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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_llm/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_llm/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 |