PassNet: Scaling Large Language Models for Graph Compiler Pass Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914612241235968 |
|---|---|
| author | Liu, Yiqun Wu, Yingsheng Yang, Ruqi Zheng, Enrong Qiu, Honglei He, Sijun Liang, Tai Wu, Jingjing Zhou, Yuhan Zhang, Yiwei Chen, Dongyan Yi, Weihan Li, Xinqi Bao, Siqi |
| author_facet | Liu, Yiqun Wu, Yingsheng Yang, Ruqi Zheng, Enrong Qiu, Honglei He, Sijun Liang, Tai Wu, Jingjing Zhou, Yuhan Zhang, Yiwei Chen, Dongyan Yi, Weihan Li, Xinqi Bao, Siqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29357 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PassNet: Scaling Large Language Models for Graph Compiler Pass Generation Liu, Yiqun Wu, Yingsheng Yang, Ruqi Zheng, Enrong Qiu, Honglei He, Sijun Liang, Tai Wu, Jingjing Zhou, Yuhan Zhang, Yiwei Chen, Dongyan Yi, Weihan Li, Xinqi Bao, Siqi Artificial Intelligence Machine Learning Programming Languages 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. |
| title | PassNet: Scaling Large Language Models for Graph Compiler Pass Generation |
| topic | Artificial Intelligence Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2605.29357 |