mirror of
https://github.com/Z3Prover/z3
synced 2025-11-03 13:07:53 +00:00
282 lines
8.1 KiB
C++
282 lines
8.1 KiB
C++
/*++
|
|
Copyright (c) 2019 Microsoft Corporation
|
|
|
|
Module Name:
|
|
|
|
sat_ddfw.h
|
|
|
|
Abstract:
|
|
|
|
DDFW Local search module for clauses
|
|
|
|
Author:
|
|
|
|
Nikolaj Bjorner, Marijn Heule 2019-4-23
|
|
|
|
Notes:
|
|
|
|
http://www.ict.griffith.edu.au/~johnt/publications/CP2006raouf.pdf
|
|
|
|
--*/
|
|
#pragma once
|
|
|
|
#include "util/uint_set.h"
|
|
#include "util/rlimit.h"
|
|
#include "util/params.h"
|
|
#include "util/ema.h"
|
|
#include "util/sat_sls.h"
|
|
#include "util/map.h"
|
|
#include "util/sat_literal.h"
|
|
#include "util/statistics.h"
|
|
#include "util/stopwatch.h"
|
|
|
|
|
|
namespace sat {
|
|
|
|
class local_search_plugin {
|
|
public:
|
|
virtual ~local_search_plugin() {}
|
|
//virtual void init_search() = 0;
|
|
//virtual void finish_search() = 0;
|
|
virtual void on_rescale() = 0;
|
|
virtual lbool on_save_model() = 0;
|
|
virtual void on_restart() = 0;
|
|
};
|
|
|
|
class ddfw {
|
|
friend class ddfw_wrapper;
|
|
protected:
|
|
|
|
struct config {
|
|
config() { reset(); }
|
|
unsigned m_use_reward_zero_pct;
|
|
unsigned m_init_clause_weight;
|
|
unsigned m_max_num_models;
|
|
unsigned m_restart_base;
|
|
unsigned m_reinit_base;
|
|
unsigned m_parsync_base;
|
|
double m_itau;
|
|
void reset() {
|
|
m_init_clause_weight = 8;
|
|
m_use_reward_zero_pct = 15;
|
|
m_max_num_models = (1 << 10);
|
|
m_restart_base = 100333;
|
|
m_reinit_base = 10000;
|
|
m_parsync_base = 333333;
|
|
m_itau = 0.5;
|
|
}
|
|
};
|
|
|
|
struct var_info {
|
|
var_info() {}
|
|
bool m_value = false;
|
|
double m_reward = 0;
|
|
double m_last_reward = 0;
|
|
unsigned m_make_count = 0;
|
|
int m_bias = 0;
|
|
ema m_reward_avg = 1e-5;
|
|
};
|
|
|
|
config m_config;
|
|
reslimit m_limit;
|
|
vector<clause_info> m_clauses;
|
|
literal_vector m_assumptions;
|
|
svector<var_info> m_vars; // var -> info
|
|
svector<double> m_probs; // var -> probability of flipping
|
|
svector<double> m_scores; // reward -> score
|
|
svector<lbool> m_model; // var -> best assignment
|
|
unsigned m_init_weight = 2;
|
|
vector<unsigned_vector> m_use_list;
|
|
unsigned_vector m_flat_use_list;
|
|
unsigned_vector m_use_list_index;
|
|
unsigned m_use_list_vars = 0, m_use_list_clauses = 0;
|
|
lbool m_last_result = l_true;
|
|
|
|
indexed_uint_set m_unsat;
|
|
indexed_uint_set m_unsat_vars; // set of variables that are in unsat clauses
|
|
random_gen m_rand;
|
|
uint64_t m_last_flips_for_shift = 0;
|
|
unsigned m_num_non_binary_clauses = 0;
|
|
unsigned m_restart_count = 0, m_reinit_count = 0;
|
|
uint64_t m_restart_next = 0, m_reinit_next = 0;
|
|
uint64_t m_flips = 0, m_last_flips = 0, m_shifts = 0;
|
|
unsigned m_min_sz = UINT_MAX;
|
|
u_map<unsigned> m_models;
|
|
stopwatch m_stopwatch;
|
|
unsigned_vector m_num_models;
|
|
bool m_save_best_values = false;
|
|
|
|
scoped_ptr<local_search_plugin> m_plugin = nullptr;
|
|
std::function<bool(void)> m_parallel_sync;
|
|
|
|
bool flatten_use_list();
|
|
|
|
/**
|
|
* TBD: map reward value to a score, possibly through an exponential function, such as
|
|
* exp(-tau/r), where tau > 0
|
|
*/
|
|
inline double score(double r) { return r; }
|
|
|
|
inline unsigned& make_count(bool_var v) { return m_vars[v].m_make_count; }
|
|
|
|
inline bool& value(bool_var v) { return m_vars[v].m_value; }
|
|
|
|
inline bool value(bool_var v) const { return m_vars[v].m_value; }
|
|
|
|
inline double& reward(bool_var v) { return m_vars[v].m_reward; }
|
|
|
|
|
|
unsigned value_hash() const;
|
|
|
|
inline bool is_true(literal lit) const { return value(lit.var()) != lit.sign(); }
|
|
|
|
inline sat::literal_vector const& get_clause(unsigned idx) const { return m_clauses[idx].m_clause; }
|
|
|
|
inline double get_weight(unsigned idx) const { return m_clauses[idx].m_weight; }
|
|
|
|
inline bool is_true(unsigned idx) const { return m_clauses[idx].is_true(); }
|
|
|
|
void update_reward_avg(bool_var v) { m_vars[v].m_reward_avg.update(reward(v)); }
|
|
|
|
unsigned select_max_same_sign(unsigned cf_idx);
|
|
|
|
inline void inc_make(literal lit) {
|
|
bool_var v = lit.var();
|
|
if (make_count(v)++ == 0) m_unsat_vars.insert_fresh(v);
|
|
}
|
|
|
|
inline void dec_make(literal lit) {
|
|
bool_var v = lit.var();
|
|
if (--make_count(v) == 0) m_unsat_vars.remove(v);
|
|
}
|
|
|
|
inline void inc_reward(literal lit, double w) { reward(lit.var()) += w; }
|
|
|
|
inline void dec_reward(literal lit, double w) { reward(lit.var()) -= w; }
|
|
|
|
void check_with_plugin();
|
|
void check_without_plugin();
|
|
|
|
// flip activity
|
|
bool do_flip();
|
|
|
|
bool_var pick_var(double& reward);
|
|
|
|
bool apply_flip(bool_var v, double reward);
|
|
|
|
|
|
void save_best_values();
|
|
void save_model();
|
|
void save_priorities();
|
|
|
|
// shift activity
|
|
void shift_weights();
|
|
inline double calculate_transfer_weight(double w);
|
|
|
|
// reinitialize weights activity
|
|
bool should_reinit_weights();
|
|
void do_reinit_weights();
|
|
inline bool select_clause(double max_weight, clause_info const& cn, unsigned& n);
|
|
|
|
// restart activity
|
|
bool should_restart();
|
|
void do_restart();
|
|
void reinit_values();
|
|
|
|
unsigned select_random_true_clause();
|
|
|
|
void log();
|
|
|
|
void init(unsigned sz, literal const* assumptions);
|
|
|
|
void init_clause_data();
|
|
|
|
void invariant();
|
|
|
|
void del();
|
|
|
|
void add_assumptions();
|
|
|
|
inline void transfer_weight(unsigned from, unsigned to, double w);
|
|
|
|
inline bool disregard_neighbor();
|
|
|
|
public:
|
|
|
|
ddfw() {}
|
|
|
|
~ddfw();
|
|
|
|
void set_plugin(local_search_plugin* p) { m_plugin = p; }
|
|
|
|
lbool check(unsigned sz, literal const* assumptions);
|
|
|
|
void updt_params(params_ref const& p);
|
|
|
|
svector<lbool> const& get_model() const { return m_model; }
|
|
|
|
reslimit& rlimit() { return m_limit; }
|
|
|
|
void set_seed(unsigned n) { m_rand.set_seed(n); }
|
|
|
|
|
|
bool get_value(bool_var v) const { return value(v); }
|
|
|
|
std::ostream& display(std::ostream& out) const;
|
|
|
|
// for parallel integration
|
|
unsigned num_non_binary_clauses() const { return m_num_non_binary_clauses; }
|
|
|
|
void collect_statistics(statistics& st) const;
|
|
|
|
void reset_statistics();
|
|
|
|
double get_priority(bool_var v) const { return m_probs[v]; }
|
|
|
|
// access clause information and state of Boolean search
|
|
indexed_uint_set& unsat_set() { return m_unsat; }
|
|
|
|
indexed_uint_set const& unsat_set() const { return m_unsat; }
|
|
|
|
vector<clause_info> const& clauses() const { return m_clauses; }
|
|
|
|
clause_info& get_clause_info(unsigned idx) { return m_clauses[idx]; }
|
|
|
|
clause_info const& get_clause_info(unsigned idx) const { return m_clauses[idx]; }
|
|
|
|
void remove_assumptions();
|
|
|
|
void flip(bool_var v);
|
|
|
|
inline double get_reward(bool_var v) const { return m_vars[v].m_reward; }
|
|
|
|
double get_reward_avg(bool_var v) const { return m_vars[v].m_reward_avg; }
|
|
|
|
inline int& bias(bool_var v) { return m_vars[v].m_bias; }
|
|
|
|
void reserve_vars(unsigned n);
|
|
|
|
void add(unsigned sz, literal const* c);
|
|
|
|
sat::bool_var add_var();
|
|
|
|
void reinit();
|
|
|
|
void force_restart() { m_restart_next = m_flips; }
|
|
|
|
inline unsigned num_vars() const { return m_vars.size(); }
|
|
|
|
void simplify();
|
|
|
|
|
|
ptr_iterator<unsigned> use_list(literal lit) {
|
|
flatten_use_list();
|
|
unsigned i = lit.index();
|
|
auto const* b = m_flat_use_list.data() + m_use_list_index[i];
|
|
auto const* e = m_flat_use_list.data() + m_use_list_index[i + 1];
|
|
return { b, e };
|
|
}
|
|
|
|
};
|
|
}
|
|
|