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pytorch/benchmarks
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Mingzhe Li c543034531 add cuda sync when ops running on gpu (#29936) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29936

This diff adds synchronization after op execution to ensure all the cuda streams complete.

Test Plan:
```
buck run mode/opt //caffe2/benchmarks/operator_benchmark:benchmark_all_test -- --iterations 1
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M64_N64_K64_cpu
# Input: M: 64, N: 64, K: 64, device: cpu
Forward Execution Time (us) : 154.412

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M64_N64_K64_cuda
# Input: M: 64, N: 64, K: 64, device: cuda
Forward Execution Time (us) : 101.115
...

Reviewed By: hl475

Differential Revision: D18542732

fbshipit-source-id: b979d26a174f488e971074dc1e16b00e17179c80
2019-11-15 18:02:48 -08:00
..
fastrnns Ignore F401 in all __init__.py without putting noqa (#25823) 2019-10-23 15:28:13 -07:00
framework_overhead_benchmark Added running via throughput benchmark options. (#23077) 2019-07-22 11:27:55 -07:00
operator_benchmark add cuda sync when ops running on gpu (#29936) 2019-11-15 18:02:48 -08:00
README.md Move fast rnn benchmark to pytorch/pytorch 2019-03-27 14:46:09 -07:00

README.md

PyTorch Benchmarks

NOTE: This folder is currently work in progress.

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 supercede 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