/*******************************************************************************
* 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
//
#include <system/pointercast.h>
#include <types/types.h>
#include <types/float16.h>
#include <system/op_boilerplate.h>
#include <loops/summarystatsreduce.h>
#include <helpers/shape.h>
#include <helpers/TAD.h>
#include <system/dll.h>
#include <system/Environment.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <helpers/DebugHelper.h>
#include <ops/specials_cuda.h>
using namespace simdOps;
namespace functions {
namespace summarystats {
template <typename X, typename Z>
void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRank, void *extraParams, void *z, Nd4jLong *zShapeInfo, int zRank, int *dimension, int dimensionLength, int postProcessOrNot,bool biasCorrected,int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
functions::summarystats::SummaryStatsReduce<X,Z>::transform(op,dx,xShapeInfo,extraParams,z,zShapeInfo,dimension,dimensionLength,biasCorrected,allocationBuffer,reductionBuffer,tadOnlyShapeInfo,tadOffsets);
}
/**
*
* @param sPartialsRef
* @param tid
* @param extraParams
*/
template<typename X, typename Z>
template<typename OpType>
_CUDA_D void SummaryStatsReduce<X,Z>::aggregatePartials(SummaryStatsData<X> **sPartialsRef, Nd4jLong tid, Nd4jLong numElements, void *vextraParams) {
// start the shared memory loop on the next power of 2 less
// than the block size. If block size is not a power of 2,
// accumulate the intermediate sums in the remainder range.
auto extraParams = static_cast<Z*>(vextraParams);
SummaryStatsData<X> *sPartials = *sPartialsRef;
Nd4jLong floorPow2 = blockDim.x;
if (floorPow2 & (floorPow2 - 1)) {
while (floorPow2 & (floorPow2 - 1)) {
floorPow2 &= floorPow2 - 1;
}
if (tid >= floorPow2) {
SummaryStatsData<X> prev = sPartials[tid - floorPow2];
SummaryStatsData<X> curr = sPartials[tid];
sPartials[tid - floorPow2] = update(prev, curr, extraParams);
}
__syncthreads();
}
for (Nd4jLong activeThreads = floorPow2 >> 1; activeThreads; activeThreads >>= 1) {
if (tid < activeThreads && tid + activeThreads < numElements) {
SummaryStatsData<X> curr = sPartials[tid];
SummaryStatsData<X> next = sPartials[tid + activeThreads];
sPartials[tid] = update(curr, next, extraParams);
}
__syncthreads();
}
};
/**
* @param n n is the number of
* elements to loop through
* @param dx the data to operate on
* @param xVectorInfo the meta data for the vector:
* 0 is the offset
* 1 is the increment/stride
* 2 is the real length of the buffer (n and dx.length won't always be the same)
* 3 is the element wise stride for the buffer
* 4 is the number of elements it takes to get to the next row/column/tensor
* @param gpuInformation
* 0 is the block size
* 1 is the grid size
* 2 is the shared memory size
* @param problemDefinition
* 0 is the number of elements per vector
* 1 is the number of vectors
*/
template<typename X, typename Z>
template<typename OpType>
_CUDA_D void SummaryStatsReduce<X,Z>::transform(void *vx, Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
int postProcessOrNot,
int *allocationBuffer, void *vreductionBuffer,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
auto dx = static_cast<X*>(vx);
auto z = static_cast<Z*>(vz);
auto extraParams = static_cast<Z*>(vextraParams);
auto reductionBuffer = static_cast<Z*>(vreductionBuffer);
int tid = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ volatile int resultScalar;
__shared__ int xElementWiseStride;
int numElements = blockDim.x;
//shared memory space for storing intermediate results
__shared__ SummaryStatsData<X> *sPartials;
if(threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = reinterpret_cast<SummaryStatsData<X>*>(shmem);
}
__syncthreads();
Z startingVal = startingValue(dx);
SummaryStatsData<X> val;
val.initWithValue(startingVal);
val.n = 0;
sPartials[threadIdx.x] = val;
//length for the tad
__shared__ volatile int xLength;
__shared__ volatile int resultLength;
SummaryStatsData<X> reduction;
reduction.initWithValue(0.0);
reduction.n = 0;
if (threadIdx.x == 0) {
if (zShapeInfo != nullptr)
resultLength = shape::length(zShapeInfo);
else resultLength = 1;
if (dimensionLength == 1) {
if (resultLength == 1 && (dimension == nullptr || dimension[0] == MAX_DIMENSION))
resultScalar = 1;
else
resultScalar = 0;
}
else
resultScalar = 0;
if (resultLength == 1)
resultScalar = 1;
auto xStride = shape::stride(xShapeInfo);
auto xOrder = shape::order(xShapeInfo);
if (dimension != nullptr && (dimension[0] != MAX_DIMENSION && dimensionLength == 1)) {
xElementWiseStride = xStride[dimension[0]];
}
else {
xElementWiseStride = shape::elementWiseStride(xShapeInfo);
}
xLength = shape::length(xShapeInfo);
}
__syncthreads();
if (!resultScalar) {
__shared__ int tadLength;
__shared__ int tadEWS;
__shared__ int numTads;
if (threadIdx.x == 0) {
tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
numTads = shape::length(xShapeInfo) / tadLength;
}
__syncthreads();
if (tadEWS == 0) {
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
auto tadOffsetForBlock = tadOffsets[r];
val.initWithValue(startingVal);
val.n = 0;
sPartials[threadIdx.x] = val;
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
SummaryStatsData<X> indexVal2;
indexVal2.initWithValue(dx[xOffset]);
sPartials[threadIdx.x] = update(sPartials[threadIdx.x], OpType::op(indexVal2, extraParams), extraParams);
}
__syncthreads();
aggregatePartials<OpType>(&sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0) {
z[r] = OpType::getValue(postProcessOrNot, sPartials[threadIdx.x]);
}
__syncthreads();
}
}
else {
for (int i = blockIdx.x; i < numTads; i += gridDim.x) {
auto tadOffsetForBlock = tadOffsets[i];
val.initWithValue(startingVal);
val.n = 0;
sPartials[threadIdx.x] = val;
for (int x = threadIdx.x; x < tadLength; x += blockDim.x) {
auto indexX = tadOffsetForBlock + x * tadEWS;
SummaryStatsData<X> indexVal2;
indexVal2.initWithValue(dx[indexX]);
sPartials[threadIdx.x] = update(sPartials[threadIdx.x], OpType::op(indexVal2, extraParams), extraParams);
}
__syncthreads();
aggregatePartials<OpType>(&sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0) {
z[i] = OpType::getValue(postProcessOrNot, sPartials[threadIdx.x]); //postProcess(sPartials[0],tadLength ,extraParams);
}
}
}
}
else if (resultScalar) {
__shared__ int n;
if (threadIdx.x == 0) {
xElementWiseStride = shape::elementWiseStride(xShapeInfo);
n = shape::length(xShapeInfo);
}
__syncthreads();
if (xElementWiseStride >= 1) {
for (Nd4jLong i = tid; i < n; i += (blockDim.x * gridDim.x)) {
SummaryStatsData<X> indexVal2;
indexVal2.initWithValue(dx[i * xElementWiseStride]);
reduction = update(reduction, indexVal2, extraParams);
}
}
else {
for (Nd4jLong i = tid; i < n; i += blockDim.x * gridDim.x) {
auto offset = shape::getIndexOffset(i, xShapeInfo);
SummaryStatsData<X> indexVal2;
indexVal2.initWithValue(dx[offset]);
reduction = update(reduction, indexVal2, extraParams);
}
}
sPartials[threadIdx.x] = reduction;
__syncthreads();
aggregatePartials<OpType>(&sPartials, threadIdx.x, blockDim.x, extraParams);
__syncthreads();
if (gridDim.x > 1) {
__shared__ bool amLast;
unsigned int *tc = (unsigned int *)reductionBuffer;
tid = threadIdx.x;
if (threadIdx.x == 0) {
SummaryStatsData<X> *pBuffer = (SummaryStatsData<X>*) reductionBuffer;
pBuffer[blockIdx.x] = sPartials[0];
}
__threadfence();
__syncthreads();
if (tid == 0) {
unsigned int ticket = atomicInc(&tc[16384], gridDim.x);
amLast = (ticket == gridDim.x - 1);
}
__syncthreads();
if (amLast) {
tc[16384] = 0;
SummaryStatsData<X>* pBuffer = (SummaryStatsData<X>*) reductionBuffer;
Z startingVal = startingValue(dx);
SummaryStatsData<X> val;
val.initWithValue(startingVal);
val.n = 0;
sPartials[threadIdx.x] = val;
for (int i = threadIdx.x; i < gridDim.x; i += blockDim.x) {
sPartials[threadIdx.x] = update(sPartials[threadIdx.x], pBuffer[i], extraParams);
}
__syncthreads();
aggregatePartials<OpType>(&sPartials, threadIdx.x, gridDim.x, extraParams);
__syncthreads();
if (tid == 0) {
z[0] = OpType::getValue(postProcessOrNot, sPartials[0]);
}
}
}
else {
if (tid == 0) {
unsigned int *tc = (unsigned *)reductionBuffer;
tc[16384] = 0;
z[0] = z[0] = OpType::getValue(postProcessOrNot, sPartials[0]);
}
}
}
};
template <typename X, typename Y>
_CUDA_D void SummaryStatsReduce<X,Y>::transform(const int opNum, void *dx, Nd4jLong *xShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, int postProcessOrNot, int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
DISPATCH_BY_OPNUM_TT(transform, PARAMS(dx, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationBuffer, reductionBuffer, tadOnlyShapeInfo, tadOffsets), SUMMARY_STATS_OPS);
};
template <typename X, typename Z>
_CUDA_H void SummaryStatsReduce<X,Z>::execSummaryStatsReduceScalar(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer) {
auto x = static_cast<X*>(vx);
auto extraParams = static_cast<Z*>(vextraParams);
auto z = reinterpret_cast<Z*>(vz);
auto reductionPointerA = reinterpret_cast<Z*>(reductionBuffer);
if (sd::Environment::getInstance()->isDebugAndVerbose())
printf("D16 opNum:[%i]\n", opNum);
summaryStatsReduceT<X,Z><<<launchDims.x,launchDims.y,launchDims.z, *stream>>>(
opNum,
x,
xShapeInfo, shape::rank(hxShapeInfo),
extraParams,
z,
zShapeInfo, shape::rank(hzShapeInfo),
nullptr,
1,
1,biasCorrected, nullptr, reductionPointerA, tadShapeInfo, tadOffsets);
// this is blocking method since method should return scalar
sd::DebugHelper::checkErrorCode(stream, "execSSReduceScalar(...) failed");
}
template <typename X, typename Z>
_CUDA_H void SummaryStatsReduce<X,Z>::execSummaryStatsReduce(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer) {
auto x = static_cast<X*>(vx);
auto z = static_cast<Z*>(vz);
auto extraParams = static_cast<Z*>(vextraParams);
if (sd::Environment::getInstance()->isDebugAndVerbose())
printf("F17 opNum:[%i]\n", opNum);
auto reductionPointerA = reinterpret_cast<Z*>(reductionBuffer);
summaryStatsReduceT<X,Z><<<launchDims.x,launchDims.y,launchDims.z, *stream>>>(
opNum,
x,
xShapeInfo, shape::rank(hxShapeInfo),
extraParams,
z,
zShapeInfo, shape::rank(hzShapeInfo),
nullptr,
1,
1,biasCorrected, nullptr, reductionPointerA, tadShapeInfo, tadOffsets);
DEBUG_KERNEL(stream, opNum);
}
template<typename X, typename Z>
_CUDA_H void SummaryStatsReduce<X,Z>::execSummaryStatsReduce(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer) {
auto x = static_cast<X*>(vx);
auto z = static_cast<Z*>(vz);
auto extraParams = static_cast<Z*>(vextraParams);
if (sd::Environment::getInstance()->isDebugAndVerbose())
printf("D18 opNum:[%i]\n", opNum);
summaryStatsReduceT<X, Z><<<launchDims.x,launchDims.y,launchDims.z, *stream>>>(
opNum,
x,
xShapeInfo, shape::rank(hxShapeInfo),
extraParams,
z,
zShapeInfo, shape::rank(hzShapeInfo),
dimension,
dimensionLength,
1, biasCorrected, nullptr, reinterpret_cast<Z*>(reductionBuffer), tadShapeInfo, tadOffsets);
DEBUG_KERNEL(stream, opNum);
}
BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT SummaryStatsReduce, , LIBND4J_TYPES, FLOAT_TYPES);
}
}