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Summary: Fixes https://github.com/pytorch/pytorch/issues/47123 Follows https://github.com/pyro-ppl/pyro/pull/2701 This turns on `Distribution` validation by default. The motivation is to favor beginners by providing helpful error messages. Advanced users focused on speed can disable validation by calling ```py torch.distributions.Distribution.set_default_validate_args(False) ``` or by disabling individual distribution validation via `MyDistribution(..., validate_args=False)`. In practice I have found many beginners forget or do not know about validation. Therefore I have [enabled it by default](https://github.com/pyro-ppl/pyro/pull/2701) in Pyro. I believe PyTorch could also benefit from this change. Indeed validation caught a number of bugs in `.icdf()` methods, in tests, and in PPL benchmarks, all of which have been fixed in this PR. ## Release concerns - This may slightly slow down some models. Concerned users may disable validation. - This may cause new `ValueErrors` in models that rely on unsupported behavior, e.g. `Categorical.log_prob()` applied to continuous-valued tensors (only {0,1}-valued tensors are supported). We should clearly note this change in release notes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/48743 Reviewed By: heitorschueroff Differential Revision: D25304247 Pulled By: neerajprad fbshipit-source-id: 8d50f28441321ae691f848c55f71aa80cb356b41 |
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| .. | ||
| cpp/tensorexpr | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| 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
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