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
image1414×523 7.31 KB
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 2
### torch.optim  optimizers changed to fix in-place checks for the changes made by the optimizer (#33640 2, #34211)
any idea how I can solve this? in my case would it be ok to switch back to pytorch 1.4?