多头注意力机制 pytorch代码

以下是使用PyTorch实现的多头注意力机制的示例代码：

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, num_heads, d_model):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        
        assert d_model % self.num_heads == 0
        
        self.depth = d_model // self.num_heads
        
        self.wq = nn.Linear(d_model, d_model)
        self.wk = nn.Linear(d_model, d_model)
        self.wv = nn.Linear(d_model, d_model)
        
        self.dense = nn.Linear(d_model, d_model)
        
    def split_heads(self, x, batch_size):
        x = x.view(batch_size, -1, self.num_heads, self.depth)
        return x.transpose(1, 2)
        
    def forward(self, v, k, q):
        batch_size = q.size(0)
        
        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)
        
        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)
        
        scaled_attention_logits = torch.matmul(q, k.transpose(-2, -1))
        scaled_attention_logits /= torch.sqrt(torch.tensor(self.depth, dtype=torch.float32))
        
        attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
        
        output = torch.matmul(attention_weights, v)
        output = output.transpose(1, 2).contiguous()
        output = output.view(batch_size, -1, self.d_model)
        
        output = self.dense(output)
        
        return output, attention_weights

在上述代码中，我们定义了一个名为`MultiHeadAttention`的自定义模块。与之前的TensorFlow示例代码类似，它接受三个输入张量`v`、`k`和`q`，并在每个头上进行注意力计算。最终的输出是多头注意力机制的结果。

请注意，这只是一个示例代码，实际使用时可能需要根据具体任务和模型的需求进行适当的修改和调整。