A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 14, 05:30 UTC
Install pre-built xformers-0.0.32.post2 built with pt-2.9.0 (#27598)
Signed-off-by: Huy Do <huydhn@gmail.com> Co-authored-by: Roger Wang <hey@rogerw.io>
Huy Do committed
Oct 28, 2025 at 19:39 UTC
f257544709a8d9ccb8947e6f2c1779988c448ae7
2 files changed
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docker/Dockerfile
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@@ -361,13 +361,6 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
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&& uv pip install --system dist/*.whl --verbose \
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--extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
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-# TODO (huydhn): Remove this once xformers is released for 2.9.0
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-RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
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- . /etc/environment
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- export TORCH_CUDA_ARCH_LIST='7.5 8.0+PTX 9.0a'
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- uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.32.post2"
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-BASH
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-
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# Install FlashInfer pre-compiled kernel cache and binaries
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# https://docs.flashinfer.ai/installation.html
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RUN --mount=type=cache,target=/root/.cache/uv \
requirements/cuda.txt
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@@ -10,7 +10,7 @@ torchaudio==2.9.0
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# These must be updated alongside torch
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torchvision==0.24.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
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# https://github.com/facebookresearch/xformers/releases/tag/v0.0.32.post1
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-# xformers==0.0.32.post1; platform_system == 'Linux' and platform_machine == 'x86_64' # Requires PyTorch >= 2.8
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+xformers==0.0.33+5d4b92a5.d20251026; platform_system == 'Linux' and platform_machine == 'x86_64' # Requires PyTorch >= 2.9
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# FlashInfer should be updated together with the Dockerfile
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flashinfer-python==0.4.1
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# Triton Kernels are needed for mxfp4 fused moe. (Should be updated alongside torch)