Using torch.nn.functional.max_pool1d on a 3D-tensor with large second dimension (>= 10^6 elements) gives a CUDA-related error I could not make sense of. Reducing the second dimension solves the problem, so it might have to do with the available GPU memory. Note that I already tried applying x-contiguous() as suggested in this issue.
With the minimal example below I can reproduce the problem with the following settings:

PyTorch nightly version 1.6.0.dev20200514
CUDA 10.1
Python 3.7.7
GPU: GeForce RTX 2080 Ti

Minimal example and traceback:
import torch
import torch.nn.functional as F

x = 6.
size = 10. ** 6
print(f"Size of second dimension: {int(size)}")
poolsize = 4
x = torch.rand((1,int(size),poolsize), requires_grad=True).to('cuda:0')
y = F.max_pool1d(x.contiguous(), kernel_size=poolsize, stride=None, padding=0)

loss = torch.sum(y)
loss.backward()

"""
Traceback (most recent call last):
  File "efficient-segmentation/minimal_example.py", line 14, in <module>
    loss.backward()
  File "/home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/tensor.py", line 184, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/autograd/__init__.py", line 123, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: CUDA error: an illegal memory access was encountered
Exception raised from copy_kernel_cuda at /opt/conda/conda-bld/pytorch_1589440011545/work/aten/src/ATen/native/cuda/Copy.cu:200 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x4d (0x7f79c52dc48d in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x254fe57 (0x7f79c7a5ae57 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cuda.so)
frame #2: <unknown function> + 0x92f489 (0x7f79f246e489 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #3: <unknown function> + 0x92d7c2 (0x7f79f246c7c2 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #4: at::native::copy_(at::Tensor&, at::Tensor const&, bool) + 0x44 (0x7f79f246db04 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #5: <unknown function> + 0x31b2198 (0x7f79f4cf1198 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0xba40a1 (0x7f79f26e30a1 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #7: at::native::to(at::Tensor const&, c10::TensorOptions const&, bool, bool, c10::optional<c10::MemoryFormat>) + 0x802 (0x7f79f26e3d72 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #8: <unknown function> + 0xef07ca (0x7f79f2a2f7ca in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #9: <unknown function> + 0x29a95ab (0x7f79f44e85ab in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #10: <unknown function> + 0xe22292 (0x7f79f2961292 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #11: torch::autograd::CopyBackwards::apply(std::vector<at::Tensor, std::allocator<at::Tensor> >&&) + 0x495 (0x7f79f489df55 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #12: <unknown function> + 0x2d58847 (0x7f79f4897847 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #13: torch::autograd::Engine::evaluate_function(std::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::shared_ptr<torch::autograd::ReadyQueue> const&) + 0x1740 (0x7f79f48929d0 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #14: torch::autograd::Engine::thread_main(std::shared_ptr<torch::autograd::GraphTask> const&, bool) + 0x4ee (0x7f79f489382e in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #15: torch::autograd::Engine::thread_init(int, std::shared_ptr<torch::autograd::ReadyQueue> const&) + 0x94 (0x7f79f4889b54 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_cpu.so)
frame #16: torch::autograd::python::PythonEngine::thread_init(int, std::shared_ptr<torch::autograd::ReadyQueue> const&) + 0x40 (0x7f79f8458820 in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #17: <unknown function> + 0xc819d (0x7f79fb12019d in /home/nfs/username/miniconda3/envs/pytorch-cuda10/lib/python3.7/site-packages/torch/lib/../../../.././libstdc++.so.6)
frame #18: <unknown function> + 0x7ea5 (0x7f7a19b1fea5 in /lib64/libpthread.so.0)
frame #19: clone + 0x6d (0x7f7a198488dd in /lib64/libc.so.6)
"""