review.tizen.org Git - platform/upstream/pytorch.git/log

update of fbcode/onnx to b29e78a4efb8e5d8995f576bbf19a846807829b6 (#18503)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18503

Previous import was c05f2ae412daf8fd64136ca354b97ccf73e0ea6c

Included changes:
- **[b29e78a4](https://github.com/onnx/onnx/commit/b29e78a4)**: update copyright for open governance (#1885) <Prasanth Pulavarthi>
- **[3b0ecd55](https://github.com/onnx/onnx/commit/3b0ecd55)**: open governance (#1881) <Prasanth Pulavarthi>
- **[bbe28349](https://github.com/onnx/onnx/commit/bbe28349)**: Revert "Adding Reverse op (#1804)" (#1882) <Lu Fang>
- **[5be3e223](https://github.com/onnx/onnx/commit/5be3e223)**: Adding Reverse op (#1804) <Peyman Manikashani>

Reviewed By: zrphercule

Differential Revision: D14632717

fbshipit-source-id: 2637a4090e7071a59caff3a910fa4f077906bf3c

Move weight offload inside backend construction functor (#18385)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18385

By moving the weight offload into the backend initialization function, we can instantiate the backend once by creating the OnnxifiOp once and then clean up the parameter workspace. And we need to keep hold of that instantiated net (OnnxifiOp) without cleaning it. Subsequent ctor of OnnxifiOp of the same model will hit the cached backend and they will not look into weight offloading, which is safe as the weight is already gone.

Reviewed By: ipiszy

Differential Revision: D14590379

fbshipit-source-id: f7f34016e09777ad3df0af487885cd14658e1044

fix #16448 (#18479)

Summary:
Fixes #16448

bddppq
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18479

Differential Revision: D14635360

Pulled By: ezyang

fbshipit-source-id: 4010319fbce050dd0bdf4da3cd1171b9737f3c4c

Add section about .code to docs

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18493

Differential Revision: D14634677

Pulled By: jamesr66a

fbshipit-source-id: 9ee065f6ce4218f725b93deb4c64b4ef55926145

how to use the `ccache` package on Ubuntu (#18495)

Summary:
Added full instructions for how to use the `ccache` package. Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18495

Differential Revision: D14635351

Pulled By: ezyang

fbshipit-source-id: 158e1052bae580e95f73644252fdbddcc0213128

Append c10 libs to TorchConfig.cmake (#18418)

Summary:
Fixes #18416.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18418

Differential Revision: D14635322

Pulled By: ezyang

fbshipit-source-id: 81cb658f73583e4cd0358173617f747ebf4f7f8a

Add some missing docs for tensor methods and attributes, new unittest to enforce tensors.rst no longer miss anything (#16057)

Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039

This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.

When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057

Differential Revision: D14619550

Pulled By: ezyang

fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc

Revert D14613517: [pytorch][PR] Updating onnxtrt submodule to master branch

Differential Revision:
D14613517

Original commit changeset: dd20d718db55

fbshipit-source-id: d6267ddfc339d04f182e2de1750a601c8d6bf8c6

Fix direct comparison of OperatorDef proto structs (#18466)

Summary:
arguments order is okay to be different

ajyu
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18466

Differential Revision: D14627258

Pulled By: bddppq

fbshipit-source-id: 430e1fb1bea2c5639a547ae7c1652368788c86b9

Revert D14605905: [pytorch][PR] Add return_counts to torch.unique

Differential Revision:
D14605905

Original commit changeset: 555f5a12a8e2

fbshipit-source-id: c7874f5987893e956c022180a37763d88bba38db

Fix typo in Github links in elementwise_ops_schema.cc (#18018)

Summary:
s/elementwise_op_schema.cc/elementwise_ops_schema.cc
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18018

Differential Revision: D14612291

Pulled By: soumith

fbshipit-source-id: 09276283b9ff92c039ce530165c62cc8421fb443

Improve numerical precision of (s)logdet (#18449)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/18448 and https://github.com/pytorch/pytorch/issues/18450
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18449

Differential Revision: D14611638

Pulled By: soumith

fbshipit-source-id: 4f1f27ab5316a92d2783e734169f599afed743cf

fix arange shape issue inconsistency across cpu and cuda (#18462)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/18363
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18462

Differential Revision: D14620263

Pulled By: soumith

fbshipit-source-id: 223524cdda2f5d55c2ca8d4cdcf6f7a05a6c15eb

Updating onnxtrt submodule to master branch

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18441

Differential Revision: D14613517

Pulled By: bddppq

fbshipit-source-id: dd20d718db55942df9cce7acd1151d6902bc57ff

Minor fix for onnx ConstantOfShape export (#18199)

Summary:
Set value as tensor of 1 element instead of scalar, according to ONNX spec.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18199

Reviewed By: dzhulgakov

Differential Revision: D14542588

Pulled By: houseroad

fbshipit-source-id: 70dc978d870ebe6ef37c519ba4a20061c3f07372

Namedtuple return for solve, slogdet, sort, topk (#17093)

Summary:
More ops for https://github.com/pytorch/pytorch/issues/394. ~~Also need to rebase after landing #16186, because we need to update the whitelist of the new unit test added in #16186.~~

cc: ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17093

Differential Revision: D14620068

Pulled By: ezyang

fbshipit-source-id: deec5ffc9bf7624e0350c85392ee59789bad4237

Expose c10 operators to caffe2 by operator name (#18160)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18160

When exposing a c10 operator to the caffe2 frontend, don't use the operator schema but use the operator name instead.
This allows us to get rid of the existing mechanism for operator schema registration in a diff stacked on top.

Reviewed By: dzhulgakov

Differential Revision: D14513420

fbshipit-source-id: 6b08a9c6d9497eaf18b62361dd44bc07c7b4b76b

Test running a CUDA build on CPU machine. (#18242)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18242
ghimport-source-id: b949d312a48226a34f90304162e910acee7c95cd

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18242 Test running a CUDA build on CPU machine.**
* #18362 Add ability to query if built with CUDA and MKL-DNN.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14584429

fbshipit-source-id: b54de5b33f0c795a7d9605d30576cdf9b74050fd

Properly use cudaGetLastError return code. (#18485)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18485

I don't know how (1) we landed the wrong version of the patch and (2) how
this passed the push blocking test

Reviewed By: pjh5

Differential Revision: D14621961

fbshipit-source-id: 0a3953d7adcdc79727a61c2acff65f436dcafe55

Move math::Axpy function to elementwise lib (#18316)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18316

Move math::Axpy function to elementwise lib

i-am-not-moving-c2-to-c10

Reviewed By: houseroad

Differential Revision: D14574697

fbshipit-source-id: 7cfbb2da295c8966c5328bd6b577cce2638eea62

Upgrade mkldnn to version 0.18.1 (#18463)

Summary:
Upgrade mkldnn to version 0.18.1
Fix the MKLDNN build issue if linking with MKL 2019.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18463

Differential Revision: D14620228

Pulled By: ezyang

fbshipit-source-id: 136074ad0e4631e1dde4ca1b0af4ee6a41e50913

Add Google tag (#17690)

Summary:
This PR adds a Global Site Tag to the site.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17690

Differential Revision: D14620816

Pulled By: zou3519

fbshipit-source-id: c02407881ce08340289123f5508f92381744e8e3

remove redundant --install_dir parameter in GEN_COMMAND (#18473)

Summary:
remove redundant --install_dir parameter in GEN_COMMAND, since "--install_dir parameter " already contained in ${GEN_COMMAND}.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18473

Differential Revision: D14620193

Pulled By: ezyang

fbshipit-source-id: ee9953b5d055f4b8beb3557f95f6539051b0028a

Resolving comments from Bool Tensor for CPU PR (#18165)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18165
ghimport-source-id: 55cb3fb63a25c2faab1725b4ec14c688bf45bd38

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18166 Bool Tensor for CUDA
* **#18165 Resolved comments from Bool Tensor for CPU PR**
-------
------------
This is a follow up PR that resolves some additional feedback on one the of previous Bool Tensor PRs.

gchanan, here is a list of almost all the comments from the original PR with respective fixes and replies:

**[utils/python_scalars.h]** why is this converting from uint8_t and not bool? (comment?)
When i was adding this, i was testing by creating a tensor and then calling its .tolist(). it worked for bool and uint8_t equally good so i left uint8_t as thought it makes more sense as we are calling PyBool_FromLong. �Changing it to bool.

**[ATen/Dispatch.h]**better name?.
fixed.

**[test/test_torch.py]** what about other factories, such as full? (and more).
There is a test that goes through the factory methods - test_tensor_factories_empty. i added some bool cases above it and added a comment that once CUDA will be done, i will unite them and it will iterate not just between CUDA and CPU but also all types. ��Adding all bool cases now. Will unite in CUDA PR.

**[generic/THTensorMath.h]** any changes in this file actually needed?
Bad merge. Fixed.

**[TH/THTensor.h]** this generates code for random, clampedRandom, and cappedRandom -- do we have tests for all of these with bool?
Added

**[c10/core/ScalarType.h]** I'm not very confident about the lack of Bool here -- can you look at the call sites and see what makes sense to do here?
Added bool to the macro and created a similar one without for a single case which fails the build with errors:

_./torch/csrc/jit/symbolic_variable.h:79:20: error: ambiguous overload for ‘operator*’ (operand types are ‘const torch::jit::SymbolicVariable’ and ‘torch::jit::Value*’)
return (*this) * insertConstant(rhs);_

Differential Revision: D14605105

fbshipit-source-id: abf82d50e8f8c50b386545ac068268651b28496d

Unify cudaGetDeviceCount implementations. (#18445)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18445
ghimport-source-id: 30d018737bf6989bc68b7e3676f44e0ca6141fde

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18242 Test running a CUDA build on CPU machine.
* **#18445 Unify cudaGetDeviceCount implementations.**

I went about doing this by searching for calls to cudaGetDeviceCount,
and then methodically replacing them with references to c10::cuda::device_count()
or at::cuda::device_count().

There is a point to doing this: the various implementations wildly differed
in their handling of what to do when cudaGetDeviceCount returns an error.
The final standardized behavior is that **all errors are swallowed** and
we return device count of zero. This indirectly fixes running CUDA builds
on CPU, which was broken in #17847.

I added 'noexcept' to the 'deviceCount' virtual method on DeviceGuardImpl.
This is a BC-breaking change for anyone inheriting from DeviceGuardImpl
but all you need to do is put 'noexcept' on your method and it is backwards
compatible with older libtorch.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14612189

fbshipit-source-id: 3c8d186e3dd623c0e27625212c7ce30f75d943cb

Use TensorIterator for unary operations

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18309

Differential Revision: D14591533

Pulled By: cpuhrsch

fbshipit-source-id: a3b0788a481bddf1803c9f2d3289263d7364f8d7

Introduce SobolEngine (#10505)

Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.

Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.

This closes #9332 .

cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505

Reviewed By: zou3519

Differential Revision: D9330179

Pulled By: ezyang

fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d

fix str of autogradzero

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18442

Differential Revision: D14602880

Pulled By: wanchaol

fbshipit-source-id: ebd00f9bb5f1f7e33964c10d8c9f165b7bb4985f

Optimize boolean expressions & unwraps (#18259)

Summary:
Simplify or eliminate boolean and/or expressions, optimize unwrapping a value that cannot be None, and optimize using `is` with a None and a non-None value

Since peephole optimize is now introducing constants, i added another constant propagation pass after running it.

Previously i had a PR that did this & optimized shape ops - i will add the shape optimizations in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18259

Differential Revision: D14602749

Pulled By: eellison

fbshipit-source-id: 1c3f5a67067d8dfdf55d7b78dcb616472ea8a267

Fix python resolution in caffe2 CI scripts

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18417

Differential Revision: D14612704

Pulled By: bddppq

fbshipit-source-id: 0942048a9c3990afc50ce73c1fa1005c4d4097aa

Support dim=None for argmax and argmin (#18264)

Summary:
Fixes: https://github.com/pytorch/pytorch/issues/18263
cc: houseroad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18264

Reviewed By: ezyang

Differential Revision: D14559234

Pulled By: houseroad

fbshipit-source-id: c5b8623752d6c6af41c6d715fd9585a65294868d

Add return_counts to torch.unique (#18391)

Summary:
Fixes: https://github.com/pytorch/pytorch/issues/12598

This PR was originally authorized by ptrblck at https://github.com/pytorch/pytorch/pull/15495, but since there was no update for months after the request change, I clone that branch and resolve the code reviews here. Hope everything is good now. Especially, the implementation of count is changed from ptrblck's original algorithm to the one ngimel suggest, i.e. using `unique_by_key` and `adjacent_difference`.

The currently implementation of `_unique_dim` is VERY slow for computing inverse index and counts, see https://github.com/pytorch/pytorch/issues/18405. I will refactor `_unique_dim` in a later PR. For this PR, please allow me to keep the implementation as is.

cc: ptrblck ezyang ngimel colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18391

Reviewed By: soumith

Differential Revision: D14605905

Pulled By: VitalyFedyunin

fbshipit-source-id: 555f5a12a8e28c38b10dfccf1b6bb16c030bfdce

change dropout lowering in symbolic_script (#18375)

Summary:
Dropout is now eligible for fusion, and generated fused kernels are just as fast as dropout in ATen. Change its lowering in symbolic script so that it can actually be fused. Still special-cased for cuda, because without fusion this lowering is less efficient than current (bernoulli_ * input). Testing is covered by the test case that ailzhang added (test_dropout_cuda).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18375

Differential Revision: D14611938

Pulled By: soumith

fbshipit-source-id: 11b18f4784e6c9265e382a8f8deca7add8df3b37

Add torch.version.git_version (#18299)

Summary:
Fixes: https://github.com/pytorch/pytorch/issues/18293
cc: colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18299

Differential Revision: D14611972

Pulled By: soumith

fbshipit-source-id: cdb48ef37c8869713a9a43ea0da08e1bed9279a2

Change deprecated IntList to IntArrayRef

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18262

Differential Revision: D14612244

Pulled By: ezyang

fbshipit-source-id: 5d21c7b94d64104fececcb15c6d38d9bd2a1fc70

Enable printing to stderr for test_proper_exit for better debugging (#18458)

Summary:
related to https://github.com/pytorch/pytorch/issues/16608
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18458

Differential Revision: D14611718

Pulled By: soumith

fbshipit-source-id: 6dc903ff2d32b9c3b76470869d1f4e9a67f706df

Don't require pygraphviz for regenerate.sh (#17485)

Summary:
closes #17336

Do not overwrite config.yml if script throws an error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17485

Differential Revision: D14604388

Pulled By: kostmo

fbshipit-source-id: 5024545e3a8711abdbc0800911c766929dbca196

Add quant-passes stubs. (#18151)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18151
ghimport-source-id: 7d12462971bdf3e5e26a3f150f1fcad05bba1a15

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18152 Initial implementation of InsertObserverNodes pass.
* **#18151 Add quant-passes stubs.**

gh-metadata: pytorch pytorch 18149 gh/zolotukhinm@gmail.com/1/head

Differential Revision: D14584224

fbshipit-source-id: b3d0b5ff797160d5ad23f91f732e627b0129086c

caffe2 - support flaky operator tests for caffe2 build (#18155)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18155

- Make a python decorator caffe2_flaky for caffe2 operator unit tests.
- The environment variable CAFFE2_RUN_FLAKY_TESTS are now used to mark flaky test mode

During test run,
- If flaky tests mode are on, only flaky tests are run
- If flaky tests mode are off, only non-flaky tests are run

Mark ctc_beam_search_decoder_op_test as flaky

Reviewed By: ezyang, salexspb

Differential Revision: D14468816

fbshipit-source-id: dceb4a48daeb5437ad9cc714bef3343e9761f3a4

Remove unused th_scalar_type (#18390)

Summary:
th_scalar_type seems to be unused anywhere so can be removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18390

Reviewed By: ezyang

Differential Revision: D14591374

Pulled By: izdeby

fbshipit-source-id: 2113aa81229cdfdfb8dc5c951ea6dea3725b8582

Porting CPU UpSample functions to ATen (#18020)

Summary:
This PR resolves partially #10482
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18020

Differential Revision: D14598029

Pulled By: ezyang

fbshipit-source-id: 513e7c6438ab6d5dc3f43241e7cb724744e9a287

Fix caffe2 build with BLAS=OpenBLAS (#18422)

Summary:
g++ complains about failing to find the declaration of cblas_sscal and cblas_dscal BLAS function
let's fix it :)

fedora 29, gcc 8.3.1, openblas 0.3.5
build with cmake -DBLAS=OpenBLAS ..
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18422

Differential Revision: D14598977

Pulled By: soumith

fbshipit-source-id: bde77bfb359d2ff38226401caeed78c114ef7468

Add addcmul, lerp to fuser, enable scalar->float specialization in symbolic script (#18081)

Summary:
This PR did two things:

1. Enable scalar->float specialization in symbolic script, so AD formula that contains scalar in the schema, should write `float` instead.
2. add addcmul, lerp to AD and fuser.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18081

Differential Revision: D14490493

Pulled By: wanchaol

fbshipit-source-id: b3b86d960d5f051b30733bc908b19786111cdaa4

Add ability to query if built with CUDA and MKL-DNN. (#18362)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18362
ghimport-source-id: 374b7ab97e2d6a894368007133201f510539296f

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18242 Test running a CUDA build on CPU machine.
* **#18362 Add ability to query if built with CUDA and MKL-DNN.**

Fixes #18108.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14584430

fbshipit-source-id: 7605a1ac4e8f2a7c70d52e5a43ad7f03f0457473

Updating submodules

Reviewed By: yns88

fbshipit-source-id: b2c5eb7dfa9048e399461c00d1103e945a30a5bc

Implement reference counting for shared IPC CUDA tensors (#16854)

Summary:
This is to fix #16141 and similar issues.

The idea is to track a reference to every shared CUDA Storage and deallocate memory only after a consumer process deallocates received Storage.

ezyang Done with cleanup. Same (insignificantly better) performance as in file-per-share solution, but handles millions of shared tensors easily. Note [ ] documentation in progress.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16854

Differential Revision: D13994490

Pulled By: VitalyFedyunin

fbshipit-source-id: 565148ec3ac4fafb32d37fde0486b325bed6fbd1

Don't segfault on trying to get data_ptr of sparse tensor. (#18347)

Summary:
Also asserts in storage_initialized that there is a storage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18347

Differential Revision: D14582028

Pulled By: gchanan

fbshipit-source-id: df3f5d181188f39e361839169fd054539c3b2839

Assert tensor isn't sparse in enforce_invariants. (#18338)

Summary:
There's no reason we can't check this, but I'm punting on implementing it for now. But it currently segfaults, so this is an improvements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18338

Differential Revision: D14580308

Pulled By: gchanan

fbshipit-source-id: 44d4cafeab12e1beeb3453a2d4068d221c2e9c4f

Only look for Caffe2 package when shared (#18421)

Summary:
Previously it would look for the Config even if it was not written.

Fixed #18419
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18421

Differential Revision: D14597139

Pulled By: ezyang

fbshipit-source-id: c212cbf5dc91564c12d9d07e507c8285e11c6bdf

Add more options to the quantization model exporter (#18383)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18383

Add command line options for different quantization schemes.

Reviewed By: amylittleyang

Differential Revision: D14476862

fbshipit-source-id: 37fbf5b4c1c550121eae313f5a71d703a0a87f0f

Revert "Specialize optional tensor inputs to graphs in the JIT (#18360)" (#18411)

Summary:
This reverts commit 7cc7ed1322405ba3c627b9c5661a330f92c4183d.

I think it's better to sort out the issues raised in #18407 firs. I'm sorry for not stopping it earlier.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18411

Differential Revision: D14594937

Pulled By: soumith

fbshipit-source-id: 3c90b7fa7694e2f59e55607acecde4a47af801ea

Fix deprecated: type() -> scalar_type() (#18406)

Summary:
Sorry for not sending these fixes in a single PR. I found this compiler warning when I was working on something else, and I just go to GitHub and modify the file directly for convenience...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18406

Differential Revision: D14594180

Pulled By: soumith

fbshipit-source-id: 92f48513bc62fbe2c67c759d68830a973296e43b

Fix deprecated: type() -> scalar_type()

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18394

Differential Revision: D14593890

Pulled By: soumith

fbshipit-source-id: 92b9a8c22008341c0cc3b7a721bef1973c528daf

Added tensor size warning to F.mse_loss() (#18349)

Summary:
To address the issue of broadcasting giving the wrong result in `nn.MSELoss()` as mentioned here https://github.com/pytorch/pytorch/issues/16045 . In particular, the issue often arises when computing the loss between tensors with shapes (n, 1) and (n,)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18349

Differential Revision: D14594176

Pulled By: soumith

fbshipit-source-id: f23ae68a4bf42f3554ad7678a314ba2c7532a6db

Fix For Requires Grad Infinite Loop (#18361)

Summary:
Previously, we would continue to run requires grad on a loop body when the outputs and inputs disagreed. This adds a check so that we don't continue running if the results haven't changed since the last run.

Fix for https://github.com/pytorch/pytorch/issues/18320
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18361

Differential Revision: D14584332

Pulled By: eellison

fbshipit-source-id: 696b225f80a2036318540946428b525985a9e735

update magma instructions (#18410)

Summary:
fixes https://github.com/pytorch/pytorch/issues/18389

cc: stas00
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18410

Differential Revision: D14594198

Pulled By: soumith

fbshipit-source-id: fb46ef77a36c90ad95e47f7066f5d32aa1f1370f

Removed some dead code (#18201)

Summary:
Removed some dead code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18201

Differential Revision: D14555251

Pulled By: izdeby

fbshipit-source-id: f49640133ef4ae1b0306f7cec6655f23869cc6e7

Specialize optional tensor inputs to graphs in the JIT (#18360)

Summary:
This specializes optional tensor inputs to either a DimensionedTensorType or, when None is passed,
UndefinedTensor (aka AutogradZeroTensorType).
This works because we already have different specs and thus separate plans for the two cases.
It enhances the shape analysis - because now unwrapped optional tensors will have DimensionedTensorType with appropriate shape and required grad etc.
Also, when combined with "if-pruning" (which I understand #18259 works towards), we actually get much nicer concrete graphs, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18360

Differential Revision: D14590577

Pulled By: soumith

fbshipit-source-id: cac204a506d1d38b15703cbcc67a6b75fd4979f4

Move pyobj_ to TensorImpl (#18225)

Summary:
Currently, `THPVariable_Wrap(…)` and `THPVariable_NewWithVar(…)` depend on the existence of `pyobj_` in the autograd metadata of a Variable to convert the Variable to a Python tensor. However, after the Variable/Tensor merge, there will be Variables that don't contain autograd metadata, and to allow the conversion from non-autograd-meta Variable to a Python tensor we need to store the `pyobj_` outside of autograd metadata and in a place where it will always be available.

This PR makes it possible by moving `pyobj_` into TensorImpl, so that `THPVariable_Wrap(…)` and `THPVariable_NewWithVar(…)` can always access a Variable's `pyobj_` and convert the Variable to a Python tensor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18225

Differential Revision: D14562616

Pulled By: yf225

fbshipit-source-id: 18d4aaace70eee6120abaf9276036d1f8f51b18d

Fix deprecated scalar type in ATen/native/Distributions.cpp

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18265

Differential Revision: D14577543

Pulled By: ezyang

fbshipit-source-id: 36674530b32366c51835e4073d7ba23d455d2fda

Revert D14446895: [C2] Implement rotated generate_proposals_op without opencv dependency (~2x faster)

Differential Revision:
D14446895

Original commit changeset: 847f2443e645

fbshipit-source-id: fc6ab5ee59e027f125f5ab0f7ee51ad7db37d4a4

Revert D14584266: [pytorch][PR] Better error message for tensor with grad as constant in tracing

Differential Revision:
D14584266

Original commit changeset: 4e7850dadc78

fbshipit-source-id: 3bb3b5006e469edff984c16e0ff8d5dac2862d88

Better error when module attr is used (#18164)

Summary:
Adds a suggestion to add to __constants__ when a torch.nn.Module attr is accessed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18164

Differential Revision: D14580060

Pulled By: eellison

fbshipit-source-id: 0c5adc21d7341a5691d4b45930947cb1ba84c8e8

Fix incorrect sparse add behavior when the sparse tensor has non-contiguous values (#18179)

Summary:
Currently, this code gives incorrect result:
```python
import torch
indices=torch.tensor([[7, 1, 3]])
values=torch.tensor([[1., 1., 1.],
               [1., 1., 1.],
               [1., 1., 1.]])
x = torch.sparse_coo_tensor(indices, values, size=(10, 3))
values=torch.tensor(1.).expand(3, 3)
y = torch.sparse_coo_tensor(indices, values, size=(10, 3))
z = x + y

tensor(indices=tensor([[7, 1, 3]]),
       values=tensor([[2., 1., 1.],
                      [1., 1., 1.],
                      [1., 1., 1.]]),
       size=(10, 3), nnz=3, layout=torch.sparse_coo)
```

This PR fixes the bug by adding special handling for sparse tensors with non-contiguous values in the addition function (specifically, by cat'ing the indices and values together).

This PR closes https://github.com/pytorch/pytorch/issues/17950 and https://github.com/pytorch/pytorch/issues/17919.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18179

Reviewed By: ezyang

Differential Revision: D14569591

Pulled By: yf225

fbshipit-source-id: f5a14c4a31337fc95eab64596212066b4fb18b1a

Implement rotated generate_proposals_op without opencv dependency (1.8x faster) (#18010)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18010

[C2] Implement rotated generate_proposals_op without opencv dependency.

Reviewed By: newstzpz

Differential Revision: D14446895

fbshipit-source-id: 847f2443e645f8cae1327dfbaa111c48875ca9be

Remove empty file (actual file_check.cpp resides in torch/csrc/jit/testing) (#18303)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18303
ghimport-source-id: 66f4402075b123e36c6ffdf806b7c93187a1a58a

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18307 Convert test_recursive_cse to use Filecheck inline annotations.
* #18306 [Filecheck] Add a feature to parse check annotations from string.
* #18305 Add python bindings for parseIR.
* **#18303 Remove empty file (actual file_check.cpp resides in torch/csrc/jit/testing)**

Differential Revision: D14586003

fbshipit-source-id: a13e57bd4302e4d3f06198068d525de25e2aa8b3

Turn script_type_parser into a class (#18211)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18211
ghimport-source-id: 73b81e9ec631937b14db1da10991831788a6894b

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18296 [jit] Add namespacing for ScriptClasses
* #18284 [jit] make test module hook use save/load
* **#18211 [jit] Turn script_type_parser into a class**
* #18148 [jit] python interop for script classes

If we are namespacing classes, the type parser will need to carry around
some state about which namespaces to look in. This PR just wraps it in a
class in preparation.

Also, subscriptToType can no longer be static, since parseTypeFromExpr
may give different results depending on the namespaces available, so
it's been made a regular function instead of a static map lookup.

Reviewed By: eellison

Differential Revision: D14581128

fbshipit-source-id: 711315472ccde1920abf9fdb5a871ac27fb86787

python interop for script classes (#18148)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18148
ghimport-source-id: 40a9d745dc9aeba53d098743323fcbd50ca65137

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18148 py interop**

Support for converting classes between the Python–TorchScript boundary. Like other TorchScript values, ScriptClasses are native Python values when used in Python and IValues when used in TorchScript.

Notably, there is a copy across this boundary, which will be surprising to users who will expect standard Python reference semantics. I have some ideas for fixing that, but it's a more involved process.

Reviewed By: jamesr66a

Differential Revision: D14526259

fbshipit-source-id: 5916e3032488a42dc7da756c1826d7c040a21ebd

Better error message for tensor with grad as constant in tracing (#18298)

Summary:
Fix for https://github.com/pytorch/pytorch/issues/17583

There's an unrelated issue right now causing a segfault when printing tensor so that might have to fixed first for this to land
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18298

Differential Revision: D14584266

Pulled By: eellison

fbshipit-source-id: 4e7850dadc78ef1e98ad40b9d8adc0fef42acf48

Support for basic list comprehensions (#17267)

Summary:
Supports the following syntax:
```
        torch.jit.script
        def comp(l):
            # type: (List[float]) -> List[float]

            n = [x * 3 for x in l]
            return n
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17267

Differential Revision: D14581119

Pulled By: Krovatkin

fbshipit-source-id: 6fd091a8a9ab607386ac58fda6ad88bf8aea380e

Make it possible to trigger XLA/slow tests via commit message. (#18345)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18345
ghimport-source-id: 9649d76bb194866859d62e6ba2a3a265c96ebba5

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18345 Make it possible to trigger XLA/slow tests via commit message.**

Four variants are supported: `[xla ci] [ci xla] [xla test] [test xla]`; substitute
xla with slow for slow tests.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14584557

fbshipit-source-id: fcbfdfb28246823135bb3d3910baae073d16e81d

Avoid refcount when looking up dispatch key

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18294

Reviewed By: ezyang

Differential Revision: D14512979

fbshipit-source-id: 45e548974f06184c375c2bb8339e3049a4ebd880

Fix DCHECK to handle dangling else (#18295)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18295

Replace "if (false)" with "while (false)" which fixes potential dangling else issue as shown in added test case.

Reviewed By: ezyang

Differential Revision: D14569608

fbshipit-source-id: 407052db9182ce27b7a59841e90fa50d3eca262e

Allow fusion of float function arguments (#18087)

Summary:
so that functions like `def fn(x, p:float)` can be fused. Fixes #9940 and #11186. Fuses only float (not integer) arguments to simplify assembling arguments for fusion launch.
CPU fusion is disabled in CI and this won't be tested, but I tested it locally.
cc t-vi, apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18087

Differential Revision: D14581206

Pulled By: wanchaol

fbshipit-source-id: ccb0cf79b1751706f9b2cdf1715115eae5a39fb6

Fix error reporting in NVRTC use of the fuser (#18327)

Summary:
Two functions were not directed ad NVRTC.
It's a bit hard to test this, as the fuser usually produces correct code - unless I try to hack on it. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18327

Differential Revision: D14579285

Pulled By: soumith

fbshipit-source-id: 1be7ba461cc473d514ba619507742a47d4d7c97e

Using sqrt for better precision in cosine_similarity (#18250)

Summary:
address comment in #18168 .
Testing in CI...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18250

Differential Revision: D14568601

Pulled By: ailzhang

fbshipit-source-id: 39fbbdb08743b53fa665c7e88e4750cbe0976ec7

Fix alignment issues for Fake BFP16 fp32 -> bfp16 rounding routines (#18321)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18321

As title.

Reviewed By: jspark1105

Differential Revision: D14575512

fbshipit-source-id: 0e33cdab54b1aef8b67f0b4c366692c5dbdf631d

Untangle internal build python and cpp dependencies

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18326

Reviewed By: ezyang

Differential Revision: D14576294

fbshipit-source-id: 186ce1e3d026d962b7386f861eddf093f583a878

Caffe2: crash op (#18207)

Summary:
this is handy when testing various core dump related
things. If in the future we want to unit test our future gdb debugger
extensions, we can use this op to generate a core dump for us within a
unit test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18207

Differential Revision: D14482186

Pulled By: salexspb

fbshipit-source-id: 39a9fffbdd4bd083597f544d1c783a82cf023a89

caffe2 - Util to cleanup external inputs and outputs from a NetDef (#18194)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18194

Add a util method to cleanup external inputs and outputs from a NetDef

The following conditions will be met after the modification
- No duplicate external inputs
- No duplicate external outputs
- Going through list of ops in order, all op inputs must be outputs
from other ops, or registered as external inputs.
- All external outputs must be outputs of some operators.

Reviewed By: ZolotukhinM

Differential Revision: D14528589

fbshipit-source-id: c8d82fda1946aa3696abcbec869a4a8bb22f09b6

End to end hack to call server side Caffe2 ops (#18267)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18267

Motivation: we don't actually want to use it for real under any circumstances. This is an idea to unblock our internal progress and parallelize workstreams. We can easily define schemas for all ops in question and implement forwarding to C2 ops which is NOT going to be performant. Then several things can be happening in parallel:
* move code of ops outside of C2 ops that depend on protobuf into c10
* development of optimization/fusion passes
* building python-level wrappers with clean API
* improving perf

This demonstrates, Relu, quant, dequant. It seems to cover all use cases necessary (maybe except weights prepacking). Ideally I'd demonstrate Conv, but will get to it later in a separate PR (contributions welcomed)

Reviewed By: ezyang

Differential Revision: D14531232

fbshipit-source-id: 4cd4a71ae0cb373c6c0e81f965c442b82a1b4069

Optimize MomentumSGDUpdate maximum block size and make it templated

Summary: Removing the maximum number of blocks limit from the operator and making the nesterov parameter templated to remove branching.

Reviewed By: BIT-silence

Differential Revision: D14567003

fbshipit-source-id: 394c2039ee214adc6ccd2e562e4e9563d307131f

Add test for #17271 (torch.exp incorrect for 2**31 size tensor) (#18292)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18292
ghimport-source-id: a3e96584db0eef7b6202a1211808f9f6e59dd529

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18292 Add test for #17271 (torch.exp incorrect for 2**31 size tensor)**
* #18291 Correctly call superclass setUp in TestCase subclasses.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14567642

fbshipit-source-id: c60ee7597a86f5d2c5c0b72cb106f17815950427

Correctly call superclass setUp in TestCase subclasses. (#18291)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18291
ghimport-source-id: d6e95e899bd320407967df41435801e54864ba62

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18292 Add test for #17271 (torch.exp incorrect for 2**31 size tensor)
* **#18291 Correctly call superclass setUp in TestCase subclasses.**

This makes PYTORCH_TEST_SKIP_FAST work correctly for more
tests, reducing the wasted testing effort on our slow_test job.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14567643

fbshipit-source-id: 40cf1d6556e0dd0a0550ff3d9ffed8b6000f8191

Verify def before infer fensor (#18129)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18129

A lot of tensor interference function assume the operator passes the schema.
So call Verity to make sure this is actually the case.

Created diff before to add checking in Concat (https://github.com/pytorch/pytorch/pull/17110), but I encountered lot more places where this is assumed (for example ElementwiseOpShapeInference)

Reviewed By: mdschatz

Differential Revision: D14503933

fbshipit-source-id: cf0097b8c3e4beb1cded6b61e092a6adee4b8fcb

add more Python interface functions to make quantization simpler (#18246)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18246

Simplifies histogram collection and quantization process.

Histogram collection before this diff was something like this
```
from caffe2.quantization.server import dnnlowp_pybind11
...
dnnlowp_pybind11.ObserveHistogramOfOutput(hist_file)
for ...
   workspace.RunNet(predict_net)
dnnlowp_pybind11.ClearNetObservers()  # This is to trigger Stop function in the observer to dump out histogram file but this can have unintended consequence of also clearing all the other useful observers we attached
```

After this diff we can
```
workspace.CreateNet(predict_net)  # Note we need to create net to have a net to attach observer
histogram_observer = dnnlowp_pybind11.AddHistogramObserver(predic_net, hist_file)
for ...
   workspace.RunNet(predict_net)
predict_net.RemoveObserver(histogram_observer)
```

Choosing quantization parameters of weights before this diff was something like this
```
dnnlowp_pybind11.ObserveHistogramOfOutput(weight_hist_file)
workspace.RunNetOnce(init_net)
dnnlowp_pybind11.ClearNetObservers() # Has same issue as the histogram collection example above

dnnlowp_pybind11.RegisterQuantizationParamsWithHistogram(
    weight_hist_file, is_weight=True, qparams_output_file_name=qparams_file
)
workspace.CreateNet(init_net, overwrite=True)
dnnlowp_pybind11.ClearNetObservers()

logger.info("Loading quantization params from {}".format(qparams_file))
blobs_to_qparams = {}
with open(qparams_file) as f:
    lines = f.readlines()
for line in lines:
    op_id, op_type, output_id, tensor_name, mini, maxi, scale, zero_point, precision = (
        line.split()
    )
    op_id = int(op_id)
    output_id = int(output_id)
    op = net.Proto().op[op_id]
    if op_type != op.type or op.output[output_id] != tensor_name:
        print(
            "Corrupt qparams file {} {} {} {} {}".format(
                qparams_file, op_type, op.type, op.output[output_id], tensor_name
            )
        )
    blobs_to_qparams[tensor_name] = QuantizationParam(float(scale), int(zero_point))

```

After this diff this can be simplified to
```
blobs_to_qparams = {}
for op in init_net.Proto().op:
    for output in op.output:
        scale, zero_point = dnnlowp_pybind11.ChooseQuantizationParams(output)
        blobs_to_qparams[output] = QuantizationParam(scale, zero_point)
```

Reviewed By: dskhudia

Differential Revision: D14544694

fbshipit-source-id: 4fd06cd63256201e2e9d15c39f503138d1be53c2

add fbgemm fp16 (fbfcpacked) support, add global_init_net in predictor_export_meta (#18257)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18257

support adding op in global_init_net. because pred_init_net is per thread, and just doesn't cut it.

Reviewed By: jspark1105

Differential Revision: D14552695

fbshipit-source-id: 53dd44c84ad019019ab9f35fc04d076b7f941ddc

update of fbcode/onnx to c05f2ae412daf8fd64136ca354b97ccf73e0ea6c (#18285)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18285

Previous import was 96c58ceeacf0f2b73d752e413e4fd78787a12da3

Included changes:
- **[c05f2ae4](https://github.com/onnx/onnx/commit/c05f2ae4)**: update both core and ml docs (#1879) <Lu Fang>
- **[f895279b](https://github.com/onnx/onnx/commit/f895279b)**: fix the problems introduced in previous PRs in operator registration (#1878) <Lu Fang>
- **[f6f80657](https://github.com/onnx/onnx/commit/f6f80657)**: Skip the schema check on ops in non-standard domain (#1876) <Lu Fang>
- **[8c8be722](https://github.com/onnx/onnx/commit/8c8be722)**: Introduce Function Body Helper (#1868) <Sherlock>
- **[b605eafb](https://github.com/onnx/onnx/commit/b605eafb)**: Support down sampling for Upsample with scales < 1. (#1773) <Ke Zhang>
- **[47f7aa71](https://github.com/onnx/onnx/commit/47f7aa71)**: Remove scaledtanh (#1866) <Ashwini Khade>
- **[4dfc56de](https://github.com/onnx/onnx/commit/4dfc56de)**: Add Ceil support for Max and Average Pooling (#1860) <Lara Haidar>
- **[552a8efc](https://github.com/onnx/onnx/commit/552a8efc)**: Add testcase generator for functions (#1862) <Raymond Yang>
- **[fdb978a5](https://github.com/onnx/onnx/commit/fdb978a5)**: Promote Thresholded Relu Op (#1856) <Ashwini Khade>
- **[ce332628](https://github.com/onnx/onnx/commit/ce332628)**: Update Slice with dynamic input & optional input steps (#1836) <Bowen Bao>
- **[3a9a8787](https://github.com/onnx/onnx/commit/3a9a8787)**: Merge function into opschema (#1834) <Raymond Yang>
- **[3dbf8fe9](https://github.com/onnx/onnx/commit/3dbf8fe9)**: Handle string comparision represented as np.objects (#1851) <Dmitri Smirnov>
- **[3b0d3bb2](https://github.com/onnx/onnx/commit/3b0d3bb2)**: remove global variable in header file (#1850) <Lu Fang>
- **[1cca8733](https://github.com/onnx/onnx/commit/1cca8733)**: bump the version for drop out - fix the issue that the version was not bumped when changing its type constraint declaration. (#1848) <Ke Zhang>
- **[1ec81bc6](https://github.com/onnx/onnx/commit/1ec81bc6)**: Change TopK operator to allow dynamic 'k' (#1829) <Hariharan Seshadri>
- **[a89a4a16](https://github.com/onnx/onnx/commit/a89a4a16)**: Remove exp op: Affine, ImageScaler,ParametricSoftplus, Crop. (#1832) <Ke Zhang>

Reviewed By: yinghai

Differential Revision: D14566202

fbshipit-source-id: b1e5912ae6887e2865fc628363071e2b9938dfa4

Cleanup TorchScript rst docs (#18234)

Summary:
* Adds more headers for easier scanning
* Adds some line breaks so things are displayed correctly
* Minor copy/spelling stuff
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18234

Reviewed By: ezyang

Differential Revision: D14567737

Pulled By: driazati

fbshipit-source-id: 046d991f7aab8e00e9887edb745968cb79a29441

Replace the remaining usages of IntList in caffe2 to IntArrayRef

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18282

Differential Revision: D14569269

Pulled By: bddppq

fbshipit-source-id: 5fc33701b83f9efdec4b456d2691764831d10e7f

Blacklist certain op types when doing bound shape inference (#18290)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18290

Some such as `Tile` will mess up our tracking of batch size and for now it makes sense to stop the shape inference on these ops so that we don't lower it and downstream ops without proper batch info.

Reviewed By: zrphercule

Differential Revision: D14463550

fbshipit-source-id: 2792481efa540f2a7dd310e677c213860c3053ca

Fix use of c10::guts::apply (#18159)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18159

In some instances, the call to forward could clash with std::forward. Fully qualify it to make sure it gets the right one

Reviewed By: ezyang

Differential Revision: D14512189

fbshipit-source-id: 6242607dbe54fcdb93229c1a4aaee8b84a88caa1

Allow using C10_DECLARE_TENSOR_TYPE and C10_DEFINE_TENSOR_TYPE from any namespace (#18158)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18158

They didn't work when called from other namespaces before because they didn't fully specify the c10 namespace.

Reviewed By: ezyang

Differential Revision: D14512187

fbshipit-source-id: a496b89a1bbe2b56137cfae03ab94a60f38d7068

Move schema inference to c10 (#18090)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18090

This schema inference is needed by the c10 operator registration mechanism. Move it to c10.
It is going to be used by diffs stacked on top.

Reviewed By: ezyang

Differential Revision: D14491454

fbshipit-source-id: 0f8ddcdbd91467c8347d315dd443a1ca8b216481

Allow registering same operator schema multiple times (#18038)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18038

Now that we have named overloads, we can allow registering the same function schema multiple times and just check it's identical.

This is going to be used in custom op registration since they register the schema every time a kernel is registered.

Reviewed By: dzhulgakov

Differential Revision: D14467494

fbshipit-source-id: 2c26cf72a64b65f120afe05e989302ec42597515

Rename trtrs to triangular_solve (#18213)

Summary:
Changelog:
- Renames `trtrs` to `triangular_solve` to remain consistent with `cholesky_solve` and `solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `triangular_solve` under the name `trtrs`, and add a deprecation warning to not promote usage.
- Move `isnan` to _torch_docs.py
- Remove unnecessary imports
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18213

Differential Revision: D14566902

Pulled By: ezyang

fbshipit-source-id: 544f57c29477df391bacd5de700bed1add456d3f

Fix contribution_guide docs (#18237)

Summary:
Fixes Typo and a Link in the `docs/source/community/contribution_guide.rst`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18237

Differential Revision: D14566907

Pulled By: ezyang

fbshipit-source-id: 3a75797ab6b27d28dd5566d9b189d80395024eaf

Updating submodules

Reviewed By: yns88

fbshipit-source-id: 80b00c33e6f6c7cfa08f645cd33419f6545f45d2

Optimize group_norm_op (#17945)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17945

Optimize group_norm_op

Reviewed By: houseroad

Differential Revision: D14419908

fbshipit-source-id: 4024b5c5dbeff97f4f026d61fc44af1f0e98ed68

Enable running of slow tests in CI. (#18236)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18236
ghimport-source-id: 2bb80d017c2ea833669a2d55b340a922b2d44685

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18236 Enable running of slow tests in CI.**
* #18231 Add a decorator for marking slow tests.

These tests only run on master, as they are slow.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14563115

fbshipit-source-id: f54ddef4abedc7e872e58657fc9ac537952773d0