/*******************************************************************************
* 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
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <op_boilerplate.h>
#include <loops/reduce_bool.h>
#include <loops/legacy_ops.h>
#include <helpers/DebugHelper.h>
#include <types/types.h>
#include <execution/LaunchContext.h>
#include <exceptions/cuda_exception.h>
#include <loops/scalar.h>
using namespace simdOps;
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z, typename OpType>
__global__ void simpleReduce(void *x, Nd4jLong *xShapeInfo,
void *extraParams,
void *z, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
void *reductionBuffer,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
functions::reduce::ReduceBoolFunction<X,Z>::template transformCudaXD<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo, tadOffsets);
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z, typename OpType>
__global__ void simpleScalar(void *x, Nd4jLong *xShapeInfo,
void *extraParams,
void *z, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
functions::reduce::ReduceBoolFunction<X, Z>::template execScalarCuda<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, reductionBuffer, tadOnlyShapeInfo);
}
namespace functions {
namespace reduce {
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
template <typename OpType>
__device__ void ReduceBoolFunction<X,Z>::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 = reinterpret_cast<Z*>(vsPartials);
auto extraParams = reinterpret_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, typename Z>
template <typename OpType>
__device__ void ReduceBoolFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *xShapeInfo,
void *vextraParams,
void *vz, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
void *vreductionBuffer,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
auto x = reinterpret_cast<X*>(vx);
auto z = reinterpret_cast<Z*>(vz);
auto extraParams = reinterpret_cast<X*>(vextraParams);
auto reductionBuffer = reinterpret_cast<Z*>(vreductionBuffer);
//shared memory space for storing intermediate results
__shared__ Z* sPartials;
__shared__ int tadLength, numTads;
__shared__ bool isPlainOutput;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = reinterpret_cast<Z*>(shmem);
isPlainOutput = shape::order(zShapeInfo) == 'c' && shape::elementWiseStride(zShapeInfo) == 1;
tadLength = shape::length(tadOnlyShapeInfo); //tadLength(xShapeInfo, dimension, dimensionLength);
numTads = shape::length(xShapeInfo) / tadLength;
}
__syncthreads();
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
Nd4jLong tadOffsetForBlock = tadOffsets[r];
sPartials[threadIdx.x] = OpType::startingValue(x + tadOffsetForBlock);
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[xOffset], extraParams), extraParams);
}
__syncthreads();
// aggregate. do NOT reduce for elements > tadLength
aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0)
z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
}
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
template <typename OpType>
__device__ void ReduceBoolFunction<X,Z>::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<Z*>(vz);
auto extraParams = reinterpret_cast<X*>(vextraParams);
auto reductionBuffer = reinterpret_cast<Z*>(vreductionBuffer);
auto tid = blockDim.x * blockIdx.x + threadIdx.x;
//shared memory space for storing intermediate results
__shared__ Z* sPartials;
__shared__ Nd4jLong xEws;
__shared__ Nd4jLong len;
if(threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = reinterpret_cast<Z*>(shmem);
xEws = shape::elementWiseStride(xShapeInfo);
len = shape::length(xShapeInfo);
}
__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);
__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);
}
}
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
template<typename OpType>
__host__ void ReduceBoolFunction<X,Z>::intermediateXD(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
if(shape::isEmpty(hXShapeInfo)) {
if(shape::isEmpty(hZShapeInfo))
return;
const auto startingVal = static_cast<Z>(OpType::startingValue(reinterpret_cast<X*>(x)));
auto res = cudaMemcpyAsync(nd4j::LaunchContext::defaultContext()->getScalarPointer(), &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream);
if (res != 0)
throw nd4j::cuda_exception::build("ReduceBoolFunction<X,Z>::intermediateXD: failed to copy temporary scalar", res);
auto ptr = nd4j::LaunchContext::defaultContext()->getScalarPointer();
// scalar assign
functions::scalar::ScalarTransform<Z, Z, Z>::executeCudaShaped(launchDims, stream, 14, z, zShapeInfo, hXShapeInfo, z, zShapeInfo, hZShapeInfo, ptr, nullptr);
}
else {
simpleReduce<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets);
nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolDim(...) failed");
}
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Z>
template<typename OpType>
__host__ void ReduceBoolFunction<X,Z>::intermediateScalar(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
if (shape::isEmpty(hXShapeInfo)) {
if (shape::isEmpty(hZShapeInfo))
return;
const auto startingVal = static_cast<Z>(OpType::startingValue(reinterpret_cast<X*>(x)));
auto res = cudaMemcpyAsync(z, &startingVal, sizeof(Z), cudaMemcpyHostToDevice, *stream);
if (res != 0)
throw nd4j::cuda_exception::build("ReduceBoolFunction<X,Z>::intermediateScalar: failed to copy resulting scalar", res);
}
else {
simpleScalar<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo);
nd4j::DebugHelper::checkErrorCode(stream, "reduceBoolScalar(...) failed");
}
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
_CUDA_H void ReduceBoolFunction<X,Y>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
DISPATCH_BY_OPNUM_TT(intermediateScalar, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_BOOL_OPS));
nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed");
}
////////////////////////////////////////////////////////////////////////
template <typename X, typename Y>
_CUDA_H void ReduceBoolFunction<X,Y>::execReduceXD(dim3 launchDims, cudaStream_t *stream, int opNum, int rank, void *x, Nd4jLong *xShapeInfo, Nd4jLong *hXShapeInfo, void *extraParams, void *z, Nd4jLong *zShapeInfo, Nd4jLong *hZShapeInfo, int *dimension, int dimensionLength, void *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
DISPATCH_BY_OPNUM_TT(intermediateXD, PARAMS(launchDims, stream, x, xShapeInfo, hXShapeInfo, extraParams, z, zShapeInfo, hZShapeInfo, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_BOOL_OPS));
DEBUG_KERNEL(stream, opNum);
}
////////////////////////////////////////////////////////////////////////
template <typename X>
__device__ void initializeShared(X *extraParams, X **sPartials, int sMemSize) {
int sPartialsLength = sMemSize / sizeof(X);
X *sPartialsDeref = (X *) *sPartials;
for (int i = 0; i < sPartialsLength; i++)
sPartialsDeref[i] = extraParams[0];
}
BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT ReduceBoolFunction, , LIBND4J_TYPES, BOOL_TYPES);
}
}