基于pytorch代码了解transformer的自注意力机制

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import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        assert d_model % num_heads == 0  # 确保d_model可以被num_heads整除
        self.d_model = d_model
        self.num_heads = num_heads
        self.d_head = d_model // num_heads  # 每个头的特征维度
        
        # 定义线性变换
        self.W_Q = nn.Linear(d_model, d_model)  # (d_model, d_model)
        self.W_K = nn.Linear(d_model, d_model)  # (d_model, d_model)
        self.W_V = nn.Linear(d_model, d_model)  # (d_model, d_model)
        self.W_O = nn.Linear(d_model, d_model)  # (d_model, d_model)
    
    def forward(self, Q, K, V, mask=None):
        batch_size = Q.size(0)  # (batch_size, seq_len, d_model)
        
        # 将输入通过线性变换得到 Q, K, V
        Q = self.W_Q(Q)  # (batch_size, seq_len, d_model)
        K = self.W_K(K)  # (batch_size, seq_len, d_model)
        V = self.W_V(V)  # (batch_size, seq_len, d_model)
        
        # 将 Q, K, V 按照头数分割
        Q = Q.view(batch_size, -1, self.num_heads, self.d_head).transpose(1, 2)  # (batch_size, num_heads, seq_len, d_head)
        K = K.view(batch_size, -1, self.num_heads, self.d_head).transpose(1, 2)  # (batch_size, num_heads, seq_len, d_head)
        V = V.view(batch_size, -1, self.num_heads, self.d_head).transpose(1, 2)  # (batch_size, num_heads, seq_len, d_head)
        
        # 计算注意力
        scores = torch.matmul(Q, K.transpose(-2, -1)) / (self.d_head ** 0.5)  # (batch_size, num_heads, seq_len, seq_len)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, float('-inf'))  # (batch_size, num_heads, seq_len, seq_len)
        attention_weights = F.softmax(scores, dim=-1)  # (batch_size, num_heads, seq_len, seq_len)
        attention_output = torch.matmul(attention_weights, V)  # (batch_size, num_heads, seq_len, d_head)
        
        # 合并所有头的输出
        attention_output = attention_output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)  # (batch_size, seq_len, d_model)
        
        # 线性变换输出
        output = self.W_O(attention_output)  # (batch_size, seq_len, d_model)
        return output

# 测试 MultiHeadAttention
d_model = 512
num_heads = 8
batch_size = 64
seq_len = 10

mha = MultiHeadAttention(d_model, num_heads)
Q = torch.randn(batch_size, seq_len, d_model)  # (64, 10, 512)
K = torch.randn(batch_size, seq_len, d_model)  # (64, 10, 512)
V = torch.randn(batch_size, seq_len, d_model)  # (64, 10, 512)
output = mha(Q, K, V)  # (64, 10, 512)
print(output.shape)  # 输出: torch.Size([64, 10, 512])

注释详细解释

nn.Linear 初始化：

self.W_Q, self.W_K, self.W_V, self.W_O 是线性层，将输入的特征维度映射到相同的特征维度（d_model），权重矩阵大小为 (d_model, d_model)。

前向传播：

Q, K, V 的初始维度是 (batch_size, seq_len, d_model)，通过线性变换后仍然是相同的维度。

view 操作将 Q, K, V 从 (batch_size, seq_len, d_model) 变为 (batch_size, num_heads, seq_len, d_head)，然后 transpose 使其变为 (batch_size, num_heads, seq_len, d_head)。

scores 是通过计算查询和键的点积得到的，维度为 (batch_size, num_heads, seq_len, seq_len)。

attention_weights 是应用 softmax 后的注意力权重，维度也为 (batch_size, num_heads, seq_len, seq_len)。

attention_output 是对值矩阵进行加权求和后的结果，维度为 (batch_size, num_heads, seq_len, d_head)。

将所有注意力头的输出合并回 (batch_size, seq_len, d_model)，最后通过线性变换 self.W_O 得到输出，维度为 (batch_size, seq_len, d_model)。

希望这可以帮助你更好地理解代码中的每个步骤和张量的尺寸变化！

 

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