320 lines
13 KiB
Python
320 lines
13 KiB
Python
import time
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import threading
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from toolbox import update_ui, Singleton
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from multiprocessing import Process, Pipe
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from contextlib import redirect_stdout
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from request_llms.queued_pipe import create_queue_pipe
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class ThreadLock(object):
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def __init__(self):
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self._lock = threading.Lock()
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def acquire(self):
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# print("acquiring", self)
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#traceback.print_tb
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self._lock.acquire()
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# print("acquired", self)
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def release(self):
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# print("released", self)
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#traceback.print_tb
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self._lock.release()
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def __enter__(self):
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self.acquire()
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def __exit__(self, type, value, traceback):
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self.release()
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@Singleton
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class GetSingletonHandle():
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def __init__(self):
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self.llm_model_already_running = {}
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def get_llm_model_instance(self, cls, *args, **kargs):
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if cls not in self.llm_model_already_running:
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self.llm_model_already_running[cls] = cls(*args, **kargs)
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return self.llm_model_already_running[cls]
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elif self.llm_model_already_running[cls].corrupted:
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self.llm_model_already_running[cls] = cls(*args, **kargs)
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return self.llm_model_already_running[cls]
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else:
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return self.llm_model_already_running[cls]
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def reset_tqdm_output():
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import sys, tqdm
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def status_printer(self, file):
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fp = file
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if fp in (sys.stderr, sys.stdout):
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getattr(sys.stderr, 'flush', lambda: None)()
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getattr(sys.stdout, 'flush', lambda: None)()
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def fp_write(s):
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print(s)
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last_len = [0]
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def print_status(s):
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from tqdm.utils import disp_len
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len_s = disp_len(s)
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fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0)))
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last_len[0] = len_s
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return print_status
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tqdm.tqdm.status_printer = status_printer
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class LocalLLMHandle(Process):
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def __init__(self):
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# ⭐run in main process
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super().__init__(daemon=True)
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self.is_main_process = True # init
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self.corrupted = False
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self.load_model_info()
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self.parent, self.child = create_queue_pipe()
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self.parent_state, self.child_state = create_queue_pipe()
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# allow redirect_stdout
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self.std_tag = "[Subprocess Message] "
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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.state = ""
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self.check_dependency()
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self.is_main_process = False # state wrap for child process
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self.start()
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self.is_main_process = True # state wrap for child process
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self.threadLock = ThreadLock()
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def get_state(self):
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# ⭐run in main process
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while self.parent_state.poll():
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self.state = self.parent_state.recv()
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return self.state
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def set_state(self, new_state):
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# ⭐run in main process or 🏃♂️🏃♂️🏃♂️ run in child process
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if self.is_main_process:
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self.state = new_state
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else:
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self.child_state.send(new_state)
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def load_model_info(self):
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# 🏃♂️🏃♂️🏃♂️ run in child process
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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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# 🏃♂️🏃♂️🏃♂️ run in child process
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raise NotImplementedError("Method not implemented yet")
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def llm_stream_generator(self, **kwargs):
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# 🏃♂️🏃♂️🏃♂️ run in child process
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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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# ⭐run in main process
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raise NotImplementedError("Method not implemented yet")
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def check_dependency(self):
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# ⭐run in main process
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try:
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self.try_to_import_special_deps()
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self.set_state("`依赖检测通过`")
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self.running = True
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except:
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self.set_state(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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# 🏃♂️🏃♂️🏃♂️ run in child process
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# 第一次运行，加载参数
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self.child.flush = lambda *args: None
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self.child.write = lambda x: self.child.send(self.std_tag + x)
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reset_tqdm_output()
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self.set_state("`尝试加载模型`")
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try:
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with redirect_stdout(self.child):
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self._model, self._tokenizer = self.load_model_and_tokenizer()
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except:
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self.set_state("`加载模型失败`")
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self.running = False
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from toolbox import trimmed_format_exc
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self.child.send(
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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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self.set_state("`准备就绪`")
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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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# print('debug' + 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(
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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 clear_pending_messages(self):
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# ⭐run in main process
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while True:
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if self.parent.poll():
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self.parent.recv()
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continue
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for _ in range(5):
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time.sleep(0.5)
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if self.parent.poll():
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r = self.parent.recv()
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continue
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break
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return
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def stream_chat(self, **kwargs):
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# ⭐run in main process
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if self.get_state() == "`准备就绪`":
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yield "`正在等待线程锁，排队中请稍候 ...`"
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with self.threadLock:
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if self.parent.poll():
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yield "`排队中请稍候 ...`"
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self.clear_pending_messages()
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self.parent.send(kwargs)
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std_out = ""
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std_out_clip_len = 4096
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while True:
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res = self.parent.recv()
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# pipe_watch_dog.feed()
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if res.startswith(self.std_tag):
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new_output = res[len(self.std_tag):]
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std_out = std_out[:std_out_clip_len]
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print(new_output, end='')
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std_out = new_output + std_out
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yield self.std_tag + '\n```\n' + std_out + '\n```\n'
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elif res == '[Finish]':
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break
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elif 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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std_out = ""
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yield res
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def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
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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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refer to request_llms/bridge_all.py
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"""
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_llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
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if len(observe_window) >= 1:
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observe_window[0] = load_message + "\n\n" + _llm_handle.get_state()
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if not _llm_handle.running:
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raise RuntimeError(_llm_handle.get_state())
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if history_format == 'classic':
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# 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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elif history_format == 'chatglm3':
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# 有 sys_prompt 接口
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conversation_cnt = len(history) // 2
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history_feedin = [{"role": "system", "content": sys_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "":
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continue
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history_feedin.append(what_i_have_asked)
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history_feedin.append(what_gpt_answer)
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else:
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history_feedin[-1]['content'] = what_gpt_answer['content']
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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:
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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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refer to request_llms/bridge_all.py
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"""
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chatbot.append((inputs, ""))
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_llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
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chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.get_state())
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yield from update_ui(chatbot=chatbot, history=[])
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if not _llm_handle.running:
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raise RuntimeError(_llm_handle.get_state())
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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(
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additional_fn, inputs, history, chatbot)
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# 处理历史信息
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if history_format == 'classic':
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# 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([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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elif history_format == 'chatglm3':
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# 有 sys_prompt 接口
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conversation_cnt = len(history) // 2
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history_feedin = [{"role": "system", "content": system_prompt}]
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if conversation_cnt:
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for index in range(0, 2*conversation_cnt, 2):
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what_i_have_asked = {}
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what_i_have_asked["role"] = "user"
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what_i_have_asked["content"] = history[index]
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what_gpt_answer = {}
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what_gpt_answer["role"] = "assistant"
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what_gpt_answer["content"] = history[index+1]
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if what_i_have_asked["content"] != "":
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if what_gpt_answer["content"] == "":
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continue
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history_feedin.append(what_i_have_asked)
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history_feedin.append(what_gpt_answer)
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else:
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history_feedin[-1]['content'] = what_gpt_answer['content']
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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
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