Operator-level performance microbenchmarks #18740
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@pytorchbot retest this please |
Summary: Pull Request resolved: #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. Differential Revision: D13887111 fbshipit-source-id: c396152e08f689a163ef3e7ca5ebdf9c10338529
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This pull request has been merged in 5f5a2aa. |
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Summary:
Test utilities for writing Caffe2/PyTorch performance microbenchmarks. Brief description of the file structure
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:
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.
Differential Revision: D13887111