Adding Test for CadenceWith16BitMatmulActivationsQuantizer (pytorch#16089)

Summary:

We test the quantizer we added in D87996796 correctly annotates the graph. 

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: D88053808

Author: RahulC7

backends/cadence/aot/TARGETS

@@ -641,6 +641,9 @@ python_unittest(
     typing = True,
     deps = [
         "//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

@@ -9,14 +9,64 @@
 import unittest
 
 import torch
+from executorch.backends.cadence.aot.graph_builder import GraphBuilder
 from executorch.backends.cadence.aot.quantizer.patterns import AddmmPattern
+from executorch.exir.pass_base import NodeMetadata
 
 from executorch.backends.cadence.aot.quantizer.quantizer import (
     CadenceAtenQuantizer,
     CadenceDefaultQuantizer,
     CadenceW8A32MixedQuantizer,
+    CadenceWith16BitMatmulActivationsQuantizer,
+    qconfig_A16,
     qconfig_A8W8,
 )
+from torchao.quantization.pt2e.quantizer.quantizer import (
+    Q_ANNOTATION_KEY,
+    QuantizationAnnotation,
+)
+
+
+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 test_matmul_16bit_quantizer_annotation(self) -> None:
+        """Test that CadenceWith16BitMatmulActivationsQuantizer correctly annotates matmul."""
+        gm, matmul_node = self._build_matmul_graph()
+
+        quantizer = CadenceWith16BitMatmulActivationsQuantizer()
+        quantizer.annotate(gm)
+
+        annotation: QuantizationAnnotation = matmul_node.meta[Q_ANNOTATION_KEY]
+        self.assertTrue(annotation._annotated)
+
+        self.assertEqual(annotation.output_qspec, qconfig_A16.output_activation)
+
+        self.assertEqual(len(annotation.input_qspec_map), 2)
+        for _, input_qspec in annotation.input_qspec_map.items():
+            self.assertEqual(input_qspec, qconfig_A16.input_activation)
 
 
 class QuantizerOpsPreserveTest(unittest.TestCase):