Fix docstring for shape of `target` for MultiLabelSoftMarginLoss (#107817)
Fixes #92000
The documentation at https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html#multilabelsoftmarginloss states:
> label targets padded by -1 ensuring same shape as the input.
However, the shape of input and target tensor are compared, and an exception is raised if they differ in either dimension 0 or 1. Meaning the label targets are never padded. See the code snippet below and the resulting output. The documentation is therefore adjusted to:
> label targets must have the same shape as the input.
```
import torch
import torch.nn as nn
# Create some example data
input = torch.tensor(
[
[0.8, 0.2, -0.5],
[0.1, 0.9, 0.3],
]
)
target1 = torch.tensor(
[
[1, 0, 1],
[0, 1, 1],
[0, 1, 1],
]
)
target2 = torch.tensor(
[
[1, 0],
[0, 1],
]
)
target3 = torch.tensor(
[
[1, 0, 1],
[0, 1, 1],
]
)
loss_func = nn.MultiLabelSoftMarginLoss()
try:
loss = loss_func(input, target1).item()
except RuntimeError as e:
print('target1 ', e)
try:
loss = loss_func(input, target2).item()
except RuntimeError as e:
print('target2 ', e)
loss = loss_func(input, target3).item()
print('target3 ', loss)
```
output:
```
target1 The size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 0
target2 The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1
target3 0.6305370926856995
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107817
Approved by: https://github.com/mikaylagawarecki
