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cavis/libnd4j/include/pairwise_util.h

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by agibsonccc on 2/15/16.
//
#ifndef NATIVEOPERATIONS_PAIRWISE_UTIL_H
#define NATIVEOPERATIONS_PAIRWISE_UTIL_H
#ifdef __CUDACC__
#include <cuda.h>
#include <cuda_runtime.h>
#endif
#ifndef _OPENMP
#define omp_get_thread_num() 0
#define omp_get_num_threads() 1
#define omp_get_max_threads() 1
#define omp_set_num_threads(threads)
#endif
#include <templatemath.h>
#include <functional>
#include <pointercast.h>
#include <op_boilerplate.h>
#include <dll.h>
#include <nd4jmemset.h>
#ifdef _OPENMP
#include <omp.h>
#endif
//Loops adapted from:
//https://github.com/numpy/numpy/blob/009b17a85a22707e63ac9ea1896413992bbf9ce5/numpy/core/src/private/lowlevel_strided_loops.h#L401-L401
/*
namespace shape {
Nd4jLong length(const Nd4jLong *shapeInfo);
Nd4jLong elementWiseStride(const Nd4jLong *shapeInfo);
char order(const Nd4jLong *shapeInfo);
bool isStrideSimple(const Nd4jLong* shapeInfo);
Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen);
}
*/
/************************************************************
* A struct used by CreateSortedStridePerm, new in 1.7.
************************************************************/
typedef struct {
int perm, stride;
} StridePermutation;
/**
* Credit to:
* http://alienryderflex.com/quicksort/
*
* The non recursive implementation is important for being able to run on cuda as well
* as host.
*
* In practice the work loads intended for
* this won't be so hard
* that it matters.
*
* We can work on optimizations later that leverage openmp,
* the gpu etc
*/
#ifdef __CUDACC__
__host__ __device__
#endif
void quickSort(StridePermutation *arr, int elements);
/* Start raw iteration */
#define ND4J_RAW_ITER_START(idim, ndim, coord, shape) \
memset((coord), 0, (ndim) * sizeof(coord[0])); \
do {
/* Increment to the next n-dimensional coordinate for one raw array */
#define ND4J_RAW_ITER_ONE_NEXT(idim, ndim, coord, shape, data, strides) \
for ((idim) = 0; (idim) < (ndim); (idim)++) { \
if (++(coord)[idim] == (shape)[idim]) { \
(coord)[idim] = 0; \
(data) -= ((shape)[idim] - 1) * (strides)[idim]; \
} \
else { \
(data) += (strides)[idim]; \
break; \
} \
} \
} while ((idim) < (ndim))
#define ND4J_RAW_ITER_ONE_NEXTF(idim, ndim, coord, shape, data, strides) \
for ((idim) = ndim - 1; (idim) >= (0); (idim)--) { \
if (++(coord)[idim] == (shape)[idim]) { \
(coord)[idim] = 0; \
(data) -= ((shape)[idim] - 1) * (strides)[idim]; \
} \
else { \
(data) += (strides)[idim]; \
break; \
} \
} \
} while ((idim) >= (0))
/* Increment to the next n-dimensional coordinate for two raw arrays */
#define ND4J_RAW_ITER_TWO_NEXT(idim, ndim, coord, shape, \
dataA, stridesA, dataB, stridesB) \
for ((idim) = 0; (idim) < (ndim); (idim)++) { \
if (++(coord)[idim] == (shape)[idim]) { \
(coord)[idim] = 0; \
(dataA) -= ((shape)[idim] - 1) * (stridesA)[idim]; \
(dataB) -= ((shape)[idim] - 1) * (stridesB)[idim]; \
} \
else { \
(dataA) += (stridesA)[idim]; \
(dataB) += (stridesB)[idim]; \
break; \
} \
} \
} while ((idim) < (ndim))
/* Increment to the next n-dimensional coordinate for three raw arrays */
#define ND4J_RAW_ITER_THREE_NEXT(idim, ndim, coord, shape, \
dataA, stridesA, \
dataB, stridesB, \
dataC, stridesC) \
for ((idim) = 0; (idim) < (ndim); (idim)++) { \
if (++(coord)[idim] == (shape)[idim]) { \
(coord)[idim] = 0; \
(dataA) -= ((shape)[idim] - 1) * (stridesA)[idim]; \
(dataB) -= ((shape)[idim] - 1) * (stridesB)[idim]; \
(dataC) -= ((shape)[idim] - 1) * (stridesC)[idim]; \
} \
else { \
(dataA) += (stridesA)[idim]; \
(dataB) += (stridesB)[idim]; \
(dataC) += (stridesC)[idim]; \
break; \
} \
} \
} while ((idim) < (ndim))
/* Increment to the next n-dimensional coordinate for four raw arrays */
#define ND4J_RAW_ITER_FOUR_NEXT(idim, ndim, coord, shape, \
dataA, stridesA, \
dataB, stridesB, \
dataC, stridesC, \
dataD, stridesD) \
for ((idim) = 0; (idim) < (ndim); (idim)++) { \
if (++(coord)[idim] == (shape)[idim]) { \
(coord)[idim] = 0; \
(dataA) -= ((shape)[idim] - 1) * (stridesA)[idim]; \
(dataB) -= ((shape)[idim] - 1) * (stridesB)[idim]; \
(dataC) -= ((shape)[idim] - 1) * (stridesC)[idim]; \
(dataD) -= ((shape)[idim] - 1) * (stridesD)[idim]; \
} \
else { \
(dataA) += (stridesA)[idim]; \
(dataB) += (stridesB)[idim]; \
(dataC) += (stridesC)[idim]; \
(dataD) += (stridesD)[idim]; \
break; \
} \
} \
} while ((idim) < (ndim))
/*NUMPY_API
*
* This function populates the first ndim elements
* of strideperm with sorted descending by their absolute values.
* For example, the stride array (4, -2, 12) becomes
* [(2, 12), (0, 4), (1, -2)].
*/
#ifdef __CUDACC__
__host__ __device__
#endif
inline void SortStrideArray(int ndim, int strides[],
StridePermutation *out_strideperm) {
/* Set up the strideperm values */
for (int i = 0; i < ndim; i++) {
out_strideperm[i].perm = i;
out_strideperm[i].stride = strides[i];
}
/* Sort them */
quickSort(out_strideperm,ndim);
}
/*
* Prepares shape and strides for a simple raw array iteration.
* This sorts the strides into FORTRAN order, reverses any negative
* strides, then coalesces axes where possible. The results are
* filled in the output parameters.
*
* This is intended for simple, lightweight iteration over arrays
* where no buffering of any kind is needed, and the array may
* not be stored as a PyArrayObject.
*
* The arrays shape, outShape, strides, and outStrides must all
* point to different data.
*
* Returns 0 on success, -1 on failure.
*/
template <typename T>
#ifdef __CUDACC__
__host__ __device__
#endif
inline int PrepareOneRawArrayIter(int ndim, Nd4jLong shape[],
T data[], Nd4jLong strides[],
int *out_ndim, Nd4jLong outShape[],
T **out_data, Nd4jLong *outStrides) {
for (int i = 0; i < ndim; i++) {
outShape[i] = shape[i];
outStrides[i] = strides[i];
}
#if 0
/* DEBUG */
{
printf("raw iter ndim %d\n", ndim);
printf("shape: ");
for (int i = 0; i < ndim; ++i) {
printf("%d ", outShape[i]);
}
printf("\n");
printf("strides: ");
for (int i = 0; i < ndim; ++i) {
printf("%d ", outStrides[i]);
}
printf("\n");
}
#endif
*out_data = data;
*out_ndim = ndim;
return 0;
}
class BlockInformation {
public:
Nd4jLong items;
int threads;
Nd4jLong chunks;
Nd4jLong modulo;
Nd4jLong remainder;
BlockInformation(Nd4jLong length, int threshold) {
threads = length / threshold;
threads = nd4j::math::nd4j_max<int>(1, threads);
threads = nd4j::math::nd4j_min<int>(threads, omp_get_max_threads());
items = length / threads;
remainder = length % threads;
if(items < 1)
items = 1;
chunks = length / items;
modulo = length % items;
//one left over chunk
if(modulo > 0)
chunks++;
}
};
class CudaBlockInformation {
};
/**
* Credit to:
* http://alienryderflex.com/quicksort/
*
* The non recursive implementation is important for being able to run on cuda as well
* as host.
*
* In practice the work loads intended for
* this won't be so hard
* that it matters.
*
* We can work on optimizations later that leverage openmp,
* the gpu etc
*/
#ifdef __CUDACC__
__host__ __device__
#endif
inline void quickSort(StridePermutation *arr, int elements) {
#define MAX_LEVELS 300
int beg[MAX_LEVELS], end[MAX_LEVELS], i= 0, L, R, swap ;
StridePermutation piv;
beg[0] = 0;
end[0] = elements;
while (i >= 0) {
L = beg[i];
R= end[i] - 1;
if (L < R) {
piv = arr[L];
while (L < R) {
while (arr[R].stride >= piv.stride && L < R)
R--;
if (L < R)
arr[L++] = arr[R];
while (arr[L].stride <= piv.stride && L < R)
L++;
if (L<R)
arr[R--] = arr[L];
}
arr[L] = piv;
beg[i + 1]= L + 1;
end[i + 1]= end[i];
end[i++] = L;
if (end[i] - beg[i] > end[i - 1] - beg[i - 1]) {
swap = beg[i];
beg[i]= beg[i - 1];
beg[i - 1] = swap;
swap = end[i];
end[i] = end[i - 1];
end[i - 1] = swap;
}
}
else {
i--;
}
}
}
/**
* The same as PrepareOneRawArrayIter, but for two
* operands instead of one. Any broadcasting of the two operands
* should have already been done before calling this function,
* as the ndim and shape is only specified once for both operands.
*
* Only the strides of the first operand are used to reorder
* the dimensions, no attempt to consider all the strides together
* is made, as is done in the NpyIter object.
*
* You can use this together with NPY_RAW_ITER_START and
* NPY_RAW_ITER_TWO_NEXT to handle the looping boilerplate of everything
* but the innermost loop (which is for idim == 0).
*
* Returns 0 on success, -1 on failure.
*/
template <typename X, typename Y>
int _CUDA_HD PrepareTwoRawArrayIter(int ndim, Nd4jLong *shape,
X *dataA, Nd4jLong *stridesA,
Y *dataB, Nd4jLong *stridesB,
int *out_ndim, Nd4jLong *outShape,
X **out_dataA, Nd4jLong *outStridesA,
Y **out_dataB, Nd4jLong *outStridesB) {
int i;
/* Sort the axes based on the destination strides */
for (i = 0; i < ndim; ++i) {
outShape[i] = shape[i];
outStridesA[i] = stridesA[i];
outStridesB[i] = stridesB[i];
}
/* Reverse any negative strides of operand A */
for (i = 0; i < ndim; i++) {
Nd4jLong stride_entryA = outStridesA[i];
Nd4jLong stride_entryB = outStridesB[i];
Nd4jLong shape_entry = outShape[i];
if (stride_entryA < 0) {
dataA += stride_entryA * (shape_entry - 1);
dataB += stride_entryB * (shape_entry - 1);
outStridesA[i] = -stride_entryA;
outStridesB[i] = -stride_entryB;
}
/* Detect 0-size arrays here */
if (shape_entry == 0) {
*out_ndim = 1;
*out_dataA = dataA;
*out_dataB = dataB;
outShape[0] = 0;
outStridesA[0] = 0;
outStridesB[0] = 0;
return 0;
}
}
*out_dataA = dataA;
*out_dataB = dataB;
*out_ndim = ndim;
#if 0
/* DEBUG */
{
printf("raw iter ndim %d\n", ndim);
printf("shape: ");
for (i = 0; i < ndim; ++i) {
printf("%d ", (int)outShape[i]);
}
printf("\n");
printf("strides a: ");
for (i = 0; i < ndim; ++i) {
printf("%d ", (int)outStridesA[i]);
}
printf("\n");
printf("strides b: ");
for (i = 0; i < ndim; ++i) {
printf("%d ", (int)outStridesB[i]);
}
printf("\n");
}
#endif
return 0;
}
/**
* The same as PrepareOneRawArrayIter, but for three
* operands instead of one. Any broadcasting of the three operands
* should have already been done before calling this function,
* as the ndim and shape is only specified once for all operands.
*
* Only the strides of the first operand are used to reorder
* the dimensions, no attempt to consider all the strides together
* is made, as is done in the NpyIter object.
*
* You can use this together with NPY_RAW_ITER_START and
* NPY_RAW_ITER_THREE_NEXT to handle the looping boilerplate of everything
* but the innermost loop (which is for idim == 0).
*
* Returns 0 on success, -1 on failure.
*/
template <typename X, typename Y, typename Z>
#ifdef __CUDACC__
__host__ __device__
#endif
int PrepareThreeRawArrayIter(int ndim, Nd4jLong shape[],
X *dataA, Nd4jLong *stridesA,
Y *dataB, Nd4jLong *stridesB,
Z *dataC, Nd4jLong *stridesC,
int &out_ndim, Nd4jLong *outShape,
X **out_dataA, Nd4jLong outStridesA[],
Y **out_dataB, Nd4jLong outStridesB[],
Z **out_dataC, Nd4jLong outStridesC[])
{
/* Special case 0 and 1 dimensions */
if (ndim == 0) {
out_ndim = 1;
*out_dataA = dataA;
*out_dataB = dataB;
*out_dataC = dataC;
outShape[0] = 1;
outStridesA[0] = 0;
outStridesB[0] = 0;
outStridesC[0] = 0;
return 0;
}
else if (ndim == 1) {
auto stride_entryA = stridesA[0];
auto stride_entryB = stridesB[0];
auto stride_entryC = stridesC[0];
auto shape_entry = shape[0];
out_ndim = 1;
outShape[0] = shape[0];
/* Always make a positive stride for the first operand */
if (stride_entryA >= 0) {
*out_dataA = dataA;
*out_dataB = dataB;
*out_dataC = dataC;
outStridesA[0] = stride_entryA;
outStridesB[0] = stride_entryB;
outStridesC[0] = stride_entryC;
}
else {
*out_dataA = dataA + stride_entryA * (shape_entry - 1);
*out_dataB = dataB + stride_entryB * (shape_entry - 1);
*out_dataC = dataC + stride_entryC * (shape_entry - 1);
outStridesA[0] = -stride_entryA;
outStridesB[0] = -stride_entryB;
outStridesC[0] = -stride_entryC;
}
return 0;
}
for (int i = 0; i < ndim; ++i) {
outShape[i] = shape[i];
outStridesA[i] = stridesA[i];
outStridesB[i] = stridesB[i];
outStridesC[i] = stridesC[i];
}
/* Reverse any negative strides of operand A */
for (int i = 0; i < ndim; ++i) {
auto stride_entryA = outStridesA[i];
auto stride_entryB = outStridesB[i];
auto stride_entryC = outStridesC[i];
auto shape_entry = outShape[i];
if (stride_entryA < 0) {
dataA += stride_entryA * (shape_entry - 1);
dataB += stride_entryB * (shape_entry - 1);
dataC += stride_entryC * (shape_entry - 1);
outStridesA[i] = -stride_entryA;
outStridesB[i] = -stride_entryB;
outStridesC[i] = -stride_entryC;
}
/* Detect 0-size arrays here */
if (shape_entry == 0) {
out_ndim = 1;
*out_dataA = dataA;
*out_dataB = dataB;
*out_dataC = dataC;
outShape[0] = 0;
outStridesA[0] = 0;
outStridesB[0] = 0;
outStridesC[0] = 0;
return 0;
}
}
*out_dataA = dataA;
*out_dataB = dataB;
*out_dataC = dataC;
out_ndim = ndim;
return 0;
}
#endif //NATIVEOPERATIONS_PAIRWISE_UTIL_H
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