BERT(cpu)代码复现_bert代码复现

逐行注释，逐行解析。可直接运行。
code from https://github.com/graykode/nlp-tutorial/tree/master/5-2.BERT

import math
import re
import time
from random import *
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
 
 
# 10. MyDataset
class MyDataset(Dataset):
    # 读数据
    def __init__(self, input_ids, segment_ids, masked_tokens, masked_pos, isNext):
        self.input_ids = input_ids
        self.segment_ids = segment_ids
        self.masked_tokens = masked_tokens
        self.masked_pos = masked_pos
        self.isNext = isNext
 
    # 返回数据长度（有几行数据）
    def __len__(self):
        return len(self.input_ids)
        # return self.input_ids.shape[0]
 
    # 返回相对位置上的元素，会比make_batch函数返回的tensor数据少一个维度
    def __getitem__(self, idx):
        return self.input_ids[idx], self.segment_ids[idx], self.masked_tokens[idx], self.masked_pos[idx], self.isNext[idx]
 
 
# 9. gelu激活函数  (GAUSSIAN ERROR LINEAR UNITS：高斯误差线性单元)
def gelu(x):
    """
      Implementation of the gelu activation function.
      For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
      0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
      Also see https://arxiv.org/abs/1606.08415
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))  # x==[6, 30, 3072]
 
 
# 8. PoswiseFeedForwardNet
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):
        # x==[6, 30, 768]
        return self.fc2(gelu(self.fc1(x)))  # 9.
 
 
# 7. ScaledDotProductAttention
class ScaledDotProductAttention(nn.Module):
    def __init__(self):
        super(ScaledDotProductAttention, self).__init__()
 
    def forward(self, Q, K, V, attn_mask):
        # Q,K,V==[6, 12, 30, 64]  attn_mask==[6, 12, 30, 30]
        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k)  # [6, 12, 30, 64]*[6, 12, 64, 30]==[6, 12, 30, 30]
        scores.masked_fill_(attn_mask, -1e9)
        # 矩阵横行 特征维度上做softmax
        attn = nn.Softmax(dim=-1)(scores)  # [6, 12, 30, 30]
        context = torch.matmul(attn, V)  # [6, 12, 30, 30]*[6, 12, 30, 64]==[6, 12, 30, 64]
        return context, attn
 
 
# 6. MultiHeadAttention
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)
        self.linear = nn.Linear(n_heads * d_v, d_model)
        self.layer_norm = nn.LayerNorm(d_model)
 
    def forward(self, Q, K, V, attn_mask):
        # Q,K,V==[6, 30, 768]  attn_mask==[6, 30, 30]
        residual = Q  # 残差连接
        batch_size = Q.size(0)
        q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1, 2)  # [6, 30, 12, 64]--->[6, 12, 30, 64]
        k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1, 2)  # [6, 30, 12, 64]--->[6, 12, 30, 64]
        v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1, 2)  # [6, 30, 12, 64]--->[6, 12, 30, 64]
        attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)  # [6, 1, 30, 30]--->[6, 12, 30, 30]
        # attn==[6, 12, 30, 30]  context==[6, 12, 30, 64]
        context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)  # 7.
        context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v)  # [6, 12, 30, 64]--->[6, 30, 12, 64]--->[6, 30, 768]
        output = self.linear(context)  # [6, 30, 768]--->[6, 30, 768]
        return self.layer_norm(output + residual), attn
 
 
# 5. EncoderLayer：包含两个部分，多头自注意力层和前馈神经网络
class EncoderLayer(nn.Module):
    def __init__(self):
        super(EncoderLayer, self).__init__()
        self.enc_self_attn = MultiHeadAttention()  # 6.
        self.pos_ffn = PoswiseFeedForwardNet()  # 8.
 
    def forward(self, enc_inputs, enc_self_attn_mask):
        # attn==[6, 12, 30, 30]  enc_outputs==[6, 30, 768]
        enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask)
        enc_outputs = self.pos_ffn(enc_outputs)  # [6, 30, 768]
        return enc_outputs, attn
 
 
# 4. get_attn_pad_mask
def get_attn_pad_mask(seq_q, seq_k):
    batch_size, len_q = seq_q.size()  # [6, 30]
    batch_size, len_k = seq_k.size()  # [6, 30]
    pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)  # [6, 30]--->[6, 1, 30]
    return pad_attn_mask.expand(batch_size, len_q, len_k)  # [6, 30, 30]
 
 
# 3. 词向量层 构建词表矩阵
class Embedding(nn.Module):
    def __init__(self):
        super(Embedding, self).__init__()
        self.tok_embed = nn.Embedding(vocab_size, d_model)  # Token Embeddings    [29, 768]
        self.seg_embed = nn.Embedding(n_segments, d_model)  # Segment Embeddings  [2, 768]
        self.pos_embed = nn.Embedding(max_len, d_model)     # Position Embeddings [30, 768]
        self.norm = nn.LayerNorm(d_model)
 
    def forward(self, x, seg):  # x==[6, 30]  seg==[6, 30]
        seq_len = x.size(1)  # 30
        pos = torch.arange(seq_len, dtype=torch.long)  # (30,) tensor([0,1,...29])
        pos = pos.unsqueeze(0).expand_as(x)  # [1, 30]--->[6, 30]
        embedding = self.tok_embed(x) + self.seg_embed(seg) + self.pos_embed(pos)  # [6, 30, 768]+[6, 30, 768]+[6, 30, 768]==[6, 30, 768]
        return self.norm(embedding)
 
 
# 2. BERT模型整体架构
class BERT(nn.Module):
    def __init__(self):
        super(BERT, self).__init__()
        self.embedding = Embedding()  # 3.
        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])  # 5.
        self.fc = nn.Linear(d_model, d_model)    # nsp任务--->cls后面接的线性层
        self.activ1 = nn.Tanh()                  # nsp任务--->cls后面接个线性层之后再接个激活函数(Tanh)
        self.classifier = nn.Linear(d_model, 2)  # nsp任务--->cls后面接完线性层和激活函数后 最后再接个线性层进行二分类
        self.linear = nn.Linear(d_model, d_model)  # mlm任务--->mlm后面接的线性层
        self.activ2 = gelu                         # mlm任务--->mlm后面接个线性层之后再接个激活函数(gelu)
        self.norm = nn.LayerNorm(d_model)          # mlm任务--->mlm后面接完线性层和激活函数后 最后在进行规范化
        # decoder is shared with embedding layer
        embed_weight = self.embedding.tok_embed.weight          # Token Embeddings [29, 768]
        n_vocab, n_dim = embed_weight.size()                    # n_vocab==29  n_dim==768
        self.decoder = nn.Linear(n_dim, n_vocab, bias=False)    # 把768映射为词表大小(29)
        self.decoder.weight = embed_weight                      # 权重w
        self.decoder_bias = nn.Parameter(torch.zeros(n_vocab))  # 偏置b：创建一个可学习参数(29个全0一维)
 
    def forward(self, input_ids, segment_ids, masked_pos):
        output = self.embedding(input_ids, segment_ids)  # [6, 30, 768]
        # get_attn_pad_mask：是为了记录句子中pad的位置信息，传给模型后面，在计算自注意力的时候去掉pad符号的影响 4.
        enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids)  # [6, 30, 30]
        for layer in self.layers:
            output, enc_self_attn = layer(output, enc_self_attn_mask)  # enc_self_attn==[6, 12, 30, 30]  output==[6, 30, 768]
        # 处理cls
        h_pooled = self.activ1(self.fc(output[:, 0]))  # [6, 30, 768]--->[6, 768]
        logits_clsf = self.classifier(h_pooled)  # [6, 2]
        # 处理mask
        masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1))  # [6, 5]--->[6, 5, 1]--->[6, 5, 768]
        h_masked = torch.gather(output, 1, masked_pos)  # get masked_tokens：[6, 5, 768]
        h_masked = self.norm(self.activ2(self.linear(h_masked)))  # [6, 5, 768]
        logits_lm = self.decoder(h_masked) + self.decoder_bias  # [6, 5, 29]+[29]==[6, 5, 29] 在最后一个维度上相加
        return logits_lm, logits_clsf
 
 
# 1. 预训练任务的数据构建
def make_batch():
    batch = []  # 最终要返回的结果
    # 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
    positive = negative = 0
    while positive != batch_size/2 or negative != batch_size/2:
        # 比如tokens_a_index=3，tokens_b_index=1；从整个样本中抽取对应的样本
        tokens_a_index = randrange(len(sentences))
        tokens_b_index = randrange(len(sentences))
        # 根据索引获取对应样本：比如tokens_a=[5, 23, 26, 20, 9, 13, 18] tokens_b=[27, 11, 23, 8, 17, 28, 12, 22, 16, 25]
        tokens_a = token_list[tokens_a_index]
        tokens_b = token_list[tokens_b_index]
        # 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]
        input_ids = [word_dict['[CLS]']] + tokens_a + [word_dict['[SEP]']] + tokens_b + [word_dict['[SEP]']]
        # 分割句子符号：[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        segment_ids = [0] * (1 + len(tokens_a) + 1) + [1] * (len(tokens_b) + 1)
 
        # MASK LM
        # 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，相当于起到截断的作用，确保每次计算损失的时候没有那么多字符以及信息充足
        n_pred = min(max_pred, max(1, int(round(len(input_ids) * 0.15))))
        # 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
        cand_maked_pos = [i for i, token in enumerate(input_ids) if token != word_dict['[CLS]'] and token != word_dict['[SEP]']]
        # 打乱顺序：cand_maked_pos=[6, 5, 17, 3, 1, 13, 16, 10, 12, 2, 9, 7, 11, 18, 4, 14, 15] 随机取mask，这里只是采用众多方式中的一种方式来取(shuffle)
        shuffle(cand_maked_pos)
        masked_tokens = []  # 被mask的元素的原始真实标签 也就是ground truth
        masked_pos = []     # 记录哪些位置被mask了
        # 随机取其中的前三个，masked_pos=[6, 5, 17]：注意这里对应的是position信息索引；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
        for pos in cand_maked_pos[:n_pred]:
            masked_pos.append(pos)
            masked_tokens.append(input_ids[pos])
            if random() < 0.8:    # 80%概率用mask替换
                input_ids[pos] = word_dict['[MASK]']
            elif random() > 0.9:  # 10%概率用其他词替换
                index = randint(0, vocab_size - 1)
                while index < 4:  # 不能小于4，因为词表里面前4个词是[PAD][CLS][SEP][MASK]，用这4个词替换没意义，所以再重新选
                    index = randint(0, vocab_size - 1)
                input_ids[pos] = word_dict[number_dict[index]]
            # 还有剩下0.8到0.9之间这10%的概率不做任何替换，所以这里没写
 
        # Zero Paddings1
        n_pad = max_len - len(input_ids)  # max_len=30  n_pad=10
        input_ids.extend([0] * n_pad)     # 在input_ids后面补零
        segment_ids.extend([0] * n_pad)   # segment_ids后面也要跟着补零，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，它其实是另一种维度的位置信息
 
        # Zero Paddings2 (mask 15%) tokens
        # 同一个batch里面每条样本的mask数量必须是相等的，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
        # 对masked_tokens补0是最好的，因为0本身对应PAD，没有意义。当然补123也是可以的([CLS][SEP][MASK])，只不过不太好
        if max_pred > n_pred:                  # n_pred不可能比max_pred大，因为MASK LM里面设置截断了
            n_pad = max_pred - n_pred
            masked_tokens.extend([0] * n_pad)  # masked_tokens= [13, 9, 16, 0, 0]
            masked_pos.extend([0] * n_pad)     # masked_pos= [6, 5, 17，0，0]
 
        # 判断这两句话是否相邻
        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
 
 
if __name__ == '__main__':
    # BERT Parameters
    max_len = 30    # 句子的最大长度 cover住95%(表示同一个batch中的所有句子都由30个token组成，不够的补PAD)
    batch_size = 6
    max_pred = 5    # 表示最多需要预测多少个mask单词，设置了一个上限(比如100个单词按照15%算有15个单词做mask，那我不做15个，只做max_pred个)
    n_layers = 12   # number of Encoder Layer
    n_heads = 12    # number of heads in Multi-Head Attention
    d_model = 768   # Embedding Size(Token Embeddings、Segment Embeddings、Position Embeddings)
    d_ff = 3072     # 4*d_model, FeedForward dimension
    d_k = d_v = 64  # dimension of Q(=k),V
    n_segments = 2  # 表示Encoder input 由几句话组成
 
    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'
    )
 
    sentences = re.sub(r'[.,!?\-]', '', text.lower()).split('\n')  # filter '.', ',', '?', '!', '-' --->['', '', '', '', '', '']
    # ' '.join(sentences)：将一个字符串返回成一个新的字符串，' '作用在字符串与字符串之间，用' '隔开
    # split()：默认以空格切分，返回字符串列表['', '', ...]
    # set()：返回去重后的集合{}，会乱序
    # list()：返回列表[]
    word_list = list(set(' '.join(sentences).split()))
    # word_to_id
    word_dict = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[MASK]': 3}
    for i, w in enumerate(word_list):
        word_dict[w] = i + 4
    # id_to_word
    number_dict = {i: w for i, w in enumerate(word_dict)}
    # 29
    vocab_size = len(word_dict)
 
    # 原始的文本转为数字(索引)，便于被计算机处理：[[15, 7, 27, 22, 8, 4, 16], ..., []]
    token_list = list()
    for sentence in sentences:
        arr = [word_dict[s] for s in sentence.split()]
        token_list.append(arr)
 
    # 数据
    # batch：[[[],[],[],[],F/T], [[],[],[],[],F/T], [[],[],[],[],F/T], [[],[],[],[],F/T], [[],[],[],[],F/T], [[],[],[],[],F/T]]
    batch = make_batch()  # 1.
    # zip(*batch)：[([],[],[],[],[],[]), ([],[],[],[],[],[]), ([],[],[],[],[],[]), ([],[],[],[],[],[]), (F/T, F/T, F/T, F/T, F/T, F/T)]
    input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(*batch))  # 转成tensor
 
    # 模型
    model = BERT()  # 2.
    # 损失函数
    criterion = nn.CrossEntropyLoss(ignore_index=0)  # 真实label中为0的部分不参与损失计算
    # 优化器
    optimizer = optim.Adam(model.parameters(), lr=0.001)
 
    # 实例化数据源
    dataset = MyDataset(input_ids, segment_ids, masked_tokens, masked_pos, isNext)  # 10.
    # 实例化
    dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True)
 
    model.train()
    start = time.time()
    for epoch in range(10):
        # 通过DataLoader把MyDataset中的__getitem__函数返回的数据拿出来后 会多增加一个维度
        for input_ids, segment_ids, masked_tokens, masked_pos, isNext in dataloader:
            logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)
            loss_lm = criterion(logits_lm.transpose(1, 2), masked_tokens)  # mlm任务损失计算
            loss_lm = (loss_lm.float()).mean()
            loss_clsf = criterion(logits_clsf, isNext)  # nsp任务损失计算(cls classification)
            loss = loss_lm + loss_clsf
            if (epoch + 1) % 2 == 0:
                print('Epoch:', '%04d' % (epoch + 1), 'cost=', '{:.6f}'.format(loss))
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    # 保存模型
    # torch.save(model.state_dict(), './wb_model/wb_bert(cpu).pt')
    end = time.time()
    print(f'cpu下共执行--->{end - start}s')
 
    print('-------------------- Predict mask tokens and isNext --------------------')
    # 加载数据
    batch = make_batch()
    # batch[0]：取出第0个批次  [[],[],[],[],F/T]
    # zip(batch[0])：[([],), ([],), ([],), ([],), (F/T,)]
    # [1, 30]     [1, 30]       [1, 5]       [1, 5]     [1,]
    input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(batch[0]))  # 转成tensor
    print(text)
    print([number_dict[w.item()] for w in input_ids[0] if number_dict[w.item()] != '[PAD]'])
 
    # 加载训练好的模型参数
    model = BERT()
    # model.load_state_dict(torch.load('./wb_model/wb_bert(cpu).pt'))
 
    # 模型预测
    with torch.no_grad():
        logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos)  # logits_lm==[1, 5, 29]  logits_clsf==[1, 2]
        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)