详细介绍transformer中的self-attention机制

Transformer模型中的self-attention机制是其核心组成部分之一。self-attention机制可以使模型更好地理解输入序列中的上下文关系，从而提高模型的性能。

self-attention机制的主要思想是将输入序列中的每个元素都看作是查询(key)、键(key)和值(value)三个向量的线性组合，然后计算每个查询向量与所有键向量的相似度，并将相似度作为权重计算出每个键向量对应的值向量的加权和，最终得到一个加权和向量。这个加权和向量就是输入序列中每个元素的表示。

具体来说，self-attention机制可以分为三个步骤：

1. 计算相似度

通过计算查询向量和键向量之间的点积得到相似度矩阵，然后对相似度矩阵进行缩放，从而避免梯度消失问题。

2. 计算权重

通过将相似度矩阵进行softmax操作，得到每个键向量对应的权重，这个权重代表了查询向量与该键向量的相似度。

3. 计算加权和

将每个值向量乘以对应的权重，然后将它们加起来，得到最终的加权和向量。

在Transformer模型中，self-attention机制被应用于多头注意力机制中，通过将输入序列划分为多个子序列，每个子序列都使用单独的self-attention机制进行编码，然后将所有子序列的编码结果拼接起来，得到最终的编码结果。

总之，self-attention机制通过计算输入序列中每个元素之间的关系，从而获得更好的上下文表示，进而提高模型的性能。

相关问题

创建attention mask是为了应用transformer中的self-attention机制吗

是的，创建attention mask是为了应用transformer中的self-attention机制。在Transformer模型中，self-attention层利用输入序列中每个单词之间的关系来计算注意力权重，以便更好地捕捉序列中的重要信息。为了确保self-attention层只关注输入序列中的有用信息，我们需要创建一个attention mask来指定哪些单词需要被忽略或屏蔽。这样，模型就可以在计算注意力权重时跳过这些无用的单词，只关注有用的信息。

Transformer and Self-Attention Pytorch代码

以下是使用PyTorch实现Transformer和Self-Attention的示例代码：

## Self-Attention

import torch
import torch.nn as nn

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        
        assert (self.head_dim * heads == embed_size), "Embed size needs to be divisible by heads"
        
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads * self.head_dim, embed_size)
        
    def forward(self, values, keys, queries, mask):
        # Get number of training examples
        N = queries.shape[0]
        
        value_len, key_len, query_len = values.shape[1], keys.shape[1], queries.shape[1]
        
        # Split embedding into self.heads pieces
        values = values.reshape(N, value_len, self.heads, self.head_dim)
        keys = keys.reshape(N, key_len, self.heads, self.head_dim)
        queries = queries.reshape(N, query_len, self.heads, self.head_dim)
        
        # Transpose to get dimensions batch_size * self.heads * seq_len * self.head_dim
        values = values.permute(0, 2, 1, 3)
        keys = keys.permute(0, 2, 1, 3)
        queries = queries.permute(0, 2, 1, 3)
        
        # Calculate energy
        energy = torch.matmul(queries, keys.permute(0, 1, 3, 2))
        
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float("-1e20"))
        
        # Apply softmax to get attention scores
        attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=-1)
        
        # Multiply attention scores with values
        out = torch.matmul(attention, values)
        
        # Concatenate and linearly transform output
        out = out.permute(0, 2, 1, 3).reshape(N, query_len, self.heads * self.head_dim)
        out = self.fc_out(out)
        
        return out

## Transformer

import torch
import torch.nn as nn
from torch.nn.modules.activation import MultiheadAttention

class TransformerBlock(nn.Module):
    def __init__(self, embed_size, heads, dropout, forward_expansion):
        super(TransformerBlock, self).__init__()
        self.attention = MultiheadAttention(embed_dim=embed_size, num_heads=heads)
        self.norm1 = nn.LayerNorm(embed_size)
        self.norm2 = nn.LayerNorm(embed_size)
        
        self.feed_forward = nn.Sequential(
            nn.Linear(embed_size, forward_expansion * embed_size),
            nn.ReLU(),
            nn.Linear(forward_expansion * embed_size, embed_size)
        )
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, value, key, query, mask):
        attention_output, _ = self.attention(query, key, value, attn_mask=mask)
        
        x = self.dropout(self.norm1(attention_output + query))
        
        forward_output = self.feed_forward(x)
        out = self.dropout(self.norm2(forward_output + x))
        
        return out
        

class Encoder(nn.Module):
    def __init__(self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length):
        super(Encoder, self).__init__()
        self.embed_size = embed_size
        self.device = device
        self.word_embedding = nn.Embedding(src_vocab_size, embed_size)
        self.position_embedding = nn.Embedding(max_length, embed_size)
        
        self.layers = nn.ModuleList([
            TransformerBlock(embed_size, heads, dropout, forward_expansion) for _ in range(num_layers)
        ])
        
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        
        out = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
        
        for layer in self.layers:
            out = layer(out, out, out, mask)
            
        return out
    
    
class DecoderBlock(nn.Module):
    def __init__(self, embed_size, heads, forward_expansion, dropout, device):
        super(DecoderBlock, self).__init__()
        self.norm = nn.LayerNorm(embed_size)
        self.attention = MultiheadAttention(embed_size, heads)
        self.transformer_block = TransformerBlock(embed_size, heads, dropout, forward_expansion)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, value, key, src_mask, trg_mask):
        attention_output, _ = self.attention(x, x, x, attn_mask=trg_mask)
        query = self.dropout(self.norm(attention_output + x))
        out = self.transformer_block(value, key, query, src_mask)
        
        return out
    
    
class Decoder(nn.Module):
    def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length):
        super(Decoder, self).__init__()
        self.embed_size = embed_size
        self.device = device
        self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
        self.position_embedding = nn.Embedding(max_length, embed_size)
        
        self.layers = nn.ModuleList([
            DecoderBlock(embed_size, heads, forward_expansion, dropout, device) for _ in range(num_layers)
        ])
        
        self.fc_out = nn.Linear(embed_size, trg_vocab_size)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, enc_out, src_mask, trg_mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        
        x = self.dropout(self.word_embedding(x) + self.position_embedding(positions))
        
        for layer in self.layers:
            x = layer(x, enc_out, enc_out, src_mask, trg_mask)
            
        out = self.fc_out(x)
        
        return out

这些代码可以用于实现Transformer和Self-Attention模型。但这只是示例，你需要根据你的数据和任务来调整这些代码中的各种超参数和结构。