Fixed bug with ConstantPad2d accepting negative padding dimensions (#12935)

### Summary
If a negative dimension is used with ConstantPad2d(), the export fails.
This functionality isn't supported by XNNPACK, so this pr changes the
node partitioner config such that negative dimension ConstantPadConfig
nodes aren't partitioned. The issue is further detailed below
#12456.

### Test plan
Added a new test class replicating the error in the github issue with
negative padding dimension.

Author: leafs1

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),