cavis/libnd4j/include/loops/cpu/reduce/reduce_same.cpp

295 lines
13 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
******************************************************************************/
//
// @author raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <types/types.h>
#include <ShapeUtils.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);
PRAGMA_OMP_PARALLEL_FOR_IF(length > nd4j::Environment::getInstance()->elementwiseThreshold())
for (uint i = 0; i < length; i++)
z[i] = startingVal;
return;
}
if (xEws >= 1) {
z[0] = execScalar<OpType>(x, xEws, length, extraParams);
}
else {
X start = OpType::startingValue(x);
const int maxThreads = nd4j::math::nd4j_min<int>(256, omp_get_max_threads());
X intermediate[256];
for (int e = 0; e < maxThreads; e++)
intermediate[e] = start;
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
for(Nd4jLong i = 0; i < length; ++i)
intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
for (int e = 0; e < maxThreads; e++)
start = OpType::update(start, intermediate[e], extraParams);
z[0] = OpType::postProcess(start, 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 {
X start = OpType::startingValue(x);
const int maxThreads = nd4j::math::nd4j_min<int>(256, omp_get_max_threads());
X intermediate[256];
for (int e = 0; e < maxThreads; e++)
intermediate[e] = start;
uint xShapeInfoCast[MAX_RANK];
const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
for(Nd4jLong i = 0; i < length; ++i)
intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
for (int e = 0; e < maxThreads; e++)
start = OpType::update(start, intermediate[e], extraParams);
return OpType::postProcess(start, shape::length(xShapeInfo), 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) {
DISPATCH_BY_OPNUM_T(exec, PARAMS(x,
xShapeInfo,
extraParams,
z,
zShapeInfo,
dimension,
dimensionLength,
tadShapeInfo,
tadOffset),
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) {
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);
PRAGMA_OMP_PARALLEL_FOR_IF(zLength > nd4j::Environment::getInstance()->elementwiseThreshold())
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);
#else
nd4j::ReductionSameLoops<X>::template innerloopReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams);
#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);
auto startingVal = OpType::startingValue(x);
nd4j::OmpLaunchHelper info(length);
if (xEws == 1) {
PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
{
auto local = OpType::startingValue(x);
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto xi = x + threadOffset;
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
for (Nd4jLong i = 0; i < ulen; i++)
local = OpType::update(local, OpType::op(xi[i], extraParams), extraParams);
PRAGMA_OMP_CRITICAL
startingVal = OpType::update(startingVal, local, extraParams);
}
}
else {
PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
{
auto local = OpType::startingValue(x);
auto threadNum = omp_get_thread_num();
auto threadOffset = info.getThreadOffset(threadNum);
auto xi = x + xEws*threadOffset;
auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
for (Nd4jLong i = 0; i < ulen; i++)
local = OpType::update(local, OpType::op(xi[i*xEws], extraParams), extraParams);
PRAGMA_OMP_CRITICAL
startingVal = OpType::update(startingVal, local, extraParams);
}
}
return OpType::postProcess(startingVal, length, extraParams);
}
BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT ReduceSameFunction, , LIBND4J_TYPES);
}
}