12. Embeddings

12. Embeddings#

Module Overview

Converting tokens to dense vector representations that capture semantic meaning for language models.

What You’ll Build#

In this module, you’ll implement the systems that transform discrete tokens into rich vector representations:

  • Embedding layers with efficient lookup table operations

  • Positional encoding systems that enable sequence understanding

  • Embedding optimization for memory-efficient vocabulary management

  • Performance profiling for embedding lookup patterns and cache efficiency

Learning Objectives#

ML Systems Focus

This module emphasizes embedding table scaling, memory bandwidth optimization, and efficient vector representations.

By completing this module, you will be able to:

  1. Build embedding layers with lookup tables that efficiently convert token indices to dense vectors

  2. Implement positional encoding systems that capture sequence information for transformer models

  3. Understand embedding scaling and how vocabulary size affects model memory and computational requirements

  4. Optimize embedding lookups for cache efficiency and memory bandwidth utilization

  5. Analyze embedding trade-offs between dimension size, vocabulary size, and model capacity

Systems Concepts#

This module covers critical ML systems concepts:

  • Memory scaling with vocabulary size and embedding dimensions

  • Cache-friendly lookup patterns for high-throughput embedding access

  • Memory bandwidth bottlenecks in embedding-heavy language models

  • Parameter sharing strategies for efficient vocabulary management

  • Vector representation efficiency and storage optimization

Prerequisites#

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

  • Module 11 (Tokenization): Token processing and vocabulary management

Time Estimate#

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

Getting Started#

Open the embeddings module and begin implementing your vector representation systems:

# Navigate to the module
cd modules/12_embeddings

# Open the development notebook
tito module view 12_embeddings

# Complete the module
tito module complete 12_embeddings

Next Steps#

After completing embeddings, you’ll be ready for:

  • Module 13 (Attention): Multi-head attention mechanisms for sequence understanding

Production Context#

Scale Reality Check

GPT-3 has embedding tables with 600M+ parameters (50k vocabulary × 12k dimensions). Understanding embedding systems is crucial for building scalable language models.

Modern language models rely heavily on efficient embedding systems:

  • Memory management: Embedding tables often represent 20-40% of total model parameters

  • Bandwidth optimization: Embedding lookups are memory-bandwidth bound operations

  • Distributed training: Large embedding tables require sophisticated parameter sharding strategies

  • Inference efficiency: Optimized embedding access patterns are critical for real-time language generation

Your embedding implementations provide the foundation for all transformer-based language models in TinyTorch.

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