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