ASP-based solver: avoid cycles in clingo using hidden directive (#40720)

The code should be functonally equivalent to what it was before,
but now to avoid cycles by design we are using a "hidden"
feature of clingo
This commit is contained in:
Massimiliano Culpo 2023-10-30 07:38:53 +01:00 committed by GitHub
parent 2a797f90b4
commit 6983db1392
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 4 additions and 50 deletions

View file

@ -8,7 +8,6 @@
import enum import enum
import itertools import itertools
import os import os
import pathlib
import pprint import pprint
import re import re
import types import types
@ -889,14 +888,6 @@ def on_model(model):
timer.start("solve") timer.start("solve")
solve_result = self.control.solve(**solve_kwargs) solve_result = self.control.solve(**solve_kwargs)
if solve_result.satisfiable and self._model_has_cycles(models):
tty.debug(f"cycles detected, falling back to slower algorithm [specs={specs}]")
self.control.load(os.path.join(parent_dir, "cycle_detection.lp"))
self.control.ground([("no_cycle", [])])
models.clear()
solve_result = self.control.solve(**solve_kwargs)
timer.stop("solve") timer.stop("solve")
# once done, construct the solve result # once done, construct the solve result
@ -950,26 +941,6 @@ def on_model(model):
return result, timer, self.control.statistics return result, timer, self.control.statistics
def _model_has_cycles(self, models):
"""Returns true if the best model has cycles in it"""
cycle_detection = clingo.Control()
parent_dir = pathlib.Path(__file__).parent
lp_file = parent_dir / "cycle_detection.lp"
min_cost, best_model = min(models)
with cycle_detection.backend() as backend:
for atom in best_model:
if atom.name == "attr" and str(atom.arguments[0]) == '"depends_on"':
symbol = fn.depends_on(atom.arguments[1], atom.arguments[2])
atom_id = backend.add_atom(symbol.symbol())
backend.add_rule([atom_id], [], choice=False)
cycle_detection.load(str(lp_file))
cycle_detection.ground([("base", []), ("no_cycle", [])])
cycle_result = cycle_detection.solve()
return cycle_result.unsatisfiable
class ConcreteSpecsByHash(collections.abc.Mapping): class ConcreteSpecsByHash(collections.abc.Mapping):
"""Mapping containing concrete specs keyed by DAG hash. """Mapping containing concrete specs keyed by DAG hash.

View file

@ -1325,6 +1325,10 @@ build_priority(PackageNode, 0) :- not build(PackageNode), attr("node", Package
#defined installed_hash/2. #defined installed_hash/2.
% This statement, which is a hidden feature of clingo, let us avoid cycles in the DAG
#edge (A, B) : depends_on(A, B).
%----------------------------------------------------------------- %-----------------------------------------------------------------
% Optimization to avoid errors % Optimization to avoid errors
%----------------------------------------------------------------- %-----------------------------------------------------------------

View file

@ -1,21 +0,0 @@
% Copyright 2013-2023 Lawrence Livermore National Security, LLC and other
% Spack Project Developers. See the top-level COPYRIGHT file for details.
%
% SPDX-License-Identifier: (Apache-2.0 OR MIT)
%=============================================================================
% Avoid cycles in the DAG
%
% Some combinations of conditional dependencies can result in cycles;
% this ensures that we solve around them. Note that these rules are quite
% demanding on both grounding and solving, since they need to compute and
% consider all possible paths between pair of nodes.
%=============================================================================
#program no_cycle.
path(Parent, Child) :- depends_on(Parent, Child).
path(Parent, Descendant) :- path(Parent, A), depends_on(A, Descendant).
:- path(A, A).
#defined depends_on/2.