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e60a7c2c88
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12 Commits

Author SHA1 Message Date
Richard Zou
e60a7c2c88 codemod tensor.type().is_cuda(), tensor.type().is_sparse() (#13590) 
Summary:
Followup to #12841

Changed these to not require type dispatch:
tensor.type().is_cuda() -> tensor.is_cuda()
tensor.type().is_sparse() -> tensor.is_sparse()
isVariable(tensor.type()) -> tensor.is_variable()

This probably does not affect performance
very much in most cases but it is nice to have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13590

Reviewed By: ezyang

Differential Revision: D12929301

Pulled By: zou3519

fbshipit-source-id: 8ac5c6200c579dd7a44fb4ee58fc9bb170feb1d7
 2018-11-07 07:27:42 -08:00
Wei Yang
807de9a1e3 fix segfault when grad to a hook fn is None (#12028) 
Summary:
- fixes https://github.com/pytorch/pytorch/issues/11751 by checking if a grad is a Python None object before getting cdata from it
- behaviors:

pre-fix
```
>>> a = torch.randn(5, requires_grad=True)
>>> a_list = a.unbind()

>>> a0 = a_list[0]
>>> a0.register_hook
...:    def hook(grad):
...:        print(grad)

>>> a_list[0].backward()
tensor(1.)

>>> print('a_list[0]', a_list[0].grad, a.grad)
('a_list[0]', None, tensor([1., 0., 0., 0., 0.]))

>>> a_list[1].backward() # segfault
```

post-fix
```
>>> a = torch.randn(5, requires_grad=True)
>>> a_list = a.unbind()

>>> a0 = a_list[0]
>>> a0.register_hook
... :   def hook(grad):
... :       print(grad)

>>> a_list[0].backward()
tensor(1.)

>>> print(a_list[0].grad, a.grad)
(None, tensor([1., 0., 0., 0., 0.]))

>>> a_list[1].backward()
None

>>> print(a_list[1].grad, a.grad)
(None, tensor([1., 1., 0., 0., 0.]))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12028

Differential Revision: D10034094

Pulled By: weiyangfb

fbshipit-source-id: 3f2135325fa7d338b920f57752057e4f6a6c0b1d
 2018-09-25 19:10:25 -07:00
Gregory Chanan
9a2b54e08b [ATen] Rename isCuda -> is_cuda. 2017-11-15 18:33:07 -08:00
Sam Gross
1290e586fb Use at::Tensor based autograd Variable (#2676) 
Variable is now a subclass of at::Tensor backed by a VariableImpl* pImpl. The implementation of the ATen functions is defined in the auto-generated VariableType.h/cpp file.

Currently, only functions which fall through to the base type, such as sizes() and isCuda() are implemented. Differentiable ops like add() and mul() will be added in a subsequent PR.
 2017-09-12 11:36:01 -04:00
Zachary DeVito
43c944acbd Remove dead THPP code that has been replaced with ATen objects. (#2235) 
THPP usage is now isolated in THD.
 2017-07-29 08:07:41 +05:30
Trevor Killeen
c304d04fc6 Replace thpp::Tensor with ATen Tensor in autograd csrc (#2170) 2017-07-28 10:18:37 -04:00
Adam Paszke
98afdcf409 Accept None values returned from grad hooks 2017-06-06 01:06:14 -04:00
Trevor Killeen
05bc877a05 make THPPointer have explicit constructors (#1636) 2017-05-25 15:35:54 -04:00
Adam Paszke
2ca787fcf4 Refactor attribute names in autograd 2017-05-01 16:44:56 -04:00
Sam Gross
4c1cdb6148 Refactor Python string utility function 2017-04-28 21:25:26 +02:00
Sam Gross
5073132837 Implement 'pre' and 'post' hooks at the C++ autograd level 2017-03-06 12:47:53 -08:00
Sam Gross
34ce58c909 Parallelize backwards 2017-03-03 11:26:00 -08:00