自注意力机制模块代码

好的，以下是自注意力机制模块的代码：

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)
        
        # Compute energy between query and key for each head
        energy = torch.einsum("nqhd,nkhd->nhqk", [query, keys])
        
        # Apply mask to energy
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))
        
        # Apply softmax to get attention scores
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3)
        
        # Compute attention output
        out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(
            N, query_len, self.heads*self.head_dim
        )
        
        # Apply final linear layer
        out = self.fc_out(out)
        
        return out

这是一个基于 PyTorch 实现的自注意力机制模块，其中包含了值、键、查询三个线性层，以及一个输出层。在 forward 方法中，我们首先将输入的 values、keys、query 分别 reshape 成 (batch_size, seq_len, heads, head_dim) 的形状，然后通过 einsum 函数计算出每个 head 的注意力得分，再将得分与 values 相乘得到每个 head 的输出，最后将所有 head 的输出拼接起来并通过输出层得到最终的输出。