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Issue: https://github.com/pytorch/pytorch/issues/144888 Torchbench of timm lcnet_050 model fails on accuracy in case of `--frezing` `--inference` `--bfloat16` `res_error==0.12` If to turn off convolution inductor constant folding - `res_error==0.016` `float16 error ~ 0.00669` `float16 without conv folding ~ 0.0018` convolution folding results in increase of error almost at one order of magnitude. I think we should revisit and try to do something to improve the accuracy for conv folding. E.g. For example doing conv folding at compilation time with float64? At the moment I am adding counters to identify if convolution folding happened, and in case of bfloat16 and conv_folding - increase multiplier to the max level (10) to pass accuracy test. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145623 Approved by: https://github.com/eellison
PyTorch Benchmarks
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 supersede 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. Links are provided where descriptions exist: