Add add-relu fusion in the quantizer

backends/cadence/aot/quantizer/fusion_pass.py

@@ -15,6 +15,8 @@
 from executorch.backends.cadence.aot.quantizer.patterns import (
     AddmmPattern,
     AddPattern,
+    AddReluPattern0,
+    AddReluPattern1,
     BmmPattern,
     CatPattern,
     Conv1dPattern,
@@ -63,6 +65,7 @@
     Conv2dReluPattern0,
     Conv2dReluPattern1,
 )
+AddReluPatterns = (AddReluPattern0, AddReluPattern1)
 
 
 def get_args_and_kwargs_add(
@@ -616,7 +619,20 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                         inputs_inputs + weights_inputs + other_inputs + bias_inputs
                     )
                     kwargs = {}
-                    if isinstance(pattern, AddPattern):
+                    if isinstance(pattern, AddReluPatterns):
+                        # For AddReLU, we are fusing Add+ReLU.
+                        # The quantized_add op performs requantization,
+                        # so the relu is implicit in the output quant params.
+                        check_out_zero_point_is_min_range(
+                            quant_node.args[2], quant_node.args[5]
+                        )
+                        args, kwargs = get_args_and_kwargs_add(
+                            graph_module,
+                            inputs_inputs,
+                            dequants_inputs,
+                            quant_node,
+                        )
+                    elif isinstance(pattern, AddPattern):
                         args, kwargs = get_args_and_kwargs_add(
                             graph_module,
                             inputs_inputs,

backends/cadence/aot/quantizer/patterns.py

@@ -153,6 +153,61 @@ def replacement_op(self) -> OpOverload:
         return torch.ops.cadence.quantized_add.per_tensor
 
 
+# This is a base class for Add+ReLU fusion, since it can be used with two different relu aten ops
+class AddReluBasePattern(QuantizationPattern):
+    @abstractmethod
+    def partition_types(self) -> List[OpOverload]:
+        pass
+
+    def get_anchors(
+        self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule]
+    ) -> Tuple[PartitionAnchors, fx.Node]:
+        # The first node should be add, the second should be relu
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        add_node = fused_partition[0].nodes[-1]
+        # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge...
+        relu_node = fused_partition[1].nodes[-1]
+
+        # Bail if:
+        #   - the add node is not a tensor add
+        #   - the add node has kwargs (e.g. alpha)
+        is_tensor_add = isinstance(add_node.args[0], fx.Node) and isinstance(
+            add_node.args[1], fx.Node
+        )
+        if not is_tensor_add or len(add_node.kwargs) > 0:
+            return (
+                PartitionAnchors(
+                    empty=True,
+                ),
+                add_node,
+            )
+
+        return (
+            PartitionAnchors(
+                inputs=[(add_node, 0), (add_node, 1)],
+                weights=[],
+                biases=[],
+                output=[(relu_node,)],  # Output is from the relu node
+            ),
+            relu_node,
+        )
+
+    def replacement_op(self) -> OpOverload:
+        return torch.ops.cadence.quantized_add.per_tensor
+
+
+# Add + regular relu op fusion
+class AddReluPattern0(AddReluBasePattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.add.Tensor, torch.ops.aten.relu.default]
+
+
+# Add + alternate relu op fusion
+class AddReluPattern1(AddReluBasePattern):
+    def partition_types(self) -> List[OpOverload]:
+        return [torch.ops.aten.add.Tensor, torch.ops.aten.relu_.default]
+
+
 class BmmPattern(QuantizationPattern):
     def partition_types(self) -> List[OpOverload]:
         return [torch.ops.aten.bmm.default]

backends/cadence/aot/quantizer/quantizer.py

@@ -13,6 +13,8 @@
 from executorch.backends.cadence.aot.quantizer.patterns import (
     AddmmPattern,
     AddPattern,
+    AddReluPattern0,
+    AddReluPattern1,
     BmmPattern,
     CatPattern,
     Conv1dPattern,
@@ -398,6 +400,8 @@ def __init__(
         quantizers.append(CadenceAtenQuantizer(Conv1dReluPattern1(), a8w8sym))
         quantizers.append(CadenceAtenQuantizer(Conv2dReluPattern0(), a8w8sym))
         quantizers.append(CadenceAtenQuantizer(Conv2dReluPattern1(), a8w8sym))
+        quantizers.append(CadenceAtenQuantizer(AddReluPattern0(), a8w8))
+        quantizers.append(CadenceAtenQuantizer(AddReluPattern1(), a8w8))
         quantizers = quantizers + get_cadence_default_quantizers(is_qat=is_qat)
         quantizers.append(CadenceAtenQuantizer(AddPattern(), a8w8))
         quantizers.append(CadenceAtenQuantizer(CatPattern(), a8w8))

backends/cadence/aot/tests/test_quantizer_ops.py

@@ -215,6 +215,15 @@
         [qconfig_A8W8.input_activation],
     ),
     # CadenceFusedConvReluQuantizer test cases
+    (
+        "fused_add_relu_A8W8",
+        lambda self: self._build_add_relu_graph(),
+        CadenceFusedConvReluQuantizer(),
+        torch.ops.aten.relu.default,
+        qconfig_A8W8.output_activation,
+        # For fused add+relu: both inputs are activations from add node
+        [qconfig_A8W8.input_activation, qconfig_A8W8.input_activation],
+    ),
     (
         "fused_conv1d_relu_A8W8sym",
         lambda self: self._build_conv1d_relu_graph(),
@@ -508,6 +517,50 @@ def _build_max_pool2d_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
         )
         return gm, max_pool_nodes[0]
 
+    def _build_add_relu_graph(
+        self,
+    ) -> tuple[torch.fx.GraphModule, torch.fx.Node, torch.fx.Node]:
+        """Build a graph with an add followed by relu (fused pattern).
+
+        Returns:
+            A tuple of (graph_module, relu_node, add_node).
+            The relu_node is the target node where the annotation is placed.
+            The add_node is the input source node whose args contain the quantized inputs.
+        """
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(1, 10))
+        y = builder.placeholder("y", torch.randn(1, 10))
+        add = builder.call_operator(
+            op=torch.ops.aten.add.Tensor,
+            args=(x, y),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("add", torch.ops.aten.add.Tensor)]}
+            ),
+        )
+        relu = builder.call_operator(
+            op=torch.ops.aten.relu.default,
+            args=(add,),
+            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")
+
+        add_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.add.Tensor,
+        )
+        self.assertEqual(len(add_nodes), 1, "Should find exactly one add node")
+
+        return gm, relu_nodes[0], add_nodes[0]
+
     def _build_conv2d_relu_graph(
         self,
     ) -> tuple[torch.fx.GraphModule, torch.fx.Node, torch.fx.Node]: