Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36631
Summary of changes
1. Moved random transformation functions to DistributionHelper.h (`uniform_int_from_to_distribution`, `uniform_int_full_range_distribution`, `uniform_int_distribution`) to avoid code duplication between default CPU, CUDA rngs and custom rng extensions
2. Made GeneratorImpl fields protected instead of private
3. Introduced `TORCH_CHECK_IF_NOT_ON_CUDA` that does the same as `TORCH_CHECK` if it is not CUDA/ROCm device
4. To test multiple rng extensions I had to move ops registration to the method `registerOps()`, expose it to python and call it `def setUp(self)`
Test Plan: Imported from OSS
Differential Revision: D21229202
Pulled By: pbelevich
fbshipit-source-id: 6aa3280f2fc3324cf3e748388b5087e3a1e49f23
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36232
The purpose of this PR is to replace `at::Generator generator = nullptr` with `c10::optional<at::Generator> = c10::nullopt` all over the code
* #36230 Replace std::shared_ptr with c10::intrusive_ptr in at::Generator
Test Plan: Imported from OSS
Differential Revision: D20943603
Pulled By: pbelevich
fbshipit-source-id: 65d335990f01fcc706867d5344e73793fad68ae6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34774
This PR provides pybind11's `type_caster<at::Generator>` that allows mapping `at::Generator` instance returned from user-defined method to python `torch::Generator`, defined as `THPGenerator ` c++ class.
This allows 1) defining custom RNG in c++ extension 2) using custom RNG in python code.
`TestRNGExtension.test_rng` shows how to use custom RNG defined in `rng_extension.cpp`
Test Plan: Imported from OSS
Differential Revision: D20549451
Pulled By: pbelevich
fbshipit-source-id: 312a6deccf8228f7f60695bbf95834620d52f5eb
