StableHLO & OpenXLA: Enhancing Hardware Portability for ML
OpenXLA converts and optimizes calculations for CPUs, GPUs, and TPUs in JAX, a Python numerical computation toolkit featuring pytorch/XLA compilation and automated differentiation
The context of OpenXLA’s function suggests that StableHLO is probably connected to the portability and stability of the Hardware Abstraction Layer (HAL) within the OpenXLA ecosystem
Between low-level hardware backends and high-level machine learning frameworks like JAX, OpenXLA serves as an abstraction layer
OpenXLA has an intermediate representation (IR) that links the backend (like XLA compilers for particular hardware) with the frontend (like JAX)
StableHLO most likely denotes an OpenXLA versioned and standardized version of the HLO (High-Level Optimizer) IR
Intel and other systems, performance optimization requires an understanding of how JAX and OpenXLA interact, especially the compilation and execution cycle
OpenXLA converts the compute graph generated by JAX (in the form of HLO) into machine code that is optimized for a variety of backends, such as CPUs, GPUs, and TPUs