| 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 | |