[MetaSchedule] Add Gradient Based Task Scheduler (apache#10366)

Co-authored-by: Junru Shao <junrushao1994@gmail.com>

Author: zxybazh

include/tvm/meta_schedule/search_strategy.h

@@ -252,21 +252,21 @@ class SearchStrategy : public runtime::ObjectRef {
   /*!
    * \brief Constructor of replay trace search strategy.
    * \param num_trials_per_iter The number of trials per iteration, i.e., the batch size.
-   * \param num_trials_total The total number of trials for trace replaying.
+   * \param max_trials_per_task The total number of trials for trace replaying.
    */
-  TVM_DLL static SearchStrategy ReplayTrace(int num_trials_per_iter, int num_trials_total);
+  TVM_DLL static SearchStrategy ReplayTrace(int num_trials_per_iter, int max_trials_per_task);
 
   /*!
    * \brief Constructor of replay func search strategy.
    * \param num_trials_per_iter The number of trials per iteration, i.e., the batch size.
-   * \param num_trials_total The total number of trials for func replaying.
+   * \param max_trials_per_task The total number of trials for func replaying.
    */
-  TVM_DLL static SearchStrategy ReplayFunc(int num_trials_per_iter, int num_trials_total);
+  TVM_DLL static SearchStrategy ReplayFunc(int num_trials_per_iter, int max_trials_per_task);
 
   /*!
    * \brief Constructor of evolutionary search strategy.
    * \param num_trials_per_iter The number of trials per iteration, i.e., the batch size.
-   * \param num_trials_total The total number of trials for evolutionary search.
+   * \param max_trials_per_task The total number of trials for evolutionary search.
    * \param population_size The initial sample population.
    * \param init_measured_ratio The ratio of measures samples in initial population.
    * \param init_min_unmeasured The minimal size of unmeasured population in the initial sampling.
@@ -276,7 +276,7 @@ class SearchStrategy : public runtime::ObjectRef {
    * \param eps_greedy The ratio to select samples in a greedy fashion via their predicted score.
    */
   TVM_DLL static SearchStrategy EvolutionarySearch(int num_trials_per_iter,     //
-                                                   int num_trials_total,        //
+                                                   int max_trials_per_task,     //
                                                    int population_size,         //
                                                    double init_measured_ratio,  //
                                                    int init_min_unmeasured,     //

include/tvm/meta_schedule/task_scheduler.h

@@ -75,10 +75,14 @@ class TaskSchedulerNode : public runtime::Object {
   Runner runner{nullptr};
   /*! \brief The database of the scheduler. */
   Database database{nullptr};
+  /*! \brief The maximum number of trials allowed. */
+  int max_trials;
   /*! \brief The cost model of the scheduler. */
   Optional<CostModel> cost_model;
   /*! \brief The list of measure callbacks of the scheduler. */
   Array<MeasureCallback> measure_callbacks;
+  /*! \brief The number of trials already conducted. */
+  int num_trials_already;
 
   /*! \brief The default destructor. */
   virtual ~TaskSchedulerNode() = default;
@@ -88,8 +92,10 @@ class TaskSchedulerNode : public runtime::Object {
     v->Visit("builder", &builder);
     v->Visit("runner", &runner);
     v->Visit("database", &database);
+    v->Visit("max_trials", &max_trials);
     v->Visit("cost_model", &cost_model);
     v->Visit("measure_callbacks", &measure_callbacks);
+    v->Visit("num_trials_already", &num_trials_already);
   }
 
   /*! \brief Auto-tuning. */
@@ -102,23 +108,16 @@ class TaskSchedulerNode : public runtime::Object {
   virtual void InitializeTask(int task_id);
 
   /*!
-   * \brief Set specific task to be stopped.
-   * \param task_id The task id to be stopped.
-   */
-  virtual void SetTaskStopped(int task_id);
-
-  /*!
-   * \brief Check whether the task is running.
+   * \brief Touch the task and update its status
    * \param task_id The task id to be checked.
-   * \return Whether the task is running.
    */
-  virtual bool IsTaskRunning(int task_id);
+  virtual void TouchTask(int task_id);
 
   /*!
    * \brief Wait until the task is finished.
    * \param task_id The task id to be joined.
    */
-  virtual void JoinRunningTask(int task_id);
+  virtual Array<RunnerResult> JoinRunningTask(int task_id);
 
   /*!
    * \brief Fetch the next task id.
@@ -142,23 +141,17 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
   using FInitializeTask = runtime::TypedPackedFunc<void(int)>;
 
   /*!
-   * \brief The function type of `SetTaskStopped` method.
-   * \param task_id The task id to be stopped.
-   */
-  using FSetTaskStopped = runtime::TypedPackedFunc<void(int)>;
-
-  /*!
-   * \brief The function type of `IsTaskRunning` method.
+   * \brief The function type of `TouchTask` method.
    * \param task_id The task id to be checked.
    * \return Whether the task is running.
    */
-  using FIsTaskRunning = runtime::TypedPackedFunc<bool(int)>;
+  using FTouchTask = runtime::TypedPackedFunc<void(int)>;
 
   /*!
    * \brief The function type of `JoinRunningTask` method.
    * \param task_id The task id to be joined.
    */
-  using FJoinRunningTask = runtime::TypedPackedFunc<void(int)>;
+  using FJoinRunningTask = runtime::TypedPackedFunc<Array<RunnerResult>(int)>;
 
   /*!
    * \brief The function type of `NextTaskId` method.
@@ -170,10 +163,8 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
   FTune f_tune;
   /*! \brief The packed function to the `InitializeTask` function. */
   FInitializeTask f_initialize_task;
-  /*! \brief The packed function to the `SetTaskStopped` function. */
-  FSetTaskStopped f_set_task_stopped;
-  /*! \brief The packed function to the `IsTaskRunning` function. */
-  FIsTaskRunning f_is_task_running;
+  /*! \brief The packed function to the `TouchTask` function. */
+  FTouchTask f_touch_task;
   /*! \brief The packed function to the `JoinRunningTask` function. */
   FJoinRunningTask f_join_running_task;
   /*! \brief The packed function to the `NextTaskId` function. */
@@ -182,8 +173,7 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
   void VisitAttrs(tvm::AttrVisitor* v) {
     // `f_tune` is not visited
     // `f_initialize_task` is not visited
-    // `f_set_task_stopped` is not visited
-    // `f_is_task_running` is not visited
+    // `f_touch_task` is not visited
     // `f_join_running_task` is not visited
     // `f_next_task_id` is not visited
   }
@@ -204,23 +194,15 @@ class PyTaskSchedulerNode : public TaskSchedulerNode {
     }
   }
 
-  void SetTaskStopped(int task_id) final {
-    if (f_set_task_stopped == nullptr) {
-      TaskSchedulerNode::SetTaskStopped(task_id);
-    } else {
-      f_set_task_stopped(task_id);
-    }
-  }
-
-  bool IsTaskRunning(int task_id) final {
-    if (f_is_task_running == nullptr) {
-      return TaskSchedulerNode::IsTaskRunning(task_id);
+  void TouchTask(int task_id) final {
+    if (f_touch_task == nullptr) {
+      return TaskSchedulerNode::TouchTask(task_id);
     } else {
-      return f_is_task_running(task_id);
+      return f_touch_task(task_id);
     }
   }
 
-  void JoinRunningTask(int task_id) final {
+  Array<RunnerResult> JoinRunningTask(int task_id) final {
     if (f_join_running_task == nullptr) {
       return TaskSchedulerNode::JoinRunningTask(task_id);
     } else {
@@ -249,6 +231,7 @@ class TaskScheduler : public runtime::ObjectRef {
    * \param builder The builder of the scheduler.
    * \param runner The runner of the scheduler.
    * \param database The database of the scheduler.
+   * \param max_trials The maximum number of trials.
    * \param cost_model The cost model of the scheduler.
    * \param measure_callbacks The measure callbacks of the scheduler.
    * \return The task scheduler created.
@@ -257,20 +240,47 @@ class TaskScheduler : public runtime::ObjectRef {
                                           Builder builder,                 //
                                           Runner runner,                   //
                                           Database database,               //
+                                          int max_trials,                  //
                                           Optional<CostModel> cost_model,  //
                                           Optional<Array<MeasureCallback>> measure_callbacks);
+  /*!
+   * \brief Create a task scheduler that fetches tasks in a gradient based fashion.
+   * \param tasks The tasks to be tuned.
+   * \param task_weights The weights of each task.
+   * \param builder The builder of the scheduler.
+   * \param runner The runner of the scheduler.
+   * \param database The database of the scheduler.
+   * \param max_trials The maximum number of trials.
+   * \param cost_model The cost model of the scheduler.
+   * \param measure_callbacks The measure callbacks of the scheduler.
+   * \param alpha The parameter alpha to control gradient computation.
+   * \param window_size The parameter to control backward window size.
+   * \param seed The random seed.
+   * \return The task scheduler created.
+   */
+  TVM_DLL static TaskScheduler GradientBased(Array<TuneContext> tasks,
+                                             Array<FloatImm> task_weights,                        //
+                                             Builder builder,                                     //
+                                             Runner runner,                                       //
+                                             Database database,                                   //
+                                             int max_trials,                                      //
+                                             Optional<CostModel> cost_model,                      //
+                                             Optional<Array<MeasureCallback>> measure_callbacks,  //
+                                             double alpha,                                        //
+                                             int window_size,                                     //
+                                             support::LinearCongruentialEngine::TRandState seed);
   /*!
    * \brief Create a task scheduler with customized methods on the python-side.
    * \param tasks The tasks to be tuned.
    * \param builder The builder of the scheduler.
    * \param runner The runner of the scheduler.
    * \param database The database of the scheduler.
+   * \param max_trials The maximum number of trials.
    * \param cost_model The cost model of the scheduler.
    * \param measure_callbacks The measure callbacks of the scheduler.
    * \param f_tune The packed function of `Tune`.
    * \param f_initialize_task The packed function of `InitializeTask`.
-   * \param f_set_task_stopped The packed function of `SetTaskStopped`.
-   * \param f_is_task_running The packed function of `IsTaskRunning`.
+   * \param f_touch_task The packed function of `TouchTask`.
    * \param f_join_running_task The packed function of `JoinRunningTask`.
    * \param f_next_task_id The packed function of `NextTaskId`.
    * \return The task scheduler created.
@@ -280,12 +290,12 @@ class TaskScheduler : public runtime::ObjectRef {
       Builder builder,                                            //
       Runner runner,                                              //
       Database database,                                          //
+      int max_trials,                                             //
       Optional<CostModel> cost_model,                             //
       Optional<Array<MeasureCallback>> measure_callbacks,         //
       PyTaskSchedulerNode::FTune f_tune,                          //
       PyTaskSchedulerNode::FInitializeTask f_initialize_task,     //
-      PyTaskSchedulerNode::FSetTaskStopped f_set_task_stopped,    //
-      PyTaskSchedulerNode::FIsTaskRunning f_is_task_running,      //
+      PyTaskSchedulerNode::FTouchTask f_touch_task,               //
       PyTaskSchedulerNode::FJoinRunningTask f_join_running_task,  //
       PyTaskSchedulerNode::FNextTaskId f_next_task_id);
   TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS(TaskScheduler, ObjectRef, TaskSchedulerNode);

include/tvm/meta_schedule/tune_context.h

@@ -62,7 +62,7 @@ class TuneContextNode : public runtime::Object {
   /*! \brief The task scheduler that owns the tune context */
   const TaskSchedulerNode* task_scheduler;
   /*! \brief Whether the tuning task has been stopped or finished. */
-  bool is_stopped;
+  bool is_terminated;
   /*! \brief The measure candidates. */
   Optional<Array<MeasureCandidate>> measure_candidates;
   /*! \brief The building results. */
@@ -81,7 +81,7 @@ class TuneContextNode : public runtime::Object {
     v->Visit("task_name", &task_name);
     v->Visit("rand_state", &rand_state);
     v->Visit("num_threads", &num_threads);
-    v->Visit("is_stopped", &is_stopped);
+    v->Visit("is_terminated", &is_terminated);
     v->Visit("builder_results", &builder_results);
     v->Visit("runner_futures", &runner_futures);
     v->Visit("measure_candidates", &measure_candidates);

include/tvm/support/random_engine.h

@@ -99,15 +99,15 @@ class LinearCongruentialEngine {
    * \brief Change the start random state of RNG with the seed of a new random state value.
    * \param rand_state The random state given in result_type.
    */
-  void Seed(TRandState rand_state = 1) {
-    ICHECK(rand_state != -1) << "The seed can't be -1 which should be changed to random seed!";
-    rand_state %= modulus;  // Make sure the seed is within the range of modulus.
-    if (rand_state == 0)
-      rand_state = 1;  // Avoid getting all 0 given the current parameter set.
-    else if (rand_state < 0)
-      rand_state += modulus;             // Make sure the rand state is non-negative.
-    ICHECK(rand_state_ptr_ != nullptr);  // Make sure the pointer is not null.
-    *rand_state_ptr_ = rand_state;       // Change pointed random state to given random state value.
+  void Seed(TRandState rand_state) {
+    if (rand_state == -1) {
+      rand_state = DeviceRandom();
+    } else if (rand_state == 0) {
+      rand_state = 1;
+    }
+    ICHECK(rand_state >= 0) << "The random state should be nonnegative";
+    ICHECK(rand_state_ptr_ != nullptr);
+    *rand_state_ptr_ = rand_state % modulus;
   }
 
   /*!

include/tvm/tir/schedule/schedule.h

@@ -128,7 +128,7 @@ class ScheduleNode : public runtime::Object {
    * \brief Seed the randomness
    * \param seed The new random seed, -1 if use device random, otherwise non-negative
    */
-  virtual void Seed(support::LinearCongruentialEngine::TRandState seed = -1) = 0;
+  virtual void Seed(support::LinearCongruentialEngine::TRandState seed) = 0;
   /*! \brief Fork the random state */
   virtual support::LinearCongruentialEngine::TRandState ForkSeed() = 0;
 

python/tvm/meta_schedule/search_strategy/evolutionary_search.py

@@ -34,7 +34,7 @@ class EvolutionarySearch(SearchStrategy):
     ----------
     num_trials_per_iter : int
         Number of trials per iteration.
-    num_trials_total : int
+    max_trials_per_task : int
         Total number of trials.
     population_size : int
         The initial population of traces from measured samples and randomly generated samples.
@@ -53,7 +53,7 @@ class EvolutionarySearch(SearchStrategy):
     """
 
     num_trials_per_iter: int
-    num_trials_total: int
+    max_trials_per_task: int
     population_size: int
     init_measured_ratio: int
     init_min_unmeasured: int
@@ -66,7 +66,7 @@ def __init__(
         self,
         *,
         num_trials_per_iter: int,
-        num_trials_total: int,
+        max_trials_per_task: int,
         population_size: int,
         init_measured_ratio: float,
         init_min_unmeasured: int,
@@ -79,7 +79,7 @@ def __init__(
         self.__init_handle_by_constructor__(
             _ffi_api.SearchStrategyEvolutionarySearch,  # type: ignore # pylint: disable=no-member
             num_trials_per_iter,
-            num_trials_total,
+            max_trials_per_task,
             population_size,
             init_measured_ratio,
             init_min_unmeasured,
@@ -94,7 +94,8 @@ class EvolutionarySearchConfig(NamedTuple):
     """Configuration for EvolutionarySearch"""
 
     num_trials_per_iter: int
-    num_trials_total: int
+    max_trials_per_task: int
+    max_trials_global: int
     population_size: int = 2048
     init_measured_ratio: float = 0.2
     init_min_unmeasured: int = 50
@@ -106,7 +107,7 @@ class EvolutionarySearchConfig(NamedTuple):
     def create_strategy(self) -> EvolutionarySearch:
         return EvolutionarySearch(
             num_trials_per_iter=self.num_trials_per_iter,
-            num_trials_total=self.num_trials_total,
+            max_trials_per_task=self.max_trials_per_task,
             population_size=self.population_size,
             init_measured_ratio=self.init_measured_ratio,
             init_min_unmeasured=self.init_min_unmeasured,

python/tvm/meta_schedule/search_strategy/replay_func.py

@@ -33,31 +33,32 @@ class ReplayFunc(SearchStrategy):
     ----------
     num_trials_per_iter : int
         Number of trials per iteration.
-    num_trials_total : int
+    max_trials_per_task : int
         Total number of trials.
     """
 
     num_trials_per_iter: int
-    num_trials_total: int
+    max_trials_per_task: int
 
     def __init__(
         self,
         num_trials_per_iter: int,
-        num_trials_total: int,
+        max_trials_per_task: int,
     ):
         """Constructor"""
         self.__init_handle_by_constructor__(
             _ffi_api.SearchStrategyReplayFunc,  # type: ignore # pylint: disable=no-member
             num_trials_per_iter,
-            num_trials_total,
+            max_trials_per_task,
         )
 
 
 class ReplayFuncConfig(NamedTuple):
     """Configuration for ReplayFunc"""
 
     num_trials_per_iter: int
-    num_trials_total: int
+    max_trials_per_task: int
+    max_trials_global: int
 
     def create_strategy(self) -> ReplayFunc:
-        return ReplayFunc(self.num_trials_per_iter, self.num_trials_total)
+        return ReplayFunc(self.num_trials_per_iter, self.max_trials_per_task)