Hello,

I created a model of 3 parts encoder, decoder and discriminator, and created a separate optimizer for each part this way

```
optimizer_encoder = optim.Adam(model.encode.parameters(), lr=args.lr)
optimizer_decoder = optim.Adam(model.decode.parameters(), lr=args.lr)
optimizer_discriminator = optim.Adam(model.dis.parameters(), lr=args.lr)
```

in the training loop I try to backpropagate losses this way

```
model.zero_grad()
loss_encoder.backward(retain_graph=True)
# # someone likes to clamp the grad here
# #[p.grad.data.clamp_(-1,1) for p in model.encode.parameters()]
# # update parameters
optimizer_encoder.step()
# clean others, so they are not afflicted by encoder loss
model.zero_grad()
#decoder
# if train_dec:
loss_decoder.backward(retain_graph=True)
#[p.grad.data.clamp_(-1,1) for p in model.decode.parameters()]
optimizer_decoder.step()
#clean the discriminator
model.dis.zero_grad()
# # #discriminator
# # # if train_dis:
loss_discriminator.backward()
# # #[p.grad.data.clamp_(-1,1) for p in model.dis.parameters()]
optimizer_discriminator.step()
```

Here a simple schematic the model

I need the parameters of each part to be updated only through backpropagating it’s own loss, that’s why the above code was implemented this way

but I get this error

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [256, 256]], which is output 0 of TBackward, is at version 3; expected version 2 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!

I found that it seems my issue is related to this release not here

**### torch.optim optimizers changed to fix in-place checks for the changes made by the optimizer (#33640, #34211)**

any idea how I can solve this? in my case would it be ok to switch back to pytorch 1.4?