wehub-resource-sync
204 lines
7.8 KiB
C++
204 lines
7.8 KiB
C++
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
|
|
//
|
|
|
|
#include <helpers/Loops.h>
|
|
#include <helpers/ShapeUtils.h>
|
|
#include <ops/declarable/helpers/transforms.h>
|
|
|
|
#include <numeric>
|
|
#include <system/selective_rendering.h>
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace helpers {
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename X, typename Y>
|
|
static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
|
|
const X* x = reinterpret_cast<X*>(input.buffer());
|
|
const Y* y = reinterpret_cast<Y*>(indices.buffer());
|
|
X* z = reinterpret_cast<X*>(output.buffer());
|
|
|
|
const sd::LongType xRank = input.rankOf();
|
|
const sd::LongType yRank = indices.rankOf();
|
|
const sd::LongType zRank = output.rankOf();
|
|
const sd::LongType maxRank = sd::math::sd_max<sd::LongType>(yRank, sd::math::sd_max<sd::LongType>(xRank, zRank));
|
|
|
|
const sd::LongType zLen = output.lengthOf();
|
|
|
|
const sd::LongType yLastDim = indices.sizeAt(-1);
|
|
|
|
const int diff = zRank - xRank;
|
|
const bool bEqual = yLastDim == xRank;
|
|
|
|
sd::LongType outputRank = output.rankOf();
|
|
sd::LongType* outputShape = shape::shapeOf(output.shapeInfo());
|
|
sd::LongType* outputStride = shape::stride(output.shapeInfo());
|
|
sd::LongType indicesRank = indices.rankOf();
|
|
sd::LongType* indicesShape = shape::shapeOf(indices.shapeInfo());
|
|
sd::LongType* indicesStride = shape::stride(indices.shapeInfo());
|
|
|
|
sd::LongType inputRank = input.rankOf();
|
|
sd::LongType* inputShape = shape::shapeOf(input.shapeInfo());
|
|
sd::LongType* inputStride = shape::stride(input.shapeInfo());
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
sd::LongType xCoords[SD_MAX_RANK], zCoords[SD_MAX_RANK], temp;
|
|
|
|
for (sd::LongType i = start; i < stop; i++) {
|
|
INDEX2COORDS(i, outputRank, outputShape, zCoords);
|
|
|
|
sd::LongType zOffset;
|
|
COORDS2INDEX(outputRank, outputStride, zCoords, zOffset);
|
|
|
|
temp = zCoords[yRank - 1];
|
|
zCoords[yRank - 1] = 0;
|
|
|
|
sd::LongType yOffset;
|
|
COORDS2INDEX(indicesRank, indicesStride, zCoords, yOffset);
|
|
|
|
zCoords[yRank - 1] = temp;
|
|
|
|
if (bEqual)
|
|
memcpy(xCoords, zCoords, zRank * sizeof(sd::LongType));
|
|
else if (diff >= 0)
|
|
memcpy(xCoords, zCoords + diff, xRank * sizeof(sd::LongType));
|
|
else
|
|
memcpy(xCoords - diff, zCoords, zRank * sizeof(sd::LongType));
|
|
|
|
for (sd::LongType j = 0; j < yLastDim; ++j)
|
|
xCoords[j] = y[yOffset + j * indicesStride[yRank - 1]]; // last stride
|
|
|
|
sd::LongType xOffset;
|
|
COORDS2INDEX(inputRank, inputStride, xCoords, xOffset);
|
|
|
|
z[zOffset] = x[xOffset];
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, zLen);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void gatherND(sd::LaunchContext* context, NDArray& input, NDArray& indices, NDArray& output) {
|
|
auto inputDType = input.dataType();
|
|
auto indicesDType = indices.dataType();
|
|
BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), SD_COMMON_TYPES,
|
|
SD_INDEXING_TYPES);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void gather_(NDArray* input, NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
|
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
|
|
const int inputRank = input->rankOf();
|
|
if (axis < 0) axis += inputRank;
|
|
|
|
const int numOfIntArgs = intArgs.size();
|
|
|
|
if (indices != nullptr) {
|
|
for (sd::LongType i = 0; i < indices->lengthOf(); ++i)
|
|
if (indices->e<sd::LongType>(i) >= input->sizeAt(axis))
|
|
THROW_EXCEPTION(
|
|
"helpers::gather function: indices array contains wrong elements, each element must be smaller than "
|
|
"corresponding dimension of input array !");
|
|
|
|
// first case: indices consist of only one scalar
|
|
if (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<sd::LongType>(0);
|
|
auto scalarNDArray = input->e(idx);
|
|
output->assign(&scalarNDArray);
|
|
} else {
|
|
// tadForDimensions expects the dimensions to create TADs along,
|
|
// NOT the dimensions to exclude
|
|
std::vector<sd::LongType> axesVec = {axis};
|
|
// Pass the axis directly - TadCalculator will handle the exclusion internally
|
|
auto tadPack = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), &axesVec);
|
|
|
|
auto tadArr = NDArray(reinterpret_cast<void*>(reinterpret_cast<T*>(input->buffer()) +
|
|
tadPack->primaryOffsets()[indices->e<sd::LongType>(0)]),
|
|
tadPack->primaryShapeInfo(), output->getContext(), 0, 0);
|
|
output->assign(&tadArr);
|
|
}
|
|
} else if (input->rankOf() == 1 && indices->isVector()) {
|
|
// special case
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto e = start; e < stop; e++) output->p(e, input->e<T>(indices->e<sd::LongType>(e)));
|
|
};
|
|
|
|
samediff::Threads::parallel_for(func, 0, indices->lengthOf());
|
|
} else {
|
|
std::vector<sd::LongType> dimsOut(indices->rankOf());
|
|
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... indices->rankOf()-1
|
|
const sd::LongType numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->shapeInfo(), dimsOut);
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i++) {
|
|
NDArray *subArrOut = (*output)(i, dimsOut);
|
|
NDArray *subArrIn = (*input)(indices->e<sd::LongType>(i), {axis});
|
|
subArrOut->assign(subArrIn);
|
|
delete subArrOut;
|
|
delete subArrIn;
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
|
}
|
|
} else {
|
|
for (int i = 1; i < numOfIntArgs; ++i)
|
|
if (intArgs[i] >= input->sizeAt(axis))
|
|
THROW_EXCEPTION(
|
|
"helpers::gather function: some of input indexes is larger than corresponding shape of input array !");
|
|
|
|
// we only allow scalar/vector case here
|
|
if (numOfIntArgs == 2) { // scalar case
|
|
NDArray *view = (*input)(intArgs[1], {axis});
|
|
output->assign(view);
|
|
delete view;
|
|
} else { // vector case
|
|
const sd::LongType numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->shapeInfo(), {axis});
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto i = start; i < stop; i++) {
|
|
NDArray *subArrOut = (*output)(i, {axis});
|
|
NDArray *subArrIn = (*input)(intArgs[i + 1], {axis});
|
|
subArrOut->assign(subArrIn);
|
|
delete subArrIn;
|
|
delete subArrOut;
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
|
}
|
|
}
|
|
}
|
|
|
|
void gather(NDArray* input, NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), gather_, (input, indices, output, intArgs), SD_COMMON_TYPES);
|
|
}
|
|
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|