diff --git a/attention.py b/attention.py deleted file mode 100644 index 15d8286..0000000 --- a/attention.py +++ /dev/null @@ -1,115 +0,0 @@ -import torch -from torch import nn -import config -import torch.nn.functional as F -class ScaledDotProductAttention(nn.Module): - """ Scaled Dot-Product Attention """ - - def __init__(self, scale_factor=16, dropout=config.dropout): - super().__init__() - self.scale_factor = scale_factor - # dropout用于防止过拟合,在前向传播的过程中,让某个神经元的激活值以一定的概率停止工作 - self.dropout = nn.Dropout(dropout) - - def forward(self, q, k, v, mask=None): - # batch_size: 批量大小 - # len_q,len_k,len_v: 序列长度 在这里他们都相等 - # n_head: 多头注意力,论文中默认为8 - # d_k,d_v: k v 的dim(维度) 默认都是64 - # 此时q的shape为(batch_size, n_head, len_q, d_k) (batch_size, 8, len_q, 64) - # 此时k的shape为(batch_size, n_head, len_k, d_k) (batch_size, 8, len_k, 64) - # 此时v的shape为(batch_size, n_head, len_k, d_v) (batch_size, 8, len_k, 64) - # q先除以self.scale_factor,再乘以k的转置(交换最后两个维度(这样才可以进行矩阵相乘))。 - # attn的shape为(batch_size, n_head, len_q, len_k) - - attn = torch.matmul(q / self.scale_factor, k.transpose(2, 3)) - - if mask is not None: - - """ - 用-1e9代替0 -1e9是一个很大的负数 经过softmax之后接近0 - # 其一:去除掉各种padding在训练过程中的影响 - # 其二,将输入进行遮盖,避免decoder看到后面要预测的东西。(只用在decoder中) - """ - attn = attn.masked_fill(mask.to('cuda:0') == 0, -1e9) - # 先在attn的最后一个维度做softmax 再dropout 得到注意力分数 - attn = self.dropout(torch.softmax(attn, dim=-1)) - # 最后attn与v矩阵相乘 - # output的shape为(batch_size, 8, len_q, 64) - output = torch.matmul(attn, v) - # 返回 output和注意力分数 - return output, attn - -class MultiHeadAttention(nn.Module): - """ Multi-Head Attention module """ - - def __init__(self, n_head=config.n_head, d_model=config.input_dim, d_k=config.input_dim//2, d_v=config.hidden_dim//2, dropout=config.dropout): - # 论文中这里的n_head, d_model, d_k, d_v分别默认为8, 512, 64, 64 - ''' - # q k v先经过不同的线性层,再用ScaledDotProductAttention,最后再经过一个线性层 - ''' - 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=config.bias) - self.w_ks = nn.Linear(d_model, n_head * d_k, bias=config.bias) - self.w_vs = nn.Linear(d_model, n_head * d_v, bias=config.bias) - self.fc = nn.Linear(n_head * d_v, d_model, bias=config.bias) - - self.attention = ScaledDotProductAttention(scale_factor=d_k ** 0.5) - - self.dropout = nn.Dropout(dropout) - self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) # 默认对最后一个维度初始化 - - def forward(self, q, k, v, mask=None): - # q, k, v初次输入为含位置信息的嵌入矩阵X,由于要堆叠N次,后面的输入则是上个多头的输出 - # q, k, v:batch_size * seq_num * d_model - d_k, d_v, n_head = self.d_k, self.d_v, self.n_head - # len_q, len_k, len_v 为输入的序列长度 - # batch_size为batch_size - batch_size, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1) - # 用作残差连接 - 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 k v 分别经过一个线性层再改变维度 - # 由(batch_size, len_q, n_head*d_k) => (batch_size, len_q, n_head, d_k) - # (batch_size, len_q, 8*64) => (batch_size, len_q, 8, 64) - q = self.layer_norm(q) - k = self.layer_norm(k) - v = self.layer_norm(v) - - # 与q,k,v相关矩阵相乘,得到相应的q,k,v向量,d_model=n_head * d_k - q = self.w_qs(q).view(batch_size, len_q, n_head, d_k) - k = self.w_ks(k).view(batch_size, len_k, n_head, d_k) - v = self.w_vs(v).view(batch_size, len_v, n_head, d_v) - - # Transpose for attention dot product: b x n x lq x dv - # 交换维度做attention - # 由(batch_size, len_q, n_head, d_k) => (batch_size, n_head, len_q, d_k) - # (batch_size, len_q, 8, 64) => (batch_size, 8, len_q, 64) - q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) - - - # 输出的q为Softmax(QK/d + (1-S)σ)V, attn 为QK/D - q, attn = self.attention(q, k, v, mask=None) - - # 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) - # (batch_size, 8, len_k, 64) => (batch_size, len_k, 8, 64) => (batch_size, len_k, 512) - q = q.transpose(1, 2).contiguous().view(batch_size, len_q, -1) - - # 经过fc和dropout - q = self.dropout(self.fc(q)) - - # 残差连接 论文中的Add & Norm中的Add - q += residual - # 论文中的Add & Norm中的Norm - q = self.layer_norm(q) - # q的shape为(batch_size, len_q, 512) - # attn的shape为(batch_size, n_head, len_q, len_k) - return q, attn -