b696e25a7a
This patch adds hermite normal form computation to Matrix. Part of this algorithm lived in LinearTransform, being used for compuing column echelon form. This patch moves the implementation to Matrix::hermiteNormalForm and generalises it to compute the hermite normal form. Reviewed By: arjunp Differential Revision: https://reviews.llvm.org/D133510
68 lines
2.1 KiB
C++
68 lines
2.1 KiB
C++
//===- LinearTransform.cpp - MLIR LinearTransform Class -------------------===//
|
|
//
|
|
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Analysis/Presburger/LinearTransform.h"
|
|
#include "mlir/Analysis/Presburger/IntegerRelation.h"
|
|
|
|
using namespace mlir;
|
|
using namespace presburger;
|
|
|
|
LinearTransform::LinearTransform(Matrix &&oMatrix) : matrix(oMatrix) {}
|
|
LinearTransform::LinearTransform(const Matrix &oMatrix) : matrix(oMatrix) {}
|
|
|
|
std::pair<unsigned, LinearTransform>
|
|
LinearTransform::makeTransformToColumnEchelon(const Matrix &m) {
|
|
// Compute the hermite normal form of m. This, is by definition, is in column
|
|
// echelon form.
|
|
auto [h, u] = m.computeHermiteNormalForm();
|
|
|
|
// Since the matrix is in column ecehlon form, a zero column means the rest of
|
|
// the columns are zero. Thus, once we find a zero column, we can stop.
|
|
unsigned col, e;
|
|
for (col = 0, e = m.getNumColumns(); col < e; ++col) {
|
|
bool zeroCol = true;
|
|
for (unsigned row = 0, f = m.getNumRows(); row < f; ++row) {
|
|
if (h(row, col) != 0) {
|
|
zeroCol = false;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (zeroCol)
|
|
break;
|
|
}
|
|
|
|
return {col, LinearTransform(std::move(u))};
|
|
}
|
|
|
|
IntegerRelation LinearTransform::applyTo(const IntegerRelation &rel) const {
|
|
IntegerRelation result(rel.getSpace());
|
|
|
|
for (unsigned i = 0, e = rel.getNumEqualities(); i < e; ++i) {
|
|
ArrayRef<MPInt> eq = rel.getEquality(i);
|
|
|
|
const MPInt &c = eq.back();
|
|
|
|
SmallVector<MPInt, 8> newEq = preMultiplyWithRow(eq.drop_back());
|
|
newEq.push_back(c);
|
|
result.addEquality(newEq);
|
|
}
|
|
|
|
for (unsigned i = 0, e = rel.getNumInequalities(); i < e; ++i) {
|
|
ArrayRef<MPInt> ineq = rel.getInequality(i);
|
|
|
|
const MPInt &c = ineq.back();
|
|
|
|
SmallVector<MPInt, 8> newIneq = preMultiplyWithRow(ineq.drop_back());
|
|
newIneq.push_back(c);
|
|
result.addInequality(newIneq);
|
|
}
|
|
|
|
return result;
|
|
}
|