From bafa2c4724bb4935612d3a94e295a9f4bf57e5d1 Mon Sep 17 00:00:00 2001
From: Kiersten Stokes <kierstenstokes@gmail.com>
Date: Tue, 25 Apr 2023 23:53:22 +0000
Subject: [PATCH] Change 'w.r.t.' to 'wrt' in function docstrings to fix doc
 rendering (#100028)

Fixes #72428 according to decision reached in comments.

I've left other instances of `w.r.t.` in tact (e.g. in parameter/return descriptions, in comments, etc) because there were many, and I didn't' want to go out-of-scope. That being said, I'm happy to change those as well if we'd prefer the consistency!

I've also fixed a typo that I came across while grepping for instances.

Will update with screenshots once docs are built.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/100028
Approved by: https://github.com/albanD
---
 torch/_tensor.py            | 2 +-
 torch/autograd/function.py  | 2 +-
 torch/autograd/gradcheck.py | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/torch/_tensor.py b/torch/_tensor.py
index 0007be9d27e..ccebfc5c441 100644
--- a/torch/_tensor.py
+++ b/torch/_tensor.py
@@ -429,7 +429,7 @@ class Tensor(torch._C._TensorBase):
     def backward(
         self, gradient=None, retain_graph=None, create_graph=False, inputs=None
     ):
-        r"""Computes the gradient of current tensor w.r.t. graph leaves.
+        r"""Computes the gradient of current tensor wrt graph leaves.
 
         The graph is differentiated using the chain rule. If the tensor is
         non-scalar (i.e. its data has more than one element) and requires
diff --git a/torch/autograd/function.py b/torch/autograd/function.py
index 880ef803ea3..c9f4529ed96 100644
--- a/torch/autograd/function.py
+++ b/torch/autograd/function.py
@@ -375,7 +375,7 @@ class _SingleLevelFunction(_C._FunctionBase, FunctionCtx, _HookMixin, metaclass=
         pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
         of booleans representing whether each input needs gradient. E.g.,
         :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the
-        first input to :func:`forward` needs gradient computated w.r.t. the
+        first input to :func:`forward` needs gradient computed w.r.t. the
         output.
         """
         raise NotImplementedError("You must implement either the backward or vjp method for "
diff --git a/torch/autograd/gradcheck.py b/torch/autograd/gradcheck.py
index 9a7a8698cff..e2382ed2962 100644
--- a/torch/autograd/gradcheck.py
+++ b/torch/autograd/gradcheck.py
@@ -1449,7 +1449,7 @@ def gradcheck(
     masked: Optional[bool] = None,
 ) -> bool:
     r"""Check gradients computed via small finite differences against analytical
-    gradients w.r.t. tensors in :attr:`inputs` that are of floating point or complex type
+    gradients wrt tensors in :attr:`inputs` that are of floating point or complex type
     and with ``requires_grad=True``.
 
     The check between numerical and analytical gradients uses :func:`~torch.allclose`.
@@ -1606,7 +1606,7 @@ def gradgradcheck(
     masked: bool = False,
 ) -> bool:
     r"""Check gradients of gradients computed via small finite differences
-    against analytical gradients w.r.t. tensors in :attr:`inputs` and
+    against analytical gradients wrt tensors in :attr:`inputs` and
     :attr:`grad_outputs` that are of floating point or complex type and with
     ``requires_grad=True``.