cavis/libnd4j/tests_cpu/layers_tests/SparseUtilsTest.cpp

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// Created by raver119 on 04.08.17.
//

#include "testlayers.h"
#include <memory>
#include <array/NDArray.h>
#include "ops/specials_sparse.h"
using namespace sd;

//////////////////////////////////////////////////////////////////////
class SparseUtilsTest : public testing::Test {
public:
    static const Nd4jLong nnz = 40;
    static const int rank = 3;
};


//////////////////////////////////////////////////////////////////////
TEST_F(SparseUtilsTest, SortCOOindices_Test) {

#ifndef __CUDABLAS__


    Nd4jLong * indicesArr = new Nd4jLong[nnz * rank]{
            0,2,7,
            2,36,35,
            3,30,17,
            5,12,22,
            5,43,45,
            6,32,11,
            8,8,32,
            9,29,11,
            5,11,22,
            15,26,16,
            17,48,49,
            24,28,31,
            26,6,23,
            31,21,31,
            35,46,45,
            37,13,14,
            6,38,18,
            7,28,20,
            8,29,39,
            8,32,30,
            9,42,43,
            11,15,18,
            13,18,45,
            29,26,39,
            30,8,25,
            42,31,24,
            28,33,5,
            31,27,1,
            35,43,26,
            36,8,37,
            39,22,14,
            39,24,42,
            42,48,2,
            43,26,48,
            44,23,49,
            45,18,34,
            46,28,5,
            46,32,17,
            48,34,44,
            49,38,39,
    };

    Nd4jLong * expIndicesArr = new Nd4jLong[nnz * rank]{
            0, 2, 7,
            2, 36, 35,
            3, 30, 17,
            5, 11, 22,
            5, 12, 22,
            5, 43, 45,
            6, 32, 11,
            6, 38, 18,
            7, 28, 20,
            8, 8, 32,
            8, 29, 39,
            8, 32, 30,
            9, 29, 11,
            9, 42, 43,
            11, 15, 18,
            13, 18, 45,
            15, 26, 16,
            17, 48, 49,
            24, 28, 31,
            26, 6, 23,
            28, 33, 5,
            29, 26, 39,
            30, 8, 25,
            31, 21, 31,
            31, 27, 1,
            35, 43, 26,
            35, 46, 45,
            36, 8, 37,
            37, 13, 14,
            39, 22, 14,
            39, 24, 42,
            42, 31, 24,
            42, 48, 2,
            43, 26, 48,
            44, 23, 49,
            45, 18, 34,
            46, 28, 5,
            46, 32, 17,
            48, 34, 44,
            49, 38, 39,
    };

    auto values  = NDArrayFactory::create<float>('c', {40}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
                                      22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39});

    auto expValues  = NDArrayFactory::create<float>('c', {40}, {0,  1,  2,  8,  3,  4,  5, 16, 17,  6, 18, 19,  7, 20, 21, 22,  9,
                                          10, 11, 12, 26, 23, 24, 13, 27, 28, 14, 29, 15, 30, 31, 25, 32, 33,
                                          34, 35, 36, 37, 38, 39
                                          });

    sd::sparse::SparseUtils<float >::sortCooIndicesGeneric(indicesArr, reinterpret_cast<float*>(values.buffer()), nnz, rank);

    for ( int i = 0; i < rank * nnz; ++i){
        ASSERT_EQ(expIndicesArr[i], indicesArr[i]);
    }

    ASSERT_TRUE(expValues.equalsTo(values));


    delete[] indicesArr;
    delete[] expIndicesArr;


#endif
}

//////////////////////////////////////////////////////////////////////
TEST_F(SparseUtilsTest, RavelIndices_Test) {

#ifndef __CUDABLAS__

    Nd4jLong * indicesArrExp = new Nd4jLong[nnz * rank]{
            0,2,7,
            2,36,35,
            3,30,17,
            5,12,22,
            5,43,45,
            6,32,11,
            8,8,32,
            9,29,11,
            5,11,22,
            15,26,16,
            17,48,49,
            24,28,31,
            26,6,23,
            31,21,31,
            35,46,45,
            37,13,14,
            6,38,18,
            7,28,20,
            8,29,39,
            8,32,30,
            9,42,43,
            11,15,18,
            13,18,45,
            29,26,39,
            30,8,25,
            42,31,24,
            28,33,5,
            31,27,1,
            35,43,26,
            36,8,37,
            39,22,14,
            39,24,42,
            42,48,2,
            43,26,48,
            44,23,49,
            45,18,34,
            46,28,5,
            46,32,17,
            48,34,44,
            49,38,39,
    };
    Nd4jLong * indicesArr = new Nd4jLong[nnz * rank];

        Nd4jLong * flatIndicesExp = new Nd4jLong[nnz]{
            147,  10955,  14717,  21862,  24055,  27451,  34192,  39841,
            21792,  64836,  74809, 102791, 109643, 131701, 150265, 156324,
            27878,  31380,  35669,  35870,  40783,  47268,  55905, 123659,
            126585, 178594, 119915, 132091, 150036, 151797, 165354, 165522,
            179762, 182468, 186459, 190294, 195165, 195457, 204024, 208499
    };

    Nd4jLong * flatIndices = new Nd4jLong[nnz];


    Nd4jLong * shape =  new Nd4jLong[rank]{50, 60, 70};
    Nd4jLong * shapeInfoBuffer = shape::shapeBuffer(rank, sd::DataType::INT64, shape);


    sd::sparse::IndexUtils::ravelMultiIndex(indicesArrExp, flatIndices, nnz, shapeInfoBuffer, ND4J_CLIPMODE_THROW);

    for ( int i = 0; i < nnz; ++i){
        ASSERT_EQ(flatIndicesExp[i], flatIndices[i]);
    }

    sd::sparse::IndexUtils::unravelIndex(indicesArr, flatIndices, nnz, shapeInfoBuffer);

    for ( int i = 0; i < nnz * rank; ++i){
        ASSERT_EQ(indicesArrExp[i], indicesArr[i]);
    }

    shape[2] = 30;
    shapeInfoBuffer = shape::shapeBuffer(rank, sd::DataType::INT64, shape);

    try {
        sd::sparse::IndexUtils::ravelMultiIndex(indicesArrExp, flatIndices, nnz, shapeInfoBuffer, ND4J_CLIPMODE_THROW);
        FAIL();
    } catch (const std::runtime_error& e) {
        // pass
    }

    delete[] indicesArrExp;
    delete[] indicesArr;
    delete[] flatIndicesExp;
    delete[] flatIndices;
    delete[] shape;
    delete[] shapeInfoBuffer;

#endif
}