[quant][fx] Support keyword arguments for functional linear (#79095)
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/78117
Fixes: https://github.com/pytorch/pytorch/issues/73463
This PR adds a normalization pass that normalizes all the args to keyword args in positional order and fixes lowering code that previously
only uses node.args to use both args and kwargs instead.
Also tried to add a test for F.conv2d, but since conv2d matches multiple schemas we are doing an extra schema match, and because we are using symbolic values
in `transform`, we don't have a schema match, so F.conv2d still fails with runtime errors. we can resolve this issue later when there is a need.
Another thing I'm considering is to do the normalization with real inputs instead of symbolic inputs and not rely on operator_schemas (which is based on torchscript),
and rely on inspect.signature, I tried this briefly but didn't get too far, it looks like we cannot get the python signature for `torch._C._nn.linear`, it might be possible to fix as well, but will need follow up discussions.
The goal for this PR is just to introduce normalization in our codebase so that we can adapt some downstream code to this, and also fix the F.linear issue.
Test Plan:
python test/test_quantization.py TestQuantizeFx.test_normalize_args
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D37163228](https://our.internmc.facebook.com/intern/diff/D37163228)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79095
Approved by: https://github.com/andrewor14
