Implement ReplaceMulTensorWithMulAndFullOpsPass.

Differential Revision: D76469624

Pull Request resolved: pytorch#11577

Author: eigen-k

backends/cadence/aot/replace_ops.py

@@ -2300,6 +2300,52 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
         return result
 
 
+@register_cadence_pass(CadencePassAttribute(opt_level=1))
+class ReplaceMulTensorWithMulAndFullOpsPass(ExportPass):
+    """
+    Extracts a single value argument of mul op to a separate full op.
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for mul_node in graph_module.graph.find_nodes(
+            op="call_function", target=torch.ops.aten.mul.Tensor
+        ):
+            x_arg, const_arg = mul_node.args
+
+            # Swap arguments if the order is wrong
+            if isinstance(const_arg, torch.fx.Node):
+                x_arg, const_arg = const_arg, x_arg
+
+            # Skip if the const_arg is not a scalar
+            if not isinstance(const_arg, (float, int)) or not isinstance(
+                x_arg, torch.fx.Node
+            ):
+                continue
+
+            # Cast the const_arg to the dtype of the x_arg
+            full_arg = self.resolve_full_arg(x_arg, const_arg)
+
+            # Extract an argument to a separate full op.
+            with graph_module.graph.inserting_before(mul_node):
+                full_tensor = graph_module.graph.call_function(
+                    exir_ops.edge.aten.full.default, args=([1], full_arg)
+                )
+                new_mul_node = graph_module.graph.call_function(
+                    torch.ops.aten.mul.Tensor, args=(x_arg, full_tensor)
+                )
+            # Replace the old mul with a newly created mul.
+            mul_node.replace_all_uses_with(new_mul_node)
+            graph_module.graph.erase_node(mul_node)
+        return super().call(graph_module)
+
+    def resolve_full_arg(self, x_arg, const_arg):
+        if x_arg.meta["val"].dtype == torch.float32 and isinstance(const_arg, int):
+            const_arg = float(const_arg)
+        if x_arg.meta["val"].dtype == torch.int32 and isinstance(const_arg, float):
+            const_arg = int(const_arg)
+        return const_arg
+
+
 # This class encapsulates all the functions that replace/switch one op in the
 # graph with another.
 class CadenceReplaceOpsInGraph:

backends/cadence/aot/tests/test_replace_ops_passes.py

@@ -15,7 +15,7 @@
     GraphBuilder,
     single_op_builder,
 )
-from executorch.backends.cadence.aot.pass_utils import count_node
+from executorch.backends.cadence.aot.pass_utils import count_node, op_counts_match
 from executorch.backends.cadence.aot.replace_ops import (
     ForceChannelLastForConvPass,
     MakeSliceAndCatDimOutermostPass,
@@ -31,6 +31,7 @@
     ReplaceLinearWithFullyConnectedOpPass,
     ReplaceMatmulWithTransposedMatmulPass,
     ReplaceMMWithAddMMPass,
+    ReplaceMulTensorWithMulAndFullOpsPass,
     ReplaceNopTransposeOrPermuteWithViewPass,
     ReplacePadWithCatPass,
     ReplacePermuteWithTransposePass,
@@ -1875,3 +1876,30 @@ def test_empty_slice(self):
             ),
             1,
         )
+
+    @parameterized.expand(
+        [
+            ("int", int(123)),
+            ("float", float(456.0)),
+        ],
+    )
+    @torch.no_grad()
+    def test_extract_mul_argument_to_full(self, _, value) -> None:
+        x = torch.randn(2, 1, 64)
+        gm = single_op_builder(
+            placeholders=(x,),
+            op=torch.ops.aten.mul.Tensor,
+            args=(x, value),
+            kwargs={},
+        )
+        p = ReplaceMulTensorWithMulAndFullOpsPass()
+        graph_after_passes = p.call(gm).graph_module
+        self.assertTrue(
+            op_counts_match(
+                graph_after_passes,
+                expected_op_counts={
+                    torch.ops.aten.mul.Tensor: 1,
+                    exir_ops.edge.aten.full.default: 1,
+                },
+            )
+        )