1318 lines
54 KiB
C++
1318 lines
54 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 20.04.2018
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//
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#include <ops/declarable/helpers/transforms.h>
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#include <array/ResultSet.h>
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#include <helpers/ShapeUtils.h>
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#include <numeric>
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#include <NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <helpers/ConstantTadHelper.h>
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#include <Loops.h>
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#include <graph/RandomGenerator.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void triuBP_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
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auto dOdI = NDArray(&gradO); // dO/dI
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const_cast<NDArray&>(input).fillAsTriangular<T>(0, diagonal, dOdI.sizeAt(-1), 'b', &dOdI);
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int dLen = dOdI.lengthOf();
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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if (dOdI.t<T>(i) != static_cast<T>(0.f))
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dOdI.t<T>(i) = static_cast<T>(1.f);
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}
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};
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samediff::Threads::parallel_for(func, 0, dLen);
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// FIXME: !!!
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gradI.assign(dOdI * gradO); // chain rule: dLoss/dI = dO/dI * dLoss/dO
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}
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void triuBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
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BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void trace_(const NDArray& input, NDArray& output) {
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const int inRank = input.rankOf();
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auto setOfSubArrs = input.allTensorsAlongDimension({inRank-2, inRank-1});
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment)
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output.p(i, setOfSubArrs->at(i)->getTrace());
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};
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samediff::Threads::parallel_for(func, 0, setOfSubArrs->size());
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delete setOfSubArrs;
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}
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void trace(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), trace_, (input, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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void randomShuffle_(NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
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// check edge cases first
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int temp;
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const int firstDim = input.sizeAt(0);
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if(input.lengthOf() == 1 || firstDim == 1) {
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if(!isInplace)
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output.assign(input);
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}
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else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) {
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// apply Fisher-Yates shuffle
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if(isInplace) {
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//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
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for(int i = firstDim-1; i > 0; --i) {
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int r = rng.relativeInt(i) % i;
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if(i == r)
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continue;
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T t0 = input.t<T>(i);
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T t1 = input.t<T>(r);
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//math::nd4j_swap<T>(input(i), input(r));
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input.t<T>(i) = t1;
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input.t<T>(r) = t0;
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}
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}
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else {
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std::vector<int> indices(firstDim);
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std::iota(indices.begin(), indices.end(), 0);
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output.p<T>(Nd4jLong(0), input.e<T>(0));
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// FIXME: parallelism!!
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for(int i = firstDim-1; i > 0; --i) {
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int r = rng.relativeInt(i) % i;
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output.t<T>(i) = input.t<T>(indices[r]);
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if(i == r)
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continue;
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output.t<T>(r) = input.t<T>(indices[i]);
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math::nd4j_swap<int>(indices[i], indices[r]);
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}
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rng.rewindH(firstDim-1);
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}
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}
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else {
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// evaluate sub-arrays list of input array through all dimensions excluding first one
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std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
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auto subArrsListIn = input.allTensorsAlongDimension(dimensions);
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// apply Fisher-Yates shuffle
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if(isInplace) {
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//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->elementwiseThreshold())
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for(int i = firstDim - 1; i > 0; --i) {
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int r = rng.relativeInt(i) % i;
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if(i == r)
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continue;
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subArrsListIn->at(i)->swapUnsafe(*subArrsListIn->at(r));
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}
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}
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else {
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// evaluate sub-arrays list of output array through all dimensions excluding first one
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auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
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std::vector<int> indices(firstDim);
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std::iota(indices.begin(), indices.end(), 0);
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bool isZeroShuffled = false;
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//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
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for(int i = firstDim - 1; i > 0; --i) {
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int r = rng.relativeInt(i) % i;
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subArrsListOut->at(i)->assign(subArrsListIn->at(indices[r]));
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if(r == 0)
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isZeroShuffled = true;
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if(i == r)
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continue;
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subArrsListOut->at(r)->assign(subArrsListIn->at(indices[i]));
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math::nd4j_swap<int>(indices[i], indices[r]);
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}
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if(!isZeroShuffled)
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subArrsListOut->at(0)->assign(subArrsListIn->at(0));
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delete subArrsListOut;
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}
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rng.rewindH(firstDim-1);
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delete subArrsListIn;
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}
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}
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void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
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BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (input, output, rng, isInplace), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
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const T* x = input.bufferAsT<T>();
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T* z = output.bufferAsT<T>();
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const Nd4jLong* xShape = input.shapeOf();
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const Nd4jLong* zShape = output.shapeOf();
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const int rank = input.rankOf(); // both input and output have the same rank
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const int rankMinusOne = rank - 1;
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const auto zLen = output.lengthOf();
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if(mode == 0) { // CONSTANT case
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const T padVal = padValue.e<T>(0);
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auto func = PRAGMA_THREADS_FOR {
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Nd4jLong coords[MAX_RANK];
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for (auto i = start; i < stop; i += increment) {
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shape::index2coords(i, output.getShapeInfo(), coords);
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const auto zOffset = shape::getOffset(output.getShapeInfo(), coords);
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bool within = true;
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for (int j = rankMinusOne; j >= 0; --j) {
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if (xShape[j] == zShape[j]) continue;
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const auto left = paddings.e<Nd4jLong>(j, 0);
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if (coords[j] < left || coords[j] >= left + xShape[j]) {
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within = false;
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break;
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}
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else { coords[j] = coords[j] - left; }
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}
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if (within)
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z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords)];
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else
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z[zOffset] = padVal;
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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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else { // REFLECT and SYMMETRIC cases
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const Nd4jLong shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
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const Nd4jLong shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
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auto func = PRAGMA_THREADS_FOR {
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Nd4jLong coords[MAX_RANK];
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for (auto i = start; i < stop; i += increment) {
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shape::index2coords(i, output.getShapeInfo(), coords);
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const auto zOffset = shape::getOffset(output.getShapeInfo(), coords);
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for (int j = rankMinusOne; j >= 0; --j) {
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if (xShape[j] == zShape[j]) continue;
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coords[j] = coords[j] - paddings.e<Nd4jLong>(j, 0); // are ready to fill middle (within input dimension range)
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if (coords[j] < 0) coords[j] = -coords[j] - shift1; // means fill from left
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else if (coords[j] >= xShape[j]) coords[j] = 2 * xShape[j] - coords[j] - shift2; // means fill from right
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}
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const auto xOffset = shape::getOffset(input.getShapeInfo(), coords);
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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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// //////////////////////////////////////////////////////////////////////////
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// template<typename T>
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// void pad2_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
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// const int rank = output.rankOf();
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// std::vector<int> dimsToExclude(rank);
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// std::iota(dimsToExclude.begin(), dimsToExclude.end(), 0); // fill with 0, 1, ... rank-1
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// Nd4jLong numLeft = paddings.e<Nd4jLong>(rank-1,0);
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// Nd4jLong numRight = paddings.e<Nd4jLong>(rank-1,1);
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// Nd4jLong inDimSize = input.sizeAt(rank-1);
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// Nd4jLong outDimSize = output.sizeAt(rank-1);
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// std::vector<std::vector<Nd4jLong>> outIdx = { std::vector<Nd4jLong>(2*rank), {numLeft, numLeft + inDimSize}, {0, numLeft}, {numLeft + inDimSize, outDimSize} };
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// for(int i = 0; i < rank-1; ++i) {
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// outIdx[0][2*i] = paddings.e<Nd4jLong>(i, 0);
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// outIdx[0][2*i + 1] = outIdx[0][2*i] + input.sizeAt(i);
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// }
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// outIdx[0][2*rank-1] = outIdx[0][2*rank-2] = 0;
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// // ***** populate innermost sub-arrays firstly ***** //
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// dimsToExclude.pop_back();
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// Nd4jLong startL = mode == 1 ? 1 : 0; // REFLECT or SYMMETRIC
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// Nd4jLong startR = mode == 1 ? inDimSize-2 : inDimSize-1; // REFLECT or SYMMETRIC
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// Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
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// NDArray outSubArr0 = output(outIdx[0], true);
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// PRAGMA_OMP_PARALLEL_FOR
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// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
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// NDArray outSubArr1 = outSubArr0(j, dimsToExclude);
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// NDArray inSubArr = input(j, dimsToExclude);
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// NDArray outSubArrMid = outSubArr1(outIdx[1]);
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// outSubArrMid.assign(inSubArr); // assign middle
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// if(mode == 0) { // CONSTANT
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// if(numLeft != 0) {
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// NDArray temp = outSubArr1(outIdx[2]);
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// temp.assign(padValue); // assign left
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// }
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// if(numRight != 0) {
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// NDArray temp = outSubArr1(outIdx[3]);
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// temp.assign(padValue); // assign right
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// }
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// }
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// else { // REFLECT or SYMMETRIC
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// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) // fill left side
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// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
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// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) // fill right side
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// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
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// }
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// }
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// // ***** fill rest of outer sub-arrays ***** //
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// std::vector<Nd4jLong> outIdxInner(2, 0);
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// std::vector<Nd4jLong> outIdxOuter(2, 0);
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// for(int i = rankBorder - 1; i >= 0; --i) {
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// dimsToExclude.pop_back();
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// outIdxInner.push_back(0), outIdxInner.push_back(0);
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// outIdxOuter.push_back(0), outIdxOuter.push_back(0);
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// Nd4jLong numLeft = paddings.e<Nd4jLong>(i, 0);
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// Nd4jLong numRight = paddings.e<Nd4jLong>(i, 1);
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// if(numLeft == 0 && numRight == 0)
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// continue;
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// Nd4jLong inDimSize = input.sizeAt(i);
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// Nd4jLong outDimSize = output.sizeAt(i);
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// if(mode == 0) {
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// outIdxOuter[0] = 0; outIdxOuter[1] = numLeft;
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// outIdxInner[0] = numLeft + inDimSize; outIdxInner[1] = outDimSize;
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// }
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// startL = mode == 1 ? numLeft + 1 : numLeft; // REFLECT or SYMMETRIC
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// startR = mode == 1 ? numLeft + inDimSize - 2 : numLeft + inDimSize-1; // REFLECT or SYMMETRIC
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// numOfSubArrs = ShapeUtils::getNumOfSubArrs(output.getShapeInfo(), dimsToExclude);
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// PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(outIdxOuter, outIdxInner))
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// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
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// NDArray outSubArr = output(j, dimsToExclude);
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// if(mode == 0) { // CONSTANT
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// if(numLeft != 0) {
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// NDArray tempO = outSubArr(outIdxOuter);
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// tempO.assign(padValue); // assign left
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// }
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// if(numRight != 0) {
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// NDArray tempI = outSubArr(outIdxInner);
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// tempI.assign(padValue); // assign right
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// }
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// }
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// else { // REFLECT or SYMMETRIC
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// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) { // fill left side
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// outIdxOuter[0] = k;
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// outIdxOuter[1] = k+1;
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// outIdxInner[0] = e;
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// outIdxInner[1] = e+1;
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// NDArray outSubArrInner = outSubArr(outIdxInner);
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// NDArray outSubArrOuter = outSubArr(outIdxOuter);
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// outSubArrOuter.assign(outSubArrInner);
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// }
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// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) { // fill right side
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// outIdxOuter[0] = k;
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// outIdxOuter[1] = k+1;
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// outIdxInner[0] = e;
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// outIdxInner[1] = e+1;
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// NDArray outSubArrInner = outSubArr(outIdxInner);
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// NDArray outSubArrOuter = outSubArr(outIdxOuter);
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// outSubArrOuter.assign(outSubArrInner);
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// }
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// }
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// }
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// }
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// }
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void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
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BUILD_SINGLE_SELECTOR(input.dataType(), pad_, (mode, input, paddings, output, padValue), LIBND4J_TYPES);
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}
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////////////////////////////////////////////////////////////////////////
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/*// initial values of inIdx, outIdx, dim must be equal to zero
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template<typename T>
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static void recursiveLoopForPad_(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
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int leftOffset;
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// dimensions are array of input dimensions, it is sorted in increasing order
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// every time at the beginning we erase first element from it (not good idea to use vector for this purpose, but luckily it is small enough)
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// then we use this array for tads building, every time while recursion the number of built tads becomes bigger
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dimensions.erase(dimensions.begin());
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// build tad basing on output array, also create auxiliary arrays pointing on required output array ranges
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shape::TAD tadOut(output.getShapeInfo(), dimensions.data(), dimensions.size());
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tadOut.createTadOnlyShapeInfo();
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tadOut.createOffsets();
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auto subArrOut = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
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auto subArr = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
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// build tad basing on input array, also create auxiliary array pointing on required input array range
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shape::TAD tadIn(input.getShapeInfo(), dimensions.data(), dimensions.size());
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tadIn.createTadOnlyShapeInfo();
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tadIn.createOffsets();
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auto subArrIn = NDArray(input.getBuffer(), tadIn.tadOnlyShapeInfo, output.getContext());
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// these indices take into account recursion and always point to actual tads numbers
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if (input.rankOf() > 1 && output.rankOf() > 1) {// only for non-vector cases
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outIdx = outIdx * output.sizeAt(dim + 1);
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inIdx = inIdx * input.sizeAt(dim + 1);
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}
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// current input tad number, we add to it unity in a loop
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int k = -1;
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// loop through current dimension
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for(int i = 0; i < output.sizeAt(dim); ++i) {
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// corresponds to outer range (relevant indices are absent in input)
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leftOffset = paddings.e<int>(dim, 0);
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if(i < leftOffset || i >= (input.sizeAt(dim) + leftOffset))
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continue;
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// increase input tads number
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++k;
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// recursion condition allows for the fact that tad can't reduce to scalar
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if(dim < input.rankOf() - 2)
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recursiveLoopForPad(mode, input, paddings, output, dimensions, dim + 1, inIdx + k, outIdx + i, padValue);
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else if (paddings.sizeAt(0) > dim + 1){
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leftOffset = paddings.e<int>(dim + 1, 0);
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// shift buffers pointers to actual element position
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if (output.rankOf() > 1) {
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|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + i]);
|
|
subArrIn.setBuffer(reinterpret_cast<T*>(input.getBuffer()) + tadIn.tadOffsets[inIdx + i - paddings.e<int>(dim, 0)]);
|
|
}
|
|
else {
|
|
subArrOut.p(i, subArrIn.e<T>(i - leftOffset));
|
|
}
|
|
// most inner loop, corresponds to last dim = rank-1
|
|
switch (mode) {
|
|
case 0: // CONSTANT mode
|
|
for(int j = 0; j < subArrOut.lengthOf(); ++j)
|
|
if(j < leftOffset || j >= (subArrIn.lengthOf() + leftOffset) ) // firstly fill with zeros outer ranges
|
|
subArrOut.p(j, (T)0.f);
|
|
else
|
|
subArrOut.p(j, subArrIn.e<T>(j - leftOffset)); // fill middle with elements of input array
|
|
break;
|
|
|
|
case 1: // REFLECT mode
|
|
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
|
|
subArrOut.p(leftOffset - j, subArrIn.e<T>(j));
|
|
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
|
|
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
|
|
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
|
|
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j - 1));
|
|
break;
|
|
|
|
case 2: // SYMMETRIC mode
|
|
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
|
|
subArrOut.p(leftOffset - j, subArrIn.e<T>(j-1));
|
|
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
|
|
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
|
|
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
|
|
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j));
|
|
break;
|
|
}
|
|
}
|
|
else {
|
|
|
|
if (mode == 0 && input.rankOf() < 2)
|
|
subArrOut.p(i, subArrIn.e<T>(i - leftOffset)); // fill middle with elements of input array
|
|
}
|
|
}
|
|
// populate sub-array formed previously
|
|
leftOffset = paddings.e<int>(dim,0);
|
|
switch (mode) {
|
|
case 0: // CONSTANT mode
|
|
for(int j = 1; j <= leftOffset; ++j) {
|
|
// fill left side with padValue
|
|
if (output.rankOf() > 1) {
|
|
subArrOut.setBuffer(
|
|
reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
|
subArrOut.assign(padValue);
|
|
}
|
|
else {
|
|
subArrOut.p(j - 1, padValue);
|
|
}
|
|
}
|
|
// output.printIndexedBuffer("Output at");
|
|
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill left side with zeros
|
|
if (output.rankOf() > 1) {
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(padValue);
|
|
}
|
|
else {
|
|
subArrOut.p(j, padValue);
|
|
}
|
|
}
|
|
break;
|
|
|
|
case 1: // REFLECT mode
|
|
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - 1 - j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
break;
|
|
|
|
case 2: // SYMMETRIC mode
|
|
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j - 1]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
|
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - j]);
|
|
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
|
subArrOut.assign(&subArr);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
*/
|
|
/*
|
|
void recursiveLoopForPad(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), recursiveLoopForPad_, (mode, input, paddings, output, dimensions, dim, inIdx, outIdx, padValue), LIBND4J_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void recursiveLoopForPad_, (const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue), LIBND4J_TYPES);
|
|
|
|
*/
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void invertPermutation(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
|
|
|
|
std::set<int> uniqueElems;
|
|
const int length = input.lengthOf();
|
|
|
|
for(int i = 0; i < length; ++i) {
|
|
|
|
int elem = input.e<int>(i);
|
|
|
|
if(!uniqueElems.insert(elem).second) // this operation forbids us to use #pragma omp
|
|
throw std::runtime_error("helpers::invertPermutation function: input array contains duplicates !");
|
|
|
|
if(elem < 0 || elem > length - 1)
|
|
throw std::runtime_error("helpers::invertPermutation function: element of input array is out of range (0, length-1) !");
|
|
|
|
output.p<int>(elem, i);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename X, typename Y>
|
|
static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
|
|
|
|
const X* x = reinterpret_cast<X*>(input.getBuffer());
|
|
const Y* y = reinterpret_cast<Y*>(indices.getBuffer());
|
|
X* z = reinterpret_cast<X*>(output.getBuffer());
|
|
|
|
const int xRank = input.rankOf();
|
|
const int yRank = indices.rankOf();
|
|
const int zRank = output.rankOf();
|
|
const int maxRank = nd4j::math::nd4j_max<int>(yRank, nd4j::math::nd4j_max<int>(xRank, zRank));
|
|
|
|
const Nd4jLong zLen = output.lengthOf();
|
|
|
|
const int yLastDim = indices.sizeAt(-1);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
Nd4jLong coords[MAX_RANK * 3];
|
|
for (auto i = start; i < stop; i += increment) {
|
|
Nd4jLong *zCoordStart, *xCoordStart;
|
|
|
|
if (yLastDim == xRank) {
|
|
zCoordStart = coords;
|
|
xCoordStart = coords;
|
|
} else if (zRank >= xRank) {
|
|
zCoordStart = coords;
|
|
xCoordStart = coords + zRank - xRank;
|
|
} else {
|
|
zCoordStart = coords + xRank - zRank;
|
|
xCoordStart = coords;
|
|
}
|
|
|
|
shape::index2coords(i, output.getShapeInfo(), zCoordStart);
|
|
|
|
const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoordStart);
|
|
|
|
// last y coordinate
|
|
uint coordToRestore;
|
|
if (yLastDim != xRank)
|
|
coordToRestore = static_cast<uint>(zCoordStart[yRank - 1]);
|
|
|
|
zCoordStart[yRank - 1] = 0;
|
|
const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoordStart);
|
|
|
|
//restore z coordinate
|
|
if (yLastDim != xRank)
|
|
zCoordStart[yRank - 1] = coordToRestore;
|
|
|
|
// construct coordinates for x
|
|
for (uint j = 0; j < yLastDim; ++j)
|
|
xCoordStart[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]]; // last stride
|
|
|
|
const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoordStart);
|
|
|
|
z[zOffset] = x[xOffset];
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, zLen);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
|
|
BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), LIBND4J_TYPES, INDEXING_TYPES);
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void gather_(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
|
|
|
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
|
|
const int inputRank = input->rankOf();
|
|
if(axis < 0)
|
|
axis += inputRank;
|
|
|
|
const int numOfIntArgs = intArgs.size();
|
|
|
|
if (indices != nullptr) {
|
|
|
|
for(int i = 0; i < indices->lengthOf(); ++i)
|
|
if(indices->e<Nd4jLong>(i) >= input->sizeAt(axis))
|
|
throw std::runtime_error("helpers::gather function: indices array contains wrong elements, each element must be smaller than corresponding dimension of input array !");
|
|
|
|
// first case: indices consist of only one scalar
|
|
if(indices->isScalar()) {
|
|
if(input->rankOf() <= 1){
|
|
//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
|
|
auto idx = indices->e<Nd4jLong>(0);
|
|
auto scalarNDArray = input->e(idx);
|
|
output->assign(scalarNDArray);
|
|
} else {
|
|
auto dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
|
|
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
|
|
|
|
auto tadArr = NDArray(reinterpret_cast<void *>(reinterpret_cast<T*>(input->getBuffer()) + tadPack.primaryOffsets()[indices->e<Nd4jLong>(0)]), tadPack.primaryShapeInfo(), output->getContext());
|
|
output->assign(&tadArr);
|
|
}
|
|
}
|
|
else if (input->rankOf() == 1 && indices->isVector()) {
|
|
// special case
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e += increment)
|
|
output->p(e, input->e<T>(indices->e<Nd4jLong>(e)));
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, indices->lengthOf());
|
|
}
|
|
else {
|
|
|
|
std::vector<int> dimsOut(indices->rankOf());
|
|
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... indices->rankOf()-1
|
|
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), dimsOut);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
NDArray subArrOut = (*output)(i, dimsOut);
|
|
NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
|
|
subArrOut.assign(subArrIn);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
|
}
|
|
}
|
|
else {
|
|
|
|
for(int i = 1; i < numOfIntArgs; ++i)
|
|
if(intArgs[i] >= input->sizeAt(axis))
|
|
throw std::runtime_error("helpers::gather function: some of input indexes is larger than corresponding shape of input array !");
|
|
|
|
// we only allow scalar/vector case here
|
|
if (numOfIntArgs == 2) { // scalar case
|
|
output->assign((*input)(intArgs[1], {axis}));
|
|
}
|
|
else { // vector case
|
|
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), {axis});
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
NDArray subArrOut = (*output)(i, {axis});
|
|
NDArray subArrIn = (*input)(intArgs[i + 1], {axis});
|
|
subArrOut.assign(subArrIn);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
|
}
|
|
}
|
|
}
|
|
|
|
void gather(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), gather_, (input, indices, output, intArgs), LIBND4J_TYPES);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void eye(nd4j::LaunchContext * context, NDArray& output) {
|
|
|
|
const int rank = output.rankOf();
|
|
auto arrs = output.allTensorsAlongDimension({rank-2, rank-1});
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment)
|
|
arrs->at(i)->setIdentity();
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, arrs->size());
|
|
|
|
delete arrs;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void scatterUpdate(nd4j::LaunchContext * context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {
|
|
|
|
int opCode = (*intArgs)[0];
|
|
int dimSize = (*intArgs)[1];
|
|
Nd4jLong e;
|
|
Nd4jLong limg = 2 + dimSize;
|
|
std::vector<int> tadDimensions(dimSize);
|
|
for (e = 2; e < limg; e++)
|
|
tadDimensions[e-2] = (*intArgs)[e];
|
|
|
|
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input.rankOf(), tadDimensions);
|
|
|
|
// increasing counter to skip numIndices
|
|
e++;
|
|
std::vector<int> indices;
|
|
for (; e < intArgs->size(); e++)
|
|
indices.push_back((*intArgs)[e]);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
auto inSubArr = input(indices[i], dimsToExclude, true);
|
|
auto updSubArr = updates(i, dimsToExclude, true);
|
|
|
|
if (inSubArr.lengthOf() != updSubArr.lengthOf())
|
|
continue;
|
|
|
|
switch (opCode) {
|
|
case 0:
|
|
inSubArr.applyPairwiseTransform(pairwise::Add, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 1:
|
|
inSubArr.applyPairwiseTransform(pairwise::Subtract, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 2:
|
|
inSubArr.applyPairwiseTransform(pairwise::Multiply, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 3:
|
|
inSubArr.applyPairwiseTransform(pairwise::Divide, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 4:
|
|
inSubArr.applyPairwiseTransform(pairwise::ReverseSubtract, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 5:
|
|
inSubArr.applyPairwiseTransform(pairwise::ReverseDivide, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
case 6:
|
|
inSubArr.applyPairwiseTransform(pairwise::CopyPws, &updSubArr, &inSubArr, nullptr);
|
|
break;
|
|
default:
|
|
continue;
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, indices.size());
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void scatterSimple(nd4j::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
|
|
|
|
// updates and indices have same length
|
|
const Nd4jLong len = indices.lengthOf();
|
|
|
|
switch (opId) {
|
|
|
|
case 6: { // copy
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
auto inSubArr = input(i, dimensions);
|
|
inSubArr.p(indices.t<Nd4jLong>(i), updates.e(i));
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, len);
|
|
}
|
|
break;
|
|
|
|
default:
|
|
throw std::invalid_argument("helpers::scatterSimple: operation is not implemented for given id !");
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void mergeMaxIndex_(const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
|
|
const Nd4jLong numArgs = inArrs.size();
|
|
auto x = inArrs[0];
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e += increment) {
|
|
T max = -DataTypeUtils::max<T>();
|
|
Nd4jLong idx = 0;
|
|
|
|
for (int i = 0; i < numArgs; i++) {
|
|
T v = inArrs[i]->e<T>(e);
|
|
if (v > max) {
|
|
max = v;
|
|
idx = i;
|
|
}
|
|
}
|
|
output.p(e, idx);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
|
}
|
|
|
|
void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), mergeMaxIndex_, (inArrs, output), LIBND4J_TYPES);
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void mergeMax_(const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
const Nd4jLong numArgs = inArrs.size();
|
|
auto x = inArrs[0];
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e += increment) {
|
|
T max = -DataTypeUtils::max<T>();
|
|
for (int i = 0; i < numArgs; i++) {
|
|
T v = inArrs[i]->e<T>(e);
|
|
if (v > max)
|
|
max = v;
|
|
}
|
|
output.p(e, max);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
|
}
|
|
|
|
void mergeMax(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (inArrs, output), LIBND4J_TYPES);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void mergeAvg_(const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
const Nd4jLong numArgs = inArrs.size();
|
|
const T factor = 1.f / numArgs;
|
|
auto x = inArrs[0];
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e += increment) {
|
|
T sum = 0.;
|
|
for (int i = 0; i < numArgs; i++) {
|
|
T v = inArrs[i]->e<T>(e);
|
|
sum += v;
|
|
}
|
|
output.p<T>(e, sum * factor);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
|
}
|
|
|
|
void mergeAvg(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (inArrs, output), LIBND4J_TYPES);
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void mergeAdd_(const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
|
|
const Nd4jLong numArgs = inArrs.size();
|
|
auto x = inArrs[0];
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e += increment) {
|
|
T sum = (T) 0.f;
|
|
for (int i = 0; i < numArgs; i++)
|
|
sum += inArrs[i]->e<T>(e);
|
|
|
|
output.p(e, sum);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
|
}
|
|
void mergeAdd(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (inArrs, output), LIBND4J_TYPES);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByNorm_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
|
|
const int rank = input.rankOf();
|
|
const auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions);
|
|
|
|
const T normActual = norm2.e<T>(0);
|
|
const T normClip = clipNorm.e<T>(0);
|
|
|
|
if (isInplace) {
|
|
|
|
if(norm2.lengthOf() == 1) {
|
|
|
|
if(normActual > normClip)
|
|
input *= (normClip / normActual);
|
|
}
|
|
else {
|
|
|
|
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
const T iNormActual = norm2.e<T>(i);
|
|
if (iNormActual > normClip)
|
|
*listOfInSubArrs->at(i) *= normClip / iNormActual;
|
|
}
|
|
};
|
|
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs->size());
|
|
|
|
delete listOfInSubArrs;
|
|
}
|
|
}
|
|
else {
|
|
|
|
if(norm2.lengthOf() == 1) {
|
|
|
|
if(normActual > normClip)
|
|
output.assign(input * (normClip / normActual));
|
|
else
|
|
output.assign(input);
|
|
}
|
|
else {
|
|
|
|
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
|
|
auto listOfOutSubArrs = output.allTensorsAlongDimension(dimensions);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
auto inputSubArr = listOfInSubArrs->at(i);
|
|
auto outputSubArr = listOfOutSubArrs->at(i);
|
|
outputSubArr->assign(inputSubArr);
|
|
|
|
const T iNormActual = norm2.e<T>(i);
|
|
|
|
if (iNormActual > clipNorm.e<T>(0))
|
|
*outputSubArr *= clipNorm / iNormActual;
|
|
}
|
|
};
|
|
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs->size());
|
|
|
|
delete listOfInSubArrs;
|
|
delete listOfOutSubArrs;
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
template <typename T>
|
|
static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
|
T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
|
|
// PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
|
|
for (size_t i = 0; i < inputs.size(); i++) {
|
|
auto input = inputs[i];
|
|
auto l2norm = input->reduceNumber(reduce::Norm2);
|
|
globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
|
|
}
|
|
|
|
//globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = nd4j::math::nd4j_sqrt(globalNorm);
|
|
auto normS = nd4j::math::nd4j_sqrt<T,T>(globalNorm);
|
|
outputs[inputs.size()]->p(0, normS);
|
|
|
|
const T factor = clipNorm / normS;
|
|
|
|
// PRAGMA_OMP_PARALLEL_FOR
|
|
for (size_t e = 0; e < inputs.size(); e++) {
|
|
// all-reduce
|
|
auto input = inputs[e];
|
|
auto output = outputs[e];
|
|
|
|
if (normS <= clipNorm) {
|
|
output->assign(input);
|
|
}
|
|
else {
|
|
|
|
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
|
input->applyLambda<T>(lambda, output);
|
|
}
|
|
}
|
|
}
|
|
void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByNormBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
|
|
|
const int rank = input.rankOf();
|
|
|
|
auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions);
|
|
|
|
if(norm2.lengthOf() == 1) {
|
|
|
|
const T N = norm2.e<T>(0);
|
|
|
|
auto cn = clipNorm.e<T>(0);
|
|
|
|
if(N > cn) {
|
|
|
|
const T sumOfProd = (input * gradO).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
|
|
const T factor1 = static_cast<T>(1.f) / N;
|
|
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
|
|
|
|
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
|
|
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
|
|
};
|
|
|
|
(const_cast<NDArray&>(input)).applyPairwiseLambda<T>(const_cast<NDArray*>(&gradO), lambda, &gradI);
|
|
}
|
|
else
|
|
gradI.assign(gradO);
|
|
}
|
|
else {
|
|
|
|
const auto gradISubArrs = gradI.allTensorsAlongDimension({dimensions});
|
|
const auto gradOSubArrs = gradO.allTensorsAlongDimension({dimensions});
|
|
const auto inputSubArrs = input.allTensorsAlongDimension({dimensions});
|
|
|
|
auto cn = clipNorm.e<T>(0);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i += increment) {
|
|
T N = norm2.e<T>(i);
|
|
|
|
auto gradOSubArr = gradOSubArrs->at(i);
|
|
auto gradISubArr = gradISubArrs->at(i);
|
|
|
|
if (N > cn) {
|
|
auto inputSubArr = inputSubArrs->at(i);
|
|
const T sumOfProd = (*inputSubArr * *gradOSubArr).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
|
|
const T factor1 = static_cast<T>(1.f) / N;
|
|
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
|
|
|
|
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
|
|
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
|
|
};
|
|
|
|
inputSubArr->applyPairwiseLambda<T>(gradOSubArr, lambda, gradISubArr);
|
|
} else
|
|
gradISubArr->assign(gradOSubArr);
|
|
}
|
|
};
|
|
samediff::Threads::parallel_tad(func, 0, gradISubArrs->size());
|
|
|
|
delete gradISubArrs;
|
|
delete gradOSubArrs;
|
|
delete inputSubArrs;
|
|
}
|
|
}
|
|
|
|
void clipByNormBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
|
BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByNormBP_, (const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm), FLOAT_TYPES);
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByAveraged_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
|
|
auto cn = clipNorm.e<T>(0);
|
|
if (dimensions.size() == 0) {
|
|
// all-reduce
|
|
T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / input.lengthOf();
|
|
if (n2 <= cn) {
|
|
if (!isInplace)
|
|
output.assign(input);
|
|
}
|
|
else {
|
|
const T factor = cn / n2;
|
|
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
|
input.applyLambda<T>(lambda, &output);
|
|
}
|
|
}
|
|
else {
|
|
// along dimension
|
|
auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions, false);
|
|
if (!isInplace)
|
|
output.assign(input);
|
|
auto tads = output.allTensorsAlongDimension(dimensions);
|
|
// TODO: make this CUDA-compliant somehow
|
|
for (int e = 0; e < tads->size(); e++) {
|
|
T n2 = norm2.e<T>(e) / tads->at(e)->lengthOf();
|
|
const T factor = cn / n2;
|
|
if (n2 > cn) {
|
|
auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
|
|
tads->at(e)->applyLambda<T>(lambda, &output);
|
|
}
|
|
}
|
|
delete tads;
|
|
}
|
|
}
|
|
|
|
void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
|
|
|
|
/*
|
|
if (d1 > params[1])
|
|
return params[1];
|
|
else if (d1 < params[0])
|
|
return params[0];
|
|
else return d1;
|
|
*/
|
|
|
|
template <typename T>
|
|
static void clipByValue_(NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
|
auto routine = LAMBDA_T(_x, leftBound, rightBound) {
|
|
if (_x > rightBound) return rightBound;
|
|
if (_x < leftBound) return leftBound;
|
|
return _x;
|
|
};
|
|
|
|
input.applyLambda<T>(routine, &output);
|
|
}
|
|
|
|
void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByValue_, (NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
|
|
|
|
// mode: 0 - REFLECT, else - SYMMETRIC
|
|
const int reflBorder = (bool)mode ? 1 : 0;
|
|
const int rank = input.rankOf();
|
|
const Nd4jLong outLen = output.lengthOf();
|
|
|
|
if(rank <= 1) {
|
|
|
|
const Nd4jLong inLen = input.lengthOf();
|
|
const auto leftSide = paddings.e<Nd4jLong>(0);
|
|
const auto leftSideCorrected = leftSide - reflBorder;
|
|
const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
|
|
|
|
for(int i = 0; i < outLen; ++i) {
|
|
|
|
if (i < leftSide) // left side
|
|
output.p(i, input.e<T>(leftSideCorrected - i));
|
|
|
|
else if(i >= leftSide && i < leftSide + inLen) // middle
|
|
output.p(i, input.e<T>(i - leftSide));
|
|
|
|
else // right side
|
|
output.p(i, input.e<T>(len - i));
|
|
}
|
|
}
|
|
else {
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
Nd4jLong inIdx[MAX_RANK];
|
|
Nd4jLong outIdx[MAX_RANK];
|
|
for (auto i = start; i < stop; i += increment) {
|
|
shape::index2coords(i, output.getShapeInfo(), outIdx);
|
|
|
|
for (int j = 0; j < rank; ++j) {
|
|
const Nd4jLong inLen = input.sizeAt(j);
|
|
const auto leftSide = paddings.e<T>(j, 0);
|
|
const auto leftSideCorrected = leftSide - reflBorder;
|
|
const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
|
|
|
|
if (outIdx[j] < leftSide) // left side
|
|
inIdx[j] = leftSideCorrected - outIdx[j];
|
|
|
|
else if (outIdx[j] >= leftSide && outIdx[j] < leftSide + inLen) // middle
|
|
inIdx[j] = outIdx[j] - leftSide;
|
|
|
|
else // right side
|
|
inIdx[j] = len - outIdx[j];
|
|
}
|
|
|
|
auto outOffset = shape::getOffset(output.getShapeInfo(), outIdx);
|
|
auto inOffset = shape::getOffset(input.getShapeInfo(), inIdx);
|
|
reinterpret_cast<T *>(output.buffer())[outOffset] = reinterpret_cast<T *>(input.getBuffer())[inOffset];
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, outLen);
|
|
}
|
|
}
|
|
|
|
void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), mirrorPad_, (input, paddings, output, mode), LIBND4J_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void mirrorPad_, (const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void concat_(const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
|
|
nd4j::SpecialMethods<T>::concatCpuGeneric(inArrs, output, axis);
|
|
}
|
|
|
|
void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), concat_,(inArrs, output, axis), LIBND4J_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void concat_, (const std::vector<NDArray*>& inArrs, NDArray& output, const int axis), LIBND4J_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
|
|
|
T* gradIBuff = reinterpret_cast<T*>(gradI.getBuffer());
|
|
const T* gradOBuff = reinterpret_cast<T*>(gradO.getBuffer());
|
|
const Nd4jLong gradILen = gradI.lengthOf();
|
|
const Nd4jLong gradOLen = gradO.lengthOf(); // gradOLen >= gradILen
|
|
const Nd4jLong gradIEWS = nd4j::math::nd4j_abs<Nd4jLong>(gradI.ews());
|
|
const Nd4jLong gradOEWS = gradO.ews();
|
|
|
|
// initial zeroing of gradI content
|
|
if(gradIEWS == 1)
|
|
memset(gradIBuff, 0, gradILen * sizeof(T));
|
|
else {
|
|
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
|
for (int i = 0; i < gradILen * gradIEWS; i += gradIEWS)
|
|
gradIBuff[i] = static_cast<T>(0.f);
|
|
}
|
|
|
|
|
|
if(gradO.ordering() == 'c' && gradOEWS == 1) {
|
|
|
|
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
|
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
|
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
|
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i]);
|
|
}
|
|
}
|
|
else if(gradO.ordering() == 'c' && gradOEWS > 1) {
|
|
|
|
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
|
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
|
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
|
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i * gradOEWS]);
|
|
}
|
|
}
|
|
else {
|
|
|
|
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
|
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
|
|
|
auto fidx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
|
gradI.p(fidx, gradI.e<T>(fidx) + gradOBuff[shape::getIndexOffset(i, gradO.getShapeInfo())]);
|
|
}
|
|
}
|
|
}
|
|
|
|
void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
|
BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBP_, (gradO, gradI, reps), FLOAT_TYPES);
|
|
}
|
|
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void tileBP_, (const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES);
|
|
|
|
|
|
|
|
|
|
|
|
}
|
|
}
|
|
}
|