mirror of
https://github.com/Z3Prover/z3
synced 2025-11-04 05:19:11 +00:00
400 lines
12 KiB
Python
400 lines
12 KiB
Python
from z3 import *
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import heapq
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# Simplistic (and fragile) converter from
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# a class of Horn clauses corresponding to
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# a transition system into a transition system
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# representation as <init, trans, goal>
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# It assumes it is given three Horn clauses
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# of the form:
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# init(x) => Invariant(x)
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# Invariant(x) and trans(x,x') => Invariant(x')
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# Invariant(x) and goal(x) => Goal(x)
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# where Invariant and Goal are uninterpreted predicates
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class Horn2Transitions:
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def __init__(self):
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self.trans = True
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self.init = True
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self.inputs = []
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self.goal = True
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self.index = 0
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def parse(self, file):
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fp = Fixedpoint()
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goals = fp.parse_file(file)
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for r in fp.get_rules():
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if not is_quantifier(r):
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continue
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b = r.body()
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if not is_implies(b):
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continue
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f = b.arg(0)
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g = b.arg(1)
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if self.is_goal(f, g):
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continue
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if self.is_transition(f, g):
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continue
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if self.is_init(f, g):
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continue
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def is_pred(self, p, name):
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return is_app(p) and p.decl().name() == name
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def is_goal(self, body, head):
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if not self.is_pred(head, "Goal"):
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return False
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pred, inv = self.is_body(body)
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if pred is None:
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return False
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self.goal = self.subst_vars("x", inv, pred)
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self.goal = self.subst_vars("i", self.goal, self.goal)
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self.inputs += self.vars
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self.inputs = list(set(self.inputs))
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return True
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def is_body(self, body):
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if not is_and(body):
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return None, None
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fmls = [f for f in body.children() if self.is_inv(f) is None]
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inv = None
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for f in body.children():
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if self.is_inv(f) is not None:
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inv = f;
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break
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return And(fmls), inv
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def is_inv(self, f):
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if self.is_pred(f, "Invariant"):
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return f
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return None
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def is_transition(self, body, head):
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pred, inv0 = self.is_body(body)
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if pred is None:
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return False
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inv1 = self.is_inv(head)
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if inv1 is None:
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return False
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pred = self.subst_vars("x", inv0, pred)
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self.xs = self.vars
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pred = self.subst_vars("xn", inv1, pred)
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self.xns = self.vars
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pred = self.subst_vars("i", pred, pred)
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self.inputs += self.vars
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self.inputs = list(set(self.inputs))
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self.trans = pred
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return True
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def is_init(self, body, head):
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for f in body.children():
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if self.is_inv(f) is not None:
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return False
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inv = self.is_inv(head)
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if inv is None:
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return False
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self.init = self.subst_vars("x", inv, body)
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return True
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def subst_vars(self, prefix, inv, fml):
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subst = self.mk_subst(prefix, inv)
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self.vars = [ v for (k,v) in subst ]
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return substitute(fml, subst)
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def mk_subst(self, prefix, inv):
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self.index = 0
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if self.is_inv(inv) is not None:
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return [(f, self.mk_bool(prefix)) for f in inv.children()]
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else:
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vars = self.get_vars(inv)
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return [(f, self.mk_bool(prefix)) for f in vars]
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def mk_bool(self, prefix):
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self.index += 1
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return Bool("%s%d" % (prefix, self.index))
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def get_vars(self, f, rs=[]):
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if is_var(f):
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return z3util.vset(rs + [f], str)
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else:
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for f_ in f.children():
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rs = self.get_vars(f_, rs)
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return z3util.vset(rs, str)
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# Produce a finite domain solver.
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# The theory QF_FD covers bit-vector formulas
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# and pseudo-Boolean constraints.
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# By default cardinality and pseudo-Boolean
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# constraints are converted to clauses. To override
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# this default for cardinality constraints
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# we set sat.cardinality.solver to True
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def fd_solver():
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s = SolverFor("QF_FD")
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s.set("sat.cardinality.solver", True)
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return s
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# negate, avoid double negation
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def negate(f):
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if is_not(f):
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return f.arg(0)
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else:
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return Not(f)
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def cube2clause(cube):
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return Or([negate(f) for f in cube])
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class State:
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def __init__(self, s):
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self.R = set([])
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self.solver = s
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def add(self, clause):
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if clause not in self.R:
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self.R |= { clause }
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self.solver.add(clause)
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class Goal:
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def __init__(self, cube, parent, level):
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self.level = level
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self.cube = cube
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self.parent = parent
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def __lt__(self, other):
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return self.level < other.level
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def is_seq(f):
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return isinstance(f, list) or isinstance(f, tuple) or isinstance(f, AstVector)
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# Check if the initial state is bad
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def check_disjoint(a, b):
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s = fd_solver()
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s.add(a)
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s.add(b)
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return unsat == s.check()
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# Remove clauses that are subsumed
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def prune(R):
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removed = set([])
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s = fd_solver()
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for f1 in R:
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s.push()
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for f2 in R:
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if f2 not in removed:
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s.add(Not(f2) if f1.eq(f2) else f2)
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if s.check() == unsat:
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removed |= { f1 }
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s.pop()
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return R - removed
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class MiniIC3:
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def __init__(self, init, trans, goal, x0, inputs, xn):
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self.x0 = x0
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self.inputs = inputs
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self.xn = xn
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self.init = init
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self.bad = goal
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self.trans = trans
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self.min_cube_solver = fd_solver()
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self.min_cube_solver.add(Not(trans))
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self.goals = []
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s = State(fd_solver())
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s.add(init)
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s.solver.add(trans)
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self.states = [s]
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self.s_bad = fd_solver()
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self.s_good = fd_solver()
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self.s_bad.add(self.bad)
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self.s_good.add(Not(self.bad))
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def next(self, f):
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if is_seq(f):
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return [self.next(f1) for f1 in f]
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return substitute(f, [p for p in zip(self.x0, self.xn)])
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def prev(self, f):
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if is_seq(f):
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return [self.prev(f1) for f1 in f]
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return substitute(f, [p for p in zip(self.xn, self.x0)])
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def add_solver(self):
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s = fd_solver()
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s.add(self.trans)
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self.states += [State(s)]
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def R(self, i):
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return And(self.states[i].R)
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# Check if there are two states next to each other that have the same clauses.
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def is_valid(self):
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i = 1
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while i + 1 < len(self.states):
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if not (self.states[i].R - self.states[i+1].R):
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return And(prune(self.states[i].R))
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i += 1
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return None
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def value2literal(self, m, x):
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value = m.eval(x)
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if is_true(value):
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return x
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if is_false(value):
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return Not(x)
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return None
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def values2literals(self, m, xs):
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p = [self.value2literal(m, x) for x in xs]
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return [x for x in p if x is not None]
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def project0(self, m):
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return self.values2literals(m, self.x0)
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def projectI(self, m):
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return self.values2literals(m, self.inputs)
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def projectN(self, m):
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return self.values2literals(m, self.xn)
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# Determine if there is a cube for the current state
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# that is potentially reachable.
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def unfold(self):
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core = []
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self.s_bad.push()
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R = self.R(len(self.states)-1)
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self.s_bad.add(R)
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is_sat = self.s_bad.check()
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if is_sat == sat:
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m = self.s_bad.model()
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cube = self.project0(m)
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props = cube + self.projectI(m)
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self.s_good.push()
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self.s_good.add(R)
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is_sat2 = self.s_good.check(props)
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assert is_sat2 == unsat
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core = self.s_good.unsat_core()
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core = [c for c in core if c in set(cube)]
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self.s_good.pop()
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self.s_bad.pop()
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return is_sat, core
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# Block a cube by asserting the clause corresponding to its negation
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def block_cube(self, i, cube):
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self.assert_clause(i, cube2clause(cube))
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# Add a clause to levels 0 until i
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def assert_clause(self, i, clause):
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for j in range(i + 1):
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self.states[j].add(clause)
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# minimize cube that is core of Dual solver.
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# this assumes that props & cube => Trans
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def minimize_cube(self, cube, inputs, lits):
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is_sat = self.min_cube_solver.check(lits + [c for c in cube] + [i for i in inputs])
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assert is_sat == unsat
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core = self.min_cube_solver.unsat_core()
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assert core
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return [c for c in core if c in set(cube)]
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# push a goal on a heap
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def push_heap(self, goal):
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heapq.heappush(self.goals, (goal.level, goal))
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# A state s0 and level f0 such that
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# not(s0) is f0-1 inductive
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def ic3_blocked(self, s0, f0):
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self.push_heap(Goal(self.next(s0), None, f0))
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while self.goals:
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f, g = heapq.heappop(self.goals)
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sys.stdout.write("%d." % f)
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sys.stdout.flush()
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# Not(g.cube) is f-1 invariant
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if f == 0:
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print("")
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return g
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cube, f, is_sat = self.is_inductive(f, g.cube)
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if is_sat == unsat:
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self.block_cube(f, self.prev(cube))
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if f < f0:
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self.push_heap(Goal(g.cube, g.parent, f + 1))
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elif is_sat == sat:
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self.push_heap(Goal(cube, g, f - 1))
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self.push_heap(g)
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else:
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return is_sat
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print("")
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return None
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# Rudimentary generalization:
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# If the cube is already unsat with respect to transition relation
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# extract a core (not necessarily minimal)
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# otherwise, just return the cube.
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def generalize(self, cube, f):
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s = self.states[f - 1].solver
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if unsat == s.check(cube):
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core = s.unsat_core()
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if not check_disjoint(self.init, self.prev(And(core))):
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return core, f
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return cube, f
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# Check if the negation of cube is inductive at level f
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def is_inductive(self, f, cube):
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s = self.states[f - 1].solver
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s.push()
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s.add(self.prev(Not(And(cube))))
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is_sat = s.check(cube)
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if is_sat == sat:
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m = s.model()
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s.pop()
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if is_sat == sat:
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cube = self.next(self.minimize_cube(self.project0(m), self.projectI(m), self.projectN(m)))
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elif is_sat == unsat:
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cube, f = self.generalize(cube, f)
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return cube, f, is_sat
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def run(self):
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if not check_disjoint(self.init, self.bad):
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return "goal is reached in initial state"
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level = 0
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while True:
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inv = self.is_valid()
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if inv is not None:
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return inv
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is_sat, cube = self.unfold()
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if is_sat == unsat:
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level += 1
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print("Unfold %d" % level)
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sys.stdout.flush()
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self.add_solver()
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elif is_sat == sat:
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cex = self.ic3_blocked(cube, level)
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if cex is not None:
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return cex
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else:
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return is_sat
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def test(file):
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h2t = Horn2Transitions()
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h2t.parse(file)
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mp = MiniIC3(h2t.init, h2t.trans, h2t.goal, h2t.xs, h2t.inputs, h2t.xns)
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result = mp.run()
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if isinstance(result, Goal):
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g = result
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print("Trace")
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while g:
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print(g.level, g.cube)
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g = g.parent
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return
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if isinstance(result, ExprRef):
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print("Invariant:\n%s " % result)
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return
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print(result)
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test("data/horn1.smt2")
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test("data/horn2.smt2")
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test("data/horn3.smt2")
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test("data/horn4.smt2")
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test("data/horn5.smt2")
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# test("data/horn6.smt2") # takes long time to finish
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