11. Tokenization

11. Tokenization#

Module Overview

Text processing systems that convert raw text into numerical sequences for language models.

What You’ll Build#

In this module, you’ll implement the fundamental text processing systems that enable language models to understand human text:

  • Character-level tokenization with special token handling for basic text processing

  • BPE (Byte Pair Encoding) tokenizer for efficient subword unit representation

  • TokenizationProfiler for analyzing performance characteristics and bottlenecks

  • OptimizedTokenizer with cache-friendly text processing patterns

Learning Objectives#

ML Systems Focus

This module emphasizes text processing pipelines, tokenization throughput, and memory-efficient vocabulary management.

By completing this module, you will be able to:

  1. Implement character-level tokenization with special token handling for basic text processing

  2. Build BPE (Byte Pair Encoding) tokenizer for subword units that balance vocabulary size and sequence length

  3. Understand tokenization trade-offs between vocabulary size and sequence length optimization

  4. Optimize tokenization performance for production text processing systems

  5. Analyze how tokenization affects model memory usage and training efficiency

Systems Concepts#

This module covers critical ML systems concepts:

  • Memory efficiency of token representations and vocabulary storage

  • Vocabulary size vs model size trade-offs in production language models

  • Tokenization throughput optimization for high-volume text processing

  • String processing performance and cache-friendly access patterns

  • Text processing pipeline design for scalable language model serving

Prerequisites#

  • Module 02 (Tensor): Understanding of tensor operations and data structures

Time Estimate#

4-5 hours - Comprehensive implementation with performance optimization and systems analysis

Getting Started#

Open the tokenization module and begin implementing your text processing pipeline:

# Navigate to the module
cd modules/11_tokenization

# Open the development notebook
tito module view 11_tokenization

# Complete the module
tito module complete 11_tokenization

Next Steps#

After completing tokenization, you’ll be ready for:

  • Module 12 (Embeddings): Converting tokens to dense vector representations

Production Context#

Modern language models like GPT-3 use sophisticated tokenization strategies with vocabularies of 50,000+ tokens. Understanding how to build efficient tokenizers is essential for:

  • Preprocessing text at scale for training large language models

  • Optimizing inference speed by reducing sequence lengths through effective subword encoding

  • Managing memory usage in production language model serving systems

  • Handling multilingual text with robust encoding strategies

Your tokenization implementations form the foundation for all text-based ML systems in the TinyTorch framework.

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