I am trying to run some code in PyTorch but I am got stacked at that point:
At first iteration, both backward operations, for Discriminator and Generator are running well
....
self.G_loss.backward(retain_graph=True)
self.D_loss.backward()
...
At the second iteration, when self.G_loss.backward(retain_graph=True) executes i got this error:
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [8192, 512]] is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
According to torch.autograd.set_detect_anomaly, the last of the following lines in the Discriminator network, is responsible for this:
bottleneck = bottleneck[:-1]
self.embedding = x.view(x.size(0), -1)
self.logit = self.layers[-1](self.embedding)
The strange thing is that I have used that network architecture in other code and worked properly. Any suggestion?
the full error:
> site-packages\torch\autograd\__init__.py", line 127, in backward
allow_unreachable=True) # allow_unreachable flag RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [8192, 512]] is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!