Meta seeks to speed up AI inference with open-source AITemplate

With out inference, a synthetic intelligence (AI) mannequin is simply math and doesn’t really execute or forecast a lot, if something.

To this point, AI inference engines have been largely tethered to particular {hardware} for which they’re designed. That diploma of {hardware} lock-in signifies that builders might want to construct particular software program for various {hardware}, and will effectively additionally sluggish the tempo of trade innovation general. 

The problem of managing inference {hardware} has not been misplaced on social media big Meta (previously Fb). Meta makes use of lots of totally different {hardware} throughout its infrastructure and has its justifiable share of challenges implementing inference options. To assist remedy that problem, Meta has been engaged on a know-how it calls AITemplate (AIT) which it defines as a unified inference system that originally will help each Nvidia TensorCore and AMD MatrixCore inference {hardware}. Meta introduced yesterday that it’s open sourcing AITemplate beneath an Apache 2.0 license.

“Our present model of AIT is concentrated on help for Nvidia and AMD GPUs, however the platform is scalable and will help Intel GPUs in future if demand was there,” Ajit Matthews, director of engineering at Meta, advised VentureBeat. “Now that we’ve open-sourced AIT, we welcome any silicon suppliers to contribute to it.”

The necessity for GPU and inference engine abstraction

The thought of lock-in for AI {hardware} will not be restricted to simply inference engines; it’s additionally a priority that others within the trade, together with Intel, even have about GPUs for accelerated computing.

Intel is among the many main backers of the open-source SYCL specification, which seeks to assist create a unified programming layer for GPUs. The Meta-led AIT effort is comparable in idea, although totally different in what it allows. Matthews defined that SYCL is nearer to the GPU programming degree, whereas AITemplate is specializing in high-performance TensorCore/MatrixCore AI primitives.

“AIT is an alternative choice to TensorRT which is the Inference engine from Nvidia,” Matthews stated. “Not like TensorRT, it’s an open-source resolution which helps each Nvidia and AMD GPU backends.”

Matthews famous that AIT first characterizes the mannequin structure, after which works on fusing and optimizing layers and operations particular to that structure. 

It’s not about competitors

AIT isn’t nearly creating a typical software program layer for inference, it’s additionally about efficiency. In early exams performed by Meta, it’s already seeing efficiency enhancements over non-AIT inference-powered fashions on each Nvidia and AMD GPUs. 

“For AIT the objective is to convey versatile, open, extra energy-efficient AI inference for GPU customers,” Matthews stated. 

Meta isn’t simply constructing AIT to serve the better good, however to additionally meet its personal AI wants. Matthews stated that Meta’s workloads are evolving and in an effort to meet these altering wants, it wants options which can be open and performant. He additionally famous that Meta tends to need the higher layers of its know-how stacks to be hardware-agnostic. AIT does that at the moment with AMD and Nvidia GPUs.

“We see alternatives with a lot of our present and future Inference workloads to profit from AIT,” he stated. “We expect AIT has the potential for broad adoption as essentially the most performant unified inference engine.”