I have an encoder-decode model and a function that calculating something iteratively:

```
f(a,b,c):
# a: [batchsize, n, channel]
res_a = torch.empty(a.shape)
res_b = torch.empty(b.shape)
for i in range(a.shape[1]):
res_a[:,i] = balabala
res_b[:,i] = balabala
return torch.cat([res_a,res_b],-1)
```

This is the main procedure of forward function:

```
def forward(x):
a, b, c = x[0:i], x[i:j], x[j:k]
q = f(a,b,c)
x' = decoder(encoder(x))
a, b, c = x'[0:i], x'[i:j], x'[j:k]
q' = f(a,b,c)
return q, q'
```

The loss is the difference of q and q1

Then an error occurs when backward for the computation of `q'`

:

```
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [64, 3]], which is output 0 of SelectBackward, is at version 21; expected version 20 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
```

I really have no idea where I modify the variables for gradient computation, all I do in function `f`

is just indexing some data from input and conduct some computations (add, multiply), then store the result to a new tensor.