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Copying what @kadeng did. ``` FINAL results... Experiment group: bmm (BS: 8, 1024x1024, 1024x1024) torch.float16 +-----------------------+--------------------+----------------------+---------------------+ | name | forward_time (us) | compilation_time (s) | perf_over_aten (%) | +-----------------------+--------------------+----------------------+---------------------+ | aten | 44.454172253608704 | 3.0991086587309837 | NA | | triton | 44.06978189945221 | 0.07496077567338943 | -0.8646890374284049 | | triton_persistent_tma | 43.598245829343796 | 0.06154991965740919 | -1.9254130284597197 | | cutlass_lvl_default | 39.91834074258804 | 0.056073310784995556 | -10.20338762612423 | +-----------------------+--------------------+----------------------+---------------------+ Experiment group: bmm (BS: 8, 1024x1024, 1024x1024) torch.bfloat16 +-----------------------+-------------------+----------------------+---------------------+ | name | forward_time (us) | compilation_time (s) | perf_over_aten (%) | +-----------------------+-------------------+----------------------+---------------------+ | aten | 49.05610531568527 | 0.160279156640172 | NA | | triton | 43.97720843553543 | 0.0660805031657219 | -10.353241145961718 | | triton_persistent_tma | 43.94153505563736 | 0.061738294549286366 | -10.425960697724962 | | cutlass_lvl_default | 40.2066633105278 | 0.034127906896173954 | -18.039430460713596 | +-----------------------+-------------------+----------------------+---------------------+ Average edge over aten (max(-edge, 0), higher is better): triton: 5.608965091695062 (from 2 valid values) triton_persistent_tma: 6.175686863092341 (from 2 valid values) cutlass_lvl_default: 14.121409043418913 (from 2 valid values) ``` Differential Revision: [D73625766](https://our.internmc.facebook.com/intern/diff/D73625766/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/152163 Approved by: https://github.com/jingsh
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
conda install pytorch torchvision -c pytorch
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop
# 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: