.

train_sft_ds.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 import os
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
 import glob
 import socket
 import argparse
@@ -158,35 +159,36 @@ class QwenChatSFTDataset(IterableDataset):
 
 # ----------------- 专用 Collator：pad inputs, pad labels=-100 -----------------
 class SFTDataCollator:
-    def __init__(self, tokenizer: AutoTokenizer):
+    def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
         self.tok = tokenizer
-        assert self.tok.pad_token_id is not None, "tokenizer.pad_token 不能为空；已在主函数里兜底为 eos_token"
+        self.pad_to_length = pad_to_length
+        assert self.tok.pad_token_id is not None
 
-    def __call__(self, features: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+    def __call__(self, features):
-        # 将变长样本对齐到 batch 内最大长度；labels 用 -100 补齐
+        def _to_list(x): return x.tolist() if isinstance(x, torch.Tensor) else list(x)
-        def _to_list(x):
+        input_ids   = [_to_list(f["input_ids"])      for f in features]
-            return x.tolist() if isinstance(x, torch.Tensor) else list(x)
+        attn_masks  = [_to_list(f["attention_mask"]) for f in features]
-        input_ids   = [ _to_list(f["input_ids"])   for f in features ]
+        labels_list = [_to_list(f["labels"])         for f in features]
-        attn_masks  = [ _to_list(f["attention_mask"]) for f in features ]
+
-        labels_list = [ _to_list(f["labels"])      for f in features ]
+        max_len_in_batch = max(len(x) for x in input_ids)
+        target_len = self.pad_to_length if self.pad_to_length is not None else max_len_in_batch
 
-        max_len = max(len(x) for x in input_ids)
         pad_id = self.tok.pad_token_id
-
         batch_inp, batch_attn, batch_lab = [], [], []
         for inp, msk, lab in zip(input_ids, attn_masks, labels_list):
-            pad_len = max_len - len(inp)
+            pad_len = target_len - len(inp)
+            if pad_len < 0:
+                inp, msk, lab = inp[:target_len], msk[:target_len], lab[:target_len]
+                pad_len = 0
             batch_inp.append(torch.tensor(inp + [pad_id]*pad_len, dtype=torch.long))
             batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
             batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
-
         return {
             "input_ids": torch.stack(batch_inp, dim=0),
             "attention_mask": torch.stack(batch_attn, dim=0),
             "labels": torch.stack(batch_lab, dim=0),
         }
 
-
 # ----------------- 参数 -----------------
 def parse_args():
     ap = argparse.ArgumentParser()
@@ -227,18 +229,27 @@ def main():
     args = parse_args()
     set_seed(args.seed)
 
-    # Tokenizer/Model
+    # 1) 先补 tokenizer 的 pad
     tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True, trust_remote_code=True)
+    
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token  # 供 padding 使用
     
+    # 可选：让警告更少
+    tokenizer.model_max_length = args.seq_len
+
+    # 2) 再加载模型
     model = AutoModelForCausalLM.from_pretrained(
         args.model_name_or_path,
         torch_dtype=(torch.bfloat16 if args.bf16 else torch.float16),
         low_cpu_mem_usage=True,
         trust_remote_code=True
     )
+
+    # 3) 最后对齐模型的 pad_token_id
+    model.config.pad_token_id = tokenizer.pad_token_id
     model.config.use_cache = False  # 训练时禁用 cache
+    
     if args.gradient_checkpointing:
         model.gradient_checkpointing_enable()
 
@@ -336,7 +347,7 @@ def main():
         if len(eval_samples) > 0:
             eval_dataset = ListDataset(eval_samples)
 
-    data_collator = SFTDataCollator(tokenizer)
+    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")
@@ -376,6 +387,8 @@ def main():
         deepspeed=args.deepspeed,
         dataloader_drop_last=True,
         dataloader_num_workers=0,
+        dataloader_prefetch_factor=None,
+        dataloader_pin_memory=False,
         report_to=([] if args.report_to == "none" else [args.report_to]),
         bf16=args.bf16,
         fp16=(not args.bf16),
@@ -392,7 +405,7 @@ def main():
         args=training_args,
         train_dataset=train_stream,
         eval_dataset=eval_dataset,
-        tokenizer=tokenizer,
+        processing_class=tokenizer,
         data_collator=data_collator
     )
     trainer.add_callback(CsvLossLogger(csv_path=os.path.join(args.output_dir, "loss.csv")))