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()
with torchprof.Profile(model, use_cuda=True) as prof:
model(x)
print(prof.display(show_events=False)) # equivalent to `print(prof)` and `print(prof.display())`
Module | Self CPU total | CPU total | CUDA total | Occurrences
---------------|----------------|-----------|------------|------------
AlexNet | | | |
├── features | | | |
│├── 0 | 1.636ms | 6.466ms | 6.447ms | 1
│├── 1 | 61.320us | 92.700us | 94.016us | 1
│├── 2 | 87.680us | 177.270us | 163.744us | 1
│├── 3 | 291.539us | 1.225ms | 1.966ms | 1
│├── 4 | 34.550us | 48.850us | 50.112us | 1
│├── 5 | 63.220us | 131.670us | 121.888us | 1
│├── 6 | 202.009us | 768.135us | 846.048us | 1
│├── 7 | 40.440us | 58.130us | 59.264us | 1
│├── 8 | 183.129us | 690.816us | 854.016us | 1
│├── 9 | 35.580us | 50.360us | 51.200us | 1
│├── 10 | 167.769us | 631.019us | 701.088us | 1
│├── 11 | 34.450us | 48.730us | 50.048us | 1
│└── 12 | 64.509us | 134.508us | 123.040us | 1
├── avgpool | 67.200us | 131.190us | 122.880us | 1
└── classifier | | | |
├── 0 | 82.110us | 172.480us | 150.848us | 1
├── 1 | 470.078us | 490.848us | 815.104us | 1
├── 2 | 44.269us | 68.289us | 59.424us | 1
├── 3 | 59.339us | 125.977us | 109.568us | 1
├── 4 | 72.319us | 86.819us | 219.136us | 1
├── 5 | 34.780us | 49.340us | 49.152us | 1
└── 6 | 70.070us | 85.290us | 95.232us | 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 | CUDA total | Occurrences
------------------------------------|----------------|-----------|------------|------------
AlexNet | | | |
├── features | | | |
│├── 0 | | | |
││├── aten::conv2d | 16.320us | 1.636ms | 1.636ms | 1
││├── aten::convolution | 11.710us | 1.619ms | 1.620ms | 1
││├── aten::_convolution | 40.950us | 1.607ms | 1.608ms | 1
││├── aten::contiguous | 2.920us | 2.920us | 2.720us | 1
││├── aten::cudnn_convolution | 1.467ms | 1.493ms | 1.554ms | 1
││├── aten::empty | 6.160us | 6.160us | 0.000us | 1
││├── aten::resize_ | 0.490us | 0.490us | 0.000us | 1
││├── aten::stride | 2.380us | 2.380us | 0.000us | 4
││├── aten::reshape | 6.820us | 18.640us | 2.048us | 1
││├── aten::view | 11.820us | 11.820us | 0.000us | 1
││└── aten::add_ | 51.060us | 51.060us | 18.432us | 1
│├── 1 | | | |
││├── aten::relu_ | 29.940us | 61.320us | 61.408us | 1
││└── aten::threshold_ | 31.380us | 31.380us | 32.608us | 1
│├── 2 | | | |
││├── aten::max_pool2d | 14.680us | 87.680us | 86.016us | 1
...
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 # of Calls
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
aten::conv2d 1.00% 16.320us 100.00% 1.636ms 1.636ms 16.032us 0.98% 1.636ms 1.636ms 1
aten::convolution 0.72% 11.710us 99.00% 1.619ms 1.619ms 12.064us 0.74% 1.620ms 1.620ms 1
aten::_convolution 2.50% 40.950us 98.29% 1.607ms 1.607ms 29.088us 1.78% 1.608ms 1.608ms 1
aten::contiguous 0.25% 4.090us 0.25% 4.090us 4.090us 4.032us 0.25% 4.032us 4.032us 1
aten::cudnn_convolution 89.71% 1.467ms 91.27% 1.493ms 1.493ms 1.548ms 94.64% 1.554ms 1.554ms 1
aten::empty 0.28% 4.590us 0.28% 4.590us 4.590us 0.000us 0.00% 0.000us 0.000us 1
aten::contiguous 0.22% 3.530us 0.22% 3.530us 3.530us 3.200us 0.20% 3.200us 3.200us 1
aten::resize_ 0.33% 5.390us 0.33% 5.390us 5.390us 0.000us 0.00% 0.000us 0.000us 1
aten::contiguous 0.18% 2.920us 0.18% 2.920us 2.920us 2.720us 0.17% 2.720us 2.720us 1
aten::resize_ 0.03% 0.490us 0.03% 0.490us 0.490us 0.000us 0.00% 0.000us 0.000us 1
aten::stride 0.09% 1.460us 0.09% 1.460us 1.460us 0.000us 0.00% 0.000us 0.000us 1
aten::stride 0.02% 0.320us 0.02% 0.320us 0.320us 0.000us 0.00% 0.000us 0.000us 1
aten::stride 0.02% 0.300us 0.02% 0.300us 0.300us 0.000us 0.00% 0.000us 0.000us 1
aten::stride 0.02% 0.300us 0.02% 0.300us 0.300us 0.000us 0.00% 0.000us 0.000us 1
aten::empty 0.38% 6.160us 0.38% 6.160us 6.160us 0.000us 0.00% 0.000us 0.000us 1
aten::reshape 0.42% 6.820us 1.14% 18.640us 18.640us 2.048us 0.13% 2.048us 2.048us 1
aten::view 0.72% 11.820us 0.72% 11.820us 11.820us 0.000us 0.00% 0.000us 0.000us 1
aten::add_ 3.12% 51.060us 3.12% 51.060us 51.060us 18.432us 1.13% 18.432us 18.432us 1
--------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 1.636ms
CUDA time total: 1.636ms
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 | CUDA total | Occurrences
---------------|----------------|-----------|------------|------------
AlexNet | | | |
├── features | | | |
│├── 0 | | | |
│├── 1 | | | |
│├── 2 | | | |
│├── 3 | 2.908ms | 11.604ms | 0.000us | 1
│├── 4 | | | |
│├── 5 | | | |
│├── 6 | | | |
│├── 7 | | | |
│├── 8 | | | |
│├── 9 | | | |
│├── 10 | | | |
│├── 11 | | | |
│└── 12 | | | |
├── avgpool | | | |
└── classifier | 12.311ms | 13.077ms | 0.000us | 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.}
}
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