cavis/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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
//
#ifndef DEV_TESTS_REDUCE_SAME_LOOPS_H
#define DEV_TESTS_REDUCE_SAME_LOOPS_H
#include <ops.h>
#include <types/types.h>
#include <op_boilerplate.h>
#include <shape.h>
using namespace simdOps;
namespace functions {
namespace reduce {
template <typename X>
class ReduceSameInplace {
public:
static FORCEINLINE void _CUDA_D execScalarCudaLegacy(int opNum, void *vx, Nd4jLong *xShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, void *vreductionBuffer, Nd4jLong *tadOnlyShapeInfo);
template <typename OpClass>
static FORCEINLINE void _CUDA_D execScalarCuda(void *vx, Nd4jLong *xShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, void *vreductionBuffer, Nd4jLong *tadOnlyShapeInfo);
template <typename OpClass>
static FORCEINLINE void _CUDA_D aggregatePartials(void *vsPartials, Nd4jLong tid, Nd4jLong numItems, void *vextraParams);
};
template <typename X>
template <typename OpType>
__device__ void ReduceSameInplace<X>::aggregatePartials(void *vsPartials, Nd4jLong tid, Nd4jLong numItems, 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 sPartials = static_cast<X*>(vsPartials);
auto extraParams = static_cast<X*>(vextraParams);
Nd4jLong floorPow2 = numItems;
if (floorPow2 & (floorPow2 - 1)) {
while (floorPow2 & (floorPow2 - 1))
floorPow2 &= floorPow2 - 1;
if (tid >= floorPow2)
sPartials[tid - floorPow2] = OpType::update(sPartials[tid - floorPow2], sPartials[tid], extraParams);
__syncthreads();
}
for (Nd4jLong activeThreads = floorPow2 >> 1; activeThreads; activeThreads >>= 1) {
if (tid < activeThreads && tid + activeThreads < numItems)
sPartials[tid] = OpType::update(sPartials[tid], sPartials[tid + activeThreads], extraParams);
__syncthreads();
}
}
template <typename X>
FORCEINLINE void _CUDA_D ReduceSameInplace<X>::execScalarCudaLegacy(int opNum, void *vx, Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong *zShapeInfo,
void *vreductionBuffer,
Nd4jLong *tadOnlyShapeInfo) {
DISPATCH_BY_OPNUM_T(execScalarCuda, PARAMS(vx, xShapeInfo, vextraParams, vz, zShapeInfo, vreductionBuffer, tadOnlyShapeInfo), REDUCE_SAME_OPS);
}
template <typename X>
template <typename OpType>
FORCEINLINE void _CUDA_D ReduceSameInplace<X>::execScalarCuda(void *vx, Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong *zShapeInfo,
void *vreductionBuffer,
Nd4jLong *tadOnlyShapeInfo) {
auto x = reinterpret_cast<X*>(vx);
auto z = reinterpret_cast<X*>(vz);
auto extraParams = reinterpret_cast<X*>(vextraParams);
auto reductionBuffer = reinterpret_cast<X*>(vreductionBuffer);
int xEws = shape::elementWiseStride(xShapeInfo);
auto len = shape::length(xShapeInfo);
auto tid = blockDim.x * blockIdx.x + threadIdx.x;
//shared memory space for storing intermediate results
__shared__ X* sPartials;
if(threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = reinterpret_cast<X*>(shmem);
}
__syncthreads();
sPartials[threadIdx.x] = OpType::startingValue(x);
if (xEws > 0)
for (int i = tid; i < len; i += (blockDim.x * gridDim.x))
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
else
for (int i = tid; i < len; i += blockDim.x * gridDim.x)
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
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__syncthreads();
aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
__syncthreads();
if (gridDim.x > 1) {
unsigned int *tc = (unsigned int *)reductionBuffer;
__shared__ bool amLast;
tid = threadIdx.x;
if (threadIdx.x == 0)
reductionBuffer[blockIdx.x] = sPartials[0];//this->postProcess(sPartials[0],len,extraParams);
__threadfence();
__syncthreads();
if (threadIdx.x == 0) {
unsigned int ticket = atomicInc(&tc[16384], gridDim.x);
amLast = (ticket == gridDim.x - 1);
}
__syncthreads();
if (amLast) {
tc[16384] = 0;
sPartials[threadIdx.x] = OpType::startingValue(x);
for (int i = threadIdx.x; i < gridDim.x; i += blockDim.x)
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], reductionBuffer[i], extraParams);
__syncthreads();
aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(gridDim.x, blockDim.x), extraParams);
__syncthreads();
if (threadIdx.x == 0) {
z[0] = OpType::postProcess(sPartials[0], len, extraParams);
}
}
}
else {
if (threadIdx.x == 0) {
unsigned int *tc = (unsigned *)reductionBuffer;
tc[16384] = 0;
z[0] = OpType::postProcess(sPartials[0], len, extraParams);
}
}
}
}
}
#endif //DEV_TESTS_REDUCE_SAME_LOOPS_H