118 lines
5.4 KiB
C++
118 lines
5.4 KiB
C++
/*
|
|
* ******************************************************************************
|
|
* *
|
|
* *
|
|
* * 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.
|
|
* *
|
|
* * See the NOTICE file distributed with this work for additional
|
|
* * information regarding copyright ownership.
|
|
* * 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);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|