OpenXLA Project is Now Available to Accelerate and Simplify Machine Learning

Over the past few years, machine learning (ML) has completely revolutionized the technology industry. Ranging from 3D protein structure prediction and prediction of tumors in cells to helping identify fraudulent credit card transactions and curating personalized experiences, there is hardly any industry that has not yet employed ML algorithms to enhance their use cases. Even though machine learning is a rapidly emerging discipline, there are still a number of challenges that need to be resolved before these ML models can be developed and put into use. Nowadays, ML development and deployment suffer for a number of reasons. Infrastructure and resource limitations are among the main causes, as the execution of ML models is frequently computationally intensive and necessitates a large amount of resources. Moreover, there is a lack of standardization when it comes to deploying ML models, as it depends greatly on the framework and hardware being used and the purpose for which the model is being designed. As a result, it takes developers a lot of time and effort to ensure that a model employing a specific framework functions properly on every piece of hardware, which requires a considerable amount of domain-specific knowledge. Such inconsistencies and inefficiencies greatly affect the speed at which developers work and places restriction on the model architecture, performance, and generalizability.

Several ML industry leaders, including Alibaba, Amazon Web Services, AMD, Apple, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, and NVIDIA, have teamed up to develop an open-source compiler and infrastructure ecosystem known as OpenXLA to close this gap by making ML frameworks compatible with a variety of hardware systems and increasing developers’ productivity. Depending on the use case, developers can choose the framework of their choice (PyTorch, TensorFlow, etc.) and build it with high performance across multiple hardware backend options like GPU, CPU, etc., using OpenXLA’s state-of-the-art compilers. The ecosystem significantly focuses on providing its users with high performance, scalability, portability, and flexibility, while making it affordable at the same time. The OpenXLA Project, which consists of the XLA compiler (a domain-specific compiler that optimizes linear algebra operations to be run across hardware) and StableHLO (a compute operation that enables the deployment of various ML frameworks across hardware), is now available to the general public and is accepting contributions from the community.

The OpenXLA community has done a fantastic job of bringing together the expertise of several developers and industry leaders across different fields in the ML world. Since ML infrastructure is so immense and vast, no single organization is capable of resolving it alone at a large scale. Thus, experts well-versed in different ML domains such as frameworks, hardware, compilers, runtime, and performance accuracy have come together to accelerate the pace of development and deployment of ML models. The OpenXLA project achieves this vision in two ways by providing: a modular and uniform compiler interface that developers can use for any framework and pluggable hardware-specific backends for model optimizations. Developers can also leverage MLIR-based components from the extensible ML compiler platform to configure them according to their particular use cases and enable hardware-specific customization throughout the compilation workflow.

OpenXLA can be employed for a spectrum of use cases. They include developing and delivering cutting-edge performance for a variety of established and new models, including, to mention a few, DeepMind’s AlphaFold and multi-modal LLMs for Amazon. These models can be scaled with OpenXLA over numerous hosts and accelerators without exceeding the deployment limits. One of the most significant uses of the ecosystem is that it provides support for a multitude of hardware devices such as AMD and NVIDIA GPUs, x86 CPU, etc., and ML accelerators like Google TPUs, AWS Trainium and Inferentia, and many more. As mentioned previously, earlier developers needed domain-specific knowledge to write device-specific code to increase the performance of models written in different frameworks to be executed across hardware. However, OpenXLA has several model enhancements that simplify a developer’s job, like streamlined linear algebra operations, enhanced scheduling, etc. Moreover, it comes with a number of modules that provide effective model parallelization across various hardware hosts and accelerators.

The developers behind the OpenXLA Project are extremely excited to see how developers use it to enhance ML development and deployment for their preferred use case.

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