cavis/libnd4j/include/ops/declarable/helpers/cpu/updaterAdaMax.cpp

114 lines
5.3 KiB
C++

/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
//
#include <ops/declarable/helpers/updatersHelpers.h>
#include <execution/Threads.h>
#include <math/platformmath.h>
#include <math/templatemath.h>
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T>
static void adaMaxUpdater_(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) {
const T* grad = gradient.bufferAsT<T>();
const T* initU = initStateU.bufferAsT<T>();
const T* initM = initStateM.bufferAsT<T>();
T* up = update.bufferAsT<T>();
T* stU = stateU.bufferAsT<T>();
T* stM = stateM.bufferAsT<T>();
const T lr = static_cast<T>(dLr);
const T beta1 = static_cast<T>(dBeta1);
const T beta2 = static_cast<T>(dBeta2);
const T epsilon = static_cast<T>(dEpsilon);
const T iteration = static_cast<T>(nIteration);
const T beta1T = sd::math::nd4j_pow<T, T, T>(beta1, (iteration + 1));
T epsilonT = lr / (1.0 - beta1T);
if (sd::math::nd4j_isnan(epsilonT) || 0 == epsilonT || sd::math::nd4j_isinf(epsilonT))
epsilonT = epsilon;
bool bEws1 = 1 == gradient.ews() && 1 == update.ews() && 1 == stateM.ews() && 1 == initStateM.ews() && 1 == stateU.ews() && 1 == initStateU.ews();
bool bSameOrdering = gradient.ordering() == update.ordering() &&
update.ordering() == stateU.ordering() &&
stateU.ordering() == initStateU.ordering() &&
stateU.ordering() == initStateM.ordering() && stateM.ordering() == initStateM.ordering();
if (bEws1 && bSameOrdering) {
auto func = PRAGMA_THREADS_FOR{
for (auto i = start; i < stop; i++) {
//m = B_1 * m + (1-B_1)*grad
stM[i] = beta1 * initM[i] + grad[i] * (1 - beta1);
//u = max(B_2 * u, |grad|)
stU[i] = sd::math::nd4j_max((beta2 * initU[i]), sd::math::nd4j_abs(grad[i])) + 1e-32;
up[i] = stM[i] * epsilonT / stU[i];
}
};
samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
return;
}
bool bXZsame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), update.shapeInfo());
bool bXInVSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), initStateU.shapeInfo());
bool bXStVSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), stateU.shapeInfo());
bool bXInMSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), initStateM.shapeInfo());
bool bXStMSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), stateM.shapeInfo());
auto func = PRAGMA_THREADS_FOR{
int coords[MAX_RANK];
for (auto i = start; i < stop; i++) {
shape::index2coordsCPU(start, i, gradient.shapeInfo(), coords);
const auto xOffset = shape::getOffset(gradient.shapeInfo(), coords);
const auto zOffset = bXZsame ? xOffset : shape::getOffset(update.shapeInfo(), coords);
const auto initUOffset = bXInVSame ? xOffset : shape::getOffset(initStateU.shapeInfo(), coords);
const auto stUOffset = bXStVSame ? xOffset : shape::getOffset(stateU.shapeInfo(), coords);
const auto initMOffset = bXInMSame ? xOffset : shape::getOffset(initStateM.shapeInfo(), coords);
const auto stMOffset = bXStMSame ? xOffset : shape::getOffset(stateM.shapeInfo(), coords);
//m = B_1 * m + (1-B_1)*grad
stM[stMOffset] = beta1 * initM[initMOffset] + grad[xOffset] * (1 - beta1);
//u = max(B_2 * u, |grad|)
stU[stUOffset] = sd::math::nd4j_max((beta2 * initU[initUOffset]), sd::math::nd4j_abs(grad[xOffset])) + 1e-32;
up[zOffset] = stM[stMOffset] * epsilonT / stU[stUOffset];
}
};
samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
return;
}
void updaterAdaMax(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) {
BUILD_SINGLE_SELECTOR(gradient.dataType(), adaMaxUpdater_, (gradient, initStateU, initStateM, update, stateU, stateM, dLr, dBeta1, dBeta2, dEpsilon, nIteration), FLOAT_TYPES);
}
}
}
}