Use .to instead of contiguous to generate channels last tensor (#96791)
Fix for https://github.com/pytorch/pytorch/issues/95693.
From https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html:
> There are minor difference between the two APIs to and contiguous. We suggest to stick with to when explicitly converting memory format of tensor.
For general cases the two APIs behave the same. However in special cases for a 4D tensor with size NCHW when either: C==1 or H==1 && W==1, only to would generate a proper stride to represent channels last memory format.
We hit this case in convolution_backward in calling `contiguous()`. Even though we were determining that we should run the backward in channels_last forward, as FakeTensor had gathered from the output of [determine_backend_memory_format](https://github.com/pytorch/pytorch/blob/master/torch/_subclasses/fake_tensor.py#L559), we were still outputting a contiguous tensor. That led to the mismatch in strides in the issue.
Should we be calling `to` instead of `contiguous` more liberally throughout the codebase, especially in convolution related code ? Not sure if there are reasons not to do this.
Another fix would be to update `cudnn_conv_suggest_memory_format` so that it would output a contiguous_format in this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96791
Approved by: https://github.com/ngimel
