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:
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
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
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 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:
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
* 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
* 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
* 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
* 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
* 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
* 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
