注意力机制学习:Multi-Head Attention

多头注意力机制(Mutil-head Attention):多头注意( Multihead Attention )是注意机制模块。

实现:通过一个注意力机制的多次并行运行,将独立的注意力输出串联起来,线性地转化为预期维度。直观看来,多个注意头允许对序列的不同部分进行注意力运算。

\text{MultiHead}\left(\textbf{Q}, \textbf{K}, \textbf{V}\right) = \left[\text{head}_{1},\dots,\text{head}_{h}\right]\textbf{W}_{0}

\text{where} \text{ head}_{i} = \text{Attention} \left(\textbf{Q}\textbf{W}_{i}^{Q}, \textbf{K}\textbf{W}_{i}^{K}, \textbf{V}\textbf{W}_{i}^{V} \right)

注意力机制学习:Multi-Head Attention_第1张图片

 

 其中,\textbf{W}都是可学习的参数矩阵。

注:缩放点积注意力(Scaled dot-product attention)在这个模块中很常用的,同时也可以将其换为其他类型的注意力机制。

 

import numpy as np
import torch.nn as nn
import torch.nn.functional as F

class ScaledDotProductAttention(nn.Module):
    ''' Scaled Dot-Product Attention '''
    def __init__(self, temperature, attn_dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = nn.Dropout(attn_dropout)
    def forward(self, q, k, v, mask=None):
        attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
        if mask is not None:
            attn = attn.masked_fill(mask == 0, -1e9)
        attn = self.dropout(F.softmax(attn, dim=-1))
        output = torch.matmul(attn, v)
        return output, attn


class MultiHeadAttention(nn.Module):
    ''' Multi-Head Attention module '''
    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
        super().__init__()
        self.n_head = n_head
        self.d_k = d_k
        self.d_v = d_v
        self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
        self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
        self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
        self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)


    def forward(self, q, k, v, mask=None):
        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
        residual = q
        # Pass through the pre-attention projection: b x lq x (n*dv)
        # Separate different heads: b x lq x n x dv
        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
        # Transpose for attention dot product: b x n x lq x dv
        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
        if mask is not None:
            mask = mask.unsqueeze(1)   # For head axis broadcasting.
        q, attn = self.attention(q, k, v, mask=mask)
        # Transpose to move the head dimension back: b x lq x n x dv
        # Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
        q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
        q = self.dropout(self.fc(q))
        q += residual
        q = self.layer_norm(q)
        return q, attn


class PositionwiseFeedForward(nn.Module):
    ''' A two-feed-forward-layer module '''
    def __init__(self, d_in, d_hid, dropout=0.1):
        super().__init__()
        self.w_1 = nn.Linear(d_in, d_hid) # position-wise
        self.w_2 = nn.Linear(d_hid, d_in) # position-wise
        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)
    def forward(self, x):
        residual = x
        x = self.w_2(F.relu(self.w_1(x)))
        x = self.dropout(x)
        x += residual
        x = self.layer_norm(x)
        return x

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