A test suite to measure TorchScript parity with PyTorch on many nn.Modules
crawled from popular GitHub projects.
-
Install conda with python>=3.8 and create/activate a conda environment
-
Install requirements:
conda install pip
pip install -r requirements.txt
conda install pytorch torchvision cpuonly -c pytorch-nightly
- Run
python main.py, you should see an output like:
TorchScript ParityBench:
total passing score
projects 1172 346 29.5%
tests 8292 4734 57.1%
A file errors.csv is generated containing the top error messages and example
generated/* files to reproduce those errors.
WARNING: this will download 10+ gigabytes of code from crawling github and take days to complete. It is likely not necessary for you to do this.
python main.py --download
python main.py --generate-all
You can limit number of github projects to download for testing and running on a smaller set of github repos
python main.py --download --download-dir <folder path> --limit 10
You can generate tests for one project folder -g. This will extract nn modules from that project and generate a test script --tests-dir
python main.py -g <folder path> --tests-dir <folder path>
You can evaluate one generated test script -e and try export the module to onnx --onnxdir
python main.py -e <test.py file> --onnxdir <folder path>
You can evaluate using different compile mode, e.g, dynamo(default) or torchscript.
python main.py -e <test.py file> --compile_mode dynamo
You can evaluate using different dynamo backends provided in torch._dynamo, please refer torch._dynamo.list_backends().
python main.py -e <test.py file> --backend eager
You can evaluate using cuda(default) or cpu.
python main.py -e <test.py file> --device cuda
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