Only call into reducer if torch.is_grad_enabled() #19897
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Differential Revision: D15118726 Differential Version: 80866244
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Differential Revision: D15118726 Differential Version: 80866256
mrshenli
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Apr 29, 2019
soumith
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Apr 29, 2019
Differential Revision: D15118726 Differential Version: 80866864
soumith
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Apr 29, 2019
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Summary: Pull Request resolved: #19897 During validation, gradient reduction is not needed, and autograd is never called. The model output will always be a detached tensor. After the new reducer was merged, this meant that it would find all model parameters unused, and kick off reduction for them. When #19799 and output where no parameters are used and it tries to kick off reduction of zeroed gradients. Test for `torch.is_grad_enabled()` and `self.training` before calling into the reducer. Reviewed By: mrshenli Differential Revision: D15118726 fbshipit-source-id: b0208f632a61cbe8110fa626fa427937b7f05924
zhangguanheng66
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Summary: Pull Request resolved: pytorch#19897 During validation, gradient reduction is not needed, and autograd is never called. The model output will always be a detached tensor. After the new reducer was merged, this meant that it would find all model parameters unused, and kick off reduction for them. When pytorch#19799 and output where no parameters are used and it tries to kick off reduction of zeroed gradients. Test for `torch.is_grad_enabled()` and `self.training` before calling into the reducer. Reviewed By: mrshenli Differential Revision: D15118726 fbshipit-source-id: b0208f632a61cbe8110fa626fa427937b7f05924
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Stack:
:black_circle: #19901 Finer grained consistency check in reducer 💛
:white_circle: #19897 Only call into reducer if torch.is_grad_enabled() 💚
During validation, gradient reduction is not needed, and autograd is
never called. The model output will always be a detached tensor. After
the new reducer was merged, this meant that it would find all model
parameters unused, and kick off reduction for them. When #19799 and
#19821 were merged it looked like model output during validation is an
output where no parameters are used and it tries to kick off reduction
of zeroed gradients. Test for
torch.is_grad_enabled()andself.trainingbefore calling into the reducer.Differential Revision: D15118726