multi-head self-attention代码

以下是一个简单的 multi-head self-attention 的代码示例：

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_k = d_model // num_heads
        
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.out = nn.Linear(d_model, d_model)
        
    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        
        # perform linear operation and split into h heads
        k = self.k_linear(k).view(bs, -1, self.num_heads, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.num_heads, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.num_heads, self.d_k)
        
        # transpose to get dimensions bs * h * sl * d_model
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)
        
        # calculate attention using function we will define next
        scores = self.attention(q, k, v, self.d_k, mask, self.num_heads)
        
        # concatenate heads and put through final linear layer
        concat = scores.transpose(1,2).contiguous().view(bs, -1, self.num_heads * self.d_k)
        output = self.out(concat)
        
        return output
    
    def attention(self, q, k, v, d_k, mask=None, num_heads=None):
        scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(d_k)
        
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == 0, -1e9)
        
        scores = nn.functional.softmax(scores, dim=-1)
        output = torch.matmul(scores, v)
        return output

这个代码实现了一个 multi-head self-attention 模块，可以用于构建 Transformer 等模型。