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[nit] fix some typo within documentation #40692

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2 changes: 1 addition & 1 deletion torch/csrc/jit/codegen/cuda/ir_printer.h
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Expand Up @@ -8,7 +8,7 @@

/*
* IRMathPrinter and IRTransformPrinter allow the splitting up of fusion print
* functions. IRMathPrinter as its name implies focuses soley on what tensor
* functions. IRMathPrinter as its name implies focuses solely on what tensor
* computations are taking place. Resulting TensorView math will reflect the
* series of split/merge/computeAts that have taken place, however these
* nodes will not be displayed in what is printed. IRTransformPrinter does not
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2 changes: 1 addition & 1 deletion torch/nn/modules/linear.py
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Expand Up @@ -96,7 +96,7 @@ def extra_repr(self) -> str:
)


# This class exists soley for Transformer; it has an annotation stating
# This class exists solely for Transformer; it has an annotation stating
# that bias is never None, which appeases TorchScript
class _LinearWithBias(Linear):
bias: Tensor
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4 changes: 2 additions & 2 deletions torch/nn/modules/loss.py
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Expand Up @@ -474,7 +474,7 @@ class BCELoss(_WeightedLoss):
However, an infinite term in the loss equation is not desirable for several reasons.
For one, if either :math:`y_n = 0` or :math:`(1 - y_n) = 0`, then we would be
multipying 0 with infinity. Secondly, if we have an infinite loss value, then
multiplying 0 with infinity. Secondly, if we have an infinite loss value, then
we would also have an infinite term in our gradient, since
:math:`\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty`.
This would make BCELoss's backward method nonlinear with respect to :math:`x_n`,
Expand Down Expand Up @@ -1316,7 +1316,7 @@ class CTCLoss(_Loss):
>>> # Initialize random batch of input vectors, for *size = (T,N,C)
>>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
>>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
>>>
>>>
>>> # Initialize random batch of targets (0 = blank, 1:C = classes)
>>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long)
>>> target = torch.randint(low=1, high=C, size=(sum(target_lengths),), dtype=torch.long)
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10 changes: 5 additions & 5 deletions torch/nn/modules/module.py
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Expand Up @@ -60,7 +60,7 @@ def register_module_forward_pre_hook(hook: Callable[..., None]) -> RemovableHand
.. warning ::

This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
and it is only intended for debugging/profiling purposes.

The hook will be called every time before :func:`forward` is invoked.
It should have the following signature::
Expand Down Expand Up @@ -92,7 +92,7 @@ def register_module_forward_hook(hook: Callable[..., None]) -> RemovableHandle:
.. warning ::

This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
and it is only intended for debugging/profiling purposes.

The hook will be called every time after :func:`forward` has computed an output.
It should have the following signature::
Expand Down Expand Up @@ -124,7 +124,7 @@ def register_module_backward_hook(

.. warning ::
This adds global state to the `nn.module` module
and it is only intended for debugging/profiling purposes.
and it is only intended for debugging/profiling purposes.

The current implementation will not have the presented behavior
for complex :class:`Module` that perform many operations.
Expand Down Expand Up @@ -977,7 +977,7 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
error_msgs.append('While copying the parameter named "{}", '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}, '
'an exception occured : {}.'
'an exception occurred : {}.'
.format(key, param.size(), input_param.size(), ex.args))
elif strict:
missing_keys.append(key)
Expand Down Expand Up @@ -1329,7 +1329,7 @@ def _get_name(self):
def extra_repr(self) -> str:
r"""Set the extra representation of the module

To print customized extra information, you should reimplement
To print customized extra information, you should re-implement
this method in your own modules. Both single-line and multi-line
strings are acceptable.
"""
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4 changes: 2 additions & 2 deletions torch/nn/quantized/modules/functional_modules.py
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Expand Up @@ -6,7 +6,7 @@


class FloatFunctional(torch.nn.Module):
r"""State collector class for float operatitons.
r"""State collector class for float operations.
The instance of this class can be used instead of the ``torch.`` prefix for
some operations. See example usage below.
Expand Down Expand Up @@ -84,7 +84,7 @@ def add_relu(self, x, y):


class QFunctional(torch.nn.Module):
r"""Wrapper class for quantized operatitons.
r"""Wrapper class for quantized operations.
The instance of this class can be used instead of the
``torch.ops.quantized`` prefix. See example usage below.
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