Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57315
This PR ports `torch.ormqr` from TH to ATen.
CUDA path will be implemented in a follow-up PR.
With ATen port, support for complex and batched inputs is added.
The tests are rewritten and OpInfo entry is added.
We can implement the least squares solver with geqrf + ormqr +
triangular_solve. So it's useful to have this function renewed at least for the
internal code.
Resolves https://github.com/pytorch/pytorch/issues/24748
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D28242070
Pulled By: mruberry
fbshipit-source-id: f070bb6ac2f5a3269b163b22f7354e9089ed3061
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56249
This PR ports `torch.geqrf` from TH to ATen. CUDA path will be
implemented in a follow-up PR.
With ATen port support for complex and batched inputs is added.
There were no correctness tests, they are
added in this PR and I added OpInfo for this operation.
We can implement the QR decomposition as a composition of geqrf and
orgqr (torch.linalg.householder_product).
Also we can implement the least squares solver with geqrf + ormqr +
trtrs. So it's useful to have this function renewed at least for the
internal code.
Resolves https://github.com/pytorch/pytorch/issues/24705
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D27907357
Pulled By: mruberry
fbshipit-source-id: 94e1806078977417e7903db76eab9d578305f585
Summary:
Fixes https://github.com/pytorch/pytorch/issues/45664
This PR adds a note to the documentation for `torch.clamp()` to alert users to a special case: If `min` is greater than `max`, all values are set to the `max` value.
Also, an example was added after the first code example. And this one is referenced in the note.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56367
Reviewed By: ezyang
Differential Revision: D27960553
Pulled By: mruberry
fbshipit-source-id: 9dc6016ccacebe87c809a0dd9f557b4aea0ae6f5
Summary:
https://github.com/pytorch/pytorch/issues/54268 removed `test_run_mypy` since now we're running `mypy` as its own job in GitHub Actions, but previously we used this `set_cwd` context manager in that test to ensure that we picked up the `mypy` config correctly. However, for some reason, we have not been doing that in `test_doc_examples`, which has been succeeding in CI for a while despite being broken.
Specifically, [`run_test.py` changes the working directory to `test/` before running test files](48aaea3359/test/run_test.py (L534-L535)), which is contrary to [what `CONTRIBUTING.md` instructs developers to do](48aaea3359/CONTRIBUTING.md (python-unit-testing)). As a result, in CI, `test/test_type_hints.py` has been passing in CI, but if you run it locally from the root of the repo, this you get this error:
```
F
======================================================================
FAIL: test_doc_examples (__main__.TestTypeHints)
Run documentation examples through mypy.
----------------------------------------------------------------------
Traceback (most recent call last):
File "test/test_type_hints.py", line 127, in test_doc_examples
self.fail(f"mypy failed:\n{stdout}")
AssertionError: mypy failed:
test/generated_type_hints_smoketest.py:851: error: Name 'tensor' is not defined [name-defined]
test/generated_type_hints_smoketest.py:853: error: Name 'tensor' is not defined [name-defined]
Found 2 errors in 1 file (checked 1 source file)
----------------------------------------------------------------------
Ran 1 test in 1.416s
FAILED (failures=1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56388
Test Plan:
Before this PR, the first of the following two commands should fail (since that is essentially what is run in CI), but the second should fail:
```
python test/run_test.py -i test_type_hints
python test/test_type_hints.py
```
After this PR, both commands should succeed.
Reviewed By: driazati
Differential Revision: D27860173
Pulled By: samestep
fbshipit-source-id: efb82fffd7ccb04d0331824b40bdef7bbc319c98
Summary:
argmax docstring previously said that it returns indexes of the first 'minimal' value, fixed typo in that line to 'maximal'
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55239
Reviewed By: albanD
Differential Revision: D27641562
Pulled By: mrshenli
fbshipit-source-id: f8b5c579400088b5210c83a05da6c4c106fbf95d
Summary:
Fixes https://github.com/pytorch/pytorch/issues/53651
I did not put much effort in improving the docs, as I will go over all these docs in future PRs
cc anjali411
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55085
Reviewed By: nikithamalgifb
Differential Revision: D27493604
Pulled By: anjali411
fbshipit-source-id: 413363013e188bc869c404b2d54ce1f87eef4425
Summary:
Reference: https://github.com/pytorch/pytorch/issues/50345
Chages:
* Alias for sigmoid and logit
* Adds out variant for C++ API
* Updates docs to link back to `special` documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54759
Reviewed By: mrshenli
Differential Revision: D27615208
Pulled By: mruberry
fbshipit-source-id: 8bba908d1bea246e4aa9dbadb6951339af353556
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49267
This PR builds upon the PR https://github.com/pytorch/pytorch/pull/48711 by RockingJavaBean. The original PR introduced a BC breaking change by making the interpolation parameter positional. Thus, previous invocations of torch.quantile that did not include the interpolation parameter failed after the PR landed.
To avoid BC breaking changes, we preserve the original signatures and make the interpolation parameter in the new signatures kwarg only. For now, interpolation cannot have a default value to avoid ambiguity with the deprecated signature. However, due to limitations of codegen and C++, we cannot have a required arg after optional ones. Thus, this PR also makes dim and keepdim requires args. Once we can remove the old signatures, dim, keepdim and interpolation parameters in the new signature will get the default values back.
__TODO__
---
- [ ] Run backward compat tests
This reverts commit 2f1d1eb7df.
Test Plan: Imported from OSS
Reviewed By: glaringlee
Differential Revision: D27337117
Pulled By: heitorschueroff
fbshipit-source-id: 7fe31f22027645e0d6cb3cab0392d532a4b362c9
Summary:
- Corrected a few errata in the SVD docs
- Made the notation more uniform (refer to `Vh` in `linalg.svd`, always use double tilts...)
- Wrote a better explanation about why the gradients of `U` and `V` are not well-defined when the input is complex or real but has repeated singular values. The previous one pointed to a somewhat obscure post on gauge theory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54002
Reviewed By: malfet
Differential Revision: D27459502
Pulled By: mruberry
fbshipit-source-id: f5c35eca02d35dadd2fc0eeadfacc8824f409400
Summary:
Reference: https://github.com/pytorch/pytorch/issues/38349
Wrapper around the existing `torch.gather` with broadcasting logic.
TODO:
* [x] Add Doc entry (see if phrasing can be improved)
* [x] Add OpInfo
* [x] Add test against numpy
* [x] Handle broadcasting behaviour and when dim is not given.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52833
Reviewed By: malfet
Differential Revision: D27319038
Pulled By: mruberry
fbshipit-source-id: 00f307825f92c679d96e264997aa5509172f5ed1
Summary:
This PR adds autograd support for `torch.orgqr`.
Since `torch.orgqr` is one of few functions that expose LAPACK's naming and all other linear algebra routines were renamed a long time ago, I also added a new function with a new name and `torch.orgqr` now is an alias for it.
The new proposed name is `householder_product`. For a matrix `input` and a vector `tau` LAPACK's orgqr operation takes columns of `input` (called Householder vectors or elementary reflectors) scalars of `tau` that together represent Householder matrices and then the product of these matrices is computed. See https://www.netlib.org/lapack/lug/node128.html.
Other linear algebra libraries that I'm aware of do not expose this LAPACK function, so there is some freedom in naming it. It is usually used internally only for QR decomposition, but can be useful for deep learning tasks now when it supports differentiation.
Resolves https://github.com/pytorch/pytorch/issues/50104
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52637
Reviewed By: agolynski
Differential Revision: D27114246
Pulled By: mruberry
fbshipit-source-id: 9ab51efe52aec7c137aa018c7bd486297e4111ce
Summary:
brianjo
- Add a javascript snippet to close the expandable left navbar sections 'Notes', 'Language Bindings', 'Libraries', 'Community'
- Fix two latex bugs that were causing output in the log that might have been misleading when looking for true doc build problems
- Change the way release versions interact with sphinx. I tested these via building docs twice: once with `export RELEASE=1` and once without.
- Remove perl scripting to turn the static version text into a link to the versions.html document. Instead, put this where it belongs in the layout.html template. This is the way the domain libraries (text, vision, audio) do it.
- There were two separate templates for master and release, with the only difference between them is that the master has an admonition "You are viewing unstable developer preview docs....". Instead toggle that with the value of `release`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53851
Reviewed By: mruberry
Differential Revision: D27085875
Pulled By: ngimel
fbshipit-source-id: c2d674deb924162f17131d895cb53cef08a1f1cb
Summary:
Close https://github.com/pytorch/pytorch/issues/51108
Related https://github.com/pytorch/pytorch/issues/38349
This PR implements the `cpu_kernel_multiple_outputs` to support returning multiple values in a CPU kernel.
```c++
auto iter = at::TensorIteratorConfig()
.add_output(out1)
.add_output(out2)
.add_input(in1)
.add_input(in2)
.build();
at::native::cpu_kernel_multiple_outputs(iter,
[=](float a, float b) -> std::tuple<float, float> {
float add = a + b;
float mul = a * b;
return std::tuple<float, float>(add, mul);
}
);
```
The `out1` will equal to `torch.add(in1, in2)`, while the result of `out2` will be `torch.mul(in1, in2)`.
It helps developers implement new torch functions that return two tensors more conveniently, such as NumPy-like functions [divmod](https://numpy.org/doc/1.18/reference/generated/numpy.divmod.html?highlight=divmod#numpy.divmod) and [frexp](https://numpy.org/doc/stable/reference/generated/numpy.frexp.html#numpy.frexp).
This PR adds `torch.frexp` function to exercise the new functionality provided by `cpu_kernel_multiple_outputs`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51097
Reviewed By: albanD
Differential Revision: D26982619
Pulled By: heitorschueroff
fbshipit-source-id: cb61c7f2c79873ab72ab5a61cbdb9203531ad469
Summary:
This PR adds the cuBLAS based path for `torch.triangular_solve`
The device dispatching helper function was removed from native_functions.yml, it is replaced with DECLARE/DEFINE_DISPATCH.
`magmaTriangularSolve` is removed and replaced with cuBLAS calls, this is not a BC-breaking change because internally MAGMA just calls the same cuBLAS function and doesn't do anything else.
Batched cuBLAS is faster than batched MAGMA for matrices of size up until 512x512, after that MAGMA is faster. For batches smaller than ~8 and matrix sizes larger than 64x64 a forloop of cuBLAS calls is faster than batched version.
Ref. https://github.com/pytorch/pytorch/issues/47953
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53147
Reviewed By: heitorschueroff
Differential Revision: D27007416
Pulled By: mruberry
fbshipit-source-id: ddfc190346e6a56b84145ed0a9af67ca9cde3506
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53276
- One of the tests had a syntax error (but the test
wasn't fine grained enough to catch this; any error
was a pass)
- Doesn't work on ROCm
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D26820048
Test Plan: Imported from OSS
Reviewed By: mruberry
Pulled By: ezyang
fbshipit-source-id: b02c4252d10191c3b1b78f141d008084dc860c45
Summary:
In design review the use of the word "true" for a "rounding mode" which actually performed no rounding was, understandably, considered confusing. This PR updates the documentation to remove references to "true." The signatures for torch.div and torch.divide are updated to reflect the future behavior where rounding_mode=None will be the default.
This is slightly inaccurate. Today when rounding mode is not specified it is effectively None, but users cannot actually specify rounding_mode=None today. That change was considered too disruptive to the 1.8 branch cut process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52202
Reviewed By: gchanan
Differential Revision: D26424979
Pulled By: mruberry
fbshipit-source-id: db3cc769c0d9c6d7e42bfad294073c99fa9168d9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51748
Adding docs for `fake_quantize_per_tensor_affine` and `fake_quantize_per_channel_affine`
functions.
Note: not documenting `fake_quantize_per_tensor_affine_cachemask` and
`fake_quantize_per_channel_affine_cachemask` since they are implementation details
of `fake_quantize_per_tensor_affine` and `fake_quantize_per_channel_affine`,
and do not need to be exposed to the user at the moment.
Test Plan: Build the docs locally on Mac OS, it looks good
Reviewed By: supriyar
Differential Revision: D26270514
Pulled By: vkuzo
fbshipit-source-id: 8e3c9815a12a3427572cb4d34a779e9f5e4facdd
