[chatgpt] fix inference demo loading bug (#2969)

* [chatgpt] fix inference demo loading bug

* polish

applications/ChatGPT/examples/inference.py

@@ -9,30 +9,34 @@ from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
 def eval(args):
     # configure model
     if args.model == 'gpt2':
-        model = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
+        actor = GPTActor().to(torch.cuda.current_device())
     elif args.model == 'bloom':
-        model = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device())
+        actor = BLOOMActor().to(torch.cuda.current_device())
     elif args.model == 'opt':
-        model = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
+        actor = OPTActor().to(torch.cuda.current_device())
     else:
         raise ValueError(f'Unsupported model "{args.model}"')
 
+    state_dict = torch.load(args.pretrain)
+    actor.model.load_state_dict(state_dict)
+    
+    
     # configure tokenizer
     if args.model == 'gpt2':
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         tokenizer.pad_token = tokenizer.eos_token
     elif args.model == 'bloom':
-        tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
+        tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')
         tokenizer.pad_token = tokenizer.eos_token
     elif args.model == 'opt':
-        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+        tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
     else:
         raise ValueError(f'Unsupported model "{args.model}"')
 
-    model.eval()
+    actor.eval()
     input = args.input
     input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device())
-    outputs = model.generate(input_ids,
+    outputs = actor.generate(input_ids,
                              max_length=args.max_length,
                              do_sample=True,
                              top_k=50,
@@ -46,7 +50,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
     parser.add_argument('--pretrain', type=str, default=None)
-    parser.add_argument('--input', type=str, default='Q: How are you ? A:')
+    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()
     eval(args)