I install pytorch 0.1.12+ac1c674 and it errors like this.

[Naruto-Sasuke]

pytorch/pytorch@ac1c674

Traceback (most recent call last):
  File "gan_toy.py", line 270, in <module>
    gradient_penalty.backward()
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 151, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables)
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/__init__.py", line 98, in backward
    variables, grad_variables, retain_graph)
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/function.py", line 90, in apply
    return self._forward_cls.backward(self, *args)
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/linear.py", line 23, in backward
    grad_input = torch.mm(grad_output, weight)
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 539, in mm
    return self._static_blas(Addmm, (output, 0, 1, self, matrix), False)
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 532, in _static_blas
    return cls.apply(*(args[:1] + args[-2:] + (alpha, beta, inplace)))
  File "/home/yan/anaconda3/lib/python3.6/site-packages/torch/autograd/_functions/blas.py", line 24, in forward
    matrix1, matrix2, out=output)
TypeError: torch.addmm received an invalid combination of arguments - got (int, torch.cuda.ByteTensor, int, torch.cuda.ByteTensor, torch.cuda.FloatTensor, out=torch.cuda.ByteT
ensor), but expected one of:
 * (torch.cuda.ByteTensor source, torch.cuda.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (torch.cuda.ByteTensor source, torch.cuda.sparse.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (int beta, torch.cuda.ByteTensor source, torch.cuda.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (torch.cuda.ByteTensor source, int alpha, torch.cuda.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (int beta, torch.cuda.ByteTensor source, torch.cuda.sparse.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (torch.cuda.ByteTensor source, int alpha, torch.cuda.sparse.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
 * (int beta, torch.cuda.ByteTensor source, int alpha, torch.cuda.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
      didn't match because some of the arguments have invalid types: (int, torch.cuda.ByteTensor, int, torch.cuda.ByteTensor, torch.cuda.FloatTensor, out=torch.cuda.ByteTensor
)
 * (int beta, torch.cuda.ByteTensor source, int alpha, torch.cuda.sparse.ByteTensor mat1, torch.cuda.ByteTensor mat2, *, torch.cuda.ByteTensor out)
      didn't match because some of the arguments have invalid types: (int, torch.cuda.ByteTensor, int, torch.cuda.ByteTensor, torch.cuda.FloatTensor, out=torch.cuda.ByteTensor
)

Any quick fix or just wait for the milestone when stable double backprop have been implemented?

[caogang]

you can refer to the issue #6 to solve this problem

[santisy]

@caogang Is this problem fixed on master branch of Pytorch? I encountered a different problem where costs soon became nan and it can also be fixed by your solution. Is there still something wired that has not been fixed about masked_fill?

[caogang]

It will soon be fixed in the pytorch branch. Maybe i will test it in the future days.