363 lines
18 KiB
Python
363 lines
18 KiB
Python
import traceback
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from toolbox import update_ui
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def input_clipping(inputs, history, max_token_limit):
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import tiktoken
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import numpy as np
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from toolbox import get_conf
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enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
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def get_token_num(txt): return len(enc.encode(txt))
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mode = 'input-and-history'
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# 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
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input_token_num = get_token_num(inputs)
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if input_token_num < max_token_limit//2:
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mode = 'only-history'
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max_token_limit = max_token_limit - input_token_num
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everything = [inputs] if mode == 'input-and-history' else ['']
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everything.extend(history)
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n_token = get_token_num('\n'.join(everything))
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everything_token = [get_token_num(e) for e in everything]
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delta = max(everything_token) // 16 # 截断时的颗粒度
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while n_token > max_token_limit:
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where = np.argmax(everything_token)
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encoded = enc.encode(everything[where])
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clipped_encoded = encoded[:len(encoded)-delta]
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everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
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everything_token[where] = get_token_num(everything[where])
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n_token = get_token_num('\n'.join(everything))
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if mode == 'input-and-history':
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inputs = everything[0]
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else:
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pass
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history = everything[1:]
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return inputs, history
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def request_gpt_model_in_new_thread_with_ui_alive(
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inputs, inputs_show_user, llm_kwargs,
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chatbot, history, sys_prompt, refresh_interval=0.2,
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handle_token_exceed=True,
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retry_times_at_unknown_error=2,
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):
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"""
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Request GPT model，请求GPT模型同时维持用户界面活跃。
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输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
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inputs (string): List of inputs （输入）
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inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
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top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
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temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
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chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
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history (list): List of chat history （历史，对话历史列表）
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sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
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refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
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handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
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retry_times_at_unknown_error：失败时的重试次数
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输出 Returns:
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future: 输出，GPT返回的结果
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"""
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import time
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from concurrent.futures import ThreadPoolExecutor
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from request_llm.bridge_chatgpt import predict_no_ui_long_connection
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# 用户反馈
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chatbot.append([inputs_show_user, ""])
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msg = '正常'
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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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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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while True:
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try:
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# 【第一种情况】：顺利完成
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result = predict_no_ui_long_connection(
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inputs=inputs, llm_kwargs=llm_kwargs,
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history=history, sys_prompt=sys_prompt, observe_window=mutable)
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return result
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except ConnectionAbortedError as token_exceeded_error:
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# 【第二种情况】：Token溢出
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if handle_token_exceed:
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exceeded_cnt += 1
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# 【选择处理】 尝试计算比例，尽可能多地保留文本
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from toolbox import get_reduce_token_percent
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p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
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MAX_TOKEN = 4096
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EXCEED_ALLO = 512 + 512 * exceeded_cnt
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inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
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mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
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continue # 返回重试
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else:
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# 【选择放弃】
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tb_str = '```\n' + traceback.format_exc() + '```'
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mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
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return mutable[0] # 放弃
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except:
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# 【第三种情况】：其他错误：重试几次
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tb_str = '```\n' + traceback.format_exc() + '```'
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mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
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if retry_op > 0:
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retry_op -= 1
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mutable[0] += f"[Local Message] 重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
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time.sleep(5)
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continue # 返回重试
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else:
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time.sleep(5)
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return mutable[0] # 放弃
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future = executor.submit(_req_gpt, inputs, history, sys_prompt)
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while True:
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# yield一次以刷新前端页面
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time.sleep(refresh_interval)
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# “喂狗”（看门狗）
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mutable[1] = time.time()
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if future.done():
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break
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chatbot[-1] = [chatbot[-1][0], mutable[0]]
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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final_result = future.result()
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chatbot[-1] = [chatbot[-1][0], final_result]
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yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
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return final_result
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def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array, inputs_show_user_array, llm_kwargs,
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chatbot, history_array, sys_prompt_array,
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refresh_interval=0.2, max_workers=10, scroller_max_len=30,
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handle_token_exceed=True, show_user_at_complete=False,
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retry_times_at_unknown_error=2,
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):
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"""
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Request GPT model using multiple threads with UI and high efficiency
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请求GPT模型的[多线程]版。
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具备以下功能：
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实时在UI上反馈远程数据流
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使用线程池，可调节线程池的大小避免openai的流量限制错误
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处理中途中止的情况
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网络等出问题时，会把traceback和已经接收的数据转入输出
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输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
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inputs_array (list): List of inputs （每个子任务的输入）
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inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
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llm_kwargs: llm_kwargs参数
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chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
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history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
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sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
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refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
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max_workers (int, optional): Maximum number of threads (default: 10) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
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scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
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handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
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handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
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show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
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retry_times_at_unknown_error：子任务失败时的重试次数
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输出 Returns:
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list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
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"""
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import time, random
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from concurrent.futures import ThreadPoolExecutor
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from request_llm.bridge_chatgpt import predict_no_ui_long_connection
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assert len(inputs_array) == len(history_array)
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assert len(inputs_array) == len(sys_prompt_array)
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executor = ThreadPoolExecutor(max_workers=max_workers)
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n_frag = len(inputs_array)
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# 用户反馈
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chatbot.append(["请开始多线程操作。", ""])
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msg = '正常'
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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# 异步原子
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mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
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def _req_gpt(index, inputs, history, sys_prompt):
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gpt_say = ""
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retry_op = retry_times_at_unknown_error
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exceeded_cnt = 0
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mutable[index][2] = "执行中"
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while True:
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try:
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# 【第一种情况】：顺利完成
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# time.sleep(10); raise RuntimeError("测试")
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gpt_say = predict_no_ui_long_connection(
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inputs=inputs, llm_kwargs=llm_kwargs, history=history,
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sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
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)
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mutable[index][2] = "已成功"
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return gpt_say
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except ConnectionAbortedError as token_exceeded_error:
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# 【第二种情况】：Token溢出，
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if handle_token_exceed:
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exceeded_cnt += 1
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# 【选择处理】 尝试计算比例，尽可能多地保留文本
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from toolbox import get_reduce_token_percent
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p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
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MAX_TOKEN = 4096
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EXCEED_ALLO = 512 + 512 * exceeded_cnt
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inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
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gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
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mutable[index][2] = f"截断重试"
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continue # 返回重试
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else:
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# 【选择放弃】
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tb_str = '```\n' + traceback.format_exc() + '```'
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gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
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if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
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mutable[index][2] = "输入过长已放弃"
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return gpt_say # 放弃
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except:
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# 【第三种情况】：其他错误
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tb_str = '```\n' + traceback.format_exc() + '```'
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gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
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if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
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if retry_op > 0:
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retry_op -= 1
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wait = random.randint(5, 20)
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for i in range(wait):# 也许等待十几秒后，情况会好转
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mutable[index][2] = f"等待重试 {wait-i}"; time.sleep(1)
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mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
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continue # 返回重试
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else:
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mutable[index][2] = "已失败"
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wait = 5
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time.sleep(5)
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return gpt_say # 放弃
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# 异步任务开始
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futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
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range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
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cnt = 0
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while True:
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# yield一次以刷新前端页面
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time.sleep(refresh_interval)
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cnt += 1
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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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# 更好的UI视觉效果
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observe_win = []
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# print([mutable[thread_index][2] for thread_index, _ in enumerate(worker_done)])
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# 每个线程都要“喂狗”（看门狗）
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for thread_index, _ in enumerate(worker_done):
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mutable[thread_index][1] = time.time()
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# 在前端打印些好玩的东西
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for thread_index, _ in enumerate(worker_done):
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print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
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replace('\n', '').replace('```', '...').replace(
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' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
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observe_win.append(print_something_really_funny)
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stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
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if not done else f'`{mutable[thread_index][2]}`\n\n'
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for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
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chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
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msg = "正常"
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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# 异步任务结束
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gpt_response_collection = []
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for inputs_show_user, f in zip(inputs_show_user_array, futures):
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gpt_res = f.result()
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gpt_response_collection.extend([inputs_show_user, gpt_res])
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if show_user_at_complete:
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for inputs_show_user, f in zip(inputs_show_user_array, futures):
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gpt_res = f.result()
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chatbot.append([inputs_show_user, gpt_res])
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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time.sleep(1)
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return gpt_response_collection
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def WithRetry(f):
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"""
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装饰器函数，用于自动重试。
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"""
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def decorated(retry, res_when_fail, *args, **kwargs):
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assert retry >= 0
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while True:
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try:
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res = yield from f(*args, **kwargs)
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return res
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except:
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retry -= 1
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if retry<0:
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print("达到最大重试次数")
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break
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else:
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print("重试中……")
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continue
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return res_when_fail
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return decorated
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def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "":
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continue
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print(cnt)
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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print('what the fuck ?')
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raise RuntimeError("存在一行极长的文本！")
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# print(len(post))
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# 列表递归接龙
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result = [prev]
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result.extend(cut(post, must_break_at_empty_line))
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return result
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try:
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return cut(txt, must_break_at_empty_line=True)
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except RuntimeError:
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return cut(txt, must_break_at_empty_line=False)
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def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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cnt = 0
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "":
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continue
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print(cnt)
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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# print('what the fuck ? 存在一行极长的文本！')
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raise RuntimeError("存在一行极长的文本！")
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# print(len(post))
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# 列表递归接龙
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result = [prev]
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result.extend(cut(post, must_break_at_empty_line))
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return result
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try:
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return cut(txt, must_break_at_empty_line=True)
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except RuntimeError:
|
||
try:
|
||
return cut(txt, must_break_at_empty_line=False)
|
||
except RuntimeError:
|
||
# 这个中文的句号是故意的，作为一个标识而存在
|
||
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
|
||
return [r.replace('。\n', '.') for r in res]
|