Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27782
Warnings show up when running `make html` to build documentation. All of
the warnings are very reasonable and point to bugs in our docs. This PR
attempts to fix most of those warnings.
In the future we will add something to the CI that asserts that there
are no warnings in our docs.
Test Plan: - build and view changes locally
Differential Revision: D17887067
Pulled By: zou3519
fbshipit-source-id: 6bf4d08764759133b20983d6cd7f5d27e5ee3166
Summary:
Added Complex support with AVX to unary ops and binary ops.
I need to add nan propagation to minimum() and maximum() in the future.
In-tree changes to pytorch to support complex numbers are being submitted here.
Out-of-tree support for complex numbers is here: pytorch-cpu-strided-complex extension
Preliminary Benchmarks are here.
I tried rrii and riri and found that riri is better in most situations.
Divide is very slow because you can't reduce 1/(x+y)
Sqrt is also very slow.
Reciprocal could be sped up after I add conj()
Everything else is typically within 20% of the real number performance.
Questions:
Why does macOS not support mil? #if AT_MKL_ENABLED() && !defined(__APPLE__) in vml.h. MKL does support some complex operations like Abs, so I was curious about trying it.
Is MKL just calling AVX?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26500
Differential Revision: D17835431
Pulled By: ezyang
fbshipit-source-id: 6746209168fbeb567af340c22bf34af28286bd54
Summary:
Changelog:
- Remove `torch.gels` which was deprecated in v1.2.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26480
Test Plan: - No tests were changed and all callsites for `torch.gels` where modified to `torch.lstsq` when `torch.lstsq` was introduced
Differential Revision: D17527207
Pulled By: zou3519
fbshipit-source-id: 28e2fa3a3bf30eb6b9029bb5aab198c4d570a950
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26240
In particular adds support for empty/empty_like which is needed for memory layouts to work.
Test Plan: Imported from OSS
Differential Revision: D17443220
Pulled By: dzhulgakov
fbshipit-source-id: 9c9e25981999c0edaf40be104a5741e9c62a1333
Summary:
This patch writes documentation for `Tensor.record_stream()`, which is not a documented API currently. I've discussed publishing it with colesbury in https://github.com/pytorch/pytorch/issues/23729.
The documentation is based on [the introduction at `CUDACachingAllocator.cpp`](25d1496d58/c10/cuda/CUDACachingAllocator.cpp (L47-L50)). ~~I didn't explain full details of the life cycle of memory blocks or stream awareness of the allocator for the consistent level of details with other documentations.~~ I explained about the stream awareness in a note block.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24078
Differential Revision: D16743526
Pulled By: zou3519
fbshipit-source-id: 05819c3cc96733e2ba93c0a7c0ca06933acb22f3
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460
Test Plan: - All tests should pass to confirm that the patch is correct
Differential Revision: D16547834
Pulled By: colesbury
fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
Summary:
Something flaky is going on with `test_inplace_view_saved_output` on Windows.
With my PR #20598 applied, the test fails, even though there is no obvious reason it should be related, so the PR was reverted.
Based on commenting out various parts of my change and re-building, I think the problem is with the name -- renaming everything from `T` to `asdf` seems to make the test stop failing. I can't be sure that this is actually the case though, since I could just be seeing patterns in non-deterministic build output...
I spoke with colesbury offline and we agreed that it is okay to just disable this test on Windows for now and not block landing the main change. He will look into why it is failing.
**Test Plan:** I will wait to make sure the Windows CI suite passes before landing this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21175
Differential Revision: D15566970
Pulled By: umanwizard
fbshipit-source-id: edf223375d41faaab0a3a14dca50841f08030da3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21196
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15577123
fbshipit-source-id: d0abeea488418fa9ab212f84b0b97ee237124240
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21156
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15558784
fbshipit-source-id: 0b194750c423f51ad1ad5e9387a12b4d58d969a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20874
A criteria for what should go in Tensor method is whether numpy has it, for this one it does not
so we are removing it as a Tensor method, we can still call it as function.
Python
```
torch.quantize_linear(t, ...), torch.dequantize(t)
```
C++
```
at::quantize_linear(t, ...), at::dequantize(t)
```
Reviewed By: dzhulgakov
Differential Revision: D15477933
fbshipit-source-id: c8aa81f681e02f038d72e44f0c700632f1af8437
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20938
Dequantize_linear need not be exposed to the front end users.
It will only be used for the jit passes for q-dq insertion and op
substitution.
Differential Revision: D15446097
fbshipit-source-id: a5fbcf2bb72115122c9653e5089d014e2a2e891d
Summary:
Changelog:
- Rename `potri` to `cholesky_inverse` to remain consistent with names of `cholesky` methods (`cholesky`, `cholesky_solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `cholesky_inverse` under the name `potri` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19498
Differential Revision: D15029901
Pulled By: ezyang
fbshipit-source-id: 2074286dc93d8744cdc9a45d54644fe57df3a57a
Summary:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726
Differential Revision: D14726237
Pulled By: zou3519
fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18230
Implementing minimum qtensor API to unblock other workstreams in quantization
Changes:
- Added Quantizer which represents different quantization schemes
- Added qint8 as a data type for QTensor
- Added a new ScalarType QInt8
- Added QTensorImpl for QTensor
- Added following user facing APIs
- quantize_linear(scale, zero_point)
- dequantize()
- q_scale()
- q_zero_point()
Reviewed By: dzhulgakov
Differential Revision: D14524641
fbshipit-source-id: c1c0ae0978fb500d47cdb23fb15b747773429e6c
Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision: D14680352
Pulled By: soumith
fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039
This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.
When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057
Differential Revision: D14619550
Pulled By: ezyang
fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc
Summary:
Changelog:
- Renames `trtrs` to `triangular_solve` to remain consistent with `cholesky_solve` and `solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `triangular_solve` under the name `trtrs`, and add a deprecation warning to not promote usage.
- Move `isnan` to _torch_docs.py
- Remove unnecessary imports
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18213
Differential Revision: D14566902
Pulled By: ezyang
fbshipit-source-id: 544f57c29477df391bacd5de700bed1add456d3f
Summary:
Changelog:
- Renames `gesv` to `solve` to remain consistent with `cholesky_solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `solve` under the name `gesv`, and add a deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18060
Differential Revision: D14503117
Pulled By: zou3519
fbshipit-source-id: 99c16d94e5970a19d7584b5915f051c030d49ff5
Summary:
Based on https://github.com/pytorch/pytorch/pull/12413, with the following additional changes:
- Inside `native_functions.yml` move those outplace operators right next to everyone's corresponding inplace operators for convenience of checking if they match when reviewing
- `matches_jit_signature: True` for them
- Add missing `scatter` with Scalar source
- Add missing `masked_fill` and `index_fill` with Tensor source.
- Add missing test for `scatter` with Scalar source
- Add missing test for `masked_fill` and `index_fill` with Tensor source by checking the gradient w.r.t source
- Add missing docs to `tensor.rst`
Differential Revision: D14069925
Pulled By: ezyang
fbshipit-source-id: bb3f0cb51cf6b756788dc4955667fead6e8796e5
Summary:
Changelog:
- Renames `potrs` to `cholesky_solve` to remain consistent with Tensorflow and Scipy (not really, they call their function chol_solve)
- Default argument for upper in cholesky_solve is False. This will allow a seamless interface between `cholesky` and `cholesky_solve`, since the `upper` argument in both function are the same.
- Rename all tests
- Create a tentative alias for `cholesky_solve` under the name `potrs`, and add deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15334
Differential Revision: D13507724
Pulled By: soumith
fbshipit-source-id: b826996541e49d2e2bcd061b72a38c39450c76d0
Summary:
Add to the Tensor doc info about `.device`, `.is_cuda`, `.requires_grad`, `.is_leaf` and `.grad`.
Update the `register_backward_hook` doc with a warning stating that it does not work in all cases.
Add support in the `_add_docstr` function to add docstring to attributes.
There is an explicit cast here but I am not sure how to handle it properly. The thing is that the doc field for getsetdescr is written as being a const char * (as all other doc fields in descriptors objects) in cpython online documentation. But in the code, it is the only one that is not const.
I assumed here that it is a bug in the code because it does not follow the doc and the convention of the others descriptors and so I cast out the const.
EDIT: the online doc I was looking at is for 3.7 and in that version both the code and the doc are const. For older versions, both are non const.
Please let me know if this should not be done. And if it should be done if there is a cleaner way to do it !
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14339
Differential Revision: D13243266
Pulled By: ezyang
fbshipit-source-id: 75b7838f7cd6c8dc72b0c61950e7a971baefaeeb
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.
