[aotd] Support mutations of the same input in fw and bw #155354
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[ghstack-poisoned]
IvanKobzarev
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Chillee,
albanD,
bdhirsh,
ezyang and
soulitzer
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/155354
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bdhirsh
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Jun 6, 2025
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
This was referenced Jun 11, 2025
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@pytorchbot revert -m "Not sure why CI was green, but it breaks tons of tests, see https://hud.pytorch.org/hud/pytorch/pytorch/930b575389f9233efddf70ea7b7804ed06af80d5/1?per_page=50&mergeEphemeralLF=true" -c nosignal |
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@pytorchbot successfully started a revert job. Check the current status here. |
pytorchmergebot
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Jun 24, 2025
)" This reverts commit 3f920f3. Reverted #155354 on behalf of https://github.com/malfet due to Not sure why CI was green, but it breaks tons of tests, see https://hud.pytorch.org/hud/pytorch/pytorch/930b575389f9233efddf70ea7b7804ed06af80d5/1?per_page=50&mergeEphemeralLF=true ([comment](#155354 (comment)))
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@IvanKobzarev your PR has been successfully reverted. |
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv voznesenskym penguinwu Guobing-Chen XiaobingSuper zhuhaozhe blzheng jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv voznesenskym penguinwu Guobing-Chen XiaobingSuper zhuhaozhe blzheng jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv voznesenskym penguinwu Guobing-Chen XiaobingSuper zhuhaozhe blzheng jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
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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 |
Merge failedReason: Command Details for Dev Infra teamRaised by workflow job |
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv voznesenskym penguinwu Guobing-Chen XiaobingSuper zhuhaozhe blzheng jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
Original issue: #154820 The issue happens when there is a mutation for the same input in forward AND in backward. AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward). After that partitioner can put it either in forward or in backward. The fix: 1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation. 2/ Exposing mutation_counter to python We want to keep invariant that copy_ exist only in the end of joint graph. 3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward. Emit post_forward mutations after joint graph fully traced. add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward. 4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward. For this set MUST_SAVE for the source of mutation in forward. proxy_tensor changes: By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained. But we want that this copy_ will be independent and applied just to primals. For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv voznesenskym penguinwu Guobing-Chen XiaobingSuper zhuhaozhe blzheng jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
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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 |
pytorchmergebot
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Jun 27, 2025
This PR adds a new config `backward_pass_autocast`, to set the backward autocast behavior. It does not change the existing behavior. The reason why we need this is that torch.compile acquires a forward and backward graph at the time of the forward pass. This means that implemented naively, if there are any context managers active outside the call to torch.compile, the backward graph will also get the behaviors from those context managers. This PR gives users a way to tweak the autocast behavior of the backward pass. Please see torch._functorch.config for the options to the `backward_pass_autocast` config. Pull Request resolved: #156356 Approved by: https://github.com/bdhirsh ghstack dependencies: #155354
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Stack from ghstack (oldest at bottom):
Original issue: #154820
The issue happens when there is a mutation for the same input in forward AND in backward.
AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward).
After that partitioner can put it either in forward or in backward.
The fix:
1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward
We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for forward mutation.
2/ Exposing mutation_counter to python
We want to keep invariant that copy_ exist only in the end of joint graph.
3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward.
Emit post_forward mutations after joint graph fully traced.
add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward.
4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward.
For this set MUST_SAVE for the source of mutation in forward.
proxy_tensor changes:
By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained.
But we want that this copy_ will be independent and applied just to primals.
For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations.
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv @voznesenskym @penguinwu @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @jiayisunx @chenyang78 @kadeng @chauhang @amjames