[meta] Add meta implementation for aten.masked_scatter #108802
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/108802
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 681d078 with merge base e471c12 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Sep 7, 2023
peterbell10
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ghstack-source-id: 40d1464ee829aaf20469792fa9797f9149282ad9 Pull Request resolved: pytorch#108802
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peterbell10
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ghstack-source-id: 136065a833938f6370a8950e5fb2c5875a4a1d30 Pull Request resolved: pytorch#108802
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]
lezcano
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cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]
pytorchmergebot
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This decomposes masked_scatter into `aten.cumsum` and a single pointwise kernel, which is similar to what is done in eager. I only do this for CUDA because on CPU it isn't split into two passes like this so would cause a slowdown. Pull Request resolved: #108803 Approved by: https://github.com/lezcano ghstack dependencies: #108802
pytorchmergebot
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This decomposes masked_scatter into `aten.cumsum` and a single pointwise kernel, which is similar to what is done in eager. I only do this for CUDA because on CPU it isn't split into two passes like this so would cause a slowdown. Pull Request resolved: #108803 Approved by: https://github.com/lezcano ghstack dependencies: #108802
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cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov