278 lines
9.1 KiB
C++
278 lines
9.1 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019-2020 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 Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
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// @author Oleh Semeniv (oleg.semeniv@gmail.com)
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//
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#include <ops/declarable/helpers/transforms.h>
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#include <helpers/Loops.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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static void mergeMaxIndex_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
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const Nd4jLong numArgs = inArrs.size();
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auto x = inArrs[0];
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) {
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T max = -DataTypeUtils::max<T>();
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Nd4jLong idx = 0;
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for (Nd4jLong i = 0; i < numArgs; i++) {
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T v = inArrs[i]->e<T>(e);
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if (v > max) {
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max = v;
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idx = i;
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}
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}
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output.p(e, idx);
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}
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};
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samediff::Threads::parallel_for(func, 0, x->lengthOf());
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}
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void mergeMaxIndex(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), mergeMaxIndex_, (inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeMax_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
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const Nd4jLong numArgs = inArrs.size();
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auto x = inArrs[0];
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) {
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T max = -DataTypeUtils::max<T>();
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for (Nd4jLong i = 0; i < numArgs; i++) {
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T v = inArrs[i]->e<T>(e);
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if (v > max)
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max = v;
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}
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output.p(e, max);
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}
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};
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samediff::Threads::parallel_for(func, 0, x->lengthOf());
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}
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void mergeMax(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeMaxBp_(const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
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// outArrs.size() == inArrs.size() - 1
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const Nd4jLong numArgs = outArrs.size();
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// last array is gradient
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const auto gradient = inArrs[numArgs]->bufferAsT<T>();
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auto length = inArrs[numArgs]->lengthOf();
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bool bSameOrderAndEws1 = (1 == inArrs[numArgs]->ews());
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if (bSameOrderAndEws1) {
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auto gradOrdering = inArrs[numArgs]->ordering();
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for (int i = 0; i < numArgs; ++i) {
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bSameOrderAndEws1 &= (gradOrdering == inArrs[i]->ordering());
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bSameOrderAndEws1 &= (1 == inArrs[i]->ews());
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bSameOrderAndEws1 &= (gradOrdering == outArrs[i]->ordering());
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bSameOrderAndEws1 &= (1 == outArrs[i]->ews());
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}
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}
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if(bSameOrderAndEws1){
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auto func = PRAGMA_THREADS_FOR{
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for (auto e = start; e < stop; e++) {
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T max = -DataTypeUtils::max<T>();
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Nd4jLong nMaxIndex = 0;
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for (Nd4jLong i = 0; i < numArgs; i++) {
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const T* v = inArrs[i]->bufferAsT<T>();
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if (v[e] > max) {
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max = v[e];
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nMaxIndex = i;
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}
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}
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T* z = outArrs[nMaxIndex]->bufferAsT<T>();
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z[e] = gradient[e];
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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return;
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}
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auto gradShape = inArrs[numArgs]->getShapeInfo();
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std::vector<bool> vbSameShaepeAndStrides(numArgs);
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for (int i = 0; i < numArgs; ++i) {
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vbSameShaepeAndStrides[i] = shape::haveSameShapeAndStrides(gradShape, inArrs[i]->getShapeInfo());
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}
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auto func = PRAGMA_THREADS_FOR{
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int coords[MAX_RANK];
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for (auto e = start; e < stop; e++) {
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shape::index2coordsCPU(start, e, gradShape, coords);
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const auto gradOffset = shape::getOffset(gradShape, coords);
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T max = -DataTypeUtils::max<T>();
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Nd4jLong nMaxIndex = 0;
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for (Nd4jLong i = 0; i < numArgs; i++) {
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const auto xOffset = vbSameShaepeAndStrides[i] ? gradOffset : shape::getOffset(inArrs[i]->getShapeInfo(), coords);
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const T* v = inArrs[i]->bufferAsT<T>();
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if (v[xOffset] > max) {
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max = v[xOffset];
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nMaxIndex = i;
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}
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}
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const auto zOffset = vbSameShaepeAndStrides[nMaxIndex] ? gradOffset : shape::getOffset(outArrs[nMaxIndex]->getShapeInfo(), coords);
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T* z = outArrs[nMaxIndex]->bufferAsT<T>();
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z[zOffset] = gradient[gradOffset];
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}
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};
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samediff::Threads::parallel_for(func, 0, length);
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return;
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}
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void mergeMaxBp(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
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BUILD_SINGLE_SELECTOR(outArrs[0]->dataType(), mergeMaxBp_, (inArrs, outArrs), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeAvg_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
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const Nd4jLong numArgs = inArrs.size();
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const T factor = 1.f / numArgs;
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auto x = inArrs[0];
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) {
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T sum = 0.;
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for (Nd4jLong i = 0; i < numArgs; i++) {
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T v = inArrs[i]->e<T>(e);
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sum += v;
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}
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output.p<T>(e, sum * factor);
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}
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};
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samediff::Threads::parallel_for(func, 0, x->lengthOf());
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}
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void mergeAvg(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeAvgBp_(const NDArray& gradient, std::vector<NDArray*>& outArrs) {
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const Nd4jLong numArgs = outArrs.size();
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auto func = PRAGMA_THREADS_FOR{
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for (auto e = start; e < stop; e++) {
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T v = gradient.e<T>(e) / numArgs;
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for (Nd4jLong i = 0; i < numArgs; i++) {
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outArrs[i]->p<T>(e, v);
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, gradient.lengthOf());
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}
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void mergeAvgBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
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BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAvgBp_, (gradient, outArrs), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeAdd_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
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const Nd4jLong numArgs = inArrs.size();
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auto x = inArrs[0];
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) {
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T sum = (T) 0.f;
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for (Nd4jLong i = 0; i < numArgs; i++)
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sum += inArrs[i]->e<T>(e);
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output.p(e, sum);
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}
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};
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samediff::Threads::parallel_for(func, 0, x->lengthOf());
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}
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void mergeAdd(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void mergeAddBp_(const NDArray& gradient, std::vector<NDArray*>& outArrs) {
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const Nd4jLong numArgs = outArrs.size();
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auto func = PRAGMA_THREADS_FOR{
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for (auto e = start; e < stop; e++) {
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T v = gradient.e<T>(e);
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for (Nd4jLong i = 0; i < numArgs; i++) {
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outArrs[i]->p<T>(e, v);
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}
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}
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};
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samediff::Threads::parallel_for(func, 0, gradient.lengthOf());
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}
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void mergeAddBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
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BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAddBp_, (gradient, outArrs), LIBND4J_TYPES);
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}
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}
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}
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}
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