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:
This PR adds the functional version of `DataParallel` (i.e. `data_parallel`) to the C++ frontend.
For this, I had to:
1. Add "differentiable" versions of scatter and gather, which perform their inverse operation in the backward pass, to C++. I've added them under `torch/csrc/autograd/functions/comm.{h,cpp}`. I had to move some utilities from `VariableType.cpp` into `torch/csrc/autograd/functions/utils.h`, and changed them a bit to fix the `const_cast`s for which there were `TODO`s,
2. Implement the `replicate`, `parallel_apply` and the combining `data_parallel` functions in C++.
`replicate` is implemented based on our existing `clone()` interface, along with the ability to set the current device via `at::OptionsGuard` (so nice).
`parallel_apply` is implemented using `at::parallel_for` (CC cpuhrsch) and [follows the code from PyTorch](https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/parallel_apply.py).
Added lots of tests for these things.
apaszke ezyang ebetica colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9234
Differential Revision: D8865182
Pulled By: goldsborough
fbshipit-source-id: 4f1fecf2b3f3bc1540c071dfb2d23dd45de433e4
