/*******************************************************************************
* 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 (iuriish@yahoo.com), created on 20.04.2018
//
#include <ops/declarable/helpers/transforms.h>
#include <array/ResultSet.h>
#include <helpers/ShapeUtils.h>
#include <numeric>
#include <array/NDArrayFactory.h>
#include <helpers/TAD.h>
#include <helpers/ConstantTadHelper.h>
#include <helpers/Loops.h>
#include <graph/RandomGenerator.h>
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void triuBP_(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
auto dOdI = NDArray(&gradO); // dO/dI
const_cast<NDArray&>(input).fillAsTriangular<T>(0, diagonal, dOdI.sizeAt(-1), dOdI, 'b');
int dLen = dOdI.lengthOf();
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
if (dOdI.t<T>(i) != static_cast<T>(0.f))
dOdI.t<T>(i) = static_cast<T>(1.f);
}
};
samediff::Threads::parallel_for(func, 0, dLen);
// FIXME: !!!
gradI.assign(dOdI * gradO); // chain rule: dLoss/dI = dO/dI * dLoss/dO
}
void triuBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void trace_(const NDArray& input, NDArray& output) {
const int inRank = input.rankOf();
auto setOfSubArrs = input.allTensorsAlongDimension({inRank-2, inRank-1});
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++)
output.p(i, setOfSubArrs.at(i)->getTrace());
};
samediff::Threads::parallel_for(func, 0, setOfSubArrs.size());
}
void trace(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
BUILD_SINGLE_SELECTOR(input.dataType(), trace_, (input, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void randomShuffle_(NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {
// check edge cases first
int temp;
const int firstDim = input.sizeAt(0);
if(input.lengthOf() == 1 || firstDim == 1) {
if(!isInplace)
output.assign(input);
}
else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) {
// apply Fisher-Yates shuffle
if(isInplace) {
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
for(int i = firstDim-1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
if(i == r)
continue;
T t0 = input.t<T>(i);
T t1 = input.t<T>(r);
//math::nd4j_swap<T>(input(i), input(r));
input.t<T>(i) = t1;
input.t<T>(r) = t0;
}
}
else {
std::vector<int> indices(firstDim);
std::iota(indices.begin(), indices.end(), 0);
output.p<T>(Nd4jLong(0), input.e<T>(0));
// FIXME: parallelism!!
for(int i = firstDim-1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
output.t<T>(i) = input.t<T>(indices[r]);
if(i == r)
continue;
output.t<T>(r) = input.t<T>(indices[i]);
math::nd4j_swap<int>(indices[i], indices[r]);
}
rng.rewindH(firstDim-1);
}
}
else {
// evaluate sub-arrays list of input array through all dimensions excluding first one
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
auto subArrsListIn = input.allTensorsAlongDimension(dimensions);
// apply Fisher-Yates shuffle
if(isInplace) {
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->elementwiseThreshold())
for(int i = firstDim - 1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
if(i == r)
continue;
subArrsListIn.at(i)->swapUnsafe(*subArrsListIn.at(r));
}
}
else {
// evaluate sub-arrays list of output array through all dimensions excluding first one
auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
std::vector<int> indices(firstDim);
std::iota(indices.begin(), indices.end(), 0);
bool isZeroShuffled = false;
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
for(int i = firstDim - 1; i > 0; --i) {
int r = rng.relativeInt(i) % i;
subArrsListOut.at(i)->assign(subArrsListIn.at(indices[r]));
if(r == 0)
isZeroShuffled = true;
if(i == r)
continue;
subArrsListOut.at(r)->assign(subArrsListIn.at(indices[i]));
math::nd4j_swap<int>(indices[i], indices[r]);
}
if(!isZeroShuffled)
subArrsListOut.at(0)->assign(subArrsListIn.at(0));
}
rng.rewindH(firstDim-1);
}
}
void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {
BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (input, output, rng, isInplace), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
const T* x = input.bufferAsT<T>();
T* z = output.bufferAsT<T>();
const Nd4jLong* xShape = input.shapeOf();
const Nd4jLong* zShape = output.shapeOf();
const int rank = input.rankOf(); // both input and output have the same rank
const int rankMinusOne = rank - 1;
const auto zLen = output.lengthOf();
if(mode == 0) { // CONSTANT case
const T padVal = padValue.e<T>(0);
auto func = PRAGMA_THREADS_FOR {
Nd4jLong coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coords(i, output.getShapeInfo(), coords);
const auto zOffset = shape::getOffset(output.getShapeInfo(), coords);
bool within = true;
for (int j = rankMinusOne; j >= 0; --j) {
if (xShape[j] == zShape[j]) continue;
const auto left = paddings.e<Nd4jLong>(j, 0);
if (coords[j] < left || coords[j] >= left + xShape[j]) {
within = false;
break;
}
else { coords[j] = coords[j] - left; }
}
if (within)
z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)];
else
z[zOffset] = padVal;
}
};
samediff::Threads::parallel_tad(func, 0, zLen);
}
else { // REFLECT and SYMMETRIC cases
const Nd4jLong shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
const Nd4jLong shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
auto func = PRAGMA_THREADS_FOR {
Nd4jLong coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coords(i, output.getShapeInfo(), coords);
const auto zOffset = shape::getOffset(output.getShapeInfo(), coords);
for (int j = rankMinusOne; j >= 0; --j) {
if (xShape[j] == zShape[j]) continue;
coords[j] = coords[j] - paddings.e<Nd4jLong>(j, 0); // are ready to fill middle (within input dimension range)
if (coords[j] < 0) coords[j] = -coords[j] - shift1; // means fill from left
else if (coords[j] >= xShape[j]) coords[j] = 2 * xShape[j] - coords[j] - shift2; // means fill from right
}
const auto xOffset = shape::getOffset(input.getShapeInfo(), coords);
z[zOffset] = x[xOffset];
}
};
samediff::Threads::parallel_tad(func, 0, zLen);
}
}
// //////////////////////////////////////////////////////////////////////////
// template<typename T>
// void pad2_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
// const int rank = output.rankOf();
// std::vector<int> dimsToExclude(rank);
// std::iota(dimsToExclude.begin(), dimsToExclude.end(), 0); // fill with 0, 1, ... rank-1
// Nd4jLong numLeft = paddings.e<Nd4jLong>(rank-1,0);
// Nd4jLong numRight = paddings.e<Nd4jLong>(rank-1,1);
// Nd4jLong inDimSize = input.sizeAt(rank-1);
// Nd4jLong outDimSize = output.sizeAt(rank-1);
// std::vector<std::vector<Nd4jLong>> outIdx = { std::vector<Nd4jLong>(2*rank), {numLeft, numLeft + inDimSize}, {0, numLeft}, {numLeft + inDimSize, outDimSize} };
// for(int i = 0; i < rank-1; ++i) {
// outIdx[0][2*i] = paddings.e<Nd4jLong>(i, 0);
// outIdx[0][2*i + 1] = outIdx[0][2*i] + input.sizeAt(i);
// }
// outIdx[0][2*rank-1] = outIdx[0][2*rank-2] = 0;
// // ***** populate innermost sub-arrays firstly ***** //
// dimsToExclude.pop_back();
// Nd4jLong startL = mode == 1 ? 1 : 0; // REFLECT or SYMMETRIC
// Nd4jLong startR = mode == 1 ? inDimSize-2 : inDimSize-1; // REFLECT or SYMMETRIC
// Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
// NDArray outSubArr0 = output(outIdx[0], true);
// PRAGMA_OMP_PARALLEL_FOR
// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
// NDArray outSubArr1 = outSubArr0(j, dimsToExclude);
// NDArray inSubArr = input(j, dimsToExclude);
// NDArray outSubArrMid = outSubArr1(outIdx[1]);
// outSubArrMid.assign(inSubArr); // assign middle
// if(mode == 0) { // CONSTANT
// if(numLeft != 0) {
// NDArray temp = outSubArr1(outIdx[2]);
// temp.assign(padValue); // assign left
// }
// if(numRight != 0) {
// NDArray temp = outSubArr1(outIdx[3]);
// temp.assign(padValue); // assign right
// }
// }
// else { // REFLECT or SYMMETRIC
// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) // fill left side
// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) // fill right side
// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
// }
// }
// // ***** fill rest of outer sub-arrays ***** //
// std::vector<Nd4jLong> outIdxInner(2, 0);
// std::vector<Nd4jLong> outIdxOuter(2, 0);
// for(int i = rankBorder - 1; i >= 0; --i) {
// dimsToExclude.pop_back();
// outIdxInner.push_back(0), outIdxInner.push_back(0);
// outIdxOuter.push_back(0), outIdxOuter.push_back(0);
// Nd4jLong numLeft = paddings.e<Nd4jLong>(i, 0);
// Nd4jLong numRight = paddings.e<Nd4jLong>(i, 1);
// if(numLeft == 0 && numRight == 0)
// continue;
// Nd4jLong inDimSize = input.sizeAt(i);
// Nd4jLong outDimSize = output.sizeAt(i);
// if(mode == 0) {
// outIdxOuter[0] = 0; outIdxOuter[1] = numLeft;
// outIdxInner[0] = numLeft + inDimSize; outIdxInner[1] = outDimSize;
// }
// startL = mode == 1 ? numLeft + 1 : numLeft; // REFLECT or SYMMETRIC
// startR = mode == 1 ? numLeft + inDimSize - 2 : numLeft + inDimSize-1; // REFLECT or SYMMETRIC
// numOfSubArrs = ShapeUtils::getNumOfSubArrs(output.getShapeInfo(), dimsToExclude);
// PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(outIdxOuter, outIdxInner))
// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
// NDArray outSubArr = output(j, dimsToExclude);
// if(mode == 0) { // CONSTANT
// if(numLeft != 0) {
// NDArray tempO = outSubArr(outIdxOuter);
// tempO.assign(padValue); // assign left
// }
// if(numRight != 0) {
// NDArray tempI = outSubArr(outIdxInner);
// tempI.assign(padValue); // assign right
// }
// }
// else { // REFLECT or SYMMETRIC
// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) { // fill left side
// outIdxOuter[0] = k;
// outIdxOuter[1] = k+1;
// outIdxInner[0] = e;
// outIdxInner[1] = e+1;
// NDArray outSubArrInner = outSubArr(outIdxInner);
// NDArray outSubArrOuter = outSubArr(outIdxOuter);
// outSubArrOuter.assign(outSubArrInner);
// }
// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) { // fill right side
// outIdxOuter[0] = k;
// outIdxOuter[1] = k+1;
// outIdxInner[0] = e;
// outIdxInner[1] = e+1;
// NDArray outSubArrInner = outSubArr(outIdxInner);
// NDArray outSubArrOuter = outSubArr(outIdxOuter);
// outSubArrOuter.assign(outSubArrInner);
// }
// }
// }
// }
// }
void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
BUILD_SINGLE_SELECTOR(input.dataType(), pad_, (mode, input, paddings, output, padValue), LIBND4J_TYPES);
}
////////////////////////////////////////////////////////////////////////
/*// initial values of inIdx, outIdx, dim must be equal to zero
template<typename T>
static void recursiveLoopForPad_(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
int leftOffset;
// dimensions are array of input dimensions, it is sorted in increasing order
// every time at the beginning we erase first element from it (not good idea to use vector for this purpose, but luckily it is small enough)
// then we use this array for tads building, every time while recursion the number of built tads becomes bigger
dimensions.erase(dimensions.begin());
// build tad basing on output array, also create auxiliary arrays pointing on required output array ranges
shape::TAD tadOut(output.getShapeInfo(), dimensions.data(), dimensions.size());
tadOut.createTadOnlyShapeInfo();
tadOut.createOffsets();
auto subArrOut = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
auto subArr = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
// build tad basing on input array, also create auxiliary array pointing on required input array range
shape::TAD tadIn(input.getShapeInfo(), dimensions.data(), dimensions.size());
tadIn.createTadOnlyShapeInfo();
tadIn.createOffsets();
auto subArrIn = NDArray(input.getBuffer(), tadIn.tadOnlyShapeInfo, output.getContext());
// these indices take into account recursion and always point to actual tads numbers
if (input.rankOf() > 1 && output.rankOf() > 1) {// only for non-vector cases
outIdx = outIdx * output.sizeAt(dim + 1);
inIdx = inIdx * input.sizeAt(dim + 1);
}
// current input tad number, we add to it unity in a loop
int k = -1;
// loop through current dimension
for(int i = 0; i < output.sizeAt(dim); ++i) {
// corresponds to outer range (relevant indices are absent in input)
leftOffset = paddings.e<int>(dim, 0);
if(i < leftOffset || i >= (input.sizeAt(dim) + leftOffset))
continue;
// increase input tads number
++k;
// recursion condition allows for the fact that tad can't reduce to scalar
if(dim < input.rankOf() - 2)
recursiveLoopForPad(mode, input, paddings, output, dimensions, dim + 1, inIdx + k, outIdx + i, padValue);
else if (paddings.sizeAt(0) > dim + 1){
leftOffset = paddings.e<int>(dim + 1, 0);
// shift buffers pointers to actual element position
if (output.rankOf() > 1) {
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + i]);
subArrIn.setBuffer(reinterpret_cast<T*>(input.getBuffer()) + tadIn.tadOffsets[inIdx + i - paddings.e<int>(dim, 0)]);
}
else {
subArrOut.p(i, subArrIn.e<T>(i - leftOffset));
}
// most inner loop, corresponds to last dim = rank-1
switch (mode) {
case 0: // CONSTANT mode
for(int j = 0; j < subArrOut.lengthOf(); ++j)
if(j < leftOffset || j >= (subArrIn.lengthOf() + leftOffset) ) // firstly fill with zeros outer ranges
subArrOut.p(j, (T)0.f);
else
subArrOut.p(j, subArrIn.e<T>(j - leftOffset)); // fill middle with elements of input array
break;
case 1: // REFLECT mode
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
subArrOut.p(leftOffset - j, subArrIn.e<T>(j));
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j - 1));
break;
case 2: // SYMMETRIC mode
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
subArrOut.p(leftOffset - j, subArrIn.e<T>(j-1));
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j));
break;
}
}
else {
if (mode == 0 && input.rankOf() < 2)
subArrOut.p(i, subArrIn.e<T>(i - leftOffset)); // fill middle with elements of input array
}
}
// populate sub-array formed previously
leftOffset = paddings.e<int>(dim,0);
switch (mode) {
case 0: // CONSTANT mode
for(int j = 1; j <= leftOffset; ++j) {
// fill left side with padValue
if (output.rankOf() > 1) {
subArrOut.setBuffer(
reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
subArrOut.assign(padValue);
}
else {
subArrOut.p(j - 1, padValue);
}
}
// output.printIndexedBuffer("Output at");
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill left side with zeros
if (output.rankOf() > 1) {
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
subArrOut.assign(padValue);
}
else {
subArrOut.p(j, padValue);
}
}
break;
case 1: // REFLECT mode
for(int j = 1; j <= leftOffset; ++j) { // fill left side
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j]);
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
subArrOut.assign(&subArr);
}
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - 1 - j]);
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
subArrOut.assign(&subArr);
}
break;
case 2: // SYMMETRIC mode
for(int j = 1; j <= leftOffset; ++j) { // fill left side
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j - 1]);
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
subArrOut.assign(&subArr);
}
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - j]);
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
subArrOut.assign(&subArr);
}
break;
}
}
*/
/*
void recursiveLoopForPad(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
BUILD_SINGLE_SELECTOR(input.dataType(), recursiveLoopForPad_, (mode, input, paddings, output, dimensions, dim, inIdx, outIdx, padValue), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void recursiveLoopForPad_, (const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue), LIBND4J_TYPES);
*/
////////////////////////////////////////////////////////////////////////
void invertPermutation(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
std::set<int> uniqueElems;
const int length = input.lengthOf();
for(int i = 0; i < length; ++i) {
int elem = input.e<int>(i);
if(!uniqueElems.insert(elem).second) // this operation forbids us to use #pragma omp
throw std::runtime_error("helpers::invertPermutation function: input array contains duplicates !");
if(elem < 0 || elem > length - 1)
throw std::runtime_error("helpers::invertPermutation function: element of input array is out of range (0, length-1) !");
output.p<int>(elem, i);
}
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Y>
static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
const X* x = reinterpret_cast<X*>(input.getBuffer());
const Y* y = reinterpret_cast<Y*>(indices.getBuffer());
X* z = reinterpret_cast<X*>(output.getBuffer());
const int xRank = input.rankOf();
const int yRank = indices.rankOf();
const int zRank = output.rankOf();
const int maxRank = sd::math::nd4j_max<int>(yRank, sd::math::nd4j_max<int>(xRank, zRank));
const Nd4jLong zLen = output.lengthOf();
const int yLastDim = indices.sizeAt(-1);
auto func = PRAGMA_THREADS_FOR {
Nd4jLong coords[MAX_RANK * 3];
for (auto i = start; i < stop; i++) {
Nd4jLong *zCoordStart, *xCoordStart;
if (yLastDim == xRank) {
zCoordStart = coords;
xCoordStart = coords;
} else if (zRank >= xRank) {
zCoordStart = coords;
xCoordStart = coords + zRank - xRank;
} else {
zCoordStart = coords + xRank - zRank;
xCoordStart = coords;
}
shape::index2coords(i, output.getShapeInfo(), zCoordStart);
const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoordStart);
// last y coordinate
uint coordToRestore;
if (yLastDim != xRank)
coordToRestore = static_cast<uint>(zCoordStart[yRank - 1]);
zCoordStart[yRank - 1] = 0;
const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoordStart);
//restore z coordinate
if (yLastDim != xRank)
zCoordStart[yRank - 1] = coordToRestore;
// construct coordinates for x
for (int j = 0; j < yLastDim; ++j)
xCoordStart[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]]; // last stride
const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoordStart);
z[zOffset] = x[xOffset];
}
};
samediff::Threads::parallel_tad(func, 0, zLen);
}
////////////////////////////////////////////////////////////////////////
void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), LIBND4J_TYPES, INDEXING_TYPES);
}
////////////////////////////////////////////////////////////////////////
template<typename T>
static void gather_(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
const int inputRank = input->rankOf();
if(axis < 0)
axis += inputRank;
const int numOfIntArgs = intArgs.size();
if (indices != nullptr) {
for(Nd4jLong i = 0; i < indices->lengthOf(); ++i)
if(indices->e<Nd4jLong>(i) >= input->sizeAt(axis))
throw std::runtime_error("helpers::gather function: indices array contains wrong elements, each element must be smaller than corresponding dimension of input array !");
// first case: indices consist of only one scalar
if(indices->isScalar()) {
if(input->rankOf() <= 1){
//For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead, we want to get a scalar
auto idx = indices->e<Nd4jLong>(0);
auto scalarNDArray = input->e(idx);
output->assign(scalarNDArray);
} else {
auto dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
auto tadArr = NDArray(reinterpret_cast<void *>(reinterpret_cast<T*>(input->getBuffer()) + tadPack.primaryOffsets()[indices->e<Nd4jLong>(0)]), tadPack.primaryShapeInfo(), output->getContext());
output->assign(&tadArr);
}
}
else if (input->rankOf() == 1 && indices->isVector()) {
// special case
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++)
output->p(e, input->e<T>(indices->e<Nd4jLong>(e)));
};
samediff::Threads::parallel_for(func, 0, indices->lengthOf());
}
else {
std::vector<int> dimsOut(indices->rankOf());
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... indices->rankOf()-1
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), dimsOut);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
NDArray subArrOut = (*output)(i, dimsOut);
NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
subArrOut.assign(subArrIn);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
else {
for(int i = 1; i < numOfIntArgs; ++i)
if(intArgs[i] >= input->sizeAt(axis))
throw std::runtime_error("helpers::gather function: some of input indexes is larger than corresponding shape of input array !");
// we only allow scalar/vector case here
if (numOfIntArgs == 2) { // scalar case
output->assign((*input)(intArgs[1], {axis}));
}
else { // vector case
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), {axis});
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
NDArray subArrOut = (*output)(i, {axis});
NDArray subArrIn = (*input)(intArgs[i + 1], {axis});
subArrOut.assign(subArrIn);
}
};
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
}
}
}
void gather(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
BUILD_SINGLE_SELECTOR(input->dataType(), gather_, (input, indices, output, intArgs), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
void eye(sd::LaunchContext * context, NDArray& output) {
const int rank = output.rankOf();
auto arrs = output.allTensorsAlongDimension({rank-2, rank-1});
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++)
arrs.at(i)->setIdentity();
};
samediff::Threads::parallel_tad(func, 0, arrs.size());
}
//////////////////////////////////////////////////////////////////////////
void scatterUpdate(sd::LaunchContext * context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {
int opCode = (*intArgs)[0];
int dimSize = (*intArgs)[1];
Nd4jLong e;
Nd4jLong limg = 2 + dimSize;
std::vector<int> tadDimensions(dimSize);
for (e = 2; e < limg; e++)
tadDimensions[e-2] = (*intArgs)[e];
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input.rankOf(), tadDimensions);
// increasing counter to skip numIndices
e++;
std::vector<int> indices;
for (; e < static_cast<Nd4jLong>(intArgs->size()); e++)
indices.push_back((*intArgs)[e]);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto inSubArr = input(indices[i], dimsToExclude, true);
auto updSubArr = updates(i, dimsToExclude, true);
if (inSubArr.lengthOf() != updSubArr.lengthOf())
continue;
switch (opCode) {
case 0:
inSubArr.applyPairwiseTransform(pairwise::Add, updSubArr, inSubArr);
break;
case 1:
inSubArr.applyPairwiseTransform(pairwise::Subtract, updSubArr, inSubArr);
break;
case 2:
inSubArr.applyPairwiseTransform(pairwise::Multiply, updSubArr, inSubArr);
break;
case 3:
inSubArr.applyPairwiseTransform(pairwise::Divide, updSubArr, inSubArr);
break;
case 4:
inSubArr.applyPairwiseTransform(pairwise::ReverseSubtract, updSubArr, inSubArr);
break;
case 5:
inSubArr.applyPairwiseTransform(pairwise::ReverseDivide, updSubArr, inSubArr);
break;
case 6:
inSubArr.applyPairwiseTransform(pairwise::CopyPws, updSubArr, inSubArr);
break;
default:
continue;
}
}
};
samediff::Threads::parallel_tad(func, 0, indices.size());
}
//////////////////////////////////////////////////////////////////////////
void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
// updates and indices have same length
const Nd4jLong len = indices.lengthOf();
switch (opId) {
case 6: { // copy
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto inSubArr = input(i, dimensions);
inSubArr.p(indices.t<Nd4jLong>(i), updates.e(i));
}
};
samediff::Threads::parallel_for(func, 0, len);
}
break;
default:
throw std::invalid_argument("helpers::scatterSimple: operation is not implemented for given id !");
}
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void mergeMaxIndex_(const std::vector<NDArray*>& inArrs, NDArray& output) {
const Nd4jLong numArgs = inArrs.size();
auto x = inArrs[0];
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
T max = -DataTypeUtils::max<T>();
Nd4jLong idx = 0;
for (Nd4jLong i = 0; i < numArgs; i++) {
T v = inArrs[i]->e<T>(e);
if (v > max) {
max = v;
idx = i;
}
}
output.p(e, idx);
}
};
samediff::Threads::parallel_for(func, 0, x->lengthOf());
}
void mergeMaxIndex(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), mergeMaxIndex_, (inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void mergeMax_(const std::vector<NDArray*>& inArrs, NDArray& output) {
const Nd4jLong numArgs = inArrs.size();
auto x = inArrs[0];
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
T max = -DataTypeUtils::max<T>();
for (Nd4jLong i = 0; i < numArgs; i++) {
T v = inArrs[i]->e<T>(e);
if (v > max)
max = v;
}
output.p(e, max);
}
};
samediff::Threads::parallel_for(func, 0, x->lengthOf());
}
void mergeMax(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void mergeAvg_(const std::vector<NDArray*>& inArrs, NDArray& output) {
const Nd4jLong numArgs = inArrs.size();
const T factor = 1.f / numArgs;
auto x = inArrs[0];
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
T sum = 0.;
for (Nd4jLong i = 0; i < numArgs; i++) {
T v = inArrs[i]->e<T>(e);
sum += v;
}
output.p<T>(e, sum * factor);
}
};
samediff::Threads::parallel_for(func, 0, x->lengthOf());
}
void mergeAvg(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void mergeAdd_(const std::vector<NDArray*>& inArrs, NDArray& output) {
const Nd4jLong numArgs = inArrs.size();
auto x = inArrs[0];
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
T sum = (T) 0.f;
for (Nd4jLong i = 0; i < numArgs; i++)
sum += inArrs[i]->e<T>(e);
output.p(e, sum);
}
};
samediff::Threads::parallel_for(func, 0, x->lengthOf());
}
void mergeAdd(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (inArrs, output), LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByNorm_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
const int rank = input.rankOf();
const auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
const T normActual = norm2.e<T>(0);
const T normClip = clipNorm.e<T>(0);
if (isInplace) {
if(norm2.lengthOf() == 1) {
if(normActual > normClip)
input *= (normClip / normActual);
}
else {
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
const T iNormActual = norm2.e<T>(i);
if (iNormActual > normClip)
*listOfInSubArrs.at(i) *= normClip / iNormActual;
}
};
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs.size());
}
}
else {
if(norm2.lengthOf() == 1) {
if(normActual > normClip)
output.assign(input * (normClip / normActual));
else
output.assign(input);
}
else {
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
auto listOfOutSubArrs = output.allTensorsAlongDimension(dimensions);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto inputSubArr = listOfInSubArrs.at(i);
auto outputSubArr = listOfOutSubArrs.at(i);
outputSubArr->assign(inputSubArr);
const T iNormActual = norm2.e<T>(i);
if (iNormActual > clipNorm.e<T>(0))
*outputSubArr *= clipNorm / iNormActual;
}
};
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs.size());
}
}
}
//////////////////////////////////////////////////////////////////////////
void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
}
template <typename T>
static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
// PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
for (size_t i = 0; i < inputs.size(); i++) {
auto input = inputs[i];
auto l2norm = input->reduceNumber(reduce::Norm2);
globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
}
//globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = sd::math::nd4j_sqrt(globalNorm);
auto normS = sd::math::nd4j_sqrt<T,T>(globalNorm);
outputs[inputs.size()]->p(0, normS);
const T factor = clipNorm / normS;
// PRAGMA_OMP_PARALLEL_FOR
for (size_t e = 0; e < inputs.size(); e++) {
// all-reduce
auto input = inputs[e];
auto output = outputs[e];
if (normS <= clipNorm) {
output->assign(input);
}
else {
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
input->applyLambda<T>(lambda, *output);
}
}
}
void clipByGlobalNorm(sd::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByNormBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
const int rank = input.rankOf();
auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
if(norm2.lengthOf() == 1) {
const T N = norm2.e<T>(0);
auto cn = clipNorm.e<T>(0);
if(N > cn) {
const T sumOfProd = (input * gradO).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
const T factor1 = static_cast<T>(1.f) / N;
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
};
(const_cast<NDArray&>(input)).applyPairwiseLambda<T>(const_cast<NDArray&>(gradO), lambda, gradI);
}
else
gradI.assign(gradO);
}
else {
auto gradISubArrs = gradI.allTensorsAlongDimension({dimensions});
auto gradOSubArrs = gradO.allTensorsAlongDimension({dimensions});
auto inputSubArrs = input.allTensorsAlongDimension({dimensions});
auto cn = clipNorm.e<T>(0);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
T N = norm2.e<T>(i);
auto gradOSubArr = gradOSubArrs.at(i);
auto gradISubArr = gradISubArrs.at(i);
if (N > cn) {
auto inputSubArr = inputSubArrs.at(i);
const T sumOfProd = (*inputSubArr * *gradOSubArr).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
const T factor1 = static_cast<T>(1.f) / N;
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
};
inputSubArr->applyPairwiseLambda<T>(*gradOSubArr, lambda, *gradISubArr);
} else
gradISubArr->assign(gradOSubArr);
}
};
samediff::Threads::parallel_tad(func, 0, gradISubArrs.size());
}
}
void clipByNormBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByNormBP_, (const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByAveraged_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
auto cn = clipNorm.e<T>(0);
if (dimensions.size() == 0) {
// all-reduce
T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / input.lengthOf();
if (n2 <= cn) {
if (!isInplace)
output.assign(input);
}
else {
const T factor = cn / n2;
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
input.applyLambda<T>(lambda, output);
}
}
else {
// along dimension
auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions, false);
if (!isInplace)
output.assign(input);
auto tads = output.allTensorsAlongDimension(dimensions);
// TODO: make this CUDA-compliant somehow
for (int e = 0; e < tads.size(); e++) {
T n2 = norm2.e<T>(e) / tads.at(e)->lengthOf();
const T factor = cn / n2;
if (n2 > cn) {
auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
tads.at(e)->applyLambda<T>(lambda, output);
}
}
}
}
void clipByAveraged(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
/*
if (d1 > params[1])
return params[1];
else if (d1 < params[0])
return params[0];
else return d1;
*/
template <typename T>
static void clipByValue_(NDArray& input, double leftBound, double rightBound, NDArray& output) {
auto routine = LAMBDA_T(_x, leftBound, rightBound) {
if (_x > rightBound) return rightBound;
if (_x < leftBound) return leftBound;
return _x;
};
input.applyLambda<T>(routine, output);
}
void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void clipByValue_, (NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
template<typename T>
static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
// mode: 0 - REFLECT, else - SYMMETRIC
const int reflBorder = (bool)mode ? 1 : 0;
const int rank = input.rankOf();
const Nd4jLong outLen = output.lengthOf();
if(rank <= 1) {
const Nd4jLong inLen = input.lengthOf();
const auto leftSide = paddings.e<Nd4jLong>(0);
const auto leftSideCorrected = leftSide - reflBorder;
const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
for(int i = 0; i < outLen; ++i) {
if (i < leftSide) // left side
output.p(i, input.e<T>(leftSideCorrected - i));
else if(i >= leftSide && i < leftSide + inLen) // middle
output.p(i, input.e<T>(i - leftSide));
else // right side
output.p(i, input.e<T>(len - i));
}
}
else {
auto func = PRAGMA_THREADS_FOR {
Nd4jLong inIdx[MAX_RANK];
Nd4jLong outIdx[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coords(i, output.getShapeInfo(), outIdx);
for (int j = 0; j < rank; ++j) {
const Nd4jLong inLen = input.sizeAt(j);
const auto leftSide = paddings.e<T>(j, 0);
const auto leftSideCorrected = leftSide - reflBorder;
const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
if (outIdx[j] < leftSide) // left side
inIdx[j] = leftSideCorrected - outIdx[j];
else if (outIdx[j] >= leftSide && outIdx[j] < leftSide + inLen) // middle
inIdx[j] = outIdx[j] - leftSide;
else // right side
inIdx[j] = len - outIdx[j];
}
auto outOffset = shape::getOffset(output.getShapeInfo(), outIdx);
auto inOffset = shape::getOffset(input.getShapeInfo(), inIdx);
reinterpret_cast<T *>(output.buffer())[outOffset] = reinterpret_cast<T *>(input.getBuffer())[inOffset];
}
};
samediff::Threads::parallel_for(func, 0, outLen);
}
}
void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
BUILD_SINGLE_SELECTOR(input.dataType(), mirrorPad_, (input, paddings, output, mode), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void mirrorPad_, (const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
T* gradIBuff = reinterpret_cast<T*>(gradI.getBuffer());
const T* gradOBuff = reinterpret_cast<T*>(gradO.getBuffer());
const Nd4jLong gradILen = gradI.lengthOf();
const Nd4jLong gradOLen = gradO.lengthOf(); // gradOLen >= gradILen
const Nd4jLong gradIEWS = sd::math::nd4j_abs<Nd4jLong>(gradI.ews());
const Nd4jLong gradOEWS = gradO.ews();
// initial zeroing of gradI content
if(gradIEWS == 1)
memset(gradIBuff, 0, gradILen * sizeof(T));
else {
//PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong i = 0; i < gradILen * gradIEWS; i += gradIEWS)
gradIBuff[i] = static_cast<T>(0.f);
}
if(gradO.ordering() == 'c' && gradOEWS == 1) {
//PRAGMA_OMP_PARALLEL_FOR_SIMD
for(Nd4jLong i=0; i<gradOLen; ++i) {
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i]);
}
}
else if(gradO.ordering() == 'c' && gradOEWS > 1) {
//PRAGMA_OMP_PARALLEL_FOR_SIMD
for(Nd4jLong i=0; i<gradOLen; ++i) {
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i * gradOEWS]);
}
}
else {
//PRAGMA_OMP_PARALLEL_FOR_SIMD
for(Nd4jLong i=0; i<gradOLen; ++i) {
auto fidx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
gradI.p(fidx, gradI.e<T>(fidx) + gradOBuff[shape::getIndexOffset(i, gradO.getShapeInfo())]);
}
}
}
void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBP_, (gradO, gradI, reps), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void tileBP_, (const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES);
}
}
}