wehub-resource-sync
121 lines
4.7 KiB
C++
121 lines
4.7 KiB
C++
/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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#ifndef TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_
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#define TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_
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#include <tvm/ffi/container/array.h>
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#include <tvm/ffi/function.h>
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#include <tvm/ffi/reflection/registry.h>
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#include <tvm/ffi/string.h>
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#include <tvm/runtime/tensor.h>
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#include <tvm/s_tir/meta_schedule/measure_candidate.h>
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namespace tvm {
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namespace s_tir {
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namespace meta_schedule {
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class TuneContext;
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/*! \brief Extractor for features from measure candidates for use in cost model. */
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class FeatureExtractorNode : public ffi::Object {
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public:
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/*! \brief Virtual destructor. */
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virtual ~FeatureExtractorNode() = default;
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static void RegisterReflection() {
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<FeatureExtractorNode>();
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}
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/*!
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* \brief Extract features from the given measure candidate.
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* \param context The tuning context for feature extraction.
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* \param candidates The measure candidates to extract features from.
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* \return The feature tensor extracted.
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*/
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virtual ffi::Array<tvm::runtime::Tensor> ExtractFrom(
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const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates) = 0;
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TVM_FFI_DECLARE_OBJECT_INFO("s_tir.meta_schedule.FeatureExtractor", FeatureExtractorNode,
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ffi::Object);
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};
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/*! \brief The feature extractor with customized methods on the python-side. */
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class PyFeatureExtractorNode : public FeatureExtractorNode {
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public:
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/*!
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* \brief Extract features from the given measure candidate.
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* \param context The tuning context for feature extraction.
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* \param candidates The measure candidates to extract features from.
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* \return The feature tensor extracted.
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*/
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using FExtractFrom = ffi::TypedFunction<ffi::Array<tvm::runtime::Tensor>(
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const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates)>;
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/*! \brief The packed function to the `ExtractFrom` function. */
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FExtractFrom f_extract_from;
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static void RegisterReflection() {
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// `f_extract_from` is not registered
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<PyFeatureExtractorNode>();
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}
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ffi::Array<tvm::runtime::Tensor> ExtractFrom(
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const TuneContext& context, const ffi::Array<MeasureCandidate>& candidates) final;
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TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.PyFeatureExtractor",
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PyFeatureExtractorNode, FeatureExtractorNode);
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};
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/*!
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* \brief Managed reference to FeatureExtractorNode
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* \sa FeatureExtractorNode
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*/
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class FeatureExtractor : public ffi::ObjectRef {
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public:
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/*!
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* \brief Create a feature extractor that extracts features from each BufferStore
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* \param buffers_per_store The number of buffers in each BufferStore; Pad or truncate if
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* necessary.
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* \param arith_intensity_curve_num_samples The number of samples used in the arithmetic intensity
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* curve.
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* \param cache_line_bytes The number of bytes in a cache line.
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* \param extract_workload Whether to extract features in the workload in tuning context or not.
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* \return The feature extractor created.
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*/
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TVM_DLL static FeatureExtractor PerStoreFeature(int buffers_per_store = 5,
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int arith_intensity_curve_num_samples = 10,
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int cache_line_bytes = 64,
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bool extract_workload = false);
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/*!
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* \brief Create a feature extractor with customized methods on the python-side.
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* \param f_extract_from The packed function of `ExtractFrom`.
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* \return The feature extractor created.
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*/
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TVM_DLL static FeatureExtractor PyFeatureExtractor(
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PyFeatureExtractorNode::FExtractFrom f_extract_from);
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TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE(FeatureExtractor, ffi::ObjectRef,
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FeatureExtractorNode);
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};
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} // namespace meta_schedule
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} // namespace s_tir
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} // namespace tvm
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#endif // TVM_S_TIR_META_SCHEDULE_FEATURE_EXTRACTOR_H_
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