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Author SHA1 Message Date
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
Will Feng
ce3d024727 Make options.name_ private, and change all callsites to use options.name() (#26419) 
Summary:
The implementation of several modules in C++ frontend currently has calls to `options.name_`,  which is bad practice because `options.name_` should be a private options field and we should use `options.name()` to access its value. This PR makes `options.name_` actually private and changes all callsites of `options.name_` to `options.name()`.

After this change, we can change all module options to have a map as the underlying data structure, and require that all options must be able to be stored in `c10::IValue`. These changes together would make serializing module options much easier.

Note that this PR is BC-breaking in the following way:

Previously, calling `options.name_` in C++ module implementation works because `options.name_`  was a public field. After this PR, `options.name_` becomes private, and to get the value of `options.name_` we should call `options.name()`, and to set the value of `options.name_` we should call `options.name(new_value)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26419

Differential Revision: D17481507

Pulled By: yf225

fbshipit-source-id: 93e4ed0e1d79ef57104ad748809d03e25da61ed3
 2019-09-19 14:48:22 -07:00
Pavel Belevich
965b97f5f0 Bidirectional GRU and LSTM C++ API forward fix (#22850) 
Summary:
Fixing https://github.com/pytorch/pytorch/issues/17998
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22850

Differential Revision: D16420854

Pulled By: pbelevich

fbshipit-source-id: 76f38be40d8479fb9cafba92939cea61d81fd336
 2019-07-22 12:59:47 -07:00
Pavel Belevich
a48cf8f52d Fixed RNNImplBase reset and flat_weights methods to handle bidirectional flag correctly (#22493) 
Summary:
Fixing https://github.com/pytorch/pytorch/issues/19545:
Changed torch/csrc/api/src/nn/modules/rnn.cpp to be consistent with torch/nn/modules/rnn.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22493

Differential Revision: D16111433

Pulled By: pbelevich

fbshipit-source-id: edfa41e8a9889d64918998dc7c46b8763fdf5765
 2019-07-08 10:34:04 -07:00
Peter Goldsborough
eb5d28ecef Pretty printing of C++ modules (#15326) 
Summary:
A long outstanding nicety: pretty printing of C++ modules. E.g.
```
  Sequential sequential(
      Linear(10, 3),
      Conv2d(1, 2, 3),
      Dropout(0.5),
      BatchNorm(5),
      Embedding(4, 10),
      LSTM(4, 5));
std::cout << sequential;
```
prints
```
torch::nn::Sequential(
  (0): torch::nn::Linear(in=10, out=3, with_bias=true)
  (1): torch::nn::Conv2d(input_channels=1, output_channels=2, kernel_size=[3, 3], stride=[1, 1])
  (2): torch::nn::Dropout(rate=0.5)
  (3): torch::nn::BatchNorm(features=5, eps=1e-05, momentum=0.1, affine=true, stateful=true)
  (4): torch::nn::Embedding(count=4, dimension=10)
  (5): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)
)
```

apaszke ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15326

Differential Revision: D13518986

Pulled By: goldsborough

fbshipit-source-id: 63bf753672f0e348951de3645208f263581de5fb
 2018-12-19 21:55:49 -08:00
Peter Goldsborough
332a7db35e Use MNIST dataset in C++ integration test (#13737) 
Summary:
We have an MNIST reader in the C++ data API, so we can get rid of the custom one currently implemented in the integration tests.

ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13737

Differential Revision: D12990936

Pulled By: goldsborough

fbshipit-source-id: 125a1910ec91d53dbf121570fc9eec6ccfba0477
 2018-11-09 09:55:02 -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
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
Peter Goldsborough
9403e0cac0 Use ATen implementation of RNNs (#10761) 
Summary:
apaszke recently ported RNNs from Python into ATen, which means we can replace our implementation in the C++ API (written by ebetica) with the ATen implementation, which cleans up a lot of code (+99, -323). Thanks apaszke!

I also added the `bidirectional` and `batch_first` options to the C++ API RNN options, just because why not.

apaszke ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10761

Differential Revision: D9443885

Pulled By: goldsborough

fbshipit-source-id: b6ef7566b9ced2b2f0b2e1f46c295b6f250c65a8
 2018-08-23 16:12:14 -07:00
Edward Yang
19031c68dc Use intrusive_ptr in Storage; replace unique_ptr<Storage> with Storage (#10488) 
Summary:
```
Use intrusive_ptr in Storage; replace unique_ptr<Storage> with Storage

This patch does two major changes:

- It replaces the use of Retainable in Storage with a new implementation
  based on intrusive_ptr.  This will be necessary because Caffe2 will
  be using this class to implement intrusive_ptrs, and we need to
  line these up for the merge.  One good thing about the new implementation is
  that the default copy/move constructors/assignment operators and destructor
  work automatically, instead of needing to be hardcoded into Storage/Tensor.

- It replaces all places where we returned std::unique_ptr<Storage> with
  Storage, collapsing an unnecessary double indirection that is no longer
  necessary now that we have correctly working copy/move constructors.

I didn't initially want to do step (2), but it was very important to
eliminate all bare uses of new Storage and new StorageImpl, and this making
the API change was the most straightforward way to do this.

HOW TO FIX YOUR CODE IN THE NEW API

- You no longer need to dereference the result of tensor.storage() to pass
  it to set.  So, instead of:

      x.set_(*y.storage());

  just write:

      x.set_(y.storage());

- If you were accessing methods on StorageImpl via the pImpl() method, you
  must use the dot operator to run pImpl().  Even better; just drop pImpl,
  we now have method forwarding.  So, instead of:

      storage->pImpl()->data();

  just do:

      storage->data();
      // storage.pImpl()->data() works too but is not as recommended

- storage->getDevice() is no more; instead use storage->device().index()

MISC CODE UPDATES

- retain, release, weak_retain, weak_release and weak_lock are now
  reimplemented using the "blessed API", and renamed to make it
  clearer that their use is discouraged.

- nvcc OS X and general OS X portability improvements to intrusive_ptr

- A new comment in intrusive_ptr describing how stack allocated
  intrusive_ptr_targets work differently than heap allocated ones
  from c10::make_intrusive

CAVEAT EMPTOR

- THStorage_weakRetain used to work on strong pointers, but it NO LONGER
  works with intrusive_ptr.  You must reclaim the strong pointer into a
  real strong pointer, construct a weak pointer from it, and then release
  the strong and weak pointers.  See StorageSharing.cpp for an example.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10488

Reviewed By: gchanan

Differential Revision: D9306134

Pulled By: ezyang

fbshipit-source-id: 02d58ef62dab8e4da6131e1a24834a65c21048e2
 2018-08-21 21:39:55 -07:00
Peter Goldsborough
2e0dd86903 Make torch::Tensor -> at::Tensor (#10516) 
Summary:
This PR removes the `using Tensor = autograd::Variable;` alias from `torch/tensor.h`, which means `torch::Tensor` is now `at::Tensor`. This PR fixes up some last uses of `.data()` and tidies up the resulting code. For example, I was able to remove `TensorListView` such that code like

```
auto loss = torch::stack(torch::TensorListView(policy_loss)).sum() +
    torch::stack(torch::TensorListView(value_loss)).sum();
```

is now

```
auto loss = torch::stack(policy_loss).sum() + torch::stack(value_loss).sum();
```

CC jgehring

ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10516

Differential Revision: D9324691

Pulled By: goldsborough

fbshipit-source-id: a7c1cb779c9c829f89cea55f07ac539b00c78449
 2018-08-15 21:25:12 -07:00
Christian Puhrsch
ef9801f32c Merge THStorage into at::Storage 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9772

Reviewed By: ezyang

Differential Revision: D9019375

Pulled By: cpuhrsch

fbshipit-source-id: d5185e29747929d648e4260db4967452cd40f563
 2018-07-27 13:53:55 -07:00
Peter Goldsborough
ae44a6b5e3 Fix Sequential::clone() (#9372) 
Summary:
I noticed that `Sequential::clone()` does not work. This is because `Sequential` does not use `reset()` which is normally where modules have to initialize and register its submodules. Further, this is because of the way `Sequential` allows its modules to be passed in the constructor, which doesn't work with `reset()` (since it does "late" initialization).

I've added some better error messages inside `Cloneable::clone()` which makes this kind of mistake clearer for other users, and tests for `Sequential::clone()`.

I also had to give `AnyModule` a deep `clone()` method.

ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9372

Differential Revision: D8865189

Pulled By: goldsborough

fbshipit-source-id: b81586e0d3157cd3c4265b19ac8dd87c5d8dcf94
 2018-07-16 21:53:42 -07:00
Peter Goldsborough
9ce15173fb Move _cudnn_init_dropout_state to TensorOptions and enable cuDNN dropout in C++ API RNNs (#9012) 
Summary:
The goal of this PR was to add support for dropout descriptors in the C++ API's RNN class.
The end result is a 4x-5x speedup for our RNN integration tests since they can now use cuDNN instead of autograd when dropout is set.

To achieve this, I had to move `_cudnn_init_dropout_state` to the `TensorOptions` API.

I also fixed a bug around `RNN::cuda()` not flattening parameters for cuDNN.

ebetica ezyang
Closes https://github.com/pytorch/pytorch/pull/9012

Reviewed By: pjh5

Differential Revision: D8689786

Pulled By: goldsborough

fbshipit-source-id: 44fb191f5a38e41c4ded5417306b5bbc012cd56c
 2018-06-29 17:25:23 -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
1f36caceb2
[C++ API] Rework optimization package (#8815) 
* Rework optim folder

* Removed TORCH_OPTIMIZER_CLASS macro

* Got rid of CRTP/Impl

* Removed TORCH_AUTOGRAD_KWARG

* Differentiate between Optimizer and LossClosureOptimizer

* Make Optimizers parameters based instead of model based

* Allow construction of optimizer from arbitrary vector

* Added test for zero grad

* Added test for external parameter vectors

* Now comparing against baseline values

* Documentation

* Post rebase fixes

* Different strategy for creating and accessing buffers in optimizers

* Fix member ordering
 2018-06-26 10:13:14 -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
a2dd707031
[C++ API] Create fixed width dtypes in torch:: namespace (#8639) 
* Create fixed width dtypes in torch:: namespace

* Make kByte -> kUInt8
 2018-06-19 12:40:58 -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
Peter Goldsborough
de4e97e89a
[C++ API] Cursors (#8190) 
* Add cursors to C++ API

* Small self nits

* s/struct/class

* Use more STL like names for cursors
 2018-06-11 09:48:43 -07:00
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
549b4069bb
[C++ API] Using new registration mechanism (#7663) 
* Using new registration mechanism

* Fix signature of param() in module.cpp

* Remove ParameterList

* Fix tests
 2018-05-21 17:59:21 -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
3414475653
[C++ API] Remove initialize_* functions (#7517) 
* Remove initialize_ functions

* Fix clone() to recursively clone children

* Small codemove
 2018-05-14 18:24:58 -07:00
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
Zeming Lin
5c575a1497
Fixes RNN shapes for C++ API (#7272) 2018-05-04 14:00:30 -04:00
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