PyTorch中怎么实现自注意力机制_维修百科_

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：

导入必要的库：

import torchimport torch.nn as nn

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)

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

使用自注意力机制模块进行实验：

# Define input tensorvalue = torch.rand(3, 10, 512)  # (N, value_len, embed_size)key = torch.rand(3, 10, 512)  # (N, key_len, embed_size)query = torch.rand(3, 10, 512)  # (N, query_len, embed_size)# Create self attention layerself_attn = SelfAttention(512, 8)# Perform self attentionoutput = self_attn(value, key, query)print(output.shape)

通过以上步骤，就可以在PyTorch中实现自注意力机制。