Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52859
This reverts commit 92a4ee1cf6.
Added support for bfloat16 for CUDA 11 and removed fast-path for empty input tensors that was affecting autograd graph.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D27402390
Pulled By: heitorschueroff
fbshipit-source-id: 73c5ccf54f3da3d29eb63c9ed3601e2fe6951034
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
This is to prepare for new language reference spec that needs to describe `torch.jit.Attribute` and `torch.jit.annotate`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54485
Reviewed By: SplitInfinity, nikithamalgifb
Differential Revision: D27406843
Pulled By: gmagogsfm
fbshipit-source-id: 98983b9df0f974ed69965ba4fcc03c1a18d1f9f5
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54645
Had to replace RRef[..] with just RRef in the return signature since
sphynx seemed to completely mess up rendering RRef[..]
ghstack-source-id: 125024783
Test Plan: View locally.
Reviewed By: SciPioneer
Differential Revision: D27314609
fbshipit-source-id: 2dd9901e79f31578ac7733f79dbeb376f686ed75
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54277
alltoall already supported in nccl backend, so update the doc to reflect it.
Test Plan: Imported from OSS
Reviewed By: divchenko
Differential Revision: D27172904
Pulled By: wanchaol
fbshipit-source-id: 9afa89583d56b247b2017ea2350936053eb30827
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54052
Introduce `fp16_compress_wrapper`, which can give some speedup on top of some gradient compression algorithms like PowerSGD.
ghstack-source-id: 124001805
Test Plan: {F509205173}
Reviewed By: iseessel
Differential Revision: D27076064
fbshipit-source-id: 4845a14854cafe2112c0caefc1e2532efe9d3ed8
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:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.
The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.
The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093
Reviewed By: albanD
Differential Revision: D26991788
Pulled By: mruberry
fbshipit-source-id: 8af9ada979240b255402f55210c0af1cba6a0a3c
Summary:
This PR proposes to improve the distributed doc:
* [x] putting the init functions together
* [x] moving post-init functions into their own sub-section as they are only available after init and moving that group to after all init sub-sections
If this is too much, could we at least put these 2 functions together:
```
.. autofunction:: init_process_group
.. autofunction:: is_initialized
```
as they are interconnected. and the other functions are not alphabetically sorted in the first place.
Thank you.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52976
Reviewed By: albanD
Differential Revision: D26993933
Pulled By: mrshenli
fbshipit-source-id: 7cacbe28172ebb5849135567b1d734870b49de77
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53855
Remove "noindex" here:
{F492926346}
ghstack-source-id: 123724419
Test Plan:
waitforbuildbot
The failure on doctest does not seem to be relevant.
Reviewed By: rohan-varma
Differential Revision: D26967086
fbshipit-source-id: adf9db1144fa1475573f617402fdbca8177b7c08
Summary:
Fixes https://github.com/pytorch/pytorch/issues/50002
The last commit adds tests for 3d conv with the `SubModelFusion` and `SubModelWithoutFusion` classes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50003
Reviewed By: mrshenli
Differential Revision: D26325953
Pulled By: jerryzh168
fbshipit-source-id: 7406dd2721c0c4df477044d1b54a6c5e128a9034
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53253
Since GradBucket class becomes public, mention this class in ddp_comm_hooks.rst.
Screenshot:
{F478201008}
ghstack-source-id: 123596842
Test Plan: viewed generated html file
Reviewed By: rohan-varma
Differential Revision: D26812210
fbshipit-source-id: 65b70a45096b39f7d41a195e65b365b722645000
Summary:
This is a more fundamental example, as we may support some amount of shape specialization in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53250
Reviewed By: navahgar
Differential Revision: D26841272
Pulled By: Chillee
fbshipit-source-id: 027c719afafc03828a657e40859cbfbf135e05c9
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Provides the implementation for feature request issue https://github.com/pytorch/pytorch/issues/28937.
Adds the `Parametrization` functionality and implements `Pruning` on top of it.
It adds the `auto` mode, on which the parametrization is just computed once per forwards pass. The previous implementation computed the pruning on every forward, which is not optimal when pruning RNNs for example.
It implements a caching mechanism for parameters. This is implemented through the mechanism proposed at the end of the discussion https://github.com/pytorch/pytorch/issues/7313. In particular, it assumes that the user will not manually change the updated parameters between the call to `backwards()` and the `optimizer.step()`. If they do so, they would need to manually call the `.invalidate()` function provided in the implementation. This could be made into a function that gets a model and invalidates all the parameters in it. It might be the case that this function has to be called in the `.cuda()` and `.to` and related functions.
As described in https://github.com/pytorch/pytorch/issues/7313, this could be used, to implement in a cleaner way the `weight_norm` and `spectral_norm` functions. It also allows, as described in https://github.com/pytorch/pytorch/issues/28937, for the implementation of constrained optimization on manifolds (i.e. orthogonal constraints, positive definite matrices, invertible matrices, weights on the sphere or the hyperbolic space...)
TODO (when implementation is validated):
- More thorough test
- Documentation
Resolves https://github.com/pytorch/pytorch/issues/28937
albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33344
Reviewed By: zhangguanheng66
Differential Revision: D26816708
Pulled By: albanD
fbshipit-source-id: 07c8f0da661f74e919767eae31335a9c60d9e8fe
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53084
Adding RemoteModule to master RPC docs since it is a prototype
feature.
ghstack-source-id: 122816689
Test Plan: waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D26743372
fbshipit-source-id: 00ce9526291dfb68494e07be3e67d7d9c2686f1b
Summary:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.
The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.
The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093
Reviewed By: H-Huang
Differential Revision: D26723384
Pulled By: mruberry
fbshipit-source-id: c9866a95f14091955cf42de22f4ac9e2da009713
