Update autograd related comments (pytorch#50166)

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Remove outdated comment and update to use new paths.

Pull Request resolved: pytorch#50166

Reviewed By: zou3519

Differential Revision: D25824942

Pulled By: albanD

fbshipit-source-id: 7dc694891409e80e1804eddcdcc50cc21b60f822

c10/core/TensorImpl.h

@@ -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;
 

tools/autograd/derivatives.yaml

@@ -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++