cavis/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp

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/*******************************************************************************
* 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
******************************************************************************/
//
// @author Yurii Shyrma, created on 21.09.2018
// @author raver119@gmail.com
//
#include <helpers/TAD.h>
#include<ops/declarable/helpers/ismax.h>
#include <helpers/ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename X, typename Z>
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static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>& dimensions) {
if (input->isVector()) {
int dimensionsLength = dimensions.size();
int length = input->lengthOf();
if (!dimensions.empty() && (input->shapeOf())[dimensions[0]] == 1) {
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for (int i = 0; i < length; i++)
output->p<Z>(i, 1);
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}
else {
int eleStride = shape::elementWiseStride(input->getShapeInfo());
if (eleStride == 1) {
int maxIdx = 0;
auto currMax = input->e<X>(0);
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if (length < ELEMENT_THRESHOLD) {
for (int i = 0; i < length; i++) {
if (currMax < input->e<X>(i)) {
currMax = input->e<X>(i);
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maxIdx = i;
}
output->p<Z>(i, 0);
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}
}
else {
{
int maxIdxLocal = maxIdx;
auto currMaxLocal = currMax;
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for (int i = 0; i < length; i++) {
if (currMaxLocal < input->e<X>(i)) {
currMaxLocal = input->e<X>(i);
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maxIdxLocal = i;
}
output->p<Z>(i, 0);
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}
PRAGMA_OMP_CRITICAL
{
if (currMax < currMaxLocal) {
currMax = currMaxLocal;
maxIdx = maxIdxLocal;
}
}
}
}
output->p<Z>(maxIdx, 1);
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}
else {
int maxIdx = 0;
auto currMax = input->e<X>(0);
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if (length < ELEMENT_THRESHOLD) {
for (int i = 0; i < length; i++) {
if (currMax < input->e<X>(i*eleStride)) {
currMax = input->e<X>(i*eleStride);
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maxIdx = i;
}
output->p<Z>(i, 0.f);
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}
}
else {
{
int maxIdxLocal = maxIdx;
auto currMaxLocal = currMax;
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for (int i = 0; i < length; i++) {
if (currMaxLocal < input->e<X>(i*eleStride)) {
currMaxLocal = input->e<X>(i*eleStride);
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maxIdxLocal = i;
}
output->p<Z>(i, 0.f);
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}
PRAGMA_OMP_CRITICAL
{
if (currMax < currMaxLocal) {
currMax = currMaxLocal;
maxIdx = maxIdxLocal;
}
}
}
}
output->p<Z>(maxIdx, 1);
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}
}
}
else {
int dimensionsLength = dimensions.size();
//int tads = tad.numTads;
//decompose in to several sub tads after
//moving all dimensions (in sorted order)
//to the back.
//permuted version of the input shape info for setting up the tad problem
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), const_cast<int*>(dimensions.data()), dimensionsLength);
auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), const_cast<int*>(dimensions.data()), dimensionsLength);
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auto tadShapeShapeInfo = tadPack.primaryShapeInfo();
auto tadOffsets = tadPack.primaryOffsets();
auto zOfsets = tadPackZ.platformOffsets();
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int tadLength = shape::length(tadShapeShapeInfo);
int tads = tadPack.numberOfTads();
int tadsPerThread = tads / TAD_THRESHOLD;
int num_threads = nd4j::math::nd4j_max<int>(1, tadsPerThread);
num_threads = nd4j::math::nd4j_min<int>(num_threads, omp_get_max_threads());
auto tadEWS = shape::elementWiseStride(tadShapeShapeInfo);
auto zEWS = shape::elementWiseStride(tadPackZ.primaryShapeInfo());
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int span = (tads / num_threads) + 8;
PRAGMA_OMP_PARALLEL_THREADS(num_threads)
{
int tid = omp_get_thread_num();
int start = span * tid;
int end = span * (tid + 1);
if (end > tads) end = tads;
for (int r = start; r < end; r++) {
if (tadEWS > 0 && zEWS > 0 && dimensionsLength == 1) {
auto rX = const_cast<NDArray*>(input)->bufferAsT<X>() + tadOffsets[r];
auto rZ = output->bufferAsT<Z>() + zOfsets[r];
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auto maxValue = rX[0];
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int maxIdx = 0;
if (tadEWS == 1 && zEWS == 1) {
for (int i = 0; i < tadLength; i++) {
if (rX[i] > maxValue) {
maxIdx = i;
maxValue = rX[i];
}
}
PRAGMA_OMP_SIMD
for (int i = 0; i < tadLength; i++) {
rZ[i] = maxIdx == i ? (Z) 1 : (Z) 0;
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}
}
else if (tadEWS > 1 && zEWS > 1) {
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for (int i = 0; i < tadLength; i++) {
if (rX[i * tadEWS] > maxValue) {
maxIdx = i;
maxValue = rX[i * tadEWS];
}
}
PRAGMA_OMP_SIMD
for (int i = 0; i < tadLength; i++) {
rZ[i * zEWS] = maxIdx == i ? (Z) 1 : (Z) 0;
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}
} else {
for (int i = 0; i < tadLength; i++) {
auto xOffset = shape::getIndexOffset(i, tadShapeShapeInfo, tadLength);
if (rX[xOffset] > maxValue) {
maxIdx = i;
maxValue = rX[xOffset];
}
}
PRAGMA_OMP_SIMD
for (int i = 0; i < tadLength; i++) {
auto zOffset = shape::getIndexOffset(i, tadPackZ.primaryShapeInfo(), tadLength);
rZ[zOffset] = maxIdx == i ? (Z) 1 : (Z) 0;
}
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}
}
else {
int tadsPerThread = tads / TAD_THRESHOLD;
int num_threads = nd4j::math::nd4j_max<int>(1, tadsPerThread);
num_threads = nd4j::math::nd4j_min<int>(num_threads, omp_get_max_threads());
Nd4jLong offset = tadOffsets[r];
Nd4jLong shapeIter[MAX_RANK];
Nd4jLong coord[MAX_RANK];
int dim;
Nd4jLong xStridesIter[MAX_RANK];
Nd4jLong resultStridesIter[MAX_RANK];
Nd4jLong *xShape = shape::shapeOf(tadShapeShapeInfo);
Nd4jLong *xStride = shape::stride(tadShapeShapeInfo);
Nd4jLong *resultStride = shape::stride(tadShapeShapeInfo);
int rank = shape::rank(tadShapeShapeInfo);
auto xPointer = const_cast<NDArray*>(input)->bufferAsT<X>() + offset;
auto resultPointer = output->bufferAsT<Z>() + offset;
auto maxValue = xPointer[0];
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auto maxCursor = resultPointer;
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Nd4jPointer maxCursorLong = reinterpret_cast<Nd4jPointer>(maxCursor);
if (PrepareTwoRawArrayIter<X,Z>(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);
{
if (maxValue < xPointer[0]) {
maxCursor = resultPointer;
maxCursorLong = reinterpret_cast<Nd4jPointer>(resultPointer);
maxValue = xPointer[0];
}
resultPointer[0] = (Z) 0;
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}
ND4J_RAW_ITER_TWO_NEXT(dim, rank, coord, shapeIter, xPointer, xStridesIter, resultPointer, resultStridesIter);
maxCursor = reinterpret_cast<Z*>(maxCursorLong);
maxCursor[0] = (Z) 1;
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}
}
}
}
}
}
void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions) {
BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), ismax_, (input, output, dimensions), LIBND4J_TYPES, LIBND4J_TYPES);
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}
}
}
}