GPT2训练自己的对话问答机器人-Toy模板网

这篇具有很好参考价值的文章主要介绍了GPT2训练自己的对话问答机器人。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

1.环境搭建

这里我搭建了虚拟的3.6环境

conda create -n gpt python=3.6
conda activate gpt
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch
pip install transformers==4.4.2 -i https://pypi.python.org/simple
pip install tensorboard
pip uninstall sklearn
pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install pandas
pip install matplotlib
pip install jieba

2.理论研究

基于GPT2的中文闲聊机器人，模型实现基于HuggingFace的transformers ,精读GPT2-Chinese的论文和代码，获益匪浅。
GPT2训练自己的对话问答机器人,机器人,python,人工智能

3.模型训练与测试

data/train.txt:默认的原始训练集文件，存放闲聊语料;data/train.pkl:对原始训练语料进行tokenize之后的文件,存储一个list对象，list的每条数据表示一个多轮对话，表示一条训练数据
model:存放对话生成的模型;epoch40:经过40轮训练之后得到的模型,config.json:模型参数的配置文件;pytorch_model.bin:模型文件
vocab/vocab.txt:字典文件。默认的字典大小为13317，若需要使用自定义字典，需要将confog.json文件中的vocab_size字段设为相应的大小。
sample:存放人机闲聊生成的历史聊天记录

3.1语料tokenize

运行preprocess.py:数据预处理代码。

from tokenizers import BertWordPieceTokenizer
from transformers import BertTokenizer
from transformers import BertTokenizerFast
import argparse
import pandas as pd
import pickle
import jieba.analyse
from tqdm import tqdm
from transformers import GPT2TokenizerFast, GPT2LMHeadModel
import logging
import numpy as np


def create_logger(log_path):
    """
    将日志输出到日志文件和控制台
    """
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.INFO)

    formatter = logging.Formatter(
        '%(asctime)s - %(levelname)s - %(message)s')

    # 创建一个handler，用于写入日志文件
    file_handler = logging.FileHandler(
        filename=log_path)
    file_handler.setFormatter(formatter)
    file_handler.setLevel(logging.INFO)
    logger.addHandler(file_handler)

    # 创建一个handler，用于将日志输出到控制台
    console = logging.StreamHandler()
    console.setLevel(logging.DEBUG)
    console.setFormatter(formatter)
    logger.addHandler(console)

    return logger


def preprocess():
    """
    对原始语料进行tokenize，将每段对话处理成如下形式："[CLS]utterance1[SEP]utterance2[SEP]utterance3[SEP]"
    """
    # 设置参数
    parser = argparse.ArgumentParser()
    parser.add_argument('--vocab_path', default='vocab/vocab.txt', type=str, required=False,
                        help='词表路径')
    parser.add_argument('--log_path', default='data/preprocess.log', type=str, required=False, help='训练日志存放位置')
    parser.add_argument('--train_path', default='data/train.txt', type=str, required=False, help='训练日志存放位置')
    parser.add_argument('--save_path', default='data/train.pkl', type=str, required=False, help='tokenize的训练数据集')
    args = parser.parse_args()

    # 初始化日志对象
    logger = create_logger(args.log_path)

    # 初始化tokenizer
    tokenizer = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
    sep_id = tokenizer.sep_token_id
    cls_id = tokenizer.cls_token_id
    logger.info("preprocessing data,data path:{}, save path:{}".format(args.train_path, args.save_path))

    # 读取训练数据集
    with open(args.train_path, 'rb') as f:
        data = f.read().decode("utf-8")

    # 需要区分linux和windows环境下的换行符
    if "\r\n" in data:
        train_data = data.split("\r\n\r\n")
    else:
        train_data = data.split("\n\n")
    logger.info("there are {} dialogue in dataset".format(len(train_data)))

    # 开始进行tokenize
    # 保存所有的对话数据,每条数据的格式为："[CLS]utterance1[SEP]utterance2[SEP]utterance3[SEP]"
    dialogue_len = []  # 记录所有对话tokenize之后的长度，用于统计中位数与均值
    dialogue_list = []
    with open(args.save_path, "w", encoding="utf-8") as f:
        for index, dialogue in enumerate(tqdm(train_data)):
            if "\r\n" in data:
                utterances = dialogue.split("\r\n")
            else:
                utterances = dialogue.split("\n")

            input_ids = [cls_id]  # 每个dialogue以[CLS]开头
            for utterance in utterances:
                input_ids += tokenizer.encode(utterance, add_special_tokens=False)
                input_ids.append(sep_id)  # 每个utterance之后添加[SEP]，表示utterance结束
            dialogue_len.append(len(input_ids))
            dialogue_list.append(input_ids)
    len_mean = np.mean(dialogue_len)
    len_median = np.median(dialogue_len)
    len_max = np.max(dialogue_len)
    with open(args.save_path, "wb") as f:
        pickle.dump(dialogue_list, f)
    logger.info("finish preprocessing data,the result is stored in {}".format(args.save_path))
    logger.info("mean of dialogue len:{},median of dialogue len:{},max len:{}".format(len_mean, len_median, len_max))


if __name__ == '__main__':
    preprocess()

3.2用GPT2训练数据

运行train.py,使用预处理后的数据，对模型进行自回归训练，模型保存在根目录下的model文件夹中。

import argparse
import math
import time
import torch
import torch.nn.functional as F
import torch.optim as optim
import logging
from datetime import datetime
import os
from torch.utils.data import Dataset, DataLoader
from os.path import join, exists
from torch.nn import CrossEntropyLoss
from tqdm import tqdm
from torch.nn import DataParallel
import transformers
import pickle
import sys
from pytorchtools import EarlyStopping
from sklearn.model_selection import train_test_split
from data_parallel import BalancedDataParallel
from transformers import GPT2TokenizerFast, GPT2LMHeadModel, GPT2Config
from transformers import BertTokenizerFast
import pandas as pd
import torch.nn.utils.rnn as rnn_utils
import numpy as np
from dataset import MyDataset


def set_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', default='3', type=str, required=False, help='设置使用哪些显卡')
    parser.add_argument('--no_cuda', action='store_true', help='不使用GPU进行训练')
    parser.add_argument('--vocab_path', default='vocab/vocab.txt', type=str, required=False,
                        help='词表路径')
    parser.add_argument('--model_config', default='config/config.json', type=str, required=False,
                        help='设置模型参数')
    parser.add_argument('--train_path', default='data/train.pkl', type=str, required=False, help='训练集路径')
    parser.add_argument('--max_len', default=150, type=int, required=False, help='训练时，输入数据的最大长度')

    parser.add_argument('--log_path', default='data/train.log', type=str, required=False, help='训练日志存放位置')
    parser.add_argument('--log', default=True, help="是否记录日志")
    parser.add_argument('--ignore_index', default=-100, type=int, required=False, help='对于ignore_index的label token不计算梯度')
    # parser.add_argument('--input_len', default=200, type=int, required=False, help='输入的长度')
    parser.add_argument('--epochs', default=15, type=int, required=False, help='训练的最大轮次')
    parser.add_argument('--batch_size', default=4, type=int, required=False, help='训练的batch size')
    parser.add_argument('--gpu0_bsz', default=10, type=int, required=False, help='0号卡的batch size')
    parser.add_argument('--lr', default=2.6e-5, type=float, required=False, help='学习率')
    parser.add_argument('--eps', default=1.0e-09, type=float, required=False, help='衰减率')
    parser.add_argument('--log_step', default=1, type=int, required=False, help='多少步汇报一次loss')
    parser.add_argument('--gradient_accumulation_steps', default=4, type=int, required=False, help='梯度积累')
    parser.add_argument('--max_grad_norm', default=2.0, type=float, required=False)
    parser.add_argument('--save_model_path', default='model', type=str, required=False,
                        help='模型输出路径')
    parser.add_argument('--pretrained_model', default='', type=str, required=False,
                        help='预训练的模型的路径')
    # parser.add_argument('--seed', type=int, default=None, help='设置种子用于生成随机数，以使得训练的结果是确定的')
    parser.add_argument('--num_workers', type=int, default=0, help="dataloader加载数据时使用的线程数量")
    parser.add_argument('--patience', type=int, default=0, help="用于early stopping,设为0时,不进行early stopping.early stop得到的模型的生成效果不一定会更好。")
    parser.add_argument('--warmup_steps', type=int, default=4000, help='warm up步数')
    # parser.add_argument('--label_smoothing', default=True, action='store_true', help='是否进行标签平滑')
    parser.add_argument('--val_num', type=int, default=8000, help='验证集大小')
    args = parser.parse_args()
    return args


def create_logger(args):
    """
    将日志输出到日志文件和控制台
    """
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.INFO)

    formatter = logging.Formatter(
        '%(asctime)s - %(levelname)s - %(message)s')

    # 创建一个handler，用于写入日志文件
    file_handler = logging.FileHandler(
        filename=args.log_path)
    file_handler.setFormatter(formatter)
    file_handler.setLevel(logging.INFO)
    logger.addHandler(file_handler)

    # 创建一个handler，用于将日志输出到控制台
    console = logging.StreamHandler()
    console.setLevel(logging.DEBUG)
    console.setFormatter(formatter)
    logger.addHandler(console)

    return logger


def collate_fn(batch):
    input_ids = rnn_utils.pad_sequence(batch, batch_first=True, padding_value=0)
    labels = rnn_utils.pad_sequence(batch, batch_first=True, padding_value=-100)
    return input_ids, labels


# def padding_batch(data_list, pad_id):
#     """
#     使用pad_id将data_list的每条数据，填充至data_list中最长的长度
#     :param data_list:
#     :param pad_id:
#     :return:
#     """
#     # 统计data_list中的最大长度
#     max_len = 0
#     for data in data_list:
#         max_len = max_len if max_len > len(data) else len(data)
#
#     # 对数据进行padding
#     new_data_list = []
#     for data in data_list:
#         new_data = data + [pad_id] * (max_len - len(data))
#         new_data_list.append(new_data)
#     return new_data_list


def load_dataset(logger, args):
    """
    加载训练集和验证集
    """
    logger.info("loading training dataset and validating dataset")
    train_path = args.train_path

    with open(train_path, "rb") as f:
        input_list = pickle.load(f)

    # 划分训练集与验证集
    val_num = args.val_num
    input_list_train = input_list[val_num:]
    input_list_val = input_list[:val_num]
    # test
    # input_list_train = input_list_train[:24]
    # input_list_val = input_list_val[:24]

    train_dataset = MyDataset(input_list_train, args.max_len)
    val_dataset = MyDataset(input_list_val, args.max_len)

    return train_dataset, val_dataset


def train_epoch(model, train_dataloader, optimizer, scheduler, logger,
                epoch, args):
    model.train()
    device = args.device
    # pad_id = args.pad_id
    # sep_id = args.sep_id
    ignore_index = args.ignore_index
    epoch_start_time = datetime.now()
    total_loss = 0  # 记录下整个epoch的loss的总和

    # epoch_correct_num:每个epoch中,output预测正确的word的数量
    # epoch_total_num: 每个epoch中,output预测的word的总数量
    epoch_correct_num, epoch_total_num = 0, 0

    for batch_idx, (input_ids, labels) in enumerate(train_dataloader):
        # 捕获cuda out of memory exception
        try:
            input_ids = input_ids.to(device)
            labels = labels.to(device)
            outputs = model.forward(input_ids, labels=labels)
            logits = outputs.logits
            loss = outputs.loss
            loss = loss.mean()

            # 统计该batch的预测token的正确数与总数
            batch_correct_num, batch_total_num = calculate_acc(logits, labels, ignore_index=ignore_index)
            # 统计该epoch的预测token的正确数与总数
            epoch_correct_num += batch_correct_num
            epoch_total_num += batch_total_num
            # 计算该batch的accuracy
            batch_acc = batch_correct_num / batch_total_num

            total_loss += loss.item()
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            loss.backward()
            # 梯度裁剪
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            # 进行一定step的梯度累计之后，更新参数
            if (batch_idx + 1) % args.gradient_accumulation_steps == 0:
                # 更新参数
                optimizer.step()
                # 更新学习率
                scheduler.step()
                # 清空梯度信息
                optimizer.zero_grad()

            if (batch_idx + 1) % args.log_step == 0:
                logger.info(
                    "batch {} of epoch {}, loss {}, batch_acc {}, lr {}".format(
                        batch_idx + 1, epoch + 1, loss.item() * args.gradient_accumulation_steps, batch_acc, scheduler.get_lr()))

            del input_ids, outputs

        except RuntimeError as exception:
            if "out of memory" in str(exception):
                logger.info("WARNING: ran out of memory")
                if hasattr(torch.cuda, 'empty_cache'):
                    torch.cuda.empty_cache()
            else:
                logger.info(str(exception))
                raise exception

    # 记录当前epoch的平均loss与accuracy
    epoch_mean_loss = total_loss / len(train_dataloader)
    epoch_mean_acc = epoch_correct_num / epoch_total_num
    logger.info(
        "epoch {}: loss {}, predict_acc {}".format(epoch + 1, epoch_mean_loss, epoch_mean_acc))

    # save model
    logger.info('saving model for epoch {}'.format(epoch + 1))
    model_path = join(args.save_model_path, 'epoch{}'.format(epoch + 1))
    if not os.path.exists(model_path):
        os.mkdir(model_path)
    model_to_save = model.module if hasattr(model, 'module') else model
    model_to_save.save_pretrained(model_path)
    logger.info('epoch {} finished'.format(epoch + 1))
    epoch_finish_time = datetime.now()
    logger.info('time for one epoch: {}'.format(epoch_finish_time - epoch_start_time))

    return epoch_mean_loss


def validate_epoch(model, validate_dataloader, logger, epoch, args):
    logger.info("start validating")
    model.eval()
    device = args.device
    # pad_id = args.pad_id
    # sep_id = args.sep_id
    ignore_index = args.ignore_index
    epoch_start_time = datetime.now()
    total_loss = 0
    # 捕获cuda out of memory exception
    try:
        with torch.no_grad():
            for batch_idx, (input_ids, labels) in enumerate(validate_dataloader):
                input_ids = input_ids.to(device)
                labels = labels.to(device)
                outputs = model.forward(input_ids, labels=labels)
                logits = outputs.logits
                loss = outputs.loss
                loss = loss.mean()

                total_loss += loss.item()
                del input_ids, outputs

            # 记录当前epoch的平均loss
            epoch_mean_loss = total_loss / len(validate_dataloader)
            logger.info(
                "validate epoch {}: loss {}".format(epoch+1, epoch_mean_loss))
            epoch_finish_time = datetime.now()
            logger.info('time for validating one epoch: {}'.format(epoch_finish_time - epoch_start_time))
            return epoch_mean_loss
    except RuntimeError as exception:
        if "out of memory" in str(exception):
            logger.info("WARNING: ran out of memory")
            if hasattr(torch.cuda, 'empty_cache'):
                torch.cuda.empty_cache()
        else:
            logger.info(str(exception))
            raise exception


def train(model, logger, train_dataset, validate_dataset, args):
    train_dataloader = DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=collate_fn,
        drop_last=True
    )
    validate_dataloader = DataLoader(validate_dataset, batch_size=args.batch_size, shuffle=True,
                                     num_workers=args.num_workers, collate_fn=collate_fn, drop_last=True)
    early_stopping = EarlyStopping(args.patience, verbose=True, save_path=args.save_model_path)
    t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.epochs
    optimizer = transformers.AdamW(model.parameters(), lr=args.lr, eps=args.eps)
    # scheduler = transformers.WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
    scheduler = transformers.get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )

    logger.info('starting training')

    # 用于记录每个epoch训练和验证的loss
    train_losses, validate_losses = [], []
    # 记录验证集的最小loss
    best_val_loss = 10000
    # 开始训练
    for epoch in range(args.epochs):
        # ========== train ========== #
        train_loss = train_epoch(
            model=model, train_dataloader=train_dataloader,
            optimizer=optimizer, scheduler=scheduler,
            logger=logger, epoch=epoch, args=args)
        train_losses.append(train_loss)

        # ========== validate ========== #
        validate_loss = validate_epoch(
            model=model, validate_dataloader=validate_dataloader,
            logger=logger, epoch=epoch, args=args)
        validate_losses.append(validate_loss)

        # 保存当前困惑度最低的模型，困惑度低，模型的生成效果不一定会越好
        if validate_loss < best_val_loss:
            best_val_loss = validate_loss
            logger.info('saving current best model for epoch {}'.format(epoch + 1))
            model_path = join(args.save_model_path, 'min_ppl_model'.format(epoch + 1))
            if not os.path.exists(model_path):
                os.mkdir(model_path)
            model_to_save = model.module if hasattr(model, 'module') else model
            model_to_save.save_pretrained(model_path)

        #  如果patience=0,则不进行early stopping
        if args.patience == 0:
            continue
        early_stopping(validate_loss, model)
        if early_stopping.early_stop:
            logger.info("Early stopping")
            break
    logger.info('training finished')
    logger.info("train_losses:{}".format(train_losses))
    logger.info("validate_losses:{}".format(validate_losses))


def caculate_loss(logit, target, pad_idx, smoothing=True):
    if smoothing:
        logit = logit[..., :-1, :].contiguous().view(-1, logit.size(2))
        target = target[..., 1:].contiguous().view(-1)

        eps = 0.1
        n_class = logit.size(-1)

        one_hot = torch.zeros_like(logit).scatter(1, target.view(-1, 1), 1)
        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
        log_prb = F.log_softmax(logit, dim=1)

        non_pad_mask = target.ne(pad_idx)
        loss = -(one_hot * log_prb).sum(dim=1)
        loss = loss.masked_select(non_pad_mask).mean()  # average later
    else:
        # loss = F.cross_entropy(predict_logit, target, ignore_index=pad_idx)
        logit = logit[..., :-1, :].contiguous().view(-1, logit.size(-1))
        labels = target[..., 1:].contiguous().view(-1)
        loss = F.cross_entropy(logit, labels, ignore_index=pad_idx)
    return loss


def calculate_acc(logit, labels, ignore_index=-100):
    logit = logit[..., :-1, :].contiguous().view(-1, logit.size(-1))
    labels = labels[..., 1:].contiguous().view(-1)

    _, logit = logit.max(dim=-1)  # 对于每条数据，返回最大的index
    # 进行非运算，返回一个tensor，若labels的第i个位置为pad_id，则置为0，否则为1
    non_pad_mask = labels.ne(ignore_index)
    n_correct = logit.eq(labels).masked_select(non_pad_mask).sum().item()
    n_word = non_pad_mask.sum().item()
    return n_correct, n_word


def main():
    # 初始化参数
    args = set_args()

    # 设置使用哪些显卡进行训练
    os.environ["CUDA_VISIBLE_DEVICES"] = args.device

    args.cuda = not args.no_cuda

    if args.batch_size < 2048 and args.warmup_steps <= 4000:
        print('[Warning] The warmup steps may be not enough.\n' \
              '(sz_b, warmup) = (2048, 4000) is the official setting.\n' \
              'Using smaller batch w/o longer warmup may cause ' \
              'the warmup stage ends with only little data trained.')

    # 创建日志对象
    logger = create_logger(args)
    # 当用户使用GPU,并且GPU可用时
    args.cuda = torch.cuda.is_available() and not args.no_cuda
    device = 'cuda:0' if args.cuda else 'cpu'
    args.device = device
    logger.info('using device:{}'.format(device))

    # 初始化tokenizer
    tokenizer = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
    args.sep_id = tokenizer.sep_token_id
    args.pad_id = tokenizer.pad_token_id
    args.cls_id = tokenizer.cls_token_id

    # 创建模型的输出目录
    if not os.path.exists(args.save_model_path):
        os.mkdir(args.save_model_path)

    # 创建模型
    if args.pretrained_model:  # 加载预训练模型
        model = GPT2LMHeadModel.from_pretrained(args.pretrained_model)
    else:  # 初始化模型
        model_config = GPT2Config.from_json_file(args.model_config)
        model = GPT2LMHeadModel(config=model_config)
    model = model.to(device)
    logger.info('model config:\n{}'.format(model.config.to_json_string()))
    assert model.config.vocab_size == tokenizer.vocab_size

    # 并行训练模型
    if args.cuda and torch.cuda.device_count() > 1:
        model = DataParallel(model).cuda()
        # model = BalancedDataParallel(args.gpu0_bsz, model, dim=0).cuda()
        logger.info("use GPU {} to train".format(args.device))

    # 计算模型参数数量
    num_parameters = 0
    parameters = model.parameters()
    for parameter in parameters:
        num_parameters += parameter.numel()
    logger.info('number of model parameters: {}'.format(num_parameters))

    # 记录参数设置
    logger.info("args:{}".format(args))

    # 加载训练集和验证集
    # ========= Loading Dataset ========= #
    train_dataset, validate_dataset = load_dataset(logger, args)

    train(model, logger, train_dataset, validate_dataset, args)


if __name__ == '__main__':
    main()

3.3人机交互

运行interact.py，使用训练好的模型，进行人机交互，输入Ctrl+Z结束对话之后，聊天记录将保存到sample目录下的sample.txt文件中。

from tokenizers import BertWordPieceTokenizer
from transformers import BertTokenizer
from transformers import BertTokenizerFast
import argparse
import pandas as pd
import pickle
import jieba.analyse
from tqdm import tqdm
from transformers import GPT2TokenizerFast, GPT2LMHeadModel
import logging
import numpy as np


def create_logger(log_path):
    """
    将日志输出到日志文件和控制台
    """
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.INFO)

    formatter = logging.Formatter(
        '%(asctime)s - %(levelname)s - %(message)s')

    # 创建一个handler，用于写入日志文件
    file_handler = logging.FileHandler(
        filename=log_path)
    file_handler.setFormatter(formatter)
    file_handler.setLevel(logging.INFO)
    logger.addHandler(file_handler)

    # 创建一个handler，用于将日志输出到控制台
    console = logging.StreamHandler()
    console.setLevel(logging.DEBUG)
    console.setFormatter(formatter)
    logger.addHandler(console)

    return logger


def preprocess():
    """
    对原始语料进行tokenize，将每段对话处理成如下形式："[CLS]utterance1[SEP]utterance2[SEP]utterance3[SEP]"
    """
    # 设置参数
    parser = argparse.ArgumentParser()
    parser.add_argument('--vocab_path', default='vocab/vocab.txt', type=str, required=False,
                        help='词表路径')
    parser.add_argument('--log_path', default='data/preprocess.log', type=str, required=False, help='训练日志存放位置')
    parser.add_argument('--train_path', default='data/train.txt', type=str, required=False, help='训练日志存放位置')
    parser.add_argument('--save_path', default='data/train.pkl', type=str, required=False, help='tokenize的训练数据集')
    args = parser.parse_args()

    # 初始化日志对象
    logger = create_logger(args.log_path)

    # 初始化tokenizer
    tokenizer = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
    sep_id = tokenizer.sep_token_id
    cls_id = tokenizer.cls_token_id
    logger.info("preprocessing data,data path:{}, save path:{}".format(args.train_path, args.save_path))

    # 读取训练数据集
    with open(args.train_path, 'rb') as f:
        data = f.read().decode("utf-8")

    # 需要区分linux和windows环境下的换行符
    if "\r\n" in data:
        train_data = data.split("\r\n\r\n")
    else:
        train_data = data.split("\n\n")
    logger.info("there are {} dialogue in dataset".format(len(train_data)))

    # 开始进行tokenize
    # 保存所有的对话数据,每条数据的格式为："[CLS]utterance1[SEP]utterance2[SEP]utterance3[SEP]"
    dialogue_len = []  # 记录所有对话tokenize之后的长度，用于统计中位数与均值
    dialogue_list = []
    with open(args.save_path, "w", encoding="utf-8") as f:
        for index, dialogue in enumerate(tqdm(train_data)):
            if "\r\n" in data:
                utterances = dialogue.split("\r\n")
            else:
                utterances = dialogue.split("\n")

            input_ids = [cls_id]  # 每个dialogue以[CLS]开头
            for utterance in utterances:
                input_ids += tokenizer.encode(utterance, add_special_tokens=False)
                input_ids.append(sep_id)  # 每个utterance之后添加[SEP]，表示utterance结束
            dialogue_len.append(len(input_ids))
            dialogue_list.append(input_ids)
    len_mean = np.mean(dialogue_len)
    len_median = np.median(dialogue_len)
    len_max = np.max(dialogue_len)
    with open(args.save_path, "wb") as f:
        pickle.dump(dialogue_list, f)
    logger.info("finish preprocessing data,the result is stored in {}".format(args.save_path))
    logger.info("mean of dialogue len:{},median of dialogue len:{},max len:{}".format(len_mean, len_median, len_max))


if __name__ == '__main__':
    preprocess()