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38 Commits

Author SHA1 Message Date
Nikita Shulga
a9b0a921d5 Disable avoid-non-const-global-variables lint check (#62008) 
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`

All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`;  do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008

Reviewed By: driazati, r-barnes

Differential Revision: D29838584

Pulled By: malfet

fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
 2021-07-22 18:04:40 -07:00
Nikita Shulga
3a66a1cb99 [clang-tidy] Exclude cppcoreguidelines-avoid-magic-numbers (#57841) 
Summary:
Add cppcoreguidelines-avoid-magic-numbers exclusion to clang-tidy
Remove existing nolint warnings using following script:
```
for file in `git ls-files | grep -v \.py`; do gsed '/^ *\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)/d' -i  $file; done
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/57841

Reviewed By: samestep

Differential Revision: D28295045

Pulled By: malfet

fbshipit-source-id: 7c6e8d1213c9593f169ed3df6a916498f1a97163
 2021-05-07 20:02:33 -07:00
Nikita Shulga
4cb534f92e Make PyTorch code-base clang-tidy compliant (#56892) 
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os

def get_compiled_files_list():
    import json
    with open("build/compile_commands.json") as f:
        data = json.load(f)
    files = [os.path.relpath(node['file']) for node in data]
    for idx, fname in enumerate(files):
        if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
            files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
    return files

def run_clang_tidy(fname):
    check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
    changes = check_output(["git", "ls-files", "-m"])
    if len(changes) == 0:
        return
    check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])

def main():
    git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
    compiled_files = get_compiled_files_list()
    for idx, fname in enumerate(git_files):
        if fname not in compiled_files:
            continue
        if fname.startswith("caffe2/contrib/aten/"):
            continue
        print(f"[{idx}/{len(git_files)}] Processing {fname}")
        run_clang_tidy(fname)

if __name__ == "__main__":
    main()
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892

Reviewed By: H-Huang

Differential Revision: D27991944

Pulled By: malfet

fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
 2021-04-28 14:10:25 -07:00
Edward Yang
8eee8460f8 codegen: Resolve overload ambiguities created by defaulted arguments (#49348) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49348

This is a redux of #45666 post refactor, based off of
d534f7d4c5
Credit goes to peterbell10 for the implementation.

Fixes #43945.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: smessmer

Differential Revision: D25594004

Pulled By: ezyang

fbshipit-source-id: c8eb876bb3348308d6dc8ba7bf091a2a3389450f
 2021-01-04 11:59:16 -08:00
Kurt Mohler
df7c059428 Throw error if torch.set_deterministic(True) is called with nondeterministic CuBLAS config (#41377) 
Summary:
For CUDA >= 10.2, the `CUBLAS_WORKSPACE_CONFIG` environment variable must be set to either `:4096:8` or `:16:8` to ensure deterministic CUDA stream usage. This PR adds some logic inside `torch.set_deterministic()` to raise an error if this environment variable is not set properly and CUDA >= 10.2.

Issue https://github.com/pytorch/pytorch/issues/15359

Pull Request resolved: https://github.com/pytorch/pytorch/pull/41377

Reviewed By: malfet

Differential Revision: D22758459

Pulled By: ezyang

fbshipit-source-id: 4b96f1e9abf85d94ba79140fd927bbd0c05c4522
 2020-08-05 12:42:24 -07:00
Kurt Mohler
124cdf2290 Add experimental deterministic flag (#38683) 
Summary:
Adds `torch.experimental.deterministic` flag to enforce deterministic algorithms across all of pytorch.
Adds `torch.experimental.deterministic_error_level` to allow users to choose between error/warning/silent if determinism for an operation is not available.
Adds `torch.experimental.alert_not_deterministic()` which should be called within operations that are not deterministic.
Offers both Python and ATen interfaces

Issue https://github.com/pytorch/pytorch/issues/15359
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38683

Differential Revision: D21998093

Pulled By: ezyang

fbshipit-source-id: 23aabbddd20f6199d846f97764ff24d728163737
 2020-06-12 08:44:06 -07:00
Dmytro Dzhulgakov
50a1850d8d [pytorch] Route default warning sync to LOG(WARNING) - second try (#36984) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36984

Follow LOG(WARNING) format for c++ side warnings in order to play well with larger services, especially when using glog. I need to hook up into GLOG internals a bit in order to override FILE/LINE without having to change the whole thing to be macros, but it seems to be stable between glog versions.

Note, this also changes caffe2_log_level to warning by default - I think it's a much better default when compiling without glog (or maybe even have info).

With glog output, stderr capture doesn't work any more in tests. That's why we instead use c10-level warnings capture.

Test Plan:
Run unittest in both glog and non-glog build mode:

glog:
```
W0416 12:06:49.778215 3311666 exception_test.cpp:23] Warning: I'm a warning (function TestBody)
```

no-glog:
```
[W exception_test.cpp:23] Warning: I'm a warning (function TestBody)
```

Reviewed By: ilia-cher

Differential Revision: D21151351

fbshipit-source-id: fa926d9e480db5ff696990dad3d80f79ef79f24a
 2020-04-23 01:08:00 -07:00
Will Feng
e8087a3060 Change C++ API test files to only include torch/torch.h (#27067) 
Summary:
One of the purposes of the C++ API tests in `test/cpp/api/` should be to check that including `torch/torch.h` is a sufficient prerequisite for using all C++ frontend features. This PR change ensures that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27067

Differential Revision: D17856815

Pulled By: yf225

fbshipit-source-id: 49c057bd807b003e4a00f6ba73131d763a0f277a
 2019-10-10 09:46:29 -07:00
Pavel Belevich
98ccae09af C++ API parity: at::Tensor::grad 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26150

Test Plan: Imported from OSS

Differential Revision: D17427579

Pulled By: pbelevich

fbshipit-source-id: 68d012076aa86dee9f23fad71a2d265d75f56d22
 2019-09-18 09:20:38 -07:00
Will Feng
085bd15880 Add TORCH_WARN_ONCE, and use it in Tensor.data<T>() (#25207) 
Summary:
This PR adds `TORCH_WARN_ONCE` macro, and use it in `Tensor.data<T>()`.

cc. gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25207

Differential Revision: D17066263

Pulled By: yf225

fbshipit-source-id: 411c6ccc8326fb27ff885fee4638df8b5ba4d449
 2019-08-27 21:42:44 -07:00
Josh Varty
1cdcdd78af Kaiming Initialization (#14718) 
Summary:
/cc goldsborough

Working on #14582

The corresponding python implementations are at: [pytorch/torch/nn/init.py](6302e4001a/torch/nn/init.py (L261-L327))

Here is my initial implementation of Kaiming Initialization. I have not been able to figure out how to successfully run tests locally so I haven't added any yet.

A couple questions:
- Are the enums defined in the right place? I copied their names from Python, but do you prefer different naming conventions for C++?
- To run tests locally do I use `python setup.py test`? Can I run just a subset of the tests somehow?
- Should I add my tests at [test/cpp/api/misc.cpp](https://github.com/pytorch/pytorch/blob/master/test/cpp/api/misc.cpp#L47-L54)?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14718

Differential Revision: D14049159

Pulled By: goldsborough

fbshipit-source-id: 966ac5126875936e69b185b5041f16476ed4cf70
 2019-02-15 14:58:22 -08:00
Dmytro Dzhulgakov
46503a7ac0 Trim libshm deps, move tempfile.h to c10 (#17019) 
Summary:
libshm_manager doesn't need to depend on all of libtorch. It only uses tiny tempfile.h which can be moved to c10. I could just duplicate the file too, but it's not worth it as c10 is small enough.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17019

Differential Revision: D14052688

Pulled By: dzhulgakov

fbshipit-source-id: 8797d15f8c7c49c49d40b7ab2f43aa3bf6becb0c
 2019-02-13 19:38:35 -08:00
Peter Goldsborough
8311bbee7f Fix Windows build and test in CI (#11716) 
Summary:
This PR adds Windows support for the C++ frontend. A lot of declarations were missing `TORCH_API` macros, and lots of code just did not compile on MSVC.

ebetica ezyang orionr
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11716

Reviewed By: orionr

Differential Revision: D13038253

Pulled By: goldsborough

fbshipit-source-id: c8e5a45efd26117aeb99e768b56fcd5a89fcb9f8
 2018-11-13 16:35:54 -08:00
Peter Goldsborough
ab0c72ab6f Replace cursors with OrderedDict (#13427) 
Summary:
This is a pre-cursor diff to Python <-> C++ frontend integration -- I have a follow-up PR coming for that. This PR changes the C++ frontend module interface to replace the custom "cursor"s I introduced some time ago with `OrderedDict`. I introduced cursors at the time as a convenient way of applying functions and query operations on a modules' parameters, buffers and modules, allowing things like `module.parameters().map(my_func)`. However, I noticed that (1) this functionality is easily implement-able on top of a regular data structure and (2) more importantly,  using OrderedDicts is much, much easier for Python integration. This is especially true given that ScriptModule today also uses OrderedDict. Since C++ frontend modules and ScriptModules will soon too share as many implementation details as possible, it is overall the best move to ditch the custom cursor datastructure and pervasively use OrderedDict everywhere.

For this I did:

1. Changed the C++ frontend module interface to more closely match the Python one by providing `parameters()`, `named_parameters()` and other methods Python provides. This is very important for the following diff which binds these into Python for inter-op with Python modules.
2. In lieu of the `Cursor::apply()` method I added `nn::Module::apply`. This again is one more unifying step between Python and C++, since Python modules have an apply function too.
3. Deleted all uses of Cursor.
4. Tidied and beefed up the `OrderedDict` class. In particular, I made `OrderedDict::Item` store an `std::pair` under the hood, because that is trivial to bind into Python and saved me a lot of headaches. `key` and `value` become methods instead of fields, which they should have been from the very start anyway because it allows exactly these kinds of changes, as per usual good software engineering principle of encapsulation.
5. Added many tests for the OrderedDict use in `nn::Module`.

ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13427

Differential Revision: D12894092

Pulled By: goldsborough

fbshipit-source-id: 715770c95a9643753a1db26d7f9da9a78619a15d
 2018-11-07 11:10:05 -08:00
Peter Goldsborough
393ad6582d Use torch:: instead of at:: in all C++ APIs (#13523) 
Summary:
In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions.

Note that since we're just talking about typedefs, this change does not break any existing code.

Once this lands I will update stuff in `pytorch/tutorials` too.

zdevito ezyang gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523

Differential Revision: D12942787

Pulled By: goldsborough

fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
 2018-11-06 14:32:25 -08:00
Peter Goldsborough
469c6b0539 Replace tmpnam usage (#13289) 
Summary:
Fix
```
/torch_shm_manager#compile-manager.cpp.oc089dac2,gcc-5-glibc-2.23-clang/manager.cpp.o:manager.cpp:function main:
warning: the use of `tmpnam' is dangerous, better use `mkstemp`
```

apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13289

Differential Revision: D12873282

Pulled By: goldsborough

fbshipit-source-id: fc64b59403d52eb271744378ef4ee8338c79312c
 2018-11-01 13:50:43 -07:00
Christian Puhrsch
a9e6a673ae Remove caffe2::Tensor::capacity_nbytes, at::Tensor::to##name##Data, (#11876) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11876

Modern C++ api instead of macros, item() is aligned with Python frontend. caffe2::Tensor::capacity_nbytes is effecitvely unused and confusing w.r.t. caffe2::Tensor::nbytes().

codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte   "item<uint8_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong   "item<int64_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt    "item<int32_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat  "item<float>"

codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData   "data<uint8_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData   "data<int64_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData    "data<int32_t>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>"
codemod -d caffe2           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData  "data<float>"

codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte   "item<uint8_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong   "item<int64_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt    "item<int32_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat  "item<float>"

codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData   "data<uint8_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData   "data<int64_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData    "data<int32_t>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>"
codemod -d hphp           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData  "data<float>"

codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCComplexDouble "item<std::complex<double>>"

codemod -d tc           --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat  "item<float>"

Reviewed By: ezyang

Differential Revision: D9948572

fbshipit-source-id: 70c9f5390d92b82c85fdd5f8a5aebca338ab413c
 2018-09-24 10:40:10 -07:00
Peter Goldsborough
825181ea9d Rewrite C++ API tests in gtest (#11953) 
Summary:
This PR is a large codemod to rewrite all C++ API tests with GoogleTest (gtest) instead of Catch.

You can largely trust me to have correctly code-modded the tests, so it's not required to review every of the 2000+ changed lines. However, additional things I changed were:

1. Moved the cmake parts for these tests into their own `CMakeLists.txt` under `test/cpp/api` and calling `add_subdirectory` from `torch/CMakeLists.txt`
2. Fixing DataParallel tests which weren't being compiled because `USE_CUDA` wasn't correctly being set at all.
3. Updated README

ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11953

Differential Revision: D9998883

Pulled By: goldsborough

fbshipit-source-id: affe3f320b0ca63e7e0019926a59076bb943db80
 2018-09-21 21:28:16 -07:00
Gregory Chanan
e00fb69b25 Use CATCH prefix to avoid name conflicts with Caffe2. 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11780

Differential Revision: D9889925

Pulled By: gchanan

fbshipit-source-id: 5eca849c36ced00b8ae7482b7945b445a3e1687e
 2018-09-18 08:12:45 -07:00
Sebastian Messmer
f51f15bb27 Update include paths for ATen/core (#10130) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10130

Update some include paths to make them internally consistent

Reviewed By: ezyang

Differential Revision: D9119906

fbshipit-source-id: b44e5cab8e8e795ee18afe9ffc6caf1f2b413467
 2018-08-03 11:57:02 -07:00
Peter Goldsborough
4a796e4430 Initialization functions (#9295) 
Summary:
To allow our C++  customers to use our initialization methods as well, this PR moves some of the code from `torch.nn.init` to ATen, calls it from Python, and adds equivalent code to the C++ frontend.

Notes:
1. Happy to hear thoughts on whether it's ok to have e.g. `torch.nn.init.dirac_` *and* `torch.dirac_` (the former has a `no_grad` guard). We have this for `ones_` and stuff too, so I don't mind it.
2. I left the exception checking in Python because they throw `ValueError`s while ATen errors show as `RuntimeError`s. I imagine this would break users' error handling if someone were to have a `try`-`except` handler for `ValueError` (or maybe it's a far fetch)

EDIT: After discussions with zdevito, the PR now simply duplicates the code in C++ exclusively for the C++ API, and we leave the Python code as-is (to make it easier for people to read/modify).

ebetica ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9295

Differential Revision: D8813793

Pulled By: goldsborough

fbshipit-source-id: 4b969f3f75952c1be4e837e19e23b8098e5fbd4b
 2018-07-12 18:53:57 -07:00
Peter Goldsborough
03d0a70a4d Set random seed at the start of C++ tests (#8903) 
Summary:
Sets the random seed at the start of C++ tests so that everything is super deterministic.

I made sure we only generate random values from torch instead of `std::`, so that this seed always applies. I.e. I do:

```
torch::randint(2, {2}, at::kInt64)
```

instead of

```
std::rand() % 2
```

Also got rid of the tests that test the random seeding, since it would interfere here. And the test is not useful since we just use ATen's seeding mechanism, which should work.

Fixes  #7288 #7286 #7289

ebetica ezyang
Closes https://github.com/pytorch/pytorch/pull/8903

Differential Revision: D8667269

Pulled By: goldsborough

fbshipit-source-id: a833e86e156d5e68dae8c53a4b1c433cb0608b6c
 2018-06-27 20:09:46 -07:00
Peter Goldsborough
fef9a66d08 Use torch:: instead of at:: (#8911) 
Summary:
This PR is the final step to making `torch::` the only  namespace users of the C++ API ever see. Basically, I did:

``` cpp

namespace torch {
using namespace at;
}
```

And then changed `torch::` to `at::` almost everywhere. This worked surprisingly well out of the box. So users can now write `torch::relu`  and `torch::log_softmax` and `torch::conv2d` instead of having to know when to use `at::` and when `torch::`. This is happy!

Another thing I did was to have `using Dtype = at::ScalarType`, which will be the eventual name anyway.

ebetica ezyang apaszke zdevito
Closes https://github.com/pytorch/pytorch/pull/8911

Reviewed By: ezyang

Differential Revision: D8668230

Pulled By: goldsborough

fbshipit-source-id: a72ccb70fca763c396c4b0997d3c4767c8cf4fd3
 2018-06-27 14:42:01 -07:00
Peter Goldsborough
55757357b2
[C++ API] Better forward methods (#8739) 
* Better forward methods in C++ API

capitalize error message in test_torch.test_flatten

Support for operator()

* Add operator() to Functional

* Get rid of SigmoidLinear

* Add BoundFunction to FunctionalImpl

* Remove macro from conv because it makes errors more nasty
 2018-06-26 13:23:16 -07:00
Peter Goldsborough
521f5111ad
[C++ API] Use torch::Tensor instead of at::Tensor/Variable mix (#8680) 
* Use torch::Tensor instead of at::Tensor/Variable mix

* TensorRange -> TensorListView
 2018-06-24 19:03:39 -07:00
Peter Goldsborough
271406f276
[C++ API] Make pImpl easy to use in modules to enable happy reference semantics (#8347) 
* Created TORCH_MODULE macro

Rewrote Linear

Rewrote Dropout and added default constructor to TORCH_MODULE macro

Turned TORCH_MODULE contens into a proper base class

Added some documentation

Got rid of the old Dropout module

Got rid of the old Embedding module

Got rid of the old BatchNorm module

Got rid of the old Conv module

Fixing optimizers

Rebase

Removed old RNN modules and the TORCH_ATTR macro

Removed temporary P:: namespace

Added cloning behavior to all modules

Got rid of some get() calls

self review nits

Remove noexcept from ModuleHolder methods that can throw

Remove spaces

Add missing override to reset() methods

Added examples to documentation in pimpl.h

* Post rebase fixes
 2018-06-18 19:45:53 -07:00
Peter Goldsborough
372d1d6735
Create ATen tensors via TensorOptions (#7869) 
* Created TensorOptions

Storing the type in TensorOptions to solve the Variable problem

Created convenience creation functions for TensorOptions and added tests

Converted zeros to TensorOptions

Converted rand to TensorOptions

Fix codegen for TensorOptions and multiple arguments

Put TensorOptions convenience functions into torch namespace too

All factory functions except *_like support TensorOptions

Integrated with recent JIT changes

Support *_like functions

Fix in place modification

Some cleanups and fixes

Support sparse_coo_tensor

Fix bug in Type.cpp

Fix .empty calls in C++ API

Fix bug in Type.cpp

Trying to fix device placement

Make AutoGPU CPU compatible

Remove some auto_gpu.h uses

Fixing some headers

Fix some remaining CUDA/AutoGPU issues

Fix some AutoGPU uses

Fixes to dispatch_tensor_conversion

Reset version of new variables to zero

Implemented parsing device strings

Random fixes to tests

Self review cleanups

flake8

Undo changes to variable.{h,cpp} because they fail on gcc7.2

Add [cuda] tag to tensor_options_cuda.cpp

Move AutoGPU::set_index_from into .cpp file because Windows is stupid and sucks

Fix linker error in AutoGPU.cpp

Fix bad merge conflict in native_functions.yaml

Fixed caffe2/contrib/aten

Fix new window functions added to TensorFactories.cpp

* Removed torch::TensorOptions

Added code to generate wrapper functions for factory methods

Add implicit constructor from Backend to TensorOptions

Remove Var() from C++ API and use torch:: functions

Use torch:: functions more subtly in C++ API

Make AutoGPU::set_device more exception safe

Check status directly in DynamicCUDAHooksInterface

Rename AutoGPU to DeviceGuard

Removed set_requires_grad from python_variables.h and warn appropriately in Variable::set_requires_grad

remove python_default_init: self.type()

Add back original factory functions, but with deprecation warnings

Disable DeviceGuard for a couple functions in ATen

Remove print statement

Fix DeviceGuard construction from undefined tensor

Fixing CUDA device compiler issues

Moved as many methods as possible into header files

Dont generate python functions for deprecated factories

Remove merge conflict artefact

Fix tensor_options_cuda.cpp

Fix set_requires_grad not being checked

Fix tensor_new.h

TEMPORARILY put some methods in .cpp files to see if it solves issues on windows and mac

Fix bug in DeviceGuard.h

Missing includes

TEMPORARILY moving a few more methods into .cpp to see if it fixes windows

Fixing linker errors

* Fix up SummaryOps to use new factories

Undo device agnostic behavior of DeviceGuard

Use -1 instead of optional for default device index

Also move DeviceGuard methods into header

Fixes around device index after optional -> int32_t switch

Fix use of DeviceGuard in new_with_tensor_copy

Fix tensor_options.cpp

* Fix Type::copy(

* Remove test_non_float_params from ONNX tests

* Set requires_grad=False in ONNX tests that use ints

* Put layout/dtype/device on Tensor

* Post merge fixes

* Change behavior of DeviceGuard to match AutoGPU

* Fix C++ API integration tests

* Fix flip functions
 2018-06-16 00:40:35 -07:00
Sam Gross
12229afd00
Record shape and type in autograd to validate gradients (#8168) 
The check that the gradient is defined is currently disabled because
TestJit.test_ge_optimized will trigger the error.
 2018-06-06 18:09:53 -04:00
Peter Goldsborough
990c6c5531 [C++ API] Improve and use OrderedDict for parameters / modules (#7823) 
* Improve OrderedDict for C++ API

* Give OrderedDict a subject and fix review comments

* Fix OrderedDict use in torch/csrc/jit/script/init.cpp
 2018-06-05 14:29:09 -04:00
Peter Goldsborough
4a80755834
Split up detail.h (#7836) 2018-05-30 08:55:34 -07:00
Peter Goldsborough
28b1a3852c
Add backward() to Tensor and Variable (#7774) 
* Add backward() to Tensor and Variable

* Add at:: in front of Tensor

* Trying to not move optional to appease windows?

* Move implementation into cpp file

* Undo some formatting changes
 2018-05-24 17:31:41 -07:00
Peter Goldsborough
b12164005f
[C++ API] Remove virtual forward and implement Sequential based on Any(Module) (#7508) 
* Remove virtual forward

* Rebase
 2018-05-24 12:46:51 -07:00
Will Feng
60745b3380 Revert #7750 and #7762 to fix Windows CI on master (#7772) 
* Revert "Add missing brace (#7762)"

This reverts commit ea27c5af50.

* Revert "[C++ API] Add backward() to Tensor and Variable  (#7750)"

This reverts commit 1e2762796f.
 2018-05-22 15:42:52 -07:00
Peter Goldsborough
1e2762796f
[C++ API] Add backward() to Tensor and Variable (#7750) 
* Add backward() to Tensor and Variable

* Added a couple tests
 2018-05-22 10:43:04 -07:00
Peter Goldsborough
cba19e59ca
[C++ API] Implement builder style construction (#7597) 
* Implemented fused builder based construction mechanism

* "weights" -> "weight"

* Use int64_t instead of size_t everywhere in RNN

* Extracted Conv::ExpandingSize into its own thing

* Rename TORCH_PARAMETER to TORCH_ATTR

* Added documentation

* Fix weight names in batchnorm module
 2018-05-17 17:10:15 -04:00
Peter Goldsborough
8fce8673bb
Rename Container to Module in autogradpp and reorg code (#7304) 
* Rename autograd namespace to torch and change torch.h into python.h

* Pave the way for torch::nn::Module

* Reorganize module code structure

* Undo ONNX update

* Remove sleef submodule
 2018-05-07 14:45:00 -07:00
Peter Goldsborough
67d0d14908
Rename autograd namespace to torch and change torch.h into python.h (#7267) 
* Rename autograd namespace to torch and change torch.h into python.h

* Include torch.h instead of python.h in test/cpp/api

* Change some mentions of torch.h to python.h in C++ extensions

* Set paths directly, without find_path
 2018-05-04 08:04:57 -07:00
Peter Goldsborough
afe3c2688f Update C++ API tests to use Catch2 (#7108) 
* Update C++ API tests to use Catch2

* Update download_mnist.py to be less verbose
 2018-04-30 21:36:35 -04:00