Fix training gradient underflow in quantization tests#13539
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jiqing-feng wants to merge 4 commits intohuggingface:mainfrom
Open
Fix training gradient underflow in quantization tests#13539jiqing-feng wants to merge 4 commits intohuggingface:mainfrom
jiqing-feng wants to merge 4 commits intohuggingface:mainfrom
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Change autocast dtype from float16 to bfloat16 in _test_quantization_training. Float16's limited dynamic range causes gradients to underflow to zero when passing through quantized tensor subclass operations.
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Hi @sayakpaul . Would you please review the PR? Thanks! |
sayakpaul
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May 11, 2026
| inputs = self.get_dummy_inputs() | ||
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| with torch.amp.autocast(torch_device, dtype=torch.float16): | ||
| # Use bfloat16 instead of float16 to avoid gradient underflow with quantized layers |
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Is this quantization backend agnostic?
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What does this PR do?
Changes autocast dtype from
float16tobfloat16in_test_quantization_training. Float16's limited dynamic range (max ~65504, min subnormal ~5.96e-8) causes gradients to underflow to zero when passing through quantized tensor subclass operations; bfloat16 shares float32's exponent range and avoids this.