vllm [MISC] Upgrade dependency to PyTorch 2.3.1

PyTorch 2.3.1 just released, and this PR upgrades the dependency to 2.3.1. The most important reason of this upgrade is PyTorch strictly depends on a particular triton version, and PyTorch 2.3.0 depends on triton 2.3.0. However, @pcmoritz pointed out a performance bug in triton 2.3.0 and it has been fixed in triton 2.3.1, so in order to achieve the best Mixtral FP8 performance, we have to use triton 2.3.1, which results in version conflict if we depend on PyTorch 2.3.0.

cc @Yard1 @pcmoritz @robertgshaw2-neuralmagic @simon-mo

FIX #4509 FIX #5535 FIX #5579 FIX #5705

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Jun 07 '24 00:06 comaniac

I think you also need requirements-cuda.txt: https://github.com/vllm-project/vllm/blob/main/requirements-cuda.txt#L7

Do we need to update vllm-flash-attention as well, or are we okay with minor, minor version? I think that xformers has not released a new package yet (latest stable is xformers==0.0.26.post1, which uses 2.3.0)

Jun 07 '24 00:06 robertgshaw2-redhat

Thanks for pointing out. I just changed all required places. Meanwhile, yeah we do need xFormers and vllm-flash-attn...

Jun 07 '24 00:06 comaniac

@comaniac vllm-flash-attn v2.5.9.post1 was built for PyTorch v2.3.1 and is now available in PyPI: https://pypi.org/project/vllm-flash-attn/

Jun 07 '24 03:06 WoosukKwon

Pending xformers to release a version against torch 2.3.1. Tracking issue https://github.com/facebookresearch/xformers/issues/1052

Closing #4509

Jun 07 '24 05:06 comaniac

CI passed, but need to double check manually whether FlashInfer supports torch 2.3.1.

Jul 10 '24 18:07 comaniac

Checked that FlashInfer works with PyTorch 2.3.1.
Performance benchmark (https://buildkite.com/vllm/performance-benchmark/builds/4569#) shows no regression compared to https://simon-mo-workspace.observablehq.cloud/vllm-dashboard-v0/perf

This PR should be good to go.

cc @Yard1 @robertgshaw2-neuralmagic @WoosukKwon @simon-mo @DarkLight1337

Jul 12 '24 18:07 comaniac