Adding Test For CadenceWith16BitLinearActivationsQuantizer

[RahulC7]

Summary:
We test the CadenceWith16BitLinearActivationQuantizer.

We use the graph builder to build the graph with metadata(that's needed for quantizer.annotate to recognize the nodes), and we ensure that the quantization params are as expected.

Reviewed By: hsharma35

Differential Revision: D88054651

[pytorch-bot]

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❌ 1 New Failure, 1 Unrelated Failure

As of commit e155ee0 with merge base 4014597 ():

NEW FAILURE - The following job has failed:

• Lint / lintrunner / linux-job (gh)
  >>> Lint for backends/cadence/aot/tests/test_quantizer_ops.py:

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.

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[Copilot]

Pull request overview

This PR adds comprehensive unit tests for the CadenceWith16BitLinearActivationsQuantizer and CadenceWith16BitMatmulActivationsQuantizer classes. The tests verify that these quantizers correctly annotate graph nodes with 16-bit quantization specifications (INT16 for activations, INT8 for weights).

Key Changes:

• Adds a new test class QuantizerAnnotationTest with helper methods to build test graphs
• Tests 16-bit quantizer annotations for both matmul and linear operations
• Updates TARGETS file with necessary dependencies for the new tests

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
backends/cadence/aot/tests/test_quantizer_ops.py	Adds QuantizerAnnotationTest class with tests for 16-bit quantizer annotations on matmul and linear operations
backends/cadence/aot/TARGETS	Adds dependencies for graph_builder, pass_base, and torchao modules

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[Copilot]

The test should verify that all inputs in input_qspec_map are expected. Currently, if an unexpected input node appears that doesn't match linear_node.args[0] or linear_node.args[1], the test will silently pass without checking its qspec. Consider adding an else clause with a self.fail() to catch unexpected inputs:

for input_node, input_qspec in annotation.input_qspec_map.items():
    if input_node == linear_node.args[0]:
        # Activation input - should be INT16
        self.assertEqual(input_qspec, qconfig_A16.input_activation)
    elif input_node == linear_node.args[1]:
        # Weight - should be INT8
        self.assertEqual(input_qspec, qconfig_A16.weight)
    else:
        self.fail(f"Unexpected input node in input_qspec_map: {input_node}")

Suggested change

	else:
	self.fail(f"Unexpected input node in input_qspec_map: {input_node}")