liaopeiyuan / tenset

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

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

Get Started with the Cost Model Experiments

See this tutorial.

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 = ...

License

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