chore: import upstream snapshot with attribution
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wehub-resource-sync
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/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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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//
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// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
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//
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#include <helpers/Loops.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/helpers/transforms.h>
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#include <numeric>
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#include <system/selective_rendering.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
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const X* x = reinterpret_cast<X*>(input.buffer());
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const Y* y = reinterpret_cast<Y*>(indices.buffer());
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X* z = reinterpret_cast<X*>(output.buffer());
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const sd::LongType xRank = input.rankOf();
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const sd::LongType yRank = indices.rankOf();
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const sd::LongType zRank = output.rankOf();
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const sd::LongType maxRank = sd::math::sd_max<sd::LongType>(yRank, sd::math::sd_max<sd::LongType>(xRank, zRank));
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const sd::LongType zLen = output.lengthOf();
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const sd::LongType yLastDim = indices.sizeAt(-1);
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const int diff = zRank - xRank;
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const bool bEqual = yLastDim == xRank;
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sd::LongType outputRank = output.rankOf();
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sd::LongType* outputShape = shape::shapeOf(output.shapeInfo());
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sd::LongType* outputStride = shape::stride(output.shapeInfo());
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sd::LongType indicesRank = indices.rankOf();
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sd::LongType* indicesShape = shape::shapeOf(indices.shapeInfo());
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sd::LongType* indicesStride = shape::stride(indices.shapeInfo());
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sd::LongType inputRank = input.rankOf();
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sd::LongType* inputShape = shape::shapeOf(input.shapeInfo());
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sd::LongType* inputStride = shape::stride(input.shapeInfo());
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auto func = PRAGMA_THREADS_FOR {
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sd::LongType xCoords[SD_MAX_RANK], zCoords[SD_MAX_RANK], temp;
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for (sd::LongType i = start; i < stop; i++) {
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INDEX2COORDS(i, outputRank, outputShape, zCoords);
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sd::LongType zOffset;
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COORDS2INDEX(outputRank, outputStride, zCoords, zOffset);
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temp = zCoords[yRank - 1];
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zCoords[yRank - 1] = 0;
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sd::LongType yOffset;
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COORDS2INDEX(indicesRank, indicesStride, zCoords, yOffset);
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zCoords[yRank - 1] = temp;
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if (bEqual)
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memcpy(xCoords, zCoords, zRank * sizeof(sd::LongType));
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else if (diff >= 0)
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memcpy(xCoords, zCoords + diff, xRank * sizeof(sd::LongType));
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else
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memcpy(xCoords - diff, zCoords, zRank * sizeof(sd::LongType));
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for (sd::LongType j = 0; j < yLastDim; ++j)
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xCoords[j] = y[yOffset + j * indicesStride[yRank - 1]]; // last stride
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sd::LongType xOffset;
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COORDS2INDEX(inputRank, inputStride, xCoords, xOffset);
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z[zOffset] = x[xOffset];
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}
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};
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samediff::Threads::parallel_tad(func, 0, zLen);
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}
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////////////////////////////////////////////////////////////////////////
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void gatherND(sd::LaunchContext* context, NDArray& input, NDArray& indices, NDArray& output) {
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auto inputDType = input.dataType();
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auto indicesDType = indices.dataType();
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BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), SD_COMMON_TYPES,
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SD_INDEXING_TYPES);
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void gather_(NDArray* input, NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
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int axis = intArgs.size() > 0 ? intArgs[0] : 0;
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const int inputRank = input->rankOf();
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if (axis < 0) axis += inputRank;
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const int numOfIntArgs = intArgs.size();
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if (indices != nullptr) {
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for (sd::LongType i = 0; i < indices->lengthOf(); ++i)
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if (indices->e<sd::LongType>(i) >= input->sizeAt(axis))
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THROW_EXCEPTION(
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"helpers::gather function: indices array contains wrong elements, each element must be smaller than "
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"corresponding dimension of input array !");
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// first case: indices consist of only one scalar
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if (indices->isScalar()) {
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if (input->rankOf() <= 1) {
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// For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead,
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// we want to get a scalar
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auto idx = indices->e<sd::LongType>(0);
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auto scalarNDArray = input->e(idx);
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output->assign(&scalarNDArray);
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} else {
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// tadForDimensions expects the dimensions to create TADs along,
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// NOT the dimensions to exclude
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std::vector<sd::LongType> axesVec = {axis};
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// Pass the axis directly - TadCalculator will handle the exclusion internally
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auto tadPack = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), &axesVec);
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auto tadArr = NDArray(reinterpret_cast<void*>(reinterpret_cast<T*>(input->buffer()) +
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tadPack->primaryOffsets()[indices->e<sd::LongType>(0)]),
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tadPack->primaryShapeInfo(), output->getContext(), 0, 0);
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output->assign(&tadArr);
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}
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} else if (input->rankOf() == 1 && indices->isVector()) {
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// special case
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) output->p(e, input->e<T>(indices->e<sd::LongType>(e)));
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};
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samediff::Threads::parallel_for(func, 0, indices->lengthOf());
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} else {
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std::vector<sd::LongType> dimsOut(indices->rankOf());
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std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... indices->rankOf()-1
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const sd::LongType numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->shapeInfo(), dimsOut);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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NDArray *subArrOut = (*output)(i, dimsOut);
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NDArray *subArrIn = (*input)(indices->e<sd::LongType>(i), {axis});
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subArrOut->assign(subArrIn);
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delete subArrOut;
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delete subArrIn;
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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} else {
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for (int i = 1; i < numOfIntArgs; ++i)
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if (intArgs[i] >= input->sizeAt(axis))
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THROW_EXCEPTION(
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"helpers::gather function: some of input indexes is larger than corresponding shape of input array !");
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// we only allow scalar/vector case here
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if (numOfIntArgs == 2) { // scalar case
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NDArray *view = (*input)(intArgs[1], {axis});
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output->assign(view);
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delete view;
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} else { // vector case
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const sd::LongType numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->shapeInfo(), {axis});
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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NDArray *subArrOut = (*output)(i, {axis});
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NDArray *subArrIn = (*input)(intArgs[i + 1], {axis});
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subArrOut->assign(subArrIn);
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delete subArrIn;
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delete subArrOut;
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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}
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}
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}
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void gather(NDArray* input, NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
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BUILD_SINGLE_SELECTOR(input->dataType(), gather_, (input, indices, output, intArgs), SD_COMMON_TYPES);
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}
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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