[ Kernel ] Fp8 Channelwise Weight Support

[robertgshaw2-neuralmagic]

SUMMARY:

• support channelwise scales for fp8 via compressed-tensors via cutlass kernels
• note: on devices without support for cutlass (ada - for now), we cannot run these models. An alternative approach is to re-enable cutlass on these devices OR fall back to per tensor quantization

TEST PLAN:

• manually verified correctness of the model below. We do not have H100s in CI so we cannot run this

from vllm import LLM
model = LLM("nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors")

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[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.
Just a reminder: PRs would not trigger full CI run by default. Instead, it would only trigger fastcheck CI to run, which consists only a small and essential subset of tests to quickly catch errors with the flexibility to run extra individual tests on top (you can do this by unblocking test steps in the Buildkite run).

Full CI run is still required to merge this PR so once the PR is ready to go, please make sure to run it. If you need all test signals in between PR commits, you can trigger full CI as well.

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🚀

[robertgshaw2-neuralmagic]

/ready

[mgoin]

Nice job!

[tlrmchlsmth]

Are you going to do per-token in a separate PR? (Waiting on the quant kernel for that one?)

[robertgshaw2-neuralmagic]

Are you going to do per-token in a separate PR? (Waiting on the quant kernel for that one?)

Separate PR, waiting on quant kernel. Do you know current state?

[tlrmchlsmth]

Are you going to do per-token in a separate PR? (Waiting on the quant kernel for that one?)

Separate PR, waiting on quant kernel. Do you know current state?

#6511

[DarkLight1337]

You should merge main to resolve the doc failure.