/*******************************************************************************
* 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
******************************************************************************/
#ifndef NDARRAY_CPP
#define NDARRAY_CPP
#include "../NDArray.h"
#include "../NDArrayFactory.h"
#include "NativeOpExecutioner.h"
#include <BroadcastPairwiseConverter.h>
#include <memory/Workspace.h>
#include <memory/MemoryRegistrator.h>
#include <ops.h>
#include <ops/gemm.h>
#include <pointercast.h>
#include <stdexcept>
#include <memory>
#include <helpers/logger.h>
#include <loops/pairwise_transform.h>
#include <loops/transform_same.h>
#include <loops/random.h>
#include <loops/broadcasting.h>
#include <indexing/NDIndex.h>
#include <indexing/IndicesList.h>
#include <helpers/ShapeUtils.h>
#include <sstream>
#include <helpers/ArrayUtils.h>
#include <MmulHelper.h>
#include <helpers/threshold.h>
#include <exceptions/datatype_exception.h>
#include <exceptions/allocation_exception.h>
#include <helpers/ConstantTadHelper.h>
#include <NDArray.hpp>
namespace nd4j {
////////////////////////////////////////////////////////////////////////
void* NDArray::platformBuffer() { return buffer(); }
void* NDArray::getPlatformBuffer() const { return getBuffer(); }
Nd4jLong* NDArray::getPlatformShapeInfo() const { return getShapeInfo(); }
Nd4jLong* NDArray::platformShapeInfo() { return shapeInfo(); }
void NDArray::syncToDevice() const { }
void NDArray::syncToHost() const { }
void NDArray::tickWriteHost() const { }
void NDArray::tickWriteDevice() const { }
void NDArray::tickReadHost() const { }
void NDArray::tickReadDevice() const { }
void NDArray::tickBothActual() const { }
bool NDArray::isActualOnHostSide() const { return true; }
bool NDArray::isActualOnDeviceSide() const { return true; }
void NDArray::makeBothBuffersActual() const { }
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::fillAsTriangular(const float val, int lower, int upper, NDArray& target, const char direction) {
if (isS())
throw std::runtime_error("NDArray::fillArrayAsTriangular: you can't use this method on String array!");
if(!isSameShape(target) && !(rankOf() == 1 && target.rankOf() == 2 && sizeAt(0) == target.sizeAt(0) && sizeAt(0) == target.sizeAt(1)))
throw std::string("NDArray::fillArrayAsTriangular method: wrong shape of target array !");
if (direction == 'u')
lower = -target.sizeAt(-2);
else if (direction == 'l')
upper = target.sizeAt(-1);
const T value = static_cast<T>(val);
const auto x = reinterpret_cast<const T*>(getBuffer());
auto z = reinterpret_cast<T*>(target.getBuffer());
const int xRank = rankOf();
const int zRank = target.rankOf();
const auto zLen = target.lengthOf();
const bool areSameOffsets = shape::haveSameShapeAndStrides(getShapeInfo(), target.getShapeInfo());
auto func = PRAGMA_THREADS_FOR {
Nd4jLong coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coords(i, target.getShapeInfo(), coords);
const auto zOffset = shape::getOffset(target.getShapeInfo(), coords);
// if( (row + upper < col) || (row + lower > col) )
if ((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1]))
z[zOffset] = value;
else if (this != &target) { // when this and target are different arrays
if (xRank != zRank)
coords[0] = coords[1];
const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(getShapeInfo(), coords);
z[zOffset] = x[xOffset];
}
}
};
samediff::Threads::parallel_for(func, 0, zLen);
}
BUILD_SINGLE_TEMPLATE(template void NDArray::fillAsTriangular, (const float val, int lower, int upper, NDArray& target, const char direction), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
void NDArray::setIdentity() {
if (isS())
throw std::runtime_error("NDArray::setIdentity: you can't use this method on String array!");
this->nullify();
int rank = rankOf();
auto shape = shapeOf();
int minDim = MAX_INT;
Nd4jLong indices[MAX_RANK];
for(int j = 0; j < rank; ++j)
indices[j] = 1;
Nd4jLong offset = shape::getOffset(getShapeInfo(), indices);
for(int i = 0; i < rank; ++i)
if(minDim > shape[i])
minDim = shape[i];
float v = 1.0f;
for(int i = 0; i < minDim; ++i)
templatedSet<float>(buffer(), i*offset, this->dataType(), &v);
}
////////////////////////////////////////////////////////////////////////
template <typename T>
static void templatedSwap(void *xBuffer, void *yBuffer, Nd4jLong length) {
auto x = reinterpret_cast<T *>(xBuffer);
auto y = reinterpret_cast<T *>(yBuffer);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto temp = x[i];
x[i] = y[i];
y[i] = temp;
}
};
samediff::Threads::parallel_for(func, 0, length);
}
BUILD_SINGLE_TEMPLATE(template void templatedSwap, (void *xBuffer, void *yBuffer, Nd4jLong length), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
void NDArray::swapUnsafe(NDArray& other) {
auto xType = this->dataType();
if (xType != other.dataType())
throw std::runtime_error("NDArray::swapUnsage method: both arrays must have the same data type");
if(buffer() == nullptr || other.buffer() == nullptr)
throw std::runtime_error("NDArray::swapUnsafe method: input array should not be empty!");
if(lengthOf() != other.lengthOf())
throw std::runtime_error("NDArray::swapUnsafe method: input arrays should have the same length!");
BUILD_SINGLE_SELECTOR(xType, templatedSwap, (buffer(), other.buffer(), this->lengthOf()), LIBND4J_TYPES);
}
////////////////////////////////////////////////////////////////////////
void NDArray::synchronize(const char* msg) const {
// no-op
}
void NDArray::prepareSpecialUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {
// no-op
}
void NDArray::registerSpecialUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList) {
// no-op
}
void NDArray::preparePrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {
// no-op
}
void NDArray::registerPrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList) {
// no-op
}
void NDArray::syncShape() const {
// no-op
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::printCurrentBuffer(const bool host, const char* msg, const int precision) const {
}
////////////////////////////////////////////////////////////////////////
void* NDArray::specialBufferWithOffset(Nd4jLong offset) const {
return nullptr;
}
////////////////////////////////////////////////////////////////////////
void* NDArray::specialBuffer() {
if (_buffer->special() == nullptr)
return getBuffer();
// FIXME: this should be fixed once CUDA backend added
return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
}
////////////////////////////////////////////////////////////////////////
void* NDArray::getSpecialBuffer() const {
if (_buffer->special() == nullptr)
return getBuffer();
// FIXME: this should be fixed once CUDA backend added
return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
}
//////////////////////////////////////////////////////////////////////////
// change an array by repeating it the number of times given by reps.
NDArray NDArray::tile(const std::vector<Nd4jLong>& reps) const {
const int repsSize = reps.size();
Nd4jLong product = 1;
for(const auto& item : reps)
product *= item;
if(product == 0)
throw std::runtime_error("NDArray::tile method: one of the elements in reps array is zero !");
int rankOld = rankOf();
int diff = rankOld - repsSize;
if(product==1) { // in this case 2 possibilities are present: just reshape or nothing to do
NDArray result(*this);
if(diff < 0) { // reshape to higher dimension
std::vector<Nd4jLong> shapeNew = reps; // there is requirement to have unities at first "diff" positions of new shape
memcpy(&shapeNew[-diff], result.getShapeInfo()+1, rankOld * sizeof(Nd4jLong)); // put old shape numbers at rest of positions
result.reshapei(ordering(), shapeNew);
}
return result; // nothing to do, if diff >= 0 -> identity tile
}
// evaluate shapeInfo for resulting array
auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
// create new buffer, in any case the memory amount new buffer points to is bigger then those for old _buffer
std::shared_ptr<DataBuffer> newBuff = std::make_shared<DataBuffer>(shape::length(newShapeInfo) * sizeOfT(), dataType(), getContext()->getWorkspace());
// assign new shape and new buffer to resulting array
NDArray result(newBuff, ShapeDescriptor(newShapeInfo), getContext());
// fill newBuff, loop through all elements of newBuff
// looping through _buffer goes automatically by means of getSubArrayIndex applying
const auto resultLen = result.lengthOf();
auto xType = this->dataType();
if(result.ordering() == 'c') { // ews == 1 always here
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto yOffset = shape::subArrayOffset(i, newShapeInfo, getShapeInfo());
BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign,(result.getBuffer(), i, this->getBuffer(), yOffset), LIBND4J_TYPES);
}
};
samediff::Threads::parallel_for(func, 0, resultLen);
}
else {
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++) {
auto xOffset = result.getOffset(i);
auto yOffset = shape::subArrayOffset(i, newShapeInfo, getShapeInfo());
BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign,(result.getBuffer(), xOffset, this->getBuffer(), yOffset), LIBND4J_TYPES);
}
};
samediff::Threads::parallel_for(func, 0, resultLen);
}
result.tickWriteHost();
return result;
}
//////////////////////////////////////////////////////////////////////////
// change an array by repeating it the number of times given by reps.
void NDArray::tile(const std::vector<Nd4jLong>& reps, NDArray& target) const {
auto repProd = shape::prodLong(reps.data(), reps.size());
if (repProd < 1)
throw std::runtime_error("NDArray::tile: reps can't contain 0s");
// evaluate true tile shapeInfo for comparison with target shapeInfo
auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
if(!shape::equalsSoft(newShapeInfo, target.getShapeInfo())) {
delete []newShapeInfo;
throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");
}
// fill newBuff, loop through all elements of newBuff
// looping through _buffer goes automatically by means of getSubArrayIndex applying
const int ews = target.ews();
const int targetLen = target.lengthOf();
if(target.ordering() == 'c' && ews == 1) { // ews == 1 always here
//#pragma omp parallel for simd if(targetLen > Environment::getInstance()->elementwiseThreshold()) schedule(guided)
for(Nd4jLong i=0; i<targetLen; ++i) {
auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
}
}
else if(target.ordering() == 'c' && ews > 1) {
for(Nd4jLong i=0; i<targetLen; ++i) {
auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i*ews, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
}
}
else {
for(Nd4jLong i=0; i<targetLen; ++i) {
auto xOffset = target.getOffset(i);
auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), xOffset, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
}
}
}
//////////////////////////////////////////////////////////////////////////
void NDArray::tile(NDArray& target) const {
if(rankOf() > target.rankOf())
throw std::runtime_error("NDArray::tile method - rank of target array must be bigger or equal to the rank of this array !");
if(!ShapeUtils::areShapesBroadcastable(*this, target))
throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");
// fill newBuff, loop through all elements of newBuff
// looping through _buffer goes automatically by means of getSubArrayIndex applying
const auto ews = target.ews();
const auto targetLen = target.lengthOf();
if(target.ordering() == 'c' && ews >= 1) {
for(Nd4jLong i=0; i<targetLen; ++i) {
auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i*ews, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
}
}
else {
for(Nd4jLong i=0; i<targetLen; ++i) {
auto xOffset = target.getOffset(i);
auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), xOffset, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
}
}
}
////////////////////////////////////////////////////////////////////////
template<typename X, typename Z>
static void repeat_(const NDArray& input, NDArray& output, const std::vector<int>& repeats, const int axis) {
const X* x = input.bufferAsT<X>();
Z* z = output.bufferAsT<Z>();
const int rank = input.rankOf(); // xRank = zRank
const int zLen = output.lengthOf(); // xLen <= zLen
const int repSize = repeats.size();
// loop through input array
auto func = PRAGMA_THREADS_FOR {
Nd4jLong coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coords(i, output.getShapeInfo(), coords);
const auto zOffset = shape::getOffset(output.getShapeInfo(), coords);
if (repSize > 1) {
for (uint j = 0; j < repSize; ++j) {
coords[axis] -= repeats[j];
if (coords[axis] < 0) {
coords[axis] = j;
break;
}
}
} else
coords[axis] /= repeats[0];
z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)];
}
};
samediff::Threads::parallel_for(func, 0, zLen);
}
//////////////////////////////////////////////////////////////////////////
// create new array by repeating it the number of times given by repeats
NDArray NDArray::repeat(const int axis, const std::vector<int>& repeats) const {
NDArray output('c', ShapeUtils::evalRepeatShape(axis, repeats, *this), dataType(), getContext());
BUILD_SINGLE_SELECTOR_TWICE(dataType(), repeat_, (*this, output, repeats, axis), LIBND4J_TYPES);
return output;
}
//////////////////////////////////////////////////////////////////////////
// fill array by repeating it the number of times given by reps
void NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) const {
if(!target.isSameShape(ShapeUtils::evalRepeatShape(axis, repeats, *this)))
throw std::invalid_argument("NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) method: wrong shape of target array!");
BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), repeat_, (*this, target, repeats, axis), LIBND4J_TYPES, LIBND4J_TYPES);
}
//////////////////////////////////////////////////////////////////////////
#ifndef __JAVACPP_HACK__
#include "NDArrayLambda.hpp"
#endif
/*
#ifndef __CLION_IDE__
#include "NDArray.macro"
#endif
*/
}
#endif