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Taylor Robie 0d81528a47 Definition infrastructure for instruction count ubenchmarks (#53296) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53296

Part 1 of the instruction count microbenchmarks. This PR is focused on benchmark definition machinery. (Though you can run `main.py` to see it in action.) A summary of the system is given in the README.

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D26907092

Pulled By: robieta

fbshipit-source-id: 0f61457b3ce89aa59a06bf1f0e7a74ccdbf17090
2021-03-23 21:59:46 -07:00
..
cpp/tensorexpr [jit][tensorexpr] Added aten::batch_norm into fuser when in inference mode (#54204) 2021-03-23 04:41:52 -07:00
distributed Forbid trailing whitespace (#53406) 2021-03-05 17:22:55 -08:00
fastrnns Forbid trailing whitespace (#53406) 2021-03-05 17:22:55 -08:00
framework_overhead_benchmark
functional_autograd_benchmark Fix typo in torchvision_models.py (#53968) 2021-03-15 11:02:06 -07:00
instruction_counts Definition infrastructure for instruction count ubenchmarks (#53296) 2021-03-23 21:59:46 -07:00
operator_benchmark fix broken quantization_test in operator_benchmark (#53153) 2021-03-08 12:12:57 -08:00
overrides_benchmark
profiler_benchmark
record_function_benchmark
serialization
sparse/dlmc matmul performance benchmarks (#51647) 2021-03-14 00:25:45 -08:00
static_runtime [Static Runtime] Fix bug in reshape_copy (#54467) 2021-03-22 22:20:55 -07:00
tensorexpr [NNC] Implementation for aten::cat without conditionals. (#53128) 2021-03-07 22:57:02 -08:00
compare-fastrnn-results.py
compare.sh
README.md
upload_scribe.py

README.md

PyTorch Benchmarks

NOTE: This folder is currently work in progress.

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