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deeplearning4j--deeplearning4j/libnd4j/include/helpers/impl/hhSequence.cpp
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2026-07-13 12:47:05 +08:00

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/* ******************************************************************************
*
*
* 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
******************************************************************************/
//
// Created by Yurii Shyrma on 02.01.2018
//
#include <helpers/hhSequence.h>
#include <helpers/householder.h>
namespace sd {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
HHsequence::HHsequence(NDArray* vectors, NDArray* coeffs, const char type)
: _vectors(vectors), _coeffs(coeffs) {
_diagSize = math::sd_min(_vectors->sizeAt(0), _vectors->sizeAt(1));
_shift = 0;
_type = type;
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void HHsequence::mulLeft_(NDArray* matrix) {
const int rows = _vectors->sizeAt(0);
const int cols = _vectors->sizeAt(1);
const int inRows = matrix->sizeAt(0);
NDArray matrixRef = *matrix;
NDArray vectorsRef = *_vectors;
for (int i = _diagSize - 1; i >= 0; --i) {
if (_type == 'u') {
NDArray *blockPtr = matrixRef({inRows - rows + _shift + i, inRows, 0, 0}, true);
NDArray block = *blockPtr;
NDArray *vectorPtr = vectorsRef({i + 1 + _shift, rows, i, i + 1}, true);
NDArray vector = *vectorPtr;
Householder<T>::mulLeft(block, vector, _coeffs->t<T>(i));
delete blockPtr;
delete vectorPtr;
} else {
NDArray *blockPtr = matrixRef({inRows - cols + _shift + i, inRows, 0, 0}, true);
NDArray block = *blockPtr;
NDArray *vectorPtr = vectorsRef({i, i + 1, i + 1 + _shift, cols}, true);
NDArray vector = *vectorPtr;
Householder<T>::mulLeft(block, vector, _coeffs->t<T>(i));
delete blockPtr;
delete vectorPtr;
}
}
}
//////////////////////////////////////////////////////////////////////////
NDArray HHsequence::getTail(const int idx) const {
int first = idx + 1 + _shift;
NDArray vectorsRef = *_vectors;
if (_type == 'u') {
NDArray *tailPtr = vectorsRef({first, -1, idx, idx + 1}, true);
NDArray tail = *tailPtr;
delete tailPtr;
return tail;
} else {
NDArray *tailPtr = vectorsRef({idx, idx + 1, first, -1}, true);
NDArray tail = *tailPtr;
delete tailPtr;
return tail;
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void HHsequence::applyTo_(NDArray* dest) {
int size = _type == 'u' ? _vectors->sizeAt(0) : _vectors->sizeAt(1);
NDArray *originalDest = dest;
NDArray destRef = *dest;
std::vector<LongType> sizeShape = {size,size};
if (dest->rankOf() != 2 || (dest->sizeAt(0) != size && dest->sizeAt(1) != size)) {
dest = new NDArray(dest->ordering(), sizeShape, dest->dataType(), dest->getContext());
destRef = *dest;
}
dest->setIdentity();
for (int k = _diagSize - 1; k >= 0; --k) {
int curNum = size - k - _shift;
if (curNum < 1 || (k + 1 + _shift) >= size) continue;
NDArray *blockPtr = destRef({dest->sizeAt(0) - curNum, dest->sizeAt(0), dest->sizeAt(1) - curNum, dest->sizeAt(1)}, true);
NDArray block = *blockPtr;
NDArray tailK = getTail(k);
Householder<T>::mulLeft(block, tailK, _coeffs->t<T>(k));
delete blockPtr;
}
if(originalDest != dest) {
delete dest;
}
}
//////////////////////////////////////////////////////////////////////////
void HHsequence::applyTo(NDArray* dest) {
auto xType = _coeffs->dataType();
BUILD_SINGLE_SELECTOR(xType, applyTo_, (dest), SD_FLOAT_TYPES);
}
//////////////////////////////////////////////////////////////////////////
void HHsequence::mulLeft(NDArray* matrix) {
auto xType = _coeffs->dataType();
BUILD_SINGLE_SELECTOR(xType, mulLeft_, (matrix), SD_FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE( void HHsequence::applyTo_, (sd::NDArray * dest), SD_FLOAT_TYPES);
BUILD_SINGLE_TEMPLATE( void HHsequence::mulLeft_, (NDArray * matrix), SD_FLOAT_TYPES);
} // namespace helpers
} // namespace ops
} // namespace sd