test.py

import torch
from torch.autograd import Variable
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F


v_in = Variable(torch.Tensor([0.1, 0.1]).view(1, 2, 1, 1), requires_grad=True)
f1 = lambda x: x * 2
f2 = nn.Conv2d(2, 1, 1, 1)

grad_out = Variable(torch.ones(1,1,1,1))
gradient = autograd.grad(outputs=f2(f1(v_in)), inputs=v_in,
                         grad_outputs=grad_out,
                         create_graph=True, retain_graph=True, 
                         only_inputs=True)[0]

gradient.sum().backward()  # undefined tensor error

# the following checks that a change in `v_in` changes `gradient`
# so the conclusion here is that `v_in`.grad shouldn't be 0
v_in = Variable(torch.Tensor([0.2, 0.1]).view(1, 2, 1, 1), requires_grad=True)
f1 = lambda x: x * 2
f2 = nn.Conv2d(2, 1, 1, 1)

grad_out = Variable(torch.ones(1,1,1,1))
gradient = autograd.grad(outputs=f2(f1(v_in)), inputs=v_in,
                        grad_outputs=grad_out,
                        create_graph=True, retain_graph=True, 
                        only_inputs=True)[0]