.

train_sft_ds.py

@@ -95,7 +95,7 @@ class QwenChatSFTDataset(IterableDataset):
             if not msgs or not isinstance(msgs, list):
                 continue
 
-            # 可选：过滤掉带有非空 <think>…</think> 的样本（避免训练真实 COT）
+            # 可选过滤 think
             bad = False
             for m in msgs:
                 if m.get("role") == "assistant" and isinstance(m.get("content"), str):
@@ -109,22 +109,16 @@ class QwenChatSFTDataset(IterableDataset):
 
             tools = ex.get("tools", None)
 
-            # 1) 按模型自带模板渲染
             rendered: str = self.tok.apply_chat_template(
-                msgs,
-                tools=tools,
-                add_generation_prompt=False,
-                tokenize=False
+                msgs, tools=tools, add_generation_prompt=False, tokenize=False
             )
             if not isinstance(rendered, str) or not rendered.strip():
                 continue
 
-            # 2) 找出 assistant 片段的字符区间
             spans = _assistant_char_spans(rendered)
             if not spans:
                 continue
 
-            # 3) 分词 + 字符/Token 对齐
             enc = self.tok(
                 rendered,
                 add_special_tokens=False,
@@ -133,11 +127,9 @@ class QwenChatSFTDataset(IterableDataset):
             input_ids: List[int] = enc["input_ids"]
             offsets: List[Tuple[int, int]] = enc["offset_mapping"]
 
-            # 空样本防御：分词后长度为 0
             if not input_ids:
                 continue
 
-            # 4) 仅 assistant 计损失
             labels = [-100] * len(input_ids)
 
             def in_any_span(lo: int, hi: int) -> bool:
@@ -150,22 +142,40 @@ class QwenChatSFTDataset(IterableDataset):
                 if in_any_span(lo, hi):
                     labels[i] = input_ids[i]
 
-            # 5) 超长裁剪（保留尾部）
+            # —— 固定长度策略：先截尾，再在 Dataset 层补到固定 seq_len ——
+            # 1) 截断到 seq_len（保留尾部）
             if len(input_ids) > self.seq_len:
                 input_ids = input_ids[-self.seq_len:]
                 labels    = labels[-self.seq_len:]
 
-            # 若没有任何可训练 token（labels 全 -100），也跳过
+            # 2) 左侧补齐到 seq_len（保证所有样本长度一致）
+            pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else self.tok.eos_token_id
+            L = len(input_ids)
+            if L < self.seq_len:
+                pad = self.seq_len - L
+                input_ids = ([pad_id] * pad) + input_ids
+                labels    = ([-100]  * pad) + labels
+                attn_mask = [0] * pad + [1] * L
+            else:
+                # 恰好等于 seq_len
+                attn_mask = [1] * self.seq_len
+
+            # 若没有任何可训练 token（labels 全 -100），跳过
             if all(v == -100 for v in labels):
                 continue
 
+            assert len(input_ids) == self.seq_len
+            assert len(labels)    == self.seq_len
+            assert len(attn_mask) == self.seq_len
+
             yield {
                 "input_ids": torch.tensor(input_ids, dtype=torch.long),
-                "attention_mask": torch.ones(len(input_ids), dtype=torch.long),
-                "labels": torch.tensor(labels, dtype=torch.long)
+                "attention_mask": torch.tensor(attn_mask, dtype=torch.long),
+                "labels": torch.tensor(labels, dtype=torch.long),
             }
 
 
+
 # ----------------- 专用 Collator：pad inputs, pad labels=-100 -----------------
 class SFTDataCollator:
     def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
@@ -387,7 +397,8 @@ def main():
             eval_dataset = ListDataset(eval_samples)
 
     # 更稳：联调阶段不强行 pad 到 4096
-    data_collator = SFTDataCollator(tokenizer, pad_to_length=None)
+    # data_collator = SFTDataCollator(tokenizer, pad_to_length=None)
+    data_collator = SFTDataCollator(tokenizer, pad_to_length=args.seq_len)
 
     os.makedirs(args.output_dir, exist_ok=True)
     logging_dir = os.path.join(args.output_dir, "logs")