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

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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
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2
torch/_tensor.py
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@ -429,7 +429,7 @@ class Tensor(torch._C._TensorBase):
def backward( def backward(
self, gradient=None, retain_graph=None, create_graph=False, inputs=None 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 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 non-scalar (i.e. its data has more than one element) and requires

2
torch/autograd/function.py
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@ -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 pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
of booleans representing whether each input needs gradient. E.g., of booleans representing whether each input needs gradient. E.g.,
:func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the :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. output.
""" """
raise NotImplementedError("You must implement either the backward or vjp method for " raise NotImplementedError("You must implement either the backward or vjp method for "

4
torch/autograd/gradcheck.py
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@ -1449,7 +1449,7 @@ def gradcheck(
masked: Optional[bool] = None, masked: Optional[bool] = None,
) -> bool: ) -> bool:
r"""Check gradients computed via small finite differences against analytical 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``. and with ``requires_grad=True``.
The check between numerical and analytical gradients uses :func:`~torch.allclose`. The check between numerical and analytical gradients uses :func:`~torch.allclose`.
@ -1606,7 +1606,7 @@ def gradgradcheck(
masked: bool = False, masked: bool = False,
) -> bool: ) -> bool:
r"""Check gradients of gradients computed via small finite differences 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 :attr:`grad_outputs` that are of floating point or complex type and with
``requires_grad=True``. ``requires_grad=True``.