wehub-resource-sync
277 lines
13 KiB
C++
277 lines
13 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_SEARCH_STRATEGY_H_
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#define TVM_S_TIR_META_SCHEDULE_SEARCH_STRATEGY_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/optional.h>
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#include <tvm/ffi/reflection/registry.h>
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#include <tvm/runtime/base.h>
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#include <tvm/s_tir/meta_schedule/arg_info.h>
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#include <tvm/s_tir/meta_schedule/cost_model.h>
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#include <tvm/s_tir/meta_schedule/database.h>
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#include <tvm/s_tir/meta_schedule/measure_candidate.h>
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#include <tvm/s_tir/meta_schedule/runner.h>
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#include <tvm/s_tir/schedule/schedule.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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// Forward declaration
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class TuneContext;
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class SearchStrategy;
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/*!
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* \brief The search strategy for measure candidates generation.
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* \note The relationship between SearchStrategy and other classes are as follows:
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+--------------------------------------------------------------+
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+--+-----------------------------------------------------------+ |
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+--+------------------ Tune Context -----------------------------+ | |
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| +---------------------+ | | |
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| | | Generate | | |
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| | Space Generator +--------------+ | | |
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| | | | | | |
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| +---------------------+ v | | |
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| Design Space | | |
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| +---------------------+ | | | |
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| Generate | | Pretuning | | | |
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| +-----------+ Search Strategy |<-------------+ | | |
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| | | | | +--+
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| | +---------------------+ +--+
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+----+----------------------------------------------------------+
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+----+---------------- Managed By Task Scheduler ---------------------+
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| | +-----------+ |
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| | Send to | | Send to |
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| v +-------------+| Builder +----------+ |
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| Measure Candidate | Builder | | Runner | |
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| | | +-----------+ | |
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| | +------------+------------+ | |
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| | | | +-----------+ | |
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| +---->| Task Scheduler | | | | |
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| | | | Runner |<-----+ |
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| +-------------------------+ | | |
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| ^ +-----+-----+ |
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| | | |
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| +---- Runner Future <-------+ |
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+---------------------------------------------------------------------+
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*/
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class SearchStrategyNode : public ffi::Object {
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public:
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/*! \brief Virtual destructor */
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virtual ~SearchStrategyNode() = default;
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/*!
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* \brief Initialize the search strategy with tuning context.
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* \param context The tuning context for initialization.
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* \note This method is supposed to be called only once before every other method.
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*/
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virtual void InitializeWithTuneContext(const TuneContext& context) = 0;
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/*!
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* \brief Pre-tuning for the search strategy.
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* \param max_trials The maximum number of trials.
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* \param num_trials_per_iter The number of trials per iteration.
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* \param design_spaces The design spaces used during tuning process.
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* \param database The database used during tuning process.
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* \param cost_model The cost model used during tuning process.
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* \note Pre-tuning is supposed to be called before the tuning process and after the
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* initialization. Because the search strategy is stateful, we can always call pretuning
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* and reset the search strategy.
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*/
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virtual void PreTuning(int max_trials, int num_trials_per_iter,
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const ffi::Array<s_tir::Schedule>& design_spaces,
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const ffi::Optional<Database>& database,
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const ffi::Optional<CostModel>& cost_model) = 0;
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/*!
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* \brief Post-tuning for the search strategy.
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* \note Post-tuning is supposed to be called after the tuning process and before we reset the
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* search strategy with another pre-tuning. Post-tuning can be empty.
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*/
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virtual void PostTuning() = 0;
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/*!
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* \brief Generate measure candidates from design spaces for measurement.
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* \return The measure candidates generated, nullptr if finished.
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*/
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virtual ffi::Optional<ffi::Array<MeasureCandidate>> GenerateMeasureCandidates() = 0;
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/*!
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* \brief Update the search strategy with measurement results.
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* \param measure_candidates The candidates to be measured.
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* \param results The measurement results from the runner.
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*/
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virtual void NotifyRunnerResults(const ffi::Array<MeasureCandidate>& measure_candidates,
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const ffi::Array<RunnerResult>& results) = 0;
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/*!
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* \brief Clone the search strategy.
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* \return The cloned search strategy.
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*/
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virtual SearchStrategy Clone() const = 0;
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static constexpr const bool _type_mutable = true;
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TVM_FFI_DECLARE_OBJECT_INFO("s_tir.meta_schedule.SearchStrategy", SearchStrategyNode,
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ffi::Object);
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};
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/*!
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* \brief Managed reference to SearchStrategyNode.
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* \sa SearchStrategyNode
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*/
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class SearchStrategy : public ffi::ObjectRef {
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public:
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/*!
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* \brief The function type of `InitializeWithTuneContext` method.
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* \param context The tuning context for initialization.
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*/
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using FInitializeWithTuneContext = ffi::TypedFunction<void(const TuneContext&)>;
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/*!
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* \brief The function type of `PreTuning` method.
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*/
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using FPreTuning = ffi::TypedFunction<void(
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int max_trials, int num_trials_per_iter, const ffi::Array<s_tir::Schedule>&,
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const ffi::Optional<Database>&, const ffi::Optional<CostModel>&)>;
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/*! \brief The function type of `PostTuning` method. */
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using FPostTuning = ffi::TypedFunction<void()>;
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/*!
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* \brief The function type of `GenerateMeasureCandidates` method.
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* \return The measure candidates generated, nullptr if finished.
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*/
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using FGenerateMeasureCandidates =
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ffi::TypedFunction<ffi::Optional<ffi::Array<MeasureCandidate>>()>;
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/*!
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* \brief The function type of `NotifyRunnerResults` method.
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* \param results The measurement results from the runner.
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*/
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using FNotifyRunnerResults = ffi::TypedFunction<void(const ffi::Array<MeasureCandidate>&,
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const ffi::Array<RunnerResult>&)>;
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/*!
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* \brief The function type of `Clone` method.
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* \return The cloned search strategy.
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*/
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using FClone = ffi::TypedFunction<SearchStrategy()>;
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/*!
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* \brief Create a search strategy with customized methods on the python-side.
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* \param f_initialize_with_tune_context The packed function of `InitializeWithTuneContext`.
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* \param f_pre_tuning The packed function of `PreTuning`.
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* \param f_post_tuning The packed function of `PostTuning`.
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* \param f_generate_measure_candidates The packed function of `GenerateMeasureCandidates`.
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* \param f_notify_runner_results The packed function of `NotifyRunnerResults`.
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* \param f_clone The packed function of `Clone`.
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* \return The search strategy created.
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*/
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TVM_DLL static SearchStrategy PySearchStrategy(
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FInitializeWithTuneContext f_initialize_with_tune_context, //
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FPreTuning f_pre_tuning, //
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FPostTuning f_post_tuning, //
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FGenerateMeasureCandidates f_generate_measure_candidates, //
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FNotifyRunnerResults f_notify_runner_results, //
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FClone f_clone);
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/*!
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* \brief Constructor of replay trace search strategy.
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* \param max_fail_count The max number of failures during trace replaying.
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*/
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TVM_DLL static SearchStrategy ReplayTrace(int max_fail_count);
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/*! \brief Constructor of replay func search strategy. */
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TVM_DLL static SearchStrategy ReplayFunc();
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/*!
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* \brief Constructor of evolutionary search strategy.
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* \param population_size The initial sample population.
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* \param init_measured_ratio The ratio of measures samples in initial population.
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* \param init_min_unmeasured The minimal size of unmeasured population in the initial sampling.
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* \param max_fail_count The max number of failure during initial sampling.
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* \param genetic_num_iters The iterations to run the genetic algorithm.
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* \param genetic_mutate_prob The probability of mutation.
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* \param genetic_max_fail_count The maximum number to try evolving the given trace.
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* \param eps_greedy The ratio to select samples in a greedy fashion via their predicted score.
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*/
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TVM_DLL static SearchStrategy EvolutionarySearch(int population_size, //
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double init_measured_ratio, //
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int init_min_unmeasured, //
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int max_fail_count, //
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int genetic_num_iters, //
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double genetic_mutate_prob, //
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int genetic_max_fail_count, //
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double eps_greedy);
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TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE(SearchStrategy, ffi::ObjectRef, SearchStrategyNode);
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};
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/*! \brief The python side customizable class for measure candidate generation */
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class PySearchStrategyNode : public SearchStrategyNode {
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public:
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using FInitializeWithTuneContext = SearchStrategy::FInitializeWithTuneContext;
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using FPreTuning = SearchStrategy::FPreTuning;
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using FPostTuning = SearchStrategy::FPostTuning;
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using FGenerateMeasureCandidates = SearchStrategy::FGenerateMeasureCandidates;
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using FNotifyRunnerResults = SearchStrategy::FNotifyRunnerResults;
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using FClone = SearchStrategy::FClone;
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/*! \brief The packed function to the `InitializeWithTuneContext` method. */
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FInitializeWithTuneContext f_initialize_with_tune_context;
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/*! \brief The packed function to the `PreTuning` method. */
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FPreTuning f_pre_tuning;
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/*! \brief The packed function to the `PostTuning` method. */
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FPostTuning f_post_tuning;
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/*! \brief The packed function to the `GenerateMeasureCandidates` method. */
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FGenerateMeasureCandidates f_generate_measure_candidates;
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/*! \brief The packed function to the `NotifyRunnerResults` method. */
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FNotifyRunnerResults f_notify_runner_results;
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/*! \brief The packed function to the `Clone` method. */
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FClone f_clone;
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static void RegisterReflection() {
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// `f_initialize_with_tune_context` is not registered
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// `f_pre_tuning` is not registered
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// `f_post_tuning` is not registered
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// `f_generate_measure_candidates` is not registered
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// `f_notify_runner_results` is not registered
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// `f_clone` is not registered
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<PySearchStrategyNode>();
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}
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void InitializeWithTuneContext(const TuneContext& context) final;
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void PreTuning(int max_trials, int num_trials_per_iter,
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const ffi::Array<s_tir::Schedule>& design_spaces,
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const ffi::Optional<Database>& database,
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const ffi::Optional<CostModel>& cost_model) final;
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void PostTuning() final;
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ffi::Optional<ffi::Array<MeasureCandidate>> GenerateMeasureCandidates() final;
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void NotifyRunnerResults(const ffi::Array<MeasureCandidate>& measure_candidates,
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const ffi::Array<RunnerResult>& results);
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SearchStrategy Clone() const final;
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TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.PySearchStrategy", PySearchStrategyNode,
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SearchStrategyNode);
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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_SEARCH_STRATEGY_H_
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