Inheritance diagram for tvm::meta_schedule::TaskScheduler:

Collaboration diagram for tvm::meta_schedule::TaskScheduler:

Public Member Functions
	TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS (TaskScheduler, ObjectRef, TaskSchedulerNode)

Public Member Functions inherited from tvm::runtime::ObjectRef
	ObjectRef ()=default
	default constructor More...

	ObjectRef (ObjectPtr< Object > data)
	Constructor from existing object ptr. More...

bool	same_as (const ObjectRef &other) const
	Comparator. More...

bool	operator== (const ObjectRef &other) const
	Comparator. More...

bool	operator!= (const ObjectRef &other) const
	Comparator. More...

bool	operator< (const ObjectRef &other) const
	Comparator. More...

bool	defined () const

const Object *	get () const

const Object *	operator-> () const

bool	unique () const

int	use_count () const

template<typename ObjectType >
const ObjectType *	as () const
	Try to downcast the internal Object to a raw pointer of a corresponding type. More...

Static Public Member Functions
static TaskScheduler	RoundRobin (Array< TuneContext > tasks, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func)
	Create a task scheduler that fetches tasks in a round-robin fashion. More...

static TaskScheduler	GradientBased (Array< TuneContext > tasks, Array< FloatImm > task_weights, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func, double alpha, int window_size, support::LinearCongruentialEngine::TRandState seed)
	Create a task scheduler that fetches tasks in a gradient based fashion. More...

static TaskScheduler	PyTaskScheduler (Array< TuneContext > tasks, Builder builder, Runner runner, Optional< Database > database, Optional< CostModel > cost_model, Optional< Array< MeasureCallback >> measure_callbacks, int max_trials, PackedFunc logging_func, PyTaskSchedulerNode::FTune f_tune, PyTaskSchedulerNode::FInitializeTask f_initialize_task, PyTaskSchedulerNode::FTouchTask f_touch_task, PyTaskSchedulerNode::FJoinRunningTask f_join_running_task, PyTaskSchedulerNode::FNextTaskId f_next_task_id)
	Create a task scheduler with customized methods on the python-side. More...

Additional Inherited Members
Public Types inherited from tvm::runtime::ObjectRef
using	ContainerType = Object
	type indicate the container type. More...

Static Public Attributes inherited from tvm::runtime::ObjectRef
static constexpr bool	_type_is_nullable = true

Protected Member Functions inherited from tvm::runtime::ObjectRef
Object *	get_mutable () const

Static Protected Member Functions inherited from tvm::runtime::ObjectRef
template<typename T >
static T	DowncastNoCheck (ObjectRef ref)
	Internal helper function downcast a ref without check. More...

static void	FFIClearAfterMove (ObjectRef *ref)
	Clear the object ref data field without DecRef after we successfully moved the field. More...

template<typename ObjectType >
static ObjectPtr< ObjectType >	GetDataPtr (const ObjectRef &ref)
	Internal helper function get data_ as ObjectPtr of ObjectType. More...

Protected Attributes inherited from tvm::runtime::ObjectRef
ObjectPtr< Object >	data_
	Internal pointer that backs the reference. More...

Detailed Description

Managed reference to TaskSchedulerNode.

Member Function Documentation

static TaskScheduler tvm::meta_schedule::TaskScheduler::GradientBased	(	Array< TuneContext >	tasks,
		Array< FloatImm >	task_weights,
		Builder	builder,
		Runner	runner,
		Optional< Database >	database,
		Optional< CostModel >	cost_model,
		Optional< Array< MeasureCallback >>	measure_callbacks,
		int	max_trials,
		PackedFunc	logging_func,
		double	alpha,
		int	window_size,
		support::LinearCongruentialEngine::TRandState	seed
	)

static

Create a task scheduler that fetches tasks in a gradient based fashion.

Parameters

tasks	The tasks to be tuned.
task_weights	The weights of each task.
builder	The builder of the scheduler.
runner	The runner of the scheduler.
database	The database of the scheduler.
max_trials	The maximum number of trials.
cost_model	The cost model of the scheduler.
measure_callbacks	The measure callbacks of the scheduler.
logging_func	The tuning task's logging function.
alpha	The parameter alpha to control gradient computation.
window_size	The parameter to control backward window size.
seed	The random seed.

static TaskScheduler tvm::meta_schedule::TaskScheduler::PyTaskScheduler	(	Array< TuneContext >	tasks,
		Builder	builder,
		Runner	runner,
		Optional< Database >	database,
		Optional< CostModel >	cost_model,
		Optional< Array< MeasureCallback >>	measure_callbacks,
		int	max_trials,
		PackedFunc	logging_func,
		PyTaskSchedulerNode::FTune	f_tune,
		PyTaskSchedulerNode::FInitializeTask	f_initialize_task,
		PyTaskSchedulerNode::FTouchTask	f_touch_task,
		PyTaskSchedulerNode::FJoinRunningTask	f_join_running_task,
		PyTaskSchedulerNode::FNextTaskId	f_next_task_id
	)

static

Create a task scheduler with customized methods on the python-side.

Parameters

tasks	The tasks to be tuned.
builder	The builder of the scheduler.
runner	The runner of the scheduler.
database	The database of the scheduler.
max_trials	The maximum number of trials.
cost_model	The cost model of the scheduler.
measure_callbacks	The measure callbacks of the scheduler.
logging_func	The tuning task's logging function.
f_tune	The packed function of `Tune`.
f_initialize_task	The packed function of `InitializeTask`.
f_touch_task	The packed function of `TouchTask`.
f_join_running_task	The packed function of `JoinRunningTask`.
f_next_task_id	The packed function of `NextTaskId`.

static TaskScheduler tvm::meta_schedule::TaskScheduler::RoundRobin	(	Array< TuneContext >	tasks,
		Builder	builder,
		Runner	runner,
		Optional< Database >	database,
		Optional< CostModel >	cost_model,
		Optional< Array< MeasureCallback >>	measure_callbacks,
		int	max_trials,
		PackedFunc	logging_func
	)

static

Create a task scheduler that fetches tasks in a round-robin fashion.

Parameters

tasks	The tasks to be tuned.
builder	The builder of the scheduler.
runner	The runner of the scheduler.
database	The database of the scheduler.
max_trials	The maximum number of trials.
cost_model	The cost model of the scheduler.
measure_callbacks	The measure callbacks of the scheduler.
logging_func	The tuning task's logging function.

tvm::meta_schedule::TaskScheduler::TVM_DEFINE_MUTABLE_NOTNULLABLE_OBJECT_REF_METHODS	(	TaskScheduler	,
		ObjectRef	,
		TaskSchedulerNode
	)

The documentation for this class was generated from the following file: