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Diffstat (limited to 'unsupported/test/cxx11_tensor_reduction_sycl.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_reduction_sycl.cpp | 138 |
1 files changed, 138 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_reduction_sycl.cpp b/unsupported/test/cxx11_tensor_reduction_sycl.cpp new file mode 100644 index 000000000..a9ef82907 --- /dev/null +++ b/unsupported/test/cxx11_tensor_reduction_sycl.cpp @@ -0,0 +1,138 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2015 +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.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_TEST_FUNC cxx11_tensor_reduction_sycl +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_USE_SYCL + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + + + +static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { + + const int num_rows = 452; + const int num_cols = 765; + array<int, 2> tensorRange = {{num_rows, num_cols}}; + + Tensor<float, 2> in(tensorRange); + Tensor<float, 0> full_redux; + Tensor<float, 0> full_redux_gpu; + + in.setRandom(); + + full_redux = in.sum(); + + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float)); + + TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data); + + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(); + sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float)); + // Check that the CPU and GPU reductions return the same result. + VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); + + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} + +static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { + + int dim_x = 145; + int dim_y = 1; + int dim_z = 67; + + array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; + Eigen::array<int, 1> red_axis; + red_axis[0] = 0; + array<int, 2> reduced_tensorRange = {{dim_y, dim_z}}; + + Tensor<float, 3> in(tensorRange); + Tensor<float, 2> redux(reduced_tensorRange); + Tensor<float, 2> redux_gpu(reduced_tensorRange); + + in.setRandom(); + + redux= in.sum(red_axis); + + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); + + TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); + + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(red_axis); + sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); + + // Check that the CPU and GPU reductions return the same result. + for(int j=0; j<reduced_tensorRange[0]; j++ ) + for(int k=0; k<reduced_tensorRange[1]; k++ ) + VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); + + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} + +static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { + + int dim_x = 567; + int dim_y = 1; + int dim_z = 47; + + array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; + Eigen::array<int, 1> red_axis; + red_axis[0] = 2; + array<int, 2> reduced_tensorRange = {{dim_x, dim_y}}; + + Tensor<float, 3> in(tensorRange); + Tensor<float, 2> redux(reduced_tensorRange); + Tensor<float, 2> redux_gpu(reduced_tensorRange); + + in.setRandom(); + + redux= in.sum(red_axis); + + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); + + TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); + + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(red_axis); + sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); + // Check that the CPU and GPU reductions return the same result. + for(int j=0; j<reduced_tensorRange[0]; j++ ) + for(int k=0; k<reduced_tensorRange[1]; k++ ) + VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); + + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); + +} + +void test_cxx11_tensor_reduction_sycl() { + cl::sycl::gpu_selector s; + Eigen::SyclDevice sycl_device(s); + CALL_SUBTEST((test_full_reductions_sycl(sycl_device))); + CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device))); + CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device))); + +} |