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.
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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.pklcontains the relay IR of resnet_50 with input shape (1, 3, 224, 224).
- For example,
*.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.pklcontains all tasks of resnet_50 on llvm backend.
- For example,
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
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Print a task
cd scripts python3 print_all_tasks.py --idx 1264output:
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)
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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.
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