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
Summary:
This PR brings to changes to the recently landed C++ Frontend dataloader:
1. Removes the `size()` method from `BatchDataset`. This makes it cleaner to implement unsized ("infinite stream") datasets. The method was not used much beyond initial configuration.
2. Makes the index type of a dataset a template parameter of `BatchDataset` and `Sampler`. This essentially allows custom index types instead of only `vector<size_t>`. This greatly improves flexibility.
See the `InfiniteStreamDataset` and `TestIndex` datasets in the tests for what this enables.
Some additional minor updates and code movements too.
apaszke SsnL
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12960
Differential Revision: D12893342
Pulled By: goldsborough
fbshipit-source-id: ef03ea0f11a93319e81fba7d52a0ef1a125d3108
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
Summary:
Documents the `AnyModule` class in the C++ API.
Also changed the API to be friendlier by default. Calling `AnyModule::forward` used to return an `AnyModule::Value` which you had to call `.get<T>()` on to cast to a concrete type. I changed the name of that `forward` method to `any_forward` and instead made `forward` templated on a `ReturnType` template parameter which you can supply to do the `.get<T>` cast for you automatically. I default this parameter to `torch::Tensor` so that it can often be omitted. So where you used to have to write
```cpp
any_module.forward(...).get<int>();
any_module.forward(...).get<torch::Tensor>();
```
you now write
```cpp
any_module.forward<int>(...);
any_module.forward(...);
```
ebetica ezyang soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11580
Differential Revision: D9798626
Pulled By: goldsborough
fbshipit-source-id: 060b4ea28facaffc417f53b80b846a9dff9acb73
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
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
Summary:
Operations on `Variable`s (or `torch::Tensor`) usually return `at::Tensor`. This is usually fine, but the `AnyModule` used in the implementation of `torch::Sequential` is very picky about types, and does not understand implicit conversions like this. This means that `sequential.forward(at_tensor_that_is_actually_a_variable)` will fail unless you wrap `at_tensor_that_is_actually_a_variable` with `torch::Tensor`.
This PR adds a special case to `AnyModule` that will convert an `at::Tensor` to `torch::Tensor` when the tensor is really a variable, and else just pass the `at::Tensor`. This is a nice little usability improvement for the often-used `Sequential` class.
ebetica ezyang
Closes https://github.com/pytorch/pytorch/pull/8968
Reviewed By: ezyang
Differential Revision: D8670407
Pulled By: goldsborough
fbshipit-source-id: 3635ed6ed28238f3900ce4a876d07f1b11713831
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
* 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
* 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
