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115 lines
4.4 KiB
C++
115 lines
4.4 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) 2016
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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_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 "OffByOneScalar.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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#include <stdint.h>
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#include <iostream>
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template <typename DataType, int DataLayout, typename IndexType>
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void test_device_memory(const Eigen::SyclDevice &sycl_device) {
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IndexType sizeDim1 = 100;
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array<IndexType, 1> tensorRange = {{sizeDim1}};
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Tensor<DataType, 1, DataLayout,IndexType> in(tensorRange);
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Tensor<DataType, 1, DataLayout,IndexType> in1(tensorRange);
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType)));
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// memset
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memset(in1.data(), 1, in1.size() * sizeof(DataType));
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sycl_device.memset(gpu_in_data, 1, in.size()*sizeof(DataType));
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sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType));
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for (IndexType i=0; i<in.size(); i++) {
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VERIFY_IS_EQUAL(in(i), in1(i));
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}
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// fill
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DataType value = DataType(7);
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std::fill_n(in1.data(), in1.size(), value);
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sycl_device.fill(gpu_in_data, gpu_in_data + in.size(), value);
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sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType));
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for (IndexType i=0; i<in.size(); i++) {
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VERIFY_IS_EQUAL(in(i), in1(i));
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}
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sycl_device.deallocate(gpu_in_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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void test_device_exceptions(const Eigen::SyclDevice &sycl_device) {
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VERIFY(sycl_device.ok());
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IndexType sizeDim1 = 100;
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array<IndexType, 1> tensorDims = {{sizeDim1}};
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DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1*sizeof(DataType)));
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sycl_device.memset(gpu_data, 1, sizeDim1*sizeof(DataType));
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TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> in(gpu_data, tensorDims);
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TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> out(gpu_data, tensorDims);
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out.device(sycl_device) = in / in.constant(0);
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sycl_device.synchronize();
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VERIFY(!sycl_device.ok());
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sycl_device.deallocate(gpu_data);
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}
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template<typename DataType, int DataLayout, typename IndexType>
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void test_device_attach_buffer(const Eigen::SyclDevice &sycl_device) {
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IndexType sizeDim1 = 100;
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array<IndexType, 1> tensorRange = {{sizeDim1}};
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Tensor<DataType, 1, DataLayout, IndexType> in(tensorRange);
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cl::sycl::buffer<buffer_scalar_t, 1> buffer(cl::sycl::range<1>(sizeDim1 * sizeof(DataType)));
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.attach_buffer(buffer));
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// fill
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DataType value = DataType(7);
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std::fill_n(in.data(), in.size(), value);
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sycl_device.fill(gpu_in_data, gpu_in_data + in.size(), value);
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// Check that buffer is filled with the correct value.
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auto reint = buffer.reinterpret<DataType>(cl::sycl::range<1>(sizeDim1));
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auto access = reint.template get_access<cl::sycl::access::mode::read>();
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for (IndexType i=0; i<in.size(); i++) {
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VERIFY_IS_EQUAL(in(i), access[i]);
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}
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sycl_device.detach_buffer(gpu_in_data);
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}
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template<typename DataType> void sycl_device_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_device_memory<DataType, RowMajor, int64_t>(sycl_device);
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test_device_memory<DataType, ColMajor, int64_t>(sycl_device);
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/// this test throw an exception. enable it if you want to see the exception
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//test_device_exceptions<DataType, RowMajor>(sycl_device);
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/// this test throw an exception. enable it if you want to see the exception
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//test_device_exceptions<DataType, ColMajor>(sycl_device);
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test_device_attach_buffer<DataType, ColMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl) {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_device_test_per_device<float>(device));
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CALL_SUBTEST(sycl_device_test_per_device<OffByOneScalar<int>>(device));
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}
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}
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