Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
cavis/libnd4j/blas/cpu/NativeOps.cpp

2799 lines
99 KiB
C++
Raw Normal View History

Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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 2/21/16.
//
#define __STDC_CONSTANT_MACROS
#include "../NativeOps.h"
#include "NativeOpExecutioner.h"
#include "../NDArray.h"
#include "../GraphExecutioner.h"
#include <graph/GraphHolder.h>
#include <templatemath.h>
#include <types/float8.h>
#include <loops/type_conversions.h>
#include <loops/aggregates.h>
#include <helpers/helper_ptrmap.h>
#include <helpers/logger.h>
#include <pointercast.h>
#include <pairwise_util.h>
#include <types/types.h>
#include <ops/declarable/helpers/transforms.h>
#include <exceptions/allocation_exception.h>
#include <fcntl.h>
#include <stdio.h>
#include <stdlib.h>
#ifndef _WIN32
#include <unistd.h>
#include <sys/mman.h>
#else
#include <io.h>
#include <helpers/mman.h>
#endif
#include <sys/types.h>
#include <ops/declarable/CustomOperations.h>
#include <errno.h>
char *name;
bool nameSet = false;
#ifdef __ND4J_EXPERIMENTAL__
bool experimentalSupport = true;
#else
bool experimentalSupport = false;
#endif
#include <ops/specials.h>
#include "../Environment.h"
#include <TAD.h>
#include <ops/declarable/OpRegistrator.h>
#include <graph/Context.h>
#include <graph/ResultWrapper.h>
#include <helpers/DebugHelper.h>
#include <helpers/ConstantTadHelper.h>
using namespace nd4j;
NativeOps::NativeOps() {
//
}
void NativeOps::setElementThreshold(int num) {
if (num > 0)
nd4j::Environment::getInstance()->setElementwiseThreshold(num);
}
void NativeOps::setTADThreshold(int num) {
if (num > 0)
nd4j::Environment::getInstance()->setTadThreshold(num);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
*/
void NativeOps::execIndexReduceScalar(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execIndexReduceScalar(nullptr, opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParams, hZ, hZShapeInfo, dZ, dZShapeInfo);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
* @param hZ
* @param hZShapeInfo
* @param dimension
* @param dimensionLength
*/
void NativeOps::execIndexReduce(Nd4jPointer *extraPointers,int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPack.primaryShapeInfo();
auto hTADOffsets = tadPack.primaryOffsets();
auto hz = reinterpret_cast<Nd4jLong*>(hZ);
NativeOpExecutioner::execIndexReduce(nullptr, opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hz,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
hTADShapeInfo,
hTADOffsets);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param hY
* @param hYShapeInfo
* @param hZ
* @param hZShapeInfo
* @param dimension
* @param dimensionLength
*/
void NativeOps::execBroadcast(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPackX.primaryShapeInfo();
auto hTADOffsets = tadPackX.primaryOffsets();
auto hTADShapeInfoZ = tadPackZ.primaryShapeInfo();
auto hTADOffsetsZ = tadPackZ.primaryOffsets();
NativeOpExecutioner::execBroadcast(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hY,
hYShapeInfo,
dY,
dYShapeInfo,
hZ, hZShapeInfo,
dZ, dZShapeInfo,
dimension,
dimensionLength, hTADShapeInfo, hTADOffsets, hTADShapeInfoZ, hTADOffsetsZ);
}
void NativeOps::execBroadcastBool(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto tadPackZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hZShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPackX.primaryShapeInfo();
auto hTADOffsets = tadPackX.primaryOffsets();
auto hTADShapeInfoZ = tadPackZ.primaryShapeInfo();
auto hTADOffsetsZ = tadPackZ.primaryOffsets();
NativeOpExecutioner::execBroadcastBool(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hY,
hYShapeInfo,
dY,
dYShapeInfo,
hZ, hZShapeInfo,
dZ, dZShapeInfo,
dimension,
dimensionLength, hTADShapeInfo, hTADOffsets, hTADShapeInfoZ, hTADOffsetsZ);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param hY
* @param hYShapeInfo
* @param hZ
* @param hZShapeInfo
* @param extraParams
* @param n
*/
void NativeOps::execPairwiseTransform(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execPairwiseTransform(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hY,
hYShapeInfo,
dY,
dYShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams);
}
void NativeOps::execPairwiseTransformBool(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execPairwiseBoolTransform(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hY,
hYShapeInfo,
dY,
dYShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
* @param hZ
* @param hZShapeInfo
*/
void NativeOps::execReduceFloat(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduceFloatScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo);
}
void NativeOps::execReduceSame(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduceSameScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo);
}
void NativeOps::execReduceBool(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduceBoolScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo);
}
void NativeOps::execReduceLong(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduceLongScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
* @param hZ
* @param hZShapeInfo
*/
void NativeOps::execReduceFloat(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPackX.primaryShapeInfo();
auto hTADOffsets = tadPackX.primaryOffsets();
NativeOpExecutioner::execReduceFloat(nullptr, opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
hTADShapeInfo,
hTADOffsets);
}
void NativeOps::execReduceBool(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPack.primaryShapeInfo();
auto hTADOffsets = tadPack.primaryOffsets();
NativeOpExecutioner::execReduceBool(nullptr, opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
hTADShapeInfo,
hTADOffsets);
}
void NativeOps::execReduceSame(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPack.primaryShapeInfo();
auto hTADOffsets = tadPack.primaryOffsets();
NativeOpExecutioner::execReduceSame(nullptr, opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
hTADShapeInfo,
hTADOffsets);
}
void NativeOps::execReduceLong(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPack.primaryShapeInfo();
auto hTADOffsets = tadPack.primaryOffsets();
NativeOpExecutioner::execReduceLong(nullptr, opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
hTADShapeInfo,
hTADOffsets);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParamsVals
* @param hY
* @param hYShapeInfo
* @param hZ
* @param hZShapeInfo
*/
void NativeOps::execReduce3(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduce3(nullptr, opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParams, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParamsVals
* @param hY
* @param hYShapeInfo
*/
void NativeOps::execReduce3Scalar(Nd4jPointer *extraPointers,int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo) {
NativeOpExecutioner::execReduce3Scalar(nullptr, opNum,hX,hXShapeInfo,dX, dXShapeInfo,extraParams,hY,hYShapeInfo,dY,dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParamsVals
* @param hY
* @param hYShapeInfo
* @param hZ
* @param hZShapeInfo
* @param dimension
* @param dimensionLength
*/
void NativeOps::execReduce3(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets,
Nd4jLong *yTadOnlyShapeInfo, Nd4jLong *yTadOffsets) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
if (extraPointers == nullptr || extraPointers[2] == 0) {
NativeOpExecutioner::execReduce3(LaunchContext::defaultContext(), opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParams, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
} else {
// going tad-way
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
auto hTADShapeInfo = tadPack.primaryShapeInfo();
auto hTADOffsets = tadPack.primaryOffsets();
NativeOpExecutioner::execReduce3TAD(LaunchContext::defaultContext(), opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParams, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo, dimension, dimensionLength, hTADShapeInfo, hTADOffsets, nullptr, nullptr);
}
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param hZ
* @param hZShapeInfo
* @param hScalar
* @param extraParams
* @param n
*/
void NativeOps::execScalar(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hScalar, Nd4jLong *hScalarShapeInfo,
void *dScalar, Nd4jLong *dScalarShapeInfo,
void *extraParams) {
NativeOpExecutioner::execScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
hScalar,
hScalarShapeInfo,
dScalar,
dScalarShapeInfo,
extraParams);
}
void NativeOps::execScalarBool(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hScalar, Nd4jLong *hScalarShapeInfo,
void *dScalar, Nd4jLong *dScalarShapeInfo,
void *extraParams) {
NativeOpExecutioner::execScalarBool(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
hScalar,
hScalarShapeInfo,
dScalar,
dScalarShapeInfo,
extraParams);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
*/
void NativeOps::execSummaryStatsScalar(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
bool biasCorrected) {
NativeOpExecutioner::execSummaryStatsScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
biasCorrected);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
* @param hZ
* @param hZShapeInfo
*/
void NativeOps::execSummaryStats(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
bool biasCorrected) {
NativeOpExecutioner::execSummaryStats(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
biasCorrected);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param extraParams
* @param hZ
* @param hZShapeInfo
* @param dimension
* @param dimensionLength
*/
void NativeOps::execSummaryStats(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParams,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape,
bool biasCorrected,
Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
NativeOpExecutioner::execSummaryStats(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
dimension,
dimensionLength,
tadShapeInfo,
tadOffsets,
biasCorrected);
}
/**
*
* @param opNum
* @param hX
* @param hXShapeInfo
* @param hZ
* @param hZShapeInfo
* @param extraParams
* @param n
*/
void NativeOps::execTransformFloat(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execTransformFloat(nullptr,
opNum,
hX,
hXShapeInfo,
dZ,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams,
nullptr,
nullptr);
}
void NativeOps::execTransformSame(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execTransformSame(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams,
nullptr,
nullptr);
}
void NativeOps::execTransformBool(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execTransformBool(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams,
nullptr,
nullptr);
}
void NativeOps::execTransformAny(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execTransformAny(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams,
nullptr,
nullptr);
}
void NativeOps::execTransformStrict(
Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraParams) {
NativeOpExecutioner::execTransformStrict(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
extraParams,
nullptr,
nullptr);
}
void NativeOps::execReduce3All(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *extraParamsVals,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape,
Nd4jLong *xTadShapeInfo,
Nd4jLong *xOffsets,
Nd4jLong *yTadShapeInfo,
Nd4jLong *yOffsets) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
NativeOpExecutioner::execReduce3All(nullptr, opNum, hX, hXShapeInfo, dX, dXShapeInfo, extraParamsVals, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo, dimension, dimensionLength, xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets);
}
template <typename T>
void flattenGeneric(Nd4jPointer *extraPointers,
int offset,
char order,
void *vresult,
Nd4jLong *hZShapeInfo,
void *vinput,
Nd4jLong *inputShapeInfo) {
auto hZ = reinterpret_cast<T *>(vresult);
auto input = reinterpret_cast<T *>(vinput);
int numOnes = 0;
auto shape = shape::shapeOf(inputShapeInfo);
int wholeRank = shape::rank(inputShapeInfo);
for(int i = 0; i < wholeRank; i++) {
if(shape[i] == 1)
numOnes++;
}
//start at the given offset
hZ += offset;
char inputOrder = shape::order(inputShapeInfo);
auto len = shape::length(inputShapeInfo);
auto resultEleStride = shape::elementWiseStride(hZShapeInfo);
auto inputEleStride = shape::elementWiseStride(inputShapeInfo);
Nd4jLong numTads, stride;
int dimension, dimensionLength;
int rank = shape::rank(inputShapeInfo);
auto xStride = shape::stride(inputShapeInfo);
auto xShape = shape::shapeOf(inputShapeInfo);
dimensionLength = 1;
if(order == 'f') {
dimension = 0;
}
else {
dimension = rank - 1;
}
stride = xStride[dimension];
// numTads is product of length of all dimensions excluding
// the one we do the tad on
numTads = 1;
for (int i = 0; i < rank; i++) {
if (i != dimension)
numTads *= xShape[i];
}
if (inputOrder == order) {
if (resultEleStride == 1 && inputEleStride == 1) {
memcpy(hZ, input, len* sizeof(T));
}
else if (resultEleStride >= 1 && inputEleStride >= 1) {
if (len < ELEMENT_THRESHOLD) {
PRAGMA_OMP_SIMD
for (int i = 0; i < len; i++) {
hZ[i * resultEleStride] = input[i * inputEleStride];
}
}
else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int i = 0; i < len; i++) {
hZ[i * resultEleStride] = input[i * inputEleStride];
}
}
}
else {
int idx = 0;
for(int i = 0; i < len; i++)
hZ[idx++] = input[shape::getIndexOffset(i, inputShapeInfo, len)];
}
}
else {
int rank = shape::rank(inputShapeInfo);
auto xShape = shape::shapeOf(inputShapeInfo);
auto tadShape = xShape[dimension];
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(inputShapeInfo, dimension);
PRAGMA_OMP_PARALLEL_FOR
for(int i = 0; i < numTads; i++) {
Nd4jLong resultOffset;
if (order == 'f') {
// 1. get c ordering coordinates
auto cIndexCoordinates = new Nd4jLong[rank - 1];
int divisor = 1;
for (int dim = rank - 1; dim > 0; dim--) {
cIndexCoordinates[dim - 1] = (i / divisor) % xShape[dim];
divisor *= xShape[dim];
}
// 2. convert to f ordering index
int fIndex = 0;
int multiplier = 1;
for (int dim = 1; dim <= rank - 1; dim++) {
fIndex += cIndexCoordinates[dim - 1] * multiplier;
multiplier *= xShape[dim];
}
resultOffset = fIndex * tadShape;
delete[] cIndexCoordinates;
}
else {
resultOffset = i * tadShape;
}
auto tadOffset = tadPack.primaryOffsets()[i];
for( int j = 0; j < tadShape; j++) {
// TAD are returned in C ordering always
hZ[resultOffset + j] = input[tadOffset + j * stride];
}
}
}
}
/**
* Concatneate multi array of the same shape together
* along a particular dimension
*/
void NativeOps::concat(
Nd4jPointer *extraPointers,
int dimension,
int numArrays,
Nd4jPointer *data, Nd4jPointer *inputShapeInfo,
Nd4jPointer *ddata, Nd4jPointer *dinputShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
Nd4jPointer *tadPointers,
Nd4jPointer *offsetPointers) {
auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo);
BUILD_SINGLE_SELECTOR(zType, nd4j::SpecialMethods, ::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES);
}
/**
* Concatneate multi array of the same shape together
* along a particular dimension
*/
void NativeOps::specialConcat(
Nd4jPointer *extraPointers,
int dimension,
int numArrays,
Nd4jPointer *data,
Nd4jPointer *inputShapeInfo,
void *hZ,
Nd4jLong *hZShapeInfo,
Nd4jPointer *tadPointers,
Nd4jPointer *offsetPointers) {
auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo);
BUILD_SINGLE_SELECTOR(zType, nd4j::SpecialMethods, ::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, hZ, hZShapeInfo), LIBND4J_TYPES);
}
/**
* Append an input array
* to the end of a flat array
* in a particular order
* @param offset the offset of the array to start at
* @param order the order
* @param hZ the hZ array
* @param hZShapeInfo the shape info for te array
* @param input the input for the array
* @param inputShapeInfo the shape information for that array
*/
void NativeOps::flatten(
Nd4jPointer *extraPointers,
int offset,
char order,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *input, Nd4jLong *inputShapeInfo,
void *dinput, Nd4jLong *dinputShapeInfo) {
auto xType = nd4j::ArrayOptions::dataType(inputShapeInfo);
auto zType = nd4j::ArrayOptions::dataType(hZShapeInfo);
if (xType != zType)
throw std::runtime_error("NativeOps::flatten requires all operands to have same data type");
BUILD_SINGLE_SELECTOR(xType, flattenGeneric, (extraPointers, offset, order, hZ, hZShapeInfo, input, inputShapeInfo), LIBND4J_TYPES);
}
/**
* This is dummy method for JNI compatibility
* Since we'll use this from java, jni compiler would like to have method no matter what.
*/
void NativeOps::initializeDevicesAndFunctions() {
}
void NativeOps::initializeFunctions(Nd4jPointer *functions) {
nd4j::BlasHelper::getInstance()->initializeFunctions(functions);
}
/**
* This method acquires memory chunk of requested size on host side
*
* @param pointer pointer that'll be used for allocation
* @param memorySize memory size, in bytes
* @param flags optional parameter
*/
Nd4jPointer NativeOps::mallocHost(Nd4jLong memorySize, int flags) {
Nd4jPointer pointer = (Nd4jPointer) malloc(memorySize);
if (pointer == 0)
return 0L;
return pointer;
}
/**
* This method acquires memory chunk of requested size on specified device
*
* PLEASE NOTE: This method is NOT supported and has NO effect in CPU-based backend.
*
* @param pointer pointer that'll be used for allocation
* @param memorySize memory size, in bytes
* @param ptrToDeviceId pointer to deviceId. For cuda that's just and int, for OpenCL that's pointer to device_id, etc
* @param flags optional parameter
*/
Nd4jPointer NativeOps::mallocDevice(Nd4jLong memorySize, int deviceId, int flags) {
// not supported
return 0L;
}
/**
* This method releases previously allocated host memory space
*
* @param pointer pointer that'll be freed
*/
int NativeOps::freeHost(Nd4jPointer pointer) {
free(reinterpret_cast<void *>(pointer));
return 1L;
}
/**
* This method releases previously allocated memory space on device
*
* PLEASE NOTE: This method is NOT supported and has NO effect in CPU-based backend.
*
* @param pointer pointer that'll be freed
* @param ptrToDeviceId pointer to deviceId.
*/
int NativeOps::freeDevice(Nd4jPointer pointer, int deviceId) {
// not supported
return 0L;
}
/**
* Returns the maximum number open mp threads
*/
int NativeOps::ompGetMaxThreads() {
return omp_get_max_threads();
}
/**
* Returns the number open mp threads
*/
int NativeOps::ompGetNumThreads() {
return omp_get_num_threads();
}
/**
* Sets the number of openmp threads
*/
void NativeOps::setOmpNumThreads(int threads) {
omp_set_num_threads(threads);
}
Nd4jPointer NativeOps::createContext() {
return 0L;
}
Nd4jPointer NativeOps::createStream() {
return 0L;
}
Nd4jPointer NativeOps::createEvent() {
return 0L;
}
int NativeOps::getDeviceMajor(int deviceId ) {
return 0;
}
int NativeOps::getDeviceMinor(int deviceId) {
return 0;
}
int NativeOps::registerEvent(Nd4jPointer event, Nd4jPointer stream) {
return 0L;
}
int NativeOps::setDevice(int deviceId) {
return 0L;
}
Nd4jLong NativeOps::getDeviceFreeMemory(int deviceId) {
return 0L;
}
Nd4jLong NativeOps::getDeviceFreeMemory() {
return 0L;
}
Nd4jLong NativeOps::getDeviceTotalMemory(int deviceId) {
return 0L;
}
int NativeOps::memcpy(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
return 0L;
}
int NativeOps::memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
return 0L;
}
int NativeOps::memset(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointer reserved) {
return 0L;
}
int NativeOps::memsetAsync(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointer reserved) {
return 0L;
}
int NativeOps::destroyEvent(Nd4jPointer event) {
return 0L;
}
int NativeOps::streamSynchronize(Nd4jPointer stream) {
return 0L;
}
int NativeOps::eventSynchronize(Nd4jPointer event) {
return 0L;
}
int NativeOps::getAvailableDevices() {
return 0L;
}
void NativeOps::enableDebugMode(bool reallyEnable) {
nd4j::Environment::getInstance()->setDebug(reallyEnable);
}
void NativeOps::enableVerboseMode(bool reallyEnable) {
nd4j::Environment::getInstance()->setVerbose(reallyEnable);
}
void NativeOps::setGridLimit(int gridSize) {
// no-op
}
nd4j::TadPack* NativeOps::tadOnlyShapeInfo(Nd4jLong *hXShapeInfo, int *dimension, int dimensionLength) {
auto pack = new TadPack();
*pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo, dimension, dimensionLength);
return pack;
}
int NativeOps::memcpyConstantAsync(Nd4jLong dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
// no-op
return 0L;
}
Nd4jPointer NativeOps::getConstantSpace() {
// no-op
return 0L;
}
template<typename T>
void pullRowsGeneric(void *vx,
Nd4jLong *hXShapeInfo,
void *vz,
Nd4jLong *hZShapeInfo,
const int n,
Nd4jLong *indexes,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffsets,
Nd4jLong *zTadShapeInfo,
Nd4jLong *zTadOffsets) {
auto hX = reinterpret_cast<T *>(vx);
auto hZ = reinterpret_cast<T *>(vz);
const auto xEWS = shape::elementWiseStride(tadShapeInfo);
const auto zEWS = shape::elementWiseStride(zTadShapeInfo);
const auto tadLength = shape::length(tadShapeInfo);
int elementsPerThread = n / TAD_THRESHOLD;
int _threads = nd4j::math::nd4j_max<int>(1, elementsPerThread);
_threads = nd4j::math::nd4j_min<int>(_threads, omp_get_max_threads());
PRAGMA_OMP_PARALLEL_FOR_THREADS(_threads)
for (int idx = 0; idx < n; idx++) {
auto xTadOffsetForBlock = tadOffsets[indexes[idx]];
auto zTadOffsetForBlock = zTadOffsets[idx];
auto rX = hX + xTadOffsetForBlock;
auto rZ = hZ + zTadOffsetForBlock;
if (xEWS == 1 && zEWS == 1) {
PRAGMA_OMP_SIMD
for (int i = 0; i < tadLength; i++ ) {
rZ[i] = rX[i];
}
} else if (xEWS >= 1 && zEWS >= 1) {
PRAGMA_OMP_SIMD
for (int i = 0; i < tadLength; i++ ) {
rZ[i * zEWS] = rX[i * xEWS];
}
}
else {
for (int i = 0; i < tadLength; i++) {
auto xOffset = xTadOffsetForBlock + shape::getIndexOffset(i, tadShapeInfo, tadLength);
auto zOffset = zTadOffsetForBlock + shape::getIndexOffset(i, zTadShapeInfo, tadLength);
hZ[zOffset] = hX[xOffset];
}
}
}
}
void NativeOps::pullRows(Nd4jPointer *extraPointers,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
Nd4jLong n,
Nd4jLong *indexes,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffsets,
Nd4jLong *zTadShapeInfo,
Nd4jLong *zTadOffsets) {
auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo);
BUILD_SINGLE_SELECTOR(xType, pullRowsGeneric, (hX, hXShapeInfo, hZ, hZShapeInfo, n, indexes, tadShapeInfo, tadOffsets, zTadShapeInfo, zTadOffsets), LIBND4J_TYPES);
}
template<typename T>
void tearGeneric(void *vx,
Nd4jLong *hXShapeInfo,
Nd4jPointer *targets,
Nd4jLong *hZShapeInfo,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffsets) {
auto hX = reinterpret_cast<T *>(vx);
const auto tadLength = shape::length(tadShapeInfo);
auto tadEWS = shape::elementWiseStride(tadShapeInfo);
auto zEWS = shape::elementWiseStride(hZShapeInfo);
auto numTads = shape::length(hXShapeInfo) / tadLength;
PRAGMA_OMP_PARALLEL_FOR
for (Nd4jLong i = 0; i < numTads; i++) {
auto hZ = reinterpret_cast<T *>(targets[i]);
auto s = hX + tadOffsets[i];
if (zEWS == 1 && tadEWS == 1) {
PRAGMA_OMP_SIMD
for (Nd4jLong j = 0; j < tadLength; j++) {
hZ[j] = s[j];
}
} else if (zEWS > 0 && tadEWS > 0) {
PRAGMA_OMP_SIMD
for (Nd4jLong j = 0; j < tadLength; j++) {
hZ[j * zEWS] = s[j * tadEWS];
}
}
else {
for (Nd4jLong j = 0; j < tadLength; j++)
hZ[shape::getIndexOffset(j, hZShapeInfo, tadLength)] = s[shape::getIndexOffset(j, tadShapeInfo, tadLength)];
}
}
}
void NativeOps::tear(Nd4jPointer *extraPointers,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
Nd4jPointer *targets,
Nd4jLong *hZShapeInfo,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffsets) {
auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo);
BUILD_SINGLE_SELECTOR(xType, tearGeneric, (hX, hXShapeInfo, targets, hZShapeInfo, tadShapeInfo, tadOffsets), LIBND4J_TYPES);
}
void NativeOps::average(Nd4jPointer *extras,
Nd4jPointer *hX, Nd4jLong *hXShapeInfo,
Nd4jPointer *dX, Nd4jLong *dXShapeInfo,
void *z, Nd4jLong *hZShapeInfo,
void *dz, Nd4jLong *dZShapeInfo,
int n,
Nd4jLong length,
bool propagate) {
auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo);
BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::averageGeneric(hX, z, hZShapeInfo, n, length, propagate), LIBND4J_TYPES);
}
void NativeOps::accumulate(Nd4jPointer *extras,
Nd4jPointer *hX, Nd4jLong *hXShapeInfo,
Nd4jPointer *dX, Nd4jLong *dXShapeInfo,
void *hz, Nd4jLong *hZShapeInfo,
void *dz, Nd4jLong *dZShapeInfo,
int n,
Nd4jLong length) {
auto xType = nd4j::ArrayOptions::dataType(hXShapeInfo);
BUILD_SINGLE_SELECTOR(xType, nd4j::SpecialMethods, ::accumulateGeneric(hX, hz, hZShapeInfo, n, length), LIBND4J_TYPES);
}
void NativeOps::enableP2P(bool enable) {
// no-op
}
void NativeOps::encodeThresholdP1(Nd4jPointer *extraPointers, void *hX, Nd4jLong *hXShapeInfo, Nd4jLong N, int *dz, float threshold) {
// TODO: to be implemented
}
void NativeOps::encodeThresholdP2Int(Nd4jPointer *extraPointers, int *hX, Nd4jLong N, int *dz) {
// TODO: to be implemented
}
void NativeOps::encodeThresholdP3(Nd4jPointer *extraPointers, void *hX, Nd4jLong *hXShapeInfo, int *offsets, Nd4jLong N, int *dz){
// offsets won't be used here
// TODO: to be implemented
}
void NativeOps::decodeThreshold(Nd4jPointer *extraPointers, void *hX, Nd4jLong N, void *dz, Nd4jLong *hZShapeInfo){
// TODO: to be implemented
}
bool NativeOps::isP2PAvailable() {
// always TRUE for cpu backend
return true;
}
void NativeOps::checkP2P() {
// no-op
}
void NativeOps::decodeBitmap(Nd4jPointer *extraPointers, void *hX, Nd4jLong N, void *dz, Nd4jLong *hZShapeInfo) {
NativeOpExecutioner::decodeBitmap(hX, N, dz, hZShapeInfo);
}
template<typename T>
void shuffleGeneric(void **hX, Nd4jLong **hXShapeInfo, void **dz, Nd4jLong **hZShapeInfo, int N, int *shuffleMap, Nd4jLong **tadOnlyShapeInfo, Nd4jLong **tadOffsets) {
auto dX = reinterpret_cast<T **>(hX);
auto dZ = reinterpret_cast<T **>(dz);
PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(N)
for (int f = 0; f < N; f++) {
auto hX = reinterpret_cast<T *>(dX[f]);
//auto hZ = reinterpret_cast<T *>(dZ[f]);
auto xShapeInfo = hXShapeInfo[f];
auto tadOffset = reinterpret_cast<Nd4jLong *>(tadOffsets[f]);
const auto tadLength = shape::length(tadOnlyShapeInfo[f]);
auto tadEWS = shape::elementWiseStride(tadOnlyShapeInfo[f]);
auto tadRank = shape::rank(tadOnlyShapeInfo[f]);
auto numTads = shape::length(hXShapeInfo[f]) / tadLength;
auto tadShape = shape::shapeOf(tadOnlyShapeInfo[f]);
auto tadStride = shape::stride(tadOnlyShapeInfo[f]);
if (shape::rank(xShapeInfo) == 1) {
auto xLength = shape::length(xShapeInfo);
auto ews = shape::elementWiseStride(xShapeInfo);
for (Nd4jLong r = 0; r < xLength; r++) {
auto swapIdx = shuffleMap[r];
if (swapIdx < 0)
continue;
nd4j::math::nd4j_swap<T>(hX[r*ews], hX[swapIdx*ews]);
}
} else {
for (Nd4jLong r = 0; r < numTads; r++) {
if (shuffleMap[r] < 0)
continue;
auto oldOffset = tadOffset[r];
auto newOffset = tadOffset[shuffleMap[r]];
auto rX = hX + oldOffset;
auto rY = hX + newOffset;
if (tadEWS == 1) {
for (Nd4jLong i = 0; i < tadLength; i++) {
nd4j::math::nd4j_swap<T>(rX[i], rY[i]);
}
} else {
for (Nd4jLong i = 0; i < tadLength; i++) {
auto offset = shape::getIndexOffset(i, tadOnlyShapeInfo[f], tadLength);
nd4j::math::nd4j_swap<T>(hX[offset + oldOffset], hX[offset + newOffset]);
}
}
}
}
}
}
void NativeOps::shuffle(Nd4jPointer *extras,
Nd4jPointer *hX, Nd4jPointer *hXShapeInfo,
Nd4jPointer *dX, Nd4jPointer *dXShapeInfo,
Nd4jPointer *hz, Nd4jPointer *hZShapeInfo,
Nd4jPointer *dz, Nd4jPointer *dZShapeInfo,
int N,
int *shuffleMap,
Nd4jPointer *tadShapeInfo,
Nd4jPointer *tadOffsets) {
auto xShape = reinterpret_cast<Nd4jLong **>(hXShapeInfo);
auto zShape = reinterpret_cast<Nd4jLong **>(hZShapeInfo);
auto tadOnlyShapeInfo = reinterpret_cast<Nd4jLong **>(tadShapeInfo);
auto tadOffset = reinterpret_cast<Nd4jLong **>(tadOffsets);
auto xType = nd4j::ArrayOptions::dataType(xShape[0]);
BUILD_SINGLE_SELECTOR(xType, shuffleGeneric, (hX, xShape, hz, zShape, N, shuffleMap, tadOnlyShapeInfo, tadOffset), LIBND4J_TYPES);
}
bool NativeOps::isExperimentalEnabled() {
return nd4j::Environment::getInstance()->isExperimentalBuild();
}
void NativeOps::setOmpMinThreads(int threads) {
// TODO: to be implemented
}
/*
void NativeOps::execMetaPredicateShape(Nd4jPointer *extras,
const int opTypeA,
const int opNumA,
const int opTypeB,
const int opNumB,
Nd4jLong N,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraA,
void *extraB,
double scalarA,
double scalarB) {
// no-op;
}
*/
int NativeOps::getDevice() {
return 0;
}
void NativeOps::execScalar(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hScalars, Nd4jLong *hScalarShapeInfo,
void *dScalars, Nd4jLong *dScalarShapeInfo,
void *extraParams,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape,
Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
NativeOpExecutioner::execScalar(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
hScalars,
hScalarShapeInfo,
dScalars,
dScalarShapeInfo,
dimension,
shape::length(hDimensionShape),
tadShapeInfo,
tadOffsets,
tadShapeInfoZ,
tadOffsetsZ);
}
void NativeOps::execScalarBool(Nd4jPointer *extraPointers,
int opNum,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *hScalars, Nd4jLong *hScalarShapeInfo,
void *dScalars, Nd4jLong *dScalarShapeInfo,
void *extraParams,
void *hDimension, Nd4jLong *hDimensionShape,
void *dDimension, Nd4jLong *dDimensionShape,
Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
auto dimension = reinterpret_cast<int *>(hDimension);
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
NativeOpExecutioner::execScalarBool(nullptr,
opNum,
hX,
hXShapeInfo,
dX,
dXShapeInfo,
extraParams,
hZ,
hZShapeInfo,
dZ,
dZShapeInfo,
hScalars,
hScalarShapeInfo,
dScalars,
dScalarShapeInfo,
dimension,
dimensionLength,
tadShapeInfo,
tadOffsets,
tadShapeInfoZ,
tadOffsetsZ);
}
const char * NativeOps::getDeviceName(int deviceId) {
if (!nameSet) {
name = reinterpret_cast<char *>(malloc(256 * sizeof(char)));
CHECK_ALLOC(name, "Failed to allocate new string buffer", 256);
std::memset(name, 0, 256 * sizeof(char));
nameSet = true;
// TODO: provide proper CPU model name here
sprintf(name, "x86-compatible CPU");
}
return name;
}
void NativeOps::execAggregate(Nd4jPointer *extraPointers,int opNum,
void **arguments,
int numArguments,
Nd4jLong **shapeArguments,
int numShapeArguments,
int *indexArguments,
int numIndexArguments,
int **intArrays,
int numIntArrays,
void *realArguments,
int numRealArguments,
nd4j::DataType dtype) {
BUILD_SINGLE_SELECTOR(dtype, NativeOpExecutioner::execAggregate, (nullptr, opNum, arguments, numArguments, shapeArguments, numShapeArguments, indexArguments, numIndexArguments, intArrays, numIntArrays, realArguments, numRealArguments), FLOAT_TYPES);
}
template <typename T>
void NativeOps::_batchExecutor(Nd4jPointer *extraPointers,
int numAggregates,
int opNum,
int maxArgs,
int maxShapes,
int maxIntArrays,
int maxIntArraySize,
int maxIdx,
int maxReals,
void *ptrToArguments,
nd4j::DataType dtype) {
// probably, we don't want too much threads as usually
int _threads = nd4j::math::nd4j_min<int>(numAggregates, omp_get_max_threads());
nd4j::PointersHelper<T> helper(ptrToArguments,
numAggregates,
maxArgs,
maxShapes,
maxIntArrays,
maxIntArraySize,
maxIdx,
maxReals);
// special case here, we prefer spread arrangement here, all threads are detached from each other
PRAGMA_OMP_PARALLEL_FOR_THREADS(_threads)
for (int i = 0; i < numAggregates; i++) {
auto intArrays = new int *[maxIntArrays];
auto arguments = helper.getArguments(i);
auto shapes = helper.getShapeArguments(i);
auto idxArg = helper.getIndexArguments(i);
auto realArg = helper.getRealArguments(i);
for (int e = 0; e < maxIntArrays; e++) {
intArrays[e] = helper.getIntArrayArguments(i, e);
}
execAggregate(extraPointers,
opNum,
reinterpret_cast<void **>(arguments),
helper.getNumArguments(i),
shapes,
helper.getNumShapeArguments(i),
idxArg,
helper.getNumIndexArguments(i),
intArrays,
helper.getNumIntArrayArguments(i),
realArg,
helper.getNumRealArguments(i),
dtype);
delete [] intArrays;
}
}
BUILD_SINGLE_TEMPLATE(template void NativeOps::_batchExecutor, (Nd4jPointer *extraPointers, int numAggregates, int opNum, int maxArgs, int maxShapes, int maxIntArrays, int maxIntArraySize, int maxIdx, int maxReals, void *ptrToArguments, nd4j::DataType dtype), FLOAT_TYPES);
void NativeOps::execAggregateBatch(Nd4jPointer *extraPointers,
int numAggregates,
int opNum,
int maxArgs,
int maxShapes,
int maxIntArrays,
int maxIntArraySize,
int maxIdx,
int maxReals,
void *ptrToArguments,
nd4j::DataType dtype) {
BUILD_SINGLE_SELECTOR(dtype, _batchExecutor, (extraPointers, numAggregates, opNum, maxArgs, maxShapes, maxIntArrays, maxIntArraySize, maxIdx, maxReals, ptrToArguments, dtype), FLOAT_TYPES);
}
void NativeOps::execRandom(Nd4jPointer *extraPointers,
int opNum,
Nd4jPointer state,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraArguments) {
NativeOpExecutioner::execRandom(nullptr, opNum, state, hZ, hZShapeInfo, dZ, dZShapeInfo, extraArguments);
}
void NativeOps::execRandom(Nd4jPointer *extraPointers,
int opNum,
Nd4jPointer state,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hY, Nd4jLong *hYShapeInfo,
void *dY, Nd4jLong *dYShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraArguments) {
NativeOpExecutioner::execRandom(nullptr, opNum, state, hX, hXShapeInfo, dX, dXShapeInfo, hY, hYShapeInfo, dY, dYShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo, extraArguments);
}
void NativeOps::execRandom(Nd4jPointer *extraPointers,
int opNum,
Nd4jPointer state,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
void *hZ, Nd4jLong *hZShapeInfo,
void *dZ, Nd4jLong *dZShapeInfo,
void *extraArguments) {
NativeOpExecutioner::execRandom(nullptr, opNum, state, hX, hXShapeInfo, dX, dXShapeInfo, hZ, hZShapeInfo, dZ, dZShapeInfo, extraArguments);
}
Nd4jPointer NativeOps::initRandom(Nd4jPointer *extraPointers, long seed, long bufferSize, Nd4jPointer ptrToBuffer) {
auto ptrBuf = reinterpret_cast<long *>(ptrToBuffer);
auto buffer = new nd4j::random::RandomBuffer(seed, bufferSize, reinterpret_cast<uint64_t *>(ptrBuf));
nd4j::random::Xoroshiro128 generator(buffer);
generator.refreshBuffer();
return (Nd4jPointer) buffer;
}
void NativeOps::refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) {
auto buffer = reinterpret_cast<nd4j::random::RandomBuffer *> (ptrRandom);
buffer->setSeed(seed);
buffer->setOffset(0);
nd4j::random::Xoroshiro128 generator(buffer);
generator.refreshBuffer();
}
void NativeOps::reSeedBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) {
auto buffer = reinterpret_cast<nd4j::random::RandomBuffer *> (ptrRandom);
buffer->reSeed(seed);
}
void NativeOps::destroyRandom(Nd4jPointer ptrBuffer) {
auto buffer = reinterpret_cast<nd4j::random::RandomBuffer *>(ptrBuffer);
delete buffer;
}
/**
* Return the length of a shape buffer
* based on the pointer
* @param buffer the buffer pointer to check
* @return
*/
int NativeOps::lengthForShapeBufferPointer(Nd4jPointer buffer) {
auto shapeBuffer = reinterpret_cast<Nd4jLong *>(buffer);
return shape::shapeInfoLength(shape::rank(shapeBuffer));
}
/**
* The pointer to get the address for
*
* @param address the address to get the pointer
* @return the pointer for the given address
*/
Nd4jPointer NativeOps::pointerForAddress(Nd4jLong address) {
return reinterpret_cast<Nd4jPointer >(address);
}
void NativeOps::sort(Nd4jPointer *extraPointers,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
bool descending) {
NativeOpExecutioner::execSort(hX, hXShapeInfo, descending);
}
void NativeOps::sortTad(Nd4jPointer *extraPointers,
void *hX, Nd4jLong *hXShapeInfo,
void *dX, Nd4jLong *dXShapeInfo,
int *dimension,
int dimensionLength,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffsets,
bool descending) {
NativeOpExecutioner::execSort(hX, hXShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffsets, descending);
}
void NativeOps::sortCooIndices(Nd4jPointer *extraPointers,
Nd4jLong *indices,
void *values,
Nd4jLong length,
int rank) {
NativeOpExecutioner::execSortCooIndices(indices, values, length, rank);
}
Nd4jLong NativeOps::encodeBitmap(Nd4jPointer *extraPointers, void *hX, Nd4jLong *hXShapeInfo, Nd4jLong N, int *dz, float threshold) {
return NativeOpExecutioner::encodeBitmap(hX, hXShapeInfo, N, dz, threshold);
}
Nd4jLong* NativeOps::mmapFile(Nd4jPointer *extraPointers, const char *fileName, Nd4jLong length) {
auto hZ = new Nd4jLong[2];errno = 0;
#if defined(_WIN32) || defined(_WIN64)
_mmap(hZ, static_cast<size_t>(length), fileName);
#else
int fd = open(fileName, O_RDWR, 0);// checking for failed fopen
if (fd < 0) {
nd4j_printf("Errno: %i\n", errno);
throw std::runtime_error("Failed to open file for MMAP");
}
void * ptr = mmap(NULL, length, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
// check for failed allocation
if (ptr == MAP_FAILED)
return nullptr;
hZ[0] = (Nd4jLong) ptr;
hZ[1] = fd;
#endif
return hZ;
}
void NativeOps::munmapFile(Nd4jPointer *extraPointers, Nd4jLong *ptrMap, Nd4jLong length) {
munmap((Nd4jPointer) ptrMap[0], length);
#if defined(_WIN32) || defined(_WIN64)
CloseHandle(reinterpret_cast<HANDLE>(ptrMap[1]));
#else
close((int) ptrMap[1]);
#endif
delete[] ptrMap;
}
nd4j::graph::ResultWrapper* NativeOps::executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) {
return nd4j::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer);
}
const char* NativeOps::getAllCustomOps() {
return nd4j::ops::OpRegistrator::getInstance()->getAllCustomOperations();
}
template <typename T>
FORCEINLINE int estimateThresholdGeneric(Nd4jPointer *extraPointers, Nd4jPointer hX, int N, T threshold) {
auto buffer = reinterpret_cast<T *>(hX);
int span = (N / 6) + 8;
int cnt = 0;
PRAGMA_OMP_PARALLEL_REDUCTION(+:cnt)
{
int tid = omp_get_thread_num();
int start = span * tid;
int stop = span * (tid + 1);
if (stop > N)
stop = N;
PRAGMA_OMP_SIMD
for (int e = start; e < stop; e++) {
auto v = nd4j::math::nd4j_abs<T>(buffer[e]);
if (v >= threshold)
cnt++;
}
}
return cnt;
}
int NativeOps::estimateThreshold(Nd4jPointer *extraPointers, Nd4jPointer hX, Nd4jLong *hXShapeInfo, int N, float threshold) {
auto xType = ArrayOptions::dataType(hXShapeInfo);
BUILD_SINGLE_SELECTOR(xType, return estimateThresholdGeneric, (extraPointers, hX, N, threshold), FLOAT_TYPES);
}
void NativeOps::deleteShapeList(Nd4jPointer shapeList) {
auto list = reinterpret_cast<nd4j::ShapeList*>(shapeList);
//list->destroy();
delete list;
}
nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs) {
nd4j::graph::VariableSpace varSpace;
Context block(2, &varSpace);
nd4j::ShapeList inShapes;
for (int e = 0; e < numIArgs; e++)
block.getIArguments()->push_back(iArgs[e]);
for (int e = 0; e < numTArgs; e++)
block.getTArguments()->push_back(tArgs[e]);
for (int e = 0; e < numBArgs; e++)
block.getBArguments()->push_back(bArgs[e]);
for (int e = 0; e < numInputShapes; e++) {
auto shape_ = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
// we shouldn't copy buffer if that's empty array
void *buffer_ = nd4j::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e];
auto array = new nd4j::NDArray(buffer_, shape_, varSpace.launchContext(), false);
// block should contain references to proper variable
varSpace.putVariable(1, e, array);
block.pickInput(1, e);
inShapes.push_back(shape_);
}
auto status = op->validateDataTypes(block);
if (status != Status::OK())
throw std::runtime_error("Data types validation failed");
auto shapeList = op->calculateOutputShape(&inShapes, block);
if (varSpace.launchContext() != nullptr)
shapeList->detach();
return shapeList;
}
nd4j::ShapeList* NativeOps::calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs) {
auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash);
return _calculateOutputShapes(extraPointers, op, inputBuffers, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs);
}
nd4j::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, nd4j::ops::DeclarableOp *op, Nd4jPointer* inputShapes, int numInputShapes, double *tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) {
Context block(1);
nd4j::ShapeList inShapes;
for (int e = 0; e < numIArgs; e++)
block.getIArguments()->push_back(iArgs[e]);
for (int e = 0; e < numTArgs; e++)
block.getTArguments()->push_back(tArgs[e]);
for (int e = 0; e < numInputShapes; e++)
inShapes.push_back(reinterpret_cast<Nd4jLong *>(inputShapes[e]));
auto shapeList = op->calculateOutputShape(&inShapes, block);
shapeList->detach();
return shapeList;
}
nd4j::ShapeList* NativeOps::calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) {
auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash);
return _calculateOutputShapes(extraPointers, op, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs);
}
int NativeOps::execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opContext) {
auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash);
auto context = reinterpret_cast<Context*>(opContext);
return op->execute(context);
}
Nd4jStatus realExec(nd4j::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) {
if (op == nullptr)
nd4j_printf("Can't find requested operation: [%lld]\n", hash);
// we're using the same fake nodeId everywhere here
std::vector<nd4j::NDArray*> inputs(numInputs);
std::vector<nd4j::NDArray*> outputs(numOutputs);
std::vector<double> ttArgs(numTArgs);
std::vector<Nd4jLong> iiArgs(numIArgs);
std::vector<bool> biArgs(numBArgs);
// filling block now with inputs
for (int e = 0; e < numInputs; e++) {
auto shape = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e];
inputs[e] = new nd4j::NDArray(buffer, shape);
}
// if not inplace - transferring output arrays
if (!isInplace)
for (int e = 0; e < numOutputs; e++) {
// we want to keep original output shape intact
auto shape = shape::copyShape(reinterpret_cast<Nd4jLong *>(outputShapes[e]));
void *buffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e];
// FIXME: revisit this.
bool canNullify = true;
for (int i = 0; i < numInputs; i++) {
void *ibuffer = nd4j::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i];
if (ibuffer == buffer) {
canNullify = false;
break;
}
}
if (canNullify)
memset((uint8_t *) buffer, '\0', shape::length(shape) * DataTypeUtils::sizeOfElement(ArrayOptions::dataType(shape)));
auto array = new nd4j::NDArray(buffer, shape);
outputs[e] = array;
// and we want to release shape copy once we're done
delete []shape;
}
for (int e = 0; e < numIArgs; e++)
iiArgs[e] = iArgs[e];
for (int e = 0; e < numTArgs; e++)
ttArgs[e] = tArgs[e];
for (int e = 0; e < numBArgs; e++)
biArgs[e] = bArgs[e];
// hypothetically at this point we have everything filled
auto hZ = op->execute(inputs, outputs, ttArgs, iiArgs, biArgs, isInplace);
//auto hZ = op->execute(inputs, ttArgs, iiArgs, isInplace);
if (!isInplace)
for (int e = 0; e < numOutputs; e++) {
//shape::printShapeInfoLinear("JVM output shape", (int *) outputShapes[e]);
//shape::printShapeInfoLinear("C++ output shape", (int *) outputs[e]->shapeInfo());
//outputs[e]->printIndexedBuffer("C++ raw output");
//outputs[e]->printBuffer("C++ indexed output");
if (outputs[e]->ordering() != shape::order(reinterpret_cast<Nd4jLong *>(outputShapes[e])))
outputs[e]->streamline(shape::order(reinterpret_cast<Nd4jLong *>(outputShapes[e])));
}
/*
if (!isInplace) {
if (hZ->size() != numOutputs) {
return ND4J_STATUS_BAD_OUTPUT;
}
for (int e = 0; e < numOutputs; e++) {
auto buffer = (T *) outputBuffers[e];
auto shape = (int *) outputShapes[e];
nd4j::NDArray<T> tmp(buffer, shape);
if (tmp.lengthOf() != hZ->at(e)->lengthOf()) {
nd4j_printf("Provided output array for [%s] has length of %i, but actual hZ has length of %i\n", op->getOpName()->c_str(), tmp.lengthOf(), hZ->at(e)->lengthOf());
return ND4J_STATUS_BAD_OUTPUT;
}
tmp.assign(hZ->at(e));
}
} else {
// if op is inplace, our ResultSet holds pointers
hZ->purge();
}
delete hZ;
*/
for (auto v: inputs)
delete v;
for (auto v: outputs)
delete v;
return hZ;
}
int NativeOps::execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) {
auto op = nd4j::ops::OpRegistrator::getInstance()->getOperation(hash);
return realExec(op, extraPointers, hash, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, isInplace);
}
int NativeOps::registerGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer flatBufferPointer) {
auto graph = nd4j::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer);
nd4j::graph::GraphHolder::getInstance()->registerGraph(graphId, graph);
return ND4J_STATUS_OK;
}
static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) {
auto graph = nd4j::graph::GraphHolder::getInstance()->cloneGraph(graphId);
auto varSpace = graph->getVariableSpace();
std::vector<nd4j::NDArray*> handles;
for (int e = 0; e < numInputs; e++) {
auto idx = inputIndices[e];
// we'll delete this array later, together with cloned VariableSpace
auto array = new nd4j::NDArray(inputBuffers[e], reinterpret_cast<Nd4jLong *>(inputShapes[e]));
handles.emplace_back(array);
if (varSpace->hasVariable(idx)) {
auto var = varSpace->getVariable(idx);
if (var->hasNDArray())
delete var->getNDArray();
var->setNDArray(array);
} else
varSpace->putVariable(idx, array);
}
auto hZ = nd4j::graph::GraphExecutioner::execute(graph, varSpace);
auto varSet = new nd4j::graph::VariablesSet(hZ);
if (hZ == ND4J_STATUS_OK) {
// pull back results, and provide them
auto outputs = graph->fetchOutputs();
for (int e = 0; e < outputs->size(); e++) {
// we're only getting variable ID/Index from original grap. values will be taken from cloned workspace
std::pair<int, int> varId(outputs->at(e)->id(), outputs->at(e)->index());
auto var = varSpace->getVariable(varId);
varSet->push_back(var->clone());
}
delete outputs;
}
delete graph;
return varSet;
}
nd4j::graph::VariablesSet* NativeOps::executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) {
return nullptr;
}
int NativeOps::unregisterGraph(Nd4jPointer *extraPointers, Nd4jLong graphId) {
nd4j::graph::GraphHolder::getInstance()->dropGraphAny(graphId);
return nd4j::Status::OK();
}
void NativeOps::deletePointerArray(Nd4jPointer pointer) {
auto ptr = reinterpret_cast<Nd4jPointer *>(pointer);
delete[] ptr;
}
void NativeOps::deleteIntArray(Nd4jPointer pointer) {
auto ptr = reinterpret_cast<int *>(pointer);
delete[] ptr;
}
void NativeOps::deleteLongArray(Nd4jPointer pointer) {
auto ptr = reinterpret_cast<Nd4jLong *>(pointer);
delete[] ptr;
}
template <typename T>
static void deleteVariablesSetT(Nd4jPointer pointer) {
auto ptr = reinterpret_cast<nd4j::graph::VariablesSet*>(pointer);
delete ptr;
}
void NativeOps::deleteVariablesSet(Nd4jPointer pointer) {
deleteVariablesSetT<double>(pointer);
}
const char* NativeOps::getAllOperations() {
return nd4j::OpTracker::getInstance()->exportOperations();
}
Nd4jPointer NativeOps::getGraphState(Nd4jLong id) {
return (Nd4jPointer) new nd4j::graph::GraphState(id);
}
void NativeOps::deleteGraphState(Nd4jPointer state) {
auto stateP = reinterpret_cast<nd4j::graph::GraphState*>(state);
delete stateP;
}
Nd4jStatus execCustomOpWithScope_(Nd4jPointer *extraPointers, nd4j::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) {
/**
* That's basically exec, with VariableSpace provided in GraphState:
* depending on operation (i.e. while of if), different logic executors could be used
*/
auto graph = state->graph();
auto varSpace = state->variableSpace();
// Node is dynamically created, and has nothing beyond it: only inputs and outputs
// this node has id of 0, and inputs are
Node node(OpType_LOGIC, opHash, 0);
// mapping inputs
for (int e = 0; e < numInputs; e++) {
auto buffer = inputBuffers[e];
auto shapeInfo = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
auto array = new nd4j::NDArray(buffer, shapeInfo, varSpace->launchContext());
// now we just put array to VarSpace
varSpace->putVariable(0, e, array);
node.pickInput(0, e);
}
// mapping scopes
for (int e = 0; e < numScopes; e++) {
// we should check scope existence in GraphState/Graph
int scopeId = (int) scopes[e];
if (!state->hasScope(scopeId)) {
// nd4j_printf("execCustomOpWithScope: referenced scope [%i] doesn't exist\n", scopeId);
return Status::THROW();
}
node.pickInput(scopeId, 0);
}
auto hZ = LogicExecutor::processNode(graph, &node);
if (hZ != Status::OK())
return hZ;
// mapping outputs
for (int e = 0; e < numOutputs; e++) {
auto buffer = outputBuffers[e];
auto shapeInfo = reinterpret_cast<Nd4jLong *>(outputShapes[e]);
NDArray array(buffer, shapeInfo, varSpace->launchContext());
// now we just put array to VarSpace to the same ID
//varSpace->putVariable(0, e, array);
auto t = varSpace->getVariable(0, e)->getNDArray();
array.assign(t);
}
// removing input variables
for (int e = 0; e < numInputs; e++) {
varSpace->dropVariable(0, e);
}
// after some bla-bla-bla we should have Graph and Node for current op
return Status::OK();
}
Nd4jStatus NativeOps::execCustomOpWithScope(Nd4jPointer *extraPointers, Nd4jPointer state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) {
return execCustomOpWithScope_(extraPointers, reinterpret_cast<nd4j::graph::GraphState*>(state), opHash, scopes, numScopes, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes, numOutputs);
}
void NativeOps::deleteResultWrapper(Nd4jPointer ptr) {
// just 0 room for compiler s@!t
auto p = reinterpret_cast<nd4j::graph::ResultWrapper *>(ptr);
delete p;
}
/*
* TypeDef:
* void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer hX, long N, int dstType, Nd4jPointer hZ);
*/
void NativeOps::convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer hX, Nd4jLong N, int dstType, Nd4jPointer hZ) {
auto hx = reinterpret_cast<void *>(hX);
auto hz = reinterpret_cast<void *>(hZ);
if (srcType == ND4J_FLOAT8) {
if (dstType == ND4J_FLOAT8) {
// convertGeneric<double, nd4j::float8>(hx, N, hz);
} else if (dstType == ND4J_INT8) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, nd4j::int8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, nd4j::uint8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, nd4j::int16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, nd4j::uint16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_FLOAT32) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
//nd4j::TypeCast::convertGeneric<nd4j::float8, double>(nullptr, hx, N, hz);
} else {
//nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_INT8) {
if (dstType == ND4J_FLOAT8) {
//nd4j::TypeCast::convertGeneric<nd4j::int8, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
//convertGeneric<nd4j::int8, nd4j::int8>(hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<int8_t, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<int8_t, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
nd4j::TypeCast::convertGeneric<int8_t, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
//nd4j::TypeCast::convertGeneric<int8_t, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
// TODO: eventually we might want to add it
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertGeneric<int8_t, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertGeneric<int8_t, double>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_UINT8) {
if (dstType == ND4J_FLOAT8) {
// nd4j::TypeCast::convertGeneric<uint8_t, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
nd4j::TypeCast::convertGeneric<uint8_t, int8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<uint8_t, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<uint8_t, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
nd4j::TypeCast::convertGeneric<uint8_t, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
// nd4j::TypeCast::convertGeneric<uint8_t, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
// TODO: still might want to add
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertGeneric<uint8_t, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertGeneric<uint8_t, double>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_FLOAT16) {
if (dstType == ND4J_FLOAT8) {
// nd4j::TypeCast::convertGeneric<float16, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
nd4j::TypeCast::convertGeneric<float16, int8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<float16, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<float16, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
nd4j::TypeCast::convertGeneric<float16, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
// nd4j::TypeCast::convertGeneric<float16, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
// TODO: .... ^^^
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertGeneric<float16, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertGeneric<float16, double>(nullptr, hx, N, hz);
} else if (dstType == ND4J_THRESHOLD) {
nd4j::TypeCast::convertToThreshold<float16>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_INT16) {
if (dstType == ND4J_FLOAT8) {
// nd4j::TypeCast::convertGeneric<int16_t, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
nd4j::TypeCast::convertGeneric<int16_t, int8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<int16_t, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<int16_t, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
//nd4j::TypeCast::convertGeneric<int16_t, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
// nd4j::TypeCast::convertGeneric<int16_t, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
// TODO...
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertGeneric<int16_t, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertGeneric<int16_t, double>(nullptr, hx, N, hz);
} else {
printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_FLOAT24) {
} else if (srcType == ND4J_FLOAT32) {
if (dstType == ND4J_FLOAT8) {
// nd4j::TypeCast::convertGeneric<float, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
nd4j::TypeCast::convertGeneric<float, int8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<float, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<float, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
nd4j::TypeCast::convertGeneric<float, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
// nd4j::TypeCast::convertGeneric<float, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertGeneric<float, double>(nullptr, hx, N, hz);
} else if (dstType == ND4J_THRESHOLD) {
nd4j::TypeCast::convertToThreshold<float>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_DOUBLE) {
if (dstType == ND4J_FLOAT8) {
// nd4j::TypeCast::convertGeneric<double, nd4j::float8>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT8) {
nd4j::TypeCast::convertGeneric<double, int8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT8) {
nd4j::TypeCast::convertGeneric<double, uint8_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertGeneric<double, float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_INT16) {
nd4j::TypeCast::convertGeneric<double, int16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_UINT16) {
// nd4j::TypeCast::convertGeneric<double, uint16_t>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertGeneric<double, float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
//
} else if (dstType == ND4J_THRESHOLD) {
nd4j::TypeCast::convertToThreshold<double>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_THRESHOLD) {
if (dstType == ND4J_FLOAT16) {
nd4j::TypeCast::convertFromThreshold<float16>(nullptr, hx, N, hz);
} else if (dstType == ND4J_FLOAT32) {
nd4j::TypeCast::convertFromThreshold<float>(nullptr, hx, N, hz);
} else if (dstType == ND4J_DOUBLE) {
nd4j::TypeCast::convertFromThreshold<double>(nullptr, hx, N, hz);
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
}
/*
void NativeOps::fillUtf8String(Nd4jPointer *extraPointers, const char **strings, int numStrings, Nd4jPointer buffer) {
auto hZ = reinterpret_cast<nd4j::utf8string**>(buffer);
for (int e = 0; e < numStrings; e++) {
hZ[e] = reinterpret_cast<nd4j::utf8string*>(createUtf8String(extraPointers, strings[e]));
}
}
*/
Nd4jPointer NativeOps::createUtf8String(Nd4jPointer *extraPointers, const char *string, int length) {
auto u = new nd4j::utf8string(string, length);
return reinterpret_cast<Nd4jPointer>(u);
}
void NativeOps::deleteUtf8String(Nd4jPointer *extraPointers, Nd4jPointer ptr) {
delete(reinterpret_cast<nd4j::utf8string*>(ptr));
}
////////////////////////////////////////////////////////////////////////
void NativeOps::scatterUpdate(Nd4jPointer *extraPointers, int opCode, int numOfSubArrs,
void* hX, Nd4jLong* hXShapeInfo, Nd4jLong* hXOffsets,
void* dX, Nd4jLong* dXShapeInfo, Nd4jLong* dXOffsets,
void* hY, Nd4jLong* hYShapeInfo, Nd4jLong* hYOffsets,
void* dY, Nd4jLong* dYShapeInfo, Nd4jLong* dYOffsets,
int* hIindexes, int* dIindexes) {
int numThreads = omp_get_max_threads();
PRAGMA_OMP_PARALLEL_THREADS(numThreads)
{
for (int i = 0; i < numOfSubArrs; ++i) {
int threadIndex = omp_get_thread_num();
const auto xIndex = hIindexes[i];
const bool isOwner = xIndex < numThreads ? threadIndex == xIndex : threadIndex == xIndex % numThreads;
if (!isOwner)
continue;
NDArray inSubArr(reinterpret_cast<int8_t *>(hX) + (hXOffsets[hIindexes[i]] * DataTypeUtils::sizeOf(hXShapeInfo)), hXShapeInfo);
NDArray updSubArr(reinterpret_cast<int8_t *>(hY) + (hYOffsets[i] * DataTypeUtils::sizeOf(hXShapeInfo)), hYShapeInfo);
if (inSubArr.lengthOf() != updSubArr.lengthOf()) {
continue;
}
switch (opCode) {
case 0:
inSubArr.applyPairwiseTransform(pairwise::Add, &updSubArr, &inSubArr, nullptr);
break;
case 1:
inSubArr.applyPairwiseTransform(pairwise::Subtract, &updSubArr, &inSubArr, nullptr);
break;
case 2:
inSubArr.applyPairwiseTransform(pairwise::Multiply, &updSubArr, &inSubArr, nullptr);
break;
case 3:
inSubArr.applyPairwiseTransform(pairwise::Divide, &updSubArr, &inSubArr, nullptr);
break;
case 4:
inSubArr.applyPairwiseTransform(pairwise::ReverseSubtract, &updSubArr, &inSubArr, nullptr);
break;
case 5:
inSubArr.applyPairwiseTransform(pairwise::ReverseDivide, &updSubArr, &inSubArr, nullptr);
break;
case 6:
inSubArr.applyPairwiseTransform(pairwise::CopyPws, &updSubArr, &inSubArr, nullptr);
break;
default:
continue;
}
}
}
}
void NativeOps::inspectArray(Nd4jPointer *extraPointers, Nd4jPointer buffer, Nd4jLong *shapeInfo, Nd4jPointer specialBuffer, Nd4jLong *specialShapeInfo, Nd4jPointer debugInfo) {
auto p = reinterpret_cast<nd4j::DebugInfo*>(debugInfo);
NDArray array(buffer, shapeInfo);
nd4j::DebugHelper::retrieveDebugStatistics(p, &array);
}
void NativeOps::tryPointer(Nd4jPointer extra, Nd4jPointer p, int len) {
auto buf = reinterpret_cast<int8_t*>(p);
int cnt = 0;
for (int i = 0; i < len; i++)
cnt += buf[cnt];
}
nd4j::ConstantDataBuffer* NativeOps::shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, nd4j::DataType dtype, char order, Nd4jLong ews, bool empty) {
auto buffer = new ConstantDataBuffer();
*buffer = nd4j::ConstantShapeHelper::getInstance()->bufferForShapeInfo(ShapeDescriptor(dtype, order, shape, strides, rank, ews, empty));
return buffer;
}
nd4j::ConstantDataBuffer* NativeOps::constantBuffer(nd4j::DataType dtype, Nd4jLong *data, int length) {
return nullptr;
}
nd4j::ConstantDataBuffer* NativeOps::constantBuffer(nd4j::DataType dtype, double *data, int length) {
return nullptr;
}
nd4j::ConstantDataBuffer* NativeOps::constantBuffer(nd4j::DataType dtype, nd4j::ConstantDescriptor *descriptor) {
return nd4j::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype);
}
int NativeOps::dataTypeFromNpyHeader(void *header) {
return (int) cnpy::dataTypeFromHeader(reinterpret_cast<char *>(header));
}
Dev branch merge: dev_20190606 (#7904) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
2019-06-15 13:34:34 +02:00
Nd4jPointer NativeOps::shapeBufferForNumpy(Nd4jPointer npyArray) {
cnpy::NpyArray arr = cnpy::loadNpyFromPointer(reinterpret_cast<char *>(npyArray));
unsigned int shapeSize = arr.shape.size();
std::vector<Nd4jLong> shape(shapeSize);
bool _empty = false;
for(unsigned int i = 0; i < shapeSize; i++) {
shape[i] = arr.shape[i];
if (arr.shape[i] == 0)
_empty = true;
}
auto dtype = cnpy::dataTypeFromHeader(reinterpret_cast<char *>(npyArray));
Nd4jLong *shapeBuffer;
if (_empty) {
if (shapeSize > 0)
shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape);
else
shapeBuffer = nd4j::ShapeBuilders::emptyShapeInfo(dtype);
} else {
shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape);
}
return reinterpret_cast<Nd4jPointer>(shapeBuffer);
}
Merge master to upstream (#7945) * Shugeo strided slice zeros (#14) * Modified strided_slice op to properly work with empty-like shapes. * Fixed test for reduce_mean with empty-like input. * [WIP] Last merge (#15) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks * [WIP] Fixing outstanding issues for NLP (#9) * Avoid using not-inited objects * Test fixed. * Redundant method avoided for models like FastText * KMeans++ implementation * KMeans++ implementation * Disable parallel execution * KMeans++ * Tests * Dev branch merge (#16) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Fix some issues on master (#17) * Fix DataVec test issue * Fix issue with dl4j SameDiff output layer * Dtype fix for lambda layers * #7912 BertIterator dtype fix (use float32 not global default) * [WIP] Next set of CUDA stuff (#7) New CUDA implementations and improvements * bad file * Dev branch master merge (#23) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * SameDiff ops, TF import and fixes (#24) * CheckNumerics tests + fixes + misc fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fake quant Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * FakeQuantWithMinMaxArgs Signed-off-by: AlexDBlack <blacka101@gmail.com> * CheckNumerics fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * Exception tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix for out of scope stack allocated var use Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignores Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignore for known failing test (already logged issue) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Merge upstream to fork (#25) * Add thousand-separator commas to TotalParams (#7915) * Add thousand-separator commas to TotalParams The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them. * Add thousand-separator commas to MultiLayerNetwork Corresponding change to MultiLayerNetwork Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com> * Update contributing and issue/PR templates (#7934) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix link to AdaDelta paper (#7942) Fix link to AdaDelta paper hosted on matthewzeiler.com Signed-off-by: Jxtps * Fixes, and ignores for known/logged failing issues (#7943) Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff + DL4J/SameDiff: Multiple fixes (#28) * #7919 HDF5 attribute buffer length fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7909 Arbiter constructor exception ux improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7925 RNN output layer length checks Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Add listener for validating inputs are not incorrectly modified Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Integrate NonInplaceValidationListener into tests * #7844 DL4J SameDiff fixes for variable minibatch size * DL4J SameDiff fixes - ensure gradient for input placeholder is available Signed-off-by: AlexDBlack <blacka101@gmail.com> * Tweaks to ExternalErrorsFunction - use placeholders, make more robust * Another fix * More fixes * More SameDiff/DL4J fixes * Scope out scalar array creation in BaseScalarOp * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] Final dev branch merge (#29) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * [WIP] Multiple dataset iterators (#27) * Splitting dataset into arbitrary number * Fixes * Multiple split of iterator * Test * Test * Some fixes * signature change * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * one more test for sequential use of DataSetIteratorSplitter Signed-off-by: raver119 <raver119@gmail.com> * Fixes * Fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * couple of assertions tweaked Signed-off-by: raver119 <raver119@gmail.com> * MDS splitter test :/ Signed-off-by: raver119 <raver119@gmail.com> * Minor refactoring * Multi dataset * Some fixes * More tests * Small number of test fixes/improvements (failures on CI) (#31) Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] More CUDA stuff (#26) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * LRN BP CUDA Signed-off-by: raver119 <raver119@gmail.com> * less memory Signed-off-by: raver119 <raver119@gmail.com> * Fixed bug with crop_and_resize op helper. * get rid of unnecessary index-calculation dunction Signed-off-by: Yurii <yurii@skymind.io> * Fixed sort with nth_element cuda-based helper. * Refactored nth_element. * Refactored nth_element op and tests. * Modified usage of dim array with sortTad routine. * Refactored main routine of helper for non_max_image_suppression op. * non_max_image_suppression op helper with cuda kernel implementation. Initial revision. * fix vol2col cuda kernel * meh Signed-off-by: raver119 <raver119@gmail.com> * topK concept Signed-off-by: raver119 <raver119@gmail.com> * unsorted topK with scanWitdh of 1 Signed-off-by: raver119 <raver119@gmail.com> * correct vol2col tests * sorted/unsorted topK Signed-off-by: raver119 <raver119@gmail.com> * implementation and fixing col2im/col2vol * Corrected usage flags with input/output with reverse op. * dup is const now Signed-off-by: raver119 <raver119@gmail.com> * percentile op Signed-off-by: raver119 <raver119@gmail.com> * group tests for mapool2d Signed-off-by: Yurii <yurii@skymind.io> * special test for george Signed-off-by: raver119 <raver119@gmail.com> * less threads for sortTad Signed-off-by: raver119 <raver119@gmail.com> * provide conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * remove auther in sort tad kernel code Signed-off-by: Yurii <yurii@skymind.io> * provide depthwise_conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * - max_pooling_with_argmax - null check for special use Signed-off-by: raver119 <raver119@gmail.com> * dts cuda Signed-off-by: raver119 <raver119@gmail.com> * provide sconv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * std cuda Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op to conform TF implementation. * Improved suppression helper. * provide pooling3d for cuda Signed-off-by: Yurii <yurii@skymind.io> * minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * more of minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * (bi)dynamic_rnn Signed-off-by: raver119 <raver119@gmail.com> * templates init order Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op. * Added cuda kernel for non_max_suppression. * CPU sort by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value tests Signed-off-by: raver119 <raver119@gmail.com> * Eliminate compiler error with cuda implementation. * - repaired gradCheck in cuda - provide conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * missed signature Signed-off-by: raver119 <raver119@gmail.com> * provide depthwise_conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * Implementation of lup helper with cuda kernel. Initial commit. * further work on backprops for convolutions Signed-off-by: Yurii <yurii@skymind.io> * CUDA linear sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * CUDA tad sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * start providing of backprop for pooling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * Added atomicAdd for bool datatype. * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition scalar CUDA Signed-off-by: raver119 <raver119@gmail.com> * important comment Signed-off-by: raver119 <raver119@gmail.com> * fix pooling2d/3d backprop helpers Signed-off-by: Yurii <yurii@skymind.io> * Added non-linear test with dynamic_partition. * Improved test for dynamic_partition. * dynamic_partition TAD concept Signed-off-by: raver119 <raver119@gmail.com> * - dynamic_partition TAD CUDA impl - dynamic_partition TAD CPU fix Signed-off-by: raver119 <raver119@gmail.com> * - rewrite cpu code for usampling2d/3d - write cuda code for usampling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * dynamic_stitch CUDA vector case Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case impl Signed-off-by: raver119 <raver119@gmail.com> * Added tests for dynamic_stitch 3D-4D cases. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * Fixed type check for dynamic stitch. * min/max bp Signed-off-by: raver119 <raver119@gmail.com> * rewrite code for upsampling2d/3d cpu Signed-off-by: Yurii <yurii@skymind.io> * reduce min/max/norm_max bp Signed-off-by: raver119 <raver119@gmail.com> * lup implementation. Additional enhancements. * provide code for upsamling2d/3d backprop Signed-off-by: Yurii <yurii@skymind.io> * weightedCrossEntropyWithLogits Signed-off-by: raver119 <raver119@gmail.com> * Fixed template math atomicMul for 64bit ints. * Refactored dynamic_partition_bp op. * inverseBroadcast fix Signed-off-by: raver119 <raver119@gmail.com> * DynamicPartitionBP test datatype fixed. * - nd4j_atomicMul Windows fix - cpu/NDArrayLambda.hpp excluded from CUDA Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
void NativeOps::sortByKey(Nd4jPointer *extraPointers,
void *x, Nd4jLong *xShapeInfo,
void *dx, Nd4jLong *dxShapeInfo,
void *y, Nd4jLong *yShapeInfo,
void *dy, Nd4jLong *dyShapeInfo,
bool descending) {
auto xType = ArrayOptions::dataType(xShapeInfo);
auto yType = ArrayOptions::dataType(yShapeInfo);
BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortByKey(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES);
}
void NativeOps::sortByValue(Nd4jPointer *extraPointers,
void *x, Nd4jLong *xShapeInfo,
void *dx, Nd4jLong *dxShapeInfo,
void *y, Nd4jLong *yShapeInfo,
void *dy, Nd4jLong *dyShapeInfo,
bool descending) {
auto xType = ArrayOptions::dataType(xShapeInfo);
auto yType = ArrayOptions::dataType(yShapeInfo);
BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortByValue(x, xShapeInfo, y, yShapeInfo, descending), LIBND4J_TYPES, LIBND4J_TYPES);
}
void NativeOps::sortTadByKey(Nd4jPointer *extraPointers,
void *x, Nd4jLong *xShapeInfo,
void *dx, Nd4jLong *dxShapeInfo,
void *y, Nd4jLong *yShapeInfo,
void *dy, Nd4jLong *dyShapeInfo,
int *dimension,
int dimensionLength,
bool descending) {
auto xType = ArrayOptions::dataType(xShapeInfo);
auto yType = ArrayOptions::dataType(yShapeInfo);
BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortTadByKey(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES);
}
void NativeOps::sortTadByValue(Nd4jPointer *extraPointers,
void *x, Nd4jLong *xShapeInfo,
void *dx, Nd4jLong *dxShapeInfo,
void *y, Nd4jLong *yShapeInfo,
void *dy, Nd4jLong *dyShapeInfo,
int *dimension,
int dimensionLength,
bool descending) {
auto xType = ArrayOptions::dataType(xShapeInfo);
auto yType = ArrayOptions::dataType(yShapeInfo);
BUILD_DOUBLE_SELECTOR(xType, yType, nd4j::DoubleMethods, ::sortTadByValue(x, xShapeInfo, y, yShapeInfo, dimension, dimensionLength, descending), LIBND4J_TYPES, LIBND4J_TYPES);
}
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
BUILD_SINGLE_TEMPLATE(template void flattenGeneric,(Nd4jPointer*, int, char, void*, Nd4jLong*, void*, Nd4jLong*), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void pullRowsGeneric, (void *, Nd4jLong*, void*, Nd4jLong*, const int, Nd4jLong*, Nd4jLong*, Nd4jLong*, Nd4jLong*, Nd4jLong*), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void tearGeneric, (void *, Nd4jLong*, Nd4jPointer*, Nd4jLong*, Nd4jLong*, Nd4jLong*), LIBND4J_TYPES);
BUILD_SINGLE_TEMPLATE(template void shuffleGeneric, (void**, Nd4jLong**, void**, Nd4jLong**, int, int*, Nd4jLong**, Nd4jLong**), LIBND4J_TYPES);