180 lines
7.2 KiB
Python
180 lines
7.2 KiB
Python
from transformers import AutoModel, AutoTokenizer
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import time
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import threading
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import importlib
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from toolbox import update_ui, get_conf, Singleton
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from multiprocessing import Process, Pipe
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def SingletonLocalLLM(cls):
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"""
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一个单实例装饰器
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"""
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_instance = {}
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def _singleton(*args, **kargs):
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if cls not in _instance:
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_instance[cls] = cls(*args, **kargs)
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return _instance[cls]
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elif _instance[cls].corrupted:
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_instance[cls] = cls(*args, **kargs)
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return _instance[cls]
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else:
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return _instance[cls]
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return _singleton
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class LocalLLMHandle(Process):
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def __init__(self):
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# ⭐主进程执行
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super().__init__(daemon=True)
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self.corrupted = False
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self.load_model_info()
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self.parent, self.child = Pipe()
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self.running = True
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self._model = None
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self._tokenizer = None
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self.info = ""
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self.check_dependency()
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self.start()
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self.threadLock = threading.Lock()
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def load_model_info(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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raise NotImplementedError("Method not implemented yet")
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self.model_name = ""
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self.cmd_to_install = ""
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def load_model_and_tokenizer(self):
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"""
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This function should return the model and the tokenizer
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"""
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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raise NotImplementedError("Method not implemented yet")
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def llm_stream_generator(self, **kwargs):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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raise NotImplementedError("Method not implemented yet")
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def try_to_import_special_deps(self, **kwargs):
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"""
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import something that will raise error if the user does not install requirement_*.txt
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"""
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# ⭐主进程执行
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raise NotImplementedError("Method not implemented yet")
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def check_dependency(self):
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# ⭐主进程执行
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try:
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self.try_to_import_special_deps()
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self.info = "依赖检测通过"
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self.running = True
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except:
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self.info = f"缺少{self.model_name}的依赖，如果要使用{self.model_name}，除了基础的pip依赖以外，您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。"
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self.running = False
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def run(self):
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# 🏃♂️🏃♂️🏃♂️ 子进程执行
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# 第一次运行，加载参数
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try:
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self._model, self._tokenizer = self.load_model_and_tokenizer()
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except:
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self.running = False
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from toolbox import trimmed_format_exc
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self.child.send(f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
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self.child.send('[FinishBad]')
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raise RuntimeError(f"不能正常加载{self.model_name}的参数！")
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while True:
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# 进入任务等待状态
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kwargs = self.child.recv()
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# 收到消息，开始请求
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try:
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for response_full in self.llm_stream_generator(**kwargs):
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self.child.send(response_full)
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self.child.send('[Finish]')
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# 请求处理结束，开始下一个循环
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except:
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from toolbox import trimmed_format_exc
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self.child.send(f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
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self.child.send('[Finish]')
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def stream_chat(self, **kwargs):
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# ⭐主进程执行
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self.threadLock.acquire()
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self.parent.send(kwargs)
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while True:
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res = self.parent.recv()
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if res == '[Finish]':
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break
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if res == '[FinishBad]':
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self.running = False
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self.corrupted = True
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break
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else:
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yield res
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self.threadLock.release()
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def get_local_llm_predict_fns(LLMSingletonClass, model_name):
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load_message = f"{model_name}尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，{model_name}消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
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"""
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⭐多线程方法
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函数的说明请见 request_llms/bridge_all.py
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"""
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_llm_handle = LLMSingletonClass()
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if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + _llm_handle.info
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if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
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# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
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history_feedin = []
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history_feedin.append([sys_prompt, "Certainly!"])
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for i in range(len(history)//2):
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history_feedin.append([history[2*i], history[2*i+1]] )
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watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
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response = ""
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for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
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if len(observe_window) >= 1:
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observe_window[0] = response
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if len(observe_window) >= 2:
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if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
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return response
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def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
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"""
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⭐单线程方法
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函数的说明请见 request_llms/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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_llm_handle = LLMSingletonClass()
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chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.info)
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yield from update_ui(chatbot=chatbot, history=[])
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if not _llm_handle.running: raise RuntimeError(_llm_handle.info)
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if additional_fn is not None:
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from core_functional import handle_core_functionality
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inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
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# 处理历史信息
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history_feedin = []
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history_feedin.append([system_prompt, "Certainly!"])
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for i in range(len(history)//2):
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history_feedin.append([history[2*i], history[2*i+1]] )
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# 开始接收回复
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response = f"[Local Message] 等待{model_name}响应中 ..."
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for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
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chatbot[-1] = (inputs, response)
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yield from update_ui(chatbot=chatbot, history=history)
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# 总结输出
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if response == f"[Local Message] 等待{model_name}响应中 ...":
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response = f"[Local Message] {model_name}响应异常 ..."
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history.extend([inputs, response])
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yield from update_ui(chatbot=chatbot, history=history)
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return predict_no_ui_long_connection, predict |