单头自注意力机制代码

单头自注意力机制是Transformer模型中的一种注意力机制，它可以用于自然语言处理任务中的编码器和解码器。下面是单头自注意力机制的代码实现：

import torch
import torch.nn as nn

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        
        assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"
        
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads*self.head_dim, embed_size)
        
    def forward(self, values, keys, query, mask):
        N = query.shape[0]
        value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
        
        # Split embedding into self.heads pieces
        values = values.reshape(N, value_len, self.heads, self.head_dim)
        keys = keys.reshape(N, key_len, self.heads, self.head_dim)
        query = query.reshape(N, query_len, self.heads, self.head_dim)
        
        values = self.values(values) # (N, value_len, heads, head_dim)
        keys = self.keys(keys) # (N, key_len, heads, head_dim)
        queries = self.queries(query) # (N, query_len, heads, head_dim)
        
        energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys]) # (N, heads, query_len, key_len)
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))
        
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3) # (N, heads, query_len, key_len)
        
        out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(N, query_len, self.heads*self.head_dim)
        
        out = self.fc_out(out)
        
        return out

其中，`embed_size`表示嵌入向量的维度，`heads`表示注意力头的数量。在`__init__`函数中，我们定义了三个线性层，分别用于计算values、keys和queries。在`forward`函数中，我们首先将输入的values、keys和query分别拆分成`heads`个部分，然后对它们进行线性变换。接着，我们计算注意力分数，并使用softmax函数进行归一化。最后，我们将注意力分数与values相乘，得到最终的输出。