model/encoder.py

import torch
import torch.nn as nn
from typing import Tuple

class Encoder(nn.Module):
    def __init__(
        self,
        vocab_size: int,
        embed_size: int,
        hidden_size: int,
        num_layers: int = 2,
        dropout: float = 0.3
    ):
        super(Encoder, self).__init__()
        
        self.vocab_size = vocab_size
        self.embed_size = embed_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        
        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=hidden_size,
            num_layers=num_layers,
            batch_first=True,
            dropout=dropout if num_layers > 1 else 0,
            bidirectional=True
        )
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(hidden_size * 2)
    
    def forward(
        self,
        input_seq: torch.Tensor,
        input_lengths: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        embedded = self.dropout(self.embedding(input_seq))
        packed_embedded = nn.utils.rnn.pack_padded_sequence(
            embedded,
            input_lengths.cpu(),
            batch_first=True,
            enforce_sorted=False
        )
        packed_output, (hidden, cell) = self.lstm(packed_embedded)
        outputs, _ = nn.utils.rnn.pad_packed_sequence(
            packed_output,
            batch_first=True
        )
        outputs = self.layer_norm(outputs)
        return outputs, hidden, cell