mirror of
https://github.com/zebrajr/pytorch.git
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58b6ab69e5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39955 resolves https://github.com/pytorch/pytorch/issues/36323 by adding `torch.sgn` for complex tensors. `torch.sgn` returns `x/abs(x)` for `x != 0` and returns `0 + 0j` for `x==0` This PR doesn't test the correctness of the gradients. It will be done as a part of auditing all the ops in future once we decide the autograd behavior (JAX vs TF) and add gradchek. Test Plan: Imported from OSS Reviewed By: mruberry Differential Revision: D23460526 Pulled By: anjali411 fbshipit-source-id: 70fc4e14e4d66196e27cf188e0422a335fc42f92 |
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| .. | ||
| distributed/ddp | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
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 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