self-attention的公式为
a t t e n t i o n ( Q , K , V ) = S o f t m a x ( Q K d k ) V attention(Q,K,V)=Softmax( \frac{QK}{\sqrt{d_{k}}})Vattention(Q,K,V)=Softmax(dkQK)V
个人理解，除以d k \sqrt{d_{k}}dk的原因有两点：
d k d_{k}dk是词向量/隐藏层的维度
1、首先要除以一个数，防止输入softmax的值过大，导致偏导数趋近于0；
2、选择根号d_k是因为可以使得q*k的结果满足期望为0，方差为1的分布，类似于归一化。
公式分析，首先假设q和k都是服从期望为0，方差为1的独立的随机变量。
Assume：X = q i X=q_{i}X=qi，Y = k i Y=k_{i}Y=ki，那么：
1、E ( X Y ) = E ( X ) E ( Y ) = 0 ∗ 0 = 0 E(XY)=E(X)E(Y)=0*0=0E(XY)=E(X)E(Y)=0∗0=0
2、D ( X Y ) = E ( X 2 Y 2 ) − [ E ( X Y ) ] 2 D(XY)=E(X^{2}Y^{2})-[E(XY)]^{2}D(XY)=E(X2Y2)−[E(XY)]2
= E ( X 2 ) E ( Y 2 ) − [ E ( X ) E ( Y ) ] 2 =E(X^{2})E(Y^{2})-[E(X)E(Y)]^{2}=E(X2)E(Y2)−[E(X)E(Y)]2
= E ( X 2 − 0 2 ) E ( Y 2 − 0 2 ) − [ E ( X ) E ( Y ) ] 2 =E(X^{2}-0^{2})E(Y^{2}-0^{2})-[E(X)E(Y)]^{2}=E(X2−02)E(Y2−02)−[E(X)E(Y)]2
= E ( X 2 − [ E ( X ) ] 2 ) E ( Y 2 − [ E ( Y ) ] 2 ) − [ E ( X ) E ( Y ) ] 2 =E(X^{2}-[E(X)]^{2})E(Y^{2}-[E(Y)]^{2})-[E(X)E(Y)]^{2}=E(X2−[E(X)]2)E(Y2−[E(Y)]2)−[E(X)E(Y)]2
= [ E ( X 2 ) − [ E ( X ) ] 2 ] [ E ( Y 2 ) − [ E ( Y ) ] 2 ] − [ E ( X ) E ( Y ) ] 2 =[E(X^{2})-[E(X)]^{2}][E(Y^{2})-[E(Y)]^{2}]-[E(X)E(Y)]^{2}=[E(X2)−[E(X)]2][E(Y2)−[E(Y)]2]−[E(X)E(Y)]2
= D ( X ) D ( Y ) − [ E ( X ) E ( Y ) ] 2 =D(X)D(Y)-[E(X)E(Y)]^{2}=D(X)D(Y)−[E(X)E(Y)]2
= 1 ∗ 1 − 0 ∗ 0 =1*1-0*0=1∗1−0∗0
= 1 =1=1
3、D ( Q K d k ) = d k ( d k ) 2 = 1 D(\frac{QK}{\sqrt{d_{k}}})=\frac{d_{k}}{(\sqrt{d_{k}})^{2}}=1D(dkQK)=(dk)2dk=1
需要注意的是，D ( Q K ) = D ( ∑ i = 0 d k q i k i ) = d k ∗ 1 = d k D(QK)=D(\sum_{i=0}^{d_{k}}q_{i}k_{i})=d_{k}*1=d_{k}D(QK)=D(∑i=0dkqiki)=dk∗1=dk