# Rust builder FROM lukemathwalker/cargo-chef:latest-rust-1.79 AS chef WORKDIR /usr/src ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse FROM chef as planner COPY Cargo.lock Cargo.lock COPY Cargo.toml Cargo.toml COPY rust-toolchain.toml rust-toolchain.toml COPY proto proto COPY benchmark benchmark COPY router router COPY launcher launcher RUN cargo chef prepare --recipe-path recipe.json FROM chef AS builder RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \ curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \ unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \ unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \ rm -f $PROTOC_ZIP COPY --from=planner /usr/src/recipe.json recipe.json RUN cargo chef cook --profile release-opt --recipe-path recipe.json ARG GIT_SHA ARG DOCKER_LABEL COPY Cargo.toml Cargo.toml COPY rust-toolchain.toml rust-toolchain.toml COPY proto proto COPY benchmark benchmark COPY router router COPY launcher launcher RUN cargo build --profile release-opt # Text Generation Inference base image for RoCm FROM rocm/dev-ubuntu-22.04:6.1.1_hip_update as base RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ build-essential \ ca-certificates \ ccache \ curl \ git \ make \ libssl-dev \ g++ \ # Needed to build VLLM & flash. rocthrust-dev \ hipsparse-dev \ hipblas-dev \ hipblaslt-dev \ rocblas-dev \ hiprand-dev \ rocrand-dev \ miopen-hip-dev \ hipfft-dev \ hipcub-dev \ hipsolver-dev \ rccl-dev \ cmake \ python3-dev && \ rm -rf /var/lib/apt/lists/* # Keep in sync with `server/pyproject.toml ARG MAMBA_VERSION=23.1.0-1 ARG PYTORCH_VERSION='2.3.0' ARG ROCM_VERSION='6.0.2' ARG PYTHON_VERSION='3.10.10' # Automatically set by buildx ARG TARGETPLATFORM ENV PATH /opt/conda/bin:$PATH # TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda. # Install mamba # translating Docker's TARGETPLATFORM into mamba arches RUN case ${TARGETPLATFORM} in \ "linux/arm64") MAMBA_ARCH=aarch64 ;; \ *) MAMBA_ARCH=x86_64 ;; \ esac && \ curl -fsSL -v -o ~/mambaforge.sh -O "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh" RUN chmod +x ~/mambaforge.sh && \ bash ~/mambaforge.sh -b -p /opt/conda && \ mamba init && \ rm ~/mambaforge.sh # Install flash-attention, torch dependencies RUN pip install numpy einops ninja --no-cache-dir RUN conda install intel::mkl-static intel::mkl-include RUN pip uninstall -y triton && \ git clone --depth 1 --single-branch https://github.com/ROCm/triton.git && \ cd triton/python && \ pip install . RUN git clone --depth 1 --recursive --single-branch --branch 2.3-patched https://github.com/fxmarty/pytorch.git pytorch && cd pytorch && pip install -r requirements.txt --no-cache-dir ARG _GLIBCXX_USE_CXX11_ABI="1" ARG CMAKE_PREFIX_PATH="/opt/conda" ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942" ARG BUILD_CAFFE2="0" \ BUILD_CAFFE2_OPS="0" \ USE_CUDA="0" \ USE_ROCM="1" \ BUILD_TEST="0" \ USE_FBGEMM="0" \ USE_NNPACK="0" \ USE_QNNPACK="0" \ USE_XNNPACK="0" \ USE_FLASH_ATTENTION="1" \ USE_MEM_EFF_ATTENTION="0" RUN cd pytorch && python tools/amd_build/build_amd.py && python setup.py install # Set as recommended: https://github.com/ROCm/triton/wiki/A-script-to-set-program-execution-environment-in-ROCm ENV HIP_FORCE_DEV_KERNARG=1 # On MI250 and MI300, performances for flash with Triton FA are slightly better than CK. # However, Triton requires a tunning for each prompt length, which is prohibitive. ENV ROCM_USE_FLASH_ATTN_V2_TRITON=0 FROM base AS kernel-builder # # Build vllm kernels FROM kernel-builder AS vllm-builder WORKDIR /usr/src COPY server/Makefile-vllm Makefile # Build specific version of vllm RUN make build-vllm-rocm # Build Flash Attention v2 kernels FROM kernel-builder AS flash-att-v2-builder WORKDIR /usr/src COPY server/Makefile-flash-att-v2 Makefile # Build specific version of flash attention v2 RUN make build-flash-attention-v2-rocm # Build Transformers CUDA kernels (gpt-neox and bloom) FROM kernel-builder as custom-kernels-builder WORKDIR /usr/src COPY server/custom_kernels/ . RUN python setup.py build # Build exllama kernels FROM kernel-builder as exllama-kernels-builder WORKDIR /usr/src COPY server/exllama_kernels/ . RUN python setup.py build # Build exllama v2 kernels FROM kernel-builder as exllamav2-kernels-builder WORKDIR /usr/src COPY server/exllamav2_kernels/ . RUN python setup.py build FROM base as base-copy # Text Generation Inference base env ENV HUGGINGFACE_HUB_CACHE=/data \ HF_HUB_ENABLE_HF_TRANSFER=1 \ PORT=80 # Copy builds artifacts from vllm builder COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages # Copy build artifacts from flash attention v2 builder COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages # Copy build artifacts from custom kernels builder COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages # Copy build artifacts from exllama kernels builder COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages # Copy build artifacts from exllamav2 kernels builder COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages # Install server COPY proto proto COPY server server COPY server/Makefile server/Makefile RUN cd server && \ make gen-server && \ pip install -r requirements_rocm.txt && \ pip install ".[accelerate, peft, outlines]" --no-cache-dir # Install benchmarker COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark # Install router COPY --from=builder /usr/src/target/release-opt/text-generation-router /usr/local/bin/text-generation-router # Install launcher COPY --from=builder /usr/src/target/release-opt/text-generation-launcher /usr/local/bin/text-generation-launcher # AWS Sagemaker compatible image FROM base as sagemaker COPY sagemaker-entrypoint.sh entrypoint.sh RUN chmod +x entrypoint.sh ENTRYPOINT ["./entrypoint.sh"] # Final image FROM base-copy COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh RUN chmod +x /tgi-entrypoint.sh ENTRYPOINT ["/tgi-entrypoint.sh"] CMD ["--json-output"]