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
Summary:
There has a description error in quantization.rst, fixed it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50187
Reviewed By: mrshenli
Differential Revision: D25895294
Pulled By: soumith
fbshipit-source-id: c0b2e7ba3fadfc0977ab2d4d4e9ed4f93694cedd
Summary:
Implements `np.diff` for single order differences only:
- method and function variants for `diff` and function variant for `diff_out`
- supports out variant, but not in-place since shape changes
- adds OpInfo entry, and test in `test_torch`
- automatic autograd because we are using the `Math` dispatch
_Update: we only support Tensors for prepend and append in this PR. See discussion below and comments for more details._
Currently there is a quirk in the c++ API based on how this is implemented: it is not possible to specify scalar prepend and appends without also specifying all 4 arguments.
That is because the goal is to match NumPy's diff signature of `diff(int n=1, int dim=-1, Union[Scalar, Tensor] prepend=None, Union[Scalar, Tensor] append)=None` where all arguments are optional, positional and in the correct order.
There are a couple blockers. One is c++ ambiguity. This prevents us from simply doing `diff(int n=1, int dim=-1, Scalar? prepend=None, Tensor? append=None)` etc for all combinations of {Tensor, Scalar} x {Tensor, Scalar}.
Why not have append, prepend not have default args and then write out the whole power set of {Tensor, Scalar, omitted} x {Tensor, Scalar, omitted} you might ask. Aside from having to write 18 overloads, this is actually illegal because arguments with defaults must come after arguments without defaults. This would mean having to write `diff(prepend, append, n, dim)` which is not desired. Finally writing out the entire power set of all arguments n, dim, prepend, append is out of the question because that would actually involve 2 * 2 * 3 * 3 = 36 combinations. And if we include the out variant, that would be 72 overloads!
With this in mind, the current way this is implemented is actually to still do `diff(int n=1, int dim=-1, Scalar? prepend=None, Tensor? append=None)`. But also make use of `cpp_no_default_args`. The idea is to only have one of the 4 {Tensor, Scalar} x {Tensor, Scalar} provide default arguments for the c++ api, and add `cpp_no_default_args` for the remaining 3 overloads. With this, Python api works as expected, but some calls such as `diff(prepend=1)` won't work on c++ api.
We can optionally add 18 more overloads that cover the {dim, n, no-args} x {scalar-tensor, tensor-scalar, scalar-scalar} x {out, non-out} cases for c++ api. _[edit: counting is hard - just realized this number is still wrong. We should try to count the cases we do cover instead and subtract that from the total: (2 * 2 * 3 * 3) - (3 + 2^4) = 17. 3 comes from the 3 of 4 combinations of {tensor, scalar}^2 that we declare to be `cpp_no_default_args`, and the one remaining case that has default arguments has covers 2^4 cases. So actual count is 34 additional overloads to support all possible calls]_
_[edit: thanks to https://github.com/pytorch/pytorch/issues/50767 hacky_wrapper is no longer necessary; it is removed in the latest commit]_
hacky_wrapper was also necessary here because `Tensor?` will cause dispatch to look for the `const optional<Tensor>&` schema but also generate a `const Tensor&` declaration in Functions.h. hacky_wrapper allows us to define our function as `const Tensor&` but wraps it in optional for us, so this avoids both the errors while linking and loading.
_[edit: rewrote the above to improve clarity and correct the fact that we actually need 18 more overloads (26 total), not 18 in total to complete the c++ api]_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50569
Reviewed By: H-Huang
Differential Revision: D26176105
Pulled By: soulitzer
fbshipit-source-id: cd8e77cc2de1117c876cd71c29b312887daca33f
