| 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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|
