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93772792e3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/57334 Here's a possibly controversial PR. These counters got in the way of generalizing the fuser tests to handle arbitrary devices, and I guess I'm just generally skeptical that they provide much value. While true that they let us observe whether fusion groups were created, we already have assertions based on the shape of the graph, and I'm not sure that I trust those any less than these counters. Test Plan: Imported from OSS Reviewed By: ZolotukhinM Differential Revision: D29471484 Pulled By: bertmaher fbshipit-source-id: f6d76f6e72dbfb581acff1d834b0c74500941b57 |
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| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| instruction_counts | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| 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