调整损失权重计算并优化训练循环终止条件

src/trainer/model.py

@@ -510,6 +510,8 @@ class MoEModel(nn.Module):
             self.to(self.device)
         if loss_weight:
             loss_weight = 1 / torch.sqrt(torch.tensor(loss_weight))
+            loss_weight = loss_weight / loss_weight.mean()
+            loss_weight = torch.clamp(loss_weight, min=0.01, max=1.0)
             self.loss_weight = loss_weight.to(self.device)
             criterion.weight = self.loss_weight
 
@@ -529,7 +531,7 @@ class MoEModel(nn.Module):
         optimizer.zero_grad()
 
         for epoch in range(num_epochs):
-            for batch_idx, batch in enumerate(tqdm(train_dataloader, total=1e6)):
+            for batch_idx, batch in enumerate(tqdm(train_dataloader, total=stop_batch)):
                 # ---------- 更新 batch 计数器 ----------
                 processed_batches += 1
 
@@ -582,6 +584,8 @@ class MoEModel(nn.Module):
                                 f"step: {global_step}, loss: {avg_loss:.4f}, acc: {acc:.4f}, eval_loss: {eval_loss:.4f}"
                             )
                         batch_loss_sum = 0.0
+                    if processed_batches >= stop_batch:
+                        break
 
     def load_from_state_dict(self, state_dict_path: Union[str, Path]):
         state_dict = torch.load(