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[CI/Build] refactor dockerfile & fix pip cache
[CI/Build] fix pip cache with vllm_nccl & refactor dockerfile to build wheels (#3859)
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youkaichao
GitHub
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@ -90,7 +90,7 @@ steps:
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- bash run-benchmarks.sh
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- label: Documentation Build
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working_dir: "/vllm-workspace/docs"
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working_dir: "/vllm-workspace/test_docs/docs"
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no_gpu: True
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commands:
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- pip install -r requirements-docs.txt
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89
Dockerfile
89
Dockerfile
@ -2,6 +2,7 @@
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# to run the OpenAI compatible server.
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#################### BASE BUILD IMAGE ####################
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# prepare basic build environment
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FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
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RUN apt-get update -y \
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@ -34,7 +35,7 @@ ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
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#################### BASE BUILD IMAGE ####################
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#################### EXTENSION BUILD IMAGE ####################
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#################### WHEEL BUILD IMAGE ####################
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FROM dev AS build
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# install build dependencies
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@ -45,14 +46,14 @@ RUN --mount=type=cache,target=/root/.cache/pip \
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# install compiler cache to speed up compilation leveraging local or remote caching
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RUN apt-get update -y && apt-get install -y ccache
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# copy input files
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# files and directories related to build wheels
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COPY csrc csrc
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COPY setup.py setup.py
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COPY cmake cmake
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COPY CMakeLists.txt CMakeLists.txt
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COPY requirements.txt requirements.txt
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COPY pyproject.toml pyproject.toml
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COPY vllm/__init__.py vllm/__init__.py
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COPY vllm vllm
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# max jobs used by Ninja to build extensions
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ARG max_jobs=2
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@ -65,7 +66,15 @@ ENV VLLM_INSTALL_PUNICA_KERNELS=1
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ENV CCACHE_DIR=/root/.cache/ccache
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RUN --mount=type=cache,target=/root/.cache/ccache \
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python3 setup.py build_ext --inplace
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--mount=type=cache,target=/root/.cache/pip \
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python3 setup.py bdist_wheel --dist-dir=dist
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# the `vllm_nccl` package must be installed from source distribution
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# pip is too smart to store a wheel in the cache, and other CI jobs
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# will directly use the wheel from the cache, which is not what we want.
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# we need to remove it manually
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip cache remove vllm_nccl*
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#################### EXTENSION Build IMAGE ####################
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#################### FLASH_ATTENTION Build IMAGE ####################
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@ -85,57 +94,59 @@ RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
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#################### FLASH_ATTENTION Build IMAGE ####################
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#################### vLLM installation IMAGE ####################
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# image with vLLM installed
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FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
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WORKDIR /vllm-workspace
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RUN apt-get update -y \
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&& apt-get install -y python3-pip git vim
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# Workaround for https://github.com/openai/triton/issues/2507 and
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# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
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# this won't be needed for future versions of this docker image
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# or future versions of triton.
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RUN ldconfig /usr/local/cuda-12.1/compat/
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# install vllm wheel first, so that torch etc will be installed
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RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
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--mount=type=cache,target=/root/.cache/pip \
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pip install dist/*.whl --verbose
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RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
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--mount=type=cache,target=/root/.cache/pip \
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pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
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#################### vLLM installation IMAGE ####################
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#################### TEST IMAGE ####################
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# image to run unit testing suite
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FROM dev AS test
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# note that this uses vllm installed by `pip`
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FROM vllm-base AS test
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# copy pytorch extensions separately to avoid having to rebuild
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# when python code changes
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WORKDIR /vllm-workspace
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# ADD is used to preserve directory structure
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ADD . /vllm-workspace/
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COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
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# Install flash attention (from pre-built wheel)
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RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
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pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
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# ignore build dependencies installation because we are using pre-complied extensions
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RUN rm pyproject.toml
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RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
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#################### TEST IMAGE ####################
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#################### RUNTIME BASE IMAGE ####################
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# We used base cuda image because pytorch installs its own cuda libraries.
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# However pynccl depends on cuda libraries so we had to switch to the runtime image
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# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
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FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
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# libnccl required for ray
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RUN apt-get update -y \
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&& apt-get install -y python3-pip
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WORKDIR /workspace
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COPY requirements.txt requirements.txt
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# install development dependencies (for testing)
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install -r requirements.txt
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pip install -r requirements-dev.txt
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# Install flash attention (from pre-built wheel)
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RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
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pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
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#################### RUNTIME BASE IMAGE ####################
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# doc requires source code
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# we hide them inside `test_docs/` , so that this source code
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# will not be imported by other tests
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RUN mkdir test_docs
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RUN mv docs test_docs/
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RUN mv vllm test_docs/
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#################### TEST IMAGE ####################
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#################### OPENAI API SERVER ####################
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# openai api server alternative
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FROM vllm-base AS vllm-openai
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# install additional dependencies for openai api server
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install accelerate hf_transfer modelscope
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COPY --from=build /workspace/vllm/*.so /workspace/vllm/
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COPY vllm vllm
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ENV VLLM_USAGE_SOURCE production-docker-image
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ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
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@ -11,13 +11,10 @@
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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import logging
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import os
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import sys
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from sphinx.ext import autodoc
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sys.path.insert(0, os.path.abspath(os.path.join('..', '..')))
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logger = logging.getLogger(__name__)
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# -- Project information -----------------------------------------------------
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