add docstrings, refactor logger

Former-commit-id: c34e489d71f8f539028543ccf8ee92cecedd6276

src/llamafactory/train/sft/trainer.py

@@ -23,6 +23,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 import numpy as np
 import torch
 from transformers import Seq2SeqTrainer
+from typing_extensions import override
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
@@ -64,17 +65,20 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+    @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:
             self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args)
         return super().create_optimizer()
 
+    @override
     def create_scheduler(
         self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
     ) -> "torch.optim.lr_scheduler.LRScheduler":
         create_custom_scheduler(self.args, num_training_steps, optimizer)
         return super().create_scheduler(num_training_steps, optimizer)
 
+    @override
     def prediction_step(
         self,
         model: "torch.nn.Module",