Creating Paramaterized Test For Quantizers For Easier Testing

backends/cadence/aot/TARGETS

@@ -640,7 +640,11 @@ python_unittest(
     ],
     typing = True,
     deps = [
+        "fbsource//third-party/pypi/parameterized:parameterized",
         "//caffe2:torch",
+        "//executorch/backends/cadence/aot:graph_builder",
         "//executorch/backends/cadence/aot/quantizer:quantizer",
+        "//executorch/exir:pass_base",
+        "//pytorch/ao:torchao",
     ],
 )

backends/cadence/aot/tests/test_quantizer_ops.py

@@ -7,16 +7,143 @@
 # pyre-strict
 
 import unittest
+from typing import Callable
 
 import torch
+from executorch.backends.cadence.aot.graph_builder import GraphBuilder
 from executorch.backends.cadence.aot.quantizer.patterns import AddmmPattern
 
 from executorch.backends.cadence.aot.quantizer.quantizer import (
     CadenceAtenQuantizer,
     CadenceDefaultQuantizer,
+    CadenceQuantizer,
     CadenceW8A32MixedQuantizer,
+    CadenceWith16BitLinearActivationsQuantizer,
+    CadenceWith16BitMatmulActivationsQuantizer,
+    qconfig_A16,
     qconfig_A8W8,
 )
+from executorch.exir.pass_base import NodeMetadata
+from parameterized import parameterized
+from torch._ops import OpOverload
+from torchao.quantization.pt2e.quantizer.quantizer import (
+    Q_ANNOTATION_KEY,
+    QuantizationAnnotation,
+    QuantizationSpec,
+)
+
+# Type alias for graph builder functions
+GraphBuilderFn = Callable[
+    ["QuantizerAnnotationTest"], tuple[torch.fx.GraphModule, torch.fx.Node]
+]
+
+
+class QuantizerAnnotationTest(unittest.TestCase):
+    """Unit tests for verifying quantizer annotations are correctly applied."""
+
+    def _build_matmul_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a matmul operation."""
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(4, 8))
+        y = builder.placeholder("y", torch.randn(8, 4))
+        matmul = builder.call_operator(
+            op=torch.ops.aten.matmul.default,
+            args=(x, y),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("matmul", torch.ops.aten.matmul.default)]}
+            ),
+        )
+        builder.output([matmul])
+        gm = builder.get_graph_module()
+
+        matmul_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.matmul.default,
+        )
+        self.assertEqual(len(matmul_nodes), 1, "Should find exactly one matmul node")
+        return gm, matmul_nodes[0]
+
+    def _build_linear_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a linear operation (no bias)."""
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 10))
+        weight = builder.placeholder("weight", torch.randn(5, 10))
+        linear = builder.call_operator(
+            op=torch.ops.aten.linear.default,
+            args=(x, weight),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("linear", torch.ops.aten.linear.default)]}
+            ),
+        )
+        builder.output([linear])
+        gm = builder.get_graph_module()
+
+        linear_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.linear.default,
+        )
+        self.assertEqual(len(linear_nodes), 1, "Should find exactly one linear node")
+        return gm, linear_nodes[0]
+
+    @parameterized.expand(
+        [
+            (
+                "matmul_A16",
+                lambda self: self._build_matmul_graph(),
+                CadenceWith16BitMatmulActivationsQuantizer(),
+                torch.ops.aten.matmul.default,
+                qconfig_A16.output_activation,
+                # For matmul, both inputs are activations
+                [qconfig_A16.input_activation, qconfig_A16.input_activation],
+            ),
+            (
+                "linear_A16",
+                lambda self: self._build_linear_graph(),
+                CadenceWith16BitLinearActivationsQuantizer(),
+                torch.ops.aten.linear.default,
+                qconfig_A16.output_activation,
+                # For linear: [input_activation, weight]
+                [qconfig_A16.input_activation, qconfig_A16.weight],
+            ),
+        ]
+    )
+    def test_quantizer_annotation(
+        self,
+        name: str,
+        graph_builder_fn: GraphBuilderFn,
+        quantizer: CadenceQuantizer,
+        target: OpOverload,
+        expected_output_qspec: QuantizationSpec,
+        expected_input_qspecs: list[QuantizationSpec],
+    ) -> None:
+        """Parameterized test for quantizer annotations."""
+        gm, op_node = graph_builder_fn(self)
+
+        quantizer.annotate(gm)
+
+        annotation: QuantizationAnnotation = op_node.meta[Q_ANNOTATION_KEY]
+        self.assertTrue(annotation._annotated)
+
+        # Verify output annotation
+        self.assertEqual(annotation.output_qspec, expected_output_qspec)
+
+        # Verify input annotations
+        # Build actual_specs in the fixed order defined by op_node.args
+        self.assertEqual(len(annotation.input_qspec_map), len(expected_input_qspecs))
+        actual_specs = [
+            annotation.input_qspec_map[op_node.args[i]]
+            for i in range(len(expected_input_qspecs))
+        ]
+
+        # Compare expected vs actual specs
+        for i, (expected, actual) in enumerate(
+            zip(expected_input_qspecs, actual_specs)
+        ):
+            self.assertEqual(
+                actual,
+                expected,
+                f"Input qspec mismatch at index {i}",
+            )
 
 
 class QuantizerOpsPreserveTest(unittest.TestCase):