/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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/op_boilerplate.h>
#include <loops/reduce_float.h>
#include <loops/legacy_ops.h>
#include <helpers/DebugHelper.h>
template <typename X, typename Z, typename OpClass>
__device__ void reduceSimpleGeneric(
void *dx,
Nd4jLong *xShapeInfo,
void *extraParams,
void *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
__shared__ UnifiedSharedMemory *manager;
if (threadIdx.x == 0) {
c __shared__ unsigned char shmem[];
manager = new(shmem) UnifiedSharedMemory((int *) shmem);
manager->init(sizeof(UnifiedSharedMemory), 0, sizeof(functions::reduce::ReduceFunction<T>), sizeof(shape::TAD), shape::rank(xShapeInfo));
}
__syncthreads();
functions::reduce::ReduceFunction<T>::template transformCudaXD<OpClass>(
dx,
xShapeInfo,
extraParams,
result,
resultShapeInfo,
dimension,
dimensionLength,
reductionBuffer,
manager,
tadOnlyShapeInfo,
tadOffsets);
}
template <typename T, typename OpClass>
__device__ void reduceSimpleGeneric1D(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer,
Nd4jLong *tadOnlyShapeInfo,
Nd4jLong *tadOffsets) {
functions::reduce::ReduceFunction<T>::template transformCuda1D<OpClass>(
dx,
xShapeInfo,
extraParams,
result,
resultShapeInfo,
dimension,
dimensionLength,
reductionBuffer, nullptr, tadOnlyShapeInfo, tadOffsets);
}
template <typename T, typename OpClass>
__device__ void reduceSimpleGeneric3D(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer,
Nd4jLong *tadOnlyShapeInfo,
Nd4jLong *tadOffsets) {
functions::reduce::ReduceFunction<T>::template transformCuda3D<OpClass>(
dx,
xShapeInfo,
extraParams,
result,
resultShapeInfo,
dimension,
dimensionLength,
reductionBuffer,
nullptr,
tadOnlyShapeInfo,
tadOffsets);
}
template <typename T, typename OpClass>
__device__ void reduceScalarGeneric(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
__shared__ UnifiedSharedMemory *manager;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
manager = new(shmem) UnifiedSharedMemory((int *) shmem);
manager->init(sizeof(UnifiedSharedMemory), 0, sizeof(functions::reduce::ReduceFunction<T>), sizeof(shape::TAD), 0);
}
__syncthreads();
functions::reduce::ReduceFunction<T>::template execScalarCuda<OpClass>(
dx,
xShapeInfo,
extraParams,
result,
resultShapeInfo,
reductionBuffer,
manager,
tadOnlyShapeInfo);
};
#ifndef __CLION_IDE__
// reduceScalar
DISPATCH_KERNEL_SIMPLE(reduceScalarSimple_, reduceScalarGeneric, float, INPUT(float *x, Nd4jLong *xShapeInfo, float *extraParams, float *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float *reductionBuffer, Nd4jLong *tadOnlyShapeInfo), PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceScalarSimple_, reduceScalarGeneric, double, INPUT(double *x, Nd4jLong *xShapeInfo, double *extraParams, double *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, double *reductionBuffer, Nd4jLong *tadOnlyShapeInfo), PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceScalarSimple_, reduceScalarGeneric, float16, INPUT(float16 *x, Nd4jLong *xShapeInfo, float16 *extraParams, float16 *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float16 *reductionBuffer, Nd4jLong *tadOnlyShapeInfo), PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, dimension, dimensionLength, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
// reduce1D
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric1D_, reduceSimpleGeneric1D, float, INPUT(float *x, Nd4jLong *xShape, float *extraParams, float *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric1D_, reduceSimpleGeneric1D, double, INPUT(double *x, Nd4jLong *xShape, double *extraParams, double *z, Nd4jLong *zShape, int *dimension, int dimensionLength, double *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric1D_, reduceSimpleGeneric1D, float16, INPUT(float16 *x, Nd4jLong *xShape, float16 *extraParams, float16 *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float16 *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
// reduce3D
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric3D_, reduceSimpleGeneric3D, float, INPUT(float *x, Nd4jLong *xShape, float *extraParams, float *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric3D_, reduceSimpleGeneric3D, double, INPUT(double *x, Nd4jLong *xShape, double *extraParams, double *z, Nd4jLong *zShape, int *dimension, int dimensionLength, double *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGeneric3D_, reduceSimpleGeneric3D, float16, INPUT(float16 *x, Nd4jLong *xShape, float16 *extraParams, float16 *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float16 *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
// reduceXD
DISPATCH_KERNEL_SIMPLE(reduceSimpleGenericXD_, reduceSimpleGeneric, float, INPUT(float *x, Nd4jLong *xShape, float *extraParams, float *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGenericXD_, reduceSimpleGeneric, double, INPUT(double *x, Nd4jLong *xShape, double *extraParams, double *z, Nd4jLong *zShape, int *dimension, int dimensionLength, double *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
DISPATCH_KERNEL_SIMPLE(reduceSimpleGenericXD_, reduceSimpleGeneric, float16, INPUT(float16 *x, Nd4jLong *xShape, float16 *extraParams, float16 *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float16 *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets), PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
#endif
namespace functions {
namespace reduce {
template <>
_CUDA_H void ReduceFunction<float>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, float *x, Nd4jLong *xShapeInfo, float *extraParams, float *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
DISPATCH_SIMPLE(reduceScalarSimple, float, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
sd::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed");
}
template <>
_CUDA_H void ReduceFunction<float16>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, float16 *x, Nd4jLong *xShapeInfo, float16 *extraParams, float16 *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float16 *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
DISPATCH_SIMPLE(reduceScalarSimple, float16, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
sd::DebugHelper::checkErrorCode(stream, "execReduceScalarHalf(...) failed");
}
template <>
_CUDA_H void ReduceFunction<double>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, double *x, Nd4jLong *xShapeInfo, double *extraParams, double *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, double *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
DISPATCH_SIMPLE(reduceScalarSimple, double, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
sd::DebugHelper::checkErrorCode(stream, "execReduceScalarDouble(...) failed");
}
template <>
_CUDA_H void ReduceFunction<float>::execReduceXD(dim3 launchDims, cudaStream_t *stream, int opNum, int rank, float *x, Nd4jLong *xShape, float *extraParams, float *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
if (rank == 1) {
DISPATCH_SIMPLE(reduceSimpleGeneric1D, float, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else if (rank <= 3) {
DISPATCH_SIMPLE(reduceSimpleGeneric3D, float, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else {
DISPATCH_SIMPLE(reduceSimpleGenericXD, float, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
}
DEBUG_KERNEL(stream, opNum);
}
template <>
_CUDA_H void ReduceFunction<float16>::execReduceXD(dim3 launchDims, cudaStream_t *stream, int opNum, int rank, float16 *x, Nd4jLong *xShape, float16 *extraParams, float16 *z, Nd4jLong *zShape, int *dimension, int dimensionLength, float16 *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
if (rank == 1) {
DISPATCH_SIMPLE(reduceSimpleGeneric1D, float16, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else if (rank <= 3) {
DISPATCH_SIMPLE(reduceSimpleGeneric3D, float16, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else {
DISPATCH_SIMPLE(reduceSimpleGenericXD, float16, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
}
DEBUG_KERNEL(stream, opNum);
}
template <>
_CUDA_H void ReduceFunction<double>::execReduceXD(dim3 launchDims, cudaStream_t *stream, int opNum, int rank, double *x, Nd4jLong *xShape, double *extraParams, double *z, Nd4jLong *zShape, int *dimension, int dimensionLength, double *reductionPointer, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
if (rank == 1) {
DISPATCH_SIMPLE(reduceSimpleGeneric1D, double, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else if (rank <= 3) {
DISPATCH_SIMPLE(reduceSimpleGeneric3D, double, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
} else {
DISPATCH_SIMPLE(reduceSimpleGenericXD, double, PARAMS(x, xShape, extraParams, z, zShape, dimension, dimensionLength, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(REDUCE_OPS))
}
DEBUG_KERNEL(stream, opNum);
}
template <typename T>
__device__ void initializeShared(T *extraParams, T **sPartials, int sMemSize) {
int sPartialsLength = sMemSize / sizeof(T);
T *sPartialsDeref = (T *) *sPartials;
for (int i = 0; i < sPartialsLength; i++) {
sPartialsDeref[i] = extraParams[0];
}
}
template <typename T>
template <typename OpType>
__device__ void ReduceFunction<T>::transformCuda1D(T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer, UnifiedSharedMemory *manager, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
if (OpType::requiresSpecialAccumulation) {
OpType::execSpecialCuda(dx, xShapeInfo, extraParams, result, resultShapeInfo, dimension, dimensionLength, reductionBuffer, manager, tadOnlyShapeInfo, tadOffsets);
return;
}
//shared memory space for storing intermediate results
__shared__ T *sPartials;// = (T *)manager->getSharedReductionBuffer();
__shared__ int tadLength;
__shared__ int tadEWS;
__shared__ int numTads;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = (T *) shmem;
tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
numTads = shape::length(xShapeInfo) / tadLength;
}
__syncthreads();
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
Nd4jLong tadOffsetForBlock = tadOffsets[r];
T *rX = dx + tadOffsetForBlock;
sPartials[threadIdx.x] = OpType::startingValue(rX);
if (tadEWS >= 1) {
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(rX[i * tadEWS], extraParams), extraParams);
}
} else {
__shared__ int tadRank;
__shared__ Nd4jLong *tadShape;
__shared__ Nd4jLong *tadStride;
Nd4jLong xCoord[MAX_RANK];
if (threadIdx.x == 0) {
tadRank = shape::rank(tadOnlyShapeInfo);
tadShape = shape::shapeOf(tadOnlyShapeInfo);
tadStride = shape::stride(tadOnlyShapeInfo);
}
__syncthreads();
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
}
}
__syncthreads();
// aggregate. do NOT reduce for elements > tadLength
aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0) {
result[r] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
}
}
}
template <typename T>
template <typename OpType>
__device__ void ReduceFunction<T>::execScalarCuda(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
T *reductionBuffer,
UnifiedSharedMemory *manager,
Nd4jLong *tadOnlyShapeInfo) {
int elementWiseStride = shape::elementWiseStride(xShapeInfo);
auto n = shape::length(xShapeInfo);
auto tid = blockDim.x * blockIdx.x + threadIdx.x;
//shared memory space for storing intermediate results
T *sPartials = (T *)manager->getSharedReductionBuffer();
sPartials[threadIdx.x] = OpType::startingValue(dx);
if (elementWiseStride >= 1) {
for (int i = tid; i < n; i += (blockDim.x * gridDim.x)) {
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[i * elementWiseStride], extraParams), extraParams);
}
}
else {
__shared__ int rank;
__shared__ Nd4jLong *xShape;
__shared__ Nd4jLong *xStride;
if (threadIdx.x == 0) {
rank = shape::rank(xShapeInfo);
xShape = shape::shapeOf(xShapeInfo);
xStride = shape::stride(xShapeInfo);
}
__syncthreads();
Nd4jLong ind2sub[MAX_RANK];
for (int i = tid; i < n; i += blockDim.x * gridDim.x) {
shape::ind2subC(rank, xShape, i, n, ind2sub);
auto offset = shape::getOffset(xShapeInfo, ind2sub);
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[offset], extraParams), extraParams);
}
}
__syncthreads();
aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, n), 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],n,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(dx);
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, sd::math::nd4j_min<int>(gridDim.x, blockDim.x), extraParams);
__syncthreads();
if (threadIdx.x == 0) {
result[0] = OpType::postProcess(sPartials[0], n, extraParams);
}
}
}
else {
if (threadIdx.x == 0) {
unsigned int *tc = (unsigned *)reductionBuffer;
tc[16384] = 0;
result[0] = OpType::postProcess(sPartials[0], n, extraParams);
}
}
}
template <typename T>
template <typename OpType>
__device__ void ReduceFunction<T>::transformCuda3D(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer, UnifiedSharedMemory *manager, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
if (OpType::requiresSpecialAccumulation) {
OpType::execSpecialCuda(dx, xShapeInfo, extraParams, result, resultShapeInfo, dimension, dimensionLength, reductionBuffer, manager, tadOnlyShapeInfo, tadOffsets);
return;
}
//shared memory space for storing intermediate results
__shared__ T *sPartials; // = (T *)manager->getSharedReductionBuffer();
__shared__ int tadLength;
__shared__ int tadRank;
__shared__ int numTads;
__shared__ Nd4jLong *tadShape;
__shared__ Nd4jLong *tadStride;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = (T *) shmem;
tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
tadRank = shape::rank(tadOnlyShapeInfo);
numTads = shape::length(xShapeInfo) / tadLength;
tadShape = shape::shapeOf(tadOnlyShapeInfo);
tadStride = shape::stride(tadOnlyShapeInfo);
}
__syncthreads();
Nd4jLong xCoord[3];
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
Nd4jLong tadOffsetForBlock = tadOffsets[r];
sPartials[threadIdx.x] = OpType::startingValue(dx + tadOffsetForBlock);
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
}
__syncthreads();
// aggregate. do NOT reduce for elements > tadLength
aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0)
result[r] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
}
}
template <typename T>
template <typename OpType>
__device__ void ReduceFunction<T>::transformCudaXD(
T *dx,
Nd4jLong *xShapeInfo,
T *extraParams,
T *result,
Nd4jLong *resultShapeInfo,
int *dimension,
int dimensionLength,
T *reductionBuffer,
UnifiedSharedMemory *manager,
Nd4jLong *tadOnlyShapeInfo,
Nd4jLong *tadOffsets) {
if (OpType::requiresSpecialAccumulation) {
OpType::execSpecialCuda(dx, xShapeInfo, extraParams, result, resultShapeInfo, dimension, dimensionLength, reductionBuffer, manager, tadOnlyShapeInfo, tadOffsets);
return;
}
//shared memory space for storing intermediate results
__shared__ T *sPartials;
// __shared__ shape::TAD *tad;
__shared__ int tadLength;
__shared__ int tadRank;
__shared__ int numTads;
__shared__ Nd4jLong *tadShape;
__shared__ Nd4jLong *tadStride;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sPartials = (T *) shmem;
tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
tadRank = shape::rank(tadOnlyShapeInfo);
numTads = shape::length(xShapeInfo) / tadLength;
tadShape = shape::shapeOf(tadOnlyShapeInfo);
tadStride = shape::stride(tadOnlyShapeInfo);
}
__syncthreads();
Nd4jLong xCoord[MAX_RANK];
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
Nd4jLong tadOffsetForBlock = tadOffsets[r];
sPartials[threadIdx.x] = OpType::startingValue(dx + tadOffsetForBlock);
for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
}
__syncthreads();
// aggregate. do NOT reduce for elements > tadLength
aggregatePartials<OpType>(sPartials, threadIdx.x, sd::math::nd4j_min<int>(blockDim.x, tadLength), extraParams);
__syncthreads();
if (threadIdx.x == 0)
result[r] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
}
}
template <typename T>
template <typename OpType>
__device__ void ReduceFunction<T>::aggregatePartials(T *sPartials, Nd4jLong tid, Nd4jLong numItems, T *extraParams) {
// 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.
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();
}
}
#ifndef __CLION_IDE__
BUILD_CALL_1(template __device__ void ReduceFunction<float>::execScalarCuda, float, (float*, Nd4jLong*, float*, float*, Nd4jLong*, float*, UnifiedSharedMemory *, Nd4jLong*), REDUCE_OPS)
BUILD_CALL_1(template __device__ void ReduceFunction<float16>::execScalarCuda, float16, (float16*, Nd4jLong*, float16*, float16*, Nd4jLong*, float16*, UnifiedSharedMemory *, Nd4jLong*), REDUCE_OPS)
BUILD_CALL_1(template __device__ void ReduceFunction<double>::execScalarCuda, double, (double*, Nd4jLong*, double*, double*, Nd4jLong*, double*, UnifiedSharedMemory *, Nd4jLong*), REDUCE_OPS)
BUILD_CALL_1(template __device__ void ReduceFunction<float>::aggregatePartials, float, (float*, Nd4jLong, Nd4jLong, float*), REDUCE_OPS)
BUILD_CALL_1(template __device__ void ReduceFunction<float16>::aggregatePartials, float16, (float16*, Nd4jLong, Nd4jLong, float16*), REDUCE_OPS)
BUILD_CALL_1(template __device__ void ReduceFunction<double>::aggregatePartials, double, (double*, Nd4jLong, Nd4jLong, double*), REDUCE_OPS)
#endif
}
}