178 lines
7.5 KiB
C++
178 lines
7.5 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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//
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// @author Yurii Shyrma (iuriish@yahoo.com), created on 07.03.2019
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//
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#include <ops/declarable/helpers/gather.h>
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#include <numeric>
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#include <execution/Threads.h>
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#include <helpers/ShapeUtils.h>
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#include <helpers/ConstantTadHelper.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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void gather(sd::LaunchContext * context, const NDArray* input, const 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)
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axis += inputRank;
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const int numOfIntArgs = intArgs.size();
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if (indices != nullptr) {
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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, we want to get a scalar
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auto idx = indices->e<Nd4jLong>(0);
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auto scalarNDArray = input->e(idx);
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output->assign(scalarNDArray);
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}
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else {
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NDArray inSubArr = (*input)(indices->e<Nd4jLong>(0), {axis});
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output->assign(inSubArr);
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}
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}
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else {
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if(input->rankOf() == 1 && output->rankOf() == 1) {
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++)
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output->p(i, input->e(indices->e<Nd4jLong>(i)));
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};
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samediff::Threads::parallel_for(func, 0, output->lengthOf());
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}
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else {
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std::vector<int> dimsOut;
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for (int i = 0; i < axis; ++i)
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dimsOut.push_back(i);
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for (int i = axis+indices->rankOf(); i < output->rankOf(); ++i)
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dimsOut.push_back(i);
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std::vector<int> dimsIn = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
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const Nd4jLong numOfSubArrs = indices->lengthOf();
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auto inTadPack = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimsIn);
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auto outTadPack = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimsOut);
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auto inTadShapeInfo = inTadPack.primaryShapeInfo();
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auto outTadShapeInfo = outTadPack.primaryShapeInfo();
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if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
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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 inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
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auto outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
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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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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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auto inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[indices->e<Nd4jLong>(i)]);
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auto outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
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inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input special*/,
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outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output special*/,
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nullptr, nullptr, nullptr, false/*allowParallelism*/);
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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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}
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else {
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// we only allow scalar/vector case here
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if (numOfIntArgs == 2) { // scalar case
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output->assign((*input)(intArgs[1], {axis}));
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}
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else { // vector case
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const Nd4jLong numOfSubArrs = intArgs.size() - 1;
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std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
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auto inTadPack = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dims);
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auto outTadPack = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dims);
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auto inTadShapeInfo = inTadPack.primaryShapeInfo();
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auto outTadShapeInfo = outTadPack.primaryShapeInfo();
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if (shape::order(inTadShapeInfo) == shape::order(outTadShapeInfo) && shape::order(inTadShapeInfo) == 'c' && input->dataType() == output->dataType() && shape::elementWiseStride(inTadShapeInfo) == 1 && shape::elementWiseStride(outTadShapeInfo) == 1) {
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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 inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
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void* outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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std::memcpy(outBuff, inBuff, shape::length(inTadShapeInfo) * input->sizeOfT());
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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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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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auto inBuff = input->bufferWithOffset(inTadPack.primaryOffsets()[intArgs[i + 1]]);
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auto outBuff = output->bufferWithOffset(outTadPack.primaryOffsets()[i]);
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NativeOpExecutioner::execTransformAny(input->getContext(), transform::Assign,
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inBuff, inTadShapeInfo, nullptr/*input specialBuffer*/, nullptr/*input special*/,
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outBuff, outTadShapeInfo, nullptr/*output specialBuffer*/, nullptr/*output special*/,
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nullptr, nullptr, nullptr, false/*allowParallelism*/);
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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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}
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}
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}
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}
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