Summary:
After being deprecated in 1.5 and throwing a runtime error in 1.6, we can now enable torch.full inferring its dtype when given bool and integer fill values. This PR enables that inference and updates the tests and docs to reflect this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41912
Reviewed By: pbelevich
Differential Revision: D22790718
Pulled By: mruberry
fbshipit-source-id: 8d1eb01574b1977f00bc0696974ac38ffdd40d9e
Summary:
The function torch.cross is a bit confusing, in particular the defaulting of the dim argument.
The default `dim` has been documented as -1 but it is actually `None`. This increases the confusion, in two possible ways depending on how carefully you read the rest. I also add a final warning to the final sentence.
This partially addresses https://github.com/pytorch/pytorch/issues/39310.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41850
Reviewed By: izdeby
Differential Revision: D22664625
Pulled By: albanD
fbshipit-source-id: b8669e026fd01de9e4ec16da1414b9edfaa76bdd
Summary:
- Removes outdated language like "BoolTensor"
- Consistently labels keyword arguments, like out
- Uses a more natural string to describe their return type
- A few bonus fixes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41626
Reviewed By: ngimel
Differential Revision: D22617322
Pulled By: mruberry
fbshipit-source-id: 03cc3562b78a07ed30bd1dc7936d7a4f4e31f01d
Summary:
https://github.com/pytorch/pytorch/issues/38349
mruberry
Not entirely sure if all the changes are necessary in how functions are added to Pytorch.
Should it throw an error when called with a non-complex tensor? Numpy allows non-complex arrays in its imag() function which is used in its isreal() function but Pytorch's imag() throws an error for non-complex arrays.
Where does assertONNX() get its expected output to compare to?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41298
Reviewed By: ngimel
Differential Revision: D22610500
Pulled By: mruberry
fbshipit-source-id: 817d61f8b1c3670788b81690636bd41335788439
Summary:
lcm was missing an abs. This adds it plus extends the test for NumPy compliance. Also includes a few doc fixes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41552
Reviewed By: ngimel
Differential Revision: D22580997
Pulled By: mruberry
fbshipit-source-id: 5ce1db56f88df4355427e1b682fcf8877458ff4e
Summary:
Implementing the quantile operator similar to [numpy.quantile](https://numpy.org/devdocs/reference/generated/numpy.quantile.html).
For this implementation I'm reducing it to existing torch operators to get free CUDA implementation. It is more efficient to implement multiple quickselect algorithm instead of sorting but this can be addressed in a future PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39417
Reviewed By: mruberry
Differential Revision: D22525217
Pulled By: heitorschueroff
fbshipit-source-id: 27a8bb23feee24fab7f8c228119d19edbb6cea33
Summary:
A small PR fixing some formatting in lcm, gcd, and the serialization note. Adds a note to lcm and gcd explaining behavior that is not always defined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41526
Reviewed By: ngimel
Differential Revision: D22569341
Pulled By: mruberry
fbshipit-source-id: 5f5ff98c0831f65e82b991ef444a5cee8e3c8b5a
Summary:
Fixes https://github.com/pytorch/pytorch/issues/36403
Copy-paste of the issue description:
* Escape hatch: Introduce unsafe_* version of the three functions above that have the current behavior (outputs not tracked as views). The documentation will explain in detail why they are unsafe and when it is safe to use them. (basically, only the outputs OR the input can be modified inplace but not both. Otherwise, you will get wrong gradients).
* Deprecation: Use the CreationMeta on views to track views created by these three ops and throw warning when any of the views is modified inplace saying that this is deprecated and will raise an error soon. For users that really need to modify these views inplace, they should look at the doc of the unsafe_* version to make sure their usecase is valid:
* If it is not, then pytorch is computing wrong gradients for their use case and they should not do inplace anymore.
* If it is, then they can use the unsafe_* version to keep the current behavior.
* Removal: Use the CreationMeta on view to prevent any inplace on these views (like we do for all other views coming from multi-output Nodes). The users will still be able to use the unsafe_ versions if they really need to do this.
Note about BC-breaking:
- This PR changes the behavior of the regular function by making them return proper views now. This is a modification that the user will be able to see.
- We skip all the view logic for these views and so the code should behave the same as before (except the change in the `._is_view()` value).
- Even though the view logic is not performed, we do raise deprecation warnings for the cases where doing these ops would throw an error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39299
Differential Revision: D22432885
Pulled By: albanD
fbshipit-source-id: 324aef091b32ce69dd067fe9b13a3f17d85d0f12
Summary:
solves most of gh-38011 in the framework of solving gh-32703.
These should only be formatting fixes, I did not try to fix grammer and syntax.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41068
Differential Revision: D22411919
Pulled By: zou3519
fbshipit-source-id: 25780316b6da2cfb4028ea8a6f649bb18b746440
Summary:
BC-breaking NOTE:
In PyTorch 1.6 bool and integral fill values given to torch.full must set the dtype our out keyword arguments. In prior versions of PyTorch these fill values would return float tensors by default, but in PyTorch 1.7 they will return a bool or long tensor, respectively. The documentation for torch.full has been updated to reflect this.
PR NOTE:
This PR causes torch.full to throw a runtime error when it would have inferred a float dtype by being given a boolean or integer value. A versioned symbol for torch.full is added to preserve the behavior of already serialized Torchscript programs. Existing tests for this behavior being deprecated have been updated to reflect it now being unsupported, and a couple new tests have been added to validate the versioned symbol behavior. The documentation of torch.full has also been updated to reflect this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40364
Differential Revision: D22176640
Pulled By: mruberry
fbshipit-source-id: b20158ebbcb4f6bf269d05a688bcf4f6c853a965
Summary:
**1.6 Deprecation Note**
In PyTorch 1.6 attempting to divide two integer tensors or an integer tensor and an integer scalar will throw a runtime error. This behavior was deprecated with a warning in PyTorch 1.5. In PyTorch 1.7 torch.div and the division operator will always perform true division like Python3 and NumPy.
To divide integer values use either torch.true_divide, for true division, or torch.floor_divide (the // operator) for floor division.
**PR Summary**
This PR updates the warning message when performing integer division to be a runtime error. Because some serialized Torchscript programs may rely on torch.div's historic behavior it also implements a "versioned symbol" for div that lets those models retain their current behavior. Extensive tests of this behavior are the majority of this PR.
Note this change bumps the produced file format version to delineate which programs should have their historic div behavior preserved.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38620
Differential Revision: D21612598
Pulled By: mruberry
fbshipit-source-id: c9c33591abce2f7e97f67f0f859901f5b03ed47d
Summary:
I added the following to the docs:
1. `torch.save`.
1. Added doc for `_use_new_zipfile_serialization` argument.
2. Added a note telling that extension does not matter while saving.
3. Added an example showing the use of above argument along with `pickle_protocol=5`.
2. `torch.split`
1. Added an example showing the use of the function.
3. `torch.squeeze`
1. Added a warning for batch_size=1 case.
4. `torch.set_printoptions`
1. Changed the docs of `sci_mode` argument from
```
sci_mode: Enable (True) or disable (False) scientific notation. If
None (default) is specified, the value is defined by `_Formatter`
```
to
```
sci_mode: Enable (True) or disable (False) scientific notation. If
None (default=False) is specified, the value is defined by
`torch._tensor_str._Formatter`.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39303
Differential Revision: D21904504
Pulled By: zou3519
fbshipit-source-id: 92a324257d09d6bcfa0b410d4578859782b94488
Summary:
This PR aims to add `arcosh`, `arcsinh` and `arctanh` support. Please see issue https://github.com/pytorch/pytorch/issues/38349 for more details.
**TODOs:**
* [x] Add test cases for `arcosh`, `arcsinh` and `arctanh`. (need help)
* [x] Overload ops if `std::op` does not work with `thrust::complex` types (like for `sinh`, `cosh`).
Note: `std::acosh, std::asinh, std::atanh` do not support `thrust::complex` types. Added support for complex types for these 3 ops (`arccosh, arcsinh, arctanh`)
cc: mruberry
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38388
Differential Revision: D21882055
Pulled By: mruberry
fbshipit-source-id: d334590b47c5a89e491a002c3e41e6ffa89000e3
Summary:
1.6 Deprecation Note:
In 1.6 attempting to perform integer division using addcdiv will throw a RuntimeError, and in 1.7 the behavior will change so that addcdiv always performs a true division of its tensor1 and tensor2 inputs. See the warning in torch.addcdiv's documentation for more information.
PR Summary:
This PR updates the warning that appears when addcdiv performs integer division to throw a RuntimeError. This is intended to prevent silent errors when torch.addcdiv's behavior is changed to always perform true division in 1.7. The documentation is updated (slightly) to reflect this, as our the addcdiv tests in test_torch and test_type_promotion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38762
Differential Revision: D21657585
Pulled By: mruberry
fbshipit-source-id: c514b44409706f2bcfeca4473424b30cc48aafbc
Summary:
xref gh-32838, gh-34032
This is a major refactor of parts of the documentation to split it up using sphinx's `autosummary` feature which will build out `autofuction` and `autoclass` stub files and link to them. The end result is that the top module pages like torch.nn.rst and torch.rst are now more like table-of-contents to the actual single-class or single-function documentations pages.
Along the way, I modified many of the docstrings to eliminate sphinx warnings when building. I think the only thing I changed from a non-documentation perspective is to add names to `__all__` when adding them to `globals()` in `torch.__init__.py`
I do not know the CI system: are the documentation build artifacts available after the build, so reviewers can preview before merging?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37419
Differential Revision: D21337640
Pulled By: ezyang
fbshipit-source-id: d4ad198780c3ae7a96a9f22651e00ff2d31a0c0f
Summary:
Adds support for generating Vandermonde matrices based off of the Numpy implementation found [here](https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/twodim_base.py#L475-L563).
Adds test to ensure generated matrix matches expected Numpy implementation. Note test are only limited to torch.long and torch.double due to differences in now PyTorch and Numpy deal with type promotion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36725
Differential Revision: D21075138
Pulled By: jessebrizzi
fbshipit-source-id: 6bb1559e8247945714469b0e2b07c6f4d5fd1fd0
Summary:
Previously torch.isclose would RuntimeError when called on complex tensors. This update updates torch.isclose to run on complex tensors and be consistent with [NumPy](https://numpy.org/doc/1.18/reference/generated/numpy.isclose.html). However, NumPy's handling of NaN, -inf, and inf values is odd, so I adopted Python's [cmath.isclose](https://docs.python.org/3/library/cmath.html) behavior when dealing with them. See https://github.com/numpy/numpy/issues/15959 for more on NumPy's behavior.
While implementing complex isclose I also simplified the isclose algorithm to:
- A is close to B if A and B are equal, if equal_nan is true then NaN is equal to NaN
- If A and B are finite, then A is close to B if `abs(a - b) <= (atol + abs(rtol * b))`
This PR also documents torch.isclose, since it was undocumented, and adds multiple tests for its behavior to test_torch.py since it had no dedicated tests.
The PR leaves equal_nan=True with complex inputs an error for now, pending the outcome of https://github.com/numpy/numpy/issues/15959.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36456
Differential Revision: D21159853
Pulled By: mruberry
fbshipit-source-id: fb18fa7048e6104cc24f5ce308fdfb0ba5e4bb30
