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pytorch/benchmarks
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Mingzhe Li 3c986dff77 introduce auto_set to simplify benchmarking the backward path of operators (#23276) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23276

This diff introduces a new feature to simplify benchmarking the backward path of ops. Here is an example:

```
...
self.input_one = torch.rand(M, N, K, requires_grad=self.auto_set())
self.input_two = torch.rand(M, N, K, requires_grad=self.auto_set())
...
```

In this way, the benchmark will generate three different test cases.
1. input_one requires grad
2. input_two requires grad
3. both inputs require grad

Here is a sample output:
```
# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwdall
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 863.744

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd1
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 727.915

# Benchmarking PyTorch: add
# Mode: Eager
# Name: add_M1_N8_K8_bwd2
# Input: M: 1, N: 8, K: 8
Backward Execution Time (us) : 687.626
```

Reviewed By: zheng-xq

Differential Revision: D16450355

fbshipit-source-id: 50ae0916e81c3ff9f0c482ed6d386319eb15b305
2019-07-29 15:58:41 -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 introduce auto_set to simplify benchmarking the backward path of operators (#23276) 2019-07-29 15:58:41 -07:00
README.md

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