mirror of
https://github.com/AdaCore/cvc5.git
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1a3a935fb5
This refactors the base Python API to expose TermManager (related to previous refactor of the C++ API to expose TermManager in #10426).
119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
#!/usr/bin/env python
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###############################################################################
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# Top contributors (to current version):
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# Aina Niemetz, Makai Mann, Mathias Preiner
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#
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# This file is part of the cvc5 project.
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#
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# Copyright (c) 2009-2024 by the authors listed in the file AUTHORS
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# in the top-level source directory and their institutional affiliations.
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# All rights reserved. See the file COPYING in the top-level source
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# directory for licensing information.
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# #############################################################################
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#
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# A simple demonstration of the solving capabilities of the cvc5
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# floating point solver through the Python API contributed by Eva
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# Darulova. This requires building cvc5 with symfpu.
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##
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import cvc5
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from cvc5 import Kind, RoundingMode
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if __name__ == "__main__":
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tm = cvc5.TermManager()
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slv = cvc5.Solver(tm)
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slv.setOption("produce-models", "true")
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slv.setLogic("QF_FP")
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# single 32-bit precision
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fp32 = tm.mkFloatingPointSort(8, 24)
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# the standard rounding mode
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rm = tm.mkRoundingMode(RoundingMode.ROUND_NEAREST_TIES_TO_EVEN)
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# create a few single-precision variables
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x = tm.mkConst(fp32, 'x')
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y = tm.mkConst(fp32, 'y')
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z = tm.mkConst(fp32, 'z')
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# check floating-point arithmetic is commutative, i.e. x + y == y + x
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commutative = tm.mkTerm(
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Kind.FLOATINGPOINT_EQ,
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tm.mkTerm(Kind.FLOATINGPOINT_ADD, rm, x, y),
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tm.mkTerm(Kind.FLOATINGPOINT_ADD, rm, y, x))
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slv.push()
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slv.assertFormula(tm.mkTerm(Kind.NOT, commutative))
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print("Checking floating-point commutativity")
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print("Expect SAT (property does not hold for NaN and Infinities).")
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print("cvc5:", slv.checkSat())
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print("Model for x:", slv.getValue(x))
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print("Model for y:", slv.getValue(y))
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# disallow NaNs and Infinities
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slv.assertFormula(tm.mkTerm(
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Kind.NOT, tm.mkTerm(Kind.FLOATINGPOINT_IS_NAN, x)))
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slv.assertFormula(tm.mkTerm(
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Kind.NOT, tm.mkTerm(Kind.FLOATINGPOINT_IS_INF, x)))
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slv.assertFormula(tm.mkTerm(
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Kind.NOT, tm.mkTerm(Kind.FLOATINGPOINT_IS_NAN, y)))
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slv.assertFormula(tm.mkTerm(
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Kind.NOT, tm.mkTerm(Kind.FLOATINGPOINT_IS_INF, y)))
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print("Checking floating-point commutativity assuming x and y are not NaN or Infinity")
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print("Expect UNSAT.")
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print("cvc5:", slv.checkSat())
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# check floating-point arithmetic is not associative
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slv.pop()
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# constrain x, y, z between -3.14 and 3.14 (also disallows NaN and infinity)
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a = tm.mkFloatingPoint(
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8,
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24,
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tm.mkBitVector(32, "11000000010010001111010111000011", 2)) # -3.14
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b = tm.mkFloatingPoint(
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8,
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24,
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tm.mkBitVector(32, "01000000010010001111010111000011", 2)) # 3.14
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bounds_x = tm.mkTerm(
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Kind.AND,
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, a, x),
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, x, b))
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bounds_y = tm.mkTerm(
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Kind.AND,
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, a, y),
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, y, b))
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bounds_z = tm.mkTerm(
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Kind.AND,
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, a, z),
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tm.mkTerm(Kind.FLOATINGPOINT_LEQ, z, b))
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slv.assertFormula(tm.mkTerm(
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Kind.AND, tm.mkTerm(Kind.AND, bounds_x, bounds_y), bounds_z))
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# (x + y) + z == x + (y + z)
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lhs = tm.mkTerm(
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Kind.FLOATINGPOINT_ADD,
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rm,
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tm.mkTerm(Kind.FLOATINGPOINT_ADD, rm, x, y),
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z)
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rhs = tm.mkTerm(
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Kind.FLOATINGPOINT_ADD,
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rm,
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x,
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tm.mkTerm(Kind.FLOATINGPOINT_ADD, rm, y, z))
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associative = tm.mkTerm(
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Kind.NOT,
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tm.mkTerm(Kind.FLOATINGPOINT_EQ, lhs, rhs))
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slv.assertFormula(associative)
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print("Checking floating-point associativity")
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print("Expect SAT.")
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print("cvc5:", slv.checkSat())
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print("Model for x:", slv.getValue(x))
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print("Model for y:", slv.getValue(y))
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print("Model for z:", slv.getValue(z))
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