.

train_sft_lora.py

@@ -329,6 +329,11 @@ class SFTDataCollator:
             batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
             batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
 
+        # ensure this batch has supervised tokens
+        has_sup = any((lab != -100).any().item() for lab in batch_lab)
+        if not has_sup:
+            raise RuntimeError("batch has zero supervised tokens; check masking or dataset.")
+
         return {
             "input_ids": torch.stack(batch_inp, dim=0),
             "attention_mask": torch.stack(batch_attn, dim=0),
@@ -604,10 +609,24 @@ def main():
     )
     model = get_peft_model(model, lora_cfg)
 
+    try:
+        model.print_trainable_parameters()
+    except Exception:
+        pass
+
     # 3) 再次配置梯度检查点（注入后调用更稳）
     if args.gradient_checkpointing:
         model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
 
+        # 关键：让输入参与梯度，从而兼容 checkpoint
+        try:
+            model.enable_input_require_grads()
+        except AttributeError:
+            # 旧版 transformers 兜底：给 embedding 输出打 requires_grad
+            emb = model.get_input_embeddings()
+            if hasattr(emb, "register_forward_hook"):
+                emb.register_forward_hook(lambda m, inp, out: out.requires_grad_(True))        
+
     # 4) 打印可训练参数占比
     try:
         from peft import get_peft_model_state_dict