Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D32588230
Pulled By: mruberry
fbshipit-source-id: 69e484849deb9ad7bb992cc97905df29c8915910
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: zou3519, JacobSzwejbka
Differential Revision: D32283178
Pulled By: mruberry
fbshipit-source-id: deb672e6e52f58b76536ab4158073927a35e43a8
Summary:
Fixes https://github.com/pytorch/pytorch/issues/46741
pytorchbot
contributors: nickleus27, yanivsagy, and khanhthien123
SmrutiSikha this is mostly your work. We just did very minor clean up.
cc mruberry
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67664
Reviewed By: gchanan
Differential Revision: D32311838
Pulled By: mruberry
fbshipit-source-id: 0e5d4d888caeccb0fd7c80e6ff11b1b1fa8e00d6
Summary:
### Create `linalg.cross`
Fixes https://github.com/pytorch/pytorch/issues/62810
As discussed in the corresponding issue, this PR adds `cross` to the `linalg` namespace (**Note**: There is no method variant) which is slightly different in behaviour compared to `torch.cross`.
**Note**: this is NOT an alias as suggested in mruberry's [https://github.com/pytorch/pytorch/issues/62810 comment](https://github.com/pytorch/pytorch/issues/62810#issuecomment-897504372) below
> linalg.cross being consistent with the Python Array API (over NumPy) makes sense because NumPy has no linalg.cross. I also think we can implement linalg.cross without immediately deprecating torch.cross, although we should definitely refer users to linalg.cross. Deprecating torch.cross will require additional review. While it's not used often it is used, and it's unclear if users are relying on its unique behavior or not.
The current default implementation of `torch.cross` is extremely weird and confusing. This has also been reported multiple times previously. (See https://github.com/pytorch/pytorch/issues/17229, https://github.com/pytorch/pytorch/issues/39310, https://github.com/pytorch/pytorch/issues/41850, https://github.com/pytorch/pytorch/issues/50273)
- [x] Add `torch.linalg.cross` with default `dim=-1`
- [x] Add OpInfo and other tests for `torch.linalg.cross`
- [x] Add broadcasting support to `torch.cross` and `torch.linalg.cross`
- [x] Remove out skip from `torch.cross` OpInfo
- [x] Add docs for `torch.linalg.cross`. Improve docs for `torch.cross` mentioning `linalg.cross` and the difference between the two. Also adds a warning to `torch.cross`, that it may change in the future (we might want to deprecate it later)
---
### Additional Fixes to `torch.cross`
- [x] Fix Doc for Tensor.cross
- [x] Fix torch.cross in `torch/overridres.py`
While working on `linalg.cross` I noticed these small issues with `torch.cross` itself.
[Tensor.cross docs](https://pytorch.org/docs/stable/generated/torch.Tensor.cross.html) still mentions `dim=-1` default which is actually wrong. It should be `dim=None` after the behaviour was updated in PR https://github.com/pytorch/pytorch/issues/17582 but the documentation for the `method` or `function` variant wasn’t updated. Later PR https://github.com/pytorch/pytorch/issues/41850 updated the documentation for the `function` variant i.e `torch.cross` and also added the following warning about the weird behaviour.
> If `dim` is not given, it defaults to the first dimension found with the size 3. Note that this might be unexpected.
But still, the `Tensor.cross` docs were missed and remained outdated. I’m finally fixing that here. Also fixing `torch/overrides.py` for `torch.cross` as well now, with `dim=None`.
To verify according to the docs the default behaviour of `dim=-1` should raise, you can try the following.
```python
a = torch.randn(3, 4)
b = torch.randn(3, 4)
b.cross(a) # this works because the implementation finds 3 in the first dimension and the default behaviour as shown in documentation is actually not true.
>>> tensor([[ 0.7171, -1.1059, 0.4162, 1.3026],
[ 0.4320, -2.1591, -1.1423, 1.2314],
[-0.6034, -1.6592, -0.8016, 1.6467]])
b.cross(a, dim=-1) # this raises as expected since the last dimension doesn't have a 3
>>> RuntimeError: dimension -1 does not have size 3
```
Please take a closer look (particularly the autograd part, this is the first time I'm dealing with `derivatives.yaml`). If there is something missing, wrong or needs more explanation, please let me know. Looking forward to the feedback.
cc mruberry Lezcano IvanYashchuk rgommers
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63285
Reviewed By: gchanan
Differential Revision: D32313346
Pulled By: mruberry
fbshipit-source-id: e68c2687c57367274e8ddb7ef28ee92dcd4c9f2c
Summary:
Adds `torch.argwhere` as an alias to `torch.nonzero`
Currently, `torch.nonzero` is actually provides equivalent functionality to `np.argwhere`.
From NumPy docs,
> np.argwhere(a) is almost the same as np.transpose(np.nonzero(a)), but produces a result of the correct shape for a 0D array.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64257
Reviewed By: qihqi
Differential Revision: D32049884
Pulled By: saketh-are
fbshipit-source-id: 016e49884698daa53b83e384435c3f8f6b5bf6bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64430
The functionalization pass needs `{view}_scatter` versions of the slice/select/diagonal ops in order to correctly propagate mutations from a view to its base. On top of that, the implementations need to be primitive w.r.t. autograd, because they look something like `...slice().copy_()`, and the functionalization pass can't use views + mutations inside of it's own alias-removal machinery!
I added some basic tests that I tried to base off of existing tests for views (particularly around testing the derivative formulas), but I'm wondering if I should add something more comprehensive.
Also, as_strided fits into this category - the functionalization pass will need an `as_strided_scatter` op that's primitive w.r.t. autograd. I didn't add it for now, because it'll involve duplicating a bunch of logic from the current `as_strided_backward()` function, and also writing a derivative formula that I wasn't sure how to write :)
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D31942092
Pulled By: bdhirsh
fbshipit-source-id: c702a57c2748a7c771c14e4bcc3e996b48fcc4c8
Summary:
Adds `torch.argwhere` as an alias to `torch.nonzero`
Currently, `torch.nonzero` is actually provides equivalent functionality to `np.argwhere`.
From NumPy docs,
> np.argwhere(a) is almost the same as np.transpose(np.nonzero(a)), but produces a result of the correct shape for a 0D array.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64257
Reviewed By: dagitses
Differential Revision: D31474901
Pulled By: saketh-are
fbshipit-source-id: 335327a4986fa327da74e1fb8624cc1e56959c70
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64181
This PR replaces all the calls to:
- `transpose(-2, -1)` or `transpose(-1, -2)` by `mT()` in C++ and `mT` in Python
- `conj().transpose(-2, -1)` or `transpose(-2, -1).conj()` or `conj().transpose(-1, -2)` or `transpose(-1, -2).conj()` by `mH()` in C++ and `mH` in Python.
It also simplifies two pieces of code, and fixes one bug where a pair
of parentheses were missing in the function `make_symmetric_matrices`.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D31692896
Pulled By: anjali411
fbshipit-source-id: e9112c42343663d442dc5bd53ff2b492094b434a
Summary:
Currently, the description of torch.any would be parsed like
```
param input
the input tensor.
```
However, it should be
```
Tests if any element in input evaluates to True.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65310
Reviewed By: ezyang
Differential Revision: D31102918
Pulled By: soulitzer
fbshipit-source-id: 678ade20ba16ad2643639fbd2420c8b36fcd8bd7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62671
Very crude first implementation of `torch.nanmean`. The current reduction kernels do not have good support for implementing nan* variants. Rather than implementing new kernels for each nan* operator, I will work on new reduction kernels with support for a `nan_policy` flag and then I will port `nanmean` to use that.
**TODO**
- [x] Fix autograd issue
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D30515181
Pulled By: heitorschueroff
fbshipit-source-id: 303004ebd7ac9cf963dc4f8e2553eaded5f013f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63567
The current implementation called trtrs for CPU and trsm for CUDA.
See https://github.com/pytorch/pytorch/issues/56326#issuecomment-825496115 for a discussion on the differences between
these two functions and why we prefer trsm vs trtrs on CUDA.
This PR also exposes the `side` argument of this function which is used
in the second PR of this stack to optimise the number copies one needs to make
when preparing the arguments to be sent to the backends.
It also changes the use of `bool`s to a common enum type to represent
whether a matrix is transposed / conj transposed, etc. This makes the API
consistent, as before, the behaviour of these functions with `transpose=True`
and `conjugate_transpose=True` it was not well defined.
Functions to transform this type into the specific types / chars for the different
libraries are provided under the names `to_blas`, `to_lapack`, `to_magma`, etc.
This is the first of a stack of PRs that aim to improve the performance of
`linalg.solve_triangular`. `trsm` has an extra parameter (`side`), which allows to
ellide the copy of the triangular matrix in many cases.
Fixes https://github.com/pytorch/pytorch/issues/56326
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D30566479
Pulled By: mruberry
fbshipit-source-id: 3831af9b51e09fbfe272c17c88c21ecf45413212
Summary:
Fixes https://github.com/pytorch/pytorch/issues/62811
Add `torch.linalg.matmul` alias to `torch.matmul`. Note that the `linalg.matmul` doesn't have a `method` variant.
Also cleaning up `torch/_torch_docs.py` when formatting is not needed.
cc IvanYashchuk Lezcano mruberry rgommers
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63227
Reviewed By: mrshenli
Differential Revision: D30770235
Pulled By: mruberry
fbshipit-source-id: bfba77dfcbb61fcd44f22ba41bd8d84c21132403
Summary:
Fixes https://github.com/pytorch/pytorch/issues/61767
## Changes
- [x] Add `torch.concat` alias to `torch.cat`
- [x] Add OpInfo for `cat`/`concat`
- [x] Fix `test_out` skips (Use `at::native::resize_output` or `at::native::resize_output_check`)
- [x] `cat`/`concat`
- [x] `stack`
- [x] `hstack`
- [x] `dstack`
- [x] `vstack`/`row_stack`
- [x] Remove redundant tests for `cat`/`stack`
~I've not added `cat`/`concat` to OpInfo `op_db` yet, since cat is a little more tricky than other OpInfos (should have a lot of tests) and currently there are no OpInfos for that. I can try to add that in a subsequent PR or maybe here itself, whatever is suggested.~
**Edit**: cat/concat OpInfo has been added.
**Note**: I've added the named tensor support for `concat` alias as well, maybe that's out of spec in `array-api` but it is still useful for consistency in PyTorch.
Thanks to krshrimali for guidance on my first PR :))
cc mruberry rgommers pmeier asmeurer leofang AnirudhDagar asi1024 emcastillo kmaehashi heitorschueroff krshrimali
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62560
Reviewed By: saketh-are
Differential Revision: D30762069
Pulled By: mruberry
fbshipit-source-id: 6985159d1d9756238890488a0ab3ae7699d94337
Summary:
Fixes https://github.com/pytorch/pytorch/issues/39329.
The documentation for torch.add and torch.mul was sorely out of date and even included deprecated references. This PR modernizes their descriptions consistent with torch.sub.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63309
Reviewed By: ngimel
Differential Revision: D30338004
Pulled By: mruberry
fbshipit-source-id: ee1c2a8106af8341253cafb0003b06e8f652624d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63258
This function supports scalar and tensor qparams
Test Plan:
CI
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D30316432
fbshipit-source-id: 8b2f5582e7e095fdda22c17d178abcbc89a2d1fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63242
These functions are part of the native functions namespace as well as the quantized namespace
Test Plan:
CI
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D30316430
fbshipit-source-id: cd9c839e5c1a961e3c6944e514c16fbc256a2f0c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63240
Op is exposed via torch.quantized_batch_norm to the end user without any existing documentation
Test Plan:
CI
Imported from OSS
Reviewed By: VitalyFedyunin
Differential Revision: D30316431
fbshipit-source-id: bf2dc8b7b6f497cf73528eaa2bedef9f65029d84
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63177
**Summary**
This commit adds a link in the documentation for `torch.transpose` that
directs to `torch.t` and vice versa. These two functions are related and
it is useful for users of one to know about the other.
**Test Plan**
Continuous integration.
**Fixes**
This commit fixes#56267.
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin
Differential Revision: D30292654
Pulled By: SplitInfinity
fbshipit-source-id: 8e60cd7a598ff8b4756cb30141399dfe8e118338
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63165
Add details about the overloads for
* list of tensors input
* supporting tensor scale/zero-point inputs
Test Plan:
CI
Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D30291045
fbshipit-source-id: 9fc6418792c5e3a35417eeb8d31de4a4bfcbb7a5
Summary:
Sphinx 4.x is out, but it seems that requires many more changes to
adopt. So instead use the latest version of 3.x, which includes
several nice features.
* Add some noindex directives to deal with warnings that would otherwise
be triggered by this change due to conflicts between the docstrings
declaring a function and the autodoc extension declaring the
same function.
* Update distributions.utils.lazy_property to make it look like a
regular property when sphinx autodoc inspects classes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61601
Reviewed By: ejguan
Differential Revision: D29801876
Pulled By: albanD
fbshipit-source-id: 544d2434a15ceb77bff236e934dbd8e4dbd9d160
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59077Fixes#58549
`from_buffer` constructs a tensor object from an already allocated buffer through
CPython's buffer protocol. Besides the standard `dtype`, `count`, and `offset` parameters,
this function also accepts:
- `device`: where the buffer lives
- `requires_grad`: should autograd record operations on the new tensor
A new test file _test_buffer_protocol.py_ was created. Currently, only CPU tests were
implemented. That's because neither PyTorch nor Numba implements CPython's buffer
protocol. Therefore, there's no way to create a CUDA buffer with the existing
dependencies (could use PyCUDA for that, though).
At the moment, if `device` differs from the device the buffer actually lives, two things
may happen:
- `RuntimeError`, if `device='cuda'`
- Segmentation fault (not tested -- see above), if `device='cpu'`
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D29870914
Pulled By: mruberry
fbshipit-source-id: 9fa8611aeffedfe39c9af74558178157a11326bb
Summary:
This PR un-reverts https://github.com/pytorch/pytorch/issues/61475 + fixes compilation with MSVC, that does not recognize alternative operator spellings (i.e. using `or` instead of `||` )
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61937
Reviewed By: albanD
Differential Revision: D29805941
Pulled By: malfet
fbshipit-source-id: 01e5963c6717c1b44b260300d87ba0bf57f26ce9
