软注意力机制复杂超长超长版代码

以下是一个基于PyTorch实现的复杂软注意力机制的代码示例，包含了超长超长版的实现：

import torch
import torch.nn as nn
import torch.nn.functional as F


class ComplexSoftAttention(nn.Module):
    def __init__(self, in_dim, hidden_dim=None):
        super(ComplexSoftAttention, self).__init__()

        if hidden_dim is None:
            hidden_dim = in_dim

        self.in_dim = in_dim
        self.hidden_dim = hidden_dim

        self.query_layer = nn.Linear(in_features=in_dim, out_features=hidden_dim, bias=False)
        self.key_layer = nn.Linear(in_features=in_dim, out_features=hidden_dim, bias=False)
        self.value_layer = nn.Linear(in_features=in_dim, out_features=hidden_dim, bias=False)

    def forward(self, query, key, value, mask=None):
        """
        :param query: [batch_size, query_len, in_dim]
        :param key: [batch_size, key_len, in_dim]
        :param value: [batch_size, key_len, in_dim]
        :param mask: [batch_size, key_len]
        :return: [batch_size, query_len, hidden_dim]
        """

        query = self.query_layer(query)  # [batch_size, query_len, hidden_dim]
        key = self.key_layer(key)  # [batch_size, key_len, hidden_dim]
        value = self.value_layer(value)  # [batch_size, key_len, hidden_dim]

        # 计算复数内积
        attention_weights = torch.einsum('bqi,bqj->biqj',
                                         query.view(query.size(0), query.size(1), 1, self.hidden_dim),
                                         key.view(key.size(0), 1, key.size(1), self.hidden_dim))  # [batch_size, query_len, key_len, 2]

        # 使用掩码处理注意力权重
        if mask is not None:
            mask = mask.unsqueeze(1).unsqueeze(-1).repeat([1, query.size(1), key.size(1), 1])  # [batch_size, query_len, key_len, 2]
            attention_weights = attention_weights.masked_fill(mask == 0, -1e9)

        # 计算复杂软注意力得分
        attention_weights = F.softmax(attention_weights, dim=2)  # [batch_size, query_len, key_len, 2]
        attention_weights = attention_weights[:, :, :, 0] + 1j * attention_weights[:, :, :, 1]  # [batch_size, query_len, key_len]
        attention_weights = torch.div(attention_weights, torch.sqrt(torch.tensor(self.hidden_dim, dtype=torch.float32)))  # [batch_size, query_len, key_len]

        # 加权求和
        output = torch.einsum('bqi,bqj->bij',
                              attention_weights,
                              value)  # [batch_size, query_len, hidden_dim]

        return output

这个模型的核心是复杂软注意力机制，它可以在处理实数和虚数的同时，充分利用输入序列之间的相关性，进而提高模型的性能。在这个实现中，我们使用了PyTorch的einsum函数来计算复数内积，使用了掩码来处理注意力权重，并且在计算复杂软注意力得分时进行了归一化和平方根缩放。