31.注意力评分函数

1.加性注意力

python 复制代码

import math
import torch
from torch import nn
from d2l import torch as d2l
def masked_softmax(X, valid_lens):
    """通过在最后一个轴上掩蔽元素来执行softmax操作"""
    # X:3D张量，valid_lens:1D或2D张量
    if valid_lens is None:
        return nn.functional.softmax(X, dim=-1)
    else:
        shape = X.shape
        if valid_lens.dim() == 1:
            valid_lens = torch.repeat_interleave(valid_lens, shape[1])
        else:
            valid_lens = valid_lens.reshape(-1)
        # 最后一轴上被掩蔽的元素使用一个非常大的负值替换，从而其softmax输出为0
        X = d2l.sequence_mask(X.reshape(-1, shape[-1]), valid_lens,
                              value=-1e6)
        return nn.functional.softmax(X.reshape(shape), dim=-1)
class AdditiveAttention(nn.Module):
    def __init__(self,key_size,query_size,num_hiddens,dropout,**kwargs):
        super(AdditiveAttention,self).__init__(**kwargs)
        self.w_k=nn.Linear(key_size,num_hiddens,bias=False)
        
        self.w_q=nn.Linear(query_size,num_hiddens,bias=False)
        self.w_v=nn.Linear(num_hiddens,1,bias=False)
        self.dropout=nn.Dropout(dropout)
    def forward(self,queries,keys,values,valid_lens):
        queries,keys=self.w_q(queries),self.w_k(keys)
        #print(queries.shape)
        # q=queries.unsqueeze(2)
        # k=keys.unsqueeze(1)
        #利用广播机制进行相加:
        #queries.unsqueeze(2):[B,sq_len,1,hiddens]
        #keys.unsqueeze(1):[B,1,sq_len,hiddens]
        features=queries.unsqueeze(2)+keys.unsqueeze(1)
        #print(features.shape)
        features=torch.tanh(features)
        scores=self.w_v(features).squeeze(-1)
        self.attention_weights=masked_softmax(scores,valid_lens)
        #批量矩阵乘乘法:bmm
        return torch.bmm(self.dropout(self.attention_weights),values)
#输入测试:
queries, keys = torch.normal(0, 1, (2, 1, 20)), torch.ones((2, 10, 2))
values = torch.arange(40, dtype=torch.float32).reshape(1, 10, 4).repeat(2, 1, 1)
valid_lens = torch.tensor([2, 6])
attention = AdditiveAttention(key_size=2, query_size=20, num_hiddens=8,dropout=0.1)
attention.eval()
print(attention(queries, keys, values, valid_lens))  
d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),
                  xlabel='Keys', ylabel='Queries')

2.缩放点积注意力

python 复制代码

import math
import torch
from torch import nn
from d2l import torch as d2l
def sequence_mask(X, valid_lens, value=float("-inf")):
    """简单实现的掩蔽功能"""
    batch_size, seq_len = X.shape
    mask = torch.arange(seq_len, device=X.device).expand(batch_size, seq_len) < valid_lens.unsqueeze(1)
    X[~mask] = value
    return X
def masked_softmax(X, valid_lens):
    """通过在最后一个轴上掩蔽元素来执行softmax操作"""
    if valid_lens is None:
        return nn.functional.softmax(X, dim=-1)
    else:
        shape = X.shape
        if valid_lens.dim() == 1:
            valid_lens = valid_lens.unsqueeze(1).repeat(1, shape[1])
        X = X.reshape(-1, shape[-1])
        X = sequence_mask(X, valid_lens.reshape(-1), value=-1e6)
        return nn.functional.softmax(X.reshape(shape), dim=-1)
class DotProductAttention(nn.Module):
    """缩放点积注意力"""
    def __init__(self, dropout, **kwargs):
        super(DotProductAttention, self).__init__(**kwargs)
        self.dropout = nn.Dropout(dropout)
    # queries的形状：(batch_size，查询的个数，d)
    # keys的形状：(batch_size，"键－值"对的个数，d)
    # values的形状：(batch_size，"键－值"对的个数，值的维度)
    # valid_lens的形状:(batch_size，)或者(batch_size，查询的个数)
    def forward(self, queries, keys, values, valid_lens=None):
        d = queries.shape[-1]
        # 设置transpose_b=True为了交换keys的最后两个维度
        scores = torch.bmm(queries, keys.transpose(1,2)) / math.sqrt(d)
        self.attention_weights = masked_softmax(scores, valid_lens)
    
        return torch.bmm(self.dropout(self.attention_weights), values)
#输入测试:
queries = torch.normal(0, 1, (2, 3, 4))
keys = torch.normal(0, 1, (2,5, 4))
values = torch.normal(0, 1, (2, 5, 6))
valid_lens = torch.tensor([3, 4]) 
attention = DotProductAttention(dropout=0.5)
attention.eval()
attention(queries, keys, values, valid_lens)
print(attention.attention_weights.shape)
d2l.show_heatmaps(attention.attention_weights[1].reshape((1, 1, 3, 5)),
                  xlabel='Keys', ylabel='Queries')