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-rw-r--r--eigen/unsupported/test/cxx11_tensor_sycl.cpp159
1 files changed, 0 insertions, 159 deletions
diff --git a/eigen/unsupported/test/cxx11_tensor_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_sycl.cpp
deleted file mode 100644
index 6a9c334..0000000
--- a/eigen/unsupported/test/cxx11_tensor_sycl.cpp
+++ /dev/null
@@ -1,159 +0,0 @@
-// 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));
-}
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