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Diffstat (limited to 'unsupported/test/cxx11_tensor_reduction_gpu.cu')
-rw-r--r-- | unsupported/test/cxx11_tensor_reduction_gpu.cu | 154 |
1 files changed, 154 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_reduction_gpu.cu b/unsupported/test/cxx11_tensor_reduction_gpu.cu new file mode 100644 index 000000000..122ac946b --- /dev/null +++ b/unsupported/test/cxx11_tensor_reduction_gpu.cu @@ -0,0 +1,154 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#define EIGEN_TEST_NO_LONGDOUBLE +#define EIGEN_TEST_NO_COMPLEX + +#define EIGEN_USE_GPU + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + + +template<typename Type, int DataLayout> +static void test_full_reductions() { + + Eigen::GpuStreamDevice stream; + Eigen::GpuDevice gpu_device(&stream); + + const int num_rows = internal::random<int>(1024, 5*1024); + const int num_cols = internal::random<int>(1024, 5*1024); + + Tensor<Type, 2, DataLayout> in(num_rows, num_cols); + in.setRandom(); + + Tensor<Type, 0, DataLayout> full_redux; + full_redux = in.sum(); + + std::size_t in_bytes = in.size() * sizeof(Type); + std::size_t out_bytes = full_redux.size() * sizeof(Type); + Type* gpu_in_ptr = static_cast<Type*>(gpu_device.allocate(in_bytes)); + Type* gpu_out_ptr = static_cast<Type*>(gpu_device.allocate(out_bytes)); + gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes); + + TensorMap<Tensor<Type, 2, DataLayout> > in_gpu(gpu_in_ptr, num_rows, num_cols); + TensorMap<Tensor<Type, 0, DataLayout> > out_gpu(gpu_out_ptr); + + out_gpu.device(gpu_device) = in_gpu.sum(); + + Tensor<Type, 0, DataLayout> full_redux_gpu; + gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes); + gpu_device.synchronize(); + + // Check that the CPU and GPU reductions return the same result. + VERIFY_IS_APPROX(full_redux(), full_redux_gpu()); + + gpu_device.deallocate(gpu_in_ptr); + gpu_device.deallocate(gpu_out_ptr); +} + +template<typename Type, int DataLayout> +static void test_first_dim_reductions() { + int dim_x = 33; + int dim_y = 1; + int dim_z = 128; + + Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z); + in.setRandom(); + + Eigen::array<int, 1> red_axis; + red_axis[0] = 0; + Tensor<Type, 2, DataLayout> redux = in.sum(red_axis); + + // Create device + Eigen::GpuStreamDevice stream; + Eigen::GpuDevice dev(&stream); + + // Create data(T) + Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type)); + Type* out_data = (Type*)dev.allocate(dim_z*dim_y*sizeof(Type)); + Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z); + Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_y, dim_z); + + // Perform operation + dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type)); + gpu_out.device(dev) = gpu_in.sum(red_axis); + gpu_out.device(dev) += gpu_in.sum(red_axis); + Tensor<Type, 2, DataLayout> redux_gpu(dim_y, dim_z); + dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type)); + dev.synchronize(); + + // Check that the CPU and GPU reductions return the same result. + for (int i = 0; i < gpu_out.size(); ++i) { + VERIFY_IS_APPROX(2*redux(i), redux_gpu(i)); + } + + dev.deallocate(in_data); + dev.deallocate(out_data); +} + +template<typename Type, int DataLayout> +static void test_last_dim_reductions() { + int dim_x = 128; + int dim_y = 1; + int dim_z = 33; + + Tensor<Type, 3, DataLayout> in(dim_x, dim_y, dim_z); + in.setRandom(); + + Eigen::array<int, 1> red_axis; + red_axis[0] = 2; + Tensor<Type, 2, DataLayout> redux = in.sum(red_axis); + + // Create device + Eigen::GpuStreamDevice stream; + Eigen::GpuDevice dev(&stream); + + // Create data + Type* in_data = (Type*)dev.allocate(dim_x*dim_y*dim_z*sizeof(Type)); + Type* out_data = (Type*)dev.allocate(dim_x*dim_y*sizeof(Type)); + Eigen::TensorMap<Eigen::Tensor<Type, 3, DataLayout> > gpu_in(in_data, dim_x, dim_y, dim_z); + Eigen::TensorMap<Eigen::Tensor<Type, 2, DataLayout> > gpu_out(out_data, dim_x, dim_y); + + // Perform operation + dev.memcpyHostToDevice(in_data, in.data(), in.size()*sizeof(Type)); + gpu_out.device(dev) = gpu_in.sum(red_axis); + gpu_out.device(dev) += gpu_in.sum(red_axis); + Tensor<Type, 2, DataLayout> redux_gpu(dim_x, dim_y); + dev.memcpyDeviceToHost(redux_gpu.data(), out_data, gpu_out.size()*sizeof(Type)); + dev.synchronize(); + + // Check that the CPU and GPU reductions return the same result. + for (int i = 0; i < gpu_out.size(); ++i) { + VERIFY_IS_APPROX(2*redux(i), redux_gpu(i)); + } + + dev.deallocate(in_data); + dev.deallocate(out_data); +} + + +EIGEN_DECLARE_TEST(cxx11_tensor_reduction_gpu) { + CALL_SUBTEST_1((test_full_reductions<float, ColMajor>())); + CALL_SUBTEST_1((test_full_reductions<double, ColMajor>())); + CALL_SUBTEST_2((test_full_reductions<float, RowMajor>())); + CALL_SUBTEST_2((test_full_reductions<double, RowMajor>())); + + CALL_SUBTEST_3((test_first_dim_reductions<float, ColMajor>())); + CALL_SUBTEST_3((test_first_dim_reductions<double, ColMajor>())); + CALL_SUBTEST_4((test_first_dim_reductions<float, RowMajor>())); +// Outer reductions of doubles aren't supported just yet. +// CALL_SUBTEST_4((test_first_dim_reductions<double, RowMajor>())) + + CALL_SUBTEST_5((test_last_dim_reductions<float, ColMajor>())); +// Outer reductions of doubles aren't supported just yet. +// CALL_SUBTEST_5((test_last_dim_reductions<double, ColMajor>())); + CALL_SUBTEST_6((test_last_dim_reductions<float, RowMajor>())); + CALL_SUBTEST_6((test_last_dim_reductions<double, RowMajor>())); +} |