Add Float8ActInt4WeightQATQuantizer#2289
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2289
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andrewor14
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| from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib # noqa: F401 | ||
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| from torchao import quantize_ | ||
| from torchao.float8.config import ScalingGranularity |
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I kinda hate that we have ScalingGranuliarty and Ganularity of the other FP8 inference APIs
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I think this is worth fixing before landing. @andrewor14 , how about just using rowwise scaling (since I assume that the one you want) and removing the option to confugure it? That will at least keep this problem away from the BC surface of QAT in a way that we can more easily fix later.
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request changes for removing ScalingGranularity from user API
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Removed ScalingGranularity from the public API |
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**Summary:** This commit adds a QAT quantizer that performs float8 dynamic activation + int4 symmetric per channel weight fake quantization. Note that there is no corresponding config for float8 QAT yet. This will be added in a future PR. **Test Plan:** python test/quantization/test_qat.py -k test_float8_fake_quantize python test/quantization/test_qat.py -k test_qat_fp8a4w_quantizer
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**Summary:** This commit adds a QAT quantizer that performs float8 dynamic activation + int4 symmetric per channel weight fake quantization. Note that there is no corresponding config for float8 QAT yet. This will be added in a future PR. **Test Plan:** python test/quantization/test_qat.py -k test_float8_fake_quantize python test/quantization/test_qat.py -k test_qat_fp8a4w_quantizer
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Summary: This commit adds a QAT quantizer that performs float8 dynamic activation + int4 symmetric per channel weight fake quantization. Note that there is no corresponding config for float8 QAT yet. This will be added in a future PR.
Test Plan:
python test/quantization/test_qat.py -k test_float8_fake_quantize
python test/quantization/test_qat.py -k test_qat_fp8a4w_quantizer