import streamlit as st
from transformers import GPT2LMHeadModel#, CpmTokenizer
#from transformers.models.gpt2.modeling_gpt2  import GPT2LMHeadModel
from transformers.models.cpm import CpmTokenizer
import argparse
import os
import torch
import time
from utils import top_k_top_p_filtering
import torch.nn.functional as F

st.set_page_config(page_title="Demo", initial_sidebar_state="auto", layout="wide")


@st.cache(allow_output_mutation=True)
def get_model(device, model_path):
    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

    model = GPT2LMHeadModel.from_pretrained(model_path)
    model.to(device)
    model.eval()
    return tokenizer, model, eod_id, sep_id, unk_id



device_ids = 0
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICE"] = str(device_ids)
device = torch.device("cuda" if torch.cuda.is_available() and int(device_ids) >= 0 else "cpu")
tokenizer, model, eod_id, sep_id, unk_id = get_model(device, "model/zuowen_epoch40")

def generate_next_token(input_ids,args):
    """
    对于给定的上文，生成下一个单词
    """
    # 只根据当前位置的前context_len个token进行生成
    input_ids = input_ids[:, -200:]
    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.top_k, top_p=args.top_p)
    # 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 predict_one_sample(model, tokenizer, device, args, title, context):
    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)
        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())
        # 超过最大长度，并且换行
        if cur_len >= args.generate_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 >= args.generate_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))
    content = result.split("<sep>")[1]  # 生成的最终内容
    return content


def writer():
    st.markdown(
        """
        ## GPT生成模型
        """
    )
    st.sidebar.subheader("配置参数")

    generate_max_len = st.sidebar.number_input("generate_max_len", min_value=0, max_value=512, value=32, step=1)
    top_k = st.sidebar.slider("top_k", min_value=0, max_value=10, value=3, step=1)
    top_p = st.sidebar.number_input("top_p", min_value=0.0, max_value=1.0, value=0.95, step=0.01)
    temperature = st.sidebar.number_input("temperature", min_value=0.0, max_value=100.0, value=1.0, step=0.1)

    parser = argparse.ArgumentParser()
    parser.add_argument('--generate_max_len', default=generate_max_len, type=int, help='生成标题的最大长度')
    parser.add_argument('--top_k', default=top_k, type=float, help='解码时保留概率最高的多少个标记')
    parser.add_argument('--top_p', default=top_p, type=float, help='解码时保留概率累加大于多少的标记')
    parser.add_argument('--max_len', type=int, default=512, help='输入模型的最大长度，要比config中n_ctx小')
    parser.add_argument('--temperature', type=float, default=temperature, help='输入模型的最大长度，要比config中n_ctx小')
    args = parser.parse_args()

    context = st.text_area("请输入标题", max_chars=512)
    title = st.text_area("请输入正文", max_chars=512)
    if st.button("点我生成结果"):
        start_message = st.empty()
        start_message.write("自毁程序启动中请稍等 10.9.8.7 ...")
        start_time = time.time()
        result = predict_one_sample(model, tokenizer, device, args, title, context)
        end_time = time.time()
        start_message.write("生成完成，耗时{}s".format(end_time - start_time))
        st.text_area("生成结果", value=result, key=None)
    else:
        st.stop()


if __name__ == '__main__':
    writer()

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 sklearn.model_selection import train_test_split
from data_parallel import BalancedDataParallel
from transformers import GPT2LMHeadModel, GPT2Config, CpmTokenizer#pip install sentencepiece
import pandas as pd   #https://www.sciencedirect.com/science/article/pii/S266665102100019X
import torch.nn.utils.rnn as rnn_utils
import numpy as np
from dataset import CPMDataset

#--epochs 40 --batch_size 8 --device 0,1 --train_path data/train.pkl
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=16, 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=10, 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', 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()))

            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
    )
    logger.info("total_steps:{}".format(len(train_dataloader)* args.epochs))
    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
    )#设置warmup

    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 https://www.sciencedirect.com/science/article/pii/S266665102100019X
    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()

import argparse
from utils import set_logger
from transformers import CpmTokenizer
import os
import pickle
from tqdm import tqdm
# --data_path data/zuowen --save_path data/train.pkl --win_size 200 --step 200
# https://huggingface.co/docs/transformers/main/en/model_doc/cpm#transformers.CpmTokenizer
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")#pip install jieba
    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()