Creating Paramaterized Test For Quantizers For Easier Testing

[RahulC7]

Summary:
We consolidate the two tests we created into a single testing function using parameterization.

This will make testing future Quantizers much easier, and there will be a lot less code duplication.

Reviewed By: hsharma35

Differential Revision: D88054917

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16098

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❗ 1 Active SEVs

There are 1 currently active SEVs. If your PR is affected, please view them below:

• Replace all macOS instances with nextjs due CVE-2025-55182

✅ You can merge normally! (1 Unrelated Failure)

As of commit c924c92 with merge base 4014597 ():

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

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

• pull / android / run-emulator (gh) (trunk failure)
  Timeout waiting for emulator to boot.

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[meta-codesync]

@RahulC7 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D88054917.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.

[Copilot]

Pull request overview

This PR consolidates quantizer annotation tests into a single parameterized test function, reducing code duplication and making it easier to add new quantizer tests. The refactoring uses the parameterized library to test both matmul and linear operations with their respective 16-bit activation quantizers.

Key changes:

• Introduced parameterized testing approach using @parameterized.expand decorator
• Created reusable graph builder helper methods (_build_matmul_graph, _build_linear_graph)
• Added new imports for GraphBuilder, parameterized library, and quantization annotation types

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
backends/cadence/aot/tests/test_quantizer_ops.py	Added parameterized test infrastructure with QuantizerAnnotationTest class containing graph builder helpers and consolidated test method for verifying quantizer annotations on matmul and linear operations
backends/cadence/aot/TARGETS	Added dependencies for parameterized testing library, graph_builder module, pass_base, and torchao to support the new test infrastructure

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

The target parameter is unused in the test function. Consider removing it from the parameter list (lines 94 and 103) if it's not needed, or use it to validate that op_node.target matches the expected target operation for additional test robustness.

[Copilot]

Potential KeyError: accessing annotation.input_qspec_map[op_node.args[i]] could fail if op_node.args[i] is not in the map. Consider adding a check or using .get() with a clear error message to make debugging easier if the key is missing.

Suggested change

	actual_specs = [
	annotation.input_qspec_map[op_node.args[i]]
	for i in range(len(expected_input_qspecs))
	]
	actual_specs = []
	for i in range(len(expected_input_qspecs)):
	key = op_node.args[i]
	if key not in annotation.input_qspec_map:
	raise KeyError(
	f"Key {key!r} not found in input_qspec_map. "
	f"Available keys: {list(annotation.input_qspec_map.keys())}"
	)
	actual_specs.append(annotation.input_qspec_map[key])