帮我一个稀疏注意力机制代码，用pytorch架构

当然，我可以帮你写稀疏注意力机制的代码，以下是一个简单的实现：

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

class SparseAttention(nn.Module):
    def __init__(self, dim, num_heads):
        super().__init__()
        
        self.dim = dim
        self.num_heads = num_heads
        
        self.q_layer = nn.Linear(dim, dim * num_heads)
        self.k_layer = nn.Linear(dim, dim * num_heads)
        self.v_layer = nn.Linear(dim, dim * num_heads)
        
    def calc_attention_scores(self, q, k):
        scores = torch.matmul(q, k.transpose(-1, -2))
        scores *= self.dim ** -0.5
        return scores
    
    def forward(self, x):
        batch_size, seq_len, dim = x.shape
        
        q = self.q_layer(x).view(batch_size, seq_len, self.num_heads, dim).permute(0, 2, 1, 3)
        k = self.k_layer(x).view(batch_size, seq_len, self.num_heads, dim).permute(0, 2, 3, 1)
        v = self.v_layer(x).view(batch_size, seq_len, self.num_heads, dim).permute(0, 2, 1, 3)
        
        attn_scores = self.calc_attention_scores(q, k) # (batch_size, num_heads, seq_len, seq_len)
        attn_weights = F.softmax(attn_scores, dim=-1)
        
        # Create random sparsity pattern for each sample in batch
        patterns = torch.bernoulli(0.5 * torch.ones(batch_size, self.num_heads, seq_len, seq_len))
        attn_weights = attn_weights * patterns
        
        out = torch.matmul(attn_weights, v).permute(0, 2, 1, 3).contiguous()
        out = out.view(batch_size, seq_len, self.num_heads * dim)
        
        return out

这是一个基础实现，你可以根据自己的具体需求进行修改和优化。希望能对你有所帮助！