model/decoder.py

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