wehub-resource-sync
302 lines
13 KiB
C++
302 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_TASK_SCHEDULER_H_
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#define TVM_S_TIR_META_SCHEDULE_TASK_SCHEDULER_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/builder.h>
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#include <tvm/s_tir/meta_schedule/cost_model.h>
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#include <tvm/s_tir/meta_schedule/measure_callback.h>
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#include <tvm/s_tir/meta_schedule/runner.h>
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#include <tvm/s_tir/meta_schedule/tune_context.h>
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#include <tvm/s_tir/random_engine.h>
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#include <string>
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#include <vector>
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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 TaskRecordNode : public ffi::Object {
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public:
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/*! \brief The tune context of the task. */
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TuneContext ctx{ffi::UnsafeInit()};
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/*! \brief The weight of the task */
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double task_weight{1.0};
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/*! \brief The FLOP count of the task */
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double flop{1.0};
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/*! \brief Whether the tuning task has been stopped or finished. */
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bool is_terminated = false;
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/*! \brief Builder errors happens in the task */
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int build_error_count = 0;
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/*! \brief Runner errors happens in the task */
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int run_error_count = 0;
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/*! \brief The latency of each run, in milliseconds. */
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std::vector<double> latency_ms = {};
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/*! \brief The measure candidates. */
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ffi::Optional<ffi::Array<MeasureCandidate>> measure_candidates = std::nullopt;
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/*! \brief The building results. */
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ffi::Optional<ffi::Array<BuilderResult>> builder_results = std::nullopt;
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/*! \brief Packed functions to fetch the runner results asynchronously. */
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ffi::Optional<ffi::Array<RunnerFuture>> runner_futures = std::nullopt;
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static void RegisterReflection() {
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<TaskRecordNode>()
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.def_ro("ctx", &TaskRecordNode::ctx)
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.def_ro("task_weight", &TaskRecordNode::task_weight)
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.def_ro("flop", &TaskRecordNode::flop)
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.def_ro("is_terminated", &TaskRecordNode::is_terminated)
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.def_ro("build_error_count", &TaskRecordNode::build_error_count)
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.def_ro("run_error_count", &TaskRecordNode::run_error_count)
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.def_ro("measure_candidates", &TaskRecordNode::measure_candidates)
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.def_ro("builder_results", &TaskRecordNode::builder_results)
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.def_ro("runner_futures", &TaskRecordNode::runner_futures);
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}
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static constexpr const bool _type_mutable = true;
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TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.TaskRecord", TaskRecordNode, ffi::Object);
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};
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/*!
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* \brief Managed reference to TaskRecordNode.
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* \sa TaskRecordNode
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*/
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class TaskRecord : public ffi::ObjectRef {
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public:
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/*! \brief Constructor */
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explicit TaskRecord(TuneContext task, double task_weight);
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TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE(TaskRecord, ffi::ObjectRef, TaskRecordNode);
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};
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/*!
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* \brief The abstract interface of task schedulers.
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* \note The relationship between SpaceGenerator 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 TaskSchedulerNode : public ffi::Object {
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public:
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/*! \brief The tuning task's logging function. */
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ffi::Function logger;
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/*! \brief Records for each task */
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ffi::Array<TaskRecord> tasks_;
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/*! \brief The list of measure callbacks of the scheduler. */
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ffi::Array<MeasureCallback> measure_callbacks_;
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/*! \brief The database used in tuning */
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ffi::Optional<Database> database_;
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/*! \brief The cost model used in tuning */
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ffi::Optional<CostModel> cost_model_;
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/*! \brief The number of remaining tasks to be tuned. */
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int remaining_tasks_;
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/*! \brief The default destructor. */
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virtual ~TaskSchedulerNode() = default;
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static void RegisterReflection() {
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<TaskSchedulerNode>()
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.def_ro("tasks_", &TaskSchedulerNode::tasks_)
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.def_ro("measure_callbacks_", &TaskSchedulerNode::measure_callbacks_)
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.def_ro("database_", &TaskSchedulerNode::database_)
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.def_ro("cost_model_", &TaskSchedulerNode::cost_model_)
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.def_ro("remaining_tasks_", &TaskSchedulerNode::remaining_tasks_);
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}
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/*!
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* \brief Fetch the next task id.
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* \return The next task id.
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*/
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virtual int NextTaskId() = 0;
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/*!
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* \brief Wait until the task is finished.
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* \param task_id The task id to be joined.
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* \return The results from the runner.
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*/
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virtual ffi::Array<RunnerResult> JoinRunningTask(int task_id);
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/*!
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* \brief Jointly tune a given list of tasks.
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* \param tasks The tasks to be tuned
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* \param task_weights The weight of each task
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* \param max_trials_global The maximum number of trials to be performed globally
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* \param max_trials_per_task The maximum number of trials to be performed for each task
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* \param num_trials_per_iter The number of trials to be performed in each iteration
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* \param builder The MetaSchedule builder
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* \param runner The MetaSchedule runner
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* \param measure_callbacks The callbacks to be called after each measurement
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* \param database The database used in tuning
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* \param cost_model The cost model used in tuning
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*/
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virtual void Tune(ffi::Array<TuneContext> tasks, //
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ffi::Array<FloatImm> task_weights, //
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int max_trials_global, //
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int max_trials_per_task, //
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int num_trials_per_iter, //
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Builder builder, //
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Runner runner, //
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ffi::Array<MeasureCallback> measure_callbacks, //
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ffi::Optional<Database> database, //
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ffi::Optional<CostModel> cost_model);
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/*!
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* \brief Terminate a task
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* \param task_id The id of the task to be terminated
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*/
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void TerminateTask(int task_id);
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/*!
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* \brief Touch the task and update its status
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* \param task_id The task id to be checked.
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*/
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void TouchTask(int task_id);
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/*! \brief Print out a human-readable format of the tuning statistics. */
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void PrintTuningStatistics();
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static constexpr const bool _type_mutable = true;
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TVM_FFI_DECLARE_OBJECT_INFO("s_tir.meta_schedule.TaskScheduler", TaskSchedulerNode, ffi::Object);
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};
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class TaskScheduler;
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/*! \brief The task scheduler with customized methods on the python-side. */
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class PyTaskSchedulerNode : public TaskSchedulerNode {
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public:
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/*!
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* \brief The function type of `NextTaskId` method.
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* \return The next task id.
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*/
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using FNextTaskId = ffi::TypedFunction<int()>;
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/*!
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* \brief The function type of `JoinRunningTask` method.
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* \param task_id The task id to be joined.
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*/
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using FJoinRunningTask = ffi::TypedFunction<ffi::Array<RunnerResult>(int)>;
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/*! \brief The function type of `Tune` method. */
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using FTune = ffi::TypedFunction<void(ffi::Array<TuneContext> tasks, //
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ffi::Array<FloatImm> task_weights, //
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int max_trials_global, //
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int max_trials_per_task, //
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int num_trials_per_iter, //
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Builder builder, //
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Runner runner, //
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ffi::Array<MeasureCallback> measure_callbacks, //
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ffi::Optional<Database> database, //
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ffi::Optional<CostModel> cost_model)>;
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/*! \brief The packed function to the `NextTaskId` function. */
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FNextTaskId f_next_task_id;
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/*! \brief The packed function to the `JoinRunningTask` function. */
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FJoinRunningTask f_join_running_task;
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/*! \brief The packed function to the `Tune` function. */
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FTune f_tune;
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static void RegisterReflection() {
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namespace refl = tvm::ffi::reflection;
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refl::ObjectDef<PyTaskSchedulerNode>();
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}
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int NextTaskId() final;
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ffi::Array<RunnerResult> JoinRunningTask(int task_id) final;
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void Tune(ffi::Array<TuneContext> tasks, ffi::Array<FloatImm> task_weights, int max_trials_global,
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int max_trials_per_task, int num_trials_per_iter, Builder builder, Runner runner,
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ffi::Array<MeasureCallback> measure_callbacks, ffi::Optional<Database> database,
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ffi::Optional<CostModel> cost_model) final;
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TVM_FFI_DECLARE_OBJECT_INFO_FINAL("s_tir.meta_schedule.PyTaskScheduler", PyTaskSchedulerNode,
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TaskSchedulerNode);
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};
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/*!
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* \brief Managed reference to TaskSchedulerNode.
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* \sa TaskSchedulerNode
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*/
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class TaskScheduler : public ffi::ObjectRef {
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public:
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explicit TaskScheduler(ffi::ObjectPtr<TaskSchedulerNode> data) : ffi::ObjectRef(data) {
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TVM_FFI_ICHECK(data != nullptr);
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}
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/*!
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* \brief Create a task scheduler that fetches tasks in a round-robin fashion.
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* \param logger The tuning task's logging function.
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* \return The task scheduler created.
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*/
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TVM_DLL static TaskScheduler RoundRobin(ffi::Function logger);
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/*!
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* \brief Create a task scheduler that fetches tasks in a gradient based fashion.
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* \param logger The tuning task's logging function.
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* \param alpha The parameter alpha to control gradient computation.
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* \param window_size The parameter to control backward window size.
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* \param seed The random seed.
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* \return The task scheduler created.
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*/
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TVM_DLL static TaskScheduler GradientBased(ffi::Function logger, double alpha, int window_size,
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LinearCongruentialEngine::TRandState seed);
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/*!
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* \brief Create a task scheduler with customized methods on the python-side.
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* \param logger The tuning task's logging function.
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* \param f_next_task_id The packed function of `NextTaskId`.
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* \param f_join_running_task The packed function of `JoinRunningTask`.
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* \param f_tune The packed function of `Tune`.
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* \return The task scheduler created.
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*/
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TVM_DLL static TaskScheduler PyTaskScheduler(
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ffi::Function logger, PyTaskSchedulerNode::FNextTaskId f_next_task_id,
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PyTaskSchedulerNode::FJoinRunningTask f_join_running_task, PyTaskSchedulerNode::FTune f_tune);
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TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE(TaskScheduler, ffi::ObjectRef, TaskSchedulerNode);
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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_TASK_SCHEDULER_H_
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