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Measure neural network device specific metrics (latency, flops, etc.)

Project description

torchprof

PyPI version CircleCI

Attention! This library is deprecated due to the PyTorch 1.9 changes to the torch profiler. Please use the official profiler. Thank you!

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}
}

LICENSE

MIT

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