BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding_BUPT-WT的博客-程序员宅基地

技术标签: NLP  Paper  

Bert历史意义:

1、获取了left-to-right和right-to-left的上下文信息

2、nlp领域正式开始pretraining+finetuning的模型训练方式

nlp领域(各个下游任务都有自身的模型) -------2018(Bert) --------nlp领域(各个下游任务统一使用Bert模型)

 

Bert衍生模型

衍生模型 模型特点
RoBERTa 模型更大,参数量更多静态mask变成动态mask
ALBERT 参数量减少,跨层的参数共享
BERT-WWM 全词mask,中文
ERINE mask实体,中文
SpanBERT 随机选取span进行mask
TinyBERT 对transformer结构进行蒸馏
Sentence-BERT 孪生网络
K-BERT 知识图谱

 

ELmo、GPT、Bert比较

模型 模型采用结构 预训练形式 优点 缺点 在GLue上表现
BERT Transformer Encoder部分(无sequence mask) fine-tuning 在各项下游任务中表现最好采用mlm的实现形式完成真正意义上的双向增加了句子级别预测的任务 在文本生成任务上表现不好 最好
ELMO Bilstm + LM feature-based 动态的词向量表征 双向只是单纯的concat两个lstm,并没有真正的双向 最差
GPT Transformer Decoder部分(含有sequence mask,去掉中间的encoder-decoder的attention) fine-tuning 在文本生成任务上表现出色,同时采用辅助目标函数和lm 单向的transformer结构,无法利用全局上下文信息 较差

 

 

论文主要结构:

一、Abstract

 介绍背景及提出bert模型,在多个数据集上的效果都表现优异

 1、提出一种新的语言表征模型bert,不同于其它的语言表征模型,bert可以同时学习到向左和向右的上下文信息

 2、预训练好的bert可以直接fine-tuning,只需加相应的输出层,无需太多模型结构的改动

3、bert模型在各项nlp下游任务中都表现的良好

二、Introduction

介绍之前的预训练模型存在单向的问题,因此提出了Bert

三、Related Work

介绍无监督的feature-based和fine-tuning

四、Architecture

Bert网络结构及其内部细节: pre-training和fine-tuning

模型结构:

 

输入:

1、在第一个句子的开头入【CLS】标价,在末尾加入【SEP】标记

2、将表示句子A或B的embedding添加到每个token上

3、给每个token准备对应的embedding和position embedding

4、句子和句子之间用【SEP】隔开

 

Bert、GPT、Elmo比较图

 

Pre-training fine-tuning

 

为了使模型学习到句子left-to-right 和right-to-left的全局的信息,采取两种策略:

策略1: Masked LM, 随机的mask一个句子中15%的词,用其上下文做预测 my dog is hairy --> my dog is [mask]

为了保证预训练和微调时的一致性,采取以下措施:

1、80%的机会是采用mask,my dog is hairy ---> my dog is [mask]

2、10% 的机会是随机一个词替代mask my dog is hairy --> my dog is apple

3、10%的机会是保持不变的 my dog is hairy --> my dog is hairy

       策略2: Next Sentence Prediction

       选择一些句子对A和B,其中50%的数据B是A的下一条句子,50%的数据是从语料库中选取的,这样做是针对句子间的任务,例如SNLI

 

Fine-tuning BERT

在句子分类任务中,就是在【CLS】对应的token处接相应的线性层,BERT结构的参数是fine-tunied,但是线性层的参数是从头到尾训练的,ner任务中每个token后面都会有对应的输出

 

模型蒸馏:

1、首先需要训练一个大的模型,这个大模型也称为teacher模型

2、利用teacher模型输出的概率分布训练小模型,小模型称为student模型

3、训练student模型时,包含两种label,soft label对应了teacher模型输出的概率分布,而hard label是原来的one-hot label

4、模型蒸馏训练的小模型会学习到大模型的表现以及泛化能力

        KL(p||q) = Ep(log(p/q)) = sumipi(logpi) - sumipilog(qi)

i 代表当前的token,p代表teacher模型的分布,q代表student模型的分布,采用KL散度作为损失函数

五、Experiments

Bert在各个nlp下游任务中的结果对比

 

          Bert在下游任务中的表现完全远超之前的模型

 

六、Analysis

通过消融分析来验证Bert的pre-training 和model size的有效性

七、Conclusions

论文验证了bert模型确实能在下游任务中良好的效果

主要思想-MLM、NSP、fine-tuning Bert思想

八、代码实现

预训练模型地址: https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz

公开数据集下载地址: http://thuctc.thunlp.org/ 

 

一、fine-tuning代码

""" fine-tuning 代码"""

from importlib import import_module
import time
import torch
import numpy as np
import torch.nn as nn
from pytorch_pretrained import BertModel, BertTokenizer

dataset = '.'  # 数据集

class Config(object):
    """ 配置参数 """
    
    def __init__(self, dataset):
        self.model_name = 'bert'
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        self.class_list = [x.strip() for x in open(dataset + '/data/class.txt').readlines()]
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
        self.require_imporovement = 1000
        self.num_classes = len(self.class_list)
        self.num_epochs = 3
        self.batch_size = 128
        self.pad_size = 32
        self.learning_rate = 5e-5
        
        self.bert_path = "/Users/apple/work/matrial/bert/Bert-Chinese-Text-Classification-Pytorch/bert_pretrain/"
        self.tokenizer = BertTokenizer.from_pretrained(self.bert_path + 'vocab.txt')
        self.hidden_size = 768


class Model(nn.Module):
    
    def __init__(self,config):
        super(Model,self).__init__()
        self.bert = BertModel.from_pretrained(config.bert_path)
        for param in self.bert.parameters():
            param.requires_grad = True

        self.fc = nn.Linear(config.hidden_size, config.num_classes)
        
    def forward(self, x):
        context = x[0]
        mask = x[2]
        _, pooled = self.bert(context, attention_mask=mask, output_all_encoded_layers=False)
        out = self.fc(pooled)
        
        return out


config = Config(dataset)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True

start_time = time.time()

print("Loading data .... ")


from tqdm import tqdm
import time
from datetime import timedelta

PAD,CLS = '[PAD]','[CLS]'


def build_dataset(config):
    
    def load_dataset(path, pad_size=32):
        contents = []
        with open(path,'r',encoding='utf-8') as f:
            for line in tqdm(f):
                lin = line.strip()
                if not lin:
                    continue
                content,label = lin.split('\t')
                token = config.tokenizer.tokenize(content)
                token = [CLS] + token
                seq_len = len(token)
                mask = []
                token_ids = config.tokenizer.convert_tokens_to_ids(token)
                
                if pad_size:
                    if len(token) < pad_size:
                        mask = [1] * len(token_ids) + [0]*(pad_size - len(token_ids))
                        token_ids += ([0] * (pad_size - len(token)))
                    else:
                        mask = [1] * pad_size
                        token_ids = token_ids[:pad_size]
                        seq_len = pad_size
                
                contents.append((token_ids, int(label), seq_len, mask))
                
        return contents
    
    train = load_dataset(config.train_path, config.pad_size)
    dev = load_dataset(config.dev_path, config.pad_size)
    test = load_dataset(config.test_path, config.pad_size)
    
    return train,dev,test
        
def build_iterator(dataset, config):
    
    iter_ = DatasetIterater(dataset, config.batch_size, config.device)
    
    return iter_

def get_time_dif(start_time):
    end_time = time.time()
    time_diff = end_time - start_time
    
    return timedelta(seconds=int(round(time_diff)))


class DatasetIterater(object):
    def __init__(self, batches, batch_size, device):
        self.batch_size = batch_size
        self.batches = batches
        self.n_batches = len(batches) // batch_size
        self.residue = False  # 记录batch数量是否为整数
        if len(batches) % self.n_batches != 0:
            self.residue = True
        self.index = 0
        self.device = device

    def _to_tensor(self, datas):
        x = torch.LongTensor([_[0] for _ in datas]).to(self.device)
        y = torch.LongTensor([_[1] for _ in datas]).to(self.device)

        # pad前的长度(超过pad_size的设为pad_size)
        seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device)
        mask = torch.LongTensor([_[3] for _ in datas]).to(self.device)
        return (x, seq_len, mask), y

    def __next__(self):
        if self.residue and self.index == self.n_batches:
            batches = self.batches[self.index * self.batch_size: len(self.batches)]
            self.index += 1
            batches = self._to_tensor(batches)
            return batches

        elif self.index >= self.n_batches:
            self.index = 0
            raise StopIteration
        else:
            batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
            self.index += 1
            batches = self._to_tensor(batches)
            return batches

    def __iter__(self):
        return self

    def __len__(self):
        if self.residue:
            return self.n_batches + 1
        else:
            return self.n_batches

train_data, dev_data, test_data = build_dataset(config)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data,config)
test_iter = build_iterator(test_data,config)
time_dif = get_time_dif(start_time)

print("Time usage:", time_dif)



import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics
import time
from pytorch_pretrained.optimization import BertAdam


# 权重初始化,默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=123):
    for name, w in model.named_parameters():
        if exclude not in name:
            if len(w.size()) < 2:
                continue
            if 'weight' in name:
                if method == 'xavier':
                    nn.init.xavier_normal_(w)
                elif method == 'kaiming':
                    nn.init.kaiming_normal_(w)
                else:
                    nn.init.normal_(w)
            elif 'bias' in name:
                nn.init.constant_(w, 0)
            else:
                pass


def train(config, model, train_iter, dev_iter, test_iter):
    start_time = time.time()
    model.train()
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}]
    # optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
    optimizer = BertAdam(optimizer_grouped_parameters,
                         lr=config.learning_rate,
                         warmup=0.05,
                         t_total=len(train_iter) * config.num_epochs)
    total_batch = 0  # 记录进行到多少batch
    dev_best_loss = float('inf')
    last_improve = 0  # 记录上次验证集loss下降的batch数
    flag = False  # 记录是否很久没有效果提升
    model.train()
    for epoch in range(config.num_epochs):
        print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
        for i, (trains, labels) in enumerate(train_iter):
            outputs = model(trains)
            model.zero_grad()
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()
            if total_batch % 100 == 0:
                # 每多少轮输出在训练集和验证集上的效果
                true = labels.data.cpu()
                predic = torch.max(outputs.data, 1)[1].cpu()
                train_acc = metrics.accuracy_score(true, predic)
                dev_acc, dev_loss = evaluate(config, model, dev_iter)
                if dev_loss < dev_best_loss:
                    dev_best_loss = dev_loss
                    torch.save(model.state_dict(), config.save_path)
                    improve = '*'
                    last_improve = total_batch
                else:
                    improve = ''
                time_dif = get_time_dif(start_time)
                msg = 'Iter: {0:>6},  Train Loss: {1:>5.2},  Train Acc: {2:>6.2%},  Val Loss: {3:>5.2},  Val Acc: {4:>6.2%},  Time: {5} {6}'
                print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
                model.train()
            total_batch += 1
            if total_batch - last_improve > config.require_improvement:
                # 验证集loss超过1000batch没下降,结束训练
                print("No optimization for a long time, auto-stopping...")
                flag = True
                break
        if flag:
            break
    test(config, model, test_iter)


def test(config, model, test_iter):
    # test
    model.load_state_dict(torch.load(config.save_path))
    model.eval()
    start_time = time.time()
    test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
    msg = 'Test Loss: {0:>5.2},  Test Acc: {1:>6.2%}'
    print(msg.format(test_loss, test_acc))
    print("Precision, Recall and F1-Score...")
    print(test_report)
    print("Confusion Matrix...")
    print(test_confusion)
    time_dif = get_time_dif(start_time)
    print("Time usage:", time_dif)


def evaluate(config, model, data_iter, test=False):
    model.eval()
    loss_total = 0
    predict_all = np.array([], dtype=int)
    labels_all = np.array([], dtype=int)
    with torch.no_grad():
        for texts, labels in data_iter:
            outputs = model(texts)
            loss = F.cross_entropy(outputs, labels)
            loss_total += loss
            labels = labels.data.cpu().numpy()
            predic = torch.max(outputs.data, 1)[1].cpu().numpy()
            labels_all = np.append(labels_all, labels)
            predict_all = np.append(predict_all, predic)

    acc = metrics.accuracy_score(labels_all, predict_all)
    if test:
        report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
        confusion = metrics.confusion_matrix(labels_all, predict_all)
        return acc, loss_total / len(data_iter), report, confusion
    return acc, loss_total / len(data_iter)


model = Model(config).to(config.device)
train(config, model, train_iter, dev_iter, test_iter)

二、Pre-training代码

""" Pre-training代码 """

from torch.utils.data import DataLoader
import torch.nn as nn
import pickle
import tqdm
from collections import Counter


class Args(object):
    def __init__(self):
        self.train_dataset='data/corpus.small'
        self.test_dataset='data/corpus.small'
        self.vocab_path='data/corpus.small.vocab'
        self.output_path='data/corpus.small.vocab'
        self.hidden=256
        self.layers=8
        self.attn_heads=8
        self.seq_len=20
        self.batch_size=64
        self.epochs=10
        self.num_workers=5
        self.with_cuda=True
        self.log_freq=10
        self.corpus_lines=""
        self.lr=1e-3
        self.adam_weight_decay=0.01
        self.adam_beta1=0.9
        self.adam_beta2=0.999
        
args=Args()


class TorchVocab(object):
    def __init__(self, counter, max_size=None, min_freq=1, specials=['<pad>', '<oov>'],
                 vectors=None, unk_init=None, vectors_cache=None):
        self.freqs = counter
        counter = counter.copy()
        min_freq = max(min_freq, 1)

        self.itos = list(specials)
        # frequencies of special tokens are not counted when building vocabulary
        # in frequency order
        for tok in specials:
            del counter[tok]

        max_size = None if max_size is None else max_size + len(self.itos)

        # sort by frequency, then alphabetically
        words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])
        words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True)

        for word, freq in words_and_frequencies:
            if freq < min_freq or len(self.itos) == max_size:
                break
            self.itos.append(word)

        # stoi is simply a reverse dict for itos
        self.stoi = {tok: i for i, tok in enumerate(self.itos)}

        self.vectors = None
        if vectors is not None:
            self.load_vectors(vectors, unk_init=unk_init, cache=vectors_cache)
        else:
            assert unk_init is None and vectors_cache is None

    def __eq__(self, other):
        if self.freqs != other.freqs:
            return False
        if self.stoi != other.stoi:
            return False
        if self.itos != other.itos:
            return False
        if self.vectors != other.vectors:
            return False
        return True

    def __len__(self):
        return len(self.itos)

    def vocab_rerank(self):
        self.stoi = {word: i for i, word in enumerate(self.itos)}

    def extend(self, v, sort=False):
        words = sorted(v.itos) if sort else v.itos
        for w in words:
            if w not in self.stoi:
                self.itos.append(w)
                self.stoi[w] = len(self.itos) - 1


class Vocab(TorchVocab):
    def __init__(self, counter, max_size=None, min_freq=1):
        self.pad_index = 0
        self.unk_index = 1
        self.eos_index = 2
        self.sos_index = 3
        self.mask_index = 4
        super().__init__(counter, specials=["<pad>", "<unk>", "<eos>", "<sos>", "<mask>"],
                         max_size=max_size, min_freq=min_freq)

    def to_seq(self, sentece, seq_len, with_eos=False, with_sos=False) -> list:
        pass

    def from_seq(self, seq, join=False, with_pad=False):
        pass

    @staticmethod
    def load_vocab(vocab_path: str) -> 'Vocab':
        with open(vocab_path, "rb") as f:
            return pickle.load(f)

    def save_vocab(self, vocab_path):
        with open(vocab_path, "wb") as f:
            pickle.dump(self, f)

class WordVocab(Vocab):
    def __init__(self, texts, max_size=None, min_freq=1):
        print("Building Vocab")
        counter = Counter()
        for line in tqdm.tqdm(texts):
            if isinstance(line, list):
                words = line
            else:
                words = line.replace("\n", "").replace("\t", "").split()

            for word in words:
                counter[word] += 1
        super().__init__(counter, max_size=max_size, min_freq=min_freq)

    def to_seq(self, sentence, seq_len=None, with_eos=False, with_sos=False, with_len=False):
        if isinstance(sentence, str):
            sentence = sentence.split()

        seq = [self.stoi.get(word, self.unk_index) for word in sentence]

        if with_eos:
            seq += [self.eos_index]  # this would be index 1
        if with_sos:
            seq = [self.sos_index] + seq

        origin_seq_len = len(seq)

        if seq_len is None:
            pass
        elif len(seq) <= seq_len:
            seq += [self.pad_index for _ in range(seq_len - len(seq))]
        else:
            seq = seq[:seq_len]

        return (seq, origin_seq_len) if with_len else seq

    def from_seq(self, seq, join=False, with_pad=False):
        words = [self.itos[idx]
                 if idx < len(self.itos)
                 else "<%d>" % idx
                 for idx in seq
                 if not with_pad or idx != self.pad_index]

        return " ".join(words) if join else words

    @staticmethod
    def load_vocab(vocab_path: str) -> 'WordVocab':
        with open(vocab_path, "rb") as f:
            return pickle.load(f)


"""构建词典 """
corpus_path=args.train_dataset
with open(corpus_path, "r") as f:
    vocab = WordVocab(f, max_size=None, min_freq=1)

print("VOCAB SIZE:", len(vocab))
vocab.save_vocab(args.output_path)


""" 加载词典 """
vocab=WordVocab.load_vocab(args.vocab_path)


from torch.utils.data import Dataset
import tqdm
import torch
import random

class BERTDataset(Dataset):
    def __init__(self, corpus_path, vocab, seq_len, encoding="utf-8", corpus_lines=None):
        self.vocab = vocab
        self.seq_len = seq_len

        with open(corpus_path, "r", encoding=encoding) as f:
            self.datas = [line[:-1].split("\t")
                          for line in tqdm.tqdm(f, desc="Loading Dataset", total=corpus_lines)]

    def __len__(self):
        return len(self.datas)

    def __getitem__(self, item):
        t1, (t2, is_next_label) = self.datas[item][0], self.random_sent(item)
        t1_random, t1_label = self.random_word(t1)
        t2_random, t2_label = self.random_word(t2)

        # [CLS] tag = SOS tag, [SEP] tag = EOS tag
        t1 = [self.vocab.sos_index] + t1_random + [self.vocab.eos_index]
        t2 = t2_random + [self.vocab.eos_index]

        t1_label = [self.vocab.pad_index] + t1_label + [self.vocab.pad_index]
        t2_label = t2_label + [self.vocab.pad_index]

        segment_label = ([1 for _ in range(len(t1))] + [2 for _ in range(len(t2))])[:self.seq_len]
        bert_input = (t1 + t2)[:self.seq_len]
        bert_label = (t1_label + t2_label)[:self.seq_len]

        padding = [self.vocab.pad_index for _ in range(self.seq_len - len(bert_input))]
        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)

        output = {"bert_input": bert_input,
                  "bert_label": bert_label,
                  "segment_label": segment_label,
                  "is_next": is_next_label}

        return {key: torch.tensor(value) for key, value in output.items()}

    def random_word(self, sentence):
        tokens = sentence.split()
        output_label = []

        for i, token in enumerate(tokens):
            prob = random.random()
            if prob < 0.15:
#                 prob/=0.15
                # 80% randomly change token to make token
                if prob < 0.8:
                    tokens[i] = self.vocab.mask_index

                # 10% randomly change token to random token
                elif  prob < 0.9:
                    tokens[i] = random.randrange(len(self.vocab))

                # 10% randomly change token to current token
                else:
                    tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)

                output_label.append(self.vocab.stoi.get(token, self.vocab.unk_index))

            else:
                tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)
                output_label.append(0)

        return tokens, output_label

    def random_sent(self, index):
        # output_text, label(isNotNext:0, isNext:1)
        if random.random() > 0.5:
            return self.datas[index][1], 1
        else:
            return self.datas[random.randrange(len(self.datas))][1], 0


print("Loading Train Dataset", args.train_dataset)
train_dataset = BERTDataset(args.train_dataset, vocab, seq_len=args.seq_len, corpus_lines=args.corpus_lines)

print("Loading Test Dataset", args.test_dataset)
test_dataset = BERTDataset(args.test_dataset, vocab,
                           seq_len=args.seq_len) if args.test_dataset is not None else None

print("Creating Dataloader")
train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers)
test_data_loader = DataLoader(test_dataset, batch_size=args.batch_size, num_workers=args.num_workers) \
    if test_dataset is not None else None


""" Bert """

import torch.nn.functional as F

class BERT(nn.Module):
    """
    BERT model : Bidirectional Encoder Representations from Transformers.
    """

    def __init__(self, vocab_size, hidden=768, n_layers=12, attn_heads=12, dropout=0.1):
        """
        :param vocab_size: vocab_size of total words
        :param hidden: BERT model hidden size
        :param n_layers: numbers of Transformer blocks(layers)
        :param attn_heads: number of attention heads
        :param dropout: dropout rate
        """

        super().__init__()
        self.hidden = hidden
        self.n_layers = n_layers
        self.attn_heads = attn_heads

        # paper noted they used 4*hidden_size for ff_network_hidden_size
        self.feed_forward_hidden = hidden * 4

        # embedding for BERT, sum of positional, segment, token embeddings
        self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=hidden)

        # multi-layers transformer blocks, deep network
        self.transformer_blocks = nn.ModuleList(
            [TransformerBlock(hidden, attn_heads, hidden * 4, dropout) for _ in range(n_layers)])

    def forward(self, x, segment_info):
        # attention masking for padded token
        # torch.ByteTensor([batch_size, 1, seq_len, seq_len)
        mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)

        # embedding the indexed sequence to sequence of vectors
        x = self.embedding(x, segment_info)

        # running over multiple transformer blocks
        for transformer in self.transformer_blocks:
            x = transformer.forward(x, mask)

        return x


""" Bert Embedding """

class BERTEmbedding(nn.Module):
    """
    BERT Embedding which is consisted with under features
        1. TokenEmbedding : normal embedding matrix
        2. PositionalEmbedding : adding positional information using sin, cos
        2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2)

        sum of all these features are output of BERTEmbedding
    """

    def __init__(self, vocab_size, embed_size, dropout=0.1):
        """
        :param vocab_size: total vocab size
        :param embed_size: embedding size of token embedding
        :param dropout: dropout rate
        """
        super().__init__()
        self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size)
        self.position = PositionalEmbedding(d_model=self.token.embedding_dim)
        self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim)
        self.dropout = nn.Dropout(p=dropout)
        self.embed_size = embed_size

    def forward(self, sequence, segment_label):
        x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)
        return self.dropout(x)
    
class TokenEmbedding(nn.Embedding):
    def __init__(self, vocab_size, embed_size=512):
        super().__init__(vocab_size, embed_size, padding_idx=0)
        
class PositionalEmbedding(nn.Module):

    def __init__(self, d_model, max_len=512):
        super().__init__()

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model)).float().exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]
    
class SegmentEmbedding(nn.Embedding):
    def __init__(self, embed_size=512):
        super().__init__(3, embed_size, padding_idx=0)


"""TransformerBlock """

class TransformerBlock(nn.Module):
    """
    Bidirectional Encoder = Transformer (self-attention)
    Transformer = MultiHead_Attention + Feed_Forward with sublayer connection
    """

    def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout):
        """
        :param hidden: hidden size of transformer
        :param attn_heads: head sizes of multi-head attention
        :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size
        :param dropout: dropout rate
        """

        super().__init__()
        self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden)
        self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout)
        self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, mask):
        x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask))
        x = self.output_sublayer(x, self.feed_forward)
        return self.dropout(x)
    
    
class MultiHeadedAttention(nn.Module):
    """
    Take in model size and number of heads.
    """

    def __init__(self, h, d_model, dropout=0.1):
        super().__init__()
        assert d_model % h == 0

        # We assume d_v always equals d_k
        self.d_k = d_model // h
        self.h = h

        self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])
        self.output_linear = nn.Linear(d_model, d_model)
        self.attention = Attention()

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)

        # 1) Do all the linear projections in batch from d_model => h x d_k
        query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
                             for l, x in zip(self.linear_layers, (query, key, value))]

        # 2) Apply attention on all the projected vectors in batch.
        x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout)

        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k)

        return self.output_linear(x)
    
class Attention(nn.Module):
    """
    Compute 'Scaled Dot Product Attention
    """

    def forward(self, query, key, value, mask=None, dropout=None):
        scores = torch.matmul(query, key.transpose(-2, -1)) \
                 / math.sqrt(query.size(-1))

        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)

        p_attn = F.softmax(scores, dim=-1)

        if dropout is not None:
            p_attn = dropout(p_attn)

        return torch.matmul(p_attn, value), p_attn
    
class PositionwiseFeedForward(nn.Module):
    "Implements FFN equation."

    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = GELU()

    def forward(self, x):
        return self.w_2(self.dropout(self.activation(self.w_1(x))))
    
class SublayerConnection(nn.Module):
    """
    A residual connection followed by a layer norm.
    Note for code simplicity the norm is first as opposed to last.
    """

    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        "Apply residual connection to any sublayer with the same size."
        return x + self.dropout(sublayer(self.norm(x)))
    
class LayerNorm(nn.Module):
    "Construct a layernorm module (See citation for details)."

    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
    
class GELU(nn.Module):
    """
    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU
    """

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))


"""Build Bert"""

import math
print("Building BERT model")
bert = BERT(len(vocab), hidden=args.hidden, n_layers=args.layers, attn_heads=args.attn_heads)

""" BertTrainer"""

from torch.optim import Adam
from torch.utils.data import DataLoader
import tqdm

class BERTTrainer:
    """
    BERTTrainer make the pretrained BERT model with two LM training method.

        1. Masked Language Model : 3.3.1 Task #1: Masked LM
        2. Next Sentence prediction : 3.3.2 Task #2: Next Sentence Prediction

    please check the details on README.md with simple example.

    """

    def __init__(self, bert: BERT, vocab_size: int,
                 train_dataloader: DataLoader, test_dataloader: DataLoader = None,
                 lr: float = 1e-4, betas=(0.9, 0.999), weight_decay: float = 0.01,
                 with_cuda: bool = True, log_freq: int = 10):
        """
        :param bert: BERT model which you want to train
        :param vocab_size: total word vocab size
        :param train_dataloader: train dataset data loader
        :param test_dataloader: test dataset data loader [can be None]
        :param lr: learning rate of optimizer
        :param betas: Adam optimizer betas
        :param weight_decay: Adam optimizer weight decay param
        :param with_cuda: traning with cuda
        :param log_freq: logging frequency of the batch iteration
        """

        # Setup cuda device for BERT training, argument -c, --cuda should be true
        cuda_condition = torch.cuda.is_available() and with_cuda
        self.device = torch.device("cuda:0" if cuda_condition else "cpu")

        # This BERT model will be saved every epoch
        self.bert = bert
        # Initialize the BERT Language Model, with BERT model
        self.model = BERTLM(bert, vocab_size).to(self.device)

        # Distributed GPU training if CUDA can detect more than 1 GPU
        if torch.cuda.device_count() > 1:
            print("Using %d GPUS for BERT" % torch.cuda.device_count())
            self.model = nn.DataParallel(self.model)

        # Setting the train and test data loader
        self.train_data = train_dataloader
        self.test_data = test_dataloader

        # Setting the Adam optimizer with hyper-param
        self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay)

        # Using Negative Log Likelihood Loss function for predicting the masked_token
        self.criterion = nn.NLLLoss(ignore_index=0)

        self.log_freq = log_freq

        print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()]))

    def train(self, epoch):
        self.iteration(epoch, self.train_data)

    def test(self, epoch):
        self.iteration(epoch, self.test_data, train=False)

    def iteration(self, epoch, data_loader, train=True):
        """
        loop over the data_loader for training or testing
        if on train status, backward operation is activated
        and also auto save the model every peoch

        :param epoch: current epoch index
        :param data_loader: torch.utils.data.DataLoader for iteration
        :param train: boolean value of is train or test
        :return: None
        """
        str_code = "train" if train else "test"

        # Setting the tqdm progress bar
        data_iter = tqdm.tqdm(enumerate(data_loader),
                              desc="EP_%s:%d" % (str_code, epoch),
                              total=len(data_loader),
                              bar_format="{l_bar}{r_bar}")

        avg_loss = 0.0
        total_correct = 0
        total_element = 0

        for i, data in data_iter:
            # 0. batch_data will be sent into the device(GPU or cpu)
            data = {key: value.to(self.device) for key, value in data.items()}

            # 1. forward the next_sentence_prediction and masked_lm model
            next_sent_output, mask_lm_output = self.model.forward(data["bert_input"], data["segment_label"])

            # 2-1. NLL(negative log likelihood) loss of is_next classification result
            next_loss = self.criterion(next_sent_output, data["is_next"])

            # 2-2. NLLLoss of predicting masked token word
            mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data["bert_label"])

            # 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure
            loss = next_loss + mask_loss

            # 3. backward and optimization only in train
            if train:
                self.optim.zero_grad()
                loss.backward()
                self.optim.step()

            # next sentence prediction accuracy
            correct = next_sent_output.argmax(dim=-1).eq(data["is_next"]).sum().item()
            avg_loss += loss.item()
            total_correct += correct
            total_element += data["is_next"].nelement()

            post_fix = {
                "epoch": epoch,
                "iter": i,
                "avg_loss": avg_loss / (i + 1),
                "avg_acc": total_correct / total_element * 100,
                "loss": loss.item()
            }

            if i % self.log_freq == 0:
                data_iter.write(str(post_fix))

        print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter), "total_acc=",
              total_correct * 100.0 / total_element)

    def save(self, epoch, file_path="output/bert_trained.model"):
        """
        Saving the current BERT model on file_path

        :param epoch: current epoch number
        :param file_path: model output path which gonna be file_path+"ep%d" % epoch
        :return: final_output_path
        """
        output_path = file_path + ".ep%d" % epoch
        torch.save(self.bert.cpu(), output_path)
        self.bert.to(self.device)
        print("EP:%d Model Saved on:" % epoch, output_path)
        return output_path


""" BERTLM """

class BERTLM(nn.Module):
    """
    BERT Language Model
    Next Sentence Prediction Model + Masked Language Model
    """

    def __init__(self, bert: BERT, vocab_size):
        """
        :param bert: BERT model which should be trained
        :param vocab_size: total vocab size for masked_lm
        """

        super().__init__()
        self.bert = bert
        self.next_sentence = NextSentencePrediction(self.bert.hidden)
        self.mask_lm = MaskedLanguageModel(self.bert.hidden, vocab_size)

    def forward(self, x, segment_label):
        x = self.bert(x, segment_label)
        return self.next_sentence(x), self.mask_lm(x)


class NextSentencePrediction(nn.Module):
    """
    2-class classification model : is_next, is_not_next
    """

    def __init__(self, hidden):
        """
        :param hidden: BERT model output size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, 2)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        return self.softmax(self.linear(x[:, 0]))


class MaskedLanguageModel(nn.Module):
    """
    predicting origin token from masked input sequence
    n-class classification problem, n-class = vocab_size
    """

    def __init__(self, hidden, vocab_size):
        """
        :param hidden: output size of BERT model
        :param vocab_size: total vocab size
        """
        super().__init__()
        self.linear = nn.Linear(hidden, vocab_size)
        self.softmax = nn.LogSoftmax(dim=-1)

    def forward(self, x):
        return self.softmax(self.linear(x))


"""Start Training """

print("Creating BERT Trainer")
trainer = BERTTrainer(bert, len(vocab), train_dataloader=train_data_loader, test_dataloader=test_data_loader,
                      lr=args.lr, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay,
                      with_cuda=args.with_cuda, log_freq=args.log_freq)

print("Training Start")
for epoch in range(args.epochs):
    trainer.train(epoch)
    trainer.save(epoch, args.output_path)

    if test_data_loader is not None:
        trainer.test(epoch)



参考代码:https://github.com/649453932/Bert-Chinese-Text-Classification-Pytorch

模型结构图片:参考李宏毅视频ppt

版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。
本文链接:https://blog.csdn.net/weixin_41362649/article/details/110204842

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