217 lines
13 KiB
C++
217 lines
13 KiB
C++
/*******************************************************************************
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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 (iuriish@yahoo.com)
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//
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#ifndef LIBND4J_SHAPEUTILS_H
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#define LIBND4J_SHAPEUTILS_H
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#include <vector>
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#include <NDArray.h>
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namespace nd4j {
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class ND4J_EXPORT ShapeUtils {
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public:
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// evaluate shape for array resulting from tensorDot operation, also evaluate shapes and permutation dimensions for transposition of two input arrays
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static std::vector<Nd4jLong> evalShapeForTensorDot(const Nd4jLong* aShapeInfo, const Nd4jLong* bShapeInfo, std::vector<int> axesA, std::vector<int> axesB, std::vector<int>& permutAt, std::vector<int>& permutBt, std::vector<Nd4jLong>& shapeAt, std::vector<Nd4jLong>& shapeBt);
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static std::vector<Nd4jLong> evalShapeForTensorDot(const NDArray* a, const NDArray* b, const std::vector<int>& axesA, const std::vector<int>& axesB, std::vector<int>& permutAt, std::vector<int>& permutBt, std::vector<Nd4jLong>& shapeAt, std::vector<Nd4jLong>& shapeBt);
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// evaluate resulting shape after reduce operation
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static Nd4jLong* evalReduceShapeInfo(const char order, std::vector<int>& dimensions, const NDArray& arr, const nd4j::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr);
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static Nd4jLong* evalReduceShapeInfo(const char order, std::vector<int>& dimensions, const Nd4jLong* shapeInfo, const nd4j::DataType dataType, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr);
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static Nd4jLong* evalReduceShapeInfo(const char order, std::vector<int>& dimensions, const NDArray& arr, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr);
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static Nd4jLong* evalReduceShapeInfo(const char order, std::vector<int>& dimensions, const Nd4jLong* shapeInfo, const bool keepDims = false, const bool supportOldShapes = false, nd4j::memory::Workspace* workspace = nullptr);
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/**
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* evaluate output shape for reduce operation when input shape is empty
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* behavior is analogous to tf
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*/
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static Nd4jLong* evalReduceShapeInfoEmpty(const char order, std::vector<int>& dimensions, const Nd4jLong *shapeInfo, const nd4j::DataType dataType, const bool keepDims, nd4j::memory::Workspace* workspace);
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// evaluate shape for array which is result of repeat operation applied to arr
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static std::vector<Nd4jLong> evalRepeatShape(int axis, const std::vector<int>& repeats, const NDArray& arr);
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// evaluate shapeInfo of permuted array
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// if setContigStrides = true, then set contiguous strides in output shapeInfo in accordance with arr order
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static Nd4jLong* evalPermShapeInfo(const int* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace, const bool setContigStrides = false);
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static Nd4jLong* evalPermShapeInfo(const Nd4jLong* dimensions, const int rank, const NDArray& arr, nd4j::memory::Workspace* workspace);
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// evaluate shapeInfo of transposed array
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// if setContigStrides = true, then set contiguous strides in output shapeInfo in accordance with arr order
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static Nd4jLong* evalTranspShapeInfo(const NDArray& arr, nd4j::memory::Workspace* workspace, const bool setContigStrides = false);
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static bool copyVectorPart(std::vector<int>& target, std::vector<int>& source, int rank, int offset);
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// return new (shorter) sorted dimensions array without dimensions that are present in input vector
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static std::vector<int> evalDimsToExclude(const int rank, const int dimsLen, const int* dimensions);
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static std::vector<int> evalDimsToExclude(const int rank, const std::vector<int>& dimensions);
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// check whether 2 arrays have mutually broadcastable shapes
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// shape comparison starts from the end
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static bool areShapesBroadcastable(const NDArray &arr1, const NDArray &arr2);
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static bool areShapesBroadcastable(Nd4jLong* shapeX, Nd4jLong* shapeY);
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static bool areShapesBroadcastable(const std::vector<Nd4jLong>& shape1, const std::vector<Nd4jLong>& shape2);
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// check the possibility of broadcast operation, if true then return shapeInfo of resulting array
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// if evalMinMax == false then array with larger rank has to be passed as first argument
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static bool evalBroadcastShapeInfo(const NDArray& max, const NDArray& min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace);
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static bool evalBroadcastShapeInfo(Nd4jLong *max, Nd4jLong *min, const bool evalMinMax, Nd4jLong*& resultShapeInfo, nd4j::memory::Workspace* workspace);
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// evaluate sorted vector of max axes to create tads along in case of simple broadcast operation
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// if simple broadcast is not possible then empty vector is returned
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// PLEASE NOTE: condition (rank_max >= rank_min) should be satisfied !
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static std::vector<int> tadAxesForSimpleBroadcast(const NDArray& max, const NDArray& min);
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// check the possibility of broadcast operation for set of arrays, if true then return resulting broadcasted shapeInfo
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static bool evalCommonBroadcastShapeInfo(const std::vector<const NDArray*>& arrays, Nd4jLong*& resultShapeInfo, memory::Workspace* workspace = nullptr);
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// return sorted vector of dimensions common (same) for two arrays, dimensions values corresponds to array with bigger rank
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// for example if arr1{2,7}, arr2{2,5,4,7} then vector = {0,3}
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static std::vector<int> getDimsWithSameShape(const NDArray& max, const NDArray& min);
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// evaluate shapeInfo for resulting array of tile operation
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static Nd4jLong* evalTileShapeInfo(const NDArray& arr, const std::vector<Nd4jLong>& reps, nd4j::memory::Workspace* workspace);
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// returns shape part of shapeInfo as std::vector
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static std::vector<Nd4jLong> pullShapeFromShapeInfo(Nd4jLong *shapeInfo);
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static std::string shapeAsString(const NDArray* array);
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static std::string shapeAsString(const std::vector<Nd4jLong>& shape);
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static std::string shapeAsString(const Nd4jLong* shapeInfo);
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static std::string shapeAsString(const int rank, const Nd4jLong* shapeInfo);
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static std::string strideAsString(const NDArray* array);
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static std::string shapeInfoAsString(const Nd4jLong* shapeInfo);
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static std::vector<Nd4jLong> shapeAsVector(const Nd4jLong* shapeInfo);
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// evaluate shapeInfo for diagonal array which is made using input arr elements as diagonal
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static Nd4jLong* evalDiagShapeInfo(const Nd4jLong* shapeInfo, nd4j::memory::Workspace* workspace);
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static std::vector<int> evalBroadcastBackwardAxis(const Nd4jLong *operand, const Nd4jLong *result);
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// utility to calculate matrix product shape with give source shapes and additional params
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// returns ShapeList pointer with result shape
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static Nd4jLong* matrixProductShape(Nd4jLong* theFirstShape, Nd4jLong* theSecondShape, bool shouldTranspondFirst, bool shouldTranspondSecond, nd4j::DataType dtype, nd4j::memory::Workspace* workspace);
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/**
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* This method evaluates permutation vector necessary for reducing of shapeFrom to shapeTo
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* if shapeFrom is identical to shapeTo (permutation is unnecessary) then empty vector is returned
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* in case of permutation is impossible an exception is thrown
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*/
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static std::vector<int> evalPermutFromTo(const std::vector<Nd4jLong>& shapeFrom, const std::vector<Nd4jLong>& shapeTo);
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/**
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* This method composes shape (shape only, not whole shapeInfo!) using dimensions values and corresponding indexes,
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* please note: the size of input vector dimsAndIdx must always be even, since the numbers of dimensions and indexes are the same,
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* for example if dimsAndIdx = {dimC,dimB,dimA, 2,1,0} then output vector = {dimA,dimB,dimC}
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*/
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static std::vector<Nd4jLong> composeShapeUsingDimsAndIdx(const std::vector<int>& dimsAndIdx);
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/**
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* x * y = c, evaluate shape for array resulting from mmul operation
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* possible cases: dot product (xRank=yRank=1), matrix-vector product (xRank=2, yRank=1), vector-matrix product (xRank=1, yRank=2), matrix-matrix product (xRank=yRank and rank >=2)
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*/
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static std::vector<Nd4jLong> evalShapeForMatmul(const Nd4jLong* xShapeInfo, const Nd4jLong* yShapeInfo, const bool transX, const bool transY);
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/**
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* evaluate number of sub-arrays along dimensions stored in dimsToExclude
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* i.e. if shape is [2,3,4,5] and dimsToExclude={0,2}, then number of sub-arrays = 8
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*/
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static Nd4jLong getNumOfSubArrs(const Nd4jLong* shapeInfo, const std::vector<int>& dimsToExclude);
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/**
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* evaluate indexes ranges that define sub-array of array having shape=shapeInfo
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* subArrIdx - index of current sub-array
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* shapeInfo - shapeInfo of array for which to evaluate sub-arrays
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* dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-arrays along, i.e. when shape is [2,3,4,5] and dimsToExclude={0,2}, then there will be 8 sub-arrays with shape [3,5],
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* if dimsToExclude is empty then idxRanges containing all zeros (means whole array) will be returned.
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* idxRanges - where to put result, the length of idxRanges must be equal to 2*shapeInfo[0]
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*/
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static void evalIdxRangesForSubArr(const Nd4jLong subArrIdx, const Nd4jLong* shapeInfo, const std::vector<int>& dimsToExclude, Nd4jLong* idxRanges);
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/**
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* return shape without unities, for example if shape is [1,2,1,3] then [2,3] will be returned
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* if unities are not present in given shapeInfo then exactly identical shape will be returned, for example [2,3] -> [2,3]
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* edge case: if given shape is [1,1,1,...,1] (all dims are unities) then output will be empty and means scalar
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*/
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static std::vector<Nd4jLong> evalDimsWithoutUnities(const Nd4jLong* shapeInfo);
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/**
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* method returns false if permut == {0,1,2,...permut.size()-1} - in that case permutation is unnecessary
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*/
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FORCEINLINE static bool isPermutNecessary(const std::vector<int>& permut);
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/**
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* calculates strides using "dest" shape and given "order", also copies data type from "source" to "dest"
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*/
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static void updateStridesAndType(Nd4jLong* dest, const Nd4jLong* source, const char order);
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/**
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* calculates strides using "dest" shape and "order", also set "dtype" into "dest"
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*/
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static void updateStridesAndType(Nd4jLong* dest, const DataType dtype, const char order);
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/**
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* This method retuns number of bytes required for string tensor
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* @param numStrings
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* @return
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*/
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static FORCEINLINE Nd4jLong stringBufferHeaderRequirements(Nd4jLong numStrings) {
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// we store +1 offset
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return (numStrings + 1) * sizeof(Nd4jLong);
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}
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/*
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* check whether arr1/arr2 is sub-array of arr2/arr1,
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* this method do not evaluate what array is sub-array, it returns true if arr1 is sub-array of arr2 or arr2 is sub-array of arr1
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* sameDims is filled (and sorted) with dimensions values that match both in arr1 and arr2 shapes (unities are ignored)
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* for example:
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* if arr1{2,3} and arr2{2,4,3,7} then return true and sameDims contains {0,2}
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* if arr1{1,1,3,1,3,1,1} and arr2{1,2,3,1,3} then return true and sameDims contains {2,4}
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* if arr1{2,1,4,1,7,5} and arr2{1,1,4,5} then return true and sameDims contains {2,5}
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static bool isSubArrayCase(const NDArray& arr1, const NDArray& arr2, std::vector<int>& sameDims);
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*/
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};
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//////////////////////////////////////////////////////////////////////////
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///// IMLEMENTATION OF INLINE METHODS /////
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//////////////////////////////////////////////////////////////////////////
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FORCEINLINE bool ShapeUtils::isPermutNecessary(const std::vector<int>& permut) {
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for(int i=0; i<permut.size(); ++i)
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if(permut[i] != i)
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return true;
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return false;
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}
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}
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#endif //LIBND4J_SHAPEUTILS_H
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