PyTorch中怎么实现自注意力机制

在PyTorch中实现自注意力机制可以使用torch.nn.MultiheadAttention模块。具体实现步骤如下：

1. 导入必要的库：

import torch
import torch.nn as nn

2. 定义自注意力机制模块：

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)

3. 实现自注意力机制的前向传播方法：

def forward(self, value, key, query, mask=None):
    N = query.shape[0]
    value_len, key_len, query_len = value.shape[1], key.shape[1], query.shape[1]
    
    # Split the embedding into self.heads pieces
    values = value.reshape(N, value_len, self.heads, self.head_dim)
    keys = key.reshape(N, key_len, self.heads, self.head_dim)
    queries = query.reshape(N, query_len, self.heads, self.head_dim)
    
    values = self.values(values)
    keys = self.keys(keys)
    queries = self.queries(queries)
    
    energy = torch.einsum("nqhd, nkhd->nhqk", [queries, keys])
    
    if mask is not None:
        energy = energy.masked_fill(mask == 0, float("-1e20"))
    
    attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3)
    
    out = torch.einsum("nhql, nlhd->nqhd", [attention, values]).reshape(
        N, query_len, self.heads * self.head_dim
    )
    
    out = self.fc_out(out)
    
    return out

4. 使用自注意力机制模块进行实验：

# Define input tensor
value = https://www.yisu.com/ask/torch.rand(3, 10, 512)  # (N, value_len, embed_size)>
通过以上步骤，就可以在PyTorch中实现自注意力机制。