KernelEvolve：Meta 的 Ranking Engineer Agent 如何优化 AI 基础设施
This is the second post in the Ranking Engineer Agent blog series exploring the autonomous AI capabilities accelerating Meta’s Ads Ranking innovation. The previous post introduced Ranking Engineer Agent’s ML exploration capability, which autonomously designs, executes, and analyzes ranking model experiments. This post covers how to optimize the low-level infrastructure that makes those models run efficiently at scale. We introduce KernelEvolve, an agentic kernel authoring system used by Ranking Engineer Agent and generally applicable to a range of AI models beyond Ads Ranking.
这是 Ranking Engineer Agent 博客系列的第二篇，探讨加速 Meta Ads Ranking 创新的自主 AI 能力。上一篇 介绍了 Ranking Engineer Agent 的 ML 探索能力，该能力自主设计、执行并分析排名模型实验。本文介绍了如何优化使这些模型在大规模下高效运行的低级基础设施。我们介绍了 KernelEvolve，这是一个代理式内核创作系统，由 Ranking Engineer Agent 使用，并普遍适用于 Ads Ranking 之外的各种 AI 模型。
Summary
总结
- Meta operates a large fleet of heterogeneous hardware — NVIDIA GPUs, AMD GPUs, Meta’s custom MTIA silicon chips, and CPUs. Using this hardware effectively and efficiently requires developing software that translates high-level model operations into efficient, chip-specific instructions called optimized kernels. Authoring and optimizing kernels must be done for each new chip generation and ML model architecture. Beyond standard kernel operators like general matrix multiplications (GEMMs) and convolutions covered by vendor libraries, production workloads require many custom operators across ranking models. With the number of models and number of hardware types and generations, hand-tuning by kernel experts doesn’t scale.
- Meta 运营着一个大型异构硬件舰队 — NVIDIA GPUs、AMD GPUs、Meta 的定制 MTIA 硅芯片和 CPUs。有效高效地使用这些硬件需要开发软件，将高级模型操作翻译成高效的、芯片特定指令，称为优化内核。为每个新芯片世代和 ML 模型架构必须编写和优化内核。除了供应商库覆盖的标准内核操作符如通用矩阵乘法 (GEMMs) 和卷积，生产工作负载需要众多跨排名模型的自定义操作符。随着模型数量和硬件类型及世代数量的增加，内核专家的手动调优无法扩展。
- To address the volume of performance optimization work required by the increasing number of models X number of hardware types & generations, we built KernelEvolve, an agent to optimize performance used by Meta’s Ranking Engineer Agent. ...