Support dynamically quantized 2D convolutions

[keyprocedure]

Summary

Add initial support for dynamically quantized Conv2d in XNNPACK:

• Add conv to DYNAMIC_OPS for annotation
• Update partitioner to support dynamically quantized Conv2d
• Add checks to ensure only 2D, non-depthwise dynamically quantized convs are partitioned and annotated
• Update NHWC permute node insertion to trace back to original input for dynamically quantized inputs
• Compute num_nonbatch_dims based on whether the node feeds into a conv
• Remove the num_nonbatch_dims check from XNNCompiler
• Add unit tests for channels-last permute and single, sequential, and parallel dynamically quantized 2D convs

Fixes #9021

Test plan

python -m unittest backends.xnnpack.test.ops.test_conv2d.TestConv2d.test_dq_conv2d
python -m unittest backends.xnnpack.test.ops.test_conv2d.TestConv2d.test_dq_conv2d_seq
python -m unittest backends.xnnpack.test.ops.test_conv2d.TestConv2d.test_dq_conv2d_parallel
python -m unittest backends.xnnpack.test.passes.test_channels_last_tagged_reshape.TestChannelsLastTaggedReshapePass.test_dq_conv2d_channels_last_tagged_reshape_pass

[pytorch-bot]

🔗 Helpful Links

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❌ 1 New Failure

As of commit d82e080 with merge base ad7cd2b ():

NEW FAILURE - The following job has failed:

• Lint / lintrunner / linux-job (gh)
  >>> Lint for util/python_profiler.py:

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[keyprocedure]

@pytorchbot label "topic: not user facing"

[mcr229]

looks good! let's wait for CI to run and then we can merge. Thank you for the contribution!

[mcr229]

lintrunner failure isn't relevant and is fixed on main. Merging changes