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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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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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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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raise NotImplementedError("Method not implemented yet") |
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def llm_stream_generator(self, **kwargs): |
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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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raise NotImplementedError("Method not implemented yet") |
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def check_dependency(self): |
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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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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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kwargs = self.child.recv() |
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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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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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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_llm/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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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 |
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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_llm/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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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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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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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 |