构建2700个真实深度学习计算图数据集，助力张量编译器评测与优化。
GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research
- 基于2700个真实计算图，覆盖六类任务与多个框架。
- 提出速度提升评分S(t)，兼顾加速效果与执行正确性。
- 支持编译器开发者定位性能瓶颈，适合研究编译优化者。
我们提出了GraphNet，一个包含2700个真实世界深度学习计算图的大规模数据集，涵盖多个主流深度学习框架及六大任务类别。为评估张量编译器在这些样本上的表现，我们引入了可调容差的运行时加速评分指标Speedup Score S(t)，综合考虑加速效果与执行正确性，提供对通用优化能力的可靠度量。进一步地，我们扩展出含误差感知的Error-aware Speedup Score ES(t)，帮助编译器开发者识别关键性能瓶颈。本报告通过在计算机视觉（CV）和自然语言处理（NLP）样本上对默认编译器CINN（PaddlePaddle）和TorchInductor（PyTorch）进行基准测试，验证了GraphNet的实际可用性。完整的数据构建流程、图提取工具与编译器评测工具已开源，地址为https://github.com/PaddlePaddle/GraphNet。
原文摘要 · Abstract (English)
We introduce GraphNet, a dataset of 2.7K real-world deep learning computational graphs with rich metadata, spanning six major task categories across multiple deep learning frameworks. To evaluate tensor compiler performance on these samples, we propose the benchmark metric Speedup Score S(t), which jointly considers runtime speedup and execution correctness under tunable tolerance levels, offering a reliable measure of general optimization capability. Furthermore, we extend S(t) to the Error-aware Speedup Score ES(t), which incorporates error information and helps compiler developers identify key performance bottlenecks. In this report, we benchmark the default tensor compilers, CINN for PaddlePaddle and TorchInductor for PyTorch, on computer vision (CV) and natural language processing (NLP) samples to demonstrate the practicality of GraphNet. The full construction pipeline with graph extraction and compiler evaluation tools is available at https://github.com/PaddlePaddle/GraphNet .
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