ChatGLM改成多进程运行
This commit is contained in:
qingxu fu
parent
294ac338bd
commit
6aba339538
@ -66,7 +66,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
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chatbot.append([inputs_show_user, ""])
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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executor = ThreadPoolExecutor(max_workers=16)
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mutable = ["", time.time()]
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mutable = ["", time.time(), ""]
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def _req_gpt(inputs, history, sys_prompt):
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retry_op = retry_times_at_unknown_error
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exceeded_cnt = 0
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@ -20,7 +20,8 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=txt, inputs_show_user=txt,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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sys_prompt=system_prompt
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sys_prompt=system_prompt,
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retry_times_at_unknown_error=0
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)
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history.append(txt)
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@ -24,7 +24,7 @@ RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
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# 下载分支
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WORKDIR /gpt
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RUN $useProxyNetwork git clone https://github.com/binary-husky/chatgpt_academic.git -b v3.0
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RUN $useProxyNetwork git clone https://github.com/binary-husky/chatgpt_academic.git -b v3.1
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WORKDIR /gpt/chatgpt_academic
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RUN $useProxyNetwork python3 -m pip install -r requirements.txt
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RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
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87
main.py
87
main.py
@ -1,56 +1,57 @@
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import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
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import gradio as gr
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from request_llm.bridge_all import predict
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
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# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
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def main():
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import gradio as gr
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from request_llm.bridge_all import predict
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from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
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# 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
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proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
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get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
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# 如果WEB_PORT是-1, 则随机选取WEB端口
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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if not AUTHENTICATION: AUTHENTICATION = None
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# 如果WEB_PORT是-1, 则随机选取WEB端口
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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if not AUTHENTICATION: AUTHENTICATION = None
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from check_proxy import get_current_version
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initial_prompt = "Serve me as a writing and programming assistant."
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title_html = f"<h1 align=\"center\">ChatGPT 学术优化 {get_current_version()}</h1>"
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description = """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)"""
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from check_proxy import get_current_version
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initial_prompt = "Serve me as a writing and programming assistant."
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title_html = f"<h1 align=\"center\">ChatGPT 学术优化 {get_current_version()}</h1>"
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description = """代码开源和更新[地址🚀](https://github.com/binary-husky/chatgpt_academic)，感谢热情的[开发者们❤️](https://github.com/binary-husky/chatgpt_academic/graphs/contributors)"""
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# 问询记录, python 版本建议3.9+（越新越好）
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import logging
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os.makedirs("gpt_log", exist_ok=True)
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try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
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except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
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print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
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# 问询记录, python 版本建议3.9+（越新越好）
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import logging
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os.makedirs("gpt_log", exist_ok=True)
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try:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO, encoding="utf-8")
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except:logging.basicConfig(filename="gpt_log/chat_secrets.log", level=logging.INFO)
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print("所有问询记录将自动保存在本地目录./gpt_log/chat_secrets.log, 请注意自我隐私保护哦！")
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# 一些普通功能模块
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from core_functional import get_core_functions
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functional = get_core_functions()
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# 一些普通功能模块
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from core_functional import get_core_functions
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functional = get_core_functions()
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# 高级函数插件
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from crazy_functional import get_crazy_functions
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crazy_fns = get_crazy_functions()
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# 高级函数插件
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from crazy_functional import get_crazy_functions
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crazy_fns = get_crazy_functions()
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# 处理markdown文本格式的转变
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gr.Chatbot.postprocess = format_io
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# 处理markdown文本格式的转变
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gr.Chatbot.postprocess = format_io
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# 做一些外观色彩上的调整
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from theme import adjust_theme, advanced_css
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set_theme = adjust_theme()
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# 做一些外观色彩上的调整
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from theme import adjust_theme, advanced_css
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set_theme = adjust_theme()
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# 代理与自动更新
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from check_proxy import check_proxy, auto_update
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proxy_info = check_proxy(proxies)
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# 代理与自动更新
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from check_proxy import check_proxy, auto_update
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proxy_info = check_proxy(proxies)
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gr_L1 = lambda: gr.Row().style()
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gr_L2 = lambda scale: gr.Column(scale=scale)
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if LAYOUT == "TOP-DOWN":
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gr_L1 = lambda: gr.Row().style()
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gr_L2 = lambda scale: gr.Column(scale=scale)
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if LAYOUT == "TOP-DOWN":
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gr_L1 = lambda: DummyWith()
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gr_L2 = lambda scale: gr.Row()
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CHATBOT_HEIGHT /= 2
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cancel_handles = []
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with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
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cancel_handles = []
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with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=False, css=advanced_css) as demo:
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gr.HTML(title_html)
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cookies = gr.State({'api_key': API_KEY, 'llm_model': LLM_MODEL})
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with gr_L1():
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@ -161,8 +162,9 @@ with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=
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# 终止按钮的回调函数注册
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stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
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stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
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# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
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def auto_opentab_delay():
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# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
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def auto_opentab_delay():
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import threading, webbrowser, time
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print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
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print(f"\t（亮色主题）: http://localhost:{PORT}")
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@ -173,5 +175,8 @@ def auto_opentab_delay():
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threading.Thread(target=open, name="open-browser", daemon=True).start()
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threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
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auto_opentab_delay()
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION)
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auto_opentab_delay()
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION)
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if __name__ == "__main__":
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main()
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@ -31,6 +31,24 @@ methods = {
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"tgui-ui": tgui_ui,
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}
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def LLM_CATCH_EXCEPTION(f):
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"""
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装饰器函数，将错误显示出来
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"""
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def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
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try:
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return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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except Exception as e:
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from toolbox import get_conf
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import traceback
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proxies, = get_conf('proxies')
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tb_str = '\n```\n' + traceback.format_exc() + '\n```\n'
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observe_window[0] = tb_str
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return tb_str
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return decorated
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colors = ['#FF00FF', '#00FFFF', '#FF0000''#990099', '#009999', '#990044']
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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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发送至LLM，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
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@ -62,17 +80,13 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
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else:
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# 如果同时询问多个大语言模型：
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executor = ThreadPoolExecutor(max_workers=16)
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executor = ThreadPoolExecutor(max_workers=4)
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models = model.split('&')
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n_model = len(models)
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window_len = len(observe_window)
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if window_len==0:
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window_mutex = [[] for _ in range(n_model)] + [True]
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elif window_len==1:
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window_mutex = [[""] for _ in range(n_model)] + [True]
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elif window_len==2:
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window_mutex = [["", time.time()] for _ in range(n_model)] + [True]
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assert window_len==3
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window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
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futures = []
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for i in range(n_model):
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@ -85,12 +99,12 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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method = methods['tgui-no-ui']
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llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
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llm_kwargs_feedin['llm_model'] = model
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future = executor.submit(method, inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
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future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
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futures.append(future)
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def mutex_manager(window_mutex, observe_window):
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while True:
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time.sleep(0.2)
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time.sleep(0.5)
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if not window_mutex[-1]: break
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# 看门狗（watchdog）
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for i in range(n_model):
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@ -98,8 +112,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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# 观察窗（window）
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chat_string = []
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for i in range(n_model):
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chat_string.append( f"[{str(models[i])} 说]: {window_mutex[i][0]}" )
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res = '\n\n---\n\n'.join(chat_string)
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chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
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res = '<br/><br/>\n\n---\n\n'.join(chat_string)
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# # # # # # # # # # #
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observe_window[0] = res
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@ -107,10 +121,18 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
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t_model.start()
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return_string_collect = []
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while True:
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worker_done = [h.done() for h in futures]
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if all(worker_done):
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executor.shutdown()
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break
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time.sleep(1)
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for i, future in enumerate(futures): # wait and get
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return_string_collect.append( f"[{str(models[i])} 说]: {future.result()}" )
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return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
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window_mutex[-1] = False # stop mutex thread
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res = '\n\n---\n\n'.join(return_string_collect)
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res = '<br/>\n\n---\n\n'.join(return_string_collect)
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return res
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@ -3,35 +3,69 @@ from transformers import AutoModel, AutoTokenizer
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import time
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import importlib
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from toolbox import update_ui, get_conf
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from multiprocessing import Process, Pipe
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#################################################################################
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class GetGLMHandle(Process):
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def __init__(self):
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super().__init__(daemon=True)
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self.parent, self.child = Pipe()
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self.chatglm_model = None
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self.chatglm_tokenizer = None
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self.start()
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print('初始化')
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global chatglm_model, chatglm_tokenizer
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def ready(self):
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return self.chatglm_model is not None
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chatglm_model = None
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chatglm_tokenizer = None
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def model_loader():
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global chatglm_model, chatglm_tokenizer
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if chatglm_tokenizer is None:
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chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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if chatglm_model is None: # 尚未加载
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def run(self):
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while True:
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try:
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if self.chatglm_model is None:
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self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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device, = get_conf('LOCAL_MODEL_DEVICE')
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if device=='cpu':
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chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
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self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
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else:
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chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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chatglm_model = chatglm_model.eval()
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chatglm_model = chatglm_model.eval()
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self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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self.chatglm_model = self.chatglm_model.eval()
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break
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else:
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break
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except:
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pass
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while True:
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kwargs = self.child.recv()
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try:
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for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
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self.child.send(response)
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except:
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self.child.send('[Local Message] Call ChatGLM fail.')
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self.child.send('[Finish]')
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def stream_chat(self, **kwargs):
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self.parent.send(kwargs)
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while True:
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res = self.parent.recv()
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if res != '[Finish]':
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yield res
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else:
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break
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return
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global glm_handle
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glm_handle = None
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#################################################################################
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def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
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"""
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多线程方法
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函数的说明请见 request_llm/bridge_all.py
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"""
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global chatglm_model, chatglm_tokenizer
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if chatglm_model is None:
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observe_window[0] = "ChatGLM尚未加载，加载需要一段时间 ……"
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global glm_handle
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if glm_handle is None:
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glm_handle = GetGLMHandle()
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observe_window[0] = "ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM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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model_loader()
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# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
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history_feedin = []
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for i in range(len(history)//2):
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@ -40,29 +74,27 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
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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, history in chatglm_model.stream_chat(chatglm_tokenizer, inputs, history=history_feedin, max_length=llm_kwargs['max_length'],
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top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
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# 观测窗，把已经获取的数据显示出去
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for response in glm_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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observe_window[0] = response
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# 看门狗 (watchdog)，如果超过期限没有喂狗，则终止
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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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# if not console_slience:
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# print(response)
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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):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
"""
|
||||
global chatglm_model, chatglm_tokenizer
|
||||
chatbot.append((inputs, ""))
|
||||
if chatglm_model is None:
|
||||
chatbot[-1] = (inputs, "ChatGLM尚未加载，加载需要一段时间 ……")
|
||||
|
||||
global glm_handle
|
||||
if glm_handle is None:
|
||||
glm_handle = GetGLMHandle()
|
||||
chatbot[-1] = (inputs, "ChatGLM尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLM消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……")
|
||||
yield from update_ui(chatbot=chatbot, history=[])
|
||||
model_loader()
|
||||
|
||||
if additional_fn is not None:
|
||||
import core_functional
|
||||
@ -71,13 +103,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||
|
||||
|
||||
history_feedin = []
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append(["What can I do?", system_prompt] )
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
for response, history in chatglm_model.stream_chat(chatglm_tokenizer, inputs, history=history_feedin, max_length=llm_kwargs['max_length'],
|
||||
top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
||||
for response in glm_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)
|
||||
Loading…
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Reference in New Issue
Block a user