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Edward Z. Yang 9eab13fc90 Reenable llama benchmark (#100877) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/100877
Approved by: https://github.com/albanD
2023-05-09 01:12:54 +00:00
..
cpp moving nvfuser benchmark to third_party/nvfuser (#96725) 2023-03-21 23:19:15 +00:00
distributed [BE]: Update flake8 and plugins and fix bugs (#97795) 2023-03-28 23:51:55 +00:00
dynamo Reenable llama benchmark (#100877) 2023-05-09 01:12:54 +00:00
fastrnns [BE] [1/3] Rewrite super() calls in caffe2 and benchmarks (#94587) 2023-02-11 18:19:48 +00:00
framework_overhead_benchmark [BE] [1/3] Rewrite super() calls in caffe2 and benchmarks (#94587) 2023-02-11 18:19:48 +00:00
functional_autograd_benchmark [BE] [1/3] Rewrite super() calls in caffe2 and benchmarks (#94587) 2023-02-11 18:19:48 +00:00
fuser
instruction_counts [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
nested Use tensor cores for NT bmm (#86856) 2022-11-02 21:51:40 +00:00
operator_benchmark [BE] Enable C419 rule for any all shortcircuiting (#99890) 2023-04-25 15:02:13 +00:00
overrides_benchmark
profiler_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
record_function_benchmark [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
serialization [PyTorch] Add annotation_str benchmark (#96496) 2023-03-23 04:18:07 +00:00
sparse [BE] Enable flake8-comprehension rule C417 (#97880) 2023-03-30 14:34:24 +00:00
static_runtime Fix flaky StaticRuntime.Nonzero test (#94418) 2023-02-16 21:25:15 +00:00
tensorexpr Fix usages of contextmanager without finally (#96170) 2023-03-08 20:59:27 +00:00
transformer [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00
compare-fastrnn-results.py
compare.sh
README.md
upload_scribe.py [BE] Prefer dash over underscore in command-line options (#94505) 2023-02-09 20:16:49 +00:00

README.md

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