/*******************************************************************************
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 07.03.2019
//
#include <ops/declarable/helpers/gather.h>
#include <numeric>
#include <PointersManager.h>
#include <ShapeUtils.h>
namespace nd4j {
namespace ops {
namespace helpers {
template<typename X, typename Y>
__global__ static void gatherCudaLinearKernel(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo,
void* vz, const Nd4jLong* zShapeInfo) {
__shared__ const X* x;
__shared__ const Y* y;
__shared__ X* z;
__shared__ Nd4jLong xLen, yLen, zLen;
if (threadIdx.x == 0) {
x = reinterpret_cast<const X*>(vx);
z = reinterpret_cast<X*>(vz);
y = reinterpret_cast<const Y *>(vy);
xLen = shape::length(xShapeInfo);
yLen = shape::length(yShapeInfo);
zLen = shape::length(zShapeInfo);
}
__syncthreads();
//const Nd4jLong zLen = shape::length(zShapeInfo);
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (int j = start; j < zLen; j += step) {
auto zIndex = shape::getIndexOffset(j, zShapeInfo);
auto yIndex = shape::getIndexOffset(j, yShapeInfo);
auto xIndex = shape::getIndexOffset(y[yIndex], xShapeInfo);
z[zIndex] = x[xIndex];
}
}
//////////////////////////////////////////////////////////////////////
template<typename X, typename Y>
__global__ static void gatherCuda(const int numOfSubArrs,
const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xOffsets,
const void* vy, const Nd4jLong* yShapeInfo,
void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong* zOffsets) {
const Y* y = reinterpret_cast<const Y*>(vy);
__shared__ const X* x;
__shared__ X* z;
const Nd4jLong len = shape::length(xShapeInfo);
//const Nd4jLong zLen = shape::length(zShapeInfo);
for (int i = blockIdx.x; i < numOfSubArrs; i += gridDim.x) {
if (threadIdx.x == 0) {
x = reinterpret_cast<const X*>(vx) + xOffsets[y[shape::getIndexOffset(i, yShapeInfo)]];
z = reinterpret_cast<X*>(vz) + zOffsets[i];
}
__syncthreads();
for (int j = threadIdx.x; j < len; j += blockDim.x) {
auto zIndex = shape::getIndexOffset(j, zShapeInfo);
auto xIndex = shape::getIndexOffset(j, xShapeInfo);
z[zIndex] = x[xIndex];
}
__syncthreads();
}
}
template<typename X, typename Y>
__host__ static void gatherCudaLinear(const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo,
void* vz, const Nd4jLong* zShapeInfo) {
gatherCudaLinearKernel<X,Y><<<128, 256, 1024, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo);
}
//////////////////////////////////////////////////////////////////////
template<typename X, typename Y>
__host__ static void gatherCudaLauncher(const cudaStream_t *stream, const int numOfSubArrs,
const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xOffsets,
const void* vy, const Nd4jLong* yShapeInfo,
void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong* zOffsets) {
gatherCuda<X,Y><<<numOfSubArrs, MAX_NUM_THREADS, 1024, *stream>>>(numOfSubArrs, vx, xShapeInfo, xOffsets, vy, yShapeInfo, vz, zShapeInfo, zOffsets);
}
//////////////////////////////////////////////////////////////////////
void gather(nd4j::LaunchContext * context, const NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
const int inputRank = input->rankOf();
const int numOfIntArgs = intArgs.size();
int axis = numOfIntArgs > 0 ? intArgs[0] : 0;
if(axis < 0)
axis += inputRank;
if (indices == nullptr && numOfIntArgs == 2) { // scalar case
output->assign((*input)(intArgs[1], {axis}));
}
else if (indices != nullptr && indices->isScalar()) {
if(input->rankOf() <= 1) { //For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead, we want to get a scalar
auto idx = indices->e<Nd4jLong>(0);
auto scalarNDArray = input->e(idx);
output->assign(scalarNDArray);
}
else {
NDArray inSubArr = (*input)(indices->e<Nd4jLong>(0), {axis});
output->assign(inSubArr);
}
}
else {
NDArray* pIndices = const_cast<NDArray*>(indices);
if(indices == nullptr)
pIndices = new NDArray(input->ordering(), {numOfIntArgs-1}, std::vector<double>(intArgs.begin() + 1, intArgs.end()), DataType::INT64, input->getContext());
std::vector<int> dimsOut(pIndices->rankOf());
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... axis+pIndices->rankOf()-1
const Nd4jLong numOfSubArrs = pIndices->lengthOf();
Nd4jLong *outSubArrShapeInfo(nullptr), *inSubArrShapeInfo(nullptr), *outSubArrOffsets(nullptr), *inSubArrOffsets(nullptr);
input-> getSubArrShapeAndOffsets({axis}, inSubArrShapeInfo, inSubArrOffsets);
output->getSubArrShapeAndOffsets(dimsOut, outSubArrShapeInfo, outSubArrOffsets);
if (output->rankOf() > 1) {
PointersManager manager(context, "gather");
auto xShapeInfo = reinterpret_cast<Nd4jLong *>(manager.replicatePointer(inSubArrShapeInfo,
shape::shapeInfoByteLength(
inSubArrShapeInfo)));
auto zShapeInfo = reinterpret_cast<Nd4jLong *>(manager.replicatePointer(outSubArrShapeInfo,
shape::shapeInfoByteLength(
outSubArrShapeInfo)));
auto xOffsets = reinterpret_cast<Nd4jLong *>(manager.replicatePointer(inSubArrOffsets, (input->lengthOf() /
shape::length(
inSubArrShapeInfo)) *
sizeof(Nd4jLong)));
auto zOffsets = reinterpret_cast<Nd4jLong *>(manager.replicatePointer(outSubArrOffsets,
(output->lengthOf() /
shape::length(outSubArrShapeInfo)) *
sizeof(Nd4jLong)));
NDArray::prepareSpecialUse({output}, {input, pIndices});
BUILD_DOUBLE_SELECTOR(input->dataType(), pIndices->dataType(), gatherCudaLauncher, (context->getCudaStream(), numOfSubArrs, input->getSpecialBuffer(), xShapeInfo, xOffsets, pIndices->getSpecialBuffer(), pIndices->getSpecialShapeInfo(), output->getSpecialBuffer(), zShapeInfo, zOffsets), LIBND4J_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, pIndices});
manager.synchronize();
}
else {
NDArray::prepareSpecialUse({output}, {input, pIndices});
BUILD_DOUBLE_SELECTOR(input->dataType(), pIndices->dataType(), gatherCudaLinear, (context->getCudaStream(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), pIndices->getSpecialBuffer(), pIndices->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo()), LIBND4J_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({output}, {input, pIndices});
}
if(indices == nullptr)
delete pIndices;
}
}
}
}
}