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 | 1.671ms | 6.589ms | 6.701ms | 1
│├── 1 | 62.430us | 62.430us | 63.264us | 1
│├── 2 | 62.909us | 109.948us | 112.640us | 1
│├── 3 | 225.389us | 858.376us | 1.814ms | 1
│├── 4 | 18.999us | 18.999us | 19.456us | 1
│├── 5 | 29.560us | 52.720us | 54.272us | 1
│├── 6 | 136.959us | 511.216us | 707.360us | 1
│├── 7 | 18.480us | 18.480us | 18.624us | 1
│├── 8 | 84.380us | 300.700us | 590.688us | 1
│├── 9 | 18.249us | 18.249us | 17.632us | 1
│├── 10 | 81.289us | 289.946us | 470.016us | 1
│├── 11 | 17.850us | 17.850us | 18.432us | 1
│└── 12 | 29.350us | 52.260us | 52.288us | 1
├── avgpool | 41.840us | 70.840us | 76.832us | 1
└── classifier | | | |
├── 0 | 66.400us | 122.110us | 125.920us | 1
├── 1 | 293.658us | 293.658us | 664.704us | 1
├── 2 | 17.600us | 17.600us | 18.432us | 1
├── 3 | 27.920us | 49.030us | 51.168us | 1
├── 4 | 40.590us | 40.590us | 208.672us | 1
├── 5 | 17.570us | 17.570us | 18.432us | 1
└── 6 | 40.489us | 40.489us | 81.920us | 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 | | | |
││├── conv2d | 13.370us | 1.671ms | 1.698ms | 1
││├── convolution | 12.730us | 1.658ms | 1.685ms | 1
││├── _convolution | 30.660us | 1.645ms | 1.673ms | 1
││├── contiguous | 6.970us | 6.970us | 7.136us | 1
││└── cudnn_convolution | 1.608ms | 1.608ms | 1.638ms | 1
│├── 1 | | | |
││└── relu_ | 62.430us | 62.430us | 63.264us | 1
│├── 2 | | | |
││├── max_pool2d | 15.870us | 62.909us | 63.488us | 1
││└── max_pool2d_with_indices | 47.039us | 47.039us | 49.152us | 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 total % Self CPU total CPU total % CPU total CPU time avg CUDA total % CUDA total CUDA time avg Number of Calls Input Shapes
--------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -----------------------------------
conv2d 0.80% 13.370us 100.00% 1.671ms 1.671ms 25.34% 1.698ms 1.698ms 1 []
convolution 0.76% 12.730us 99.20% 1.658ms 1.658ms 25.15% 1.685ms 1.685ms 1 []
_convolution 1.83% 30.660us 98.44% 1.645ms 1.645ms 24.97% 1.673ms 1.673ms 1 []
contiguous 0.42% 6.970us 0.42% 6.970us 6.970us 0.11% 7.136us 7.136us 1 []
cudnn_convolution 96.19% 1.608ms 96.19% 1.608ms 1.608ms 24.44% 1.638ms 1.638ms 1 []
--------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- -----------------------------------
Self CPU time total: 1.671ms
CUDA time total: 6.701ms
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 | 3.189ms | 12.717ms | 0.000us | 1
│├── 4 | | | |
│├── 5 | | | |
│├── 6 | | | |
│├── 7 | | | |
│├── 8 | | | |
│├── 9 | | | |
│├── 10 | | | |
│├── 11 | | | |
│└── 12 | | | |
├── avgpool | | | |
└── classifier | 13.403ms | 14.011ms | 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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