请用pytorch写出SparseAttention的代码

好的，以下是使用PyTorch实现SparseAttention的代码：

import torch
import torch.nn.functional as F

class SparseAttention(torch.nn.Module):
    def __init__(self, num_heads, d_model, dropout=0.1):
        super().__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        self.dropout = torch.nn.Dropout(dropout)
        self.q_linear = torch.nn.Linear(d_model, d_model, bias=False)
        self.k_linear = torch.nn.Linear(d_model, d_model, bias=False)
        self.v_linear = torch.nn.Linear(d_model, d_model, bias=False)
        self.out_linear = torch.nn.Linear(d_model, d_model, bias=False)

    def forward(self, q, k, v, mask=None):
        # q, k, and v are of shape (batch_size, seq_len, d_model)
        # mask is of shape (batch_size, seq_len)
        
        batch_size = q.size(0)
        seq_len = q.size(1)

        # Linear projection for the queries, keys, and values
        q_proj = self.q_linear(q).view(batch_size, seq_len, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)
        k_proj = self.k_linear(k).view(batch_size, seq_len, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)
        v_proj = self.v_linear(v).view(batch_size, seq_len, self.num_heads, self.d_model // self.num_heads).transpose(1, 2)

        # Compute the attention scores
        attn_scores = torch.matmul(q_proj, k_proj.transpose(-2, -1)) / (self.d_model // self.num_heads)**0.5
        
        # Apply the mask
        if mask is not None:
            attn_scores = attn_scores.masked_fill(mask.unsqueeze(1).unsqueeze(2) == 0, float('-inf'))

        # Apply the softmax function
        attn_probs = F.softmax(attn_scores, dim=-1)

        # Apply the dropout
        attn_probs = self.dropout(attn_probs)

        # Compute the weighted sum of the values
        attn_output = torch.matmul(attn_probs, v_proj)

        # Concatenate the heads and apply the output projection
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.num_heads * (self.d_model // self.num_heads))
        attn_output = self.out_linear(attn_output)

        return attn_output

在这个实现中，我们先使用线性投影将输入的queries、keys和values的维度变为d_model，然后将它们分别拆分成num_heads个头，每个头的维度为d_model/num_heads。接着，我们计算每个头的attention得分，然后将它们拼接在一起，再通过一个输出投影层获得最终的attention输出。如果有mask，则在计算attention得分时将mask的位置设置为负无穷，以忽略这些位置的信息。最后，在softmax和输出投影层之前应用dropout以防止过拟合。