Summary:
Changelog:
- Changes the behavior of returning a zero tensor when eigenvectors=False, matching behavior of torch.eig
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28338
Test Plan: - test_symeig has been modified appropriately for this change
Differential Revision: D18085280
Pulled By: ezyang
fbshipit-source-id: 43129a96dd01743997157974100e5a7270742b46
Summary:
The types don't appear in the docstrings for other functions in the `torch` namespace so I think this was included here because of a copy/paste error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28479
Differential Revision: D18086150
Pulled By: ezyang
fbshipit-source-id: 2481bccba6df36b12779a330f8c43d4aea68495f
Summary:
f362a5a04b reverted
5ca612b55e due to build time conerns (also
see https://github.com/pytorch/pytorch/issues/25254). Now we come back to this by reusing the underlying code in
comparison operators: Logical operators on non-bool variables are
essentially comparison operators that semantically output bool
values. Compared with the previous implementation, we compromise by
always applying XOR on the same input type, while output can be either
the input type or the bool type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27248
Differential Revision: D17929356
Pulled By: ezyang
fbshipit-source-id: dbac08c7614b36f05d24c69104fee9df9ca523d5
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:
Currently when an integral tensor is divided by zero, it emits a
"floating point exception" (which can be different from system to
system). Clarify in the document that nothing would be guaranteed under
this circumstance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25968
Differential Revision: D17888097
Pulled By: ezyang
fbshipit-source-id: 7c3ce3ac4080479d637cc2710b6aa3ae7e42431d
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:
We do support inputs with dim > 2 in _out variants
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26989
Differential Revision: D17785632
Pulled By: soumith
fbshipit-source-id: d42ba7ca9c225ad1a26ff3b410d0c5c08eaed001
Summary:
Change the doc of torch.where. The parameters are x and y instead of input and other
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25554
Differential Revision: D17227193
Pulled By: soumith
fbshipit-source-id: 96d8a6f60ae8e788648247320ae715d0058de2b4
Summary:
Changelog:
- Enable broadcasting of RHS and LHS tensors for lu_solve. This means that you can now have RHS with size `3 x 2` and LHS with size `4 x 3 x 3` for instance
- Remove deprecated behavior of having 2D tensors for RHS. Now all tensors have to have a last dimension which equals the number of right hand sides
- Modified docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24333
Test Plan: - Add tests for new behavior in test_torch.py with a port to test_cuda.py
Differential Revision: D17165463
Pulled By: zou3519
fbshipit-source-id: cda5d5496ddb29ed0182bab250b5d90f8f454aa6
Summary:
As in https://github.com/pytorch/pytorch/issues/23439, some descriptions of arguments in `_torch_docs.py` have been replaced by `common_args`, it would be helpful to check if any descriptions can be replaced for new docs in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24161
Differential Revision: D16889293
Pulled By: ezyang
fbshipit-source-id: bf6f581494482d6eb32e634f73e84a4586766230
Summary:
Many descriptions of arguments could be replaced by items in the template such as `factory_common_args`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23439
Differential Revision: D16688527
Pulled By: ezyang
fbshipit-source-id: 406ce45d72e297f46b5fa9ea5472b3284c8d4324
Summary:
Changelog:
- Add batching for det / logdet / slogdet operations
- Update derivative computation to support batched inputs (and consequently batched outputs)
- Update docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22909
Test Plan:
- Add a `test_det_logdet_slogdet_batched` method in `test_torch.py` to test `torch.det`, `torch.logdet` and `torch.slogdet` on batched inputs. This relies on the correctness of `torch.det` on single matrices (tested by `test_det_logdet_slogdet`). A port of this test is added to `test_cuda.py`
- Add autograd tests for batched inputs
Differential Revision: D16580988
Pulled By: ezyang
fbshipit-source-id: b76c87212fbe621f42a847e3b809b5e60cfcdb7a
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:
I manually went through all functions in `torch.*` and corrected any mismatch between the arguments mentioned in doc and the ones actually taken by the function. This fixes https://github.com/pytorch/pytorch/issues/8698.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22973
Differential Revision: D16419602
Pulled By: yf225
fbshipit-source-id: 5562c9b0b95a0759abee41f967c45efacf2267c2
Summary:
Asterisks start emphases in rst. We should either escape them or put them as interpreted text.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22896
Differential Revision: D16282869
Pulled By: zou3519
fbshipit-source-id: 15ec4286434db55fb8357b1a12e6f70ef54f8c66
Summary:
Changelog:
- Port SVD TH implementation to ATen/native/BatchLinearAlgebra.cpp
- Port SVD THC implementation to ATen/native/cuda/BatchLinearAlgebra.cu
- Allow batches of matrices as arguments to `torch.svd`
- Remove existing implementations in TH and THC
- Update doc string
- Update derivatives to support batching
- Modify nuclear norm implementation to use at::svd instead of _batch_svd
- Remove _batch_svd as it is redundant
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21588
Test Plan:
- Add new test suite for SVD in test_torch.py with port to test_cuda.py
- Add tests in common_methods_invocations.py for derivative testing
Differential Revision: D16266115
Pulled By: nairbv
fbshipit-source-id: e89bb0dbd8f2d58bd758b7830d2389c477aa61fb
Summary:
This has been requested in https://github.com/pytorch/pytorch/issues/20323
(It is still not exactly the same as NumPy, which allows you to pass tensors at mean/std and broadcast them with size, but the present PR is extremely simple and does the main thing people are asking for)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20545
Differential Revision: D15358736
Pulled By: zhangguanheng66
fbshipit-source-id: 762ea5eab5b8667afbac2df0137df017ba6e413c
Summary:
Changelog:
- Port `symeig` from TH/THC to ATen
- Enable batching of matrix inputs for `symeig`
- Modify derivative computation based on batching
- Update docs to reflect the change
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21858
Test Plan: - Added additional tests in `test_torch.py` (with a port to `test_cuda.py`) and `common_methods_invocations.py` to test if both the port and batching work.
Differential Revision: D15981789
Pulled By: soumith
fbshipit-source-id: ab9af8361f8608db42318aabc8421bd99a1ca7ae
