mirror of
https://github.com/zebrajr/pytorch.git
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a0495b3cdb
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/67001 The overload of `operator()` taking `std::vector<at::Tensor>` was only used for testing. In a diff following this one, I will add a new overload that takes `std::vector<c10::IValue> args` and no `kwargs` so we can avoid default-constructing `kwargs` everywhere. This new overload will probably take a forwarding reference, so to avoid problems with overloading on forwarding reference and simplify the interface, it's best to remove this unused one. Test Plan: `buck test caffe2/benchmarks/static_runtime/...` `buck test caffe2/test:static_runtime` Reviewed By: hlu1 Differential Revision: D31821990 fbshipit-source-id: 6d2e4a75ca4abe6e262651532eb96c3b274c6f4a |
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| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| instruction_counts | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| 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