Dynamically Add Parameters to Optimizer


#1

Hi, I have a dynamic graph that adds/removes layers after some epochs.
I realized that the optimizer is not aware of newly added layer.

Q. Is it safe to call opt.add_param_group() after a few iterations of opt.step() in runtime?
Q. How can I delete some parameters from opt? (To save the memory in runtime)

There was a recommentation to reconstruct the optimizer:


but I think it will lose optimizer states like momentum for existing parameters. (Cloning the optimizer would be the last resort)


(MirandaAgent) #2

Yes I want to know how to do this too. Did you find a solution? @m75

cross posted:


(MirandaAgent) #3

@colesbury from our discussion about when fresh new graphs are created in Pytorch (What does the backward() function do?), what I am truly interested to do correctly is to make sure that the new parameters that I am creating dynamically as the training happens are correctly included in the forward computation correctly. So you seem to imply that what I should do is for each iteration that I do an update to create a new loss function basically. Right? As follows:

    ## new parameters
    add_new_parameters(mdl,W_new)

    #making sure the parameters are included
    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')

   # Reset gradient
    optimizer.zero_grad()

    # Forward
    fx = model.forward(x)
    output = loss.forward(fx, y)

    # Backward
    output.backward()

    # Update parameters
    optimizer.step()