runtime functions for NDArray More...

#include <imperative.h>

Collaboration diagram for mxnet::Imperative:

Classes
class	AGInfo

class	DCInfo
	DCInfo datastructure to enable deferred computation. More...

Public Member Functions
bool	is_training () const
	whether operator recording is on. More...

bool	set_is_training (bool is_train)
	turn on or turn off operator recording for autograd. More...

bool	is_recording () const
	whether operator recording is on. More...

bool	set_is_recording (bool is_recording)
	turn on or turn off operator recording for autograd. More...

bool	is_deferred_compute () const
	whether deferred compute mode is on. More...

bool	set_is_deferred_compute (bool is_deferred_compute)
	turn on or turn off operator recording for autograd. More...

int	is_np_shape () const
	return current numpy compatibility status, GlobalOn(2), ThreadLocalOn(1), Off(0). More...

bool	set_is_np_shape (int is_np_shape)
	specify numpy compatibility off, thread local on or global on. More...

bool	is_np_default_dtype () const
	return current numpy default dtype compatibility status. More...

bool	set_is_np_default_dtype (bool is_np_default_dtype)
	specify numpy default dtype off or global on. More...

OptConstraint	get_opt_constraints () const
	return current optimization constraints. More...

OptConstraint	set_opt_constraints (OptConstraint constraints)
	set optimization constraints. More...

void	RecordOp (nnvm::NodeAttrs &&attrs, const std::vector< NDArray * > &inputs, const std::vector< NDArray * > &outputs, const OpStatePtr &state=OpStatePtr(), std::vector< bool > p_save_inputs=nullptr, std::vector< bool > p_save_outputs=nullptr)
	to record operator, return corresponding node. More...

void	RecordDeferredCompute (nnvm::NodeAttrs &&attrs, const std::vector< NDArray * > &inputs, const std::vector< NDArray * > &outputs)
	to record operator, return corresponding node. More...

nnvm::Symbol	GetDeferredComputeSymbol (const std::vector< NDArray * > &outputs)
	obtain symbol representation of deferred compute session. More...

void	SetDeferredComputeVariable (NDArrayHandle arrays, SymbolHandle variables, const int num)
	associate arrays with variables for deferred compute More...

void	DeferredComputeClear (NDArrayHandle *arrays, const int num)
	clear info node associated with array More...

OpStatePtr	Invoke (const Context &default_ctx, const nnvm::NodeAttrs &attrs, const std::vector< NDArray * > &inputs, const std::vector< NDArray * > &outputs)

OpStatePtr	InvokeOp (const Context &ctx, const nnvm::NodeAttrs &attrs, const std::vector< NDArray * > &inputs, const std::vector< NDArray * > &outputs, const std::vector< OpReqType > &req, const DispatchMode dispatch_mode, OpStatePtr state=OpStatePtr())

void	MarkVariables (const std::vector< NDArray * > &variables, const std::vector< uint32_t > &grad_reqs, const std::vector< NDArray * > &gradients)
	mark variables for computing gradients. More...

void	DropGrads (const std::vector< NDArray * > &variables)
	unmark nonleaf variables to free the memory. More...

std::vector< NDArray * >	Backward (const std::vector< NDArray * > &outputs, const std::vector< NDArray * > &ograds, const std::vector< NDArray * > &variables, bool is_train, bool retain_graph, bool create_graph)
	compute the gradient of outputs w.r.t variables. More...

std::vector< nnvm::ObjectPtr >	ListNonleafVariables (const nnvm::Symbol &sym) const
	Return the marked nonleaf nodes. More...

Static Public Member Functions
static Imperative *	Get ()

static bool	PreferBulkExecInference ()
	Should op execution bulking be employed during inference. More...

static bool	PreferBulkExecTrain ()
	Should op execution bulking be employed during training. More...

static int	BulkExecMaxNodeTrainFwd ()
	The max number of op nodes in a bulk during forward pass of training. More...

static int	BulkExecMaxNodeTrainBwd ()
	The max number of op nodes in a bulk during backward pass of training. More...

Friends
class	NDArray

Detailed Description

runtime functions for NDArray

Member Function Documentation

◆ Backward()

std::vector<NDArray*> mxnet::Imperative::Backward	(	const std::vector< NDArray * > &	outputs,
		const std::vector< NDArray * > &	ograds,
		const std::vector< NDArray * > &	variables,
		bool	is_train,
		bool	retain_graph,
		bool	create_graph
	)

compute the gradient of outputs w.r.t variables.

◆ BulkExecMaxNodeTrainBwd()

static int mxnet::Imperative::BulkExecMaxNodeTrainBwd ( )

inlinestatic

The max number of op nodes in a bulk during backward pass of training.

◆ BulkExecMaxNodeTrainFwd()

static int mxnet::Imperative::BulkExecMaxNodeTrainFwd ( )

inlinestatic

The max number of op nodes in a bulk during forward pass of training.

◆ DeferredComputeClear()

void mxnet::Imperative::DeferredComputeClear	(	NDArrayHandle *	arrays,
		const int	num
	)

clear info node associated with array

◆ DropGrads()

void mxnet::Imperative::DropGrads ( const std::vector< NDArray * > & variables )

unmark nonleaf variables to free the memory.

◆ Get()

static Imperative* mxnet::Imperative::Get ( )

static

Returns: AutogradRuntime singleton

◆ get_opt_constraints()

OptConstraint mxnet::Imperative::get_opt_constraints ( ) const

inline

return current optimization constraints.

◆ GetDeferredComputeSymbol()

nnvm::Symbol mxnet::Imperative::GetDeferredComputeSymbol ( const std::vector< NDArray * > & outputs )

obtain symbol representation of deferred compute session.

◆ Invoke()

OpStatePtr mxnet::Imperative::Invoke	(	const Context &	default_ctx,
		const nnvm::NodeAttrs &	attrs,
		const std::vector< NDArray * > &	inputs,
		const std::vector< NDArray * > &	outputs
	)

◆ InvokeOp()

OpStatePtr mxnet::Imperative::InvokeOp	(	const Context &	ctx,
		const nnvm::NodeAttrs &	attrs,
		const std::vector< NDArray * > &	inputs,
		const std::vector< NDArray * > &	outputs,
		const std::vector< OpReqType > &	req,
		const DispatchMode	dispatch_mode,
		OpStatePtr	state = `OpStatePtr()`
	)

◆ is_deferred_compute()

bool mxnet::Imperative::is_deferred_compute ( ) const

inline

whether deferred compute mode is on.

◆ is_np_default_dtype()

bool mxnet::Imperative::is_np_default_dtype ( ) const

inline

return current numpy default dtype compatibility status.

◆ is_np_shape()

int mxnet::Imperative::is_np_shape ( ) const

inline

return current numpy compatibility status, GlobalOn(2), ThreadLocalOn(1), Off(0).

◆ is_recording()

bool mxnet::Imperative::is_recording ( ) const

inline

whether operator recording is on.

◆ is_training()

bool mxnet::Imperative::is_training ( ) const

inline

whether operator recording is on.

◆ ListNonleafVariables()

std::vector<nnvm::ObjectPtr> mxnet::Imperative::ListNonleafVariables ( const nnvm::Symbol & sym ) const

Return the marked nonleaf nodes.

◆ MarkVariables()

void mxnet::Imperative::MarkVariables	(	const std::vector< NDArray * > &	variables,
		const std::vector< uint32_t > &	grad_reqs,
		const std::vector< NDArray * > &	gradients
	)

mark variables for computing gradients.

◆ PreferBulkExecInference()

static bool mxnet::Imperative::PreferBulkExecInference ( )

inlinestatic

Should op execution bulking be employed during inference.

◆ PreferBulkExecTrain()

static bool mxnet::Imperative::PreferBulkExecTrain ( )

inlinestatic

Should op execution bulking be employed during training.

◆ RecordDeferredCompute()

void mxnet::Imperative::RecordDeferredCompute	(	nnvm::NodeAttrs &&	attrs,
		const std::vector< NDArray * > &	inputs,
		const std::vector< NDArray * > &	outputs
	)

to record operator, return corresponding node.

◆ RecordOp()

void mxnet::Imperative::RecordOp	(	nnvm::NodeAttrs &&	attrs,
		const std::vector< NDArray * > &	inputs,
		const std::vector< NDArray * > &	outputs,
		const OpStatePtr &	state = `OpStatePtr()`,
		std::vector< bool > *	p_save_inputs = `nullptr`,
		std::vector< bool > *	p_save_outputs = `nullptr`
	)

to record operator, return corresponding node.

◆ set_is_deferred_compute()

bool mxnet::Imperative::set_is_deferred_compute ( bool is_deferred_compute )

inline

turn on or turn off operator recording for autograd.

◆ set_is_np_default_dtype()

bool mxnet::Imperative::set_is_np_default_dtype ( bool is_np_default_dtype )

inline

specify numpy default dtype off or global on.

◆ set_is_np_shape()

bool mxnet::Imperative::set_is_np_shape ( int is_np_shape )

inline

specify numpy compatibility off, thread local on or global on.

◆ set_is_recording()

bool mxnet::Imperative::set_is_recording ( bool is_recording )

inline

turn on or turn off operator recording for autograd.

◆ set_is_training()

bool mxnet::Imperative::set_is_training ( bool is_train )

inline

turn on or turn off operator recording for autograd.

◆ set_opt_constraints()

OptConstraint mxnet::Imperative::set_opt_constraints ( OptConstraint constraints )

inline

set optimization constraints.

◆ SetDeferredComputeVariable()

void mxnet::Imperative::SetDeferredComputeVariable	(	NDArrayHandle *	arrays,
		SymbolHandle *	variables,
		const int	num
	)

associate arrays with variables for deferred compute

Friends And Related Function Documentation

◆ NDArray

friend class NDArray

friend

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

/work/mxnet/include/mxnet/imperative.h

Classes

Public Member Functions

Static Public Member Functions

Friends

Detailed Description

Member Function Documentation

◆ Backward()

◆ BulkExecMaxNodeTrainBwd()

◆ BulkExecMaxNodeTrainFwd()

◆ DeferredComputeClear()

◆ DropGrads()

◆ Get()

◆ get_opt_constraints()

◆ GetDeferredComputeSymbol()

◆ Invoke()

◆ InvokeOp()

◆ is_deferred_compute()

◆ is_np_default_dtype()

◆ is_np_shape()

◆ is_recording()

◆ is_training()

◆ ListNonleafVariables()

◆ MarkVariables()

◆ PreferBulkExecInference()

◆ PreferBulkExecTrain()

◆ RecordDeferredCompute()

◆ RecordOp()

◆ set_is_deferred_compute()

◆ set_is_np_default_dtype()

◆ set_is_np_shape()

◆ set_is_recording()

◆ set_is_training()

◆ set_opt_constraints()

◆ SetDeferredComputeVariable()

Friends And Related Function Documentation

◆ NDArray