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[ Misc ] Support Fp8 via llm-compressor #6110

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robertgshaw2-redhat
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@robertgshaw2-redhat robertgshaw2-redhat commented Jul 3, 2024

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

TEST PLAN:

  • added regression test to lm-eval-harness automation

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@robertgshaw2-redhat robertgshaw2-redhat marked this pull request as ready for review July 3, 2024 17:00
@robertgshaw2-redhat robertgshaw2-redhat changed the title [ Misc] Support Fp8 via llm-compressor [ Misc ] Support Fp8 via llm-compressor Jul 3, 2024
@pavanimajety
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@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?

from vllm.platforms import current_platform


def cutlass_fp8_supported() -> bool:
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Given that this is no longer in linear method, we should make its name more informative, such as cutless_scaled_mm_fp8_supported.

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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
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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 and others added 11 commits July 6, 2024 12:43
@robertgshaw2-redhat
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@comaniac @simon-mo this should be ready to go pending CI

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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

self.input_dynamic = input_dynamic
self.cutlass_fp8_supported = cutlass_fp8_supported()

# W8A8-Fp8 kernels support only per-tensor and per-channel cases.
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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?

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@robertgshaw2-redhat robertgshaw2-redhat Jul 7, 2024

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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

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LGTM

return scale


def convert_to_channelwise(
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Suggested change
def convert_to_channelwise(
def convert_to_channelwise_scale(

@robertgshaw2-redhat robertgshaw2-redhat enabled auto-merge (squash) July 7, 2024 16:31
robertgshaw2-redhat and others added 4 commits July 7, 2024 12:56
@robertgshaw2-redhat robertgshaw2-redhat merged commit abfe705 into vllm-project:main Jul 7, 2024
70 checks passed
xjpang pushed a commit to xjpang/vllm that referenced this pull request Jul 24, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic>
Alvant pushed a commit to compressa-ai/vllm that referenced this pull request Oct 26, 2024
Co-authored-by: Robert Shaw <rshaw@neuralmagic>
Signed-off-by: Alvant <alvasian@yandex.ru>
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5 participants