rename folder

request_llms/local_llm_class.py

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