[FSDP] Relax post-backward assert #89791
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awgu
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/89791
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit a510d8d: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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ghstack-source-id: ee0fd21a02c0fc671ca900e2b57083a1ef60edd2 Pull Request resolved: #89791
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| @@ -482,9 +482,13 @@ def _post_backward_hook( | |||
| "FullyShardedDataParallel._post_backward_hook" | |||
| ): | |||
| _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD]) | |||
| # For reentrant AC, the post-backward hook may run multiple times in | |||
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nit: For reentrant AC multiple times
This assert was accidentally made stricter when transitioning from per-FSDP-instance training state to per-handle training state. This PR relaxes it again, which should restore compatibility for some reentrant AC plus FSDP cases. [ghstack-poisoned]
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ghstack-source-id: 90b4cb6e62e1dad7afccb625996cc797692c1db2 Pull Request resolved: #89791
This assert was accidentally made stricter when transitioning from per-FSDP-instance training state to per-handle training state. This PR relaxes it again, which should restore compatibility for some reentrant AC plus FSDP cases. [ghstack-poisoned]
awgu
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ghstack-source-id: 3d8a27dc946209d413b1dd5e6ed0d6815bb71721 Pull Request resolved: #89791
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@pytorchbot rebase -s |
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This assert was accidentally made stricter when transitioning from per-FSDP-instance training state to per-handle training state. This PR relaxes it again, which should restore compatibility for some reentrant AC plus FSDP cases. [ghstack-poisoned]
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Successfully rebased |
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ghstack-source-id: 268645d4e371a830a9857b981311dbfd6455d05a Pull Request resolved: #89791
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@pytorchbot merge |
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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With combining FSDP with reentrant checkpointing, the post backward hook might run twice, and then hit [this error](https://github.com/pytorch/pytorch/blob/e20ec44544c17d6d3d411f88b870e05043bda731/torch/distributed/fsdp/_runtime_utils.py#L487). This is because reentrant backward uses nested autograd GraphTasks. The inner GraphTask is not aware of the outer one and therefore will flush pending `AccumulateGrad` invocations on exit, which in turn triggers the post backward hooks registered by FSDP. Later, the outer GraphTask will trigger that again, leading to the above error. PR #89791 relaxes the FSDP training state check, but we still run into grad value check failures occasionally. Therefore, this PR only lands the test for non-reentrant test, and we can enable the reentrant test when the accuracy issues are addressed. [ghstack-poisoned]
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With combining FSDP with reentrant checkpointing, the post backward hook might run twice, and then hit [this error](https://github.com/pytorch/pytorch/blob/e20ec44544c17d6d3d411f88b870e05043bda731/torch/distributed/fsdp/_runtime_utils.py#L487). This is because reentrant backward uses nested autograd GraphTasks. The inner GraphTask is not aware of the outer one and therefore will flush pending `AccumulateGrad` invocations on exit, which in turn triggers the post backward hooks registered by FSDP. Later, the outer GraphTask will trigger that again, leading to the above error. PR #89791 relaxes the FSDP training state check, but we still run into grad value check failures occasionally. Therefore, this PR only lands the test for non-reentrant test, and we can enable the reentrant test when the accuracy issues are addressed. ghstack-source-id: 8848c4cbf572c3a5acd8a9c2fd2b22539a65375f Pull Request resolved: #89781
Merge failedReason: 1 additional jobs have failed, first few of them are: trunk Details for Dev Infra teamRaised by workflow job |
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With combining FSDP with reentrant checkpointing, the post backward hook might run twice, and then hit [this error](https://github.com/pytorch/pytorch/blob/e20ec44544c17d6d3d411f88b870e05043bda731/torch/distributed/fsdp/_runtime_utils.py#L487). This is because reentrant backward uses nested autograd GraphTasks. The inner GraphTask is not aware of the outer one and therefore will flush pending `AccumulateGrad` invocations on exit, which in turn triggers the post backward hooks registered by FSDP. Later, the outer GraphTask will trigger that again, leading to the above error. PR #89791 relaxes the FSDP training state check, but we still run into grad value check failures occasionally. Therefore, this PR only lands the test for non-reentrant test, and we can enable the reentrant test when the accuracy issues are addressed. Pull Request resolved: #89781 Approved by: https://github.com/rohan-varma
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@pytorchbot merge |
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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With combining FSDP with reentrant checkpointing, the post backward hook might run twice, and then hit [this error](https://github.com/pytorch/pytorch/blob/e20ec44544c17d6d3d411f88b870e05043bda731/torch/distributed/fsdp/_runtime_utils.py#L487). This is because reentrant backward uses nested autograd GraphTasks. The inner GraphTask is not aware of the outer one and therefore will flush pending `AccumulateGrad` invocations on exit, which in turn triggers the post backward hooks registered by FSDP. Later, the outer GraphTask will trigger that again, leading to the above error. PR pytorch#89791 relaxes the FSDP training state check, but we still run into grad value check failures occasionally. Therefore, this PR only lands the test for non-reentrant test, and we can enable the reentrant test when the accuracy issues are addressed. Pull Request resolved: pytorch#89781 Approved by: https://github.com/rohan-varma
kulinseth
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Dec 10, 2022
This assert was accidentally made stricter when transitioning from per-FSDP-instance training state to per-handle training state. This PR relaxes it again, which should restore compatibility for some reentrant AC plus FSDP cases. Pull Request resolved: pytorch#89791 Approved by: https://github.com/zhaojuanmao
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Stack from ghstack (oldest at bottom):
This assert was accidentally made stricter when transitioning from per-FSDP-instance training state to per-handle training state. This PR relaxes it again, which should restore compatibility for some reentrant AC plus FSDP cases.