Measure neural network device specific metrics (latency, flops, etc.)
Project description
torchprof
A minimal dependency library for layer-by-layer profiling of PyTorch models.
All metrics are derived using the PyTorch autograd profiler.
Quickstart
pip install torchprof
import torch
import torchvision
import torchprof
model = torchvision.models.alexnet(pretrained=False).cuda()
x = torch.rand([1, 3, 224, 224]).cuda()
# `profile_memory` was added in PyTorch 1.6, this will output a runtime warning if unsupported.
with torchprof.Profile(model, use_cuda=True, profile_memory=True) as prof:
model(x)
# equivalent to `print(prof)` and `print(prof.display())`
print(prof.display(show_events=False))
Module | Self CPU total | CPU total | Self CUDA total | CUDA total | Self CPU Mem | CPU Mem | Self CUDA Mem | CUDA Mem | Number of Calls
---------------|----------------|-----------|-----------------|------------|--------------|---------|---------------|-----------|----------------
AlexNet | | | | | | | | |
├── features | | | | | | | | |
│├── 0 | 1.831ms | 7.260ms | 1.830ms | 7.230ms | 0 b | 0 b | 3.71 Mb | 756.50 Kb | 1
│├── 1 | 46.768us | 68.950us | 46.976us | 70.528us | 0 b | 0 b | 0 b | 0 b | 1
│├── 2 | 80.361us | 166.213us | 79.872us | 149.696us | 0 b | 0 b | 1.60 Mb | 547.00 Kb | 1
│├── 3 | 277.412us | 1.205ms | 492.544us | 1.932ms | 0 b | 0 b | 2.68 Mb | 547.00 Kb | 1
│├── 4 | 28.274us | 40.156us | 27.872us | 41.184us | 0 b | 0 b | 0 b | 0 b | 1
│├── 5 | 57.138us | 124.176us | 56.512us | 109.536us | 0 b | 0 b | 1.11 Mb | 380.50 Kb | 1
│├── 6 | 173.517us | 674.434us | 210.880us | 809.824us | 0 b | 0 b | 8.27 Mb | 253.50 Kb | 1
│├── 7 | 27.382us | 38.754us | 27.648us | 39.936us | 0 b | 0 b | 0 b | 0 b | 1
│├── 8 | 144.863us | 556.345us | 207.872us | 798.368us | 0 b | 0 b | 10.20 Mb | 169.00 Kb | 1
│├── 9 | 27.552us | 39.224us | 26.752us | 39.072us | 0 b | 0 b | 0 b | 0 b | 1
│├── 10 | 138.752us | 531.703us | 173.056us | 661.568us | 0 b | 0 b | 7.08 Mb | 169.00 Kb | 1
│├── 11 | 27.743us | 39.515us | 27.648us | 39.936us | 0 b | 0 b | 0 b | 0 b | 1
│└── 12 | 60.333us | 133.099us | 59.392us | 116.768us | 0 b | 0 b | 324.00 Kb | 108.00 Kb | 1
├── avgpool | 55.655us | 110.770us | 57.344us | 107.456us | 0 b | 0 b | 108.00 Kb | 36.00 Kb | 1
└── classifier | | | | | | | | |
├── 0 | 77.746us | 165.089us | 77.696us | 144.064us | 0 b | 0 b | 171.00 Kb | 45.00 Kb | 1
├── 1 | 405.262us | 425.012us | 796.672us | 796.672us | 0 b | 0 b | 32.00 Kb | 16.00 Kb | 1
├── 2 | 29.455us | 42.329us | 29.472us | 42.976us | 0 b | 0 b | 0 b | 0 b | 1
├── 3 | 53.601us | 120.870us | 53.248us | 99.328us | 0 b | 0 b | 76.00 Kb | 20.00 Kb | 1
├── 4 | 63.981us | 79.811us | 232.448us | 232.448us | 0 b | 0 b | 32.00 Kb | 16.00 Kb | 1
├── 5 | 27.853us | 39.445us | 27.648us | 40.928us | 0 b | 0 b | 0 b | 0 b | 1
└── 6 | 61.656us | 76.714us | 96.256us | 96.256us | 0 b | 0 b | 8.00 Kb | 4.00 Kb | 1
To see the low level operations that occur within each layer, print the contents of prof.display(show_events=True).
Module | Self CPU total | CPU total | Self CUDA total | CUDA total | Self CPU Mem | CPU Mem | Self CUDA Mem | CUDA Mem | Number of Calls
------------------------------------|----------------|-----------|-----------------|------------|--------------|---------|---------------|-----------|----------------
AlexNet | | | | | | | | |
├── features | | | | | | | | |
│├── 0 | | | | | | | | |
││├── aten::conv2d | 15.779us | 1.831ms | 14.336us | 1.830ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::convolution | 10.139us | 1.815ms | 8.512us | 1.816ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::_convolution | 45.115us | 1.805ms | 36.288us | 1.808ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::contiguous | 8.586us | 8.586us | 8.160us | 8.160us | 0 b | 0 b | 0 b | 0 b | 3
││├── aten::cudnn_convolution | 1.646ms | 1.682ms | 1.745ms | 1.749ms | 0 b | 0 b | 756.50 Kb | -18.00 Kb | 1
││├── aten::empty | 21.821us | 21.821us | 0.000us | 0.000us | 0 b | 0 b | 774.50 Kb | 774.50 Kb | 2
││├── aten::resize_ | 7.324us | 7.324us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 2
││├── aten::stride | 2.073us | 2.073us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 4
││├── aten::reshape | 5.701us | 17.603us | 1.056us | 1.056us | 0 b | 0 b | 0 b | 0 b | 1
││├── aten::view | 11.902us | 11.902us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 1
││└── aten::add_ | 56.837us | 56.837us | 17.408us | 17.408us | 0 b | 0 b | 0 b | 0 b | 1
│├── 1 | | | | | | | | |
││├── aten::relu_ | 24.586us | 46.768us | 23.424us | 46.976us | 0 b | 0 b | 0 b | 0 b | 1
││└── aten::threshold_ | 22.182us | 22.182us | 23.552us | 23.552us | 0 b | 0 b | 0 b | 0 b | 1
│├── 2 | | | | | | | | |
...
The original Pytorch EventList can be returned by calling raw() on the profile instance.
trace, event_lists_dict = prof.raw()
print(trace[2])
# Trace(path=('AlexNet', 'features', '0'), leaf=True, module=Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)))
print(event_lists_dict[trace[2].path][0])
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg CPU Mem Self CPU Mem CUDA Mem Self CUDA Mem # of Calls
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
aten::conv2d 0.86% 15.779us 100.00% 1.831ms 1.831ms 14.336us 0.78% 1.830ms 1.830ms 0 b 0 b 756.50 Kb 0 b 1
aten::convolution 0.55% 10.139us 99.14% 1.815ms 1.815ms 8.512us 0.47% 1.816ms 1.816ms 0 b 0 b 756.50 Kb 0 b 1
aten::_convolution 2.46% 45.115us 98.58% 1.805ms 1.805ms 36.288us 1.98% 1.808ms 1.808ms 0 b 0 b 756.50 Kb 0 b 1
aten::contiguous 0.20% 3.697us 0.20% 3.697us 3.697us 3.616us 0.20% 3.616us 3.616us 0 b 0 b 0 b 0 b 1
aten::cudnn_convolution 89.88% 1.646ms 91.85% 1.682ms 1.682ms 1.745ms 95.31% 1.749ms 1.749ms 0 b 0 b 756.50 Kb -18.00 Kb 1
aten::empty 0.67% 12.313us 0.67% 12.313us 12.313us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 756.50 Kb 756.50 Kb 1
aten::contiguous 0.14% 2.575us 0.14% 2.575us 2.575us 2.464us 0.13% 2.464us 2.464us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.37% 6.843us 0.37% 6.843us 6.843us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::contiguous 0.13% 2.314us 0.13% 2.314us 2.314us 2.080us 0.11% 2.080us 2.080us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.03% 0.481us 0.03% 0.481us 0.481us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.07% 1.203us 0.07% 1.203us 1.203us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.02% 0.300us 0.02% 0.300us 0.300us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.02% 0.290us 0.02% 0.290us 0.290us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.02% 0.280us 0.02% 0.280us 0.280us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::empty 0.52% 9.508us 0.52% 9.508us 9.508us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 18.00 Kb 18.00 Kb 1
aten::reshape 0.31% 5.701us 0.96% 17.603us 17.603us 1.056us 0.06% 1.056us 1.056us 0 b 0 b 0 b 0 b 1
aten::view 0.65% 11.902us 0.65% 11.902us 11.902us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::add_ 3.10% 56.837us 3.10% 56.837us 56.837us 17.408us 0.95% 17.408us 17.408us 0 b 0 b 0 b 0 b 1
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.831ms
CUDA time total: 1.830ms
Layers can be selected for individually using the optional paths kwarg. Profiling is ignored for all other layers.
model = torchvision.models.alexnet(pretrained=False)
x = torch.rand([1, 3, 224, 224])
# Layer does not have to be a leaf layer
paths = [("AlexNet", "features", "3"), ("AlexNet", "classifier")]
with torchprof.Profile(model, paths=paths) as prof:
model(x)
print(prof)
Module | Self CPU total | CPU total | Number of Calls
---------------|----------------|-----------|----------------
AlexNet | | |
├── features | | |
│├── 0 | | |
│├── 1 | | |
│├── 2 | | |
│├── 3 | 2.079ms | 8.296ms | 1
│├── 4 | | |
│├── 5 | | |
│├── 6 | | |
│├── 7 | | |
│├── 8 | | |
│├── 9 | | |
│├── 10 | | |
│├── 11 | | |
│└── 12 | | |
├── avgpool | | |
└── classifier | 10.734ms | 11.282ms | 1
├── 0 | | |
├── 1 | | |
├── 2 | | |
├── 3 | | |
├── 4 | | |
├── 5 | | |
└── 6 | | |
Citation
If this software is useful to your research, I would greatly appreciate a citation in your work.
@misc{awwong1-torchprof,
title = {torchprof},
author = {Alexander William Wong},
month = 12,
year = 2020,
url = {https://github.com/awwong1/torchprof}
note = {https://github.com/awwong1/torchprof}
}
LICENSE
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
torchprof-1.3.1.tar.gz
(12.5 kB
view details)
Built Distribution
torchprof-1.3.1-py3-none-any.whl
(11.1 kB
view details)
File details
Details for the file torchprof-1.3.1.tar.gz.
File metadata
- Download URL: torchprof-1.3.1.tar.gz
- Upload date:
- Size: 12.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.53.0 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b09a9b8c422fe7719553e32e14d9c6c565ea456766236ce9ee8d0f8402525020 |
|
| MD5 | 0336d0b760bd2c71b8c2cbb668d7b9ef |
|
| BLAKE2b-256 | 3b9c5c7eabd433b4cc568e4bd8cb4f9897d8e293cf8255d7d914079c1a388f77 |
File details
Details for the file torchprof-1.3.1-py3-none-any.whl.
File metadata
- Download URL: torchprof-1.3.1-py3-none-any.whl
- Upload date:
- Size: 11.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.53.0 CPython/3.7.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b594f13ab33c2d85bca3126c088f447087457756f5287a0290864187a4268a3d |
|
| MD5 | d79168bc0bf91ab4dd530126f0deebf0 |
|
| BLAKE2b-256 | ee38e55eec4f7a61d2b265c4ec9fe7eb713318d7559d30f3240dcd844c5ee30e |
