Summary:
This PR restructures the public-facing C++ headers in a backwards compatible way. The problem right now is that the C++ extension header `torch/extension.h` does not include the C++ frontend headers from `torch/torch.h`. However, those C++ frontend headers can be convenient. Further, including the C++ frontend main header `torch/torch.h` in a C++ extension currently raises a warning because we want to move people away from exclusively including `torch/torch.h` in extensions (which was the correct thing 6 months ago), since that *used* to be the main C++ extension header but is now the main C++ frontend header. In short: it should be possible to include the C++ frontend functionality from `torch/torch.h`, but without including that header directly because it's deprecated for extensions.
For clarification: why is `torch/torch.h` deprecated for extensions? Because for extensions we need to include Python stuff, but for the C++ frontend we don't want this Python stuff. For now the python stuff is included in `torch/torch.h` whenever the header is used from a C++ extension (enabled by a macro passed by `cpp_extensions.py`) to not break existing users, but this should change in the future.
The overall fix is simple:
1. C++ frontend sub-headers move from `torch/torch.h` into `torch/all.h`.
2. `torch/all.h` is included in:
1. `torch/torch.h`, as is.
2. `torch/extensions.h`, to now also give C++ extension users this functionality.
With the next release we can then:
1. Remove the Python includes from `torch/torch.h`
2. Move C++-only sub-headers from `all.h` back into `torch.h`
3. Make `extension.h` include `torch.h` and `Python.h`
This will then break old C++ extensions that include `torch/torch.h`, since the correct header for C++ extensions is `torch/extension.h`.
I've also gone ahead and deprecated `torch::CPU` et al. since those are long due to die.
ezyang soumith apaszke fmassa
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13482
Differential Revision: D12924999
Pulled By: goldsborough
fbshipit-source-id: 5bb7bdc005fcb7b525195b769065176514efad8a
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/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
* 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
