add mxfp4 qat, mainly packing code. #2347
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Pull request overview
This PR adds support for MXFP4 (4-bit microscaling floating point) quantization-aware training (QAT), extending the existing MXFP8 support. The implementation includes packing utilities to convert FP4 values into packed uint8 format and export functionality for serialization.
Key changes:
- Added MXFP4 to QAT module mappings alongside MXFP8
- Implemented FP4 packing/unpacking utilities with bit manipulation
- Extended export logic to handle MXFP4 format with packed weight buffers
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.
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| File | Description |
|---|---|
| neural_compressor/torch/quantization/config.py | Adds MXFP4 to QAT module mappings for torch.nn.Linear |
| neural_compressor/torch/export/export_hf.py | Adds MXFP4 export path that packs weights into buffers |
| neural_compressor/torch/algorithms/qat/tensor_quantizer.py | Implements MXFP4 weight packing using new packing utilities |
| neural_compressor/torch/algorithms/qat/quant_utils.py | Adds MXFP4 detection and sets float-quantized format |
| neural_compressor/torch/algorithms/qat/mxfp4_packing.py | New file with FP4 casting and uint4-to-uint8 packing functions |
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add ut |
yiliu30
reviewed
Dec 5, 2025
.../pytorch/nlp/huggingface_models/language-modeling/quantization/llm_qat/quantize_autoround.py
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yiliu30
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Dec 5, 2025
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Description
add mxfp4 qat