NPU] Update prompt format for baichuan2-pipeline (#12625)

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/baichuan2.py

@@ -25,19 +25,6 @@
 
 logger = logging.get_logger(__name__)
 
-def get_prompt(message: str, chat_history: list[tuple[str, str]],
-               system_prompt: str) -> str:
-    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
-    # The first user input is _not_ stripped
-    do_strip = False
-    for user_input, response in chat_history:
-        user_input = user_input.strip() if do_strip else user_input
-        do_strip = True
-        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
-    message = message.strip() if do_strip else message
-    texts.append(f'{message} [/INST]')
-    return ''.join(texts)
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Predict Tokens using `generate()` API for npu model"
@@ -108,11 +95,15 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
     with torch.inference_mode():
         print("finish to load")
         for i in range(3):
-            prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
-            _input_ids = tokenizer.encode(prompt, return_tensors="pt")
+            messages = [{"role": "system", "content": "You are a helpful assistant."},
+                        {"role": "user", "content": args.prompt}]
+            text = tokenizer.apply_chat_template(messages,
+                                                 tokenize=False,
+                                                 add_generation_prompt=True)
+            _input_ids = tokenizer([text], return_tensors="pt").input_ids
             print("-" * 20, "Input", "-" * 20)
             print("input length:", len(_input_ids[0]))
-            print(prompt)
+            print(args.prompt)
             print("-" * 20, "Output", "-" * 20)
             st = time.time()
             output = model.generate(