/*******************************************************************************
* 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
******************************************************************************/
/*
* shape.h
*
* Created on: Dec 28, 2015
* Author: agibsonccc
*/
#ifndef SHAPE_H_
#define SHAPE_H_
#include <cstring>
#include <cstdio>
#include "../dll.h"
#include "../nd4jmalloc.h"
#include "../templatemath.h"
#include "../helpers/logger.h"
#include "../pointercast.h"
#include "../cnpy/cnpy.h"
#include <op_boilerplate.h>
#define MAX_DIMENSION 0x7fffffff
#define MAX_NUM_THREADS 1024
#define MAX_RANK 32
#define MAX_SHAPEINFOLENGTH 2*MAX_RANK+4
#define MAX_COORD 3
#define PREALLOC_SIZE 33554432
#ifdef __CUDACC__
#include <cuda.h>
#include <cuda_runtime.h>
#endif
#ifdef __CUDACC__
#define INLINEDEF inline
#else
#define INLINEDEF inline
#endif
#include "../pairwise_util.h"
#include <stdint.h>
#include <array/ArrayOptions.h>
typedef unsigned int uint;
namespace shape {
/**
* Shape information approximating
* the information on an ndarray
*/
struct ND4J_EXPORT ShapeInformation {
_CUDA_HD ShapeInformation(Nd4jLong *shape_ = nullptr, Nd4jLong *stride_ = nullptr, char order_ = 0, int rank_ = 0, int offset_ = 0, int elementWiseStride_ = 0)
: shape(shape_), stride(stride_), order(order_), rank(rank_), offset(offset_), elementWiseStride(elementWiseStride_)
{}
Nd4jLong *shape;
Nd4jLong *stride;
char order;
int rank;
int offset;
int elementWiseStride;
};
/**
* Indexing information
* for bounds checking
*/
struct ND4J_EXPORT CurrentIndexing {
int numElementsPerThread;
int blockStartingIndex;
int startingThreadIndex;
int endingThreadIndex;
};
ND4J_EXPORT _CUDA_HD bool shapeEquals(const int shape1Rank, const Nd4jLong *shape1, const int shape2Rank, const Nd4jLong *shape2);
ND4J_EXPORT _CUDA_HD Nd4jLong* detachShape(Nd4jLong *originalShape);
ND4J_EXPORT _CUDA_HD Nd4jLong* copyShape(Nd4jLong *originalShape);
ND4J_EXPORT _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2);
ND4J_EXPORT _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3);
ND4J_EXPORT _CUDA_HD bool strideEquals(int shape1Rank,Nd4jLong *shape1,int shape2Rank,Nd4jLong *shape2);
ND4J_EXPORT _CUDA_HD bool strideEquals(Nd4jLong *shapeInfo1,Nd4jLong *shapeInfo2);
ND4J_EXPORT _CUDA_HD bool strideEquals(Nd4jLong *stride1,int rank1,Nd4jLong *stride2,int rank2);
ND4J_EXPORT _CUDA_HD bool equalsSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
ND4J_EXPORT _CUDA_HD bool equalsTypesAndShapesSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
ND4J_EXPORT _CUDA_HD bool equalsStrict(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
// returns true if ranks, shapes and strides are the same
ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2);
ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3);
ND4J_EXPORT _CUDA_HD int sizeAt(const Nd4jLong *shape, const int dim);
template <typename T>
ND4J_EXPORT _CUDA_HD void fill(T* buffer, T value, Nd4jLong length);
ND4J_EXPORT _CUDA_HD void traceNew(int id);
ND4J_EXPORT _CUDA_HD int tadIndexForLinear(int linearIndex, int tadLength);
ND4J_EXPORT _CUDA_HD Nd4jLong tadLength(Nd4jLong *shapeInfo, int *dimension, int dimensionLength);
ND4J_EXPORT _CUDA_HD bool canReshape(const int oldRank, Nd4jLong* oldShape, const int newRank, Nd4jLong* newShape, bool isFOrder);
ND4J_EXPORT _CUDA_HD bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo, const int newRank, const Nd4jLong* newShape, Nd4jLong* newShapeInfo);
/**
* Get the shape info buffer
* for the given rank and shape.
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape);
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer);
/**
* Get the shape info buffer
* for the given rank and shape.
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape);
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *output);
#ifdef __CUDACC__
__device__ ND4J_EXPORT Nd4jLong *cuMalloc(Nd4jLong *buffer, long size);
#endif
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
ND4J_EXPORT _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank);
ND4J_EXPORT _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank, Nd4jLong* ret);
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank);
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, Nd4jLong* ret);
ND4J_EXPORT _CUDA_HD void updateStrides(Nd4jLong *shape, const char order);
ND4J_EXPORT _CUDA_HD void updateStrides(const int rank, const Nd4jLong *shapeOnly, Nd4jLong *stridesOnly, const char order);
// check whether input dimensions are permuted, not permuted dimensions order have to be 0,....,rank-1
template <typename T>
ND4J_EXPORT _CUDA_HD bool isDimPermuted(const T* dimensions, const int dimSize);
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStridesFortran(Nd4jLong *shape, int rank, int startNum);
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStridesFortran(Nd4jLong *shape, int rank, int startNum, Nd4jLong* ret);
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, int startNum);
ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, int startNum, Nd4jLong* ret);
/**
* @param toCopy the shape to copy
* @return a copy of the original struct
*/
ND4J_EXPORT _CUDA_HD ShapeInformation *shapeCopy( ShapeInformation *toCopy);
ND4J_EXPORT _CUDA_HD bool strideDescendingCAscendingF(const Nd4jLong *shapeBuffer);
ND4J_EXPORT _CUDA_HD bool isContiguous(const Nd4jLong* shapeInfo);
/**
* copy-past from java hasDefaultStridesForShape function
* check whether array is not permuted and has contiguous elements in memory
*/
ND4J_EXPORT _CUDA_HD bool areStridesDefault(const Nd4jLong* shapeInfo);
/**
* Compute the element wise stride
* for a given shape/stride configuration
* @param rank the rank of the shape/stride
* @param shape the shape
* @param stride the stride
* @param isFOrder 0 or 1 for whether the array is f
* ordered or not
* @return 0 if there is no element wise stride the
* element wise stride of reshape(1,length) otherwise
*/
ND4J_EXPORT _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder);
/**
* Compute the element wise stride
* for a given shape/stride configuration
* @param rank the rank of the shape/stride
* @param shape the shape
* @param stride the stride
* @param isFOrder 0 or 1 for whether the array is f
* ordered or not
* @return 0 if there is no element wise stride the
* element wise stride of reshape(1,length) otherwise
*/
ND4J_EXPORT _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder, Nd4jLong *dimension, int dimensionLength);
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride);
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride, Nd4jLong *buffer);
/**
*
* @param length
* @param shape
* @param rearrange
* @return
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *doPermuteSwap(int length, Nd4jLong *shape, int* rearrange);
/**
* In place permute swap
* @param length
* @param shape
* @param rearrange
*/
ND4J_EXPORT _CUDA_HD void doPermuteSwap(int length, Nd4jLong **shape, int* rearrange);
ND4J_EXPORT _CUDA_HD Nd4jLong *permuteShapeBuffer(Nd4jLong *shapeBuffer, int* rearrange);
ND4J_EXPORT _CUDA_HD void permuteShapeBufferInPlace(Nd4jLong *shapeBuffer, int* rearrange, Nd4jLong *out);
ND4J_EXPORT _CUDA_HD void doPermuteShapeInfo(Nd4jLong *shapeBuffer, const int *rearrange, Nd4jLong len = -1);
/**
* Rearrange the permute indexes
* according to which dimensions are specified.
*
* For example, dimension is implicitly:
* 0,1,2
*
* If you want to do a reduce along dimensions 0 and 1,
* you need to permute the indexes to be:
* 2,0,1
*
* which will give us the ability to ierate along an element
* wise stride.
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* createPermuteIndexes(int originalRank, int *dimension,int dimensionLength);
ND4J_EXPORT _CUDA_HD Nd4jLong* computeResultShape(Nd4jLong *originalShapeBuffer, int *dimension,int dimensionLength);
/**
* This method does inplace transpose of given shapeBuffer
*
* @param shapeBuffer
*/
ND4J_EXPORT _CUDA_HD void transposeInplace(Nd4jLong *shapeBuffer);
/**
* Get the ordering for the device
* @param length
* @param shape
* @param stride
* @param elementStride
* @return
*/
ND4J_EXPORT _CUDA_HD char getOrder(int length, Nd4jLong *shape, Nd4jLong *stride, int elementStride);
/**
* Ensure that every value in the re arrange
* array is unique
* @param arr
* @param shape
* @param arrLength
* @param shapeLength
* @return
*/
template <typename T>
ND4J_EXPORT _CUDA_HD int checkArrangeArray(T *arr, int arrLength, int shapeLength);
/**
* Permute the shape information
* @param info the shape information to permute
* @param rearrange the order to re arrange
* @param rank the rank of the rearrange array
*/
ND4J_EXPORT _CUDA_HD void permute(ShapeInformation **info, int *rearrange, int rank);
/**
* Returns whether the
* given shape is a vector or not
* @param shape the shape of the array
* @param rank the rank of cthe shape
*/
ND4J_EXPORT _CUDA_HD int isVector(Nd4jLong *shape, int rank);
/**
* When 1 dimension is the whole length of the
* array
*/
ND4J_EXPORT _CUDA_HD int oneDimEqualToLength(Nd4jLong *shape, int rank);
ND4J_EXPORT _CUDA_HD int oneDimEqualToLength(Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD int isVector(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD bool isLikeVector(Nd4jLong *shapeInfo, int& posOfNonUnityDim);
ND4J_EXPORT _CUDA_HD bool isCommonVector(const Nd4jLong *shapeInfo, int& posOfNonUnityDim);
ND4J_EXPORT _CUDA_HD bool isRowVector(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD bool isColumnVector(Nd4jLong *shapeInfo);
/**
* Returns whether the
* given shape is a vector or not
* @param shape the shape of the array
* @param rank the rank of the shape
*/
ND4J_EXPORT _CUDA_HD int isMatrix(Nd4jLong *shape, int rank);
INLINEDEF _CUDA_HD int isMatrix(Nd4jLong *shapeInfo);
/**
* Returns the shape portion of an information
* buffer
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeOf(Nd4jLong *buffer);
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* copyOf(Nd4jLong length, T *toCopy);
template <typename T>
ND4J_EXPORT _CUDA_HD T* copyOf(Nd4jLong length, T *toCopy, T *ret);
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
template <typename T>
ND4J_EXPORT _CUDA_HD void copyTo(Nd4jLong length, T *from, T *to);
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
ND4J_EXPORT _CUDA_HD void copyTo(int length, Nd4jLong *from, Nd4jLong *to, Nd4jLong *indexes);
/**
* Permute the given strides
* in the given rearrange order
* @param toPermute the buffer to permute
* @param shapeRank the length of the buffer to permute
* @param rearrange the rearrange order (must be 0 based indexes
* and all must be filled in)
* @return the rearranged array
*/
//ND4J_EXPORT _CUDA_HD Nd4jLong *permutedStrides(Nd4jLong *toPermute, int shapeRank, Nd4jLong *rearrange);
/**
* Return the slice (shape + 1 in pointer arithmetic)
* @param shape the shape to take the slice of
* @return the shape array - the first entry
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *slice(Nd4jLong *shape);
ND4J_EXPORT _CUDA_HD int slices(Nd4jLong *shapeBuffer);
ND4J_EXPORT _CUDA_HD Nd4jLong *sliceOfShapeBuffer(Nd4jLong sliceIdx, Nd4jLong *shapeBuffer);
/**
* Returns the length of the
* shape information buffer:
* rank * 2 + 3
* @param rank the rank to get the shape
* info length for
* @return rank * 2 + 4
*/
ND4J_EXPORT _CUDA_HD int shapeInfoLength(int rank);
ND4J_EXPORT _CUDA_HD int shapeInfoLength(Nd4jLong* shapeInfo);
ND4J_EXPORT _CUDA_HD int shapeInfoLength(const Nd4jLong* shapeInfo);
ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(int rank);
ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(const Nd4jLong* shapeInfo);
ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(const Nd4jLong* shapeInfo);
/**
* Returns the rank portion of
* an information buffer
*/
ND4J_EXPORT _CUDA_HD int rank(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD int rank(const int *shapeInfo);
ND4J_EXPORT _CUDA_HD int rank(const unsigned int *shapeInfo);
// returns pointer on elementWiseStride
ND4J_EXPORT _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo);
/**
* returns pointer on elementWiseStride
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo);
/**
* Converts a raw int buffer of the layout:
* rank
* shape
* stride
* offset
* elementWiseStride
*
* where shape and stride are both straight int pointers
*/
ND4J_EXPORT _CUDA_HD ShapeInformation *infoFromBuffer(Nd4jLong *buffer);
/**
* Returns the stride portion of an information
* buffer
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *stride(Nd4jLong *buffer);
ND4J_EXPORT _CUDA_HD Nd4jLong *stride(const Nd4jLong *buffer);
/**
* Compute the length of the given shape
*/
ND4J_EXPORT _CUDA_HD bool isEmpty(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD Nd4jLong length(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD Nd4jLong length(std::initializer_list<int>& shape);
ND4J_EXPORT _CUDA_HD Nd4jLong length(std::initializer_list<Nd4jLong>& shape);
/***
* Returns the offset portion of an information buffer
*/
ND4J_EXPORT _CUDA_HD Nd4jLong offset(Nd4jLong *buffer);
ND4J_EXPORT _CUDA_HD Nd4jLong& extra(Nd4jLong *buffer);
/**
* Returns the ordering
* for this shape information buffer
*/
ND4J_EXPORT _CUDA_HD char order(const Nd4jLong *buffer);
/**
* Returns the type
*/
ND4J_EXPORT _CUDA_HD Nd4jLong type(const Nd4jLong* shapeInfo);
/**
* Returns the element wise stride for this information
* buffer
*/
ND4J_EXPORT _CUDA_HD Nd4jLong elementWiseStride(const Nd4jLong *buffer);
/**
* Returns the element wise stride for this information
* buffer
* relative to a dimension and ordering for a reduction index
*/
ND4J_EXPORT _CUDA_HD Nd4jLong reductionIndexElementWiseStride(Nd4jLong *buffer, int *dimension, int dimensionLength);
/**
* Returns whether
* the given shape info buffer
* represents a scalar shape
*/
ND4J_EXPORT _CUDA_HD int isScalar(Nd4jLong *info);
/**
* Returns whether
* the given shape information
* represents a scalar
* shape or not
*/
ND4J_EXPORT _CUDA_HD int isScalar(volatile ShapeInformation *info);
/**
* Return a copy of this array with the
* given index omitted
*
* @param data the data to copy
* @param indexes the index of the item to remove
* @param dataLength the length of the data array
* @param indexesLength the length of the data array
* @return the new array with the omitted
*
* item
*/
template <typename T1, typename T2>
ND4J_EXPORT _CUDA_HD void removeIndex(T1 *data, T2 *indexes, Nd4jLong dataLength, Nd4jLong indexesLength, T1 *out);
/**
* Return a copy of this array with the
* given index omitted
*
* @param data the data to copy
* @param indexes the index of the item to remove
* @param dataLength the length of the data array
* @param indexesLength the length of the data array
* @return the new array with the omitted
*
* item
*/
template <typename T1, typename T2>
ND4J_EXPORT _CUDA_HD T1* removeIndex(T1 *data, T2 *indexes, Nd4jLong dataLength, Nd4jLong indexesLength);
/**
* Iterate over a given set of indexes
* the begin and end indexes are 0 based.
* 1 padding is automatically assumed for the ending.
*
* For example if you want to iterate over 0 to 4
* it will go to 4 rather than 3.
*
* indexes should be the indexes to exclude
* indexes length should be the length of indexes
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* everyIndexBut(Nd4jLong *indexes,int indexesLength,int begin,int end);
/**
* Computes the offset for accessing
* a global element given the shape information
* and the offset to be read.
*/
//#ifdef __CUDACC__
// __device__
//#endif
// ND4J_EXPORT int tadOffset(shape::ShapeInformation *xInfo, int offset);
/**
* Returns a shape
* forces the given length to be 2.
* @param shape the shape to modify
* @param dimension the dimension (row or column)
* for the shape to be returned as
* @return the new shape
*/
ND4J_EXPORT _CUDA_HD Nd4jLong* ensureVectorShape(Nd4jLong *shape);
ND4J_EXPORT _CUDA_HD Nd4jLong* createScalarShapeInfo();
ND4J_EXPORT _CUDA_HD Nd4jLong* createScalarShapeInfo(Nd4jLong *ret);
/**
* Generate an int buffer
* up to the given length
* at the specified increment
*
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* range(int from, int to, int increment);
/**
* Range between from and two with an
* increment of 1
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* range(int from, int to);
/**
* Keep the given indexes
* in the data
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *keep(volatile Nd4jLong *data, int* index, int indexLength, int dataLength);
/**
* Generate reverse copy of the data
* @param data
* @param length
* @return
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* reverseCopy(T *data, Nd4jLong length);
template <typename T>
ND4J_EXPORT _CUDA_HD void reverseCopyTo(T *from, T *to, Nd4jLong length);
template <typename T>
ND4J_EXPORT _CUDA_HD void reverseCopyTo(T *from, T *to, Nd4jLong *indexes, Nd4jLong length);
template <typename T1, typename T2>
ND4J_EXPORT _CUDA_H void convertT(T1 *from, T2 *to, Nd4jLong length);
/**
*
* @param arr1
* @param arr1Length
* @param arr2
* @param arr2Length
* @return
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* concat(T* arr1, Nd4jLong arr1Length, T* arr2, Nd4jLong arr2Length);
/**
*
* @param numArrays
* @param numTotalElements
* @param arr
* @param lengths
* @return
*/
template <typename T>
ND4J_EXPORT _CUDA_HD T* concat(int numArrays, int numTotalElements, Nd4jLong **arr, Nd4jLong *lengths);
/**
* Get the length per slice of the
* given shape and the dimension
* @param rank the rank of the shape
* @param shape the shape of to get
* the length per slice for
* @param dimension the dimension to
* get the length per slice for
* @param dimensionLength the length of the dimension array
* @return the length per slice of the given shape
* along the given dimension
*/
ND4J_EXPORT _CUDA_HD Nd4jLong lengthPerSlice(int rank, Nd4jLong *shape, int *dimension, int dimensionLength);
/**
* calculates the offset for a tensor
* @param index
* @param arr
* @param tensorShape
* @return
*/
ND4J_EXPORT _CUDA_HD Nd4jLong sliceOffsetForTensor(int rank,
int index,
Nd4jLong *shape,
Nd4jLong *tensorShape,
int tensorShapeLength,
int *dimension,
int dimensionLength);
/**
* calculates the offset for a tensor
* @param index
* @param arr
* @param tensorShape
* @return
*/
ND4J_EXPORT _CUDA_HD Nd4jLong sliceOffsetForTensor(int index,int tensorLength,int lengthPerSlice2);
/**
* Computes the tensor along dimension
* offset
* @param index the index to get the offset for the tad for
* @param rank the rank of the shapes and strides
* @param info the shape information to use for tad
* @param dimension the dimensions to use for computing the tensor along dimensions
*/
// ND4J_EXPORT _CUDA_HD int offset(int index,
// int rank,
// shape::ShapeInformation *info,
// Nd4jLong *dimension,
// int dimensionLength);
/**
* Computes the number
* of tensors along
* a given dimension
*/
ND4J_EXPORT _CUDA_HD Nd4jLong tensorsAlongDimension(int rank,
volatile int length,
volatile Nd4jLong *shape,
int *dimension,
int dimensionLength);
/**
* Computes the number
* of tensors along
* a given dimension
*/
ND4J_EXPORT _CUDA_HD Nd4jLong tensorsAlongDimension(Nd4jLong *shapeInfo, int *dimension, int dimensionLength);
/**
* Returns the tensor along dimension
* for the given block index
* @param blockSize
* @param blockIdx
* @param i
* @return
*/
ND4J_EXPORT _CUDA_HD int tadForBlockIndex(int blockSize, int blockIdx, int i);
/**
* Computes the number of tads per block
*
*/
ND4J_EXPORT _CUDA_HD int tadsPerBlock(int blockSize, int tads);
// ND4J_EXPORT _CUDA_HD Nd4jLong *tadShapeInfo(int index, Nd4jLong *xShapeInfo, Nd4jLong *dimension,
// int dimensionLength);
/**
* Returns a shape buffer
* for the shape information metadata.
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *toShapeBuffer( ShapeInformation *info);
ND4J_EXPORT _CUDA_HD Nd4jLong *toShapeBuffer( ShapeInformation *info, Nd4jLong* ret);
/**
* Returns the number of elements per thread
*/
//#ifdef __CUDACC__
// __device__
//#endif
// int numElementsPerThread(int N);
/**
* Returns the block starting index
*/
//#ifdef __CUDACC__
// __device__
//#endif
// int blockStartingIndex(int N);
/**
* Returns the thread starting index
*/
//#ifdef __CUDACC__
// __device__
//#endif
// int threadStartingIndex(int N, int stride, int offset);
/**
* Returns the thread ending index
*/
//#ifdef __CUDACC__
// __device__
//#endif
// int threadEndingIndex(int N, int stride, int offset);
/**
* Returns indexing information
* for the current kernel invocation
*/
//#ifdef __CUDACC__
// __device__
//#endif
// CurrentIndexing *currentIndex(int N, int offset, int stride);
/** Given an linear index, element wise stride
* and the length of each tad
* map a linear index to a tad
* @param i the index to map
* @param the element wise stride for the tads
* @param numElementsPerTad the number of elements
* per tad
*/
ND4J_EXPORT _CUDA_HD int tadIndex(int i, int elementWiseStride, int numElementsPerTad);
/**
* Map a tad to a
* reduction index.
* @param tadIndexForOriginal the original tad index for the
* split up problem (eg: split is dimension 3 mapping to a 2,3 problem)
* @param tadsForReduced the number of tads for the shrunk down problem (eg: 2,3)
* @param tadsForOriginal the number of tads for the smaller problem (eg: 3)
*/
ND4J_EXPORT _CUDA_HD int reductionIndexForTad(int tadIndexForOriginal, int tadsForReduced,
int tadsForOriginal);
/**
* Computes the number of tads
* per reduce index for the
* reduction tad.
*/
ND4J_EXPORT _CUDA_HD int tadsPerReduceIndex(int tadsForReduce, int tadsForOriginal);
/**
* Maps a linear index to a reduction index
* @param i the linear index to map
* @param elementWiseStride the element wise stride
* for the multiple problem
* @param tadNum the number of tads for the shrunken problem
* @param originalTadNum the tad number for the reduced version of the problem
*/
ND4J_EXPORT _CUDA_HD int reductionIndexForLinear(int i, int elementWiseStride, int numElementsPerTad,
int tadNum, int originalTadNum);
/**
* Returns the prod of the data
* up to the given length
*/
ND4J_EXPORT _CUDA_HD Nd4jLong prodLong(const Nd4jLong *data, int length);
/**
* Returns the rear most left over item not present in
* the dimension array. This assumes that the dimension array is sorted.
*
* For example, given a dimension array of:
* 0,2
*
* and
*
* 12,4,2,1 in data
*
* You end up with 1 (data[3])
* since the first item won't match
* the last item of the dimension array
*/
// ND4J_EXPORT _CUDA_HD int rearMostLeftOverItem(Nd4jLong *data,int length,Nd4jLong *dimension,int dimensionLength);
/**
* Get an offset for retrieval
* from a data buffer
* based on the given
* shape stride and given indices
* @param baseOffset the offset to start from
* @param shape the shape of the array
* @param stride the stride of the array
* @param indices the indices to iterate over
* @return the double at the specified index
*/
ND4J_EXPORT _CUDA_HD Nd4jLong getOffset(Nd4jLong baseOffset, const Nd4jLong *shape, const Nd4jLong *stride, const Nd4jLong *indices, const int rank);
ND4J_EXPORT _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const Nd4jLong *indices, Nd4jLong baseOffset = 0);
ND4J_EXPORT Nd4jLong getOffset(const Nd4jLong *shapeInfo, const std::vector<uint>& indices);
ND4J_EXPORT _CUDA_HD Nd4jLong* createShapeInfo(Nd4jLong *shape, Nd4jLong *stride, int rank);
ND4J_EXPORT _CUDA_HD Nd4jLong* createShapeInfo(Nd4jLong *shape, Nd4jLong *stride, int rank, Nd4jLong *buffer);
/**
* Convert a linear index to the corresponding coordinates
* for example if shape is {2, 4}, then index 5 corresponds to following coordinates
* -> [1, 1] in case of c order
* -> [1, 2] in case of f order
*/
ND4J_EXPORT _CUDA_HD void index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong arrLen, Nd4jLong *coords, const char order = 'c');
ND4J_EXPORT _CUDA_HD void index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong *coords, const char order = 'c');
/**
* Convert coordinates to the corresponding linear index (sequence number in other words)
* for example if shape is {2, 4}, then:
* in case of c order and coordinates [1, 1] index 5 is returned
* in case of f order and coordinates [1, 2] index 5 is returned
*/
ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *coords, const char order = 'c');
/**
* increment n-dimensional array by one iteration by changing coord appropriately
* for example we have array with shape {2, 3}:
* - if input coord = {0,1}, then output coord = {0,2}
* - if input coord = {0,2}, then output coord = {1,0}
* so the aim is to produce following subsequence of coord: {0,0}, {0,1}, {0,2}, {1,0}, {1,1}, {1,2}
*/
/* calculates an array buffer offset for given "index" using following formula: offset = coord_0*stride_0 + coord_1*stride_1 + ... + coord_{rank-1}*stride_{rank-1}
* arrLen - array length
*/
ND4J_EXPORT _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen);
ND4J_EXPORT _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen);
ND4J_EXPORT _CUDA_HD Nd4jLong getIndexOrderOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen, const char order);
ND4J_EXPORT _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, Nd4jLong arrLen, const bool useUnsigned);
/**
* Compute the real linear indices for the given shape and stride
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *computeIndices(int rank, Nd4jLong *shape, Nd4jLong *stride);
/**
* Compute the real linear indices for the
* given shape buffer. Shape,stride and rank are derived
* from the buffer
*/
ND4J_EXPORT _CUDA_HD Nd4jLong *computeIndices( Nd4jLong *shapeBuffer);
ND4J_EXPORT _CUDA_HD void printShapeInfo(Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD void printShapeInfoLinear(const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD void printShapeInfoLinear(const char *msg, const Nd4jLong *shapeInfo);
ND4J_EXPORT _CUDA_HD void printShapeInfoLinear(const char *msg, int rank, const Nd4jLong *shape, const Nd4jLong *strides);
ND4J_EXPORT _CUDA_HD void printIntArray(const Nd4jLong *arr, const int length);
ND4J_EXPORT _CUDA_HD void printIntArray(const int *arr, const int length);
ND4J_EXPORT _CUDA_HD void printArray(float *arr,int length);
template<typename T>
ND4J_EXPORT _CUDA_HD void printArray(T *arr,int length, const char *message);
ND4J_EXPORT _CUDA_HD Nd4jLong* shapeBufferOfNpy(int rank, unsigned int *shape,bool fortranOrder);
ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferOfNpy(cnpy::NpyArray arr);
// ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferOfNpyBuffer(char *buffer);
// this function checks the consistence of dimensions with array rank (negative dimensions, too large dimensions, too big number of dimensions)
// also sort input array of dimensions, this operation is also necessary for creating TAD object
ND4J_EXPORT _CUDA_H void checkDimensions(const int rank, std::vector<int>& dimensions);
// function calculates linear index of array min, min is sub-array of max, index to be returned is min-array's index and corresponds to maxIdx of max array
// dimsToExclude - should be sorted in increasing order
ND4J_EXPORT _CUDA_HD Nd4jLong subArrayIndex(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr, const int dimsLen = -1);
// function calculates absolute offset of min array, min is sub-array of max, offset to be returned corresponds to maxIdx of max array
// dimsToExclude - should be sorted in increasing order
ND4J_EXPORT _CUDA_HD Nd4jLong subArrayOffset(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr, const int dimsLen = -1);
// max array is outer for min array, min array is sub-array of max array
// function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs)
// dimsToExclude - should be sorted in increasing order
// dimsLen - length of dimsToExclude, if not set (= -1), then it is calculated as maxRank - minRank
ND4J_EXPORT _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr, const int dimsLen = -1);
// calculate indexes of max-array, these output indexes correspond to one minIdx index of min-array which is sub-array of max-array
// dimsToExclude - should be sorted in increasing order
ND4J_EXPORT _CUDA_HD int outerArrayIndexes(Nd4jLong* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude = nullptr);
// calculate offsets of max-array, these offsets correspond to one minIdx index of min-array which is sub-array of max-array
// maxOffsets - will contain calculated offsets of max-array, buffer for maxOffsets should be allocated beforehand
// dimsToExclude - should be sorted in increasing order
// memBuff - auxiliary memory buffer (size = 2 * max_rank) for coordinates and increments storing, should be allocated beforehand
ND4J_EXPORT _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, Nd4jLong* memBuff, const int* dimsToExclude = nullptr);
// calculates offsets for entities (elements or sub-arrays), shape in context of sub-array means dimensions excluded from outer array
// rank is equal to size of shape
ND4J_EXPORT void calcOffsets(const int rank, const Nd4jLong* shape, const Nd4jLong* strides, Nd4jLong* offsets, const char order = 'c');
ND4J_EXPORT void calcOffsets(const Nd4jLong* shapeInfo, Nd4jLong* offsets, const char order = 'c');
ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const char order = 'c');
ND4J_EXPORT void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order = 'c');
ND4J_EXPORT _CUDA_HD void shapeOldScalar(nd4j::DataType dtype, Nd4jLong* const buffer, const char order);
// deduce element-wise stride
// if array is scalar or unit length vector then ews = 1
// if array is common vector then ews = stride of non-unity dimension
// if strides are normal set ews = 1, otherwise ews = 0
ND4J_EXPORT _CUDA_HD void setEws(Nd4jLong* shapeInfo, Nd4jLong len);
// deduce order and element-wise stride
// if array is scalar or unit length vector then ews = 1 and order is preserved
// if array is common vector then ews = stride of non-unity dimension and order is preserved
// if strides are normal/contiguous then ews = 1 and corresponding order is set, otherwise ews = 0 and order is preserved
ND4J_EXPORT _CUDA_HD void setOrderAndEws(Nd4jLong* shapeInfo, Nd4jLong len = -1);
/**
* processes whole set of sub-arrays
* evaluates shapeInfo of sub-arrays (all sub-arrays have the same shapeInfo) and their buffer offsets (each sub-array has its own unique offset from original this-buffer)
* arguments:
* wholeShapeInfo - original shapeInfo of whole array
* numOfSubArrs - number of sub-arrays, size of subArrOffsets is equal to numOfSubArrs
* dimsSize - size of dimsToExclude, if dimsSize = array rank or dimsSize = 0 it means sub-array is whole array, copy of wholeShapeInfo and one zero offset will be returned
* dimsToExclude - MUST BE SORTED, dimensions to evaluate sub-array 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]
* subArrShapeInfo - output argument, contains shapeInfo common for all sub-arrays
* subArrOffsets - output argument, contains successive sub-arrays offsets from original this-buffer
* keepUnitiesInShape - if false then eliminate unities from sub-array shapeInfo, for example {1,a,1,b} -> {a,b}
*/
ND4J_EXPORT _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape = false);
//END HEADERS
//BEGIN IMPLEMENTATIONS
#ifdef __CUDACC__
/**
* BEWARE: THIS METHOD DOES NOT CHECKS ALLOCATION BOUNDARIES
*/
__device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) {
Nd4jLong *ret = buffer;
ret += (threadIdx.x * size);
return ret;
}
#endif
/**
* Length of a tad given
* the shape information
*/
INLINEDEF _CUDA_HD Nd4jLong tadLength(Nd4jLong *shapeInfo, int *dimension, int dimensionLength) {
if(dimensionLength == 1) {
return shape::shapeOf(shapeInfo)[dimension[0]];
}
else {
Nd4jLong ret = 1;
for(int i = 0; i < shape::rank(shapeInfo); i++) {
for(int j = 0; j < dimensionLength; j++) {
if(i == dimension[j])
ret *= shape::shapeOf(shapeInfo)[dimension[j]];
}
}
return ret;
}
}
/**
* Tad element wise stride:
* given the inner most dimension (the sorted dimension of the last)
* the element wise stride of the tad (disregarding order) is the
* last dimension's stride.
*
* For a given singular dimension this will just be the only entry.
* For example, given the following c order shape/stride:
* 2,2,3,2
* 12,6,2,1
*
* The tad element wise stride for 3 will be 1.
* For zero it wil be 12
*
* For 2,3 it's 1
*
* Note here that the multi dimensional 2,3 case
* is equivalent to the singular 3 case.
*
*
* Note that this is for the dimension that ultimately
* ends up removed.
*
* Again: this may not preserve ordering of the tad
* but maybe used for reductions.
*/
INLINEDEF _CUDA_HD int tadElementWiseStride(Nd4jLong *shapeInfo, int *dimension,int dimensionLength) {
return reductionIndexElementWiseStride(shapeInfo,dimension,dimensionLength);
}
INLINEDEF _CUDA_HD bool shapeEquals(const int shape1Rank, const Nd4jLong *shape1, const int shape2Rank, const Nd4jLong *shape2) {
if(shape1Rank != shape2Rank)
return false;
//rank not equals
for(int i = 0; i < shape1Rank; i++) {
if(shape1[i] != shape2[i])
return false;
}
return true;
}
INLINEDEF _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2) {
return shape::shapeEquals(shape::rank(shapeInfo1), shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo1)), shape::rank(shapeInfo2), shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo2)));
}
INLINEDEF _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3) {
return shape::shapeEquals(shapeInfo1, shapeInfo2) && shape::shapeEquals(shapeInfo1, shapeInfo3);
}
INLINEDEF _CUDA_HD bool strideEquals(int shape1Rank,Nd4jLong *shape1,int shape2Rank,Nd4jLong *shape2) {
if(shape1Rank != shape2Rank)
return false;
//rank not equals
for(int i = 0; i < shape1Rank; i++) {
if(shape1[i] != shape2[i])
return false;
}
return true;
}
INLINEDEF _CUDA_HD bool strideEquals(Nd4jLong *shapeInfo1,Nd4jLong *shapeInfo2) {
return shape::strideEquals(shape::rank(shapeInfo1),shape::stride(shapeInfo1),shape::rank(shapeInfo2),shape::stride(shapeInfo2));
}
INLINEDEF _CUDA_HD bool strideEquals(Nd4jLong *stride1,int rank1 , Nd4jLong *stride2, int rank2) {
if(rank1 != rank2)
return false;
for(int i = 0; i < rank1; i++) {
if(stride1[i] != stride2[i])
return false;
}
return true;
}
INLINEDEF _CUDA_HD Nd4jLong *computeResultShape(Nd4jLong *originalShapeBuffer, int* dimension,int dimensionLength) {
Nd4jLong *retShape;
int retShapeLength;
if(dimensionLength == 1 && dimension[0] == 2147483647) {
retShape = new Nd4jLong[2];
retShape[0] = 1;
retShape[1] = 1;
retShapeLength = 2;
}
else {
retShape = shape::removeIndex<Nd4jLong, int>(shape::shapeOf(originalShapeBuffer), dimension, shape::shapeInfoLength(shape::rank(originalShapeBuffer)), dimensionLength);
retShapeLength = shape::rank(originalShapeBuffer) - dimensionLength;
}
//ensure vector is proper shape
if (retShapeLength == 1) {
if (dimension[0] == 0) {
auto newRetShape = new Nd4jLong[2]{1, retShape[0]};
delete[] retShape;
retShape = newRetShape;
retShapeLength = 2;
}
else {
auto newRetShape = new Nd4jLong[2]{retShape[0], 1};
delete[] retShape;
retShape = newRetShape;
retShapeLength = 2;
}
} else if (retShapeLength == 0) {
auto newRetShape = new Nd4jLong[2]{1, 1};
delete[] retShape;
retShape = newRetShape;
retShapeLength = 2;
}
auto ret = shape::shapeBuffer(retShapeLength, nd4j::ArrayOptions::dataType(originalShapeBuffer), retShape);
delete[] retShape;
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride, Nd4jLong *buffer) {
Nd4jLong *theShape = shape::shapeOf(shapeInfo);
Nd4jLong *theStride = shape::stride(shapeInfo);
int rank = dimensionLength == 1 ? 2 : dimensionLength;
Nd4jLong *ret = buffer;
//set the rank
ret[0] = rank;
Nd4jLong *retShape = shape::shapeOf(ret);
Nd4jLong *retStride = shape::stride(ret);
int len = rank;
if(dimensionLength == 1) {
if(shape::isMatrix(theShape,shape::rank(shapeInfo))) {
if(dimension[0] == 0) {
Nd4jLong newStride[2] = {theStride[dimension[0]],1};
Nd4jLong newShape[2] = {theShape[dimension[0]],1};
retShape[0] = newShape[0];
retShape[1] = newShape[1];
retStride[0] = newStride[0];
retStride[1] = newStride[1];
}
else {
Nd4jLong newStride[2] = {theStride[dimension[0]],1};
Nd4jLong newShape[2] = {theShape[dimension[0]],1};
retShape[0] = newShape[0];
retShape[1] = newShape[1];
retStride[0] = newStride[0];
retStride[1] = newStride[1];
}
}
else {
Nd4jLong newStride[2] = {1,theStride[dimension[0]]};
Nd4jLong newShape[2] = {1,theShape[dimension[0]]};
retShape[0] = newShape[0];
retShape[1] = newShape[1];
retStride[0] = newStride[0];
retStride[1] = newStride[1];
}
}
else {
Nd4jLong *newIndexes = dimension;
if(reverseCopyStride)
shape::reverseCopyTo(theStride, retStride, newIndexes, len);
else
shape::copyTo(len, theStride, retStride, newIndexes);
shape::copyTo(len, theShape, retShape, newIndexes);
}
ret[shape::shapeInfoLength(rank) - 1] = shape::order(shapeInfo);
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride) {
int rank = dimensionLength == 1 ? 2 : dimensionLength;
traceNew(4);
Nd4jLong *ret = new Nd4jLong[shape::shapeInfoLength(rank)];
return shapeInfoOnlyShapeAndStride(shapeInfo, dimension, dimensionLength, reverseCopyStride, ret);
}
INLINEDEF _CUDA_HD Nd4jLong * createShapeInfo(Nd4jLong *shape, Nd4jLong *stride, int rank) {
traceNew(5);
Nd4jLong *ret = new Nd4jLong[shape::shapeInfoLength(rank)];
return createShapeInfo(shape, stride, rank, ret);
}
INLINEDEF _CUDA_HD Nd4jLong * createShapeInfo(Nd4jLong *shape, Nd4jLong *stride, int rank, Nd4jLong *buffer) {
buffer[0] = rank;
Nd4jLong *retShape = shape::shapeOf(buffer);
Nd4jLong *retStride = shape::stride(buffer);
for(int i = 0;i < rank; i++) {
retShape[i] = shape[i];
retStride[i] = stride[i];
}
return buffer;
}
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
INLINEDEF _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank, int startNum) {
if (isVector(shape, rank)) {
traceNew(5);
Nd4jLong *ret = new Nd4jLong[2];
for (int i = 0; i < 2; i++)
ret[i] = 1;
return ret;
}
int dimensions = rank;
traceNew(6);
Nd4jLong *stride = new Nd4jLong[dimensions];
Nd4jLong st = startNum;
for (int j = 0; j < rank; j++) {
stride[j] = st;
st *= shape[j];
}
return stride;
}
INLINEDEF _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank, int startNum, Nd4jLong *ret) {
if (isVector(shape, rank)) {
for (int i = 0; i < rank; i++)
ret[i] = 1;
return ret;
}
//int dimensions = rank;
Nd4jLong st = startNum;
for (int j = 0; j < rank; j++) {
ret[j] = st;
st *= shape[j];
}
return ret;
}
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
INLINEDEF _CUDA_HD Nd4jLong * calcStrides(Nd4jLong *shape, int rank, int startNum) {
traceNew(7);
Nd4jLong *stride = new Nd4jLong[rank];
if (rank == 1) {
stride[0] = 1;
return stride;
}
// if (shape::isVector(shape, rank)) {
// for (int i = 0; i < 2; i++)
// stride[i] = 1;
// return stride;
// }
Nd4jLong st = startNum;
for (int j = rank - 1; j >= 0; j--) {
stride[j] = st;
st *= shape[j];
}
return stride;
}
INLINEDEF _CUDA_HD Nd4jLong * calcStrides(Nd4jLong *shape, int rank, int startNum, Nd4jLong* ret) {
if (rank == 1) {
ret[0] = 1;
return ret;
}
// if (shape::isVector(shape, rank)) {
// for (int i = 0; i < 2; i++)
// ret[i] = 1;
// return ret;
// }
Nd4jLong st = startNum;
for (int j = rank - 1; j >= 0; j--) {
ret[j] = st;
st *= shape[j];
}
return ret;
}
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
INLINEDEF _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank) {
return calcStridesFortran(shape, rank, 1);
}
INLINEDEF _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank, Nd4jLong* ret) {
return calcStridesFortran(shape, rank, 1, ret);
}
/**
* Computes the standard packed array strides for a given shape.
*
* @param shape the shape of a matrix:
* @param startNum the start number for the strides
* @return the strides for a matrix of n dimensions
*/
INLINEDEF _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank) {
return calcStrides(shape, rank, 1);
}
INLINEDEF _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, Nd4jLong* ret) {
return calcStrides(shape, rank, 1, ret);
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void updateStrides(Nd4jLong *shapeInfo, const char order) {
int rank = shapeInfo[0];
int doubleRank = 2*rank;
if (rank > 0) {
if (order == 'c') {
shapeInfo[doubleRank] = 1; // set unity as last stride for c order
for (int j = 1; j < rank; ++j) {
shapeInfo[doubleRank - j] = shapeInfo[doubleRank - j + 1] * shapeInfo[rank + 1 - j];
}
} else {
shapeInfo[rank + 1] = 1; // set unity as first stride for f order
for (int j = rank + 1; j < doubleRank; ++j) {
shapeInfo[j + 1] = shapeInfo[j] * shapeInfo[j - rank];
}
}
}
// set last 2 elements in shapeInfo
shapeInfo[doubleRank + 2] = 1;
shapeInfo[doubleRank + 3] = (int)order;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void updateStrides(const int rank, const Nd4jLong *shapeOnly, Nd4jLong *stridesOnly, const char order) {
if (rank > 0) {
if (order == 'c') {
stridesOnly[rank - 1] = 1; // set unity as last stride for c order
for (int j = 1; j < rank; ++j)
stridesOnly[rank - 1 - j] = stridesOnly[rank - j] * shapeOnly[rank - j];
}
else {
stridesOnly[0] = 1; // set unity as first stride for f order
for (int j = 1; j < rank; ++j) {
stridesOnly[j] = stridesOnly[j - 1] * shapeOnly[j - 1];
}
}
}
}
// check whether input dimensions are permuted, not permuted dimensions order have to be 0,....,rank-1
template <typename T>
INLINEDEF _CUDA_HD bool isDimPermuted(const T* dimensions, const Nd4jLong dimSize ) {
for(int i=0; i<dimSize-1; ++i)
if(dimensions[i] > dimensions[i+1])
return true;
return false;
}
/**
* @param toCopy the shape to copy
* @return a copy of the original struct
*/
INLINEDEF _CUDA_HD ShapeInformation *shapeCopy( ShapeInformation *toCopy) {
auto copy = new ShapeInformation;
traceNew(8);
copy->shape = new Nd4jLong[toCopy->rank];
memcpy(copy->shape, toCopy->shape, toCopy->rank * sizeof(Nd4jLong));
traceNew(9);
copy->stride = new Nd4jLong[toCopy->rank];
for (int i = 0; i < toCopy->rank; i++) {
copy->stride[i] = toCopy->stride[i];
}
copy->order = toCopy->order;
copy->rank = toCopy->rank;
copy->offset = toCopy->offset;
copy->elementWiseStride = toCopy->elementWiseStride;
return copy;
}
INLINEDEF _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder) {
if (rank == 0)
return 1;
if(shape::isVector(shape,rank)) {
return stride[rank - 1];
}
else {
int oldnd;
Nd4jLong *oldDims = shape::copyOf(rank, shape);
Nd4jLong *oldStrides = shape::copyOf(rank, stride);
Nd4jLong np, op, last_stride;
Nd4jLong oldStart, oldStop, ok, newStart, newStop, nk;
traceNew(10);
auto newStrides = new Nd4jLong[rank];
oldnd = 0;
//set the shape to be 1 x length
int newShapeRank = 2;
auto newShape = new Nd4jLong[newShapeRank];
newShape[0] = 1;
newShape[1] = shape::prodLong(shape, rank);
/*
* Remove axes with dimension 1 from the old array. They have no effect
* but would need special cases since their strides do not matter.
*/
for (oldStart = 0; oldStart < rank; oldStart++) {
if (shape[oldStart] != 1) {
oldDims[oldnd] = shape[oldStart];
oldStrides[oldnd] = stride[oldStart];
oldnd++;
}
}
np = 1;
for (newStart = 0; newStart < newShapeRank; newStart++) {
np *= newShape[newStart];
}
op = 1;
for (oldStart = 0; oldStart < oldnd; oldStart++) {
op *= oldDims[oldStart];
}
if (np != op) {
/* different total sizes; no hope */
delete[] newStrides;
delete[] newShape;
delete[] oldStrides;
delete[] oldDims;
return 0;
}
if (np == 0) {
/* the current code does not handle 0-sized arrays, so give up */
delete[] newStrides;
delete[] newShape;
delete[] oldStrides;
delete[] oldDims;
return 0;
}
/* oldStart to oldStop and newStart to newStop give the axis ranges currently worked with */
oldStart = 0;
oldStop = 1;
newStart = 0;
newStop = 1;
while (newStart < newShapeRank && oldStart < oldnd) {
np = newShape[newStart];
op = oldDims[oldStart];
while (np != op) {
if (np < op) {
/* Misses trailing 1s, these are handled later */
np *= newShape[newStop++];
} else {
op *= oldDims[oldStop++];
}
}
/* Check whether the original axes can be combined */
for (ok = oldStart; ok < oldStop - 1; ok++) {
if (isFOrder) {
if (oldStrides[ok + 1] != oldDims[ok] * oldStrides[ok]) {
/* not contiguous enough */
delete[] newStrides;
delete[] newShape;
delete[] oldStrides;
delete[] oldDims;
return 0;
}
} else {
/* C order */
if (oldStrides[ok] != oldDims[ok + 1] * oldStrides[ok + 1]) {
/* not contiguous enough */
delete[] newStrides;
delete[] newShape;
delete[] oldStrides;
delete[] oldDims;
return 0;
}
}
}
/* Calculate new strides for all axes currently worked with */
if (isFOrder) {
newStrides[newStart] = oldStrides[oldStart];
for (nk = newStart + 1; nk < newStop; nk++) {
newStrides[nk] = newStrides[nk - 1] * newShape[nk - 1];
}
} else {
/* C order */
newStrides[newStop - 1] = oldStrides[oldStop - 1];
for (nk = newStop - 1; nk > newStart; nk--) {
newStrides[nk - 1] = newStrides[nk] * newShape[nk];
}
}
newStart = newStop++;
oldStart = oldStop++;
}
/*
* Set strides corresponding to trailing 1s of the new shape.
*/
if (newStart >= 1) {
last_stride = newStrides[newStart - 1];
} else {
last_stride = stride[rank - 1];
}
if (isFOrder) {
if (newStart >= 1)
last_stride *= newShape[newStart - 1];
}
for (nk = newStart; nk < newShapeRank; nk++) {
newStrides[nk] = last_stride;
}
//returns the last element of the new stride array
int ret = last_stride;
delete[] newStrides;
delete[] newShape;
delete[] oldStrides;
delete[] oldDims;
return ret;
}
}
INLINEDEF _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder,
Nd4jLong *dimension, int dimensionLength) {
if(dimensionLength == 1) {
return stride[dimension[0]];
}
return 0;
}
/**
* Get the shape info buffer
* for the given rank and shape.
*/
INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape) {
Nd4jLong *stride = shape::calcStrides(shape, rank);
traceNew(11);
auto shapeInfo = new shape::ShapeInformation();
shapeInfo->shape = shape;
shapeInfo->stride = stride;
shapeInfo->offset = 0;
shapeInfo->rank = rank;
int elementWiseStride = shape::computeElementWiseStride(rank, shape, stride, 0);
shapeInfo->order = 'c';
shapeInfo->elementWiseStride = elementWiseStride;
auto shapeInfoBuffer = shape::toShapeBuffer(shapeInfo);
delete[] stride;
delete shapeInfo;
nd4j::ArrayOptions::setDataType(shapeInfoBuffer, dtype);
return shapeInfoBuffer;
}
/**
* This is special method, it returns ONLY 2D shapebuffer.
*
* This method is used only for SoftMax
*/
INLINEDEF _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer) {
Nd4jLong stride[MAX_RANK];
shape::calcStrides(shape,rank, stride);
shape::ShapeInformation shapeInfo;
shapeInfo.shape = shape;
shapeInfo.stride = stride;
shapeInfo.offset = 0;
shapeInfo.rank = rank;
auto elementWiseStride = shape::computeElementWiseStride(rank, shape, stride, 0);
shapeInfo.order = 'c';
shapeInfo.elementWiseStride = elementWiseStride;
shape::toShapeBuffer(&shapeInfo, buffer);
nd4j::ArrayOptions::setDataType(buffer, dtype);
return buffer;
}
/**
* Get the shape info buffer
* for the given rank and shape.
*/
INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape) {
auto stride = shape::calcStridesFortran(shape,rank);
traceNew(12);
auto shapeInfo = new shape::ShapeInformation();
shapeInfo->shape = shape;
shapeInfo->stride = stride;
shapeInfo->offset = 0;
shapeInfo->rank = rank;
int elementWiseStride = shape::computeElementWiseStride(rank, shape, stride, 0);
shapeInfo->order = 'f';
shapeInfo->elementWiseStride = elementWiseStride;
auto shapeInfoBuffer = shape::toShapeBuffer(shapeInfo);
delete[] stride;
delete shapeInfo;
nd4j::ArrayOptions::setDataType(shapeInfoBuffer, dtype);
return shapeInfoBuffer;
}
INLINEDEF _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *output) {
Nd4jLong stride[MAX_RANK];
shape::calcStridesFortran(shape,rank, stride);
shape::ShapeInformation shapeInfo;
shapeInfo.shape = shape;
shapeInfo.stride = stride;
shapeInfo.offset = 0;
shapeInfo.rank = rank;
auto elementWiseStride = shape::computeElementWiseStride(rank, shape, stride, 0);
shapeInfo.order = 'f';
shapeInfo.elementWiseStride = elementWiseStride;
shape::toShapeBuffer(&shapeInfo, output);
nd4j::ArrayOptions::setDataType(output, dtype);
return output;
}
/**
* Compute the real linear indices for the given shape and stride
*/
INLINEDEF _CUDA_HD Nd4jLong *computeIndices(int rank, Nd4jLong *shape, Nd4jLong *stride) {
Nd4jLong length = shape::prodLong(shape,rank);
traceNew(13);
Nd4jLong *ret = new Nd4jLong[length];
for(int i = 0; i < length; i++) {
Nd4jLong *idx = new Nd4jLong[rank];
shape::index2coords(rank, shape, i, idx, 'f');
ret[i] = shape::getOffset(0, shape, stride, idx, rank);
delete[] idx;
}
return ret;
}
/**
* Compute the real linear indices for the given shape and stride
*/
INLINEDEF _CUDA_HD Nd4jLong *computeIndices(Nd4jLong *shapeBuffer) {
return computeIndices(shape::rank(shapeBuffer),shape::shapeOf(shapeBuffer),shape::stride(shapeBuffer));
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *indices, const char order) {
Nd4jLong index, shift = 1;;
if(order == 'c') {
index = indices[rank - 1];
for(int i = rank - 2; i >= 0; --i) {
shift *= shape[i + 1];
index += shift * indices[i];
}
}
else {
index = indices[0];
for(int i = 1; i < rank; ++i) {
shift *= shape[i - 1];
index += shift * indices[i];
}
}
return index;
}
template <typename T>
INLINEDEF _CUDA_HD void fill(T* buffer, T value, Nd4jLong length) {
PRAGMA_OMP_SIMD
for (int e = 0; e < length; e++)
buffer[e] = value;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen) {
const Nd4jLong ews = shapeInfo[shapeInfo[0] + shapeInfo[0] + 2];
if(ews > 0 && order(shapeInfo) == 'c')
if (ews == 1)
return index;
else
return ews * index;
Nd4jLong offset = 0;
Nd4jLong rank = shapeInfo[0];
for(int i = 1; i <= shapeInfo[0]; ++i) {
arrLen /= shapeInfo[i];
if(arrLen > 0 && shapeInfo[i] > 1) {
offset += (index / arrLen) * shapeInfo[i + rank];
index %= arrLen;
}
}
return offset;
}
INLINEDEF _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen) {
const uint rank = shapeInfo[0];
const uint ews = shapeInfo[rank + rank + 2];
if(ews > 0 && shapeInfo[rank + rank + 3] == 99)
if (ews == 1)
return index;
else
return ews * index;
uint offset = 0;
for(uint i = 1; i <= rank; ++i) {
arrLen /= shapeInfo[i];
if(arrLen > 0 && shapeInfo[i] > 1) {
offset += (index / arrLen) * shapeInfo[i + rank];
index %= arrLen;
}
}
return offset;
}
INLINEDEF _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, Nd4jLong arrLen, const bool useUnsigned) {
if(useUnsigned)
return getIndexOffset(static_cast<uint>(index), uShapeInfo, static_cast<uint>(arrLen));
return getIndexOffset(index, lShapeInfo, arrLen);
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD Nd4jLong getIndexOrderOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen, const char order) {
Nd4jLong offset = 0;
if(order == 'c') {
for(int i = 1; i <= *shapeInfo; ++i) {
arrLen /= shapeInfo[i];
if(arrLen > 0 && shapeInfo[i] > 1) {
offset += (index / arrLen) * shapeInfo[i + *shapeInfo];
index %= arrLen;
}
}
}
else {
for(int i = *shapeInfo; i >= 1 ; --i) {
arrLen /= shapeInfo[i];
if(arrLen > 0 && shapeInfo[i] > 1) {
offset += (index / arrLen) * shapeInfo[i + *shapeInfo];
index %= arrLen;
}
}
}
return offset;
}
/**
*
* @param length
* @param shape
* @param rearrange
* @return
*/
INLINEDEF _CUDA_HD Nd4jLong *doPermuteSwap(int length, Nd4jLong *shape, int *rearrange) {
traceNew(16);
Nd4jLong *ret = new Nd4jLong[length];
for (int i = 0; i < length; i++) {
ret[i] = shape[rearrange[i]];
}
return ret;
}
/**
*
* @param length
* @param shape
* @param rearrange
* @return
*/
INLINEDEF _CUDA_HD void doPermuteSwap(int length, Nd4jLong **shape, int *rearrange) {
if(length == 1) {
return;
}
else {
Nd4jLong *shapeDeref = *shape;
if(shape::prodLong(shapeDeref,length) < 2) {
return;
}
}
bool inOrder = true;
for(int i = 0; i < length - 1; i++) {
inOrder = inOrder && rearrange[i] + 1 == rearrange[i + 1];
}
//all in order, nothing to do
if(inOrder)
return;
Nd4jLong *shapeDeref = *shape;
//we know they are just reversed, dimension length of 2
if(length == 2) {
auto shapeFirst = shapeDeref[0];
auto shapeSecond = shapeDeref[1];
shapeDeref[0] = shapeSecond;
shapeDeref[1] = shapeFirst;
return;
}
else if(length == 1) {
//no permute
return;
}
auto temp = new Nd4jLong[length];
memcpy(temp,shapeDeref,sizeof(Nd4jLong) * length);
for (int i = 0; i < length; i++) {
shapeDeref[i] = temp[rearrange[i]];
}
delete[] temp;
}
INLINEDEF _CUDA_HD void permuteShapeBufferInPlace(Nd4jLong *shapeBuffer, int *rearrange, Nd4jLong *out) {
if(shapeBuffer != out)
memcpy(out,shapeBuffer,sizeof(Nd4jLong) * shape::shapeInfoLength(shapeBuffer));
shape::doPermuteShapeInfo(out, rearrange);
}
INLINEDEF _CUDA_HD Nd4jLong *permuteShapeBuffer(Nd4jLong *shapeBuffer, int* rearrange) {
auto len = shape::shapeInfoLength(shape::rank(shapeBuffer));
Nd4jLong *copy = shape::copyOf(len, shapeBuffer);
shape::doPermuteShapeInfo(copy,rearrange);
return copy;
}
INLINEDEF _CUDA_HD void doPermuteShapeInfo(Nd4jLong *shapeInfo, const int *rearrange, Nd4jLong len) {
if(len == -1) // calculate array length if it is not given
len = shape::length(shapeInfo);
//check whether shape is like {1} or {1,1} or {1,1,1,1,...} - in this case we don't need permute
if(len == 1)
return;
const int rank = shape::rank(shapeInfo);
// check whether rearrange is like {0,1,2,3,...} - in this case we don't need permute as well
bool isPermutNecessary = false;
for(int i = 0; i < rank; ++i)
if(rearrange[i] != i) {
isPermutNecessary = true;
break;
}
if(!isPermutNecessary)
return;
// check whether rearrange contains correct indexes
for(int i = 0; i < rank; ++i)
if(rearrange[i] >= rank || rearrange[i] < 0) {
printf("shape::doPermuteShapeInfo function failed: rearrange indexes are incorrect !\n");
return;
}
// if everything is ok then perform permute
auto temp = new Nd4jLong[shape::shapeInfoLength(rank) - 3];
memcpy(temp, shapeInfo, sizeof(Nd4jLong) * (shape::shapeInfoLength(rank) - 3));
for (int i = 0; i < rank; ++i) {
shapeInfo[i + 1] = temp[rearrange[i] + 1];
shapeInfo[i + 1 + rank] = temp[rearrange[i] + 1 + rank];
}
shape::setOrderAndEws(shapeInfo, len);
delete[] temp;
}
INLINEDEF _CUDA_HD Nd4jLong *createPermuteIndexes(int originalRank, int *dimension,int dimensionLength) {
int delta = originalRank - dimensionLength;
traceNew(17);
Nd4jLong *ret = new Nd4jLong[originalRank];
for(int i = 0; i < delta; i++) {
ret[i] = i + dimensionLength;
}
for(int i = delta; i < originalRank; i++) {
ret[i] = i - delta;
}
return ret;
}
/**
* Get the ordering for the device
* @param length
* @param shape
* @param stride
* @param elementStride
* @return
*/
INLINEDEF _CUDA_HD char getOrder(int length, Nd4jLong *shape, Nd4jLong *stride, int elementStride) {
Nd4jLong sd = 1;
int dim = -1;
int i = -1;
int cContiguous = 1;
int isFortran = 1;
for (i = length - 1; i >= 0; --i) {
dim = shape[i];
if (stride[i] != sd) {
cContiguous = 0;
break;
}
/* contiguous, if it got this far */
if (dim == 0) {
break;
}
sd *= dim;
}
/* check if fortran contiguous */
sd = elementStride;
for (i = 0; i < length; ++i) {
dim = shape[i];
if (stride[i] != sd) {
isFortran = 0;
}
if (dim == 0) {
break;
}
sd *= dim;
}
if (isFortran && cContiguous)
return 'a';
else if (isFortran && !cContiguous)
return 'f';
else if (!isFortran && !cContiguous)
return 'c';
else
return 'c';
}
/**
* Ensure that every value in the re arrange
* array is unique
* @param arr
* @param shape
* @param arrLength
* @param shapeLength
* @return
*/
template <typename T>
INLINEDEF _CUDA_HD int checkArrangeArray(T *arr, int arrLength, int shapeLength) {
if (arrLength != shapeLength)
return -1;
for (int i = 0; i < arrLength; i++) {
if (arr[i] >= arrLength || arr[i] < 0)
return -1;
}
for (int i = 0; i < arrLength; i++) {
for (int j = 0; j < arrLength; j++) {
if (i != j && arr[i] == arr[j])
return -1;
}
}
return 1;
}
INLINEDEF _CUDA_HD void traceNew(int id) {
//printf("new happened: [%i]\n", id);
#ifndef __CUDACC__
//fflush(stdout);
#endif
}
/**
* Permute the shape information
* @param info the shape information to permute
* @param rearrange the order to re arrange
* @param rank the rank of the rearrange array
*/
INLINEDEF _CUDA_HD void permute(ShapeInformation **info, int *rearrange, int rank) {
ShapeInformation *infoDeref = *info;
checkArrangeArray(rearrange, rank, rank);
shape::doPermuteSwap(rank, &infoDeref->shape, rearrange);
shape::doPermuteSwap(rank, &infoDeref->stride, rearrange);
char order = getOrder(rank,
infoDeref->shape,
infoDeref->stride,
infoDeref->elementWiseStride);
infoDeref->order = order;
}
/**
* Returns whether the
* given shape is a vector or not
* @param shape the shape of the array
* @param rank the rank of the shape
*/
INLINEDEF _CUDA_HD int isVector(Nd4jLong *shape, int rank) {
if (rank == 0)
return 0;
if (rank == 1)
return 1;
if (rank > 2)
return 0;
else if (rank <= 2) {
if (shape[0] == 1 || shape[1] == 1)
return 1;
}
return 0;
}
INLINEDEF _CUDA_HD bool isLikeVector(Nd4jLong *shapeInfo, int& posOfNonUnityDim) {
int numOfNonUnity = 0;
for(int i = 1; i <= shapeInfo[0]; ++i) {
if(shapeInfo[i] != 1) {
++numOfNonUnity;
posOfNonUnityDim = i-1;
}
}
return numOfNonUnity == 1 && shapeInfo[0] > 2;
}
INLINEDEF _CUDA_HD bool isCommonVector(const Nd4jLong *shapeInfo, int& posOfNonUnityDim) {
if(rank(shapeInfo) > 0 && length(shapeInfo) == 1) {
posOfNonUnityDim = 0;
return true;
}
int numOfNonUnity = 0;
for(int i = 1; i <= shapeInfo[0]; ++i) {
if(shapeInfo[i] != 1) {
++numOfNonUnity;
posOfNonUnityDim = i-1;
}
}
return numOfNonUnity == 1;
}
INLINEDEF _CUDA_H Nd4jLong* detachShape(Nd4jLong *originalShape) {
Nd4jLong *newShape = new Nd4jLong[shape::shapeInfoLength(originalShape)];
memcpy(newShape, originalShape, shape::shapeInfoByteLength(originalShape));
return newShape;
}
INLINEDEF _CUDA_H Nd4jLong* copyShape(Nd4jLong *originalShape) {
Nd4jLong *newShape = new Nd4jLong[shape::shapeInfoLength(originalShape)];
memcpy(newShape, originalShape, shape::shapeInfoByteLength(originalShape));
return newShape;
}
INLINEDEF _CUDA_HD int isVector(const Nd4jLong *shapeInfo) {
return isVector(shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo)), shape::rank(shapeInfo));
}
INLINEDEF _CUDA_HD bool isRowVector(const Nd4jLong *shapeInfo) {
bool isVector = shape::isVector(shapeInfo) == 1;
bool shapeFirstOne = shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo))[0] == 1;
return isVector && shapeFirstOne;
}
INLINEDEF _CUDA_HD bool isColumnVector(Nd4jLong *shapeInfo) {
bool isVector = shape::isVector(shapeInfo) == 1;
bool shapeFirstOne = shape::shapeOf(shapeInfo)[0] == 1;
return isVector && !shapeFirstOne;
}
INLINEDEF _CUDA_HD int oneDimEqualToLength(Nd4jLong *shape, int rank) {
for(int i = 0; i < rank; i++) {
if(shape[i] == shape::prodLong(shape,rank))
return 1;
}
return 0;
}
INLINEDEF _CUDA_HD int oneDimEqualToLength(Nd4jLong *shapeInfo) {
return oneDimEqualToLength(shape::shapeOf(shapeInfo),shape::rank(shapeInfo));
}
/**
* Returns whether the
* given shape is a vector or not
* @param shape the shape of the array
* @param rank the rank of the shape
*/
INLINEDEF _CUDA_HD int isMatrix(Nd4jLong *shape, int rank) {
if (rank > 2)
return 0;
else if (rank <= 2) {
if (shape[0] == 1 || shape[1] == 1)
return 0;
}
return 1;
}
INLINEDEF _CUDA_HD int isMatrix(Nd4jLong *shapeInfo) {
return isMatrix(shape::shapeOf(shapeInfo),shape::rank(shapeInfo));
}
/**
* Returns the shape portion of an information
* buffer
*/
INLINEDEF _CUDA_HD Nd4jLong *shapeOf(Nd4jLong *buffer) {
return buffer + 1;
}
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
template <typename T>
INLINEDEF _CUDA_HD T *copyOf(Nd4jLong length, T *toCopy) {
traceNew(18);
T *ret = new T[length];
return copyOf(length, toCopy, ret);
}
template <typename T>
INLINEDEF _CUDA_HD T* copyOf(Nd4jLong length, T *toCopy, T *ret) {
memcpy(ret, toCopy, sizeof(T)*length);
return ret;
}
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
template <typename T>
INLINEDEF _CUDA_HD void copyTo(Nd4jLong length, T *from, T *to) {
memcpy(to, from, sizeof(T)*length);
}
/**
* Return a copy of a buffer.
* This buffer allocates memory
* that must be freed elsewhere.
*/
INLINEDEF _CUDA_HD void copyTo(int length, Nd4jLong *from, Nd4jLong *to, Nd4jLong *indexes) {
for(int i = 0; i < length; i++) {
to[i] = from[indexes[i]];
}
}
/**
* Permute the given strides
* in the given rearrange order
* @param toPermute the buffer to permute
* @param shapeRank the length of the buffer to permute
* @param rearrange the rearrange order (must be 0 based indexes
* and all must be filled in)
* @return the rearranged array
*/
/*
INLINEDEF _CUDA_HD Nd4jLong *permutedStrides(Nd4jLong *toPermute, int shapeRank, int *rearrange) {
Nd4jLong *strideCopy = copyOf(shapeRank, toPermute);
checkArrangeArray(rearrange, shapeRank, shapeRank);
Nd4jLong *newStride = doPermuteSwap(shapeRank, strideCopy, rearrange);
delete[] strideCopy;
return newStride;
}
*/
/**
* Return the slice (shape + 1 in pointer arithmetic)
* @param shape the shape to take the slice of
* @return the shape array - the first entry
*/
INLINEDEF _CUDA_HD Nd4jLong *slice(Nd4jLong *shape) {
return shape + 1;
}
INLINEDEF _CUDA_HD int slices(Nd4jLong *shapeBuffer) {
return static_cast<int>(shape::shapeOf(shapeBuffer)[0]);
}
INLINEDEF _CUDA_HD Nd4jLong *sliceOfShapeBuffer(Nd4jLong sliceIdx, Nd4jLong *shapeBuffer) {
int rank = shape::rank(shapeBuffer);
int newRank = rank - 1;
if(newRank < 2)
newRank = 2;
Nd4jLong *newShapeBuffer = new Nd4jLong[shape::shapeInfoLength(newRank)];
newShapeBuffer[0] = newRank;
Nd4jLong *currShape = shape::shapeOf(shapeBuffer);
Nd4jLong *currStride = shape::stride(shapeBuffer);
//initialize new shape and stride by taking the shape and stride + 1
//and adding to the shape information
//a slice is always just taking the existing shape and cutting the first index off
//of the shape and stride
Nd4jLong *newShape = shape::shapeOf(newShapeBuffer);
Nd4jLong *newStride = shape::stride(newShapeBuffer);
if(shape::isVector(shapeBuffer)) {
Nd4jLong *currShape = shape::shapeOf(shapeBuffer);
//row vector: slice index 0 is a valid index, just copy the whole thing
if(currShape[0] == 1) {
if(sliceIdx == 0) {
memcpy(newShapeBuffer,shapeBuffer,shape::shapeInfoByteLength(shape::rank(shapeBuffer)));
return newShapeBuffer;
}
}
//column vector: this will be a scalar
else {
delete[] newShapeBuffer;
Nd4jLong *scalar = shape::createScalarShapeInfo();
int offset = shape::offset(shapeBuffer);
scalar[shape::shapeInfoLength(2) - 3] = offset + sliceIdx;
return scalar;
}
}
else if(shape::isMatrix(shapeBuffer)) {
newShape[0] = 1;
newShape[1] = currShape[1];
newStride[0] = 1;
newStride[1] = currStride[1];
}
else {
for(int i = 0; i < newRank; i++) {
newShape[i] = currShape[i + 1];
newStride[i] = currStride[i + 1];
}
}
auto indices = new Nd4jLong[rank];
memset((void *) indices,0,rank * sizeof(Nd4jLong));
indices[0] = sliceIdx;
Nd4jLong offset = shape::getOffset(0,newShape,newStride,indices,rank);
newShapeBuffer[shape::shapeInfoLength(newRank) - 3] = offset;
// set current order and ews
newShapeBuffer[2 * newRank + 2] = shape::elementWiseStride(shapeBuffer);
newShapeBuffer[2 * newRank + 3] = shape::order(shapeBuffer);
// correct order and ews if necessary
shape::setOrderAndEws(newShapeBuffer);
delete[] indices;
return newShapeBuffer;
}
/**
* Returns the length of the
* shape information buffer:
* rank * 2 + 3
* @param rank the rank to get the shape
* info length for
* @return rank * 2 + 4
*/
INLINEDEF _CUDA_HD int shapeInfoLength(int rank) {
//FIXME magic numbers
return rank * 2 + 4;
}
INLINEDEF _CUDA_HD int shapeInfoLength(Nd4jLong* shape) {
return shapeInfoLength(static_cast<int>(shape[0]));
}
INLINEDEF _CUDA_HD int shapeInfoLength(const Nd4jLong* shape) {
return shapeInfoLength(static_cast<int>(shape[0]));
}
INLINEDEF _CUDA_HD size_t shapeInfoByteLength(int rank) {
//FIXME magic numbers
return (rank * 2 + 4) * sizeof(Nd4jLong);
}
INLINEDEF _CUDA_HD size_t shapeInfoByteLength(const Nd4jLong* shapeInfo) {
//FIXME magic numbers
return shapeInfoByteLength((int) shapeInfo[0]);
}
/**
* Returns the rank portion of
* an information buffer
*/
INLINEDEF _CUDA_HD int rank(const Nd4jLong *buffer) {
return static_cast<int>(buffer[0]);
}
INLINEDEF _CUDA_HD int rank(const int *buffer) {
return buffer[0];
}
INLINEDEF _CUDA_HD int rank(const unsigned int *buffer) {
return static_cast<int>(buffer[0]);
}
INLINEDEF _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo) {
return shapeInfo + 2 * shapeInfo[0] + 2;
}
/**
* Converts a raw int buffer of the layout:
* rank
* shape
* stride
* offset
* elementWiseStride
*
* where shape and stride are both straight int pointers
*/
INLINEDEF _CUDA_HD ShapeInformation *infoFromBuffer(Nd4jLong *buffer) {
traceNew(19);
auto info = new ShapeInformation;
auto length = shapeInfoLength(rank(buffer));
auto rank = buffer[0];
//start after rank
info->shape = buffer + 1;
info->stride = buffer + (1 + rank);
info->rank = rank;
info->offset = buffer[length - 3];
info->elementWiseStride = buffer[length - 2];
Nd4jLong *stride = buffer + 1 + rank;
info->stride = stride;
info->order = (char) buffer[length - 1];
return info;
}
/**
* Returns the stride portion of an information
* buffer
*/
INLINEDEF _CUDA_HD Nd4jLong *stride(Nd4jLong *buffer) {
return buffer + (1 + rank(buffer));
}
INLINEDEF _CUDA_HD Nd4jLong *stride(const Nd4jLong *buffer) {
return stride(const_cast<Nd4jLong *>(buffer));
}
INLINEDEF _CUDA_HD bool isEmpty(const Nd4jLong *shapeInfo) {
return ((shape::extra(const_cast<Nd4jLong*>(shapeInfo)) & ARRAY_EMPTY) == ARRAY_EMPTY);
}
/**
* Compute the length of the given shape
*/
INLINEDEF _CUDA_HD Nd4jLong length(const Nd4jLong *shapeInfo) {
const int rank = shape::rank(shapeInfo);
if (rank == 0) {
if (isEmpty(shapeInfo))
return 0L;
return 1L;
}
if (rank == 1)
return shapeInfo[1];
// if(shape::elementWiseStride(shapeInfo) == 1) { // contiguous
// if(shape::order(shapeInfo) == 'c')
// return shapeInfo[1] * shapeInfo[rank + 1]; // first dim * first stride
// return shapeInfo[rank] * shapeInfo[2 * rank]; // last dim * last stride
// }
return shape::prodLong(shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo)), rank);
}
INLINEDEF _CUDA_HD Nd4jLong length(std::initializer_list<int>& shape) {
Nd4jLong ret = 1;
for (auto v : shape) {
ret *= v;
}
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong length(std::initializer_list<Nd4jLong>& shape) {
Nd4jLong ret = 1;
for (auto v : shape) {
ret *= v;
}
return ret;
}
/***
* Returns the offset
* portion of an information buffer
*/
INLINEDEF _CUDA_HD Nd4jLong offset(Nd4jLong *buffer) {
return buffer[shape::shapeInfoLength(shape::rank(buffer)) - 3];
}
INLINEDEF _CUDA_HD Nd4jLong& extra(Nd4jLong *buffer) {
return buffer[shape::shapeInfoLength(shape::rank(buffer)) - 3];
}
/**
* Returns the ordering
* for this shape information buffer
*/
INLINEDEF _CUDA_HD char order(const Nd4jLong *buffer) {
//FIXME magic numbers
return static_cast<char>(buffer[buffer[0] * 2 + 3]);
}
/**
* Returns type
*/
INLINEDEF _CUDA_HD Nd4jLong type(const Nd4jLong *shapeInfo) {
return shapeInfo[2 * shapeInfo[0] + 1];
}
/**
* Returns the element wise stride for this information
* buffer
*/
INLINEDEF _CUDA_HD Nd4jLong elementWiseStride(const Nd4jLong *buffer) {
return buffer[shapeInfoLength(static_cast<int>(buffer[0])) - 2];
}
/**
* Returns the element wise stride for this information
* buffer relative to a dimension and reduction index
*/
INLINEDEF _CUDA_HD Nd4jLong reductionIndexElementWiseStride(Nd4jLong* buffer, int* dimension, int dimensionLength) {
if(dimensionLength > 1) {
if(shape::order(buffer) == 'f') {
/**
* The element wise stride belongs to a reduction index.
* When used out of order, we can get rid of the data
* dependencies and rely on using the max dimension
* specified for stride instead.
* Say we take the sum(0,1) along arr
* we can use arr.stride(1) as a representation
* along which to iterate.
*/
if(shape::shapeOf(buffer)[dimension[dimensionLength - 1]] != 1) {
//int tadElementWiseStride = shape::stride(buffer)[dimension[dimensionLength - 1]];
//return tadElementWiseStride;
auto tadElementWiseStride = shape::stride(buffer)[dimension[0]];
return tadElementWiseStride;
}
return 1;
}
else {
/**
* The element wise stride belongs to a reduction index.
* When used out of order, we can get rid of the data
* dependencies and rely on using the max dimension
* specified for stride instead.
* Say we take the sum(0,1) along arr
* we can use arr.stride(1) as a representation
* along which to iterate.
*/
if(shape::shapeOf(buffer)[dimension[dimensionLength - 1]] != 1) {
auto tadElementWiseStride = shape::stride(buffer)[dimension[dimensionLength - 1]];
return tadElementWiseStride;
}
return 1;
}
}
else {
if(shape::order(buffer) == 'f') {
/**
* The element wise stride belongs to a reduction index.
* When used out of order, we can get rid of the data
* dependencies and rely on using the max dimension
* specified for stride instead.
* Say we take the sum(0,1) along arr
* we can use arr.stride(1) as a representation
* along which to iterate.
*/
auto tadElementWiseStride = shape::stride(buffer)[dimension[0]];
return tadElementWiseStride;
}
else {
/**
* The element wise stride belongs to a reduction index.
* When used out of order, we can get rid of the data
* dependencies and rely on using the max dimension
* specified for stride instead.
* Say we take the sum(0,1) along arr
* we can use arr.stride(1) as a representation
* along which to iterate.
*/
auto tadElementWiseStride = shape::stride(buffer)[dimension[dimensionLength - 1]];
return tadElementWiseStride;
}
}
}
/**
* Returns whether
* the given shape info buffer
* represents a scalar shape
*/
INLINEDEF _CUDA_HD int isScalar(Nd4jLong *info) {
const int rank = shape::rank(info);
if(rank > 2)
return 0;
if(rank == 0)
return 1;
if(rank == 1)
return shape::shapeOf(info)[0] == 1;
if(rank == 2)
return shape::shapeOf(info)[0] == 1 && shape::shapeOf(info)[1] == 1;
return 0;
}
/**
* Returns whether
* the given shape information
* represents a scalar
* shape or not
*/
INLINEDEF _CUDA_HD int isScalar(volatile ShapeInformation *info) {
const int rank = info->rank;
if(rank > 2)
return 0;
if(rank == 1)
return info->shape[0] == 1;
if(rank == 2)
return info->shape[0] == 1 && info->shape[1] == 1;
return 0;
}
/**
* Return a copy of this array with the
* given index omitted
*
* @param data the data to copy
* @param indexes the index of the item to remove
* @param dataLength the length of the data array
* @param indexesLength the length of the data array
* @return the new array with the omitted
*
* item
*/
template <typename T1, typename T2>
INLINEDEF _CUDA_HD void removeIndex(T1* data, T2 *indexes, Nd4jLong dataLength, Nd4jLong indexesLength, T1 *ret) {
int count = 0;
int absLength = dataLength - indexesLength;
for (int i = 0; i < dataLength && count < absLength; i++) {
int contains = 0;
for (int j = 0; j < indexesLength; j++) {
if (i == indexes[j]) {
contains = 1;
break;
}
}
if (!contains) {
ret[count] = data[i];
count++;
}
}
}
/**
* Return a copy of this array with the
* given index omitted
*
* @param data the data to copy
* @param indexes the index of the item to remove
* @param dataLength the length of the data array
* @param indexesLength the length of the data array
* @return the new array with the omitted
*
* item
*/
template <typename T1, typename T2>
INLINEDEF _CUDA_HD T1* removeIndex(T1 *data, T2 *indexes, Nd4jLong dataLength, Nd4jLong indexesLength) {
auto lengthOfArr = dataLength - indexesLength;
if(lengthOfArr < 0) {
printf("Remove index call created a <= 0 length array. This was likely not intended.");
}
auto ret = new T1[lengthOfArr];
memset(ret,0,sizeof(T1) * lengthOfArr);
removeIndex<T1, T2>(data, indexes, dataLength, indexesLength, ret);
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong* everyIndexBut(Nd4jLong *indexes,int indexesLength,int begin,int end) {
int len = end - indexesLength;
traceNew(20);
auto ret = new Nd4jLong[len];
int retIdx = 0;
//not here that we do 0 based indexing for end - this assumes things like:
//0 to 4 are specified
for(int i = begin; i < end ; i++) {
bool found = false;
for(int j = 0; j < indexesLength; j++) {
if(indexes[j] == i) {
found = true;
break;
}
}
if(!found) {
ret[retIdx++] = i;
}
}
return ret;
}
/**
* Computes the offset for accessing
* a global element given the shape information
* and the offset to be read.
*/
#ifdef __CUDACC__
INLINEDEF __device__ int tadOffset(ShapeInformation *xInfo, int offset) {
return offset + threadIdx.x * xInfo->elementWiseStride;
}
#endif
/**
* Returns a shape
* forces the given length to be 2.
* @param shape the shape to modify
* @param dimension the dimension (row or column)
* for the shape to be returned as
* @return the new shape
*/
INLINEDEF _CUDA_HD Nd4jLong *ensureVectorShape(Nd4jLong *shape, int dimension) {
traceNew(21);
Nd4jLong *ret = new Nd4jLong[2];
if (dimension == 0) {
ret[0] = 1;
ret[1] = shape[0];
} else {
ret[0] = shape[0];
ret[1] = 1;
}
return ret;
}
/**
* Returns a shape
* forces the given length to be 2.
* @param shape the shape to modify
* @param dimension the dimension (row or column)
* for the shape to be returned as
* @return the new shape
*/
INLINEDEF _CUDA_HD Nd4jLong *ensureVectorShape(Nd4jLong *shape) {
return ensureVectorShape(shape, 0);
}
/**
* This method does STRICT comparison for two shape buffers
*
* @param shape
* @return
*/
INLINEDEF _CUDA_HD bool equalsStrict(const Nd4jLong *shapeA, const Nd4jLong *shapeB) {
if (shapeA[0] != shapeB[0])
return false;
if (shapeA[0] == 0)
return true;
// we do full comparison here
int length = shape::shapeInfoLength(shapeA[0]);
for (int e = 1; e < length; e++)
if (shapeA[e] != shapeB[e])
return false;
return true;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2) {
if (shapeInfo1[0] != shapeInfo2[0])
return false;
if (shapeInfo1[0] == 0)
return true;
int range = 2 * shapeInfo1[0];
for (int e = 1; e <= range; e++)
if (shapeInfo1[e] != shapeInfo2[e])
return false;
return true;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3) {
return shape::haveSameShapeAndStrides(shapeInfo1, shapeInfo2) && shape::haveSameShapeAndStrides(shapeInfo1, shapeInfo3);
}
INLINEDEF _CUDA_HD int sizeAt(const Nd4jLong *shape, const int dim) {
if (0 == rank(shape))
return 1;
if (dim >= 0)
return shape[1+dim];
else
return shape[1+(rank(shape) + dim)];
}
/**
* This method does SOFT comparison for two shape buffers, we compare only rank & shapes
*
* @param shape
* @return
*/
INLINEDEF _CUDA_HD bool equalsSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB) {
if (shapeA[0] != shapeB[0])
return false;
if (shapeA[0] == 0)
return true;
// we compare only shapes, and ignoring stride & ews
auto length = shapeA[0];
for (int e = 1; e <= length; e++)
if (shapeA[e] != shapeB[e])
return false;
return true;
}
INLINEDEF _CUDA_HD bool equalsTypesAndShapesSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB) {
return equalsSoft(shapeA, shapeB) && shapeA[shapeInfoLength(shapeA) - 3] == shapeB[shapeInfoLength(shapeB) - 3];
}
/**
* Generate an int buffer
* up to the given length
* at the specified increment
*
*/
template <typename T>
INLINEDEF _CUDA_HD T* range(int from, int to, int increment) {
int diff = nd4j::math::nd4j_abs<int>(from - to);
int retLength = diff / increment;
T *ret;
traceNew(22);
if(diff / increment < 1)
ret = new T[1];
else
ret = new T[diff / increment];
if (from < to) {
int count = 0;
for (int i = from; i < to; i += increment) {
if (count >= retLength)
break;
ret[count++] = i;
}
} else if (from > to) {
int count = 0;
for (int i = from - 1; i >= to; i -= increment) {
if (count >= retLength)
break;
ret[count++] = i;
}
}
return ret;
}
/**
* Generate a range
* beginning at from and ending at to
* incrementing by 1
* @param from the start
* @param to the end
* @return the int array starting at from and ending at to
*/
template <typename T>
INLINEDEF _CUDA_HD T* range(int from, int to) {
return range<T>(from, to, 1);
}
/**
* Keep the given indexes in the data
* @param data
* @param index
* @param indexLength
* @param dataLength
* @return
*/
INLINEDEF _CUDA_HD Nd4jLong *keep(volatile Nd4jLong *data, int* index, int indexLength, int dataLength) {
traceNew(23);
Nd4jLong *ret = new Nd4jLong[indexLength];
int count = 0;
for (int i = 0; i < dataLength; i++) {
int contains = 0;
for (int j = 0; j < indexLength; j++) {
if (i == index[j]) {
contains = 1;
break;
}
}
if (contains)
ret[count++] = data[i];
}
return ret;
}
/**
* Generate a reverse
* copy of the data
*/
template <typename T>
INLINEDEF _CUDA_HD T* reverseCopy(T *data, Nd4jLong length) {
if (length < 1)
return nullptr;
traceNew(24);
T *copy = new T[length];
for (Nd4jLong i = 0; i <= length / 2; i++) {
T temp = data[i];
copy[i] = data[length - i - 1];
copy[length - i - 1] = temp;
}
return copy;
}
template <typename T>
INLINEDEF _CUDA_HD void reverseCopyTo(T *from, T *to, Nd4jLong length) {
if (length < 1)
return;
for (Nd4jLong i = 0; i <= length / 2; i++) {
T temp = from[i];
to[i] = from[length - i - 1];
to[length - i - 1] = temp;
}
}
template <typename T>
INLINEDEF _CUDA_HD void reverseCopyTo(T *from, T *to, Nd4jLong *indexes, Nd4jLong length) {
if (length < 1)
return;
for (Nd4jLong i = 0; i <= length / 2; i++) {
T temp = from[indexes[i]];
to[i] = from[indexes[length - i - 1]];
to[length - i - 1] = temp;
}
}
/**
*
* @param arr1
* @param arr1Length
* @param arr2
* @param arr2Length
* @return
*/
template <typename T>
INLINEDEF _CUDA_HD T* concat(T* arr1, Nd4jLong arr1Length, T* arr2, Nd4jLong arr2Length) {
traceNew(25);
T *ret = new T[arr1Length + arr2Length];
std::memcpy(ret, arr1, arr1Length * sizeof(T));
std::memcpy(ret + arr1Length, arr2, arr2Length * sizeof(T));
return ret;
}
/**
*
* @param numArrays
* @param numTotalElements
* @param arr
* @param lengths
* @return
*/
template <typename T>
INLINEDEF _CUDA_HD T *concat(Nd4jLong numArrays, Nd4jLong numTotalElements, T **arr, Nd4jLong *lengths) {
T* ret = new T[numTotalElements];
Nd4jLong count = 0;
for (Nd4jLong i = 0; i < numArrays; i++) {
for (Nd4jLong j = 0; j < lengths[i]; j++) {
ret[count++] = arr[i][j];
}
}
return ret;
}
/**
* Get the length per slice of the
* given shape and the dimension
* @param rank the rank of the shape
* @param shape the shape of to get
* the length per slice for
* @param dimension the dimension to
* get the length per slice for
* @param dimensionLength the length of the dimension array
* @return the length per slice of the given shape
* along the given dimension
*/
INLINEDEF _CUDA_HD Nd4jLong lengthPerSlice(int rank, Nd4jLong *shape, int* dimension, int dimensionLength) {
if(shape::isVector(shape,rank)) {
//return total length for row vectors
if(dimensionLength == 1 && shape[0] == 1) {
return shape::prodLong(shape,rank);
}
}
else if(rank == dimensionLength)
return shape::prodLong(shape,rank);
int absSelta = nd4j::math::nd4j_abs<int>(rank - dimensionLength);
traceNew(27);
auto ret2 = shape::removeIndex<Nd4jLong>(shape, dimension, rank, dimensionLength);
auto ret = prodLong(ret2, absSelta);
delete[] ret2;
return ret;
}
/**
* calculates the offset for a tensor
* @param index
* @param arr
* @param tensorShape
* @return
*/
INLINEDEF _CUDA_HD Nd4jLong sliceOffsetForTensor(int rank, int index, Nd4jLong *shape, Nd4jLong *tensorShape, int tensorShapeLength, int* dimension, int dimensionLength) {
auto tensorLength = prodLong(tensorShape, tensorShapeLength);
auto lengthPerSlice2 = lengthPerSlice(rank, shape, dimension, dimensionLength);
if (lengthPerSlice2 <= 0) {
return 0;
}
Nd4jLong offset = index * tensorLength / lengthPerSlice2;
return offset;
}
/**
* calculates the offset for a tensor
* @param index
* @param arr
* @param tensorShape
* @return
*/
INLINEDEF _CUDA_HD Nd4jLong sliceOffsetForTensor(int index,int tensorLength,int lengthPerSlice2) {
Nd4jLong offset = index * tensorLength / lengthPerSlice2;
return offset;
}
#ifdef __CUDACC__
/**
* Computes the offset for accessing
* a global element given the shape information
* and the offset to be read.
*/
INLINEDEF _CUDA_D int tadOffset(Nd4jLong *xInfo, int offset) {
return offset + threadIdx.x * elementWiseStride(xInfo);
}
#endif
/**
* Computes the number
* of tensors along
* a given dimension
*/
INLINEDEF _CUDA_HD Nd4jLong tensorsAlongDimension(volatile int rank, volatile int length,
volatile Nd4jLong *shape, int *dimension, int dimensionLength) {
Nd4jLong *tensorShape = shape::keep(shape, dimension, dimensionLength, rank);
Nd4jLong ret = length / shape::prodLong(tensorShape, dimensionLength);
delete[] tensorShape;
return ret;
}
/**
* Computes the number
* of tensors along
* a given dimension
*/
INLINEDEF _CUDA_HD Nd4jLong tensorsAlongDimension(Nd4jLong *shapeInfo, int *dimension, int dimensionLength) {
Nd4jLong *keepShape = shape::shapeOf(shapeInfo);
Nd4jLong *tensorShape = shape::keep(keepShape, dimension, dimensionLength, rank(shapeInfo));
Nd4jLong ret = shape::length(shapeInfo) / shape::prodLong(tensorShape, dimensionLength);
delete[] tensorShape;
return ret;
}
/**
* Get an offset for retrieval
* from a data buffer
* based on the given
* shape stride and given indices
* @param baseOffset the offset to start from
* @param shape the shape of the array
* @param stride the stride of the array
* @param indices the indices to iterate over
* @return the double at the specified index
*/
INLINEDEF _CUDA_HD Nd4jLong getOffset(Nd4jLong baseOffset, const Nd4jLong *shape, const Nd4jLong *stride, const Nd4jLong *indices, const int rank) {
Nd4jLong offset = baseOffset;
for(int i = 0; i < rank; i++) {
if(shape[i] != 1)
offset += indices[i] * stride[i];
}
return offset;
}
INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const Nd4jLong *indices, Nd4jLong baseOffset) {
return shape::getOffset(baseOffset, shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo)), shape::stride(const_cast<Nd4jLong*>(shapeInfo)), indices, shapeInfo[0]);
}
INLINEDEF Nd4jLong getOffset(const Nd4jLong *shapeInfo, const std::vector<uint>& indices) {
Nd4jLong offset = 0;
for(uint i = 0; i < shapeInfo[0]; ++i)
if(shapeInfo[i + 1] != 1)
offset += indices[i] * shapeInfo[shapeInfo[0] + i + 1];
return offset;
}
/**
* Returns the tensor along dimension
* for the given block index
* @param blockSize
* @param blockIdx
* @param i
* @return
*/
INLINEDEF _CUDA_HD int tadForBlockIndex(int blockSize, int blockIdx, int i) {
return blockIdx + i * blockSize;
}
/**
* Computes the number of tads per block
*
*/
INLINEDEF _CUDA_HD int tadsPerBlock(int blockSize, int tads) {
return nd4j::math::nd4j_ceil<double, int>(tads / (double) blockSize);
}
/**
* Returns a shape buffer
* for the shape information metadata.
*/
INLINEDEF _CUDA_HD Nd4jLong *toShapeBuffer( ShapeInformation *info) {
traceNew(29);
auto ret = new Nd4jLong[shapeInfoLength(info->rank)];
int count = 1;
int rank = info->rank;
ret[0] = info->rank;
for (int i = 0; i < rank; i++) {
ret[count++] = info->shape[i];
}
for (int i = 0; i < rank; i++) {
ret[count++] = info->stride[i];
}
ret[count++] = info->offset;
ret[count++] = info->elementWiseStride;
ret[count] = info->order;
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong *toShapeBuffer( ShapeInformation *info, Nd4jLong* ret) {
int count = 1;
int rank = info->rank;
ret[0] = info->rank;
if (ret[0] == 0) {
ret[1] = 0;
ret[2] = 1;
ret[3] = 99;
return ret;
}
for (int i = 0; i < rank; i++) {
ret[count++] = info->shape[i];
}
for (int i = 0; i < rank; i++) {
ret[count++] = info->stride[i];
}
ret[count++] = info->offset;
ret[count++] = info->elementWiseStride;
ret[count++] = info->order;
return ret;
}
INLINEDEF _CUDA_HD void printIntArray(const Nd4jLong *arr, const int length) {
for(int i = 0; i < length; i++) {
printf(" %lld ", (long long) arr[i]);
}
printf("\n");
}
INLINEDEF _CUDA_HD void printIntArray(const int *arr, const int length) {
for(int i = 0; i < length; i++) {
printf(" %i ", arr[i]);
}
printf("\n");
}
INLINEDEF _CUDA_HD void printShapeInfo(Nd4jLong *shapeInfo) {
int rank = shape::rank(shapeInfo);
Nd4jLong *shape = shape::shapeOf(shapeInfo);
printf("Rank %d\n",rank);
printf("Shape:\n");
for(int i = 0; i < rank; i++) {
printf(" %lld ",(long long) shape[i]);
}
printf("\n");
Nd4jLong *stride = shape::stride(shapeInfo);
printf("Stride:\n");
for(int i = 0; i < rank; i++) {
printf(" %lld ", (long long) stride[i]);
}
printf("\n");
printf("Order %c\n",shape::order(shapeInfo));
}
INLINEDEF _CUDA_HD void printShapeInfoLinear(const Nd4jLong *shapeInfo) {
int rank = shape::rank(shapeInfo);
int lim = shape::shapeInfoLength(rank);
printf("ShapeInfo: [");
for (int i = 0; i < lim; i++) {
printf("%lld", (long long) shapeInfo[i]);
if (i < lim - 1) {
printf(", ");
}
}
printf("]\n");
#ifndef __CUDA_ARCH__
fflush(stdout);
#endif
}
INLINEDEF _CUDA_HD void printShapeInfoLinear(const char *msg, int rank, const Nd4jLong *shape, const Nd4jLong *strides) {
printf("%s : [", msg);
for (int i = 0; i < rank; i++) {
printf("%lld, ", (long long) shape[i]);
}
for (int i = 0; i < rank; i++) {
printf("%lld", (long long) strides[i]);
if (i < rank - 1)
printf(", ");
}
printf("]\n");
#ifndef __CUDA_ARCH__
fflush(stdout);
#endif
}
INLINEDEF _CUDA_HD void printShapeInfoLinear(const char *msg, const Nd4jLong *shapeInfo) {
int rank = shape::rank(shapeInfo);
int lim = shape::shapeInfoLength(rank);
printf("%s : [", msg);
for (int i = 0; i < lim; i++) {
printf("%lld", (long long) shapeInfo[i]);
if (i < lim - 1) {
printf(", ");
}
}
printf("]\n");
#ifndef __CUDACC__
fflush(stdout);
#endif
}
template <typename T>
INLINEDEF _CUDA_HD void printArray(void *varr,int length, const char * message) {
auto arr = reinterpret_cast<T*>(varr);
if (message != nullptr)
printf("%s: [", message);
else
printf("Array: [");
for (int i = 0; i < length; i ++) {
printf("%f", (float) arr[i]);
if (i + 1 < length) printf(", ");
}
printf("]\n");
#ifndef __CUDACC__
fflush(stdout);
#endif
}
INLINEDEF _CUDA_HD void printArray(float *arr,int length) {
printf("Array: [");
for (int i = 0; i < length; i ++) {
printf("%f", arr[i]);
if (i + 1 < length) printf(", ");
}
printf("]\n");
}
/**
* Given an linear index, element wise stride
* and the length of each tad
* map a linear index to a tad
* @param i the index to map
* @param the element wise stride for the tads
* @param numElementsPerTad the number of elements
* per tad
*/
INLINEDEF _CUDA_HD int tadIndex(int i, int elementWiseStride, int numElementsPerTad) {
return i / (numElementsPerTad * elementWiseStride);
}
/**
* Map a tad to a
* reduction index.
* @param tadIndexForOriginal the original tad index for the
* split up problem (eg: split is dimension 3 mapping to a 2,3 problem)
* @param tadsForReduced the number of tads for the shrunk down problem (eg: 2,3)
* @param tadsForOriginal the number of tads for the smaller problem (eg: 3)
*/
INLINEDEF _CUDA_HD int reductionIndexForTad(int tadIndexForOriginal, int tadsForReduced,
int tadsForOriginal) {
if (tadIndexForOriginal == 0)
return 0;
return tadIndexForOriginal / (tadsForOriginal / tadsForReduced);
}
INLINEDEF _CUDA_HD void transposeInplace(Nd4jLong *shapeBuffer) {
int rank = shape::rank(shapeBuffer);
Nd4jLong *shape = shape::shapeOf(shapeBuffer);
Nd4jLong *strides = shape::stride(shapeBuffer);
// swap shape
for (int e = 0; e < rank / 2; e++) {
int idx1 = rank - e - 1;
int idx2 = e;
int tmp = shape[idx2];
shape[idx2] = shape[idx1];
shape[idx1] = tmp;
}
// swap strides
for (int e = 0; e < rank / 2; e++) {
int idx1 = rank - e - 1;
int idx2 = e;
int tmp = strides[idx2];
strides[idx2] = strides[idx1];
strides[idx1] = tmp;
}
if (shape::order(shapeBuffer) == 'c')
shapeBuffer[shape::shapeInfoLength(shapeBuffer) - 1] = 102;
else
shapeBuffer[shape::shapeInfoLength(shapeBuffer) - 1] = 99;
}
/**
* Tad index for linear
* @param linearIndex
* @param tadLength
* @return
*/
INLINEDEF _CUDA_HD int tadIndexForLinear(int linearIndex, int tadLength) {
return linearIndex % tadLength;
}
/**
* Computes the number of tads
* per reduce index for the
* reduction tad.
*/
INLINEDEF _CUDA_HD int tadsPerReduceIndex(int tadsForReduce, int tadsForOriginal) {
return tadsForOriginal / tadsForReduce;
}
/**
* Maps a linear index to a reduction index
* @param i the linear index to map
* @param elementWiseStride the element wise stride
* for the multiple problem
* @param tadNum the number of tads for the shrunken problem
* @param originalTadNum the tad number for the reduced version of the problem
*/
INLINEDEF _CUDA_HD int reductionIndexForLinear(int i, int elementWiseStride, int numElementsPerTad,
int tadNum, int originalTadNum) {
int tad = tadIndex(i, elementWiseStride, numElementsPerTad);
return reductionIndexForTad(tad, tadNum, originalTadNum);
}
INLINEDEF _CUDA_HD Nd4jLong* createScalarShapeInfo() {
traceNew(30);
auto shape = new Nd4jLong[1];
shape[0] = 1;
auto stride = new Nd4jLong[1];
stride[0] = 1;
auto shapeInformation2 = new ShapeInformation();
shapeInformation2->rank = 1;
shapeInformation2->offset = 0;
shapeInformation2->stride = stride;
shapeInformation2->shape = shape;
shapeInformation2->elementWiseStride = 1;
shapeInformation2->order = 99;
Nd4jLong *ret = shape::toShapeBuffer(shapeInformation2);
delete shapeInformation2;
delete[] shape;
delete[] stride;
return ret;
}
INLINEDEF _CUDA_HD Nd4jLong* createScalarShapeInfo(Nd4jLong *ret) {
ret[0] = 2;
ret[1] = 1;
ret[2] = 1;
ret[3] = 1;
ret[4] = 1;
ret[5] = 0;
ret[6] = 1;
ret[7] = 99;
return ret;
}
/**
* Returns the prod of the data
* up to the given length
*/
INLINEDEF _CUDA_HD Nd4jLong prodLong(const Nd4jLong *data, int length) {
Nd4jLong prod = 1;
for (int i = 0; i < length; i++) {
prod *= data[i];
}
return prod;
}
INLINEDEF _CUDA_HD int rearMostLeftOverItem(Nd4jLong *data, Nd4jLong *dimension,int dimensionLength) {
Nd4jLong *stride = shape::stride(data);
//corner case: return the final item when its greater than the max, since its guaranteed to be left over
//note here that strides are interpreted in reverse for tad
//start from the front rather than the back
int rank = shape::rank(data);
if(shape::order(data) == 'f') {
int dimIdx = dimensionLength - 1;
for(int i = rank - 1; i >= 0; i--) {
/**
* Needs to find an algorithm such that:
* looping backwards will find the highest dimension left
* that isn't included in the dimension index list.
*
* This can also be thought of as the last item of the first index
* of the difference between the full list of indices and
* the dimension indices.
*
* We should avoid excessive object creation by only looping backwards.
*/
if(dimension[dimIdx--] != i) {
int ret = stride[i];
return ret;
}
}
}
else {
int dimIdx = dimensionLength - 1;
for(int i = rank - 1; i >= 0; i--) {
/**
* Needs to find an algorithm such that:
* looping backwards will find the highest dimension left
* that isn't included in the dimension index list.
*
* This can also be thought of as the last item of the first index
* of the difference between the full list of indices and
* the dimension indices.
*
* We should avoid excessive object creation by only looping backwards.
*/
if(dimension[dimIdx--] != i) {
int ret = stride[i];
return ret;
}
}
}
int ret = stride[0];
return ret;
}
#ifdef __CUDACC__
__device__ INLINEDEF void sweepShapeInfoBuffer(Nd4jLong *shapeInfoBuffer, Nd4jLong *targetBuffer) {
// we read first element, to find out length of our shapeInfoBuffer
int rank = shapeInfoBuffer[0];
int len = shape::shapeInfoLength(rank);
for (int i = threadIdx.x; i < len; i += blockDim.x)
targetBuffer[i] = shapeInfoBuffer[i];
}
#endif
INLINEDEF _CUDA_HD Nd4jLong *shapeBufferOfNpy(cnpy::NpyArray arr) {
return shape::shapeBufferOfNpy(arr.shape.size(),(unsigned int*) arr.shape.data(),arr.fortranOrder);
}
// INLINEDEF _CUDA_HD Nd4jLong *shapeBufferOfNpyBuffer(char *buffer) {
// unsigned Nd4jLong *shape;
// unsigned int ndims, wordSize;
// bool fortranOrder;
// cnpy::parseNpyHeaderStr(std::string(buffer),wordSize,shape,ndims,fortranOrder);
// Nd4jLong * ret = shape::shapeBufferOfNpy(ndims,shape,fortranOrder);
// delete[] shape;
// return ret;
// }
INLINEDEF _CUDA_HD Nd4jLong *shapeBufferOfNpy(int rank, unsigned int* shape,bool fortranOrder) {
if(fortranOrder) {
Nd4jLong *shapeBufferRet = shape::shapeBufferFortran(rank, nd4j::FLOAT32,(Nd4jLong *) shape);
return shapeBufferRet;
}
else {
Nd4jLong *newShape = new Nd4jLong[rank];
for(int i = 0; i < rank; i++) {
newShape[i] = shape[i];
}
Nd4jLong *shapeBufferRet = shape::shapeBuffer(rank, nd4j::FLOAT32, newShape);
delete[] newShape;
return shapeBufferRet;
}
}
INLINEDEF _CUDA_HD bool strideDescendingCAscendingF(const Nd4jLong *shapeBuffer) {
int rank = shape::rank(shapeBuffer);
Nd4jLong *strides = shape::stride(const_cast<Nd4jLong*>(shapeBuffer));
char order = shape::order(shapeBuffer);
if (shape::isRowVector(shapeBuffer) && strides[0] == 1 && strides[1] == 1)
return true;
if (order == 'c') {
for (int i = 1; i < rank; i++)
if (strides[i-1] <= strides[i])
return false;
return true;
} else if (order == 'f') {
for (int i = 1; i < rank; i++)
if (strides[i-1] >= strides[i])
return false;
return true;
} else {
printf("Unknown order for array!\n");
return false;
}
}
INLINEDEF _CUDA_HD bool isContiguous(const Nd4jLong* shapeInfo) {
return (order(shapeInfo) == 'c') && (elementWiseStride(shapeInfo) > 0);
}
//////////////////////////////////////////////////////////////////////////
// copy-past from java hasDefaultStridesForShape function
INLINEDEF _CUDA_HD bool areStridesDefault(const Nd4jLong* shapeInfo) {
const int rank = shape::rank(shapeInfo);
if(rank == 0)
return true;
if(!strideDescendingCAscendingF(shapeInfo))
return false;
Nd4jLong defaultShapeInfo[MAX_SHAPEINFOLENGTH];
memcpy(defaultShapeInfo, shapeInfo, shape::shapeInfoByteLength(shapeInfo));
shape::updateStrides(defaultShapeInfo, shape::order(shapeInfo));
bool result = true;
for(int i = rank+1; i <= 2*rank; ++i)
if(defaultShapeInfo[i] != shapeInfo[i]) {
result = false;
break;
}
return result;
}
// INLINEDEF _CUDA_H bool reshapeC(const int oldRank, Nd4jLong* oldShape, const int newRank, Nd4jLong* newShapeOf, bool isFOrder, Nd4jLong* target) {
// int oldnd;
// Nd4jLong* olddims = shape::copyOf(oldRank, shape::shapeOf(oldShape));
// Nd4jLong* oldstrides = shape::copyOf(oldRank, shape::stride(oldShape));
// int np, op, last_stride;
// int oi, oj, ok, ni, nj, nk;
// Nd4jLong* newStrides = new Nd4jLong[newRank];
// oldnd = 0;
// /*
// * Remove axes with dimension 1 from the old array. They have no effect
// * but would need special cases since their strides do not matter.
// */
// for (oi = 0; oi < oldRank; oi++) {
// if (shape::shapeOf(oldShape)[oi] != 1) {
// olddims[oldnd] = shape::shapeOf(oldShape)[oi];
// oldstrides[oldnd] = shape::stride(oldShape)[oi];
// oldnd++;
// }
// }
// np = 1;
// for (ni = 0; ni < newRank; ni++) {
// np *= newShapeOf[ni];
// }
// op = 1;
// for (oi = 0; oi < oldnd; oi++) {
// op *= olddims[oi];
// }
// if (np != op) {
// /* different total sizes; no hope */
// delete[] olddims;
// delete[] oldstrides;
// delete[] newStrides;
// return false;
// }
// if (np == 0) {
// /* the current code does not handle 0-sized arrays, so give up */
// delete[] olddims;
// delete[] oldstrides;
// delete[] newStrides;
// return false;
// }
// /* oi to oj and ni to nj give the axis ranges currently worked with */
// oi = 0;
// oj = 1;
// ni = 0;
// nj = 1;
// while (ni < newRank && oi < oldnd) {
// np = newShapeOf[ni];
// op = olddims[oi];
// while (np != op) {
// if (np < op) {
// /* Misses trailing 1s, these are handled later */
// np *= newShapeOf[nj++];
// } else {
// op *= olddims[oj++];
// }
// }
// /* Check whether the original axes can be combined */
// for (ok = oi; ok < oj - 1; ok++) {
// if (isFOrder) {
// if (oldstrides[ok + 1] != olddims[ok] * oldstrides[ok]) {
// /* not contiguous enough */
// delete[] olddims;
// delete[] oldstrides;
// delete[] newStrides;
// return false;
// }
// } else {
// /* C order */
// if (oldstrides[ok] != olddims[ok + 1] * oldstrides[ok + 1]) {
// /* not contiguous enough */
// delete[] olddims;
// delete[] oldstrides;
// delete[] newStrides;
// return false;
// }
// }
// }
// /* Calculate new strides for all axes currently worked with */
// if (isFOrder) {
// newStrides[ni] = oldstrides[oi];
// for (nk = ni + 1; nk < nj; nk++) {
// newStrides[nk] = newStrides[nk - 1] * newShapeOf[nk - 1];
// }
// } else {
// /* C order */
// newStrides[nj - 1] = oldstrides[oj - 1];
// for (nk = nj - 1; nk > ni; nk--) {
// newStrides[nk - 1] = newStrides[nk] * newShapeOf[nk];
// }
// }
// ni = nj++;
// oi = oj++;
// }
// if (ni >= 1) {
// last_stride = newStrides[ni - 1];
// } else {
// last_stride = shape::elementWiseStride(oldShape);
// }
// if (isFOrder && ni >= 1) {
// last_stride *= newShapeOf[ni - 1];
// }
// for (nk = ni; nk < newRank; nk++) {
// newStrides[nk] = last_stride;
// }
// target[0] = newRank;
// int cnt = 1;
// for (int e = 0; e < newRank; e++)
// target[cnt++] = newShapeOf[e];
// for (int e = 0; e < newRank; e++)
// target[cnt++] = newStrides[e];
// target[shape::shapeInfoLength(newRank) - 3] = 0;
// target[shape::shapeInfoLength(newRank) - 2] = 0;
// target[shape::shapeInfoLength(newRank) - 1] = isFOrder ? 102 : 99;
// nd4j::ArrayOptions::setDataType(target, nd4j::ArrayOptions::dataType(oldShape));
// delete[] olddims;
// delete[] oldstrides;
// delete[] newStrides;
// return true;
// }
// INLINEDEF _CUDA_H bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo, const int newRank, const Nd4jLong* newShape, const bool isFOrder, Nd4jLong* newShapeInfo) {
// // PLEASE NOTE !: reshaping not-permuted (ews=1) array in f order (except insertion/elimination of unities) will definitely cause allocation of new buffer for array elements
// // also this function takes into account identical shapes automatically, namely in that case oldShapeInfo is completely copied to newShapeInfo
// const int newOrder = isFOrder ? 102 : 99;
// const int oldOrder = oldShapeInfo[2 * oldRank + 3];
// newShapeInfo[0] = newRank;
// memcpy(newShapeInfo + 1, newShape, newRank * sizeof(Nd4jLong));
// Nd4jLong* newStrides = shape::stride(newShapeInfo);
// const Nd4jLong* oldShape = shape::shapeOf(const_cast<Nd4jLong*>(oldShapeInfo));
// const Nd4jLong* oldStrides = shape::stride(const_cast<Nd4jLong*>(oldShapeInfo));
// int oldStart(0), oldStop(1), newStart(0), newStop(1), newDim, oldDim;
// while (newStart < newRank && oldStart < oldRank) {
// newDim = newShape[newStart];
// oldDim = oldShape[oldStart];
// while (newDim != oldDim)
// if (newDim < oldDim) newDim *= newShape[newStop++];
// else oldDim *= oldShape[oldStop++];
// // ------ Check whether the original axes can be combined ------ //
// for (int i = oldStart; i < oldStop - 1; i++) {
// if(oldShape[i] == 1) { // ignore strides like {...,1,1,...}
// if(oldOrder == 102) ++oldStart;
// continue;
// }
// if(oldOrder == 102 && oldStrides[i + 1] != oldShape[i] * oldStrides[i])
// return false; // not contiguous enough
// if(oldOrder == 99 && oldStrides[i] != oldShape[i + 1] * oldStrides[i + 1])
// return false; // not contiguous enough
// }
// // ------ Calculate new strides for all axes currently worked with ------ //
// if(isFOrder) {
// newStrides[newStart] = oldStrides[oldStart];
// for (int i = newStart + 1; i < newStop; ++i)
// newStrides[i] = newStrides[i - 1] * newShape[i - 1];
// }
// else {
// newStrides[newStop - 1] = oldStrides[oldStop - 1];
// for (int i = newStop - 1; i > newStart; --i)
// newStrides[i - 1] = newStrides[i] * newShape[i];
// }
// newStart = newStop++;
// oldStart = oldStop++;
// }
// newShapeInfo[2 * newRank + 3] = shape::order(oldShapeInfo); // order
// newShapeInfo[2 * newRank + 2] = shape::elementWiseStride(oldShapeInfo); // ews
// newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type
// return true;
// }
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_H bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo, const int newRank, const Nd4jLong* newShape, Nd4jLong* newShapeInfo) {
// PLEASE NOTE !: reshaping not-permuted (ews=1) array in f order (except insertion/elimination of unities) will definitely cause allocation of new buffer for array elements
// also this function takes into account identical shapes automatically, namely in that case oldShapeInfo is completely copied to newShapeInfo
newShapeInfo[0] = newRank;
memcpy(newShapeInfo + 1, newShape, newRank * sizeof(Nd4jLong));
Nd4jLong* newStrides = shape::stride(newShapeInfo);
const Nd4jLong* oldShape = shape::shapeOf(const_cast<Nd4jLong*>(oldShapeInfo));
const Nd4jLong* oldStrides = shape::stride(const_cast<Nd4jLong*>(oldShapeInfo));
Nd4jLong oldStart(0), oldStop(1), newStart(0), newStop(1), newDim, oldDim;
while (newStart < newRank && oldStart < oldRank) {
newDim = newShape[newStart];
oldDim = oldShape[oldStart];
while (newDim != oldDim && newDim > 0 && oldDim > 0)
if (newDim < oldDim) newDim *= newShape[newStop++];
else oldDim *= oldShape[oldStop++];
// ------ Check whether the original axes can be combined ------ //
for (int step = 1, i = oldStart; i < oldStop - 1; ++i) {
if(oldShape[i] == 1) // skip unity-dimension and its stride
continue;
while((i + step) < oldRank && oldShape[i + step] == 1)
++step; // skip following unity-dimensions and its strides if such are present
if((i + step) < oldRank && oldStrides[i] != oldShape[i + step] * oldStrides[i + step])
return false; // not contiguous enough
}
newStrides[newStop - 1] = oldStrides[oldStop - 1];
for (int i = newStop - 1; i > newStart; --i)
newStrides[i - 1] = newStrides[i] * newShape[i];
newStart = newStop++;
oldStart = oldStop++;
}
newShapeInfo[2 * newRank + 3] = shape::order(oldShapeInfo); // order
newShapeInfo[2 * newRank + 2] = shape::elementWiseStride(oldShapeInfo); // ews
newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type
return true;
}
INLINEDEF _CUDA_H bool canReshape(const int oldRank, Nd4jLong* oldShape, const int newRank, Nd4jLong* newShapeOf, bool isFOrder) {
Nd4jLong oldnd;
Nd4jLong* oldDims = shape::copyOf(oldRank, shape::shapeOf(oldShape));
Nd4jLong* oldStrides = shape::copyOf(oldRank, shape::stride(oldShape));
Nd4jLong np, op, last_stride;
Nd4jLong oldStart, oldStop, ok, newStart, newStop, nk;
auto newStrides = new Nd4jLong[newRank];
oldnd = 0;
/*
* Remove axes with dimension 1 from the old array. They have no effect
* but would need special cases since their strides do not matter.
*/
for (oldStart = 0; oldStart < oldRank; oldStart++) {
if (shape::shapeOf(oldShape)[oldStart] != 1) {
oldDims[oldnd] = shape::shapeOf(oldShape)[oldStart];
oldStrides[oldnd] = shape::stride(oldShape)[oldStart];
oldnd++;
}
}
np = 1;
for (newStart = 0; newStart < newRank; newStart++) {
np *= newShapeOf[newStart];
}
op = 1;
for (oldStart = 0; oldStart < oldnd; oldStart++) {
op *= oldDims[oldStart];
}
if (np != op) {
/* different total sizes; no hope */
delete[] oldDims;
delete[] oldStrides;
delete[] newStrides;
return false;
}
if (np == 0) {
/* the current code does not handle 0-sized arrays, so give up */
delete[] oldDims;
delete[] oldStrides;
delete[] newStrides;
return false;
}
/* oldStart to oldStop and newStart to newStop give the axis ranges currently worked with */
oldStart = 0;
oldStop = 1;
newStart = 0;
newStop = 1;
while (newStart < newRank && oldStart < oldnd) {
np = newShapeOf[newStart];
op = oldDims[oldStart];
while (np != op) {
if (np < op) {
/* Misses trailing 1s, these are handled later */
np *= newShapeOf[newStop++];
} else {
op *= oldDims[oldStop++];
}
}
/* Check whether the original axes can be combined */
for (ok = oldStart; ok < oldStop - 1; ok++) {
if (isFOrder) {
if (oldStrides[ok + 1] != oldDims[ok] * oldStrides[ok]) {
/* not contiguous enough */
delete[] oldDims;
delete[] oldStrides;
delete[] newStrides;
return false;
}
} else {
/* C order */
if (oldStrides[ok] != oldDims[ok + 1] * oldStrides[ok + 1]) {
/* not contiguous enough */
delete[] oldDims;
delete[] oldStrides;
delete[] newStrides;
return false;
}
}
}
/* Calculate new strides for all axes currently worked with */
if (isFOrder) {
newStrides[newStart] = oldStrides[oldStart];
for (nk = newStart + 1; nk < newStop; nk++) {
newStrides[nk] = newStrides[nk - 1] * newShapeOf[nk - 1];
}
} else {
/* C order */
newStrides[newStop - 1] = oldStrides[oldStop - 1];
for (nk = newStop - 1; nk > newStart; nk--) {
newStrides[nk - 1] = newStrides[nk] * newShapeOf[nk];
}
}
newStart = newStop++;
oldStart = oldStop++;
}
delete[] oldDims;
delete[] oldStrides;
delete[] newStrides;
return true;
}
// this function checks the consistence of dimensions with array rank (negative dimensions, too large dimensions, too big number of dimensions)
// also it sorts input array of dimensions, this operation is also necessary for creating TAD object
INLINEDEF _CUDA_H void checkDimensions(const int rank, std::vector<int>& dimensions) {
int dimSize = dimensions.size();
if(dimSize == 0)
throw std::runtime_error("shape::checkDimensions method: array of dimensions is empty!");
// check presence of negative dimensions and if they are present transform them to positive ones -dim -> rank - |dim|
for(auto& dim : dimensions)
if(dim < 0)
dim += rank;
// sort input array of dimensions, this operation is also necessary for creating TAD object in external methods
if (dimSize > 1) {
std::sort(dimensions.begin(), dimensions.end());
// remove duplicates if they are present
dimensions.erase(std::unique(dimensions.begin(), dimensions.end()), dimensions.end());
}
// check whether number of dimensions is to big (>rank)
dimSize = dimensions.size();
if(dimSize > rank)
throw std::runtime_error("shape::checkDimensions method: number of input dimensions is too big ( > rank of array)!");
// check if min dimension is still negative and whether max dimension is bigger then rank-1
if(dimensions[0] < 0 || dimensions.back() > (rank-1))
throw std::runtime_error("shape::checkDimensions method: the negative dimension is still present in input array after transform or the too big dimension is present ( > rank of array) !");
}
// max array is outer for min array, min array is sub-array of max array
// function calculates the coordinates of min array (and saves them into minIdxs) given coordinates of max array (already stored in maxIdxs)
INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, int dimsLen) {
const auto maxRank = shape::rank(maxShapeInfo);
const auto minRank = shape::rank(minShapeInfo);
// if(minRank >= maxRank)
// throw std::runtime_error("shape::maxIndToMinInd method: rank of min array should be smaller then rank of max array!");
if(dimsLen == -1)
dimsLen = maxRank - minRank; // if size is not given (= -1) then it is equal to ranks difference
if(maxRank == minRank) {
if(dimsToExclude == nullptr) { // --> means dimsToExclude == {0,1,2,...,dimsLen-1}
for (int i = 0; i < maxRank; ++i) {
if(i < dimsLen)
minIdxs[i] = maxIdxs[i];
else {
if(maxIdxs[i] > minShapeInfo[i + 1])
minIdxs[i] = maxIdxs[i] % minShapeInfo[i + 1];
else if(maxIdxs[i] == minShapeInfo[i + 1])
minIdxs[i] = 0;
else
minIdxs[i] = maxIdxs[i];
}
}
}
else {
for (int i = 0, dim = 0; i < maxRank; ++i) {
if(dim < dimsLen && dimsToExclude[dim] == i) {
minIdxs[i] = maxIdxs[i];
++dim;
continue;
}
if(maxIdxs[i] > minShapeInfo[i + 1])
minIdxs[i] = maxIdxs[i] % minShapeInfo[i + 1];
else if(maxIdxs[i] == minShapeInfo[i + 1])
minIdxs[i] = 0;
else
minIdxs[i] = maxIdxs[i];
}
}
}
else {
if(dimsToExclude == nullptr) { // --> means dimsToExclude == {0,1,2,...,dimsLen-1}
for (int i = 0; i < minRank; ++i) {
if(maxIdxs[i + dimsLen] > minShapeInfo[i + 1])
minIdxs[i] = maxIdxs[i + dimsLen] % minShapeInfo[i + 1];
else if(maxIdxs[i + dimsLen] == minShapeInfo[i + 1])
minIdxs[i] = 0;
else
minIdxs[i] = maxIdxs[i + dimsLen];
}
}
else {
for (int minI = 0, maxI = 0, dim = 0; maxI < maxRank; ++maxI) {
if(dim < dimsLen && dimsToExclude[dim] == maxI) {
++dim;
continue;
}
if(maxIdxs[maxI] == minShapeInfo[minI + 1])
minIdxs[minI] = 0;
else if(maxIdxs[maxI] > minShapeInfo[minI + 1])
minIdxs[minI] = maxIdxs[maxI] % minShapeInfo[minI + 1];
else
minIdxs[minI] = maxIdxs[maxI];
++minI;
}
}
}
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD Nd4jLong subArrayIndex(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) {
Nd4jLong maxIdxs[MAX_RANK];
shape::index2coords(shape::rank(maxShapeInfo), const_cast<Nd4jLong *>(maxShapeInfo)+1, const_cast<Nd4jLong&>(maxIdx), maxIdxs, shape::order(maxShapeInfo));
Nd4jLong minIdxs[MAX_RANK];
maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen);
return coords2index(shape::rank(minShapeInfo), minShapeInfo + 1, minIdxs);
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD Nd4jLong subArrayOffset(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) {
Nd4jLong maxIdxs[MAX_RANK];
shape::index2coords(shape::rank(maxShapeInfo), const_cast<Nd4jLong *>(maxShapeInfo)+1, const_cast<Nd4jLong&>(maxIdx), maxIdxs, shape::order(maxShapeInfo));
Nd4jLong minIdxs[MAX_RANK];
maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen);
return getOffset(0, minShapeInfo + 1, minShapeInfo + shape::rank(minShapeInfo) + 1, minIdxs, shape::rank(minShapeInfo));
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD int outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, Nd4jLong* memBuff, const int* dimsToExclude) {
const auto rankMin = shape::rank(minShapeInfo);
const auto rankMax = shape::rank(maxShapeInfo);
// if(rankMin >= rankMax)
// throw std::runtime_error("shape::subArrayIndex method: rank of min array should be smaller then rank of max array!");
const auto diff = rankMax - rankMin; // the size of dimsToExclude is equal to diff
Nd4jLong* indices = memBuff;
Nd4jLong* increment = memBuff + rankMax;
int N, minI, maxI;
// calculate min per-dim-indices which corresponds to absolute minIdx index
shape::index2coords(rankMin, minShapeInfo + 1, minIdx, indices, order(minShapeInfo));
// transform storage indices to contain per-dim max indices, purpose - memory saving
// fill increment array as well
if(dimsToExclude == nullptr) { // means dimsToExclude == {0,1,2,...,diff-1}
for(minI = rankMin - 1, maxI = rankMax-1; maxI >= diff; --maxI, --minI) {
increment[maxI] = (maxShapeInfo[maxI+1] == minShapeInfo[minI+1]) ? 0 : minShapeInfo[minI+1];
indices[maxI] = indices[minI];
}
for(maxI = 0; maxI < diff; ++maxI) {
increment[maxI] = 1;
indices[maxI] = 0;
}
}
else {
for(N = diff-1, minI = rankMin - 1, maxI = rankMax - 1; maxI >= 0; --maxI) {
if(N >= 0 && dimsToExclude[N] == maxI) {
increment[maxI] = 1;
indices[maxI] = 0;
--N;
}
else {
increment[maxI] = (maxShapeInfo[maxI+1] == minShapeInfo[minI+1]) ? 0 : minShapeInfo[minI+1];
indices[maxI] = indices[minI--];
}
}
}
maxI = rankMax-1;
N = 0;
int step;
maxOffsets[N++] = shape::getOffset(0, maxShapeInfo + 1, maxShapeInfo + rankMax + 1, indices, rankMax);
// nested loops - producing of absolute indices for max array
while(maxI >= 0) {
if(increment[maxI] != 0) {
indices[maxI] += increment[maxI];
if(indices[maxI] >= maxShapeInfo[maxI+1]) {
indices[maxI] %= increment[maxI]; // restore initial value of indices[maxI]
step = -1;
}
else {
maxOffsets[N++] = shape::getOffset(0, maxShapeInfo + 1, maxShapeInfo + rankMax + 1, indices, rankMax);
step = rankMax - 1 - maxI;
}
}
else if(maxI == rankMax - 1)
step = -1;
maxI += step;
}
return N;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD int outerArrayIndexes(Nd4jLong* maxIdxs, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude) {
const auto rankMin = shape::rank(minShapeInfo);
const auto rankMax = shape::rank(maxShapeInfo);
// if(rankMin >= rankMax)
// throw std::runtime_error("shape::subArrayIndex method: rank of min array should be smaller then rank of max array!");
// if(rankMax > MAX_RANK/2)
// throw std::runtime_error("shape::subArrayIndex method: rank of max array should be <= MAX_RANK/2 !");
const auto diff = rankMax - rankMin; // the size of dimsToExclude is equal to diff
Nd4jLong buffer[MAX_RANK];
Nd4jLong* indices = buffer;
Nd4jLong* increment = buffer + MAX_RANK/2;
int N, minI, maxI;
// calculate min per-dim-indices which corresponds to absolute minIdx index
shape::index2coords(rankMin, minShapeInfo + 1, minIdx, indices, order(minShapeInfo));
// transform storage indices to contain per-dim max indices, purpose - memory saving
// fill increment array as well
if(dimsToExclude == nullptr) { // means dimsToExclude == {0,1,2,...,diff-1}
for(minI = rankMin - 1, maxI = rankMax-1; maxI >= diff; --maxI, --minI) {
increment[maxI] = (maxShapeInfo[maxI+1] == minShapeInfo[minI+1]) ? 0 : minShapeInfo[minI+1];
indices[maxI] = indices[minI];
}
for(maxI = 0; maxI < diff; ++maxI) {
increment[maxI] = 1;
indices[maxI] = 0;
}
}
else {
for(N = diff-1, minI = rankMin - 1, maxI = rankMax - 1; maxI >= 0; --maxI) {
if(N >= 0 && dimsToExclude[N] == maxI) {
increment[maxI] = 1;
indices[maxI] = 0;
--N;
}
else {
increment[maxI] = (maxShapeInfo[maxI+1] == minShapeInfo[minI+1]) ? 0 : minShapeInfo[minI+1];
indices[maxI] = indices[minI--];
}
}
}
maxI = rankMax-1;
N = 0;
int step;
maxIdxs[N++] = coords2index(rankMax, maxShapeInfo + 1, indices);
// nested loops - producing of absolute indices for max array
while(maxI >= 0) {
if(increment[maxI] != 0) {
indices[maxI] += increment[maxI];
if(indices[maxI] >= maxShapeInfo[maxI+1]) {
indices[maxI] %= increment[maxI]; // restore initial value of indices[maxI]
step = -1;
}
else {
maxIdxs[N++] = coords2index(rankMax, maxShapeInfo + 1, indices);
step = rankMax - 1 - maxI;
}
}
else if(maxI == rankMax - 1)
step = -1;
maxI += step;
}
return N;
}
INLINEDEF _CUDA_HD void shapeOldScalar(nd4j::DataType dataType, Nd4jLong* const buffer, const char order) {
buffer[0] = 2;
buffer[1] = 1;
buffer[2] = 1;
buffer[3] = 1;
buffer[4] = 1;
buffer[6] = 1;
buffer[7] = (int)order;
nd4j::ArrayOptions::setDataType(buffer, dataType);
}
template <typename T1, typename T2>
INLINEDEF _CUDA_H void convertT(T1 *from, T2 *to, Nd4jLong length) {
for (Nd4jLong e = 0; e < length; e++)
to[e] = (T2) from[e];
};
//////////////////////////////////////////////////////////////////////
INLINEDEF void calcOffsets(const Nd4jLong* shapeInfo, Nd4jLong* offsets, const char order) {
// firstly consider simple case when ews > 0
const Nd4jLong ews = shape::elementWiseStride(shapeInfo);
if(ews > 0) {
// set offset for first sub-array, it is equal to zero always
offsets[0] = 0;
Nd4jLong e = 0;
if(order != shape::order(shapeInfo))
for(int i = 1; i <= shape::rank(shapeInfo); ++i)
if(shapeInfo[i] != 1)
++e; //check whether input is CommonVector
if(order == shape::order(shapeInfo) || e == 1) { // e==1 means common vector
e = 1;
Nd4jLong len = shape::length(shapeInfo);
while(e < len)
offsets[e++] = offsets[e - 1] + ews;
return;
}
}
shape::calcOffsets(shape::rank(shapeInfo), shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo)), shape::stride(const_cast<Nd4jLong*>(shapeInfo)), offsets, order);
}
//////////////////////////////////////////////////////////////////////
INLINEDEF void calcOffsets(const int rank, const Nd4jLong* shape, const Nd4jLong* strides, Nd4jLong* offsets, const char order) {
// if(false) { // tests showed that this code did calculation notably slower even for big N
// Nd4jLong indexes[MAX_RANK];
// PRAGMA_OMP_PARALLEL_FOR_ARGS(private(indexes))
// for (Nd4jLong i = 0; i < N; ++i) {
// shape::index2coords(rank, shape, i, indexes);
// subArrOffsets[i] = 0;
// for (int j = 0; j < rank; ++j)
// if(shape[j] != 1)
// subArrOffsets[i] += indexes[j] * strides[j];
// }
// return;
// }
// set offset for first sub-array, it is equal to zero always
offsets[0] = 0;
Nd4jLong * idx = new Nd4jLong[rank];
Nd4jLong* offsetPerDim = new Nd4jLong[rank];
memset(idx, 0, sizeof(Nd4jLong) * rank);
PRAGMA_OMP_SIMD
for (int k = 0; k < rank; ++k)
offsetPerDim[k] = (shape[k] - 1) * strides[k];
Nd4jLong init = 0, i = 1;
// nested loops - calculation of sub-array offsets
if(order == 'c') {
Nd4jLong rankMinusOne = rank - 1, j = rankMinusOne;
while(j >= 0) {
if(shape[j] == 1) { --j; continue; } // ignore dimensions equal to unity
if(j == rankMinusOne) { // last dimension
for(int l = 1; l < shape[j]; ++l)
offsets[i++] = offsets[i - 1] + strides[j];
--j;
}
else if(idx[j] < shape[j] - 1) {
init += strides[j];
offsets[i++] = init;
++idx[j];
j = rankMinusOne;
}
else {
init -= offsetPerDim[j];
idx[j--] = 0;
}
}
}
else {
Nd4jLong j = 0;
while(j < rank) {
if(shape[j] == 1) { ++j; continue; } // ignore dimensions equal to unity
if(j == 0) { // last dimension
for(int l = 1; l < shape[j]; ++l)
offsets[i++] = offsets[i - 1] + strides[j];
++j;
}
else if(idx[j] < shape[j] - 1) {
init += strides[j];
offsets[i++] = init;
++idx[j];
j = 0;
}
else {
init -= offsetPerDim[j];
idx[j++] = 0;
}
}
}
delete []idx;
delete []offsetPerDim;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF void _CUDA_HD setEws(Nd4jLong* shapeInfo, Nd4jLong len) {
const int rank = shape::rank(shapeInfo);
const Nd4jLong* shape = shape::shapeOf(shapeInfo);
const Nd4jLong* strides = shape::stride(shapeInfo);
const char order = shape::order(shapeInfo);
Nd4jLong* ews = shape::ews(shapeInfo);
if(len == -1) // calculate array length if it is not given
len = shape::length(shapeInfo);
if(len <= 1) { // empty, scalar or unity-vector case
*ews = 1;
return;
}
int nonUnityDim(0);
if(shape::isCommonVector(shapeInfo, nonUnityDim)) {
*ews = strides[nonUnityDim];
return;
}
// check last(c)/first(f) dimension, it should be equal to 1
if((order == 'c' && shape[rank - 1] != 1 && strides[rank - 1] != 1) || (order == 'f' && shape[0] != 1 && strides[0] != 1)) {
*ews = 0;
return;
}
Nd4jLong correctStride = 1;
if(order == 'c') {
for (int i = rank - 2; i >= 0 ; i--) {
correctStride *= shape[i + 1];
if(shape[i] == 1)
continue;
if(correctStride != strides[i]) {
*ews = 0;
return;
}
}
}
else {
for (int i = 1; i < rank; ++i) {
correctStride *= shape[i - 1];
if(shape[i] == 1)
continue;
if(correctStride != strides[i]) {
*ews = 0;
return;
}
}
}
*ews = 1;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void setOrderAndEws(Nd4jLong* shapeInfo, Nd4jLong len) {
const int rank = shape::rank(shapeInfo);
const Nd4jLong* shape = shape::shapeOf(shapeInfo);
const Nd4jLong* strides = shape::stride(shapeInfo);
const char order = shape::order(shapeInfo);
Nd4jLong* ews = shape::ews(shapeInfo);
if(len == -1) // calculate array length if it is not given
len = shape::length(shapeInfo);
if(len <= 1) { // empty, scalar or unity-vector case
*ews = 1;
return;
}
int nonUnityDim(0);
if(shape::isCommonVector(shapeInfo, nonUnityDim)) { // in this case we don't change order
*ews = strides[nonUnityDim];
return;
}
// check if strides are contiguous in respect to c-order
// firstly check last stride, it should be equal to 1
if (strides[rank - 1] == 1 || shape[rank - 1] == 1) { // last dimension is ok, go on through the rest dimensions in reverse order
Nd4jLong correctStride = 1;
bool cContiguous = true;
for (int i = rank - 2; i >= 0 ; i--) {
correctStride *= shape[i + 1];
if(shape[i] == 1)
continue;
if(correctStride != strides[i]) {
cContiguous = false;
break;
}
}
if(cContiguous) {
*ews = 1;
shapeInfo[shape::shapeInfoLength(rank) - 1] = 99;
return;
}
}
// now check if strides are contiguous in respect to f-order
// firstly check first stride, it should be equal to 1
if(strides[0] == 1 || shape[0] == 1) { // first dimension is ok, go on through the rest dimensions
Nd4jLong correctStride = 1;
bool fContiguous = true;
for (int i = 1; i < rank; ++i) {
correctStride *= shape[i - 1];
if(shape[i] == 1)
continue;
if(correctStride != strides[i]) {
fContiguous = false;
break;
}
}
if(fContiguous) {
*ews = 1;
shapeInfo[shape::shapeInfoLength(rank) - 1] = 102;
return;
}
}
*ews = 0;
// if both cContiguous and fContiguous are false then order is preserved
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo, const Nd4jLong numOfSubArrs, const int dimsSize, const int* dimsToExclude, Nd4jLong* subArrShapeInfo, Nd4jLong* subArrOffsets, bool keepUnitiesInShape) {
const int rank = shape::rank(wholeShapeInfo);
if(dimsSize == rank || dimsSize == 0) { // means there is one sub-array and it coincides with whole array, return copy of wholeShapeInfo and one zero offset in this case
memcpy(subArrShapeInfo, wholeShapeInfo, shape::shapeInfoLength(rank) * sizeof(Nd4jLong));
*subArrOffsets = 0;
return;
}
Nd4jLong *outShapeInfo = new Nd4jLong[shape::shapeInfoLength(wholeShapeInfo)];
memcpy(outShapeInfo, wholeShapeInfo, shape::shapeInfoByteLength(wholeShapeInfo));
Nd4jLong* shape = new Nd4jLong[dimsSize];
Nd4jLong* strides = new Nd4jLong[dimsSize];
const int subArrRank = keepUnitiesInShape ? rank : rank - dimsSize;
Nd4jLong* shapeNoUnities = nullptr;
if(!keepUnitiesInShape)
shapeNoUnities = new Nd4jLong[subArrRank];
Nd4jLong subArrLen = 1;
for(int k = subArrRank - 1, j = dimsSize - 1, i = rank - 1; i >= 0; --i) {
if(j >= 0 && i == dimsToExclude[j]) {
strides[j] = shape::stride(outShapeInfo)[i];
shape[j--] = shape::shapeOf(outShapeInfo)[i];
shape::shapeOf(outShapeInfo)[i] = 1;
}
else {
subArrLen *= shape::shapeOf(outShapeInfo)[i];
if(!keepUnitiesInShape)
shapeNoUnities[k--] = shape::shapeOf(outShapeInfo)[i];
}
}
// evaluate ews
shape::setEws(outShapeInfo, subArrLen);
// calculation of sub-array offsets (subArrOffsets)
shape::calcOffsets(dimsSize, shape, strides, subArrOffsets);
// remove unities from outShapeInfo if required
if(!keepUnitiesInShape) {
shape::reshapeC(rank, outShapeInfo, subArrRank, shapeNoUnities, subArrShapeInfo);
delete []shapeNoUnities;
}
else
memcpy(subArrShapeInfo, outShapeInfo, shape::shapeInfoLength(subArrRank) * sizeof(Nd4jLong));
delete []strides;
delete []shape;
delete []outShapeInfo;
}
//////////////////////////////////////////////////////////////////////
INLINEDEF void _CUDA_HD index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong *coords, const char order) {
Nd4jLong arrLen = shape::prodLong(shape, rank);
shape::index2coords(rank, shape, index, arrLen, coords, order);
}
INLINEDEF void _CUDA_HD index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong arrLen, Nd4jLong *coords, const char order) {
if(order == 'c') {
for(int i = 0; i < rank; i++) {
arrLen /= shape[i];
if(arrLen > 0 && shape[i] > 1) {
coords[i] = index / arrLen;
index %= arrLen;
}
else
coords[i] = 0;
}
}
else {
for(int i = rank - 1; i >= 0; i--) {
arrLen /= shape[i];
if(arrLen > 0 && shape[i] > 1) {
coords[i] = index / arrLen;
index %= arrLen;
}
else
coords[i] = 0;
}
}
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order) {
// we assume all array have same length
const Nd4jLong len = shape::length(xShapeInfo);
const Nd4jLong xEws = shape::elementWiseStride(xShapeInfo);
const Nd4jLong yEws = shape::elementWiseStride(yShapeInfo);
const Nd4jLong zEws = shape::elementWiseStride(zShapeInfo);
const char xOrder = shape::order(xShapeInfo);
const char yOrder = shape::order(yShapeInfo);
const char zOrder = shape::order(zShapeInfo);
const bool shapesSame = shape::shapeEquals(xShapeInfo, yShapeInfo, zShapeInfo);
if (xEws == 1 && yEws == 1 && zEws == 1 && xOrder == yOrder && xOrder == zOrder && (xOrder == 'c' || shapesSame)) {
xOffsets = yOffsets = zOffsets = nullptr;
}
else if(xEws == 1 && yEws == 1 && xOrder == yOrder && (xOrder == 'c' || shape::shapeEquals(xShapeInfo, yShapeInfo))) {
xOffsets = yOffsets = nullptr;
zOffsets = new Nd4jLong[len];
shape::calcOffsets(zShapeInfo, zOffsets, xOrder);
}
else if(xEws == 1 && zEws == 1 && xOrder == zOrder && (xOrder == 'c' || shape::shapeEquals(xShapeInfo, zShapeInfo))) {
xOffsets = zOffsets = nullptr;
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets, xOrder);
}
else if(yEws == 1 && zEws == 1 && yOrder == zOrder && (yOrder == 'c' || shape::shapeEquals(yShapeInfo, zShapeInfo))) {
yOffsets = zOffsets = nullptr;
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets, yOrder);
}
else if(xEws == 1) {
xOffsets = nullptr;
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets, xOrder);
}
PRAGMA_OMP_SECTION
{
zOffsets = new Nd4jLong[len];
shape::calcOffsets(zShapeInfo, zOffsets, xOrder);
}
}
}
else if(yEws == 1) {
yOffsets = nullptr;
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets, yOrder);
}
PRAGMA_OMP_SECTION
{
zOffsets = new Nd4jLong[len];
shape::calcOffsets(zShapeInfo, zOffsets, yOrder);
}
}
}
else if(zEws == 1) {
zOffsets = nullptr;
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets, zOrder);
}
PRAGMA_OMP_SECTION
{
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets, zOrder);
}
}
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo, zShapeInfo)) {
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
yOffsets = zOffsets = xOffsets;
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
}
PRAGMA_OMP_SECTION
{
zOffsets = new Nd4jLong[len];
shape::calcOffsets(zShapeInfo, zOffsets);
}
}
yOffsets = xOffsets;
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
}
PRAGMA_OMP_SECTION
{
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets);
}
}
zOffsets = xOffsets;
}
else {
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
}
PRAGMA_OMP_SECTION
{
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets);
}
PRAGMA_OMP_SECTION
{
zOffsets = new Nd4jLong[len];
shape::calcOffsets(zShapeInfo, zOffsets);
}
}
}
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const char order) {
// we assume all array have same length
const Nd4jLong len = shape::length(xShapeInfo);
const Nd4jLong xEws = shape::elementWiseStride(xShapeInfo);
const Nd4jLong yEws = shape::elementWiseStride(yShapeInfo);
const char xOrder = shape::order(xShapeInfo);
const char yOrder = shape::order(yShapeInfo);
const bool shapesSame = shape::shapeEquals(xShapeInfo, yShapeInfo);
if (xEws == 1 && yEws == 1 && xOrder == yOrder && (xOrder == 'c' || shapesSame)) {
xOffsets = yOffsets = nullptr;
}
else if(xEws == 1) {
xOffsets = nullptr;
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets, xOrder);
}
else if(yEws == 1) {
yOffsets = nullptr;
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets, yOrder);
}
else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
yOffsets = xOffsets;
}
else {
PRAGMA_OMP_PARALLEL_SECTIONS
{
PRAGMA_OMP_SECTION
{
xOffsets = new Nd4jLong[len];
shape::calcOffsets(xShapeInfo, xOffsets);
}
PRAGMA_OMP_SECTION
{
yOffsets = new Nd4jLong[len];
shape::calcOffsets(yShapeInfo, yOffsets);
}
}
}
}
}
#endif /* SHAPE_H_ */