multi head attention原理

Multi-head attention是一种在Transformer模型中广泛使用的自注意力机制。它允许模型同时关注不同的位置和表示层次，以捕捉更丰富的语义信息。

Multi-head attention的原理如下：

1. 对输入进行线性映射
通过将输入向量乘以权重矩阵，得到三个不同的向量：query、key和value。

2. 分成多个头
将这三个向量分别切分成多个头，每个头的维度相同。

3. 计算注意力分数
对于每个头，通过计算query和key之间的点积，得到该头的注意力分数。注意力分数体现了query和key之间的相似度。

4. 归一化注意力分数
为了保证注意力分数的值在[0,1]之间，需要将注意力分数进行softmax计算，得到每个头的注意力权重。

5. 加权求和
将每个头的value向量乘以对应的注意力权重，然后将所有加权后的向量相加，得到最终的输出向量。

通过将注意力机制分成多个头，模型可以对不同的信息进行并行处理，提高了模型的并行性和表达能力。同时，由于每个头只关注部分信息，模型可以更加准确地捕捉输入的语义信息。

相关问题

Multi head attention code

Here is a simple implementation of multi-head attention in PyTorch:

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        
        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
        
        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads
        
        self.query = nn.Linear(d_model, d_model)
        self.key = nn.Linear(d_model, d_model)
        self.value = 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.head_dim)
        x = x.permute(0, 2, 1, 3)
        return x
    
    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)
        
        # linear transformations
        query = self.query(query)
        key = self.key(key)
        value = self.value(value)
        
        # split into multiple heads
        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)
        
        # dot product attention
        scores = torch.matmul(query, key.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.head_dim).float())
        if mask is not None:
            scores = scores.masked_fill(mask == 0, float("-inf"))
        attention = torch.softmax(scores, dim=-1)
        x = torch.matmul(attention, value)
        
        # concatenate attention heads
        x = x.permute(0, 2, 1, 3).contiguous()
        x = x.view(batch_size, -1, self.d_model)
        
        # final linear transformation
        x = self.fc(x)
        return x

This implementation takes as input a `d_model` dimension tensor and splits it into `num_heads` attention heads. The `query`, `key`, and `value` matrices are linearly transformed and split into heads as well. Then, the dot product attention is calculated and the attention heads are concatenated and linearly transformed again.

To use this module in your Transformer, you can simply call it like this:

attn = MultiHeadAttention(d_model=512, num_heads=8)
output = attn(query, key, value)

Multi-Head Attention

Multi-Head Attention是由多个Self-Attention组成的模块，它可以同时关注不同的位置和表示子空间，从而提高模型的表现力。在Multi-Head Attention中，输入首先被分成多个头，每个头都进行Self-Attention计算，然后将它们的输出连接起来并通过一个线性变换得到最终的输出。这样做的好处是，每个头可以关注不同的信息，从而提高模型的泛化能力和鲁棒性。同时，Multi-Head Attention还可以通过调整头的数量和维度来平衡计算量和模型表现力。