Modern tensor compilers such as TorchInductor deliver substantial speedups on mainstream models, yet face a systematic performance ceiling on long-tail workloads -- our profiling shows that 43% of real-world subgraphs experience end-to-end slowdowns under default compilation. While LLMs offer a path toward automated optimization, existing efforts focus on standalone kernel generation. We argue that pass generation -- where LLMs author structured graph transformations that integrate directly into compiler pipelines -- is the more appropriate abstraction. We propose PassNet, the first large-scale ecosystem for LLM-based compiler pass generation, comprising: (1) PassNet-Dataset, over 18K unique computational graphs from 100K real-world models; and (2) PassBench, 200 curated long-tail fusible tasks (comprising 2,060 subgraphs in total) evaluated under the Error-aware Speedup Score (ES_t) -- a metric unifying correctness, stability, and performance -- with layered integrity defenses against systematic LLM exploitation. Experiments reveal that PassBench is both highly discriminative and genuinely unsaturated: the best frontier model trails TorchInductor by 37% in aggregate, yet on individual subgraphs LLMs achieve up to 3x speedup over the same compiler -- indicating that the bottleneck is consistency, not capability. Fine-tuning a small model on merely ~4K PassNet trajectories yields a 2.67x improvement approaching frontier-model performance, demonstrating substantial headroom and validating PassNet as live training infrastructure for advancing LLM-driven compiler optimization. All data, benchmarks, and tooling are publicly available.

翻译：现代张量编译器（如TorchInductor）在主流模型上可显著提速，但在长尾负载中面临系统性性能瓶颈——我们的性能分析表明，43%的真实世界子图在默认编译下出现端到端性能退化。尽管大语言模型为自动优化提供了新路径，现有工作聚焦于独立内核生成。我们认为，Pass生成（即由大语言模型编写可直接集成到编译器流程的结构化图变换）是更合适的抽象范式。为此提出PassNet——首个面向LLM驱动编译器Pass生成的大规模生态系统，包含：（1）PassNet-Dataset，包含来自10万个真实世界模型的超过1.8万张独特计算图；（2）PassBench，包含200个精心筛选的长尾可融合任务（涵盖2060个子图），采用错误感知加速得分（ES_t）进行评估——该指标统一衡量正确性、稳定性与性能——并配备分层完整性防御机制以防范系统性LLM攻击。实验表明，PassBench具有高度区分性与实质性未饱和特性：最强基础模型整体落后TorchInductor达37%，但在单个子图上，LLM相比同一编译器最高可实现3倍加速——这表明瓶颈在于一致性而非能力。在仅约4000条PassNet轨迹数据上微调小型模型，即可带来2.67倍的性能提升，逼近前沿模型水平，彰显出巨大提升空间，并验证了PassNet作为推动LLM驱动编译器优化的实时训练基础设施的有效性。所有数据、基准测试与工具均已开源。