unflatten closure #159418
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unflatten closure #159418
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avikchaudhuri
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/159418
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit eee811d with merge base d3ce450 ( UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:
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angelayi
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Jul 30, 2025
| gm = export(m, (torch.rand(64, 64),)) | ||
| torch.export.unflatten(gm) | ||
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| def test_unflatten_closure(self): |
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nit: why not put this in test_unflatten.py?
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I guess I could? test_unflatten checks strictly fewer variants though, right?
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Summary: Sometimes the call history recorded in a `nn_module_stack` does not have the stack property, where each FQN is a prefix of the next FQN. This can cause errors during `unflatten`. Instead of erroring we now drop entries from such a `nn_module_stack` to restore the stack property. This effectively leads to less unflattening: the last FQN in the call history before the stack property was broken keeps the entire flat subgraph of its call. Test Plan: added test, updated another Rollback Plan: Reviewed By: angelayi Differential Revision: D79204669
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Summary: Pull Request resolved: pytorch#159418 Sometimes the call history recorded in a `nn_module_stack` does not have the stack property, where each FQN is a prefix of the next FQN. This can cause errors during `unflatten`. Instead of erroring we now drop entries from such a `nn_module_stack` to restore the stack property. This effectively leads to less unflattening: the last FQN in the call history before the stack property was broken keeps the entire flat subgraph of its call. Test Plan: added test, updated another Rollback Plan: Reviewed By: angelayi Differential Revision: D79204669
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This pull request was exported from Phabricator. Differential Revision: D79204669 |
Summary: Pull Request resolved: pytorch#159418 Sometimes the call history recorded in a `nn_module_stack` does not have the stack property, where each FQN is a prefix of the next FQN. This can cause errors during `unflatten`. Instead of erroring we now drop entries from such a `nn_module_stack` to restore the stack property. This effectively leads to less unflattening: the last FQN in the call history before the stack property was broken keeps the entire flat subgraph of its call. Test Plan: added test, updated another Rollback Plan: Reviewed By: angelayi Differential Revision: D79204669
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@pytorchbot merge (Initiating merge automatically since Phabricator Diff has merged) |
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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Summary: Sometimes the call history recorded in a `nn_module_stack` does not have the stack property, where each FQN is a prefix of the next FQN. This can cause errors during `unflatten`. Instead of erroring we now drop entries from such a `nn_module_stack` to restore the stack property. This effectively leads to less unflattening: the last FQN in the call history before the stack property was broken keeps the entire flat subgraph of its call. Test Plan: added test, updated another Rollback Plan: Differential Revision: D79204669 Pull Request resolved: #159418 Approved by: https://github.com/angelayi
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Summary: Sometimes the call history recorded in a
nn_module_stackdoes not have the stack property, where each FQN is a prefix of the next FQN. This can cause errors duringunflatten. Instead of erroring we now drop entries from such ann_module_stackto restore the stack property. This effectively leads to less unflattening: the last FQN in the call history before the stack property was broken keeps the entire flat subgraph of its call.Test Plan:
added test, updated another
Rollback Plan:
Differential Revision: D79204669