XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning. The XLA compiler takes models from popular frameworks such as PyTorch, TensorFlow, and JAX, and optimizes the models for high-performance execution across different hardware platforms including GPUs, CPUs, and ML accelerators. For example, in a BERT MLPerf submission, using XLA with 8 Volta V100 GPUs achieved a ~7x performance improvement and ~5x batch-size improvement compared to the same GPUs without XLA.

As a part of the OpenXLA project, XLA is built collaboratively by industry-leading ML hardware and software companies, including Alibaba, Amazon Web Services, AMD, Apple, Arm, Google, Intel, Meta, and NVIDIA.

Key benefits

  • Build anywhere: XLA is already integrated into leading ML frameworks such as TensorFlow, PyTorch, and JAX.

  • Run anywhere: It supports various backends including GPUs, CPUs, and ML accelerators, and includes a pluggable infrastructure to add support for more.

  • Maximize and scale performance: It optimizes a model's performance with production-tested optimization passes and automated partitioning for model parallelism.

  • Eliminate complexity: It leverages the power of MLIR to bring the best capabilities into a single compiler toolchain, so you don't have to manage a range of domain-specific compilers.

  • Future ready: As an open source project, built through a collaboration of leading ML hardware and software vendors, XLA is designed to operate at the cutting-edge of the ML industry.


To learn more about XLA, check out the links on the left. If you're a new XLA developer, you might want to start with XLA architecture and then read Code reviews.