Add 16a4w_block QAT config

[sxu]

Summary: Introduce a FakeQuantizer subclass. It falls back to LPBQ observer's convert. _derived_bias_quant_spec also looks for it to correctly derive bias scale.

Open to suggestions on how to test. Naveen launched a QAT run and it seems to produce reasonable results.

Differential Revision: D87194388

[pytorch-bot]

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❌ 1 New Failure, 2 Unrelated Failures

As of commit eb2e9f9 with merge base 529a265 ():

NEW FAILURE - The following job has failed:

• Lint / lintrunner / linux-job (gh)
  >>> Lint for backends/qualcomm/quantizer/quantizer.py:

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / unittest / linux / linux-job (gh) (trunk failure)
  exir/backend/test/test_lowered_backend_module.py::TestBackendAPI::test_emit_nested_lowered_backend_module
• pull / unittest-editable / linux / linux-job (gh) (trunk failure)
  exir/backend/test/test_lowered_backend_module.py::TestBackendAPI::test_emit_nested_lowered_backend_module

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[meta-codesync]

@sxu has exported this pull request. If you are a Meta employee, you can view the originating Diff in D87194388.

[billmguo]

@winskuo-quic can you review and approve this diff?

[haowhsu-quic]

Would it be simpler if calling torchao.quantization.quant_primitives._fake_quantize_affine directly?

[DannyYuyang-quic]

I think for QAT testing we can use pseudo labels generated by the FP32 model, do a few mini training steps on the fake-quant model, and then compare outputs against the FP32 baseline (pseudo labels) within acceptable atol/rtol thresholds as usual.