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e8aab00 2e4aa16 e8aab00 2e4aa16 e8aab00 2e4aa16 e8aab00 2e4aa16 | 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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import torch
import torch.nn as nn
from typing import Tuple, Optional
class Decoder(nn.Module):
def __init__(
self,
vocab_size: int,
embed_size: int,
hidden_size: int,
attention: nn.Module,
num_layers: int = 2,
dropout: float = 0.3
):
super(Decoder, self).__init__()
self.vocab_size = vocab_size
self.embed_size = embed_size
self.hidden_size = hidden_size
self.attention = attention
self.embedding = nn.Embedding(
num_embeddings=vocab_size,
embedding_dim=embed_size,
padding_idx=0
)
self.lstm = nn.LSTM(
input_size=embed_size + hidden_size * 2,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0
)
self.fc_out = nn.Linear(
hidden_size + hidden_size * 2 + embed_size,
vocab_size
)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(hidden_size + hidden_size * 2 + embed_size)
def forward(
self,
input_token: torch.Tensor,
decoder_hidden: torch.Tensor,
decoder_cell: torch.Tensor,
encoder_outputs: torch.Tensor,
mask: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
embedded = self.dropout(self.embedding(input_token.unsqueeze(1)))
top_hidden = decoder_hidden[-1]
context, attention_weights = self.attention(
top_hidden, encoder_outputs, mask
)
context = self.dropout(context)
lstm_input = torch.cat((embedded, context.unsqueeze(1)), dim=2)
output, (decoder_hidden, decoder_cell) = self.lstm(
lstm_input,
(decoder_hidden, decoder_cell)
)
output = output.squeeze(1)
embedded = embedded.squeeze(1)
output_context = torch.cat((output, context, embedded), dim=1)
output_context = self.layer_norm(output_context)
prediction = self.fc_out(output_context)
return prediction, decoder_hidden, decoder_cell, attention_weights
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