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This adds a PR time benchmark that checks for runtime overhead on a very small graph. This will help track regressions in runtime overhead. Example Results: ``` runtime_overhead_inductor,instruction_count,222645 runtime_overhead_inductor_inference_mode,instruction_count,234998 runtime_overhead_inductor_requires_grad,instruction_count,293556 runtime_overhead_inductor_requires_grad_backward,instruction_count,78181 runtime_overhead_inductor_dynamic,instruction_count,234870 runtime_overhead_inductor_inference_mode_dynamic,instruction_count,248711 runtime_overhead_inductor_requires_grad_dynamic,instruction_count,309979 runtime_overhead_inductor_requires_grad_backward_dynamic,instruction_count,77599 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/163866 Approved by: https://github.com/jansel, https://github.com/mlazos, https://github.com/anijain2305 |
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|---|---|---|
| .. | ||
| data | ||
| distributed/ddp | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inductor_backends | ||
| inference | ||
| instruction_counts | ||
| nested | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| transformer | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
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: