cavis/libnd4j/include/ops/declarable/helpers/cuda/updaterNesterovs.cu

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/*******************************************************************************
* Copyright (c) 2019 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 <system/op_boilerplate.h>
#include <ops/declarable/helpers/updatersHelpers.h>
#include <helpers/PointersManager.h>
#include <math/platformmath.h>
#include <math/templatemath.h>
namespace sd {
namespace ops {
namespace helpers {
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ void nesterovsUpdaterCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vin, const Nd4jLong* inShapeInfo,
void* vz, const Nd4jLong* zShapeInfo, void* vst, const Nd4jLong* stShapeInfo, const T lr, const T momentum) {
const auto grad = reinterpret_cast<const T*>(vx);
const auto init = reinterpret_cast<const T*>(vin);
auto up = reinterpret_cast<T*>(vz);
auto st = reinterpret_cast<T*>(vst);
__shared__ Nd4jLong xLen;
__shared__ T momentumT;
__shared__ bool bEWS, bOrdering, bXZsame, bXInSame, bXStSame;
if (threadIdx.x == 0) {
xLen = shape::length(xShapeInfo);
momentumT = (-momentum - 1);
bEWS = 1 == shape::elementWiseStride(xShapeInfo) && 1 == shape::elementWiseStride(zShapeInfo) &&
1 == shape::elementWiseStride(stShapeInfo) && 1 == shape::elementWiseStride(inShapeInfo);
bOrdering = shape::order(xShapeInfo) == shape::order(zShapeInfo) && shape::order(xShapeInfo) == shape::order(inShapeInfo) &&
shape::order(xShapeInfo) == shape::order(stShapeInfo);
bXZsame = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
bXInSame = shape::haveSameShapeAndStrides(xShapeInfo, inShapeInfo);
bXStSame = shape::haveSameShapeAndStrides(xShapeInfo, stShapeInfo);
}
__syncthreads();
int coords[MAX_RANK];
for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < xLen; i += gridDim.x * blockDim.x) {
auto xOffset = i, zOffset = i, initOffset = i, stOffset = i;
if (!bEWS || !bOrdering) {
shape::index2coords(i, xShapeInfo, coords);
xOffset = shape::getOffset(xShapeInfo, coords);
zOffset = bXZsame ? xOffset : shape::getOffset(zShapeInfo, coords);
initOffset = bXInSame ? xOffset : shape::getOffset(inShapeInfo, coords);
stOffset = bXStSame ? xOffset : shape::getOffset(stShapeInfo, coords);
}
T prevState = momentum * init[initOffset];
st[stOffset] = prevState - lr * grad[xOffset];
up[zOffset] = prevState + momentumT * st[stOffset];
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
linkage void nesterovsUpdaterCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t* stream,
const void* vx, const Nd4jLong* xShapeInfo, const void* vin, const Nd4jLong* inShapeInfo,
void* vz, const Nd4jLong* zShapeInfo, void* vst, const Nd4jLong* stShapeInfo,
const double dLr, const double dMomentum) {
const T lr = static_cast<T>(dLr);
const T momentum = static_cast<T>(dMomentum);
nesterovsUpdaterCuda<T><<<blocksPerGrid, threadsPerBlock, 256, * stream>>>(vx, xShapeInfo, vin, inShapeInfo,
vz, zShapeInfo, vst, stShapeInfo, lr, momentum);
}
///////////////////////////////////////////////////////////////////
void updaterNesterovs(sd::LaunchContext* context, const NDArray& gradient, const NDArray& initState,
NDArray& update, NDArray& stateV, const double dLr, const double dMomentum) {
PointersManager manager(context, "nesterovsUpdater");
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (gradient.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
NDArray::prepareSpecialUse({ &update, &stateV }, { &gradient, &initState });
BUILD_SINGLE_SELECTOR(gradient.dataType(), nesterovsUpdaterCudaLauncher, (blocksPerGrid, threadsPerBlock,
context->getCudaStream(), gradient.specialBuffer(), gradient.specialShapeInfo(),
initState.specialBuffer(), initState.specialShapeInfo(),
update.specialBuffer(), update.specialShapeInfo(),
stateV.specialBuffer(), stateV.specialShapeInfo(), dLr, dMomentum), FLOAT_TYPES);
NDArray::registerSpecialUse({ &update, &stateV }, { &gradient, &initState });
manager.synchronize();
}
}
}
}