多头注意力机制如何插入代码中

在深度学习框架如PyTorch或TensorFlow中，实现多头注意力（Multi-Head Attention）通常会涉及到自定义层或者是利用已有的库函数。这里提供一个简单的示例，假设我们正在使用PyTorch：

import torch
from torch.nn import Linear, Dropout

class MultiHeadAttention(torch.nn.Module):
    def __init__(self, d_model, num_heads, dropout=0.1):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.wq = Linear(d_model, d_model) # Query weight matrix
        self.wk = Linear(d_model, d_model) # Key weight matrix
        self.wv = Linear(d_model, d_model) # Value weight matrix
        self.fc = Linear(d_model, d_model) # Output linear layer
        self.dropout = Dropout(dropout)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        qkv_same_dim = query.size(-1) == key.size(-1) and query.size(-1) == value.size(-1)

        if not qkv_same_dim or query.size(-1) != self.d_model:
            raise ValueError("Incompatible dimension")

        Q, K, V = self.split_heads(query), self.split_heads(key), self.split_heads(value)
        
        attention_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_model // self.num_heads)
        if mask is not None:
            attention_scores = attention_scores.masked_fill(mask == 0, -1e9)  # Apply masking
        
        attention_probs = self.dropout(F.softmax(attention_scores, dim=-1))
        context_layer = torch.matmul(attention_probs, V)
        
        context_layer = self.merge_heads(context_layer)
        return self.fc(context_layer)

    def split_heads(self, x):
        # Reshape from (batch_size, seq_len, d_model) to (batch_size, num_heads, seq_len, head_dim)
        return x.view(batch_size, -1, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)

    def merge_heads(self, x):
        # Transpose and reshape back to (batch_size, seq_len, d_model)
        return x.permute(0, 2, 1, 3).contiguous().view(batch_size, -1, self.d_model)

这个例子中，`split_heads`用于将输入展平到各个注意力头，`merge_heads`则是将所有头的信息整合回来。`forward`函数的核心是计算注意力得分、添加mask（如果存在），然后经过softmax和dropout得到最终的context vector。

请注意，这只是一个基础版的实现，实际使用时可能需要调整以适应更大的模型和更复杂的任务配置。在TensorFlow中也有相应的API，例如`tf.keras.layers.MultiHeadAttention`。