帮我用pytorch写出SparseAttention代码

以下是使用PyTorch实现的Sparse Attention代码：

import torch

class SparseAttention(torch.nn.Module):
    def __init__(self, dim_in, dim_out, n_heads, dropout=0.0):
        super(SparseAttention, self).__init__()
        
        self.dim_in = dim_in
        self.dim_out = dim_out
        self.n_heads = n_heads
        self.dropout = dropout
        
        self.Q = torch.nn.Linear(dim_in, dim_out * n_heads)
        self.K = torch.nn.Linear(dim_in, dim_out * n_heads)
        self.V = torch.nn.Linear(dim_in, dim_out * n_heads)
        
        self.dropout_layer = torch.nn.Dropout(dropout)
        self.out = torch.nn.Linear(dim_out * n_heads, dim_in)
        
    def forward(self, x):
        batch_size, seq_len, _ = x.size()
        
        Q = self.Q(x).view(batch_size, seq_len, self.n_heads, self.dim_out)
        K = self.K(x).view(batch_size, seq_len, self.n_heads, self.dim_out)
        V = self.V(x).view(batch_size, seq_len, self.n_heads, self.dim_out)
        
        attn = (Q @ K.transpose(-2, -1)) / (self.dim_out ** 0.5)
        attn = torch.nn.functional.softmax(attn, dim=-1)
        attn = self.dropout_layer(attn)
        
        x = (attn @ V).transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
        x = self.out(x)
        
        return x

这个代码实现了一个带有dropout的稀疏自注意力层，可以用于NLP中的文本分类、序列标注等任务。在这个实现中，我们使用了线性层来进行Q、K、V的变换，并使用了PyTorch的softmax函数来计算注意力权重。注意力权重和V的乘积采用了batched matrix multiplication的方式进行计算，以提高计算效率。最后，我们使用了一个线性层来将注意力计算的结果映射回原始维度。