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