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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/74987 Add specializations to `prim::If` operator at runtime to save resources when some of subblocks are empty Test Plan: `buck build //caffe2:torch-cpp-cpu` `buck test //caffe2/benchmarks/static_runtime/...` Add unit test: `buck test //caffe2/benchmarks/static_runtime:static_runtime_cpptest -- StaticRuntime.EmptyIfBlock` Reviewed By: mikeiovine Differential Revision: D35262952 fbshipit-source-id: 324f88471f33f035f4d8a9b212716530d8e59df2 (cherry picked from commit 2db1b1a6833b1376fa376f54791effc8e12fb77f) |
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| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| 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