[cadence][aot]Implement mul.Tensor to quant fusion.

Differential Revision: D76302365

Pull Request resolved: pytorch#11580

Author: eigen-k

backends/cadence/aot/fuse_ops.py

@@ -863,6 +863,76 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
         return result
 
 
+@register_cadence_pass(CadencePassAttribute(opt_level=1))
+class FuseMulTensorIntoQuantPass(ExportPass):
+    """
+    Looks for the pattern where aten.mul.Tensor is followed by quant node.
+    If found, updates the quant scale to reflect the multiplication and
+    removes the mul node.
+    """
+
+    def attempt_fusion(
+        self, graph_module: torch.fx.GraphModule, mul_node: torch.fx.Node
+    ) -> None:
+        full_nodes = [
+            arg
+            for arg in mul_node.args
+            if isinstance(arg, torch.fx.Node)
+            and arg.target == exir_ops.edge.aten.full.default
+        ]
+
+        if len(full_nodes) != 1 or len(mul_node.users) != 1:
+            return
+
+        full_node = full_nodes[0]
+        mul_user = list(mul_node.users.keys())[0]
+
+        if mul_user.target not in {
+            exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+            exir_ops.edge.cadence.quantize_per_tensor.default,
+        }:
+            return
+
+        quant_node = mul_user
+
+        # Calculate the new scale value.
+        prev_scale = quant_node.args[1]
+        assert isinstance(prev_scale, (int, float))
+        mul_scalar = full_node.args[1]
+        assert isinstance(mul_scalar, (int, float))
+        new_scale = float(prev_scale) * float(mul_scalar)
+
+        logging.debug(
+            f"Fused {mul_node} and {full_node} into {quant_node}. Updated scale from {quant_node.args[1]} to {new_scale}"
+        )
+
+        # Replace the input first
+        quant_node.replace_input_with(
+            cast(torch.fx.Node, quant_node.args[0]),
+            cast(torch.fx.Node, mul_node.args[0]),
+        )
+
+        # Now update the scale in the args
+        new_quant_args = list(quant_node.args)
+        new_quant_args[1] = new_scale
+        quant_node.args = tuple(new_quant_args)
+
+        # Clean up the mul_node
+        mul_node.args = ()
+        mul_node.users = {}
+
+        graph_module.graph.erase_node(mul_node)
+        graph_module.graph.erase_node(full_node)
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.find_nodes(
+            op="call_function", target=exir_ops.edge.aten.mul.Tensor
+        ):
+            self.attempt_fusion(graph_module, node)
+        graph_module.graph.eliminate_dead_code()
+        return super().call(graph_module)
+
+
 @register_cadence_pass(CadencePassAttribute(opt_level=1))
 class FuseMulTensorIntoDequantPass(ExportPass):
     """

backends/cadence/aot/tests/test_fusion_ops_passes.py

@@ -20,6 +20,7 @@
     FuseMMWithAdd,
     FuseMulScalarIntoDequantPass,
     FuseMulTensorIntoDequantPass,
+    FuseMulTensorIntoQuantPass,
     FuseQuantDequantToRequantizePass,
     FuseTransposeOrPermuteOpPairsPass,
 )
@@ -587,6 +588,48 @@ def test_fuse_mul_scalar_into_dequant(self):
                 deq_scale = node.args[1]
         self.assertEqual(deq_scale, dequant_scale * mul_value)
 
+    def test_fuse_mul_into_quant(self):
+        quant_scale = 1.5
+        mul_value = 10
+
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(4, 32, dtype=torch.float32))
+        full = builder.call_operator(
+            op=exir_ops.edge.aten.full.default,
+            args=([1], mul_value),
+        )
+        mul = builder.call_operator(
+            op=exir_ops.edge.aten.mul.Tensor,
+            args=(x, full),
+        )
+        quant = builder.call_operator(
+            op=exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+            args=(mul, quant_scale, 0, 0, 255, torch.uint8),
+        )
+        builder.output(quant)
+        graph_module = FuseMulTensorIntoQuantPass()(
+            builder.get_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.quantize_per_tensor.default: 1,
+                exir_ops.edge.aten.full.default: 0,
+                exir_ops.edge.aten.mul.Tensor: 0,
+            },
+        )
+
+        # verify that the quant scale value was updated correctly
+        for node in graph_module.graph.nodes:
+            if (
+                node.target
+                == exir_ops.edge.quantized_decomposed.quantize_per_tensor.default
+            ):
+                deq_scale = node.args[1]
+        self.assertEqual(deq_scale, quant_scale * mul_value)
+
     def test_fuse_then_transpose_pass(self):
         # Create a graph with full -> transpose.
         builder = GraphBuilder()