142 lines
6.2 KiB
Python
142 lines
6.2 KiB
Python
from toolbox import CatchException, update_ui, promote_file_to_downloadzone
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
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import datetime, json
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def fetch_items(list_of_items, batch_size):
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for i in range(0, len(list_of_items), batch_size):
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yield list_of_items[i:i + batch_size]
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def string_to_options(arguments):
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import argparse
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import shlex
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# Create an argparse.ArgumentParser instance
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parser = argparse.ArgumentParser()
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# Add command-line arguments
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parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
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parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
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parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
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parser.add_argument("--batch", type=int, help="System prompt", default=50)
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parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
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parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
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parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
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parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
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parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
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# Parse the arguments
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args = parser.parse_args(shlex.split(arguments))
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return args
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@CatchException
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def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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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
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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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web_port 当前软件运行的端口号
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"""
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history = [] # 清空历史，以免输入溢出
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chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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args = plugin_kwargs.get("advanced_arg", None)
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if args is None:
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chatbot.append(("没给定指令", "退出"))
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yield from update_ui(chatbot=chatbot, history=history); return
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else:
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arguments = string_to_options(arguments=args)
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dat = []
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with open(txt, 'r', encoding='utf8') as f:
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for line in f.readlines():
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json_dat = json.loads(line)
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dat.append(json_dat["content"])
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llm_kwargs['llm_model'] = arguments.llm_to_learn
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for batch in fetch_items(dat, arguments.batch):
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res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
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inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history_array=[[] for _ in (batch)],
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sys_prompt_array=[arguments.system_prompt for _ in (batch)],
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max_workers=10 # OpenAI所允许的最大并行过载
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)
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with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
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for b, r in zip(batch, res[1::2]):
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f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
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promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
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return
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@CatchException
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def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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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
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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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web_port 当前软件运行的端口号
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"""
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import subprocess
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history = [] # 清空历史，以免输入溢出
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chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
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if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
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args = plugin_kwargs.get("advanced_arg", None)
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if args is None:
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chatbot.append(("没给定指令", "退出"))
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yield from update_ui(chatbot=chatbot, history=history); return
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else:
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arguments = string_to_options(arguments=args)
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pre_seq_len = arguments.pre_seq_len # 128
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learning_rate = arguments.learning_rate # 2e-2
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num_gpus = arguments.num_gpus # 1
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json_dataset = arguments.json_dataset # 't_code.json'
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ptuning_directory = arguments.ptuning_directory # '/home/hmp/ChatGLM2-6B/ptuning'
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command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
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--do_train \
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--train_file AdvertiseGen/{json_dataset} \
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--validation_file AdvertiseGen/{json_dataset} \
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--preprocessing_num_workers 20 \
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--prompt_column content \
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--response_column summary \
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--overwrite_cache \
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--model_name_or_path THUDM/chatglm2-6b \
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--output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
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--overwrite_output_dir \
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--max_source_length 256 \
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--max_target_length 256 \
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--per_device_train_batch_size 1 \
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--per_device_eval_batch_size 1 \
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--gradient_accumulation_steps 16 \
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--predict_with_generate \
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--max_steps 100 \
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--logging_steps 10 \
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--save_steps 20 \
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--learning_rate {learning_rate} \
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--pre_seq_len {pre_seq_len} \
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--quantization_bit 4"
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process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
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try:
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process.communicate(timeout=3600*24)
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except subprocess.TimeoutExpired:
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process.kill()
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return
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