mirror of
https://git.cs.ou.nl/joshua.moerman/mealy-decompose.git
synced 2025-04-29 17:57:44 +02:00
400 lines
14 KiB
Python
400 lines
14 KiB
Python
from pysat.solvers import Solver
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from pysat.card import ITotalizer
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from pysat.formula import IDPool
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from pysat.formula import CNF
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import signal
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import math
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import argparse
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### Gebruik:
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# Stap 1: pip3 install python-sat
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# Stap 2: python3 decompose_fsm.py -h
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keep_log = True
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def main():
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record_file = './results/log.txt' if keep_log else None
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parser = argparse.ArgumentParser(description="Decomposes a FSM into smaller components by remapping its outputs. Uses a SAT solver.")
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parser.add_argument('-c', '--components', type=int, default=2, help='number of components')
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parser.add_argument('-w', '--weak', default=False, action="store_true", help='look for weak decomposition')
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parser.add_argument('--add-state-trans', default=False, action="store_true", help='adds state transitivity constraints')
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parser.add_argument('-v', '--verbose', default=False, action="store_true", help='prints more info')
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parser.add_argument('-t', '--timeout', type=int, default=None, help='timeout (in seconds)')
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parser.add_argument('filename', help='path to .dot file')
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args = parser.parse_args()
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# Aantal componenten. c = 1 is zinloos, maar zou moeten werken
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c = args.components
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assert c >= 1
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with open(args.filename) as file:
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machine = parse_dot_file(file)
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if args.verbose:
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print(machine)
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print(f'Input FSM: {len(machine.states)} states, {len(machine.inputs)} inputs, and {len(machine.outputs)} outputs')
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if args.timeout != None:
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def timeout_handler(*_):
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with open(record_file, 'a') as file:
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last_two_comps = '/'.join(args.filename.split('/')[-2:])
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file.write(f'{last_two_comps}\t{len(machine.states)}\t{len(machine.inputs)}\t{len(machine.outputs)}\t{args.weak}\t{c}\tTIMEOUT\n')
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print('TIMEOUT')
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exit()
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signal.signal(signal.SIGALRM, timeout_handler)
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signal.alarm(args.timeout)
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encoder = Encoder(machine, args, record_file=record_file)
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encoder.solve()
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###################################
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# .dot file parser (heuristic)
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class FSM:
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def __init__(self, initial_state, states, inputs, outputs, transition_map, output_map):
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self.initial_state = initial_state
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self.states = states
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self.inputs = inputs
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self.outputs = outputs
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self.transition_map = transition_map
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self.output_map = output_map
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def __str__(self):
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return f'FSM({self.initial_state}, {self.states}, {self.inputs}, {self.outputs}, {self.transition_map}, {self.output_map})'
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def transition(self, s, a):
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return self.transition_map[(s, a)]
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def output(self, s, a):
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return self.output_map[(s, a)]
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def parse_dot_file(lines):
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def parse_transition(line):
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(l, _, r) = line.partition('->')
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s = l.strip()
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(l, _, r) = r.partition('[label="')
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t = l.strip()
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(l, _, _) = r.partition('"]')
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(i, _, o) = l.partition('/')
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return (s, i, o, t)
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initial_state = None
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states, inputs, outputs = set(), set(), set()
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transition_map, output_map = {}, {}
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for line in lines:
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(s, i, o, t) = parse_transition(line)
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if s and i and o and t:
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states.add(s)
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inputs.add(i)
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outputs.add(o)
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states.add(t)
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transition_map[(s, i)] = t
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output_map[(s, i)] = o
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if not initial_state:
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initial_state = s
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assert initial_state in states
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assert len(transition_map) == len(states) * len(inputs)
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assert len(output_map) == len(states) * len(inputs)
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return FSM(initial_state, states, inputs, outputs, transition_map, output_map)
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###################################
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# Utility functions
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def print_table(cell, rs, cs):
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first_col_size = max([len(str(r)) for r in rs])
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col_size = 1 + max([len(str(c)) for c in cs] + [len(cell(r, c)) for c in cs for r in rs])
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print(''.rjust(first_col_size), end='')
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for c in cs:
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print(str(c).rjust(col_size), end='')
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print('')
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for r in rs:
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print(str(r).rjust(first_col_size), end='')
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for c in cs:
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print(cell(r, c).rjust(col_size), end='')
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print('')
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def print_eqrel(rel, xs):
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print_table(lambda r, c: 'Y' if rel(r, c) else '·', xs, xs)
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class Progress:
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def __init__(self, name, guess):
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self.reset(name, guess, show=False)
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def reset(self, name, guess, show=True):
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self.name = name
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self.guess = math.ceil(guess)
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self.count = 0
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self.percentage = None
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if show:
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print(name)
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def add(self, n=1):
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self.count += n
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percentage = math.floor(100 * self.count / self.guess)
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if percentage != self.percentage:
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self.percentage = percentage
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print(f'{self.percentage}%', end='', flush=True)
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print('\r', end='')
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###################################
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# Main logic
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class Encoder:
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def __init__(self, machine, args, record_file=None, progress=None):
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self.machine = machine
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self.args = args
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self.record_file = record_file
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self.progress = progress if progress else Progress('', 1)
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self.c = self.args.components
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self.N = len(self.machine.states)
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self.os = list(machine.outputs) # outputs
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self.rids = [i for i in range(self.c)] # components
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self.vpool = IDPool()
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self.solver = Solver()
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self.encode()
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def add_clause(self, cls, no_return=True):
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self.solver.add_clause(cls, no_return)
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def add_clauses(self, clss, no_return=True):
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self.solver.append_formula(clss, no_return)
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# Een hulp variabele voor False en True, maakt de andere variabelen eenvoudiger
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def var_const(self, b):
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return(self.vpool.id(('const', b)))
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# Voor elke relatie en elke twee elementen o1 en o2, is er een variabele die
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# aangeeft of o1 en o2 gerelateerd zijn. Er is 1 variabele voor xRy en yRx, dus
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# symmetrie is al ingebouwd. Reflexiviteit is ook ingebouwd.
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def var_rel(self, rid, o1, o2):
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if o1 == o2:
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return self.var_const(True)
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[so1, so2] = sorted([o1, o2])
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return self.vpool.id(('rel', rid, so1, so2))
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# De relatie op outputs geeft een relaties op states. Deze relatie moet ook een
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# bisimulatie zijn.
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def var_state_rel(self, rid, s1, s2):
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if s1 == s2:
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return self.var_const(True)
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[ss1, ss2] = sorted([s1, s2])
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return self.vpool.id(('state_rel', rid, ss1, ss2))
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# Voor elke relatie, en elke equivalentie-klasse, kiezen we precies 1 state
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# als representant. Deze variabele geeft aan welk element.
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def var_state_rep(self, rid, s):
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return self.vpool.id(('state_rep', rid, s))
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def encode(self):
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print('===============')
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print('Start encoding')
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self.add_clause([self.var_const(True)])
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self.add_clause([-self.var_const(False)])
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# Contraints zodat de relatie een equivalentie relatie is. We hoeven alleen
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# maar transitiviteit te encoderen, want refl en symm zijn ingebouwd in de var.
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self.progress.reset('transitivity (o)', guess=len(self.rids) * len(self.os) ** 3)
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for rid in self.rids:
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for xo in self.os:
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for yo in self.os:
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for zo in self.os:
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# als xo R yo en yo R zo dan xo R zo
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self.add_clause([-self.var_rel(rid, xo, yo), -self.var_rel(rid, yo, zo), self.var_rel(rid, xo, zo)])
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self.progress.add()
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if self.args.add_state_trans:
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self.progress.reset('transitivity (s)', guess=len(self.rids) * len(self.machine.states) ** 3)
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for rid in self.rids:
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for sx in self.machine.states:
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for sy in self.machine.states:
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for sz in self.machine.states:
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# als sx R sy en sy R sz dan sx R sz
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self.add_clause([-self.var_state_rel(rid, sx, sy), -self.var_state_rel(rid, sy, sz), self.var_state_rel(rid, sx, sz)])
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self.progress.add()
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# Constraint zodat de relaties samen alle elementen kunnen onderscheiden.
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# (Aka: the bijbehorende quotienten zijn joint-injective.)
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self.progress.reset('injectivity', guess=len(self.os) * (len(self.os) - 1) / 2)
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for xi, xo in enumerate(self.os):
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for yo in self.os[xi+1:]:
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# Tenminste een rid moet een verschil maken
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self.add_clause([-self.var_rel(rid, xo, yo) for rid in self.rids])
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self.progress.add()
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# sx ~ sy => for each input: (1) outputs equivalent AND (2) successors related
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# Momenteel hebben we niet de inverse implicatie, is misschien ook niet nodig?
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self.progress.reset('bisimulation modulo rel', guess=len(self.rids) * len(self.machine.states) * len(self.machine.states) * len(self.machine.inputs))
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for rid in self.rids:
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for sx in self.machine.states:
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for sy in self.machine.states:
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for i in self.machine.inputs:
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# sx ~ sy => output(sx, i) ~ output(sy, i)
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ox = self.machine.output(sx, i)
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oy = self.machine.output(sy, i)
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self.add_clause([-self.var_state_rel(rid, sx, sy), self.var_rel(rid, ox, oy)])
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# sx ~ sy => delta(sx, i) ~ delta(sy, i)
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tx = self.machine.transition(sx, i)
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ty = self.machine.transition(sy, i)
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self.add_clause([-self.var_state_rel(rid, sx, sy), self.var_state_rel(rid, tx, ty)])
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self.progress.add()
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# De constraints die zorgen dat representanten ook echt representanten zijn.
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states = list(self.machine.states)
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self.progress.reset('representatives', guess=len(self.rids) * len(states))
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for rid in self.rids:
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for ix, sx in enumerate(states):
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# Belangrijkste: een element is een representant, of equivalent met een
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# eerder element. We forceren hiermee dat de solver representanten moet
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# kiezen (voor aan de lijst).
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self.add_clause([self.var_state_rep(rid, sx)] + [self.var_state_rel(rid, sx, sy) for sy in states[:ix]] )
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for sy in states[:ix]:
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# rx en ry kunnen niet beide een representant zijn, tenzij ze
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# niet gerelateerd zijn.
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self.add_clause([-self.var_state_rep(rid, sx), -self.var_state_rep(rid, sy), -self.var_state_rel(rid, sx, sy)])
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self.progress.add()
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# Tot slot willen we weinig representanten. Dit doen we met een "atmost"
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# formule. We gaan een binaire zoek doen met incremental sat solving.
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self.rhs = None
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if self.args.weak:
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self.lower_bound = int(math.floor((self.N-1)**(1/self.c)))
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self.upper_bound = int(self.N)
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print(f'weak size constraints {self.lower_bound} {self.upper_bound}')
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self.rhs = []
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for rid in self.rids:
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with ITotalizer([self.var_state_rep(rid, sx) for sx in self.machine.states], ubound=self.upper_bound, top_id=self.vpool.top) as cnf_optim:
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self.vpool.occupy(self.vpool.top + 1, cnf_optim.top_id)
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self.vpool.top = cnf_optim.top_id
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self.add_clauses(cnf_optim.cnf.clauses)
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self.rhs.append(cnf_optim.rhs)
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else:
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self.lower_bound = int(math.floor(self.c * (self.N-1)**(1/self.c)))
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self.upper_bound = int(self.N + self.c - 1)
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print(f'size constraints {self.lower_bound} {self.upper_bound}')
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with ITotalizer([self.var_state_rep(rid, sx) for rid in self.rids for sx in self.machine.states], ubound=self.upper_bound, top_id=self.vpool.top) as cnf_optim:
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self.add_clauses(cnf_optim.cnf.clauses)
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self.rhs = cnf_optim.rhs
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def solve(self):
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print('===============')
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print('Start solving')
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sat = None
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while self.upper_bound - self.lower_bound >= 2:
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self.mid_size = int((self.lower_bound + self.upper_bound) / 2)
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print(f'Trying {self.lower_bound} < {self.mid_size} < {self.upper_bound}', end='', flush=True)
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if self.args.weak:
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assumptions = [-self.rhs[rid][self.mid_size] for rid in self.rids]
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sat = self.solver.solve(assumptions=assumptions)
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else:
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sat = self.solver.solve(assumptions=[-self.rhs[self.mid_size]])
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if sat:
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print('\tdown')
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self.upper_bound = self.mid_size
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continue
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else:
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print('\tup')
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self.lower_bound = self.mid_size
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continue
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self.bound = self.upper_bound
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print(f'done searching, found bound = {self.bound}')
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if self.args.weak:
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assumptions = [-self.rhs[rid][self.bound] for rid in self.rids]
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sat = self.solver.solve(assumptions=assumptions)
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else:
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sat = self.solver.solve(assumptions=[-self.rhs[self.bound]])
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assert sat
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# Even omzetten in een makkelijkere data structuur
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m = self.solver.get_model()
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model = {}
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for l in m:
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if l < 0: model[-l] = False
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else: model[l] = True
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if self.args.verbose:
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for rid in self.rids:
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print(f'Relation {rid}:')
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print_eqrel(lambda x, y: model[self.var_rel(rid, x, y)], self.os)
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for rid in self.rids:
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print(f'State relation {rid}:')
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print_eqrel(lambda x, y: model[self.var_state_rel(rid, x, y)], self.machine.states)
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# Precieze groottes van elk component tellen
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counts = []
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for rid in self.rids:
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count = 0
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# Eerst verzamelen we de representanten
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for s in self.machine.states:
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if model[self.var_state_rep(rid, s)]:
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count += 1
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if self.args.verbose:
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print(f'comp {rid} -> representative state {s}')
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counts.append(count)
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print(f'Reduced sizes = {counts} = {sum(counts)}')
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if self.record_file != None:
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with open(self.record_file, 'a') as file:
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last_two_comps = '/'.join(self.args.filename.split('/')[-2:])
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file.write(f'{last_two_comps}\t{self.N}\t{len(self.machine.inputs)}\t{len(self.machine.outputs)}\t{self.args.weak}\t{self.c}\t{sum(counts)}\t{sorted(counts, reverse=True)}\n')
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projections = {}
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for rid in self.rids:
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local_outputs = self.machine.outputs.copy()
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projections[rid] = {}
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count = 0
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while local_outputs:
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repr = local_outputs.pop()
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if repr in projections[rid]:
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continue
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projections[rid][repr] = f'cls_{rid}_{count}'
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others = False
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for o in local_outputs:
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if model[self.var_rel(rid, o, repr)]:
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others = True
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projections[rid][o] = f'cls_{rid}_{count}'
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if not others:
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# Aangeven dat het een unieke output is
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projections[rid][repr] = f'cls_{rid}_{count}_u'
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count += 1
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print('===============')
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print('Output mapping:')
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print_table(lambda o, rid: projections[rid][o], self.machine.outputs, self.rids)
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###################################
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# Run script
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if __name__ == "__main__":
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main()
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