345 lines
11 KiB
C++
Executable File
345 lines
11 KiB
C++
Executable File
/*******************************************************************************
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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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* 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 <templatemath.h>
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#include <dll.h>
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#include <helpers/shape.h>
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#ifdef __CUDACC__
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#include <cuda.h>
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#include <cuda_runtime.h>
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#define host_and_device inline __host__ __device__
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#else
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#define host_and_device inline
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#endif
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#ifdef __JNI__
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#include <jni.h>
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#endif
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#include <ops/ops.h>
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#include <op_boilerplate.h>
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#include "legacy_ops.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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_CUDA_HD SummaryStatsData() {
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initialize();
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}
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// initialize to the identity element
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_CUDA_HD void initialize() {
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n = mean = M2 = M3 = M4 = bias = 0;
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}
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_CUDA_HD 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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_CUDA_HD 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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_CUDA_HD double variance() {
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if (n <= 1.0)
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return 0.0;
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return M2 / (n);
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}
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_CUDA_HD 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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_CUDA_HD double variance_n() {
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if (n <= 1.0)
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return 0.0;
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return M2 / n;
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}
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_CUDA_HD double skewness() { return M2 > 0.0 ? nd4j::math::nd4j_sqrt<double, double>(n) * M3 / nd4j::math::nd4j_pow<double, double, double>(M2, 1.5) : 0.0; }
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_CUDA_HD double kurtosis() { return M2 > 0.0 ? n * M4 / (M2 * M2) : 0; }
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_CUDA_HD double getM2() {
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return M2;
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}
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_CUDA_HD void setM2(X m2) {
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M2 = m2;
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}
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_CUDA_HD double getM3() {
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return M3;
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}
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_CUDA_HD void setM3(X m3) {
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M3 = m3;
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}
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_CUDA_HD double getM4() {
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return M4;
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}
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_CUDA_HD void setM4(X m4) {
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M4 = m4;
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}
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_CUDA_HD double getMax() {
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return max;
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}
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_CUDA_HD void setMax(X max) {
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this->max = max;
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}
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_CUDA_HD double getMean() {
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return mean;
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}
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_CUDA_HD void setMean(X mean) {
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this->mean = mean;
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}
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_CUDA_HD double getMin() {
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return min;
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}
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_CUDA_HD void setMin(X min) {
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this->min = min;
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}
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_CUDA_HD double getN() {
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return n;
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}
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_CUDA_HD void setN(X n) {
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this->n = n;
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}
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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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__device__ T * getPointer() {
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extern __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, uint, 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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__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, uint, 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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__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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_CUDA_HD static SummaryStatsData<X> update(SummaryStatsData<X> x, SummaryStatsData<X> y,
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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 = nd4j::math::nd4j_min(x.min, y.min);
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vz.max = nd4j::math::nd4j_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 inline _CUDA_D Z startingValue(X *input) {
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return static_cast<Z>(0);
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}
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template<typename OpType>
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static _CUDA_D void aggregatePartials(SummaryStatsData<X> **sPartialsRef, Nd4jLong tid, Nd4jLong numElements, void *extraParams);
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template<typename OpType>
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static _CUDA_D void transform(void *dx, Nd4jLong *xShapeInfo, void *extraParams, void *vz, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, int postProcessOrNot, int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets);
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static _CUDA_D void transform(const int opNum, void *dx, Nd4jLong *xShapeInfo, void *extraParams, void *vz, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, int postProcessOrNot, int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets);
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static _CUDA_H void execSummaryStatsReduceScalar(dim3& launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *extraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer);
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static _CUDA_H void execSummaryStatsReduce(dim3& launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *extraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer);
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static _CUDA_H void execSummaryStatsReduce(dim3& launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *extraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer);
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#endif
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static Z execScalar(int opNum,
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bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams);
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static void execScalar(int opNum,
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bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vz,
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Nd4jLong *resultShapeInfoBuffer);
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static void exec(int opNum,
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bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vz,
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Nd4jLong *resultShapeInfoBuffer,
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int *dimension, int dimensionLength);
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template<typename OpType>
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static Z execScalar(bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams);
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template<typename OpType>
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static void execScalar(bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vz,
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Nd4jLong *resultShapeInfoBuffer);
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template<typename OpType>
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static void exec(bool biasCorrected,
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void *x,
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Nd4jLong *xShapeInfo,
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void *extraParams,
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void *vz,
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Nd4jLong *resultShapeInfoBuffer,
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int *dimension,
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int dimensionLength);
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};
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}
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}
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#endif /* SUMMARYSTATSREDUCE_H_ */
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