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182 lines
7.7 KiB
C++
182 lines
7.7 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_reduction_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_full_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 452;
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const IndexType num_cols = 765;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 0, DataLayout, IndexType> full_redux;
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Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
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in.setRandom();
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full_redux = in.mean();
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.mean();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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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_full_reductions_min_sycl(const Eigen::SyclDevice& sycl_device) {
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const IndexType num_rows = 876;
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const IndexType num_cols = 953;
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array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
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Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 0, DataLayout, IndexType> full_redux;
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Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
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in.setRandom();
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full_redux = in.minimum();
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.minimum();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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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_first_dim_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) {
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IndexType dim_x = 145;
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IndexType dim_y = 1;
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IndexType dim_z = 67;
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array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
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Eigen::array<IndexType, 1> red_axis;
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red_axis[0] = 0;
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array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
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Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
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in.setRandom();
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redux= in.maximum(red_axis);
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
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sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.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 j=0; j<reduced_tensorRange[0]; j++ )
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for(IndexType k=0; k<reduced_tensorRange[1]; k++ )
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VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
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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_last_dim_reductions_sum_sycl(const Eigen::SyclDevice &sycl_device) {
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IndexType dim_x = 567;
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IndexType dim_y = 1;
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IndexType dim_z = 47;
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array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
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Eigen::array<IndexType, 1> red_axis;
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red_axis[0] = 2;
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array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
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Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
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Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
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in.setRandom();
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redux= in.sum(red_axis);
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DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
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DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
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out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
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sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.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 j=0; j<reduced_tensorRange[0]; j++ )
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for(IndexType k=0; k<reduced_tensorRange[1]; k++ )
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VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
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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> void sycl_reduction_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_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
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test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device);
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test_first_dim_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device);
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test_last_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
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}
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void test_cxx11_tensor_reduction_sycl() {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_reduction_test_per_device<float>(device));
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}
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}
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