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[ Misc ] Support Fp8 via llm-compressor
#6110
[ Misc ] Support Fp8 via llm-compressor
#6110
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llm-compressor
llm-compressor
@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? |
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
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vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
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vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
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from vllm.platforms import current_platform | ||
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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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We are little delayed on the model available on HF HUB. But here's the quantization config for us:
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@pavanimajety I would suggest |
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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() | ||
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# 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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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 | ||
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def convert_to_channelwise( |
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def convert_to_channelwise( | |
def convert_to_channelwise_scale( |
Co-authored-by: Robert Shaw <rshaw@neuralmagic>
Co-authored-by: Robert Shaw <rshaw@neuralmagic> Signed-off-by: Alvant <alvasian@yandex.ru>
SUMMARY:
autofp8
,compressed-tensors
,nvidia-ammo
compressed-tensors
Fp8 models (dense only)Relevant PR: main...pavanimajety:vllm:ammo_ckpt_rev2
TEST PLAN:
lm-eval-harness
automationBEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
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