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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015
// 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_TEST_FUNC cxx11_tensor_reduction_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
template <typename DataType, int DataLayout, typename IndexType>
static void test_full_reductions_mean_sycl(const Eigen::SyclDevice& sycl_device) {
const IndexType num_rows = 452;
const IndexType num_cols = 765;
array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
Tensor<DataType, 0, DataLayout, IndexType> full_redux;
Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
in.setRandom();
full_redux = in.mean();
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
out_gpu.device(sycl_device) = in_gpu.mean();
sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
// Check that the CPU and GPU reductions return the same result.
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_full_reductions_min_sycl(const Eigen::SyclDevice& sycl_device) {
const IndexType num_rows = 876;
const IndexType num_cols = 953;
array<IndexType, 2> tensorRange = {{num_rows, num_cols}};
Tensor<DataType, 2, DataLayout, IndexType> in(tensorRange);
Tensor<DataType, 0, DataLayout, IndexType> full_redux;
Tensor<DataType, 0, DataLayout, IndexType> full_redux_gpu;
in.setRandom();
full_redux = in.minimum();
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
DataType* gpu_out_data =(DataType*)sycl_device.allocate(sizeof(DataType));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
TensorMap<Tensor<DataType, 0, DataLayout, IndexType> > out_gpu(gpu_out_data);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
out_gpu.device(sycl_device) = in_gpu.minimum();
sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(DataType));
// Check that the CPU and GPU reductions return the same result.
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_first_dim_reductions_max_sycl(const Eigen::SyclDevice& sycl_device) {
IndexType dim_x = 145;
IndexType dim_y = 1;
IndexType dim_z = 67;
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
Eigen::array<IndexType, 1> red_axis;
red_axis[0] = 0;
array<IndexType, 2> reduced_tensorRange = {{dim_y, dim_z}};
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
in.setRandom();
redux= in.maximum(red_axis);
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType)));
TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
out_gpu.device(sycl_device) = in_gpu.maximum(red_axis);
sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(DataType));
// Check that the CPU and GPU reductions return the same result.
for(IndexType j=0; j<reduced_tensorRange[0]; j++ )
for(IndexType k=0; k<reduced_tensorRange[1]; k++ )
VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_last_dim_reductions_sum_sycl(const Eigen::SyclDevice &sycl_device) {
IndexType dim_x = 567;
IndexType dim_y = 1;
IndexType dim_z = 47;
array<IndexType, 3> tensorRange = {{dim_x, dim_y, dim_z}};
Eigen::array<IndexType, 1> red_axis;
red_axis[0] = 2;
array<IndexType, 2> reduced_tensorRange = {{dim_x, dim_y}};
Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
Tensor<DataType, 2, DataLayout, IndexType> redux(reduced_tensorRange);
Tensor<DataType, 2, DataLayout, IndexType> redux_gpu(reduced_tensorRange);
in.setRandom();
redux= in.sum(red_axis);
DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(DataType)));
DataType* gpu_out_data = static_cast<DataType*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(DataType)));
TensorMap<Tensor<DataType, 3, DataLayout, IndexType> > in_gpu(gpu_in_data, tensorRange);
TensorMap<Tensor<DataType, 2, DataLayout, IndexType> > out_gpu(gpu_out_data, reduced_tensorRange);
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(DataType));
out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(DataType));
// Check that the CPU and GPU reductions return the same result.
for(IndexType j=0; j<reduced_tensorRange[0]; j++ )
for(IndexType k=0; k<reduced_tensorRange[1]; k++ )
VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
template<typename DataType> void sycl_reduction_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_full_reductions_mean_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_full_reductions_min_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_first_dim_reductions_max_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_last_dim_reductions_sum_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_full_reductions_mean_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_full_reductions_min_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_first_dim_reductions_max_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_last_dim_reductions_sum_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
void test_cxx11_tensor_reduction_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_reduction_test_per_device<float>(device));
}
}
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