Support whitelist of dynamic sources #147979
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[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/147979
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 5401645 with merge base 5220d40 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
laithsakka
reviewed
Feb 27, 2025
laithsakka
reviewed
Feb 27, 2025
laithsakka
reviewed
Feb 27, 2025
laithsakka
reviewed
Feb 27, 2025
laithsakka
reviewed
Feb 27, 2025
laithsakka
reviewed
Feb 27, 2025
laithsakka
previously requested changes
Feb 27, 2025
This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic. NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic. NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic. NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
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This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic. NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time. Pull Request resolved: pytorch#147979 Approved by: https://github.com/Mingming-Ding
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Stack from ghstack (oldest at bottom):
This PR introduces the ability to whitelist sources as dynamic. This is particularly useful for large models with graph breaks, as you can keep the dynamism across graph breaks since source names stay consistent. Additionally you can use this to mark ints as dynamic.
NB: I intentionally didn't complicate the interface by supporting specification of per dimension dynamism. There is virtue in keeping true to the standard way of representing sources (eg. L['x']). If we find in practice that we need more more fine grained control, we can explore further affordances at that time.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames