mirror of
https://github.com/Z3Prover/z3
synced 2025-12-03 10:39:07 +00:00
* Make spacer_sem_matcher::reset() public * Add .clang-format for src/muz/spacer * Mark substitution::get_bindings() as const * Fix in spacer_antiunify * Various helper methods in spacer_util Minor functions to compute number of free variables, detect presence of certain sub-expressions, etc. The diff is ugly because of clang-format * Add spacer_cluster for clustering lemmas A cluster of lemmas is a set of lemmas that are all instances of the same pattern, where a pattern is a qff formula with free variables. Currently, the instances are required to be explicit, that is, they are all obtained by substituting concrete values (i.e., numbers) for free variables of the pattern. Lemmas are clustered in cluster_db in each predicate transformer. * Integrate spacer_cluster into spacer_context * Custom clang-format pragmas for spacer_context spacer_context.(cpp|h) are large and have inconsistent formatting. Disable clang-format for them until merge with main z3 branch and re-format. * Computation of convex closure and matrix kernel Various LA functions. The implementations are somewhat preliminary. Convex closure is simplemented via syntactic convex closure procedure. Kernel computation considers many common cases. spacer_arith_kernel_sage implements kernel computation by call external Sage binary. It is used only for debugging and experiments. There is no link dependence on Sage. If desired, it can be removed. * Add spacer_concretize * Utility methods for spacer conjecture rule * Add spacer_expand_bnd_generalizer Generalizes arithmetic inequality literals of the form x <= c, by changing constant c to other constants found in the problem. * Add spacer_global_generalizer Global generalizer checks every new lemma against a cluster of previously learned lemmas, and, if possible, conjectures a new pob, that, when blocked, generalizes multiple existing lemmas. * Remove fp.spacer.print_json option The option is used to dump state of spacer into json for debugging. It has been replaced by `fp.spacer.trace_file` that allows dumping an execution of spacer. The json file can be reconstructed from the trace file elsewhere. * Workaround for segfault in spacer_proof_utils Issue #3 in hgvk94/z3 Segfault in some proof reduction. Avoid by bailing out on reduction. * Revert bug for incomplete models * Use local fresh variables in spacer_global_generalizer * Cleanup of spacer_convex_closure * Allow arbitrary expressions to name cols in convex_closure * WIP: convex closure * WIP: convex closure * Fix bindings order in spacer_global_generalizer The matcher creates substitution using std_order, which is reverse of expected order (variable 0 is last). Adjust the code appropriately for that. * Increase verbosity level for smt_context stats * Dead code in qe_mbp * bug fixes in spacer_global_generalizer::subsumer * Partially remove dependence of size of m_alphas I want m_alphas to potentially be greater than currently used alpha variables. This is helpful for reusing them across multiple calls to convex closure * Subtle bug in kernel computation Coefficient was being passed by reference and, therefore, was being changed indirectly. In the process, updated the code to be more generic to avoid rational computation in the middle of matrix manipulation. * another test for sparse_matrix_ops::kernel * Implementation of matrix kernel using Fraction Free Elimination Ensures that the kernel is int for int matrices. All divisions are exact. * clang-format sparse_matrix_ops.h * another implementation of ffe kernel in sparse_matrix_ops * Re-do arith_kernel and convex_closure * update spacer_global_generalization for new subsumer * remove spacer.gg.use_sage parameter * cleanup of spacer_global_generalizer * Removed dependency on sage * fix in spacer_convex_closure * spacer_sem_matcher: consider an additional semantic matching disabled until it is shown useful * spacer_global_generalizer: improve do_conjecture - if conjecture does not apply to pob, use lemma instead - better normalization - improve debug prints * spacer_conjecture: formatting * spacer_cluster: improve debug prints * spacer_context: improve debug prints * spacer_context: re-queue may pobs enabled even if global re-queue is disabled * spacer_cluster print formatting * reset methods on pob * cleanup of print and local variable names * formatting * reset generalization data once it has been used * refactored extra pob creation during global guidance * fix bug copying sparse matrix into spacer matrix * bug fix in spacer_convex_closure * formatting change in spacer_context * spacer_cluster: get_min_lvl chose level based on pob as well as lemmas * spacer_context: add desired_level to pob desired_level indicates at which level pob should be proved. A pob will be pushed to desired_level if necessary * spacer_context: renamed subsume stats the name of success/failed was switched * spacer_convex_closure: fix prototype of is_congruent_mod() * spacer_convex_closure: hacks in infer_div_pred() * spacer_util: do not expand literals with mod By default, equality literal t=p is expanded into t<=p && t>=p Disable the expansion in case t contains 'mod' operator since such expansion is usually not helpful for divisibility * spacer_util: rename m_util into m_arith * spacer_util: cleanup normalize() * spacer_util: formatting * spacer_context: formatting cleanup on subsume and conjecture * spacer_context: fix handling may pobs when abs_weakness is enabled A pob might be undef, so weakness must be bumped up * spacer_arith_kernel: enhance debug print * spacer_global_generalizer: improve matching on conjecture * spacer_global_generalizer: set desired level on conjecture pob * spacer_global_generalizer: debug print * spacer_global_generalizer: set min level on new pobs the new level should not be higher than the pob that was generalized * spacer_global_generalizer: do no re-create closed pobs If a generalized pob exist and closed, do not re-create it. * spacer_context: normalize twice * spacer_context: forward propagate only same kind of pobs * sketch of inductive generalizer A better implementation of inductive generalizer that in addition to dropping literals also attempts to weaken them. Current implementation is a sketch to be extended based on examples/requirements. * fix ordering in spacer_cluster_util * fix resetting of substitution matcher in spacer_conjecture Old code would forget to reset the substitution provided to the sem_matcher. Thus, if the substitution was matched once (i.e., one literal of interest is found), no other literal would be matched. * add spacer_util is_normalized() method used for debugging only * simplify normalization of pob expressions pob expressions are normalized to increase syntactic matching. Some of the normalization rules seem out of place, so removing them for now. * fix in spacer_global_generalizer If conjecture fails, do not try other generalization strategies -- they will not apply. * fix in spacer_context do not check that may pob is blocked by existing lemmas. It is likely to be blocked. Our goal is to block it again and generalize to a new lemma. This can be further improved by moving directly to generalization when pob is blocked by existing lemmas... Co-authored-by: hgvk94 <hgvk94@gmail.com>
796 lines
26 KiB
C++
796 lines
26 KiB
C++
/*++
|
|
Copyright (c) 2020 Arie Gurfinkel
|
|
|
|
Module Name:
|
|
|
|
spacer_global_generalizer.cpp
|
|
|
|
Abstract:
|
|
|
|
Global Guidance for Spacer
|
|
|
|
Author:
|
|
|
|
Hari Govind V K
|
|
Arie Gurfinkel
|
|
|
|
|
|
--*/
|
|
#include "muz/spacer/spacer_global_generalizer.h"
|
|
#include "ast/arith_decl_plugin.h"
|
|
#include "ast/ast_util.h"
|
|
#include "ast/for_each_expr.h"
|
|
#include "ast/rewriter/expr_safe_replace.h"
|
|
#include "muz/spacer/spacer_cluster.h"
|
|
#include "muz/spacer/spacer_concretize.h"
|
|
#include "muz/spacer/spacer_context.h"
|
|
#include "muz/spacer/spacer_manager.h"
|
|
#include "muz/spacer/spacer_matrix.h"
|
|
#include "muz/spacer/spacer_util.h"
|
|
#include "smt/smt_solver.h"
|
|
|
|
using namespace spacer;
|
|
|
|
namespace {
|
|
|
|
// LOCAL HELPER FUNCTIONS IN ANONYMOUS NAMESPACE
|
|
|
|
class to_real_stripper {
|
|
ast_manager &m;
|
|
arith_util m_arith;
|
|
|
|
public:
|
|
to_real_stripper(ast_manager &_m) : m(_m), m_arith(m) {}
|
|
bool operator()(expr_ref &e, unsigned depth = 8) {
|
|
rational num;
|
|
if (m_arith.is_int(e)) return true;
|
|
if (depth == 0) return false;
|
|
if (!is_app(e)) return false;
|
|
|
|
if (m_arith.is_to_real(e)) {
|
|
// strip to_real()
|
|
e = to_app(e)->get_arg(0);
|
|
return true;
|
|
} else if (m_arith.is_numeral(e, num)) {
|
|
// convert number to an integer
|
|
if (denominator(num).is_one()) {
|
|
e = m_arith.mk_int(num);
|
|
return true;
|
|
} else {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
app *e_app = to_app(e);
|
|
expr_ref_buffer args(m);
|
|
expr_ref kid(m);
|
|
bool dirty = false;
|
|
for (unsigned i = 0, sz = e_app->get_num_args(); i < sz; ++i) {
|
|
auto *arg = e_app->get_arg(i);
|
|
kid = arg;
|
|
if (this->operator()(kid, depth - 1)) {
|
|
dirty |= (kid.get() != arg);
|
|
args.push_back(std::move(kid));
|
|
} else {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (dirty)
|
|
e = m.mk_app(e_app->get_family_id(), e_app->get_decl_kind(),
|
|
args.size(), args.data());
|
|
|
|
return true;
|
|
}
|
|
|
|
bool operator()(expr_ref_vector &vec, unsigned depth = 8) {
|
|
bool res = true;
|
|
expr_ref e(m);
|
|
for (unsigned i = 0, sz = vec.size(); res && i < sz; ++i) {
|
|
e = vec.get(i);
|
|
res = this->operator()(e, depth);
|
|
if (res) { vec[i] = e; }
|
|
}
|
|
return res;
|
|
}
|
|
};
|
|
|
|
// Check whether \p sub contains a mapping to a bv_numeral.
|
|
// return bv_size of the bv_numeral in the first such mapping.
|
|
bool contains_bv(ast_manager &m, const substitution &sub, unsigned &sz) {
|
|
bv_util m_bv(m);
|
|
std::pair<unsigned, unsigned> v;
|
|
expr_offset r;
|
|
rational num;
|
|
for (unsigned j = 0, sz = sub.get_num_bindings(); j < sz; j++) {
|
|
sub.get_binding(j, v, r);
|
|
if (m_bv.is_numeral(r.get_expr(), num, sz)) return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
// Check whether 1) all expressions in the range of \p sub are bv_numerals 2)
|
|
// all bv_numerals in range are of size sz
|
|
bool all_same_sz(ast_manager &m, const substitution &sub, unsigned sz) {
|
|
bv_util m_bv(m);
|
|
std::pair<unsigned, unsigned> v;
|
|
expr_offset r;
|
|
rational num;
|
|
unsigned n_sz;
|
|
for (unsigned j = 0; j < sub.get_num_bindings(); j++) {
|
|
sub.get_binding(j, v, r);
|
|
if (!m_bv.is_numeral(r.get_expr(), num, n_sz) || n_sz != sz)
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
} // namespace
|
|
|
|
namespace spacer {
|
|
lemma_global_generalizer::subsumer::subsumer(ast_manager &a_m, bool ground_pob)
|
|
: m(a_m), m_arith(m), m_bv(m), m_tags(m), m_used_tags(0), m_col_names(m),
|
|
m_ground_pob(ground_pob) {
|
|
scoped_ptr<solver_factory> factory(
|
|
mk_smt_strategic_solver_factory(symbol::null));
|
|
m_solver = (*factory)(m, params_ref::get_empty(), false, true, false,
|
|
symbol::null);
|
|
}
|
|
|
|
app *lemma_global_generalizer::subsumer::mk_fresh_tag() {
|
|
if (m_used_tags == m_tags.size()) {
|
|
auto *bool_sort = m.mk_bool_sort();
|
|
// -- create 4 new tags
|
|
m_tags.push_back(m.mk_fresh_const(symbol("t"), bool_sort));
|
|
m_tags.push_back(m.mk_fresh_const(symbol("t"), bool_sort));
|
|
m_tags.push_back(m.mk_fresh_const(symbol("t"), bool_sort));
|
|
m_tags.push_back(m.mk_fresh_const(symbol("t"), bool_sort));
|
|
}
|
|
|
|
return m_tags.get(m_used_tags++);
|
|
}
|
|
|
|
lemma_global_generalizer::lemma_global_generalizer(context &ctx)
|
|
: lemma_generalizer(ctx), m(ctx.get_ast_manager()),
|
|
m_subsumer(m, ctx.use_ground_pob()), m_do_subsume(ctx.do_subsume()) {}
|
|
|
|
void lemma_global_generalizer::operator()(lemma_ref &lemma) {
|
|
scoped_watch _w_(m_st.watch);
|
|
generalize(lemma);
|
|
}
|
|
|
|
void lemma_global_generalizer::subsumer::mk_col_names(const lemma_cluster &lc) {
|
|
|
|
expr_offset r;
|
|
std::pair<unsigned, unsigned> v;
|
|
|
|
auto &lemmas = lc.get_lemmas();
|
|
SASSERT(!lemmas.empty());
|
|
const substitution &sub = lemmas.get(0).get_sub();
|
|
|
|
m_col_names.reserve(sub.get_num_bindings());
|
|
for (unsigned j = 0, sz = sub.get_num_bindings(); j < sz; j++) {
|
|
// get var id (sub is in reverse order)
|
|
sub.get_binding(sz - 1 - j, v, r);
|
|
auto *sort = r.get_expr()->get_sort();
|
|
|
|
if (!m_col_names.get(j) || m_col_names.get(j)->get_sort() != sort) {
|
|
// create a fresh skolem constant for the jth variable
|
|
// reuse variables if they are already here and have matching sort
|
|
m_col_names[j] = m.mk_fresh_const("mrg_cvx!!", sort);
|
|
}
|
|
}
|
|
|
|
// -- lcm corresponds to a column, reset them since names have potentially
|
|
// changed
|
|
// -- this is a just-in-case
|
|
m_col_lcm.reset();
|
|
}
|
|
|
|
// Populate m_cvx_cls by 1) collecting all substitutions in the cluster \p lc
|
|
// 2) normalizing them to integer numerals
|
|
void lemma_global_generalizer::subsumer::setup_cvx_closure(
|
|
convex_closure &cc, const lemma_cluster &lc) {
|
|
expr_offset r;
|
|
std::pair<unsigned, unsigned> v;
|
|
|
|
mk_col_names(lc);
|
|
const lemma_info_vector &lemmas = lc.get_lemmas();
|
|
|
|
m_col_lcm.reset();
|
|
|
|
unsigned n_vars = 0;
|
|
rational num;
|
|
bool is_first = true;
|
|
for (const auto &lemma : lemmas) {
|
|
const substitution &sub = lemma.get_sub();
|
|
if (is_first) {
|
|
n_vars = sub.get_num_bindings();
|
|
m_col_lcm.reserve(n_vars, rational::one());
|
|
is_first = false;
|
|
}
|
|
|
|
for (unsigned j = 0; j < n_vars; j++) {
|
|
sub.get_binding(n_vars - 1 - j, v, r);
|
|
if (is_numeral(r.get_expr(), num)) {
|
|
m_col_lcm[j] = lcm(m_col_lcm.get(j), abs(denominator(num)));
|
|
}
|
|
}
|
|
}
|
|
|
|
cc.reset(n_vars);
|
|
|
|
unsigned bv_width;
|
|
if (contains_bv(m, lc.get_lemmas()[0].get_sub(), bv_width)) {
|
|
cc.set_bv(bv_width);
|
|
}
|
|
|
|
for (unsigned j = 0; j < n_vars; ++j)
|
|
cc.set_col_var(j, mk_rat_mul(m_col_lcm.get(j), m_col_names.get(j)));
|
|
|
|
vector<rational> row;
|
|
for (const auto &lemma : lemmas) {
|
|
row.reset();
|
|
|
|
const substitution &sub = lemma.get_sub();
|
|
for (unsigned j = 0, sz = sub.get_num_bindings(); j < sz; j++) {
|
|
sub.get_binding(sz - 1 - j, v, r);
|
|
VERIFY(is_numeral(r.get_expr(), num));
|
|
row.push_back(m_col_lcm.get(j) * num);
|
|
}
|
|
|
|
// -- add normalized row to convex closure
|
|
cc.add_row(row);
|
|
}
|
|
}
|
|
|
|
/// Find a representative for \p c
|
|
// TODO: replace with a symbolic representative
|
|
expr *lemma_global_generalizer::subsumer::find_repr(const model_ref &mdl,
|
|
const app *c) {
|
|
return mdl->get_const_interp(c->get_decl());
|
|
}
|
|
|
|
/// Skolemize implicitly existentially quantified constants
|
|
///
|
|
/// Constants in \p m_dim_frsh_cnsts are existentially quantified in \p f. They
|
|
/// are replaced by specific skolem constants. The \p out vector is populated
|
|
/// with corresponding instantiations. Currently, instantiations are values
|
|
/// chosen from the model
|
|
void lemma_global_generalizer::subsumer::skolemize_for_quic3(
|
|
expr_ref &f, const model_ref &mdl, app_ref_vector &out) {
|
|
unsigned idx = out.size();
|
|
app_ref sk(m);
|
|
expr_ref eval(m);
|
|
expr_safe_replace sub(m);
|
|
|
|
expr_ref_vector f_cnsts(m);
|
|
spacer::collect_uninterp_consts(f, f_cnsts);
|
|
|
|
expr_fast_mark2 marks;
|
|
for (auto *c : f_cnsts) { marks.mark(c); }
|
|
|
|
for (unsigned i = 0, sz = m_col_names.size(); i < sz; i++) {
|
|
app *c = m_col_names.get(i);
|
|
if (!marks.is_marked(c)) continue;
|
|
|
|
SASSERT(m_arith.is_int(c));
|
|
// Make skolem constants for ground pob
|
|
sk = mk_zk_const(m, i + idx, c->get_sort());
|
|
eval = find_repr(mdl, c);
|
|
SASSERT(is_app(eval));
|
|
out.push_back(to_app(eval));
|
|
sub.insert(c, sk);
|
|
}
|
|
sub(f.get(), f);
|
|
TRACE("subsume", tout << "skolemized into " << f << "\n";);
|
|
m_col_names.reset();
|
|
}
|
|
|
|
bool lemma_global_generalizer::subsumer::find_model(
|
|
const expr_ref_vector &cc, const expr_ref_vector &alphas, expr *bg,
|
|
model_ref &out_model) {
|
|
|
|
// push because we re-use the solver
|
|
solver::scoped_push _sp(*m_solver);
|
|
if (bg) m_solver->assert_expr(bg);
|
|
|
|
// -- assert syntactic convex closure constraints
|
|
m_solver->assert_expr(cc);
|
|
|
|
// if there are alphas, we have syntactic convex closure
|
|
if (!alphas.empty()) {
|
|
SASSERT(alphas.size() >= 2);
|
|
|
|
// try to get an interior point in convex closure that also satisfies bg
|
|
{
|
|
// push because this might be unsat
|
|
solver::scoped_push _sp2(*m_solver);
|
|
expr_ref zero(m_arith.mk_real(0), m);
|
|
|
|
for (auto *alpha : alphas) {
|
|
m_solver->assert_expr(m_arith.mk_gt(alpha, zero));
|
|
}
|
|
|
|
auto res = m_solver->check_sat();
|
|
if (res == l_true) {
|
|
m_solver->get_model(out_model);
|
|
return true;
|
|
}
|
|
}
|
|
}
|
|
|
|
// failed, try to get any point in convex closure
|
|
auto res = m_solver->check_sat();
|
|
|
|
if (res == l_true) {
|
|
m_solver->get_model(out_model);
|
|
return true;
|
|
}
|
|
|
|
UNREACHABLE();
|
|
|
|
// something went wrong and there is no model, even though one was expected
|
|
return false;
|
|
}
|
|
|
|
/// Returns false if subsumption is not supported for \p lc
|
|
bool lemma_global_generalizer::subsumer::is_handled(const lemma_cluster &lc) {
|
|
// check whether all substitutions are to bv_numerals
|
|
unsigned sz = 0;
|
|
bool bv_clus = contains_bv(m, lc.get_lemmas()[0].get_sub(), sz);
|
|
// If there are no BV numerals, cases are handled.
|
|
// TODO: put restriction on Arrays, non linear arithmetic etc
|
|
if (!bv_clus) return true;
|
|
if (!all_same_sz(m, lc.get_lemmas()[0].get_sub(), sz)) {
|
|
TRACE("subsume",
|
|
tout << "cannot compute cvx cls of different size variables\n";);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void lemma_global_generalizer::subsumer::reset() {
|
|
m_used_tags = 0;
|
|
m_col_lcm.reset();
|
|
}
|
|
|
|
bool lemma_global_generalizer::subsumer::subsume(const lemma_cluster &lc,
|
|
expr_ref_vector &new_post,
|
|
app_ref_vector &bindings) {
|
|
if (!is_handled(lc)) return false;
|
|
|
|
convex_closure cvx_closure(m);
|
|
|
|
reset();
|
|
setup_cvx_closure(cvx_closure, lc);
|
|
|
|
// compute convex closure
|
|
if (!cvx_closure.compute()) { return false; }
|
|
bool is_syntactic = cvx_closure.has_implicit();
|
|
if (is_syntactic) { m_st.m_num_syn_cls++; }
|
|
|
|
CTRACE("subsume_verb", is_syntactic,
|
|
tout << "Convex closure introduced new variables. Implicit part of "
|
|
"closure is: "
|
|
<< mk_and(cvx_closure.get_implicit()) << "\n";);
|
|
|
|
expr_ref grounded(m);
|
|
ground_free_vars(lc.get_pattern(), grounded);
|
|
|
|
expr_ref_vector vec(m);
|
|
auto &implicit_cc = cvx_closure.get_implicit();
|
|
auto &explicit_cc = cvx_closure.get_explicit();
|
|
vec.append(implicit_cc.size(), implicit_cc.data());
|
|
vec.append(explicit_cc.size(), explicit_cc.data());
|
|
|
|
// get a model for mbp
|
|
model_ref mdl;
|
|
auto &alphas = cvx_closure.get_alphas();
|
|
find_model(vec, alphas, grounded, mdl);
|
|
|
|
app_ref_vector vars(m);
|
|
expr_ref conj(m);
|
|
vec.reset();
|
|
|
|
// eliminate real-valued alphas from syntactic convex closure
|
|
if (!implicit_cc.empty()) {
|
|
vec.append(implicit_cc.size(), implicit_cc.data());
|
|
conj = mk_and(vec);
|
|
vars.append(alphas.size(),
|
|
reinterpret_cast<app *const *>(alphas.data()));
|
|
qe_project(m, vars, conj, *mdl.get(), true, true, !m_ground_pob);
|
|
|
|
// mbp failed, not expected, bail out
|
|
if (!vars.empty()) return false;
|
|
}
|
|
|
|
// vec = [implicit_cc]
|
|
// store full cc, this is what we want to over-approximate explicitly
|
|
vec.append(explicit_cc.size(), explicit_cc.data());
|
|
flatten_and(grounded, vec);
|
|
// vec = [implicit_cc(alpha_j, v_i), explicit_cc(v_i), phi(v_i)]
|
|
expr_ref full_cc(mk_and(vec), m);
|
|
|
|
vec.reset();
|
|
if (conj) {
|
|
// if explicit version of implicit cc was successfully computed
|
|
// conj is it, but need to ensure it has no to_real()
|
|
to_real_stripper stripper(m);
|
|
flatten_and(conj, vec);
|
|
stripper(vec);
|
|
}
|
|
vec.append(explicit_cc.size(), explicit_cc.data());
|
|
|
|
flatten_and(grounded, vec);
|
|
// here vec is [cc(v_i), phi(v_i)], and we need to eliminate v_i from it
|
|
|
|
vars.reset();
|
|
vars.append(m_col_names.size(),
|
|
reinterpret_cast<app *const *>(m_col_names.data()));
|
|
conj = mk_and(vec);
|
|
qe_project(m, vars, conj, *mdl.get(), true, true, !m_ground_pob);
|
|
|
|
// failed
|
|
if (!vars.empty()) return false;
|
|
|
|
// at the end, new_post must over-approximate the implicit convex closure
|
|
flatten_and(conj, new_post);
|
|
return over_approximate(new_post, full_cc);
|
|
}
|
|
|
|
/// Find a weakening of \p a such that \p b ==> a
|
|
///
|
|
/// Returns true on success and sets \p a to the result
|
|
bool lemma_global_generalizer::subsumer::over_approximate(expr_ref_vector &a,
|
|
const expr_ref b) {
|
|
|
|
// B && !(A1 && A2 && A3) is encoded as
|
|
// B && ((tag1 && !A1) || (tag2 && !A2) || (tag3 && !A3))
|
|
// iterate and check tags
|
|
expr_ref_vector tags(m), tagged_a(m);
|
|
std::string tag_prefix = "o";
|
|
for (auto *lit : a) {
|
|
tags.push_back(mk_fresh_tag());
|
|
tagged_a.push_back(m.mk_implies(tags.back(), lit));
|
|
}
|
|
|
|
TRACE("subsume_verb", tout << "weakening " << mk_and(a)
|
|
<< " to over approximate " << b << "\n";);
|
|
solver::scoped_push _sp(*m_solver);
|
|
m_solver->assert_expr(b);
|
|
m_solver->assert_expr(push_not(mk_and(tagged_a)));
|
|
|
|
while (true) {
|
|
lbool res = m_solver->check_sat(tags.size(), tags.data());
|
|
if (res == l_false) {
|
|
break;
|
|
} else if (res == l_undef) {
|
|
break;
|
|
}
|
|
|
|
// flip tags for all satisfied literals of !A
|
|
model_ref mdl;
|
|
m_solver->get_model(mdl);
|
|
|
|
for (unsigned i = 0, sz = a.size(); i < sz; ++i) {
|
|
if (!m.is_not(tags.get(i)) && mdl->is_false(a.get(i))) {
|
|
tags[i] = m.mk_not(tags.get(i));
|
|
}
|
|
}
|
|
}
|
|
|
|
expr_ref_buffer res(m);
|
|
// remove all expressions whose tags are false
|
|
for (unsigned i = 0, sz = tags.size(); i < sz; i++) {
|
|
if (!m.is_not(tags.get(i))) { res.push_back(a.get(i)); }
|
|
}
|
|
a.reset();
|
|
a.append(res.size(), res.data());
|
|
|
|
if (a.empty()) {
|
|
// could not find an over approximation
|
|
TRACE("subsume",
|
|
tout << "mbp did not over-approximate convex closure\n";);
|
|
m_st.m_num_no_ovr_approx++;
|
|
return false;
|
|
}
|
|
|
|
TRACE("subsume",
|
|
tout << "over approximate produced " << mk_and(a) << "\n";);
|
|
return true;
|
|
}
|
|
|
|
/// Attempt to set a conjecture on pob \p n.
|
|
///
|
|
/// Done by dropping literal \p lit from
|
|
/// post of \p n. \p lvl is level for conjecture pob. \p gas is the gas for
|
|
/// the conjecture pob returns true if conjecture is set
|
|
bool lemma_global_generalizer::do_conjecture(pob_ref &n, lemma_ref &lemma,
|
|
const expr_ref &lit, unsigned lvl,
|
|
unsigned gas) {
|
|
arith_util arith(m);
|
|
expr_ref_vector fml_vec(m);
|
|
expr_ref n_post(n->post(), m);
|
|
normalize(n_post, n_post, false, false);
|
|
// normalize_order(n_post, n_post);
|
|
fml_vec.push_back(n_post);
|
|
flatten_and(fml_vec);
|
|
|
|
expr_ref_vector conj(m);
|
|
bool is_filtered = filter_out_lit(fml_vec, lit, conj);
|
|
expr *e1 = nullptr, *e2 = nullptr;
|
|
if (!is_filtered &&
|
|
(arith.is_le(lit, e1, e2) || arith.is_ge(lit, e1, e2))) {
|
|
|
|
// if lit is '<=' or '>=', try matching '=='
|
|
is_filtered =
|
|
filter_out_lit(fml_vec, expr_ref(m.mk_eq(e1, e2), m), conj);
|
|
}
|
|
|
|
if (!is_filtered) {
|
|
// -- try using the corresponding lemma instead
|
|
conj.reset();
|
|
n_post = mk_and(lemma->get_cube());
|
|
normalize_order(n_post, n_post);
|
|
fml_vec.reset();
|
|
fml_vec.push_back(n_post);
|
|
flatten_and(fml_vec);
|
|
is_filtered = filter_out_lit(fml_vec, lit, conj);
|
|
}
|
|
|
|
SASSERT(0 < gas && gas < UINT_MAX);
|
|
if (conj.empty()) {
|
|
// If the pob cannot be abstracted, stop using generalization on
|
|
// it
|
|
TRACE("global", tout << "stop local generalization on pob " << n_post
|
|
<< " id is " << n_post->get_id() << "\n";);
|
|
n->disable_local_gen();
|
|
return false;
|
|
} else if (!is_filtered) {
|
|
// The literal to be abstracted is not in the pob
|
|
TRACE("global", tout << "Conjecture failed:\n"
|
|
<< lit << "\n"
|
|
<< n_post << "\n"
|
|
<< "conj:" << conj << "\n";);
|
|
n->disable_local_gen();
|
|
m_st.m_num_cant_abs++;
|
|
return false;
|
|
}
|
|
|
|
pob *root = n->parent();
|
|
while (root->parent()) root = root->parent();
|
|
scoped_ptr<pob> new_pob = alloc(pob, root, n->pt(), lvl, n->depth(), false);
|
|
if (!new_pob) return false;
|
|
|
|
new_pob->set_desired_level(n->level());
|
|
|
|
new_pob->set_post(mk_and(conj));
|
|
new_pob->set_conjecture();
|
|
|
|
// -- register with current pob
|
|
n->set_data(new_pob.detach());
|
|
|
|
// -- update properties of the current pob itself
|
|
n->set_expand_bnd();
|
|
n->set_gas(gas);
|
|
n->disable_local_gen();
|
|
TRACE("global", tout << "set conjecture " << mk_pp(n->get_data()->post(), m)
|
|
<< " at level " << n->get_data()->level() << "\n";);
|
|
return true;
|
|
}
|
|
|
|
// Decide global guidance based on lemma
|
|
void lemma_global_generalizer::generalize(lemma_ref &lemma) {
|
|
// -- pob that the lemma blocks
|
|
pob_ref &pob = lemma->get_pob();
|
|
// -- cluster that the lemma belongs to
|
|
lemma_cluster *cluster = pob->pt().clstr_match(lemma);
|
|
|
|
/// Lemma does not belong to any cluster. return
|
|
if (!cluster) return;
|
|
|
|
// if the cluster does not have enough gas, stop local generalization
|
|
// and return
|
|
if (cluster->get_gas() == 0) {
|
|
m_st.m_num_cls_ofg++;
|
|
pob->disable_local_gen();
|
|
TRACE("global", tout << "stop local generalization on pob "
|
|
<< mk_pp(pob->post(), m) << " id is "
|
|
<< pob->post()->get_id() << "\n";);
|
|
return;
|
|
}
|
|
|
|
// -- local cluster that includes the new lemma
|
|
lemma_cluster lc(*cluster);
|
|
// XXX most of the time lemma clustering happens before generalization
|
|
// XXX so `add_lemma` is likely to return false, but this does not mean
|
|
// XXX that the lemma is not new
|
|
bool is_new = lc.add_lemma(lemma, true);
|
|
(void)is_new;
|
|
|
|
const expr_ref &pat = lc.get_pattern();
|
|
|
|
TRACE("global", {
|
|
tout << "Global generalization of:\n"
|
|
<< mk_and(lemma->get_cube()) << "\n"
|
|
<< "at lvl: " << lemma->level() << "\n"
|
|
<< (is_new ? "new" : "old") << "\n"
|
|
<< "Using cluster:\n"
|
|
<< pat << "\n"
|
|
<< "Existing lemmas in the cluster:\n";
|
|
for (const auto &li : cluster->get_lemmas()) {
|
|
tout << mk_and(li.get_lemma()->get_cube())
|
|
<< " lvl:" << li.get_lemma()->level() << "\n";
|
|
}
|
|
});
|
|
|
|
// Concretize
|
|
if (has_nonlinear_var_mul(pat, m)) {
|
|
m_st.m_num_non_lin++;
|
|
|
|
TRACE("global",
|
|
tout << "Found non linear pattern. Marked to concretize \n";);
|
|
// not constructing the concrete pob here since we need a model for
|
|
// n->post()
|
|
pob->set_concretize_pattern(pat);
|
|
pob->set_concretize(true);
|
|
pob->set_gas(cluster->get_pob_gas());
|
|
cluster->dec_gas();
|
|
return;
|
|
}
|
|
|
|
// Conjecture
|
|
expr_ref lit(m);
|
|
if (find_unique_mono_var_lit(pat, lit)) {
|
|
// Create a conjecture by dropping literal from pob.
|
|
TRACE("global", tout << "Conjecture with pattern\n"
|
|
<< mk_pp(pat, m) << "\n"
|
|
<< "with gas " << cluster->get_gas() << "\n";);
|
|
unsigned gas = cluster->get_pob_gas();
|
|
unsigned lvl = lc.get_min_lvl();
|
|
if (pob) lvl = std::min(lvl, pob->level());
|
|
if (do_conjecture(pob, lemma, lit, lvl, gas)) {
|
|
// decrease the number of times this cluster is going to be used
|
|
// for conjecturing
|
|
cluster->dec_gas();
|
|
return;
|
|
} else {
|
|
// -- if conjecture failed, there is nothing else to do.
|
|
// -- the pob matched pre-condition for conjecture, so it should not
|
|
// be subsumed
|
|
return;
|
|
}
|
|
}
|
|
|
|
// if subsumption removed all the other lemmas, there is nothing to
|
|
// generalize
|
|
if (lc.get_size() < 2) return;
|
|
|
|
if (!m_do_subsume) return;
|
|
// -- new pob that is blocked by generalized lemma
|
|
expr_ref_vector new_post(m);
|
|
// -- bindings for free variables of new_pob
|
|
// -- subsumer might introduce extra free variables
|
|
app_ref_vector bindings(lemma->get_bindings());
|
|
|
|
if (m_subsumer.subsume(lc, new_post, bindings)) {
|
|
class pob *root = pob->parent();
|
|
while (root->parent()) root = root->parent();
|
|
|
|
unsigned new_lvl = lc.get_min_lvl();
|
|
if (pob) new_lvl = std::min(new_lvl, pob->level());
|
|
scoped_ptr<class pob> new_pob =
|
|
alloc(class pob, root, pob->pt(), new_lvl, pob->depth(), false);
|
|
if (!new_pob) return;
|
|
|
|
new_pob->set_desired_level(pob->level());
|
|
new_pob->set_post(mk_and(new_post), bindings);
|
|
new_pob->set_subsume();
|
|
pob->set_data(new_pob.detach());
|
|
|
|
// -- update properties of the pob itself
|
|
pob->set_gas(cluster->get_pob_gas() + 1);
|
|
pob->set_expand_bnd();
|
|
// Stop local generalization. Perhaps not the best choice in general.
|
|
// Helped with one instance on our benchmarks
|
|
pob->disable_local_gen();
|
|
cluster->dec_gas();
|
|
|
|
TRACE("global", tout << "Create subsume pob at level " << new_lvl
|
|
<< "\n"
|
|
<< mk_and(new_post) << "\n";);
|
|
}
|
|
}
|
|
|
|
/// Replace bound vars in \p fml with uninterpreted constants
|
|
void lemma_global_generalizer::subsumer::ground_free_vars(expr *pat,
|
|
expr_ref &out) {
|
|
SASSERT(!is_ground(pat));
|
|
var_subst vs(m, false);
|
|
// m_col_names might be bigger since it contains previously used constants
|
|
// relying on the fact that m_col_lcm was just set. Better to compute free
|
|
// vars of pat
|
|
SASSERT(m_col_lcm.size() <= m_col_names.size());
|
|
out = vs(pat, m_col_lcm.size(),
|
|
reinterpret_cast<expr *const *>(m_col_names.data()));
|
|
SASSERT(is_ground(out));
|
|
}
|
|
|
|
pob *lemma_global_generalizer::mk_concretize_pob(pob &n, model_ref &model) {
|
|
expr_ref_vector new_post(m);
|
|
spacer::pob_concretizer proc(m, model, n.get_concretize_pattern());
|
|
if (proc.apply(n.post(), new_post)) {
|
|
pob *new_pob = n.pt().mk_pob(n.parent(), n.level(), n.depth(),
|
|
mk_and(new_post), n.get_binding());
|
|
|
|
TRACE("concretize", tout << "pob:\n"
|
|
<< mk_pp(n.post(), m)
|
|
<< " is concretized into:\n"
|
|
<< mk_pp(new_pob->post(), m) << "\n";);
|
|
return new_pob;
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
pob *lemma_global_generalizer::mk_subsume_pob(pob &n) {
|
|
if (!(n.get_gas() >= 0 && n.has_data() && n.get_data()->is_subsume()))
|
|
return nullptr;
|
|
|
|
pob *data = n.get_data();
|
|
|
|
pob *f = n.pt().find_pob(data->parent(), data->post());
|
|
if (f && (f->is_in_queue() || f->is_closed())) {
|
|
n.reset_data();
|
|
return nullptr;
|
|
}
|
|
|
|
TRACE("global", tout << "mk_subsume_pob at level " << data->level()
|
|
<< " with post state:\n"
|
|
<< mk_pp(data->post(), m) << "\n";);
|
|
f = n.pt().mk_pob(data->parent(), data->level(), data->depth(),
|
|
data->post(), n.get_binding());
|
|
f->set_subsume();
|
|
f->inherit(*data);
|
|
|
|
n.reset_data();
|
|
return f;
|
|
}
|
|
|
|
pob *lemma_global_generalizer::mk_conjecture_pob(pob &n) {
|
|
if (!(n.has_data() && n.get_data()->is_conjecture() && n.get_gas() > 0))
|
|
return nullptr;
|
|
|
|
pob *data = n.get_data();
|
|
pob *f = n.pt().find_pob(data->parent(), data->post());
|
|
if (f && (f->is_in_queue() || f->is_closed())) {
|
|
n.reset_data();
|
|
return nullptr;
|
|
}
|
|
|
|
f = n.pt().mk_pob(data->parent(), data->level(), data->depth(),
|
|
data->post(), {m});
|
|
|
|
// inherit all metadata from new_pob
|
|
f->inherit(*data);
|
|
|
|
n.reset_data();
|
|
return f;
|
|
}
|
|
|
|
void lemma_global_generalizer::subsumer::collect_statistics(
|
|
statistics &st) const {
|
|
st.update("SPACER num no over approximate", m_st.m_num_no_ovr_approx);
|
|
st.update("SPACER num sync cvx cls", m_st.m_num_syn_cls);
|
|
st.update("SPACER num mbp failed", m_st.m_num_mbp_failed);
|
|
// m_cvx_closure.collect_statistics(st);
|
|
}
|
|
|
|
void lemma_global_generalizer::collect_statistics(statistics &st) const {
|
|
st.update("time.spacer.solve.reach.gen.global", m_st.watch.get_seconds());
|
|
st.update("SPACER cluster out of gas", m_st.m_num_cls_ofg);
|
|
st.update("SPACER num non lin", m_st.m_num_non_lin);
|
|
st.update("SPACER num cant abstract", m_st.m_num_cant_abs);
|
|
}
|
|
|
|
} // namespace spacer
|