import numpy as np import torch import torch.nn as nn 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) #输入512维，输出512维 self.w_ks = nn.Linear(d_model, n_head * d_k) self.w_vs = nn.Linear(d_model, n_head * d_v) nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k))) #权重初始化。mean=0：正态分布的均值；std：标准差 nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))#np.sqrt():开根号 nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v))) self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5), attn_dropout=dropout) #np.power(a,b):a的b次方 self.layer_norm = nn.LayerNorm(d_model) #归一化 self.fc = nn.Linear(n_head * d_v, d_model) #线性变换，输入512维，输出512维 nn.init.xavier_normal_(self.fc.weight) #初始化线性变换的权重 self.dropout = nn.Dropout(dropout) #信息漏失率 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, _ = q.size() #获取查询矩阵的大小，长度 sz_b, len_k, _ = k.size() sz_b, len_v, _ = v.size() residual = q q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) #重塑tensor的shape 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) #.permute（）：改变tensor维度；contiguous方法改变了多维数组在内存中的存储顺序，以便配合view方法使用 #torch.contiguous()方法首先拷贝了一份张量在内存中的地址，然后将地址按照形状改变后的张量的语义进行排列。 q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv if mask is not None: mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. output, attn = self.attention(q, k, v, mask=mask) #进入attention得到输出和注意力关系 output = output.view(n_head, sz_b, len_q, d_v) #对输出重塑 output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv) output = self.dropout(self.fc(output)) #输出进行线性变换和dropout output = self.layer_norm(output + residual) #残差连接和归一化 return output, attn 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) self.softmax = nn.Softmax(dim=2) def forward(self, q, k, v, mask=None): attn = torch.bmm(q, k.transpose(1, 2)) #三维矩阵相乘（qxk） attn = attn / self.temperature #qxk除以根号k的维度 if mask is not None: attn = attn.masked_fill(mask.bool(), -np.inf) #-np.inf：负无穷的浮点数 attn = self.softmax(attn) attn = self.dropout(attn) output = torch.bmm(attn, v) return output, attn