xref: /aosp_15_r20/external/pytorch/test/nn/test_pooling.py (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

# Owner(s): ["module: nn"]
import itertools
import math
import operator
import os
import random
import subprocess
import sys
import unittest
from functools import partial, reduce
from itertools import repeat

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import inf, nan
from torch.autograd import gradcheck, gradgradcheck
from torch.testing import make_tensor
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.testing._internal.common_device_type import (
    dtypes,
    dtypesIfCUDA,
    expectedFailureMeta,
    instantiate_device_type_tests,
    largeTensorTest,
    onlyCPU,
    onlyCUDA,
    onlyNativeDeviceTypes,
    skipCUDAIfRocm,
    TEST_WITH_ROCM,
)
from torch.testing._internal.common_dtype import floating_types_and
from torch.testing._internal.common_nn import (
    _test_bfloat16_ops,
    _test_module_empty_input,
    NNTestCase,
)
from torch.testing._internal.common_utils import (
    gcIfJetson,
    instantiate_parametrized_tests,
    parametrize as parametrize_test,
    run_tests,
    set_default_dtype,
    skipIfMps,
    skipIfTorchDynamo,
    slowTest,
    subtest,
    TEST_WITH_UBSAN,
    TestCase,
)


class TestAvgPool(TestCase):
    def _sum_pool2d(self, x, kernel_size):
        windows = torch.nn.functional.unfold(
            x, kernel_size=kernel_size, stride=kernel_size
        )
        return torch.sum(windows, dim=1)

    def _sum_pool3d(self, x, kernel_size):
        # Because unfold does not support 3D sliding window we will split tensor to multiple tensors and calculate sum
        h = kernel_size[0]
        splited_x = [t.sum(0) for t in x.split(h) if t.size(0) == h]
        # sum_pool2d assumes tensor in (1, 1, n, m) view, so unsqueeze two times
        splited_x = [
            self._sum_pool2d(t.unsqueeze(0).unsqueeze(0), kernel_size[1:])
            for t in splited_x
        ]
        joined_x = torch.cat(splited_x)
        return joined_x.view(1, joined_x.numel())

    def _avg_pool2d(self, x, kernel_size):
        size = reduce(operator.mul, kernel_size)
        return self._sum_pool2d(x, kernel_size) / size

    def _avg_pool3d(self, x, kernel_size):
        size = reduce(operator.mul, kernel_size)
        return self._sum_pool3d(x, kernel_size) / size

    def test_doubletensor_avg_pool2d(self):
        n, m = 5, 8
        input = torch.rand(1, 1, n, m, dtype=torch.double)
        for i in range(1, n + 1):
            for j in range(1, m + 1):
                actual = torch.nn.functional.avg_pool2d(input[0], (i, j))
                actual = actual.view(1, actual.numel())
                expected = self._avg_pool2d(input, (i, j))
                self.assertEqual(actual, expected, rtol=0, atol=1e-5)

    def test_doubletensor_avg_pool2d_with_divisor(self):
        n, m = 3, 3
        input = torch.rand(1, 1, n, m, dtype=torch.double)
        for i in range(1, n + 1):
            for j in range(1, m + 1):
                for divisor in [1, 7, i * j]:
                    actual = F.avg_pool2d(input[0], (i, j), divisor_override=divisor)
                    actual = actual.view(1, actual.numel())
                    expected = self._sum_pool2d(input, (i, j)) / divisor
                    self.assertEqual(actual, expected, rtol=0, atol=1e-5)

    def test_doubletensor_avg_pool3d(self):
        h, w, d = 5, 6, 7
        input = torch.rand(h, w, d, dtype=torch.double)
        for i in range(1, h + 1):
            for j in range(1, w + 1):
                for k in range(1, d + 1):
                    actual = torch.nn.functional.avg_pool3d(
                        input.unsqueeze(0), (i, j, k)
                    )
                    actual = actual.view(1, actual.numel())
                    expected = self._avg_pool3d(input, (i, j, k))
                    self.assertEqual(actual, expected, rtol=0, atol=1e-5)

    def test_doubletensor_avg_pool3d_with_divisor(self):
        h, w, d = 6, 5, 7
        input = torch.rand(h, w, d, dtype=torch.double)
        for i in range(1, h + 1):
            for j in range(1, w + 1):
                for k in range(1, d + 1):
                    for divisor in [1, 7, i * j]:
                        actual = torch.nn.functional.avg_pool3d(
                            input.unsqueeze(0), (i, j, k), divisor_override=divisor
                        )
                        actual = actual.view(1, actual.numel())
                        expected = self._sum_pool3d(input, (i, j, k)) / divisor
                        self.assertEqual(actual, expected, rtol=0, atol=1e-5)

    def test_avg_pool1d_ceil_mode(self):
        # Regression test for gh-36977
        x = 10 * torch.randn((1, 16, 4))
        y = torch.nn.functional.avg_pool1d(
            x, ceil_mode=True, count_include_pad=True, kernel_size=1, stride=2
        )
        self.assertTrue(not torch.isnan(y).any())

        if TEST_CUDA:
            y = torch.nn.functional.avg_pool1d(
                x.to("cuda"),
                ceil_mode=True,
                count_include_pad=True,
                kernel_size=1,
                stride=2,
            )
            self.assertTrue(not torch.isnan(y).any())

    def test_avg_pool2d_ceil_mode(self):
        # Regression test for gh-36977
        x = 10 * torch.randn((1, 16, 4, 4))
        y = torch.nn.functional.avg_pool2d(
            x,
            ceil_mode=True,
            count_include_pad=True,
            kernel_size=(1, 2),
            padding=(0, 1),
            stride=2,
        )
        self.assertTrue(not torch.isnan(y).any())

        if TEST_CUDA:
            y = torch.nn.functional.avg_pool2d(
                x.to("cuda"),
                ceil_mode=True,
                count_include_pad=True,
                kernel_size=(1, 2),
                padding=(0, 1),
                stride=2,
            )
            self.assertTrue(not torch.isnan(y).any())

    def test_avg_pool3d_ceil_mode(self):
        # Regression test for gh-36977
        x = 10 * torch.randn((1, 16, 4, 4, 4))
        y = torch.nn.functional.avg_pool3d(
            x, ceil_mode=True, count_include_pad=True, kernel_size=(1, 2, 3), stride=2
        )
        self.assertTrue(not torch.isnan(y).any())

        if TEST_CUDA:
            y = torch.nn.functional.avg_pool3d(
                x.to("cuda"),
                ceil_mode=True,
                count_include_pad=True,
                kernel_size=(1, 2, 3),
                stride=2,
            )
            self.assertTrue(not torch.isnan(y).any())


class TestPoolingNN(NNTestCase):
    _do_cuda_memory_leak_check = True
    _do_cuda_non_default_stream = True

    def test_adaptive_pooling_size_none(self):
        for numel in (2, 3):
            for pool_type in ("Max", "Avg"):
                cls_name = f"Adaptive{pool_type}Pool{numel}d"
                module_cls = getattr(nn, cls_name)
                output_size = (2,) * (numel - 1) + (None,)
                module = module_cls(output_size)

                input = torch.randn((4,) * (numel + 1))
                output = module(input)
                self.assertEqual(output.size(), (4,) + (2,) * (numel - 1) + (4,))

    @unittest.skipIf(TEST_WITH_UBSAN, "signed integer overflow error with UBSAN")
    def test_adaptive_pooling_size_overflow(self):
        # 0x0x3fffffffffffffff * 2 * 2 = 0xfffffffffffffffc = -4 as int64_t
        # Tensor::numel() return int64_t, so following check that negative allocs are correctly handled
        self.assertRaises(
            RuntimeError,
            lambda: torch.nn.AdaptiveMaxPool1d(0x3FFFFFFFFFFFFFFF)(
                torch.empty([2, 2, 2])
            ),
        )

    def test_adaptive_pooling_avg_nhwc(self):
        device_list = ["cpu"]
        if TEST_CUDA:
            device_list.append("cuda")

        for device in device_list:
            input = torch.randint(1, 10, (4, 8, 8, 8), dtype=torch.float32).to(device)
            input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
            grad = torch.randint(1, 10, (4, 8, 7, 7), dtype=torch.float32).to(device)
            pool = torch.nn.AdaptiveAvgPool2d((7, 7)).to(device)

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.AdaptiveAvgPool2d((7, 7)).to(device)

            out = pool(input)
            out.backward(grad)
            ref_out = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(input.grad, ref_input.grad)

    def test_adaptive_pooling_avg_nhwc_non_contiguous(self):
        device_list = ["cpu"]
        if TEST_CUDA:
            device_list.append("cuda")

        for device in device_list:
            input = torch.randint(1, 10, (4, 8, 8, 8), dtype=torch.float32).to(device)
            input = input.contiguous(memory_format=torch.channels_last)
            input = input[:, ::2, :, :].requires_grad_()
            grad = torch.randint(1, 10, (4, 8, 7, 7), dtype=torch.float32).to(device)
            grad = grad[:, ::2, :, :]
            pool = torch.nn.AdaptiveAvgPool2d((7, 7)).to(device)

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.AdaptiveAvgPool2d((7, 7)).to(device)

            out = pool(input)
            out.backward(grad)
            ref_out = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(input.grad, ref_input.grad)

    def test_adaptive_pooling_lower_precision(self):
        def _test_adaptive_pooling_lower_precision(
            self, device, dtype, mod, memory_format
        ):
            input = torch.randint(1, 10, (3, 19, 8, 8), dtype=torch.float32)
            input = input.to(device).to(memory_format=memory_format).requires_grad_()
            pool = mod((7, 7)).to(device)

            input2 = input.detach().clone().to(dtype=dtype).requires_grad_(True)

            out = pool(input)
            out.sum().backward()
            out2 = pool(input2)
            out2.sum().backward()

            self.assertTrue(out2.is_contiguous(memory_format=memory_format))
            self.assertEqual(out2.dtype, dtype)
            self.assertEqual(input2.grad.dtype, dtype)
            self.assertEqual(out, out2.float(), atol=0.1, rtol=0)
            self.assertEqual(input.grad, input2.grad.float(), atol=0.1, rtol=0)

        device_list = ["cpu"]
        for device in device_list:
            for dtype in [torch.bfloat16, torch.float16]:
                _test_adaptive_pooling_lower_precision(
                    self,
                    device,
                    dtype,
                    torch.nn.AdaptiveAvgPool2d,
                    torch.contiguous_format,
                )
                _test_adaptive_pooling_lower_precision(
                    self, device, dtype, torch.nn.AdaptiveAvgPool2d, torch.channels_last
                )
                _test_adaptive_pooling_lower_precision(
                    self,
                    device,
                    dtype,
                    torch.nn.AdaptiveMaxPool2d,
                    torch.contiguous_format,
                )
                _test_adaptive_pooling_lower_precision(
                    self, device, dtype, torch.nn.AdaptiveMaxPool2d, torch.channels_last
                )

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    @largeTensorTest("12GB", device="cuda")
    def test_adaptive_pooling_avg_nhwc_launch_config_backward(self):
        input = torch.randint(
            1, 10, (1, 32, 2**17 + 1, 32), dtype=torch.float32, device="cuda"
        )
        input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
        grad = torch.randint(1, 10, (1, 32, 10, 32), dtype=torch.float32, device="cuda")

        pool = torch.nn.AdaptiveAvgPool2d((10, 32)).cuda()

        ref_input = input.detach().clone().contiguous().requires_grad_(True)
        ref_grad = grad.detach().clone().contiguous()
        ref_pool = torch.nn.AdaptiveAvgPool2d((10, 32)).cuda()

        out = pool(input)
        out.backward(grad)
        ref_out = ref_pool(ref_input)
        ref_out.backward(ref_grad)

        self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
        self.assertTrue(ref_out.is_contiguous())
        self.assertEqual(out, ref_out)
        self.assertEqual(input.grad, ref_input.grad)

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    @largeTensorTest("12GB", device="cuda")
    def test_adaptive_pooling_avg_nhwc_launch_config_forward(self):
        input = torch.randint(
            1, 10, (1, 32, 16, 16), dtype=torch.float32, device="cuda"
        )
        input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
        pool = torch.nn.AdaptiveAvgPool2d((2**17 + 1, 32)).cuda()

        ref_input = input.detach().clone().contiguous().requires_grad_(True)
        ref_pool = torch.nn.AdaptiveAvgPool2d((2**17 + 1, 32)).cuda()

        out = pool(input)
        ref_out = ref_pool(ref_input)

        self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
        self.assertTrue(ref_out.is_contiguous())
        self.assertEqual(out, ref_out)

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_adaptive_avg_pooling_overflow(self):
        input = torch.randint(
            -256, 256, (20, 32, 256, 256), dtype=torch.half, device="cuda"
        )
        avg_pool = torch.nn.AdaptiveAvgPool2d((2, 2))
        out = avg_pool(input)
        self.assertFalse(torch.isinf(out).any())
        self.assertFalse(torch.isnan(out).any())

    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_adaptive_avg_pooling_nhwc_overflow(self):
        input = torch.randint(
            -256, 256, (20, 32, 256, 256), dtype=torch.half, device="cuda"
        )
        input = input.contiguous(memory_format=torch.channels_last)
        avg_pool = torch.nn.AdaptiveAvgPool2d((2, 2))
        out = avg_pool(input)
        self.assertFalse(torch.isinf(out).any())
        self.assertFalse(torch.isnan(out).any())

    def test_MaxUnpool2d_output_size(self):
        m = nn.MaxPool2d(3, stride=2, return_indices=True)
        mu = nn.MaxUnpool2d(3, stride=2)
        big_t = torch.rand(1, 1, 6, 6)
        big_t[0][0][4][4] = 100
        output_big, indices_big = m(big_t)
        self.assertRaises(RuntimeError, lambda: mu(output_big, indices_big))

        small_t = torch.rand(1, 1, 5, 5)
        for i in range(0, 4, 2):
            for j in range(0, 4, 2):
                small_t[:, :, i, j] = 100
        output_small, indices_small = m(small_t)
        for h in range(3, 10):
            for w in range(3, 10):
                if 4 <= h <= 6 and 4 <= w <= 6:
                    size = (h, w)
                    if h == 6:
                        size = (1, 1) + size

                    mu(output_small, indices_small, output_size=size)
                else:
                    self.assertRaises(
                        ValueError, lambda: mu(output_small, indices_small, (h, w))
                    )

    def test_max_unpool2d_nhwc_cpu(self):
        input = torch.randn(2, 10, 9, 9).float().cpu()
        input = input.contiguous(memory_format=torch.channels_last)
        ref_input = input.clone().contiguous()

        pool = nn.MaxPool2d(3, stride=2, return_indices=True).cpu()
        ref_pool = nn.MaxPool2d(3, stride=2, return_indices=True).cpu()

        out, ind = pool(input)
        ref_out, ref_ind = ref_pool(ref_input)
        out.requires_grad_()
        ref_out.requires_grad_()

        unpool = nn.MaxUnpool2d(3, stride=2).cpu()
        ref_unpool = nn.MaxUnpool2d(3, stride=2).cpu()

        upout = unpool(out, ind)
        ref_upout = ref_unpool(ref_out, ref_ind)

        grad = torch.randn(upout.size()).float().cpu()
        grad = grad.contiguous(memory_format=torch.channels_last)
        ref_grad = grad.clone().contiguous()

        upout.backward(grad)
        ref_upout.backward(ref_grad)

        self.assertTrue(upout.is_contiguous(memory_format=torch.channels_last))
        self.assertTrue(ref_upout.is_contiguous())
        self.assertTrue(torch.allclose(upout, ref_upout))
        self.assertTrue(torch.allclose(out.grad, ref_out.grad))

    def test_max_unpool(self):
        with set_default_dtype(torch.double):
            # Test 1D
            output, indices = F.max_pool1d(
                torch.randn([1, 1, 4]), 2, stride=2, return_indices=True
            )
            self.assertEqual(
                F.max_unpool1d(output, indices, 2),
                F.max_unpool1d(output, indices, 2, stride=2),
            )

            # Test list / tuple passed as argument to max_unpool1d
            input = torch.randn([1, 1, 5], requires_grad=True)
            output, indices = F.max_pool1d(input, 2, stride=2, return_indices=True)
            self.assertEqual(
                F.max_unpool1d(output, indices, 2, stride=2, output_size=input.shape),
                F.max_unpool1d(output, indices, 2, stride=2, output_size=input.size()),
            )
            gradcheck(F.max_unpool1d, (output, indices, 2), check_forward_ad=True)

            # Test 2D
            output, indices = F.max_pool2d(
                torch.randn([1, 1, 4, 4], requires_grad=True),
                2,
                stride=2,
                return_indices=True,
            )
            self.assertEqual(
                F.max_unpool2d(output, indices, 2),
                F.max_unpool2d(output, indices, 2, stride=2),
            )
            gradcheck(F.max_unpool2d, (output, indices, 2), check_forward_ad=True)

            # Test 3D
            output, indices = F.max_pool3d(
                torch.randn([4, 4, 4, 4, 4], requires_grad=True),
                2,
                stride=2,
                return_indices=True,
            )
            self.assertEqual(
                F.max_unpool3d(output, indices, 2),
                F.max_unpool3d(output, indices, 2, stride=2),
            )
            gradcheck(F.max_unpool3d, (output, indices, 2), check_forward_ad=True)

    def test_max_unpool3d_input_check(self):
        x = torch.ones(1, 3, 1, 1, 1)
        with self.assertRaises(RuntimeError):
            F.max_unpool3d(x, torch.zeros(x.shape, dtype=int), [1, 1])

    def test_quantized_max_pool1d_empty_kernel(self):
        # This used to segfault when called with an empty kernel
        # see https://github.com/pytorch/pytorch/issues/116323
        base = torch.randn(1)
        temp_tensor = torch.quantize_per_tensor(base, 0.1, 10, torch.quint2x4)
        with self.assertRaises(RuntimeError):
            torch.quantized_max_pool1d(temp_tensor, [])


class TestPoolingNNDeviceType(NNTestCase):
    @onlyNativeDeviceTypes
    @dtypes(torch.float, torch.double)
    def test_adaptive_pooling_zero_batch(self, dtype, device):
        inp = torch.ones(0, 10, dtype=dtype, device=device)
        mod = torch.nn.AdaptiveAvgPool1d(5).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        inp = torch.ones(0, 10, 10, dtype=dtype, device=device)
        mod = torch.nn.AdaptiveAvgPool2d((5, 5)).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        inp = torch.ones(0, 10, 10, 10, dtype=dtype, device=device)
        mod = torch.nn.AdaptiveAvgPool3d((5, 5, 5)).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

    # The tests are used to verify the functions raises errors for backward propagation
    # when output_size = 0, in adaptive_{avg, max}_pool and its variants.
    # These tests are explicitly written because ErrorInputs does not support backward calls
    # Issue: https://github.com/pytorch/pytorch/issues/78868
    @onlyNativeDeviceTypes
    @dtypes(torch.float32, torch.float64)
    @dtypesIfCUDA(torch.float32, torch.float64, torch.bfloat16, torch.float16)
    def test_adaptive_pooling_empty_output_size(self, dtype, device):
        error_msg = (
            "Expected grad_output to have non-zero size for non-batch dimensions"
        )

        make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=True)
        input = make_arg((1, 64, 10, 9))
        output_size = 0

        fns = (
            nn.functional.adaptive_avg_pool2d,
            nn.functional.adaptive_avg_pool3d,
            nn.functional.adaptive_max_pool2d,
            nn.functional.adaptive_max_pool3d,
        )

        for fn in fns:
            with self.assertRaisesRegex(RuntimeError, error_msg):
                fn(input, output_size).sum().backward()

        fns2 = (
            nn.functional.adaptive_avg_pool1d,
            nn.functional.adaptive_max_pool1d,
        )
        input2 = make_arg((1, 64))

        for fn in fns2:
            with self.assertRaisesRegex(RuntimeError, error_msg):
                fn(input2, output_size).sum().backward()

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool2d_zero_batch(self, device):
        mod = nn.FractionalMaxPool2d(3, output_ratio=(0.5, 0.5))
        inp = torch.ones(0, 16, 50, 32, device=device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected input"):
            inp = torch.randn(1, 0, 50, 32, device=device)
            mod(inp)

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool3d_zero_batch(self, device):
        mod = nn.FractionalMaxPool3d(3, output_ratio=(0.5, 0.5, 0.5)).to(device)
        inp = torch.ones(0, 16, 50, 32, 32, device=device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected input"):
            inp = torch.randn(1, 0, 50, 32, 32, device=device)
            mod(inp)

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool2d_zero_out_size(self, device):
        mod = nn.FractionalMaxPool2d([2, 2], output_size=[0, 1])
        inp = torch.rand([16, 50, 32, 32], device=device)
        out = mod(inp)
        self.assertEqual(out, torch.empty((16, 50, 0, 1), device=device))

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool3d_zero_out_size(self, device):
        mod = nn.FractionalMaxPool3d([3, 2, 2], output_size=[0, 1, 1])
        inp = torch.rand([16, 50, 32, 32], device=device)
        out = mod(inp)
        self.assertEqual(out, torch.empty((16, 0, 1, 1), device=device))

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool2d_zero_samples(self, device):
        samples = torch.rand([0, 16, 2], device=device)
        mod = nn.FractionalMaxPool2d(
            [2, 2], output_size=[1, 1], _random_samples=samples
        )
        inp = torch.randn([0, 16, 32, 32], device=device)
        out = mod(inp)
        self.assertEqual(out, torch.empty((0, 16, 1, 1), device=device))

        inp1 = torch.randn([1, 16, 32, 32], device=device)
        with self.assertRaisesRegex(RuntimeError, "Expect _random_samples"):
            out1 = mod(inp1)

    @onlyNativeDeviceTypes
    def test_FractionalMaxPool3d_zero_samples(self, device):
        samples = torch.rand([0, 16, 3], device=device)
        mod = nn.FractionalMaxPool3d(
            [3, 2, 2], output_size=[1, 1, 1], _random_samples=samples
        )
        inp = torch.randn([0, 16, 50, 32, 32], device=device)
        out = mod(inp)
        self.assertEqual(out, torch.empty((0, 16, 1, 1, 1), device=device))

        inp1 = torch.randn([1, 16, 50, 32, 32], device=device)
        with self.assertRaisesRegex(RuntimeError, "Expect _random_samples"):
            out1 = mod(inp1)

    @onlyNativeDeviceTypes
    def test_MaxPool_zero_batch_dim(self, device):
        inp = torch.randn(0, 16, 50, device=device)
        mod = torch.nn.MaxPool1d(3, stride=2).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        # 1D is supposed to be okay with 0 numel() inputs so dont test
        # error raising for that case.

        inp = torch.randn(0, 16, 50, 32, device=device)
        mod = torch.nn.MaxPool2d(3, stride=2).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected"):
            inp = torch.randn(1, 0, 50, 32, device=device)
            mod(inp)

        inp = torch.ones(0, 16, 50, 44, 31, device=device)
        mod = torch.nn.MaxPool3d(3, stride=2).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected"):
            inp = torch.ones(1, 0, 50, 44, 31, device=device)
            mod(inp)

    @onlyNativeDeviceTypes
    def test_MaxUnpool_zero_batch_dim(self, device):
        pool = torch.nn.MaxPool1d(2, stride=2, return_indices=True).to(device)
        unpool = torch.nn.MaxUnpool1d(2, stride=2).to(device)
        inp = torch.randn(0, 10, 10, requires_grad=True, device=device)
        output, indices = pool(inp)
        output.requires_grad_(True)
        unpool_out = unpool(output, indices)
        unpool_out.sum().backward()

        self.assertEqual(inp.grad, torch.zeros_like(inp))
        self.assertEqual(unpool_out, torch.zeros_like(unpool_out))

        pool = torch.nn.MaxPool2d(2, stride=2, return_indices=True).to(device)
        unpool = torch.nn.MaxUnpool2d(2, stride=2).to(device)
        inp = torch.randn(0, 10, 10, 10, requires_grad=True, device=device)
        output, indices = pool(inp)
        unpool_out = unpool(output, indices)
        unpool_out.sum().backward()

        self.assertEqual(inp.grad, torch.zeros_like(inp))
        self.assertEqual(unpool_out, torch.zeros_like(unpool_out))

        pool = torch.nn.MaxPool3d(2, stride=2, return_indices=True).to(device)
        unpool = torch.nn.MaxUnpool3d(2, stride=2).to(device)
        inp = torch.randn(0, 10, 10, 10, 10, requires_grad=True, device=device)
        output, indices = pool(inp)
        output.requires_grad_(True)
        unpool_out = unpool(output, indices)
        unpool_out.sum().backward()

        self.assertEqual(inp.grad, torch.zeros_like(inp))
        self.assertEqual(unpool_out, torch.zeros_like(unpool_out))

    @slowTest
    @onlyNativeDeviceTypes
    @skipCUDAIfRocm
    @parametrize_test(
        "module_name,module_size,output_size,test_index,should_error",
        [
            # Some tests are failing in trunk https://github.com/pytorch/pytorch/issues/103854
            subtest(
                ("MaxUnpool2d", (2, 2), (1, 3, 4, 5), -1, True),
                name="case1",
            ),
            subtest(
                ("MaxUnpool2d", (2, 2), (1, 3, 4, 5), 2 * 2 * 4 * 5, True),
                name="case2",
            ),
            subtest(
                ("MaxUnpool2d", (2, 2), (1, 3, 4, 5), (2 * 2 * 4 * 5) - 1, False),
                name="case3",
            ),
            subtest(
                ("MaxUnpool2d", (2, 3), (2, 1, 4, 2), 2 * 3 * 4 * 2, True),
                name="case4",
            ),
            subtest(
                ("MaxUnpool2d", (2, 3), (2, 1, 4, 2), (2 * 3 * 4 * 2) - 1, False),
                name="case5",
            ),
            subtest(
                ("MaxUnpool3d", (2, 2, 2), (1, 3, 4, 5), -1, True),
                name="case6",
            ),
            subtest(
                ("MaxUnpool3d", (2, 2, 2), (1, 3, 4, 5), 2 * 2 * 2 * 3 * 4 * 5, True),
                name="case7",
            ),
            subtest(
                (
                    "MaxUnpool3d",
                    (2, 2, 2),
                    (1, 3, 4, 5),
                    (2 * 2 * 2 * 3 * 4 * 5) - 1,
                    False,
                ),
                name="case8",
            ),
            subtest(
                ("MaxUnpool3d", (2, 2, 2), (2, 3, 4, 1), 2 * 2 * 2 * 3 * 4 * 1, True),
                name="case9",
            ),
            subtest(
                (
                    "MaxUnpool3d",
                    (2, 2, 2),
                    (2, 3, 4, 1),
                    (2 * 2 * 2 * 3 * 4 * 1) - 1,
                    False,
                ),
                name="case10",
            ),
        ],
    )
    def test_MaxUnpool_index_errors(
        self, device, module_name, module_size, output_size, test_index, should_error
    ):
        # NOTE: CUDA tests need to be run in a subprocess because they cause device asserts
        if torch.device(device).type == "cuda":
            error_msgs = {
                "MaxUnpool2d": r"Assertion `maxind >= 0 && maxind < outputImageSize` failed",
                "MaxUnpool3d": r"Assertion `index >= 0 && index < outputImageSize` failed",
            }

            script = f"""
import torch
unpool = torch.nn.{module_name}({module_size}).to('{device}')
output = torch.rand({output_size}, dtype=torch.float32, device='{device}')
indices = torch.zeros({output_size}, dtype=torch.int64, device='{device}')
indices.flatten()[0] = {test_index}
unpool(output, indices)
torch.cuda.synchronize()
"""
            p = subprocess.run(
                [sys.executable, "-c", script],
                cwd=os.path.dirname(os.path.realpath(__file__)),
                capture_output=True,
                text=True,
            )

            output = p.stdout + "\n" + p.stderr

            error_msg = error_msgs[module_name]

            if should_error:
                self.assertIn(error_msg, output, "The expected error was not found")
            else:
                self.assertNotIn("Error", output, "Should not have produced an error")
        else:
            module_class = getattr(torch.nn, module_name)
            unpool = module_class(module_size).to(device)
            output = torch.rand(output_size, dtype=torch.float32, device=device)
            indices = torch.zeros(output_size, dtype=torch.int64, device=device)
            indices.flatten()[0] = test_index

            if should_error:
                with self.assertRaisesRegex(
                    RuntimeError, r"Found an invalid max index:"
                ):
                    unpool(output, indices)
            else:
                unpool(output, indices)

    @onlyNativeDeviceTypes
    def test_AdaptiveMaxPool_zero_batch_dim(self, device):
        inp = torch.randn(0, 16, 50, device=device)
        mod = torch.nn.AdaptiveMaxPool1d(3).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected"):
            inp = torch.randn(1, 0, 50, device=device)
            mod(inp)

        inp = torch.randn(0, 16, 50, 32, device=device)
        mod = torch.nn.AdaptiveMaxPool2d(3).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected"):
            inp = torch.randn(1, 0, 50, 32, device=device)
            mod(inp)

        inp = torch.ones(0, 16, 50, 44, 31, device=device)
        mod = torch.nn.AdaptiveMaxPool3d(3).to(device)
        _test_module_empty_input(self, mod, inp, check_size=False)

        with self.assertRaisesRegex(RuntimeError, "Expected"):
            inp = torch.ones(1, 0, 50, 44, 31, device=device)
            mod(inp)

    @onlyNativeDeviceTypes
    def test_AvgPool2d_empty(self, device):
        avgpool = torch.nn.AvgPool2d(3, stride=2).to(device)
        inp = torch.randn(0, 16, 20, 32, device=device)
        _test_module_empty_input(self, avgpool, inp, check_size=False)

        clast_inp = torch.randn(0, 16, 20, 32, device=device).contiguous(
            memory_format=torch.channels_last
        )
        _test_module_empty_input(self, avgpool, clast_inp, check_size=False)

        # test with empty non-batch input
        with self.assertRaisesRegex(RuntimeError, "3D or 4D"):
            inp = torch.randn(16, 0, 20, 32, device=device)
            avgpool(inp)

    def test_pooling_shape(self, device):
        """Test the output shape calculation for pooling functions"""

        # Checks output shape against expected for 1D, 2D and 3D
        def check(expected_out_shape, sizes, *args, **kwargs):
            for kernel in ["max", "avg"]:
                for i in [1, 2, 3]:
                    if hasattr(torch.nn.functional, f"{kernel}_pool{i}d"):
                        op = getattr(torch.nn.functional, f"{kernel}_pool{i}d")
                        t = torch.randn(sizes[: i + 2], device=device)
                        self.assertEqual(
                            op(t, *args, **kwargs).shape, expected_out_shape[: i + 2]
                        )

        check(
            (1, 1, 3, 3, 4),
            (1, 1, 5, 6, 7),
            kernel_size=1,
            stride=2,
            padding=0,
            ceil_mode=True,
        )
        check(
            (1, 1, 2, 3, 3),
            (1, 1, 3, 4, 5),
            kernel_size=2,
            stride=2,
            padding=1,
            ceil_mode=False,
        )
        check(
            (1, 1, 2, 3, 3),
            (1, 1, 3, 4, 5),
            kernel_size=2,
            stride=2,
            padding=1,
            ceil_mode=True,
        )

        # Test case from issue https://github.com/pytorch/pytorch/issues/45357
        x = torch.randn(1, 1, 6, 7, device=device)
        y = torch.nn.functional.max_pool2d(
            x, 1, stride=(2, 2), padding=0, ceil_mode=True
        )
        self.assertEqual(y.size(), (1, 1, 3, 4))

    @onlyNativeDeviceTypes  # TODO: fix on XLA
    def test_adaptive_avg_pool2d_output_size_one(self, device):
        def helper(size, memory_format):
            x = torch.randint(
                1, 10, size, dtype=torch.float, device=device, requires_grad=True
            )
            if memory_format == "non_contiguous":
                x = x[::2, ::2, ::2, ::2]
            else:
                x = x.to(memory_format=memory_format)

            net = torch.nn.AdaptiveAvgPool2d((1, 1))
            out = net(x)
            ref_out = x.contiguous().mean((-1, -2)).view((x.size(0), x.size(1), 1, 1))

            out.sum().backward()  # make sure it doesn't crash

            self.assertEqual(out, ref_out)
            if memory_format == torch.channels_last:
                self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
                c = out.size(1)
                self.assertEqual(out.stride(), [c, 1, c, c])
            else:
                self.assertTrue(out.is_contiguous())
                c = out.size(1)
                self.assertEqual(out.stride(), [c, 1, 1, 1])

        for mf in (torch.contiguous_format, torch.channels_last, "non_contiguous"):
            helper((2, 3, 6, 6), mf)

    @onlyNativeDeviceTypes
    def test_adaptive_avg_pool3d_output_size_one(self, device):
        x = torch.randn(
            (2, 3, 6, 6, 6), dtype=torch.float, device=device, requires_grad=True
        )

        net = torch.nn.AdaptiveAvgPool3d(1)
        out = net(x)
        ref_out = x.contiguous().mean((-1, -2, -3)).view(out.shape)

        out.sum().backward()  # make sure it doesn't crash

        self.assertEqual(out, ref_out)
        self.assertTrue(out.is_contiguous())
        c = out.size(1)
        self.assertEqual(out.stride(), [c, 1, 1, 1, 1])

    @expectedFailureMeta  # Runtime Error not raised for meta
    @onlyNativeDeviceTypes
    @dtypes(torch.uint8, torch.int8, torch.short, torch.int, torch.long)
    def test_adaptive_pooling_no_suppot_input(self, device, dtype):
        for numel in (2, 3):
            for pool_type in ("Max", "Avg"):
                cls_name = f"Adaptive{pool_type}Pool{numel}d"
                module_cls = getattr(nn, cls_name)
                output_size = (2,) * numel
                module = module_cls(output_size)
                input = torch.randn((4,) * (numel + 1), device=device).to(dtype)
                with self.assertRaisesRegex(RuntimeError, "not implemented"):
                    output = module(input)

    @onlyNativeDeviceTypes
    @gcIfJetson
    @dtypes(torch.float, torch.double)
    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    def test_avg_pool2d_nhwc(self, device, dtype):
        def helper(
            n,
            c,
            h,
            w,
            kernel_size,
            stride=None,
            count_include_pad=True,
            divisor_override=None,
            padding=0,
        ):
            if stride is None:
                stride = kernel_size
            input = torch.randn(n, c, h, w, dtype=dtype, device=device)
            input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
            grad = torch.randn(
                n,
                c,
                (h - kernel_size) // stride + 1,
                (w - kernel_size) // stride + 1,
                dtype=dtype,
                device=device,
            )
            pool = torch.nn.AvgPool2d(
                kernel_size,
                stride=stride,
                count_include_pad=count_include_pad,
                divisor_override=divisor_override,
            ).to(device)

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.AvgPool2d(
                kernel_size,
                stride=stride,
                count_include_pad=count_include_pad,
                divisor_override=divisor_override,
            ).to(device)

            out = pool(input)
            out.backward(grad)
            ref_out = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(input.grad, ref_input.grad)

        helper(4, 8, 8, 8, 3)
        helper(4, 8, 8, 8, 3, count_include_pad=False, padding=1)
        helper(4, 8, 8, 8, 3, count_include_pad=False, padding=2, stride=2)
        helper(4, 8, 8, 8, 3, divisor_override=42)
        helper(4, 8, 8, 8, 7)
        # ROCm 16GB MI25 hits OOM error. Clear caching allocator prior to running large subtest.
        if TEST_WITH_ROCM and "cuda" in device:
            torch.cuda.empty_cache()
        helper(200, 512, 28, 28, 2)
        helper(4, 8, 7, 7, 3, stride=1)
        helper(4, 8, 7, 7, 3, padding=2, stride=1)
        helper(10, 512, 31, 31, 3, stride=2)
        helper(1, 129, 8, 8, 3, stride=2)

    @onlyCPU
    @dtypes(torch.float, torch.double)
    def test_max_pool1d_corner_cases(self, device, dtype):
        def check(x, args, expected):
            model = torch.nn.MaxPool1d(*args)
            if isinstance(x, list):
                x = torch.tensor(x, device=device, dtype=dtype)
                expected = torch.tensor(expected, device=device, dtype=dtype)
            self.assertEqual(model(x), expected)

        # Pooling args: (kernel_size, stride, padding, dilation, return_indices, ceil_mode)
        check([[1]], (1, None, 0, 1, False, False), [[1]])
        check([[1]], (2, None, 1, 2, False, False), [[float("-inf")]])
        check(
            [[1], [1]],
            (2, None, 1, 2, False, False),
            [[float("-inf")], [float("-inf")]],
        )
        check([[1, 2]], (2, 1, 1, 2, False, False), [[2, 1]])
        check([[1, 2]], (2, 2, 1, 2, False, True), [[2, 2]])

    @onlyCPU
    @dtypes(torch.float, torch.double)
    @skipIfTorchDynamo("OOMs https://github.com/pytorch/pytorch/issues/111320")
    def test_max_pool1d(self, device, dtype):
        # FIXME For now compare against max_pool1d with indices
        def check(x, *args, **kwargs):
            model = torch.nn.MaxPool1d(*args, **kwargs)
            ref_model = torch.nn.MaxPool1d(*args, **kwargs, return_indices=True)
            self.assertEqual(model(x), ref_model(x)[0])

        sizes = [random.sample(range(8, 128), 3) for _ in range(3)]
        kernel_sizes = random.sample(range(1, 5), 3)
        strides = random.sample(range(1, 5), 3)
        dilations = random.sample(range(1, 5), 3)
        ceil_modes = [True, False]

        for size, kernel_size, stride, dilation, ceil_mode in itertools.product(
            sizes, kernel_sizes, strides, dilations, ceil_modes
        ):
            padding = random.sample(range(0, math.floor(kernel_size / 2) + 1), 1)
            check(
                torch.randn(size, device=device, dtype=dtype),
                kernel_size,
                stride,
                padding,
                dilation,
                ceil_mode=ceil_mode,
            )

        # Non-contiguous test
        tensor = torch.randn(5, 151, 33, device=device, dtype=dtype)[::2, ::3, ::2]
        check(tensor, 3, 2, 1, 2, ceil_mode=True)
        check(tensor.transpose(1, 2), 3, 2, 1, 2, ceil_mode=True)

    @onlyCUDA
    @gcIfJetson
    def test_max_pool2d(self, device):
        def helper(n, c, h, w, ks):
            x = torch.randn(
                n, c, h, w, device="cuda", dtype=torch.float, requires_grad=True
            )
            ref_x = x.detach().clone().cpu().requires_grad_()

            pool = torch.nn.MaxPool2d(kernel_size=ks)

            y = pool(x)
            ref_y = pool(ref_x)

            y.sum().backward()
            ref_y.sum().backward()

            self.assertEqual(y, ref_y)
            self.assertEqual(x.grad, ref_x.grad)

        helper(2, 8, 4, 4, ks=2)
        helper(1, 100000, 32, 32, ks=4)
        helper(1, 100000, 1, 4, ks=(1, 4))  # test for max_pool1d

    @onlyNativeDeviceTypes
    @dtypes(torch.half, torch.bfloat16, torch.float, torch.double)
    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @gcIfJetson
    def test_max_pool2d_nhwc(self, device, dtype):
        def helper(n, c, h, w, kernel_size, stride=None):
            if stride is None:
                stride = kernel_size
            input = torch.randn(n, c, h, w, dtype=dtype, device=device)
            input = input.contiguous(memory_format=torch.channels_last).requires_grad_()
            grad = torch.randn(
                n,
                c,
                (h - kernel_size) // stride + 1,
                (w - kernel_size) // stride + 1,
                dtype=dtype,
                device=device,
            )
            pool = torch.nn.MaxPool2d(kernel_size, stride, return_indices=True).to(
                device
            )

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.MaxPool2d(kernel_size, stride, return_indices=True).to(
                device
            )

            out, ind = pool(input)
            out.backward(grad)
            ref_out, ref_ind = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertTrue(ind.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_ind.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(ind, ref_ind)
            self.assertEqual(input.grad, ref_input.grad)

        helper(4, 8, 8, 8, 7)
        helper(200, 512, 28, 28, 2)
        helper(4, 8, 7, 7, 3, stride=1)
        helper(10, 512, 31, 31, 3, stride=2)
        helper(1, 129, 8, 8, 3, stride=2)

    @onlyCPU
    @dtypes(torch.int32, torch.int64)
    def test_max_pool2d_corner_cases(self, device, dtype):
        def check(x, args, expected, memory_format):
            model = torch.nn.MaxPool2d(*args)
            if isinstance(x, list):
                x = torch.tensor(x, device=device, dtype=dtype).to(
                    memory_format=memory_format
                )
                expected = torch.tensor(expected, device=device, dtype=dtype).to(
                    memory_format=memory_format
                )
            self.assertEqual(model(x), expected)

        # Pooling args: (kernel_size, stride, padding, dilation, return_indices, ceil_mode)
        check(
            [[[[-1, -2], [-3, -4]]]],
            (2, 2, 1, 2, False, True),
            [[[[-4, -4], [-4, -4]]]],
            torch.contiguous_format,
        )
        check(
            [[[[-1, -2], [-3, -4]]]],
            (2, 2, 1, 2, False, True),
            [[[[-4, -4], [-4, -4]]]],
            torch.channels_last,
        )

    @onlyNativeDeviceTypes
    @dtypes(torch.half, torch.bfloat16, torch.float, torch.double)
    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @gcIfJetson
    def test_max_pool3d_ndhwc(self, device, dtype):
        def helper(n, c, h, w, d, kernel_size, stride=None):
            batch = n
            if not batch:
                batch = 1
            input = torch.randn(batch, c, d, h, w, dtype=dtype, device=device)
            input = input.contiguous(
                memory_format=torch.channels_last_3d
            ).requires_grad_()
            if not n:
                input = input.squeeze(0).detach().clone().requires_grad_()
            if isinstance(kernel_size, int):
                kernel_size = [kernel_size] * 3
            if stride is None:
                stride = kernel_size
            elif isinstance(stride, int):
                stride = [stride] * 3
            grad = torch.randn(
                batch,
                c,
                (d - kernel_size[0]) // stride[0] + 1,
                (h - kernel_size[1]) // stride[1] + 1,
                (w - kernel_size[2]) // stride[2] + 1,
                dtype=dtype,
                device=device,
            )
            grad = grad.contiguous(memory_format=torch.channels_last_3d)
            if not n:
                grad = grad.squeeze(0)
            pool = torch.nn.MaxPool3d(kernel_size, stride, return_indices=True).to(
                device
            )

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.MaxPool3d(kernel_size, stride, return_indices=True).to(
                device
            )
            out, ind = pool(input)
            out.backward(grad)
            ref_out, ref_ind = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            if len(out.shape) == 4:
                self.assertTrue(
                    out.unsqueeze(0).is_contiguous(memory_format=torch.channels_last_3d)
                )
            else:
                self.assertTrue(out.is_contiguous(memory_format=torch.channels_last_3d))
            self.assertTrue(ref_out.is_contiguous())
            if len(ind.shape) == 4:
                self.assertTrue(
                    ind.unsqueeze(0).is_contiguous(memory_format=torch.channels_last_3d)
                )
            else:
                self.assertTrue(ind.is_contiguous(memory_format=torch.channels_last_3d))
            self.assertTrue(ref_ind.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(ind, ref_ind)
            if dtype == torch.half:
                self.assertEqual(input.grad, ref_input.grad, atol=0.05, rtol=0.01)
            else:
                self.assertEqual(input.grad, ref_input.grad)

        helper(4, 8, 8, 8, 8, 7)
        helper(4, 8, 8, 8, 8, (5, 6, 7))
        helper(1, 8, 8, 8, 8, (5, 6, 7))
        helper(0, 6, 12, 13, 14, (5, 6, 7))
        helper(4, 8, 7, 7, 7, 3, stride=1)
        helper(10, 128, 19, 19, 19, 3, stride=2)
        helper(10, 128, 19, 19, 19, (1, 2, 3), stride=2)
        helper(1, 128, 19, 19, 19, (1, 2, 3), stride=2)
        helper(0, 128, 19, 19, 19, (1, 2, 3), stride=2)
        helper(1, 79, 4, 4, 4, 3, stride=2)
        helper(0, 79, 4, 4, 4, 3, stride=2)

    @onlyCPU
    @dtypes(torch.half, torch.bfloat16)
    def test_max_pool_bfloat16_half(self, device, dtype):
        def helper(shape, kernel_size, stride, memory_format, dtype):
            input = torch.randn(shape, dtype=dtype, device=device)
            input = input.to(memory_format=memory_format).requires_grad_()
            if len(shape) == 4:
                pool = torch.nn.MaxPool2d(kernel_size, stride, return_indices=True).to(
                    device
                )
            else:
                pool = torch.nn.MaxPool3d(kernel_size, stride, return_indices=True).to(
                    device
                )

            input2 = input.detach().clone().float().requires_grad_(True)

            out, ind = pool(input)
            out.sum().backward()
            out2, ind2 = pool(input2)
            out2.sum().backward()

            self.assertTrue(out.is_contiguous(memory_format=memory_format))
            self.assertEqual(out.dtype, dtype)
            self.assertEqual(input.grad.dtype, dtype)
            self.assertEqual(out, out2.to(dtype=dtype))
            self.assertEqual(ind, ind2)
            self.assertEqual(input.grad, input2.grad.to(dtype=dtype))

        helper((4, 30, 8, 8), 7, 1, torch.contiguous_format, dtype)
        helper((4, 65, 8, 8), 7, 1, torch.channels_last, dtype)
        helper((1, 19, 20, 10), 8, 2, torch.contiguous_format, dtype)
        helper((1, 19, 20, 10), 8, 2, torch.channels_last, dtype)
        helper((4, 30, 8, 8), 7, 1, torch.contiguous_format, dtype)
        helper((4, 65, 8, 8), 7, 1, torch.channels_last, dtype)
        helper((1, 19, 10, 10, 10), 8, 2, torch.contiguous_format, dtype)
        helper((1, 19, 10, 9, 14), 8, 2, torch.channels_last_3d, dtype)
        helper((4, 10, 3, 8, 8), 3, 1, torch.contiguous_format, dtype)
        helper((4, 10, 8, 8, 8), 7, 1, torch.channels_last_3d, dtype)

    @onlyCUDA
    @gcIfJetson
    def test_max_pool2d_indices(self, device):
        def helper(n, c, h, w, ks):
            if n is None:
                x = torch.randn(
                    c, h, w, device="cuda", dtype=torch.float, requires_grad=True
                )
            else:
                x = torch.randn(
                    n, c, h, w, device="cuda", dtype=torch.float, requires_grad=True
                )

            ref_x = x.detach().clone().cpu().requires_grad_()

            pool = torch.nn.MaxPool2d(kernel_size=ks, return_indices=True)

            y, idx = pool(x)
            ref_y, ref_idx = pool(ref_x)

            y.sum().backward()
            ref_y.sum().backward()

            self.assertEqual(y, ref_y)
            self.assertEqual(
                idx, ref_idx
            )  # assertEqual implicitly compares shape for tensors
            self.assertEqual(x.grad, ref_x.grad)

        helper(2, 8, 4, 4, ks=2)
        helper(None, 3, 50, 50, ks=5)

    @onlyCPU
    @dtypes(torch.half, torch.bfloat16)
    def test_avg_pool2d_reduced_floating(self, device, dtype):
        def helper(n, c, h, w, kernel_size, stride, memory_format):
            input = torch.randn(n, c, h, w, dtype=torch.float32, device=device).to(
                dtype=dtype
            )
            input = input.to(memory_format=memory_format).requires_grad_()
            pool = torch.nn.AvgPool2d(kernel_size, stride).to(device)

            input2 = input.detach().clone().float().requires_grad_(True)

            out = pool(input)
            out.sum().backward()
            out2 = pool(input2)
            out2.sum().backward()

            self.assertTrue(out.is_contiguous(memory_format=memory_format))
            self.assertEqual(out.dtype, dtype)
            self.assertEqual(input.grad.dtype, dtype)
            self.assertEqual(out, out2.to(dtype=dtype))
            self.assertEqual(input.grad, input2.grad.to(dtype=dtype))

        helper(4, 30, 8, 8, 7, 1, torch.contiguous_format)
        helper(4, 65, 8, 8, 7, 1, torch.channels_last)
        helper(1, 19, 20, 10, 8, 2, torch.contiguous_format)
        helper(1, 19, 20, 10, 8, 2, torch.channels_last)

    @dtypes(torch.float, torch.double)
    def test_adaptive_pooling_max_nhwc(self, device, dtype):
        def helper(input_size, output_plane_size, contig):
            n_plane_dims = len(output_plane_size)
            mod = (
                torch.nn.AdaptiveMaxPool2d
                if n_plane_dims == 2
                else torch.nn.AdaptiveMaxPool3d
            )
            channels_last = (
                torch.channels_last if n_plane_dims == 2 else torch.channels_last_3d
            )
            output_size = input_size[:2] + output_plane_size
            input = torch.randint(1, 10, input_size, device=device, dtype=dtype)
            input = input.contiguous(memory_format=channels_last)
            grad = torch.randint(1, 10, output_size, device=device, dtype=dtype)
            grad = grad.contiguous(memory_format=channels_last)
            if not contig:
                input = input[:, ::2]
                grad = grad[:, ::2]
            input.requires_grad_(True)
            pool = mod(output_plane_size, return_indices=True).to(device)

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = mod(output_plane_size, return_indices=True).to(device)

            out, ind = pool(input)
            out.backward(grad)
            ref_out, ref_ind = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            # channels_last_3d case does not return channels_last_3d outputs
            if n_plane_dims == 2:
                self.assertTrue(out.is_contiguous(memory_format=channels_last))
                self.assertTrue(ind.is_contiguous(memory_format=channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertTrue(ref_ind.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(ind, ref_ind)
            self.assertEqual(input.grad, ref_input.grad)

        for contig in [True, False]:
            helper((4, 8, 10, 10), (7, 7), contig)
            helper((4, 8, 9, 14), (5, 8), contig)
            helper((4, 8, 11, 11), (1, 1), contig)
            helper((2, 1, 3, 3), (1, 1), contig)
            helper((4, 8, 10, 10, 10), (7, 7, 7), contig)
            helper((4, 8, 11, 11, 11), (1, 1, 1), contig)
            helper((2, 1, 3, 3, 3), (1, 1, 1), contig)

    @dtypes(torch.float, torch.double)
    def test_pooling_max_nhwc(self, device, dtype):
        def helper(n, c, h, w, kernel_size, stride, padding, dilation, contig, device):
            output_height = math.floor(
                (h + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1)
                / stride[0]
                + 1
            )
            output_width = math.floor(
                (w + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1)
                / stride[1]
                + 1
            )

            input = torch.randint(1, 10, (n, c, h, w), device=device, dtype=dtype)
            input = input.contiguous(memory_format=torch.channels_last)
            grad = torch.randint(
                1, 10, (n, c, output_height, output_width), device=device, dtype=dtype
            )
            grad = grad.contiguous(memory_format=torch.channels_last)
            if not contig:
                input = input[:, ::2, :, :]
                grad = grad[:, ::2, :, :]
            input.requires_grad_(True)
            pool = torch.nn.MaxPool2d(
                kernel_size,
                stride,
                padding,
                dilation,
                return_indices=True,
                ceil_mode=False,
            )

            ref_input = input.detach().clone().contiguous().requires_grad_(True)
            ref_grad = grad.detach().clone().contiguous()
            ref_pool = torch.nn.MaxPool2d(
                kernel_size,
                stride,
                padding,
                dilation,
                return_indices=True,
                ceil_mode=False,
            ).to(device)

            out, ind = pool(input)
            out.backward(grad)
            ref_out, ref_ind = ref_pool(ref_input)
            ref_out.backward(ref_grad)

            self.assertTrue(out.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_out.is_contiguous())
            self.assertTrue(ind.is_contiguous(memory_format=torch.channels_last))
            self.assertTrue(ref_ind.is_contiguous())
            self.assertEqual(out, ref_out)
            self.assertEqual(ind, ref_ind)
            self.assertEqual(input.grad, ref_input.grad)

        for contig in [True, False]:
            helper(4, 8, 10, 10, (2, 2), (1, 1), (1, 1), (2, 2), contig, device)
            helper(4, 8, 9, 14, (2, 2), (1, 1), (1, 1), (2, 2), contig, device)
            helper(4, 8, 11, 11, (4, 4), (2, 2), (2, 2), (2, 2), contig, device)

    @onlyCUDA
    def test_pool3d_size_one_feature_dim(self, device):
        # Tests crazy strides for feature dim of size 1
        x = torch.randn(7, 1, 5, 3, 2, device=device)
        strange_strides = [30, 1234, 6, 2, 1]
        y = x.as_strided(x.size(), strange_strides)
        x = x.cpu().as_strided(x.size(), strange_strides)

        to_test = {
            "max_pool3d": lambda t: F.max_pool3d(t, (5, 1, 1), stride=(5, 1, 1)),
            "avg_pool3d": lambda t: F.avg_pool3d(t, (5, 1, 1), stride=(5, 1, 1)),
        }

        for test, fn in to_test.items():
            # Should not crash
            out_y = fn(y)
            out_x = fn(x)
            self.assertEqual(out_y, out_x.to(device), msg=test)

    @onlyCUDA
    @largeTensorTest("18GB")
    @largeTensorTest("180GB", "cpu")
    def test_pool3d_large_size_int64(self, device):
        # See https://github.com/pytorch/pytorch/issues/52822
        x = torch.randn(
            70, 32, 100, 100, 100, dtype=torch.half, device=device, requires_grad=True
        )
        y = torch.nn.functional.max_pool3d(x, 5)
        g = torch.randn_like(y, dtype=torch.half)
        torch.cuda.synchronize()
        y.backward(g)
        torch.cuda.synchronize()

        ref_x = x.detach().cpu().float()  # max_pool3d_cpu is not implemented for half
        ref_x.requires_grad = True
        ref_g = g.cpu().float()
        ref_y = torch.nn.functional.max_pool3d(ref_x, 5)
        ref_y.backward(ref_g)

        self.assertEqual(y, ref_y, exact_dtype=False)
        self.assertEqual(x.grad, ref_x.grad, exact_dtype=False)

    @onlyCUDA
    def test_AvgPool3d_backward_after_cat_dim1_device(self, device):
        # x has to have batch_size 1 to test contiguous checks
        x = torch.randn(1, 3, 4, 4, 4, device=device, requires_grad=True)
        y = F.avg_pool3d(x, kernel_size=3, padding=1, stride=2)

        grad = torch.randn(y.size(), device=device)
        # increase the stride in dimension 0. the tensor is still contiguous because size[0] is 1
        stride = list(grad.stride())
        stride[0] = stride[0] * 2
        grad.set_(grad.storage(), 0, grad.size(), stride)
        assert grad.is_contiguous()

        y.backward(grad)

    def _test_maxpool_indices(
        self, num_dim, adaptive=False, device="cpu", dtype=torch.float
    ):
        def expected_indices(dim, dtype):
            if dim == 1:
                return torch.tensor([1, 3], dtype=dtype).repeat(2, 2, 1)
            if dim == 2:
                return torch.tensor([[5, 7], [13, 15]], dtype=dtype).repeat(2, 2, 1, 1)

        def expected_grad(dim, dtype):
            if dim == 1:
                return torch.tensor([0, 1, 0, 1], dtype=dtype).repeat(2, 2, 1)
            grad = expected_grad(dim - 1, dtype=dtype)
            zero = torch.zeros(grad.size(), dtype=dtype)
            return torch.stack((zero, grad, zero, grad), 2)

        def expected_output(dim, dtype):
            if dim == 1:
                return torch.arange(2, 17, 2, dtype=dtype).view(2, 2, 2)
            if dim == 2:
                col = torch.arange(6, 63, 8, dtype=dtype)
                return torch.stack([col, col + 2], 1).view(2, 2, 2, 2)

        if adaptive:
            cls_name = "AdaptiveMaxPool{}d".format(num_dim)  # noqa: UP032
        else:
            # FIXME(#105716): Test fails when using f-string
            cls_name = "MaxPool{}d".format(num_dim)  # noqa: UP032
        module_cls = getattr(nn, cls_name)
        module = module_cls(2, return_indices=True).to(device, dtype=dtype)
        numel = 4 ** (num_dim + 1)
        input = (
            torch.arange(1, numel + 1)
            .view(2, 2, *repeat(4, num_dim))
            .to(device, dtype=dtype)
        )
        input_var = input.clone().detach().requires_grad_()

        # Check forward
        output, indices = module(input_var)
        if num_dim != 3:
            expected_indices = expected_indices(num_dim, dtype=indices.data.dtype)
            expected_output = expected_output(num_dim, dtype=output.data.dtype)
            self.assertEqual(indices.dim(), input.dim())
            self.assertEqual(indices.data.squeeze(), expected_indices)
            self.assertEqual(output.data.squeeze(), expected_output)
        self.assertTrue(output.requires_grad)
        self.assertFalse(indices.requires_grad)

        # Make sure backward works
        grad_output = torch.ones(output.size(), device=device, dtype=dtype)
        output.backward(grad_output, retain_graph=True)
        expected_grad = expected_grad(num_dim, dtype=input_var.grad.data.dtype)
        self.assertEqual(input_var.grad.data, expected_grad.view_as(input))

        # Make sure backward after changing indices will result in an error
        indices.add_(1)
        self.assertRaises(RuntimeError, lambda: output.backward(grad_output))

        # Make sure -Infinity is handled correctly
        t = torch.tensor([[[float("-inf")]]])
        m = nn.MaxPool1d(kernel_size=1, return_indices=True)
        output, indices = m(t)
        self.assertEqual(output[0, 0, 0], float("-inf"))
        self.assertEqual(indices[0, 0, 0], 0)

        t = torch.tensor([[[float("-inf")]]])
        m = nn.MaxPool2d(kernel_size=1, return_indices=True)
        output, indices = m(t)
        self.assertEqual(output[0, 0, 0], float("-inf"))
        self.assertEqual(indices[0, 0, 0], 0)

        t = torch.tensor([[[[float("-inf")]]]])
        m = nn.MaxPool3d(kernel_size=1, return_indices=True)
        output, indices = m(t)
        self.assertEqual(output[0, 0, 0, 0], float("-inf"))
        self.assertEqual(indices[0, 0, 0, 0], 0)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @dtypes(torch.float)
    def test_MaxPool1d_indices(self, device, dtype):
        self._test_maxpool_indices(1, device=device, dtype=dtype)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @dtypes(torch.float)
    def test_MaxPool2d_indices(self, device, dtype):
        self._test_maxpool_indices(2, device=device, dtype=dtype)

    @skipIfMps
    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @dtypes(torch.float)
    def test_MaxPool3d_indices(self, device, dtype):
        self._test_maxpool_indices(3, device=device, dtype=dtype)

    @skipIfMps
    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @dtypes(torch.float)
    def test_AdaptiveMaxPool1d_indices(self, device, dtype):
        self._test_maxpool_indices(1, adaptive=True, device=device, dtype=dtype)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @skipIfMps
    @dtypes(torch.float)
    def test_AdaptiveMaxPool2d_indices(self, device, dtype):
        self._test_maxpool_indices(2, adaptive=True, device=device, dtype=dtype)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @skipIfMps
    @dtypes(torch.float)
    def test_AdaptiveMaxPool3d_indices(self, device, dtype):
        self._test_maxpool_indices(3, adaptive=True, device=device, dtype=dtype)

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @skipIfMps
    @dtypes(torch.float)
    def test_maxpool_indices_no_batch_dim(self, device, dtype):
        """Check that indices with no batch dim is consistent with a single batch."""
        max_pool_cases = [
            (
                nn.MaxPool1d(3, return_indices=True),
                torch.randn(3, 5, device=device, dtype=dtype),
            ),
            (
                nn.MaxPool2d(3, return_indices=True),
                torch.randn(3, 5, 6, device=device, dtype=dtype),
            ),
            (
                nn.MaxPool3d(3, return_indices=True),
                torch.randn(3, 5, 6, 7, device=device, dtype=dtype),
            ),
            (
                nn.AdaptiveMaxPool1d(3, return_indices=True),
                torch.randn(3, 5, device=device, dtype=dtype),
            ),
            (
                nn.AdaptiveMaxPool2d(3, return_indices=True),
                torch.randn(3, 5, 6, device=device, dtype=dtype),
            ),
            (
                nn.AdaptiveMaxPool3d(3, return_indices=True),
                torch.randn(3, 5, 6, 7, device=device, dtype=dtype),
            ),
        ]

        for module, input in max_pool_cases:
            _, indices_no_batch = module(input)
            _, indicies_single_batch = module(input.unsqueeze(0))
            self.assertEqual(indices_no_batch, indicies_single_batch.squeeze(0))

    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @dtypes(torch.float)
    @onlyNativeDeviceTypes  # TODO: Fails on XLA
    @gcIfJetson
    def test_max_pool_nan_inf(self, device, dtype):
        for adaptive in ["", "adaptive_"]:
            for num_dim in [1, 2, 3]:
                fn_name = f"{adaptive}max_pool{num_dim}d"
                fn = getattr(F, fn_name)

                x = torch.full(
                    [1, 1] + num_dim * [3],
                    nan,
                    device=device,
                    dtype=dtype,
                    requires_grad=True,
                )
                res = fn(x, 1 if adaptive else 3)
                res.backward(torch.randn_like(res))
                self.assertTrue(math.isnan(res.item()))
                x.requires_grad_(False)
                res = fn(x, 1 if adaptive else 3)
                self.assertTrue(math.isnan(res.item()))

                x2 = torch.full(
                    [1, 1] + num_dim * [3],
                    -inf,
                    device=device,
                    dtype=dtype,
                    requires_grad=True,
                )
                res2 = fn(x2, 1 if adaptive else 3)
                res2.backward(torch.randn_like(res2))
                self.assertTrue(math.isinf(res2.item()))
                x2.requires_grad_(False)
                res2 = fn(x2, 1 if adaptive else 3)
                self.assertTrue(math.isinf(res2.item()))

    @expectedFailureMeta  # RuntimeError: Unrecognized tensor type ID: Meta
    @onlyNativeDeviceTypes
    def test_fractional_max_pool2d(self, device):
        with set_default_dtype(torch.double):
            x = torch.randn(1, 2, 7, 7, requires_grad=True, device=device)
            samples = x.new(1, 2, 2).uniform_()

            def func(x):
                return F.fractional_max_pool2d(
                    x, (2, 2), output_size=(3, 3), _random_samples=samples
                )

            self.assertEqual(func(x).shape, (1, 2, 3, 3))
            gradcheck(func, [x])
            gradgradcheck(func, [x])

            x = torch.randn(2, 7, 7, requires_grad=True, device=device)
            self.assertEqual(func(x).shape, (2, 3, 3))
            if self.device_type != "cuda":
                # Reference: https://github.com/pytorch/pytorch/issues/52427
                # Raises -> RuntimeError: TensorAccessor expected 4 dims but tensor has 3
                # on CUDA in gradcheck
                gradcheck(func, [x])
                gradgradcheck(func, [x])

            for kernel_size in [(), (1,)]:
                with self.assertRaisesRegex(RuntimeError, "kernel_size must either"):
                    # Incorrect kernel_size
                    F.fractional_max_pool2d(
                        x,
                        kernel_size=kernel_size,
                        output_size=(3, 3),
                        _random_samples=samples,
                    )

            err_large_msg = "too large relative to input "
            err_out_size_msg = "output_size must either"
            for output_size, msg in [
                ((9, 3), err_large_msg + "height"),
                ((3, 9), err_large_msg + "width"),
                ((3,), err_out_size_msg),
                ((), err_out_size_msg),
            ]:
                with self.assertRaisesRegex(RuntimeError, msg):
                    # Incorrect output_size
                    F.fractional_max_pool2d(
                        x, (2, 2), output_size=output_size, _random_samples=samples
                    )

    @expectedFailureMeta  # RuntimeError: Unrecognized tensor type ID: Meta
    @onlyNativeDeviceTypes
    def test_fractional_max_pool3d(self, device):
        with set_default_dtype(torch.double):
            x = torch.randn(1, 2, 7, 7, 7, requires_grad=True, device=device)
            samples = x.new(1, 2, 3).uniform_()

            def func(x):
                return F.fractional_max_pool3d(
                    x, (2, 2, 2), output_size=(3, 3, 3), _random_samples=samples
                )

            self.assertEqual(func(x).shape, (1, 2, 3, 3, 3))
            gradcheck(func, [x])
            gradgradcheck(func, [x])

            x = torch.randn(2, 7, 7, 7, requires_grad=True, device=device)
            self.assertEqual(func(x).shape, (2, 3, 3, 3))
            gradcheck(func, [x])
            gradgradcheck(func, [x])

            for kernel_size in [(), (1,), (1, 1)]:
                with self.assertRaisesRegex(RuntimeError, "kernel_size must either"):
                    # Incorrect kernel_size
                    F.fractional_max_pool3d(
                        x,
                        kernel_size=kernel_size,
                        output_size=(3, 3, 3),
                        _random_samples=samples,
                    )

            err_large_msg = "too large relative to input "
            err_out_size_msg = "output_size must either"
            for output_size, msg in [
                ((9, 3, 3), err_large_msg + "time"),
                ((3, 9, 3), err_large_msg + "height"),
                ((3, 3, 9), err_large_msg + "width"),
                ((3, 3), err_out_size_msg),
                ((3,), err_out_size_msg),
                ((), err_out_size_msg),
            ]:
                with self.assertRaisesRegex(RuntimeError, msg):
                    # Incorrect output_size
                    F.fractional_max_pool3d(
                        x, (2, 2, 2), output_size=output_size, _random_samples=samples
                    )

    @dtypesIfCUDA(torch.half, torch.float, torch.double)
    @dtypes(torch.float)
    @onlyNativeDeviceTypes  # TODO: Fails on XLA
    def test_fractional_max_pool_nan_inf(self, device, dtype):
        for num_dim in [2, 3]:
            fn_name = f"FractionalMaxPool{num_dim}d"
            fn = getattr(nn, fn_name)(kernel_size=2, output_size=1)
            x = torch.full(
                [1, 1] + num_dim * [3],
                nan,
                device=device,
                dtype=dtype,
                requires_grad=True,
            )
            res = fn(x)
            res.backward(torch.randn_like(res))
            self.assertTrue(math.isnan(res.item()))

            x2 = torch.full(
                [1, 1] + num_dim * [3],
                -inf,
                device=device,
                dtype=dtype,
                requires_grad=True,
            )
            res2 = fn(x2)
            res2.backward(torch.randn_like(res2))
            self.assertTrue(math.isinf(res2.item()))

    @onlyNativeDeviceTypes  # TODO: RuntimeError message different on XLA
    def test_pooling_zero_stride(self, device):
        for op in ("max", "avg"):
            for num_dim in [1, 2, 3]:
                fn_name = f"{op}_pool{num_dim}d"
                fn = getattr(F, fn_name)
                x = torch.ones([1, 2] + num_dim * [4], device=device, dtype=torch.float)
                self.assertRaisesRegex(
                    RuntimeError,
                    r"stride should not be zero|stride must be greater than zero",
                    lambda: fn(x, kernel_size=2, stride=0),
                )

                fn_module_name = f"{op.title()}Pool{num_dim}d"
                fn_module = getattr(nn, fn_module_name)(kernel_size=2, stride=0)
                self.assertRaisesRegex(
                    RuntimeError,
                    r"stride should not be zero|stride must be greater than zero",
                    lambda: fn_module(x),
                )

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @skipIfMps
    @dtypes(torch.float)
    def test_pool_large_size(self, device, dtype):
        for op in ("max", "avg"):
            for num_dim in [1, 2, 3]:
                fn_name = f"{op}_pool{num_dim}d"
                fn = getattr(F, fn_name)
                # 16777217 is the smallest integer not expressible in float32
                x = torch.ones(
                    [1, 1, 16777217] + (num_dim - 1) * [1], device=device, dtype=dtype
                )
                res = fn(x, 1, stride=1, padding=0)
                # check if the output shape was still computed correctly
                self.assertEqual(x.shape[2], res.shape[2])

    @onlyCUDA
    @largeTensorTest("6GB")
    def test_pooling_large(self, device):
        def helper(pool):
            inp = torch.randn(
                2**7 + 10, 2**8, 2**8, 2**8, dtype=torch.half, device="cuda"
            )
            self.assertTrue(inp.numel() > 2**31 - 1)
            out = pool(inp)
            torch.cuda.synchronize()  # asserts test finishes normally without raising errors

        helper(nn.MaxPool2d(4, 4))
        helper(nn.AvgPool2d(4, 4))
        helper(nn.FractionalMaxPool2d(4, 4))
        helper(nn.AdaptiveMaxPool2d((2**6, 2**6)))
        helper(nn.AdaptiveAvgPool2d((2**6, 2**6)))

    @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
    @skipIfMps
    @dtypes(torch.float)
    def test_pool_invalid_size(self, device, dtype):
        for op in ("max", "avg"):
            for num_dim in [1, 2, 3]:
                fn_name = f"{op}_pool{num_dim}d"
                if op == "max":
                    # New implementation without indices supports empty tensors
                    # TODO(Heitor) change once with_indices code is updated
                    fn_name += "_with_indices"
                fn = getattr(F, fn_name)
                # use a configuration that gives zero outputs only
                # when doing a correct floor division by the stride
                x = torch.ones([1, 1] + num_dim * [4], device=device, dtype=dtype)
                with self.assertRaisesRegex(RuntimeError, r"too small|smaller than"):
                    try:
                        res = fn(x, 3, stride=2, padding=0, dilation=2)
                    except TypeError:
                        # some implementations do not support dilation
                        res = fn(x, 6, stride=2, padding=0)

    @onlyCUDA
    def test_pooling_bfloat16(self, device):
        _test_bfloat16_ops(
            self,
            torch.nn.AvgPool1d(3, stride=2),
            device,
            inp_dims=(8, 4, 16),
            prec=0.05,
        )
        _test_bfloat16_ops(
            self,
            torch.nn.AvgPool2d(3, stride=2),
            device,
            inp_dims=(8, 4, 16, 16),
            prec=0.05,
        )
        _test_bfloat16_ops(
            self,
            torch.nn.AvgPool3d(3, stride=2),
            device,
            inp_dims=(8, 4, 16, 16, 16),
            prec=0.05,
        )
        _test_bfloat16_ops(
            self, torch.nn.AdaptiveAvgPool1d(3), device, inp_dims=(8, 4, 16), prec=0.05
        )
        _test_bfloat16_ops(
            self,
            torch.nn.AdaptiveAvgPool2d((3, 5)),
            device,
            inp_dims=(8, 4, 16, 16),
            prec=0.05,
        )
        _test_bfloat16_ops(
            self,
            torch.nn.AdaptiveAvgPool3d((3, 5, 7)),
            device,
            inp_dims=(8, 4, 16, 16, 16),
            prec=0.05,
        )

    def test_maxpool3d_non_square_backward(self, device):
        # previous CUDA routine of this backward calculates kernel launch grid size
        # with last two dimensions interchanged, so the tailing along the longer dim
        # get ignored. Here we test whether every position gets gradient.
        for dim in (2, 3, 4):
            shape = tuple(32 if i != dim else 256 for i in range(4))
            x = torch.randn(shape, device=device, requires_grad=True)
            F.max_pool3d(x, kernel_size=(1, 1, 1)).sum().backward()
            self.assertEqual(x.grad, torch.ones_like(x.grad))

    @slowTest
    def test_adaptive_pool_odd_size(self, device):
        # See https://github.com/pytorch/pytorch/issues/81409
        Ih, Iw, Oh, Ow = 5873, 3693, 3527, 2219
        imgs = torch.randint(low=0, high=256, size=(11, Ih, Iw), dtype=torch.float)
        imgs_ = F.adaptive_avg_pool2d(imgs, (Oh, Ow))
        imgs_ = F.adaptive_max_pool2d(imgs, (Oh, Ow))

        Id, Ih, Iw, Od, Oh, Ow = 3, 5873, 3693, 3, 3527, 2219
        imgs = torch.randint(low=0, high=256, size=(3, Id, Ih, Iw), dtype=torch.float)
        imgs_ = F.adaptive_avg_pool3d(imgs, (Od, Oh, Ow))
        imgs_ = F.adaptive_max_pool3d(imgs, (Od, Oh, Ow))


instantiate_device_type_tests(TestPoolingNNDeviceType, globals())
instantiate_parametrized_tests(TestPoolingNN)

if __name__ == "__main__":
    run_tests()