool { + sink.write(mm->data() + offset, length); + return true; + }); + } + if (detail::range_error(req, res)) { res.body.clear(); res.content_length_ = 0; @@ -6769,12 +7356,21 @@ Server::process_request(Stream &strm, bool close_connection, inline bool Server::is_valid() const { return true; } inline bool Server::process_and_close_socket(socket_t sock) { + std::string remote_addr; + int remote_port = 0; + detail::get_remote_ip_and_port(sock, remote_addr, remote_port); + + std::string local_addr; + int local_port = 0; + detail::get_local_ip_and_port(sock, local_addr, local_port); + auto ret = detail::process_server_socket( svr_sock_, sock, keep_alive_max_count_, keep_alive_timeout_sec_, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, - [this](Stream &strm, bool close_connection, bool &connection_closed) { - return process_request(strm, close_connection, connection_closed, + [&](Stream &strm, bool close_connection, bool &connection_closed) { + return process_request(strm, remote_addr, remote_port, local_addr, + local_port, close_connection, connection_closed, nullptr); }); @@ -6793,8 +7389,8 @@ inline ClientImpl::ClientImpl(const std::string &host, int port) inline ClientImpl::ClientImpl(const std::string &host, int port, const std::string &client_cert_path, const std::string &client_key_path) - : host_(host), port_(port), - host_and_port_(adjust_host_string(host) + ":" + std::to_string(port)), + : host_(detail::escape_abstract_namespace_unix_domain(host)), port_(port), + host_and_port_(adjust_host_string(host_) + ":" + std::to_string(port)), client_cert_path_(client_cert_path), client_key_path_(client_key_path) {} inline ClientImpl::~ClientImpl() { @@ -6813,6 +7409,7 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) { read_timeout_usec_ = rhs.read_timeout_usec_; write_timeout_sec_ = rhs.write_timeout_sec_; write_timeout_usec_ = rhs.write_timeout_usec_; + max_timeout_msec_ = rhs.max_timeout_msec_; basic_auth_username_ = rhs.basic_auth_username_; basic_auth_password_ = rhs.basic_auth_password_; bearer_token_auth_token_ = rhs.bearer_token_auth_token_; @@ -6825,6 +7422,7 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) { url_encode_ = rhs.url_encode_; address_family_ = rhs.address_family_; tcp_nodelay_ = rhs.tcp_nodelay_; + ipv6_v6only_ = rhs.ipv6_v6only_; socket_options_ = rhs.socket_options_; compress_ = rhs.compress_; decompress_ = rhs.decompress_; @@ -6845,6 +7443,8 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) { #endif #ifdef CPPHTTPLIB_OPENSSL_SUPPORT server_certificate_verification_ = rhs.server_certificate_verification_; + server_hostname_verification_ = rhs.server_hostname_verification_; + server_certificate_verifier_ = rhs.server_certificate_verifier_; #endif logger_ = rhs.logger_; } @@ -6853,9 +7453,9 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const { if (!proxy_host_.empty() && proxy_port_ != -1) { return detail::create_client_socket( proxy_host_, std::string(), proxy_port_, address_family_, tcp_nodelay_, - socket_options_, connection_timeout_sec_, connection_timeout_usec_, - read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, - write_timeout_usec_, interface_, error); + ipv6_v6only_, socket_options_, connection_timeout_sec_, + connection_timeout_usec_, read_timeout_sec_, read_timeout_usec_, + write_timeout_sec_, write_timeout_usec_, interface_, error); } // Check is custom IP specified for host_ @@ -6864,10 +7464,10 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const { if (it != addr_map_.end()) { ip = it->second; } return detail::create_client_socket( - host_, ip, port_, address_family_, tcp_nodelay_, socket_options_, - connection_timeout_sec_, connection_timeout_usec_, read_timeout_sec_, - read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, interface_, - error); + host_, ip, port_, address_family_, tcp_nodelay_, ipv6_v6only_, + socket_options_, connection_timeout_sec_, connection_timeout_usec_, + read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, + write_timeout_usec_, interface_, error); } inline bool ClientImpl::create_and_connect_socket(Socket &socket, @@ -6967,6 +7567,15 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) { auto is_alive = false; if (socket_.is_open()) { is_alive = detail::is_socket_alive(socket_.sock); + +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + if (is_alive && is_ssl()) { + if (detail::is_ssl_peer_could_be_closed(socket_.ssl, socket_.sock)) { + is_alive = false; + } + } +#endif + if (!is_alive) { // Attempt to avoid sigpipe by shutting down nongracefully if it seems // like the other side has already closed the connection Also, there @@ -6988,7 +7597,8 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) { auto &scli = static_cast(*this); if (!proxy_host_.empty() && proxy_port_ != -1) { auto success = false; - if (!scli.connect_with_proxy(socket_, res, success, error)) { + if (!scli.connect_with_proxy(socket_, req.start_time_, res, success, + error)) { return success; } } @@ -7034,7 +7644,7 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) { } }); - ret = process_socket(socket_, [&](Stream &strm) { + ret = process_socket(socket_, req.start_time_, [&](Stream &strm) { return handle_request(strm, req, res, close_connection, error); }); @@ -7144,7 +7754,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { if (location.empty()) { return false; } const static std::regex re( - R"((?:(https?):)?(?://(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)"); + R"((?:(https?):)?(?://(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)"); std::smatch m; if (!std::regex_match(location, m, re)) { return false; } @@ -7243,12 +7853,26 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); } + if (!req.content_receiver) { + if (!req.has_header("Accept-Encoding")) { + std::string accept_encoding; +#ifdef CPPHTTPLIB_BROTLI_SUPPORT + accept_encoding = "br"; +#endif +#ifdef CPPHTTPLIB_ZLIB_SUPPORT + if (!accept_encoding.empty()) { accept_encoding += ", "; } + accept_encoding += "gzip, deflate"; +#endif + req.set_header("Accept-Encoding", accept_encoding); + } + #ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT - if (!req.has_header("User-Agent")) { - auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION; - req.set_header("User-Agent", agent); - } + if (!req.has_header("User-Agent")) { + auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION; + req.set_header("User-Agent", agent); + } #endif + }; if (req.body.empty()) { if (req.content_provider_) { @@ -7308,8 +7932,14 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, { detail::BufferStream bstrm; - const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path; - bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str()); + const auto &path_with_query = + req.params.empty() ? req.path + : append_query_params(req.path, req.params); + + const auto &path = + url_encode_ ? detail::encode_url(path_with_query) : path_with_query; + + detail::write_request_line(bstrm, req.method, path); header_writer_(bstrm, req.headers); @@ -7417,11 +8047,15 @@ inline Result ClientImpl::send_with_content_provider( const std::string &method, const std::string &path, const Headers &headers, const char *body, size_t content_length, ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, - const std::string &content_type) { + const std::string &content_type, Progress progress) { Request req; req.method = method; req.headers = headers; req.path = path; + req.progress = progress; + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } auto error = Error::Success; @@ -7448,9 +8082,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, if (is_ssl()) { auto is_proxy_enabled = !proxy_host_.empty() && proxy_port_ != -1; if (!is_proxy_enabled) { - char buf[1]; - if (SSL_peek(socket_.ssl, buf, 1) == 0 && - SSL_get_error(socket_.ssl, 0) == SSL_ERROR_ZERO_RETURN) { + if (detail::is_ssl_peer_could_be_closed(socket_.ssl, socket_.sock)) { error = Error::SSLPeerCouldBeClosed_; return false; } @@ -7468,7 +8100,9 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, // Body if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" && req.method != "CONNECT") { - auto redirect = 300 < res.status && res.status < 400 && follow_location_; + auto redirect = 300 < res.status && res.status < 400 && + res.status != StatusCode::NotModified_304 && + follow_location_; if (req.response_handler && !redirect) { if (!req.response_handler(res)) { @@ -7489,9 +8123,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, : static_cast( [&](const char *buf, size_t n, uint64_t /*off*/, uint64_t /*len*/) { - if (res.body.size() + n > res.body.max_size()) { - return false; - } + assert(res.body.size() + n <= res.body.max_size()); res.body.append(buf, n); return true; }); @@ -7503,12 +8135,25 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, return ret; }; - int dummy_status; - if (!detail::read_content(strm, res, (std::numeric_limits::max)(), - dummy_status, std::move(progress), std::move(out), - decompress_)) { - if (error != Error::Canceled) { error = Error::Read; } - return false; + if (res.has_header("Content-Length")) { + if (!req.content_receiver) { + auto len = res.get_header_value_u64("Content-Length"); + if (len > res.body.max_size()) { + error = Error::Read; + return false; + } + res.body.reserve(static_cast(len)); + } + } + + if (res.status != StatusCode::NotModified_304) { + int dummy_status; + if (!detail::read_content(strm, res, (std::numeric_limits::max)(), + dummy_status, std::move(progress), + std::move(out), decompress_)) { + if (error != Error::Canceled) { error = Error::Read; } + return false; + } } } @@ -7562,12 +8207,14 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider( }; } -inline bool -ClientImpl::process_socket(const Socket &socket, - std::function callback) { +inline bool ClientImpl::process_socket( + const Socket &socket, + std::chrono::time_point start_time, + std::function callback) { return detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, - write_timeout_usec_, std::move(callback)); + write_timeout_usec_, max_timeout_msec_, start_time, + std::move(callback)); } inline bool ClientImpl::is_ssl() const { return false; } @@ -7591,6 +8238,9 @@ inline Result ClientImpl::Get(const std::string &path, const Headers &headers, req.path = path; req.headers = headers; req.progress = std::move(progress); + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } return send_(std::move(req)); } @@ -7656,6 +8306,9 @@ inline Result ClientImpl::Get(const std::string &path, const Headers &headers, return content_receiver(data, data_length); }; req.progress = std::move(progress); + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } return send_(std::move(req)); } @@ -7701,6 +8354,9 @@ inline Result ClientImpl::Head(const std::string &path, req.method = "HEAD"; req.headers = headers; req.path = path; + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } return send_(std::move(req)); } @@ -7717,14 +8373,22 @@ inline Result ClientImpl::Post(const std::string &path, inline Result ClientImpl::Post(const std::string &path, const char *body, size_t content_length, const std::string &content_type) { - return Post(path, Headers(), body, content_length, content_type); + return Post(path, Headers(), body, content_length, content_type, nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return send_with_content_provider("POST", path, headers, body, content_length, - nullptr, nullptr, content_type); + nullptr, nullptr, content_type, nullptr); +} + +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("POST", path, headers, body, content_length, + nullptr, nullptr, content_type, progress); } inline Result ClientImpl::Post(const std::string &path, const std::string &body, @@ -7732,12 +8396,27 @@ inline Result ClientImpl::Post(const std::string &path, const std::string &body, return Post(path, Headers(), body, content_type); } +inline Result ClientImpl::Post(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return Post(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return send_with_content_provider("POST", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + nullptr); +} + +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("POST", path, headers, body.data(), + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Post(const std::string &path, const Params ¶ms) { @@ -7763,14 +8442,15 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("POST", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("POST", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, @@ -7779,6 +8459,13 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, return Post(path, headers, query, "application/x-www-form-urlencoded"); } +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + auto query = detail::params_to_query_str(params); + return Post(path, headers, query, "application/x-www-form-urlencoded", + progress); +} + inline Result ClientImpl::Post(const std::string &path, const MultipartFormDataItems &items) { return Post(path, Headers(), items); @@ -7816,7 +8503,7 @@ ClientImpl::Post(const std::string &path, const Headers &headers, return send_with_content_provider( "POST", path, headers, nullptr, 0, nullptr, get_multipart_content_provider(boundary, items, provider_items), - content_type); + content_type, nullptr); } inline Result ClientImpl::Put(const std::string &path) { @@ -7833,7 +8520,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, body, content_length, - nullptr, nullptr, content_type); + nullptr, nullptr, content_type, nullptr); +} + +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("PUT", path, headers, body, content_length, + nullptr, nullptr, content_type, progress); } inline Result ClientImpl::Put(const std::string &path, const std::string &body, @@ -7841,12 +8536,27 @@ inline Result ClientImpl::Put(const std::string &path, const std::string &body, return Put(path, Headers(), body, content_type); } +inline Result ClientImpl::Put(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return Put(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + nullptr); +} + +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("PUT", path, headers, body.data(), + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Put(const std::string &path, size_t content_length, @@ -7868,14 +8578,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Put(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Put(const std::string &path, const Params ¶ms) { @@ -7888,6 +8599,13 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, return Put(path, headers, query, "application/x-www-form-urlencoded"); } +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + auto query = detail::params_to_query_str(params); + return Put(path, headers, query, "application/x-www-form-urlencoded", + progress); +} + inline Result ClientImpl::Put(const std::string &path, const MultipartFormDataItems &items) { return Put(path, Headers(), items); @@ -7925,7 +8643,7 @@ ClientImpl::Put(const std::string &path, const Headers &headers, return send_with_content_provider( "PUT", path, headers, nullptr, 0, nullptr, get_multipart_content_provider(boundary, items, provider_items), - content_type); + content_type, nullptr); } inline Result ClientImpl::Patch(const std::string &path) { return Patch(path, std::string(), std::string()); @@ -7937,12 +8655,26 @@ inline Result ClientImpl::Patch(const std::string &path, const char *body, return Patch(path, Headers(), body, content_length, content_type); } +inline Result ClientImpl::Patch(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return Patch(path, Headers(), body, content_length, content_type, progress); +} + inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { + return Patch(path, headers, body, content_length, content_type, nullptr); +} + +inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { return send_with_content_provider("PATCH", path, headers, body, content_length, nullptr, nullptr, - content_type); + content_type, progress); } inline Result ClientImpl::Patch(const std::string &path, @@ -7951,12 +8683,26 @@ inline Result ClientImpl::Patch(const std::string &path, return Patch(path, Headers(), body, content_type); } +inline Result ClientImpl::Patch(const std::string &path, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Patch(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { + return Patch(path, headers, body, content_type, nullptr); +} + +inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { return send_with_content_provider("PATCH", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Patch(const std::string &path, size_t content_length, @@ -7978,14 +8724,15 @@ inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("PATCH", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("PATCH", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Delete(const std::string &path) { @@ -8003,14 +8750,33 @@ inline Result ClientImpl::Delete(const std::string &path, const char *body, return Delete(path, Headers(), body, content_length, content_type); } +inline Result ClientImpl::Delete(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return Delete(path, Headers(), body, content_length, content_type, progress); +} + inline Result ClientImpl::Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { + return Delete(path, headers, body, content_length, content_type, nullptr); +} + +inline Result ClientImpl::Delete(const std::string &path, + const Headers &headers, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { Request req; req.method = "DELETE"; req.headers = headers; req.path = path; + req.progress = progress; + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } if (!content_type.empty()) { req.set_header("Content-Type", content_type); } req.body.assign(body, content_length); @@ -8024,6 +8790,14 @@ inline Result ClientImpl::Delete(const std::string &path, return Delete(path, Headers(), body.data(), body.size(), content_type); } +inline Result ClientImpl::Delete(const std::string &path, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Delete(path, Headers(), body.data(), body.size(), content_type, + progress); +} + inline Result ClientImpl::Delete(const std::string &path, const Headers &headers, const std::string &body, @@ -8031,6 +8805,15 @@ inline Result ClientImpl::Delete(const std::string &path, return Delete(path, headers, body.data(), body.size(), content_type); } +inline Result ClientImpl::Delete(const std::string &path, + const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Delete(path, headers, body.data(), body.size(), content_type, + progress); +} + inline Result ClientImpl::Options(const std::string &path) { return Options(path, Headers()); } @@ -8041,6 +8824,9 @@ inline Result ClientImpl::Options(const std::string &path, req.method = "OPTIONS"; req.headers = headers; req.path = path; + if (max_timeout_msec_ > 0) { + req.start_time_ = std::chrono::steady_clock::now(); + } return send_(std::move(req)); } @@ -8094,6 +8880,10 @@ inline void ClientImpl::set_write_timeout(time_t sec, time_t usec) { write_timeout_usec_ = usec; } +inline void ClientImpl::set_max_timeout(time_t msec) { + max_timeout_msec_ = msec; +} + inline void ClientImpl::set_basic_auth(const std::string &username, const std::string &password) { basic_auth_username_ = username; @@ -8138,6 +8928,8 @@ inline void ClientImpl::set_address_family(int family) { inline void ClientImpl::set_tcp_nodelay(bool on) { tcp_nodelay_ = on; } +inline void ClientImpl::set_ipv6_v6only(bool on) { ipv6_v6only_ = on; } + inline void ClientImpl::set_socket_options(SocketOptions socket_options) { socket_options_ = std::move(socket_options); } @@ -8187,13 +8979,11 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) { inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert, std::size_t size) const { auto mem = BIO_new_mem_buf(ca_cert, static_cast(size)); + auto se = detail::scope_exit([&] { BIO_free_all(mem); }); if (!mem) { return nullptr; } auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr); - if (!inf) { - BIO_free_all(mem); - return nullptr; - } + if (!inf) { return nullptr; } auto cts = X509_STORE_new(); if (cts) { @@ -8207,13 +8997,21 @@ inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert, } sk_X509_INFO_pop_free(inf, X509_INFO_free); - BIO_free_all(mem); return cts; } inline void ClientImpl::enable_server_certificate_verification(bool enabled) { server_certificate_verification_ = enabled; } + +inline void ClientImpl::enable_server_hostname_verification(bool enabled) { + server_hostname_verification_ = enabled; +} + +inline void ClientImpl::set_server_certificate_verifier( + std::function verifier) { + server_certificate_verifier_ = verifier; +} #endif inline void ClientImpl::set_logger(Logger logger) { @@ -8257,13 +9055,26 @@ inline SSL *ssl_new(socket_t sock, SSL_CTX *ctx, std::mutex &ctx_mutex, return ssl; } -inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl, +inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl, socket_t sock, bool shutdown_gracefully) { // sometimes we may want to skip this to try to avoid SIGPIPE if we know // the remote has closed the network connection // Note that it is not always possible to avoid SIGPIPE, this is merely a // best-efforts. - if (shutdown_gracefully) { SSL_shutdown(ssl); } + if (shutdown_gracefully) { +#ifdef _WIN32 + (void)(sock); + SSL_shutdown(ssl); +#else + detail::set_socket_opt_time(sock, SOL_SOCKET, SO_RCVTIMEO, 1, 0); + + auto ret = SSL_shutdown(ssl); + while (ret == 0) { + std::this_thread::sleep_for(std::chrono::milliseconds{100}); + ret = SSL_shutdown(ssl); + } +#endif + } std::lock_guard guard(ctx_mutex); SSL_free(ssl); @@ -8307,12 +9118,14 @@ inline bool process_server_socket_ssl( } template -inline bool -process_client_socket_ssl(SSL *ssl, socket_t sock, time_t read_timeout_sec, - time_t read_timeout_usec, time_t write_timeout_sec, - time_t write_timeout_usec, T callback) { +inline bool process_client_socket_ssl( + SSL *ssl, socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec, + std::chrono::time_point start_time, T callback) { SSLSocketStream strm(sock, ssl, read_timeout_sec, read_timeout_usec, - write_timeout_sec, write_timeout_usec); + write_timeout_sec, write_timeout_usec, + max_timeout_msec, start_time); return callback(strm); } @@ -8325,27 +9138,37 @@ class SSLInit { }; // SSL socket stream implementation -inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl, - time_t read_timeout_sec, - time_t read_timeout_usec, - time_t write_timeout_sec, - time_t write_timeout_usec) +inline SSLSocketStream::SSLSocketStream( + socket_t sock, SSL *ssl, time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, time_t write_timeout_usec, + time_t max_timeout_msec, + std::chrono::time_point start_time) : sock_(sock), ssl_(ssl), read_timeout_sec_(read_timeout_sec), read_timeout_usec_(read_timeout_usec), write_timeout_sec_(write_timeout_sec), - write_timeout_usec_(write_timeout_usec) { + write_timeout_usec_(write_timeout_usec), + max_timeout_msec_(max_timeout_msec), start_time(start_time) { SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY); } inline SSLSocketStream::~SSLSocketStream() = default; inline bool SSLSocketStream::is_readable() const { - return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; + if (max_timeout_msec_ <= 0) { + return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; + } + + time_t read_timeout_sec; + time_t read_timeout_usec; + calc_actual_timeout(max_timeout_msec_, duration(), read_timeout_sec_, + read_timeout_usec_, read_timeout_sec, read_timeout_usec); + + return select_read(sock_, read_timeout_sec, read_timeout_usec) > 0; } inline bool SSLSocketStream::is_writable() const { return select_write(sock_, write_timeout_sec_, write_timeout_usec_) > 0 && - is_socket_alive(sock_); + is_socket_alive(sock_) && !is_ssl_peer_could_be_closed(ssl_, sock_); } inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { @@ -8366,7 +9189,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { if (SSL_pending(ssl_) > 0) { return SSL_read(ssl_, ptr, static_cast(size)); } else if (is_readable()) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{10}); ret = SSL_read(ssl_, ptr, static_cast(size)); if (ret >= 0) { return ret; } err = SSL_get_error(ssl_, ret); @@ -8376,8 +9199,9 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { } } return ret; + } else { + return -1; } - return -1; } inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) { @@ -8397,7 +9221,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) { while (--n >= 0 && err == SSL_ERROR_WANT_WRITE) { #endif if (is_writable()) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{10}); ret = SSL_write(ssl_, ptr, static_cast(handle_size)); if (ret >= 0) { return ret; } err = SSL_get_error(ssl_, ret); @@ -8423,6 +9247,12 @@ inline void SSLSocketStream::get_local_ip_and_port(std::string &ip, inline socket_t SSLSocketStream::socket() const { return sock_; } +inline time_t SSLSocketStream::duration() const { + return std::chrono::duration_cast( + std::chrono::steady_clock::now() - start_time) + .count(); +} + static SSLInit sslinit_; } // namespace detail @@ -8439,7 +9269,7 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path, SSL_OP_NO_COMPRESSION | SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION); - SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); if (private_key_password != nullptr && (private_key_password[0] != '\0')) { SSL_CTX_set_default_passwd_cb_userdata( @@ -8449,7 +9279,8 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path, if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 || SSL_CTX_use_PrivateKey_file(ctx_, private_key_path, SSL_FILETYPE_PEM) != - 1) { + 1 || + SSL_CTX_check_private_key(ctx_) != 1) { SSL_CTX_free(ctx_); ctx_ = nullptr; } else if (client_ca_cert_file_path || client_ca_cert_dir_path) { @@ -8471,7 +9302,7 @@ inline SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key, SSL_OP_NO_COMPRESSION | SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION); - SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); if (SSL_CTX_use_certificate(ctx_, cert) != 1 || SSL_CTX_use_PrivateKey(ctx_, private_key) != 1) { @@ -8505,6 +9336,19 @@ inline bool SSLServer::is_valid() const { return ctx_; } inline SSL_CTX *SSLServer::ssl_context() const { return ctx_; } +inline void SSLServer::update_certs(X509 *cert, EVP_PKEY *private_key, + X509_STORE *client_ca_cert_store) { + + std::lock_guard guard(ctx_mutex_); + + SSL_CTX_use_certificate(ctx_, cert); + SSL_CTX_use_PrivateKey(ctx_, private_key); + + if (client_ca_cert_store != nullptr) { + SSL_CTX_set_cert_store(ctx_, client_ca_cert_store); + } +} + inline bool SSLServer::process_and_close_socket(socket_t sock) { auto ssl = detail::ssl_new( sock, ctx_, ctx_mutex_, @@ -8516,20 +9360,29 @@ inline bool SSLServer::process_and_close_socket(socket_t sock) { auto ret = false; if (ssl) { + std::string remote_addr; + int remote_port = 0; + detail::get_remote_ip_and_port(sock, remote_addr, remote_port); + + std::string local_addr; + int local_port = 0; + detail::get_local_ip_and_port(sock, local_addr, local_port); + ret = detail::process_server_socket_ssl( svr_sock_, ssl, sock, keep_alive_max_count_, keep_alive_timeout_sec_, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, - [this, ssl](Stream &strm, bool close_connection, - bool &connection_closed) { - return process_request(strm, close_connection, connection_closed, + [&](Stream &strm, bool close_connection, bool &connection_closed) { + return process_request(strm, remote_addr, remote_port, local_addr, + local_port, close_connection, + connection_closed, [&](Request &req) { req.ssl = ssl; }); }); // Shutdown gracefully if the result seemed successful, non-gracefully if // the connection appeared to be closed. const bool shutdown_gracefully = ret; - detail::ssl_delete(ctx_mutex_, ssl, shutdown_gracefully); + detail::ssl_delete(ctx_mutex_, ssl, sock, shutdown_gracefully); } detail::shutdown_socket(sock); @@ -8551,6 +9404,8 @@ inline SSLClient::SSLClient(const std::string &host, int port, : ClientImpl(host, port, client_cert_path, client_key_path) { ctx_ = SSL_CTX_new(TLS_client_method()); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); + detail::split(&host_[0], &host_[host_.size()], '.', [&](const char *b, const char *e) { host_components_.emplace_back(b, e); @@ -8638,16 +9493,22 @@ inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) { } // Assumes that socket_mutex_ is locked and that there are no requests in flight -inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, - bool &success, Error &error) { +inline bool SSLClient::connect_with_proxy( + Socket &socket, + std::chrono::time_point start_time, + Response &res, bool &success, Error &error) { success = true; Response proxy_res; if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, - write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { + write_timeout_sec_, write_timeout_usec_, max_timeout_msec_, + start_time, [&](Stream &strm) { Request req2; req2.method = "CONNECT"; req2.path = host_and_port_; + if (max_timeout_msec_ > 0) { + req2.start_time_ = std::chrono::steady_clock::now(); + } return process_request(strm, req2, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are no @@ -8667,7 +9528,8 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, proxy_res = Response(); if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, - write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { + write_timeout_sec_, write_timeout_usec_, max_timeout_msec_, + start_time, [&](Stream &strm) { Request req3; req3.method = "CONNECT"; req3.path = host_and_port_; @@ -8675,6 +9537,9 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, req3, auth, 1, detail::random_string(10), proxy_digest_auth_username_, proxy_digest_auth_password_, true)); + if (max_timeout_msec_ > 0) { + req3.start_time_ = std::chrono::steady_clock::now(); + } return process_request(strm, req3, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are @@ -8758,36 +9623,47 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) { } if (server_certificate_verification_) { - verify_result_ = SSL_get_verify_result(ssl2); + if (server_certificate_verifier_) { + if (!server_certificate_verifier_(ssl2)) { + error = Error::SSLServerVerification; + return false; + } + } else { + verify_result_ = SSL_get_verify_result(ssl2); - if (verify_result_ != X509_V_OK) { - error = Error::SSLServerVerification; - return false; - } + if (verify_result_ != X509_V_OK) { + error = Error::SSLServerVerification; + return false; + } - auto server_cert = SSL_get1_peer_certificate(ssl2); + auto server_cert = SSL_get1_peer_certificate(ssl2); + auto se = detail::scope_exit([&] { X509_free(server_cert); }); - if (server_cert == nullptr) { - error = Error::SSLServerVerification; - return false; - } + if (server_cert == nullptr) { + error = Error::SSLServerVerification; + return false; + } - if (!verify_host(server_cert)) { - X509_free(server_cert); - error = Error::SSLServerVerification; - return false; + if (server_hostname_verification_) { + if (!verify_host(server_cert)) { + error = Error::SSLServerHostnameVerification; + return false; + } + } } - X509_free(server_cert); } return true; }, [&](SSL *ssl2) { +#if defined(OPENSSL_IS_BORINGSSL) + SSL_set_tlsext_host_name(ssl2, host_.c_str()); +#else // NOTE: Direct call instead of using the OpenSSL macro to suppress // -Wold-style-cast warning - // SSL_set_tlsext_host_name(ssl2, host_.c_str()); SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name, static_cast(const_cast(host_.c_str()))); +#endif return true; }); @@ -8812,19 +9688,22 @@ inline void SSLClient::shutdown_ssl_impl(Socket &socket, return; } if (socket.ssl) { - detail::ssl_delete(ctx_mutex_, socket.ssl, shutdown_gracefully); + detail::ssl_delete(ctx_mutex_, socket.ssl, socket.sock, + shutdown_gracefully); socket.ssl = nullptr; } assert(socket.ssl == nullptr); } -inline bool -SSLClient::process_socket(const Socket &socket, - std::function callback) { +inline bool SSLClient::process_socket( + const Socket &socket, + std::chrono::time_point start_time, + std::function callback) { assert(socket.ssl); return detail::process_client_socket_ssl( socket.ssl, socket.sock, read_timeout_sec_, read_timeout_usec_, - write_timeout_sec_, write_timeout_usec_, std::move(callback)); + write_timeout_sec_, write_timeout_usec_, max_timeout_msec_, start_time, + std::move(callback)); } inline bool SSLClient::is_ssl() const { return true; } @@ -8861,8 +9740,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { auto type = GEN_DNS; - struct in6_addr addr6 {}; - struct in_addr addr {}; + struct in6_addr addr6{}; + struct in_addr addr{}; size_t addr_len = 0; #ifndef __MINGW32__ @@ -8965,7 +9844,7 @@ inline Client::Client(const std::string &scheme_host_port, const std::string &client_cert_path, const std::string &client_key_path) { const static std::regex re( - R"((?:([a-z]+):\/\/)?(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)"); + R"((?:([a-z]+):\/\/)?(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)"); std::smatch m; if (std::regex_match(scheme_host_port, m, re)) { @@ -9002,10 +9881,12 @@ inline Client::Client(const std::string &scheme_host_port, client_key_path); } } else { + // NOTE: Update TEST(UniversalClientImplTest, Ipv6LiteralAddress) + // if port param below changes. cli_ = detail::make_unique(scheme_host_port, 80, client_cert_path, client_key_path); } -} +} // namespace detail inline Client::Client(const std::string &host, int port) : cli_(detail::make_unique(host, port)) {} @@ -9111,15 +9992,30 @@ inline Result Client::Post(const std::string &path, const Headers &headers, const std::string &content_type) { return cli_->Post(path, headers, body, content_length, content_type); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress) { + return cli_->Post(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Post(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Post(path, body, content_type); } +inline Result Client::Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Post(path, body, content_type, progress); +} inline Result Client::Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Post(path, headers, body, content_type); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Post(path, headers, body, content_type, progress); +} inline Result Client::Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -9150,6 +10046,10 @@ inline Result Client::Post(const std::string &path, const Headers &headers, const Params ¶ms) { return cli_->Post(path, headers, params); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + return cli_->Post(path, headers, params, progress); +} inline Result Client::Post(const std::string &path, const MultipartFormDataItems &items) { return cli_->Post(path, items); @@ -9180,15 +10080,29 @@ inline Result Client::Put(const std::string &path, const Headers &headers, const std::string &content_type) { return cli_->Put(path, headers, body, content_length, content_type); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress) { + return cli_->Put(path, headers, body, content_length, content_type, progress); +} inline Result Client::Put(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Put(path, body, content_type); } +inline Result Client::Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Put(path, body, content_type, progress); +} inline Result Client::Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Put(path, headers, body, content_type); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Put(path, headers, body, content_type, progress); +} inline Result Client::Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -9219,6 +10133,10 @@ inline Result Client::Put(const std::string &path, const Headers &headers, const Params ¶ms) { return cli_->Put(path, headers, params); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + return cli_->Put(path, headers, params, progress); +} inline Result Client::Put(const std::string &path, const MultipartFormDataItems &items) { return cli_->Put(path, items); @@ -9246,20 +10164,44 @@ inline Result Client::Patch(const std::string &path, const char *body, const std::string &content_type) { return cli_->Patch(path, body, content_length, content_type); } +inline Result Client::Patch(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, body, content_length, content_type, progress); +} inline Result Client::Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return cli_->Patch(path, headers, body, content_length, content_type); } +inline Result Client::Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Patch(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Patch(path, body, content_type); } +inline Result Client::Patch(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, body, content_type, progress); +} inline Result Client::Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Patch(path, headers, body, content_type); } +inline Result Client::Patch(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, headers, body, content_type, progress); +} inline Result Client::Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -9294,20 +10236,44 @@ inline Result Client::Delete(const std::string &path, const char *body, const std::string &content_type) { return cli_->Delete(path, body, content_length, content_type); } +inline Result Client::Delete(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, body, content_length, content_type, progress); +} inline Result Client::Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return cli_->Delete(path, headers, body, content_length, content_type); } +inline Result Client::Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Delete(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Delete(path, body, content_type); } +inline Result Client::Delete(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, body, content_type, progress); +} inline Result Client::Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Delete(path, headers, body, content_type); } +inline Result Client::Delete(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, headers, body, content_type, progress); +} inline Result Client::Options(const std::string &path) { return cli_->Options(path); } @@ -9417,6 +10383,15 @@ inline void Client::set_proxy_digest_auth(const std::string &username, inline void Client::enable_server_certificate_verification(bool enabled) { cli_->enable_server_certificate_verification(enabled); } + +inline void Client::enable_server_hostname_verification(bool enabled) { + cli_->enable_server_hostname_verification(enabled); +} + +inline void Client::set_server_certificate_verifier( + std::function verifier) { + cli_->set_server_certificate_verifier(verifier); +} #endif inline void Client::set_logger(Logger logger) { diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js deleted file mode 100644 index 54a0f22f58e6f..0000000000000 --- a/examples/server/public/completion.js +++ /dev/null @@ -1,225 +0,0 @@ -const paramDefaults = { - stream: true, - temperature: 0.2, -}; - -let generation_settings = null; - -export class CompletionError extends Error { - constructor(message, name, data) { - super(message); - this.name = name; - } -}; - -// Completes the prompt as a generator. Recommended for most use cases. -// -// Example: -// -// import { llama } from '/completion.js' -// -// const request = llama("Tell me a joke", {n_predict: 800}) -// for await (const chunk of request) { -// document.write(chunk.data.content) -// } -// -export async function* llama(prompt, params = {}, config = {}) { - let controller = config.controller; - const api_url = config.api_url?.replace(/\/+$/, '') || ""; - - if (!controller) { - controller = new AbortController(); - } - - const completionParams = { ...paramDefaults, ...params, prompt }; - - const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { - method: 'POST', - body: JSON.stringify(completionParams), - headers: { - 'Connection': 'keep-alive', - 'Content-Type': 'application/json', - 'Accept': 'text/event-stream', - ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) - }, - signal: controller.signal, - }); - - const status = response.status; - if (status !== 200) { - try { - const body = await response.json(); - if (body && body.error && body.error.message) { - throw new CompletionError(body.error.message, 'ServerError'); - } - } catch (err) { - throw new CompletionError(err.message, 'ServerError'); - } - } - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - - let content = ""; - let leftover = ""; // Buffer for partially read lines - - try { - let cont = true; - - while (cont) { - const result = await reader.read(); - if (result.done) { - break; - } - - // Add any leftover data to the current chunk of data - const text = leftover + decoder.decode(result.value); - - // Check if the last character is a line break - const endsWithLineBreak = text.endsWith('\n'); - - // Split the text into lines - let lines = text.split('\n'); - - // If the text doesn't end with a line break, then the last line is incomplete - // Store it in leftover to be added to the next chunk of data - if (!endsWithLineBreak) { - leftover = lines.pop(); - } else { - leftover = ""; // Reset leftover if we have a line break at the end - } - - // Parse all sse events and add them to result - const regex = /^(\S+):\s(.*)$/gm; - for (const line of lines) { - const match = regex.exec(line); - if (match) { - result[match[1]] = match[2]; - if (result.data === '[DONE]') { - cont = false; - break; - } - - // since we know this is llama.cpp, let's just decode the json in data - if (result.data) { - result.data = JSON.parse(result.data); - content += result.data.content; - - // yield - yield result; - - // if we got a stop token from server, we will break here - if (result.data.stop) { - if (result.data.generation_settings) { - generation_settings = result.data.generation_settings; - } - cont = false; - break; - } - } - if (result.error) { - try { - result.error = JSON.parse(result.error); - if (result.error.message.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); - } - } catch(e) { - console.error(`llama.cpp error ${result.error}`) - } - } - } - } - } - } catch (e) { - if (e.name !== 'AbortError') { - console.error("llama error: ", e); - } - throw e; - } - finally { - controller.abort(); - } - - return content; -} - -// Call llama, return an event target that you can subscribe to -// -// Example: -// -// import { llamaEventTarget } from '/completion.js' -// -// const conn = llamaEventTarget(prompt) -// conn.addEventListener("message", (chunk) => { -// document.write(chunk.detail.content) -// }) -// -export const llamaEventTarget = (prompt, params = {}, config = {}) => { - const eventTarget = new EventTarget(); - (async () => { - let content = ""; - for await (const chunk of llama(prompt, params, config)) { - if (chunk.data) { - content += chunk.data.content; - eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); - } - if (chunk.data.generation_settings) { - eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); - } - if (chunk.data.timings) { - eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); - } - } - eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); - })(); - return eventTarget; -} - -// Call llama, return a promise that resolves to the completed text. This does not support streaming -// -// Example: -// -// llamaPromise(prompt).then((content) => { -// document.write(content) -// }) -// -// or -// -// const content = await llamaPromise(prompt) -// document.write(content) -// -export const llamaPromise = (prompt, params = {}, config = {}) => { - return new Promise(async (resolve, reject) => { - let content = ""; - try { - for await (const chunk of llama(prompt, params, config)) { - content += chunk.data.content; - } - resolve(content); - } catch (error) { - reject(error); - } - }); -}; - -/** - * (deprecated) - */ -export const llamaComplete = async (params, controller, callback) => { - for await (const chunk of llama(params.prompt, params, { controller })) { - callback(chunk); - } -} - -// Get the model info from the server. This is useful for getting the context window and so on. -export const llamaModelInfo = async (config = {}) => { - if (!generation_settings) { - const api_url = config.api_url?.replace(/\/+$/, '') || ""; - const props = await fetch(`${api_url}/props`).then(r => r.json()); - generation_settings = props.default_generation_settings; - } - return generation_settings; -} diff --git a/examples/server/public/deps_daisyui.min.css b/examples/server/public/deps_daisyui.min.css deleted file mode 100644 index bc85296519d9a..0000000000000 --- a/examples/server/public/deps_daisyui.min.css +++ /dev/null @@ -1,13 +0,0 @@ -.alert{display:grid;width:100%;grid-auto-flow:row;align-content:flex-start;align-items:center;justify-items:center;gap:1rem;text-align:center}@media (min-width:640px){.alert{grid-auto-flow:column;grid-template-columns:auto 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scaleY(var(--tw-scale-y));border-bottom-right-radius:var(--rounded-box,1rem);border-bottom-left-radius:var(--rounded-box,1rem);border-top-left-radius:var(--rounded-box,1rem);border-top-right-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));padding:1.5rem;transition-property:color,background-color,border-color,text-decoration-color,fill,stroke,opacity,box-shadow,transform,filter,backdrop-filter;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-duration:.2s;transition-timing-function:cubic-bezier(0,0,.2,1);box-shadow:rgba(0,0,0,.25) 0 25px 50px -12px;overflow-y:auto;overscroll-behavior:contain}.modal-open .modal-box,.modal-toggle:checked+.modal .modal-box,.modal:target .modal-box,.modal[open] .modal-box{--tw-translate-y:0px;--tw-scale-x:1;--tw-scale-y:1;transform:translate(var(--tw-translate-x),var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y))}.modal-action{margin-top:1.5rem;justify-content:flex-end}.modal-action>:not([hidden])~:not([hidden]){--tw-space-x-reverse:0;margin-right:calc(.5rem * var(--tw-space-x-reverse));margin-left:calc(.5rem * calc(1 - var(--tw-space-x-reverse)))}@keyframes modal-pop{0%{opacity:0}}.navbar{padding:var(--navbar-padding,.5rem);min-height:4rem;width:100%}.progress{height:.5rem;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.2))}.progress::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)))}.progress-primary::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)))}.progress-secondary::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)))}.progress-accent::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)))}.progress-info::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)))}.progress-success::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)))}.progress-warning::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)))}.progress-error::-moz-progress-bar{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)))}.progress:indeterminate{--progress-color:var(--fallback-bc,oklch(var(--bc)/1))}.progress-primary:indeterminate{--progress-color:var(--fallback-p,oklch(var(--p)/1))}.progress-secondary:indeterminate{--progress-color:var(--fallback-s,oklch(var(--s)/1))}.progress-accent:indeterminate{--progress-color:var(--fallback-a,oklch(var(--a)/1))}.progress-info:indeterminate{--progress-color:var(--fallback-in,oklch(var(--in)/1))}.progress-success:indeterminate{--progress-color:var(--fallback-su,oklch(var(--su)/1))}.progress-warning:indeterminate{--progress-color:var(--fallback-wa,oklch(var(--wa)/1))}.progress-error:indeterminate{--progress-color:var(--fallback-er,oklch(var(--er)/1))}.progress::-webkit-progress-bar{border-radius:var(--rounded-box,1rem);background-color:transparent}.progress::-webkit-progress-value{border-radius:var(--rounded-box,1rem);--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)))}.progress-primary::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)))}.progress-secondary::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)))}.progress-accent::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)))}.progress-info::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)))}.progress-success::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)))}.progress-warning::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)))}.progress-error::-webkit-progress-value{--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)))}.progress:indeterminate{background-image:repeating-linear-gradient(90deg,var(--progress-color) -1%,var(--progress-color) 10%,transparent 10%,transparent 90%);background-size:200%;background-position-x:15%;animation:progress-loading 5s ease-in-out infinite}.progress:indeterminate::-moz-progress-bar{background-color:transparent;background-image:repeating-linear-gradient(90deg,var(--progress-color) -1%,var(--progress-color) 10%,transparent 10%,transparent 90%);background-size:200%;background-position-x:15%;animation:progress-loading 5s ease-in-out infinite}@keyframes progress-loading{50%{background-position-x:-115%}}.radial-progress{--value:0;--size:5rem;--thickness:calc(var(--size) / 10)}.radial-progress:after{background-color:currentColor}.radio{--chkbg:var(--bc);height:1.5rem;width:1.5rem;cursor:pointer;appearance:none;border-radius:9999px;border-width:1px;border-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-border-opacity)));--tw-border-opacity:0.2}.radio:focus{box-shadow:none}.radio:focus-visible{outline-style:solid;outline-width:2px;outline-offset:2px;outline-color:var(--fallback-bc,oklch(var(--bc)/1))}.radio:checked,.radio[aria-checked=true]{--tw-bg-opacity:1;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));background-image:none;animation:radiomark var(--animation-input,.2s) ease-out;box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}.radio-primary{--chkbg:var(--p);--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)))}@media(hover:hover){.radio-primary:hover{--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)))}}.radio-primary:focus-visible{outline-color:var(--fallback-p,oklch(var(--p)/1))}.radio-primary:checked,.radio-primary[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-p,oklch(var(--p)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-p,oklch(var(--p)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-pc,oklch(var(--pc)/var(--tw-text-opacity)))}.radio-secondary{--chkbg:var(--s);--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)))}@media(hover:hover){.radio-secondary:hover{--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)))}}.radio-secondary:focus-visible{outline-color:var(--fallback-s,oklch(var(--s)/1))}.radio-secondary:checked,.radio-secondary[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-s,oklch(var(--s)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-s,oklch(var(--s)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-sc,oklch(var(--sc)/var(--tw-text-opacity)))}.radio-accent{--chkbg:var(--a);--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)))}@media(hover:hover){.radio-accent:hover{--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)))}}.radio-accent:focus-visible{outline-color:var(--fallback-a,oklch(var(--a)/1))}.radio-accent:checked,.radio-accent[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-a,oklch(var(--a)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-a,oklch(var(--a)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-ac,oklch(var(--ac)/var(--tw-text-opacity)))}.radio-success{--chkbg:var(--su);--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)))}@media(hover:hover){.radio-success:hover{--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)))}}.radio-success:focus-visible{outline-color:var(--fallback-su,oklch(var(--su)/1))}.radio-success:checked,.radio-success[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-su,oklch(var(--su)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-su,oklch(var(--su)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-suc,oklch(var(--suc)/var(--tw-text-opacity)))}.radio-warning{--chkbg:var(--wa);--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)))}@media(hover:hover){.radio-warning:hover{--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)))}}.radio-warning:focus-visible{outline-color:var(--fallback-wa,oklch(var(--wa)/1))}.radio-warning:checked,.radio-warning[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-wa,oklch(var(--wa)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-wac,oklch(var(--wac)/var(--tw-text-opacity)))}.radio-info{--chkbg:var(--in);--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)))}@media(hover:hover){.radio-info:hover{--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)))}}.radio-info:focus-visible{outline-color:var(--fallback-in,oklch(var(--in)/1))}.radio-info:checked,.radio-info[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-in,oklch(var(--in)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-in,oklch(var(--in)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-inc,oklch(var(--inc)/var(--tw-text-opacity)))}.radio-error{--chkbg:var(--er);--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)))}@media(hover:hover){.radio-error:hover{--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)))}}.radio-error:focus-visible{outline-color:var(--fallback-er,oklch(var(--er)/1))}.radio-error:checked,.radio-error[aria-checked=true]{--tw-border-opacity:1;border-color:var(--fallback-er,oklch(var(--er)/var(--tw-border-opacity)));--tw-bg-opacity:1;background-color:var(--fallback-er,oklch(var(--er)/var(--tw-bg-opacity)));--tw-text-opacity:1;color:var(--fallback-erc,oklch(var(--erc)/var(--tw-text-opacity)))}.radio:disabled{cursor:not-allowed;opacity:.2}@keyframes radiomark{0%{box-shadow:0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 12px var(--fallback-b1,oklch(var(--b1)/1)) inset}50%{box-shadow:0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 3px var(--fallback-b1,oklch(var(--b1)/1)) inset}100%{box-shadow:0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 4px var(--fallback-b1,oklch(var(--b1)/1)) inset}}.radio-mark{display:none}.range{appearance:none;-webkit-appearance:none;--range-shdw:var(--fallback-bc,oklch(var(--bc)/1));overflow:hidden;border-radius:var(--rounded-box,1rem);background-color:transparent}.range:focus-visible::-webkit-slider-thumb{--focus-shadow:0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 2rem var(--range-shdw) inset}.range:focus-visible::-moz-range-thumb{--focus-shadow:0 0 0 6px var(--fallback-b1,oklch(var(--b1)/1)) inset,0 0 0 2rem var(--range-shdw) inset}.range::-webkit-slider-runnable-track{height:.5rem;width:100%;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-moz-range-track{height:.5rem;width:100%;border-radius:var(--rounded-box,1rem);background-color:var(--fallback-bc,oklch(var(--bc)/.1))}.range::-webkit-slider-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box,1rem);border-style:none;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));appearance:none;-webkit-appearance:none;top:50%;color:var(--range-shdw);transform:translateY(-50%);--filler-size:100rem;--filler-offset:0.6rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow,0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}.range::-moz-range-thumb{position:relative;height:1.5rem;width:1.5rem;border-radius:var(--rounded-box,1rem);border-style:none;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));top:50%;color:var(--range-shdw);--filler-size:100rem;--filler-offset:0.5rem;box-shadow:0 0 0 3px var(--range-shdw) inset,var(--focus-shadow,0 0),calc(var(--filler-size) * -1 - var(--filler-offset)) 0 0 var(--filler-size)}.range-primary{--range-shdw:var(--fallback-p,oklch(var(--p)/1))}.range-secondary{--range-shdw:var(--fallback-s,oklch(var(--s)/1))}.range-accent{--range-shdw:var(--fallback-a,oklch(var(--a)/1))}.range-success{--range-shdw:var(--fallback-su,oklch(var(--su)/1))}.range-warning{--range-shdw:var(--fallback-wa,oklch(var(--wa)/1))}.range-info{--range-shdw:var(--fallback-in,oklch(var(--in)/1))}.range-error{--range-shdw:var(--fallback-er,oklch(var(--er)/1))}.rating input{appearance:none;-webkit-appearance:none}.rating :where(input){animation:rating-pop var(--animation-input,.25s) ease-out;height:1.5rem;width:1.5rem;background-color:var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));--tw-bg-opacity:1}.rating .rating-hidden{width:.5rem;background-color:transparent}.rating input[type=radio]:checked{background-image:none}.rating input:checked~input,.rating input[aria-checked=true]~input{--tw-bg-opacity:0.2}.rating input:focus-visible{transition-property:transform;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-duration:.3s;transition-timing-function:cubic-bezier(0,0,.2,1);transform:translateY(-.125em)}.rating input:active:focus{animation:none;transform:translateY(-.125em)}.rating-half :where(input:not(.rating-hidden)){width:.75rem}@keyframes rating-pop{0%{transform:translateY(-.125em)}40%{transform:translateY(-.125em)}100%{transform:translateY(0)}}.select{border-radius:var(--rounded-btn,.5rem);border-width:1px;border-color:transparent;--tw-bg-opacity:1;background-color:var(--fallback-b1,oklch(var(--b1)/var(--tw-bg-opacity)));padding-inline-end:2.5rem}.select-bordered{border-color:var(--fallback-bc,oklch(var(--bc)/.2))}.select{background-image:linear-gradient(45deg,transparent 50%,currentColor 50%),linear-gradient(135deg,currentColor 50%,transparent 50%);background-position:calc(100% - 20px) calc(1px + 50%),calc(100% - 16.1px) calc(1px + 50%);background-size:4px 4px,4px 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globalThis : global || self, - global.markdownit = factory()); -})(this, (function() { - "use strict"; - function createCommonjsModule(fn, basedir, module) { - return module = { - path: basedir, - exports: {}, - require: function(path, base) { - return commonjsRequire(path, base === undefined || base === null ? module.path : base); - } - }, fn(module, module.exports), module.exports; - } - function getAugmentedNamespace(n) { - if (n.__esModule) return n; - var a = Object.defineProperty({}, "__esModule", { - value: true - }); - Object.keys(n).forEach((function(k) { - var d = Object.getOwnPropertyDescriptor(n, k); - Object.defineProperty(a, k, d.get ? d : { - enumerable: true, - get: function() { - return n[k]; - } - }); - })); - return a; - } - function commonjsRequire() { - throw new Error("Dynamic requires are not currently supported by @rollup/plugin-commonjs"); - } - var require$$0 = { - Aacute: "\xc1", - aacute: "\xe1", - Abreve: "\u0102", - abreve: "\u0103", - ac: "\u223e", - acd: "\u223f", - acE: "\u223e\u0333", - Acirc: "\xc2", - acirc: "\xe2", - acute: "\xb4", - Acy: "\u0410", - acy: "\u0430", - AElig: "\xc6", - aelig: "\xe6", - af: "\u2061", - Afr: "\ud835\udd04", - afr: "\ud835\udd1e", - Agrave: "\xc0", - agrave: "\xe0", - alefsym: "\u2135", - aleph: "\u2135", - Alpha: "\u0391", - alpha: "\u03b1", - Amacr: "\u0100", - amacr: "\u0101", - amalg: "\u2a3f", - amp: "&", - AMP: "&", - andand: "\u2a55", - And: "\u2a53", - and: "\u2227", - andd: "\u2a5c", - andslope: "\u2a58", - andv: "\u2a5a", - ang: "\u2220", - ange: "\u29a4", - angle: "\u2220", - angmsdaa: "\u29a8", - angmsdab: "\u29a9", - angmsdac: "\u29aa", - angmsdad: "\u29ab", - angmsdae: "\u29ac", - angmsdaf: "\u29ad", - angmsdag: "\u29ae", - angmsdah: "\u29af", - angmsd: "\u2221", - angrt: "\u221f", - angrtvb: "\u22be", - angrtvbd: "\u299d", - angsph: "\u2222", - angst: "\xc5", - angzarr: "\u237c", - Aogon: "\u0104", - aogon: "\u0105", - Aopf: "\ud835\udd38", - aopf: "\ud835\udd52", - apacir: "\u2a6f", - ap: "\u2248", - apE: "\u2a70", - ape: "\u224a", - apid: "\u224b", - apos: "'", - ApplyFunction: "\u2061", - approx: "\u2248", - approxeq: "\u224a", - Aring: "\xc5", - aring: "\xe5", - Ascr: "\ud835\udc9c", - ascr: "\ud835\udcb6", - Assign: "\u2254", - ast: "*", - asymp: "\u2248", - asympeq: "\u224d", - Atilde: "\xc3", - atilde: "\xe3", - Auml: "\xc4", - auml: "\xe4", - awconint: "\u2233", - awint: "\u2a11", - backcong: "\u224c", - backepsilon: "\u03f6", - backprime: "\u2035", - backsim: "\u223d", - backsimeq: "\u22cd", - Backslash: "\u2216", - Barv: "\u2ae7", - barvee: "\u22bd", - barwed: "\u2305", - Barwed: "\u2306", - barwedge: "\u2305", - bbrk: "\u23b5", - bbrktbrk: "\u23b6", - bcong: "\u224c", - Bcy: "\u0411", - bcy: "\u0431", - bdquo: "\u201e", - becaus: "\u2235", - because: "\u2235", - Because: "\u2235", - bemptyv: "\u29b0", - bepsi: "\u03f6", - bernou: "\u212c", - Bernoullis: "\u212c", - Beta: "\u0392", - beta: "\u03b2", - beth: "\u2136", - between: "\u226c", - Bfr: "\ud835\udd05", - bfr: "\ud835\udd1f", - bigcap: "\u22c2", - bigcirc: "\u25ef", - bigcup: "\u22c3", - bigodot: "\u2a00", - bigoplus: "\u2a01", - bigotimes: "\u2a02", - bigsqcup: "\u2a06", - bigstar: "\u2605", - bigtriangledown: "\u25bd", - bigtriangleup: "\u25b3", - biguplus: "\u2a04", - bigvee: "\u22c1", - bigwedge: "\u22c0", - bkarow: "\u290d", - blacklozenge: "\u29eb", - blacksquare: "\u25aa", - blacktriangle: "\u25b4", - blacktriangledown: "\u25be", - blacktriangleleft: "\u25c2", - blacktriangleright: "\u25b8", - blank: "\u2423", - blk12: "\u2592", - blk14: "\u2591", - blk34: "\u2593", - block: "\u2588", - bne: "=\u20e5", - bnequiv: "\u2261\u20e5", - bNot: "\u2aed", - bnot: "\u2310", - Bopf: "\ud835\udd39", - bopf: "\ud835\udd53", - bot: "\u22a5", - bottom: "\u22a5", - bowtie: "\u22c8", - boxbox: "\u29c9", - boxdl: "\u2510", - boxdL: "\u2555", - boxDl: "\u2556", - boxDL: "\u2557", - boxdr: "\u250c", - boxdR: "\u2552", - boxDr: "\u2553", - boxDR: "\u2554", - boxh: "\u2500", - boxH: "\u2550", - boxhd: "\u252c", - boxHd: "\u2564", - boxhD: "\u2565", - boxHD: "\u2566", - boxhu: "\u2534", - boxHu: "\u2567", - boxhU: "\u2568", - boxHU: "\u2569", - boxminus: "\u229f", - boxplus: "\u229e", - boxtimes: "\u22a0", - boxul: "\u2518", - boxuL: "\u255b", - boxUl: "\u255c", - boxUL: "\u255d", - boxur: "\u2514", - boxuR: "\u2558", - boxUr: "\u2559", - boxUR: "\u255a", - boxv: "\u2502", - boxV: "\u2551", - boxvh: "\u253c", - boxvH: "\u256a", - boxVh: "\u256b", - boxVH: "\u256c", - boxvl: "\u2524", - boxvL: "\u2561", - boxVl: "\u2562", - boxVL: "\u2563", - boxvr: "\u251c", - boxvR: "\u255e", - boxVr: "\u255f", - boxVR: "\u2560", - bprime: "\u2035", - breve: "\u02d8", - Breve: "\u02d8", - brvbar: "\xa6", - bscr: "\ud835\udcb7", - Bscr: "\u212c", - bsemi: "\u204f", - bsim: "\u223d", - bsime: "\u22cd", - bsolb: "\u29c5", - bsol: "\\", - bsolhsub: "\u27c8", - bull: "\u2022", - bullet: "\u2022", - bump: "\u224e", - bumpE: "\u2aae", - bumpe: "\u224f", - Bumpeq: "\u224e", - bumpeq: "\u224f", - Cacute: "\u0106", - cacute: "\u0107", - capand: "\u2a44", - capbrcup: "\u2a49", - capcap: "\u2a4b", - cap: "\u2229", - Cap: "\u22d2", - capcup: "\u2a47", - capdot: "\u2a40", - CapitalDifferentialD: "\u2145", - caps: "\u2229\ufe00", - caret: "\u2041", - caron: "\u02c7", - Cayleys: "\u212d", - ccaps: "\u2a4d", - Ccaron: "\u010c", - ccaron: "\u010d", - Ccedil: "\xc7", - ccedil: "\xe7", - Ccirc: "\u0108", - ccirc: "\u0109", - Cconint: "\u2230", - ccups: "\u2a4c", - ccupssm: "\u2a50", - Cdot: "\u010a", - cdot: "\u010b", - cedil: "\xb8", - Cedilla: "\xb8", - cemptyv: "\u29b2", - cent: "\xa2", - centerdot: "\xb7", - CenterDot: "\xb7", - cfr: "\ud835\udd20", - Cfr: "\u212d", - CHcy: "\u0427", - chcy: "\u0447", - check: "\u2713", - checkmark: "\u2713", - Chi: "\u03a7", - chi: "\u03c7", - circ: "\u02c6", - circeq: "\u2257", - circlearrowleft: "\u21ba", - circlearrowright: "\u21bb", - circledast: "\u229b", - circledcirc: "\u229a", - circleddash: "\u229d", - CircleDot: "\u2299", - circledR: "\xae", - circledS: "\u24c8", - CircleMinus: "\u2296", - CirclePlus: "\u2295", - CircleTimes: "\u2297", - cir: "\u25cb", - cirE: "\u29c3", - cire: "\u2257", - cirfnint: "\u2a10", - cirmid: "\u2aef", - cirscir: "\u29c2", - ClockwiseContourIntegral: "\u2232", - CloseCurlyDoubleQuote: "\u201d", - CloseCurlyQuote: "\u2019", - clubs: "\u2663", - clubsuit: "\u2663", - colon: ":", - Colon: "\u2237", - Colone: "\u2a74", - colone: "\u2254", - coloneq: "\u2254", - comma: ",", - commat: "@", - comp: "\u2201", - compfn: "\u2218", - complement: "\u2201", - complexes: "\u2102", - cong: "\u2245", - congdot: "\u2a6d", - Congruent: "\u2261", - conint: "\u222e", - Conint: "\u222f", - ContourIntegral: "\u222e", - copf: "\ud835\udd54", - Copf: "\u2102", - coprod: "\u2210", - Coproduct: "\u2210", - copy: "\xa9", - COPY: "\xa9", - copysr: "\u2117", - CounterClockwiseContourIntegral: "\u2233", - crarr: "\u21b5", - cross: "\u2717", - Cross: "\u2a2f", - Cscr: "\ud835\udc9e", - cscr: "\ud835\udcb8", - csub: "\u2acf", - csube: "\u2ad1", - csup: "\u2ad0", - csupe: "\u2ad2", - ctdot: "\u22ef", - cudarrl: "\u2938", - cudarrr: "\u2935", - cuepr: "\u22de", - cuesc: "\u22df", - cularr: "\u21b6", - cularrp: "\u293d", - cupbrcap: "\u2a48", - cupcap: "\u2a46", - CupCap: "\u224d", - cup: "\u222a", - Cup: "\u22d3", - cupcup: "\u2a4a", - cupdot: "\u228d", - cupor: "\u2a45", - cups: "\u222a\ufe00", - curarr: "\u21b7", - curarrm: "\u293c", - curlyeqprec: "\u22de", - curlyeqsucc: "\u22df", - curlyvee: "\u22ce", - curlywedge: "\u22cf", - curren: "\xa4", - curvearrowleft: "\u21b6", - curvearrowright: "\u21b7", - cuvee: "\u22ce", - cuwed: "\u22cf", - cwconint: "\u2232", - cwint: "\u2231", - cylcty: "\u232d", - dagger: "\u2020", - Dagger: "\u2021", - daleth: "\u2138", - darr: "\u2193", - Darr: "\u21a1", - dArr: "\u21d3", - dash: "\u2010", - Dashv: "\u2ae4", - dashv: "\u22a3", - dbkarow: "\u290f", - dblac: "\u02dd", - Dcaron: "\u010e", - dcaron: "\u010f", - Dcy: "\u0414", - dcy: "\u0434", - ddagger: "\u2021", - ddarr: "\u21ca", - DD: "\u2145", - dd: "\u2146", - DDotrahd: "\u2911", - ddotseq: "\u2a77", - deg: "\xb0", - Del: "\u2207", - Delta: "\u0394", - delta: "\u03b4", - demptyv: "\u29b1", - dfisht: "\u297f", - Dfr: "\ud835\udd07", - dfr: "\ud835\udd21", - dHar: "\u2965", - dharl: "\u21c3", - dharr: "\u21c2", - DiacriticalAcute: "\xb4", - DiacriticalDot: "\u02d9", - DiacriticalDoubleAcute: "\u02dd", - DiacriticalGrave: "`", - DiacriticalTilde: "\u02dc", - diam: "\u22c4", - diamond: "\u22c4", - Diamond: "\u22c4", - diamondsuit: "\u2666", - diams: "\u2666", - die: "\xa8", - DifferentialD: "\u2146", - digamma: "\u03dd", - disin: "\u22f2", - div: "\xf7", - divide: "\xf7", - divideontimes: "\u22c7", - divonx: "\u22c7", - DJcy: "\u0402", - djcy: "\u0452", - dlcorn: "\u231e", - dlcrop: "\u230d", - dollar: "$", - Dopf: "\ud835\udd3b", - dopf: "\ud835\udd55", - Dot: "\xa8", - dot: "\u02d9", - DotDot: "\u20dc", - doteq: "\u2250", - doteqdot: "\u2251", - DotEqual: "\u2250", - dotminus: "\u2238", - dotplus: "\u2214", - dotsquare: "\u22a1", - doublebarwedge: "\u2306", - DoubleContourIntegral: "\u222f", - DoubleDot: "\xa8", - DoubleDownArrow: "\u21d3", - DoubleLeftArrow: "\u21d0", - DoubleLeftRightArrow: "\u21d4", - DoubleLeftTee: "\u2ae4", - DoubleLongLeftArrow: "\u27f8", - DoubleLongLeftRightArrow: "\u27fa", - DoubleLongRightArrow: "\u27f9", - DoubleRightArrow: "\u21d2", - DoubleRightTee: "\u22a8", - DoubleUpArrow: "\u21d1", - DoubleUpDownArrow: "\u21d5", - DoubleVerticalBar: "\u2225", - DownArrowBar: "\u2913", - downarrow: "\u2193", - DownArrow: "\u2193", - Downarrow: "\u21d3", - DownArrowUpArrow: "\u21f5", - DownBreve: "\u0311", - downdownarrows: "\u21ca", - downharpoonleft: "\u21c3", - downharpoonright: "\u21c2", - DownLeftRightVector: "\u2950", - DownLeftTeeVector: "\u295e", - DownLeftVectorBar: "\u2956", - DownLeftVector: "\u21bd", - DownRightTeeVector: "\u295f", - DownRightVectorBar: "\u2957", - DownRightVector: "\u21c1", - DownTeeArrow: "\u21a7", - DownTee: "\u22a4", - drbkarow: "\u2910", - drcorn: "\u231f", - drcrop: "\u230c", - Dscr: "\ud835\udc9f", - dscr: "\ud835\udcb9", - DScy: "\u0405", - dscy: "\u0455", - dsol: "\u29f6", - Dstrok: "\u0110", - dstrok: "\u0111", - dtdot: "\u22f1", - dtri: "\u25bf", - dtrif: "\u25be", - duarr: "\u21f5", - duhar: "\u296f", - dwangle: "\u29a6", - DZcy: "\u040f", - dzcy: "\u045f", - dzigrarr: "\u27ff", - Eacute: "\xc9", - eacute: "\xe9", - easter: "\u2a6e", - Ecaron: "\u011a", - ecaron: "\u011b", - Ecirc: "\xca", - ecirc: "\xea", - ecir: "\u2256", - ecolon: "\u2255", - Ecy: "\u042d", - ecy: "\u044d", - eDDot: "\u2a77", - Edot: "\u0116", - edot: "\u0117", - eDot: "\u2251", - ee: "\u2147", - efDot: "\u2252", - Efr: "\ud835\udd08", - efr: "\ud835\udd22", - eg: "\u2a9a", - Egrave: "\xc8", - egrave: "\xe8", - egs: "\u2a96", - egsdot: "\u2a98", - el: "\u2a99", - Element: "\u2208", - elinters: "\u23e7", - ell: "\u2113", - els: "\u2a95", - elsdot: "\u2a97", - Emacr: "\u0112", - emacr: "\u0113", - empty: "\u2205", - emptyset: "\u2205", - EmptySmallSquare: "\u25fb", - emptyv: "\u2205", - EmptyVerySmallSquare: "\u25ab", - emsp13: "\u2004", - emsp14: "\u2005", - emsp: "\u2003", - ENG: "\u014a", - eng: "\u014b", - ensp: "\u2002", - Eogon: "\u0118", - eogon: "\u0119", - Eopf: "\ud835\udd3c", - eopf: "\ud835\udd56", - epar: "\u22d5", - eparsl: "\u29e3", - eplus: "\u2a71", - epsi: "\u03b5", - Epsilon: "\u0395", - epsilon: "\u03b5", - epsiv: "\u03f5", - eqcirc: "\u2256", - eqcolon: "\u2255", - eqsim: "\u2242", - eqslantgtr: "\u2a96", - eqslantless: "\u2a95", - Equal: "\u2a75", - equals: "=", - EqualTilde: "\u2242", - equest: "\u225f", - Equilibrium: "\u21cc", - equiv: "\u2261", - equivDD: "\u2a78", - eqvparsl: "\u29e5", - erarr: "\u2971", - erDot: "\u2253", - escr: "\u212f", - Escr: "\u2130", - esdot: "\u2250", - Esim: "\u2a73", - esim: "\u2242", - Eta: "\u0397", - eta: "\u03b7", - ETH: "\xd0", - eth: "\xf0", - Euml: "\xcb", - euml: "\xeb", - euro: "\u20ac", - excl: "!", - exist: "\u2203", - Exists: "\u2203", - expectation: "\u2130", - exponentiale: "\u2147", - ExponentialE: "\u2147", - fallingdotseq: "\u2252", - Fcy: "\u0424", - fcy: "\u0444", - female: "\u2640", - ffilig: "\ufb03", - fflig: "\ufb00", - ffllig: "\ufb04", - Ffr: "\ud835\udd09", - ffr: "\ud835\udd23", - filig: "\ufb01", - FilledSmallSquare: "\u25fc", - FilledVerySmallSquare: "\u25aa", - fjlig: "fj", - flat: "\u266d", - fllig: "\ufb02", - fltns: "\u25b1", - fnof: "\u0192", - Fopf: "\ud835\udd3d", - fopf: "\ud835\udd57", - forall: "\u2200", - ForAll: "\u2200", - fork: "\u22d4", - forkv: "\u2ad9", - Fouriertrf: "\u2131", - fpartint: "\u2a0d", - frac12: "\xbd", - frac13: "\u2153", - frac14: "\xbc", - frac15: "\u2155", - frac16: "\u2159", - frac18: "\u215b", - frac23: "\u2154", - frac25: "\u2156", - frac34: "\xbe", - frac35: "\u2157", - frac38: "\u215c", - frac45: "\u2158", - frac56: "\u215a", - frac58: "\u215d", - frac78: "\u215e", - frasl: "\u2044", - frown: "\u2322", - fscr: "\ud835\udcbb", - Fscr: "\u2131", - gacute: "\u01f5", - Gamma: "\u0393", - gamma: "\u03b3", - Gammad: "\u03dc", - gammad: "\u03dd", - gap: "\u2a86", - Gbreve: "\u011e", - gbreve: "\u011f", - Gcedil: "\u0122", - Gcirc: "\u011c", - gcirc: "\u011d", - Gcy: "\u0413", - gcy: "\u0433", - Gdot: "\u0120", - gdot: "\u0121", - ge: "\u2265", - gE: "\u2267", - gEl: "\u2a8c", - gel: "\u22db", - geq: "\u2265", - geqq: "\u2267", - geqslant: "\u2a7e", - gescc: "\u2aa9", - ges: "\u2a7e", - gesdot: "\u2a80", - gesdoto: "\u2a82", - gesdotol: "\u2a84", - gesl: "\u22db\ufe00", - gesles: "\u2a94", - Gfr: "\ud835\udd0a", - gfr: "\ud835\udd24", - gg: "\u226b", - Gg: "\u22d9", - ggg: "\u22d9", - gimel: "\u2137", - GJcy: "\u0403", - gjcy: "\u0453", - gla: "\u2aa5", - gl: "\u2277", - glE: "\u2a92", - glj: "\u2aa4", - gnap: "\u2a8a", - gnapprox: "\u2a8a", - gne: "\u2a88", - gnE: "\u2269", - gneq: "\u2a88", - gneqq: "\u2269", - gnsim: "\u22e7", - Gopf: "\ud835\udd3e", - gopf: "\ud835\udd58", - grave: "`", - GreaterEqual: "\u2265", - GreaterEqualLess: "\u22db", - GreaterFullEqual: "\u2267", - GreaterGreater: "\u2aa2", - GreaterLess: "\u2277", - GreaterSlantEqual: "\u2a7e", - GreaterTilde: "\u2273", - Gscr: "\ud835\udca2", - gscr: "\u210a", - gsim: "\u2273", - gsime: "\u2a8e", - gsiml: "\u2a90", - gtcc: "\u2aa7", - gtcir: "\u2a7a", - gt: ">", - GT: ">", - Gt: "\u226b", - gtdot: "\u22d7", - gtlPar: "\u2995", - gtquest: "\u2a7c", - gtrapprox: "\u2a86", - gtrarr: "\u2978", - gtrdot: "\u22d7", - gtreqless: "\u22db", - gtreqqless: "\u2a8c", - gtrless: "\u2277", - gtrsim: "\u2273", - gvertneqq: "\u2269\ufe00", - gvnE: "\u2269\ufe00", - Hacek: "\u02c7", - hairsp: "\u200a", - half: "\xbd", - hamilt: "\u210b", - HARDcy: "\u042a", - hardcy: "\u044a", - harrcir: "\u2948", - harr: "\u2194", - hArr: "\u21d4", - harrw: "\u21ad", - Hat: "^", - hbar: "\u210f", - Hcirc: "\u0124", - hcirc: "\u0125", - hearts: "\u2665", - heartsuit: "\u2665", - hellip: "\u2026", - hercon: "\u22b9", - hfr: "\ud835\udd25", - Hfr: "\u210c", - HilbertSpace: "\u210b", - hksearow: "\u2925", - hkswarow: "\u2926", - hoarr: "\u21ff", - homtht: "\u223b", - hookleftarrow: "\u21a9", - hookrightarrow: "\u21aa", - hopf: "\ud835\udd59", - Hopf: "\u210d", - horbar: "\u2015", - HorizontalLine: "\u2500", - hscr: "\ud835\udcbd", - Hscr: "\u210b", - hslash: "\u210f", - Hstrok: "\u0126", - hstrok: "\u0127", - HumpDownHump: "\u224e", - HumpEqual: "\u224f", - hybull: "\u2043", - hyphen: "\u2010", - Iacute: "\xcd", - iacute: "\xed", - ic: "\u2063", - Icirc: "\xce", - icirc: "\xee", - Icy: "\u0418", - icy: "\u0438", - Idot: "\u0130", - IEcy: "\u0415", - iecy: "\u0435", - iexcl: "\xa1", - iff: "\u21d4", - ifr: "\ud835\udd26", - Ifr: "\u2111", - Igrave: "\xcc", - igrave: "\xec", - ii: "\u2148", - iiiint: "\u2a0c", - iiint: "\u222d", - iinfin: "\u29dc", - iiota: "\u2129", - IJlig: "\u0132", - ijlig: "\u0133", - Imacr: "\u012a", - imacr: "\u012b", - image: "\u2111", - ImaginaryI: "\u2148", - imagline: "\u2110", - imagpart: "\u2111", - imath: "\u0131", - Im: "\u2111", - imof: "\u22b7", - imped: "\u01b5", - Implies: "\u21d2", - incare: "\u2105", - in: "\u2208", - infin: "\u221e", - infintie: "\u29dd", - inodot: "\u0131", - intcal: "\u22ba", - int: "\u222b", - Int: "\u222c", - integers: "\u2124", - Integral: "\u222b", - intercal: "\u22ba", - Intersection: "\u22c2", - intlarhk: "\u2a17", - intprod: "\u2a3c", - InvisibleComma: "\u2063", - InvisibleTimes: "\u2062", - IOcy: "\u0401", - iocy: "\u0451", - Iogon: "\u012e", - iogon: "\u012f", - Iopf: "\ud835\udd40", - iopf: "\ud835\udd5a", - Iota: "\u0399", - iota: "\u03b9", - iprod: "\u2a3c", - iquest: "\xbf", - iscr: "\ud835\udcbe", - Iscr: "\u2110", - isin: "\u2208", - isindot: "\u22f5", - isinE: "\u22f9", - isins: "\u22f4", - isinsv: "\u22f3", - isinv: "\u2208", - it: "\u2062", - Itilde: "\u0128", - itilde: "\u0129", - Iukcy: "\u0406", - iukcy: "\u0456", - Iuml: "\xcf", - iuml: "\xef", - Jcirc: "\u0134", - jcirc: "\u0135", - Jcy: "\u0419", - jcy: "\u0439", - Jfr: "\ud835\udd0d", - jfr: "\ud835\udd27", - jmath: "\u0237", - Jopf: "\ud835\udd41", - jopf: "\ud835\udd5b", - Jscr: "\ud835\udca5", - jscr: "\ud835\udcbf", - Jsercy: "\u0408", - jsercy: "\u0458", - Jukcy: "\u0404", - jukcy: "\u0454", - Kappa: "\u039a", - kappa: "\u03ba", - kappav: "\u03f0", - Kcedil: "\u0136", - kcedil: "\u0137", - Kcy: "\u041a", - kcy: "\u043a", - Kfr: "\ud835\udd0e", - kfr: "\ud835\udd28", - kgreen: "\u0138", - KHcy: "\u0425", - khcy: "\u0445", - KJcy: "\u040c", - kjcy: "\u045c", - Kopf: "\ud835\udd42", - kopf: "\ud835\udd5c", - Kscr: "\ud835\udca6", - kscr: "\ud835\udcc0", - lAarr: "\u21da", - Lacute: "\u0139", - lacute: "\u013a", - laemptyv: "\u29b4", - lagran: "\u2112", - Lambda: "\u039b", - lambda: "\u03bb", - lang: "\u27e8", - Lang: "\u27ea", - langd: "\u2991", - langle: "\u27e8", - lap: "\u2a85", - Laplacetrf: "\u2112", - laquo: "\xab", - larrb: "\u21e4", - larrbfs: "\u291f", - larr: "\u2190", - Larr: "\u219e", - lArr: "\u21d0", - larrfs: "\u291d", - larrhk: "\u21a9", - larrlp: "\u21ab", - larrpl: "\u2939", - larrsim: "\u2973", - larrtl: "\u21a2", - latail: "\u2919", - lAtail: "\u291b", - lat: "\u2aab", - late: "\u2aad", - lates: "\u2aad\ufe00", - lbarr: "\u290c", - lBarr: "\u290e", - lbbrk: "\u2772", - lbrace: "{", - lbrack: "[", - lbrke: "\u298b", - lbrksld: "\u298f", - lbrkslu: "\u298d", - Lcaron: "\u013d", - lcaron: "\u013e", - Lcedil: "\u013b", - lcedil: "\u013c", - lceil: "\u2308", - lcub: "{", - Lcy: "\u041b", - lcy: "\u043b", - ldca: "\u2936", - ldquo: "\u201c", - ldquor: "\u201e", - ldrdhar: "\u2967", - ldrushar: "\u294b", - ldsh: "\u21b2", - le: "\u2264", - lE: "\u2266", - LeftAngleBracket: "\u27e8", - LeftArrowBar: "\u21e4", - leftarrow: "\u2190", - LeftArrow: "\u2190", - Leftarrow: "\u21d0", - LeftArrowRightArrow: "\u21c6", - leftarrowtail: "\u21a2", - LeftCeiling: "\u2308", - LeftDoubleBracket: "\u27e6", - LeftDownTeeVector: "\u2961", - LeftDownVectorBar: "\u2959", - LeftDownVector: "\u21c3", - LeftFloor: "\u230a", - leftharpoondown: "\u21bd", - leftharpoonup: "\u21bc", - leftleftarrows: "\u21c7", - leftrightarrow: "\u2194", - LeftRightArrow: "\u2194", - Leftrightarrow: "\u21d4", - leftrightarrows: "\u21c6", - leftrightharpoons: "\u21cb", - leftrightsquigarrow: "\u21ad", - LeftRightVector: "\u294e", - LeftTeeArrow: "\u21a4", - LeftTee: "\u22a3", - LeftTeeVector: "\u295a", - leftthreetimes: "\u22cb", - LeftTriangleBar: "\u29cf", - LeftTriangle: "\u22b2", - LeftTriangleEqual: "\u22b4", - LeftUpDownVector: "\u2951", - LeftUpTeeVector: "\u2960", - LeftUpVectorBar: "\u2958", - LeftUpVector: "\u21bf", - LeftVectorBar: "\u2952", - LeftVector: "\u21bc", - lEg: "\u2a8b", - leg: "\u22da", - leq: "\u2264", - leqq: "\u2266", - leqslant: "\u2a7d", - lescc: "\u2aa8", - les: "\u2a7d", - lesdot: "\u2a7f", - lesdoto: "\u2a81", - lesdotor: "\u2a83", - lesg: "\u22da\ufe00", - lesges: "\u2a93", - lessapprox: "\u2a85", - lessdot: "\u22d6", - lesseqgtr: "\u22da", - lesseqqgtr: "\u2a8b", - LessEqualGreater: "\u22da", - LessFullEqual: "\u2266", - LessGreater: "\u2276", - lessgtr: "\u2276", - LessLess: "\u2aa1", - lesssim: "\u2272", - LessSlantEqual: "\u2a7d", - LessTilde: "\u2272", - lfisht: "\u297c", - lfloor: "\u230a", - Lfr: "\ud835\udd0f", - lfr: "\ud835\udd29", - lg: "\u2276", - lgE: "\u2a91", - lHar: "\u2962", - lhard: "\u21bd", - lharu: "\u21bc", - lharul: "\u296a", - lhblk: "\u2584", - LJcy: "\u0409", - ljcy: "\u0459", - llarr: "\u21c7", - ll: "\u226a", - Ll: "\u22d8", - llcorner: "\u231e", - Lleftarrow: "\u21da", - llhard: "\u296b", - lltri: "\u25fa", - Lmidot: "\u013f", - lmidot: "\u0140", - lmoustache: "\u23b0", - lmoust: "\u23b0", - lnap: "\u2a89", - lnapprox: "\u2a89", - lne: "\u2a87", - lnE: "\u2268", - lneq: "\u2a87", - lneqq: "\u2268", - lnsim: "\u22e6", - loang: "\u27ec", - loarr: "\u21fd", - lobrk: "\u27e6", - longleftarrow: "\u27f5", - LongLeftArrow: "\u27f5", - Longleftarrow: "\u27f8", - longleftrightarrow: "\u27f7", - LongLeftRightArrow: "\u27f7", - Longleftrightarrow: "\u27fa", - longmapsto: "\u27fc", - longrightarrow: "\u27f6", - LongRightArrow: "\u27f6", - Longrightarrow: "\u27f9", - looparrowleft: "\u21ab", - looparrowright: "\u21ac", - lopar: "\u2985", - Lopf: "\ud835\udd43", - lopf: "\ud835\udd5d", - loplus: "\u2a2d", - lotimes: "\u2a34", - lowast: "\u2217", - lowbar: "_", - LowerLeftArrow: "\u2199", - LowerRightArrow: "\u2198", - loz: "\u25ca", - lozenge: "\u25ca", - lozf: "\u29eb", - lpar: "(", - lparlt: "\u2993", - lrarr: "\u21c6", - lrcorner: "\u231f", - lrhar: "\u21cb", - lrhard: "\u296d", - lrm: "\u200e", - lrtri: "\u22bf", - lsaquo: "\u2039", - lscr: "\ud835\udcc1", - Lscr: "\u2112", - lsh: "\u21b0", - Lsh: "\u21b0", - lsim: "\u2272", - lsime: "\u2a8d", - lsimg: "\u2a8f", - lsqb: "[", - lsquo: "\u2018", - lsquor: "\u201a", - Lstrok: "\u0141", - lstrok: "\u0142", - ltcc: "\u2aa6", - ltcir: "\u2a79", - lt: "<", - LT: "<", - Lt: "\u226a", - ltdot: "\u22d6", - lthree: "\u22cb", - ltimes: "\u22c9", - ltlarr: "\u2976", - ltquest: "\u2a7b", - ltri: "\u25c3", - ltrie: "\u22b4", - ltrif: "\u25c2", - ltrPar: "\u2996", - lurdshar: "\u294a", - luruhar: "\u2966", - lvertneqq: "\u2268\ufe00", - lvnE: "\u2268\ufe00", - macr: "\xaf", - male: "\u2642", - malt: "\u2720", - maltese: "\u2720", - Map: "\u2905", - map: "\u21a6", - mapsto: "\u21a6", - mapstodown: "\u21a7", - mapstoleft: "\u21a4", - mapstoup: "\u21a5", - marker: "\u25ae", - mcomma: "\u2a29", - Mcy: "\u041c", - mcy: "\u043c", - mdash: "\u2014", - mDDot: "\u223a", - measuredangle: "\u2221", - MediumSpace: "\u205f", - Mellintrf: "\u2133", - Mfr: "\ud835\udd10", - mfr: "\ud835\udd2a", - mho: "\u2127", - micro: "\xb5", - midast: "*", - midcir: "\u2af0", - mid: "\u2223", - middot: "\xb7", - minusb: "\u229f", - minus: "\u2212", - minusd: "\u2238", - minusdu: "\u2a2a", - MinusPlus: "\u2213", - mlcp: "\u2adb", - mldr: "\u2026", - mnplus: "\u2213", - models: "\u22a7", - Mopf: "\ud835\udd44", - mopf: "\ud835\udd5e", - mp: "\u2213", - mscr: "\ud835\udcc2", - Mscr: "\u2133", - mstpos: "\u223e", - Mu: "\u039c", - mu: "\u03bc", - multimap: "\u22b8", - mumap: "\u22b8", - nabla: "\u2207", - Nacute: "\u0143", - nacute: "\u0144", - nang: "\u2220\u20d2", - nap: "\u2249", - napE: "\u2a70\u0338", - napid: "\u224b\u0338", - napos: "\u0149", - napprox: "\u2249", - natural: "\u266e", - naturals: "\u2115", - natur: "\u266e", - nbsp: "\xa0", - nbump: "\u224e\u0338", - nbumpe: "\u224f\u0338", - ncap: "\u2a43", - Ncaron: "\u0147", - ncaron: "\u0148", - Ncedil: "\u0145", - ncedil: "\u0146", - ncong: "\u2247", - ncongdot: "\u2a6d\u0338", - ncup: "\u2a42", - Ncy: "\u041d", - ncy: "\u043d", - ndash: "\u2013", - nearhk: "\u2924", - nearr: "\u2197", - neArr: "\u21d7", - nearrow: "\u2197", - ne: "\u2260", - nedot: "\u2250\u0338", - NegativeMediumSpace: "\u200b", - NegativeThickSpace: "\u200b", - NegativeThinSpace: "\u200b", - NegativeVeryThinSpace: "\u200b", - nequiv: "\u2262", - nesear: "\u2928", - nesim: "\u2242\u0338", - NestedGreaterGreater: "\u226b", - NestedLessLess: "\u226a", - NewLine: "\n", - nexist: "\u2204", - nexists: "\u2204", - Nfr: "\ud835\udd11", - nfr: "\ud835\udd2b", - ngE: "\u2267\u0338", - nge: "\u2271", - ngeq: "\u2271", - ngeqq: "\u2267\u0338", - ngeqslant: "\u2a7e\u0338", - nges: "\u2a7e\u0338", - nGg: "\u22d9\u0338", - ngsim: "\u2275", - nGt: "\u226b\u20d2", - ngt: "\u226f", - ngtr: "\u226f", - nGtv: "\u226b\u0338", - nharr: "\u21ae", - nhArr: "\u21ce", - nhpar: "\u2af2", - ni: "\u220b", - nis: "\u22fc", - nisd: "\u22fa", - niv: "\u220b", - NJcy: "\u040a", - njcy: "\u045a", - nlarr: "\u219a", - nlArr: "\u21cd", - nldr: "\u2025", - nlE: "\u2266\u0338", - nle: "\u2270", - nleftarrow: "\u219a", - nLeftarrow: "\u21cd", - nleftrightarrow: "\u21ae", - nLeftrightarrow: "\u21ce", - nleq: "\u2270", - nleqq: "\u2266\u0338", - nleqslant: "\u2a7d\u0338", - nles: "\u2a7d\u0338", - nless: "\u226e", - nLl: "\u22d8\u0338", - nlsim: "\u2274", - nLt: "\u226a\u20d2", - nlt: "\u226e", - nltri: "\u22ea", - nltrie: "\u22ec", - nLtv: "\u226a\u0338", - nmid: "\u2224", - NoBreak: "\u2060", - NonBreakingSpace: "\xa0", - nopf: "\ud835\udd5f", - Nopf: "\u2115", - Not: "\u2aec", - not: "\xac", - NotCongruent: "\u2262", - NotCupCap: "\u226d", - NotDoubleVerticalBar: "\u2226", - NotElement: "\u2209", - NotEqual: "\u2260", - NotEqualTilde: "\u2242\u0338", - NotExists: "\u2204", - NotGreater: "\u226f", - NotGreaterEqual: "\u2271", - NotGreaterFullEqual: "\u2267\u0338", - NotGreaterGreater: "\u226b\u0338", - NotGreaterLess: "\u2279", - NotGreaterSlantEqual: "\u2a7e\u0338", - NotGreaterTilde: "\u2275", - NotHumpDownHump: "\u224e\u0338", - NotHumpEqual: "\u224f\u0338", - notin: "\u2209", - notindot: "\u22f5\u0338", - notinE: "\u22f9\u0338", - notinva: "\u2209", - notinvb: "\u22f7", - notinvc: "\u22f6", - NotLeftTriangleBar: "\u29cf\u0338", - NotLeftTriangle: "\u22ea", - NotLeftTriangleEqual: "\u22ec", - NotLess: "\u226e", - NotLessEqual: "\u2270", - NotLessGreater: "\u2278", - NotLessLess: "\u226a\u0338", - NotLessSlantEqual: "\u2a7d\u0338", - NotLessTilde: "\u2274", - NotNestedGreaterGreater: "\u2aa2\u0338", - NotNestedLessLess: "\u2aa1\u0338", - notni: "\u220c", - notniva: "\u220c", - notnivb: "\u22fe", - notnivc: "\u22fd", - NotPrecedes: "\u2280", - NotPrecedesEqual: "\u2aaf\u0338", - NotPrecedesSlantEqual: "\u22e0", - NotReverseElement: "\u220c", - NotRightTriangleBar: "\u29d0\u0338", - NotRightTriangle: "\u22eb", - NotRightTriangleEqual: "\u22ed", - NotSquareSubset: "\u228f\u0338", - NotSquareSubsetEqual: "\u22e2", - NotSquareSuperset: "\u2290\u0338", - NotSquareSupersetEqual: "\u22e3", - NotSubset: "\u2282\u20d2", - NotSubsetEqual: "\u2288", - NotSucceeds: "\u2281", - NotSucceedsEqual: "\u2ab0\u0338", - NotSucceedsSlantEqual: "\u22e1", - NotSucceedsTilde: "\u227f\u0338", - NotSuperset: "\u2283\u20d2", - NotSupersetEqual: "\u2289", - NotTilde: "\u2241", - NotTildeEqual: "\u2244", - NotTildeFullEqual: "\u2247", - NotTildeTilde: "\u2249", - NotVerticalBar: "\u2224", - nparallel: "\u2226", - npar: "\u2226", - nparsl: "\u2afd\u20e5", - npart: "\u2202\u0338", - npolint: "\u2a14", - npr: "\u2280", - nprcue: "\u22e0", - nprec: "\u2280", - npreceq: "\u2aaf\u0338", - npre: "\u2aaf\u0338", - nrarrc: "\u2933\u0338", - nrarr: "\u219b", - nrArr: "\u21cf", - nrarrw: "\u219d\u0338", - nrightarrow: "\u219b", - nRightarrow: "\u21cf", - nrtri: "\u22eb", - nrtrie: "\u22ed", - nsc: "\u2281", - nsccue: "\u22e1", - nsce: "\u2ab0\u0338", - Nscr: "\ud835\udca9", - nscr: "\ud835\udcc3", - nshortmid: "\u2224", - nshortparallel: "\u2226", - nsim: "\u2241", - nsime: "\u2244", - nsimeq: "\u2244", - nsmid: "\u2224", - nspar: "\u2226", - nsqsube: "\u22e2", - nsqsupe: "\u22e3", - nsub: "\u2284", - nsubE: "\u2ac5\u0338", - nsube: "\u2288", - nsubset: "\u2282\u20d2", - nsubseteq: "\u2288", - nsubseteqq: "\u2ac5\u0338", - nsucc: "\u2281", - nsucceq: "\u2ab0\u0338", - nsup: "\u2285", - nsupE: "\u2ac6\u0338", - nsupe: "\u2289", - nsupset: "\u2283\u20d2", - nsupseteq: "\u2289", - nsupseteqq: "\u2ac6\u0338", - ntgl: "\u2279", - Ntilde: "\xd1", - ntilde: "\xf1", - ntlg: "\u2278", - ntriangleleft: "\u22ea", - ntrianglelefteq: "\u22ec", - ntriangleright: "\u22eb", - ntrianglerighteq: "\u22ed", - Nu: "\u039d", - nu: "\u03bd", - num: "#", - numero: "\u2116", - numsp: "\u2007", - nvap: "\u224d\u20d2", - nvdash: "\u22ac", - nvDash: "\u22ad", - nVdash: "\u22ae", - nVDash: "\u22af", - nvge: "\u2265\u20d2", - nvgt: ">\u20d2", - nvHarr: "\u2904", - nvinfin: "\u29de", - nvlArr: "\u2902", - nvle: "\u2264\u20d2", - nvlt: "<\u20d2", - nvltrie: "\u22b4\u20d2", - nvrArr: "\u2903", - nvrtrie: "\u22b5\u20d2", - nvsim: "\u223c\u20d2", - nwarhk: "\u2923", - nwarr: "\u2196", - nwArr: "\u21d6", - nwarrow: "\u2196", - nwnear: "\u2927", - Oacute: "\xd3", - oacute: "\xf3", - oast: "\u229b", - Ocirc: "\xd4", - ocirc: "\xf4", - ocir: "\u229a", - Ocy: "\u041e", - ocy: "\u043e", - odash: "\u229d", - Odblac: "\u0150", - odblac: "\u0151", - odiv: "\u2a38", - odot: "\u2299", - odsold: "\u29bc", - OElig: "\u0152", - oelig: "\u0153", - ofcir: "\u29bf", - Ofr: "\ud835\udd12", - ofr: "\ud835\udd2c", - ogon: "\u02db", - Ograve: "\xd2", - ograve: "\xf2", - ogt: "\u29c1", - ohbar: "\u29b5", - ohm: "\u03a9", - oint: "\u222e", - olarr: "\u21ba", - olcir: "\u29be", - olcross: "\u29bb", - oline: "\u203e", - olt: "\u29c0", - Omacr: "\u014c", - omacr: "\u014d", - Omega: "\u03a9", - omega: "\u03c9", - Omicron: "\u039f", - omicron: "\u03bf", - omid: "\u29b6", - ominus: "\u2296", - Oopf: "\ud835\udd46", - oopf: "\ud835\udd60", - opar: "\u29b7", - OpenCurlyDoubleQuote: "\u201c", - OpenCurlyQuote: "\u2018", - operp: "\u29b9", - oplus: "\u2295", - orarr: "\u21bb", - Or: "\u2a54", - or: "\u2228", - ord: "\u2a5d", - order: "\u2134", - orderof: "\u2134", - ordf: "\xaa", - ordm: "\xba", - origof: "\u22b6", - oror: "\u2a56", - orslope: "\u2a57", - orv: "\u2a5b", - oS: "\u24c8", - Oscr: "\ud835\udcaa", - oscr: "\u2134", - Oslash: "\xd8", - oslash: "\xf8", - osol: "\u2298", - Otilde: "\xd5", - otilde: "\xf5", - otimesas: "\u2a36", - Otimes: "\u2a37", - otimes: "\u2297", - Ouml: "\xd6", - ouml: "\xf6", - ovbar: "\u233d", - OverBar: "\u203e", - OverBrace: "\u23de", - OverBracket: "\u23b4", - OverParenthesis: "\u23dc", - para: "\xb6", - parallel: "\u2225", - par: "\u2225", - parsim: "\u2af3", - parsl: "\u2afd", - part: "\u2202", - PartialD: "\u2202", - Pcy: "\u041f", - pcy: "\u043f", - percnt: "%", - period: ".", - permil: "\u2030", - perp: "\u22a5", - pertenk: "\u2031", - Pfr: "\ud835\udd13", - pfr: "\ud835\udd2d", - Phi: "\u03a6", - phi: "\u03c6", - phiv: "\u03d5", - phmmat: "\u2133", - phone: "\u260e", - Pi: "\u03a0", - pi: "\u03c0", - pitchfork: "\u22d4", - piv: "\u03d6", - planck: "\u210f", - planckh: "\u210e", - plankv: "\u210f", - plusacir: "\u2a23", - plusb: "\u229e", - pluscir: "\u2a22", - plus: "+", - plusdo: "\u2214", - plusdu: "\u2a25", - pluse: "\u2a72", - PlusMinus: "\xb1", - plusmn: "\xb1", - plussim: "\u2a26", - plustwo: "\u2a27", - pm: "\xb1", - Poincareplane: "\u210c", - pointint: "\u2a15", - popf: "\ud835\udd61", - Popf: "\u2119", - pound: "\xa3", - prap: "\u2ab7", - Pr: "\u2abb", - pr: "\u227a", - prcue: "\u227c", - precapprox: "\u2ab7", - prec: "\u227a", - preccurlyeq: "\u227c", - Precedes: "\u227a", - PrecedesEqual: "\u2aaf", - PrecedesSlantEqual: "\u227c", - PrecedesTilde: "\u227e", - preceq: "\u2aaf", - precnapprox: "\u2ab9", - precneqq: "\u2ab5", - precnsim: "\u22e8", - pre: "\u2aaf", - prE: "\u2ab3", - precsim: "\u227e", - prime: "\u2032", - Prime: "\u2033", - primes: "\u2119", - prnap: "\u2ab9", - prnE: "\u2ab5", - prnsim: "\u22e8", - prod: "\u220f", - Product: "\u220f", - profalar: "\u232e", - profline: "\u2312", - profsurf: "\u2313", - prop: "\u221d", - Proportional: "\u221d", - Proportion: "\u2237", - propto: "\u221d", - prsim: "\u227e", - prurel: "\u22b0", - Pscr: "\ud835\udcab", - pscr: "\ud835\udcc5", - Psi: "\u03a8", - psi: "\u03c8", - puncsp: "\u2008", - Qfr: "\ud835\udd14", - qfr: "\ud835\udd2e", - qint: "\u2a0c", - qopf: "\ud835\udd62", - Qopf: "\u211a", - qprime: "\u2057", - Qscr: "\ud835\udcac", - qscr: "\ud835\udcc6", - quaternions: "\u210d", - quatint: "\u2a16", - quest: "?", - questeq: "\u225f", - quot: '"', - QUOT: '"', - rAarr: "\u21db", - race: "\u223d\u0331", - Racute: "\u0154", - racute: "\u0155", - radic: "\u221a", - raemptyv: "\u29b3", - rang: "\u27e9", - Rang: "\u27eb", - rangd: "\u2992", - range: "\u29a5", - rangle: "\u27e9", - raquo: "\xbb", - rarrap: "\u2975", - rarrb: "\u21e5", - rarrbfs: "\u2920", - rarrc: "\u2933", - rarr: "\u2192", - Rarr: "\u21a0", - rArr: "\u21d2", - rarrfs: "\u291e", - rarrhk: "\u21aa", - rarrlp: "\u21ac", - rarrpl: "\u2945", - rarrsim: "\u2974", - Rarrtl: "\u2916", - rarrtl: "\u21a3", - rarrw: "\u219d", - ratail: "\u291a", - rAtail: "\u291c", - ratio: "\u2236", - rationals: "\u211a", - rbarr: "\u290d", - rBarr: "\u290f", - RBarr: "\u2910", - rbbrk: "\u2773", - rbrace: "}", - rbrack: "]", - rbrke: "\u298c", - rbrksld: "\u298e", - rbrkslu: "\u2990", - Rcaron: "\u0158", - rcaron: "\u0159", - Rcedil: "\u0156", - rcedil: "\u0157", - rceil: "\u2309", - rcub: "}", - Rcy: "\u0420", - rcy: "\u0440", - rdca: "\u2937", - rdldhar: "\u2969", - rdquo: "\u201d", - rdquor: "\u201d", - rdsh: "\u21b3", - real: "\u211c", - realine: "\u211b", - realpart: "\u211c", - reals: "\u211d", - Re: "\u211c", - rect: "\u25ad", - reg: "\xae", - REG: "\xae", - ReverseElement: "\u220b", - ReverseEquilibrium: "\u21cb", - ReverseUpEquilibrium: "\u296f", - rfisht: "\u297d", - rfloor: "\u230b", - rfr: "\ud835\udd2f", - Rfr: "\u211c", - rHar: "\u2964", - rhard: "\u21c1", - rharu: "\u21c0", - rharul: "\u296c", - Rho: "\u03a1", - rho: "\u03c1", - rhov: "\u03f1", - RightAngleBracket: "\u27e9", - RightArrowBar: "\u21e5", - rightarrow: "\u2192", - RightArrow: "\u2192", - Rightarrow: "\u21d2", - RightArrowLeftArrow: "\u21c4", - rightarrowtail: "\u21a3", - RightCeiling: "\u2309", - RightDoubleBracket: "\u27e7", - RightDownTeeVector: "\u295d", - RightDownVectorBar: "\u2955", - RightDownVector: "\u21c2", - RightFloor: "\u230b", - rightharpoondown: "\u21c1", - rightharpoonup: "\u21c0", - rightleftarrows: "\u21c4", - rightleftharpoons: "\u21cc", - rightrightarrows: "\u21c9", - rightsquigarrow: "\u219d", - RightTeeArrow: "\u21a6", - RightTee: "\u22a2", - RightTeeVector: "\u295b", - rightthreetimes: "\u22cc", - RightTriangleBar: "\u29d0", - RightTriangle: "\u22b3", - RightTriangleEqual: "\u22b5", - RightUpDownVector: "\u294f", - RightUpTeeVector: "\u295c", - RightUpVectorBar: "\u2954", - RightUpVector: "\u21be", - RightVectorBar: "\u2953", - RightVector: "\u21c0", - ring: "\u02da", - risingdotseq: "\u2253", - rlarr: "\u21c4", - rlhar: "\u21cc", - rlm: "\u200f", - rmoustache: "\u23b1", - rmoust: "\u23b1", - rnmid: "\u2aee", - roang: "\u27ed", - roarr: "\u21fe", - robrk: "\u27e7", - ropar: "\u2986", - ropf: "\ud835\udd63", - Ropf: "\u211d", - roplus: "\u2a2e", - rotimes: "\u2a35", - RoundImplies: "\u2970", - rpar: ")", - rpargt: "\u2994", - rppolint: "\u2a12", - rrarr: "\u21c9", - Rrightarrow: "\u21db", - rsaquo: "\u203a", - rscr: "\ud835\udcc7", - Rscr: "\u211b", - rsh: "\u21b1", - Rsh: "\u21b1", - rsqb: "]", - rsquo: "\u2019", - rsquor: "\u2019", - rthree: "\u22cc", - rtimes: "\u22ca", - rtri: "\u25b9", - rtrie: "\u22b5", - rtrif: "\u25b8", - rtriltri: "\u29ce", - RuleDelayed: "\u29f4", - ruluhar: "\u2968", - rx: "\u211e", - Sacute: "\u015a", - sacute: "\u015b", - sbquo: "\u201a", - scap: "\u2ab8", - Scaron: "\u0160", - scaron: "\u0161", - Sc: "\u2abc", - sc: "\u227b", - sccue: "\u227d", - sce: "\u2ab0", - scE: "\u2ab4", - Scedil: "\u015e", - scedil: "\u015f", - Scirc: "\u015c", - scirc: "\u015d", - scnap: "\u2aba", - scnE: "\u2ab6", - scnsim: "\u22e9", - scpolint: "\u2a13", - scsim: "\u227f", - Scy: "\u0421", - scy: "\u0441", - sdotb: "\u22a1", - sdot: "\u22c5", - sdote: "\u2a66", - searhk: "\u2925", - searr: "\u2198", - seArr: "\u21d8", - searrow: "\u2198", - sect: "\xa7", - semi: ";", - seswar: "\u2929", - setminus: "\u2216", - setmn: "\u2216", - sext: "\u2736", - Sfr: "\ud835\udd16", - sfr: "\ud835\udd30", - sfrown: "\u2322", - sharp: "\u266f", - SHCHcy: "\u0429", - shchcy: "\u0449", - SHcy: "\u0428", - shcy: "\u0448", - ShortDownArrow: "\u2193", - ShortLeftArrow: "\u2190", - shortmid: "\u2223", - shortparallel: "\u2225", - ShortRightArrow: "\u2192", - ShortUpArrow: "\u2191", - shy: "\xad", - Sigma: "\u03a3", - sigma: "\u03c3", - sigmaf: "\u03c2", - sigmav: "\u03c2", - sim: "\u223c", - simdot: "\u2a6a", - sime: "\u2243", - simeq: "\u2243", - simg: "\u2a9e", - simgE: "\u2aa0", - siml: "\u2a9d", - simlE: "\u2a9f", - simne: "\u2246", - simplus: "\u2a24", - simrarr: "\u2972", - slarr: "\u2190", - SmallCircle: "\u2218", - smallsetminus: "\u2216", - smashp: "\u2a33", - smeparsl: "\u29e4", - smid: "\u2223", - smile: "\u2323", - smt: "\u2aaa", - smte: "\u2aac", - smtes: "\u2aac\ufe00", - SOFTcy: "\u042c", - softcy: "\u044c", - solbar: "\u233f", - solb: "\u29c4", - sol: "/", - Sopf: "\ud835\udd4a", - sopf: "\ud835\udd64", - spades: "\u2660", - spadesuit: "\u2660", - spar: "\u2225", - sqcap: "\u2293", - sqcaps: "\u2293\ufe00", - sqcup: "\u2294", - sqcups: "\u2294\ufe00", - Sqrt: "\u221a", - sqsub: "\u228f", - sqsube: "\u2291", - sqsubset: "\u228f", - sqsubseteq: "\u2291", - sqsup: "\u2290", - sqsupe: "\u2292", - sqsupset: "\u2290", - sqsupseteq: "\u2292", - square: "\u25a1", - Square: "\u25a1", - SquareIntersection: "\u2293", - SquareSubset: "\u228f", - SquareSubsetEqual: "\u2291", - SquareSuperset: "\u2290", - SquareSupersetEqual: "\u2292", - SquareUnion: "\u2294", - squarf: "\u25aa", - squ: "\u25a1", - squf: "\u25aa", - srarr: "\u2192", - Sscr: "\ud835\udcae", - sscr: "\ud835\udcc8", - ssetmn: "\u2216", - ssmile: "\u2323", - sstarf: "\u22c6", - Star: "\u22c6", - star: "\u2606", - starf: "\u2605", - straightepsilon: "\u03f5", - straightphi: "\u03d5", - strns: "\xaf", - sub: "\u2282", - Sub: "\u22d0", - subdot: "\u2abd", - subE: "\u2ac5", - sube: "\u2286", - subedot: "\u2ac3", - submult: "\u2ac1", - subnE: "\u2acb", - subne: "\u228a", - subplus: "\u2abf", - subrarr: "\u2979", - subset: "\u2282", - Subset: "\u22d0", - subseteq: "\u2286", - subseteqq: "\u2ac5", - SubsetEqual: "\u2286", - subsetneq: "\u228a", - subsetneqq: "\u2acb", - subsim: "\u2ac7", - subsub: "\u2ad5", - subsup: "\u2ad3", - succapprox: "\u2ab8", - succ: "\u227b", - succcurlyeq: "\u227d", - Succeeds: "\u227b", - SucceedsEqual: "\u2ab0", - SucceedsSlantEqual: "\u227d", - SucceedsTilde: "\u227f", - succeq: "\u2ab0", - succnapprox: "\u2aba", - succneqq: "\u2ab6", - succnsim: "\u22e9", - succsim: "\u227f", - SuchThat: "\u220b", - sum: "\u2211", - Sum: "\u2211", - sung: "\u266a", - sup1: "\xb9", - sup2: "\xb2", - sup3: "\xb3", - sup: "\u2283", - Sup: "\u22d1", - supdot: "\u2abe", - supdsub: "\u2ad8", - supE: "\u2ac6", - supe: "\u2287", - supedot: "\u2ac4", - Superset: "\u2283", - SupersetEqual: "\u2287", - suphsol: "\u27c9", - suphsub: "\u2ad7", - suplarr: "\u297b", - supmult: "\u2ac2", - supnE: "\u2acc", - supne: "\u228b", - supplus: "\u2ac0", - supset: "\u2283", - Supset: "\u22d1", - supseteq: "\u2287", - supseteqq: "\u2ac6", - supsetneq: "\u228b", - supsetneqq: "\u2acc", - supsim: "\u2ac8", - supsub: "\u2ad4", - supsup: "\u2ad6", - swarhk: "\u2926", - swarr: "\u2199", - swArr: "\u21d9", - swarrow: "\u2199", - swnwar: "\u292a", - szlig: "\xdf", - Tab: "\t", - target: "\u2316", - Tau: "\u03a4", - tau: "\u03c4", - tbrk: "\u23b4", - Tcaron: "\u0164", - tcaron: "\u0165", - Tcedil: "\u0162", - tcedil: "\u0163", - Tcy: "\u0422", - tcy: "\u0442", - tdot: "\u20db", - telrec: "\u2315", - Tfr: "\ud835\udd17", - tfr: "\ud835\udd31", - there4: "\u2234", - therefore: "\u2234", - Therefore: "\u2234", - Theta: "\u0398", - theta: "\u03b8", - thetasym: "\u03d1", - thetav: "\u03d1", - thickapprox: "\u2248", - thicksim: "\u223c", - ThickSpace: "\u205f\u200a", - ThinSpace: "\u2009", - thinsp: "\u2009", - thkap: "\u2248", - thksim: "\u223c", - THORN: "\xde", - thorn: "\xfe", - tilde: "\u02dc", - Tilde: "\u223c", - TildeEqual: "\u2243", - TildeFullEqual: "\u2245", - TildeTilde: "\u2248", - timesbar: "\u2a31", - timesb: "\u22a0", - times: "\xd7", - timesd: "\u2a30", - tint: "\u222d", - toea: "\u2928", - topbot: "\u2336", - topcir: "\u2af1", - top: "\u22a4", - Topf: "\ud835\udd4b", - topf: "\ud835\udd65", - topfork: "\u2ada", - tosa: "\u2929", - tprime: "\u2034", - trade: "\u2122", - TRADE: "\u2122", - triangle: "\u25b5", - triangledown: "\u25bf", - triangleleft: "\u25c3", - trianglelefteq: "\u22b4", - triangleq: "\u225c", - triangleright: "\u25b9", - trianglerighteq: "\u22b5", - tridot: "\u25ec", - trie: "\u225c", - triminus: "\u2a3a", - TripleDot: "\u20db", - triplus: "\u2a39", - trisb: "\u29cd", - tritime: "\u2a3b", - trpezium: "\u23e2", - Tscr: "\ud835\udcaf", - tscr: "\ud835\udcc9", - TScy: "\u0426", - tscy: "\u0446", - TSHcy: "\u040b", - tshcy: "\u045b", - Tstrok: "\u0166", - tstrok: "\u0167", - twixt: "\u226c", - twoheadleftarrow: "\u219e", - twoheadrightarrow: "\u21a0", - Uacute: "\xda", - uacute: "\xfa", - uarr: "\u2191", - Uarr: "\u219f", - uArr: "\u21d1", - Uarrocir: "\u2949", - Ubrcy: "\u040e", - ubrcy: "\u045e", - Ubreve: "\u016c", - ubreve: "\u016d", - Ucirc: "\xdb", - ucirc: "\xfb", - Ucy: "\u0423", - ucy: "\u0443", - udarr: "\u21c5", - Udblac: "\u0170", - udblac: "\u0171", - udhar: "\u296e", - ufisht: "\u297e", - Ufr: "\ud835\udd18", - ufr: "\ud835\udd32", - Ugrave: "\xd9", - ugrave: "\xf9", - uHar: "\u2963", - uharl: "\u21bf", - uharr: "\u21be", - uhblk: "\u2580", - ulcorn: "\u231c", - ulcorner: "\u231c", - ulcrop: "\u230f", - ultri: "\u25f8", - Umacr: "\u016a", - umacr: "\u016b", - uml: "\xa8", - UnderBar: "_", - UnderBrace: "\u23df", - UnderBracket: "\u23b5", - UnderParenthesis: "\u23dd", - Union: "\u22c3", - UnionPlus: "\u228e", - Uogon: "\u0172", - uogon: "\u0173", - Uopf: "\ud835\udd4c", - uopf: "\ud835\udd66", - UpArrowBar: "\u2912", - uparrow: "\u2191", - UpArrow: "\u2191", - Uparrow: "\u21d1", - UpArrowDownArrow: "\u21c5", - updownarrow: "\u2195", - UpDownArrow: "\u2195", - Updownarrow: "\u21d5", - UpEquilibrium: "\u296e", - upharpoonleft: "\u21bf", - upharpoonright: "\u21be", - uplus: "\u228e", - UpperLeftArrow: "\u2196", - UpperRightArrow: "\u2197", - upsi: "\u03c5", - Upsi: "\u03d2", - upsih: "\u03d2", - Upsilon: "\u03a5", - upsilon: "\u03c5", - UpTeeArrow: "\u21a5", - UpTee: "\u22a5", - upuparrows: "\u21c8", - urcorn: "\u231d", - urcorner: "\u231d", - urcrop: "\u230e", - Uring: "\u016e", - uring: "\u016f", - urtri: "\u25f9", - Uscr: "\ud835\udcb0", - uscr: "\ud835\udcca", - utdot: "\u22f0", - Utilde: "\u0168", - utilde: "\u0169", - utri: "\u25b5", - utrif: "\u25b4", - uuarr: "\u21c8", - Uuml: "\xdc", - uuml: "\xfc", - uwangle: "\u29a7", - vangrt: "\u299c", - varepsilon: "\u03f5", - varkappa: "\u03f0", - varnothing: "\u2205", - varphi: "\u03d5", - varpi: "\u03d6", - varpropto: "\u221d", - varr: "\u2195", - vArr: "\u21d5", - varrho: "\u03f1", - varsigma: "\u03c2", - varsubsetneq: "\u228a\ufe00", - varsubsetneqq: "\u2acb\ufe00", - varsupsetneq: "\u228b\ufe00", - varsupsetneqq: "\u2acc\ufe00", - vartheta: "\u03d1", - vartriangleleft: "\u22b2", - vartriangleright: "\u22b3", - vBar: "\u2ae8", - Vbar: "\u2aeb", - vBarv: "\u2ae9", - Vcy: "\u0412", - vcy: "\u0432", - vdash: "\u22a2", - vDash: "\u22a8", - Vdash: "\u22a9", - VDash: "\u22ab", - Vdashl: "\u2ae6", - veebar: "\u22bb", - vee: "\u2228", - Vee: "\u22c1", - veeeq: "\u225a", - vellip: "\u22ee", - verbar: "|", - Verbar: "\u2016", - vert: "|", - Vert: "\u2016", - VerticalBar: "\u2223", - VerticalLine: "|", - VerticalSeparator: "\u2758", - VerticalTilde: "\u2240", - VeryThinSpace: "\u200a", - Vfr: "\ud835\udd19", - vfr: "\ud835\udd33", - vltri: "\u22b2", - vnsub: "\u2282\u20d2", - vnsup: "\u2283\u20d2", - Vopf: "\ud835\udd4d", - vopf: "\ud835\udd67", - vprop: "\u221d", - vrtri: "\u22b3", - Vscr: "\ud835\udcb1", - vscr: "\ud835\udccb", - vsubnE: "\u2acb\ufe00", - vsubne: "\u228a\ufe00", - vsupnE: "\u2acc\ufe00", - vsupne: "\u228b\ufe00", - Vvdash: "\u22aa", - vzigzag: "\u299a", - Wcirc: "\u0174", - wcirc: "\u0175", - wedbar: "\u2a5f", - wedge: "\u2227", - Wedge: "\u22c0", - wedgeq: "\u2259", - weierp: "\u2118", - Wfr: "\ud835\udd1a", - wfr: "\ud835\udd34", - Wopf: "\ud835\udd4e", - wopf: "\ud835\udd68", - wp: "\u2118", - wr: "\u2240", - wreath: "\u2240", - Wscr: "\ud835\udcb2", - wscr: "\ud835\udccc", - xcap: "\u22c2", - xcirc: "\u25ef", - xcup: "\u22c3", - xdtri: "\u25bd", - Xfr: "\ud835\udd1b", - xfr: "\ud835\udd35", - xharr: "\u27f7", - xhArr: "\u27fa", - Xi: "\u039e", - xi: "\u03be", - xlarr: "\u27f5", - xlArr: "\u27f8", - xmap: "\u27fc", - xnis: "\u22fb", - xodot: "\u2a00", - Xopf: "\ud835\udd4f", - xopf: "\ud835\udd69", - xoplus: "\u2a01", - xotime: "\u2a02", - xrarr: "\u27f6", - xrArr: "\u27f9", - Xscr: "\ud835\udcb3", - xscr: "\ud835\udccd", - xsqcup: "\u2a06", - xuplus: "\u2a04", - xutri: "\u25b3", - xvee: "\u22c1", - xwedge: "\u22c0", - Yacute: "\xdd", - yacute: "\xfd", - YAcy: "\u042f", - yacy: "\u044f", - Ycirc: "\u0176", - ycirc: "\u0177", - Ycy: "\u042b", - ycy: "\u044b", - yen: "\xa5", - Yfr: "\ud835\udd1c", - yfr: "\ud835\udd36", - YIcy: "\u0407", - yicy: "\u0457", - Yopf: "\ud835\udd50", - yopf: "\ud835\udd6a", - Yscr: "\ud835\udcb4", - yscr: "\ud835\udcce", - YUcy: "\u042e", - yucy: "\u044e", - yuml: "\xff", - Yuml: "\u0178", - Zacute: "\u0179", - zacute: "\u017a", - Zcaron: "\u017d", - zcaron: "\u017e", - Zcy: "\u0417", - zcy: "\u0437", - Zdot: "\u017b", - zdot: "\u017c", - zeetrf: "\u2128", - ZeroWidthSpace: "\u200b", - Zeta: "\u0396", - zeta: "\u03b6", - zfr: "\ud835\udd37", - Zfr: "\u2128", - ZHcy: "\u0416", - zhcy: "\u0436", - zigrarr: "\u21dd", - zopf: "\ud835\udd6b", - Zopf: "\u2124", - Zscr: "\ud835\udcb5", - zscr: "\ud835\udccf", - zwj: "\u200d", - zwnj: "\u200c" - }; - /*eslint quotes:0*/ var entities = require$$0; - var regex$4 = /[!-#%-\*,-\/:;\?@\[-\]_\{\}\xA1\xA7\xAB\xB6\xB7\xBB\xBF\u037E\u0387\u055A-\u055F\u0589\u058A\u05BE\u05C0\u05C3\u05C6\u05F3\u05F4\u0609\u060A\u060C\u060D\u061B\u061E\u061F\u066A-\u066D\u06D4\u0700-\u070D\u07F7-\u07F9\u0830-\u083E\u085E\u0964\u0965\u0970\u09FD\u0A76\u0AF0\u0C84\u0DF4\u0E4F\u0E5A\u0E5B\u0F04-\u0F12\u0F14\u0F3A-\u0F3D\u0F85\u0FD0-\u0FD4\u0FD9\u0FDA\u104A-\u104F\u10FB\u1360-\u1368\u1400\u166D\u166E\u169B\u169C\u16EB-\u16ED\u1735\u1736\u17D4-\u17D6\u17D8-\u17DA\u1800-\u180A\u1944\u1945\u1A1E\u1A1F\u1AA0-\u1AA6\u1AA8-\u1AAD\u1B5A-\u1B60\u1BFC-\u1BFF\u1C3B-\u1C3F\u1C7E\u1C7F\u1CC0-\u1CC7\u1CD3\u2010-\u2027\u2030-\u2043\u2045-\u2051\u2053-\u205E\u207D\u207E\u208D\u208E\u2308-\u230B\u2329\u232A\u2768-\u2775\u27C5\u27C6\u27E6-\u27EF\u2983-\u2998\u29D8-\u29DB\u29FC\u29FD\u2CF9-\u2CFC\u2CFE\u2CFF\u2D70\u2E00-\u2E2E\u2E30-\u2E4E\u3001-\u3003\u3008-\u3011\u3014-\u301F\u3030\u303D\u30A0\u30FB\uA4FE\uA4FF\uA60D-\uA60F\uA673\uA67E\uA6F2-\uA6F7\uA874-\uA877\uA8CE\uA8CF\uA8F8-\uA8FA\uA8FC\uA92E\uA92F\uA95F\uA9C1-\uA9CD\uA9DE\uA9DF\uAA5C-\uAA5F\uAADE\uAADF\uAAF0\uAAF1\uABEB\uFD3E\uFD3F\uFE10-\uFE19\uFE30-\uFE52\uFE54-\uFE61\uFE63\uFE68\uFE6A\uFE6B\uFF01-\uFF03\uFF05-\uFF0A\uFF0C-\uFF0F\uFF1A\uFF1B\uFF1F\uFF20\uFF3B-\uFF3D\uFF3F\uFF5B\uFF5D\uFF5F-\uFF65]|\uD800[\uDD00-\uDD02\uDF9F\uDFD0]|\uD801\uDD6F|\uD802[\uDC57\uDD1F\uDD3F\uDE50-\uDE58\uDE7F\uDEF0-\uDEF6\uDF39-\uDF3F\uDF99-\uDF9C]|\uD803[\uDF55-\uDF59]|\uD804[\uDC47-\uDC4D\uDCBB\uDCBC\uDCBE-\uDCC1\uDD40-\uDD43\uDD74\uDD75\uDDC5-\uDDC8\uDDCD\uDDDB\uDDDD-\uDDDF\uDE38-\uDE3D\uDEA9]|\uD805[\uDC4B-\uDC4F\uDC5B\uDC5D\uDCC6\uDDC1-\uDDD7\uDE41-\uDE43\uDE60-\uDE6C\uDF3C-\uDF3E]|\uD806[\uDC3B\uDE3F-\uDE46\uDE9A-\uDE9C\uDE9E-\uDEA2]|\uD807[\uDC41-\uDC45\uDC70\uDC71\uDEF7\uDEF8]|\uD809[\uDC70-\uDC74]|\uD81A[\uDE6E\uDE6F\uDEF5\uDF37-\uDF3B\uDF44]|\uD81B[\uDE97-\uDE9A]|\uD82F\uDC9F|\uD836[\uDE87-\uDE8B]|\uD83A[\uDD5E\uDD5F]/; - var encodeCache = {}; - // Create a lookup array where anything but characters in `chars` string - // and alphanumeric chars is percent-encoded. - - function getEncodeCache(exclude) { - var i, ch, cache = encodeCache[exclude]; - if (cache) { - return cache; - } - cache = encodeCache[exclude] = []; - for (i = 0; i < 128; i++) { - ch = String.fromCharCode(i); - if (/^[0-9a-z]$/i.test(ch)) { - // always allow unencoded alphanumeric characters - cache.push(ch); - } else { - cache.push("%" + ("0" + i.toString(16).toUpperCase()).slice(-2)); - } - } - for (i = 0; i < exclude.length; i++) { - cache[exclude.charCodeAt(i)] = exclude[i]; - } - return cache; - } - // Encode unsafe characters with percent-encoding, skipping already - // encoded sequences. - - // - string - string to encode - // - exclude - list of characters to ignore (in addition to a-zA-Z0-9) - // - keepEscaped - don't encode '%' in a correct escape sequence (default: true) - - function encode$2(string, exclude, keepEscaped) { - var i, l, code, nextCode, cache, result = ""; - if (typeof exclude !== "string") { - // encode(string, keepEscaped) - keepEscaped = exclude; - exclude = encode$2.defaultChars; - } - if (typeof keepEscaped === "undefined") { - keepEscaped = true; - } - cache = getEncodeCache(exclude); - for (i = 0, l = string.length; i < l; i++) { - code = string.charCodeAt(i); - if (keepEscaped && code === 37 /* % */ && i + 2 < l) { - if (/^[0-9a-f]{2}$/i.test(string.slice(i + 1, i + 3))) { - result += string.slice(i, i + 3); - i += 2; - continue; - } - } - if (code < 128) { - result += cache[code]; - continue; - } - if (code >= 55296 && code <= 57343) { - if (code >= 55296 && code <= 56319 && i + 1 < l) { - nextCode = string.charCodeAt(i + 1); - if (nextCode >= 56320 && nextCode <= 57343) { - result += encodeURIComponent(string[i] + string[i + 1]); - i++; - continue; - } - } - result += "%EF%BF%BD"; - continue; - } - result += encodeURIComponent(string[i]); - } - return result; - } - encode$2.defaultChars = ";/?:@&=+$,-_.!~*'()#"; - encode$2.componentChars = "-_.!~*'()"; - var encode_1 = encode$2; - /* eslint-disable no-bitwise */ var decodeCache = {}; - function getDecodeCache(exclude) { - var i, ch, cache = decodeCache[exclude]; - if (cache) { - return cache; - } - cache = decodeCache[exclude] = []; - for (i = 0; i < 128; i++) { - ch = String.fromCharCode(i); - cache.push(ch); - } - for (i = 0; i < exclude.length; i++) { - ch = exclude.charCodeAt(i); - cache[ch] = "%" + ("0" + ch.toString(16).toUpperCase()).slice(-2); - } - return cache; - } - // Decode percent-encoded string. - - function decode$2(string, exclude) { - var cache; - if (typeof exclude !== "string") { - exclude = decode$2.defaultChars; - } - cache = getDecodeCache(exclude); - return string.replace(/(%[a-f0-9]{2})+/gi, (function(seq) { - var i, l, b1, b2, b3, b4, chr, result = ""; - for (i = 0, l = seq.length; i < l; i += 3) { - b1 = parseInt(seq.slice(i + 1, i + 3), 16); - if (b1 < 128) { - result += cache[b1]; - continue; - } - if ((b1 & 224) === 192 && i + 3 < l) { - // 110xxxxx 10xxxxxx - b2 = parseInt(seq.slice(i + 4, i + 6), 16); - if ((b2 & 192) === 128) { - chr = b1 << 6 & 1984 | b2 & 63; - if (chr < 128) { - result += "\ufffd\ufffd"; - } else { - result += String.fromCharCode(chr); - } - i += 3; - continue; - } - } - if ((b1 & 240) === 224 && i + 6 < l) { - // 1110xxxx 10xxxxxx 10xxxxxx - b2 = parseInt(seq.slice(i + 4, i + 6), 16); - b3 = parseInt(seq.slice(i + 7, i + 9), 16); - if ((b2 & 192) === 128 && (b3 & 192) === 128) { - chr = b1 << 12 & 61440 | b2 << 6 & 4032 | b3 & 63; - if (chr < 2048 || chr >= 55296 && chr <= 57343) { - result += "\ufffd\ufffd\ufffd"; - } else { - result += String.fromCharCode(chr); - } - i += 6; - continue; - } - } - if ((b1 & 248) === 240 && i + 9 < l) { - // 111110xx 10xxxxxx 10xxxxxx 10xxxxxx - b2 = parseInt(seq.slice(i + 4, i + 6), 16); - b3 = parseInt(seq.slice(i + 7, i + 9), 16); - b4 = parseInt(seq.slice(i + 10, i + 12), 16); - if ((b2 & 192) === 128 && (b3 & 192) === 128 && (b4 & 192) === 128) { - chr = b1 << 18 & 1835008 | b2 << 12 & 258048 | b3 << 6 & 4032 | b4 & 63; - if (chr < 65536 || chr > 1114111) { - result += "\ufffd\ufffd\ufffd\ufffd"; - } else { - chr -= 65536; - result += String.fromCharCode(55296 + (chr >> 10), 56320 + (chr & 1023)); - } - i += 9; - continue; - } - } - result += "\ufffd"; - } - return result; - })); - } - decode$2.defaultChars = ";/?:@&=+$,#"; - decode$2.componentChars = ""; - var decode_1 = decode$2; - var format$1 = function format(url) { - var result = ""; - result += url.protocol || ""; - result += url.slashes ? "//" : ""; - result += url.auth ? url.auth + "@" : ""; - if (url.hostname && url.hostname.indexOf(":") !== -1) { - // ipv6 address - result += "[" + url.hostname + "]"; - } else { - result += url.hostname || ""; - } - result += url.port ? ":" + url.port : ""; - result += url.pathname || ""; - result += url.search || ""; - result += url.hash || ""; - return result; - }; - // Copyright Joyent, Inc. and other Node contributors. - - // Changes from joyent/node: - - // 1. No leading slash in paths, - // e.g. in `url.parse('http://foo?bar')` pathname is ``, not `/` - - // 2. Backslashes are not replaced with slashes, - // so `http:\\example.org\` is treated like a relative path - - // 3. Trailing colon is treated like a part of the path, - // i.e. in `http://example.org:foo` pathname is `:foo` - - // 4. Nothing is URL-encoded in the resulting object, - // (in joyent/node some chars in auth and paths are encoded) - - // 5. `url.parse()` does not have `parseQueryString` argument - - // 6. Removed extraneous result properties: `host`, `path`, `query`, etc., - // which can be constructed using other parts of the url. - - function Url() { - this.protocol = null; - this.slashes = null; - this.auth = null; - this.port = null; - this.hostname = null; - this.hash = null; - this.search = null; - this.pathname = null; - } - // Reference: RFC 3986, RFC 1808, RFC 2396 - // define these here so at least they only have to be - // compiled once on the first module load. - var protocolPattern = /^([a-z0-9.+-]+:)/i, portPattern = /:[0-9]*$/, - // Special case for a simple path URL - simplePathPattern = /^(\/\/?(?!\/)[^\?\s]*)(\?[^\s]*)?$/, - // RFC 2396: characters reserved for delimiting URLs. - // We actually just auto-escape these. - delims = [ "<", ">", '"', "`", " ", "\r", "\n", "\t" ], - // RFC 2396: characters not allowed for various reasons. - unwise = [ "{", "}", "|", "\\", "^", "`" ].concat(delims), - // Allowed by RFCs, but cause of XSS attacks. Always escape these. - autoEscape = [ "'" ].concat(unwise), - // Characters that are never ever allowed in a hostname. - // Note that any invalid chars are also handled, but these - // are the ones that are *expected* to be seen, so we fast-path - // them. - nonHostChars = [ "%", "/", "?", ";", "#" ].concat(autoEscape), hostEndingChars = [ "/", "?", "#" ], hostnameMaxLen = 255, hostnamePartPattern = /^[+a-z0-9A-Z_-]{0,63}$/, hostnamePartStart = /^([+a-z0-9A-Z_-]{0,63})(.*)$/, - // protocols that can allow "unsafe" and "unwise" chars. - /* eslint-disable no-script-url */ - // protocols that never have a hostname. - hostlessProtocol = { - javascript: true, - "javascript:": true - }, - // protocols that always contain a // bit. - slashedProtocol = { - http: true, - https: true, - ftp: true, - gopher: true, - file: true, - "http:": true, - "https:": true, - "ftp:": true, - "gopher:": true, - "file:": true - }; - /* eslint-enable no-script-url */ function urlParse(url, slashesDenoteHost) { - if (url && url instanceof Url) { - return url; - } - var u = new Url; - u.parse(url, slashesDenoteHost); - return u; - } - Url.prototype.parse = function(url, slashesDenoteHost) { - var i, l, lowerProto, hec, slashes, rest = url; - // trim before proceeding. - // This is to support parse stuff like " http://foo.com \n" - rest = rest.trim(); - if (!slashesDenoteHost && url.split("#").length === 1) { - // Try fast path regexp - var simplePath = simplePathPattern.exec(rest); - if (simplePath) { - this.pathname = simplePath[1]; - if (simplePath[2]) { - this.search = simplePath[2]; - } - return this; - } - } - var proto = protocolPattern.exec(rest); - if (proto) { - proto = proto[0]; - lowerProto = proto.toLowerCase(); - this.protocol = proto; - rest = rest.substr(proto.length); - } - // figure out if it's got a host - // user@server is *always* interpreted as a hostname, and url - // resolution will treat //foo/bar as host=foo,path=bar because that's - // how the browser resolves relative URLs. - if (slashesDenoteHost || proto || rest.match(/^\/\/[^@\/]+@[^@\/]+/)) { - slashes = rest.substr(0, 2) === "//"; - if (slashes && !(proto && hostlessProtocol[proto])) { - rest = rest.substr(2); - this.slashes = true; - } - } - if (!hostlessProtocol[proto] && (slashes || proto && !slashedProtocol[proto])) { - // there's a hostname. - // the first instance of /, ?, ;, or # ends the host. - // If there is an @ in the hostname, then non-host chars *are* allowed - // to the left of the last @ sign, unless some host-ending character - // comes *before* the @-sign. - // URLs are obnoxious. - // ex: - // http://a@b@c/ => user:a@b host:c - // http://a@b?@c => user:a host:c path:/?@c - // v0.12 TODO(isaacs): This is not quite how Chrome does things. - // Review our test case against browsers more comprehensively. - // find the first instance of any hostEndingChars - var hostEnd = -1; - for (i = 0; i < hostEndingChars.length; i++) { - hec = rest.indexOf(hostEndingChars[i]); - if (hec !== -1 && (hostEnd === -1 || hec < hostEnd)) { - hostEnd = hec; - } - } - // at this point, either we have an explicit point where the - // auth portion cannot go past, or the last @ char is the decider. - var auth, atSign; - if (hostEnd === -1) { - // atSign can be anywhere. - atSign = rest.lastIndexOf("@"); - } else { - // atSign must be in auth portion. - // http://a@b/c@d => host:b auth:a path:/c@d - atSign = rest.lastIndexOf("@", hostEnd); - } - // Now we have a portion which is definitely the auth. - // Pull that off. - if (atSign !== -1) { - auth = rest.slice(0, atSign); - rest = rest.slice(atSign + 1); - this.auth = auth; - } - // the host is the remaining to the left of the first non-host char - hostEnd = -1; - for (i = 0; i < nonHostChars.length; i++) { - hec = rest.indexOf(nonHostChars[i]); - if (hec !== -1 && (hostEnd === -1 || hec < hostEnd)) { - hostEnd = hec; - } - } - // if we still have not hit it, then the entire thing is a host. - if (hostEnd === -1) { - hostEnd = rest.length; - } - if (rest[hostEnd - 1] === ":") { - hostEnd--; - } - var host = rest.slice(0, hostEnd); - rest = rest.slice(hostEnd); - // pull out port. - this.parseHost(host); - // we've indicated that there is a hostname, - // so even if it's empty, it has to be present. - this.hostname = this.hostname || ""; - // if hostname begins with [ and ends with ] - // assume that it's an IPv6 address. - var ipv6Hostname = this.hostname[0] === "[" && this.hostname[this.hostname.length - 1] === "]"; - // validate a little. - if (!ipv6Hostname) { - var hostparts = this.hostname.split(/\./); - for (i = 0, l = hostparts.length; i < l; i++) { - var part = hostparts[i]; - if (!part) { - continue; - } - if (!part.match(hostnamePartPattern)) { - var newpart = ""; - for (var j = 0, k = part.length; j < k; j++) { - if (part.charCodeAt(j) > 127) { - // we replace non-ASCII char with a temporary placeholder - // we need this to make sure size of hostname is not - // broken by replacing non-ASCII by nothing - newpart += "x"; - } else { - newpart += part[j]; - } - } - // we test again with ASCII char only - if (!newpart.match(hostnamePartPattern)) { - var validParts = hostparts.slice(0, i); - var notHost = hostparts.slice(i + 1); - var bit = part.match(hostnamePartStart); - if (bit) { - validParts.push(bit[1]); - notHost.unshift(bit[2]); - } - if (notHost.length) { - rest = notHost.join(".") + rest; - } - this.hostname = validParts.join("."); - break; - } - } - } - } - if (this.hostname.length > hostnameMaxLen) { - this.hostname = ""; - } - // strip [ and ] from the hostname - // the host field still retains them, though - if (ipv6Hostname) { - this.hostname = this.hostname.substr(1, this.hostname.length - 2); - } - } - // chop off from the tail first. - var hash = rest.indexOf("#"); - if (hash !== -1) { - // got a fragment string. - this.hash = rest.substr(hash); - rest = rest.slice(0, hash); - } - var qm = rest.indexOf("?"); - if (qm !== -1) { - this.search = rest.substr(qm); - rest = rest.slice(0, qm); - } - if (rest) { - this.pathname = rest; - } - if (slashedProtocol[lowerProto] && this.hostname && !this.pathname) { - this.pathname = ""; - } - return this; - }; - Url.prototype.parseHost = function(host) { - var port = portPattern.exec(host); - if (port) { - port = port[0]; - if (port !== ":") { - this.port = port.substr(1); - } - host = host.substr(0, host.length - port.length); - } - if (host) { - this.hostname = host; - } - }; - var parse$1 = urlParse; - var encode$1 = encode_1; - var decode$1 = decode_1; - var format = format$1; - var parse = parse$1; - var mdurl = { - encode: encode$1, - decode: decode$1, - format: format, - parse: parse - }; - var regex$3 = /[\0-\uD7FF\uE000-\uFFFF]|[\uD800-\uDBFF][\uDC00-\uDFFF]|[\uD800-\uDBFF](?![\uDC00-\uDFFF])|(?:[^\uD800-\uDBFF]|^)[\uDC00-\uDFFF]/; - var regex$2 = /[\0-\x1F\x7F-\x9F]/; - var regex$1 = /[\xAD\u0600-\u0605\u061C\u06DD\u070F\u08E2\u180E\u200B-\u200F\u202A-\u202E\u2060-\u2064\u2066-\u206F\uFEFF\uFFF9-\uFFFB]|\uD804[\uDCBD\uDCCD]|\uD82F[\uDCA0-\uDCA3]|\uD834[\uDD73-\uDD7A]|\uDB40[\uDC01\uDC20-\uDC7F]/; - var regex = /[ \xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/; - var Any = regex$3; - var Cc = regex$2; - var Cf = regex$1; - var P = regex$4; - var Z = regex; - var uc_micro = { - Any: Any, - Cc: Cc, - Cf: Cf, - P: P, - Z: Z - }; - var utils = createCommonjsModule((function(module, exports) { - function _class(obj) { - return Object.prototype.toString.call(obj); - } - function isString(obj) { - return _class(obj) === "[object String]"; - } - var _hasOwnProperty = Object.prototype.hasOwnProperty; - function has(object, key) { - return _hasOwnProperty.call(object, key); - } - // Merge objects - - function assign(obj /*from1, from2, from3, ...*/) { - var sources = Array.prototype.slice.call(arguments, 1); - sources.forEach((function(source) { - if (!source) { - return; - } - if (typeof source !== "object") { - throw new TypeError(source + "must be object"); - } - Object.keys(source).forEach((function(key) { - obj[key] = source[key]; - })); - })); - return obj; - } - // Remove element from array and put another array at those position. - // Useful for some operations with tokens - function arrayReplaceAt(src, pos, newElements) { - return [].concat(src.slice(0, pos), newElements, src.slice(pos + 1)); - } - //////////////////////////////////////////////////////////////////////////////// - function isValidEntityCode(c) { - /*eslint no-bitwise:0*/ - // broken sequence - if (c >= 55296 && c <= 57343) { - return false; - } - // never used - if (c >= 64976 && c <= 65007) { - return false; - } - if ((c & 65535) === 65535 || (c & 65535) === 65534) { - return false; - } - // control codes - if (c >= 0 && c <= 8) { - return false; - } - if (c === 11) { - return false; - } - if (c >= 14 && c <= 31) { - return false; - } - if (c >= 127 && c <= 159) { - return false; - } - // out of range - if (c > 1114111) { - return false; - } - return true; - } - function fromCodePoint(c) { - /*eslint no-bitwise:0*/ - if (c > 65535) { - c -= 65536; - var surrogate1 = 55296 + (c >> 10), surrogate2 = 56320 + (c & 1023); - return String.fromCharCode(surrogate1, surrogate2); - } - return String.fromCharCode(c); - } - var UNESCAPE_MD_RE = /\\([!"#$%&'()*+,\-.\/:;<=>?@[\\\]^_`{|}~])/g; - var ENTITY_RE = /&([a-z#][a-z0-9]{1,31});/gi; - var UNESCAPE_ALL_RE = new RegExp(UNESCAPE_MD_RE.source + "|" + ENTITY_RE.source, "gi"); - var DIGITAL_ENTITY_TEST_RE = /^#((?:x[a-f0-9]{1,8}|[0-9]{1,8}))$/i; - function replaceEntityPattern(match, name) { - var code; - if (has(entities, name)) { - return entities[name]; - } - if (name.charCodeAt(0) === 35 /* # */ && DIGITAL_ENTITY_TEST_RE.test(name)) { - code = name[1].toLowerCase() === "x" ? parseInt(name.slice(2), 16) : parseInt(name.slice(1), 10); - if (isValidEntityCode(code)) { - return fromCodePoint(code); - } - } - return match; - } - /*function replaceEntities(str) { - if (str.indexOf('&') < 0) { return str; } - - return str.replace(ENTITY_RE, replaceEntityPattern); - }*/ function unescapeMd(str) { - if (str.indexOf("\\") < 0) { - return str; - } - return str.replace(UNESCAPE_MD_RE, "$1"); - } - function unescapeAll(str) { - if (str.indexOf("\\") < 0 && str.indexOf("&") < 0) { - return str; - } - return str.replace(UNESCAPE_ALL_RE, (function(match, escaped, entity) { - if (escaped) { - return escaped; - } - return replaceEntityPattern(match, entity); - })); - } - //////////////////////////////////////////////////////////////////////////////// - var HTML_ESCAPE_TEST_RE = /[&<>"]/; - var HTML_ESCAPE_REPLACE_RE = /[&<>"]/g; - var HTML_REPLACEMENTS = { - "&": "&", - "<": "<", - ">": ">", - '"': """ - }; - function replaceUnsafeChar(ch) { - return HTML_REPLACEMENTS[ch]; - } - function escapeHtml(str) { - if (HTML_ESCAPE_TEST_RE.test(str)) { - return str.replace(HTML_ESCAPE_REPLACE_RE, replaceUnsafeChar); - } - return str; - } - //////////////////////////////////////////////////////////////////////////////// - var REGEXP_ESCAPE_RE = /[.?*+^$[\]\\(){}|-]/g; - function escapeRE(str) { - return str.replace(REGEXP_ESCAPE_RE, "\\$&"); - } - //////////////////////////////////////////////////////////////////////////////// - function isSpace(code) { - switch (code) { - case 9: - case 32: - return true; - } - return false; - } - // Zs (unicode class) || [\t\f\v\r\n] - function isWhiteSpace(code) { - if (code >= 8192 && code <= 8202) { - return true; - } - switch (code) { - case 9: - // \t - case 10: - // \n - case 11: - // \v - case 12: - // \f - case 13: - // \r - case 32: - case 160: - case 5760: - case 8239: - case 8287: - case 12288: - return true; - } - return false; - } - //////////////////////////////////////////////////////////////////////////////// - /*eslint-disable max-len*/ - // Currently without astral characters support. - function isPunctChar(ch) { - return regex$4.test(ch); - } - // Markdown ASCII punctuation characters. - - // !, ", #, $, %, &, ', (, ), *, +, ,, -, ., /, :, ;, <, =, >, ?, @, [, \, ], ^, _, `, {, |, }, or ~ - // http://spec.commonmark.org/0.15/#ascii-punctuation-character - - // Don't confuse with unicode punctuation !!! It lacks some chars in ascii range. - - function isMdAsciiPunct(ch) { - switch (ch) { - case 33 /* ! */ : - case 34 /* " */ : - case 35 /* # */ : - case 36 /* $ */ : - case 37 /* % */ : - case 38 /* & */ : - case 39 /* ' */ : - case 40 /* ( */ : - case 41 /* ) */ : - case 42 /* * */ : - case 43 /* + */ : - case 44 /* , */ : - case 45 /* - */ : - case 46 /* . */ : - case 47 /* / */ : - case 58 /* : */ : - case 59 /* ; */ : - case 60 /* < */ : - case 61 /* = */ : - case 62 /* > */ : - case 63 /* ? */ : - case 64 /* @ */ : - case 91 /* [ */ : - case 92 /* \ */ : - case 93 /* ] */ : - case 94 /* ^ */ : - case 95 /* _ */ : - case 96 /* ` */ : - case 123 /* { */ : - case 124 /* | */ : - case 125 /* } */ : - case 126 /* ~ */ : - return true; - - default: - return false; - } - } - // Hepler to unify [reference labels]. - - function normalizeReference(str) { - // Trim and collapse whitespace - str = str.trim().replace(/\s+/g, " "); - // In node v10 'ẞ'.toLowerCase() === 'Ṿ', which is presumed to be a bug - // fixed in v12 (couldn't find any details). - - // So treat this one as a special case - // (remove this when node v10 is no longer supported). - - if ("\u1e9e".toLowerCase() === "\u1e7e") { - str = str.replace(/\u1e9e/g, "\xdf"); - } - // .toLowerCase().toUpperCase() should get rid of all differences - // between letter variants. - - // Simple .toLowerCase() doesn't normalize 125 code points correctly, - // and .toUpperCase doesn't normalize 6 of them (list of exceptions: - // İ, ϴ, ẞ, Ω, K, Å - those are already uppercased, but have differently - // uppercased versions). - - // Here's an example showing how it happens. Lets take greek letter omega: - // uppercase U+0398 (Θ), U+03f4 (ϴ) and lowercase U+03b8 (θ), U+03d1 (ϑ) - - // Unicode entries: - // 0398;GREEK CAPITAL LETTER THETA;Lu;0;L;;;;;N;;;;03B8; - // 03B8;GREEK SMALL LETTER THETA;Ll;0;L;;;;;N;;;0398;;0398 - // 03D1;GREEK THETA SYMBOL;Ll;0;L; 03B8;;;;N;GREEK SMALL LETTER SCRIPT THETA;;0398;;0398 - // 03F4;GREEK CAPITAL THETA SYMBOL;Lu;0;L; 0398;;;;N;;;;03B8; - - // Case-insensitive comparison should treat all of them as equivalent. - - // But .toLowerCase() doesn't change ϑ (it's already lowercase), - // and .toUpperCase() doesn't change ϴ (already uppercase). - - // Applying first lower then upper case normalizes any character: - // '\u0398\u03f4\u03b8\u03d1'.toLowerCase().toUpperCase() === '\u0398\u0398\u0398\u0398' - - // Note: this is equivalent to unicode case folding; unicode normalization - // is a different step that is not required here. - - // Final result should be uppercased, because it's later stored in an object - // (this avoid a conflict with Object.prototype members, - // most notably, `__proto__`) - - return str.toLowerCase().toUpperCase(); - } - //////////////////////////////////////////////////////////////////////////////// - // Re-export libraries commonly used in both markdown-it and its plugins, - // so plugins won't have to depend on them explicitly, which reduces their - // bundled size (e.g. a browser build). - - exports.lib = {}; - exports.lib.mdurl = mdurl; - exports.lib.ucmicro = uc_micro; - exports.assign = assign; - exports.isString = isString; - exports.has = has; - exports.unescapeMd = unescapeMd; - exports.unescapeAll = unescapeAll; - exports.isValidEntityCode = isValidEntityCode; - exports.fromCodePoint = fromCodePoint; - // exports.replaceEntities = replaceEntities; - exports.escapeHtml = escapeHtml; - exports.arrayReplaceAt = arrayReplaceAt; - exports.isSpace = isSpace; - exports.isWhiteSpace = isWhiteSpace; - exports.isMdAsciiPunct = isMdAsciiPunct; - exports.isPunctChar = isPunctChar; - exports.escapeRE = escapeRE; - exports.normalizeReference = normalizeReference; - })); - // Parse link label - var parse_link_label = function parseLinkLabel(state, start, disableNested) { - var level, found, marker, prevPos, labelEnd = -1, max = state.posMax, oldPos = state.pos; - state.pos = start + 1; - level = 1; - while (state.pos < max) { - marker = state.src.charCodeAt(state.pos); - if (marker === 93 /* ] */) { - level--; - if (level === 0) { - found = true; - break; - } - } - prevPos = state.pos; - state.md.inline.skipToken(state); - if (marker === 91 /* [ */) { - if (prevPos === state.pos - 1) { - // increase level if we find text `[`, which is not a part of any token - level++; - } else if (disableNested) { - state.pos = oldPos; - return -1; - } - } - } - if (found) { - labelEnd = state.pos; - } - // restore old state - state.pos = oldPos; - return labelEnd; - }; - var unescapeAll$2 = utils.unescapeAll; - var parse_link_destination = function parseLinkDestination(str, start, max) { - var code, level, pos = start, result = { - ok: false, - pos: 0, - lines: 0, - str: "" - }; - if (str.charCodeAt(pos) === 60 /* < */) { - pos++; - while (pos < max) { - code = str.charCodeAt(pos); - if (code === 10 /* \n */) { - return result; - } - if (code === 60 /* < */) { - return result; - } - if (code === 62 /* > */) { - result.pos = pos + 1; - result.str = unescapeAll$2(str.slice(start + 1, pos)); - result.ok = true; - return result; - } - if (code === 92 /* \ */ && pos + 1 < max) { - pos += 2; - continue; - } - pos++; - } - // no closing '>' - return result; - } - // this should be ... } else { ... branch - level = 0; - while (pos < max) { - code = str.charCodeAt(pos); - if (code === 32) { - break; - } - // ascii control characters - if (code < 32 || code === 127) { - break; - } - if (code === 92 /* \ */ && pos + 1 < max) { - if (str.charCodeAt(pos + 1) === 32) { - break; - } - pos += 2; - continue; - } - if (code === 40 /* ( */) { - level++; - if (level > 32) { - return result; - } - } - if (code === 41 /* ) */) { - if (level === 0) { - break; - } - level--; - } - pos++; - } - if (start === pos) { - return result; - } - if (level !== 0) { - return result; - } - result.str = unescapeAll$2(str.slice(start, pos)); - result.pos = pos; - result.ok = true; - return result; - }; - var unescapeAll$1 = utils.unescapeAll; - var parse_link_title = function parseLinkTitle(str, start, max) { - var code, marker, lines = 0, pos = start, result = { - ok: false, - pos: 0, - lines: 0, - str: "" - }; - if (pos >= max) { - return result; - } - marker = str.charCodeAt(pos); - if (marker !== 34 /* " */ && marker !== 39 /* ' */ && marker !== 40 /* ( */) { - return result; - } - pos++; - // if opening marker is "(", switch it to closing marker ")" - if (marker === 40) { - marker = 41; - } - while (pos < max) { - code = str.charCodeAt(pos); - if (code === marker) { - result.pos = pos + 1; - result.lines = lines; - result.str = unescapeAll$1(str.slice(start + 1, pos)); - result.ok = true; - return result; - } else if (code === 40 /* ( */ && marker === 41 /* ) */) { - return result; - } else if (code === 10) { - lines++; - } else if (code === 92 /* \ */ && pos + 1 < max) { - pos++; - if (str.charCodeAt(pos) === 10) { - lines++; - } - } - pos++; - } - return result; - }; - var parseLinkLabel = parse_link_label; - var parseLinkDestination = parse_link_destination; - var parseLinkTitle = parse_link_title; - var helpers = { - parseLinkLabel: parseLinkLabel, - parseLinkDestination: parseLinkDestination, - parseLinkTitle: parseLinkTitle - }; - var assign$1 = utils.assign; - var unescapeAll = utils.unescapeAll; - var escapeHtml = utils.escapeHtml; - //////////////////////////////////////////////////////////////////////////////// - var default_rules = {}; - default_rules.code_inline = function(tokens, idx, options, env, slf) { - var token = tokens[idx]; - return "" + escapeHtml(token.content) + ""; - }; - default_rules.code_block = function(tokens, idx, options, env, slf) { - var token = tokens[idx]; - return "" + escapeHtml(tokens[idx].content) + "\n"; - }; - default_rules.fence = function(tokens, idx, options, env, slf) { - var token = tokens[idx], info = token.info ? unescapeAll(token.info).trim() : "", langName = "", langAttrs = "", highlighted, i, arr, tmpAttrs, tmpToken; - if (info) { - arr = info.split(/(\s+)/g); - langName = arr[0]; - langAttrs = arr.slice(2).join(""); - } - if (options.highlight) { - highlighted = options.highlight(token.content, langName, langAttrs) || escapeHtml(token.content); - } else { - highlighted = escapeHtml(token.content); - } - if (highlighted.indexOf("" + highlighted + "\n"; - } - return "
" + highlighted + "
\n"; - }; - default_rules.image = function(tokens, idx, options, env, slf) { - var token = tokens[idx]; - // "alt" attr MUST be set, even if empty. Because it's mandatory and - // should be placed on proper position for tests. - - // Replace content with actual value - token.attrs[token.attrIndex("alt")][1] = slf.renderInlineAsText(token.children, options, env); - return slf.renderToken(tokens, idx, options); - }; - default_rules.hardbreak = function(tokens, idx, options /*, env */) { - return options.xhtmlOut ? "
\n" : "
\n"; - }; - default_rules.softbreak = function(tokens, idx, options /*, env */) { - return options.breaks ? options.xhtmlOut ? "
\n" : "
\n" : "\n"; - }; - default_rules.text = function(tokens, idx /*, options, env */) { - return escapeHtml(tokens[idx].content); - }; - default_rules.html_block = function(tokens, idx /*, options, env */) { - return tokens[idx].content; - }; - default_rules.html_inline = function(tokens, idx /*, options, env */) { - return tokens[idx].content; - }; - /** - * new Renderer() - * - * Creates new [[Renderer]] instance and fill [[Renderer#rules]] with defaults. - **/ function Renderer() { - /** - * Renderer#rules -> Object - * - * Contains render rules for tokens. Can be updated and extended. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')(); - * - * md.renderer.rules.strong_open = function () { return ''; }; - * md.renderer.rules.strong_close = function () { return ''; }; - * - * var result = md.renderInline(...); - * ``` - * - * Each rule is called as independent static function with fixed signature: - * - * ```javascript - * function my_token_render(tokens, idx, options, env, renderer) { - * // ... - * return renderedHTML; - * } - * ``` - * - * See [source code](https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js) - * for more details and examples. - **/ - this.rules = assign$1({}, default_rules); - } - /** - * Renderer.renderAttrs(token) -> String - * - * Render token attributes to string. - **/ Renderer.prototype.renderAttrs = function renderAttrs(token) { - var i, l, result; - if (!token.attrs) { - return ""; - } - result = ""; - for (i = 0, l = token.attrs.length; i < l; i++) { - result += " " + escapeHtml(token.attrs[i][0]) + '="' + escapeHtml(token.attrs[i][1]) + '"'; - } - return result; - }; - /** - * Renderer.renderToken(tokens, idx, options) -> String - * - tokens (Array): list of tokens - * - idx (Numbed): token index to render - * - options (Object): params of parser instance - * - * Default token renderer. Can be overriden by custom function - * in [[Renderer#rules]]. - **/ Renderer.prototype.renderToken = function renderToken(tokens, idx, options) { - var nextToken, result = "", needLf = false, token = tokens[idx]; - // Tight list paragraphs - if (token.hidden) { - return ""; - } - // Insert a newline between hidden paragraph and subsequent opening - // block-level tag. - - // For example, here we should insert a newline before blockquote: - // - a - // > - - if (token.block && token.nesting !== -1 && idx && tokens[idx - 1].hidden) { - result += "\n"; - } - // Add token name, e.g. ``. - needLf = false; - } - } - } - } - result += needLf ? ">\n" : ">"; - return result; - }; - /** - * Renderer.renderInline(tokens, options, env) -> String - * - tokens (Array): list on block tokens to render - * - options (Object): params of parser instance - * - env (Object): additional data from parsed input (references, for example) - * - * The same as [[Renderer.render]], but for single token of `inline` type. - **/ Renderer.prototype.renderInline = function(tokens, options, env) { - var type, result = "", rules = this.rules; - for (var i = 0, len = tokens.length; i < len; i++) { - type = tokens[i].type; - if (typeof rules[type] !== "undefined") { - result += rules[type](tokens, i, options, env, this); - } else { - result += this.renderToken(tokens, i, options); - } - } - return result; - }; - /** internal - * Renderer.renderInlineAsText(tokens, options, env) -> String - * - tokens (Array): list on block tokens to render - * - options (Object): params of parser instance - * - env (Object): additional data from parsed input (references, for example) - * - * Special kludge for image `alt` attributes to conform CommonMark spec. - * Don't try to use it! Spec requires to show `alt` content with stripped markup, - * instead of simple escaping. - **/ Renderer.prototype.renderInlineAsText = function(tokens, options, env) { - var result = ""; - for (var i = 0, len = tokens.length; i < len; i++) { - if (tokens[i].type === "text") { - result += tokens[i].content; - } else if (tokens[i].type === "image") { - result += this.renderInlineAsText(tokens[i].children, options, env); - } else if (tokens[i].type === "softbreak") { - result += "\n"; - } - } - return result; - }; - /** - * Renderer.render(tokens, options, env) -> String - * - tokens (Array): list on block tokens to render - * - options (Object): params of parser instance - * - env (Object): additional data from parsed input (references, for example) - * - * Takes token stream and generates HTML. Probably, you will never need to call - * this method directly. - **/ Renderer.prototype.render = function(tokens, options, env) { - var i, len, type, result = "", rules = this.rules; - for (i = 0, len = tokens.length; i < len; i++) { - type = tokens[i].type; - if (type === "inline") { - result += this.renderInline(tokens[i].children, options, env); - } else if (typeof rules[type] !== "undefined") { - result += rules[type](tokens, i, options, env, this); - } else { - result += this.renderToken(tokens, i, options, env); - } - } - return result; - }; - var renderer = Renderer; - /** - * class Ruler - * - * Helper class, used by [[MarkdownIt#core]], [[MarkdownIt#block]] and - * [[MarkdownIt#inline]] to manage sequences of functions (rules): - * - * - keep rules in defined order - * - assign the name to each rule - * - enable/disable rules - * - add/replace rules - * - allow assign rules to additional named chains (in the same) - * - cacheing lists of active rules - * - * You will not need use this class directly until write plugins. For simple - * rules control use [[MarkdownIt.disable]], [[MarkdownIt.enable]] and - * [[MarkdownIt.use]]. - **/ - /** - * new Ruler() - **/ function Ruler() { - // List of added rules. Each element is: - // { - // name: XXX, - // enabled: Boolean, - // fn: Function(), - // alt: [ name2, name3 ] - // } - this.__rules__ = []; - // Cached rule chains. - - // First level - chain name, '' for default. - // Second level - diginal anchor for fast filtering by charcodes. - - this.__cache__ = null; - } - //////////////////////////////////////////////////////////////////////////////// - // Helper methods, should not be used directly - // Find rule index by name - - Ruler.prototype.__find__ = function(name) { - for (var i = 0; i < this.__rules__.length; i++) { - if (this.__rules__[i].name === name) { - return i; - } - } - return -1; - }; - // Build rules lookup cache - - Ruler.prototype.__compile__ = function() { - var self = this; - var chains = [ "" ]; - // collect unique names - self.__rules__.forEach((function(rule) { - if (!rule.enabled) { - return; - } - rule.alt.forEach((function(altName) { - if (chains.indexOf(altName) < 0) { - chains.push(altName); - } - })); - })); - self.__cache__ = {}; - chains.forEach((function(chain) { - self.__cache__[chain] = []; - self.__rules__.forEach((function(rule) { - if (!rule.enabled) { - return; - } - if (chain && rule.alt.indexOf(chain) < 0) { - return; - } - self.__cache__[chain].push(rule.fn); - })); - })); - }; - /** - * Ruler.at(name, fn [, options]) - * - name (String): rule name to replace. - * - fn (Function): new rule function. - * - options (Object): new rule options (not mandatory). - * - * Replace rule by name with new function & options. Throws error if name not - * found. - * - * ##### Options: - * - * - __alt__ - array with names of "alternate" chains. - * - * ##### Example - * - * Replace existing typographer replacement rule with new one: - * - * ```javascript - * var md = require('markdown-it')(); - * - * md.core.ruler.at('replacements', function replace(state) { - * //... - * }); - * ``` - **/ Ruler.prototype.at = function(name, fn, options) { - var index = this.__find__(name); - var opt = options || {}; - if (index === -1) { - throw new Error("Parser rule not found: " + name); - } - this.__rules__[index].fn = fn; - this.__rules__[index].alt = opt.alt || []; - this.__cache__ = null; - }; - /** - * Ruler.before(beforeName, ruleName, fn [, options]) - * - beforeName (String): new rule will be added before this one. - * - ruleName (String): name of added rule. - * - fn (Function): rule function. - * - options (Object): rule options (not mandatory). - * - * Add new rule to chain before one with given name. See also - * [[Ruler.after]], [[Ruler.push]]. - * - * ##### Options: - * - * - __alt__ - array with names of "alternate" chains. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')(); - * - * md.block.ruler.before('paragraph', 'my_rule', function replace(state) { - * //... - * }); - * ``` - **/ Ruler.prototype.before = function(beforeName, ruleName, fn, options) { - var index = this.__find__(beforeName); - var opt = options || {}; - if (index === -1) { - throw new Error("Parser rule not found: " + beforeName); - } - this.__rules__.splice(index, 0, { - name: ruleName, - enabled: true, - fn: fn, - alt: opt.alt || [] - }); - this.__cache__ = null; - }; - /** - * Ruler.after(afterName, ruleName, fn [, options]) - * - afterName (String): new rule will be added after this one. - * - ruleName (String): name of added rule. - * - fn (Function): rule function. - * - options (Object): rule options (not mandatory). - * - * Add new rule to chain after one with given name. See also - * [[Ruler.before]], [[Ruler.push]]. - * - * ##### Options: - * - * - __alt__ - array with names of "alternate" chains. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')(); - * - * md.inline.ruler.after('text', 'my_rule', function replace(state) { - * //... - * }); - * ``` - **/ Ruler.prototype.after = function(afterName, ruleName, fn, options) { - var index = this.__find__(afterName); - var opt = options || {}; - if (index === -1) { - throw new Error("Parser rule not found: " + afterName); - } - this.__rules__.splice(index + 1, 0, { - name: ruleName, - enabled: true, - fn: fn, - alt: opt.alt || [] - }); - this.__cache__ = null; - }; - /** - * Ruler.push(ruleName, fn [, options]) - * - ruleName (String): name of added rule. - * - fn (Function): rule function. - * - options (Object): rule options (not mandatory). - * - * Push new rule to the end of chain. See also - * [[Ruler.before]], [[Ruler.after]]. - * - * ##### Options: - * - * - __alt__ - array with names of "alternate" chains. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')(); - * - * md.core.ruler.push('my_rule', function replace(state) { - * //... - * }); - * ``` - **/ Ruler.prototype.push = function(ruleName, fn, options) { - var opt = options || {}; - this.__rules__.push({ - name: ruleName, - enabled: true, - fn: fn, - alt: opt.alt || [] - }); - this.__cache__ = null; - }; - /** - * Ruler.enable(list [, ignoreInvalid]) -> Array - * - list (String|Array): list of rule names to enable. - * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. - * - * Enable rules with given names. If any rule name not found - throw Error. - * Errors can be disabled by second param. - * - * Returns list of found rule names (if no exception happened). - * - * See also [[Ruler.disable]], [[Ruler.enableOnly]]. - **/ Ruler.prototype.enable = function(list, ignoreInvalid) { - if (!Array.isArray(list)) { - list = [ list ]; - } - var result = []; - // Search by name and enable - list.forEach((function(name) { - var idx = this.__find__(name); - if (idx < 0) { - if (ignoreInvalid) { - return; - } - throw new Error("Rules manager: invalid rule name " + name); - } - this.__rules__[idx].enabled = true; - result.push(name); - }), this); - this.__cache__ = null; - return result; - }; - /** - * Ruler.enableOnly(list [, ignoreInvalid]) - * - list (String|Array): list of rule names to enable (whitelist). - * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. - * - * Enable rules with given names, and disable everything else. If any rule name - * not found - throw Error. Errors can be disabled by second param. - * - * See also [[Ruler.disable]], [[Ruler.enable]]. - **/ Ruler.prototype.enableOnly = function(list, ignoreInvalid) { - if (!Array.isArray(list)) { - list = [ list ]; - } - this.__rules__.forEach((function(rule) { - rule.enabled = false; - })); - this.enable(list, ignoreInvalid); - }; - /** - * Ruler.disable(list [, ignoreInvalid]) -> Array - * - list (String|Array): list of rule names to disable. - * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. - * - * Disable rules with given names. If any rule name not found - throw Error. - * Errors can be disabled by second param. - * - * Returns list of found rule names (if no exception happened). - * - * See also [[Ruler.enable]], [[Ruler.enableOnly]]. - **/ Ruler.prototype.disable = function(list, ignoreInvalid) { - if (!Array.isArray(list)) { - list = [ list ]; - } - var result = []; - // Search by name and disable - list.forEach((function(name) { - var idx = this.__find__(name); - if (idx < 0) { - if (ignoreInvalid) { - return; - } - throw new Error("Rules manager: invalid rule name " + name); - } - this.__rules__[idx].enabled = false; - result.push(name); - }), this); - this.__cache__ = null; - return result; - }; - /** - * Ruler.getRules(chainName) -> Array - * - * Return array of active functions (rules) for given chain name. It analyzes - * rules configuration, compiles caches if not exists and returns result. - * - * Default chain name is `''` (empty string). It can't be skipped. That's - * done intentionally, to keep signature monomorphic for high speed. - **/ Ruler.prototype.getRules = function(chainName) { - if (this.__cache__ === null) { - this.__compile__(); - } - // Chain can be empty, if rules disabled. But we still have to return Array. - return this.__cache__[chainName] || []; - }; - var ruler = Ruler; - // Normalize input string - // https://spec.commonmark.org/0.29/#line-ending - var NEWLINES_RE = /\r\n?|\n/g; - var NULL_RE = /\0/g; - var normalize = function normalize(state) { - var str; - // Normalize newlines - str = state.src.replace(NEWLINES_RE, "\n"); - // Replace NULL characters - str = str.replace(NULL_RE, "\ufffd"); - state.src = str; - }; - var block = function block(state) { - var token; - if (state.inlineMode) { - token = new state.Token("inline", "", 0); - token.content = state.src; - token.map = [ 0, 1 ]; - token.children = []; - state.tokens.push(token); - } else { - state.md.block.parse(state.src, state.md, state.env, state.tokens); - } - }; - var inline = function inline(state) { - var tokens = state.tokens, tok, i, l; - // Parse inlines - for (i = 0, l = tokens.length; i < l; i++) { - tok = tokens[i]; - if (tok.type === "inline") { - state.md.inline.parse(tok.content, state.md, state.env, tok.children); - } - } - }; - var arrayReplaceAt = utils.arrayReplaceAt; - function isLinkOpen$1(str) { - return /^\s]/i.test(str); - } - function isLinkClose$1(str) { - return /^<\/a\s*>/i.test(str); - } - var linkify$1 = function linkify(state) { - var i, j, l, tokens, token, currentToken, nodes, ln, text, pos, lastPos, level, htmlLinkLevel, url, fullUrl, urlText, blockTokens = state.tokens, links; - if (!state.md.options.linkify) { - return; - } - for (j = 0, l = blockTokens.length; j < l; j++) { - if (blockTokens[j].type !== "inline" || !state.md.linkify.pretest(blockTokens[j].content)) { - continue; - } - tokens = blockTokens[j].children; - htmlLinkLevel = 0; - // We scan from the end, to keep position when new tags added. - // Use reversed logic in links start/end match - for (i = tokens.length - 1; i >= 0; i--) { - currentToken = tokens[i]; - // Skip content of markdown links - if (currentToken.type === "link_close") { - i--; - while (tokens[i].level !== currentToken.level && tokens[i].type !== "link_open") { - i--; - } - continue; - } - // Skip content of html tag links - if (currentToken.type === "html_inline") { - if (isLinkOpen$1(currentToken.content) && htmlLinkLevel > 0) { - htmlLinkLevel--; - } - if (isLinkClose$1(currentToken.content)) { - htmlLinkLevel++; - } - } - if (htmlLinkLevel > 0) { - continue; - } - if (currentToken.type === "text" && state.md.linkify.test(currentToken.content)) { - text = currentToken.content; - links = state.md.linkify.match(text); - // Now split string to nodes - nodes = []; - level = currentToken.level; - lastPos = 0; - // forbid escape sequence at the start of the string, - // this avoids http\://example.com/ from being linkified as - // http://example.com/ - if (links.length > 0 && links[0].index === 0 && i > 0 && tokens[i - 1].type === "text_special") { - links = links.slice(1); - } - for (ln = 0; ln < links.length; ln++) { - url = links[ln].url; - fullUrl = state.md.normalizeLink(url); - if (!state.md.validateLink(fullUrl)) { - continue; - } - urlText = links[ln].text; - // Linkifier might send raw hostnames like "example.com", where url - // starts with domain name. So we prepend http:// in those cases, - // and remove it afterwards. - - if (!links[ln].schema) { - urlText = state.md.normalizeLinkText("http://" + urlText).replace(/^http:\/\//, ""); - } else if (links[ln].schema === "mailto:" && !/^mailto:/i.test(urlText)) { - urlText = state.md.normalizeLinkText("mailto:" + urlText).replace(/^mailto:/, ""); - } else { - urlText = state.md.normalizeLinkText(urlText); - } - pos = links[ln].index; - if (pos > lastPos) { - token = new state.Token("text", "", 0); - token.content = text.slice(lastPos, pos); - token.level = level; - nodes.push(token); - } - token = new state.Token("link_open", "a", 1); - token.attrs = [ [ "href", fullUrl ] ]; - token.level = level++; - token.markup = "linkify"; - token.info = "auto"; - nodes.push(token); - token = new state.Token("text", "", 0); - token.content = urlText; - token.level = level; - nodes.push(token); - token = new state.Token("link_close", "a", -1); - token.level = --level; - token.markup = "linkify"; - token.info = "auto"; - nodes.push(token); - lastPos = links[ln].lastIndex; - } - if (lastPos < text.length) { - token = new state.Token("text", "", 0); - token.content = text.slice(lastPos); - token.level = level; - nodes.push(token); - } - // replace current node - blockTokens[j].children = tokens = arrayReplaceAt(tokens, i, nodes); - } - } - } - }; - // Simple typographic replacements - // TODO: - // - fractionals 1/2, 1/4, 3/4 -> ½, ¼, ¾ - // - multiplications 2 x 4 -> 2 × 4 - var RARE_RE = /\+-|\.\.|\?\?\?\?|!!!!|,,|--/; - // Workaround for phantomjs - need regex without /g flag, - // or root check will fail every second time - var SCOPED_ABBR_TEST_RE = /\((c|tm|r)\)/i; - var SCOPED_ABBR_RE = /\((c|tm|r)\)/gi; - var SCOPED_ABBR = { - c: "\xa9", - r: "\xae", - tm: "\u2122" - }; - function replaceFn(match, name) { - return SCOPED_ABBR[name.toLowerCase()]; - } - function replace_scoped(inlineTokens) { - var i, token, inside_autolink = 0; - for (i = inlineTokens.length - 1; i >= 0; i--) { - token = inlineTokens[i]; - if (token.type === "text" && !inside_autolink) { - token.content = token.content.replace(SCOPED_ABBR_RE, replaceFn); - } - if (token.type === "link_open" && token.info === "auto") { - inside_autolink--; - } - if (token.type === "link_close" && token.info === "auto") { - inside_autolink++; - } - } - } - function replace_rare(inlineTokens) { - var i, token, inside_autolink = 0; - for (i = inlineTokens.length - 1; i >= 0; i--) { - token = inlineTokens[i]; - if (token.type === "text" && !inside_autolink) { - if (RARE_RE.test(token.content)) { - token.content = token.content.replace(/\+-/g, "\xb1").replace(/\.{2,}/g, "\u2026").replace(/([?!])\u2026/g, "$1..").replace(/([?!]){4,}/g, "$1$1$1").replace(/,{2,}/g, ",").replace(/(^|[^-])---(?=[^-]|$)/gm, "$1\u2014").replace(/(^|\s)--(?=\s|$)/gm, "$1\u2013").replace(/(^|[^-\s])--(?=[^-\s]|$)/gm, "$1\u2013"); - } - } - if (token.type === "link_open" && token.info === "auto") { - inside_autolink--; - } - if (token.type === "link_close" && token.info === "auto") { - inside_autolink++; - } - } - } - var replacements = function replace(state) { - var blkIdx; - if (!state.md.options.typographer) { - return; - } - for (blkIdx = state.tokens.length - 1; blkIdx >= 0; blkIdx--) { - if (state.tokens[blkIdx].type !== "inline") { - continue; - } - if (SCOPED_ABBR_TEST_RE.test(state.tokens[blkIdx].content)) { - replace_scoped(state.tokens[blkIdx].children); - } - if (RARE_RE.test(state.tokens[blkIdx].content)) { - replace_rare(state.tokens[blkIdx].children); - } - } - }; - var isWhiteSpace$1 = utils.isWhiteSpace; - var isPunctChar$1 = utils.isPunctChar; - var isMdAsciiPunct$1 = utils.isMdAsciiPunct; - var QUOTE_TEST_RE = /['"]/; - var QUOTE_RE = /['"]/g; - var APOSTROPHE = "\u2019"; - /* ’ */ function replaceAt(str, index, ch) { - return str.slice(0, index) + ch + str.slice(index + 1); - } - function process_inlines(tokens, state) { - var i, token, text, t, pos, max, thisLevel, item, lastChar, nextChar, isLastPunctChar, isNextPunctChar, isLastWhiteSpace, isNextWhiteSpace, canOpen, canClose, j, isSingle, stack, openQuote, closeQuote; - stack = []; - for (i = 0; i < tokens.length; i++) { - token = tokens[i]; - thisLevel = tokens[i].level; - for (j = stack.length - 1; j >= 0; j--) { - if (stack[j].level <= thisLevel) { - break; - } - } - stack.length = j + 1; - if (token.type !== "text") { - continue; - } - text = token.content; - pos = 0; - max = text.length; - /*eslint no-labels:0,block-scoped-var:0*/ OUTER: while (pos < max) { - QUOTE_RE.lastIndex = pos; - t = QUOTE_RE.exec(text); - if (!t) { - break; - } - canOpen = canClose = true; - pos = t.index + 1; - isSingle = t[0] === "'"; - // Find previous character, - // default to space if it's the beginning of the line - - lastChar = 32; - if (t.index - 1 >= 0) { - lastChar = text.charCodeAt(t.index - 1); - } else { - for (j = i - 1; j >= 0; j--) { - if (tokens[j].type === "softbreak" || tokens[j].type === "hardbreak") break; - // lastChar defaults to 0x20 - if (!tokens[j].content) continue; - // should skip all tokens except 'text', 'html_inline' or 'code_inline' - lastChar = tokens[j].content.charCodeAt(tokens[j].content.length - 1); - break; - } - } - // Find next character, - // default to space if it's the end of the line - - nextChar = 32; - if (pos < max) { - nextChar = text.charCodeAt(pos); - } else { - for (j = i + 1; j < tokens.length; j++) { - if (tokens[j].type === "softbreak" || tokens[j].type === "hardbreak") break; - // nextChar defaults to 0x20 - if (!tokens[j].content) continue; - // should skip all tokens except 'text', 'html_inline' or 'code_inline' - nextChar = tokens[j].content.charCodeAt(0); - break; - } - } - isLastPunctChar = isMdAsciiPunct$1(lastChar) || isPunctChar$1(String.fromCharCode(lastChar)); - isNextPunctChar = isMdAsciiPunct$1(nextChar) || isPunctChar$1(String.fromCharCode(nextChar)); - isLastWhiteSpace = isWhiteSpace$1(lastChar); - isNextWhiteSpace = isWhiteSpace$1(nextChar); - if (isNextWhiteSpace) { - canOpen = false; - } else if (isNextPunctChar) { - if (!(isLastWhiteSpace || isLastPunctChar)) { - canOpen = false; - } - } - if (isLastWhiteSpace) { - canClose = false; - } else if (isLastPunctChar) { - if (!(isNextWhiteSpace || isNextPunctChar)) { - canClose = false; - } - } - if (nextChar === 34 /* " */ && t[0] === '"') { - if (lastChar >= 48 /* 0 */ && lastChar <= 57 /* 9 */) { - // special case: 1"" - count first quote as an inch - canClose = canOpen = false; - } - } - if (canOpen && canClose) { - // Replace quotes in the middle of punctuation sequence, but not - // in the middle of the words, i.e.: - // 1. foo " bar " baz - not replaced - // 2. foo-"-bar-"-baz - replaced - // 3. foo"bar"baz - not replaced - canOpen = isLastPunctChar; - canClose = isNextPunctChar; - } - if (!canOpen && !canClose) { - // middle of word - if (isSingle) { - token.content = replaceAt(token.content, t.index, APOSTROPHE); - } - continue; - } - if (canClose) { - // this could be a closing quote, rewind the stack to get a match - for (j = stack.length - 1; j >= 0; j--) { - item = stack[j]; - if (stack[j].level < thisLevel) { - break; - } - if (item.single === isSingle && stack[j].level === thisLevel) { - item = stack[j]; - if (isSingle) { - openQuote = state.md.options.quotes[2]; - closeQuote = state.md.options.quotes[3]; - } else { - openQuote = state.md.options.quotes[0]; - closeQuote = state.md.options.quotes[1]; - } - // replace token.content *before* tokens[item.token].content, - // because, if they are pointing at the same token, replaceAt - // could mess up indices when quote length != 1 - token.content = replaceAt(token.content, t.index, closeQuote); - tokens[item.token].content = replaceAt(tokens[item.token].content, item.pos, openQuote); - pos += closeQuote.length - 1; - if (item.token === i) { - pos += openQuote.length - 1; - } - text = token.content; - max = text.length; - stack.length = j; - continue OUTER; - } - } - } - if (canOpen) { - stack.push({ - token: i, - pos: t.index, - single: isSingle, - level: thisLevel - }); - } else if (canClose && isSingle) { - token.content = replaceAt(token.content, t.index, APOSTROPHE); - } - } - } - } - var smartquotes = function smartquotes(state) { - /*eslint max-depth:0*/ - var blkIdx; - if (!state.md.options.typographer) { - return; - } - for (blkIdx = state.tokens.length - 1; blkIdx >= 0; blkIdx--) { - if (state.tokens[blkIdx].type !== "inline" || !QUOTE_TEST_RE.test(state.tokens[blkIdx].content)) { - continue; - } - process_inlines(state.tokens[blkIdx].children, state); - } - }; - // Join raw text tokens with the rest of the text - var text_join = function text_join(state) { - var j, l, tokens, curr, max, last, blockTokens = state.tokens; - for (j = 0, l = blockTokens.length; j < l; j++) { - if (blockTokens[j].type !== "inline") continue; - tokens = blockTokens[j].children; - max = tokens.length; - for (curr = 0; curr < max; curr++) { - if (tokens[curr].type === "text_special") { - tokens[curr].type = "text"; - } - } - for (curr = last = 0; curr < max; curr++) { - if (tokens[curr].type === "text" && curr + 1 < max && tokens[curr + 1].type === "text") { - // collapse two adjacent text nodes - tokens[curr + 1].content = tokens[curr].content + tokens[curr + 1].content; - } else { - if (curr !== last) { - tokens[last] = tokens[curr]; - } - last++; - } - } - if (curr !== last) { - tokens.length = last; - } - } - }; - // Token class - /** - * class Token - **/ - /** - * new Token(type, tag, nesting) - * - * Create new token and fill passed properties. - **/ function Token(type, tag, nesting) { - /** - * Token#type -> String - * - * Type of the token (string, e.g. "paragraph_open") - **/ - this.type = type; - /** - * Token#tag -> String - * - * html tag name, e.g. "p" - **/ this.tag = tag; - /** - * Token#attrs -> Array - * - * Html attributes. Format: `[ [ name1, value1 ], [ name2, value2 ] ]` - **/ this.attrs = null; - /** - * Token#map -> Array - * - * Source map info. Format: `[ line_begin, line_end ]` - **/ this.map = null; - /** - * Token#nesting -> Number - * - * Level change (number in {-1, 0, 1} set), where: - * - * - `1` means the tag is opening - * - `0` means the tag is self-closing - * - `-1` means the tag is closing - **/ this.nesting = nesting; - /** - * Token#level -> Number - * - * nesting level, the same as `state.level` - **/ this.level = 0; - /** - * Token#children -> Array - * - * An array of child nodes (inline and img tokens) - **/ this.children = null; - /** - * Token#content -> String - * - * In a case of self-closing tag (code, html, fence, etc.), - * it has contents of this tag. - **/ this.content = ""; - /** - * Token#markup -> String - * - * '*' or '_' for emphasis, fence string for fence, etc. - **/ this.markup = ""; - /** - * Token#info -> String - * - * Additional information: - * - * - Info string for "fence" tokens - * - The value "auto" for autolink "link_open" and "link_close" tokens - * - The string value of the item marker for ordered-list "list_item_open" tokens - **/ this.info = ""; - /** - * Token#meta -> Object - * - * A place for plugins to store an arbitrary data - **/ this.meta = null; - /** - * Token#block -> Boolean - * - * True for block-level tokens, false for inline tokens. - * Used in renderer to calculate line breaks - **/ this.block = false; - /** - * Token#hidden -> Boolean - * - * If it's true, ignore this element when rendering. Used for tight lists - * to hide paragraphs. - **/ this.hidden = false; - } - /** - * Token.attrIndex(name) -> Number - * - * Search attribute index by name. - **/ Token.prototype.attrIndex = function attrIndex(name) { - var attrs, i, len; - if (!this.attrs) { - return -1; - } - attrs = this.attrs; - for (i = 0, len = attrs.length; i < len; i++) { - if (attrs[i][0] === name) { - return i; - } - } - return -1; - }; - /** - * Token.attrPush(attrData) - * - * Add `[ name, value ]` attribute to list. Init attrs if necessary - **/ Token.prototype.attrPush = function attrPush(attrData) { - if (this.attrs) { - this.attrs.push(attrData); - } else { - this.attrs = [ attrData ]; - } - }; - /** - * Token.attrSet(name, value) - * - * Set `name` attribute to `value`. Override old value if exists. - **/ Token.prototype.attrSet = function attrSet(name, value) { - var idx = this.attrIndex(name), attrData = [ name, value ]; - if (idx < 0) { - this.attrPush(attrData); - } else { - this.attrs[idx] = attrData; - } - }; - /** - * Token.attrGet(name) - * - * Get the value of attribute `name`, or null if it does not exist. - **/ Token.prototype.attrGet = function attrGet(name) { - var idx = this.attrIndex(name), value = null; - if (idx >= 0) { - value = this.attrs[idx][1]; - } - return value; - }; - /** - * Token.attrJoin(name, value) - * - * Join value to existing attribute via space. Or create new attribute if not - * exists. Useful to operate with token classes. - **/ Token.prototype.attrJoin = function attrJoin(name, value) { - var idx = this.attrIndex(name); - if (idx < 0) { - this.attrPush([ name, value ]); - } else { - this.attrs[idx][1] = this.attrs[idx][1] + " " + value; - } - }; - var token = Token; - function StateCore(src, md, env) { - this.src = src; - this.env = env; - this.tokens = []; - this.inlineMode = false; - this.md = md; - // link to parser instance - } - // re-export Token class to use in core rules - StateCore.prototype.Token = token; - var state_core = StateCore; - var _rules$2 = [ [ "normalize", normalize ], [ "block", block ], [ "inline", inline ], [ "linkify", linkify$1 ], [ "replacements", replacements ], [ "smartquotes", smartquotes ], - // `text_join` finds `text_special` tokens (for escape sequences) - // and joins them with the rest of the text - [ "text_join", text_join ] ]; - /** - * new Core() - **/ function Core() { - /** - * Core#ruler -> Ruler - * - * [[Ruler]] instance. Keep configuration of core rules. - **/ - this.ruler = new ruler; - for (var i = 0; i < _rules$2.length; i++) { - this.ruler.push(_rules$2[i][0], _rules$2[i][1]); - } - } - /** - * Core.process(state) - * - * Executes core chain rules. - **/ Core.prototype.process = function(state) { - var i, l, rules; - rules = this.ruler.getRules(""); - for (i = 0, l = rules.length; i < l; i++) { - rules[i](state); - } - }; - Core.prototype.State = state_core; - var parser_core = Core; - var isSpace$a = utils.isSpace; - function getLine(state, line) { - var pos = state.bMarks[line] + state.tShift[line], max = state.eMarks[line]; - return state.src.slice(pos, max); - } - function escapedSplit(str) { - var result = [], pos = 0, max = str.length, ch, isEscaped = false, lastPos = 0, current = ""; - ch = str.charCodeAt(pos); - while (pos < max) { - if (ch === 124 /* | */) { - if (!isEscaped) { - // pipe separating cells, '|' - result.push(current + str.substring(lastPos, pos)); - current = ""; - lastPos = pos + 1; - } else { - // escaped pipe, '\|' - current += str.substring(lastPos, pos - 1); - lastPos = pos; - } - } - isEscaped = ch === 92 /* \ */; - pos++; - ch = str.charCodeAt(pos); - } - result.push(current + str.substring(lastPos)); - return result; - } - var table = function table(state, startLine, endLine, silent) { - var ch, lineText, pos, i, l, nextLine, columns, columnCount, token, aligns, t, tableLines, tbodyLines, oldParentType, terminate, terminatorRules, firstCh, secondCh; - // should have at least two lines - if (startLine + 2 > endLine) { - return false; - } - nextLine = startLine + 1; - if (state.sCount[nextLine] < state.blkIndent) { - return false; - } - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[nextLine] - state.blkIndent >= 4) { - return false; - } - // first character of the second line should be '|', '-', ':', - // and no other characters are allowed but spaces; - // basically, this is the equivalent of /^[-:|][-:|\s]*$/ regexp - pos = state.bMarks[nextLine] + state.tShift[nextLine]; - if (pos >= state.eMarks[nextLine]) { - return false; - } - firstCh = state.src.charCodeAt(pos++); - if (firstCh !== 124 /* | */ && firstCh !== 45 /* - */ && firstCh !== 58 /* : */) { - return false; - } - if (pos >= state.eMarks[nextLine]) { - return false; - } - secondCh = state.src.charCodeAt(pos++); - if (secondCh !== 124 /* | */ && secondCh !== 45 /* - */ && secondCh !== 58 /* : */ && !isSpace$a(secondCh)) { - return false; - } - // if first character is '-', then second character must not be a space - // (due to parsing ambiguity with list) - if (firstCh === 45 /* - */ && isSpace$a(secondCh)) { - return false; - } - while (pos < state.eMarks[nextLine]) { - ch = state.src.charCodeAt(pos); - if (ch !== 124 /* | */ && ch !== 45 /* - */ && ch !== 58 /* : */ && !isSpace$a(ch)) { - return false; - } - pos++; - } - lineText = getLine(state, startLine + 1); - columns = lineText.split("|"); - aligns = []; - for (i = 0; i < columns.length; i++) { - t = columns[i].trim(); - if (!t) { - // allow empty columns before and after table, but not in between columns; - // e.g. allow ` |---| `, disallow ` ---||--- ` - if (i === 0 || i === columns.length - 1) { - continue; - } else { - return false; - } - } - if (!/^:?-+:?$/.test(t)) { - return false; - } - if (t.charCodeAt(t.length - 1) === 58 /* : */) { - aligns.push(t.charCodeAt(0) === 58 /* : */ ? "center" : "right"); - } else if (t.charCodeAt(0) === 58 /* : */) { - aligns.push("left"); - } else { - aligns.push(""); - } - } - lineText = getLine(state, startLine).trim(); - if (lineText.indexOf("|") === -1) { - return false; - } - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - columns = escapedSplit(lineText); - if (columns.length && columns[0] === "") columns.shift(); - if (columns.length && columns[columns.length - 1] === "") columns.pop(); - // header row will define an amount of columns in the entire table, - // and align row should be exactly the same (the rest of the rows can differ) - columnCount = columns.length; - if (columnCount === 0 || columnCount !== aligns.length) { - return false; - } - if (silent) { - return true; - } - oldParentType = state.parentType; - state.parentType = "table"; - // use 'blockquote' lists for termination because it's - // the most similar to tables - terminatorRules = state.md.block.ruler.getRules("blockquote"); - token = state.push("table_open", "table", 1); - token.map = tableLines = [ startLine, 0 ]; - token = state.push("thead_open", "thead", 1); - token.map = [ startLine, startLine + 1 ]; - token = state.push("tr_open", "tr", 1); - token.map = [ startLine, startLine + 1 ]; - for (i = 0; i < columns.length; i++) { - token = state.push("th_open", "th", 1); - if (aligns[i]) { - token.attrs = [ [ "style", "text-align:" + aligns[i] ] ]; - } - token = state.push("inline", "", 0); - token.content = columns[i].trim(); - token.children = []; - token = state.push("th_close", "th", -1); - } - token = state.push("tr_close", "tr", -1); - token = state.push("thead_close", "thead", -1); - for (nextLine = startLine + 2; nextLine < endLine; nextLine++) { - if (state.sCount[nextLine] < state.blkIndent) { - break; - } - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - break; - } - lineText = getLine(state, nextLine).trim(); - if (!lineText) { - break; - } - if (state.sCount[nextLine] - state.blkIndent >= 4) { - break; - } - columns = escapedSplit(lineText); - if (columns.length && columns[0] === "") columns.shift(); - if (columns.length && columns[columns.length - 1] === "") columns.pop(); - if (nextLine === startLine + 2) { - token = state.push("tbody_open", "tbody", 1); - token.map = tbodyLines = [ startLine + 2, 0 ]; - } - token = state.push("tr_open", "tr", 1); - token.map = [ nextLine, nextLine + 1 ]; - for (i = 0; i < columnCount; i++) { - token = state.push("td_open", "td", 1); - if (aligns[i]) { - token.attrs = [ [ "style", "text-align:" + aligns[i] ] ]; - } - token = state.push("inline", "", 0); - token.content = columns[i] ? columns[i].trim() : ""; - token.children = []; - token = state.push("td_close", "td", -1); - } - token = state.push("tr_close", "tr", -1); - } - if (tbodyLines) { - token = state.push("tbody_close", "tbody", -1); - tbodyLines[1] = nextLine; - } - token = state.push("table_close", "table", -1); - tableLines[1] = nextLine; - state.parentType = oldParentType; - state.line = nextLine; - return true; - }; - // Code block (4 spaces padded) - var code = function code(state, startLine, endLine /*, silent*/) { - var nextLine, last, token; - if (state.sCount[startLine] - state.blkIndent < 4) { - return false; - } - last = nextLine = startLine + 1; - while (nextLine < endLine) { - if (state.isEmpty(nextLine)) { - nextLine++; - continue; - } - if (state.sCount[nextLine] - state.blkIndent >= 4) { - nextLine++; - last = nextLine; - continue; - } - break; - } - state.line = last; - token = state.push("code_block", "code", 0); - token.content = state.getLines(startLine, last, 4 + state.blkIndent, false) + "\n"; - token.map = [ startLine, state.line ]; - return true; - }; - // fences (``` lang, ~~~ lang) - var fence = function fence(state, startLine, endLine, silent) { - var marker, len, params, nextLine, mem, token, markup, haveEndMarker = false, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - if (pos + 3 > max) { - return false; - } - marker = state.src.charCodeAt(pos); - if (marker !== 126 /* ~ */ && marker !== 96 /* ` */) { - return false; - } - // scan marker length - mem = pos; - pos = state.skipChars(pos, marker); - len = pos - mem; - if (len < 3) { - return false; - } - markup = state.src.slice(mem, pos); - params = state.src.slice(pos, max); - if (marker === 96 /* ` */) { - if (params.indexOf(String.fromCharCode(marker)) >= 0) { - return false; - } - } - // Since start is found, we can report success here in validation mode - if (silent) { - return true; - } - // search end of block - nextLine = startLine; - for (;;) { - nextLine++; - if (nextLine >= endLine) { - // unclosed block should be autoclosed by end of document. - // also block seems to be autoclosed by end of parent - break; - } - pos = mem = state.bMarks[nextLine] + state.tShift[nextLine]; - max = state.eMarks[nextLine]; - if (pos < max && state.sCount[nextLine] < state.blkIndent) { - // non-empty line with negative indent should stop the list: - // - ``` - // test - break; - } - if (state.src.charCodeAt(pos) !== marker) { - continue; - } - if (state.sCount[nextLine] - state.blkIndent >= 4) { - // closing fence should be indented less than 4 spaces - continue; - } - pos = state.skipChars(pos, marker); - // closing code fence must be at least as long as the opening one - if (pos - mem < len) { - continue; - } - // make sure tail has spaces only - pos = state.skipSpaces(pos); - if (pos < max) { - continue; - } - haveEndMarker = true; - // found! - break; - } - // If a fence has heading spaces, they should be removed from its inner block - len = state.sCount[startLine]; - state.line = nextLine + (haveEndMarker ? 1 : 0); - token = state.push("fence", "code", 0); - token.info = params; - token.content = state.getLines(startLine + 1, nextLine, len, true); - token.markup = markup; - token.map = [ startLine, state.line ]; - return true; - }; - var isSpace$9 = utils.isSpace; - var blockquote = function blockquote(state, startLine, endLine, silent) { - var adjustTab, ch, i, initial, l, lastLineEmpty, lines, nextLine, offset, oldBMarks, oldBSCount, oldIndent, oldParentType, oldSCount, oldTShift, spaceAfterMarker, terminate, terminatorRules, token, isOutdented, oldLineMax = state.lineMax, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - // check the block quote marker - if (state.src.charCodeAt(pos) !== 62 /* > */) { - return false; - } - // we know that it's going to be a valid blockquote, - // so no point trying to find the end of it in silent mode - if (silent) { - return true; - } - oldBMarks = []; - oldBSCount = []; - oldSCount = []; - oldTShift = []; - terminatorRules = state.md.block.ruler.getRules("blockquote"); - oldParentType = state.parentType; - state.parentType = "blockquote"; - // Search the end of the block - - // Block ends with either: - // 1. an empty line outside: - // ``` - // > test - - // ``` - // 2. an empty line inside: - // ``` - // > - // test - // ``` - // 3. another tag: - // ``` - // > test - // - - - - // ``` - for (nextLine = startLine; nextLine < endLine; nextLine++) { - // check if it's outdented, i.e. it's inside list item and indented - // less than said list item: - // ``` - // 1. anything - // > current blockquote - // 2. checking this line - // ``` - isOutdented = state.sCount[nextLine] < state.blkIndent; - pos = state.bMarks[nextLine] + state.tShift[nextLine]; - max = state.eMarks[nextLine]; - if (pos >= max) { - // Case 1: line is not inside the blockquote, and this line is empty. - break; - } - if (state.src.charCodeAt(pos++) === 62 /* > */ && !isOutdented) { - // This line is inside the blockquote. - // set offset past spaces and ">" - initial = state.sCount[nextLine] + 1; - // skip one optional space after '>' - if (state.src.charCodeAt(pos) === 32 /* space */) { - // ' > test ' - // ^ -- position start of line here: - pos++; - initial++; - adjustTab = false; - spaceAfterMarker = true; - } else if (state.src.charCodeAt(pos) === 9 /* tab */) { - spaceAfterMarker = true; - if ((state.bsCount[nextLine] + initial) % 4 === 3) { - // ' >\t test ' - // ^ -- position start of line here (tab has width===1) - pos++; - initial++; - adjustTab = false; - } else { - // ' >\t test ' - // ^ -- position start of line here + shift bsCount slightly - // to make extra space appear - adjustTab = true; - } - } else { - spaceAfterMarker = false; - } - offset = initial; - oldBMarks.push(state.bMarks[nextLine]); - state.bMarks[nextLine] = pos; - while (pos < max) { - ch = state.src.charCodeAt(pos); - if (isSpace$9(ch)) { - if (ch === 9) { - offset += 4 - (offset + state.bsCount[nextLine] + (adjustTab ? 1 : 0)) % 4; - } else { - offset++; - } - } else { - break; - } - pos++; - } - lastLineEmpty = pos >= max; - oldBSCount.push(state.bsCount[nextLine]); - state.bsCount[nextLine] = state.sCount[nextLine] + 1 + (spaceAfterMarker ? 1 : 0); - oldSCount.push(state.sCount[nextLine]); - state.sCount[nextLine] = offset - initial; - oldTShift.push(state.tShift[nextLine]); - state.tShift[nextLine] = pos - state.bMarks[nextLine]; - continue; - } - // Case 2: line is not inside the blockquote, and the last line was empty. - if (lastLineEmpty) { - break; - } - // Case 3: another tag found. - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - // Quirk to enforce "hard termination mode" for paragraphs; - // normally if you call `tokenize(state, startLine, nextLine)`, - // paragraphs will look below nextLine for paragraph continuation, - // but if blockquote is terminated by another tag, they shouldn't - state.lineMax = nextLine; - if (state.blkIndent !== 0) { - // state.blkIndent was non-zero, we now set it to zero, - // so we need to re-calculate all offsets to appear as - // if indent wasn't changed - oldBMarks.push(state.bMarks[nextLine]); - oldBSCount.push(state.bsCount[nextLine]); - oldTShift.push(state.tShift[nextLine]); - oldSCount.push(state.sCount[nextLine]); - state.sCount[nextLine] -= state.blkIndent; - } - break; - } - oldBMarks.push(state.bMarks[nextLine]); - oldBSCount.push(state.bsCount[nextLine]); - oldTShift.push(state.tShift[nextLine]); - oldSCount.push(state.sCount[nextLine]); - // A negative indentation means that this is a paragraph continuation - - state.sCount[nextLine] = -1; - } - oldIndent = state.blkIndent; - state.blkIndent = 0; - token = state.push("blockquote_open", "blockquote", 1); - token.markup = ">"; - token.map = lines = [ startLine, 0 ]; - state.md.block.tokenize(state, startLine, nextLine); - token = state.push("blockquote_close", "blockquote", -1); - token.markup = ">"; - state.lineMax = oldLineMax; - state.parentType = oldParentType; - lines[1] = state.line; - // Restore original tShift; this might not be necessary since the parser - // has already been here, but just to make sure we can do that. - for (i = 0; i < oldTShift.length; i++) { - state.bMarks[i + startLine] = oldBMarks[i]; - state.tShift[i + startLine] = oldTShift[i]; - state.sCount[i + startLine] = oldSCount[i]; - state.bsCount[i + startLine] = oldBSCount[i]; - } - state.blkIndent = oldIndent; - return true; - }; - var isSpace$8 = utils.isSpace; - var hr = function hr(state, startLine, endLine, silent) { - var marker, cnt, ch, token, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - marker = state.src.charCodeAt(pos++); - // Check hr marker - if (marker !== 42 /* * */ && marker !== 45 /* - */ && marker !== 95 /* _ */) { - return false; - } - // markers can be mixed with spaces, but there should be at least 3 of them - cnt = 1; - while (pos < max) { - ch = state.src.charCodeAt(pos++); - if (ch !== marker && !isSpace$8(ch)) { - return false; - } - if (ch === marker) { - cnt++; - } - } - if (cnt < 3) { - return false; - } - if (silent) { - return true; - } - state.line = startLine + 1; - token = state.push("hr", "hr", 0); - token.map = [ startLine, state.line ]; - token.markup = Array(cnt + 1).join(String.fromCharCode(marker)); - return true; - }; - var isSpace$7 = utils.isSpace; - // Search `[-+*][\n ]`, returns next pos after marker on success - // or -1 on fail. - function skipBulletListMarker(state, startLine) { - var marker, pos, max, ch; - pos = state.bMarks[startLine] + state.tShift[startLine]; - max = state.eMarks[startLine]; - marker = state.src.charCodeAt(pos++); - // Check bullet - if (marker !== 42 /* * */ && marker !== 45 /* - */ && marker !== 43 /* + */) { - return -1; - } - if (pos < max) { - ch = state.src.charCodeAt(pos); - if (!isSpace$7(ch)) { - // " -test " - is not a list item - return -1; - } - } - return pos; - } - // Search `\d+[.)][\n ]`, returns next pos after marker on success - // or -1 on fail. - function skipOrderedListMarker(state, startLine) { - var ch, start = state.bMarks[startLine] + state.tShift[startLine], pos = start, max = state.eMarks[startLine]; - // List marker should have at least 2 chars (digit + dot) - if (pos + 1 >= max) { - return -1; - } - ch = state.src.charCodeAt(pos++); - if (ch < 48 /* 0 */ || ch > 57 /* 9 */) { - return -1; - } - for (;;) { - // EOL -> fail - if (pos >= max) { - return -1; - } - ch = state.src.charCodeAt(pos++); - if (ch >= 48 /* 0 */ && ch <= 57 /* 9 */) { - // List marker should have no more than 9 digits - // (prevents integer overflow in browsers) - if (pos - start >= 10) { - return -1; - } - continue; - } - // found valid marker - if (ch === 41 /* ) */ || ch === 46 /* . */) { - break; - } - return -1; - } - if (pos < max) { - ch = state.src.charCodeAt(pos); - if (!isSpace$7(ch)) { - // " 1.test " - is not a list item - return -1; - } - } - return pos; - } - function markTightParagraphs(state, idx) { - var i, l, level = state.level + 2; - for (i = idx + 2, l = state.tokens.length - 2; i < l; i++) { - if (state.tokens[i].level === level && state.tokens[i].type === "paragraph_open") { - state.tokens[i + 2].hidden = true; - state.tokens[i].hidden = true; - i += 2; - } - } - } - var list = function list(state, startLine, endLine, silent) { - var ch, contentStart, i, indent, indentAfterMarker, initial, isOrdered, itemLines, l, listLines, listTokIdx, markerCharCode, markerValue, max, offset, oldListIndent, oldParentType, oldSCount, oldTShift, oldTight, pos, posAfterMarker, prevEmptyEnd, start, terminate, terminatorRules, token, nextLine = startLine, isTerminatingParagraph = false, tight = true; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[nextLine] - state.blkIndent >= 4) { - return false; - } - // Special case: - // - item 1 - // - item 2 - // - item 3 - // - item 4 - // - this one is a paragraph continuation - if (state.listIndent >= 0 && state.sCount[nextLine] - state.listIndent >= 4 && state.sCount[nextLine] < state.blkIndent) { - return false; - } - // limit conditions when list can interrupt - // a paragraph (validation mode only) - if (silent && state.parentType === "paragraph") { - // Next list item should still terminate previous list item; - // This code can fail if plugins use blkIndent as well as lists, - // but I hope the spec gets fixed long before that happens. - if (state.sCount[nextLine] >= state.blkIndent) { - isTerminatingParagraph = true; - } - } - // Detect list type and position after marker - if ((posAfterMarker = skipOrderedListMarker(state, nextLine)) >= 0) { - isOrdered = true; - start = state.bMarks[nextLine] + state.tShift[nextLine]; - markerValue = Number(state.src.slice(start, posAfterMarker - 1)); - // If we're starting a new ordered list right after - // a paragraph, it should start with 1. - if (isTerminatingParagraph && markerValue !== 1) return false; - } else if ((posAfterMarker = skipBulletListMarker(state, nextLine)) >= 0) { - isOrdered = false; - } else { - return false; - } - // If we're starting a new unordered list right after - // a paragraph, first line should not be empty. - if (isTerminatingParagraph) { - if (state.skipSpaces(posAfterMarker) >= state.eMarks[nextLine]) return false; - } - // For validation mode we can terminate immediately - if (silent) { - return true; - } - // We should terminate list on style change. Remember first one to compare. - markerCharCode = state.src.charCodeAt(posAfterMarker - 1); - // Start list - listTokIdx = state.tokens.length; - if (isOrdered) { - token = state.push("ordered_list_open", "ol", 1); - if (markerValue !== 1) { - token.attrs = [ [ "start", markerValue ] ]; - } - } else { - token = state.push("bullet_list_open", "ul", 1); - } - token.map = listLines = [ nextLine, 0 ]; - token.markup = String.fromCharCode(markerCharCode); - - // Iterate list items - - prevEmptyEnd = false; - terminatorRules = state.md.block.ruler.getRules("list"); - oldParentType = state.parentType; - state.parentType = "list"; - while (nextLine < endLine) { - pos = posAfterMarker; - max = state.eMarks[nextLine]; - initial = offset = state.sCount[nextLine] + posAfterMarker - (state.bMarks[nextLine] + state.tShift[nextLine]); - while (pos < max) { - ch = state.src.charCodeAt(pos); - if (ch === 9) { - offset += 4 - (offset + state.bsCount[nextLine]) % 4; - } else if (ch === 32) { - offset++; - } else { - break; - } - pos++; - } - contentStart = pos; - if (contentStart >= max) { - // trimming space in "- \n 3" case, indent is 1 here - indentAfterMarker = 1; - } else { - indentAfterMarker = offset - initial; - } - // If we have more than 4 spaces, the indent is 1 - // (the rest is just indented code block) - if (indentAfterMarker > 4) { - indentAfterMarker = 1; - } - // " - test" - // ^^^^^ - calculating total length of this thing - indent = initial + indentAfterMarker; - // Run subparser & write tokens - token = state.push("list_item_open", "li", 1); - token.markup = String.fromCharCode(markerCharCode); - token.map = itemLines = [ nextLine, 0 ]; - if (isOrdered) { - token.info = state.src.slice(start, posAfterMarker - 1); - } - // change current state, then restore it after parser subcall - oldTight = state.tight; - oldTShift = state.tShift[nextLine]; - oldSCount = state.sCount[nextLine]; - // - example list - // ^ listIndent position will be here - // ^ blkIndent position will be here - - oldListIndent = state.listIndent; - state.listIndent = state.blkIndent; - state.blkIndent = indent; - state.tight = true; - state.tShift[nextLine] = contentStart - state.bMarks[nextLine]; - state.sCount[nextLine] = offset; - if (contentStart >= max && state.isEmpty(nextLine + 1)) { - // workaround for this case - // (list item is empty, list terminates before "foo"): - // ~~~~~~~~ - // - - // foo - // ~~~~~~~~ - state.line = Math.min(state.line + 2, endLine); - } else { - state.md.block.tokenize(state, nextLine, endLine, true); - } - // If any of list item is tight, mark list as tight - if (!state.tight || prevEmptyEnd) { - tight = false; - } - // Item become loose if finish with empty line, - // but we should filter last element, because it means list finish - prevEmptyEnd = state.line - nextLine > 1 && state.isEmpty(state.line - 1); - state.blkIndent = state.listIndent; - state.listIndent = oldListIndent; - state.tShift[nextLine] = oldTShift; - state.sCount[nextLine] = oldSCount; - state.tight = oldTight; - token = state.push("list_item_close", "li", -1); - token.markup = String.fromCharCode(markerCharCode); - nextLine = state.line; - itemLines[1] = nextLine; - if (nextLine >= endLine) { - break; - } - - // Try to check if list is terminated or continued. - - if (state.sCount[nextLine] < state.blkIndent) { - break; - } - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[nextLine] - state.blkIndent >= 4) { - break; - } - // fail if terminating block found - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - break; - } - // fail if list has another type - if (isOrdered) { - posAfterMarker = skipOrderedListMarker(state, nextLine); - if (posAfterMarker < 0) { - break; - } - start = state.bMarks[nextLine] + state.tShift[nextLine]; - } else { - posAfterMarker = skipBulletListMarker(state, nextLine); - if (posAfterMarker < 0) { - break; - } - } - if (markerCharCode !== state.src.charCodeAt(posAfterMarker - 1)) { - break; - } - } - // Finalize list - if (isOrdered) { - token = state.push("ordered_list_close", "ol", -1); - } else { - token = state.push("bullet_list_close", "ul", -1); - } - token.markup = String.fromCharCode(markerCharCode); - listLines[1] = nextLine; - state.line = nextLine; - state.parentType = oldParentType; - // mark paragraphs tight if needed - if (tight) { - markTightParagraphs(state, listTokIdx); - } - return true; - }; - var normalizeReference$2 = utils.normalizeReference; - var isSpace$6 = utils.isSpace; - var reference = function reference(state, startLine, _endLine, silent) { - var ch, destEndPos, destEndLineNo, endLine, href, i, l, label, labelEnd, oldParentType, res, start, str, terminate, terminatorRules, title, lines = 0, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine], nextLine = startLine + 1; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - if (state.src.charCodeAt(pos) !== 91 /* [ */) { - return false; - } - // Simple check to quickly interrupt scan on [link](url) at the start of line. - // Can be useful on practice: https://github.com/markdown-it/markdown-it/issues/54 - while (++pos < max) { - if (state.src.charCodeAt(pos) === 93 /* ] */ && state.src.charCodeAt(pos - 1) !== 92 /* \ */) { - if (pos + 1 === max) { - return false; - } - if (state.src.charCodeAt(pos + 1) !== 58 /* : */) { - return false; - } - break; - } - } - endLine = state.lineMax; - // jump line-by-line until empty one or EOF - terminatorRules = state.md.block.ruler.getRules("reference"); - oldParentType = state.parentType; - state.parentType = "reference"; - for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { - // this would be a code block normally, but after paragraph - // it's considered a lazy continuation regardless of what's there - if (state.sCount[nextLine] - state.blkIndent > 3) { - continue; - } - // quirk for blockquotes, this line should already be checked by that rule - if (state.sCount[nextLine] < 0) { - continue; - } - // Some tags can terminate paragraph without empty line. - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - break; - } - } - str = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); - max = str.length; - for (pos = 1; pos < max; pos++) { - ch = str.charCodeAt(pos); - if (ch === 91 /* [ */) { - return false; - } else if (ch === 93 /* ] */) { - labelEnd = pos; - break; - } else if (ch === 10 /* \n */) { - lines++; - } else if (ch === 92 /* \ */) { - pos++; - if (pos < max && str.charCodeAt(pos) === 10) { - lines++; - } - } - } - if (labelEnd < 0 || str.charCodeAt(labelEnd + 1) !== 58 /* : */) { - return false; - } - // [label]: destination 'title' - // ^^^ skip optional whitespace here - for (pos = labelEnd + 2; pos < max; pos++) { - ch = str.charCodeAt(pos); - if (ch === 10) { - lines++; - } else if (isSpace$6(ch)) ; else { - break; - } - } - // [label]: destination 'title' - // ^^^^^^^^^^^ parse this - res = state.md.helpers.parseLinkDestination(str, pos, max); - if (!res.ok) { - return false; - } - href = state.md.normalizeLink(res.str); - if (!state.md.validateLink(href)) { - return false; - } - pos = res.pos; - lines += res.lines; - // save cursor state, we could require to rollback later - destEndPos = pos; - destEndLineNo = lines; - // [label]: destination 'title' - // ^^^ skipping those spaces - start = pos; - for (;pos < max; pos++) { - ch = str.charCodeAt(pos); - if (ch === 10) { - lines++; - } else if (isSpace$6(ch)) ; else { - break; - } - } - // [label]: destination 'title' - // ^^^^^^^ parse this - res = state.md.helpers.parseLinkTitle(str, pos, max); - if (pos < max && start !== pos && res.ok) { - title = res.str; - pos = res.pos; - lines += res.lines; - } else { - title = ""; - pos = destEndPos; - lines = destEndLineNo; - } - // skip trailing spaces until the rest of the line - while (pos < max) { - ch = str.charCodeAt(pos); - if (!isSpace$6(ch)) { - break; - } - pos++; - } - if (pos < max && str.charCodeAt(pos) !== 10) { - if (title) { - // garbage at the end of the line after title, - // but it could still be a valid reference if we roll back - title = ""; - pos = destEndPos; - lines = destEndLineNo; - while (pos < max) { - ch = str.charCodeAt(pos); - if (!isSpace$6(ch)) { - break; - } - pos++; - } - } - } - if (pos < max && str.charCodeAt(pos) !== 10) { - // garbage at the end of the line - return false; - } - label = normalizeReference$2(str.slice(1, labelEnd)); - if (!label) { - // CommonMark 0.20 disallows empty labels - return false; - } - // Reference can not terminate anything. This check is for safety only. - /*istanbul ignore if*/ if (silent) { - return true; - } - if (typeof state.env.references === "undefined") { - state.env.references = {}; - } - if (typeof state.env.references[label] === "undefined") { - state.env.references[label] = { - title: title, - href: href - }; - } - state.parentType = oldParentType; - state.line = startLine + lines + 1; - return true; - }; - // List of valid html blocks names, accorting to commonmark spec - var html_blocks = [ "address", "article", "aside", "base", "basefont", "blockquote", "body", "caption", "center", "col", "colgroup", "dd", "details", "dialog", "dir", "div", "dl", "dt", "fieldset", "figcaption", "figure", "footer", "form", "frame", "frameset", "h1", "h2", "h3", "h4", "h5", "h6", "head", "header", "hr", "html", "iframe", "legend", "li", "link", "main", "menu", "menuitem", "nav", "noframes", "ol", "optgroup", "option", "p", "param", "section", "source", "summary", "table", "tbody", "td", "tfoot", "th", "thead", "title", "tr", "track", "ul" ]; - // Regexps to match html elements - var attr_name = "[a-zA-Z_:][a-zA-Z0-9:._-]*"; - var unquoted = "[^\"'=<>`\\x00-\\x20]+"; - var single_quoted = "'[^']*'"; - var double_quoted = '"[^"]*"'; - var attr_value = "(?:" + unquoted + "|" + single_quoted + "|" + double_quoted + ")"; - var attribute = "(?:\\s+" + attr_name + "(?:\\s*=\\s*" + attr_value + ")?)"; - var open_tag = "<[A-Za-z][A-Za-z0-9\\-]*" + attribute + "*\\s*\\/?>"; - var close_tag = "<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>"; - var comment = "\x3c!----\x3e|\x3c!--(?:-?[^>-])(?:-?[^-])*--\x3e"; - var processing = "<[?][\\s\\S]*?[?]>"; - var declaration = "]*>"; - var cdata = ""; - var HTML_TAG_RE$1 = new RegExp("^(?:" + open_tag + "|" + close_tag + "|" + comment + "|" + processing + "|" + declaration + "|" + cdata + ")"); - var HTML_OPEN_CLOSE_TAG_RE$1 = new RegExp("^(?:" + open_tag + "|" + close_tag + ")"); - var HTML_TAG_RE_1 = HTML_TAG_RE$1; - var HTML_OPEN_CLOSE_TAG_RE_1 = HTML_OPEN_CLOSE_TAG_RE$1; - var html_re = { - HTML_TAG_RE: HTML_TAG_RE_1, - HTML_OPEN_CLOSE_TAG_RE: HTML_OPEN_CLOSE_TAG_RE_1 - }; - var HTML_OPEN_CLOSE_TAG_RE = html_re.HTML_OPEN_CLOSE_TAG_RE; - // An array of opening and corresponding closing sequences for html tags, - // last argument defines whether it can terminate a paragraph or not - - var HTML_SEQUENCES = [ [ /^<(script|pre|style|textarea)(?=(\s|>|$))/i, /<\/(script|pre|style|textarea)>/i, true ], [ /^/, true ], [ /^<\?/, /\?>/, true ], [ /^/, true ], [ /^/, true ], [ new RegExp("^|$))", "i"), /^$/, true ], [ new RegExp(HTML_OPEN_CLOSE_TAG_RE.source + "\\s*$"), /^$/, false ] ]; - var html_block = function html_block(state, startLine, endLine, silent) { - var i, nextLine, token, lineText, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - if (!state.md.options.html) { - return false; - } - if (state.src.charCodeAt(pos) !== 60 /* < */) { - return false; - } - lineText = state.src.slice(pos, max); - for (i = 0; i < HTML_SEQUENCES.length; i++) { - if (HTML_SEQUENCES[i][0].test(lineText)) { - break; - } - } - if (i === HTML_SEQUENCES.length) { - return false; - } - if (silent) { - // true if this sequence can be a terminator, false otherwise - return HTML_SEQUENCES[i][2]; - } - nextLine = startLine + 1; - // If we are here - we detected HTML block. - // Let's roll down till block end. - if (!HTML_SEQUENCES[i][1].test(lineText)) { - for (;nextLine < endLine; nextLine++) { - if (state.sCount[nextLine] < state.blkIndent) { - break; - } - pos = state.bMarks[nextLine] + state.tShift[nextLine]; - max = state.eMarks[nextLine]; - lineText = state.src.slice(pos, max); - if (HTML_SEQUENCES[i][1].test(lineText)) { - if (lineText.length !== 0) { - nextLine++; - } - break; - } - } - } - state.line = nextLine; - token = state.push("html_block", "", 0); - token.map = [ startLine, nextLine ]; - token.content = state.getLines(startLine, nextLine, state.blkIndent, true); - return true; - }; - var isSpace$5 = utils.isSpace; - var heading = function heading(state, startLine, endLine, silent) { - var ch, level, tmp, token, pos = state.bMarks[startLine] + state.tShift[startLine], max = state.eMarks[startLine]; - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - ch = state.src.charCodeAt(pos); - if (ch !== 35 /* # */ || pos >= max) { - return false; - } - // count heading level - level = 1; - ch = state.src.charCodeAt(++pos); - while (ch === 35 /* # */ && pos < max && level <= 6) { - level++; - ch = state.src.charCodeAt(++pos); - } - if (level > 6 || pos < max && !isSpace$5(ch)) { - return false; - } - if (silent) { - return true; - } - // Let's cut tails like ' ### ' from the end of string - max = state.skipSpacesBack(max, pos); - tmp = state.skipCharsBack(max, 35, pos); - // # - if (tmp > pos && isSpace$5(state.src.charCodeAt(tmp - 1))) { - max = tmp; - } - state.line = startLine + 1; - token = state.push("heading_open", "h" + String(level), 1); - token.markup = "########".slice(0, level); - token.map = [ startLine, state.line ]; - token = state.push("inline", "", 0); - token.content = state.src.slice(pos, max).trim(); - token.map = [ startLine, state.line ]; - token.children = []; - token = state.push("heading_close", "h" + String(level), -1); - token.markup = "########".slice(0, level); - return true; - }; - // lheading (---, ===) - var lheading = function lheading(state, startLine, endLine /*, silent*/) { - var content, terminate, i, l, token, pos, max, level, marker, nextLine = startLine + 1, oldParentType, terminatorRules = state.md.block.ruler.getRules("paragraph"); - // if it's indented more than 3 spaces, it should be a code block - if (state.sCount[startLine] - state.blkIndent >= 4) { - return false; - } - oldParentType = state.parentType; - state.parentType = "paragraph"; - // use paragraph to match terminatorRules - // jump line-by-line until empty one or EOF - for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { - // this would be a code block normally, but after paragraph - // it's considered a lazy continuation regardless of what's there - if (state.sCount[nextLine] - state.blkIndent > 3) { - continue; - } - - // Check for underline in setext header - - if (state.sCount[nextLine] >= state.blkIndent) { - pos = state.bMarks[nextLine] + state.tShift[nextLine]; - max = state.eMarks[nextLine]; - if (pos < max) { - marker = state.src.charCodeAt(pos); - if (marker === 45 /* - */ || marker === 61 /* = */) { - pos = state.skipChars(pos, marker); - pos = state.skipSpaces(pos); - if (pos >= max) { - level = marker === 61 /* = */ ? 1 : 2; - break; - } - } - } - } - // quirk for blockquotes, this line should already be checked by that rule - if (state.sCount[nextLine] < 0) { - continue; - } - // Some tags can terminate paragraph without empty line. - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - break; - } - } - if (!level) { - // Didn't find valid underline - return false; - } - content = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); - state.line = nextLine + 1; - token = state.push("heading_open", "h" + String(level), 1); - token.markup = String.fromCharCode(marker); - token.map = [ startLine, state.line ]; - token = state.push("inline", "", 0); - token.content = content; - token.map = [ startLine, state.line - 1 ]; - token.children = []; - token = state.push("heading_close", "h" + String(level), -1); - token.markup = String.fromCharCode(marker); - state.parentType = oldParentType; - return true; - }; - // Paragraph - var paragraph = function paragraph(state, startLine, endLine) { - var content, terminate, i, l, token, oldParentType, nextLine = startLine + 1, terminatorRules = state.md.block.ruler.getRules("paragraph"); - oldParentType = state.parentType; - state.parentType = "paragraph"; - // jump line-by-line until empty one or EOF - for (;nextLine < endLine && !state.isEmpty(nextLine); nextLine++) { - // this would be a code block normally, but after paragraph - // it's considered a lazy continuation regardless of what's there - if (state.sCount[nextLine] - state.blkIndent > 3) { - continue; - } - // quirk for blockquotes, this line should already be checked by that rule - if (state.sCount[nextLine] < 0) { - continue; - } - // Some tags can terminate paragraph without empty line. - terminate = false; - for (i = 0, l = terminatorRules.length; i < l; i++) { - if (terminatorRules[i](state, nextLine, endLine, true)) { - terminate = true; - break; - } - } - if (terminate) { - break; - } - } - content = state.getLines(startLine, nextLine, state.blkIndent, false).trim(); - state.line = nextLine; - token = state.push("paragraph_open", "p", 1); - token.map = [ startLine, state.line ]; - token = state.push("inline", "", 0); - token.content = content; - token.map = [ startLine, state.line ]; - token.children = []; - token = state.push("paragraph_close", "p", -1); - state.parentType = oldParentType; - return true; - }; - var isSpace$4 = utils.isSpace; - function StateBlock(src, md, env, tokens) { - var ch, s, start, pos, len, indent, offset, indent_found; - this.src = src; - // link to parser instance - this.md = md; - this.env = env; - - // Internal state vartiables - - this.tokens = tokens; - this.bMarks = []; - // line begin offsets for fast jumps - this.eMarks = []; - // line end offsets for fast jumps - this.tShift = []; - // offsets of the first non-space characters (tabs not expanded) - this.sCount = []; - // indents for each line (tabs expanded) - // An amount of virtual spaces (tabs expanded) between beginning - // of each line (bMarks) and real beginning of that line. - - // It exists only as a hack because blockquotes override bMarks - // losing information in the process. - - // It's used only when expanding tabs, you can think about it as - // an initial tab length, e.g. bsCount=21 applied to string `\t123` - // means first tab should be expanded to 4-21%4 === 3 spaces. - - this.bsCount = []; - // block parser variables - this.blkIndent = 0; - // required block content indent (for example, if we are - // inside a list, it would be positioned after list marker) - this.line = 0; - // line index in src - this.lineMax = 0; - // lines count - this.tight = false; - // loose/tight mode for lists - this.ddIndent = -1; - // indent of the current dd block (-1 if there isn't any) - this.listIndent = -1; - // indent of the current list block (-1 if there isn't any) - // can be 'blockquote', 'list', 'root', 'paragraph' or 'reference' - // used in lists to determine if they interrupt a paragraph - this.parentType = "root"; - this.level = 0; - // renderer - this.result = ""; - // Create caches - // Generate markers. - s = this.src; - indent_found = false; - for (start = pos = indent = offset = 0, len = s.length; pos < len; pos++) { - ch = s.charCodeAt(pos); - if (!indent_found) { - if (isSpace$4(ch)) { - indent++; - if (ch === 9) { - offset += 4 - offset % 4; - } else { - offset++; - } - continue; - } else { - indent_found = true; - } - } - if (ch === 10 || pos === len - 1) { - if (ch !== 10) { - pos++; - } - this.bMarks.push(start); - this.eMarks.push(pos); - this.tShift.push(indent); - this.sCount.push(offset); - this.bsCount.push(0); - indent_found = false; - indent = 0; - offset = 0; - start = pos + 1; - } - } - // Push fake entry to simplify cache bounds checks - this.bMarks.push(s.length); - this.eMarks.push(s.length); - this.tShift.push(0); - this.sCount.push(0); - this.bsCount.push(0); - this.lineMax = this.bMarks.length - 1; - // don't count last fake line - } - // Push new token to "stream". - - StateBlock.prototype.push = function(type, tag, nesting) { - var token$1 = new token(type, tag, nesting); - token$1.block = true; - if (nesting < 0) this.level--; - // closing tag - token$1.level = this.level; - if (nesting > 0) this.level++; - // opening tag - this.tokens.push(token$1); - return token$1; - }; - StateBlock.prototype.isEmpty = function isEmpty(line) { - return this.bMarks[line] + this.tShift[line] >= this.eMarks[line]; - }; - StateBlock.prototype.skipEmptyLines = function skipEmptyLines(from) { - for (var max = this.lineMax; from < max; from++) { - if (this.bMarks[from] + this.tShift[from] < this.eMarks[from]) { - break; - } - } - return from; - }; - // Skip spaces from given position. - StateBlock.prototype.skipSpaces = function skipSpaces(pos) { - var ch; - for (var max = this.src.length; pos < max; pos++) { - ch = this.src.charCodeAt(pos); - if (!isSpace$4(ch)) { - break; - } - } - return pos; - }; - // Skip spaces from given position in reverse. - StateBlock.prototype.skipSpacesBack = function skipSpacesBack(pos, min) { - if (pos <= min) { - return pos; - } - while (pos > min) { - if (!isSpace$4(this.src.charCodeAt(--pos))) { - return pos + 1; - } - } - return pos; - }; - // Skip char codes from given position - StateBlock.prototype.skipChars = function skipChars(pos, code) { - for (var max = this.src.length; pos < max; pos++) { - if (this.src.charCodeAt(pos) !== code) { - break; - } - } - return pos; - }; - // Skip char codes reverse from given position - 1 - StateBlock.prototype.skipCharsBack = function skipCharsBack(pos, code, min) { - if (pos <= min) { - return pos; - } - while (pos > min) { - if (code !== this.src.charCodeAt(--pos)) { - return pos + 1; - } - } - return pos; - }; - // cut lines range from source. - StateBlock.prototype.getLines = function getLines(begin, end, indent, keepLastLF) { - var i, lineIndent, ch, first, last, queue, lineStart, line = begin; - if (begin >= end) { - return ""; - } - queue = new Array(end - begin); - for (i = 0; line < end; line++, i++) { - lineIndent = 0; - lineStart = first = this.bMarks[line]; - if (line + 1 < end || keepLastLF) { - // No need for bounds check because we have fake entry on tail. - last = this.eMarks[line] + 1; - } else { - last = this.eMarks[line]; - } - while (first < last && lineIndent < indent) { - ch = this.src.charCodeAt(first); - if (isSpace$4(ch)) { - if (ch === 9) { - lineIndent += 4 - (lineIndent + this.bsCount[line]) % 4; - } else { - lineIndent++; - } - } else if (first - lineStart < this.tShift[line]) { - // patched tShift masked characters to look like spaces (blockquotes, list markers) - lineIndent++; - } else { - break; - } - first++; - } - if (lineIndent > indent) { - // partially expanding tabs in code blocks, e.g '\t\tfoobar' - // with indent=2 becomes ' \tfoobar' - queue[i] = new Array(lineIndent - indent + 1).join(" ") + this.src.slice(first, last); - } else { - queue[i] = this.src.slice(first, last); - } - } - return queue.join(""); - }; - // re-export Token class to use in block rules - StateBlock.prototype.Token = token; - var state_block = StateBlock; - var _rules$1 = [ - // First 2 params - rule name & source. Secondary array - list of rules, - // which can be terminated by this one. - [ "table", table, [ "paragraph", "reference" ] ], [ "code", code ], [ "fence", fence, [ "paragraph", "reference", "blockquote", "list" ] ], [ "blockquote", blockquote, [ "paragraph", "reference", "blockquote", "list" ] ], [ "hr", hr, [ "paragraph", "reference", "blockquote", "list" ] ], [ "list", list, [ "paragraph", "reference", "blockquote" ] ], [ "reference", reference ], [ "html_block", html_block, [ "paragraph", "reference", "blockquote" ] ], [ "heading", heading, [ "paragraph", "reference", "blockquote" ] ], [ "lheading", lheading ], [ "paragraph", paragraph ] ]; - /** - * new ParserBlock() - **/ function ParserBlock() { - /** - * ParserBlock#ruler -> Ruler - * - * [[Ruler]] instance. Keep configuration of block rules. - **/ - this.ruler = new ruler; - for (var i = 0; i < _rules$1.length; i++) { - this.ruler.push(_rules$1[i][0], _rules$1[i][1], { - alt: (_rules$1[i][2] || []).slice() - }); - } - } - // Generate tokens for input range - - ParserBlock.prototype.tokenize = function(state, startLine, endLine) { - var ok, i, prevLine, rules = this.ruler.getRules(""), len = rules.length, line = startLine, hasEmptyLines = false, maxNesting = state.md.options.maxNesting; - while (line < endLine) { - state.line = line = state.skipEmptyLines(line); - if (line >= endLine) { - break; - } - // Termination condition for nested calls. - // Nested calls currently used for blockquotes & lists - if (state.sCount[line] < state.blkIndent) { - break; - } - // If nesting level exceeded - skip tail to the end. That's not ordinary - // situation and we should not care about content. - if (state.level >= maxNesting) { - state.line = endLine; - break; - } - // Try all possible rules. - // On success, rule should: - - // - update `state.line` - // - update `state.tokens` - // - return true - prevLine = state.line; - for (i = 0; i < len; i++) { - ok = rules[i](state, line, endLine, false); - if (ok) { - if (prevLine >= state.line) { - throw new Error("block rule didn't increment state.line"); - } - break; - } - } - // this can only happen if user disables paragraph rule - if (!ok) throw new Error("none of the block rules matched"); - // set state.tight if we had an empty line before current tag - // i.e. latest empty line should not count - state.tight = !hasEmptyLines; - // paragraph might "eat" one newline after it in nested lists - if (state.isEmpty(state.line - 1)) { - hasEmptyLines = true; - } - line = state.line; - if (line < endLine && state.isEmpty(line)) { - hasEmptyLines = true; - line++; - state.line = line; - } - } - }; - /** - * ParserBlock.parse(str, md, env, outTokens) - * - * Process input string and push block tokens into `outTokens` - **/ ParserBlock.prototype.parse = function(src, md, env, outTokens) { - var state; - if (!src) { - return; - } - state = new this.State(src, md, env, outTokens); - this.tokenize(state, state.line, state.lineMax); - }; - ParserBlock.prototype.State = state_block; - var parser_block = ParserBlock; - // Skip text characters for text token, place those to pending buffer - // Rule to skip pure text - // '{}$%@~+=:' reserved for extentions - // !, ", #, $, %, &, ', (, ), *, +, ,, -, ., /, :, ;, <, =, >, ?, @, [, \, ], ^, _, `, {, |, }, or ~ - // !!!! Don't confuse with "Markdown ASCII Punctuation" chars - // http://spec.commonmark.org/0.15/#ascii-punctuation-character - function isTerminatorChar(ch) { - switch (ch) { - case 10 /* \n */ : - case 33 /* ! */ : - case 35 /* # */ : - case 36 /* $ */ : - case 37 /* % */ : - case 38 /* & */ : - case 42 /* * */ : - case 43 /* + */ : - case 45 /* - */ : - case 58 /* : */ : - case 60 /* < */ : - case 61 /* = */ : - case 62 /* > */ : - case 64 /* @ */ : - case 91 /* [ */ : - case 92 /* \ */ : - case 93 /* ] */ : - case 94 /* ^ */ : - case 95 /* _ */ : - case 96 /* ` */ : - case 123 /* { */ : - case 125 /* } */ : - case 126 /* ~ */ : - return true; - - default: - return false; - } - } - var text = function text(state, silent) { - var pos = state.pos; - while (pos < state.posMax && !isTerminatorChar(state.src.charCodeAt(pos))) { - pos++; - } - if (pos === state.pos) { - return false; - } - if (!silent) { - state.pending += state.src.slice(state.pos, pos); - } - state.pos = pos; - return true; - }; - // Process links like https://example.org/ - // RFC3986: scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) - var SCHEME_RE = /(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$/i; - var linkify = function linkify(state, silent) { - var pos, max, match, proto, link, url, fullUrl, token; - if (!state.md.options.linkify) return false; - if (state.linkLevel > 0) return false; - pos = state.pos; - max = state.posMax; - if (pos + 3 > max) return false; - if (state.src.charCodeAt(pos) !== 58 /* : */) return false; - if (state.src.charCodeAt(pos + 1) !== 47 /* / */) return false; - if (state.src.charCodeAt(pos + 2) !== 47 /* / */) return false; - match = state.pending.match(SCHEME_RE); - if (!match) return false; - proto = match[1]; - link = state.md.linkify.matchAtStart(state.src.slice(pos - proto.length)); - if (!link) return false; - url = link.url; - // invalid link, but still detected by linkify somehow; - // need to check to prevent infinite loop below - if (url.length <= proto.length) return false; - // disallow '*' at the end of the link (conflicts with emphasis) - url = url.replace(/\*+$/, ""); - fullUrl = state.md.normalizeLink(url); - if (!state.md.validateLink(fullUrl)) return false; - if (!silent) { - state.pending = state.pending.slice(0, -proto.length); - token = state.push("link_open", "a", 1); - token.attrs = [ [ "href", fullUrl ] ]; - token.markup = "linkify"; - token.info = "auto"; - token = state.push("text", "", 0); - token.content = state.md.normalizeLinkText(url); - token = state.push("link_close", "a", -1); - token.markup = "linkify"; - token.info = "auto"; - } - state.pos += url.length - proto.length; - return true; - }; - var isSpace$3 = utils.isSpace; - var newline = function newline(state, silent) { - var pmax, max, ws, pos = state.pos; - if (state.src.charCodeAt(pos) !== 10 /* \n */) { - return false; - } - pmax = state.pending.length - 1; - max = state.posMax; - // ' \n' -> hardbreak - // Lookup in pending chars is bad practice! Don't copy to other rules! - // Pending string is stored in concat mode, indexed lookups will cause - // convertion to flat mode. - if (!silent) { - if (pmax >= 0 && state.pending.charCodeAt(pmax) === 32) { - if (pmax >= 1 && state.pending.charCodeAt(pmax - 1) === 32) { - // Find whitespaces tail of pending chars. - ws = pmax - 1; - while (ws >= 1 && state.pending.charCodeAt(ws - 1) === 32) ws--; - state.pending = state.pending.slice(0, ws); - state.push("hardbreak", "br", 0); - } else { - state.pending = state.pending.slice(0, -1); - state.push("softbreak", "br", 0); - } - } else { - state.push("softbreak", "br", 0); - } - } - pos++; - // skip heading spaces for next line - while (pos < max && isSpace$3(state.src.charCodeAt(pos))) { - pos++; - } - state.pos = pos; - return true; - }; - var isSpace$2 = utils.isSpace; - var ESCAPED = []; - for (var i = 0; i < 256; i++) { - ESCAPED.push(0); - } - "\\!\"#$%&'()*+,./:;<=>?@[]^_`{|}~-".split("").forEach((function(ch) { - ESCAPED[ch.charCodeAt(0)] = 1; - })); - var _escape = function escape(state, silent) { - var ch1, ch2, origStr, escapedStr, token, pos = state.pos, max = state.posMax; - if (state.src.charCodeAt(pos) !== 92 /* \ */) return false; - pos++; - // '\' at the end of the inline block - if (pos >= max) return false; - ch1 = state.src.charCodeAt(pos); - if (ch1 === 10) { - if (!silent) { - state.push("hardbreak", "br", 0); - } - pos++; - // skip leading whitespaces from next line - while (pos < max) { - ch1 = state.src.charCodeAt(pos); - if (!isSpace$2(ch1)) break; - pos++; - } - state.pos = pos; - return true; - } - escapedStr = state.src[pos]; - if (ch1 >= 55296 && ch1 <= 56319 && pos + 1 < max) { - ch2 = state.src.charCodeAt(pos + 1); - if (ch2 >= 56320 && ch2 <= 57343) { - escapedStr += state.src[pos + 1]; - pos++; - } - } - origStr = "\\" + escapedStr; - if (!silent) { - token = state.push("text_special", "", 0); - if (ch1 < 256 && ESCAPED[ch1] !== 0) { - token.content = escapedStr; - } else { - token.content = origStr; - } - token.markup = origStr; - token.info = "escape"; - } - state.pos = pos + 1; - return true; - }; - // Parse backticks - var backticks = function backtick(state, silent) { - var start, max, marker, token, matchStart, matchEnd, openerLength, closerLength, pos = state.pos, ch = state.src.charCodeAt(pos); - if (ch !== 96 /* ` */) { - return false; - } - start = pos; - pos++; - max = state.posMax; - // scan marker length - while (pos < max && state.src.charCodeAt(pos) === 96 /* ` */) { - pos++; - } - marker = state.src.slice(start, pos); - openerLength = marker.length; - if (state.backticksScanned && (state.backticks[openerLength] || 0) <= start) { - if (!silent) state.pending += marker; - state.pos += openerLength; - return true; - } - matchEnd = pos; - // Nothing found in the cache, scan until the end of the line (or until marker is found) - while ((matchStart = state.src.indexOf("`", matchEnd)) !== -1) { - matchEnd = matchStart + 1; - // scan marker length - while (matchEnd < max && state.src.charCodeAt(matchEnd) === 96 /* ` */) { - matchEnd++; - } - closerLength = matchEnd - matchStart; - if (closerLength === openerLength) { - // Found matching closer length. - if (!silent) { - token = state.push("code_inline", "code", 0); - token.markup = marker; - token.content = state.src.slice(pos, matchStart).replace(/\n/g, " ").replace(/^ (.+) $/, "$1"); - } - state.pos = matchEnd; - return true; - } - // Some different length found, put it in cache as upper limit of where closer can be found - state.backticks[closerLength] = matchStart; - } - // Scanned through the end, didn't find anything - state.backticksScanned = true; - if (!silent) state.pending += marker; - state.pos += openerLength; - return true; - }; - // ~~strike through~~ - // Insert each marker as a separate text token, and add it to delimiter list - - var tokenize$1 = function strikethrough(state, silent) { - var i, scanned, token, len, ch, start = state.pos, marker = state.src.charCodeAt(start); - if (silent) { - return false; - } - if (marker !== 126 /* ~ */) { - return false; - } - scanned = state.scanDelims(state.pos, true); - len = scanned.length; - ch = String.fromCharCode(marker); - if (len < 2) { - return false; - } - if (len % 2) { - token = state.push("text", "", 0); - token.content = ch; - len--; - } - for (i = 0; i < len; i += 2) { - token = state.push("text", "", 0); - token.content = ch + ch; - state.delimiters.push({ - marker: marker, - length: 0, - // disable "rule of 3" length checks meant for emphasis - token: state.tokens.length - 1, - end: -1, - open: scanned.can_open, - close: scanned.can_close - }); - } - state.pos += scanned.length; - return true; - }; - function postProcess$1(state, delimiters) { - var i, j, startDelim, endDelim, token, loneMarkers = [], max = delimiters.length; - for (i = 0; i < max; i++) { - startDelim = delimiters[i]; - if (startDelim.marker !== 126 /* ~ */) { - continue; - } - if (startDelim.end === -1) { - continue; - } - endDelim = delimiters[startDelim.end]; - token = state.tokens[startDelim.token]; - token.type = "s_open"; - token.tag = "s"; - token.nesting = 1; - token.markup = "~~"; - token.content = ""; - token = state.tokens[endDelim.token]; - token.type = "s_close"; - token.tag = "s"; - token.nesting = -1; - token.markup = "~~"; - token.content = ""; - if (state.tokens[endDelim.token - 1].type === "text" && state.tokens[endDelim.token - 1].content === "~") { - loneMarkers.push(endDelim.token - 1); - } - } - // If a marker sequence has an odd number of characters, it's splitted - // like this: `~~~~~` -> `~` + `~~` + `~~`, leaving one marker at the - // start of the sequence. - - // So, we have to move all those markers after subsequent s_close tags. - - while (loneMarkers.length) { - i = loneMarkers.pop(); - j = i + 1; - while (j < state.tokens.length && state.tokens[j].type === "s_close") { - j++; - } - j--; - if (i !== j) { - token = state.tokens[j]; - state.tokens[j] = state.tokens[i]; - state.tokens[i] = token; - } - } - } - // Walk through delimiter list and replace text tokens with tags - - var postProcess_1$1 = function strikethrough(state) { - var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; - postProcess$1(state, state.delimiters); - for (curr = 0; curr < max; curr++) { - if (tokens_meta[curr] && tokens_meta[curr].delimiters) { - postProcess$1(state, tokens_meta[curr].delimiters); - } - } - }; - var strikethrough = { - tokenize: tokenize$1, - postProcess: postProcess_1$1 - }; - // Process *this* and _that_ - // Insert each marker as a separate text token, and add it to delimiter list - - var tokenize = function emphasis(state, silent) { - var i, scanned, token, start = state.pos, marker = state.src.charCodeAt(start); - if (silent) { - return false; - } - if (marker !== 95 /* _ */ && marker !== 42 /* * */) { - return false; - } - scanned = state.scanDelims(state.pos, marker === 42); - for (i = 0; i < scanned.length; i++) { - token = state.push("text", "", 0); - token.content = String.fromCharCode(marker); - state.delimiters.push({ - // Char code of the starting marker (number). - marker: marker, - // Total length of these series of delimiters. - length: scanned.length, - // A position of the token this delimiter corresponds to. - token: state.tokens.length - 1, - // If this delimiter is matched as a valid opener, `end` will be - // equal to its position, otherwise it's `-1`. - end: -1, - // Boolean flags that determine if this delimiter could open or close - // an emphasis. - open: scanned.can_open, - close: scanned.can_close - }); - } - state.pos += scanned.length; - return true; - }; - function postProcess(state, delimiters) { - var i, startDelim, endDelim, token, ch, isStrong, max = delimiters.length; - for (i = max - 1; i >= 0; i--) { - startDelim = delimiters[i]; - if (startDelim.marker !== 95 /* _ */ && startDelim.marker !== 42 /* * */) { - continue; - } - // Process only opening markers - if (startDelim.end === -1) { - continue; - } - endDelim = delimiters[startDelim.end]; - // If the previous delimiter has the same marker and is adjacent to this one, - // merge those into one strong delimiter. - - // `whatever` -> `whatever` - - isStrong = i > 0 && delimiters[i - 1].end === startDelim.end + 1 && - // check that first two markers match and adjacent - delimiters[i - 1].marker === startDelim.marker && delimiters[i - 1].token === startDelim.token - 1 && - // check that last two markers are adjacent (we can safely assume they match) - delimiters[startDelim.end + 1].token === endDelim.token + 1; - ch = String.fromCharCode(startDelim.marker); - token = state.tokens[startDelim.token]; - token.type = isStrong ? "strong_open" : "em_open"; - token.tag = isStrong ? "strong" : "em"; - token.nesting = 1; - token.markup = isStrong ? ch + ch : ch; - token.content = ""; - token = state.tokens[endDelim.token]; - token.type = isStrong ? "strong_close" : "em_close"; - token.tag = isStrong ? "strong" : "em"; - token.nesting = -1; - token.markup = isStrong ? ch + ch : ch; - token.content = ""; - if (isStrong) { - state.tokens[delimiters[i - 1].token].content = ""; - state.tokens[delimiters[startDelim.end + 1].token].content = ""; - i--; - } - } - } - // Walk through delimiter list and replace text tokens with tags - - var postProcess_1 = function emphasis(state) { - var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; - postProcess(state, state.delimiters); - for (curr = 0; curr < max; curr++) { - if (tokens_meta[curr] && tokens_meta[curr].delimiters) { - postProcess(state, tokens_meta[curr].delimiters); - } - } - }; - var emphasis = { - tokenize: tokenize, - postProcess: postProcess_1 - }; - var normalizeReference$1 = utils.normalizeReference; - var isSpace$1 = utils.isSpace; - var link = function link(state, silent) { - var attrs, code, label, labelEnd, labelStart, pos, res, ref, token, href = "", title = "", oldPos = state.pos, max = state.posMax, start = state.pos, parseReference = true; - if (state.src.charCodeAt(state.pos) !== 91 /* [ */) { - return false; - } - labelStart = state.pos + 1; - labelEnd = state.md.helpers.parseLinkLabel(state, state.pos, true); - // parser failed to find ']', so it's not a valid link - if (labelEnd < 0) { - return false; - } - pos = labelEnd + 1; - if (pos < max && state.src.charCodeAt(pos) === 40 /* ( */) { - // Inline link - // might have found a valid shortcut link, disable reference parsing - parseReference = false; - // [link]( "title" ) - // ^^ skipping these spaces - pos++; - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace$1(code) && code !== 10) { - break; - } - } - if (pos >= max) { - return false; - } - // [link]( "title" ) - // ^^^^^^ parsing link destination - start = pos; - res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax); - if (res.ok) { - href = state.md.normalizeLink(res.str); - if (state.md.validateLink(href)) { - pos = res.pos; - } else { - href = ""; - } - // [link]( "title" ) - // ^^ skipping these spaces - start = pos; - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace$1(code) && code !== 10) { - break; - } - } - // [link]( "title" ) - // ^^^^^^^ parsing link title - res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax); - if (pos < max && start !== pos && res.ok) { - title = res.str; - pos = res.pos; - // [link]( "title" ) - // ^^ skipping these spaces - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace$1(code) && code !== 10) { - break; - } - } - } - } - if (pos >= max || state.src.charCodeAt(pos) !== 41 /* ) */) { - // parsing a valid shortcut link failed, fallback to reference - parseReference = true; - } - pos++; - } - if (parseReference) { - // Link reference - if (typeof state.env.references === "undefined") { - return false; - } - if (pos < max && state.src.charCodeAt(pos) === 91 /* [ */) { - start = pos + 1; - pos = state.md.helpers.parseLinkLabel(state, pos); - if (pos >= 0) { - label = state.src.slice(start, pos++); - } else { - pos = labelEnd + 1; - } - } else { - pos = labelEnd + 1; - } - // covers label === '' and label === undefined - // (collapsed reference link and shortcut reference link respectively) - if (!label) { - label = state.src.slice(labelStart, labelEnd); - } - ref = state.env.references[normalizeReference$1(label)]; - if (!ref) { - state.pos = oldPos; - return false; - } - href = ref.href; - title = ref.title; - } - - // We found the end of the link, and know for a fact it's a valid link; - // so all that's left to do is to call tokenizer. - - if (!silent) { - state.pos = labelStart; - state.posMax = labelEnd; - token = state.push("link_open", "a", 1); - token.attrs = attrs = [ [ "href", href ] ]; - if (title) { - attrs.push([ "title", title ]); - } - state.linkLevel++; - state.md.inline.tokenize(state); - state.linkLevel--; - token = state.push("link_close", "a", -1); - } - state.pos = pos; - state.posMax = max; - return true; - }; - var normalizeReference = utils.normalizeReference; - var isSpace = utils.isSpace; - var image = function image(state, silent) { - var attrs, code, content, label, labelEnd, labelStart, pos, ref, res, title, token, tokens, start, href = "", oldPos = state.pos, max = state.posMax; - if (state.src.charCodeAt(state.pos) !== 33 /* ! */) { - return false; - } - if (state.src.charCodeAt(state.pos + 1) !== 91 /* [ */) { - return false; - } - labelStart = state.pos + 2; - labelEnd = state.md.helpers.parseLinkLabel(state, state.pos + 1, false); - // parser failed to find ']', so it's not a valid link - if (labelEnd < 0) { - return false; - } - pos = labelEnd + 1; - if (pos < max && state.src.charCodeAt(pos) === 40 /* ( */) { - // Inline link - // [link]( "title" ) - // ^^ skipping these spaces - pos++; - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace(code) && code !== 10) { - break; - } - } - if (pos >= max) { - return false; - } - // [link]( "title" ) - // ^^^^^^ parsing link destination - start = pos; - res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax); - if (res.ok) { - href = state.md.normalizeLink(res.str); - if (state.md.validateLink(href)) { - pos = res.pos; - } else { - href = ""; - } - } - // [link]( "title" ) - // ^^ skipping these spaces - start = pos; - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace(code) && code !== 10) { - break; - } - } - // [link]( "title" ) - // ^^^^^^^ parsing link title - res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax); - if (pos < max && start !== pos && res.ok) { - title = res.str; - pos = res.pos; - // [link]( "title" ) - // ^^ skipping these spaces - for (;pos < max; pos++) { - code = state.src.charCodeAt(pos); - if (!isSpace(code) && code !== 10) { - break; - } - } - } else { - title = ""; - } - if (pos >= max || state.src.charCodeAt(pos) !== 41 /* ) */) { - state.pos = oldPos; - return false; - } - pos++; - } else { - // Link reference - if (typeof state.env.references === "undefined") { - return false; - } - if (pos < max && state.src.charCodeAt(pos) === 91 /* [ */) { - start = pos + 1; - pos = state.md.helpers.parseLinkLabel(state, pos); - if (pos >= 0) { - label = state.src.slice(start, pos++); - } else { - pos = labelEnd + 1; - } - } else { - pos = labelEnd + 1; - } - // covers label === '' and label === undefined - // (collapsed reference link and shortcut reference link respectively) - if (!label) { - label = state.src.slice(labelStart, labelEnd); - } - ref = state.env.references[normalizeReference(label)]; - if (!ref) { - state.pos = oldPos; - return false; - } - href = ref.href; - title = ref.title; - } - - // We found the end of the link, and know for a fact it's a valid link; - // so all that's left to do is to call tokenizer. - - if (!silent) { - content = state.src.slice(labelStart, labelEnd); - state.md.inline.parse(content, state.md, state.env, tokens = []); - token = state.push("image", "img", 0); - token.attrs = attrs = [ [ "src", href ], [ "alt", "" ] ]; - token.children = tokens; - token.content = content; - if (title) { - attrs.push([ "title", title ]); - } - } - state.pos = pos; - state.posMax = max; - return true; - }; - // Process autolinks '' - /*eslint max-len:0*/ var EMAIL_RE = /^([a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/; - var AUTOLINK_RE = /^([a-zA-Z][a-zA-Z0-9+.\-]{1,31}):([^<>\x00-\x20]*)$/; - var autolink = function autolink(state, silent) { - var url, fullUrl, token, ch, start, max, pos = state.pos; - if (state.src.charCodeAt(pos) !== 60 /* < */) { - return false; - } - start = state.pos; - max = state.posMax; - for (;;) { - if (++pos >= max) return false; - ch = state.src.charCodeAt(pos); - if (ch === 60 /* < */) return false; - if (ch === 62 /* > */) break; - } - url = state.src.slice(start + 1, pos); - if (AUTOLINK_RE.test(url)) { - fullUrl = state.md.normalizeLink(url); - if (!state.md.validateLink(fullUrl)) { - return false; - } - if (!silent) { - token = state.push("link_open", "a", 1); - token.attrs = [ [ "href", fullUrl ] ]; - token.markup = "autolink"; - token.info = "auto"; - token = state.push("text", "", 0); - token.content = state.md.normalizeLinkText(url); - token = state.push("link_close", "a", -1); - token.markup = "autolink"; - token.info = "auto"; - } - state.pos += url.length + 2; - return true; - } - if (EMAIL_RE.test(url)) { - fullUrl = state.md.normalizeLink("mailto:" + url); - if (!state.md.validateLink(fullUrl)) { - return false; - } - if (!silent) { - token = state.push("link_open", "a", 1); - token.attrs = [ [ "href", fullUrl ] ]; - token.markup = "autolink"; - token.info = "auto"; - token = state.push("text", "", 0); - token.content = state.md.normalizeLinkText(url); - token = state.push("link_close", "a", -1); - token.markup = "autolink"; - token.info = "auto"; - } - state.pos += url.length + 2; - return true; - } - return false; - }; - var HTML_TAG_RE = html_re.HTML_TAG_RE; - function isLinkOpen(str) { - return /^\s]/i.test(str); - } - function isLinkClose(str) { - return /^<\/a\s*>/i.test(str); - } - function isLetter(ch) { - /*eslint no-bitwise:0*/ - var lc = ch | 32; - // to lower case - return lc >= 97 /* a */ && lc <= 122 /* z */; - } - var html_inline = function html_inline(state, silent) { - var ch, match, max, token, pos = state.pos; - if (!state.md.options.html) { - return false; - } - // Check start - max = state.posMax; - if (state.src.charCodeAt(pos) !== 60 /* < */ || pos + 2 >= max) { - return false; - } - // Quick fail on second char - ch = state.src.charCodeAt(pos + 1); - if (ch !== 33 /* ! */ && ch !== 63 /* ? */ && ch !== 47 /* / */ && !isLetter(ch)) { - return false; - } - match = state.src.slice(pos).match(HTML_TAG_RE); - if (!match) { - return false; - } - if (!silent) { - token = state.push("html_inline", "", 0); - token.content = match[0]; - if (isLinkOpen(token.content)) state.linkLevel++; - if (isLinkClose(token.content)) state.linkLevel--; - } - state.pos += match[0].length; - return true; - }; - var has = utils.has; - var isValidEntityCode = utils.isValidEntityCode; - var fromCodePoint = utils.fromCodePoint; - var DIGITAL_RE = /^&#((?:x[a-f0-9]{1,6}|[0-9]{1,7}));/i; - var NAMED_RE = /^&([a-z][a-z0-9]{1,31});/i; - var entity = function entity(state, silent) { - var ch, code, match, token, pos = state.pos, max = state.posMax; - if (state.src.charCodeAt(pos) !== 38 /* & */) return false; - if (pos + 1 >= max) return false; - ch = state.src.charCodeAt(pos + 1); - if (ch === 35 /* # */) { - match = state.src.slice(pos).match(DIGITAL_RE); - if (match) { - if (!silent) { - code = match[1][0].toLowerCase() === "x" ? parseInt(match[1].slice(1), 16) : parseInt(match[1], 10); - token = state.push("text_special", "", 0); - token.content = isValidEntityCode(code) ? fromCodePoint(code) : fromCodePoint(65533); - token.markup = match[0]; - token.info = "entity"; - } - state.pos += match[0].length; - return true; - } - } else { - match = state.src.slice(pos).match(NAMED_RE); - if (match) { - if (has(entities, match[1])) { - if (!silent) { - token = state.push("text_special", "", 0); - token.content = entities[match[1]]; - token.markup = match[0]; - token.info = "entity"; - } - state.pos += match[0].length; - return true; - } - } - } - return false; - }; - // For each opening emphasis-like marker find a matching closing one - function processDelimiters(delimiters) { - var closerIdx, openerIdx, closer, opener, minOpenerIdx, newMinOpenerIdx, isOddMatch, lastJump, openersBottom = {}, max = delimiters.length; - if (!max) return; - // headerIdx is the first delimiter of the current (where closer is) delimiter run - var headerIdx = 0; - var lastTokenIdx = -2; - // needs any value lower than -1 - var jumps = []; - for (closerIdx = 0; closerIdx < max; closerIdx++) { - closer = delimiters[closerIdx]; - jumps.push(0); - // markers belong to same delimiter run if: - // - they have adjacent tokens - // - AND markers are the same - - if (delimiters[headerIdx].marker !== closer.marker || lastTokenIdx !== closer.token - 1) { - headerIdx = closerIdx; - } - lastTokenIdx = closer.token; - // Length is only used for emphasis-specific "rule of 3", - // if it's not defined (in strikethrough or 3rd party plugins), - // we can default it to 0 to disable those checks. - - closer.length = closer.length || 0; - if (!closer.close) continue; - // Previously calculated lower bounds (previous fails) - // for each marker, each delimiter length modulo 3, - // and for whether this closer can be an opener; - // https://github.com/commonmark/cmark/commit/34250e12ccebdc6372b8b49c44fab57c72443460 - if (!openersBottom.hasOwnProperty(closer.marker)) { - openersBottom[closer.marker] = [ -1, -1, -1, -1, -1, -1 ]; - } - minOpenerIdx = openersBottom[closer.marker][(closer.open ? 3 : 0) + closer.length % 3]; - openerIdx = headerIdx - jumps[headerIdx] - 1; - newMinOpenerIdx = openerIdx; - for (;openerIdx > minOpenerIdx; openerIdx -= jumps[openerIdx] + 1) { - opener = delimiters[openerIdx]; - if (opener.marker !== closer.marker) continue; - if (opener.open && opener.end < 0) { - isOddMatch = false; - // from spec: - - // If one of the delimiters can both open and close emphasis, then the - // sum of the lengths of the delimiter runs containing the opening and - // closing delimiters must not be a multiple of 3 unless both lengths - // are multiples of 3. - - if (opener.close || closer.open) { - if ((opener.length + closer.length) % 3 === 0) { - if (opener.length % 3 !== 0 || closer.length % 3 !== 0) { - isOddMatch = true; - } - } - } - if (!isOddMatch) { - // If previous delimiter cannot be an opener, we can safely skip - // the entire sequence in future checks. This is required to make - // sure algorithm has linear complexity (see *_*_*_*_*_... case). - lastJump = openerIdx > 0 && !delimiters[openerIdx - 1].open ? jumps[openerIdx - 1] + 1 : 0; - jumps[closerIdx] = closerIdx - openerIdx + lastJump; - jumps[openerIdx] = lastJump; - closer.open = false; - opener.end = closerIdx; - opener.close = false; - newMinOpenerIdx = -1; - // treat next token as start of run, - // it optimizes skips in **<...>**a**<...>** pathological case - lastTokenIdx = -2; - break; - } - } - } - if (newMinOpenerIdx !== -1) { - // If match for this delimiter run failed, we want to set lower bound for - // future lookups. This is required to make sure algorithm has linear - // complexity. - // See details here: - // https://github.com/commonmark/cmark/issues/178#issuecomment-270417442 - openersBottom[closer.marker][(closer.open ? 3 : 0) + (closer.length || 0) % 3] = newMinOpenerIdx; - } - } - } - var balance_pairs = function link_pairs(state) { - var curr, tokens_meta = state.tokens_meta, max = state.tokens_meta.length; - processDelimiters(state.delimiters); - for (curr = 0; curr < max; curr++) { - if (tokens_meta[curr] && tokens_meta[curr].delimiters) { - processDelimiters(tokens_meta[curr].delimiters); - } - } - }; - // Clean up tokens after emphasis and strikethrough postprocessing: - var fragments_join = function fragments_join(state) { - var curr, last, level = 0, tokens = state.tokens, max = state.tokens.length; - for (curr = last = 0; curr < max; curr++) { - // re-calculate levels after emphasis/strikethrough turns some text nodes - // into opening/closing tags - if (tokens[curr].nesting < 0) level--; - // closing tag - tokens[curr].level = level; - if (tokens[curr].nesting > 0) level++; - // opening tag - if (tokens[curr].type === "text" && curr + 1 < max && tokens[curr + 1].type === "text") { - // collapse two adjacent text nodes - tokens[curr + 1].content = tokens[curr].content + tokens[curr + 1].content; - } else { - if (curr !== last) { - tokens[last] = tokens[curr]; - } - last++; - } - } - if (curr !== last) { - tokens.length = last; - } - }; - var isWhiteSpace = utils.isWhiteSpace; - var isPunctChar = utils.isPunctChar; - var isMdAsciiPunct = utils.isMdAsciiPunct; - function StateInline(src, md, env, outTokens) { - this.src = src; - this.env = env; - this.md = md; - this.tokens = outTokens; - this.tokens_meta = Array(outTokens.length); - this.pos = 0; - this.posMax = this.src.length; - this.level = 0; - this.pending = ""; - this.pendingLevel = 0; - // Stores { start: end } pairs. Useful for backtrack - // optimization of pairs parse (emphasis, strikes). - this.cache = {}; - // List of emphasis-like delimiters for current tag - this.delimiters = []; - // Stack of delimiter lists for upper level tags - this._prev_delimiters = []; - // backtick length => last seen position - this.backticks = {}; - this.backticksScanned = false; - // Counter used to disable inline linkify-it execution - // inside and markdown links - this.linkLevel = 0; - } - // Flush pending text - - StateInline.prototype.pushPending = function() { - var token$1 = new token("text", "", 0); - token$1.content = this.pending; - token$1.level = this.pendingLevel; - this.tokens.push(token$1); - this.pending = ""; - return token$1; - }; - // Push new token to "stream". - // If pending text exists - flush it as text token - - StateInline.prototype.push = function(type, tag, nesting) { - if (this.pending) { - this.pushPending(); - } - var token$1 = new token(type, tag, nesting); - var token_meta = null; - if (nesting < 0) { - // closing tag - this.level--; - this.delimiters = this._prev_delimiters.pop(); - } - token$1.level = this.level; - if (nesting > 0) { - // opening tag - this.level++; - this._prev_delimiters.push(this.delimiters); - this.delimiters = []; - token_meta = { - delimiters: this.delimiters - }; - } - this.pendingLevel = this.level; - this.tokens.push(token$1); - this.tokens_meta.push(token_meta); - return token$1; - }; - // Scan a sequence of emphasis-like markers, and determine whether - // it can start an emphasis sequence or end an emphasis sequence. - - // - start - position to scan from (it should point at a valid marker); - // - canSplitWord - determine if these markers can be found inside a word - - StateInline.prototype.scanDelims = function(start, canSplitWord) { - var pos = start, lastChar, nextChar, count, can_open, can_close, isLastWhiteSpace, isLastPunctChar, isNextWhiteSpace, isNextPunctChar, left_flanking = true, right_flanking = true, max = this.posMax, marker = this.src.charCodeAt(start); - // treat beginning of the line as a whitespace - lastChar = start > 0 ? this.src.charCodeAt(start - 1) : 32; - while (pos < max && this.src.charCodeAt(pos) === marker) { - pos++; - } - count = pos - start; - // treat end of the line as a whitespace - nextChar = pos < max ? this.src.charCodeAt(pos) : 32; - isLastPunctChar = isMdAsciiPunct(lastChar) || isPunctChar(String.fromCharCode(lastChar)); - isNextPunctChar = isMdAsciiPunct(nextChar) || isPunctChar(String.fromCharCode(nextChar)); - isLastWhiteSpace = isWhiteSpace(lastChar); - isNextWhiteSpace = isWhiteSpace(nextChar); - if (isNextWhiteSpace) { - left_flanking = false; - } else if (isNextPunctChar) { - if (!(isLastWhiteSpace || isLastPunctChar)) { - left_flanking = false; - } - } - if (isLastWhiteSpace) { - right_flanking = false; - } else if (isLastPunctChar) { - if (!(isNextWhiteSpace || isNextPunctChar)) { - right_flanking = false; - } - } - if (!canSplitWord) { - can_open = left_flanking && (!right_flanking || isLastPunctChar); - can_close = right_flanking && (!left_flanking || isNextPunctChar); - } else { - can_open = left_flanking; - can_close = right_flanking; - } - return { - can_open: can_open, - can_close: can_close, - length: count - }; - }; - // re-export Token class to use in block rules - StateInline.prototype.Token = token; - var state_inline = StateInline; - //////////////////////////////////////////////////////////////////////////////// - // Parser rules - var _rules = [ [ "text", text ], [ "linkify", linkify ], [ "newline", newline ], [ "escape", _escape ], [ "backticks", backticks ], [ "strikethrough", strikethrough.tokenize ], [ "emphasis", emphasis.tokenize ], [ "link", link ], [ "image", image ], [ "autolink", autolink ], [ "html_inline", html_inline ], [ "entity", entity ] ]; - // `rule2` ruleset was created specifically for emphasis/strikethrough - // post-processing and may be changed in the future. - - // Don't use this for anything except pairs (plugins working with `balance_pairs`). - - var _rules2 = [ [ "balance_pairs", balance_pairs ], [ "strikethrough", strikethrough.postProcess ], [ "emphasis", emphasis.postProcess ], - // rules for pairs separate '**' into its own text tokens, which may be left unused, - // rule below merges unused segments back with the rest of the text - [ "fragments_join", fragments_join ] ]; - /** - * new ParserInline() - **/ function ParserInline() { - var i; - /** - * ParserInline#ruler -> Ruler - * - * [[Ruler]] instance. Keep configuration of inline rules. - **/ this.ruler = new ruler; - for (i = 0; i < _rules.length; i++) { - this.ruler.push(_rules[i][0], _rules[i][1]); - } - /** - * ParserInline#ruler2 -> Ruler - * - * [[Ruler]] instance. Second ruler used for post-processing - * (e.g. in emphasis-like rules). - **/ this.ruler2 = new ruler; - for (i = 0; i < _rules2.length; i++) { - this.ruler2.push(_rules2[i][0], _rules2[i][1]); - } - } - // Skip single token by running all rules in validation mode; - // returns `true` if any rule reported success - - ParserInline.prototype.skipToken = function(state) { - var ok, i, pos = state.pos, rules = this.ruler.getRules(""), len = rules.length, maxNesting = state.md.options.maxNesting, cache = state.cache; - if (typeof cache[pos] !== "undefined") { - state.pos = cache[pos]; - return; - } - if (state.level < maxNesting) { - for (i = 0; i < len; i++) { - // Increment state.level and decrement it later to limit recursion. - // It's harmless to do here, because no tokens are created. But ideally, - // we'd need a separate private state variable for this purpose. - state.level++; - ok = rules[i](state, true); - state.level--; - if (ok) { - if (pos >= state.pos) { - throw new Error("inline rule didn't increment state.pos"); - } - break; - } - } - } else { - // Too much nesting, just skip until the end of the paragraph. - // NOTE: this will cause links to behave incorrectly in the following case, - // when an amount of `[` is exactly equal to `maxNesting + 1`: - // [[[[[[[[[[[[[[[[[[[[[foo]() - // TODO: remove this workaround when CM standard will allow nested links - // (we can replace it by preventing links from being parsed in - // validation mode) - state.pos = state.posMax; - } - if (!ok) { - state.pos++; - } - cache[pos] = state.pos; - }; - // Generate tokens for input range - - ParserInline.prototype.tokenize = function(state) { - var ok, i, prevPos, rules = this.ruler.getRules(""), len = rules.length, end = state.posMax, maxNesting = state.md.options.maxNesting; - while (state.pos < end) { - // Try all possible rules. - // On success, rule should: - // - update `state.pos` - // - update `state.tokens` - // - return true - prevPos = state.pos; - if (state.level < maxNesting) { - for (i = 0; i < len; i++) { - ok = rules[i](state, false); - if (ok) { - if (prevPos >= state.pos) { - throw new Error("inline rule didn't increment state.pos"); - } - break; - } - } - } - if (ok) { - if (state.pos >= end) { - break; - } - continue; - } - state.pending += state.src[state.pos++]; - } - if (state.pending) { - state.pushPending(); - } - }; - /** - * ParserInline.parse(str, md, env, outTokens) - * - * Process input string and push inline tokens into `outTokens` - **/ ParserInline.prototype.parse = function(str, md, env, outTokens) { - var i, rules, len; - var state = new this.State(str, md, env, outTokens); - this.tokenize(state); - rules = this.ruler2.getRules(""); - len = rules.length; - for (i = 0; i < len; i++) { - rules[i](state); - } - }; - ParserInline.prototype.State = state_inline; - var parser_inline = ParserInline; - var re = function(opts) { - var re = {}; - opts = opts || {}; - // Use direct extract instead of `regenerate` to reduse browserified size - re.src_Any = regex$3.source; - re.src_Cc = regex$2.source; - re.src_Z = regex.source; - re.src_P = regex$4.source; - // \p{\Z\P\Cc\CF} (white spaces + control + format + punctuation) - re.src_ZPCc = [ re.src_Z, re.src_P, re.src_Cc ].join("|"); - // \p{\Z\Cc} (white spaces + control) - re.src_ZCc = [ re.src_Z, re.src_Cc ].join("|"); - // Experimental. List of chars, completely prohibited in links - // because can separate it from other part of text - var text_separators = "[><\uff5c]"; - // All possible word characters (everything without punctuation, spaces & controls) - // Defined via punctuation & spaces to save space - // Should be something like \p{\L\N\S\M} (\w but without `_`) - re.src_pseudo_letter = "(?:(?!" + text_separators + "|" + re.src_ZPCc + ")" + re.src_Any + ")"; - // The same as abothe but without [0-9] - // var src_pseudo_letter_non_d = '(?:(?![0-9]|' + src_ZPCc + ')' + src_Any + ')'; - //////////////////////////////////////////////////////////////////////////////// - re.src_ip4 = "(?:(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)"; - // Prohibit any of "@/[]()" in user/pass to avoid wrong domain fetch. - re.src_auth = "(?:(?:(?!" + re.src_ZCc + "|[@/\\[\\]()]).)+@)?"; - re.src_port = "(?::(?:6(?:[0-4]\\d{3}|5(?:[0-4]\\d{2}|5(?:[0-2]\\d|3[0-5])))|[1-5]?\\d{1,4}))?"; - re.src_host_terminator = "(?=$|" + text_separators + "|" + re.src_ZPCc + ")" + "(?!" + (opts["---"] ? "-(?!--)|" : "-|") + "_|:\\d|\\.-|\\.(?!$|" + re.src_ZPCc + "))"; - re.src_path = "(?:" + "[/?#]" + "(?:" + "(?!" + re.src_ZCc + "|" + text_separators + "|[()[\\]{}.,\"'?!\\-;]).|" + "\\[(?:(?!" + re.src_ZCc + "|\\]).)*\\]|" + "\\((?:(?!" + re.src_ZCc + "|[)]).)*\\)|" + "\\{(?:(?!" + re.src_ZCc + "|[}]).)*\\}|" + '\\"(?:(?!' + re.src_ZCc + '|["]).)+\\"|' + "\\'(?:(?!" + re.src_ZCc + "|[']).)+\\'|" + "\\'(?=" + re.src_pseudo_letter + "|[-])|" + // allow `I'm_king` if no pair found - "\\.{2,}[a-zA-Z0-9%/&]|" + // google has many dots in "google search" links (#66, #81). - // github has ... in commit range links, - // Restrict to - // - english - // - percent-encoded - // - parts of file path - // - params separator - // until more examples found. - "\\.(?!" + re.src_ZCc + "|[.]|$)|" + (opts["---"] ? "\\-(?!--(?:[^-]|$))(?:-*)|" : "\\-+|") + ",(?!" + re.src_ZCc + "|$)|" + // allow `,,,` in paths - ";(?!" + re.src_ZCc + "|$)|" + // allow `;` if not followed by space-like char - "\\!+(?!" + re.src_ZCc + "|[!]|$)|" + // allow `!!!` in paths, but not at the end - "\\?(?!" + re.src_ZCc + "|[?]|$)" + ")+" + "|\\/" + ")?"; - // Allow anything in markdown spec, forbid quote (") at the first position - // because emails enclosed in quotes are far more common - re.src_email_name = '[\\-;:&=\\+\\$,\\.a-zA-Z0-9_][\\-;:&=\\+\\$,\\"\\.a-zA-Z0-9_]*'; - re.src_xn = "xn--[a-z0-9\\-]{1,59}"; - // More to read about domain names - // http://serverfault.com/questions/638260/ - re.src_domain_root = - // Allow letters & digits (http://test1) - "(?:" + re.src_xn + "|" + re.src_pseudo_letter + "{1,63}" + ")"; - re.src_domain = "(?:" + re.src_xn + "|" + "(?:" + re.src_pseudo_letter + ")" + "|" + "(?:" + re.src_pseudo_letter + "(?:-|" + re.src_pseudo_letter + "){0,61}" + re.src_pseudo_letter + ")" + ")"; - re.src_host = "(?:" + - // Don't need IP check, because digits are already allowed in normal domain names - // src_ip4 + - // '|' + - "(?:(?:(?:" + re.src_domain + ")\\.)*" + re.src_domain /*_root*/ + ")" + ")"; - re.tpl_host_fuzzy = "(?:" + re.src_ip4 + "|" + "(?:(?:(?:" + re.src_domain + ")\\.)+(?:%TLDS%))" + ")"; - re.tpl_host_no_ip_fuzzy = "(?:(?:(?:" + re.src_domain + ")\\.)+(?:%TLDS%))"; - re.src_host_strict = re.src_host + re.src_host_terminator; - re.tpl_host_fuzzy_strict = re.tpl_host_fuzzy + re.src_host_terminator; - re.src_host_port_strict = re.src_host + re.src_port + re.src_host_terminator; - re.tpl_host_port_fuzzy_strict = re.tpl_host_fuzzy + re.src_port + re.src_host_terminator; - re.tpl_host_port_no_ip_fuzzy_strict = re.tpl_host_no_ip_fuzzy + re.src_port + re.src_host_terminator; - //////////////////////////////////////////////////////////////////////////////// - // Main rules - // Rude test fuzzy links by host, for quick deny - re.tpl_host_fuzzy_test = "localhost|www\\.|\\.\\d{1,3}\\.|(?:\\.(?:%TLDS%)(?:" + re.src_ZPCc + "|>|$))"; - re.tpl_email_fuzzy = "(^|" + text_separators + '|"|\\(|' + re.src_ZCc + ")" + "(" + re.src_email_name + "@" + re.tpl_host_fuzzy_strict + ")"; - re.tpl_link_fuzzy = - // Fuzzy link can't be prepended with .:/\- and non punctuation. - // but can start with > (markdown blockquote) - "(^|(?![.:/\\-_@])(?:[$+<=>^`|\uff5c]|" + re.src_ZPCc + "))" + "((?![$+<=>^`|\uff5c])" + re.tpl_host_port_fuzzy_strict + re.src_path + ")"; - re.tpl_link_no_ip_fuzzy = - // Fuzzy link can't be prepended with .:/\- and non punctuation. - // but can start with > (markdown blockquote) - "(^|(?![.:/\\-_@])(?:[$+<=>^`|\uff5c]|" + re.src_ZPCc + "))" + "((?![$+<=>^`|\uff5c])" + re.tpl_host_port_no_ip_fuzzy_strict + re.src_path + ")"; - return re; - }; - //////////////////////////////////////////////////////////////////////////////// - // Helpers - // Merge objects - - function assign(obj /*from1, from2, from3, ...*/) { - var sources = Array.prototype.slice.call(arguments, 1); - sources.forEach((function(source) { - if (!source) { - return; - } - Object.keys(source).forEach((function(key) { - obj[key] = source[key]; - })); - })); - return obj; - } - function _class(obj) { - return Object.prototype.toString.call(obj); - } - function isString(obj) { - return _class(obj) === "[object String]"; - } - function isObject(obj) { - return _class(obj) === "[object Object]"; - } - function isRegExp(obj) { - return _class(obj) === "[object RegExp]"; - } - function isFunction(obj) { - return _class(obj) === "[object Function]"; - } - function escapeRE(str) { - return str.replace(/[.?*+^$[\]\\(){}|-]/g, "\\$&"); - } - //////////////////////////////////////////////////////////////////////////////// - var defaultOptions = { - fuzzyLink: true, - fuzzyEmail: true, - fuzzyIP: false - }; - function isOptionsObj(obj) { - return Object.keys(obj || {}).reduce((function(acc, k) { - return acc || defaultOptions.hasOwnProperty(k); - }), false); - } - var defaultSchemas = { - "http:": { - validate: function(text, pos, self) { - var tail = text.slice(pos); - if (!self.re.http) { - // compile lazily, because "host"-containing variables can change on tlds update. - self.re.http = new RegExp("^\\/\\/" + self.re.src_auth + self.re.src_host_port_strict + self.re.src_path, "i"); - } - if (self.re.http.test(tail)) { - return tail.match(self.re.http)[0].length; - } - return 0; - } - }, - "https:": "http:", - "ftp:": "http:", - "//": { - validate: function(text, pos, self) { - var tail = text.slice(pos); - if (!self.re.no_http) { - // compile lazily, because "host"-containing variables can change on tlds update. - self.re.no_http = new RegExp("^" + self.re.src_auth + - // Don't allow single-level domains, because of false positives like '//test' - // with code comments - "(?:localhost|(?:(?:" + self.re.src_domain + ")\\.)+" + self.re.src_domain_root + ")" + self.re.src_port + self.re.src_host_terminator + self.re.src_path, "i"); - } - if (self.re.no_http.test(tail)) { - // should not be `://` & `///`, that protects from errors in protocol name - if (pos >= 3 && text[pos - 3] === ":") { - return 0; - } - if (pos >= 3 && text[pos - 3] === "/") { - return 0; - } - return tail.match(self.re.no_http)[0].length; - } - return 0; - } - }, - "mailto:": { - validate: function(text, pos, self) { - var tail = text.slice(pos); - if (!self.re.mailto) { - self.re.mailto = new RegExp("^" + self.re.src_email_name + "@" + self.re.src_host_strict, "i"); - } - if (self.re.mailto.test(tail)) { - return tail.match(self.re.mailto)[0].length; - } - return 0; - } - } - }; - /*eslint-disable max-len*/ - // RE pattern for 2-character tlds (autogenerated by ./support/tlds_2char_gen.js) - var tlds_2ch_src_re = "a[cdefgilmnoqrstuwxz]|b[abdefghijmnorstvwyz]|c[acdfghiklmnoruvwxyz]|d[ejkmoz]|e[cegrstu]|f[ijkmor]|g[abdefghilmnpqrstuwy]|h[kmnrtu]|i[delmnoqrst]|j[emop]|k[eghimnprwyz]|l[abcikrstuvy]|m[acdeghklmnopqrstuvwxyz]|n[acefgilopruz]|om|p[aefghklmnrstwy]|qa|r[eosuw]|s[abcdeghijklmnortuvxyz]|t[cdfghjklmnortvwz]|u[agksyz]|v[aceginu]|w[fs]|y[et]|z[amw]"; - // DON'T try to make PRs with changes. Extend TLDs with LinkifyIt.tlds() instead - var tlds_default = "biz|com|edu|gov|net|org|pro|web|xxx|aero|asia|coop|info|museum|name|shop|\u0440\u0444".split("|"); - /*eslint-enable max-len*/ - //////////////////////////////////////////////////////////////////////////////// - function resetScanCache(self) { - self.__index__ = -1; - self.__text_cache__ = ""; - } - function createValidator(re) { - return function(text, pos) { - var tail = text.slice(pos); - if (re.test(tail)) { - return tail.match(re)[0].length; - } - return 0; - }; - } - function createNormalizer() { - return function(match, self) { - self.normalize(match); - }; - } - // Schemas compiler. Build regexps. - - function compile(self) { - // Load & clone RE patterns. - var re$1 = self.re = re(self.__opts__); - // Define dynamic patterns - var tlds = self.__tlds__.slice(); - self.onCompile(); - if (!self.__tlds_replaced__) { - tlds.push(tlds_2ch_src_re); - } - tlds.push(re$1.src_xn); - re$1.src_tlds = tlds.join("|"); - function untpl(tpl) { - return tpl.replace("%TLDS%", re$1.src_tlds); - } - re$1.email_fuzzy = RegExp(untpl(re$1.tpl_email_fuzzy), "i"); - re$1.link_fuzzy = RegExp(untpl(re$1.tpl_link_fuzzy), "i"); - re$1.link_no_ip_fuzzy = RegExp(untpl(re$1.tpl_link_no_ip_fuzzy), "i"); - re$1.host_fuzzy_test = RegExp(untpl(re$1.tpl_host_fuzzy_test), "i"); - - // Compile each schema - - var aliases = []; - self.__compiled__ = {}; - // Reset compiled data - function schemaError(name, val) { - throw new Error('(LinkifyIt) Invalid schema "' + name + '": ' + val); - } - Object.keys(self.__schemas__).forEach((function(name) { - var val = self.__schemas__[name]; - // skip disabled methods - if (val === null) { - return; - } - var compiled = { - validate: null, - link: null - }; - self.__compiled__[name] = compiled; - if (isObject(val)) { - if (isRegExp(val.validate)) { - compiled.validate = createValidator(val.validate); - } else if (isFunction(val.validate)) { - compiled.validate = val.validate; - } else { - schemaError(name, val); - } - if (isFunction(val.normalize)) { - compiled.normalize = val.normalize; - } else if (!val.normalize) { - compiled.normalize = createNormalizer(); - } else { - schemaError(name, val); - } - return; - } - if (isString(val)) { - aliases.push(name); - return; - } - schemaError(name, val); - })); - - // Compile postponed aliases - - aliases.forEach((function(alias) { - if (!self.__compiled__[self.__schemas__[alias]]) { - // Silently fail on missed schemas to avoid errons on disable. - // schemaError(alias, self.__schemas__[alias]); - return; - } - self.__compiled__[alias].validate = self.__compiled__[self.__schemas__[alias]].validate; - self.__compiled__[alias].normalize = self.__compiled__[self.__schemas__[alias]].normalize; - })); - - // Fake record for guessed links - - self.__compiled__[""] = { - validate: null, - normalize: createNormalizer() - }; - - // Build schema condition - - var slist = Object.keys(self.__compiled__).filter((function(name) { - // Filter disabled & fake schemas - return name.length > 0 && self.__compiled__[name]; - })).map(escapeRE).join("|"); - // (?!_) cause 1.5x slowdown - self.re.schema_test = RegExp("(^|(?!_)(?:[><\uff5c]|" + re$1.src_ZPCc + "))(" + slist + ")", "i"); - self.re.schema_search = RegExp("(^|(?!_)(?:[><\uff5c]|" + re$1.src_ZPCc + "))(" + slist + ")", "ig"); - self.re.schema_at_start = RegExp("^" + self.re.schema_search.source, "i"); - self.re.pretest = RegExp("(" + self.re.schema_test.source + ")|(" + self.re.host_fuzzy_test.source + ")|@", "i"); - - // Cleanup - - resetScanCache(self); - } - /** - * class Match - * - * Match result. Single element of array, returned by [[LinkifyIt#match]] - **/ function Match(self, shift) { - var start = self.__index__, end = self.__last_index__, text = self.__text_cache__.slice(start, end); - /** - * Match#schema -> String - * - * Prefix (protocol) for matched string. - **/ this.schema = self.__schema__.toLowerCase(); - /** - * Match#index -> Number - * - * First position of matched string. - **/ this.index = start + shift; - /** - * Match#lastIndex -> Number - * - * Next position after matched string. - **/ this.lastIndex = end + shift; - /** - * Match#raw -> String - * - * Matched string. - **/ this.raw = text; - /** - * Match#text -> String - * - * Notmalized text of matched string. - **/ this.text = text; - /** - * Match#url -> String - * - * Normalized url of matched string. - **/ this.url = text; - } - function createMatch(self, shift) { - var match = new Match(self, shift); - self.__compiled__[match.schema].normalize(match, self); - return match; - } - /** - * class LinkifyIt - **/ - /** - * new LinkifyIt(schemas, options) - * - schemas (Object): Optional. Additional schemas to validate (prefix/validator) - * - options (Object): { fuzzyLink|fuzzyEmail|fuzzyIP: true|false } - * - * Creates new linkifier instance with optional additional schemas. - * Can be called without `new` keyword for convenience. - * - * By default understands: - * - * - `http(s)://...` , `ftp://...`, `mailto:...` & `//...` links - * - "fuzzy" links and emails (example.com, foo@bar.com). - * - * `schemas` is an object, where each key/value describes protocol/rule: - * - * - __key__ - link prefix (usually, protocol name with `:` at the end, `skype:` - * for example). `linkify-it` makes shure that prefix is not preceeded with - * alphanumeric char and symbols. Only whitespaces and punctuation allowed. - * - __value__ - rule to check tail after link prefix - * - _String_ - just alias to existing rule - * - _Object_ - * - _validate_ - validator function (should return matched length on success), - * or `RegExp`. - * - _normalize_ - optional function to normalize text & url of matched result - * (for example, for @twitter mentions). - * - * `options`: - * - * - __fuzzyLink__ - recognige URL-s without `http(s):` prefix. Default `true`. - * - __fuzzyIP__ - allow IPs in fuzzy links above. Can conflict with some texts - * like version numbers. Default `false`. - * - __fuzzyEmail__ - recognize emails without `mailto:` prefix. - * - **/ function LinkifyIt(schemas, options) { - if (!(this instanceof LinkifyIt)) { - return new LinkifyIt(schemas, options); - } - if (!options) { - if (isOptionsObj(schemas)) { - options = schemas; - schemas = {}; - } - } - this.__opts__ = assign({}, defaultOptions, options); - // Cache last tested result. Used to skip repeating steps on next `match` call. - this.__index__ = -1; - this.__last_index__ = -1; - // Next scan position - this.__schema__ = ""; - this.__text_cache__ = ""; - this.__schemas__ = assign({}, defaultSchemas, schemas); - this.__compiled__ = {}; - this.__tlds__ = tlds_default; - this.__tlds_replaced__ = false; - this.re = {}; - compile(this); - } - /** chainable - * LinkifyIt#add(schema, definition) - * - schema (String): rule name (fixed pattern prefix) - * - definition (String|RegExp|Object): schema definition - * - * Add new rule definition. See constructor description for details. - **/ LinkifyIt.prototype.add = function add(schema, definition) { - this.__schemas__[schema] = definition; - compile(this); - return this; - }; - /** chainable - * LinkifyIt#set(options) - * - options (Object): { fuzzyLink|fuzzyEmail|fuzzyIP: true|false } - * - * Set recognition options for links without schema. - **/ LinkifyIt.prototype.set = function set(options) { - this.__opts__ = assign(this.__opts__, options); - return this; - }; - /** - * LinkifyIt#test(text) -> Boolean - * - * Searches linkifiable pattern and returns `true` on success or `false` on fail. - **/ LinkifyIt.prototype.test = function test(text) { - // Reset scan cache - this.__text_cache__ = text; - this.__index__ = -1; - if (!text.length) { - return false; - } - var m, ml, me, len, shift, next, re, tld_pos, at_pos; - // try to scan for link with schema - that's the most simple rule - if (this.re.schema_test.test(text)) { - re = this.re.schema_search; - re.lastIndex = 0; - while ((m = re.exec(text)) !== null) { - len = this.testSchemaAt(text, m[2], re.lastIndex); - if (len) { - this.__schema__ = m[2]; - this.__index__ = m.index + m[1].length; - this.__last_index__ = m.index + m[0].length + len; - break; - } - } - } - if (this.__opts__.fuzzyLink && this.__compiled__["http:"]) { - // guess schemaless links - tld_pos = text.search(this.re.host_fuzzy_test); - if (tld_pos >= 0) { - // if tld is located after found link - no need to check fuzzy pattern - if (this.__index__ < 0 || tld_pos < this.__index__) { - if ((ml = text.match(this.__opts__.fuzzyIP ? this.re.link_fuzzy : this.re.link_no_ip_fuzzy)) !== null) { - shift = ml.index + ml[1].length; - if (this.__index__ < 0 || shift < this.__index__) { - this.__schema__ = ""; - this.__index__ = shift; - this.__last_index__ = ml.index + ml[0].length; - } - } - } - } - } - if (this.__opts__.fuzzyEmail && this.__compiled__["mailto:"]) { - // guess schemaless emails - at_pos = text.indexOf("@"); - if (at_pos >= 0) { - // We can't skip this check, because this cases are possible: - // 192.168.1.1@gmail.com, my.in@example.com - if ((me = text.match(this.re.email_fuzzy)) !== null) { - shift = me.index + me[1].length; - next = me.index + me[0].length; - if (this.__index__ < 0 || shift < this.__index__ || shift === this.__index__ && next > this.__last_index__) { - this.__schema__ = "mailto:"; - this.__index__ = shift; - this.__last_index__ = next; - } - } - } - } - return this.__index__ >= 0; - }; - /** - * LinkifyIt#pretest(text) -> Boolean - * - * Very quick check, that can give false positives. Returns true if link MAY BE - * can exists. Can be used for speed optimization, when you need to check that - * link NOT exists. - **/ LinkifyIt.prototype.pretest = function pretest(text) { - return this.re.pretest.test(text); - }; - /** - * LinkifyIt#testSchemaAt(text, name, position) -> Number - * - text (String): text to scan - * - name (String): rule (schema) name - * - position (Number): text offset to check from - * - * Similar to [[LinkifyIt#test]] but checks only specific protocol tail exactly - * at given position. Returns length of found pattern (0 on fail). - **/ LinkifyIt.prototype.testSchemaAt = function testSchemaAt(text, schema, pos) { - // If not supported schema check requested - terminate - if (!this.__compiled__[schema.toLowerCase()]) { - return 0; - } - return this.__compiled__[schema.toLowerCase()].validate(text, pos, this); - }; - /** - * LinkifyIt#match(text) -> Array|null - * - * Returns array of found link descriptions or `null` on fail. We strongly - * recommend to use [[LinkifyIt#test]] first, for best speed. - * - * ##### Result match description - * - * - __schema__ - link schema, can be empty for fuzzy links, or `//` for - * protocol-neutral links. - * - __index__ - offset of matched text - * - __lastIndex__ - index of next char after mathch end - * - __raw__ - matched text - * - __text__ - normalized text - * - __url__ - link, generated from matched text - **/ LinkifyIt.prototype.match = function match(text) { - var shift = 0, result = []; - // Try to take previous element from cache, if .test() called before - if (this.__index__ >= 0 && this.__text_cache__ === text) { - result.push(createMatch(this, shift)); - shift = this.__last_index__; - } - // Cut head if cache was used - var tail = shift ? text.slice(shift) : text; - // Scan string until end reached - while (this.test(tail)) { - result.push(createMatch(this, shift)); - tail = tail.slice(this.__last_index__); - shift += this.__last_index__; - } - if (result.length) { - return result; - } - return null; - }; - /** - * LinkifyIt#matchAtStart(text) -> Match|null - * - * Returns fully-formed (not fuzzy) link if it starts at the beginning - * of the string, and null otherwise. - **/ LinkifyIt.prototype.matchAtStart = function matchAtStart(text) { - // Reset scan cache - this.__text_cache__ = text; - this.__index__ = -1; - if (!text.length) return null; - var m = this.re.schema_at_start.exec(text); - if (!m) return null; - var len = this.testSchemaAt(text, m[2], m[0].length); - if (!len) return null; - this.__schema__ = m[2]; - this.__index__ = m.index + m[1].length; - this.__last_index__ = m.index + m[0].length + len; - return createMatch(this, 0); - }; - /** chainable - * LinkifyIt#tlds(list [, keepOld]) -> this - * - list (Array): list of tlds - * - keepOld (Boolean): merge with current list if `true` (`false` by default) - * - * Load (or merge) new tlds list. Those are user for fuzzy links (without prefix) - * to avoid false positives. By default this algorythm used: - * - * - hostname with any 2-letter root zones are ok. - * - biz|com|edu|gov|net|org|pro|web|xxx|aero|asia|coop|info|museum|name|shop|рф - * are ok. - * - encoded (`xn--...`) root zones are ok. - * - * If list is replaced, then exact match for 2-chars root zones will be checked. - **/ LinkifyIt.prototype.tlds = function tlds(list, keepOld) { - list = Array.isArray(list) ? list : [ list ]; - if (!keepOld) { - this.__tlds__ = list.slice(); - this.__tlds_replaced__ = true; - compile(this); - return this; - } - this.__tlds__ = this.__tlds__.concat(list).sort().filter((function(el, idx, arr) { - return el !== arr[idx - 1]; - })).reverse(); - compile(this); - return this; - }; - /** - * LinkifyIt#normalize(match) - * - * Default normalizer (if schema does not define it's own). - **/ LinkifyIt.prototype.normalize = function normalize(match) { - // Do minimal possible changes by default. Need to collect feedback prior - // to move forward https://github.com/markdown-it/linkify-it/issues/1 - if (!match.schema) { - match.url = "http://" + match.url; - } - if (match.schema === "mailto:" && !/^mailto:/i.test(match.url)) { - match.url = "mailto:" + match.url; - } - }; - /** - * LinkifyIt#onCompile() - * - * Override to modify basic RegExp-s. - **/ LinkifyIt.prototype.onCompile = function onCompile() {}; - var linkifyIt = LinkifyIt; - /*! https://mths.be/punycode v1.4.1 by @mathias */ - /** Highest positive signed 32-bit float value */ var maxInt = 2147483647; - // aka. 0x7FFFFFFF or 2^31-1 - /** Bootstring parameters */ var base = 36; - var tMin = 1; - var tMax = 26; - var skew = 38; - var damp = 700; - var initialBias = 72; - var initialN = 128; - // 0x80 - var delimiter = "-"; - // '\x2D' - /** Regular expressions */ var regexPunycode = /^xn--/; - var regexNonASCII = /[^\x20-\x7E]/; - // unprintable ASCII chars + non-ASCII chars - var regexSeparators = /[\x2E\u3002\uFF0E\uFF61]/g; - // RFC 3490 separators - /** Error messages */ var errors = { - overflow: "Overflow: input needs wider integers to process", - "not-basic": "Illegal input >= 0x80 (not a basic code point)", - "invalid-input": "Invalid input" - }; - /** Convenience shortcuts */ var baseMinusTMin = base - tMin; - var floor = Math.floor; - var stringFromCharCode = String.fromCharCode; - /*--------------------------------------------------------------------------*/ - /** - * A generic error utility function. - * @private - * @param {String} type The error type. - * @returns {Error} Throws a `RangeError` with the applicable error message. - */ function error(type) { - throw new RangeError(errors[type]); - } - /** - * A generic `Array#map` utility function. - * @private - * @param {Array} array The array to iterate over. - * @param {Function} callback The function that gets called for every array - * item. - * @returns {Array} A new array of values returned by the callback function. - */ function map(array, fn) { - var length = array.length; - var result = []; - while (length--) { - result[length] = fn(array[length]); - } - return result; - } - /** - * A simple `Array#map`-like wrapper to work with domain name strings or email - * addresses. - * @private - * @param {String} domain The domain name or email address. - * @param {Function} callback The function that gets called for every - * character. - * @returns {Array} A new string of characters returned by the callback - * function. - */ function mapDomain(string, fn) { - var parts = string.split("@"); - var result = ""; - if (parts.length > 1) { - // In email addresses, only the domain name should be punycoded. Leave - // the local part (i.e. everything up to `@`) intact. - result = parts[0] + "@"; - string = parts[1]; - } - // Avoid `split(regex)` for IE8 compatibility. See #17. - string = string.replace(regexSeparators, "."); - var labels = string.split("."); - var encoded = map(labels, fn).join("."); - return result + encoded; - } - /** - * Creates an array containing the numeric code points of each Unicode - * character in the string. While JavaScript uses UCS-2 internally, - * this function will convert a pair of surrogate halves (each of which - * UCS-2 exposes as separate characters) into a single code point, - * matching UTF-16. - * @see `punycode.ucs2.encode` - * @see - * @memberOf punycode.ucs2 - * @name decode - * @param {String} string The Unicode input string (UCS-2). - * @returns {Array} The new array of code points. - */ function ucs2decode(string) { - var output = [], counter = 0, length = string.length, value, extra; - while (counter < length) { - value = string.charCodeAt(counter++); - if (value >= 55296 && value <= 56319 && counter < length) { - // high surrogate, and there is a next character - extra = string.charCodeAt(counter++); - if ((extra & 64512) == 56320) { - // low surrogate - output.push(((value & 1023) << 10) + (extra & 1023) + 65536); - } else { - // unmatched surrogate; only append this code unit, in case the next - // code unit is the high surrogate of a surrogate pair - output.push(value); - counter--; - } - } else { - output.push(value); - } - } - return output; - } - /** - * Creates a string based on an array of numeric code points. - * @see `punycode.ucs2.decode` - * @memberOf punycode.ucs2 - * @name encode - * @param {Array} codePoints The array of numeric code points. - * @returns {String} The new Unicode string (UCS-2). - */ function ucs2encode(array) { - return map(array, (function(value) { - var output = ""; - if (value > 65535) { - value -= 65536; - output += stringFromCharCode(value >>> 10 & 1023 | 55296); - value = 56320 | value & 1023; - } - output += stringFromCharCode(value); - return output; - })).join(""); - } - /** - * Converts a basic code point into a digit/integer. - * @see `digitToBasic()` - * @private - * @param {Number} codePoint The basic numeric code point value. - * @returns {Number} The numeric value of a basic code point (for use in - * representing integers) in the range `0` to `base - 1`, or `base` if - * the code point does not represent a value. - */ function basicToDigit(codePoint) { - if (codePoint - 48 < 10) { - return codePoint - 22; - } - if (codePoint - 65 < 26) { - return codePoint - 65; - } - if (codePoint - 97 < 26) { - return codePoint - 97; - } - return base; - } - /** - * Converts a digit/integer into a basic code point. - * @see `basicToDigit()` - * @private - * @param {Number} digit The numeric value of a basic code point. - * @returns {Number} The basic code point whose value (when used for - * representing integers) is `digit`, which needs to be in the range - * `0` to `base - 1`. If `flag` is non-zero, the uppercase form is - * used; else, the lowercase form is used. The behavior is undefined - * if `flag` is non-zero and `digit` has no uppercase form. - */ function digitToBasic(digit, flag) { - // 0..25 map to ASCII a..z or A..Z - // 26..35 map to ASCII 0..9 - return digit + 22 + 75 * (digit < 26) - ((flag != 0) << 5); - } - /** - * Bias adaptation function as per section 3.4 of RFC 3492. - * https://tools.ietf.org/html/rfc3492#section-3.4 - * @private - */ function adapt(delta, numPoints, firstTime) { - var k = 0; - delta = firstTime ? floor(delta / damp) : delta >> 1; - delta += floor(delta / numPoints); - for (;delta > baseMinusTMin * tMax >> 1; k += base) { - delta = floor(delta / baseMinusTMin); - } - return floor(k + (baseMinusTMin + 1) * delta / (delta + skew)); - } - /** - * Converts a Punycode string of ASCII-only symbols to a string of Unicode - * symbols. - * @memberOf punycode - * @param {String} input The Punycode string of ASCII-only symbols. - * @returns {String} The resulting string of Unicode symbols. - */ function decode(input) { - // Don't use UCS-2 - var output = [], inputLength = input.length, out, i = 0, n = initialN, bias = initialBias, basic, j, index, oldi, w, k, digit, t, - /** Cached calculation results */ - baseMinusT; - // Handle the basic code points: let `basic` be the number of input code - // points before the last delimiter, or `0` if there is none, then copy - // the first basic code points to the output. - basic = input.lastIndexOf(delimiter); - if (basic < 0) { - basic = 0; - } - for (j = 0; j < basic; ++j) { - // if it's not a basic code point - if (input.charCodeAt(j) >= 128) { - error("not-basic"); - } - output.push(input.charCodeAt(j)); - } - // Main decoding loop: start just after the last delimiter if any basic code - // points were copied; start at the beginning otherwise. - for (index = basic > 0 ? basic + 1 : 0; index < inputLength; ) { - // `index` is the index of the next character to be consumed. - // Decode a generalized variable-length integer into `delta`, - // which gets added to `i`. The overflow checking is easier - // if we increase `i` as we go, then subtract off its starting - // value at the end to obtain `delta`. - for (oldi = i, w = 1, k = base; ;k += base) { - if (index >= inputLength) { - error("invalid-input"); - } - digit = basicToDigit(input.charCodeAt(index++)); - if (digit >= base || digit > floor((maxInt - i) / w)) { - error("overflow"); - } - i += digit * w; - t = k <= bias ? tMin : k >= bias + tMax ? tMax : k - bias; - if (digit < t) { - break; - } - baseMinusT = base - t; - if (w > floor(maxInt / baseMinusT)) { - error("overflow"); - } - w *= baseMinusT; - } - out = output.length + 1; - bias = adapt(i - oldi, out, oldi == 0); - // `i` was supposed to wrap around from `out` to `0`, - // incrementing `n` each time, so we'll fix that now: - if (floor(i / out) > maxInt - n) { - error("overflow"); - } - n += floor(i / out); - i %= out; - // Insert `n` at position `i` of the output - output.splice(i++, 0, n); - } - return ucs2encode(output); - } - /** - * Converts a string of Unicode symbols (e.g. a domain name label) to a - * Punycode string of ASCII-only symbols. - * @memberOf punycode - * @param {String} input The string of Unicode symbols. - * @returns {String} The resulting Punycode string of ASCII-only symbols. - */ function encode(input) { - var n, delta, handledCPCount, basicLength, bias, j, m, q, k, t, currentValue, output = [], - /** `inputLength` will hold the number of code points in `input`. */ - inputLength, - /** Cached calculation results */ - handledCPCountPlusOne, baseMinusT, qMinusT; - // Convert the input in UCS-2 to Unicode - input = ucs2decode(input); - // Cache the length - inputLength = input.length; - // Initialize the state - n = initialN; - delta = 0; - bias = initialBias; - // Handle the basic code points - for (j = 0; j < inputLength; ++j) { - currentValue = input[j]; - if (currentValue < 128) { - output.push(stringFromCharCode(currentValue)); - } - } - handledCPCount = basicLength = output.length; - // `handledCPCount` is the number of code points that have been handled; - // `basicLength` is the number of basic code points. - // Finish the basic string - if it is not empty - with a delimiter - if (basicLength) { - output.push(delimiter); - } - // Main encoding loop: - while (handledCPCount < inputLength) { - // All non-basic code points < n have been handled already. Find the next - // larger one: - for (m = maxInt, j = 0; j < inputLength; ++j) { - currentValue = input[j]; - if (currentValue >= n && currentValue < m) { - m = currentValue; - } - } - // Increase `delta` enough to advance the decoder's state to , - // but guard against overflow - handledCPCountPlusOne = handledCPCount + 1; - if (m - n > floor((maxInt - delta) / handledCPCountPlusOne)) { - error("overflow"); - } - delta += (m - n) * handledCPCountPlusOne; - n = m; - for (j = 0; j < inputLength; ++j) { - currentValue = input[j]; - if (currentValue < n && ++delta > maxInt) { - error("overflow"); - } - if (currentValue == n) { - // Represent delta as a generalized variable-length integer - for (q = delta, k = base; ;k += base) { - t = k <= bias ? tMin : k >= bias + tMax ? tMax : k - bias; - if (q < t) { - break; - } - qMinusT = q - t; - baseMinusT = base - t; - output.push(stringFromCharCode(digitToBasic(t + qMinusT % baseMinusT, 0))); - q = floor(qMinusT / baseMinusT); - } - output.push(stringFromCharCode(digitToBasic(q, 0))); - bias = adapt(delta, handledCPCountPlusOne, handledCPCount == basicLength); - delta = 0; - ++handledCPCount; - } - } - ++delta; - ++n; - } - return output.join(""); - } - /** - * Converts a Punycode string representing a domain name or an email address - * to Unicode. Only the Punycoded parts of the input will be converted, i.e. - * it doesn't matter if you call it on a string that has already been - * converted to Unicode. - * @memberOf punycode - * @param {String} input The Punycoded domain name or email address to - * convert to Unicode. - * @returns {String} The Unicode representation of the given Punycode - * string. - */ function toUnicode(input) { - return mapDomain(input, (function(string) { - return regexPunycode.test(string) ? decode(string.slice(4).toLowerCase()) : string; - })); - } - /** - * Converts a Unicode string representing a domain name or an email address to - * Punycode. Only the non-ASCII parts of the domain name will be converted, - * i.e. it doesn't matter if you call it with a domain that's already in - * ASCII. - * @memberOf punycode - * @param {String} input The domain name or email address to convert, as a - * Unicode string. - * @returns {String} The Punycode representation of the given domain name or - * email address. - */ function toASCII(input) { - return mapDomain(input, (function(string) { - return regexNonASCII.test(string) ? "xn--" + encode(string) : string; - })); - } - var version = "1.4.1"; - /** - * An object of methods to convert from JavaScript's internal character - * representation (UCS-2) to Unicode code points, and back. - * @see - * @memberOf punycode - * @type Object - */ var ucs2 = { - decode: ucs2decode, - encode: ucs2encode - }; - var punycode$1 = { - version: version, - ucs2: ucs2, - toASCII: toASCII, - toUnicode: toUnicode, - encode: encode, - decode: decode - }; - var punycode$2 = Object.freeze({ - __proto__: null, - decode: decode, - encode: encode, - toUnicode: toUnicode, - toASCII: toASCII, - version: version, - ucs2: ucs2, - default: punycode$1 - }); - // markdown-it default options - var _default = { - options: { - html: false, - // Enable HTML tags in source - xhtmlOut: false, - // Use '/' to close single tags (
) - breaks: false, - // Convert '\n' in paragraphs into
- langPrefix: "language-", - // CSS language prefix for fenced blocks - linkify: false, - // autoconvert URL-like texts to links - // Enable some language-neutral replacements + quotes beautification - typographer: false, - // Double + single quotes replacement pairs, when typographer enabled, - // and smartquotes on. Could be either a String or an Array. - // For example, you can use '«»„“' for Russian, '„“‚‘' for German, - // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). - quotes: "\u201c\u201d\u2018\u2019", - /* “”‘’ */ - // Highlighter function. Should return escaped HTML, - // or '' if the source string is not changed and should be escaped externaly. - // If result starts with ) - breaks: false, - // Convert '\n' in paragraphs into
- langPrefix: "language-", - // CSS language prefix for fenced blocks - linkify: false, - // autoconvert URL-like texts to links - // Enable some language-neutral replacements + quotes beautification - typographer: false, - // Double + single quotes replacement pairs, when typographer enabled, - // and smartquotes on. Could be either a String or an Array. - // For example, you can use '«»„“' for Russian, '„“‚‘' for German, - // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). - quotes: "\u201c\u201d\u2018\u2019", - /* “”‘’ */ - // Highlighter function. Should return escaped HTML, - // or '' if the source string is not changed and should be escaped externaly. - // If result starts with ) - breaks: false, - // Convert '\n' in paragraphs into
- langPrefix: "language-", - // CSS language prefix for fenced blocks - linkify: false, - // autoconvert URL-like texts to links - // Enable some language-neutral replacements + quotes beautification - typographer: false, - // Double + single quotes replacement pairs, when typographer enabled, - // and smartquotes on. Could be either a String or an Array. - // For example, you can use '«»„“' for Russian, '„“‚‘' for German, - // and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp). - quotes: "\u201c\u201d\u2018\u2019", - /* “”‘’ */ - // Highlighter function. Should return escaped HTML, - // or '' if the source string is not changed and should be escaped externaly. - // If result starts with = 0) { - try { - parsed.hostname = punycode.toASCII(parsed.hostname); - } catch (er) {} - } - } - return mdurl.encode(mdurl.format(parsed)); - } - function normalizeLinkText(url) { - var parsed = mdurl.parse(url, true); - if (parsed.hostname) { - // Encode hostnames in urls like: - // `http://host/`, `https://host/`, `mailto:user@host`, `//host/` - // We don't encode unknown schemas, because it's likely that we encode - // something we shouldn't (e.g. `skype:name` treated as `skype:host`) - if (!parsed.protocol || RECODE_HOSTNAME_FOR.indexOf(parsed.protocol) >= 0) { - try { - parsed.hostname = punycode.toUnicode(parsed.hostname); - } catch (er) {} - } - } - // add '%' to exclude list because of https://github.com/markdown-it/markdown-it/issues/720 - return mdurl.decode(mdurl.format(parsed), mdurl.decode.defaultChars + "%"); - } - /** - * class MarkdownIt - * - * Main parser/renderer class. - * - * ##### Usage - * - * ```javascript - * // node.js, "classic" way: - * var MarkdownIt = require('markdown-it'), - * md = new MarkdownIt(); - * var result = md.render('# markdown-it rulezz!'); - * - * // node.js, the same, but with sugar: - * var md = require('markdown-it')(); - * var result = md.render('# markdown-it rulezz!'); - * - * // browser without AMD, added to "window" on script load - * // Note, there are no dash. - * var md = window.markdownit(); - * var result = md.render('# markdown-it rulezz!'); - * ``` - * - * Single line rendering, without paragraph wrap: - * - * ```javascript - * var md = require('markdown-it')(); - * var result = md.renderInline('__markdown-it__ rulezz!'); - * ``` - **/ - /** - * new MarkdownIt([presetName, options]) - * - presetName (String): optional, `commonmark` / `zero` - * - options (Object) - * - * Creates parser instanse with given config. Can be called without `new`. - * - * ##### presetName - * - * MarkdownIt provides named presets as a convenience to quickly - * enable/disable active syntax rules and options for common use cases. - * - * - ["commonmark"](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/commonmark.js) - - * configures parser to strict [CommonMark](http://commonmark.org/) mode. - * - [default](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/default.js) - - * similar to GFM, used when no preset name given. Enables all available rules, - * but still without html, typographer & autolinker. - * - ["zero"](https://github.com/markdown-it/markdown-it/blob/master/lib/presets/zero.js) - - * all rules disabled. Useful to quickly setup your config via `.enable()`. - * For example, when you need only `bold` and `italic` markup and nothing else. - * - * ##### options: - * - * - __html__ - `false`. Set `true` to enable HTML tags in source. Be careful! - * That's not safe! You may need external sanitizer to protect output from XSS. - * It's better to extend features via plugins, instead of enabling HTML. - * - __xhtmlOut__ - `false`. Set `true` to add '/' when closing single tags - * (`
`). This is needed only for full CommonMark compatibility. In real - * world you will need HTML output. - * - __breaks__ - `false`. Set `true` to convert `\n` in paragraphs into `
`. - * - __langPrefix__ - `language-`. CSS language class prefix for fenced blocks. - * Can be useful for external highlighters. - * - __linkify__ - `false`. Set `true` to autoconvert URL-like text to links. - * - __typographer__ - `false`. Set `true` to enable [some language-neutral - * replacement](https://github.com/markdown-it/markdown-it/blob/master/lib/rules_core/replacements.js) + - * quotes beautification (smartquotes). - * - __quotes__ - `“”‘’`, String or Array. Double + single quotes replacement - * pairs, when typographer enabled and smartquotes on. For example, you can - * use `'«»„“'` for Russian, `'„“‚‘'` for German, and - * `['«\xA0', '\xA0»', '‹\xA0', '\xA0›']` for French (including nbsp). - * - __highlight__ - `null`. Highlighter function for fenced code blocks. - * Highlighter `function (str, lang)` should return escaped HTML. It can also - * return empty string if the source was not changed and should be escaped - * externaly. If result starts with `): - * - * ```javascript - * var hljs = require('highlight.js') // https://highlightjs.org/ - * - * // Actual default values - * var md = require('markdown-it')({ - * highlight: function (str, lang) { - * if (lang && hljs.getLanguage(lang)) { - * try { - * return '
' +
-	 *                hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
-	 *                '
'; - * } catch (__) {} - * } - * - * return '
' + md.utils.escapeHtml(str) + '
'; - * } - * }); - * ``` - * - **/ function MarkdownIt(presetName, options) { - if (!(this instanceof MarkdownIt)) { - return new MarkdownIt(presetName, options); - } - if (!options) { - if (!utils.isString(presetName)) { - options = presetName || {}; - presetName = "default"; - } - } - /** - * MarkdownIt#inline -> ParserInline - * - * Instance of [[ParserInline]]. You may need it to add new rules when - * writing plugins. For simple rules control use [[MarkdownIt.disable]] and - * [[MarkdownIt.enable]]. - **/ this.inline = new parser_inline; - /** - * MarkdownIt#block -> ParserBlock - * - * Instance of [[ParserBlock]]. You may need it to add new rules when - * writing plugins. For simple rules control use [[MarkdownIt.disable]] and - * [[MarkdownIt.enable]]. - **/ this.block = new parser_block; - /** - * MarkdownIt#core -> Core - * - * Instance of [[Core]] chain executor. You may need it to add new rules when - * writing plugins. For simple rules control use [[MarkdownIt.disable]] and - * [[MarkdownIt.enable]]. - **/ this.core = new parser_core; - /** - * MarkdownIt#renderer -> Renderer - * - * Instance of [[Renderer]]. Use it to modify output look. Or to add rendering - * rules for new token types, generated by plugins. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')(); - * - * function myToken(tokens, idx, options, env, self) { - * //... - * return result; - * }; - * - * md.renderer.rules['my_token'] = myToken - * ``` - * - * See [[Renderer]] docs and [source code](https://github.com/markdown-it/markdown-it/blob/master/lib/renderer.js). - **/ this.renderer = new renderer; - /** - * MarkdownIt#linkify -> LinkifyIt - * - * [linkify-it](https://github.com/markdown-it/linkify-it) instance. - * Used by [linkify](https://github.com/markdown-it/markdown-it/blob/master/lib/rules_core/linkify.js) - * rule. - **/ this.linkify = new linkifyIt; - /** - * MarkdownIt#validateLink(url) -> Boolean - * - * Link validation function. CommonMark allows too much in links. By default - * we disable `javascript:`, `vbscript:`, `file:` schemas, and almost all `data:...` schemas - * except some embedded image types. - * - * You can change this behaviour: - * - * ```javascript - * var md = require('markdown-it')(); - * // enable everything - * md.validateLink = function () { return true; } - * ``` - **/ this.validateLink = validateLink; - /** - * MarkdownIt#normalizeLink(url) -> String - * - * Function used to encode link url to a machine-readable format, - * which includes url-encoding, punycode, etc. - **/ this.normalizeLink = normalizeLink; - /** - * MarkdownIt#normalizeLinkText(url) -> String - * - * Function used to decode link url to a human-readable format` - **/ this.normalizeLinkText = normalizeLinkText; - // Expose utils & helpers for easy acces from plugins - /** - * MarkdownIt#utils -> utils - * - * Assorted utility functions, useful to write plugins. See details - * [here](https://github.com/markdown-it/markdown-it/blob/master/lib/common/utils.js). - **/ this.utils = utils; - /** - * MarkdownIt#helpers -> helpers - * - * Link components parser functions, useful to write plugins. See details - * [here](https://github.com/markdown-it/markdown-it/blob/master/lib/helpers). - **/ this.helpers = utils.assign({}, helpers); - this.options = {}; - this.configure(presetName); - if (options) { - this.set(options); - } - } - /** chainable - * MarkdownIt.set(options) - * - * Set parser options (in the same format as in constructor). Probably, you - * will never need it, but you can change options after constructor call. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')() - * .set({ html: true, breaks: true }) - * .set({ typographer, true }); - * ``` - * - * __Note:__ To achieve the best possible performance, don't modify a - * `markdown-it` instance options on the fly. If you need multiple configurations - * it's best to create multiple instances and initialize each with separate - * config. - **/ MarkdownIt.prototype.set = function(options) { - utils.assign(this.options, options); - return this; - }; - /** chainable, internal - * MarkdownIt.configure(presets) - * - * Batch load of all options and compenent settings. This is internal method, - * and you probably will not need it. But if you will - see available presets - * and data structure [here](https://github.com/markdown-it/markdown-it/tree/master/lib/presets) - * - * We strongly recommend to use presets instead of direct config loads. That - * will give better compatibility with next versions. - **/ MarkdownIt.prototype.configure = function(presets) { - var self = this, presetName; - if (utils.isString(presets)) { - presetName = presets; - presets = config[presetName]; - if (!presets) { - throw new Error('Wrong `markdown-it` preset "' + presetName + '", check name'); - } - } - if (!presets) { - throw new Error("Wrong `markdown-it` preset, can't be empty"); - } - if (presets.options) { - self.set(presets.options); - } - if (presets.components) { - Object.keys(presets.components).forEach((function(name) { - if (presets.components[name].rules) { - self[name].ruler.enableOnly(presets.components[name].rules); - } - if (presets.components[name].rules2) { - self[name].ruler2.enableOnly(presets.components[name].rules2); - } - })); - } - return this; - }; - /** chainable - * MarkdownIt.enable(list, ignoreInvalid) - * - list (String|Array): rule name or list of rule names to enable - * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. - * - * Enable list or rules. It will automatically find appropriate components, - * containing rules with given names. If rule not found, and `ignoreInvalid` - * not set - throws exception. - * - * ##### Example - * - * ```javascript - * var md = require('markdown-it')() - * .enable(['sub', 'sup']) - * .disable('smartquotes'); - * ``` - **/ MarkdownIt.prototype.enable = function(list, ignoreInvalid) { - var result = []; - if (!Array.isArray(list)) { - list = [ list ]; - } - [ "core", "block", "inline" ].forEach((function(chain) { - result = result.concat(this[chain].ruler.enable(list, true)); - }), this); - result = result.concat(this.inline.ruler2.enable(list, true)); - var missed = list.filter((function(name) { - return result.indexOf(name) < 0; - })); - if (missed.length && !ignoreInvalid) { - throw new Error("MarkdownIt. Failed to enable unknown rule(s): " + missed); - } - return this; - }; - /** chainable - * MarkdownIt.disable(list, ignoreInvalid) - * - list (String|Array): rule name or list of rule names to disable. - * - ignoreInvalid (Boolean): set `true` to ignore errors when rule not found. - * - * The same as [[MarkdownIt.enable]], but turn specified rules off. - **/ MarkdownIt.prototype.disable = function(list, ignoreInvalid) { - var result = []; - if (!Array.isArray(list)) { - list = [ list ]; - } - [ "core", "block", "inline" ].forEach((function(chain) { - result = result.concat(this[chain].ruler.disable(list, true)); - }), this); - result = result.concat(this.inline.ruler2.disable(list, true)); - var missed = list.filter((function(name) { - return result.indexOf(name) < 0; - })); - if (missed.length && !ignoreInvalid) { - throw new Error("MarkdownIt. Failed to disable unknown rule(s): " + missed); - } - return this; - }; - /** chainable - * MarkdownIt.use(plugin, params) - * - * Load specified plugin with given params into current parser instance. - * It's just a sugar to call `plugin(md, params)` with curring. - * - * ##### Example - * - * ```javascript - * var iterator = require('markdown-it-for-inline'); - * var md = require('markdown-it')() - * .use(iterator, 'foo_replace', 'text', function (tokens, idx) { - * tokens[idx].content = tokens[idx].content.replace(/foo/g, 'bar'); - * }); - * ``` - **/ MarkdownIt.prototype.use = function(plugin /*, params, ... */) { - var args = [ this ].concat(Array.prototype.slice.call(arguments, 1)); - plugin.apply(plugin, args); - return this; - }; - /** internal - * MarkdownIt.parse(src, env) -> Array - * - src (String): source string - * - env (Object): environment sandbox - * - * Parse input string and return list of block tokens (special token type - * "inline" will contain list of inline tokens). You should not call this - * method directly, until you write custom renderer (for example, to produce - * AST). - * - * `env` is used to pass data between "distributed" rules and return additional - * metadata like reference info, needed for the renderer. It also can be used to - * inject data in specific cases. Usually, you will be ok to pass `{}`, - * and then pass updated object to renderer. - **/ MarkdownIt.prototype.parse = function(src, env) { - if (typeof src !== "string") { - throw new Error("Input data should be a String"); - } - var state = new this.core.State(src, this, env); - this.core.process(state); - return state.tokens; - }; - /** - * MarkdownIt.render(src [, env]) -> String - * - src (String): source string - * - env (Object): environment sandbox - * - * Render markdown string into html. It does all magic for you :). - * - * `env` can be used to inject additional metadata (`{}` by default). - * But you will not need it with high probability. See also comment - * in [[MarkdownIt.parse]]. - **/ MarkdownIt.prototype.render = function(src, env) { - env = env || {}; - return this.renderer.render(this.parse(src, env), this.options, env); - }; - /** internal - * MarkdownIt.parseInline(src, env) -> Array - * - src (String): source string - * - env (Object): environment sandbox - * - * The same as [[MarkdownIt.parse]] but skip all block rules. It returns the - * block tokens list with the single `inline` element, containing parsed inline - * tokens in `children` property. Also updates `env` object. - **/ MarkdownIt.prototype.parseInline = function(src, env) { - var state = new this.core.State(src, this, env); - state.inlineMode = true; - this.core.process(state); - return state.tokens; - }; - /** - * MarkdownIt.renderInline(src [, env]) -> String - * - src (String): source string - * - env (Object): environment sandbox - * - * Similar to [[MarkdownIt.render]] but for single paragraph content. Result - * will NOT be wrapped into `

` tags. - **/ MarkdownIt.prototype.renderInline = function(src, env) { - env = env || {}; - return this.renderer.render(this.parseInline(src, env), this.options, env); - }; - var lib = MarkdownIt; - var markdownIt = lib; - return markdownIt; -})); - diff --git a/examples/server/public/deps_tailwindcss.js b/examples/server/public/deps_tailwindcss.js deleted file mode 100644 index 6736cb8ca7d16..0000000000000 --- a/examples/server/public/deps_tailwindcss.js +++ /dev/null @@ -1,82 +0,0 @@ -(()=>{var Iv=Object.create;var Ui=Object.defineProperty;var Dv=Object.getOwnPropertyDescriptor;var qv=Object.getOwnPropertyNames;var $v=Object.getPrototypeOf,Lv=Object.prototype.hasOwnProperty;var cf=r=>Ui(r,"__esModule",{value:!0});var pf=r=>{if(typeof require!="undefined")return require(r);throw new Error('Dynamic require of "'+r+'" is not supported')};var R=(r,e)=>()=>(r&&(e=r(r=0)),e);var x=(r,e)=>()=>(e||r((e={exports:{}}).exports,e),e.exports),Ge=(r,e)=>{cf(r);for(var t in 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- const length = Math.max( - 1, - end > count ? lineLength - pad : end - start - ); - res.push(` | ` + " ".repeat(pad) + "^".repeat(length)); - } else if (j > i) { - if (end > count) { - const length = Math.max(Math.min(end - count, lineLength), 1); - res.push(` | ` + "^".repeat(length)); - } - count += lineLength + newLineSeqLength; - } - } - break; - } - } - return res.join("\n"); -} - -function normalizeStyle(value) { - if (isArray(value)) { - const res = {}; - for (let i = 0; i < value.length; i++) { - const item = value[i]; - const normalized = isString(item) ? parseStringStyle(item) : normalizeStyle(item); - if (normalized) { - for (const key in normalized) { - res[key] = normalized[key]; - } - } - } - return res; - } else if (isString(value) || isObject(value)) { - return value; - } -} -const listDelimiterRE = /;(?![^(]*\))/g; -const propertyDelimiterRE = /:([^]+)/; -const styleCommentRE = /\/\*[^]*?\*\//g; -function parseStringStyle(cssText) { - const ret = {}; - cssText.replace(styleCommentRE, "").split(listDelimiterRE).forEach((item) => { - if (item) { - const tmp = item.split(propertyDelimiterRE); - tmp.length > 1 && (ret[tmp[0].trim()] = tmp[1].trim()); - } - }); - return ret; -} -function stringifyStyle(styles) { - let ret = ""; - if (!styles || isString(styles)) { - return ret; - } - for (const key in styles) { - const value = styles[key]; - if (isString(value) || typeof value === "number") { - const normalizedKey = key.startsWith(`--`) ? key : hyphenate(key); - ret += `${normalizedKey}:${value};`; - } - } - return ret; -} -function normalizeClass(value) { - let res = ""; - if (isString(value)) { - res = value; - } else if (isArray(value)) { - for (let i = 0; i < value.length; i++) { - const normalized = normalizeClass(value[i]); - if (normalized) { - res += normalized + " "; - } - } - } else if (isObject(value)) { - for (const name in value) { - if (value[name]) { - res += name + " "; - } - } - } - return res.trim(); -} -function normalizeProps(props) { - if (!props) return null; - let { class: klass, style } = props; - if (klass && !isString(klass)) { - props.class = normalizeClass(klass); - } - if (style) { - props.style = normalizeStyle(style); - } - return props; -} - -const HTML_TAGS = "html,body,base,head,link,meta,style,title,address,article,aside,footer,header,hgroup,h1,h2,h3,h4,h5,h6,nav,section,div,dd,dl,dt,figcaption,figure,picture,hr,img,li,main,ol,p,pre,ul,a,b,abbr,bdi,bdo,br,cite,code,data,dfn,em,i,kbd,mark,q,rp,rt,ruby,s,samp,small,span,strong,sub,sup,time,u,var,wbr,area,audio,map,track,video,embed,object,param,source,canvas,script,noscript,del,ins,caption,col,colgroup,table,thead,tbody,td,th,tr,button,datalist,fieldset,form,input,label,legend,meter,optgroup,option,output,progress,select,textarea,details,dialog,menu,summary,template,blockquote,iframe,tfoot"; -const SVG_TAGS = "svg,animate,animateMotion,animateTransform,circle,clipPath,color-profile,defs,desc,discard,ellipse,feBlend,feColorMatrix,feComponentTransfer,feComposite,feConvolveMatrix,feDiffuseLighting,feDisplacementMap,feDistantLight,feDropShadow,feFlood,feFuncA,feFuncB,feFuncG,feFuncR,feGaussianBlur,feImage,feMerge,feMergeNode,feMorphology,feOffset,fePointLight,feSpecularLighting,feSpotLight,feTile,feTurbulence,filter,foreignObject,g,hatch,hatchpath,image,line,linearGradient,marker,mask,mesh,meshgradient,meshpatch,meshrow,metadata,mpath,path,pattern,polygon,polyline,radialGradient,rect,set,solidcolor,stop,switch,symbol,text,textPath,title,tspan,unknown,use,view"; -const MATH_TAGS = "annotation,annotation-xml,maction,maligngroup,malignmark,math,menclose,merror,mfenced,mfrac,mfraction,mglyph,mi,mlabeledtr,mlongdiv,mmultiscripts,mn,mo,mover,mpadded,mphantom,mprescripts,mroot,mrow,ms,mscarries,mscarry,msgroup,msline,mspace,msqrt,msrow,mstack,mstyle,msub,msubsup,msup,mtable,mtd,mtext,mtr,munder,munderover,none,semantics"; -const VOID_TAGS = "area,base,br,col,embed,hr,img,input,link,meta,param,source,track,wbr"; -const isHTMLTag = /* @__PURE__ */ makeMap(HTML_TAGS); -const isSVGTag = /* @__PURE__ */ makeMap(SVG_TAGS); -const isMathMLTag = /* @__PURE__ */ makeMap(MATH_TAGS); -const isVoidTag = /* @__PURE__ */ makeMap(VOID_TAGS); - -const specialBooleanAttrs = `itemscope,allowfullscreen,formnovalidate,ismap,nomodule,novalidate,readonly`; -const isSpecialBooleanAttr = /* @__PURE__ */ makeMap(specialBooleanAttrs); -const isBooleanAttr = /* @__PURE__ */ makeMap( - specialBooleanAttrs + `,async,autofocus,autoplay,controls,default,defer,disabled,hidden,inert,loop,open,required,reversed,scoped,seamless,checked,muted,multiple,selected` -); -function includeBooleanAttr(value) { - return !!value || value === ""; -} -const isKnownHtmlAttr = /* @__PURE__ */ makeMap( - `accept,accept-charset,accesskey,action,align,allow,alt,async,autocapitalize,autocomplete,autofocus,autoplay,background,bgcolor,border,buffered,capture,challenge,charset,checked,cite,class,code,codebase,color,cols,colspan,content,contenteditable,contextmenu,controls,coords,crossorigin,csp,data,datetime,decoding,default,defer,dir,dirname,disabled,download,draggable,dropzone,enctype,enterkeyhint,for,form,formaction,formenctype,formmethod,formnovalidate,formtarget,headers,height,hidden,high,href,hreflang,http-equiv,icon,id,importance,inert,integrity,ismap,itemprop,keytype,kind,label,lang,language,loading,list,loop,low,manifest,max,maxlength,minlength,media,min,multiple,muted,name,novalidate,open,optimum,pattern,ping,placeholder,poster,preload,radiogroup,readonly,referrerpolicy,rel,required,reversed,rows,rowspan,sandbox,scope,scoped,selected,shape,size,sizes,slot,span,spellcheck,src,srcdoc,srclang,srcset,start,step,style,summary,tabindex,target,title,translate,type,usemap,value,width,wrap` -); -const isKnownSvgAttr = /* @__PURE__ */ makeMap( - `xmlns,accent-height,accumulate,additive,alignment-baseline,alphabetic,amplitude,arabic-form,ascent,attributeName,attributeType,azimuth,baseFrequency,baseline-shift,baseProfile,bbox,begin,bias,by,calcMode,cap-height,class,clip,clipPathUnits,clip-path,clip-rule,color,color-interpolation,color-interpolation-filters,color-profile,color-rendering,contentScriptType,contentStyleType,crossorigin,cursor,cx,cy,d,decelerate,descent,diffuseConstant,direction,display,divisor,dominant-baseline,dur,dx,dy,edgeMode,elevation,enable-background,end,exponent,fill,fill-opacity,fill-rule,filter,filterRes,filterUnits,flood-color,flood-opacity,font-family,font-size,font-size-adjust,font-stretch,font-style,font-variant,font-weight,format,from,fr,fx,fy,g1,g2,glyph-name,glyph-orientation-horizontal,glyph-orientation-vertical,glyphRef,gradientTransform,gradientUnits,hanging,height,href,hreflang,horiz-adv-x,horiz-origin-x,id,ideographic,image-rendering,in,in2,intercept,k,k1,k2,k3,k4,kernelMatrix,kernelUnitLength,kerning,keyPoints,keySplines,keyTimes,lang,lengthAdjust,letter-spacing,lighting-color,limitingConeAngle,local,marker-end,marker-mid,marker-start,markerHeight,markerUnits,markerWidth,mask,maskContentUnits,maskUnits,mathematical,max,media,method,min,mode,name,numOctaves,offset,opacity,operator,order,orient,orientation,origin,overflow,overline-position,overline-thickness,panose-1,paint-order,path,pathLength,patternContentUnits,patternTransform,patternUnits,ping,pointer-events,points,pointsAtX,pointsAtY,pointsAtZ,preserveAlpha,preserveAspectRatio,primitiveUnits,r,radius,referrerPolicy,refX,refY,rel,rendering-intent,repeatCount,repeatDur,requiredExtensions,requiredFeatures,restart,result,rotate,rx,ry,scale,seed,shape-rendering,slope,spacing,specularConstant,specularExponent,speed,spreadMethod,startOffset,stdDeviation,stemh,stemv,stitchTiles,stop-color,stop-opacity,strikethrough-position,strikethrough-thickness,string,stroke,stroke-dasharray,stroke-dashoffset,stroke-linecap,stroke-linejoin,stroke-miterlimit,stroke-opacity,stroke-width,style,surfaceScale,systemLanguage,tabindex,tableValues,target,targetX,targetY,text-anchor,text-decoration,text-rendering,textLength,to,transform,transform-origin,type,u1,u2,underline-position,underline-thickness,unicode,unicode-bidi,unicode-range,units-per-em,v-alphabetic,v-hanging,v-ideographic,v-mathematical,values,vector-effect,version,vert-adv-y,vert-origin-x,vert-origin-y,viewBox,viewTarget,visibility,width,widths,word-spacing,writing-mode,x,x-height,x1,x2,xChannelSelector,xlink:actuate,xlink:arcrole,xlink:href,xlink:role,xlink:show,xlink:title,xlink:type,xmlns:xlink,xml:base,xml:lang,xml:space,y,y1,y2,yChannelSelector,z,zoomAndPan` -); -function isRenderableAttrValue(value) { - if (value == null) { - return false; - } - const type = typeof value; - return type === "string" || type === "number" || type === "boolean"; -} - -const cssVarNameEscapeSymbolsRE = /[ !"#$%&'()*+,./:;<=>?@[\\\]^`{|}~]/g; -function getEscapedCssVarName(key, doubleEscape) { - return key.replace( - cssVarNameEscapeSymbolsRE, - (s) => `\\${s}` - ); -} - -function looseCompareArrays(a, b) { - if (a.length !== b.length) return false; - let equal = true; - for (let i = 0; equal && i < a.length; i++) { - equal = looseEqual(a[i], b[i]); - } - return equal; -} -function looseEqual(a, b) { - if (a === b) return true; - let aValidType = isDate(a); - let bValidType = isDate(b); - if (aValidType || bValidType) { - return aValidType && bValidType ? a.getTime() === b.getTime() : false; - } - aValidType = isSymbol(a); - bValidType = isSymbol(b); - if (aValidType || bValidType) { - return a === b; - } - aValidType = isArray(a); - bValidType = isArray(b); - if (aValidType || bValidType) { - return aValidType && bValidType ? looseCompareArrays(a, b) : false; - } - aValidType = isObject(a); - bValidType = isObject(b); - if (aValidType || bValidType) { - if (!aValidType || !bValidType) { - return false; - } - const aKeysCount = Object.keys(a).length; - const bKeysCount = Object.keys(b).length; - if (aKeysCount !== bKeysCount) { - return false; - } - for (const key in a) { - const aHasKey = a.hasOwnProperty(key); - const bHasKey = b.hasOwnProperty(key); - if (aHasKey && !bHasKey || !aHasKey && bHasKey || !looseEqual(a[key], b[key])) { - return false; - } - } - } - return String(a) === String(b); -} -function looseIndexOf(arr, val) { - return arr.findIndex((item) => looseEqual(item, val)); -} - -const isRef$1 = (val) => { - return !!(val && val["__v_isRef"] === true); -}; -const toDisplayString = (val) => { - return isString(val) ? val : val == null ? "" : isArray(val) || isObject(val) && (val.toString === objectToString || !isFunction(val.toString)) ? isRef$1(val) ? toDisplayString(val.value) : JSON.stringify(val, replacer, 2) : String(val); -}; -const replacer = (_key, val) => { - if (isRef$1(val)) { - return replacer(_key, val.value); - } else if (isMap(val)) { - return { - [`Map(${val.size})`]: [...val.entries()].reduce( - (entries, [key, val2], i) => { - entries[stringifySymbol(key, i) + " =>"] = val2; - return entries; - }, - {} - ) - }; - } else if (isSet(val)) { - return { - [`Set(${val.size})`]: [...val.values()].map((v) => stringifySymbol(v)) - }; - } else if (isSymbol(val)) { - return stringifySymbol(val); - } else if (isObject(val) && !isArray(val) && !isPlainObject(val)) { - return String(val); - } - return val; -}; -const stringifySymbol = (v, i = "") => { - var _a; - return ( - // Symbol.description in es2019+ so we need to cast here to pass - // the lib: es2016 check - isSymbol(v) ? `Symbol(${(_a = v.description) != null ? _a : i})` : v - ); -}; - -function warn$2(msg, ...args) { - console.warn(`[Vue warn] ${msg}`, ...args); -} - -let activeEffectScope; -class EffectScope { - constructor(detached = false) { - this.detached = detached; - /** - * @internal - */ - this._active = true; - /** - * @internal - */ - this.effects = []; - /** - * @internal - */ - this.cleanups = []; - this._isPaused = false; - this.parent = activeEffectScope; - if (!detached && activeEffectScope) { - this.index = (activeEffectScope.scopes || (activeEffectScope.scopes = [])).push( - this - ) - 1; - } - } - get active() { - return this._active; - } - pause() { - if (this._active) { - this._isPaused = true; - let i, l; - if (this.scopes) { - for (i = 0, l = this.scopes.length; i < l; i++) { - this.scopes[i].pause(); - } - } - for (i = 0, l = this.effects.length; i < l; i++) { - this.effects[i].pause(); - } - } - } - /** - * Resumes the effect scope, including all child scopes and effects. - */ - resume() { - if (this._active) { - if (this._isPaused) { - this._isPaused = false; - let i, l; - if (this.scopes) { - for (i = 0, l = this.scopes.length; i < l; i++) { - this.scopes[i].resume(); - } - } - for (i = 0, l = this.effects.length; i < l; i++) { - this.effects[i].resume(); - } - } - } - } - run(fn) { - if (this._active) { - const currentEffectScope = activeEffectScope; - try { - activeEffectScope = this; - return fn(); - } finally { - activeEffectScope = currentEffectScope; - } - } else { - warn$2(`cannot run an inactive effect scope.`); - } - } - /** - * This should only be called on non-detached scopes - * @internal - */ - on() { - activeEffectScope = this; - } - /** - * This should only be called on non-detached scopes - * @internal - */ - off() { - activeEffectScope = this.parent; - } - stop(fromParent) { - if (this._active) { - let i, l; - for (i = 0, l = this.effects.length; i < l; i++) { - this.effects[i].stop(); - } - for (i = 0, l = this.cleanups.length; i < l; i++) { - this.cleanups[i](); - } - if (this.scopes) { - for (i = 0, l = this.scopes.length; i < l; i++) { - this.scopes[i].stop(true); - } - } - if (!this.detached && this.parent && !fromParent) { - const last = this.parent.scopes.pop(); - if (last && last !== this) { - this.parent.scopes[this.index] = last; - last.index = this.index; - } - } - this.parent = void 0; - this._active = false; - } - } -} -function effectScope(detached) { - return new EffectScope(detached); -} -function getCurrentScope() { - return activeEffectScope; -} -function onScopeDispose(fn, failSilently = false) { - if (activeEffectScope) { - activeEffectScope.cleanups.push(fn); - } else if (!failSilently) { - warn$2( - `onScopeDispose() is called when there is no active effect scope to be associated with.` - ); - } -} - -let activeSub; -const pausedQueueEffects = /* @__PURE__ */ new WeakSet(); -class ReactiveEffect { - constructor(fn) { - this.fn = fn; - /** - * @internal - */ - this.deps = void 0; - /** - * @internal - */ - this.depsTail = void 0; - /** - * @internal - */ - this.flags = 1 | 4; - /** - * @internal - */ - this.next = void 0; - /** - * @internal - */ - this.cleanup = void 0; - this.scheduler = void 0; - if (activeEffectScope && activeEffectScope.active) { - activeEffectScope.effects.push(this); - } - } - pause() { - this.flags |= 64; - } - resume() { - if (this.flags & 64) { - this.flags &= ~64; - if (pausedQueueEffects.has(this)) { - pausedQueueEffects.delete(this); - this.trigger(); - } - } - } - /** - * @internal - */ - notify() { - if (this.flags & 2 && !(this.flags & 32)) { - return; - } - if (!(this.flags & 8)) { - batch(this); - } - } - run() { - if (!(this.flags & 1)) { - return this.fn(); - } - this.flags |= 2; - cleanupEffect(this); - prepareDeps(this); - const prevEffect = activeSub; - const prevShouldTrack = shouldTrack; - activeSub = this; - shouldTrack = true; - try { - return this.fn(); - } finally { - if (activeSub !== this) { - warn$2( - "Active effect was not restored correctly - this is likely a Vue internal bug." - ); - } - cleanupDeps(this); - activeSub = prevEffect; - shouldTrack = prevShouldTrack; - this.flags &= ~2; - } - } - stop() { - if (this.flags & 1) { - for (let link = this.deps; link; link = link.nextDep) { - removeSub(link); - } - this.deps = this.depsTail = void 0; - cleanupEffect(this); - this.onStop && this.onStop(); - this.flags &= ~1; - } - } - trigger() { - if (this.flags & 64) { - pausedQueueEffects.add(this); - } else if (this.scheduler) { - this.scheduler(); - } else { - this.runIfDirty(); - } - } - /** - * @internal - */ - runIfDirty() { - if (isDirty(this)) { - this.run(); - } - } - get dirty() { - return isDirty(this); - } -} -let batchDepth = 0; -let batchedSub; -let batchedComputed; -function batch(sub, isComputed = false) { - sub.flags |= 8; - if (isComputed) { - sub.next = batchedComputed; - batchedComputed = sub; - return; - } - sub.next = batchedSub; - batchedSub = sub; -} -function startBatch() { - batchDepth++; -} -function endBatch() { - if (--batchDepth > 0) { - return; - } - if (batchedComputed) { - let e = batchedComputed; - batchedComputed = void 0; - while (e) { - const next = e.next; - e.next = void 0; - e.flags &= ~8; - e = next; - } - } - let error; - while (batchedSub) { - let e = batchedSub; - batchedSub = void 0; - while (e) { - const next = e.next; - e.next = void 0; - e.flags &= ~8; - if (e.flags & 1) { - try { - ; - e.trigger(); - } catch (err) { - if (!error) error = err; - } - } - e = next; - } - } - if (error) throw error; -} -function prepareDeps(sub) { - for (let link = sub.deps; link; link = link.nextDep) { - link.version = -1; - link.prevActiveLink = link.dep.activeLink; - link.dep.activeLink = link; - } -} -function cleanupDeps(sub) { - let head; - let tail = sub.depsTail; - let link = tail; - while (link) { - const prev = link.prevDep; - if (link.version === -1) { - if (link === tail) tail = prev; - removeSub(link); - removeDep(link); - } else { - head = link; - } - link.dep.activeLink = link.prevActiveLink; - link.prevActiveLink = void 0; - link = prev; - } - sub.deps = head; - sub.depsTail = tail; -} -function isDirty(sub) { - for (let link = sub.deps; link; link = link.nextDep) { - if (link.dep.version !== link.version || link.dep.computed && (refreshComputed(link.dep.computed) || link.dep.version !== link.version)) { - return true; - } - } - if (sub._dirty) { - return true; - } - return false; -} -function refreshComputed(computed) { - if (computed.flags & 4 && !(computed.flags & 16)) { - return; - } - computed.flags &= ~16; - if (computed.globalVersion === globalVersion) { - return; - } - computed.globalVersion = globalVersion; - const dep = computed.dep; - computed.flags |= 2; - if (dep.version > 0 && !computed.isSSR && computed.deps && !isDirty(computed)) { - computed.flags &= ~2; - return; - } - const prevSub = activeSub; - const prevShouldTrack = shouldTrack; - activeSub = computed; - shouldTrack = true; - try { - prepareDeps(computed); - const value = computed.fn(computed._value); - if (dep.version === 0 || hasChanged(value, computed._value)) { - computed._value = value; - dep.version++; - } - } catch (err) { - dep.version++; - throw err; - } finally { - activeSub = prevSub; - shouldTrack = prevShouldTrack; - cleanupDeps(computed); - computed.flags &= ~2; - } -} -function removeSub(link, soft = false) { - const { dep, prevSub, nextSub } = link; - if (prevSub) { - prevSub.nextSub = nextSub; - link.prevSub = void 0; - } - if (nextSub) { - nextSub.prevSub = prevSub; - link.nextSub = void 0; - } - if (dep.subsHead === link) { - dep.subsHead = nextSub; - } - if (dep.subs === link) { - dep.subs = prevSub; - if (!prevSub && dep.computed) { - dep.computed.flags &= ~4; - for (let l = dep.computed.deps; l; l = l.nextDep) { - removeSub(l, true); - } - } - } - if (!soft && !--dep.sc && dep.map) { - dep.map.delete(dep.key); - } -} -function removeDep(link) { - const { prevDep, nextDep } = link; - if (prevDep) { - prevDep.nextDep = nextDep; - link.prevDep = void 0; - } - if (nextDep) { - nextDep.prevDep = prevDep; - link.nextDep = void 0; - } -} -function effect(fn, options) { - if (fn.effect instanceof ReactiveEffect) { - fn = fn.effect.fn; - } - const e = new ReactiveEffect(fn); - if (options) { - extend(e, options); - } - try { - e.run(); - } catch (err) { - e.stop(); - throw err; - } - const runner = e.run.bind(e); - runner.effect = e; - return runner; -} -function stop(runner) { - runner.effect.stop(); -} -let shouldTrack = true; -const trackStack = []; -function pauseTracking() { - trackStack.push(shouldTrack); - shouldTrack = false; -} -function resetTracking() { - const last = trackStack.pop(); - shouldTrack = last === void 0 ? true : last; -} -function cleanupEffect(e) { - const { cleanup } = e; - e.cleanup = void 0; - if (cleanup) { - const prevSub = activeSub; - activeSub = void 0; - try { - cleanup(); - } finally { - activeSub = prevSub; - } - } -} - -let globalVersion = 0; -class Link { - constructor(sub, dep) { - this.sub = sub; - this.dep = dep; - this.version = dep.version; - this.nextDep = this.prevDep = this.nextSub = this.prevSub = this.prevActiveLink = void 0; - } -} -class Dep { - constructor(computed) { - this.computed = computed; - this.version = 0; - /** - * Link between this dep and the current active effect - */ - this.activeLink = void 0; - /** - * Doubly linked list representing the subscribing effects (tail) - */ - this.subs = void 0; - /** - * For object property deps cleanup - */ - this.map = void 0; - this.key = void 0; - /** - * Subscriber counter - */ - this.sc = 0; - { - this.subsHead = void 0; - } - } - track(debugInfo) { - if (!activeSub || !shouldTrack || activeSub === this.computed) { - return; - } - let link = this.activeLink; - if (link === void 0 || link.sub !== activeSub) { - link = this.activeLink = new Link(activeSub, this); - if (!activeSub.deps) { - activeSub.deps = activeSub.depsTail = link; - } else { - link.prevDep = activeSub.depsTail; - activeSub.depsTail.nextDep = link; - activeSub.depsTail = link; - } - addSub(link); - } else if (link.version === -1) { - link.version = this.version; - if (link.nextDep) { - const next = link.nextDep; - next.prevDep = link.prevDep; - if (link.prevDep) { - link.prevDep.nextDep = next; - } - link.prevDep = activeSub.depsTail; - link.nextDep = void 0; - activeSub.depsTail.nextDep = link; - activeSub.depsTail = link; - if (activeSub.deps === link) { - activeSub.deps = next; - } - } - } - if (activeSub.onTrack) { - activeSub.onTrack( - extend( - { - effect: activeSub - }, - debugInfo - ) - ); - } - return link; - } - trigger(debugInfo) { - this.version++; - globalVersion++; - this.notify(debugInfo); - } - notify(debugInfo) { - startBatch(); - try { - if (true) { - for (let head = this.subsHead; head; head = head.nextSub) { - if (head.sub.onTrigger && !(head.sub.flags & 8)) { - head.sub.onTrigger( - extend( - { - effect: head.sub - }, - debugInfo - ) - ); - } - } - } - for (let link = this.subs; link; link = link.prevSub) { - if (link.sub.notify()) { - ; - link.sub.dep.notify(); - } - } - } finally { - endBatch(); - } - } -} -function addSub(link) { - link.dep.sc++; - if (link.sub.flags & 4) { - const computed = link.dep.computed; - if (computed && !link.dep.subs) { - computed.flags |= 4 | 16; - for (let l = computed.deps; l; l = l.nextDep) { - addSub(l); - } - } - const currentTail = link.dep.subs; - if (currentTail !== link) { - link.prevSub = currentTail; - if (currentTail) currentTail.nextSub = link; - } - if (link.dep.subsHead === void 0) { - link.dep.subsHead = link; - } - link.dep.subs = link; - } -} -const targetMap = /* @__PURE__ */ new WeakMap(); -const ITERATE_KEY = Symbol( - "Object iterate" -); -const MAP_KEY_ITERATE_KEY = Symbol( - "Map keys iterate" -); -const ARRAY_ITERATE_KEY = Symbol( - "Array iterate" -); -function track(target, type, key) { - if (shouldTrack && activeSub) { - let depsMap = targetMap.get(target); - if (!depsMap) { - targetMap.set(target, depsMap = /* @__PURE__ */ new Map()); - } - let dep = depsMap.get(key); - if (!dep) { - depsMap.set(key, dep = new Dep()); - dep.map = depsMap; - dep.key = key; - } - { - dep.track({ - target, - type, - key - }); - } - } -} -function trigger(target, type, key, newValue, oldValue, oldTarget) { - const depsMap = targetMap.get(target); - if (!depsMap) { - globalVersion++; - return; - } - const run = (dep) => { - if (dep) { - { - dep.trigger({ - target, - type, - key, - newValue, - oldValue, - oldTarget - }); - } - } - }; - startBatch(); - if (type === "clear") { - depsMap.forEach(run); - } else { - const targetIsArray = isArray(target); - const isArrayIndex = targetIsArray && isIntegerKey(key); - if (targetIsArray && key === "length") { - const newLength = Number(newValue); - depsMap.forEach((dep, key2) => { - if (key2 === "length" || key2 === ARRAY_ITERATE_KEY || !isSymbol(key2) && key2 >= newLength) { - run(dep); - } - }); - } else { - if (key !== void 0 || depsMap.has(void 0)) { - run(depsMap.get(key)); - } - if (isArrayIndex) { - run(depsMap.get(ARRAY_ITERATE_KEY)); - } - switch (type) { - case "add": - if (!targetIsArray) { - run(depsMap.get(ITERATE_KEY)); - if (isMap(target)) { - run(depsMap.get(MAP_KEY_ITERATE_KEY)); - } - } else if (isArrayIndex) { - run(depsMap.get("length")); - } - break; - case "delete": - if (!targetIsArray) { - run(depsMap.get(ITERATE_KEY)); - if (isMap(target)) { - run(depsMap.get(MAP_KEY_ITERATE_KEY)); - } - } - break; - case "set": - if (isMap(target)) { - run(depsMap.get(ITERATE_KEY)); - } - break; - } - } - } - endBatch(); -} -function getDepFromReactive(object, key) { - const depMap = targetMap.get(object); - return depMap && depMap.get(key); -} - -function reactiveReadArray(array) { - const raw = toRaw(array); - if (raw === array) return raw; - track(raw, "iterate", ARRAY_ITERATE_KEY); - return isShallow(array) ? raw : raw.map(toReactive); -} -function shallowReadArray(arr) { - track(arr = toRaw(arr), "iterate", ARRAY_ITERATE_KEY); - return arr; -} -const arrayInstrumentations = { - __proto__: null, - [Symbol.iterator]() { - return iterator(this, Symbol.iterator, toReactive); - }, - concat(...args) { - return reactiveReadArray(this).concat( - ...args.map((x) => isArray(x) ? reactiveReadArray(x) : x) - ); - }, - entries() { - return iterator(this, "entries", (value) => { - value[1] = toReactive(value[1]); - return value; - }); - }, - every(fn, thisArg) { - return apply(this, "every", fn, thisArg, void 0, arguments); - }, - filter(fn, thisArg) { - return apply(this, "filter", fn, thisArg, (v) => v.map(toReactive), arguments); - }, - find(fn, thisArg) { - return apply(this, "find", fn, thisArg, toReactive, arguments); - }, - findIndex(fn, thisArg) { - return apply(this, "findIndex", fn, thisArg, void 0, arguments); - }, - findLast(fn, thisArg) { - return apply(this, "findLast", fn, thisArg, toReactive, arguments); - }, - findLastIndex(fn, thisArg) { - return apply(this, "findLastIndex", fn, thisArg, void 0, arguments); - }, - // flat, flatMap could benefit from ARRAY_ITERATE but are not straight-forward to implement - forEach(fn, thisArg) { - return apply(this, "forEach", fn, thisArg, void 0, arguments); - }, - includes(...args) { - return searchProxy(this, "includes", args); - }, - indexOf(...args) { - return searchProxy(this, "indexOf", args); - }, - join(separator) { - return reactiveReadArray(this).join(separator); - }, - // keys() iterator only reads `length`, no optimisation required - lastIndexOf(...args) { - return searchProxy(this, "lastIndexOf", args); - }, - map(fn, thisArg) { - return apply(this, "map", fn, thisArg, void 0, arguments); - }, - pop() { - return noTracking(this, "pop"); - }, - push(...args) { - return noTracking(this, "push", args); - }, - reduce(fn, ...args) { - return reduce(this, "reduce", fn, args); - }, - reduceRight(fn, ...args) { - return reduce(this, "reduceRight", fn, args); - }, - shift() { - return noTracking(this, "shift"); - }, - // slice could use ARRAY_ITERATE but also seems to beg for range tracking - some(fn, thisArg) { - return apply(this, "some", fn, thisArg, void 0, arguments); - }, - splice(...args) { - return noTracking(this, "splice", args); - }, - toReversed() { - return reactiveReadArray(this).toReversed(); - }, - toSorted(comparer) { - return reactiveReadArray(this).toSorted(comparer); - }, - toSpliced(...args) { - return reactiveReadArray(this).toSpliced(...args); - }, - unshift(...args) { - return noTracking(this, "unshift", args); - }, - values() { - return iterator(this, "values", toReactive); - } -}; -function iterator(self, method, wrapValue) { - const arr = shallowReadArray(self); - const iter = arr[method](); - if (arr !== self && !isShallow(self)) { - iter._next = iter.next; - iter.next = () => { - const result = iter._next(); - if (result.value) { - result.value = wrapValue(result.value); - } - return result; - }; - } - return iter; -} -const arrayProto = Array.prototype; -function apply(self, method, fn, thisArg, wrappedRetFn, args) { - const arr = shallowReadArray(self); - const needsWrap = arr !== self && !isShallow(self); - const methodFn = arr[method]; - if (methodFn !== arrayProto[method]) { - const result2 = methodFn.apply(self, args); - return needsWrap ? toReactive(result2) : result2; - } - let wrappedFn = fn; - if (arr !== self) { - if (needsWrap) { - wrappedFn = function(item, index) { - return fn.call(this, toReactive(item), index, self); - }; - } else if (fn.length > 2) { - wrappedFn = function(item, index) { - return fn.call(this, item, index, self); - }; - } - } - const result = methodFn.call(arr, wrappedFn, thisArg); - return needsWrap && wrappedRetFn ? wrappedRetFn(result) : result; -} -function reduce(self, method, fn, args) { - const arr = shallowReadArray(self); - let wrappedFn = fn; - if (arr !== self) { - if (!isShallow(self)) { - wrappedFn = function(acc, item, index) { - return fn.call(this, acc, toReactive(item), index, self); - }; - } else if (fn.length > 3) { - wrappedFn = function(acc, item, index) { - return fn.call(this, acc, item, index, self); - }; - } - } - return arr[method](wrappedFn, ...args); -} -function searchProxy(self, method, args) { - const arr = toRaw(self); - track(arr, "iterate", ARRAY_ITERATE_KEY); - const res = arr[method](...args); - if ((res === -1 || res === false) && isProxy(args[0])) { - args[0] = toRaw(args[0]); - return arr[method](...args); - } - return res; -} -function noTracking(self, method, args = []) { - pauseTracking(); - startBatch(); - const res = toRaw(self)[method].apply(self, args); - endBatch(); - resetTracking(); - return res; -} - -const isNonTrackableKeys = /* @__PURE__ */ makeMap(`__proto__,__v_isRef,__isVue`); -const builtInSymbols = new Set( - /* @__PURE__ */ Object.getOwnPropertyNames(Symbol).filter((key) => key !== "arguments" && key !== "caller").map((key) => Symbol[key]).filter(isSymbol) -); -function hasOwnProperty(key) { - if (!isSymbol(key)) key = String(key); - const obj = toRaw(this); - track(obj, "has", key); - return obj.hasOwnProperty(key); -} -class BaseReactiveHandler { - constructor(_isReadonly = false, _isShallow = false) { - this._isReadonly = _isReadonly; - this._isShallow = _isShallow; - } - get(target, key, receiver) { - const isReadonly2 = this._isReadonly, isShallow2 = this._isShallow; - if (key === "__v_isReactive") { - return !isReadonly2; - } else if (key === "__v_isReadonly") { - return isReadonly2; - } else if (key === "__v_isShallow") { - return isShallow2; - } else if (key === "__v_raw") { - if (receiver === (isReadonly2 ? isShallow2 ? shallowReadonlyMap : readonlyMap : isShallow2 ? shallowReactiveMap : reactiveMap).get(target) || // receiver is not the reactive proxy, but has the same prototype - // this means the receiver is a user proxy of the reactive proxy - Object.getPrototypeOf(target) === Object.getPrototypeOf(receiver)) { - return target; - } - return; - } - const targetIsArray = isArray(target); - if (!isReadonly2) { - let fn; - if (targetIsArray && (fn = arrayInstrumentations[key])) { - return fn; - } - if (key === "hasOwnProperty") { - return hasOwnProperty; - } - } - const res = Reflect.get( - target, - key, - // if this is a proxy wrapping a ref, return methods using the raw ref - // as receiver so that we don't have to call `toRaw` on the ref in all - // its class methods - isRef(target) ? target : receiver - ); - if (isSymbol(key) ? builtInSymbols.has(key) : isNonTrackableKeys(key)) { - return res; - } - if (!isReadonly2) { - track(target, "get", key); - } - if (isShallow2) { - return res; - } - if (isRef(res)) { - return targetIsArray && isIntegerKey(key) ? res : res.value; - } - if (isObject(res)) { - return isReadonly2 ? readonly(res) : reactive(res); - } - return res; - } -} -class MutableReactiveHandler extends BaseReactiveHandler { - constructor(isShallow2 = false) { - super(false, isShallow2); - } - set(target, key, value, receiver) { - let oldValue = target[key]; - if (!this._isShallow) { - const isOldValueReadonly = isReadonly(oldValue); - if (!isShallow(value) && !isReadonly(value)) { - oldValue = toRaw(oldValue); - value = toRaw(value); - } - if (!isArray(target) && isRef(oldValue) && !isRef(value)) { - if (isOldValueReadonly) { - return false; - } else { - oldValue.value = value; - return true; - } - } - } - const hadKey = isArray(target) && isIntegerKey(key) ? Number(key) < target.length : hasOwn(target, key); - const result = Reflect.set( - target, - key, - value, - isRef(target) ? target : receiver - ); - if (target === toRaw(receiver)) { - if (!hadKey) { - trigger(target, "add", key, value); - } else if (hasChanged(value, oldValue)) { - trigger(target, "set", key, value, oldValue); - } - } - return result; - } - deleteProperty(target, key) { - const hadKey = hasOwn(target, key); - const oldValue = target[key]; - const result = Reflect.deleteProperty(target, key); - if (result && hadKey) { - trigger(target, "delete", key, void 0, oldValue); - } - return result; - } - has(target, key) { - const result = Reflect.has(target, key); - if (!isSymbol(key) || !builtInSymbols.has(key)) { - track(target, "has", key); - } - return result; - } - ownKeys(target) { - track( - target, - "iterate", - isArray(target) ? "length" : ITERATE_KEY - ); - return Reflect.ownKeys(target); - } -} -class ReadonlyReactiveHandler extends BaseReactiveHandler { - constructor(isShallow2 = false) { - super(true, isShallow2); - } - set(target, key) { - { - warn$2( - `Set operation on key "${String(key)}" failed: target is readonly.`, - target - ); - } - return true; - } - deleteProperty(target, key) { - { - warn$2( - `Delete operation on key "${String(key)}" failed: target is readonly.`, - target - ); - } - return true; - } -} -const mutableHandlers = /* @__PURE__ */ new MutableReactiveHandler(); -const readonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(); -const shallowReactiveHandlers = /* @__PURE__ */ new MutableReactiveHandler(true); -const shallowReadonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(true); - -const toShallow = (value) => value; -const getProto = (v) => Reflect.getPrototypeOf(v); -function createIterableMethod(method, isReadonly2, isShallow2) { - return function(...args) { - const target = this["__v_raw"]; - const rawTarget = toRaw(target); - const targetIsMap = isMap(rawTarget); - const isPair = method === "entries" || method === Symbol.iterator && targetIsMap; - const isKeyOnly = method === "keys" && targetIsMap; - const innerIterator = target[method](...args); - const wrap = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive; - !isReadonly2 && track( - rawTarget, - "iterate", - isKeyOnly ? MAP_KEY_ITERATE_KEY : ITERATE_KEY - ); - return { - // iterator protocol - next() { - const { value, done } = innerIterator.next(); - return done ? { value, done } : { - value: isPair ? [wrap(value[0]), wrap(value[1])] : wrap(value), - done - }; - }, - // iterable protocol - [Symbol.iterator]() { - return this; - } - }; - }; -} -function createReadonlyMethod(type) { - return function(...args) { - { - const key = args[0] ? `on key "${args[0]}" ` : ``; - warn$2( - `${capitalize(type)} operation ${key}failed: target is readonly.`, - toRaw(this) - ); - } - return type === "delete" ? false : type === "clear" ? void 0 : this; - }; -} -function createInstrumentations(readonly, shallow) { - const instrumentations = { - get(key) { - const target = this["__v_raw"]; - const rawTarget = toRaw(target); - const rawKey = toRaw(key); - if (!readonly) { - if (hasChanged(key, rawKey)) { - track(rawTarget, "get", key); - } - track(rawTarget, "get", rawKey); - } - const { has } = getProto(rawTarget); - const wrap = shallow ? toShallow : readonly ? toReadonly : toReactive; - if (has.call(rawTarget, key)) { - return wrap(target.get(key)); - } else if (has.call(rawTarget, rawKey)) { - return wrap(target.get(rawKey)); - } else if (target !== rawTarget) { - target.get(key); - } - }, - get size() { - const target = this["__v_raw"]; - !readonly && track(toRaw(target), "iterate", ITERATE_KEY); - return Reflect.get(target, "size", target); - }, - has(key) { - const target = this["__v_raw"]; - const rawTarget = toRaw(target); - const rawKey = toRaw(key); - if (!readonly) { - if (hasChanged(key, rawKey)) { - track(rawTarget, "has", key); - } - track(rawTarget, "has", rawKey); - } - return key === rawKey ? target.has(key) : target.has(key) || target.has(rawKey); - }, - forEach(callback, thisArg) { - const observed = this; - const target = observed["__v_raw"]; - const rawTarget = toRaw(target); - const wrap = shallow ? toShallow : readonly ? toReadonly : toReactive; - !readonly && track(rawTarget, "iterate", ITERATE_KEY); - return target.forEach((value, key) => { - return callback.call(thisArg, wrap(value), wrap(key), observed); - }); - } - }; - extend( - instrumentations, - readonly ? { - add: createReadonlyMethod("add"), - set: createReadonlyMethod("set"), - delete: createReadonlyMethod("delete"), - clear: createReadonlyMethod("clear") - } : { - add(value) { - if (!shallow && !isShallow(value) && !isReadonly(value)) { - value = toRaw(value); - } - const target = toRaw(this); - const proto = getProto(target); - const hadKey = proto.has.call(target, value); - if (!hadKey) { - target.add(value); - trigger(target, "add", value, value); - } - return this; - }, - set(key, value) { - if (!shallow && !isShallow(value) && !isReadonly(value)) { - value = toRaw(value); - } - const target = toRaw(this); - const { has, get } = getProto(target); - let hadKey = has.call(target, key); - if (!hadKey) { - key = toRaw(key); - hadKey = has.call(target, key); - } else { - checkIdentityKeys(target, has, key); - } - const oldValue = get.call(target, key); - target.set(key, value); - if (!hadKey) { - trigger(target, "add", key, value); - } else if (hasChanged(value, oldValue)) { - trigger(target, "set", key, value, oldValue); - } - return this; - }, - delete(key) { - const target = toRaw(this); - const { has, get } = getProto(target); - let hadKey = has.call(target, key); - if (!hadKey) { - key = toRaw(key); - hadKey = has.call(target, key); - } else { - checkIdentityKeys(target, has, key); - } - const oldValue = get ? get.call(target, key) : void 0; - const result = target.delete(key); - if (hadKey) { - trigger(target, "delete", key, void 0, oldValue); - } - return result; - }, - clear() { - const target = toRaw(this); - const hadItems = target.size !== 0; - const oldTarget = isMap(target) ? new Map(target) : new Set(target) ; - const result = target.clear(); - if (hadItems) { - trigger( - target, - "clear", - void 0, - void 0, - oldTarget - ); - } - return result; - } - } - ); - const iteratorMethods = [ - "keys", - "values", - "entries", - Symbol.iterator - ]; - iteratorMethods.forEach((method) => { - instrumentations[method] = createIterableMethod(method, readonly, shallow); - }); - return instrumentations; -} -function createInstrumentationGetter(isReadonly2, shallow) { - const instrumentations = createInstrumentations(isReadonly2, shallow); - return (target, key, receiver) => { - if (key === "__v_isReactive") { - return !isReadonly2; - } else if (key === "__v_isReadonly") { - return isReadonly2; - } else if (key === "__v_raw") { - return target; - } - return Reflect.get( - hasOwn(instrumentations, key) && key in target ? instrumentations : target, - key, - receiver - ); - }; -} -const mutableCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(false, false) -}; -const shallowCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(false, true) -}; -const readonlyCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(true, false) -}; -const shallowReadonlyCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(true, true) -}; -function checkIdentityKeys(target, has, key) { - const rawKey = toRaw(key); - if (rawKey !== key && has.call(target, rawKey)) { - const type = toRawType(target); - warn$2( - `Reactive ${type} contains both the raw and reactive versions of the same object${type === `Map` ? ` as keys` : ``}, which can lead to inconsistencies. Avoid differentiating between the raw and reactive versions of an object and only use the reactive version if possible.` - ); - } -} - -const reactiveMap = /* @__PURE__ */ new WeakMap(); -const shallowReactiveMap = /* @__PURE__ */ new WeakMap(); -const readonlyMap = /* @__PURE__ */ new WeakMap(); -const shallowReadonlyMap = /* @__PURE__ */ new WeakMap(); -function targetTypeMap(rawType) { - switch (rawType) { - case "Object": - case "Array": - return 1 /* COMMON */; - case "Map": - case "Set": - case "WeakMap": - case "WeakSet": - return 2 /* COLLECTION */; - default: - return 0 /* INVALID */; - } -} -function getTargetType(value) { - return value["__v_skip"] || !Object.isExtensible(value) ? 0 /* INVALID */ : targetTypeMap(toRawType(value)); -} -function reactive(target) { - if (isReadonly(target)) { - return target; - } - return createReactiveObject( - target, - false, - mutableHandlers, - mutableCollectionHandlers, - reactiveMap - ); -} -function shallowReactive(target) { - return createReactiveObject( - target, - false, - shallowReactiveHandlers, - shallowCollectionHandlers, - shallowReactiveMap - ); -} -function readonly(target) { - return createReactiveObject( - target, - true, - readonlyHandlers, - readonlyCollectionHandlers, - readonlyMap - ); -} -function shallowReadonly(target) { - return createReactiveObject( - target, - true, - shallowReadonlyHandlers, - shallowReadonlyCollectionHandlers, - shallowReadonlyMap - ); -} -function createReactiveObject(target, isReadonly2, baseHandlers, collectionHandlers, proxyMap) { - if (!isObject(target)) { - { - warn$2( - `value cannot be made ${isReadonly2 ? "readonly" : "reactive"}: ${String( - target - )}` - ); - } - return target; - } - if (target["__v_raw"] && !(isReadonly2 && target["__v_isReactive"])) { - return target; - } - const existingProxy = proxyMap.get(target); - if (existingProxy) { - return existingProxy; - } - const targetType = getTargetType(target); - if (targetType === 0 /* INVALID */) { - return target; - } - const proxy = new Proxy( - target, - targetType === 2 /* COLLECTION */ ? collectionHandlers : baseHandlers - ); - proxyMap.set(target, proxy); - return proxy; -} -function isReactive(value) { - if (isReadonly(value)) { - return isReactive(value["__v_raw"]); - } - return !!(value && value["__v_isReactive"]); -} -function isReadonly(value) { - return !!(value && value["__v_isReadonly"]); -} -function isShallow(value) { - return !!(value && value["__v_isShallow"]); -} -function isProxy(value) { - return value ? !!value["__v_raw"] : false; -} -function toRaw(observed) { - const raw = observed && observed["__v_raw"]; - return raw ? toRaw(raw) : observed; -} -function markRaw(value) { - if (!hasOwn(value, "__v_skip") && Object.isExtensible(value)) { - def(value, "__v_skip", true); - } - return value; -} -const toReactive = (value) => isObject(value) ? reactive(value) : value; -const toReadonly = (value) => isObject(value) ? readonly(value) : value; - -function isRef(r) { - return r ? r["__v_isRef"] === true : false; -} -function ref(value) { - return createRef(value, false); -} -function shallowRef(value) { - return createRef(value, true); -} -function createRef(rawValue, shallow) { - if (isRef(rawValue)) { - return rawValue; - } - return new RefImpl(rawValue, shallow); -} -class RefImpl { - constructor(value, isShallow2) { - this.dep = new Dep(); - this["__v_isRef"] = true; - this["__v_isShallow"] = false; - this._rawValue = isShallow2 ? value : toRaw(value); - this._value = isShallow2 ? value : toReactive(value); - this["__v_isShallow"] = isShallow2; - } - get value() { - { - this.dep.track({ - target: this, - type: "get", - key: "value" - }); - } - return this._value; - } - set value(newValue) { - const oldValue = this._rawValue; - const useDirectValue = this["__v_isShallow"] || isShallow(newValue) || isReadonly(newValue); - newValue = useDirectValue ? newValue : toRaw(newValue); - if (hasChanged(newValue, oldValue)) { - this._rawValue = newValue; - this._value = useDirectValue ? newValue : toReactive(newValue); - { - this.dep.trigger({ - target: this, - type: "set", - key: "value", - newValue, - oldValue - }); - } - } - } -} -function triggerRef(ref2) { - if (ref2.dep) { - { - ref2.dep.trigger({ - target: ref2, - type: "set", - key: "value", - newValue: ref2._value - }); - } - } -} -function unref(ref2) { - return isRef(ref2) ? ref2.value : ref2; -} -function toValue(source) { - return isFunction(source) ? source() : unref(source); -} -const shallowUnwrapHandlers = { - get: (target, key, receiver) => key === "__v_raw" ? target : unref(Reflect.get(target, key, receiver)), - set: (target, key, value, receiver) => { - const oldValue = target[key]; - if (isRef(oldValue) && !isRef(value)) { - oldValue.value = value; - return true; - } else { - return Reflect.set(target, key, value, receiver); - } - } -}; -function proxyRefs(objectWithRefs) { - return isReactive(objectWithRefs) ? objectWithRefs : new Proxy(objectWithRefs, shallowUnwrapHandlers); -} -class CustomRefImpl { - constructor(factory) { - this["__v_isRef"] = true; - this._value = void 0; - const dep = this.dep = new Dep(); - const { get, set } = factory(dep.track.bind(dep), dep.trigger.bind(dep)); - this._get = get; - this._set = set; - } - get value() { - return this._value = this._get(); - } - set value(newVal) { - this._set(newVal); - } -} -function customRef(factory) { - return new CustomRefImpl(factory); -} -function toRefs(object) { - if (!isProxy(object)) { - warn$2(`toRefs() expects a reactive object but received a plain one.`); - } - const ret = isArray(object) ? new Array(object.length) : {}; - for (const key in object) { - ret[key] = propertyToRef(object, key); - } - return ret; -} -class ObjectRefImpl { - constructor(_object, _key, _defaultValue) { - this._object = _object; - this._key = _key; - this._defaultValue = _defaultValue; - this["__v_isRef"] = true; - this._value = void 0; - } - get value() { - const val = this._object[this._key]; - return this._value = val === void 0 ? this._defaultValue : val; - } - set value(newVal) { - this._object[this._key] = newVal; - } - get dep() { - return getDepFromReactive(toRaw(this._object), this._key); - } -} -class GetterRefImpl { - constructor(_getter) { - this._getter = _getter; - this["__v_isRef"] = true; - this["__v_isReadonly"] = true; - this._value = void 0; - } - get value() { - return this._value = this._getter(); - } -} -function toRef(source, key, defaultValue) { - if (isRef(source)) { - return source; - } else if (isFunction(source)) { - return new GetterRefImpl(source); - } else if (isObject(source) && arguments.length > 1) { - return propertyToRef(source, key, defaultValue); - } else { - return ref(source); - } -} -function propertyToRef(source, key, defaultValue) { - const val = source[key]; - return isRef(val) ? val : new ObjectRefImpl(source, key, defaultValue); -} - -class ComputedRefImpl { - constructor(fn, setter, isSSR) { - this.fn = fn; - this.setter = setter; - /** - * @internal - */ - this._value = void 0; - /** - * @internal - */ - this.dep = new Dep(this); - /** - * @internal - */ - this.__v_isRef = true; - // TODO isolatedDeclarations "__v_isReadonly" - // A computed is also a subscriber that tracks other deps - /** - * @internal - */ - this.deps = void 0; - /** - * @internal - */ - this.depsTail = void 0; - /** - * @internal - */ - this.flags = 16; - /** - * @internal - */ - this.globalVersion = globalVersion - 1; - /** - * @internal - */ - this.next = void 0; - // for backwards compat - this.effect = this; - this["__v_isReadonly"] = !setter; - this.isSSR = isSSR; - } - /** - * @internal - */ - notify() { - this.flags |= 16; - if (!(this.flags & 8) && // avoid infinite self recursion - activeSub !== this) { - batch(this, true); - return true; - } - } - get value() { - const link = this.dep.track({ - target: this, - type: "get", - key: "value" - }) ; - refreshComputed(this); - if (link) { - link.version = this.dep.version; - } - return this._value; - } - set value(newValue) { - if (this.setter) { - this.setter(newValue); - } else { - warn$2("Write operation failed: computed value is readonly"); - } - } -} -function computed$1(getterOrOptions, debugOptions, isSSR = false) { - let getter; - let setter; - if (isFunction(getterOrOptions)) { - getter = getterOrOptions; - } else { - getter = getterOrOptions.get; - setter = getterOrOptions.set; - } - const cRef = new ComputedRefImpl(getter, setter, isSSR); - if (debugOptions && !isSSR) { - cRef.onTrack = debugOptions.onTrack; - cRef.onTrigger = debugOptions.onTrigger; - } - return cRef; -} - -const TrackOpTypes = { - "GET": "get", - "HAS": "has", - "ITERATE": "iterate" -}; -const TriggerOpTypes = { - "SET": "set", - "ADD": "add", - "DELETE": "delete", - "CLEAR": "clear" -}; - -const INITIAL_WATCHER_VALUE = {}; -const cleanupMap = /* @__PURE__ */ new WeakMap(); -let activeWatcher = void 0; -function getCurrentWatcher() { - return activeWatcher; -} -function onWatcherCleanup(cleanupFn, failSilently = false, owner = activeWatcher) { - if (owner) { - let cleanups = cleanupMap.get(owner); - if (!cleanups) cleanupMap.set(owner, cleanups = []); - cleanups.push(cleanupFn); - } else if (!failSilently) { - warn$2( - `onWatcherCleanup() was called when there was no active watcher to associate with.` - ); - } -} -function watch$1(source, cb, options = EMPTY_OBJ) { - const { immediate, deep, once, scheduler, augmentJob, call } = options; - const warnInvalidSource = (s) => { - (options.onWarn || warn$2)( - `Invalid watch source: `, - s, - `A watch source can only be a getter/effect function, a ref, a reactive object, or an array of these types.` - ); - }; - const reactiveGetter = (source2) => { - if (deep) return source2; - if (isShallow(source2) || deep === false || deep === 0) - return traverse(source2, 1); - return traverse(source2); - }; - let effect; - let getter; - let cleanup; - let boundCleanup; - let forceTrigger = false; - let isMultiSource = false; - if (isRef(source)) { - getter = () => source.value; - forceTrigger = isShallow(source); - } else if (isReactive(source)) { - getter = () => reactiveGetter(source); - forceTrigger = true; - } else if (isArray(source)) { - isMultiSource = true; - forceTrigger = source.some((s) => isReactive(s) || isShallow(s)); - getter = () => source.map((s) => { - if (isRef(s)) { - return s.value; - } else if (isReactive(s)) { - return reactiveGetter(s); - } else if (isFunction(s)) { - return call ? call(s, 2) : s(); - } else { - warnInvalidSource(s); - } - }); - } else if (isFunction(source)) { - if (cb) { - getter = call ? () => call(source, 2) : source; - } else { - getter = () => { - if (cleanup) { - pauseTracking(); - try { - cleanup(); - } finally { - resetTracking(); - } - } - const currentEffect = activeWatcher; - activeWatcher = effect; - try { - return call ? call(source, 3, [boundCleanup]) : source(boundCleanup); - } finally { - activeWatcher = currentEffect; - } - }; - } - } else { - getter = NOOP; - warnInvalidSource(source); - } - if (cb && deep) { - const baseGetter = getter; - const depth = deep === true ? Infinity : deep; - getter = () => traverse(baseGetter(), depth); - } - const scope = getCurrentScope(); - const watchHandle = () => { - effect.stop(); - if (scope) { - remove(scope.effects, effect); - } - }; - if (once && cb) { - const _cb = cb; - cb = (...args) => { - _cb(...args); - watchHandle(); - }; - } - let oldValue = isMultiSource ? new Array(source.length).fill(INITIAL_WATCHER_VALUE) : INITIAL_WATCHER_VALUE; - const job = (immediateFirstRun) => { - if (!(effect.flags & 1) || !effect.dirty && !immediateFirstRun) { - return; - } - if (cb) { - const newValue = effect.run(); - if (deep || forceTrigger || (isMultiSource ? newValue.some((v, i) => hasChanged(v, oldValue[i])) : hasChanged(newValue, oldValue))) { - if (cleanup) { - cleanup(); - } - const currentWatcher = activeWatcher; - activeWatcher = effect; - try { - const args = [ - newValue, - // pass undefined as the old value when it's changed for the first time - oldValue === INITIAL_WATCHER_VALUE ? void 0 : isMultiSource && oldValue[0] === INITIAL_WATCHER_VALUE ? [] : oldValue, - boundCleanup - ]; - call ? call(cb, 3, args) : ( - // @ts-expect-error - cb(...args) - ); - oldValue = newValue; - } finally { - activeWatcher = currentWatcher; - } - } - } else { - effect.run(); - } - }; - if (augmentJob) { - augmentJob(job); - } - effect = new ReactiveEffect(getter); - effect.scheduler = scheduler ? () => scheduler(job, false) : job; - boundCleanup = (fn) => onWatcherCleanup(fn, false, effect); - cleanup = effect.onStop = () => { - const cleanups = cleanupMap.get(effect); - if (cleanups) { - if (call) { - call(cleanups, 4); - } else { - for (const cleanup2 of cleanups) cleanup2(); - } - cleanupMap.delete(effect); - } - }; - { - effect.onTrack = options.onTrack; - effect.onTrigger = options.onTrigger; - } - if (cb) { - if (immediate) { - job(true); - } else { - oldValue = effect.run(); - } - } else if (scheduler) { - scheduler(job.bind(null, true), true); - } else { - effect.run(); - } - watchHandle.pause = effect.pause.bind(effect); - watchHandle.resume = effect.resume.bind(effect); - watchHandle.stop = watchHandle; - return watchHandle; -} -function traverse(value, depth = Infinity, seen) { - if (depth <= 0 || !isObject(value) || value["__v_skip"]) { - return value; - } - seen = seen || /* @__PURE__ */ new Set(); - if (seen.has(value)) { - return value; - } - seen.add(value); - depth--; - if (isRef(value)) { - traverse(value.value, depth, seen); - } else if (isArray(value)) { - for (let i = 0; i < value.length; i++) { - traverse(value[i], depth, seen); - } - } else if (isSet(value) || isMap(value)) { - value.forEach((v) => { - traverse(v, depth, seen); - }); - } else if (isPlainObject(value)) { - for (const key in value) { - traverse(value[key], depth, seen); - } - for (const key of Object.getOwnPropertySymbols(value)) { - if (Object.prototype.propertyIsEnumerable.call(value, key)) { - traverse(value[key], depth, seen); - } - } - } - return value; -} - -const stack$1 = []; -function pushWarningContext(vnode) { - stack$1.push(vnode); -} -function popWarningContext() { - stack$1.pop(); -} -let isWarning = false; -function warn$1(msg, ...args) { - if (isWarning) return; - isWarning = true; - pauseTracking(); - const instance = stack$1.length ? stack$1[stack$1.length - 1].component : null; - const appWarnHandler = instance && instance.appContext.config.warnHandler; - const trace = getComponentTrace(); - if (appWarnHandler) { - callWithErrorHandling( - appWarnHandler, - instance, - 11, - [ - // eslint-disable-next-line no-restricted-syntax - msg + args.map((a) => { - var _a, _b; - return (_b = (_a = a.toString) == null ? void 0 : _a.call(a)) != null ? _b : JSON.stringify(a); - }).join(""), - instance && instance.proxy, - trace.map( - ({ vnode }) => `at <${formatComponentName(instance, vnode.type)}>` - ).join("\n"), - trace - ] - ); - } else { - const warnArgs = [`[Vue warn]: ${msg}`, ...args]; - if (trace.length && // avoid spamming console during tests - true) { - warnArgs.push(` -`, ...formatTrace(trace)); - } - console.warn(...warnArgs); - } - resetTracking(); - isWarning = false; -} -function getComponentTrace() { - let currentVNode = stack$1[stack$1.length - 1]; - if (!currentVNode) { - return []; - } - const normalizedStack = []; - while (currentVNode) { - const last = normalizedStack[0]; - if (last && last.vnode === currentVNode) { - last.recurseCount++; - } else { - normalizedStack.push({ - vnode: currentVNode, - recurseCount: 0 - }); - } - const parentInstance = currentVNode.component && currentVNode.component.parent; - currentVNode = parentInstance && parentInstance.vnode; - } - return normalizedStack; -} -function formatTrace(trace) { - const logs = []; - trace.forEach((entry, i) => { - logs.push(...i === 0 ? [] : [` -`], ...formatTraceEntry(entry)); - }); - return logs; -} -function formatTraceEntry({ vnode, recurseCount }) { - const postfix = recurseCount > 0 ? `... (${recurseCount} recursive calls)` : ``; - const isRoot = vnode.component ? vnode.component.parent == null : false; - const open = ` at <${formatComponentName( - vnode.component, - vnode.type, - isRoot - )}`; - const close = `>` + postfix; - return vnode.props ? [open, ...formatProps(vnode.props), close] : [open + close]; -} -function formatProps(props) { - const res = []; - const keys = Object.keys(props); - keys.slice(0, 3).forEach((key) => { - res.push(...formatProp(key, props[key])); - }); - if (keys.length > 3) { - res.push(` ...`); - } - return res; -} -function formatProp(key, value, raw) { - if (isString(value)) { - value = JSON.stringify(value); - return raw ? value : [`${key}=${value}`]; - } else if (typeof value === "number" || typeof value === "boolean" || value == null) { - return raw ? value : [`${key}=${value}`]; - } else if (isRef(value)) { - value = formatProp(key, toRaw(value.value), true); - return raw ? value : [`${key}=Ref<`, value, `>`]; - } else if (isFunction(value)) { - return [`${key}=fn${value.name ? `<${value.name}>` : ``}`]; - } else { - value = toRaw(value); - return raw ? value : [`${key}=`, value]; - } -} -function assertNumber(val, type) { - if (val === void 0) { - return; - } else if (typeof val !== "number") { - warn$1(`${type} is not a valid number - got ${JSON.stringify(val)}.`); - } else if (isNaN(val)) { - warn$1(`${type} is NaN - the duration expression might be incorrect.`); - } -} - -const ErrorCodes = { - "SETUP_FUNCTION": 0, - "0": "SETUP_FUNCTION", - "RENDER_FUNCTION": 1, - "1": "RENDER_FUNCTION", - "NATIVE_EVENT_HANDLER": 5, - "5": "NATIVE_EVENT_HANDLER", - "COMPONENT_EVENT_HANDLER": 6, - "6": "COMPONENT_EVENT_HANDLER", - "VNODE_HOOK": 7, - "7": "VNODE_HOOK", - "DIRECTIVE_HOOK": 8, - "8": "DIRECTIVE_HOOK", - "TRANSITION_HOOK": 9, - "9": "TRANSITION_HOOK", - "APP_ERROR_HANDLER": 10, - "10": "APP_ERROR_HANDLER", - "APP_WARN_HANDLER": 11, - "11": "APP_WARN_HANDLER", - "FUNCTION_REF": 12, - "12": "FUNCTION_REF", - "ASYNC_COMPONENT_LOADER": 13, - "13": "ASYNC_COMPONENT_LOADER", - "SCHEDULER": 14, - "14": "SCHEDULER", - "COMPONENT_UPDATE": 15, - "15": "COMPONENT_UPDATE", - "APP_UNMOUNT_CLEANUP": 16, - "16": "APP_UNMOUNT_CLEANUP" -}; -const ErrorTypeStrings$1 = { - ["sp"]: "serverPrefetch hook", - ["bc"]: "beforeCreate hook", - ["c"]: "created hook", - ["bm"]: "beforeMount hook", - ["m"]: "mounted hook", - ["bu"]: "beforeUpdate hook", - ["u"]: "updated", - ["bum"]: "beforeUnmount hook", - ["um"]: "unmounted hook", - ["a"]: "activated hook", - ["da"]: "deactivated hook", - ["ec"]: "errorCaptured hook", - ["rtc"]: "renderTracked hook", - ["rtg"]: "renderTriggered hook", - [0]: "setup function", - [1]: "render function", - [2]: "watcher getter", - [3]: "watcher callback", - [4]: "watcher cleanup function", - [5]: "native event handler", - [6]: "component event handler", - [7]: "vnode hook", - [8]: "directive hook", - [9]: "transition hook", - [10]: "app errorHandler", - [11]: "app warnHandler", - [12]: "ref function", - [13]: "async component loader", - [14]: "scheduler flush", - [15]: "component update", - [16]: "app unmount cleanup function" -}; -function callWithErrorHandling(fn, instance, type, args) { - try { - return args ? fn(...args) : fn(); - } catch (err) { - handleError(err, instance, type); - } -} -function callWithAsyncErrorHandling(fn, instance, type, args) { - if (isFunction(fn)) { - const res = callWithErrorHandling(fn, instance, type, args); - if (res && isPromise(res)) { - res.catch((err) => { - handleError(err, instance, type); - }); - } - return res; - } - if (isArray(fn)) { - const values = []; - for (let i = 0; i < fn.length; i++) { - values.push(callWithAsyncErrorHandling(fn[i], instance, type, args)); - } - return values; - } else { - warn$1( - `Invalid value type passed to callWithAsyncErrorHandling(): ${typeof fn}` - ); - } -} -function handleError(err, instance, type, throwInDev = true) { - const contextVNode = instance ? instance.vnode : null; - const { errorHandler, throwUnhandledErrorInProduction } = instance && instance.appContext.config || EMPTY_OBJ; - if (instance) { - let cur = instance.parent; - const exposedInstance = instance.proxy; - const errorInfo = ErrorTypeStrings$1[type] ; - while (cur) { - const errorCapturedHooks = cur.ec; - if (errorCapturedHooks) { - for (let i = 0; i < errorCapturedHooks.length; i++) { - if (errorCapturedHooks[i](err, exposedInstance, errorInfo) === false) { - return; - } - } - } - cur = cur.parent; - } - if (errorHandler) { - pauseTracking(); - callWithErrorHandling(errorHandler, null, 10, [ - err, - exposedInstance, - errorInfo - ]); - resetTracking(); - return; - } - } - logError(err, type, contextVNode, throwInDev, throwUnhandledErrorInProduction); -} -function logError(err, type, contextVNode, throwInDev = true, throwInProd = false) { - { - const info = ErrorTypeStrings$1[type]; - if (contextVNode) { - pushWarningContext(contextVNode); - } - warn$1(`Unhandled error${info ? ` during execution of ${info}` : ``}`); - if (contextVNode) { - popWarningContext(); - } - if (throwInDev) { - throw err; - } else { - console.error(err); - } - } -} - -const queue = []; -let flushIndex = -1; -const pendingPostFlushCbs = []; -let activePostFlushCbs = null; -let postFlushIndex = 0; -const resolvedPromise = /* @__PURE__ */ Promise.resolve(); -let currentFlushPromise = null; -const RECURSION_LIMIT = 100; -function nextTick(fn) { - const p = currentFlushPromise || resolvedPromise; - return fn ? p.then(this ? fn.bind(this) : fn) : p; -} -function findInsertionIndex(id) { - let start = flushIndex + 1; - let end = queue.length; - while (start < end) { - const middle = start + end >>> 1; - const middleJob = queue[middle]; - const middleJobId = getId(middleJob); - if (middleJobId < id || middleJobId === id && middleJob.flags & 2) { - start = middle + 1; - } else { - end = middle; - } - } - return start; -} -function queueJob(job) { - if (!(job.flags & 1)) { - const jobId = getId(job); - const lastJob = queue[queue.length - 1]; - if (!lastJob || // fast path when the job id is larger than the tail - !(job.flags & 2) && jobId >= getId(lastJob)) { - queue.push(job); - } else { - queue.splice(findInsertionIndex(jobId), 0, job); - } - job.flags |= 1; - queueFlush(); - } -} -function queueFlush() { - if (!currentFlushPromise) { - currentFlushPromise = resolvedPromise.then(flushJobs); - } -} -function queuePostFlushCb(cb) { - if (!isArray(cb)) { - if (activePostFlushCbs && cb.id === -1) { - activePostFlushCbs.splice(postFlushIndex + 1, 0, cb); - } else if (!(cb.flags & 1)) { - pendingPostFlushCbs.push(cb); - cb.flags |= 1; - } - } else { - pendingPostFlushCbs.push(...cb); - } - queueFlush(); -} -function flushPreFlushCbs(instance, seen, i = flushIndex + 1) { - { - seen = seen || /* @__PURE__ */ new Map(); - } - for (; i < queue.length; i++) { - const cb = queue[i]; - if (cb && cb.flags & 2) { - if (instance && cb.id !== instance.uid) { - continue; - } - if (checkRecursiveUpdates(seen, cb)) { - continue; - } - queue.splice(i, 1); - i--; - if (cb.flags & 4) { - cb.flags &= ~1; - } - cb(); - if (!(cb.flags & 4)) { - cb.flags &= ~1; - } - } - } -} -function flushPostFlushCbs(seen) { - if (pendingPostFlushCbs.length) { - const deduped = [...new Set(pendingPostFlushCbs)].sort( - (a, b) => getId(a) - getId(b) - ); - pendingPostFlushCbs.length = 0; - if (activePostFlushCbs) { - activePostFlushCbs.push(...deduped); - return; - } - activePostFlushCbs = deduped; - { - seen = seen || /* @__PURE__ */ new Map(); - } - for (postFlushIndex = 0; postFlushIndex < activePostFlushCbs.length; postFlushIndex++) { - const cb = activePostFlushCbs[postFlushIndex]; - if (checkRecursiveUpdates(seen, cb)) { - continue; - } - if (cb.flags & 4) { - cb.flags &= ~1; - } - if (!(cb.flags & 8)) cb(); - cb.flags &= ~1; - } - activePostFlushCbs = null; - postFlushIndex = 0; - } -} -const getId = (job) => job.id == null ? job.flags & 2 ? -1 : Infinity : job.id; -function flushJobs(seen) { - { - seen = seen || /* @__PURE__ */ new Map(); - } - const check = (job) => checkRecursiveUpdates(seen, job) ; - try { - for (flushIndex = 0; flushIndex < queue.length; flushIndex++) { - const job = queue[flushIndex]; - if (job && !(job.flags & 8)) { - if (check(job)) { - continue; - } - if (job.flags & 4) { - job.flags &= ~1; - } - callWithErrorHandling( - job, - job.i, - job.i ? 15 : 14 - ); - if (!(job.flags & 4)) { - job.flags &= ~1; - } - } - } - } finally { - for (; flushIndex < queue.length; flushIndex++) { - const job = queue[flushIndex]; - if (job) { - job.flags &= ~1; - } - } - flushIndex = -1; - queue.length = 0; - flushPostFlushCbs(seen); - currentFlushPromise = null; - if (queue.length || pendingPostFlushCbs.length) { - flushJobs(seen); - } - } -} -function checkRecursiveUpdates(seen, fn) { - const count = seen.get(fn) || 0; - if (count > RECURSION_LIMIT) { - const instance = fn.i; - const componentName = instance && getComponentName(instance.type); - handleError( - `Maximum recursive updates exceeded${componentName ? ` in component <${componentName}>` : ``}. This means you have a reactive effect that is mutating its own dependencies and thus recursively triggering itself. Possible sources include component template, render function, updated hook or watcher source function.`, - null, - 10 - ); - return true; - } - seen.set(fn, count + 1); - return false; -} - -let isHmrUpdating = false; -const hmrDirtyComponents = /* @__PURE__ */ new Map(); -{ - getGlobalThis().__VUE_HMR_RUNTIME__ = { - createRecord: tryWrap(createRecord), - rerender: tryWrap(rerender), - reload: tryWrap(reload) - }; -} -const map = /* @__PURE__ */ new Map(); -function registerHMR(instance) { - const id = instance.type.__hmrId; - let record = map.get(id); - if (!record) { - createRecord(id, instance.type); - record = map.get(id); - } - record.instances.add(instance); -} -function unregisterHMR(instance) { - map.get(instance.type.__hmrId).instances.delete(instance); -} -function createRecord(id, initialDef) { - if (map.has(id)) { - return false; - } - map.set(id, { - initialDef: normalizeClassComponent(initialDef), - instances: /* @__PURE__ */ new Set() - }); - return true; -} -function normalizeClassComponent(component) { - return isClassComponent(component) ? component.__vccOpts : component; -} -function rerender(id, newRender) { - const record = map.get(id); - if (!record) { - return; - } - record.initialDef.render = newRender; - [...record.instances].forEach((instance) => { - if (newRender) { - instance.render = newRender; - normalizeClassComponent(instance.type).render = newRender; - } - instance.renderCache = []; - isHmrUpdating = true; - instance.update(); - isHmrUpdating = false; - }); -} -function reload(id, newComp) { - const record = map.get(id); - if (!record) return; - newComp = normalizeClassComponent(newComp); - updateComponentDef(record.initialDef, newComp); - const instances = [...record.instances]; - for (let i = 0; i < instances.length; i++) { - const instance = instances[i]; - const oldComp = normalizeClassComponent(instance.type); - let dirtyInstances = hmrDirtyComponents.get(oldComp); - if (!dirtyInstances) { - if (oldComp !== record.initialDef) { - updateComponentDef(oldComp, newComp); - } - hmrDirtyComponents.set(oldComp, dirtyInstances = /* @__PURE__ */ new Set()); - } - dirtyInstances.add(instance); - instance.appContext.propsCache.delete(instance.type); - instance.appContext.emitsCache.delete(instance.type); - instance.appContext.optionsCache.delete(instance.type); - if (instance.ceReload) { - dirtyInstances.add(instance); - instance.ceReload(newComp.styles); - dirtyInstances.delete(instance); - } else if (instance.parent) { - queueJob(() => { - isHmrUpdating = true; - instance.parent.update(); - isHmrUpdating = false; - dirtyInstances.delete(instance); - }); - } else if (instance.appContext.reload) { - instance.appContext.reload(); - } else if (typeof window !== "undefined") { - window.location.reload(); - } else { - console.warn( - "[HMR] Root or manually mounted instance modified. Full reload required." - ); - } - if (instance.root.ce && instance !== instance.root) { - instance.root.ce._removeChildStyle(oldComp); - } - } - queuePostFlushCb(() => { - hmrDirtyComponents.clear(); - }); -} -function updateComponentDef(oldComp, newComp) { - extend(oldComp, newComp); - for (const key in oldComp) { - if (key !== "__file" && !(key in newComp)) { - delete oldComp[key]; - } - } -} -function tryWrap(fn) { - return (id, arg) => { - try { - return fn(id, arg); - } catch (e) { - console.error(e); - console.warn( - `[HMR] Something went wrong during Vue component hot-reload. Full reload required.` - ); - } - }; -} - -let devtools$1; -let buffer = []; -let devtoolsNotInstalled = false; -function emit$1(event, ...args) { - if (devtools$1) { - devtools$1.emit(event, ...args); - } else if (!devtoolsNotInstalled) { - buffer.push({ event, args }); - } -} -function setDevtoolsHook$1(hook, target) { - var _a, _b; - devtools$1 = hook; - if (devtools$1) { - devtools$1.enabled = true; - buffer.forEach(({ event, args }) => devtools$1.emit(event, ...args)); - buffer = []; - } else if ( - // handle late devtools injection - only do this if we are in an actual - // browser environment to avoid the timer handle stalling test runner exit - // (#4815) - typeof window !== "undefined" && // some envs mock window but not fully - window.HTMLElement && // also exclude jsdom - // eslint-disable-next-line no-restricted-syntax - !((_b = (_a = window.navigator) == null ? void 0 : _a.userAgent) == null ? void 0 : _b.includes("jsdom")) - ) { - const replay = target.__VUE_DEVTOOLS_HOOK_REPLAY__ = target.__VUE_DEVTOOLS_HOOK_REPLAY__ || []; - replay.push((newHook) => { - setDevtoolsHook$1(newHook, target); - }); - setTimeout(() => { - if (!devtools$1) { - target.__VUE_DEVTOOLS_HOOK_REPLAY__ = null; - devtoolsNotInstalled = true; - buffer = []; - } - }, 3e3); - } else { - devtoolsNotInstalled = true; - buffer = []; - } -} -function devtoolsInitApp(app, version) { - emit$1("app:init" /* APP_INIT */, app, version, { - Fragment, - Text, - Comment, - Static - }); -} -function devtoolsUnmountApp(app) { - emit$1("app:unmount" /* APP_UNMOUNT */, app); -} -const devtoolsComponentAdded = /* @__PURE__ */ createDevtoolsComponentHook("component:added" /* COMPONENT_ADDED */); -const devtoolsComponentUpdated = /* @__PURE__ */ createDevtoolsComponentHook("component:updated" /* COMPONENT_UPDATED */); -const _devtoolsComponentRemoved = /* @__PURE__ */ createDevtoolsComponentHook( - "component:removed" /* COMPONENT_REMOVED */ -); -const devtoolsComponentRemoved = (component) => { - if (devtools$1 && typeof devtools$1.cleanupBuffer === "function" && // remove the component if it wasn't buffered - !devtools$1.cleanupBuffer(component)) { - _devtoolsComponentRemoved(component); - } -}; -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function createDevtoolsComponentHook(hook) { - return (component) => { - emit$1( - hook, - component.appContext.app, - component.uid, - component.parent ? component.parent.uid : void 0, - component - ); - }; -} -const devtoolsPerfStart = /* @__PURE__ */ createDevtoolsPerformanceHook("perf:start" /* PERFORMANCE_START */); -const devtoolsPerfEnd = /* @__PURE__ */ createDevtoolsPerformanceHook("perf:end" /* PERFORMANCE_END */); -function createDevtoolsPerformanceHook(hook) { - return (component, type, time) => { - emit$1(hook, component.appContext.app, component.uid, component, type, time); - }; -} -function devtoolsComponentEmit(component, event, params) { - emit$1( - "component:emit" /* COMPONENT_EMIT */, - component.appContext.app, - component, - event, - params - ); -} - -let currentRenderingInstance = null; -let currentScopeId = null; -function setCurrentRenderingInstance(instance) { - const prev = currentRenderingInstance; - currentRenderingInstance = instance; - currentScopeId = instance && instance.type.__scopeId || null; - return prev; -} -function pushScopeId(id) { - currentScopeId = id; -} -function popScopeId() { - currentScopeId = null; -} -const withScopeId = (_id) => withCtx; -function withCtx(fn, ctx = currentRenderingInstance, isNonScopedSlot) { - if (!ctx) return fn; - if (fn._n) { - return fn; - } - const renderFnWithContext = (...args) => { - if (renderFnWithContext._d) { - setBlockTracking(-1); - } - const prevInstance = setCurrentRenderingInstance(ctx); - let res; - try { - res = fn(...args); - } finally { - setCurrentRenderingInstance(prevInstance); - if (renderFnWithContext._d) { - setBlockTracking(1); - } - } - { - devtoolsComponentUpdated(ctx); - } - return res; - }; - renderFnWithContext._n = true; - renderFnWithContext._c = true; - renderFnWithContext._d = true; - return renderFnWithContext; -} - -function validateDirectiveName(name) { - if (isBuiltInDirective(name)) { - warn$1("Do not use built-in directive ids as custom directive id: " + name); - } -} -function withDirectives(vnode, directives) { - if (currentRenderingInstance === null) { - warn$1(`withDirectives can only be used inside render functions.`); - return vnode; - } - const instance = getComponentPublicInstance(currentRenderingInstance); - const bindings = vnode.dirs || (vnode.dirs = []); - for (let i = 0; i < directives.length; i++) { - let [dir, value, arg, modifiers = EMPTY_OBJ] = directives[i]; - if (dir) { - if (isFunction(dir)) { - dir = { - mounted: dir, - updated: dir - }; - } - if (dir.deep) { - traverse(value); - } - bindings.push({ - dir, - instance, - value, - oldValue: void 0, - arg, - modifiers - }); - } - } - return vnode; -} -function invokeDirectiveHook(vnode, prevVNode, instance, name) { - const bindings = vnode.dirs; - const oldBindings = prevVNode && prevVNode.dirs; - for (let i = 0; i < bindings.length; i++) { - const binding = bindings[i]; - if (oldBindings) { - binding.oldValue = oldBindings[i].value; - } - let hook = binding.dir[name]; - if (hook) { - pauseTracking(); - callWithAsyncErrorHandling(hook, instance, 8, [ - vnode.el, - binding, - vnode, - prevVNode - ]); - resetTracking(); - } - } -} - -const TeleportEndKey = Symbol("_vte"); -const isTeleport = (type) => type.__isTeleport; -const isTeleportDisabled = (props) => props && (props.disabled || props.disabled === ""); -const isTeleportDeferred = (props) => props && (props.defer || props.defer === ""); -const isTargetSVG = (target) => typeof SVGElement !== "undefined" && target instanceof SVGElement; -const isTargetMathML = (target) => typeof MathMLElement === "function" && target instanceof MathMLElement; -const resolveTarget = (props, select) => { - const targetSelector = props && props.to; - if (isString(targetSelector)) { - if (!select) { - warn$1( - `Current renderer does not support string target for Teleports. (missing querySelector renderer option)` - ); - return null; - } else { - const target = select(targetSelector); - if (!target && !isTeleportDisabled(props)) { - warn$1( - `Failed to locate Teleport target with selector "${targetSelector}". Note the target element must exist before the component is mounted - i.e. the target cannot be rendered by the component itself, and ideally should be outside of the entire Vue component tree.` - ); - } - return target; - } - } else { - if (!targetSelector && !isTeleportDisabled(props)) { - warn$1(`Invalid Teleport target: ${targetSelector}`); - } - return targetSelector; - } -}; -const TeleportImpl = { - name: "Teleport", - __isTeleport: true, - process(n1, n2, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, internals) { - const { - mc: mountChildren, - pc: patchChildren, - pbc: patchBlockChildren, - o: { insert, querySelector, createText, createComment } - } = internals; - const disabled = isTeleportDisabled(n2.props); - let { shapeFlag, children, dynamicChildren } = n2; - if (isHmrUpdating) { - optimized = false; - dynamicChildren = null; - } - if (n1 == null) { - const placeholder = n2.el = createComment("teleport start") ; - const mainAnchor = n2.anchor = createComment("teleport end") ; - insert(placeholder, container, anchor); - insert(mainAnchor, container, anchor); - const mount = (container2, anchor2) => { - if (shapeFlag & 16) { - if (parentComponent && parentComponent.isCE) { - parentComponent.ce._teleportTarget = container2; - } - mountChildren( - children, - container2, - anchor2, - parentComponent, - parentSuspense, - namespace, - slotScopeIds, - optimized - ); - } - }; - const mountToTarget = () => { - const target = n2.target = resolveTarget(n2.props, querySelector); - const targetAnchor = prepareAnchor(target, n2, createText, insert); - if (target) { - if (namespace !== "svg" && isTargetSVG(target)) { - namespace = "svg"; - } else if (namespace !== "mathml" && isTargetMathML(target)) { - namespace = "mathml"; - } - if (!disabled) { - mount(target, targetAnchor); - updateCssVars(n2, false); - } - } else if (!disabled) { - warn$1( - "Invalid Teleport target on mount:", - target, - `(${typeof target})` - ); - } - }; - if (disabled) { - mount(container, mainAnchor); - updateCssVars(n2, true); - } - if (isTeleportDeferred(n2.props)) { - queuePostRenderEffect(mountToTarget, parentSuspense); - } else { - mountToTarget(); - } - } else { - n2.el = n1.el; - n2.targetStart = n1.targetStart; - const mainAnchor = n2.anchor = n1.anchor; - const target = n2.target = n1.target; - const targetAnchor = n2.targetAnchor = n1.targetAnchor; - const wasDisabled = isTeleportDisabled(n1.props); - const currentContainer = wasDisabled ? container : target; - const currentAnchor = wasDisabled ? mainAnchor : targetAnchor; - if (namespace === "svg" || isTargetSVG(target)) { - namespace = "svg"; - } else if (namespace === "mathml" || isTargetMathML(target)) { - namespace = "mathml"; - } - if (dynamicChildren) { - patchBlockChildren( - n1.dynamicChildren, - dynamicChildren, - currentContainer, - parentComponent, - parentSuspense, - namespace, - slotScopeIds - ); - traverseStaticChildren(n1, n2, true); - } else if (!optimized) { - patchChildren( - n1, - n2, - currentContainer, - currentAnchor, - parentComponent, - parentSuspense, - namespace, - slotScopeIds, - false - ); - } - if (disabled) { - if (!wasDisabled) { - moveTeleport( - n2, - container, - mainAnchor, - internals, - 1 - ); - } else { - if (n2.props && n1.props && n2.props.to !== n1.props.to) { - n2.props.to = n1.props.to; - } - } - } else { - if ((n2.props && n2.props.to) !== (n1.props && n1.props.to)) { - const nextTarget = n2.target = resolveTarget( - n2.props, - querySelector - ); - if (nextTarget) { - moveTeleport( - n2, - nextTarget, - null, - internals, - 0 - ); - } else { - warn$1( - "Invalid Teleport target on update:", - target, - `(${typeof target})` - ); - } - } else if (wasDisabled) { - moveTeleport( - n2, - target, - targetAnchor, - internals, - 1 - ); - } - } - updateCssVars(n2, disabled); - } - }, - remove(vnode, parentComponent, parentSuspense, { um: unmount, o: { remove: hostRemove } }, doRemove) { - const { - shapeFlag, - children, - anchor, - targetStart, - targetAnchor, - target, - props - } = vnode; - if (target) { - hostRemove(targetStart); - hostRemove(targetAnchor); - } - doRemove && hostRemove(anchor); - if (shapeFlag & 16) { - const shouldRemove = doRemove || !isTeleportDisabled(props); - for (let i = 0; i < children.length; i++) { - const child = children[i]; - unmount( - child, - parentComponent, - parentSuspense, - shouldRemove, - !!child.dynamicChildren - ); - } - } - }, - move: moveTeleport, - hydrate: hydrateTeleport -}; -function moveTeleport(vnode, container, parentAnchor, { o: { insert }, m: move }, moveType = 2) { - if (moveType === 0) { - insert(vnode.targetAnchor, container, parentAnchor); - } - const { el, anchor, shapeFlag, children, props } = vnode; - const isReorder = moveType === 2; - if (isReorder) { - insert(el, container, parentAnchor); - } - if (!isReorder || isTeleportDisabled(props)) { - if (shapeFlag & 16) { - for (let i = 0; i < children.length; i++) { - move( - children[i], - container, - parentAnchor, - 2 - ); - } - } - } - if (isReorder) { - insert(anchor, container, parentAnchor); - } -} -function hydrateTeleport(node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized, { - o: { nextSibling, parentNode, querySelector, insert, createText } -}, hydrateChildren) { - const target = vnode.target = resolveTarget( - vnode.props, - querySelector - ); - if (target) { - const disabled = isTeleportDisabled(vnode.props); - const targetNode = target._lpa || target.firstChild; - if (vnode.shapeFlag & 16) { - if (disabled) { - vnode.anchor = hydrateChildren( - nextSibling(node), - vnode, - parentNode(node), - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - vnode.targetStart = targetNode; - vnode.targetAnchor = targetNode && nextSibling(targetNode); - } else { - vnode.anchor = nextSibling(node); - let targetAnchor = targetNode; - while (targetAnchor) { - if (targetAnchor && targetAnchor.nodeType === 8) { - if (targetAnchor.data === "teleport start anchor") { - vnode.targetStart = targetAnchor; - } else if (targetAnchor.data === "teleport anchor") { - vnode.targetAnchor = targetAnchor; - target._lpa = vnode.targetAnchor && nextSibling(vnode.targetAnchor); - break; - } - } - targetAnchor = nextSibling(targetAnchor); - } - if (!vnode.targetAnchor) { - prepareAnchor(target, vnode, createText, insert); - } - hydrateChildren( - targetNode && nextSibling(targetNode), - vnode, - target, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - } - } - updateCssVars(vnode, disabled); - } - return vnode.anchor && nextSibling(vnode.anchor); -} -const Teleport = TeleportImpl; -function updateCssVars(vnode, isDisabled) { - const ctx = vnode.ctx; - if (ctx && ctx.ut) { - let node, anchor; - if (isDisabled) { - node = vnode.el; - anchor = vnode.anchor; - } else { - node = vnode.targetStart; - anchor = vnode.targetAnchor; - } - while (node && node !== anchor) { - if (node.nodeType === 1) node.setAttribute("data-v-owner", ctx.uid); - node = node.nextSibling; - } - ctx.ut(); - } -} -function prepareAnchor(target, vnode, createText, insert) { - const targetStart = vnode.targetStart = createText(""); - const targetAnchor = vnode.targetAnchor = createText(""); - targetStart[TeleportEndKey] = targetAnchor; - if (target) { - insert(targetStart, target); - insert(targetAnchor, target); - } - return targetAnchor; -} - -const leaveCbKey = Symbol("_leaveCb"); -const enterCbKey$1 = Symbol("_enterCb"); -function useTransitionState() { - const state = { - isMounted: false, - isLeaving: false, - isUnmounting: false, - leavingVNodes: /* @__PURE__ */ new Map() - }; - onMounted(() => { - state.isMounted = true; - }); - onBeforeUnmount(() => { - state.isUnmounting = true; - }); - return state; -} -const TransitionHookValidator = [Function, Array]; -const BaseTransitionPropsValidators = { - mode: String, - appear: Boolean, - persisted: Boolean, - // enter - onBeforeEnter: TransitionHookValidator, - onEnter: TransitionHookValidator, - onAfterEnter: TransitionHookValidator, - onEnterCancelled: TransitionHookValidator, - // leave - onBeforeLeave: TransitionHookValidator, - onLeave: TransitionHookValidator, - onAfterLeave: TransitionHookValidator, - onLeaveCancelled: TransitionHookValidator, - // appear - onBeforeAppear: TransitionHookValidator, - onAppear: TransitionHookValidator, - onAfterAppear: TransitionHookValidator, - onAppearCancelled: TransitionHookValidator -}; -const recursiveGetSubtree = (instance) => { - const subTree = instance.subTree; - return subTree.component ? recursiveGetSubtree(subTree.component) : subTree; -}; -const BaseTransitionImpl = { - name: `BaseTransition`, - props: BaseTransitionPropsValidators, - setup(props, { slots }) { - const instance = getCurrentInstance(); - const state = useTransitionState(); - return () => { - const children = slots.default && getTransitionRawChildren(slots.default(), true); - if (!children || !children.length) { - return; - } - const child = findNonCommentChild(children); - const rawProps = toRaw(props); - const { mode } = rawProps; - if (mode && mode !== "in-out" && mode !== "out-in" && mode !== "default") { - warn$1(`invalid mode: ${mode}`); - } - if (state.isLeaving) { - return emptyPlaceholder(child); - } - const innerChild = getInnerChild$1(child); - if (!innerChild) { - return emptyPlaceholder(child); - } - let enterHooks = resolveTransitionHooks( - innerChild, - rawProps, - state, - instance, - // #11061, ensure enterHooks is fresh after clone - (hooks) => enterHooks = hooks - ); - if (innerChild.type !== Comment) { - setTransitionHooks(innerChild, enterHooks); - } - const oldChild = instance.subTree; - const oldInnerChild = oldChild && getInnerChild$1(oldChild); - if (oldInnerChild && oldInnerChild.type !== Comment && !isSameVNodeType(innerChild, oldInnerChild) && recursiveGetSubtree(instance).type !== Comment) { - const leavingHooks = resolveTransitionHooks( - oldInnerChild, - rawProps, - state, - instance - ); - setTransitionHooks(oldInnerChild, leavingHooks); - if (mode === "out-in" && innerChild.type !== Comment) { - state.isLeaving = true; - leavingHooks.afterLeave = () => { - state.isLeaving = false; - if (!(instance.job.flags & 8)) { - instance.update(); - } - delete leavingHooks.afterLeave; - }; - return emptyPlaceholder(child); - } else if (mode === "in-out" && innerChild.type !== Comment) { - leavingHooks.delayLeave = (el, earlyRemove, delayedLeave) => { - const leavingVNodesCache = getLeavingNodesForType( - state, - oldInnerChild - ); - leavingVNodesCache[String(oldInnerChild.key)] = oldInnerChild; - el[leaveCbKey] = () => { - earlyRemove(); - el[leaveCbKey] = void 0; - delete enterHooks.delayedLeave; - }; - enterHooks.delayedLeave = delayedLeave; - }; - } - } - return child; - }; - } -}; -function findNonCommentChild(children) { - let child = children[0]; - if (children.length > 1) { - let hasFound = false; - for (const c of children) { - if (c.type !== Comment) { - if (hasFound) { - warn$1( - " can only be used on a single element or component. Use for lists." - ); - break; - } - child = c; - hasFound = true; - } - } - } - return child; -} -const BaseTransition = BaseTransitionImpl; -function getLeavingNodesForType(state, vnode) { - const { leavingVNodes } = state; - let leavingVNodesCache = leavingVNodes.get(vnode.type); - if (!leavingVNodesCache) { - leavingVNodesCache = /* @__PURE__ */ Object.create(null); - leavingVNodes.set(vnode.type, leavingVNodesCache); - } - return leavingVNodesCache; -} -function resolveTransitionHooks(vnode, props, state, instance, postClone) { - const { - appear, - mode, - persisted = false, - onBeforeEnter, - onEnter, - onAfterEnter, - onEnterCancelled, - onBeforeLeave, - onLeave, - onAfterLeave, - onLeaveCancelled, - onBeforeAppear, - onAppear, - onAfterAppear, - onAppearCancelled - } = props; - const key = String(vnode.key); - const leavingVNodesCache = getLeavingNodesForType(state, vnode); - const callHook = (hook, args) => { - hook && callWithAsyncErrorHandling( - hook, - instance, - 9, - args - ); - }; - const callAsyncHook = (hook, args) => { - const done = args[1]; - callHook(hook, args); - if (isArray(hook)) { - if (hook.every((hook2) => hook2.length <= 1)) done(); - } else if (hook.length <= 1) { - done(); - } - }; - const hooks = { - mode, - persisted, - beforeEnter(el) { - let hook = onBeforeEnter; - if (!state.isMounted) { - if (appear) { - hook = onBeforeAppear || onBeforeEnter; - } else { - return; - } - } - if (el[leaveCbKey]) { - el[leaveCbKey]( - true - /* cancelled */ - ); - } - const leavingVNode = leavingVNodesCache[key]; - if (leavingVNode && isSameVNodeType(vnode, leavingVNode) && leavingVNode.el[leaveCbKey]) { - leavingVNode.el[leaveCbKey](); - } - callHook(hook, [el]); - }, - enter(el) { - let hook = onEnter; - let afterHook = onAfterEnter; - let cancelHook = onEnterCancelled; - if (!state.isMounted) { - if (appear) { - hook = onAppear || onEnter; - afterHook = onAfterAppear || onAfterEnter; - cancelHook = onAppearCancelled || onEnterCancelled; - } else { - return; - } - } - let called = false; - const done = el[enterCbKey$1] = (cancelled) => { - if (called) return; - called = true; - if (cancelled) { - callHook(cancelHook, [el]); - } else { - callHook(afterHook, [el]); - } - if (hooks.delayedLeave) { - hooks.delayedLeave(); - } - el[enterCbKey$1] = void 0; - }; - if (hook) { - callAsyncHook(hook, [el, done]); - } else { - done(); - } - }, - leave(el, remove) { - const key2 = String(vnode.key); - if (el[enterCbKey$1]) { - el[enterCbKey$1]( - true - /* cancelled */ - ); - } - if (state.isUnmounting) { - return remove(); - } - callHook(onBeforeLeave, [el]); - let called = false; - const done = el[leaveCbKey] = (cancelled) => { - if (called) return; - called = true; - remove(); - if (cancelled) { - callHook(onLeaveCancelled, [el]); - } else { - callHook(onAfterLeave, [el]); - } - el[leaveCbKey] = void 0; - if (leavingVNodesCache[key2] === vnode) { - delete leavingVNodesCache[key2]; - } - }; - leavingVNodesCache[key2] = vnode; - if (onLeave) { - callAsyncHook(onLeave, [el, done]); - } else { - done(); - } - }, - clone(vnode2) { - const hooks2 = resolveTransitionHooks( - vnode2, - props, - state, - instance, - postClone - ); - if (postClone) postClone(hooks2); - return hooks2; - } - }; - return hooks; -} -function emptyPlaceholder(vnode) { - if (isKeepAlive(vnode)) { - vnode = cloneVNode(vnode); - vnode.children = null; - return vnode; - } -} -function getInnerChild$1(vnode) { - if (!isKeepAlive(vnode)) { - if (isTeleport(vnode.type) && vnode.children) { - return findNonCommentChild(vnode.children); - } - return vnode; - } - if (vnode.component) { - return vnode.component.subTree; - } - const { shapeFlag, children } = vnode; - if (children) { - if (shapeFlag & 16) { - return children[0]; - } - if (shapeFlag & 32 && isFunction(children.default)) { - return children.default(); - } - } -} -function setTransitionHooks(vnode, hooks) { - if (vnode.shapeFlag & 6 && vnode.component) { - vnode.transition = hooks; - setTransitionHooks(vnode.component.subTree, hooks); - } else if (vnode.shapeFlag & 128) { - vnode.ssContent.transition = hooks.clone(vnode.ssContent); - vnode.ssFallback.transition = hooks.clone(vnode.ssFallback); - } else { - vnode.transition = hooks; - } -} -function getTransitionRawChildren(children, keepComment = false, parentKey) { - let ret = []; - let keyedFragmentCount = 0; - for (let i = 0; i < children.length; i++) { - let child = children[i]; - const key = parentKey == null ? child.key : String(parentKey) + String(child.key != null ? child.key : i); - if (child.type === Fragment) { - if (child.patchFlag & 128) keyedFragmentCount++; - ret = ret.concat( - getTransitionRawChildren(child.children, keepComment, key) - ); - } else if (keepComment || child.type !== Comment) { - ret.push(key != null ? cloneVNode(child, { key }) : child); - } - } - if (keyedFragmentCount > 1) { - for (let i = 0; i < ret.length; i++) { - ret[i].patchFlag = -2; - } - } - return ret; -} - -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function defineComponent(options, extraOptions) { - return isFunction(options) ? ( - // #8236: extend call and options.name access are considered side-effects - // by Rollup, so we have to wrap it in a pure-annotated IIFE. - /* @__PURE__ */ (() => extend({ name: options.name }, extraOptions, { setup: options }))() - ) : options; -} - -function useId() { - const i = getCurrentInstance(); - if (i) { - return (i.appContext.config.idPrefix || "v") + "-" + i.ids[0] + i.ids[1]++; - } else { - warn$1( - `useId() is called when there is no active component instance to be associated with.` - ); - } - return ""; -} -function markAsyncBoundary(instance) { - instance.ids = [instance.ids[0] + instance.ids[2]++ + "-", 0, 0]; -} - -const knownTemplateRefs = /* @__PURE__ */ new WeakSet(); -function useTemplateRef(key) { - const i = getCurrentInstance(); - const r = shallowRef(null); - if (i) { - const refs = i.refs === EMPTY_OBJ ? i.refs = {} : i.refs; - let desc; - if ((desc = Object.getOwnPropertyDescriptor(refs, key)) && !desc.configurable) { - warn$1(`useTemplateRef('${key}') already exists.`); - } else { - Object.defineProperty(refs, key, { - enumerable: true, - get: () => r.value, - set: (val) => r.value = val - }); - } - } else { - warn$1( - `useTemplateRef() is called when there is no active component instance to be associated with.` - ); - } - const ret = readonly(r) ; - { - knownTemplateRefs.add(ret); - } - return ret; -} - -function setRef(rawRef, oldRawRef, parentSuspense, vnode, isUnmount = false) { - if (isArray(rawRef)) { - rawRef.forEach( - (r, i) => setRef( - r, - oldRawRef && (isArray(oldRawRef) ? oldRawRef[i] : oldRawRef), - parentSuspense, - vnode, - isUnmount - ) - ); - return; - } - if (isAsyncWrapper(vnode) && !isUnmount) { - return; - } - const refValue = vnode.shapeFlag & 4 ? getComponentPublicInstance(vnode.component) : vnode.el; - const value = isUnmount ? null : refValue; - const { i: owner, r: ref } = rawRef; - if (!owner) { - warn$1( - `Missing ref owner context. ref cannot be used on hoisted vnodes. A vnode with ref must be created inside the render function.` - ); - return; - } - const oldRef = oldRawRef && oldRawRef.r; - const refs = owner.refs === EMPTY_OBJ ? owner.refs = {} : owner.refs; - const setupState = owner.setupState; - const rawSetupState = toRaw(setupState); - const canSetSetupRef = setupState === EMPTY_OBJ ? () => false : (key) => { - { - if (hasOwn(rawSetupState, key) && !isRef(rawSetupState[key])) { - warn$1( - `Template ref "${key}" used on a non-ref value. It will not work in the production build.` - ); - } - if (knownTemplateRefs.has(rawSetupState[key])) { - return false; - } - } - return hasOwn(rawSetupState, key); - }; - if (oldRef != null && oldRef !== ref) { - if (isString(oldRef)) { - refs[oldRef] = null; - if (canSetSetupRef(oldRef)) { - setupState[oldRef] = null; - } - } else if (isRef(oldRef)) { - oldRef.value = null; - } - } - if (isFunction(ref)) { - callWithErrorHandling(ref, owner, 12, [value, refs]); - } else { - const _isString = isString(ref); - const _isRef = isRef(ref); - if (_isString || _isRef) { - const doSet = () => { - if (rawRef.f) { - const existing = _isString ? canSetSetupRef(ref) ? setupState[ref] : refs[ref] : ref.value; - if (isUnmount) { - isArray(existing) && remove(existing, refValue); - } else { - if (!isArray(existing)) { - if (_isString) { - refs[ref] = [refValue]; - if (canSetSetupRef(ref)) { - setupState[ref] = refs[ref]; - } - } else { - ref.value = [refValue]; - if (rawRef.k) refs[rawRef.k] = ref.value; - } - } else if (!existing.includes(refValue)) { - existing.push(refValue); - } - } - } else if (_isString) { - refs[ref] = value; - if (canSetSetupRef(ref)) { - setupState[ref] = value; - } - } else if (_isRef) { - ref.value = value; - if (rawRef.k) refs[rawRef.k] = value; - } else { - warn$1("Invalid template ref type:", ref, `(${typeof ref})`); - } - }; - if (value) { - doSet.id = -1; - queuePostRenderEffect(doSet, parentSuspense); - } else { - doSet(); - } - } else { - warn$1("Invalid template ref type:", ref, `(${typeof ref})`); - } - } -} - -let hasLoggedMismatchError = false; -const logMismatchError = () => { - if (hasLoggedMismatchError) { - return; - } - console.error("Hydration completed but contains mismatches."); - hasLoggedMismatchError = true; -}; -const isSVGContainer = (container) => container.namespaceURI.includes("svg") && container.tagName !== "foreignObject"; -const isMathMLContainer = (container) => container.namespaceURI.includes("MathML"); -const getContainerType = (container) => { - if (container.nodeType !== 1) return void 0; - if (isSVGContainer(container)) return "svg"; - if (isMathMLContainer(container)) return "mathml"; - return void 0; -}; -const isComment = (node) => node.nodeType === 8; -function createHydrationFunctions(rendererInternals) { - const { - mt: mountComponent, - p: patch, - o: { - patchProp, - createText, - nextSibling, - parentNode, - remove, - insert, - createComment - } - } = rendererInternals; - const hydrate = (vnode, container) => { - if (!container.hasChildNodes()) { - warn$1( - `Attempting to hydrate existing markup but container is empty. Performing full mount instead.` - ); - patch(null, vnode, container); - flushPostFlushCbs(); - container._vnode = vnode; - return; - } - hydrateNode(container.firstChild, vnode, null, null, null); - flushPostFlushCbs(); - container._vnode = vnode; - }; - const hydrateNode = (node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized = false) => { - optimized = optimized || !!vnode.dynamicChildren; - const isFragmentStart = isComment(node) && node.data === "["; - const onMismatch = () => handleMismatch( - node, - vnode, - parentComponent, - parentSuspense, - slotScopeIds, - isFragmentStart - ); - const { type, ref, shapeFlag, patchFlag } = vnode; - let domType = node.nodeType; - vnode.el = node; - { - def(node, "__vnode", vnode, true); - def(node, "__vueParentComponent", parentComponent, true); - } - if (patchFlag === -2) { - optimized = false; - vnode.dynamicChildren = null; - } - let nextNode = null; - switch (type) { - case Text: - if (domType !== 3) { - if (vnode.children === "") { - insert(vnode.el = createText(""), parentNode(node), node); - nextNode = node; - } else { - nextNode = onMismatch(); - } - } else { - if (node.data !== vnode.children) { - warn$1( - `Hydration text mismatch in`, - node.parentNode, - ` - - rendered on server: ${JSON.stringify( - node.data - )} - - expected on client: ${JSON.stringify(vnode.children)}` - ); - logMismatchError(); - node.data = vnode.children; - } - nextNode = nextSibling(node); - } - break; - case Comment: - if (isTemplateNode(node)) { - nextNode = nextSibling(node); - replaceNode( - vnode.el = node.content.firstChild, - node, - parentComponent - ); - } else if (domType !== 8 || isFragmentStart) { - nextNode = onMismatch(); - } else { - nextNode = nextSibling(node); - } - break; - case Static: - if (isFragmentStart) { - node = nextSibling(node); - domType = node.nodeType; - } - if (domType === 1 || domType === 3) { - nextNode = node; - const needToAdoptContent = !vnode.children.length; - for (let i = 0; i < vnode.staticCount; i++) { - if (needToAdoptContent) - vnode.children += nextNode.nodeType === 1 ? nextNode.outerHTML : nextNode.data; - if (i === vnode.staticCount - 1) { - vnode.anchor = nextNode; - } - nextNode = nextSibling(nextNode); - } - return isFragmentStart ? nextSibling(nextNode) : nextNode; - } else { - onMismatch(); - } - break; - case Fragment: - if (!isFragmentStart) { - nextNode = onMismatch(); - } else { - nextNode = hydrateFragment( - node, - vnode, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - } - break; - default: - if (shapeFlag & 1) { - if ((domType !== 1 || vnode.type.toLowerCase() !== node.tagName.toLowerCase()) && !isTemplateNode(node)) { - nextNode = onMismatch(); - } else { - nextNode = hydrateElement( - node, - vnode, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - } - } else if (shapeFlag & 6) { - vnode.slotScopeIds = slotScopeIds; - const container = parentNode(node); - if (isFragmentStart) { - nextNode = locateClosingAnchor(node); - } else if (isComment(node) && node.data === "teleport start") { - nextNode = locateClosingAnchor(node, node.data, "teleport end"); - } else { - nextNode = nextSibling(node); - } - mountComponent( - vnode, - container, - null, - parentComponent, - parentSuspense, - getContainerType(container), - optimized - ); - if (isAsyncWrapper(vnode)) { - let subTree; - if (isFragmentStart) { - subTree = createVNode(Fragment); - subTree.anchor = nextNode ? nextNode.previousSibling : container.lastChild; - } else { - subTree = node.nodeType === 3 ? createTextVNode("") : createVNode("div"); - } - subTree.el = node; - vnode.component.subTree = subTree; - } - } else if (shapeFlag & 64) { - if (domType !== 8) { - nextNode = onMismatch(); - } else { - nextNode = vnode.type.hydrate( - node, - vnode, - parentComponent, - parentSuspense, - slotScopeIds, - optimized, - rendererInternals, - hydrateChildren - ); - } - } else if (shapeFlag & 128) { - nextNode = vnode.type.hydrate( - node, - vnode, - parentComponent, - parentSuspense, - getContainerType(parentNode(node)), - slotScopeIds, - optimized, - rendererInternals, - hydrateNode - ); - } else { - warn$1("Invalid HostVNode type:", type, `(${typeof type})`); - } - } - if (ref != null) { - setRef(ref, null, parentSuspense, vnode); - } - return nextNode; - }; - const hydrateElement = (el, vnode, parentComponent, parentSuspense, slotScopeIds, optimized) => { - optimized = optimized || !!vnode.dynamicChildren; - const { type, props, patchFlag, shapeFlag, dirs, transition } = vnode; - const forcePatch = type === "input" || type === "option"; - { - if (dirs) { - invokeDirectiveHook(vnode, null, parentComponent, "created"); - } - let needCallTransitionHooks = false; - if (isTemplateNode(el)) { - needCallTransitionHooks = needTransition( - null, - // no need check parentSuspense in hydration - transition - ) && parentComponent && parentComponent.vnode.props && parentComponent.vnode.props.appear; - const content = el.content.firstChild; - if (needCallTransitionHooks) { - transition.beforeEnter(content); - } - replaceNode(content, el, parentComponent); - vnode.el = el = content; - } - if (shapeFlag & 16 && // skip if element has innerHTML / textContent - !(props && (props.innerHTML || props.textContent))) { - let next = hydrateChildren( - el.firstChild, - vnode, - el, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - let hasWarned = false; - while (next) { - if (!isMismatchAllowed(el, 1 /* CHILDREN */)) { - if (!hasWarned) { - warn$1( - `Hydration children mismatch on`, - el, - ` -Server rendered element contains more child nodes than client vdom.` - ); - hasWarned = true; - } - logMismatchError(); - } - const cur = next; - next = next.nextSibling; - remove(cur); - } - } else if (shapeFlag & 8) { - let clientText = vnode.children; - if (clientText[0] === "\n" && (el.tagName === "PRE" || el.tagName === "TEXTAREA")) { - clientText = clientText.slice(1); - } - if (el.textContent !== clientText) { - if (!isMismatchAllowed(el, 0 /* TEXT */)) { - warn$1( - `Hydration text content mismatch on`, - el, - ` - - rendered on server: ${el.textContent} - - expected on client: ${vnode.children}` - ); - logMismatchError(); - } - el.textContent = vnode.children; - } - } - if (props) { - { - const isCustomElement = el.tagName.includes("-"); - for (const key in props) { - if (// #11189 skip if this node has directives that have created hooks - // as it could have mutated the DOM in any possible way - !(dirs && dirs.some((d) => d.dir.created)) && propHasMismatch(el, key, props[key], vnode, parentComponent)) { - logMismatchError(); - } - if (forcePatch && (key.endsWith("value") || key === "indeterminate") || isOn(key) && !isReservedProp(key) || // force hydrate v-bind with .prop modifiers - key[0] === "." || isCustomElement) { - patchProp(el, key, null, props[key], void 0, parentComponent); - } - } - } - } - let vnodeHooks; - if (vnodeHooks = props && props.onVnodeBeforeMount) { - invokeVNodeHook(vnodeHooks, parentComponent, vnode); - } - if (dirs) { - invokeDirectiveHook(vnode, null, parentComponent, "beforeMount"); - } - if ((vnodeHooks = props && props.onVnodeMounted) || dirs || needCallTransitionHooks) { - queueEffectWithSuspense(() => { - vnodeHooks && invokeVNodeHook(vnodeHooks, parentComponent, vnode); - needCallTransitionHooks && transition.enter(el); - dirs && invokeDirectiveHook(vnode, null, parentComponent, "mounted"); - }, parentSuspense); - } - } - return el.nextSibling; - }; - const hydrateChildren = (node, parentVNode, container, parentComponent, parentSuspense, slotScopeIds, optimized) => { - optimized = optimized || !!parentVNode.dynamicChildren; - const children = parentVNode.children; - const l = children.length; - let hasWarned = false; - for (let i = 0; i < l; i++) { - const vnode = optimized ? children[i] : children[i] = normalizeVNode(children[i]); - const isText = vnode.type === Text; - if (node) { - if (isText && !optimized) { - if (i + 1 < l && normalizeVNode(children[i + 1]).type === Text) { - insert( - createText( - node.data.slice(vnode.children.length) - ), - container, - nextSibling(node) - ); - node.data = vnode.children; - } - } - node = hydrateNode( - node, - vnode, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - } else if (isText && !vnode.children) { - insert(vnode.el = createText(""), container); - } else { - if (!isMismatchAllowed(container, 1 /* CHILDREN */)) { - if (!hasWarned) { - warn$1( - `Hydration children mismatch on`, - container, - ` -Server rendered element contains fewer child nodes than client vdom.` - ); - hasWarned = true; - } - logMismatchError(); - } - patch( - null, - vnode, - container, - null, - parentComponent, - parentSuspense, - getContainerType(container), - slotScopeIds - ); - } - } - return node; - }; - const hydrateFragment = (node, vnode, parentComponent, parentSuspense, slotScopeIds, optimized) => { - const { slotScopeIds: fragmentSlotScopeIds } = vnode; - if (fragmentSlotScopeIds) { - slotScopeIds = slotScopeIds ? slotScopeIds.concat(fragmentSlotScopeIds) : fragmentSlotScopeIds; - } - const container = parentNode(node); - const next = hydrateChildren( - nextSibling(node), - vnode, - container, - parentComponent, - parentSuspense, - slotScopeIds, - optimized - ); - if (next && isComment(next) && next.data === "]") { - return nextSibling(vnode.anchor = next); - } else { - logMismatchError(); - insert(vnode.anchor = createComment(`]`), container, next); - return next; - } - }; - const handleMismatch = (node, vnode, parentComponent, parentSuspense, slotScopeIds, isFragment) => { - if (!isMismatchAllowed(node.parentElement, 1 /* CHILDREN */)) { - warn$1( - `Hydration node mismatch: -- rendered on server:`, - node, - node.nodeType === 3 ? `(text)` : isComment(node) && node.data === "[" ? `(start of fragment)` : ``, - ` -- expected on client:`, - vnode.type - ); - logMismatchError(); - } - vnode.el = null; - if (isFragment) { - const end = locateClosingAnchor(node); - while (true) { - const next2 = nextSibling(node); - if (next2 && next2 !== end) { - remove(next2); - } else { - break; - } - } - } - const next = nextSibling(node); - const container = parentNode(node); - remove(node); - patch( - null, - vnode, - container, - next, - parentComponent, - parentSuspense, - getContainerType(container), - slotScopeIds - ); - return next; - }; - const locateClosingAnchor = (node, open = "[", close = "]") => { - let match = 0; - while (node) { - node = nextSibling(node); - if (node && isComment(node)) { - if (node.data === open) match++; - if (node.data === close) { - if (match === 0) { - return nextSibling(node); - } else { - match--; - } - } - } - } - return node; - }; - const replaceNode = (newNode, oldNode, parentComponent) => { - const parentNode2 = oldNode.parentNode; - if (parentNode2) { - parentNode2.replaceChild(newNode, oldNode); - } - let parent = parentComponent; - while (parent) { - if (parent.vnode.el === oldNode) { - parent.vnode.el = parent.subTree.el = newNode; - } - parent = parent.parent; - } - }; - const isTemplateNode = (node) => { - return node.nodeType === 1 && node.tagName === "TEMPLATE"; - }; - return [hydrate, hydrateNode]; -} -function propHasMismatch(el, key, clientValue, vnode, instance) { - let mismatchType; - let mismatchKey; - let actual; - let expected; - if (key === "class") { - actual = el.getAttribute("class"); - expected = normalizeClass(clientValue); - if (!isSetEqual(toClassSet(actual || ""), toClassSet(expected))) { - mismatchType = 2 /* CLASS */; - mismatchKey = `class`; - } - } else if (key === "style") { - actual = el.getAttribute("style") || ""; - expected = isString(clientValue) ? clientValue : stringifyStyle(normalizeStyle(clientValue)); - const actualMap = toStyleMap(actual); - const expectedMap = toStyleMap(expected); - if (vnode.dirs) { - for (const { dir, value } of vnode.dirs) { - if (dir.name === "show" && !value) { - expectedMap.set("display", "none"); - } - } - } - if (instance) { - resolveCssVars(instance, vnode, expectedMap); - } - if (!isMapEqual(actualMap, expectedMap)) { - mismatchType = 3 /* STYLE */; - mismatchKey = "style"; - } - } else if (el instanceof SVGElement && isKnownSvgAttr(key) || el instanceof HTMLElement && (isBooleanAttr(key) || isKnownHtmlAttr(key))) { - if (isBooleanAttr(key)) { - actual = el.hasAttribute(key); - expected = includeBooleanAttr(clientValue); - } else if (clientValue == null) { - actual = el.hasAttribute(key); - expected = false; - } else { - if (el.hasAttribute(key)) { - actual = el.getAttribute(key); - } else if (key === "value" && el.tagName === "TEXTAREA") { - actual = el.value; - } else { - actual = false; - } - expected = isRenderableAttrValue(clientValue) ? String(clientValue) : false; - } - if (actual !== expected) { - mismatchType = 4 /* ATTRIBUTE */; - mismatchKey = key; - } - } - if (mismatchType != null && !isMismatchAllowed(el, mismatchType)) { - const format = (v) => v === false ? `(not rendered)` : `${mismatchKey}="${v}"`; - const preSegment = `Hydration ${MismatchTypeString[mismatchType]} mismatch on`; - const postSegment = ` - - rendered on server: ${format(actual)} - - expected on client: ${format(expected)} - Note: this mismatch is check-only. The DOM will not be rectified in production due to performance overhead. - You should fix the source of the mismatch.`; - { - warn$1(preSegment, el, postSegment); - } - return true; - } - return false; -} -function toClassSet(str) { - return new Set(str.trim().split(/\s+/)); -} -function isSetEqual(a, b) { - if (a.size !== b.size) { - return false; - } - for (const s of a) { - if (!b.has(s)) { - return false; - } - } - return true; -} -function toStyleMap(str) { - const styleMap = /* @__PURE__ */ new Map(); - for (const item of str.split(";")) { - let [key, value] = item.split(":"); - key = key.trim(); - value = value && value.trim(); - if (key && value) { - styleMap.set(key, value); - } - } - return styleMap; -} -function isMapEqual(a, b) { - if (a.size !== b.size) { - return false; - } - for (const [key, value] of a) { - if (value !== b.get(key)) { - return false; - } - } - return true; -} -function resolveCssVars(instance, vnode, expectedMap) { - const root = instance.subTree; - if (instance.getCssVars && (vnode === root || root && root.type === Fragment && root.children.includes(vnode))) { - const cssVars = instance.getCssVars(); - for (const key in cssVars) { - expectedMap.set( - `--${getEscapedCssVarName(key)}`, - String(cssVars[key]) - ); - } - } - if (vnode === root && instance.parent) { - resolveCssVars(instance.parent, instance.vnode, expectedMap); - } -} -const allowMismatchAttr = "data-allow-mismatch"; -const MismatchTypeString = { - [0 /* TEXT */]: "text", - [1 /* CHILDREN */]: "children", - [2 /* CLASS */]: "class", - [3 /* STYLE */]: "style", - [4 /* ATTRIBUTE */]: "attribute" -}; -function isMismatchAllowed(el, allowedType) { - if (allowedType === 0 /* TEXT */ || allowedType === 1 /* CHILDREN */) { - while (el && !el.hasAttribute(allowMismatchAttr)) { - el = el.parentElement; - } - } - const allowedAttr = el && el.getAttribute(allowMismatchAttr); - if (allowedAttr == null) { - return false; - } else if (allowedAttr === "") { - return true; - } else { - const list = allowedAttr.split(","); - if (allowedType === 0 /* TEXT */ && list.includes("children")) { - return true; - } - return allowedAttr.split(",").includes(MismatchTypeString[allowedType]); - } -} - -const requestIdleCallback = getGlobalThis().requestIdleCallback || ((cb) => setTimeout(cb, 1)); -const cancelIdleCallback = getGlobalThis().cancelIdleCallback || ((id) => clearTimeout(id)); -const hydrateOnIdle = (timeout = 1e4) => (hydrate) => { - const id = requestIdleCallback(hydrate, { timeout }); - return () => cancelIdleCallback(id); -}; -function elementIsVisibleInViewport(el) { - const { top, left, bottom, right } = el.getBoundingClientRect(); - const { innerHeight, innerWidth } = window; - return (top > 0 && top < innerHeight || bottom > 0 && bottom < innerHeight) && (left > 0 && left < innerWidth || right > 0 && right < innerWidth); -} -const hydrateOnVisible = (opts) => (hydrate, forEach) => { - const ob = new IntersectionObserver((entries) => { - for (const e of entries) { - if (!e.isIntersecting) continue; - ob.disconnect(); - hydrate(); - break; - } - }, opts); - forEach((el) => { - if (!(el instanceof Element)) return; - if (elementIsVisibleInViewport(el)) { - hydrate(); - ob.disconnect(); - return false; - } - ob.observe(el); - }); - return () => ob.disconnect(); -}; -const hydrateOnMediaQuery = (query) => (hydrate) => { - if (query) { - const mql = matchMedia(query); - if (mql.matches) { - hydrate(); - } else { - mql.addEventListener("change", hydrate, { once: true }); - return () => mql.removeEventListener("change", hydrate); - } - } -}; -const hydrateOnInteraction = (interactions = []) => (hydrate, forEach) => { - if (isString(interactions)) interactions = [interactions]; - let hasHydrated = false; - const doHydrate = (e) => { - if (!hasHydrated) { - hasHydrated = true; - teardown(); - hydrate(); - e.target.dispatchEvent(new e.constructor(e.type, e)); - } - }; - const teardown = () => { - forEach((el) => { - for (const i of interactions) { - el.removeEventListener(i, doHydrate); - } - }); - }; - forEach((el) => { - for (const i of interactions) { - el.addEventListener(i, doHydrate, { once: true }); - } - }); - return teardown; -}; -function forEachElement(node, cb) { - if (isComment(node) && node.data === "[") { - let depth = 1; - let next = node.nextSibling; - while (next) { - if (next.nodeType === 1) { - const result = cb(next); - if (result === false) { - break; - } - } else if (isComment(next)) { - if (next.data === "]") { - if (--depth === 0) break; - } else if (next.data === "[") { - depth++; - } - } - next = next.nextSibling; - } - } else { - cb(node); - } -} - -const isAsyncWrapper = (i) => !!i.type.__asyncLoader; -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function defineAsyncComponent(source) { - if (isFunction(source)) { - source = { loader: source }; - } - const { - loader, - loadingComponent, - errorComponent, - delay = 200, - hydrate: hydrateStrategy, - timeout, - // undefined = never times out - suspensible = true, - onError: userOnError - } = source; - let pendingRequest = null; - let resolvedComp; - let retries = 0; - const retry = () => { - retries++; - pendingRequest = null; - return load(); - }; - const load = () => { - let thisRequest; - return pendingRequest || (thisRequest = pendingRequest = loader().catch((err) => { - err = err instanceof Error ? err : new Error(String(err)); - if (userOnError) { - return new Promise((resolve, reject) => { - const userRetry = () => resolve(retry()); - const userFail = () => reject(err); - userOnError(err, userRetry, userFail, retries + 1); - }); - } else { - throw err; - } - }).then((comp) => { - if (thisRequest !== pendingRequest && pendingRequest) { - return pendingRequest; - } - if (!comp) { - warn$1( - `Async component loader resolved to undefined. If you are using retry(), make sure to return its return value.` - ); - } - if (comp && (comp.__esModule || comp[Symbol.toStringTag] === "Module")) { - comp = comp.default; - } - if (comp && !isObject(comp) && !isFunction(comp)) { - throw new Error(`Invalid async component load result: ${comp}`); - } - resolvedComp = comp; - return comp; - })); - }; - return defineComponent({ - name: "AsyncComponentWrapper", - __asyncLoader: load, - __asyncHydrate(el, instance, hydrate) { - const doHydrate = hydrateStrategy ? () => { - const teardown = hydrateStrategy( - hydrate, - (cb) => forEachElement(el, cb) - ); - if (teardown) { - (instance.bum || (instance.bum = [])).push(teardown); - } - } : hydrate; - if (resolvedComp) { - doHydrate(); - } else { - load().then(() => !instance.isUnmounted && doHydrate()); - } - }, - get __asyncResolved() { - return resolvedComp; - }, - setup() { - const instance = currentInstance; - markAsyncBoundary(instance); - if (resolvedComp) { - return () => createInnerComp(resolvedComp, instance); - } - const onError = (err) => { - pendingRequest = null; - handleError( - err, - instance, - 13, - !errorComponent - ); - }; - if (suspensible && instance.suspense || isInSSRComponentSetup) { - return load().then((comp) => { - return () => createInnerComp(comp, instance); - }).catch((err) => { - onError(err); - return () => errorComponent ? createVNode(errorComponent, { - error: err - }) : null; - }); - } - const loaded = ref(false); - const error = ref(); - const delayed = ref(!!delay); - if (delay) { - setTimeout(() => { - delayed.value = false; - }, delay); - } - if (timeout != null) { - setTimeout(() => { - if (!loaded.value && !error.value) { - const err = new Error( - `Async component timed out after ${timeout}ms.` - ); - onError(err); - error.value = err; - } - }, timeout); - } - load().then(() => { - loaded.value = true; - if (instance.parent && isKeepAlive(instance.parent.vnode)) { - instance.parent.update(); - } - }).catch((err) => { - onError(err); - error.value = err; - }); - return () => { - if (loaded.value && resolvedComp) { - return createInnerComp(resolvedComp, instance); - } else if (error.value && errorComponent) { - return createVNode(errorComponent, { - error: error.value - }); - } else if (loadingComponent && !delayed.value) { - return createVNode(loadingComponent); - } - }; - } - }); -} -function createInnerComp(comp, parent) { - const { ref: ref2, props, children, ce } = parent.vnode; - const vnode = createVNode(comp, props, children); - vnode.ref = ref2; - vnode.ce = ce; - delete parent.vnode.ce; - return vnode; -} - -const isKeepAlive = (vnode) => vnode.type.__isKeepAlive; -const KeepAliveImpl = { - name: `KeepAlive`, - // Marker for special handling inside the renderer. We are not using a === - // check directly on KeepAlive in the renderer, because importing it directly - // would prevent it from being tree-shaken. - __isKeepAlive: true, - props: { - include: [String, RegExp, Array], - exclude: [String, RegExp, Array], - max: [String, Number] - }, - setup(props, { slots }) { - const instance = getCurrentInstance(); - const sharedContext = instance.ctx; - if (!sharedContext.renderer) { - return () => { - const children = slots.default && slots.default(); - return children && children.length === 1 ? children[0] : children; - }; - } - const cache = /* @__PURE__ */ new Map(); - const keys = /* @__PURE__ */ new Set(); - let current = null; - { - instance.__v_cache = cache; - } - const parentSuspense = instance.suspense; - const { - renderer: { - p: patch, - m: move, - um: _unmount, - o: { createElement } - } - } = sharedContext; - const storageContainer = createElement("div"); - sharedContext.activate = (vnode, container, anchor, namespace, optimized) => { - const instance2 = vnode.component; - move(vnode, container, anchor, 0, parentSuspense); - patch( - instance2.vnode, - vnode, - container, - anchor, - instance2, - parentSuspense, - namespace, - vnode.slotScopeIds, - optimized - ); - queuePostRenderEffect(() => { - instance2.isDeactivated = false; - if (instance2.a) { - invokeArrayFns(instance2.a); - } - const vnodeHook = vnode.props && vnode.props.onVnodeMounted; - if (vnodeHook) { - invokeVNodeHook(vnodeHook, instance2.parent, vnode); - } - }, parentSuspense); - { - devtoolsComponentAdded(instance2); - } - }; - sharedContext.deactivate = (vnode) => { - const instance2 = vnode.component; - invalidateMount(instance2.m); - invalidateMount(instance2.a); - move(vnode, storageContainer, null, 1, parentSuspense); - queuePostRenderEffect(() => { - if (instance2.da) { - invokeArrayFns(instance2.da); - } - const vnodeHook = vnode.props && vnode.props.onVnodeUnmounted; - if (vnodeHook) { - invokeVNodeHook(vnodeHook, instance2.parent, vnode); - } - instance2.isDeactivated = true; - }, parentSuspense); - { - devtoolsComponentAdded(instance2); - } - }; - function unmount(vnode) { - resetShapeFlag(vnode); - _unmount(vnode, instance, parentSuspense, true); - } - function pruneCache(filter) { - cache.forEach((vnode, key) => { - const name = getComponentName(vnode.type); - if (name && !filter(name)) { - pruneCacheEntry(key); - } - }); - } - function pruneCacheEntry(key) { - const cached = cache.get(key); - if (cached && (!current || !isSameVNodeType(cached, current))) { - unmount(cached); - } else if (current) { - resetShapeFlag(current); - } - cache.delete(key); - keys.delete(key); - } - watch( - () => [props.include, props.exclude], - ([include, exclude]) => { - include && pruneCache((name) => matches(include, name)); - exclude && pruneCache((name) => !matches(exclude, name)); - }, - // prune post-render after `current` has been updated - { flush: "post", deep: true } - ); - let pendingCacheKey = null; - const cacheSubtree = () => { - if (pendingCacheKey != null) { - if (isSuspense(instance.subTree.type)) { - queuePostRenderEffect(() => { - cache.set(pendingCacheKey, getInnerChild(instance.subTree)); - }, instance.subTree.suspense); - } else { - cache.set(pendingCacheKey, getInnerChild(instance.subTree)); - } - } - }; - onMounted(cacheSubtree); - onUpdated(cacheSubtree); - onBeforeUnmount(() => { - cache.forEach((cached) => { - const { subTree, suspense } = instance; - const vnode = getInnerChild(subTree); - if (cached.type === vnode.type && cached.key === vnode.key) { - resetShapeFlag(vnode); - const da = vnode.component.da; - da && queuePostRenderEffect(da, suspense); - return; - } - unmount(cached); - }); - }); - return () => { - pendingCacheKey = null; - if (!slots.default) { - return current = null; - } - const children = slots.default(); - const rawVNode = children[0]; - if (children.length > 1) { - { - warn$1(`KeepAlive should contain exactly one component child.`); - } - current = null; - return children; - } else if (!isVNode(rawVNode) || !(rawVNode.shapeFlag & 4) && !(rawVNode.shapeFlag & 128)) { - current = null; - return rawVNode; - } - let vnode = getInnerChild(rawVNode); - if (vnode.type === Comment) { - current = null; - return vnode; - } - const comp = vnode.type; - const name = getComponentName( - isAsyncWrapper(vnode) ? vnode.type.__asyncResolved || {} : comp - ); - const { include, exclude, max } = props; - if (include && (!name || !matches(include, name)) || exclude && name && matches(exclude, name)) { - vnode.shapeFlag &= ~256; - current = vnode; - return rawVNode; - } - const key = vnode.key == null ? comp : vnode.key; - const cachedVNode = cache.get(key); - if (vnode.el) { - vnode = cloneVNode(vnode); - if (rawVNode.shapeFlag & 128) { - rawVNode.ssContent = vnode; - } - } - pendingCacheKey = key; - if (cachedVNode) { - vnode.el = cachedVNode.el; - vnode.component = cachedVNode.component; - if (vnode.transition) { - setTransitionHooks(vnode, vnode.transition); - } - vnode.shapeFlag |= 512; - keys.delete(key); - keys.add(key); - } else { - keys.add(key); - if (max && keys.size > parseInt(max, 10)) { - pruneCacheEntry(keys.values().next().value); - } - } - vnode.shapeFlag |= 256; - current = vnode; - return isSuspense(rawVNode.type) ? rawVNode : vnode; - }; - } -}; -const KeepAlive = KeepAliveImpl; -function matches(pattern, name) { - if (isArray(pattern)) { - return pattern.some((p) => matches(p, name)); - } else if (isString(pattern)) { - return pattern.split(",").includes(name); - } else if (isRegExp(pattern)) { - pattern.lastIndex = 0; - return pattern.test(name); - } - return false; -} -function onActivated(hook, target) { - registerKeepAliveHook(hook, "a", target); -} -function onDeactivated(hook, target) { - registerKeepAliveHook(hook, "da", target); -} -function registerKeepAliveHook(hook, type, target = currentInstance) { - const wrappedHook = hook.__wdc || (hook.__wdc = () => { - let current = target; - while (current) { - if (current.isDeactivated) { - return; - } - current = current.parent; - } - return hook(); - }); - injectHook(type, wrappedHook, target); - if (target) { - let current = target.parent; - while (current && current.parent) { - if (isKeepAlive(current.parent.vnode)) { - injectToKeepAliveRoot(wrappedHook, type, target, current); - } - current = current.parent; - } - } -} -function injectToKeepAliveRoot(hook, type, target, keepAliveRoot) { - const injected = injectHook( - type, - hook, - keepAliveRoot, - true - /* prepend */ - ); - onUnmounted(() => { - remove(keepAliveRoot[type], injected); - }, target); -} -function resetShapeFlag(vnode) { - vnode.shapeFlag &= ~256; - vnode.shapeFlag &= ~512; -} -function getInnerChild(vnode) { - return vnode.shapeFlag & 128 ? vnode.ssContent : vnode; -} - -function injectHook(type, hook, target = currentInstance, prepend = false) { - if (target) { - const hooks = target[type] || (target[type] = []); - const wrappedHook = hook.__weh || (hook.__weh = (...args) => { - pauseTracking(); - const reset = setCurrentInstance(target); - const res = callWithAsyncErrorHandling(hook, target, type, args); - reset(); - resetTracking(); - return res; - }); - if (prepend) { - hooks.unshift(wrappedHook); - } else { - hooks.push(wrappedHook); - } - return wrappedHook; - } else { - const apiName = toHandlerKey(ErrorTypeStrings$1[type].replace(/ hook$/, "")); - warn$1( - `${apiName} is called when there is no active component instance to be associated with. Lifecycle injection APIs can only be used during execution of setup().` + (` If you are using async setup(), make sure to register lifecycle hooks before the first await statement.` ) - ); - } -} -const createHook = (lifecycle) => (hook, target = currentInstance) => { - if (!isInSSRComponentSetup || lifecycle === "sp") { - injectHook(lifecycle, (...args) => hook(...args), target); - } -}; -const onBeforeMount = createHook("bm"); -const onMounted = createHook("m"); -const onBeforeUpdate = createHook( - "bu" -); -const onUpdated = createHook("u"); -const onBeforeUnmount = createHook( - "bum" -); -const onUnmounted = createHook("um"); -const onServerPrefetch = createHook( - "sp" -); -const onRenderTriggered = createHook("rtg"); -const onRenderTracked = createHook("rtc"); -function onErrorCaptured(hook, target = currentInstance) { - injectHook("ec", hook, target); -} - -const COMPONENTS = "components"; -const DIRECTIVES = "directives"; -function resolveComponent(name, maybeSelfReference) { - return resolveAsset(COMPONENTS, name, true, maybeSelfReference) || name; -} -const NULL_DYNAMIC_COMPONENT = Symbol.for("v-ndc"); -function resolveDynamicComponent(component) { - if (isString(component)) { - return resolveAsset(COMPONENTS, component, false) || component; - } else { - return component || NULL_DYNAMIC_COMPONENT; - } -} -function resolveDirective(name) { - return resolveAsset(DIRECTIVES, name); -} -function resolveAsset(type, name, warnMissing = true, maybeSelfReference = false) { - const instance = currentRenderingInstance || currentInstance; - if (instance) { - const Component = instance.type; - if (type === COMPONENTS) { - const selfName = getComponentName( - Component, - false - ); - if (selfName && (selfName === name || selfName === camelize(name) || selfName === capitalize(camelize(name)))) { - return Component; - } - } - const res = ( - // local registration - // check instance[type] first which is resolved for options API - resolve(instance[type] || Component[type], name) || // global registration - resolve(instance.appContext[type], name) - ); - if (!res && maybeSelfReference) { - return Component; - } - if (warnMissing && !res) { - const extra = type === COMPONENTS ? ` -If this is a native custom element, make sure to exclude it from component resolution via compilerOptions.isCustomElement.` : ``; - warn$1(`Failed to resolve ${type.slice(0, -1)}: ${name}${extra}`); - } - return res; - } else { - warn$1( - `resolve${capitalize(type.slice(0, -1))} can only be used in render() or setup().` - ); - } -} -function resolve(registry, name) { - return registry && (registry[name] || registry[camelize(name)] || registry[capitalize(camelize(name))]); -} - -function renderList(source, renderItem, cache, index) { - let ret; - const cached = cache && cache[index]; - const sourceIsArray = isArray(source); - if (sourceIsArray || isString(source)) { - const sourceIsReactiveArray = sourceIsArray && isReactive(source); - let needsWrap = false; - if (sourceIsReactiveArray) { - needsWrap = !isShallow(source); - source = shallowReadArray(source); - } - ret = new Array(source.length); - for (let i = 0, l = source.length; i < l; i++) { - ret[i] = renderItem( - needsWrap ? toReactive(source[i]) : source[i], - i, - void 0, - cached && cached[i] - ); - } - } else if (typeof source === "number") { - if (!Number.isInteger(source)) { - warn$1(`The v-for range expect an integer value but got ${source}.`); - } - ret = new Array(source); - for (let i = 0; i < source; i++) { - ret[i] = renderItem(i + 1, i, void 0, cached && cached[i]); - } - } else if (isObject(source)) { - if (source[Symbol.iterator]) { - ret = Array.from( - source, - (item, i) => renderItem(item, i, void 0, cached && cached[i]) - ); - } else { - const keys = Object.keys(source); - ret = new Array(keys.length); - for (let i = 0, l = keys.length; i < l; i++) { - const key = keys[i]; - ret[i] = renderItem(source[key], key, i, cached && cached[i]); - } - } - } else { - ret = []; - } - if (cache) { - cache[index] = ret; - } - return ret; -} - -function createSlots(slots, dynamicSlots) { - for (let i = 0; i < dynamicSlots.length; i++) { - const slot = dynamicSlots[i]; - if (isArray(slot)) { - for (let j = 0; j < slot.length; j++) { - slots[slot[j].name] = slot[j].fn; - } - } else if (slot) { - slots[slot.name] = slot.key ? (...args) => { - const res = slot.fn(...args); - if (res) res.key = slot.key; - return res; - } : slot.fn; - } - } - return slots; -} - -function renderSlot(slots, name, props = {}, fallback, noSlotted) { - if (currentRenderingInstance.ce || currentRenderingInstance.parent && isAsyncWrapper(currentRenderingInstance.parent) && currentRenderingInstance.parent.ce) { - if (name !== "default") props.name = name; - return openBlock(), createBlock( - Fragment, - null, - [createVNode("slot", props, fallback && fallback())], - 64 - ); - } - let slot = slots[name]; - if (slot && slot.length > 1) { - warn$1( - `SSR-optimized slot function detected in a non-SSR-optimized render function. You need to mark this component with $dynamic-slots in the parent template.` - ); - slot = () => []; - } - if (slot && slot._c) { - slot._d = false; - } - openBlock(); - const validSlotContent = slot && ensureValidVNode(slot(props)); - const slotKey = props.key || // slot content array of a dynamic conditional slot may have a branch - // key attached in the `createSlots` helper, respect that - validSlotContent && validSlotContent.key; - const rendered = createBlock( - Fragment, - { - key: (slotKey && !isSymbol(slotKey) ? slotKey : `_${name}`) + // #7256 force differentiate fallback content from actual content - (!validSlotContent && fallback ? "_fb" : "") - }, - validSlotContent || (fallback ? fallback() : []), - validSlotContent && slots._ === 1 ? 64 : -2 - ); - if (!noSlotted && rendered.scopeId) { - rendered.slotScopeIds = [rendered.scopeId + "-s"]; - } - if (slot && slot._c) { - slot._d = true; - } - return rendered; -} -function ensureValidVNode(vnodes) { - return vnodes.some((child) => { - if (!isVNode(child)) return true; - if (child.type === Comment) return false; - if (child.type === Fragment && !ensureValidVNode(child.children)) - return false; - return true; - }) ? vnodes : null; -} - -function toHandlers(obj, preserveCaseIfNecessary) { - const ret = {}; - if (!isObject(obj)) { - warn$1(`v-on with no argument expects an object value.`); - return ret; - } - for (const key in obj) { - ret[preserveCaseIfNecessary && /[A-Z]/.test(key) ? `on:${key}` : toHandlerKey(key)] = obj[key]; - } - return ret; -} - -const getPublicInstance = (i) => { - if (!i) return null; - if (isStatefulComponent(i)) return getComponentPublicInstance(i); - return getPublicInstance(i.parent); -}; -const publicPropertiesMap = ( - // Move PURE marker to new line to workaround compiler discarding it - // due to type annotation - /* @__PURE__ */ extend(/* @__PURE__ */ Object.create(null), { - $: (i) => i, - $el: (i) => i.vnode.el, - $data: (i) => i.data, - $props: (i) => shallowReadonly(i.props) , - $attrs: (i) => shallowReadonly(i.attrs) , - $slots: (i) => shallowReadonly(i.slots) , - $refs: (i) => shallowReadonly(i.refs) , - $parent: (i) => getPublicInstance(i.parent), - $root: (i) => getPublicInstance(i.root), - $host: (i) => i.ce, - $emit: (i) => i.emit, - $options: (i) => resolveMergedOptions(i) , - $forceUpdate: (i) => i.f || (i.f = () => { - queueJob(i.update); - }), - $nextTick: (i) => i.n || (i.n = nextTick.bind(i.proxy)), - $watch: (i) => instanceWatch.bind(i) - }) -); -const isReservedPrefix = (key) => key === "_" || key === "$"; -const hasSetupBinding = (state, key) => state !== EMPTY_OBJ && !state.__isScriptSetup && hasOwn(state, key); -const PublicInstanceProxyHandlers = { - get({ _: instance }, key) { - if (key === "__v_skip") { - return true; - } - const { ctx, setupState, data, props, accessCache, type, appContext } = instance; - if (key === "__isVue") { - return true; - } - let normalizedProps; - if (key[0] !== "$") { - const n = accessCache[key]; - if (n !== void 0) { - switch (n) { - case 1 /* SETUP */: - return setupState[key]; - case 2 /* DATA */: - return data[key]; - case 4 /* CONTEXT */: - return ctx[key]; - case 3 /* PROPS */: - return props[key]; - } - } else if (hasSetupBinding(setupState, key)) { - accessCache[key] = 1 /* SETUP */; - return setupState[key]; - } else if (data !== EMPTY_OBJ && hasOwn(data, key)) { - accessCache[key] = 2 /* DATA */; - return data[key]; - } else if ( - // only cache other properties when instance has declared (thus stable) - // props - (normalizedProps = instance.propsOptions[0]) && hasOwn(normalizedProps, key) - ) { - accessCache[key] = 3 /* PROPS */; - return props[key]; - } else if (ctx !== EMPTY_OBJ && hasOwn(ctx, key)) { - accessCache[key] = 4 /* CONTEXT */; - return ctx[key]; - } else if (shouldCacheAccess) { - accessCache[key] = 0 /* OTHER */; - } - } - const publicGetter = publicPropertiesMap[key]; - let cssModule, globalProperties; - if (publicGetter) { - if (key === "$attrs") { - track(instance.attrs, "get", ""); - markAttrsAccessed(); - } else if (key === "$slots") { - track(instance, "get", key); - } - return publicGetter(instance); - } else if ( - // css module (injected by vue-loader) - (cssModule = type.__cssModules) && (cssModule = cssModule[key]) - ) { - return cssModule; - } else if (ctx !== EMPTY_OBJ && hasOwn(ctx, key)) { - accessCache[key] = 4 /* CONTEXT */; - return ctx[key]; - } else if ( - // global properties - globalProperties = appContext.config.globalProperties, hasOwn(globalProperties, key) - ) { - { - return globalProperties[key]; - } - } else if (currentRenderingInstance && (!isString(key) || // #1091 avoid internal isRef/isVNode checks on component instance leading - // to infinite warning loop - key.indexOf("__v") !== 0)) { - if (data !== EMPTY_OBJ && isReservedPrefix(key[0]) && hasOwn(data, key)) { - warn$1( - `Property ${JSON.stringify( - key - )} must be accessed via $data because it starts with a reserved character ("$" or "_") and is not proxied on the render context.` - ); - } else if (instance === currentRenderingInstance) { - warn$1( - `Property ${JSON.stringify(key)} was accessed during render but is not defined on instance.` - ); - } - } - }, - set({ _: instance }, key, value) { - const { data, setupState, ctx } = instance; - if (hasSetupBinding(setupState, key)) { - setupState[key] = value; - return true; - } else if (setupState.__isScriptSetup && hasOwn(setupState, key)) { - warn$1(`Cannot mutate - - - - -

- - - - - - - - - diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz new file mode 100644 index 0000000000000..e6a22a4e3770f Binary files /dev/null and b/examples/server/public/index.html.gz differ diff --git a/examples/server/public_legacy/index-new.html b/examples/server/public_legacy/index-new.html index 8bfa380e57388..cbfbbdf2806fa 100644 --- a/examples/server/public_legacy/index-new.html +++ b/examples/server/public_legacy/index-new.html @@ -39,7 +39,6 @@ temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower repeat_last_n: 0, // 0 = disable penalty, -1 = context size repeat_penalty: 1.0, // 1.0 = disabled - penalize_nl: false, // true only useful for infinite completion dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well dry_base: 1.75, // 0.0 = disabled dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well diff --git a/examples/server/public_legacy/index.html b/examples/server/public_legacy/index.html index a95f5c6df8775..75f39330a789d 100644 --- a/examples/server/public_legacy/index.html +++ b/examples/server/public_legacy/index.html @@ -303,7 +303,6 @@ temperature: 0.7, repeat_last_n: 256, // 0 = disable penalty, -1 = context size repeat_penalty: 1.18, // 1.0 = disabled - penalize_nl: false, dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well dry_base: 1.75, // 0.0 = disabled dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well @@ -1006,7 +1005,6 @@ ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} - ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js index 8e0df3b61df2b..2fcd24a860bd4 100644 --- a/examples/server/public_simplechat/simplechat.js +++ b/examples/server/public_simplechat/simplechat.js @@ -407,6 +407,9 @@ class SimpleChat { if (curLine.startsWith("data:")) { curLine = curLine.substring(5); } + if (curLine.trim() === "[DONE]") { + break; + } let curJson = JSON.parse(curLine); console.debug("DBUG:SC:PART:Json:", curJson); this.append_response(this.response_extract_stream(curJson, apiEP)); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f9d20fee5a6f6..2306dc26fe431 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -14,16 +14,12 @@ // mime type for sending response #define MIMETYPE_JSON "application/json; charset=utf-8" -// auto generated files (update with ./deps.sh) -#include "index.html.hpp" -#include "completion.js.hpp" +// auto generated files (see README.md for details) +#include "index.html.gz.hpp" #include "loading.html.hpp" -#include "deps_daisyui.min.css.hpp" -#include "deps_markdown-it.js.hpp" -#include "deps_tailwindcss.js.hpp" -#include "deps_vue.esm-browser.js.hpp" #include +#include #include #include #include @@ -37,12 +33,16 @@ using json = nlohmann::ordered_json; +constexpr int HTTP_POLLING_SECONDS = 1; + enum stop_type { - STOP_TYPE_FULL, - STOP_TYPE_PARTIAL, + STOP_TYPE_NONE, + STOP_TYPE_EOS, + STOP_TYPE_WORD, + STOP_TYPE_LIMIT, }; -// state diagram: https://github.com/ggerganov/llama.cpp/pull/9283 +// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283 enum slot_state { SLOT_STATE_IDLE, SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future @@ -51,92 +51,1176 @@ enum slot_state { SLOT_STATE_GENERATING, }; -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded +}; + +enum server_task_type { + SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_EMBEDDING, + SERVER_TASK_TYPE_RERANK, + SERVER_TASK_TYPE_INFILL, + SERVER_TASK_TYPE_CANCEL, + SERVER_TASK_TYPE_NEXT_RESPONSE, + SERVER_TASK_TYPE_METRICS, + SERVER_TASK_TYPE_SLOT_SAVE, + SERVER_TASK_TYPE_SLOT_RESTORE, + SERVER_TASK_TYPE_SLOT_ERASE, + SERVER_TASK_TYPE_SET_LORA, +}; + +enum oaicompat_type { + OAICOMPAT_TYPE_NONE, + OAICOMPAT_TYPE_CHAT, + OAICOMPAT_TYPE_COMPLETION, + OAICOMPAT_TYPE_EMBEDDING, +}; + +// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 +enum error_type { + ERROR_TYPE_INVALID_REQUEST, + ERROR_TYPE_AUTHENTICATION, + ERROR_TYPE_SERVER, + ERROR_TYPE_NOT_FOUND, + ERROR_TYPE_PERMISSION, + ERROR_TYPE_UNAVAILABLE, // custom error + ERROR_TYPE_NOT_SUPPORTED, // custom error +}; + +struct slot_params { + bool stream = true; + bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt + bool return_tokens = false; + + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half + int32_t n_predict = -1; // new tokens to predict + int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters + + int64_t t_max_prompt_ms = -1; // TODO: implement + int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit + + std::vector lora; + + std::vector antiprompt; + std::vector response_fields; + bool timings_per_token = false; + bool post_sampling_probs = false; + bool ignore_eos = false; + + struct common_params_sampling sampling; + struct common_params_speculative speculative; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + + json to_json() const { + std::vector samplers; + samplers.reserve(sampling.samplers.size()); + for (const auto & sampler : sampling.samplers) { + samplers.emplace_back(common_sampler_type_to_str(sampler)); + } + + json lora = json::array(); + for (size_t i = 0; i < this->lora.size(); ++i) { + lora.push_back({{"id", i}, {"scale", this->lora[i].scale}}); + } + + std::vector grammar_trigger_words; + for (const auto & trigger : sampling.grammar_trigger_words) { + grammar_trigger_words.push_back(trigger.word); + } + + return json { + {"n_predict", n_predict}, // Server configured n_predict + {"seed", sampling.seed}, + {"temperature", sampling.temp}, + {"dynatemp_range", sampling.dynatemp_range}, + {"dynatemp_exponent", sampling.dynatemp_exponent}, + {"top_k", sampling.top_k}, + {"top_p", sampling.top_p}, + {"min_p", sampling.min_p}, + {"xtc_probability", sampling.xtc_probability}, + {"xtc_threshold", sampling.xtc_threshold}, + {"typical_p", sampling.typ_p}, + {"repeat_last_n", sampling.penalty_last_n}, + {"repeat_penalty", sampling.penalty_repeat}, + {"presence_penalty", sampling.penalty_present}, + {"frequency_penalty", sampling.penalty_freq}, + {"dry_multiplier", sampling.dry_multiplier}, + {"dry_base", sampling.dry_base}, + {"dry_allowed_length", sampling.dry_allowed_length}, + {"dry_penalty_last_n", sampling.dry_penalty_last_n}, + {"dry_sequence_breakers", sampling.dry_sequence_breakers}, + {"mirostat", sampling.mirostat}, + {"mirostat_tau", sampling.mirostat_tau}, + {"mirostat_eta", sampling.mirostat_eta}, + {"stop", antiprompt}, + {"max_tokens", n_predict}, // User configured n_predict + {"n_keep", n_keep}, + {"n_discard", n_discard}, + {"ignore_eos", sampling.ignore_eos}, + {"stream", stream}, + {"logit_bias", format_logit_bias(sampling.logit_bias)}, + {"n_probs", sampling.n_probs}, + {"min_keep", sampling.min_keep}, + {"grammar", sampling.grammar}, + {"grammar_trigger_words", grammar_trigger_words}, + {"grammar_trigger_tokens", sampling.grammar_trigger_tokens}, + {"preserved_tokens", sampling.preserved_tokens}, + {"chat_format", common_chat_format_name(oaicompat_chat_format)}, + {"samplers", samplers}, + {"speculative.n_max", speculative.n_max}, + {"speculative.n_min", speculative.n_min}, + {"speculative.p_min", speculative.p_min}, + {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, + {"lora", lora}, + }; + } +}; + +struct server_task { + int id = -1; // to be filled by server_queue + int index = -1; // used when there are multiple prompts (batch request) + + server_task_type type; + + // used by SERVER_TASK_TYPE_CANCEL + int id_target = -1; + + // used by SERVER_TASK_TYPE_INFERENCE + slot_params params; + llama_tokens prompt_tokens; + int id_selected_slot = -1; + + // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE + struct slot_action { + int slot_id; + std::string filename; + std::string filepath; + }; + slot_action slot_action; + + // used by SERVER_TASK_TYPE_METRICS + bool metrics_reset_bucket = false; + + // used by SERVER_TASK_TYPE_SET_LORA + std::vector set_lora; + + server_task(server_task_type type) : type(type) {} + + static slot_params params_from_json_cmpl( + const llama_context * ctx, + const common_params & params_base, + const json & data) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + slot_params params; + + // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them) + slot_params defaults; + defaults.sampling = params_base.sampling; + defaults.speculative = params_base.speculative; + + // enabling this will output extra debug information in the HTTP responses from the server + params.verbose = params_base.verbosity > 9; + params.timings_per_token = json_value(data, "timings_per_token", false); + + params.stream = json_value(data, "stream", false); + params.cache_prompt = json_value(data, "cache_prompt", true); + params.return_tokens = json_value(data, "return_tokens", false); + params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); + params.n_indent = json_value(data, "n_indent", defaults.n_indent); + params.n_keep = json_value(data, "n_keep", defaults.n_keep); + params.n_discard = json_value(data, "n_discard", defaults.n_discard); + //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement + params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); + params.response_fields = json_value(data, "response_fields", std::vector()); + + params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); + params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); + params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); + params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); + params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); + params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); + params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); + params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); + params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); + params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); + params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); + params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); + params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); + params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); + params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); + params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); + params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); + params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); + params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); + params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); + params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); + params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); + + params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); + params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); + params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); + + params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min); + params.speculative.n_min = std::max(params.speculative.n_min, 0); + params.speculative.n_max = std::max(params.speculative.n_max, 0); + + // Use OpenAI API logprobs only if n_probs wasn't provided + if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){ + params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs); + } + + if (data.contains("lora")) { + if (data.at("lora").is_array()) { + params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora")); + } else { + throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields"); + } + } else { + params.lora = params_base.lora_adapters; + } + + // TODO: add more sanity checks for the input parameters + + if (params.sampling.penalty_last_n < -1) { + throw std::runtime_error("Error: repeat_last_n must be >= -1"); + } + + if (params.sampling.dry_penalty_last_n < -1) { + throw std::runtime_error("Error: dry_penalty_last_n must be >= -1"); + } + + if (params.sampling.penalty_last_n == -1) { + // note: should be the slot's context and not the full context, but it's ok + params.sampling.penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + params.sampling.dry_penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_base < 1.0f) { + params.sampling.dry_base = defaults.sampling.dry_base; + } + + // sequence breakers for DRY + { + // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format + // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 + + if (data.contains("dry_sequence_breakers")) { + params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (params.sampling.dry_sequence_breakers.empty()) { + throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings"); + } + } + } + + // process "json_schema" and "grammar" + if (data.contains("json_schema") && !data.contains("grammar")) { + try { + auto schema = json_value(data, "json_schema", json::object()); + SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str()); + params.sampling.grammar = json_schema_to_grammar(schema); + SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str()); + } catch (const std::exception & e) { + throw std::runtime_error(std::string("\"json_schema\": ") + e.what()); + } + } else { + params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); + SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str()); + params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy); + SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false"); + } + + { + auto it = data.find("chat_format"); + if (it != data.end()) { + params.oaicompat_chat_format = static_cast(it->get()); + SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str()); + } else { + params.oaicompat_chat_format = defaults.oaicompat_chat_format; + } + } + + { + const auto grammar_triggers = data.find("grammar_triggers"); + if (grammar_triggers != data.end()) { + for (const auto & t : *grammar_triggers) { + common_grammar_trigger trigger; + trigger.word = t.at("word"); + trigger.at_start = t.at("at_start"); + + auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + SRV_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str()); + params.sampling.grammar_trigger_tokens.push_back(ids[0]); + params.sampling.preserved_tokens.insert(ids[0]); + continue; + } + SRV_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str()); + params.sampling.grammar_trigger_words.push_back(trigger); + } + } + const auto preserved_tokens = data.find("preserved_tokens"); + if (preserved_tokens != data.end()) { + for (const auto & t : *preserved_tokens) { + auto ids = common_tokenize(vocab, t.get(), /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + SRV_DBG("Preserved token: %d\n", ids[0]); + params.sampling.preserved_tokens.insert(ids[0]); + } else { + // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens. + SRV_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get().c_str()); + } + } + } + if (params.sampling.grammar_lazy) { + GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0); + } + } + + { + params.sampling.logit_bias.clear(); + params.ignore_eos = json_value(data, "ignore_eos", false); + + const auto & logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { + const int n_vocab = llama_vocab_n_tokens(vocab); + for (const auto & el : *logit_bias) { + // TODO: we may want to throw errors here, in case "el" is incorrect + if (el.is_array() && el.size() == 2) { + float bias; + if (el[1].is_number()) { + bias = el[1].get(); + } else if (el[1].is_boolean() && !el[1].get()) { + bias = -INFINITY; + } else { + continue; + } + + if (el[0].is_number_integer()) { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } else if (el[0].is_string()) { + auto toks = common_tokenize(vocab, el[0].get(), false); + for (auto tok : toks) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } + } + } + } + } + + { + params.antiprompt.clear(); + + const auto & stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) { + for (const auto & word : *stop) { + if (!word.empty()) { + params.antiprompt.push_back(word); + } + } + } + } + + { + const auto samplers = data.find("samplers"); + if (samplers != data.end()) { + if (samplers->is_array()) { + params.sampling.samplers = common_sampler_types_from_names(*samplers, false); + } else if (samplers->is_string()){ + params.sampling.samplers = common_sampler_types_from_chars(samplers->get()); + } + } else { + params.sampling.samplers = defaults.sampling.samplers; + } + } + + std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias; + params.oaicompat_model = json_value(data, "model", model_name); + + return params; + } + + // utility function + static std::unordered_set get_list_id(const std::vector & tasks) { + std::unordered_set ids(tasks.size()); + for (size_t i = 0; i < tasks.size(); i++) { + ids.insert(tasks[i].id); + } + return ids; + } +}; + +struct result_timings { + int32_t prompt_n = -1; + double prompt_ms; + double prompt_per_token_ms; + double prompt_per_second; + + int32_t predicted_n = -1; + double predicted_ms; + double predicted_per_token_ms; + double predicted_per_second; + + json to_json() const { + return { + {"prompt_n", prompt_n}, + {"prompt_ms", prompt_ms}, + {"prompt_per_token_ms", prompt_per_token_ms}, + {"prompt_per_second", prompt_per_second}, + + {"predicted_n", predicted_n}, + {"predicted_ms", predicted_ms}, + {"predicted_per_token_ms", predicted_per_token_ms}, + {"predicted_per_second", predicted_per_second}, + }; + } +}; + +struct server_task_result { + int id = -1; + int id_slot = -1; + virtual bool is_error() { + // only used by server_task_result_error + return false; + } + virtual bool is_stop() { + // only used by server_task_result_cmpl_* + return false; + } + virtual int get_index() { + return -1; + } + virtual json to_json() = 0; + virtual ~server_task_result() = default; +}; + +// using shared_ptr for polymorphism of server_task_result +using server_task_result_ptr = std::unique_ptr; + +inline std::string stop_type_to_str(stop_type type) { + switch (type) { + case STOP_TYPE_EOS: return "eos"; + case STOP_TYPE_WORD: return "word"; + case STOP_TYPE_LIMIT: return "limit"; + default: return "none"; + } +} + +struct completion_token_output { + llama_token tok; + float prob; + std::string text_to_send; + struct prob_info { + llama_token tok; + std::string txt; + float prob; + }; + std::vector probs; + + json to_json(bool post_sampling_probs) const { + json probs_for_token = json::array(); + for (const auto & p : probs) { + std::string txt(p.txt); + txt.resize(validate_utf8(txt)); + probs_for_token.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.txt)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + }); + } + return probs_for_token; + } + + static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { + json out = json::array(); + for (const auto & p : probs) { + std::string txt(p.text_to_send); + txt.resize(validate_utf8(txt)); + out.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.text_to_send)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + { + post_sampling_probs ? "top_probs" : "top_logprobs", + p.to_json(post_sampling_probs) + }, + }); + } + return out; + } + + static float logarithm(float x) { + // nlohmann::json converts -inf to null, so we need to prevent that + return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); + } + + static std::vector str_to_bytes(const std::string & str) { + std::vector bytes; + for (unsigned char c : str) { + bytes.push_back(c); + } + return bytes; + } +}; + +struct server_task_result_cmpl_final : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + bool stream; + result_timings timings; + std::string prompt; + + bool truncated; + int32_t n_decoded; + int32_t n_prompt_tokens; + int32_t n_tokens_cached; + bool has_new_line; + std::string stopping_word; + stop_type stop = STOP_TYPE_NONE; + + bool post_sampling_probs; + std::vector probs_output; + std::vector response_fields; + + slot_params generation_params; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return true; // in stream mode, final responses are considered stop + } + + virtual json to_json() override { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } + } + + json to_json_non_oaicompat() { + json res = json { + {"index", index}, + {"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"tokens", stream ? llama_tokens {} : tokens}, + {"id_slot", id_slot}, + {"stop", true}, + {"model", oaicompat_model}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + {"generation_settings", generation_params.to_json()}, + {"prompt", prompt}, + {"has_new_line", has_new_line}, + {"truncated", truncated}, + {"stop_type", stop_type_to_str(stop)}, + {"stopping_word", stopping_word}, + {"tokens_cached", n_tokens_cached}, + {"timings", timings.to_json()}, + }; + if (!stream && !probs_output.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); + } + return response_fields.empty() ? res : json_get_nested_values(response_fields, res); + } + + json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (!stream && probs_output.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + json finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + json res = json { + {"choices", json::array({ + json{ + {"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", finish_reason}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat() { + std::string finish_reason = "length"; + common_chat_msg msg; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + SRV_DBG("Parsing chat message: %s\n", content.c_str()); + msg = common_chat_parse(content, oaicompat_chat_format); + finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls"; + } else { + msg.content = content; + } + + json message { + {"role", "assistant"}, + }; + if (!msg.reasoning_content.empty()) { + message["reasoning_content"] = msg.reasoning_content; + } + if (msg.content.empty() && !msg.tool_calls.empty()) { + message["content"] = json(); + } else { + message["content"] = msg.content; + } + if (!msg.tool_calls.empty()) { + auto tool_calls = json::array(); + for (const auto & tc : msg.tool_calls) { + tool_calls.push_back({ + {"type", "function"}, + {"function", { + {"name", tc.name}, + {"arguments", tc.arguments}, + }}, + {"id", tc.id}, + }); + } + message["tool_calls"] = tool_calls; + } + + json choice { + {"finish_reason", finish_reason}, + {"index", 0}, + {"message", message}, + }; + + if (!stream && probs_output.size() > 0) { + choice["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + + std::time_t t = std::time(0); + + json res = json { + {"choices", json::array({choice})}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat_stream() { + std::time_t t = std::time(0); + std::string finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + + json choice = json { + {"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()} + }; + + json ret = json { + {"choices", json::array({choice})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens}, + }}, + }; + + if (timings.prompt_n >= 0) { + ret.push_back({"timings", timings.to_json()}); + } + + return ret; + } +}; + +struct server_task_result_cmpl_partial : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + int32_t n_decoded; + int32_t n_prompt_tokens; + + bool post_sampling_probs; + completion_token_output prob_output; + result_timings timings; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return false; // in stream mode, partial responses are not considered stop + } + + virtual json to_json() override { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } + } + + json to_json_non_oaicompat() { + // non-OAI-compat JSON + json res = json { + {"index", index}, + {"content", content}, + {"tokens", tokens}, + {"stop", false}, + {"id_slot", id_slot}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + }; + // populate the timings object when needed (usually for the last response or with timings_per_token enabled) + if (timings.prompt_n > 0) { + res.push_back({"timings", timings.to_json()}); + } + if (!prob_output.probs.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); + } + return res; + } + + json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (prob_output.probs.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json res = json { + {"choices", json::array({ + json{ + {"text", content}, + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", nullptr}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat() { + bool first = n_decoded == 0; + std::time_t t = std::time(0); + json choices; + + if (first) { + if (content.empty()) { + choices = json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{{"role", "assistant"}}}}}); + } else { + // We have to send this as two updates to conform to openai behavior + json initial_ret = json{{"choices", json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"role", "assistant"} + }}}})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"object", "chat.completion.chunk"}}; + + json second_ret = json{ + {"choices", json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json { + {"content", content}}} + }})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"object", "chat.completion.chunk"}}; + + return std::vector({initial_ret, second_ret}); + } + } else { + choices = json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", + json { + {"content", content}, + }}, + }}); + } + + GGML_ASSERT(choices.size() >= 1); + + if (prob_output.probs.size() > 0) { + choices[0]["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + + json ret = json { + {"choices", choices}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"} + }; + + if (timings.prompt_n >= 0) { + ret.push_back({"timings", timings.to_json()}); + } + + return std::vector({ret}); + } +}; + +struct server_task_result_embd : server_task_result { + int index = 0; + std::vector> embedding; + + int32_t n_tokens; + + // OAI-compat fields + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override { + return oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? to_json_oaicompat() + : to_json_non_oaicompat(); + } + + json to_json_non_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding}, + }; + } + + json to_json_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding[0]}, + {"tokens_evaluated", n_tokens}, + }; + } +}; + +struct server_task_result_rerank : server_task_result { + int index = 0; + float score = -1e6; + + int32_t n_tokens; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override { + return json { + {"index", index}, + {"score", score}, + {"tokens_evaluated", n_tokens}, + }; + } +}; + +// this function maybe used outside of server_task_result_error +static json format_error_response(const std::string & message, const enum error_type type) { + std::string type_str; + int code = 500; + switch (type) { + case ERROR_TYPE_INVALID_REQUEST: + type_str = "invalid_request_error"; + code = 400; + break; + case ERROR_TYPE_AUTHENTICATION: + type_str = "authentication_error"; + code = 401; + break; + case ERROR_TYPE_NOT_FOUND: + type_str = "not_found_error"; + code = 404; + break; + case ERROR_TYPE_SERVER: + type_str = "server_error"; + code = 500; + break; + case ERROR_TYPE_PERMISSION: + type_str = "permission_error"; + code = 403; + break; + case ERROR_TYPE_NOT_SUPPORTED: + type_str = "not_supported_error"; + code = 501; + break; + case ERROR_TYPE_UNAVAILABLE: + type_str = "unavailable_error"; + code = 503; + break; + } + return json { + {"code", code}, + {"message", message}, + {"type", type_str}, + }; +} + +struct server_task_result_error : server_task_result { + int index = 0; + error_type err_type = ERROR_TYPE_SERVER; + std::string err_msg; + + virtual bool is_error() override { + return true; + } + + virtual json to_json() override { + return format_error_response(err_msg, err_type); + } }; -enum server_task_type { - SERVER_TASK_TYPE_INFERENCE, - SERVER_TASK_TYPE_CANCEL, - SERVER_TASK_TYPE_NEXT_RESPONSE, - SERVER_TASK_TYPE_METRICS, - SERVER_TASK_TYPE_SLOT_SAVE, - SERVER_TASK_TYPE_SLOT_RESTORE, - SERVER_TASK_TYPE_SLOT_ERASE, - SERVER_TASK_TYPE_SET_LORA, -}; +struct server_task_result_metrics : server_task_result { + int n_idle_slots; + int n_processing_slots; + int n_tasks_deferred; + int64_t t_start; -enum server_task_inf_type { - SERVER_TASK_INF_TYPE_COMPLETION, - SERVER_TASK_INF_TYPE_EMBEDDING, - SERVER_TASK_INF_TYPE_RERANK, - SERVER_TASK_INF_TYPE_INFILL, -}; + int32_t kv_cache_tokens_count; + int32_t kv_cache_used_cells; -struct server_task { - int id = -1; // to be filled by server_queue - int id_target = -1; // used by SERVER_TASK_TYPE_CANCEL + // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; + uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; - llama_tokens prompt_tokens; - server_task_type type; - json data; + uint64_t n_prompt_tokens_processed = 0; + uint64_t t_prompt_processing = 0; - server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; - // utility function - static std::unordered_set get_list_id(const std::vector & tasks) { - std::unordered_set ids(tasks.size()); - for (size_t i = 0; i < tasks.size(); i++) { - ids.insert(tasks[i].id); - } - return ids; - } -}; + uint64_t n_decode_total = 0; + uint64_t n_busy_slots_total = 0; -struct server_task_result { - int id = -1; + // while we can also use std::vector this requires copying the slot object which can be quite messy + // therefore, we use json to temporarily store the slot.to_json() result + json slots_data = json::array(); - json data; + virtual json to_json() override { + return json { + { "idle", n_idle_slots }, + { "processing", n_processing_slots }, + { "deferred", n_tasks_deferred }, + { "t_start", t_start }, - bool stop; - bool error; -}; + { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total }, + { "t_tokens_generation_total", t_tokens_generation_total }, + { "n_tokens_predicted_total", n_tokens_predicted_total }, + { "t_prompt_processing_total", t_prompt_processing_total }, -struct server_static_file { - const unsigned char * data; - unsigned int size; - const char * mime_type; -}; + { "n_prompt_tokens_processed", n_prompt_tokens_processed }, + { "t_prompt_processing", t_prompt_processing }, + { "n_tokens_predicted", n_tokens_predicted }, + { "t_tokens_generation", t_tokens_generation }, -struct slot_params { - bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + { "n_decode_total", n_decode_total }, + { "n_busy_slots_total", n_busy_slots_total }, - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half - int32_t n_predict = -1; // new tokens to predict - int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters + { "kv_cache_tokens_count", kv_cache_tokens_count }, + { "kv_cache_used_cells", kv_cache_used_cells }, - int64_t t_max_prompt_ms = -1; // TODO: implement - int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit + { "slots", slots_data }, + }; + } +}; - std::vector antiprompt; +struct server_task_result_slot_save_load : server_task_result { + std::string filename; + bool is_save; // true = save, false = load + + size_t n_tokens; + size_t n_bytes; + double t_ms; + + virtual json to_json() override { + if (is_save) { + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_saved", n_tokens }, + { "n_written", n_bytes }, + { "timings", { + { "save_ms", t_ms } + }}, + }; + } else { + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_restored", n_tokens }, + { "n_read", n_bytes }, + { "timings", { + { "restore_ms", t_ms } + }}, + }; + } + } +}; - struct common_params_sampling sampling; - struct common_params_speculative speculative; +struct server_task_result_slot_erase : server_task_result { + size_t n_erased; + + virtual json to_json() override { + return json { + { "id_slot", id_slot }, + { "n_erased", n_erased }, + }; + } +}; + +struct server_task_result_apply_lora : server_task_result { + virtual json to_json() override { + return json {{ "success", true }}; + } }; struct server_slot { int id; int id_task = -1; - llama_batch batch_spec; + // only used for completion/embedding/infill/rerank + server_task_type task_type = SERVER_TASK_TYPE_COMPLETION; + llama_batch batch_spec = {}; + + llama_context * ctx = nullptr; llama_context * ctx_dft = nullptr; common_speculative * spec = nullptr; + std::vector lora; + // the index relative to completion multi-task request size_t index = 0; @@ -164,22 +1248,18 @@ struct server_slot { size_t last_nl_pos = 0; - std::string generated_text; + std::string generated_text; + llama_tokens generated_tokens; + llama_tokens cache_tokens; - std::vector generated_token_probs; - server_task_inf_type inf_type = SERVER_TASK_INF_TYPE_COMPLETION; + std::vector generated_token_probs; bool has_next_token = true; bool has_new_line = false; bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; + stop_type stop; - bool oaicompat = false; - - std::string oaicompat_model; std::string stopping_word; // sampling @@ -189,9 +1269,10 @@ struct server_slot { llama_token sampled; + common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + // stats size_t n_sent_text = 0; // number of sent text character - size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_generation; @@ -209,18 +1290,25 @@ struct server_slot { generated_text = ""; has_new_line = false; truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; + stop = STOP_TYPE_NONE; stopping_word = ""; n_past = 0; n_sent_text = 0; - n_sent_token_probs = 0; - inf_type = SERVER_TASK_INF_TYPE_COMPLETION; + task_type = SERVER_TASK_TYPE_COMPLETION; + generated_tokens.clear(); generated_token_probs.clear(); } + bool is_non_causal() const { + return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK; + } + + bool can_batch_with(server_slot & other_slot) { + return is_non_causal() == other_slot.is_non_causal() + && are_lora_equal(lora, other_slot.lora); + } + bool has_budget(const common_params & global_params) { if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless @@ -264,38 +1352,40 @@ struct server_slot { } } - json get_formated_timings() const { - return json { - {"prompt_n", n_prompt_tokens_processed}, - {"prompt_ms", t_prompt_processing}, - {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, - - {"predicted_n", n_decoded}, - {"predicted_ms", t_token_generation}, - {"predicted_per_token_ms", t_token_generation / n_decoded}, - {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, - }; + result_timings get_timings() const { + result_timings timings; + timings.prompt_n = n_prompt_tokens_processed; + timings.prompt_ms = t_prompt_processing; + timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed; + timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; + + timings.predicted_n = n_decoded; + timings.predicted_ms = t_token_generation; + timings.predicted_per_token_ms = t_token_generation / n_decoded; + timings.predicted_per_second = 1e3 / t_token_generation * n_decoded; + + return timings; } - size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) { + size_t find_stopping_strings(const std::string & text, const size_t last_token_size, bool is_full_stop) { size_t stop_pos = std::string::npos; for (const std::string & word : params.antiprompt) { size_t pos; - if (type == STOP_TYPE_FULL) { + if (is_full_stop) { const size_t tmp = word.size() + last_token_size; const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; pos = text.find(word, from_pos); } else { + // otherwise, partial stop pos = find_partial_stop_string(word, text); } if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { - if (type == STOP_TYPE_FULL) { - stopped_word = true; + if (is_full_stop) { + stop = STOP_TYPE_WORD; stopping_word = word; has_next_token = false; } @@ -315,13 +1405,35 @@ struct server_slot { SLT_INF(*this, "\n" - "\rprompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" - "\r eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" - "\r total time = %10.2f ms / %5d tokens\n", + "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" + " eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" + " total time = %10.2f ms / %5d tokens\n", t_prompt_processing, n_prompt_tokens_processed, t_prompt, n_prompt_second, t_token_generation, n_decoded, t_gen, n_gen_second, t_prompt_processing + t_token_generation, n_prompt_tokens_processed + n_decoded); } + + json to_json() const { + return json { + {"id", id}, + {"id_task", id_task}, + {"n_ctx", n_ctx}, + {"speculative", can_speculate()}, + {"is_processing", is_processing()}, + {"non_causal", is_non_causal()}, + {"params", params.to_json()}, + {"prompt", common_detokenize(ctx, prompt_tokens)}, + {"next_token", + { + {"has_next_token", has_next_token}, + {"has_new_line", has_new_line}, + {"n_remain", n_remaining}, + {"n_decoded", n_decoded}, + {"stopping_word", stopping_word}, + } + }, + }; + } }; struct server_metrics { @@ -394,8 +1506,10 @@ struct server_queue { // Add a new task to the end of the queue int post(server_task task, bool front = false) { std::unique_lock lock(mutex_tasks); - if (task.id == -1) { - task.id = id++; + GGML_ASSERT(task.id != -1); + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); } QUE_DBG("new task, id = %d, front = %d\n", task.id, front); if (front) { @@ -414,6 +1528,10 @@ struct server_queue { if (task.id == -1) { task.id = id++; } + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); + } QUE_DBG("new task, id = %d/%d, front = %d\n", task.id, (int) tasks.size(), front); if (front) { queue_tasks.push_front(std::move(task)); @@ -482,6 +1600,10 @@ struct server_queue { while (true) { std::unique_lock lock(mutex_tasks); + if (!running) { + QUE_DBG("%s", "terminate\n"); + return; + } if (queue_tasks.empty()) { lock.unlock(); break; @@ -502,11 +1624,11 @@ struct server_queue { QUE_DBG("%s", "waiting for new tasks\n"); { std::unique_lock lock(mutex_tasks); + if (!running) { + QUE_DBG("%s", "terminate\n"); + return; + } if (queue_tasks.empty()) { - if (!running) { - QUE_DBG("%s", "terminate\n"); - return; - } condition_tasks.wait(lock, [&]{ return (!queue_tasks.empty() || !running); }); @@ -514,14 +1636,28 @@ struct server_queue { } } } + +private: + void cleanup_pending_task(int id_target) { + // no need lock because this is called exclusively by post() + auto rm_func = [id_target](const server_task & task) { + return task.id_target == id_target; + }; + queue_tasks.erase( + std::remove_if(queue_tasks.begin(), queue_tasks.end(), rm_func), + queue_tasks.end()); + queue_tasks_deferred.erase( + std::remove_if(queue_tasks_deferred.begin(), queue_tasks_deferred.end(), rm_func), + queue_tasks_deferred.end()); + } }; struct server_response { // for keeping track of all tasks waiting for the result std::unordered_set waiting_task_ids; - // the main result queue - std::vector queue_results; + // the main result queue (using ptr for polymorphism) + std::vector queue_results; std::mutex mutex_results; std::condition_variable condition_results; @@ -549,6 +1685,12 @@ struct server_response { std::unique_lock lock(mutex_results); waiting_task_ids.erase(id_task); + // make sure to clean up all pending results + queue_results.erase( + std::remove_if(queue_results.begin(), queue_results.end(), [id_task](const server_task_result_ptr & res) { + return res->id == id_task; + }), + queue_results.end()); } void remove_waiting_task_ids(const std::unordered_set & id_tasks) { @@ -561,41 +1703,64 @@ struct server_response { } // This function blocks the thread until there is a response for one of the id_tasks - server_task_result recv(const std::unordered_set & id_tasks) { + server_task_result_ptr recv(const std::unordered_set & id_tasks) { while (true) { std::unique_lock lock(mutex_results); condition_results.wait(lock, [&]{ return !queue_results.empty(); }); + for (size_t i = 0; i < queue_results.size(); i++) { + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here + } + + // same as recv(), but have timeout in seconds + // if timeout is reached, nullptr is returned + server_task_result_ptr recv_with_timeout(const std::unordered_set & id_tasks, int timeout) { + while (true) { + std::unique_lock lock(mutex_results); + for (int i = 0; i < (int) queue_results.size(); i++) { - if (id_tasks.find(queue_results[i].id) != id_tasks.end()) { - server_task_result res = queue_results[i]; + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); queue_results.erase(queue_results.begin() + i); return res; } } + + std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout)); + if (cr_res == std::cv_status::timeout) { + return nullptr; + } } // should never reach here } // single-task version of recv() - server_task_result recv(int id_task) { + server_task_result_ptr recv(int id_task) { std::unordered_set id_tasks = {id_task}; return recv(id_tasks); } // Send a new result to a waiting id_task - void send(server_task_result & result) { - SRV_DBG("sending result for task id = %d\n", result.id); + void send(server_task_result_ptr && result) { + SRV_DBG("sending result for task id = %d\n", result->id); std::unique_lock lock(mutex_results); for (const auto & id_task : waiting_task_ids) { - if (result.id == id_task) { - SRV_DBG("task id = %d moved to result queue\n", result.id); + if (result->id == id_task) { + SRV_DBG("task id = %d pushed to result queue\n", result->id); - queue_results.push_back(std::move(result)); + queue_results.emplace_back(std::move(result)); condition_results.notify_all(); return; } @@ -606,11 +1771,17 @@ struct server_response { struct server_context { common_params params_base; + // note: keep these alive - they determine the lifetime of the model, context, etc. + common_init_result llama_init; + common_init_result llama_init_dft; + llama_model * model = nullptr; llama_context * ctx = nullptr; - std::vector loras; + + const llama_vocab * vocab = nullptr; llama_model * model_dft = nullptr; + llama_context_params cparams_dft; llama_batch batch = {}; @@ -633,22 +1804,9 @@ struct server_context { // Necessary similarity of prompt for slot selection float slot_prompt_similarity = 0.0f; - ~server_context() { - if (ctx) { - llama_free(ctx); - ctx = nullptr; - } - - if (model) { - llama_free_model(model); - model = nullptr; - } - - if (model_dft) { - llama_free_model(model_dft); - model_dft = nullptr; - } + common_chat_templates_ptr chat_templates; + ~server_context() { // Clear any sampling context for (server_slot & slot : slots) { common_sampler_free(slot.smpl); @@ -671,70 +1829,74 @@ struct server_context { params_base = params; - common_init_result llama_init = common_init_from_params(params_base); + llama_init = common_init_from_params(params_base); - model = llama_init.model; - ctx = llama_init.context; - loras = llama_init.lora_adapters; + model = llama_init.model.get(); + ctx = llama_init.context.get(); if (model == nullptr) { SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str()); return false; } + vocab = llama_model_get_vocab(model); + n_ctx = llama_n_ctx(ctx); - add_bos_token = llama_add_bos_token(model); - has_eos_token = !llama_add_eos_token(model); + add_bos_token = llama_vocab_get_add_bos(vocab); + has_eos_token = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; - if (!params_base.speculative.model.empty()) { + if (!params_base.speculative.model.empty() || !params_base.speculative.hf_repo.empty()) { SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str()); auto params_dft = params_base; + params_dft.devices = params_base.speculative.devices; + params_dft.hf_file = params_base.speculative.hf_file; + params_dft.hf_repo = params_base.speculative.hf_repo; params_dft.model = params_base.speculative.model; - params_dft.n_ctx = params_base.speculative.n_ctx; + params_dft.model_url = params_base.speculative.model_url; + params_dft.n_ctx = params_base.speculative.n_ctx == 0 ? params_base.n_ctx / params_base.n_parallel : params_base.speculative.n_ctx; params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers; + params_dft.n_parallel = 1; - common_init_result llama_init_dft = common_init_from_params(params_dft); + llama_init_dft = common_init_from_params(params_dft); - model_dft = llama_init_dft.model; + model_dft = llama_init_dft.model.get(); if (model_dft == nullptr) { SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.c_str()); return false; } - if (!common_speculative_are_compatible(ctx, llama_init_dft.context)) { + if (!common_speculative_are_compatible(ctx, llama_init_dft.context.get())) { SRV_ERR("the draft model '%s' is not compatible with the target model '%s'\n", params_base.speculative.model.c_str(), params_base.model.c_str()); - llama_free (llama_init_dft.context); - llama_free_model(llama_init_dft.model); - return false; } - cparams_dft = common_context_params_to_llama(params_base); - cparams_dft.n_batch = llama_n_ctx(llama_init_dft.context); + const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get()); + + cparams_dft = common_context_params_to_llama(params_dft); + cparams_dft.n_batch = n_ctx_dft; + + // force F16 KV cache for the draft model for extra performance + cparams_dft.type_k = GGML_TYPE_F16; + cparams_dft.type_v = GGML_TYPE_F16; // the context is not needed - we will create one for each slot - llama_free(llama_init_dft.context); + llama_init_dft.context.reset(); } - return true; - } - - bool validate_model_chat_template() const { - std::vector model_template(2048, 0); // longest known template is about 1200 bytes - std::string template_key = "tokenizer.chat_template"; - int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); - if (res >= 0) { - llama_chat_message chat[] = {{"user", "test"}}; - std::string tmpl = std::string(model_template.data(), model_template.size()); - int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0); - return chat_res > 0; + chat_templates = common_chat_templates_init(model, params_base.chat_template); + try { + common_chat_format_example(chat_templates.get(), params.use_jinja); + } catch (const std::exception & e) { + SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__); + chat_templates = common_chat_templates_init(model, "chatml"); } - return false; + + return true; } void init() { @@ -746,13 +1908,14 @@ struct server_context { server_slot slot; slot.id = i; + slot.ctx = ctx; slot.n_ctx = n_ctx_slot; slot.n_predict = params_base.n_predict; if (model_dft) { slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1); - slot.ctx_dft = llama_new_context_with_model(model_dft, cparams_dft); + slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft); if (slot.ctx_dft == nullptr) { SRV_ERR("%s", "failed to create draft context\n"); return; @@ -778,8 +1941,7 @@ struct server_context { slots.push_back(slot); } - default_generation_settings_for_props = get_formated_generation(slots.front()); - default_generation_settings_for_props["seed"] = -1; + default_generation_settings_for_props = slots[0].to_json(); // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) @@ -866,174 +2028,29 @@ struct server_context { } bool launch_slot_with_task(server_slot & slot, const server_task & task) { - // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them) - slot_params defaults; - defaults.sampling = params_base.sampling; - defaults.speculative = params_base.speculative; - - const auto & data = task.data; - - if (data.count("__oaicompat") != 0) { - slot.oaicompat = true; - slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - } else { - slot.oaicompat = false; - slot.oaicompat_model = ""; - } - - slot.params.stream = json_value(data, "stream", false); - slot.params.cache_prompt = json_value(data, "cache_prompt", false); - slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); - slot.params.n_indent = json_value(data, "n_indent", defaults.n_indent); - slot.params.n_keep = json_value(data, "n_keep", defaults.n_keep); - slot.params.n_discard = json_value(data, "n_discard", defaults.n_discard); - //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement - slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); - - slot.params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); - slot.params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); - slot.params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); - slot.params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); - slot.params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); - slot.params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); - slot.params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); - slot.params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); - slot.params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); - slot.params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); - slot.params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); - slot.params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); - slot.params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); - slot.params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); - slot.params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); - slot.params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); - slot.params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); - slot.params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); - slot.params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); - slot.params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); - slot.params.sampling.penalize_nl = json_value(data, "penalize_nl", defaults.sampling.penalize_nl); - slot.params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); - slot.params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); - slot.params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); - - slot.params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); - slot.params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); - slot.params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); - - slot.params.speculative.n_min = std::min(slot.params.speculative.n_max, slot.params.speculative.n_min); - - if (slot.params.sampling.dry_base < 1.0f) { - slot.params.sampling.dry_base = defaults.sampling.dry_base; - } - - // sequence breakers for DRY - { - // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format - // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 - - if (data.contains("dry_sequence_breakers")) { - slot.params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); - if (slot.params.sampling.dry_sequence_breakers.empty()) { - send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST); - return false; - } - } + slot.reset(); + slot.id_task = task.id; + slot.index = task.index; + slot.task_type = task.type; + slot.params = std::move(task.params); + slot.prompt_tokens = std::move(task.prompt_tokens); + + if (!are_lora_equal(task.params.lora, slot.lora)) { + // if lora is changed, we cannot reuse cached tokens + slot.cache_tokens.clear(); + slot.lora = task.params.lora; } - // process "json_schema" and "grammar" - if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) { - send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST); - return false; - } - if (data.contains("json_schema") && !data.contains("grammar")) { - try { - auto schema = json_value(data, "json_schema", json::object()); - slot.params.sampling.grammar = json_schema_to_grammar(schema); - } catch (const std::exception & e) { - send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST); - return false; - } - } else { - slot.params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); - } + SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str()); if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { // Might be better to reject the request with a 400 ? + SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.params.n_predict, slot.n_predict); slot.params.n_predict = slot.n_predict; - SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.n_predict, slot.n_predict); - } - - { - slot.params.sampling.logit_bias.clear(); - - if (json_value(data, "ignore_eos", false) && has_eos_token) { - slot.params.sampling.logit_bias.push_back({llama_token_eos(model), -INFINITY}); - } - - const auto & logit_bias = data.find("logit_bias"); - if (logit_bias != data.end() && logit_bias->is_array()) { - const int n_vocab = llama_n_vocab(model); - for (const auto & el : *logit_bias) { - // TODO: we may want to throw errors here, in case "el" is incorrect - if (el.is_array() && el.size() == 2) { - float bias; - if (el[1].is_number()) { - bias = el[1].get(); - } else if (el[1].is_boolean() && !el[1].get()) { - bias = -INFINITY; - } else { - continue; - } - - if (el[0].is_number_integer()) { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) { - slot.params.sampling.logit_bias.push_back({tok, bias}); - } - } else if (el[0].is_string()) { - auto toks = common_tokenize(model, el[0].get(), false); - for (auto tok : toks) { - slot.params.sampling.logit_bias.push_back({tok, bias}); - } - } - } - } - } - } - - { - slot.params.antiprompt.clear(); - - const auto & stop = data.find("stop"); - if (stop != data.end() && stop->is_array()) { - for (const auto & word : *stop) { - if (!word.empty()) { - slot.params.antiprompt.push_back(word); - } - } - } } - { - const auto & samplers = data.find("samplers"); - if (samplers != data.end()) { - if (samplers->is_array()) { - std::vector sampler_names; - for (const auto & name : *samplers) { - if (name.is_string()) { - sampler_names.emplace_back(name); - } - } - slot.params.sampling.samplers = common_sampler_types_from_names(sampler_names, false); - } else if (samplers->is_string()){ - std::string sampler_string; - for (const auto & name : *samplers) { - sampler_string += name; - } - slot.params.sampling.samplers = common_sampler_types_from_chars(sampler_string); - } - } else { - slot.params.sampling.samplers = defaults.sampling.samplers; - } + if (slot.params.ignore_eos && has_eos_token) { + slot.params.sampling.logit_bias.push_back({llama_vocab_eos(vocab), -INFINITY}); } { @@ -1072,49 +2089,33 @@ struct server_context { bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special); + const std::string token_str = result.text_to_send; slot.sampled = result.tok; - // search stop word and delete it slot.generated_text += token_str; + if (slot.params.return_tokens) { + slot.generated_tokens.push_back(result.tok); + } slot.has_next_token = true; // check if there is incomplete UTF-8 character at the end - bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { - unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) { - // continuation byte: 10xxxxxx - continue; - } - if ((c & 0xE0) == 0xC0) { - // 2-byte character: 110xxxxx ... - incomplete = i < 2; - } else if ((c & 0xF0) == 0xE0) { - // 3-byte character: 1110xxxx ... - incomplete = i < 3; - } else if ((c & 0xF8) == 0xF0) { - // 4-byte character: 11110xxx ... - incomplete = i < 4; - } - // else 1-byte character or invalid byte - break; - } + bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size(); + // search stop word and delete it if (!incomplete) { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool send_text = true; - size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL); + size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), true); if (stop_pos != std::string::npos) { slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); } else if (slot.has_next_token) { - stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL); + stop_pos = slot.find_stopping_strings(str_test, token_str.size(), false); send_text = stop_pos == std::string::npos; } @@ -1124,6 +2125,8 @@ struct server_context { result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache + } else { + result.text_to_send = ""; } slot.add_token(result); @@ -1138,7 +2141,7 @@ struct server_context { // check the limits if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params_base)) { - slot.stopped_limit = true; + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict); @@ -1147,7 +2150,7 @@ struct server_context { if (slot.has_new_line) { // if we have already seen a new line, we stop after a certain time limit if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { - slot.stopped_limit = true; + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); @@ -1167,7 +2170,7 @@ struct server_context { } if (pos < slot.generated_text.size() && n_indent < slot.params.n_indent) { - slot.stopped_limit = true; + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; // cut the last line @@ -1196,25 +2199,25 @@ struct server_context { // if context shift is disabled, we stop when it reaches the context limit if (slot.n_past >= slot.n_ctx) { slot.truncated = true; - slot.stopped_limit = true; + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; SLT_DBG(slot, "stopped due to running out of context capacity, n_past = %d, n_prompt_tokens = %d, n_decoded = %d, n_ctx = %d\n", slot.n_decoded, slot.n_prompt_tokens, slot.n_past, slot.n_ctx); } - if (llama_token_is_eog(model, result.tok)) { - slot.stopped_eos = true; + if (llama_vocab_is_eog(vocab, result.tok)) { + slot.stop = STOP_TYPE_EOS; slot.has_next_token = false; SLT_DBG(slot, "%s", "stopped by EOS\n"); } - const auto n_ctx_train = llama_n_ctx_train(model); + const auto n_ctx_train = llama_model_n_ctx_train(model); if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) { slot.truncated = true; - slot.stopped_limit = true; + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; // stop prediction SLT_WRN(slot, @@ -1228,57 +2231,53 @@ struct server_context { return slot.has_next_token; // continue } - json get_formated_generation(const server_slot & slot) const { - std::vector samplers; - samplers.reserve(slot.params.sampling.samplers.size()); - for (const auto & sampler : slot.params.sampling.samplers) { - samplers.emplace_back(common_sampler_type_to_str(sampler)); - } + void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) { + size_t n_probs = slot.params.sampling.n_probs; + size_t n_vocab = llama_vocab_n_tokens(vocab); + if (post_sampling) { + const auto * cur_p = common_sampler_get_candidates(slot.smpl); + const size_t max_probs = cur_p->size; + + // set probability for sampled token + for (size_t i = 0; i < max_probs; i++) { + if (cur_p->data[i].id == result.tok) { + result.prob = cur_p->data[i].p; + break; + } + } - return json { - {"n_ctx", slot.n_ctx}, - {"n_predict", slot.n_predict}, // Server configured n_predict - {"model", params_base.model_alias}, - {"seed", slot.params.sampling.seed}, - {"seed_cur", slot.smpl ? common_sampler_get_seed(slot.smpl) : 0}, - {"temperature", slot.params.sampling.temp}, - {"dynatemp_range", slot.params.sampling.dynatemp_range}, - {"dynatemp_exponent", slot.params.sampling.dynatemp_exponent}, - {"top_k", slot.params.sampling.top_k}, - {"top_p", slot.params.sampling.top_p}, - {"min_p", slot.params.sampling.min_p}, - {"xtc_probability", slot.params.sampling.xtc_probability}, - {"xtc_threshold", slot.params.sampling.xtc_threshold}, - {"typical_p", slot.params.sampling.typ_p}, - {"repeat_last_n", slot.params.sampling.penalty_last_n}, - {"repeat_penalty", slot.params.sampling.penalty_repeat}, - {"presence_penalty", slot.params.sampling.penalty_present}, - {"frequency_penalty", slot.params.sampling.penalty_freq}, - {"dry_multiplier", slot.params.sampling.dry_multiplier}, - {"dry_base", slot.params.sampling.dry_base}, - {"dry_allowed_length", slot.params.sampling.dry_allowed_length}, - {"dry_penalty_last_n", slot.params.sampling.dry_penalty_last_n}, - {"dry_sequence_breakers", slot.params.sampling.dry_sequence_breakers}, - {"mirostat", slot.params.sampling.mirostat}, - {"mirostat_tau", slot.params.sampling.mirostat_tau}, - {"mirostat_eta", slot.params.sampling.mirostat_eta}, - {"penalize_nl", slot.params.sampling.penalize_nl}, - {"stop", slot.params.antiprompt}, - {"max_tokens", slot.params.n_predict}, // User configured n_predict - {"n_keep", slot.params.n_keep}, - {"n_discard", slot.params.n_discard}, - {"ignore_eos", slot.params.sampling.ignore_eos}, - {"stream", slot.params.stream}, - //{"logit_bias", slot.params.sampling.logit_bias}, - {"n_probs", slot.params.sampling.n_probs}, - {"min_keep", slot.params.sampling.min_keep}, - {"grammar", slot.params.sampling.grammar}, - {"samplers", samplers}, - {"speculative", slot.can_speculate()}, - {"speculative.n_max", slot.params.speculative.n_max}, - {"speculative.n_min", slot.params.speculative.n_min}, - {"speculative.p_min", slot.params.speculative.p_min}, - }; + // set probability for top n_probs tokens + result.probs.reserve(max_probs); + for (size_t i = 0; i < std::min(max_probs, n_probs); i++) { + result.probs.push_back({ + cur_p->data[i].id, + common_token_to_piece(ctx, cur_p->data[i].id, special), + cur_p->data[i].p + }); + } + } else { + // TODO: optimize this with min-p optimization + std::vector cur = get_token_probabilities(ctx, idx); + + // set probability for sampled token + for (size_t i = 0; i < n_vocab; i++) { + // set probability for sampled token + if (cur[i].id == result.tok) { + result.prob = cur[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(n_probs); + for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + result.probs.push_back({ + cur[i].id, + common_token_to_piece(ctx, cur[i].id, special), + cur[i].p + }); + } + } } void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { @@ -1292,110 +2291,100 @@ struct server_context { void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { SRV_ERR("task id = %d, error: %s\n", id_task, error.c_str()); - server_task_result res; - res.id = id_task; - res.stop = false; - res.error = true; - res.data = format_error_response(error, type); - - queue_results.send(res); - } - - void send_partial_response(server_slot & slot, completion_token_output tkn) { - server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = false; - res.data = json { - {"content", tkn.text_to_send}, - {"stop", false}, - {"id_slot", slot.id}, - {"multimodal", false}, - {"index", slot.index}, - }; + auto res = std::make_unique(); + res->id = id_task; + res->err_type = type; + res->err_msg = error; - if (slot.params.sampling.n_probs > 0) { - const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); - const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); - const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); + queue_results.send(std::move(res)); + } - std::vector probs_output; - if (probs_pos < probs_stop_pos) { - probs_output = std::vector( - slot.generated_token_probs.begin() + probs_pos, - slot.generated_token_probs.begin() + probs_stop_pos); - } - slot.n_sent_token_probs = probs_stop_pos; + void send_partial_response(server_slot & slot, const completion_token_output & tkn) { + auto res = std::make_unique(); + + res->id = slot.id_task; + res->index = slot.index; + res->content = tkn.text_to_send; + res->tokens = { tkn.tok }; - res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->post_sampling_probs = slot.params.post_sampling_probs; + + res->verbose = slot.params.verbose; + res->oaicompat = slot.params.oaicompat; + res->oaicompat_model = slot.params.oaicompat_model; + res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; + + // populate res.probs_output + if (slot.params.sampling.n_probs > 0) { + res->prob_output = tkn; // copy the token probs } - if (slot.oaicompat) { - res.data["oaicompat_token_ctr"] = slot.n_decoded; - res.data["model"] = slot.oaicompat_model; + // populate timings if this is final response or timings_per_token is enabled + if (slot.stop != STOP_TYPE_NONE || slot.params.timings_per_token) { + res->timings = slot.get_timings(); } - queue_results.send(res); + queue_results.send(std::move(res)); } - void send_final_response(const server_slot & slot) { - server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; - res.data = json { - {"content", !slot.params.stream ? slot.generated_text : ""}, - {"id_slot", slot.id}, - {"stop", true}, - {"model", params_base.model_alias}, - {"tokens_predicted", slot.n_decoded}, - {"tokens_evaluated", slot.n_prompt_tokens}, - {"generation_settings", get_formated_generation(slot)}, - {"prompt", common_detokenize(ctx, slot.prompt_tokens)}, - {"has_new_line", slot.has_new_line}, - {"truncated", slot.truncated}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - {"tokens_cached", slot.n_past}, - {"timings", slot.get_formated_timings()}, - {"index", slot.index}, - }; - + void send_final_response(server_slot & slot) { + auto res = std::make_unique(); + res->id = slot.id_task; + res->id_slot = slot.id; + + res->index = slot.index; + res->content = std::move(slot.generated_text); + res->tokens = std::move(slot.generated_tokens); + res->timings = slot.get_timings(); + res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); + res->response_fields = std::move(slot.params.response_fields); + + res->truncated = slot.truncated; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_tokens_cached = slot.n_past; + res->has_new_line = slot.has_new_line; + res->stopping_word = slot.stopping_word; + res->stop = slot.stop; + res->post_sampling_probs = slot.params.post_sampling_probs; + + res->verbose = slot.params.verbose; + res->stream = slot.params.stream; + res->oaicompat = slot.params.oaicompat; + res->oaicompat_model = slot.params.oaicompat_model; + res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; + res->oaicompat_chat_format = slot.params.oaicompat_chat_format; + // populate res.probs_output if (slot.params.sampling.n_probs > 0) { - std::vector probs; - if (!slot.params.stream && slot.stopped_word) { + if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) { const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); - probs = std::vector( + res->probs_output = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.end() - safe_offset); } else { - probs = std::vector( + res->probs_output = std::vector( slot.generated_token_probs.begin(), slot.generated_token_probs.end()); } - - res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs); } - if (slot.oaicompat) { - res.data["oaicompat_token_ctr"] = slot.n_decoded; - res.data["model"] = slot.oaicompat_model; - } + res->generation_params = slot.params; // copy the parameters - queue_results.send(res); + queue_results.send(std::move(res)); } void send_embedding(const server_slot & slot, const llama_batch & batch) { - server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + auto res = std::make_unique(); + res->id = slot.id_task; + res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; + res->oaicompat = slot.params.oaicompat; - const int n_embd = llama_n_embd(model); + const int n_embd = llama_model_n_embd(model); std::vector embd_res(n_embd, 0.0f); @@ -1412,32 +2401,30 @@ struct server_context { if (embd == NULL) { SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]); - res.data = json { - {"embedding", std::vector(n_embd, 0.0f)}, - {"index", slot.index}, - }; - + res->embedding.push_back(std::vector(n_embd, 0.0f)); continue; } - common_embd_normalize(embd, embd_res.data(), n_embd); - - res.data = json { - {"embedding", embd_res}, - {"index", slot.index}, - }; + // normalize only when there is pooling + // TODO: configurable + if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) { + common_embd_normalize(embd, embd_res.data(), n_embd, 2); + res->embedding.push_back(embd_res); + } else { + res->embedding.push_back({ embd, embd + n_embd }); + } } SLT_DBG(slot, "%s", "sending embeddings\n"); - queue_results.send(res); + queue_results.send(std::move(res)); } void send_rerank(const server_slot & slot, const llama_batch & batch) { - server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + auto res = std::make_unique(); + res->id = slot.id_task; + res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { @@ -1452,156 +2439,110 @@ struct server_context { if (embd == NULL) { SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]); - res.data = json { - {"index", slot.index}, - {"score", -1e6}, - }; - + res->score = -1e6; continue; } - res.data = json { - {"index", slot.index}, - {"score", embd[0]}, - }; + res->score = embd[0]; } - SLT_DBG(slot, "sending rerank result, res = '%s'\n", res.data.dump().c_str()); + SLT_DBG(slot, "sending rerank result, res.score = %f\n", res->score); - queue_results.send(res); + queue_results.send(std::move(res)); } // // Functions to create new task(s) and receive result(s) // - // break the input "prompt" into multiple tasks if needed, then format and tokenize the input prompt(s) - std::vector create_tasks_inference(json data, server_task_inf_type inf_type) { - std::vector tasks; - auto create_task = [&](json & task_data, llama_tokens & prompt_tokens) { - SRV_DBG("create task, n_tokens = %d\n", (int) prompt_tokens.size()); - server_task task; - task.id = queue_tasks.get_new_id(); - task.inf_type = inf_type; - task.type = SERVER_TASK_TYPE_INFERENCE; - task.data = task_data; - task.prompt_tokens = std::move(prompt_tokens); - tasks.push_back(std::move(task)); - }; - - static constexpr const char * error_msg = "\"prompt\" must be a string, an array of token ids or an array of prompts"; - if (!data.contains("prompt")) { - throw std::runtime_error(error_msg); - } - - // because llama_tokenize api is thread-safe, we can tokenize the prompt from HTTP thread - bool add_special = inf_type != SERVER_TASK_INF_TYPE_RERANK && inf_type != SERVER_TASK_INF_TYPE_INFILL; - std::vector tokenized_prompts = tokenize_input_prompts(ctx, data.at("prompt"), add_special, true); - switch (inf_type) { - case SERVER_TASK_INF_TYPE_RERANK: - { - // prompts[0] is the question - // the rest are the answers/documents - GGML_ASSERT(tokenized_prompts.size() > 1); - SRV_DBG("creating rerank tasks, n_prompts = %d\n", (int) tokenized_prompts.size() - 1); - for (size_t i = 1; i < tokenized_prompts.size(); i++) { - data["index"] = i - 1; - auto tokens = format_rerank(model, tokenized_prompts[0], tokenized_prompts[i]); - create_task(data, tokens); - } - } break; - case SERVER_TASK_INF_TYPE_INFILL: - { - SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); - for (size_t i = 0; i < tokenized_prompts.size(); i++) { - data["index"] = i; - auto tokens = format_infill( - ctx, - data.at("input_prefix"), - data.at("input_suffix"), - data.at("input_extra"), - params_base.n_batch, - params_base.n_predict, - slots[0].n_ctx, // TODO: there should be a better way - params_base.spm_infill, - tokenized_prompts[i] - ); - create_task(data, tokens); - } - } break; - default: - { - SRV_DBG("creating multi-prompt tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); - for (size_t i = 0; i < tokenized_prompts.size(); i++) { - data["index"] = i; - create_task(data, tokenized_prompts[i]); - } - } - } - - return tasks; - } - void cancel_tasks(const std::unordered_set & id_tasks) { std::vector cancel_tasks; cancel_tasks.reserve(id_tasks.size()); for (const auto & id_task : id_tasks) { SRV_WRN("cancel task, id_task = %d\n", id_task); - server_task task; - task.type = SERVER_TASK_TYPE_CANCEL; + server_task task(SERVER_TASK_TYPE_CANCEL); task.id_target = id_task; - cancel_tasks.push_back(task); queue_results.remove_waiting_task_id(id_task); + cancel_tasks.push_back(task); } // push to beginning of the queue, so it has highest priority queue_tasks.post(cancel_tasks, true); } - // receive the results from task(s) created by create_tasks_inference - void receive_cmpl_results( + // receive the results from task(s) + void receive_multi_results( const std::unordered_set & id_tasks, - const std::function&)> & result_handler, - const std::function & error_handler) { - // TODO: currently, there is no way to detect the client has cancelled the request - std::vector results(id_tasks.size()); - for (size_t i = 0; i < id_tasks.size(); i++) { - server_task_result result = queue_results.recv(id_tasks); - - if (result.error) { - error_handler(result.data); + const std::function&)> & result_handler, + const std::function & error_handler, + const std::function & is_connection_closed) { + std::vector results(id_tasks.size()); + for (int i = 0; i < (int)id_tasks.size(); i++) { + server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS); + + if (is_connection_closed()) { cancel_tasks(id_tasks); return; } - const size_t idx = result.data["index"]; - GGML_ASSERT(idx < results.size() && "index out of range"); + if (result == nullptr) { + i--; // retry + continue; + } + + if (result->is_error()) { + error_handler(result->to_json()); + cancel_tasks(id_tasks); + return; + } - results[idx] = result; + GGML_ASSERT( + dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + ); + const size_t idx = result->get_index(); + GGML_ASSERT(idx < results.size() && "index out of range"); + results[idx] = std::move(result); } result_handler(results); } - // receive the results from task(s) created by create_tasks_inference, in stream mode + // receive the results from task(s), in stream mode void receive_cmpl_results_stream( - const std::unordered_set & id_tasks, const - std::function & result_handler, const - std::function & error_handler) { + const std::unordered_set & id_tasks, + const std::function & result_handler, + const std::function & error_handler, + const std::function & is_connection_closed) { size_t n_finished = 0; while (true) { - server_task_result result = queue_results.recv(id_tasks); - if (!result_handler(result)) { + server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS); + + if (is_connection_closed()) { cancel_tasks(id_tasks); - break; + return; + } + + if (result == nullptr) { + continue; // retry + } + + if (result->is_error()) { + error_handler(result->to_json()); + cancel_tasks(id_tasks); + return; } - if (result.error) { - error_handler(result.data); + GGML_ASSERT( + dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + ); + if (!result_handler(result)) { cancel_tasks(id_tasks); break; } - if (result.stop) { + if (result->is_stop()) { if (++n_finished == id_tasks.size()) { break; } @@ -1615,9 +2556,12 @@ struct server_context { void process_single_task(server_task task) { switch (task.type) { - case SERVER_TASK_TYPE_INFERENCE: + case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_EMBEDDING: + case SERVER_TASK_TYPE_RERANK: { - const int id_slot = json_value(task.data, "id_slot", -1); + const int id_slot = task.id_selected_slot; server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); @@ -1634,13 +2578,6 @@ struct server_context { break; } - slot->reset(); - - slot->id_task = task.id; - slot->inf_type = task.inf_type; - slot->index = json_value(task.data, "index", 0); - slot->prompt_tokens = std::move(task.prompt_tokens); - if (!launch_slot_with_task(*slot, task)) { SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id); break; @@ -1668,21 +2605,7 @@ struct server_context { int n_processing_slots = 0; for (server_slot & slot : slots) { - json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["id_task"] = slot.id_task; - slot_data["is_processing"] = slot.is_processing(); - slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens); - slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"has_new_line", slot.has_new_line}, - {"n_remain", slot.n_remaining}, - {"n_decoded", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }; + json slot_data = slot.to_json(); if (slot.is_processing()) { n_processing_slots++; @@ -1694,43 +2617,38 @@ struct server_context { } SRV_DBG("n_idle_slots = %d, n_processing_slots = %d\n", n_idle_slots, n_processing_slots); - server_task_result res; - res.id = task.id; - res.stop = true; - res.error = false; - res.data = { - { "idle", n_idle_slots }, - { "processing", n_processing_slots }, - { "deferred", queue_tasks.queue_tasks_deferred.size() }, - { "t_start", metrics.t_start}, + auto res = std::make_unique(); + res->id = task.id; + res->slots_data = std::move(slots_data); + res->n_idle_slots = n_idle_slots; + res->n_processing_slots = n_processing_slots; + res->n_tasks_deferred = queue_tasks.queue_tasks_deferred.size(); + res->t_start = metrics.t_start; - { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, - { "t_tokens_generation_total", metrics.t_tokens_generation_total}, - { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, - { "t_prompt_processing_total", metrics.t_prompt_processing_total}, + res->kv_cache_tokens_count = llama_get_kv_cache_token_count(ctx); + res->kv_cache_used_cells = llama_get_kv_cache_used_cells(ctx); - { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, - { "t_prompt_processing", metrics.t_prompt_processing}, - { "n_tokens_predicted", metrics.n_tokens_predicted}, - { "t_tokens_generation", metrics.t_tokens_generation}, + res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total; + res->t_prompt_processing_total = metrics.t_prompt_processing_total; + res->n_tokens_predicted_total = metrics.n_tokens_predicted_total; + res->t_tokens_generation_total = metrics.t_tokens_generation_total; - { "n_decode_total", metrics.n_decode_total}, - { "n_busy_slots_total", metrics.n_busy_slots_total}, + res->n_prompt_tokens_processed = metrics.n_prompt_tokens_processed; + res->t_prompt_processing = metrics.t_prompt_processing; + res->n_tokens_predicted = metrics.n_tokens_predicted; + res->t_tokens_generation = metrics.t_tokens_generation; - { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, - { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, + res->n_decode_total = metrics.n_decode_total; + res->n_busy_slots_total = metrics.n_busy_slots_total; - { "slots", slots_data }, - }; - - if (json_value(task.data, "reset_bucket", false)) { + if (task.metrics_reset_bucket) { metrics.reset_bucket(); } - queue_results.send(res); + queue_results.send(std::move(res)); } break; case SERVER_TASK_TYPE_SLOT_SAVE: { - int id_slot = task.data.at("id_slot"); + int id_slot = task.slot_action.slot_id; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1746,32 +2664,27 @@ struct server_context { const size_t token_count = slot->cache_tokens.size(); const int64_t t_start = ggml_time_us(); - std::string filename = task.data.at("filename"); - std::string filepath = task.data.at("filepath"); + std::string filename = task.slot_action.filename; + std::string filepath = task.slot_action.filepath; const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count); const int64_t t_end = ggml_time_us(); const double t_save_ms = (t_end - t_start) / 1000.0; - server_task_result result; - result.id = task.id; - result.stop = true; - result.error = false; - result.data = json { - { "id_slot", id_slot }, - { "filename", filename }, - { "n_saved", token_count }, // tokens saved - { "n_written", nwrite }, // bytes written - { "timings", { - { "save_ms", t_save_ms } - } } - }; - queue_results.send(result); + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->filename = filename; + res->is_save = true; + res->n_tokens = token_count; + res->n_bytes = nwrite; + res->t_ms = t_save_ms; + queue_results.send(std::move(res)); } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { - int id_slot = task.data.at("id_slot"); + int id_slot = task.slot_action.slot_id; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1786,8 +2699,8 @@ struct server_context { const int64_t t_start = ggml_time_us(); - std::string filename = task.data.at("filename"); - std::string filepath = task.data.at("filepath"); + std::string filename = task.slot_action.filename; + std::string filepath = task.slot_action.filepath; slot->cache_tokens.resize(slot->n_ctx); size_t token_count = 0; @@ -1802,24 +2715,19 @@ struct server_context { const int64_t t_end = ggml_time_us(); const double t_restore_ms = (t_end - t_start) / 1000.0; - server_task_result result; - result.id = task.id; - result.stop = true; - result.error = false; - result.data = json { - { "id_slot", id_slot }, - { "filename", filename }, - { "n_restored", token_count }, // tokens restored - { "n_read", nread }, // bytes read - { "timings", { - { "restore_ms", t_restore_ms } - } } - }; - queue_results.send(result); + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->filename = filename; + res->is_save = false; + res->n_tokens = token_count; + res->n_bytes = nread; + res->t_ms = t_restore_ms; + queue_results.send(std::move(res)); } break; case SERVER_TASK_TYPE_SLOT_ERASE: { - int id_slot = task.data.at("id_slot"); + int id_slot = task.slot_action.slot_id; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1837,25 +2745,18 @@ struct server_context { llama_kv_cache_seq_rm(ctx, slot->id, -1, -1); slot->cache_tokens.clear(); - server_task_result result; - result.id = task.id; - result.stop = true; - result.error = false; - result.data = json { - { "id_slot", id_slot }, - { "n_erased", n_erased } - }; - queue_results.send(result); + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->n_erased = n_erased; + queue_results.send(std::move(res)); } break; case SERVER_TASK_TYPE_SET_LORA: { - common_lora_adapters_apply(ctx, loras); - server_task_result result; - result.id = task.id; - result.stop = true; - result.error = false; - result.data = json{{ "success", true }}; - queue_results.send(result); + params_base.lora_adapters = std::move(task.set_lora); + auto res = std::make_unique(); + res->id = task.id; + queue_results.send(std::move(res)); } break; } } @@ -1885,10 +2786,8 @@ struct server_context { { SRV_DBG("%s", "posting NEXT_RESPONSE\n"); - server_task task; - task.type = SERVER_TASK_TYPE_NEXT_RESPONSE; - task.id_target = -1; - + server_task task(SERVER_TASK_TYPE_NEXT_RESPONSE); + task.id = queue_tasks.get_new_id(); queue_tasks.post(task); } @@ -1931,12 +2830,26 @@ struct server_context { // start populating the batch for this iteration common_batch_clear(batch); + // track if given slot can be batched with slots already in the batch + server_slot * slot_batched = nullptr; + + auto accept_special_token = [&](server_slot & slot, llama_token token) { + return params_base.special || slot.params.sampling.preserved_tokens.find(token) != slot.params.sampling.preserved_tokens.end(); + }; + // frist, add sampled tokens from any ongoing sequences for (auto & slot : slots) { if (slot.state != SLOT_STATE_GENERATING) { continue; } + // check if we can batch this slot with the previous one + if (!slot_batched) { + slot_batched = &slot; + } else if (!slot_batched->can_batch_with(slot)) { + continue; + } + slot.i_batch = batch.n_tokens; common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true); @@ -1955,15 +2868,18 @@ struct server_context { int32_t n_batch = llama_n_batch(ctx); int32_t n_ubatch = llama_n_ubatch(ctx); - // track if this is an embedding or non-embedding batch - // if we've added sampled tokens above, we are in non-embedding mode - // -1: none, 0: non-embedding, 1: embedding - // TODO: make enum - int32_t batch_type = batch.n_tokens > 0 ? 0 : -1; - // next, batch any pending prompts without exceeding n_batch if (params_base.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { + // check if we can batch this slot with the previous one + if (slot.is_processing()) { + if (!slot_batched) { + slot_batched = &slot; + } else if (!slot_batched->can_batch_with(slot)) { + continue; + } + } + // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { auto & prompt_tokens = slot.prompt_tokens; @@ -2002,7 +2918,7 @@ struct server_context { continue; } - if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { + if (slot.is_non_causal()) { if (slot.n_prompt_tokens > n_ubatch) { slot.release(); send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); @@ -2117,24 +3033,13 @@ struct server_context { } // non-causal tasks require to fit the entire prompt in the physical batch - if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { + if (slot.is_non_causal()) { // cannot fit the prompt in the current batch - will try next iter if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { continue; } } - // check that we are in the right batch_type, if not defer the slot - const bool slot_type = - slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING || - slot.inf_type == SERVER_TASK_INF_TYPE_RERANK ? 1 : 0; - - if (batch_type == -1) { - batch_type = slot_type; - } else if (batch_type != slot_type) { - continue; - } - // keep only the common part if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) { // could not partially delete (likely using a non-Transformer model) @@ -2151,7 +3056,10 @@ struct server_context { // add prompt tokens for processing in the current batch while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { - common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false); + // without pooling, we want to output the embeddings for all the tokens in the batch + const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE; + + common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, need_embd); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -2199,8 +3107,12 @@ struct server_context { SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens); - // make sure we're in the right embedding mode - llama_set_embeddings(ctx, batch_type == 1); + if (slot_batched) { + // make sure we're in the right embedding mode + llama_set_embeddings(ctx, slot_batched->is_non_causal()); + // apply lora, only need to do it once per batch + common_set_adapter_lora(ctx, slot_batched->lora); + } // process the created batch of tokens for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { @@ -2245,7 +3157,7 @@ struct server_context { } if (slot.state == SLOT_STATE_DONE_PROMPT) { - if (slot.inf_type == SERVER_TASK_INF_TYPE_EMBEDDING) { + if (slot.task_type == SERVER_TASK_TYPE_EMBEDDING) { // prompt evaluated for embedding send_embedding(slot, batch_view); slot.release(); @@ -2253,7 +3165,7 @@ struct server_context { continue; // continue loop of slots } - if (slot.inf_type == SERVER_TASK_INF_TYPE_RERANK) { + if (slot.task_type == SERVER_TASK_TYPE_RERANK) { send_rerank(slot, batch_view); slot.release(); slot.i_batch = -1; @@ -2266,52 +3178,78 @@ struct server_context { continue; // continue loop of slots } - llama_token id; + const int tok_idx = slot.i_batch - i; - { - completion_token_output result; + llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); - id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i); + slot.i_batch = -1; - slot.i_batch = -1; + common_sampler_accept(slot.smpl, id, true); - common_sampler_accept(slot.smpl, id, true); + slot.n_decoded += 1; - slot.n_decoded += 1; - if (slot.n_decoded == 1) { - slot.t_start_generation = ggml_time_us(); - slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; - metrics.on_prompt_eval(slot); - } + const int64_t t_current = ggml_time_us(); - result.tok = id; + if (slot.n_decoded == 1) { + slot.t_start_generation = t_current; + slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; + metrics.on_prompt_eval(slot); + } - const auto * cur_p = common_sampler_get_candidates(slot.smpl); + slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; - for (size_t i = 0; i < (size_t) slot.params.sampling.n_probs; ++i) { - result.probs.push_back({ - cur_p->data[i].id, - i >= cur_p->size ? 0.0f : cur_p->data[i].p, - }); - } + completion_token_output result; + result.tok = id; + result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok)); + result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs - if (!process_token(result, slot)) { - // release slot because of stop condition - slot.release(); - slot.print_timings(); - send_final_response(slot); - metrics.on_prediction(slot); - continue; - } + if (slot.params.sampling.n_probs > 0) { + populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx); + } + + if (!process_token(result, slot)) { + // release slot because of stop condition + slot.release(); + slot.print_timings(); + send_final_response(slot); + metrics.on_prediction(slot); + continue; + } + } + + // do speculative decoding + for (auto & slot : slots) { + if (!slot.is_processing() || !slot.can_speculate()) { + continue; } - // check if the slot supports speculative decoding - if (!slot.can_speculate()) { + if (slot.state != SLOT_STATE_GENERATING) { + continue; + } + + // determine the max draft that fits the current slot state + int n_draft_max = slot.params.speculative.n_max; + + // note: n_past is not yet increased for the `id` token sampled above + // also, need to leave space for 1 extra token to allow context shifts + n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.n_past - 2); + + if (slot.n_remaining > 0) { + n_draft_max = std::min(n_draft_max, slot.n_remaining - 1); + } + + SLT_DBG(slot, "max possible draft: %d\n", n_draft_max); + + if (n_draft_max < slot.params.speculative.n_min) { + SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.params.speculative.n_min); + continue; } + llama_token id = slot.sampled; + struct common_speculative_params params_spec; - params_spec.n_draft = slot.params.speculative.n_max; + params_spec.n_draft = n_draft_max; params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.params.speculative.n_max; params_spec.p_min = slot.params.speculative.p_min; @@ -2319,6 +3257,8 @@ struct server_context { // ignore small drafts if (slot.params.speculative.n_min > (int) draft.size()) { + SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.params.speculative.n_min); + continue; } @@ -2330,6 +3270,8 @@ struct server_context { common_batch_add(slot.batch_spec, draft[i], slot.n_past + 1 + i, { slot.id }, true); } + SLT_DBG(slot, "decoding speculative batch, size = %d\n", slot.batch_spec.n_tokens); + llama_decode(ctx, slot.batch_spec); // the accepted tokens from the speculation @@ -2346,7 +3288,11 @@ struct server_context { for (size_t i = 0; i < ids.size(); ++i) { completion_token_output result; - result.tok = ids[i]; + result.tok = ids[i]; + result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok)); + result.prob = 1.0f; // set later + + // TODO: set result.probs if (!process_token(result, slot)) { // release slot because of stop condition @@ -2358,7 +3304,7 @@ struct server_context { } } - SRV_DBG("accepted %d/%d draft tokens\n", (int) ids.size() - 1, (int) draft.size()); + SLT_DBG(slot, "accepted %d/%d draft tokens, new n_past = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.n_past); } } @@ -2367,12 +3313,12 @@ struct server_context { json model_meta() const { return json { - {"vocab_type", llama_vocab_type (model)}, - {"n_vocab", llama_n_vocab (model)}, - {"n_ctx_train", llama_n_ctx_train (model)}, - {"n_embd", llama_n_embd (model)}, - {"n_params", llama_model_n_params(model)}, - {"size", llama_model_size (model)}, + {"vocab_type", llama_vocab_type (vocab)}, + {"n_vocab", llama_vocab_n_tokens (vocab)}, + {"n_ctx_train", llama_model_n_ctx_train(model)}, + {"n_embd", llama_model_n_embd (model)}, + {"n_params", llama_model_n_params (model)}, + {"size", llama_model_size (model)}, }; } }; @@ -2383,10 +3329,12 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp return; } - LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status); + // reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch + + SRV_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status); - LOG_DBG("request: %s\n", req.body.c_str()); - LOG_DBG("response: %s\n", res.body.c_str()); + SRV_DBG("request: %s\n", req.body.c_str()); + SRV_DBG("response: %s\n", res.body.c_str()); } std::function shutdown_handler; @@ -2413,17 +3361,9 @@ int main(int argc, char ** argv) { common_init(); - // enabling this will output extra debug information in the HTTP responses from the server - // see format_final_response_oaicompat() - const bool verbose = params.verbosity > 9; - // struct that contains llama context and inference server_context ctx_server; - if (params.model_alias == "unknown") { - params.model_alias = params.model; - } - llama_backend_init(); llama_numa_init(params.numa); @@ -2432,16 +3372,6 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); LOG_INF("\n"); - // static files - std::map static_files = { - { "/", { index_html, index_html_len, "text/html; charset=utf-8" }}, - { "/completion.js", { completion_js, completion_js_len, "text/javascript; charset=utf-8" }}, - { "/deps_daisyui.min.css", { deps_daisyui_min_css, deps_daisyui_min_css_len, "text/css; charset=utf-8" }}, - { "/deps_markdown-it.js", { deps_markdown_it_js, deps_markdown_it_js_len, "text/javascript; charset=utf-8" }}, - { "/deps_tailwindcss.js", { deps_tailwindcss_js, deps_tailwindcss_js_len, "text/javascript; charset=utf-8" }}, - { "/deps_vue.esm-browser.js", { deps_vue_esm_browser_js, deps_vue_esm_browser_js_len, "text/javascript; charset=utf-8" }}, - }; - std::unique_ptr svr; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT if (params.ssl_file_key != "" && params.ssl_file_cert != "") { @@ -2468,28 +3398,32 @@ int main(int argc, char ** argv) { auto res_error = [](httplib::Response & res, const json & error_data) { json final_response {{"error", error_data}}; - res.set_content(final_response.dump(-1, ' ', false, json::error_handler_t::replace), MIMETYPE_JSON); + res.set_content(safe_json_to_str(final_response), MIMETYPE_JSON); res.status = json_value(error_data, "code", 500); }; auto res_ok = [](httplib::Response & res, const json & data) { - res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace), MIMETYPE_JSON); + res.set_content(safe_json_to_str(data), MIMETYPE_JSON); res.status = 200; }; - svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) { + svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, const std::exception_ptr & ep) { std::string message; try { std::rethrow_exception(ep); - } catch (std::exception & e) { + } catch (const std::exception & e) { message = e.what(); } catch (...) { message = "Unknown Exception"; } - json formatted_error = format_error_response(message, ERROR_TYPE_SERVER); - LOG_WRN("got exception: %s\n", formatted_error.dump().c_str()); - res_error(res, formatted_error); + try { + json formatted_error = format_error_response(message, ERROR_TYPE_SERVER); + LOG_WRN("got exception: %s\n", formatted_error.dump().c_str()); + res_error(res, formatted_error); + } catch (const std::exception & e) { + LOG_ERR("got another exception: %s | while hanlding exception: %s\n", e.what(), message.c_str()); + } }); svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) { @@ -2522,7 +3456,7 @@ int main(int argc, char ** argv) { // Middlewares // - auto middleware_validate_api_key = [¶ms, &res_error, &static_files](const httplib::Request & req, httplib::Response & res) { + auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { static const std::unordered_set public_endpoints = { "/health", "/models", @@ -2535,7 +3469,7 @@ int main(int argc, char ** argv) { } // If path is public or is static file, skip validation - if (public_endpoints.find(req.path) != public_endpoints.end() || static_files.find(req.path) != static_files.end()) { + if (public_endpoints.find(req.path) != public_endpoints.end() || req.path == "/") { return true; } @@ -2610,27 +3544,33 @@ int main(int argc, char ** argv) { } // request slots data using task queue - server_task task; + server_task task(SERVER_TASK_TYPE_METRICS); task.id = ctx_server.queue_tasks.get_new_id(); - task.type = SERVER_TASK_TYPE_METRICS; - ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task, true); // high-priority task // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + // TODO: get rid of this dynamic_cast + auto res_metrics = dynamic_cast(result.get()); + GGML_ASSERT(res_metrics != nullptr); + // optionally return "fail_on_no_slot" error - const int n_idle_slots = result.data.at("idle"); if (req.has_param("fail_on_no_slot")) { - if (n_idle_slots == 0) { + if (res_metrics->n_idle_slots == 0) { res_error(res, format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE)); return; } } - res_ok(res, result.data.at("slots")); + res_ok(res, res_metrics->slots_data); }; const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { @@ -2640,83 +3580,77 @@ int main(int argc, char ** argv) { } // request slots data using task queue - server_task task; + server_task task(SERVER_TASK_TYPE_METRICS); task.id = ctx_server.queue_tasks.get_new_id(); - task.id_target = -1; - task.type = SERVER_TASK_TYPE_METRICS; - task.data.push_back({{"reset_bucket", true}}); + task.metrics_reset_bucket = true; ctx_server.queue_results.add_waiting_task_id(task.id); ctx_server.queue_tasks.post(task, true); // high-priority task // get the result - server_task_result result = ctx_server.queue_results.recv(task.id); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); - json data = result.data; - - const uint64_t n_prompt_tokens_processed = data.at("n_prompt_tokens_processed"); - const uint64_t t_prompt_processing = data.at("t_prompt_processing"); - - const uint64_t n_tokens_predicted = data.at("n_tokens_predicted"); - const uint64_t t_tokens_generation = data.at("t_tokens_generation"); - - const uint64_t n_decode_total = data.at("n_decode_total"); - const uint64_t n_busy_slots_total = data.at("n_busy_slots_total"); + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } - const int32_t kv_cache_used_cells = data.at("kv_cache_used_cells"); + // TODO: get rid of this dynamic_cast + auto res_metrics = dynamic_cast(result.get()); + GGML_ASSERT(res_metrics != nullptr); // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, - {"value", (uint64_t) data.at("n_prompt_tokens_processed_total")} + {"value", (uint64_t) res_metrics->n_prompt_tokens_processed_total} }, { {"name", "prompt_seconds_total"}, {"help", "Prompt process time"}, - {"value", (uint64_t) data.at("t_prompt_processing_total") / 1.e3} + {"value", (uint64_t) res_metrics->t_prompt_processing_total / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, - {"value", (uint64_t) data.at("n_tokens_predicted_total")} + {"value", (uint64_t) res_metrics->n_tokens_predicted_total} }, { {"name", "tokens_predicted_seconds_total"}, {"help", "Predict process time"}, - {"value", (uint64_t) data.at("t_tokens_generation_total") / 1.e3} + {"value", (uint64_t) res_metrics->t_tokens_generation_total / 1.e3} }, { {"name", "n_decode_total"}, {"help", "Total number of llama_decode() calls"}, - {"value", n_decode_total} + {"value", res_metrics->n_decode_total} }, { {"name", "n_busy_slots_per_decode"}, {"help", "Average number of busy slots per llama_decode() call"}, - {"value", (float) n_busy_slots_total / (float) n_decode_total} + {"value", (float) res_metrics->n_busy_slots_total / std::max((float) res_metrics->n_decode_total, 1.f)} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.} + {"value", res_metrics->n_prompt_tokens_processed ? 1.e3 / res_metrics->t_prompt_processing * res_metrics->n_prompt_tokens_processed : 0.} },{ {"name", "predicted_tokens_seconds"}, {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.} + {"value", res_metrics->n_tokens_predicted ? 1.e3 / res_metrics->t_tokens_generation * res_metrics->n_tokens_predicted : 0.} },{ {"name", "kv_cache_usage_ratio"}, {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} + {"value", 1. * res_metrics->kv_cache_used_cells / params.n_ctx} },{ {"name", "kv_cache_tokens"}, {"help", "KV-cache tokens."}, - {"value", (uint64_t) data.at("kv_cache_tokens_count")} + {"value", (uint64_t) res_metrics->kv_cache_tokens_count} },{ {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", (uint64_t) data.at("processing")} + {"help", "Number of requests processing."}, + {"value", (uint64_t) res_metrics->n_processing_slots} },{ {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", (uint64_t) data.at("deferred")} + {"help", "Number of requests deferred."}, + {"value", (uint64_t) res_metrics->n_tasks_deferred} }}} }; @@ -2737,8 +3671,7 @@ int main(int argc, char ** argv) { } } - const int64_t t_start = data.at("t_start"); - res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); + res.set_header("Process-Start-Time-Unix", std::to_string(res_metrics->t_start)); res.set_content(prometheus.str(), "text/plain; version=0.0.4"); res.status = 200; // HTTP OK @@ -2753,25 +3686,24 @@ int main(int argc, char ** argv) { } std::string filepath = params.slot_save_path + filename; - server_task task; - task.type = SERVER_TASK_TYPE_SLOT_SAVE; - task.data = { - { "id_slot", id_slot }, - { "filename", filename }, - { "filepath", filepath }, - }; + server_task task(SERVER_TASK_TYPE_SLOT_SAVE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; + task.slot_action.filename = filename; + task.slot_action.filepath = filepath; - const int id_task = ctx_server.queue_tasks.post(task); - ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - if (result.error) { - res_error(res, result.data); - } else { - res_ok(res, result.data); + if (result->is_error()) { + res_error(res, result->to_json()); + return; } + + res_ok(res, result->to_json()); }; const auto handle_slots_restore = [&ctx_server, &res_error, &res_ok, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { @@ -2783,45 +3715,45 @@ int main(int argc, char ** argv) { } std::string filepath = params.slot_save_path + filename; - server_task task; - task.type = SERVER_TASK_TYPE_SLOT_RESTORE; - task.data = { - { "id_slot", id_slot }, - { "filename", filename }, - { "filepath", filepath }, - }; + server_task task(SERVER_TASK_TYPE_SLOT_RESTORE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; + task.slot_action.filename = filename; + task.slot_action.filepath = filepath; - const int id_task = ctx_server.queue_tasks.post(task); - ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - if (result.error) { - res_error(res, result.data); - } else { - res_ok(res, result.data); + if (result->is_error()) { + res_error(res, result->to_json()); + return; } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); }; const auto handle_slots_erase = [&ctx_server, &res_error, &res_ok](const httplib::Request & /* req */, httplib::Response & res, int id_slot) { - server_task task; - task.type = SERVER_TASK_TYPE_SLOT_ERASE; - task.data = { - { "id_slot", id_slot }, - }; + server_task task(SERVER_TASK_TYPE_SLOT_ERASE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; - const int id_task = ctx_server.queue_tasks.post(task); - ctx_server.queue_results.add_waiting_task_id(id_task); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - if (result.error) { - res_error(res, result.data); - } else { - res_ok(res, result.data); + if (result->is_error()) { + res_error(res, result->to_json()); + return; } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); }; const auto handle_slots_action = [¶ms, &res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) { @@ -2854,11 +3786,21 @@ int main(int argc, char ** argv) { }; const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { + // this endpoint is publicly available, please only return what is safe to be exposed json data = { { "default_generation_settings", ctx_server.default_generation_settings_for_props }, { "total_slots", ctx_server.params_base.n_parallel }, - { "chat_template", llama_get_chat_template(ctx_server.model) }, + { "model_path", ctx_server.params_base.model }, + { "chat_template", common_chat_templates_source(ctx_server.chat_templates.get()) }, + { "bos_token", common_token_to_piece(ctx_server.ctx, llama_vocab_bos(ctx_server.vocab), /* special= */ true)}, + { "eos_token", common_token_to_piece(ctx_server.ctx, llama_vocab_eos(ctx_server.vocab), /* special= */ true)}, + { "build_info", build_info }, }; + if (ctx_server.params_base.use_jinja) { + if (auto tool_use_src = common_chat_templates_source(ctx_server.chat_templates.get(), "tool_use")) { + data["chat_template_tool_use"] = tool_use_src; + } + } res_ok(res, data); }; @@ -2876,13 +3818,56 @@ int main(int argc, char ** argv) { res_ok(res, {{ "success", true }}); }; - const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) { + // handle completion-like requests (completion, chat, infill) + // we can optionally provide a custom format for partial results and final results + const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok]( + server_task_type type, + json & data, + std::function is_connection_closed, + httplib::Response & res, + oaicompat_type oaicompat) { + GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL); + if (ctx_server.params_base.embedding) { res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } - std::vector tasks = ctx_server.create_tasks_inference(data, inf_type); + auto completion_id = gen_chatcmplid(); + std::vector tasks; + + try { + const auto & prompt = data.at("prompt"); + // TODO: this log can become very long, put it behind a flag or think about a more compact format + //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get().c_str() : prompt.dump(2).c_str()); + + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true); + tasks.reserve(tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + server_task task = server_task(type); + + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + + task.prompt_tokens = std::move(tokenized_prompts[i]); + task.params = server_task::params_from_json_cmpl( + ctx_server.ctx, + ctx_server.params_base, + data); + task.id_selected_slot = json_value(data, "id_slot", -1); + + // OAI-compat + task.params.oaicompat = oaicompat; + task.params.oaicompat_cmpl_id = completion_id; + // oaicompat_model is already populated by params_from_json_cmpl + + tasks.push_back(task); + } + } catch (const std::exception & e) { + res_error(res, format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST)); + return; + } + ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); @@ -2890,30 +3875,48 @@ int main(int argc, char ** argv) { const auto task_ids = server_task::get_list_id(tasks); if (!stream) { - ctx_server.receive_cmpl_results(task_ids, [&](std::vector & results) { + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { if (results.size() == 1) { // single result - res_ok(res, results[0].data); + res_ok(res, results[0]->to_json()); } else { // multiple results (multitask) json arr = json::array(); - for (const auto & res : results) { - arr.push_back(res.data); + for (auto & res : results) { + arr.push_back(res->to_json()); } res_ok(res, arr); } }, [&](const json & error_data) { res_error(res, error_data); - }); + }, is_connection_closed); ctx_server.queue_results.remove_waiting_task_ids(task_ids); } else { - const auto chunked_content_provider = [task_ids, &ctx_server](size_t, httplib::DataSink & sink) { - ctx_server.receive_cmpl_results_stream(task_ids, [&](const server_task_result & result) -> bool { - return server_sent_event(sink, "data", result.data); + const auto chunked_content_provider = [task_ids, &ctx_server, oaicompat](size_t, httplib::DataSink & sink) { + ctx_server.receive_cmpl_results_stream(task_ids, [&](server_task_result_ptr & result) -> bool { + json res_json = result->to_json(); + if (res_json.is_array()) { + for (const auto & res : res_json) { + if (!server_sent_event(sink, "data", res)) { + // sending failed (HTTP connection closed), cancel the generation + return false; + } + } + return true; + } else { + return server_sent_event(sink, "data", res_json); + } }, [&](const json & error_data) { server_sent_event(sink, "error", error_data); + }, [&sink]() { + // note: do not use req.is_connection_closed here because req is already destroyed + return !sink.is_writable(); }); + if (oaicompat != OAICOMPAT_TYPE_NONE) { + static const std::string ev_done = "data: [DONE]\n\n"; + sink.write(ev_done.data(), ev_done.size()); + } sink.done(); return false; }; @@ -2926,21 +3929,36 @@ int main(int argc, char ** argv) { } }; - const auto handle_completions = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { json data = json::parse(req.body); - return handle_completions_generic(SERVER_TASK_INF_TYPE_COMPLETION, data, res); + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_NONE); }; - const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) { + const auto handle_completions_oai = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + json data = oaicompat_completion_params_parse(json::parse(req.body)); + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_COMPLETION); + }; + + const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { // check model compatibility std::string err; - if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) { + if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) { err += "prefix token is missing. "; } - if (llama_token_fim_suf(ctx_server.model) == LLAMA_TOKEN_NULL) { + if (llama_vocab_fim_suf(ctx_server.vocab) == LLAMA_TOKEN_NULL) { err += "suffix token is missing. "; } - if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) { + if (llama_vocab_fim_mid(ctx_server.vocab) == LLAMA_TOKEN_NULL) { err += "middle token is missing. "; } if (!err.empty()) { @@ -2951,6 +3969,11 @@ int main(int argc, char ** argv) { json data = json::parse(req.body); // validate input + if (data.contains("prompt") && !data.at("prompt").is_string()) { + // prompt is optional + res_error(res, format_error_response("\"prompt\" must be a string", ERROR_TYPE_INVALID_REQUEST)); + } + if (!data.contains("input_prefix")) { res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST)); } @@ -2960,9 +3983,11 @@ int main(int argc, char ** argv) { } if (data.contains("input_extra") && !data.at("input_extra").is_array()) { + // input_extra is optional res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST)); return; } + json input_extra = json_value(data, "input_extra", json::array()); for (const auto & chunk : input_extra) { // { "text": string, "filename": string } @@ -2978,72 +4003,60 @@ int main(int argc, char ** argv) { } data["input_extra"] = input_extra; // default to empty array if it's not exist - return handle_completions_generic(SERVER_TASK_INF_TYPE_INFILL, data, res); + std::string prompt = json_value(data, "prompt", std::string()); + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, false, true); + SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + data["prompt"] = format_infill( + ctx_server.vocab, + data.at("input_prefix"), + data.at("input_suffix"), + data.at("input_extra"), + ctx_server.params_base.n_batch, + ctx_server.params_base.n_predict, + ctx_server.slots[0].n_ctx, // TODO: there should be a better way + ctx_server.params_base.spm_infill, + tokenized_prompts[0] + ); + + return handle_completions_impl( + SERVER_TASK_TYPE_INFILL, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_NONE); // infill is not OAI compatible }; - // TODO: maybe merge this function with "handle_completions_generic" - const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + LOG_DBG("request: %s\n", req.body.c_str()); if (ctx_server.params_base.embedding) { res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); - - std::vector tasks = ctx_server.create_tasks_inference(data, SERVER_TASK_INF_TYPE_COMPLETION); - ctx_server.queue_results.add_waiting_tasks(tasks); - ctx_server.queue_tasks.post(tasks); - - bool stream = json_value(data, "stream", false); - const auto task_ids = server_task::get_list_id(tasks); - const auto completion_id = gen_chatcmplid(); - - if (!stream) { - ctx_server.receive_cmpl_results(task_ids, [&](const std::vector & results) { - // multitask is never support in chat completion, there is only one result - json result_oai = format_final_response_oaicompat(data, results[0].data, completion_id, /*.streaming =*/ false, verbose); - res_ok(res, result_oai); - }, [&](const json & error_data) { - res_error(res, error_data); - }); - - ctx_server.queue_results.remove_waiting_task_ids(task_ids); - } else { - const auto chunked_content_provider = [task_ids, &ctx_server, completion_id](size_t, httplib::DataSink & sink) { - ctx_server.receive_cmpl_results_stream(task_ids, [&](const server_task_result & result) -> bool { - std::vector result_array = format_partial_response_oaicompat(result.data, completion_id); - for (auto & event_data : result_array) { - if (event_data.empty()) { - continue; // skip the stop token - } - if (!server_sent_event(sink, "data", event_data)) { - return false; // connection is closed - } - } - return true; // ok - }, [&](const json & error_data) { - server_sent_event(sink, "error", error_data); - }); - static const std::string ev_done = "data: [DONE]\n\n"; - sink.write(ev_done.data(), ev_done.size()); - sink.done(); - return true; - }; + auto body = json::parse(req.body); + json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get()); - auto on_complete = [task_ids, &ctx_server] (bool) { - ctx_server.queue_results.remove_waiting_task_ids(task_ids); - }; + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_CHAT); + }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } + // same with handle_chat_completions, but without inference part + const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) { + auto body = json::parse(req.body); + json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get()); + res_ok(res, {{ "prompt", std::move(data.at("prompt")) }}); }; - const auto handle_models = [¶ms, &ctx_server](const httplib::Request &, httplib::Response & res) { + const auto handle_models = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { json models = { {"object", "list"}, {"data", { { - {"id", params.model_alias}, + {"id", params.model_alias.empty() ? params.model : params.model_alias}, {"object", "model"}, {"created", std::time(0)}, {"owned_by", "llamacpp"}, @@ -3052,7 +4065,7 @@ int main(int argc, char ** argv) { }} }; - res.set_content(models.dump(), MIMETYPE_JSON); + res_ok(res, models); }; const auto handle_tokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) { @@ -3063,7 +4076,7 @@ int main(int argc, char ** argv) { const bool add_special = json_value(body, "add_special", false); const bool with_pieces = json_value(body, "with_pieces", false); - llama_tokens tokens = tokenize_mixed(ctx_server.ctx, body.at("content"), add_special, true); + llama_tokens tokens = tokenize_mixed(ctx_server.vocab, body.at("content"), add_special, true); if (with_pieces) { for (const auto& token : tokens) { @@ -3108,42 +4121,79 @@ int main(int argc, char ** argv) { res_ok(res, data); }; - const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { + const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) { const json body = json::parse(req.body); - bool is_openai = false; - // an input prompt can be a string or a list of tokens (integer) + if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { + res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + // for the shape of input/content, see tokenize_input_prompts() json prompt; if (body.count("input") != 0) { - is_openai = true; prompt = body.at("input"); - } else if (body.count("content") != 0) { - // with "content", we only support single prompt - prompt = std::vector{body.at("content")}; + } else if (body.contains("content")) { + oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible + prompt = body.at("content"); } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); return; } + bool use_base64 = false; + if (body.count("encoding_format") != 0) { + const std::string& format = body.at("encoding_format"); + if (format == "base64") { + use_base64 = true; + } else if (format != "float") { + res_error(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true); + for (const auto & tokens : tokenized_prompts) { + // this check is necessary for models that do not add BOS token to the input + if (tokens.empty()) { + res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + // create and queue the task json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_EMBEDDING); + std::vector tasks; + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); + + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.prompt_tokens = std::move(tokenized_prompts[i]); + + // OAI-compat + task.params.oaicompat = oaicompat; + + tasks.push_back(task); + } + ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); // get the result std::unordered_set task_ids = server_task::get_list_id(tasks); - ctx_server.receive_cmpl_results(task_ids, [&](std::vector & results) { - for (const auto & res : results) { - responses.push_back(res.data); + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { + for (auto & res : results) { + GGML_ASSERT(dynamic_cast(res.get()) != nullptr); + responses.push_back(res->to_json()); } }, [&](const json & error_data) { res_error(res, error_data); error = true; - }); + }, req.is_connection_closed); ctx_server.queue_results.remove_waiting_task_ids(task_ids); } @@ -3153,12 +4203,20 @@ int main(int argc, char ** argv) { } // write JSON response - json root = is_openai - ? format_embeddings_response_oaicompat(body, responses) - : responses[0]; + json root = oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? format_embeddings_response_oaicompat(body, responses, use_base64) + : json(responses); res_ok(res, root); }; + const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE); + }; + + const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_EMBEDDING); + }; + const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) { res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED)); @@ -3176,6 +4234,11 @@ int main(int argc, char ** argv) { // return; //} + // if true, use TEI API format, otherwise use Jina API format + // Jina: https://jina.ai/reranker/ + // TEI: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/rerank + bool is_tei_format = body.contains("texts"); + json query; if (body.count("query") == 1) { query = body.at("query"); @@ -3188,40 +4251,45 @@ int main(int argc, char ** argv) { return; } - std::vector documents = json_value(body, "documents", std::vector()); + std::vector documents = json_value(body, "documents", + json_value(body, "texts", std::vector())); if (documents.empty()) { res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST)); return; } - // construct prompt object: array of ["query", "doc0", "doc1", ...] - json prompt; - prompt.push_back(query); - for (const auto & doc : documents) { - prompt.push_back(doc); - } - - LOG_DBG("rerank prompt: %s\n", prompt.dump().c_str()); + llama_tokens tokenized_query = tokenize_input_prompts(ctx_server.vocab, query, /* add_special */ false, true)[0]; // create and queue the task json responses = json::array(); bool error = false; { - std::vector tasks = ctx_server.create_tasks_inference({{"prompt", prompt}}, SERVER_TASK_INF_TYPE_RERANK); + std::vector tasks; + std::vector tokenized_docs = tokenize_input_prompts(ctx_server.vocab, documents, /* add_special */ false, true); + tasks.reserve(tokenized_docs.size()); + for (size_t i = 0; i < tokenized_docs.size(); i++) { + server_task task = server_task(SERVER_TASK_TYPE_RERANK); + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.prompt_tokens = format_rerank(ctx_server.vocab, tokenized_query, tokenized_docs[i]); + tasks.push_back(task); + } + ctx_server.queue_results.add_waiting_tasks(tasks); ctx_server.queue_tasks.post(tasks); // get the result std::unordered_set task_ids = server_task::get_list_id(tasks); - ctx_server.receive_cmpl_results(task_ids, [&](std::vector & results) { - for (const auto & res : results) { - responses.push_back(res.data); + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { + for (auto & res : results) { + GGML_ASSERT(dynamic_cast(res.get()) != nullptr); + responses.push_back(res->to_json()); } }, [&](const json & error_data) { res_error(res, error_data); error = true; - }); + }, req.is_connection_closed); } if (error) { @@ -3229,14 +4297,20 @@ int main(int argc, char ** argv) { } // write JSON response - json root = format_response_rerank(body, responses); + json root = format_response_rerank( + body, + responses, + is_tei_format, + documents); + res_ok(res, root); }; const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) { json result = json::array(); - for (size_t i = 0; i < ctx_server.loras.size(); ++i) { - auto & lora = ctx_server.loras[i]; + const auto & loras = ctx_server.params_base.lora_adapters; + for (size_t i = 0; i < loras.size(); ++i) { + auto & lora = loras[i]; result.push_back({ {"id", i}, {"path", lora.path}, @@ -3248,55 +4322,56 @@ int main(int argc, char ** argv) { }; const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) { - const std::vector body = json::parse(req.body); - int max_idx = ctx_server.loras.size(); - - // clear existing value - for (auto & lora : ctx_server.loras) { - lora.scale = 0.0f; - } - - // set value - for (auto entry : body) { - int id = entry.at("id"); - float scale = entry.at("scale"); - if (0 <= id && id < max_idx) { - ctx_server.loras[id].scale = scale; - } else { - throw std::runtime_error("invalid adapter id"); - } + const json body = json::parse(req.body); + if (!body.is_array()) { + res_error(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST)); + return; } + server_task task(SERVER_TASK_TYPE_SET_LORA); + task.id = ctx_server.queue_tasks.get_new_id(); + task.set_lora = parse_lora_request(ctx_server.params_base.lora_adapters, body); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); - server_task task; - task.type = SERVER_TASK_TYPE_SET_LORA; - const int id_task = ctx_server.queue_tasks.post(task); - ctx_server.queue_results.add_waiting_task_id(id_task); + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - server_task_result result = ctx_server.queue_results.recv(id_task); - ctx_server.queue_results.remove_waiting_task_id(id_task); + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } - res_ok(res, result.data); - res.status = 200; // HTTP OK + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); }; // // Router // - // register static assets routes - if (!params.public_path.empty()) { - // Set the base directory for serving static files - bool is_found = svr->set_mount_point("/", params.public_path); - if (!is_found) { - LOG_ERR("%s: static assets path not found: %s\n", __func__, params.public_path.c_str()); - return 1; - } + if (!params.webui) { + LOG_INF("Web UI is disabled\n"); } else { - // using embedded static files - for (const auto & it : static_files) { - const server_static_file & static_file = it.second; - svr->Get(it.first.c_str(), [&static_file](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(static_file.data), static_file.size, static_file.mime_type); + // register static assets routes + if (!params.public_path.empty()) { + // Set the base directory for serving static files + bool is_found = svr->set_mount_point("/", params.public_path); + if (!is_found) { + LOG_ERR("%s: static assets path not found: %s\n", __func__, params.public_path.c_str()); + return 1; + } + } else { + // using embedded static index.html + svr->Get("/", [](const httplib::Request & req, httplib::Response & res) { + if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) { + res.set_content("Error: gzip is not supported by this browser", "text/plain"); + } else { + res.set_header("Content-Encoding", "gzip"); + // COEP and COOP headers, required by pyodide (python interpreter) + res.set_header("Cross-Origin-Embedder-Policy", "require-corp"); + res.set_header("Cross-Origin-Opener-Policy", "same-origin"); + res.set_content(reinterpret_cast(index_html_gz), index_html_gz_len, "text/html; charset=utf-8"); + } return false; }); } @@ -3311,19 +4386,20 @@ int main(int argc, char ** argv) { svr->Get ("/v1/models", handle_models); // public endpoint (no API key check) svr->Post("/completion", handle_completions); // legacy svr->Post("/completions", handle_completions); - svr->Post("/v1/completions", handle_completions); + svr->Post("/v1/completions", handle_completions_oai); svr->Post("/chat/completions", handle_chat_completions); svr->Post("/v1/chat/completions", handle_chat_completions); svr->Post("/infill", handle_infill); svr->Post("/embedding", handle_embeddings); // legacy svr->Post("/embeddings", handle_embeddings); - svr->Post("/v1/embeddings", handle_embeddings); + svr->Post("/v1/embeddings", handle_embeddings_oai); svr->Post("/rerank", handle_rerank); svr->Post("/reranking", handle_rerank); svr->Post("/v1/rerank", handle_rerank); svr->Post("/v1/reranking", handle_rerank); svr->Post("/tokenize", handle_tokenize); svr->Post("/detokenize", handle_detokenize); + svr->Post("/apply-template", handle_apply_template); // LoRA adapters hotswap svr->Get ("/lora-adapters", handle_lora_adapters_list); svr->Post("/lora-adapters", handle_lora_adapters_apply); @@ -3343,20 +4419,29 @@ int main(int argc, char ** argv) { // clean up function, to be called before exit auto clean_up = [&svr]() { + SRV_INF("%s: cleaning up before exit...\n", __func__); svr->stop(); llama_backend_free(); }; - // bind HTTP listen port, run the HTTP server in a thread - if (!svr->bind_to_port(params.hostname, params.port)) { - //LOG_ERROR("couldn't bind HTTP server socket", { - // {"hostname", params.hostname}, - // {"port", params.port}, - //}); + // bind HTTP listen port + bool was_bound = false; + if (params.port == 0) { + int bound_port = svr->bind_to_any_port(params.hostname); + if ((was_bound = (bound_port >= 0))) { + params.port = bound_port; + } + } else { + was_bound = svr->bind_to_port(params.hostname, params.port); + } + + if (!was_bound) { LOG_ERR("%s: couldn't bind HTTP server socket, hostname: %s, port: %d\n", __func__, params.hostname.c_str(), params.port); clean_up(); return 1; } + + // run the HTTP server in a thread std::thread t([&]() { svr->listen_after_bind(); }); svr->wait_until_ready(); @@ -3367,7 +4452,7 @@ int main(int argc, char ** argv) { if (!ctx_server.load_model(params)) { clean_up(); - t.join(); + // t.join(); // FIXME: see below LOG_ERR("%s: exiting due to model loading error\n", __func__); return 1; } @@ -3377,31 +4462,24 @@ int main(int argc, char ** argv) { LOG_INF("%s: model loaded\n", __func__); - // if a custom chat template is not supplied, we will use the one that comes with the model (if any) - if (params.chat_template.empty()) { - if (!ctx_server.validate_model_chat_template()) { - LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__); - params.chat_template = "chatml"; - } - } - // print sample chat example to make it clear which template is used - LOG_INF("%s: chat template, built_in: %d, chat_example: '%s'\n", __func__, params.chat_template.empty(), common_chat_format_example(ctx_server.model, params.chat_template).c_str()); + LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__, + common_chat_templates_source(ctx_server.chat_templates.get()), + common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params_base.use_jinja).c_str()); - ctx_server.queue_tasks.on_new_task(std::bind( - &server_context::process_single_task, &ctx_server, std::placeholders::_1)); + ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) { + ctx_server.process_single_task(task); + }); - ctx_server.queue_tasks.on_update_slots(std::bind( - &server_context::update_slots, &ctx_server)); + ctx_server.queue_tasks.on_update_slots([&ctx_server]() { + ctx_server.update_slots(); + }); shutdown_handler = [&](int) { + // this will unblock start_loop() ctx_server.queue_tasks.terminate(); }; - LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); - - ctx_server.queue_tasks.start_loop(); - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = signal_handler; @@ -3416,8 +4494,13 @@ int main(int argc, char ** argv) { SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif + LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); + + // this call blocks the main thread until queue_tasks.terminate() is called + ctx_server.queue_tasks.start_loop(); + clean_up(); - t.join(); + // t.join(); // FIXME: http thread may stuck if there is an on-going request. we don't need to care about this for now as the HTTP connection will already be closed at this point, but it's better to fix this return 0; } diff --git a/examples/server/tests/.gitignore b/examples/server/tests/.gitignore index 1d17dae13b53a..90ee7fe6d971a 100644 --- a/examples/server/tests/.gitignore +++ b/examples/server/tests/.gitignore @@ -1 +1,2 @@ .venv +tmp diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 10f22c4471ea7..12816bfa8705b 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,19 +1,9 @@ # Server tests -Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) -and [behave](https://behave.readthedocs.io/en/latest/): - -* [issues.feature](./features/issues.feature) Pending issues scenario -* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests -* [security.feature](./features/security.feature) Security, CORS and API Key -* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... +Python based server tests scenario using [pytest](https://docs.pytest.org/en/stable/). Tests target GitHub workflows job runners with 4 vCPU. -Requests are -using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) -based http client. - Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. @@ -39,26 +29,38 @@ It's possible to override some scenario steps values with environment variables: |--------------------------|------------------------------------------------------------------------------------------------| | `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | | `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/llama-server` | -| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | +| `DEBUG` | to enable steps and server verbose mode `--verbose` | | `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | +| `LLAMA_CACHE` | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this | + +To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed): + +```shell +SLOW_TESTS=1 ./tests.sh +``` -### Run @bug, @wip or @wrong_usage annotated scenario +To run with stdout/stderr display in real time (verbose output, but useful for debugging): -Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. +```shell +DEBUG=1 ./tests.sh -s -v -x +``` + +To run all the tests in a file: -- `@bug` annotation aims to link a scenario with a GitHub issue. -- `@wrong_usage` are meant to show user issue that are actually an expected behavior -- `@wip` to focus on a scenario working in progress -- `@slow` heavy test, disabled by default +```shell +./tests.sh unit/test_chat_completion.py -v -x +``` -To run a scenario annotated with `@bug`, start: +To run a single test: ```shell -DEBUG=ON ./tests.sh --no-skipped --tags bug --stop +./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req ``` -After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. +Hint: You can compile and run test in single command, useful for local developement: ```shell -./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile" +cmake --build build -j --target llama-server && ./examples/server/tests/tests.sh ``` + +To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html) diff --git a/examples/server/tests/conftest.py b/examples/server/tests/conftest.py new file mode 100644 index 0000000000000..017d1bb841efd --- /dev/null +++ b/examples/server/tests/conftest.py @@ -0,0 +1,15 @@ +import pytest +from utils import * + + +# ref: https://stackoverflow.com/questions/22627659/run-code-before-and-after-each-test-in-py-test +@pytest.fixture(autouse=True) +def stop_server_after_each_test(): + # do nothing before each test + yield + # stop all servers after each test + instances = set( + server_instances + ) # copy the set to prevent 'Set changed size during iteration' + for server in instances: + server.stop() diff --git a/examples/server/tests/features/ctx_shift.feature b/examples/server/tests/features/ctx_shift.feature deleted file mode 100644 index ae6c6b01b0221..0000000000000 --- a/examples/server/tests/features/ctx_shift.feature +++ /dev/null @@ -1,66 +0,0 @@ -@llama.cpp -@ctx_shift -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a model file test-model.gguf - And a model alias tinyllama-2 - And BOS token is 1 - And 42 as server seed - And 256 KV cache size - And 32 as batch size - And 2 slots - - # the prompt is 301 tokens - # the slot context is 256/2 = 128 tokens - # the prompt is truncated to keep the last 109 tokens - # 64 tokens are generated thanks to shifting the context when it gets full - Scenario: Inference with context shift - And 64 server max tokens to predict - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl - And the completion is truncated - And 109 prompt tokens are processed - - Scenario Outline: Inference without context shift - And server max tokens to predict - And disable context shifting - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Hi how are you - """ - And a completion request with no api error - Then tokens are predicted matching twind|Anna - And the completion is truncated - And 8 prompt tokens are processed - Examples: - | n_predict | n_token_output | truncated | - | 64 | 64 | not | - | -1 | 120 | | - - Scenario: Inference without context shift (expected error: prompt too long) - And disable context shifting - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with 400 api error - diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature deleted file mode 100644 index f4fe2ee4335ff..0000000000000 --- a/examples/server/tests/features/embeddings.feature +++ /dev/null @@ -1,113 +0,0 @@ -@llama.cpp -@embeddings -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf - And a model file bert-bge-small.gguf - And a model alias bert-bge-small - And 42 as server seed - And 2 slots - # the bert-bge-small model has context size of 512 - # since the generated prompts are as big as the batch size, we need to set the batch size to <= 512 - # ref: https://huggingface.co/BAAI/bge-small-en-v1.5/blob/5c38ec7c405ec4b44b94cc5a9bb96e735b38267a/config.json#L20 - And 128 as batch size - And 128 as ubatch size - And 512 KV cache size - And enable embeddings endpoint - Then the server is starting - Then the server is healthy - - Scenario: Embedding - When embeddings are computed for: - """ - What is the capital of Bulgaria ? - """ - Then embeddings are generated - - Scenario: Embedding (error: prompt too long) - When embeddings are computed for: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And embeddings request with 500 api error - - Scenario: OAI Embeddings compatibility - Given a model bert-bge-small - When an OAI compatible embeddings computation request for: - """ - What is the capital of Spain ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility with multiple inputs - Given a model bert-bge-small - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - When an OAI compatible embeddings computation request for multiple inputs - Then embeddings are generated - - Scenario: Multi users embeddings - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - Given concurrent embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: Multi users OAI compatibility embeddings - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - And a prompt: - """ - What is the biggest US city ? - """ - And a prompt: - """ - What is the capital of Bulgaria ? - """ - And a model bert-bge-small - Given concurrent OAI embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: All embeddings should be the same - Given 10 fixed prompts - And a model bert-bge-small - Given concurrent OAI embedding requests - Then all embeddings are the same diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py deleted file mode 100644 index e7845dc2f51fc..0000000000000 --- a/examples/server/tests/features/environment.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import signal -import socket -import sys -import time -import traceback -from contextlib import closing -from subprocess import TimeoutExpired - - -def before_scenario(context, scenario): - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' - if context.debug: - print("DEBUG=ON") - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") - port = 8080 - if 'PORT' in os.environ: - port = int(os.environ['PORT']) - if is_server_listening("localhost", port): - assert False, "Server already started" - - -def after_scenario(context, scenario): - try: - if 'server_process' not in context or context.server_process is None: - return - if scenario.status == "failed": - if 'GITHUB_ACTIONS' in os.environ: - print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n") - if os.path.isfile('llama.log'): - with closing(open('llama.log', 'r')) as f: - for line in f: - print(line) - if not is_server_listening(context.server_fqdn, context.server_port): - print("\x1b[33;101mERROR: Server stopped listening\x1b[0m") - - if context.server_process.poll() is not None: - assert False, f"Server not running pid={context.server_process.pid} ..." - - server_graceful_shutdown(context) # SIGINT - - try: - context.server_process.wait(0.5) - except TimeoutExpired: - print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...") - context.server_process.kill() # SIGKILL - context.server_process.wait() - - while is_server_listening(context.server_fqdn, context.server_port): - time.sleep(0.1) - except Exception: - print("ignoring error in after_scenario:") - traceback.print_exc(file=sys.stdout) - - -def server_graceful_shutdown(context): - print(f"shutting down server pid={context.server_process.pid} ...") - if os.name == 'nt': - interrupt = signal.CTRL_C_EVENT - else: - interrupt = signal.SIGINT - context.server_process.send_signal(interrupt) - - -def is_server_listening(server_fqdn, server_port): - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: - result = sock.connect_ex((server_fqdn, server_port)) - _is_server_listening = result == 0 - if _is_server_listening: - print(f"server is listening on {server_fqdn}:{server_port}...") - return _is_server_listening diff --git a/examples/server/tests/features/infill.feature b/examples/server/tests/features/infill.feature deleted file mode 100644 index a0bbfef77707b..0000000000000 --- a/examples/server/tests/features/infill.feature +++ /dev/null @@ -1,36 +0,0 @@ -@llama.cpp -@infill -Feature: llama.cpp server - - # The current model is made by adding FIM tokens to the existing stories260K - # We may want to use a better model in the future, maybe something like SmolLM 360M - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models - And a model file test-model-infill.gguf - And a model alias tinyllama-infill - And 42 as server seed - And 1024 as batch size - And 1024 as ubatch size - And 2048 KV cache size - And 64 max tokens to predict - And 0.0 temperature - Then the server is starting - Then the server is healthy - - Scenario: Infill without input_extra - Given a prompt "Complete this" - And an infill input extra none none - And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" - And an infill input suffix "}\n" - And an infill request with no api error - Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird - - Scenario: Infill with input_extra - Given a prompt "Complete this" - And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n" - And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" - And an infill input suffix "}\n" - And an infill request with no api error - Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room" diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature deleted file mode 100644 index 7b13e44cad395..0000000000000 --- a/examples/server/tests/features/issues.feature +++ /dev/null @@ -1,5 +0,0 @@ -# List of ongoing issues -# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug -@bug -Feature: Issues - # No confirmed issue at the moment diff --git a/examples/server/tests/features/lora.feature b/examples/server/tests/features/lora.feature deleted file mode 100644 index 7b85988ac6e87..0000000000000 --- a/examples/server/tests/features/lora.feature +++ /dev/null @@ -1,36 +0,0 @@ -@llama.cpp -@lora -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/stories15M_MOE-F16.gguf - And a model file stories15M_MOE-F16.gguf - And a model alias stories15M_MOE - And a lora adapter file from https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf - And 42 as server seed - And 1024 as batch size - And 1024 as ubatch size - And 2048 KV cache size - And 64 max tokens to predict - And 0.0 temperature - Then the server is starting - Then the server is healthy - - Scenario: Completion LoRA disabled - Given switch off lora adapter 0 - Given a prompt: - """ - Look in thy glass - """ - And a completion request with no api error - Then 64 tokens are predicted matching little|girl|three|years|old - - Scenario: Completion LoRA enabled - Given switch on lora adapter 0 - Given a prompt: - """ - Look in thy glass - """ - And a completion request with no api error - Then 64 tokens are predicted matching eye|love|glass|sun diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature deleted file mode 100644 index 423d0f1d42f55..0000000000000 --- a/examples/server/tests/features/parallel.feature +++ /dev/null @@ -1,131 +0,0 @@ -@llama.cpp -@parallel -Feature: Parallel - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models - And a model file test-model-00001-of-00003.gguf - And 42 as server seed - And 128 as batch size - And 256 KV cache size - And 2 slots - And continuous batching - Then the server is starting - Then the server is healthy - - Scenario Outline: Multi users completion - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And max tokens to predict - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all prompts are predicted with tokens - Examples: - | n_predict | - | 128 | - - Scenario Outline: Multi users OAI completions compatibility - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario Outline: Multi users OAI completions compatibility no v1 - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests no v1 - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario Outline: Multi users with number of prompts exceeding number of slots - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And a prompt: - """ - What is LLM? - """ - And a prompt: - """ - The sky is blue and I love it. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - And 128 max tokens to predict - Given concurrent completion requests - Then the server is busy - Then the server is idle - Then all prompts are predicted diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature deleted file mode 100644 index ff0a82cc46581..0000000000000 --- a/examples/server/tests/features/passkey.feature +++ /dev/null @@ -1,56 +0,0 @@ -# run with: ./tests.sh --no-skipped --tags passkey -@passkey -@slow -Feature: Passkey / Self-extend with context shift - - Background: Server startup - Given a server listening on localhost:8080 - - # Generates a long text of junk and inserts a secret passkey number inside it. - # Then we query the LLM for the secret passkey. - # see #3856 and #4810 - Scenario Outline: Passkey - Given a model file from HF repo - And as batch size - And as number of junk - And server max tokens to predict - And 42 as seed - And 0.0 temperature - And KV cache size - And 1 slots - And group attention factor to extend context size through self-extend - And group attention width to extend context size through self-extend - # Can be override with N_GPU_LAYERS - And GPU offloaded layers - Then the server is starting - # Higher timeout because the model may need to be downloaded from the internet - Then the server is healthy with timeout 120 seconds - Given available models - Then model 0 is trained on tokens context - Given a prefix prompt: - """ - here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there. - """ - And a passkey prompt template: - """ - The pass key is Remember it. is the pass key. - """ - And a junk suffix prompt: - """ - The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. - """ - And a suffix prompt: - """ - What is the pass key? The pass key is - """ - Given a "" passkey challenge prompt with the passkey inserted every junk - And a completion request with no api error - Then tokens are predicted matching - - Examples: - | hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content | - | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 | - | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b | - #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | - #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 - # 987 | diff --git a/examples/server/tests/features/rerank.feature b/examples/server/tests/features/rerank.feature deleted file mode 100644 index c36cc8e215fa6..0000000000000 --- a/examples/server/tests/features/rerank.feature +++ /dev/null @@ -1,42 +0,0 @@ -@llama.cpp -@rerank -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/models/resolve/main/jina-reranker-v1-tiny-en/ggml-model-f16.gguf - And a model file jina-reranker-v1-tiny-en.gguf - And a model alias jina-reranker-v1-tiny-en - And 42 as server seed - And 2 slots - And 512 as batch size - And 512 as ubatch size - And 512 KV cache size - And enable reranking endpoint - Then the server is starting - Then the server is healthy - - Scenario: Rerank - Given a rerank query: - """ - Machine learning is - """ - And a rerank document: - """ - A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines. - """ - And a rerank document: - """ - Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. - """ - And a rerank document: - """ - Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. - """ - And a rerank document: - """ - Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine. - """ - When reranking request - Then reranking results are returned - Then reranking highest score is index 2 and lowest score is index 3 diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature deleted file mode 100644 index e8e1b54147b05..0000000000000 --- a/examples/server/tests/features/results.feature +++ /dev/null @@ -1,118 +0,0 @@ -@llama.cpp -@results -Feature: Results - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models - And a model file test-model-00001-of-00003.gguf - And 128 as batch size - And 1024 KV cache size - And 128 max tokens to predict - And continuous batching - - Scenario Outline: consistent results with same seed - Given slots - And 1.0 temperature - Then the server is starting - Then the server is healthy - - Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 - - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all predictions are equal - Examples: - | n_slots | - | 1 | - # FIXME: unified KV cache nondeterminism - # | 2 | - - Scenario Outline: different results with different seed - Given slots - And 1.0 temperature - Then the server is starting - Then the server is healthy - - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45 - - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all predictions are different - Examples: - | n_slots | - | 1 | - | 2 | - - Scenario Outline: consistent results with same seed and varying batch size - Given 4 slots - And temperature - # And 0 as draft - Then the server is starting - Then the server is healthy - - Given 1 prompts "Write a very long story about AI." with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Given prompts "Write a very long story about AI." with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Then all predictions are equal - Examples: - | n_parallel | temp | - | 1 | 0.0 | - | 1 | 1.0 | - # FIXME: unified KV cache nondeterminism - # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 - # and https://github.com/ggerganov/llama.cpp/pull/7347 . - # | 2 | 0.0 | - # | 4 | 0.0 | - # | 2 | 1.0 | - # | 4 | 1.0 | - - Scenario Outline: consistent token probs with same seed and prompt - Given slots - And KV cache size - And 1.0 temperature - And max tokens to predict - Then the server is starting - Then the server is healthy - - Given 1 prompts "The meaning of life is" with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Given prompts "The meaning of life is" with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Then all token probabilities are equal - Examples: - | n_slots | n_kv | n_predict | n_parallel | - | 4 | 1024 | 1 | 1 | - # FIXME: unified KV cache nondeterminism - # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 - # and https://github.com/ggerganov/llama.cpp/pull/7347 . - # | 4 | 1024 | 1 | 4 | - # | 4 | 1024 | 100 | 1 | - # This test still fails even the above patches; the first token probabilities are already different. - # | 4 | 1024 | 100 | 4 | diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature deleted file mode 100644 index ef30007c3eddb..0000000000000 --- a/examples/server/tests/features/security.feature +++ /dev/null @@ -1,68 +0,0 @@ -@llama.cpp -@security -Feature: Security - - Background: Server startup with an api key defined - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a server api key THIS_IS_THE_KEY - Then the server is starting - Then the server is healthy - - Scenario Outline: Completion with some user api key - Given a prompt test - And a user api key - And 4 max tokens to predict - And a completion request with api error - - Examples: Prompts - | api_key | api_error | - | THIS_IS_THE_KEY | no | - | THIS_IS_THE_KEY | no | - | hackeme | raised | - | | raised | - - Scenario Outline: OAI Compatibility - Given a system prompt test - And a user prompt test - And a model test - And 2 max tokens to predict - And streaming is disabled - And a user api key - Given an OAI compatible chat completions request with api error - - Examples: Prompts - | api_key | api_error | - | THIS_IS_THE_KEY | no | - | THIS_IS_THE_KEY | no | - | hackme | raised | - - Scenario Outline: OAI Compatibility (invalid response formats) - Given a system prompt test - And a user prompt test - And a response format - And a model test - And 2 max tokens to predict - And streaming is disabled - Given an OAI compatible chat completions request with raised api error - - Examples: Prompts - | response_format | - | {"type": "sound"} | - | {"type": "json_object", "schema": 123} | - | {"type": "json_object", "schema": {"type": 123}} | - | {"type": "json_object", "schema": {"type": "hiccup"}} | - - - Scenario Outline: CORS Options - Given a user api key THIS_IS_THE_KEY - When an OPTIONS request is sent from - Then CORS header is set to - - Examples: Headers - | origin | cors_header | cors_header_value | - | localhost | Access-Control-Allow-Origin | localhost | - | web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr | - | origin | Access-Control-Allow-Credentials | true | - | web.mydomain.fr | Access-Control-Allow-Methods | GET, POST | - | web.mydomain.fr | Access-Control-Allow-Headers | * | diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature deleted file mode 100644 index 15e24c624af37..0000000000000 --- a/examples/server/tests/features/server.feature +++ /dev/null @@ -1,120 +0,0 @@ -@llama.cpp -@server -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a model file test-model.gguf - And a model alias tinyllama-2 - And BOS token is 1 - And 42 as server seed - # KV Cache corresponds to the total amount of tokens - # that can be stored across all independent sequences: #4130 - # see --ctx-size and #5568 - And 256 KV cache size - And 32 as batch size - And 2 slots - And 64 server max tokens to predict - And prometheus compatible metrics exposed - Then the server is starting - Then the server is healthy - - Scenario: Health - Then the server is ready - And all slots are idle - - - Scenario Outline: Completion - Given a prompt - And max tokens to predict - And a completion request with no api error - Then tokens are predicted matching - And the completion is truncated - And prompt tokens are processed - And prometheus metrics are exposed - And metric llamacpp:tokens_predicted is - - Examples: Prompts - | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | - | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | - | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not | - - Scenario: Completion prompt truncated - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl - And the completion is truncated - And 109 prompt tokens are processed - - - Scenario Outline: OAI Compatibility - Given a model - And a system prompt - And a user prompt - And max tokens to predict - And streaming is - Given an OAI compatible chat completions request with no api error - Then tokens are predicted matching - And prompt tokens are processed - And the completion is truncated - - Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | - | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | - - - Scenario Outline: OAI Compatibility w/ response format - Given a model test - And a system prompt test - And a user prompt test - And a response format - And 10 max tokens to predict - Given an OAI compatible chat completions request with no api error - Then tokens are predicted matching - - Examples: Prompts - | response_format | n_predicted | re_content | - | {"type": "json_object", "schema": {"const": "42"}} | 6 | "42" | - | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | - | {"type": "json_object"} | 10 | \{ " Jacky. | - - - Scenario: Tokenize / Detokenize - When tokenizing: - """ - What is the capital of France ? - """ - Then tokens can be detokenized - And tokens do not begin with BOS - - Scenario: Tokenize w/ BOS - Given adding special tokens - When tokenizing: - """ - What is the capital of Germany? - """ - Then tokens begin with BOS - Given first token is removed - Then tokens can be detokenized - - Scenario: Tokenize with pieces - When tokenizing with pieces: - """ - What is the capital of Germany? - 媽 - """ - Then tokens are given with pieces - - Scenario: Models available - Given available models - Then 1 models are supported - Then model 0 is identified by tinyllama-2 - Then model 0 is trained on 128 tokens context diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature deleted file mode 100644 index 1c281c0741afe..0000000000000 --- a/examples/server/tests/features/slotsave.feature +++ /dev/null @@ -1,58 +0,0 @@ -@llama.cpp -@slotsave -Feature: llama.cpp server slot management - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And prompt caching is enabled - And 2 slots - And . as slot save path - And 2048 KV cache size - And 42 as server seed - And 24 max tokens to predict - Then the server is starting - Then the server is healthy - - Scenario: Save and Restore Slot - # First prompt in slot 1 should be fully processed - Given a user prompt "What is the capital of France?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed - When the slot 1 is saved with filename "slot1.bin" - Then the server responds with status code 200 - # Since we have cache, this should only process the last tokens - Given a user prompt "What is the capital of Germany?" - And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) - And 7 prompt tokens are processed - # Loading the original cache into slot 0, - # we should only be processing 1 prompt token and get the same output - When the slot 0 is restored with filename "slot1.bin" - Then the server responds with status code 200 - Given a user prompt "What is the capital of France?" - And using slot id 0 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 1 prompt tokens are processed - # For verification that slot 1 was not corrupted during slot 0 load, same thing - Given a user prompt "What is the capital of Germany?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) - And 1 prompt tokens are processed - - Scenario: Erase Slot - Given a user prompt "What is the capital of France?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed - When the slot 1 is erased - Then the server responds with status code 200 - Given a user prompt "What is the capital of France?" - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py deleted file mode 100644 index 687b163f487b6..0000000000000 --- a/examples/server/tests/features/steps/steps.py +++ /dev/null @@ -1,1518 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import asyncio -import json -import os -import re -import socket -import subprocess -import sys -import threading -import time -import requests -from collections.abc import Sequence -from contextlib import closing -from re import RegexFlag -from typing import Any, Literal, cast - -import aiohttp -import numpy as np -import openai -from openai.types.chat import ChatCompletionChunk -from behave import step # pyright: ignore[reportAttributeAccessIssue] -from behave.api.async_step import async_run_until_complete -from prometheus_client import parser - -# pyright: reportRedeclaration=false - -DEFAULT_TIMEOUT_SECONDS = aiohttp.ClientTimeout(total=600) - -@step("a server listening on {server_fqdn}:{server_port}") -def step_server_config(context, server_fqdn: str, server_port: str): - context.server_fqdn = server_fqdn - context.server_port = int(server_port) - context.n_threads = None - context.n_gpu_layer = None - if 'PORT' in os.environ: - context.server_port = int(os.environ['PORT']) - print(f"$PORT set, overriding server port with to {context.server_port}") - if 'FQDN' in os.environ: - context.server_fqdn = os.environ['FQDN'] - print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}") - if 'N_GPU_LAYERS' in os.environ: - context.n_gpu_layer = int(os.environ['N_GPU_LAYERS']) - print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}") - - context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - - context.model_alias = None - context.model_file = None - context.model_hf_repo = None - context.model_hf_file = None - context.model_url = None - context.n_batch = None - context.n_ubatch = None - context.n_ctx = None - context.n_ga = None - context.n_ga_w = None - context.n_predict = None - context.n_prompts = 0 - context.n_server_predict = None - context.slot_save_path = None - context.id_slot = None - context.cache_prompt = None - context.n_slots = None - context.prompt_prefix = None - context.prompt_suffix = None - context.server_api_key = None - context.server_continuous_batching = False - context.server_embeddings = False - context.server_reranking = False - context.server_metrics = False - context.server_process = None - context.seed = None - context.draft = None - context.server_seed = None - context.user_api_key = None - context.response_format = None - context.temperature = None - context.lora_file = None - context.disable_ctx_shift = False - - # infill - context.infill_input_extra = None - context.infill_input_suffix = '' - context.infill_input_prefix = '' - - context.tasks_result = [] - context.concurrent_tasks = [] - context.prompts = [] - - context.reranking_query = None - context.reranking_documents = [] - context.reranking_results = None - - -@step('a model file {hf_file} from HF repo {hf_repo}') -def step_download_hf_model(context, hf_file: str, hf_repo: str): - context.model_hf_repo = hf_repo - context.model_hf_file = hf_file - context.model_file = os.path.basename(hf_file) - -@step('a lora adapter file from {lora_file_url}') -def step_download_lora_file(context, lora_file_url: str): - file_name = lora_file_url.split('/').pop() - context.lora_file = f'../../../{file_name}' - with open(context.lora_file, 'wb') as f: - f.write(requests.get(lora_file_url).content) - -@step('a model file {model_file}') -def step_model_file(context, model_file: str): - context.model_file = model_file - - -@step('a model url {model_url}') -def step_model_url(context, model_url: str): - context.model_url = model_url - - -@step('a model alias {model_alias}') -def step_model_alias(context, model_alias: str): - context.model_alias = model_alias - - -@step('{seed:d} as server seed') -def step_seed(context, seed: int): - context.server_seed = seed - - -@step('{ngl:d} GPU offloaded layers') -def step_n_gpu_layer(context, ngl: int): - if 'N_GPU_LAYERS' in os.environ: - new_ngl = int(os.environ['N_GPU_LAYERS']) - if context.debug: - print(f"-ngl upgraded from {ngl} to {new_ngl}") - ngl = new_ngl - context.n_gpu_layer = ngl - - -@step('{n_threads:d} threads') -def step_n_threads(context, n_threads: int): - context.n_thread = n_threads - - -@step('{draft:d} as draft') -def step_draft(context, draft: int): - context.draft = draft - - -@step('{n_ctx:d} KV cache size') -def step_n_ctx(context, n_ctx: int): - context.n_ctx = n_ctx - - -@step('{n_slots:d} slots') -def step_n_slots(context, n_slots: int): - context.n_slots = n_slots - - -@step('{n_predict:d} server max tokens to predict') -def step_server_n_predict(context, n_predict: int): - context.n_server_predict = n_predict if n_predict > 0 else None - - -@step('{slot_save_path} as slot save path') -def step_slot_save_path(context, slot_save_path: str): - context.slot_save_path = slot_save_path - - -@step('using slot id {id_slot:d}') -def step_id_slot(context, id_slot: int): - context.id_slot = id_slot - - -@step('prompt caching is enabled') -def step_enable_prompt_cache(context): - context.cache_prompt = True - - -@step('continuous batching') -def step_server_continuous_batching(context): - context.server_continuous_batching = True - - -@step('enable embeddings endpoint') -def step_server_embeddings(context): - context.server_embeddings = True - -@step('enable reranking endpoint') -def step_server_reranking(context): - context.server_reranking = True - -@step('prometheus compatible metrics exposed') -def step_server_metrics(context): - context.server_metrics = True - -@step('disable context shifting') -def step_server_disable_ctx_shift(context): - context.disable_ctx_shift = True - -@step("the server is starting") -def step_start_server(context): - start_server_background(context) - attempts = 0 - max_attempts = 20 - if 'GITHUB_ACTIONS' in os.environ: - max_attempts *= 2 - - addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM) - family, typ, proto, _, sockaddr = addrs[0] - - while True: - with closing(socket.socket(family, typ, proto)) as sock: - result = sock.connect_ex(sockaddr) - if result == 0: - print("\x1b[33;46mserver started!\x1b[0m") - return - attempts += 1 - if attempts > max_attempts: - assert False, "server not started" - print(f"waiting for server to start, connect error code = {result}...") - time.sleep(0.1) - - -async def wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int): - match expecting_status: - case 'healthy': - await wait_for_slots_status(context, context.base_url, 200, - timeout=timeout) - - case 'ready' | 'idle': - await wait_for_slots_status(context, context.base_url, 200, - timeout=timeout, - params={'fail_on_no_slot': 1}, - slots_idle=context.n_slots, - slots_processing=0) - case 'busy': - await wait_for_slots_status(context, context.base_url, 503, - params={'fail_on_no_slot': 1}, - slots_idle=0, - slots_processing=context.n_slots) - case _: - assert False, "unknown status" - - -@step("the server is {expecting_status} with timeout {timeout:d} seconds") -@async_run_until_complete -async def step_wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int): - await wait_for_server_status_with_timeout(context, expecting_status, timeout) - - -@step("the server is {expecting_status}") -@async_run_until_complete -async def step_wait_for_server_status(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str): - await wait_for_server_status_with_timeout(context, expecting_status, 30) - - -@step('all slots are {expected_slot_status_string}') -@async_run_until_complete -async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str): - match expected_slot_status_string: - case 'idle': - expected_slot_status = False - case 'busy': - expected_slot_status = True - case _: - assert False, "unknown status" - - expected_slots = [{'id': slot_id, 'is_processing': expected_slot_status} - for slot_id in range(context.n_slots)] - await request_slots_status(context, expected_slots) - - -@step('a completion request with {api_error} api error') -@async_run_until_complete -async def step_request_completion(context, api_error: Literal['raised'] | str): - expect_api_error = api_error == 'raised' or api_error != 'no' - seeds = await completions_seed(context, num_seeds=1) - completion = await request_completion(context.prompts.pop(), - seeds[0] if seeds is not None else seeds, - context.base_url, - debug=context.debug, - n_predict=context.n_predict, - cache_prompt=context.cache_prompt, - id_slot=context.id_slot, - expect_api_error=expect_api_error, - user_api_key=context.user_api_key, - temperature=context.temperature) - context.tasks_result.append(completion) - if context.debug: - print(f"Completion response: {completion}") - if api_error == 'raised': - assert completion == 401, f"completion must be an 401 status code: {completion}" - elif api_error.isdigit(): - api_error_code = int(api_error) - assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}" - - -@step('an infill request with {api_error} api error') -@async_run_until_complete -async def step_request_completion(context, api_error: Literal['raised'] | str): - if api_error != 'no': - raise ValueError(f'api_error={api_error} is not yet implemented') - payload = { - "prompt": context.prompts[0], - "input_suffix": context.infill_input_suffix, - "input_prefix": context.infill_input_prefix, - "n_predict": context.n_predict, - "seed": context.seed, - "temperature": context.temperature, - } - if context.infill_input_extra is not None: - payload['input_extra'] = context.infill_input_extra - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/infill', - json=payload) as response: - assert response.status == 200 - context.tasks_result = [await response.json()] - - -@step('{predicted_n:d} tokens are predicted matching {re_content}') -def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - context.completion = context.tasks_result.pop() - assert_n_tokens_predicted(context.completion, predicted_n, re_content) - - -@step('{predicted_n:d} tokens are predicted') -def step_n_tokens_predicted(context, predicted_n): - context.completion = context.tasks_result.pop() - assert_n_tokens_predicted(context.completion, predicted_n) - - -@step('all predictions are equal') -@async_run_until_complete -async def step_predictions_equal(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_predictions_equal(context.tasks_result) - context.tasks_result = [] - - -@step('all predictions are different') -@async_run_until_complete -async def step_predictions_different(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_predictions_different(context.tasks_result) - context.tasks_result = [] - - -@step('all token probabilities are equal') -@async_run_until_complete -async def step_token_probabilities_equal(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_token_probabilities_equal(context.tasks_result) - context.tasks_result = [] - - -@step('the completion is truncated') -def step_assert_completion_truncated(context): - step_assert_completion_truncated(context, '') - - -@step('the completion is {truncated} truncated') -def step_assert_completion_truncated(context, truncated): - truncated = truncated != "not" - assert context.completion['truncated'] == truncated, f'{context.completion}' - - -@step('{n_prompt:d} prompt tokens are processed') -def step_impl(context, n_prompt): - assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}" - - -@step('a user prompt {user_prompt}') -def step_user_prompt(context, user_prompt): - context.prompts.append(user_prompt) - context.n_prompts = len(context.prompts) - - -@step('a system prompt {system_prompt}') -def step_system_prompt(context, system_prompt): - context.system_prompt = system_prompt - - -@step('a model {model}') -def step_model(context, model): - context.model = model - - -@step('{max_tokens:d} max tokens to predict') -def step_max_tokens(context, max_tokens): - context.n_predict = max_tokens - - -@step('a response format {response_format}') -def step_response_format(context, response_format): - context.response_format = json.loads(response_format) - - -@step('{temperature:f} temperature') -def step_temperature(context, temperature): - context.temperature = temperature - - -@step('streaming is {enable_streaming}') -def step_streaming(context, enable_streaming): - context.enable_streaming = enable_streaming == 'enabled' - - -@step('a user api key {user_api_key}') -def step_user_api_key(context, user_api_key): - context.user_api_key = user_api_key - - -@step('no user api key') -def step_no_user_api_key(context): - context.user_api_key = None - - -@step('a user api key ') -def step_no_user_api_key_space(context): - context.user_api_key = None - - -@step('a server api key {server_api_key}') -def step_server_api_key(context, server_api_key): - context.server_api_key = server_api_key - - -@step('{n_junk:d} as number of junk') -def step_n_junk(context, n_junk): - context.n_junk = n_junk - - -@step('{n_batch:d} as batch size') -def step_n_batch(context, n_batch): - context.n_batch = n_batch - - -@step('{n_ubatch:d} as ubatch size') -def step_n_ubatch(context, n_ubatch): - context.n_ubatch = n_ubatch - - -@step('{seed:d} as seed') -def step_seed(context, seed): - if context.seed is None: - context.seed = [seed] - else: - context.seed.append(seed) - - -@step('BOS token is {bos:d}') -def step_bos_token(context, bos): - context.bos = bos - - -@step('a prefix prompt') -def step_prompt_prefix(context): - context.prompt_prefix = context_text(context) - - -@step('a junk suffix prompt') -def step_prompt_junk_suffix(context): - context.prompt_junk_suffix = context_text(context) - - -@step('a suffix prompt') -def step_prompt_suffix(context): - context.prompt_suffix = context_text(context) - - -@step('{n_ga:d} group attention factor' - ' to extend context size through self-extend') -def step_impl(context, n_ga): - context.n_ga = n_ga - - -@step('{n_ga_w:d} group attention width to extend context size through self-extend') -def step_impl(context, n_ga_w): - context.n_ga_w = n_ga_w - - -@step('a passkey prompt template') -def step_prompt_passkey(context): - context.prompt_passkey = context_text(context) - -@step('a rerank query') -def step_set_rerank_query(context): - context.reranking_query = context_text(context) - context.reranking_documents = [] - -@step('a rerank document') -def step_set_rerank_document(context): - context.reranking_documents.append(context_text(context)) - -@step('{n_prompts:d} fixed prompts') -def step_fixed_prompts(context, n_prompts): - context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)]) - context.n_prompts = n_prompts - - -@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') -def step_prompt_passkey(context, passkey, i_pos): - prompt = "" - for i in range(context.n_junk): - if i % context.n_junk == i_pos: - prompt += context.prompt_passkey # the passkey is already substituted - prompt += context.prompt_junk_suffix - if context.debug: - passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" - print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```") - context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) - context.n_prompts = len(context.prompts) - - -@step('an OAI compatible chat completions request with {api_error} api error') -@async_run_until_complete -async def step_oai_chat_completions(context, api_error): - if context.debug: - print(f"Submitting OAI compatible completions request...") - expect_api_error = api_error == 'raised' - seeds = await completions_seed(context, num_seeds=1), - completion = await oai_chat_completions(context.prompts.pop(), - seeds[0] if seeds is not None else seeds, - context.system_prompt, - context.base_url, - '/v1/chat', - False, - model=context.model if hasattr(context, 'model') else None, - - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - - response_format=context.response_format - if hasattr(context, 'response_format') else None, - - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None, - - expect_api_error=expect_api_error) - context.tasks_result.append(completion) - if context.debug: - print(f"Completion response: {completion}") - if expect_api_error: - assert completion == 401, f"completion must be an 401 status code: {completion}" - - if context.debug: - print(f"Completion response: {completion}") - - -@step('a prompt') -def step_a_prompt(context): - context.prompts.append(context_text(context)) - context.n_prompts = len(context.prompts) - - -@step('a prompt {prompt}') -def step_a_prompt_prompt(context, prompt): - context.prompts.append(prompt) - context.n_prompts = len(context.prompts) - - -# TODO: allow this to be repeated -@step('an infill input extra {filename} {text}') -def step_infill_input_extra(context, filename, text): - if filename == 'none': - context.infill_input_extra = None - else: - context.infill_input_extra = [{'filename': filename, 'text': text}] - - -@step('an infill input suffix {text}') -def step_infill_input_suffix(context, text): - context.infill_input_suffix = text - - -@step('an infill input prefix {text}') -def step_infill_input_prefix(context, text): - context.infill_input_prefix = text - - -@step('{num_prompts:d} prompts {prompt} with seed {seed:d}') -def step_many_prompts(context, num_prompts, prompt, seed): - if context.seed is None: - context.seed = [] - for _ in range(num_prompts): - context.seed.append(seed) - context.prompts.append(prompt) - context.n_prompts = len(context.prompts) - - -@step('concurrent completion requests') -@async_run_until_complete() -async def step_concurrent_completion_requests(context): - await concurrent_requests( - context, - request_completion, - # prompt is inserted automatically - context.base_url, - debug=context.debug, - prompt_prefix=context.prompt_prefix, - prompt_suffix=context.prompt_suffix, - n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, - temperature=context.temperature, - ) - - -@step('concurrent OAI completions requests') -@async_run_until_complete -async def step_oai_chat_completions(context): - await concurrent_requests(context, oai_chat_completions, - # user_prompt is inserted automatically - context.system_prompt, - context.base_url, - '/v1/chat/completions', - True, # async_client - model=context.model - if hasattr(context, 'model') else None, - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - response_format=context.response_format - if hasattr(context, 'response_format') else None, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) - - -@step('concurrent OAI completions requests no v1') -@async_run_until_complete -async def step_oai_chat_completions(context): - await concurrent_requests(context, oai_chat_completions, - # user_prompt is inserted automatically - context.system_prompt, - context.base_url, - '/chat/completions', - True, # async_client - model=context.model - if hasattr(context, 'model') else None, - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - response_format=context.response_format - if hasattr(context, 'response_format') else None, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) - - -@step('all prompts are predicted') -@async_run_until_complete -async def step_all_prompts_are_predicted(context): - await all_prompts_are_predicted(context) - - -@step('all prompts are predicted with {n_expected_predicted:d} tokens') -@async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): - await all_prompts_are_predicted(context, n_expected_predicted) - - -async def all_prompts_are_predicted(context, expected_predicted_n=None): - n_completions = await gather_tasks_results(context) - assert n_completions > 0 - for i in range(n_completions): - assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n) - assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" - - -@step('embeddings are computed for') -@async_run_until_complete -async def step_compute_embedding(context): - context.n_prompts = 1 - context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url) - - -@step('reranking request') -@async_run_until_complete -async def step_compute_reranking(context): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/reranking', - json={ - "query": context.reranking_query, - "documents": context.reranking_documents, - }) as response: - if response.status == 200: - response_json = await response.json() - context.reranking_results = response_json['results'] - else: - context.reranking_results = response.status - - -@step('all embeddings are the same') -@async_run_until_complete -async def step_all_embeddings_are_the_same(context): - n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests > 0 - embeddings = [] - for i in range(n_embedding_requests): - embedding = context.tasks_result.pop().pop() - embeddings.append(embedding) - assert_embeddings(embedding) - n = len(embeddings) - for i in range(n-1): - for j in range(i+1, n): - embedding1 = np.array(embeddings[i]) - embedding2 = np.array(embeddings[j]) - if context.debug: - print(f"embedding1: {embedding1[-8:]}") - print(f"embedding2: {embedding2[-8:]}") - similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) - msg = f"Similarity between {i} and {j}: {similarity:.10f}" - if context.debug: - print(f"{msg}") - assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg - - -@step('embeddings are generated') -def step_assert_embeddings(context): - assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n" - f"context.n_prompts={context.n_prompts}\n" - f"context.embeddings={context.embeddings}") - for embedding in context.embeddings: - assert_embeddings(embedding) - -@step('embeddings request with {api_error_code:d} api error') -def step_assert_embeddings(context, api_error_code: int): - assert context.embeddings == api_error_code, f"embeddings request must return code {api_error_code}, but got {context.embeddings}" - -@step('an OAI compatible embeddings computation request for') -@async_run_until_complete -async def step_oai_compute_embeddings(context): - context.n_prompts = 1 - context.embeddings = await request_oai_embeddings(context_text(context), None, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - - -@step('an OAI compatible embeddings computation request for multiple inputs') -@async_run_until_complete -async def step_oai_compute_embeddings_multiple_inputs(context): - context.embeddings = await request_oai_embeddings(context.prompts, None, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - context.prompts.clear() - - -@step('concurrent embedding requests') -@async_run_until_complete() -async def step_concurrent_embedding_requests(context): - await concurrent_requests(context, - request_embedding, - # prompt is inserted automatically - base_url=context.base_url) - - -@step('concurrent OAI embedding requests') -@async_run_until_complete() -async def step_concurrent_oai_embedding_requests(context): - await concurrent_requests(context, - request_oai_embeddings, - # prompt is inserted automatically - base_url=context.base_url, - async_client=True, - model=context.model) - - -@step('all embeddings are generated') -@async_run_until_complete() -async def all_embeddings_are_generated(context): - n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests == context.n_prompts - for i in range(n_embedding_requests): - assert_embeddings(context.tasks_result.pop().pop()) - -@step('reranking results are returned') -def reranking_results_are_returned(context): - assert len(context.reranking_results) == len(context.reranking_documents) - -@step('reranking highest score is index {idx_high:d} and lowest score is index {idx_low:d}') -def reranking_results_are_returned(context, idx_high: int, idx_low: int): - max_score, max_idx = 0, 0 - min_score, min_idx = 0, 0 - for res in context.reranking_results: - if max_score < res['relevance_score']: - max_score = res['relevance_score'] - max_idx = res['index'] - if min_score > res['relevance_score']: - min_score = res['relevance_score'] - min_idx = res['index'] - print(context.reranking_results) - assert max_idx == idx_high - assert min_idx == idx_low - -@step('adding special tokens') -def step_tokenize_set_add_special(context): - context.tokenize_add_special = True - - -@step("tokenizing with pieces") -@async_run_until_complete -async def step_tokenize_with_pieces(context): - context.tokenized_text = context_text(context) - async with aiohttp.ClientSession() as session: - tokenize_args = {"content": context.tokenized_text, "with_pieces": True} - if getattr(context, "tokenize_add_special", None) is not None: - tokenize_args["add_special"] = context.tokenize_add_special - - async with session.post( - f"{context.base_url}/tokenize", json=tokenize_args - ) as response: - assert response.status == 200 - tokenize_json = await response.json() - context.tokens_with_pieces = tokenize_json["tokens"] - - -@step("tokens are given with pieces") -@async_run_until_complete -async def step_tokenize_with_pieces(context): - # Verify that the response contains both token IDs and pieces - assert all( - "id" in token and "piece" in token for token in context.tokens_with_pieces - ) - - -@step('tokenizing') -@async_run_until_complete -async def step_tokenize(context): - context.tokenized_text = context_text(context) - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - tokenize_args = { - "content": context.tokenized_text, - } - if getattr(context, 'tokenize_add_special', None) is not None: - tokenize_args['add_special'] = context.tokenize_add_special - async with session.post(f'{context.base_url}/tokenize', - json=tokenize_args) as response: - assert response.status == 200 - tokenize_json = await response.json() - context.tokens = tokenize_json['tokens'] - - -@step('tokens can be detokenized') -@async_run_until_complete -async def step_detokenize(context): - assert len(context.tokens) > 0 - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/detokenize', - json={ - "tokens": context.tokens, - }) as response: - assert response.status == 200 - detokenize_json = await response.json() - # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15 - assert context.tokenized_text == detokenize_json['content'].strip() - - -@step('tokens begin with BOS') -def step_strings_for_tokenization(context): - assert context.tokens[0] == context.bos - - -@step('tokens do not begin with BOS') -def step_strings_for_tokenization(context): - assert context.tokens[0] != context.bos - - -@step('first token is removed') -def step_strings_for_tokenization(context): - context.tokens = context.tokens[1:] - - -@step('an OPTIONS request is sent from {origin}') -@async_run_until_complete -async def step_options_request(context, origin): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin} - async with session.options(f'{context.base_url}/v1/chat/completions', - headers=headers) as response: - assert response.status == 200 - context.options_response = response - - -@step('CORS header {cors_header} is set to {cors_header_value}') -def step_check_options_header_value(context, cors_header, cors_header_value): - assert context.options_response.headers[cors_header] == cors_header_value - - -@step('prometheus metrics are exposed') -@async_run_until_complete -async def step_prometheus_metrics_exported(context): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with await session.get(f'{context.base_url}/metrics') as metrics_response: - assert metrics_response.status == 200 - assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" - metrics_raw = await metrics_response.text() - metric_exported = False - if context.debug: - print(f"/metrics answer:\n{metrics_raw}") - context.metrics = {} - for metric in parser.text_string_to_metric_families(metrics_raw): - match metric.name: - case "llamacpp:kv_cache_usage_ratio": - assert len(metric.samples) > 0 - metric_exported = True - context.metrics[metric.name] = metric - assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time" - assert metric_exported, "No metrics exported" - - -@step('metric {metric_name} is {metric_value:d}') -def step_assert_metric_value(context, metric_name, metric_value): - if metric_name not in context.metrics: - assert False, f"no metric {metric_name} in {context.metrics.keys()}" - assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}" - - -@step('available models') -def step_available_models(context): - # openai client always expects an api_key - openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' - openai.base_url = f'{context.base_url}/v1/' - context.models = openai.models.list().data - - -@step('{n_model:d} models are supported') -def step_supported_models(context, n_model): - if context.debug: - print("server models available:", context.models) - assert len(context.models) == n_model - - -@step('model {i_model:d} is {param} {preposition} {param_value}') -def step_supported_models(context, i_model: int, param: Literal['identified', 'trained'] | str, preposition: str, param_value: str): - assert i_model < len(context.models) - model = context.models[i_model] - - param_value = param_value.split(' ', 1)[0] - match param: - case 'identified': - value = model.id - case 'trained': - value = str(model.meta["n_ctx_train"]) - case _: - assert False, "param {param} not supported" - assert param_value == value, f"model param {param} {value} != {param_value}" - - -async def concurrent_requests(context, f_completion, *args, **kwargs): - context.n_prompts = len(context.prompts) - if context.debug: - print(f"starting {context.n_prompts} concurrent completion requests...") - assert context.n_prompts > 0 - seeds = await completions_seed(context) - assert seeds is not None - for prompt_no in range(context.n_prompts): - shifted_args = [context.prompts.pop(), seeds[prompt_no], *args] - context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) - await asyncio.sleep(0.01) - - -@step('the slot {slot_id:d} is saved with filename "{filename}"') -@async_run_until_complete -async def step_save_slot(context, slot_id, filename): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=save', - json={"filename": filename}, - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('the slot {slot_id:d} is restored with filename "{filename}"') -@async_run_until_complete -async def step_restore_slot(context, slot_id, filename): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore', - json={"filename": filename}, - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('the slot {slot_id:d} is erased') -@async_run_until_complete -async def step_erase_slot(context, slot_id): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase', - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('switch {on_or_off} lora adapter {lora_id:d}') -@async_run_until_complete -async def toggle_lora_adapter(context, on_or_off: str, lora_id: int): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/lora-adapters', - json=[{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}], - headers={"Content-Type": "application/json"}) as response: - context.response = response - print([{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}]) - - -@step('the server responds with status code {status_code:d}') -def step_server_responds_with_status_code(context, status_code): - assert context.response.status == status_code - - -async def request_completion(prompt, - seed, - base_url, - debug=False, - prompt_prefix=None, - prompt_suffix=None, - n_predict=None, - cache_prompt=False, - id_slot=None, - expect_api_error=None, - user_api_key=None, - temperature=None) -> int | dict[str, Any]: - if debug: - print(f"Sending completion request: {prompt}") - origin = "my.super.domain" - headers = { - 'Origin': origin - } - if user_api_key is not None: - if debug: - print(f"Set user_api_key: {user_api_key}") - headers['Authorization'] = f'Bearer {user_api_key}' - - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/completion', - json={ - "input_prefix": prompt_prefix, - "prompt": prompt, - "input_suffix": prompt_suffix, - "n_predict": n_predict if n_predict is not None else -1, - "cache_prompt": cache_prompt, - "id_slot": id_slot, - "seed": seed if seed is not None else 42, - "temperature": temperature if temperature is not None else 0.8, - "n_probs": 2, - }, - headers=headers) as response: - if expect_api_error is None or not expect_api_error: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - return await response.json() - else: - return response.status - - -async def oai_chat_completions(user_prompt, - seed, - system_prompt, - base_url: str, - base_path: str, - async_client, - debug=False, - temperature=None, - model=None, - n_predict=None, - enable_streaming=None, - response_format=None, - user_api_key=None, - expect_api_error=None) -> int | dict[str, Any]: - if debug: - print(f"Sending OAI Chat completions request: {user_prompt}") - # openai client always expects an api key - user_api_key = user_api_key if user_api_key is not None else 'nope' - seed = seed if seed is not None else 42 - enable_streaming = enable_streaming if enable_streaming is not None else False - payload = { - "messages": [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": user_prompt, - } - ], - "model": model, - "max_tokens": n_predict, - "stream": enable_streaming, - "temperature": temperature if temperature is not None else 0.0, - "seed": seed, - } - if response_format is not None: - payload['response_format'] = response_format - completion_response = { - 'content': '', - 'timings': { - 'predicted_n': 0, - 'prompt_n': 0 - } - } - if async_client: - origin = 'llama.cpp' - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}{base_path}', - json=payload, - headers=headers) as response: - if enable_streaming: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "text/event-stream" - event_received = True - while event_received: - event_received = False - async for line_in_bytes in response.content: - line = line_in_bytes.decode('utf-8') - line = line.rstrip('\n').rstrip('\r') - if line == '': - continue - event_data = line.split(': ', 1) - assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```' - chunk_raw = event_data[1] - if chunk_raw == '[DONE]': - break - - chunk = json.loads(chunk_raw) - assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```" - delta = chunk['choices'][0]['delta'] - if 'content' in delta: - completion_response['content'] += delta['content'] - completion_response['timings']['predicted_n'] += 1 - else: - if expect_api_error is None or not expect_api_error: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "application/json; charset=utf-8" - chat_completion_raw = await response.json() - completion_response = { - 'content': chat_completion_raw['choices'][0]['message'], - 'timings': { - 'predicted_n': chat_completion_raw['usage']['completion_tokens'], - 'prompt_n': chat_completion_raw['usage']['prompt_tokens'] - } - } - else: - return response.status - else: - try: - openai.api_key = user_api_key - openai.base_url = f'{base_url}{base_path.removesuffix("chat")}' - assert model is not None - chat_completion = openai.chat.completions.create( - messages=payload['messages'], - model=model, - max_tokens=n_predict, - stream=enable_streaming, - response_format=payload.get('response_format') or openai.NOT_GIVEN, - seed=seed, - temperature=payload['temperature'] - ) - except openai.AuthenticationError as e: - if expect_api_error is not None and expect_api_error: - return 401 - else: - assert False, f'error raised: {e}' - - if enable_streaming: - chat_completion = cast(openai.Stream[ChatCompletionChunk], chat_completion) - for chunk in chat_completion: - assert len(chunk.choices) == 1 - delta = chunk.choices[0].delta - if delta.content is not None: - completion_response['content'] += delta.content - completion_response['timings']['predicted_n'] += 1 - completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop' - else: - assert len(chat_completion.choices) == 1 - assert chat_completion.usage is not None - completion_response = { - 'content': chat_completion.choices[0].message.content, - 'timings': { - 'predicted_n': chat_completion.usage.completion_tokens, - 'prompt_n': chat_completion.usage.prompt_tokens - }, - 'truncated': chat_completion.choices[0].finish_reason != 'stop' - } - if debug: - print("OAI response formatted to llama.cpp:", completion_response) - return completion_response - - -async def request_embedding(content, seed, base_url=None) -> list[list[float]] | int: - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/embedding', - json={ - "content": content, - }) as response: - if response.status == 200: - response_json = await response.json() - return [response_json['embedding']] - else: - return response.status - - -async def request_oai_embeddings(input, seed, - base_url=None, user_api_key=None, - model=None, async_client=False) -> list[list[float]]: - # openai client always expects an api_key - user_api_key = user_api_key if user_api_key is not None else 'nope' - if async_client: - origin = 'llama.cpp' - headers=[] - if user_api_key is not None: - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/v1/embeddings', - json={ - "input": input, - "model": model, - }, - headers=headers) as response: - assert response.status == 200, f"received status code not expected: {response.status}" - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "application/json; charset=utf-8" - response_json = await response.json() - assert response_json['model'] == model, f"invalid model received: {response_json['model']}" - assert response_json['object'] == 'list' - if isinstance(input, Sequence): - embeddings = [] - for an_oai_embeddings in response_json['data']: - embeddings.append(an_oai_embeddings['embedding']) - else: - embeddings = [response_json['data']['embedding']] - return embeddings - else: - openai.api_key = user_api_key - openai.base_url = f'{base_url}/v1/' - assert model is not None - oai_embeddings = openai.embeddings.create( - model=model, - input=input, - ) - - return [e.embedding for e in oai_embeddings.data] - - -def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None): - content = completion_response['content'] - n_predicted = completion_response['timings']['predicted_n'] - assert len(content) > 0, "no token predicted" - if re_content is not None: - p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL) - matches = p.finditer(content) - last_match = 0 - highlighted = '' - for match in matches: - start, end = match.span() - highlighted += content[last_match: start] - highlighted += '\x1b[33m' - highlighted += content[start: end] - highlighted += '\x1b[0m' - last_match = end - highlighted += content[last_match:] - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - print(f"Checking completion response: {highlighted}") - assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' - if expected_predicted_n and expected_predicted_n > 0: - assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' - f' {n_predicted} <> {expected_predicted_n}') - -def assert_all_predictions_equal(completion_responses): - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - print(f"content {i}: {content_i}") - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - content_j = response_j['content'] - assert content_i == content_j, "contents not equal" - - -def assert_all_predictions_different(completion_responses): - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - print(f"content {i}: {content_i}") - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - content_j = response_j['content'] - assert content_i != content_j, "contents not different" - - -def assert_all_token_probabilities_equal(completion_responses): - n_predict = len(completion_responses[0]['completion_probabilities']) - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for pos in range(n_predict): - for i, response_i in enumerate(completion_responses): - probs_i = response_i['completion_probabilities'][pos]['probs'] - print(f"pos {pos}, probs {i}: {probs_i}") - for pos in range(n_predict): - for i, response_i in enumerate(completion_responses): - probs_i = response_i['completion_probabilities'][pos]['probs'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - probs_j = response_j['completion_probabilities'][pos]['probs'] - assert probs_i == probs_j, "contents not equal" - - -async def gather_tasks_results(context): - n_tasks = len(context.concurrent_tasks) - if context.debug: - print(f"Waiting for all {n_tasks} tasks results...") - for task_no in range(n_tasks): - context.tasks_result.append(await context.concurrent_tasks.pop()) - n_completions = len(context.tasks_result) - return n_completions - - -async def wait_for_slots_status(context, - base_url, - expected_http_status_code, - timeout=3, - params=None, - slots_idle=None, - slots_processing=None): - if context.debug: - print(f"Starting checking for health for expected_http_status_code={expected_http_status_code}") - interval = 0.5 - counter = 0 - if 'GITHUB_ACTIONS' in os.environ: - timeout *= 2 - - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - while True: - headers = {'Authorization': f'Bearer {context.server_api_key}'} - async with await session.get(f'{base_url}/slots', params=params, headers=headers) as slots_response: - status_code = slots_response.status - slots = await slots_response.json() - if context.debug: - print(f"slots responses {slots}\n") - if status_code == 503 and status_code == expected_http_status_code: - return - if status_code == 200 and status_code == expected_http_status_code: - n_slots_idle = sum(1 if not slot["is_processing"] else 0 for slot in slots) - n_slots_processing = sum(1 if slot["is_processing"] else 0 for slot in slots) - if ((slots_idle is None or slots_idle == n_slots_idle) - and (slots_processing is None or slots_processing == n_slots_processing)): - return - await asyncio.sleep(interval) - - counter += interval - if counter >= timeout: - # Sometimes health requests are triggered after completions are predicted - if expected_http_status_code == 503: - if len(context.tasks_result) == 0: - print("\x1b[5;37;43mWARNING: forcing concurrent tasks," - " busy health check missed, probably too fast inference\x1b[0m\n") - n_completions = await gather_tasks_results(context) - if n_completions > 0: - return - - assert False, f'slots check timeout exceeded {counter}s>={timeout}' - - -def assert_embeddings(embeddings): - assert len(embeddings) > 0 - embeddings_computed = False - for emb in embeddings: - if not isinstance(emb, float): - assert False, f"Bad embeddings: {embeddings}" - if emb != 0: - embeddings_computed = True - assert embeddings_computed, f"Embeddings: {embeddings}" - - -async def request_slots_status(context, expected_slots): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with await session.get(f'{context.base_url}/slots') as slots_response: - assert slots_response.status == 200 - slots = await slots_response.json() - assert_slots_status(slots, expected_slots) - - -def assert_slots_status(slots, expected_slots): - assert len(slots) == len(expected_slots) - for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)): - for key in expected: - assert expected[key] == slot[key], (f"invalid slot {slot_id}" - f" expected[{key}] != slot[{key}]" - f" = {expected[key]} != {slot[key]}") - - -async def completions_seed(context, num_seeds=None): - if hasattr(context, "seed") and context.seed is not None: - assert len(context.seed) == context.n_prompts - if num_seeds is None: - num_seeds = context.n_prompts - assert num_seeds <= context.n_prompts - seeds = context.seed[:num_seeds] - context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None - return seeds - - if hasattr(context, "server_seed") and context.server_seed is not None: - if num_seeds is None: - return [context.server_seed] * context.n_prompts - else: - return [context.server_seed] * num_seeds - return None - - -def context_text(context): - return context.text.replace('\r', '') - - -def start_server_background(context): - if os.name == 'nt': - context.server_path = '../../../build/bin/Release/llama-server.exe' - else: - context.server_path = '../../../build/bin/llama-server' - if 'LLAMA_SERVER_BIN_PATH' in os.environ: - context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] - server_listen_addr = context.server_fqdn - server_args = [ - '--slots', # requires to get slot status via /slots endpoint - '--host', server_listen_addr, - '--port', context.server_port, - ] - if context.model_file: - server_args.extend(['--model', context.model_file]) - if context.model_url: - server_args.extend(['--model-url', context.model_url]) - if context.model_hf_repo: - server_args.extend(['--hf-repo', context.model_hf_repo]) - if context.model_hf_file: - server_args.extend(['--hf-file', context.model_hf_file]) - if context.n_batch: - server_args.extend(['--batch-size', context.n_batch]) - if context.n_ubatch: - server_args.extend(['--ubatch-size', context.n_ubatch]) - if context.n_threads: - server_args.extend(['--threads', context.threads]) - if context.n_gpu_layer: - server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) - if context.draft is not None: - server_args.extend(['--draft', context.draft]) - if context.server_continuous_batching: - server_args.append('--cont-batching') - if context.server_embeddings: - server_args.append('--embedding') - if context.server_reranking: - server_args.append('--reranking') - if context.server_metrics: - server_args.append('--metrics') - if context.model_alias: - server_args.extend(['--alias', context.model_alias]) - if context.n_ctx: - server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots: - server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict: - server_args.extend(['--n-predict', context.n_server_predict]) - if context.slot_save_path: - server_args.extend(['--slot-save-path', context.slot_save_path]) - if context.server_api_key: - server_args.extend(['--api-key', context.server_api_key]) - if context.n_ga: - server_args.extend(['--grp-attn-n', context.n_ga]) - if context.n_ga_w: - server_args.extend(['--grp-attn-w', context.n_ga_w]) - if context.debug: - server_args.append('--verbose') - if context.lora_file: - server_args.extend(['--lora', context.lora_file]) - if context.disable_ctx_shift: - server_args.extend(['--no-context-shift']) - - args = [str(arg) for arg in [context.server_path, *server_args]] - print(f"bench: starting server with: {' '.join(args)}") - - flags = 0 - if 'nt' == os.name: - flags |= subprocess.DETACHED_PROCESS - flags |= subprocess.CREATE_NEW_PROCESS_GROUP - flags |= subprocess.CREATE_NO_WINDOW - - pkwargs = { - 'creationflags': flags, - 'stdout': subprocess.PIPE, - 'stderr': subprocess.PIPE - } - context.server_process = subprocess.Popen( - [str(arg) for arg in [context.server_path, *server_args]], - **pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue] - - def server_log(in_stream, out_stream): - for line in iter(in_stream.readline, b''): - print(line.decode('utf-8'), end='', file=out_stream) - - thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout)) - thread_stdout.start() - - thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr)) - thread_stderr.start() - - print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}") diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature deleted file mode 100644 index 61d5f315e1567..0000000000000 --- a/examples/server/tests/features/wrong_usages.feature +++ /dev/null @@ -1,25 +0,0 @@ -# run with: ./tests.sh --no-skipped --tags wrong_usage -@wrong_usage -Feature: Wrong usage of llama.cpp server - - #3969 The user must always set --n-predict option - # to cap the number of tokens any completion request can generate - # or pass n_predict/max_tokens in the request. - Scenario: Infinite loop - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And 42 as server seed - And 2048 KV cache size - # Uncomment below to fix the issue - #And 64 server max tokens to predict - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Go to: infinite loop - """ - # Uncomment below to fix the issue - #And 128 max tokens to predict - Given concurrent completion requests - Then the server is idle - Then all prompts are predicted diff --git a/examples/server/tests/pytest.ini b/examples/server/tests/pytest.ini new file mode 100644 index 0000000000000..6df308df74d57 --- /dev/null +++ b/examples/server/tests/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + slow: marks tests as slow (deselect with '-m "not slow"') + serial diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 5539548720ff1..15d024914e841 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,7 +1,8 @@ aiohttp~=3.9.3 -behave~=1.2.6 +pytest~=8.3.3 huggingface_hub~=0.23.2 numpy~=1.26.4 -openai~=1.30.3 +openai~=1.55.3 prometheus-client~=0.20.0 requests~=2.32.3 +wget~=3.2 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 72a0fbad827db..33fa8cc6464e2 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -1,11 +1,23 @@ #!/bin/bash +# make sure we are in the right directory +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +cd $SCRIPT_DIR + set -eu +if [[ "${SLOW_TESTS:-0}" == 1 ]]; then + # Slow tests for tool calls need quite a few models ahead of time to avoid timing out. + python $SCRIPT_DIR/../../../scripts/fetch_server_test_models.py +fi + if [ $# -lt 1 ] then - # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp + if [[ "${SLOW_TESTS:-0}" == 1 ]]; then + pytest -v -x + else + pytest -v -x -m "not slow" + fi else - behave "$@" + pytest "$@" fi diff --git a/examples/server/tests/unit/test_basic.py b/examples/server/tests/unit/test_basic.py new file mode 100644 index 0000000000000..1485de8ceb3fc --- /dev/null +++ b/examples/server/tests/unit/test_basic.py @@ -0,0 +1,96 @@ +import pytest +import requests +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +def test_server_start_simple(): + global server + server.start() + res = server.make_request("GET", "/health") + assert res.status_code == 200 + + +def test_server_props(): + global server + server.start() + res = server.make_request("GET", "/props") + assert res.status_code == 200 + assert ".gguf" in res.body["model_path"] + assert res.body["total_slots"] == server.n_slots + default_val = res.body["default_generation_settings"] + assert server.n_ctx is not None and server.n_slots is not None + assert default_val["n_ctx"] == server.n_ctx / server.n_slots + assert default_val["params"]["seed"] == server.seed + + +def test_server_models(): + global server + server.start() + res = server.make_request("GET", "/models") + assert res.status_code == 200 + assert len(res.body["data"]) == 1 + assert res.body["data"][0]["id"] == server.model_alias + + +def test_server_slots(): + global server + + # without slots endpoint enabled, this should return error + server.server_slots = False + server.start() + res = server.make_request("GET", "/slots") + assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED + assert "error" in res.body + server.stop() + + # with slots endpoint enabled, this should return slots info + server.server_slots = True + server.n_slots = 2 + server.start() + res = server.make_request("GET", "/slots") + assert res.status_code == 200 + assert len(res.body) == server.n_slots + assert server.n_ctx is not None and server.n_slots is not None + assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots + assert "params" in res.body[0] + assert res.body[0]["params"]["seed"] == server.seed + + +def test_load_split_model(): + global server + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/split/stories15M-q8_0-00001-of-00003.gguf" + server.model_alias = "tinyllama-split" + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 16, + "prompt": "Hello", + "temperature": 0.0, + }) + assert res.status_code == 200 + assert match_regex("(little|girl)+", res.body["content"]) + + +def test_no_webui(): + global server + # default: webui enabled + server.start() + url = f"http://{server.server_host}:{server.server_port}" + res = requests.get(url) + assert res.status_code == 200 + assert "" in res.text + server.stop() + + # with --no-webui + server.no_webui = True + server.start() + res = requests.get(url) + assert res.status_code == 404 diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py new file mode 100644 index 0000000000000..af1dcb5b96554 --- /dev/null +++ b/examples/server/tests/unit/test_chat_completion.py @@ -0,0 +1,310 @@ +import pytest +from openai import OpenAI +from utils import * + +server: ServerProcess + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +@pytest.mark.parametrize( + "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template", + [ + (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None), + (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'), + (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None), + (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", False, None), + (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", True, None), + ] +) +def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template): + global server + server.jinja = jinja + server.chat_template = chat_template + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "model": model, + "max_tokens": max_tokens, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + }) + assert res.status_code == 200 + assert "cmpl" in res.body["id"] # make sure the completion id has the expected format + assert res.body["system_fingerprint"].startswith("b") + assert res.body["model"] == model if model is not None else server.model_alias + assert res.body["usage"]["prompt_tokens"] == n_prompt + assert res.body["usage"]["completion_tokens"] == n_predicted + choice = res.body["choices"][0] + assert "assistant" == choice["message"]["role"] + assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}' + assert choice["finish_reason"] == finish_reason + + +@pytest.mark.parametrize( + "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason", + [ + ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"), + ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"), + ] +) +def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason): + global server + server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL + server.start() + res = server.make_stream_request("POST", "/chat/completions", data={ + "max_tokens": max_tokens, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + "stream": True, + }) + content = "" + last_cmpl_id = None + for data in res: + choice = data["choices"][0] + assert data["system_fingerprint"].startswith("b") + assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future + if last_cmpl_id is None: + last_cmpl_id = data["id"] + assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream + if choice["finish_reason"] in ["stop", "length"]: + assert data["usage"]["prompt_tokens"] == n_prompt + assert data["usage"]["completion_tokens"] == n_predicted + assert "content" not in choice["delta"] + assert match_regex(re_content, content) + assert choice["finish_reason"] == finish_reason + else: + assert choice["finish_reason"] is None + content += choice["delta"]["content"] + + +def test_chat_completion_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=8, + seed=42, + temperature=0.8, + ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") + assert res.choices[0].finish_reason == "length" + assert res.choices[0].message.content is not None + assert match_regex("(Suddenly)+", res.choices[0].message.content) + + +def test_chat_template(): + global server + server.chat_template = "llama3" + server.debug = True # to get the "__verbose" object in the response + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": 8, + "messages": [ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ] + }) + assert res.status_code == 200 + assert "__verbose" in res.body + assert res.body["__verbose"]["prompt"] == " <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" + + +def test_apply_chat_template(): + global server + server.chat_template = "command-r" + server.start() + res = server.make_request("POST", "/apply-template", data={ + "messages": [ + {"role": "system", "content": "You are a test."}, + {"role": "user", "content":"Hi there"}, + ] + }) + assert res.status_code == 200 + assert "prompt" in res.body + assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" + + +@pytest.mark.parametrize("response_format,n_predicted,re_content", [ + ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""), + ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"), + ({"type": "json_object"}, 10, "(\\{|John)+"), + ({"type": "sound"}, 0, None), + # invalid response format (expected to fail) + ({"type": "json_object", "schema": 123}, 0, None), + ({"type": "json_object", "schema": {"type": 123}}, 0, None), + ({"type": "json_object", "schema": {"type": "hiccup"}}, 0, None), +]) +def test_completion_with_response_format(response_format: dict, n_predicted: int, re_content: str | None): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predicted, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "response_format": response_format, + }) + if re_content is not None: + assert res.status_code == 200 + choice = res.body["choices"][0] + assert match_regex(re_content, choice["message"]["content"]) + else: + assert res.status_code != 200 + assert "error" in res.body + + +@pytest.mark.parametrize("jinja,json_schema,n_predicted,re_content", [ + (False, {"const": "42"}, 6, "\"42\""), + (True, {"const": "42"}, 6, "\"42\""), +]) +def test_completion_with_json_schema(jinja: bool, json_schema: dict, n_predicted: int, re_content: str): + global server + server.jinja = jinja + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predicted, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "json_schema": json_schema, + }) + assert res.status_code == 200, f'Expected 200, got {res.status_code}' + choice = res.body["choices"][0] + assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}' + + +@pytest.mark.parametrize("jinja,grammar,n_predicted,re_content", [ + (False, 'root ::= "a"{5,5}', 6, "a{5,5}"), + (True, 'root ::= "a"{5,5}', 6, "a{5,5}"), +]) +def test_completion_with_grammar(jinja: bool, grammar: str, n_predicted: int, re_content: str): + global server + server.jinja = jinja + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predicted, + "messages": [ + {"role": "user", "content": "Does not matter what I say, does it?"}, + ], + "grammar": grammar, + }) + assert res.status_code == 200, res.body + choice = res.body["choices"][0] + assert match_regex(re_content, choice["message"]["content"]), choice["message"]["content"] + + +@pytest.mark.parametrize("messages", [ + None, + "string", + [123], + [{}], + [{"role": 123}], + [{"role": "system", "content": 123}], + # [{"content": "hello"}], # TODO: should not be a valid case + [{"role": "system", "content": "test"}, {}], +]) +def test_invalid_chat_completion_req(messages): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "messages": messages, + }) + assert res.status_code == 400 or res.status_code == 500 + assert "error" in res.body + + +def test_chat_completion_with_timings_per_token(): + global server + server.start() + res = server.make_stream_request("POST", "/chat/completions", data={ + "max_tokens": 10, + "messages": [{"role": "user", "content": "test"}], + "stream": True, + "timings_per_token": True, + }) + for data in res: + assert "timings" in data + assert "prompt_per_second" in data["timings"] + assert "predicted_per_second" in data["timings"] + assert "predicted_n" in data["timings"] + assert data["timings"]["predicted_n"] <= 10 + + +def test_logprobs(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + ) + output_text = res.choices[0].message.content + aggregated_text = '' + assert res.choices[0].logprobs is not None + assert res.choices[0].logprobs.content is not None + for token in res.choices[0].logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text + + +def test_logprobs_stream(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + stream=True, + ) + output_text = '' + aggregated_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + if choice.delta.content: + output_text += choice.delta.content + assert choice.logprobs is not None + assert choice.logprobs.content is not None + for token in choice.logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert token.top_logprobs is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py new file mode 100644 index 0000000000000..0ed5b99bef4e4 --- /dev/null +++ b/examples/server/tests/unit/test_completion.py @@ -0,0 +1,428 @@ +import pytest +import requests +import time +from openai import OpenAI +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + +@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True), +]) +def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": prompt, + "return_tokens": return_tokens, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == n_prompt + assert res.body["timings"]["predicted_n"] == n_predicted + assert res.body["truncated"] == truncated + assert type(res.body["has_new_line"]) == bool + assert match_regex(re_content, res.body["content"]) + if return_tokens: + assert len(res.body["tokens"]) > 0 + assert all(type(tok) == int for tok in res.body["tokens"]) + else: + assert res.body["tokens"] == [] + + +@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), +]) +def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": prompt, + "stream": True, + }) + content = "" + for data in res: + assert "stop" in data and type(data["stop"]) == bool + if data["stop"]: + assert data["timings"]["prompt_n"] == n_prompt + assert data["timings"]["predicted_n"] == n_predicted + assert data["truncated"] == truncated + assert data["stop_type"] == "limit" + assert type(data["has_new_line"]) == bool + assert "generation_settings" in data + assert server.n_predict is not None + assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict) + assert data["generation_settings"]["seed"] == server.seed + assert match_regex(re_content, content) + else: + assert len(data["tokens"]) > 0 + assert all(type(tok) == int for tok in data["tokens"]) + content += data["content"] + + +def test_completion_stream_vs_non_stream(): + global server + server.start() + res_stream = server.make_stream_request("POST", "/completion", data={ + "n_predict": 8, + "prompt": "I believe the meaning of life is", + "stream": True, + }) + res_non_stream = server.make_request("POST", "/completion", data={ + "n_predict": 8, + "prompt": "I believe the meaning of life is", + }) + content_stream = "" + for data in res_stream: + content_stream += data["content"] + assert content_stream == res_non_stream.body["content"] + + +def test_completion_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") + assert res.choices[0].finish_reason == "length" + assert res.choices[0].text is not None + assert match_regex("(going|bed)+", res.choices[0].text) + + +def test_completion_stream_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + stream=True, + ) + output_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + assert choice.text is not None + output_text += choice.text + assert match_regex("(going|bed)+", output_text) + + +@pytest.mark.parametrize("n_slots", [1, 2]) +def test_consistent_result_same_seed(n_slots: int): + global server + server.n_slots = n_slots + server.start() + last_res = None + for _ in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 0.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] == last_res.body["content"] + last_res = res + + +@pytest.mark.parametrize("n_slots", [1, 2]) +def test_different_result_different_seed(n_slots: int): + global server + server.n_slots = n_slots + server.start() + last_res = None + for seed in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": seed, + "temperature": 1.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] != last_res.body["content"] + last_res = res + +# TODO figure why it don't work with temperature = 1 +# @pytest.mark.parametrize("temperature", [0.0, 1.0]) +@pytest.mark.parametrize("n_batch", [16, 32]) +@pytest.mark.parametrize("temperature", [0.0]) +def test_consistent_result_different_batch_size(n_batch: int, temperature: float): + global server + server.n_batch = n_batch + server.start() + last_res = None + for _ in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": temperature, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] == last_res.body["content"] + last_res = res + + +@pytest.mark.skip(reason="This test fails on linux, need to be fixed") +def test_cache_vs_nocache_prompt(): + global server + server.start() + res_cache = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 1.0, + "cache_prompt": True, + }) + res_no_cache = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 1.0, + "cache_prompt": False, + }) + assert res_cache.body["content"] == res_no_cache.body["content"] + + +def test_completion_with_tokens_input(): + global server + server.temperature = 0.0 + server.start() + prompt_str = "I believe the meaning of life is" + res = server.make_request("POST", "/tokenize", data={ + "content": prompt_str, + "add_special": True, + }) + assert res.status_code == 200 + tokens = res.body["tokens"] + + # single completion + res = server.make_request("POST", "/completion", data={ + "prompt": tokens, + }) + assert res.status_code == 200 + assert type(res.body["content"]) == str + + # batch completion + res = server.make_request("POST", "/completion", data={ + "prompt": [tokens, tokens], + }) + assert res.status_code == 200 + assert type(res.body) == list + assert len(res.body) == 2 + assert res.body[0]["content"] == res.body[1]["content"] + + # mixed string and tokens + res = server.make_request("POST", "/completion", data={ + "prompt": [tokens, prompt_str], + }) + assert res.status_code == 200 + assert type(res.body) == list + assert len(res.body) == 2 + assert res.body[0]["content"] == res.body[1]["content"] + + # mixed string and tokens in one sequence + res = server.make_request("POST", "/completion", data={ + "prompt": [1, 2, 3, 4, 5, 6, prompt_str, 7, 8, 9, 10, prompt_str], + }) + assert res.status_code == 200 + assert type(res.body["content"]) == str + + +@pytest.mark.parametrize("n_slots,n_requests", [ + (1, 3), + (2, 2), + (2, 4), + (4, 2), # some slots must be idle + (4, 6), +]) +def test_completion_parallel_slots(n_slots: int, n_requests: int): + global server + server.n_slots = n_slots + server.temperature = 0.0 + server.start() + + PROMPTS = [ + ("Write a very long book.", "(very|special|big)+"), + ("Write another a poem.", "(small|house)+"), + ("What is LLM?", "(Dad|said)+"), + ("The sky is blue and I love it.", "(climb|leaf)+"), + ("Write another very long music lyrics.", "(friends|step|sky)+"), + ("Write a very long joke.", "(cat|Whiskers)+"), + ] + def check_slots_status(): + should_all_slots_busy = n_requests >= n_slots + time.sleep(0.1) + res = server.make_request("GET", "/slots") + n_busy = sum([1 for slot in res.body if slot["is_processing"]]) + if should_all_slots_busy: + assert n_busy == n_slots + else: + assert n_busy <= n_slots + + tasks = [] + for i in range(n_requests): + prompt, re_content = PROMPTS[i % len(PROMPTS)] + tasks.append((server.make_request, ("POST", "/completion", { + "prompt": prompt, + "seed": 42, + "temperature": 1.0, + }))) + tasks.append((check_slots_status, ())) + results = parallel_function_calls(tasks) + + # check results + for i in range(n_requests): + prompt, re_content = PROMPTS[i % len(PROMPTS)] + res = results[i] + assert res.status_code == 200 + assert type(res.body["content"]) == str + assert len(res.body["content"]) > 10 + # FIXME: the result is not deterministic when using other slot than slot 0 + # assert match_regex(re_content, res.body["content"]) + + +@pytest.mark.parametrize( + "prompt,n_predict,response_fields", + [ + ("I believe the meaning of life is", 8, []), + ("I believe the meaning of life is", 32, ["content", "generation_settings/n_predict", "prompt"]), + ], +) +def test_completion_response_fields( + prompt: str, n_predict: int, response_fields: list[str] +): + global server + server.start() + res = server.make_request( + "POST", + "/completion", + data={ + "n_predict": n_predict, + "prompt": prompt, + "response_fields": response_fields, + }, + ) + assert res.status_code == 200 + assert "content" in res.body + assert len(res.body["content"]) + if len(response_fields): + assert res.body["generation_settings/n_predict"] == n_predict + assert res.body["prompt"] == " " + prompt + assert isinstance(res.body["content"], str) + assert len(res.body) == len(response_fields) + else: + assert len(res.body) + assert "generation_settings" in res.body + + +def test_n_probs(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_stream(): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "stream": True, + }) + for data in res: + if data["stop"] == False: + assert "completion_probabilities" in data + assert len(data["completion_probabilities"]) == 1 + for tok in data["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_post_sampling(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "post_sampling_probs": True, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "prob" in tok and 0.0 < tok["prob"] <= 1.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_probs"]) == 10 + for prob in tok["top_probs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0 + assert "bytes" in prob and type(prob["bytes"]) == list + # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs + assert any(prob["prob"] == 1.0 for prob in tok["top_probs"]) + + +def test_cancel_request(): + global server + server.n_ctx = 4096 + server.n_predict = -1 + server.n_slots = 1 + server.server_slots = True + server.start() + # send a request that will take a long time, but cancel it before it finishes + try: + server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + }, timeout=0.1) + except requests.exceptions.ReadTimeout: + pass # expected + # make sure the slot is free + time.sleep(1) # wait for HTTP_POLLING_SECONDS + res = server.make_request("GET", "/slots") + assert res.body[0]["is_processing"] == False diff --git a/examples/server/tests/unit/test_ctx_shift.py b/examples/server/tests/unit/test_ctx_shift.py new file mode 100644 index 0000000000000..be93a6d31f410 --- /dev/null +++ b/examples/server/tests/unit/test_ctx_shift.py @@ -0,0 +1,67 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +LONG_TEXT = """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. +Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. +Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. +""".strip() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.n_ctx = 256 + server.n_slots = 2 + + +def test_ctx_shift_enabled(): + # the prompt is 301 tokens + # the slot context is 256/2 = 128 tokens + # the prompt is truncated to keep the last 109 tokens + # 64 tokens are generated thanks to shifting the context when it gets full + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 64, + "prompt": LONG_TEXT, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == 109 + assert res.body["timings"]["predicted_n"] == 64 + assert res.body["truncated"] is True + + +@pytest.mark.parametrize("n_predict,n_token_output,truncated", [ + (64, 64, False), + (-1, 120, True), +]) +def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool): + global server + server.disable_ctx_shift = True + server.n_predict = -1 + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": "Hi how are you", + }) + assert res.status_code == 200 + assert res.body["timings"]["predicted_n"] == n_token_output + assert res.body["truncated"] == truncated + + +def test_ctx_shift_disabled_long_prompt(): + global server + server.disable_ctx_shift = True + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 64, + "prompt": LONG_TEXT, + }) + assert res.status_code != 200 + assert "error" in res.body + assert "exceeds the available context size" in res.body["error"]["message"] diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py new file mode 100644 index 0000000000000..8b0eb42b0926f --- /dev/null +++ b/examples/server/tests/unit/test_embedding.py @@ -0,0 +1,237 @@ +import base64 +import struct +import pytest +from openai import OpenAI +from utils import * + +server = ServerPreset.bert_bge_small() + +EPSILON = 1e-3 + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.bert_bge_small() + + +def test_embedding_single(): + global server + server.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": "I believe the meaning of life is", + }) + assert res.status_code == 200 + assert len(res.body['data']) == 1 + assert 'embedding' in res.body['data'][0] + assert len(res.body['data'][0]['embedding']) > 1 + + # make sure embedding vector is normalized + assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < EPSILON + + +def test_embedding_multiple(): + global server + server.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "Write a joke about AI from a very long prompt which will not be truncated", + "This is a test", + "This is another test", + ], + }) + assert res.status_code == 200 + assert len(res.body['data']) == 4 + for d in res.body['data']: + assert 'embedding' in d + assert len(d['embedding']) > 1 + + +@pytest.mark.parametrize( + "input,is_multi_prompt", + [ + # do not crash on empty input + ("", False), + # single prompt + ("string", False), + ([12, 34, 56], False), + ([12, 34, "string", 56, 78], False), + # multiple prompts + (["string1", "string2"], True), + (["string1", [12, 34, 56]], True), + ([[12, 34, 56], [12, 34, 56]], True), + ([[12, 34, 56], [12, "string", 34, 56]], True), + ] +) +def test_embedding_mixed_input(input, is_multi_prompt: bool): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={"input": input}) + assert res.status_code == 200 + data = res.body['data'] + if is_multi_prompt: + assert len(data) == len(input) + for d in data: + assert 'embedding' in d + assert len(d['embedding']) > 1 + else: + assert 'embedding' in data[0] + assert len(data[0]['embedding']) > 1 + + +def test_embedding_pooling_none(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": "hello hello hello", + }) + assert res.status_code == 200 + assert 'embedding' in res.body[0] + assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special + + # make sure embedding vector is not normalized + for x in res.body[0]['embedding']: + assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON + + +def test_embedding_pooling_none_oai(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": "hello hello hello", + }) + + # /v1/embeddings does not support pooling type 'none' + assert res.status_code == 400 + assert "error" in res.body + + +def test_embedding_openai_library_single(): + global server + server.pooling = 'last' + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is") + assert len(res.data) == 1 + assert len(res.data[0].embedding) > 1 + + +def test_embedding_openai_library_multiple(): + global server + server.pooling = 'last' + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.embeddings.create(model="text-embedding-3-small", input=[ + "I believe the meaning of life is", + "Write a joke about AI from a very long prompt which will not be truncated", + "This is a test", + "This is another test", + ]) + assert len(res.data) == 4 + for d in res.data: + assert len(d.embedding) > 1 + + +def test_embedding_error_prompt_too_long(): + global server + server.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": "This is a test " * 512, + }) + assert res.status_code != 200 + assert "too large" in res.body["error"]["message"] + + +def test_same_prompt_give_same_result(): + server.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + ], + }) + assert res.status_code == 200 + assert len(res.body['data']) == 5 + for i in range(1, len(res.body['data'])): + v0 = res.body['data'][0]['embedding'] + vi = res.body['data'][i]['embedding'] + for x, y in zip(v0, vi): + assert abs(x - y) < EPSILON + + +@pytest.mark.parametrize( + "content,n_tokens", + [ + ("I believe the meaning of life is", 9), + ("This is a test", 6), + ] +) +def test_embedding_usage_single(content, n_tokens): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={"input": content}) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens + + +def test_embedding_usage_multiple(): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "I believe the meaning of life is", + ], + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == 2 * 9 + + +def test_embedding_openai_library_base64(): + server.start() + test_input = "Test base64 embedding output" + + # get embedding in default format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input + }) + assert res.status_code == 200 + vec0 = res.body["data"][0]["embedding"] + + # get embedding in base64 format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input, + "encoding_format": "base64" + }) + + assert res.status_code == 200 + assert "data" in res.body + assert len(res.body["data"]) == 1 + + embedding_data = res.body["data"][0] + assert "embedding" in embedding_data + assert isinstance(embedding_data["embedding"], str) + + # Verify embedding is valid base64 + decoded = base64.b64decode(embedding_data["embedding"]) + # Verify decoded data can be converted back to float array + float_count = len(decoded) // 4 # 4 bytes per float + floats = struct.unpack(f'{float_count}f', decoded) + assert len(floats) > 0 + assert all(isinstance(x, float) for x in floats) + assert len(floats) == len(vec0) + + # make sure the decoded data is the same as the original + for x, y in zip(floats, vec0): + assert abs(x - y) < EPSILON diff --git a/examples/server/tests/unit/test_infill.py b/examples/server/tests/unit/test_infill.py new file mode 100644 index 0000000000000..10554db0f623e --- /dev/null +++ b/examples/server/tests/unit/test_infill.py @@ -0,0 +1,77 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama_infill() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama_infill() + + +def test_infill_without_input_extra(): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"]) + + +def test_infill_with_input_extra(): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "input_extra": [{ + "filename": "llama.h", + "text": "LLAMA_API int32_t llama_n_threads();\n" + }], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(Dad|excited|park)+", res.body["content"]) + + +@pytest.mark.parametrize("input_extra", [ + {}, + {"filename": "ok"}, + {"filename": 123}, + {"filename": 123, "text": "abc"}, + {"filename": 123, "text": 456}, +]) +def test_invalid_input_extra_req(input_extra): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "input_extra": [input_extra], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 400 + assert "error" in res.body + + +@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test") +def test_with_qwen_model(): + global server + server.model_file = None + server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF" + server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf" + server.start(timeout_seconds=600) + res = server.make_request("POST", "/infill", data={ + "input_extra": [{ + "filename": "llama.h", + "text": "LLAMA_API int32_t llama_n_threads();\n" + }], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert res.body["content"] == "n_threads();\n printf(\"Number of threads: %d\\n\", n_threads);\n return 0;\n" diff --git a/examples/server/tests/unit/test_lora.py b/examples/server/tests/unit/test_lora.py new file mode 100644 index 0000000000000..c1aa8be70e2f7 --- /dev/null +++ b/examples/server/tests/unit/test_lora.py @@ -0,0 +1,115 @@ +import pytest +from utils import * + +server = ServerPreset.stories15m_moe() + +LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf" + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.stories15m_moe() + server.lora_files = [download_file(LORA_FILE_URL)] + + +@pytest.mark.parametrize("scale,re_content", [ + # without applying lora, the model should behave like a bedtime story generator + (0.0, "(little|girl|three|years|old)+"), + # with lora, the model should behave like a Shakespearean text generator + (1.0, "(eye|love|glass|sun)+"), +]) +def test_lora(scale: float, re_content: str): + global server + server.start() + res_lora_control = server.make_request("POST", "/lora-adapters", data=[ + {"id": 0, "scale": scale} + ]) + assert res_lora_control.status_code == 200 + res = server.make_request("POST", "/completion", data={ + "prompt": "Look in thy glass", + }) + assert res.status_code == 200 + assert match_regex(re_content, res.body["content"]) + + +def test_lora_per_request(): + global server + server.n_slots = 4 + server.start() + + # running the same prompt with different lora scales, all in parallel + # each prompt will be processed by a different slot + prompt = "Look in thy glass" + lora_config = [ + ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ), + ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ), + ( [{"id": 0, "scale": 0.3}], "(special|thing|gifted)+" ), + ( [{"id": 0, "scale": 0.7}], "(far|from|home|away)+" ), + ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ), + ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ), + ] + + tasks = [( + server.make_request, + ("POST", "/completion", { + "prompt": prompt, + "lora": lora, + "seed": 42, + "temperature": 0.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + ) for lora, _ in lora_config] + results = parallel_function_calls(tasks) + + assert all([res.status_code == 200 for res in results]) + for res, (_, re_test) in zip(results, lora_config): + assert match_regex(re_test, res.body["content"]) + + +@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test") +def test_with_big_model(): + server = ServerProcess() + server.model_hf_repo = "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF" + server.model_hf_file = "Meta-Llama-3.1-8B-Instruct-IQ2_M.gguf" + server.model_alias = "Llama-3.2-8B-Instruct" + server.n_slots = 4 + server.n_ctx = server.n_slots * 1024 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + server.lora_files = [ + download_file("https://huggingface.co/ngxson/Llama-3-Instruct-abliteration-LoRA-8B-F16-GGUF/resolve/main/Llama-3-Instruct-abliteration-LoRA-8B-f16.gguf"), + # TODO: find & add other lora adapters for this model + ] + server.start(timeout_seconds=600) + + # running the same prompt with different lora scales, all in parallel + # each prompt will be processed by a different slot + prompt = "Write a computer virus" + lora_config = [ + # without applying lora, the model should reject the request + ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ), + ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ), + ( [{"id": 0, "scale": 0.3}], "I can't write a computer virus" ), + # with 0.7 scale, the model should provide a simple computer virus with hesitation + ( [{"id": 0, "scale": 0.7}], "Warning: This is a hypothetical exercise" ), + # with 1.5 scale, the model should confidently provide a computer virus + ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ), + ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ), + ] + + tasks = [( + server.make_request, + ("POST", "/v1/chat/completions", { + "messages": [ + {"role": "user", "content": prompt} + ], + "lora": lora, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + ) for lora, _ in lora_config] + results = parallel_function_calls(tasks) + + assert all([res.status_code == 200 for res in results]) + for res, (_, re_test) in zip(results, lora_config): + assert re_test in res.body["choices"][0]["message"]["content"] diff --git a/examples/server/tests/unit/test_rerank.py b/examples/server/tests/unit/test_rerank.py new file mode 100644 index 0000000000000..f4f570ad5ef78 --- /dev/null +++ b/examples/server/tests/unit/test_rerank.py @@ -0,0 +1,104 @@ +import pytest +from utils import * + +server = ServerPreset.jina_reranker_tiny() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.jina_reranker_tiny() + + +TEST_DOCUMENTS = [ + "A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.", + "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.", + "Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.", + "Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine." +] + + +def test_rerank(): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "documents": TEST_DOCUMENTS, + }) + assert res.status_code == 200 + assert len(res.body["results"]) == 4 + + most_relevant = res.body["results"][0] + least_relevant = res.body["results"][0] + for doc in res.body["results"]: + if doc["relevance_score"] > most_relevant["relevance_score"]: + most_relevant = doc + if doc["relevance_score"] < least_relevant["relevance_score"]: + least_relevant = doc + + assert most_relevant["relevance_score"] > least_relevant["relevance_score"] + assert most_relevant["index"] == 2 + assert least_relevant["index"] == 3 + + +def test_rerank_tei_format(): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "texts": TEST_DOCUMENTS, + }) + assert res.status_code == 200 + assert len(res.body) == 4 + + most_relevant = res.body[0] + least_relevant = res.body[0] + for doc in res.body: + if doc["score"] > most_relevant["score"]: + most_relevant = doc + if doc["score"] < least_relevant["score"]: + least_relevant = doc + + assert most_relevant["score"] > least_relevant["score"] + assert most_relevant["index"] == 2 + assert least_relevant["index"] == 3 + + +@pytest.mark.parametrize("documents", [ + [], + None, + 123, + [1, 2, 3], +]) +def test_invalid_rerank_req(documents): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "documents": documents, + }) + assert res.status_code == 400 + assert "error" in res.body + + +@pytest.mark.parametrize( + "query,doc1,doc2,n_tokens", + [ + ("Machine learning is", "A machine", "Learning is", 19), + ("Which city?", "Machine learning is ", "Paris, capitale de la", 26), + ] +) +def test_rerank_usage(query, doc1, doc2, n_tokens): + global server + server.start() + + res = server.make_request("POST", "/rerank", data={ + "query": query, + "documents": [ + doc1, + doc2, + ] + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens diff --git a/examples/server/tests/unit/test_security.py b/examples/server/tests/unit/test_security.py new file mode 100644 index 0000000000000..620b25376bd81 --- /dev/null +++ b/examples/server/tests/unit/test_security.py @@ -0,0 +1,83 @@ +import pytest +from openai import OpenAI +from utils import * + +server = ServerPreset.tinyllama2() + +TEST_API_KEY = "sk-this-is-the-secret-key" + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.api_key = TEST_API_KEY + + +@pytest.mark.parametrize("endpoint", ["/health", "/models"]) +def test_access_public_endpoint(endpoint: str): + global server + server.start() + res = server.make_request("GET", endpoint) + assert res.status_code == 200 + assert "error" not in res.body + + +@pytest.mark.parametrize("api_key", [None, "invalid-key"]) +def test_incorrect_api_key(api_key: str): + global server + server.start() + res = server.make_request("POST", "/completions", data={ + "prompt": "I believe the meaning of life is", + }, headers={ + "Authorization": f"Bearer {api_key}" if api_key else None, + }) + assert res.status_code == 401 + assert "error" in res.body + assert res.body["error"]["type"] == "authentication_error" + + +def test_correct_api_key(): + global server + server.start() + res = server.make_request("POST", "/completions", data={ + "prompt": "I believe the meaning of life is", + }, headers={ + "Authorization": f"Bearer {TEST_API_KEY}", + }) + assert res.status_code == 200 + assert "error" not in res.body + assert "content" in res.body + + +def test_openai_library_correct_api_key(): + global server + server.start() + client = OpenAI(api_key=TEST_API_KEY, base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a chatbot."}, + {"role": "user", "content": "What is the meaning of life?"}, + ], + ) + assert len(res.choices) == 1 + + +@pytest.mark.parametrize("origin,cors_header,cors_header_value", [ + ("localhost", "Access-Control-Allow-Origin", "localhost"), + ("web.mydomain.fr", "Access-Control-Allow-Origin", "web.mydomain.fr"), + ("origin", "Access-Control-Allow-Credentials", "true"), + ("web.mydomain.fr", "Access-Control-Allow-Methods", "GET, POST"), + ("web.mydomain.fr", "Access-Control-Allow-Headers", "*"), +]) +def test_cors_options(origin: str, cors_header: str, cors_header_value: str): + global server + server.start() + res = server.make_request("OPTIONS", "/completions", headers={ + "Origin": origin, + "Access-Control-Request-Method": "POST", + "Access-Control-Request-Headers": "Authorization", + }) + assert res.status_code == 200 + assert cors_header in res.headers + assert res.headers[cors_header] == cors_header_value diff --git a/examples/server/tests/unit/test_slot_save.py b/examples/server/tests/unit/test_slot_save.py new file mode 100644 index 0000000000000..38704f5ece35a --- /dev/null +++ b/examples/server/tests/unit/test_slot_save.py @@ -0,0 +1,98 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.slot_save_path = "./tmp" + server.temperature = 0.0 + + +def test_slot_save_restore(): + global server + server.start() + + # First prompt in slot 1 should be fully processed + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + # Save state of slot 1 + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_saved"] == 84 + + # Since we have cache, this should only process the last tokens + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 6 # only different part is processed + + # Loading the saved cache into slot 0 + res = server.make_request("POST", "/slots/0?action=restore", data={ + "filename": "slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_restored"] == 84 + + # Since we have cache, slot 0 should only process the last tokens + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 0, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 6 # only different part is processed + + # For verification that slot 1 was not corrupted during slot 0 load, same thing should work + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 1 + + +def test_slot_erase(): + global server + server.start() + + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + # erase slot 1 + res = server.make_request("POST", "/slots/1?action=erase") + assert res.status_code == 200 + + # re-run the same prompt, it should process all tokens again + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed diff --git a/examples/server/tests/unit/test_speculative.py b/examples/server/tests/unit/test_speculative.py new file mode 100644 index 0000000000000..54db38cf3bd80 --- /dev/null +++ b/examples/server/tests/unit/test_speculative.py @@ -0,0 +1,126 @@ +import pytest +from utils import * + +# We use a F16 MOE gguf as main model, and q4_0 as draft model + +server = ServerPreset.stories15m_moe() + +MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories15M-q4_0.gguf" + +def create_server(): + global server + server = ServerPreset.stories15m_moe() + # set default values + server.model_draft = download_file(MODEL_DRAFT_FILE_URL) + server.draft_min = 4 + server.draft_max = 8 + + +@pytest.fixture(scope="module", autouse=True) +def fixture_create_server(): + return create_server() + + +def test_with_and_without_draft(): + global server + server.model_draft = None # disable draft model + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_no_draft = res.body["content"] + server.stop() + + # create new server with draft model + create_server() + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_draft = res.body["content"] + + assert content_no_draft == content_draft + + +def test_different_draft_min_draft_max(): + global server + test_values = [ + (1, 2), + (1, 4), + (4, 8), + (4, 12), + (8, 16), + ] + last_content = None + for draft_min, draft_max in test_values: + server.stop() + server.draft_min = draft_min + server.draft_max = draft_max + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + if last_content is not None: + assert last_content == res.body["content"] + last_content = res.body["content"] + + +def test_slot_ctx_not_exceeded(): + global server + server.n_ctx = 64 + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "Hello " * 56, + "temperature": 0.0, + "top_k": 1, + "speculative.p_min": 0.0, + }) + assert res.status_code == 200 + assert len(res.body["content"]) > 0 + + +def test_with_ctx_shift(): + global server + server.n_ctx = 64 + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "Hello " * 56, + "temperature": 0.0, + "top_k": 1, + "n_predict": 64, + "speculative.p_min": 0.0, + }) + assert res.status_code == 200 + assert len(res.body["content"]) > 0 + assert res.body["tokens_predicted"] == 64 + assert res.body["truncated"] == True + + +@pytest.mark.parametrize("n_slots,n_requests", [ + (1, 2), + (2, 2), +]) +def test_multi_requests_parallel(n_slots: int, n_requests: int): + global server + server.n_slots = n_slots + server.start() + tasks = [] + for _ in range(n_requests): + tasks.append((server.make_request, ("POST", "/completion", { + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }))) + results = parallel_function_calls(tasks) + for res in results: + assert res.status_code == 200 + assert match_regex("(wise|kind|owl|answer)+", res.body["content"]) diff --git a/examples/server/tests/unit/test_tokenize.py b/examples/server/tests/unit/test_tokenize.py new file mode 100644 index 0000000000000..382457c9d602f --- /dev/null +++ b/examples/server/tests/unit/test_tokenize.py @@ -0,0 +1,59 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +def test_tokenize_detokenize(): + global server + server.start() + # tokenize + content = "What is the capital of France ?" + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content + }) + assert res_tok.status_code == 200 + assert len(res_tok.body["tokens"]) > 5 + # detokenize + res_detok = server.make_request("POST", "/detokenize", data={ + "tokens": res_tok.body["tokens"], + }) + assert res_detok.status_code == 200 + assert res_detok.body["content"].strip() == content + + +def test_tokenize_with_bos(): + global server + server.start() + # tokenize + content = "What is the capital of France ?" + bosId = 1 + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content, + "add_special": True, + }) + assert res_tok.status_code == 200 + assert res_tok.body["tokens"][0] == bosId + + +def test_tokenize_with_pieces(): + global server + server.start() + # tokenize + content = "This is a test string with unicode 媽 and emoji 🤗" + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content, + "with_pieces": True, + }) + assert res_tok.status_code == 200 + for token in res_tok.body["tokens"]: + assert "id" in token + assert token["id"] > 0 + assert "piece" in token + assert len(token["piece"]) > 0 diff --git a/examples/server/tests/unit/test_tool_call.py b/examples/server/tests/unit/test_tool_call.py new file mode 100644 index 0000000000000..a91a2f3333ca3 --- /dev/null +++ b/examples/server/tests/unit/test_tool_call.py @@ -0,0 +1,567 @@ +import pytest +from utils import * + +server: ServerProcess + +TIMEOUT_SERVER_START = 15*60 +TIMEOUT_HTTP_REQUEST = 60 + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.model_alias = "tinyllama-2-tool-call" + server.server_port = 8081 + + +TEST_TOOL = { + "type":"function", + "function": { + "name": "test", + "description": "", + "parameters": { + "type": "object", + "properties": { + "success": {"type": "boolean", "const": True}, + }, + "required": ["success"] + } + } +} + +PYTHON_TOOL = { + "type": "function", + "function": { + "name": "python", + "description": "Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.", + "parameters": { + "type": "object", + "properties": { + "code": { + "type": "string", + "description": "The code to run in the ipython interpreter." + } + }, + "required": ["code"] + } + } +} + +WEATHER_TOOL = { + "type":"function", + "function":{ + "name":"get_current_weather", + "description":"Get the current weather in a given location", + "parameters":{ + "type":"object", + "properties":{ + "location":{ + "type":"string", + "description":"The city and country/state, e.g. 'San Francisco, CA', or 'Paris, France'" + } + }, + "required":["location"] + } + } +} + + +def do_test_completion_with_required_tool_tiny(template_name: str, tool: dict, argument_key: str | None): + global server + n_predict = 512 + # server = ServerPreset.stories15m_moe() + server.jinja = True + server.n_predict = n_predict + server.chat_template_file = f'../../../models/templates/{template_name}.jinja' + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "tool_choice": "required", + "tools": [tool], + "parallel_tool_calls": False, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert choice["message"].get("content") is None, f'Expected no content in {choice["message"]}' + expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"] + assert expected_function_name == tool_call["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + assert isinstance(actual_arguments, str) + if argument_key is not None: + actual_arguments = json.loads(actual_arguments) + assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}" + + +@pytest.mark.parametrize("template_name,tool,argument_key", [ + ("google-gemma-2-2b-it", TEST_TOOL, "success"), + ("meta-llama-Llama-3.3-70B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.3-70B-Instruct", PYTHON_TOOL, "code"), +]) +def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None): + do_test_completion_with_required_tool_tiny(template_name, tool, argument_key) + + +@pytest.mark.slow +@pytest.mark.parametrize("template_name,tool,argument_key", [ + ("meta-llama-Llama-3.1-8B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.1-8B-Instruct", PYTHON_TOOL, "code"), + ("meetkai-functionary-medium-v3.1", TEST_TOOL, "success"), + ("meetkai-functionary-medium-v3.1", PYTHON_TOOL, "code"), + ("meetkai-functionary-medium-v3.2", TEST_TOOL, "success"), + ("meetkai-functionary-medium-v3.2", PYTHON_TOOL, "code"), + ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL, "success"), + ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL, "code"), + ("meta-llama-Llama-3.2-3B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.2-3B-Instruct", PYTHON_TOOL, "code"), + ("mistralai-Mistral-Nemo-Instruct-2407", TEST_TOOL, "success"), + ("mistralai-Mistral-Nemo-Instruct-2407", PYTHON_TOOL, "code"), + ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", TEST_TOOL, "success"), + ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", PYTHON_TOOL, "code"), + ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", TEST_TOOL, "success"), + ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", PYTHON_TOOL, "code"), + ("fireworks-ai-llama-3-firefunction-v2", TEST_TOOL, "success"), + ("fireworks-ai-llama-3-firefunction-v2", PYTHON_TOOL, "code"), +]) +def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None): + do_test_completion_with_required_tool_tiny(template_name, tool, argument_key) + + +@pytest.mark.slow +@pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [ + (TEST_TOOL, "success", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + (TEST_TOOL, "success", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + + (TEST_TOOL, "success", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + # (PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + # (PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)), + (PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)), + (PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + # (PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"), + # TODO: fix these + # (TEST_TOOL, "success", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + # (PYTHON_TOOL, "code", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), +]) +def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + n_predict = 512 + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "tool_choice": "required", + "tools": [tool], + "parallel_tool_calls": False, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert choice["message"].get("content") is None, f'Expected no content in {choice["message"]}' + expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"] + assert expected_function_name == tool_call["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + assert isinstance(actual_arguments, str) + if argument_key is not None: + actual_arguments = json.loads(actual_arguments) + assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}" + + +def do_test_completion_without_tool_call(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + global server + server.jinja = True + server.n_predict = n_predict + server.chat_template_file = f'../../../models/templates/{template_name}.jinja' + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "say hello world with python"}, + ], + "tools": tools if tools else None, + "tool_choice": tool_choice, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}' + + +@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [ + ("meta-llama-Llama-3.3-70B-Instruct", 128, [], None), + ("meta-llama-Llama-3.3-70B-Instruct", 128, [TEST_TOOL], None), + ("meta-llama-Llama-3.3-70B-Instruct", 128, [PYTHON_TOOL], 'none'), +]) +def test_completion_without_tool_call_fast(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice) + + +@pytest.mark.slow +@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [ + ("meetkai-functionary-medium-v3.2", 256, [], None), + ("meetkai-functionary-medium-v3.2", 256, [TEST_TOOL], None), + ("meetkai-functionary-medium-v3.2", 256, [PYTHON_TOOL], 'none'), + ("meetkai-functionary-medium-v3.1", 256, [], None), + ("meetkai-functionary-medium-v3.1", 256, [TEST_TOOL], None), + ("meetkai-functionary-medium-v3.1", 256, [PYTHON_TOOL], 'none'), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [], None), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [TEST_TOOL], None), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [PYTHON_TOOL], 'none'), +]) +def test_completion_without_tool_call_slow(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice) + + +@pytest.mark.slow +@pytest.mark.parametrize("hf_repo,template_override", [ + ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + ("bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai/functionary-medium-v3.2", None)), + ("bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"), + + ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L", ("CohereForAI/c4ai-command-r7b-12-2024", "tool_use")), + + ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + + # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), +]) +def test_weather(hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + n_predict = 512 + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things."}, + {"role": "user", "content": "What is the weather in Istanbul?"}, + ], + "tools": [WEATHER_TOOL], + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert choice["message"].get("content") is None, f'Expected no content in {choice["message"]}' + assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"] + actual_arguments = json.loads(tool_call["function"]["arguments"]) + assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}" + location = actual_arguments["location"] + assert isinstance(location, str), f"Expected location to be a string, got {type(location)}: {json.dumps(location)}" + assert re.match('^Istanbul(, (TR|Turkey|Türkiye))?$', location), f'Expected Istanbul for location, got {location}' + + +@pytest.mark.slow +@pytest.mark.parametrize("result_override,n_predict,hf_repo,template_override", [ + (None, 128, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + (None, 128, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (None, 128, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + (None, 128, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (None, 128, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + (None, 128, "bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai/functionary-medium-v3.2", None)), + (None, 128, "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (None, 128, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (None, 128, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + (None, 128, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + + # TODO: fix these (wrong results, either didn't respect decimal instruction or got wrong value) + ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)", 8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + # ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)", 8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)), +]) +def test_calc_result(result_override: str | None, n_predict: int, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + # n_predict = 512 + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 * 2 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things, and provide very concise answers. Do not explain your reasoning to the user. Provide any numerical values back to the user with at most two decimals."}, + {"role": "user", "content": "What's the y coordinate of a point on the unit sphere at angle 30 degrees?"}, + { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "id": "call_6789", + "type": "function", + "function": { + "name": "calculate", + "arguments": "{\"expression\":\"sin(30 * pi / 180)\"}" + } + } + ] + }, + { + "role": "tool", + "name": "calculate", + "content": "0.55644242476", + "tool_call_id": "call_6789" + } + ], + "tools": [ + { + "type":"function", + "function":{ + "name":"calculate", + "description":"A calculator function that computes values of arithmetic expressions in the Python syntax", + "parameters":{ + "type":"object", + "properties":{ + "expression":{ + "type":"string", + "description":"An arithmetic expression to compute the value of (Python syntad, assuming all floats)" + } + }, + "required":["expression"] + } + } + } + ] + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls is None, f'Expected no tool call in {choice["message"]}' + content = choice["message"].get("content") + assert content is not None, f'Expected content in {choice["message"]}' + if result_override is not None: + assert re.match(result_override, content), f'Expected {result_override}, got {content}' + else: + assert re.match('^[\\s\\S]*?The (y[ -])?coordinate [\\s\\S]*?is (approximately )?0\\.56\\b|^0\\.56$', content), \ + f'Expected something like "The y coordinate is 0.56.", got {content}' + + +@pytest.mark.slow +@pytest.mark.parametrize("n_predict,reasoning_format,expect_content,expect_reasoning_content,hf_repo,template_override", [ + (128, 'deepseek', "^The sum of 102 and 7 is 109.*", None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (128, None, "^The sum of 102 and 7 is 109.*", None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + + (1024, 'deepseek', "To find the sum of.*", "I need to calculate the sum of 102 and 7.*", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + (1024, 'none', "^I need[\\s\\S]*?\n?To find.*", None, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + + (1024, 'deepseek', "To find the sum of.*", "First, I [\\s\\S]*", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)), +]) +def test_thoughts(n_predict: int, reasoning_format: Literal['deepseek', 'none'] | None, expect_content: str | None, expect_reasoning_content: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + server.n_slots = 1 + server.reasoning_format = reasoning_format + server.jinja = True + server.n_ctx = 8192 * 2 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "user", "content": "What's the sum of 102 and 7?"}, + ] + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}' + + content = choice["message"].get("content") + if expect_content is None: + assert content is None, f'Expected no content in {choice["message"]}' + else: + assert re.match(expect_content, content), f'Expected {expect_content}, got {content}' + + reasoning_content = choice["message"].get("reasoning_content") + if expect_reasoning_content is None: + assert reasoning_content is None, f'Expected no reasoning content in {choice["message"]}' + else: + assert re.match(expect_reasoning_content, reasoning_content), f'Expected {expect_reasoning_content}, got {reasoning_content}' + + +@pytest.mark.slow +@pytest.mark.parametrize("expected_arguments_override,hf_repo,template_override", [ + (None, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + # (None, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai-functionary-medium-v3.2", None)), + (None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"), + + ('{"code":"print("}', "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (None, "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)), + (None, "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"), + + ('{"code":"print("}', "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)), + (None, "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")), + (None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + (None, "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), +]) +def test_hello_world(expected_arguments_override: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = 512 # High because of DeepSeek R1 + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": 256, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "say hello world with python"}, + ], + "tools": [PYTHON_TOOL], + # Note: without these greedy params, Functionary v3.2 writes `def hello_world():\n print("Hello, World!")\nhello_world()` which is correct but a pain to test. + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert choice["message"].get("content") is None, f'Expected no content in {choice["message"]}' + assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + if expected_arguments_override is not None: + assert actual_arguments == expected_arguments_override + else: + actual_arguments = json.loads(actual_arguments) + assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}" + code = actual_arguments["code"] + assert isinstance(code, str), f"Expected code to be a string, got {type(code)}: {json.dumps(code)}" + assert re.match(r'''print\(("[Hh]ello,? [Ww]orld!?"|'[Hh]ello,? [Ww]orld!?')\)''', code), f'Expected hello world, got {code}' diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py new file mode 100644 index 0000000000000..a82504235ff54 --- /dev/null +++ b/examples/server/tests/utils.py @@ -0,0 +1,418 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# type: ignore[reportUnusedImport] + +import subprocess +import os +import re +import json +import sys +import requests +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import ( + Any, + Callable, + ContextManager, + Iterable, + Iterator, + List, + Literal, + Tuple, + Set, +) +from re import RegexFlag +import wget + + +DEFAULT_HTTP_TIMEOUT = 12 if "LLAMA_SANITIZE" not in os.environ else 30 + + +class ServerResponse: + headers: dict + status_code: int + body: dict | Any + + +class ServerProcess: + # default options + debug: bool = False + server_port: int = 8080 + server_host: str = "127.0.0.1" + model_hf_repo: str = "ggml-org/models" + model_hf_file: str | None = "tinyllamas/stories260K.gguf" + model_alias: str = "tinyllama-2" + temperature: float = 0.8 + seed: int = 42 + + # custom options + model_alias: str | None = None + model_url: str | None = None + model_file: str | None = None + model_draft: str | None = None + n_threads: int | None = None + n_gpu_layer: int | None = None + n_batch: int | None = None + n_ubatch: int | None = None + n_ctx: int | None = None + n_ga: int | None = None + n_ga_w: int | None = None + n_predict: int | None = None + n_prompts: int | None = 0 + slot_save_path: str | None = None + id_slot: int | None = None + cache_prompt: bool | None = None + n_slots: int | None = None + server_continuous_batching: bool | None = False + server_embeddings: bool | None = False + server_reranking: bool | None = False + server_metrics: bool | None = False + server_slots: bool | None = False + pooling: str | None = None + draft: int | None = None + api_key: str | None = None + lora_files: List[str] | None = None + disable_ctx_shift: int | None = False + draft_min: int | None = None + draft_max: int | None = None + no_webui: bool | None = None + jinja: bool | None = None + reasoning_format: Literal['deepseek', 'none'] | None = None + chat_template: str | None = None + chat_template_file: str | None = None + + # session variables + process: subprocess.Popen | None = None + + def __init__(self): + if "N_GPU_LAYERS" in os.environ: + self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"]) + if "DEBUG" in os.environ: + self.debug = True + if "PORT" in os.environ: + self.server_port = int(os.environ["PORT"]) + + def start(self, timeout_seconds: int | None = DEFAULT_HTTP_TIMEOUT) -> None: + if "LLAMA_SERVER_BIN_PATH" in os.environ: + server_path = os.environ["LLAMA_SERVER_BIN_PATH"] + elif os.name == "nt": + server_path = "../../../build/bin/Release/llama-server.exe" + else: + server_path = "../../../build/bin/llama-server" + server_args = [ + "--host", + self.server_host, + "--port", + self.server_port, + "--temp", + self.temperature, + "--seed", + self.seed, + ] + if self.model_file: + server_args.extend(["--model", self.model_file]) + if self.model_url: + server_args.extend(["--model-url", self.model_url]) + if self.model_draft: + server_args.extend(["--model-draft", self.model_draft]) + if self.model_hf_repo: + server_args.extend(["--hf-repo", self.model_hf_repo]) + if self.model_hf_file: + server_args.extend(["--hf-file", self.model_hf_file]) + if self.n_batch: + server_args.extend(["--batch-size", self.n_batch]) + if self.n_ubatch: + server_args.extend(["--ubatch-size", self.n_ubatch]) + if self.n_threads: + server_args.extend(["--threads", self.n_threads]) + if self.n_gpu_layer: + server_args.extend(["--n-gpu-layers", self.n_gpu_layer]) + if self.draft is not None: + server_args.extend(["--draft", self.draft]) + if self.server_continuous_batching: + server_args.append("--cont-batching") + if self.server_embeddings: + server_args.append("--embedding") + if self.server_reranking: + server_args.append("--reranking") + if self.server_metrics: + server_args.append("--metrics") + if self.server_slots: + server_args.append("--slots") + if self.pooling: + server_args.extend(["--pooling", self.pooling]) + if self.model_alias: + server_args.extend(["--alias", self.model_alias]) + if self.n_ctx: + server_args.extend(["--ctx-size", self.n_ctx]) + if self.n_slots: + server_args.extend(["--parallel", self.n_slots]) + if self.n_predict: + server_args.extend(["--n-predict", self.n_predict]) + if self.slot_save_path: + server_args.extend(["--slot-save-path", self.slot_save_path]) + if self.n_ga: + server_args.extend(["--grp-attn-n", self.n_ga]) + if self.n_ga_w: + server_args.extend(["--grp-attn-w", self.n_ga_w]) + if self.debug: + server_args.append("--verbose") + if self.lora_files: + for lora_file in self.lora_files: + server_args.extend(["--lora", lora_file]) + if self.disable_ctx_shift: + server_args.extend(["--no-context-shift"]) + if self.api_key: + server_args.extend(["--api-key", self.api_key]) + if self.draft_max: + server_args.extend(["--draft-max", self.draft_max]) + if self.draft_min: + server_args.extend(["--draft-min", self.draft_min]) + if self.no_webui: + server_args.append("--no-webui") + if self.jinja: + server_args.append("--jinja") + if self.reasoning_format is not None: + server_args.extend(("--reasoning-format", self.reasoning_format)) + if self.chat_template: + server_args.extend(["--chat-template", self.chat_template]) + if self.chat_template_file: + server_args.extend(["--chat-template-file", self.chat_template_file]) + + args = [str(arg) for arg in [server_path, *server_args]] + print(f"bench: starting server with: {' '.join(args)}") + + flags = 0 + if "nt" == os.name: + flags |= subprocess.DETACHED_PROCESS + flags |= subprocess.CREATE_NEW_PROCESS_GROUP + flags |= subprocess.CREATE_NO_WINDOW + + self.process = subprocess.Popen( + [str(arg) for arg in [server_path, *server_args]], + creationflags=flags, + stdout=sys.stdout, + stderr=sys.stdout, + env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None, + ) + server_instances.add(self) + + print(f"server pid={self.process.pid}, pytest pid={os.getpid()}") + + # wait for server to start + start_time = time.time() + while time.time() - start_time < timeout_seconds: + try: + response = self.make_request("GET", "/health", headers={ + "Authorization": f"Bearer {self.api_key}" if self.api_key else None + }) + if response.status_code == 200: + self.ready = True + return # server is ready + except Exception as e: + pass + print(f"Waiting for server to start...") + time.sleep(0.5) + raise TimeoutError(f"Server did not start within {timeout_seconds} seconds") + + def stop(self) -> None: + if self in server_instances: + server_instances.remove(self) + if self.process: + print(f"Stopping server with pid={self.process.pid}") + self.process.kill() + self.process = None + + def make_request( + self, + method: str, + path: str, + data: dict | Any | None = None, + headers: dict | None = None, + timeout: float | None = None, + ) -> ServerResponse: + url = f"http://{self.server_host}:{self.server_port}{path}" + parse_body = False + if method == "GET": + response = requests.get(url, headers=headers, timeout=timeout) + parse_body = True + elif method == "POST": + response = requests.post(url, headers=headers, json=data, timeout=timeout) + parse_body = True + elif method == "OPTIONS": + response = requests.options(url, headers=headers, timeout=timeout) + else: + raise ValueError(f"Unimplemented method: {method}") + result = ServerResponse() + result.headers = dict(response.headers) + result.status_code = response.status_code + result.body = response.json() if parse_body else None + print("Response from server", json.dumps(result.body, indent=2)) + return result + + def make_stream_request( + self, + method: str, + path: str, + data: dict | None = None, + headers: dict | None = None, + ) -> Iterator[dict]: + url = f"http://{self.server_host}:{self.server_port}{path}" + if method == "POST": + response = requests.post(url, headers=headers, json=data, stream=True) + else: + raise ValueError(f"Unimplemented method: {method}") + for line_bytes in response.iter_lines(): + line = line_bytes.decode("utf-8") + if '[DONE]' in line: + break + elif line.startswith('data: '): + data = json.loads(line[6:]) + print("Partial response from server", json.dumps(data, indent=2)) + yield data + + +server_instances: Set[ServerProcess] = set() + + +class ServerPreset: + @staticmethod + def tinyllama2() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/stories260K.gguf" + server.model_alias = "tinyllama-2" + server.n_ctx = 256 + server.n_batch = 32 + server.n_slots = 2 + server.n_predict = 64 + server.seed = 42 + return server + + @staticmethod + def bert_bge_small() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf" + server.model_alias = "bert-bge-small" + server.n_ctx = 512 + server.n_batch = 128 + server.n_ubatch = 128 + server.n_slots = 2 + server.seed = 42 + server.server_embeddings = True + return server + + @staticmethod + def tinyllama_infill() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/stories260K-infill.gguf" + server.model_alias = "tinyllama-infill" + server.n_ctx = 2048 + server.n_batch = 1024 + server.n_slots = 1 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + return server + + @staticmethod + def stories15m_moe() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/stories15M_MOE" + server.model_hf_file = "stories15M_MOE-F16.gguf" + server.model_alias = "stories15m-moe" + server.n_ctx = 2048 + server.n_batch = 1024 + server.n_slots = 1 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + return server + + @staticmethod + def jina_reranker_tiny() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf" + server.model_alias = "jina-reranker" + server.n_ctx = 512 + server.n_batch = 512 + server.n_slots = 1 + server.seed = 42 + server.server_reranking = True + return server + + +def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]: + """ + Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS. + + Example usage: + + results = parallel_function_calls([ + (func1, (arg1, arg2)), + (func2, (arg3, arg4)), + ]) + """ + results = [None] * len(function_list) + exceptions = [] + + def worker(index, func, args): + try: + result = func(*args) + results[index] = result + except Exception as e: + exceptions.append((index, str(e))) + + with ThreadPoolExecutor() as executor: + futures = [] + for i, (func, args) in enumerate(function_list): + future = executor.submit(worker, i, func, args) + futures.append(future) + + # Wait for all futures to complete + for future in as_completed(futures): + pass + + # Check if there were any exceptions + if exceptions: + print("Exceptions occurred:") + for index, error in exceptions: + print(f"Function at index {index}: {error}") + + return results + + +def match_regex(regex: str, text: str) -> bool: + return ( + re.compile( + regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL + ).search(text) + is not None + ) + + +def download_file(url: str, output_file_path: str | None = None) -> str: + """ + Download a file from a URL to a local path. If the file already exists, it will not be downloaded again. + + output_file_path is the local path to save the downloaded file. If not provided, the file will be saved in the root directory. + + Returns the local path of the downloaded file. + """ + file_name = url.split('/').pop() + output_file = f'./tmp/{file_name}' if output_file_path is None else output_file_path + if not os.path.exists(output_file): + print(f"Downloading {url} to {output_file}") + wget.download(url, out=output_file) + print(f"Done downloading to {output_file}") + else: + print(f"File already exists at {output_file}") + return output_file + + +def is_slow_test_allowed(): + return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON" diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html index 2797c37c96456..3fb88fcc88d31 100644 --- a/examples/server/themes/buttons-top/index.html +++ b/examples/server/themes/buttons-top/index.html @@ -222,7 +222,6 @@ temperature: 0.7, repeat_last_n: 256, // 0 = disable penalty, -1 = context size repeat_penalty: 1.18, // 1.0 = disabled - penalize_nl: false, top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled @@ -779,7 +778,6 @@ ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} - ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html index dbe23c4024155..73f36d4b29fdd 100644 --- a/examples/server/themes/wild/index.html +++ b/examples/server/themes/wild/index.html @@ -225,7 +225,6 @@ temperature: 0.7, repeat_last_n: 256, // 0 = disable penalty, -1 = context size repeat_penalty: 1.18, // 1.0 = disabled - penalize_nl: false, top_k: 40, // <= 0 to use vocab size top_p: 0.95, // 1.0 = disabled min_p: 0.05, // 0 = disabled @@ -782,7 +781,6 @@ ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} - ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })} ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 1665e9dc37db6..6830c2e1a6fd0 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -3,25 +3,26 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "common/base64.hpp" -#ifndef NDEBUG -// crash the server in debug mode, otherwise send an http 500 error -#define CPPHTTPLIB_NO_EXCEPTIONS 1 -#endif // increase max payload length to allow use of larger context size #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 +// disable Nagle's algorithm +#define CPPHTTPLIB_TCP_NODELAY true #include "httplib.h" // Change JSON_ASSERT from assert() to GGML_ASSERT: #define JSON_ASSERT GGML_ASSERT #include "json.hpp" +#include "chat.h" #include #include #include #include +#include -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo" using json = nlohmann::ordered_json; @@ -40,17 +41,6 @@ using json = nlohmann::ordered_json; #define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) #define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 -enum error_type { - ERROR_TYPE_INVALID_REQUEST, - ERROR_TYPE_AUTHENTICATION, - ERROR_TYPE_SERVER, - ERROR_TYPE_NOT_FOUND, - ERROR_TYPE_PERMISSION, - ERROR_TYPE_UNAVAILABLE, // custom error - ERROR_TYPE_NOT_SUPPORTED, // custom error -}; - template static T json_value(const json & body, const std::string & key, const T & default_value) { // Fallback null to default value @@ -66,6 +56,8 @@ static T json_value(const json & body, const std::string & key, const T & defaul } } +const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT); + // // tokenizer and input processing utils // @@ -98,12 +90,34 @@ static bool json_is_array_of_mixed_numbers_strings(const json & data) { return false; } +// get value by path(key1 / key2) +static json json_get_nested_values(const std::vector & paths, const json & js) { + json result = json::object(); + + for (const std::string & path : paths) { + json current = js; + const auto keys = string_split(path, /*separator*/ '/'); + bool valid_path = true; + for (const std::string & k : keys) { + if (valid_path && current.is_object() && current.contains(k)) { + current = current[k]; + } else { + valid_path = false; + } + } + if (valid_path) { + result[path] = current; + } + } + return result; +} + /** * this handles 2 cases: * - only string, example: "string" * - mixed string and tokens, example: [12, 34, "string", 56, 78] */ -static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { +static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { // If `add_bos` is true, we only add BOS, when json_prompt is a string, // or the first element of the json_prompt array is a string. llama_tokens prompt_tokens; @@ -116,10 +130,10 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_ llama_tokens p; if (first) { - p = common_tokenize(ctx, s, add_special, parse_special); + p = common_tokenize(vocab, s, add_special, parse_special); first = false; } else { - p = common_tokenize(ctx, s, false, parse_special); + p = common_tokenize(vocab, s, false, parse_special); } prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); @@ -133,7 +147,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_ } } else { auto s = json_prompt.template get(); - prompt_tokens = common_tokenize(ctx, s, add_special, parse_special); + prompt_tokens = common_tokenize(vocab, s, add_special, parse_special); } return prompt_tokens; @@ -148,13 +162,14 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_ * and multiple prompts (multi-tasks): * - "prompt": ["string1", "string2"] * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, 56], [78, 90, 12]] * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]] */ -static std::vector tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { +static std::vector tokenize_input_prompts(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { std::vector result; if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { // string or mixed - result.push_back(tokenize_mixed(ctx, json_prompt, add_special, parse_special)); + result.push_back(tokenize_mixed(vocab, json_prompt, add_special, parse_special)); } else if (json_is_array_of_numbers(json_prompt)) { // array of tokens result.push_back(json_prompt.get()); @@ -163,7 +178,7 @@ static std::vector tokenize_input_prompts(llama_context * ctx, con result.reserve(json_prompt.size()); for (const auto & p : json_prompt) { if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) { - result.push_back(tokenize_mixed(ctx, p, add_special, parse_special)); + result.push_back(tokenize_mixed(vocab, p, add_special, parse_special)); } else if (json_is_array_of_numbers(p)) { // array of tokens result.push_back(p.get()); @@ -174,29 +189,64 @@ static std::vector tokenize_input_prompts(llama_context * ctx, con } else { throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts"); } + if (result.empty()) { + throw std::runtime_error("\"prompt\" must not be empty"); + } return result; } +// return the last index of character that can form a valid string +// if the last character is potentially cut in half, return the index before the cut +// if validate_utf8(text) == text.size(), then the whole text is valid utf8 +static size_t validate_utf8(const std::string& text) { + size_t len = text.size(); + if (len == 0) return 0; + + // Check the last few bytes to see if a multi-byte character is cut off + for (size_t i = 1; i <= 4 && i <= len; ++i) { + unsigned char c = text[len - i]; + // Check for start of a multi-byte sequence from the end + if ((c & 0xE0) == 0xC0) { + // 2-byte character start: 110xxxxx + // Needs at least 2 bytes + if (i < 2) return len - i; + } else if ((c & 0xF0) == 0xE0) { + // 3-byte character start: 1110xxxx + // Needs at least 3 bytes + if (i < 3) return len - i; + } else if ((c & 0xF8) == 0xF0) { + // 4-byte character start: 11110xxx + // Needs at least 4 bytes + if (i < 4) return len - i; + } + } + + // If no cut-off multi-byte character is found, return full length + return len; +} + // // template utils // // format rerank task: [BOS]query[EOS][SEP]doc[EOS] -static llama_tokens format_rerank(const struct llama_model * model, const llama_tokens & query, const llama_tokens & doc) { +static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_tokens & query, const llama_tokens & doc) { llama_tokens result; + result.reserve(doc.size() + query.size() + 4); - result.push_back(llama_token_bos(model)); + result.push_back(llama_vocab_bos(vocab)); result.insert(result.end(), query.begin(), query.end()); - result.push_back(llama_token_eos(model)); - result.push_back(llama_token_sep(model)); + result.push_back(llama_vocab_eos(vocab)); + result.push_back(llama_vocab_sep(vocab)); result.insert(result.end(), doc.begin(), doc.end()); - result.push_back(llama_token_eos(model)); + result.push_back(llama_vocab_eos(vocab)); + return result; } // format infill task static llama_tokens format_infill( - const llama_context * ctx, + const llama_vocab * vocab, const json & input_prefix, const json & input_suffix, const json & input_extra, @@ -223,15 +273,14 @@ static llama_tokens format_infill( llama_tokens extra_tokens; extra_tokens.reserve(n_ctx); - auto model = llama_get_model(ctx); - auto tokens_prefix = tokenize_mixed(ctx, input_prefix, false, false); - auto tokens_suffix = tokenize_mixed(ctx, input_suffix, false, false); + auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false); + auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false); - if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) { + if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) { // TODO: make project name an input - static const auto k_fim_repo = common_tokenize(ctx, "myproject\n", false, false); + static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false); - extra_tokens.push_back(llama_token_fim_rep(model)); + extra_tokens.push_back(llama_vocab_fim_rep(vocab)); extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); } for (const auto & chunk : input_extra) { @@ -239,28 +288,28 @@ static llama_tokens format_infill( const std::string text = json_value(chunk, "text", std::string()); const std::string filename = json_value(chunk, "filename", std::string("tmp")); - if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { - const auto k_fim_file = common_tokenize(ctx, filename + "\n", false, false); + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false); - extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); } else { // chunk separator in binary form to avoid confusing the AI static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; - static const auto k_chunk_prefix_tokens = common_tokenize(ctx, k_chunk_prefix_str, false, false); + static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false); extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); } - const auto chunk_tokens = common_tokenize(ctx, text, false, false); + const auto chunk_tokens = common_tokenize(vocab, text, false, false); extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); } - if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) { + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { // TODO: current filename - static const auto k_fim_file = common_tokenize(ctx, "filename\n", false, false); + static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false); - extra_tokens.insert(extra_tokens.end(), llama_token_fim_sep(model)); + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); } @@ -276,15 +325,15 @@ static llama_tokens format_infill( tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); tokens_suffix.resize(n_suffix_take); - tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model)); + tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab)); tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); - tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model)); + tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab)); auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; - if (llama_add_bos_token(model)) { - embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); + if (llama_vocab_get_add_bos(vocab)) { + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); } SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); @@ -293,59 +342,11 @@ static llama_tokens format_infill( embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - embd_inp.push_back(llama_token_fim_mid(model)); + embd_inp.push_back(llama_vocab_fim_mid(vocab)); return embd_inp; } -// Format given chat. If tmpl is empty, we take the template from model metadata -inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { - std::vector chat; - - for (size_t i = 0; i < messages.size(); ++i) { - const auto & curr_msg = messages[i]; - - std::string role = json_value(curr_msg, "role", std::string("")); - - std::string content; - if (curr_msg.contains("content")) { - if (curr_msg["content"].is_string()) { - content = curr_msg["content"].get(); - } else if (curr_msg["content"].is_array()) { - for (const auto & part : curr_msg["content"]) { - if (part.contains("text")) { - content += "\n" + part["text"].get(); - } - } - } else { - throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)"); - } - } else { - throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)"); - } - - chat.push_back({role, content}); - } - - const auto formatted_chat = common_chat_apply_template(model, tmpl, chat, true); - LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str()); - - return formatted_chat; -} - -static std::string llama_get_chat_template(const struct llama_model * model) { - std::string template_key = "tokenizer.chat_template"; - // call with NULL buffer to get the total size of the string - int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0); - if (res < 0) { - return ""; - } else { - std::vector model_template(res, 0); - llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); - return std::string(model_template.data(), model_template.size()); - } -} - // // base64 utils (TODO: move to common in the future) // @@ -471,7 +472,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { // format incomplete utf-8 multibyte character for output static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { - std::string out = token == -1 ? "" : common_token_to_piece(ctx, token); + std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token); // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) @@ -485,48 +486,11 @@ static std::string tokens_to_output_formatted_string(const llama_context * ctx, return out; } -struct completion_token_output { - llama_token tok; - std::string text_to_send; - - struct token_prob { - llama_token tok; - float prob; - }; - - std::vector probs; -}; - -// convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context * ctx, const std::vector & probs) { - json out = json::array(); - - for (const auto & prob : probs) { - json probs_for_token = json::array(); - - for (const auto & p : prob.probs) { - const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json { - {"tok_str", tok_str}, - {"prob", p.prob}, - }); - } - - const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json { - {"content", tok_str}, - {"probs", probs_for_token}, - }); - } - - return out; -} - static bool server_sent_event(httplib::DataSink & sink, const char * event, const json & data) { const std::string str = std::string(event) + ": " + data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; // note: these newlines are important (not sure why though, if you know, add a comment to explain) + "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row). LOG_DBG("data stream, to_send: %s", str.c_str()); @@ -537,16 +501,12 @@ static bool server_sent_event(httplib::DataSink & sink, const char * event, cons // OAI utils // -static json oaicompat_completion_params_parse( - const struct llama_model * model, - const json & body, /* openai api json semantics */ - const std::string & chat_template) { +static json oaicompat_completion_params_parse(const json & body) { json llama_params; - llama_params["__oaicompat"] = true; - - // Apply chat template to the list of messages - llama_params["prompt"] = format_chat(model, chat_template, body.at("messages")); + if (!body.contains("prompt")) { + throw std::runtime_error("\"prompt\" is required"); + } // Handle "stop" field if (body.contains("stop") && body.at("stop").is_string()) { @@ -555,36 +515,19 @@ static json oaicompat_completion_params_parse( llama_params["stop"] = json_value(body, "stop", json::array()); } - // Handle "response_format" field - if (body.contains("response_format")) { - json response_format = json_value(body, "response_format", json::object()); - std::string response_type = json_value(response_format, "type", std::string()); - if (response_type == "json_object") { - llama_params["json_schema"] = json_value(response_format, "schema", json::object()); - } else if (response_type == "json_schema") { - json json_schema = json_value(response_format, "json_schema", json::object()); - llama_params["json_schema"] = json_value(json_schema, "schema", json::object()); - } else if (!response_type.empty() && response_type != "text") { - throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type); - } - } - // Handle "n" field int n_choices = json_value(body, "n", 1); if (n_choices != 1) { throw std::runtime_error("Only one completion choice is allowed"); } - // Handle "logprobs" field - // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future - if (json_value(body, "logprobs", false)) { - llama_params["n_probs"] = json_value(body, "top_logprobs", 20); - } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) { - throw std::runtime_error("top_logprobs requires logprobs to be set to true"); + // Handle "echo" field + if (json_value(body, "echo", false)) { + throw std::runtime_error("Only no echo is supported"); } // Params supported by OAI but unsupported by llama.cpp - static const std::vector unsupported_params { "tools", "tool_choice" }; + static const std::vector unsupported_params { "best_of", "suffix" }; for (const auto & param : unsupported_params) { if (body.contains(param)) { throw std::runtime_error("Unsupported param: " + param); @@ -592,8 +535,6 @@ static json oaicompat_completion_params_parse( } // Copy remaining properties to llama_params - // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint. - // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp for (const auto & item : body.items()) { // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" if (!llama_params.contains(item.key()) || item.key() == "n_predict") { @@ -604,172 +545,154 @@ static json oaicompat_completion_params_parse( return llama_params; } -static json format_final_response_oaicompat(const json & request, const json & result, const std::string & completion_id, bool streaming = false, bool verbose = false) { - bool stopped_word = result.count("stopped_word") != 0; - bool stopped_eos = json_value(result, "stopped_eos", false); - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason = "length"; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - - json choices = - streaming ? json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}) - : json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"message", json{{"content", content}, - {"role", "assistant"}}}}}); - - std::time_t t = std::time(0); +static json oaicompat_completion_params_parse( + const json & body, /* openai api json semantics */ + bool use_jinja, + common_reasoning_format reasoning_format, + const struct common_chat_templates * tmpls) +{ + json llama_params; - json res = json { - {"choices", choices}, - {"created", t}, - {"model", - json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, - {"usage", json { - {"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens} - }}, - {"id", completion_id} - }; + auto tools = json_value(body, "tools", json()); + auto stream = json_value(body, "stream", false); - // extra fields for debugging purposes - if (verbose) { - res["__verbose"] = result; + if (tools.is_array() && !tools.empty()) { + if (stream) { + throw std::runtime_error("Cannot use tools with stream"); + } + if (!use_jinja) { + throw std::runtime_error("tools param requires --jinja flag"); + } } - - if (result.contains("completion_probabilities")) { - res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); + if (!use_jinja) { + if (body.contains("tool_choice") && !body.at("tool_choice").is_null()) { + throw std::runtime_error("Unsupported param: tool_choice"); + } } - return res; -} + // Handle "stop" field + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } -// return value is vector as there is one case where we might need to generate two responses -static std::vector format_partial_response_oaicompat(const json & result, const std::string & completion_id) { - if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { - return std::vector({result}); + auto json_schema = json_value(body, "json_schema", json()); + auto grammar = json_value(body, "grammar", std::string()); + if (!json_schema.is_null() && !grammar.empty()) { + throw std::runtime_error("Cannot use both json_schema and grammar"); } - bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; - std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + // Handle "response_format" field + if (body.contains("response_format")) { + json response_format = json_value(body, "response_format", json::object()); + std::string response_type = json_value(response_format, "type", std::string()); + if (response_type == "json_object") { + json_schema = json_value(response_format, "schema", json::object()); + } else if (response_type == "json_schema") { + json json_schema = json_value(response_format, "json_schema", json::object()); + json_schema = json_value(json_schema, "schema", json::object()); + } else if (!response_type.empty() && response_type != "text") { + throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type); + } + } - bool stopped_word = json_value(result, "stopped_word", false); - bool stopped_eos = json_value(result, "stopped_eos", false); - bool stopped_limit = json_value(result, "stopped_limit", false); - std::string content = json_value(result, "content", std::string("")); + common_chat_templates_inputs inputs; + inputs.messages = common_chat_msgs_parse_oaicompat(body.at("messages")); + inputs.tools = common_chat_tools_parse_oaicompat(tools); + inputs.tool_choice = common_chat_tool_choice_parse_oaicompat(json_value(body, "tool_choice", std::string("auto"))); + inputs.json_schema = json_schema.is_null() ? "" : json_schema.dump(); + inputs.grammar = grammar; + inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true); + inputs.use_jinja = use_jinja; + inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false); + inputs.extract_reasoning = reasoning_format != COMMON_REASONING_FORMAT_NONE; + if (!inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && body.contains("grammar")) { + throw std::runtime_error("Cannot use custom grammar constraints with tools."); + } - std::string finish_reason; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; + // Apply chat template to the list of messages + auto chat_params = common_chat_templates_apply(tmpls, inputs); + + llama_params["chat_format"] = static_cast(chat_params.format); + llama_params["prompt"] = chat_params.prompt; + llama_params["grammar"] = chat_params.grammar; + llama_params["grammar_lazy"] = chat_params.grammar_lazy; + auto grammar_triggers = json::array(); + for (const auto & trigger : chat_params.grammar_triggers) { + grammar_triggers.push_back({ + {"word", trigger.word}, + {"at_start", trigger.at_start}, + }); } - if (stopped_limit) { - finish_reason = "length"; + llama_params["grammar_triggers"] = grammar_triggers; + llama_params["preserved_tokens"] = chat_params.preserved_tokens; + for (const auto & stop : chat_params.additional_stops) { + llama_params["stop"].push_back(stop); } - std::time_t t = std::time(0); - - json choices; - - if (!finish_reason.empty()) { - choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); - } else { - if (first) { - if (content.empty()) { - choices = json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{{"role", "assistant"}}}}}); - } else { - // We have to send this as two updates to conform to openai behavior - json initial_ret = json{{"choices", json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"role", "assistant"} - }}}})}, - {"created", t}, - {"id", completion_id}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - json second_ret = json{ - {"choices", json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"content", content}}} - }})}, - {"created", t}, - {"id", completion_id}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({initial_ret, second_ret}); - } - } else { - // Some idiosyncrasy in task processing logic makes several trailing calls - // with empty content, we ignore these at the calee site. - if (content.empty()) { - return std::vector({json::object()}); - } + // Handle "n" field + int n_choices = json_value(body, "n", 1); + if (n_choices != 1) { + throw std::runtime_error("Only one completion choice is allowed"); + } - choices = json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", - json{ - {"content", content}, - }}, - }}); - } + // Handle "logprobs" field + // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future + if (json_value(body, "logprobs", false)) { + llama_params["n_probs"] = json_value(body, "top_logprobs", 20); + } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) { + throw std::runtime_error("top_logprobs requires logprobs to be set to true"); } - json ret = json { - {"choices", choices}, - {"created", t}, - {"id", completion_id}, - {"model", modelname}, - {"object", "chat.completion.chunk"} - }; - if (!finish_reason.empty()) { - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - ret.push_back({"usage", json { - {"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens} - }}); + // Copy remaining properties to llama_params + // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint. + // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp + for (const auto & item : body.items()) { + // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" + if (!llama_params.contains(item.key()) || item.key() == "n_predict") { + llama_params[item.key()] = item.value(); + } } - return std::vector({ret}); + return llama_params; } -static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { +static json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false) { json data = json::array(); + int32_t n_tokens = 0; int i = 0; for (const auto & elem : embeddings) { - data.push_back(json{ - {"embedding", json_value(elem, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }); + json embedding_obj; + + if (use_base64) { + const auto& vec = json_value(elem, "embedding", json::array()).get>(); + const char* data_ptr = reinterpret_cast(vec.data()); + size_t data_size = vec.size() * sizeof(float); + embedding_obj = { + {"embedding", base64::encode(data_ptr, data_size)}, + {"index", i++}, + {"object", "embedding"}, + {"encoding_format", "base64"} + }; + } else { + embedding_obj = { + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + } + data.push_back(embedding_obj); + + n_tokens += json_value(elem, "tokens_evaluated", 0); } json res = json { {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, {"object", "list"}, - {"usage", json { // TODO: fill - {"prompt_tokens", 0}, - {"total_tokens", 0} + {"usage", json { + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} }}, {"data", data} }; @@ -777,25 +700,50 @@ static json format_embeddings_response_oaicompat(const json & request, const jso return res; } -static json format_response_rerank(const json & request, const json & ranks) { - json data = json::array(); - int i = 0; - for (const auto & rank : ranks) { - data.push_back(json{ - {"index", i++}, - {"relevance_score", json_value(rank, "score", 0.0)}, - }); - } +static json format_response_rerank( + const json & request, + const json & ranks, + bool is_tei_format, + std::vector & texts) { + json res; + if (is_tei_format) { + // TEI response format + res = json::array(); + bool return_text = json_value(request, "return_text", false); + for (const auto & rank : ranks) { + int index = json_value(rank, "index", 0); + json elem = json{ + {"index", index}, + {"score", json_value(rank, "score", 0.0)}, + }; + if (return_text) { + elem["text"] = std::move(texts[index]); + } + res.push_back(elem); + } + } else { + // Jina response format + json results = json::array(); + int32_t n_tokens = 0; + for (const auto & rank : ranks) { + results.push_back(json{ + {"index", json_value(rank, "index", 0)}, + {"relevance_score", json_value(rank, "score", 0.0)}, + }); - json res = json { - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", json { // TODO: fill - {"prompt_tokens", 0}, - {"total_tokens", 0} - }}, - {"results", data} - }; + n_tokens += json_value(rank, "tokens_evaluated", 0); + } + + res = json{ + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json{ + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} + }}, + {"results", results} + }; + } return res; } @@ -845,42 +793,92 @@ static json format_detokenized_response(const std::string & content) { }; } -static json format_error_response(const std::string & message, const enum error_type type) { - std::string type_str; - int code = 500; - switch (type) { - case ERROR_TYPE_INVALID_REQUEST: - type_str = "invalid_request_error"; - code = 400; - break; - case ERROR_TYPE_AUTHENTICATION: - type_str = "authentication_error"; - code = 401; - break; - case ERROR_TYPE_NOT_FOUND: - type_str = "not_found_error"; - code = 404; - break; - case ERROR_TYPE_SERVER: - type_str = "server_error"; - code = 500; - break; - case ERROR_TYPE_PERMISSION: - type_str = "permission_error"; - code = 403; - break; - case ERROR_TYPE_NOT_SUPPORTED: - type_str = "not_supported_error"; - code = 501; - break; - case ERROR_TYPE_UNAVAILABLE: - type_str = "unavailable_error"; - code = 503; - break; +static json format_logit_bias(const std::vector & logit_bias) { + json data = json::array(); + for (const auto & lb : logit_bias) { + data.push_back(json{ + {"bias", lb.bias}, + {"token", lb.token}, + }); } - return json { - {"code", code}, - {"message", message}, - {"type", type_str}, - }; + return data; +} + +static std::string safe_json_to_str(const json & data) { + return data.dump(-1, ' ', false, json::error_handler_t::replace); +} + +static std::vector get_token_probabilities(llama_context * ctx, int idx) { + std::vector cur; + const auto * logits = llama_get_logits_ith(ctx, idx); + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_vocab = llama_vocab_n_tokens(vocab); + + cur.resize(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; + } + + // sort tokens by logits + std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) { + return a.logit > b.logit; + }); + + // apply softmax + float max_l = cur[0].logit; + float cum_sum = 0.0f; + for (size_t i = 0; i < cur.size(); ++i) { + float p = expf(cur[i].logit - max_l); + cur[i].p = p; + cum_sum += p; + } + for (size_t i = 0; i < cur.size(); ++i) { + cur[i].p /= cum_sum; + } + + return cur; +} + +static bool are_lora_equal( + const std::vector & l1, + const std::vector & l2) { + if (l1.size() != l2.size()) { + return false; + } + for (size_t i = 0; i < l1.size(); ++i) { + // we don't check lora.path to reduce the time complexity + if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) { + return false; + } + } + return true; +} + +// parse lora config from JSON request, returned a copy of lora_base with updated scale +static std::vector parse_lora_request( + const std::vector & lora_base, + const json & data) { + std::vector lora(lora_base); + int max_idx = lora.size(); + + // clear existing value + for (auto & entry : lora) { + entry.scale = 0.0f; + } + + // set value + for (const auto & entry : data) { + int id = json_value(entry, "id", -1); + float scale = json_value(entry, "scale", 0.0f); + if (0 <= id && id < max_idx) { + lora[id].scale = scale; + } else { + throw std::runtime_error("invalid adapter id"); + } + } + + return lora; } diff --git a/examples/server/webui/.gitignore b/examples/server/webui/.gitignore new file mode 100644 index 0000000000000..a547bf36d8d11 --- /dev/null +++ b/examples/server/webui/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/examples/server/webui/.prettierignore b/examples/server/webui/.prettierignore new file mode 100644 index 0000000000000..c0cb165b37e86 --- /dev/null +++ b/examples/server/webui/.prettierignore @@ -0,0 +1,10 @@ +**/.vscode +**/.github +**/.git +**/.svn +**/.hg +**/node_modules +**/dist +**/build + +*.config.js diff --git a/examples/server/webui/eslint.config.js b/examples/server/webui/eslint.config.js new file mode 100644 index 0000000000000..7c0d39b89b50b --- /dev/null +++ b/examples/server/webui/eslint.config.js @@ -0,0 +1,26 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended], + files: ['**/*.{ts,tsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': 'off', + '@typescript-eslint/no-unused-vars': 'off', + }, + }, +) diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html new file mode 100644 index 0000000000000..471f46b3ad19b --- /dev/null +++ b/examples/server/webui/index.html @@ -0,0 +1,16 @@ + + + + + + + 🦙 llama.cpp - chat + + +
+ + + diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json new file mode 100644 index 0000000000000..056592dd4775d --- /dev/null +++ b/examples/server/webui/package-lock.json @@ -0,0 +1,6615 @@ +{ + "name": "webui", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "webui", + "version": "0.0.0", + "dependencies": { + "@heroicons/react": "^2.2.0", + "@sec-ant/readable-stream": "^0.6.0", + "@vscode/markdown-it-katex": "^1.1.1", + "autoprefixer": "^10.4.20", + "daisyui": "^4.12.14", + "dexie": "^4.0.11", + "highlight.js": "^11.10.0", + "katex": "^0.16.15", + "postcss": "^8.4.49", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^9.0.3", + "react-router": "^7.1.5", + "rehype-highlight": "^7.0.2", + "rehype-katex": "^7.0.1", + "remark-breaks": "^4.0.0", + "remark-gfm": "^4.0.0", + "remark-math": "^6.0.0", + "tailwindcss": "^3.4.15", + "textlinestream": "^1.1.1", + 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"https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", + "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", + "license": "MIT", + "funding": { + "type": "github", + "url": "https://github.com/sponsors/wooorm" + } + } + } +} diff --git a/examples/server/webui/package.json b/examples/server/webui/package.json new file mode 100644 index 0000000000000..8c833d0241e1c --- /dev/null +++ b/examples/server/webui/package.json @@ -0,0 +1,60 @@ +{ + "name": "webui", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "format": "eslint . && prettier --write .", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "@heroicons/react": "^2.2.0", + "@sec-ant/readable-stream": "^0.6.0", + "@vscode/markdown-it-katex": "^1.1.1", + "autoprefixer": "^10.4.20", + "daisyui": "^4.12.14", + "dexie": "^4.0.11", + "highlight.js": "^11.10.0", + "katex": "^0.16.15", + "postcss": "^8.4.49", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^9.0.3", + "react-router": "^7.1.5", + "rehype-highlight": "^7.0.2", + "rehype-katex": "^7.0.1", + "remark-breaks": "^4.0.0", + "remark-gfm": "^4.0.0", + "remark-math": "^6.0.0", + "tailwindcss": "^3.4.15", + "textlinestream": "^1.1.1", + "vite-plugin-singlefile": "^2.0.3" + }, + "devDependencies": { + "@eslint/js": "^9.17.0", + "@types/markdown-it": "^14.1.2", + "@types/node": "^22.13.1", + "@types/react": "^18.3.18", + "@types/react-dom": "^18.3.5", + "@vitejs/plugin-react": "^4.3.4", + "eslint": "^9.17.0", + "eslint-plugin-react-hooks": "^5.0.0", + "eslint-plugin-react-refresh": "^0.4.16", + "globals": "^15.14.0", + "prettier": "^3.4.2", + "sass-embedded": "^1.83.4", + "typescript": "~5.6.2", + "typescript-eslint": "^8.18.2", + "vite": "^6.0.5" + }, + "prettier": { + "trailingComma": "es5", + "tabWidth": 2, + "semi": true, + "singleQuote": true, + "bracketSameLine": false + } +} diff --git a/examples/server/webui/postcss.config.js b/examples/server/webui/postcss.config.js new file mode 100644 index 0000000000000..2e7af2b7f1a6f --- /dev/null +++ b/examples/server/webui/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/examples/server/webui/public/demo-conversation.json b/examples/server/webui/public/demo-conversation.json new file mode 100644 index 0000000000000..338b4aea590f2 --- /dev/null +++ b/examples/server/webui/public/demo-conversation.json @@ -0,0 +1,33 @@ +{ + "demo": true, + "id": "conv-1734086746930", + "lastModified": 1734087548943, + "messages": [ + { + "id": 1734086764521, + "role": "user", + "content": "this is a demo conversation, used in dev mode" + }, + { + "id": 1734087548327, + "role": "assistant", + "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\n$2x + y = z$\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$", + "timings": { + "prompt_n": 1, + "prompt_ms": 28.923, + "predicted_n": 25, + "predicted_ms": 573.016 + } + }, + { + "id": 1734087548328, + "role": "user", + "content": "this is a demo conversation, used in dev mode" + }, + { + "id": 1734087548329, + "role": "assistant", + "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```" + } + ] +} diff --git a/examples/server/webui/src/App.tsx b/examples/server/webui/src/App.tsx new file mode 100644 index 0000000000000..2ce734682cff0 --- /dev/null +++ b/examples/server/webui/src/App.tsx @@ -0,0 +1,47 @@ +import { HashRouter, Outlet, Route, Routes } from 'react-router'; +import Header from './components/Header'; +import Sidebar from './components/Sidebar'; +import { AppContextProvider, useAppContext } from './utils/app.context'; +import ChatScreen from './components/ChatScreen'; +import SettingDialog from './components/SettingDialog'; + +function App() { + return ( + +
+ + + }> + } /> + } /> + + + +
+ + ); +} + +function AppLayout() { + const { showSettings, setShowSettings } = useAppContext(); + return ( + <> + +
+
+ +
+ { + setShowSettings(false)} + /> + } + + ); +} + +export default App; diff --git a/examples/server/webui/src/Config.ts b/examples/server/webui/src/Config.ts new file mode 100644 index 0000000000000..779ed9bf7840c --- /dev/null +++ b/examples/server/webui/src/Config.ts @@ -0,0 +1,92 @@ +import daisyuiThemes from 'daisyui/src/theming/themes'; +import { isNumeric } from './utils/misc'; + +export const isDev = import.meta.env.MODE === 'development'; + +// constants +export const BASE_URL = new URL('.', document.baseURI).href + .toString() + .replace(/\/$/, ''); + +export const CONFIG_DEFAULT = { + // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value. + // Do not use nested objects, keep it single level. Prefix the key if you need to group them. + apiKey: '', + systemMessage: 'You are a helpful assistant.', + showTokensPerSecond: false, + showThoughtInProgress: false, + excludeThoughtOnReq: true, + // make sure these default values are in sync with `common.h` + samplers: 'edkypmxt', + temperature: 0.8, + dynatemp_range: 0.0, + dynatemp_exponent: 1.0, + top_k: 40, + top_p: 0.95, + min_p: 0.05, + xtc_probability: 0.0, + xtc_threshold: 0.1, + typical_p: 1.0, + repeat_last_n: 64, + repeat_penalty: 1.0, + presence_penalty: 0.0, + frequency_penalty: 0.0, + dry_multiplier: 0.0, + dry_base: 1.75, + dry_allowed_length: 2, + dry_penalty_last_n: -1, + max_tokens: -1, + custom: '', // custom json-stringified object + // experimental features + pyIntepreterEnabled: false, +}; +export const CONFIG_INFO: Record = { + apiKey: 'Set the API Key if you are using --api-key option for the server.', + systemMessage: 'The starting message that defines how model should behave.', + samplers: + 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature', + temperature: + 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.', + dynatemp_range: + 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.', + dynatemp_exponent: + 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.', + top_k: 'Keeps only k top tokens.', + top_p: + 'Limits tokens to those that together have a cumulative probability of at least p', + min_p: + 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.', + xtc_probability: + 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.', + xtc_threshold: + 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.', + typical_p: + 'Sorts and limits tokens based on the difference between log-probability and entropy.', + repeat_last_n: 'Last n tokens to consider for penalizing repetition', + repeat_penalty: + 'Controls the repetition of token sequences in the generated text', + presence_penalty: + 'Limits tokens based on whether they appear in the output or not.', + frequency_penalty: + 'Limits tokens based on how often they appear in the output.', + dry_multiplier: + 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.', + dry_base: + 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.', + dry_allowed_length: + 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.', + dry_penalty_last_n: + 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.', + max_tokens: 'The maximum number of token per output.', + custom: '', // custom json-stringified object +}; +// config keys having numeric value (i.e. temperature, top_k, top_p, etc) +export const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT) + .filter((e) => isNumeric(e[1])) + .map((e) => e[0]); +// list of themes supported by daisyui +export const THEMES = ['light', 'dark'] + // make sure light & dark are always at the beginning + .concat( + Object.keys(daisyuiThemes).filter((t) => t !== 'light' && t !== 'dark') + ); diff --git a/examples/server/webui/src/components/CanvasPyInterpreter.tsx b/examples/server/webui/src/components/CanvasPyInterpreter.tsx new file mode 100644 index 0000000000000..c2707fe20fcec --- /dev/null +++ b/examples/server/webui/src/components/CanvasPyInterpreter.tsx @@ -0,0 +1,195 @@ +import { useEffect, useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { OpenInNewTab, XCloseButton } from '../utils/common'; +import { CanvasType } from '../utils/types'; +import { PlayIcon, StopIcon } from '@heroicons/react/24/outline'; +import StorageUtils from '../utils/storage'; + +const canInterrupt = typeof SharedArrayBuffer === 'function'; + +// adapted from https://pyodide.org/en/stable/usage/webworker.html +const WORKER_CODE = ` +importScripts("https://cdn.jsdelivr.net/pyodide/v0.27.2/full/pyodide.js"); + +let stdOutAndErr = []; + +let pyodideReadyPromise = loadPyodide({ + stdout: (data) => stdOutAndErr.push(data), + stderr: (data) => stdOutAndErr.push(data), +}); + +let alreadySetBuff = false; + +self.onmessage = async (event) => { + stdOutAndErr = []; + + // make sure loading is done + const pyodide = await pyodideReadyPromise; + const { id, python, context, interruptBuffer } = event.data; + + if (interruptBuffer && !alreadySetBuff) { + pyodide.setInterruptBuffer(interruptBuffer); + alreadySetBuff = true; + } + + // Now load any packages we need, run the code, and send the result back. + await pyodide.loadPackagesFromImports(python); + + // make a Python dictionary with the data from content + const dict = pyodide.globals.get("dict"); + const globals = dict(Object.entries(context)); + try { + self.postMessage({ id, running: true }); + // Execute the python code in this context + const result = pyodide.runPython(python, { globals }); + self.postMessage({ result, id, stdOutAndErr }); + } catch (error) { + self.postMessage({ error: error.message, id }); + } + interruptBuffer[0] = 0; +}; +`; + +let worker: Worker; +const interruptBuffer = canInterrupt + ? new Uint8Array(new SharedArrayBuffer(1)) + : null; + +const startWorker = () => { + if (!worker) { + worker = new Worker( + URL.createObjectURL(new Blob([WORKER_CODE], { type: 'text/javascript' })) + ); + } +}; + +if (StorageUtils.getConfig().pyIntepreterEnabled) { + startWorker(); +} + +const runCodeInWorker = ( + pyCode: string, + callbackRunning: () => void +): { + donePromise: Promise; + interrupt: () => void; +} => { + startWorker(); + const id = Math.random() * 1e8; + const context = {}; + if (interruptBuffer) { + interruptBuffer[0] = 0; + } + + const donePromise = new Promise((resolve) => { + worker.onmessage = (event) => { + const { error, stdOutAndErr, running } = event.data; + if (id !== event.data.id) return; + if (running) { + callbackRunning(); + return; + } else if (error) { + resolve(error.toString()); + } else { + resolve(stdOutAndErr.join('\n')); + } + }; + worker.postMessage({ id, python: pyCode, context, interruptBuffer }); + }); + + const interrupt = () => { + console.log('Interrupting...'); + console.trace(); + if (interruptBuffer) { + interruptBuffer[0] = 2; + } + }; + + return { donePromise, interrupt }; +}; + +export default function CanvasPyInterpreter() { + const { canvasData, setCanvasData } = useAppContext(); + + const [code, setCode] = useState(canvasData?.content ?? ''); // copy to avoid direct mutation + const [running, setRunning] = useState(false); + const [output, setOutput] = useState(''); + const [interruptFn, setInterruptFn] = useState<() => void>(); + const [showStopBtn, setShowStopBtn] = useState(false); + + const runCode = async (pycode: string) => { + interruptFn?.(); + setRunning(true); + setOutput('Loading Pyodide...'); + const { donePromise, interrupt } = runCodeInWorker(pycode, () => { + setOutput('Running...'); + setShowStopBtn(canInterrupt); + }); + setInterruptFn(() => interrupt); + const out = await donePromise; + setOutput(out); + setRunning(false); + setShowStopBtn(false); + }; + + // run code on mount + useEffect(() => { + setCode(canvasData?.content ?? ''); + runCode(canvasData?.content ?? ''); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [canvasData?.content]); + + if (canvasData?.type !== CanvasType.PY_INTERPRETER) { + return null; + } + + return ( +
+
+
+ Python Interpreter + setCanvasData(null)} + /> +
+
+ +
+
+ + {showStopBtn && ( + + )} + + + Report a bug + + +
+ +
+
+
+
+ ); +} diff --git a/examples/server/webui/src/components/ChatMessage.tsx b/examples/server/webui/src/components/ChatMessage.tsx new file mode 100644 index 0000000000000..40ea74711f349 --- /dev/null +++ b/examples/server/webui/src/components/ChatMessage.tsx @@ -0,0 +1,296 @@ +import { useMemo, useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { Message, PendingMessage } from '../utils/types'; +import { classNames } from '../utils/misc'; +import MarkdownDisplay, { CopyButton } from './MarkdownDisplay'; +import { ChevronLeftIcon, ChevronRightIcon } from '@heroicons/react/24/outline'; + +interface SplitMessage { + content: PendingMessage['content']; + thought?: string; + isThinking?: boolean; +} + +export default function ChatMessage({ + msg, + siblingLeafNodeIds, + siblingCurrIdx, + id, + onRegenerateMessage, + onEditMessage, + onChangeSibling, + isPending, +}: { + msg: Message | PendingMessage; + siblingLeafNodeIds: Message['id'][]; + siblingCurrIdx: number; + id?: string; + onRegenerateMessage(msg: Message): void; + onEditMessage(msg: Message, content: string): void; + onChangeSibling(sibling: Message['id']): void; + isPending?: boolean; +}) { + const { viewingChat, config } = useAppContext(); + const [editingContent, setEditingContent] = useState(null); + const timings = useMemo( + () => + msg.timings + ? { + ...msg.timings, + prompt_per_second: + (msg.timings.prompt_n / msg.timings.prompt_ms) * 1000, + predicted_per_second: + (msg.timings.predicted_n / msg.timings.predicted_ms) * 1000, + } + : null, + [msg.timings] + ); + const nextSibling = siblingLeafNodeIds[siblingCurrIdx + 1]; + const prevSibling = siblingLeafNodeIds[siblingCurrIdx - 1]; + + // for reasoning model, we split the message into content and thought + // TODO: implement this as remark/rehype plugin in the future + const { content, thought, isThinking }: SplitMessage = useMemo(() => { + if (msg.content === null || msg.role !== 'assistant') { + return { content: msg.content }; + } + let actualContent = ''; + let thought = ''; + let isThinking = false; + let thinkSplit = msg.content.split('', 2); + actualContent += thinkSplit[0]; + while (thinkSplit[1] !== undefined) { + // tag found + thinkSplit = thinkSplit[1].split('', 2); + thought += thinkSplit[0]; + isThinking = true; + if (thinkSplit[1] !== undefined) { + // closing tag found + isThinking = false; + thinkSplit = thinkSplit[1].split('', 2); + actualContent += thinkSplit[0]; + } + } + return { content: actualContent, thought, isThinking }; + }, [msg]); + + if (!viewingChat) return null; + + return ( +
+
+
+ {/* textarea for editing message */} + {editingContent !== null && ( + <> + +
+ + + + )} + {/* not editing content, render message */} + {editingContent === null && ( + <> + {content === null ? ( + <> + {/* show loading dots for pending message */} + + + ) : ( + <> + {/* render message as markdown */} +
+ {thought && ( +
+ + {isPending && isThinking ? ( + + + Thinking + + ) : ( + Thought Process + )} + +
+ +
+
+ )} + + {msg.extra && msg.extra.length > 0 && ( +
+ + Extra content + +
+ {msg.extra.map( + (extra, i) => + extra.type === 'textFile' ? ( +
+ {extra.name} + 
{extra.content}
+
+ ) : extra.type === 'context' ? ( +
+ 
{extra.content}
+
+ ) : null // TODO: support other extra types + )} +
+
+ )} + + +
+ + )} + {/* render timings if enabled */} + {timings && config.showTokensPerSecond && ( +
+
+ Speed: {timings.predicted_per_second.toFixed(1)} t/s +
+
+ Prompt +
- Tokens: {timings.prompt_n} +
- Time: {timings.prompt_ms} ms +
- Speed: {timings.prompt_per_second.toFixed(1)} t/s +
+ Generation +
- Tokens: {timings.predicted_n} +
- Time: {timings.predicted_ms} ms +
- Speed: {timings.predicted_per_second.toFixed(1)} t/s +
+
+
+ )} + + )} +
+
+ + {/* actions for each message */} + {msg.content !== null && ( +
+ {siblingLeafNodeIds && siblingLeafNodeIds.length > 1 && ( +
+ + + {siblingCurrIdx + 1} / {siblingLeafNodeIds.length} + + +
+ )} + {/* user message */} + {msg.role === 'user' && ( + + )} + {/* assistant message */} + {msg.role === 'assistant' && ( + <> + {!isPending && ( + + )} + + )} + +
+ )} +
+ ); +} diff --git a/examples/server/webui/src/components/ChatScreen.tsx b/examples/server/webui/src/components/ChatScreen.tsx new file mode 100644 index 0000000000000..d7a246cf6a986 --- /dev/null +++ b/examples/server/webui/src/components/ChatScreen.tsx @@ -0,0 +1,266 @@ +import { useEffect, useMemo, useRef, useState } from 'react'; +import { CallbackGeneratedChunk, useAppContext } from '../utils/app.context'; +import ChatMessage from './ChatMessage'; +import { CanvasType, Message, PendingMessage } from '../utils/types'; +import { classNames, throttle } from '../utils/misc'; +import CanvasPyInterpreter from './CanvasPyInterpreter'; +import StorageUtils from '../utils/storage'; +import { useVSCodeContext } from '../utils/llama-vscode'; + +/** + * A message display is a message node with additional information for rendering. + * For example, siblings of the message node are stored as their last node (aka leaf node). + */ +export interface MessageDisplay { + msg: Message | PendingMessage; + siblingLeafNodeIds: Message['id'][]; + siblingCurrIdx: number; + isPending?: boolean; +} + +function getListMessageDisplay( + msgs: Readonly, + leafNodeId: Message['id'] +): MessageDisplay[] { + const currNodes = StorageUtils.filterByLeafNodeId(msgs, leafNodeId, true); + const res: MessageDisplay[] = []; + const nodeMap = new Map(); + for (const msg of msgs) { + nodeMap.set(msg.id, msg); + } + // find leaf node from a message node + const findLeafNode = (msgId: Message['id']): Message['id'] => { + let currNode: Message | undefined = nodeMap.get(msgId); + while (currNode) { + if (currNode.children.length === 0) break; + currNode = nodeMap.get(currNode.children.at(-1) ?? -1); + } + return currNode?.id ?? -1; + }; + // traverse the current nodes + for (const msg of currNodes) { + const parentNode = nodeMap.get(msg.parent ?? -1); + if (!parentNode) continue; + const siblings = parentNode.children; + if (msg.type !== 'root') { + res.push({ + msg, + siblingLeafNodeIds: siblings.map(findLeafNode), + siblingCurrIdx: siblings.indexOf(msg.id), + }); + } + } + return res; +} + +const scrollToBottom = throttle( + (requiresNearBottom: boolean, delay: number = 80) => { + const mainScrollElem = document.getElementById('main-scroll'); + if (!mainScrollElem) return; + const spaceToBottom = + mainScrollElem.scrollHeight - + mainScrollElem.scrollTop - + mainScrollElem.clientHeight; + if (!requiresNearBottom || spaceToBottom < 50) { + setTimeout( + () => mainScrollElem.scrollTo({ top: mainScrollElem.scrollHeight }), + delay + ); + } + }, + 80 +); + +export default function ChatScreen() { + const { + viewingChat, + sendMessage, + isGenerating, + stopGenerating, + pendingMessages, + canvasData, + replaceMessageAndGenerate, + } = useAppContext(); + const [inputMsg, setInputMsg] = useState(''); + const inputRef = useRef(null); + + const { extraContext, clearExtraContext } = useVSCodeContext( + inputRef, + setInputMsg + ); + // TODO: improve this when we have "upload file" feature + const currExtra: Message['extra'] = extraContext ? [extraContext] : undefined; + + // keep track of leaf node for rendering + const [currNodeId, setCurrNodeId] = useState(-1); + const messages: MessageDisplay[] = useMemo(() => { + if (!viewingChat) return []; + else return getListMessageDisplay(viewingChat.messages, currNodeId); + }, [currNodeId, viewingChat]); + + const currConvId = viewingChat?.conv.id ?? null; + const pendingMsg: PendingMessage | undefined = + pendingMessages[currConvId ?? '']; + + useEffect(() => { + // reset to latest node when conversation changes + setCurrNodeId(-1); + // scroll to bottom when conversation changes + scrollToBottom(false, 1); + }, [currConvId]); + + const onChunk: CallbackGeneratedChunk = (currLeafNodeId?: Message['id']) => { + if (currLeafNodeId) { + setCurrNodeId(currLeafNodeId); + } + scrollToBottom(true); + }; + + const sendNewMessage = async () => { + if (inputMsg.trim().length === 0 || isGenerating(currConvId ?? '')) return; + const lastInpMsg = inputMsg; + setInputMsg(''); + scrollToBottom(false); + setCurrNodeId(-1); + // get the last message node + const lastMsgNodeId = messages.at(-1)?.msg.id ?? null; + if ( + !(await sendMessage( + currConvId, + lastMsgNodeId, + inputMsg, + currExtra, + onChunk + )) + ) { + // restore the input message if failed + setInputMsg(lastInpMsg); + } + // OK + clearExtraContext(); + }; + + const handleEditMessage = async (msg: Message, content: string) => { + if (!viewingChat) return; + setCurrNodeId(msg.id); + scrollToBottom(false); + await replaceMessageAndGenerate( + viewingChat.conv.id, + msg.parent, + content, + msg.extra, + onChunk + ); + setCurrNodeId(-1); + scrollToBottom(false); + }; + + const handleRegenerateMessage = async (msg: Message) => { + if (!viewingChat) return; + setCurrNodeId(msg.parent); + scrollToBottom(false); + await replaceMessageAndGenerate( + viewingChat.conv.id, + msg.parent, + null, + msg.extra, + onChunk + ); + setCurrNodeId(-1); + scrollToBottom(false); + }; + + const hasCanvas = !!canvasData; + + // due to some timing issues of StorageUtils.appendMsg(), we need to make sure the pendingMsg is not duplicated upon rendering (i.e. appears once in the saved conversation and once in the pendingMsg) + const pendingMsgDisplay: MessageDisplay[] = + pendingMsg && messages.at(-1)?.msg.id !== pendingMsg.id + ? [ + { + msg: pendingMsg, + siblingLeafNodeIds: [], + siblingCurrIdx: 0, + isPending: true, + }, + ] + : []; + + return ( +
+
+ {/* chat messages */} +
+
+ {/* placeholder to shift the message to the bottom */} + {viewingChat ? '' : 'Send a message to start'} +
+ {[...messages, ...pendingMsgDisplay].map((msg) => ( + + ))} +
+ + {/* chat input */} +
+ + {isGenerating(currConvId ?? '') ? ( + + ) : ( + + )} +
+
+
+ {canvasData?.type === CanvasType.PY_INTERPRETER && ( + + )} +
+
+ ); +} diff --git a/examples/server/webui/src/components/Header.tsx b/examples/server/webui/src/components/Header.tsx new file mode 100644 index 0000000000000..cbee394ba2e0c --- /dev/null +++ b/examples/server/webui/src/components/Header.tsx @@ -0,0 +1,179 @@ +import { useEffect, useState } from 'react'; +import StorageUtils from '../utils/storage'; +import { useAppContext } from '../utils/app.context'; +import { classNames } from '../utils/misc'; +import daisyuiThemes from 'daisyui/src/theming/themes'; +import { THEMES } from '../Config'; +import { useNavigate } from 'react-router'; + +export default function Header() { + const navigate = useNavigate(); + const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme()); + const { setShowSettings } = useAppContext(); + + const setTheme = (theme: string) => { + StorageUtils.setTheme(theme); + setSelectedTheme(theme); + }; + + useEffect(() => { + document.body.setAttribute('data-theme', selectedTheme); + document.body.setAttribute( + 'data-color-scheme', + // @ts-expect-error daisyuiThemes complains about index type, but it should work + daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto' + ); + }, [selectedTheme]); + + const { isGenerating, viewingChat } = useAppContext(); + const isCurrConvGenerating = isGenerating(viewingChat?.conv.id ?? ''); + + const removeConversation = () => { + if (isCurrConvGenerating || !viewingChat) return; + const convId = viewingChat?.conv.id; + if (window.confirm('Are you sure to delete this conversation?')) { + StorageUtils.remove(convId); + navigate('/'); + } + }; + + const downloadConversation = () => { + if (isCurrConvGenerating || !viewingChat) return; + const convId = viewingChat?.conv.id; + const conversationJson = JSON.stringify(viewingChat, null, 2); + const blob = new Blob([conversationJson], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `conversation_${convId}.json`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + }; + + return ( +
+ {/* open sidebar button */} + + +
llama.cpp
+ + {/* action buttons (top right) */} +
+ {viewingChat && ( +
+ {/* "..." button */} + + {/* dropdown menu */} + +
+ )} + +
+ +
+ + {/* theme controller is copied from https://daisyui.com/components/theme-controller/ */} +
+
+
+ + + +
+
    +
  • + +
    • + {THEMES.map((theme) => ( +
  • + e.target.checked && setTheme(theme)} + /> +
    • + ))} +
+
+
+
+
+ ); +} diff --git a/examples/server/webui/src/components/MarkdownDisplay.tsx b/examples/server/webui/src/components/MarkdownDisplay.tsx new file mode 100644 index 0000000000000..5b7a725914e80 --- /dev/null +++ b/examples/server/webui/src/components/MarkdownDisplay.tsx @@ -0,0 +1,310 @@ +import React, { useMemo, useState } from 'react'; +import Markdown, { ExtraProps } from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import rehypeHightlight from 'rehype-highlight'; +import rehypeKatex from 'rehype-katex'; +import remarkMath from 'remark-math'; +import remarkBreaks from 'remark-breaks'; +import 'katex/dist/katex.min.css'; +import { classNames, copyStr } from '../utils/misc'; +import { ElementContent, Root } from 'hast'; +import { visit } from 'unist-util-visit'; +import { useAppContext } from '../utils/app.context'; +import { CanvasType } from '../utils/types'; + +export default function MarkdownDisplay({ + content, + isGenerating, +}: { + content: string; + isGenerating?: boolean; +}) { + const preprocessedContent = useMemo( + () => preprocessLaTeX(content), + [content] + ); + return ( + ( + + ), + // note: do not use "pre", "p" or other basic html elements here, it will cause the node to re-render when the message is being generated (this should be a bug with react-markdown, not sure how to fix it) + }} + > + {preprocessedContent} + + ); +} + +const CodeBlockButtons: React.ElementType< + React.ClassAttributes & + React.HTMLAttributes & + ExtraProps & { origContent: string; isGenerating?: boolean } +> = ({ node, origContent, isGenerating }) => { + const { config } = useAppContext(); + const startOffset = node?.position?.start.offset ?? 0; + const endOffset = node?.position?.end.offset ?? 0; + + const copiedContent = useMemo( + () => + origContent + .substring(startOffset, endOffset) + .replace(/^```[^\n]+\n/g, '') + .replace(/```$/g, ''), + [origContent, startOffset, endOffset] + ); + + const codeLanguage = useMemo( + () => + origContent + .substring(startOffset, startOffset + 10) + .match(/^```([^\n]+)\n/)?.[1] ?? '', + [origContent, startOffset] + ); + + const canRunCode = + !isGenerating && + config.pyIntepreterEnabled && + codeLanguage.startsWith('py'); + + return ( +
+ + {canRunCode && ( + + )} +
+ ); +}; + +export const CopyButton = ({ + content, + className, +}: { + content: string; + className?: string; +}) => { + const [copied, setCopied] = useState(false); + return ( + + ); +}; + +export const RunPyCodeButton = ({ + content, + className, +}: { + content: string; + className?: string; +}) => { + const { setCanvasData } = useAppContext(); + return ( + <> + + + ); +}; + +/** + * This injects the "button" element before each "pre" element. + * The actual button will be replaced with a react component in the MarkdownDisplay. + * We don't replace "pre" node directly because it will cause the node to re-render, which causes this bug: https://github.com/ggerganov/llama.cpp/issues/9608 + */ +function rehypeCustomCopyButton() { + return function (tree: Root) { + visit(tree, 'element', function (node) { + if (node.tagName === 'pre' && !node.properties.visited) { + const preNode = { ...node }; + // replace current node + preNode.properties.visited = 'true'; + node.tagName = 'div'; + node.properties = {}; + // add node for button + const btnNode: ElementContent = { + type: 'element', + tagName: 'button', + properties: {}, + children: [], + position: node.position, + }; + node.children = [btnNode, preNode]; + } + }); + }; +} + +/** + * The part below is copied and adapted from: + * https://github.com/danny-avila/LibreChat/blob/main/client/src/utils/latex.ts + * (MIT License) + */ + +// Regex to check if the processed content contains any potential LaTeX patterns +const containsLatexRegex = + /\\\(.*?\\\)|\\\[.*?\\\]|\$.*?\$|\\begin\{equation\}.*?\\end\{equation\}/; + +// Regex for inline and block LaTeX expressions +const inlineLatex = new RegExp(/\\\((.+?)\\\)/, 'g'); +const blockLatex = new RegExp(/\\\[(.*?[^\\])\\\]/, 'gs'); + +// Function to restore code blocks +const restoreCodeBlocks = (content: string, codeBlocks: string[]) => { + return content.replace( + /<>/g, + (_, index) => codeBlocks[index] + ); +}; + +// Regex to identify code blocks and inline code +const codeBlockRegex = /(```[\s\S]*?```|`.*?`)/g; + +export const processLaTeX = (_content: string) => { + let content = _content; + // Temporarily replace code blocks and inline code with placeholders + const codeBlocks: string[] = []; + let index = 0; + content = content.replace(codeBlockRegex, (match) => { + codeBlocks[index] = match; + return `<>`; + }); + + // Escape dollar signs followed by a digit or space and digit + let processedContent = content.replace(/(\$)(?=\s?\d)/g, '\\$'); + + // If no LaTeX patterns are found, restore code blocks and return the processed content + if (!containsLatexRegex.test(processedContent)) { + return restoreCodeBlocks(processedContent, codeBlocks); + } + + // Convert LaTeX expressions to a markdown compatible format + processedContent = processedContent + .replace(inlineLatex, (_: string, equation: string) => `$${equation}$`) // Convert inline LaTeX + .replace(blockLatex, (_: string, equation: string) => `$$${equation}$$`); // Convert block LaTeX + + // Restore code blocks + return restoreCodeBlocks(processedContent, codeBlocks); +}; + +/** + * Preprocesses LaTeX content by replacing delimiters and escaping certain characters. + * + * @param content The input string containing LaTeX expressions. + * @returns The processed string with replaced delimiters and escaped characters. + */ +export function preprocessLaTeX(content: string): string { + // Step 1: Protect code blocks + const codeBlocks: string[] = []; + content = content.replace(/(```[\s\S]*?```|`[^`\n]+`)/g, (_, code) => { + codeBlocks.push(code); + return `<>`; + }); + + // Step 2: Protect existing LaTeX expressions + const latexExpressions: string[] = []; + + // Protect block math ($$...$$), \[...\], and \(...\) as before. + content = content.replace( + /(\$\$[\s\S]*?\$\$|\\\[[\s\S]*?\\\]|\\\(.*?\\\))/g, + (match) => { + latexExpressions.push(match); + return `<>`; + } + ); + + // Protect inline math ($...$) only if it does NOT match a currency pattern. + // We assume a currency pattern is one where the inner content is purely numeric (with optional decimals). + content = content.replace(/\$([^$]+)\$/g, (match, inner) => { + if (/^\s*\d+(?:\.\d+)?\s*$/.test(inner)) { + // This looks like a currency value (e.g. "$123" or "$12.34"), + // so don't protect it. + return match; + } else { + // Otherwise, treat it as a LaTeX expression. + latexExpressions.push(match); + return `<>`; + } + }); + + // Step 3: Escape dollar signs that are likely currency indicators. + // (Now that inline math is protected, this will only escape dollars not already protected) + content = content.replace(/\$(?=\d)/g, '\\$'); + + // Step 4: Restore LaTeX expressions + content = content.replace( + /<>/g, + (_, index) => latexExpressions[parseInt(index)] + ); + + // Step 5: Restore code blocks + content = content.replace( + /<>/g, + (_, index) => codeBlocks[parseInt(index)] + ); + + // Step 6: Apply additional escaping functions + content = escapeBrackets(content); + content = escapeMhchem(content); + + return content; +} + +export function escapeBrackets(text: string): string { + const pattern = + /(```[\S\s]*?```|`.*?`)|\\\[([\S\s]*?[^\\])\\]|\\\((.*?)\\\)/g; + return text.replace( + pattern, + ( + match: string, + codeBlock: string | undefined, + squareBracket: string | undefined, + roundBracket: string | undefined + ): string => { + if (codeBlock != null) { + return codeBlock; + } else if (squareBracket != null) { + return `$$${squareBracket}$$`; + } else if (roundBracket != null) { + return `$${roundBracket}$`; + } + return match; + } + ); +} + +export function escapeMhchem(text: string) { + return text.replaceAll('$\\ce{', '$\\\\ce{').replaceAll('$\\pu{', '$\\\\pu{'); +} diff --git a/examples/server/webui/src/components/SettingDialog.tsx b/examples/server/webui/src/components/SettingDialog.tsx new file mode 100644 index 0000000000000..592b93fa39309 --- /dev/null +++ b/examples/server/webui/src/components/SettingDialog.tsx @@ -0,0 +1,536 @@ +import { useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config'; +import { isDev } from '../Config'; +import StorageUtils from '../utils/storage'; +import { classNames, isBoolean, isNumeric, isString } from '../utils/misc'; +import { + BeakerIcon, + ChatBubbleOvalLeftEllipsisIcon, + Cog6ToothIcon, + FunnelIcon, + HandRaisedIcon, + SquaresPlusIcon, +} from '@heroicons/react/24/outline'; +import { OpenInNewTab } from '../utils/common'; + +type SettKey = keyof typeof CONFIG_DEFAULT; + +const BASIC_KEYS: SettKey[] = [ + 'temperature', + 'top_k', + 'top_p', + 'min_p', + 'max_tokens', +]; +const SAMPLER_KEYS: SettKey[] = [ + 'dynatemp_range', + 'dynatemp_exponent', + 'typical_p', + 'xtc_probability', + 'xtc_threshold', +]; +const PENALTY_KEYS: SettKey[] = [ + 'repeat_last_n', + 'repeat_penalty', + 'presence_penalty', + 'frequency_penalty', + 'dry_multiplier', + 'dry_base', + 'dry_allowed_length', + 'dry_penalty_last_n', +]; + +enum SettingInputType { + SHORT_INPUT, + LONG_INPUT, + CHECKBOX, + CUSTOM, +} + +interface SettingFieldInput { + type: Exclude; + label: string | React.ReactElement; + help?: string | React.ReactElement; + key: SettKey; +} + +interface SettingFieldCustom { + type: SettingInputType.CUSTOM; + key: SettKey; + component: + | string + | React.FC<{ + value: string | boolean | number; + onChange: (value: string) => void; + }>; +} + +interface SettingSection { + title: React.ReactElement; + fields: (SettingFieldInput | SettingFieldCustom)[]; +} + +const ICON_CLASSNAME = 'w-4 h-4 mr-1 inline'; + +const SETTING_SECTIONS: SettingSection[] = [ + { + title: ( + <> + + General + + ), + fields: [ + { + type: SettingInputType.SHORT_INPUT, + label: 'API Key', + key: 'apiKey', + }, + { + type: SettingInputType.LONG_INPUT, + label: 'System Message (will be disabled if left empty)', + key: 'systemMessage', + }, + ...BASIC_KEYS.map( + (key) => + ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + }) as SettingFieldInput + ), + ], + }, + { + title: ( + <> + + Samplers + + ), + fields: [ + { + type: SettingInputType.SHORT_INPUT, + label: 'Samplers queue', + key: 'samplers', + }, + ...SAMPLER_KEYS.map( + (key) => + ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + }) as SettingFieldInput + ), + ], + }, + { + title: ( + <> + + Penalties + + ), + fields: PENALTY_KEYS.map((key) => ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + })), + }, + { + title: ( + <> + + Reasoning + + ), + fields: [ + { + type: SettingInputType.CHECKBOX, + label: 'Expand though process by default for generating message', + key: 'showThoughtInProgress', + }, + { + type: SettingInputType.CHECKBOX, + label: + 'Exclude thought process when sending request to API (Recommended for DeepSeek-R1)', + key: 'excludeThoughtOnReq', + }, + ], + }, + { + title: ( + <> + + Advanced + + ), + fields: [ + { + type: SettingInputType.CUSTOM, + key: 'custom', // dummy key, won't be used + component: () => { + const debugImportDemoConv = async () => { + const res = await fetch('/demo-conversation.json'); + const demoConv = await res.json(); + StorageUtils.remove(demoConv.id); + for (const msg of demoConv.messages) { + StorageUtils.appendMsg(demoConv.id, msg); + } + }; + return ( + + ); + }, + }, + { + type: SettingInputType.CHECKBOX, + label: 'Show tokens per second', + key: 'showTokensPerSecond', + }, + { + type: SettingInputType.LONG_INPUT, + label: ( + <> + Custom JSON config (For more info, refer to{' '} + + server documentation + + ) + + ), + key: 'custom', + }, + ], + }, + { + title: ( + <> + + Experimental + + ), + fields: [ + { + type: SettingInputType.CUSTOM, + key: 'custom', // dummy key, won't be used + component: () => ( + <> +

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

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

Settings

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

+ Settings are saved in browser's localStorage +

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