请用pytorch写出SparseAttention的代码
时间: 2023-06-30 20:12:06 浏览: 44
好的,下面是用PyTorch实现Sparse Attention的代码:
```python
import torch
import torch.nn as nn
class SparseAttention(nn.Module):
def __init__(self, num_heads, head_size, dropout=0.1):
super(SparseAttention, self).__init__()
self.num_heads = num_heads
self.head_size = head_size
self.all_head_size = num_heads * head_size
self.dropout = nn.Dropout(dropout)
self.query = nn.Linear(self.all_head_size, self.all_head_size)
self.key = nn.Linear(self.all_head_size, self.all_head_size)
self.value = nn.Linear(self.all_head_size, self.all_head_size)
self.softmax = nn.Softmax(dim=-1)
def forward(self, query, key, value, attention_mask=None):
batch_size = query.size(0)
seq_len = query.size(1)
# Calculate scores
query = self.query(query).view(batch_size, seq_len, self.num_heads, self.head_size)
key = self.key(key).view(batch_size, seq_len, self.num_heads, self.head_size)
value = self.value(value).view(batch_size, seq_len, self.num_heads, self.head_size)
scores = torch.einsum('bqnh,bknh->bnqk', [query, key])
# Apply attention mask
if attention_mask is not None:
scores.masked_fill_(attention_mask.unsqueeze(1).unsqueeze(2), float('-inf'))
# Calculate attention weights
attn_weights = self.softmax(scores)
# Apply dropout
attn_weights = self.dropout(attn_weights)
# Calculate context vectors
context = torch.einsum('bnqk,bknh->bqnh', [attn_weights, value])
# Reshape context vectors
context = context.view(batch_size, seq_len, self.all_head_size)
return context, attn_weights
```
这里我们实现了一个SparseAttention类,其中包含了query、key、value三个线性层,以及softmax和dropout操作。在forward函数中,我们首先将query、key、value线性变换后reshape为(batch_size, seq_len, num_heads, head_size)的形状。然后通过einsum函数计算出注意力得分scores,再根据注意力掩码attention_mask进行过滤。接着计算注意力权重attn_weights,进行dropout操作后,再通过einsum函数计算出上下文向量context。最后将上下文向量reshape为(batch_size, seq_len, num_heads * head_size)的形状并返回。
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