Support ceil_mode=True on maxpool2d in XNNPACK delegate (#7355)

Summary:
Support ceil_mode=True for MaxPool2d in XNNPACK delegate by conditionally updating padding. This works only when the input shape is static, as it requires statically computing the change in padding. As such, the partitioner constraints are also updated to reflect this.


Test Plan: Added a new test (test_fp32_maxpool2d_ceilmode) that exercises various permutations of input with ceil_mode=True.

Reviewed By: digantdesai, mcr229

Differential Revision: D67386151

Pulled By: GregoryComer

Author: GregoryComer

backends/xnnpack/operators/op_max_pool2d.py

@@ -4,6 +4,8 @@
 # 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 cast, Dict, List
 
 import torch
@@ -51,9 +53,9 @@ def define_node(
         kwargs["output_id"] = vals_to_ids[node]
 
         # kernel info
-        kernal_shape = cast(List[int], node.args[1])
-        kwargs["pooling_height"] = kernal_shape[0]
-        kwargs["pooling_width"] = kernal_shape[1]
+        kernel_shape = cast(List[int], node.args[1])
+        kwargs["pooling_height"] = kernel_shape[0]
+        kwargs["pooling_width"] = kernel_shape[1]
 
         # stride info
         stride = cast(List[int], node.args[2])
@@ -81,6 +83,26 @@ def define_node(
             kwargs["dilation_height"] = dilation[0]
             kwargs["dilation_width"] = dilation[1]
 
+        # ceil mode
+        ceil_mode = node.args[5] if len(node.args) > 5 else False
+        if ceil_mode:
+            # use original input shape as xnnpack input may be permuted
+            orig_input_shape = node.all_input_nodes[0].meta["val"].shape
+            kwargs["padding_bottom"] += self.calculate_pad_amount_1d(
+                orig_input_shape[2],
+                kernel_shape[0],
+                stride[0],
+                padding_shape[0],
+                dilation[0],
+            )
+            kwargs["padding_right"] += self.calculate_pad_amount_1d(
+                orig_input_shape[3],
+                kernel_shape[1],
+                stride[1],
+                padding_shape[1],
+                dilation[1],
+            )
+
         kwargs["flags"] = XNN_FLAG_KEEP_DIMS
 
         ser_node = XNode(
@@ -90,3 +112,25 @@ def define_node(
             debug_handle=debug_handle,
         )
         xnn_graph.xnodes.append(ser_node)
+
+    def calculate_pad_amount_1d(self, in_size, kernel_size, stride, padding, dilation):
+        # Determine the number of padding elements to add along a single dimension
+        # to match the ceil_mode=True behavior.
+        # See https://pytorch.org/docs/stable/generated/torch.nn.MaxPool1d.html
+
+        # Determine the number of input elements to exactly bump up the output size
+        # by 1. Note that there is an additional condition to substract 1 from the
+        # output when ceil_mode=True and (output_size - 1) * stride >= in_size + padding
+        # In this case, we don't need to pad, as ceil_mode=False and True give the
+        # same behavior.
+        numerator_no_ceil = in_size + 2 * padding - dilation * (kernel_size - 1) - 1
+        numerator = numerator_no_ceil + stride - 1
+        output_size = numerator // stride + 1
+
+        needs_adjust = (output_size - 1) * stride >= in_size + padding
+        partial_stride = numerator_no_ceil % stride
+        pad_out = (
+            (stride - partial_stride) if partial_stride > 0 and not needs_adjust else 0
+        )
+
+        return pad_out

backends/xnnpack/partition/config/generic_node_configs.py

@@ -4,6 +4,8 @@
 # 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
+
 import logging
 from typing import cast, List, Optional
 
@@ -287,9 +289,13 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
         if not self.check_common_constraints(node, ep):
             return False
 
+        # Ceil mode is supported via op padding, which must be statically known.
         is_ceil_mode = len(node.args) >= 6 and cast(bool, node.args[5])
-        if is_ceil_mode:
-            why(node, reason="ceil mode is not supported")
+        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:
+            why(node, reason="ceil mode is not supported for dynamic shapes")
             return False
         return True
 

backends/xnnpack/test/ops/test_maxpool2d.py

@@ -4,10 +4,14 @@
 # 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
+
+import itertools
 import unittest
 
 import torch
-from executorch.backends.xnnpack.test.tester import Tester
+from executorch.backends.xnnpack.test.tester import Export, Tester
+from torch.export.dynamic_shapes import Dim
 
 
 class TestMaxPool2d(unittest.TestCase):
@@ -38,10 +42,12 @@ 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):
+    class MaxPool2dCeilMode(torch.nn.Module):
+        def __init__(self, kernel_size=3, stride=1, padding=0, dilation=1):
             super().__init__()
-            self.max_pool2d_module = torch.nn.MaxPool2d(2, stride=2, ceil_mode=True)
+            self.max_pool2d_module = torch.nn.MaxPool2d(
+                kernel_size, stride, padding, dilation, ceil_mode=True
+            )
 
         def forward(self, x):
             return self.max_pool2d_module(x)
@@ -93,14 +99,56 @@ def test_fp32_maxpool2d_unsupported(self):
             )
         )
 
-    def test_fp32_maxpool2d_unsupported_ceilmode(self):
+    def test_fp32_maxpool2d_ceilmode(self):
+        input_sizes = [[17, 32], [32, 37]]
+        kernel_sizes = [2, 3, 12]
+        strides = [1, 2, 4]
+        padding = [0, 1, 5]
+        dilations = [1, 2, 3]
+
+        for input_size, kernel_size, stride, pad, dilation in itertools.product(
+            input_sizes, kernel_sizes, strides, padding, dilations
+        ):
+            # Check XNNPACK and PyTorch constraints
+            if pad > ((kernel_size - 1) * dilation + 1) / 2:
+                continue
+            if stride > kernel_size:
+                continue
+            if any(
+                (size + 2 * pad - dilation * (kernel_size - 1) - 1) // stride + 1 <= 0
+                for size in input_size
+            ):  # Output size too small
+                continue
+
+            inputs = (torch.randn(1, 1, input_size[0], input_size[1]),)
+            (
+                Tester(
+                    self.MaxPool2dCeilMode(kernel_size, stride, pad, dilation), inputs
+                )
+                .export()
+                .check_count({"torch.ops.aten.max_pool2d.default": 1})
+                .to_edge_transform_and_lower()
+                .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+                .check_not(
+                    [
+                        "executorch_exir_dialects_edge__ops_aten_max_pool2d_with_indices_default"
+                    ]
+                )
+                .to_executorch()
+                .serialize()
+                .run_method_and_compare_outputs()
+            )
+
+    def test_fp32_maxpool2d_unsupported_dynamic_ceilmode(self):
         """
-        MaxPool2d with ceil mode is not generally supported (see maxpool2d constraint).
+        MaxPool2d with ceil mode is supported with dynamic shape (see maxpool2d constraint).
         """
         inputs = (torch.randn(1, 32, 23, 23),)
+        dim3 = Dim("_dim3", min=11, max=50)
+        dynamic_shapes = {"x": {3: 2 * dim3 - 1}}
         (
-            Tester(self.MaxPool2dUnsupportedCeilMode(), inputs)
-            .export()
+            Tester(self.MaxPool2dCeilMode(), inputs)
+            .export(Export(dynamic_shapes=dynamic_shapes))
             .check_count({"torch.ops.aten.max_pool2d.default": 1})
             .to_edge_transform_and_lower()
             # We expect it not be be delegated.