相关代码或算法

以下是使用PyTorch实现自注意力机制的代码：

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, queries, mask):
        N = queries.shape[0]
        value_len, key_len, query_len = values.shape[1], keys.shape[1], queries.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)
        queries = queries.reshape(N, query_len, self.heads, self.head_dim)
        # Compute dot product of queries and keys for each head
        energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])
        # Apply mask to exclude padded tokens from attention
        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 weighted sum of values for each head
        out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(N, query_len, self.heads*self.head_dim)
        # Concatenate heads and pass through final linear layer
        out = self.fc_out(out)
        return out

以上代码实现了一个SelfAttention模块，可以在神经网络中使用自注意力机制来加强对特定词的关注。在模型的forward中，需要传入待加权的values、keys、queries，以及mask（如果需要）。其中，values、keys、queries的shape均为[N, seq_len, embed_size]，表示batch大小、序列长度和词嵌入维度，mask的shape为[N, 1, seq_len]，表示哪些位置需要被mask。模型输出的shape为[N, seq_len, embed_size]，表示加权后的词嵌入。