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| from toolbox import CatchException, update_ui, promote_file_to_downloadzone | |
| from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency | |
| import datetime, json | |
| def fetch_items(list_of_items, batch_size): | |
| for i in range(0, len(list_of_items), batch_size): | |
| yield list_of_items[i:i + batch_size] | |
| def string_to_options(arguments): | |
| import argparse | |
| import shlex | |
| # Create an argparse.ArgumentParser instance | |
| parser = argparse.ArgumentParser() | |
| # Add command-line arguments | |
| parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo") | |
| parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='') | |
| parser.add_argument("--system_prompt", type=str, help="System prompt", default='') | |
| parser.add_argument("--batch", type=int, help="System prompt", default=50) | |
| parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50) | |
| parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2) | |
| parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1) | |
| parser.add_argument("--json_dataset", type=str, help="json_dataset", default="") | |
| parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="") | |
| # Parse the arguments | |
| args = parser.parse_args(shlex.split(arguments)) | |
| return args | |
| def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request): | |
| """ | |
| 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 | |
| llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 | |
| plugin_kwargs 插件模型的参数 | |
| chatbot 聊天显示框的句柄，用于显示给用户 | |
| history 聊天历史，前情提要 | |
| system_prompt 给gpt的静默提醒 | |
| user_request 当前用户的请求信息（IP地址等） | |
| """ | |
| history = [] # 清空历史，以免输入溢出 | |
| chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成")) | |
| if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg") | |
| args = plugin_kwargs.get("advanced_arg", None) | |
| if args is None: | |
| chatbot.append(("没给定指令", "退出")) | |
| yield from update_ui(chatbot=chatbot, history=history); return | |
| else: | |
| arguments = string_to_options(arguments=args) | |
| dat = [] | |
| with open(txt, 'r', encoding='utf8') as f: | |
| for line in f.readlines(): | |
| json_dat = json.loads(line) | |
| dat.append(json_dat["content"]) | |
| llm_kwargs['llm_model'] = arguments.llm_to_learn | |
| for batch in fetch_items(dat, arguments.batch): | |
| res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( | |
| inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)], | |
| inputs_show_user_array=[f"Show Nothing" for _ in (batch)], | |
| llm_kwargs=llm_kwargs, | |
| chatbot=chatbot, | |
| history_array=[[] for _ in (batch)], | |
| sys_prompt_array=[arguments.system_prompt for _ in (batch)], | |
| max_workers=10 # OpenAI所允许的最大并行过载 | |
| ) | |
| with open(txt+'.generated.json', 'a+', encoding='utf8') as f: | |
| for b, r in zip(batch, res[1::2]): | |
| f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n') | |
| promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot) | |
| return | |
| def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request): | |
| """ | |
| 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 | |
| llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 | |
| plugin_kwargs 插件模型的参数 | |
| chatbot 聊天显示框的句柄，用于显示给用户 | |
| history 聊天历史，前情提要 | |
| system_prompt 给gpt的静默提醒 | |
| user_request 当前用户的请求信息（IP地址等） | |
| """ | |
| import subprocess | |
| history = [] # 清空历史，以免输入溢出 | |
| chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成")) | |
| if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg") | |
| args = plugin_kwargs.get("advanced_arg", None) | |
| if args is None: | |
| chatbot.append(("没给定指令", "退出")) | |
| yield from update_ui(chatbot=chatbot, history=history); return | |
| else: | |
| arguments = string_to_options(arguments=args) | |
| pre_seq_len = arguments.pre_seq_len # 128 | |
| learning_rate = arguments.learning_rate # 2e-2 | |
| num_gpus = arguments.num_gpus # 1 | |
| json_dataset = arguments.json_dataset # 't_code.json' | |
| ptuning_directory = arguments.ptuning_directory # '/home/hmp/ChatGLM2-6B/ptuning' | |
| command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \ | |
| --do_train \ | |
| --train_file AdvertiseGen/{json_dataset} \ | |
| --validation_file AdvertiseGen/{json_dataset} \ | |
| --preprocessing_num_workers 20 \ | |
| --prompt_column content \ | |
| --response_column summary \ | |
| --overwrite_cache \ | |
| --model_name_or_path THUDM/chatglm2-6b \ | |
| --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \ | |
| --overwrite_output_dir \ | |
| --max_source_length 256 \ | |
| --max_target_length 256 \ | |
| --per_device_train_batch_size 1 \ | |
| --per_device_eval_batch_size 1 \ | |
| --gradient_accumulation_steps 16 \ | |
| --predict_with_generate \ | |
| --max_steps 100 \ | |
| --logging_steps 10 \ | |
| --save_steps 20 \ | |
| --learning_rate {learning_rate} \ | |
| --pre_seq_len {pre_seq_len} \ | |
| --quantization_bit 4" | |
| process = subprocess.Popen(command, shell=True, cwd=ptuning_directory) | |
| try: | |
| process.communicate(timeout=3600*24) | |
| except subprocess.TimeoutExpired: | |
| process.kill() | |
| return | |