232 lines
9.9 KiB
C++
232 lines
9.9 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 <system/op_boilerplate.h>
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#include <loops/reduce_float.h>
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#include <loops/legacy_ops.h>
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#include <helpers/OmpLaunchHelper.h>
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#include <helpers/Loops.h>
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#include <helpers/ConstantTadHelper.h>
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#include <helpers/ShapeBuilders.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 ReduceFloatFunction<X,Z>::execScalar(const void *vx, const Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vz, const Nd4jLong *zShapeInfo) {
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auto x = reinterpret_cast<const X *>(vx);
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auto z = reinterpret_cast<Z *>(vz);
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auto extraParams = reinterpret_cast<Z *>(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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if (std::is_same<OpType, simdOps::Mean<X,Z>>::value) {
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z[0] = sd::DataTypeUtils::nanOrZero<Z>();
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} else {
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z[0] = OpType::startingValue(x);
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}
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return;
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}
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if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) {
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if(sd::ArrayOptions::arrayType(zShapeInfo) == sd::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 > 0) {
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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 = sd::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
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int maxThreads = sd::math::nd4j_min<int>(64, sd::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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for (auto i = start; i < stop; i++)
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intermediate[thread_id] = OpType::update(intermediate[thread_id], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
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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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// write out results
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z[0] = OpType::postProcess(intermediate[0], 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 ReduceFloatFunction<X, Z>::execScalar(const void *vx, const Nd4jLong *xShapeInfo, void *vextraParams) {
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auto x = reinterpret_cast<const X *>(vx);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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const Nd4jLong length = shape::length(xShapeInfo);
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int xEws = shape::elementWiseStride(xShapeInfo);
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if (xEws > 0) {
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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 = sd::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 ReduceFloatFunction<X, Y>::execScalar(const int opNum,
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const void *x, const Nd4jLong *xShapeInfo,
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void *extraParams) {
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RETURNING_DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(x, xShapeInfo, extraParams), REDUCE_FLOAT_OPS);
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}
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template <typename X, typename Y>
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void ReduceFloatFunction<X, Y>::execScalar(const int opNum,
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const void *x, const Nd4jLong *xShapeInfo,
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void *extraParams,
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void *z, const Nd4jLong *zShapeInfo) {
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DISPATCH_BY_OPNUM_TT(execScalar, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo), REDUCE_FLOAT_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 ReduceFloatFunction<X,Z>::exec(const void *x, const Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vresult, const 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 ReduceFloatFunction<X, Z>::execScalar(const void *vx, Nd4jLong xEws, Nd4jLong length, void *vextraParams) {
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auto x = reinterpret_cast<const X *>(vx);
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auto extraParams = reinterpret_cast<Z *>(vextraParams);
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int maxThreads = sd::math::nd4j_min<int>(64, sd::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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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Z>
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template<typename OpType>
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void _CUDA_H ReduceFloatFunction<X, Z>::exec(sd::memory::Workspace* workspace, const void *vx, const Nd4jLong *xShapeInfo, void *vextraParams, void *vz, const Nd4jLong *zShapeInfo, const int* dims) {
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const X* x = reinterpret_cast<const X*>(vx);
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Z* z = reinterpret_cast<Z*>(vz);
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Z* extraParams = reinterpret_cast<Z*>(vextraParams);
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const int xRank = shape::rank(xShapeInfo);
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const int zRank = shape::rank(zShapeInfo);
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if(sd::ArrayOptions::arrayType(xShapeInfo) == sd::ArrayType::EMPTY) {
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const auto startingVal = std::is_same<OpType, simdOps::Mean<X,Z>>::value ? sd::DataTypeUtils::nanOrZero<Z>() : static_cast<Z>(OpType::startingValue(x));
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const auto zLen = shape::length(zShapeInfo);
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for (Nd4jLong i = 0; i < zLen; i++)
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z[i] = startingVal;
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return;
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}
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if (shape::length(zShapeInfo) == 1) {
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z[0] = execScalar<OpType>(x, xShapeInfo, extraParams);
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return;
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}
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if (OpType::requiresSpecialAccumulation) {
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OpType::execSpecial(x, xShapeInfo, extraParams, z, zShapeInfo, const_cast<int*>(dims)+zRank, xRank-zRank, nullptr, nullptr);
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return;
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}
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#ifdef INLINE_LOOPS
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sd::ReductionLoops<X,Z,Z>::template loopReduce<OpType>(workspace, x, xShapeInfo, z, zShapeInfo, dims, extraParams);
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#else
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sd::ReductionFloatLoops<X,Z>::template innerloopReduce<OpType>(workspace, x, xShapeInfo, z, zShapeInfo, dims, extraParams);
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#endif
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}
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////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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void ReduceFloatFunction<X, Y>::exec(const int opNum, sd::memory::Workspace* workspace, const void *vx, const Nd4jLong *xShapeInfo, void *vextraParams, void *vz, const Nd4jLong *zShapeInfo, const int *dims) {
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DISPATCH_BY_OPNUM_TT(exec, PARAMS(workspace, vx, xShapeInfo, vextraParams, vz, zShapeInfo, dims), REDUCE_FLOAT_OPS);
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}
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}
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} |