Introduction#

This guide gives a brief overview of XLA and how XLA relates to Jax.For in-depth details please refer toXLA documentation.

XLA: The Powerhouse Behind Jax#

XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that plays a pivotal role in Jax’s performance and flexibility. It enables Jax to generate optimized code for various hardware backends (CPUs, GPUs, TPUs) by transforming and compiling your Python/NumPy-like code into efficient machine instructions.

Jax uses XLA’s JIT compilation capabilities to transform your Python functions into optimized XLA computations at runtime.

Configuring XLA in Jax:#

You can influence XLA’s behavior in Jax by setting XLA_FLAGS environment variables before running your Python script or colab notebook.

For the colab notebooks:

Provide flags usingos.environ['XLA_FLAGS']:

importos# Set multiple flags separated by spacesos.environ['XLA_FLAGS']='--flag1=value1 --flag2=value2'

For the python scripts:

SpecifyXLA_FLAGS as a part of cli command:

XLA_FLAGS='--flag1=value1 --flag2=value2'python3source.py

Important Notes:

• SetXLA_FLAGS before importing Jax or other relevant libraries. ChangingXLA_FLAGS after backend initialization will have no effect and given backend initialization time is not clearly defined it is usually safer to setXLA_FLAGS before executing any Jax code.

• Experiment with different flags to optimize performance for your specific use case.

For further information:

• Complete and up to date documentation about XLA can be found in the officialXLA documentation.

• For backends supported by open-source version of XLA (CPU, GPU), XLA flags are defined with their default values inxla/debug_options_flags.cc, and a complete list of flags could be foundhere.

• A guide on how to use key XLA flags can be foundhere.

Additional reading:

• GPU performance tips