241 lines
10 KiB
C++
241 lines
10 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <types/types.h>
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#include <op_boilerplate.h>
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#include <loops/reduce_bool.h>
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#include <loops/legacy_ops.h>
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#include <OmpLaunchHelper.h>
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#include <helpers/Loops.h>
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#include <helpers/ConstantTadHelper.h>
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using namespace simdOps;
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namespace functions {
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namespace reduce {
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template <typename X, typename Z>
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template <typename OpType>
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void _CUDA_H ReduceBoolFunction<X,Z>::execScalar(void *vx,
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Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vz,
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Nd4jLong *zShapeInfo) {
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auto x = reinterpret_cast<X *>(vx);
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auto z = reinterpret_cast<Z *>(vz);
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auto extraParams = reinterpret_cast<X *>(vextraParams);
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const Nd4jLong length = shape::length(xShapeInfo);
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auto xEws = shape::elementWiseStride(xShapeInfo);
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if (shape::isEmpty(xShapeInfo)) {
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z[0] = OpType::startingValue(x);
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return;
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}
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if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) {
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if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY)
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return;
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const auto startingVal = OpType::startingValue(x);
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for (Nd4jLong i = 0; i < length; i++)
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z[i] = startingVal;
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return;
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}
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if (xEws >= 1) {
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z[0] = execScalar<OpType>(x, xEws, length, extraParams);
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}
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else {
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auto startingValue = OpType::startingValue(x);
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uint xShapeInfoCast[MAX_RANK];
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const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
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for (Nd4jLong i = 0; i < length; i++)
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startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
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z[0] = OpType::postProcess(startingValue, length, extraParams);
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}
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}
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template <typename X, typename Z>
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template <typename OpType>
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Z _CUDA_H ReduceBoolFunction<X, Z>::execScalar(void *vx, Nd4jLong *xShapeInfo, void *vextraParams) {
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auto x = reinterpret_cast<X *>(vx);
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auto extraParams = reinterpret_cast<X *>(vextraParams);
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const Nd4jLong length = shape::length(xShapeInfo);
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auto xEws = shape::elementWiseStride(xShapeInfo);
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if (xEws >= 1) {
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return execScalar<OpType>(x, xEws, length, extraParams);
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}
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else {
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auto startingValue = OpType::startingValue(x);
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uint xShapeInfoCast[MAX_RANK];
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bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
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for (Nd4jLong i = 0; i < length; i++)
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startingValue = OpType::update(startingValue, OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
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return OpType::postProcess(startingValue, length, extraParams);
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}
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}
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template <typename X, typename Y>
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Y ReduceBoolFunction<X, Y>::execScalar(const int opNum,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams) {
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RETURNING_DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(x, xShapeInfo, extraParams), REDUCE_BOOL_OPS);
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}
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template <typename X, typename Y>
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void ReduceBoolFunction<X, Y>::execScalar(const int opNum,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z,
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Nd4jLong *zShapeInfo) {
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DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo), REDUCE_BOOL_OPS);
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}
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template <typename X, typename Y>
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void ReduceBoolFunction<X, Y>::exec(const int opNum,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z,
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Nd4jLong *zShapeInfo,
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int *dimension,
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int dimensionLength,
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Nd4jLong *tadShapeInfo,
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Nd4jLong *tadOffset, int64_t start, int64_t stop) {
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DISPATCH_BY_OPNUM_TT(exec, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, tadShapeInfo, tadOffset, start, stop), REDUCE_BOOL_OPS);
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}
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template <typename X, typename Z>
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template <typename OpType>
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void _CUDA_H ReduceBoolFunction<X,Z>::exec(void *vx,
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Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vresult,
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Nd4jLong *zShapeInfo,
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int *dimension,
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int dimensionLength,
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Nd4jLong *tadShapeInfo,
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Nd4jLong *tadOffset, int64_t start, int64_t stop) {
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auto x = reinterpret_cast<X *>(vx);
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auto z = reinterpret_cast<Z *>(vresult);
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auto extraParams = reinterpret_cast<X *>(vextraParams);
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auto resultLength = shape::length(zShapeInfo);
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if(nd4j::ArrayOptions::arrayType(xShapeInfo) == nd4j::ArrayType::EMPTY) {
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if(nd4j::ArrayOptions::arrayType(zShapeInfo) == nd4j::ArrayType::EMPTY)
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return;
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const auto startingVal = OpType::startingValue(x);
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for (Nd4jLong i = 0; i < resultLength; i++)
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z[i] = startingVal;
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return;
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}
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//pre squeezed: this is for keeping the pointer to the original
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//shape information for tad offset
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//the squeezed information doesn't render the right strides for
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//tad offset
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// || tad.wholeThing
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if (resultLength == 1 || dimension == nullptr || dimensionLength == shape::rank(xShapeInfo)) {
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z[0] = execScalar<OpType>(x, xShapeInfo, extraParams);
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return;
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}
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auto tadOnlyShapeInfo = tadShapeInfo;
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auto tadOffsets = tadOffset;
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if (tadOnlyShapeInfo == nullptr || tadOffsets == nullptr) {
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if (dimensionLength < 1)
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return;
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auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
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tadOnlyShapeInfo = tadPack.primaryShapeInfo();
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tadOffsets = tadPack.primaryOffsets();
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}
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#ifdef INLINE_LOOPS
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nd4j::ReductionLoops<X,Z,X>::template loopReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop);
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#else
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nd4j::ReductionBoolLoops<X,Z>::template innerloopReduce<OpType>(x, xShapeInfo, z, zShapeInfo, tadOnlyShapeInfo, tadOffsets, extraParams, start, stop);
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#endif
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}
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template <typename X, typename Z>
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template<typename OpType>
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void _CUDA_H ReduceBoolFunction<X,Z>::exec(void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vresult,
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Nd4jLong *resultShapeInfo) {
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// FIXME: wtf???
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auto z = reinterpret_cast<Z*>(vresult);
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z[0] = execScalar<OpType>(x, xShapeInfo, extraParams);
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}
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template <typename X, typename Z>
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template <typename OpType>
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Z _CUDA_H ReduceBoolFunction<X, Z>::execScalar(void *vx, Nd4jLong xEws, Nd4jLong length, void *vextraParams) {
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auto x = reinterpret_cast<X *>(vx);
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auto extraParams = reinterpret_cast<X *>(vextraParams);
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int maxThreads = nd4j::math::nd4j_min<int>(64, nd4j::Environment::getInstance()->maxThreads());
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Z intermediate[64];
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PRAGMA_OMP_SIMD
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for (auto e = 0; e < maxThreads; e++)
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intermediate[e] = OpType::startingValue(x);
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auto func = PRAGMA_THREADS_FOR {
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if (xEws == 1) {
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for (auto i = start; i < stop; i++)
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intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[i], extraParams), extraParams);
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} else {
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for (auto i = start; i < stop; i++)
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intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[i * xEws], extraParams), extraParams);
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}
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};
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maxThreads = samediff::Threads::parallel_for(func, 0, length, 1, maxThreads);
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// merge results
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for (int e = 1; e < maxThreads; e++)
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intermediate[0] = OpType::update(intermediate[0], intermediate[e], extraParams);
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// return result
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return OpType::postProcess(intermediate[0], length, extraParams);
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}
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT ReduceBoolFunction, , LIBND4J_TYPES, BOOL_TYPES);
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}
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} |