[ Misc ] Support Fp8 via llm-compressor

[robertgshaw2-redhat]

SUMMARY:

• refactors fp8 utilities for shared use across autofp8, compressed-tensors, nvidia-ammo
• adds support for compressed-tensors Fp8 models (dense only)

Relevant PR: main...pavanimajety:vllm:ammo_ckpt_rev2

• cc @pavanimajety this will help land nvidia-ammo

TEST PLAN:

• added regression test to lm-eval-harness automation

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[pavanimajety]

@robertgshaw2-neuralmagic Thanks for the cc and the utils you created. Here's the WIP PR: #6112

Do we have an agreement about adding the "quant_source" parameter in "quantization_config" for static scaled checkpoints?

[comaniac]

Given that this is no longer in linear method, we should make its name more informative, such as cutless_scaled_mm_fp8_supported.

[pavanimajety]

Let me get this merged in + then we can use these utils for ModelOpt

We are little delayed on the model available on HF HUB. But here's the quantization config for us:

 "quantization_config": {
     "activation_scheme": "static",
     "quant_method": "fp8",
     "quant_source":"modelopt"
 }

[robertgshaw2-redhat]

Let me get this merged in + then we can use these utils for ModelOpt

We are little delayed on the model available on HF HUB. But here's the quantization config for us:

 "quantization_config": {
     "activation_scheme": "static",
     "quant_method": "fp8",
     "quant_source":"modelopt"
 }

@pavanimajety I would suggest "quant_method": "modelopt" in the config. This will allows us to know to use the ModelOptLinearMethod rather than going down the AutoFP8 pathway

[robertgshaw2-redhat]

@comaniac @simon-mo this should be ready to go pending CI

[mgoin]

Great job with the refactoring cleanup into utility functions here. This is a definite improvement now and sets a better structure for future variations, no qualms

[mgoin]

Just to be clear, in the per channel case there is no max reduction done? It isn't clear from the code in this function. Is per channel actually supported?

[robertgshaw2-redhat]

The cutlass kernels do actually support the per-channel case.

We currently do not allow this at the Python layer so far but should be easy to add via compressed-tensors

[comaniac]

LGTM

[comaniac]

Suggested change

	def convert_to_channelwise(
	def convert_to_channelwise_scale(