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Daya Khudia bf7ebc5a53 Set number of threads for operator_benchmarks (#27010) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27010

Setting OMP_NUM_THREADS programmatically doesn't do the right thing because initialization is already done. Fixing this by calling torch.set_num_threads explicitly.

Passing --omp_num_threads works as expected now.

In dir benchmarks/operator_benchmark/

python -m pt.qconv_test --tag_filter resnext101_32x4 --wipe_cache --test_name QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0 --omp_num_threads 1
```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : None

# Benchmarking PyTorch: QConv2d
# Mode: Eager
# Name: QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0
# Input: N: 1, IC: 64, OC: 128, H: 56, W: 56, G: 1, kernel: 1, stride: 1, pad: 0
Forward Execution Time (us) : 509.965

# Benchmarking PyTorch: QConv2d
# Mode: Eager
# Name: QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0
# Input: N: 1, IC: 64, OC: 128, H: 56, W: 56, G: 1, kernel: 1, stride: 1, pad: 0
Forward Execution Time (us) : 576.007
```

python -m pt.qconv_test --tag_filter resnext101_32x4 --wipe_cache --test_name QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0 --omp_num_threads 4

```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : None

# Benchmarking PyTorch: QConv2d
# Mode: Eager
# Name: QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0
# Input: N: 1, IC: 64, OC: 128, H: 56, W: 56, G: 1, kernel: 1, stride: 1, pad: 0
Forward Execution Time (us) : 195.002

# Benchmarking PyTorch: QConv2d
# Mode: Eager
# Name: QConv2d_N1_IC64_OC128_H56_W56_G1_kernel1_stride1_pad0
# Input: N: 1, IC: 64, OC: 128, H: 56, W: 56, G: 1, kernel: 1, stride: 1, pad: 0
Forward Execution Time (us) : 189.788
```
ghstack-source-id: 91050434

Test Plan: See summary

Differential Revision: D17647391

fbshipit-source-id: e00de1151902291ed94fd34446995ea1f0199d14
2019-09-30 17:04:51 -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 Set number of threads for operator_benchmarks (#27010) 2019-09-30 17:04:51 -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