Fixed bug with ConstantPad2d accepting negative padding dimensions

backends/xnnpack/partition/config/generic_node_configs.py

@@ -589,6 +589,21 @@ def supported_precision_types(self) -> List[ConfigPrecisionType]:
 class ConstantPadConfig(GenericNodePartitionerConfig):
     target_name = "constant_pad_nd.default"
 
+    def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
+        """
+        XNNPACK does not support cropping with negative padding sizes.
+        """
+        if not self.check_common_constraints(node, ep):
+            return False
+
+        # Check for negative padding values
+        padding = cast(List[int], node.args[1])
+        if any(p < 0 for p in padding):
+            why(node, reason="XNNPACK does not support negative padding values")
+            return False
+
+        return True
+
     def supported_precision_types(self) -> List[ConfigPrecisionType]:
         return [ConfigPrecisionType.FP32]
 

backends/xnnpack/test/ops/test_static_constant_pad.py

@@ -7,7 +7,10 @@
 import unittest
 
 import torch
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
 from executorch.backends.xnnpack.test.tester import Tester
+from executorch.exir import to_edge_transform_and_lower
+from torch.export import export
 
 
 class TestStaticConstantPad(unittest.TestCase):
@@ -125,6 +128,45 @@ def _test_static_constant_pad_functional(self, inputs):
             .run_method_and_compare_outputs()
         )
 
+    class NegativePadModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.pad = torch.nn.ConstantPad2d((0, 0, -2, 2), 0.0)
+
+        def forward(self, input):
+            input = self.pad(input)
+            return input
+
+    def test_negative_pad_model_with_ints(self):
+        """Test that negative padding with integer inputs falls back to PyTorch implementation as XNNPACK does not support negative padding dimensions"""
+        input_tensor = torch.tensor([[4], [5], [6]])
+        model = self.NegativePadModel()
+        model.eval()
+        model.to("cpu")
+
+        exported_model = export(model, (input_tensor,))
+
+        executorch_program = to_edge_transform_and_lower(
+            exported_model, partitioner=[XnnpackPartitioner()]
+        ).to_executorch()
+
+        self.assertIsNotNone(executorch_program)
+
+    def test_negative_pad_model_with_floats(self):
+        """Test that negative padding with float inputs is now rejected by XNNPACK partitioner as XNNPACK does not support negative padding dimensions"""
+        input_tensor = torch.tensor([[4.0], [5.0], [6.0]])
+        model = self.NegativePadModel()
+        model.eval()
+        model.to("cpu")
+
+        exported_model = export(model, (input_tensor,))
+
+        executorch_program = to_edge_transform_and_lower(
+            exported_model, partitioner=[XnnpackPartitioner()]
+        ).to_executorch()
+
+        self.assertIsNotNone(executorch_program)
+
     def test_fp16_static_constant_pad_functional(self):
         inputs = (
             torch.randn(size=(5, 4, 3, 2)).to(torch.float16),