diff options
Diffstat (limited to 'unsupported/test/cxx11_tensor_sycl.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_sycl.cpp | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_sycl.cpp b/unsupported/test/cxx11_tensor_sycl.cpp new file mode 100644 index 000000000..6a9c33422 --- /dev/null +++ b/unsupported/test/cxx11_tensor_sycl.cpp @@ -0,0 +1,159 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 +// Mehdi Goli Codeplay Software Ltd. +// Ralph Potter Codeplay Software Ltd. +// Luke Iwanski Codeplay Software Ltd. +// Contact: <eigen@codeplay.com> +// 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_TEST_FUNC cxx11_tensor_sycl +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_USE_SYCL + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::array; +using Eigen::SyclDevice; +using Eigen::Tensor; +using Eigen::TensorMap; + +void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) { + + int sizeDim1 = 100; + int sizeDim2 = 100; + int sizeDim3 = 100; + array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; + Tensor<float, 3> in1(tensorRange); + Tensor<float, 3> in2(tensorRange); + Tensor<float, 3> in3(tensorRange); + Tensor<float, 3> out(tensorRange); + + in2 = in2.random(); + in3 = in3.random(); + + float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float))); + float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float))); + float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float))); + float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float))); + + TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange); + TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange); + TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange); + TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange); + + /// a=1.2f + gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f); + sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(in1(i,j,k), 1.2f); + } + } + } + printf("a=1.2f Test passed\n"); + + /// a=b*1.2f + gpu_out.device(sycl_device) = gpu_in1 * 1.2f; + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i,j,k), + in1(i,j,k) * 1.2f); + } + } + } + printf("a=b*1.2f Test Passed\n"); + + /// c=a*b + sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float)); + gpu_out.device(sycl_device) = gpu_in1 * gpu_in2; + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i,j,k), + in1(i,j,k) * + in2(i,j,k)); + } + } + } + printf("c=a*b Test Passed\n"); + + /// c=a+b + gpu_out.device(sycl_device) = gpu_in1 + gpu_in2; + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i,j,k), + in1(i,j,k) + + in2(i,j,k)); + } + } + } + printf("c=a+b Test Passed\n"); + + /// c=a*a + gpu_out.device(sycl_device) = gpu_in1 * gpu_in1; + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i,j,k), + in1(i,j,k) * + in1(i,j,k)); + } + } + } + printf("c= a*a Test Passed\n"); + + //a*3.14f + b*2.7f + gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f); + sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i,j,k), + in1(i,j,k) * 3.14f + + in2(i,j,k) * 2.7f); + } + } + } + printf("a*3.14f + b*2.7f Test Passed\n"); + + ///d= (a>0.5? b:c) + sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.dimensions().TotalSize())*sizeof(float)); + gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3); + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float)); + for (int i = 0; i < sizeDim1; ++i) { + for (int j = 0; j < sizeDim2; ++j) { + for (int k = 0; k < sizeDim3; ++k) { + VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f) + ? in2(i, j, k) + : in3(i, j, k)); + } + } + } + printf("d= (a>0.5? b:c) Test Passed\n"); + sycl_device.deallocate(gpu_in1_data); + sycl_device.deallocate(gpu_in2_data); + sycl_device.deallocate(gpu_in3_data); + sycl_device.deallocate(gpu_out_data); +} +void test_cxx11_tensor_sycl() { + cl::sycl::gpu_selector s; + Eigen::SyclDevice sycl_device(s); + CALL_SUBTEST(test_sycl_cpu(sycl_device)); +} |