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27793b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch.nn as nn
import torch.nn.functional as F
class DecoderLayer(nn.Module):
def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
dropout=0.1, activation="relu"):
super(DecoderLayer, self).__init__()
d_ff = d_ff or 4*d_model
self.self_attention = self_attention
self.cross_attention = cross_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.norm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
def forward(self, x, cross, x_mask=None, cross_mask=None):
x = x + self.dropout(self.self_attention(
x, x, x,
attn_mask=x_mask
)[0])
x = self.norm1(x)
x = x + self.dropout(self.cross_attention(
x, cross, cross,
attn_mask=cross_mask
)[0])
y = x = self.norm2(x)
y = self.dropout(self.activation(self.conv1(y.transpose(-1,1))))
y = self.dropout(self.conv2(y).transpose(-1,1))
return self.norm3(x+y)
class Decoder(nn.Module):
def __init__(self, layers, norm_layer=None):
super(Decoder, self).__init__()
self.layers = nn.ModuleList(layers)
self.norm = norm_layer
def forward(self, x, cross, x_mask=None, cross_mask=None):
for layer in self.layers:
x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
if self.norm is not None:
x = self.norm(x)
return x |