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include chronological backtracking, two-phase sat, xor inprocessing, probsat, ddfw
Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>
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Nikolaj Bjorner
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32 changed files with 3246 additions and 654 deletions
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src/sat/sat_ddfw.h
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src/sat/sat_ddfw.h
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/*++
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Copyright (c) 2019 Microsoft Corporation
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Module Name:
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sat_ddfw.h
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Abstract:
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DDFW Local search module for clauses
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Author:
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Nikolaj Bjorner, Marijn Heule 2019-4-23
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Notes:
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http://www.ict.griffith.edu.au/~johnt/publications/CP2006raouf.pdf
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--*/
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#ifndef _SAT_DDFW_
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#define _SAT_DDFW_
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#include "util/uint_set.h"
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#include "util/rlimit.h"
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#include "util/params.h"
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#include "util/ema.h"
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#include "sat/sat_clause.h"
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#include "sat/sat_types.h"
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namespace sat {
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class solver;
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class parallel;
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class ddfw : public i_local_search {
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struct clause_info {
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clause_info(clause* cl, unsigned init_weight): m_weight(init_weight), m_trues(0), m_num_trues(0), m_clause(cl) {}
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unsigned m_weight; // weight of clause
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unsigned m_trues; // set of literals that are true
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unsigned m_num_trues; // size of true set
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clause* m_clause;
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bool is_true() const { return m_num_trues > 0; }
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void add(literal lit) { ++m_num_trues; m_trues += lit.index(); }
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void del(literal lit) { SASSERT(m_num_trues > 0); --m_num_trues; m_trues -= lit.index(); }
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};
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struct config {
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config() { reset(); }
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unsigned m_use_reward_zero_pct;
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unsigned m_init_clause_weight;
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unsigned m_max_num_models;
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unsigned m_restart_base;
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unsigned m_reinit_base;
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unsigned m_parsync_base;
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double m_itau;
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void reset() {
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m_init_clause_weight = 8;
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m_use_reward_zero_pct = 15;
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m_max_num_models = (1 << 10);
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m_restart_base = 100333;
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m_reinit_base = 10000;
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m_parsync_base = 333333;
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m_itau = 0.5;
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}
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};
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struct var_info {
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var_info(): m_value(false), m_reward(0), m_make_count(0), m_bias(0), m_reward_avg(1e-5) {}
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bool m_value;
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int m_reward;
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unsigned m_make_count;
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int m_bias;
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ema m_reward_avg;
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};
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config m_config;
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reslimit m_limit;
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clause_allocator m_alloc;
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svector<clause_info> m_clauses;
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literal_vector m_assumptions;
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svector<var_info> m_vars; // var -> info
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svector<double> m_probs; // var -> probability of flipping
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svector<double> m_scores; // reward -> score
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model m_model; // var -> best assignment
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vector<unsigned_vector> m_use_list;
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unsigned_vector m_flat_use_list;
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unsigned_vector m_use_list_index;
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indexed_uint_set m_unsat;
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indexed_uint_set m_unsat_vars; // set of variables that are in unsat clauses
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random_gen m_rand;
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unsigned m_num_non_binary_clauses;
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unsigned m_restart_count, m_reinit_count, m_parsync_count;
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uint64_t m_restart_next, m_reinit_next, m_parsync_next;
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uint64_t m_flips, m_last_flips, m_shifts;
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unsigned m_min_sz;
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hashtable<unsigned, unsigned_hash, default_eq<unsigned>> m_models;
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stopwatch m_stopwatch;
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parallel* m_par;
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class use_list {
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ddfw& p;
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unsigned i;
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public:
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use_list(ddfw& p, literal lit):
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p(p), i(lit.index()) {}
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unsigned const* begin() { return p.m_flat_use_list.c_ptr() + p.m_use_list_index[i]; }
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unsigned const* end() { return p.m_flat_use_list.c_ptr() + p.m_use_list_index[i+1]; }
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};
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void flatten_use_list();
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double mk_score(unsigned r);
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inline double score(unsigned r) { return r; } // TBD: { for (unsigned sz = m_scores.size(); sz <= r; ++sz) m_scores.push_back(mk_score(sz)); return m_scores[r]; }
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inline unsigned num_vars() const { return m_vars.size(); }
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inline unsigned& make_count(bool_var v) { return m_vars[v].m_make_count; }
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inline bool& value(bool_var v) { return m_vars[v].m_value; }
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inline bool value(bool_var v) const { return m_vars[v].m_value; }
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inline int& reward(bool_var v) { return m_vars[v].m_reward; }
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inline int reward(bool_var v) const { return m_vars[v].m_reward; }
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inline int& bias(bool_var v) { return m_vars[v].m_bias; }
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unsigned value_hash() const;
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inline bool is_true(literal lit) const { return value(lit.var()) != lit.sign(); }
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inline clause const& get_clause(unsigned idx) const { return *m_clauses[idx].m_clause; }
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inline unsigned get_weight(unsigned idx) const { return m_clauses[idx].m_weight; }
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inline bool is_true(unsigned idx) const { return m_clauses[idx].is_true(); }
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void update_reward_avg(bool_var v) { m_vars[v].m_reward_avg.update(reward(v)); }
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unsigned select_max_same_sign(unsigned cf_idx);
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inline void inc_make(literal lit) {
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bool_var v = lit.var();
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if (make_count(v)++ == 0) m_unsat_vars.insert(v);
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}
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inline void dec_make(literal lit) {
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bool_var v = lit.var();
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if (--make_count(v) == 0) m_unsat_vars.remove(v);
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}
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inline void inc_reward(literal lit, int inc) { reward(lit.var()) += inc; }
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inline void dec_reward(literal lit, int inc) { reward(lit.var()) -= inc; }
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// flip activity
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bool do_flip();
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bool_var pick_var();
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void flip(bool_var v);
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void save_best_values();
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// shift activity
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void shift_weights();
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// reinitialize weights activity
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bool should_reinit_weights();
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void do_reinit_weights();
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inline bool select_clause(unsigned max_weight, unsigned max_trues, clause_info const& cn, unsigned& n);
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// restart activity
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bool should_restart();
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void do_restart();
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void reinit_values();
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// parallel integration
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bool should_parallel_sync();
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void do_parallel_sync();
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void log();
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void init(unsigned sz, literal const* assumptions);
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void init_clause_data();
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void invariant();
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void add(unsigned sz, literal const* c);
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void add_assumptions();
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public:
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ddfw(): m_par(nullptr) {}
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~ddfw() override;
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lbool check(unsigned sz, literal const* assumptions, parallel* p) override;
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void updt_params(params_ref const& p) override;
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model const& get_model() const override { return m_model; }
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reslimit& rlimit() override { return m_limit; }
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void set_seed(unsigned n) override { m_rand.set_seed(n); }
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void add(solver const& s) override;
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std::ostream& display(std::ostream& out) const;
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// for parallel integration
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unsigned num_non_binary_clauses() const override { return m_num_non_binary_clauses; }
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void reinit(solver& s) override;
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void collect_statistics(statistics& st) const override {}
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double get_priority(bool_var v) const override { return m_probs[v]; }
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};
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}
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#endif
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