Gradient computation sees a variable modified but it doesn't seem o

[KFrank]

Hi iftg!

@iftg

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [64, 1, 6]], which is output 0 of TanhBackward0, is at version 1; expected version 0 instead.
…

My forward functions is:

def forward(self, x: torch.Tensor):
        ...
        return x # [Batch_size, pred_len, predict_size]

Most likely, the output of forward() is being modified inplace after
forward() has run.

If I remove x = self.activation (x) the code works fine.

self.activation(x) isn’t causing the inplace modification error, per se.
Rather, the presence of self.activation (x) in the computation graph
is causing an inplace modification – that exists with or without the call
to self.activation (x) – to matter.

There is a reasonable chance that adding a .clone(), specifically
x = self.activation (x).clone(), will fix your problem.

Or ask pytorch to sweep this inplace modification error under the rug
for you.

Sometimes these errors are a symptom of something incorrect or
sub-optimal in what you are doing. If you want to track down the root
cause, take a look at the debugging techniques in the following post:

"RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [64, 1]], which is output 0 of AsStridedBackward0, is at version 3; expected version 2 instead. Hint: the backtrace further a

Hi Fahmyadan and Sangyoon! Here are some suggestions about how to track down (and maybe fix) inplace-modification errors. Note that an inplace modification in the forward pass is not necessarily* an error – it depends on whether and how the tensor that was modified is used in the backward pass. Note that inplace operations can be useful for saving memory – if you replace an innocent inplace operation with an out-of-place equivalent, your training will use more memory (and, to a minor e…

Best.

K. Frank