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Advanced JAX: Performance, Optimization, and Scale

Chapter 1: Advanced JAX Transformations and Control Flow

Review of Core Transformations: jit, grad, vmap

Mastering lax.scan for Sequential Operations

Conditional Execution with lax.cond

Looping with lax.while_loop

Combining Control Flow and Transformations

Advanced Masking Techniques

Understanding Closures and JAX Staging

Practical: Implementing Complex Recurrent Logic

Chapter 2: Optimizing JAX Code for Performance

Profiling JAX Code on CPU, GPU, and TPU

Understanding JAX Computation Graphs (jaxpr)

The Role of XLA Compilation

Memory Layout and Its Impact on Performance

Avoiding Recompilation

Fusion and Operator Optimization

Asynchronous Dispatch

Practice: Optimizing a Numerical Computation

Chapter 3: Distributed Computing with JAX

Introduction to Parallelism Concepts

Device Management in JAX

Single-Program Multiple-Data (SPMD) with pmap

Implementing Data Parallelism using pmap

Collective Communication Primitives (psum, pmean, etc.)

Handling Axis Names in pmap

Nested pmap and Advanced Partitioning

Introduction to Multi-Host Programming (Conceptual)

Practice: Distributed Data-Parallel Training

Chapter 4: Advanced Automatic Differentiation Techniques

Review of Forward- and Reverse-Mode Autodiff

Jacobian-Vector Products (JVPs) with jax.jvp

Vector-Jacobian Products (VJPs) with jax.vjp

Higher-Order Derivatives

Computing Full Jacobians and Hessians

Custom Differentiation Rules with jax.custom_vjp

Custom Differentiation Rules with jax.custom_jvp

Differentiation through Control Flow Primitives

Handling Non-Differentiable Functions

Practice: Implementing a Custom Gradient

Chapter 5: Interoperability and Custom Operations

Integrating JAX with NumPy

Zero-Copy Data Sharing with DLPack

Calling External CPU/GPU Code with jax.experimental.host_callback

Using jax.pure_callback for Side-Effect Free Calls

Introduction to JAX Primitives

Defining Custom Primitives

Implementing Abstract Evaluation Rules

Implementing Lowering Rules for Backends (CPU/GPU/TPU)

Defining Differentiation Rules for Custom Primitives

Practice: Integrating a C++ Function

Chapter 6: Large-Scale Model Training Techniques

Overview of Challenges in Large Model Training

Introduction to JAX Ecosystem Libraries (Flax, Haiku)

Managing Model Parameters and State

Combining pmap with Training Frameworks

Gradient Accumulation

Gradient Checkpointing (Re-materialization)

Mixed Precision Training

Conceptual Model Parallelism Strategies

Optimization Algorithms for Large Scale

Practice: Implementing Gradient Checkpointing

Advanced JAX: Performance, Optimization, and Scale

Prerequisites: Strong foundation in JAX fundamentals (jit, grad, vmap), Python, NumPy, and machine learning concepts. Familiarity with GPU/TPU architecture is beneficial.

Level: Advanced

What You'll Learn

Performance Optimization
Profile and optimize JAX code for maximal performance on accelerators like GPUs and TPUs.
Distributed Computing
Implement data and model parallelism using pmap and other distributed primitives for multi-device execution.
Advanced Transformations
Utilize complex control flow primitives (scan, cond, while_loop) and understand their interaction with JAX transformations.
Custom Autodiff Rules
Define custom vector-Jacobian products (VJPs) and Jacobian-vector products (JVPs) for non-standard operations.
JAX Internals
Gain a deeper understanding of JAX's compilation process (XLA) and internal representations (jaxprs).
Large-Scale Training
Apply advanced JAX patterns and libraries (like Flax or Haiku) for training large neural networks efficiently.

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