120 lines
5.6 KiB
C++
120 lines
5.6 KiB
C++
/*
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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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//
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// @author Oleh Semeniv (oleg.semeniv@gmail.com)
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// @author Abdelrauf (rauf@konduit.ai)
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// https://arxiv.org/pdf/2010.07468.pdf
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#include <ops/declarable/helpers/updatersHelpers.h>
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#include <execution/Threads.h>
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#include <math/platformmath.h>
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#include <math/templatemath.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 T>
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static void adaBeliefUpdater_(const NDArray& gradient, const NDArray& initStateU, const NDArray& initStateM, NDArray& update,
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NDArray& stateU, NDArray& stateM, const double dLr, const double dBeta1, const double dBeta2,
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const double dEpsilon, const int nIteration) {
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const T* grad = gradient.bufferAsT<T>();
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const T* initU = initStateU.bufferAsT<T>();
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const T* initM = initStateM.bufferAsT<T>();
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T* up = update.bufferAsT<T>();
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T* stU = stateU.bufferAsT<T>();
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T* stM = stateM.bufferAsT<T>();
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const T lr = static_cast<T>(dLr);
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const T beta1 = static_cast<T>(dBeta1);
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const T beta2 = static_cast<T>(dBeta2);
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const T epsilon = static_cast<T>(dEpsilon);
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const T iteration = static_cast<T>(nIteration);
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const T beta1T = sd::math::nd4j_pow<T, T, T>(beta1, (iteration + 1));
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const T beta2T = sd::math::nd4j_pow<T, T, T>(beta2, (iteration + 1));
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T epsilonT = lr * sd::math::nd4j_sqrt<T, T>(1. - beta2T) / (1.0 - beta1T);
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if (sd::math::nd4j_isnan(epsilonT) || 0 == epsilonT || sd::math::nd4j_isinf(epsilonT))
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epsilonT = epsilon;
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bool bEws1 = 1 == gradient.ews() && 1 == update.ews() && 1 == stateM.ews() && 1 == initStateM.ews() && 1 == stateU.ews() && 1 == initStateU.ews();
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bool bSameOrdering = gradient.ordering() == update.ordering() &&
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update.ordering() == stateU.ordering() &&
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stateU.ordering() == initStateU.ordering() &&
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stateU.ordering() == initStateM.ordering() && stateM.ordering() == initStateM.ordering();
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if (bEws1 && bSameOrdering) {
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auto func = PRAGMA_THREADS_FOR{
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for (auto i = start; i < stop; i++) {
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stM[i] = beta1 * initM[i] + grad[i] * (1 - beta1);
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stU[i] = beta2 * initU[i] + (grad[i] - stM[i]) * (grad[i] - stM[i]) * (1 - beta2) + epsilon;
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up[i] = (stM[i] * epsilonT) / (sd::math::nd4j_sqrt<T, T>(stU[i]) + epsilon);
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}
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};
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samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
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return;
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}
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bool bXZsame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), update.shapeInfo());
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bool bXInVSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), initStateU.shapeInfo());
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bool bXStVSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), stateU.shapeInfo());
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bool bXInMSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), initStateM.shapeInfo());
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bool bXStMSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), stateM.shapeInfo());
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auto func = PRAGMA_THREADS_FOR{
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int coords[MAX_RANK];
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for (auto i = start; i < stop; i++) {
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shape::index2coordsCPU(start, i, gradient.shapeInfo(), coords);
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const auto xOffset = shape::getOffset(gradient.shapeInfo(), coords);
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const auto zOffset = bXZsame ? xOffset : shape::getOffset(update.shapeInfo(), coords);
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const auto initUOffset = bXInVSame ? xOffset : shape::getOffset(initStateU.shapeInfo(), coords);
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const auto stUOffset = bXStVSame ? xOffset : shape::getOffset(stateU.shapeInfo(), coords);
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const auto initMOffset = bXInVSame ? xOffset : shape::getOffset(initStateM.shapeInfo(), coords);
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const auto stMOffset = bXStMSame ? xOffset : shape::getOffset(stateM.shapeInfo(), coords);
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stM[stMOffset] = beta1 * initM[initMOffset] + grad[xOffset] * (1 - beta1);
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stU[stUOffset] = beta2 * initU[initUOffset] + (grad[xOffset] - stM[stMOffset]) * (grad[xOffset] - stM[stMOffset]) * (1 - beta2) + epsilon;
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up[zOffset] = (stM[stMOffset] * epsilonT) / (sd::math::nd4j_sqrt<T, T>(stU[stUOffset]) + epsilon);
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}
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};
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samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
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return;
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}
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void updaterAdaBelief(sd::LaunchContext* context, const NDArray& gradient, const NDArray& initStateU, const NDArray& initStateM, NDArray& update, NDArray& stateU, NDArray& stateM, const double dLr, const double dBeta1, const double dBeta2, const double dEpsilon, const int nIteration) {
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BUILD_SINGLE_SELECTOR(gradient.dataType(), adaBeliefUpdater_, (gradient, initStateU, initStateM, update, stateU, stateM, dLr, dBeta1, dBeta2, dEpsilon, nIteration), FLOAT_TYPES);
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}
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}
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}
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}
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