设计文档就是你所需的全部：一款原生AI的机器学习性能工具

中文摘要 AI 辅助

机器学习性能建模对长期运行的软件而言是极为不利的领域：当今抽象层中内置的假设会被未来的模型和系统所推翻，迫使人们不断重构性能建模框架。与此同时，AI编码智能体已变得足够快速和强大，以至于重新生成整个库的成本要低于逐步修补技术债务的成本。我们描述了SMART，这是一款用于ML系统的严谨符号性能建模库，其主分支几乎没有代码：该仓库是由自包含的自然语言设计文档构成的有向无环图（DAG），编码子智能体仅从新版本更新的文档中重新生成实现，且所有人类变更都是对文档的自然语言编辑——从结构上实现了自文档化。有两个要素使重新生成过程可靠：（i）围绕逐步计算示例构建的设计文档风格，这些示例作为生成智能体的上下文演示；（ii）最小的、递归定义的算子中间表示（IR），带有符号（SymPy）成本表达式、用于大规模扫描的快速分析汇总模式，以及用于细粒度调度研究的慢速模调度模式。重新生成的实现可复现经过人工审计的参考模型——包括在TPU pod分片上部署的DeepSeek-V3——直至舍入精度，这表明设计文档而非代码可成为ML系统协同设计工具的持久产物。

英文摘要

Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models and systems, forcing perpetual refactoring of performance-modeling frameworks. Meanwhile, AI coding agents have become fast and capable enough that regenerating an entire library is cheaper than paying down the tech debt of incrementally patching it. We describe SMART, a rigorous symbolic performance-modeling library for ML systems whose main branch contains almost no code: the repository is a DAG of self-contained natural-language design docs, coding sub-agents regenerate the implementation from only the docs on new version updates, and every human change is a natural-language edit to a doc--self-documenting by construction. Two ingredients make regeneration reliable: (i) a design-doc style built around step-by-step worked examples that act as in-context demonstrations for the generating agents, and (ii) a minimal, recursively defined operator IR with symbolic (SymPy) cost expressions, a fast analytical roll-up mode for large sweeps, and a slow modulo-scheduling mode for fine-grained schedule studies. Regenerated implementations reproduce hand-audited reference models--including DeepSeek-V3 serving on a TPU pod slice--to round-off precision, suggesting that design docs--not code--can be the durable artifact for ML-systems co-design tools.

发表机构

• MIT（麻省理工学院）
• Google（谷歌）
• Stanford（斯坦福大学）
• Google DeepMind（谷歌DeepMind）

机构由 AI 辅助整理，请以论文原文为准。