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222 lines
8.3 KiB
C++
222 lines
8.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2015
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_reverse_sycl
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_simple_reverse(const Eigen::SyclDevice& sycl_device) {
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IndexType dim1 = 2;
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IndexType dim2 = 3;
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IndexType dim3 = 5;
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IndexType dim4 = 7;
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array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
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Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
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Tensor<DataType, 4, DataLayout, IndexType> reversed_tensor(tensorRange);
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tensor.setRandom();
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array<bool, 4> dim_rev;
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dim_rev[0] = false;
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dim_rev[1] = true;
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dim_rev[2] = true;
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dim_rev[3] = false;
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data =static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu(gpu_out_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
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sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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for (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 3; ++j) {
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for (IndexType k = 0; k < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l));
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}
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}
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}
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}
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dim_rev[0] = true;
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dim_rev[1] = false;
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dim_rev[2] = false;
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dim_rev[3] = false;
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out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
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sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
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for (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 3; ++j) {
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for (IndexType k = 0; k < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l));
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}
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}
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}
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}
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dim_rev[0] = true;
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dim_rev[1] = false;
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dim_rev[2] = false;
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dim_rev[3] = true;
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out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
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sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
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for (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 3; ++j) {
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for (IndexType k = 0; k < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l));
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}
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}
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}
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}
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_expr_reverse(const Eigen::SyclDevice& sycl_device, bool LValue)
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{
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IndexType dim1 = 2;
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IndexType dim2 = 3;
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IndexType dim3 = 5;
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IndexType dim4 = 7;
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array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
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Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
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Tensor<DataType, 4, DataLayout, IndexType> expected(tensorRange);
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Tensor<DataType, 4, DataLayout, IndexType> result(tensorRange);
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tensor.setRandom();
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array<bool, 4> dim_rev;
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dim_rev[0] = false;
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dim_rev[1] = true;
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dim_rev[2] = false;
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dim_rev[3] = true;
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data_expected =static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data_result =static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu_expected(gpu_out_data_expected, tensorRange);
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType> > out_gpu_result(gpu_out_data_result, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
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if (LValue) {
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out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
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} else {
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out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
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}
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sycl_device.memcpyDeviceToHost(expected.data(), gpu_out_data_expected, expected.dimensions().TotalSize()*sizeof(DataType));
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array<IndexType, 4> src_slice_dim;
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src_slice_dim[0] = 2;
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src_slice_dim[1] = 3;
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src_slice_dim[2] = 1;
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src_slice_dim[3] = 7;
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array<IndexType, 4> src_slice_start;
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src_slice_start[0] = 0;
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src_slice_start[1] = 0;
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src_slice_start[2] = 0;
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src_slice_start[3] = 0;
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array<IndexType, 4> dst_slice_dim = src_slice_dim;
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array<IndexType, 4> dst_slice_start = src_slice_start;
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for (IndexType i = 0; i < 5; ++i) {
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if (LValue) {
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out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
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in_gpu.slice(src_slice_start, src_slice_dim);
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} else {
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out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
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in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
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}
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src_slice_start[2] += 1;
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dst_slice_start[2] += 1;
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}
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sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
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for (IndexType i = 0; i < expected.dimension(0); ++i) {
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for (IndexType j = 0; j < expected.dimension(1); ++j) {
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for (IndexType k = 0; k < expected.dimension(2); ++k) {
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for (IndexType l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
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}
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}
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}
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}
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dst_slice_start[2] = 0;
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result.setRandom();
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sycl_device.memcpyHostToDevice(gpu_out_data_result, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
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for (IndexType i = 0; i < 5; ++i) {
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if (LValue) {
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out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
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in_gpu.slice(dst_slice_start, dst_slice_dim);
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} else {
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out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
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in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
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}
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dst_slice_start[2] += 1;
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}
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sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));
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for (IndexType i = 0; i < expected.dimension(0); ++i) {
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for (IndexType j = 0; j < expected.dimension(1); ++j) {
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for (IndexType k = 0; k < expected.dimension(2); ++k) {
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for (IndexType l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
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}
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}
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}
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}
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}
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template<typename DataType> void sycl_reverse_test_per_device(const cl::sycl::device& d){
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std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
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QueueInterface queueInterface(d);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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test_simple_reverse<DataType, RowMajor, int64_t>(sycl_device);
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test_simple_reverse<DataType, ColMajor, int64_t>(sycl_device);
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test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, false);
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test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, false);
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test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, true);
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test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, true);
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}
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void test_cxx11_tensor_reverse_sycl() {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_reverse_test_per_device<float>(device));
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}
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}
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