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

add reference to flake8-mypy in contributing.md

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17759

Differential Revision: D14376813

Pulled By: eellison

fbshipit-source-id: cca1128e967ef7368633b94a3fa3c8e76a4a16f4

Move lerp to ATen, add functionality for tensor weights (#17348)

Summary:
Changelog:
- Remove TH/THC bindings
- Add tensor weights for `lerp`
- Modify derivatives appropriately
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17348

Differential Revision: D14355845

Pulled By: soumith

fbshipit-source-id: eaede4c09ee589d77ba6cf52583510ea8e3a2fcf

Refactor dispatcher (#17753)

Summary:
This is a side PR for a bool tensor feature. The idea of this change came from a feedback received in this [PR](https://github.com/pytorch/pytorch/pull/17376).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17753

Differential Revision: D14367989

Pulled By: izdeby

fbshipit-source-id: 4fa380e56e20f18e480be68920170dbc3a4eb91c

add layernorm to AD

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17702

Differential Revision: D14368472

Pulled By: wanchaol

fbshipit-source-id: 8db390e39444078258ad1d34ba74d6ddafa5d02b

move half<->float conversions to oss operators (#17548)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17548

expose half float operators to OSS

common/math/Float16.h is the original implementation
this is substituted by caffe2/c10/util/Half.h

from the comments seems like the both implementations don't handle denormals

Reviewed By: jspark1105

Differential Revision: D14244200

fbshipit-source-id: f90ba28c5bf6a2b451b429cc4925b8cc376ac651

Fix the update ONNX expect files (#17767)

Summary:
Fix the CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17767

Reviewed By: zrphercule

Differential Revision: D14370483

Pulled By: houseroad

fbshipit-source-id: e7b0bbde0797c41f5a010fa206fab80fe2792eb7

Cleanup testFusion/testOne: there are unused arguments.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17737

Differential Revision: D14366584

Pulled By: ZolotukhinM

fbshipit-source-id: 3c2dd2aabfecca475909e4eec4a077d900795da9

update of fbcode/onnx to 96c58ceeacf0f2b73d752e413e4fd78787a12da3 (#17676)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17676

Previous import was e18bb41d255a23daf368ffd62a2645db55db4c72

Included changes:
- **[96c58ce](https://github.com/onnx/onnx/commit/96c58ce)**: Fix shape inference when auto_pad is notset again (#1830) <Li-Wen Chang>
- **[873ddbb](https://github.com/onnx/onnx/commit/873ddbb)**: More extendable Runner (#1809) <Michał Karzyński>

Reviewed By: zrphercule

Differential Revision: D14321241

fbshipit-source-id: 12de9021afc61f5435f1b719cccf7b0f4ad73a84

Set the default ONNX opset to the latest stable opset (i.e., 9) (#17736)

Summary:
1) The changes in the new opset won't affect internal pipeline.
2) The CI won't be affected by the ONNX changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17736

Reviewed By: zrphercule

Differential Revision: D14358710

Pulled By: houseroad

fbshipit-source-id: 4ef15d2246b50f6875ee215ce37ecf92d555ca6a

Add module attributes (#17309)

Summary:
Similar to `nn.Parameter`s, this PR lets you store any `IValue` on a module as an attribute on a `ScriptModule` (only from the Python front-end currently). To mark something as an attribute, it should wrapped in `jit.Attribute(value, type)` (ex. `self.table = torch.jit.Attribute(table, Dict[str, torch.Tensor])`)

Followup Work:
* (de)serializing for use in C++
* change `self.training` to be a `bool` attribute instead of a buffer
* mutable attributes
* string frontend support
* documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17309

Differential Revision: D14354316

Pulled By: driazati

fbshipit-source-id: 67e08ab5229366b67fbc837e67b58831a4fb3318

- refactoring serialization of ONNX initializers to be name-based (#17420)

Summary:
Currently, serialization of model parameters in ONNX export depends on the order in which they are stored in a container (`list` on Python side and `std::vector` on C++ side). This has worked fine till now, but if we need to do any pass on that graph that mutates the parameter list, then strictly order-based serialization may not work.

This PR is the first in a set to bring in more passes (such as constant folding) related to ONNX export. This PR lays the groundwork by moving the serialization in ONNX export from order-based to name based approach, which is more amenable to some of the passes.

houseroad - As discussed this change uses a map for export, and removes the code from `export.cpp` that relies on the order to compute initializer names.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17420

Differential Revision: D14361993

Pulled By: houseroad

fbshipit-source-id: da93e945d55755c126de06641f35df87d1648cc4

ONNX Export for Max and Average Pooling in CEIL_MODE

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16769

Differential Revision: D14362175

Pulled By: houseroad

fbshipit-source-id: 65cfb1dfba6a43d39cc85374add368fe8e4e5645

use flake8-mypy (#17721)

Summary:
Use flake8 installed with mypy checks so that our linter matches fbcode. Mypy type errors also provide valuable signal
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17721

Differential Revision: D14357778

Pulled By: eellison

fbshipit-source-id: d8c9ea3fe3b5f550c3b70fe259e0eabf95e4c92d

use fp16<->fp32 intrinsic (#17496)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17496

As title.

Reviewed By: hyuen

Differential Revision: D14222907

fbshipit-source-id: d5d6c032e725ca8b52aca2be7401ec3c59f6a242

Implement a Caffe2 standalone LSTM operator (#17726)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17726

Pull Request resolved: https://github.com/pytorch/pytorch/pull/17725

Pull Request resolved: https://github.com/pytorch/pytorch/pull/17461

Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions.

Two things missing:

- Profiling this implementation against the current ONNXified LSTM op
- Make this operator available to use in PyTorch

Reviewed By: dzhulgakov

Differential Revision: D14351575

fbshipit-source-id: 3b99b53212cf593c7a49e45580b5a07b90809e64

caffe2:libtorch_cuda depends on caffe2:caffe2_gpu (#17729)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17729

When doing "import torch" in fbcode, previously the caffe2 cuda kernels weren't loaded because libcaffe2_gpu.so wasn't loaded.
Once you also did "from caffe2.python import workspace", then the cuda kernels were loaded because that triggered a runtime mechanism for loading libcaffe2_gpu.so.

We want the cuda kernels to always be available, so this diff adds a dependency from caffe2:libtorch_cuda to caffe2:caffe2_gpu.

Reviewed By: ezyang

Differential Revision: D14353498

fbshipit-source-id: 76a9fe69f231b308ab40eac393bb216c6fad3658

add tensor and cost inference functions (#17684)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17684

Adding tensor and cost inference functions to more int8 operators.

Reviewed By: yinghai

Differential Revision: D14174746

fbshipit-source-id: dfad975fa75899565c8fb61f1b7747a9206ebd22

ONNX Export Narrow op

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17550

Differential Revision: D14350401

Pulled By: houseroad

fbshipit-source-id: 4d88079bb7a8bbd270b0272009826eb3b202cc33

Keep the dim_type of hinted shape as BATCH if possible (#17734)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17734

If input is not BATCH, we will skip adjust its batch size during onnxifi transformation. So when we take hints, we take it as CONSTANT but later need to change it to BATCH if possible.

Reviewed By: jackm321

Differential Revision: D14355983

fbshipit-source-id: 63eb54a44afb1565c71486fdd73db07ca0ac4fd4

fix different round behavior on CPU and GPU #16498 (#17443)

Summary:
xxtemp, colesbury, bhushan23, zou3519, convert gpu round behavior to half-to-even, consistent with torch cpu version and numpy. You feedback are welcomed.
See #16498
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17443

Differential Revision: D14261786

Pulled By: VitalyFedyunin

fbshipit-source-id: 98156436b545d72769831a89e2775d43ad913ebc

Warn about memory overlaps on expanded tensors (#17576)

Summary:
Eventually we should remove these when we're certain that all our ops
handle memory overlaps correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17576

Differential Revision: D14349990

Pulled By: zou3519

fbshipit-source-id: c3a09f6113b9b1bf93e7f13c0b426c45b2cdf21f

fix exp fam. formula

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17719

Differential Revision: D14349029

Pulled By: soumith

fbshipit-source-id: cf016756a9319436f7379e8377f8bd1e1b672b40

refactor caffe2 operator constructors - 10/9 (#17659)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17659

clangr codemod

Reviewed By: ezyang

Differential Revision: D14304675

fbshipit-source-id: 45fbd84c50651a70ae29bf46df3322715e99d225

Improve ONNX symbolic for logsoftmax and softmax (#17672)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17672

support dtype in the onnx symbolic

Reviewed By: zrphercule

Differential Revision: D14313987

fbshipit-source-id: e9364621b3f795191d880599711dfbcb220d0e31

Enable using CMD when building cpp extensions on Windows

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17706

Differential Revision: D14346482

Pulled By: ezyang

fbshipit-source-id: 7c85e51c701f6c0947ad324ef19fafda40ae1cb9

Do not rename net boundary inputs/outputs during ssaRewrite. (#17545)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17545

This diff avoids renaming boundary inputs of net during onnxifi transform.
It also removes adding mappings for the initializer during onnxifi op creation.
Thus gets read of the mapped ws creation during onnxifi op creation.

Reviewed By: zrphercule

Differential Revision: D14243161

fbshipit-source-id: 6eafa920c45f6a6bfacbbb443e8e84cf9778644c

Reapply D14078519 (#17596)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17596

Was reverted before, now fixed version.

Reviewed By: ezyang

Differential Revision: D14270288

fbshipit-source-id: c72490b5d02cc6098cb60145fa9a842b3c9a24c5

Batch of expect file removals (#17581)

Summary:
Another batch of removing expect files.

One note - I removed the Batched expect files without adding equivalent tests since they are already being tested in another ways, and we are no longer actively maintaining that project.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17581

Differential Revision: D14343578

Pulled By: eellison

fbshipit-source-id: ce0b1fd2b5b4ec80ad9003bab1b58f41645d3da6

(#14267)

Summary:
- Summary:

Added synchronized batch normalization, allows synchronization of stats across mini-batches between processes within a process group.
Current implementation uses a mixture of extended ATen native functions (cpp cuda extension) + torch.nn.modules (c10d python API)

- User-facing api:

1. torch.nn.utils.convert_sync_batchnorm(modules, process_group=None)

2. torch.nn.SyncBatchNorm(num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, ***process_group=None***)

- supported use case:
DistributedDataParallel with ***single-gpu multi-process***

a. User creates model containing `torch.nn.SyncBatchNorm` layers through one of the ways listed below:

  1. use layers directly:

     torch.nn.SyncBatchNorm(...)

     similar API as with torch.nn.BatchNormXd(...)
     with added argument `process_group` which is used to limit the scope of
     synchronization within each process group. Default value is None, which
     implies synchronization across all GPUs

  2. use torch.nn.utils.convert_sync_batchnorm(modules, process_group)

     recursively convert all `torch.nn.BatchNormXd` into `torch.nn.SyncBatchNorm`
     preserving values of parameters/buffers.
     the utility function also allows user to specify process_group value to all
     converted layers.

b. user wraps their model with
   `torch.distributed.parallel.DataParallelDistributed`, from this point, user
   should follow the general guidelines for DDP use guide

- Error checking

For use cases not supported, we error out:

1. Application launched without ddp:
   > import torch
   > sbn = torch.nn.SyncBatchNorm(10).cuda()
   > inp = torch.randn(5, 10, 3, 3).cuda()
   > sbn(inp) --> Error!
   > AttributeError: SyncBatchNorm is only supported within torch.nn.parallel.DistributedDataParallel

2. Application launched using DDP with multi-GPU per-process:
   > ddp_module = nn.parallel.DistributedDataParallel(module, device_ids=device_ids, output_device=args.local_rank)
   > ValueError: SyncBatchNorm is only supported for DDP with single GPU per process
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14267

Differential Revision: D14270035

Pulled By: ezyang

fbshipit-source-id: 4956d8fa565c32e9df5408d53719ff9f945f4d6d

Update ModuleDict doc about order

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17717

Differential Revision: D14346557

Pulled By: ezyang

fbshipit-source-id: 2484c7d8105f9aa8bce5567d1fa2d4f587cc9cc2

Update CODEOWNERS (#17720)

Summary:
teng-li is passing the baton to mrshenli. Thanks for all your work on distributed teng-li!! :tada:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17720

Differential Revision: D14350120

Pulled By: pietern

fbshipit-source-id: edfe784520c54630203cc8fbb296455d3dbf341b

ONNX Export Argmin and Argmax ops

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17382

Differential Revision: D14338811

Pulled By: houseroad

fbshipit-source-id: be07548d8063d1aa94f1801c18137738365b85fb

Turn atol to 1e-5 when comparing the end to end results (#17708)

Summary:
results smaller than 1e-5 don't make sense.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17708

Differential Revision: D14348893

Pulled By: houseroad

fbshipit-source-id: 5e07c38e5b58b27b61fae63bfc3c21e2fe5629fe

remove loop expects (#17695)

Summary:
Replace loop unrolling expect files with assertions on the output IR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17695

Differential Revision: D14347105

Pulled By: eellison

fbshipit-source-id: 1703b4ca32bc1c67c01fc4330b0e6eb66feaa103

typo fix

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17653

Differential Revision: D14302003

Pulled By: ezyang

fbshipit-source-id: 8ad90985a392b07127c7e315d4e74ce77962b573

omit group conv NHWC test for GPU (#17715)

Summary:
Observed the test `TestGroupConvolution.test_group_convolution` to fail with the following error:

```
Falsifying example: test_group_convolution(self=<caffe2.python.operator_test.group_conv_test.TestGroupConvolution testMethod=test_group_convolution>, stride=3, pad=0, kernel=5, size=8, group=4, input_channels_per_group=7, output_channels_per_group=8, batch_size=2, order='NHWC', engine='', use_bias=False, gc=, dc=[, device_type: 1])

You can reproduce this example by temporarily adding reproduce_failure('3.59.1', b'AAAA') as a decorator on your test case
```
This example generated by hypothesis has `group=2, order='NHWC' and dc=[, device_type: 1])`.
I think this example should be skipped.

I have mimicked the change corresponding to [PR#13554](https://github.com/pytorch/pytorch/pull/13554) to skip this example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17715

Differential Revision: D14346642

Pulled By: ezyang

fbshipit-source-id: b1f1fef09f625fdb43d31c7213854e61a96381ba

fix tuple matching (#17687)

Summary:
Check for Tuple Matching in isSubvalueOf, since they may contain container types that need to be recursed within isSubvalueOf

Fix for https://github.com/pytorch/pytorch/issues/17650
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17687

Differential Revision: D14324642

Pulled By: eellison

fbshipit-source-id: 7f1e019875286b2640a3b9c003d1635dda8cf543

Temporarily disable Upsample operator tests in pytorch-onnx tests (#17696)

Summary:
In discussion with houseroad, because Upsample op is being updated in ONNX https://github.com/onnx/onnx/pull/1773 and these tests are blocking it. These tests will be updated once the ONNX PR goes in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17696

Differential Revision: D14338845

Pulled By: houseroad

fbshipit-source-id: cfaf8cf1ab578ae69dd3bf21b1c0681b572b9b6f

Add check for x64 Python before setup (#17707)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/17657.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17707

Differential Revision: D14346705

Pulled By: ezyang

fbshipit-source-id: 5daafacdb99eb9a9c6517263d10f20c79f920d24

Replace caffe2::DeviceGuard with c10::cuda::CUDAGuard (#17623)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17623

Despite it's generic sounding name, caffe2::DeviceGuard actually
only worked on CUDA devices. Rename it to something that more
clearly spells out its applicability.

I'm not sure if it's the right call, but in this patch I added
'using CUDAGuard = c10::cuda::CUDAGuard', as this seems to be more
in-line with how the Caffe2 codebase is currently written. More
idiomatic c10 namespace style would be to say cuda::CUDAGuard.
Willing to change this if people shout.

This is a respin of D13156470 (#14284)

Reviewed By: dzhulgakov

Differential Revision: D14285504

fbshipit-source-id: 93b8ab938b064572b3b010c307e1261fde0fff3d

Remove nomscheduler (#17693)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17693

Remove nomscheduler tool

Reviewed By: yinghai

Differential Revision: D14328168

fbshipit-source-id: 674d0e18596a4dc2bbb6b8d321f4066c4fc454ab

index operation support for torch.HalfTensor (#17645)

Summary:
- Test cases added
1. indexing for half tensor
2. setting for half tensor

fixes #17161
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17645

Differential Revision: D14302069

Pulled By: ezyang

fbshipit-source-id: 100f141c07046f200c904e27c5882a9417bccda0

Revert D14160172: Implement a Caffe2 standalone LSTM operator

Differential Revision:
D14160172

Original commit changeset: c33e3f9e8aea

fbshipit-source-id: cffe35d93f0ac75ca93aa98a3b82af3d372f2fc1

fix typo in hub doc

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17705

Differential Revision: D14338380

Pulled By: ailzhang

fbshipit-source-id: d53eece30bede88a642e718ee6f829ba29c7d1c4

fix dropout AD & rename range to rangelist (#17691)

Summary:
fixes #17669
Address apaszke 's comments in #17523
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17691

Differential Revision: D14328083

Pulled By: ailzhang

fbshipit-source-id: 9ec4a54f13bfd1aaf4b1821dd00c31793ac07a44

enable use of MIOpen for depthwise convolutions (#17685)

Summary:
* added miopen conv mode to be used for setConvDescriptor
* added miopen depthwise convolutions
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17685

Differential Revision: D14327811

Pulled By: bddppq

fbshipit-source-id: d5bdc1abafd5f39694fadf3f9275b9d880c5b115

Implement a Caffe2 standalone LSTM operator (#17461)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17461

Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions.

Two things missing:

- Profiling this implementation against the current ONNXified LSTM op
- Make this operator available to use in PyTorch

Reviewed By: dzhulgakov

Differential Revision: D14160172

fbshipit-source-id: c33e3f9e8aeae578b64d97593cb031a251216029

Fix nll_loss crash on cpu where ignore_index is out of bounds (#17328)

Summary:
Fixes https://github.com/pytorch/pytorch/issues/15508
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17328

Differential Revision: D14322629

Pulled By: soumith

fbshipit-source-id: 7d02f372be78794782c18affcfc109ce30b1e91c

Add '--hip-clang-launch' to favor <<<>>>-based launch. (#17686)

Summary:
hip-clang uses triple chevron kernel dispatch syntax. Add an option to the hipification script to skip translating triple chevron to hipLaunchKernelGGL.

Once we switch to hip-clang, this option will be default and subsequently removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17686

Differential Revision: D14327810

Pulled By: bddppq

fbshipit-source-id: 5e1512325077dd3ebb8fb9b5bf35fd1f8d9a4dc3

Improve caching allocator for Pascal and newer GPUs. (#17120)

Summary:
```
NVIDIA changed the CUDA allocation behavior on Pascal GPUs. The
page size increased from 1MB to 2MB and allocations larger than 1MB
are now always page-aligned. Previously, allocations larger than 1MB
were aligned to 128KB boundaries.

This interacted poorly with the caching allocator. The remaining
memory in a page could only be filled by small cudaMalloc calls, but
the caching allocator never cudaMalloc's a chunk smaller than 1MB.
This behavior could also cause a large discrepancy between the memory
usage reported by nvidia-smi and the memory usage reported by
PyTorch, because nvidia-smi counts a partially used page as "full",
while PyTorch only counts the actual memory requested.

This PR makes a few changes to the caching allocator to better support
Pascal and Volta GPUs:

- All cudaMalloc calls are now multiples of 2MB (the page size)
- Requests between 1-10MB allocate (and split) a 20MB block to
reduce wasted space due to rounding
- Small requests are now packed into 2MB blocks (instead of 1MB)

This improves Mask R-CNN memory usage by 10-20% in internal tests on
Volta GPUs. Maxwell performance seems to be largely unchanged, but
it's possible that some use cases suffer slightly.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17120

Differential Revision: D14301536

Pulled By: colesbury

fbshipit-source-id: a8282315ea8f7b8ca149b5066fdeaecd0d404edf

Turn the Half::from_bits into a constexpr function to avoid unresolve… (#17661)

Summary:
…d symbol errors when building in DEBUG mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17661

Differential Revision: D14319610

Pulled By: soumith

fbshipit-source-id: 6c508a37155e29260f403d7174f343aa1ff32385

Remove Expect Files from python / tracing / script interop

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17622

Differential Revision: D14308307

Pulled By: eellison

fbshipit-source-id: bda249d38ac2570000a12b0ca328c26233ecefe8

Enable apex on Windows

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17675

Differential Revision: D14320473

Pulled By: soumith

fbshipit-source-id: cb696984f5196f9b8b50722b4fe927bb6407c322

bump docker build to upgrade magma to 2.5.0 (#17674)

Summary:
upgrades magma in docker build.

vishwakftw
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17674

Differential Revision: D14320187

Pulled By: soumith

fbshipit-source-id: 7887f65fb703b802fc6231408b55ad9c4039882b

refactor caffe2 operator constructors - 1/9 (#17082)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17082

clangr codemod

Reviewed By: ezyang

Differential Revision: D14078498

fbshipit-source-id: f7f65d6d81c7942293f53fdaa61f756d8b7360c1

Expose cuda kernel for caffe2::GenerateProposals

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17066

Reviewed By: ezyang, wat3rBro

Differential Revision: D14071130

fbshipit-source-id: 6fe26503f6069c36ec31d6c09b549b932d5db242

print warnings when DNNLOWP_16 or DNNLOWP_ROWWISE_16 engine is used (#17176)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17176

As title

Reviewed By: csummersea

Differential Revision: D14111616

fbshipit-source-id: 1282cb2452c4ad385fd2dc6d3f8c19e9fec715ff

Fix XOutput/XOutputTensor for ivalue based c2 operators (#17599)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17599

XOutput/XOutputTensor was broken for ivalue based operators. This diff fixes that.

Reviewed By: ezyang

Differential Revision: D14274003

fbshipit-source-id: b99f020244c66c4e2551dbd32ae0f665cc91b338

Fix InputSize/OutputSize for ivalue based operators (#17579)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17579

These methods previously just returned 0 when it was not a legacy operator,
making it impossible to convert some operators.

Reviewed By: dzhulgakov

Differential Revision: D14253094

fbshipit-source-id: 72bfdcf6da291a4ab80d1e0ceb20984b86edc408

Fix clamp fusion on missing limits (#17533)

Summary:
Fixes #17449

Context: before #17186, we don't fuse `clamp` for the case when `min/max` are missing inputs, because they are `prim::None` node, after #17186, we make None a `prim::Constant` node which enables the fusion for `clamp`. But codegen.cpp does not handle the case when `prim::Constant` is not a Double/Int/Bool, this PR makes it so that missing inputs are handled correctly, it is done in the following way:

1. emit nothing when you see `type? = prim::Constant()`
2. when emitRHS, do special casing for aten::clamp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17533

Differential Revision: D14238450

Pulled By: wanchaol

fbshipit-source-id: 61a272154754b13e89021bb86002927f02cde19c

int32 indexing for Tensor Iterator Reduction (#17428)

Summary:
1. Enabling int32 indexing for cases where TI cannot accumulate in output due to
incompatible data types (e.g. Welford).
2. Updating Welford kernel to use int32 instead of int64 indexing on GPU.

This change improves performance for torch.var / torch.std

Implementation:
1. Allocated extra buffer to handle accumulation between sub Tensor Iterators.
2. Removed int64 indexing in gpu_reduce_kernel
3. WelfordOps now supports index type / combination typeas a template parameter.
While GPU uses int32_t and float, CPU implementation uses int64_t and double.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17428

Differential Revision: D14264608

Pulled By: umanwizard

fbshipit-source-id: 3eb54451de925b469dbc1127e5ea7443c4431036

Removed all usages of TH_Index_Base (#17591)

Summary:
TH_Index_Base is hard coded to 0 and can be removed from the code base.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17591

Differential Revision: D14269273

Pulled By: izdeby

fbshipit-source-id: d844e261f4af7297bad8a81e7d6dcf0a391b94e6

PyTorch/Caffe2 tensor interop in Python (#17190)

Summary:
Because of two separate python extensions with different pybind
instances I have to go through void* conversion. Since it's hidden from
user, it's fine.

New APIs added on C2 side:
- workspace.FetchTorch('blob')
- workspace.Workspace.current.blobs['blob'].to_torch()
- workspace.FeedBlob('blob', pytorch_tensor)

Works on CPU an GPU.

The only glitches are with resizing because of variable/tensor split.
But data sharing works properly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17190

Reviewed By: ezyang

Differential Revision: D14163882

Pulled By: dzhulgakov

fbshipit-source-id: d18e5b8fcae026f393c842a1149e972515732de2

Fixed typo in aten/src/ATen/native_parse.py (#17641)

Summary:
Hi, there.
There is a typo in aten/src/ATen/native_parse.py, and I fix it.
`std::aray` -> `std::array`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17641

Differential Revision: D14301981

Pulled By: ezyang

fbshipit-source-id: a37859cdedcbf6c29333b954486dfa086d6c2176

Remove GPU dependency from ProfileObserver (#17592)

Summary:
Remove GPU dependency and register ProfileObserver.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17592

Reviewed By: ezyang

Differential Revision: D14265801

Pulled By: mdschatz

fbshipit-source-id: f98c0c32653c64a8b087c58ece4f864dfbe1d4b8

Don't make factory methods create a tensor and then immediately copy it (#17565)

Summary:
Create a `make_variable` override that moves out of a tensor instead of going through `shallow_copy_and_detach`. Call this override from factory methods like `empty` that create a brand new tensor, do nothing with it, and then copy it into a variable.

Will update this with actual numbers, but it seems to get rid of around 20-40% of the overhead of calling `torch.empty(0)`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17565

Differential Revision: D14266130

Pulled By: umanwizard

fbshipit-source-id: f57d5f2ca3f80ee8ee96d50f905e852fd10db941

Fixed typo in torch/functional.py w/r/t broadcast_tensors (#17642)

Summary:
In reference to #17574
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17642

Differential Revision: D14297177

Pulled By: ezyang

fbshipit-source-id: 968176ea3b46a0153da0fd9e6b40db314d29e51c

Change fake tqdm constructor to match real tqdm (#17636)

Summary:
Currently, the fake tqdm implementation requires an input (whereas real tqdm does not).

This caused a problem in torchvision (https://github.com/pytorch/vision/pull/770), and seems likely to cause minor irritations elsewhere.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17636

Differential Revision: D14296530

Pulled By: ezyang

fbshipit-source-id: bc077d898773c93dab34c985a7b30525a43e558a

Mark native_functions as matched if uncaptured by JIT (#17631)

Summary:
Various functions aren't used by the JIT, so they're jit-compliant w.r.t. their schema by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17631

Differential Revision: D14295559

Pulled By: cpuhrsch

fbshipit-source-id: a2ecdcb5df47eb67c54ec642d88d42e985515142

Ban std::array from native_functions.yaml

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17629

Differential Revision: D14292941

Pulled By: cpuhrsch

fbshipit-source-id: 3c3eed57a5505a4e1da3aea682092677ab0e73e3

Remove more usages of BoolTensor and IndexTensor from native_functions.yaml

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16468

Differential Revision: D14095405

Pulled By: cpuhrsch

fbshipit-source-id: ea4d6bb7a4e81c05fe9861190ddbf52201612bbf

Implement kthvalue in ATen (#17544)

Summary:
The CPU version is based on the TH version.
The GPU version is based on #8406 by Pararth Shah (thank you).

CPU quickselect based on that in TH's THTensorMoreMath.cpp, but with C++ (quickselectnoindex will be achieved by a different swap)
CPU kthvalue is based on the THTensor function in the same file.
The dim_apply function is a C++ replacement for TH_TENSOR_DIM_APPLYx macros.
The CUDA kernel uses functions adapted from the THCTensorSortK implementation.
In particular radixSelect is from THCTensorTopK.cuh.
The CUDA launcher code replaces a bunch of macros with C++. It will be re-used in one of the following patches.

Plan for further PRs:
- This
- Sort
- TopK + Mode + Median in any order
- Rip out THC stuff.

There may be utility functions / structs in the SortingCommon.cuh that come into
relevance only with sort.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17544

Differential Revision: D14286934

Pulled By: ezyang

fbshipit-source-id: 35dbea050b097e88777ac5fa5c0f499d5e23c738

Change vml.h to support sizes greater than 2**32 - 1

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17280

Differential Revision: D14154997

Pulled By: cpuhrsch

fbshipit-source-id: c19b15d18da59c9ee87e82765d3244d2a4ef6729

msvc_fixes (#17201)

Summary:
Fixing MSVC errors

```
  D:\pytorch-scripts\caffe2_builders\v141\pytorch\aten\src\THC/THCReduce.cuh(144): error C4002: too many actual paramet
ers for macro 'C10_LAUNCH_BOUNDS_1' [D:\pytorch-scripts\caffe2_builders\v141\pytorch\build\Debug\caffe2\caffe2_gpu.vcxp
roj]
  D:\pytorch-scripts\caffe2_builders\v141\pytorch\aten\src\THC/THCReduce.cuh(259): error C4002: too many actual paramet
ers for macro 'C10_LAUNCH_BOUNDS_1' [D:\pytorch-scripts\caffe2_builders\v141\pytorch\build\Debug\caffe2\caffe2_gpu.vcxp
roj]
  D:/pytorch-scripts/caffe2_builders/v141/pytorch/aten/src/THCUNN/SpatialDilatedMaxPooling.cu(51): error C4002: too man
y actual parameters for macro 'C10_LAUNCH_BOUNDS_1' [D:\pytorch-scripts\caffe2_builders\v141\pytorch\build\Debug\caffe2
\caffe2_gpu.vcxproj]
```

on variadic C10_LAUNCH_BOUNDS as well as Debug linking issues with at::Half in pool_op_cudnn.cc like this one

```
pool_op_cudnn.obj : error LNK2019: unresolved external symbol "public: bool __cdecl caffe2::MaxPoolFunctor<class caff
e2::CUDAContext>::GlobalPoolingBackward<struct c10::Half,2>(int,int,int,struct c10::Half const *,struct c10::Half const
,struct c10::Half const ,struct c10::Half ,class caffe2::CUDAContext )const " (??$GlobalPoolingBackward@UHalf@c10@
@$01@?$MaxPoolFunctor@VCUDAContext@caffe2@@caffe2@QEBA_NHHHPEBUHalf@c10@00PEAU23@PEAVCUDAContext@1@Z) referenced in
function "public: bool __cdecl caffe2::`anonymous namespace'::CuDNNMaxPoolFunctor::GlobalPoolingBackward<struct c10::H
alf,2>(int,int,int,struct c10::Half const ,struct c10::Half const ,struct c10::Half const ,struct c10::Half ,class
caffe2::CUDAContext *)const " (??$GlobalPoolingBackward@UHalf@c10@@$01@CuDNNMaxPoolFunctor@?A0xb936404a@caffe2@QEBA_NH
HHPEBUHalf@c10@00PEAU34@PEAVCUDAContext@2@Z) [D:\pytorch-scripts\caffe2_builders\v141\pytorch\build\Debug\caffe2\caff
e2_gpu.vcxproj]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17201

Differential Revision: D14165732

Pulled By: ezyang

fbshipit-source-id: 875fd9a5b2db6f83fc483f6d750d2c011260eb8b

Hipify fixes for Masquerade logic (#17598)

Summary:
ezyang Please review.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17598

Differential Revision: D14287724

Pulled By: ezyang

fbshipit-source-id: 46e5083854a827370bb4c81b82e5a4ede511e473

Rename prim::Undefined to prim::AutogradZero (#17611)

Summary:
supersedes #17245
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17611

Differential Revision: D14283581

Pulled By: wanchaol

fbshipit-source-id: 8022d02b8a021ea2fee9a18a2c8920eb123200c5

Add python test for extension backend tensor.device (#17602)

Summary:
Adding a test for #17361
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17602

Differential Revision: D14287373

Pulled By: li-roy

fbshipit-source-id: 544ecf17eb310aed22ba0ea5f86f46b8e3bb69b5

Revert D13935403: Call c10 cuda op from test_torch

Differential Revision:
D13935403

Original commit changeset: b2915ec8a366

fbshipit-source-id: 0f3409d5c102d719bc1f0483695aee93e7d613c9

add command line option to use hive filler; add README (#17619)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17619

--filler hive --iter -1 will let debugger exhaust all batches from a hive partition before exiting.
add README that summarizes command line options and usage.

Reviewed By: yinghai

Differential Revision: D14220166

fbshipit-source-id: daa23b7e8a9184481c6d7b67acf1599e5c99d74a

Remove TH(CU)NN Sparse Linear (#17610)

Summary:
Sparse Linear in TH(CU)NN implements sparse linear layers without
using sparse matrices.
It is currently not documented in PyTorch and there is no functional or
module interface. This means it is unused from a PyTorch point of view.

The reason for removing it is twofold:
- The module uses sort, which I would like to move to ATen.
- When we implement a SparseLinear layer, we would want to do it
using sparse tensors, so it's not all that useful, anyway.

I checked this on slack with soumith, I hope the above is an accurate
representation. All bad ideas are my own.

This is part of the ongoing work to move
sort/topk/mode/median/kthvalue to ATen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17610

Differential Revision: D14280663

Pulled By: gchanan

fbshipit-source-id: 289231d2c20626855ce2ceecd4f204b460c32378

Correct docstring of vision/init functions

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17351

Differential Revision: D14276355

Pulled By: soumith

fbshipit-source-id: 9b572b6a04eeb1e44cd93961edac76ed10f7b24e

Call c10 cuda op from test_torch

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16692

Reviewed By: ezyang

Differential Revision: D13935403

fbshipit-source-id: b2915ec8a3664bb6e918ed357908cc33d8f9449a

Revert #17191 and #17215 that no longer apply on Windows (#17567)

Summary:
They are previously merged to resolve #17051. However, since it was resolved by the upstream, and it was causing some issues like https://github.com/abjer/tsds/issues/8, I think it's time to revert these changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17567

Differential Revision: D14265241

Pulled By: kostmo

fbshipit-source-id: 7fa2b7dd4ebc5148681acb439cf82d983898694e

usertype -> class (#17528)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17528

as title. register_prim_ops is messy because someone ruined clang-format, but I figured it's okay to include here since this is such a mechanical change

Reviewed By: driazati

Differential Revision: D14236943

fbshipit-source-id: c2b22845837b7f830015510e48ec2ee5202fa407

alias analysis refactor take 2 (#17594)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17594

The original version of this broke things because a concurrent change raced with it in CI.

Reviewed By: ezyang

Differential Revision: D14266663

fbshipit-source-id: e8ac5dfcb7349b4f2c425d9f0eabbfc964314063

Fix the missing Windows CPU job in the build status section (#17608)

Summary:
It will be better to split the CPU job on CI. But unluckily, we are out of Windows machines.
cc, davidbrownellWork yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17608

Differential Revision: D14281393

Pulled By: soumith

fbshipit-source-id: ae9a6140b7207ce56cfb2da3d812bc3fe060764a

Update magma to 2.5.0 for Windows

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17607

Differential Revision: D14281291

Pulled By: yf225

fbshipit-source-id: 51209c5540932871e45e54ba6d61b3b7d264aa8c

Adding support for 0-d tensor for transpose (.t()) (#17535)

Summary:
- Test updates
1. test_torch: added 0-d test case and t_() test cases
2. test_jit : updated error message for TestAsync.test_async_script_error

- Updating documentation for torch.t()
Adding information regarding new support of 0-D and 1-D tenso

Fixes #17520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17535

Differential Revision: D14269984

Pulled By: gchanan

fbshipit-source-id: 38b723f31484be939261c88edb33575d242eca65

Updating submodules

Reviewed By: yns88

fbshipit-source-id: 05fafcfb34c76f425ac5c8ef24a5f920641c2cf7

Mark cudaGetLastError return value unused in C10_CUDA_CHECK

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17605

Reviewed By: xw285cornell

Differential Revision: D14277586

Pulled By: bddppq

fbshipit-source-id: 38879208f2ab83cf39d8a8a61b288cd09fcafd9a

add dropout during eval (#17549)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17549

Currently Dropout is only enabled in training, we enable the option of having dropout in Eval.

This is to follow [1]. This functionality would be used for uncertainty estimation in exploration project.

[1] Gal, Yarin, and Zoubin Ghahramani. "Dropout as a bayesian approximation: Representing model uncertainty in deep learning." international conference on machine learning. 2016.

Reviewed By: Wakeupbuddy

Differential Revision: D14216216

fbshipit-source-id: 87c8c9cc522a82df467b685805f0775c86923d8b

Adjust launch_bounds annotation for AMD hardware. (#17555)

Summary:
The max pooling backwards kernel is currently annotated with launch bounds (256,8).

Adjust the number of waves to 4 (4 times 64 is 256) for ROCm. This improves training performance for torchvision models by up to 15% (AlexNet) on a gfx906 GPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17555

Differential Revision: D14277744

Pulled By: bddppq

fbshipit-source-id: 2a62088f7b8a87d1e350c432bf655288967c7883

Fix verbose compiler warning in flat_hash_map (#17562)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17562

fixes https://github.com/pytorch/pytorch/issues/17332

Reviewed By: ezyang

Differential Revision: D14254499

fbshipit-source-id: 9d5d7408c2ce510ac20cd438c6514dc2bbe3a854

Fix diagnostic pragmas (#17561)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17561

The push at the top of the file was missing a corresponding pop

Reviewed By: ezyang

Differential Revision: D14254500

fbshipit-source-id: ff20359b563d6d6dcc68273dc754ab31aa8fad12

Allow dispatch based on tensor list args (#17522)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17522

Dispatch is still based on the first tensor arg, but that first "tensor arg" is now allowed to be a tensor list.
That is, the first argument that is either Tensor or TensorList will be the deciding factor for dispatch.
If it is a TensorList, then that TensorList must not be empty or dispatch will fail.

Reviewed By: ezyang

Differential Revision: D14235840

fbshipit-source-id: 266c18912d56ce77aa84306c5605c4191f3d882b

Allow exposing caffe2 operators with variable number of input tensors to c10 (#17491)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17491

Before, there was no way to expose a caffe2 operator that had a variable number of inputs.
Now, this is allowed by giving the operator one tensor list input.
Note that the tensor list must be the first input, and that any other tensor inputs will be ignored and inaccessible in this case.

Reviewed By: ezyang

Differential Revision: D14220705

fbshipit-source-id: 7f921bfb581caf46b229888c409bbcc40f7dda80

blacklist fft algorithms for strided dgrad (#17016)

Summary:
Applies https://github.com/pytorch/pytorch/pull/16626 from v1.0.1
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17016

Differential Revision: D14270100

Pulled By: ezyang

fbshipit-source-id: 1137899dd1551d33d16f39e8dde76cad8192af46

Revert D14078519: [codemod][caffe2] [clangr] refactor caffe2 operator constructors - 5/9

Differential Revision:
D14078519

Original commit changeset: b0ca31a52e4a

fbshipit-source-id: 713ae108d3dd6f33abdbf98a5f213e57e2b64642

Add generic list/dict custom op bindings (#17587)

Summary:
Fixes #17017

Sandcastle refuses to land #17037, so trying fresh here
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17587

Differential Revision: D14265402

Pulled By: driazati

fbshipit-source-id: b942721aa9360ac6b3862f552ac95529eb0cf52c

refactor caffe2 operator constructors - 8/9 (#17089)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17089

clangr codemod

Reviewed By: ezyang

Differential Revision: D14078539

fbshipit-source-id: 9ca196af4af7f26fc82e6cf82b35d478d0597752