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// 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_TEST_FUNC cxx11_tensor_striding_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include <iostream>
#include <chrono>
#include <ctime>
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout, typename IndexType>
static void test_simple_striding(const Eigen::SyclDevice& sycl_device)
{
Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}};
Eigen::array<IndexType, 4> stride_dims = {{1,1,3,3}};
Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensor_dims);
Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims);
std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
std::size_t stride_bytes = stride.size() * sizeof(DataType);
DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, tensor_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
tensor.setRandom();
array<IndexType, 4> strides;
strides[0] = 1;
strides[1] = 1;
strides[2] = 1;
strides[3] = 1;
sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
gpu_no_stride.device(sycl_device)=gpu_tensor.stride(strides);
sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
//no_stride = tensor.stride(strides);
VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
for (IndexType k = 0; k < 5; ++k) {
for (IndexType l = 0; l < 7; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l));
}
}
}
}
strides[0] = 2;
strides[1] = 4;
strides[2] = 2;
strides[3] = 3;
//Tensor<float, 4, DataLayout> stride;
// stride = tensor.stride(strides);
gpu_stride.device(sycl_device)=gpu_tensor.stride(strides);
sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
VERIFY_IS_EQUAL(stride.dimension(0), 1);
VERIFY_IS_EQUAL(stride.dimension(1), 1);
VERIFY_IS_EQUAL(stride.dimension(2), 3);
VERIFY_IS_EQUAL(stride.dimension(3), 3);
for (IndexType i = 0; i < 1; ++i) {
for (IndexType j = 0; j < 1; ++j) {
for (IndexType k = 0; k < 3; ++k) {
for (IndexType l = 0; l < 3; ++l) {
VERIFY_IS_EQUAL(tensor(2*i,4*j,2*k,3*l), stride(i,j,k,l));
}
}
}
}
sycl_device.deallocate(d_tensor);
sycl_device.deallocate(d_no_stride);
sycl_device.deallocate(d_stride);
}
template <typename DataType, int DataLayout, typename IndexType>
static void test_striding_as_lvalue(const Eigen::SyclDevice& sycl_device)
{
Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}};
Eigen::array<IndexType, 4> stride_dims = {{3,12,10,21}};
Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
Tensor<DataType, 4, DataLayout,IndexType> no_stride(stride_dims);
Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims);
std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
std::size_t stride_bytes = stride.size() * sizeof(DataType);
DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, stride_dims);
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
//Tensor<float, 4, DataLayout> tensor(2,3,5,7);
tensor.setRandom();
array<IndexType, 4> strides;
strides[0] = 2;
strides[1] = 4;
strides[2] = 2;
strides[3] = 3;
// Tensor<float, 4, DataLayout> result(3, 12, 10, 21);
// result.stride(strides) = tensor;
sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
gpu_stride.stride(strides).device(sycl_device)=gpu_tensor;
sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
for (IndexType k = 0; k < 5; ++k) {
for (IndexType l = 0; l < 7; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), stride(2*i,4*j,2*k,3*l));
}
}
}
}
array<IndexType, 4> no_strides;
no_strides[0] = 1;
no_strides[1] = 1;
no_strides[2] = 1;
no_strides[3] = 1;
// Tensor<float, 4, DataLayout> result2(3, 12, 10, 21);
// result2.stride(strides) = tensor.stride(no_strides);
gpu_no_stride.stride(strides).device(sycl_device)=gpu_tensor.stride(no_strides);
sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 3; ++j) {
for (IndexType k = 0; k < 5; ++k) {
for (IndexType l = 0; l < 7; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(2*i,4*j,2*k,3*l));
}
}
}
}
sycl_device.deallocate(d_tensor);
sycl_device.deallocate(d_no_stride);
sycl_device.deallocate(d_stride);
}
template <typename Dev_selector> void tensorStridingPerDevice(Dev_selector& s){
QueueInterface queueInterface(s);
auto sycl_device=Eigen::SyclDevice(&queueInterface);
test_simple_striding<float, ColMajor, int64_t>(sycl_device);
test_simple_striding<float, RowMajor, int64_t>(sycl_device);
test_striding_as_lvalue<float, ColMajor, int64_t>(sycl_device);
test_striding_as_lvalue<float, RowMajor, int64_t>(sycl_device);
}
void test_cxx11_tensor_striding_sycl() {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(tensorStridingPerDevice(device));
}
}
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