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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327
main.py
327
main.py
@ -1,177 +1,182 @@
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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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get_conf('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: DummyWith()
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gr_L2 = lambda scale: gr.Row()
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CHATBOT_HEIGHT /= 2
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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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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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with gr_L2(scale=2):
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chatbot = gr.Chatbot()
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chatbot.style(height=CHATBOT_HEIGHT)
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history = gr.State([])
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with gr_L2(scale=1):
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with gr.Accordion("输入区", open=True) as area_input_primary:
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with gr.Row():
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txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
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with gr.Row():
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submitBtn = gr.Button("提交", variant="primary")
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with gr.Row():
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resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
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stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
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with gr.Row():
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status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}")
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with gr.Accordion("基础功能区", open=True) as area_basic_fn:
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with gr.Row():
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for k in functional:
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variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
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functional[k]["Button"] = gr.Button(k, variant=variant)
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with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
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with gr.Row():
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gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.")
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with gr.Row():
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for k in crazy_fns:
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if not crazy_fns[k].get("AsButton", True): continue
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variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
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crazy_fns[k]["Button"] = gr.Button(k, variant=variant)
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crazy_fns[k]["Button"].style(size="sm")
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with gr.Row():
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with gr.Accordion("更多函数插件", open=True):
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dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
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with gr.Column(scale=1):
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dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
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with gr.Column(scale=1):
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switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
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with gr.Row():
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with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
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file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
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with gr.Accordion("展开SysPrompt & 交互界面布局 & Github地址", open=(LAYOUT == "TOP-DOWN")):
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system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
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top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
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max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",)
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checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
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md_dropdown = gr.Dropdown(["gpt-3.5-turbo", "chatglm"], value=LLM_MODEL, label="").style(container=False)
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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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with gr_L2(scale=2):
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chatbot = gr.Chatbot()
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chatbot.style(height=CHATBOT_HEIGHT)
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history = gr.State([])
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with gr_L2(scale=1):
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with gr.Accordion("输入区", open=True) as area_input_primary:
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with gr.Row():
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txt = gr.Textbox(show_label=False, placeholder="Input question here.").style(container=False)
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with gr.Row():
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submitBtn = gr.Button("提交", variant="primary")
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with gr.Row():
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resetBtn = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
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stopBtn = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
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with gr.Row():
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status = gr.Markdown(f"Tip: 按Enter提交, 按Shift+Enter换行。当前模型: {LLM_MODEL} \n {proxy_info}")
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with gr.Accordion("基础功能区", open=True) as area_basic_fn:
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with gr.Row():
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for k in functional:
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variant = functional[k]["Color"] if "Color" in functional[k] else "secondary"
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functional[k]["Button"] = gr.Button(k, variant=variant)
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with gr.Accordion("函数插件区", open=True) as area_crazy_fn:
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with gr.Row():
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gr.Markdown("注意：以下“红颜色”标识的函数插件需从输入区读取路径作为参数.")
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with gr.Row():
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for k in crazy_fns:
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if not crazy_fns[k].get("AsButton", True): continue
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variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
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crazy_fns[k]["Button"] = gr.Button(k, variant=variant)
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crazy_fns[k]["Button"].style(size="sm")
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with gr.Row():
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with gr.Accordion("更多函数插件", open=True):
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dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
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with gr.Column(scale=1):
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dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
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with gr.Column(scale=1):
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switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
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with gr.Row():
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with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
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file_upload = gr.Files(label="任何文件, 但推荐上传压缩文件(zip, tar)", file_count="multiple")
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with gr.Accordion("展开SysPrompt & 交互界面布局 & Github地址", open=(LAYOUT == "TOP-DOWN")):
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system_prompt = gr.Textbox(show_label=True, placeholder=f"System Prompt", label="System prompt", value=initial_prompt)
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top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
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max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",)
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checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
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md_dropdown = gr.Dropdown(["gpt-3.5-turbo", "chatglm"], value=LLM_MODEL, label="").style(container=False)
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gr.Markdown(description)
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with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
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with gr.Row():
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txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
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with gr.Row():
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submitBtn2 = gr.Button("提交", variant="primary")
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with gr.Row():
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resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
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stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
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# 功能区显示开关与功能区的互动
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def fn_area_visibility(a):
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ret = {}
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ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
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ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
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ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))})
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ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
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if "底部输入区" in a: ret.update({txt: gr.update(value="")})
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return ret
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checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2] )
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# 整理反复出现的控件句柄组合
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input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt]
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output_combo = [cookies, chatbot, history, status]
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predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
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# 提交按钮、重置按钮
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cancel_handles.append(txt.submit(**predict_args))
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cancel_handles.append(txt2.submit(**predict_args))
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cancel_handles.append(submitBtn.click(**predict_args))
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cancel_handles.append(submitBtn2.click(**predict_args))
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resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
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resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
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# 基础功能区的回调函数注册
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for k in functional:
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click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
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cancel_handles.append(click_handle)
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# 文件上传区，接收文件后与chatbot的互动
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file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt], [chatbot, txt])
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# 函数插件-固定按钮区
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for k in crazy_fns:
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if not crazy_fns[k].get("AsButton", True): continue
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click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo)
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gr.Markdown(description)
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with gr.Accordion("备选输入区", open=True, visible=False) as area_input_secondary:
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with gr.Row():
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txt2 = gr.Textbox(show_label=False, placeholder="Input question here.", label="输入区2").style(container=False)
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with gr.Row():
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submitBtn2 = gr.Button("提交", variant="primary")
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with gr.Row():
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resetBtn2 = gr.Button("重置", variant="secondary"); resetBtn.style(size="sm")
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stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn.style(size="sm")
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# 功能区显示开关与功能区的互动
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def fn_area_visibility(a):
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ret = {}
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ret.update({area_basic_fn: gr.update(visible=("基础功能区" in a))})
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ret.update({area_crazy_fn: gr.update(visible=("函数插件区" in a))})
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ret.update({area_input_primary: gr.update(visible=("底部输入区" not in a))})
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ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
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if "底部输入区" in a: ret.update({txt: gr.update(value="")})
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return ret
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checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2] )
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# 整理反复出现的控件句柄组合
|
||||
input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt]
|
||||
output_combo = [cookies, chatbot, history, status]
|
||||
predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
|
||||
# 提交按钮、重置按钮
|
||||
cancel_handles.append(txt.submit(**predict_args))
|
||||
cancel_handles.append(txt2.submit(**predict_args))
|
||||
cancel_handles.append(submitBtn.click(**predict_args))
|
||||
cancel_handles.append(submitBtn2.click(**predict_args))
|
||||
resetBtn.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
|
||||
resetBtn2.click(lambda: ([], [], "已重置"), None, [chatbot, history, status])
|
||||
# 基础功能区的回调函数注册
|
||||
for k in functional:
|
||||
click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
|
||||
cancel_handles.append(click_handle)
|
||||
# 文件上传区，接收文件后与chatbot的互动
|
||||
file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt], [chatbot, txt])
|
||||
# 函数插件-固定按钮区
|
||||
for k in crazy_fns:
|
||||
if not crazy_fns[k].get("AsButton", True): continue
|
||||
click_handle = crazy_fns[k]["Button"].click(ArgsGeneralWrapper(crazy_fns[k]["Function"]), [*input_combo, gr.State(PORT)], output_combo)
|
||||
click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
|
||||
cancel_handles.append(click_handle)
|
||||
# 函数插件-下拉菜单与随变按钮的互动
|
||||
def on_dropdown_changed(k):
|
||||
variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
|
||||
return {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
|
||||
# 随变按钮的回调函数注册
|
||||
def route(k, *args, **kwargs):
|
||||
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
|
||||
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
|
||||
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
|
||||
click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
|
||||
# def expand_file_area(file_upload, area_file_up):
|
||||
# if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
|
||||
# click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
|
||||
cancel_handles.append(click_handle)
|
||||
# 函数插件-下拉菜单与随变按钮的互动
|
||||
def on_dropdown_changed(k):
|
||||
variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
|
||||
return {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
|
||||
# 随变按钮的回调函数注册
|
||||
def route(k, *args, **kwargs):
|
||||
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
|
||||
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
|
||||
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
|
||||
click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
|
||||
# def expand_file_area(file_upload, area_file_up):
|
||||
# if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
|
||||
# click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
|
||||
cancel_handles.append(click_handle)
|
||||
# 终止按钮的回调函数注册
|
||||
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
|
||||
def auto_opentab_delay():
|
||||
import threading, webbrowser, time
|
||||
print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
|
||||
print(f"\t（亮色主题）: http://localhost:{PORT}")
|
||||
print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true")
|
||||
def open():
|
||||
time.sleep(2) # 打开浏览器
|
||||
webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
|
||||
threading.Thread(target=open, name="open-browser", daemon=True).start()
|
||||
threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
|
||||
# 终止按钮的回调函数注册
|
||||
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
stopBtn2.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
|
||||
auto_opentab_delay()
|
||||
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION)
|
||||
# gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
|
||||
def auto_opentab_delay():
|
||||
import threading, webbrowser, time
|
||||
print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
|
||||
print(f"\t（亮色主题）: http://localhost:{PORT}")
|
||||
print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true")
|
||||
def open():
|
||||
time.sleep(2) # 打开浏览器
|
||||
webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
|
||||
threading.Thread(target=open, name="open-browser", daemon=True).start()
|
||||
threading.Thread(target=auto_update, name="self-upgrade", daemon=True).start()
|
||||
|
||||
auto_opentab_delay()
|
||||
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -31,6 +31,24 @@ methods = {
|
||||
"tgui-ui": tgui_ui,
|
||||
}
|
||||
|
||||
def LLM_CATCH_EXCEPTION(f):
|
||||
"""
|
||||
装饰器函数，将错误显示出来
|
||||
"""
|
||||
def decorated(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience):
|
||||
try:
|
||||
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
|
||||
except Exception as e:
|
||||
from toolbox import get_conf
|
||||
import traceback
|
||||
proxies, = get_conf('proxies')
|
||||
tb_str = '\n```\n' + traceback.format_exc() + '\n```\n'
|
||||
observe_window[0] = tb_str
|
||||
return tb_str
|
||||
return decorated
|
||||
|
||||
colors = ['#FF00FF', '#00FFFF', '#FF0000''#990099', '#009999', '#990044']
|
||||
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience=False):
|
||||
"""
|
||||
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。
|
||||
@ -62,17 +80,13 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
return method(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
|
||||
else:
|
||||
# 如果同时询问多个大语言模型：
|
||||
executor = ThreadPoolExecutor(max_workers=16)
|
||||
executor = ThreadPoolExecutor(max_workers=4)
|
||||
models = model.split('&')
|
||||
n_model = len(models)
|
||||
|
||||
window_len = len(observe_window)
|
||||
if window_len==0:
|
||||
window_mutex = [[] for _ in range(n_model)] + [True]
|
||||
elif window_len==1:
|
||||
window_mutex = [[""] for _ in range(n_model)] + [True]
|
||||
elif window_len==2:
|
||||
window_mutex = [["", time.time()] for _ in range(n_model)] + [True]
|
||||
assert window_len==3
|
||||
window_mutex = [["", time.time(), ""] for _ in range(n_model)] + [True]
|
||||
|
||||
futures = []
|
||||
for i in range(n_model):
|
||||
@ -85,12 +99,12 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
method = methods['tgui-no-ui']
|
||||
llm_kwargs_feedin = copy.deepcopy(llm_kwargs)
|
||||
llm_kwargs_feedin['llm_model'] = model
|
||||
future = executor.submit(method, inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
|
||||
future = executor.submit(LLM_CATCH_EXCEPTION(method), inputs, llm_kwargs_feedin, history, sys_prompt, window_mutex[i], console_slience)
|
||||
futures.append(future)
|
||||
|
||||
def mutex_manager(window_mutex, observe_window):
|
||||
while True:
|
||||
time.sleep(0.2)
|
||||
time.sleep(0.5)
|
||||
if not window_mutex[-1]: break
|
||||
# 看门狗（watchdog）
|
||||
for i in range(n_model):
|
||||
@ -98,8 +112,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
# 观察窗（window）
|
||||
chat_string = []
|
||||
for i in range(n_model):
|
||||
chat_string.append( f"[{str(models[i])} 说]: {window_mutex[i][0]}" )
|
||||
res = '\n\n---\n\n'.join(chat_string)
|
||||
chat_string.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {window_mutex[i][0]} </font>" )
|
||||
res = '<br/><br/>\n\n---\n\n'.join(chat_string)
|
||||
# # # # # # # # # # #
|
||||
observe_window[0] = res
|
||||
|
||||
@ -107,10 +121,18 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
t_model.start()
|
||||
|
||||
return_string_collect = []
|
||||
while True:
|
||||
worker_done = [h.done() for h in futures]
|
||||
if all(worker_done):
|
||||
executor.shutdown()
|
||||
break
|
||||
time.sleep(1)
|
||||
|
||||
for i, future in enumerate(futures): # wait and get
|
||||
return_string_collect.append( f"[{str(models[i])} 说]: {future.result()}" )
|
||||
return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
|
||||
|
||||
window_mutex[-1] = False # stop mutex thread
|
||||
res = '\n\n---\n\n'.join(return_string_collect)
|
||||
res = '<br/>\n\n---\n\n'.join(return_string_collect)
|
||||
return res
|
||||
|
||||
|
||||
|
||||
@ -3,35 +3,69 @@ from transformers import AutoModel, AutoTokenizer
|
||||
import time
|
||||
import importlib
|
||||
from toolbox import update_ui, get_conf
|
||||
from multiprocessing import Process, Pipe
|
||||
|
||||
#################################################################################
|
||||
class GetGLMHandle(Process):
|
||||
def __init__(self):
|
||||
super().__init__(daemon=True)
|
||||
self.parent, self.child = Pipe()
|
||||
self.chatglm_model = None
|
||||
self.chatglm_tokenizer = None
|
||||
self.start()
|
||||
print('初始化')
|
||||
|
||||
def ready(self):
|
||||
return self.chatglm_model is not None
|
||||
|
||||
global chatglm_model, chatglm_tokenizer
|
||||
|
||||
chatglm_model = None
|
||||
chatglm_tokenizer = None
|
||||
|
||||
def model_loader():
|
||||
global chatglm_model, chatglm_tokenizer
|
||||
if chatglm_tokenizer is None:
|
||||
chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
|
||||
if chatglm_model is None: # 尚未加载
|
||||
device, = get_conf('LOCAL_MODEL_DEVICE')
|
||||
if device=='cpu':
|
||||
chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
|
||||
else:
|
||||
chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
|
||||
chatglm_model = chatglm_model.eval()
|
||||
chatglm_model = chatglm_model.eval()
|
||||
def run(self):
|
||||
while True:
|
||||
try:
|
||||
if self.chatglm_model is None:
|
||||
self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
|
||||
device, = get_conf('LOCAL_MODEL_DEVICE')
|
||||
if device=='cpu':
|
||||
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
|
||||
else:
|
||||
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
|
||||
self.chatglm_model = self.chatglm_model.eval()
|
||||
break
|
||||
else:
|
||||
break
|
||||
except:
|
||||
pass
|
||||
while True:
|
||||
kwargs = self.child.recv()
|
||||
try:
|
||||
for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
|
||||
self.child.send(response)
|
||||
except:
|
||||
self.child.send('[Local Message] Call ChatGLM fail.')
|
||||
self.child.send('[Finish]')
|
||||
|
||||
def stream_chat(self, **kwargs):
|
||||
self.parent.send(kwargs)
|
||||
while True:
|
||||
res = self.parent.recv()
|
||||
if res != '[Finish]':
|
||||
yield res
|
||||
else:
|
||||
break
|
||||
return
|
||||
|
||||
global glm_handle
|
||||
glm_handle = None
|
||||
#################################################################################
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
"""
|
||||
global chatglm_model, chatglm_tokenizer
|
||||
if chatglm_model is None:
|
||||
observe_window[0] = "ChatGLM尚未加载，加载需要一段时间 ……"
|
||||
global glm_handle
|
||||
if glm_handle is None:
|
||||
glm_handle = GetGLMHandle()
|
||||
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 ……"
|
||||
|
||||
model_loader()
|
||||
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
|
||||
history_feedin = []
|
||||
for i in range(len(history)//2):
|
||||
@ -40,29 +74,27 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
|
||||
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
|
||||
response = ""
|
||||
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']):
|
||||
# 观测窗，把已经获取的数据显示出去
|
||||
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']):
|
||||
observe_window[0] = response
|
||||
# 看门狗 (watchdog)，如果超过期限没有喂狗，则终止
|
||||
if len(observe_window) >= 2:
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("程序终止。")
|
||||
# if not console_slience:
|
||||
# print(response)
|
||||
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
|
||||
"""
|
||||
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…
x
Reference in New Issue
Block a user