BERT - Bert模型框架复现

本节将实现一个基于Transformer架构的BERT模型。

1. MultiHeadAttention 类

这个类实现了多头自注意力机制（Multi-Head Self-Attention），是Transformer架构的核心部分。

在前几篇文章中均有讲解，直接上代码

class MultiHeadAttention(nn.Module):def __init__(self, d_model, num_heads, dropout):super().__init__()self.num_heads = num_headsself.d_k = d_model // num_headsself.q_proj = nn.Linear(d_model, d_model)self.k_proj = nn.Linear(d_model, d_model)self.v_proj = nn.Linear(d_model, d_model)self.o_proj = nn.Linear(d_model, d_model)self.dropout = nn.Dropout(dropout)def forward(self, x, mask=None):batch_size, seq_len, d_model = x.shapeQ = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)K = self.k_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)V = self.v_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)atten_scores = (Q @ K.transpose(-2, -1)) / math.sqrt(self.d_k)if mask is not None:mask = mask.unsqueeze(1).unsqueeze(1)atten_scores = atten_scores.masked_fill(mask == 0, -1e9)atten_scores = torch.softmax(atten_scores, dim=-1)out = atten_scores @ Vout = out.transpose(1, 2).contiguous().view(batch_size, seq_len, d_model)return self.dropout(self.o_proj(out))

2. FeedForward 类

这个类实现了Transformer中的前馈网络（Feed-Forward Network, FFN）。

在前几篇文章中均有讲解，直接上代码

class FeedForward(nn.Module):def __init__(self, d_model, dff, dropout):super().__init__()self.W1 = nn.Linear(d_model, dff)self.act = nn.GELU()self.W2 = nn.Linear(dff, d_model)self.dropout = nn.Dropout(dropout)def forward(self, x):return self.W2(self.dropout(self.act(self.W1(x))))

3. TransformerEncoderBlock 类

这个类实现了Transformer架构中的一个编码器块（Encoder Block）。

在前几篇文章中有Decoder的讲解（与Encoder原理基本相似），直接上代码

class TransformerEncoderBlock(nn.Module):def __init__(self, d_model, num_heads, dropout, dff):super().__init__()self.mha_block = MultiHeadAttention(d_model, num_heads, dropout)self.ffn_block = FeedForward(d_model, dff, dropout)self.norm1 = nn.LayerNorm(d_model)self.norm2 = nn.LayerNorm(d_model)self.dropout1 = nn.Dropout(dropout)self.dropout2 = nn.Dropout(dropout)def forward(self, x, mask=None):res1 = self.norm1(x + self.dropout1(self.mha_block(x, mask)))res2 = self.norm2(res1 + self.dropout2(self.ffn_block(res1)))return res2

4. BertModel 类

这个类实现了BERT模型的整体架构。

class BertModel(nn.Module):def __init__(self, vocab_size, d_model, seq_len, N_blocks, num_heads, dropout, dff):super().__init__()self.tok_emb = nn.Embedding(vocab_size, d_model)self.seg_emb = nn.Embedding(3, d_model)self.pos_emb = nn.Embedding(seq_len, d_model)self.layers = nn.ModuleList([TransformerEncoderBlock(d_model, num_heads, dropout, dff)for _ in range(N_blocks)])self.norm = nn.LayerNorm(d_model)self.drop = nn.Dropout(dropout)def forward(self, x, seg_ids, mask):pos = torch.arange(x.shape[1])tok_emb = self.tok_emb(x)seg_emb = self.seg_emb(seg_ids)pos_emb = self.pos_emb(pos)x = tok_emb + seg_emb + pos_embfor layer in self.layers:x = layer(x, mask)x = self.norm(x)return x

• 词嵌入、段嵌入和位置嵌入：

  • tok_emb：将输入的词索引映射到词嵌入空间。

  • seg_emb：用于区分不同的句子（例如在BERT中，用于区分句子A和句子B）。

  • pos_emb：将位置信息编码到嵌入空间，使模型能够捕捉到序列中的位置信息。

• Transformer编码器层：通过nn.ModuleList堆叠了N_blocks个TransformerEncoderBlock，每个块都负责对输入序列进行进一步的特征提取。

• 层归一化和Dropout：在所有编码器层处理完毕后，对输出进行层归一化和Dropout处理，进一步稳定模型的输出。

Bert完整代码（标红部分为本节所提到部分）

import re
import math
import torch
import random
import torch.nn as nnfrom transformers import BertTokenizer
from torch.utils.data import Dataset, DataLoader# nn.TransformerEncoderLayerclass MultiHeadAttention(nn.Module):def __init__(self, d_model, num_heads, dropout):super().__init__()self.num_heads = num_headsself.d_k = d_model // num_headsself.q_proj = nn.Linear(d_model, d_model)self.k_proj = nn.Linear(d_model, d_model)self.v_proj = nn.Linear(d_model, d_model)self.o_proj = nn.Linear(d_model, d_model)self.dropout = nn.Dropout(dropout)def forward(self, x, mask=None):batch_size, seq_len, d_model = x.shapeQ = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)K = self.k_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)V = self.v_proj(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)atten_scores = (Q @ K.transpose(-2, -1)) / math.sqrt(self.d_k)if mask is not None:mask = mask.unsqueeze(1).unsqueeze(1)atten_scores = atten_scores.masked_fill(mask == 0, -1e9)atten_scores = torch.softmax(atten_scores, dim=-1)out = atten_scores @ Vout = out.transpose(1, 2).contiguous().view(batch_size, seq_len, d_model)return self.dropout(self.o_proj(out))class FeedForward(nn.Module):def __init__(self, d_model, dff, dropout):super().__init__()self.W1 = nn.Linear(d_model, dff)self.act = nn.GELU()self.W2 = nn.Linear(dff, d_model)self.dropout = nn.Dropout(dropout)def forward(self, x):return self.W2(self.dropout(self.act(self.W1(x))))class TransformerEncoderBlock(nn.Module):def __init__(self, d_model, num_heads, dropout, dff):super().__init__()self.mha_block = MultiHeadAttention(d_model, num_heads, dropout)self.ffn_block = FeedForward(d_model, dff, dropout)self.norm1 = nn.LayerNorm(d_model)self.norm2 = nn.LayerNorm(d_model)self.dropout1 = nn.Dropout(dropout)self.dropout2 = nn.Dropout(dropout)def forward(self, x, mask=None):res1 = self.norm1(x + self.dropout1(self.mha_block(x, mask)))res2 = self.norm2(res1 + self.dropout2(self.ffn_block(res1)))return res2class BertModel(nn.Module):def __init__(self, vocab_size, d_model, seq_len, N_blocks, num_heads, dropout, dff):super().__init__()self.tok_emb = nn.Embedding(vocab_size, d_model)self.seg_emb = nn.Embedding(3, d_model)self.pos_emb = nn.Embedding(seq_len, d_model)self.layers = nn.ModuleList([TransformerEncoderBlock(d_model, num_heads, dropout, dff)for _ in range(N_blocks)])self.norm = nn.LayerNorm(d_model)self.drop = nn.Dropout(dropout)def forward(self, x, seg_ids, mask):pos = torch.arange(x.shape[1])tok_emb = self.tok_emb(x)seg_emb = self.seg_emb(seg_ids)pos_emb = self.pos_emb(pos)x = tok_emb + seg_emb + pos_embfor layer in self.layers:x = layer(x, mask)x = self.norm(x)return xclass BERT(nn.Module):def __init__(self, vocab_size, d_model, seq_len, N_blocks, num_heads, dropout, dff):super().__init__()self.bert = BertModel(vocab_size, d_model, seq_len, N_blocks, num_heads, dropout, dff)self.mlm_head = nn.Linear(d_model, vocab_size)self.nsp_head = nn.Linear(d_model, 2)def forward(self, mlm_tok_ids, seg_ids, mask):bert_out = self.bert(mlm_tok_ids, seg_ids, mask)cls_token = bert_out[:, 0, :]mlm_logits = self.mlm_head(bert_out)nsp_logits = self.nsp_head(cls_token)return mlm_logits, nsp_logitsdef read_data(file):with open(file, "r", encoding="utf-8") as f:data = f.read().strip().replace("\n", "")corpus = re.split(r'[。，“”：；！、]', data)corpus = [sentence for sentence in corpus if sentence.strip()]return corpusdef create_nsp_dataset(corpus):nsp_dataset = []for i in range(len(corpus)-1):next_sentence = corpus[i+1]rand_id = random.randint(0, len(corpus) - 1)while abs(rand_id - i) <= 1:rand_id = random.randint(0, len(corpus) - 1)negt_sentence = corpus[rand_id]nsp_dataset.append((corpus[i], next_sentence, 1)) # 正样本nsp_dataset.append((corpus[i], negt_sentence, 0)) # 负样本return nsp_datasetclass BERTDataset(Dataset):def __init__(self, nsp_dataset, tokenizer: BertTokenizer, max_length):self.nsp_dataset = nsp_datasetself.tokenizer = tokenizerself.max_length = max_lengthself.cls_id = tokenizer.cls_token_idself.sep_id = tokenizer.sep_token_idself.pad_id = tokenizer.pad_token_idself.mask_id = tokenizer.mask_token_iddef __len__(self):return len(self.nsp_dataset)def __getitem__(self, idx):sent1, sent2, nsp_label = self.nsp_dataset[idx]sent1_ids = self.tokenizer.encode(sent1, add_special_tokens=False)sent2_ids = self.tokenizer.encode(sent2, add_special_tokens=False)tok_ids = [self.cls_id] + sent1_ids + [self.sep_id] + sent2_ids + [self.sep_id]seg_ids = [0]*(len(sent1_ids)+2) + [1]*(len(sent2_ids) + 1)mlm_tok_ids, mlm_labels = self.build_mlm_dataset(tok_ids)mlm_tok_ids = self.pad_to_seq_len(mlm_tok_ids, 0)seg_ids = self.pad_to_seq_len(seg_ids, 2)mlm_labels = self.pad_to_seq_len(mlm_labels, -100)mask = (mlm_tok_ids != 0)return {"mlm_tok_ids": mlm_tok_ids,"seg_ids": seg_ids,"mask": mask,"mlm_labels": mlm_labels,"nsp_labels": torch.tensor(nsp_label)}def pad_to_seq_len(self, seq, pad_value):seq = seq[:self.max_length]pad_num = self.max_length - len(seq)return torch.tensor(seq + pad_num * [pad_value])def build_mlm_dataset(self, tok_ids):mlm_tok_ids = tok_ids.copy()mlm_labels = [-100] * len(tok_ids)for i in range(len(tok_ids)):if tok_ids[i] not in [self.cls_id, self.sep_id, self.pad_id]:if random.random() < 0.15:mlm_labels[i] = tok_ids[i]if random.random() < 0.8:mlm_tok_ids[i] = self.mask_idelif random.random() < 0.9:mlm_tok_ids[i] = random.randint(106, self.tokenizer.vocab_size - 1)return mlm_tok_ids, mlm_labelsif __name__ == "__main__":data_file = "4.10-BERT/背影.txt"model_path = "/Users/azen/Desktop/llm/models/bert-base-chinese"tokenizer = BertTokenizer.from_pretrained(model_path)corpus = read_data(data_file)max_length = 25 # len(max(corpus, key=len))print("Max length of dataset: {}".format(max_length))nsp_dataset = create_nsp_dataset(corpus)trainset = BERTDataset(nsp_dataset, tokenizer, max_length)batch_size = 16trainloader = DataLoader(trainset, batch_size, shuffle=True)vocab_size = tokenizer.vocab_sized_model = 768N_blocks = 2num_heads = 12dropout = 0.1dff = 4*d_modelmodel = BERT(vocab_size, d_model, max_length, N_blocks, num_heads, dropout, dff)lr = 1e-3optim = torch.optim.Adam(model.parameters(), lr=lr)loss_fn = nn.CrossEntropyLoss()epochs = 20for epoch in range(epochs):for batch in trainloader:batch_mlm_tok_ids = batch["mlm_tok_ids"]batch_seg_ids = batch["seg_ids"]batch_mask = batch["mask"]batch_mlm_labels = batch["mlm_labels"]batch_nsp_labels = batch["nsp_labels"]mlm_logits, nsp_logits = model(batch_mlm_tok_ids, batch_seg_ids, batch_mask)loss_mlm = loss_fn(mlm_logits.view(-1, vocab_size), batch_mlm_labels.view(-1))loss_nsp = loss_fn(nsp_logits, batch_nsp_labels)loss = loss_mlm + loss_nsploss.backward()optim.step()optim.zero_grad()print("Epoch: {}, MLM Loss: {}, NSP Loss: {}".format(epoch, loss_mlm, loss_nsp))passpass