TReduceMax.cpp

/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northestern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*   http://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.
*/

/*
* $Created by: Xu Chen (email: hello_master1954@163.com) 2018-06-30
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-06 float16 added
*/

#include "TReduceMax.h"
#include "../core/getandset/ConvertDataType.h"

namespace nts { // namespace nts(NiuTrans.Tensor)

/* 
case 1: get the max value of the items along a dimension of the tensor. 
In this case,
(2, 4) -> (4), dim = 0
(2, 4) -> (2), dim = 1
*/
bool TestReduceMax1()
{
    /* a input tensor of size (2, 4) */
    int sOrder = 2;
    int * sDimSize = new int[sOrder];
    sDimSize[0] = 2;
    sDimSize[1] = 4;

    int sUnitNum = 1;
    for (int i = 0; i < sOrder; i++)
        sUnitNum *= sDimSize[i];

    /* a output tensor of size (4) */
    int tOrder1 = 1;
    int * tDimSize1 = new int[tOrder1];
    tDimSize1[0] = 4;

    int tUnitNum1 = 1;
    for (int i = 0; i < tOrder1; i++)
        tUnitNum1 *= tDimSize1[i];

    /* a output tensor of size (2) */
    int tOrder2 = 1;
    int * tDimSize2 = new int[tOrder2];
    tDimSize2[0] = 2;

    int tUnitNum2 = 1;
    for (int i = 0; i < tOrder2; i++)
        tUnitNum2 *= tDimSize2[i];

    DTYPE sData[2][4] = { {0.0F, 5.0F, 2.0F, 3.0F},
                          {4.0F, 1.0F, 6.0F, 7.0F} };
    DTYPE answer1[4] = {4.0F, 5.0F, 6.0F, 7.0F};
    DTYPE answer2[2] = {5.0F, 7.0F};

    /* CPU test */
    bool cpuTest = true;

    /* create tensors */
    XTensor * s = NewTensor(sOrder, sDimSize);
    XTensor * t1 = NewTensor(tOrder1, tDimSize1);
    XTensor * t2 = NewTensor(tOrder2, tDimSize2);
    XTensor tUser1;
    XTensor tUser2;

    /* initialize variables */
    s->SetData(sData, sUnitNum);
    t1->SetZeroAll();
    t2->SetZeroAll();

    /* call ReduceMax function */
    _ReduceMax(s, t1, 0);
    _ReduceMax(s, t2, 1);
    tUser1 = ReduceMax(*s, 0);
    tUser2 = ReduceMax(*s, 1);

    /* check results */
    cpuTest = t1->CheckData(answer1, tUnitNum1) && tUser1.CheckData(answer1, tUnitNum1) && 
              t2->CheckData(answer2, tUnitNum2) && tUser2.CheckData(answer2, tUnitNum2);

#ifdef USE_CUDA
    /* GPU test */
    bool gpuTest = true;

    /* create tensors */
    XTensor * sGPU = NewTensor(sOrder, sDimSize, X_FLOAT, 1.0F, 0);
    XTensor * tGPU1 = NewTensor(tOrder1, tDimSize1, X_FLOAT, 1.0F, 0);
    XTensor * tGPU2 = NewTensor(tOrder2, tDimSize2, X_FLOAT, 1.0F, 0);
    XTensor tUserGPU1;
    XTensor tUserGPU2;

    /* initialize variables */
    sGPU->SetData(sData, sUnitNum);
    tGPU1->SetZeroAll();
    tGPU2->SetZeroAll();

    /* call ReduceMax function */
    _ReduceMax(sGPU, tGPU1, 0);
    _ReduceMax(sGPU, tGPU2, 1);
    tUserGPU1 = ReduceMax(*sGPU, 0);
    tUserGPU2 = ReduceMax(*sGPU, 1);

    /* check results */
    gpuTest = tGPU1->CheckData(answer1, tUnitNum1) && tUserGPU1.CheckData(answer1, tUnitNum1) && 
              tGPU2->CheckData(answer2, tUnitNum2) && tUserGPU2.CheckData(answer2, tUnitNum2);

    /* destroy variables */
    delete s;
    delete t1;
    delete t2;
    delete sGPU;
    delete tGPU1;
    delete tGPU2;
    delete[] sDimSize;
    delete[] tDimSize1;
    delete[] tDimSize2;

    return cpuTest && gpuTest;
#else
    /* destroy variables */
    delete s;
    delete t1;
    delete t2;
    delete[] sDimSize;
    delete[] tDimSize1;
    delete[] tDimSize2;

    return cpuTest;
#endif // USE_CUDA
}

/*
case 2: float16 get the max value of the items along a dimension of the tensor.
In this case,
(2, 4) -> (4), dim = 0
(2, 4) -> (2), dim = 1
*/
bool TestReduceMax2()
{
    /* a input tensor of size (2, 4) */
    int sOrder = 2;
    int * sDimSize = new int[sOrder];
    sDimSize[0] = 2;
    sDimSize[1] = 4;

    int sUnitNum = 1;
    for (int i = 0; i < sOrder; i++)
        sUnitNum *= sDimSize[i];

    /* a output tensor of size (4) */
    int tOrder1 = 1;
    int * tDimSize1 = new int[tOrder1];
    tDimSize1[0] = 4;

    int tUnitNum1 = 1;
    for (int i = 0; i < tOrder1; i++)
        tUnitNum1 *= tDimSize1[i];

    /* a output tensor of size (2) */
    int tOrder2 = 1;
    int * tDimSize2 = new int[tOrder2];
    tDimSize2[0] = 2;

    int tUnitNum2 = 1;
    for (int i = 0; i < tOrder2; i++)
        tUnitNum2 *= tDimSize2[i];

    DTYPE sData[2][4] = { {0.0F, 5.0F, 2.0F, 3.0F},
                          {4.0F, 1.0F, 6.0F, 7.0F} };
    DTYPE answer1[4] = {4.0F, 5.0F, 6.0F, 7.0F};
    DTYPE answer2[2] = {5.0F, 7.0F};

    /* CPU test */
    bool cpuTest = true;

#ifdef USE_CUDA
    /* GPU test */
    bool gpuTest = true;

    /* create tensors */
    XTensor * sGPU = NewTensor(sOrder, sDimSize, X_FLOAT, 1.0F, 0);
    XTensor * tGPU1 = NewTensor(tOrder1, tDimSize1, X_FLOAT, 1.0F, 0);
    XTensor * tGPU2 = NewTensor(tOrder2, tDimSize2, X_FLOAT, 1.0F, 0);
    XTensor tUserGPU1;
    XTensor tUserGPU2;

    /* create float16 tensors */
    XTensor sHalfGPU;
    XTensor tHalfGPU1;
    XTensor tHalfGPU2;
    XTensor tUserHalfGPU1;
    XTensor tUserHalfGPU2;

    /* initialize variables */
    sGPU->SetData(sData, sUnitNum);
    tGPU1->SetZeroAll();
    tGPU2->SetZeroAll();

    /* convert data type form float to float16 */
    sHalfGPU = ConvertDataType(*sGPU, X_FLOAT16);
    tHalfGPU1 = ConvertDataType(*tGPU1, X_FLOAT16);
    tHalfGPU2 = ConvertDataType(*tGPU2, X_FLOAT16);

    /* call reducemax function */
    _ReduceMax(&sHalfGPU, &tHalfGPU1, 0);
    _ReduceMax(&sHalfGPU, &tHalfGPU2, 1);
    tUserHalfGPU1 = ReduceMax(sHalfGPU, 0);
    tUserHalfGPU2 = ReduceMax(sHalfGPU, 1);

    /* convert data type from float16 to float */
    _ConvertDataType(&tHalfGPU1, tGPU1);
    _ConvertDataType(&tHalfGPU2, tGPU2);
    tUserGPU1 = ConvertDataType(tUserHalfGPU1, X_FLOAT);
    tUserGPU2 = ConvertDataType(tUserHalfGPU2, X_FLOAT);

    /* check results */
    gpuTest = tGPU1->CheckData(answer1, tUnitNum1) && tUserGPU1.CheckData(answer1, tUnitNum1) &&
              tGPU2->CheckData(answer2, tUnitNum2) && tUserGPU2.CheckData(answer2, tUnitNum2);

    /* destroy variables */
    delete sGPU;
    delete tGPU1;
    delete tGPU2;
    delete[] sDimSize;
    delete[] tDimSize1;
    delete[] tDimSize2;

    return cpuTest && gpuTest;
#else
    /* destroy variables */
    delete[] sDimSize;
    delete[] tDimSize1;
    delete[] tDimSize2;

    return cpuTest;
#endif // USE_CUDA
}

/* other cases */
/*
TODO!!
*/

/* test for ReduceMax Function */
bool TestReduceMax()
{
    XPRINT(0, stdout, "[TEST ReduceMax] get the max value of the items along a dimension of the tensor\n");
    bool returnFlag = true, caseFlag = true;

    /* case 1 test */
    caseFlag = TestReduceMax1();
    if (!caseFlag) {
        returnFlag = false;
        XPRINT(0, stdout, ">> case 1 failed!\n");
    }
    else
        XPRINT(0, stdout, ">> case 1 passed!\n");

    /* case 2 test */
    caseFlag = TestReduceMax2();
    if (!caseFlag) {
        returnFlag = false;
        XPRINT(0, stdout, ">> case 2 failed!\n");
    }
    else
        XPRINT(0, stdout, ">> case 2 passed!\n");

    /* other cases test */
    /*
    TODO!!
    */

    if (returnFlag) {
        XPRINT(0, stdout, ">> All Passed!\n");
    }
    else
        XPRINT(0, stdout, ">> Failed!\n");

    XPRINT(0, stdout, "\n");

    return returnFlag;
    }

} // namespace nts(NiuTrans.Tensor)