pytorch/benchmarks
Bert Maher 10e11dbdcd Reland D29190420: [nnc][tests] Tests and benchmarks for computeSum (#60550)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60550

Original commit changeset: ed655497a981

Whatever gcc version OSS Bazel uses wasn't happy move-constructing the
SimpleIREvaluator, so use a unique_ptr instead.

Test Plan:
CI.  Hope that the gcc version used by OSS Bazel build is
happier with this (it should be), since actually testing it locally is
an intractable pain.

Reviewed By: navahgar

Differential Revision: D29333116

fbshipit-source-id: c3e4b5d8c91eb96a43ae5315a01ca0c0f4d4a99d
2021-06-23 10:50:03 -07:00
..
cpp Reland D29190420: [nnc][tests] Tests and benchmarks for computeSum (#60550) 2021-06-23 10:50:03 -07:00
distributed Open json config file in context manager (#58077) 2021-05-26 08:58:40 -07:00
fastrnns Add lint for unqualified noqa (#56272) 2021-04-19 13:16:18 -07:00
framework_overhead_benchmark
functional_autograd_benchmark Add lint for unqualified type: ignore (#56290) 2021-04-21 08:07:23 -07:00
instruction_counts Allow instruction counting to use shared memory as a staging ground. (And a couple other tweaks.) (#56711) 2021-05-12 20:37:41 -07:00
operator_benchmark [Pytorch benchmark] Add BMM benchmark (#59595) 2021-06-10 08:24:29 -07:00
overrides_benchmark Remove legacy constructor calls from pytorch codebase. (#54142) 2021-04-11 15:45:17 -07:00
profiler_benchmark Use libkineto in profiler (#46470) 2020-11-25 04:32:16 -08:00
record_function_benchmark Fix D23995953 import. 2020-09-29 19:30:23 -07:00
serialization
sparse Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
static_runtime [static runtime] out variant for full_like (#58079) 2021-05-20 16:17:40 -07:00
tensorexpr [nnc] Added micro-benchmark to show perf improvement with cat subgraph optimization (#59581) 2021-06-18 14:32:09 -07:00
compare-fastrnn-results.py
compare.sh
README.md Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
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
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