wehub-resource-sync
282 lines
9.6 KiB
C++
282 lines
9.6 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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* summarystatsreduce.h
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*
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* Created on: Jan 19, 2016
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* Author: agibsonccc
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*/
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#ifndef SUMMARYSTATSREDUCE_H_
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#define SUMMARYSTATSREDUCE_H_
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#include <helpers/shape.h>
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#include <math/templatemath.h>
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#ifdef __JNI__
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#include <jni.h>
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#endif
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#include <loops/legacy_ops.h>
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#include <ops/ops.h>
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#include <system/op_boilerplate.h>
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namespace functions {
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namespace summarystats {
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// This example computes several statistical properties of a data
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// series in a single reduction. The algorithm is described in detail here:
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// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
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//
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// Thanks to Joseph Rhoads for contributing this example
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// structure used to accumulate the moments and other
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// statistical properties encountered so far.
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template <typename X>
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class SummaryStatsData {
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public:
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double n;
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double min;
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double max;
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double mean;
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double M2;
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double M3;
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double M4;
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double bias;
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SD_HOST_DEVICE SummaryStatsData() { initialize(); }
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// initialize to the identity element
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SD_HOST_DEVICE void initialize() { n = mean = M2 = M3 = M4 = bias = 0; }
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SD_HOST_DEVICE void initWithValue(X val) {
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n = 1;
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min = val;
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max = val;
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mean = val;
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M2 = 0;
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M3 = 0;
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M4 = 0;
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bias = 0;
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}
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SD_HOST_DEVICE void setValues(SummaryStatsData<X>* target) {
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n = target->n;
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min = target->min;
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max = target->max;
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mean = target->mean;
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M2 = target->M2;
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M3 = target->M3;
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M4 = target->M4;
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bias = target->bias;
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}
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SD_HOST_DEVICE double variance() {
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if (n <= 1.0) return 0.0;
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return M2 / (n);
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}
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SD_HOST_DEVICE double varianceBiasCorrected() {
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if (this->n <= 1.0) {
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return 0.0;
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}
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return M2 / (n - 1.0);
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}
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SD_HOST_DEVICE double variance_n() {
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if (n <= 1.0) return 0.0;
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return M2 / n;
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}
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SD_HOST_DEVICE double skewness() {
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return M2 > 0.0 ? sd::math::sd_sqrt<double, double>(n) * M3 / sd::math::sd_pow<double, double, double>(M2, 1.5)
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: 0.0;
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}
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SD_HOST_DEVICE double kurtosis() { return M2 > 0.0 ? n * M4 / (M2 * M2) : 0; }
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SD_HOST_DEVICE double getM2() { return M2; }
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SD_HOST_DEVICE void setM2(X m2) { M2 = m2; }
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SD_HOST_DEVICE double getM3() { return M3; }
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SD_HOST_DEVICE void setM3(X m3) { M3 = m3; }
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SD_HOST_DEVICE double getM4() { return M4; }
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SD_HOST_DEVICE void setM4(X m4) { M4 = m4; }
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SD_HOST_DEVICE double getMax() { return max; }
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SD_HOST_DEVICE void setMax(X maxI) { this->max = maxI; }
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SD_HOST_DEVICE double getMean() { return mean; }
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SD_HOST_DEVICE void setMean(X meanI) { this->mean = meanI; }
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SD_HOST_DEVICE double getMin() { return min; }
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SD_HOST_DEVICE void setMin(X minI) { this->min = minI; }
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SD_HOST_DEVICE double getN() { return n; }
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SD_HOST_DEVICE void setN(X nI) { this->n = nI; }
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};
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#ifdef __CUDACC__
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// This is the un-specialized struct. Note that we prevent instantiation of this
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// struct by putting an undefined symbol in the function body so it won't compile.
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template <typename T>
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struct SharedSummaryStatsData {
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// Ensure that we won't compile any un-specialized types
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SD_DEVICE T* getPointer() {
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extern SD_DEVICE void error(void);
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error();
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return 0;
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}
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};
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// Following are the specializations for the following types.
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// int, sd::Unsigned, char, uchar, short, ushort, long long, ulong long, bool, float, and double
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// One could also specialize it for user-defined types.
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template <>
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struct SharedSummaryStatsData<float> {
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SD_DEVICE SummaryStatsData<float>* getPointer() {
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extern __shared__ SummaryStatsData<float> s_int2[];
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return s_int2;
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}
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};
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// Following are the specializations for the following types.
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// int, sd::Unsigned, char, uchar, short, ushort, long long, ulong long, bool, float, and double
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// One could also specialize it for user-defined types.
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template <>
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struct SharedSummaryStatsData<double> {
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SD_DEVICE SummaryStatsData<double>* getPointer() {
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extern __shared__ SummaryStatsData<double> s_int6[];
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return s_int6;
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}
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};
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#endif
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/**
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* Standard deviation or variance 1 pass
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*/
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template <typename X, typename Z>
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class SummaryStatsReduce {
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public:
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// calculate an update of the reduce operation
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SD_HOST_DEVICE static SummaryStatsData<X> update(SummaryStatsData<X> x, SummaryStatsData<X> y, void* extraParams) {
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if ((long)x.n == 0 && (long)y.n > 0)
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return y;
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else if ((long)x.n > 0 && (long)y.n == 0)
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return x;
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SummaryStatsData<X> vz;
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double n = x.n + y.n;
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double n2 = n * n;
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double n3 = n2 * n;
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double delta = y.mean - x.mean;
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double delta2 = delta * delta;
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double delta3 = delta2 * delta;
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double delta4 = delta3 * delta;
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// Basic number of samples (n), min, and max
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vz.n = n;
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vz.min = sd::math::sd_min(x.min, y.min);
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vz.max = sd::math::sd_max(x.max, y.max);
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double meanD = x.mean + delta * y.n / n;
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vz.mean = meanD;
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double M2D = x.M2 + y.M2;
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M2D += delta2 * x.n * y.n / n;
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vz.M2 = M2D;
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vz.M3 = x.M3 + y.M3;
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vz.M3 += delta3 * x.n * y.n * (x.n - y.n) / n2;
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vz.M3 += 3.0 * delta * (x.n * y.M2 - y.n * x.M2) / n;
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vz.M4 = x.M4 + y.M4;
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vz.M4 += delta4 * x.n * y.n * (x.n * x.n - x.n * y.n + y.n * y.n) / n3;
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vz.M4 += 6.0 * delta2 * (x.n * x.n * y.M2 + y.n * y.n * x.M2) / n2;
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vz.M4 += 4.0 * delta * (x.n * y.M3 - y.n * x.M3) / n;
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return vz;
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}
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#ifdef __CUDACC__
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static SD_INLINE SD_DEVICE Z startingValue(X * input) { return static_cast<Z>(0); }
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template <typename OpType>
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static SD_DEVICE void aggregatePartials(SummaryStatsData<X>* sPartials, sd::LongType tid, sd::LongType numElements,
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void* extraParams);
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template <typename OpType>
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static SD_DEVICE void transform(void * dx, sd::LongType * xShapeInfo, void* extraParams, void* vz,
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sd::LongType * zShapeInfo, sd::LongType* dimension,
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sd::LongType dimensionLength,
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int postProcessOrNot, sd::LongType* allocationBuffer, void* reductionBuffer,
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sd::LongType * tadOnlyShapeInfo, sd::LongType * tadOffsets);
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static SD_DEVICE void transform( int opNum, void * dx, sd::LongType * xShapeInfo, void* extraParams,
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void* vz, sd::LongType * zShapeInfo, sd::LongType* dimension,
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sd::LongType dimensionLength,
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int postProcessOrNot, sd::LongType* allocationBuffer, void* reductionBuffer,
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sd::LongType * tadOnlyShapeInfo, sd::LongType * tadOffsets);
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static SD_HOST void execSummaryStatsReduceScalar(dim3& launchDims, cudaStream_t* stream, int opNum, void * x,
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sd::LongType * xShapeInfo, sd::LongType * hxShapeInfo,
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void* extraParams, void* vz, sd::LongType * zShapeInfo,
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sd::LongType * hzShapeInfo, sd::LongType * tadShapeInfo,
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sd::LongType * tadOffsets, bool biasCorrected,
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void* reductionBuffer);
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static SD_HOST void execSummaryStatsReduce(dim3& launchDims, cudaStream_t* stream, int opNum, void * x,
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sd::LongType * xShapeInfo, sd::LongType * hxShapeInfo,
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void* extraParams, void* vz, sd::LongType * zShapeInfo,
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sd::LongType * hzShapeInfo, sd::LongType * tadShapeInfo,
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sd::LongType * tadOffsets, bool biasCorrected, void* reductionBuffer);
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#else
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static Z execScalar(int opNum, bool biasCorrected, void *x, sd::LongType *xShapeInfo, void *extraParams);
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static void execScalar(int opNum, bool biasCorrected, void *x, sd::LongType *xShapeInfo,
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void *extraParams, void *vz, sd::LongType *resultShapeInfoBuffer);
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static void exec(int opNum, bool biasCorrected, void *x, sd::LongType *xShapeInfo, void *extraParams,
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void *vz, sd::LongType *resultShapeInfoBuffer, sd::LongType *dimension,
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sd::LongType dimensionLength);
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template <typename OpType>
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static Z execScalar(bool biasCorrected, void *x, sd::LongType *xShapeInfo, void *extraParams);
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template <typename OpType>
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static void execScalar(bool biasCorrected, void *x, sd::LongType *xShapeInfo, void *extraParams, void *vz,
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sd::LongType *resultShapeInfoBuffer);
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template <typename OpType>
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static void exec(bool biasCorrected, void *x, sd::LongType *xShapeInfo, void *extraParams, void *vz,
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sd::LongType *resultShapeInfoBuffer, sd::LongType *dimension, sd::LongType dimensionLength);
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#endif
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};
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} // namespace summarystats
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} // namespace functions
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#endif /* SUMMARYSTATSREDUCE_H_ */
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