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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class EncoderLayer(nn.Module): | |
| def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"): | |
| super(EncoderLayer, self).__init__() | |
| d_ff = d_ff or 4 * d_model | |
| self.attention = attention | |
| self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1) | |
| self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.dropout = nn.Dropout(dropout) | |
| self.activation = F.relu if activation == "relu" else F.gelu | |
| def forward(self, x, attn_mask=None, tau=None, delta=None): | |
| new_x, attn = self.attention(x, x, x, attn_mask=attn_mask, tau=tau, delta=delta) | |
| x = x + self.dropout(new_x) | |
| y = x = self.norm1(x) | |
| y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) | |
| y = self.dropout(self.conv2(y).transpose(-1, 1)) | |
| return self.norm2(x + y), attn | |
| class Encoder(nn.Module): | |
| def __init__(self, attn_layers, conv_layers=None, norm_layer=None): | |
| super(Encoder, self).__init__() | |
| self.attn_layers = nn.ModuleList(attn_layers) | |
| self.conv_layers = ( | |
| nn.ModuleList(conv_layers) if conv_layers is not None else None | |
| ) | |
| self.norm = norm_layer | |
| def forward(self, x, attn_mask=None, tau=None, delta=None): | |
| # x [B, L, D] | |
| attns = [] | |
| if self.conv_layers is not None: | |
| for i, (attn_layer, conv_layer) in enumerate( | |
| zip(self.attn_layers, self.conv_layers) | |
| ): | |
| delta = delta if i == 0 else None | |
| x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta) | |
| x = conv_layer(x) | |
| attns.append(attn) | |
| x, attn = self.attn_layers[-1](x, tau=tau, delta=None) | |
| attns.append(attn) | |
| else: | |
| for attn_layer in self.attn_layers: | |
| x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta) | |
| attns.append(attn) | |
| if self.norm is not None: | |
| x = self.norm(x) | |
| return x, attns | |