[Doc] Deprecation notice for only_inputs argument (#63631)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/63544.

Changed docstring accordingly. I'm new here, not sure if the style is okay. Please check.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/63631

Reviewed By: ejguan

Differential Revision: D30459439

Pulled By: soulitzer

fbshipit-source-id: 8df3c509d1dd39764815b099ab47229550126cbe

diff --git a/torch/autograd/__init__.py b/torch/autograd/__init__.py
index 0d4f153..d11e261 100644 (file)
--- a/torch/autograd/__init__.py
+++ b/torch/autograd/__init__.py
@@ -173,17 +173,18 @@ def grad(
     gradients w.r.t. each of the outputs. If an output doesn't require_grad,
     then the gradient can be ``None``).
 
-    If ``only_inputs`` is ``True``, the function will only return a list of gradients
-    w.r.t the specified inputs. If it's ``False``, then gradient w.r.t. all remaining
-    leaves will still be computed, and will be accumulated into their ``.grad``
-    attribute.
-
     .. note::
 
         If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
         in a user-specified CUDA stream context, see
         :ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.
 
+    .. note::
+
+        ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
+        To accumulate gradient for other parts of the graph, please use
+        ``torch.autograd.backward``.
+
     Args:
         outputs (sequence of Tensor): outputs of the differentiated function.
         inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be