The reason for enabling sparse/dense_dim() for strided tensors is to have more meaningful error messages:
For instance, compare
```
NotImplementedError: Could not run 'aten::sparse_dim' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::sparse_dim' is only available for these backends: [SparseCPU, SparseCUDA, SparseMeta, SparseCsrCPU, SparseCsrCUDA, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMeta, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].
```
[master] vs
```
RuntimeError: addmm: matrices expected, got 0D tensor
```
[this PR] where the latter message gives a hint of which function is to blame for dealing with unexpected inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86203
Approved by: https://github.com/cpuhrsch
**Reopened** to help with merge issues. See #59790 for full context.
Fixes#20778. Helps #71688.
Finalizes @martinPasen's force argument for `Tensor.numpy()`. It is set to False by default. If it's set to True then we:
1. detatch the Tensor, if requires_grad == True
2. move to cpu, if not on cpu already
3. Uses .resolve_conj() if .is_conj() == True
4. Uses .resolve_neg() if .is_neg() == True
cc @albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78564
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74226
Update signature of `scatter_reduce_` to match `scatter_/scatter_add_`
`Tensor.scatter_reduce_(int64 dim, Tensor index, Tensor src, str reduce)`
- Add new reduction options in ScatterGatherKernel.cpp and update `scatter_reduce` to call into the cpu kernel for `scatter.reduce`
- `scatter_reduce` now has the same shape constraints as `scatter_` and `scatter_add_`
- Migrate `test/test_torch.py:test_scatter_reduce` to `test/test_scatter_gather_ops.py`
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D35222842
Pulled By: mikaylagawarecki
fbshipit-source-id: 84930add2ad30baf872c495251373313cb7428bd
(cherry picked from commit 1b45139482e22eb0dc8b6aec2a7b25a4b58e31df)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/65908
Added a new overload instead of updating the current signature. (Had issues with JIT and **maybe** it would have been FC breaking)
TODO:
* [x] Don't compute `std::pow(10, decimals)` for each element.
* [x] Update docs (https://docs-preview.pytorch.org/66195/generated/torch.round.html?highlight=round#torch.round)
* [x] Add tests
* ~~Should we try to make it composite?~~
* ~~Should we add specialized test with more values of `decimals` outside of OpInfo with larger range of values in input tensor?~~
cc mruberry rgommers
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66195
Reviewed By: anjali411
Differential Revision: D31821385
Pulled By: mruberry
fbshipit-source-id: 9a03fcb809440f0c83530108284e69c345e1850f
(cherry picked from commit 50b67c6968)
Summary:
Hi, PyTorch Team!
I am very much interested in starting up my contribution to PyTorch. I made several contributions in NumPy and CuPy, but this is my first PR towards PyTorch. I aim to contribute more in the upcoming future.
The PR fixes https://github.com/pytorch/pytorch/issues/70972https://github.com/pytorch/pytorch/issues/70975.
#### Aim of PR
The functions like `Tensor.ravel`, `Tensor.tril`, `Tensor.tril_`, `Tensor.triu`, and `Tensor.triu_` had a couple of typos in docs. The PR aims to resolve that.
I'm looking forward to your viewpoints. Thanks!
cc: kshitij12345 vadimkantorov Lezcano TestSomething22
cc brianjo mruberry
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71057
Reviewed By: preeti1205
Differential Revision: D33502911
Pulled By: mruberry
fbshipit-source-id: 8ce0b68a29658a5a0be79bc807dfa7d71653532d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65993
This PR attempts to port `index_add` to structured kernels, but does more than that:
* Adds an `out=` variant to `index_add`
* Revises `native_functions.yaml` registrations, to not have multiple entries and instead pass default value to `alpha`.
* Changes in `derivatives.yaml` file for autograd functioning
* Revises error messages, please see: https://github.com/pytorch/pytorch/pull/65993#issuecomment-945441615
Follow-up PRs in near future will attempt to refactor the OpInfo test, and will give another look at tests in `test/test_torch.py` for this function. (hence the use of ghstack for this)
~This is WIP because there are tests failing for `Dimname` variant on mobile/android builds, and I'm working on fixing them.~
Issue tracker: https://github.com/pytorch/pytorch/issues/55070
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D32646426
fbshipit-source-id: b035ecf843a9a27d4d1e18b202b035adc2a49ab5
