models/chatglm/test.py

import os
import platform
import signal
import time
from transformers import AutoTokenizer, AutoModel
from multi_input import MultiInputInCmd

if __name__ == "__main__":

    tokenizer = AutoTokenizer.from_pretrained("E:\ProjectEX\LLM\ChatGLM-6B\chatglm-6b-int4", trust_remote_code=True)
    model = AutoModel.from_pretrained("E:\ProjectEX\LLM\ChatGLM-6B\chatglm-6b-int4", trust_remote_code=True).float()
    model = model.quantize(bits=4,
                           kernel_file="E:\ProjectEX\LLM\ChatGLM-6B\chatglm-6b-int4\quantization_kernels_parallel.so")
    model = model.eval()

    os_name = platform.system()
    clear_command = 'cls' if os_name == 'Windows' else 'clear'
    stop_stream = False


    def build_prompt(history):
        prompt = "欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
        for query, response in history:
            prompt += f"\n\n用户：{query}"
            prompt += f"\n\nChatGLM-6B：{response}"
        return prompt


    def signal_handler(signal, frame):
        global stop_stream
        stop_stream = True


    def main():
        history = []
        global stop_stream
        print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
        while True:

            # query = input("\n用户：")

            input_fun = MultiInputInCmd("\n用户：")
            all_input_lines = input_fun.run()
            # handle as normal message
            query = ''
            for index in range(len(all_input_lines)):
                if index == len(all_input_lines) - 1:
                    query = query + all_input_lines[index]
                else:
                    query = query + all_input_lines[index] + '\n'

            if query.strip() == "stop":
                break
            if query.strip() == "clear":
                history = []
                os.system(clear_command)
                print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
                continue
            last_index = 0
            start = time.time()
            for response, history in model.stream_chat(tokenizer, query, history=history):
                if stop_stream:
                    stop_stream = False
                    break
                else:
                    print(response[last_index:], end='', flush=True)
                    last_index = len(response)
                    signal.signal(signal.SIGINT, signal_handler)
            print((time.time() - start) / last_index)
            print('')

    main()