don't partition max pool with ceil mode (#3578)

Summary:
Pull Request resolved: #3578

XNNPACK doesn't support max pooling with ceil mode, so we should not be partitioning these nodes where ceil mode is True

Resolving this issue:
#3567

Reviewed By: mergennachin, digantdesai

Differential Revision: D57228128

fbshipit-source-id: ee57a783d314d69ebef57f0e1707c0d038582a31

Author: mcr229

backends/xnnpack/partition/xnnpack_partitioner.py

@@ -166,6 +166,8 @@ def _check_outputs_are_valid_dtypes(self, node, valid_dtypes):
         return True
 
     def check_node_has_valid_dtype(self, node):
+        # max_pool2d_with_indicies returns indicies which is int64
+        # this is supportable within XNNPACK
         if node.target in {exir_ops.edge.aten.max_pool2d_with_indices.default}:
             return True
 
@@ -268,13 +270,16 @@ def maxpool2d_with_indices(
     ) -> bool:
         """
         Only if the first output value is consumed in the graph
+        and it is not in ceil mode
         """
         users = list(node.users.keys())
+        is_ceil_mode = len(node.args) >= 6 and node.args[5]
         return (
             True
             if len(users) == 1
             and users[0].target == operator.getitem
             and users[0].args[1] == 0
+            and not is_ceil_mode
             else False
         )
 

backends/xnnpack/test/ops/maxpool2d.py

@@ -38,6 +38,14 @@ def __init__(self, kernel_size=3, stride=1, padding=0, dilation=1):
         def forward(self, x):
             return self.max_pool2d_module(x)[1]
 
+    class MaxPool2dUnsupportedCeilMode(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.max_pool2d_module = torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
+
+        def forward(self, x):
+            return self.max_pool2d_module(x)
+
     def _test_maxpool2d(self, inputs):
         """
         Note that the export process generates aten.max_pool2d_with_indices. The remove_getitem_op
@@ -99,6 +107,34 @@ def test_fp32_maxpool2d_unsupported(self):
             )
         )
 
+    def test_fp32_maxpool2d_unsupported_ceilmode(self):
+        """
+        MaxPool2d with ceil mode is not generally supported (see maxpool2d constraint).
+        """
+        inputs = (torch.randn(1, 32, 23, 23),)
+        (
+            Tester(self.MaxPool2dUnsupportedCeilMode(), inputs)
+            .export()
+            .check_count({"torch.ops.aten.max_pool2d_with_indices.default": 1})
+            .to_edge()
+            .check_count(
+                {
+                    "executorch_exir_dialects_edge__ops_aten_max_pool2d_with_indices_default": 1
+                }
+            )
+            .partition()
+            # 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):