454 lines
18 KiB
C++
454 lines
18 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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#ifndef NDARRAY_CPP
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#define NDARRAY_CPP
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#include "../NDArray.h"
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#include "../NDArrayFactory.h"
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#include "NativeOpExecutioner.h"
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#include <BroadcastPairwiseConverter.h>
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#include <memory/Workspace.h>
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#include <memory/MemoryRegistrator.h>
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#include <ops.h>
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#include <ops/gemm.h>
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#include <pointercast.h>
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#include <stdexcept>
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#include <memory>
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#include <helpers/logger.h>
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#include <loops/pairwise_transform.h>
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#include <loops/transform_same.h>
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#include <loops/random.h>
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#include <loops/broadcasting.h>
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#include <indexing/NDIndex.h>
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#include <indexing/IndicesList.h>
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#include <helpers/ShapeUtils.h>
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#include <sstream>
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#include <helpers/ArrayUtils.h>
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#include <MmulHelper.h>
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#include <helpers/threshold.h>
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#include <exceptions/datatype_exception.h>
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#include <exceptions/allocation_exception.h>
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#include <helpers/ConstantTadHelper.h>
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#include <NDArray.hpp>
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namespace nd4j {
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////////////////////////////////////////////////////////////////////////
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void* NDArray::platformBuffer() { return buffer(); }
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void* NDArray::getPlatformBuffer() const { return getBuffer(); }
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Nd4jLong* NDArray::getPlatformShapeInfo() const { return getShapeInfo(); }
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Nd4jLong* NDArray::platformShapeInfo() { return shapeInfo(); }
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void NDArray::syncToDevice() const { }
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void NDArray::syncToHost() const { }
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void NDArray::tickWriteHost() const { }
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void NDArray::tickWriteDevice() const { }
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void NDArray::tickReadHost() const { }
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void NDArray::tickReadDevice() const { }
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void NDArray::tickBothActual() const { }
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bool NDArray::isActualOnHostSide() const { return true; }
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bool NDArray::isActualOnDeviceSide() const { return true; }
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void NDArray::makeBothBuffersActual() const { }
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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void NDArray::fillAsTriangular(const float val, int lower, int upper, const char direction, NDArray* target) {
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if (isS())
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throw std::runtime_error("NDArray::fillArrayAsTriangular: you can't use this method on String array!");
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if(target == nullptr)
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target = this;
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if(!isSameShape(target) && !(rankOf() == 1 && target->rankOf() == 2 && sizeAt(0) == target->sizeAt(0) && sizeAt(0) == target->sizeAt(1)))
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throw std::string("NDArray::fillArrayAsTriangular method: wrong shape of target array !");
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if (direction == 'u')
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lower = -target->sizeAt(-2);
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else if (direction == 'l')
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upper = target->sizeAt(-1);
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const T value = static_cast<T>(val);
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const auto x = reinterpret_cast<const T*>(getBuffer());
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auto z = reinterpret_cast<T*>(target->getBuffer());
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const int xRank = rankOf();
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const int zRank = target->rankOf();
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const auto zLen = target->lengthOf();
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const bool areSameOffsets = shape::haveSameShapeAndStrides(getShapeInfo(), target->getShapeInfo());
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std::vector<Nd4jLong> coords(zRank);
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PRAGMA_OMP_PARALLEL_FOR_ARGS(if(zLen > Environment::getInstance()->elementwiseThreshold()) firstprivate(coords))
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for (Nd4jLong i = 0; i < zLen; ++i) {
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shape::index2coords(zRank, target->shapeOf(), i, zLen, coords.data());
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const auto zOffset = shape::getOffset(0, target->shapeOf(), target->stridesOf(), coords.data(), zRank);
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// if( (row + upper < col) || (row + lower > col) )
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if((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1]))
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z[zOffset] = value;
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else if(this != target) { // when this and target are different arrays
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if(xRank != zRank)
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coords[0] = coords[1];
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const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(0, shapeOf(), stridesOf(), coords.data(), xRank);
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z[zOffset] = x[xOffset];
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}
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}
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}
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BUILD_SINGLE_TEMPLATE(template void NDArray::fillAsTriangular, (const float val, int lower, int upper, const char direction, NDArray* target), LIBND4J_TYPES);
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////////////////////////////////////////////////////////////////////////
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void NDArray::setIdentity() {
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if (isS())
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throw std::runtime_error("NDArray::setIdentity: you can't use this method on String array!");
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this->nullify();
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int rank = rankOf();
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auto shape = shapeOf();
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auto strides = stridesOf();
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int minDim = MAX_INT;
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Nd4jLong indices[MAX_RANK];
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for(int j = 0; j < rank; ++j)
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indices[j] = 1;
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Nd4jLong offset = shape::getOffset(0, shape, strides, indices, rank);
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for(int i = 0; i < rank; ++i)
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if(minDim > shape[i])
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minDim = shape[i];
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float v = 1.0f;
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PRAGMA_OMP_PARALLEL_FOR_ARGS(if(minDim > Environment::getInstance()->elementwiseThreshold()) schedule(guided))
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for(int i = 0; i < minDim; ++i)
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templatedSet<float>(buffer(), i*offset, this->dataType(), &v);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void templatedSwap(void *xBuffer, void *yBuffer, Nd4jLong length) {
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auto x = reinterpret_cast<T *>(xBuffer);
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auto y = reinterpret_cast<T *>(yBuffer);
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PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(schedule(static))
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for (Nd4jLong i = 0; i < length; ++i) {
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auto temp = x[i];
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x[i] = y[i];
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y[i] = temp;
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}
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}
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BUILD_SINGLE_TEMPLATE(template void templatedSwap, (void *xBuffer, void *yBuffer, Nd4jLong length), LIBND4J_TYPES);
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////////////////////////////////////////////////////////////////////////
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void NDArray::swapUnsafe(NDArray& other) {
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auto xType = this->dataType();
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if (xType != other.dataType())
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throw std::runtime_error("NDArray::swapUnsage method: both arrays must have the same data type");
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if(buffer() == nullptr || other.buffer() == nullptr)
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throw std::runtime_error("NDArray::swapUnsafe method: input array should not be empty!");
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if(lengthOf() != other.lengthOf())
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throw std::runtime_error("NDArray::swapUnsafe method: input arrays should have the same length!");
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BUILD_SINGLE_SELECTOR(xType, templatedSwap, (buffer(), other.buffer(), this->lengthOf()), LIBND4J_TYPES);
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}
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////////////////////////////////////////////////////////////////////////
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void NDArray::synchronize(const char* msg) const {
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// no-op
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}
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void NDArray::prepareSpecialUse(const std::initializer_list<const NDArray*>& writeList, const std::initializer_list<const NDArray*>& readList, bool synchronizeWritables) {
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// no-op
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}
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void NDArray::registerSpecialUse(const std::initializer_list<const NDArray*>& writeList, const std::initializer_list<const NDArray*>& readList) {
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// no-op
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}
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void NDArray::preparePrimaryUse(const std::initializer_list<const NDArray*>& writeList, const std::initializer_list<const NDArray*>& readList, bool synchronizeWritables) {
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// no-op
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}
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void NDArray::registerPrimaryUse(const std::initializer_list<const NDArray*>& writeList, const std::initializer_list<const NDArray*>& readList) {
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// no-op
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}
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void NDArray::syncShape() const {
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// no-op
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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void NDArray::printCurrentBuffer(const bool host, const char* msg, const int precision) const {
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}
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////////////////////////////////////////////////////////////////////////
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void* NDArray::specialBufferWithOffset(Nd4jLong offset) const {
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return nullptr;
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}
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////////////////////////////////////////////////////////////////////////
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void* NDArray::specialBuffer() {
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if (_buffer->special() == nullptr)
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return getBuffer();
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// FIXME: this should be fixed once CUDA backend added
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return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
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}
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////////////////////////////////////////////////////////////////////////
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void* NDArray::getSpecialBuffer() const {
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if (_buffer->special() == nullptr)
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return getBuffer();
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// FIXME: this should be fixed once CUDA backend added
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return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
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}
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//////////////////////////////////////////////////////////////////////////
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// change an array by repeating it the number of times given by reps.
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NDArray NDArray::tile(const std::vector<Nd4jLong>& reps) const {
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const int repsSize = reps.size();
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int product = 1;
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for(const auto& item : reps)
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product *= item;
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if(product == 0)
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throw std::runtime_error("NDArray::tile method: one of the elements in reps array is zero !");
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int rankOld = rankOf();
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int diff = rankOld - repsSize;
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if(product==1) { // in this case 2 possibilities are present: just reshape or nothing to do
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NDArray result(*this);
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if(diff < 0) { // reshape to higher dimension
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std::vector<Nd4jLong> shapeNew = reps; // there is requirement to have unities at first "diff" positions of new shape
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memcpy(&shapeNew[-diff], result.getShapeInfo()+1, rankOld * sizeof(Nd4jLong)); // put old shape numbers at rest of positions
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result.reshapei(ordering(), shapeNew);
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}
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return result; // nothing to do, if diff >= 0 -> identity tile
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}
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// evaluate shapeInfo for resulting array
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auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
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// create new buffer, in any case the memory amount new buffer points to is bigger then those for old _buffer
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std::shared_ptr<DataBuffer> newBuff = std::make_shared<DataBuffer>(shape::length(newShapeInfo) * sizeOfT(), dataType(), getContext()->getWorkspace());
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// assign new shape and new buffer to resulting array
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NDArray result(newBuff, ShapeDescriptor(newShapeInfo), getContext());
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// fill newBuff, loop through all elements of newBuff
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// looping through _buffer goes automatically by means of getSubArrayIndex applying
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const auto resultLen = result.lengthOf();
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auto xType = this->dataType();
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if(result.ordering() == 'c') { // ews == 1 always here
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for(Nd4jLong i = 0; i < resultLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, newShapeInfo, getShapeInfo());
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BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign, (result.getBuffer(), i, this->getBuffer(), yOffset), LIBND4J_TYPES);
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}
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}
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else {
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PRAGMA_OMP_PARALLEL_FOR_SIMD
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for(Nd4jLong i=0; i<resultLen; ++i) {
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auto xOffset = result.getOffset(i);
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auto yOffset = shape::subArrayOffset(i, newShapeInfo, getShapeInfo());
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BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign, (result.getBuffer(), xOffset, this->getBuffer(), yOffset), LIBND4J_TYPES);
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}
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}
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result.tickWriteHost();
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return result;
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}
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//////////////////////////////////////////////////////////////////////////
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// change an array by repeating it the number of times given by reps.
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void NDArray::tile(const std::vector<Nd4jLong>& reps, NDArray& target) const {
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// evaluate true tile shapeInfo for comparison with target shapeInfo
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auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
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if(!shape::equalsSoft(newShapeInfo, target.getShapeInfo())) {
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delete []newShapeInfo;
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throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");
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}
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// fill newBuff, loop through all elements of newBuff
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// looping through _buffer goes automatically by means of getSubArrayIndex applying
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const int ews = target.ews();
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const int targetLen = target.lengthOf();
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if(target.ordering() == 'c' && ews == 1) { // ews == 1 always here
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//#pragma omp parallel for simd if(targetLen > Environment::getInstance()->elementwiseThreshold()) schedule(guided)
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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else if(target.ordering() == 'c' && ews > 1) {
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i*ews, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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else {
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto xOffset = target.getOffset(i);
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), xOffset, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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void NDArray::tile(NDArray& target) const {
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if(rankOf() > target.rankOf())
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throw std::runtime_error("NDArray::tile method - rank of target array must be bigger or equal to the rank of this array !");
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if(!ShapeUtils::areShapesBroadcastable(*this, target))
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throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");
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// fill newBuff, loop through all elements of newBuff
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// looping through _buffer goes automatically by means of getSubArrayIndex applying
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const auto ews = target.ews();
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const auto targetLen = target.lengthOf();
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if(target.ordering() == 'c' && ews == 1) { // ews == 1 always here
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for (Nd4jLong i = 0; i < targetLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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else if(target.ordering() == 'c' && ews > 1) {
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), i*ews, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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else {
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto xOffset = target.getOffset(i);
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auto yOffset = shape::subArrayOffset(i, target.getShapeInfo(), getShapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.getBuffer(), xOffset, getBuffer(), yOffset), LIBND4J_TYPES, LIBND4J_TYPES);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// create new array by repeating it the number of times given by reps
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NDArray* NDArray::repeat(int dimension, const std::vector<Nd4jLong>& repeats) const {
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auto outShape = ShapeUtils::evalRepeatShape(dimension, repeats, *this);
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// the size of outShape == rank
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int rank = rankOf(); // = outShape.size()
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auto ret = new NDArray('c', outShape, dataType(), getContext());
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auto retArrs = ret->allTensorsAlongDimension({dimension});
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auto thisArrs = this->allTensorsAlongDimension({dimension});
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auto repeatDelta = shape::prodLong(outShape.data(), rank) / this->lengthOf();
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auto numTads = retArrs->size();
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for (int i = 0; i < numTads; i++) {
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auto thisTensor = thisArrs->at(i);
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auto retTensor = retArrs->at(i);
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Nd4jLong retIdx = 0;
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for (Nd4jLong k = 0; k < thisTensor->lengthOf(); k++) {
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auto s = thisTensor->e(k);
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for (Nd4jLong j = 0; j < repeatDelta; j++)
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retTensor->p(retIdx++, s);
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}
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}
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delete retArrs;
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delete thisArrs;
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return ret;
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}
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//////////////////////////////////////////////////////////////////////////
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// fill array by repeating it the number of times given by reps
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void NDArray::repeat(int dimension, NDArray& target) const {
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if(dimension < 0)
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dimension += rankOf();
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if(rankOf() != target.rankOf())
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throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong rank of target array it must be equal to this array rank!");
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Nd4jLong repeatDelta = target.sizeAt(dimension) / sizeAt(dimension);
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if(repeatDelta == 0)
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throw std::invalid_argument("NDArray::repeat(int dimension, NDArray& target) method: wrong shape of target array!");
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std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rankOf(), {dimension});
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const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(getShapeInfo(), dimsToExclude);
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for (int i = 0; i < numTads; i++) {
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auto thisTensor = (*this)(i, dimsToExclude);
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auto retTensor = target(i, dimsToExclude);
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int tensorLength = thisTensor.lengthOf();
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int retIdx = 0;
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if (isR()) {
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for (int k = 0; k < tensorLength; k++) {
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auto s = thisTensor.e<double>(k);
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for (int j = 0; j < repeatDelta; j++) {
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retTensor.p<double>(retIdx++, s);
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}
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}
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} else {
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for (int k = 0; k < tensorLength; k++) {
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auto s = thisTensor.e<Nd4jLong>(k);
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for (int j = 0; j < repeatDelta; j++) {
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retTensor.p<Nd4jLong>(retIdx++, s);
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}
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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#ifndef __JAVACPP_HACK__
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#include "NDArrayLambda.hpp"
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#endif
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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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