Summary:
This makes a few changes wrt Type, with the ultimate goal of removing Type from the public Methods/Functions. In particular:
1) Removes factory functions from Type, into TypeExtendedInterface.
2) sparse_coo_tensor is now a first class at:: namespace function, with TensorOptions overloads.
3) We move from Type-based sparse_coo_tensor dispatch to function-based.
Note we still require a number of changes to get rid of tType in the public interface, in particular TensorOptions needs to support CUDA vs non-CUDA dispatch. That is coming in a future patch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12025
Reviewed By: ezyang
Differential Revision: D10017205
Pulled By: gchanan
fbshipit-source-id: 00807a37b09ed33f0656aaa165bb925abb026320
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11096
To discourage willy-nilly use, and make it clearer that it
is not a Variable
Reviewed By: cpuhrsch
Differential Revision: D9583699
fbshipit-source-id: 4fbde0c01ae3deb2c7ef8c125a9028f089b203ae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11095
We used getType to mean a lot of things.
- getVariableTypeFromBaseType: given a base Type (non-Variable type)
compute the Variable Type which corresponds to it.
- getVariableType: like at::getType, but return the Variable type
rather than the plain type.
This rename makes it clearer at the use-site what things are what,
and will make a subsequent rename of at::getType easier.
Reviewed By: gchanan, cpuhrsch
Differential Revision: D9583630
fbshipit-source-id: 2667ec98e7607bc466920c7415a8c651fd56dfca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10478
- Removed Backend constructor from Device, and fixed all
use-sites to use DeviceType::CPU instead of kCPU, or
use a new function backendToDeviceType to perform
the conversion.
- New method device_type() on Type; it gives you the
underlying device type, e.g., CPU for SparseCPU.
- We add backward compatibility for kCPU/kCUDA uses,
by introducing a new special type which is implicitly
convertible to both DeviceType and Backend. As long as
you don't define a function that's overloaded on both
DeviceType and Backend (but not on BackendOrDeviceType),
the implicit conversions will ensure that uses
of at::Device(at::kCPU) keep working. We fixed use-sites in
the library, but did NOT fix sites in the test code, so that
we can exercise this BC code.
Reviewed By: Yangqing
Differential Revision: D9301861
fbshipit-source-id: 9a9d88620500715c7b37e655b4fd761f6dd72716
* this removes the flag controlling whether the interpreter works on variables.
* now the interpreter _always_ works on variables
* constants in the IR are still _always_ non-variables, and an assert was added to ensure this.
* as_tensor was split into as_variable and as_tensor since it is sometimes used
to construct constants in the IR
* I tried changing the IR to also always use variables but that change was much more
cross cutting and fragile and I never got it working
* Rename autograd namespace to torch and change torch.h into python.h
* Include torch.h instead of python.h in test/cpp/api
* Change some mentions of torch.h to python.h in C++ extensions
* Set paths directly, without find_path
* PyObject* <--> at::Tensor no longer unwraps variables, instead we expect end uses to always work with variable types, and we will only unwrap the variables when we optimize.
* Add torch::CPU, torch::CUDA and torch::getType
* at::CPU -> torch::CPU in extensions
