cross attention GRU pytorch代码

Cross-Attention GRU (长短时记忆网络) 结合了注意力机制，常用于处理序列到序列的问题，在PyTorch框架中实现这样的模型需要一些步骤。首先，你需要安装必要的库，如PyTorch、transformers等。

下面是一个简单的示例代码片段，展示了如何创建一个基于GRU的自注意力模块：

import torch
from torch import nn
from transformers import MultiheadAttention

class CrossAttentionGRU(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads=8, dropout=0.1):
        super(CrossAttentionGRU, self).__init__()
        
        # 使用MultiheadAttention层作为注意力部分
        self.self_attention = MultiheadAttention(input_size, num_heads, dropout=dropout)
        self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
        self.dropout = nn.Dropout(dropout)

    def forward(self, query, key, value, mask=None):
        # 自注意力计算
        context, _ = self.self_attention(query, key, value, attn_mask=mask)

        # 将注意力上下文输入GRU
        gru_input = torch.cat((query, context), dim=-1)
        gru_output, _ = self.gru(gru_input, None)

        return gru_output, context

# 创建一个实例并传递数据
model = CrossAttentionGRU(128, 64)
query = torch.randn(10, 5, 128)
key = torch.randn(10, 15, 128)
value = torch.randn(10, 15, 128)
mask = torch.BoolTensor(query.size(1), key.size(1)).triu().unsqueeze(0)
output, context = model(query, key, value, mask)