132 lines
5.9 KiB
Plaintext
132 lines
5.9 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
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//
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#include<ops/declarable/helpers/transforms.h>
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#include <array/ResultSet.h>
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#include <helpers/ShapeUtils.h>
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#include <numeric>
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#include <array/NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <helpers/PointersManager.h>
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#include <helpers/ConstantTadHelper.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__ static void scatterUpdateCuda(const int opCode, const int numOfInd,
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void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets,
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void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets,
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const int* indexes) {
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__shared__ T *x, *y;
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__shared__ Nd4jLong arrLenX, arrLenY;
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for (int e = 0; e < numOfInd; e++ ) {
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const auto xIndex = indexes[e];
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const bool isOwner = xIndex < gridDim.x ? blockIdx.x == xIndex : blockIdx.x == xIndex % gridDim.x;
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if (!isOwner)
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continue;
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if (threadIdx.x == 0) {
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x = reinterpret_cast<T*>(vx) + xOffsets[xIndex];
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y = reinterpret_cast<T*>(vy) + yOffsets[e];
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arrLenX = shape::length(xShapeInfo);
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arrLenY = shape::length(yShapeInfo);
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}
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__syncthreads();
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if (arrLenX != arrLenY)
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return;
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for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
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const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
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switch (opCode) {
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case 0:
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x[xOffset] += y[yOffset];
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break;
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case 1:
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x[xOffset] -= y[yOffset];
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break;
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case 2:
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x[xOffset] *= y[yOffset];
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break;
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case 3:
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x[xOffset] /= y[yOffset];
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break;
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case 4:
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x[xOffset] = y[yOffset] - x[xOffset];
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break;
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case 5:
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x[xOffset] = y[yOffset] / x[xOffset];
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break;
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case 6:
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x[xOffset] = y[yOffset];
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break;
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default:
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continue;
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}
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}
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__syncthreads();
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}
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}
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template<typename T>
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__host__ static void scatterUpdateCudaLauncher(const cudaStream_t* stream, const int opCode, const int numOfInd, void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets, void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets, const int* indexes) {
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scatterUpdateCuda<T><<<512, 256, MAX_NUM_THREADS, *stream>>>(opCode, numOfInd, vx, xShapeInfo, xOffsets, vy, yShapeInfo, yOffsets, indexes);
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}
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//////////////////////////////////////////////////////////////////////////
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void scatterUpdate(sd::LaunchContext* context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {
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const int opCode = (*intArgs)[0];
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const int numOfDims = (*intArgs)[1];
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const int numOfInd = (*intArgs)[2 + numOfDims];
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std::vector<int> tadDimensions(numOfDims);
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for (int e = 2; e < 2 + numOfDims; e++)
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tadDimensions[e-2] = (*intArgs)[e];
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auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.shapeInfo(), tadDimensions);
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auto packY = ConstantTadHelper::getInstance()->tadForDimensions(updates.shapeInfo(), tadDimensions);
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NDArray indices(const_cast<int*>(intArgs->data()) + numOfDims + 3, 'c', {numOfInd}, sd::DataType::INT32, context);
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PointersManager manager(context, "scatterUpdate");
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NDArray::prepareSpecialUse({&input}, {&input, &updates, &indices});
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BUILD_SINGLE_SELECTOR(input.dataType(), scatterUpdateCudaLauncher, (context->getCudaStream(), opCode, numOfInd, input.specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), updates.specialBuffer(), packY.platformShapeInfo(), packY.platformOffsets(), reinterpret_cast<int*>(indices.specialBuffer())), LIBND4J_TYPES);
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NDArray::registerSpecialUse({&input}, {&input, &updates, &indices});
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manager.synchronize();
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}
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}
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}
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} |