379 lines
17 KiB
Plaintext
379 lines
17 KiB
Plaintext
/*******************************************************************************
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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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// Created by raver on 4/9/2018.
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//
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#include <Environment.h>
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#include "../indexreduce.h"
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#include <op_boilerplate.h>
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#include <helpers/DebugHelper.h>
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#include <types/types.h>
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#include "../legacy_ops.h"
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using namespace simdOps;
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template <typename X, typename Z>
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static __global__ void simpleIndexReduceGeneric(const int op,
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void *dx,
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Nd4jLong *xShapeInfo, int xRank,
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void *extraParams,
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void *result,
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Nd4jLong *zShapeInfo, int zRank,
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int *dimension,
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int dimensionLength,
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int postProcessOrNot, int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
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functions::indexreduce::IndexReduce<X, Z>::transform(op,dx,xShapeInfo,extraParams,result,zShapeInfo,dimension,dimensionLength,postProcessOrNot,allocationBuffer,reductionBuffer,tadOnlyShapeInfo,tadOffsets);
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}
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namespace functions {
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namespace indexreduce {
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template <typename X, typename Z>
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_CUDA_H void IndexReduce<X,Z>::executeIndexReduceScalar(dim3 launchDims, cudaStream_t *stream,
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const int opNum,
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void *dx, Nd4jLong *xShapeInfo,
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int xRank,
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void *extraParams,
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void *result, Nd4jLong *zShapeInfo,
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int zRank,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationBuffer, void *reductionBuffer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
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simpleIndexReduceGeneric<X, Z><<<launchDims.x,launchDims.y,launchDims.z, *stream>>>(opNum,
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dx, xShapeInfo, xRank,
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extraParams,
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result, zShapeInfo, 0,
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nullptr, 0,
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1,
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allocationBuffer, reductionBuffer,
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tadOnlyShapeInfo, tadOffsets);
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}
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template <typename X, typename Z>
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_CUDA_H void IndexReduce<X, Z>::executeIndexReduce(dim3 launchDims, cudaStream_t *stream, const int opNum, void *dx, Nd4jLong *xShapeInfo, int xRank, void *extraParams, void *result, Nd4jLong *zShapeInfo, int zRank, int *dimension, int dimensionLength, int postProcessOrNot, int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
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simpleIndexReduceGeneric<X, Z><<<launchDims.x,launchDims.y,launchDims.z, *stream>>>(
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opNum,
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dx,
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xShapeInfo, xRank,
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extraParams,
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result,
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zShapeInfo, zRank,
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dimension,
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dimensionLength,
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1, allocationBuffer, reductionBuffer, tadOnlyShapeInfo, tadOffsets);
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}
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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 SharedIndexValue {
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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 SharedIndexValue<float> {
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__device__ IndexValue<float> * getPointer() {
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extern __shared__ IndexValue<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 SharedIndexValue<double> {
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__device__ IndexValue<double> * getPointer() {
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extern __shared__ IndexValue<double> s_int6[];
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return s_int6;
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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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__device__ void IndexReduce<X, Z>::aggregatePartials(IndexValue<X> **sPartialsRef, Nd4jLong tid, Nd4jLong numElements, void *vextraParams) {
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// start the shared memory loop on the next power of 2 less
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// than the block size. If block size is not a power of 2,
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// accumulate the intermediate sums in the remainder range.
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auto extraParams = static_cast<X*>(vextraParams);
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IndexValue<X> *sPartials = *sPartialsRef;
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Nd4jLong floorPow2 = blockDim.x;
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if (floorPow2 & (floorPow2 - 1)) {
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while ( floorPow2 & (floorPow2 - 1) ) {
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floorPow2 &= floorPow2 - 1;
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}
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if (tid >= floorPow2) {
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IndexValue<X> prev = sPartials[tid - floorPow2];
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IndexValue<X> curr = sPartials[tid];
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sPartials[tid - floorPow2] = OpType::update(prev,curr,extraParams);
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}
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__syncthreads();
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}
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for (int activeThreads = floorPow2 >> 1;activeThreads; activeThreads >>= 1) {
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if (tid < activeThreads && tid + activeThreads < numElements) {
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IndexValue<X> curr = sPartials[tid];
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IndexValue<X> next = sPartials[tid + activeThreads];
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sPartials[tid] = OpType::update(curr,next,extraParams);
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}
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__syncthreads();
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}
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}
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template <typename X, typename Y>
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__device__ void IndexReduce<X, Y>::transform(
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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 *result,
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Nd4jLong *zShapeInfo,
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int *dimension,
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int dimensionLength,
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int postProcessOrNot,
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int *allocationBuffer,
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void *reductionBuffer,
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Nd4jLong *tadShapeInfo,
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Nd4jLong *tadOffset) {
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DISPATCH_BY_OPNUM_TT(transform, PARAMS(x, xShapeInfo, extraParams, result, zShapeInfo, dimension, dimensionLength, postProcessOrNot, allocationBuffer, reductionBuffer, tadShapeInfo, tadOffset), INDEX_REDUCE_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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__device__ void IndexReduce<X, Z>::transform(void *vdx, Nd4jLong *xShapeInfo,
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void *vextraParams,
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void *vz, Nd4jLong *zShapeInfo,
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int *dimension, int dimensionLength,
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int postProcessOrNot,
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int *allocationBuffer, void *vreductionBuffer,
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Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets){
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/**int
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* Gpu information for the problem
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*/
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auto dx = reinterpret_cast<X*>(vdx);
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auto z = reinterpret_cast<Z*>(vz);
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auto extraParams = static_cast<X*>(vextraParams);
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auto reductionBuffer = static_cast<X*>(vreductionBuffer);
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auto order = shape::order(xShapeInfo);
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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__shared__ volatile int resultScalar;
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//shared memory space for storing intermediate results
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__shared__ IndexValue<X>* sPartials;
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if(threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sPartials = reinterpret_cast<IndexValue<X>*>(shmem);
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}
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__syncthreads();
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sPartials[threadIdx.x] = OpType::startingIndexValue(dx);
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//length for the tad
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__shared__ volatile Nd4jLong xLength;
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__shared__ volatile Nd4jLong zLen;
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//only compute the tad indexes once
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IndexValue<X> reduction = OpType::startingIndexValue(dx);
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if (threadIdx.x == 0) {
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if (zShapeInfo != nullptr)
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zLen = shape::length(zShapeInfo);
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else zLen = 1;
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if (dimensionLength == 1) {
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if (zLen == 1 && (dimension == nullptr || dimension[0] == MAX_DIMENSION))
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resultScalar = 1;
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else
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resultScalar = 0;
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}
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else
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resultScalar = 0;
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if (zLen == 1)
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resultScalar = 1;
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xLength = shape::length(xShapeInfo);
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}
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__syncthreads();
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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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for (uint i = blockIdx.x * blockDim.x + threadIdx.x; i < zLen; i += gridDim.x * blockDim.x)
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z[i] = (Z) reduction.index;
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return;
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}
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if (!resultScalar) {
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__shared__ Nd4jLong tadLength;
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__shared__ int tadEWS;
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__shared__ int numTads;
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if (threadIdx.x == 0) {
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tadLength = shape::length(tadOnlyShapeInfo);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = shape::length(xShapeInfo) / tadLength;
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}
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__syncthreads();
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if (dimensionLength > 1 || tadEWS < 1) {
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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auto tadOffsetForBlock = tadOffsets[r];
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sPartials[threadIdx.x] = OpType::startingIndexValue(dx);
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for(int i = threadIdx.x;i < tadLength; i += blockDim.x) {
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auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
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IndexValue<X> comp {dx[xOffset], i};
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], comp, extraParams);
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}
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__syncthreads();
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aggregatePartials<OpType>(&sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, tadLength),extraParams);
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__syncthreads();
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if (threadIdx.x == 0) {
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z[r] = (Z) sPartials[threadIdx.x].index;
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}
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__syncthreads();
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}
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} else {
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for(int i = blockIdx.x; i < numTads; i+= gridDim.x) {
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Nd4jLong tadOffsetForBlock = tadOffsets[i];
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sPartials[threadIdx.x] = OpType::startingIndexValue(dx);
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for (int x = threadIdx.x; x < tadLength; x+= blockDim.x) {
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IndexValue<X> comp {dx[tadOffsetForBlock + x * tadEWS], x};
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], comp, extraParams);
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}
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__syncthreads();
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aggregatePartials<OpType>(&sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, tadLength),extraParams);
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__syncthreads();
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if (threadIdx.x == 0) {
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z[i] = (Z) sPartials[threadIdx.x].index; //postProcess(sPartials[0],tadLength ,extraParams);
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}
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__syncthreads();
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}
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}
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} else {
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auto n = shape::length(xShapeInfo);
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auto xElementWiseStride = shape::elementWiseStride(xShapeInfo);
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if(xElementWiseStride >= 1 && order == 'c') {
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for(Nd4jLong i = tid;i < n; i += (blockDim.x * gridDim.x)) {
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IndexValue<X> indexVal = {dx[i * xElementWiseStride], i};
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reduction = OpType::update(reduction, indexVal, extraParams);
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}
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} else {
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for(Nd4jLong i = tid;i < n; i += blockDim.x * gridDim.x) {
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auto offset = shape::getIndexOffset(i, xShapeInfo);
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IndexValue<X> indexVal = {dx[offset], i};
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reduction = OpType::update(reduction, indexVal, extraParams);
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}
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}
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sPartials[threadIdx.x] = reduction;
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__syncthreads();
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aggregatePartials<OpType>(&sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, (int) n),extraParams);
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__syncthreads();
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if (gridDim.x > 1) {
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__shared__ bool amLast;
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unsigned int *tc = (unsigned int *) reductionBuffer;
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tid = threadIdx.x;
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if (threadIdx.x == 0) {
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auto pBuffer = reinterpret_cast<IndexValue<X> *>(reductionBuffer);
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pBuffer[blockIdx.x] = {sPartials[0].value, sPartials[0].index};
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}
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__threadfence();
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__syncthreads();
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if (tid==0) {
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unsigned int ticket = atomicInc(&tc[16384], gridDim.x);
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amLast = (ticket == gridDim.x-1);
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}
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__syncthreads();
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if (amLast) {
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tc[16384] = 0;
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IndexValue<X> *pBuffer = (IndexValue<X> *) reductionBuffer;
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sPartials[threadIdx.x] = OpType::startingIndexValue(dx);
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for (Nd4jLong i = threadIdx.x; i < gridDim.x; i += blockDim.x) {
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sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], pBuffer[i], extraParams);
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}
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__syncthreads();
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aggregatePartials<OpType>(&sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(gridDim.x, blockDim.x),extraParams);
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__syncthreads();
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if (tid == 0) {
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z[0] = (Z) sPartials[0].index;
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}
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}
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} else {
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if (tid == 0) {
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auto tc = reinterpret_cast<unsigned int *>(reductionBuffer);
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tc[16384] = 0;
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z[0] = (Z) sPartials[0].index;
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}
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}
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}
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}
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT IndexReduce, , LIBND4J_TYPES, INDEXING_TYPES);
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}
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}
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