cavis/libnd4j/include/helpers/cpu/hhSequence.cpp

125 lines
3.8 KiB
C++
Raw Normal View History

2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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
******************************************************************************/
//
// Created by Yurii Shyrma on 02.01.2018
//
#include <helpers/hhSequence.h>
#include <helpers/householder.h>
#include <array/NDArrayFactory.h>
2019-06-06 14:21:15 +02:00
namespace sd {
2019-06-06 14:21:15 +02:00
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
HHsequence::HHsequence(const NDArray& vectors, const NDArray& coeffs, const char type): _vectors(vectors), _coeffs(coeffs) {
_diagSize = sd::math::nd4j_min(_vectors.sizeAt(0), _vectors.sizeAt(1));
2019-06-06 14:21:15 +02:00
_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* block(nullptr);
for(int i = _diagSize - 1; i >= 0; --i) {
if(_type == 'u') {
block = new NDArray(matrix({inRows-rows+_shift+ i,inRows, 0,0}, true));
T _x = _coeffs.e<T>(i);
Householder<T>::mulLeft(*block, _vectors({i + 1 + _shift, rows, i, i+1}, true), _x);
_coeffs.p<T>(i, _x);
}
else {
block = new NDArray(matrix({inRows-cols+_shift+i,inRows, 0,0}, true));
T _x = _coeffs.e<T>(i);
Householder<T>::mulLeft(*block, _vectors({i, i+1, i + 1 + _shift, cols}, true), _x);
_coeffs.p<T>(i, _x);
}
delete block;
}
}
//////////////////////////////////////////////////////////////////////////
NDArray HHsequence::getTail(const int idx) const {
int first = idx + 1 + _shift;
if(_type == 'u')
return _vectors({first, -1, idx, idx+1}, true);
else
return _vectors({idx, idx+1, first, -1}, true);
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
void HHsequence::_applyTo(NDArray& dest) {
int size = _type == 'u' ? _vectors.sizeAt(0) : _vectors.sizeAt(1);
if(dest.rankOf() != 2 || (dest.sizeAt(0) != size && dest.sizeAt(1) != size))
dest = NDArrayFactory::create(dest.ordering(), {size, size}, dest.dataType(), dest.getContext());
dest.setIdentity();
for(int k = _diagSize - 1; k >= 0; --k) {
int curNum = size - k - _shift;
if(curNum < 1 || (k + 1 + _shift) >= size )
continue;
auto block = dest({dest.sizeAt(0)-curNum,dest.sizeAt(0), dest.sizeAt(1)-curNum,dest.sizeAt(1)}, true);
T _x = _coeffs.e<T>(k);
Householder<T>::mulLeft(block, getTail(k), _x);
_coeffs.p<T>(k, _x);
}
}
void HHsequence::applyTo(NDArray& dest) {
auto xType = _coeffs.dataType();
BUILD_SINGLE_SELECTOR(xType, _applyTo, (dest), FLOAT_TYPES);
}
void HHsequence::mulLeft(NDArray& matrix) {
auto xType = _coeffs.dataType();
BUILD_SINGLE_SELECTOR(xType, _mulLeft, (matrix), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void HHsequence::_applyTo, (sd::NDArray &dest), FLOAT_TYPES);
2019-06-06 14:21:15 +02:00
BUILD_SINGLE_TEMPLATE(template void HHsequence::_mulLeft, (NDArray& matrix), FLOAT_TYPES);
}
}
}