Update loading instructions

Author: duzx16

ptuning/README.md

@@ -152,15 +152,16 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b", config=config, trust_remot
 prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"))
 new_prefix_state_dict = {}
 for k, v in prefix_state_dict.items():
-    new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+    if k.startswith("transformer.prefix_encoder."):
+        new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
 model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
 ```
 注意你可能需要将 `pre_seq_len` 改成你训练时的实际值。如果你是[从本地加载模型的话](https://github.com/THUDM/ChatGLM-6B#%E4%BB%8E%E6%9C%AC%E5%9C%B0%E5%8A%A0%E8%BD%BD%E6%A8%A1%E5%9E%8B)，需要将 `THUDM/chatglm-6b` 改成本地的模型路径（注意不是checkpoint路径）。
 
 (2) 如果需要加载的是旧 Checkpoint（包含 ChatGLM-6B 以及 PrefixEncoder 参数），或者进行的全参数微调，则直接加载整个 Checkpoint：
 
 ```python
-model = AutoModel.from_pretrained(CHECKPOINT_PATH, config=config, trust_remote_code=True)
+model = AutoModel.from_pretrained(CHECKPOINT_PATH, trust_remote_code=True)
 ```
 
 之后根据需求可以进行量化，也可以直接使用：

ptuning/main.py

@@ -118,7 +118,8 @@ def main():
         prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
         new_prefix_state_dict = {}
         for k, v in prefix_state_dict.items():
-            new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+            if k.startswith("transformer.prefix_encoder."):
+                new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
         model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
     else:
         model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)