deeplearning4j--deeplearning4j/libnd4j/include/loops/cpu/summarystatsreduce.cpp

/* ******************************************************************************
*
*
* 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.
*
*  See the NOTICE file distributed with this work for additional
*  information regarding copyright ownership.
* 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 18.12.17.
//
#include <execution/Threads.h>
#include <helpers/ConstantTadHelper.h>

#include <helpers/shape.h>
#include <loops/summarystatsreduce.h>
#include <system/op_boilerplate.h>
#include <types/types.h>

using namespace simdOps;

namespace functions {
namespace summarystats {

template <typename X, typename Z>
Z SummaryStatsReduce<X, Z>::execScalar(const int opNum, const bool biasCorrected,  void *x,
                                       sd::LongType *xShapeInfo, void *extraParams) {
 RETURNING_DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(biasCorrected, x, xShapeInfo, extraParams), SUMMARY_STATS_OPS);
}

template <typename X, typename Z>
void SummaryStatsReduce<X, Z>::execScalar(const int opNum, const bool biasCorrected,  void *x,
                                          sd::LongType *xShapeInfo, void *extraParams, void *z,
                                          sd::LongType *zShapeInfo) {
 DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(biasCorrected, x, xShapeInfo, extraParams, z, zShapeInfo), SUMMARY_STATS_OPS);
}

template <typename X, typename Z>
void SummaryStatsReduce<X, Z>::exec(int opNum, bool biasCorrected,  void *x,
                                    sd::LongType *xShapeInfo, void *extraParams, void *z,
                                    sd::LongType *zShapeInfo,
                                   sd::LongType *dimension, sd::LongType dimensionLength) {
 DISPATCH_BY_OPNUM_TT(exec,
                      PARAMS(biasCorrected, x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength),
                      SUMMARY_STATS_OPS);
}

template <typename X, typename Z>
template <typename OpType>
void SummaryStatsReduce<X, Z>::execScalar(const bool biasCorrected,  void *vx,  sd::LongType *xShapeInfo,
                                         void *vextraParams, void *vz,  sd::LongType *zShapeInfo) {
 auto z = reinterpret_cast<Z *>(vz);
 z[0] = execScalar<OpType>(biasCorrected, vx, xShapeInfo, vextraParams);
}

template <typename X, typename Z>
template <typename OpType>
Z SummaryStatsReduce<X, Z>::execScalar(const bool biasCorrected,  void *vx,  sd::LongType *xShapeInfo,
                                      void *vextraParams) {
 auto x = reinterpret_cast<const X *>(vx);
 auto extraParams = reinterpret_cast<Z *>(vextraParams);

 // Cache shape-related values
 sd::LongType xRank = shape::rank(xShapeInfo);
 sd::LongType *xShape = shape::shapeOf(xShapeInfo);
 sd::LongType *xStride = shape::stride(xShapeInfo);

 SummaryStatsData<X> startingIndex;
 startingIndex.initialize();
 auto length = shape::length(xShapeInfo);

 for (sd::LongType i = 0; i < length; i++) {
   sd::LongType coords[SD_MAX_RANK];
   INDEX2COORDS(i, xRank, xShape, coords);
   sd::LongType xOffset;
   COORDS2INDEX(xRank, xStride, coords, xOffset);

   SummaryStatsData<X> curr;
   curr.initWithValue(x[xOffset]);
   startingIndex = update(startingIndex, curr, extraParams);
 }

 return OpType::getValue(biasCorrected, startingIndex);
}

template <typename X, typename Z>
template <typename OpType>
void SummaryStatsReduce<X, Z>::exec(bool biasCorrected,  void *vx,  sd::LongType *xShapeInfo,
                                   void *vextraParams, void *vz,  sd::LongType *zShapeInfo,
                                   sd::LongType *dimension, sd::LongType dimensionLength) {
 auto x = reinterpret_cast< X *>(vx);
 auto z = reinterpret_cast<Z *>(vz);
 auto extraParams = reinterpret_cast<Z *>(vextraParams);
 auto resultLength = shape::length(zShapeInfo);

 if (sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) {
   if (sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY) return;
   SummaryStatsData<X> comp;
   comp.initWithValue(x[0]);

   for (sd::LongType i = 0; i < resultLength; i++) z[i] = OpType::getValue(biasCorrected, comp);
   return;
 }

 if (shape::isScalar(zShapeInfo)) {
   z[0] = execScalar<OpType>(biasCorrected, (void *)x, xShapeInfo, extraParams);
   return;
 }

 if (dimensionLength < 1) return;

 // When shared_ptr goes out of scope, it deletes the TadPack and invalidates pointers!
 auto tadPack = sd::ConstantTadHelper::getInstance().tadForDimensions(const_cast<sd::LongType*>(xShapeInfo), dimension, dimensionLength);
 if (resultLength == 1 || dimensionLength == shape::rank(xShapeInfo) || tadPack->numberOfTads() == 1) {
   z[0] = execScalar<OpType>(biasCorrected, x, xShapeInfo, extraParams);
   return;
 }

 auto tadShapeShapeInfo = tadPack->primaryShapeInfo();
 auto tadLength = shape::length(tadPack->primaryShapeInfo());

 // Cache TAD shape-related values
 sd::LongType tadRank = shape::rank(tadShapeShapeInfo);
 sd::LongType *tadShape = shape::shapeOf(tadShapeShapeInfo);
 sd::LongType *tadStride = shape::stride(tadShapeShapeInfo);

 auto func = PRAGMA_THREADS_FOR {
   for (auto r = start; r < stop; r++) {
     auto tadOffsetForBlock = tadPack->primaryOffsets()[r];
     auto tx = x + tadOffsetForBlock;
     SummaryStatsData<X> comp;
     comp.initWithValue(tx[0]);

     for (sd::LongType i = 1; i < tadLength; i++) {
       sd::LongType coords[SD_MAX_RANK];
       INDEX2COORDS(i, tadRank, tadShape, coords);
       sd::LongType xOffset;
       COORDS2INDEX(tadRank, tadStride, coords, xOffset);

       SummaryStatsData<X> indexVal2;
       indexVal2.initWithValue(tx[xOffset]);

       comp = update(comp, OpType::op(indexVal2, extraParams), extraParams);
     }

     z[r] = OpType::getValue(biasCorrected, comp);
   }
 };

 samediff::Threads::parallel_tad(func, 0, resultLength, 1);
}

BUILD_DOUBLE_TEMPLATE( class SummaryStatsReduce, , SD_COMMON_TYPES, SD_FLOAT_TYPES);
}  // namespace summarystats
}  // namespace functions