保姆级讲解BERT

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预训练 & 语言模型概述

Bert前世篇：从Word Embedding到Word2Vec、ELMo和GPT

关于Attention的超详细讲解（Attention、Self-Attention、Multi-Head Attention）

保姆级讲解Transformer

Transformer文本分类推理流程及复现

• 保姆级讲解BERT：

保姆级讲解BERT

• BERT示例代码:

## from https://github.com/graykode/nlp-tutorial/tree/master/5-2.BERT
import math
import re
from random import *
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import pkuseg

# sample IsNext and NotNext to be same in small batch size
# 通过这个函数，看一下bert预训练任务中的数据是如何被构建出来的
def make_batch():
    batch = []
    positive = negative = 0 ## 为了记录NSP任务中的正样本和负样本的个数，比例最好是在一个batch中接近1：1
    while positive != batch_size/2 or negative != batch_size/2:
        tokens_a_index, tokens_b_index= randrange(len(sentences)), randrange(len(sentences)) # 比如tokens_a_index=3，tokens_b_index=1；从整个样本中抽取对应的样本的索引；
        tokens_a, tokens_b= token_list[tokens_a_index], token_list[tokens_b_index]## 根据索引获取对应样本：tokens_a=[5, 23, 26, 20, 9, 13, 18] tokens_b=[27, 11, 23, 8, 17, 28, 12, 22, 16, 25]
        input_ids = [word_dict['[CLS]']] + tokens_a + [word_dict['[SEP]']] + tokens_b + [word_dict['[SEP]']] ## 加上特殊符号，CLS符号是1，sep符号是2：[1, 5, 23, 26, 20, 9, 13, 18, 2, 27, 11, 23, 8, 17, 28, 12, 22, 16, 25, 2]
        segment_ids = [0] * (1 + len(tokens_a) + 1) + [1] * (len(tokens_b) + 1)##分割句子符号（用0表示第一个句子，用1表示第二个句子）：[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

        # MASK LM
        n_pred =  min(max_pred, max(1, int(round(len(input_ids) * 0.15)))) # n_pred=3；整个句子的15%的字符可以被mask掉，这里取和max_pred中的最小值，确保每次计算损失的时候没有那么多字符以及信息充足，有15%做控制就够了；其实可以不用加这个，单个句子少了，就要加上足够的训练样本
        cand_maked_pos = [i for i, token in enumerate(input_ids)
                          if token != word_dict['[CLS]'] and token != word_dict['[SEP]']] ## cand_maked_pos=[1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]；整个句子input_ids中可以被mask的符号必须是非cls和sep符号的，要不然没意义
        shuffle(cand_maked_pos)## 打乱顺序：cand_maked_pos=[6, 5, 17, 3, 1, 13, 16, 10, 12, 2, 9, 7, 11, 18, 4, 14, 15]  其实取mask对应的位置有很多方法，这里只是一种使用shuffle的方式
        masked_tokens, masked_pos = [], []  # 被mask字符的原始值、被mask字符的位置
        for pos in cand_maked_pos[:n_pred]:## 取其中的三个；masked_pos=[6, 5, 17] 注意这里对应的是position信息；masked_tokens=[13, 9, 16] 注意这里是被mask的元素之前对应的原始单字数字；
            masked_pos.append(pos)
            masked_tokens.append(input_ids[pos])
            if random() < 0.8:  # 80%
                input_ids[pos] = word_dict['[MASK]'] # make mask
            elif random() < 0.5:  # 10%
                index = randint(0, vocab_size - 1) # random index in vocabulary
                input_ids[pos] = word_dict[number_dict[index]] # replace

        # Zero Paddings（补0：可以让没有被mask的位置不参与到损失函数的计算）
        n_pad = maxlen - len(input_ids)##maxlen=30；n_pad=10
        input_ids.extend([0] * n_pad)#在input_ids后面补零
        segment_ids.extend([0] * n_pad)# 在segment_ids 后面补零；这里有一个问题，0和之前的重了，这里主要是为了区分不同的句子，所以无所谓啊；他其实是另一种维度的位置信息；

        # Zero Padding (100% - 15%) tokens 是为了计算一个batch中句子的mlm损失的时候可以组成一个有效矩阵放进去；不然第一个句子预测5个字符，第二句子预测7个字符，第三个句子预测8个字符，组不成一个有效的矩阵；
        ## 这里非常重要，为什么是对masked_tokens是补零，而不是补其他的字符？？？？我补1可不可以？？
        if max_pred > n_pred:
            n_pad = max_pred - n_pred
            masked_tokens.extend([0] * n_pad)##  masked_tokens= [13, 9, 16, 0, 0] masked_tokens 对应的是被mask的元素的原始真实标签是啥，也就是groundtruth
            masked_pos.extend([0] * n_pad)## masked_pos= [6, 5, 17，0，0] masked_pos是记录哪些位置被mask了

        if tokens_a_index + 1 == tokens_b_index and positive < batch_size/2:
            batch.append([input_ids, segment_ids, masked_tokens, masked_pos, True]) # IsNext
            positive += 1
        elif tokens_a_index + 1 != tokens_b_index and negative < batch_size/2:
            batch.append([input_ids, segment_ids, masked_tokens, masked_pos, False]) # NotNext
            negative += 1
    return batch
# Proprecessing Finished

def get_attn_pad_mask(seq_q, seq_k):
    batch_size, len_q = seq_q.size()
    batch_size, len_k = seq_k.size()
    # eq(zero) is PAD token
    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # batch_size x 1 x len_k(=len_q), one is masking
    return pad_attn_mask.expand(batch_size, len_q, len_k)  # batch_size x len_q x len_k

def gelu(x):
    "Implementation of the gelu activation function by Hugging Face"
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))

class Embedding(nn.Module):
    def __init__(self):
        super(Embedding, self).__init__()
        self.tok_embed = nn.Embedding(vocab_size, d_model)  # token embedding
        self.pos_embed = nn.Embedding(maxlen, d_model)  # position embedding
        self.seg_embed = nn.Embedding(n_segments, d_model)  # segment(token type) embedding
        self.norm = nn.LayerNorm(d_model)

    def forward(self, x, seg):
        seq_len = x.size(1)
        pos = torch.arange(seq_len, dtype=torch.long)
        pos = pos.unsqueeze(0).expand_as(x)  # (seq_len,) -> (batch_size, seq_len)
        embedding = self.tok_embed(x) + self.pos_embed(pos) + self.seg_embed(seg)
        return self.norm(embedding)

class ScaledDotProductAttention(nn.Module):
    def __init__(self):
        super(ScaledDotProductAttention, self).__init__()

    def forward(self, Q, K, V, attn_mask):
        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.
        attn = nn.Softmax(dim=-1)(scores)
        context = torch.matmul(attn, V)
        return context, attn

class MultiHeadAttention(nn.Module):
    def __init__(self):
        super(MultiHeadAttention, self).__init__()
        self.W_Q = nn.Linear(d_model, d_k * n_heads)
        self.W_K = nn.Linear(d_model, d_k * n_heads)
        self.W_V = nn.Linear(d_model, d_v * n_heads)
    def forward(self, Q, K, V, attn_mask):
        # q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]
        residual, batch_size = Q, Q.size(0)
        # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
        q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # q_s: [batch_size x n_heads x len_q x d_k]
        k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)  # k_s: [batch_size x n_heads x len_k x d_k]
        v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)  # v_s: [batch_size x n_heads x len_k x d_v]

        attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]

        # context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
        context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
        output = nn.Linear(n_heads * d_v, d_model)(context)
        return nn.LayerNorm(d_model)(output + residual), attn # output: [batch_size x len_q x d_model]

class PoswiseFeedForwardNet(nn.Module):
    def __init__(self):
        super(PoswiseFeedForwardNet, self).__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.fc2 = nn.Linear(d_ff, d_model)

    def forward(self, x):
        # (batch_size, len_seq, d_model) -> (batch_size, len_seq, d_ff) -> (batch_size, len_seq, d_model)
        return self.fc2(gelu(self.fc1(x)))

class EncoderLayer(nn.Module):
    def __init__(self):
        super(EncoderLayer, self).__init__()
        self.enc_self_attn = MultiHeadAttention()
        self.pos_ffn = PoswiseFeedForwardNet()

    def forward(self, enc_inputs, enc_self_attn_mask):
        enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V
        enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]
        return enc_outputs, attn

## 1. BERT模型整体架构
class BERT(nn.Module):
    def __init__(self):
        super(BERT, self).__init__()
        self.embedding = Embedding() ## 词向量层，构建词表矩阵
        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)]) ## 把N个encoder堆叠起来，具体encoder实现一会看
        self.fc = nn.Linear(d_model, d_model) ## 前馈神经网络-cls
        self.activ1 = nn.Tanh() ## 激活函数-cls
        self.linear = nn.Linear(d_model, d_model)#-mlm
        self.activ2 = gelu ## 激活函数--mlm
        self.norm = nn.LayerNorm(d_model)
        self.classifier = nn.Linear(d_model, 2)## cls 这是一个分类层，维度是从d_model到2，对应我们架构图中就是这种：
        # decoder is shared with embedding layer
        embed_weight = self.embedding.tok_embed.weight
        n_vocab, n_dim = embed_weight.size()
        self.decoder = nn.Linear(n_dim, n_vocab, bias=False)
        self.decoder.weight = embed_weight
        self.decoder_bias = nn.Parameter(torch.zeros(n_vocab))

    def forward(self, input_ids, segment_ids, masked_pos):
        output = self.embedding(input_ids, segment_ids)## 生成input_ids对应的embdding；和segment_ids对应的embedding
        enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids)
        for layer in self.layers:
            output, enc_self_attn = layer(output, enc_self_attn_mask)
        # output : [batch_size, len, d_model], attn : [batch_size, n_heads, d_mode, d_model]
        # it will be decided by first token(CLS)
        h_pooled = self.activ1(self.fc(output[:, 0])) # [batch_size, d_model]
        logits_clsf = self.classifier(h_pooled) # [batch_size, 2]

        masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1)) # [batch_size, max_pred, d_model]  其中一个 masked_pos= [6, 5, 17，0，0]
        # get masked position from final output of transformer.
        h_masked = torch.gather(output, 1, masked_pos) # masking position [batch_size, max_pred, d_model]
        h_masked = self.norm(self.activ2(self.linear(h_masked)))
        logits_lm = self.decoder(h_masked) + self.decoder_bias # [batch_size, max_pred, n_vocab]

        return logits_lm, logits_clsf

seg = pkuseg.pkuseg()
def pk_tokenizer(text):
    return seg.cut(text)

if __name__ == '__main__':
    # BERT Parameters
    maxlen = 30 # 句子的最大长度 cover住95% 不要看平均数 或者99%  直接取最大可以吗？当然也可以，看你自己
    batch_size = 6 # 每一组有多少个句子一起送进去模型

    # 为什么会有这个：比如一个句子被mask了3个token，一个句子被mask了7个token，在计算损失的时候，很难把这两个组成一个有效的矩阵输入进去
    # 可以理解为截断的意思，大于这个参数了就截断在这里
    max_pred = 5  # max tokens of prediction，限制一个句子中最大可以预测多少个token（控制每个句子中，最多能有多少被mask掉）

    n_layers = 6 # number of Encoder of Encoder Layer，encoder block堆叠个数
    n_heads = 12 # number of heads in Multi-Head Attention
    d_model = 768 # Embedding Size
    d_ff = 3072  # 4*d_model, FeedForward dimension，全连接的维度
    d_k = d_v = 64  # dimension of K(=Q), V，Q、K、V的维度
    n_segments = 2  # bert预训练中有两个任务，其中一个是NSP，输入是两个句子，需要通过segment embedding区分不同的两个句子，E_A对应就是0，E_B对应就是1

    text = (
        'Hello, how are you? I am Romeo.\n'
        'Hello, Romeo My name is Juliet. Nice to meet you.\n'
        'Nice meet you too. How are you today?\n'
        'Great. My baseball team won the competition.\n'
        'Oh Congratulations, Juliet\n'
        'Thanks you Romeo'
    )

    # 模拟的是数据预处理，去除掉原始文本中没有用的字符
    sentences = re.sub("[。?.,!?\\-]", '', text.lower()).split('\n')  # filter '.', ',', '?', '!'
    word_list = list(set(" ".join(sentences).split()))
    print(word_list)

    # 构建词表
    word_dict = {
    
    '[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[MASK]': 3}
    for i, w in enumerate(word_list):
        word_dict[w] = i + 4
    number_dict = {
    
    i: w for i, w in enumerate(word_dict)}
    vocab_size = len(word_dict)

    token_list = list()
    for sentence in sentences:
        arr = [word_dict[s] for s in sentence.split()]
        token_list.append(arr)

    # 预训练任务的数据构建部分
    batch = make_batch()
    input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(*batch))

    model = BERT()
    criterion = nn.CrossEntropyLoss(ignore_index=0)   # ignore_index：忽略哪些索引，就是索引0不参与损失的计算
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # for epoch in range(100):
    for epoch in range(3):
        optimizer.zero_grad()
        logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)## logits_lm 【6，5，29】 bs*max_pred*voca  logits_clsf:[6*2]
        loss_lm = criterion(logits_lm.transpose(1, 2), masked_tokens) # for masked LM ;masked_tokens [6,5]
        loss_lm = (loss_lm.float()).mean()
        loss_clsf = criterion(logits_clsf, isNext) # for sentence classification
        loss = loss_lm + loss_clsf
        # if (epoch + 1) % 10 == 0:
        if (epoch + 1) % 1 == 0:
            print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
        loss.backward()
        optimizer.step()

    # Predict mask tokens ans isNext
    input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(batch[0]))
    print(text)
    print([number_dict[w.item()] for w in input_ids[0] if number_dict[w.item()] != '[PAD]'])

    logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)
    logits_lm = logits_lm.data.max(2)[1][0].data.numpy()
    print('masked tokens list : ',[pos.item() for pos in masked_tokens[0] if pos.item() != 0])
    print('predict masked tokens list : ',[pos for pos in logits_lm if pos != 0])

    logits_clsf = logits_clsf.data.max(1)[1].data.numpy()[0]
    print('isNext : ', True if isNext else False)
    print('predict isNext : ',True if logits_clsf else False)