device of 'edge_index' is changed when' edge_index' is empty

🐛 Bug

To Reproduce

Steps to reproduce the behavior:

import torch
from torch_geometric.nn import EdgeConv
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.data import Data

class MLP(nn.Module):
    def __init__(self, F_in, F_out):
        super(MLP, self).__init__()
        self.mlp = nn.Sequential(
            nn.Linear(2 * F_in, F_out),
            nn.ReLU(inplace=True))

    def forward(self, x):
        return self.mlp(x)

class TestBlock(nn.Module):
    def __init__(self, F_in, F_out):
        super(TestBlock, self).__init__()
        self.edge_conv1 = EdgeConv(MLP(F_in, F_out),aggr='mean')
        self.edge_conv2 = EdgeConv(MLP(F_out, F_out), aggr='mean') 
        self.fc = nn.Linear(F_out, 16)
        
    def forward(self, data):
        x = data.x
        edge_index = data.edge_index 
        x = self.edge_conv1(x, edge_index)
        x = self.fc(self.edge_conv2(x, edge_index))
        return x

d = torch.device('cuda:1')
block = TestBlock(4, 8).to(d)
edge_index = torch.tensor([[], []], dtype=torch.long)
data = Data(x=x, edge_index=edge_index)
y = block(data.to(d))

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-34-d1a9b2a113f2> in <module>
      1 edge_index = torch.tensor([[], []], dtype=torch.long)
      2 data = Data(x=x, edge_index=edge_index)
----> 3 y = block(data.to(d))
      4 print(y)
      5 print(data.edge_index.data)

~/miniconda3/envs/py36_pytorch110/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

<ipython-input-31-b450444a54a6> in forward(self, data)
     24         edge_index = data.edge_index
     25         x = self.edge_conv1(x, edge_index)
---> 26         x = self.fc(self.edge_conv2(x, edge_index))
     27         return x

~/miniconda3/envs/py36_pytorch110/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/py36_pytorch110/lib/python3.6/site-packages/torch_geometric/nn/conv/edge_conv.py in forward(self, x, edge_index)
     42         x = x.unsqueeze(-1) if x.dim() == 1 else x
     43 
---> 44         return self.propagate(edge_index, x=x)
     45 
     46     def message(self, x_i, x_j):

~/miniconda3/envs/py36_pytorch110/lib/python3.6/site-packages/torch_geometric/nn/conv/message_passing.py in propagate(self, edge_index, size, **kwargs)
     99                         raise ValueError(__size_error_msg__)
    100 
--> 101                     tmp = torch.index_select(tmp, 0, edge_index[idx])
    102                     message_args.append(tmp)
    103             else:

RuntimeError: arguments are located on different GPUs at /opt/conda/conda-bld/pytorch_1556653183467/work/aten/src/THC/generic/THCTensorIndex.cu:521
'''

## Expected behavior

<!-- A clear and concise description of what you expected to happen. -->

## Environment

* OS:   Linux 16.04
* Python version:  3.6
* PyTorch version: 1.1.0
* CUDA/cuDNN version:  9.0
* GCC version:  5.4.0
* Any other relevant information:

## Additional context

<!-- Add any other context about the problem here. -->
Maybe there are some problems in initialization, and I find some interesting thing in the function:
 `gen` in  `torch_scatter/utils/gen.py`

def gen(src, index, dim=-1, out=None, dim_size=None, fill_value=0): dim = range(src.dim())[dim] # Get real dim value.

# Automatically expand index tensor to the right dimensions.
if index.dim() == 1:
    index_size = list(repeat(1, src.dim()))
    index_size[dim] = src.size(dim)
    index = index.view(index_size).expand_as(src)

# Generate output tensor if not given.
if out is None:
    out_size = list(src.size())
    dim_size = maybe_dim_size(index, dim_size)
    out_size[dim] = dim_size
    out = src.new_full(out_size, fill_value)

return src, out, index, dim

The device of the index.data is 'cuda:1' before `index.view(index_size)` , but after that it changes to 'cuda:0' while the device of `index` is still 'cuda:1'. 


B.T.W, if the 'edge_index` of the first sample fed to the network is not empty , the bug is gone.

edge_index = torch.tensor([[0], [1]], dtype=torch.long) data = Data(x=x, edge_index=edge_index) y = block(data.to(d)) edge_index = torch.tensor([[], []], dtype=torch.long) data = Data(x=x, edge_index=edge_index) y = block(data.to(d)) print(y.device) print(data.edge_index.data)

The output:

cuda:1 tensor([], device=‘cuda:1’, size=(2, 0), dtype=torch.int64)

[WMF1997]

p.s. @ZongweiZhou1 in most of time, edge_index may not be [[],[]]. (i.e. may not be empty…) while testing… (meaningful datasets, or random edge_index)

indeed, after adding something to the null edge_index, it works. (no error, and showing the device is cuda:1.

yours sincerely: @WMF1997

[rusty1s]

I fixed the bug in torch-scatter master. In the end, this is not a PyTorch Geometric bug but rather a PyTorch bug, where view() seems to make stupid things when applied on zero-element tensors.

I do not think that bug is critical, so I wait till releasing a new torch-scatter release. Thank you very much!