127 lines
6.3 KiB
C++
127 lines
6.3 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019-2020 Konduit K.K.
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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 Oleh Semeniv (oleg.semeniv@gmail.com)
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//
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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 amsGradUpdater_(const NDArray& gradient, const NDArray& initStateV, const NDArray& initStateM, const NDArray& initStateH,
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NDArray& update, NDArray& stateV, NDArray& stateM, NDArray& stateH, const double dLr, const double dBeta1, const double dBeta2, const double dEpsilon, const int nIteration) {
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const T* grad = gradient.bufferAsT<T>();
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const T* initV = initStateV.bufferAsT<T>();
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const T* initM = initStateM.bufferAsT<T>();
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const T* initH = initStateH.bufferAsT<T>();
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T* up = update.bufferAsT<T>();
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T* stV = stateV.bufferAsT<T>();
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T* stM = stateM.bufferAsT<T>();
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T* stH = stateH.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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T epsilonT = lr * sd::math::nd4j_sqrt<T, T>(1.0 - sd::math::nd4j_pow<T, T, T>(beta2, (iteration + 1))) / (1.0 - sd::math::nd4j_pow<T, T, T>(beta1, (iteration + 1)));
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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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const T mbeta1 = (1 - beta1);
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const T mbeta2 = (1 - beta2);
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bool bEws1 = 1 == gradient.ews() && 1 == update.ews() && 1 == stateM.ews() && 1 == initStateM.ews() &&
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1 == stateV.ews() && 1 == initStateV.ews() && 1 == stateH.ews() && 1 == initStateH.ews();
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bool bSameOrdering = gradient.ordering() == update.ordering() &&
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update.ordering() == stateV.ordering() &&
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stateV.ordering() == initStateV.ordering() &&
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stateV.ordering() == initStateM.ordering() &&
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stateM.ordering() == initStateM.ordering() &&
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stateM.ordering() == initStateH.ordering() && stateH.ordering() == initStateH.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] * mbeta1;
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stV[i] = beta2 * initV[i] + grad[i] * grad[i] * mbeta2;
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stH[i] = sd::math::nd4j_max(initH[i], stV[i]);
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up[i] = epsilonT * stM[i] / (sd::math::nd4j_sqrt<T, T>(stH[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.getShapeInfo(), update.getShapeInfo());
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bool bXInVSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), initStateV.getShapeInfo());
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bool bXStVSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), stateV.getShapeInfo());
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bool bXInMSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), initStateM.getShapeInfo());
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bool bXStMSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), stateM.getShapeInfo());
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bool bXInHSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), initStateH.getShapeInfo());
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bool bXStHSame = shape::haveSameShapeAndStrides(gradient.getShapeInfo(), stateH.getShapeInfo());
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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.getShapeInfo(), coords);
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const auto xOffset = shape::getOffset(gradient.getShapeInfo(), coords);
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const auto zOffset = bXZsame ? xOffset : shape::getOffset(update.getShapeInfo(), coords);
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const auto initVOffset = bXInVSame ? xOffset : shape::getOffset(initStateV.getShapeInfo(), coords);
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const auto stVOffset = bXStVSame ? xOffset : shape::getOffset(stateV.getShapeInfo(), coords);
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const auto initMOffset = bXInMSame ? xOffset : shape::getOffset(initStateM.getShapeInfo(), coords);
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const auto stMOffset = bXStMSame ? xOffset : shape::getOffset(stateM.getShapeInfo(), coords);
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const auto initHOffset = bXInHSame ? xOffset : shape::getOffset(initStateH.getShapeInfo(), coords);
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const auto stHOffset = bXStHSame ? xOffset : shape::getOffset(stateH.getShapeInfo(), coords);
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stM[stMOffset] = beta1 * initM[initMOffset] + grad[xOffset] * mbeta1;
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stV[stVOffset] = beta2 * initV[initVOffset] + grad[xOffset] * grad[xOffset] * mbeta2;
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stH[stHOffset] = sd::math::nd4j_max(initH[initHOffset], stV[stVOffset]);
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up[zOffset] = epsilonT * stM[stMOffset] / (sd::math::nd4j_sqrt<T, T>(stH[stHOffset]) + 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 updaterAmsGrad(sd::LaunchContext* context, const NDArray& gradient, const NDArray& initStateV, const NDArray& initStateM, const NDArray& initStateH,
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NDArray& update, NDArray& stateV, NDArray& stateM, NDArray& stateH, const double dLr, const double dBeta1, const double dBeta2, const double dEpsilon, const int nIteration) {
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BUILD_SINGLE_SELECTOR(gradient.dataType(), amsGradUpdater_, (gradient, initStateV, initStateM, initStateH, update, stateV, stateM, stateH, 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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