[update] mask log

dataset/lm_dataset.py

@@ -86,12 +86,9 @@ class SFTDataset(Dataset):
         Y = torch.tensor(input_ids[1:], dtype=torch.long)
         loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long)  # 对齐预测位置
         # # === 打印每个token的掩码情况 ===
-        # print(f"\n--- Sample {index} Token Loss Mask (length: {len(input_ids)}) ---")
-        # for i, (token_id, mask) in enumerate(zip(input_ids, loss_mask)):
-        #     token_str = self.tokenizer.decode([token_id], skip_special_tokens=False)
-        #     token_str = token_str.replace('\n', '\\n').replace('\t', '\\t')  # 处理换行等不可见字符
-        #     print(f"Token {i:3d}: {token_id:5d} -> '{token_str:10s}' | mask: {mask}")
-        # print(f"--- End of Sample {index} ---")
+        # print(f"\n--- Sample {index} ---")
+        # for i, (x, y, m) in enumerate(zip(X, Y, loss_mask)):
+        #     print(f"{i:3d}: X={self.tokenizer.decode([x])!r:16s} ---> Y={self.tokenizer.decode([y])!r:16s} mask={m}")
         # # ================================
         return X, Y, loss_mask