[Quant][PT2E][X86] Enable annotation of aten.mul.tensor with X86InductorQuantizer

test/quantization/pt2e/test_x86inductor_quantizer.py

@@ -2876,6 +2876,73 @@ def test_lowering_to_x86(self):
                 lower=True,
             )
 
+    @skipIfNoX86
+    def test_annotate_mul_tensor(self):
+        class M1(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                return x * y
+
+        class M2(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                return x * y.sum(-1)
+
+        class M3(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                return x * y.sum()
+
+        class M4(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, y):
+                return x * y.sum().item()
+
+        for Mod in [M1, M2, M3, M4]:
+            with override_quantized_engine("x86"), torch.no_grad():
+                m = Mod().eval()
+                example_inputs = (torch.randn(64, 64), torch.randn(64, 64))
+                quantizer = X86InductorQuantizer().set_global(
+                    xiq.get_default_x86_inductor_quantization_config()
+                )
+                quantizer.set_function_type_qconfig(
+                    torch.mul, quantizer.get_global_quantization_config()
+                )
+                node_occurrence = {
+                    torch.ops.quantized_decomposed.quantize_per_tensor.default: 2
+                    if isinstance(m, M1)
+                    else 0,
+                    torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2
+                    if isinstance(m, M1)
+                    else 0,
+                    torch.ops.quantized_decomposed.quantize_per_channel.default: 0,
+                    torch.ops.quantized_decomposed.dequantize_per_channel.default: 0,
+                }
+                node_list = [
+                    torch.ops.aten.mul.Tensor,
+                ]
+                if isinstance(m, M1):
+                    node_list = [
+                        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+                        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+                    ] + node_list
+
+                self._test_quantizer(
+                    m,
+                    example_inputs,
+                    quantizer,
+                    node_occurrence,
+                    node_list,
+                )
+
 
 if __name__ == "__main__":
     run_tests()

torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py

@@ -93,6 +93,7 @@ class _X86InductorQuantizationAnnotation(QuantizationAnnotation):
     torch.ops.aten.conv1d.default,
     torch.ops.aten.conv2d.default,
     torch.ops.aten.linear.default,
+    torch.ops.aten.mul.Tensor,
 }
 
 # A superset of default_quantizable_ops includes operators support the int8 data type
@@ -219,6 +220,12 @@ def _map_module_function_to_aten_operator_type():
             ],
             torch.ops.aten.matmul.default,
         ),
+        (
+            [
+                torch.mul,
+            ],
+            torch.ops.aten.mul.Tensor,
+        ),
     )
     for map_item in map_list:
         module_function_to_aten_operator.update(dict.fromkeys(map_item[0], map_item[1]))  # type: ignore[arg-type, call-overload]
@@ -735,6 +742,7 @@ def _annotate_with_config(
         self._annotate_conv2d_fusion_pattern(model, quantization_config, filter_fn)
         self._annotate_linear_fusion_pattern(model, quantization_config, filter_fn)
         self._annotate_matmul(model, quantization_config, filter_fn)
+        self._annotate_mul_tensor(model, quantization_config, filter_fn)
 
         # Step2: Recipe to propagate annotation for patterns beside conv/linear.
         # Go through all the nodes from start to end.
@@ -1577,5 +1585,47 @@ def _annotate_linear_binary_unary(
                         )
                     )
 
+    def _annotate_mul_tensor(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: Optional[QuantizationConfig],
+        filter_fn: Optional[FilterFn] = None,
+    ):
+        def _is_tensor(n: Node):
+            return isinstance(n, Node) and isinstance(
+                n.meta["val"], torch._subclasses.fake_tensor.FakeTensor
+            )
+
+        def _same_shape(n1: Node, n2: Node):
+            return n1.meta["val"].shape == n2.meta["val"].shape
+
+        for node in model.graph.nodes:
+            if node.target != torch.ops.aten.mul.Tensor:
+                continue
+
+            if _skip_annotate([node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize(node)
+                continue
+
+            assert len(node.args) == 2
+            if not (_is_tensor(node.args[0]) and _is_tensor(node.args[1])):
+                continue
+
+            if not _same_shape(node.args[0], node.args[1]):
+                continue
+
+            input_qspec_map = {}
+            mul_node = node
+            for input_node in mul_node.args:
+                input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+            mul_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
     def validate(self, model: torch.fx.GraphModule) -> None:
         pass