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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 |
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| .. | ||
| distributed | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inference | ||
| instruction_counts | ||
| nested | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| transformer | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
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: