The warning is raised, because the GradScaler might use an initial scaling factor, which could be too large for the first batches and will thus reduce it as well as skip the optimizer.step(). Due to this, the scheduler.step() would be executed before the first optimizer.step(), which will raise the warning.
You could ignore or disable it, or on the other hand, check if the scaler decreased the scale factor and also skip the scheduler.step().