fix(trainer): 优化检查点保存逻辑避免重复写入和进度条重置问题

src/model/trainer.py

@@ -355,12 +355,18 @@ class Trainer:
         }
 
         torch.save(checkpoint, checkpoint_path)
-        logger.info(f"Checkpoint saved to {checkpoint_path}")
 
         if is_best:
             best_path = self.checkpoint_dir / "best_model.pt"
+            # 如果已经保存到best_model.pt，则不再重复保存
+            if checkpoint_path != best_path:
+                logger.info(f"Checkpoint saved to {checkpoint_path}")
                 torch.save(checkpoint, best_path)
                 logger.info(f"Best model saved to {best_path}")
+            else:
+                logger.info(f"Best model saved to {checkpoint_path}")
+        else:
+            logger.info(f"Checkpoint saved to {checkpoint_path}")
 
     def load_checkpoint(
         self, checkpoint_path: Union[str, Path], reset_training_state: bool = False
@@ -667,8 +673,7 @@ class Trainer:
                         progress.update(epoch_task, completed=self.total_steps)
                         break
 
-                # 重置进度条
-                progress.reset(epoch_task)
+                # 进度条不重置，显示整体训练进度
 
                 # 每个epoch结束后保存检查点
                 self.save_checkpoint(f"epoch_{epoch + 1}.pt")
@@ -1797,3 +1802,4 @@ def expand_finetune(
 
 if __name__ == "__main__":
     app()
+

test.py

@@ -2,6 +2,7 @@ import sys
 
 sys.path.append("src")
 
+import time
 import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
@@ -95,7 +96,9 @@ for k, v in sample.items():
     if isinstance(v, str):
         print(f"{k}: {v}")
 
+start = time.time()
 res = model(input_ids, token_type_ids, attention_mask, pinyin_ids, history_slot_ids)
+print(f'计算时长： {(time.time() - start) * 1000:4f}ms')
 sort_res = sorted(
     [(i, v) for i, v in enumerate(res[0])], key=lambda x: x[1], reverse=True
 )
@@ -125,32 +128,3 @@ for i in range(20):
     print(
         f"  {i + 1:2d}. ID:{idx}\t字符: {query_engine.query_by_id(idx).char}\t概率: {prob:.6f}"
     )
-
-print("\n" + "=" * 60)
-print("测试 history_slot_ids 全零情况")
-print("=" * 60)
-
-masked_labels = [0, 0, 0, 0, 0, 0, 0, 0]
-history_slot_ids = torch.tensor(masked_labels, dtype=torch.long).unsqueeze(0)
-sample["history_slot_ids"] = history_slot_ids
-
-res_zero = model(
-    input_ids, token_type_ids, attention_mask, pinyin_ids, history_slot_ids
-)
-probs_zero = F.softmax(res_zero, dim=-1)
-
-print(f"\n📊 概率分布分析 (全零历史):")
-print(f"  形状: {probs_zero.shape}")
-print(f"  总概率和: {probs_zero.sum().item():.6f}")
-print(f"  最大概率: {probs_zero.max().item():.6f}")
-print(f"  最小概率: {probs_zero.min().item():.6f}")
-print(f"  平均概率: {probs_zero.mean().item():.6f}")
-
-top_probs_zero, top_indices_zero = torch.topk(probs_zero, k=20)
-print(f"\n🏆 Top-20预测 (全零历史):")
-for i in range(20):
-    idx = top_indices_zero[0, i].item()
-    prob = top_probs_zero[0, i].item()
-    print(
-        f"  {i + 1:2d}. ID:{idx}\t字符: {query_engine.query_by_id(idx).char}\t概率: {prob:.6f}"
-    )