text: ''' Define the sublayers in encoder/decoder layer '''
  import numpy as np
  import torch.nn as nn
  import torch.nn.functional as F
  from transformer.Modules import ScaledDotProductAttention
  
  __author__ = "Yu-Hsiang Huang"
  
  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, bias=False)
          self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
          self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
          self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
  
          self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
  
          self.dropout = nn.Dropout(dropout)
          self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
  
  
      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, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
  
          residual = q
          q = self.layer_norm(q)
  
          # Pass through the pre-attention projection: b x lq x (n*dv)
          # Separate different heads: b x lq x n x dv
          q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
          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)
  
          # Transpose for attention dot product: b x n x lq x dv
          q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
          
          if mask is not None:
              mask = mask.unsqueeze(1)   # For head axis broadcasting.
  
          q, attn = self.attention(q, k, v, mask=mask)
  
          # 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)
          q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
          q = self.dropout(self.fc(q))
          q += residual
  
          return q, attn
  
  
  class PositionwiseFeedForward(nn.Module):
      ''' A two-feed-forward-layer module '''
  
      def __init__(self, d_in, d_hid, dropout=0.1):
          super().__init__()
          self.w_1 = nn.Linear(d_in, d_hid) # position-wise
          self.w_2 = nn.Linear(d_hid, d_in) # position-wise
          self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
          self.dropout = nn.Dropout(dropout)
  
      def forward(self, x):
  
          residual = x
          x = self.layer_norm(x)
  
          x = self.w_2(F.relu(self.w_1(x)))
          x = self.dropout(x)
          x += residual
  
          return x
  