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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52141
Remove BufferShuffleDataSet, as it's not being used anywhere within PyTorch (no usage on Github based on a search) and it's not included in the release of PyTorch 1.7.1.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D26710940
Pulled By: ejguan
fbshipit-source-id: 90023b4bfb105d6aa392753082100f9181ecebd0
Summary:
Fixes https://github.com/pytorch/pytorch/issues/52724.
This fixes the following for the LKJCholesky distribution in master:
- `log_prob` does sample validation when `validate_args=True`.
- exposes documentation for the LKJCholesky distribution.
cc. fehiepsi, fritzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52763
Reviewed By: anjali411
Differential Revision: D26657216
Pulled By: neerajprad
fbshipit-source-id: 12e8f8384cf0c3df8a29564c1e1718d2d6a5833f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51807
Implemented torch.linalg.multi_dot similar to [numpy.linalg.multi_dot](https://numpy.org/doc/stable/reference/generated/numpy.linalg.multi_dot.html).
This function does not support broadcasting or batched inputs at the moment.
**NOTE**
numpy.linalg.multi_dot allows the first and last tensors to have more than 2 dimensions despite their docs stating these must be either 1D or 2D. This PR diverges from NumPy in that it enforces this restriction.
**TODO**
- [ ] Benchmark against NumPy
- [x] Add OpInfo testing
- [x] Remove unnecessary copy for out= argument
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D26375734
Pulled By: heitorschueroff
fbshipit-source-id: 839642692424c4b1783606c76dd5b34455368f0b
Summary:
Toward fixing https://github.com/pytorch/pytorch/issues/47624
~Step 1: add `TORCH_WARN_MAYBE` which can either warn once or every time in c++, and add a c++ function to toggle the value.
Step 2 will be to expose this to python for tests. Should I continue in this PR or should we take a different approach: add the python level exposure without changing any c++ code and then over a series of PRs change each call site to use the new macro and change the tests to make sure it is being checked?~
Step 1: add a python and c++ toggle to convert TORCH_WARN_ONCE into TORCH_WARN so the warnings can be caught in tests
Step 2: add a python-level decorator to use this toggle in tests
Step 3: (in future PRs): use the decorator to catch the warnings instead of `maybeWarnsRegex`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48560
Reviewed By: ngimel
Differential Revision: D26171175
Pulled By: mruberry
fbshipit-source-id: d83c18f131d282474a24c50f70a6eee82687158f
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
Summary:
Add some much needed documentation on the Timer callgrind output format, and expand what is shown on the website.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51664
Reviewed By: tugsbayasgalan
Differential Revision: D26246675
Pulled By: robieta
fbshipit-source-id: 7a07ff35cae07bd2da111029242a5dc8de21403c
Summary:
Notes the module is in beta and that the policy for returning optionally computed tensors may change in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51620
Reviewed By: heitorschueroff
Differential Revision: D26220254
Pulled By: mruberry
fbshipit-source-id: edf78fe448d948b43240e138d6d21b780324e41e
