5661 lines
263 KiB
C++
5661 lines
263 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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// $NDArray.hpp - architech-independent implementations (both cuda and cpu).
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//
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#ifndef __NDARRAY__HPP__
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#define __NDARRAY__HPP__
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#include <array/ShapeDescriptor.h>
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#include <helpers/ConstantShapeHelper.h>
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#include <helpers/ConstantShapeHelper.h>
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#include <helpers/ConstantTadHelper.h>
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#include <loops/BroadcastPairwiseConverter.h>
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#include <helpers/PointersManager.h>
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namespace sd {
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template <>
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ND4J_EXPORT utf8string NDArray::e(const Nd4jLong i) const;
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template <>
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ND4J_EXPORT std::string NDArray::e(const Nd4jLong i) const;
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template <>
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ND4J_EXPORT std::u16string NDArray::e(const Nd4jLong i) const;
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template <>
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ND4J_EXPORT std::u32string NDArray::e(const Nd4jLong i) const;
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////////////////////////////////////////////////////////////////////////
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// copy constructor
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NDArray::NDArray(const NDArray& other) {
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_context = other._context;
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_offset = 0;
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setShapeInfo(ShapeDescriptor(other.dataType(), other.ordering(), other.shapeOf(), other.rankOf()));
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if(!isEmpty()) {
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_buffer = std::make_shared<DataBuffer>(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace());
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this->assign(&other);
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}
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else
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_buffer = std::make_shared<DataBuffer>();
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}
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////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(const char order, const std::vector<Nd4jLong> &shape, sd::DataType dtype, sd::LaunchContext * context) {
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("Rank of NDArray can't exceed 32");
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_context = context;
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_isAttached = _context->getWorkspace() != nullptr;
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_offset = 0;
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if (shape.empty())
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setShapeInfo(ShapeDescriptor::emptyDescriptor(dtype));
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else
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setShapeInfo(ShapeDescriptor(dtype, order, shape));
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_buffer = std::make_shared<DataBuffer>(lengthOf() * DataTypeUtils::sizeOf(dtype), dtype, getContext()->getWorkspace());
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_buffer->setToZeroBuffers();
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}
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////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(const char order, const std::vector<Nd4jLong> &shape, const std::vector<double>& data, sd::DataType dtype, sd::LaunchContext * context) {
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("Rank of NDArray can't exceed 32");
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_context = context;
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_offset = 0;
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if (shape.size() == 0) {
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if (data.size() == 0)
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setShapeInfo(ShapeDescriptor::emptyDescriptor(dtype));
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else
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setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
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} else {
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setShapeInfo(ShapeDescriptor(dtype, order, shape));
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}
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if (lengthOf() != data.size()) {
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nd4j_printf("NDArray constructor: data size [%i] doesn't match shape length [%i]\n", data.size(), lengthOf());
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throw std::runtime_error("Data size doesn't match shape");
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}
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_buffer = std::make_shared<DataBuffer>(lengthOf() * DataTypeUtils::sizeOf(dtype), dtype, getContext()->getWorkspace(), true);
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for(Nd4jLong i=0; i < lengthOf(); ++i) {
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BUILD_SINGLE_PARTIAL_SELECTOR(dtype, templatedDoubleAssign<, double>(buffer(), i, reinterpret_cast<const void *>(data.data()), i), LIBND4J_TYPES);
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}
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tickWriteHost();
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syncToDevice();
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}
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////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(const NDArray *other, const bool copyStrides, sd::LaunchContext* context) {
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_context = context;
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_offset = 0;
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_isAttached = getContext()->getWorkspace() != nullptr;
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if (copyStrides)
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setShapeInfo(ShapeDescriptor(other->_shapeInfo));
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else
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setShapeInfo(ShapeDescriptor(other->dataType(), other->ordering(), other->shapeOf(), other->rankOf()));
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if (!isEmpty())
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_buffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dataType(), getContext()->getWorkspace());
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}
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////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(void* buffer, const char order, const std::vector<Nd4jLong> &shape, sd::DataType dtype, sd::LaunchContext * context, const bool isBuffAlloc) {
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if (shape.empty())
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throw std::runtime_error("NDArray constructor: input shape is empty !");
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("Rank of NDArray can't exceed 32");
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_context = context;
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_offset = 0;
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_isAttached = getContext()->getWorkspace() != nullptr;
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setShapeInfo(ShapeDescriptor(dtype, order, shape));
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_buffer = std::make_shared<DataBuffer>(buffer, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, getContext()->getWorkspace());
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}
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////////////////////////////////////////////////////////////////////////
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// creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros
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NDArray::NDArray(const Nd4jLong* shapeInfo, const sd::DataType dtype, const bool copyStrides, sd::LaunchContext * context, const bool nullify) {
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if (shapeInfo == nullptr)
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throw std::runtime_error("NDArray constructor: can't be initalized without shapeinfo");
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if ((int) shapeInfo[0] > MAX_RANK)
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throw std::invalid_argument("Rank of NDArray can't exceed 32");
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_context = context;
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_offset = 0;
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if (copyStrides)
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setShapeInfo(ShapeDescriptor(shapeInfo, dtype));
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else
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setShapeInfo(ShapeDescriptor(dtype, shape::order(shapeInfo), shape::shapeOf(shapeInfo), shape::rank(shapeInfo)));
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if (!isEmpty()) {
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_buffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dtype, getContext()->getWorkspace());
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if (nullify)
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_buffer->setToZeroBuffers();
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}
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}
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////////////////////////////////////////////////////////////////////////
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// scalar constructor
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NDArray::NDArray(sd::DataType dtype, sd::LaunchContext* context, const bool isScalar) {
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_context = context;
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_offset = 0;
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_isAttached = getContext()->getWorkspace() != nullptr;
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if (isScalar) {
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setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
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_buffer = std::make_shared<DataBuffer>(sizeOfT(), dtype, getContext()->getWorkspace());
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_buffer->setToZeroBuffers();
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}
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else
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setShapeInfo(ConstantShapeHelper::getInstance().emptyShapeInfo(dtype));
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}
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//////////////////////////////////////////////////////////////////////////
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// move constructor
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NDArray::NDArray(NDArray&& other) noexcept {
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_isView = other._isView;
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_buffer = other._buffer;
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_shapeInfo = other._shapeInfo;
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_shapeInfoD = other._shapeInfoD;
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_context = other._context;
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_dataType = other._dataType;
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_length = other._length;
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_offset = other._offset;
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other._buffer = std::make_shared<DataBuffer>();
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other._shapeInfo = other._shapeInfoD = nullptr;
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other._length = 0;
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}
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////////////////////////////////////////////////////////////////////////
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//constructor, create empty array at given workspace
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NDArray::NDArray(sd::LaunchContext * context) {
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_buffer = std::make_shared<DataBuffer>();
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_shapeInfo = nullptr;
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_shapeInfoD = nullptr;
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_offset = 0;
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_context = context;
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_length = 0;
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}
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////////////////////////////////////////////////////////////////////////
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// creates new NDArray using shape information from "shapeInfo" array, set all elements in new array to be zeros, set dtype as array type
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NDArray::NDArray(const Nd4jLong* shapeInfo, const bool copyStrides, sd::LaunchContext * context, const bool nullify):
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NDArray(shapeInfo, ArrayOptions::dataType(shapeInfo), copyStrides, context) {
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}
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////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(std::shared_ptr<DataBuffer> buffer, const ShapeDescriptor& descriptor, sd::LaunchContext* context, const Nd4jLong offset) {
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_context = context;
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_offset = offset;
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setShapeInfo(descriptor);
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_buffer = buffer;
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_isView = offset > 0 || _length * DataTypeUtils::sizeOf(_dataType) < buffer->getLenInBytes();
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}
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NDArray::NDArray(void *buffer, Nd4jLong *shapeInfo, sd::LaunchContext * context, const bool isBuffAlloc) : NDArray::NDArray(buffer, const_cast<const Nd4jLong *>(shapeInfo), context, isBuffAlloc) {
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//
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}
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////////////////////////////////////////////////////////////////////////
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// do not allocate memory, memory for array is passed from outside
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NDArray::NDArray(void *buffer, const Nd4jLong *shapeInfo, sd::LaunchContext * context, const bool isBuffAlloc) {
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if (buffer == nullptr && ArrayOptions::arrayType(shapeInfo) != ArrayType::EMPTY)
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throw std::runtime_error("NDArray constructor: can't be initalized with nullptr buffer !");
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if (shapeInfo == nullptr)
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throw std::runtime_error("NDArray constructor: can't be initalized without shapeinfo !");
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if ((int) shapeInfo[0] > MAX_RANK)
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throw std::invalid_argument("NDArray constructor: rank of NDArray can't exceed 32 !");
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_context = context;
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_isAttached = getContext()->getWorkspace() != nullptr;
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_offset = 0;
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setShapeInfo(ShapeDescriptor(shapeInfo));
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if (this->isEmpty()) {
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tickReadDevice();
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tickReadHost();
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}
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else {
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_buffer = std::make_shared<DataBuffer>(buffer, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, getContext()->getWorkspace());
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}
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}
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////////////////////////////////////////////////////////////////////////
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// do not allocate memory, memory for array is passed from outside
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// we suppose the content of both (device and host) buffers is identical
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NDArray::NDArray(void *buffer, void* bufferD, const Nd4jLong *shapeInfo, sd::LaunchContext * context, const bool isBuffAlloc, const bool isBuffDAlloc) {
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if (shapeInfo == nullptr)
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throw std::runtime_error("NDArray constructor cuda: can't be initalized without shapeinfo");
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if ((int) shapeInfo[0] > MAX_RANK)
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throw std::invalid_argument("NDArray constructor cuda: rank of NDArray can't exceed 32");
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_context = context;
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_offset = 0;
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setShapeInfo(ShapeDescriptor(shapeInfo));
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if (!isEmpty())
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_buffer = std::make_shared<DataBuffer>(buffer, bufferD, lengthOf() * sizeOfT(), dataType(), isBuffAlloc, isBuffDAlloc, getContext()->getWorkspace());
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}
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//////////////////////////////////////////////////////////////////////////
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NDArray::NDArray(std::shared_ptr<DataBuffer> buffer, const char order, const std::vector<Nd4jLong> &shape, sd::LaunchContext* context) {
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if (shape.empty())
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throw std::runtime_error("NDArray constructor: input shape is empty !");
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("NDArray constructor: rank of NDArray can't exceed 32");
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_context = context;
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_offset = 0;
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setShapeInfo(ShapeDescriptor(buffer->getDataType(), order, shape));
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_buffer = buffer;
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_isView = _length * DataTypeUtils::sizeOf(_dataType) < buffer->getLenInBytes();
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}
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/////////////////////////////////////////////////////////////////////////
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// u16 string constructors
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NDArray::NDArray(const std::u16string& u16string, sd::DataType dtype, sd::LaunchContext* context) {
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if (!DataTypeUtils::isS(dtype)) {
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throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
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}
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if (!unicode::isStringValidU16(u16string.data(), u16string.data() + u16string.size())) {
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throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
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}
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// one word that is why used 1
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Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(1);
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Nd4jLong dataLength = [&] {
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if (dtype == DataType::UTF16) {
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return static_cast<Nd4jLong>(u16string.size() * sizeof(uint16_t));
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}
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if (dtype == DataType::UTF32) {
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return unicode::offsetUtf16StringInUtf32(u16string.data(), u16string.size());
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}
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return unicode::offsetUtf16StringInUtf8(u16string.data(), u16string.size());
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}();
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Nd4jLong offsets[2] = { 0 , dataLength };
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_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
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_context = context;
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_isAttached = getContext()->getWorkspace() != nullptr;
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_offset = 0;
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setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
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memcpy(bufferAsT<int8_t>(), &offsets[0], 2 * sizeof(Nd4jLong));
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auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
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if (dtype == DataType::UTF8) {
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unicode::utf16to8(u16string.data(), data, u16string.size());
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}
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else if (dtype == DataType::UTF16) {
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memcpy(data, u16string.data(), dataLength);
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}
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else {
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unicode::utf16to32(u16string.data(), data, u16string.size());
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}
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tickWriteHost();
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syncToDevice();
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}
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/////////////////////////////////////////////////////////////////////////
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// u32 string constructors
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NDArray::NDArray(const std::u32string& u32string, sd::DataType dtype, sd::LaunchContext* context) {
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if (!DataTypeUtils::isS(dtype)) {
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throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
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}
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if (!unicode::isStringValidU32(u32string.data(), u32string.data() + u32string.size())) {
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throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
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}
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// one word that is why used 1
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Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(1);
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Nd4jLong dataLength = [&] {
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if (dtype == DataType::UTF16) {
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return unicode::offsetUtf32StringInUtf16(u32string.data(), u32string.size());
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}
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if (dtype == DataType::UTF32) {
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return static_cast<Nd4jLong>(sizeof(uint32_t) * u32string.size());
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}
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return unicode::offsetUtf32StringInUtf8(u32string.data(), u32string.size());
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}();
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Nd4jLong offsets[2] = { 0 , dataLength };
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_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
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_context = context;
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_isAttached = getContext()->getWorkspace() != nullptr;
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_offset = 0;
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setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
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memcpy(bufferAsT<int8_t>(), &offsets[0], 2 * sizeof(Nd4jLong));
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auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
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if (dtype == DataType::UTF8) {
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unicode::utf32to8(u32string.data(), data, u32string.size());
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}
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else if (dtype == DataType::UTF16) {
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unicode::utf32to16(u32string.data(), data, u32string.size());
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}
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else {
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memcpy(data, u32string.data(), u32string.size() * sizeof(uint32_t));
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}
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tickWriteHost();
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syncToDevice();
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}
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/////////////////////////////////////////////////////////////////////////
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// u8 string constructors
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NDArray::NDArray(const std::string& str, sd::DataType dtype, sd::LaunchContext* context) {
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if (!DataTypeUtils::isS(dtype)) {
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throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
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}
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if (!unicode::isStringValidU8(str.data(), str.data() + str.size())) {
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throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
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}
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// one word that is why used 1
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auto headerLength = ShapeUtils::stringBufferHeaderRequirements(1);
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Nd4jLong dataLength = [&] {
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if (dtype == DataType::UTF16) {
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return unicode::offsetUtf8StringInUtf16(str.data(), str.size());
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}
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if (dtype == DataType::UTF32) {
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return unicode::offsetUtf8StringInUtf32(str.data(), str.size());
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}
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return static_cast<Nd4jLong>(str.size());
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}();
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Nd4jLong offsets[2] = { 0 , dataLength };
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_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
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_context = context;
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_isAttached = getContext()->getWorkspace() != nullptr;
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_offset = 0;
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setShapeInfo(ShapeDescriptor::scalarDescriptor(dtype));
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memcpy(bufferAsT<int8_t>(), &offsets[0], 2 * sizeof(Nd4jLong));
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auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
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if (dtype == DataType::UTF8) {
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memcpy(data, str.data(), str.size());
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}
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else if (dtype == DataType::UTF16) {
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unicode::utf8to16(str.data(), data, str.size());
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}
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else {
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unicode::utf8to32(str.data(), data, str.size());
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}
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tickWriteHost();
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syncToDevice();
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}
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/////////////////////////////////////////////////////////////////////////
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// constructors for vector of strings
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NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<const char*>& string, const sd::DataType dataType, sd::LaunchContext* context) {
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if (!DataTypeUtils::isS(dataType))
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throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
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if (shape::prodLong(shape.data(), shape.size()) != string.size())
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throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
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for (const auto& str : string) {
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if (!unicode::isStringValidU8(str, str + std::char_traits<char>::length(str)) ) {
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throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
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}
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}
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Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
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std::vector<Nd4jLong> offsets(string.size() + 1);
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Nd4jLong dataLength = 0;
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for (int e = 0; e < string.size(); e++) {
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offsets[e] = dataLength;
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dataLength += [&] {
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if (dataType == DataType::UTF16)
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return unicode::offsetUtf8StringInUtf16(string[e], std::char_traits<char>::length(string[e]));
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if (dataType == DataType::UTF32)
|
|
return unicode::offsetUtf8StringInUtf32(string[e], std::char_traits<char>::length(string[e]));
|
|
return static_cast<Nd4jLong>(std::char_traits<char>::length(string[e]));
|
|
}();
|
|
}
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dataType, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dataType, 'c', shape));
|
|
|
|
_isView = false;
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dataType == DataType::UTF16) {
|
|
unicode::utf8to16(string[e], cdata, std::char_traits<char>::length(string[e]));
|
|
}
|
|
else if (dataType == DataType::UTF32) {
|
|
unicode::utf8to32(string[e], cdata, std::char_traits<char>::length(string[e]));
|
|
}
|
|
else {
|
|
memcpy(cdata, string[e], std::char_traits<char>::length(string[e]));
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<std::string>& string, const sd::DataType dataType, sd::LaunchContext* context) {
|
|
|
|
if (!DataTypeUtils::isS(dataType))
|
|
throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
|
|
|
|
if (shape::prodLong(shape.data(), shape.size()) != string.size())
|
|
throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
|
|
|
|
for (const auto& str : string) {
|
|
if (!unicode::isStringValidU8(str.data(), str.data() + str.size())) {
|
|
throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
|
|
}
|
|
}
|
|
|
|
Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
Nd4jLong dataLength = 0;
|
|
for (int e = 0; e < string.size(); e++) {
|
|
offsets[e] = dataLength;
|
|
dataLength += [&] {
|
|
if (dataType == DataType::UTF16)
|
|
return unicode::offsetUtf8StringInUtf16(string[e].data(), string[e].size());
|
|
if (dataType == DataType::UTF32)
|
|
return unicode::offsetUtf8StringInUtf32(string[e].data(), string[e].size());
|
|
return static_cast<Nd4jLong>(string[e].size());
|
|
}();
|
|
}
|
|
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dataType, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dataType, 'c', shape));
|
|
|
|
_isView = false;
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dataType == DataType::UTF16) {
|
|
unicode::utf8to16(string[e].data(), cdata, string[e].size());
|
|
}
|
|
else if (dataType == DataType::UTF32) {
|
|
unicode::utf8to32(string[e].data(), cdata, string[e].size());
|
|
}
|
|
else {
|
|
memcpy(cdata, string[e].data(), string[e].size());
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<std::u16string>& string, sd::DataType dtype, sd::LaunchContext* context) {
|
|
|
|
if (!DataTypeUtils::isS(dtype))
|
|
throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
|
|
|
|
if (shape::prodLong(shape.data(), shape.size()) != string.size())
|
|
throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
|
|
|
|
for (const auto& str : string) {
|
|
if (!unicode::isStringValidU16(str.data(), str.data() + str.size())) {
|
|
throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
|
|
}
|
|
}
|
|
|
|
Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
Nd4jLong dataLength = 0;
|
|
for (int e = 0; e < string.size(); e++) {
|
|
offsets[e] = dataLength;
|
|
dataLength += [&] {
|
|
if (dtype == DataType::UTF16)
|
|
return static_cast<Nd4jLong>(sizeof(uint16_t) * string[e].size());
|
|
if (dtype == DataType::UTF32)
|
|
return unicode::offsetUtf16StringInUtf32(string[e].data(), string[e].size());
|
|
return unicode::offsetUtf16StringInUtf8(string[e].data(), string[e].size());
|
|
}();
|
|
}
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dtype, 'c', shape));
|
|
|
|
_isView = false;
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dtype == DataType::UTF16) {
|
|
memcpy(cdata, string[e].data(), string[e].size() * sizeof(uint16_t));
|
|
}
|
|
else if (dtype == DataType::UTF32) {
|
|
unicode::utf16to32(string[e].data(), cdata, string[e].size());
|
|
}
|
|
else {
|
|
unicode::utf16to8(string[e].data(), cdata, string[e].size());
|
|
}
|
|
}
|
|
};
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<const char16_t*>& string, sd::DataType dtype, sd::LaunchContext* context) {
|
|
|
|
if (!DataTypeUtils::isS(dtype))
|
|
throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
|
|
|
|
if (shape::prodLong(shape.data(), shape.size()) != string.size())
|
|
throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
|
|
|
|
for (const auto& str : string) {
|
|
if (!unicode::isStringValidU16(str, str + std::char_traits<char16_t>::length(str))) {
|
|
throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
|
|
}
|
|
}
|
|
|
|
Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
Nd4jLong dataLength = 0;
|
|
for (int e = 0; e < string.size(); e++) {
|
|
offsets[e] = dataLength;
|
|
dataLength += [&] {
|
|
if (dtype == DataType::UTF16)
|
|
return static_cast<Nd4jLong>(sizeof(uint16_t) * std::char_traits<char16_t>::length(string[e]));
|
|
if (dtype == DataType::UTF32)
|
|
return unicode::offsetUtf16StringInUtf32(string[e], std::char_traits<char16_t>::length(string[e]));
|
|
return unicode::offsetUtf16StringInUtf8(string[e], std::char_traits<char16_t>::length(string[e]));
|
|
}();
|
|
}
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dtype, 'c', shape));
|
|
|
|
_isView = false;
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dtype == DataType::UTF16) {
|
|
memcpy(cdata, string[e], std::char_traits<char16_t>::length(string[e]) * sizeof(uint16_t));
|
|
}
|
|
else if (dtype == DataType::UTF32) {
|
|
unicode::utf16to32(string[e], cdata, std::char_traits<char16_t>::length(string[e]));
|
|
}
|
|
else {
|
|
unicode::utf16to8(string[e], cdata, std::char_traits<char16_t>::length(string[e]));
|
|
}
|
|
}
|
|
};
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<std::u32string>& string, sd::DataType dtype, sd::LaunchContext* context) {
|
|
|
|
if (!DataTypeUtils::isS(dtype))
|
|
throw std::invalid_argument("NDArray::NDArray: invalid DataType, only string dataTypes have to be used");
|
|
|
|
if (shape::prodLong(shape.data(), shape.size()) != string.size())
|
|
throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
|
|
|
|
for (auto str : string) {
|
|
if (!unicode::isStringValidU32(str.data(), str.data() + str.size())) {
|
|
throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
|
|
}
|
|
}
|
|
|
|
Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
|
|
Nd4jLong dataLength = 0;
|
|
for (int e = 0; e < string.size(); e++) {
|
|
offsets[e] = dataLength;
|
|
dataLength += [&] {
|
|
if (dtype == DataType::UTF16)
|
|
return unicode::offsetUtf32StringInUtf16(string[e].data(), string[e].size());
|
|
if (dtype == DataType::UTF32)
|
|
return static_cast<Nd4jLong>(sizeof(uint32_t) * string[e].size());
|
|
return unicode::offsetUtf32StringInUtf16(string[e].data(), string[e].size());
|
|
}();
|
|
}
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dtype, 'c', shape));
|
|
|
|
_isView = false;
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dtype == DataType::UTF16) {
|
|
unicode::utf32to16(string[e].data(), cdata, string[e].size());
|
|
}
|
|
else if (dtype == DataType::UTF32) {
|
|
memcpy(cdata, string[e].data(), string[e].size() * sizeof(uint32_t));
|
|
}
|
|
else {
|
|
unicode::utf32to8(string[e].data(), cdata, string[e].size());
|
|
}
|
|
}
|
|
};
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray::NDArray(const std::vector<Nd4jLong>& shape, const std::vector<const char32_t *>& string, sd::DataType dtype, sd::LaunchContext* context) {
|
|
|
|
if (!DataTypeUtils::isS(dtype))
|
|
throw std::invalid_argument("NDArray::NDArray: invalid DataType used");
|
|
|
|
if (shape::prodLong(shape.data(), shape.size()) != string.size())
|
|
throw std::invalid_argument("NDArray::NDArray: Number of strings should match length of array");
|
|
|
|
for (const auto& str : string) {
|
|
if (!unicode::isStringValidU32(str, str + std::char_traits<char32_t>::length(str))) {
|
|
throw std::invalid_argument("NDArray::NDArray: invalid character in input string");
|
|
}
|
|
}
|
|
|
|
Nd4jLong headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
|
|
Nd4jLong dataLength = 0;
|
|
for (int e = 0; e < string.size(); e++) {
|
|
offsets[e] = dataLength;
|
|
dataLength += [&] {
|
|
if (dtype == DataType::UTF16)
|
|
return unicode::offsetUtf32StringInUtf16(string[e], std::char_traits<char32_t>::length(string[e]));
|
|
if (dtype == DataType::UTF32)
|
|
return static_cast<Nd4jLong>(sizeof(uint32_t) * std::char_traits<char32_t>::length(string[e]));
|
|
return unicode::offsetUtf32StringInUtf16(string[e], std::char_traits<char32_t>::length(string[e]));
|
|
}();
|
|
}
|
|
offsets[string.size()] = dataLength;
|
|
|
|
_buffer = std::make_shared<DataBuffer>(headerLength + dataLength, dtype, context->getWorkspace(), true);
|
|
|
|
_context = context;
|
|
_offset = 0;
|
|
|
|
setShapeInfo(ShapeDescriptor(dtype, 'c', shape));
|
|
|
|
_isView = _length * DataTypeUtils::sizeOf(_dataType) < _buffer->getLenInBytes();
|
|
|
|
setAttached(context->getWorkspace() != nullptr);
|
|
|
|
memcpy(bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto data = reinterpret_cast<int8_t*>(bufferAsT<int8_t>() + headerLength);
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto e = start; e < stop; e++) {
|
|
auto cdata = data + offsets[e];
|
|
if (dtype == DataType::UTF16) {
|
|
unicode::utf32to16(string[e], cdata, std::char_traits<char32_t>::length(string[e]));
|
|
}
|
|
else if (dtype == DataType::UTF32) {
|
|
memcpy(cdata, string[e], std::char_traits<char32_t>::length(string[e]) * sizeof(uint32_t));
|
|
}
|
|
else {
|
|
unicode::utf32to8(string[e], cdata, std::char_traits<char32_t>::length(string[e]));
|
|
}
|
|
}
|
|
};
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
tickWriteHost();
|
|
syncToDevice();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// assignment operator
|
|
NDArray& NDArray::operator=(const NDArray& other) {
|
|
|
|
if (this == &other || (_shapeInfo == other._shapeInfo && _shapeInfo == nullptr))
|
|
return *this;
|
|
|
|
if (_shapeInfo != nullptr && shape::equalsTypesAndShapesSoft(_shapeInfo, other._shapeInfo)) {
|
|
if(!other.isEmpty())
|
|
this->assign(&other);
|
|
}
|
|
else {
|
|
_context = other._context;
|
|
_offset = 0;
|
|
setShapeInfo(ShapeDescriptor(other.dataType(), other.ordering(), other.shapeOf(), other.rankOf()));
|
|
|
|
if(!other.isEmpty()) {
|
|
_buffer = std::make_shared<DataBuffer>(other.lengthOf() * other.sizeOfT(), other.dataType(), other.getContext()->getWorkspace());
|
|
this->assign(&other);
|
|
}
|
|
else
|
|
_buffer = std::make_shared<DataBuffer>();
|
|
}
|
|
return *this;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isC() const {
|
|
// TODO: this method must be implemented once we add support for complex numbers
|
|
return false;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isS() const {
|
|
return (dataType() == DataType::UTF8 ||
|
|
dataType() == DataType::UTF16 ||
|
|
dataType() == DataType::UTF32);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isR() const {
|
|
auto xType = ArrayOptions::dataType(this->_shapeInfo);
|
|
return xType == FLOAT32 || xType == HALF || xType == DOUBLE || xType == FLOAT8 || xType == BFLOAT16;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isZ() const {
|
|
// TODO: decide if we really want to exclude Bool here
|
|
return !isC() && !isR() && !isB() && !isS();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isB() const {
|
|
return ArrayOptions::dataType(this->_shapeInfo) == BOOL;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
std::string NDArray::toStringValue(T value) {
|
|
std::ostringstream os ;
|
|
//throw the value into the string stream
|
|
os << value ;
|
|
//convert the string stream into a string and return
|
|
return os.str() ;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<>
|
|
std::string NDArray::toStringValue(float16 value) {
|
|
std::ostringstream os ;
|
|
//throw the value into the string stream
|
|
os << (float) value ;
|
|
//convert the string stream into a string and return
|
|
return os.str() ;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<>
|
|
std::string NDArray::toStringValue(bfloat16 value) {
|
|
std::ostringstream os ;
|
|
//throw the value into the string stream
|
|
os << (float) value ;
|
|
//convert the string stream into a string and return
|
|
return os.str() ;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
std::string NDArray::asIndexedString(Nd4jLong limit) {
|
|
std::ostringstream os;
|
|
os << "[";
|
|
if (limit < 1 || limit > this->lengthOf())
|
|
limit = this->lengthOf();
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
os << toStringValue(this->e<float>(e));
|
|
if (e < limit - 1)
|
|
os << ", ";
|
|
}
|
|
os << "]";
|
|
return os.str();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
std::string NDArray::asString(Nd4jLong limit) {
|
|
std::ostringstream os;
|
|
os << "[";
|
|
if (limit < 1 || limit > this->lengthOf())
|
|
limit = this->lengthOf();
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
if (this->isR())
|
|
os << toStringValue(this->e<float>(e));
|
|
else if (this->isZ())
|
|
os << toStringValue(this->e<Nd4jLong>(e));
|
|
else if (this->isB())
|
|
os << toStringValue(this->e<bool>(e));
|
|
else if (this->isS()) // todo add utf16 and utf32
|
|
os << this->e<std::string>(e);
|
|
if (e < limit - 1)
|
|
os << ", ";
|
|
}
|
|
os << "]";
|
|
return os.str();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
std::vector<T> NDArray::getBufferAsVector() const {
|
|
std::vector<T> vector(lengthOf());
|
|
for (Nd4jLong e = 0; e < lengthOf(); e++)
|
|
vector[e] = this->e<T>(e);
|
|
return vector;
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT std::vector, NDArray::getBufferAsVector() const, LIBND4J_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<int64_t> NDArray::getShapeAsFlatVector() const {
|
|
std::vector<int64_t> vector(this->rankOf());
|
|
for (int e = 0; e < this->rankOf(); e++)
|
|
vector[e] = static_cast<int64_t>(this->sizeAt(e));
|
|
return vector;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<Nd4jLong> NDArray::getShapeAsVector() const {
|
|
|
|
std::vector<Nd4jLong> vector(this->rankOf());
|
|
for (int e = 0; e < this->rankOf(); e++)
|
|
vector[e] = this->sizeAt(e);
|
|
|
|
return vector;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<int> NDArray::getShapeAsVectorInt() const {
|
|
|
|
std::vector<int> vector(this->rankOf());
|
|
for (int e = 0; e < this->rankOf(); e++)
|
|
vector[e] = static_cast<int>(this->sizeAt(e));
|
|
|
|
return vector;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<int64_t> NDArray::getShapeInfoAsFlatVector() const {
|
|
int magicNumber = shape::shapeInfoLength(this->rankOf());
|
|
std::vector<int64_t> vector(magicNumber);
|
|
|
|
for (int e = 0; e < magicNumber; e++)
|
|
vector[e] = static_cast<int64_t>(_shapeInfo[e]);
|
|
|
|
return vector;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<Nd4jLong> NDArray::getShapeInfoAsVector() const {
|
|
int magicNumber = shape::shapeInfoLength(this->rankOf());
|
|
std::vector<Nd4jLong> vector(magicNumber);
|
|
for (int e = 0; e < magicNumber; e++)
|
|
vector[e] = this->_shapeInfo[e];
|
|
return vector;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
std::vector<int8_t> NDArray::asByteVector() {
|
|
|
|
if (isS()) {
|
|
// string data type requires special treatment
|
|
syncToHost();
|
|
auto numWords = this->lengthOf();
|
|
auto offsetsBuffer = this->bufferAsT<Nd4jLong>();
|
|
auto headerLength = ShapeUtils::stringBufferHeaderRequirements(numWords);
|
|
auto dataLength = offsetsBuffer[numWords];
|
|
std::vector<int8_t> result(headerLength + dataLength);
|
|
|
|
memcpy(result.data(), buffer(), headerLength + dataLength);
|
|
|
|
return result;
|
|
} else {
|
|
// all other types are linear
|
|
std::vector<int8_t> result((unsigned long long) this->lengthOf() * sizeOfT());
|
|
|
|
if (this->isView()) {
|
|
auto tmp = this->dup(this->ordering());
|
|
syncToHost();
|
|
memcpy(result.data(), tmp.buffer(), (unsigned long long) lengthOf() * sizeOfT());
|
|
} else {
|
|
syncToHost();
|
|
memcpy(result.data(), buffer(), (unsigned long long) lengthOf() * sizeOfT());
|
|
}
|
|
return result;
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::linspace(const double start) {
|
|
linspace(start, 1);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::linspace(const double start, const double step) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::linspace: you can't use this method on String array!");
|
|
Nd4jLong numElements = this->lengthOf();
|
|
for (Nd4jLong e = 0; e < numElements; e++)
|
|
this->p(e, start + (step * e));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::streamline(char o) {
|
|
char order = o == 'a' ? this->ordering() : o;
|
|
syncToDevice();
|
|
std::shared_ptr<DataBuffer> newBuffer = std::make_shared<DataBuffer>(this->lengthOf() * sizeOfT(), dataType(), getContext()->getWorkspace());
|
|
auto shapeBuffer = ConstantShapeHelper::getInstance().bufferForShapeInfo(dataType(), order, rankOf(), shapeOf());
|
|
NativeOpExecutioner::execTransformSame(getContext(), transform::Copy, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), newBuffer->primary(),
|
|
shapeBuffer.primary(), newBuffer->special(),
|
|
shapeBuffer.special(), nullptr, nullptr, nullptr);
|
|
setShapeInfo(shapeBuffer);
|
|
_buffer = newBuffer;
|
|
_offset = 0;
|
|
tickWriteDevice();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// move assignment operator
|
|
NDArray& NDArray::operator=(NDArray&& other) noexcept {
|
|
if (this == &other)
|
|
return *this;
|
|
|
|
_isView = other._isView;
|
|
_buffer = other._buffer;
|
|
_shapeInfo = other._shapeInfo;
|
|
_shapeInfoD = other._shapeInfoD;
|
|
_context = other._context;
|
|
_dataType = other._dataType;
|
|
_length = other._length;
|
|
_offset = other._offset;
|
|
|
|
other._buffer = std::make_shared<DataBuffer>();
|
|
other._shapeInfo = other._shapeInfoD = nullptr;
|
|
other._length = 0;
|
|
|
|
return *this;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
NDArray& NDArray::operator=(const T scalar) {
|
|
this->assign(scalar);
|
|
return *this;
|
|
}
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const double scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const float scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const float16 scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const bfloat16 scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const Nd4jLong scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const int scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const int8_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const uint8_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const uint16_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const uint32_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const uint64_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const int16_t scalar);
|
|
template ND4J_EXPORT NDArray& NDArray::operator=(const bool scalar);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::copyBuffersContinuouslyFrom(const NDArray& other, size_t sizeToCopyInBytes, Nd4jLong offsetThis, Nd4jLong offsetOther) {
|
|
|
|
if(offsetThis == 0)
|
|
offsetThis = bufferOffset();
|
|
if(offsetOther == 0)
|
|
offsetOther = other.bufferOffset();
|
|
|
|
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()) {
|
|
throw std::runtime_error("Cannot assign empty array to non-empty array");
|
|
}
|
|
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.buffer(), 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.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr, allowParallelism);
|
|
NDArray::registerSpecialUse({this}, {});
|
|
}
|
|
else {
|
|
NDArray::prepareSpecialUse({this}, {&other});
|
|
NativeOpExecutioner::execScalar(getContext(), scalar::CopyPws, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr, allowParallelism);
|
|
NDArray::registerSpecialUse({this}, {&other});
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
if (other.lengthOf() != lengthOf()) {
|
|
auto shapeThis = ShapeUtils::shapeAsString(this);
|
|
auto shapeThat = ShapeUtils::shapeAsString(&other);
|
|
nd4j_printf("Can't assign array: this shape %s; other shape: %s\n", shapeThis.c_str(), shapeThat.c_str());
|
|
throw std::runtime_error("NDArray::assign: lengths of arrays are mismatched");
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({this}, {&other});
|
|
NativeOpExecutioner::execTransformAny(getContext(), transform::Assign, other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, nullptr, nullptr, allowParallelism);
|
|
NDArray::registerSpecialUse({this}, {&other});
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// This method assigns values of given NDArray to this one, wrt order
|
|
void NDArray::assign(const NDArray *other, bool allowParallelism) {
|
|
assign(*other, allowParallelism);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
void NDArray::assign(const T& value, bool allowParallelism) {
|
|
// just fire scalar
|
|
auto temp = NDArrayFactory::create(dataType(), value, this->getContext());
|
|
|
|
NDArray::prepareSpecialUse({this}, {&temp});
|
|
NativeOpExecutioner::execScalar(getContext(), sd::scalar::CopyPws, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), temp.buffer(), temp.shapeInfo(), temp.specialBuffer(), temp.specialShapeInfo(), nullptr, allowParallelism);
|
|
NDArray::registerSpecialUse({this}, {&temp});
|
|
}
|
|
template ND4J_EXPORT void NDArray::assign(const double& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const float& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const float16& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const bfloat16& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const Nd4jLong& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const int& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const int8_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const int16_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const uint8_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const uint16_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const uint32_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const uint64_t& value, bool allowParallelism);
|
|
template ND4J_EXPORT void NDArray::assign(const bool& value, bool allowParallelism);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray* NDArray::detach() {
|
|
|
|
if (!isAttached())
|
|
return this;
|
|
|
|
std::shared_ptr<DataBuffer> newBuffer = std::make_shared<DataBuffer>(lengthOf() * sizeOfT(), dataType());
|
|
|
|
auto result = new NDArray(newBuffer, ShapeDescriptor(dataType(), ordering(), shapeOf(), rankOf()));
|
|
|
|
result->assign(*this);
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::varianceNumber(sd::variance::Ops op, bool biasCorrected) {
|
|
|
|
NDArray res(DataTypeUtils::pickFloatingType(dataType()), getContext());
|
|
|
|
NDArray::prepareSpecialUse({&res}, {this});
|
|
NativeOpExecutioner::execSummaryStatsScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), 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(), sd::reduce::SameOps::Sum, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), 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(), sd::reduce::FloatOps::Mean, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), 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(sd::reduce::IsNan, nullptr).e<int>(0) > 0;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::hasInfs() {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::hasInfs: you can't use this method on String array!");
|
|
return this->reduceNumber(sd::reduce::IsInf, nullptr).e<int>(0) > 0;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::isFinite() {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::isFinite: you can't use this method on String array!");
|
|
return this->reduceNumber(sd::reduce::IsInfOrNan, nullptr).e<int>(0) == 0;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename Y>
|
|
void NDArray::templatedSet(void *buffer, const Nd4jLong *indices, const void *value) {
|
|
auto t = reinterpret_cast<T *>(buffer);
|
|
const auto y = *(reinterpret_cast<const Y *>(value));
|
|
|
|
auto xOffset = shape::getOffset(shapeInfo(), indices);
|
|
t[xOffset] = static_cast<T>(y);
|
|
}
|
|
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong *indices, const void *value), LIBND4J_TYPES, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename Y>
|
|
void NDArray::templatedSet(void *buffer, const Nd4jLong offset, const void *value) {
|
|
auto t = reinterpret_cast<T *>(buffer);
|
|
const auto y = *(reinterpret_cast<const Y *>(value));
|
|
|
|
t[offset] = static_cast<T>(y);
|
|
}
|
|
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedSet, (void *buffer, const Nd4jLong offset, const void *value), LIBND4J_TYPES, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::setContext(sd::LaunchContext *context) {
|
|
|
|
_context = context;
|
|
if (getContext() == nullptr)
|
|
_context = sd::LaunchContext ::defaultContext(); // empty context for default cases
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void const* NDArray::bufferWithOffset(Nd4jLong offset) const {
|
|
return const_cast<int8_t *>(buffer() != nullptr ? static_cast<const int8_t*>(buffer()) + (offset * sizeOfT()) : nullptr);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void* NDArray::bufferWithOffset(Nd4jLong offset) {
|
|
return const_cast<int8_t *>(buffer() != nullptr ? static_cast<const int8_t*>(buffer()) + (offset * sizeOfT()) : nullptr);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// eventually method reduces array by excluding its shapes along axes present in dimensions vector
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::FloatOps op, const std::vector<int>& dimensions, const bool keepDims) const {
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, isR() ? dataType() : Environment::getInstance().defaultFloatDataType(), keepDims, false, getContext()->getWorkspace());
|
|
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
this->reduceAlongDimension(op, result, copy, keepDims, false);
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::SameOps op, const std::vector<int>& dimensions, const bool keepDims) const {
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, keepDims, false, getContext()->getWorkspace());
|
|
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
reduceAlongDimension(op, result, copy, keepDims, false);
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::BoolOps op, const std::vector<int>& dimensions, const bool keepDims) const {
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::BOOL, keepDims, false, getContext()->getWorkspace());
|
|
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
reduceAlongDimension(op, result, copy, keepDims, false);
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::LongOps op, const std::vector<int>& dimensions, const bool keepDims) const {
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::INT64, keepDims, false, getContext()->getWorkspace());
|
|
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
reduceAlongDimension(op, result, copy, keepDims, false);
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// method reduces array by excluding its shapes along axes present in dimensions vector
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::FloatOps op, const std::initializer_list<int>& dimensions, const bool keepDims) const {
|
|
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::SameOps op, const std::initializer_list<int>& dimensions, const bool keepDims) const {
|
|
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::BoolOps op, const std::initializer_list<int>& dimensions, const bool keepDims) const {
|
|
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceAlongDimension(sd::reduce::LongOps op, const std::initializer_list<int>& dimensions, const bool keepDims) const {
|
|
return reduceAlongDimension(op, std::vector<int>(dimensions), keepDims);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::reduceNumber(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::indexReduceNumber(sd::indexreduce::Ops op, ExtraArguments *extraParams) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::indexReduceNumber: you can't use this method on String array!");
|
|
|
|
auto res = NDArrayFactory::create<Nd4jLong>(0);
|
|
|
|
NDArray::NDArray::prepareSpecialUse({&res}, {this});
|
|
NativeOpExecutioner::execIndexReduceScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams == nullptr ? nullptr : extraParams->argumentsAsT(this->dataType()), res.buffer(), res.shapeInfo(), res.specialBuffer(), res.specialShapeInfo());
|
|
NDArray::NDArray::registerSpecialUse({&res}, {this});
|
|
|
|
return res;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::tensorsAlongDimension(std::initializer_list<int> dimensions) const {
|
|
return tensorsAlongDimension(std::vector<int>(dimensions));
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::tensorsAlongDimension(const std::vector<int>& dimensions) const {
|
|
std::vector<int> copy(dimensions);
|
|
shape::checkDimensions(rankOf(), copy);
|
|
|
|
Nd4jLong tadLength = shape::tadLength(this->_shapeInfo, copy.data(), copy.size());
|
|
Nd4jLong numTads = this->lengthOf() / tadLength;
|
|
|
|
return numTads;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::printShapeInfo(const char * msg) const {
|
|
|
|
int rank = shape::rank(_shapeInfo);
|
|
int lim = shape::shapeInfoLength(rank);
|
|
|
|
if(msg != nullptr)
|
|
printf("shapeInfo %s: [", msg);
|
|
else
|
|
printf("shapeInfo: [");
|
|
|
|
printf("%i, ", rank);
|
|
for (int i = 1; i < shape::shapeInfoLength(rank) - 3; i++){
|
|
if(i == rank + 1)
|
|
printf(" ");
|
|
printf("%lld,", _shapeInfo[i]);
|
|
}
|
|
printf(" %lld,", shape::type(_shapeInfo));
|
|
printf("%lld,", shape::elementWiseStride(_shapeInfo));
|
|
printf("%lld]\n", (Nd4jLong)shape::order(_shapeInfo));
|
|
|
|
fflush(stdout);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::printBuffer(const char* msg, Nd4jLong limit, const bool sync) const{
|
|
if (sync)
|
|
syncToHost();
|
|
|
|
if (limit == -1)
|
|
limit = (int) this->lengthOf();
|
|
|
|
if (msg != nullptr)
|
|
printf("%s: [", msg);
|
|
else
|
|
printf("[");
|
|
if (this->isR()) {
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
if (e)
|
|
printf(", ");
|
|
printf("%f", this->e<float>(e));
|
|
}
|
|
}
|
|
else if (this->isZ()) {
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
if (this->dataType() != sd::DataType::INT64 && this->dataType() != sd::DataType::UINT64)
|
|
printf("%d", this->e<int>(e));
|
|
else
|
|
printf("%llu", this->e<Nd4jLong>(e));
|
|
if (e < limit - 1)
|
|
printf(", ");
|
|
}
|
|
}
|
|
else if (this->isB()) {
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
if (this->e<bool>(e))
|
|
printf("true");
|
|
else
|
|
printf("false");
|
|
if (e < limit - 1)
|
|
printf(", ");
|
|
}
|
|
}
|
|
else if (this->isS()) {
|
|
// todo do we need this print offsets
|
|
/*
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
printf("\"%lld\"", this->getOffset(e));
|
|
if (e < limit - 1)
|
|
printf(", ");
|
|
}
|
|
printf("]\n[");
|
|
*/
|
|
for (Nd4jLong e = 0; e < limit; e++) {
|
|
printf("\"%s\"", this->e<std::string>(e).c_str());
|
|
if (e < limit - 1)
|
|
printf(", ");
|
|
}
|
|
}
|
|
printf("]\n");
|
|
fflush(stdout);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// print element by element consequently in a way they (elements) are stored in physical memory
|
|
void NDArray::printLinearBuffer() const {
|
|
|
|
syncToHost();
|
|
|
|
const auto ews = this->ews() > 0 ? this->ews() : 1;
|
|
const auto len = this->lengthOf();
|
|
|
|
printf("[");
|
|
|
|
if (this->dataType() == sd::DataType::INT32) {
|
|
for(Nd4jLong e = 0; e < len; e++)
|
|
printf("%d, ", this->bufferAsT<int>()[e * ews]);
|
|
}
|
|
else if(this->dataType() == sd::DataType::INT64) {
|
|
for(Nd4jLong e = 0; e < len; e++)
|
|
printf("%lld, ", this->bufferAsT<Nd4jLong>()[e * ews]);
|
|
}
|
|
else if(this->dataType() == sd::DataType::FLOAT32) {
|
|
for(Nd4jLong e = 0; e < len; e++)
|
|
printf("%.8f, ", this->bufferAsT<float>()[e * ews]);
|
|
}
|
|
else if(this->dataType() == sd::DataType::DOUBLE) {
|
|
for(Nd4jLong e = 0; e < len; e++)
|
|
printf("%.8f, ", this->bufferAsT<double>()[e * ews]);
|
|
}
|
|
else
|
|
throw std::invalid_argument("NDArray::printLinearBuffer: not implemented yet for this data type !");
|
|
|
|
printf("]\n");
|
|
fflush(stdout);
|
|
}
|
|
//////////////////////////////////////////////////////////////////////////
|
|
static void printFormatted(NDArray const* arr, int depth, int limit) {
|
|
|
|
if (arr->rankOf() == 1) {
|
|
printf("[ ");
|
|
for (Nd4jLong i = 0; i < arr->lengthOf(); ++i) {
|
|
if (arr->isR())
|
|
printf("%f, ", arr->e<float>(i));
|
|
else if (arr->isZ())
|
|
printf("%lld, ", arr->e<Nd4jLong>(i));
|
|
else if (arr->isB())
|
|
printf("%s, ", arr->e<bool>(i)?"true":"false");
|
|
else if (arr->isS()) {
|
|
printf("\"%s\", ", arr->e<std::string>(i).c_str());
|
|
}
|
|
}
|
|
printf("]\n");
|
|
}
|
|
else if (arr->rankOf() == 2) {
|
|
Nd4jLong rows = arr->rows();
|
|
Nd4jLong cols = arr->columns();
|
|
char* padding = new char[depth + 1];
|
|
memset(padding, ' ', depth);
|
|
padding[depth] = 0;
|
|
printf("[");
|
|
for (Nd4jLong row = 0; row < rows; ++row) {
|
|
if (row && depth > 0)
|
|
printf("%s", padding);
|
|
printf("[");
|
|
Nd4jLong colLimit = cols > limit?cols:limit;
|
|
for (Nd4jLong col = 0; col < colLimit; ++col) {
|
|
if (col)
|
|
printf(", ");
|
|
if (arr->isR())
|
|
printf("%f", arr->e<float>(row, col));
|
|
else if (arr->isZ())
|
|
printf("%lld", arr->e<Nd4jLong>(row, col));
|
|
else if (arr->isB())
|
|
printf("%s", arr->e<bool>(row, col)?"true":"false");
|
|
else if (arr->isS()) {
|
|
printf("\"%s\"", arr->e<std::string>(row * cols + col).c_str());
|
|
}
|
|
}
|
|
if (row < rows - 1)
|
|
printf("]\n");
|
|
else
|
|
printf("]");
|
|
}
|
|
printf("]");
|
|
if (padding)
|
|
delete [] padding;
|
|
}
|
|
else {
|
|
//std::unique_ptr<ResultSet> arrs(arr->allTensorsAlongDimension({0}));
|
|
size_t restCount = 2;
|
|
printf("[");
|
|
restCount = ShapeUtils::getNumOfSubArrs(arr->shapeInfo(), {0});
|
|
for (size_t arrIndex = 0; arrIndex < restCount; ++arrIndex) {
|
|
NDArray subArr = (*arr)(arrIndex, {0});
|
|
printFormatted(&subArr, depth + 1, limit);
|
|
if (arrIndex < restCount - 1) {
|
|
for (Nd4jLong i = 1; i < arr->rankOf(); ++i)
|
|
printf("\n");
|
|
for (Nd4jLong i = 0; i < depth - 2; ++i)
|
|
printf(" ");
|
|
}
|
|
}
|
|
printf("]");
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::printIndexedBuffer(const char* msg, Nd4jLong limit) const {
|
|
|
|
syncToHost();
|
|
|
|
Nd4jLong rank = this->rankOf();
|
|
|
|
bool rowFlag = (rank < 2) || (rank == 2 && this->sizeAt(0) == 1);
|
|
|
|
if (msg)
|
|
printf("%s: ", msg);
|
|
|
|
if (this->isEmpty()) {
|
|
printf("Empty\n");
|
|
}
|
|
else if (this->rankOf() == 0) {
|
|
if (this->isZ())
|
|
printf("%lld\n", this->e<Nd4jLong>(0));
|
|
else if (this->isR())
|
|
printf("%.8f\n", this->e<float>(0));
|
|
else if (this->isB()) {
|
|
printf("%s\n", this->e<bool>(0)?"true":"false");
|
|
}
|
|
else if (this->isS()) {
|
|
// todo do we need this
|
|
// printf("\"%lld\"\n", this->getOffset(e));
|
|
printf("\"%s\"\n", this->e<std::string>(0).c_str());
|
|
}
|
|
}
|
|
else if (rowFlag && ews()==1)
|
|
printBuffer(nullptr, limit);
|
|
else {
|
|
if (msg)
|
|
printf("\n");
|
|
printFormatted(this, 1, limit);
|
|
printf("\n");
|
|
}
|
|
fflush(stdout);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void* NDArray::templatedPointerShift(const Nd4jLong offset) const {
|
|
return const_cast<T*>(reinterpret_cast<T const*>(buffer()) + 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(shapeInfo()), getContext(), bufferOffset());
|
|
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.shapeInfo()))
|
|
throw std::runtime_error("NDArray::transpose method: the shapeInfo of target array is wrong !");
|
|
|
|
target._buffer = _buffer;
|
|
target._offset = _offset;
|
|
target._isView = true;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// This method applies in-place transpose to this array, so this array becomes transposed
|
|
void NDArray::transposei() {
|
|
std::vector<int> perm;
|
|
for (int e = this->rankOf() - 1; e >= 0; e--)
|
|
perm.emplace_back(e);
|
|
|
|
this->permutei(perm);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::equalsTo(const NDArray &other, double eps) const {
|
|
return equalsTo(&other, eps);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::setAttached(bool reallyAttached) {
|
|
_isAttached = reallyAttached;
|
|
};
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// calculate strides
|
|
void NDArray::updateStrides(const char order) {
|
|
throw std::runtime_error("Forbidden method");
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// set new order and shape in case of suitable array length
|
|
bool NDArray::reshapei(const char order, const std::initializer_list<Nd4jLong>& shape, const bool copyToNewBuff) {
|
|
std::vector<Nd4jLong> vShape(shape);
|
|
return reshapei(order, vShape, copyToNewBuff);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::reshapei(const std::initializer_list<Nd4jLong>& shape, const bool copyToNewBuff) {
|
|
return reshapei(ordering(), shape, copyToNewBuff);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::reshapei(const std::vector<Nd4jLong>& shape, const bool copyToNewBuff) {
|
|
return reshapei(ordering(), shape, copyToNewBuff);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::enforce(const std::initializer_list<Nd4jLong> &dimensions, char order) {
|
|
std::vector<Nd4jLong> dims(dimensions);
|
|
enforce(dims, order);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::enforce(std::vector<Nd4jLong> &dimensions, char o) {
|
|
|
|
Nd4jLong prod = 1;
|
|
for (int e = 0; e < dimensions.size(); e++)
|
|
prod *= dimensions[e];
|
|
|
|
if (prod != this->lengthOf()) {
|
|
std::string current = ShapeUtils::shapeAsString(this);
|
|
std::string enforced = ShapeUtils::shapeAsString(dimensions);
|
|
nd4j_printf("Can't enforce new shape, lengths mismatch. Original shape: %s; Requested shape: %s\n", current.c_str(), enforced.c_str());
|
|
throw std::runtime_error("Incompatible shape");
|
|
}
|
|
|
|
char order = o == 'a' ? this->ordering() : o;
|
|
setShapeInfo(ShapeDescriptor(dataType(), order, dimensions));
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::argMax(std::initializer_list<int> dimensions) {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::argMax: you can't use this method on String array!");
|
|
|
|
if (dimensions.size() == 0) {
|
|
Nd4jLong max = 0;
|
|
auto mv = -DataTypeUtils::max<float>();
|
|
for (Nd4jLong e = 0; e < this->lengthOf(); e++) {
|
|
auto val = this->e<float>(e);
|
|
if (mv < val) {
|
|
mv = val;
|
|
max = e;
|
|
}
|
|
}
|
|
return max;
|
|
}
|
|
else
|
|
throw std::runtime_error("Not implemented yet");
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// create new array with corresponding order and shape, new array will point to the same _buffer as this array
|
|
NDArray NDArray::reshape(const char order, const std::vector<Nd4jLong>& shape, const bool copyToNewBuff) const & {
|
|
|
|
NDArray newArr(getDataBuffer(), ShapeDescriptor(shapeInfo()), getContext(), bufferOffset());
|
|
newArr.reshapei(order, shape, copyToNewBuff);
|
|
|
|
return newArr;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::reshape(const char order, const std::vector<Nd4jLong>& shape, const bool copyToNewBuff) && {
|
|
|
|
this->reshapei(order, shape, copyToNewBuff);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// change an array by repeating it the number of times given by reps.
|
|
void NDArray::tilei(const std::vector<Nd4jLong>& reps) {
|
|
*this = this->tile(reps);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::sizeAt(const int dim) const {
|
|
|
|
if (dim >= this->rankOf() || dim < -this->rankOf())
|
|
throw std::runtime_error("NDArray::sizeAt: bad size index requested");
|
|
|
|
if (dim >= 0)
|
|
return shape::shapeOf(_shapeInfo)[dim];
|
|
else
|
|
return shape::shapeOf(_shapeInfo)[this->rankOf() + dim];
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::strideAt(const int dim) const {
|
|
|
|
if (dim >= this->rankOf() || dim < -this->rankOf())
|
|
throw std::runtime_error("NDArray::strideAt: Bad size index requested");
|
|
|
|
if (dim >= 0)
|
|
return shape::stride(_shapeInfo)[dim];
|
|
else
|
|
return shape::stride(_shapeInfo)[this->rankOf() + dim];
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const std::initializer_list<int>& dimensions) {
|
|
std::vector<int> vec(dimensions);
|
|
return permutei(vec);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const std::vector<int>& dimensions) {
|
|
return permutei(dimensions.data(), rankOf());
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const std::initializer_list<Nd4jLong>& dimensions) {
|
|
std::vector<Nd4jLong> vec(dimensions);
|
|
std::vector<int> ivec(dimensions.size());
|
|
|
|
for (int e = 0; e < vec.size(); e++)
|
|
ivec[e] = static_cast<int>(vec[e]);
|
|
|
|
return permutei(ivec);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const std::vector<Nd4jLong>& dimensions) {
|
|
|
|
std::vector<int> ivec(dimensions.size());
|
|
|
|
for (int e = 0; e < dimensions.size(); e++)
|
|
ivec[e] = dimensions[e];
|
|
|
|
return permutei(ivec.data(), rankOf());
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
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(), bufferOffset());
|
|
ret._isView = true;
|
|
return ret;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const int* dimensions, const int rank) && {
|
|
|
|
this->permutei(dimensions, rank);
|
|
return std::move(*this);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const Nd4jLong* dimensions, const int rank) const &{
|
|
int tempDims[MAX_RANK];
|
|
shape::convertT<Nd4jLong, int>(const_cast<Nd4jLong *>(dimensions), tempDims, rank);
|
|
return permute(tempDims, rank);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const Nd4jLong* dimensions, const int rank) && {
|
|
|
|
this->permutei(dimensions, rank);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::vector<int>& dimensions) const &{
|
|
|
|
return permute(dimensions.data(), rankOf());
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::vector<int>& dimensions) && {
|
|
|
|
this->permutei(dimensions);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::vector<Nd4jLong>& dimensions) const & {
|
|
|
|
return permute(dimensions.data(), rankOf());
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::vector<Nd4jLong>& dimensions) && {
|
|
|
|
this->permutei(dimensions);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::initializer_list<int>& dimensions) const &{
|
|
|
|
std::vector<int> vec(dimensions);
|
|
return permute(vec);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::initializer_list<int>& dimensions) && {
|
|
|
|
this->permutei(dimensions);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::initializer_list<Nd4jLong>& dimensions) const & {
|
|
std::vector<Nd4jLong> vec(dimensions);
|
|
return permute(vec);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::permute(const std::initializer_list<Nd4jLong>& dimensions) && {
|
|
|
|
this->permutei(dimensions);
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::permute(const int* dimensions, const int rank, NDArray& target) const {
|
|
if (!nonNull() || !target.nonNull() || rank != rankOf() || rank != target.rankOf() )
|
|
throw std::runtime_error("NDArray<T>::permute method: either arrays are nullptr or ranks are not suitable!");
|
|
|
|
auto shapeInfoNew = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, target.getContext()->getWorkspace());
|
|
|
|
target.setShapeInfo(shapeInfoNew);
|
|
target._buffer = _buffer;
|
|
target._offset = _offset;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::permute(const Nd4jLong *dimensions, const int rank, NDArray& target) const {
|
|
if (!nonNull() || !target.nonNull() || rank != rankOf() || rank != target.rankOf() )
|
|
throw std::runtime_error("NDArray<T>::permute method: either arrays are nullptr or ranks are not suitable!");
|
|
|
|
auto shapeInfoNew = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, target.getContext()->getWorkspace());
|
|
|
|
target.setShapeInfo(shapeInfoNew);
|
|
target._buffer = _buffer;
|
|
target._offset = _offset;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::permute(const std::vector<int>& dimensions, NDArray& target) const {
|
|
permute(dimensions.data(), rankOf(), target);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::permute(const std::vector<Nd4jLong>& dimensions, NDArray& target) const {
|
|
permute(dimensions.data(), rankOf(), 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(Nd4jLong i=0; i<rows(); ++i)
|
|
if(sd::math::nd4j_abs(e<double>(i,i) - 1.f) > eps)
|
|
return false;
|
|
|
|
for(Nd4jLong i=0; i<rows(); ++i) {
|
|
for(Nd4jLong j=0; j<columns(); ++j) {
|
|
if (i == j)
|
|
continue;
|
|
if(sd::math::nd4j_abs(e<double>(i,j)) > eps)
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// check whether array is unitary matrix
|
|
bool NDArray::isUnitary() {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::isUnitary: you can't use this method on String array!");
|
|
if(rankOf() != 2 || rows() != columns())
|
|
throw std::runtime_error("isUnitary method: matrix must be square and have rank = 2 !");
|
|
|
|
auto tr = this->transpose();
|
|
auto trMul = MmulHelper::mmul(this, &tr, nullptr, 1.f, 0.f);
|
|
|
|
bool result = trMul->isIdentityMatrix();
|
|
delete trMul;
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <>
|
|
const std::string* ND4J_EXPORT NDArray::bufferAsT() const {
|
|
throw std::runtime_error("This method is NOT supposed to be used");
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
const T* NDArray::bufferAsT() const {
|
|
// FIXME: do we REALLY want sync here?
|
|
// syncToHost();
|
|
|
|
return reinterpret_cast<const T*>(buffer());
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT const, * NDArray::bufferAsT() const, LIBND4J_TYPES);
|
|
|
|
template <typename T>
|
|
T* NDArray::bufferAsT() {
|
|
syncToHost();
|
|
return reinterpret_cast<T*>(buffer());
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT, * NDArray::bufferAsT(), LIBND4J_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::subarray(IndicesList& idx) const {
|
|
|
|
const int idxSize = idx.size();
|
|
if (idxSize != this->rankOf())
|
|
throw std::runtime_error("NDArray::subarray: number of indices should match");
|
|
|
|
std::vector<Nd4jLong> indexes(3 * idxSize);
|
|
|
|
// convert IndicesList to vector
|
|
for (int d = 0; d < idxSize; ++d) {
|
|
|
|
if (idx.at(d)->isAll()) {
|
|
indexes[3 * d] = 0; // first
|
|
indexes[3 * d + 1] = 0; // last
|
|
indexes[3 * d + 2] = 1; // stride
|
|
}
|
|
else if (idx.at(d)->isPoint()) {
|
|
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = indexes[3 * d] + 1; // last
|
|
indexes[3 * d + 2] = 1; // stride
|
|
}
|
|
else if (idx.at(d)->isInterval()) {
|
|
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = idx.at(d)->getIndices().size();// last
|
|
indexes[3 * d + 2] = idx.at(d)->stride(); // stride
|
|
}
|
|
else {
|
|
indexes[3 * d] = idx.at(d)->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = idx.at(d)->getIndices().at(1); // last
|
|
indexes[3 * d + 2] = idx.at(d)->getIndices().at(2); // stride
|
|
}
|
|
}
|
|
return NDArray((*this)(indexes, true, true));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::subarray(const std::initializer_list<NDIndex*>& idx) const {
|
|
|
|
const int idxSize = idx.size();
|
|
if (idxSize != this->rankOf())
|
|
throw std::runtime_error("NDArray::subarray: number of indices should match the array rank");
|
|
|
|
std::vector<Nd4jLong> indexes(3 * idxSize);
|
|
|
|
// convert NDIndex to vector
|
|
int d = 0;
|
|
for (const auto& item : idx) {
|
|
|
|
if (item->isAll()) {
|
|
indexes[3 * d] = 0; // first
|
|
indexes[3 * d + 1] = 0; // last
|
|
indexes[3 * d + 2] = 1; // stride
|
|
}
|
|
else if (item->isPoint()) {
|
|
indexes[3 * d] = item->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = indexes[3 * d] + 1; // last
|
|
indexes[3 * d + 2] = 1; // stride
|
|
}
|
|
else if (item->isInterval()) {
|
|
indexes[3 * d] = item->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = item->getIndices().size(); // last
|
|
indexes[3 * d + 2] = item->stride(); // stride
|
|
}
|
|
else {
|
|
indexes[3 * d] = item->getIndices().at(0); // first
|
|
indexes[3 * d + 1] = item->getIndices().at(1); // last
|
|
indexes[3 * d + 2] = item->getIndices().at(2); // stride
|
|
}
|
|
++d;
|
|
}
|
|
|
|
// release NDIndices
|
|
for (auto i: idx)
|
|
delete i;
|
|
|
|
return NDArray((*this)(indexes, true, true));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::subarray(const Intervals& idx) const {
|
|
|
|
const int idxSize = idx.size();
|
|
if (idxSize != this->rankOf())
|
|
throw std::runtime_error("NDArray::subarray: number of indices should match the rank of array!");
|
|
|
|
std::vector<Nd4jLong> indexes(2 * idxSize);
|
|
|
|
// convert Intervals to vector
|
|
for (int d = 0; d < idxSize; ++d) {
|
|
|
|
if (idx[d].empty()) {
|
|
indexes[2 * d] = 0; // first
|
|
indexes[2 * d + 1] = 0; // last
|
|
}
|
|
else {
|
|
indexes[2 * d] = idx[d][0]; // first
|
|
indexes[2 * d + 1] = idx[d][1]; // last
|
|
}
|
|
}
|
|
|
|
return NDArray((*this)(indexes, true));
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
NDArray NDArray::asT() const{
|
|
|
|
auto result = isScalar() ? NDArray('c', {}, std::vector<double>{0.}, DataTypeUtils::fromT<T>(), this->getContext()) : NDArray(ordering(), getShapeAsVector(), DataTypeUtils::fromT<T>(), this->getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this});
|
|
NativeOpExecutioner::execTransformAny(getContext(), transform::AnyOps::Assign, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), nullptr, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArray::asT, () const, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
NDArray NDArray::asS() const {
|
|
|
|
if (!isS())
|
|
throw std::runtime_error("NDArray::asS: you can use this method only for String array!");
|
|
|
|
auto dtype = DataTypeUtils::fromT<T>();
|
|
|
|
if (!(DataTypeUtils::isS(dtype)))
|
|
throw std::invalid_argument("NDArray::asS: invalid DataType used");
|
|
|
|
if (dtype == dataType()) {
|
|
|
|
Nd4jLong offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
|
|
const auto nInputoffsets = bufferAsT<Nd4jLong>();
|
|
std::shared_ptr<DataBuffer> pBuffer = std::make_shared<DataBuffer>(offsetsLength + nInputoffsets[lengthOf()], dtype, getContext()->getWorkspace(), true);
|
|
|
|
NDArray res(pBuffer, ShapeDescriptor(dtype, ordering(), getShapeAsVector()), getContext());
|
|
res.setAttached(getContext()->getWorkspace() != nullptr);
|
|
|
|
preparePrimaryUse({ &res }, { this });
|
|
memcpy(res.bufferAsT<int8_t>(), nInputoffsets, offsetsLength);
|
|
auto data = res.bufferAsT<int8_t>() + offsetsLength;
|
|
const auto inData = bufferAsT<int8_t>() + offsetsLength;
|
|
memcpy(data, inData, nInputoffsets[lengthOf()]);
|
|
|
|
registerPrimaryUse({ &res }, { this });
|
|
return res;
|
|
}
|
|
|
|
Nd4jLong offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
|
|
|
|
std::vector<Nd4jLong> offsets(lengthOf() + 1);
|
|
|
|
const auto nInputoffsets = bufferAsT<Nd4jLong>();
|
|
|
|
Nd4jLong start = 0, stop = 0;
|
|
Nd4jLong dataLength = 0;
|
|
|
|
auto data = bufferAsT<int8_t>() + offsetsLength;
|
|
for (Nd4jLong e = 0; e < lengthOf(); e++) {
|
|
offsets[e] = dataLength;
|
|
start = nInputoffsets[e];
|
|
stop = nInputoffsets[e + 1];
|
|
if (dataType() == DataType::UTF8) {
|
|
dataLength += (dtype == DataType::UTF16) ? unicode::offsetUtf8StringInUtf16(data + start, stop)
|
|
: unicode::offsetUtf8StringInUtf32(data + start, stop);
|
|
}
|
|
else if (dataType() == DataType::UTF16) {
|
|
dataLength += (dtype == DataType::UTF32) ? unicode::offsetUtf16StringInUtf32(data + start, (stop / sizeof(char16_t)) )
|
|
: unicode::offsetUtf16StringInUtf8(data + start, (stop / sizeof(char16_t)));
|
|
}
|
|
else {
|
|
dataLength += (dtype == DataType::UTF16) ? unicode::offsetUtf32StringInUtf16(data + start, (stop / sizeof(char32_t)))
|
|
: unicode::offsetUtf32StringInUtf8(data + start, (stop / sizeof(char32_t)));
|
|
}
|
|
}
|
|
offsets[lengthOf()] = dataLength;
|
|
|
|
std::shared_ptr<DataBuffer> pBuffer = std::make_shared<DataBuffer>(offsetsLength + dataLength, dtype, getContext()->getWorkspace(), true);
|
|
|
|
NDArray res(pBuffer, ShapeDescriptor(dtype, ordering(), getShapeAsVector()), getContext());
|
|
res.setAttached(getContext()->getWorkspace() != nullptr);
|
|
|
|
preparePrimaryUse({ &res }, { this });
|
|
|
|
memcpy(res.bufferAsT<int8_t>(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
auto outData = res.bufferAsT<int8_t>() + offsetsLength;
|
|
const auto inData = bufferAsT<int8_t>() + offsetsLength;
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (int e = start; e < stop; e++) {
|
|
auto cdata = outData + offsets[e];
|
|
auto end = nInputoffsets[e + 1];
|
|
auto idata = inData + nInputoffsets[e];
|
|
if (dtype == DataType::UTF16) {
|
|
if (dataType() == DataType::UTF8) {
|
|
unicode::utf8to16(idata, outData, end);
|
|
}
|
|
else {
|
|
unicode::utf32to16(idata, outData, (end / sizeof(char32_t)));
|
|
}
|
|
}
|
|
else if (dtype == DataType::UTF32) {
|
|
if (dataType() == DataType::UTF8) {
|
|
unicode::utf8to32(idata, cdata, end);
|
|
}
|
|
else {
|
|
unicode::utf16to32(idata, outData, (end / sizeof(char16_t)));
|
|
}
|
|
}
|
|
else {
|
|
if (dataType() == DataType::UTF16) {
|
|
unicode::utf16to8(idata, outData, (end / sizeof(char16_t)));
|
|
}
|
|
else {
|
|
unicode::utf32to8(idata, outData, (end / sizeof(char32_t)));
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
registerPrimaryUse({ &res }, { this });
|
|
|
|
return res;
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArray::asS, () const, LIBND4J_STRINGTYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::asT(DataType dtype) const {
|
|
|
|
if (isS() && !DataTypeUtils::isS(dtype))
|
|
throw std::runtime_error("NDArray::asT: you can't use this method on String array with not string DataType!");
|
|
|
|
if (!isS() && DataTypeUtils::isS(dtype))
|
|
throw std::runtime_error("NDArray::asT: you can't use this method on not String array with string DataType!");
|
|
|
|
if (isS()){
|
|
BUILD_SINGLE_SELECTOR(dtype, return asS, (), LIBND4J_STRINGTYPES);
|
|
} else {
|
|
BUILD_SINGLE_SELECTOR(dtype, return asT, (), LIBND4J_TYPES);
|
|
}
|
|
|
|
return NDArray();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::cast(DataType dtype) const {
|
|
|
|
if (isS() && !DataTypeUtils::isS(dtype))
|
|
throw std::runtime_error("NDArray::cast: you can't use this method on String array with not string DataType!");
|
|
|
|
if (!isS() && DataTypeUtils::isS(dtype))
|
|
throw std::runtime_error("NDArray::cast: you can't use this method on not String array with string DataType!");
|
|
|
|
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 sd::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(), sd::scalar::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else{
|
|
const 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(sd::BroadcastOpsTuple::Add(), other, *this, false);
|
|
}
|
|
else {
|
|
NDArray result(bShape, true, getContext());
|
|
this->applyTrueBroadcast(sd::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 sd::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(), sd::scalar::Subtract, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Subtract, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else{
|
|
const 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(sd::BroadcastOpsTuple::Subtract(), other, *this, false);
|
|
}
|
|
else {
|
|
NDArray result(bShape, true, getContext());
|
|
this->applyTrueBroadcast(sd::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 sd::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(), sd::scalar::Multiply, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Multiply, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else{
|
|
const 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(sd::BroadcastOpsTuple::Multiply(), other, *this, false);
|
|
}
|
|
else {
|
|
NDArray result(bShape, true, getContext());
|
|
this->applyTrueBroadcast(sd::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 sd::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(), sd::scalar::Divide, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else if (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execPairwiseTransform(getContext(), sd::pairwise::Divide, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
else{
|
|
const 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(sd::BroadcastOpsTuple::Divide(), other, *this, false);
|
|
}
|
|
else {
|
|
NDArray result(bShape, true, getContext());
|
|
this->applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), other, result, false);
|
|
*this = std::move(result); // move assignment operator, zero cost copy
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::operator+=(const T value) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::operator+=: you can't use this method on String array!");
|
|
|
|
auto other = NDArrayFactory::create(this->dataType(), value, getContext());
|
|
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execScalar(getContext(), sd::scalar::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
template ND4J_EXPORT void NDArray::operator+=(const double value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const float value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const float16 value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const bfloat16 value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const Nd4jLong value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const int value);
|
|
template ND4J_EXPORT void NDArray::operator+=(const bool value);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
void NDArray::operator-=(const T value) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::operator-=: you can't use this method on String array!");
|
|
|
|
auto other = NDArrayFactory::create(dataType(), value, getContext());
|
|
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execScalar(getContext(), sd::scalar::Subtract, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
template ND4J_EXPORT void NDArray::operator-=(const double value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const float value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const float16 value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const bfloat16 value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const Nd4jLong value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const int value);
|
|
template ND4J_EXPORT void NDArray::operator-=(const bool value);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
void NDArray::operator*=(const T scalar) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::operator*=: you can't use this method on String array!");
|
|
|
|
auto other = NDArrayFactory::create(this->dataType(), scalar, getContext());
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execScalar(getContext(), sd::scalar::Multiply, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
template ND4J_EXPORT void NDArray::operator*=(const double scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const float scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const float16 scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const bfloat16 scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const Nd4jLong scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const int scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const int16_t scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const int8_t scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const uint8_t scalar);
|
|
template ND4J_EXPORT void NDArray::operator*=(const bool scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
void NDArray::operator/=(const T scalar) {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::operator/=: you can't use this method on String array!");
|
|
|
|
auto other = NDArrayFactory::create(this->dataType(), scalar, getContext());
|
|
NDArray::prepareSpecialUse({this}, {this, &other});
|
|
NativeOpExecutioner::execScalar(getContext(), sd::scalar::Divide, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({this}, {this, &other});
|
|
}
|
|
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(shapeInfo(), false, getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this});
|
|
NativeOpExecutioner::execTransformSame(getContext(), sd::transform::Neg, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), 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(), sd::transform::Neg, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {this});
|
|
|
|
return std::move(*this);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// mathematical multiplication of two arrays
|
|
NDArray mmul(const NDArray& left, const NDArray& right) {
|
|
if (left.isS() || right.isS())
|
|
throw std::runtime_error("mmul friend function: you can't use this function on String array!");
|
|
auto ptr = MmulHelper::mmul(const_cast<NDArray*>(&left), const_cast<NDArray*>(&right), nullptr, 1., 0.);
|
|
NDArray result(std::move(*ptr));
|
|
delete ptr;
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::tileToShape(const std::vector<Nd4jLong>& shape, NDArray& target) {
|
|
if(&target != this) {
|
|
this->tile(target);
|
|
return;
|
|
}
|
|
|
|
std::vector<Nd4jLong> thisShape(rankOf());
|
|
for(int i = 0; i < rankOf(); ++i)
|
|
thisShape[i] = sizeAt(i);
|
|
|
|
if(!ShapeUtils::areShapesBroadcastable(shape, thisShape))
|
|
throw std::runtime_error("NDArray::tileToShape method: the shape of this array and input shape are not suitable for broadcast operation !");
|
|
|
|
const int newRank = shape.size();
|
|
std::vector<Nd4jLong> repeats(newRank);
|
|
|
|
for(int i = 1; i <= newRank; ++i) {
|
|
if(i > rankOf())
|
|
repeats[newRank-i] = shape[newRank - i];
|
|
else
|
|
repeats[newRank-i] = shape[newRank - i] / thisShape[rankOf() - i];
|
|
}
|
|
|
|
tilei(repeats);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::tileToShape(const std::initializer_list<Nd4jLong>& shape, NDArray& target) {
|
|
tileToShape(std::vector<Nd4jLong>(shape), target);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::tileToShape(const Nd4jLong* shapeInfo) {
|
|
|
|
NDArray result(const_cast<Nd4jLong*>(shapeInfo), false, getContext());
|
|
tile(result);
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
double NDArray::getTrace() const {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::getTrace: you can't use this method on String array!");
|
|
|
|
int rank = rankOf();
|
|
auto shape = shapeOf();
|
|
int minDim = 100000000;
|
|
|
|
Nd4jLong indices[MAX_RANK];
|
|
for(int j = 0; j < rank; ++j)
|
|
indices[j] = 1;
|
|
|
|
auto offset = shape::getOffset(shapeInfo(), indices);
|
|
|
|
for(int i = 0; i < rank; ++i)
|
|
if(minDim > shape[i])
|
|
minDim = shape[i];
|
|
|
|
double sum = 0.;
|
|
|
|
for(int i = 0; i < minDim; ++i)
|
|
sum += e<double>(i * offset);
|
|
|
|
return sum;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::quantize(const NDArray& array) {
|
|
|
|
if(!array.isR())
|
|
throw std::invalid_argument("NDArray::quantize: type of array should be from real space!");
|
|
|
|
auto ws = array.getContext()->getWorkspace();
|
|
|
|
Nd4jLong* shapeInfo = ShapeBuilders::copyShapeInfo(array.shapeInfo(), true, ws);
|
|
ArrayOptions::setPropertyBit(shapeInfo, ARRAY_QUANTIZED);
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(TypeCast::estimateQuantizedSize(array.lengthOf()), ArrayOptions::dataType(shapeInfo), ws);
|
|
|
|
NDArray result(buffer, ShapeDescriptor(shapeInfo), array.getContext());
|
|
|
|
return result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, NDArray& target, const bool checkTargetShape, ExtraArguments *extraArgs) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::applyTrueBroadcast: you can't use this method on String array!");
|
|
|
|
if(((op.s == scalar::Divide || op.s == scalar::FloorDiv || op.s == scalar::FloorMod) && other.isB()) || (op.s == scalar::ReverseDivide && this->isB()))
|
|
throw std::runtime_error("NDArray::applyTrueBroadcast method: you can't divide by bool array !");
|
|
|
|
if (isEmpty() || other.isEmpty())
|
|
return;
|
|
|
|
// if (lengthOf() == 1) {
|
|
// target.assign(this);
|
|
// target.applyPairwiseTransform(op.p, other, extraArgs);
|
|
// return;
|
|
// }
|
|
// if (other.lengthOf() == 1) {
|
|
// const_cast<NDArray*>(this)->applyScalarArr(op.s, other, target, extraArgs);
|
|
// return;
|
|
// }
|
|
|
|
if(checkTargetShape) {
|
|
const 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.shapeInfo(), newShapeInfo))
|
|
throw std::runtime_error("NDArray::applyTrueBroadcast method: the shape or type of target array is wrong !");
|
|
}
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace());
|
|
xShapeInfoH = xPack.primary();
|
|
xShapeInfoD = xPack.special();
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace());
|
|
yShapeInfoH = yPack.primary();
|
|
yShapeInfoD = yPack.special();
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcast(getContext(), op.b, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyTrueBroadcast(sd::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) {
|
|
const 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 !");
|
|
}
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace());
|
|
xShapeInfoH = xPack.primary();
|
|
xShapeInfoD = xPack.special();
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace());
|
|
yShapeInfoH = yPack.primary();
|
|
yShapeInfoD = yPack.special();
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcastBool(getContext(), op.b, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr);
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyTrueBroadcast(sd::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) {
|
|
const 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 !");
|
|
}
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace());
|
|
xShapeInfoH = reinterpret_cast<Nd4jLong const*>(xPack.primary());
|
|
xShapeInfoD = reinterpret_cast<Nd4jLong const*>(xPack.special());
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace());
|
|
yShapeInfoH = reinterpret_cast<Nd4jLong const*>(yPack.primary());
|
|
yShapeInfoD = reinterpret_cast<Nd4jLong const*>(yPack.special());
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcastInt(getContext(), op.b, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::applyTrueBroadcast(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) const & {
|
|
if (isEmpty() || other.isEmpty()) {
|
|
if (isEmpty())
|
|
return NDArray(*this);
|
|
else
|
|
return NDArray(other);
|
|
}
|
|
|
|
const 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(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) const & {
|
|
if (isEmpty() || other.isEmpty()) {
|
|
if (isEmpty())
|
|
return NDArray(*this);
|
|
else
|
|
return NDArray(other);
|
|
}
|
|
|
|
const 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.shapeInfo())) {
|
|
|
|
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(sd::BroadcastOpsTuple op, const NDArray& other, ExtraArguments *extraArgs) && {
|
|
if (isEmpty() || other.isEmpty()) {
|
|
if (isEmpty())
|
|
return NDArray(*this);
|
|
else
|
|
return NDArray(other);
|
|
}
|
|
|
|
const 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, shapeInfo())) {
|
|
|
|
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(sd::BroadcastOpsTuple op, NDArray&& other, ExtraArguments *extraArgs) && {
|
|
if (isEmpty() || other.isEmpty()) {
|
|
if (isEmpty())
|
|
return NDArray(*this);
|
|
else
|
|
return NDArray(other);
|
|
}
|
|
|
|
const 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, shapeInfo());
|
|
const bool otherMove = shape::shapeEquals(newShapeInfo, other.shapeInfo());
|
|
|
|
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(sd::broadcast::Ops op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
|
|
|
|
if (dimensions.size() == 0)
|
|
return;
|
|
|
|
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 (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
// NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
// NativeOpExecutioner::execPairwiseTransform(getContext(), fromBroadcastToPairwise(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.special(), nullptr);
|
|
// NDArray::registerSpecialUse({&target}, {this, &other});
|
|
// return;
|
|
// }
|
|
|
|
if(target.dataType() != DataTypeUtils::pickPairwiseResultType(shapeInfo(), other.shapeInfo()))
|
|
throw std::invalid_argument("NDArray::applyBroadcast method: wrong type of target array !");
|
|
if(!target.isSameShape(this) && !target.isSameShape(other))
|
|
throw std::invalid_argument("NDArray::applyBroadcast method: one of of two input arrays (this or other) should has the same shape as target array!");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if (dimensions.size() > 1)
|
|
std::sort(copy.begin(), copy.end());
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace(), copy);
|
|
xShapeInfoH = reinterpret_cast<Nd4jLong const*>(xPack.primary());
|
|
xShapeInfoD = reinterpret_cast<Nd4jLong const*>(xPack.special());
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace(), copy);
|
|
yShapeInfoH = reinterpret_cast<Nd4jLong const*>(yPack.primary());
|
|
yShapeInfoD = reinterpret_cast<Nd4jLong const*>(yPack.special());
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcast(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyBroadcast(sd::broadcast::BoolOps op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
|
|
|
|
if (dimensions.size() == 0)
|
|
return;
|
|
|
|
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 (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
// NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
// NativeOpExecutioner::execPairwiseBoolTransform(getContext(), fromBroadcastToPairwiseBool(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.special(), nullptr);
|
|
// NDArray::registerSpecialUse({&target}, {this, &other});
|
|
// return;
|
|
// }
|
|
|
|
if(target.dataType() != DataType::BOOL)
|
|
throw std::invalid_argument("NDArray::applyBroadcast bool method: type of target array must be BOOL!");
|
|
if(!target.isSameShape(this) && !target.isSameShape(other))
|
|
throw std::invalid_argument("NDArray::applyBroadcast bool method: one of of two input arrays (this or other) should has the same shape as target array!");
|
|
if(_dataType != other._dataType)
|
|
throw std::invalid_argument("NDArray::applyBroadcast bool method: this and other arrays must have the same type !");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if (dimensions.size() > 1)
|
|
std::sort(copy.begin(), copy.end());
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace(), copy);
|
|
xShapeInfoH = reinterpret_cast<Nd4jLong const*>(xPack.primary());
|
|
xShapeInfoD = reinterpret_cast<Nd4jLong const*>(xPack.special());
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace(), copy);
|
|
yShapeInfoH = reinterpret_cast<Nd4jLong const*>(yPack.primary());
|
|
yShapeInfoD = reinterpret_cast<Nd4jLong const*>(yPack.special());
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcastBool(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), nullptr);
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyBroadcast(sd::broadcast::IntOps op, const std::vector<int>& dimensions, const NDArray& other, NDArray& target, ExtraArguments* extraArgs) {
|
|
|
|
if (dimensions.empty())
|
|
return;
|
|
|
|
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 (other.lengthOf() == lengthOf() && this->rankOf() == other.rankOf()) {
|
|
// NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
// NativeOpExecutioner::execPairwiseIntTransform(getContext(), fromBroadcastToPairwiseInt(op), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.special(), nullptr);
|
|
// NDArray::registerSpecialUse({&target}, {this, &other});
|
|
// return;
|
|
// }
|
|
|
|
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(this) && !target.isSameShape(other))
|
|
throw std::invalid_argument("NDArray::applyBroadcast int method: one of of two input arrays (this or other) should has the same shape as target array!");
|
|
if(_dataType != other._dataType)
|
|
throw std::invalid_argument("NDArray::applyBroadcast int method: this and other arrays must have the same type !");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if (dimensions.size() > 1)
|
|
std::sort(copy.begin(), copy.end());
|
|
|
|
Nd4jLong const* xShapeInfoH = shapeInfo();
|
|
Nd4jLong const* yShapeInfoH = other.shapeInfo();
|
|
Nd4jLong const* xShapeInfoD = specialShapeInfo();
|
|
Nd4jLong const* yShapeInfoD = other.specialShapeInfo();
|
|
|
|
if(!isSameShape(target)) {
|
|
auto xPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), shapeInfo(), getContext()->getWorkspace(), copy);
|
|
xShapeInfoH = reinterpret_cast<Nd4jLong const*>(xPack.primary());
|
|
xShapeInfoD = reinterpret_cast<Nd4jLong const*>(xPack.special());
|
|
}
|
|
if(!other.isSameShape(target)) {
|
|
auto yPack = ConstantShapeHelper::getInstance().createShapeInfoWithUnitiesForBroadcast(target.shapeInfo(), other.shapeInfo(), other.getContext()->getWorkspace(), copy);
|
|
yShapeInfoH = reinterpret_cast<Nd4jLong const*>(yPack.primary());
|
|
yShapeInfoD = reinterpret_cast<Nd4jLong const*>(yPack.special());
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &other});
|
|
NativeOpExecutioner::execBroadcastInt(getContext(), op, buffer(), xShapeInfoH, specialBuffer(), xShapeInfoD, other.buffer(), yShapeInfoH, other.specialBuffer(), yShapeInfoD, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyBroadcast(sd::broadcast::Ops op, const std::initializer_list<int> dimensions, const NDArray& tadArray, NDArray& target, ExtraArguments* extraArgs) {
|
|
std::vector<int> vec(dimensions);
|
|
applyBroadcast(op, vec, tadArray, target, extraArgs);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void* NDArray::operator new(size_t i) {
|
|
if (sd::memory::MemoryRegistrator::getInstance().hasWorkspaceAttached()) {
|
|
sd::memory::Workspace* ws = sd::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 (!sd::memory::MemoryRegistrator::getInstance().hasWorkspaceAttached())
|
|
free(p);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
std::vector<T> NDArray::asVectorT() {
|
|
|
|
std::vector<T> result(this->lengthOf());
|
|
|
|
PRAGMA_OMP_SIMD
|
|
for (int e = 0; e < this->lengthOf(); e++)
|
|
result[e] = this->e<T>(e);
|
|
|
|
return result;
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT std::vector, NDArray::asVectorT(), LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// set new order and shape in case of suitable array length
|
|
bool NDArray::reshapei(const char order, const std::vector<Nd4jLong>& cshape, const bool copyToNewBuff) {
|
|
|
|
// check firstly whether cshape is identical to shape of array, if yes then reshape is unnecessary
|
|
if(order == ordering() && shape::shapeEquals(rankOf(), shapeOf(), cshape.size(), cshape.data()))
|
|
return true;
|
|
|
|
const bool isOutShapeEmpty = std::find(cshape.begin(), cshape.end(), 0) != cshape.end();
|
|
|
|
if(isEmpty() && !isOutShapeEmpty)
|
|
throw std::invalid_argument("NDArray::reshapei: can't reshape empty array to non-empty !");
|
|
if(!isEmpty() && isOutShapeEmpty)
|
|
throw std::invalid_argument("NDArray::reshapei: can't reshape non-empty array to empty !");
|
|
if(isEmpty() && isOutShapeEmpty) {
|
|
Nd4jLong* shapeInfoNew = ShapeBuilders::emptyShapeInfo(dataType(), order, cshape, getContext()->getWorkspace());
|
|
setShapeInfo(shapeInfoNew);
|
|
RELEASE(shapeInfoNew, getContext()->getWorkspace());
|
|
return true;
|
|
}
|
|
|
|
std::vector<Nd4jLong> shape(cshape);
|
|
int rank = shape.size();
|
|
|
|
// looking for negative in shape
|
|
|
|
int numberNegativesOnes = 0;
|
|
|
|
Nd4jLong* shape_ = shape.data();
|
|
for (int i = 0; i < (int) shape.size(); i++) {
|
|
if (shape[i] < 0) {
|
|
if (numberNegativesOnes >= 1)
|
|
throw std::runtime_error("NDArray::reshapei: only one dimension can be negative at once");
|
|
|
|
numberNegativesOnes++;
|
|
|
|
int shapeLength = 1;
|
|
for (int j = 0; j < (int) shape.size(); j++)
|
|
if (i != j)
|
|
shapeLength *= shape_[j];
|
|
|
|
Nd4jLong realShape = sd::math::nd4j_abs<int>(lengthOf() / shapeLength);
|
|
auto thisNewShape = new Nd4jLong[shape.size()];
|
|
|
|
for (int j = 0; j < (int) shape.size(); j++)
|
|
if (i != j)
|
|
thisNewShape[j] = shape_[j];
|
|
else
|
|
thisNewShape[j] = realShape;
|
|
|
|
shape_ = thisNewShape;
|
|
}
|
|
}
|
|
|
|
for (int e = 0; e < (int) shape.size(); e++)
|
|
shape[e] = shape_[e];
|
|
|
|
if (numberNegativesOnes > 0)
|
|
delete[] shape_;
|
|
|
|
Nd4jLong arrLength = 1;
|
|
for(const auto& item : shape)
|
|
arrLength *= item;
|
|
|
|
if(platformBuffer() == nullptr || arrLength != this->lengthOf()) {
|
|
this->printShapeInfo("Mismatched shape");
|
|
sd::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(shapeInfo(), order, shape.size(), shape.data(), shapeInfoNew);
|
|
|
|
if (canReshape) {
|
|
setShapeInfo(shapeInfoNew);
|
|
}
|
|
else {
|
|
NDArray temp(order, shape, dataType(), getContext());
|
|
if(copyToNewBuff)
|
|
this->applyTransform(transform::Assign, temp, nullptr);
|
|
*this = std::move(temp);
|
|
}
|
|
|
|
RELEASE(shapeInfoNew, getContext()->getWorkspace());
|
|
|
|
return canReshape;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::nullify() {
|
|
if (isEmpty())
|
|
return;
|
|
|
|
if (isView() || ews() != 1)
|
|
assign(0);
|
|
else
|
|
_buffer->setToZeroBuffers();
|
|
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::templatedSet(void *buffer, const Nd4jLong xOfsset, sd::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, sd::DataType dtype, const void *value), LIBND4J_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyPairwiseTransform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), 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(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr);
|
|
NDArray::registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyPairwiseTransform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()) : nullptr);
|
|
NDArray::registerSpecialUse({&target}, {this, &other});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyPairwiseTransform(sd::pairwise::Ops op, const NDArray& other, ExtraArguments *extraParams) {
|
|
applyPairwiseTransform(op, other, *this, extraParams);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename X, typename Y>
|
|
void NDArray::templatedDoubleAssign(void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const {
|
|
auto x = reinterpret_cast<X *>(xBuffer);
|
|
const auto y = reinterpret_cast<const Y *>(yBuffer);
|
|
x[xOffset] = static_cast<X>(y[yOffset]);
|
|
}
|
|
BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedDoubleAssign, (void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const, LIBND4J_TYPES, LIBND4J_TYPES);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::varianceAlongDimension(sd::variance::Ops op, NDArray& target, const bool biasCorrected, const std::vector<int>& dimensions) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!");
|
|
|
|
if (!target.isR())
|
|
throw std::runtime_error("NDArray::varianceAlongDimension: target array must have FLOAT type");
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this});
|
|
|
|
if(rankOf() == dimensions.size() || dimensions.empty())
|
|
NativeOpExecutioner::execSummaryStatsScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), biasCorrected);
|
|
else {
|
|
std::vector<int> copy(dimensions);
|
|
auto pDims = sd::Environment::getInstance().isCPU() ? copy.data() : nullptr;
|
|
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimensions);
|
|
NativeOpExecutioner::execSummaryStats(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), pDims, dimensions.size(), packX.platformShapeInfo(), packX.platformOffsets(), biasCorrected);
|
|
synchronize("NDArray::varianceAlongDimension");
|
|
}
|
|
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::vector<int>& dimensions) const {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::varianceAlongDimension: you can't use this method on String array!");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
if (copy.size() > 1)
|
|
std::sort(copy.begin(), copy.end());
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataTypeUtils::pickFloatingType(dataType()), false, false, getContext()->getWorkspace());
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
this->varianceAlongDimension(op, result, biasCorrected, dimensions);
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::varianceAlongDimension(sd::variance::Ops op, const bool biasCorrected, const std::initializer_list<int>& dimensions) const {
|
|
return varianceAlongDimension(op, biasCorrected, std::vector<int>(dimensions));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::varianceAlongDimension(sd::variance::Ops op, NDArray &target, const bool biasCorrected, const std::initializer_list<int>& dimensions) const {
|
|
varianceAlongDimension(op, target, biasCorrected, std::vector<int>(dimensions));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// This method returns new copy of this NDArray, optionally in different order
|
|
NDArray NDArray::dup(const char newOrder) const {
|
|
|
|
if (isEmpty())
|
|
return NDArrayFactory::empty(dataType(), getContext());
|
|
|
|
char order = newOrder == 'a' ? ordering() : newOrder;
|
|
|
|
// for now string arrays require special treatment
|
|
if (isS()) {
|
|
if (dataType() == DataType::UTF8) {
|
|
std::vector<std::string> strings(lengthOf());
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto i = start; i < stop; i++) {
|
|
strings[i] = std::move(this->e<std::string>(i));
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
return NDArray(getShapeAsVector(), strings, dataType(), getContext());
|
|
}
|
|
if (dataType() == DataType::UTF16) {
|
|
std::vector<std::u16string> strings(lengthOf());
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto i = start; i < stop; i++) {
|
|
strings[i] = std::move(this->e<std::u16string>(i));
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
return NDArray(getShapeAsVector(), strings, dataType(), getContext());
|
|
}
|
|
|
|
std::vector<std::u32string> strings(lengthOf());
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto i = start; i < stop; i++) {
|
|
strings[i] = std::move(this->e<std::u32string>(i));
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, lengthOf(), 1);
|
|
|
|
return NDArray(getShapeAsVector(), strings, dataType(), getContext());
|
|
}
|
|
|
|
NDArray result(order, isScalar() ? std::vector<Nd4jLong>({0}) : getShapeAsVector(), dataType(), getContext());
|
|
result.assign(*this);
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// This method returns true if two arrays are equal, with custom or default Eps value of 1e-5, false otherwise
|
|
bool NDArray::equalsTo(const NDArray *other, double eps) const {
|
|
|
|
if (dataType() != other->dataType() || lengthOf() != other->lengthOf())
|
|
return false;
|
|
|
|
// we need to be able to compare [1, len] to [len]
|
|
if ((rankOf() == 1 && other->rankOf() == 2) || (rankOf() == 2 && other->rankOf() == 1)) {
|
|
// FIXME: do something here?
|
|
} else if (!shape::equalsSoft(shapeInfo(), other->shapeInfo()))
|
|
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
|
|
|
|
if (dataType() == DataType::UTF8) {
|
|
for (Nd4jLong e = 0; e < this->lengthOf(); e++) {
|
|
auto s1 = this->e<std::string>(e);
|
|
auto s2 = other->e<std::string>(e);
|
|
|
|
if (s1 != s2)
|
|
return false;
|
|
}
|
|
}
|
|
else if (dataType() == DataType::UTF16) {
|
|
for (Nd4jLong e = 0; e < this->lengthOf(); e++) {
|
|
auto s1 = this->e<std::u16string>(e);
|
|
auto s2 = other->e<std::u16string>(e);
|
|
|
|
if (s1 != s2)
|
|
return false;
|
|
}
|
|
}
|
|
else {
|
|
for (Nd4jLong e = 0; e < this->lengthOf(); e++) {
|
|
auto s1 = this->e<std::u32string>(e);
|
|
auto s2 = other->e<std::u32string>(e);
|
|
|
|
if (s1 != s2)
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
} else {
|
|
// regular numeric types
|
|
NDArray tmp(sd::DataType::FLOAT32, getContext()); // scalar = 0
|
|
|
|
ExtraArguments extras({0.0, 0.0, eps});
|
|
|
|
NDArray::prepareSpecialUse({&tmp}, {this, other});
|
|
NativeOpExecutioner::execReduce3Scalar(getContext(), reduce3::EqualsWithEps, buffer(), shapeInfo(),
|
|
specialBuffer(), specialShapeInfo(),
|
|
extras.argumentsAsT(DataType::FLOAT32), other->buffer(),
|
|
other->shapeInfo(), other->specialBuffer(),
|
|
other->specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(),
|
|
tmp.specialBuffer(), tmp.specialShapeInfo());
|
|
NDArray::registerSpecialUse({&tmp}, {this, other});
|
|
|
|
synchronize("NDArray::equalsTo");
|
|
|
|
if (tmp.e<Nd4jLong>(0) != 0)
|
|
return false;
|
|
|
|
return true;
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <>
|
|
std::string NDArray::e(const Nd4jLong i) const {
|
|
|
|
if (!isS())
|
|
throw std::runtime_error("Can't get std::string out of non-string array");
|
|
|
|
if (i == lengthOf())
|
|
throw std::runtime_error("Can't get std::string for index out of range");
|
|
|
|
|
|
if (this->dataType() == DataType::UTF16) {
|
|
auto u16 = this->e<std::u16string>(i);
|
|
std::string s;
|
|
StringUtils::u16StringToU8String(u16, s);
|
|
return s;
|
|
}
|
|
|
|
if (this->dataType() == DataType::UTF32) {
|
|
auto u32 = this->e<std::u32string>(i);
|
|
std::string s;
|
|
StringUtils::u32StringToU8String(u32, s);
|
|
return s;
|
|
}
|
|
|
|
NDArray::preparePrimaryUse({}, {this});
|
|
|
|
auto offsets = bufferAsT<Nd4jLong>();
|
|
auto offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
|
|
auto start = offsets[i];
|
|
auto end = offsets[i + 1];
|
|
auto data = bufferAsT<int8_t>() + offsetsLength + start;
|
|
|
|
std::string r(reinterpret_cast<const char*>(data), (end - start));
|
|
|
|
registerPrimaryUse({}, {this});
|
|
|
|
return r;
|
|
}
|
|
|
|
template <>
|
|
std::u16string NDArray::e(const Nd4jLong i) const {
|
|
|
|
if (!isS())
|
|
throw std::runtime_error("Can't get std::u16string out of non-string array");
|
|
|
|
if(i == lengthOf())
|
|
throw std::runtime_error("Can't get std::u16string for index out of range");
|
|
|
|
if (this->dataType() == DataType::UTF8) {
|
|
auto u = this->e<std::string>(i);
|
|
std::u16string s;
|
|
StringUtils::u8StringToU16String(u, s);
|
|
return s;
|
|
}
|
|
|
|
if (this->dataType() == DataType::UTF32) {
|
|
auto u32 = this->e<std::u32string>(i);
|
|
std::u16string s;
|
|
StringUtils::u32StringToU16String(u32, s);
|
|
return s;
|
|
}
|
|
|
|
NDArray::preparePrimaryUse({}, { this });
|
|
|
|
auto offsets = bufferAsT<Nd4jLong>();
|
|
Nd4jLong offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
|
|
Nd4jLong start = offsets[i];
|
|
Nd4jLong end = offsets[i + 1];
|
|
auto data = bufferAsT<int8_t>() + offsetsLength + start;
|
|
|
|
std::u16string r(reinterpret_cast<const char16_t*>(data), (end - start) / sizeof(char16_t));
|
|
|
|
registerPrimaryUse({}, { this });
|
|
|
|
return r;
|
|
}
|
|
|
|
template <>
|
|
std::u32string NDArray::e(const Nd4jLong i) const {
|
|
|
|
if (!isS())
|
|
throw std::runtime_error("Can't get std::u32string out of non-string array");
|
|
|
|
if (i == lengthOf())
|
|
throw std::runtime_error("Can't get std::u32string for index out of range");
|
|
|
|
if (this->dataType() == DataType::UTF8) {
|
|
auto u = this->e<std::string>(i);
|
|
std::u32string s;
|
|
StringUtils::u8StringToU32String(u, s);
|
|
return s;
|
|
}
|
|
|
|
if (this->dataType() == DataType::UTF16) {
|
|
auto u16 = this->e<std::u16string>(i);
|
|
std::u32string s;
|
|
StringUtils::u16StringToU32String(u16, s);
|
|
return s;
|
|
}
|
|
|
|
NDArray::preparePrimaryUse({}, { this });
|
|
|
|
auto offsets = bufferAsT<Nd4jLong>();
|
|
Nd4jLong offsetsLength = ShapeUtils::stringBufferHeaderRequirements(lengthOf());
|
|
Nd4jLong start = offsets[i];
|
|
Nd4jLong end = offsets[i + 1];
|
|
|
|
auto data = bufferAsT<int8_t>() + offsetsLength + start;
|
|
|
|
std::u32string r(reinterpret_cast<const char32_t*>(data), (end - start) / sizeof(char32_t));
|
|
|
|
registerPrimaryUse({}, { this });
|
|
|
|
return r;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <>
|
|
utf8string NDArray::e(const Nd4jLong i) const {
|
|
|
|
if (!isS())
|
|
throw std::runtime_error("This method is available for String arrays only");
|
|
|
|
auto rp = getOffset(i);
|
|
|
|
syncToHost();
|
|
tickReadHost();
|
|
|
|
return *(reinterpret_cast<utf8string* const*>(buffer())[rp]);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
T NDArray::e(const Nd4jLong i) const {
|
|
|
|
const auto rp = getOffset(i);
|
|
|
|
NDArray::preparePrimaryUse({}, {this});
|
|
NDArray::registerPrimaryUse({}, {this});
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(buffer(), rp), LIBND4J_TYPES);
|
|
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong) const, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// Returns value from 2D matrix by coordinates/indexes
|
|
template <typename T>
|
|
T NDArray::e(const Nd4jLong i, const Nd4jLong j) const {
|
|
|
|
if (rankOf() != 2 || i >= shapeOf()[0] || j >= shapeOf()[1])
|
|
throw std::invalid_argument("NDArray::e(i,j): one of input indexes is out of array length or rank!=2 !");
|
|
|
|
const auto xOffset = i * strideAt(0) + j * strideAt(1);
|
|
|
|
NDArray::preparePrimaryUse({}, {this});
|
|
NDArray::registerPrimaryUse({}, {this});
|
|
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(buffer(), xOffset), LIBND4J_TYPES);
|
|
|
|
return static_cast<T>(119);
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// returns value from 3D tensor by coordinates
|
|
template <typename T>
|
|
T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) const {
|
|
|
|
if (rankOf() != 3 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2])
|
|
throw std::invalid_argument("NDArray::e(i,j,k): one of input indexes is out of array length or rank!=3 !");
|
|
|
|
const auto xOffset = i * strideAt(0) + j * strideAt(1) + k * strideAt(2);
|
|
|
|
NDArray::preparePrimaryUse({}, {this});
|
|
NDArray::registerPrimaryUse({}, {this});
|
|
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(buffer(), xOffset), LIBND4J_TYPES);
|
|
|
|
return static_cast<T>(119);
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// returns value from 3D tensor by coordinates
|
|
template <typename T>
|
|
T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l) const {
|
|
|
|
if (rankOf() != 4 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2] || l >= shapeOf()[3])
|
|
throw std::invalid_argument("NDArray::e(i,j,k,l): one of input indexes is out of array length or rank!=4 !");
|
|
|
|
const auto xOffset = i * strideAt(0) + j * strideAt(1) + k * strideAt(2) + l * strideAt(3);
|
|
|
|
NDArray::preparePrimaryUse({}, {this});
|
|
NDArray::registerPrimaryUse({}, {this});
|
|
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), return templatedGet<, T>(buffer(), xOffset), LIBND4J_TYPES);
|
|
|
|
return static_cast<T>(119);
|
|
}
|
|
BUILD_SINGLE_UNCHAINED_TEMPLATE(template ND4J_EXPORT , NDArray::e(const Nd4jLong, const Nd4jLong, const Nd4jLong, const Nd4jLong) const, LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::e(const Nd4jLong i) const {
|
|
|
|
const auto offset = getOffset(i);
|
|
|
|
NDArray scalar(dataType(), getContext());
|
|
|
|
scalar.copyBuffersContinuouslyFrom(*this, sizeOfT(), 0, bufferOffset() + offset);
|
|
|
|
return scalar;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// perform array transformation
|
|
void NDArray::applyTransform(sd::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(sd::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(sd::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(sd::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(sd::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(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {this});
|
|
|
|
return std::move(*this);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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(shapeInfo(), false, getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this});
|
|
NativeOpExecutioner::execTransformSame(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {this});
|
|
|
|
return std::move(*this);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::transform::StrictOps op, void *extraParams) const & {
|
|
if (!this->isR())
|
|
throw std::runtime_error("Source array must have one of FLOAT types");
|
|
|
|
NDArray result(shapeInfo(), false, getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this});
|
|
NativeOpExecutioner::execTransformStrict(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {this});
|
|
|
|
return std::move(*this);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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(), sd::DataType::BOOL, getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this});
|
|
NativeOpExecutioner::execTransformBool(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({&result}, {this});
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::transform(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), extraParams, nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {this});
|
|
|
|
return std::move(*this);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyScalarArr(sd::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.shapeInfo()) && !(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.buffer(), scalar.shapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
|
|
NDArray::registerSpecialUse({&target}, {this, &scalar});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyScalarArr(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), scalar.buffer(), scalar.shapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
|
|
NDArray::registerSpecialUse({&target}, {this, &scalar});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyScalarArr(sd::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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), scalar.buffer(), scalar.shapeInfo(), scalar.specialBuffer(), scalar.specialShapeInfo(), extraParams != nullptr ? extraParams->argumentsAsT(target.dataType()): nullptr);
|
|
NDArray::registerSpecialUse({&target}, {this, &scalar});
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::applyScalar(sd::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(sd::scalar::IntOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
|
|
template ND4J_EXPORT void NDArray::applyScalar<double>(sd::scalar::IntOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<float>(sd::scalar::IntOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<float16>(sd::scalar::IntOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<bfloat16>(sd::scalar::IntOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<Nd4jLong>(sd::scalar::IntOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int>(sd::scalar::IntOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int16_t>(sd::scalar::IntOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int8_t>(sd::scalar::IntOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<uint8_t>(sd::scalar::IntOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<bool>(sd::scalar::IntOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::applyScalar(sd::scalar::Ops op, const T scalar, NDArray& target, ExtraArguments *extraParams) {
|
|
|
|
auto scalarArr = NDArrayFactory::create<T>(dataType(), scalar, this->getContext());
|
|
applyScalarArr(op, scalarArr, target, extraParams);
|
|
}
|
|
template <> ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const double scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const float scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const float16 scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams);
|
|
template ND4J_EXPORT void NDArray::applyScalar(sd::scalar::Ops op, const bool scalar, NDArray &target, ExtraArguments *extraParams);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::applyScalar(sd::scalar::BoolOps op, const T scalar, NDArray &target, ExtraArguments *extraParams) const {
|
|
|
|
NDArray scalarArr = NDArrayFactory::create<T>(scalar, getContext());
|
|
applyScalarArr(op, scalarArr, target, extraParams);
|
|
}
|
|
|
|
template <> ND4J_EXPORT void NDArray::applyScalar(sd::scalar::BoolOps op, const NDArray& scalar, NDArray &target, ExtraArguments *extraParams) const { throw std::runtime_error("NDArray::applyScalar<NDArray*> method: do not use me!");}
|
|
template ND4J_EXPORT void NDArray::applyScalar<double>(sd::scalar::BoolOps op, const double scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<float>(sd::scalar::BoolOps op, const float scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<float16>(sd::scalar::BoolOps op, const float16 scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<bfloat16>(sd::scalar::BoolOps op, const bfloat16 scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<Nd4jLong>(sd::scalar::BoolOps op, const Nd4jLong scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int>(sd::scalar::BoolOps op, const int scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int16_t>(sd::scalar::BoolOps op, const int16_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<int8_t>(sd::scalar::BoolOps op, const int8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<uint8_t>(sd::scalar::BoolOps op, const uint8_t scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
template ND4J_EXPORT void NDArray::applyScalar<bool>(sd::scalar::BoolOps op, const bool scalar, NDArray &target, ExtraArguments *extraParams) const;
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::applyIndexReduce(sd::indexreduce::Ops op, NDArray& target, const std::vector<int>& dimensions, const ExtraArguments *extraParams) const {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::applyIndexReduce: you can't use this method on String array!");
|
|
|
|
if (target.dataType() != sd::DataType::INT64 && target.dataType() != sd::DataType::INT32)
|
|
throw std::runtime_error("NDArray::applyIndexReduce operations return INT32/INT64");
|
|
|
|
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(this->dataType()) : nullptr;
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this});
|
|
|
|
if (target.lengthOf() == 1) {
|
|
NativeOpExecutioner::execIndexReduceScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), params, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
}
|
|
else {
|
|
std::vector<int> copy = dimensions;
|
|
shape::checkDimensions(rankOf(), copy);
|
|
auto pDims = sd::Environment::getInstance().isCPU() ? copy.data() : nullptr;
|
|
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(shapeInfo(), copy);
|
|
NativeOpExecutioner::execIndexReduce(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), 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(sd::indexreduce::Ops op, const std::vector<int>& dimensions, const ExtraArguments* extraParams ) const {
|
|
|
|
std::vector<int> copy = dimensions;
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataType::INT64, false, false, getContext()->getWorkspace());
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
applyIndexReduce(op, result, copy, extraParams);
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// apply reduce3 operations to this and other array, return result in new output array
|
|
NDArray NDArray::applyReduce3(sd::reduce3::Ops op, const NDArray& other, const ExtraArguments* extraParams) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::applyReduce3 method: you can't use this method on String array!");
|
|
if(dataType() != other.dataType())
|
|
throw std::runtime_error("NDArray::applyReduce3 method: the types of this and other arrays must be the same !");
|
|
// check shapes consistency
|
|
if(!isSameShape(other))
|
|
throw std::runtime_error("NDArray::applyReduce3 method: the shapes of this and other arrays must be the same !");
|
|
// create shapeInfo for scalar
|
|
auto newShape = ShapeBuilders::createScalarShapeInfo(DataTypeUtils::pickFloatingType(dataType()), getContext()->getWorkspace());
|
|
// create output array (scalar)
|
|
NDArray result(newShape, true, getContext());
|
|
RELEASE(newShape, getContext()->getWorkspace());
|
|
// create dynamic array of extra parameters if array extraParams is empty (==nullptr)
|
|
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this, &other});
|
|
NativeOpExecutioner::execReduce3Scalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), params, other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), 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(sd::reduce3::Ops op, const NDArray& other, const std::vector<int>& dimensions, const ExtraArguments* extraParams) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::applyReduce3: you can't use this method on String array!");
|
|
if(dataType() != other.dataType())
|
|
throw std::runtime_error("NDArray::applyReduce3 method: the types of this and other arrays must be the same !");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
shape::checkDimensions(rankOf(), copy);
|
|
shape::checkDimensions(other.rankOf(), copy);
|
|
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo('c', copy, *this, DataTypeUtils::pickFloatingType(dataType()), false, false, getContext()->getWorkspace());
|
|
NDArray result(newShape, true, getContext());
|
|
// create temporary dynamic array of extra parameters if array extraParams is empty (==nullptr)
|
|
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this, &other});
|
|
|
|
// perform calculations
|
|
if(rankOf() == copy.size() && other.rankOf() == copy.size()) {
|
|
NativeOpExecutioner::execReduce3Scalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), params, other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo());
|
|
}
|
|
else {
|
|
|
|
auto pDims = sd::Environment::getInstance().isCPU() ? copy.data() : nullptr;
|
|
|
|
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(shapeInfo(), copy);
|
|
auto packY = sd::ConstantTadHelper::getInstance().tadForDimensions(other.shapeInfo(), 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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), params, other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), 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(sd::reduce3::Ops op, const NDArray& other, const std::vector<int>& dimensions, const ExtraArguments* extraParams) const {
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::applyAllReduce3: you can't use this method on String array!");
|
|
if(dataType() != other.dataType())
|
|
throw std::runtime_error("NDArray::applyAllReduce3 method: the types of this and other arrays must be the same !");
|
|
|
|
// be careful, copy array may undergo changes (sort, transformation of negative dimensions to positive, duplicates removing )
|
|
std::vector<int> copy(dimensions);
|
|
shape::checkDimensions(rankOf(), copy);
|
|
shape::checkDimensions(other.rankOf(), copy);
|
|
|
|
auto packX = ConstantTadHelper::getInstance().tadForDimensions(shapeInfo(), copy);
|
|
auto packY = ConstantTadHelper::getInstance().tadForDimensions(other.shapeInfo(), copy);
|
|
|
|
// check tads shapes
|
|
if(!shape::equalsSoft(packX.primaryShapeInfo(), packY.primaryShapeInfo()))
|
|
throw std::runtime_error("NDArray::applyAllReduce3 method: the shapes of array tads are different !");
|
|
|
|
// set newShape for output array
|
|
auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(DataTypeUtils::pickFloatingType(dataType()), 'c', {packX.numberOfTads(), packY.numberOfTads()});
|
|
|
|
// create output array
|
|
NDArray result(newShape, true, getContext());
|
|
|
|
// create dynamic array of extra parameters if array extraParams is empty (==nullptr)
|
|
void* params = extraParams != nullptr ? const_cast<ExtraArguments*>(extraParams)->argumentsAsT(dataType()) : nullptr;
|
|
|
|
auto pDims = sd::Environment::getInstance().isCPU() ? copy.data() : nullptr;
|
|
|
|
NDArray::prepareSpecialUse({&result}, {this, &other});
|
|
NativeOpExecutioner::execReduce3All(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), params, other.buffer(), other.shapeInfo(), other.specialBuffer(), other.specialShapeInfo(), 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(sd::reduce::FloatOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool checkTargetShape) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::reduceAlongDimension FloatOps: you can't use this method on String array!");
|
|
if (!target.isR())
|
|
throw std::invalid_argument("NDArray::reduceAlongDimension FloatOps: requires target array to be present and have type form real space!");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if(checkTargetShape) {
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, false, getContext()->getWorkspace());
|
|
if(!shape::shapeEquals(newShape, target.shapeInfo()))
|
|
throw std::runtime_error("NDArray::reduceAlongDimension FloatOps: wrong target shape!");
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this});
|
|
|
|
if(rankOf() == copy.size() || copy.empty()) {
|
|
NativeOpExecutioner::execReduceFloatScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(),nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
}
|
|
else {
|
|
const Nd4jLong* zShapeInfoH = target.shapeInfo();
|
|
const Nd4jLong* zShapeInfoD = target.specialShapeInfo();
|
|
|
|
if(rankOf() - dimensions.size() != target.rankOf()) {
|
|
auto zPack = ConstantShapeHelper::getInstance().createShapeInfoWithNoUnitiesForReduce(target.shapeInfo(), copy, target.getContext()->getWorkspace());
|
|
zShapeInfoH = reinterpret_cast<Nd4jLong const*>(zPack.primary());
|
|
zShapeInfoD = reinterpret_cast<Nd4jLong const*>(zPack.special());
|
|
}
|
|
|
|
std::vector<int> dims = ShapeUtils::evalDimsForReduceOp(rankOf(), copy);
|
|
NativeOpExecutioner::execReduceFloat(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), zShapeInfoH, target.specialBuffer(), zShapeInfoD, dims.data(), dims.size());
|
|
|
|
}
|
|
synchronize("NDArray::reduceAlongDimension FloatOps");
|
|
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// method reduces array by excluding its shapes along axes present in dimensions vector
|
|
void NDArray::reduceAlongDimension(sd::reduce::SameOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool checkTargetShape) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: you can't use this method on String array!");
|
|
if (target.dataType() != dataType())
|
|
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: requires target array to be present and have same dtype as input");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if(checkTargetShape) {
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, false, getContext()->getWorkspace());
|
|
if(!shape::shapeEquals(newShape, target.shapeInfo()))
|
|
throw std::runtime_error("NDArray::reduceAlongDimension SameOps: wrong target shape!");
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this});
|
|
|
|
if(rankOf() == copy.size() || copy.empty()) {
|
|
NativeOpExecutioner::execReduceSameScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
}
|
|
else {
|
|
|
|
const Nd4jLong* zShapeInfoH = target.shapeInfo();
|
|
const Nd4jLong* zShapeInfoD = target.specialShapeInfo();
|
|
|
|
if(rankOf() - dimensions.size() != target.rankOf()) {
|
|
auto zPack = ConstantShapeHelper::getInstance().createShapeInfoWithNoUnitiesForReduce(target.shapeInfo(), copy, target.getContext()->getWorkspace());
|
|
zShapeInfoH = reinterpret_cast<Nd4jLong const*>(zPack.primary());
|
|
zShapeInfoD = reinterpret_cast<Nd4jLong const*>(zPack.special());
|
|
}
|
|
|
|
std::vector<int> dims = ShapeUtils::evalDimsForReduceOp(rankOf(), copy);
|
|
NativeOpExecutioner::execReduceSame(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), zShapeInfoH, target.specialBuffer(), zShapeInfoD, dims.data(), dims.size());
|
|
}
|
|
synchronize("NDArray::reduceAlongDimension SameOps");
|
|
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// method reduces array by excluding its shapes along axes present in dimensions vector
|
|
void NDArray::reduceAlongDimension(sd::reduce::LongOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool checkTargetShape) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: you can't use this method on String array!");
|
|
if (target.dataType() != DataType::INT64)
|
|
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: requires target array to be present and have type of INT64");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if(checkTargetShape) {
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, false, getContext()->getWorkspace());
|
|
if(!shape::shapeEquals(newShape, target.shapeInfo()))
|
|
throw std::runtime_error("NDArray::reduceAlongDimension LongOps: wrong target shape!");
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this});
|
|
|
|
if(rankOf() == copy.size() || copy.empty()) {
|
|
NativeOpExecutioner::execReduceLongScalar(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
}
|
|
else {
|
|
const Nd4jLong* zShapeInfoH = target.shapeInfo();
|
|
const Nd4jLong* zShapeInfoD = target.specialShapeInfo();
|
|
|
|
if(rankOf() - dimensions.size() != target.rankOf()) {
|
|
auto zPack = ConstantShapeHelper::getInstance().createShapeInfoWithNoUnitiesForReduce(target.shapeInfo(), copy, target.getContext()->getWorkspace());
|
|
zShapeInfoH = reinterpret_cast<Nd4jLong const*>(zPack.primary());
|
|
zShapeInfoD = reinterpret_cast<Nd4jLong const*>(zPack.special());
|
|
}
|
|
|
|
std::vector<int> dims = ShapeUtils::evalDimsForReduceOp(rankOf(), copy);
|
|
NativeOpExecutioner::execReduceLong(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), zShapeInfoH, target.specialBuffer(), zShapeInfoD, dims.data(), dims.size());
|
|
}
|
|
synchronize("NDArray::reduceAlongDimension LongOps");
|
|
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// method reduces array by excluding its shapes along axes present in dimensions vector
|
|
void NDArray::reduceAlongDimension(sd::reduce::BoolOps op, NDArray& target, const std::vector<int>& dimensions, const bool keepDims, const bool checkTargetShape) const {
|
|
|
|
if (isS())
|
|
throw std::runtime_error("NDArray::reduceAlongDimension BoolOps cuda: you can't use this method on String array!");
|
|
if (!target.isB())
|
|
throw std::invalid_argument("NDArray::reduceAlongDimension BoolOps cuda: requires target array to be present and have BOOL type!");
|
|
|
|
std::vector<int> copy(dimensions);
|
|
|
|
if(checkTargetShape) {
|
|
auto newShape = ShapeUtils::evalReduceShapeInfo(target.ordering(), copy, *this, keepDims, false, getContext()->getWorkspace());
|
|
if(!shape::shapeEquals(newShape, target.shapeInfo()))
|
|
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, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo());
|
|
}
|
|
else {
|
|
const Nd4jLong* zShapeInfoH = target.shapeInfo();
|
|
const Nd4jLong* zShapeInfoD = target.specialShapeInfo();
|
|
|
|
if(rankOf() - dimensions.size() != target.rankOf()) {
|
|
auto zPack = ConstantShapeHelper::getInstance().createShapeInfoWithNoUnitiesForReduce(target.shapeInfo(), copy, target.getContext()->getWorkspace());
|
|
zShapeInfoH = reinterpret_cast<Nd4jLong const*>(zPack.primary());
|
|
zShapeInfoD = reinterpret_cast<Nd4jLong const*>(zPack.special());
|
|
}
|
|
|
|
std::vector<int> dims = ShapeUtils::evalDimsForReduceOp(rankOf(), copy);
|
|
NativeOpExecutioner::execReduceBool(getContext(), op, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), nullptr, target.buffer(), zShapeInfoH, target.specialBuffer(), zShapeInfoD, dims.data(), dims.size());
|
|
}
|
|
synchronize("NDArray::reduceAlongDimension LongOps");
|
|
|
|
NDArray::registerSpecialUse({&target}, {this});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// This method sets value in linear buffer to position i
|
|
template <typename T>
|
|
void NDArray::p(const Nd4jLong i, const T value) {
|
|
|
|
if (i >= lengthOf())
|
|
throw std::invalid_argument("NDArray::p(i, value): input index is out of array length !");
|
|
|
|
auto rp = getOffset(i);
|
|
const void *pV = reinterpret_cast<const void*>(const_cast<T *>(&value));
|
|
|
|
NDArray::preparePrimaryUse({this}, {}, true);
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(this->dataType(), templatedSet<, T>(this->buffer(), rp, pV), LIBND4J_TYPES);
|
|
NDArray::registerPrimaryUse({this}, {});
|
|
}
|
|
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const double value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const float value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const float16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const bfloat16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint32_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const uint64_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const int16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const bool value);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// This method sets value in 2D matrix to position i, j
|
|
template <typename T>
|
|
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const T value) {
|
|
|
|
if (rankOf() != 2 || i >= shapeOf()[0] || j >= shapeOf()[1])
|
|
throw std::invalid_argument("NDArray:pe(i,j, value): one of input indexes is out of array length or rank!=2 !");
|
|
|
|
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
|
|
auto xOffset = i * strideAt(0) + j * strideAt(1);
|
|
|
|
NDArray::preparePrimaryUse({this}, {}, true);
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->buffer(), xOffset, p), LIBND4J_TYPES);
|
|
NDArray::registerPrimaryUse({this}, {});
|
|
}
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const double value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const float value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const float16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const bfloat16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint32_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const uint64_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const int16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const bool value);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// This method sets value in 3D matrix to position i,j,k
|
|
template <typename T>
|
|
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const T value) {
|
|
//(*this)(i,j,k) = value;
|
|
if (rankOf() != 3 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2])
|
|
throw std::invalid_argument("NDArray:pe(i,j,k, value): one of input indexes is out of array length or rank!=3 !");
|
|
|
|
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
|
|
auto xOffset = i * strideAt(0) + j * strideAt(1) + k * strideAt(2);
|
|
|
|
NDArray::preparePrimaryUse({this}, {}, true);
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->buffer(), xOffset, p), LIBND4J_TYPES);
|
|
NDArray::registerPrimaryUse({this}, {});
|
|
}
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const double value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const float value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const float16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const bfloat16 value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint8_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint32_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const uint64_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const int16_t value);
|
|
template ND4J_EXPORT void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const bool value);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const T value) {
|
|
//(*this)(i,j,k) = value;
|
|
if (rankOf() != 4 || i >= shapeOf()[0] || j >= shapeOf()[1] || k >= shapeOf()[2] || l >= shapeOf()[3])
|
|
throw std::invalid_argument("NDArray::p(i,j,k,l, value): one of input indexes is out of array length or rank!=4 !");
|
|
|
|
void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
|
|
auto xOffset = i * strideAt(0) + j * strideAt(1) + k * strideAt(2) + l * strideAt(3);
|
|
|
|
NDArray::preparePrimaryUse({this}, {}, true);
|
|
BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->buffer(), 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, (buffer(), rp, scalar.dataType(), scalar.buffer()), LIBND4J_TYPES);
|
|
NDArray::registerPrimaryUse({this}, {&scalar});
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong l, const NDArray& scalar) {
|
|
|
|
if(scalar.lengthOf() != 1)
|
|
throw std::invalid_argument("NDArray::p method: input array must be scalar!");
|
|
if (i >= _length)
|
|
throw std::invalid_argument("NDArray::p(i, NDArray_scalar): input index is out of array length !");
|
|
|
|
// void *p = reinterpret_cast<void *>(scalar.buffer());
|
|
Nd4jLong coords[4] = {i, j, k, l};
|
|
auto xOffset = shape::getOffset(shapeInfo(), coords);
|
|
|
|
NDArray::preparePrimaryUse({this}, {&scalar}, true);
|
|
// BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->buffer(), xOffset, p), LIBND4J_TYPES);
|
|
BUILD_SINGLE_SELECTOR(scalar.dataType(), templatedSet, (this->buffer(), xOffset, scalar.dataType(), scalar.buffer()), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &row});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), row.buffer(), row.shapeInfo(), row.specialBuffer(), row.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &row});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Subtract, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), row.buffer(), row.shapeInfo(), row.specialBuffer(), row.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), &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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &row});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Multiply, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), row.buffer(), row.shapeInfo(), row.specialBuffer(), row.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &row});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Divide, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), row.buffer(), row.shapeInfo(), row.specialBuffer(), row.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({this}, {&row});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), row.buffer(), row.shapeInfo(), row.specialBuffer(), row.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({&target}, {this, &column});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), column.buffer(), column.shapeInfo(), column.specialBuffer(), column.specialShapeInfo(), target.buffer(), target.shapeInfo(), target.specialBuffer(), target.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({this}, {&column});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Add, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), column.buffer(), column.shapeInfo(), column.specialBuffer(), column.specialShapeInfo(), 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 = sd::ConstantTadHelper::getInstance().tadForDimensions(this->shapeInfo(), dimension);
|
|
|
|
NDArray::prepareSpecialUse({this}, {&column});
|
|
NativeOpExecutioner::execBroadcast(getContext(), sd::broadcast::Ops::Multiply, buffer(), shapeInfo(), specialBuffer(), specialShapeInfo(), column.buffer(), column.shapeInfo(), column.specialBuffer(), column.specialShapeInfo(), this->buffer(), this->shapeInfo(), this->specialBuffer(), this->specialShapeInfo(), nullptr, 1, packX.platformShapeInfo(), packX.platformOffsets(), nullptr, nullptr);
|
|
NDArray::registerSpecialUse({this}, {&column});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void NDArray::templatedAssign(void *xBuffer, Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const {
|
|
if (xBuffer != nullptr && yBuffer != nullptr)
|
|
*(reinterpret_cast<T*>(xBuffer) + xOffset) = *(reinterpret_cast<const T*>(yBuffer) + yOffset);
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::templatedAssign, (void *xBuffer, const Nd4jLong xOffset, const void *yBuffer, const Nd4jLong yOffset) const, LIBND4J_TYPES);
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const int* dimensions, const int rank) {
|
|
|
|
auto shapeInfo = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, getContext()->getWorkspace());
|
|
setShapeInfo(shapeInfo);
|
|
|
|
return true;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
bool NDArray::permutei(const Nd4jLong* dimensions, const int rank) {
|
|
|
|
auto shapeInfo = ShapeUtils::evalPermShapeInfo(dimensions, rank, *this, getContext()->getWorkspace());
|
|
setShapeInfo(shapeInfo);
|
|
|
|
return true;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
ResultSet NDArray::multipleTensorsAlongDimension(const std::vector<int> &indices, const std::vector<int> &dimensions) const {
|
|
ResultSet result;
|
|
|
|
if (indices.size() == 0)
|
|
return result;
|
|
|
|
auto pack = ConstantTadHelper::getInstance().tadForDimensions(shapeInfo(), const_cast<int*>(dimensions.data()), dimensions.size());
|
|
|
|
auto tadLength = shape::length(pack.primaryShapeInfo());
|
|
auto numTads = lengthOf() / tadLength;
|
|
|
|
for (auto idx: indices) {
|
|
if (idx >= numTads) {
|
|
nd4j_printf("NDArray::multipleTensorsAlongDimension: index %i is higher then number of TADs: %i\n", idx, numTads);
|
|
throw std::runtime_error("Bad index");
|
|
}
|
|
|
|
auto array = new NDArray(getDataBuffer(), ShapeDescriptor(pack.primaryShapeInfo()), getContext(), pack.primaryOffsets()[idx] + bufferOffset());
|
|
result.push_back(array);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
ResultSet NDArray::allTensorsAlongDimension(const std::initializer_list<int>& dimensions) const {
|
|
return allTensorsAlongDimension(std::vector<int>(dimensions));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
ResultSet NDArray::allExamples() const {
|
|
std::vector<int> dimensions(rankOf() - 1);
|
|
for (int e = 1; e < rankOf(); e++)
|
|
dimensions[e-1] = e;
|
|
|
|
return allTensorsAlongDimension(dimensions);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
Nd4jLong NDArray::getOffset(const Nd4jLong i) const {
|
|
|
|
if (i >= lengthOf())
|
|
throw std::invalid_argument("NDArray::getOffset: input index is out of array length !");
|
|
|
|
return shape::getIndexOffset(i, _shapeInfo);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::like() {
|
|
|
|
return NDArray(shapeInfo(), this->dataType(), false, getContext());
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::ulike() const{
|
|
|
|
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(shapeInfo(), 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(), bufferOffset());
|
|
|
|
RELEASE(outShapeInfo, getContext()->getWorkspace());
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
ResultSet NDArray::allTensorsAlongDimension(const std::vector<int> &dimensions) const {
|
|
|
|
ResultSet result;
|
|
|
|
if(dimensions.size() == 0)
|
|
return result;
|
|
|
|
if(dimensions.back() >= rankOf())
|
|
throw std::runtime_error("NDArray::allTensorsAlongDimension static function: all input dimensions must be smaller than rank of input array !");
|
|
|
|
|
|
auto pack = ConstantTadHelper::getInstance().tadForDimensions(_shapeInfo, const_cast<int*>(dimensions.data()), dimensions.size());
|
|
auto numTads = pack.numberOfTads();
|
|
|
|
for (Nd4jLong idx = 0; idx < numTads; idx++ ) {
|
|
auto array = new NDArray(_buffer, ShapeDescriptor(pack.primaryShapeInfo()), getContext(), pack.primaryOffsets()[idx] + bufferOffset());
|
|
array->_isView = true;
|
|
result.push_back(array);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// operator returns sub-array with buffer pointing at this->_buffer + certain offset
|
|
NDArray NDArray::operator()(const std::vector<Nd4jLong>& idx, const bool keepUnitiesInShape, const bool isStrided) const {
|
|
|
|
if(isEmpty())
|
|
throw std::invalid_argument("NDArray::operator(sub-arrays): array is empty !");
|
|
|
|
// Nd4jLong *outShapeInfo = nullptr;
|
|
// ALLOCATE(outShapeInfo, workspace, shape::shapeInfoLength(inShapeInfo), Nd4jLong);
|
|
|
|
int numOfUntiesInSubArrShape = 0;
|
|
|
|
Nd4jLong* subArrShapeInfo = nullptr;
|
|
|
|
if(!keepUnitiesInShape) {
|
|
|
|
int n(isStrided ? 3 : 2), first, last;
|
|
|
|
// calculate the number of unities in shape
|
|
for (uint d = 0; d < rankOf(); ++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;
|
|
if(last - first == 1)
|
|
++numOfUntiesInSubArrShape;
|
|
}
|
|
}
|
|
}
|
|
|
|
ALLOCATE(subArrShapeInfo, getContext()->getWorkspace(), shape::shapeInfoLength(rankOf() - numOfUntiesInSubArrShape), Nd4jLong);
|
|
|
|
Nd4jLong offset;
|
|
|
|
shape::calcSubArrShapeInfoAndOffset(idx.data(), shapeInfo(), subArrShapeInfo, offset, keepUnitiesInShape, isStrided, numOfUntiesInSubArrShape);
|
|
|
|
NDArray result(_buffer, ShapeDescriptor(subArrShapeInfo), getContext(), offset + bufferOffset());
|
|
result._isView = true;
|
|
|
|
RELEASE(subArrShapeInfo, getContext()->getWorkspace());
|
|
|
|
return result;
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
NDArray NDArray::operator()(const Nd4jLong subArrIdx, const std::vector<int>& dimsToExclude, bool keepUnitiesInShape) const {
|
|
|
|
std::vector<Nd4jLong> idxRanges(2 * rankOf());
|
|
|
|
const auto rank = rankOf();
|
|
const auto subArrRank = static_cast<int>(dimsToExclude.size());
|
|
|
|
if(subArrRank > rank)
|
|
throw std::invalid_argument("NDArray::operator(const Nd4jLong subArrIdx, const std::vector<int>& dimsToExclude, bool keepUnitiesInShape): static method: dimsToExclude is empty or has size > rank of array !");
|
|
|
|
memset(idxRanges.data(), 0, 2 * rank * sizeof(Nd4jLong));
|
|
|
|
// subArrRank == 0 means whole array, idxRanges should contain zeros only
|
|
|
|
if(subArrRank != 0) {
|
|
|
|
std::vector<Nd4jLong> shapeOfSubArr(subArrRank), indexes(subArrRank);
|
|
for(int i = 0; i < subArrRank; ++i)
|
|
shapeOfSubArr[i] = sizeAt(dimsToExclude[i]);
|
|
|
|
shape::index2coords(subArrIdx, subArrRank, shapeOfSubArr.data(), indexes.data());
|
|
|
|
for(int i = 0; i < subArrRank; ++i) {
|
|
int currIdx = 2 * dimsToExclude[i];
|
|
idxRanges[currIdx] = indexes[i];
|
|
idxRanges[currIdx + 1] = indexes[i] + 1;
|
|
}
|
|
}
|
|
|
|
return (*this)(idxRanges, keepUnitiesInShape);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::getSubArrShapeAndOffsets(const std::vector<int>& dimsToExclude, Nd4jLong* &subArrShapeInfo, Nd4jLong* &subArrOffsets, bool keepUnitiesInShape) const {
|
|
|
|
if(isEmpty())
|
|
throw std::invalid_argument("NDArray::getSubArrShapeAndOffsets: array is empty !");
|
|
|
|
const int rank = rankOf();
|
|
const int subArrRank = (rank == dimsToExclude.size() || keepUnitiesInShape) ? rank : rank - dimsToExclude.size();
|
|
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(_shapeInfo, dimsToExclude);
|
|
|
|
// allocate memory
|
|
ALLOCATE(subArrShapeInfo, getContext()->getWorkspace(), shape::shapeInfoLength(subArrRank), Nd4jLong);
|
|
ALLOCATE(subArrOffsets, getContext()->getWorkspace(), numOfSubArrs, Nd4jLong);
|
|
|
|
shape::calcSubArrsShapeInfoAndOffsets(_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 = shapeBuffer.primary();
|
|
#ifdef __CUDABLAS__
|
|
_shapeInfoD = shapeBuffer.special();
|
|
#endif
|
|
|
|
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
|
|
_length = 0;
|
|
else
|
|
_length = shape::length(_shapeInfo);
|
|
|
|
_dataType = ArrayOptions::dataType(_shapeInfo);
|
|
}
|
|
else {
|
|
_dataType = sd::DataType::INHERIT;
|
|
_shapeInfoD = _shapeInfo = nullptr;
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void NDArray::setShapeInfo(const Nd4jLong *shapeInfo, const sd::DataType dtype) {
|
|
|
|
if (shapeInfo != nullptr) {
|
|
|
|
Nd4jLong* shapeInfoTemp = ShapeBuilders::copyShapeInfoAndType(shapeInfo, dtype, true, getContext()->getWorkspace());
|
|
ShapeDescriptor descriptor(shapeInfoTemp);
|
|
auto shapeBuffer = ConstantShapeHelper::getInstance().bufferForShapeInfo(descriptor);
|
|
|
|
_shapeInfo = shapeBuffer.primary();
|
|
#ifdef __CUDABLAS__
|
|
_shapeInfoD = shapeBuffer.special();
|
|
#endif
|
|
|
|
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
|
|
_length = 0;
|
|
else
|
|
_length = shape::length(_shapeInfo);
|
|
|
|
_dataType = dtype;
|
|
}
|
|
else {
|
|
_dataType = sd::DataType::INHERIT;
|
|
_shapeInfoD = _shapeInfo = nullptr;
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::setShapeInfo(const ShapeDescriptor& descriptor) {
|
|
|
|
auto shapeBuffer = ConstantShapeHelper::getInstance().bufferForShapeInfo(const_cast<ShapeDescriptor &>(descriptor));
|
|
|
|
_shapeInfo = shapeBuffer.primary();
|
|
#ifdef __CUDABLAS__
|
|
_shapeInfoD = shapeBuffer.special();
|
|
#endif
|
|
|
|
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
|
|
_length = 0;
|
|
else
|
|
_length = shape::length(_shapeInfo);
|
|
|
|
_dataType = ArrayOptions::dataType(_shapeInfo);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void NDArray::setShapeInfo(const ConstantShapeBuffer& shapeBuffer) {
|
|
|
|
_shapeInfo = shapeBuffer.primary();
|
|
#ifdef __CUDABLAS__
|
|
_shapeInfoD = shapeBuffer.special();
|
|
#endif
|
|
|
|
if(ArrayOptions::arrayType(_shapeInfo) == ArrayType::EMPTY)
|
|
_length = 0;
|
|
else
|
|
_length = shape::length(_shapeInfo);
|
|
|
|
_dataType = ArrayOptions::dataType(_shapeInfo);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////
|
|
// addition operator array + scalar
|
|
template <typename T, typename>
|
|
NDArray operator+(NDArray&& arr, const T& scalar) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(arr + scalar); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator+(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
|
|
throw std::runtime_error("operator+(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Add, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), 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+(NDArray&& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator+(NDArray&& arr, const int& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator+(const NDArray& arr, const T& scalar) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator+(const NDArray& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Add, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), 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 NDArray& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator+(const NDArray& arr, const int& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator+(const T& scalar, NDArray&& arr) {
|
|
return std::move(arr) + scalar;
|
|
}
|
|
template ND4J_EXPORT NDArray operator+(const double& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator+(const float& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator+(const float16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator+(const bfloat16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator+(const int& scalar, NDArray&& arr);
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator+(const T& scalar, const NDArray& arr) {
|
|
return arr + scalar;
|
|
}
|
|
template ND4J_EXPORT NDArray operator+(const double& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator+(const float& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator+(const int& scalar, const NDArray& arr);
|
|
|
|
///////////////////////////////////////////////////////////////////////
|
|
// addition operator array - scalar
|
|
template <typename T, typename>
|
|
NDArray operator-(NDArray&& arr, const T& scalar) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(arr - scalar); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator-(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
|
|
throw std::runtime_error("operator-(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Subtract, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), 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-(NDArray&& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator-(NDArray&& arr, const float& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator-(const NDArray& arr, const T& scalar) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator-(const NDArray& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Subtract, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), 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 NDArray& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator-(const NDArray& arr, const int& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator-(const T& scalar, NDArray&& arr) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(scalar - arr); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator-(const T& scalar, NDArray&& arr): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseSubtract, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
|
|
|
|
return std::move(arr);
|
|
|
|
}
|
|
template ND4J_EXPORT NDArray operator-(const double& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator-(const float& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator-(const float16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator-(const bfloat16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator-(const int& scalar, NDArray&& arr);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator-(const T& scalar, const NDArray& arr) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator-(const T& scalar, const NDArray& arr): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseSubtract, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
|
|
|
|
return result;
|
|
}
|
|
template ND4J_EXPORT NDArray operator-(const double& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator-(const float& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator-(const int& scalar, const NDArray& arr);
|
|
|
|
///////////////////////////////////////////////////////////////////////
|
|
// addition operator array + scalar
|
|
template <typename T, typename>
|
|
NDArray operator*(NDArray&& arr, const T& scalar) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(arr * scalar); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator*(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
|
|
throw std::runtime_error("operator*(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Multiply, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), 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*(NDArray&& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const int& scalar);
|
|
template ND4J_EXPORT NDArray operator*(NDArray&& arr, const long long& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator*(const NDArray& arr, const T& scalar) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator*(const NDArray& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Multiply, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), 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 NDArray& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const int& scalar);
|
|
template ND4J_EXPORT NDArray operator*(const NDArray& arr, const long long& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator*(const T& scalar, NDArray&& arr) {
|
|
return std::move(arr) * scalar;
|
|
}
|
|
template ND4J_EXPORT NDArray operator*(const double& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator*(const float& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator*(const float16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator*(const bfloat16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator*(const int& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator*(const long long& scalar, NDArray&& arr);
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator*(const T& scalar, const NDArray& arr) {
|
|
return arr * scalar;
|
|
}
|
|
template ND4J_EXPORT NDArray operator*(const double& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator*(const float& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator*(const float16& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator*(const bfloat16& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator*(const int& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator*(const long long& scalar, const NDArray& arr);
|
|
|
|
///////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator/(NDArray&& arr, const T& scalar) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(arr / scalar); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator/(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
if (arr.dataType() != DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()))
|
|
throw std::runtime_error("operator/(NDArray&& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Divide, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), 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/(NDArray&& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator/(NDArray&& arr, const long long& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator/(const NDArray& arr, const T& scalar) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator/(const NDArray& arr, const T& scalar): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::Divide, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), 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 NDArray& arr, const double& scalar);
|
|
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const float& scalar);
|
|
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const float16& scalar);
|
|
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const bfloat16& scalar);
|
|
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const int& scalar);
|
|
template ND4J_EXPORT NDArray operator/(const NDArray& arr, const long long& scalar);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator/(const T& scalar, NDArray&& arr) {
|
|
|
|
if(arr.isView()) // do not use resources of arrays which use buffers of other original arrays
|
|
return std::move(scalar / arr); // arr is lvalue inside function body
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator/(const T& scalar, NDArray&& arr): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&arr}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseDivide, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({&arr}, {&arr, &tmp});
|
|
|
|
return std::move(arr);
|
|
|
|
}
|
|
template ND4J_EXPORT NDArray operator/(const double& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator/(const float& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator/(const float16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator/(const bfloat16& scalar, NDArray&& arr);
|
|
template ND4J_EXPORT NDArray operator/(const int& scalar, NDArray&& arr);
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T, typename>
|
|
NDArray operator/(const T& scalar, const NDArray& arr) {
|
|
|
|
if (arr.isS())
|
|
throw std::runtime_error("operator/(const T& scalar, const NDArray& arr): you can't use this method on String array!");
|
|
|
|
auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getContext());
|
|
NDArray result(arr.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<T>()), false, arr.getContext());
|
|
|
|
NDArray::prepareSpecialUse({&result}, {&arr, &tmp});
|
|
NativeOpExecutioner::execScalar(arr.getContext(), sd::scalar::ReverseDivide, arr.buffer(), arr.shapeInfo(), arr.specialBuffer(), arr.specialShapeInfo(), result.buffer(), result.shapeInfo(), result.specialBuffer(), result.specialShapeInfo(), tmp.buffer(), tmp.shapeInfo(), tmp.specialBuffer(), tmp.specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({&result}, {&arr, &tmp});
|
|
|
|
return result;
|
|
}
|
|
template ND4J_EXPORT NDArray operator/(const double& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator/(const float& scalar, const NDArray& arr);
|
|
template ND4J_EXPORT NDArray operator/(const int& scalar, const NDArray& arr);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// addition operator array + array
|
|
template <typename T1, typename T2, typename>
|
|
NDArray operator+(T1&& arr1, T2&& arr2) {
|
|
|
|
if (arr1.isS() || arr2.isS())
|
|
throw std::runtime_error("operator+(T&& arr1, T&& arr2): you can't use this method on String arrays!");
|
|
if (!Environment::getInstance().isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
|
|
throw sd::datatype_exception::build("operator+(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
|
|
|
|
PointersManager pointersManager(arr1.getContext(), "operator+(T&& arr1, T&& arr2)");
|
|
|
|
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
|
|
|
|
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
|
|
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
|
|
|
|
NDArray* result = nullptr;
|
|
if(isArr1Rvalue)
|
|
result = const_cast<NDArray*>(&arr1);
|
|
else if(isArr2Rvalue)
|
|
result = const_cast<NDArray*>(&arr2);
|
|
else
|
|
result = new NDArray(arr1.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.shapeInfo(), arr2.shapeInfo()), false, arr1.getContext());
|
|
|
|
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
|
|
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Add, arr1.buffer(), arr1.shapeInfo(), arr1.specialBuffer(), arr1.specialShapeInfo(), arr2.buffer(), arr2.shapeInfo(), arr2.specialBuffer(), arr2.specialShapeInfo(), result->buffer(), result->shapeInfo(), result->specialBuffer(), result->specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
|
|
|
|
if(!isArr1Rvalue && !isArr2Rvalue) {
|
|
NDArray res = std::move(*result);
|
|
delete result;
|
|
return std::move(res);
|
|
}
|
|
|
|
return std::move(*result);
|
|
}
|
|
|
|
return std::forward<T1>(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Add(), std::forward<T2>(arr2));
|
|
}
|
|
template ND4J_EXPORT NDArray operator+<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
|
|
template ND4J_EXPORT NDArray operator+<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
|
|
template ND4J_EXPORT NDArray operator+<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator+<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// addition operator array - array
|
|
template <typename T1, typename T2, typename>
|
|
NDArray operator-(T1&& arr1, T2&& arr2) {
|
|
|
|
if (arr1.isS() || arr2.isS())
|
|
throw std::runtime_error("operator-(T&& arr1, T&& arr2): you can't use this method on String arrays!");
|
|
if (!Environment::getInstance().isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
|
|
throw sd::datatype_exception::build("operator-(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
|
|
|
|
PointersManager pointersManager(arr1.getContext(), "operator-(T&& arr1, T&& arr2)");
|
|
|
|
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
|
|
|
|
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
|
|
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
|
|
|
|
NDArray* result = nullptr;
|
|
if(isArr1Rvalue)
|
|
result = const_cast<NDArray*>(&arr1);
|
|
else if(isArr2Rvalue)
|
|
result = const_cast<NDArray*>(&arr2);
|
|
else
|
|
result = new NDArray(arr1.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.shapeInfo(), arr2.shapeInfo()), false, arr1.getContext());
|
|
|
|
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
|
|
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Subtract, arr1.buffer(), arr1.shapeInfo(), arr1.specialBuffer(), arr1.specialShapeInfo(), arr2.buffer(), arr2.shapeInfo(), arr2.specialBuffer(), arr2.specialShapeInfo(), result->buffer(), result->shapeInfo(), result->specialBuffer(), result->specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
|
|
|
|
if(!isArr1Rvalue && !isArr2Rvalue) {
|
|
NDArray res = std::move(*result);
|
|
delete result;
|
|
return std::move(res);
|
|
}
|
|
|
|
return std::move(*result);
|
|
}
|
|
|
|
return std::forward<T1>(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), std::forward<T2>(arr2));
|
|
}
|
|
template ND4J_EXPORT NDArray operator-<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
|
|
template ND4J_EXPORT NDArray operator-<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
|
|
template ND4J_EXPORT NDArray operator-<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator-<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// multiplication operator array*array
|
|
template <typename T1, typename T2, typename>
|
|
NDArray operator*(T1&& arr1, T2&& arr2) {
|
|
|
|
if (arr1.isS() || arr2.isS())
|
|
throw std::runtime_error("operator*(T&& arr1, T&& arr2): you can't use this method on String arrays!");
|
|
if (!Environment::getInstance().isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
|
|
throw sd::datatype_exception::build("operator*(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
|
|
|
|
PointersManager pointersManager(arr1.getContext(), "operator*(T&& arr1, T&& arr2)");
|
|
|
|
if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
|
|
|
|
const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
|
|
const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
|
|
|
|
NDArray* result = nullptr;
|
|
if(isArr1Rvalue)
|
|
result = const_cast<NDArray*>(&arr1);
|
|
else if(isArr2Rvalue)
|
|
result = const_cast<NDArray*>(&arr2);
|
|
else
|
|
result = new NDArray(arr1.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.shapeInfo(), arr2.shapeInfo()), false, arr1.getContext());
|
|
|
|
NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
|
|
NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Multiply, arr1.buffer(), arr1.shapeInfo(), arr1.specialBuffer(), arr1.specialShapeInfo(), arr2.buffer(), arr2.shapeInfo(), arr2.specialBuffer(), arr2.specialShapeInfo(), result->buffer(), result->shapeInfo(), result->specialBuffer(), result->specialShapeInfo(), nullptr);
|
|
NDArray::registerSpecialUse({result}, {&arr1, &arr2});
|
|
|
|
if(!isArr1Rvalue && !isArr2Rvalue) {
|
|
NDArray res = std::move(*result);
|
|
delete result;
|
|
return std::move(res);
|
|
}
|
|
|
|
return std::move(*result);
|
|
}
|
|
|
|
return std::forward<T1>(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), std::forward<T2>(arr2));
|
|
}
|
|
template ND4J_EXPORT NDArray operator*<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
|
|
template ND4J_EXPORT NDArray operator*<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
|
|
template ND4J_EXPORT NDArray operator*<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
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template ND4J_EXPORT NDArray operator*<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator*<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
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template ND4J_EXPORT NDArray operator*<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
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template ND4J_EXPORT NDArray operator*<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator*<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator*<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
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////////////////////////////////////////////////////////////////////////
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// multiplication operator array*array
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template <typename T1, typename T2, typename>
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NDArray operator/(T1&& arr1, T2&& arr2) {
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if (arr1.isS() || arr2.isS())
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throw std::runtime_error("operator/(T&& arr1, T&& arr2): you can't use this method on String arrays!");
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if (!Environment::getInstance().isExperimentalBuild() && arr1.dataType() != arr2.dataType() && (arr1.dataType() != DataType::BOOL || arr2.dataType() != BOOL))
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throw sd::datatype_exception::build("operator/(T&& arr1, T&& arr2): Cannot multiply different types", arr1.dataType(), arr2.dataType());
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PointersManager pointersManager(arr1.getContext(), "operator/(T&& arr1, T&& arr2)");
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if (arr1.lengthOf() == arr2.lengthOf() && arr1.rankOf() == arr2.rankOf()) {
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const bool isArr1Rvalue = !std::is_reference<T1>::value && !arr1.isView();
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const bool isArr2Rvalue = !std::is_reference<T2>::value && !arr2.isView();
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NDArray* result = nullptr;
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if(isArr1Rvalue)
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result = const_cast<NDArray*>(&arr1);
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else if(isArr2Rvalue)
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result = const_cast<NDArray*>(&arr2);
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else
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result = new NDArray(arr1.shapeInfo(), DataTypeUtils::pickPairwiseResultType(arr1.shapeInfo(), arr2.shapeInfo()), false, arr1.getContext());
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NDArray::prepareSpecialUse({result}, {&arr1, &arr2});
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NativeOpExecutioner::execPairwiseTransform(arr1.getContext(), sd::pairwise::Divide, arr1.buffer(), arr1.shapeInfo(), arr1.specialBuffer(), arr1.specialShapeInfo(), arr2.buffer(), arr2.shapeInfo(), arr2.specialBuffer(), arr2.specialShapeInfo(), result->buffer(), result->shapeInfo(), result->specialBuffer(), result->specialShapeInfo(), nullptr);
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NDArray::registerSpecialUse({result}, {&arr1, &arr2});
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if(!isArr1Rvalue && !isArr2Rvalue) {
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NDArray res = std::move(*result);
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delete result;
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return std::move(res);
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}
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return std::move(*result);
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}
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return std::forward<T1>(arr1).applyTrueBroadcast(sd::BroadcastOpsTuple::Divide(), std::forward<T2>(arr2));
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}
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template ND4J_EXPORT NDArray operator/<NDArray&, NDArray&, void>(NDArray& arr1, NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<NDArray&, NDArray, void>(NDArray& arr1, NDArray&& arr2);
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template ND4J_EXPORT NDArray operator/<NDArray, NDArray&, void>(NDArray&& arr1, NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<NDArray&, const NDArray&, void>(NDArray& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<const NDArray&, NDArray&, void>(const NDArray& arr1, NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<const NDArray&, NDArray, void>(const NDArray& arr1, NDArray&& arr2);
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template ND4J_EXPORT NDArray operator/<const NDArray&, const NDArray&, void>(const NDArray& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<NDArray, const NDArray&, void>(NDArray&& arr1, const NDArray& arr2);
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template ND4J_EXPORT NDArray operator/<NDArray, NDArray, void>(NDArray&& arr1, NDArray&& arr2);
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/*
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#ifndef __CLION_IDE__
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#include "NDArray.macro"
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#endif
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*/
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}
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#endif
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//////////////////////////////////////////////////////////////////////////
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// check whether array's rows (arg=0) or columns (arg=1) create orthogonal basis
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// bool NDArray::hasOrthonormalBasis(const int arg) {
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// if (isS())
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// throw std::runtime_error("NDArray::hasOrthonormalBasis: you can't use this method on String array!");
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// if(rankOf() !=2 )
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// throw std::runtime_error("NDArray::hasOrthBasis method: rank of ndarray is not equal 2 !");
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// if(arg!=0 && arg!=1)
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// throw std::runtime_error("NDArray::hasOrthBasis method: input argument is not equal to 0 or 1 !");
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// const double eps = 1e-5;
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// double dot = 0.f;
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// if(arg) { // check whether columns create orthogonal basis
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// for(int j=0; j<columns()-1; ++j)
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// for(int k=j+1; k<columns(); ++k) {
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// for(int i=0; i<rows(); ++i)
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// dot += e<double>(i,j)*e<double>(i,k);
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// if(sd::math::nd4j_abs(dot) > eps )
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// return false;
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// dot = 0.f;
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// }
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// for(int j=0; j<columns(); ++j) { // check whether norm of column vector = 1
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// for(int i=0; i<rows(); ++i)
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// dot += e<double>(i,j)*e<double>(i,j);
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// if(dot != 0.f && sd::math::nd4j_abs(sd::math::nd4j_sqrt<double, double>(dot) - 1.f) > eps)
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// return false;
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// dot = 0.f;
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// }
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// }
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// else { // check whether rows create orthogonal basis
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// for(int i=0; i<rows()-1; ++i)
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// for(int k=i+1; k<rows(); ++k) {
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// for(int j=0; j<columns(); ++j)
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// dot += e<double>(i,j)*e<double>(k,j);
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// if(sd::math::nd4j_abs(dot) > eps )
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// return false;
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// dot = 0.;
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// }
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// for(int i=0; i<rows(); ++i) { // check whether norm of row vector = 1
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// for(int j=0; j<columns(); ++j)
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// dot += e<double>(i,j)*e<double>(i,j);
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// if(dot!= 0. && sd::math::nd4j_abs(sd::math::nd4j_sqrt<double, double>(dot) - 1.) > eps)
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// return false;
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// dot = 0.;
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// }
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// }
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// return true;
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// }
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