[chatgpt]fix inference model load (#2988)

* fix lora bug

* polish

* fix lora gemini

* fix inference laod model bug

applications/ChatGPT/examples/README.md

@@ -69,10 +69,13 @@ torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy
 ## Inference example(After Stage3)
 We support naive inference demo after training.
 ```shell
-# inference
-python inference.py --pretrain <your actor model path> --model <your model type>
+# inference, using pretrain path to configure model
+python inference.py --model_path <your actor model path> --model <your model type> --pretrain <your pretrain model name/path>
+# example
+python inference.py --model_path ./actor_checkpoint_prompts.pt --pretrain bigscience/bloom-560m --model bloom
 ```
 
+
 #### data
 - [x] [rm-static](https://huggingface.co/datasets/Dahoas/rm-static)
 - [x] [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)

applications/ChatGPT/examples/inference.py

 import argparse
-import torch
 
+import torch
 from chatgpt.nn import BLOOMActor, GPTActor, OPTActor
 from transformers import AutoTokenizer
 from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
@@ -9,18 +9,17 @@ from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
 def eval(args):
     # configure model
     if args.model == 'gpt2':
-        actor = GPTActor().to(torch.cuda.current_device())
+        actor = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
     elif args.model == 'bloom':
-        actor = BLOOMActor().to(torch.cuda.current_device())
+        actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device())
     elif args.model == 'opt':
-        actor = OPTActor().to(torch.cuda.current_device())
+        actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
     else:
         raise ValueError(f'Unsupported model "{args.model}"')
 
-    state_dict = torch.load(args.pretrain)
+    state_dict = torch.load(args.model_path)
     actor.model.load_state_dict(state_dict)
-    
-    
+
     # configure tokenizer
     if args.model == 'gpt2':
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
@@ -49,7 +48,9 @@ def eval(args):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
+    # We suggest to use the pretrained model from HuggingFace, use pretrain to configure model
     parser.add_argument('--pretrain', type=str, default=None)
+    parser.add_argument('--model_path', type=str, default=None)
     parser.add_argument('--input', type=str, default='Question: How are you ? Answer:')
     parser.add_argument('--max_length', type=int, default=100)
     args = parser.parse_args()