| // 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)); |
| } |
| } |