Adding Tests for CadenceDefaultQuantizer

backends/cadence/aot/tests/test_quantizer_ops.py

@@ -33,6 +33,7 @@
     CadenceWithSoftmaxQuantizer,
     qconfig_A16,
     qconfig_A8W8,
+    qconfig_A8W8sym,
 )
 from executorch.exir.pass_base import NodeMetadata
 from parameterized import parameterized
@@ -53,7 +54,6 @@
 # Quantizers intentionally excluded from annotation testing.
 # These should be explicitly justified when added.
 EXCLUDED_FROM_ANNOTATION_TESTING: set[type[CadenceQuantizer]] = {
-    CadenceDefaultQuantizer,  # TODO: T247438143 Add test coverage
     CadenceFusedConvReluQuantizer,  # TODO: T247438151 Add test coverage
     CadenceNopQuantizer,  # No-op quantizer, doesn't annotate anything
     CadenceW8A32MixedQuantizer,  # TODO: T247438158 Add test coverage
@@ -137,6 +137,61 @@
         # For add: both inputs are activations
         [qconfig_A8W8.input_activation, qconfig_A8W8.input_activation],
     ),
+    # CadenceDefaultQuantizer test cases
+    (
+        "default_matmul_A8W8",
+        lambda self: self._build_matmul_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.matmul.default,
+        qconfig_A8W8.output_activation,
+        # For matmul: both inputs are activations
+        [qconfig_A8W8.input_activation, qconfig_A8W8.input_activation],
+    ),
+    (
+        "default_linear_A8W8",
+        lambda self: self._build_linear_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.linear.default,
+        qconfig_A8W8.output_activation,
+        # For linear: [input_activation, weight]
+        [qconfig_A8W8.input_activation, qconfig_A8W8.weight],
+    ),
+    (
+        "default_conv1d_A8W8sym",
+        lambda self: self._build_conv1d_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.conv1d.default,
+        qconfig_A8W8sym.output_activation,
+        # For conv1d: [input_activation, weight] with symmetric weights
+        [qconfig_A8W8sym.input_activation, qconfig_A8W8sym.weight],
+    ),
+    (
+        "default_conv2d_A8W8sym",
+        lambda self: self._build_conv2d_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.conv2d.default,
+        qconfig_A8W8sym.output_activation,
+        # For conv2d: [input_activation, weight] with symmetric weights
+        [qconfig_A8W8sym.input_activation, qconfig_A8W8sym.weight],
+    ),
+    (
+        "default_bmm_A8W8",
+        lambda self: self._build_bmm_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.bmm.default,
+        qconfig_A8W8.output_activation,
+        # For bmm: both inputs are activations
+        [qconfig_A8W8.input_activation, qconfig_A8W8.input_activation],
+    ),
+    (
+        "default_relu_A8W8",
+        lambda self: self._build_relu_graph(),
+        CadenceDefaultQuantizer(),
+        torch.ops.aten.relu.default,
+        qconfig_A8W8.output_activation,
+        # For relu: only input_activation
+        [qconfig_A8W8.input_activation],
+    ),
 ]
 
 # Derive the set of tested quantizer classes from the test cases.
@@ -309,6 +364,50 @@ def _build_add_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
         self.assertEqual(len(add_nodes), 1, "Should find exactly one add node")
         return gm, add_nodes[0]
 
+    def _build_bmm_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a bmm (batch matrix multiply) operation."""
+        builder = GraphBuilder()
+        # BMM requires 3D tensors: (batch, n, m) @ (batch, m, p) -> (batch, n, p)
+        x = builder.placeholder("x", torch.randn(2, 4, 8))
+        y = builder.placeholder("y", torch.randn(2, 8, 4))
+        bmm = builder.call_operator(
+            op=torch.ops.aten.bmm.default,
+            args=(x, y),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("bmm", torch.ops.aten.bmm.default)]}
+            ),
+        )
+        builder.output([bmm])
+        gm = builder.get_graph_module()
+
+        bmm_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.bmm.default,
+        )
+        self.assertEqual(len(bmm_nodes), 1, "Should find exactly one bmm node")
+        return gm, bmm_nodes[0]
+
+    def _build_relu_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a relu operation."""
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 10))
+        relu = builder.call_operator(
+            op=torch.ops.aten.relu.default,
+            args=(x,),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("relu", torch.ops.aten.relu.default)]}
+            ),
+        )
+        builder.output([relu])
+        gm = builder.get_graph_module()
+
+        relu_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.relu.default,
+        )
+        self.assertEqual(len(relu_nodes), 1, "Should find exactly one relu node")
+        return gm, relu_nodes[0]
+
     @parameterized.expand(QUANTIZER_ANNOTATION_TEST_CASES)
     def test_quantizer_annotation(
         self,