[inductor] remove no_x_dim #159810
[inductor] remove no_x_dim #159810
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cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben Differential Revision: [D79599286](https://our.internmc.facebook.com/intern/diff/D79599286) [ghstack-poisoned]
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@davidberard98 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben Differential Revision: [D79599286](https://our.internmc.facebook.com/intern/diff/D79599286) [ghstack-poisoned]
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben Differential Revision: [D79599286](https://our.internmc.facebook.com/intern/diff/D79599286) [ghstack-poisoned]
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perf run h100: https://github.com/pytorch/pytorch/actions/runs/16850631639 |
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@pytorchbot 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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@davidberard98 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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@pytorchbot merge |
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@pytorchbot merge |
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@pytorchbot merge |
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Stack from ghstack (oldest at bottom):
no_x_dim is used to indicate that a reduction operates on a single row, and data loaded for the reduction is 1-dimensional.
no_x_dim was introduced in #102444 - in which there was bad perf in some reductions, and using 1D tensors fixed the perf issue.
However, it appears that this perf issue no longer exists in current Triton versions. #118822 checked this, and we can also check this on H100 benchmarks (linked below). And another motivation for removing this behavior is that it enables larger loads, which we observe is necessary for good performance on certain shapes on Blackwell.
H100 inference benchmarks:
https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Mon%2C%2004%20Aug%202025%2004%3A13%3A24%20GMT&stopTime=Mon%2C%2011%20Aug%202025%2004%3A13%3A24%20GMT&granularity=hour&mode=inference&dtype=bfloat16&deviceName=cuda%20(h100)&lBranch=gh/davidberard98/396/orig&lCommit=a6bcd4692fb39fa2fad260f290bff545d4425829&rBranch=main&rCommit=e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a
H100 training benchmarks:
https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Mon%2C%2004%20Aug%202025%2004%3A13%3A24%20GMT&stopTime=Mon%2C%2011%20Aug%202025%2004%3A13%3A24%20GMT&granularity=hour&mode=training&dtype=amp&deviceName=cuda%20(h100)&lBranch=gh/davidberard98/396/orig&lCommit=a6bcd4692fb39fa2fad260f290bff545d4425829&rBranch=main&rCommit=e96c7c4bb0f6aeae2ab3b6f040f7d67edbec199a
Overall, the benchmarks show minimal change in performance.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @mlazos
Differential Revision: D79599286