Qwen/jacobi_demo/pipeline.py

import argparse

from BaseModel.base_model import BaseModel

class Qwen(BaseModel):
 def __init__(self, args):
 super().__init__(args)
 # preprocess parameters, such as prompt & tokenizer
 self.system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
 self.prompt = (
 "<|im_start|>user\n{}<|im_end|>\n"
 "<|im_start|>assistant\n"
 )
 self.EOS = self.tokenizer.im_end_id # tokenizer.encode("<|im_end|>")
 self.history = [self.system_prompt]

 # load model
 self.load_model(args)

 def load_model(self, args):
 if args.decode_mode == "jacobi":
 from Qwen.python_demo import chat_jacobi
 self.model = chat_jacobi.Qwen()
 elif args.decode_mode == "basic":
 from Qwen.python_demo import chat
 self.model = chat.Qwen()
 self.model.init(self.devices, args.model_path)
 self.model.temperature = args.temperature
 self.model.top_p = args.top_p
 self.model.repeat_penalty = args.repeat_penalty
 self.model.repeat_last_n = args.repeat_last_n
 self.model.max_new_tokens = args.max_new_tokens
 self.model.generation_mode = args.generation_mode
 self.model.prompt_mode = args.prompt_mode
 self.SEQLEN = self.model.SEQLEN

 def update_history(self):
 if self.model.token_length >= self.SEQLEN:
 print("... (reach the maximal length)", flush=True, end='')
 self.history = [self.system_prompt]
 else:
 self.history[-1] = self.history[-1] + self.answer_cur

 def encode_tokens(self):
 self.history.append(self.prompt.format(self.input_str))
 text = "".join(self.history)
 tokens = self.tokenizer(text).input_ids
 return tokens

def main(args):
 model = Qwen(args)
 model.chat()

if __name__ == "__main__":
 parser = argparse.ArgumentParser()
 parser.add_argument('-m', '--model_path', type=str, required=True, help='path to the bmodel file')
 parser.add_argument('-t', '--tokenizer_path', type=str, default="../support/token_config", help='path to the tokenizer file')
 parser.add_argument('-d', '--devid', type=str, default='0', help='device ID to use')
 parser.add_argument('--temperature', type=float, default=1.0, help='temperature scaling factor for the likelihood distribution')
 parser.add_argument('--top_p', type=float, default=1.0, help='cumulative probability of token words to consider as a set of candidates')
 parser.add_argument('--repeat_penalty', type=float, default=1.0, help='penalty for repeated tokens')
 parser.add_argument('--repeat_last_n', type=int, default=32, help='repeat penalty for recent n tokens')
 parser.add_argument('--max_new_tokens', type=int, default=1024, help='max new token length to generate')
 parser.add_argument('--generation_mode', type=str, choices=["greedy", "penalty_sample"], default="greedy", help='mode for generating next token')
 parser.add_argument('--prompt_mode', type=str, choices=["prompted", "unprompted"], default="prompted", help='use prompt format or original input')
 parser.add_argument('--decode_mode', type=str, default="basic", choices=["basic", "jacobi"], help='mode for decoding')
 args = parser.parse_args()
 main(args)