What is JAX?

Key Concepts:

• JAX: A numerical computation library focused on flexibility and speed, especially on accelerators.
• NumPy-like API: JAX offers a familiar interface for scientific computing and machine learning.
• Function Transformations: JAX uses transformations like grad, vmap, and jit to modify functions.
• Automatic Differentiation (Autodiff): Automatically computes gradients of functions.
• Vectorization/Batching: Processing multiple elements simultaneously.
• Just-In-Time (JIT) Compilation: Compiling code for optimized performance.
• Parallelization: Distributing computation across multiple devices.
• XLA (Accelerated Linear Algebra): Compiler used by JAX for performance.
• shard_map: Provides per-device control over computation and communication.
• Kernel Languages: Low-level languages for hardware control (e.g., Pallas, Triton).
• Memory Pipeline Control: Explicitly managing data loading into memory.
• Mosaic: Compiler for Pallas programs.

1. Introduction to JAX

• JAX is an open-source numerical computing library from Google DeepMind.
• It balances rapid iteration with high-performance execution on accelerators.
• Offers a NumPy-like API for scientific computing and machine learning.
• Example: A multilayer perceptron (MLP) model with predict (dot products, biases, tanh activation) and loss (sum of squared differences) functions.
• Switching from NumPy to JAX NumPy can provide a speedup.

2. Core Features and Function Transformations

• Grad Transformation:
  • Computes the gradients of a function using automatic differentiation (autodiff).
  • Example: Wrapping the loss function with grad to compute gradients for model weight adjustment.
• Vmap Transformation:
  • Vectorizes a function to work on batches of elements without explicit loops.
  • Transforms array and matrix dimensions for batched inputs.
  • Example: Vectorizing the gradient of the loss function to compute gradients for multiple batches.
• JIT Compilation:
  • Compiles code for optimized performance.
  • Advantages: Reduced latency, efficient hardware utilization, elimination of unused outputs and unnecessary recomputations, operation fusion, optimized backwards pass.
  • Example: JIT(vmap(grad(loss)))
  • Supports automatic and manual splitting of computation across devices and hosts.

3. Scaling and Parallelization

• JAX provides a unified API for scaling across CPUs, GPUs, and TPUs.
• The same JIT transform works regardless of the hardware or scale.
• Program with a global view; JAX handles splitting the computation.
• XLA compiler enables high compute performance on TPUs and GPUs.
• Supports data parallelism, tensor parallelism, and pipeline parallelism.
• Example: Splitting neural network parameters across chips using JIT.

4. JAX Ecosystem

• JAX is the core of a larger ecosystem of libraries.
• Libraries focus on specific tasks and can be used together.
• Examples:
  • NNX: Neural network library.
  • Optax: Optimizer library.
  • Grain: Data loading library.
  • Orbax: Model checkpoint management library.
• jax.dev provides resources and recommendations.

5. Advanced Features: Manual Control

• JAX offers features for manual control and customization.
• Trade-offs between flexibility and ease of use.
• Features include shard_map and the Pallas kernel language.
• shard_map:
  • Provides a per-device view of computation.
  • Allows explicit control over communication between devices.
  • Example: Collective matrix multiply for tensor parallelism in a transformer layer.
  • Supports composable function transformations like autodifferentiation.
  • Can be used for parallelized mixture of expert layers and custom distributed scientific computations.
• Kernel Languages (Pallas):
  • Low-level control over hardware.
  • Operates close to the hardware level but programmed through Python.
  • Pallas (Google DeepMind) targets GPUs and TPUs.
  • Offers explicit memory pipeline control.
  • Allows specifying the order in which data is loaded into memory.
  • Syntax similar to NumPy.
  • Supports operation fusion and batching.
  • Requires explicit differentiation rules.
  • Uses the Mosaic compiler to target GPUs and TPUs.

6. Pallas in Practice

• Used for rewriting operations and developing building-block operations.
• Examples of open-source Pallas kernels exist.
• Enables customization of computation and efficient implementation of new operations.

7. Conclusion

• JAX offers a balance of high-level abstractions and low-level control.
• It provides a scalable, unified parallel API compatible with various hardware configurations.
• JAX is at the forefront of research and scientific computing.
• The ecosystem of libraries built on JAX is constantly evolving.
• Advanced features like shard_map and Pallas provide fine-grained control over hardware.
• jax.dev provides resources and information for getting started.