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Natalia Gimelshein 3875e1ba45 try to make at::cat in mm_tree_reduction operate on contig tensors (#18816) 
Summary:
Sometimes at::cat gets transposed inputs and goes on a slow path. Also, make jit_premul lstm benchmark add bias to the whole input tensor to avoid separate reduction kernels in the backward pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18816

Differential Revision: D15013576

Pulled By: wanchaol

fbshipit-source-id: bcfa1cf44180b11b05b0f55f034707012f66281a
2019-04-24 23:44:25 -07:00
..
fastrnns try to make at::cat in mm_tree_reduction operate on contig tensors (#18816) 2019-04-24 23:44:25 -07:00
operator_benchmark Fix op benchmarks error in OSS environment (#19518) 2019-04-19 16:25:16 -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