owen/worker-vllm
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

0.5.3, any vllm arg as env var, refactor and fixes, moving away from building separate image from vLLM fork

Browse Source
This commit is contained in:
alpayariyak
2024-07-25 12:41:48 -07:00
parent a08d83f600
commit 5bd6f3a75e
11 changed files with 210 additions and 224 deletions
Download Patch File Download Diff File Expand all files Collapse all files
vllm-base-image/Dockerfile
-149
View File
@@ -1,149 +0,0 @@
################### vLLM Base Dockerfile ###################
# This Dockerfile is for building the image that the
# vLLM worker container will use as its base image.
# If your changes are outside of the vLLM source code, you
# do not need to build this image.
##########################################################
# Define the CUDA version for the build
ARG WORKER_CUDA_VERSION=11.8.0
FROM nvidia/cuda:${WORKER_CUDA_VERSION}-devel-ubuntu22.04 AS dev
# Re-declare ARG after FROM
ARG WORKER_CUDA_VERSION
# Update and install dependencies
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Set working directory
WORKDIR /vllm-installation
RUN ldconfig /usr/local/cuda-$(echo "$WORKER_CUDA_VERSION" | sed 's/\.0$//')/compat/
# Install build and runtime dependencies
COPY vllm/requirements-common.txt requirements-common.txt
COPY vllm/requirements-cuda${WORKER_CUDA_VERSION}.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# Install development dependencies
COPY vllm/requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
FROM dev AS build
# Re-declare ARG after FROM
ARG WORKER_CUDA_VERSION
# Install build dependencies
COPY vllm/requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# Copy necessary files
COPY vllm/csrc csrc
COPY vllm/setup.py setup.py
COPY vllm/cmake cmake
COPY vllm/CMakeLists.txt CMakeLists.txt
COPY vllm/requirements-common.txt requirements-common.txt
COPY vllm/requirements-cuda${WORKER_CUDA_VERSION}.txt requirements-cuda.txt
COPY vllm/pyproject.toml pyproject.toml
COPY vllm/vllm vllm
# Set environment variables for building extensions
ENV WORKER_CUDA_VERSION=${WORKER_CUDA_VERSION}
ENV VLLM_INSTALL_PUNICA_KERNELS=0
# Build extensions
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
FROM dev as flash-attn-builder
# max jobs used for build
# flash attention version
ARG flash_attn_version=v2.5.8
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
FROM dev as NCCL-installer
# Re-declare ARG after FROM
ARG WORKER_CUDA_VERSION
# Update and install necessary libraries
RUN apt-get update -y \
&& apt-get install -y wget
# Install NCCL library
RUN if [ "$WORKER_CUDA_VERSION" = "11.8.0" ]; then \
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb \
&& dpkg -i cuda-keyring_1.0-1_all.deb \
&& apt-get update \
&& apt install -y libnccl2=2.15.5-1+cuda11.8 libnccl-dev=2.15.5-1+cuda11.8; \
elif [ "$WORKER_CUDA_VERSION" = "12.1.0" ]; then \
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb \
&& dpkg -i cuda-keyring_1.0-1_all.deb \
&& apt-get update \
&& apt install -y libnccl2=2.17.1-1+cuda12.1 libnccl-dev=2.17.1-1+cuda12.1; \
else \
echo "Unsupported CUDA version: $WORKER_CUDA_VERSION"; \
exit 1; \
fi
FROM nvidia/cuda:${WORKER_CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
# Re-declare ARG after FROM
ARG WORKER_CUDA_VERSION
# Update and install necessary libraries
RUN apt-get update -y \
&& apt-get install -y python3-pip
# Set working directory
WORKDIR /vllm-workspace
RUN ldconfig /usr/local/cuda-$(echo "$WORKER_CUDA_VERSION" | sed 's/\.0$//')/compat/
RUN --mount=type=bind,from=build,src=/vllm-installation/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
FROM vllm-base AS runtime
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope tensorizer
# Set PYTHONPATH environment variable
ENV PYTHONPATH="/"
# Copy NCCL library
COPY --from=NCCL-installer /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/x86_64-linux-gnu/libnccl.so.2
# Set the VLLM_NCCL_SO_PATH environment variable
ENV VLLM_NCCL_SO_PATH="/usr/lib/x86_64-linux-gnu/libnccl.so.2"
# Validate the installation
RUN python3 -c "import vllm; print(vllm.__file__)"
vllm-base-image/README.md
-1
View File
@@ -1 +0,0 @@
This directory is for building the vllm-base image utilized by the worker.
vllm-base-image/vllm
+1 -1
Submodule vllm-base-image/vllm updated: ba8f5e79e1...6a1a31c41e
vllm-base-image/vllm-metadata.yml
-2
View File
@@ -1,2 +0,0 @@
version: '0.4.2'
dev_version: '0.4.2'