split_strategy.hpp

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// Copyright 2023 Pierre Talbot
 
#ifndef LALA_POWER_SPLIT_STRATEGY_HPP
#define LALA_POWER_SPLIT_STRATEGY_HPP
 
#include "battery/vector.hpp"
#include "battery/shared_ptr.hpp"
#include "branch.hpp"
#include "lala/logic/logic.hpp"
#include "lala/abstract_deps.hpp"
 
namespace lala {
 
enum class VariableOrder {
  INPUT_ORDER,
  FIRST_FAIL,
  ANTI_FIRST_FAIL,
  SMALLEST,
  LARGEST,
  // unsupported:
  // OCCURRENCE,
  // MOST_CONSTRAINED,
  // MAX_REGRET,
  // DOM_W_DEG,
  // RANDOM
};
 
enum class ValueOrder {
  MIN,
  MAX,
  MEDIAN,
  SPLIT,
  REVERSE_SPLIT,
  // unsupported:
  // INTERVAL,
  // RANDOM,
  // MIDDLE,
};
 
template <class A, class Allocator = typename A::allocator_type>
class SplitStrategy {
public:
  using allocator_type = Allocator;
  using sub_type = A;
  using sub_allocator_type = typename sub_type::allocator_type;
  using sub_tell_type = sub_type::template tell_type<allocator_type>;
  using branch_type = Branch<sub_tell_type, allocator_type>;
  using this_type = SplitStrategy<sub_type, allocator_type>;
 
  constexpr static const bool is_abstract_universe = false;
  constexpr static const bool sequential = sub_type::sequential;
  constexpr static const bool is_totally_ordered = false;
  constexpr static const bool preserve_bot = true;
  constexpr static const bool preserve_top = true;
  // The next properties should be checked more seriously, relying on the sub-domain might be uneccessarily restrictive.
  constexpr static const bool preserve_join = sub_type::preserve_join;
  constexpr static const bool preserve_meet = sub_type::preserve_meet;
  constexpr static const bool injective_concretization = sub_type::injective_concretization;
  constexpr static const bool preserve_concrete_covers = sub_type::preserve_concrete_covers;
 
  constexpr static const char* name = "SplitStrategy";
 
  template <class Alloc>
  struct snapshot_type {
    size_t num_strategies;
    int current_strategy;
    int next_unassigned_var;
 
    CUDA snapshot_type(size_t num_strategies, int current_strategy, int next_unassigned_var)
      : num_strategies(num_strategies)
      , current_strategy(current_strategy)
      , next_unassigned_var(next_unassigned_var)
    {}
 
    snapshot_type(const snapshot_type<Alloc>&) = default;
    snapshot_type(snapshot_type<Alloc>&&) = default;
    snapshot_type<Alloc>& operator=(snapshot_type<Alloc>&&) = default;
    snapshot_type<Alloc>& operator=(const snapshot_type<Alloc>&) = default;
 
    template<class SplitSnapshot>
    CUDA snapshot_type(const SplitSnapshot& other, const Alloc&)
      : num_strategies(other.num_strategies)
      , current_strategy(other.current_strategy)
      , next_unassigned_var(other.next_unassigned_var)
    {}
  };
 
  /** A split strategy consists of a variable order and value order on a subset of the variables. */
  template <class Alloc2>
  struct strategy_type {
    VariableOrder var_order;
    ValueOrder val_order;
    battery::vector<AVar, Alloc2> vars;
 
    CUDA strategy_type(const Alloc2& alloc = Alloc2{}): var_order(VariableOrder::INPUT_ORDER), val_order(ValueOrder::MIN), vars(alloc) {}
    strategy_type(const strategy_type<Alloc2>&) = default;
    strategy_type(strategy_type<Alloc2>&&) = default;
    strategy_type& operator=(strategy_type<Alloc2>&&) = default;
 
    CUDA strategy_type(VariableOrder var_order, ValueOrder val_order, battery::vector<AVar, Alloc2>&& vars)
      : var_order(var_order), val_order(val_order), vars(std::move(vars)) {}
 
    using allocator_type = Alloc2;
    CUDA allocator_type get_allocator() const {
      return vars.get_allocator();
    }
 
    template <class StrategyType>
    CUDA strategy_type(const StrategyType& other, const Alloc2& alloc = Alloc2{})
    : var_order(other.var_order), val_order(other.val_order), vars(other.vars, alloc) {}
 
    template <class Alloc3>
    friend class strategy_type;
  };
 
  template <class Alloc2>
  using tell_type = battery::vector<strategy_type<Alloc2>, Alloc2>;
 
  template <class A2, class Alloc2>
  friend class SplitStrategy;
 
private:
  using universe_type = typename A::universe_type;
  using LB = typename universe_type::LB;
  using UB = typename universe_type::UB;
 
  AType atype;
  abstract_ptr<A> a;
  battery::vector<strategy_type<allocator_type>, allocator_type> strategies;
  int current_strategy;
  int next_unassigned_var;
 
  CUDA const battery::vector<AVar, allocator_type>& current_vars() const {
    return strategies[current_strategy].vars;
  }
 
  CUDA NI void move_to_next_unassigned_var() {
    while(current_strategy < strategies.size()) {
      const auto& vars = current_vars();
      while(next_unassigned_var < vars.size()) {
        universe_type v{};
        a->project(vars[next_unassigned_var], v);
        if(v.lb().value() != v.ub().value()) {
          return;
        }
        next_unassigned_var++;
      }
      current_strategy++;
      next_unassigned_var = 0;
    }
  }
 
  template <class MapFunction>
  CUDA NI AVar var_map_fold_left(const battery::vector<AVar, allocator_type>& vars, MapFunction op) {
    int i = next_unassigned_var;
    int best_i = i;
    auto best = op(a->project(vars[i]));
    for(++i; i < vars.size(); ++i) {
      const auto& u = a->project(vars[i]);
      if(u.lb().value() != u.ub().value()) {
        if(best.meet(op(u))) {
          best_i = i;
        }
      }
    }
    return vars[best_i];
  }
 
  CUDA AVar select_var() {
    const auto& strat = strategies[current_strategy];
    const auto& vars = strat.vars;
    switch(strat.var_order) {
      case VariableOrder::INPUT_ORDER: return vars[next_unassigned_var];
      case VariableOrder::FIRST_FAIL: return var_map_fold_left(vars, [](const universe_type& u) { return u.width().ub(); });
      case VariableOrder::ANTI_FIRST_FAIL: return var_map_fold_left(vars, [](const universe_type& u) { return dual_bound<LB>(u.width().ub()); });
      case VariableOrder::LARGEST: return var_map_fold_left(vars, [](const universe_type& u) { return dual_bound<LB>(u.ub()); });
      case VariableOrder::SMALLEST: return var_map_fold_left(vars, [](const universe_type& u) { return dual_bound<UB>(u.lb()); });
      default: printf("BUG: unsupported variable order strategy\n"); assert(false); return AVar{};
    }
  }
 
  template <class U>
  CUDA NI branch_type make_branch(AVar x, Sig left_op, Sig right_op, const U& u) {
    if((u.is_top() && U::preserve_top) || (u.is_bot() && U::preserve_bot)) {
      if(u.is_top()) {
        printf("%% WARNING: Cannot currently branch on unbounded variables.\n");
      }
      return branch_type(get_allocator());
    }
    using F = TFormula<allocator_type>;
    using branch_vector = battery::vector<sub_tell_type, allocator_type>;
    VarEnv<allocator_type> empty_env{};
    auto k = u.template deinterpret<F>();
    IDiagnostics diagnostics;
    sub_tell_type left(get_allocator());
    sub_tell_type right(get_allocator());
    bool res = a->interpret_tell(F::make_binary(F::make_avar(x), left_op, k, x.aty(), get_allocator()), empty_env, left, diagnostics);
    res &= a->interpret_tell(F::make_binary(F::make_avar(x), right_op, k, x.aty(), get_allocator()), empty_env, right, diagnostics);
    if(res) {
      return Branch(branch_vector({std::move(left), std::move(right)}, get_allocator()));
    }
    // Fallback on a more standard split search strategy.
    // We don't print anything because it might interfere with the output (without lock).
    else if(left_op != LEQ || right_op != GT) {
      return make_branch(x, LEQ, GT, u);
    }
    else {
      printf("%% WARNING: The subdomain does not support the underlying search strategy.\n");
      // Diagnostics mode.
      a->template interpret_tell<true>(F::make_binary(F::make_avar(x), left_op, k, x.aty(), get_allocator()), empty_env, left, diagnostics);
      a->template interpret_tell<true>(F::make_binary(F::make_avar(x), right_op, k, x.aty(), get_allocator()), empty_env, right, diagnostics);
      diagnostics.print();
      return branch_type(get_allocator());
    }
  }
 
public:
  CUDA SplitStrategy(AType atype, abstract_ptr<A> a, const allocator_type& alloc = allocator_type()):
    atype(atype), a(a), current_strategy(0), next_unassigned_var(0), strategies(alloc)
  {}
 
  template<class A2, class Alloc2, class... Allocators>
  CUDA SplitStrategy(const SplitStrategy<A2, Alloc2>& other, AbstractDeps<Allocators...>& deps)
   : atype(other.atype),
     a(deps.template clone<A>(other.a)),
     strategies(other.strategies, deps.template get_allocator<allocator_type>()),
     current_strategy(other.current_strategy),
     next_unassigned_var(other.next_unassigned_var)
  {}
 
  CUDA AType aty() const {
    return atype;
  }
 
  CUDA allocator_type get_allocator() const {
    return strategies.get_allocator();
  }
 
  template <class Alloc2 = allocator_type>
  CUDA snapshot_type<Alloc2> snapshot(const Alloc2& alloc = Alloc2()) const {
    return snapshot_type<Alloc2>{strategies.size(), current_strategy, next_unassigned_var};
  }
 
  template <class Alloc2 = allocator_type>
  CUDA void restore(const snapshot_type<Alloc2>& snap) {
    while(strategies.size() > snap.num_strategies) {
      strategies.pop_back();
    }
    current_strategy = snap.current_strategy;
    next_unassigned_var = snap.next_unassigned_var;
  }
 
  /** Restart the search from the first variable. */
  CUDA void reset() {
    current_strategy = 0;
    next_unassigned_var = 0;
  }
 
  /** This interpretation function expects `f` to be a predicate of the form `search(VariableOrder, ValueOrder, x_1, x_2, ..., x_n)`. */
  template <bool diagnose = false, class F, class Env, class Alloc2>
  CUDA NI bool interpret_tell(const F& f, Env& env, tell_type<Alloc2>& tell, IDiagnostics& diagnostics) const {
    if(!(f.is(F::ESeq)
      && f.eseq().size() >= 2
      && f.esig() == "search"
      && f.eseq()[0].is(F::ESeq) && f.eseq()[0].eseq().size() == 0
      && f.eseq()[1].is(F::ESeq) && f.eseq()[1].eseq().size() == 0))
    {
      RETURN_INTERPRETATION_ERROR("SplitStrategy can only interpret predicates of the form `search(input_order, indomain_min, x1, ..., xN)`.");
    }
    strategy_type<Alloc2> strat{tell.get_allocator()};
    const auto& var_order_str = f.eseq()[0].esig();
    const auto& val_order_str = f.eseq()[1].esig();
    if(var_order_str == "input_order") { strat.var_order = VariableOrder::INPUT_ORDER; }
    else if(var_order_str == "first_fail") { strat.var_order = VariableOrder::FIRST_FAIL; }
    else if(var_order_str == "anti_first_fail") { strat.var_order = VariableOrder::ANTI_FIRST_FAIL; }
    else if(var_order_str == "smallest") { strat.var_order = VariableOrder::SMALLEST; }
    else if(var_order_str == "largest") { strat.var_order = VariableOrder::LARGEST; }
    else {
      RETURN_INTERPRETATION_ERROR("This variable order strategy is unsupported.");
    }
    if(val_order_str == "indomain_min") { strat.val_order = ValueOrder::MIN; }
    else if(val_order_str == "indomain_max") { strat.val_order = ValueOrder::MAX; }
    else if(val_order_str == "indomain_median") { strat.val_order = ValueOrder::MEDIAN; }
    else if(val_order_str == "indomain_split") { strat.val_order = ValueOrder::SPLIT; }
    else if(val_order_str == "indomain_reverse_split") { strat.val_order = ValueOrder::REVERSE_SPLIT; }
    else {
      RETURN_INTERPRETATION_ERROR("This value order strategy is unsupported.");
    }
    for(int i = 2; i < f.eseq().size(); ++i) {
      if(f.eseq(i).is(F::LV)) {
        strat.vars.push_back(AVar{});
        if(!env.interpret(f.eseq(i), strat.vars.back(), diagnostics)) {
          return false;
        }
      }
      else if(f.eseq(i).is(F::V)) {
        strat.vars.push_back(f.eseq(i).v());
      }
      else if(num_vars(f.eseq(i)) > 0) {
        RETURN_INTERPRETATION_ERROR("The predicate `search` only supports variables or constants, but an expression containing a variable was passed to it.");
      }
      // Ignore constant expressions.
      else {}
    }
    if(strat.vars.size() == 0) {
      RETURN_INTERPRETATION_WARNING("The predicate `search` has no variable, and thus it is ignored.");
    }
    tell.push_back(std::move(strat));
    return true;
  }
 
  template <IKind kind, bool diagnose = false, class F, class Env, class I>
  CUDA bool interpret(const F& f, Env& env, I& intermediate, IDiagnostics& diagnostics) const {
    static_assert(kind == IKind::TELL);
    return interpret_tell<diagnose>(f, env, intermediate, diagnostics);
  }
 
  /** This deduce method adds new strategies, and therefore do not satisfy the PCCP model.
   * Calling this function multiple times will create multiple strategies, that will be called in sequence along a branch of the search tree.
   * @sequential
  */
  template <class Alloc2>
  CUDA local::B deduce(const tell_type<Alloc2>& t) {
    local::B has_changed = false;
    for(int i = 0; i < t.size(); ++i) {
      if(t[i].vars.size() > 0) {
        strategies.push_back(t[i]);
        has_changed = true;
      }
    }
    return has_changed;
  }
 
  /** Split the next unassigned variable according to the current strategy.
   * If all variables of the current strategy are assigned, use the next strategy.
   * If no strategy remains, returns an empty set of branches.
 
   If the next unassigned variable cannot be split, for instance because the value ordering strategy maps to `bot` or `top`, an empty set of branches is returned.
   This also means that you cannot suppose `split(a) = {}` to mean `a` is at `bot`. */
  CUDA NI branch_type split() {
    if(a->is_bot()) {
      return branch_type(get_allocator());
    }
    move_to_next_unassigned_var();
    if(current_strategy < strategies.size()) {
      AVar x = select_var();
      // printf("split on %d\n", x.vid());
      switch(strategies[current_strategy].val_order) {
        case ValueOrder::MIN: return make_branch(x, EQ, GT, a->project(x).lb());
        case ValueOrder::MAX: return make_branch(x, EQ, LT, a->project(x).ub());
        case ValueOrder::MEDIAN: return make_branch(x, EQ, NEQ, a->project(x).median().lb());
        case ValueOrder::SPLIT: return make_branch(x, LEQ, GT, a->project(x).median().lb());
        case ValueOrder::REVERSE_SPLIT: return make_branch(x, GT, LEQ, a->project(x).median().lb());
        default: printf("BUG: unsupported value order strategy\n"); assert(false); return branch_type(get_allocator());
      }
    }
    else {
      // printf("%% All variables are already assigned, we could not split anymore. It means the underlying abstract domain has not detected the satisfiability or unsatisfiability of the problem although all variables were assigned.\n");
      return branch_type(get_allocator());
    }
  }
 
  CUDA size_t num_strategies() const {
    return strategies.size();
  }
};
 
}
 
#endif