深度学习 / 默认 · 4月 13, 2022 0

DotProductAttention

内容纲要

用所有值计算查询的点积,并应用 softmax 函数来获得值的权重

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
import numpy as np
from typing import Optional, Tuple

class DotProductAttention(nn.Module):
    """
    Compute the dot products of the query with all values and apply a softmax function to obtain the weights on the values
    https://github.com/napoler/attentions/blob/d6a6b12b8a2b473e5a23e37a13c9547f00f8ffb4/attentions.py#L45
    """
    def __init__(self, hidden_dim):
        super(DotProductAttention, self).__init__()
        self.normalize = nn.LayerNorm(hidden_dim)
        self.out_projection = nn.Linear(hidden_dim * 2, hidden_dim)

    def forward(self, query: Tensor, value: Tensor) -> Tuple[Tensor, Tensor]:
        batch_size, hidden_dim, input_size = query.size(0), query.size(2), value.size(1)

        score = torch.bmm(query, value.transpose(1, 2))
        attn = F.softmax(score.view(-1, input_size), dim=1).view(batch_size, -1, input_size)
        context = torch.bmm(attn, value)

        return context, attn

出处论文

https://arxiv.org/pdf/1508.04025.pdf

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