search_strategy.h

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 * distributed with this work for additional information
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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
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#ifndef TVM_META_SCHEDULE_SEARCH_STRATEGY_H_
#define TVM_META_SCHEDULE_SEARCH_STRATEGY_H_

#include <tvm/meta_schedule/arg_info.h>
#include <tvm/meta_schedule/cost_model.h>
#include <tvm/meta_schedule/database.h>
#include <tvm/meta_schedule/measure_candidate.h>
#include <tvm/meta_schedule/runner.h>
#include <tvm/node/reflection.h>
#include <tvm/runtime/container/array.h>
#include <tvm/runtime/container/optional.h>
#include <tvm/runtime/object.h>
#include <tvm/runtime/packed_func.h>
#include <tvm/tir/schedule/schedule.h>

namespace tvm {
namespace meta_schedule {

// Forward declaration
class TuneContext;
class SearchStrategy;

class SearchStrategyNode : public runtime::Object {
 public:
  virtual ~SearchStrategyNode() = default;

  virtual void InitializeWithTuneContext(const TuneContext& context) = 0;

  virtual void PreTuning(int max_trials, int num_trials_per_iter,
                         const Array<tir::Schedule>& design_spaces,
                         const Optional<Database>& database,
                         const Optional<CostModel>& cost_model) = 0;

  virtual void PostTuning() = 0;

  virtual Optional<Array<MeasureCandidate>> GenerateMeasureCandidates() = 0;

  virtual void NotifyRunnerResults(const Array<MeasureCandidate>& measure_candidates,
                                   const Array<RunnerResult>& results) = 0;

  virtual SearchStrategy Clone() const = 0;

  static constexpr const char* _type_key = "meta_schedule.SearchStrategy";
  TVM_DECLARE_BASE_OBJECT_INFO(SearchStrategyNode, Object);
};

class SearchStrategy : public runtime::ObjectRef {
 public:
  using FInitializeWithTuneContext = runtime::TypedPackedFunc<void(const TuneContext&)>;
  using FPreTuning = runtime::TypedPackedFunc<void(
      int max_trials, int num_trials_per_iter, const Array<tir::Schedule>&,
      const Optional<Database>&, const Optional<CostModel>&)>;
  using FPostTuning = runtime::TypedPackedFunc<void()>;
  using FGenerateMeasureCandidates = runtime::TypedPackedFunc<Optional<Array<MeasureCandidate>>()>;
  using FNotifyRunnerResults =
      runtime::TypedPackedFunc<void(const Array<MeasureCandidate>&, const Array<RunnerResult>&)>;
  using FClone = runtime::TypedPackedFunc<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_DEFINE_MUTABLE_OBJECT_REF_METHODS(SearchStrategy, 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;

  void VisitAttrs(tvm::AttrVisitor* v) {
    // `f_initialize_with_tune_context` is not visited
    // `f_pre_tuning` is not visited
    // `f_post_tuning` is not visited
    // `f_generate_measure_candidates` is not visited
    // `f_notify_runner_results` is not visited
    // `f_clone` is not visited
  }

  void InitializeWithTuneContext(const TuneContext& context) final;
  void PreTuning(int max_trials, int num_trials_per_iter, const Array<tir::Schedule>& design_spaces,
                 const Optional<Database>& database, const Optional<CostModel>& cost_model) final;
  void PostTuning() final;
  Optional<Array<MeasureCandidate>> GenerateMeasureCandidates() final;
  void NotifyRunnerResults(const Array<MeasureCandidate>& measure_candidates,
                           const Array<RunnerResult>& results);
  SearchStrategy Clone() const final;

  static constexpr const char* _type_key = "meta_schedule.PySearchStrategy";
  TVM_DECLARE_FINAL_OBJECT_INFO(PySearchStrategyNode, SearchStrategyNode);
};

}  // namespace meta_schedule
}  // namespace tvm

#endif  // TVM_META_SCHEDULE_SEARCH_STRATEGY_H_