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

Refactor CPU embedding_bag implementation (#18734)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18734
ghimport-source-id: e0e50d4b47f2fb8c86e464aacb950521d601f8d3

Reviewed By: cpuhrsch

Differential Revision: D14851413

Pulled By: zou3519

fbshipit-source-id: 8ac4e4de590a363e9807dc552fe4ca52b92652ed

Make BlackBoxPredictor handle networks throwing exceptions (#19080)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19080

OSS: add a tiny unit test utility function to create tensors given shape and data outside of any workspace. I use it in an internal test

Reviewed By: dzhulgakov

Differential Revision: D14814194

fbshipit-source-id: 6d53b235d99a97da812215f5c7f11fecad363c8c

Remind users to set map_location properly when using DDP

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19084

Differential Revision: D14861702

Pulled By: mrshenli

fbshipit-source-id: 10ca4a9b41e707050a6bce228ccca4177c9fa4a6

Rename btrisolve to lu_solve (#18726)

Summary:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726

Differential Revision: D14726237

Pulled By: zou3519

fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b

Avoid calling tensor.data.set_() in DDP

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18961

Differential Revision: D14811208

Pulled By: mrshenli

fbshipit-source-id: c1c46dfa13e0a6ec83aefd35696ee31a7ea3d810

Reapply Wrap workaround for cpp custom types a bit prettier and add an example" (#19062)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19062

As a temporary demonstration on how to extend this hack further until custom C types are ready.

Reviewed By: ezyang

Differential Revision: D14817809

fbshipit-source-id: 6eaf731e9135313eb858e178abcd9f25380ab8fe

Propagate ProcessGroup timeout to Store (#16571)

Summary:
closes #16520

Hi pietern, I am not sure if this is the expected way to pass timeout to `Store`, could you please help take a look? Thanks!

Questions:
1. How do I write tests for this? I wanted to do something like `test_barrier_timeout_global`, but it seems I need to set the pg's timeout larger than the `Store`'s default timeout (3 min) to see a difference, which is too long for a unit test. And I do not want to change the `Store`'s default timeout either. Any suggestion?
2. Should I also propagate timeout configuration down to `PrefixStore` in `_new_process_group_helper`?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16571

Differential Revision: D13954527

Pulled By: mrshenli

fbshipit-source-id: 77f2653903f24255207233eb298f7c0321119a87

make test_jit_fuser runnable

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19036

Differential Revision: D14839800

Pulled By: wanchaol

fbshipit-source-id: b52c131b58e1b42a8c3da5d1117217c3dc2e5f5b

Fix documentation for unfold(dimension=..., ...), fixes #18793 (#19020)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19020
ghimport-source-id: 8f31e51b79daba11939aa7992450984054713b9c

Differential Revision: D14851890

Pulled By: ezyang

fbshipit-source-id: 8498e86a63633fdfd9ecae9b7f85b773b75fe27a

Debugging: Increase process reporting for apt/dpkg. (#18880)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18880
ghimport-source-id: b43a33c12df379ec75c1fd4c713c1fc723a763e1

Differential Revision: D14856296

Pulled By: ezyang

fbshipit-source-id: 30691eb14dddfe998b2605b416aaa1b14d1b6ad5

Add torch.__config__.show(), reporting detailed version of all libraries. (#18579)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18579
ghimport-source-id: 65124c95e49423de4ad1008c65e75057fea09b94

Differential Revision: D14778507

Pulled By: ezyang

fbshipit-source-id: 1e4bb79f4800a116ce8fb7af2fefbd34da8d102c

Fix torch::nn::init::orthogonal_ with CNNs (#18915)

Summary:
Fixes #18518

I changed the C++ API torch::nn::init::orthogonal_ implementation to match the Python implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18915

Differential Revision: D14851833

Pulled By: ezyang

fbshipit-source-id: 45b5e9741582777c203e9ebed564ab3ac1f94baf

move nightlies to 1.1.0xxx

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19069

Differential Revision: D14854600

Pulled By: soumith

fbshipit-source-id: 85c703bddbd47c1b3914d58ab9521ed22ddeb62a

add an utility function to check whether it's in the middle of onnx export or not

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19050

Reviewed By: yinghai

Differential Revision: D14849878

Pulled By: houseroad

fbshipit-source-id: a0a4a57f5f9f315ba1334edfccc9284a8099d17f

remove interned_string.h dep (#19061)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19061

remove the deps on interned_string.h

Reviewed By: BIT-silence

Differential Revision: D14850078

fbshipit-source-id: 07e6ad72a7de369049ea56f32b72276fb4c59b32

add logging to make the saving action visible (#19042)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19042

show the model saving step in the log.

Reviewed By: kennyhorror

Differential Revision: D14809385

fbshipit-source-id: c7a1e50ff92bb45b16b1c501d9325b304b07fbd3

Namedtuple return for gels, triangular_solve, and test refactor (#17195)

Summary:
Partial fix of: https://github.com/pytorch/pytorch/issues/394
- `gels` and `triangular_solve` now returns namedtuple
- refactor test for namedtuple API for better coverage and maintainability
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17195

Differential Revision: D14851875

Pulled By: ezyang

fbshipit-source-id: 9b2cba95564269d2c3a15324ba48751d68ed623c

Convert all tabs to spaces, add CI. (#18959)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18959
ghimport-source-id: a934163fa34cb2019732d5f49dc7290c376bf156

Differential Revision: D14831246

Pulled By: ezyang

fbshipit-source-id: beb92dc4ee8c82f4c8259c081dd72e477fe7a9d0

Fix BN tests for >= 8 GPU test environments (#19049)

Summary:
DDP does not support replicating BN layers within a process. Existing BN tests fail if the test environment has more than 8 GPUs. This is fixed by explicitly setting each process to use a single replica.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19049

Differential Revision: D14845286

Pulled By: mrshenli

fbshipit-source-id: 937dda5081d415ece48b21f2781b6b4e008dd42f

do not use constexpr with CUDA >= 9.2 compiler on Windows. (#18986)

Summary:
Define `AT_CPP14_CONSTEXPR` from `constexpr` to empty on Windows with CUDA >= 9.2 as workaround.

Discussed in #18425.

When using CUDA 10.1 on Windows, I faced following errors:
~~~
D:/data/source/pytorch\c10/util/ArrayRef.h(144): error: variable in constexpr function does not have automatic storage duration
detected during instantiation of "const T &c10::ArrayRef<T>::front() const [with T=at::Tensor]"
D:/data/source/pytorch/aten/src\ATen/DeviceGuard.h(30): here
~~~

From documentation of CUDA Toolkit v10.1.105, compiler supports `constexpr` and relaxing requirements (in C++14), but compilation failed.

I suppose this could be compiler bug and require this workaround.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18986

Differential Revision: D14821836

Pulled By: ezyang

fbshipit-source-id: 9800da2fe7291e7c09e8e5e882adebab08d83ae3

Add torch/lib/protobuf to gitignore, fixes #18700 (#19019)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19019
ghimport-source-id: 84d36f8d27912d1d094d5672154b82187dd88761

Differential Revision: D14846615

Pulled By: ezyang

fbshipit-source-id: e402557ec321c85be3b28c8602b680246c8eecfe

update of fbcode/onnx to 971311db58f2fa8306d15e1458b5fd47dbc8d11c (#19046)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19046

Previous import was 079c2639f9bb79b1774d1e3bfa05b0c093816ca7

Included changes:
- **[971311db](https://github.com/onnx/onnx/commit/971311db)**: use ONNX_NAMESPACE::to_string instead of std::to_string (#1915) <Lu Fang>
- **[65227446](https://github.com/onnx/onnx/commit/65227446)**: Remove all the experimental ops (#1909) <Lu Fang>
- **[bdb28f29](https://github.com/onnx/onnx/commit/bdb28f29)**: opset converter backward compatibility support for opset versions 9 and 8 (#1847) <Peyman Manikashani>
- **[47692338](https://github.com/onnx/onnx/commit/47692338)**: Create CODEOWNERS for automatic reviewer assignment for PRs (#1910) <Prasanth Pulavarthi>
- **[8121c731](https://github.com/onnx/onnx/commit/8121c731)**: Revert "quantization support in onnx (#1872)" (#1911) <Lu Fang>
- **[4cfa5426](https://github.com/onnx/onnx/commit/4cfa5426)**: quantization support in onnx (#1872) <Ke Zhang>
- **[030bbb80](https://github.com/onnx/onnx/commit/030bbb80)**: Update LICENSE formatting and clarify # of WG chairs (#1907) <Prasanth Pulavarthi>

Reviewed By: yinghai

Differential Revision: D14843284

fbshipit-source-id: 96c1c79abb62beff227a9fc8b2af9382c4673755

Fix default CXX for Windows in cpp_extensions.py (#19052)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/19017.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19052

Differential Revision: D14846702

Pulled By: soumith

fbshipit-source-id: b0e4dadaa749da0fa2d0405a1a064820d094220a

fix the onnx ci

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19048

Reviewed By: yinghai

Differential Revision: D14844917

Pulled By: houseroad

fbshipit-source-id: 30719e05a443981284dedf34a9e51213271aa934

Add gelu op (#18992)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18992

Add gelu op

Reviewed By: houseroad

Differential Revision: D14814811

fbshipit-source-id: 00f126b8b83763c57ebbf28fbd2de5a8fab6d491

Add MKL-DNN Tensor (#17748)

Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038

Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748

Reviewed By: dzhulgakov

Differential Revision: D14614640

Pulled By: bddppq

fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533

detect C++ ABI flag for cpp extensions from available runtime information (#18994)

Summary:
Previously, when a user built PyTorch from source, but set the version string manually to be binary-formatted, it would've simply used CXX11_ABI=0 incorrectly.

We have this information available at runtime with `torch._C._GLIBCXX_USE_CXX11_ABI`, so this PR improves the situation by simply using that information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18994

Differential Revision: D14839393

Pulled By: soumith

fbshipit-source-id: ca92e0810b29ffe688be82326e02a64a5649a3ad

Fix momentum setting in BatchNorm forward pass. (#18764)

Summary:
This is a fix for issue https://github.com/pytorch/pytorch/issues/18525. The issue is related not only to ONNX export, but can manifest in other scenarios.
An existing test point in test/onnx/test_operators.py has been updated to cover this scenario as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18764

Reviewed By: zrphercule

Differential Revision: D14735166

Pulled By: houseroad

fbshipit-source-id: 5a737c648f64355929ff31eb12bd4869e744768d

add android build workflow to pytorch CI jobs (#18919)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18919
ghimport-source-id: 3f0ce4334c899d262403d88bd8bd7513e99570f0

Reviewed By: kostmo

Differential Revision: D14800728

Pulled By: ljk53

fbshipit-source-id: fec2e34c192181b8fa31c9a30f60c9bf7388f083

Export C10 operator in PyTorch Model (#18210)

Summary:
Almost there, feel free to review.

these c10 operators are exported to _caffe2 domain.

TODO:

- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210

Reviewed By: zrphercule

Differential Revision: D14600916

Pulled By: houseroad

fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144

Fix interpolate tracing (#19034)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19034
ghimport-source-id: 874e0b0a8685184416152a77fc1850d9a06516ae

Differential Revision: D14837282

Pulled By: zdevito

fbshipit-source-id: b0ed82b607c288a54eecec3d6ed62c4626e5a563

Fix default dtype in shape analysis (#18968)

Summary:
Fix for https://github.com/pytorch/pytorch/issues/18823

Previously we were setting the dtype to Float when in torchscript the default is double. When the problem in https://github.com/pytorch/pytorch/issues/17662 gets landed, we will have to reevalute (and this test will fail).

We should still be consistent in shape_analysis in the meantime.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18968

Differential Revision: D14837939

Pulled By: eellison

fbshipit-source-id: 32383b55c14bdc7753e26dec33c39ab10124c255

Renamed bool tensors into byte tensors (#19021)

Summary:
Renamed bool tensors into byte tensors to represent the correct type in generated code
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19021

Differential Revision: D14835188

Pulled By: izdeby

fbshipit-source-id: 0252d2c69dab35ac2f076cf9a87423463e902c76

Handle None indexing in TorchScript (#18615)

Summary:
t[None], t[None, 1:, None] and friends for unsqueezing

Fixes: #12810
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18615

Differential Revision: D14837039

Pulled By: wanchaol

fbshipit-source-id: ab3862c41629f087b0a46b7c59c93dac4018e6fe

Turn on mkldnn in most builds except rocm

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18965

Differential Revision: D14836931

Pulled By: bddppq

fbshipit-source-id: 463a9bc5043a1f3194158f7bbfae3b71c6cd4b20

Remove dead code in module.cpp (#19022)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19022
ghimport-source-id: cdf694c1b426eb9f82d4c148c9f2c2cfc180cedd

Reviewed By: eellison

Differential Revision: D14833409

Pulled By: driazati

fbshipit-source-id: 8914c7227add7f3e07f56b21a513ba7727fb6800

Convert test_recursive_cse to use Filecheck inline annotations. (#19032)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19032
ghimport-source-id: 58a146542deb08dd3057d099167ba530a5e51400

Differential Revision: D14836689

Pulled By: ZolotukhinM

fbshipit-source-id: e65ca5f09193eb7c16c204aedd50c474ea31210c

Add a document 'How to Write Tests Using FileCheck' (#19005)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19005
ghimport-source-id: f9c3eff54adc8eef3ead2c77be62c44d88d22a00

Differential Revision: D14826845

Pulled By: ZolotukhinM

fbshipit-source-id: 62cc3657ee89acc979403da15e39bd4cd09a866d

caffe2 - Expose tensor filler util to Python (#18886)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18886

Expose tensor filler util to Python and add a unit test (both C++/Python)

Reviewed By: salexspb

Differential Revision: D14784470

fbshipit-source-id: bb8e013d1755c27c166e87d5a8491a97c65d3d8d

call build_android.sh from pytorch CI build script (#18918)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18918
ghimport-source-id: 98c63da263adbbc6ac74a69ac117740c852833cd

Reviewed By: dreiss

Differential Revision: D14800727

Pulled By: ljk53

fbshipit-source-id: 4d06f845bb34bcdb74b0602404f2a0782f8c8783

Type annotations for `util.data`. (#18963)

Summary:
I haven't had a chance to rigorously try these out yet so don't merge yet.
Closes #18725.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18963

Differential Revision: D14832897

Pulled By: ezyang

fbshipit-source-id: 4780e7a34126bc66ddbfd9d808dfc9e0edd77e68

ifdef guard some explicit pragma unrolls (#19018)

Summary:
the ROCm compiler cannot and will not satisfy them, causing compile time warnings.

Reason being a runtime loop trip count.

Some warnings remain arising from other parts of the ROCm stack - tickets are filed and they will be resolved within these components.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19018

Differential Revision: D14832859

Pulled By: ezyang

fbshipit-source-id: 0d66e4aebe4e56af14dd5e2967d3c374a82be25c

Fix a dev mode bug in activation distribution observer (#19004)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19004

Handling the exception case when the data has min 3.40282e+38 max -3.40282e+38

Reviewed By: jspark1105

Differential Revision: D14822193

fbshipit-source-id: b9771d1584fdf8317f5b8c7f5806be5d27314386

Clean up some sparse code. (#18962)

Summary:
1) sparse_dispatches in native_parse was not used anymore, got rid of it.
2) got rid of overloaded sizes_ in SparseTensorImpl, which just uses the base implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18962

Differential Revision: D14811545

Pulled By: gchanan

fbshipit-source-id: 2fa60ef50456b5f605caa63beae1d8d2542fd527

Remove tensorWithAllocator() from Type (#18780)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18780
ghimport-source-id: 7d18a11ce87d988bd32f6ebb96acd878ab8d61be

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18780 Remove tensorWithAllocator() from Type**
* #18779 Remove tensorFromBlob() from Type

Differential Revision: D14739336

fbshipit-source-id: 429ab10bb9f6ac9f97b5a11c7a836b6b6336cb2d

Fix sparse mm for ROCm (#18985)

Summary:
* Annotate also two pass reduction with launch bounds
* ifdef some shortcomings of ROCm w.r.t. short-circuit returns - internal tickets filed
* while there, plug memory leak by destroying matrix descriptor after the sparse call (applicable to cuSPARSE)
* while there, fix types for cusparseXcoo2csr as per cuSPARSE documentation
* enable test_dsmm in test_sparse which now passes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18985

Differential Revision: D14822009

Pulled By: bddppq

fbshipit-source-id: 757267a47a63ee56ef396c33059f7eca099f4833

Check if profiler is disabled in push/pop event (#18908)

Summary:
Make sure to check if profiler is disabled in push/pop and mark event
functions
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18908

Differential Revision: D14791931

Pulled By: ilia-cher

fbshipit-source-id: e4f5149e69999ee2b9238c21cccad6d27c6a714a

Implement Observer pass on simple model and validate stats (#18848)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18848

The Observer Module is based on eager mode compute qparam implementation.
Goal is to validate QParam result for EagerMode and Script Mode for simple
model

Observer stats are collected and qparam computed only for activations only at this point

Reviewed By: zafartahirov

Differential Revision: D14720805

fbshipit-source-id: cb2f321b4b9927b37905fdb8eb55c5610d41b351

AVX2 with GCC9 fix. (#18991)

Summary:
Dear All,

The proposed patch fixes the test code snippets used in cmake infrastructure, and implicit failure to set properly the ```CAFFE2_COMPILER_SUPPORTS_AVX2_EXTENSIONS``` flag. The libcaffe2.so will have some ```UND``` avx2 related references, rendering it unusable.

* Using GCC 9 test code from cmake build infra always fails:
```
$ gcc  -O2 -g -pipe -Wall -m64 -mtune=generic -fopenmp -DCXX_HAS_AVX_1 -fPIE -o test.o -c test.c -mavx2
test.c: In function ‘main’:
test.c:11:26: error: incompatible type for argument 1 of ‘_mm256_extract_epi64’
   11 |     _mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code
      |                          ^
      |                          |
      |                          __m256 {aka __vector(8) float}
In file included from /usr/lib/gcc/x86_64-redhat-linux/9/include/immintrin.h:51,
                 from test.c:4:
/usr/lib/gcc/x86_64-redhat-linux/9/include/avxintrin.h:550:31: note: expected ‘__m256i’ {aka ‘__vector(4) long long int’} but argument is of type ‘__m256’ {aka ‘__vector(8) float’}
  550 | _mm256_extract_epi64 (__m256i __X, const int __N)
      |

$ gcc -v
Using built-in specs.
COLLECT_GCC=gcc
COLLECT_LTO_WRAPPER=/usr/libexec/gcc/x86_64-redhat-linux/9/lto-wrapper
OFFLOAD_TARGET_NAMES=nvptx-none
OFFLOAD_TARGET_DEFAULT=1
Target: x86_64-redhat-linux
Configured with: ../configure --enable-bootstrap --enable-languages=c,c++,fortran,objc,obj-c++,ada,go,d,lto --prefix=/usr --mandir=/usr/share/man --infodir=/usr/share/info --with-bugurl=http://bugzilla.redhat.com/bugzilla --enable-shared --enable-threads=posix --enable-checking=release --enable-multilib --with-system-zlib --enable-__cxa_atexit --disable-libunwind-exceptions --enable-gnu-unique-object --enable-linker-build-id --with-gcc-major-version-only --with-linker-hash-style=gnu --enable-plugin --enable-initfini-array --with-isl --enable-offload-targets=nvptx-none --without-cuda-driver --enable-gnu-indirect-function --enable-cet --with-tune=generic --with-arch_32=i686 --build=x86_64-redhat-linux
Thread model: posix
gcc version 9.0.1 20190328 (Red Hat 9.0.1-0.12) (GCC)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18991

Differential Revision: D14821838

Pulled By: ezyang

fbshipit-source-id: 7eb3a854a1a831f6fda8ed7ad089746230b529d7

Remove tensorFromBlob() from Type (#18779)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18779
ghimport-source-id: e7453b74fcce0e4f4a9cbce0324992a85272a426

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18780 Remove tensorWithAllocator() from Type
* **#18779 Remove tensorFromBlob() from Type**

Differential Revision: D14739335

fbshipit-source-id: 8a0619a5b412332efa3b2d60c1edebd53d089d50

Improve precision of emitted code for prim::Constant (#18817)

Summary:
Stacked on https://github.com/pytorch/pytorch/pull/18815 and https://github.com/pytorch/pytorch/pull/18811.

This makes it so that we emit a higher-precision literal for float values in the fusion kernel, as well as assign that to a `double` variable. This prevents us from losing precision for values such as `pi`, but with the previous fixes this will also get downcasted to `float` if downstream operations require it. Therefore, we should not lose performance because of implicit promotions
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18817

Differential Revision: D14820842

Pulled By: jamesr66a

fbshipit-source-id: 519671c6ca5e7adac746a4c4c72760a6d91e332f

convert_sync_batch_norm to SyncBatchNorm (#18787)

Summary:
Closes #18382

Please let me know if any changes are required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18787

Differential Revision: D14821147

Pulled By: soumith

fbshipit-source-id: edd98eab1b3f4151c4ae5148146435ddb2ae678d

fix bug when falling back to acc32 when weight is prepacked (#18974)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18974

When the weight is prepacked and it doesn't contain a prepacked weight for acc32, we shouldn't fallback to acc32.

Reviewed By: bddppq

Differential Revision: D14814067

fbshipit-source-id: aec917322de695e283f0aca1e930c5603d196404

move 2ops back to autodiff (#18969)

Summary:
Move these 2 ops back to autodiff to unblock xla CI.
I will leave them for my next PR to cleanup symbolic_variable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18969

Differential Revision: D14816811

Pulled By: ailzhang

fbshipit-source-id: dd8a7e133dcad29560d3d1d25691883960117299

Preserve naming for inputs/outputs with observer insertion (#18713)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18713

- Quantizer observer node output is hooked up to following node
which mutates the naming for input/output. This is not desired and
required because observer op can be a sync node

- Quantizer is aimed for quantizing tensors so we should insert observer
op for Values that are type tensor

Reviewed By: zafartahirov

Differential Revision: D14715916

fbshipit-source-id: feca04c65a43103b46084d3548998498b19ee599

Emit math functions specific to output type (#18815)

Summary:
Stacked on https://github.com/pytorch/pytorch/pull/18811

This makes it so that we only emit the *f variants of math functions if the output value's type is FloatTensor, otherwise we call the double variants to prevent loss of precision. This fixes more numerical issues
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18815

Differential Revision: D14816965

Pulled By: jamesr66a

fbshipit-source-id: 464be644168875ede987142281fb2168f4041e81

add instructions for NVIDIA Jetson platforms (#18990)

Summary:
Thanks to dusty-nv , we now have Stable and Weekly wheels provided for the NVIDIA Jetson Platform. They require JetPack 4.2.

He's also maintaining source build instructions.

This PR adds links to the binaries and source build instructions to the README.

The links are dynamic, so when new stable / weekly wheels are available, Dustin will update the same URL to point to the new files
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18990

Differential Revision: D14820158

Pulled By: soumith

fbshipit-source-id: 761a56557decb72ad9c1b9f8a2745667f558eec3

Quantizer pass to insert quant-dequant nodes into IR (#18446)

Summary:
- Quantizer pass to mutate IR by inserting quant-dequant nodes
before and after nodes which support quantized ops. This information
will be used by jit compiler to substitute with quantized ops

- This currently covers simple model. It will be expanded later
for subgraph pattern matching to cover more complex patterns
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18446

Differential Revision: D14592265

Pulled By: nishantpdce

fbshipit-source-id: c9ba6c12aa96cb9c117826e386721eec83a55ea6

add SyncBatchNorm to docs (#18988)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/18983
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18988

Differential Revision: D14820042

Pulled By: soumith

fbshipit-source-id: 356169f554a42303b266d700d3379a5288f9671d

Add c10_cuda to libraries in CUDAExtension for Windows (#18982)

Summary:
This change was necessary for me to compile [apex](https://github.com/NVIDIA/apex) on Windows.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18982

Differential Revision: D14819818

Pulled By: soumith

fbshipit-source-id: 37ff9b93a72ab2b7c87f23a61e9f776c71c4c1a8

Remove Trainer from README.md (#18980)

Summary:
Trainer has been removed long time ago
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18980

Differential Revision: D14819855

Pulled By: ezyang

fbshipit-source-id: f62020e688ebf6663416aec7435bf1f531607941

Create Object that represents a Module (#18469)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18469
ghimport-source-id: 73cb8b58f43f10b1dcfca805fd5b25c4fa977632

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18469 Create Object that represents a Module**
* #18468 slots with explicit value/setValue make more sense in future patches
* #18467 Make Object hold its ClassType
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.

This changes the underlying storage for script::Module to hold
a ivalue::Object which has slots for all the parameters and attributes.

NamedIValue and Slot are now merged together into one class Slot that stores
the tuple (ivalue::Object, offset) and can be used to read the name, type,
or value of the slot and also to set the value. This cleans up a bunch
of client uses.

This PR does not actually use the module object in any generated code.
A future PR will switch how code is generated to treat modules as
first class.

Differential Revision: D14613508

fbshipit-source-id: d853a7559f58d244de2ef54a781427fcd1060ed0

Add numpy like repeat as torch.repeat_interleave (#18395)

Summary:
Fixes: https://github.com/pytorch/pytorch/issues/14093
cc: SsnL
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18395

Differential Revision: D14599509

Pulled By: umanwizard

fbshipit-source-id: 2391a1cc135fe5bab38475f1c8ed87c4a96222f3

Fix interpolate trace (#18875)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/10654

The issue is that in tracing `.size` returns an int tensor, and when an int tensor is multiplied by a scalar the int dominates and the scalar gets casted 0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18875

Differential Revision: D14814441

Pulled By: eellison

fbshipit-source-id: a4e96a2698f2fcbf3ec4b2bb4c43a30250f30ad9

Code string API for fuser testing (#18884)

Summary:
This adds a C++ function `debugGetFusedKernelCode` as well as a Python binding `_jit_fuser_get_fused_kernel_code` that will, given a FusionGroup graph and a set of specified inputs, return the compiled kernel source code. We can then check the contents of this source code for verification of the fuser codegen backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18884

Differential Revision: D14795508

Pulled By: jamesr66a

fbshipit-source-id: 8f6e9dd13ebbb517737d893b0b5f5e9aa06af124

remove unused func (#18712)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18712
ghimport-source-id: e435150a501b20695a5276addee93d795e04b532

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18712 [jit][easy] remove unused func**
* #18711 [jit] fix side-effects and aliasing for custom ops

as title

Differential Revision: D14730979

fbshipit-source-id: 381d16ea2a45779bf6d5fc6d90a4f8585461e902

Revert D14778810: [caffe2/int8] fix bug when falling back to acc32 when weight is prepacked

Differential Revision:
D14778810

Original commit changeset: d49a8c4b7c81

fbshipit-source-id: 15568b084848de74437582548bec42aadc74080d

slots with explicit value/setValue make more sense in future patches (#18468)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18468
ghimport-source-id: d4b41c521f2269a695e03c8e7d05d5542731ee48

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18469 Create Object that represents a Module
* **#18468 slots with explicit value/setValue make more sense in future patches**
* #18467 Make Object hold its ClassType
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.

Reviewed By: suo

Differential Revision: D14613509

fbshipit-source-id: 9f2208d0efd01465c78cebdc3e8365a9e0adf9ff

Make Object hold its ClassType (#18467)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18467
ghimport-source-id: d51bdd64d2529d08c634c58df1a0870b54ad49fb

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18469 Create Object that represents a Module
* #18468 slots with explicit value/setValue make more sense in future patches
* **#18467 Make Object hold its ClassType**
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.

Currently it holds a symbol whose unqualified name is the name of the
class. This will get confusing when there are multiple possible registries,
and it makes getting the class type from the object difficult.
The pointer to the class is only 4 more bytes so this patch just puts
it in the object.

Reviewed By: suo

Differential Revision: D14613510

fbshipit-source-id: b35175ba4be83d2522deaa6dad5070d6ec691fed

Enforce single parent for script submodules (#18379) (#18860)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18860
ghimport-source-id: 96305349bf3db564f43df2263b1e5bddcc9e9dae

Reviewed By: suo

Differential Revision: D14780421

Pulled By: zdevito

fbshipit-source-id: 2bdd89b35866ba035ebea0adab037e441c1006e2

CUDA_NVCC_EXECUTABLE is not needed, as nvcc is in PATH (#18958)

Summary:
As indicated by f0k: https://github.com/pytorch/pytorch/pull/18495#issuecomment-480178763
nvcc via ccache is already first in the PATH in the instructions I provided, so CUDA_NVCC_EXECUTABLE is not needed.

I re-built to test that it's so.

Thank you!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18958

Differential Revision: D14810732

Pulled By: ezyang

fbshipit-source-id: 3758ae2253c745c5d7cfccedd49fa00cc4629965

Fix precision issue with expansion that prefers 'probs' over 'logits' (#18614)

Summary:
I have experienced that sometimes both were in `__dict__`, but it chose to copy `probs` which loses precision over `logits`. This is especially important when training (bayesian) neural networks or doing other type of optimization, since the loss is heavily affected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18614

Differential Revision: D14793486

Pulled By: ezyang

fbshipit-source-id: d4ff5e34fbb4021ea9de9f58af09a7de00d80a63

Method is supposed to be in-place (#18684)

Summary:
Tracing models which attempts to return this in-place value doesn't turn out well.

I haven't run any tests to confirm the results to be honest, but regardless of the outcome, the operation happens in-place, so it should work as before.

Sample output from traced model attempting to set `max_norm` on `Embedding`:
```
a leaf Variable that requires grad has been used in an in-place operation. (check_inplace at /pytorch/torch/csrc/autograd/VariableTypeUtils.h:49)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f0ecc5cc021 in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f0ecc5cb8ea in /usr/local/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #2: <unknown function> + 0x38ab2f (0x7f0ecb55ab2f in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #3: torch::autograd::VariableType::embedding_renorm_(at::Tensor&, at::Tensor const&, double, double) const + 0x76 (0x7f0ecb5b5966 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #4: <unknown function> + 0x56c958 (0x7f0ecb73c958 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #5: <unknown function> + 0x672286 (0x7f0ecb842286 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #6: torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocator<c10::IValue> >&) + 0x22 (0x7f0ecb83d842 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #7: <unknown function> + 0x65c6ac (0x7f0ecb82c6ac in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
frame #8: <unknown function> + 0x3c8ab4 (0x7f0f06bc0ab4 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #9: <unknown function> + 0x3ad2c3 (0x7f0f06ba52c3 in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #10: <unknown function> + 0x11663e (0x7f0f0690e63e in /usr/local/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #39: python_call + 0x11 (0x5563c3c521c1 in uwsgi)
frame #40: uwsgi_request_wsgi + 0x100 (0x5563c3c54410 in uwsgi)
frame #41: wsgi_req_recv + 0xac (0x5563c3becabc in uwsgi)
frame #42: simple_loop_run + 0xc4 (0x5563c3c35be4 in uwsgi)
frame #43: simple_loop + 0x10 (0x5563c3c35a00 in uwsgi)
frame #44: uwsgi_ignition + 0x241 (0x5563c3c3a3a1 in uwsgi)
frame #45: uwsgi_worker_run + 0x275 (0x5563c3c3ec35 in uwsgi)
frame #46: <unknown function> + 0x8f22c (0x5563c3c3f22c in uwsgi)
frame #47: <unknown function> + 0x3c13e (0x5563c3bec13e in uwsgi)
frame #48: __libc_start_main + 0xf1 (0x7f0f138922e1 in /lib/x86_64-linux-gnu/libc.so.6)
frame #49: _start + 0x2a (0x5563c3bec16a in uwsgi)
:
operation failed in interpreter:
op_version_set = 0
def forward(self,
    input_1: Tensor) -> Tensor:
  _0 = torch.norm(self.item_embedding.weight, 2, 1, True)
  _1 = torch.div(self.item_embedding.weight, _0)
  m_weight = torch.t(_1)
  input_2 = torch.contiguous(input_1)
  weight_1 = torch.embedding_renorm_(self.item_embedding.weight, input_2, 1., 2.)
             ~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  x = torch.embedding(weight_1, input_2, -1, False, False)
  input_3 = torch.div(x, torch.norm(x, 2, 2, True))
  max_batch_size = ops.prim.NumToTensor(torch.size(input_3, 0))
  hx = torch.zeros([2, int(max_batch_size), 70], dtype=6, layout=0, device=torch.device("cpu"))
  _2 = [self.lstm_layer.weight_ih_l0, self.lstm_layer.weight_hh_l0, self.lstm_layer.weight_ih_l1, self.lstm_layer.weight_hh_l1]
  input_4, _3, _4 = torch.lstm(input_3, [hx, hx], _2, False, 2, 0.10000000000000001, False, False, True)
  input = torch.matmul(input_4, torch.t(self.rnn2item.weight))
  tastevec = torch.div(input, torch.norm(input, 2, 2, True))
  outputs = torch.matmul(tastevec, m_weight)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18684

Differential Revision: D14782041

Pulled By: ezyang

fbshipit-source-id: 7b2fc19b7d5b6600263644498bb728319a19f39d

fix bug when falling back to acc32 when weight is prepacked (#18881)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18881

Pull Request resolved: https://github.com/pytorch/pytorch/pull/18878

When the weight is prepacked and it doesn't contain a prepacked weight for acc32, we shouldn't fallback to acc32.

TODO: add unit tests with better coverage

Reviewed By: feiyu1990

Differential Revision: D14778810

fbshipit-source-id: d49a8c4b7c815ab29b77feb53ee730ad63780488

More numerically stable lerp (#18871)

Summary:
The C++ and CUDA implementations of the lerp are not numerically stable. This is discussed on Wikipedia [here](https://en.wikipedia.org/wiki/Linear_interpolation#Programming_language_support). I checked the GPU SASS output and there's no overhead from using the more precise implementation, from Kepler all the way to Turing. I haven't looked at CPU ASM though.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18871

Differential Revision: D14793438

Pulled By: ezyang

fbshipit-source-id: 2ddc2e026c5285466cae7d1b4101174253100445

Increase default c10d/ProcessGroupGloo test timeout (#18916)

Summary:
See #18659.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18916

Differential Revision: D14808749

Pulled By: pietern

fbshipit-source-id: 9a9c8beddb2dbbb1bf4c5e575743d9e1fa3f07fa

remove symbolic variable part 1 (#17986)

Summary:
As discussed with gchanan we should deduplicate symbolic_variable and symbolic_script to prepare for the future merge with derivatives.yaml.

This PR moves most easy formulas to symbolic_script.

TODO: run benchmarks to make sure no perf regression

cc: apaszke zdevito wanchaol
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17986

Differential Revision: D14766412

Pulled By: ailzhang

fbshipit-source-id: d95a3f876e256c0f505779a71587c985571d3b8f

Revert D14742020: Wrap workaround for cpp custom types a bit prettier and add an example

Differential Revision:
D14742020

Original commit changeset: 0f2fd83ae56a

fbshipit-source-id: 5640255aef0319b7d8996e07132e87213130d31c

Decompose more Windows scripts (#18917)

Summary:
This PR:

* pulls four distinct installation steps out of `build_pytorch.bat` and into their own scripts.
* eliminates the copy step for helper scripts called by `win-build.sh` and `win-test.sh`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18917

Differential Revision: D14807236

Pulled By: kostmo

fbshipit-source-id: 03e91a5834dfd6d68903ad9725eacc099bbf6d53

Wrap workaround for cpp custom types a bit prettier and add an example (#18791)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18791

As a temporary demonstration on how to extend this hack further until custom C types are ready.

Reviewed By: jamesr66a

Differential Revision: D14742020

fbshipit-source-id: 0f2fd83ae56ab2abe16977a1829ed421e6abe74b

Remove cuda::compat functions in aten (#18905)

Summary:
Looks like the issue of using `std::` functions is fixed in new rocm version
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18905

Differential Revision: D14792943

Pulled By: bddppq

fbshipit-source-id: af11acbb85872943f23b6e55415db1f0699e7b8f

fix side-effects and aliasing for custom ops (#18711)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18711
ghimport-source-id: c9caedc0660b2b7ba3730cd0e1a2e0e9c3cf422b

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18711 [jit] fix side-effects and aliasing for custom ops**

Previously we didn't track aliasing, mutation, or side effects for
custom ops. This PR adds in guards with the most conservative
assumptions possible: the op will
1) have side effects,
2) write to everything
3) produce a wildcard.

In order to tell whether a given operator is a custom op, this PR introduces
the concept of a "reserved" namespace (basically all our builtin namespaces).
Custom ops live in non-reserved namespaces, so a check on the namespace
is sufficient to tell whether a schema/node is "custom" or not.

This is just to get things correct for now. Follow-ups to this:
- Users should be able to specify aliasing/mutability without having to learn
the whole alias annotation schema.
- Relax assumptions a bit. In particular outputs can only alias input tensors,
they don't have to be wildcards.

Fixes #18490

Differential Revision: D14730978

fbshipit-source-id: 540b47a24ccf24145051609bdcc99c97e46e0fe0

Expand the list of ops that mutate an inputs shape (#18812)

Summary:
Expand the list of ops that resize an input in-place to include broadcasting ops and other ops that affect shape. Whoever is reviewing the PR could you please look through pytorch in place ops and see if I missed any.

Expanding the PR from: https://github.com/pytorch/pytorch/pull/17518

This is already being tested in test_resize_input_ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18812

Differential Revision: D14793410

Pulled By: eellison

fbshipit-source-id: 125f4f5375ac1036fb96fabc9da2aaccc9adc778

add launch bounds, enable more tests (#18909)

Summary:
Add launch bounds annotations for ROCm arising from maxThreadsPerBlock and apply threads use.

Enable tests that now work.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18909

Differential Revision: D14801490

Pulled By: ezyang

fbshipit-source-id: b81c97fc783a2627bc7e31b32036a364cfe40cc7

Add backward pass to infer single missing input shape for Concat opportunitiscally (#18911)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18911

Att.

Reviewed By: bddppq

Differential Revision: D14791295

fbshipit-source-id: 4b7a775924f0eadb0cb73aa6c434a6a5be8b92be

change to use clang if NDK >= 18 (#18914)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18914
ghimport-source-id: 4d9d9322ee5559d96e13533ec37ff3be86a0227c

Reviewed By: ezyang

Differential Revision: D14794162

Pulled By: ljk53

fbshipit-source-id: caac55e12b1e62bf6ebcc6e2062d5ed122ad4e64

Revert D14673459: [pytorch][PR] [jit] Replace Slot on script::Method with NamedIValue

Differential Revision:
D14673459

Original commit changeset: 21200180c47f

fbshipit-source-id: 9c01de4cf5bb7c87ac0c55705b901db990cd917b

Disable flaky test_proper_exit test. (#18950)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18950
ghimport-source-id: 27bd575fd3c73a51ace1360aa020fa63a792a5d2

Differential Revision: D14802009

Pulled By: ezyang

fbshipit-source-id: 051e1d038892c2c6e8337357fa80771b8dc42680

Checkout pytorch_sphinx_theme with https. (#18859)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18859
ghimport-source-id: fbbcb8a2dd9c9f0a317de489b6bbb83e9071a7d8

Differential Revision: D14801989

Pulled By: ezyang

fbshipit-source-id: a9bc02e1383adafcac01994e6346b28551d95c71

Add tests for reducer class (#18845)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18845

This adds a few CPU only test cases for the reducer class.

Reviewed By: mrshenli

Differential Revision: D14768432

fbshipit-source-id: c008a52206826304e634a95bc14167ed94c97662

Fix a few instances of notifying on a CV while holding the lock (#18857)

Summary:
Fix a few instances of notifying on a CV while holding the lock to release the lock before notifying. This avoids an extra thread suspension when the notified thread tries to grab the lock.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18857

Differential Revision: D14779132

Pulled By: resistor

fbshipit-source-id: b18a05c4c15be1426ebfdffac1c8f002b771cfd7

Unify caffe2 and libtorch build scripts on Windows (#18683)

Summary:
`scripts/build_windows.bat` is the original way to build caffe2 on Windows, but since it is merged into libtorch, the build scripts should be unified because they actually do the same thing except there are some different flags.

The follow-up is to add the tests. Looks like the CI job for caffe2 windows is defined [here](https://github.com/pytorch/ossci-job-dsl/blob/master/src/jobs/caffe2.groovy#L906). Could we make them a separate file, just like what we've done in `.jenkins/pytorch/win-build.sh`? There's a bunch of things we can do there, like using ninja and sccache to accelerate build.

cc orionr yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18683

Differential Revision: D14730188

Pulled By: ezyang

fbshipit-source-id: ea287d7f213d66c49faac307250c31f9abeb0ebe

Simplify storage wrapping in TH. (#18855)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18855
ghimport-source-id: 01faa229fa4db901ab8539d3778b716d909ba4cf

Reviewed By: dzhulgakov

Differential Revision: D14790669

Pulled By: gchanan

fbshipit-source-id: 167b9bc9c9872743fa8f6040a26ddf7ff5789c27

Cache device on TensorImpl; clean up TensorImpl constructors. (#18833)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18833
ghimport-source-id: 6f2be25fcc5e6be3ffe20582e604bd2c1fbab66b

Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18833 [STACK] Cache device on TensorImpl; clean up TensorImpl constructors.**
* #18832 [STACK] Disallow changing the device of a tensor via set_.
* #18831 [STACK] Stop swapping in Storages of the wrong device for Tensors.

1) We cache device on TensorImpl. This means we can access the device without a virtual function and allows us to more easily extend TensorImpls (because they don't need to figure out how to store the Device for themselves).

2) Clean up TensorImpl APIs. We had a constructor that took a TensorTypeId and an allocator and would allocate a Storage based on the recognized types of TensorTypeIds. Instead, we just have two different constructors: one for types with a storage, one without.

Reviewed By: dzhulgakov

Differential Revision: D14766230

fbshipit-source-id: 745b8db84dcd6cb58f1a8675ad3ff8d033bc50df

Revert "Adding pin_memory kwarg to zeros, ones, empty,... (#18854)

Summary:
This reverts commit c484cf43a02863efd2f4a76aad43246fb0191ab5.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18854

Differential Revision: D14778393

Pulled By: VitalyFedyunin

fbshipit-source-id: 4b5a1f5b1c091bbc4a8e75614734cc011d26b452

Silence compiler warnings (#18912)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18912

We intentionally test a deprecated API, no need to show the warnings here.

Reviewed By: dzhulgakov

Differential Revision: D14792617

fbshipit-source-id: 9ea2a4106d566064283726eed2c274b98f49a2e5

ScriptModuleOp in caffe2 (#18716)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18716

Might be useful as an intermediate stage for some systems that currently use Caffe2 nets as an execution mechanism.

Not sure it's a good idea all together, please comment.

Limitations:
- only Tensor types as inputs/outputs
- the entire module is serialized as a zip archive inside a proto in Caffe2 db, it'd be subject to 4Gb limit and is likely very slow. For small models it'd work though.
- no autograd, though it can be attached in principle
- no way to retrieve parameters inside the script module from C2 runtime perspective (though they potentially can be alias-fetched and stored as individual blobs)
- after deserialization, python wrappers returned don't have correct type (as we don't do module_lookup trick)

Build-wise, I had to add dependency from pybind_state to libtorch.so. I don't think we build Caffe2 python frontend independently anymore, so it should be fine.

Reviewed By: amirshim, houseroad

Differential Revision: D14339599

fbshipit-source-id: 88a37a8abd1f1c4703e5ef937031f222535d4080

flake8 fix on extracted python script

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18931

Differential Revision: D14796114

Pulled By: kostmo

fbshipit-source-id: 25971be5a36fffc61e29db981af7298a0fe0ed8c

Replace Slot on script::Method with NamedIValue (#18252)

Summary:
This refactor lets us track the types of initial values added onto a `Method`. The main motivation for this is the change in `module.cpp`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18252

Differential Revision: D14673459

Pulled By: driazati

fbshipit-source-id: 21200180c47f25bb70898771adfb569856e6c34a

U/kostmo/windows offload scripts 3

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18754

Differential Revision: D14794893

Pulled By: kostmo

fbshipit-source-id: 05187d9b53615ffbcc7253accdc692c4ecaf25d9