Use FusedMovingAvgObsFakeQuantize instead of FakeQuantize for faster QAT #14740
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14740
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Differential Revision: D83583655
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Differential Revision: D83583655
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Differential Revision: D83583655
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| ) -> QuantizationConfig: | ||
| extra_args: Dict[str, Any] = {"eps": 2**-20} | ||
| act_fake_quant_ctr = FakeQuantize.with_args( | ||
| act_fake_quant_ctr = FusedMovingAvgObsFakeQuantize.with_args( |
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What's the difference between FakeQuantize and FusedMovingAvgObsFakeQuantize
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FusedMovingAvgObsFakeQuantize - as the name suggests, has a combined op for FakeQuantize and MovingAvgObserver which makes it faster than two separate ops: FakeQuantize and MovingAvgObserver.
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Reviewed By: billmguo Differential Revision: D83583655
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Reviewed By: billmguo Differential Revision: D83583655
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…QAT (pytorch#14740) Summary: FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly. Tested on llama model on HTP and got ~4x QAT speedup. Reviewed By: billmguo Differential Revision: D83583655
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Summary:
FusedMovingAvgObsFakeQuantize speeds up by fusing FakeQuantize and MovingAverageMinMaxObserver into one CUDA op. Using it should give good speedups. This change updates the QAT qconfigs to accordingly.
Tested on llama model on HTP and got ~4x QAT speedup.
Differential Revision: D83583655