I got the following error RuntimeError: cuda runtime error (700) : an illegal memory access was encountered at /opt/conda/conda-bld/pytorch_1595629411241/work/aten/src/THC/THCCachingHostAllocator.cpp:278 while running my code with cuda().
So I decided to check the device number for the variables. I printed following variables from forward() function
input_ device no:  1
support device no:  1
weight device no:  -1
Here is the code that caused the error.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GNNLinear(Module):
def init(self, in_features, out_features, num_supports, act_fn, dropout=False, batch_norm=False):
 ¦   super(GNNLinear, self).init()
 ¦   self.num_supports = num_supports
 ¦   self.in_features = in_features
 ¦   self.out_features = out_features
 ¦   self.dropout = dropout
 ¦   if self.dropout:
 ¦   ¦   self.dropout_fn = nn.Dropout(p=0.5, inplace=True)
 ¦   self.batch_norm = batch_norm
 ¦   if self.batch_norm:
 ¦   ¦   self.batch_norm_fn = nn.BatchNorm1d(out_features)
 ¦   self.act_fn = act_fn
 ¦   self.weight = []
 ¦   for i in range(num_supports):
 ¦   ¦   self.weight.append(Parameter(torch.FloatTensor(in_features, out_features)))
 ¦   if bias:
 ¦   ¦   self.bias = Parameter(torch.FloatTensor(out_features))
 ¦   else:
 ¦   ¦   self.register_parameter('bias', None)
 ¦   self.reset_parameters()

 def reset_parameters(self):
 ¦   for i in range(self.num_supports):
 ¦   ¦   ¦bound = 1. / math.sqrt(self.weight[i].size(1))
 ¦   ¦   ¦self.weight[i].data.uniform_(-bound, bound)
 ¦   if self.bias is not None:
 ¦   ¦   self.bias = Parameter(torch.zeros(self.bias.shape, dtype=torch.float32))

 def forward(self, input_, support):
 ¦   if self.dropout:
 ¦   ¦   input_ = self.dropout_fn(input_)
 ¦   z = 0
 ¦   for i in range(self.num_supports):
 ¦   ¦   print('input_ device no: ', input_.get_device())
 ¦   ¦   print('support device no: ', support[i].get_device())
 ¦   ¦   print('weight device no: ', self.weight[i].get_device())
 ¦   ¦   h_i = torch.mm(input_, self.weight[i])
         print(h_i)
 ¦   ¦   z_i = torch.sparse.mm(support[i], h_i)
 ¦   ¦   z += z_i
 ¦   if self.bias is not None:
 ¦   ¦   z = z + self.bias
 ¦   if self.act_fn:
 ¦   ¦   z = self.act_fn(z)
 ¦   if self.batch_norm:
 ¦   ¦   z = self.batch_norm_fn(z)
 ¦   return z
 ¦

 def __repr__(self):
 ¦   return self.__class__.__name__ + ' (' \
 ¦   ¦   ¦  + str(self.in_features) + ' -> ' \
 ¦   ¦   ¦  + str(self.out_features) + ')'