Use new parametrization based weight norm if available
# What does this PR do?

In https://github.com/pytorch/pytorch/pull/103001 I introduce a new
parametrization based version of `weight_norm`.  One big benefit of
the new API is that the resulting model is deepcopy'able; today, you can't
deepcopy Wav2Vec2 models.

Since the new API isn't even in PyTorch main yet, I'd like to feature
gate it here, so that it gets used whenever PyTorch is recent enough to
support it.  It would be a big help for me if you could take this change
earlier rather than later; otherwise I will have to patch transformers
in our own CI to get our benchmark harness working on Wav2Vec2.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

## Before submitting
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      Pull Request section?
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- [ ] Did you write any new necessary tests?


## Who can review?

cc @sanchit-gandhi

_The documentation is not available anymore as the PR was closed or merged._<|||||>PyTorch side PR has landed!<|||||>@ezyang 

With nightly pytorch, we get 

> AttributeError: encoder.pos_conv_embed.conv.weight_v not found in PyTorch model

when trying to load a pytorch model into a TF model.

The TF model is looking for `encoder.pos_conv_embed.conv.weight_v` but we now have `encoder.pos_conv_embed.conv.parametrizations.weight.original0`. (This is from our `(TF)Wav2Vec2Model` model class).

**Question**: In your PR https://github.com/pytorch/pytorch/pull/103001, is this part

`def _weight_norm_compat_hook()`

that deals with the backward compatibility? If so, we will copy it :-) <|||||>Yep. The change here is not FC so the ingester needs updating.