Reduce mish error by an alternative without softplus op#2618
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ChinChangYang wants to merge 1 commit intoapple:mainfrom
Open
Reduce mish error by an alternative without softplus op#2618ChinChangYang wants to merge 1 commit intoapple:mainfrom
ChinChangYang wants to merge 1 commit intoapple:mainfrom
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TobyRoseman
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Mar 2, 2026
| inputs = _get_inputs(context, node, expected=1) | ||
| x = inputs[0] | ||
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| softplus = mb.softplus(x=x) |
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Looking at the PyTorch documentation, it seems the existing implementation is correct:
https://docs.pytorch.org/docs/stable/generated/torch.nn.Mish.html
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If the existing (software) implementation is correct, it must be a hardware precision issue in the Neural Engine. This PR provides a (software) workaround to circumvent the precision issue. I anticipate that Apple’s low-level (hardware) developers will investigate this issue.
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Fix the high numerical error in mish activation #2359.
Algorithm:
Evaluation:
In the following experiments, the mean absolute errors are evaluated by the method in #2359 (comment).
Before this change, NE generates high numerical error:
With the new algorithm, NE generates low numerical error:
A tester reported that the new mish function generates
NaNonly whenxis-Infin the float16 format.Performance:
This change has been adopted in KataGo Core ML backend ChinChangYang/KataGo#7. The performance of the KataGo model with the new mish activation (7.15 ms) is similar to the original mish implementation (7.03 ms).
Conclusion:
Overall, the change enhances the accuracy and reliability of the mish activation in Core ML models.