Yes, if the scaler detects invalid gradients, the optimizer.step() call is skipped and thus the learning rate scheduler is called before the first parameter update.
We are working on a method to get the last status of the scaler. In the meantime you could try to check if the scale value was reduced and if so skip the learning rate scheduler step.