multihead attention
时间: 2023-03-19 14:30:01 浏览: 95
多头注意力(multihead attention)是一种深度学习中的注意力机制,它可以同时关注输入序列的不同部分,从而提高模型的性能。在多头注意力中,输入序列会被分成多个头(head),每个头都会计算出一个注意力向量,最后将这些向量拼接起来作为输出。这种方法可以让模型在处理输入序列时更加灵活和准确。
相关问题
视觉Transfomer中multihead Attention 的 pytorch代码
以下是一个简单的PyTorch代码示例,用于实现视觉Transformer中的多头注意力机制:
``` 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
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 scaled_dot_product_attention(self, Q, K, V, mask=None):
d_k = Q.size(-1)
scores = torch.matmul(Q, K.transpose(-1, -2)) / torch.sqrt(torch.tensor(d_k, dtype=torch.float32))
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
attention = torch.softmax(scores, dim=-1)
output = torch.matmul(attention, V)
return output, attention
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, attention = self.scaled_dot_product_attention(Q, K, V, mask)
scaled_attention = scaled_attention.permute(0, 2, 1, 3).contiguous()
scaled_attention = scaled_attention.view(batch_size, -1, self.d_model)
output = self.fc(scaled_attention)
return output, attention
```
在这个代码中,我们定义了一个 `MultiHeadAttention` 类,它包含了多头注意力机制的实现。在 `__init__` 函数中,我们定义了注意力机制中的一些参数,包括输入向量的维度 `d_model` 和头的数量 `num_heads`。我们还定义了一些线性层,用于将输入向量映射到查询、键和值向量。最后,我们定义了一个全连接层,用于将多头注意力机制的输出向量映射回原始向量的维度。
在 `scaled_dot_product_attention` 函数中,我们计算了查询向量 `Q` 和键向量 `K` 之间的点积相似度,并对其进行了缩放。我们还可以选择对注意力矩阵应用一个掩码矩阵,以排除一些不必要的信息。最后,我们将注意力矩阵与值向量 `V` 相乘,得到多头注意力机制的输出向量。
在 `split_heads` 函数中,我们将输入向量分成多个头,并将它们重新排列成一个更高维的张量。这样做是为了使每个头可以独立地进行注意力计算,从而提高模型的效率。
在 `forward` 函数中,我们首先将输入向量通过线性层映射到查询、键和值向量。然后,我们将它们分成多个头,并将它们传递给 `scaled_dot_product_attention` 函数。最后,我们将多头注意力机制的输出向量通过全连接层映射回原始向量的维度,并返回它们以及注意力矩阵。
multihead masked attention mechanism
Multi-head masked attention mechanism is a type of attention mechanism used in deep learning models, particularly in transformer-based models like BERT and GPT. It is a variant of the standard attention mechanism used in sequence-to-sequence models.
In multi-head masked attention, the input sequence is split into multiple sub-sequences and each sub-sequence is processed independently using the standard attention mechanism. The output of each sub-sequence is then concatenated and passed through a linear layer to produce the final output.
The "masked" part of the mechanism refers to the fact that during training, some of the input tokens are randomly masked, meaning that they are ignored during the attention calculation. This is done to prevent the model from simply memorizing the input sequence and instead forces it to learn more robust representations.
Overall, multi-head masked attention allows the model to attend to multiple parts of the input sequence simultaneously while also incorporating the concept of masking for improved performance.