Replacement is irrelevant for 1-sample multinomial #86342
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So use fast path, both on CPU and on MPS Also, remove some spurious copy-n-paste checks from MPS codepath
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/86342
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@kulinseth thank you for pointing this out, will do in a separate PR, as it does not have anything to do with this optimization. |
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@pytorchbot merge -g |
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Reuse existing RandomCachedGraph to keep RNG state as part of the graph Add `CreateCachedGraphAs` convenience wrapper Addresses #86342 (review)
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Reuse existing RandomCachedGraph to keep RNG state as part of the graph Add `CreateCachedGraphAs` convenience wrapper Addresses #86342 (review)
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Reuse existing RandomCachedGraph to keep RNG state as part of the graph Add `CreateCachedGraphAs` convenience wrapper Addresses #86342 (review) Pull Request resolved: #86437 Approved by: https://github.com/kulinseth
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Summary: So use fast path, both on CPU and on MPS Also, remove some spurious copy-n-paste checks from MPS codepath CUDA already has this optimization, see https://github.com/pytorch/pytorch/blob/dc9c507d24d0c833cb09105177326f1f6bbe99c4/aten/src/ATen/native/cuda/MultinomialKernel.cu#L355-L356 Pull Request resolved: #86342 Approved by: https://github.com/ngimel Test Plan: contbuild & OSS CI, see https://hud.pytorch.org/commit/pytorch/pytorch/2232db7fc12301a2226d1921948917d5b23b6888 Reviewed By: seemethere Differential Revision: D40167219 Pulled By: seemethere fbshipit-source-id: 52e66c95e74b7f6b2eefbc232e9ac6cac8ced73c
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Summary: Reuse existing RandomCachedGraph to keep RNG state as part of the graph Add `CreateCachedGraphAs` convenience wrapper Addresses #86342 (review) Pull Request resolved: #86437 Approved by: https://github.com/kulinseth Test Plan: contbuild & OSS CI, see https://hud.pytorch.org/commit/pytorch/pytorch/10aead9adc20bd45b7692e97a64cb76f114c8e16 Reviewed By: seemethere Differential Revision: D40196735 Pulled By: seemethere fbshipit-source-id: 89af8392d697def2a8bef21c79147767b3a8d098
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So use fast path, both on CPU and on MPS
Also, remove some spurious copy-n-paste checks from MPS codepath
CUDA already has this optimization, see
pytorch/aten/src/ATen/native/cuda/MultinomialKernel.cu
Lines 355 to 356 in dc9c507