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memory_planning algos take the specs as inputs instead of calculating them themselves #9952
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memory_planning algos take the specs as inputs instead of calculating them themselves #9952
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9952
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit 7a8e8bf with merge base a1af1ff ( NEW FAILURE - The following job has failed:
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Differential Revision: D72600295
This pull request was exported from Phabricator. Differential Revision: D72600295 |
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
This pull request was exported from Phabricator. Differential Revision: D72600295 |
… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
… them themselves (pytorch#9952) Summary: Pull Request resolved: pytorch#9952 Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
… them themselves (pytorch#9952) Summary: Pull Request resolved: pytorch#9952 Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: skrtskrtfb Differential Revision: D72600295
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: tarun292, skrtskrtfb Differential Revision: D72600295
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… them themselves (pytorch#9952) Summary: Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: tarun292, skrtskrtfb Differential Revision: D72600295
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
… them themselves (pytorch#9952) Summary: Pull Request resolved: pytorch#9952 Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow. Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function. Reviewed By: tarun292, skrtskrtfb Differential Revision: D72600295
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This pull request was exported from Phabricator. Differential Revision: D72600295 |
… them themselves Differential Revision: D72600295 Pull Request resolved: #9952
Summary:
Refactor the algos to not calculate the active set of nodes themselves. Future work should refactor apply_algo more to not be recursive and actually smartly handle lifespans on control flow.
Made the memory planning suite a class so that the algo list is easily configureable without having to make a wrapper function.
Differential Revision: D72600295
cc @angelayi