frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-21 05:34:18 +08:00
Code Issues Packages Projects Releases Wiki Activity

Change 'w.r.t.' to 'wrt' in function docstrings to fix doc rendering (#100028)

Browse Source 
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
This commit is contained in:
Kiersten Stokes
2023-04-25 23:53:22 +00:00
committed by PyTorch MergeBot
parent 676a23f452
commit bafa2c4724
3 changed files with 4 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
torch/_tensor.py
View File

@ -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

2
torch/autograd/function.py
View File

@ -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 "

4
torch/autograd/gradcheck.py
View File

@ -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``.