NLP之基于BERT的预测掩码标记和句间关系判断
BERT
@
目录
设置基本变量值,数据预处理
构建输入样本
构建BERT模型
训练: 求任务1的损失值loss_lm和任务2的损失值loss_clsf的和,然后进行梯度下降,直到连续三次的loss都小于阈值0.01
测试: 遍历每个样本句子对,将其输入模型,从返回的logits_lm和logits_clsf中选择每行最大值,即为预测结果.
"""
Task: 基于BERT的完形填空和句间关系判断
Author: ChengJunkai @github.com/Cheng0829
Email: chengjunkai829@gmail.com
Date: 2022/09/21
Reference: Tae Hwan Jung(Jeff Jung) @graykode
"""import math, re, random, torch, time, os, sys
import numpy as np
import torch.nn as nn
import torch.optim as optimdef get_attn_pad_mask(seq_q, seq_k):
'''mask大小和(len_q,len_k)一致,
是为了在点积注意力中,与torch.matmul(Q,K)的大小一致'''
# (seq_q, seq_k): (dec_inputs, enc_inputs)
# dec_inputs:[batch_size, tgt_len] # [1,5]
# enc_inputs:[batch_size, src_len] # [1,6]
batch_size, len_q = seq_q.size() # 1,5
batch_size, len_k = seq_k.size() # 1,6
"""Tensor.data.eq(element)
eq即equal,对Tensor中所有元素进行判断,和element相等即为True,否则为False,返回二值矩阵
Examples:
>>> tensor = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
>>> tensor.data.eq(1)
tensor([[ True, False, False],
[False, False, False]])
"""
# eq(zero) is PAD token
pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # 升维 enc: [1,6] -> [1,1,6]
# 矩阵扩充: enc: pad_attn_mask: [1,1,6] -> [1,5,6]
return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size, len_q, len_k'''Attention = Softmax(Q * K^T) * V '''
def Scaled_Dot_Product_Attention(Q, K, V, attn_mask):
# Q_s: [batch_size, n_heads, len_q, d_k] # [1,8,5,64]
# K_s: [batch_size, n_heads, len_k, d_k] # [1,8,6,64]
# attn_mask: [batch_size, n_heads, len_q, len_k] # [1,8,5,6]"""torch.matmul(Q, K) torch.matmul是tensor的乘法,输入可以是高维的. 当输入是都是二维时,就是普通的矩阵乘法. 当输入有多维时,把多出的一维作为batch提出来,其他部分做矩阵乘法. Exeamples: >>> a = torch.ones(3,4) >>> b = torch.ones(4,2) >>> torch.matmul(a,b).shape torch.Size([3,2])
# [1,8,5,64] * [1,8,64,6] -> [1,8,5,6] # scores : [batch_size, n_heads, len_q, len_k] scores = torch.matmul(Q, K.transpose(2,3)) / np.sqrt(d_k) # divided by scale """scores.masked_fill_(attn_mask, -1e9) 由于scores和attn_mask维度相同,根据attn_mask中的元素值,把和attn_mask中值为True的元素的 位置相同的scores元素的值赋为-1e9 """ scores.masked_fill_(attn_mask, -1e9) # 'P'的scores元素值为-1e9, softmax值即为0 softmax = nn.Softmax(dim=-1) # 求行的softmax attn = softmax(scores) # [1,8,6,6] # [1,8,6,6] * [1,8,6,64] -> [1,8,6,64] context = torch.matmul(attn, V) # [1,8,6,64] return context, attn>>> a = torch.ones(5,3,4) >>> b = torch.ones(4,2) >>> torch.matmul(a,b).shape torch.Size([5,3,2]) >>> a = torch.ones(2,5,3) >>> b = torch.ones(1,3,4) >>> torch.matmul(a,b).shape torch.Size([2,5,4]) """class MultiHeadAttention(nn.Module):
# dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)
def init(self):
super().init()
self.W_Q = nn.Linear(d_model, d_kn_heads) # (512, 648) # d_q必等于d_k
self.W_K = nn.Linear(d_model, d_kn_heads) # (512, 648) # 保持维度不变
self.W_V = nn.Linear(d_model, d_vn_heads) # (512, 648)
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): # dec_outputs: [batch_size, tgt_len, d_model] # [1,5,512] # enc_outputs: [batch_size, src_len, d_model] # [1,6,512] # dec_enc_attn_mask: [batch_size, tgt_len, src_len] # [1,5,6] # q/k/v: [batch_size, len_q/k/v, d_model] residual, batch_size = Q, len(Q) '''用n_heads=8把512拆成64*8,在不改变计算成本的前提下,让各注意力头相互独立,更有利于学习到不同的特征''' # Q_s: [batch_size, len_q, n_heads, d_q] # [1,5,8,64] # new_Q_s: [batch_size, n_heads, len_q, d_q] # [1,8,5,64] Q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # K_s: [batch_size, n_heads, len_k, d_k] # [1,8,6,64] K_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # V_s: [batch_size, n_heads, len_k, d_v] # [1,8,6,64] V_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)# attn_mask : [1,5,6] -> [1,1,5,6] -> [1,8,5,6] # attn_mask : [batch_size, n_heads, len_q, len_k] attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # context: [batch_size, n_heads, len_q, d_v] # attn: [batch_size, n_heads, len_q(=len_k), len_k(=len_q)] # context: [1,8,5,64] attn: [1,8,5,6] context, attn = Scaled_Dot_Product_Attention(Q_s, K_s, V_s, attn_mask) """contiguous() 连续的 contiguous: view只能用在连续(contiguous)的变量上. 如果在view之前用了transpose, permute等, 需要用contiguous()来返回一个contiguous copy """ # context: [1,8,5,64] -> [1,5,512] context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [1,5,512] -> [1,5,512] output = self.linear(context) """nn.LayerNorm(output) 样本归一化 和对所有样本的某一特征进行归一化的BatchNorm不同, LayerNorm是对每个样本进行归一化,而不是一个特征 Tips: 归一化Normalization和Standardization标准化区别: Normalization(X[i]) = (X[i] - np.min(X)) / (np.max(X) - np.min(X)) Standardization(X[i]) = (X[i] - np.mean(X)) / np.var(X) """ output = self.layer_norm(output + residual) return output, attn </code></pre>class Position_wise_Feed_Forward_Networks(nn.Module):
def init(self):
super().init()
'''输出层相当于1*1卷积层,也就是全连接层'''
"""nn.Conv1d
in_channels应该理解为嵌入向量维度,out_channels才是卷积核的个数(厚度)
"""
# 512 -> 2048
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
# 2048 -> 512
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
self.layer_norm = nn.LayerNorm(d_model)def forward(self, inputs): # enc_outputs: [batch_size, source_len, d_model] # [1,6,512] residual = inputs relu = nn.ReLU() # output: 512 -> 2048 [1,2048,6] output = relu(self.conv1(inputs.transpose(1, 2))) # output: 2048 -> 512 [1,6,512] output = self.conv2(output).transpose(1, 2) return self.layer_norm(output + residual)class EncoderLayer(nn.Module):
def init(self):
super(EncoderLayer, self).init()
self.enc_attn = MultiHeadAttention()
self.pos_ffn = Position_wise_Feed_Forward_Networks()def forward(self, enc_outputs, enc_attn_mask): # enc_attn_mask: [1,6,6] # enc_outputs to same Q,K,V # enc_outputs: [batch_size, source_len, d_model] # [1, 6, 512] enc_outputs, attn = self.enc_attn(enc_outputs, \ enc_outputs, enc_outputs, enc_attn_mask) # enc_outputs: [batch_size , len_q , d_model] enc_outputs = self.pos_ffn(enc_outputs) return enc_outputs, attn"""以上为Transformer架构代码**"""
'''1.数据预处理'''
def pre_process(text):
# sentences 共有6个样本句子
# 去除字符
sentences = re.sub("[.,!?\-]", '', text.lower()).split('\n') # filter '.', ',', '?', '!'
word_sequence = " ".join(sentences).split()
word_list = []
'''
如果用list(set(word_sequence))来去重,得到的将是一个随机顺序的列表(因为set无序),
这样得到的字典不同,保存的上一次训练的模型很有可能在这一次不能用
(比如上一次的模型预测碰见i:0,love:1,就输出you:2,但这次模型you在字典3号位置,也就无法输出正确结果)
'''
for word in word_sequence:
if word not in word_list:
word_list.append(word)
word_dict = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[MASK]': 3}
# word_dict前4个均为特殊字符
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)# 二维列表 sentences_list = [] for sentence in sentences: arr = [word_dict[s] for s in sentence.split()] sentences_list.append(arr) # 用序号token来替代单词 print(word_dict) return sentences, word_list, word_dict, number_dict, vocab_size, sentences_list'''论文所用激活函数,在BERT中优于RELU'''
def gelu(x):
"Implementation of the gelu activate_ation function by Hugging Face"
# erf(x):计算x的误差函数,
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))'''根据句子数据,构建词元的输入向量'''
def make_batch():
batch = []
positive = negative = 0
a_b = []
'''由于随机选取句子,所以可能依照pt文件中已训练的模型也误差较大'''
while positive != batch_size/2 or negative != batch_size/2:
"""random.randrange(start, stop=None, step=1)
在(start, stop)范围内随即返回一个数
Args:
start: 指定范围内的开始值,包含在范围内
stop: 指定范围内的结束值,不包含在范围内,若无参数stop则默认范围为0~start
step: 指定递增基数,默认为1
Examples:
>>> random.randrange(1, 100, 2)
67 # 以2递增,只能返回奇数
"""
# 在样本集中随机选取a和b两个句子
sentence_a_index, sentence_b_index = random.randrange(len(sentences)), random.randrange(len(sentences))
# sentence_ab是包含一个句子中所含单词的序号列表
sentence_a, sentence_b = sentences_list[sentence_a_index], sentences_list[sentence_b_index]if (sentence_a_index, sentence_b_index) not in a_b: # 屏蔽已经选择过的 a_b.append((sentence_a_index, sentence_b_index)) elif len(a_b) < batch_size: # 达到要求的样本值 continue else: break # 加入分类符和分隔符 input_ids = [word_dict['[CLS]']] + sentence_a + [word_dict['[SEP]']] + sentence_b + [word_dict['[SEP]']] # segment_ids用来标识a和b两个句子 segment_ids = [0] * (1 + len(sentence_a) + 1) + [1] * (len(sentence_b) + 1)
# batch: [6,5] return batch# MASK LM # 把句子15%单词准备用于mask # max_words_pred(=5): 最大预测单词数 n_pred: 预测个数 n_pred = int(0.15 * len(input_ids)) n_pred = min(max_words_pred, max(1, n_pred)) # 预测个数不能为0,也不能大于最大预测单词数 # 构建候选词列表(随机打乱后只有前n_pred个会被选择) # 要屏蔽cls和seq,所以不能用torch.arange(len(input_ids)) candidate_mask_tokens = [i for i, token in enumerate(input_ids) if token != word_dict['[CLS]'] and token != word_dict['[SEP]']] # [1,2,3......](不包含0([CLS])和[SEP]) # 随机打乱candidate_mask_tokens random.shuffle(candidate_mask_tokens) # random.shuffle打乱候选表后,直接选择前n_pred个 masked_tokens, masked_pos = [], [] for pos in candidate_mask_tokens[:n_pred]: # 不管是被mask还是被随机还是不变,都会加入masked列表(masked中的不会被替换,替换的是input_ids) # 记录原始单词,用于和模型输出比对计算loss masked_pos.append(pos) masked_tokens.append(input_ids[pos]) if random.random() < 0.8: # 80% -> MASK input_ids[pos] = word_dict['[MASK]'] # mask elif random.random() < 0.5: # 10% -> random random_index = random.randint(0, vocab_size - 1) # random index in vocabulary input_ids[pos] = word_dict[number_dict[random_index]] # replace '''把所有存储单词的变量都填充至最大长度,有利于统一处理.''' # Zero Paddings n_pad = max_len - len(input_ids) # max_len: 允许的句子最大长度 input_ids.extend([0] * n_pad) # word_dict['Pad'] = 0 segment_ids.extend([0] * n_pad) # Zero Padding (100% - 15%) tokens if max_words_pred > n_pred: # max_words_pred(=5): 最大预测长度 n_pad = max_words_pred - n_pred masked_tokens.extend([0] * n_pad) masked_pos.extend([0] * n_pad) # 判断句间关系 if sentence_a_index + 1 == sentence_b_index and positive < batch_size/2: # 论文要求严格保持50% 50% batch.append([input_ids, segment_ids, masked_tokens, masked_pos, True]) # IsNext positive += 1 elif sentence_a_index + 1 != sentence_b_index and negative < batch_size/2: # 论文要求严格保持50% 50% batch.append([input_ids, segment_ids, masked_tokens, masked_pos, False]) # NotNext negative += 1'''论文图2所设计的输入encoder的嵌入向量'''
class Embedding(nn.Module):
def init(self):
super().init()
# vocab_size: len(input_ids)
# vocab_size: 29 max_len: 30 n_segments: 2
self.tok_embed = nn.Embedding(vocab_size, d_model) # token embedding
self.pos_embed = nn.Embedding(max_len, d_model) # position embedding
self.seg_embed = nn.Embedding(n_segments, d_model) # segment(token type) embedding
self.norm = nn.LayerNorm(d_model) # 对每个样本的特征归一化def forward(self, x, seg): # embedding(input_ids, segment_ids) # [6,30] [6,30] seq_len = x.size(1) # 30 # torch.arange(n): 生成0~n-1的一维张量,默认类型为int # 加入序列信息,不用transformer的positional encoding pos = torch.arange(seq_len, dtype=torch.long).to(device) # [0,1,2,3...seq-1] ''' expand_as/expand拓展后,每一个(1,seq_len)都是同步变化的,不能单独修改某一个 单独修改某一个只能用repeat(重复倍数) ''' pos = pos.unsqueeze(0).expand_as(x) # (seq_len,) -> (batch_size, seq_len) [6,30] ''' 虽然tok_embed是(29,768),但embedding首参数是指单词类别,x虽然长度=30>29, 但是单词总类别依然肯定不大于29,所以tok_embed可以作为x的嵌入矩阵 seg_embed同理 ''' # [6,30,768], [6,30,768], [6,30,768], [6,30,768] '''论文图2, Embedding: input = word + position + segment''' embedding = self.tok_embed(x) + self.pos_embed(pos) + self.seg_embed(seg) return self.norm(embedding)'''2.构建模型'''
class BERT(nn.Module):
def init(self):
super().init()
self.embedding = Embedding()
self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
self.fc = nn.Linear(d_model, d_model)
self.norm = nn.LayerNorm(d_model)
self.classifier = nn.Linear(d_model, 2)
self.activate_1 = gelu
self.activate_2 = nn.Tanh()# decoder is shared with embedding layer token_embed_weight = self.embedding.tok_embed.weight vocab_size, n_dim = token_embed_weight.size() # 29, 768 self.decoder = nn.Linear(n_dim, vocab_size, bias=False) self.decoder.weight = token_embed_weight self.decoder_bias = nn.Parameter(torch.zeros(vocab_size)) def forward(self, input_ids, segment_ids, masked_pos):# 按照论文图2构建输入encoder的嵌入矩阵, Embedding: input = word + position + segment output = self.embedding(input_ids, segment_ids).to(device) # padding mask, 使其在softmax中为0而不影响注意力机制 enc_self_attn_mask = get_attn_pad_mask(input_ids, input_ids) for layer in self.layers: # output : [batch_size, max_len, d_model] [6,30,768] # attn : [batch_size, n_heads, d_mode, d_model] output, enc_self_attn = layer(output, enc_self_attn_mask) '''Task1''' # masked_pos -> [batch_size, max_words_pred, d_model] [6,5,768] # [:, :, None]在第三个维度增加一维 masked_pos: [6,5] -> [6,5,768] masked_pos = masked_pos[:, :, None].expand(-1, -1, output.size(-1)) # expand(-1)表示不变 # 从最终输出output中提取mask信息 """torch.gather() 收集输入的特定维度指定位置的数值 Args: input(tensor): 待操作数.不妨设其维度为(x1, x2, ···, xn) dim(int): 待操作的维度 index(LongTensor): 如何对input进行操作 """ # predict_masked: [batch_size, max_words_pred, d_model] # [6,5,768] # output:[6,30,768] -> predict_masked:[6,5,768] predict_masked = torch.gather(output, 1, masked_pos) predict_masked = self.norm(self.activate_1(self.fc(predict_masked))) # activate_1: gelu # (self.decoder:nn.Linear(768,29), self.decoder_bias: nn.Parameter(torch.zeros(29))) # logits_lm: [batch_size, max_words_pred, vocab_size] [6,5,29] '''所谓的语言模型就是vocab_size个单词的嵌入矩阵''' logits_lm = self.decoder(predict_masked) + self.decoder_bias '''Task2''' '''只训练第一列? CLS能训练? input是cls,但输出不是''' # 从第一个CLS值得到结果 # output[:, 0]取output第一列的数据 [6,30,768] -> [6,768] # h_pooled: [batch_size, d_model] [6,768] h_pooled = self.activate_2(self.fc(output[:, 0])) # activate_2: Tanh # 全连接分类 (classifier:768->2) logits_clsf = self.classifier(h_pooled) # [batch_size, 2] return logits_lm, logits_clsf</code></pre>if name == 'main':
device = ['cuda:0' if torch.cuda.is_available() else 'cpu'][0]
# BERT Parameters
max_len = 30 # 允许的句子最大长度:9+9+1+2=21
batch_size = 10 # batch中的句子对个数,因为positive数最多为5,所以batch_size最大为10 # 必须是偶数, 否则make_batch陷入死循环
max_words_pred = 5 # 最大标记预测长度 210.15=3.15 n_layers = 12 # number of Encoder of Encoder Layer n_heads = 12 # number of heads in Multi-Head Attention d_model = 768 # Embedding Size d_ff = 4d_model # 4*d_model, FeedForward dimension
d_k = d_v = 64 # dimension of K(=Q), V
n_segments = 2 # ab两个句子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' ) # print(text) '''1.数据预处理''' sentences, word_list, word_dict, number_dict, vocab_size, sentences_list = pre_process(text) batch = make_batch() '''2.构建模型''' model = BERT() model.to(device) criterion = nn.CrossEntropyLoss() '''lr如果是0.001,将会很快陷入局部收敛!''' optimizer = optim.AdamW(model.parameters(), lr=0.0001) # AdamW: 论文采用的Adam扩展版本 if os.path.exists('model_param.pt') == True: # 加载模型参数到模型结构 model.load_state_dict(torch.load('model_param.pt', map_location=device)) """map(function, iterable, ...) map()会根据提供的函数对指定序列做映射。第一个参数function以参数序列中的每一个元素调用function函数, 返回包含每次function函数返回值的新列表。 Args: function: 函数 iterable: 一个或多个序列 """ """zip([iterable, ...]) 用于将可迭代的对象作为参数,将对象中对应的元素打包成一个个元组,然后返回由这些元组组成的列表. Examples >>> a = [1,2,3] >>> b = [4,5,6] >>> list(zip([a,b])) [(1, 4), (2, 5), (3, 6)]
""" # 因为batch二维,所以要用*,不然zip会把batch里的每个样本整体当作一个迭代对象返回一个元组 # map(torch.LongTensor, zip(*batch))相当于torch.LongTensor(input_ids)...... # batch: [6,5] input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(*batch)) input_ids, segment_ids, masked_tokens, masked_pos, isNext = \ input_ids.to(device), segment_ids.to(device), masked_tokens.to(device), masked_pos.to(device), isNext.to(device) # print(masked_tokens.shape, isNext.shape) [6,5] [6,] '''3.训练''' print('{}\nTrain\n{}'.format('*'*30, '*'*30)) loss_record = [] for epoch in range(1000): optimizer.zero_grad() logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos) # Task1: 对比完形填空准确率 # logits_lm:[6,5,29]->[6,29,5] masked_tokens:[6,5] """nn.CrossEntropyLoss()(input,target) 如果target是一维,则只要求input.size(0)=target.size 如果是多维,那么要求input.size(0)=target.size(0)且input.size(-1)=target.size(-1) """ loss_lm = criterion(logits_lm.transpose(1, 2), masked_tokens) # Task2: 对比句间关系分类准确率 # logits_clsf:[6,2] isNext:[6,] loss_clsf = criterion(logits_clsf, isNext) loss = loss_lm + loss_clsf loss.backward() optimizer.step()>>> c = [a,b] # [[1,2,3],[4,5,6]] >>> list(zip(*c)) [(1, 4), (2, 5), (3, 6)] >>> list(zip(c)) [([1, 2, 3],), ([4, 5, 6],)]
'''4.测试''' print('{}\nTest\n{}'.format('*'*30, '*'*30)) print('text:\n%s'%text) for i in range(len(batch)): # 遍历每个句子对 print('*'*30) # Predict mask tokens ans isNext input_ids, segment_ids, masked_tokens, masked_pos, isNext = map(torch.LongTensor, zip(batch[i])) input_ids, segment_ids, masked_tokens, masked_pos, isNext = \ input_ids.to(device), segment_ids.to(device), masked_tokens.to(device), masked_pos.to(device), isNext.to(device)if loss >= 0.01: # 连续30轮loss小于0.01则提前结束训练 loss_record = [] else: loss_record.append(loss.item()) if len(loss_record) == 30: torch.save(model.state_dict(), 'model_param.pt') break if (epoch + 1) % 10 == 0: print('Epoch:', '%04d' % (epoch + 1), 'Loss = {:.6f}'.format(loss)) if(epoch + 1) % 100 ==0: torch.save(model.state_dict(), 'model_param.pt')# 输出input_ids # w.item() != 0 不是'PAD' print('input_ids :', [number_dict[w.item()] for w in input_ids[0] if w.item() != 0]) logits_lm, logits_clsf = model(input_ids, segment_ids, masked_pos) logits_lm, logits_clsf = logits_lm.to('cpu'), logits_clsf.to('cpu') # Task1 logits_lm = logits_lm.data.max(2)[1][0].data.numpy() print('real masked tokens list :', \ [(pos.item(), number_dict[pos.item()]) for pos in masked_tokens[0] if pos.item() != 0]) # 不是PAD print('predict masked tokens list :', \ [(pos, number_dict[pos]) for pos in logits_lm if pos != 0]) # 不是PAD # Task2 logits_clsf = logits_clsf.data.max(1)[1].data.numpy()[0] print('Real isNext :', bool(isNext)) print('predict isNext :', bool(logits_clsf))</code></pre></li>
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