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Bert Maher f2e569461b [te] Tiled (m=32 x n=32) gemm benchmark (#45905) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45905

Test Plan: Imported from OSS

Reviewed By: SplitInfinity

Differential Revision: D24142402

Pulled By: bertmaher

fbshipit-source-id: b39e18b6985ee1c1f654fba4498ed91ff14d8d5f
2020-10-06 16:57:31 -07:00
..
cpp/tensorexpr [te] Tiled (m=32 x n=32) gemm benchmark (#45905) 2020-10-06 16:57:31 -07:00
distributed/ddp Add distributed data parallel benchmark tool (#35198) 2020-04-08 15:07:03 -07:00
fastrnns Benchmarks: tweak PE config settings. (#45349) 2020-09-26 23:13:29 -07:00
framework_overhead_benchmark Remove py2 compatible future imports (#44735) 2020-09-16 12:55:57 -07:00
functional_autograd_benchmark Reland of benchmark code (#43428) 2020-08-24 13:27:26 -07:00
operator_benchmark [op-bench] fix jit mode (#45774) 2020-10-05 09:29:41 -07:00
overrides_benchmark Add __torch_function__ for methods (#37091) 2020-08-05 20:44:13 -07:00
profiler_benchmark Source code level attribution in profiler (#43898) 2020-09-30 00:57:35 -07:00
record_function_benchmark Fix D23995953 import. 2020-09-29 19:30:23 -07:00
serialization [JIT] Make new zip serialization for torch save/load significantly (~70%) faster (#38379) 2020-05-29 01:56:18 -07:00
static_runtime [StaticRuntime] Integrate Static Runtime into PyTorchPredictor (#45640) 2020-10-02 23:03:05 -07:00
tensorexpr [WIP][JIT] Add benchmarking support of NV Fuser with FP16 dtype support (#44101) 2020-09-15 15:10:49 -07:00
compare-fastrnn-results.py Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
compare.sh Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
README.md
upload_scribe.py Benchmarks: make fuser and executor configurable from command line. (#44291) 2020-09-09 11:59:35 -07:00

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