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 | 1.832ms | 7.264ms | 1.831ms | 7.235ms | 0 b | 0 b | 756.50 Kb | 3.71 Mb | 1
│├── 1 | 51.858us | 76.564us | 51.296us | 76.896us | 0 b | 0 b | 0 b | 0 b | 1
│├── 2 | 75.993us | 157.855us | 77.600us | 145.184us | 0 b | 0 b | 547.00 Kb | 1.60 Mb | 1
│├── 3 | 263.526us | 1.142ms | 489.472us | 1.918ms | 0 b | 0 b | 547.00 Kb | 2.68 Mb | 1
│├── 4 | 28.824us | 41.197us | 28.672us | 43.008us | 0 b | 0 b | 0 b | 0 b | 1
│├── 5 | 55.264us | 120.016us | 55.200us | 106.400us | 0 b | 0 b | 380.50 Kb | 1.11 Mb | 1
│├── 6 | 175.591us | 681.011us | 212.896us | 818.080us | 0 b | 0 b | 253.50 Kb | 8.27 Mb | 1
│├── 7 | 27.622us | 39.494us | 26.848us | 39.296us | 0 b | 0 b | 0 b | 0 b | 1
│├── 8 | 140.204us | 537.162us | 204.832us | 781.280us | 0 b | 0 b | 169.00 Kb | 10.20 Mb | 1
│├── 9 | 27.532us | 39.364us | 26.816us | 39.136us | 0 b | 0 b | 0 b | 0 b | 1
│├── 10 | 138.621us | 530.929us | 171.008us | 650.432us | 0 b | 0 b | 169.00 Kb | 7.08 Mb | 1
│├── 11 | 27.712us | 39.645us | 27.648us | 39.936us | 0 b | 0 b | 0 b | 0 b | 1
│└── 12 | 54.813us | 118.823us | 55.296us | 107.360us | 0 b | 0 b | 108.00 Kb | 324.00 Kb | 1
├── avgpool | 58.329us | 116.577us | 58.368us | 111.584us | 0 b | 0 b | 36.00 Kb | 108.00 Kb | 1
└── classifier | | | | | | | | |
├── 0 | 79.169us | 167.495us | 78.848us | 145.408us | 0 b | 0 b | 45.00 Kb | 171.00 Kb | 1
├── 1 | 404.070us | 423.755us | 793.600us | 793.600us | 0 b | 0 b | 16.00 Kb | 32.00 Kb | 1
├── 2 | 30.097us | 43.512us | 29.792us | 43.904us | 0 b | 0 b | 0 b | 0 b | 1
├── 3 | 53.390us | 121.042us | 53.248us | 99.328us | 0 b | 0 b | 20.00 Kb | 76.00 Kb | 1
├── 4 | 64.622us | 79.902us | 236.544us | 236.544us | 0 b | 0 b | 16.00 Kb | 32.00 Kb | 1
├── 5 | 28.854us | 41.067us | 28.544us | 41.856us | 0 b | 0 b | 0 b | 0 b | 1
└── 6 | 62.258us | 77.356us | 95.232us | 95.232us | 0 b | 0 b | 4.00 Kb | 8.00 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 | 15.630us | 1.832ms | 14.176us | 1.831ms | 0 b | 0 b | 0 b | 756.50 Kb | 1
││├── aten::convolution | 9.768us | 1.816ms | 9.056us | 1.817ms | 0 b | 0 b | 0 b | 756.50 Kb | 1
││├── aten::_convolution | 45.005us | 1.807ms | 34.432us | 1.808ms | 0 b | 0 b | 0 b | 756.50 Kb | 1
││├── aten::contiguous | 8.738us | 8.738us | 8.480us | 8.480us | 0 b | 0 b | 0 b | 0 b | 3
││├── aten::cudnn_convolution | 1.647ms | 1.683ms | 1.745ms | 1.750ms | 0 b | 0 b | -18.00 Kb | 756.50 Kb | 1
││├── aten::empty | 21.249us | 21.249us | 0.000us | 0.000us | 0 b | 0 b | 774.50 Kb | 774.50 Kb | 2
││├── aten::resize_ | 7.635us | 7.635us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 2
││├── aten::stride | 1.902us | 1.902us | 0.000us | 0.000us | 0 b | 0 b | 0 b | 0 b | 4
││├── aten::reshape | 6.081us | 17.833us | 2.048us | 2.048us | 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_ | 57.248us | 57.248us | 18.432us | 18.432us | 0 b | 0 b | 0 b | 0 b | 1
│├── 1 | | | | | | | | |
││├── aten::relu_ | 27.152us | 51.858us | 25.696us | 51.296us | 0 b | 0 b | 0 b | 0 b | 1
││└── aten::threshold_ | 24.706us | 24.706us | 25.600us | 25.600us | 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 0.85% 15.630us 100.00% 1.832ms 1.832ms 14.176us 0.77% 1.831ms 1.831ms 0 b 0 b 756.50 Kb 0 b 1
aten::convolution 0.53% 9.768us 99.15% 1.816ms 1.816ms 9.056us 0.49% 1.817ms 1.817ms 0 b 0 b 756.50 Kb 0 b 1
aten::_convolution 2.46% 45.005us 98.61% 1.807ms 1.807ms 34.432us 1.88% 1.808ms 1.808ms 0 b 0 b 756.50 Kb 0 b 1
aten::contiguous 0.20% 3.707us 0.20% 3.707us 3.707us 3.680us 0.20% 3.680us 3.680us 0 b 0 b 0 b 0 b 1
aten::cudnn_convolution 89.90% 1.647ms 91.86% 1.683ms 1.683ms 1.745ms 95.27% 1.750ms 1.750ms 0 b 0 b 756.50 Kb -18.00 Kb 1
aten::empty 0.66% 12.102us 0.66% 12.102us 12.102us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 756.50 Kb 756.50 Kb 1
aten::contiguous 0.15% 2.706us 0.15% 2.706us 2.706us 2.560us 0.14% 2.560us 2.560us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.39% 7.164us 0.39% 7.164us 7.164us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::contiguous 0.13% 2.325us 0.13% 2.325us 2.325us 2.240us 0.12% 2.240us 2.240us 0 b 0 b 0 b 0 b 1
aten::resize_ 0.03% 0.471us 0.03% 0.471us 0.471us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::stride 0.06% 1.092us 0.06% 1.092us 1.092us 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::stride 0.01% 0.270us 0.01% 0.270us 0.270us 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::empty 0.50% 9.147us 0.50% 9.147us 9.147us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 18.00 Kb 18.00 Kb 1
aten::reshape 0.33% 6.081us 0.97% 17.833us 17.833us 2.048us 0.11% 2.048us 2.048us 0 b 0 b 0 b 0 b 1
aten::view 0.64% 11.752us 0.64% 11.752us 11.752us 0.000us 0.00% 0.000us 0.000us 0 b 0 b 0 b 0 b 1
aten::add_ 3.12% 57.248us 3.12% 57.248us 57.248us 18.432us 1.01% 18.432us 18.432us 0 b 0 b 0 b 0 b 1
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.832ms
CUDA time total: 1.831ms
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 | 3.162ms | 12.626ms | 1
│├── 4 | | |
│├── 5 | | |
│├── 6 | | |
│├── 7 | | |
│├── 8 | | |
│├── 9 | | |
│├── 10 | | |
│├── 11 | | |
│└── 12 | | |
├── avgpool | | |
└── classifier | 11.398ms | 12.130ms | 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}
}
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