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

Introduce LegacyTHDispatcher for dispatching to TH functions. (#14754)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14754

This isn't hooked up to anything yet, this is just putting the skeleton in place.
The idea here is that the functions generated via Declarations.cwrap and nn.yaml are not actually operators, they are implementation details of operators, and thus don't need to participate in VariableType, JIT dispatch generation.

So, we will split these functions out from the usual Type/operator hierarchy; for now the dispatch will be done by a Type-like class called LegacyTHDispatcher. Once this is done this probably means we can collapse Type to be backend-specific, not Type/ScalarType specific, because all the ScalarType specific code will live in the LegacyTHDispatcher.

Reviewed By: ezyang

Differential Revision: D13321605

fbshipit-source-id: 25d1bbc9827a42d6ab5d69aabbad3eac72bf364c

disable batch mm if we have mutable ops (#14771)

Summary:
Just to be safe, disable batch mm for mutable ops. We don't lose much for doing this, and we can go back at a calmer time to re-enable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14771

Reviewed By: eellison

Differential Revision: D13327641

Pulled By: suo

fbshipit-source-id: 96611e21ed3cb8492a2cd040f7d33fb58c52bd5e

Replace at::Half non-vectorized conversions with implementations from FP16 (#14411)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14411
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14579

Folded the fp16 codes into c10.

Reviewed By: ezyang

Differential Revision: D13206450

fbshipit-source-id: 472208dd230dc49d33935622ff3286b17eeb0894

Use .to to convert new tensors in new_tensor (#14097)

Summary:
This would solve the tracing problems of #13969.
Fixes: #14732

I would appreciate if this got good scrutiny before applied.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14097

Differential Revision: D13323181

Pulled By: ezyang

fbshipit-source-id: dcd104b497c0bfddb751923c6166a3824b7a3702

Export generator constructor (#14041)

Summary:
Missed a spot :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14041

Reviewed By: ezyang

Differential Revision: D13283803

Pulled By: ebetica

fbshipit-source-id: 482e245f57b0cea6ca3886355ea3ae487d024d4b

c10d doesn't work with torch namespace (#14042)

Summary:
If both `Utils.hpp` and the `torch` namespace is included in the same file, the compiler won't know which fmap to use. I believe this is because of ADL. This change fixes that issue for me.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14042

Reviewed By: ezyang

Differential Revision: D13283810

Pulled By: ebetica

fbshipit-source-id: b68233336518230ba730e83ddac1226a66896533

Add resnet test, convert more modules (#14437)

Summary:
This PR add resnet to test_jit and convert more nn modules, stacked on #14533 and #14715
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14437

Differential Revision: D13325871

Pulled By: wanchaol

fbshipit-source-id: 6c94a988b36794a373af6541c0c262a07291f7b1

Add missing test skip

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14763

Differential Revision: D13325350

Pulled By: driazati

fbshipit-source-id: 4d64a7616b227983c2fc2748c5fbecd1bcbff832

Rename _local_scalar to item() (#13676)

Summary:
Make `at::_local_scalar` more "official" by renaming it to `item()`.

gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13676

Differential Revision: D13003020

Pulled By: goldsborough

fbshipit-source-id: 0ac25f5237fb81a1576304a0a02f840ff44168a4

Remove use of hipify_caffe2, in favor of file path test. (#14757)

Summary:
This is towards unifying build_pytorch_amd.py and build_caffe2_amd.py
scripts. There is only one use of hipify_caffe2 left, which is just
to control which files actually get HIPified.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14757

Differential Revision: D13323486

Pulled By: ezyang

fbshipit-source-id: 958cd91be32dfc3c0a9ba9eda507adb5937aebcd

Add inplace FeedTensor for python frontend (#14512)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14512

att

Reviewed By: dzhulgakov

Differential Revision: D13243278

fbshipit-source-id: 78af417d0fcd9b9791ee839d62095903e49205cb

Loss (#14720)

Summary:
Adding Loss modules to script. Some of the modules have an optional tensor parameter. I will wait until wanchao's diff to support optional tensors is landed before landing this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14720

Differential Revision: D13317990

Pulled By: eellison

fbshipit-source-id: 535925bdf126d28d9e7d64077b83ebd836a5beba

Add new reduction mode in kl_div (#14457)

Summary:
Fixes #6622 .
We used to average over all elements for kl divergence, which is not aligned with its math definition.
This PR corrects the default reduction behavior of KL divergence that it now naverages over batch dimension.

- In KL, default behavior `reduction=mean` averages over batch dimension. While for most other loss functions, `reduction=mean` averages over all elements.
- We used to support scalar tensor as well. For BC purpose, we still support it, no reduction is performed on scalar tensor.
- Added a new reduction mode called `batchmean` which has the correct behavior for KL. Add a warning to make `batchmean` as default for KL instead of `mean` in next major release.
- [deprecated]I chose to not add a new reduction option, since "mean over batch dimension" is kinda special, and it only makes sense in few cases like KL. We don't want to explain why there's a option "batchmean" but it's not applicable for all other functions. I'm open to discussion on this one, as I cannot think of a perfect solution for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14457

Differential Revision: D13236016

Pulled By: ailzhang

fbshipit-source-id: 905cc7b3bfc35a11d7cf098b1ebc382170a087a7

Implements Gather operator for arbitrary axis, sharing the code with BatchGather. (#13756)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13756

This implements general Gather operator for arbitrary axis, sharing the code with BatchGather.
- CPU gather & batch gather logic is now shared through caffe2::gather_helper, for any axis.
- Shared CUDA kernel moved to gather_op.cuh, for any axis.
- Gradients of axis > 0 delegate to BatchGatherGradientOp which now has axis argument.
- BatchGatherOp doc strings updated to have correct rank (q + (r -1)) and output.
- Added tests for axis == 2.

GatherOp supports index wrapping for axis == 0 by default, which was earlier for ONNX.
This diff also extends it to work in Cuda kernel. Added "wrap_indices" argument which specifies
wheather this wrapping should be done; set it to true if you'd like wrapping for any axis.

TBD: Update gradients to support negative indices (separate diff).
TBD: Once we have operator versioning, we'd like to update GatherOp to NOT support axis 0 wrapping
by default, but rather do it only if wrap_indices is set.

Reviewed By: dzhulgakov

Differential Revision: D12983815

fbshipit-source-id: 8add9d67b47fe8c5ba7a335f581ca0530b205cd7

Refactor dataloader.py (#14668)

Summary:
As I am working on tasks in https://github.com/pytorch/pytorch/issues/13023, I realized how unreadable the code is because all functions to be run in multiprocessing must be at top global level. Adding more functionalities to `dataloader.py` will only make things worse.

So in this PR, I refactor `dataloader.py` and move much of it into `data._utils`. E.g., the `_worker_loop` and related methods are now in `data._utils.worker`, signal handling code in `data._utils.signal_handling`, collating code in `data._utils.collate`, etc. This split, IMHO, makes code much clearer. I will base my future changes to DataLoader on top of this.

No functionality is changed, except that I added `torch._six.queue`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14668

Reviewed By: soumith

Differential Revision: D13289919

Pulled By: ailzhang

fbshipit-source-id: d701bc7bb48f5dd7b163b5be941a9d27eb277a4c

Back out "Move TensorOptions, DefaultTensorOptions and OptionsGuard to c10" (#14745)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14745

Original commit changeset: c62e7f9b0255

Reviewed By: suo

Differential Revision: D13318594

fbshipit-source-id: 4d7dc35ca01b627accc3ee512bfcd6f2e805a533

Back out "Fix include paths for TensorOptions, DefaultTensorOptions, OptionsGuard" (#14744)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14744

Original commit changeset: d236d5351ecf

Reviewed By: suo

Differential Revision: D13318596

fbshipit-source-id: 55f1e9472d05fb5a9c47dc82c32e9a66b5e4308c

Disable randn_like fusion in the JIT (#14752)

Summary:
Fixes #14674. We won't have time for a proper fix before the release, so at least disable fusion of nodes that trigger incorrect behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14752

Differential Revision: D13320407

Pulled By: zou3519

fbshipit-source-id: 2400f7c2cd332b957c248e755fdb0dadee68da5d

fix import failure in hub test (#14742)

Summary:
Fix #14610

I can repro the test failure following the steps provided, and this fixes the issue for me. Seems the timing of inserting has to happen after the downloading.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14742

Differential Revision: D13318533

Pulled By: ailzhang

fbshipit-source-id: b9207b4572d5a9443e516d9a84632e3d7b68e477

Revert D13304654: [pytorch][PR] Introduce LegacyTHDispatcher for dispatching to TH functions.

Differential Revision:
D13304654

Original commit changeset: cfe3e1a28adc

fbshipit-source-id: 06669d3c88f83e1d959e2c266fd608316539d42a

Introduce LegacyTHDispatcher for dispatching to TH functions. (#14708)

Summary:
This isn't hooked up to anything yet, this is just putting the skeleton in place.
The idea here is that the functions generated via Declarations.cwrap and nn.yaml are not actually operators, they are implementation details of operators, and thus don't need to participate in VariableType, JIT dispatch generation.

So, we will split these functions out from the usual Type/operator hierarchy; for now the dispatch will be done by a Type-like class called LegacyTHDispatcher. Once this is done this probably means we can collapse Type to be backend-specific, not Type/ScalarType specific, because all the ScalarType specific code will live in the LegacyTHDispatcher.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14708

Reviewed By: ezyang

Differential Revision: D13304654

Pulled By: gchanan

fbshipit-source-id: cfe3e1a28adcc355f67fe143495ee7e5c5118606

add .code property to ScriptModule (#14735)

Summary:
simple change to allow `print(foo.code)` to give a pretty-printed description of all the methods on a module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14735

Differential Revision: D13317619

Pulled By: zdevito

fbshipit-source-id: dc7f7ba12ba070f2dfccf362995c2a9e0e573cb7

Fix clamp when min/max are both None (#14716)

Summary:
Before this PR, tensor.clamp() would return an empty tensor if min and
max were not specified. This is a regression from 0.4.1, which would
throw an error. This PR restores that error message.

Fixes #14470
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14716

Differential Revision: D13311031

Pulled By: zou3519

fbshipit-source-id: 87894db582d5749eaccfc22ba06aac4e10983880

Restore device in cpp API (#14711)

Summary:
This is a stack PR based on https://github.com/pytorch/pytorch/pull/14454.

It enables the restoring the storage to appropriate device.

~~[TODO]: add/modify appropriate tests~~ Done
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14711

Reviewed By: dzhulgakov

Differential Revision: D13315746

Pulled By: houseroad

fbshipit-source-id: fe6f24a45c35e88fd1a2eebc09950d4430fac185

move structs to header file (#14728)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14728

Move IndexBlob,Index to header file so it can reused.

Differential Revision: D13315898

fbshipit-source-id: 34432c9b8fa08af3d3387f32a940d35b02a59760

improve the restore device test, and relax the assertion (#14734)

Summary:
Only compare the device index if device has it.

Test the tensor restore with some computation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14734

Reviewed By: dzhulgakov

Differential Revision: D13317949

Pulled By: houseroad

fbshipit-source-id: 26b2f2912a9bbc3b660a62283fb403ddab437e49

Reduce broadcasted inputs in derivative code (#14485)

Summary:
Previously symbolic AD formulas assumed that no broadcasting happened,
and would return gradients of incorrect shapes (possibly leading to
silent errors later).

Fixes a few bugs (known and unknown):
- #11736
- ArgumentSpec didn't compute the input types correctly [(it didn't advance the offset for non-tensor args)](https://github.com/pytorch/pytorch/pull/14485/files#diff-4fd3157a056596aefb8cdf41022a208bR153)
- Symbolic AD could suffer from use after free (dangling pointers in grad map), because [`EliminateDeadCode` could have removed nodes](https://github.com/pytorch/pytorch/pull/14485/files#diff-25d33ad1ed6855684dec79d927ca6142L781) that referenced gradients of certain values.
- Undefined behavior in `aten::size`

During my tests I've also found a few new problems, and I have opened issues for them:
- FusionGroup seems to think that cat nodes broadcast their inputs (#14483)
- `prim::ConstantChunk` derivative formula doesn't handle undefined inputs (#14484)

This patch unfortunately deoptimizes some of our code (Fusion doesn't happen past chunk nodes, and outputs more tensors only because we have to get their size). I know how to fix those issues, but wanted to fix this terrible bug quickly.

cc zou3519 zdevito ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14485

Reviewed By: eellison

Differential Revision: D13312888

Pulled By: suo

fbshipit-source-id: ad46bfb4d0a306ad9451002f8270f7a790f72d58

interpolate (#14123)

Summary:
Add support for interpolate and upsampling in weak_script mode.

Because the function parameters are overloaded, i had to add it as a builtin op. For interpolate:
size can be ?int | int[]?, and scale_factor can be ?float | float[]?. Every combination of the two parameters needs to be supported.

The same logic applies for upsample_nearest, upsample_bilinear, and upsample.

There are a few fixes that I came to along the way.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14123

Differential Revision: D13278923

Pulled By: eellison

fbshipit-source-id: e59729034369be4ce4b747291a3d1c74e135b869

Add Pooling modules to Script (#14527)

Summary:
Depends on #14584
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14527

Differential Revision: D13270773

Pulled By: driazati

fbshipit-source-id: e4acd43ccbce0f4b62d41c30ce8d5c721171e19a

Add fractional_max_pool2d to standard lib

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14591

Differential Revision: D13270755

Pulled By: driazati

fbshipit-source-id: 138a60256795f5ef8d236c75be2cfd929059b98f

Add GroupNorm to standard library (#14722)

Summary:
Depends on #14715 for the excluded tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14722

Differential Revision: D13317714

Pulled By: driazati

fbshipit-source-id: bf1cdbc0a3803f82befed41925e91ab60e20ec82

basic testing of builtin alias annotations (#14588)

Summary:
Check whether the codegen'd alias annotations actually track alias creation and writes correctly. This could be made more exhaustive, but it's good enough for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14588

Differential Revision: D13312653

Pulled By: suo

fbshipit-source-id: 98de1610ea86deada71957c75c222fff331a0888

Remove TensorImpl -> LegacyTypeDispatch dependency

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14651

Reviewed By: ezyang

Differential Revision: D13285370

fbshipit-source-id: cc93c3ca95e7260762c1cabca17b8973d52c4e22

Fix include paths for TensorOptions, DefaultTensorOptions, OptionsGuard

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14647

Reviewed By: ezyang

Differential Revision: D13283497

fbshipit-source-id: d236d5351ecf7ab9712a55e9ef12d8bba48eb53f

Move TensorOptions, DefaultTensorOptions and OptionsGuard to c10

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14646

Reviewed By: ezyang

Differential Revision: D13283494

fbshipit-source-id: c62e7f9b02551926bf8f1e3ddf6ede4ec925d28d

Fix include paths for Layout.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14645

Reviewed By: ezyang

Differential Revision: D13283496

fbshipit-source-id: d70881e957c886a6c2befe3ef1d2c5a3fac18e7f

Move Layout to c10

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14644

Reviewed By: ezyang

Differential Revision: D13283493

fbshipit-source-id: bb02f156d6a5b5129db5743c756acc84c38eca83

Fix include paths for Backend.h

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14643

Reviewed By: ezyang

Differential Revision: D13283492

fbshipit-source-id: 9919af9707d094118efc963543320e01b07d7bc5

Moved Backend to c10 (#14642)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14642

Unfortunately, TensorOptions depends on this, so we need it in c10.

Reviewed By: ezyang

Differential Revision: D13283495

fbshipit-source-id: 433cd47eb18aac1131be9c5cd650efc583870a20

enable NoneValue parameter assignment for WeakScriptModule (#14715)

Summary:
This PR:

1. Handle None value attr in the WeakScriptModuleProxy
2. add back module tests that now passing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14715

Differential Revision: D13313573

Pulled By: wanchaol

fbshipit-source-id: a6b7892707350290a6d69b6f6270ad089bfc954b

WAR for self.training (#14719)

Summary:
To enable self.training in script modules, this PR automatically adds a buffer called 'training' if a script method requests self.training. Assignment to self.training is overloaded to assign both to the boolean property and the tensor value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14719

Differential Revision: D13310569

Pulled By: zdevito

fbshipit-source-id: 406387bb602f8ce5794eeff37642863c75928be5

fix expect

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14730

Differential Revision: D13316463

Pulled By: zdevito

fbshipit-source-id: 8b11bdb22d354c17bf2de4bded352bb6eb086ec7

update of fbcode/onnx to 6b34743d2e361bbc0acb29dd73536478cb92562e (#14637)

Summary:
Previous import was f461f7aad9987635b4aff108620ed7918f002d19

Included changes:
- **[6b34743](https://github.com/onnx/onnx/commit/6b34743)**: fix the const map initializatoin (#1662) <Lu Fang>
- **[ae80999](https://github.com/onnx/onnx/commit/ae80999)**: Fuse Pad into Conv optimizer (#1580) <vloncar>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14637

Differential Revision: D13281338

Pulled By: houseroad

fbshipit-source-id: c31429914bf5954fdc85e0c02168836ef47d635c

Skip CUDA tests when built with CUDA but no GPUs available; rename cuda tests so they're obvious.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14706

Reviewed By: soumith

Differential Revision: D13304398

fbshipit-source-id: d5e2cda965ce8bc1721489b282336ea3ca7f0471

Move manual_seed into ATen/Context.h; delete reimplementation in test_seed.h (#14625)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14625

I want to reorg the test files, but I am too lazy to make the include
paths for test_seed.h work out. So just delete it.

Reviewed By: gchanan

Differential Revision: D13277567

fbshipit-source-id: a3e8e46e4816b6fc0fe926b20779839f9e0a1a06

Implement Device as a type in the script (#14666)

Summary:
[ note: stacked on expect files changes, will unstack once they land ]
This adds DeviceObjType (cannot use DeviceType it is already an enum)
to the type hierarchy and an isDevice/toDevice pair to IValue.
Previous hacks which used an int[] to represent Device are removed
and at::Device is used instead.

Note: the behavior or .to is only a subset of python, we need to
fix the aten op so that it accepts Option[Device] and Optional[ScalarType].
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14666

Reviewed By: suo

Differential Revision: D13290405

Pulled By: zdevito

fbshipit-source-id: 68b4381b292f5418a6a46aaa077f1c902750b134

Meta programming on If Stmt cond to enable conditional emit blocks (#14533)

Summary:
This PR is a part of task to unblock standard library export. Basically we want enable the ability to meta program IF stmt to dynamically emit different branches base on `cond`. This is primarily used to disable certain branch compilation on If, like the below

```python
import torch

class Test(torch.jit.ScriptModule):
  def __init__(self, b = None):
    self.b = b
  def forward(self, input):
    x = input
    if self.b is not None:
      x = self.b(input)

    return x

  Test()(torch.randn(2, 3))
```
This is also the first step for us to bridge the gap between none simple value and any sugared value in JIT.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14533

Differential Revision: D13310526

Pulled By: wanchaol

fbshipit-source-id: 78d1a8127acda5e44d2a8a88f7627c43d29ff244

Delete temporary ATenCoreTest. (#14622)

Summary:
It was previously used to sure that ATen/core was working;
but now we have plenty of headers and C++ files in ATen/core
so this is no longer necessary.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14622

Differential Revision: D13276899

Pulled By: ezyang

fbshipit-source-id: 9bef7eb1882ccdfa3ee7681a3d5b048ea94b59d3

Revert D13280899: [pytorch][PR] Reduce broadcasted inputs in derivative code

Differential Revision:
D13280899

Original commit changeset: 80cc5ec9331b

fbshipit-source-id: 2335093cca8fd7db95470fd83b9299adfa17aa8e

Restore device when import jit script module (#14454)

Summary:
We align the restore logic to `torch.load`, we try to restore to the right device, and if the device is not available, an exception is raised. We allow user to remap the device through a parameter `map_location`, it can be 1) a string like 'cuda:0`, `cpu`, 2) a device, torch.device('cpu'), 3) a dict, {'cuda:1', 'cuda:0'}, and a function, and its signature looks like string map_location(tensor, saved_device_string).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14454

Reviewed By: zrphercule

Differential Revision: D13271956

Pulled By: houseroad

fbshipit-source-id: dfd6b6049b0dc07549ddeddf2dea03ac53ba6d49

Add linear + pixelshuffle modules to standard lib

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14654

Differential Revision: D13300968

Pulled By: driazati

fbshipit-source-id: 2c36aab91ea99681687f8da6d318981fee49785b

Reduce broadcasted inputs in derivative code (#14485)

Summary:
Previously symbolic AD formulas assumed that no broadcasting happened,
and would return gradients of incorrect shapes (possibly leading to
silent errors later).

Fixes a few bugs (known and unknown):
- #11736
- ArgumentSpec didn't compute the input types correctly [(it didn't advance the offset for non-tensor args)](https://github.com/pytorch/pytorch/pull/14485/files#diff-4fd3157a056596aefb8cdf41022a208bR153)
- Symbolic AD could suffer from use after free (dangling pointers in grad map), because [`EliminateDeadCode` could have removed nodes](https://github.com/pytorch/pytorch/pull/14485/files#diff-25d33ad1ed6855684dec79d927ca6142L781) that referenced gradients of certain values.
- Undefined behavior in `aten::size`

During my tests I've also found a few new problems, and I have opened issues for them:
- FusionGroup seems to think that cat nodes broadcast their inputs (#14483)
- `prim::ConstantChunk` derivative formula doesn't handle undefined inputs (#14484)

This patch unfortunately deoptimizes some of our code (Fusion doesn't happen past chunk nodes, and outputs more tensors only because we have to get their size). I know how to fix those issues, but wanted to fix this terrible bug quickly.

cc zou3519 zdevito ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14485

Differential Revision: D13280899

Pulled By: soumith

fbshipit-source-id: 80cc5ec9331be80e1bb9ddfe85b81c2b997e0b0c

Allow DCE to clean up some mutable ops (#14601)

Summary:
This PR makes DCE a little smarter in the presence of mutable ops. Previously mutable ops could never be cleaned up, now they can be cleaned up if we can prove there are no live uses of any alias sets that the op writes to.

This behavior is optional; if you pass DCE a block instead of a graph, it will do the same thing as before. Also changed `InlineAutographSubgraph` to use the common subgraph utils.

Tested on traced ResNet, and it gets rid of the dead code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14601

Differential Revision: D13309118

Pulled By: suo

fbshipit-source-id: dac2791e7d2ecf219ae717a2759b83c1e927f254

Revert D13272203: [pytorch][PR] [jit] Meta programming on If Stmt cond to enable conditional emit blocks

Differential Revision:
D13272203

Original commit changeset: 44a545abb766

fbshipit-source-id: 8861eb4810a6c9ea4aba8427b3a07d2fa0d69a15

Move global-constructor to lazily initialized (mobile restriction) (#14650)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14650

this fixes the build for mobile

Reviewed By: dzhulgakov

Differential Revision: D13267458

fbshipit-source-id: 83e7e76e3c875134395b6c43ea791c5b56871642

Make convertable to list also accepts optional

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14598

Differential Revision: D13308254

Pulled By: wanchaol

fbshipit-source-id: bd0b6f9f20294d3d589cf68732dbd8c57b67e0e9

add avx512 option (but no avx512 kernel yet) (#14664)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14664

This diff just adds a framework to add avx512 kernels.
Please be really really careful about using avx512 kernels unless you're convinced using avx512 will bring good enough *overall* speedups because it can backfire because of cpu frequency going down.

Reviewed By: duc0

Differential Revision: D13281944

fbshipit-source-id: 04fce8619c63f814944b727a99fbd7d35538eac6

Meta programming on If Stmt cond to enable conditional emit blocks (#14533)

Summary:
This PR is a part of task to unblock standard library export. Basically we want enable the ability to meta program IF stmt to dynamically emit different branches base on `cond`. This is primarily used to disable certain branch compilation on If, like the below

```python
import torch

class Test(torch.jit.ScriptModule):
  def __init__(self, b = None):
    self.b = b
  def forward(self, input):
    x = input
    if self.b is not None:
      x = self.b(input)

    return x

  Test()(torch.randn(2, 3))
```
This is also the first step for us to bridge the gap between none simple value and any sugared value in JIT.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14533

Differential Revision: D13272203

Pulled By: wanchaol

fbshipit-source-id: 44a545abb766bbd39b762a6e19f9ebaa295e324b

Fixed DistributedDataParallel state pickling for multi-gpus (#14690)

Summary:
Fixed: https://github.com/pytorch/pytorch/issues/14678

This PR fixed DDP doesn't work after save() and load() for multiple GPUs, because, it needs all these replicating logics and bucketing in the constructor.

So I refactored some of the logics in the constructor to a helper function. And this will be used for load().

Added test too. Tested on 8 GPU machines.

```
tengli@learnfair062:~/pytorch/test$ python run_test.py -i distributed --verbose
Test executor: ['/private/home/tengli/miniconda3/bin/python']
Selected tests: distributed
Running test_distributed ... [2018-12-02 18:33:55.833580]
/public/apps/openmpi/2.1.1/gcc.5.4.0/bin/mpiexec
Running distributed tests for the mpi backend
test_Backend_enum_class (__main__.TestMPI) ... test_Backend_enum_class (__main__.TestMPI) ... test_Backend_enum_class (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok

----------------------------------------------------------------------
Ran 68 tests in 6.315s

OK (skipped=15)
ok

----------------------------------------------------------------------
Ran 68 tests in 6.315s

OK (skipped=15)
ok

----------------------------------------------------------------------
Ran 68 tests in 6.315s

OK (skipped=15)
Running distributed tests for the mpi backend with file init_method
test_Backend_enum_class (__main__.TestMPI) ... test_Backend_enum_class (__main__.TestMPI) ... test_Backend_enum_class (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_DistributedDataParallel (__main__.TestMPI) ... skipped 'Only Nccl & Gloo backend support DistributedDataParallel'
test_DistributedDataParallelCPU (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA all gather'
test_all_gather_full_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_group (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_gather_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports allgather multigpu'
test_all_reduce_full_group_max (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_min (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_product (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_full_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_max (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_min (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_product (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_group_sum (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_max (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_min (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_all_reduce_product (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_all_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_full_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_full_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_group (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... ok
test_barrier_group_cuda (__main__.TestMPI) ... skipped "MPI doesn't supports GPU barrier"
test_barrier_timeout_full_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestMPI) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_cuda (__main__.TestMPI) ... skipped 'Only Gloo and Nccl backend supports CUDA allReduce'
test_broadcast_full_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_broadcast_multigpu (__main__.TestMPI) ... skipped "MPI doesn't support broadcast multigpu"
test_destroy_full_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_destroy_group (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_full_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_gather_group (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_backend (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_default_group (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_full_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_get_rank_size_group (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_irecv (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_isend (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_max (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_min (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_product (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_full_group_sum (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_max (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_min (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_product (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_group_sum (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_max (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_min (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_multigpu (__main__.TestMPI) ... skipped 'Only Nccl backend supports reduce multigpu'
test_reduce_product (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_reduce_sum_cuda (__main__.TestMPI) ... skipped 'Only Nccl supports CUDA reduce'
test_scatter (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_full_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_scatter_group (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_any_source (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok
test_send_recv_with_tag (__main__.TestMPI) ... ok

----------------------------------------------------------------------
Ran 68 tests in 6.415s

OK (skipped=15)
ok

----------------------------------------------------------------------
Ran 68 tests in 6.415s

OK (skipped=15)
ok

----------------------------------------------------------------------
Ran 68 tests in 6.415s

OK (skipped=15)
Running distributed tests for the nccl backend
test_Backend_enum_class (__main__.TestDistBackend) ... ok
test_DistributedDataParallel (__main__.TestDistBackend) ... ok
test_DistributedDataParallelCPU (__main__.TestDistBackend) ... skipped 'nccl does not support DistributedDataParallelCPU'
test_all_gather (__main__.TestDistBackend) ... skipped 'Only MPI supports CPU all gather'
test_all_gather_cuda (__main__.TestDistBackend) ... skipped 'CUDA all gather skipped for NCCL'
test_all_gather_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_gather_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_gather_multigpu (__main__.TestDistBackend) ... ok
test_all_reduce_full_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_multigpu (__main__.TestDistBackend) ... skipped 'CUDA all_reduce multigpu skipped for NCCL'
test_all_reduce_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_sum_cuda (__main__.TestDistBackend) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_cuda (__main__.TestDistBackend) ... ok
test_barrier_full_group (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_full_group_cuda (__main__.TestDistBackend) ... ok
test_barrier_group (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_group_cuda (__main__.TestDistBackend) ... ok
test_barrier_timeout_full_group (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_cuda (__main__.TestDistBackend) ... ok
test_broadcast_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_multigpu (__main__.TestDistBackend) ... skipped 'NCCL broadcast multigpu skipped'
test_destroy_full_group (__main__.TestDistBackend) ... ok
test_destroy_group (__main__.TestDistBackend) ... ok
test_gather (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_gather_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_gather_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_get_backend (__main__.TestDistBackend) ... ok
test_get_default_group (__main__.TestDistBackend) ... ok
test_get_rank (__main__.TestDistBackend) ... ok
test_get_rank_size_full_group (__main__.TestDistBackend) ... ok
test_get_rank_size_group (__main__.TestDistBackend) ... ok
test_irecv (__main__.TestDistBackend) ... skipped 'Nccl does not support irecv'
test_isend (__main__.TestDistBackend) ... skipped 'Nccl does not support isend'
test_reduce_full_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_multigpu (__main__.TestDistBackend) ... ok
test_reduce_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_sum_cuda (__main__.TestDistBackend) ... ok
test_scatter (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_scatter_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_scatter_group (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_send_recv (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv'
test_send_recv_any_source (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv from any source'
test_send_recv_with_tag (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv'

----------------------------------------------------------------------
Ran 68 tests in 69.549s

OK (skipped=52)
Running distributed tests for the nccl backend with file init_method
test_Backend_enum_class (__main__.TestDistBackend) ... ok
test_DistributedDataParallel (__main__.TestDistBackend) ... ok
test_DistributedDataParallelCPU (__main__.TestDistBackend) ... skipped 'nccl does not support DistributedDataParallelCPU'
test_all_gather (__main__.TestDistBackend) ... skipped 'Only MPI supports CPU all gather'
test_all_gather_cuda (__main__.TestDistBackend) ... skipped 'CUDA all gather skipped for NCCL'
test_all_gather_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_gather_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_gather_multigpu (__main__.TestDistBackend) ... ok
test_all_reduce_full_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_full_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_multigpu (__main__.TestDistBackend) ... skipped 'CUDA all_reduce multigpu skipped for NCCL'
test_all_reduce_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_all_reduce_sum_cuda (__main__.TestDistBackend) ... skipped 'Only Gloo backend will have CUDA allReduce tested'
test_barrier (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_cuda (__main__.TestDistBackend) ... ok
test_barrier_full_group (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_full_group_cuda (__main__.TestDistBackend) ... ok
test_barrier_group (__main__.TestDistBackend) ... skipped 'NCCL does not support CPU barrier'
test_barrier_group_cuda (__main__.TestDistBackend) ... ok
test_barrier_timeout_full_group (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_global (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_barrier_timeout_group (__main__.TestDistBackend) ... skipped 'Only gloo backend supports timeouts'
test_broadcast (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_cuda (__main__.TestDistBackend) ... ok
test_broadcast_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_broadcast_multigpu (__main__.TestDistBackend) ... skipped 'NCCL broadcast multigpu skipped'
test_destroy_full_group (__main__.TestDistBackend) ... ok
test_destroy_group (__main__.TestDistBackend) ... ok
test_gather (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_gather_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_gather_group (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_get_backend (__main__.TestDistBackend) ... ok
test_get_default_group (__main__.TestDistBackend) ... ok
test_get_rank (__main__.TestDistBackend) ... ok
test_get_rank_size_full_group (__main__.TestDistBackend) ... ok
test_get_rank_size_group (__main__.TestDistBackend) ... ok
test_irecv (__main__.TestDistBackend) ... skipped 'Nccl does not support irecv'
test_isend (__main__.TestDistBackend) ... skipped 'Nccl does not support isend'
test_reduce_full_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_full_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_group_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_max (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_min (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_multigpu (__main__.TestDistBackend) ... ok
test_reduce_product (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_sum (__main__.TestDistBackend) ... skipped 'Nccl does not support CPU tensors'
test_reduce_sum_cuda (__main__.TestDistBackend) ... ok
test_scatter (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_scatter_full_group (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_scatter_group (__main__.TestDistBackend) ... skipped 'Nccl does not support scatter'
test_send_recv (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv'
test_send_recv_any_source (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv from any source'
test_send_recv_with_tag (__main__.TestDistBackend) ... skipped 'Nccl does not support send/recv'

----------------------------------------------------------------------
Ran 68 tests in 70.381s

OK (skipped=52)
``
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14690

Differential Revision: D13294169

Pulled By: teng-li

fbshipit-source-id: 69ccac34c6c016899bfe8fbc50b48d4bfd1d3876

Add (unused) HIP API to the Context object. (#14623)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14623

This is the last piece we need before we can start doing out-of-place
HIPify on ATen. These APIs are not actually used at the moment;
as we still do in-place HIPify which uses CUDA.

Reviewed By: gchanan

Differential Revision: D13277246

fbshipit-source-id: 771efa81c2d2022e29350f25a5b4bb8f49ac6df0

Replace THCState_getCurrentStream with direct at::cuda::getCurrentCUDAStream()

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14500

Reviewed By: gchanan

Differential Revision: D13241401

fbshipit-source-id: d78cf8ddce96876bedc1d14507b0646bcfd41aed

Delete hasCuDNN from Context. (#14499)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14499

It still needs to stay in hooks, since it's part of the public
C++ API, but I want library code to try to arrange for CuDNN checks
to occur inside CUDA code, where we it's statically obvious if
CuDNN is available (and you don't need to dynamic dispatch.

Reviewed By: gchanan

Differential Revision: D13241355

fbshipit-source-id: 4e668a5914ab890463a12d9e528ba4ecbb7dd7c2

Delete at::current_device(), Context::current_device() and Context::getNumGPUs() (#14414)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14414

The previous functions were CUDA-centric, and lead to lots of places
where we improperly assumed that CUDA is the only game in town (it's not).
Best to delete them.

What are your alternatives? This diff fix some use sites which may give
you some ideas. In particular, the "given a device type, give me the
current device for that device type" might be a good function to enshrine
for real.

Reviewed By: gchanan

Differential Revision: D13218540

fbshipit-source-id: 2f42cd6b9bdab4930d25166b8041c9466a1c6e0a

sparse.mm(), reland #14526 (#14661)

Summary:
- reland reverted PR #14526 with doc fixes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14661

Differential Revision: D13289047

Pulled By: weiyangfb

fbshipit-source-id: 5b843a11a58b56aeada3af2680a27cf89ecef4d8

Fix multi-argument allreduce in ProcessGroupGloo (#14688)

Summary:
If multiple arguments are specified to c10d allreduce, they are
interpreted as if they are expanding the ranks in the process group.
Therefore, not only is every argument to allreduce an input that must
be considered, it is also an output. The problem that this commit
fixes is that they were not correctly considered as outputs.

The upstream problem is tracked in facebookincubator/gloo#152. Once
this is fixed there we can remove the copies that this commit adds.

This fixes #14676.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14688

Differential Revision: D13294405

Pulled By: pietern

fbshipit-source-id: 078a2a0a0ff12d051392461438f1496201ec3cb9

Assert all legacy operators are 'extended_method', remove codegen for… (#14649)

Summary:
… other paths.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14649

Differential Revision: D13285183

Pulled By: gchanan

fbshipit-source-id: 91a58a22cba7e00eb0931bc277b0cb9d6f05cfdc

Remove 'type_method_inline_definitions' which isn't used.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14648

Differential Revision: D13284176

Pulled By: gchanan

fbshipit-source-id: e6b8f9410fab57164259f97de2fd46f6bdf88d5a

Delete defunct DynamicCUDAInterface (#14621)

Summary:
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14621

Differential Revision: D13276723

Pulled By: ezyang

fbshipit-source-id: b666b2cdf4c45ccec7c802e268878eb2f3e028aa

Get rid of deprecated_factory_method in codegen, which is no longer u… (#14641)

Summary:
…sed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14641

Differential Revision: D13283449

Pulled By: gchanan

fbshipit-source-id: 35cedc48940fa6144b4eab6402d9e1dc74a67b65

inline adagrad functions (#14194)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14194

Inline some of perfkernels/adagrad.h functions for better performance

Reviewed By: hyuen

Differential Revision: D13096351

fbshipit-source-id: b4da8053278d585eabc5389b8a8dcae0f253b413

Increase test barrier timeout for barrier test (#14689)

Summary:
The CUDA initialization for the participating processes can
take long enough for the barrier timeout to trigger on the
process that doesn't participate in the group.

See #14676.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14689

Reviewed By: teng-li

Differential Revision: D13293695

Pulled By: pietern

fbshipit-source-id: 6268dc9acfdb22f70c027e5e4be082f7127c0db4

Fixed DistributedDataParallel cannot kick off all-reduce in a corner case (#14675)

Summary:
Ok, this corner happens for translation guys, and it only happens in the following corner case:

(1) when the module is registered a parameter that does not requires grad

and

(2) this registered parameter has a unique type (say, double, or half) and it's the only unique type such that itself alone will be put into a separate bucket.

and

(3) it is the last parameter that got registered in the module, such that its bucket reduction is the first to be kicked off.

Once this corner case happens, since it does not require grad, the backward hook won't be kicked off. Now that all other buckets are waiting for its bucket to be kicked off, in this case, no bucket will be kicked off since it's blocked by the first bucket (the unique type parameter).

This PR fixes two things:
(1) Make sure that we will only bucket parameters that requires_grad
(2) Make all-reduction checks in the next iteration. As long as we detect the previous iteration's all-reduction has not been fully kicked off, we will issue an error in the next iteration.
(3) Also removed some unused variables

With this bug fixed, the only case when this error can happen is when the user changed parameters later after wrapping up the module with DDP, like the case in:
https://github.com/pytorch/pytorch/issues/12603

Test covered as well

Without the first fix, I varied that the repro in fbcode hit this error message:

```
result = self.forward(*input, **kwargs)
File "/data/users/tengli/fbsource/fbcode/buck-out/dev/gen/language_technology/neural_mt/os/pytorch_translate/train#link-tree/torch/nn/parallel/distributed.py", line 312, in forward
raise RuntimeError("Not all gradients are all-reduced from "
RuntimeError: Not all gradients are all-reduced from the backward of the previous iteration. This is unexpected and fatal error. Please check and ensure that the model's parameters are not changed after you wrap up the model with DistributedDataParallel.

```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14675

Differential Revision: D13291083

Pulled By: teng-li

fbshipit-source-id: 2539b699fae843f104b4b8d22721ae82502ba684

Fix CUDA 8 build on Windows (#14665)

Summary:
Fixes #14663.
Test for CUDA 8 is running here: https://dev.azure.com/pytorch/PyTorch/_build/results?buildId=54
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14665

Differential Revision: D13290392

Pulled By: soumith

fbshipit-source-id: 57f0d5b704e5d1fcb4927cbc007327b4ed74f443

Fixed typo in README.md (#14346)

Summary:
Fixed the typo in the Docker image section of README.md file
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14346

Differential Revision: D13290403

Pulled By: soumith

fbshipit-source-id: 1d848027a773f0cfc875c33d69a66e96abc7ac8b

Use Type::str() in Type::operator<< (#14657)

Summary:
Stacked on zip commit because it also changes expect files, read only the last commit.

This reduces the number of ways we can print a Type from 3 (python_str, str, operator<<) to 2.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14657

Differential Revision: D13288912

Pulled By: zdevito

fbshipit-source-id: f8dd610cea798c511c1d4327395bba54b1aa1697

Updating submodules

Reviewed By: yns88

fbshipit-source-id: 6b3905b999b1211196c9138d7236700a1b308491

Use a zip archive as our container format (#14521)

Summary:
After consulting with Owen, who pointed out the existence of the miniz library, I decided to take one last shot at using zip as our container format.
miniz makes this surprisingly feasible and I think the benefits of using zip are large enough that we should do it.

This replaces our custom container format with a zip archive, preserving all of the
desirable features of our custom format, such as append-oriented writing, and
mmap'able tensor data while adding a bunch of debugging advantages:

1. You can unzip and explore the container to debug what is going on with a model.
2. You can edit the model using a text editor (e.g. change the definition of a method,
   or editing the json-serialized meta-data), re-zip the file use OSX's native 'Compress'
   option, and re-load the result into pytorch. Note: this enables you to, e.g., print-debug
   serialized models.
3. We can easily enable features like compression in the future.
4. Stock python , without pytorch installed, and other programming languages
   can reasonably consume this format,using json  and zipfile packages, which enables
   people to build tools like visualizers without those visualizers depending on pytorch.
   This will be especially useful if you want to, for instance, write a visualizer in javascript.

Notes:

*  This add miniz (https://github.com/richgel999/miniz) as a dependency. miniz is a self-contained
   library for reading/writing zipfiles that unlike other zip libraries also includes libz
   compatible compress/decompress support. It is a single header and a single C file without
   any other dependencies. Note that the instructions for miniz explicitly state:

   > Please use the files from the releases page in your projects. Do not use the git checkout directly!

   So we have checked in the 'release' source. Miniz supports zip64, and its API is amenable
   to doing zip-align style things to align data.

*  Removes 'size' from RecordRef. This allows you to edit files in the zip archive without
   editing the meta-data file. Very important if you want to print-debug serialized models.

*  PyTorchStreamReader/PyTorchStreamWriter keep mostly the same API (though keys become strings)
   However, their implementation is completely swapped out to use miniz.

*  Code exists to check for the old magic number to give a decent warning to our preview users
   after we change the format.

*  Container version information is now put in a stand-alone 'version' file in the archive
   and serves a similar purpose to the other container version info.

*  All files in the zip archive start at 64-byte boundaries, using an approach similar to
   zip-align. Tests check that this property remains true. While the writer does this,
   the reader doesn't depend on it, allowing user-created archives that can use compression,
   and do not have to align data.

*  Added test to check for > 4GB files and archives. Disabled by default because it takes
   almost 2 minutes to run.

*  torchscript files are now optional: if a submodule does not have methods, it will
   not be written.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14521

Reviewed By: jamesr66a

Differential Revision: D13252945

Pulled By: zdevito

fbshipit-source-id: 01209294c0f6543d0fd716f85a38532249c52f8c

Revert D13252990: [pytorch][PR] [sparse] sparse.mm(S, D)

Differential Revision:
D13252990

Original commit changeset: 8fdb14144405

fbshipit-source-id: 49b8b0759a6e647854689962ffa72a205b4a2088

Tensor construction codemod - caffe2/caffe2/fb/operators - 2/3

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

Reviewed By: smessmer

Differential Revision: D13229251

fbshipit-source-id: 88b3984ea8ca82b9489c0ee9a338fd3f41dee615

Fix 'unknown type name 'optional'' (#14383)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14383

D11669870 seems to have missed a spot that wasn't triggered before the stacked code above

Reviewed By: smessmer

Differential Revision: D13198269

fbshipit-source-id: 74592bedae0721acee744e31ca95253ea6efdedb

fix double precision cast from pybind (#14417)

Summary:
JIT world only have double, not float, so when insertConstant, we need to cast the python `float_` to double instead of float. This will fix the incorrect `math.pi` and other high precision constants value
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14417

Differential Revision: D13282975

Pulled By: wanchaol

fbshipit-source-id: 26a4c89ffc044d28598af673aebfec95153a869e

Revert existing no_grad_embedding_renorm_ from aten (#14639)

Summary:
Remove no_grad_embedding_renorm_ from aten. Setting the derivatives of the inputs to false has different semantics from calling with no_grad(), because it will not error if an input is modified and then has it's grad accessed.

Instead, make a custom op, and use NoGradGuard.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14639

Differential Revision: D13285604

Pulled By: eellison

fbshipit-source-id: c7d343fe8f22e369669e92799f167674f124ffe7

cuda implementation for PackSegment to support presence mask (#14635)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14635

as title

Reviewed By: enosair

Differential Revision: D13254097

fbshipit-source-id: b9f40109e2889907c925f9a4df9da14f67f45f38

Updating submodules

Reviewed By: yns88

fbshipit-source-id: 17487c327cbe48969dff397656fe90efcf23b699

Build distributed libs in build_libtorch.py (#14037)

Summary:
This patch detects and builds c10d and gloo for the C++ API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14037

Reviewed By: ezyang

Differential Revision: D13283801

Pulled By: ebetica

fbshipit-source-id: 006dbb691344819833da6b4b844c1f0572942135

Remove methods from _th_triu_ and _th_addcmul_. (#14624)

Summary:
These somehow slipped through when we moved all of Declarations.cwrap to functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14624

Reviewed By: ezyang

Differential Revision: D13277434

Pulled By: gchanan

fbshipit-source-id: e83451e2d0fdafb55635d4b757688a501454bf8c

sparse.mm(S, D) (#14526)

Summary:
- add `sparse.mm(S, D)` with backward
- for `sparse.addmm()`, relax input constraint so that sparse matrix input doesn't have to coalesced
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14526

Reviewed By: ezyang

Differential Revision: D13252990

Pulled By: weiyangfb

fbshipit-source-id: 8fdb14144405a2122d4b8447ad4055cd0330e6e8

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

Summary:
See #14539
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14569

Differential Revision: D13282161

Pulled By: ezyang

fbshipit-source-id: 13a1131b26fa300b037f66d1919b97d14033f9e5

Remove OptionsGuard from ATen (#14524)

Summary:
Resubmission of https://github.com/pytorch/pytorch/pull/13738
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14524

Differential Revision: D13268031

Pulled By: goldsborough

fbshipit-source-id: fb306464b673c05ebd26d0f44d688ccd92d1d8c5

Explicitly ban uninitialized tensors when invoking Predictor classes (#14377)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14377

att

Reviewed By: dzhulgakov

Differential Revision: D13197348

fbshipit-source-id: 85a451bde3a57a8acdd3af548606c05e223896a6

Report timer in benchmarking when requested

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14570

Reviewed By: llyfacebook

Differential Revision: D13264904

Pulled By: sf-wind

fbshipit-source-id: fd05bc32202b7734dc911e3c792357ddf9ecedee

Fix inheritance for SharedDataset (#14629)

Summary:
ezyang ebetica

CC jaliyae
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14629

Differential Revision: D13278988

Pulled By: goldsborough

fbshipit-source-id: 53afbcd1f3fc5cb23046ff92c4345cd90abd4584

Move module tests to common_nn (#14578)

Summary:
This moves `new_module_tests` from `test_nn.py` to `common_nn.py` so
that they can be used in `test_jit.py` without running any of
`test_nn.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14578

Differential Revision: D13268286

Pulled By: driazati

fbshipit-source-id: 6e8654a4c29ab754d656ac83820c14d1c1843e03

Updating submodules

Reviewed By: yns88

fbshipit-source-id: 863e9e2a1f0810f96494cabae1724622b9eb91ff

Remove default constructor lines that do nothing, and fix warnings with clang trunk (#14300)

Summary:
The lines removed in this diff were no-op, but confusing: the default constructors in `store_handler.h` are implicitly deleted, since `std::runtime_error` has no default constructor.

Clang added a warning for this behavior [in September 2018](https://reviews.llvm.org/rL343285) (note that the warning is not just for cxx2a, despite the slightly confusing commit message), so building pytorch with a recent build of clang trunk causes spew of this warning, which is fixed by the present PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14300

Differential Revision: D13260039

Pulled By: umanwizard

fbshipit-source-id: 92788dbd6794253e788ef26bde250a66d8fb917e

remove copy_wrapper (#13937)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13937

We can now replace s_copy_ with our new _copy_ function. Experimented with moving s_copy_ out of VariableManualType.cpp, but seemed like there was enough special casing to warrant it staying.

Reviewed By: ezyang

Differential Revision: D13053648

fbshipit-source-id: e9e04d460baf4ee49b500212cf91b95221acd769

Move non_blocking copies to aten (#13866)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13866

just a straightforward port

Reviewed By: ezyang

Differential Revision: D13011878

fbshipit-source-id: f288efebf78fa634abfb681b938b44277064d5b6

Move cuda copy to aten (#13348)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13348

Move cross device, cpu to device, device to cpu copies to aten. Most of it is a direct port, main difference is that we dispatch from a single _copy_ function for copies.

Reviewed By: ezyang

Differential Revision: D12850690

fbshipit-source-id: c2e3f336796b4ae38be6027d2ec131a274a6aa8c

Move THTensor_(copy) to aten (#13603)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13603
P
Moved vectorized CPU copy to aten. Notable changes mainly in _copy_same_type_.

Reviewed By: ezyang

Differential Revision: D12936031

fbshipit-source-id: 00d28813e3160595e73d104f76685e13154971c1

Changes based on @gchanan's review of #13420 (#14441)

Summary:
```
The most significant change is that this fixes the error message when
indexing an empty tensor with an out-of-bounds index. For example:

x = torch.ones(10, 0)
x[:, [3, 4]]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14441

Differential Revision: D13226737

Pulled By: colesbury

fbshipit-source-id: d1c4a35a30e3217e3d1727d13f6b354a4a3b2a24