Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

Related tags

Deep Learningpytorch-benchmark
Overview

⏱ pytorch-benchmark

Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption

Install

pip install pytorch-benchmark

Usage

import torch
from torchvision.models import efficientnet_b0
from pytorch_benchmark import benchmark


model = efficientnet_b0()
sample = torch.randn(8, 3, 224, 224)  # (B, C, H, W)
results = benchmark(model, sample, num_runs=100)

Sample results đŸ’»

Macbook Pro (16-inch, 2019), 2.6 GHz 6-Core Intel Core i7 
device: cpu
flops: 401669732
machine_info:
  cpu:
    architecture: x86_64
    cores:
      physical: 6
      total: 12
    frequency: 2.60 GHz
    model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  gpus: null
  memory:
    available: 5.86 GB
    total: 16.00 GB
    used: 7.29 GB
  system:
    node: d40049
    release: 21.2.0
    system: Darwin
params: 5288548
timing:
  batch_size_1:
    on_device_inference:
      human_readable:
        batch_latency: 74.439 ms +/- 6.459 ms [64.604 ms, 96.681 ms]
        batches_per_second: 13.53 +/- 1.09 [10.34, 15.48]
      metrics:
        batches_per_second_max: 15.478907181264278
        batches_per_second_mean: 13.528026359855625
        batches_per_second_min: 10.343281300091244
        batches_per_second_std: 1.0922382209314958
        seconds_per_batch_max: 0.09668111801147461
        seconds_per_batch_mean: 0.07443853378295899
        seconds_per_batch_min: 0.06460404396057129
        seconds_per_batch_std: 0.006458734193132054
  batch_size_8:
    on_device_inference:
      human_readable:
        batch_latency: 509.410 ms +/- 30.031 ms [405.296 ms, 621.773 ms]
        batches_per_second: 1.97 +/- 0.11 [1.61, 2.47]
      metrics:
        batches_per_second_max: 2.4673319862230025
        batches_per_second_mean: 1.9696935126370148
        batches_per_second_min: 1.6083039834656554
        batches_per_second_std: 0.11341204895590185
        seconds_per_batch_max: 0.6217730045318604
        seconds_per_batch_mean: 0.509410228729248
        seconds_per_batch_min: 0.40529608726501465
        seconds_per_batch_std: 0.030031445467788704
Server with NVIDIA GeForce RTX 2080 and Intel Xeon 2.10GHz CPU 
device: cuda
flops: 401669732
machine_info:
  cpu:
    architecture: x86_64
    cores:
      physical: 16
      total: 32
    frequency: 3.00 GHz
    model: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
  gpus:
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  - memory: 8192.0 MB
    name: NVIDIA GeForce RTX 2080
  memory:
    available: 119.98 GB
    total: 125.78 GB
    used: 4.78 GB
  system:
    node: monster
    release: 4.15.0-167-generic
    system: Linux
max_inference_memory: 736250368
params: 5288548
post_inference_memory: 21402112
pre_inference_memory: 21402112
timing:
  batch_size_1:
    cpu_to_gpu:
      human_readable:
        batch_latency: "144.815 \xB5s +/- 16.103 \xB5s [136.614 \xB5s, 272.751 \xB5\
          s]"
        batches_per_second: 6.96 K +/- 535.06 [3.67 K, 7.32 K]
      metrics:
        batches_per_second_max: 7319.902268760908
        batches_per_second_mean: 6962.865857677197
        batches_per_second_min: 3666.3496503496503
        batches_per_second_std: 535.0581873859935
        seconds_per_batch_max: 0.0002727508544921875
        seconds_per_batch_mean: 0.00014481544494628906
        seconds_per_batch_min: 0.0001366138458251953
        seconds_per_batch_std: 1.6102982159292097e-05
    gpu_to_cpu:
      human_readable:
        batch_latency: "106.168 \xB5s +/- 17.829 \xB5s [53.167 \xB5s, 248.909 \xB5\
          s]"
        batches_per_second: 9.64 K +/- 1.60 K [4.02 K, 18.81 K]
      metrics:
        batches_per_second_max: 18808.538116591928
        batches_per_second_mean: 9639.942102368092
        batches_per_second_min: 4017.532567049808
        batches_per_second_std: 1595.7983033708472
        seconds_per_batch_max: 0.00024890899658203125
        seconds_per_batch_mean: 0.00010616779327392578
        seconds_per_batch_min: 5.316734313964844e-05
        seconds_per_batch_std: 1.7829135190772566e-05
    on_device_inference:
      human_readable:
        batch_latency: "15.567 ms +/- 546.154 \xB5s [15.311 ms, 19.261 ms]"
        batches_per_second: 64.31 +/- 1.96 [51.92, 65.31]
      metrics:
        batches_per_second_max: 65.31149174711928
        batches_per_second_mean: 64.30692850265713
        batches_per_second_min: 51.918698784442846
        batches_per_second_std: 1.9599322351815833
        seconds_per_batch_max: 0.019260883331298828
        seconds_per_batch_mean: 0.015567030906677246
        seconds_per_batch_min: 0.015311241149902344
        seconds_per_batch_std: 0.0005461537255227954
    total:
      human_readable:
        batch_latency: "15.818 ms +/- 549.873 \xB5s [15.561 ms, 19.461 ms]"
        batches_per_second: 63.29 +/- 1.92 [51.38, 64.26]
      metrics:
        batches_per_second_max: 64.26476266356143
        batches_per_second_mean: 63.28565696640637
        batches_per_second_min: 51.38378232692614
        batches_per_second_std: 1.9198343850767468
        seconds_per_batch_max: 0.019461393356323242
        seconds_per_batch_mean: 0.01581801414489746
        seconds_per_batch_min: 0.015560626983642578
        seconds_per_batch_std: 0.0005498731526138171
  batch_size_8:
    cpu_to_gpu:
      human_readable:
        batch_latency: "805.674 \xB5s +/- 157.254 \xB5s [773.191 \xB5s, 2.303 ms]"
        batches_per_second: 1.26 K +/- 97.51 [434.24, 1.29 K]
      metrics:
        batches_per_second_max: 1293.3407338883749
        batches_per_second_mean: 1259.5653105357776
        batches_per_second_min: 434.23791282741485
        batches_per_second_std: 97.51424036939879
        seconds_per_batch_max: 0.002302885055541992
        seconds_per_batch_mean: 0.000805673599243164
        seconds_per_batch_min: 0.0007731914520263672
        seconds_per_batch_std: 0.0001572538140613121
    gpu_to_cpu:
      human_readable:
        batch_latency: "104.215 \xB5s +/- 12.658 \xB5s [59.605 \xB5s, 128.031 \xB5\
          s]"
        batches_per_second: 9.81 K +/- 1.76 K [7.81 K, 16.78 K]
      metrics:
        batches_per_second_max: 16777.216
        batches_per_second_mean: 9806.840626578907
        batches_per_second_min: 7810.621973929236
        batches_per_second_std: 1761.6008872740726
        seconds_per_batch_max: 0.00012803077697753906
        seconds_per_batch_mean: 0.00010421514511108399
        seconds_per_batch_min: 5.9604644775390625e-05
        seconds_per_batch_std: 1.2658293070174213e-05
    on_device_inference:
      human_readable:
        batch_latency: "16.623 ms +/- 759.017 \xB5s [16.301 ms, 22.584 ms]"
        batches_per_second: 60.26 +/- 2.22 [44.28, 61.35]
      metrics:
        batches_per_second_max: 61.346243290283894
        batches_per_second_mean: 60.25881046175457
        batches_per_second_min: 44.27827629162004
        batches_per_second_std: 2.2193085956672296
        seconds_per_batch_max: 0.02258443832397461
        seconds_per_batch_mean: 0.01662288188934326
        seconds_per_batch_min: 0.01630091667175293
        seconds_per_batch_std: 0.0007590167680596548
    total:
      human_readable:
        batch_latency: "17.533 ms +/- 836.015 \xB5s [17.193 ms, 23.896 ms]"
        batches_per_second: 57.14 +/- 2.20 [41.85, 58.16]
      metrics:
        batches_per_second_max: 58.16374528511205
        batches_per_second_mean: 57.140338855126565
        batches_per_second_min: 41.84762740950632
        batches_per_second_std: 2.1985066663972677
        seconds_per_batch_max: 0.023896217346191406
        seconds_per_batch_mean: 0.01753277063369751
        seconds_per_batch_min: 0.017192840576171875
        seconds_per_batch_std: 0.0008360147274630088

Limitations

Usage assumptions:

  • The model has as a __call__ method that takes the sample, i.e. model(sample).
  • The Model also works if the sample had a batch size of 1 (first dimension).

Feature limitations:

  • Allocated memory uses torch.cuda.max_memory_allocated, which is only available if the model resides on a CUDA device.
  • Energy consumption can only be measured on NVIDIA Jetson platforms at the moment.

Citation

If you like the tool and use it in you research, please consider citing it:

@article{hedegaard2022torchbenchmark,
  title={PyTorch Benchmark},
  author={Lukas Hedegaard},
  journal={GitHub. Note: https://github.com/LukasHedegaard/pytorch-benchmark},
  year={2022}
}
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Comments
  • torch cuda synchronize on GPUs?

    torch cuda synchronize on GPUs?

    Hello,

    Very happy to see your repo.

    I have tested the code and found that for the GPU tests, there may lack of torch synchronize when computing the device time. I am not sure how this may impact the results but I think it would make difference.

    What do you think?

    Best,

    opened by jizongFox 1
Releases(0.3.5)
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Lukas Hedegaard
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