92 lines
3.7 KiB
C++
92 lines
3.7 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 adaGradUpdater_(const NDArray& gradient, const NDArray& initState, NDArray& update, NDArray& stateH, const double dLr, const double dEpsilon) {
|
|
|
|
const T* grad = gradient.bufferAsT<T>();
|
|
const T* init = initState.bufferAsT<T>();
|
|
|
|
T* up = update.bufferAsT<T>();
|
|
T* st = stateH.bufferAsT<T>();
|
|
|
|
const T lr = static_cast<T>(dLr);
|
|
const T epsilon = static_cast<T>(dEpsilon);
|
|
|
|
bool bEws1 = 1 == gradient.ews() && 1 == update.ews() && 1 == stateH.ews() && 1 == initState.ews();
|
|
bool bSameOrdering = gradient.ordering() == update.ordering() && update.ordering() == stateH.ordering() && stateH.ordering() == initState.ordering();
|
|
|
|
if (bEws1 && bSameOrdering) {
|
|
|
|
auto func = PRAGMA_THREADS_FOR{
|
|
for (auto i = start; i < stop; i++) {
|
|
st[i] = init[i] + grad[i] * grad[i];
|
|
up[i] = (lr * grad[i]) / (math::nd4j_sqrt<T, T>(st[i]) + epsilon);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
|
|
return;
|
|
}
|
|
|
|
bool bXZsame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), update.shapeInfo());
|
|
bool bXInSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), initState.shapeInfo());
|
|
bool bXStSame = shape::haveSameShapeAndStrides(gradient.shapeInfo(), stateH.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 initOffset = bXInSame ? xOffset : shape::getOffset(initState.shapeInfo(), coords);
|
|
const auto stOffset = bXStSame ? xOffset : shape::getOffset(stateH.shapeInfo(), coords);
|
|
|
|
st[stOffset] = init[initOffset] + grad[xOffset] * grad[xOffset];
|
|
up[zOffset] = (lr * grad[xOffset]) / (math::nd4j_sqrt<T, T>(st[stOffset]) + epsilon);
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, gradient.lengthOf(), 1);
|
|
return;
|
|
}
|
|
|
|
void updaterAdaGrad(sd::LaunchContext* context, const NDArray& gradient, const NDArray& initState, NDArray& update, NDArray& stateH,
|
|
const double dLr, const double dEpsilon) {
|
|
BUILD_SINGLE_SELECTOR(gradient.dataType(), adaGradUpdater_, (gradient, initState, update, stateH, dLr, dEpsilon), FLOAT_TYPES);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|