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https://github.com/LukasHedegaard/pytorch-benchmark
Easily benchmark PyTorch model FLOPs, latency, throughput, allocated gpu memory and energy consumption
https://github.com/LukasHedegaard/pytorch-benchmark
benchmark deep-learning flops gpu jetson python pytorch timing-analysis
Last synced: 3 months ago
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Easily benchmark PyTorch model FLOPs, latency, throughput, allocated gpu memory and energy consumption
- Host: GitHub
- URL: https://github.com/LukasHedegaard/pytorch-benchmark
- Owner: LukasHedegaard
- License: apache-2.0
- Created: 2022-02-09T13:17:50.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-25T09:38:10.000Z (about 1 year ago)
- Last Synced: 2024-04-25T00:36:31.135Z (7 months ago)
- Topics: benchmark, deep-learning, flops, gpu, jetson, python, pytorch, timing-analysis
- Language: Python
- Homepage:
- Size: 85.9 KB
- Stars: 74
- Watchers: 4
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# ⏱ pytorch-benchmark
__Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption__
*
###### \*Actual coverage is higher as GPU-related code is skipped by Codecov
## Install
```bash
pip install pytorch-benchmark
```
## Usage
```python
import torch
from torchvision.models import efficientnet_b0
from pytorch_benchmark import benchmark
model = efficientnet_b0().to("cpu") # Model device sets benchmarking device
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
```
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
```
... Your turn
## How we benchmark
The overall flow can be summarized with the diagram shown below (best viewed on GitHub):
```mermaid
flowchart TB;
A([Start]) --> B
B(prepare_samples)
B --> C[get_machine_info]
C --> D[measure_params]
D --> E[warm_up, batch_size=1]
E --> F[measure_flops]
subgraph SG[Repeat for batch_size 1 and x]
direction TB
G[measure_allocated_memory]
G --> H[warm_up, given batch_size]
H --> I[measure_detailed_inference_timing]
I --> J[measure_repeated_inference_timing]
J --> K[measure_energy]
end
F --> SG
SG --> END([End])
```
Usually, the sample and model don't reside on the same device initially (e.g., a GPU holds the model while the sample is on CPU after being loaded from disk or collected as live data). Accordingly, we measure timing in three parts: `cpu_to_gpu`, `on_device_inference`, and `gpu_to_cpu`, as well as a sum of the three, `total`. Note that the `model.device()` determines the execution device. The inference flow is shown below:
```mermaid
flowchart LR;
A([sample])
A --> B[cpu -> gpu]
B --> C[model __call__]
C --> D[gpu -> cpu]
D --> E([result])
```
## Advanced use
Trying to benchmark a custom class, which is not a `torch.nn.Module`?
You can pass custom functions to `benchmark` as seen in [this example](tests/test_custom_class.py).
## Limitations
- Allocated memory measurements are only available on CUDA devices.
- Energy consumption can only be measured on NVIDIA Jetson platforms at the moment.
- FLOPs and parameter count is not support for custom classes.
## Acknowledgement
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR).
It was developed for benchmarking tools in [OpenDR](https://github.com/opendr-eu/opendr), a non-proprietary toolkit for deep learning based functionalities for robotics and vision.
## Citation
If you like the tool and use it in research, please consider citing it:
```bibtex
@software{hedegaard2022pytorchbenchmark,
author = {Hedegaard, Lukas},
doi = {10.5281/zenodo.7223585},
month = {10},
title = {{PyTorch-Benchmark}},
version = {0.3.5},
year = {2022}
}
```