LSTM Text Generation with PyTorch - Complete Guide

LSTM Text Generation with PyTorch

A comprehensive guide to building word-level language models with Long Short-Term Memory networks

Introduction to LSTM Text Generation

Long Short-Term Memory (LSTM) networks are a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. They are particularly useful for sequence prediction problems like text generation, where the context from previous words is crucial for predicting the next word.

Key Concepts

Word-level modeling: Predicts the next word given previous words
Embeddings: Dense vector representations of words
Sequence learning: Captures patterns in word sequences
Probability distribution: Outputs likelihood of each possible next word

Applications

Autocomplete systems
Chatbot responses
Creative writing assistance
Code generation
Text summarization

Prerequisites

Before diving into this implementation, you should be familiar with:

Python programming basics
Fundamentals of neural networks
Basic PyTorch operations
Understanding of word embeddings

Note: This guide focuses on word-level prediction. For character-level models, the approach would be similar but with characters instead of words as the basic unit.

Implementation Details

Data Preparation

Model Architecture

Training Process

Prediction

1. Data Preparation

Proper data preparation is crucial for training effective language models. Here's the detailed process:

# Sample text data
text = """Elara gazed at the stars, wondering if other civilizations 
might be looking back at her. The telescope revealed countless 
points of light, each potentially hosting worlds beyond imagination."""

# Preprocessing steps
def preprocess_text(text):
    # Convert to lowercase and remove punctuation
    text = text.lower()
    text = ''.join([char for char in text if char.isalpha() or char.isspace()])
    
    # Split into words
    words = text.split()
    
    # Create vocabulary
    word_counts = Counter(words)
    vocab = sorted(word_counts, key=word_counts.get, reverse=True)
    
    # Create word-to-index and index-to-word mappings
    word_to_idx = {word: idx for idx, word in enumerate(vocab)}
    idx_to_word = {idx: word for idx, word in enumerate(vocab)}
    
    return words, word_to_idx, idx_to_word, vocab

words, word_to_idx, idx_to_word, vocab = preprocess_text(text)

Key Steps:

Text normalization (lowercase, punctuation removal)
Tokenization into words
Vocabulary creation with word frequencies
Mapping between words and numerical indices

Data Structure:

Training pairs: (input_word, target_word)
Input representation: Word indices
Target representation: One-hot encoded vectors

2. Model Architecture

The LSTM model consists of three main components: embedding layer, LSTM layer, and fully connected layer.

import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)
        
    def forward(self, x, hidden=None):
        # Embed the input words
        embedded = self.embedding(x)
        
        # Pass through LSTM
        lstm_out, hidden = self.lstm(embedded, hidden)
        
        # Reshape and pass through fully connected layer
        out = self.fc(lstm_out.contiguous().view(-1, lstm_out.shape[2]))
        
        return out, hidden

Components:

Embedding Layer: Maps word indices to dense vectors
LSTM Layer: Processes sequential data with memory
Linear Layer: Produces scores for each vocabulary word

Parameters:

vocab_size: Number of unique words
embedding_dim: Size of word vectors (typically 50-300)
hidden_dim: Size of LSTM hidden state (typically 100-1000)

The LSTM maintains a hidden state that captures information about the sequence seen so far, allowing it to learn dependencies between words.

3. Training Process

The training involves hyperparameter tuning, loss calculation, and optimization.

# Hyperparameter tuning setup
embedding_sizes = [8, 16, 32]
hidden_sizes = [32, 64, 128]
learning_rates = [0.01, 0.005, 0.001]

# Training loop
def train_model(embedding_dim, hidden_dim, lr, epochs=50):
    model = LSTMModel(len(vocab), embedding_dim, hidden_dim)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    
    for epoch in range(epochs):
        total_loss = 0
        hidden = None
        
        for word, target in training_pairs:
            # Convert to tensors
            word_tensor = torch.tensor([word_to_idx[word]], dtype=torch.long)
            target_tensor = torch.tensor([word_to_idx[target]], dtype=torch.long)
            
            # Forward pass
            output, hidden = model(word_tensor, hidden)
            loss = criterion(output, target_tensor)
            
            # Backward pass and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
        
        if (epoch+1) % 10 == 0:
            print(f'Epoch {epoch+1}, Loss: {total_loss/len(training_pairs):.4f}')
    
    return model, total_loss

Important: The hidden state should be detached between sequences to prevent backpropagation through the entire sequence history, which can cause memory issues.

4. Text Generation

After training, the model can generate new text sequences.

def predict_sequence(start_word, num_words, temperature=1.0):
    model.eval()
    words = [start_word]
    
    with torch.no_grad():
        hidden = None
        word_tensor = torch.tensor([word_to_idx[start_word]], dtype=torch.long)
        
        for _ in range(num_words):
            output, hidden = model(word_tensor, hidden)
            
            # Apply temperature to logits
            output = output / temperature
            probabilities = torch.softmax(output, dim=1).squeeze()
            
            # Sample from the probability distribution
            word_idx = torch.multinomial(probabilities, 1).item()
            next_word = idx_to_word[word_idx]
            
            words.append(next_word)
            word_tensor = torch.tensor([word_idx], dtype=torch.long)
    
    return words

Try It Out

Generated text will appear here...

Temperature controls the randomness of predictions. Lower values make the model more confident (but potentially repetitive), while higher values increase diversity (but may reduce coherence).

Advanced Topics

Improving the Model

Model Enhancements

Bidirectional LSTM: Processes sequence in both directions
Stacked LSTM: Multiple LSTM layers for deeper learning
Attention Mechanism: Focus on relevant parts of the sequence
Pretrained Embeddings: Use Word2Vec or GloVe vectors

Training Improvements

Teacher Forcing: Alternate between model predictions and actual targets
Curriculum Learning: Start with shorter sequences
Beam Search: Consider multiple sequence possibilities
Regularization: Dropout, weight decay

Evaluation Metrics

Assessing text generation quality can be challenging. Common approaches include:

Perplexity: Measures how well the model predicts the test data
BLEU Score: Compares generated text to reference texts
Human Evaluation: Subjective assessment of coherence and relevance

Limitations and Considerations

Current Limitations

Limited context window (only previous word considered)
Small vocabulary size in this simple implementation
Potential for generating nonsensical or repetitive text
No understanding of grammar rules or world knowledge

Scaling Up

Use larger datasets (millions of words)
Implement n-gram context (2-5 previous words)
Add character-level features for rare words
Use transformer architectures for long-range dependencies

Ethical Considerations: Text generation models can potentially be used to generate misleading or harmful content. Always consider the ethical implications of your models and implement appropriate safeguards.

code exercises

Long-short-term-memory (LSTM) Word Prediction With PyTorch