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TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

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Song HanAuthors Info & Claims

MICRO '23: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture

Pages 225 - 239

https://doi.org/10.1145/3613424.3614303

Published: 08 December 2023 Publication History

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Abstract

Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. Since the computation pattern is sparse and irregular, specialized high-performance kernels are required. Existing GPU libraries offer two dataflow types for sparse convolution. The gather-GEMM-scatter dataflow is easy to implement but not optimal in performance, while the dataflows with overlapped computation and memory access (e.g. implicit GEMM) are highly performant but have very high engineering costs. In this paper, we introduce TorchSparse++, a new GPU library that achieves the best of both worlds. We create a highly efficient Sparse Kernel Generator that generates performant sparse convolution kernels at less than one-tenth of the engineering cost of the current state-of-the-art system. On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads. Consequently, TorchSparse++ achieves 2.9 ×, 3.3 ×, 2.2 × and 1.7 × measured end-to-end speedup on an NVIDIA A100 GPU over state-of-the-art MinkowskiEngine, SpConv 1.2, TorchSparse and SpConv v2 in inference; and is 1.2-1.3 × faster than SpConv v2 in mixed precision training across seven representative autonomous driving benchmarks. It also seamlessly supports graph convolutions, achieving 2.6-7.6 × faster inference speed compared with state-of-the-art graph deep learning libraries. Our code is publicly released at https://github.com/mit-han-lab/torchsparse.

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Cited By

Williams FHuang JSwartz JKlar GThakkar VCong MRen XLi RFuji-Tsang CFidler SSifakis EMuseth K(2024)fVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial IntelligenceACM Transactions on Graphics10.1145/365822643:4(1-15)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658226
Monica RAleotti J(2024)A Sparse Octree-Based CNN for Probabilistic Occupancy Prediction Applied to Next Best View PlanningIEEE Robotics and Automation Letters10.1109/LRA.2024.34604329:11(9359-9366)Online publication date: Nov-2024
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Feng XSun WTang CLin XYue JYang HLiu Y(2024)A 28-nm Energy-Efficient Sparse Neural Network Processor for Point Cloud Applications Using Block-Wise Online Neighbor SearchingIEEE Journal of Solid-State Circuits10.1109/JSSC.2024.338687859:9(3070-3081)Online publication date: Sep-2024
https://doi.org/10.1109/JSSC.2024.3386878
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Index Terms

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks

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cover image ACM Conferences

MICRO '23: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture

October 2023

1528 pages

ISBN:9798400703294

DOI:10.1145/3613424

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 08 December 2023

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MICRO '23: 56th Annual IEEE/ACM International Symposium on Microarchitecture

October 28 - November 1, 2023

ON, Toronto, Canada

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Cited By

Williams FHuang JSwartz JKlar GThakkar VCong MRen XLi RFuji-Tsang CFidler SSifakis EMuseth K(2024)fVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial IntelligenceACM Transactions on Graphics10.1145/365822643:4(1-15)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658226
Monica RAleotti J(2024)A Sparse Octree-Based CNN for Probabilistic Occupancy Prediction Applied to Next Best View PlanningIEEE Robotics and Automation Letters10.1109/LRA.2024.34604329:11(9359-9366)Online publication date: Nov-2024
https://doi.org/10.1109/LRA.2024.3460432
Feng XSun WTang CLin XYue JYang HLiu Y(2024)A 28-nm Energy-Efficient Sparse Neural Network Processor for Point Cloud Applications Using Block-Wise Online Neighbor SearchingIEEE Journal of Solid-State Circuits10.1109/JSSC.2024.338687859:9(3070-3081)Online publication date: Sep-2024
https://doi.org/10.1109/JSSC.2024.3386878
Sun SZhuang BJiang ZLiu BXie XChandraker M(2024)LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01850(19563-19572)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01850
Liu MShi RChen LZhang ZXu CWei XChen HZeng CGu JSu H(2024)One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00960(10072-10083)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00960
Chen YWang JYang ZManivasagam SUrtasun R(2024)G3R: Gradient Guided Generalizable ReconstructionComputer Vision – ECCV 202410.1007/978-3-031-72658-3_18(305-323)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-3-031-72658-3_18

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