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Summary: Adding NEON specializations of Vectorized<T> for int8, int16, int32 and int64. Correcness has been checked using test_ops.py and the comprehensive torch test operator_benchmark_test.py has been enhanced by adding cases of bitwise operations, boolean ops and integer ops. The benchmark, which uses the PyTorch API, shows significant enhancements in a wide variety of operations: Before: bitwise xor: 779.882us boolean any: 636.209us boolean all: 538.621us integer mul: 304.457us integer asr: 447.997us After: bitwise xor: 680.221us ---> 15% higher throughput boolean any: 391.468us ---> 63% higher throughput boolean all: 390.189us ---> 38% higher throughput integer mul: 193.532us ---> 57% higher throughput integer asr: 179.929us---> 149% higher throughput Test Plan: Correctness: buck2 test @mode/opt //caffe2/test:test_ops buck2 test @mode/opt //caffe2/test:torch buck2 test @mode/opt //caffe2/test/distributed/launcher/fb:fb_run_test Performance: buck2 run mode/opt //caffe2/benchmarks/operator_benchmark/fb:operator_benchmark_test Differential Revision: D84424638 Pull Request resolved: https://github.com/pytorch/pytorch/pull/165273 Approved by: https://github.com/malfet
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
python -m pip install torch torchvision
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python -m pip install --no-build-isolation -v -e .
# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"
Benchmark List
Please refer to each subfolder to discover each benchmark suite. Links are provided where descriptions exist: