不要自注意力机制,需要多头注意力机制的代码
时间: 2023-11-13 22:13:45 浏览: 42
当然,下面是一个示例代码,展示了如何实现多头注意力机制:
```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
self.d_k = d_model // num_heads
self.W_q = nn.Linear(d_model, d_model)
self.W_k = nn.Linear(d_model, d_model)
self.W_v = nn.Linear(d_model, d_model)
self.W_o = nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask=None):
batch_size = query.size(0)
# Linear transformation
query = self.W_q(query)
key = self.W_k(key)
value = self.W_v(value)
# Split into multiple heads
query = query.view(batch_size * self.num_heads, -1, self.d_k)
key = key.view(batch_size * self.num_heads, -1, self.d_k)
value = value.view(batch_size * self.num_heads, -1, self.d_k)
# Scaled Dot-Product Attention
scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_k).float())
if mask is not None:
scores = scores.masked_fill(mask == 0, float('-inf'))
attention_weights = torch.softmax(scores, dim=-1)
attention_scores = torch.matmul(attention_weights, value)
# Concatenate heads and linear transformation
attention_scores = attention_scores.view(batch_size, -1, self.d_model)
output = self.W_o(attention_scores)
return output
# Example usage
d_model = 512
num_heads = 8
query = torch.randn(16, 10, d_model)
key = torch.randn(16, 20, d_model)
value = torch.randn(16, 20, d_model)
attention = MultiHeadAttention(d_model, num_heads)
output = attention(query, key, value)
```
在这个示例中,我们定义了一个名为`MultiHeadAttention`的类,它继承自`nn.Module`。在类的`__init__`方法中,我们初始化了一些必要的参数,并定义了四个线性变换层来学习查询(query)、键(key)、值(value)和输出(output)。在`forward`方法中,我们首先对输入进行线性变换,并将其分成多个头部。然后,我们计算注意力分数并进行缩放。如果提供了掩码(mask),则在计算注意力分数时将其应用于分数矩阵。最后,我们通过加权求和和线性变换来生成最终输出。
请注意,上述代码仅供示例参考,具体实现可能因您的任务和模型结构而有所不同。
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