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cavis/libnd4j/include/ops/declarable/helpers/cpu/merge.cpp

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