cost_model.h

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 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * 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
 * specific language governing permissions and limitations
 * under the License.
 */
 
#ifndef TVM_META_SCHEDULE_COST_MODEL_H_
#define TVM_META_SCHEDULE_COST_MODEL_H_
 
#include <tvm/meta_schedule/arg_info.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/string.h>
#include <tvm/runtime/object.h>
#include <tvm/runtime/packed_func.h>
#include <tvm/tir/schedule/schedule.h>
 
#include <vector>
 
namespace tvm {
namespace meta_schedule {
 
class TuneContext;
 
class CostModelNode : public runtime::Object {
 public:
  virtual ~CostModelNode() = default;
 
  void VisitAttrs(tvm::AttrVisitor* v) {}
 
  virtual void Load(const String& path) = 0;
 
  virtual void Save(const String& path) = 0;
 
  virtual void Update(const TuneContext& context, const Array<MeasureCandidate>& candidates,
                      const Array<RunnerResult>& results) = 0;
 
  virtual std::vector<double> Predict(const TuneContext& context,
                                      const Array<MeasureCandidate>& candidates) = 0;
 
  static constexpr const char* _type_key = "meta_schedule.CostModel";
  TVM_DECLARE_BASE_OBJECT_INFO(CostModelNode, Object);
};
 
class PyCostModelNode : public CostModelNode {
 public:
  using FLoad = runtime::TypedPackedFunc<void(String)>;
  using FSave = runtime::TypedPackedFunc<void(String)>;
  using FUpdate = runtime::TypedPackedFunc<void(const TuneContext&, const Array<MeasureCandidate>&,
                                                const Array<RunnerResult>&)>;
  using FPredict = runtime::TypedPackedFunc<void(const TuneContext&, const Array<MeasureCandidate>&,
                                                 void* p_addr)>;
  using FAsString = runtime::TypedPackedFunc<String()>;
 
  FLoad f_load;
  FSave f_save;
  FUpdate f_update;
  FPredict f_predict;
  FAsString f_as_string;
 
  void VisitAttrs(tvm::AttrVisitor* v) {
    // `f_load` is not visited
    // `f_save` is not visited
    // `f_update` is not visited
    // `f_predict` is not visited
    // `f_as_string` is not visited
  }
 
  void Load(const String& path);
  void Save(const String& path);
  void Update(const TuneContext& context, const Array<MeasureCandidate>& candidates,
              const Array<RunnerResult>& results);
  std::vector<double> Predict(const TuneContext& context,
                              const Array<MeasureCandidate>& candidates);
 
  static constexpr const char* _type_key = "meta_schedule.PyCostModel";
  TVM_DECLARE_FINAL_OBJECT_INFO(PyCostModelNode, CostModelNode);
};
 
class CostModel : public runtime::ObjectRef {
 public:
  TVM_DLL static CostModel PyCostModel(PyCostModelNode::FLoad f_load,        //
                                       PyCostModelNode::FSave f_save,        //
                                       PyCostModelNode::FUpdate f_update,    //
                                       PyCostModelNode::FPredict f_predict,  //
                                       PyCostModelNode::FAsString f_as_string);
  TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(CostModel, ObjectRef, CostModelNode);
};
 
}  // namespace meta_schedule
}  // namespace tvm
 
#endif  // TVM_META_SCHEDULE_COST_MODEL_H_