cavis/libnd4j/include/loops/cpu/indexreduce.hpp

155 lines
5.9 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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
******************************************************************************/
//
// Created by raver on 4/9/2018.
//
#include <loops/indexreduce.h>
#include <system/op_boilerplate.h>
#include <helpers/Loops.h>
#include <types/types.h>
#include <helpers/ConstantTadHelper.h>
#include <execution/Threads.h>
#include <loops/legacy_ops.h>
using namespace simdOps;
namespace functions {
namespace indexreduce {
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
Nd4jLong IndexReduce<X,Y>::execScalar( const int opNum, const void *x, const Nd4jLong *xShapeInfo, void *extraParams) {
RETURNING_DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(x, xShapeInfo, extraParams), INDEX_REDUCE_OPS);
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
void IndexReduce<X,Y>::exec(const int opNum,
const void *x, const Nd4jLong *xShapeInfo,
void *extraParams,
void *z, const Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
const Nd4jLong *tadShapeInfo, const Nd4jLong *tadOffset) {
DISPATCH_BY_OPNUM_TT(exec, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffset), INDEX_REDUCE_OPS);
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
template<typename OpType>
Nd4jLong IndexReduce<X, Y>::execScalar(const void *vx, const Nd4jLong *xShapeInfo, void *vextraParams) {
auto x = reinterpret_cast<const X *>(vx);
auto extraParams = reinterpret_cast<X *>(vextraParams);
//T startingVal = OpType::startingValue(x);
auto startingIndex = OpType::startingIndexValue(x);
auto len = shape::length(xShapeInfo);
auto xEws = shape::elementWiseStride(xShapeInfo);
sd::OmpLaunchHelper info(len);
uint xShapeInfoCast[MAX_RANK];
bool canCastX = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
int maxThreads = sd::math::nd4j_min<int>(64, sd::Environment::getInstance()->maxThreads());
IndexValue<X> intermediatery[64];
for (int e = 0; e < maxThreads; e++)
intermediatery[e].index = -1;
if (xEws == 1 && shape::order(xShapeInfo) == 'c') {
auto func = PRAGMA_THREADS_FOR {
intermediatery[thread_id] = OpType::startingIndexValue(x);
for (auto i = start; i < stop; i++) {
IndexValue<X> curr(x[i], i);
intermediatery[thread_id] = OpType::update(intermediatery[thread_id], curr, extraParams);
}
};
maxThreads = samediff::Threads::parallel_for(func, 0, len, 1, maxThreads);
for (int e = 0; e < maxThreads; e++)
startingIndex = OpType::update(startingIndex, intermediatery[e], extraParams);
} else {
auto func = PRAGMA_THREADS_FOR {
intermediatery[thread_id] = OpType::startingIndexValue(x);
for (auto i = start; i < stop; i++) {
auto offset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
IndexValue<X> curr(x[offset], i);
intermediatery[thread_id] = OpType::update(intermediatery[thread_id], curr, extraParams);
}
};
maxThreads = samediff::Threads::parallel_for(func, 0, len, 1, maxThreads);
for (int e = 0; e < maxThreads; e++)
startingIndex = OpType::update(startingIndex, intermediatery[e], extraParams);
}
return startingIndex.index;
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
template<typename OpType>
void IndexReduce<X, Z>::exec(const void *vx, const Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, const Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
const Nd4jLong *tadShapeInfo, const Nd4jLong *tadOffset) {
auto x = reinterpret_cast<const X *>(vx);
auto z = reinterpret_cast<Z *>(vz);
auto extraParams = reinterpret_cast<X *>(vextraParams);
const Nd4jLong zLen = shape::length(zShapeInfo);
if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) {
if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::ArrayType::EMPTY)
return;
const auto indexValue = OpType::startingIndexValue(x);
for (Nd4jLong i = 0; i < zLen; i++)
z[i] = (Z) indexValue.index;
return;
}
if(shape::isScalar(zShapeInfo)) {
z[0] = (Z) execScalar<OpType>(x,xShapeInfo,extraParams);
return;
}
auto tadOnlyShapeInfo = tadShapeInfo;
auto tadOffsets = tadOffset;
if (tadOnlyShapeInfo == nullptr || tadOffsets == nullptr) {
if (dimensionLength < 1)
return;
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
tadOnlyShapeInfo = tadPack.primaryShapeInfo();
tadOffsets = tadPack.primaryOffsets();
}
sd::IndexReductionLoops<X,Z>::template loopIndexReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams);
}
}
}