XLA 实战一线：超大规模 TPU 工作负载下的编译器正确性保障

XLA from the Trenches: Correctness on TPU Workloads at Scale

arXiv ID: arXiv:2611.00624 [cs.AI]
Submitted: October 31, 2026
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Programming Languages (cs.PL)
Authors: Ignacio Cano, Amir Saadat, Rishipal Singh Bhatia, Samuel Ginzburg, Patrick C. Toulme, Ioannis Zarkadas, Praveen Narayanan, Pratik Bhatu, Nicolas Basile, Alexander Dietmüller, Ruishan Wang, Jacob Wahlgren, Victor Barros, Subhankar Shah, Michael Kuperstein, Farzin Houshmand, Emma Sevastian, Peter Gavin, Karthik Srinivasa Murthy, Berkin Ilbeyi, Amit Sabne, Eli Bendersky

文章背景与核心概要

在当今前沿基础大模型不断膨胀的背景下，专用编译器 (如 Google 的 XLA:TPU) 正扮演着“幕后引擎”的关键角色，通过高度复杂的优化流水线将庞大计算图转化为硬件极致性能。然而，一旦编译器在代码生成阶段潜藏细微缺陷，往往不会抛出任何传统报错，却会导致静默的数值漂移或内存损坏，足以让耗资数百万美元的分布式训练前功尽弃。来自 Google 的核心编译器团队在这篇实战论文中，首次系统公开了在超大规模 TPU 集群上保障 XLA 编译器绝对正确性的工业级全套方案，深度融合了真实运行轨迹与合成程序的混合模糊测试、自主 AI 智能体集群调试，以及多层级差分计算图二分定位算法。该体系已在生产环境中成功锁定并修复了超过 142 个隐蔽编译器缺陷，为全球超大规模异构 AI 编译工程树立了兼顾极致性能与坚实正确性的全新工业标杆。

摘要概览

Abstract Summary

随着现代基础大模型的体量持续膨胀，领域专用编译器 (Domain-Specific Compilers) 愈发依赖繁复且多层级的优化流水线来榨取硬件的极致算力。然而，在追求巅峰性能与确保绝对正确性之间取得平衡，是一项极其艰巨的挑战。代码生成 (Code Generation, Codegen) 阶段潜藏的细微缺陷，往往不会触发传统的编译崩溃或报错，却会导致静默的数值精度衰退、内存异常或缓冲区损坏——这种“无声故障”足以直接击垮耗资数百万美元的大规模分布式模型训练，并悄然破坏线上实时模型推理结果的准确性。

As modern foundation models continue to grow, domain-specific compilers increasingly rely on intricate, multi-tiered optimization pipelines. However, balancing peak performance with absolute correctness presents significant challenges. Subtle code-generation (codegen) defects can lead to silent numerical degradation, memory issues, or buffer corruption—disrupting expensive multi-million-dollar training runs and impairing live model inference without throwing traditional compiler errors.

本篇论文详尽阐述了一套历经严苛实战考验的自动化验证与缺陷分流方法论，专门面向在超大规模 (Hyperscale) 环境中运行的 Google XLA:TPU 编译器而设计。其核心贡献包括：

This paper details a battle-tested verification and triage methodology designed for Google's XLA:TPU compiler operating at hyperscale. Key contributions include:

• 混合模糊测试 (Hybrid Fuzzing)：创新性地将合成程序生成机制与线上真实生产运行轨迹 (Production Traces) 深度融合；
• 自主 AI 智能体 (Autonomous AI Agents)：部署协同作战的 AI 智能体 (AI Agents) 集群，实现自主构建压力测试用例、精准复现失败场景并草拟修复补丁；
• 多层级差分流水线 (Hierarchical Differential Pipeline)：采用高精度的计算图二分定位算法，将庞大的计算图层层下钻剖析至融合算子 (Fused Kernels) 内的具体失败原子操作，高效隔离根本致错原因。

• Hybrid Fuzzing: Combining synthetic program generation with real-world production traces.
• Autonomous AI Agents: Deploying swarms of AI agents to autonomously construct stress tests, replicate failures, and draft patches.
• Hierarchical Differential Pipeline: Bisecting massive computation graphs down to individual failing operations within fused kernels to isolate root causes.

在实际生产部署中，该框架已成功推动解决了 超过 142 个编译器缺陷 (Compiler Bugs)，另有 103 项新发现的潜在问题正处于审查确认中，为超大规模 AI 系统的编译器工程化落地贡献了极具实操价值的宝贵经验。

In production deployment, this framework successfully drove the resolution of over 142 compiler bugs, with another 103 findings currently under review, yielding valuable pragmatic lessons for compiler engineering at scale.

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