Core Concepts

JAX and Transformer LLM: Core Concepts

A summary of the key ideas behind building a Transformer LLM from scratch using JAX/FLAX, providing context for the live application.

1. Why JAX? The High-Performance Advantage

JAX is a high-performance numerical computing library designed for machine learning research and production. It focuses on functional purity to enable powerful program transformations, which are critical for scaling LLMs on TPUs and GPUs.

JIT (jax.jit): Just-In-Time compilation. Takes your Python function and compiles it into highly optimized, device-specific XLA code, drastically improving runtime speed.
Vectorization (jax.vmap): Automatically batches operations. This is essential in a Transformer, for instance, applying the Feed-Forward Network to all tokens in a sequence simultaneously.
Parallelism (jax.pmap / SPMD): Enables efficient sharding of models and data across multiple accelerators, allowing for the training of billion-parameter models.

2. The Transformer Encoder Block (The Core)

The Transformer architecture abandons recurrent neural networks (RNNs) entirely, relying only on self-attention and feed-forward layers.

Embedding

Converts discrete tokens into dense vectors.

Positional Encoding

Injects sequence order information.

Multi-Head Attention

Weighs the importance of all other tokens.

Position-wise FFN

Refines each token's contextual representation.

The two main challenges optimized by the AI builder are the choices for the model dimension (d_model) and number of heads (N_heads). These must be balanced for task complexity and head dimension (d_k) to ensure efficient sharding on a TPU.

The Multi-Head Attention formula is:

Attention(Q, K, V) = σ( (Q * Kᵀ) / √dₖ ) * V