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Update autograd related comments (#50166)

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Summary:
Remove outdated comment and update to use new paths.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/50166

Reviewed By: zou3519

Differential Revision: D25824942

Pulled By: albanD

fbshipit-source-id: 7dc694891409e80e1804eddcdcc50cc21b60f822
This commit is contained in:
Alban Desmaison 2021-01-08 06:36:01 -08:00 committed by Facebook GitHub Bot
parent 9f832c8d3e
commit 006cfebf3d
2 changed files with 1 additions and 13 deletions
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12
c10/core/TensorImpl.h
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@ -582,9 +582,6 @@ struct C10_API TensorImpl : public c10::intrusive_ptr_target {
/**
* Set whether or not a tensor requires gradient.
*
* It is only valid to call this method on a Variable.
* See Note [Tensor versus Variable in C++].
*/
void set_requires_grad(bool requires_grad);
@ -594,27 +591,18 @@ struct C10_API TensorImpl : public c10::intrusive_ptr_target {
* we can automatically differentiate back to them. A tensor that
* requires gradient and has no history is a "leaf" tensor, which we
* accumulate gradients into.
*
* It is only valid to call this method on a Variable.
* See Note [Tensor versus Variable in C++].
*/
bool requires_grad() const;
/**
* Return a mutable reference to the gradient. This is conventionally
* used as `t.grad() = x` to set a gradient to a completely new tensor.
*
* It is only valid to call this method on a Variable.
* See Note [Tensor versus Variable in C++].
*/
at::Tensor& mutable_grad();
/**
* Return the accumulated gradient of a tensor. This gradient is written
* into when performing backwards, when this tensor is a leaf tensor.
*
* It is only valid to call this method on a Variable.
* See Note [Tensor versus Variable in C++].
*/
const at::Tensor& grad() const;

2
tools/autograd/derivatives.yaml
View file

@ -86,7 +86,7 @@
# e.g., it is used by _cudnn_rnn
#
# If you need a complex expression, e.g., with local variables,
# write a _backward function in tools/autograd/templates/Functions.cpp
# write a _backward function in torch/csrc/autograd/FunctionsManual.cpp
# and invoke it from here. By the way, go read
# https://github.com/zdevito/ATen/issues/163; this describes an
# important hazard that occurs when porting backwards from Python to C++