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from torch import nn
import numpy as np
import torch
import torch.nn.functional as F
def to_var(x):
if torch.cuda.is_available():
x = x.cuda()
return x
class MultiHeadAttentionSequence(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_model = d_model
self.d_k = d_k
self.d_v = d_v
self.W_Q = nn.Linear(d_model, n_head*d_k)
self.W_K = nn.Linear(d_model, n_head*d_k)
self.W_V = nn.Linear(d_model, n_head*d_v)
self.W_O = nn.Linear(n_head*d_v, d_model)
self.layer_norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v):
batch, len_q, _ = q.size()
batch, len_k, _ = k.size()
batch, len_v, _ = v.size()
Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k])
K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k])
V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v])
Q = Q.transpose(1, 2)
K = K.transpose(1, 2).transpose(2, 3)
V = V.transpose(1, 2)
attention = torch.matmul(Q, K)
attention = attention / np.sqrt(self.d_k)
attention = F.softmax(attention, dim=-1)
output = torch.matmul(attention, V)
output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head])
output = self.W_O(output)
output = self.dropout(output)
output = self.layer_norm(output + q)
return output, attention
class MultiHeadAttentionReciprocal(nn.Module):
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_model = d_model
self.d_k = d_k
self.d_v = d_v
self.W_Q = nn.Linear(d_model, n_head*d_k)
self.W_K = nn.Linear(d_model, n_head*d_k)
self.W_V = nn.Linear(d_model, n_head*d_v)
self.W_O = nn.Linear(n_head*d_v, d_model)
self.W_V_2 = nn.Linear(d_model, n_head*d_v)
self.W_O_2 = nn.Linear(n_head*d_v, d_model)
self.layer_norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.layer_norm_2 = nn.LayerNorm(d_model)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, q, k, v, v_2):
batch, len_q, _ = q.size()
batch, len_k, _ = k.size()
batch, len_v, _ = v.size()
batch, len_v_2, _ = v_2.size()
Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k])
K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k])
V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v])
V_2 = self.W_V_2(v_2).view([batch, len_v_2, self.n_head, self.d_v])
Q = Q.transpose(1, 2)
K = K.transpose(1, 2).transpose(2, 3)
V = V.transpose(1, 2)
V_2 = V_2.transpose(1,2)
attention = torch.matmul(Q, K)
attention = attention /np.sqrt(self.d_k)
attention_2 = attention.transpose(-2, -1)
attention = F.softmax(attention, dim=-1)
attention_2 = F.softmax(attention_2, dim=-1)
output = torch.matmul(attention, V)
output_2 = torch.matmul(attention_2, V_2)
output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head])
output_2 = output_2.transpose(1, 2).reshape([batch, len_k, self.d_v*self.n_head])
output = self.W_O(output)
output_2 = self.W_O_2(output_2)
output = self.dropout(output)
output = self.layer_norm(output + q)
output_2 = self.dropout(output_2)
output_2 = self.layer_norm(output_2 + k)
return output, output_2, attention, attention_2
class FFN(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.layer_1 = nn.Conv1d(d_in, d_hid,1)
self.layer_2 = nn.Conv1d(d_hid, d_in,1)
self.relu = nn.ReLU()
self.layer_norm = nn.LayerNorm(d_in)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
output = self.layer_1(x.transpose(1, 2))
output = self.relu(output)
output = self.layer_2(output)
output = self.dropout(output)
output = self.layer_norm(output.transpose(1, 2)+residual)
return output
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