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Mingzhe Li c1ed0150c5 canonical example of torch.add benchmark (#23402) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23402

This diff tries to make torch.add as a canonical example for op benchmark. Once it lands, we will also modify all other op benchmarks to be uniform with this example. With that, when people are adding new ops, they can copy paste any existing code.

Test Plan:
buck run mode/dev-nosan caffe2/benchmarks/operator_benchmark/pt:add_test -- --iterations 3

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : short

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu
# Input: M: 8, N: 16, K: 32, device: cpu
Forward Execution Time (us) : 146.586

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecuda
# Input: M: 8, N: 16, K: 32, device: cuda
Forward Execution Time (us) : 92.151

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M16_N16_K64_devicecpu
# Input: M: 16, N: 16, K: 64, device: cpu
Forward Execution Time (us) : 428.421

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M16_N16_K64_devicecuda
# Input: M: 16, N: 16, K: 64, device: cuda
Forward Execution Time (us) : 89.811

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M64_N64_K128_devicecpu
# Input: M: 64, N: 64, K: 128, device: cpu
Forward Execution Time (us) : 11857.012

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M64_N64_K128_devicecuda
# Input: M: 64, N: 64, K: 128, device: cuda
Forward Execution Time (us) : 93.918

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu_bwdall
# Input: M: 8, N: 16, K: 32, device: cpu
Backward Execution Time (us) : 990.125

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu_bwd1
# Input: M: 8, N: 16, K: 32, device: cpu
Backward Execution Time (us) : 781.217

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M8_N16_K32_devicecpu_bwd2
# Input: M: 8, N: 16, K: 32, device: cpu
Backward Execution Time (us) : 777.307
```

Reviewed By: zheng-xq

Differential Revision: D16501974

fbshipit-source-id: f1eec010eabf11ce4fcf6cfe6f85cd5241a7022d
2019-10-09 11:24:10 -07:00
..
fastrnns Fix spelling errors (#21665) 2019-06-13 15:21:55 -07:00
framework_overhead_benchmark Added running via throughput benchmark options. (#23077) 2019-07-22 11:27:55 -07:00
operator_benchmark canonical example of torch.add benchmark (#23402) 2019-10-09 11:24:10 -07:00
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