Commit Graph

4 Commits

Author SHA1 Message Date
Edward Yang
6bdbad93b9 Refactor Device to not depend on Backend. (#10478)
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
2018-08-18 17:39:14 -07:00
Sam Gross
30ec06c140
Merge Variable and Tensor classes (#5225)
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.

To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.

There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:

 https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
2018-02-23 18:03:31 -05:00
Tongzhou Wang
1848cad108 [ready] Layer Normalization (#4922)
* at::maybe_data_ptr and Check.h => TensorUtils.h

* THNN support for optional BN running_*

* ATen support for optional BN running_*

* Python nn.* support for optional BN running_*; Improve IN and BN doc

* Add tests for IN and BN new option

* Layer Norm

* Fix LRN doc

* functional interface for LN and IN

* Layer norm tests

* fix BN double backward returning undefined tensors

* fix jit test using wrong dim inputs for BN

* add/improve BN, IN and LN GPU tests with half type

* Udpate docs to be consistent with Conv notation
Fix onnx
Clarified onnx symbokic wrapper

* fix typo

* Address comments
2018-02-22 11:56:41 -05:00
Sam Gross
60c03bc09c
Implement apply_, map_, and map2_ in Variable (#4057) 2017-12-07 14:48:56 -05:00