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TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers

TenSet is a large-scale multi-platform tensor program performance dataset. TenSet contains 52 million program performance records collected from 6 hardware platforms. This repo is based on a fork of TVM.

Dataset Information

  • Statics

    Item Number
    Networks 120
    Hardware Platforms 6
    Tasks 13,848
    Measurement records 51,577,248
  • Hardware Platforms

    Hardware Platform Cloud Instance Other Comments
    Intel Platinum 8272CL @ 2.60GHz (16 cores) Azure D32s_v4 AVX-512
    Intel E5-2673 v4 @ 2.30GHz (8 cores) Azure F16s AVX-2
    AMD EPYC 7452 @ 2.35GHz (4 cores) Azure D16as_v4 AVX-2
    ARM Graviton2 (16 cores) AWS c6g.4xlarge Neon
    NVIDIA Tesla K80 AWS p2.xlarge Kepler Architecture
    NVIDIA Tesla T4 AWS g4dn.xlarge Turing Architecture

Tutorials

Organization

Follow the above tutorial to download the dataset. The dataset is stored under tenset/scripts/dataset folder.

  • dataset/network_info: The metadata for networks
    • *.relay.pkl: The relay IR of a network. One network per file.
      • For example, (resnet_50,[(1,3,224,224)]).relay.pkl contains the relay IR of resnet_50 with input shape (1, 3, 224, 224).
    • *.task.pkl: The tasks and their weights in a network. One (network, targte) pair per file.
      • For example, ((resnet_50,[(1,3,224,224)]),llvm).task.pkl contains all tasks of resnet_50 on llvm backend.
    • all_tasks.pkl: A file containing all tasks. It is used an an index for all tasks.
  • dataset/to_measure_programs: The generated random programs for measurement.
    • *.json: The randomly generated programs (schedules) for measurement. One file per task.
  • dataset/measure_records: Collected measurement records.
    • e5-2666/*.json: measurement records collected on an Intel e5-2673. One file per task.
    • platinum-8272/*.json: measurement records collected on an Intel platinum-8272. One file per task.
    • ...: other hardware platforms

Inspect Tasks and Programs in the Dataset

Follow the above tutorial to download the dataset. You can then inspect the tasks and programs in the dataset

  • Print a task

    cd scripts
    python3 print_all_tasks.py --idx 1264

    output:

    Index: 1264
    flop_ct: 115806208.0
    workload_key: ["12b88bedece6984af589a28b43e0f3c4", 1, 56, 56, 64, 3, 3, 64, 128, 1, 1, 1, 128, 1, 28, 28, 128]
    Compute DAG:
    placeholder = PLACEHOLDER [1, 56, 56, 64]
    PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
    placeholder = PLACEHOLDER [3, 3, 64, 128]
    Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
    placeholder = PLACEHOLDER [1, 1, 1, 128]
    T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
    T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)
  • Print a program

    cd scripts
    python3 print_programs.py --filename 'dataset/measure_records/e5-2673/([12b88bedece6984af589a28b43e0f3c4,1,56,56,64,3,3,64,128,1,1,1,128,1,28,28,128],llvm).json' --idx 31

    output:

    Index: 31
    Time cost (second): [0.000990787, 0.000826989, 0.00082599, 0.00083999, 0.000827089, 0.000831189, 0.00083599, 0.000853589]
    Program:
    Placeholder: placeholder, placeholder, placeholder
    parallel ax0.0@ax1.0@ax2.0@ (0,4)
      for i1 (0,57)
        for i2 ((floormod(ax0.outer.outer.ax1.outer.outer.fused.ax2.outer.outer.fused, 4)*14),15)
          for i3 (0,64)
            PaddedInput = ...
      for ax3.0 (0,2)
        for ax2.1 (0,7)
          for ax3.1 (0,8)
            Conv2dOutput auto_unroll: 16
            for rx.0 (0,3)
              for rc.0 (0,4)
                for ry.1 (0,3)
                  for rc.1 (0,16)
                    for yy.3 (0,28)
                      vectorize ff.3 (0,8)
                        Conv2dOutput = ...
            for ax1.2 (0,28)
              vectorize ax3.2 (0,8)
                T_relu = ...

Citation

@inproceedings{zheng2021tenset,
  title={Tenset: A large-scale program performance dataset for learned tensor compilers},
  author={Zheng, Lianmin and Liu, Ruochen and Shao, Junru and Chen, Tianqi and Gonzalez, Joseph E and Stoica, Ion and Ali, Ameer Haj},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
  year={2021}
}

License

The code is licensed under an Apache-2.0 license.
The dataset is licensed under a CC BY 4.0 license.

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