Create kernel on-device for transforms.functional.gaussian_blur#8426
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NicolasHug merged 4 commits intopytorch:mainfrom May 29, 2024
Merged
Create kernel on-device for transforms.functional.gaussian_blur#8426NicolasHug merged 4 commits intopytorch:mainfrom
transforms.functional.gaussian_blur#8426NicolasHug merged 4 commits intopytorch:mainfrom
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NicolasHug
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Thank you for the PR @drhead , I'll merge after the CI passes.
Note that the V2 version in torchvision.transforms.v2.functional.gaussian_blur would already create the kernel on-device, so perhaps you can migrate to the V2 version already.
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…blur` (#8426) Reviewed By: vmoens Differential Revision: D58283858 fbshipit-source-id: a4df173fcafe9bce4b35478a7eab5f66f2579180 Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com> Co-authored-by: Nicolas Hug <nicolashug@fb.com>
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The current implementation of gaussian blur creates the kernels on CPU and then moves them to the device and dtype of the image. This goes against best practices for pytorch optimization, since it induces a forced device sync.
To fix it, I have passed the device and dtype parameters over to the linspace operation that creates the initial tensor. This should remove the forced device sync. It is worth noting that this may result in it being calculated in a lower precision than normal. This would of course involve an extra call for the cast to get it to the correct precision.
There's probably room for more optimization in this, especially when GaussianBlur is used as a module -- really, I think it would be more appropriate to create the kernels one time and reuse them instead of constructing new ones. This PR solves the more critical issue as is, though.
Closes #8401
cc @vfdev-5