Fix tensor.__deepcopy__ for lazy device #73197
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@wconstab Can you please take a look? |
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@comaniac Thanks, this looks right at first glance but can you say how you tested it? I will verify it on our staging branch too. |
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- parallel change to that submitted on master by @comaniac
Thanks @wconstab for the review. I basically just did the following. model = torchvision.models.resnet18(num_classes=1000)
model.to(device="lazy")
model.train()
copy.deepcopy(model) # errorThe above code snippet will trigger the following error. Looks like PyTorch tries to copy a lazy tensor when deep copying a model, although lazy tensor being a model parameter doesn't make any sense to me. Note 1: I register the Lazy backend by myself. It's pretty much the same way as Torch/XLA. |
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Summary: A small bug that misses `lazy` in tensor.__deepcopy__, which results in segmentation when deepcopy a lazy model. Pull Request resolved: #73197 Reviewed By: jbschlosser Differential Revision: D34394482 Pulled By: wconstab fbshipit-source-id: c84fdb9b3a827677971fd3477a92679d7dbce3c0
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Summary: A small bug that misses `lazy` in tensor.__deepcopy__, which results in segmentation when deepcopy a lazy model. Pull Request resolved: pytorch/pytorch#73197 Reviewed By: jbschlosser Differential Revision: D34394482 Pulled By: wconstab fbshipit-source-id: c84fdb9b3a827677971fd3477a92679d7dbce3c0 (cherry picked from commit c003d150cea969a6595ef8004ea82596fb9431b6)
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Summary: A small bug that misses `lazy` in tensor.__deepcopy__, which results in segmentation when deepcopy a lazy model. Pull Request resolved: pytorch/pytorch#73197 Reviewed By: jbschlosser Differential Revision: D34394482 Pulled By: wconstab fbshipit-source-id: c84fdb9b3a827677971fd3477a92679d7dbce3c0 (cherry picked from commit c003d150cea969a6595ef8004ea82596fb9431b6)
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A small bug that misses
lazyin tensor.deepcopy, which results in segmentation when deepcopy a lazy model.