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Summary: Before https://github.com/pytorch/pytorch/pull/57833, calls to backward() or grad() synced only the calling thread's default stream with autograd leaf streams at the end of backward. This made the following weird pattern safe: ```python with torch.cuda.stream(s): # imagine forward used many streams, so backward leaf nodes may run on many streams loss.backward() # no sync use grads ``` but a more benign-looking pattern was unsafe: ```python with torch.cuda.stream(s): # imagine forward used a lot of streams, so backward leaf nodes may run on many streams loss.backward() # backward() syncs the default stream with all the leaf streams, but does not sync s with anything, # so counterintuitively (even though we're in the same stream context as backward()!) # it is NOT SAFE to use grads here, and there's no easy way to make it safe, # unless you manually sync on all the streams you used in forward, # or move "use grads" back to default stream outside the context. use grads ``` mruberry ngimel and I decided backward() should have the [same user-facing stream semantics as any cuda op](https://pytorch.org/docs/master/notes/cuda.html#stream-semantics-of-backward-passes).** In other words, the weird pattern should be unsafe, and the benign-looking pattern should be safe. Implementationwise, this meant backward() should sync its calling thread's current stream, not default stream, with the leaf streams. After https://github.com/pytorch/pytorch/pull/57833, backward syncs the calling thread's current stream AND default stream with all leaf streams at the end of backward. The default stream syncs were retained for temporary backward compatibility. This PR finishes https://github.com/pytorch/pytorch/pull/57833's work by deleting syncs on the default stream. With this PR, graph-capturing an entire backward() call should be possible (see the [test_graph_grad_scaling diffs](https://github.com/pytorch/pytorch/compare/master...mcarilli:streaming_backwards_remove_default_syncs?expand=1#diff-893b1eea27352f336f4cd832919e48d721e4e90186e63400b8596db6b82e7450R3641-R3642)). ** first paragraph has a formatting error which this PR should also fix. Pull Request resolved: https://github.com/pytorch/pytorch/pull/60421 Reviewed By: albanD Differential Revision: D29370344 Pulled By: ngimel fbshipit-source-id: 3248bc5fb92fc517db0c15c897e5d7250f67d7fe |
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| .. | ||
| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| ddp.rst | ||
| extending.rst | ||
| faq.rst | ||
| gradcheck.rst | ||
| hip.rst | ||
| large_scale_deployments.rst | ||
| modules.rst | ||
| multiprocessing.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
