| // 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 "OffByOneScalar.h" | 
 | #include <unsupported/Eigen/CXX11/Tensor> | 
 | #include <stdint.h> | 
 | #include <iostream> | 
 |  | 
 | template <typename DataType, int DataLayout, typename IndexType> | 
 | void test_device_memory(const Eigen::SyclDevice &sycl_device) { | 
 |   IndexType sizeDim1 = 100; | 
 |   array<IndexType, 1> tensorRange = {{sizeDim1}}; | 
 |   Tensor<DataType, 1, DataLayout,IndexType> in(tensorRange); | 
 |   Tensor<DataType, 1, DataLayout,IndexType> in1(tensorRange); | 
 |   DataType* gpu_in_data  = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType))); | 
 |    | 
 |   // memset | 
 |   memset(in1.data(), 1, in1.size() * sizeof(DataType)); | 
 |   sycl_device.memset(gpu_in_data, 1, in.size()*sizeof(DataType)); | 
 |   sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType)); | 
 |   for (IndexType i=0; i<in.size(); i++) { | 
 |     VERIFY_IS_EQUAL(in(i), in1(i)); | 
 |   } | 
 |    | 
 |   // fill | 
 |   DataType value = DataType(7); | 
 |   std::fill_n(in1.data(), in1.size(), value); | 
 |   sycl_device.fill(gpu_in_data, gpu_in_data + in.size(), value); | 
 |   sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType)); | 
 |   for (IndexType i=0; i<in.size(); i++) { | 
 |     VERIFY_IS_EQUAL(in(i), in1(i)); | 
 |   } | 
 |  | 
 |   sycl_device.deallocate(gpu_in_data); | 
 | } | 
 |  | 
 | template <typename DataType, int DataLayout, typename IndexType> | 
 | void test_device_exceptions(const Eigen::SyclDevice &sycl_device) { | 
 |   VERIFY(sycl_device.ok()); | 
 |   IndexType sizeDim1 = 100; | 
 |   array<IndexType, 1> tensorDims = {{sizeDim1}}; | 
 |   DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1*sizeof(DataType))); | 
 |   sycl_device.memset(gpu_data, 1, sizeDim1*sizeof(DataType)); | 
 |  | 
 |   TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> in(gpu_data, tensorDims); | 
 |   TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> out(gpu_data, tensorDims); | 
 |   out.device(sycl_device) = in / in.constant(0); | 
 |  | 
 |   sycl_device.synchronize(); | 
 |   VERIFY(!sycl_device.ok()); | 
 |   sycl_device.deallocate(gpu_data); | 
 | } | 
 |  | 
 | template<typename DataType, int DataLayout, typename IndexType> | 
 | void test_device_attach_buffer(const Eigen::SyclDevice &sycl_device) { | 
 |   IndexType sizeDim1 = 100; | 
 |    | 
 |   array<IndexType, 1> tensorRange = {{sizeDim1}}; | 
 |   Tensor<DataType, 1, DataLayout, IndexType> in(tensorRange); | 
 |    | 
 |   cl::sycl::buffer<buffer_scalar_t, 1> buffer(cl::sycl::range<1>(sizeDim1 * sizeof(DataType))); | 
 |   DataType* gpu_in_data = static_cast<DataType*>(sycl_device.attach_buffer(buffer)); | 
 |    | 
 |   // fill | 
 |   DataType value = DataType(7); | 
 |   std::fill_n(in.data(), in.size(), value); | 
 |   sycl_device.fill(gpu_in_data, gpu_in_data + in.size(), value); | 
 |    | 
 |   // Check that buffer is filled with the correct value. | 
 |   auto reint = buffer.reinterpret<DataType>(cl::sycl::range<1>(sizeDim1)); | 
 |   auto access = reint.template get_access<cl::sycl::access::mode::read>(); | 
 |   for (IndexType i=0; i<in.size(); i++) { | 
 |     VERIFY_IS_EQUAL(in(i), access[i]); | 
 |   } | 
 |    | 
 |   sycl_device.detach_buffer(gpu_in_data); | 
 | } | 
 |  | 
 | template<typename DataType> void sycl_device_test_per_device(const cl::sycl::device& d){ | 
 |   std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl; | 
 |   QueueInterface queueInterface(d); | 
 |   auto sycl_device = Eigen::SyclDevice(&queueInterface); | 
 |   test_device_memory<DataType, RowMajor, int64_t>(sycl_device); | 
 |   test_device_memory<DataType, ColMajor, int64_t>(sycl_device); | 
 |   /// this test throw an exception. enable it if you want to see the exception | 
 |   //test_device_exceptions<DataType, RowMajor>(sycl_device); | 
 |   /// this test throw an exception. enable it if you want to see the exception | 
 |   //test_device_exceptions<DataType, ColMajor>(sycl_device); | 
 |   test_device_attach_buffer<DataType, ColMajor, int64_t>(sycl_device); | 
 | } | 
 |  | 
 | EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl) { | 
 |   for (const auto& device :Eigen::get_sycl_supported_devices()) { | 
 |     CALL_SUBTEST(sycl_device_test_per_device<float>(device)); | 
 |     CALL_SUBTEST(sycl_device_test_per_device<OffByOneScalar<int>>(device)); | 
 |   } | 
 | } |