Add stride constraint to XNN MaxPool (#7354)

Summary:
Add an XNNPACK partitioner constraint for MaxPool2d to enforce stride <= kernel_size.

See https://github.com/google/XNNPACK/blob/860f2b9ad9d3602599aff49a41d0131d2a350e00/src/subgraph/max-pooling-2d.c#L327.


Test Plan:
Added an operator-level test (test_fp32_maxpool2d_unsupported_stride) to verify the new constraint.

```
buck test executorch/backends/xnnpack/test:test_xnnpack_ops -- maxpool2d
```

Reviewed By: digantdesai

Differential Revision: D67380978

Pulled By: GregoryComer

Author: GregoryComer

backends/xnnpack/partition/config/generic_node_configs.py

@@ -19,7 +19,7 @@
 from executorch.exir.backend.canonical_partitioners.config_partitioner import (
     format_target_name,
 )
-from executorch.exir.backend.utils import WhyNoPartition
+from executorch.exir.backend.utils import WhyNoPartition, is_shape_dynamic
 from torch.export import ExportedProgram
 
 logger = logging.getLogger(__name__)
@@ -284,19 +284,24 @@ class MaxPool2dConfig(GenericNodePartitionerConfig):
 
     def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
         """
-        XNNPACK's maxpool2d does not support ceil mode
+        XNNPACK's maxpool2d does not support ceil mode and requires stride <= kernel_size
         """
         if not self.check_common_constraints(node, ep):
             return False
 
-        # Ceil mode is supported via op padding, which must be statically known.
+        kernel_size = node.args[1]
+        stride = node.args[2]
         is_ceil_mode = len(node.args) >= 6 and cast(bool, node.args[5])
-        is_dynamic = "val" in node.meta and any(
-            isinstance(d, torch.SymInt) for d in node.meta["val"].shape
-        )
-        if is_ceil_mode and is_dynamic:
+
+        # Ceil mode is supported via op padding, which must be statically known.
+        if is_ceil_mode and is_shape_dynamic(node):
             why(node, reason="ceil mode is not supported for dynamic shapes")
             return False
+
+        if stride[0] > kernel_size[0] or stride[1] > kernel_size[1]:
+            why(node, reason=f"stride ({stride}) must be less than or equal to kernel size ({kernel_size})")
+            return False
+
         return True
 
     def supported_precision_types(self) -> List[ConfigPrecisionType]:
@@ -316,10 +321,7 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
         if not self.check_common_constraints(node, ep):
             return False
 
-        is_output_dynamic = "val" in node.meta and any(
-            isinstance(d, torch.SymInt) for d in node.meta["val"].shape
-        )
-        if is_output_dynamic:
+        if is_shape_dynamic(node):
             why(node, reason="dynamic output sizes are not supported")
             return False
         return True

backends/xnnpack/test/ops/test_maxpool2d.py

@@ -163,6 +163,28 @@ def test_fp32_maxpool2d_unsupported_dynamic_ceilmode(self):
             .run_method_and_compare_outputs()
         )
 
+    def test_fp32_maxpool2d_unsupported_stride(self):
+        """
+        XNNPACK MaxPool2d requires stride <= kernel_size.
+        """
+        inputs = (torch.randn(1, 32, 23, 23),)
+        (
+            Tester(self.MaxPool2d(kernel_size=2, stride=3), inputs)
+            .export()
+            .check_count({"torch.ops.aten.max_pool2d.default": 1})
+            .to_edge_transform_and_lower()
+            # We expect it not be be delegated.
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 0})
+            .check_count(
+                {
+                    "executorch_exir_dialects_edge__ops_aten_max_pool2d_with_indices_default": 1
+                }
+            )
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )
+
     def test_qs8_maxpool2d(self):
         class MaxPool(torch.nn.Module):
             def __init__(self, maxpool_params):

exir/backend/utils.py

@@ -416,6 +416,16 @@ def tag_mutated_buffer(edge_program: ExportedProgram) -> None:
             if len(user_tags) > 0:
                 node.meta["delegation_tag"] = user_tags.pop()
 
+def is_shape_dynamic(node: torch.fx.Node) -> bool:
+    """
+    Check if the node shape is dynamic.
+    """
+    return "val" in node.meta and any(
+        isinstance(d, torch.SymInt) for d in node.meta["val"].shape
+    )
+
+    # Shape is dynamic if any of the dimensions don't evaluate to a static value
+
 
 # TODO - style: use templated types
 class DelegateMappingBuilder: