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As in https://github.com/pytorch/pytorch/issues/23439, some descriptions of arguments in `_torch_docs.py` have been replaced by `common_args`, it would be helpful to check if any descriptions can be replaced for new docs in the future.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
Summary:
This PR covers two important points with respect to the QR decomposition:
- batching of input matrices (#7500)
- adding `some` as an option in `torch.qr` akin to NumPy's `mode` option (#10538)
Changelog:
- Enable batching for inputs to `torch.qr`
- Move QR decomposition implementation to ATen (CPU and CUDA)
- Remove existing implementations in TH/THC
- Add a `some` option to `torch.qr` that will enable users to switch between complete and reduced decomposition
- Modify doc strings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20689
Differential Revision: D15529230
Pulled By: soumith
fbshipit-source-id: 16af82b1d2db8a3a758fa8a5f798d83f5f950efb
Summary:
The current variance kernels compute mean at the same time. Many times we want both statistics together, so it seems reasonable to have a kwarg/function that allows us to get both values without launching an extra kernel.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18731
Differential Revision: D14726082
Pulled By: ifedan
fbshipit-source-id: 473cba0227b69eb2240dca5e61a8f4366df0e029
Summary:
Add base support for torch.logspace. See #19220 for details.
SsnL can you feedback? Thanks a lot.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19542
Differential Revision: D15028484
Pulled By: soumith
fbshipit-source-id: fe5a58a203b279103abbc192c754c25d5031498e
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:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Now compatible with both torch scripts:
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"), pin_memory=False)`
and
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))`
Same checked for all similar functions `rand_like`, `empty_like` and others
It is fixed version of #18455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18952
Differential Revision: D14801792
Pulled By: VitalyFedyunin
fbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba
