Automatic Mixed Precision with two optimisers that step unevenly

Hi there,

I have a question regarding the amp scaler working with multiple optimisers that step unevenly. The patterns looks as follows:

def do_train_step(wrapper, model_optimiser, contraint_optimiser, inputs):
    # wrapper contains two nn.Modules: model and constraint, 
    # that both have their own optimiser: model_optimiser, constraint_optimiser
    # and both interact with the inputs to form the loss

    with torch.set_grad_enabled(True):
        with autocast(enabled=True):
            loss = wrapper(inputs)

    # Gradients accumulate in here

    # The model optimsier should only update once every < accumulate_n_batches_grad >
    if (global_step + 1) % accumulate_n_batches_grad == 0:

        if gradient_clipping:
            # Unscales the loss of optimizer's assigned params in-place

            # Since the gradients of optimizer's assigned params are unscaled, clips as usual:
            torch.nn.utils.clip_grad_norm_(wrapper.model.parameters(), 1.0)

        # OPTIMISER STEP (first unscaled)


        # Zero the gradients only here

        # LR Scheduler

    # The constraint optimiser should update every train step

    return loss

But, this alternating pattern seems to break the amp scaling. I get the following error:

  File "/home/cbarkhof/.local/lib/python3.6/site-packages/torch/cuda/amp/", line 302, in step
    raise RuntimeError("step() has already been called since the last update().")
RuntimeError: step() has already been called since the last update().

which is about the scaler.step(constraint_optimiser).

If there is anyone that spots what might be going wrong here that would be very helpful.

Thanks in advance!

Cheers, Claartje

I think you could either use two different scalers for each optimizer or alternatively use scaler.step() on the constraint_optimizer, if the inner block wasn’t executed.
After the inner code was already executed, the gradients should be already unscaled, which are used in the first optimizer.
However, I’m currently unsure about your use case and don’t know, if both optimizers are using a different subset of all parameters, are partially overlapping, or reuse the same parameters.
Could you explain the use case a bit more?

Also, I think you have a typo in the posted code snippet, as you are calling scaler.step(loss), while an optimizer would be needed.

Hi @ptrblck, thanks for your answer. You are right about the typo, I corrected it for clarity.

A bit more context:

The wrapper wraps two nn.Modules, the constraint and the model. They do not have any shared parameters. They do both interact with (parts of) the total loss. The loss composition looks something like:

loss_1, loss_2 = model(input)
loss_2 = constraint(loss_2)
total_loss = loss_1 + loss_2

The model optimises with gradients from the total_loss, while the constraint optimises only part of the loss (loss_2). I hope this makes it clearer.

On your suggested solutions:

1. Using two scalers.
Would the following be valid?

model_scaled_loss = model_scaler.scale(loss)
constraint_scaled_loss = constraint_scaler.scale(loss)

if (global_step + 1) % accumulate_n_batches_grad == 0:
        # OPTIMISER STEP (first unscaled)


        # Zero the gradients only here


2. use scaler.step() on the constraint_optimizer , if the inner block wasn’t executed.
I am not sure what you mean by this. Are you saying only to execute scaler.step(constraint_optimiser) if scaler.step(model_optimiser) is not called (inner block execution)? I would think that both optimisers need explicit unscaling of the gradients, or don’t they? If both optimisers do not share parameters, would scaler.unscale_(model_optimiser) affect scaler.unscale_(constraint_optimiser) in any way then?

Hopefully this is clear enough, otherwise I can provide more information.