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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_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::Tensor;
// Inflation Definition for each dimension the inflated val would be
//((dim-1)*strid[dim] +1)
// for 1 dimension vector of size 3 with value (4,4,4) with the inflated stride value of 3 would be changed to
// tensor of size (2*3) +1 = 7 with the value of
// (4, 0, 0, 4, 0, 0, 4).
template <typename DataType, int DataLayout, typename IndexType>
void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
IndexType sizeDim4 = 7;
array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange);
Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensorRange);
tensor.setRandom();
array<IndexType, 4> strides;
strides[0] = 1;
strides[1] = 1;
strides[2] = 1;
strides[3] = 1;
const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
gpu_no_stride.device(sycl_device)=gpu_tensor.inflate(strides);
sycl_device.memcpyDeviceToHost(no_stride.data(), gpu_data_no_stride, tensorBuffSize);
VERIFY_IS_EQUAL(no_stride.dimension(0), sizeDim1);
VERIFY_IS_EQUAL(no_stride.dimension(1), sizeDim2);
VERIFY_IS_EQUAL(no_stride.dimension(2), sizeDim3);
VERIFY_IS_EQUAL(no_stride.dimension(3), sizeDim4);
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;
IndexType inflatedSizeDim1 = 3;
IndexType inflatedSizeDim2 = 9;
IndexType inflatedSizeDim3 = 9;
IndexType inflatedSizeDim4 = 19;
array<IndexType, 4> inflatedTensorRange = {{inflatedSizeDim1, inflatedSizeDim2, inflatedSizeDim3, inflatedSizeDim4}};
Tensor<DataType, 4, DataLayout, IndexType> inflated(inflatedTensorRange);
const size_t inflatedTensorBuffSize =inflated.size()*sizeof(DataType);
DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize));
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_inflated(gpu_data_inflated, inflatedTensorRange);
gpu_inflated.device(sycl_device)=gpu_tensor.inflate(strides);
sycl_device.memcpyDeviceToHost(inflated.data(), gpu_data_inflated, inflatedTensorBuffSize);
VERIFY_IS_EQUAL(inflated.dimension(0), inflatedSizeDim1);
VERIFY_IS_EQUAL(inflated.dimension(1), inflatedSizeDim2);
VERIFY_IS_EQUAL(inflated.dimension(2), inflatedSizeDim3);
VERIFY_IS_EQUAL(inflated.dimension(3), inflatedSizeDim4);
for (IndexType i = 0; i < inflatedSizeDim1; ++i) {
for (IndexType j = 0; j < inflatedSizeDim2; ++j) {
for (IndexType k = 0; k < inflatedSizeDim3; ++k) {
for (IndexType l = 0; l < inflatedSizeDim4; ++l) {
if (i % strides[0] == 0 &&
j % strides[1] == 0 &&
k % strides[2] == 0 &&
l % strides[3] == 0) {
VERIFY_IS_EQUAL(inflated(i,j,k,l),
tensor(i/strides[0], j/strides[1], k/strides[2], l/strides[3]));
} else {
VERIFY_IS_EQUAL(0, inflated(i,j,k,l));
}
}
}
}
}
sycl_device.deallocate(gpu_data_tensor);
sycl_device.deallocate(gpu_data_no_stride);
sycl_device.deallocate(gpu_data_inflated);
}
template<typename DataType, typename dev_Selector> void sycl_inflation_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_inflation_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_simple_inflation_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_inflation_test_per_device<float>(device));
}
}
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