I have the following implementation of page rank using sparse tensors:
i = torch.LongTensor(idx)
values = torch.FloatTensor([1] * len(idx))
M = torch.sparse.FloatTensor(i.t(), values, torch.Size([4847571, 4847571]))
N = M.shape[1]
v = torch.rand(N, 1).float()
values = torch.FloatTensor([(1 - self.d)/N] * len(indices))
temp = torch.sparse.FloatTensor(i.t(), values, torch.Size([4847571,
                                                                   4847571]))
if torch.cuda.is_available():
     v = v.cuda()
     M = M.cuda()
     temp = temp.cuda()

v = v / torch.norm(v, 1)
M_hat = self.d * M + temp
for i in range(self.num_iter):
     v = torch.mm(M_hat, v)

On CPU everything runs fine. On GPU I am getting the following error:
    v = torch.mm(M_hat, v)
RuntimeError: sub_iter.strides(0)[0] == 0 INTERNAL ASSERT FAILED at /pytorch/aten/src/ATen/native/cuda/Reduce.cuh:706, please report a bug to PyTorch.

Is this a known issue? Should I do something differently?
This is my configuration:
Collecting environment information...
PyTorch version: 1.5.0
Is debug build: No
CUDA used to build PyTorch: 10.2
OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2
Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: Tesla P100-PCIE-16GB
Nvidia driver version: 440.82
cuDNN version: Could not collect
Versions of relevant libraries:
[pip3] numpy==1.18.3
[pip3] torch==1.5.0

Thanks very much for the help!