Files
pytorch/benchmarks
Yiming Zhou 9d882fd9ff [benchmark] Add torchscript jit.trace to benchmark option (#161223)
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
2025-08-22 21:38:28 +00:00
..
2025-04-27 09:56:42 +00:00

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: