/******************************************************************************* * 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 #include #include #include #include #include #include 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(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace()); this->assign(&other); } else _buffer = std::make_shared(); } //////////////////////////////////////////////////////////////////////// NDArray::NDArray(const char order, const std::vector &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(lengthOf() * DataTypeUtils::sizeOf(dtype), dtype, getContext()->getWorkspace()); _buffer->setToZeroBuffers(); } //////////////////////////////////////////////////////////////////////// NDArray::NDArray(const char order, const std::vector &shape, const std::vector& 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(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(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(lengthOf() * sizeOfT(), dataType(), getContext()->getWorkspace()); } //////////////////////////////////////////////////////////////////////// NDArray::NDArray(void* buffer, const char order, const std::vector &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(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(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(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(); other._shapeInfo = other._shapeInfoD = nullptr; other._length = 0; } //////////////////////////////////////////////////////////////////////// //constructor, create empty array at given workspace NDArray::NDArray(nd4j::LaunchContext * context) { _buffer = std::make_shared(); _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 buffer, const ShapeDescriptor& descriptor, nd4j::LaunchContext* context, const Nd4jLong offset) { _context = context; _offset = offset; setShapeInfo(descriptor); _buffer = buffer; } //////////////////////////////////////////////////////////////////////// // 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(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(buffer, bufferD, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, isBuffDAlloc, getContext()->getWorkspace()); } ////////////////////////////////////////////////////////////////////////// NDArray::NDArray(std::shared_ptr buffer, const char order, const std::vector &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; } //////////////////////////////////////////////////////////////////////// // 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(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace()); this->assign(&other); } else _buffer = std::make_shared(); } 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 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(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(e)); else if (this->isZ()) os << toStringValue(this->e(e)); else if (this->isB()) os << toStringValue(this->e(e)); else if (this->isS()) os << this->e(e); if (e < limit - 1) os << ", "; } os << "]"; return os.str(); } //////////////////////////////////////////////////////////////////////// template std::vector NDArray::getBufferAsVector() { std::vector vector(lengthOf()); for (int e = 0; e < lengthOf(); e++) vector[e] = this->e(e); return vector; } BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT std::vector, NDArray::getBufferAsVector(), LIBND4J_TYPES); //////////////////////////////////////////////////////////////////////// std::vector NDArray::getShapeAsFlatVector() { std::vector vector(this->rankOf()); for (int e = 0; e < this->rankOf(); e++) vector[e] = static_cast(this->sizeAt(e)); return vector; } //////////////////////////////////////////////////////////////////////// std::vector NDArray::getShapeAsVector() const { std::vector vector(this->rankOf()); for (int e = 0; e < this->rankOf(); e++) vector[e] = this->sizeAt(e); return vector; } //////////////////////////////////////////////////////////////////////// std::vector NDArray::getShapeInfoAsFlatVector() { int magicNumber = shape::shapeInfoLength(this->rankOf()); std::vector vector(magicNumber); for (int e = 0; e < magicNumber; e++) vector[e] = static_cast(_shapeInfo[e]); return vector; } //////////////////////////////////////////////////////////////////////// std::vector NDArray::getShapeInfoAsVector() { int magicNumber = shape::shapeInfoLength(this->rankOf()); std::vector vector(magicNumber); for (int e = 0; e < magicNumber; e++) vector[e] = this->_shapeInfo[e]; return vector; } //////////////////////////////////////////////////////////////////////// std::vector NDArray::asByteVector() { if (isS()) { // string data type requires special treatment syncToHost(); auto numWords = this->lengthOf(); auto offsetsBuffer = this->bufferAsT(); auto headerLength = ShapeUtils::stringBufferHeaderRequirements(numWords); auto dataLength = offsetsBuffer[numWords]; std::vector result(headerLength + dataLength); memcpy(result.data(), getBuffer(), headerLength + dataLength); return result; } else { // all other types are linear std::vector 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 newBuffer = std::make_shared(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(shapeBuffer.primary()), newBuffer->special(), static_cast(shapeBuffer.special()), nullptr, nullptr, nullptr); setShapeInfo(static_cast(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(); other._shapeInfo = other._shapeInfoD = nullptr; other._length = 0; return *this; } //////////////////////////////////////////////////////////////////////// template 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(); _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 new value to the 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 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 newBuffer = std::make_shared(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(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(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(0) == 0; } ////////////////////////////////////////////////////////////////////////// template void NDArray::templatedSet(void *buffer, const Nd4jLong *indices, const void *value) { auto t = reinterpret_cast(buffer); const auto y = *(reinterpret_cast(value)); auto xOffset = shape::getOffset(getShapeInfo(), indices); t[xOffset] = static_cast(y); } BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong *indices, const void *value), LIBND4J_TYPES, LIBND4J_TYPES); ////////////////////////////////////////////////////////////////////////// template void NDArray::templatedSet(void *buffer, const Nd4jLong offset, const void *value) { auto t = reinterpret_cast(buffer); const auto y = *(reinterpret_cast(value)); t[offset] = static_cast(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(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& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector 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& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector 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& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector 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& dimensions, const bool keepDims, const bool supportOldShapes) const { std::vector 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& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::reduceAlongDimension(nd4j::reduce::SameOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::reduceAlongDimension(nd4j::reduce::BoolOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(dimensions), keepDims, supportOldShapes); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::reduceAlongDimension(nd4j::reduce::LongOps op, const std::initializer_list& dimensions, const bool keepDims, const bool supportOldShapes) const { return reduceAlongDimension(op, std::vector(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(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 dimensions) const { return tensorsAlongDimension(std::vector(dimensions)); } ////////////////////////////////////////////////////////////////////////// Nd4jLong NDArray::tensorsAlongDimension(const std::vector& dimensions) const { std::vector 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(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(e)); else printf("%llu", this->e(e)); if (e < limit - 1) printf(", "); } } else if (this->isB()) { for (Nd4jLong e = 0; e < limit; e++) { if (this->e(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(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()[e * ews]); } else if(this->dataType() == nd4j::DataType::INT64) { for(Nd4jLong e = 0; e < len; e++) printf("%lld, ", this->bufferAsT()[e * ews]); } else if(this->dataType() == nd4j::DataType::FLOAT32) { for(Nd4jLong e = 0; e < len; e++) printf("%.3f, ", this->bufferAsT()[e * ews]); } else if(this->dataType() == nd4j::DataType::DOUBLE) { for(Nd4jLong e = 0; e < len; e++) printf("%.3f, ", this->bufferAsT()[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(i)); else if (arr->isZ()) printf("%lld, ", arr->e(i)); else if (arr->isB()) printf("%s, ", arr->e(i)?"true":"false"); else if (arr->isS()) printf("\"%s\", ", arr->e(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(row, col)); else if (arr->isZ()) printf("%lld", arr->e(row, col)); else if (arr->isB()) printf("%s", arr->e(row, col)?"true":"false"); else if (arr->isS()) printf("\"%s\"", arr->e(row * cols + col).c_str()); } if (row < rows - 1) printf("]\n"); else printf("]"); } printf("]"); if (padding) delete [] padding; } else { //std::unique_ptr 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(0)); else if (this->isR()) printf("%f\n", this->e(0)); else if (this->isB()) { printf("%s\n", this->e(0)?"true":"false"); } else if (this->isS()) { printf("\"%s\"\n", this->e(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 void* NDArray::templatedPointerShift(const Nd4jLong offset) const { return reinterpret_cast(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 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& shape) { std::vector vShape(shape); return reshapei(order, vShape); } ////////////////////////////////////////////////////////////////////////// bool NDArray::reshapei(const std::initializer_list& shape) { return reshapei('c', shape); } ////////////////////////////////////////////////////////////////////////// bool NDArray::reshapei(const std::vector& shape) { return reshapei('c', shape); } ////////////////////////////////////////////////////////////////////////// void NDArray::enforce(const std::initializer_list &dimensions, char order) { std::vector dims(dimensions); enforce(dims, order); } ////////////////////////////////////////////////////////////////////////// void NDArray::enforce(std::vector &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 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(); for (Nd4jLong e = 0; e < this->lengthOf(); e++) { auto val = this->e(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& shape) const & { NDArray newArr(getDataBuffer(), ShapeDescriptor(getShapeInfo()), getContext(), getBufferOffset()); newArr.reshapei(order, shape); return newArr; } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::reshape(const char order, const std::vector& 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& 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& dimensions) { std::vector vec(dimensions); return permutei(vec); } ////////////////////////////////////////////////////////////////////////// bool NDArray::permutei(const std::vector& dimensions) { return permutei(dimensions.data(), dimensions.size()); } ////////////////////////////////////////////////////////////////////////// bool NDArray::permutei(const std::initializer_list& dimensions) { std::vector vec(dimensions); std::vector ivec(dimensions.size()); for (int e = 0; e < vec.size(); e++) ivec[e] = static_cast(vec[e]); return permutei(ivec); } ////////////////////////////////////////////////////////////////////////// bool NDArray::permutei(const std::vector& dimensions) { std::vector 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(const_cast(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& dimensions) const &{ auto data = dimensions.data(); auto size = dimensions.size(); return permute(data, size); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::vector& dimensions) && { this->permutei(dimensions); return std::move(*this); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::vector& dimensions) const & { return permute(dimensions.data(), dimensions.size()); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::vector& dimensions) && { this->permutei(dimensions); return std::move(*this); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::initializer_list& dimensions) const &{ std::vector vec(dimensions); return permute(vec); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::initializer_list& dimensions) && { this->permutei(dimensions); return std::move(*this); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::initializer_list& dimensions) const & { std::vector vec(dimensions); return permute(vec); } ////////////////////////////////////////////////////////////////////////// NDArray NDArray::permute(const std::initializer_list& 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::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::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& dimensions, NDArray& target) const { permute(dimensions.data(), dimensions.size(), target); } ////////////////////////////////////////////////////////////////////////// void NDArray::permute(const std::vector& 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(i,i) - 1.f) > eps) return false; for(int i=0; i(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 T* NDArray::bufferAsT() const { // FIXME: do we REALLY want sync here? syncToHost(); return reinterpret_cast(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 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& 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 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 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 NDArray NDArray::asT() const{ auto result = isScalar() ? NDArray('c', {}, {0.}, DataTypeUtils::fromT(), this->getContext()) : NDArray(ordering(), getShapeAsVector(), DataTypeUtils::fromT(), 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 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 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 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 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(&left), const_cast(&right), nullptr, 1., 0.); NDArray result(std::move(*ptr)); delete ptr; return result; } //////////////////////////////////////////////////////////////////////// void NDArray::tileToShape(const std::vector& shape, NDArray& target) { if(&target != this) { this->tile(target); return; } std::vector 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 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& shape, NDArray& target) { tileToShape(std::vector(shape), target); } //////////////////////////////////////////////////////////////////////// NDArray NDArray::tileToShape(const Nd4jLong* shapeInfo) { NDArray result(const_cast(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(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 buffer = std::make_shared(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(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(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(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(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& 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(&other); } else { max = const_cast(&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 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& 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(&other); } else { max = const_cast(&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 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& 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(&other); } else { max = const_cast(&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 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 dimensions, const NDArray& tadArray, NDArray& target, ExtraArguments* extraArgs) { std::vector 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 std::vector NDArray::asVectorT() { std::vector result(this->lengthOf()); PRAGMA_OMP_SIMD for (int e = 0; e < this->lengthOf(); e++) result[e] = this->e(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& 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 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(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 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 void NDArray::templatedDoubleAssign(void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const { auto x = reinterpret_cast(xBuffer); const auto y = reinterpret_cast(yBuffer); x[xOffset] = static_cast(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& 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 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& dimensions) const { if (isS()) throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!"); std::vector 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& dimensions) const { return varianceAlongDimension(op, biasCorrected, std::vector(dimensions)); } //////////////////////////////////////////////////////////////////////// void NDArray::varianceAlongDimension(nd4j::variance::Ops op, NDArray &target, const bool biasCorrected, const std::initializer_list& dimensions) const { varianceAlongDimension(op, target, biasCorrected, std::vector(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 strings(lengthOf()); for (int e = 0; e < lengthOf(); e++) strings[e] = this->e(e); auto result = NDArrayFactory::string(order, getShapeAsVector(), strings, getContext()); return result; } NDArray result(order, isScalar() ? std::vector({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(e); auto s2 = other->e(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(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(getBuffer()); auto offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf()); auto start = offsets[offset]; auto end = offsets[offset + 1]; auto data = static_cast(getBuffer()) + offsetsLength + start; std::string r(reinterpret_cast(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(getBuffer())[rp]); } ///////////////////////////////////////////////////////////////////////// template 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 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(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 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(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 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(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 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 method: do not use me!");} template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::IntOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; //////////////////////////////////////////////////////////////////////// template void NDArray::applyScalar(nd4j::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams) { auto scalarArr = NDArrayFactory::create(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 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 void NDArray::applyScalar(nd4j::scalar::BoolOps op, const T scalar, NDArray &target, ExtraArguments *extraParams) const { NDArray scalarArr = NDArrayFactory::create(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 method: do not use me!");} template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const; template ND4J_EXPORT void NDArray::applyScalar(nd4j::scalar::BoolOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const; //////////////////////////////////////////////////////////////////////// void NDArray::applyIndexReduce(nd4j::indexreduce::Ops op, NDArray& target, const std::vector& 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(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 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& dimensions, const ExtraArguments* extraParams ) const { std::vector 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(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& 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 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(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& 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 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(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& 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 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& 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 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& 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 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& 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 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 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_cast(&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 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(const_cast(&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 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(const_cast(&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 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(const_cast(&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(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 void NDArray::templatedAssign(void *xBuffer, Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const { if (xBuffer != nullptr && yBuffer != nullptr) *(reinterpret_cast(xBuffer) + xOffset) = *(reinterpret_cast(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 &indices, const std::vector &dimensions) const { ResultSet result; if (indices.size() == 0) return result; auto pack = ConstantTadHelper::getInstance()->tadForDimensions(getShapeInfo(), const_cast(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& dimensions) const { return allTensorsAlongDimension(std::vector(dimensions)); } //////////////////////////////////////////////////////////////////////// ResultSet NDArray::allExamples() const { std::vector 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 &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(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& dimensions) const { std::vector 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& dimensions) const { return tensorAlongDimension(index, std::vector(dimensions)); } //////////////////////////////////////////////////////////////////////// // operator returns sub-array with buffer pointing at this->_buffer + certain offset NDArray NDArray::operator()(const std::vector& 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 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& dimsToExclude, bool keepUnitiesInShape) const { std::vector idxRanges(2 * rankOf()); ShapeUtils::evalIdxRangesForSubArr(subArrIdx, _shapeInfo, dimsToExclude, idxRanges.data()); return (*this)(idxRanges, keepUnitiesInShape); } //////////////////////////////////////////////////////////////////////// void NDArray::getSubArrShapeAndOffsets(const std::vector& 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(shapeBuffer.primary()); #ifdef __CUDABLAS__ _shapeInfoD = reinterpret_cast(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(shapeBuffer.primary()); #ifdef __CUDABLAS__ _shapeInfoD = reinterpret_cast(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(descriptor)); _shapeInfo = reinterpret_cast(shapeBuffer.primary()); #ifdef __CUDABLAS__ _shapeInfoD = reinterpret_cast(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(const_cast(shapeBuffer).primary()); #ifdef __CUDABLAS__ _shapeInfoD = reinterpret_cast(const_cast(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 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())) 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 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()), 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 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 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 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())) 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 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()), 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 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 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()), 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 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())) 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 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()), 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 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 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 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())) 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 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()), 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 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 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()), 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 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::value && !arr1.isView(); const bool isArr2Rvalue = !std::is_reference::value && !arr2.isView(); NDArray* result = nullptr; if(isArr1Rvalue) result = const_cast(&arr1); else if(isArr2Rvalue) result = const_cast(&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(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator+(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator+(NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator+(NDArray&& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator+(NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator+(const NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator+(const NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator+(const NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator+(NDArray&& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator+(NDArray&& arr1, NDArray&& arr2); //////////////////////////////////////////////////////////////////////// // addition operator array - array template 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::value && !arr1.isView(); const bool isArr2Rvalue = !std::is_reference::value && !arr2.isView(); NDArray* result = nullptr; if(isArr1Rvalue) result = const_cast(&arr1); else if(isArr2Rvalue) result = const_cast(&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(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator-(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator-(NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator-(NDArray&& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator-(NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator-(const NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator-(const NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator-(const NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator-(NDArray&& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator-(NDArray&& arr1, NDArray&& arr2); //////////////////////////////////////////////////////////////////////// // multiplication operator array*array template 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::value && !arr1.isView(); const bool isArr2Rvalue = !std::is_reference::value && !arr2.isView(); NDArray* result = nullptr; if(isArr1Rvalue) result = const_cast(&arr1); else if(isArr2Rvalue) result = const_cast(&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(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Multiply(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator*(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator*(NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator*(NDArray&& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator*(NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator*(const NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator*(const NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator*(const NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator*(NDArray&& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator*(NDArray&& arr1, NDArray&& arr2); //////////////////////////////////////////////////////////////////////// // multiplication operator array*array template 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::value && !arr1.isView(); const bool isArr2Rvalue = !std::is_reference::value && !arr2.isView(); NDArray* result = nullptr; if(isArr1Rvalue) result = const_cast(&arr1); else if(isArr2Rvalue) result = const_cast(&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(arr1).applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), std::forward(arr2)); } template ND4J_EXPORT NDArray operator/(NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator/(NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator/(NDArray&& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator/(NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator/(const NDArray& arr1, NDArray& arr2); template ND4J_EXPORT NDArray operator/(const NDArray& arr1, NDArray&& arr2); template ND4J_EXPORT NDArray operator/(const NDArray& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator/(NDArray&& arr1, const NDArray& arr2); template ND4J_EXPORT NDArray operator/(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(i,j)*e(i,k); // if(nd4j::math::nd4j_abs(dot) > eps ) // return false; // dot = 0.f; // } // for(int j=0; j(i,j)*e(i,j); // if(dot != 0.f && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt(dot) - 1.f) > eps) // return false; // dot = 0.f; // } // } // else { // check whether rows create orthogonal basis // for(int i=0; i(i,j)*e(k,j); // if(nd4j::math::nd4j_abs(dot) > eps ) // return false; // dot = 0.; // } // for(int i=0; i(i,j)*e(i,j); // if(dot!= 0. && nd4j::math::nd4j_abs(nd4j::math::nd4j_sqrt(dot) - 1.) > eps) // return false; // dot = 0.; // } // } // return true; // }