search_strategy.h

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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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#ifndef TVM_S_TIR_META_SCHEDULE_SEARCH_STRATEGY_H_
#define TVM_S_TIR_META_SCHEDULE_SEARCH_STRATEGY_H_
 
#include <tvm/ffi/container/array.h>
#include <tvm/ffi/function.h>
#include <tvm/ffi/optional.h>
#include <tvm/ffi/reflection/registry.h>
#include <tvm/ir/prim/expr.h>
#include <tvm/runtime/base.h>
#include <tvm/s_tir/meta_schedule/arg_info.h>
#include <tvm/s_tir/meta_schedule/cost_model.h>
#include <tvm/s_tir/meta_schedule/database.h>
#include <tvm/s_tir/meta_schedule/measure_candidate.h>
#include <tvm/s_tir/meta_schedule/runner.h>
#include <tvm/s_tir/schedule/schedule.h>
 
namespace tvm {
namespace s_tir {
using namespace tvm::prim;
namespace meta_schedule {
 
// Forward declaration
class TuneContext;
class SearchStrategy;
 
class SearchStrategyNode : public ffi::Object {
 public:
  virtual ~SearchStrategyNode() = default;
 
  virtual void InitializeWithTuneContext(const TuneContext& context) = 0;
 
  virtual void PreTuning(int max_trials, int num_trials_per_iter,
                         const ffi::Array<s_tir::Schedule>& design_spaces,
                         const ffi::Optional<Database>& database,
                         const ffi::Optional<CostModel>& cost_model) = 0;
 
  virtual void PostTuning() = 0;
 
  virtual ffi::Optional<ffi::Array<MeasureCandidate>> GenerateMeasureCandidates() = 0;
 
  virtual void NotifyRunnerResults(const ffi::Array<MeasureCandidate>& measure_candidates,
                                   const ffi::Array<RunnerResult>& results) = 0;
 
  virtual SearchStrategy Clone() const = 0;
 
  static constexpr const bool _type_mutable = true;
  TVM_FFI_DECLARE_OBJECT_INFO("s_tir.meta_schedule.SearchStrategy", SearchStrategyNode,
                              ffi::Object);
};
 
class SearchStrategy : public ffi::ObjectRef {
 public:
  using FInitializeWithTuneContext = ffi::TypedFunction<void(const TuneContext&)>;
  using FPreTuning = ffi::TypedFunction<void(
      int max_trials, int num_trials_per_iter, const ffi::Array<s_tir::Schedule>&,
      const ffi::Optional<Database>&, const ffi::Optional<CostModel>&)>;
  using FPostTuning = ffi::TypedFunction<void()>;
  using FGenerateMeasureCandidates =
      ffi::TypedFunction<ffi::Optional<ffi::Array<MeasureCandidate>>()>;
  using FNotifyRunnerResults = ffi::TypedFunction<void(const ffi::Array<MeasureCandidate>&,
                                                       const ffi::Array<RunnerResult>&)>;
  using FClone = ffi::TypedFunction<SearchStrategy()>;
  TVM_DLL static SearchStrategy PySearchStrategy(
      FInitializeWithTuneContext f_initialize_with_tune_context,  //
      FPreTuning f_pre_tuning,                                    //
      FPostTuning f_post_tuning,                                  //
      FGenerateMeasureCandidates f_generate_measure_candidates,   //
      FNotifyRunnerResults f_notify_runner_results,               //
      FClone f_clone);
 
  TVM_DLL static SearchStrategy ReplayTrace(int max_fail_count);
 
  TVM_DLL static SearchStrategy ReplayFunc();
 
  TVM_DLL static SearchStrategy EvolutionarySearch(int population_size,         //
                                                   double init_measured_ratio,  //
                                                   int init_min_unmeasured,     //
                                                   int max_fail_count,          //
                                                   int genetic_num_iters,       //
                                                   double genetic_mutate_prob,  //
                                                   int genetic_max_fail_count,  //
                                                   double eps_greedy);
 
  TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE(SearchStrategy, ffi::ObjectRef, SearchStrategyNode);
};
 
class PySearchStrategyNode : public SearchStrategyNode {
 public:
  using FInitializeWithTuneContext = SearchStrategy::FInitializeWithTuneContext;
  using FPreTuning = SearchStrategy::FPreTuning;
  using FPostTuning = SearchStrategy::FPostTuning;
  using FGenerateMeasureCandidates = SearchStrategy::FGenerateMeasureCandidates;
  using FNotifyRunnerResults = SearchStrategy::FNotifyRunnerResults;
  using FClone = SearchStrategy::FClone;
 
  FInitializeWithTuneContext f_initialize_with_tune_context;
  FPreTuning f_pre_tuning;
  FPostTuning f_post_tuning;
  FGenerateMeasureCandidates f_generate_measure_candidates;
  FNotifyRunnerResults f_notify_runner_results;
  FClone f_clone;
 
  static void RegisterReflection() {
    // `f_initialize_with_tune_context` is not registered
    // `f_pre_tuning` is not registered
    // `f_post_tuning` is not registered
    // `f_generate_measure_candidates` is not registered
    // `f_notify_runner_results` is not registered
    // `f_clone` is not registered
    namespace refl = tvm::ffi::reflection;
    refl::ObjectDef<PySearchStrategyNode>();
  }
 
  void InitializeWithTuneContext(const TuneContext& context) final;
  void PreTuning(int max_trials, int num_trials_per_iter,
                 const ffi::Array<s_tir::Schedule>& design_spaces,
                 const ffi::Optional<Database>& database,
                 const ffi::Optional<CostModel>& cost_model) final;
  void PostTuning() final;
  ffi::Optional<ffi::Array<MeasureCandidate>> GenerateMeasureCandidates() final;
  void NotifyRunnerResults(const ffi::Array<MeasureCandidate>& measure_candidates,
                           const ffi::Array<RunnerResult>& results);
  SearchStrategy Clone() const final;
  TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.PySearchStrategy", PySearchStrategyNode,
                                    SearchStrategyNode);
};
 
}  // namespace meta_schedule
}  // namespace s_tir
}  // namespace tvm
 
#endif  // TVM_S_TIR_META_SCHEDULE_SEARCH_STRATEGY_H_