以下是BERT模型的主要组件：

1. Embedding Layer - 包括词嵌入、位置嵌入和段落嵌入。
2. Transformer Encoder Layer - 多个Transformer编码器堆叠而成，每个编码器包含多头自注意力机制（Multi-head Self-Attention）和前馈神经网络（Feed-Forward Network）。

我们将从定义这些组件开始，逐步构建整个模型。请注意，这只是一个教学目的的简化版本，不适用于生产环境。

步骤 1: 定义嵌入层

import torch
import torch.nn as nn
import torch.nn.functional as F

class Embeddings(nn.Module):
    def __init__(self, vocab_size, hidden_size):
        super(Embeddings, self).__init__()
        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
        self.position_embeddings = nn.Embedding(512, hidden_size)  # 假设最大序列长度为512
        self.segment_embeddings = nn.Embedding(2, hidden_size)     # 假设两个段落
        
    def forward(self, input_ids, segment_ids):
        seq_length = input_ids.size(1)
        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        
        word_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        segment_embeddings = self.segment_embeddings(segment_ids)
        
        embeddings = word_embeddings + position_embeddings + segment_embeddings
        return embeddings

步骤 2: 定义Transformer编码器

class TransformerEncoderLayer(nn.Module):
    def __init__(self, hidden_size, num_heads, feedforward_dim):
        super(TransformerEncoderLayer, self).__init__()
        self.self_attention = nn.MultiheadAttention(hidden_size, num_heads)
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, feedforward_dim),
            nn.ReLU(),
            nn.Linear(feedforward_dim, hidden_size)
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
    
    def forward(self, x, mask=None):
        attn_output, _ = self.self_attention(x, x, x, attn_mask=mask)
        x = x + attn_output
        x = self.norm1(x)
        
        ff_output = self.feed_forward(x)
        x = x + ff_output
        x = self.norm2(x)
        
        return x

步骤 3: 定义完整的BERT模型

class BERT(nn.Module):
    def __init__(self, vocab_size, hidden_size, num_layers, num_heads, feedforward_dim):
        super(BERT, self).__init__()
        self.embeddings = Embeddings(vocab_size, hidden_size)
        self.encoders = nn.ModuleList([TransformerEncoderLayer(hidden_size, num_heads, feedforward_dim) for _ in range(num_layers)])
        self.pooler = nn.Linear(hidden_size, hidden_size)
    
    def forward(self, input_ids, segment_ids, mask=None):
        embeddings = self.embeddings(input_ids, segment_ids)
        encoder_outputs = embeddings
        for encoder in self.encoders:
            encoder_outputs = encoder(encoder_outputs, mask)
        
        pooled_output = self.pooler(encoder_outputs[:, 0])
        return pooled_output, encoder_outputs

步骤 4: 实例化模型并测试

vocab_size = 30522  # BERT base uncased词汇表大小
hidden_size = 768
num_layers = 12
num_heads = 12
feedforward_dim = 3072

model = BERT(vocab_size, hidden_size, num_layers, num_heads, feedforward_dim)
input_ids = torch.randint(low=0, high=vocab_size, size=(1, 128))  # 假定输入序列长度为128
segment_ids = torch.zeros((1, 128), dtype=torch.long)

pooled_output, all_encoder_layers = model(input_ids, segment_ids)
print(pooled_output.shape)  # 应该输出 (1, 768)
print(all_encoder_layers.shape)  # 应该输出 (1, 128, 768)

以上代码定义了一个简化的BERT模型结构。在实际应用中，你需要根据具体任务调整模型，并且可能还需要添加额外的任务特定层，如分类头等。

此外，还需要实现训练和评估的逻辑，这通常涉及损失函数的选择、优化器配置以及数据集的准备等步骤。

基于torch的bert神经网络的实现 - 集智数据集