6d23cc7e7c32d1a6aa1d2d4a4c94abe50c980126,fairseq/trainer.py,Trainer,save_checkpoint,#Trainer#Any#Any#,277

Before Change

            checkpoint_utils.save_state(
                filename,
                self.cfg,
                self.model.state_dict(),
                self.get_criterion(),
                self.optimizer,
                self.lr_scheduler,
                self.get_num_updates(),
                optim_history=self._optim_history,
                extra_state=extra_state,
                task=self.task,
            )
            logger.info(f"Finished saving checkpoint to {filename}")

After Change

            state_dict["last_optimizer_state"] = self.optimizer.state_dict()
        return state_dict

    def save_checkpoint(self, filename, extra_state):
        Save all training state in a checkpoint file.
        logger.info(f"Saving checkpoint to {filename}")
        // call state_dict on all ranks in case it needs internal communication
        state_dict = utils.move_to_cpu(self.state_dict())
        state_dict["extra_state"].update(extra_state)
        if self.should_save_checkpoint_on_current_rank:
            checkpoint_utils.torch_persistent_save(
                state_dict,
                filename,
                async_write=self.cfg.checkpoint.write_checkpoints_asynchronously,
            )
        logger.info(f"Finished saving checkpoint to {filename}")

    def load_checkpoint(
        self,

Instances