pytorch多头注意力机制代码
时间: 2023-12-07 09:38:04 浏览: 51
以下是使用PyTorch实现多头注意力机制的代码:
```python
import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.Wq = nn.Linear(d_model, d_model)
self.Wk = nn.Linear(d_model, d_model)
self.Wv = nn.Linear(d_model, d_model)
self.fc = nn.Linear(d_model, d_model)
def split_heads(self, x, batch_size):
x = x.view(batch_size, -1, self.num_heads, self.depth)
return x.permute(0, 2, 1, 3)
def forward(self, q, k, v, mask=None):
batch_size = q.size(0)
q = self.Wq(q)
k = self.Wk(k)
v = self.Wv(v)
q = self.split_heads(q, batch_size)
k = self.split_heads(k, batch_size)
v = self.split_heads(v, batch_size)
scaled_attention_logits = torch.matmul(q, k.permute(0, 1, 3, 2)) / torch.sqrt(torch.tensor(self.depth, dtype=torch.float32))
if mask is not None:
scaled_attention_logits += (mask * -1e9)
attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
output = torch.matmul(attention_weights, v)
output = output.permute(0, 2, 1, 3).contiguous()
output = output.view(batch_size, -1, self.d_model)
output = self.fc(output)
return output, attention_weights
```
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