int/gpt_academic
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镜像自地址 https://github.com/binary-husky/gpt_academic.git 已同步 2025-12-06 22:46:48 +00:00
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better local model interaction

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binary-husky
2023-10-31 16:17:52 +08:00
父节点 08f036aafd
当前提交 136162ec0d
共有 3 个文件被更改，包括 146 次插入 和 187 次删除
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121
request_llms/local_llm_class.py
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@@ -1,15 +1,16 @@
from transformers import AutoModel, AutoTokenizer
import time
import threading
import importlib
from toolbox import update_ui, get_conf, Singleton
from toolbox import update_ui
from multiprocessing import Process, Pipe
from contextlib import redirect_stdout
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)
@@ -21,6 +22,28 @@ def SingletonLocalLLM(cls):
return _instance[cls]
return _singleton
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):
# ⭐主进程执行
@@ -28,6 +51,9 @@ class LocalLLMHandle(Process):
self.corrupted = False
self.load_model_info()
self.parent, self.child = Pipe()
# allow redirect_stdout
self.std_tag = "[Subprocess Message] "
self.child.write = lambda x: self.child.send(self.std_tag + x)
self.running = True
self._model = None
self._tokenizer = None
@@ -52,7 +78,7 @@ class LocalLLMHandle(Process):
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
@@ -64,7 +90,7 @@ class LocalLLMHandle(Process):
# ⭐主进程执行
try:
self.try_to_import_special_deps()
self.info = "依赖检测通过"
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。"
@@ -73,15 +99,21 @@ class LocalLLMHandle(Process):
def run(self):
# 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
# 第一次运行，加载参数
reset_tqdm_output()
self.info = "`尝试加载模型`"
try:
self._model, self._tokenizer = self.load_model_and_tokenizer()
with redirect_stdout(self.child):
self._model, self._tokenizer = self.load_model_and_tokenizer()
except:
self.info = "`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(
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.info = "`准备就绪`"
while True:
# 进入任务等待状态
kwargs = self.child.recv()
@@ -93,25 +125,35 @@ class LocalLLMHandle(Process):
# 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(
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()
if self.info == "`准备就绪`":
yield "`正在等待线程锁，排队中请稍后 ...`"
with self.threadLock:
self.parent.send(kwargs)
std_out = ""
std_out_clip_len = 4096
while True:
res = self.parent.recv()
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'):
@@ -123,15 +165,17 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
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)
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)
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]] )
history_feedin.append([history[2*i], history[2*i+1]])
elif history_format == 'chatglm3':
# 有 sys_prompt 接口
conversation_cnt = len(history) // 2
@@ -145,24 +189,24 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
what_gpt_answer["role"] = "assistant"
what_gpt_answer["content"] = history[index+1]
if what_i_have_asked["content"] != "":
if what_gpt_answer["content"] == "": continue
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
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。")
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):
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
@@ -172,11 +216,13 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
_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 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)
inputs, history = handle_core_functionality(
additional_fn, inputs, history, chatbot)
# 处理历史信息
if history_format == 'classic':
@@ -184,7 +230,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
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]] )
history_feedin.append([history[2*i], history[2*i+1]])
elif history_format == 'chatglm3':
# 有 sys_prompt 接口
conversation_cnt = len(history) // 2
@@ -198,7 +244,8 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
what_gpt_answer["role"] = "assistant"
what_gpt_answer["content"] = history[index+1]
if what_i_have_asked["content"] != "":
if what_gpt_answer["content"] == "": continue
if what_gpt_answer["content"] == "":
continue
history_feedin.append(what_i_have_asked)
history_feedin.append(what_gpt_answer)
else:
@@ -216,4 +263,4 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='cla
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)
return predict_no_ui_long_connection, predict
return predict_no_ui_long_connection, predict