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.

• 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

• Get Started with the Cost Model Experiments
• Using Pre-trained Models on AWS c5.9xlarge (or Any Other x86 CPUs with AVX-512)

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

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 = ...

@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}
}

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