| // 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_DEFAULT_DENSE_INDEX_TYPE int64_t |
| #define EIGEN_USE_SYCL |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| |
| template<typename DataType, int DataLayout, typename IndexType> |
| static void test_simple_concatenation(const Eigen::SyclDevice& sycl_device) |
| { |
| IndexType leftDim1 = 2; |
| IndexType leftDim2 = 3; |
| IndexType leftDim3 = 1; |
| Eigen::array<IndexType, 3> leftRange = {{leftDim1, leftDim2, leftDim3}}; |
| IndexType rightDim1 = 2; |
| IndexType rightDim2 = 3; |
| IndexType rightDim3 = 1; |
| Eigen::array<IndexType, 3> rightRange = {{rightDim1, rightDim2, rightDim3}}; |
| |
| //IndexType concatDim1 = 3; |
| // IndexType concatDim2 = 3; |
| // IndexType concatDim3 = 1; |
| //Eigen::array<IndexType, 3> concatRange = {{concatDim1, concatDim2, concatDim3}}; |
| |
| Tensor<DataType, 3, DataLayout, IndexType> left(leftRange); |
| Tensor<DataType, 3, DataLayout, IndexType> right(rightRange); |
| left.setRandom(); |
| right.setRandom(); |
| |
| DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType))); |
| DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType))); |
| |
| Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange); |
| sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType)); |
| sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType)); |
| /// |
| Tensor<DataType, 3, DataLayout, IndexType> concatenation1(leftDim1+rightDim1, leftDim2, leftDim3); |
| DataType * gpu_out_data1 = static_cast<DataType*>(sycl_device.allocate(concatenation1.dimensions().TotalSize()*sizeof(DataType))); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out1(gpu_out_data1, concatenation1.dimensions()); |
| |
| //concatenation = left.concatenate(right, 0); |
| gpu_out1.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 0); |
| sycl_device.memcpyDeviceToHost(concatenation1.data(), gpu_out_data1,(concatenation1.dimensions().TotalSize())*sizeof(DataType)); |
| |
| VERIFY_IS_EQUAL(concatenation1.dimension(0), 4); |
| VERIFY_IS_EQUAL(concatenation1.dimension(1), 3); |
| VERIFY_IS_EQUAL(concatenation1.dimension(2), 1); |
| for (IndexType j = 0; j < 3; ++j) { |
| for (IndexType i = 0; i < 2; ++i) { |
| VERIFY_IS_EQUAL(concatenation1(i, j, 0), left(i, j, 0)); |
| } |
| for (IndexType i = 2; i < 4; ++i) { |
| VERIFY_IS_EQUAL(concatenation1(i, j, 0), right(i - 2, j, 0)); |
| } |
| } |
| |
| sycl_device.deallocate(gpu_out_data1); |
| Tensor<DataType, 3, DataLayout, IndexType> concatenation2(leftDim1, leftDim2 +rightDim2, leftDim3); |
| DataType * gpu_out_data2 = static_cast<DataType*>(sycl_device.allocate(concatenation2.dimensions().TotalSize()*sizeof(DataType))); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out2(gpu_out_data2, concatenation2.dimensions()); |
| gpu_out2.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 1); |
| sycl_device.memcpyDeviceToHost(concatenation2.data(), gpu_out_data2,(concatenation2.dimensions().TotalSize())*sizeof(DataType)); |
| |
| //concatenation = left.concatenate(right, 1); |
| VERIFY_IS_EQUAL(concatenation2.dimension(0), 2); |
| VERIFY_IS_EQUAL(concatenation2.dimension(1), 6); |
| VERIFY_IS_EQUAL(concatenation2.dimension(2), 1); |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| VERIFY_IS_EQUAL(concatenation2(i, j, 0), left(i, j, 0)); |
| } |
| for (IndexType j = 3; j < 6; ++j) { |
| VERIFY_IS_EQUAL(concatenation2(i, j, 0), right(i, j - 3, 0)); |
| } |
| } |
| sycl_device.deallocate(gpu_out_data2); |
| Tensor<DataType, 3, DataLayout, IndexType> concatenation3(leftDim1, leftDim2, leftDim3+rightDim3); |
| DataType * gpu_out_data3 = static_cast<DataType*>(sycl_device.allocate(concatenation3.dimensions().TotalSize()*sizeof(DataType))); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out3(gpu_out_data3, concatenation3.dimensions()); |
| gpu_out3.device(sycl_device) =gpu_in1.concatenate(gpu_in2, 2); |
| sycl_device.memcpyDeviceToHost(concatenation3.data(), gpu_out_data3,(concatenation3.dimensions().TotalSize())*sizeof(DataType)); |
| |
| //concatenation = left.concatenate(right, 2); |
| VERIFY_IS_EQUAL(concatenation3.dimension(0), 2); |
| VERIFY_IS_EQUAL(concatenation3.dimension(1), 3); |
| VERIFY_IS_EQUAL(concatenation3.dimension(2), 2); |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| VERIFY_IS_EQUAL(concatenation3(i, j, 0), left(i, j, 0)); |
| VERIFY_IS_EQUAL(concatenation3(i, j, 1), right(i, j, 0)); |
| } |
| } |
| sycl_device.deallocate(gpu_out_data3); |
| sycl_device.deallocate(gpu_in1_data); |
| sycl_device.deallocate(gpu_in2_data); |
| } |
| template<typename DataType, int DataLayout, typename IndexType> |
| static void test_concatenation_as_lvalue(const Eigen::SyclDevice& sycl_device) |
| { |
| |
| IndexType leftDim1 = 2; |
| IndexType leftDim2 = 3; |
| Eigen::array<IndexType, 2> leftRange = {{leftDim1, leftDim2}}; |
| |
| IndexType rightDim1 = 2; |
| IndexType rightDim2 = 3; |
| Eigen::array<IndexType, 2> rightRange = {{rightDim1, rightDim2}}; |
| |
| IndexType concatDim1 = 4; |
| IndexType concatDim2 = 3; |
| Eigen::array<IndexType, 2> resRange = {{concatDim1, concatDim2}}; |
| |
| Tensor<DataType, 2, DataLayout, IndexType> left(leftRange); |
| Tensor<DataType, 2, DataLayout, IndexType> right(rightRange); |
| Tensor<DataType, 2, DataLayout, IndexType> result(resRange); |
| |
| left.setRandom(); |
| right.setRandom(); |
| result.setRandom(); |
| |
| DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(left.dimensions().TotalSize()*sizeof(DataType))); |
| DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(right.dimensions().TotalSize()*sizeof(DataType))); |
| DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType))); |
| |
| |
| Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in1(gpu_in1_data, leftRange); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_in2(gpu_in2_data, rightRange); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(gpu_out_data, resRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_in1_data, left.data(),(left.dimensions().TotalSize())*sizeof(DataType)); |
| sycl_device.memcpyHostToDevice(gpu_in2_data, right.data(),(right.dimensions().TotalSize())*sizeof(DataType)); |
| sycl_device.memcpyHostToDevice(gpu_out_data, result.data(),(result.dimensions().TotalSize())*sizeof(DataType)); |
| |
| // t1.concatenate(t2, 0) = result; |
| gpu_in1.concatenate(gpu_in2, 0).device(sycl_device) =gpu_out; |
| sycl_device.memcpyDeviceToHost(left.data(), gpu_in1_data,(left.dimensions().TotalSize())*sizeof(DataType)); |
| sycl_device.memcpyDeviceToHost(right.data(), gpu_in2_data,(right.dimensions().TotalSize())*sizeof(DataType)); |
| |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| VERIFY_IS_EQUAL(left(i, j), result(i, j)); |
| VERIFY_IS_EQUAL(right(i, j), result(i+2, j)); |
| } |
| } |
| sycl_device.deallocate(gpu_in1_data); |
| sycl_device.deallocate(gpu_in2_data); |
| sycl_device.deallocate(gpu_out_data); |
| } |
| |
| |
| template <typename DataType, typename Dev_selector> void tensorConcat_perDevice(Dev_selector s){ |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| test_simple_concatenation<DataType, RowMajor, int64_t>(sycl_device); |
| test_simple_concatenation<DataType, ColMajor, int64_t>(sycl_device); |
| test_concatenation_as_lvalue<DataType, ColMajor, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_concatenation_sycl) { |
| for (const auto& device :Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(tensorConcat_perDevice<half>(device)); |
| CALL_SUBTEST(tensorConcat_perDevice<float>(device)); |
| } |
| } |