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 | 1.808ms | 7.171ms | 1.807ms | 7.133ms | 0 b | 0 b | 3.71 Mb | 756.50 Kb | 1
│├── 1 | 49.693us | 72.366us | 49.152us | 72.608us | 0 b | 0 b | 0 b | 0 b | 1
│├── 2 | 78.267us | 162.737us | 77.824us | 147.232us | 0 b | 0 b | 1.60 Mb | 547.00 Kb | 1
│├── 3 | 281.690us | 1.226ms | 506.880us | 1.992ms | 0 b | 0 b | 2.68 Mb | 547.00 Kb | 1
│├── 4 | 29.124us | 41.487us | 29.632us | 43.968us | 0 b | 0 b | 0 b | 0 b | 1
│├── 5 | 56.055us | 121.788us | 55.296us | 108.544us | 0 b | 0 b | 1.11 Mb | 380.50 Kb | 1
│├── 6 | 175.320us | 678.494us | 213.856us | 818.016us | 0 b | 0 b | 8.27 Mb | 253.50 Kb | 1
│├── 7 | 28.434us | 40.487us | 28.672us | 40.960us | 0 b | 0 b | 0 b | 0 b | 1
│├── 8 | 147.237us | 564.774us | 209.920us | 801.984us | 0 b | 0 b | 10.20 Mb | 169.00 Kb | 1
│├── 9 | 28.043us | 40.005us | 27.648us | 40.928us | 0 b | 0 b | 0 b | 0 b | 1
│├── 10 | 141.357us | 541.427us | 177.152us | 671.552us | 0 b | 0 b | 7.08 Mb | 169.00 Kb | 1
│├── 11 | 28.503us | 40.405us | 28.672us | 41.152us | 0 b | 0 b | 0 b | 0 b | 1
│└── 12 | 55.224us | 119.865us | 55.296us | 106.880us | 0 b | 0 b | 324.00 Kb | 108.00 Kb | 1
├── avgpool | 55.585us | 110.217us | 57.344us | 106.464us | 0 b | 0 b | 108.00 Kb | 36.00 Kb | 1
└── classifier | | | | | | | | |
├── 0 | 78.037us | 165.510us | 76.896us | 142.432us | 0 b | 0 b | 171.00 Kb | 45.00 Kb | 1
├── 1 | 399.993us | 419.901us | 795.648us | 795.648us | 0 b | 0 b | 32.00 Kb | 16.00 Kb | 1
├── 2 | 29.937us | 43.122us | 29.664us | 42.944us | 0 b | 0 b | 0 b | 0 b | 1
├── 3 | 53.331us | 120.781us | 52.384us | 99.488us | 0 b | 0 b | 76.00 Kb | 20.00 Kb | 1
├── 4 | 64.231us | 79.479us | 232.448us | 232.448us | 0 b | 0 b | 32.00 Kb | 16.00 Kb | 1
├── 5 | 29.045us | 41.238us | 29.664us | 41.952us | 0 b | 0 b | 0 b | 0 b | 1
└── 6 | 63.289us | 78.356us | 97.280us | 97.280us | 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 | 16.481us | 1.808ms | 14.368us | 1.807ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::convolution | 10.450us | 1.792ms | 10.880us | 1.792ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::_convolution | 41.480us | 1.781ms | 34.240us | 1.782ms | 0 b | 0 b | 756.50 Kb | 0 b | 1
││├── aten::contiguous | 2.514us | 2.514us | 2.304us | 2.304us | 0 b | 0 b | 0 b | 0 b | 1
││├── aten::cudnn_convolution | 1.619ms | 1.657ms | 1.718ms | 1.723ms | 0 b | 0 b | 756.50 Kb | -18.00 Kb | 1
││├── aten::empty | 9.859us | 9.859us | 0.000us | 0.000us | 0 b | 0 b | 18.00 Kb | 18.00 Kb | 1
││├── aten::resize_ | 0.410us | 0.410us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 1
││├── aten::stride | 1.773us | 1.773us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 4
││├── aten::reshape | 6.101us | 17.853us | 1.024us | 1.024us | 0 b | 0 b | 0 b | 0 b | 1
││├── aten::view | 11.752us | 11.752us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 1
││└── aten::add_ | 61.024us | 61.024us | 19.456us | 19.456us | 0 b | 0 b | 0 b | 0 b | 1
│├── 1 | | | | | | | | |
││├── aten::relu_ | 27.020us | 49.693us | 25.696us | 49.152us | 0 b | 0 b | 0 b | 0 b | 1
││└── aten::threshold_ | 22.673us | 22.673us | 23.456us | 23.456us | 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 0.91% 16.481us 100.00% 1.808ms 1.808ms 14.368us 0.80% 1.807ms 1.807ms 0 b 0 b 756.50 Kb 0 b 1
aten::convolution 0.58% 10.450us 99.09% 1.792ms 1.792ms 10.880us 0.60% 1.792ms 1.792ms 0 b 0 b 756.50 Kb 0 b 1
aten::_convolution 2.29% 41.480us 98.51% 1.781ms 1.781ms 34.240us 1.90% 1.782ms 1.782ms 0 b 0 b 756.50 Kb 0 b 1
aten::contiguous 0.21% 3.817us 0.21% 3.817us 3.817us 3.680us 0.20% 3.680us 3.680us 0 b 0 b 0 b 0 b 1
aten::cudnn_convolution 89.53% 1.619ms 91.64% 1.657ms 1.657ms 1.718ms 95.09% 1.723ms 1.723ms 0 b 0 b 756.50 Kb -18.00 Kb 1
aten::empty 0.73% 13.125us 0.73% 13.125us 13.125us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 756.50 Kb 756.50 Kb 1
aten::contiguous 0.15% 2.745us 0.15% 2.745us 2.745us 2.720us 0.15% 2.720us 2.720us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.43% 7.835us 0.43% 7.835us 7.835us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::contiguous 0.14% 2.514us 0.14% 2.514us 2.514us 2.304us 0.13% 2.304us 2.304us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.02% 0.410us 0.02% 0.410us 0.410us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.05% 0.982us 0.05% 0.982us 0.982us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.02% 0.281us 0.02% 0.281us 0.281us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.01% 0.260us 0.01% 0.260us 0.260us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.01% 0.250us 0.01% 0.250us 0.250us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::empty 0.55% 9.859us 0.55% 9.859us 9.859us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 18.00 Kb 18.00 Kb 1
aten::reshape 0.34% 6.101us 0.99% 17.853us 17.853us 1.024us 0.06% 1.024us 1.024us 0 b 0 b 0 b 0 b 1
aten::view 0.65% 11.752us 0.65% 11.752us 11.752us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::add_ 3.37% 61.024us 3.37% 61.024us 61.024us 19.456us 1.08% 19.456us 19.456us 0 b 0 b 0 b 0 b 1
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.808ms
CUDA time total: 1.807ms
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{torchprof,
author = {Alexander William Wong},
title = {torchprof},
howpublished = {github.com},
month = 4,
year = 2020,
note = {A minimal dependency library for layer-by-layer profiling of Pytorch models.}
}
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-1.3.0.tar.gz
(12.3 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
torchprof-1.3.0-py3-none-any.whl
(11.1 kB
view details)
File details
Details for the file torchprof-1.3.0.tar.gz.
File metadata
- Download URL: torchprof-1.3.0.tar.gz
- Upload date:
- Size: 12.3 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 |
76346d30c8a82850dcb9cefc35d3094907eba5f6810fa3bd3f50074e0141be2c
|
|
| MD5 |
5bcd70e95c79f6f1a7a607438c03c78d
|
|
| BLAKE2b-256 |
c9b5be066453caaf2222a12b849be681bd51fe82191aa6938aa7d25c93328c86
|
File details
Details for the file torchprof-1.3.0-py3-none-any.whl.
File metadata
- Download URL: torchprof-1.3.0-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 |
59657bb882a65a834af0a60f1cc0af2a490596a3c270e47a7e086b832b1f463a
|
|
| MD5 |
a2268f9cae9cab2e7b3398d846c4be02
|
|
| BLAKE2b-256 |
a277335dec4f25a931a4dc3aa23d4d9d4714f500baa2444df373e157eb1e1bc5
|
