[CUDA][AMP] Speed up fp16/bf16 casts on H100+ #137053
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eqy
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/137053
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 8b45b71 with merge base 3f9f604 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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mind adding a quick perf sweep? |
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@drisspg sorry for the delay, here's a quick run on basically mostly for larger sizes: after |
drisspg
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@pytorchmergebot rebase |
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@pytorchbot started a rebase job onto refs/remotes/origin/viable/strict. Check the current status here |
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Successfully rebased |
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@pytorchmergebot merge |
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Similar to pytorch#110251 we're seeing cases where vectorization can benefit casts to fp16/bf16 Pull Request resolved: pytorch#137053 Approved by: https://github.com/drisspg
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Similar to pytorch#110251 we're seeing cases where vectorization can benefit casts to fp16/bf16 Pull Request resolved: pytorch#137053 Approved by: https://github.com/drisspg Co-authored-by: eqy <[email protected]>
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Similar to #110251 we're seeing cases where vectorization can benefit casts to fp16/bf16
cc @ptrblck @msaroufim @mcarilli @leslie-fang-intel @jgong5