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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
- @CatchException
- def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- 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
- web_port 当前软件运行的端口号
- """
- 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
- @CatchException
- def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- 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
- web_port 当前软件运行的端口号
- """
- 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
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