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
Summary:
This PR bundles a number of errata detected in the linalg docs over the last few weeks.
- Simpler Cholesky deprecation rule
- Remove repeated consecutive words
- Correct cond with rcond in lstsq
- Correct examples of lstsq
- More concise examples
- Use the names of the inputs / outputs in the variables of the examples
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61578
Reviewed By: mrshenli
Differential Revision: D29757988
Pulled By: mruberry
fbshipit-source-id: a740a64826c065c1d7c1b8b498364d147008d76d
Summary:
torch.chunk may return less than the requested number of chunks silently if some undocumented division constraints are not met. The functionality that users expect is provided by another function: torch.tensor_split
This has led to confusion countless times and who knows how many systems out there are fragile because of this.
My changes describe the discrepancy, show an example and direct users to the usually preferred function.
Issues mentioning this problem:
https://github.com/pytorch/pytorch/issues/9382https://github.com/torch/torch7/issues/617
I considered documenting the constraint for when an unexpected number of chunks may be returned (it is chunks*chunks>input.size[dim] ), so that users could quickly tell if their code may be affected. Please let me know if you think this should be in the docs or not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61097
Reviewed By: heitorschueroff
Differential Revision: D29660280
Pulled By: ezyang
fbshipit-source-id: 675086bc8a8882c1685a50a2c083ae8dd1854384
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56058
User facing changes:
1. Adds a negative bit and corresponding new API (`is_neg()`,`resolve_neg()`)
2. `tensor.conj().imag` now returns a floating point tensor with neg bit set to 1 instead of a tensor with no notion of negative bit. Note that imag is still a view and all the view properties still hold for imag.
Non user facing changes:
1. Added a new Negative dispatch key and a backend fallback to handle it
2. Updated copy kernel to handle negative bit
3. Merged conjugate and negative bit fallback kernel
4. fixed https://github.com/pytorch/pytorch/issues/60478 (caused due to https://github.com/pytorch/pytorch/pull/54987)
Testing:
1. Added a new OpInfo based test `test_neg_view` (verifies that out-of-place and in-place operations work correctly for all operations when the input is a neg view tensor by checking the result against an actually negated tensor, verifies that autograd returns the same output for both neg view and actually negated tensors as well as it works fine when grad_out is a neg view).
2. Added a new test class containing `test_conj_view`, `test_neg_view`.
Test Plan: Imported from OSS
Reviewed By: soulitzer
Differential Revision: D29636403
fbshipit-source-id: 12214c9dc4806c51850f4a72a109db9527c0ca63
Summary:
Based from https://github.com/pytorch/pytorch/pull/50466
Adds the initial implementation of `torch.cov` similar to `numpy.cov`. For simplicity, we removed support for many parameters in `numpy.cov` that are either redundant such as `bias`, or have simple workarounds such as `y` and `rowvar`.
cc PandaBoi
closes https://github.com/pytorch/pytorch/issues/19037
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58311
Reviewed By: jbschlosser
Differential Revision: D29431651
Pulled By: heitorschueroff
fbshipit-source-id: 167dea880f534934b145ba94291a9d634c25b01b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60507
Fix incorrect documentation about the dtype for `torch.randint` described in issue #56347
Test Plan: Review documentation to make sure formatting is right
Reviewed By: bdhirsh
Differential Revision: D29321181
fbshipit-source-id: caae69a9bbb30052da518a3f5d22a7ed3504cdd2
Summary:
There is a very common error when writing docs: One forgets to write a matching `` ` ``, and something like ``:attr:`x`` is rendered in the docs. This PR fixes most (all?) of these errors (and a few others).
I found these running ``grep -r ">[^#<][^<]*\`"`` on the `docs/build/html/generated` folder. The regex finds an HTML tag that does not start with `#` (as python comments in example code may contain backticks) and that contains a backtick in the rendered HTML.
This regex has not given any false positive in the current codebase, so I am inclined to suggest that we should add this check to the CI. Would this be possible / reasonable / easy to do malfet ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60474
Reviewed By: mrshenli
Differential Revision: D29309633
Pulled By: albanD
fbshipit-source-id: 9621e0e9f87590cea060dd084fa367442b6bd046
Summary:
Fixes https://github.com/pytorch/pytorch/issues/56036
Fixes https://github.com/pytorch/pytorch/issues/56130
* All the interior points are computed using second order accurate central differences method for gradient operator. However, currently we only have first order method computation for edge points. In this PR we are adding second order methods for edge points as well.
* Currently, there is no detailed description of how gradient operator computed using second order method, and how to use parameters correctly. We add detailed explanation of meaning of each parameter, and return of the gradient operator, meanwhile giving description of the second-order computation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58165
Reviewed By: mruberry
Differential Revision: D29305321
Pulled By: iramazanli
fbshipit-source-id: 0e0e418eed801c8510b8babe2ad3d064479fb4d6
Summary:
Fixes https://github.com/pytorch/pytorch/issues/3025
## Background
This PR implements a function similar to numpy's [`isin()`](https://numpy.org/doc/stable/reference/generated/numpy.isin.html#numpy.isin).
The op supports integral and floating point types on CPU and CUDA (+ half & bfloat16 for CUDA). Inputs can be one of:
* (Tensor, Tensor)
* (Tensor, Scalar)
* (Scalar, Tensor)
Internally, one of two algorithms is selected based on the number of elements vs. test elements. The heuristic for deciding which algorithm to use is taken from [numpy's implementation](fb215c7696/numpy/lib/arraysetops.py (L575)): if `len(test_elements) < 10 * len(elements) ** 0.145`, then a naive brute-force checking algorithm is used. Otherwise, a stablesort-based algorithm is used.
I've done some preliminary benchmarking to verify this heuristic on a devgpu, and determined for a limited set of tests that a power value of `0.407` instead of `0.145` is a better inflection point. For now, the heuristic has been left to match numpy's, but input is welcome for the best way to select it or whether it should be left the same as numpy's.
Tests are adapted from numpy's [isin and in1d tests](7dcd29aaaf/numpy/lib/tests/test_arraysetops.py).
Note: my locally generated docs look terrible for some reason, so I'm not including the screenshot for them until I figure out why.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53125
Test Plan:
```
python test/test_ops.py # Ex: python test/test_ops.py TestOpInfoCPU.test_supported_dtypes_isin_cpu_int32
python test/test_sort_and_select.py # Ex: python test/test_sort_and_select.py TestSortAndSelectCPU.test_isin_cpu_int32
```
Reviewed By: soulitzer
Differential Revision: D29101165
Pulled By: jbschlosser
fbshipit-source-id: 2dcc38d497b1e843f73f332d837081e819454b4e
Summary:
Based from https://github.com/pytorch/pytorch/pull/50466
Adds the initial implementation of `torch.cov` similar to `numpy.cov`. For simplicity, we removed support for many parameters in `numpy.cov` that are either redundant such as `bias`, or have simple workarounds such as `y` and `rowvar`.
cc PandaBoi
TODO
- [x] Improve documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58311
Reviewed By: mruberry
Differential Revision: D28994140
Pulled By: heitorschueroff
fbshipit-source-id: 1890166c0a9c01e0a536acd91571cd704d632f44
