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## Summary
This PR updates the expected accuracy CSV files for inductor benchmarks based on CI results from PyTorch commit 3c98eef883.
These files serve as reference points for dynamo/inductor CI to track:
- Graph breaks
- Model accuracy
## Changes
- Updated CUDA expected accuracy files in `benchmarks/dynamo/ci_expected_accuracy/`
- Updated ROCm expected accuracy files in `benchmarks/dynamo/ci_expected_accuracy/rocm/`
## Test Plan
- [ ] Verify that the CI jobs pass with the updated expected accuracy files
- [ ] Review the diff to ensure changes are reasonable and expected
- [ ] Check that no unexpected regressions are being marked as "expected"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/171533
Approved by: https://github.com/jataylo, https://github.com/atalman
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: