1) Using items stored in torch._tensor_classes to check item passed from python side;
2) Add SparsePrivateUse1 in backend_to_string, layout_from_backend and check_base_legacy_new;
3) Using more general API to get python module name in get_storage_obj and get_name functions.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119263
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61500
libstdc++ defines a static variable called `std::__ioinit` in iostream that adds global constructor size overhead to each translation that includes iostream. To reduce the size overhead from that, we can often include ostream instead.
ghstack-source-id: 136163529
Test Plan: buildsizebot some mobile apps
Reviewed By: dhruvbird
Differential Revision: D29648016
fbshipit-source-id: 9c3139712c71248513cc5032d21e77f3ecbae8fe
Summary:
Add a new device type 'XPU' ('xpu' for lower case) to PyTorch. Changes are needed for code related to device model and kernel dispatch, e.g. DeviceType, Backend and DispatchKey etc.
https://github.com/pytorch/pytorch/issues/48246
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49786
Reviewed By: mrshenli
Differential Revision: D25893962
Pulled By: ezyang
fbshipit-source-id: 7ff0a316ee34cf0ed6fc7ead08ecdeb7df4b0052
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37527
This is yet another place that needs to be updated for adding a new "Backend" and is unnecessary. Instead, just use layout_from_backend and have a map from Layout -> THPLayout.
Other changes:
- rename torch::getDtype and torch::getLayout to torch::getTHPDtype and torch::getTHPLayout since e.g. for layout you are both passing in and returning a "layout" type.
- add NumOptions to Layout to match the dtype/ScalarType formulation.
Test Plan: Imported from OSS
Differential Revision: D21309836
Pulled By: gchanan
fbshipit-source-id: ede0e4f3bf7ff2cd04a9b17df020f0d4fd654ba3
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
