- 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!