Update no_grad usage to inference_mode if possible#2534
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rwightman merged 2 commits intohuggingface:mainfrom Jul 4, 2025
Merged
Update no_grad usage to inference_mode if possible#2534rwightman merged 2 commits intohuggingface:mainfrom
rwightman merged 2 commits intohuggingface:mainfrom
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rwightman
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Jul 3, 2025
train.py
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| ) | ||
| if args.head_init_scale is not None: | ||
| with torch.no_grad(): | ||
| with torch.inference_mode(): |
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This one should probably remain as no_grad
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I think it works. It does not interfer with the graph / properties of existing variable, and the mul just scale and does not create new params.
I just tried to run train.py for some steps with those params and saw no error
--model resnet18 --dataset hfds/imagenet-1k --head-init-scale .1
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All the same, would like to revert that to previous... everything else looks good assuming no test fails, which I doubt.
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I updated no_grad references in the repository to inference_mode for some slight performance gain.
References for no_grad / inference_mode difference:
https://discuss.pytorch.org/t/pytorch-torch-no-grad-vs-torch-inference-mode/134099/2?u=timgianitsos
https://stackoverflow.com/questions/74191070/why-are-there-two-different-flags-to-disable-gradient-computation-in-pytorch/74197846#74197846