Summary:
This PR performs a renaming of the function `potrf` responsible for the Cholesky
decomposition on positive definite matrices to `cholesky` as NumPy and TF do.
Billing of changes
- make potrf cname for cholesky in Declarations.cwrap
- modify the function names in ATen/core
- modify the function names in Python frontend
- issue warnings when potrf is called to notify users of the change
Reviewed By: soumith
Differential Revision: D10528361
Pulled By: zou3519
fbshipit-source-id: 19d9bcf8ffb38def698ae5acf30743884dda0d88
Summary:
Here is my stab at ```dense.to_sparse```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12171
Differential Revision: D10859078
Pulled By: weiyangfb
fbshipit-source-id: 5df72f72ba4f8f10e283402ff7731fd535682664
Summary:
vishwakftw Your patch needed some updates because the default native function dispatches changed from `[function, method]` to `[function]`. The CI was run before that change happened so it still shows green, but the internal test caught it.
I did some changes when rebasing and updating so I didn't just force push to your branch. Let's see if this passes CI and internal test. If it does, let me know if you want me to force push to your branch or use this PR instead.
Note to reviewers: patch was already approved at #10068 .
cc yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11421
Differential Revision: D9733407
Pulled By: SsnL
fbshipit-source-id: cf2ed293bb9942dcc5158934ff4def2f63252599
Summary:
fixes#4176 cc vishwakftw
I didn't do `:math:` and `\neg` because I am using double ticks so they render more similarly with `:attr:`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9630
Differential Revision: D8933022
Pulled By: SsnL
fbshipit-source-id: 31d8551f415b624c2ff66b25d886f20789846508
Summary:
Closes#9147
Added a test to prevent regression in test_torch
Added entries in docs
cc ezyang weiyangfb
Closes https://github.com/pytorch/pytorch/pull/9156
Differential Revision: D8732095
Pulled By: soumith
fbshipit-source-id: 7a6892853cfc0ccb0142b4fd25015818849adf61
* Implement torch.as_tensor, similar to numpy.asarray.
torch.as_tensor behaves like torch.tensor except it avoids copies if possible; so also somewhat like tensor.new but without the size overloads.
I didn't add a requires_grad field, because we haven't decided on the semantics such as as_param.
* Remove requires_grad for doc.
Changes:
- Deleted docs for old constructor. Add link to new `torch.tensor` ctor
- Add docs for `torch.tensor`
- Add some info on dtypes to the top of `tensors.rst`.
* Add device docs; match constructor parameter names with attribute names.
* Use double quotes for strings.
* Update printing.
* Separate device ordinal-only construction into a separate note.
* Use current device.
* Implemented log2 and log10
* Re-add incorrectly removed files
* Fix minor bugs
* Fix log1p docs
* Add a try-except for python2 math module in log2 test
* Revert changes made to aten/doc/*
* Fix docstring errors
* Fix windows build
* Add max_values and argmax convenience functions to ATen
* Add documentation for torch.argmax/argmin and skip max_values
* Add tests for argmax/argmin
* Dont default the dim argument
* Use dim=0 in test_torch.py for argmax tests
* Implement argmin() and argmax() without dim
* Call .contiguous() before .view(-1)
* Implement torch.reshape and Tensor.reshape
This implements reshape which has similar semantics to numpy.reshape. It
will return a view of the source tensor if possible. Otherwise, it
returns a copy.
* Remove in-place reshape_ that was an alias for resize_
* Update documentation
Questions/possible future works:
How to template-ize to extend support beyond LongTensor?
How to check if autograd works (and if not, how to add explicit gradient)?
CUDA support?
Testing command:
DEBUG=1 NO_CUDA=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build && DEBUG=1 NO_CUDA=1 MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop && python3 test/test_torch.py
Partially fixes#2031
* Initial commit for unique op
* Working unique with test
* Make inverse indices shape conform to input
* flake8 whitespace removal
* address review comment nits
* Expose fn and add docs. Explicitly declare no gradients
* Trial generic dispatch implementation
* Add tests for generics
* flake8 whitespace
* Add basic CUDA error throwing and templateize set
* Explicit contiguous and AT_DISPATCH_ALL_TYPES return
* Remove extraneous numpy conversion
* Refactor out .data calls
* Refactored to variable return length API with wrapper fn as opposed to returning a 0-length tensor, per off-line reviewer comments
* Remove A
* Don't use hidden torch._unique() in test
* Fix documentations
* Add torch.take and Tensor.put_
These are similar to numpy.take and numpy.put. The take function allows
you to linearly index into a tensor without viewing it as a 1D tensor
first. The output has the same shape as the indices. The put function
copies value into a tensor also using linear indices.
* Add torch.matmul function.
Includes test_torch, test_autograd and docs changes.
* Add __all__ to functional so imports are accidentally imported.
* Include unbind in __all__.
* Add matmul case for when one argument is 1-dimensional and the other
at least 3-dimensional.
* Add squeeze_ to Variable.
* Use squeeze_ instead of squeeze for matmul.
