GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler
  Research
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 .
6902b7db72739622ee92a649
Xinqi Li
6902b7db72739622ee92a64a
Yiqun Liu
6902b7db72739622ee92a64b
Shan Jiang
claimed_verified
2025-10-30T14:32:56.334Z
689500e06f99de530a5b5f28
/avatars/09bbadc0cfe116c2443db73e527e7afa.svg
Shan jiang
shanjiang7
user
shanjiang7
6902b7db72739622ee92a64c
Enrong Zheng
6902b7db72739622ee92a64d
Huaijin Zheng
6902b7db72739622ee92a64e
Wenhao Dai
6902b7db72739622ee92a64f
Haodong Deng
6902b7db72739622ee92a650
Dianhai Yu
6902b7db72739622ee92a651
Yanjun Ma
GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler
  Research
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, a dataset of real-world deep learning computational graphs, provides a benchmark for evaluating tensor compiler performance using Speedup Score and Error-aware Speedup Score.
GraphNet computational graphs tensor compiler Speedup Score Error-aware Speedup Score CINN TorchInductor computer vision natural language processing