[aot autograd] refactor CompiledFunction.backward: prologue (1/N) #139331
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/139331
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit c7ee4a0 with merge base a951d99 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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…39343) Pull Request resolved: #139343 Approved by: https://github.com/zou3519 ghstack dependencies: #139331
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…#139347) Pull Request resolved: #139347 Approved by: https://github.com/zou3519 ghstack dependencies: #139331, #139343
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…torch#139331) So for functional autograd + CA, most nodes are inlined in aot autograd. But user-defined callables aren't safe to make_fx unless dynamo traces through them. The AOT backward must be inlined by dynamo time. We plan to directly insert calls to the backward in the graph: - call prologue - call bwd graph - call epilogue Restructuring our AOT bwd implementation will make this implementation easier. Pull Request resolved: pytorch#139331 Approved by: https://github.com/zou3519
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…torch#139343) Pull Request resolved: pytorch#139343 Approved by: https://github.com/zou3519 ghstack dependencies: pytorch#139331
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…pytorch#139347) Pull Request resolved: pytorch#139347 Approved by: https://github.com/zou3519 ghstack dependencies: pytorch#139331, pytorch#139343
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ghstack-source-id: 4aa455f Pull Request resolved: pytorch/pytorch#139331
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So for functional autograd + CA, most nodes are inlined in aot autograd. But user-defined callables aren't safe to make_fx unless dynamo traces through them. The AOT backward must be inlined by dynamo time. We plan to directly insert calls to the backward in the graph:
Restructuring our AOT bwd implementation will make this implementation easier.
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