    def lr_step_update(self):
        """Update the learning rate after each update."""
        new_lr = self.lr_scheduler.step_update(self.get_num_updates())
        if isinstance(new_lr, dict):
            for k, v in new_lr.items():
                metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300)
            new_lr = new_lr.get("default", next(iter(new_lr.values())))
        else:
            metrics.log_scalar("lr", new_lr, weight=0, priority=300)
        return new_lr
Update the learning rate after each update.
fairseq/trainer.py