256 lines
12 KiB
Plaintext
256 lines
12 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, typename Z>
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static __global__ void global_mergeMaxIndex_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<Z*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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Z mIdx(0);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape)];;
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if (mVal < val) {
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mIdx = static_cast<Z>(i);
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mVal = val;
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}
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}
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__syncthreads();
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output[shape::getIndexOffset(e, outputShape)] = mIdx;
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}
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}
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template <typename T, typename Z>
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static void mergeMaxIndex_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMaxIndex");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeMaxIndex_<T,Z><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeMaxIndex(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({&output}, {});
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for (auto v:inArrs)
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v->syncToDevice();
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BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({&output}, {});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void global_mergeMax_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape)];;
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if (mVal < val)
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mVal = val;
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}
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__syncthreads();
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output[shape::getIndexOffset(e, outputShape)] = mVal;
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}
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}
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template<typename T>
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static void mergeMax_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMax");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeMax_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeMax(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({&output}, {});
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for (auto v:inArrs)
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v->syncToDevice();
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
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NDArray::registerSpecialUse({&output}, {});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void global_mergeAvg_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T sum(0.0f);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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sum += x[shape::getIndexOffset(e, xShape)];
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}
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output[shape::getIndexOffset(e, outputShape)] = sum / numArrays;
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}
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}
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template<typename T>
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static void mergeAvg_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAvg");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeAvg_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeAvg(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({&output}, {});
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for (auto v:inArrs)
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v->syncToDevice();
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), FLOAT_TYPES);
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NDArray::registerSpecialUse({&output}, {});
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void global_mergeAdd_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T sum(0.0f);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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sum += x[shape::getIndexOffset(e, xShape)];
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}
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output[shape::getIndexOffset(e, outputShape)] = sum;
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}
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}
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template<typename T>
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static void mergeAdd_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAdd");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeAdd_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), NUMERIC_TYPES);
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void mergeAdd(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({&output}, {});
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for (auto v:inArrs)
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v->syncToDevice();
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), NUMERIC_TYPES);
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NDArray::registerSpecialUse({&output}, {});
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}
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}
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}
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} |