GraphNet：面向张量编译器研究的大规模计算图数据集

摘要

我们推出GraphNet数据集，该数据集包含2.7K个真实场景的深度学习计算图，涵盖六大任务类别并跨越多类深度学习框架，且配备丰富的元数据。为评估张量编译器在这些样本上的性能，我们提出基准指标加速比分数S(t)，该指标在可调容错阈值下综合考量运行时加速效果与执行正确性，为通用优化能力提供可靠度量。进一步地，我们将S(t)扩展为误差感知加速比分数ES(t)，通过融入误差信息帮助编译器开发者定位关键性能瓶颈。本报告以计算机视觉（CV）和自然语言处理（NLP）样本为例，对PaddlePaddle的默认张量编译器CINN和PyTorch的TorchInductor进行基准测试，验证GraphNet的实用性。包含计算图提取与编译器评估工具的完整构建流程已开源：https://github.com/PaddlePaddle/GraphNet。

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 .

GraphNet：面向张量编译器研究的大规模计算图数据集

GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research

摘要

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