Pytorch profiler

Pytorchures is a simple model profiler intended for any pytorch model. It measures execution time of model layers individually. Every layer of a model is wrapped with timing class which measures latency when called.

TLDR;

Install

pip install pytorchures

Run

from pytorchures import TimedLayer

model = TimedLayer(model)

_output_ = model(inputs)

profiling_data = model.get_timings()

with open(profiling_filename, "w") as f:
    json.dump(profiling_data, f, indent=4)

One layer extract of sample output .json

        {
            "module_name": "Conv2dNormActivation",
            "device_type": "cuda",
            "execution_times_ms": [
                110.07571220397949,
                0.8006095886230469,
                0.8091926574707031
            ],
            "mean_time_ms": 37.22850481669108,
            "median_time_ms": 0.8091926574707031,
        }

Setup

This repo was developed under WSL 2 running Ubuntu 20.04 LTS, and Ubuntu 22.04 LTS. The editor of choice is VS Code.

Install python

The code was tested for Python 3.10, if you want to run Python 3.11 or other version please subsitute the python version in the command below.

In case of running new WSL below are required packages and commands.

sudo apt-get update

sudo apt-get install python3.10

sudo apt-get install python3.10-venv

Install for PIL image.show() to work on WSL sudo apt install imagemagick

Install relevant VS Code extentions.

If you choose to use the recommended VS Code as editor please install the extensions from extensions.json.

Create virtual environment

Create venv

python3.10 -m venv .venv

To activate venv type - VS Code should automatically detect your new venv, so select it as your default interpreter.

source venv/bin/activate

Install package in editable mode

In order to be able to develop and run the code install this repo in editable mode.

pip install -e .

To install in editable mode with additional dependencies for development use the command below.

pip install -e .[dev]

Running

The entry point to profiling the sample object detection models is run_profiling.py file.

Examples

Running on CPU python pytorchures/run_profiling.py --device 'cpu' --nr_images 3

Running on GPU python pytorchures/run_profiling.py --device 'cuda' --nr_images 3

The script will print CPU wall time of every layer encountered in the model. Values are printed in a nested manner.

TimedLayer wrapper

from pytorchures import TimedLayer

model = TimedLayer(model)

_output = model(inputs)

profiling_data = model.get_timings()

with open(profiling_filename, "w") as f:
    json.dump(profiling_data, f, indent=4)

In the code above the model and all it's sublayers are recursively wrapped with TimedLayer class which measures execution times when a layers are called and stores them for every time the model is called. Execution times of every wrapped layer are retrieved as hierarchical dictionary using model.get_timings(). This dictionary can be saved to json file.

Testing

All tests are located in 'tests' folder. Please follow Arange-Act-Assert pattern for all tests. The tests should load in the test explorer.

Formatting

This repo uses 'Black' code formatter.

Publishing to Pypi

Build the package. This command will create a dist folder with pytorchures package as .whl and tar.gz.

python -m build

Check if the build pacakge were build correctly.

twine check dist/*

Optionally upload the new package to testpypi server.

twine upload -r testpypi dist/*

To test the package from use testpypi the command:

pip install --index-url https://test.pypi.org/simple/ pytorchures

Upload the new package to production pypi server.

twine upload dist/*