feature_extractor.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
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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,
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 * KIND, either express or implied.  See the License for the
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#ifndef TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_
#define TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_
 
#include <tvm/ffi/container/array.h>
#include <tvm/ffi/function.h>
#include <tvm/ffi/reflection/registry.h>
#include <tvm/ffi/string.h>
#include <tvm/runtime/object.h>
#include <tvm/runtime/tensor.h>
#include <tvm/s_tir/meta_schedule/measure_candidate.h>
 
namespace tvm {
namespace s_tir {
namespace meta_schedule {
 
class TuneContext;
 
class FeatureExtractorNode : public runtime::Object {
 public:
  virtual ~FeatureExtractorNode() = default;
 
  static void RegisterReflection() {
    namespace refl = tvm::ffi::reflection;
    refl::ObjectDef<FeatureExtractorNode>();
  }
 
  virtual ffi::Array<tvm::runtime::Tensor> ExtractFrom(
      const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates) = 0;
  TVM_FFI_DECLARE_OBJECT_INFO("s_tir.meta_schedule.FeatureExtractor", FeatureExtractorNode, Object);
};
 
class PyFeatureExtractorNode : public FeatureExtractorNode {
 public:
  using FExtractFrom = ffi::TypedFunction<ffi::Array<tvm::runtime::Tensor>(
      const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates)>;
  using FAsString = ffi::TypedFunction<ffi::String()>;
 
  FExtractFrom f_extract_from;
  FAsString f_as_string;
 
  static void RegisterReflection() {
    // `f_extract_from` is not registered
    // `f_as_string` is not registered
    namespace refl = tvm::ffi::reflection;
    refl::ObjectDef<PyFeatureExtractorNode>();
  }
 
  ffi::Array<tvm::runtime::Tensor> ExtractFrom(
      const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates) final;
  TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.PyFeatureExtractor",
                                    PyFeatureExtractorNode, FeatureExtractorNode);
};
 
class FeatureExtractor : public runtime::ObjectRef {
 public:
  TVM_DLL static FeatureExtractor PerStoreFeature(int buffers_per_store = 5,
                                                  int arith_intensity_curve_num_samples = 10,
                                                  int cache_line_bytes = 64,
                                                  bool extract_workload = false);
  TVM_DLL static FeatureExtractor PyFeatureExtractor(
      PyFeatureExtractorNode::FExtractFrom f_extract_from,
      PyFeatureExtractorNode::FAsString f_as_string);
  TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE(FeatureExtractor, ObjectRef, FeatureExtractorNode);
};
 
}  // namespace meta_schedule
}  // namespace s_tir
}  // namespace tvm
 
#endif  // TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_