Mingzhe Li 5f5a2aaab9 Operator-level performance microbenchmarks (#18740) ...

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18740

Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure

* benchmark_core.py : core utiltiites for running microbenchmark tests
* benchmark_caffe2.py : Caffe2 specific benchmark utilitites
* benchmark_pytorch.py: PyTorch specific benchmark utilities
* benchmark_runner.py : Main function. Currently it can run the microbenchmark tests in a stand-alone mode. The next step is to have this integrate with AI-PEP.

The utilities are located at https://github.com/pytorch/pytorch/tree/master/test to have access to both Caffe2/PyTorch Python's frontend.

Include two operator microbenchmarks; support both Caffe2/PyTorch:
* MatMul
* Add

Reference: PyTorch benchmarks : https://github.com/pytorch/benchmark/tree/master/timing/python. In this work, we start with two example binary operators MatMul and Add, but eventually we should to cover unary operators like in the PyTorch benchmark repo.

Reviewed By: zheng-xq

Differential Revision: D13887111

fbshipit-source-id: b7a56b95448c9ec3e674b0de0ffb96af4439bfce

2019-04-02 17:06:19 -07:00

0 lines

Python

Raw Blame History