update spark v3.5, fix glm parallel problem
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binary-husky
parent
e359fff040
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@ -1,232 +0,0 @@
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from collections.abc import Callable, Iterable, Mapping
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from typing import Any
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from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc
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from toolbox import promote_file_to_downloadzone, get_log_folder
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from .crazy_utils import input_clipping, try_install_deps
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from multiprocessing import Process, Pipe
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import os
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import time
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templete = """
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```python
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import ... # Put dependencies here, e.g. import numpy as np
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class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
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def run(self, path): # The name of the function must be `run`, it takes only a positional argument.
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# rewrite the function you have just written here
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...
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return generated_file_path
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```
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"""
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def inspect_dependency(chatbot, history):
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return True
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def get_code_block(reply):
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import re
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pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
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matches = re.findall(pattern, reply) # find all code blocks in text
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if len(matches) == 1:
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return matches[0].strip('python') # code block
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for match in matches:
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if 'class TerminalFunction' in match:
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return match.strip('python') # code block
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raise RuntimeError("GPT is not generating proper code.")
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def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
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# 输入
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prompt_compose = [
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f'Your job:\n'
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f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
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f"2. You should write this function to perform following task: " + txt + "\n",
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f"3. Wrap the output python function with markdown codeblock."
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]
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i_say = "".join(prompt_compose)
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demo = []
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# 第一步
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say, inputs_show_user=i_say,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo,
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sys_prompt= r"You are a programmer."
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)
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history.extend([i_say, gpt_say])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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# 第二步
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prompt_compose = [
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"If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
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templete
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]
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i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say, inputs_show_user=inputs_show_user,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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sys_prompt= r"You are a programmer."
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)
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code_to_return = gpt_say
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history.extend([i_say, gpt_say])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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# # 第三步
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# i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
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# i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
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# installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
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# inputs=i_say, inputs_show_user=inputs_show_user,
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# llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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# sys_prompt= r"You are a programmer."
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# )
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# # # 第三步
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# i_say = "Show me how to use `pip` to install packages to run the code above. "
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# i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
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# installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
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# inputs=i_say, inputs_show_user=i_say,
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# llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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# sys_prompt= r"You are a programmer."
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# )
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installation_advance = ""
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return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
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def make_module(code):
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module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
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with open(f'{get_log_folder()}/{module_file}.py', 'w', encoding='utf8') as f:
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f.write(code)
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def get_class_name(class_string):
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import re
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# Use regex to extract the class name
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class_name = re.search(r'class (\w+)\(', class_string).group(1)
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return class_name
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class_name = get_class_name(code)
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return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}"
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def init_module_instance(module):
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import importlib
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module_, class_ = module.split('->')
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init_f = getattr(importlib.import_module(module_), class_)
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return init_f()
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def for_immediate_show_off_when_possible(file_type, fp, chatbot):
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if file_type in ['png', 'jpg']:
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image_path = os.path.abspath(fp)
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chatbot.append(['这是一张图片, 展示如下:',
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f'本地文件地址: <br/>`{image_path}`<br/>'+
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f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
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])
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return chatbot
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def subprocess_worker(instance, file_path, return_dict):
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return_dict['result'] = instance.run(file_path)
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def have_any_recent_upload_files(chatbot):
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_5min = 5 * 60
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if not chatbot: return False # chatbot is None
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most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
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if not most_recent_uploaded: return False # most_recent_uploaded is None
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if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
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else: return False # most_recent_uploaded is too old
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def get_recent_file_prompt_support(chatbot):
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most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
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path = most_recent_uploaded['path']
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return path
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@CatchException
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def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
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"""
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txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
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llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
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plugin_kwargs 插件模型的参数，暂时没有用武之地
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chatbot 聊天显示框的句柄，用于显示给用户
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history 聊天历史，前情提要
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system_prompt 给gpt的静默提醒
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user_request 当前用户的请求信息（IP地址等）
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"""
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raise NotImplementedError
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# 清空历史，以免输入溢出
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history = []; clear_file_downloadzone(chatbot)
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# 基本信息：功能、贡献者
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chatbot.append([
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"函数插件功能？",
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"CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..."
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])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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if have_any_recent_upload_files(chatbot):
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file_path = get_recent_file_prompt_support(chatbot)
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else:
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chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 读取文件
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if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files")
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recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None)
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file_path = recently_uploaded_files[-1]
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file_type = file_path.split('.')[-1]
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# 粗心检查
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if is_the_upload_folder(txt):
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chatbot.append([
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"...",
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f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）"
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])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 开始干正事
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for j in range(5): # 最多重试5次
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try:
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code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
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yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
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code = get_code_block(code)
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res = make_module(code)
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instance = init_module_instance(res)
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break
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except Exception as e:
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chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 代码生成结束, 开始执行
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try:
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import multiprocessing
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manager = multiprocessing.Manager()
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return_dict = manager.dict()
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p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict))
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# only has 10 seconds to run
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p.start(); p.join(timeout=10)
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if p.is_alive(): p.terminate(); p.join()
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p.close()
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res = return_dict['result']
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# res = instance.run(file_path)
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except Exception as e:
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chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
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# chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 顺利完成，收尾
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res = str(res)
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if os.path.exists(res):
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chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
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new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
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chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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else:
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chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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"""
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测试：
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裁剪图像，保留下半部分
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交换图像的蓝色通道和红色通道
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将图像转为灰度图像
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将csv文件转excel表格
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"""
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@ -140,7 +140,7 @@ def can_multi_process(llm):
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if llm.startswith('api2d-'): return True
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if llm.startswith('azure-'): return True
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if llm.startswith('spark'): return True
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if llm.startswith('zhipuai'): return True
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if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
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return False
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def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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@ -560,7 +560,7 @@ if "sparkv2" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
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})
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except:
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print(trimmed_format_exc())
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if "sparkv3" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
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if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
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try:
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from .bridge_spark import predict_no_ui_long_connection as spark_noui
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from .bridge_spark import predict as spark_ui
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@ -572,6 +572,14 @@ if "sparkv3" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"sparkv3.5": {
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"fn_with_ui": spark_ui,
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"fn_without_ui": spark_noui,
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"endpoint": None,
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"max_token": 4096,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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})
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except:
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@ -65,6 +65,7 @@ class SparkRequestInstance():
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self.gpt_url = "ws://spark-api.xf-yun.com/v1.1/chat"
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self.gpt_url_v2 = "ws://spark-api.xf-yun.com/v2.1/chat"
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self.gpt_url_v3 = "ws://spark-api.xf-yun.com/v3.1/chat"
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self.gpt_url_v35 = "wss://spark-api.xf-yun.com/v3.5/chat"
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self.gpt_url_img = "wss://spark-api.cn-huabei-1.xf-yun.com/v2.1/image"
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self.time_to_yield_event = threading.Event()
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@ -91,6 +92,8 @@ class SparkRequestInstance():
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gpt_url = self.gpt_url_v2
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elif llm_kwargs['llm_model'] == 'sparkv3':
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gpt_url = self.gpt_url_v3
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elif llm_kwargs['llm_model'] == 'sparkv3.5':
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gpt_url = self.gpt_url_v35
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else:
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gpt_url = self.gpt_url
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file_manifest = []
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@ -190,6 +193,7 @@ def gen_params(appid, inputs, llm_kwargs, history, system_prompt, file_manifest)
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"spark": "general",
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"sparkv2": "generalv2",
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"sparkv3": "generalv3",
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"sparkv3.5": "generalv3.5",
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}
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domains_select = domains[llm_kwargs['llm_model']]
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if file_manifest: domains_select = 'image'
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