Finer grained consistency check in reducer #19901
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Differential Revision: D15118871 Differential Version: 80866996
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mrshenli
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Differential Revision: D15118871 Differential Version: 80867290
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Summary: Pull Request resolved: #19901 The existing code used `expect_autograd_hooks_` as a proxy for the situation where finalization of the previous iteration is needed. This is not correct, however, since you may decide to completely ignore the output of a DDP wrapped module. If this is the case, and no gradients have been passed to the reducer, it is fine to keep going. This commit adds a new variable `require_finalize_` that tracks whether the finalization is really needed. Reviewed By: mrshenli Differential Revision: D15118871 fbshipit-source-id: 25938eaf1fe13e2940feae1312892b9d3da8a67d
zhangguanheng66
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May 6, 2019
Summary: Pull Request resolved: pytorch#19901 The existing code used `expect_autograd_hooks_` as a proxy for the situation where finalization of the previous iteration is needed. This is not correct, however, since you may decide to completely ignore the output of a DDP wrapped module. If this is the case, and no gradients have been passed to the reducer, it is fine to keep going. This commit adds a new variable `require_finalize_` that tracks whether the finalization is really needed. Reviewed By: mrshenli Differential Revision: D15118871 fbshipit-source-id: 25938eaf1fe13e2940feae1312892b9d3da8a67d
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Stack:
:white_circle: #19901 Finer grained consistency check in reducer 💛
:black_circle: #19897 Only call into reducer if torch.is_grad_enabled() 💚
The existing code used
expect_autograd_hooks_as a proxy for thesituation where finalization of the previous iteration is needed. This
is not correct, however, since you may decide to completely ignore the
output of a DDP wrapped module. If this is the case, and no gradients
have been passed to the reducer, it is fine to keep going. This commit
adds a new variable
require_finalize_that tracks whether thefinalization is really needed.
Differential Revision: D15118871