pytorch-benchmark 0.1.1

Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption in one go.

⏱ 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 💻

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

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 limitataions:

• 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 ntel CPU with RAPL support, a NVIDIA GPU.

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}
}