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[model] Support fp8 with fuse model#4616

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[model] Support fp8 with fuse model#4616
mgilmore-relace wants to merge 1 commit into
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Code Review

This pull request introduces support for FP8 quantization in the fused model forward pass. The changes involve detecting if FP8 is enabled via the model configuration and then passing a flag to preprocess_packed_seqs to trigger special padding logic required for FP8 performance and correctness. My review identified a potential issue where the logic to detect if FP8 padding should be used might not work correctly with the documented configuration, which could prevent the feature from functioning as intended. I've provided a suggestion to make this check more robust.

) # vision model does not need pre_process, because we pack the input_ids to thd in the forward function
post_process: bool = unwrap_model(model).post_process
fp8 = unwrap_model(model).config.fp8
use_fp8_padding = fp8 in ["e4m3", "hybrid"]

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high

The condition to enable FP8 padding appears to be inconsistent with the project's documentation. According to docs/advance/fp8.md, FP8 quantization is enabled by setting quantization: "fp8". However, the current code checks if fp8 in ["e4m3", "hybrid"]. If the configuration value for fp8 is the string "fp8", use_fp8_padding will be False, and the necessary padding for FP8-optimized kernels will not be applied. This could lead to incorrect behavior or performance issues.

To align with the documentation and make the feature work as expected, the condition should be updated to correctly handle the "fp8" configuration value.

Suggested change
use_fp8_padding = fp8 in ["e4m3", "hybrid"]
use_fp8_padding = bool(fp8)

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