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b770156a7a
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
12 lines
307 B
C++
12 lines
307 B
C++
#include <catch.hpp>
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#include <ATen/ATen.h>
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#include <cmath>
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TEST_CASE("Tensor/AllocatesTensorOnTheCorrectDevice", "[multi-cuda]") {
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auto tensor = at::tensor({1, 2, 3}, at::device({at::kCUDA, 1}));
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REQUIRE(tensor.device().type() == at::Device::Type::CUDA);
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REQUIRE(tensor.device().index() == 1);
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}
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