4922 lines
159 KiB
C++
4922 lines
159 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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/*
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* shape.h
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*
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* Created on: Dec 28, 2015
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* Author: agibsonccc
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*/
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#ifndef SHAPE_H_
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#define SHAPE_H_
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#include <cstring>
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#include <cstdio>
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#include "../dll.h"
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#include "../nd4jmalloc.h"
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#include "../templatemath.h"
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#include "../helpers/logger.h"
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#include "../pointercast.h"
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#include "../cnpy/cnpy.h"
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#include <op_boilerplate.h>
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#define MAX_DIMENSION 0x7fffffff
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#define MAX_NUM_THREADS 1024
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#define MAX_RANK 32
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#define MAX_SHAPEINFOLENGTH 2*MAX_RANK+4
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#define MAX_COORD 3
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#define PREALLOC_SIZE 33554432
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#ifdef __CUDACC__
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#include <cuda.h>
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#include <cuda_runtime.h>
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#endif
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#ifdef __CUDACC__
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#define INLINEDEF inline
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#else
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#define INLINEDEF inline
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#endif
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#include "../pairwise_util.h"
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#include <stdint.h>
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#include <array/ArrayOptions.h>
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typedef unsigned int uint;
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namespace shape {
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/**
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* Shape information approximating
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* the information on an ndarray
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*/
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struct ND4J_EXPORT ShapeInformation {
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_CUDA_HD ShapeInformation(Nd4jLong *shape_ = nullptr, Nd4jLong *stride_ = nullptr, char order_ = 0, int rank_ = 0, int offset_ = 0, int elementWiseStride_ = 0)
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: shape(shape_), stride(stride_), order(order_), rank(rank_), offset(offset_), elementWiseStride(elementWiseStride_)
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{}
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Nd4jLong *shape;
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Nd4jLong *stride;
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char order;
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int rank;
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int offset;
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int elementWiseStride;
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};
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/**
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* Indexing information
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* for bounds checking
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*/
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struct ND4J_EXPORT CurrentIndexing {
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int numElementsPerThread;
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int blockStartingIndex;
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int startingThreadIndex;
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int endingThreadIndex;
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};
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ND4J_EXPORT _CUDA_HD bool shapeEquals(const int shape1Rank, const Nd4jLong *shape1, const int shape2Rank, const Nd4jLong *shape2);
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ND4J_EXPORT _CUDA_HD Nd4jLong* detachShape(Nd4jLong *originalShape);
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ND4J_EXPORT _CUDA_HD Nd4jLong* copyShape(Nd4jLong *originalShape);
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ND4J_EXPORT _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2);
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ND4J_EXPORT _CUDA_HD bool shapeEquals(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3);
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ND4J_EXPORT _CUDA_HD bool strideEquals(int shape1Rank,Nd4jLong *shape1,int shape2Rank,Nd4jLong *shape2);
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ND4J_EXPORT _CUDA_HD bool strideEquals(Nd4jLong *shapeInfo1,Nd4jLong *shapeInfo2);
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ND4J_EXPORT _CUDA_HD bool strideEquals(Nd4jLong *stride1,int rank1,Nd4jLong *stride2,int rank2);
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ND4J_EXPORT _CUDA_HD bool equalsSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
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ND4J_EXPORT _CUDA_HD bool equalsTypesAndShapesSoft(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
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ND4J_EXPORT _CUDA_HD bool equalsStrict(const Nd4jLong *shapeA, const Nd4jLong *shapeB);
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// returns true if ranks, shapes and strides are the same
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ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2);
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ND4J_EXPORT _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, const Nd4jLong *shapeInfo2, const Nd4jLong *shapeInfo3);
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ND4J_EXPORT _CUDA_HD int sizeAt(const Nd4jLong *shape, const int dim);
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template <typename T>
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ND4J_EXPORT _CUDA_HD void fill(T* buffer, T value, Nd4jLong length);
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ND4J_EXPORT _CUDA_HD void traceNew(int id);
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ND4J_EXPORT _CUDA_HD int tadIndexForLinear(int linearIndex, int tadLength);
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ND4J_EXPORT _CUDA_HD int tadLength(Nd4jLong *shapeInfo, int *dimension, int dimensionLength);
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ND4J_EXPORT _CUDA_HD bool canReshape(const int oldRank, Nd4jLong* oldShape, const int newRank, Nd4jLong* newShape, bool isFOrder);
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ND4J_EXPORT _CUDA_HD bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo, const int newRank, const Nd4jLong* newShape, Nd4jLong* newShapeInfo);
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/**
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* Get the shape info buffer
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* for the given rank and shape.
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape);
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBuffer(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *buffer);
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/**
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* Get the shape info buffer
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* for the given rank and shape.
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape);
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeBufferFortran(int rank, nd4j::DataType dtype, Nd4jLong *shape, Nd4jLong *output);
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#ifdef __CUDACC__
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__device__ ND4J_EXPORT Nd4jLong *cuMalloc(Nd4jLong *buffer, long size);
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#endif
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/**
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* Computes the standard packed array strides for a given shape.
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*
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* @param shape the shape of a matrix:
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* @param startNum the start number for the strides
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* @return the strides for a matrix of n dimensions
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank);
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ND4J_EXPORT _CUDA_HD Nd4jLong * calcStridesFortran(Nd4jLong *shape, int rank, Nd4jLong* ret);
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/**
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* Computes the standard packed array strides for a given shape.
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*
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* @param shape the shape of a matrix:
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* @param startNum the start number for the strides
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* @return the strides for a matrix of n dimensions
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank);
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, Nd4jLong* ret);
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ND4J_EXPORT _CUDA_HD void updateStrides(Nd4jLong *shape, const char order);
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ND4J_EXPORT _CUDA_HD void updateStrides(const int rank, const Nd4jLong *shapeOnly, Nd4jLong *stridesOnly, const char order);
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// check whether input dimensions are permuted, not permuted dimensions order have to be 0,....,rank-1
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template <typename T>
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ND4J_EXPORT _CUDA_HD bool isDimPermuted(const T* dimensions, const int dimSize);
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/**
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* Computes the standard packed array strides for a given shape.
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*
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* @param shape the shape of a matrix:
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* @param startNum the start number for the strides
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* @return the strides for a matrix of n dimensions
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStridesFortran(Nd4jLong *shape, int rank, int startNum);
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStridesFortran(Nd4jLong *shape, int rank, int startNum, Nd4jLong* ret);
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/**
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* Computes the standard packed array strides for a given shape.
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*
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* @param shape the shape of a matrix:
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* @param startNum the start number for the strides
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* @return the strides for a matrix of n dimensions
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, int startNum);
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ND4J_EXPORT _CUDA_HD Nd4jLong* calcStrides(Nd4jLong *shape, int rank, int startNum, Nd4jLong* ret);
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/**
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* @param toCopy the shape to copy
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* @return a copy of the original struct
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*/
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ND4J_EXPORT _CUDA_HD ShapeInformation *shapeCopy( ShapeInformation *toCopy);
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ND4J_EXPORT _CUDA_HD bool strideDescendingCAscendingF(const Nd4jLong *shapeBuffer);
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ND4J_EXPORT _CUDA_HD bool isContiguous(const Nd4jLong* shapeInfo);
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/**
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* copy-past from java hasDefaultStridesForShape function
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* check whether array is not permuted and has contiguous elements in memory
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*/
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ND4J_EXPORT _CUDA_HD bool areStridesDefault(const Nd4jLong* shapeInfo);
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/**
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* Compute the element wise stride
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* for a given shape/stride configuration
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* @param rank the rank of the shape/stride
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* @param shape the shape
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* @param stride the stride
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* @param isFOrder 0 or 1 for whether the array is f
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* ordered or not
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* @return 0 if there is no element wise stride the
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* element wise stride of reshape(1,length) otherwise
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*/
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ND4J_EXPORT _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder);
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/**
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* Compute the element wise stride
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* for a given shape/stride configuration
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* @param rank the rank of the shape/stride
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* @param shape the shape
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* @param stride the stride
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* @param isFOrder 0 or 1 for whether the array is f
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* ordered or not
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* @return 0 if there is no element wise stride the
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* element wise stride of reshape(1,length) otherwise
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*/
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ND4J_EXPORT _CUDA_HD int computeElementWiseStride(int rank, Nd4jLong *shape, Nd4jLong *stride, int isFOrder, Nd4jLong *dimension, int dimensionLength);
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride);
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeInfoOnlyShapeAndStride(Nd4jLong *shapeInfo, Nd4jLong *dimension, int dimensionLength,bool reverseCopyStride, Nd4jLong *buffer);
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/**
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*
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* @param length
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* @param shape
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* @param rearrange
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* @return
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong *doPermuteSwap(int length, Nd4jLong *shape, int* rearrange);
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/**
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* In place permute swap
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* @param length
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* @param shape
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* @param rearrange
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*/
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ND4J_EXPORT _CUDA_HD void doPermuteSwap(int length, Nd4jLong **shape, int* rearrange);
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ND4J_EXPORT _CUDA_HD Nd4jLong *permuteShapeBuffer(Nd4jLong *shapeBuffer, int* rearrange);
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ND4J_EXPORT _CUDA_HD void permuteShapeBufferInPlace(Nd4jLong *shapeBuffer, int* rearrange, Nd4jLong *out);
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ND4J_EXPORT _CUDA_HD void doPermuteShapeInfo(Nd4jLong *shapeBuffer, const int *rearrange, Nd4jLong len = -1);
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/**
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* Rearrange the permute indexes
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* according to which dimensions are specified.
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*
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* For example, dimension is implicitly:
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* 0,1,2
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*
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* If you want to do a reduce along dimensions 0 and 1,
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* you need to permute the indexes to be:
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* 2,0,1
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*
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* which will give us the ability to ierate along an element
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* wise stride.
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong* createPermuteIndexes(int originalRank, int *dimension,int dimensionLength);
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ND4J_EXPORT _CUDA_HD Nd4jLong* computeResultShape(Nd4jLong *originalShapeBuffer, int *dimension,int dimensionLength);
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/**
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* This method does inplace transpose of given shapeBuffer
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*
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* @param shapeBuffer
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*/
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ND4J_EXPORT _CUDA_HD void transposeInplace(Nd4jLong *shapeBuffer);
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/**
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* Get the ordering for the device
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* @param length
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* @param shape
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* @param stride
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* @param elementStride
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* @return
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*/
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ND4J_EXPORT _CUDA_HD char getOrder(int length, Nd4jLong *shape, Nd4jLong *stride, int elementStride);
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/**
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* Ensure that every value in the re arrange
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* array is unique
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* @param arr
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* @param shape
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* @param arrLength
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* @param shapeLength
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* @return
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*/
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template <typename T>
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ND4J_EXPORT _CUDA_HD int checkArrangeArray(T *arr, int arrLength, int shapeLength);
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/**
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* Permute the shape information
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* @param info the shape information to permute
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* @param rearrange the order to re arrange
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* @param rank the rank of the rearrange array
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*/
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ND4J_EXPORT _CUDA_HD void permute(ShapeInformation **info, int *rearrange, int rank);
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/**
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* Returns whether the
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* given shape is a vector or not
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* @param shape the shape of the array
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* @param rank the rank of cthe shape
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*/
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ND4J_EXPORT _CUDA_HD int isVector(Nd4jLong *shape, int rank);
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/**
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* When 1 dimension is the whole length of the
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* array
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*/
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ND4J_EXPORT _CUDA_HD int oneDimEqualToLength(Nd4jLong *shape, int rank);
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ND4J_EXPORT _CUDA_HD int oneDimEqualToLength(Nd4jLong *shapeInfo);
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ND4J_EXPORT _CUDA_HD int isVector(const Nd4jLong *shapeInfo);
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ND4J_EXPORT _CUDA_HD bool isLikeVector(Nd4jLong *shapeInfo, int& posOfNonUnityDim);
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ND4J_EXPORT _CUDA_HD bool isCommonVector(const Nd4jLong *shapeInfo, int& posOfNonUnityDim);
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ND4J_EXPORT _CUDA_HD bool isRowVector(const Nd4jLong *shapeInfo);
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ND4J_EXPORT _CUDA_HD bool isColumnVector(Nd4jLong *shapeInfo);
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/**
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* Returns whether the
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* given shape is a vector or not
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* @param shape the shape of the array
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* @param rank the rank of the shape
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*/
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ND4J_EXPORT _CUDA_HD int isMatrix(Nd4jLong *shape, int rank);
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INLINEDEF _CUDA_HD int isMatrix(Nd4jLong *shapeInfo);
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/**
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* Returns the shape portion of an information
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* buffer
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong *shapeOf(Nd4jLong *buffer);
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/**
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* Return a copy of a buffer.
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* This buffer allocates memory
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* that must be freed elsewhere.
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*/
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template <typename T>
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ND4J_EXPORT _CUDA_HD T* copyOf(Nd4jLong length, T *toCopy);
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template <typename T>
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ND4J_EXPORT _CUDA_HD T* copyOf(Nd4jLong length, T *toCopy, T *ret);
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/**
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* Return a copy of a buffer.
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* This buffer allocates memory
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* that must be freed elsewhere.
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*/
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template <typename T>
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ND4J_EXPORT _CUDA_HD void copyTo(Nd4jLong length, T *from, T *to);
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/**
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* Return a copy of a buffer.
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* This buffer allocates memory
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* that must be freed elsewhere.
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*/
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ND4J_EXPORT _CUDA_HD void copyTo(int length, Nd4jLong *from, Nd4jLong *to, Nd4jLong *indexes);
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/**
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* Permute the given strides
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* in the given rearrange order
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* @param toPermute the buffer to permute
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* @param shapeRank the length of the buffer to permute
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* @param rearrange the rearrange order (must be 0 based indexes
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* and all must be filled in)
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* @return the rearranged array
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*/
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//ND4J_EXPORT _CUDA_HD Nd4jLong *permutedStrides(Nd4jLong *toPermute, int shapeRank, Nd4jLong *rearrange);
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/**
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* Return the slice (shape + 1 in pointer arithmetic)
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* @param shape the shape to take the slice of
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* @return the shape array - the first entry
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong *slice(Nd4jLong *shape);
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ND4J_EXPORT _CUDA_HD int slices(Nd4jLong *shapeBuffer);
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ND4J_EXPORT _CUDA_HD Nd4jLong *sliceOfShapeBuffer(Nd4jLong sliceIdx, Nd4jLong *shapeBuffer);
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/**
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* Returns the length of the
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* shape information buffer:
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* rank * 2 + 3
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* @param rank the rank to get the shape
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* info length for
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* @return rank * 2 + 4
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*/
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ND4J_EXPORT _CUDA_HD int shapeInfoLength(int rank);
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ND4J_EXPORT _CUDA_HD int shapeInfoLength(Nd4jLong* shapeInfo);
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ND4J_EXPORT _CUDA_HD int shapeInfoLength(const Nd4jLong* shapeInfo);
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ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(int rank);
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ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(const Nd4jLong* shapeInfo);
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ND4J_EXPORT _CUDA_HD size_t shapeInfoByteLength(const Nd4jLong* shapeInfo);
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/**
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* Returns the rank portion of
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* an information buffer
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*/
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ND4J_EXPORT _CUDA_HD int rank(const Nd4jLong *shapeInfo);
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ND4J_EXPORT _CUDA_HD int rank(const int *shapeInfo);
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ND4J_EXPORT _CUDA_HD int rank(const unsigned int *shapeInfo);
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// returns pointer on elementWiseStride
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ND4J_EXPORT _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo);
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/**
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* returns pointer on elementWiseStride
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*/
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ND4J_EXPORT _CUDA_HD Nd4jLong* ews(Nd4jLong* shapeInfo);
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/**
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* Converts a raw int buffer of the layout:
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* rank
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* shape
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* stride
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* offset
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* elementWiseStride
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*
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* where shape and stride are both straight int pointers
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*/
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ND4J_EXPORT _CUDA_HD ShapeInformation *infoFromBuffer(Nd4jLong *buffer);
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|
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/**
|
|
* 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 int prod(Nd4jLong *data, int 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,int rank);
|
|
|
|
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 output offsets 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 outerArrayOffsets(Nd4jLong* maxOffsets, const Nd4jLong minIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, 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 int tadLength(Nd4jLong *shapeInfo, int *dimension, int dimensionLength) {
|
|
if(dimensionLength == 1) {
|
|
return shape::shapeOf(shapeInfo)[dimension[0]];
|
|
}
|
|
else {
|
|
int 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];
|
|
int 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;
|
|
|
|
int 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;
|
|
|
|
// }
|
|
|
|
int 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;
|
|
|
|
// }
|
|
|
|
int 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);
|
|
int np, op, last_stride;
|
|
int 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) {
|
|
int sd = -1;
|
|
int dim = -1;
|
|
int i = -1;
|
|
int cContiguous = 1;
|
|
int isFortran = 1;
|
|
|
|
sd = 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::prod(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::prod(shape,rank);
|
|
}
|
|
}
|
|
else if(rank == dimensionLength)
|
|
return shape::prod(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, int rank) {
|
|
Nd4jLong offset = baseOffset;
|
|
for(int i = 0; i < rank; i++) {
|
|
if(shape[i] != 1)
|
|
offset += indices[i] * stride[i];
|
|
}
|
|
|
|
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 int prod(Nd4jLong *data, int length) {
|
|
int prod = 1;
|
|
for (int i = 0; i < length; i++) {
|
|
prod *= data[i];
|
|
}
|
|
|
|
return prod;
|
|
}
|
|
|
|
/**
|
|
* 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));
|
|
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 && 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) {
|
|
int oldnd;
|
|
Nd4jLong* oldDims = shape::copyOf(oldRank, shape::shapeOf(oldShape));
|
|
Nd4jLong* oldStrides = shape::copyOf(oldRank, shape::stride(oldShape));
|
|
int np, op, last_stride;
|
|
int 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, 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;
|
|
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_ */ |