Patch the _is_conv_node function (#2257)

Summary:

Add the conv padding ops in torch/ao only, will add a separate PR for the ones in pytorch

Differential Revision: D75323215

Author: cccclai

test/quantization/pt2e/test_quantize_pt2e.py

@@ -2570,7 +2570,125 @@ def forward(self, x):
             node_occurrence,
             node_list,
         )
+                    
+    def test_conv_padding_bn_relu(self):
+        class BackendAQuantizer(Quantizer):
+            def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+                act_qspec = QuantizationSpec(
+                    dtype=torch.uint8,
+                    quant_min=0,
+                    quant_max=255,
+                    qscheme=torch.per_tensor_affine,
+                    is_dynamic=False,
+                    observer_or_fake_quant_ctr=observer.default_observer,
+                )
+                weight_qspec = QuantizationSpec(
+                    dtype=torch.int8,
+                    quant_min=-128,
+                    quant_max=127,
+                    qscheme=torch.per_tensor_affine,
+                    is_dynamic=False,
+                    observer_or_fake_quant_ctr=observer.default_weight_observer,
+                )
+                bias_qspec = QuantizationSpec(
+                    dtype=torch.float32,
+                    is_dynamic=False,
+                    observer_or_fake_quant_ctr=observer.PlaceholderObserver,
+                )
 
+                for n in model.graph.nodes:
+                    if (
+                        n.op != "call_function"
+                        or n.target != torch.ops.aten.relu.default
+                    ):
+                        continue
+                    relu_node = n
+                    n = n.args[0]
+
+                    # Check for any of the conv operations
+                    conv_ops = [
+                        torch.ops.aten.conv1d.padding,
+                        torch.ops.aten.conv2d.padding,
+                        torch.ops.aten.conv3d.padding,
+                    ]
+                    if n.op != "call_function" or n.target not in conv_ops:
+                        continue
+
+                    conv_node = n
+                    input_act = conv_node.args[0]
+                    weight = conv_node.args[1]
+                    bias = conv_node.args[2]
+                    conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+                        input_qspec_map={
+                            input_act: act_qspec,
+                            weight: weight_qspec,
+                            bias: bias_qspec,
+                        },
+                        _annotated=True,
+                    )
+                    relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+                        output_qspec=act_qspec,
+                        _annotated=True,
+                    )
+
+            def validate(self, model: torch.fx.GraphModule) -> None:
+                pass
+
+        # Test cases for Conv1d, Conv2d, Conv3d
+        test_cases = [
+            {
+                "conv_type": torch.nn.Conv1d,
+                "bn_type": torch.nn.BatchNorm1d,
+                "example_input": (torch.randn(1, 3, 5),),
+                "conv_op": torch.ops.aten.conv1d.padding,
+            },
+            {
+                "conv_type": torch.nn.Conv2d,
+                "bn_type": torch.nn.BatchNorm2d,
+                "example_input": (torch.randn(1, 3, 5, 5),),
+                "conv_op": torch.ops.aten.conv2d.padding,
+            },
+            {
+                "conv_type": torch.nn.Conv3d,
+                "bn_type": torch.nn.BatchNorm3d,
+                "example_input": (torch.randn(1, 3, 5, 5, 5),),
+                "conv_op": torch.ops.aten.conv3d.padding,
+            },
+        ]
+
+        for test_case in test_cases:
+            with self.subTest(conv_type=test_case["conv_type"].__name__):
+
+                class M(torch.nn.Module):
+                    def __init__(self):
+                        super().__init__()
+                        self.conv = test_case["conv_type"](3, 3, 3, padding="same")
+                        self.bn = test_case["bn_type"](3)
+
+                    def forward(self, x):
+                        return torch.nn.functional.relu(self.bn(self.conv(x)))
+
+                node_occurrence = {
+                    torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
+                    torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3,
+                }
+                node_list = [
+                    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+                    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+                    test_case["conv_op"],
+                    torch.ops.aten.relu.default,
+                    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+                ]
+
+                model = M().eval()
+                self._test_quantizer(
+                    model,
+                    test_case["example_input"],
+                    BackendAQuantizer(),
+                    node_occurrence,
+                    node_list,
+                )
+                
     def test_multi_users_without_output_observer(self):
         """
         Test the case in which a node is used by multiple users,

torchao/quantization/pt2e/utils.py

@@ -625,8 +625,11 @@ def _is_conv_node(n: Node):
     """
     return n.op == "call_function" and n.target in [
         torch.ops.aten.conv1d.default,
+        torch.ops.aten.conv1d.padding,
         torch.ops.aten.conv2d.default,
+        torch.ops.aten.conv2d.padding,
         torch.ops.aten.conv3d.default,
+        torch.ops.aten.conv3d.padding,
     ]