Fallback to native batchnorm implementation if input shape unsupported by cudnn

[ptrblck]

Should fix #29744 by falling back to native batch norm implementation, if cudnn cannot execute the provided shape.

Shape numbers were verified for cudnn 7.6.5.32 with tensor shapes:

# for spatial bn
x = torch.Size([880801, 256, 5])
x = torch.Size([65535, 256, 5])
x = torch.Size([880801, 64, 4, 4])
x = torch.Size([65535, 64, 4, 4])

# for per-act bn
x = torch.Size([131070, 2048])
x = torch.Size([262136, 2048])

for training() and eval() mode using torch.float32 and torch.float16.

I've increased the shape of our current smoke test to, but I can also add all use cases of the support matrix, if wanted.

[kostmo]

💊 CircleCI build failures summary and remediations

As of commit 3692fd2:

None of the build failures appear to be your fault.

• 1/1 broken upstream at merge base c729614 since Jan 29

  Please rebase on the viable/strict branch (expand for instructions)

  If your commit is newer than viable/strict, you can try basing on an older, stable commit:

  git fetch origin viable/strict
  git rebase --onto viable/strict $(git merge-base origin/master HEAD)
  

  If your commit is older than viable/strict:

  git fetch origin viable/strict
  git rebase viable/strict
  

  Check out the recency history of this "viable master" tracking branch.

Detailed failure analysis

One may explore the probable reasons each build failed interactively on the Dr. CI website.

🚧 1 upstream failure recognized by patterns:

These builds matched patterns, but were probably caused by upstream breakages:

• pytorch_windows_build from Jan 29

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This comment has been revised 3 times.

[ptrblck]

Ignore this, I'll rebase it properly.