/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* 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
******************************************************************************/
// $NDArray.hpp - architech-independent implementations (both cuda and cpu).
//
#ifndef __NDARRAY__HPP__
#define __NDARRAY__HPP__
#include <array/ShapeDescriptor.h>
#include <ConstantShapeHelper.h>
#include <ConstantShapeHelper.h>
#include <ConstantTadHelper.h>
#include <BroadcastPairwiseConverter.h>
#include <helpers/PointersManager.h>
#include <TrueBroadcastHelper.h>
namespace nd4j {
template <>
ND4J_EXPORT utf8string NDArray::e(const Nd4jLong i) const;
template <>
ND4J_EXPORT std::string NDArray::e(const Nd4jLong i) const;
////////////////////////////////////////////////////////////////////////
// copy constructor
NDArray::NDArray(const NDArray& other) {
_context = other._context;
_offset = 0;
setShapeInfo(ShapeDescriptor(other.dataType(), other.ordering(), other.shapeOf(), other.rankOf()));
if(!isEmpty()) {
_buffer = std::make_shared<DataBuffer>(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace());
this->assign(&other);
}
else
_buffer = std::make_shared<DataBuffer>();
}
////////////////////////////////////////////////////////////////////////
NDArray::NDArray(const char order, const std::vector<Nd4jLong> &shape, nd4j::DataType dtype, nd4j::LaunchContext * context) {
if ((int) shape.size() > MAX_RANK)
throw std::invalid_argument("Rank of NDArray can't exceed 32");
_context = context;
_isAttached = _context->getWorkspace() != nullptr;
_offset = 0;
if (shape.empty())
setShapeInfo(ShapeDescriptor::emptyDescriptor(dtype));
else
setShapeInfo(ShapeDescriptor(dtype, order, shape));
_buffer = std::make_shared<DataBuffer>(lengthOf() * DataTypeUtils::sizeOf(dtype), dtype, getContext()->getWorkspace());
_buffer->setToZeroBuffers();
}
////////////////////////////////////////////////////////////////////////
NDArray::NDArray(const char order, const std::vector<Nd4jLong> &shape, const std::vector<double>& data, nd4j::DataType dtype, nd4j::LaunchContext * context) {
if ((int) shape.size() > MAX_RANK)
throw std::invalid_argument("Rank of NDArray can't exceed 32");
_context = context;
_offset = 0;
if (shape.size() == 0) {
if (data.size() == 0)
setShapeInfo(ShapeDescriptor::emptyDescriptor(dtype));
else
setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
} else {
setShapeInfo(ShapeDescriptor(dtype, order, shape));
}
if (lengthOf() != data.size()) {
nd4j_printf("NDArray constructor: data size [%i] doesn't match shape length [%i]\n", data.size(), lengthOf());
throw std::runtime_error("Data size doesn't match shape");
}
_buffer = std::make_shared<DataBuffer>(lengthOf() * DataTypeUtils::sizeOf(dtype), dtype, getContext()->getWorkspace(), true);
for(Nd4jLong i=0; i < lengthOf(); ++i) {
BUILD_SINGLE_PARTIAL_SELECTOR(dtype, templatedDoubleAssign<, double>(buffer(), i, reinterpret_cast<const void *>(data.data()), i), LIBND4J_TYPES);
}
tickWriteHost();
syncToDevice();
}
////////////////////////////////////////////////////////////////////////
NDArray::NDArray(const NDArray *other, const bool copyStrides, nd4j::LaunchContext* context) {
_context = context;
_offset = 0;
_isAttached = getContext()->getWorkspace() != nullptr;
if (copyStrides)
setShapeInfo(ShapeDescriptor(other->_shapeInfo));
else
setShapeInfo(ShapeDescriptor(other->dataType(), other->ordering(), other->shapeOf(), other->rankOf()));
if (!isEmpty())
_buffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dataType(), getContext()->getWorkspace());
}
////////////////////////////////////////////////////////////////////////
NDArray::NDArray(void* buffer, const char order, const std::vector<Nd4jLong> &shape, nd4j::DataType dtype, nd4j::LaunchContext * context, const bool isBuffAlloc) {
if (shape.empty())
throw std::runtime_error("NDArray constructor: input shape is empty !");
if ((int) shape.size() > MAX_RANK)
throw std::invalid_argument("Rank of NDArray can't exceed 32");
_context = context;
_offset = 0;
_isAttached = getContext()->getWorkspace() != nullptr;
setShapeInfo(ShapeDescriptor(dtype, order, shape));
_buffer = std::make_shared<DataBuffer>(buffer, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, getContext()->getWorkspace());
}
////////////////////////////////////////////////////////////////////////
// creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros
NDArray::NDArray(Nd4jLong* shapeInfo, const nd4j::DataType dtype, const bool copyStrides, nd4j::LaunchContext * context) {
if (shapeInfo == nullptr)
throw std::runtime_error("NDArray constructor: can't be initalized without shapeinfo");
if ((int) shapeInfo[0] > MAX_RANK)
throw std::invalid_argument("Rank of NDArray can't exceed 32");
_context = context;
_offset = 0;
if (copyStrides)
setShapeInfo(ShapeDescriptor(shapeInfo, dtype));
else
setShapeInfo(ShapeDescriptor(dtype, shape::order(shapeInfo), shape::shapeOf(shapeInfo), shape::rank(shapeInfo)));
if (!isEmpty()) {
_buffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dtype, getContext()->getWorkspace());
_buffer->setToZeroBuffers();
}
}
////////////////////////////////////////////////////////////////////////
// scalar constructor
NDArray::NDArray(nd4j::DataType dtype, nd4j::LaunchContext* context, const bool isScalar) {
_context = context;
_offset = 0;
_isAttached = getContext()->getWorkspace() != nullptr;
if (isScalar) {
setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
_buffer = std::make_shared<DataBuffer>(sizeOfT(), dtype, getContext()->getWorkspace());
_buffer->setToZeroBuffers();
}
else
setShapeInfo(ConstantShapeHelper::getInstance()->emptyShapeInfo(dtype));
}
//////////////////////////////////////////////////////////////////////////
// move constructor
NDArray::NDArray(NDArray&& other) noexcept {
_isView = other._isView;
_buffer = other._buffer;
_shapeInfo = other._shapeInfo;
_shapeInfoD = other._shapeInfoD;
_context = other._context;
_dataType = other._dataType;
_length = other._length;
_offset = other._offset;
other._buffer = std::make_shared<DataBuffer>();
other._shapeInfo = other._shapeInfoD = nullptr;
other._length = 0;
}
////////////////////////////////////////////////////////////////////////
//constructor, create empty array at given workspace
NDArray::NDArray(nd4j::LaunchContext * context) {
_buffer = std::make_shared<DataBuffer>();
_shapeInfo = nullptr;
_shapeInfoD = nullptr;
_offset = 0;
_context = context;
_length = 0;
}
////////////////////////////////////////////////////////////////////////
// creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros, set dtype as array type
NDArray::NDArray(Nd4jLong* shapeInfo, const bool copyStrides, nd4j::LaunchContext * context):
NDArray(shapeInfo, ArrayOptions::dataType(shapeInfo), copyStrides, context) {
}
////////////////////////////////////////////////////////////////////////
NDArray::NDArray(std::shared_ptr<DataBuffer> buffer, const ShapeDescriptor& descriptor, nd4j::LaunchContext* context, const Nd4jLong offset) {
_context = context;
_offset = offset;
setShapeInfo(descriptor);
_buffer = buffer;
_isView = offset > 0 || _length * DataTypeUtils::sizeOf(_dataType) < buffer->getLenInBytes();
}
////////////////////////////////////////////////////////////////////////
// do not allocate memory, memory for array is passed from outside
NDArray::NDArray(void *buffer, Nd4jLong *shapeInfo, nd4j::LaunchContext * context, const bool isBuffAlloc) {
if (buffer == nullptr && ArrayOptions::arrayType(shapeInfo) != ArrayType::EMPTY)
throw std::runtime_error("NDArray constructor: can't be initalized with nullptr buffer !");
if (shapeInfo == nullptr)
throw std::runtime_error("NDArray constructor: can't be initalized without shapeinfo !");
if ((int) shapeInfo[0] > MAX_RANK)
throw std::invalid_argument("NDArray constructor: rank of NDArray can't exceed 32 !");
_context = context;
_isAttached = getContext()->getWorkspace() != nullptr;
_offset = 0;
setShapeInfo(ShapeDescriptor(shapeInfo));
if (this->isEmpty()) {
tickReadDevice();
tickReadHost();
}
else {
_buffer = std::make_shared<DataBuffer>(buffer, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, getContext()->getWorkspace());
}
}
////////////////////////////////////////////////////////////////////////
// do not allocate memory, memory for array is passed from outside
// we suppose the content of both (device and host) buffers is identical
NDArray::NDArray(void *buffer, void* bufferD, Nd4jLong *shapeInfo, nd4j::LaunchContext * context, const bool isBuffAlloc, const bool isBuffDAlloc) {
if (shapeInfo == nullptr)
throw std::runtime_error("NDArray constructor cuda: can't be initalized without shapeinfo");
if ((int) shapeInfo[0] > MAX_RANK)
throw std::invalid_argument("NDArray constructor cuda: rank of NDArray can't exceed 32");
_context = context;
_offset = 0;
setShapeInfo(ShapeDescriptor(shapeInfo));
if (!isEmpty())
_buffer = std::make_shared<DataBuffer>(buffer, bufferD, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, isBuffDAlloc, getContext()->getWorkspace());
}
//////////////////////////////////////////////////////////////////////////
NDArray::NDArray(std::shared_ptr<DataBuffer> buffer, const char order, const std::vector<Nd4jLong> &shape, nd4j::LaunchContext* context) {
if (shape.empty())
throw std::runtime_error("NDArray constructor: input shape is empty !");
if ((int) shape.size() > MAX_RANK)
throw std::invalid_argument("NDArray constructor: rank of NDArray can't exceed 32");
_context = context;
_offset = 0;
setShapeInfo(ShapeDescriptor(buffer->getDataType(), order, shape));
_buffer = buffer;
_isView = _length * DataTypeUtils::sizeOf(_dataType) < buffer->getLenInBytes();
}
////////////////////////////////////////////////////////////////////////
// assignment operator
NDArray& NDArray::operator=(const NDArray& other) {
if (this == &other || (_shapeInfo == other._shapeInfo && _shapeInfo == nullptr))
return *this;
if (_shapeInfo != nullptr && shape::equalsTypesAndShapesSoft(_shapeInfo, other._shapeInfo)) {
if(!other.isEmpty())
this->assign(&other);
}
else {
_context = other._context;
_offset = 0;
setShapeInfo(ShapeDescriptor(other.dataType(), other.ordering(), other.shapeOf(), other.rankOf()));
if(!other.isEmpty()) {
_buffer = std::make_shared<DataBuffer>(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace());
this->assign(&other);
}
else
_buffer = std::make_shared<DataBuffer>();
}
return *this;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isC() const {
// TODO: this method must be implemented once we add support for complex numbers
return false;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isS() const {
return dataType() == DataType::UTF8;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isR() const {
auto xType = ArrayOptions::dataType(this->_shapeInfo);
return xType == FLOAT32 || xType == HALF || xType == DOUBLE || xType == FLOAT8 || xType == BFLOAT16;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isZ() const {
// TODO: decide if we really want to exclude Bool here
return !isC() && !isR() && !isB() && !isS();
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isB() const {
return ArrayOptions::dataType(this->_shapeInfo) == BOOL;
}
//////////////////////////////////////////////////////////////////////////
template<typename T>
std::string NDArray::toStringValue(T value) {
std::ostringstream os ;
//throw the value into the string stream
os << value ;
//convert the string stream into a string and return
return os.str() ;
}
//////////////////////////////////////////////////////////////////////////
template<>
std::string NDArray::toStringValue(float16 value) {
std::ostringstream os ;
//throw the value into the string stream
os << (float) value ;
//convert the string stream into a string and return
return os.str() ;
}
//////////////////////////////////////////////////////////////////////////
template<>
std::string NDArray::toStringValue(bfloat16 value) {
std::ostringstream os ;
//throw the value into the string stream
os << (float) value ;
//convert the string stream into a string and return
return os.str() ;
}
//////////////////////////////////////////////////////////////////////////
std::string NDArray::asIndexedString(Nd4jLong limit) {
std::ostringstream os;
os << "[";
if (limit < 1 || limit > this->lengthOf())
limit = this->lengthOf();
for (Nd4jLong e = 0; e < limit; e++) {
os << toStringValue(this->e<float>(e));
if (e < limit - 1)
os << ", ";
}
os << "]";
return os.str();
}
//////////////////////////////////////////////////////////////////////////
std::string NDArray::asString(Nd4jLong limit) {
std::ostringstream os;
os << "[";
if (limit < 1 || limit > this->lengthOf())
limit = this->lengthOf();
for (Nd4jLong e = 0; e < limit; e++) {
if (this->isR())
os << toStringValue(this->e<float>(e));
else if (this->isZ())
os << toStringValue(this->e<Nd4jLong>(e));
else if (this->isB())
os << toStringValue(this->e<bool>(e));
else if (this->isS())
os << this->e<std::string>(e);
if (e < limit - 1)
os << ", ";
}
os << "]";
return os.str();
}
////////////////////////////////////////////////////////////////////////
template<typename T>
std::vector<T> NDArray::getBufferAsVector() {
std::vector<T> vector(lengthOf());
for (int e = 0; e < lengthOf(); e++)
vector[e] = this->e<T>(e);
return vector;
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT std::vector, NDArray::getBufferAsVector(), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
std::vector<int64_t> NDArray::getShapeAsFlatVector() {
std::vector<int64_t> vector(this->rankOf());
for (int e = 0; e < this->rankOf(); e++)
vector[e] = static_cast<int64_t>(this->sizeAt(e));
return vector;
}
////////////////////////////////////////////////////////////////////////
std::vector<Nd4jLong> NDArray::getShapeAsVector() const {
std::vector<Nd4jLong> vector(this->rankOf());
for (int e = 0; e < this->rankOf(); e++)
vector[e] = this->sizeAt(e);
return vector;
}
////////////////////////////////////////////////////////////////////////
std::vector<int> NDArray::getShapeAsVectorInt() const {
std::vector<int> vector(this->rankOf());
for (int e = 0; e < this->rankOf(); e++)
vector[e] = static_cast<int>(this->sizeAt(e));
return vector;
}
////////////////////////////////////////////////////////////////////////
std::vector<int64_t> NDArray::getShapeInfoAsFlatVector() {
int magicNumber = shape::shapeInfoLength(this->rankOf());
std::vector<int64_t> vector(magicNumber);
for (int e = 0; e < magicNumber; e++)
vector[e] = static_cast<int64_t>(_shapeInfo[e]);
return vector;
}
////////////////////////////////////////////////////////////////////////
std::vector<Nd4jLong> NDArray::getShapeInfoAsVector() {
int magicNumber = shape::shapeInfoLength(this->rankOf());
std::vector<Nd4jLong> vector(magicNumber);
for (int e = 0; e < magicNumber; e++)
vector[e] = this->_shapeInfo[e];
return vector;
}
////////////////////////////////////////////////////////////////////////
std::vector<int8_t> NDArray::asByteVector() {
if (isS()) {
// string data type requires special treatment
syncToHost();
auto numWords = this->lengthOf();
auto offsetsBuffer = this->bufferAsT<Nd4jLong>();
auto headerLength = ShapeUtils::stringBufferHeaderRequirements(numWords);
auto dataLength = offsetsBuffer[numWords];
std::vector<int8_t> result(headerLength + dataLength);
memcpy(result.data(), getBuffer(), headerLength + dataLength);
return result;
} else {
// all other types are linear
std::vector<int8_t> result((unsigned long long) this->lengthOf() * sizeOfT());
if (this->isView()) {
auto tmp = this->dup(this->ordering());
syncToHost();
memcpy(result.data(), tmp.getBuffer(), (unsigned long long) lengthOf() * sizeOfT());
} else {
syncToHost();
memcpy(result.data(), getBuffer(), (unsigned long long) lengthOf() * sizeOfT());
}
return result;
}
}
//////////////////////////////////////////////////////////////////////////
void NDArray::linspace(const double start) {
linspace(start, 1);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::linspace(const double start, const double step) {
if (isS())
throw std::runtime_error("NDArray::linspace: you can't use this method on String array!");
Nd4jLong numElements = this->lengthOf();
for (Nd4jLong e = 0; e < numElements; e++)
this->p(e, start + (step * e));
}
////////////////////////////////////////////////////////////////////////
void NDArray::streamline(char o) {
char order = o == 'a' ? this->ordering() : o;
syncToDevice();
std::shared_ptr<DataBuffer> newBuffer = std::make_shared<DataBuffer>(this->lengthOf() * sizeOfT(), dataType(), getContext()->getWorkspace());
auto shapeBuffer = ConstantShapeHelper::getInstance()->bufferForShapeInfo(dataType(), order, rankOf(), shapeOf());
NativeOpExecutioner::execTransformSame(getContext(), transform::Copy, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), newBuffer->primary(), static_cast<Nd4jLong*>(shapeBuffer.primary()), newBuffer->special(), static_cast<Nd4jLong*>(shapeBuffer.special()), nullptr, nullptr, nullptr);
setShapeInfo(static_cast<Nd4jLong*>(shapeBuffer.primary()));
_buffer = newBuffer;
_offset = 0;
tickWriteDevice();
}
////////////////////////////////////////////////////////////////////////
// move assignment operator
NDArray& NDArray::operator=(NDArray&& other) noexcept {
if (this == &other)
return *this;
_isView = other._isView;
_buffer = other._buffer;
_shapeInfo = other._shapeInfo;
_shapeInfoD = other._shapeInfoD;
_context = other._context;
_dataType = other._dataType;
_length = other._length;
_offset = other._offset;
other._buffer = std::make_shared<DataBuffer>();
other._shapeInfo = other._shapeInfoD = nullptr;
other._length = 0;
return *this;
}
////////////////////////////////////////////////////////////////////////
template<typename T>
NDArray& NDArray::operator=(const T scalar) {
this->assign(scalar);
return *this;
}
template ND4J_EXPORT NDArray& NDArray::operator=(const double scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const float scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const float16 scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const bfloat16 scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const Nd4jLong scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const int scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const int8_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const uint8_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const uint16_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const uint32_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const uint64_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const int16_t scalar);
template ND4J_EXPORT NDArray& NDArray::operator=(const bool scalar);
//////////////////////////////////////////////////////////////////////////
void NDArray::copyBuffersContinuouslyFrom(const NDArray& other, size_t sizeToCopyInBytes, Nd4jLong offsetThis, Nd4jLong offsetOther) {
if(offsetThis == 0)
offsetThis = bufferOffset();
if(offsetOther == 0)
offsetOther = other.getBufferOffset();
dataBuffer()->copyBufferFrom(*other.getDataBuffer(), sizeToCopyInBytes, offsetThis, offsetOther);
}
////////////////////////////////////////////////////////////////////
// This method assigns values of given NDArray to this one
void NDArray::assign(const NDArray& other, bool allowParallelism) {
if (this == &other)
return;
if (other.isEmpty()) {
if (!isEmpty()) {
ArrayOptions::setPropertyBit(shapeInfo(), ARRAY_EMPTY);
syncShape();
_buffer = std::make_shared<DataBuffer>();
_offset = 0;
}
return;
}
if(isEmpty()) {
*this = other;
return;
}
if (other.lengthOf() == 1) {
if(lengthOf() == 1) {
NDArray::preparePrimaryUse({this}, {&other});
BUILD_DOUBLE_SELECTOR(dataType(), other.dataType(), templatedDoubleAssign, (buffer(), 0, other.getBuffer(), 0), LIBND4J_TYPES, LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {&other});
this->syncToDevice();
}
else {
if (dataType() != other.dataType()) {
auto tmp = other.cast(dataType());
NDArray::prepareSpecialUse({this}, {&tmp});
NativeOpExecutioner::execScalar(getContext(), scalar::CopyPws, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), tmp.getSpecialBuffer(), tmp.getSpecialShapeInfo(), nullptr, allowParallelism);
NDArray::registerSpecialUse({this}, {});
}
else {
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execScalar(getContext(), scalar::CopyPws, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr, allowParallelism);
NDArray::registerSpecialUse({this}, {&other});
}
}
}
else {
if (other.lengthOf() != lengthOf()) {
auto shapeThis = ShapeUtils::shapeAsString(this);
auto shapeThat = ShapeUtils::shapeAsString(&other);
nd4j_printf("Can't assign array: this shape %s; other shape: %s\n", shapeThis.c_str(), shapeThat.c_str());
throw std::runtime_error("NDArray::assign: lengths of arrays are mismatched");
}
// memcpy is allowed only for same order && same ews (being equal to 1)
if (ordering() == other.ordering() && dataType() == other.dataType() && ews() == 1 && other.ews() == 1)
copyBuffersContinuouslyFrom(other, other.lengthOf() * other.sizeOfT());
else {
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execTransformAny(getContext(), transform::Assign, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, nullptr, nullptr, allowParallelism);
NDArray::registerSpecialUse({this}, {&other});
}
}
}
//////////////////////////////////////////////////////////////////////////
// This method assigns values of given NDArray to this one, wrt order
void NDArray::assign(const NDArray *other, bool allowParallelism) {
assign(*other, allowParallelism);
}
//////////////////////////////////////////////////////////////////////////
template <typename T, typename>
void NDArray::assign(const T& value, bool allowParallelism) {
// just fire scalar
auto temp = NDArrayFactory::create(dataType(), value, this->getContext());
NDArray::prepareSpecialUse({this}, {&temp});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::CopyPws, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), temp.buffer(), temp.shapeInfo(), temp.specialBuffer(), temp.getSpecialShapeInfo(), nullptr, allowParallelism);
NDArray::registerSpecialUse({this}, {&temp});
}
template ND4J_EXPORT void NDArray::assign(const double& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const float& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const float16& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const bfloat16& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const Nd4jLong& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const int& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const int8_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const int16_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const uint8_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const uint16_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const uint32_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const uint64_t& value, bool allowParallelism);
template ND4J_EXPORT void NDArray::assign(const bool& value, bool allowParallelism);
//////////////////////////////////////////////////////////////////////////
NDArray* NDArray::detach() {
if (!isAttached())
return this;
std::shared_ptr<DataBuffer> newBuffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dataType());
auto result = new NDArray(newBuffer, ShapeDescriptor(dataType(), ordering(), shapeOf(), rankOf()));
result->assign(*this);
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::varianceNumber(nd4j::variance::Ops op, bool biasCorrected) {
NDArray res(DataTypeUtils::pickFloatingType(dataType()), getContext());
NDArray::prepareSpecialUse({&res}, {this});
NativeOpExecutioner::execSummaryStatsScalar(getContext(), op, buffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo(), biasCorrected);
NDArray::registerSpecialUse({&res}, {this});
return res;
}
//////////////////////////////////////////////////////////////////////////
// This method returns sum of all elements of this NDArray
NDArray NDArray::sumNumber() const {
if (isS())
throw std::runtime_error("NDArray::sumNumber: you can't use this method on String array!");
NDArray res(dataType(), getContext());
NDArray::prepareSpecialUse({&res}, {this});
NativeOpExecutioner::execReduceSameScalar(getContext(), nd4j::reduce::SameOps::Sum, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo());
NDArray::registerSpecialUse({&res}, {this});
return res;
}
//////////////////////////////////////////////////////////////////////////
// This method returns mean number of this NDArray
NDArray NDArray::meanNumber() const {
if (isS())
throw std::runtime_error("NDArray::meanNumber: you can't use this method on String array!");
NDArray res(DataTypeUtils::pickFloatingType(dataType()), getContext());
NDArray::prepareSpecialUse({&res}, {this});
NativeOpExecutioner::execReduceFloatScalar(getContext(), nd4j::reduce::FloatOps::Mean, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo());
NDArray::registerSpecialUse({&res}, {this});
return res;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::hasNaNs() {
if (isS())
throw std::runtime_error("NDArray::hasNaNs: you can't use this method on String array!");
return this->reduceNumber(nd4j::reduce::IsNan, nullptr).e<int>(0) > 0;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::hasInfs() {
if (isS())
throw std::runtime_error("NDArray::hasInfs: you can't use this method on String array!");
return this->reduceNumber(nd4j::reduce::IsInf, nullptr).e<int>(0) > 0;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::isFinite() {
if (isS())
throw std::runtime_error("NDArray::isFinite: you can't use this method on String array!");
return this->reduceNumber(nd4j::reduce::IsInfOrNan, nullptr).e<int>(0) == 0;
}
//////////////////////////////////////////////////////////////////////////
template <typename T, typename Y>
void NDArray::templatedSet(void *buffer, const Nd4jLong *indices, const void *value) {
auto t = reinterpret_cast<T *>(buffer);
const auto y = *(reinterpret_cast<const Y *>(value));
auto xOffset = shape::getOffset(getShapeInfo(), indices);
t[xOffset] = static_cast<T>(y);
}
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong *indices, const void *value), LIBND4J_TYPES, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
template <typename T, typename Y>
void NDArray::templatedSet(void *buffer, const Nd4jLong offset, const void *value) {
auto t = reinterpret_cast<T *>(buffer);
const auto y = *(reinterpret_cast<const Y *>(value));
t[offset] = static_cast<T>(y);
}
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong offset, const void *value), LIBND4J_TYPES, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
void NDArray::setContext(nd4j::LaunchContext *context) {
_context = context;
if (getContext() == nullptr)
_context = nd4j::LaunchContext ::defaultContext(); // empty context for default cases
}
//////////////////////////////////////////////////////////////////////////
void* NDArray::bufferWithOffset(Nd4jLong offset) const {
return getBuffer() != nullptr ? static_cast<int8_t*>(getBuffer()) + (offset * sizeOfT()) : nullptr;
}
//////////////////////////////////////////////////////////////////////////
// eventually method reduces array by excluding its shapes along axes present in dimensions vector
NDArray NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
std::vector<int> copy(dimensions);
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, isR() ? dataType() : Environment::getInstance()->defaultFloatDataType(), keepDims, supportOldShapes, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
this->reduceAlongDimension(op, result, copy, keepDims, supportOldShapes, false);
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
std::vector<int> copy(dimensions);
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, keepDims, supportOldShapes, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
reduceAlongDimension(op, result, copy, keepDims, supportOldShapes, false);
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
std::vector<int> copy(dimensions);
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::BOOL, keepDims, supportOldShapes, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
reduceAlongDimension(op, result, copy, keepDims, supportOldShapes, false);
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
std::vector<int> copy(dimensions);
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::INT64, keepDims, supportOldShapes, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
reduceAlongDimension(op, result, copy, keepDims, supportOldShapes, false);
return result;
}
//////////////////////////////////////////////////////////////////////////
// method reduces array by excluding its shapes along axes present in dimensions vector
NDArray NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, const std::initializer_list<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims, supportOldShapes);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::initializer_list<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims, supportOldShapes);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::initializer_list<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims, supportOldShapes);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::initializer_list<int>& dimensions, const bool keepDims, const bool supportOldShapes) const {
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims, supportOldShapes);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceNumber(nd4j::reduce::FloatOps op, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber FloatOps: you can't use this method on String array!");
auto shape = ConstantShapeHelper::getInstance()->scalarShapeInfo(DataTypeUtils::pickFloatingType(dataType()));
NDArray result(shape, true, this->getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execReduceFloatScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
NDArray::registerSpecialUse({&result}, {this});
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceNumber(nd4j::reduce::SameOps op, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber SameOps: you can't use this method on String array!");
NDArray result(dataType(), getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execReduceSameScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
NDArray::registerSpecialUse({&result}, {this});
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceNumber(nd4j::reduce::BoolOps op, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber BoolOps: you can't use this method on String array!");
auto shape = ConstantShapeHelper::getInstance()->scalarShapeInfo(DataType::BOOL);
NDArray result(shape, true, this->getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execReduceBoolScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
NDArray::registerSpecialUse({&result}, {this});
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reduceNumber(nd4j::reduce::LongOps op, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber LongOps: you can't use this method on String array!");
auto shape = ConstantShapeHelper::getInstance()->scalarShapeInfo(DataType::INT64);
NDArray result(shape, true, this->getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execReduceLongScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
NDArray::registerSpecialUse({&result}, {this});
return result;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::reduceNumber(nd4j::reduce::FloatOps op, NDArray& target, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber FloatOps: you can't use this method on String array!");
if(target.lengthOf() != 1 || target.dataType() != DataTypeUtils::pickFloatingType(dataType()))
throw std::invalid_argument("NDArray::reduceNumber FloatOps: target array should be scalar and have corresponding float type!");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execReduceFloatScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::reduceNumber(nd4j::reduce::SameOps op, NDArray& target, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber SameOps: you can't use this method on String array!");
if(target.lengthOf() != 1 || target.dataType() != dataType())
throw std::invalid_argument("NDArray::reduceNumber SameOps: target array should be scalar and have same type as this array!");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execReduceSameScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, target.getBuffer(), target.getShapeInfo(), target.specialBuffer(), target.getSpecialShapeInfo());
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::reduceNumber(nd4j::reduce::BoolOps op, NDArray& target, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber BoolOps: you can't use this method on String array!");
if(target.lengthOf() != 1 || target.dataType() != DataType::BOOL)
throw std::invalid_argument("NDArray::reduceNumber BoolOps: target array should be scalar and have bool type!");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execReduceBoolScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, target.getBuffer(), target.getShapeInfo(), target.specialBuffer(), target.getSpecialShapeInfo());
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::reduceNumber(nd4j::reduce::LongOps op, NDArray& target, void *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::reduceNumber LongOps: you can't use this method on String array!");
if(target.lengthOf() != 1 || target.dataType() != DataType::INT64)
throw std::invalid_argument("NDArray::reduceNumber LongOps: target array should be scalar and have long type!");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execReduceLongScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams, target.getBuffer(), target.getShapeInfo(), target.specialBuffer(), target.getSpecialShapeInfo());
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::indexReduceNumber(nd4j::indexreduce::Ops op, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::indexReduceNumber: you can't use this method on String array!");
auto res = NDArrayFactory::create<Nd4jLong>(0);
NDArray::NDArray::prepareSpecialUse({&res}, {this});
NativeOpExecutioner::execIndexReduceScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), extraParams == nullptr ? nullptr : extraParams->argumentsAsT(this->dataType()), res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo());
NDArray::NDArray::registerSpecialUse({&res}, {this});
return res;
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::tensorsAlongDimension(std::initializer_list<int> dimensions) const {
return tensorsAlongDimension(std::vector<int>(dimensions));
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::tensorsAlongDimension(const std::vector<int>& dimensions) const {
std::vector<int> copy(dimensions);
shape::checkDimensions(rankOf(), copy);
Nd4jLong tadLength = shape::tadLength(this->_shapeInfo, copy.data(), copy.size());
Nd4jLong numTads = this->lengthOf() / tadLength;
return numTads;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::printShapeInfo(const char * msg) const {
//shape::printShapeInfo(_shapeInfo);
if (msg == nullptr)
shape::printShapeInfoLinear(_shapeInfo);
else {
int rank = shape::rank(_shapeInfo);
int lim = shape::shapeInfoLength(rank);
printf("%s: [", msg);
for (int i = 0; i < shape::shapeInfoLength(rank); i++) {
printf("%lld", (long long) _shapeInfo[i]);
if (i < lim - 1)
printf(", ");
}
printf("]\n");
}
fflush(stdout);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::printBuffer(const char* msg, Nd4jLong limit, const bool sync) const{
if (sync)
syncToHost();
if (limit == -1)
limit = (int) this->lengthOf();
if (msg != nullptr)
printf("%s: [", msg);
else
printf("[");
if (this->isR()) {
for (Nd4jLong e = 0; e < limit; e++) {
if (e)
printf(", ");
printf("%f", this->e<float>(e));
}
}
else if (this->isZ()) {
for (Nd4jLong e = 0; e < limit; e++) {
if (this->dataType() != nd4j::DataType::INT64 && this->dataType() != nd4j::DataType::UINT64)
printf("%d", this->e<int>(e));
else
printf("%llu", this->e<Nd4jLong>(e));
if (e < limit - 1)
printf(", ");
}
}
else if (this->isB()) {
for (Nd4jLong e = 0; e < limit; e++) {
if (this->e<bool>(e))
printf("true");
else
printf("false");
if (e < limit - 1)
printf(", ");
}
}
else if (this->isS()) {
for (Nd4jLong e = 0; e < limit; e++) {
printf("\"%s\"", this->e<std::string>(e).c_str());
if (e < limit - 1)
printf(", ");
}
}
printf("]\n");
fflush(stdout);
}
//////////////////////////////////////////////////////////////////////////
// print element by element consequently in a way they (elements) are stored in physical memory
void NDArray::printLinearBuffer() const {
syncToHost();
const auto ews = this->ews() > 0 ? this->ews() : 1;
const auto len = this->lengthOf();
printf("[");
if (this->dataType() == nd4j::DataType::INT32) {
for(Nd4jLong e = 0; e < len; e++)
printf("%d, ", this->bufferAsT<int>()[e * ews]);
}
else if(this->dataType() == nd4j::DataType::INT64) {
for(Nd4jLong e = 0; e < len; e++)
printf("%lld, ", this->bufferAsT<Nd4jLong>()[e * ews]);
}
else if(this->dataType() == nd4j::DataType::FLOAT32) {
for(Nd4jLong e = 0; e < len; e++)
printf("%.3f, ", this->bufferAsT<float>()[e * ews]);
}
else if(this->dataType() == nd4j::DataType::DOUBLE) {
for(Nd4jLong e = 0; e < len; e++)
printf("%.3f, ", this->bufferAsT<double>()[e * ews]);
}
else
throw std::invalid_argument("NDArray::printLinearBuffer: not implemented yet for this data type !");
printf("]\n");
fflush(stdout);
}
//////////////////////////////////////////////////////////////////////////
static void printFormatted(NDArray const* arr, int depth, int limit) {
if (arr->rankOf() == 1) {
printf("[ ");
for (Nd4jLong i = 0; i < arr->lengthOf(); ++i) {
if (arr->isR())
printf("%f, ", arr->e<float>(i));
else if (arr->isZ())
printf("%lld, ", arr->e<Nd4jLong>(i));
else if (arr->isB())
printf("%s, ", arr->e<bool>(i)?"true":"false");
else if (arr->isS())
printf("\"%s\", ", arr->e<std::string>(i).c_str());
}
printf("]\n");
}
else if (arr->rankOf() == 2) {
Nd4jLong rows = arr->rows();
Nd4jLong cols = arr->columns();
char* padding = new char[depth + 1];
memset(padding, ' ', depth);
padding[depth] = 0;
printf("[");
for (Nd4jLong row = 0; row < rows; ++row) {
if (row && depth > 0)
printf("%s", padding);
printf("[");
Nd4jLong colLimit = cols > limit?cols:limit;
for (Nd4jLong col = 0; col < colLimit; ++col) {
if (col)
printf(", ");
if (arr->isR())
printf("%f", arr->e<float>(row, col));
else if (arr->isZ())
printf("%lld", arr->e<Nd4jLong>(row, col));
else if (arr->isB())
printf("%s", arr->e<bool>(row, col)?"true":"false");
else if (arr->isS())
printf("\"%s\"", arr->e<std::string>(row * cols + col).c_str());
}
if (row < rows - 1)
printf("]\n");
else
printf("]");
}
printf("]");
if (padding)
delete [] padding;
}
else {
//std::unique_ptr<ResultSet> arrs(arr->allTensorsAlongDimension({0}));
size_t restCount = 2;
printf("[");
restCount = ShapeUtils::getNumOfSubArrs(arr->getShapeInfo(), {0});
for (size_t arrIndex = 0; arrIndex < restCount; ++arrIndex) {
NDArray subArr = (*arr)(arrIndex, {0});
printFormatted(&subArr, depth + 1, limit);
if (arrIndex < restCount - 1) {
for (Nd4jLong i = 1; i < arr->rankOf(); ++i)
printf("\n");
for (Nd4jLong i = 0; i < depth - 2; ++i)
printf(" ");
}
}
printf("]");
}
}
//////////////////////////////////////////////////////////////////////////
void NDArray::printIndexedBuffer(const char* msg, Nd4jLong limit) const {
syncToHost();
Nd4jLong rank = this->rankOf();
bool rowFlag = (rank < 2) || (rank == 2 && this->sizeAt(0) == 1);
if (msg)
printf("%s: ", msg);
if (this->isEmpty()) {
printf("Empty\n");
}
else if (this->rankOf() == 0) {
if (this->isZ())
printf("%lld\n", this->e<Nd4jLong>(0));
else if (this->isR())
printf("%f\n", this->e<float>(0));
else if (this->isB()) {
printf("%s\n", this->e<bool>(0)?"true":"false");
}
else if (this->isS()) {
printf("\"%s\"\n", this->e<std::string>(0).c_str());
}
}
else if (rowFlag && ews()==1)
printBuffer(nullptr, limit);
else {
if (msg)
printf("\n");
printFormatted(this, 1, limit);
printf("\n");
}
fflush(stdout);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void* NDArray::templatedPointerShift(const Nd4jLong offset) const {
return reinterpret_cast<T*>(getBuffer()) + offset;
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void* NDArray::templatedPointerShift, (const Nd4jLong offset) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// method makes copy of this array and applies to the copy transpose operation, this array remains unaffected
NDArray NDArray::transpose() const &{
NDArray newArr(getDataBuffer(), ShapeDescriptor(getShapeInfo()), getContext(), getBufferOffset());
newArr.transposei();
return newArr;
}
//////////////////////////////////////////////////////////////////////////
// method makes copy of this array and applies to the copy transpose operation, this array remains unaffected
NDArray NDArray::transpose() && {
this->transposei();
return std::move(*this);
}
////////////////////////////////////////////////////////////////////////
// method performs transpose operation based on this array and store result in target, this array remains unaffected
void NDArray::transpose(NDArray& target) const {
auto correctShape = ShapeUtils::evalTranspShapeInfo(*this, getContext()->getWorkspace());
if(!shape::equalsStrict(correctShape, target.getShapeInfo()))
throw std::runtime_error("NDArray::transpose method: the shapeInfo of target array is wrong !");
target._buffer = _buffer;
target._offset = _offset;
target._isView = true;
}
////////////////////////////////////////////////////////////////////////
// This method applies in-place transpose to this array, so this array becomes transposed
void NDArray::transposei() {
std::vector<int> perm;
for (int e = this->rankOf() - 1; e >= 0; e--)
perm.emplace_back(e);
this->permutei(perm);
}
////////////////////////////////////////////////////////////////////////
bool NDArray::equalsTo(const NDArray &other, double eps) const {
return equalsTo(&other, eps);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::setAttached(bool reallyAttached) {
_isAttached = reallyAttached;
};
//////////////////////////////////////////////////////////////////////////
// calculate strides
void NDArray::updateStrides(const char order) {
shape::updateStrides(_shapeInfo, order);
syncShape();
}
//////////////////////////////////////////////////////////////////////////
// set new order and shape in case of suitable array length
bool NDArray::reshapei(const char order, const std::initializer_list<Nd4jLong>& shape) {
std::vector<Nd4jLong> vShape(shape);
return reshapei(order, vShape);
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::reshapei(const std::initializer_list<Nd4jLong>& shape) {
return reshapei('c', shape);
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::reshapei(const std::vector<Nd4jLong>& shape) {
return reshapei('c', shape);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::enforce(const std::initializer_list<Nd4jLong> &dimensions, char order) {
std::vector<Nd4jLong> dims(dimensions);
enforce(dims, order);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::enforce(std::vector<Nd4jLong> &dimensions, char o) {
Nd4jLong prod = 1;
for (int e = 0; e < dimensions.size(); e++)
prod *= dimensions[e];
if (prod != this->lengthOf()) {
std::string current = ShapeUtils::shapeAsString(this);
std::string enforced = ShapeUtils::shapeAsString(dimensions);
nd4j_printf("Can't enforce new shape, lengths mismatch. Original shape: %s; Requested shape: %s\n", current.c_str(), enforced.c_str());
throw std::runtime_error("Incompatible shape");
}
char order = o == 'a' ? this->ordering() : o;
setShapeInfo(ShapeDescriptor(dataType(), order, dimensions));
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::argMax(std::initializer_list<int> dimensions) {
if (isS())
throw std::runtime_error("NDArray::argMax: you can't use this method on String array!");
if (dimensions.size() == 0) {
Nd4jLong max = 0;
auto mv = -DataTypeUtils::max<float>();
for (Nd4jLong e = 0; e < this->lengthOf(); e++) {
auto val = this->e<float>(e);
if (mv < val) {
mv = val;
max = e;
}
}
return max;
}
else
throw std::runtime_error("Not implemented yet");
}
//////////////////////////////////////////////////////////////////////////
// create new array with corresponding order and shape, new array will point to the same _buffer as this array
NDArray NDArray::reshape(const char order, const std::vector<Nd4jLong>& shape) const & {
NDArray newArr(getDataBuffer(), ShapeDescriptor(getShapeInfo()), getContext(), getBufferOffset());
newArr.reshapei(order, shape);
return newArr;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::reshape(const char order, const std::vector<Nd4jLong>& shape) && {
this->reshapei(order, shape);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
// change an array by repeating it the number of times given by reps.
void NDArray::tilei(const std::vector<Nd4jLong>& reps) {
*this = this->tile(reps);
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::sizeAt(const int dim) const {
if (dim >= this->rankOf() || dim < -this->rankOf())
throw std::runtime_error("Bad size index requested");
if (dim >= 0)
return shape::shapeOf(_shapeInfo)[dim];
else
return shape::shapeOf(_shapeInfo)[this->rankOf() + dim];
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::strideAt(const int dim) const {
if (dim >= this->rankOf() || dim < -this->rankOf())
throw std::runtime_error("NDArray::strideAt: Bad size index requested");
if (dim >= 0)
return shape::stride(_shapeInfo)[dim];
else
return shape::stride(_shapeInfo)[this->rankOf() + dim];
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const std::initializer_list<int>& dimensions) {
std::vector<int> vec(dimensions);
return permutei(vec);
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const std::vector<int>& dimensions) {
return permutei(dimensions.data(), dimensions.size());
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const std::initializer_list<Nd4jLong>& dimensions) {
std::vector<Nd4jLong> vec(dimensions);
std::vector<int> ivec(dimensions.size());
for (int e = 0; e < vec.size(); e++)
ivec[e] = static_cast<int>(vec[e]);
return permutei(ivec);
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const std::vector<Nd4jLong>& dimensions) {
std::vector<int> ivec(dimensions.size());
for (int e = 0; e < dimensions.size(); e++)
ivec[e] = dimensions[e];
return permutei(ivec.data(), ivec.size());
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const int* dimensions, const int rank) const & {
// evaluate shapeInfo for output (permuted) array ret
auto shapeInfoPermuted = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, getContext()->getWorkspace());
NDArray ret(getDataBuffer(), ShapeDescriptor(shapeInfoPermuted), getContext(), getBufferOffset());
ret._isView = true;
return ret;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const int* dimensions, const int rank) && {
this->permutei(dimensions, rank);
return std::move(*this);
}
/////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const Nd4jLong* dimensions, const int rank) const &{
int tempDims[MAX_RANK];
shape::convertT<Nd4jLong, int>(const_cast<Nd4jLong *>(dimensions), tempDims, rank);
return permute(tempDims, rank);
}
/////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const Nd4jLong* dimensions, const int rank) && {
this->permutei(dimensions, rank);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::vector<int>& dimensions) const &{
auto data = dimensions.data();
auto size = dimensions.size();
return permute(data, size);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::vector<int>& dimensions) && {
this->permutei(dimensions);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::vector<Nd4jLong>& dimensions) const & {
return permute(dimensions.data(), dimensions.size());
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::vector<Nd4jLong>& dimensions) && {
this->permutei(dimensions);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::initializer_list<int>& dimensions) const &{
std::vector<int> vec(dimensions);
return permute(vec);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::initializer_list<int>& dimensions) && {
this->permutei(dimensions);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::initializer_list<Nd4jLong>& dimensions) const & {
std::vector<Nd4jLong> vec(dimensions);
return permute(vec);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::permute(const std::initializer_list<Nd4jLong>& dimensions) && {
this->permutei(dimensions);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::permute(const int* dimensions, const int rank, NDArray& target) const {
if (!nonNull() || !target.nonNull() || rank != rankOf() || rank != target.rankOf() )
throw std::runtime_error("NDArray<T>::permute method: either arrays are nullptr or ranks are not suitable!");
auto shapeInfoNew = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, target.getContext()->getWorkspace());
target.setShapeInfo(shapeInfoNew);
target._buffer = _buffer;
target._offset = _offset;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::permute(const Nd4jLong *dimensions, const int rank, NDArray& target) const {
if (!nonNull() || !target.nonNull() || rank != rankOf() || rank != target.rankOf() )
throw std::runtime_error("NDArray<T>::permute method: either arrays are nullptr or ranks are not suitable!");
auto shapeInfoNew = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, target.getContext()->getWorkspace());
target.setShapeInfo(shapeInfoNew);
target._buffer = _buffer;
target._offset = _offset;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::permute(const std::vector<int>& dimensions, NDArray& target) const {
permute(dimensions.data(), dimensions.size(), target);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::permute(const std::vector<Nd4jLong>& dimensions, NDArray& target) const {
permute(dimensions.data(), dimensions.size(), target);
}
//////////////////////////////////////////////////////////////////////////
// check whether array is identity matrix
bool NDArray::isIdentityMatrix() {
if (isS())
throw std::runtime_error("NDArray::isIdentityMatrix: you can't use this method on String array!");
if(rankOf() !=2 || rows() != columns())
throw std::runtime_error("isIdentityMatrix method: matrix must be square and have rank = 2 !");
const double eps = 1e-5f;
for(int i=0; i<rows(); ++i)
if(nd4j::math::nd4j_abs(e<double>(i,i) - 1.f) > eps)
return false;
for(int i=0; i<rows(); ++i) {
for(int j=0; j<columns(); ++j) {
if (i == j)
continue;
if(nd4j::math::nd4j_abs(e<double>(i,j)) > eps)
return false;
}
}
return true;
}
//////////////////////////////////////////////////////////////////////////
// check whether array is unitary matrix
bool NDArray::isUnitary() {
if (isS())
throw std::runtime_error("NDArray::isUnitary: you can't use this method on String array!");
if(rankOf() != 2 || rows() != columns())
throw std::runtime_error("isUnitary method: matrix must be square and have rank = 2 !");
auto tr = this->transpose();
auto trMul = MmulHelper::mmul(this, &tr, nullptr, 1.f, 0.f);
bool result = trMul->isIdentityMatrix();
delete trMul;
return result;
}
//////////////////////////////////////////////////////////////////////////
template <>
std::string* ND4J_EXPORT NDArray::bufferAsT() const {
throw std::runtime_error("This method is NOT supposed to be used");
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
T* NDArray::bufferAsT() const {
// FIXME: do we REALLY want sync here?
syncToHost();
return reinterpret_cast<T*>(getBuffer());
}
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , * NDArray::bufferAsT() const, LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
NDArray NDArray::subarray(IndicesList& idx) const {
const int idxSize = idx.size();
if (idxSize != this->rankOf())
throw std::runtime_error("NDArray::subarray: number of indices should match");
std::vector<Nd4jLong> indexes(3 * idxSize);
// convert IndicesList to vector
for (int d = 0; d < idxSize; ++d) {
if (idx.at(d)->isAll()) {
indexes[3 * d] = 0; // first
indexes[3 * d + 1] = 0; // last
indexes[3 * d + 2] = 1; // stride
}
else if (idx.at(d)->isPoint()) {
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
indexes[3 * d + 1] = indexes[3 * d] + 1; // last
indexes[3 * d + 2] = 1; // stride
}
else if (idx.at(d)->isInterval()) {
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
indexes[3 * d + 1] = idx.at(d)->getIndices().size();// last
indexes[3 * d + 2] = idx.at(d)->stride(); // stride
}
else {
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
indexes[3 * d + 1] = idx.at(d)->getIndices().at(1); // last
indexes[3 * d + 2] = idx.at(d)->getIndices().at(2); // stride
}
}
return NDArray((*this)(indexes, true, true));
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::subarray(const std::initializer_list<NDIndex*>& idx) const {
const int idxSize = idx.size();
if (idxSize != this->rankOf())
throw std::runtime_error("NDArray::subarray: number of indices should match the array rank");
std::vector<Nd4jLong> indexes(3 * idxSize);
// convert NDIndex to vector
int d = 0;
for (const auto& item : idx) {
if (item->isAll()) {
indexes[3 * d] = 0; // first
indexes[3 * d + 1] = 0; // last
indexes[3 * d + 2] = 1; // stride
}
else if (item->isPoint()) {
indexes[3 * d] = item->getIndices().at(0); // first
indexes[3 * d + 1] = indexes[3 * d] + 1; // last
indexes[3 * d + 2] = 1; // stride
}
else if (item->isInterval()) {
indexes[3 * d] = item->getIndices().at(0); // first
indexes[3 * d + 1] = item->getIndices().size(); // last
indexes[3 * d + 2] = item->stride(); // stride
}
else {
indexes[3 * d] = item->getIndices().at(0); // first
indexes[3 * d + 1] = item->getIndices().at(1); // last
indexes[3 * d + 2] = item->getIndices().at(2); // stride
}
++d;
}
// release NDIndices
for (auto i: idx)
delete i;
return NDArray((*this)(indexes, true, true));
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::subarray(const Intervals& idx) const {
const int idxSize = idx.size();
if (idxSize != this->rankOf())
throw std::runtime_error("NDArray::subarray: number of indices should match the rank of array!");
std::vector<Nd4jLong> indexes(2 * idxSize);
// convert Intervals to vector
for (int d = 0; d < idxSize; ++d) {
if (idx[d].empty()) {
indexes[2 * d] = 0; // first
indexes[2 * d + 1] = 0; // last
}
else {
indexes[2 * d] = idx[d][0]; // first
indexes[2 * d + 1] = idx[d][1]; // last
}
}
return NDArray((*this)(indexes, true));
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
NDArray NDArray::asT() const{
auto result = isScalar() ? NDArray('c', {}, {0.}, DataTypeUtils::fromT<T>(), this->getContext()) : NDArray(ordering(), getShapeAsVector(), DataTypeUtils::fromT<T>(), this->getContext());
auto l = this->lengthOf();
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformAny(getContext(), transform::AnyOps::Assign, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.getSpecialBuffer(), result.getSpecialShapeInfo(), nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArray::asT, () const, LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
NDArray NDArray::asT(DataType dtype) const {
if (isS())
throw std::runtime_error("NDArray::asT: you can't use this method on String array!");
BUILD_SINGLE_SELECTOR(dtype, return asT, (), LIBND4J_TYPES);
return NDArray();
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::cast(DataType dtype) const {
if (isS())
throw std::runtime_error("NDArray::cast: you can't use this method on String array!");
return this->asT(dtype);
}
////////////////////////////////////////////////////////////////////////
void NDArray::cast(NDArray& target, DataType dtype) {
if (isS())
throw std::runtime_error("NDArray::cast: you can't use this method on String array!");
// TODO: to be implemented properly
target.assign(this);
}
////////////////////////////////////////////////////////////////////////
void NDArray::operator+=(const NDArray& other) {
if (isS())
throw std::runtime_error("NDArray::operator+=: you can't use this method on String array!");
if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL))
throw nd4j::datatype_exception::build("NDArray operator+=: Cannot add different types", this->dataType(), other.dataType());
if (this->lengthOf() != 1 && other.lengthOf() == 1) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else{
Nd4jLong *bShape = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, bShape, getContext()->getWorkspace()))
throw std::invalid_argument("NDArray::operator+=: the shapes of this and other arrays are not suitable for broadcast operation !");
if(shape::equalsTypesAndShapesSoft(getShapeInfo(), bShape)) {
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), other, *this, false);
}
else {
NDArray result(bShape, true, getContext());
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), other, result, false);
*this = std::move(result); // move assignment operator, zero cost copy
}
}
}
////////////////////////////////////////////////////////////////////////
void NDArray::operator-=(const NDArray& other) {
if (isS())
throw std::runtime_error("NDArray::operator-=: you can't use this method on String array!");
if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL))
throw nd4j::datatype_exception::build("NDArray operator-=: Cannot subtract different types", this->dataType(), other.dataType());
if (lengthOf() != 1 && other.lengthOf() == 1) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else{
Nd4jLong *bShape = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, bShape, getContext()->getWorkspace()))
throw std::invalid_argument("NDArray::operator-=: the shapes of this and other arrays are not suitable for broadcast operation !");
if(shape::equalsTypesAndShapesSoft(getShapeInfo(), bShape)) {
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), other, *this, false);
}
else {
NDArray result(bShape, true, getContext());
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), other, result, false);
*this = std::move(result); // move assignment operator, zero cost copy
}
}
}
////////////////////////////////////////////////////////////////////////
void NDArray::operator*=(const NDArray& other) {
if (isS())
throw std::runtime_error("NDArray::operator*=: you can't use this method on String array!");
if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType() && (this->dataType() != DataType::BOOL || other.dataType() != BOOL))
throw nd4j::datatype_exception::build("NDArray operator*=: Cannot multiply different types", this->dataType(), other.dataType());
if (lengthOf() != 1 && other.lengthOf() == 1) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else{
Nd4jLong *bShape = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, bShape, getContext()->getWorkspace()))
throw std::invalid_argument("NDArray::operator*=: the shapes of this and other arrays are not suitable for broadcast operation !");
if(shape::equalsTypesAndShapesSoft(_shapeInfo, bShape)) {
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), other, *this, false);
}
else {
NDArray result(bShape, true, getContext());
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), other, result, false);
*this = std::move(result); // move assignment operator, zero cost copy
}
}
}
////////////////////////////////////////////////////////////////////////
void NDArray::operator/=(const NDArray& other) {
if (isS() || other.isS())
throw std::runtime_error("NDArray::operator/=: you can't use this method on String array!");
if (other.isB())
throw std::runtime_error("NDArray::operator/=: you can't divide by bool array!");
if (!Environment::getInstance()->isExperimentalBuild() && this->dataType() != other.dataType()) {
throw nd4j::datatype_exception::build("NDArray operator/=: Cannot divide different types", this->dataType(), other.dataType());
}
if (lengthOf() != 1 && other.lengthOf() == 1) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({this}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {this, &other});
}
else{
Nd4jLong *bShape = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, bShape, getContext()->getWorkspace()))
throw std::invalid_argument("NDArray::operator/=: the shapes of this and other arrays are not suitable for broadcast operation !");
if(shape::equalsTypesAndShapesSoft(_shapeInfo, bShape)) {
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), other, *this, false);
}
else {
NDArray result(bShape, true, getContext());
this->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), other, result, false);
*this = std::move(result); // move assignment operator, zero cost copy
}
}
}
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::operator+=(const T value) {
if (isS())
throw std::runtime_error("NDArray::operator+=: you can't use this method on String array!");
auto other = NDArrayFactory::create(this->dataType(), value, getContext());
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Add, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {});
}
template ND4J_EXPORT void NDArray::operator+=(const double value);
template ND4J_EXPORT void NDArray::operator+=(const float value);
template ND4J_EXPORT void NDArray::operator+=(const float16 value);
template ND4J_EXPORT void NDArray::operator+=(const bfloat16 value);
template ND4J_EXPORT void NDArray::operator+=(const Nd4jLong value);
template ND4J_EXPORT void NDArray::operator+=(const int value);
template ND4J_EXPORT void NDArray::operator+=(const bool value);
////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::operator-=(const T value) {
if (isS())
throw std::runtime_error("NDArray::operator-=: you can't use this method on String array!");
auto other = NDArrayFactory::create(dataType(), value, getContext());
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Subtract, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {});
}
template ND4J_EXPORT void NDArray::operator-=(const double value);
template ND4J_EXPORT void NDArray::operator-=(const float value);
template ND4J_EXPORT void NDArray::operator-=(const float16 value);
template ND4J_EXPORT void NDArray::operator-=(const bfloat16 value);
template ND4J_EXPORT void NDArray::operator-=(const Nd4jLong value);
template ND4J_EXPORT void NDArray::operator-=(const int value);
template ND4J_EXPORT void NDArray::operator-=(const bool value);
////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::operator*=(const T scalar) {
if (isS())
throw std::runtime_error("NDArray::operator*=: you can't use this method on String array!");
auto other = NDArrayFactory::create(this->dataType(), scalar, getContext());
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Multiply, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {});
}
template ND4J_EXPORT void NDArray::operator*=(const double scalar);
template ND4J_EXPORT void NDArray::operator*=(const float scalar);
template ND4J_EXPORT void NDArray::operator*=(const float16 scalar);
template ND4J_EXPORT void NDArray::operator*=(const bfloat16 scalar);
template ND4J_EXPORT void NDArray::operator*=(const Nd4jLong scalar);
template ND4J_EXPORT void NDArray::operator*=(const int scalar);
template ND4J_EXPORT void NDArray::operator*=(const int16_t scalar);
template ND4J_EXPORT void NDArray::operator*=(const int8_t scalar);
template ND4J_EXPORT void NDArray::operator*=(const uint8_t scalar);
template ND4J_EXPORT void NDArray::operator*=(const bool scalar);
////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::operator/=(const T scalar) {
if (isS())
throw std::runtime_error("NDArray::operator/=: you can't use this method on String array!");
auto other = NDArrayFactory::create(this->dataType(), scalar, getContext());
NDArray::prepareSpecialUse({this}, {&other});
NativeOpExecutioner::execScalar(getContext(), nd4j::scalar::Divide, buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({this}, {});
}
template ND4J_EXPORT void NDArray::operator/=(const double scalar);
template ND4J_EXPORT void NDArray::operator/=(const float scalar);
template ND4J_EXPORT void NDArray::operator/=(const float16 scalar);
template ND4J_EXPORT void NDArray::operator/=(const bfloat16 scalar);
template ND4J_EXPORT void NDArray::operator/=(const Nd4jLong scalar);
template ND4J_EXPORT void NDArray::operator/=(const int scalar);
template ND4J_EXPORT void NDArray::operator/=(const int16_t scalar);
template ND4J_EXPORT void NDArray::operator/=(const int8_t scalar);
template ND4J_EXPORT void NDArray::operator/=(const uint8_t scalar);
template ND4J_EXPORT void NDArray::operator/=(const bool scalar);
////////////////////////////////////////////////////////////////////////
// negative operator, it makes all array elements = -elements
NDArray NDArray::operator-() const & {
if (isS())
throw std::runtime_error("NDArray::negative-: you can't use this method on String array!");
NDArray result(getShapeInfo(), false, getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformSame(getContext(), nd4j::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::operator-() && {
if (isS())
throw std::runtime_error("NDArray::negative-: you can't use this method on String array!");
NDArray::prepareSpecialUse({this}, {this});
NativeOpExecutioner::execTransformSame(getContext(), nd4j::transform::Neg, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), getShapeInfo(), specialBuffer(), getSpecialShapeInfo(), nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({this}, {this});
return std::move(*this);
}
////////////////////////////////////////////////////////////////////////
// mathematical multiplication of two arrays
NDArray mmul(const NDArray& left, const NDArray& right) {
if (left.isS() || right.isS())
throw std::runtime_error("mmul friend function: you can't use this function on String array!");
auto ptr = MmulHelper::mmul(const_cast<NDArray*>(&left), const_cast<NDArray*>(&right), nullptr, 1., 0.);
NDArray result(std::move(*ptr));
delete ptr;
return result;
}
////////////////////////////////////////////////////////////////////////
void NDArray::tileToShape(const std::vector<Nd4jLong>& shape, NDArray& target) {
if(&target != this) {
this->tile(target);
return;
}
std::vector<Nd4jLong> thisShape(rankOf());
for(int i = 0; i < rankOf(); ++i)
thisShape[i] = sizeAt(i);
if(!ShapeUtils::areShapesBroadcastable(shape, thisShape))
throw std::runtime_error("NDArray::tileToShape method: the shape of this array and input shape are not suitable for broadcast operation !");
const int newRank = shape.size();
std::vector<Nd4jLong> repeats(newRank);
for(int i = 1; i <= newRank; ++i) {
if(i > rankOf())
repeats[newRank-i] = shape[newRank - i];
else
repeats[newRank-i] = shape[newRank - i] / thisShape[rankOf() - i];
}
tilei(repeats);
}
////////////////////////////////////////////////////////////////////////
void NDArray::tileToShape(const std::initializer_list<Nd4jLong>& shape, NDArray& target) {
tileToShape(std::vector<Nd4jLong>(shape), target);
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::tileToShape(const Nd4jLong* shapeInfo) {
NDArray result(const_cast<Nd4jLong*>(shapeInfo), false, getContext());
tile(result);
return result;
}
////////////////////////////////////////////////////////////////////////
double NDArray::getTrace() const {
if (isS())
throw std::runtime_error("NDArray::getTrace: you can't use this method on String array!");
int rank = rankOf();
auto shape = shapeOf();
int minDim = 100000000;
Nd4jLong indices[MAX_RANK];
for(int j = 0; j < rank; ++j)
indices[j] = 1;
auto offset = shape::getOffset(getShapeInfo(), indices);
for(int i = 0; i < rank; ++i)
if(minDim > shape[i])
minDim = shape[i];
double sum = 0.;
for(int i = 0; i < minDim; ++i)
sum += e<double>(i * offset);
return sum;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::quantize(const NDArray& array) {
if(!array.isR())
throw std::invalid_argument("NDArray::quantize: type of array should be from real space!");
auto ws = array.getContext()->getWorkspace();
Nd4jLong* shapeInfo = ShapeBuilders::copyShapeInfo(array.getShapeInfo(), true, ws);
ArrayOptions::setPropertyBit(shapeInfo, ARRAY_QUANTIZED);
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(TypeCast::estimateQuantizedSize(array.lengthOf()), ArrayOptions::dataType(shapeInfo), ws);
NDArray result(buffer, ShapeDescriptor(shapeInfo), array.getContext());
return result;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const {
if (isS())
throw std::runtime_error("NDArray::applyTrueBroadcast: you can't use this method on String array!");
if(((op.s == scalar::Divide || op.s == scalar::FloorDiv || op.s == scalar::FloorMod) && other.isB()) || (op.s == scalar::ReverseDivide && this->isB()))
throw std::runtime_error("NDArray::applyTrueBroadcast method: you can't divide by bool array !");
if (isEmpty() || other.isEmpty())
return;
if (lengthOf() == 1) {
target.assign(this);
target.applyPairwiseTransform(op.p, other, extraArgs);
return;
}
if (other.lengthOf() == 1) {
const_cast<NDArray*>(this)->applyScalarArr(op.s, other, target, extraArgs);
return;
}
if(checkTargetShape) {
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of target array must be equal to max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
if(!shape::equalsTypesAndShapesSoft(target.getShapeInfo(), newShapeInfo))
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shape or type of target array is wrong !");
}
if(target.isSameShape(this) || target.isSameShape(other)) {
const_cast<NDArray*>(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs);
return;
}
#ifdef __ND4J_EXPERIMENTAL__
BUILD_PAIRWISE_SELECTOR(dataType(), other.dataType(), target.dataType(), helpers::TrueBroadcastHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES, LIBND4J_TYPES);
#else
BUILD_SINGLE_SELECTOR_THRICE(dataType(), helpers::TrueBroadcastHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES);
#endif
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyTrueBroadcast(nd4j::BroadcastBoolOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const {
if (isS())
throw std::runtime_error("NDArray::applyTrueBroadcast bool: you can't use this method on String array!");
if (isEmpty() || other.isEmpty())
return;
if (lengthOf() == 1) {
NDArray temp(target._shapeInfo, dataType(), false, getContext());
temp.assign(this);
temp.applyPairwiseTransform(op.p, other, target, extraArgs);
return;
}
if (other.lengthOf() == 1) {
this->applyScalarArr(op.s, other, target, extraArgs);
return;
}
if(checkTargetShape) {
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of target array must be equal to max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
if(!shape::equalsSoft(target._shapeInfo, newShapeInfo) || target.dataType() != DataType::BOOL)
throw std::runtime_error("NDArray::applyTrueBroadcast bool method: the shape or type of target array is wrong !");
if(dataType() != other.dataType())
throw std::invalid_argument("NDArray::applyTrueBroadcast bool method: this and other arrays must have the same type !");
}
if(target.isSameShape(this) || target.isSameShape(other)) {
const_cast<NDArray*>(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs);
return;
}
BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), helpers::TrueBroadcastBoolHelper, ::exec(op.b, *this, other, target), LIBND4J_TYPES, BOOL_TYPES);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyTrueBroadcast(nd4j::BroadcastIntOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const {
if (isS())
throw std::runtime_error("NDArray::applyTrueBroadcast bool: you can't use this method on String array!");
if (isEmpty() || other.isEmpty())
return;
if (lengthOf() == 1) {
NDArray temp(target._shapeInfo, dataType(), false, getContext());
temp.assign(this);
temp.applyPairwiseTransform(op.p, other, target, extraArgs);
return;
}
if (other.lengthOf() == 1) {
this->applyScalarArr(op.s, other, target, extraArgs);
return;
}
if(checkTargetShape) {
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, false, newShapeInfo, getContext()->getWorkspace())) // the rank of target array must be equal to max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
if(!shape::equalsSoft(target._shapeInfo, newShapeInfo) || target.dataType() != this->dataType())
throw std::runtime_error("NDArray::applyTrueBroadcast int method: the shape or type of target array is wrong !");
if(dataType() != other.dataType())
throw std::invalid_argument("NDArray::applyTrueBroadcast int method: this and other arrays must have the same type !");
}
if(target.isSameShape(this) || target.isSameShape(other)) {
const_cast<NDArray*>(this)->applyBroadcast(op.b, ShapeUtils::getDimsWithSameShape(*this, other), other, target, extraArgs);
return;
}
BUILD_SINGLE_SELECTOR(dataType(), helpers::TrueBroadcastIntHelper, ::exec(op.b, *this, other, target), INTEGER_TYPES);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) const & {
if (isEmpty() || other.isEmpty()) {
if (isEmpty())
return NDArray(*this);
else
return NDArray(other);
}
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of new array = max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
NDArray result(newShapeInfo, true, getContext());
this->applyTrueBroadcast(op, other, result, false, extraArgs);
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) const & {
if (isEmpty() || other.isEmpty()) {
if (isEmpty())
return NDArray(*this);
else
return NDArray(other);
}
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of new array = max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
if(!shape::shapeEquals(newShapeInfo, other.getShapeInfo())) {
NDArray result(newShapeInfo, true, getContext());
this->applyTrueBroadcast(op, other, result, false, extraArgs);
return std::move(result);
}
this->applyTrueBroadcast(op, other, other, false, extraArgs);
return std::move(other);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) && {
if (isEmpty() || other.isEmpty()) {
if (isEmpty())
return NDArray(*this);
else
return NDArray(other);
}
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of new array = max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
if(!shape::shapeEquals(newShapeInfo, getShapeInfo())) {
NDArray result(newShapeInfo, true, getContext());
this->applyTrueBroadcast(op, other, result, false, extraArgs);
return std::move(result);
}
this->applyTrueBroadcast(op, other, *this, false, extraArgs);
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::applyTrueBroadcast(nd4j::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) && {
if (isEmpty() || other.isEmpty()) {
if (isEmpty())
return NDArray(*this);
else
return NDArray(other);
}
Nd4jLong* newShapeInfo = nullptr;
if(!ShapeUtils::evalBroadcastShapeInfo(*this, other, true, newShapeInfo, getContext()->getWorkspace())) // the rank of new array = max->rankOf)()
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shapes of this and other arrays are not suitable for broadcast operation !");
const bool thisMove = shape::shapeEquals(newShapeInfo, getShapeInfo());
const bool otherMove = shape::shapeEquals(newShapeInfo, other.getShapeInfo());
if(!thisMove && !otherMove) {
NDArray result(newShapeInfo, true, getContext());
this->applyTrueBroadcast(op, other, result, false, extraArgs);
return std::move(result);
}
if(thisMove) {
this->applyTrueBroadcast(op, other, *this, false, extraArgs);
return std::move(*this);
}
// otherMove
this->applyTrueBroadcast(op, other, other, false, extraArgs);
return std::move(other);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyBroadcast(nd4j::broadcast::Ops op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
if (isS())
throw std::runtime_error("NDArray::applyBroadcast: you can't use this method on String array!");
if(((op == broadcast::Divide || op == broadcast::FloorDiv || op == broadcast::FloorMod) && other.isB()) || (op == broadcast::ReverseDivide && this->isB()))
throw std::runtime_error("NDArray::applyBroadcast: you can't divide by array!");
if(isEmpty() || other.isEmpty()) {
if(!target.isEmpty())
throw std::runtime_error("NDArray::applyBroadcast method: when some of input arrays (or both) is empty, target array must be empty as well !");
return;
}
if (dimensions.size() == 0)
return;
if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), fromBroadcastToPairwise(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
return;
}
NDArray *min(nullptr), *max(nullptr);
if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) {
max = this;
min = const_cast<NDArray*>(&other);
}
else {
max = const_cast<NDArray*>(&other);
min = this;
}
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(shapeInfo(), other.getShapeInfo()))
throw std::invalid_argument("NDArray::applyBroadcast method: wrong type of target array !");
if(!target.isSameShape(max))
throw std::invalid_argument("NDArray::applyBroadcast method: max and target arrays must have the same shape !");
std::vector<int> copy(dimensions);
if (dimensions.size() > 1)
std::sort(copy.begin(), copy.end());
Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size());
if (tadLength != min->lengthOf())
throw std::runtime_error("NDArray::applyBroadcast method: tad length mismatch !");
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy);
NDArray::prepareSpecialUse({&target}, {this, &other});
if(max == this)
NativeOpExecutioner::execBroadcast( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
else
NativeOpExecutioner::execInverseBroadcast(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
registerSpecialUse({&target}, {this, &other});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyBroadcast(nd4j::broadcast::BoolOps op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
if (isS())
throw std::runtime_error("NDArray::applyBroadcast BoolOps: you can't use this method on String array!");
if(isEmpty() || other.isEmpty()) {
if(!target.isEmpty())
throw std::runtime_error("NDArray::applyBroadcast BoolOps: when some of input arrays (or both) is empty, target array must be empty as well !");
return;
}
if (dimensions.size() == 0)
return;
if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseBoolTransform(getContext(), fromBroadcastToPairwiseBool(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
return;
}
NDArray *min(nullptr), *max(nullptr);
if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) {
max = this;
min = const_cast<NDArray*>(&other);
}
else {
max = const_cast<NDArray*>(&other);
min = this;
}
if(target.dataType() != DataType::BOOL)
throw std::invalid_argument("NDArray::applyBroadcast bool method: type of target array must be BOOL!");
if(!target.isSameShape(max))
throw std::invalid_argument("NDArray::applyBroadcast bool method: max and target arrays must have the same shape !");
if(_dataType != other._dataType)
throw std::invalid_argument("NDArray::applyBroadcast bool method: this and other arrays must have the same type !");
std::vector<int> copy(dimensions);
if (dimensions.size() > 1)
std::sort(copy.begin(), copy.end());
Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size());
if (tadLength != min->lengthOf())
throw std::runtime_error("Tad length mismatch");
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy);
// TODO: eventually we want separate tads here
NDArray::prepareSpecialUse({&target}, {this, &other});
if(max == this)
NativeOpExecutioner::execBroadcastBool( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr, copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
else
NativeOpExecutioner::execInverseBroadcastBool(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr, copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
registerSpecialUse({&target}, {this, &other});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyBroadcast(nd4j::broadcast::IntOps op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
if (!isZ())
throw std::runtime_error("NDArray::applyBroadcast IntOps: you can't use this method on non-Integer array!");
if(isEmpty() || other.isEmpty()) {
if(!target.isEmpty())
throw std::runtime_error("NDArray::applyBroadcast IntOps: when some of input arrays (or both) is empty, target array must be empty as well !");
return;
}
if (dimensions.empty())
return;
if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseIntTransform(getContext(), fromBroadcastToPairwiseInt(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
return;
}
NDArray *min(nullptr), *max(nullptr);
if((lengthOf() > other.lengthOf()) || (lengthOf() == other.lengthOf() && rankOf() >= other.rankOf())) {
max = this;
min = const_cast<NDArray*>(&other);
}
else {
max = const_cast<NDArray*>(&other);
min = this;
}
if(target.dataType() != dataType())
throw std::invalid_argument("NDArray::applyBroadcast int method: type of target array must be the same as input!");
if(!target.isSameShape(max))
throw std::invalid_argument("NDArray::applyBroadcast int method: max and target arrays must have the same shape !");
if(_dataType != other._dataType)
throw std::invalid_argument("NDArray::applyBroadcast int method: this and other arrays must have the same type !");
std::vector<int> copy(dimensions);
if (dimensions.size() > 1)
std::sort(copy.begin(), copy.end());
Nd4jLong tadLength = shape::tadLength(max->shapeInfo(), copy.data(), (int) copy.size());
if (tadLength != min->lengthOf())
throw std::runtime_error("Tad length mismatch");
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(max->shapeInfo(), copy);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(target.shapeInfo(), copy);
// TODO: eventually we want separate tads here
NDArray::prepareSpecialUse({&target}, {this, &other});
if(max == this)
NativeOpExecutioner::execBroadcastInt( getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
else
NativeOpExecutioner::execInverseBroadcastInt(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), copy.data(), (int)copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets());
registerSpecialUse({&target}, {this, &other});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyBroadcast(nd4j::broadcast::Ops op, const std::initializer_list<int> dimensions, const NDArray& tadArray, NDArray& target, ExtraArguments* extraArgs) {
std::vector<int> vec(dimensions);
applyBroadcast(op, vec, tadArray, target, extraArgs);
}
////////////////////////////////////////////////////////////////////////
void* NDArray::operator new(size_t i) {
if (nd4j::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached()) {
nd4j::memory::Workspace* ws = nd4j::memory::MemoryRegistrator::getInstance()->getWorkspace();
return ws->allocateBytes((Nd4jLong) i);
}
else {
auto p = malloc(i);
CHECK_ALLOC(p, "Failed to allocate new NDArray", i);
return p;
}
}
////////////////////////////////////////////////////////////////////////
void NDArray::operator delete(void* p) {
if (!nd4j::memory::MemoryRegistrator::getInstance()->hasWorkspaceAttached())
free(p);
}
////////////////////////////////////////////////////////////////////////
template <typename T>
std::vector<T> NDArray::asVectorT() {
std::vector<T> result(this->lengthOf());
PRAGMA_OMP_SIMD
for (int e = 0; e < this->lengthOf(); e++)
result[e] = this->e<T>(e);
return result;
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT std::vector, NDArray::asVectorT(), LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// set new order and shape in case of suitable array length
bool NDArray::reshapei(const char order, const std::vector<Nd4jLong>& cshape) {
// check firstly whether cshape is identical to shape of array, if yes then reshape is unnecessary
if(order == ordering() && shape::shapeEquals(rankOf(), shapeOf(), cshape.size(), cshape.data()))
return true;
const bool isOutShapeEmpty = std::find(cshape.begin(), cshape.end(), 0) != cshape.end();
if(isEmpty() && !isOutShapeEmpty)
throw std::invalid_argument("NDArray::reshapei: can't reshape empty array to non-empty !");
if(!isEmpty() && isOutShapeEmpty)
throw std::invalid_argument("NDArray::reshapei: can't reshape non-empty array to empty !");
if(isEmpty() && isOutShapeEmpty) {
Nd4jLong* shapeInfoNew = ShapeBuilders::emptyShapeInfo(dataType(), order, cshape, getContext()->getWorkspace());
setShapeInfo(shapeInfoNew);
RELEASE(shapeInfoNew, getContext()->getWorkspace());
return true;
}
std::vector<Nd4jLong> shape(cshape);
int rank = shape.size();
// looking for negative in shape
int numberNegativesOnes = 0;
Nd4jLong* shape_ = shape.data();
for (int i = 0; i < (int) shape.size(); i++) {
if (shape[i] < 0) {
if (numberNegativesOnes >= 1)
throw std::runtime_error("NDArray::reshapei: only one dimension can be negative at once");
numberNegativesOnes++;
int shapeLength = 1;
for (int j = 0; j < (int) shape.size(); j++)
if (i != j)
shapeLength *= shape_[j];
Nd4jLong realShape = nd4j::math::nd4j_abs<int>(lengthOf() / shapeLength);
auto thisNewShape = new Nd4jLong[shape.size()];
for (int j = 0; j < (int) shape.size(); j++)
if (i != j)
thisNewShape[j] = shape_[j];
else
thisNewShape[j] = realShape;
shape_ = thisNewShape;
}
}
for (int e = 0; e < (int) shape.size(); e++)
shape[e] = shape_[e];
if (numberNegativesOnes > 0)
delete[] shape_;
Nd4jLong arrLength = 1;
for(const auto& item : shape)
arrLength *= item;
if(platformBuffer() == nullptr || arrLength != this->lengthOf()) {
this->printShapeInfo("Mismatched shape");
nd4j::Logger::printv("Shape requested: ", shape);
nd4j_debug("Requested length in reshape: %i; Existing length: %i;\n", arrLength, this->lengthOf());
throw std::runtime_error("NDArray::reshapei: bad input shape!");
}
Nd4jLong *shapeInfoNew;
ALLOCATE(shapeInfoNew, getContext()->getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong);
bool canReshape = shape::reshapeC(rankOf(), shapeInfo(), shape.size(), shape.data(), shapeInfoNew);
// we can do this only if there was no permute applied, or there are no weird strides
if (canReshape) {
if(ordering() == 'c' && order == 'f')
throw std::invalid_argument("NDArray::reshapei(order, shape): in case of reshapeC it doesn't make sense to reshape from c order to f order !");
shape::setEws(shapeInfoNew, arrLength);
setShapeInfo(shapeInfoNew);
}
else {
NDArray temp(order, shape, dataType(), getContext());
this->applyTransform(transform::Assign, temp, nullptr);
*this = std::move(temp);
}
RELEASE(shapeInfoNew, getContext()->getWorkspace());
return canReshape;
}
//////////////////////////////////////////////////////////////////////////
void NDArray::nullify() {
if (isEmpty())
return;
if (isS())
throw std::runtime_error("NDArray::nullify: can't nullify string array");
if (isView() || ews() != 1)
assign(0);
else
_buffer->setToZeroBuffers();
}
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::templatedSet(void *buffer, const Nd4jLong xOfsset, nd4j::DataType dtype, const void *value) {
BUILD_SINGLE_PARTIAL_SELECTOR(dtype, templatedSet< , T>(buffer, xOfsset, value), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong xOfsset, nd4j::DataType dtype, const void *value), LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
void NDArray::applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{
if (isS())
throw std::runtime_error("NDArray::applyPairwiseTransform: you can't use this method on String array!");
if (other.lengthOf() != target.lengthOf())
throw std::invalid_argument("NDArray::applyPairwiseTransform method - lengths of arrays are mismatched");
if (target.dataType() != this->dataType() && target.dataType() != other.dataType())
throw std::invalid_argument("NDArray::applyPairwiseTransform method - type of target array must be the same as type of this or other array !");
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseTransform(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
if (extraParams != nullptr)
synchronize("NDArray::applyPairwiseTransform");
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{
if (isS())
throw std::runtime_error("NDArray::applyPairwiseTransform BoolOps: you can't use this method on String array!");
if (other.lengthOf() != target.lengthOf())
throw std::invalid_argument("NDArray::applyPairwiseTransform BoolOps method - lengths of arrays are mismatched");
if (!target.isB())
throw std::invalid_argument("NDArray::applyPairwiseTransform BoolOps method - result must have bool type");
if (dataType() != other.dataType())
throw std::invalid_argument("NDArray::applyPairwiseTransform BoolOps method - this and other arrays must have the same type !");
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseBoolTransform(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyPairwiseTransform(nd4j::pairwise::IntOps op, const NDArray& other, NDArray& target, ExtraArguments *extraParams) const{
if (isS())
throw std::runtime_error("NDArray::applyPairwiseTransform IntOps: you can't use this method on String array!");
if (other.lengthOf() != target.lengthOf())
throw std::invalid_argument("NDArray::applyPairwiseTransform IntOps method - lengths of arrays are mismatched");
if (!target.isZ())
throw std::invalid_argument("NDArray::applyPairwiseTransform IntOps method - result must have bool type");
if (dataType() != other.dataType())
throw std::invalid_argument("NDArray::applyPairwiseTransform IntOps method - this and other arrays must have the same type !");
NDArray::prepareSpecialUse({&target}, {this, &other});
NativeOpExecutioner::execPairwiseIntTransform(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr);
NDArray::registerSpecialUse({&target}, {this, &other});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyPairwiseTransform(nd4j::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams) {
applyPairwiseTransform(op, other, *this, extraParams);
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
void NDArray::templatedDoubleAssign(void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const {
auto x = reinterpret_cast<X *>(xBuffer);
const auto y = reinterpret_cast<const Y *>(yBuffer);
x[xOffset] = static_cast<X>(y[yOffset]);
}
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedDoubleAssign, (void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const, LIBND4J_TYPES, LIBND4J_TYPES);
////////////////////////////////////////////////////////////////////////
void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector<int>& dimensions) const {
if (isS())
throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!");
if (!target.isR())
throw std::runtime_error("NDArray::varianceAlongDimension: target array must have FLOAT type");
NDArray::prepareSpecialUse({&target}, {this});
if(rankOf() == dimensions.size() || dimensions.empty())
NativeOpExecutioner::execSummaryStatsScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), biasCorrected);
else {
std::vector<int> copy(dimensions);
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimensions);
NativeOpExecutioner::execSummaryStats(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.getSpecialBuffer(), target.specialShapeInfo(), pDims, dimensions.size(), packX.platformShapeInfo(), packX.platformOffsets(), biasCorrected);
synchronize("NDArray::varianceAlongDimension");
}
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::vector<int>& dimensions) const {
if (isS())
throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!");
std::vector<int> copy(dimensions);
if (copy.size() > 1)
std::sort(copy.begin(), copy.end());
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataTypeUtils::pickFloatingType(dataType()), false, false, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
this->varianceAlongDimension(op, result, biasCorrected, dimensions);
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::varianceAlongDimension(nd4j::variance::Ops op, const bool biasCorrected, const std::initializer_list<int>& dimensions) const {
return varianceAlongDimension(op, biasCorrected, std::vector<int>(dimensions));
}
////////////////////////////////////////////////////////////////////////
void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray &target, const bool biasCorrected, const std::initializer_list<int>& dimensions) const {
varianceAlongDimension(op, target, biasCorrected, std::vector<int>(dimensions));
}
////////////////////////////////////////////////////////////////////////
// This method returns new copy of this NDArray, optionally in different order
NDArray NDArray::dup(const char newOrder) const {
if (isEmpty())
return NDArrayFactory::empty(dataType(), getContext());
char order = newOrder == 'a' ? ordering() : newOrder;
// for now string arrays require special treatment
if (dataType() == DataType::UTF8) {
std::vector<std::string> strings(lengthOf());
for (int e = 0; e < lengthOf(); e++)
strings[e] = this->e<std::string>(e);
auto result = NDArrayFactory::string(order, getShapeAsVector(), strings, getContext());
return result;
}
NDArray result(order, isScalar() ? std::vector<Nd4jLong>({0}) : getShapeAsVector(), dataType(), getContext());
result.assign(*this);
return result;
}
////////////////////////////////////////////////////////////////////////
// This method returns true if two arrays are equal, with custom or default Eps value of 1e-5, false otherwise
bool NDArray::equalsTo(const NDArray *other, double eps) const {
if (dataType() != other->dataType() || lengthOf() != other->lengthOf())
return false;
// we need to be able to compare [1, len] to [len]
if ((rankOf() == 1 && other->rankOf() == 2) || (rankOf() == 2 && other->rankOf() == 1)) {
// FIXME: do something here?
} else if (!shape::equalsSoft(getShapeInfo(), other->getShapeInfo()))
return false;
if (isS()) {
// string is special case, we'll compare them one by one, considering both arrays are guaranteed to have the same length
for (int e = 0; e < this->lengthOf(); e++) {
auto s1 = this->e<std::string>(e);
auto s2 = other->e<std::string>(e);
if (s1 != s2)
return false;
}
return true;
} else {
// regular numeric types
NDArray tmp(nd4j::DataType::FLOAT32, getContext()); // scalar = 0
ExtraArguments extras({0.0, 0.0, eps});
NDArray::prepareSpecialUse({&tmp}, {this, other});
NativeOpExecutioner::execReduce3Scalar(getContext(), reduce3::EqualsWithEps, getBuffer(), getShapeInfo(),
getSpecialBuffer(), getSpecialShapeInfo(),
extras.argumentsAsT(DataType::FLOAT32), other->getBuffer(),
other->getShapeInfo(), other->getSpecialBuffer(),
other->getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(),
tmp.specialBuffer(), tmp.specialShapeInfo());
NDArray::registerSpecialUse({&tmp}, {this, other});
synchronize("NDArray::equalsTo");
if (tmp.e<Nd4jLong>(0) != 0)
return false;
return true;
}
}
//////////////////////////////////////////////////////////////////////////
template <>
std::string NDArray::e(const Nd4jLong i) const {
if (!isS())
throw std::runtime_error("Can't get std::string out of non-string array");
NDArray::preparePrimaryUse({}, {this});
// getting "virtual" offset. it's not real though,since it doesn't take lengths into account
auto offset = getOffset(i);
auto offsets = reinterpret_cast<Nd4jLong *>(getBuffer());
auto offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
auto start = offsets[offset];
auto end = offsets[offset + 1];
auto data = static_cast<int8_t*>(getBuffer()) + offsetsLength + start;
std::string r(reinterpret_cast<const char*>(data), (end - start));
registerPrimaryUse({}, {this});
return r;
}
//////////////////////////////////////////////////////////////////////////
template <>
utf8string NDArray::e(const Nd4jLong i) const {
if (!isS())
throw std::runtime_error("This method is available for String arrays only");
auto rp = getOffset(i);
syncToHost();
tickReadHost();
return *(reinterpret_cast<utf8string**>(getBuffer())[rp]);
}
/////////////////////////////////////////////////////////////////////////
template <typename T>
T NDArray::e(const Nd4jLong i) const {
const auto rp = getOffset(i);
NDArray::preparePrimaryUse({}, {this});
NDArray::registerPrimaryUse({}, {this});
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(getBuffer(), rp), LIBND4J_TYPES);
}
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// Returns value from 2D matrix by coordinates/indexes
template <typename T>
T NDArray::e(const Nd4jLong i, const Nd4jLong j) const {
if (rankOf() != 2 || i >= shapeOf()[0] || j >= shapeOf()[1])
throw std::invalid_argument("NDArray::e(i,j): one of input indexes is out of array length or rank!=2 !");
const Nd4jLong coords[2] = {i, j};
const auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({}, {this});
NDArray::registerPrimaryUse({}, {this});
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(getBuffer(), xOffset), LIBND4J_TYPES);
return static_cast<T>(119);
}
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// returns value from 3D tensor by coordinates
template <typename T>
T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) const {
if (rankOf() != 3 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2])
throw std::invalid_argument("NDArray::e(i,j,k): one of input indexes is out of array length or rank!=3 !");
const Nd4jLong coords[3] = {i, j, k};
const auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({}, {this});
NDArray::registerPrimaryUse({}, {this});
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(getBuffer(), xOffset), LIBND4J_TYPES);
return static_cast<T>(119);
}
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
// returns value from 3D tensor by coordinates
template <typename T>
T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l) const {
if (rankOf() != 4 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2] || l >= shapeOf()[3])
throw std::invalid_argument("NDArray::e(i,j,k,l): one of input indexes is out of array length or rank!=4 !");
const Nd4jLong coords[4] = {i, j, k, l};
const auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({}, {this});
NDArray::registerPrimaryUse({}, {this});
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(getBuffer(), xOffset), LIBND4J_TYPES);
return static_cast<T>(119);
}
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong, const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::e(const Nd4jLong i) const {
const auto offset = getOffset(i);
NDArray scalar(dataType(), getContext());
scalar.copyBuffersContinuouslyFrom(*this, sizeOfT(), 0, getBufferOffset() + offset);
return scalar;
}
//////////////////////////////////////////////////////////////////////////
// perform array transformation
void NDArray::applyTransform(nd4j::transform::FloatOps op, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyTransform FloatOps: you can't use this method on String array!");
if (!target.isR())
throw std::runtime_error("NDArray::applyTransform FloatOps: target array must have one of FLOAT types");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execTransformFloat(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyTransform(nd4j::transform::AnyOps op, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyTransform AnyOps: you can't use this method on String array!");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execTransformAny(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyTransform(nd4j::transform::SameOps op, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyTransform SameOps: you can't use this method on String array!");
if (target.dataType() != dataType())
throw std::runtime_error("NDArray::applyTransform SameOps: target array must have the same data type as original array");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execTransformSame(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyTransform(nd4j::transform::StrictOps op, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyTransform StrictOps: you can't use this method on String array!");
if (!this->isR() || !target.isR() || (this->dataType() != target.dataType()))
throw std::runtime_error("NDArray::applyTransform StrictOps: both Source and Target array must have same FLOAT type !");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execTransformStrict(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
void NDArray::applyTransform(nd4j::transform::BoolOps op, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyTransform BoolOps: you can't use this method on String array!");
if (!target.isB())
throw std::runtime_error("NDArray::applyTransform BoolOps: target array must have one of BOOL types");
NDArray::prepareSpecialUse({&target}, {this});
NativeOpExecutioner::execTransformBool(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr, nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) const & {
if (isS())
throw std::runtime_error("NDArray::transform FloatOps: you can't use this method on String array!");
NDArray result(ordering(), getShapeAsVector(), DataTypeUtils::pickFloatingType(dataType()), getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformFloat(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::FloatOps op, void *extraParams) && {
if (isS())
throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!");
NDArray::prepareSpecialUse({this}, {this});
NativeOpExecutioner::execTransformFloat(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({this}, {this});
return std::move(*this);
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) const & {
if (isS())
throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!");
NDArray result(getShapeInfo(), false, getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformSame(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::SameOps op, void *extraParams) && {
if (isS())
throw std::runtime_error("NDArray::transform SameOps: you can't use this method on String array!");
NDArray::prepareSpecialUse({this}, {this});
NativeOpExecutioner::execTransformSame(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({this}, {this});
return std::move(*this);
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) const & {
if (!this->isR())
throw std::runtime_error("Source array must have one of FLOAT types");
NDArray result(getShapeInfo(), false, getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformStrict(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::StrictOps op, void *extraParams) && {
if (!this->isR())
throw std::runtime_error("Source array must have one of FLOAT types");
NDArray::prepareSpecialUse({this}, {this});
NativeOpExecutioner::execTransformStrict(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({this}, {this});
return std::move(*this);
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) const & {
if (isS())
throw std::runtime_error("NDArray::transform BoolOps: you can't use this method on String array!");
NDArray result(ordering(), getShapeAsVector(), nd4j::DataType::BOOL, getContext());
NDArray::prepareSpecialUse({&result}, {this});
NativeOpExecutioner::execTransformBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({&result}, {this});
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::transform(nd4j::transform::BoolOps op, void *extraParams) && {
if (isS())
throw std::runtime_error("NDArray::transform BoolOps: you can't use this method on String array!");
NDArray::prepareSpecialUse({this}, {this});
NativeOpExecutioner::execTransformBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
NDArray::registerSpecialUse({this}, {this});
return std::move(*this);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyScalarArr(nd4j::scalar::Ops op, const NDArray& scalar, NDArray& target, ExtraArguments *extraParams) {
if (isS())
throw std::runtime_error("NDArray::applyScalarArr: you can't use this method on String array!");
if (scalar.lengthOf() != 1)
throw std::invalid_argument("NDArray::applyScalarArr method: operand is not a scalar!");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(shapeInfo(), scalar.getShapeInfo()) && !(target.dataType() == dataType() || target.dataType() == scalar.dataType()))
throw std::invalid_argument("NDArray::applyScalarArr method: wrong type of target array!");
NDArray::prepareSpecialUse({&target}, {this, &scalar});
NativeOpExecutioner::execScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), scalar.getBuffer(), scalar.getShapeInfo(), scalar.getSpecialBuffer(), scalar.getSpecialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
NDArray::registerSpecialUse({&target}, {this, &scalar});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyScalarArr(nd4j::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyScalarArr BoolOps: you can't use this method on String array!");
if (!target.isB())
throw std::invalid_argument("NDArray::applyScalarArr bool method: target has not bool type!");
if (dataType() != scalar.dataType()) {
nd4j_printf("NDArray::applyScalarArr BoolOps: this dtype: [%i]; scalar dtype: [%i]\n", this->dataType(), scalar.dataType());
throw std::invalid_argument("NDArray::applyScalarArr bool method: this and scalar arrays must have the same type!");
}
NDArray::prepareSpecialUse({&target}, {this, &scalar});
NativeOpExecutioner::execScalarBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), scalar.getBuffer(), scalar.getShapeInfo(), scalar.getSpecialBuffer(), scalar.getSpecialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
NDArray::registerSpecialUse({&target}, {this, &scalar});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::applyScalarArr(nd4j::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyScalarArr IntOps: you can't use this method on String array!");
if (target.dataType() != this->dataType())
throw std::invalid_argument("NDArray::applyScalarArr int method: target has not bool type!");
if (dataType() != scalar.dataType()) {
nd4j_printf("NDArray::applyScalarArr IntOps: this dtype: [%i]; scalar dtype: [%i]\n", this->dataType(), scalar.dataType());
throw std::invalid_argument("NDArray::applyScalarArr int method: this and scalar arrays must have the same type!");
}
NDArray::prepareSpecialUse({&target}, {this, &scalar});
NativeOpExecutioner::execScalarInt(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), scalar.getBuffer(), scalar.getShapeInfo(), scalar.getSpecialBuffer(), scalar.getSpecialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
NDArray::registerSpecialUse({&target}, {this, &scalar});
}
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::applyScalar(nd4j::scalar::IntOps op, const T scalar, NDArray& target, ExtraArguments *extraParams) const {
NDArray scalarArr = NDArrayFactory::create(this->dataType(), scalar, getContext());
applyScalarArr(op, scalarArr, target, extraParams);
}
template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
template ND4J_EXPORT void NDArray::applyScalar<double>(nd4j::scalar::IntOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<float>(nd4j::scalar::IntOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<float16>(nd4j::scalar::IntOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<bfloat16>(nd4j::scalar::IntOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<Nd4jLong>(nd4j::scalar::IntOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int>(nd4j::scalar::IntOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int16_t>(nd4j::scalar::IntOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int8_t>(nd4j::scalar::IntOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<uint8_t>(nd4j::scalar::IntOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<bool>(nd4j::scalar::IntOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const;
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::applyScalar(nd4j::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams) {
auto scalarArr = NDArrayFactory::create<T>(dataType(), scalar, this->getContext());
applyScalarArr(op, scalarArr, target, extraParams);
}
template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const double scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const float scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const float16 scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams);
template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::Ops op, const bool scalar, NDArray &target, ExtraArguments *extraParams);
////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::applyScalar(nd4j::scalar::BoolOps op, const T scalar, NDArray &target, ExtraArguments *extraParams) const {
NDArray scalarArr = NDArrayFactory::create<T>(scalar, getContext());
applyScalarArr(op, scalarArr, target, extraParams);
}
template <> ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
template ND4J_EXPORT void NDArray::applyScalar<double>(nd4j::scalar::BoolOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<float>(nd4j::scalar::BoolOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<float16>(nd4j::scalar::BoolOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<bfloat16>(nd4j::scalar::BoolOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<Nd4jLong>(nd4j::scalar::BoolOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int>(nd4j::scalar::BoolOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int16_t>(nd4j::scalar::BoolOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<int8_t>(nd4j::scalar::BoolOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<uint8_t>(nd4j::scalar::BoolOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
template ND4J_EXPORT void NDArray::applyScalar<bool>(nd4j::scalar::BoolOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const;
////////////////////////////////////////////////////////////////////////
void NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const std::vector<int>& dimensions, const ExtraArguments *extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyIndexReduce: you can't use this method on String array!");
if (target.dataType() != nd4j::DataType::INT64 && target.dataType() != nd4j::DataType::INT32)
throw std::runtime_error("NDArray::applyIndexReduce operations return INT32/INT64");
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(this->dataType()) : nullptr;
NDArray::prepareSpecialUse({&target}, {this});
if (target.lengthOf() == 1) {
NativeOpExecutioner::execIndexReduceScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
}
else {
std::vector<int> copy = dimensions;
shape::checkDimensions(rankOf(), copy);
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy);
NativeOpExecutioner::execIndexReduce(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets());
synchronize("NDArray::applyIndexReduce");
}
registerSpecialUse({&target}, {this});
}
////////////////////////////////////////////////////////////////////////
// reduce dimensions in this array relying on index operations
NDArray NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, const std::vector<int>& dimensions, const ExtraArguments* extraParams ) const {
std::vector<int> copy = dimensions;
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::INT64, false, false, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
applyIndexReduce(op, result, copy, extraParams);
return result;
}
////////////////////////////////////////////////////////////////////////
// apply reduce3 operations to this and other array, return result in new output array
NDArray NDArray::applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const ExtraArguments* extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyReduce3 method: you can't use this method on String array!");
if(dataType() != other.dataType())
throw std::runtime_error("NDArray::applyReduce3 method: the types of this and other arrays must be the same !");
// check shapes consistency
if(!isSameShape(other))
throw std::runtime_error("NDArray::applyReduce3 method: the shapes of this and other arrays must be the same !");
// create shapeInfo for scalar
auto newShape = ShapeBuilders::createScalarShapeInfo(DataTypeUtils::pickFloatingType(dataType()), getContext()->getWorkspace());
// create output array (scalar)
NDArray result(newShape, true, getContext());
RELEASE(newShape, getContext()->getWorkspace());
// create dynamic array of extra parameters if array extraParams is empty (==nullptr)
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
NDArray::prepareSpecialUse({&result}, {this, &other});
NativeOpExecutioner::execReduce3Scalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
NDArray::registerSpecialUse({&result}, {this, &other});
return result;
}
////////////////////////////////////////////////////////////////////////
// apply reduce3 (exec) operations to this and other array, return result in new output array
NDArray NDArray::applyReduce3(nd4j::reduce3::Ops op, const NDArray& other, const std::vector<int>& dimensions, const ExtraArguments* extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyReduce3: you can't use this method on String array!");
if(dataType() != other.dataType())
throw std::runtime_error("NDArray::applyReduce3 method: the types of this and other arrays must be the same !");
std::vector<int> copy(dimensions);
shape::checkDimensions(rankOf(), copy);
shape::checkDimensions(other.rankOf(), copy);
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataTypeUtils::pickFloatingType(dataType()), false, false, getContext()->getWorkspace());
NDArray result(newShape, true, getContext());
// create temporary dynamic array of extra parameters if array extraParams is empty (==nullptr)
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
NDArray::prepareSpecialUse({&result}, {this, &other});
// perform calculations
if(rankOf() == copy.size() && other.rankOf() == copy.size()) {
NativeOpExecutioner::execReduce3Scalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
}
else {
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy);
auto packY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(other.getShapeInfo(), copy);
if(!shape::equalsSoft(packX.primaryShapeInfo(), packY.primaryShapeInfo()) || (packX.numberOfTads() != packY.numberOfTads() && packX.numberOfTads() != 1 && packY.numberOfTads() != 1))
throw std::runtime_error("NDArray::applyReduce3 cuda method: arrays tads are inconsistent !");
NativeOpExecutioner::execReduce3(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packY.platformShapeInfo(), packY.platformOffsets());
}
registerSpecialUse({&result}, {this, &other});
return result;
}
////////////////////////////////////////////////////////////////////////
// apply reduce3 (execAll) operations to this and other array, return result in new output array
NDArray NDArray::applyAllReduce3(nd4j::reduce3::Ops op, const NDArray& other, const std::vector<int>& dimensions, const ExtraArguments* extraParams) const {
if (isS())
throw std::runtime_error("NDArray::applyAllReduce3: you can't use this method on String array!");
if(dataType() != other.dataType())
throw std::runtime_error("NDArray::applyAllReduce3 method: the types of this and other arrays must be the same !");
// be careful, copy array may undergo changes (sort, transformation of negative dimensions to positive, duplicates removing )
std::vector<int> copy(dimensions);
shape::checkDimensions(rankOf(), copy);
shape::checkDimensions(other.rankOf(), copy);
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy);
auto packY = ConstantTadHelper::getInstance()->tadForDimensions(other.getShapeInfo(), copy);
// check tads shapes
if(!shape::equalsSoft(packX.primaryShapeInfo(), packY.primaryShapeInfo()))
throw std::runtime_error("NDArray::applyAllReduce3 method: the shapes of array tads are different !");
// set newShape for output array
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(DataTypeUtils::pickFloatingType(dataType()), 'c', {packX.numberOfTads(), packY.numberOfTads()});
// create output array
NDArray result(newShape, true, getContext());
// create dynamic array of extra parameters if array extraParams is empty (==nullptr)
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
NDArray::prepareSpecialUse({&result}, {this, &other});
NativeOpExecutioner::execReduce3All(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), params, other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets(), packY.platformShapeInfo(), packY.platformOffsets());
NDArray::registerSpecialUse({&result}, {this, &other});
return result;
}
//////////////////////////////////////////////////////////////////////////
// method reduces array by excluding its shapes along axes present in dimensions vector
void NDArray::reduceAlongDimension(nd4j::reduce::FloatOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const {
if (isS())
throw std::runtime_error("NDArray::reduceAlongDimension FloatOps: you can't use this method on String array!");
if (!target.isR())
throw std::invalid_argument("NDArray::reduceAlongDimension FloatOps: requires target array to be present and have type form real space!");
std::vector<int> copy(dimensions);
if(checkTargetShape) {
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, supportOldShapes, getContext()->getWorkspace());
if(!shape::shapeEquals(newShape, target.getShapeInfo()))
throw std::runtime_error("NDArray::reduceAlongDimension FloatOps: wrong target shape!");
}
NDArray::prepareSpecialUse({&target}, {this});
if(rankOf() == copy.size() || copy.empty()) {
NativeOpExecutioner::execReduceFloatScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(),nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo());
}
else {
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), copy);
NativeOpExecutioner::execReduceFloat(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), copy.data(), copy.size(), packX.platformShapeInfo(), packX.platformOffsets());
}
synchronize("NDArray::reduceAlongDimension FloatOps");
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
// method reduces array by excluding its shapes along axes present in dimensions vector
void NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const {
if (isS())
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: you can't use this method on String array!");
if (target.dataType() != dataType())
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: requires target array to be present and have same dtype as input");
std::vector<int> copy(dimensions);
if(checkTargetShape) {
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, supportOldShapes, getContext()->getWorkspace());
if(!shape::shapeEquals(newShape, target.getShapeInfo()))
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: wrong target shape!");
}
NDArray::prepareSpecialUse({&target}, {this});
if(rankOf() == copy.size() || copy.empty()) {
NativeOpExecutioner::execReduceSameScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo());
}
else { //if (!isEmpty()) {
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy);
NativeOpExecutioner::execReduceSame(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets());
}
synchronize("NDArray::reduceAlongDimension SameOps");
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
// method reduces array by excluding its shapes along axes present in dimensions vector
void NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const {
if (isS())
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: you can't use this method on String array!");
if (target.dataType() != DataType::INT64)
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: requires target array to be present and have type of INT64");
std::vector<int> copy(dimensions);
if(checkTargetShape) {
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, supportOldShapes, getContext()->getWorkspace());
if(!shape::shapeEquals(newShape, target.getShapeInfo()))
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: wrong target shape!");
}
NDArray::prepareSpecialUse({&target}, {this});
if(rankOf() == copy.size() || copy.empty()) {
NativeOpExecutioner::execReduceLongScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo());
}
else {
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy);
NativeOpExecutioner::execReduceLong(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets());
}
synchronize("NDArray::reduceAlongDimension LongOps");
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
// method reduces array by excluding its shapes along axes present in dimensions vector
void NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool supportOldShapes, const bool checkTargetShape) const {
if (isS())
throw std::runtime_error("NDArray::reduceAlongDimension BoolOps cuda: you can't use this method on String array!");
if (!target.isB())
throw std::invalid_argument("NDArray::reduceAlongDimension BoolOps cuda: requires target array to be present and have BOOL type!");
std::vector<int> copy(dimensions);
if(checkTargetShape) {
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, supportOldShapes, getContext()->getWorkspace());
if(!shape::shapeEquals(newShape, target.getShapeInfo()))
throw std::runtime_error("NDArray::reduceAlongDimension BoolOps cuda: wrong target shape!");
}
NDArray::prepareSpecialUse({&target}, {this});
if(rankOf() == copy.size() || copy.empty()) {
NativeOpExecutioner::execReduceBoolScalar(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo());
}
else {
auto pDims = nd4j::Environment::getInstance()->isCPU() ? copy.data() : nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy);
NativeOpExecutioner::execReduceBool(getContext(), op, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), nullptr, target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), pDims, copy.size(), packX.platformShapeInfo(), packX.platformOffsets());
}
synchronize("NDArray::reduceAlongDimension LongOps");
NDArray::registerSpecialUse({&target}, {this});
}
//////////////////////////////////////////////////////////////////////////
// This method sets value in linear buffer to position i
template <typename T>
void NDArray::p(const Nd4jLong i, const T value) {
if (i >= lengthOf())
throw std::invalid_argument("NDArray::p(i, value): input index is out of array length !");
auto rp = getOffset(i);
const void *pV = reinterpret_cast<const void*>(const_cast<T *>(&value));
NDArray::preparePrimaryUse({this}, {}, true);
BUILD_SINGLE_PARTIAL_SELECTOR(this->dataType(), templatedSet<, T>(this->getBuffer(), rp, pV), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {});
}
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const double value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const float value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const float16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const bfloat16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint32_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint64_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const bool value);
//////////////////////////////////////////////////////////////////////////
// This method sets value in 2D matrix to position i, j
template <typename T>
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const T value) {
if (rankOf() != 2 || i >= shapeOf()[0] || j >= shapeOf()[1])
throw std::invalid_argument("NDArray:pe(i,j, value): one of input indexes is out of array length or rank!=2 !");
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
Nd4jLong coords[2] = {i, j};
auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({this}, {}, true);
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {});
}
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const double value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const float value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const float16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const bfloat16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint32_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint64_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const bool value);
//////////////////////////////////////////////////////////////////////////
// This method sets value in 3D matrix to position i,j,k
template <typename T>
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const T value) {
//(*this)(i,j,k) = value;
if (rankOf() != 3 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2])
throw std::invalid_argument("NDArray:pe(i,j,k, value): one of input indexes is out of array length or rank!=3 !");
NDArray::preparePrimaryUse({this}, {}, true);
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
Nd4jLong coords[3] = {i, j, k};
auto xOffset = shape::getOffset(getShapeInfo(), coords);
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {});
}
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const double value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const float value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const float16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const bfloat16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint32_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint64_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const bool value);
//////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const T value) {
//(*this)(i,j,k) = value;
if (rankOf() != 4 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2] || l >= shapeOf()[3])
throw std::invalid_argument("NDArray::p(i,j,k,l, value): one of input indexes is out of array length or rank!=4 !");
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
Nd4jLong coords[4] = {i, j, k, l};
auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({this}, {}, true);
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {});
}
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const double value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const float value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const float16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const bfloat16 value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const Nd4jLong value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const int value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const int8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const uint8_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const uint16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const uint32_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const uint64_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const int16_t value);
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const bool value);
////////////////////////////////////////////////////////////////////////
void NDArray::p(const Nd4jLong i, const NDArray& scalar) {
if(scalar.lengthOf() != 1)
throw std::invalid_argument("NDArray::p method: input array must be scalar!");
if (i >= _length)
throw std::invalid_argument("NDArray::p(i, NDArray_scalar): input index is out of array length !");
NDArray::preparePrimaryUse({this}, {&scalar}, true);
auto rp = getOffset(i);
BUILD_SINGLE_SELECTOR(scalar.dataType(), templatedSet, (getBuffer(), rp, scalar.dataType(), scalar.getBuffer()), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {&scalar});
}
////////////////////////////////////////////////////////////////////////
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const NDArray& scalar) {
if(scalar.lengthOf() != 1)
throw std::invalid_argument("NDArray::p method: input array must be scalar!");
if (i >= _length)
throw std::invalid_argument("NDArray::p(i, NDArray_scalar): input index is out of array length !");
// void *p = reinterpret_cast<void *>(scalar.getBuffer());
Nd4jLong coords[4] = {i, j, k, l};
auto xOffset = shape::getOffset(getShapeInfo(), coords);
NDArray::preparePrimaryUse({this}, {&scalar}, true);
// BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
BUILD_SINGLE_SELECTOR(scalar.dataType(), templatedSet, (this->getBuffer(), xOffset, scalar.dataType(), scalar.getBuffer()), LIBND4J_TYPES);
NDArray::registerPrimaryUse({this}, {&scalar});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::addRowVector(const NDArray& row, NDArray& target) const {
if (isS())
throw std::runtime_error("NDArray::addRowVector: you can't use this method on String array!");
if (rankOf() != 2 || target.rankOf() != 2 || rows() != target.rows() || columns() != target.columns() || !row.isRowVector() || columns() != row.lengthOf())
throw std::invalid_argument("NDArray::addRowVector: wrong arguments !");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(dataType(), row.dataType()) && !(isR() && row.isR() && target.isR()))
throw std::invalid_argument("NDArray::addRowVector: wrong type of target array !");
int dimension = 1;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({&target}, {this, &row});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this, &row});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::subRowVector(const NDArray& row, NDArray& target) const {
if (isS())
throw std::runtime_error("NDArray::addRowVector: you can't use this method on String array!");
if (rankOf() != 2 || target.rankOf() != 2 || rows() != target.rows() || columns() != target.columns() || !row.isRowVector() || columns() != row.lengthOf())
throw std::invalid_argument("NDArray::addRowVector: wrong arguments !");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(dataType(), row.dataType()) && !(isR() && row.isR() && target.isR()))
throw std::invalid_argument("NDArray::addRowVector: wrong type of target array !");
int dimension = 1;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({&target}, {this, &row});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Subtract, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), &dimension, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this, &row});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::mulRowVector(const NDArray &row, NDArray &target) const {
if (isS())
throw std::runtime_error("NDArray::mulRowVector: you can't use this method on String array!");
if (rankOf() != 2 || target.rankOf() != 2 || rows() != target.rows() || columns() != target.columns() || !row.isRowVector() || columns() != row.columns())
throw std::invalid_argument("NDArray::divRowVector: wrong arguments !");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(dataType(), row.dataType()))
throw std::invalid_argument("NDArray::mulRowVector: wrong type of target array !");
int dimension = 1;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({&target}, {this, &row});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this, &row});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::divRowVector(const NDArray &row, NDArray &target) const {
if (isS())
throw std::runtime_error("NDArray::divRowVector: you can't use this method on String array!");
if (row.isB())
throw std::runtime_error("NDArray::divRowVector: you can't divide by bool row!");
if (rankOf() != 2 || target.rankOf() != 2 || rows() != target.rows() || columns() != target.columns() || !row.isRowVector() || columns() != row.columns())
throw std::invalid_argument("NDArray::divRowVector: wrong arguments !");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(dataType(), row.dataType()))
throw std::invalid_argument("NDArray::divRowVector: wrong type of target array !");
int dimension = 1;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({&target}, {this, &row});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Divide, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this, &row});
}
//////////////////////////////////////////////////////////////////////////
// This method adds given row to all rows in this NDArray, this array becomes affected
void NDArray::addiRowVector(const NDArray& row) {
if (isS())
throw std::runtime_error("NDArray::addiRowVector: you can't use this method on String array!");
if (rankOf() != 2 || !row.isRowVector() || columns() != row.lengthOf())
throw std::invalid_argument("NDArray::addiRowVector: wrong arguments !");
int dimension = 1;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({this}, {&row});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), row.getBuffer(), row.getShapeInfo(), row.getSpecialBuffer(), row.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({this}, {&row});
}
//////////////////////////////////////////////////////////////////////////
void NDArray::addColumnVector(const NDArray &column, NDArray &target) const {
if (isS())
throw std::runtime_error("NDArray::addColumnVector: you can't use this method on String array!");
if (rankOf() != 2 || target.rankOf() != 2 || rows() != target.rows() || columns() != target.columns() || !column.isColumnVector() || rows() != column.lengthOf())
throw std::invalid_argument("NDArray::addColumnVector: wrong arguments !");
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(dataType(), column.dataType()))
throw std::invalid_argument("NDArray::addColumnVector: wrong type of target array !");
int dimension = 0;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({&target}, {this, &column});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), target.getBuffer(), target.getShapeInfo(), target.getSpecialBuffer(), target.getSpecialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({&target}, {this, &column});
}
//////////////////////////////////////////////////////////////////////////
// This method adds given column to all columns in this NDArray, this array becomes affected
void NDArray::addiColumnVector(const NDArray &column) {
if (isS())
throw std::runtime_error("NDArray::addiColumnVector: you can't use this method on String array!");
if (rankOf() != 2 || !column.isColumnVector() || rows() != column.lengthOf())
throw std::invalid_argument("NDArray::addiColumnVector: wrong arguments !");
int dimension = 0;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({this}, {&column});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Add, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({this}, {&column});
}
//////////////////////////////////////////////////////////////////////////
// This method multiplies each column of this array by given argument-column, this array becomes affected
void NDArray::muliColumnVector(const NDArray& column) {
if (isS())
throw std::runtime_error("NDArray::muliColumnVector: you can't use this method on String array!");
if (rankOf() != 2 || !column.isColumnVector() || rows() != column.lengthOf())
throw std::invalid_argument("NDArray::muliColumnVector: wrong arguments !");
int dimension = 0;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), dimension);
NDArray::prepareSpecialUse({this}, {&column});
NativeOpExecutioner::execBroadcast(getContext(), nd4j::broadcast::Ops::Multiply, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), column.getBuffer(), column.getShapeInfo(), column.getSpecialBuffer(), column.getSpecialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
NDArray::registerSpecialUse({this}, {&column});
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void NDArray::templatedAssign(void *xBuffer, Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const {
if (xBuffer != nullptr && yBuffer != nullptr)
*(reinterpret_cast<T*>(xBuffer) + xOffset) = *(reinterpret_cast<const T*>(yBuffer) + yOffset);
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedAssign, (void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const, LIBND4J_TYPES);
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const int* dimensions, const int rank) {
auto shapeInfo = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, getContext()->getWorkspace());
setShapeInfo(shapeInfo);
return true;
}
//////////////////////////////////////////////////////////////////////////
bool NDArray::permutei(const Nd4jLong* dimensions, const int rank) {
auto shapeInfo = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, getContext()->getWorkspace());
setShapeInfo(shapeInfo);
return true;
}
////////////////////////////////////////////////////////////////////////
ResultSet NDArray::multipleTensorsAlongDimension(const std::vector<int> &indices, const std::vector<int> &dimensions) const {
ResultSet result;
if (indices.size() == 0)
return result;
auto pack = ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), const_cast<int*>(dimensions.data()), dimensions.size());
auto tadLength = shape::length(pack.primaryShapeInfo());
auto numTads = lengthOf() / tadLength;
for (auto idx: indices) {
if (idx >= numTads) {
nd4j_printf("NDArray::multipleTensorsAlongDimension: index %i is higher then number of TADs: %i\n", idx, numTads);
throw std::runtime_error("Bad index");
}
auto array = new NDArray(getDataBuffer(), ShapeDescriptor(pack.primaryShapeInfo()), getContext(), pack.primaryOffsets()[idx] + getBufferOffset());
result.push_back(array);
}
return result;
}
////////////////////////////////////////////////////////////////////////
ResultSet NDArray::allTensorsAlongDimension(const std::initializer_list<int>& dimensions) const {
return allTensorsAlongDimension(std::vector<int>(dimensions));
}
////////////////////////////////////////////////////////////////////////
ResultSet NDArray::allExamples() const {
std::vector<int> dimensions(rankOf() - 1);
for (int e = 1; e < rankOf(); e++)
dimensions[e-1] = e;
return allTensorsAlongDimension(dimensions);
}
////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::getOffset(const Nd4jLong i) const {
if (i >= lengthOf())
throw std::invalid_argument("NDArray::getOffset: input index is out of array length !");
return shape::getIndexOffset(i, _shapeInfo);
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::like() {
return NDArray(shapeInfo(), this->dataType(), false, getContext());
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::ulike() {
return NDArray(this, false, getContext());
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::diagonal(const char type) const {
if (isS())
throw std::runtime_error("NDArray::diagonal: you can't use this method on String array!");
const char order = ordering();
const int rank = rankOf();
Nd4jLong *outShapeInfo;
ALLOCATE(outShapeInfo, getContext()->getWorkspace(), 8, Nd4jLong);
outShapeInfo[0] = 2;
outShapeInfo[5] = 0;
if(isVector() || isScalar()) {
outShapeInfo[1] = outShapeInfo[2] = outShapeInfo[3] = outShapeInfo[4] = 1;
outShapeInfo[6] = 1;
outShapeInfo[7] = (int)order;
}
else {
int diagSize = 100000000;
Nd4jLong indices[MAX_RANK];
for(int i = 0; i < rank; ++i) {
if(diagSize > shapeOf()[i])
diagSize = shapeOf()[i];
indices[i] = 1;
}
auto step = shape::getOffset(getShapeInfo(), indices);
if(type == 'c') {
outShapeInfo[1] = diagSize;
outShapeInfo[2] = 1;
}
else {
outShapeInfo[1] = 1;
outShapeInfo[2] = diagSize;
}
shape::updateStrides(outShapeInfo, order);
outShapeInfo[3] *= step;
outShapeInfo[4] *= step;
outShapeInfo[6] = 0;
}
ArrayOptions::setDataType(outShapeInfo, this->dataType());
NDArray result(_buffer, ShapeDescriptor(outShapeInfo), getContext(), getBufferOffset());
RELEASE(outShapeInfo, getContext()->getWorkspace());
return result;
}
////////////////////////////////////////////////////////////////////////
ResultSet NDArray::allTensorsAlongDimension(const std::vector<int> &dimensions) const {
ResultSet result;
if(dimensions.size() == 0)
return result;
if(dimensions.back() >= rankOf())
throw std::runtime_error("NDArray::allTensorsAlongDimension static function: all input dimensions must be smaller than rank of input array !");
auto pack = ConstantTadHelper::getInstance()->tadForDimensions(_shapeInfo, const_cast<int*>(dimensions.data()), dimensions.size());
auto numTads = pack.numberOfTads();
for (int idx = 0; idx < numTads; idx++ ) {
auto array = new NDArray(_buffer, ShapeDescriptor(pack.primaryShapeInfo()), getContext(), pack.primaryOffsets()[idx] + getBufferOffset());
array->_isView = true;
result.push_back(array);
}
return result;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::tensorAlongDimension(Nd4jLong index, const std::vector<int>& dimensions) const {
std::vector<int> copy(dimensions);
shape::checkDimensions(rankOf(), copy);
Nd4jLong tadLength = shape::tadLength(this->getShapeInfo(), copy.data(), copy.size());
Nd4jLong numTads = this->lengthOf() / tadLength;
if (index >= numTads)
throw std::runtime_error("Can't get index higher than total number of TADs");
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(this->getShapeInfo(), copy);
NDArray array(_buffer, ShapeDescriptor(packX.primaryShapeInfo()), getContext(), packX.primaryOffsets()[index] + getBufferOffset());
array._isView = true;
return array;
}
//////////////////////////////////////////////////////////////////////////
NDArray NDArray::tensorAlongDimension(Nd4jLong index, const std::initializer_list<int>& dimensions) const {
return tensorAlongDimension(index, std::vector<int>(dimensions));
}
////////////////////////////////////////////////////////////////////////
// operator returns sub-array with buffer pointing at this->_buffer + certain offset
NDArray NDArray::operator()(const std::vector<Nd4jLong>& idx, const bool keepUnitiesInShape, const bool isStrided) const {
if(isEmpty())
throw std::invalid_argument("NDArray::operator(sub-arrays): array is empty !");
const int rank = rankOf();
Nd4jLong *newShapeInfo = ShapeBuilders::copyShapeInfo(getShapeInfo(), true, getContext()->getWorkspace());
auto shapeOf = shape::shapeOf(newShapeInfo);
auto stridesOf = shape::stride(newShapeInfo);
Nd4jLong offset(0), subArrLen(1);
int n(isStrided ? 3 : 2), first, last, stride;
for (int d = rank - 1; d >= 0; --d) {
if (idx[n * d] != idx[n * d + 1]) {
first = idx[n * d] >= 0 ? idx[n * d] : idx[n * d] + sizeAt(d) + 1;
last = idx[n * d + 1] >= 0 ? idx[n * d + 1] : idx[n * d + 1] + sizeAt(d) + 1;
stride = isStrided ? idx[n * d + 2] : 1;
shapeOf[d] = (last - first + stride - 1) / stride; // ceil (last - first) / stride;
offset += first * stridesOf[d];
if(shapeOf[d] != 1)
stridesOf[d] *= stride;
}
subArrLen *= shapeOf[d];
}
// check if there is possibility to set ews = 1
shape::setEws(newShapeInfo, subArrLen);
NDArray result(_buffer, ShapeDescriptor(newShapeInfo), getContext(), offset + getBufferOffset());
result._isView = true;
if(!keepUnitiesInShape) {
const int coeff = isStrided ? 3 : 2;
std::vector<Nd4jLong> nonUnitDims;
for (int d = 0; d < rank; ++d)
if(!(idx[coeff*d] != idx[coeff*d+1] && newShapeInfo[d+1] == 1))
nonUnitDims.push_back(newShapeInfo[d+1]);
if(nonUnitDims.size() != rank)
result.reshapei(nonUnitDims);
}
RELEASE(newShapeInfo, getContext()->getWorkspace());
return result;
}
////////////////////////////////////////////////////////////////////////
NDArray NDArray::operator()(const Nd4jLong subArrIdx, const std::vector<int>& dimsToExclude, bool keepUnitiesInShape) const {
std::vector<Nd4jLong> idxRanges(2 * rankOf());
ShapeUtils::evalIdxRangesForSubArr(subArrIdx, _shapeInfo, dimsToExclude, idxRanges.data());
return (*this)(idxRanges, keepUnitiesInShape);
}
////////////////////////////////////////////////////////////////////////
void NDArray::getSubArrShapeAndOffsets(const std::vector<int>& dimsToExclude, Nd4jLong* &subArrShapeInfo, Nd4jLong* &subArrOffsets, bool keepUnitiesInShape) const {
if(isEmpty())
throw std::invalid_argument("NDArray::getSubArrShapeAndOffsets: array is empty !");
const int rank = rankOf();
const int subArrRank = (rank == dimsToExclude.size() || keepUnitiesInShape) ? rank : rank - dimsToExclude.size();
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(_shapeInfo, dimsToExclude);
// allocate memory
ALLOCATE(subArrShapeInfo, getContext()->getWorkspace(), shape::shapeInfoLength(subArrRank), Nd4jLong);
ALLOCATE(subArrOffsets, getContext()->getWorkspace(), numOfSubArrs, Nd4jLong);
shape::calcSubArrShapeAndOffsets(_shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), subArrShapeInfo, subArrOffsets, keepUnitiesInShape);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::setShapeInfo(const Nd4jLong *shapeInfo) {
if (shapeInfo != nullptr) {
ShapeDescriptor descriptor(shapeInfo);
auto shapeBuffer = ConstantShapeHelper::getInstance()->bufferForShapeInfo(descriptor);
_shapeInfo = reinterpret_cast<Nd4jLong *>(shapeBuffer.primary());
#ifdef __CUDABLAS__
_shapeInfoD = reinterpret_cast<Nd4jLong *>(shapeBuffer.special());
#endif
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
_length = 0;
else
_length = shape::length(_shapeInfo);
_dataType = ArrayOptions::dataType(_shapeInfo);
}
else {
_dataType = nd4j::DataType::INHERIT;
_shapeInfoD = _shapeInfo = nullptr;
}
}
////////////////////////////////////////////////////////////////////////
void NDArray::setShapeInfo(const Nd4jLong *shapeInfo, const nd4j::DataType dtype) {
if (shapeInfo != nullptr) {
Nd4jLong* shapeInfoTemp = ShapeBuilders::copyShapeInfoAndType(shapeInfo, dtype, true, getContext()->getWorkspace());
ShapeDescriptor descriptor(shapeInfoTemp);
auto shapeBuffer = ConstantShapeHelper::getInstance()->bufferForShapeInfo(descriptor);
_shapeInfo = reinterpret_cast<Nd4jLong *>(shapeBuffer.primary());
#ifdef __CUDABLAS__
_shapeInfoD = reinterpret_cast<Nd4jLong *>(shapeBuffer.special());
#endif
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
_length = 0;
else
_length = shape::length(_shapeInfo);
_dataType = dtype;
}
else {
_dataType = nd4j::DataType::INHERIT;
_shapeInfoD = _shapeInfo = nullptr;
}
}
//////////////////////////////////////////////////////////////////////////
void NDArray::setShapeInfo(const ShapeDescriptor& descriptor) {
auto shapeBuffer = ConstantShapeHelper::getInstance()->bufferForShapeInfo(const_cast<ShapeDescriptor &>(descriptor));
_shapeInfo = reinterpret_cast<Nd4jLong *>(shapeBuffer.primary());
#ifdef __CUDABLAS__
_shapeInfoD = reinterpret_cast<Nd4jLong *>(shapeBuffer.special());
#endif
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
_length = 0;
else
_length = shape::length(_shapeInfo);
_dataType = ArrayOptions::dataType(_shapeInfo);
}
//////////////////////////////////////////////////////////////////////////
void NDArray::setShapeInfo(const ConstantDataBuffer& shapeBuffer) {
_shapeInfo = reinterpret_cast<Nd4jLong *>(const_cast<ConstantDataBuffer&>(shapeBuffer).primary());
#ifdef __CUDABLAS__
_shapeInfoD = reinterpret_cast<Nd4jLong *>(const_cast<ConstantDataBuffer&>(shapeBuffer).special());
#endif
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
_length = 0;
else
_length = shape::length(_shapeInfo);
_dataType = ArrayOptions::dataType(_shapeInfo);
}
///////////////////////////////////////////////////////////////////////
// addition operator array + scalar
template <typename T, typename>
NDArray operator+(NDArray&& arr, const T& scalar) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(arr + scalar); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator+(NDArray&& arr, const T& scalar): you can't use this method on String array!");
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
throw std::runtime_error("operator+(NDArray&& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const double& scalar);
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const float& scalar);
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const int& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator+(const NDArray& arr, const T& scalar) {
if (arr.isS())
throw std::runtime_error("operator+(const NDArray& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Add, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const double& scalar);
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const float& scalar);
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const int& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator+(const T& scalar, NDArray&& arr) {
return std::move(arr) + scalar;
}
template ND4J_EXPORT NDArray operator+(const double& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator+(const float& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator+(const float16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator+(const bfloat16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator+(const int& scalar, NDArray&& arr);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator+(const T& scalar, const NDArray& arr) {
return arr + scalar;
}
template ND4J_EXPORT NDArray operator+(const double& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator+(const float& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator+(const int& scalar, const NDArray& arr);
///////////////////////////////////////////////////////////////////////
// addition operator array - scalar
template <typename T, typename>
NDArray operator-(NDArray&& arr, const T& scalar) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(arr - scalar); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator-(NDArray&& arr, const T& scalar): you can't use this method on String array!");
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
throw std::runtime_error("operator-(NDArray&& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator-(NDArray&& arr, const double& scalar);
template ND4J_EXPORT NDArray operator-(NDArray&& arr, const float& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator-(const NDArray& arr, const T& scalar) {
if (arr.isS())
throw std::runtime_error("operator-(const NDArray& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Subtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const double& scalar);
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const float& scalar);
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const int& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator-(const T& scalar, NDArray&& arr) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(scalar - arr); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator-(const T& scalar, NDArray&& arr): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator-(const double& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator-(const float& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator-(const float16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator-(const bfloat16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator-(const int& scalar, NDArray&& arr);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator-(const T& scalar, const NDArray& arr) {
if (arr.isS())
throw std::runtime_error("operator-(const T& scalar, const NDArray& arr): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator-(const double& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator-(const float& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator-(const int& scalar, const NDArray& arr);
///////////////////////////////////////////////////////////////////////
// addition operator array + scalar
template <typename T, typename>
NDArray operator*(NDArray&& arr, const T& scalar) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(arr * scalar); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator*(NDArray&& arr, const T& scalar): you can't use this method on String array!");
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
throw std::runtime_error("operator*(NDArray&& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const double& scalar);
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const float& scalar);
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const int& scalar);
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const long long& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator*(const NDArray& arr, const T& scalar) {
if (arr.isS())
throw std::runtime_error("operator*(const NDArray& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Multiply, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const double& scalar);
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const float& scalar);
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const int& scalar);
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const long long& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator*(const T& scalar, NDArray&& arr) {
return std::move(arr) * scalar;
}
template ND4J_EXPORT NDArray operator*(const double& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator*(const float& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator*(const float16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator*(const bfloat16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator*(const int& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator*(const long long& scalar, NDArray&& arr);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator*(const T& scalar, const NDArray& arr) {
return arr * scalar;
}
template ND4J_EXPORT NDArray operator*(const double& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator*(const float& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator*(const float16& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator*(const bfloat16& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator*(const int& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator*(const long long& scalar, const NDArray& arr);
///////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator/(NDArray&& arr, const T& scalar) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(arr / scalar); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator/(NDArray&& arr, const T& scalar): you can't use this method on String array!");
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
throw std::runtime_error("operator/(NDArray&& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.buffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const double& scalar);
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const float& scalar);
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const long long& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator/(const NDArray& arr, const T& scalar) {
if (arr.isS())
throw std::runtime_error("operator/(const NDArray& arr, const T& scalar): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::Divide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const double& scalar);
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const float& scalar);
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const float16& scalar);
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const bfloat16& scalar);
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const int& scalar);
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const long long& scalar);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator/(const T& scalar, NDArray&& arr) {
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
return std::move(scalar / arr); // arr is lvalue inside function body
if (arr.isS())
throw std::runtime_error("operator/(const T& scalar, NDArray&& arr): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), arr.getBuffer(), arr.getShapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
return std::move(arr);
}
template ND4J_EXPORT NDArray operator/(const double& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator/(const float& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator/(const float16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator/(const bfloat16& scalar, NDArray&& arr);
template ND4J_EXPORT NDArray operator/(const int& scalar, NDArray&& arr);
////////////////////////////////////////////////////////////////////////
template <typename T, typename>
NDArray operator/(const T& scalar, const NDArray& arr) {
if (arr.isS())
throw std::runtime_error("operator/(const T& scalar, const NDArray& arr): you can't use this method on String array!");
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
NativeOpExecutioner::execScalar(arr.getContext(), nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), arr.getSpecialBuffer(), arr.getSpecialShapeInfo(), result.getBuffer(), result.getShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
return result;
}
template ND4J_EXPORT NDArray operator/(const double& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator/(const float& scalar, const NDArray& arr);
template ND4J_EXPORT NDArray operator/(const int& scalar, const NDArray& arr);
////////////////////////////////////////////////////////////////////////
// addition operator array + array
template <typename T1, typename T2, typename>
NDArray operator+(T1&& arr1, T2&& arr2) {
if (arr1.isS() || arr2.isS())
throw std::runtime_error("operator+(T&& arr1, T&& arr2): you can't use this method on String arrays!");
if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
throw nd4j::datatype_exception::build("operator+(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
PointersManager pointersManager(arr1.getContext(), "operator+(T&& arr1, T&& arr2)");
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
NDArray* result = nullptr;
if(isArr1Rvalue)
result = const_cast<NDArray*>(&arr1);
else if(isArr2Rvalue)
result = const_cast<NDArray*>(&arr2);
else
result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext());
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Add, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
if(!isArr1Rvalue && !isArr2Rvalue) {
NDArray res = std::move(*result);
delete result;
return std::move(res);
}
return std::move(*result);
}
return std::forward<T1>(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), std::forward<T2>(arr2));
}
template ND4J_EXPORT NDArray operator+<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator+<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator+<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator+<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator+<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator+<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator+<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator+<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator+<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
////////////////////////////////////////////////////////////////////////
// addition operator array - array
template <typename T1, typename T2, typename>
NDArray operator-(T1&& arr1, T2&& arr2) {
if (arr1.isS() || arr2.isS())
throw std::runtime_error("operator-(T&& arr1, T&& arr2): you can't use this method on String arrays!");
if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
throw nd4j::datatype_exception::build("operator-(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
PointersManager pointersManager(arr1.getContext(), "operator-(T&& arr1, T&& arr2)");
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
NDArray* result = nullptr;
if(isArr1Rvalue)
result = const_cast<NDArray*>(&arr1);
else if(isArr2Rvalue)
result = const_cast<NDArray*>(&arr2);
else
result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext());
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Subtract, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
if(!isArr1Rvalue && !isArr2Rvalue) {
NDArray res = std::move(*result);
delete result;
return std::move(res);
}
return std::move(*result);
}
return std::forward<T1>(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), std::forward<T2>(arr2));
}
template ND4J_EXPORT NDArray operator-<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator-<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator-<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator-<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator-<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator-<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator-<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator-<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator-<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
////////////////////////////////////////////////////////////////////////
// multiplication operator array*array
template <typename T1, typename T2, typename>
NDArray operator*(T1&& arr1, T2&& arr2) {
if (arr1.isS() || arr2.isS())
throw std::runtime_error("operator*(T&& arr1, T&& arr2): you can't use this method on String arrays!");
if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
throw nd4j::datatype_exception::build("operator*(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
PointersManager pointersManager(arr1.getContext(), "operator*(T&& arr1, T&& arr2)");
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
NDArray* result = nullptr;
if(isArr1Rvalue)
result = const_cast<NDArray*>(&arr1);
else if(isArr2Rvalue)
result = const_cast<NDArray*>(&arr2);
else
result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext());
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Multiply, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
if(!isArr1Rvalue && !isArr2Rvalue) {
NDArray res = std::move(*result);
delete result;
return std::move(res);
}
return std::move(*result);
}
return std::forward<T1>(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), std::forward<T2>(arr2));
}
template ND4J_EXPORT NDArray operator*<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator*<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator*<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator*<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator*<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator*<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator*<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator*<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator*<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
////////////////////////////////////////////////////////////////////////
// multiplication operator array*array
template <typename T1, typename T2, typename>
NDArray operator/(T1&& arr1, T2&& arr2) {
if (arr1.isS() || arr2.isS())
throw std::runtime_error("operator/(T&& arr1, T&& arr2): you can't use this method on String arrays!");
if (!Environment::getInstance()->isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
throw nd4j::datatype_exception::build("operator/(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
PointersManager pointersManager(arr1.getContext(), "operator/(T&& arr1, T&& arr2)");
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
NDArray* result = nullptr;
if(isArr1Rvalue)
result = const_cast<NDArray*>(&arr1);
else if(isArr2Rvalue)
result = const_cast<NDArray*>(&arr2);
else
result = new NDArray(arr1.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.getShapeInfo(), arr2.getShapeInfo()), false, arr1.getContext());
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), nd4j::pairwise::Divide, arr1.getBuffer(), arr1.getShapeInfo(), arr1.getSpecialBuffer(), arr1.getSpecialShapeInfo(), arr2.getBuffer(), arr2.getShapeInfo(), arr2.getSpecialBuffer(), arr2.getSpecialShapeInfo(), result->buffer(), result->getShapeInfo(), result->specialBuffer(), result->getSpecialShapeInfo(), nullptr);
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
if(!isArr1Rvalue && !isArr2Rvalue) {
NDArray res = std::move(*result);
delete result;
return std::move(res);
}
return std::move(*result);
}
return std::forward<T1>(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), std::forward<T2>(arr2));
}
template ND4J_EXPORT NDArray operator/<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator/<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator/<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator/<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator/<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
template ND4J_EXPORT NDArray operator/<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
template ND4J_EXPORT NDArray operator/<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator/<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
template ND4J_EXPORT NDArray operator/<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
/*
#ifndef __CLION_IDE__
#include "NDArray.macro"
#endif
*/
}
#endif
//////////////////////////////////////////////////////////////////////////
// check whether array's rows (arg=0) or columns (arg=1) create orthogonal basis
// bool NDArray::hasOrthonormalBasis(const int arg) {
// if (isS())
// throw std::runtime_error("NDArray::hasOrthonormalBasis: you can't use this method on String array!");
// if(rankOf() !=2 )
// throw std::runtime_error("NDArray::hasOrthBasis method: rank of ndarray is not equal 2 !");
// if(arg!=0 && arg!=1)
// throw std::runtime_error("NDArray::hasOrthBasis method: input argument is not equal to 0 or 1 !");
// const double eps = 1e-5;
// double dot = 0.f;
// if(arg) { // check whether columns create orthogonal basis
// for(int j=0; j<columns()-1; ++j)
// for(int k=j+1; k<columns(); ++k) {
// for(int i=0; i<rows(); ++i)
// dot += e<double>(i,j)*e<double>(i,k);
// if(nd4j::math::nd4j_abs(dot) > eps )
// return false;
// dot = 0.f;
// }
// for(int j=0; j<columns(); ++j) { // check whether norm of column vector = 1
// for(int i=0; i<rows(); ++i)
// dot += e<double>(i,j)*e<double>(i,j);
// if(dot != 0.f && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt<double, double>(dot) - 1.f) > eps)
// return false;
// dot = 0.f;
// }
// }
// else { // check whether rows create orthogonal basis
// for(int i=0; i<rows()-1; ++i)
// for(int k=i+1; k<rows(); ++k) {
// for(int j=0; j<columns(); ++j)
// dot += e<double>(i,j)*e<double>(k,j);
// if(nd4j::math::nd4j_abs(dot) > eps )
// return false;
// dot = 0.;
// }
// for(int i=0; i<rows(); ++i) { // check whether norm of row vector = 1
// for(int j=0; j<columns(); ++j)
// dot += e<double>(i,j)*e<double>(i,j);
// if(dot!= 0. && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt<double, double>(dot) - 1.) > eps)
// return false;
// dot = 0.;
// }
// }
// return true;
// }