[Mlir-commits] [mlir] [MLIR][Presburger] Generating functions and quasi-polynomials for Barvinok's algorithm (PR #75702)

[Arjun P]

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+//===- QuasiPolynomial.h - QuasiPolynomial Class ----------------*- C++ -*-===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+//
+// Definition of the QuasiPolynomial class for Barvinok's algorithm,
+// which represents a single-valued function on a set of parameters.
+// It is an expression of the form
+// f(x) = \sum_i c_i * \prod_j ⌊g_{ij}(x)⌋
+// where c_i \in Q and
+// g_{ij} : Q^d -> Q are affine functionals over d parameters.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_ANALYSIS_PRESBURGER_QUASIPOLYNOMIAL_H
+#define MLIR_ANALYSIS_PRESBURGER_QUASIPOLYNOMIAL_H
+
+#include "mlir/Analysis/Presburger/Fraction.h"
+#include "mlir/Analysis/Presburger/Matrix.h"
+
+namespace mlir {
+namespace presburger {
+
+// A class to describe quasi-polynomials.
+// A quasipolynomial consists of a set of terms.
+// The ith term is a constant `coefficients[i]`, multiplied
+// by the product of a set of affine functions on n parameters.
+// Represents functions f : Q^n -> Q of the form
+//
+// f(x) = \sum_i c_i * \prod_j ⌊g_{ij}(x)⌋
+//
+// where c_i \in Q and
+// g_{ij} : Q^n -> Q are affine functionals.
+class QuasiPolynomial {
+public:
+  QuasiPolynomial(unsigned numParam, SmallVector<Fraction> coeffs = {},
+                  std::vector<std::vector<SmallVector<Fraction>>> aff = {});
+
+  // Find the number of parameters involved in the polynomial.
+  unsigned getNumParams() const { return numParam; }
+
+  SmallVector<Fraction> getCoefficients() const { return coefficients; }
+
+  std::vector<std::vector<SmallVector<Fraction>>> getAffine() const {
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Superty wrote:

You can return const references here.

https://github.com/llvm/llvm-project/pull/75702