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
Interoperability and Custom Operations
JAX computations often need to interact with other parts of the scientific Python stack or require specialized low-level functionality. This chapter addresses how to bridge JAX with external systems and extend its core operations.
You will learn practical techniques for:
- Converting data between JAX and NumPy arrays.
- Employing the DLPack standard for efficient, zero-copy data sharing with other libraries like PyTorch or TensorFlow.
- Executing external Python functions within JAX programs using
host_callbackandpure_callback, recognizing their use cases and limitations. - Understanding JAX primitives as the foundational operations known to the system.
- Building custom primitives by defining their abstract evaluation behavior (shapes, dtypes), implementing backend-specific lowering rules (XLA HLO generation), and specifying custom differentiation rules (JVPs/VJPs) to ensure they integrate fully with JAX's transformation system.
Sections
5.1 Integrating JAX with NumPy
5.2 Zero-Copy Data Sharing with DLPack
5.3 Calling External CPU/GPU Code with jax.experimental.host_callback
5.4 Using jax.pure_callback for Side-Effect Free Calls
5.5 Introduction to JAX Primitives
5.6 Defining Custom Primitives
5.7 Implementing Abstract Evaluation Rules
5.8 Implementing Lowering Rules for Backends (CPU/GPU/TPU)
5.9 Defining Differentiation Rules for Custom Primitives
5.10 Practice: Integrating a C++ Function
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