请用pytorch写出SparseAttention
时间: 2023-06-21 15:18:25 浏览: 308
SparseAttention是由Google等机构提出的一种注意力机制,与传统的Self-Attention不同,SparseAttention通过随机采样的方式来计算注意力,从而减小了计算量,提高了效率。下面是一个用PyTorch实现SparseAttention的例子:
```python
import torch
import torch.nn.functional as F
class SparseAttention(torch.nn.Module):
def __init__(self, dim, num_heads, dropout):
super(SparseAttention, self).__init__()
self.dim = dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = dim // num_heads
self.qkv = torch.nn.Linear(dim, dim * 3)
self.proj = torch.nn.Linear(dim, dim)
self.dropout_layer = torch.nn.Dropout(dropout)
def forward(self, x):
# x: [batch_size, seq_len, dim]
batch_size, seq_len, dim = x.size()
# q, k, v: [batch_size, num_heads, seq_len, head_dim]
qkv = self.qkv(x).view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
q, k, v = qkv.permute(2, 0, 3, 1, 4)
# attn: [batch_size, num_heads, seq_len, seq_len]
attn = torch.einsum('bhid,bhjd->bhij', q, k) / (self.head_dim ** 0.5)
attn = F.softmax(attn, dim=-1)
attn = self.dropout_layer(attn)
# sparse_attn: [batch_size, num_heads, seq_len, sparse_seq_len]
sparse_attn = torch.zeros(batch_size, self.num_heads, seq_len, seq_len // 2, device=x.device)
indices = torch.multinomial(attn.view(-1, seq_len), seq_len // 2, replacement=True)
sparse_attn.view(-1, seq_len // 2)[torch.arange(batch_size * self.num_heads).unsqueeze(1), indices] = 1
sparse_attn = sparse_attn / sparse_attn.sum(dim=-1, keepdim=True)
# sparse_v: [batch_size, num_heads, seq_len, head_dim]
sparse_v = torch.einsum('bhij,bhjd->bhid', sparse_attn, v)
sparse_v = sparse_v.view(batch_size, seq_len, dim)
# output: [batch_size, seq_len, dim]
output = self.proj(sparse_v)
return output
```
在上述代码中,我们首先通过`self.qkv`将输入`x`线性映射到`dim * 3`的维度,然后将结果变形成`[batch_size, seq_len, 3, num_heads, head_dim]`的形式。接着,我们将`qkv`按照`dim`的维度进行切分,得到`q`, `k`, `v`三个张量。然后我们通过`einsum`计算得到注意力矩阵`attn`,并对其进行`softmax`和`dropout`操作。接着,我们通过随机采样得到稀疏注意力矩阵`sparse_attn`,并将其应用到`v`上,得到稀疏的`v`张量,最后再将其线性映射到`dim`维度输出。
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