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- ''' 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)
- self.w_ks = nn.Linear(d_model, n_head * d_k)
- self.w_vs = nn.Linear(d_model, n_head * d_v)
- nn.init.xavier_normal_(self.w_qs.weight)
- nn.init.xavier_normal_(self.w_ks.weight)
- nn.init.xavier_normal_(self.w_vs.weight)
-
- self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
- self.layer_norm = nn.LayerNorm(d_model)
-
- self.fc = nn.Linear(n_head * d_v, d_model)
- nn.init.xavier_normal_(self.fc.weight)
-
- self.dropout = nn.Dropout(dropout)
-
-
- 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()
- 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)
- 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)
-
- 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)
-
- output = output.view(n_head, sz_b, len_q, d_v)
- 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))
- output = self.layer_norm(output + residual)
-
- return output, 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.Conv1d(d_in, d_hid, 1) # position-wise
- self.w_2 = nn.Conv1d(d_hid, d_in, 1) # position-wise
- self.layer_norm = nn.LayerNorm(d_in)
- self.dropout = nn.Dropout(dropout)
-
- def forward(self, x):
- residual = x
- output = x.transpose(1, 2)
- output = self.w_2(F.relu(self.w_1(output)))
- output = output.transpose(1, 2)
- output = self.dropout(output)
- output = self.layer_norm(output + residual)
- return output
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