Add a pass to fuse mul.Scalar into dequant

backends/cadence/aot/fuse_ops.py

@@ -814,11 +814,61 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=1))
-class FuseMulIntoDequantPass(ExportPass):
+class FuseMulScalarIntoDequantPass(ExportPass):
     """
-    Looks for the pattern where atem.mul is multiplying the outputs of dequantize
-    and aten.full. If found, updates the dequant scale to reflect the multiplication
-    and removes the full and mul nodes.
+    Looks for the pattern where aten.mul.Scalar is multiplying the
+     outputs of dequantize. If found, updates the dequant scale
+    to reflect the multiplication and removes the mul node.
+    """
+
+    def attempt_fusion(
+        self, graph_module: torch.fx.GraphModule, node: torch.fx.Node
+    ) -> None:
+        if node.target not in {
+            exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+            exir_ops.edge.cadence.dequantize_per_tensor.default,
+        }:
+            return
+
+        # ensure that the single user of dequant is aten.mul.Scalar
+        user = list(node.users.keys())[0]
+        if len(node.users) != 1 or user.target != exir_ops.edge.aten.mul.Scalar:
+            return
+
+        # ensure that the other arg to mul is a node (i.e. not a constant)
+        if len(user.args) > 1 and isinstance(user.args[1], torch.fx.Node):
+            return
+
+        new_deq_args = list(node.args)
+        assert isinstance(node.args[1], Number)
+        assert isinstance(user.args[1], Number)
+        # pyre-ignore[58]: Unsupported operand *
+        new_deq_args[1] = node.args[1] * user.args[1]
+
+        logging.debug(
+            f"Fused {node} and {user} into {node}. Updated scale from {node.args[1]} to {new_deq_args[1]}"
+        )
+
+        user.replace_all_uses_with(node)
+        node.args = tuple(new_deq_args)
+
+        graph_module.graph.erase_node(user)
+
+        graph_module.recompile()
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            self.attempt_fusion(graph_module, node)
+        result = super().call(graph_module)
+        return result
+
+
+@register_cadence_pass(CadencePassAttribute(opt_level=1))
+class FuseMulTensorIntoDequantPass(ExportPass):
+    """
+    Looks for the pattern where aten.mul is multiplying the outputs of dequantize
+    and aten.full, or vice versa. If found, updates the dequant scale to reflect
+    the multiplication and removes the full and mul nodes.
     """
 
     def attempt_fusion(
@@ -1017,7 +1067,8 @@ class CadenceFuseOpsInGraph:
         FuseCascadedTransposeOrPermuteOps,
         FuseCascadedViewOps,
         FuseQuantDequantToRequantizePass,
-        FuseMulIntoDequantPass,
+        FuseMulTensorIntoDequantPass,
+        FuseMulScalarIntoDequantPass,
         FuseFullThenReshapePass,
         FuseTransposeOrPermuteOpPairsPass,
     ]

backends/cadence/aot/tests/test_fusion_ops_passes.py

@@ -19,7 +19,8 @@
 )
 from executorch.backends.cadence.aot.fuse_ops import (
     FuseFullThenReshapePass,
-    FuseMulIntoDequantPass,
+    FuseMulScalarIntoDequantPass,
+    FuseMulTensorIntoDequantPass,
     FuseQuantDequantToRequantizePass,
     FuseTransposeOrPermuteOpPairsPass,
 )
@@ -446,7 +447,7 @@ def forward(self, x):
 
         inputs = (torch.randint(0, 255, [4, 32], dtype=torch.uint8),)
         graph_module = export_to_edge(M(), inputs).exported_program().graph_module
-        graph_module = FuseMulIntoDequantPass()(graph_module).graph_module
+        graph_module = FuseMulTensorIntoDequantPass()(graph_module).graph_module
 
         # verify that the mul and full ops were removed
         self.check_op_counts(
@@ -467,6 +468,47 @@ def forward(self, x):
                 deq_scale = node.args[1]
         self.assertEqual(deq_scale, 4.5)
 
+    def test_fuse_mul_scalar_into_dequant(self):
+        dequant_scale = 0.006
+        mul_value = 0.3
+
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(2, 3, 4, dtype=torch.float32))
+        quant = builder.call_operator(
+            op=exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+            args=(x, 1, 0, -128, 127, torch.int8),
+        )
+        dequant = builder.call_operator(
+            op=exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+            args=(quant, dequant_scale, 5, -128, 127, torch.int8),
+        )
+        mul_scalar = builder.call_operator(
+            op=exir_ops.edge.aten.mul.Scalar,
+            args=(dequant, mul_value),
+        )
+        builder.output(mul_scalar)
+        graph_module = builder.get_graph_module()
+
+        graph_module = FuseMulScalarIntoDequantPass()(graph_module).graph_module
+
+        # verify that the mul and full ops were removed
+        self.check_op_counts(
+            graph_module,
+            expected_op_counts={
+                exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default: 1,
+                exir_ops.edge.aten.mul.Scalar: 0,
+            },
+        )
+
+        # verify that the dequant scale value was updated correctly
+        for node in graph_module.graph.nodes:
+            if (
+                node.target
+                == exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default
+            ):
+                deq_scale = node.args[1]
+        self.assertEqual(deq_scale, dequant_scale * mul_value)
+
     def test_fuse_then_transpose_pass(self):
         # Create a graph with full -> transpose.
         builder = GraphBuilder()