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Sign upChange activation modules in C++ from using Tensor& to Tensor #28501
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Sequential does not like modules added to it to take Tensor& (const Tensor& and Tensor are both OK). Functional and others use Tensor when they want to potentially change things in-place. This changes ReLU and friends to also do that. Unfortunately, this seems to be BC breaking on the ABI level. On the other hand, use of the module ReLU seems rare enough outside Sequential (in particular in C++ models, the standard seems to be to use torch::relu instead).
Thank you, Will Co-Authored-By: Will Feng <yf225@cornell.edu>
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…h#28501) Summary: Sequential does not like modules added to it to take Tensor& (const Tensor& and Tensor are both OK). Functional and others use Tensor when they want to potentially change things in-place. This changes ReLU and friends to also do that. Unfortunately, this seems to be BC breaking on the ABI level. On the other hand, use of the module ReLU seems rare enough outside Sequential (in particular in C++ models, the standard seems to be to use torch::relu instead). is the BC breaking OK here? (yf225 or anyone else) Pull Request resolved: pytorch#28501 Differential Revision: D18089978 Pulled By: yf225 fbshipit-source-id: ac9aba6dc2081117dece57cd8a15bafe14ec8f51
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t-vi commentedOct 23, 2019
Sequential does not like modules added to it to take Tensor&
(const Tensor& and Tensor are both OK).
Functional and others use Tensor when they want to potentially
change things in-place.
This changes ReLU and friends to also do that.
Unfortunately, this seems to be BC breaking on the ABI level.
On the other hand, use of the module ReLU seems rare enough outside
Sequential (in particular in C++ models, the standard seems to be
to use torch::relu instead).
is the BC breaking OK here? (@yf225 or anyone else)