35 Commits

Author	SHA1	Message	Date
anjali411	be6ffac1b6	Adagrad optimizer - updated step function, added param_groups, state to optimizers ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29335 Differential Revision: D19449382 Pulled By: anjali411 fbshipit-source-id: ee238801ed9cdf15a80f2ce31cc4aab8ba582aea	2020-01-21 14:41:12 -08:00
Will Feng	2bcac59a30	Use default dtype for torch::tensor(floating_point_values) and torch::tensor(empty braced-init-list) when dtype is not specified (#29632) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/29632 This PR is BC-breaking in the following way: Previously, C++ `torch::tensor` with a floating-point literal with no suffix (e.g. `torch::tensor(1.1)`) or a (nested) braced-init-list of floating-point literals with no suffix (e.g. `torch::tensor({{1.1, 2.2}})` produces a tensor with dtype `at::kDouble`. After this PR, it produces a tensor with dtype `torch::get_default_dtype()`, matching Python `torch.tensor` behavior. Test Plan: Imported from OSS Differential Revision: D18465819 Pulled By: yf225 fbshipit-source-id: 6834fe50335c677bc3832f2a5e9cf8d1ede9f665	2019-11-13 15:17:11 -08:00
Will Feng	57a4b7c55d	Re-organize C++ API torch::nn folder structure (#26262) ... Summary: This PR aims to re-organize C++ API `torch::nn` folder structure in the following way: - Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example: `torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py` `torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py` `torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py` `torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py` - Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API) - `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder. - `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`. - `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262 Differential Revision: D17422426 Pulled By: yf225 fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f	2019-09-17 10:07:29 -07:00
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	bcb62cb525	Lazily create tensors in optim_baseline (#12301) ... Summary: Tensors cannot be created globally because of static initialization order issues. So tensors for the optim_baseline test must be created lazily instead. This is fine because these functions will only be called once (in the respective test). ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/12301 Differential Revision: D10201008 Pulled By: goldsborough fbshipit-source-id: 59a041f437354e7c6600e5655b3e2d0647dbde9e	2018-10-04 10:55:53 -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
Peter Goldsborough	dccd0f2de6	Bag of clang tidy fixes for torch/csrc/ and torch/csrc/autograd (#11050) ... Summary: Linting `torch/csrc/` (non-recursive) and `torch/csrc/autograd` (non-recursive). Fixed things like: - `typedef` vs `using` - Use `.empty()` instead of comparing with empty string/using `.size() == 0` - Use range for loops instead of old style loops (`modernize-`) - Remove some `virtual` + `override` - Replace `stdint.h` with `cstdint` - Replace `return Type(x, y)` with `return {x, y}` - Use boolean values (`true`/`false`) instead of numbers (1/0) - More ... ezyang apaszke cpuhrsch Pull Request resolved: https://github.com/pytorch/pytorch/pull/11050 Differential Revision: D9597505 Pulled By: goldsborough fbshipit-source-id: cb0fb4793ade885a8dbf4b10484487b84c64c7f2	2018-09-05 19:55:50 -07:00
Peter Goldsborough	13814d6744	Remove use of data() in optimizers (#10490) ... Summary: After talking to users of the C++ API we found that having the tensor type be `autograd::Variable` causes more complications than having it be `at::Tensor`. It used to be a problem because `at::Tensor` didn't have the "autograd API" of variable (e.g. `detach()` or `grad()` methods), but those methods are now on `at::Tensor`. As such, we want to make a last big breaking change to have the tensor type be `at::Tensor`, while factory methods like `torch::ones` will return `Variable`s disguised as `at::Tensor`. This will make many things easier, like calling functions in ATen that take vectors of tensors. This PR makes a small step in this direction by updating the optimizer classes to not use `.data()` on `Variable` to access the underlying `at::Tensor`. Using `.data()` is effectively a hack to work around our modification rules for tensors that require grad. The proper way of doing things is to use `with torch.no_grad` or equivalently `NoGradGuard` in C++ to guard in-place operations. The next step can then simply redefine `torch::Tensor` to be `at::Tensor`. This transition should be smooth, since all methods available on `Variable` are at this point available on `at::Tensor`. For this PR I: 1. Modified the implementations of optimizers to not use `.data()`. This means the implementations are now different from PyTorch, which still uses the legacy method of using `.data`. 2. To properly verify (1), I added more fine-grained test cases to our optimizer tests, e.g. `SGD` with and without `weight_decay`, then with `nesterov` etc. Generally more tests = more happy! 3. Minor cleanup of the optimizer codebase ebetica apaszke Pull Request resolved: https://github.com/pytorch/pytorch/pull/10490 Differential Revision: D9318229 Pulled By: goldsborough fbshipit-source-id: fb386700f37840542bc5d323f308ea88fe5ea5c5	2018-08-14 13:10:19 -07:00
Zeming Lin	b8530dc1f0	A few additions (#9837) ... Summary: This PR provides 4 fixes / features: 1. torch::nn::Cloneable inherits virtually from torch::nn::Module. We want to pass around a module with new functions, and the best way to do this is to do a diamond inheritance pattern, i.e. ```c++ struct MySuperModuleImpl : virtual public torch::nn::Module { virtual void myFunction() = 0; } struct MySuperModule : public torch::nn::Cloneable<MySuperModule>, MySuperModuleImple {}; struct MyModule : public MySuperModule<MyModule> { void myFunction() override; }; ``` This way, we can simply pass around MySuperModuleImpl around instead of torch::nn::Module. 2. Optimizer options are public now, since there's no way to decay the LR or modify it during training otherwise 3. Serialization functions creates autograd history and calls copy_! Bad! 4. Optimizers did not create buffers after add_parameters was called. Pull Request resolved: https://github.com/pytorch/pytorch/pull/9837 Reviewed By: goldsborough Differential Revision: D9199746 Pulled By: ebetica fbshipit-source-id: 76d6b22e589a42637b7cc0b5bcd3c6b6662fb299	2018-08-13 10:24:58 -07:00
Peter Goldsborough	2249751422	Add OptimizerBase::add_parameters (#9472) ... Summary: ebetica asked for a way to add parameters to `Optimizer`s after they are created. ebetica ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/9472 Differential Revision: D8872176 Pulled By: goldsborough fbshipit-source-id: 39a4032c519a6d3b458dd3596361b04afea10365	2018-07-17 14:10:22 -07:00
Peter Goldsborough	153e2e96d4	Make Sequential ref-counted (#9151) ... Summary: In the C++ API, `Sequential` currently was not refcounted itself, but stored `shared_ptr<AnyModule>` to get the reference semantics. This is unfortunate because most modules in the API are accessed via `->`, e.g. `Linear l(1, 2); l->forward(...);`. `Sequential` was different in that it had value semantics itself, thus was accessed via `.`. This PR makes `Sequential` store `AnyModule` (without extra indirection), and uses the same pImpl mechanism we use for all other modules to make `Sequential` have reference semantics itself. This makes it consistent with the rest of the library. It also removes one level of indirection inside of `Sequential`, which is cool. One thing I had to change was that the `ModuleHolder` with which the whole pImpl thing is implemented previously did some tricks to make `Linear(3, 4)` actually construct `Linear(LinearOptions(3, 4))`. This doesn't work well with `Sequential` since it takes a variadic parameter pack. Instead, I made `ModuleHolder` forward all arguments to the underlying module, and then further pushed the trick to forward parameters to modules' options types into the actual Modules. This adds one constructor per Module in the library. This is not something user modules have to do (unless they want this nice forwarding themselves). It makes the code simpler overall. ezyang ebetica apaszke Pull Request resolved: https://github.com/pytorch/pytorch/pull/9151 Reviewed By: ezyang Differential Revision: D8809298 Pulled By: goldsborough fbshipit-source-id: da68452c3de912fbc67af330ba93b5220de6909f	2018-07-11 17:24:59 -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	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	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
Matt Le	562d9971c9	Add LBFGS optimization algorithm to C++ API (#7596) ... * Adding LBFGS to cpp API * Adding stop conditions * Test cases now passing and adding closure to all algs * Addressing code review * Set seeds to make optim tests more deterministic	2018-05-17 14:03:08 -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
Edward Z. Yang	157d7499e7	Disable two flaky C++ API tests. (#7290) ... Signed-off-by: Edward Z. Yang <ezyang@fb.com>	2018-05-04 10:23:52 -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