Files
pytorch/benchmarks
Nichols A. Romero 0298ebc97a [ROCm][inductor][dashboard] Add GPT2ForSequenceClassification to use_larger_multiplier_for_smaller_tensor list (#160001)
GPT2ForSequenceClassification Hugging Face (HF) model fails on ROCm for bfloat16. The failure is numerically small.  This PRs adds this model to an exception list for small tensors. The exception list already includes two models. This increases the multiplier factor to 10.0 instead of 3 (default) for this model used in `torch/_dynamo/utils.py`.

In the PR comment below, I include a short analysis of the numerics.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/160001
Approved by: https://github.com/anijain2305, https://github.com/jataylo, https://github.com/jeffdaily
2025-08-18 15:33:30 +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: