/*******************************************************************************
* 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
******************************************************************************/
//
// @author raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <types/types.h>
#include <op_boilerplate.h>
#include <loops/reduce_same.h>
#include <loops/legacy_ops.h>
#include <OmpLaunchHelper.h>
#include <chrono>
#include <helpers/Loops.h>
#include <helpers/ConstantTadHelper.h>
using namespace simdOps;
namespace functions {
namespace reduce {
template <typename X>
template <typename OpType>
void _CUDA_H ReduceSameFunction<X>::execScalar(void *vx,
Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz,
Nd4jLong *zShapeInfo) {
auto x = reinterpret_cast<X *>(vx);
auto z = reinterpret_cast<X *>(vz);
auto extraParams = reinterpret_cast<X *>(vextraParams);
const auto length = shape::length(xShapeInfo);
const auto xEws = shape::elementWiseStride(xShapeInfo);
const int rank = shape::rank(xShapeInfo);
if (shape::isEmpty(xShapeInfo)) {
z[0] = OpType::startingValue(x);
return;
}
if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) {
if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY)
return;
const auto startingVal = OpType::startingValue(x);
for (uint i = 0; i < length; i++)
z[i] = startingVal;
return;
}
if (xEws >= 1) {
z[0] = execScalar<OpType>(x, xEws, length, extraParams);
}
else {
auto startingValue = OpType::startingValue(x);
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
int maxThreads = nd4j::math::nd4j_min<int>(64, nd4j::Environment::getInstance()->maxThreads());
X intermediate[64];
PRAGMA_OMP_SIMD
for (auto e = 0; e < maxThreads; e++)
intermediate[e] = OpType::startingValue(x);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; i++)
intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
};
maxThreads = samediff::Threads::parallel_for(func, 0, length, 1, maxThreads);
// merge results
for (int e = 1; e < maxThreads; e++)
intermediate[0] = OpType::update(intermediate[0], intermediate[e], extraParams);
// write out results
z[0] = OpType::postProcess(intermediate[0], length, extraParams);
}
}
template <typename X>
template <typename OpType>
X _CUDA_H ReduceSameFunction<X>::execScalar(void *vx,
Nd4jLong *xShapeInfo,
void *vextraParams) {
auto x = reinterpret_cast<X *>(vx);
auto extraParams = reinterpret_cast<X *>(vextraParams);
const Nd4jLong length = shape::length(xShapeInfo);
const auto xEws = shape::elementWiseStride(xShapeInfo);
if (xEws >= 1) {
return execScalar<OpType>(x, xEws, length, extraParams);
} else {
auto startingValue = OpType::startingValue(x);
uint xShapeInfoCast[MAX_RANK];
bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
for (auto i = 0; i < length; i++)
startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
return OpType::postProcess(startingValue, length, extraParams);
}
}
template <typename X>
X ReduceSameFunction<X>::execScalar(const int opNum,
void *x,
Nd4jLong *xShapeInfo,
void *extraParams) {
RETURNING_DISPATCH_BY_OPNUM_T(execScalar, PARAMS(x, xShapeInfo, extraParams), REDUCE_SAME_OPS);
}
template <typename X>
void ReduceSameFunction<X>::execScalar(const int opNum,
void *x,
Nd4jLong *xShapeInfo,
void *extraParams,
void *z,
Nd4jLong *zShapeInfo) {
DISPATCH_BY_OPNUM_T(execScalar, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo), REDUCE_SAME_OPS);
}
template <typename X>
void ReduceSameFunction<X>::exec(const int opNum,
void *x,
Nd4jLong *xShapeInfo,
void *extraParams,
void *z,
Nd4jLong *zShapeInfo,
int *dimension,
int dimensionLength,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffset, int64_t start, int64_t stop) {
DISPATCH_BY_OPNUM_T(exec, PARAMS(x,
xShapeInfo,
extraParams,
z,
zShapeInfo,
dimension,
dimensionLength,
tadShapeInfo,
tadOffset, start, stop),
REDUCE_SAME_OPS);
}
template <typename X>
template <typename OpType>
void _CUDA_H ReduceSameFunction<X>::exec(void *vx,
Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz,
Nd4jLong *zShapeInfo,
int *dimension,
int dimensionLength,
Nd4jLong *tadShapeInfo,
Nd4jLong *tadOffset, int64_t start, int64_t stop) {
auto x = reinterpret_cast<X *>(vx);
auto z = reinterpret_cast<X *>(vz);
auto extraParams = reinterpret_cast<X *>(vextraParams);
auto zLength = shape::length(zShapeInfo);
if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) {
if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY)
return;
const auto startingVal = OpType::startingValue(x);
for (uint i = 0; i < zLength; i++)
z[i] = startingVal;
return;
}
//pre squeezed: this is for keeping the pointer to the original
//shape information for tad offset
//the squeezed information doesn't render the right strides for
//tad offset
// || tad.wholeThing
if (zLength == 1 || dimension == nullptr || dimensionLength == shape::rank(xShapeInfo)) {
z[0] = execScalar<OpType>(x, xShapeInfo, extraParams);
return;
}
if (OpType::requiresSpecialAccumulation) {
OpType::execSpecial(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffset);
return;
}
auto tadOnlyShapeInfo = tadShapeInfo;
auto tadOffsets = tadOffset;
if (tadOnlyShapeInfo == nullptr || tadOffsets == nullptr) {
if (dimensionLength < 1)
return;
auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
tadOnlyShapeInfo = tadPack.primaryShapeInfo();
tadOffsets = tadPack.primaryOffsets();
}
#ifdef INLINE_LOOPS
nd4j::ReductionLoops<X,X,X>::template loopReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop);
#else
nd4j::ReductionSameLoops<X>::template innerloopReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop);
#endif
}
template <typename X>
template<typename OpType>
void _CUDA_H ReduceSameFunction<X>::exec(void *x,
Nd4jLong *xShapeInfo,
void *extraParams,
void *vz,
Nd4jLong *zShapeInfo) {
// FIXME: wtf???
auto z = reinterpret_cast<X*>(vz);
z[0] = execScalar<OpType>(x, xShapeInfo, extraParams);
}
template <typename X>
template <typename OpType>
X _CUDA_H ReduceSameFunction<X>::execScalar(void *vx, Nd4jLong xEws, Nd4jLong length, void *vextraParams) {
auto x = reinterpret_cast<X *>(vx);
auto extraParams = reinterpret_cast<X *>(vextraParams);
int maxThreads = nd4j::math::nd4j_min<int>(64, nd4j::Environment::getInstance()->maxThreads());
X intermediate[64];
PRAGMA_OMP_SIMD
for (auto e = 0; e < maxThreads; e++)
intermediate[e] = OpType::startingValue(x);
auto func = PRAGMA_THREADS_FOR {
if (xEws == 1) {
for (auto i = start; i < stop; i++)
intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[i], extraParams), extraParams);
} else {
for (auto i = start; i < stop; i++)
intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[i * xEws], extraParams), extraParams);
}
};
maxThreads = samediff::Threads::parallel_for(func, 0, length, 1, maxThreads);
// merge results
for (int e = 1; e < maxThreads; e++)
intermediate[0] = OpType::update(intermediate[0], intermediate[e], extraParams);
// return result
return OpType::postProcess(intermediate[0], length, extraParams);
}
BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT ReduceSameFunction, , LIBND4J_TYPES);
}
}