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author | Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-09-30 08:22:10 -0700 |
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committer | Benoit Steiner <benoit.steiner.goog@gmail.com> | 2016-09-30 08:22:10 -0700 |
commit | 422530946f437b6cfb73a09d3932bc0f3ac8af80 (patch) | |
tree | 65a64316c8262260cb79eca6b5a07db9e9be0df3 /unsupported/test/cxx11_tensor_forced_eval_sycl.cpp | |
parent | dd602e62c80ede4e193ccb93e395645f0f28e54b (diff) |
Renamed the SYCL tests to follow the standard naming convention.
Diffstat (limited to 'unsupported/test/cxx11_tensor_forced_eval_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_forced_eval_sycl.cpp | 68 |
1 files changed, 68 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp new file mode 100644 index 000000000..59fe743e0 --- /dev/null +++ b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp @@ -0,0 +1,68 @@ +// 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> +// +// 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_forced_eval +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int +#define EIGEN_USE_SYCL + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::Tensor; + +void test_sycl_gpu() { + cl::sycl::gpu_selector s; + cl::sycl::queue q(s, [=](cl::sycl::exception_list l) { + for (const auto& e : l) { + try { + std::rethrow_exception(e); + } catch (cl::sycl::exception e) { + std::cout << e.what() << std::endl; + } + } + }); + SyclDevice sycl_device(q); + + int sizeDim1 = 100; + int sizeDim2 = 200; + int sizeDim3 = 200; + Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}}; + Eigen::Tensor<float, 3> in1(tensorRange); + Eigen::Tensor<float, 3> in2(tensorRange); + Eigen::Tensor<float, 3> out(tensorRange); + + in1 = in1.random() + in1.constant(10.0f); + in2 = in2.random() + in2.constant(10.0f); + + // creating TensorMap from tensor + Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(in1.data(), tensorRange); + Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(in2.data(), tensorRange); + Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(out.data(), tensorRange); + + /// c=(a+b)*b + gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2; + sycl_device.deallocate(out.data()); + 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)) * in2(i, j, k)); + } + } + } + printf("(a+b)*b Test Passed\n"); +} + +void test_cxx11_tensor_sycl_forced_eval() { CALL_SUBTEST(test_sycl_gpu()); } |