238 lines
9.3 KiB
C++
238 lines
9.3 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma, created on 21.09.2018
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// @author raver119@gmail.com
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//
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#include <helpers/TAD.h>
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#include<ops/declarable/helpers/ismax.h>
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#include <helpers/ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>& dimensions) {
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if (input->isVector()) {
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int dimensionsLength = dimensions.size();
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int length = input->lengthOf();
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if ((input->shapeOf())[dimensions[0]] == 1) {
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for (int i = 0; i < length; i++)
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output->p<T>(i, 1.f);
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}
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else {
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int eleStride = shape::elementWiseStride(input->getShapeInfo());
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if (eleStride == 1) {
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int maxIdx = 0;
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T currMax = input->e<T>(0);
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if (length < ELEMENT_THRESHOLD) {
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for (int i = 0; i < length; i++) {
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if (currMax < input->e<T>(i)) {
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currMax = input->e<T>(i);
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maxIdx = i;
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}
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output->p<T>(i, 0.f);
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}
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}
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else {
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{
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int maxIdxLocal = maxIdx;
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T currMaxLocal = currMax;
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for (int i = 0; i < length; i++) {
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if (currMaxLocal < input->e<T>(i)) {
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currMaxLocal = input->e<T>(i);
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maxIdxLocal = i;
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}
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output->p<T>(i, 0.f);
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}
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PRAGMA_OMP_CRITICAL
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{
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if (currMax < currMaxLocal) {
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currMax = currMaxLocal;
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maxIdx = maxIdxLocal;
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}
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}
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}
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}
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output->p<T>(maxIdx, 1.f);
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}
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else {
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int maxIdx = 0;
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T currMax = input->e<T>(0);
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if (length < ELEMENT_THRESHOLD) {
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for (int i = 0; i < length; i++) {
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if (currMax < input->e<T>(i*eleStride)) {
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currMax = input->e<T>(i*eleStride);
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maxIdx = i;
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}
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output->p<T>(i, 0.f);
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}
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}
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else {
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{
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int maxIdxLocal = maxIdx;
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T currMaxLocal = currMax;
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for (int i = 0; i < length; i++) {
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if (currMaxLocal < input->e<T>(i*eleStride)) {
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currMaxLocal = input->e<T>(i*eleStride);
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maxIdxLocal = i;
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}
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output->p<T>(i, 0.f);
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}
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PRAGMA_OMP_CRITICAL
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{
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if (currMax < currMaxLocal) {
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currMax = currMaxLocal;
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maxIdx = maxIdxLocal;
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}
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}
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}
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}
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output->p<T>(maxIdx, 1.f);
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}
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}
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}
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else {
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int dimensionsLength = dimensions.size();
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//int tads = tad.numTads;
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//decompose in to several sub tads after
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//moving all dimensions (in sorted order)
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//to the back.
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//permuted version of the input shape info for setting up the tad problem
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auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), const_cast<int*>(dimensions.data()), dimensionsLength);
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auto tadShapeShapeInfo = tadPack.primaryShapeInfo();
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auto tadOffsets = tadPack.primaryOffsets();
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int tadLength = shape::length(tadShapeShapeInfo);
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int tads = tadPack.numberOfTads();
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int tadsPerThread = tads / TAD_THRESHOLD;
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int num_threads = nd4j::math::nd4j_max<int>(1, tadsPerThread);
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num_threads = nd4j::math::nd4j_min<int>(num_threads, omp_get_max_threads());
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auto tadEWS = shape::elementWiseStride(tadShapeShapeInfo);
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auto zEWS = tadEWS;
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int span = (tads / num_threads) + 8;
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PRAGMA_OMP_PARALLEL_THREADS(num_threads)
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{
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int tid = omp_get_thread_num();
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int start = span * tid;
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int end = span * (tid + 1);
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if (end > tads) end = tads;
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for (int r = start; r < end; r++) {
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if (tadEWS > 0 && zEWS > 0 && dimensionsLength == 1) {
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T *rX = const_cast<NDArray*>(input)->bufferAsT<T>() + tadOffsets[r];
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T *rZ = output->bufferAsT<T>() + tadOffsets[r];
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T maxValue = rX[0];
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int maxIdx = 0;
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if (tadEWS == 1 && zEWS == 1) {
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for (int i = 0; i < tadLength; i++) {
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if (rX[i] > maxValue) {
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maxIdx = i;
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maxValue = rX[i];
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}
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < tadLength; i++) {
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rZ[i] = maxIdx == i ? (T) 1.0 : (T) 0.0;
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}
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}
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else {
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for (int i = 0; i < tadLength; i++) {
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if (rX[i * tadEWS] > maxValue) {
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maxIdx = i;
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maxValue = rX[i * tadEWS];
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}
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < tadLength; i++) {
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rZ[i * zEWS] = maxIdx == i ? (T) 1.0 : (T) 0.0;
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}
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}
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}
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else {
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int tadsPerThread = tads / TAD_THRESHOLD;
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int num_threads = nd4j::math::nd4j_max<int>(1, tadsPerThread);
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num_threads = nd4j::math::nd4j_min<int>(num_threads, omp_get_max_threads());
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Nd4jLong offset = tadOffsets[r];
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Nd4jLong shapeIter[MAX_RANK];
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Nd4jLong coord[MAX_RANK];
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int dim;
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Nd4jLong xStridesIter[MAX_RANK];
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Nd4jLong resultStridesIter[MAX_RANK];
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Nd4jLong *xShape = shape::shapeOf(tadShapeShapeInfo);
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Nd4jLong *xStride = shape::stride(tadShapeShapeInfo);
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Nd4jLong *resultStride = shape::stride(tadShapeShapeInfo);
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int rank = shape::rank(tadShapeShapeInfo);
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T *xPointer = const_cast<NDArray*>(input)->bufferAsT<T>() + offset;
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T *resultPointer = output->bufferAsT<T>() + offset;
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T maxValue = xPointer[0];
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T *maxCursor = resultPointer;
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Nd4jPointer maxCursorLong = reinterpret_cast<Nd4jPointer>(maxCursor);
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if (PrepareTwoRawArrayIter<T>(rank, xShape, xPointer, xStride, resultPointer, resultStride, &rank, shapeIter, &xPointer, xStridesIter, &resultPointer, resultStridesIter) >= 0) {
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ND4J_RAW_ITER_START(dim, rank, coord, shapeIter);
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{
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if (maxValue < xPointer[0]) {
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maxCursor = resultPointer;
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maxCursorLong = reinterpret_cast<Nd4jPointer>(resultPointer);
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maxValue = xPointer[0];
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}
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resultPointer[0] = 0.0;
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}
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ND4J_RAW_ITER_TWO_NEXT(dim, rank, coord, shapeIter, xPointer, xStridesIter, resultPointer, resultStridesIter);
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maxCursor = reinterpret_cast<T*>(maxCursorLong);
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maxCursor[0] = 1.0;
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}
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}
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}
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}
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}
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}
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void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions) {
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BUILD_SINGLE_SELECTOR(input->dataType(), ismax_, (input, output, dimensions), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void ismax_, (const NDArray *input, NDArray *output, const std::vector<int>& dimensions), LIBND4J_TYPES);
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}
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}
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}
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