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
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:
This PR:
1. Documents `BatchNorm`,
2. Makes a number of API changes after reconsidering some quirks:
1. The default value for the `stateful` parameter used to be `false`, but the most common usage of `BatchNorm` out of the wild is certainly stateful, and the default in Python is also statefulness. So we change the default to stateful.
2. The `pure_forward` function used to use the internal running mean and variance variables instead of the ones supplied to that function call when `stateful` was true, which certainly seems odd. When you call `pure_forward` you would certainly expect the values you pass explicitly to be used. This is now fixed.
3. Adds tests for `BatchNorm`, finally.
ebetica apaszke ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11484
Reviewed By: pjh5
Differential Revision: D9779618
Pulled By: goldsborough
fbshipit-source-id: 59ba760e085c01454b75644b24b22317b688e459
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:
I renamed the variable in the `Embedding` module from `weight` to `table` a few months ago, because it seemed like a more meaningful name. Turns out it's not such a good idea because it deviates from PyTorch, which unnecessarily breaks C++->Python translated code.
ebetica ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9720
Differential Revision: D8955647
Pulled By: goldsborough
fbshipit-source-id: 77228b07d2b733866e8cdecaa6d0686eef4cc3ea
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
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:
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
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
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
* 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
* 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
