多头自注意力机制模型的代码

多头自注意力机制模型的代码可以使用PyTorch来实现。下面是一个示例代码：

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        
        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)
        self.output_linear = nn.Linear(d_model, d_model)
        
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        
        # 线性变换
        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)
        
        # 将输入分割成多个头
        query = query.view(batch_size, -1, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)
        key = key.view(batch_size, -1, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)
        value = value.view(batch_size, -1, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)
        
        # 计算注意力得分
        scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_model // self.num_heads, dtype=torch.float32))
        
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        
        # 注意力权重
        attention_weights = torch.softmax(scores, dim=-1)
        
        # 加权求和
        attention_output = torch.matmul(attention_weights, value)
        
        # 将多个头的输出拼接起来
        attention_output = attention_output.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * (self.d_model // self.num_heads))
        
        # 线性变换
        output = self.output_linear(attention_output)
        
        return output

这段代码定义了一个名为`MultiHeadAttention`的PyTorch模型类，它包含了多头自注意力机制的实现。在`forward`方法中，首先对输入进行线性变换，然后将输入分割成多个头，计算注意力得分，根据得分计算注意力权重，最后将多个头的输出拼接起来并进行线性变换得到最终的输出。