LSTM.py

import torch
from torch import nn
from typing import List
import os
import json



class LSTM(nn.Module):
    """
    Bidirectional LSTM running over word embeddings.
    """
    def __init__(self, word_embedding_dimension: int, hidden_dim: int, num_layers: int = 1, dropout: float = 0, bidirectional: bool = True):
        nn.Module.__init__(self)
        self.config_keys = ['word_embedding_dimension', 'hidden_dim', 'num_layers', 'dropout', 'bidirectional']
        self.word_embedding_dimension = word_embedding_dimension
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.dropout = dropout
        self.bidirectional = bidirectional

        self.embeddings_dimension = hidden_dim
        if self.bidirectional:
            self.embeddings_dimension *= 2

        self.encoder = nn.LSTM(word_embedding_dimension, hidden_dim, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional, batch_first=True)

    def forward(self, features):
        token_embeddings = features['token_embeddings']
        sentence_lengths = torch.clamp(features['sentence_lengths'], min=1)

        packed = nn.utils.rnn.pack_padded_sequence(token_embeddings, sentence_lengths, batch_first=True, enforce_sorted=False)
        packed = self.encoder(packed)
        unpack = nn.utils.rnn.pad_packed_sequence(packed[0], batch_first=True)[0]
        features.update({'token_embeddings': unpack})
        return features

    def get_word_embedding_dimension(self) -> int:
        return self.embeddings_dimension

    def tokenize(self, text: str) -> List[int]:
        raise NotImplementedError()

    def save(self, output_path: str):
        with open(os.path.join(output_path, 'lstm_config.json'), 'w') as fOut:
            json.dump(self.get_config_dict(), fOut, indent=2)

        torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin'))

    def get_config_dict(self):
        return {key: self.__dict__[key] for key in self.config_keys}

    @staticmethod
    def load(input_path: str):
        with open(os.path.join(input_path, 'lstm_config.json'), 'r') as fIn:
            config = json.load(fIn)

        weights = torch.load(os.path.join(input_path, 'pytorch_model.bin'))
        model = LSTM(**config)
        model.load_state_dict(weights)
        return model