Here is a piece of test code:

```
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1_weight = Parameter(torch.randn(10,1,3,3))
self.conv1_bias = Parameter(torch.randn(10))
def forward(self, x):
out = x
out = F.conv2d(out, self.conv1_weight, bias=self.conv1_bias)
return out
if __name__ == '__main__':
from torch.autograd import grad
model = ConvNet()
model.cuda()
x = Variable(torch.randn(1,1,28,28)).cuda()
print(x.size())
y = model(x)
print(y.size())
loss = torch.mean(y.pow(2))
g = grad(loss, model.parameters(), create_graph=True, retain_graph=True)[0]
gg = grad(g[0,0,0,0], model.parameters(), retain_graph=True)[0] ## bug
```

If the following conditions meet, the second order gradient cannot be computed:

- you want to compute second order grad (not first order grad)
- you network contains convolution (fc layer functions normally)
- Computation on gpu (on cpu, everything was fine)

And the error is:

```
RuntimeError: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
```

Add `.contiguous()`

wonâ€™t fix anything.

I will be very grateful if someone could offer some help !