Files
pytorch/benchmarks
Han, Xu bfcdbd0a97 fix wrong accuracy_status when exception. (#165731)
When I debug `XPU` accruacy issue, I found the script output wrong accuracy_status.
When the `try` block raise an exception, we should process the exception, but not return the `fail_accuracy`.

Before fixing, it returned as `fail_accuracy`:
<img width="1109" height="216" alt="image" src="https://github.com/user-attachments/assets/385c354f-fbf6-48e4-a1be-3e37e987341b" />

After fixing, it returned the exception message:
<img width="1101" height="292" alt="image" src="https://github.com/user-attachments/assets/f18c0e3c-8358-4ec7-a6bb-c2e01b69d27f" />

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165731
Approved by: https://github.com/Stonepia, https://github.com/chuanqi129, https://github.com/Lucaskabela
2025-10-17 16:37:06 +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: