Dropout(p=drop_prob) def forward(self, x): x = self.linear1(x) x = self.relu(x) x = self.dropout(x) x = self.linear2(x) return x 3.详细解读 在本小节,我们将逐步分析复现Position Wise Feed Forward每行/块代码所起的作用: 3.1 初步思考 根据原文,我们实现FFN,需要两个线性变换,并在...
5.每个编码器由两个子层组成:Self-Attention 层(自注意力层)和 Position-wise Feed Forward Network(FFN) 6.每个编码器的结构都是相同的,但是它们使用不同的权重参数。 7.编码器的输入会先流入 Self-Attention 层。它可以让编码器在对特定词进行编码时使用输入句子中的其他词的信息(可以理解为:当我们翻译一个...
In this paper, we propose the first hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and the position-wise feed-forward network (FFN) ResBlock, which are the two most complex layers in the Transformer. Firstly, an efficient method is introduced to...