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Allow tracing with fork/wait #15184

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commented Dec 13, 2018

There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052

@highker highker requested review from zdevito and apaszke Dec 13, 2018

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@highker highker force-pushed the highker:trace3 branch from 0309d86 to d845965 Dec 14, 2018

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@highker highker force-pushed the highker:trace3 branch 3 times, most recently from b0200d8 to c82a546 Dec 14, 2018

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commented Dec 15, 2018

@zdevito ready for review, thanks.

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Looks good -- minor code structure comments.


// No need to trace a script module.
std::shared_ptr<tracer::TracingState> saved_state = state;
tracer::setTracingState(nullptr);

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This should be a ResourceGuard object defined in tracer.h:

{
    ResourceGuard guard(tracer::pauseTracing())
    getOrCompileFallback().run(stack);
}
self.assertGraphContainsExactly(module.graph, kind='aten::neg', num_kind_nodes=1)
self.assertGraphContainsExactly(module.graph, kind='aten::neg', num_kind_nodes=3, consider_subgraphs=True)
# Make sure we are unpacking
self.assertGraphContainsExactly(module.graph, kind='prim::ListUnpack', num_kind_nodes=1)

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I would drop these asserts. We sometimes do peephole optimization that might delete trivial TupleUnpack(TupleCreate()) pairs, and this would just add more work to do to add those. It is sufficient to test that the functionality works.

inline void setValueTrace(const Variable& var, Value *value) {
JIT_ASSERT(var.defined());
getTracingState()->value_map[var] = value;
inline void setValueTrace(const IValue &v, Value *value) {

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This should be moved to tracer.cpp, it is getting big:

TORCH_API void setValueTrace(const IValue& v, Value* value);
Allow tracing with fork/wait
- Disable tracing with script module. That is not necessary.
- Set value trace for tensor list and tuple of tensors as well.

@highker highker force-pushed the highker:trace3 branch from 537378d to cc627f5 Dec 17, 2018

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zdevito pushed a commit to zdevito/ATen that referenced this pull request Dec 18, 2018
Allow tracing with fork/wait (#15184)
Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: pytorch/pytorch#15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e
jaciefan added a commit to jaciefan/pytorch that referenced this pull request Dec 27, 2018
Squash and push master to WIP branch
* silence unreachable code warnings (#15036)

Summary:
Stack:
&nbsp;&nbsp;&nbsp;&nbsp;:black_circle:&nbsp; **#15036 silence unreachable code warnings**&nbsp;&nbsp;[:yellow_heart:](https://our.intern.facebook.com/intern/diff/D13411100/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15036

Differential Revision: D13414712

Pulled By: li-roy

fbshipit-source-id: d4aa84571fa94c66f3c5bfa9575a10c6ee398f9e

* tox.ini -> .flake8 (#15065)

Summary:
We were only using this file to configure flake8, and fbcode linters do not recognize tox.ini which causes spurious linter warnings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15065

Differential Revision: D13420774

Pulled By: suo

fbshipit-source-id: e43a46befa36862c8b3c0a90074aec6a66531492

* Update onnx coverage script for more accurate result (#15029)

Summary:
The coverage of scalar-input test cases were not accurate. This patch fixed that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15029

Differential Revision: D13419764

Pulled By: zrphercule

fbshipit-source-id: a14a5cbef432bea8c9126156f5deb1125e1aeb47

* Issue 14984: Remove divide by zero error in index_put_ (#14986)

Summary:
No check for zero index tensor was done in the accumulate=True (serial) case in the new TensorIterator code since https://github.com/pytorch/pytorch/pull/13420.

https://github.com/pytorch/pytorch/issues/14984
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14986

Differential Revision: D13417861

Pulled By: colesbury

fbshipit-source-id: e6ed1af8f708b53a35803fc157ed1f043169ec89

* Supress warnings on generated tests

Summary: Removes all warnings spew for the TestJitGenerated tests

Differential Revision: D13420919

fbshipit-source-id: f251c12f923088ccc5daa2984c15003a67cbd1c1

* Split off fuser tests in test_jit.py to their own test case (#15072)

Summary:
This PR creates TestFuser inside test_jit.py to be a home for graph fuser
specific tests.

This was a useful exercise because now that all the fuser tests are in
one place, I can spot redundant and bitrotting tests for cleanup in a
future PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15072

Differential Revision: D13421458

Pulled By: zou3519

fbshipit-source-id: 80b1a7712feff75a0c186d1664601c4edbbca694

* re-enable copy of python files, but be careful that the copy is only … (#14982)

Summary:
…done once

This allow no-op build to work correctly even when BUILD_CAFFE2_OPS is on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14982

Differential Revision: D13413960

Pulled By: zdevito

fbshipit-source-id: 6e5412a8c375af8a47c76f548cdd31cff15f3853

* add gloo scatter support on GPU (#14917)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14917

as titled

Reviewed By: pietern

Differential Revision: D13271560

fbshipit-source-id: 0187a3390f8ebd72a2c074e7a651432159d427c0

* Remove deprecated variable_tensor_functions (#15003)

Summary:
Removing the deprecated functions in `torch/csrc/variable_tensor_functions.h` (like `torch::CPU`) and corresponding implementations from `torch/csrc/torch.cpp` from master after the release.

ezyang gchanan soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15003

Differential Revision: D13418086

Pulled By: goldsborough

fbshipit-source-id: a0accdf6f7b0efa1ec07ac7b74b86ff2da37543f

* Add error type to raise statement

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15039

Differential Revision: D13419566

Pulled By: zou3519

fbshipit-source-id: f67a3aebce937e3e640e91e81eb3e184cfdf269c

* Make ATen HIPify out-of-place, but still reuse CUDA names. (#14866)

Summary:
```
    This diff changes the HIPification of ATen to be out-of-place.
    We now have the following mappings:

    - ATen/cuda => ATen/hip
    - ATen/native/cuda => ATen/native/hip
    - ATen/native/sparse/cuda => ATen/native/sparse/hip
    - THC => THH
    - THCUNN => THHUNN

    The build system is adjusted to know about these new build paths,
    and HIPify is taught how to adjust include paths and
    THC_GENERIC_FILE appropriately.  ATen_hip is now built as
    the ATen_hip library, rather than reusing ATen_cuda.

    However, despite these new filepaths, none of the identifiers in ATen
    have actually changed.  So, e.g., THHGeneral.h still defines functions
    named THC_blahblah, and HIP still shows up as CUDA in PyTorch itself.
    We'll tackle this in a subsequent PR; this diff is just to get the files
    out-of-place.

    Minor extra improvements:

    - Don't edit tmp_install when hipifying
    - HIP no longer builds native_cudnn_cpp; it was unnecessary
    - Caffe2_HIP_INCLUDES is now Caffe2_HIP_INCLUDE, for consistency
      with all the other variables.
    - HIP build now properly respects ATEN_CUDA_FILES_GEN_LIB (it
      did not previously.)
    - You can now override file extension matching in pyHIPIFY
      by explicitly specifying its full name in the matching list.
      This is used so we can HIPify CMakeLists.txt in some situations.

    A little bit of string and ceiling wax:

    - gen.py grows a --rocm flag so that it knows to generate CUDA
      files which actually refer to the HIP headers (e.g., THH.h)
      We'll get rid of this eventually and generate real HIP files,
      but not for this PR.
    - Management of HIP dependencies is now completely deleted
      from the ATen CMakeLists.txt.  The old code was dead (because
      it was shoveled in ATen_CUDA_DEPENDENCY_LIBS and promptly
      ignored by the Caffe2 build system) and didn't actually work.
```

Stacked on https://github.com/pytorch/pytorch/pull/14849 review last commit only
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14866

Differential Revision: D13419475

Pulled By: ezyang

fbshipit-source-id: cb4c843df69a1d8369314c9fab1b7719520fa3db

* Add at::scalar_tensor factory function, use it instead of Type.scalar… (#15074)

Summary:
…_tensor.

This is part of a long series of paring down the Type interface.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15074

Differential Revision: D13421482

Pulled By: gchanan

fbshipit-source-id: 84010ee71fef2cb74d32d5de7858d8ed9f36b885

* Move TensorImpl to c10 (yay!)

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14795

Reviewed By: ezyang

Differential Revision: D13336856

fbshipit-source-id: 5375d0e42312ff7564f4df06210a5e49542d59e3

* Fix include paths for TensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14816

Reviewed By: ezyang

Differential Revision: D13348040

fbshipit-source-id: a7204d89c2dd277d13093b0ed862f40b53dee82f

* Move UndefinedTensorImpl to c10 (meh) (#14817)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14817

unfortunately, we still need this.

Reviewed By: ezyang

Differential Revision: D13348041

fbshipit-source-id: e8dcc89f5c71bd1ea2c9813990dac6e58e63b1fd

* Fix include paths for UndefinedTensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14818

Reviewed By: ezyang

Differential Revision: D13348042

fbshipit-source-id: 11bdfc755767ce9d0a6fa95b2cf49d50adde8d60

* add gloo support for gather on GPU (#14916)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14916

as titled

Reviewed By: pietern

Differential Revision: D13267832

fbshipit-source-id: 3b89d08af93f74941f17ff892c33fc2a4a023c19

* Pre-commit flake8/clang-tidy (#15102)

Summary:
Provide a pre-commit hook that does flake8 and clang tidy checks. Enables the clang-tidy script to run in parallel to make it fast enough to be used in a pre-commit hook.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15102

Reviewed By: soumith

Differential Revision: D13429629

Pulled By: zdevito

fbshipit-source-id: bd52fe5652f29b033de8d9926d78350b2da4c2fc

* Update the output format for benchmark_helper. It outputs the dimensi… (#15108)

Summary:
…on first and all the values in the next line. This way, it can output arbitrary blob
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15108

Reviewed By: llyfacebook

Differential Revision: D13429346

Pulled By: sf-wind

fbshipit-source-id: 5e0bba2a46fbe8d997dfc3d55a698484552e3af8

* Fix serialization (#15033)

Summary:
Fixes a bug where (de-)/serializing a hierarchy of submodules where one submodule doesn't have any parameters, but its submodules do, doesn't get properly loaded. This had to do with the fact that the old protobuf format couldn't store empty parameters.

Fixes https://github.com/pytorch/pytorch/issues/14891

soumith ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15033

Differential Revision: D13411322

Pulled By: goldsborough

fbshipit-source-id: 2ef73b2aa93fa9e46b1cbe1fd47d9f134d6016d5

* Remove linker and dlopen flags that allowed undefined symbols in rocm build (#15091)

Summary:
Previously the undefined symbols were caused by disabled_modules in tools/amd_build/disabled_features.json (now it's cleared).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15091

Differential Revision: D13429595

Pulled By: bddppq

fbshipit-source-id: b341e83f9e5a8d16440a364e837b045a8a4fd6e1

* Add EmptyNameScope to allow you jump out from current scope. (#14631)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14631

adding a empty name scope to allow people jump out from current namescope.

This could be useful when you want to access blob from parent or sibling scope.

 Facebook:

e.g: we encoutered a potential usecase in D13124249 (it's a large diff, please search by EmptyNameScope in that diff), we need to access to a blob declared in root namescope from a device namescope (device namescope has been used by parallel_GPU API). `EmptyNameScope` can help us do that with ease.

I referenced to `EmptyDeviceScope` D6103412 while implementing this one.

Reviewed By: yinghai

Differential Revision: D13272240

fbshipit-source-id: d4cde5abcc2336e456b6c6ef086266ef94d86da8

* Use c10::to_string that works cross platform (#15117)

Summary:
Fix master breakage introduced in #15108
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15117

Differential Revision: D13430568

Pulled By: bddppq

fbshipit-source-id: ce10bc552f085d1bf0afbc13119991bee014ac95

* Don't setup x86_64-linux-gnu-gcc as an sccache wrapper. (#15078)

Summary:
When I do this setup in a local Docker development environment,
I get the following error:

    x86_64-linux-gnu-gcc: error trying to exec 'cc1plus': execvp: No such file or directory

Somehow, gcc seems to get confused when it gets run from the wrong
directory.  Best not to do it.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15078

Differential Revision: D13432143

Pulled By: ezyang

fbshipit-source-id: b18e15f493503a4c8205c85f92a214e49762a7bc

* fix some tests that I accidentally disabled (#15077)

Summary:
While moving these scenarios into `_test_dim_ops` I accidentally left an empty loop in the actual tests, causing them to do nothing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15077

Differential Revision: D13428759

Pulled By: umanwizard

fbshipit-source-id: 08f53068981d9192c1408878b168e9053f4dc92e

* Add better support for bools in the graph fuser (#15057)

Summary:
Fixes #15038.

aten::_cast_Float(tensor, non_blocking) support was added in #14336.
Its second argument is a bool, but because we don't support generating values
of type bool in the fuser codegen, the codegen errored out.

aten::_cast_Float in the fuser never actually uses its non_blocking
argument, so another way to fix this would be to have a special op for a
fused cast but I thought that we might have fusible ops that do take
bool arguments in the future so this would be good to have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15057

Differential Revision: D13432091

Pulled By: zou3519

fbshipit-source-id: 455fe574f5f080aca9a112e346b841a2534a8dc3

* Ensure there aren't variables in checked_tensor_unwrap, checked_tenso… (#15105)

Summary:
…r_list_unwrap.

These functions use unsafeGetTensorImpl(), which doesn't work with Variables (in a silent way that may blow up later).
So let's do early checking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15105

Reviewed By: ezyang

Differential Revision: D13429149

Pulled By: gchanan

fbshipit-source-id: b85f6f5b7cdb9a6dd0c40205b924c840a3920ba0

* fix infinite loop when get_max_threads is nonzero but num_threads is 1

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15114

Differential Revision: D13431891

Pulled By: umanwizard

fbshipit-source-id: f968b8e50cf776c346d4a28d72b12e7856c95839

* Kill Type.storage. (#15075)

Summary:
It's not used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15075

Reviewed By: ezyang

Differential Revision: D13422487

Pulled By: gchanan

fbshipit-source-id: 272aa0a10e96f3ffb97d571490b517f972b9dcf7

* Move CUDAGuard, CUDAStream and CUDAGuardImpl to c10/cuda (#14248)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14248

This diff also introduces a horrifying hack to override CUDA's DeviceGuardImpl
with a HIPGuardImplMasqueradingAsCUDA, to accommodate PyTorch's current
behavior of pretending CUDA is HIP when you build with ROCm enabled.

Reviewed By: bddppq

Differential Revision: D13145293

fbshipit-source-id: ee0e207b6fd132f0d435512957424a002d588f02

* Stop erroneously running aten::warn (#15124)

Summary:
Fixes #15119. Before this PR, we were propagating constants through
aten::warn AND running it as a part of shape analysis.
This caused aten::warn to be run regardless of if it is
supposed to be run dynamically. This PR adds an exclusion for aten::warn
in constant propagation and shape analysis, similar to that of prim::RaiseException.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15124

Differential Revision: D13432815

Pulled By: zou3519

fbshipit-source-id: 15ab533ce2accb2da3fd4e569070c7979ce61708

* Move numa.{h, cc} to c10/util (#15024)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15024

Pull Request resolved: https://github.com/pytorch/pytorch/pull/14393

att

Reviewed By: dzhulgakov

Differential Revision: D13380559

fbshipit-source-id: abc3fc7321cf37323f756dfd614c7b41978734e4

* Move adaptive avg pooling 2d to ATen native (#14714)

Summary:
adaptive_avg_pool1d, adaptive_avg_pool2d, and adaptive_avgpool3d are neural network functions that are currently implemented in our legacy THNN (CPU) / THCUNN (CUDA) libraries.  It is generally better if these live in our new library ATen, since it is more feature complete and reduces cognitive overhead.

This change moves currently to adaptive_avg_pool1d and adaptive_avg_pool2d to ATen.

timed relevant cpu tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 6.273s

OK (skipped=7)

real	0m7.164s
user	3m1.289s
sys	0m0.905s
```

compared to master:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 7.232s

OK (skipped=7)

real	0m8.065s
user	3m34.714s
sys	0m2.440s
```

also timed relevant cuda tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 21.049s

OK

real	0m24.106s
user	0m20.890s
sys	0m4.026s
```

compared to master
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 23.021s

OK

real	0m27.095s
user	0m20.121s
sys	0m3.668s
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14714

Differential Revision: D13384084

Pulled By: xnder

fbshipit-source-id: 344442103ccbbda72d3c010d2feea00e9985d226

* Add script standard library documentation + cleanup (#14912)

Summary:
Documents what is supported in the script standard library.

* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912

Differential Revision: D13385928

Pulled By: driazati

fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da

* Minor documentation mistake (#15068)

Summary:
keepdim is a optional parameter for torch.max()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15068

Differential Revision: D13437745

Pulled By: zou3519

fbshipit-source-id: b5198c7d4ae17758cd136f6e5aecc6cb5838f174

* Implement torch.tril_indices and torch.triu_indices (#12653) (#14904)

Summary:
This is an optimized implementation that does the following:

1. created an empty Tensor of correct size.
2. fill the Tensor with correct values.

The following three designs to fill in the Tensor result in roughly the same performance. Hence, the 2nd option is taken for simpler code, and to return contiguous tensors.

1. Sequential: fill row coordinates first, then columns. This results in two for-loop and more arithmetic operations.
2. Interleaved: fill in index coordinates one by one, which jumps between the two output Tensor rows in every iteration.
3. Transpose: create a n X 2 Tensor, fill the Tensor sequentially, and then transpose it.

<img width="352" alt="screen shot 2018-12-10 at 3 54 39 pm" src="https://user-images.githubusercontent.com/16999635/49769172-07bd3580-fc94-11e8-8164-41839185e9f9.png">

NOTE:

This implementation returns a 2D tensor, instead of a tuple of two tensors. It means that users will not be able to do the following:

```python
x = torch.ones(3, 3)
i = torch.tril_indices(3, 3)
x[i]  # need to first convert the 2D tensor into a tuple of two 1D tensors.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14904

Reviewed By: zou3519

Differential Revision: D13433027

Pulled By: mrshenli

fbshipit-source-id: 41c876aafcf584832d7069f7c5929ffb59e0ae6a

* Optimize CPU GenerateProposals op by lazily generating anchors (3-5x faster) (#15103)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15103

There are two main optimizations in this diff:
1. We generate all anchors for every single spatial grid first, and then apply
NMS to pick 2000 anchors according to RPN_PRE_NMS_TOP_N. By first sorting the
score and picking the 2000 top ones and then lazily generating only the
corresponding anchors is much faster.
2. Transposing bbox_deltas from (num_anchors * 4, H, W) to
(H, W, num_anchors * 4) was also quite slow - taking about 20ms in the RRPN
case when there are lots of anchors which it's negligible for RPN case (like
0.1 ms). Instead of transponsing, performing all operations in the
(num_anchors, H, W) format speeds things up.

For regular RPN scenario, this gives 5x speedup from 5.84ms to 1.18ms a case
with 35 anchors over a 600x600 image.

For rotated boxes with 245 anchors, the runtime down from 80ms to 27ms per
iter.

Reviewed By: newstzpz

Differential Revision: D13428688

fbshipit-source-id: 6006b332925e01a7c9433ded2ff5dc9e6d96f7d3

* use ROCm 1.9.2 fp16 capabilities in rocBLAS and MIOpen interfaces (#14994)

Summary:
* relax MIOpen if statement to allow fp16/fp32 mixed precision training now supported by ROCm 1.9.2
* use gemm_ex API of rocBLAS in ROCm 1.9.2 instead of the previous hgemm API
* with this: enable all but one half test in test_nn

While there, fix also:
* a group convolution issue w/ MIOpen pertaining to initializing MIOpen on multi-GPU systems properly we detected while working on this
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14994

Differential Revision: D13439869

Pulled By: bddppq

fbshipit-source-id: 75e4eb51a59488882e64b5eabdc30555b25be25e

* Add back c2 string_utils include header to benchmark_helper

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15143

Differential Revision: D13439694

fbshipit-source-id: 78698b66d52a0178118cbf3e79a7a5ad1763d47b

* Export defs.bzl to open source for pytorch (#15132)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15132

Pull Request resolved: https://github.com/facebook/fbshipit/pull/64

Reviewed By: dzhulgakov

Differential Revision: D13424093

fbshipit-source-id: bbebef964b9f3aef8f59cd394eca068680c36b5a

* docs: minor spelling tweaks

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15148

Differential Revision: D13443708

Pulled By: suo

fbshipit-source-id: 5e3ec0afd3416ab8ce207f2d04105c49e1c04611

* don't compile dnnlowp.cc in avx2 option (#15147)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15147

Forgot to take out dnnlowp.cc from avx2 list in a previous diff.

Reviewed By: dskhudia

Differential Revision: D13440686

fbshipit-source-id: 9ada98b6e885c7d5f22c91a735ff60304480b4cb

* Autoformat build_variables.py (#15152)

Summary:
autoformat `tools/build_variables.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15152

Differential Revision: D13445343

Pulled By: goldsborough

fbshipit-source-id: fd63588de114cb92deda03fa1a0b36f5f9082b2f

* Fix resize for edge case tensors (#14874)

Summary:
Certain tensor shapes failed when being resized. This pull request addresses the bug found in #13404.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14874

Differential Revision: D13429788

Pulled By: soumith

fbshipit-source-id: 8aa6451dbadce46d6d1c47a01cb26e6559bcfc8c

* Implementation of ChannelShuffle Op for MKLDNN (#15106)

Summary:
the speed-up of a single operation is up to 3X .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15106

Differential Revision: D13429596

Pulled By: bddppq

fbshipit-source-id: f8d987cafeac9bef9c3daf7e43ede8c6a4ee2ce5

* support casting to string (#15110)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15110

support casting to string on CPU

Reviewed By: intermilan

Differential Revision: D13429381

fbshipit-source-id: b737a1ba1237b10f692d5c42b42a544b94ba9fd1

* Remove "early-release beta" disclaimer from README (#15136)

Summary:
Now that PyTorch 1.0 is out, this should be updated :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15136

Differential Revision: D13447377

Pulled By: soumith

fbshipit-source-id: bd4e662c53d0699f25d4d90c1b4c1e182b4427c2

* Disable strict-overflow flag to avoid compilation error (#14977)

Summary:
Disable strict-overflow flag to avoid compilation error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14977

Differential Revision: D13447577

Pulled By: soumith

fbshipit-source-id: 1957bd5aa3c7b79219da3dd53560464977c89526

* minimize header file includes from _avx2.cc (#14950)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14950

Minimize the number of headers included from _avx2.cc files to avoid accidental compilation of functions defined the header files reused by other translation units that can lead to illegal instruction errors.

Reviewed By: dskhudia

Differential Revision: D13394483

fbshipit-source-id: 67149a6fb51f7f047e745bfe395cb6dd4ae7c1ae

* Removes THCNumerics usages in RNN.cu (#15085)

Summary:
We don't need THCNumerics here since at::Half can be implicitly converted to float and the cuda math dispatches are handled by `/usr/local/cuda/include/crt/math_functions.hpp` and `cmath`. ATen should be free of THCNumerics after this and when porting kernels from THC, one should not use THCNumerics.

Should close: https://github.com/pytorch/pytorch/issues/11878
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15085

Differential Revision: D13447558

Pulled By: soumith

fbshipit-source-id: 4ff5cbf838edcd01e2d1397e4d7f4f920e9e9fc3

* Reuse KernelSpec for FusionGroups with equivalent graphs (#14541)

Summary:
Before this PR, loop unrolling + the graph fuser was creating multiple
FusionGroups with the same bodies (with different variable names) for
JIT LSTMs. Each FusionGroup got registered to a separate fusion key;
each key resulted in a different compilation for the same
specializations.

This PR makes it so that when registering FusionGroups with the fusion
compiler, the compiler first checks the KernelSpec cache to see if the
FusionGroup's graph exists already. If it does, then return the
corresponding KernelSpec's key to share compiled kernels.

In addition, graphs in the KernelSpec cache are canonicalized before
being cached. I added a flag to the canonicalize pass to remove unique
names of values.

This shortens the compile time for a JIT LSTM (seq_len of 100, loop
unroll factor of 8) from 5.3s to 2.3s. Most of this compile time is
running the graph fuser and/or fusion compiler; while this PR
makes it so that there is only one unique kernel in the forward pass,
there are a lot of different kernels (6) in the backward pass
(after loop unrolling) that should be investigated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14541

Differential Revision: D13324487

Pulled By: zou3519

fbshipit-source-id: b841d82ed35a959b5cfc72db033bf5a7b42cc4fb

* Python <-> C++ Frontend inter-op (#13481)

Summary:
This PR enables C++ frontend modules to be bound into Python and added as submodules of Python modules. For this, I added lots of pybind11 bindings for the `torch::nn::Module` class, and modified the `torch.nn.Module` class in Python to have a new Metaclass that makes `isinstance(m, torch.nn.Module)` return true when `m` is a C++ frontend module. The methods and fields of C++ modules are bound in such a way that they work seamlessly as submodules of Python modules for most operations (one exception I know of: calling `.to()` ends up calling `.apply()` on each submodule with a Python lambda, which cannot be used in C++ -- this may require small changes on Python side).

I've added quite a bunch of tests to verify the bindings and equality with Python. I think I should also try out adding a C++ module as part of some large PyTorch module, like a WLM or something, and see if everything works smoothly.

The next step for inter-op across our system is ScriptModule <-> C++ Frontend Module inter-op. I think this will then also allow using C++ frontend modules from TorchScript.

apaszke zdevito

CC dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13481

Differential Revision: D12981996

Pulled By: goldsborough

fbshipit-source-id: 147370d3596ebb0e94c82cec92993a148fee50a7

* Unify SparseTensorImpl::size_ and TensorImpl::sizes_

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15130

Differential Revision: D13434981

Pulled By: VitalyFedyunin

fbshipit-source-id: 98bd4d66834a3c3d2ea577adb0c8413852da095d

* Fix bincount for non-contiguous inputs on CPU (#15109)

Summary:
Fixes #15058.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15109

Differential Revision: D13447448

Pulled By: soumith

fbshipit-source-id: 56e8d42934538fb00465105a2c5ccfeb7c18a651

* Use a pool of per-thread cudnn handles for each device, updated (#15080)

Summary:
Rebased version of https://github.com/pytorch/pytorch/pull/14861, hopefully addressing ezyang's comments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15080

Differential Revision: D13440858

Pulled By: ezyang

fbshipit-source-id: 1c6af5c53538b81c6b92cf1dda231ed333f28035

* Fix typo (#15045)

Summary:
Simple typo fix
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15045

Reviewed By: dzhulgakov

Differential Revision: D13413509

Pulled By: houseroad

fbshipit-source-id: be66700c30d038368b1433232a4e3fd9299c83d6

* Delete defunct USE_SIMPLE_BASE_CTOR_DTOR (#15144)

Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15144

Differential Revision: D13440872

Pulled By: ezyang

fbshipit-source-id: 2b1d73fac0c63729ba01d8f129642334ae9d9cf3

* Kill non-forward, non-backward functions generated from nn.yaml (#15127)

Summary:
Updating binding to legacy functions.
Remove unused declarations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15127

Differential Revision: D13433405

Pulled By: VitalyFedyunin

fbshipit-source-id: 58544d38affd20818742338c9eb789d9d14ccbaa

* Fix old tensor OutputTensorCopyFrom usage in ImageInput operator (#15094)

Summary:
cc jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15094

Differential Revision: D13451898

Pulled By: bddppq

fbshipit-source-id: 27906be62fb88aaa13c257441a2e35a285b445ee

* Use std::vector instead of alloca to work around hcc crash

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15175

Differential Revision: D13453708

Pulled By: bddppq

fbshipit-source-id: f8c147ae9f679e395fee9d4c73ebcca052c9a752

* Tensor construction codemod(ResizeLike) - 5/7 (#15084)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15084

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419711

fbshipit-source-id: dd2b740c3f13d8087085bafc5571aaf908d1af42

* Tensor construction codemod(ResizeLike) - 6/7 (#15137)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15137

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419736

fbshipit-source-id: f4ad7b9582c2f809258169b7fef9adbca7063d99

* Replace non-printable-ascii characters in ProtoDebugString (#14918)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14918

When ProtoBuf-Lite is in use, ProtoDebugString just calls SerializeAsString.
This produces binary output, which is not a very suitable "debug" string.
Specifically, we've observed it causing problems when calling code tries to
add the debug string to a Java exception message (which requires valid UTF-8).
Now, we replace all non-ASCII bytes with "?".

This is not a very fast implementation, but generating debug strings shouldn't
be a performance-sensitive operation in any application.

Reviewed By: dzhulgakov

Differential Revision: D13385540

fbshipit-source-id: 8868172baf20efaf53fecf7d666a6980f59b64f5

* Tensor construction codemod(ResizeLike) - 4/7 (#15088)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15088

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419682

fbshipit-source-id: 3e59403bc1c0e71e5cb66df932ed0c6a0a72e643

* Remove _finfo; replace _finfo usage with torch.finfo (#15165)

Summary:
This PR removes the usage of _finfo defined in torch.distributions.utils and changes the call sites
to use torch.finfo instead

Differential Revision: D13451936

Pulled By: soumith

fbshipit-source-id: 6dbda3a6179d9407bc3396bf1a2baf3e85bc4cf2

* Run ONNX cuda backend test cases via ROCm

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15069

Differential Revision: D13427757

Pulled By: bddppq

fbshipit-source-id: ba0273d75986cd5b146f7041a83c63ddf9c6c0cf

* Remove disabled_features in hipify

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15098

Reviewed By: ezyang

Differential Revision: D13453762

Pulled By: bddppq

fbshipit-source-id: e177042c78f5bf393163d660c25b80285353853d

* Add missing caffe2_hip extension in setup.py

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15189

Reviewed By: orionr

Differential Revision: D13457644

Pulled By: bddppq

fbshipit-source-id: c2363e9b8fd21709b62777e5b2199f01ec1c65f8

* Enable performance-unnecessary-value-param in .clang-tidy (#15026)

Summary:
This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small).

ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026

Differential Revision: D13458784

Pulled By: goldsborough

fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b

* Remove TensorImpl -> Type dependency

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15086

Reviewed By: dzhulgakov

Differential Revision: D13425628

fbshipit-source-id: 08a8a774d17b071367454e027012a02f96d177d4

* Support torch.tensor in script (#14913)

Summary:
Adding support for torch.tensor in script.

The input list is typed as t[], because it can be arbitrarily nested. I added a check a compile time check  that the inner type of the list is a bool, float, or int.

Also adds specialization for Boolean Lists, which already existed at the ivalue level but had not been added to the compiler yet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14913

Differential Revision: D13407930

Pulled By: eellison

fbshipit-source-id: d17f1195a22149d5b0d08d76c89a7fab8444f7c5

* For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem (#15113)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15113

cv::rotatedRectangleIntersection has a known float underflow bug that would cause failure in ```CV_Assert(intersection.size() <= 8)```

For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem.

Otherwise, when ```USE_CPP_GENERATE_PROPOSALS = true```, the training would fail.

Reviewed By: viswanathgs

Differential Revision: D13429770

fbshipit-source-id: 5e95d059f3c668f14059a0a83e8e53d8554cdb99

* Move TensorImpl::CopyFrom to caffe2::Tensor (1/2) (#14656)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14656

This diff doesn't move it yet, but prepares it to be moved, i.e. removes all access to class internals.

dzhulgakov: Please comment on if you think it still makes sense to land this even though it's not blocking anymore since we're going to move at::CopyBytes anyhow.

ezyang: There's some changes in the implementation, especially handling undefined dest tensors. Please review carefully.

Reviewed By: ezyang

Differential Revision: D13287688

fbshipit-source-id: 17800ca8a79ab1633f23be58d96f99a160d8ed24

* Move TensorImpl::CopyFrom to caffe2::Tensor (2/2) (#14858)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14858

This diff doesn't change logic but just takes the existing code and moves it to caffe2::Tensor

Reviewed By: ezyang

Differential Revision: D13365817

fbshipit-source-id: bc73b27a793602cb14200dcdf357aa63233da43c

* add erf and erfc to fuser/autodiff

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15139

Differential Revision: D13455690

Pulled By: soumith

fbshipit-source-id: b06e5f5d362869c2e5fa11a52f9450d77c30d4cb

* Fix numpy conversion for int8 tensor

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15194

Differential Revision: D13459270

Pulled By: li-roy

fbshipit-source-id: 605534add263860a3ad9a7fa70888301ee0bf8e4

* Fix derivative for mvlgamma (#15049)

Summary:
Fixes #15015.

Added tests to validate derivative.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15049

Reviewed By: soumith

Differential Revision: D13434117

Pulled By: zou3519

fbshipit-source-id: 4a292600af9eb08b67c0f8b5482e9512aac95e72

* caffe2 - easy - Create test_util to make it easier to write C++ unit tests (#15014)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15014

Currently it looks like many of the simple operations such as comparing tensors, creating tensors, fetching tensors... are too verbose and took effort to write correctly in unit tests.
Easy to use utilities are often more important to increase productivity writing unit tests. While caffe2 python unit tests are relatively easier to write at the moment, the C++ side seems lacking.
In this change I create a test_util, started with assertsTensorEquals, getTensor, createTensor, and we can start putting more easy to use utilities there.

Reviewed By: salexspb

Differential Revision: D13370461

fbshipit-source-id: bee467a127e1d032ef19482f98aa5c776cf508c0

* caffe2 - easy - test utils to create operator (#15180)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15180

Test utils to create an operator

On top of D13370461

Reviewed By: ZolotukhinM

Differential Revision: D13382773

fbshipit-source-id: a88040ed5a60f31d3e73f1f958219cd7338dc52e

* caffe2 - easy - test utils to fill tensors (#15019)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15019

Put some utils to fill tensors to test_utils

Reviewed By: salexspb

Differential Revision: D13386691

fbshipit-source-id: 51d891aad1ca12dc5133c0352df65b8db4f96edb

* caffe2 - easy - test utils to compare tensors in two workspaces (#15181)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15181

Add test utils to compare tensors in two workspaces

Reviewed By: ZolotukhinM

Differential Revision: D13387212

fbshipit-source-id: e19d932a1ecc696bd0a08ea14d9a7485cce67bb2

* caffe2 - easy - test utils for tensor assertion (#15020)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15020

Add test utils for assertion of a tensor (sizes and values)

Reviewed By: salexspb

Differential Revision: D13401146

fbshipit-source-id: bc385df074043e03ea884940b5631b96de4a607e

* caffe2 - easy - utils to set argument of operator (#15022)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15022

Add setArgument testing utils to make it easy to set argument for an operator

Reviewed By: yinghai

Differential Revision: D13405225

fbshipit-source-id: b5c1859c6819d53c1a44718e2868e3137067df36

* caffe2 - make DataRandomFiller usable in unit tests (#15027)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15027

- Make DataRandomFiller able to accept input_dims and input_types for only non intermediate inputs. Add a helper to fill input directly to a workspace

Reviewed By: highker

Differential Revision: D13408345

fbshipit-source-id: 5fc54d33da12e3f0a200e79380d4c695b0339b17

* Revert D13407930: [pytorch][PR] Support torch.tensor in script

Differential Revision:
D13407930

Original commit changeset: d17f1195a221

fbshipit-source-id: f4458872c48ec4a2c9983b21ed90bcdc0ae665b7

* Tensor construction codemod(ResizeLike) - 3/7 (#15122)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15122

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530

* Better tests/support for Python/C++ inter-op (#15193)

Summary:
Methods like `module.named_modules()` returns a container of `shared_ptr<nn::Module>`. Currently the `nn::Module` base class does  not have Python bindings. This PR fixes this, and adds more unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15193

Differential Revision: D13458713

Pulled By: goldsborough

fbshipit-source-id: 4091fe1b96a1be8db14c6a4307fbacc2b41ff6fe

* Refactor caffe2 CI scripts and add benchmark scripts

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14575

Differential Revision: D13468049

Pulled By: bddppq

fbshipit-source-id: e73bc8742c8a03f498816eee8a72b06a3e19fe48

* Enable all clang-tidy performance checks (#15198)

Summary:
This PR adds the final set of clang-tidy checks we should add for our codebase: a last set of performance-related checks. Most fixes here are around changing `auto` to `const auto&` in a few places where unnecessary copies were made, and adding `reserve()` calls before loops doing repeated `push_back()`. Also a few cases of calling `std::string::find` with a single-character string literal instead of a single char, which uses a less efficient string search algorithm meant for searching larger substrings.

![image](https://user-images.githubusercontent.com/6429851/49978940-adc1a780-ff01-11e8-99da-a4e431361f07.png)

ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15198

Differential Revision: D13468797

Pulled By: goldsborough

fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2

* Remove __forceinline__ hipification step. (#15229)

Summary:
The HIP definition now correctly contains the inline attribute.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15229

Differential Revision: D13470962

Pulled By: bddppq

fbshipit-source-id: 34f8361bda5f3dce20a2eeb530c3a25d1b1bdd06

* Fix jit doc codeblocks and tables (#15227)

Summary:
Some of the codeblocks were showing up as normal text and the "unsupported modules" table was formatted incorrectly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15227

Differential Revision: D13468847

Pulled By: driazati

fbshipit-source-id: eb7375710d4f6eca1d0f44dfc43c7c506300cb1e

* enabled tests in test_nn, test_cuda and test_sparse (#15232)

Summary:
tests work on ROCm 1.9.2 as present on CI (fp16 bringup, hipMemset and sparse improvements)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15232

Differential Revision: D13470991

Pulled By: bddppq

fbshipit-source-id: 45acc4f9ea5baaaf7672b86eb022948055779925

* Revert D13440858: [pytorch][PR] Use a pool of per-thread cudnn handles for each device, updated

Differential Revision:
D13440858

Original commit changeset: 1c6af5c53538

fbshipit-source-id: fda42ea75000d4a4e9c4a8eeaaa5518f7ad9c298

* Do not ifdef __launch_bounds__ out for ROCm. (#15228)

Summary:
The compiler understands it and profits from knowing it by not using too
many VGPRs as it defaults to 256 default workgroup size.

Fixes a problem in bringup of ROCm 2.0 on gfx906.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15228

Differential Revision: D13470950

Pulled By: bddppq

fbshipit-source-id: f9aa44c7c95299a099c0ea9317b9044cc056acc5

* fix an issue where two rules build the same .py files

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15230

Differential Revision: D13471625

Pulled By: zdevito

fbshipit-source-id: a982413a308c7a9bb5b6a82fe96fd3de44f555aa

* Preserve module hierarchy on traced modules (#15101)

Summary:
We need this, for example, to properly call `_unpack` when we have a traced module in the hierarchy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15101

Differential Revision: D13468467

Pulled By: jamesr66a

fbshipit-source-id: c2b6740b12cde6e23395d12e42d4fc2c4c7ca3f2

* record unit time in torch.cuda.event (#15221)

Summary: Record unit of time for torch.cuda.Event's elapsed_time

Differential Revision: D13467646

Pulled By: zou3519

fbshipit-source-id: 4f1f4ef5fa4bc5a1b4775dfcec6ab155e5bf8d6e

* Build c10 HIP test

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15233

Reviewed By: ezyang

Differential Revision: D13471002

Pulled By: bddppq

fbshipit-source-id: b42c3bc2b9db672ce50a52eb700cc6ed13d3535f

* Start unittesting our main observer (#15191)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15191

OSS:

just splitting out basic flags from a unit test. So I can extend them in another test where I need to add additional flags.

Reviewed By: yinghai

Differential Revision: D13159184

fbshipit-source-id: 9823e792cf0ed8d0379235c44564862b7d784845

* FP16MomentumSGDUpdate Op fix and enable for ROCm (#15150)

Summary:
1. Fix a bug in FP16MomentumSGDUpdate operator
2. Enable operator for ROCm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15150

Differential Revision: D13473145

Pulled By: bddppq

fbshipit-source-id: 4c5c5f30cb9bba658e3639dbe193fa08a304d306

* Supply static shape info to Reshape when doing onnxGetCompatibility (#15242)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15242

Newer version ONNX Reshape gets shape info from a tensor. Hence for static backend, we need to provide this info to it when doing `onnxGetCompatibility` too.

Reviewed By: jackm321

Differential Revision: D13471959

fbshipit-source-id: 8a58e28edd900b6ad54a1dbd63ff2579fbe0e820

* Add several features to converting images to blobs (#15204)

Summary:
Several enhancements are implemented:

* Resize the images to be within a boundary between min-size and max-size (can be height and weight). It tries to resize the minimum size to match the min-size and keep the aspect ratio. However, if in that case the maximum size is more than the max-size, then resize the maximum size to be equal to the max-size (and the minimum size is less than min-size). The min/max sizes are specified in argument scale, in a comma separated form. If one of the size is -1, then that size is not a restriction.

* Change the OpenCV resize function arguments from using cv::Size() to the x, y scale. Theoretically they should be the same. But in reality, the two ways of specifying them may result to different resized outputs.

* Once the image is read in, change the data to floats. That means, after resize and other preprocessing steps, the float values are preserved (not truncated to int).

* It is possible to convert data in text format to the blob format.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15204

Reviewed By: llyfacebook

Differential Revision: D13467225

Pulled By: sf-wind

fbshipit-source-id: 7da34a72d43a9603cd7ab953f5821c1222d0178f

* Create parser.cpp (#15238)

Summary:
Moves implementation into .cpp file. Parser was getting included in several compilation units.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15238

Differential Revision: D13474635

Pulled By: zdevito

fbshipit-source-id: 7dc824eea8f506d6c8ae1aa67aeec0c34d5285fc

* Tensor method rename dims()->sizes() (#15246)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15246

Codemod generated with clangr shard mode, 25 files per diff,

Reviewed By: igorsugak

Differential Revision: D13470369

fbshipit-source-id: ce995beab7c64bebe8b234fb5e6d015940ec2952

* Mention Jacobian-vector product in the doc of torch.autograd (#15197)

Summary:
A friend of me is learning deep learning and pytorch, and he is confused by the following piece of code from the tutorial https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients :

```python
x = torch.randn(3, requires_grad=True)

y = x * 2
while y.data.norm() < 1000:
    y = y * 2

print(y)

gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(gradients)

print(x.grad)
```

He don't know where the following line comes from:
```python
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
```

What are we computing? Why don't we compute "the gradient of `y` w.r.t `x`"?

In the tutorial, it only says
> You can do many crazy things with autograd!

Which does not explain anything. It seems to be hard for some beginners of deep learning to understand why do we ever do backwards with external gradient fed in and what is the meaning of doing so. So I modified the tutorial in https://github.com/pytorch/tutorials/pull/385
and the docstring correspondingly in this PR, explaining the Jacobian vector product. Please review this PR and https://github.com/pytorch/tutorials/pull/385 together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15197

Differential Revision: D13476513

Pulled By: soumith

fbshipit-source-id: bee62282e9ab72403247384e4063bcdf59d40c3c

* value-based mark and sweep DCE (#14910)

Summary:
This makes DCE more granular by tracking live values/aliases through the graph (rather than just nodes). So we can be more aggressive in DCE around control flow blocks. For example, in:
```
%a0 = aten::foo()
%b = aten::foo()
%a2, %b2 = prim::If(%cond) {
  block0() {
    %a1 = aten::foo(%.0)
    %b1 = aten::foo(%b)
  } -> (%a1, %b1)
}
return (%a2)
```
we will now dce all the `%b` stuff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14910

Differential Revision: D13476445

Pulled By: suo

fbshipit-source-id: 2bf5db19711c07dde946697a4f4b270bd8baf791

* fix cholesky call in potrs example (#15215)

Summary:
Cholesky by default returns the lower triangular matrix, see [docs](https://pytorch.org/docs/stable/torch.html#torch.cholesky).

However `torch.potrs` by default requires the upper triangular matrix. The naming of the variable `u` suggests that the example expects the upper to be returned, so I've added the flag to make that happen in the example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15215

Differential Revision: D13476468

Pulled By: soumith

fbshipit-source-id: 7b68035f435a2b1be4d363b3f63e407394af949d

* Fix a typo in the assert

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15265

Reviewed By: llyfacebook

Differential Revision: D13477029

Pulled By: sf-wind

fbshipit-source-id: 9c5571a583c01f9701625541ebec0c836cb923f2

* Delete ffi documentation (#15220)

Summary: Deleting FFI documentation since its deprecated.

Differential Revision: D13477329

Pulled By: soumith

fbshipit-source-id: 0b3d485eb7cef1f05b6b397dff50f21a49d6409e

* Trivial comment correction in dataloader (#15276)

Summary:
Trivial comment correction in dataloader
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15276

Differential Revision: D13477324

Pulled By: soumith

fbshipit-source-id: 2a74a014999655d129311d611f2a09411339cb13

* Refactor hotpatch_vars and apply it to libtorch (#14976)

Summary:
Fixes #14801.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14976

Differential Revision: D13485381

Pulled By: soumith

fbshipit-source-id: 0af3c2e1b90988d56f6f85632328d1e4b788ffd2

* Fix tensor printing bug in Python 2 (#12732)

Summary:
`rsplit` doesn't have kwargs in Python 2 so this line raises an error

Fixes #15135
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12732

Differential Revision: D10458630

Pulled By: driazati

fbshipit-source-id: a63e42fbc0e39e4291480775b516c98122ec05a1

* Tighten up invariants regarding StreamId. (#15125)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15125

I realized that it is really bad juju if you fake a StreamId
out of thin air, because in general this isn't going to work.
So, make the constructor a lot scarier.

Most "faking StreamId out of thin air" happens because someone
just wants to put something on the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432800

fbshipit-source-id: a86991d6fc1d8aa4e54e8175e5f06f90856238e6

* Adding ONNX export for torch.expand and torch.ne (#15050)

Summary:
`torch.expand` and `torch.ne` are used often in models and this PR adds ONNX export support for them. ArmenAg has created issue https://github.com/pytorch/pytorch/issues/10882 for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15050

Differential Revision: D13453036

Pulled By: houseroad

fbshipit-source-id: 4724b4ffcebda6cd6b2acac51d6733cb27318daf

* Minor fixes in .jenkins/caffe2/bench.sh

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15304

Differential Revision: D13493876

Pulled By: bddppq

fbshipit-source-id: 7146eb2587e526af65b4b0290c25bd55653a3088

* Fix for issue 14829 (#14908)

Summary:
* Modify the testcase as outlined in the issue
   * Issue url: https://github.com/pytorch/pytorch/issues/14829
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14908

Differential Revision: D13490360

Pulled By: ezyang

fbshipit-source-id: ff11a72e19b49223652182e82c2b4e65fe444ca7

* Don't enforce docstrings on bool dispatch (#15306)

Summary:
Allows 2 functions that are boolean dispatched to have no docstrings (the only case that will fail now is if both functions have docstrings)

Fixes #15281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15306

Differential Revision: D13494884

Pulled By: driazati

fbshipit-source-id: 65fec39ae03a7d6a68ad617c9b270faeb1617930

* Replace SwitchToDevice(0) with SwitchToDevice() (#15126)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15126

I want to make people stop manufacturing StreamId from thin air,
and a first step is to make people use the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432922

fbshipit-source-id: 9f0d8d70646c50d979bde5ba3c3addeebac48a3d

* Fix the missing caffe2 proto files for Windows (#15157)

Summary:
Fixes #15156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15157

Differential Revision: D13490420

Pulled By: orionr

fbshipit-source-id: 4387d707f634a5975238af915b1befb2277f8ec7

* add isinstance static type checking for jit (#15076)

Summary:
This PR add isinstance to do static type checking in JIT.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15076

Differential Revision: D13471067

Pulled By: wanchaol

fbshipit-source-id: d39b7ed5db9fcca4b503659d02cf7795950ea8ea

* Bicubic interpolation for nn.functional.interpolate (#9849)

Summary:
Addresses #918, interpolation results should be similar to tf

* Adds bicubic interpolation operator to `nn.functional.interpolate`
* Corresponding test in `test_nn.py`

The operator is added in legacy `TH` to be aligned with the other upsampling operators; they can be refactored/moved to ATen all at once when #10482 is resolved
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9849

Differential Revision: D9007525

Pulled By: driazati

fbshipit-source-id: 93ef49a34ce4e5ffd4bda94cd9a6ddc939f0a4cc

* Removing BUILD_C10_EXPERIMENTAL_OPS option and unglobbing experimental/c10d ops

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15064

Reviewed By: orionr

Differential Revision: D13474801

Pulled By: pjh5

fbshipit-source-id: 9d3664c3a3a1b6c2d9f083f8476fe3b037296b98

* Allow future type parsing

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14887

Differential Revision: D13490984

Pulled By: highker

fbshipit-source-id: 165fe995867be273793f983154aa6cbce13e4396

* Port nn fold and unfold to c++

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14597

Reviewed By: ezyang

Differential Revision: D13272227

fbshipit-source-id: 6eccab5ff5830a977398a96393b778095120edc6

* caffe2/python/task: added __repr__ methods to all task definitions (#15250)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15250

This adds `__repr__` methods to all of the classes under task.py. This makes the objects much easier to interact with when using them in an interactive manner, such as in a Jupyter notebook.

The default `__repr__` method just returns the object ID which is very unhelpful.

Reviewed By: hanli0612

Differential Revision: D13475758

fbshipit-source-id: 6e1b166ec35163b9776c797b6a2e0d002560cd29

* Add a correctness check for C++ types to custom operators (#15247)

Summary:
The JIT uses `int64_t` for its integer type and `double` for its floating point type, but users quite often want to write `int` or `float` and that currently fails in not-so-nice ways for custom ops. This PR adds a simple `static_assert` to catch these common failure cases.

zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15247

Differential Revision: D13493941

Pulled By: goldsborough

fbshipit-source-id: c1cd0d10ab5838c75f167c0bdb57e45a0bc1344e

* Fix _apply in nn.Module (#15305)

Summary:
Fixes an issue that arose from https://github.com/pytorch/pytorch/pull/13481 where `.shared_memory()` couldn't be called. Effectively undoes all changes to `nn.Module` from that PR and solve the relevant problem in a different way (the goal was to be able to call `._apply()` on the Python wrapper for a C++ module).

soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15305

Differential Revision: D13493937

Pulled By: goldsborough

fbshipit-source-id: 4cb8687f90fc8709a536c5e7eacd0dc8edf6f750

* Reenable OpenMP by reverting the following two commits. (#15315)

Summary:
Revert "Put back linker flag for OpenMP to prevent build break on ppc64le (#14569)"

This reverts commit a84e873bb156080ea76ab182171b1f3b4d5395f6.

Revert "Update OpenMP cmake setting for xcode 9 compiler(AppleClang 9.0) (#14473)"

This reverts commit 8901935ad42fe9bf093d1106ea43606008a4024d.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15315

Differential Revision: D13495852

Pulled By: ezyang

fbshipit-source-id: bcd3f60088b14831c53d3c171f10cd1ab6b35dee

* [TensorIterator fixing mean to output correct result for half precisi… (#14878)

Summary:
…on](#12115)

mean is calculated in two step sum()/numel(). For half precision, data gets
casted back to half after sum().
We fused the division into the reduction kernel by adding pre_op/post_op.

This allows us to do torch.ones(65536).cuda().half().mean() to return correct
result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14878

Differential Revision: D13491159

Pulled By: soumith

fbshipit-source-id: e83802e1628b6d2615c45e18d7acf991d143a09e

* Allow tracing with fork/wait (#15184)

Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e

* improve script/no script save error (#15321)

Summary:
Improves the error message for #15116
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15321

Differential Revision: D13499379

Pulled By: zdevito

fbshipit-source-id: b8dc0a83efabff74199f4aab2ee98aa41c42608b

* Updating submodules

Reviewed By: cdelahousse

fbshipit-source-id: 4bf66581d07d839f459869bc9c6428011063cc5b

* Revert D13383102: [pytorch][PR] Upgrade MKL-DNN to version 0.17

Differential Revision:
D13383102

Original commit changeset: c434f0e0ddff

fbshipit-source-id: 690f46ca0710954fa591a5ea77535e9759db4de5

* caffe2 mobile opengl (#15322)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15322

caffe2 mobile opengl code is not used, deleting it to reduce complications when we perform other changes

Reviewed By: Maratyszcza

Differential Revision: D13499943

fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2

* Method returns a single argument (#15289)

Summary:
This PR changes Method (just Method not all graphs) to always have a single
return argument.

This is part 1 in a set of changes that will enable us to have better handling if early return statements.
The simplification that this change provides greatly reduces the work for the next step.

This change makes it so that Method and Python handle multiple returns in the same way:
* 0 - None
* 1 - <single value>
* many - Tuple[...]

The result is that a lot of special-case handling in compiler.cpp and its
bindings can be removed. It also fixes several bugs in return handling,
including one where return values were not always checked against their
attributed values.

Notes:
…
jaciefan added a commit to jaciefan/pytorch that referenced this pull request Dec 27, 2018
resolve conflicts in legacy hist func namespace
* tox.ini -> .flake8 (#15065)

Summary:
We were only using this file to configure flake8, and fbcode linters do not recognize tox.ini which causes spurious linter warnings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15065

Differential Revision: D13420774

Pulled By: suo

fbshipit-source-id: e43a46befa36862c8b3c0a90074aec6a66531492

* Update onnx coverage script for more accurate result (#15029)

Summary:
The coverage of scalar-input test cases were not accurate. This patch fixed that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15029

Differential Revision: D13419764

Pulled By: zrphercule

fbshipit-source-id: a14a5cbef432bea8c9126156f5deb1125e1aeb47

* Issue 14984: Remove divide by zero error in index_put_ (#14986)

Summary:
No check for zero index tensor was done in the accumulate=True (serial) case in the new TensorIterator code since https://github.com/pytorch/pytorch/pull/13420.

https://github.com/pytorch/pytorch/issues/14984
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14986

Differential Revision: D13417861

Pulled By: colesbury

fbshipit-source-id: e6ed1af8f708b53a35803fc157ed1f043169ec89

* Supress warnings on generated tests

Summary: Removes all warnings spew for the TestJitGenerated tests

Differential Revision: D13420919

fbshipit-source-id: f251c12f923088ccc5daa2984c15003a67cbd1c1

* Split off fuser tests in test_jit.py to their own test case (#15072)

Summary:
This PR creates TestFuser inside test_jit.py to be a home for graph fuser
specific tests.

This was a useful exercise because now that all the fuser tests are in
one place, I can spot redundant and bitrotting tests for cleanup in a
future PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15072

Differential Revision: D13421458

Pulled By: zou3519

fbshipit-source-id: 80b1a7712feff75a0c186d1664601c4edbbca694

* re-enable copy of python files, but be careful that the copy is only … (#14982)

Summary:
…done once

This allow no-op build to work correctly even when BUILD_CAFFE2_OPS is on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14982

Differential Revision: D13413960

Pulled By: zdevito

fbshipit-source-id: 6e5412a8c375af8a47c76f548cdd31cff15f3853

* add gloo scatter support on GPU (#14917)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14917

as titled

Reviewed By: pietern

Differential Revision: D13271560

fbshipit-source-id: 0187a3390f8ebd72a2c074e7a651432159d427c0

* Remove deprecated variable_tensor_functions (#15003)

Summary:
Removing the deprecated functions in `torch/csrc/variable_tensor_functions.h` (like `torch::CPU`) and corresponding implementations from `torch/csrc/torch.cpp` from master after the release.

ezyang gchanan soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15003

Differential Revision: D13418086

Pulled By: goldsborough

fbshipit-source-id: a0accdf6f7b0efa1ec07ac7b74b86ff2da37543f

* Add error type to raise statement

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15039

Differential Revision: D13419566

Pulled By: zou3519

fbshipit-source-id: f67a3aebce937e3e640e91e81eb3e184cfdf269c

* Make ATen HIPify out-of-place, but still reuse CUDA names. (#14866)

Summary:
```
    This diff changes the HIPification of ATen to be out-of-place.
    We now have the following mappings:

    - ATen/cuda => ATen/hip
    - ATen/native/cuda => ATen/native/hip
    - ATen/native/sparse/cuda => ATen/native/sparse/hip
    - THC => THH
    - THCUNN => THHUNN

    The build system is adjusted to know about these new build paths,
    and HIPify is taught how to adjust include paths and
    THC_GENERIC_FILE appropriately.  ATen_hip is now built as
    the ATen_hip library, rather than reusing ATen_cuda.

    However, despite these new filepaths, none of the identifiers in ATen
    have actually changed.  So, e.g., THHGeneral.h still defines functions
    named THC_blahblah, and HIP still shows up as CUDA in PyTorch itself.
    We'll tackle this in a subsequent PR; this diff is just to get the files
    out-of-place.

    Minor extra improvements:

    - Don't edit tmp_install when hipifying
    - HIP no longer builds native_cudnn_cpp; it was unnecessary
    - Caffe2_HIP_INCLUDES is now Caffe2_HIP_INCLUDE, for consistency
      with all the other variables.
    - HIP build now properly respects ATEN_CUDA_FILES_GEN_LIB (it
      did not previously.)
    - You can now override file extension matching in pyHIPIFY
      by explicitly specifying its full name in the matching list.
      This is used so we can HIPify CMakeLists.txt in some situations.

    A little bit of string and ceiling wax:

    - gen.py grows a --rocm flag so that it knows to generate CUDA
      files which actually refer to the HIP headers (e.g., THH.h)
      We'll get rid of this eventually and generate real HIP files,
      but not for this PR.
    - Management of HIP dependencies is now completely deleted
      from the ATen CMakeLists.txt.  The old code was dead (because
      it was shoveled in ATen_CUDA_DEPENDENCY_LIBS and promptly
      ignored by the Caffe2 build system) and didn't actually work.
```

Stacked on https://github.com/pytorch/pytorch/pull/14849 review last commit only
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14866

Differential Revision: D13419475

Pulled By: ezyang

fbshipit-source-id: cb4c843df69a1d8369314c9fab1b7719520fa3db

* Add at::scalar_tensor factory function, use it instead of Type.scalar… (#15074)

Summary:
…_tensor.

This is part of a long series of paring down the Type interface.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15074

Differential Revision: D13421482

Pulled By: gchanan

fbshipit-source-id: 84010ee71fef2cb74d32d5de7858d8ed9f36b885

* Move TensorImpl to c10 (yay!)

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14795

Reviewed By: ezyang

Differential Revision: D13336856

fbshipit-source-id: 5375d0e42312ff7564f4df06210a5e49542d59e3

* Fix include paths for TensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14816

Reviewed By: ezyang

Differential Revision: D13348040

fbshipit-source-id: a7204d89c2dd277d13093b0ed862f40b53dee82f

* Move UndefinedTensorImpl to c10 (meh) (#14817)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14817

unfortunately, we still need this.

Reviewed By: ezyang

Differential Revision: D13348041

fbshipit-source-id: e8dcc89f5c71bd1ea2c9813990dac6e58e63b1fd

* Fix include paths for UndefinedTensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14818

Reviewed By: ezyang

Differential Revision: D13348042

fbshipit-source-id: 11bdfc755767ce9d0a6fa95b2cf49d50adde8d60

* add gloo support for gather on GPU (#14916)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14916

as titled

Reviewed By: pietern

Differential Revision: D13267832

fbshipit-source-id: 3b89d08af93f74941f17ff892c33fc2a4a023c19

* Pre-commit flake8/clang-tidy (#15102)

Summary:
Provide a pre-commit hook that does flake8 and clang tidy checks. Enables the clang-tidy script to run in parallel to make it fast enough to be used in a pre-commit hook.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15102

Reviewed By: soumith

Differential Revision: D13429629

Pulled By: zdevito

fbshipit-source-id: bd52fe5652f29b033de8d9926d78350b2da4c2fc

* Update the output format for benchmark_helper. It outputs the dimensi… (#15108)

Summary:
…on first and all the values in the next line. This way, it can output arbitrary blob
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15108

Reviewed By: llyfacebook

Differential Revision: D13429346

Pulled By: sf-wind

fbshipit-source-id: 5e0bba2a46fbe8d997dfc3d55a698484552e3af8

* Fix serialization (#15033)

Summary:
Fixes a bug where (de-)/serializing a hierarchy of submodules where one submodule doesn't have any parameters, but its submodules do, doesn't get properly loaded. This had to do with the fact that the old protobuf format couldn't store empty parameters.

Fixes https://github.com/pytorch/pytorch/issues/14891

soumith ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15033

Differential Revision: D13411322

Pulled By: goldsborough

fbshipit-source-id: 2ef73b2aa93fa9e46b1cbe1fd47d9f134d6016d5

* Remove linker and dlopen flags that allowed undefined symbols in rocm build (#15091)

Summary:
Previously the undefined symbols were caused by disabled_modules in tools/amd_build/disabled_features.json (now it's cleared).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15091

Differential Revision: D13429595

Pulled By: bddppq

fbshipit-source-id: b341e83f9e5a8d16440a364e837b045a8a4fd6e1

* Add EmptyNameScope to allow you jump out from current scope. (#14631)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14631

adding a empty name scope to allow people jump out from current namescope.

This could be useful when you want to access blob from parent or sibling scope.

 Facebook:

e.g: we encoutered a potential usecase in D13124249 (it's a large diff, please search by EmptyNameScope in that diff), we need to access to a blob declared in root namescope from a device namescope (device namescope has been used by parallel_GPU API). `EmptyNameScope` can help us do that with ease.

I referenced to `EmptyDeviceScope` D6103412 while implementing this one.

Reviewed By: yinghai

Differential Revision: D13272240

fbshipit-source-id: d4cde5abcc2336e456b6c6ef086266ef94d86da8

* Use c10::to_string that works cross platform (#15117)

Summary:
Fix master breakage introduced in #15108
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15117

Differential Revision: D13430568

Pulled By: bddppq

fbshipit-source-id: ce10bc552f085d1bf0afbc13119991bee014ac95

* Don't setup x86_64-linux-gnu-gcc as an sccache wrapper. (#15078)

Summary:
When I do this setup in a local Docker development environment,
I get the following error:

    x86_64-linux-gnu-gcc: error trying to exec 'cc1plus': execvp: No such file or directory

Somehow, gcc seems to get confused when it gets run from the wrong
directory.  Best not to do it.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15078

Differential Revision: D13432143

Pulled By: ezyang

fbshipit-source-id: b18e15f493503a4c8205c85f92a214e49762a7bc

* fix some tests that I accidentally disabled (#15077)

Summary:
While moving these scenarios into `_test_dim_ops` I accidentally left an empty loop in the actual tests, causing them to do nothing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15077

Differential Revision: D13428759

Pulled By: umanwizard

fbshipit-source-id: 08f53068981d9192c1408878b168e9053f4dc92e

* Add better support for bools in the graph fuser (#15057)

Summary:
Fixes #15038.

aten::_cast_Float(tensor, non_blocking) support was added in #14336.
Its second argument is a bool, but because we don't support generating values
of type bool in the fuser codegen, the codegen errored out.

aten::_cast_Float in the fuser never actually uses its non_blocking
argument, so another way to fix this would be to have a special op for a
fused cast but I thought that we might have fusible ops that do take
bool arguments in the future so this would be good to have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15057

Differential Revision: D13432091

Pulled By: zou3519

fbshipit-source-id: 455fe574f5f080aca9a112e346b841a2534a8dc3

* Ensure there aren't variables in checked_tensor_unwrap, checked_tenso… (#15105)

Summary:
…r_list_unwrap.

These functions use unsafeGetTensorImpl(), which doesn't work with Variables (in a silent way that may blow up later).
So let's do early checking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15105

Reviewed By: ezyang

Differential Revision: D13429149

Pulled By: gchanan

fbshipit-source-id: b85f6f5b7cdb9a6dd0c40205b924c840a3920ba0

* fix infinite loop when get_max_threads is nonzero but num_threads is 1

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15114

Differential Revision: D13431891

Pulled By: umanwizard

fbshipit-source-id: f968b8e50cf776c346d4a28d72b12e7856c95839

* Kill Type.storage. (#15075)

Summary:
It's not used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15075

Reviewed By: ezyang

Differential Revision: D13422487

Pulled By: gchanan

fbshipit-source-id: 272aa0a10e96f3ffb97d571490b517f972b9dcf7

* Move CUDAGuard, CUDAStream and CUDAGuardImpl to c10/cuda (#14248)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14248

This diff also introduces a horrifying hack to override CUDA's DeviceGuardImpl
with a HIPGuardImplMasqueradingAsCUDA, to accommodate PyTorch's current
behavior of pretending CUDA is HIP when you build with ROCm enabled.

Reviewed By: bddppq

Differential Revision: D13145293

fbshipit-source-id: ee0e207b6fd132f0d435512957424a002d588f02

* Stop erroneously running aten::warn (#15124)

Summary:
Fixes #15119. Before this PR, we were propagating constants through
aten::warn AND running it as a part of shape analysis.
This caused aten::warn to be run regardless of if it is
supposed to be run dynamically. This PR adds an exclusion for aten::warn
in constant propagation and shape analysis, similar to that of prim::RaiseException.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15124

Differential Revision: D13432815

Pulled By: zou3519

fbshipit-source-id: 15ab533ce2accb2da3fd4e569070c7979ce61708

* Move numa.{h, cc} to c10/util (#15024)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15024

Pull Request resolved: https://github.com/pytorch/pytorch/pull/14393

att

Reviewed By: dzhulgakov

Differential Revision: D13380559

fbshipit-source-id: abc3fc7321cf37323f756dfd614c7b41978734e4

* Move adaptive avg pooling 2d to ATen native (#14714)

Summary:
adaptive_avg_pool1d, adaptive_avg_pool2d, and adaptive_avgpool3d are neural network functions that are currently implemented in our legacy THNN (CPU) / THCUNN (CUDA) libraries.  It is generally better if these live in our new library ATen, since it is more feature complete and reduces cognitive overhead.

This change moves currently to adaptive_avg_pool1d and adaptive_avg_pool2d to ATen.

timed relevant cpu tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 6.273s

OK (skipped=7)

real	0m7.164s
user	3m1.289s
sys	0m0.905s
```

compared to master:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 7.232s

OK (skipped=7)

real	0m8.065s
user	3m34.714s
sys	0m2.440s
```

also timed relevant cuda tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 21.049s

OK

real	0m24.106s
user	0m20.890s
sys	0m4.026s
```

compared to master
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 23.021s

OK

real	0m27.095s
user	0m20.121s
sys	0m3.668s
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14714

Differential Revision: D13384084

Pulled By: xnder

fbshipit-source-id: 344442103ccbbda72d3c010d2feea00e9985d226

* Add script standard library documentation + cleanup (#14912)

Summary:
Documents what is supported in the script standard library.

* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912

Differential Revision: D13385928

Pulled By: driazati

fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da

* Minor documentation mistake (#15068)

Summary:
keepdim is a optional parameter for torch.max()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15068

Differential Revision: D13437745

Pulled By: zou3519

fbshipit-source-id: b5198c7d4ae17758cd136f6e5aecc6cb5838f174

* Implement torch.tril_indices and torch.triu_indices (#12653) (#14904)

Summary:
This is an optimized implementation that does the following:

1. created an empty Tensor of correct size.
2. fill the Tensor with correct values.

The following three designs to fill in the Tensor result in roughly the same performance. Hence, the 2nd option is taken for simpler code, and to return contiguous tensors.

1. Sequential: fill row coordinates first, then columns. This results in two for-loop and more arithmetic operations.
2. Interleaved: fill in index coordinates one by one, which jumps between the two output Tensor rows in every iteration.
3. Transpose: create a n X 2 Tensor, fill the Tensor sequentially, and then transpose it.

<img width="352" alt="screen shot 2018-12-10 at 3 54 39 pm" src="https://user-images.githubusercontent.com/16999635/49769172-07bd3580-fc94-11e8-8164-41839185e9f9.png">

NOTE:

This implementation returns a 2D tensor, instead of a tuple of two tensors. It means that users will not be able to do the following:

```python
x = torch.ones(3, 3)
i = torch.tril_indices(3, 3)
x[i]  # need to first convert the 2D tensor into a tuple of two 1D tensors.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14904

Reviewed By: zou3519

Differential Revision: D13433027

Pulled By: mrshenli

fbshipit-source-id: 41c876aafcf584832d7069f7c5929ffb59e0ae6a

* Optimize CPU GenerateProposals op by lazily generating anchors (3-5x faster) (#15103)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15103

There are two main optimizations in this diff:
1. We generate all anchors for every single spatial grid first, and then apply
NMS to pick 2000 anchors according to RPN_PRE_NMS_TOP_N. By first sorting the
score and picking the 2000 top ones and then lazily generating only the
corresponding anchors is much faster.
2. Transposing bbox_deltas from (num_anchors * 4, H, W) to
(H, W, num_anchors * 4) was also quite slow - taking about 20ms in the RRPN
case when there are lots of anchors which it's negligible for RPN case (like
0.1 ms). Instead of transponsing, performing all operations in the
(num_anchors, H, W) format speeds things up.

For regular RPN scenario, this gives 5x speedup from 5.84ms to 1.18ms a case
with 35 anchors over a 600x600 image.

For rotated boxes with 245 anchors, the runtime down from 80ms to 27ms per
iter.

Reviewed By: newstzpz

Differential Revision: D13428688

fbshipit-source-id: 6006b332925e01a7c9433ded2ff5dc9e6d96f7d3

* use ROCm 1.9.2 fp16 capabilities in rocBLAS and MIOpen interfaces (#14994)

Summary:
* relax MIOpen if statement to allow fp16/fp32 mixed precision training now supported by ROCm 1.9.2
* use gemm_ex API of rocBLAS in ROCm 1.9.2 instead of the previous hgemm API
* with this: enable all but one half test in test_nn

While there, fix also:
* a group convolution issue w/ MIOpen pertaining to initializing MIOpen on multi-GPU systems properly we detected while working on this
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14994

Differential Revision: D13439869

Pulled By: bddppq

fbshipit-source-id: 75e4eb51a59488882e64b5eabdc30555b25be25e

* Add back c2 string_utils include header to benchmark_helper

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15143

Differential Revision: D13439694

fbshipit-source-id: 78698b66d52a0178118cbf3e79a7a5ad1763d47b

* Export defs.bzl to open source for pytorch (#15132)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15132

Pull Request resolved: https://github.com/facebook/fbshipit/pull/64

Reviewed By: dzhulgakov

Differential Revision: D13424093

fbshipit-source-id: bbebef964b9f3aef8f59cd394eca068680c36b5a

* docs: minor spelling tweaks

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15148

Differential Revision: D13443708

Pulled By: suo

fbshipit-source-id: 5e3ec0afd3416ab8ce207f2d04105c49e1c04611

* don't compile dnnlowp.cc in avx2 option (#15147)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15147

Forgot to take out dnnlowp.cc from avx2 list in a previous diff.

Reviewed By: dskhudia

Differential Revision: D13440686

fbshipit-source-id: 9ada98b6e885c7d5f22c91a735ff60304480b4cb

* Autoformat build_variables.py (#15152)

Summary:
autoformat `tools/build_variables.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15152

Differential Revision: D13445343

Pulled By: goldsborough

fbshipit-source-id: fd63588de114cb92deda03fa1a0b36f5f9082b2f

* Fix resize for edge case tensors (#14874)

Summary:
Certain tensor shapes failed when being resized. This pull request addresses the bug found in #13404.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14874

Differential Revision: D13429788

Pulled By: soumith

fbshipit-source-id: 8aa6451dbadce46d6d1c47a01cb26e6559bcfc8c

* Implementation of ChannelShuffle Op for MKLDNN (#15106)

Summary:
the speed-up of a single operation is up to 3X .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15106

Differential Revision: D13429596

Pulled By: bddppq

fbshipit-source-id: f8d987cafeac9bef9c3daf7e43ede8c6a4ee2ce5

* support casting to string (#15110)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15110

support casting to string on CPU

Reviewed By: intermilan

Differential Revision: D13429381

fbshipit-source-id: b737a1ba1237b10f692d5c42b42a544b94ba9fd1

* Remove "early-release beta" disclaimer from README (#15136)

Summary:
Now that PyTorch 1.0 is out, this should be updated :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15136

Differential Revision: D13447377

Pulled By: soumith

fbshipit-source-id: bd4e662c53d0699f25d4d90c1b4c1e182b4427c2

* Disable strict-overflow flag to avoid compilation error (#14977)

Summary:
Disable strict-overflow flag to avoid compilation error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14977

Differential Revision: D13447577

Pulled By: soumith

fbshipit-source-id: 1957bd5aa3c7b79219da3dd53560464977c89526

* minimize header file includes from _avx2.cc (#14950)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14950

Minimize the number of headers included from _avx2.cc files to avoid accidental compilation of functions defined the header files reused by other translation units that can lead to illegal instruction errors.

Reviewed By: dskhudia

Differential Revision: D13394483

fbshipit-source-id: 67149a6fb51f7f047e745bfe395cb6dd4ae7c1ae

* Removes THCNumerics usages in RNN.cu (#15085)

Summary:
We don't need THCNumerics here since at::Half can be implicitly converted to float and the cuda math dispatches are handled by `/usr/local/cuda/include/crt/math_functions.hpp` and `cmath`. ATen should be free of THCNumerics after this and when porting kernels from THC, one should not use THCNumerics.

Should close: https://github.com/pytorch/pytorch/issues/11878
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15085

Differential Revision: D13447558

Pulled By: soumith

fbshipit-source-id: 4ff5cbf838edcd01e2d1397e4d7f4f920e9e9fc3

* Reuse KernelSpec for FusionGroups with equivalent graphs (#14541)

Summary:
Before this PR, loop unrolling + the graph fuser was creating multiple
FusionGroups with the same bodies (with different variable names) for
JIT LSTMs. Each FusionGroup got registered to a separate fusion key;
each key resulted in a different compilation for the same
specializations.

This PR makes it so that when registering FusionGroups with the fusion
compiler, the compiler first checks the KernelSpec cache to see if the
FusionGroup's graph exists already. If it does, then return the
corresponding KernelSpec's key to share compiled kernels.

In addition, graphs in the KernelSpec cache are canonicalized before
being cached. I added a flag to the canonicalize pass to remove unique
names of values.

This shortens the compile time for a JIT LSTM (seq_len of 100, loop
unroll factor of 8) from 5.3s to 2.3s. Most of this compile time is
running the graph fuser and/or fusion compiler; while this PR
makes it so that there is only one unique kernel in the forward pass,
there are a lot of different kernels (6) in the backward pass
(after loop unrolling) that should be investigated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14541

Differential Revision: D13324487

Pulled By: zou3519

fbshipit-source-id: b841d82ed35a959b5cfc72db033bf5a7b42cc4fb

* Python <-> C++ Frontend inter-op (#13481)

Summary:
This PR enables C++ frontend modules to be bound into Python and added as submodules of Python modules. For this, I added lots of pybind11 bindings for the `torch::nn::Module` class, and modified the `torch.nn.Module` class in Python to have a new Metaclass that makes `isinstance(m, torch.nn.Module)` return true when `m` is a C++ frontend module. The methods and fields of C++ modules are bound in such a way that they work seamlessly as submodules of Python modules for most operations (one exception I know of: calling `.to()` ends up calling `.apply()` on each submodule with a Python lambda, which cannot be used in C++ -- this may require small changes on Python side).

I've added quite a bunch of tests to verify the bindings and equality with Python. I think I should also try out adding a C++ module as part of some large PyTorch module, like a WLM or something, and see if everything works smoothly.

The next step for inter-op across our system is ScriptModule <-> C++ Frontend Module inter-op. I think this will then also allow using C++ frontend modules from TorchScript.

apaszke zdevito

CC dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13481

Differential Revision: D12981996

Pulled By: goldsborough

fbshipit-source-id: 147370d3596ebb0e94c82cec92993a148fee50a7

* Unify SparseTensorImpl::size_ and TensorImpl::sizes_

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15130

Differential Revision: D13434981

Pulled By: VitalyFedyunin

fbshipit-source-id: 98bd4d66834a3c3d2ea577adb0c8413852da095d

* Fix bincount for non-contiguous inputs on CPU (#15109)

Summary:
Fixes #15058.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15109

Differential Revision: D13447448

Pulled By: soumith

fbshipit-source-id: 56e8d42934538fb00465105a2c5ccfeb7c18a651

* Use a pool of per-thread cudnn handles for each device, updated (#15080)

Summary:
Rebased version of https://github.com/pytorch/pytorch/pull/14861, hopefully addressing ezyang's comments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15080

Differential Revision: D13440858

Pulled By: ezyang

fbshipit-source-id: 1c6af5c53538b81c6b92cf1dda231ed333f28035

* Fix typo (#15045)

Summary:
Simple typo fix
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15045

Reviewed By: dzhulgakov

Differential Revision: D13413509

Pulled By: houseroad

fbshipit-source-id: be66700c30d038368b1433232a4e3fd9299c83d6

* Delete defunct USE_SIMPLE_BASE_CTOR_DTOR (#15144)

Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15144

Differential Revision: D13440872

Pulled By: ezyang

fbshipit-source-id: 2b1d73fac0c63729ba01d8f129642334ae9d9cf3

* Kill non-forward, non-backward functions generated from nn.yaml (#15127)

Summary:
Updating binding to legacy functions.
Remove unused declarations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15127

Differential Revision: D13433405

Pulled By: VitalyFedyunin

fbshipit-source-id: 58544d38affd20818742338c9eb789d9d14ccbaa

* Fix old tensor OutputTensorCopyFrom usage in ImageInput operator (#15094)

Summary:
cc jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15094

Differential Revision: D13451898

Pulled By: bddppq

fbshipit-source-id: 27906be62fb88aaa13c257441a2e35a285b445ee

* Use std::vector instead of alloca to work around hcc crash

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15175

Differential Revision: D13453708

Pulled By: bddppq

fbshipit-source-id: f8c147ae9f679e395fee9d4c73ebcca052c9a752

* Tensor construction codemod(ResizeLike) - 5/7 (#15084)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15084

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419711

fbshipit-source-id: dd2b740c3f13d8087085bafc5571aaf908d1af42

* Tensor construction codemod(ResizeLike) - 6/7 (#15137)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15137

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419736

fbshipit-source-id: f4ad7b9582c2f809258169b7fef9adbca7063d99

* Replace non-printable-ascii characters in ProtoDebugString (#14918)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14918

When ProtoBuf-Lite is in use, ProtoDebugString just calls SerializeAsString.
This produces binary output, which is not a very suitable "debug" string.
Specifically, we've observed it causing problems when calling code tries to
add the debug string to a Java exception message (which requires valid UTF-8).
Now, we replace all non-ASCII bytes with "?".

This is not a very fast implementation, but generating debug strings shouldn't
be a performance-sensitive operation in any application.

Reviewed By: dzhulgakov

Differential Revision: D13385540

fbshipit-source-id: 8868172baf20efaf53fecf7d666a6980f59b64f5

* Tensor construction codemod(ResizeLike) - 4/7 (#15088)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15088

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419682

fbshipit-source-id: 3e59403bc1c0e71e5cb66df932ed0c6a0a72e643

* Remove _finfo; replace _finfo usage with torch.finfo (#15165)

Summary:
This PR removes the usage of _finfo defined in torch.distributions.utils and changes the call sites
to use torch.finfo instead

Differential Revision: D13451936

Pulled By: soumith

fbshipit-source-id: 6dbda3a6179d9407bc3396bf1a2baf3e85bc4cf2

* Run ONNX cuda backend test cases via ROCm

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15069

Differential Revision: D13427757

Pulled By: bddppq

fbshipit-source-id: ba0273d75986cd5b146f7041a83c63ddf9c6c0cf

* Remove disabled_features in hipify

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15098

Reviewed By: ezyang

Differential Revision: D13453762

Pulled By: bddppq

fbshipit-source-id: e177042c78f5bf393163d660c25b80285353853d

* Add missing caffe2_hip extension in setup.py

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15189

Reviewed By: orionr

Differential Revision: D13457644

Pulled By: bddppq

fbshipit-source-id: c2363e9b8fd21709b62777e5b2199f01ec1c65f8

* Enable performance-unnecessary-value-param in .clang-tidy (#15026)

Summary:
This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small).

ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026

Differential Revision: D13458784

Pulled By: goldsborough

fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b

* Remove TensorImpl -> Type dependency

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15086

Reviewed By: dzhulgakov

Differential Revision: D13425628

fbshipit-source-id: 08a8a774d17b071367454e027012a02f96d177d4

* Support torch.tensor in script (#14913)

Summary:
Adding support for torch.tensor in script.

The input list is typed as t[], because it can be arbitrarily nested. I added a check a compile time check  that the inner type of the list is a bool, float, or int.

Also adds specialization for Boolean Lists, which already existed at the ivalue level but had not been added to the compiler yet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14913

Differential Revision: D13407930

Pulled By: eellison

fbshipit-source-id: d17f1195a22149d5b0d08d76c89a7fab8444f7c5

* For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem (#15113)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15113

cv::rotatedRectangleIntersection has a known float underflow bug that would cause failure in ```CV_Assert(intersection.size() <= 8)```

For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem.

Otherwise, when ```USE_CPP_GENERATE_PROPOSALS = true```, the training would fail.

Reviewed By: viswanathgs

Differential Revision: D13429770

fbshipit-source-id: 5e95d059f3c668f14059a0a83e8e53d8554cdb99

* Move TensorImpl::CopyFrom to caffe2::Tensor (1/2) (#14656)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14656

This diff doesn't move it yet, but prepares it to be moved, i.e. removes all access to class internals.

dzhulgakov: Please comment on if you think it still makes sense to land this even though it's not blocking anymore since we're going to move at::CopyBytes anyhow.

ezyang: There's some changes in the implementation, especially handling undefined dest tensors. Please review carefully.

Reviewed By: ezyang

Differential Revision: D13287688

fbshipit-source-id: 17800ca8a79ab1633f23be58d96f99a160d8ed24

* Move TensorImpl::CopyFrom to caffe2::Tensor (2/2) (#14858)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14858

This diff doesn't change logic but just takes the existing code and moves it to caffe2::Tensor

Reviewed By: ezyang

Differential Revision: D13365817

fbshipit-source-id: bc73b27a793602cb14200dcdf357aa63233da43c

* add erf and erfc to fuser/autodiff

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15139

Differential Revision: D13455690

Pulled By: soumith

fbshipit-source-id: b06e5f5d362869c2e5fa11a52f9450d77c30d4cb

* Fix numpy conversion for int8 tensor

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15194

Differential Revision: D13459270

Pulled By: li-roy

fbshipit-source-id: 605534add263860a3ad9a7fa70888301ee0bf8e4

* Fix derivative for mvlgamma (#15049)

Summary:
Fixes #15015.

Added tests to validate derivative.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15049

Reviewed By: soumith

Differential Revision: D13434117

Pulled By: zou3519

fbshipit-source-id: 4a292600af9eb08b67c0f8b5482e9512aac95e72

* caffe2 - easy - Create test_util to make it easier to write C++ unit tests (#15014)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15014

Currently it looks like many of the simple operations such as comparing tensors, creating tensors, fetching tensors... are too verbose and took effort to write correctly in unit tests.
Easy to use utilities are often more important to increase productivity writing unit tests. While caffe2 python unit tests are relatively easier to write at the moment, the C++ side seems lacking.
In this change I create a test_util, started with assertsTensorEquals, getTensor, createTensor, and we can start putting more easy to use utilities there.

Reviewed By: salexspb

Differential Revision: D13370461

fbshipit-source-id: bee467a127e1d032ef19482f98aa5c776cf508c0

* caffe2 - easy - test utils to create operator (#15180)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15180

Test utils to create an operator

On top of D13370461

Reviewed By: ZolotukhinM

Differential Revision: D13382773

fbshipit-source-id: a88040ed5a60f31d3e73f1f958219cd7338dc52e

* caffe2 - easy - test utils to fill tensors (#15019)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15019

Put some utils to fill tensors to test_utils

Reviewed By: salexspb

Differential Revision: D13386691

fbshipit-source-id: 51d891aad1ca12dc5133c0352df65b8db4f96edb

* caffe2 - easy - test utils to compare tensors in two workspaces (#15181)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15181

Add test utils to compare tensors in two workspaces

Reviewed By: ZolotukhinM

Differential Revision: D13387212

fbshipit-source-id: e19d932a1ecc696bd0a08ea14d9a7485cce67bb2

* caffe2 - easy - test utils for tensor assertion (#15020)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15020

Add test utils for assertion of a tensor (sizes and values)

Reviewed By: salexspb

Differential Revision: D13401146

fbshipit-source-id: bc385df074043e03ea884940b5631b96de4a607e

* caffe2 - easy - utils to set argument of operator (#15022)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15022

Add setArgument testing utils to make it easy to set argument for an operator

Reviewed By: yinghai

Differential Revision: D13405225

fbshipit-source-id: b5c1859c6819d53c1a44718e2868e3137067df36

* caffe2 - make DataRandomFiller usable in unit tests (#15027)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15027

- Make DataRandomFiller able to accept input_dims and input_types for only non intermediate inputs. Add a helper to fill input directly to a workspace

Reviewed By: highker

Differential Revision: D13408345

fbshipit-source-id: 5fc54d33da12e3f0a200e79380d4c695b0339b17

* Revert D13407930: [pytorch][PR] Support torch.tensor in script

Differential Revision:
D13407930

Original commit changeset: d17f1195a221

fbshipit-source-id: f4458872c48ec4a2c9983b21ed90bcdc0ae665b7

* Tensor construction codemod(ResizeLike) - 3/7 (#15122)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15122

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530

* Better tests/support for Python/C++ inter-op (#15193)

Summary:
Methods like `module.named_modules()` returns a container of `shared_ptr<nn::Module>`. Currently the `nn::Module` base class does  not have Python bindings. This PR fixes this, and adds more unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15193

Differential Revision: D13458713

Pulled By: goldsborough

fbshipit-source-id: 4091fe1b96a1be8db14c6a4307fbacc2b41ff6fe

* Refactor caffe2 CI scripts and add benchmark scripts

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14575

Differential Revision: D13468049

Pulled By: bddppq

fbshipit-source-id: e73bc8742c8a03f498816eee8a72b06a3e19fe48

* Enable all clang-tidy performance checks (#15198)

Summary:
This PR adds the final set of clang-tidy checks we should add for our codebase: a last set of performance-related checks. Most fixes here are around changing `auto` to `const auto&` in a few places where unnecessary copies were made, and adding `reserve()` calls before loops doing repeated `push_back()`. Also a few cases of calling `std::string::find` with a single-character string literal instead of a single char, which uses a less efficient string search algorithm meant for searching larger substrings.

![image](https://user-images.githubusercontent.com/6429851/49978940-adc1a780-ff01-11e8-99da-a4e431361f07.png)

ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15198

Differential Revision: D13468797

Pulled By: goldsborough

fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2

* Remove __forceinline__ hipification step. (#15229)

Summary:
The HIP definition now correctly contains the inline attribute.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15229

Differential Revision: D13470962

Pulled By: bddppq

fbshipit-source-id: 34f8361bda5f3dce20a2eeb530c3a25d1b1bdd06

* Fix jit doc codeblocks and tables (#15227)

Summary:
Some of the codeblocks were showing up as normal text and the "unsupported modules" table was formatted incorrectly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15227

Differential Revision: D13468847

Pulled By: driazati

fbshipit-source-id: eb7375710d4f6eca1d0f44dfc43c7c506300cb1e

* enabled tests in test_nn, test_cuda and test_sparse (#15232)

Summary:
tests work on ROCm 1.9.2 as present on CI (fp16 bringup, hipMemset and sparse improvements)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15232

Differential Revision: D13470991

Pulled By: bddppq

fbshipit-source-id: 45acc4f9ea5baaaf7672b86eb022948055779925

* Revert D13440858: [pytorch][PR] Use a pool of per-thread cudnn handles for each device, updated

Differential Revision:
D13440858

Original commit changeset: 1c6af5c53538

fbshipit-source-id: fda42ea75000d4a4e9c4a8eeaaa5518f7ad9c298

* Do not ifdef __launch_bounds__ out for ROCm. (#15228)

Summary:
The compiler understands it and profits from knowing it by not using too
many VGPRs as it defaults to 256 default workgroup size.

Fixes a problem in bringup of ROCm 2.0 on gfx906.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15228

Differential Revision: D13470950

Pulled By: bddppq

fbshipit-source-id: f9aa44c7c95299a099c0ea9317b9044cc056acc5

* fix an issue where two rules build the same .py files

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15230

Differential Revision: D13471625

Pulled By: zdevito

fbshipit-source-id: a982413a308c7a9bb5b6a82fe96fd3de44f555aa

* Preserve module hierarchy on traced modules (#15101)

Summary:
We need this, for example, to properly call `_unpack` when we have a traced module in the hierarchy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15101

Differential Revision: D13468467

Pulled By: jamesr66a

fbshipit-source-id: c2b6740b12cde6e23395d12e42d4fc2c4c7ca3f2

* record unit time in torch.cuda.event (#15221)

Summary: Record unit of time for torch.cuda.Event's elapsed_time

Differential Revision: D13467646

Pulled By: zou3519

fbshipit-source-id: 4f1f4ef5fa4bc5a1b4775dfcec6ab155e5bf8d6e

* Build c10 HIP test

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15233

Reviewed By: ezyang

Differential Revision: D13471002

Pulled By: bddppq

fbshipit-source-id: b42c3bc2b9db672ce50a52eb700cc6ed13d3535f

* Start unittesting our main observer (#15191)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15191

OSS:

just splitting out basic flags from a unit test. So I can extend them in another test where I need to add additional flags.

Reviewed By: yinghai

Differential Revision: D13159184

fbshipit-source-id: 9823e792cf0ed8d0379235c44564862b7d784845

* FP16MomentumSGDUpdate Op fix and enable for ROCm (#15150)

Summary:
1. Fix a bug in FP16MomentumSGDUpdate operator
2. Enable operator for ROCm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15150

Differential Revision: D13473145

Pulled By: bddppq

fbshipit-source-id: 4c5c5f30cb9bba658e3639dbe193fa08a304d306

* Supply static shape info to Reshape when doing onnxGetCompatibility (#15242)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15242

Newer version ONNX Reshape gets shape info from a tensor. Hence for static backend, we need to provide this info to it when doing `onnxGetCompatibility` too.

Reviewed By: jackm321

Differential Revision: D13471959

fbshipit-source-id: 8a58e28edd900b6ad54a1dbd63ff2579fbe0e820

* Add several features to converting images to blobs (#15204)

Summary:
Several enhancements are implemented:

* Resize the images to be within a boundary between min-size and max-size (can be height and weight). It tries to resize the minimum size to match the min-size and keep the aspect ratio. However, if in that case the maximum size is more than the max-size, then resize the maximum size to be equal to the max-size (and the minimum size is less than min-size). The min/max sizes are specified in argument scale, in a comma separated form. If one of the size is -1, then that size is not a restriction.

* Change the OpenCV resize function arguments from using cv::Size() to the x, y scale. Theoretically they should be the same. But in reality, the two ways of specifying them may result to different resized outputs.

* Once the image is read in, change the data to floats. That means, after resize and other preprocessing steps, the float values are preserved (not truncated to int).

* It is possible to convert data in text format to the blob format.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15204

Reviewed By: llyfacebook

Differential Revision: D13467225

Pulled By: sf-wind

fbshipit-source-id: 7da34a72d43a9603cd7ab953f5821c1222d0178f

* Create parser.cpp (#15238)

Summary:
Moves implementation into .cpp file. Parser was getting included in several compilation units.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15238

Differential Revision: D13474635

Pulled By: zdevito

fbshipit-source-id: 7dc824eea8f506d6c8ae1aa67aeec0c34d5285fc

* Tensor method rename dims()->sizes() (#15246)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15246

Codemod generated with clangr shard mode, 25 files per diff,

Reviewed By: igorsugak

Differential Revision: D13470369

fbshipit-source-id: ce995beab7c64bebe8b234fb5e6d015940ec2952

* Mention Jacobian-vector product in the doc of torch.autograd (#15197)

Summary:
A friend of me is learning deep learning and pytorch, and he is confused by the following piece of code from the tutorial https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients :

```python
x = torch.randn(3, requires_grad=True)

y = x * 2
while y.data.norm() < 1000:
    y = y * 2

print(y)

gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(gradients)

print(x.grad)
```

He don't know where the following line comes from:
```python
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
```

What are we computing? Why don't we compute "the gradient of `y` w.r.t `x`"?

In the tutorial, it only says
> You can do many crazy things with autograd!

Which does not explain anything. It seems to be hard for some beginners of deep learning to understand why do we ever do backwards with external gradient fed in and what is the meaning of doing so. So I modified the tutorial in https://github.com/pytorch/tutorials/pull/385
and the docstring correspondingly in this PR, explaining the Jacobian vector product. Please review this PR and https://github.com/pytorch/tutorials/pull/385 together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15197

Differential Revision: D13476513

Pulled By: soumith

fbshipit-source-id: bee62282e9ab72403247384e4063bcdf59d40c3c

* value-based mark and sweep DCE (#14910)

Summary:
This makes DCE more granular by tracking live values/aliases through the graph (rather than just nodes). So we can be more aggressive in DCE around control flow blocks. For example, in:
```
%a0 = aten::foo()
%b = aten::foo()
%a2, %b2 = prim::If(%cond) {
  block0() {
    %a1 = aten::foo(%.0)
    %b1 = aten::foo(%b)
  } -> (%a1, %b1)
}
return (%a2)
```
we will now dce all the `%b` stuff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14910

Differential Revision: D13476445

Pulled By: suo

fbshipit-source-id: 2bf5db19711c07dde946697a4f4b270bd8baf791

* fix cholesky call in potrs example (#15215)

Summary:
Cholesky by default returns the lower triangular matrix, see [docs](https://pytorch.org/docs/stable/torch.html#torch.cholesky).

However `torch.potrs` by default requires the upper triangular matrix. The naming of the variable `u` suggests that the example expects the upper to be returned, so I've added the flag to make that happen in the example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15215

Differential Revision: D13476468

Pulled By: soumith

fbshipit-source-id: 7b68035f435a2b1be4d363b3f63e407394af949d

* Fix a typo in the assert

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15265

Reviewed By: llyfacebook

Differential Revision: D13477029

Pulled By: sf-wind

fbshipit-source-id: 9c5571a583c01f9701625541ebec0c836cb923f2

* Delete ffi documentation (#15220)

Summary: Deleting FFI documentation since its deprecated.

Differential Revision: D13477329

Pulled By: soumith

fbshipit-source-id: 0b3d485eb7cef1f05b6b397dff50f21a49d6409e

* Trivial comment correction in dataloader (#15276)

Summary:
Trivial comment correction in dataloader
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15276

Differential Revision: D13477324

Pulled By: soumith

fbshipit-source-id: 2a74a014999655d129311d611f2a09411339cb13

* Refactor hotpatch_vars and apply it to libtorch (#14976)

Summary:
Fixes #14801.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14976

Differential Revision: D13485381

Pulled By: soumith

fbshipit-source-id: 0af3c2e1b90988d56f6f85632328d1e4b788ffd2

* Fix tensor printing bug in Python 2 (#12732)

Summary:
`rsplit` doesn't have kwargs in Python 2 so this line raises an error

Fixes #15135
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12732

Differential Revision: D10458630

Pulled By: driazati

fbshipit-source-id: a63e42fbc0e39e4291480775b516c98122ec05a1

* Tighten up invariants regarding StreamId. (#15125)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15125

I realized that it is really bad juju if you fake a StreamId
out of thin air, because in general this isn't going to work.
So, make the constructor a lot scarier.

Most "faking StreamId out of thin air" happens because someone
just wants to put something on the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432800

fbshipit-source-id: a86991d6fc1d8aa4e54e8175e5f06f90856238e6

* Adding ONNX export for torch.expand and torch.ne (#15050)

Summary:
`torch.expand` and `torch.ne` are used often in models and this PR adds ONNX export support for them. ArmenAg has created issue https://github.com/pytorch/pytorch/issues/10882 for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15050

Differential Revision: D13453036

Pulled By: houseroad

fbshipit-source-id: 4724b4ffcebda6cd6b2acac51d6733cb27318daf

* Minor fixes in .jenkins/caffe2/bench.sh

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15304

Differential Revision: D13493876

Pulled By: bddppq

fbshipit-source-id: 7146eb2587e526af65b4b0290c25bd55653a3088

* Fix for issue 14829 (#14908)

Summary:
* Modify the testcase as outlined in the issue
   * Issue url: https://github.com/pytorch/pytorch/issues/14829
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14908

Differential Revision: D13490360

Pulled By: ezyang

fbshipit-source-id: ff11a72e19b49223652182e82c2b4e65fe444ca7

* Don't enforce docstrings on bool dispatch (#15306)

Summary:
Allows 2 functions that are boolean dispatched to have no docstrings (the only case that will fail now is if both functions have docstrings)

Fixes #15281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15306

Differential Revision: D13494884

Pulled By: driazati

fbshipit-source-id: 65fec39ae03a7d6a68ad617c9b270faeb1617930

* Replace SwitchToDevice(0) with SwitchToDevice() (#15126)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15126

I want to make people stop manufacturing StreamId from thin air,
and a first step is to make people use the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432922

fbshipit-source-id: 9f0d8d70646c50d979bde5ba3c3addeebac48a3d

* Fix the missing caffe2 proto files for Windows (#15157)

Summary:
Fixes #15156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15157

Differential Revision: D13490420

Pulled By: orionr

fbshipit-source-id: 4387d707f634a5975238af915b1befb2277f8ec7

* add isinstance static type checking for jit (#15076)

Summary:
This PR add isinstance to do static type checking in JIT.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15076

Differential Revision: D13471067

Pulled By: wanchaol

fbshipit-source-id: d39b7ed5db9fcca4b503659d02cf7795950ea8ea

* Bicubic interpolation for nn.functional.interpolate (#9849)

Summary:
Addresses #918, interpolation results should be similar to tf

* Adds bicubic interpolation operator to `nn.functional.interpolate`
* Corresponding test in `test_nn.py`

The operator is added in legacy `TH` to be aligned with the other upsampling operators; they can be refactored/moved to ATen all at once when #10482 is resolved
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9849

Differential Revision: D9007525

Pulled By: driazati

fbshipit-source-id: 93ef49a34ce4e5ffd4bda94cd9a6ddc939f0a4cc

* Removing BUILD_C10_EXPERIMENTAL_OPS option and unglobbing experimental/c10d ops

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15064

Reviewed By: orionr

Differential Revision: D13474801

Pulled By: pjh5

fbshipit-source-id: 9d3664c3a3a1b6c2d9f083f8476fe3b037296b98

* Allow future type parsing

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14887

Differential Revision: D13490984

Pulled By: highker

fbshipit-source-id: 165fe995867be273793f983154aa6cbce13e4396

* Port nn fold and unfold to c++

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14597

Reviewed By: ezyang

Differential Revision: D13272227

fbshipit-source-id: 6eccab5ff5830a977398a96393b778095120edc6

* caffe2/python/task: added __repr__ methods to all task definitions (#15250)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15250

This adds `__repr__` methods to all of the classes under task.py. This makes the objects much easier to interact with when using them in an interactive manner, such as in a Jupyter notebook.

The default `__repr__` method just returns the object ID which is very unhelpful.

Reviewed By: hanli0612

Differential Revision: D13475758

fbshipit-source-id: 6e1b166ec35163b9776c797b6a2e0d002560cd29

* Add a correctness check for C++ types to custom operators (#15247)

Summary:
The JIT uses `int64_t` for its integer type and `double` for its floating point type, but users quite often want to write `int` or `float` and that currently fails in not-so-nice ways for custom ops. This PR adds a simple `static_assert` to catch these common failure cases.

zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15247

Differential Revision: D13493941

Pulled By: goldsborough

fbshipit-source-id: c1cd0d10ab5838c75f167c0bdb57e45a0bc1344e

* Fix _apply in nn.Module (#15305)

Summary:
Fixes an issue that arose from https://github.com/pytorch/pytorch/pull/13481 where `.shared_memory()` couldn't be called. Effectively undoes all changes to `nn.Module` from that PR and solve the relevant problem in a different way (the goal was to be able to call `._apply()` on the Python wrapper for a C++ module).

soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15305

Differential Revision: D13493937

Pulled By: goldsborough

fbshipit-source-id: 4cb8687f90fc8709a536c5e7eacd0dc8edf6f750

* Reenable OpenMP by reverting the following two commits. (#15315)

Summary:
Revert "Put back linker flag for OpenMP to prevent build break on ppc64le (#14569)"

This reverts commit a84e873bb156080ea76ab182171b1f3b4d5395f6.

Revert "Update OpenMP cmake setting for xcode 9 compiler(AppleClang 9.0) (#14473)"

This reverts commit 8901935ad42fe9bf093d1106ea43606008a4024d.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15315

Differential Revision: D13495852

Pulled By: ezyang

fbshipit-source-id: bcd3f60088b14831c53d3c171f10cd1ab6b35dee

* [TensorIterator fixing mean to output correct result for half precisi… (#14878)

Summary:
…on](#12115)

mean is calculated in two step sum()/numel(). For half precision, data gets
casted back to half after sum().
We fused the division into the reduction kernel by adding pre_op/post_op.

This allows us to do torch.ones(65536).cuda().half().mean() to return correct
result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14878

Differential Revision: D13491159

Pulled By: soumith

fbshipit-source-id: e83802e1628b6d2615c45e18d7acf991d143a09e

* Allow tracing with fork/wait (#15184)

Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e

* improve script/no script save error (#15321)

Summary:
Improves the error message for #15116
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15321

Differential Revision: D13499379

Pulled By: zdevito

fbshipit-source-id: b8dc0a83efabff74199f4aab2ee98aa41c42608b

* Updating submodules

Reviewed By: cdelahousse

fbshipit-source-id: 4bf66581d07d839f459869bc9c6428011063cc5b

* Revert D13383102: [pytorch][PR] Upgrade MKL-DNN to version 0.17

Differential Revision:
D13383102

Original commit changeset: c434f0e0ddff

fbshipit-source-id: 690f46ca0710954fa591a5ea77535e9759db4de5

* caffe2 mobile opengl (#15322)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15322

caffe2 mobile opengl code is not used, deleting it to reduce complications when we perform other changes

Reviewed By: Maratyszcza

Differential Revision: D13499943

fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2

* Method returns a single argument (#15289)

Summary:
This PR changes Method (just Method not all graphs) to always have a single
return argument.

This is part 1 in a set of changes that will enable us to have better handling if early return statements.
The simplification that this change provides greatly reduces the work for the next step.

This change makes it so that Method and Python handle multiple returns in the same way:
* 0 - None
* 1 - <single value>
* many - Tuple[...]

The result is that a lot of special-case handling in compiler.cpp and its
bindings can be removed. It also fixes several bugs in return handling,
including one where return values were not always checked against their
attributed values.

Notes:
* inferTypeFrom is renamed to be more accurate and discourage use.
* This has uncovered some bugs in other components, which are noted in
  the diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15289

Differential Revision: D13481649

Pulled By: zdevito

fbshipit-source-id: 0e2242a40bb28cca2d0e8be48bede96195e4858c

* Fix the (reduce)min and (reduce)max ONNX exporting (#15241)

Summary:
m…
jaciefan added a commit to jaciefan/pytorch that referenced this pull request Jan 8, 2019
resolve conflicts in legacy hist func namespace
* tox.ini -> .flake8 (#15065)

Summary:
We were only using this file to configure flake8, and fbcode linters do not recognize tox.ini which causes spurious linter warnings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15065

Differential Revision: D13420774

Pulled By: suo

fbshipit-source-id: e43a46befa36862c8b3c0a90074aec6a66531492

* Update onnx coverage script for more accurate result (#15029)

Summary:
The coverage of scalar-input test cases were not accurate. This patch fixed that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15029

Differential Revision: D13419764

Pulled By: zrphercule

fbshipit-source-id: a14a5cbef432bea8c9126156f5deb1125e1aeb47

* Issue 14984: Remove divide by zero error in index_put_ (#14986)

Summary:
No check for zero index tensor was done in the accumulate=True (serial) case in the new TensorIterator code since https://github.com/pytorch/pytorch/pull/13420.

https://github.com/pytorch/pytorch/issues/14984
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14986

Differential Revision: D13417861

Pulled By: colesbury

fbshipit-source-id: e6ed1af8f708b53a35803fc157ed1f043169ec89

* Supress warnings on generated tests

Summary: Removes all warnings spew for the TestJitGenerated tests

Differential Revision: D13420919

fbshipit-source-id: f251c12f923088ccc5daa2984c15003a67cbd1c1

* Split off fuser tests in test_jit.py to their own test case (#15072)

Summary:
This PR creates TestFuser inside test_jit.py to be a home for graph fuser
specific tests.

This was a useful exercise because now that all the fuser tests are in
one place, I can spot redundant and bitrotting tests for cleanup in a
future PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15072

Differential Revision: D13421458

Pulled By: zou3519

fbshipit-source-id: 80b1a7712feff75a0c186d1664601c4edbbca694

* re-enable copy of python files, but be careful that the copy is only … (#14982)

Summary:
…done once

This allow no-op build to work correctly even when BUILD_CAFFE2_OPS is on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14982

Differential Revision: D13413960

Pulled By: zdevito

fbshipit-source-id: 6e5412a8c375af8a47c76f548cdd31cff15f3853

* add gloo scatter support on GPU (#14917)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14917

as titled

Reviewed By: pietern

Differential Revision: D13271560

fbshipit-source-id: 0187a3390f8ebd72a2c074e7a651432159d427c0

* Remove deprecated variable_tensor_functions (#15003)

Summary:
Removing the deprecated functions in `torch/csrc/variable_tensor_functions.h` (like `torch::CPU`) and corresponding implementations from `torch/csrc/torch.cpp` from master after the release.

ezyang gchanan soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15003

Differential Revision: D13418086

Pulled By: goldsborough

fbshipit-source-id: a0accdf6f7b0efa1ec07ac7b74b86ff2da37543f

* Add error type to raise statement

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15039

Differential Revision: D13419566

Pulled By: zou3519

fbshipit-source-id: f67a3aebce937e3e640e91e81eb3e184cfdf269c

* Make ATen HIPify out-of-place, but still reuse CUDA names. (#14866)

Summary:
```
    This diff changes the HIPification of ATen to be out-of-place.
    We now have the following mappings:

    - ATen/cuda => ATen/hip
    - ATen/native/cuda => ATen/native/hip
    - ATen/native/sparse/cuda => ATen/native/sparse/hip
    - THC => THH
    - THCUNN => THHUNN

    The build system is adjusted to know about these new build paths,
    and HIPify is taught how to adjust include paths and
    THC_GENERIC_FILE appropriately.  ATen_hip is now built as
    the ATen_hip library, rather than reusing ATen_cuda.

    However, despite these new filepaths, none of the identifiers in ATen
    have actually changed.  So, e.g., THHGeneral.h still defines functions
    named THC_blahblah, and HIP still shows up as CUDA in PyTorch itself.
    We'll tackle this in a subsequent PR; this diff is just to get the files
    out-of-place.

    Minor extra improvements:

    - Don't edit tmp_install when hipifying
    - HIP no longer builds native_cudnn_cpp; it was unnecessary
    - Caffe2_HIP_INCLUDES is now Caffe2_HIP_INCLUDE, for consistency
      with all the other variables.
    - HIP build now properly respects ATEN_CUDA_FILES_GEN_LIB (it
      did not previously.)
    - You can now override file extension matching in pyHIPIFY
      by explicitly specifying its full name in the matching list.
      This is used so we can HIPify CMakeLists.txt in some situations.

    A little bit of string and ceiling wax:

    - gen.py grows a --rocm flag so that it knows to generate CUDA
      files which actually refer to the HIP headers (e.g., THH.h)
      We'll get rid of this eventually and generate real HIP files,
      but not for this PR.
    - Management of HIP dependencies is now completely deleted
      from the ATen CMakeLists.txt.  The old code was dead (because
      it was shoveled in ATen_CUDA_DEPENDENCY_LIBS and promptly
      ignored by the Caffe2 build system) and didn't actually work.
```

Stacked on https://github.com/pytorch/pytorch/pull/14849 review last commit only
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14866

Differential Revision: D13419475

Pulled By: ezyang

fbshipit-source-id: cb4c843df69a1d8369314c9fab1b7719520fa3db

* Add at::scalar_tensor factory function, use it instead of Type.scalar… (#15074)

Summary:
…_tensor.

This is part of a long series of paring down the Type interface.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15074

Differential Revision: D13421482

Pulled By: gchanan

fbshipit-source-id: 84010ee71fef2cb74d32d5de7858d8ed9f36b885

* Move TensorImpl to c10 (yay!)

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14795

Reviewed By: ezyang

Differential Revision: D13336856

fbshipit-source-id: 5375d0e42312ff7564f4df06210a5e49542d59e3

* Fix include paths for TensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14816

Reviewed By: ezyang

Differential Revision: D13348040

fbshipit-source-id: a7204d89c2dd277d13093b0ed862f40b53dee82f

* Move UndefinedTensorImpl to c10 (meh) (#14817)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14817

unfortunately, we still need this.

Reviewed By: ezyang

Differential Revision: D13348041

fbshipit-source-id: e8dcc89f5c71bd1ea2c9813990dac6e58e63b1fd

* Fix include paths for UndefinedTensorImpl.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14818

Reviewed By: ezyang

Differential Revision: D13348042

fbshipit-source-id: 11bdfc755767ce9d0a6fa95b2cf49d50adde8d60

* add gloo support for gather on GPU (#14916)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14916

as titled

Reviewed By: pietern

Differential Revision: D13267832

fbshipit-source-id: 3b89d08af93f74941f17ff892c33fc2a4a023c19

* Pre-commit flake8/clang-tidy (#15102)

Summary:
Provide a pre-commit hook that does flake8 and clang tidy checks. Enables the clang-tidy script to run in parallel to make it fast enough to be used in a pre-commit hook.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15102

Reviewed By: soumith

Differential Revision: D13429629

Pulled By: zdevito

fbshipit-source-id: bd52fe5652f29b033de8d9926d78350b2da4c2fc

* Update the output format for benchmark_helper. It outputs the dimensi… (#15108)

Summary:
…on first and all the values in the next line. This way, it can output arbitrary blob
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15108

Reviewed By: llyfacebook

Differential Revision: D13429346

Pulled By: sf-wind

fbshipit-source-id: 5e0bba2a46fbe8d997dfc3d55a698484552e3af8

* Fix serialization (#15033)

Summary:
Fixes a bug where (de-)/serializing a hierarchy of submodules where one submodule doesn't have any parameters, but its submodules do, doesn't get properly loaded. This had to do with the fact that the old protobuf format couldn't store empty parameters.

Fixes https://github.com/pytorch/pytorch/issues/14891

soumith ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15033

Differential Revision: D13411322

Pulled By: goldsborough

fbshipit-source-id: 2ef73b2aa93fa9e46b1cbe1fd47d9f134d6016d5

* Remove linker and dlopen flags that allowed undefined symbols in rocm build (#15091)

Summary:
Previously the undefined symbols were caused by disabled_modules in tools/amd_build/disabled_features.json (now it's cleared).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15091

Differential Revision: D13429595

Pulled By: bddppq

fbshipit-source-id: b341e83f9e5a8d16440a364e837b045a8a4fd6e1

* Add EmptyNameScope to allow you jump out from current scope. (#14631)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14631

adding a empty name scope to allow people jump out from current namescope.

This could be useful when you want to access blob from parent or sibling scope.

 Facebook:

e.g: we encoutered a potential usecase in D13124249 (it's a large diff, please search by EmptyNameScope in that diff), we need to access to a blob declared in root namescope from a device namescope (device namescope has been used by parallel_GPU API). `EmptyNameScope` can help us do that with ease.

I referenced to `EmptyDeviceScope` D6103412 while implementing this one.

Reviewed By: yinghai

Differential Revision: D13272240

fbshipit-source-id: d4cde5abcc2336e456b6c6ef086266ef94d86da8

* Use c10::to_string that works cross platform (#15117)

Summary:
Fix master breakage introduced in #15108
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15117

Differential Revision: D13430568

Pulled By: bddppq

fbshipit-source-id: ce10bc552f085d1bf0afbc13119991bee014ac95

* Don't setup x86_64-linux-gnu-gcc as an sccache wrapper. (#15078)

Summary:
When I do this setup in a local Docker development environment,
I get the following error:

    x86_64-linux-gnu-gcc: error trying to exec 'cc1plus': execvp: No such file or directory

Somehow, gcc seems to get confused when it gets run from the wrong
directory.  Best not to do it.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15078

Differential Revision: D13432143

Pulled By: ezyang

fbshipit-source-id: b18e15f493503a4c8205c85f92a214e49762a7bc

* fix some tests that I accidentally disabled (#15077)

Summary:
While moving these scenarios into `_test_dim_ops` I accidentally left an empty loop in the actual tests, causing them to do nothing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15077

Differential Revision: D13428759

Pulled By: umanwizard

fbshipit-source-id: 08f53068981d9192c1408878b168e9053f4dc92e

* Add better support for bools in the graph fuser (#15057)

Summary:
Fixes #15038.

aten::_cast_Float(tensor, non_blocking) support was added in #14336.
Its second argument is a bool, but because we don't support generating values
of type bool in the fuser codegen, the codegen errored out.

aten::_cast_Float in the fuser never actually uses its non_blocking
argument, so another way to fix this would be to have a special op for a
fused cast but I thought that we might have fusible ops that do take
bool arguments in the future so this would be good to have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15057

Differential Revision: D13432091

Pulled By: zou3519

fbshipit-source-id: 455fe574f5f080aca9a112e346b841a2534a8dc3

* Ensure there aren't variables in checked_tensor_unwrap, checked_tenso… (#15105)

Summary:
…r_list_unwrap.

These functions use unsafeGetTensorImpl(), which doesn't work with Variables (in a silent way that may blow up later).
So let's do early checking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15105

Reviewed By: ezyang

Differential Revision: D13429149

Pulled By: gchanan

fbshipit-source-id: b85f6f5b7cdb9a6dd0c40205b924c840a3920ba0

* fix infinite loop when get_max_threads is nonzero but num_threads is 1

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15114

Differential Revision: D13431891

Pulled By: umanwizard

fbshipit-source-id: f968b8e50cf776c346d4a28d72b12e7856c95839

* Kill Type.storage. (#15075)

Summary:
It's not used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15075

Reviewed By: ezyang

Differential Revision: D13422487

Pulled By: gchanan

fbshipit-source-id: 272aa0a10e96f3ffb97d571490b517f972b9dcf7

* Move CUDAGuard, CUDAStream and CUDAGuardImpl to c10/cuda (#14248)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14248

This diff also introduces a horrifying hack to override CUDA's DeviceGuardImpl
with a HIPGuardImplMasqueradingAsCUDA, to accommodate PyTorch's current
behavior of pretending CUDA is HIP when you build with ROCm enabled.

Reviewed By: bddppq

Differential Revision: D13145293

fbshipit-source-id: ee0e207b6fd132f0d435512957424a002d588f02

* Stop erroneously running aten::warn (#15124)

Summary:
Fixes #15119. Before this PR, we were propagating constants through
aten::warn AND running it as a part of shape analysis.
This caused aten::warn to be run regardless of if it is
supposed to be run dynamically. This PR adds an exclusion for aten::warn
in constant propagation and shape analysis, similar to that of prim::RaiseException.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15124

Differential Revision: D13432815

Pulled By: zou3519

fbshipit-source-id: 15ab533ce2accb2da3fd4e569070c7979ce61708

* Move numa.{h, cc} to c10/util (#15024)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15024

Pull Request resolved: https://github.com/pytorch/pytorch/pull/14393

att

Reviewed By: dzhulgakov

Differential Revision: D13380559

fbshipit-source-id: abc3fc7321cf37323f756dfd614c7b41978734e4

* Move adaptive avg pooling 2d to ATen native (#14714)

Summary:
adaptive_avg_pool1d, adaptive_avg_pool2d, and adaptive_avgpool3d are neural network functions that are currently implemented in our legacy THNN (CPU) / THCUNN (CUDA) libraries.  It is generally better if these live in our new library ATen, since it is more feature complete and reduces cognitive overhead.

This change moves currently to adaptive_avg_pool1d and adaptive_avg_pool2d to ATen.

timed relevant cpu tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 6.273s

OK (skipped=7)

real	0m7.164s
user	3m1.289s
sys	0m0.905s
```

compared to master:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.s.s.s.s.s.s.s...
----------------------------------------------------------------------
Ran 17 tests in 7.232s

OK (skipped=7)

real	0m8.065s
user	3m34.714s
sys	0m2.440s
```

also timed relevant cuda tests with this change:
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 21.049s

OK

real	0m24.106s
user	0m20.890s
sys	0m4.026s
```

compared to master
```
[ialex@devgpu064.ash5 ~/pytorch] time python test/test_nn.py
test_AdaptiveAvgPool1d (__main__.TestNN)
test_AdaptiveAvgPool1d_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_single (__main__.TestNN)
test_AdaptiveAvgPool2d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool2d_tuple_none_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_single (__main__.TestNN)
test_AdaptiveAvgPool3d_single_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_cuda (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none (__main__.TestNN)
test_AdaptiveAvgPool3d_tuple_none_cuda (__main__.TestNN)
test_adaptive_log_softmax (__main__.TestNN)
test_adaptive_pooling_input_size (__main__.TestNN)
test_adaptive_pooling_size_none (__main__.TestNN)
.................
----------------------------------------------------------------------
Ran 17 tests in 23.021s

OK

real	0m27.095s
user	0m20.121s
sys	0m3.668s
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14714

Differential Revision: D13384084

Pulled By: xnder

fbshipit-source-id: 344442103ccbbda72d3c010d2feea00e9985d226

* Add script standard library documentation + cleanup (#14912)

Summary:
Documents what is supported in the script standard library.

* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912

Differential Revision: D13385928

Pulled By: driazati

fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da

* Minor documentation mistake (#15068)

Summary:
keepdim is a optional parameter for torch.max()
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15068

Differential Revision: D13437745

Pulled By: zou3519

fbshipit-source-id: b5198c7d4ae17758cd136f6e5aecc6cb5838f174

* Implement torch.tril_indices and torch.triu_indices (#12653) (#14904)

Summary:
This is an optimized implementation that does the following:

1. created an empty Tensor of correct size.
2. fill the Tensor with correct values.

The following three designs to fill in the Tensor result in roughly the same performance. Hence, the 2nd option is taken for simpler code, and to return contiguous tensors.

1. Sequential: fill row coordinates first, then columns. This results in two for-loop and more arithmetic operations.
2. Interleaved: fill in index coordinates one by one, which jumps between the two output Tensor rows in every iteration.
3. Transpose: create a n X 2 Tensor, fill the Tensor sequentially, and then transpose it.

<img width="352" alt="screen shot 2018-12-10 at 3 54 39 pm" src="https://user-images.githubusercontent.com/16999635/49769172-07bd3580-fc94-11e8-8164-41839185e9f9.png">

NOTE:

This implementation returns a 2D tensor, instead of a tuple of two tensors. It means that users will not be able to do the following:

```python
x = torch.ones(3, 3)
i = torch.tril_indices(3, 3)
x[i]  # need to first convert the 2D tensor into a tuple of two 1D tensors.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14904

Reviewed By: zou3519

Differential Revision: D13433027

Pulled By: mrshenli

fbshipit-source-id: 41c876aafcf584832d7069f7c5929ffb59e0ae6a

* Optimize CPU GenerateProposals op by lazily generating anchors (3-5x faster) (#15103)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15103

There are two main optimizations in this diff:
1. We generate all anchors for every single spatial grid first, and then apply
NMS to pick 2000 anchors according to RPN_PRE_NMS_TOP_N. By first sorting the
score and picking the 2000 top ones and then lazily generating only the
corresponding anchors is much faster.
2. Transposing bbox_deltas from (num_anchors * 4, H, W) to
(H, W, num_anchors * 4) was also quite slow - taking about 20ms in the RRPN
case when there are lots of anchors which it's negligible for RPN case (like
0.1 ms). Instead of transponsing, performing all operations in the
(num_anchors, H, W) format speeds things up.

For regular RPN scenario, this gives 5x speedup from 5.84ms to 1.18ms a case
with 35 anchors over a 600x600 image.

For rotated boxes with 245 anchors, the runtime down from 80ms to 27ms per
iter.

Reviewed By: newstzpz

Differential Revision: D13428688

fbshipit-source-id: 6006b332925e01a7c9433ded2ff5dc9e6d96f7d3

* use ROCm 1.9.2 fp16 capabilities in rocBLAS and MIOpen interfaces (#14994)

Summary:
* relax MIOpen if statement to allow fp16/fp32 mixed precision training now supported by ROCm 1.9.2
* use gemm_ex API of rocBLAS in ROCm 1.9.2 instead of the previous hgemm API
* with this: enable all but one half test in test_nn

While there, fix also:
* a group convolution issue w/ MIOpen pertaining to initializing MIOpen on multi-GPU systems properly we detected while working on this
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14994

Differential Revision: D13439869

Pulled By: bddppq

fbshipit-source-id: 75e4eb51a59488882e64b5eabdc30555b25be25e

* Add back c2 string_utils include header to benchmark_helper

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15143

Differential Revision: D13439694

fbshipit-source-id: 78698b66d52a0178118cbf3e79a7a5ad1763d47b

* Export defs.bzl to open source for pytorch (#15132)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15132

Pull Request resolved: https://github.com/facebook/fbshipit/pull/64

Reviewed By: dzhulgakov

Differential Revision: D13424093

fbshipit-source-id: bbebef964b9f3aef8f59cd394eca068680c36b5a

* docs: minor spelling tweaks

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15148

Differential Revision: D13443708

Pulled By: suo

fbshipit-source-id: 5e3ec0afd3416ab8ce207f2d04105c49e1c04611

* don't compile dnnlowp.cc in avx2 option (#15147)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15147

Forgot to take out dnnlowp.cc from avx2 list in a previous diff.

Reviewed By: dskhudia

Differential Revision: D13440686

fbshipit-source-id: 9ada98b6e885c7d5f22c91a735ff60304480b4cb

* Autoformat build_variables.py (#15152)

Summary:
autoformat `tools/build_variables.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15152

Differential Revision: D13445343

Pulled By: goldsborough

fbshipit-source-id: fd63588de114cb92deda03fa1a0b36f5f9082b2f

* Fix resize for edge case tensors (#14874)

Summary:
Certain tensor shapes failed when being resized. This pull request addresses the bug found in #13404.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14874

Differential Revision: D13429788

Pulled By: soumith

fbshipit-source-id: 8aa6451dbadce46d6d1c47a01cb26e6559bcfc8c

* Implementation of ChannelShuffle Op for MKLDNN (#15106)

Summary:
the speed-up of a single operation is up to 3X .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15106

Differential Revision: D13429596

Pulled By: bddppq

fbshipit-source-id: f8d987cafeac9bef9c3daf7e43ede8c6a4ee2ce5

* support casting to string (#15110)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15110

support casting to string on CPU

Reviewed By: intermilan

Differential Revision: D13429381

fbshipit-source-id: b737a1ba1237b10f692d5c42b42a544b94ba9fd1

* Remove "early-release beta" disclaimer from README (#15136)

Summary:
Now that PyTorch 1.0 is out, this should be updated :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15136

Differential Revision: D13447377

Pulled By: soumith

fbshipit-source-id: bd4e662c53d0699f25d4d90c1b4c1e182b4427c2

* Disable strict-overflow flag to avoid compilation error (#14977)

Summary:
Disable strict-overflow flag to avoid compilation error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14977

Differential Revision: D13447577

Pulled By: soumith

fbshipit-source-id: 1957bd5aa3c7b79219da3dd53560464977c89526

* minimize header file includes from _avx2.cc (#14950)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14950

Minimize the number of headers included from _avx2.cc files to avoid accidental compilation of functions defined the header files reused by other translation units that can lead to illegal instruction errors.

Reviewed By: dskhudia

Differential Revision: D13394483

fbshipit-source-id: 67149a6fb51f7f047e745bfe395cb6dd4ae7c1ae

* Removes THCNumerics usages in RNN.cu (#15085)

Summary:
We don't need THCNumerics here since at::Half can be implicitly converted to float and the cuda math dispatches are handled by `/usr/local/cuda/include/crt/math_functions.hpp` and `cmath`. ATen should be free of THCNumerics after this and when porting kernels from THC, one should not use THCNumerics.

Should close: https://github.com/pytorch/pytorch/issues/11878
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15085

Differential Revision: D13447558

Pulled By: soumith

fbshipit-source-id: 4ff5cbf838edcd01e2d1397e4d7f4f920e9e9fc3

* Reuse KernelSpec for FusionGroups with equivalent graphs (#14541)

Summary:
Before this PR, loop unrolling + the graph fuser was creating multiple
FusionGroups with the same bodies (with different variable names) for
JIT LSTMs. Each FusionGroup got registered to a separate fusion key;
each key resulted in a different compilation for the same
specializations.

This PR makes it so that when registering FusionGroups with the fusion
compiler, the compiler first checks the KernelSpec cache to see if the
FusionGroup's graph exists already. If it does, then return the
corresponding KernelSpec's key to share compiled kernels.

In addition, graphs in the KernelSpec cache are canonicalized before
being cached. I added a flag to the canonicalize pass to remove unique
names of values.

This shortens the compile time for a JIT LSTM (seq_len of 100, loop
unroll factor of 8) from 5.3s to 2.3s. Most of this compile time is
running the graph fuser and/or fusion compiler; while this PR
makes it so that there is only one unique kernel in the forward pass,
there are a lot of different kernels (6) in the backward pass
(after loop unrolling) that should be investigated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14541

Differential Revision: D13324487

Pulled By: zou3519

fbshipit-source-id: b841d82ed35a959b5cfc72db033bf5a7b42cc4fb

* Python <-> C++ Frontend inter-op (#13481)

Summary:
This PR enables C++ frontend modules to be bound into Python and added as submodules of Python modules. For this, I added lots of pybind11 bindings for the `torch::nn::Module` class, and modified the `torch.nn.Module` class in Python to have a new Metaclass that makes `isinstance(m, torch.nn.Module)` return true when `m` is a C++ frontend module. The methods and fields of C++ modules are bound in such a way that they work seamlessly as submodules of Python modules for most operations (one exception I know of: calling `.to()` ends up calling `.apply()` on each submodule with a Python lambda, which cannot be used in C++ -- this may require small changes on Python side).

I've added quite a bunch of tests to verify the bindings and equality with Python. I think I should also try out adding a C++ module as part of some large PyTorch module, like a WLM or something, and see if everything works smoothly.

The next step for inter-op across our system is ScriptModule <-> C++ Frontend Module inter-op. I think this will then also allow using C++ frontend modules from TorchScript.

apaszke zdevito

CC dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13481

Differential Revision: D12981996

Pulled By: goldsborough

fbshipit-source-id: 147370d3596ebb0e94c82cec92993a148fee50a7

* Unify SparseTensorImpl::size_ and TensorImpl::sizes_

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15130

Differential Revision: D13434981

Pulled By: VitalyFedyunin

fbshipit-source-id: 98bd4d66834a3c3d2ea577adb0c8413852da095d

* Fix bincount for non-contiguous inputs on CPU (#15109)

Summary:
Fixes #15058.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15109

Differential Revision: D13447448

Pulled By: soumith

fbshipit-source-id: 56e8d42934538fb00465105a2c5ccfeb7c18a651

* Use a pool of per-thread cudnn handles for each device, updated (#15080)

Summary:
Rebased version of https://github.com/pytorch/pytorch/pull/14861, hopefully addressing ezyang's comments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15080

Differential Revision: D13440858

Pulled By: ezyang

fbshipit-source-id: 1c6af5c53538b81c6b92cf1dda231ed333f28035

* Fix typo (#15045)

Summary:
Simple typo fix
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15045

Reviewed By: dzhulgakov

Differential Revision: D13413509

Pulled By: houseroad

fbshipit-source-id: be66700c30d038368b1433232a4e3fd9299c83d6

* Delete defunct USE_SIMPLE_BASE_CTOR_DTOR (#15144)

Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15144

Differential Revision: D13440872

Pulled By: ezyang

fbshipit-source-id: 2b1d73fac0c63729ba01d8f129642334ae9d9cf3

* Kill non-forward, non-backward functions generated from nn.yaml (#15127)

Summary:
Updating binding to legacy functions.
Remove unused declarations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15127

Differential Revision: D13433405

Pulled By: VitalyFedyunin

fbshipit-source-id: 58544d38affd20818742338c9eb789d9d14ccbaa

* Fix old tensor OutputTensorCopyFrom usage in ImageInput operator (#15094)

Summary:
cc jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15094

Differential Revision: D13451898

Pulled By: bddppq

fbshipit-source-id: 27906be62fb88aaa13c257441a2e35a285b445ee

* Use std::vector instead of alloca to work around hcc crash

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15175

Differential Revision: D13453708

Pulled By: bddppq

fbshipit-source-id: f8c147ae9f679e395fee9d4c73ebcca052c9a752

* Tensor construction codemod(ResizeLike) - 5/7 (#15084)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15084

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419711

fbshipit-source-id: dd2b740c3f13d8087085bafc5571aaf908d1af42

* Tensor construction codemod(ResizeLike) - 6/7 (#15137)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15137

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419736

fbshipit-source-id: f4ad7b9582c2f809258169b7fef9adbca7063d99

* Replace non-printable-ascii characters in ProtoDebugString (#14918)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14918

When ProtoBuf-Lite is in use, ProtoDebugString just calls SerializeAsString.
This produces binary output, which is not a very suitable "debug" string.
Specifically, we've observed it causing problems when calling code tries to
add the debug string to a Java exception message (which requires valid UTF-8).
Now, we replace all non-ASCII bytes with "?".

This is not a very fast implementation, but generating debug strings shouldn't
be a performance-sensitive operation in any application.

Reviewed By: dzhulgakov

Differential Revision: D13385540

fbshipit-source-id: 8868172baf20efaf53fecf7d666a6980f59b64f5

* Tensor construction codemod(ResizeLike) - 4/7 (#15088)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15088

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: ezyang

Differential Revision: D13419682

fbshipit-source-id: 3e59403bc1c0e71e5cb66df932ed0c6a0a72e643

* Remove _finfo; replace _finfo usage with torch.finfo (#15165)

Summary:
This PR removes the usage of _finfo defined in torch.distributions.utils and changes the call sites
to use torch.finfo instead

Differential Revision: D13451936

Pulled By: soumith

fbshipit-source-id: 6dbda3a6179d9407bc3396bf1a2baf3e85bc4cf2

* Run ONNX cuda backend test cases via ROCm

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15069

Differential Revision: D13427757

Pulled By: bddppq

fbshipit-source-id: ba0273d75986cd5b146f7041a83c63ddf9c6c0cf

* Remove disabled_features in hipify

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15098

Reviewed By: ezyang

Differential Revision: D13453762

Pulled By: bddppq

fbshipit-source-id: e177042c78f5bf393163d660c25b80285353853d

* Add missing caffe2_hip extension in setup.py

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15189

Reviewed By: orionr

Differential Revision: D13457644

Pulled By: bddppq

fbshipit-source-id: c2363e9b8fd21709b62777e5b2199f01ec1c65f8

* Enable performance-unnecessary-value-param in .clang-tidy (#15026)

Summary:
This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small).

ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026

Differential Revision: D13458784

Pulled By: goldsborough

fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b

* Remove TensorImpl -> Type dependency

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15086

Reviewed By: dzhulgakov

Differential Revision: D13425628

fbshipit-source-id: 08a8a774d17b071367454e027012a02f96d177d4

* Support torch.tensor in script (#14913)

Summary:
Adding support for torch.tensor in script.

The input list is typed as t[], because it can be arbitrarily nested. I added a check a compile time check  that the inner type of the list is a bool, float, or int.

Also adds specialization for Boolean Lists, which already existed at the ivalue level but had not been added to the compiler yet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14913

Differential Revision: D13407930

Pulled By: eellison

fbshipit-source-id: d17f1195a22149d5b0d08d76c89a7fab8444f7c5

* For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem (#15113)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15113

cv::rotatedRectangleIntersection has a known float underflow bug that would cause failure in ```CV_Assert(intersection.size() <= 8)```

For rotated proposals, replace cv::rotatedRectangleIntersection with a correct version that doesn't have underflow problem.

Otherwise, when ```USE_CPP_GENERATE_PROPOSALS = true```, the training would fail.

Reviewed By: viswanathgs

Differential Revision: D13429770

fbshipit-source-id: 5e95d059f3c668f14059a0a83e8e53d8554cdb99

* Move TensorImpl::CopyFrom to caffe2::Tensor (1/2) (#14656)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14656

This diff doesn't move it yet, but prepares it to be moved, i.e. removes all access to class internals.

dzhulgakov: Please comment on if you think it still makes sense to land this even though it's not blocking anymore since we're going to move at::CopyBytes anyhow.

ezyang: There's some changes in the implementation, especially handling undefined dest tensors. Please review carefully.

Reviewed By: ezyang

Differential Revision: D13287688

fbshipit-source-id: 17800ca8a79ab1633f23be58d96f99a160d8ed24

* Move TensorImpl::CopyFrom to caffe2::Tensor (2/2) (#14858)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14858

This diff doesn't change logic but just takes the existing code and moves it to caffe2::Tensor

Reviewed By: ezyang

Differential Revision: D13365817

fbshipit-source-id: bc73b27a793602cb14200dcdf357aa63233da43c

* add erf and erfc to fuser/autodiff

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15139

Differential Revision: D13455690

Pulled By: soumith

fbshipit-source-id: b06e5f5d362869c2e5fa11a52f9450d77c30d4cb

* Fix numpy conversion for int8 tensor

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15194

Differential Revision: D13459270

Pulled By: li-roy

fbshipit-source-id: 605534add263860a3ad9a7fa70888301ee0bf8e4

* Fix derivative for mvlgamma (#15049)

Summary:
Fixes #15015.

Added tests to validate derivative.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15049

Reviewed By: soumith

Differential Revision: D13434117

Pulled By: zou3519

fbshipit-source-id: 4a292600af9eb08b67c0f8b5482e9512aac95e72

* caffe2 - easy - Create test_util to make it easier to write C++ unit tests (#15014)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15014

Currently it looks like many of the simple operations such as comparing tensors, creating tensors, fetching tensors... are too verbose and took effort to write correctly in unit tests.
Easy to use utilities are often more important to increase productivity writing unit tests. While caffe2 python unit tests are relatively easier to write at the moment, the C++ side seems lacking.
In this change I create a test_util, started with assertsTensorEquals, getTensor, createTensor, and we can start putting more easy to use utilities there.

Reviewed By: salexspb

Differential Revision: D13370461

fbshipit-source-id: bee467a127e1d032ef19482f98aa5c776cf508c0

* caffe2 - easy - test utils to create operator (#15180)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15180

Test utils to create an operator

On top of D13370461

Reviewed By: ZolotukhinM

Differential Revision: D13382773

fbshipit-source-id: a88040ed5a60f31d3e73f1f958219cd7338dc52e

* caffe2 - easy - test utils to fill tensors (#15019)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15019

Put some utils to fill tensors to test_utils

Reviewed By: salexspb

Differential Revision: D13386691

fbshipit-source-id: 51d891aad1ca12dc5133c0352df65b8db4f96edb

* caffe2 - easy - test utils to compare tensors in two workspaces (#15181)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15181

Add test utils to compare tensors in two workspaces

Reviewed By: ZolotukhinM

Differential Revision: D13387212

fbshipit-source-id: e19d932a1ecc696bd0a08ea14d9a7485cce67bb2

* caffe2 - easy - test utils for tensor assertion (#15020)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15020

Add test utils for assertion of a tensor (sizes and values)

Reviewed By: salexspb

Differential Revision: D13401146

fbshipit-source-id: bc385df074043e03ea884940b5631b96de4a607e

* caffe2 - easy - utils to set argument of operator (#15022)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15022

Add setArgument testing utils to make it easy to set argument for an operator

Reviewed By: yinghai

Differential Revision: D13405225

fbshipit-source-id: b5c1859c6819d53c1a44718e2868e3137067df36

* caffe2 - make DataRandomFiller usable in unit tests (#15027)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15027

- Make DataRandomFiller able to accept input_dims and input_types for only non intermediate inputs. Add a helper to fill input directly to a workspace

Reviewed By: highker

Differential Revision: D13408345

fbshipit-source-id: 5fc54d33da12e3f0a200e79380d4c695b0339b17

* Revert D13407930: [pytorch][PR] Support torch.tensor in script

Differential Revision:
D13407930

Original commit changeset: d17f1195a221

fbshipit-source-id: f4458872c48ec4a2c9983b21ed90bcdc0ae665b7

* Tensor construction codemod(ResizeLike) - 3/7 (#15122)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15122

Codemod generated with clangr shard mode, 25 files per diff,
motivation: https://github.com/pytorch/pytorch/pull/12407

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530

* Better tests/support for Python/C++ inter-op (#15193)

Summary:
Methods like `module.named_modules()` returns a container of `shared_ptr<nn::Module>`. Currently the `nn::Module` base class does  not have Python bindings. This PR fixes this, and adds more unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15193

Differential Revision: D13458713

Pulled By: goldsborough

fbshipit-source-id: 4091fe1b96a1be8db14c6a4307fbacc2b41ff6fe

* Refactor caffe2 CI scripts and add benchmark scripts

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14575

Differential Revision: D13468049

Pulled By: bddppq

fbshipit-source-id: e73bc8742c8a03f498816eee8a72b06a3e19fe48

* Enable all clang-tidy performance checks (#15198)

Summary:
This PR adds the final set of clang-tidy checks we should add for our codebase: a last set of performance-related checks. Most fixes here are around changing `auto` to `const auto&` in a few places where unnecessary copies were made, and adding `reserve()` calls before loops doing repeated `push_back()`. Also a few cases of calling `std::string::find` with a single-character string literal instead of a single char, which uses a less efficient string search algorithm meant for searching larger substrings.

![image](https://user-images.githubusercontent.com/6429851/49978940-adc1a780-ff01-11e8-99da-a4e431361f07.png)

ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15198

Differential Revision: D13468797

Pulled By: goldsborough

fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2

* Remove __forceinline__ hipification step. (#15229)

Summary:
The HIP definition now correctly contains the inline attribute.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15229

Differential Revision: D13470962

Pulled By: bddppq

fbshipit-source-id: 34f8361bda5f3dce20a2eeb530c3a25d1b1bdd06

* Fix jit doc codeblocks and tables (#15227)

Summary:
Some of the codeblocks were showing up as normal text and the "unsupported modules" table was formatted incorrectly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15227

Differential Revision: D13468847

Pulled By: driazati

fbshipit-source-id: eb7375710d4f6eca1d0f44dfc43c7c506300cb1e

* enabled tests in test_nn, test_cuda and test_sparse (#15232)

Summary:
tests work on ROCm 1.9.2 as present on CI (fp16 bringup, hipMemset and sparse improvements)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15232

Differential Revision: D13470991

Pulled By: bddppq

fbshipit-source-id: 45acc4f9ea5baaaf7672b86eb022948055779925

* Revert D13440858: [pytorch][PR] Use a pool of per-thread cudnn handles for each device, updated

Differential Revision:
D13440858

Original commit changeset: 1c6af5c53538

fbshipit-source-id: fda42ea75000d4a4e9c4a8eeaaa5518f7ad9c298

* Do not ifdef __launch_bounds__ out for ROCm. (#15228)

Summary:
The compiler understands it and profits from knowing it by not using too
many VGPRs as it defaults to 256 default workgroup size.

Fixes a problem in bringup of ROCm 2.0 on gfx906.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15228

Differential Revision: D13470950

Pulled By: bddppq

fbshipit-source-id: f9aa44c7c95299a099c0ea9317b9044cc056acc5

* fix an issue where two rules build the same .py files

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15230

Differential Revision: D13471625

Pulled By: zdevito

fbshipit-source-id: a982413a308c7a9bb5b6a82fe96fd3de44f555aa

* Preserve module hierarchy on traced modules (#15101)

Summary:
We need this, for example, to properly call `_unpack` when we have a traced module in the hierarchy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15101

Differential Revision: D13468467

Pulled By: jamesr66a

fbshipit-source-id: c2b6740b12cde6e23395d12e42d4fc2c4c7ca3f2

* record unit time in torch.cuda.event (#15221)

Summary: Record unit of time for torch.cuda.Event's elapsed_time

Differential Revision: D13467646

Pulled By: zou3519

fbshipit-source-id: 4f1f4ef5fa4bc5a1b4775dfcec6ab155e5bf8d6e

* Build c10 HIP test

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15233

Reviewed By: ezyang

Differential Revision: D13471002

Pulled By: bddppq

fbshipit-source-id: b42c3bc2b9db672ce50a52eb700cc6ed13d3535f

* Start unittesting our main observer (#15191)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15191

OSS:

just splitting out basic flags from a unit test. So I can extend them in another test where I need to add additional flags.

Reviewed By: yinghai

Differential Revision: D13159184

fbshipit-source-id: 9823e792cf0ed8d0379235c44564862b7d784845

* FP16MomentumSGDUpdate Op fix and enable for ROCm (#15150)

Summary:
1. Fix a bug in FP16MomentumSGDUpdate operator
2. Enable operator for ROCm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15150

Differential Revision: D13473145

Pulled By: bddppq

fbshipit-source-id: 4c5c5f30cb9bba658e3639dbe193fa08a304d306

* Supply static shape info to Reshape when doing onnxGetCompatibility (#15242)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15242

Newer version ONNX Reshape gets shape info from a tensor. Hence for static backend, we need to provide this info to it when doing `onnxGetCompatibility` too.

Reviewed By: jackm321

Differential Revision: D13471959

fbshipit-source-id: 8a58e28edd900b6ad54a1dbd63ff2579fbe0e820

* Add several features to converting images to blobs (#15204)

Summary:
Several enhancements are implemented:

* Resize the images to be within a boundary between min-size and max-size (can be height and weight). It tries to resize the minimum size to match the min-size and keep the aspect ratio. However, if in that case the maximum size is more than the max-size, then resize the maximum size to be equal to the max-size (and the minimum size is less than min-size). The min/max sizes are specified in argument scale, in a comma separated form. If one of the size is -1, then that size is not a restriction.

* Change the OpenCV resize function arguments from using cv::Size() to the x, y scale. Theoretically they should be the same. But in reality, the two ways of specifying them may result to different resized outputs.

* Once the image is read in, change the data to floats. That means, after resize and other preprocessing steps, the float values are preserved (not truncated to int).

* It is possible to convert data in text format to the blob format.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15204

Reviewed By: llyfacebook

Differential Revision: D13467225

Pulled By: sf-wind

fbshipit-source-id: 7da34a72d43a9603cd7ab953f5821c1222d0178f

* Create parser.cpp (#15238)

Summary:
Moves implementation into .cpp file. Parser was getting included in several compilation units.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15238

Differential Revision: D13474635

Pulled By: zdevito

fbshipit-source-id: 7dc824eea8f506d6c8ae1aa67aeec0c34d5285fc

* Tensor method rename dims()->sizes() (#15246)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15246

Codemod generated with clangr shard mode, 25 files per diff,

Reviewed By: igorsugak

Differential Revision: D13470369

fbshipit-source-id: ce995beab7c64bebe8b234fb5e6d015940ec2952

* Mention Jacobian-vector product in the doc of torch.autograd (#15197)

Summary:
A friend of me is learning deep learning and pytorch, and he is confused by the following piece of code from the tutorial https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients :

```python
x = torch.randn(3, requires_grad=True)

y = x * 2
while y.data.norm() < 1000:
    y = y * 2

print(y)

gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(gradients)

print(x.grad)
```

He don't know where the following line comes from:
```python
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
```

What are we computing? Why don't we compute "the gradient of `y` w.r.t `x`"?

In the tutorial, it only says
> You can do many crazy things with autograd!

Which does not explain anything. It seems to be hard for some beginners of deep learning to understand why do we ever do backwards with external gradient fed in and what is the meaning of doing so. So I modified the tutorial in https://github.com/pytorch/tutorials/pull/385
and the docstring correspondingly in this PR, explaining the Jacobian vector product. Please review this PR and https://github.com/pytorch/tutorials/pull/385 together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15197

Differential Revision: D13476513

Pulled By: soumith

fbshipit-source-id: bee62282e9ab72403247384e4063bcdf59d40c3c

* value-based mark and sweep DCE (#14910)

Summary:
This makes DCE more granular by tracking live values/aliases through the graph (rather than just nodes). So we can be more aggressive in DCE around control flow blocks. For example, in:
```
%a0 = aten::foo()
%b = aten::foo()
%a2, %b2 = prim::If(%cond) {
  block0() {
    %a1 = aten::foo(%.0)
    %b1 = aten::foo(%b)
  } -> (%a1, %b1)
}
return (%a2)
```
we will now dce all the `%b` stuff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14910

Differential Revision: D13476445

Pulled By: suo

fbshipit-source-id: 2bf5db19711c07dde946697a4f4b270bd8baf791

* fix cholesky call in potrs example (#15215)

Summary:
Cholesky by default returns the lower triangular matrix, see [docs](https://pytorch.org/docs/stable/torch.html#torch.cholesky).

However `torch.potrs` by default requires the upper triangular matrix. The naming of the variable `u` suggests that the example expects the upper to be returned, so I've added the flag to make that happen in the example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15215

Differential Revision: D13476468

Pulled By: soumith

fbshipit-source-id: 7b68035f435a2b1be4d363b3f63e407394af949d

* Fix a typo in the assert

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15265

Reviewed By: llyfacebook

Differential Revision: D13477029

Pulled By: sf-wind

fbshipit-source-id: 9c5571a583c01f9701625541ebec0c836cb923f2

* Delete ffi documentation (#15220)

Summary: Deleting FFI documentation since its deprecated.

Differential Revision: D13477329

Pulled By: soumith

fbshipit-source-id: 0b3d485eb7cef1f05b6b397dff50f21a49d6409e

* Trivial comment correction in dataloader (#15276)

Summary:
Trivial comment correction in dataloader
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15276

Differential Revision: D13477324

Pulled By: soumith

fbshipit-source-id: 2a74a014999655d129311d611f2a09411339cb13

* Refactor hotpatch_vars and apply it to libtorch (#14976)

Summary:
Fixes #14801.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14976

Differential Revision: D13485381

Pulled By: soumith

fbshipit-source-id: 0af3c2e1b90988d56f6f85632328d1e4b788ffd2

* Fix tensor printing bug in Python 2 (#12732)

Summary:
`rsplit` doesn't have kwargs in Python 2 so this line raises an error

Fixes #15135
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12732

Differential Revision: D10458630

Pulled By: driazati

fbshipit-source-id: a63e42fbc0e39e4291480775b516c98122ec05a1

* Tighten up invariants regarding StreamId. (#15125)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15125

I realized that it is really bad juju if you fake a StreamId
out of thin air, because in general this isn't going to work.
So, make the constructor a lot scarier.

Most "faking StreamId out of thin air" happens because someone
just wants to put something on the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432800

fbshipit-source-id: a86991d6fc1d8aa4e54e8175e5f06f90856238e6

* Adding ONNX export for torch.expand and torch.ne (#15050)

Summary:
`torch.expand` and `torch.ne` are used often in models and this PR adds ONNX export support for them. ArmenAg has created issue https://github.com/pytorch/pytorch/issues/10882 for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15050

Differential Revision: D13453036

Pulled By: houseroad

fbshipit-source-id: 4724b4ffcebda6cd6b2acac51d6733cb27318daf

* Minor fixes in .jenkins/caffe2/bench.sh

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15304

Differential Revision: D13493876

Pulled By: bddppq

fbshipit-source-id: 7146eb2587e526af65b4b0290c25bd55653a3088

* Fix for issue 14829 (#14908)

Summary:
* Modify the testcase as outlined in the issue
   * Issue url: https://github.com/pytorch/pytorch/issues/14829
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14908

Differential Revision: D13490360

Pulled By: ezyang

fbshipit-source-id: ff11a72e19b49223652182e82c2b4e65fe444ca7

* Don't enforce docstrings on bool dispatch (#15306)

Summary:
Allows 2 functions that are boolean dispatched to have no docstrings (the only case that will fail now is if both functions have docstrings)

Fixes #15281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15306

Differential Revision: D13494884

Pulled By: driazati

fbshipit-source-id: 65fec39ae03a7d6a68ad617c9b270faeb1617930

* Replace SwitchToDevice(0) with SwitchToDevice() (#15126)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15126

I want to make people stop manufacturing StreamId from thin air,
and a first step is to make people use the default stream.

Reviewed By: dzhulgakov

Differential Revision: D13432922

fbshipit-source-id: 9f0d8d70646c50d979bde5ba3c3addeebac48a3d

* Fix the missing caffe2 proto files for Windows (#15157)

Summary:
Fixes #15156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15157

Differential Revision: D13490420

Pulled By: orionr

fbshipit-source-id: 4387d707f634a5975238af915b1befb2277f8ec7

* add isinstance static type checking for jit (#15076)

Summary:
This PR add isinstance to do static type checking in JIT.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15076

Differential Revision: D13471067

Pulled By: wanchaol

fbshipit-source-id: d39b7ed5db9fcca4b503659d02cf7795950ea8ea

* Bicubic interpolation for nn.functional.interpolate (#9849)

Summary:
Addresses #918, interpolation results should be similar to tf

* Adds bicubic interpolation operator to `nn.functional.interpolate`
* Corresponding test in `test_nn.py`

The operator is added in legacy `TH` to be aligned with the other upsampling operators; they can be refactored/moved to ATen all at once when #10482 is resolved
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9849

Differential Revision: D9007525

Pulled By: driazati

fbshipit-source-id: 93ef49a34ce4e5ffd4bda94cd9a6ddc939f0a4cc

* Removing BUILD_C10_EXPERIMENTAL_OPS option and unglobbing experimental/c10d ops

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15064

Reviewed By: orionr

Differential Revision: D13474801

Pulled By: pjh5

fbshipit-source-id: 9d3664c3a3a1b6c2d9f083f8476fe3b037296b98

* Allow future type parsing

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14887

Differential Revision: D13490984

Pulled By: highker

fbshipit-source-id: 165fe995867be273793f983154aa6cbce13e4396

* Port nn fold and unfold to c++

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14597

Reviewed By: ezyang

Differential Revision: D13272227

fbshipit-source-id: 6eccab5ff5830a977398a96393b778095120edc6

* caffe2/python/task: added __repr__ methods to all task definitions (#15250)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15250

This adds `__repr__` methods to all of the classes under task.py. This makes the objects much easier to interact with when using them in an interactive manner, such as in a Jupyter notebook.

The default `__repr__` method just returns the object ID which is very unhelpful.

Reviewed By: hanli0612

Differential Revision: D13475758

fbshipit-source-id: 6e1b166ec35163b9776c797b6a2e0d002560cd29

* Add a correctness check for C++ types to custom operators (#15247)

Summary:
The JIT uses `int64_t` for its integer type and `double` for its floating point type, but users quite often want to write `int` or `float` and that currently fails in not-so-nice ways for custom ops. This PR adds a simple `static_assert` to catch these common failure cases.

zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15247

Differential Revision: D13493941

Pulled By: goldsborough

fbshipit-source-id: c1cd0d10ab5838c75f167c0bdb57e45a0bc1344e

* Fix _apply in nn.Module (#15305)

Summary:
Fixes an issue that arose from https://github.com/pytorch/pytorch/pull/13481 where `.shared_memory()` couldn't be called. Effectively undoes all changes to `nn.Module` from that PR and solve the relevant problem in a different way (the goal was to be able to call `._apply()` on the Python wrapper for a C++ module).

soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15305

Differential Revision: D13493937

Pulled By: goldsborough

fbshipit-source-id: 4cb8687f90fc8709a536c5e7eacd0dc8edf6f750

* Reenable OpenMP by reverting the following two commits. (#15315)

Summary:
Revert "Put back linker flag for OpenMP to prevent build break on ppc64le (#14569)"

This reverts commit a84e873bb156080ea76ab182171b1f3b4d5395f6.

Revert "Update OpenMP cmake setting for xcode 9 compiler(AppleClang 9.0) (#14473)"

This reverts commit 8901935ad42fe9bf093d1106ea43606008a4024d.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15315

Differential Revision: D13495852

Pulled By: ezyang

fbshipit-source-id: bcd3f60088b14831c53d3c171f10cd1ab6b35dee

* [TensorIterator fixing mean to output correct result for half precisi… (#14878)

Summary:
…on](#12115)

mean is calculated in two step sum()/numel(). For half precision, data gets
casted back to half after sum().
We fused the division into the reduction kernel by adding pre_op/post_op.

This allows us to do torch.ones(65536).cuda().half().mean() to return correct
result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14878

Differential Revision: D13491159

Pulled By: soumith

fbshipit-source-id: e83802e1628b6d2615c45e18d7acf991d143a09e

* Allow tracing with fork/wait (#15184)

Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e

* improve script/no script save error (#15321)

Summary:
Improves the error message for #15116
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15321

Differential Revision: D13499379

Pulled By: zdevito

fbshipit-source-id: b8dc0a83efabff74199f4aab2ee98aa41c42608b

* Updating submodules

Reviewed By: cdelahousse

fbshipit-source-id: 4bf66581d07d839f459869bc9c6428011063cc5b

* Revert D13383102: [pytorch][PR] Upgrade MKL-DNN to version 0.17

Differential Revision:
D13383102

Original commit changeset: c434f0e0ddff

fbshipit-source-id: 690f46ca0710954fa591a5ea77535e9759db4de5

* caffe2 mobile opengl (#15322)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15322

caffe2 mobile opengl code is not used, deleting it to reduce complications when we perform other changes

Reviewed By: Maratyszcza

Differential Revision: D13499943

fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2

* Method returns a single argument (#15289)

Summary:
This PR changes Method (just Method not all graphs) to always have a single
return argument.

This is part 1 in a set of changes that will enable us to have better handling if early return statements.
The simplification that this change provides greatly reduces the work for the next step.

This change makes it so that Method and Python handle multiple returns in the same way:
* 0 - None
* 1 - <single value>
* many - Tuple[...]

The result is that a lot of special-case handling in compiler.cpp and its
bindings can be removed. It also fixes several bugs in return handling,
including one where return values were not always checked against their
attributed values.

Notes:
* inferTypeFrom is renamed to be more accurate and discourage use.
* This has uncovered some bugs in other components, which are noted in
  the diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15289

Differential Revision: D13481649

Pulled By: zdevito

fbshipit-source-id: 0e2242a40bb28cca2d0e8be48bede96195e4858c

* Fix the (reduce)min and (reduce)max ONNX exporting (#15241)

Summary:
max and reducemax are smashed together, we need to support one input case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15241

Reviewed By: yinghai

Differential Revision: D13473312

Pulled By: houseroad

fbshipit-source-id: 9b8c847286a2631b006ca900271bc0d26574101a

* Add (Un)Fold modules to standard library (#14759)

Summary:
Depends on #14597 for the corresponding aten ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14759

Differential Revision: D13325356

Pulled By: driazati

fbshipit-source-id: 99e39449c1ccfa293de05672c31a11e580bdd11f

* Port torch.linspace to ATen and parallelize it on CPU.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15320

Reviewed By: ezyang

Differential Revision: D13498995

Pulled By: gchanan

fbshipit-source-id: fba655d51d978fffaa53a5e4cae4a99ebfb0eddc

* fix clang-tidy script for python 3

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15360

Differential Revision: D13509668

Pulled By: suo

fbshipit-source-id: a3448a115eaac8dd4c3f179901a23bdbc5098408

* add dense vector to id_list operator (#15090)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15090

as title
step 2 of the linked task

Reviewed By: ellie-wen

Differential Revision: D13425977

fbshipit-source-id: f3538ed68f42470ba39c5b779af764d4a5591a9d

* Minor cleanup for TestFuser tests (#15134)

Summary:
Changelog:
- change some expect tests that didn't have to be expect tests,
  instead use self.assertAllFused
- Some of the fuser tests weren't using self.assertAllFused.
- Minor test renames

cc apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15134

Differential Revision: D13507481

Pulled By: zou3519

fbshipit-source-id: dd0788530a60bb5ed2f42b961fae3db2b4404b64

* Replace resize_dim() with set_sizes_and_strides() in (#15348)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15348

We have a function resize_dim() on TensorImpl in c10/core/TensorImpl.h which lets you chan…

@soumith soumith added this to the 1.0.1 milestone Jan 18, 2019

suo added a commit that referenced this pull request Jan 18, 2019
Allow tracing with fork/wait (#15184)
Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: #15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e
soumith added a commit that referenced this pull request Jan 19, 2019
Allow tracing with fork/wait (#15184)
Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: #15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e
soumith added a commit that referenced this pull request Jan 29, 2019
Allow tracing with fork/wait (#15184)
Summary:
There is still limitation on this: if a script module is somewhere
in the trace, the inputs/outputs can only be tensors or tuples of
tensors.

resolves #15052
Pull Request resolved: #15184

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e

@ezyang ezyang added the merged label Jun 25, 2019

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