Add op_constant_pad_nd visitor

backends/arm/operator_support/tosa_supported_operators.py

@@ -138,6 +138,7 @@ def is_node_supported(self, submodules, node: fx.Node) -> bool:
             operator.getitem,
             exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
             exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+            exir_ops.edge.aten.constant_pad_nd.default,
         ]
 
         return supported

backends/arm/operators/__init__.py

@@ -12,6 +12,7 @@
     op_bmm,
     op_cat,
     op_clamp,
+    op_constant_pad_nd,
     op_conv2d,
     op_eq,
     op_exp,

backends/arm/operators/op_constant_pad_nd.py

@@ -0,0 +1,74 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+from typing import List
+
+import serializer.tosa_serializer as ts
+import torch
+
+from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
+    get_input_qparams,
+)
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from serializer.tosa_serializer import TosaOp
+
+
+@register_node_visitor
+class ConstantPadNDVisitor(NodeVisitor):
+
+    target = "aten.constant_pad_nd.default"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        if inputs[0].dtype == ts.DType.INT8:
+            input_qparams = get_input_qparams(node)
+            qargs = input_qparams[0]
+            pad_const_qs = qargs.quantize_value(inputs[2].number).item()
+            pad_const_fp = 0.0
+        else:
+            pad_const_fp = inputs[2].number
+            pad_const_qs = 0
+
+        rank = len(output.shape)
+        # Each dim needs 2 padding values. For example, to pad the last dimension, the pad has the form
+        # (padding_left, padding_right); to pad the last two dimensions, the pad has the form
+        # (padding_left, padding_right, padding_top, padding_bottom), and so on. For PyTorch NCHW format, the padding
+        # values are in the reverse order. So, firstly we need to reverse the input padding parameters.
+        input_pad = sum(
+            [
+                [inputs[1].special[i], inputs[1].special[i + 1]]
+                for i in range(0, len(inputs[1].special), 2)
+            ][::-1],
+            [],
+        )
+        # Then, add dummy zeros to make sure that both input_pad and output_pad has the same size.
+        input_pad = [0] * (rank * 2 - len(inputs[1].special)) + input_pad
+        # For PyTorch NCHW format, dim order is [0,...,rank-1]
+        input_dim_order = list(range(rank))
+        output_pad = [0] * rank * 2
+
+        # Map input padding parameters into output padding parameters. TOSA is NHWC format.
+        for input_dim_idx, input_dim in enumerate(input_dim_order):
+            output_dim_idx = output.dim_order.index(input_dim)
+            output_pad[output_dim_idx * 2 : (output_dim_idx + 1) * 2] = input_pad[
+                input_dim_idx * 2 : (input_dim_idx + 1) * 2
+            ]
+
+        attr = ts.TosaSerializerAttribute()
+        attr.PadAttribute(tosa_graph.builder, output_pad, pad_const_qs, pad_const_fp)
+
+        tosa_graph.addOperator(TosaOp.Op().PAD, [inputs[0].name], [output.name], attr)

backends/arm/quantizer/quantization_annotator.py

@@ -172,6 +172,7 @@ def _match_pattern(
     torch.ops.aten.chunk.default,
     torch.ops.aten.contiguous.default,
     torch.ops.aten.upsample_nearest2d.vec,
+    torch.ops.aten.pad.default,
 ]
 
 # Operators that can inherit the quantization specs from its parent node
@@ -216,6 +217,7 @@ def any_or_hardtanh_min_zero(n: Node):
                 torch.ops.aten.conv1d.default,
                 torch.ops.aten.conv2d.default,
                 torch.ops.aten.linear.default,
+                torch.ops.aten.conv2d.padding,
             ],
             [torch.ops.aten.relu.default, torch.ops.aten.hardtanh.default],
         ],
@@ -225,6 +227,7 @@ def any_or_hardtanh_min_zero(n: Node):
             torch.ops.aten.conv1d.default,
             torch.ops.aten.conv2d.default,
             torch.ops.aten.linear.default,
+            torch.ops.aten.conv2d.padding,
         ):
             quant_properties.quant_inputs = [
                 _QuantProperty(0, input_act_qspec),
@@ -237,6 +240,7 @@ def any_or_hardtanh_min_zero(n: Node):
         torch.ops.aten.conv1d.default,
         torch.ops.aten.conv2d.default,
         torch.ops.aten.linear.default,
+        torch.ops.aten.conv2d.padding,
     ):
         quant_properties.quant_inputs = [
             _QuantProperty(0, input_act_qspec),

backends/arm/quantizer/quantization_config.py

@@ -78,6 +78,7 @@ def _derive_qparams_fn(
             torch.ops.aten.conv1d.default,
             torch.ops.aten.conv2d.default,
             torch.ops.aten.linear.default,
+            torch.ops.aten.conv2d.padding,
         ]:
             input_act = node.args[0]
             weight = node.args[1]

backends/arm/test/ops/test_constant_pad_nd.py

@@ -0,0 +1,144 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+#
+# Test the pad_constant_nd op which pads the input tensor at specific dimension(s).
+#
+import unittest
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.arm_tester import ArmTester
+from parameterized import parameterized
+
+test_data_suite = [
+    ("4dim_last1dim", torch.rand(1, 1, 16, 16), (1, 1, 0, 0, 0, 0, 0, 0), 1),
+    ("4dim_last2dim", torch.rand(1, 1, 16, 16), (1, 0, 1, 0, 0, 0, 0, 0), 2),
+    ("4dim_last3dim", torch.rand(1, 1, 16, 16), (1, 1, 0, 2, 0, 2, 0, 0), 3),
+    ("4dim_last4dim", torch.rand(1, 1, 16, 16), (1, 0, 1, 1, 0, 2, 0, 2), 4),
+    ("3dim_last1dim", torch.rand(1, 1, 16), (1, 1, 0, 0, 0, 0), 1),
+    ("3dim_last2dim", torch.rand(1, 1, 16), (1, 0, 1, 1, 0, 0), 2),
+    ("3dim_last3dim", torch.rand(1, 1, 16), (1, 0, 1, 0, 1, 1), 3),
+    ("2dim_last1dim", torch.rand(1, 1, 16), (1, 1, 0, 0), 1),
+    ("2dim_last2dim", torch.rand(1, 1, 16), (1, 0, 1, 1), 2),
+]
+
+
+class TestConstantPadND(unittest.TestCase):
+    """Tests pad."""
+
+    class ConstantPadND(torch.nn.Module):
+        def __init__(self, pad: Tuple, value: float | None = None):
+            super().__init__()
+            self.dim = len(pad) // 2
+            self.value = value
+            in_channels = 1
+            # Only apply conv2d when the input dim = 4.
+            if self.dim == 4:
+                in_channels += pad[-3] + pad[-4]
+
+                self.conv2d = nn.Conv2d(
+                    in_channels=in_channels,
+                    out_channels=3,
+                    kernel_size=3,
+                    bias=True,
+                    stride=(2, 2),
+                    padding=0,
+                )
+
+                in_channels = 3
+                in_channels += pad[-3] + pad[-4]
+                self.conv2d_1 = nn.Conv2d(
+                    in_channels=in_channels,
+                    out_channels=3,
+                    kernel_size=3,
+                    bias=True,
+                    padding="same",
+                )
+
+            nonzero_idx = len(pad)
+            for i in range(0, len(pad), 2):
+                if pad[i] + pad[i + 1] == 0:
+                    nonzero_idx = i
+                    break
+            self.pad = pad[:nonzero_idx]
+            self.relu = nn.ReLU()
+            self.sigmoid = nn.Sigmoid()
+
+        def forward(self, x: torch.Tensor):
+            x = F.pad(x, pad=self.pad, mode="constant", value=self.value)
+            if self.dim == 4:
+                x = self.conv2d(x)
+            x = self.relu(x)
+
+            x = F.pad(x, pad=self.pad, mode="constant", value=self.value)
+            if self.dim == 4:
+                x = self.conv2d_1(x)
+            x = self.sigmoid(x)
+            return x
+
+    def _test_constant_pad_nd_tosa_MI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
+    ):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=common.get_tosa_compile_spec("TOSA-0.80+MI"),
+            )
+            .export()
+            .check_count({"torch.ops.aten.pad.default": 2})
+            .to_edge()
+            .partition()
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .run_method_and_compare_outputs(inputs=test_data)
+        )
+
+    def _test_constant_pad_nd_tosa_BI_pipeline(
+        self, module: torch.nn.Module, test_data: Tuple[torch.Tensor]
+    ):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=common.get_tosa_compile_spec("TOSA-0.80+BI"),
+            )
+            .quantize()
+            .export()
+            .check_count({"torch.ops.aten.pad.default": 2})
+            .to_edge()
+            .partition()
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .run_method_and_compare_outputs(inputs=test_data, qtol=1)
+        )
+
+    @parameterized.expand(test_data_suite)
+    def test_constant_pad_nd_tosa_MI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+        padding: Tuple,
+        value: float | None = None,
+    ):
+        self._test_constant_pad_nd_tosa_MI_pipeline(
+            self.ConstantPadND(padding, value), (test_data,)
+        )
+
+    @parameterized.expand(test_data_suite)
+    def test_constant_pad_nd_tosa_BI(
+        self,
+        test_name: str,
+        test_data: torch.Tensor,
+        padding: Tuple,
+        value: float | None = None,
+    ):
+        self._test_constant_pad_nd_tosa_BI_pipeline(
+            self.ConstantPadND(padding, value), (test_data,)
+        )