114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
import os
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import json
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import time
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from tqdm import tqdm
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import concurrent.futures
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from datetime import datetime
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import numpy as np
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from config.config import result_dir, storage_interval, window_size, overlap_size, multi_process_num
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from model.qwen import call_qwen_single_turn
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from util.logger import get_logger
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from util.data_loader import get_file_list, get_txt_content, capture_qa, merge_sub_qa_generation, save_to_file
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logger = get_logger()
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"""
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每个线程产生 QA 对以及存储到子文件中
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"""
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def single_thread_generate(thread_num, interval, model_caller, storage_jsonl_path, contents):
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storage_counter = 0
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storage_list = []
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for content in tqdm(contents):
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try:
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response = model_caller(content)
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captured_qa = capture_qa(response)
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if captured_qa is None:
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continue
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storage_list.extend(captured_qa)
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storage_counter += 1
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if storage_counter % interval == 0:
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save_to_file(storage_jsonl_path, storage_list)
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storage_counter = 0
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storage_list = []
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except Exception as exc:
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logger.error("QA generation error : %s" % (exc))
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if storage_list:
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save_to_file(storage_jsonl_path, storage_list)
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storage_list = []
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"""
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生成 QA 对
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model_name: 可调用的模型名称，暂时只实现了 qwen
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interval: 存储间隔，即每隔多少条存一次文件，过密的间隔会增大 IO 开销
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"""
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def generate_qa(
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model_name: str = 'qwen',
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interval: int = 10,
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):
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current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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if model_name == 'qwen':
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model_caller = call_qwen_single_turn
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else:
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logger.warning('This model is currently not supported and will call the default model - qwen.')
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model_caller = call_qwen_single_turn
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model_name = 'qwen'
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logger.info(f'The called model is: {model_name}.')
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logger.info(f'The storage interval is: {interval}.')
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file_list = get_file_list()
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storage_counter = 0
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storage_list = []
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for file_path in file_list:
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contents = get_txt_content(file_path, window_size=window_size, overlap_size=overlap_size)
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storage_list = []
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_, file_name = os.path.split(file_path)
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storage_jsonl_path = os.path.join(
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result_dir, f'{current_time}-{file_name}-{model_name}.jsonl')
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logger.info(f'The generated QA will be stored in {storage_jsonl_path}.')
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contents_array = np.array(contents)
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chunks = np.array_split(contents_array, multi_process_num)
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# 构建并发参数 list
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parameters_list = list()
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for thread_num, chunk in enumerate(chunks):
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parameters_list.append(
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[thread_num, interval, model_caller, storage_jsonl_path + f'-{thread_num}', list(chunk)]
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)
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# 并发生成 QA 对
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# 使用 ThreadPoolExecutor 创建一个线程池，其中 max_workers=multi_process_num 指定了线程池中最大的线程数。
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with concurrent.futures.ThreadPoolExecutor(max_workers=multi_process_num) as executor:
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# 循环调用 single_thread_generate 函数，每次赋予参数 parameters
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futures = [executor.submit(single_thread_generate, *parameters) for parameters in parameters_list]
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for future in concurrent.futures.as_completed(futures):
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try:
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future.result()
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except Exception as exc:
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logger.error("Thread generated an exception: %s" % (exc))
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# 最后调用 merge_sub_qa_generation 函数，将各个子任务生成的 QA 对合并到一个文件中。汇总整个处理过程的结果。
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merge_sub_qa_generation(result_dir, storage_jsonl_path)
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if __name__ == '__main__':
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# 创建generated文件夹
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os.makedirs('./data/generated', exist_ok=True)
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generate_qa(interval=storage_interval)
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