476 lines
18 KiB
C++
476 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 <array/NDArray.h>
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#include <array/NDArrayFactory.h>
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#include <legacy/NativeOpExecutioner.h>
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#include <loops/BroadcastPairwiseConverter.h>
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#include <memory/Workspace.h>
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#include <memory/MemoryRegistrator.h>
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#include <ops/ops.h>
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#include <ops/gemm.h>
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#include <system/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 <helpers/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 <array/NDArray.hXX>
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namespace sd {
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////////////////////////////////////////////////////////////////////////
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void* NDArray::platformBuffer() { return buffer(); }
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void const* NDArray::platformBuffer() const { return buffer(); }
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Nd4jLong const* NDArray::platformShapeInfo() const { 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, NDArray& target, const char direction) {
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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(!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*>(buffer());
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auto z = reinterpret_cast<T*>(target.buffer());
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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(shapeInfo(), target.shapeInfo());
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auto func = PRAGMA_THREADS_FOR {
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int coords[MAX_RANK], temp;
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, target.shapeInfo(), coords);
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const auto zOffset = shape::getOffset(target.shapeInfo(), coords);
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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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temp = coords[0];
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coords[0] = coords[1];
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}
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const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(shapeInfo(), coords);
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z[zOffset] = x[xOffset];
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if (xRank != zRank) // restore first coordinate
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coords[0] = temp;
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, zLen);
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}
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BUILD_SINGLE_TEMPLATE(template void NDArray::fillAsTriangular, (const float val, int lower, int upper, NDArray& target, const char direction), 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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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(shapeInfo(), indices);
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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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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, const Nd4jLong* xShapeInfo, const Nd4jLong* yShapeInfo, 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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const bool isSameOrders = shape::order(xShapeInfo) == shape::order(xShapeInfo);
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const auto xEws = shape::elementWiseStride(xShapeInfo);
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const auto yEws = shape::elementWiseStride(yShapeInfo);
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auto func = PRAGMA_THREADS_FOR {
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if(isSameOrders && xEws > 0 && yEws > 0) {
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for(auto i = start; i < stop; i++)
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sd::math::nd4j_swap(x[i*xEws], y[i*yEws]);
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}
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else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
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for(auto i = start; i < stop; i++) {
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const auto ind = shape::getIndexOffset(i, xShapeInfo);
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sd::math::nd4j_swap(x[ind], y[ind]);
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}
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}
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else {
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for(auto i = start; i < stop; i++) {
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const auto xInd = shape::getIndexOffset(i, xShapeInfo);
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const auto yInd = shape::getIndexOffset(i, yShapeInfo);
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sd::math::nd4j_swap(x[xInd], y[yInd]);
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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}
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BUILD_SINGLE_TEMPLATE(template void templatedSwap, (void *xBuffer, void *yBuffer, const Nd4jLong* xShapeInfo, const Nd4jLong* yShapeInfo, 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(), shapeInfo(), other.shapeInfo(), 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::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {
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// no-op
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}
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void NDArray::registerSpecialUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList) {
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// no-op
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}
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void NDArray::preparePrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {
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// no-op
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}
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void NDArray::registerPrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<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) {
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return nullptr;
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}
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////////////////////////////////////////////////////////////////////////
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const 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 buffer();
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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 const* NDArray::specialBuffer() const {
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if (_buffer->special() == nullptr)
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return buffer();
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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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Nd4jLong 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.shapeInfo()+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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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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auto yOffset = shape::subArrayOffset(i, newShapeInfo, shapeInfo());
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BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign,(result.buffer(), i, this->buffer(), yOffset), LIBND4J_TYPES);
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}
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};
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samediff::Threads::parallel_for(func, 0, resultLen);
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}
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else {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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auto xOffset = result.getOffset(i);
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auto yOffset = shape::subArrayOffset(i, newShapeInfo, shapeInfo());
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BUILD_SINGLE_SELECTOR(xType, this->template templatedAssign,(result.buffer(), xOffset, this->buffer(), yOffset), LIBND4J_TYPES);
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}
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};
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samediff::Threads::parallel_for(func, 0, resultLen);
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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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auto repProd = shape::prodLong(reps.data(), reps.size());
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if (repProd < 1)
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throw std::runtime_error("NDArray::tile: reps can't contain 0s");
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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.shapeInfo())) {
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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 auto 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.shapeInfo(), shapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.buffer(), i, buffer(), 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.shapeInfo(), shapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.buffer(), i*ews, buffer(), 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.shapeInfo(), shapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.buffer(), xOffset, buffer(), 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) {
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for(Nd4jLong i=0; i<targetLen; ++i) {
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auto yOffset = shape::subArrayOffset(i, target.shapeInfo(), shapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.buffer(), i*ews, buffer(), 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.shapeInfo(), shapeInfo());
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BUILD_DOUBLE_SELECTOR(target.dataType(), dataType(), templatedDoubleAssign, (target.buffer(), xOffset, buffer(), 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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template<typename X, typename Z>
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static void repeat_(const NDArray& input, NDArray& output, const std::vector<int>& repeats, const int axis) {
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const X* x = input.bufferAsT<X>();
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Z* z = output.bufferAsT<Z>();
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const int rank = input.rankOf(); // xRank = zRank
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const int zLen = output.lengthOf(); // xLen <= zLen
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const uint repSize = repeats.size();
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// loop through input array
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auto func = PRAGMA_THREADS_FOR {
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int coords[MAX_RANK], temp;
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, output.shapeInfo(), coords);
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const auto zOffset = shape::getOffset(output.shapeInfo(), coords);
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temp = coords[axis];
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if (repSize > 1) {
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for (uint j = 0; j < repSize; ++j) {
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coords[axis] -= repeats[j];
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if (coords[axis] < 0) {
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coords[axis] = j;
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break;
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}
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}
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} else
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coords[axis] /= repeats[0];
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z[zOffset] = x[shape::getOffset(input.shapeInfo(), coords)];
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coords[axis] = temp;
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}
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};
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samediff::Threads::parallel_for(func, 0, zLen);
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}
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//////////////////////////////////////////////////////////////////////////
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// create new array by repeating it the number of times given by repeats
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NDArray NDArray::repeat(const int axis, const std::vector<int>& repeats) const {
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NDArray output('c', ShapeUtils::evalRepeatShape(axis, repeats, *this), dataType(), getContext());
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BUILD_SINGLE_SELECTOR_TWICE(dataType(), repeat_, (*this, output, repeats, axis), LIBND4J_TYPES);
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return output;
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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(const int axis, const std::vector<int>& repeats, NDArray& target) const {
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if(!target.isSameShape(ShapeUtils::evalRepeatShape(axis, repeats, *this)))
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throw std::invalid_argument("NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) method: wrong shape of target array!");
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BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), repeat_, (*this, target, repeats, axis), LIBND4J_TYPES, LIBND4J_TYPES);
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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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