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

add whitelisted clang-format checks (#15254)

Summary:
This PR adds clang-format automation:
- It only checks on whitelisted files, so we can enable incrementally without noise
- There is a pre-commit hook provided that will do the same check, plus prompt users to apply the clang-format changes (no change is made without the user agreeing).

My plan is to migrate over whole files at a time, clang-formatting them and then adding them to the whitelist. Doing it this way should avoid too many merge pains (the most you'll have to is run clang-format on the affected file before rebasing).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15254

Differential Revision: D13515888

Pulled By: suo

fbshipit-source-id: d098eabcc97aa228c4dfce8fc096c3b5a45b591f

build fix

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15384

Differential Revision: D13515708

Pulled By: zdevito

fbshipit-source-id: ea077cfec30edf41b85dc83c0a969d1146434145

Split up compiler.cpp (#15355)

Summary:
This separates the different parts of compiler.cpp to make their relationship more clear. In particular it adds:

* sugared_value.{h,cpp} - all the public SugaredValues that the compiler defines and a few that were inside compiler.cpp
* type_parser.{h, cpp} - Turns TreeRef's defining types into TypePtr
* schema_matching.{h, cpp} - infrastructure for matching arguments against overloaded schema and emitting builtin operators with a particular schema.
Retains:
* compiler.{h, cpp} - now responsible simply for the `defineMethodsInModule` infra structure.

Some utility functions like inlineCallTo have moved to ir.h.

Only thing that is not a move is some changes in module.h/cpp that remove multiple returns from `Method::emit_call_to`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15355

Reviewed By: suo, wanchaol

Differential Revision: D13507524

Pulled By: zdevito

fbshipit-source-id: 69ec936a9ff1a383c12a883616346b219c72e393

Autograd using torchscript (#14604)

Summary:
This PR enables autodiff to use the forward/backward graph compiled from python code, instead of using symbolic gradients(modifying the original graph directly).

We put the map in a separate .h file for now to wait for the native_functions.yaml and derivatives.yaml merge. This should ideally go into native_functions.yaml eventually.

This PR should be enough to unblock us for now, we can start writing gradients for aten functions in python.

Differential Revision: D13494635

Pulled By: ailzhang

fbshipit-source-id: f8d51a15243ac46afd09d930c573ccdfcd9fdaaf

Minor clean up for test_jit (#15368)

Summary:
* remove None args in functional tests
* remove some expect files that are not necessary
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15368

Differential Revision: D13512349

Pulled By: wanchaol

fbshipit-source-id: 304cffff966487d15c373057ae8ad114ef8aa7f9

Add RNNCell modules to Script standard library (#14695)

Summary:
Adds RNNCell modules to script standard lib

cc apaszke for argument_spec changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14695

Differential Revision: D13467680

Pulled By: driazati

fbshipit-source-id: 13a14da87714325cc4c3d49e5fde8a850d5d757b

Remove fully qualified weak script names (#15364)

Summary:
Cleanup to make references to `weak_script` consistent across codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15364

Differential Revision: D13509676

Pulled By: driazati

fbshipit-source-id: 93dbbbe57e9b9b6587895f3cc6fac678babd21de

Redefine scheduler to set learning rate using recursive formula (#14010)

Summary:
Modified step_lr for StepLR, MultiStepLR, ExponentialLR and CosineAnnealingLR. In this way, multiple schedulers can be used simultaneously to modify the learning rates.

Related issue: https://github.com/pytorch/pytorch/issues/13022

Added unit tests combining multiple schedulers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14010

Reviewed By: ezyang

Differential Revision: D13494941

Pulled By: chandlerzuo

fbshipit-source-id: 7561270245639ba1f2c00748f8e4a5f7dec7160c

Replace resize_dim() with set_sizes_and_strides() in (#15348)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15348

We have a function resize_dim() on TensorImpl in c10/core/TensorImpl.h which lets you change the dimensionality of a tensor, resizing both sizes and strides. Unfortunately, this API is fairly easy to misuse, because it fills in the new entries with garbage when you size it larger. We want to refactor the call sites to use set_sizes_and_strides() instead, so that there is never an intermediate tensor state where the sizes/strides don't make sense. In this diff, resize_dim() is
replaced with set_sizes_and_strides() in aten/src/TH/THTensor.hpp.

Reviewed By: ezyang

Differential Revision: D13505512

fbshipit-source-id: 193bab89f0018c13ca07488be336d8e967746b76

Minor cleanup for TestFuser tests (#15134)

Summary:
Changelog:
- change some expect tests that didn't have to be expect tests,
instead use self.assertAllFused
- Some of the fuser tests weren't using self.assertAllFused.
- Minor test renames

cc apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15134

Differential Revision: D13507481

Pulled By: zou3519

fbshipit-source-id: dd0788530a60bb5ed2f42b961fae3db2b4404b64

add dense vector to id_list operator (#15090)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15090

as title
step 2 of the linked task

Reviewed By: ellie-wen

Differential Revision: D13425977

fbshipit-source-id: f3538ed68f42470ba39c5b779af764d4a5591a9d

fix clang-tidy script for python 3

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15360

Differential Revision: D13509668

Pulled By: suo

fbshipit-source-id: a3448a115eaac8dd4c3f179901a23bdbc5098408

Port torch.linspace to ATen and parallelize it on CPU.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15320

Reviewed By: ezyang

Differential Revision: D13498995

Pulled By: gchanan

fbshipit-source-id: fba655d51d978fffaa53a5e4cae4a99ebfb0eddc

Add (Un)Fold modules to standard library (#14759)

Summary:
Depends on #14597 for the corresponding aten ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14759

Differential Revision: D13325356

Pulled By: driazati

fbshipit-source-id: 99e39449c1ccfa293de05672c31a11e580bdd11f

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

Summary:
max and reducemax are smashed together, we need to support one input case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15241

Reviewed By: yinghai

Differential Revision: D13473312

Pulled By: houseroad

fbshipit-source-id: 9b8c847286a2631b006ca900271bc0d26574101a

Method returns a single argument (#15289)

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

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

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

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

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

Differential Revision: D13481649

Pulled By: zdevito

fbshipit-source-id: 0e2242a40bb28cca2d0e8be48bede96195e4858c

caffe2 mobile opengl (#15322)

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

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

Reviewed By: Maratyszcza

Differential Revision: D13499943

fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2

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

Differential Revision:
D13383102

Original commit changeset: c434f0e0ddff

fbshipit-source-id: 690f46ca0710954fa591a5ea77535e9759db4de5

Updating submodules

Reviewed By: cdelahousse

fbshipit-source-id: 4bf66581d07d839f459869bc9c6428011063cc5b

improve script/no script save error (#15321)

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

Differential Revision: D13499379

Pulled By: zdevito

fbshipit-source-id: b8dc0a83efabff74199f4aab2ee98aa41c42608b

Allow tracing with fork/wait (#15184)

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

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

Differential Revision: D13457691

Pulled By: highker

fbshipit-source-id: 8fe46afc41357a0eb8eadd83f687b31d074deb0e

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

Summary:
…on](#12115)

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

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

Differential Revision: D13491159

Pulled By: soumith

fbshipit-source-id: e83802e1628b6d2615c45e18d7acf991d143a09e

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

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

This reverts commit a84e873bb156080ea76ab182171b1f3b4d5395f6.

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

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

Differential Revision: D13495852

Pulled By: ezyang

fbshipit-source-id: bcd3f60088b14831c53d3c171f10cd1ab6b35dee

Fix _apply in nn.Module (#15305)

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

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

Differential Revision: D13493937

Pulled By: goldsborough

fbshipit-source-id: 4cb8687f90fc8709a536c5e7eacd0dc8edf6f750

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

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

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

Differential Revision: D13493941

Pulled By: goldsborough

fbshipit-source-id: c1cd0d10ab5838c75f167c0bdb57e45a0bc1344e

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

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

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

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

Reviewed By: hanli0612

Differential Revision: D13475758

fbshipit-source-id: 6e1b166ec35163b9776c797b6a2e0d002560cd29

Port nn fold and unfold to c++

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

Reviewed By: ezyang

Differential Revision: D13272227

fbshipit-source-id: 6eccab5ff5830a977398a96393b778095120edc6

Allow future type parsing

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

Differential Revision: D13490984

Pulled By: highker

fbshipit-source-id: 165fe995867be273793f983154aa6cbce13e4396

Removing BUILD_C10_EXPERIMENTAL_OPS option and unglobbing experimental/c10d ops

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

Reviewed By: orionr

Differential Revision: D13474801

Pulled By: pjh5

fbshipit-source-id: 9d3664c3a3a1b6c2d9f083f8476fe3b037296b98

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

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

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

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

Differential Revision: D9007525

Pulled By: driazati

fbshipit-source-id: 93ef49a34ce4e5ffd4bda94cd9a6ddc939f0a4cc

add isinstance static type checking for jit (#15076)

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

Differential Revision: D13471067

Pulled By: wanchaol

fbshipit-source-id: d39b7ed5db9fcca4b503659d02cf7795950ea8ea

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

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

Differential Revision: D13490420

Pulled By: orionr

fbshipit-source-id: 4387d707f634a5975238af915b1befb2277f8ec7

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

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

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

Reviewed By: dzhulgakov

Differential Revision: D13432922

fbshipit-source-id: 9f0d8d70646c50d979bde5ba3c3addeebac48a3d

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

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

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

Differential Revision: D13494884

Pulled By: driazati

fbshipit-source-id: 65fec39ae03a7d6a68ad617c9b270faeb1617930

Fix for issue 14829 (#14908)

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

Differential Revision: D13490360

Pulled By: ezyang

fbshipit-source-id: ff11a72e19b49223652182e82c2b4e65fe444ca7

Minor fixes in .jenkins/caffe2/bench.sh

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

Differential Revision: D13493876

Pulled By: bddppq

fbshipit-source-id: 7146eb2587e526af65b4b0290c25bd55653a3088

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

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

Differential Revision: D13453036

Pulled By: houseroad

fbshipit-source-id: 4724b4ffcebda6cd6b2acac51d6733cb27318daf

Tighten up invariants regarding StreamId. (#15125)

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

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

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

Reviewed By: dzhulgakov

Differential Revision: D13432800

fbshipit-source-id: a86991d6fc1d8aa4e54e8175e5f06f90856238e6

Fix tensor printing bug in Python 2 (#12732)

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

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

Differential Revision: D10458630

Pulled By: driazati

fbshipit-source-id: a63e42fbc0e39e4291480775b516c98122ec05a1

Refactor hotpatch_vars and apply it to libtorch (#14976)

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

Differential Revision: D13485381

Pulled By: soumith

fbshipit-source-id: 0af3c2e1b90988d56f6f85632328d1e4b788ffd2

Trivial comment correction in dataloader (#15276)

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

Differential Revision: D13477324

Pulled By: soumith

fbshipit-source-id: 2a74a014999655d129311d611f2a09411339cb13

Delete ffi documentation (#15220)

Summary: Deleting FFI documentation since its deprecated.

Differential Revision: D13477329

Pulled By: soumith

fbshipit-source-id: 0b3d485eb7cef1f05b6b397dff50f21a49d6409e

Fix a typo in the assert

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

Reviewed By: llyfacebook

Differential Revision: D13477029

Pulled By: sf-wind

fbshipit-source-id: 9c5571a583c01f9701625541ebec0c836cb923f2

fix cholesky call in potrs example (#15215)

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

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

Differential Revision: D13476468

Pulled By: soumith

fbshipit-source-id: 7b68035f435a2b1be4d363b3f63e407394af949d

value-based mark and sweep DCE (#14910)

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

Differential Revision: D13476445

Pulled By: suo

fbshipit-source-id: 2bf5db19711c07dde946697a4f4b270bd8baf791

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

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

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

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

print(y)

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

print(x.grad)
```

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

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

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

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

Differential Revision: D13476513

Pulled By: soumith

fbshipit-source-id: bee62282e9ab72403247384e4063bcdf59d40c3c

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

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

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

Reviewed By: igorsugak

Differential Revision: D13470369

fbshipit-source-id: ce995beab7c64bebe8b234fb5e6d015940ec2952

Create parser.cpp (#15238)

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

Differential Revision: D13474635

Pulled By: zdevito

fbshipit-source-id: 7dc824eea8f506d6c8ae1aa67aeec0c34d5285fc

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

Summary:
Several enhancements are implemented:

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

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

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

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

Reviewed By: llyfacebook

Differential Revision: D13467225

Pulled By: sf-wind

fbshipit-source-id: 7da34a72d43a9603cd7ab953f5821c1222d0178f

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

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

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

Reviewed By: jackm321

Differential Revision: D13471959

fbshipit-source-id: 8a58e28edd900b6ad54a1dbd63ff2579fbe0e820

FP16MomentumSGDUpdate Op fix and enable for ROCm (#15150)

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

Differential Revision: D13473145

Pulled By: bddppq

fbshipit-source-id: 4c5c5f30cb9bba658e3639dbe193fa08a304d306

Start unittesting our main observer (#15191)

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

OSS:

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

Reviewed By: yinghai

Differential Revision: D13159184

fbshipit-source-id: 9823e792cf0ed8d0379235c44564862b7d784845

Build c10 HIP test

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

Reviewed By: ezyang

Differential Revision: D13471002

Pulled By: bddppq

fbshipit-source-id: b42c3bc2b9db672ce50a52eb700cc6ed13d3535f

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

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

Differential Revision: D13467646

Pulled By: zou3519

fbshipit-source-id: 4f1f4ef5fa4bc5a1b4775dfcec6ab155e5bf8d6e

Preserve module hierarchy on traced modules (#15101)

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

Differential Revision: D13468467

Pulled By: jamesr66a

fbshipit-source-id: c2b6740b12cde6e23395d12e42d4fc2c4c7ca3f2

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

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

Differential Revision: D13471625

Pulled By: zdevito

fbshipit-source-id: a982413a308c7a9bb5b6a82fe96fd3de44f555aa

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

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

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

Differential Revision: D13470950

Pulled By: bddppq

fbshipit-source-id: f9aa44c7c95299a099c0ea9317b9044cc056acc5

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

Differential Revision:
D13440858

Original commit changeset: 1c6af5c53538

fbshipit-source-id: fda42ea75000d4a4e9c4a8eeaaa5518f7ad9c298

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

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

Differential Revision: D13470991

Pulled By: bddppq

fbshipit-source-id: 45acc4f9ea5baaaf7672b86eb022948055779925

Fix jit doc codeblocks and tables (#15227)

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

Differential Revision: D13468847

Pulled By: driazati

fbshipit-source-id: eb7375710d4f6eca1d0f44dfc43c7c506300cb1e

Remove __forceinline__ hipification step. (#15229)

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

Differential Revision: D13470962

Pulled By: bddppq

fbshipit-source-id: 34f8361bda5f3dce20a2eeb530c3a25d1b1bdd06

Enable all clang-tidy performance checks (#15198)

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

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

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

Differential Revision: D13468797

Pulled By: goldsborough

fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2

Refactor caffe2 CI scripts and add benchmark scripts

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

Differential Revision: D13468049

Pulled By: bddppq

fbshipit-source-id: e73bc8742c8a03f498816eee8a72b06a3e19fe48

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

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

Differential Revision: D13458713

Pulled By: goldsborough

fbshipit-source-id: 4091fe1b96a1be8db14c6a4307fbacc2b41ff6fe

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

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

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

Reviewed By: dzhulgakov

Differential Revision: D13419643

fbshipit-source-id: 65b5a037b94d458b944d51f790ba2829db1fb530

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

Differential Revision:
D13407930

Original commit changeset: d17f1195a221

fbshipit-source-id: f4458872c48ec4a2c9983b21ed90bcdc0ae665b7

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

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

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

Reviewed By: highker

Differential Revision: D13408345

fbshipit-source-id: 5fc54d33da12e3f0a200e79380d4c695b0339b17

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

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

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

Reviewed By: yinghai

Differential Revision: D13405225

fbshipit-source-id: b5c1859c6819d53c1a44718e2868e3137067df36

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

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

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

Reviewed By: salexspb

Differential Revision: D13401146

fbshipit-source-id: bc385df074043e03ea884940b5631b96de4a607e

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

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

Add test utils to compare tensors in two workspaces

Reviewed By: ZolotukhinM

Differential Revision: D13387212

fbshipit-source-id: e19d932a1ecc696bd0a08ea14d9a7485cce67bb2

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

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

Put some utils to fill tensors to test_utils

Reviewed By: salexspb

Differential Revision: D13386691

fbshipit-source-id: 51d891aad1ca12dc5133c0352df65b8db4f96edb

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

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

Test utils to create an operator

On top of D13370461

Reviewed By: ZolotukhinM

Differential Revision: D13382773

fbshipit-source-id: a88040ed5a60f31d3e73f1f958219cd7338dc52e

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

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

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

Reviewed By: salexspb

Differential Revision: D13370461

fbshipit-source-id: bee467a127e1d032ef19482f98aa5c776cf508c0

Fix derivative for mvlgamma (#15049)

Summary:
Fixes #15015.

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

Reviewed By: soumith

Differential Revision: D13434117

Pulled By: zou3519

fbshipit-source-id: 4a292600af9eb08b67c0f8b5482e9512aac95e72

Fix numpy conversion for int8 tensor

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

Differential Revision: D13459270

Pulled By: li-roy

fbshipit-source-id: 605534add263860a3ad9a7fa70888301ee0bf8e4

add erf and erfc to fuser/autodiff

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

Differential Revision: D13455690

Pulled By: soumith

fbshipit-source-id: b06e5f5d362869c2e5fa11a52f9450d77c30d4cb

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

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

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

Reviewed By: ezyang

Differential Revision: D13365817

fbshipit-source-id: bc73b27a793602cb14200dcdf357aa63233da43c

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

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

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

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

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

Reviewed By: ezyang

Differential Revision: D13287688

fbshipit-source-id: 17800ca8a79ab1633f23be58d96f99a160d8ed24

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

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

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

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

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

Reviewed By: viswanathgs

Differential Revision: D13429770

fbshipit-source-id: 5e95d059f3c668f14059a0a83e8e53d8554cdb99

Support torch.tensor in script (#14913)

Summary:
Adding support for torch.tensor in script.

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

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

Differential Revision: D13407930

Pulled By: eellison

fbshipit-source-id: d17f1195a22149d5b0d08d76c89a7fab8444f7c5

Remove TensorImpl -> Type dependency

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

Reviewed By: dzhulgakov

Differential Revision: D13425628

fbshipit-source-id: 08a8a774d17b071367454e027012a02f96d177d4

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

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

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

Differential Revision: D13458784

Pulled By: goldsborough

fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b

Add missing caffe2_hip extension in setup.py

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

Reviewed By: orionr

Differential Revision: D13457644

Pulled By: bddppq

fbshipit-source-id: c2363e9b8fd21709b62777e5b2199f01ec1c65f8

Remove disabled_features in hipify

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

Reviewed By: ezyang

Differential Revision: D13453762

Pulled By: bddppq

fbshipit-source-id: e177042c78f5bf393163d660c25b80285353853d

Run ONNX cuda backend test cases via ROCm

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

Differential Revision: D13427757

Pulled By: bddppq

fbshipit-source-id: ba0273d75986cd5b146f7041a83c63ddf9c6c0cf

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

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

Differential Revision: D13451936

Pulled By: soumith

fbshipit-source-id: 6dbda3a6179d9407bc3396bf1a2baf3e85bc4cf2

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

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

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

Reviewed By: ezyang

Differential Revision: D13419682

fbshipit-source-id: 3e59403bc1c0e71e5cb66df932ed0c6a0a72e643

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

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

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

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

Reviewed By: dzhulgakov

Differential Revision: D13385540

fbshipit-source-id: 8868172baf20efaf53fecf7d666a6980f59b64f5

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

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

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

Reviewed By: ezyang

Differential Revision: D13419736

fbshipit-source-id: f4ad7b9582c2f809258169b7fef9adbca7063d99

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

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

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

Reviewed By: ezyang

Differential Revision: D13419711

fbshipit-source-id: dd2b740c3f13d8087085bafc5571aaf908d1af42

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

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

Differential Revision: D13453708

Pulled By: bddppq

fbshipit-source-id: f8c147ae9f679e395fee9d4c73ebcca052c9a752

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

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

Differential Revision: D13451898

Pulled By: bddppq

fbshipit-source-id: 27906be62fb88aaa13c257441a2e35a285b445ee

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

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

Differential Revision: D13433405

Pulled By: VitalyFedyunin

fbshipit-source-id: 58544d38affd20818742338c9eb789d9d14ccbaa

Delete defunct USE_SIMPLE_BASE_CTOR_DTOR (#15144)

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

Differential Revision: D13440872

Pulled By: ezyang

fbshipit-source-id: 2b1d73fac0c63729ba01d8f129642334ae9d9cf3

Fix typo (#15045)

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

Reviewed By: dzhulgakov

Differential Revision: D13413509

Pulled By: houseroad

fbshipit-source-id: be66700c30d038368b1433232a4e3fd9299c83d6

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

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

Differential Revision: D13440858

Pulled By: ezyang

fbshipit-source-id: 1c6af5c53538b81c6b92cf1dda231ed333f28035

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

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

Differential Revision: D13447448

Pulled By: soumith

fbshipit-source-id: 56e8d42934538fb00465105a2c5ccfeb7c18a651

Unify SparseTensorImpl::size_ and TensorImpl::sizes_

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

Differential Revision: D13434981

Pulled By: VitalyFedyunin

fbshipit-source-id: 98bd4d66834a3c3d2ea577adb0c8413852da095d

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

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

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

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

apaszke zdevito

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

Differential Revision: D12981996

Pulled By: goldsborough

fbshipit-source-id: 147370d3596ebb0e94c82cec92993a148fee50a7

Reuse KernelSpec for FusionGroups with equivalent graphs (#14541)

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

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

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

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

Differential Revision: D13324487

Pulled By: zou3519

fbshipit-source-id: b841d82ed35a959b5cfc72db033bf5a7b42cc4fb