Fix NVFP4 QAT mixed precision #3501
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3501
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 4a5913b with merge base f3342a0 ( BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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| x = x.view(-1, x.shape[-1]) | ||
| else: | ||
| batch_size = None | ||
| fq = _NVFP4QuantizedForwardFakeQuantizedBackward.apply( |
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should the cast be inside _NVFP4QuantizedForwardFakeQuantizedBackward?
**Summary:** This commit adds support for bf16 activations + fp32 weights mixed precision for NVFP4 QAT, which previously threw a dtype assertion error: ``` File "ao/torchao/prototype/qat/nvfp4.py", line 159, in forward assert fq.dtype == x.dtype ``` **Test Plan:** ``` python test/quantization/test_qat.py -k test_nvfp4_fake_quanitzed_linear_mixed_precision ```
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| ctx.save_for_backward(_input, weight) | ||
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| return _addmm_nvfp4_dispatch( |
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why is this returning the wrong dtype?
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Seems to be caused by adding the bias:
Before this line result was bf16, after this line it's fp32. Do you think we should cast the bias here instead?
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Summary: This commit adds support for bf16 activations + fp32 weights mixed precision for NVFP4 QAT, which previously threw a dtype assertion error:
Test Plan: