/*******************************************************************************
* 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 <op_boilerplate.h>
#include <loops/broadcasting.h>
#include <loops/legacy_ops.h>
#include <types/types.h>
#include <Environment.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <string>
#include <stdexcept>
#include <StringUtils.h>
#include <specials_cuda.h>
using namespace simdOps;
template<typename X, typename Y, typename Z, typename OpClass>
static __global__ void broadcastSimple(
void *x,
Nd4jLong *xShapeInfo,
void *y,
Nd4jLong *yShapeInfo,
void *z,
Nd4jLong *zShapeInfo,
int *dimension,
int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
functions::broadcast::Broadcast<X,Y,Z>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
}
template<typename X, typename Y, typename Z, typename OpClass>
static __global__ void broadcastInverseSimple(
void *x,
Nd4jLong *xShapeInfo,
void *y,
Nd4jLong *yShapeInfo,
void *z,
Nd4jLong *zShapeInfo,
int *dimension,
int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
functions::broadcast::Broadcast<X,Y,Z>::template transformInverseCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
}
namespace functions {
namespace broadcast {
template<typename X, typename Y, typename Z>
template <typename OpClass>
__host__ void Broadcast<X,Y,Z>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
broadcastSimple<X, Y, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
}
template<typename X, typename Y, typename Z>
__host__ void Broadcast<X,Y,Z>::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
DISPATCH_BY_OPNUM_TTT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_OPS))
DEBUG_KERNEL(stream, opNum);
}
template<typename X, typename Y, typename Z>
template <typename OpClass>
__host__ void Broadcast<X,Y,Z>::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
broadcastInverseSimple<X, Y, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
}
template<typename X, typename Y, typename Z>
__host__ void Broadcast<X,Y,Z>::execInverseBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShapeInfo, void *y, Nd4jLong *yShapeInfo, void *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
DISPATCH_BY_OPNUM_TTT(intermediateInverseBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_OPS))
DEBUG_KERNEL(stream, opNum);
}
template<typename X, typename Y, typename Z>
template <typename OpType>
__device__ void Broadcast<X,Y,Z>::transformInverseCuda(
void *vx, Nd4jLong *xShapeInfo,
void *vy, Nd4jLong *yShapeInfo,
void *vz, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
if (tadOnlyShapeInfoZ == nullptr) {
tadOnlyShapeInfoZ = tadOnlyShapeInfo;
tadOffsetsZ = tadOffsets;
}
auto x = reinterpret_cast<X*>(vx);
auto y = reinterpret_cast<Y*>(vy);
auto z = reinterpret_cast<Z*>(vz);
//decompose in to several sub tads after
//moving all dimensions (in sorted order)
//to the back.
//permuted version of the x shape info for setting up the tad problem
__shared__ Nd4jLong tadLength;
__shared__ Nd4jLong tadEWS;
__shared__ int numTads;
__shared__ Nd4jLong xEWS;
__shared__ Nd4jLong zEWS;
if (threadIdx.x == 0) {
tadLength = shape::length(tadOnlyShapeInfo);
tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
numTads = shape::length(yShapeInfo) / tadLength;
xEWS = shape::elementWiseStride(xShapeInfo);
zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
}
__syncthreads();
auto xOrder = shape::order(xShapeInfo);
auto yOrder = shape::order(tadOnlyShapeInfo);
auto zOrder = shape::order(tadOnlyShapeInfoZ);
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
auto rY = y + tadOffsets[r];
auto rZ = z + tadOffsetsZ[r];
if(tadEWS > 0 && zEWS > 0 && xEWS > 0 && dimensionLength == 1 && xOrder == yOrder && xOrder == zOrder) {
for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
rZ[i * zEWS] = OpType::op(x[i * xEWS], rY[i * tadEWS]);
}
else {
// it is expected that x and z tads and y array all have the same length
for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
auto xOffset = shape::getIndexOffset(i, xShapeInfo, tadLength);
auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]);
}
}
}
}
template<typename X, typename Y, typename Z>
template <typename OpType>
__device__ void Broadcast<X,Y,Z>::transformCuda(
void *vx, Nd4jLong *xShapeInfo,
void *vy, Nd4jLong *yShapeInfo,
void *vz, Nd4jLong *zShapeInfo,
int *dimension, int dimensionLength,
Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
if (tadOnlyShapeInfoZ == nullptr) {
tadOnlyShapeInfoZ = tadOnlyShapeInfo;
tadOffsetsZ = tadOffsets;
}
auto x = reinterpret_cast<X*>(vx);
auto y = reinterpret_cast<Y*>(vy);
auto z = reinterpret_cast<Z*>(vz);
//decompose in to several sub tads after
//moving all dimensions (in sorted order)
//to the back.
//permuted version of the x shape info for setting up the tad problem
__shared__ Nd4jLong tadLength;
__shared__ Nd4jLong tadEWS;
__shared__ int numTads;
__shared__ Nd4jLong yEWS;
__shared__ Nd4jLong zEWS;
if (threadIdx.x == 0) {
tadLength = shape::length(tadOnlyShapeInfo);
tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
numTads = shape::length(xShapeInfo) / tadLength;
yEWS = shape::elementWiseStride(yShapeInfo);
zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
}
__syncthreads();
auto xOrder = shape::order(tadOnlyShapeInfo);
auto yOrder = shape::order(yShapeInfo);
auto zOrder = shape::order(tadOnlyShapeInfoZ);
for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
auto rX = x + tadOffsets[r];
auto rZ = z + tadOffsetsZ[r];
if(tadEWS > 0 && zEWS > 0 && yEWS > 0 && xOrder == yOrder && xOrder == zOrder) {
for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
rZ[i * zEWS] = OpType::op(rX[i * tadEWS], y[i * yEWS]);
}
else {
// it is expected that x and z tads and y array all have the same length
for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
auto yOffset = shape::getIndexOffset(i, yShapeInfo, tadLength);
auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]);
}
}
}
}
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_0);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_1);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_2);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_3);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_4);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_5);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_6);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_7);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_8);
BUILD_PAIRWISE_TEMPLATE(template class ND4J_EXPORT Broadcast, , PAIRWISE_TYPES_9);
}
}