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Edvard Ghazaryan e62cdae469 Static Runtime support for aten::matmul (#57291) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57291

aten::matmul support for static runtime

Test Plan: buck test caffe2/benchmarks/static_runtime:static_runtime_cpptest -- IndividualOps_Binary_MatMul

Reviewed By: hlu1

Differential Revision: D28099671

fbshipit-source-id: 784035060c8c24953df47ca4227d2bca5094da22
2021-04-30 10:49:55 -07:00
..
cpp Make PyTorch code-base clang-tidy compliant (#56892) 2021-04-28 14:10:25 -07:00
distributed Add lint for unqualified type: ignore (#56290) 2021-04-21 08:07:23 -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 Add lint for unqualified type: ignore (#56290) 2021-04-21 08:07:23 -07:00
operator_benchmark Add lint for unqualified noqa (#56272) 2021-04-19 13:16:18 -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
serialization
sparse Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
static_runtime Static Runtime support for aten::matmul (#57291) 2021-04-30 10:49:55 -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 Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
upload_scribe.py

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