[torch.compile] Fuse RMSNorm with quant

[ProExpertProg]

This PR enables fusing rms_norm and quant ops in the torch.compile backend. It adds all required infrastructure and new fused rms_norm_quant kernels. Only static FP8 quantization is supported in this PR, with more formats and datatypes to be added later to keep this PR as short as possible.

To enable fusion, TORCH_COMPILE_LEVEL needs to be at least 3, and the RMSNorm custom op needs to be enabled (by setting TORCH_CUSTOM_OPS either to all or +rms_norm). TORCH_ENABLE_FUSION needs to be set as well (on by default).

This PR gives roughly 2% end-to-end speedups: TODO detailed numbers.

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[github-actions]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. @ProExpertProg please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[youkaichao]

we can use pytorch's built-in lazy_format_graph_code, see

vllm/vllm/compilation/backends.py

Line 365 in f677862

logger.debug("%s", lazy_format_graph_code("before split", self.graph))

[ProExpertProg]

For me, I like the graph going to a file because I can keep around multiple versions, compare them, and navigate more easily. If it's all printed to the console, I'd most likely need to copy each of the graphs to a file manually.

But maybe we can make it configurable so we could do both?

[youkaichao]

thinks is quite similar to what i did in https://github.com/thuml/depyf . I did quite a lot hacking to make sure the dumped code is readable and parsable by IDEs.

I think the logging infra can be designed together with compilation cache infra.

[youkaichao]

LGTM, I left some comments, but we don't need to address them in this PR. thanks for your hard work!