CPM-main 部署实践 AI生成小说3_cpm-bee模型环境部署教程

作者：知新_RL | 2024-05-03 03:06:03

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cpm-bee模型环境部署教程

CPM-main是用来写作文的,改一下训练集就可以生成小说.

1下载项目

打开后下面代码和模型,下载模型解压后放到model目录

GitHub - yangjianxin1/CPM: Easy-to-use CPM for Chinese text generation（基于CPM的中文文本生成）Easy-to-use CPM for Chinese text generation（基于CPM的中文文本生成） - GitHub - yangjianxin1/CPM: Easy-to-use CPM for Chinese text generation（基于CPM的中文文本生成）https://github.com/yangjianxin1/CPM

2安装其他依赖包


安装SentencePiece
 
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple SentencePiece
 
 
安装jieba
输入命令更新pip：python -m pip install --upgrade pip -i https://pypi.douban.com/simple
输入命令下载jieba：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple jieba
 
 
 
如果报这个错:AttributeError: 'CpmTokenizer' object has no attribute 'convert_tokens_to_ids'
 
重启电脑,看看还有没有,有的话报错引用 改成下面这个
from transformers.models.cpm.tokenization_cpm import CpmTokenizer

3preprocess.py对数据集进行预处理

把小说放到:'\CPM-main\data\zuowen' 目录,utf-8格式


import argparse
from utils import set_logger
from transformers import CpmTokenizer
import os
import pickle
from tqdm import tqdm
 
 
def preprocess():
    """
    对故事数据集进行预处理
    """
    # 设置参数
    parser = argparse.ArgumentParser()
    parser.add_argument('--vocab_file', default='vocab/chinese_vocab.model', type=str, required=False,
                        help='词表路径')
    parser.add_argument('--log_path', default='log/preprocess.log', type=str, required=False, help='日志存放位置')
    parser.add_argument('--data_path', default='data/zuowen', type=str, required=False, help='数据集存放位置')
    parser.add_argument('--save_path', default='data/train.pkl', type=str, required=False, help='对训练数据集进行tokenize之后的数据存放位置')
    parser.add_argument('--win_size', default=200, type=int, required=False, help='滑动窗口的大小，相当于每条数据的最大长度')
    parser.add_argument('--step', default=200, type=int, required=False, help='滑动窗口的滑动步幅')
    args = parser.parse_args()
 
    # 初始化日志对象
    logger = set_logger(args.log_path)
 
    # 初始化tokenizer
    tokenizer = CpmTokenizer(vocab_file="vocab/chinese_vocab.model")
    eod_id = tokenizer.convert_tokens_to_ids("<eod>")   # 文档结束符
    sep_id = tokenizer.sep_token_id
 
    # 读取作文数据集目录下的所有文件
    train_list = []
    logger.info("start tokenizing data")
    for file in tqdm(os.listdir(args.data_path)):
        file = os.path.join(args.data_path, file)
        with open(file, "r", encoding="utf8")as reader:
            lines = reader.readlines()
            title = lines[1][3:].strip()    # 取出标题
            lines = lines[7:]   # 取出正文内容
            article = ""
            for line in lines:
                if line.strip() != "":  # 去除换行
                    article += line
            title_ids = tokenizer.encode(title, add_special_tokens=False)
            article_ids = tokenizer.encode(article, add_special_tokens=False)
            token_ids = title_ids + [sep_id] + article_ids + [eod_id]
            # train_list.append(token_ids)
 
            # 对于每条数据，使用滑动窗口对其进行截断
            win_size = args.win_size
            step = args.step
            start_index = 0
            end_index = win_size
            data = token_ids[start_index:end_index]
            train_list.append(data)
            start_index += step
            end_index += step
            while end_index+50 < len(token_ids):  # 剩下的数据长度，大于或等于50，才加入训练数据集
                data = token_ids[start_index:end_index]
                train_list.append(data)
                start_index += step
                end_index += step
 
    # 序列化训练数据
    with open(args.save_path, "wb") as f:
        pickle.dump(train_list, f)
 
 
if __name__ == '__main__':
    preprocess()

4train.py 训练小说


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 utils import set_logger, set_random_seed
from data_parallel import BalancedDataParallel
from transformers import GPT2LMHeadModel, GPT2Config, CpmTokenizer
import pandas as pd
import torch.nn.utils.rnn as rnn_utils
import numpy as np
from dataset import CPMDataset
 
 
def set_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', default='0,1', 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/chinese_vocab.model', type=str, required=False,
                        help='sp模型路径')
    parser.add_argument('--model_config', default='config/cpm-small.json', type=str, required=False,
                        help='需要从头训练一个模型时，模型参数的配置文件')
    parser.add_argument('--train_path', default='data/train.pkl', type=str, required=False, help='经过预处理之后的数据存放路径')
    parser.add_argument('--max_len', default=200, type=int, required=False, help='训练时，输入数据的最大长度')
 
    parser.add_argument('--log_path', default='log/train.log', type=str, required=False, help='训练日志存放位置')
    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('--epochs', default=100, type=int, required=False, help='训练的最大轮次')
    parser.add_argument('--batch_size', default=1, type=int, required=False, help='训练的batch size')
    parser.add_argument('--gpu0_bsz', default=6, type=int, required=False, help='0号卡的batch size')
    parser.add_argument('--lr', default=1.5e-4, type=float, required=False, help='学习率')
    parser.add_argument('--eps', default=1.0e-09, type=float, required=False, help='AdamW优化器的衰减率')
    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=6, type=int, required=False, help='梯度积累的步数')
    parser.add_argument('--max_grad_norm', default=1.0, type=float, required=False)
    parser.add_argument('--save_model_path', default='model/zuowen_epoch40', type=str, required=False,
                        help='模型输出路径')
    parser.add_argument('--pretrained_model', default='model/zuowen_epoch40', type=str, required=False,
                        help='预训练的模型的路径')
    parser.add_argument('--seed', type=int, default=1234, help='设置随机种子')
    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')
    args = parser.parse_args()
    return args
 
 
def collate_fn(batch):
    input_ids = rnn_utils.pad_sequence(batch, batch_first=True, padding_value=5)
    labels = rnn_utils.pad_sequence(batch, batch_first=True, padding_value=-100)
    return input_ids, labels
 
 
def load_dataset(logger, args):
    """
    加载训练集
    """
    logger.info("loading training dataset")
    train_path = args.train_path
 
    with open(train_path, "rb") as f:
        train_list = pickle.load(f)
 
    # test
    # train_list = train_list[:24]
 
    train_dataset = CPMDataset(train_list, args.max_len)
 
    return train_dataset
 
 
def train_epoch(model, train_dataloader, optimizer, scheduler, logger,
                epoch, args):
    model.train()
    device = args.device
    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 = 0   # 每个epoch中,预测正确的word的数量
    epoch_total_num = 0  # 每个epoch中,预测的word的总数量
 
    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()))
            
            if batch_idx % 1000 == 0:
                model.save_pretrained(args.save_model_path)
 
            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 train(model, logger, train_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
    )
    t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.epochs
    optimizer = transformers.AdamW(model.parameters(), lr=args.lr, eps=args.eps)
    scheduler = transformers.get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )
 
    logger.info('start training')
 
    train_losses = []   # 记录每个epoch的平均loss
    # ========== start training ========== #
    for epoch in range(args.epochs):
        train_loss = train_epoch(
            model=model, train_dataloader=train_dataloader,
            optimizer=optimizer, scheduler=scheduler,
            logger=logger, epoch=epoch, args=args)
        train_losses.append(round(train_loss, 4))
        logger.info("train loss list:{}".format(train_losses))
 
    logger.info('training finished')
    logger.info("train_losses:{}".format(train_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 = set_logger(args.log_path)
    # 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))
 
    # 设置随机种子
    set_random_seed(args.seed, args.cuda)
 
    # 初始化tokenizer
    tokenizer = CpmTokenizer(vocab_file="vocab/chinese_vocab.model")
    args.eod_id = tokenizer.convert_tokens_to_ids("<eod>")  # 文档结束符
    args.pad_id = tokenizer.pad_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
 
    # 多卡并行训练模型
    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 = load_dataset(logger, args)
 
    train(model, logger, train_dataset, args)
 
 
if __name__ == '__main__':
    main()

5generate.py生成小说


import torch
import torch.nn.functional as F
import os
import argparse
from tqdm import trange
from transformers import GPT2LMHeadModel, GPT2Config
 
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from utils import top_k_top_p_filtering, set_logger
from os.path import join, exists
 
 
def generate_next_token(input_ids):
    """
    对于给定的上文，生成下一个单词
    """
    outputs = model(input_ids=input_ids)
    logits = outputs.logits
    # next_token_logits表示最后一个token的hidden_state对应的prediction_scores,也就是模型要预测的下一个token的概率
    next_token_logits = logits[0, -1, :]
    next_token_logits = next_token_logits / args.temperature
    # 对于<unk>的概率设为无穷小，也就是说模型的预测结果不可能是[UNK]这个token
    next_token_logits[unk_id] = -float('Inf')
    filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=args.topk, top_p=args.topp)
    # torch.multinomial表示从候选集合中选出无放回地进行抽取num_samples个元素，权重越高，抽到的几率越高，返回元素的下标
    next_token_id = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
    return next_token_id
 
 
def generate(max_len):
    # 对title与context进行tokenize
    title_ids = tokenizer.encode(title, add_special_tokens=False)
    context_ids = tokenizer.encode(context, add_special_tokens=False)
    input_ids = title_ids + [sep_id] + context_ids
    cur_len = len(input_ids)
    last_token_id = input_ids[-1]  # 已生成的内容的最后一个token
    input_ids = torch.tensor([input_ids], dtype=torch.long, device=device)
 
    while True:
        next_token_id = generate_next_token(input_ids[:, -args.context_len:])
        input_ids = torch.cat((input_ids, next_token_id.unsqueeze(0)), dim=1)
        cur_len += 1
        word = tokenizer.convert_ids_to_tokens(next_token_id.item())
        # if cur_len >= max_len:
        #     break
        # 超过最大长度，并且换行
        if cur_len >= max_len and last_token_id == 8 and next_token_id == 3:
            break
        # 超过最大长度，并且生成标点符号
        if cur_len >= max_len and word in [".", "。", "！", "!", "?", "？", ",", "，"]:
            break
        # 生成结束符
        if next_token_id == eod_id:
            break
    result = tokenizer.decode(input_ids.squeeze(0))
    return result
 
 
if __name__ == '__main__':
    # 参数设置
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', default='0', type=str, required=False, help='生成设备')
    parser.add_argument('--temperature', default=1, type=float, required=False, help='生成温度')
    parser.add_argument('--topk', default=0, type=int, required=False, help='最高几选一')
    parser.add_argument('--topp', default=0.85, type=float, required=False, help='最高积累概率')
    parser.add_argument('--repetition_penalty', default=1.0, type=float, required=False, help='重复惩罚参数')
    parser.add_argument('--context_len', default=800, type=int, required=False, help='每一步生成时，参考的上文的长度')
    parser.add_argument('--max_len', default=300, type=int, required=False, help='生成的最长长度')
    parser.add_argument('--log_path', default='log/generate.log', type=str, required=False, help='日志存放位置')
    parser.add_argument('--no_cuda', action='store_true', help='不使用GPU进行预测')
    parser.add_argument('--model_path', type=str, default='model/zuowen_epoch40', help='模型存放位置')
    # parser.add_argument('--title', type=str, default='徜徉在书籍的阳光世界', help='作文标题')
    # parser.add_argument('--context', type=str, default='一本书是一个人的眼睛，它可以让你看到另一个世界的奇妙', help='作文上文')
    parser.add_argument('--title', type=str, default='罗峰', help='作文标题')
    parser.add_argument('--context', type=str, default='罗峰刚修炼完毕', help='作文上文')
    args = parser.parse_args()
 
    os.environ["CUDA_VISIBLE_DEVICES"] = args.device  # 此处设置程序使用哪些显卡
    args.cuda = torch.cuda.is_available() and not args.no_cuda  # 当用户使用GPU,并且GPU可用时
    device = 'cuda:0' if args.cuda else 'cpu'
    # device = 'cpu'
 
    # 创建日志对象
    logger = set_logger(args.log_path)
 
    # 初始化tokenizer
    tokenizer = CpmTokenizer(vocab_file="vocab/chinese_vocab.model")
    eod_id = tokenizer.convert_tokens_to_ids("<eod>")  # 文档结束符
 
    sep_id = tokenizer.sep_token_id
    unk_id = tokenizer.unk_token_id
 
    # 加载模型
    model = GPT2LMHeadModel.from_pretrained(args.model_path)
    model.eval()
    model = model.to(device)
 
    title = args.title
    context = args.context
    logger.info("title:{}".format(title))
    logger.info("context:{}".format(context))
 
    # 开始生成
    result = generate(args.max_len)
    result = result.split("<sep>")[1]
    logger.info("result:{}\n".format(result))
 
    # 通过控制台循环生成
    # print('开始生成，输入CTRL + Z以退出')
    # while True:
    #     try:
    #         # 用户输入title与context
    #         title = input("请输入作文标题：")
    #         context = input("请输入作文起始句子：")
    #
    #         logger.info("title:{}".format(title))
    #         logger.info("context:{}".format(context))
    #
    #         # 开始生成
    #         result = generate(args.max_len)
    #         result = result.split("<sep>")[1]
    #         logger.info("result:{}\n".format(result))
    #         break
    #
    #     except KeyboardInterrupt:
    #         break

训练5个小时,生成效果


罗峰刚修炼完毕,正端着果酒微笑看着眼前这个老者。
“银河,你说这是什么意思?”混沌城主连追问。
“这是《九劫秘典》,我已经在第十五劫第三劫第四劫第三劫的第四劫核心。”罗峰恭敬道,“接下来的日子,你可不能乱来。”
“这《九劫秘典》我还是有些疑惑的,《九劫秘典》,你可得认真观看。”混沌城主微笑道。
罗峰摇头:“没兴趣,没兴趣。”
“哈哈哈......”混沌城主也忍不住笑起来,“哈哈......”
罗峰也笑了。。
 
那般自然无比此时境界主根本没了,他也笑。,《九劫秘典御柯南龙行,这一套陨落在他的神力也笑着罗峰都笑着笑了。。,完全是。“哈哈哈..........................................这第七劫完了!
都笑声色的笑声讨了
虚拟宇宙中正透过虚拟宇宙也是你们自己。

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