mirror of
https://github.com/Z3Prover/z3
synced 2025-04-27 02:45:51 +00:00
remove dead code
Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>
This commit is contained in:
Nikolaj Bjorner
parent
b22fb74c5c
commit
cadfb804c5
6 changed files with 37 additions and 187 deletions
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@ -395,7 +395,6 @@ namespace pdr {
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lbool is_sat = m_solver.check_conjunction_as_assumptions(n.state());
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if (is_sat == l_true && core) {
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core->reset();
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model2cube(*model,*core);
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n.set_model(model);
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}
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return is_sat;
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@ -697,34 +696,6 @@ namespace pdr {
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}
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}
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void pred_transformer::model2cube(app* c, expr* val, expr_ref_vector& res) const {
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if (m.is_bool(val)) {
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res.push_back(m.is_true(val)?c:m.mk_not(c));
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}
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else {
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res.push_back(m.mk_eq(c, val));
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}
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}
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void pred_transformer::model2cube(const model_core& mdl, func_decl * d, expr_ref_vector& res) const {
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expr_ref interp(m);
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get_value_from_model(mdl, d, interp);
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app* c = m.mk_const(d);
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model2cube(c, interp, res);
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}
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void pred_transformer::model2cube(const model_core & mdl, expr_ref_vector & res) const {
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unsigned sz = mdl.get_num_constants();
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for (unsigned i = 0; i < sz; i++) {
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func_decl * d = mdl.get_constant(i);
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SASSERT(d->get_arity()==0);
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if (!m_solver.is_aux_symbol(d)) {
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model2cube(mdl, d, res);
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}
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}
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}
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// ----------------
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// model_node
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@ -1103,11 +1074,9 @@ namespace pdr {
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m_inductive_lvl(0),
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m_cancel(false)
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{
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m_use_model_generalizer = m_params.get_bool("use-model-generalizer", false);
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}
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context::~context() {
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reset_model_generalizers();
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reset_core_generalizers();
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reset();
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}
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@ -1169,7 +1138,6 @@ namespace pdr {
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void context::update_rules(datalog::rule_set& rules) {
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decl2rel rels;
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init_model_generalizers(rules);
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init_core_generalizers(rules);
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init_rules(rules, rels);
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decl2rel::iterator it = rels.begin(), end = rels.end();
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@ -1294,18 +1262,6 @@ namespace pdr {
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};
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void context::reset_model_generalizers() {
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std::for_each(m_model_generalizers.begin(), m_model_generalizers.end(), delete_proc<model_generalizer>());
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m_model_generalizers.reset();
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}
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void context::init_model_generalizers(datalog::rule_set& rules) {
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reset_model_generalizers();
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if (m_use_model_generalizer) {
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m_model_generalizers.push_back(alloc(model_evaluation_generalizer, *this, m));
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}
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}
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void context::reset_core_generalizers() {
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std::for_each(m_core_generalizers.begin(), m_core_generalizers.end(), delete_proc<core_generalizer>());
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m_core_generalizers.reset();
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@ -1552,11 +1508,7 @@ namespace pdr {
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}
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else {
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TRACE("pdr", tout << "node: " << &n << "\n";);
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for (unsigned i = 0; i < m_model_generalizers.size(); ++i) {
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(*m_model_generalizers[i])(n, cube);
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}
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create_children(n, m_pm.mk_and(cube));
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create_children(n);
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}
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break;
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case l_false: {
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@ -1627,45 +1579,6 @@ namespace pdr {
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}
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}
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// create children states from model cube.
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void context::create_children(model_node& n, expr* model) {
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if (!m_use_model_generalizer) {
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create_children2(n);
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return;
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}
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expr_ref_vector literals(m), sub_lits(m);
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expr_ref o_cube(m), n_cube(m);
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datalog::flatten_and(model, literals);
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ptr_vector<func_decl> preds;
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unsigned level = n.level();
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SASSERT(level > 0);
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n.pt().find_predecessors(n.model(), preds);
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n.pt().remove_predecessors(literals);
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TRACE("pdr",
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model_v2_pp(tout, n.model());
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tout << "Model cube\n";
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tout << mk_pp(model, m) << "\n";
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tout << "Predecessors\n";
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for (unsigned i = 0; i < preds.size(); ++i) {
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tout << preds[i]->get_name() << "\n";
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}
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);
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for (unsigned i = 0; i < preds.size(); ++i) {
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pred_transformer& pt = *m_rels.find(preds[i]);
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SASSERT(pt.head() == preds[i]);
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assign_ref_vector(sub_lits, literals);
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m_pm.filter_o_atoms(sub_lits, i);
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o_cube = m_pm.mk_and(sub_lits);
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m_pm.formula_o2n(o_cube, n_cube, i);
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model_node* child = alloc(model_node, &n, n_cube, pt, level-1);
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++m_stats.m_num_nodes;
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m_search.add_leaf(*child);
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}
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check_pre_closed(n);
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TRACE("pdr", m_search.display(tout););
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}
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/**
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\brief create children states from model cube.
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@ -1715,7 +1628,7 @@ namespace pdr {
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- Create sub-goals for L0 and L1.
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*/
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void context::create_children2(model_node& n) {
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void context::create_children(model_node& n) {
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SASSERT(n.level() > 0);
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pred_transformer& pt = n.pt();
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@ -1731,12 +1644,17 @@ namespace pdr {
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verbose_stream() << "Phi:\n" << mk_pp(phi, m) << "\n";);
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model_evaluator mev(m);
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expr_ref_vector mdl(m), forms(m);
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expr_ref_vector mdl(m), forms(m), Phi(m);
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forms.push_back(T);
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forms.push_back(phi);
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datalog::flatten_and(forms);
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ptr_vector<expr> forms1(forms.size(), forms.c_ptr());
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expr_ref_vector Phi = mev.minimize_literals(forms1, M);
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if (m_params.get_bool(":use-model-generalizer", false)) {
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Phi.append(mev.minimize_model(forms1, M));
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}
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else {
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Phi.append(mev.minimize_literals(forms1, M));
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}
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ptr_vector<func_decl> preds;
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pt.find_predecessors(r, preds);
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pt.remove_predecessors(Phi);
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@ -1842,9 +1760,6 @@ namespace pdr {
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st.update("PDR max depth", m_stats.m_max_depth);
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m_pm.collect_statistics(st);
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for (unsigned i = 0; i < m_model_generalizers.size(); ++i) {
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m_model_generalizers[i]->collect_statistics(st);
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}
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for (unsigned i = 0; i < m_core_generalizers.size(); ++i) {
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m_core_generalizers[i]->collect_statistics(st);
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}
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@ -98,9 +98,6 @@ namespace pdr {
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void init_atom(decl2rel const& pts, app * atom, app_ref_vector& var_reprs, expr_ref_vector& conj, unsigned tail_idx);
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void ground_free_vars(expr* e, app_ref_vector& vars, ptr_vector<app>& aux_vars);
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void model2cube(const model_core& md, func_decl * d, expr_ref_vector& res) const;
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void model2cube(app* c, expr* val, expr_ref_vector& res) const;
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void simplify_formulas(tactic& tac, expr_ref_vector& fmls);
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// Debugging
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@ -157,8 +154,6 @@ namespace pdr {
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manager& get_pdr_manager() const { return pm; }
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ast_manager& get_manager() const { return m; }
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void model2cube(const model_core & mdl, expr_ref_vector & res) const;
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void add_premises(decl2rel const& pts, unsigned lvl, expr_ref_vector& r);
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void close(expr* e);
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@ -266,18 +261,6 @@ namespace pdr {
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class context;
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// 'state' is satisifiable with predecessor 'cube'.
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// Generalize predecessor still forcing satisfiability.
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class model_generalizer {
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protected:
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context& m_ctx;
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public:
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model_generalizer(context& ctx): m_ctx(ctx) {}
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virtual ~model_generalizer() {}
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virtual void operator()(model_node& n, expr_ref_vector& cube) = 0;
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virtual void collect_statistics(statistics& st) {}
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};
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// 'state' is unsatisfiable at 'level' with 'core'.
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// Minimize or weaken core.
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class core_generalizer {
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@ -317,9 +300,7 @@ namespace pdr {
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pred_transformer* m_query;
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model_search m_search;
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lbool m_last_result;
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bool m_use_model_generalizer;
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unsigned m_inductive_lvl;
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ptr_vector<model_generalizer> m_model_generalizers;
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ptr_vector<core_generalizer> m_core_generalizers;
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stats m_stats;
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volatile bool m_cancel;
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@ -336,8 +317,7 @@ namespace pdr {
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void check_pre_closed(model_node& n);
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void expand_node(model_node& n);
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lbool expand_state(model_node& n, expr_ref_vector& cube);
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void create_children(model_node& n, expr* model);
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void create_children2(model_node& n);
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void create_children(model_node& n);
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expr_ref mk_sat_answer() const;
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expr_ref mk_unsat_answer() const;
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@ -347,7 +327,6 @@ namespace pdr {
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// Initialization
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class classifier_proc;
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void init_model_generalizers(datalog::rule_set& rules);
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void init_core_generalizers(datalog::rule_set& rules);
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bool check_invariant(unsigned lvl);
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@ -359,7 +338,6 @@ namespace pdr {
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void simplify_formulas();
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void reset_model_generalizers();
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void reset_core_generalizers();
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public:
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@ -29,19 +29,6 @@ Revision History:
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namespace pdr {
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//
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// eliminate conjuncts from cube as long as state is satisfied.
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//
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void model_evaluation_generalizer::operator()(model_node& n, expr_ref_vector& cube) {
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expr_ref_vector forms(cube.get_manager());
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forms.push_back(n.state());
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forms.push_back(n.pt().transition());
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datalog::flatten_and(forms);
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ptr_vector<expr> forms1(forms.size(), forms.c_ptr());
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model_ref mdl = n.model_ptr();
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m_model_evaluator.minimize_model(forms1, mdl, cube);
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}
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//
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// main propositional induction generalizer.
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// drop literals one by one from the core and check if the core is still inductive.
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@ -25,14 +25,6 @@ Revision History:
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namespace pdr {
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class model_evaluation_generalizer : public model_generalizer {
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model_evaluator m_model_evaluator;
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public:
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model_evaluation_generalizer(context& ctx, ast_manager& m) : model_generalizer(ctx), m_model_evaluator(m) {}
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virtual ~model_evaluation_generalizer() {}
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virtual void operator()(model_node& n, expr_ref_vector& cube);
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};
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class core_bool_inductive_generalizer : public core_generalizer {
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unsigned m_failure_limit;
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public:
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@ -90,33 +90,6 @@ std::string pp_cube(unsigned sz, expr * const * lits, ast_manager& m) {
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//
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bool model_evaluator::get_assignment(expr* e, expr*& var, expr*& val) {
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if (m.is_eq(e, var, val)) {
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if (!is_uninterp(var)) {
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std::swap(var, val);
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}
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if (m.is_true(val) || m.is_false(val) || m_arith.is_numeral(val)) {
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return true;
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}
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TRACE("pdr_verbose", tout << "no value for " << mk_pp(val, m) << "\n";);
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return false;
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}
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else if (m.is_not(e, var)) {
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val = m.mk_false();
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return true;
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}
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else if (m.is_bool(e)) {
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val = m.mk_true();
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var = e;
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return true;
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}
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else {
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TRACE("pdr_verbose", tout << "no value set of " << mk_pp(e, m) << "\n";);
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return false;
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}
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}
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void model_evaluator::assign_value(expr* e, expr* val) {
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rational r;
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if (m.is_true(val)) {
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@ -166,7 +139,7 @@ void model_evaluator::reset() {
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m_model = 0;
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}
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void model_evaluator::minimize_model(ptr_vector<expr> const & formulas, model_ref& mdl, expr_ref_vector & model) {
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expr_ref_vector model_evaluator::minimize_model(ptr_vector<expr> const & formulas, model_ref& mdl) {
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setup_model(mdl);
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TRACE("pdr_verbose",
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@ -174,7 +147,7 @@ void model_evaluator::minimize_model(ptr_vector<expr> const & formulas, model_re
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for (unsigned i = 0; i < formulas.size(); ++i) tout << mk_pp(formulas[i], m) << "\n";
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);
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prune_by_cone_of_influence(formulas, model);
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expr_ref_vector model = prune_by_cone_of_influence(formulas);
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TRACE("pdr_verbose",
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tout << "pruned model:\n";
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for (unsigned i = 0; i < model.size(); ++i) tout << mk_pp(model[i].get(), m) << "\n";);
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@ -185,6 +158,8 @@ void model_evaluator::minimize_model(ptr_vector<expr> const & formulas, model_re
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setup_model(mdl);
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VERIFY(check_model(formulas));
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reset(););
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return model;
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}
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expr_ref_vector model_evaluator::minimize_literals(ptr_vector<expr> const& formulas, model_ref& mdl) {
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@ -340,7 +315,7 @@ void model_evaluator::collect(ptr_vector<expr> const& formulas, ptr_vector<expr>
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m_visited.reset();
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}
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void model_evaluator::prune_by_cone_of_influence(ptr_vector<expr> const & formulas, expr_ref_vector& model) {
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expr_ref_vector model_evaluator::prune_by_cone_of_influence(ptr_vector<expr> const & formulas) {
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ptr_vector<expr> tocollect;
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collect(formulas, tocollect);
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m1.reset();
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@ -349,19 +324,23 @@ void model_evaluator::prune_by_cone_of_influence(ptr_vector<expr> const & formul
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TRACE("pdr_verbose", tout << "collect: " << mk_pp(tocollect[i], m) << "\n";);
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for_each_expr(*this, m_visited, tocollect[i]);
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}
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unsigned sz1 = model.size();
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for (unsigned i = 0; i < model.size(); ++i) {
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expr* e = model[i].get(), *var, *val;
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if (get_assignment(e, var, val)) {
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if (!m_visited.is_marked(var)) {
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model[i] = model.back();
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model.pop_back();
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--i;
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}
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unsigned sz = m_model->get_num_constants();
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expr_ref e(m), eq(m);
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expr_ref_vector model(m);
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bool_rewriter rw(m);
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for (unsigned i = 0; i < sz; i++) {
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func_decl * d = m_model->get_constant(i);
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expr* val = m_model->get_const_interp(d);
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e = m.mk_const(d);
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if (m_visited.is_marked(e)) {
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rw.mk_eq(e, val, eq);
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model.push_back(eq);
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}
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}
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m_visited.reset();
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TRACE("pdr", tout << sz1 << " ==> " << model.size() << "\n";);
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TRACE("pdr", tout << sz << " ==> " << model.size() << "\n";);
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return model;
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}
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void model_evaluator::eval_arith(app* e) {
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@ -194,10 +194,9 @@ class model_evaluator {
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void reset();
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void setup_model(model_ref& model);
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void assign_value(expr* e, expr* v);
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bool get_assignment(expr* e, expr*& var, expr*& val);
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void collect(ptr_vector<expr> const& formulas, ptr_vector<expr>& tocollect);
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void process_formula(app* e, ptr_vector<expr>& todo, ptr_vector<expr>& tocollect);
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void prune_by_cone_of_influence(ptr_vector<expr> const & formulas, expr_ref_vector& model);
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expr_ref_vector prune_by_cone_of_influence(ptr_vector<expr> const & formulas);
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void eval_arith(app* e);
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void eval_basic(app* e);
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void eval_iff(app* e, expr* arg1, expr* arg2);
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@ -230,7 +229,13 @@ protected:
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public:
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model_evaluator(ast_manager& m) : m(m), m_arith(m), m_bv(m), m_refs(m) {}
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virtual void minimize_model(ptr_vector<expr> const & formulas, model_ref& mdl, expr_ref_vector& model);
|
||||
/**
|
||||
\brief extract equalities from model that suffice to satisfy formula.
|
||||
|
||||
\pre model satisfies formulas
|
||||
*/
|
||||
|
||||
expr_ref_vector minimize_model(ptr_vector<expr> const & formulas, model_ref& mdl);
|
||||
|
||||
/**
|
||||
\brief extract literals from formulas that satisfy formulas.
|
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|
|
@ -239,12 +244,6 @@ public:
|
|||
*/
|
||||
expr_ref_vector minimize_literals(ptr_vector<expr> const & formulas, model_ref& mdl);
|
||||
|
||||
/**
|
||||
\brief extract literals from formulas that satisfy formulas.
|
||||
|
||||
\pre model satisfies formulas
|
||||
*/
|
||||
expr_ref_vector minimize_literals(ptr_vector<expr> const & formulas, expr_ref_vector const & model);
|
||||
|
||||
// for_each_expr visitor.
|
||||
void operator()(expr* e) {}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue