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For comparing NativeRT and TorchScript. We add `torchscript-jit-trace` as an option in the benchmark. With this option, we can run trace a model and run inference with the traced module using TorchScript interpreter ``` python ./benchmarks/dynamo/huggingface.py --performance --inference --torchscript-jit-trace python ./benchmarks/dynamo/timm_models.py --performance --inference --torchscript-jit-trace python ./benchmarks/dynamo/torchbench.py --performance --inference --torchscript-jit-trace ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/161223 Approved by: https://github.com/huydhn
PyTorch Benchmarks
This folder contains scripts that produce reproducible timings of various PyTorch features.
It also provides mechanisms to compare PyTorch with other frameworks.
Setup environment
Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
# Install torchvision. It comes with the pytorch stable release binary
python -m pip install torch torchvision
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python -m pip install --no-build-isolation -v -e .
# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"
Benchmark List
Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist: