XNN MaxPool2d fails to lower with single element kernel_size

[GregoryComer]

🐛 Describe the bug

When attempting to lower an AvgPool2d module with a single-element kernel size list, XNN's partitioning logic will error out due to assuming that if kernel_size is a list, it has two elements. In the case where the is contains one element, it should use it for both height and width. The partition does this for scalar values, but not single-element lists.

Repro:

import torch
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
from executorch.exir import to_edge_transform_and_lower

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.pool = torch.nn.MaxPool2d([3])
    
    def forward(self, x):
        return self.pool(x)

inputs = (
    torch.randn(1, 3, 16, 16),
)

ep = torch.export.export(Model(), inputs)
print(ep)
et_program = to_edge_transform_and_lower(
    ep,  
    partitioner=[XnnpackPartitioner()]
).to_executorch()

Output:

File ~/miniconda3/envs/pytorch/lib/python3.11/site-packages/executorch/backends/xnnpack/partition/config/generic_node_configs.py:301, in MaxPool2dConfig.check_constraints(self, node, ep)
    298     why(node, reason="ceil mode is not supported for dynamic shapes")
    299     return False
--> 301 if stride[0] > kernel_size[0] or stride[1] > kernel_size[1]:  # pyre-ignore[16]
    302     why(
    303         node,
    304         reason=f"stride ({stride}) must be less than or equal to kernel size ({kernel_size})",
    305     )
    306     return False

IndexError: list index out of range

Versions

N/A

cc @digantdesai @mcr229 @cbilgin