def step()

in optimum/neuron/accelerate/scheduler.py [0:0]

• 21 lines of code
• 12 McCabe index (conditional complexity)

    def step(self, *args, **kwargs):
        if not self.step_with_optimizer:
            # No link between scheduler and optimizer -> just step
            self.scheduler.step(*args, **kwargs)
            return

        # Otherwise, first make sure the optimizer was stepped.
        if not self.gradient_state.sync_gradients:
            if self.gradient_state.adjust_scheduler:
                self.scheduler._step_count += 1
            return

        for opt in self.optimizers:
            if opt.step_was_skipped:
                return
        if self.split_batches:
            # Split batches -> the training dataloader batch size is not changed so one step per training step
            self.scheduler.step(*args, **kwargs)
        else:
            # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
            # num_processes steps per training step
            num_processes = NeuronAcceleratorState().num_processes
            for _ in range(num_processes):
                # Special case when using OneCycle and `drop_last` was not used
                if hasattr(self.scheduler, "total_steps"):
                    if self.scheduler._step_count <= self.scheduler.total_steps:
                        self.scheduler.step(*args, **kwargs)
                else:
                    self.scheduler.step(*args, **kwargs)