Executor of a computation graph. Executor can be created by Binding a symbol. More...

#include <executor.h>

Collaboration diagram for mxnet::Executor:

Public Types
typedef std::function< void(const char , void )>	MonitorCallback
	the prototype of user-defined monitor callback More...

Public Member Functions
virtual	~Executor ()
	destructor More...

virtual void	Forward (bool is_train)=0
	Perform a Forward operation of Operator After this operation, user can get the result by using function head. More...

virtual void	PartialForward (bool is_train, int step, int *step_left)=0
	Perform a Partial Forward operation of Operator. Only issue operation specified by step. The caller must keep calling PartialForward with increasing steps, until step_left=0. More...

virtual void	Backward (const std::vector< NDArray > &head_grads, bool is_train=true)=0
	Perform a Backward operation of the Operator. This must be called after Forward. After this operation, NDArrays specified by grad_in_args_store will be updated accordingly. User is allowed to pass in an empty Array if the head node is loss function and head gradeitn is not needed. More...

virtual void	Print (std::ostream &os) const
	print the execution plan info to output stream. More...

virtual const std::vector< NDArray > &	outputs () const =0
	get array of outputs in the executor. More...

virtual const std::unordered_map< std::string, NDArray > &	in_arg_map () const =0
	get input argument map, key is arg name, value is arg's NDArray. More...

virtual const std::unordered_map< std::string, NDArray > &	arg_grad_map () const =0
	get input argument graident map, key is arg name, value is gradient's NDArray. More...

virtual const std::unordered_map< std::string, NDArray > &	aux_state_map () const =0
	get aux state map, key is arg name, value is aux state's NDArray. More...

virtual void	SetMonitorCallback (const MonitorCallback &callback)
	Install a callback to notify the completion of operation. More...

Static Public Member Functions
static Executor *	Bind (nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< NDArray > &in_args, const std::vector< NDArray > &arg_grad_store, const std::vector< OpReqType > &grad_req_type, const std::vector< NDArray > &aux_states, Executor *shared_exec=NULL)
	Create an operator by bind symbol with context and arguments. If user do not want to compute the gradients of i-th argument, grad_req_type[i] can be kNullOp. More...

static Executor *	SimpleBind (nnvm::Symbol symbol, const Context &default_ctx, const std::map< std::string, Context > &group2ctx, const std::vector< Context > &in_arg_ctxes, const std::vector< Context > &arg_grad_ctxes, const std::vector< Context > &aux_state_ctxes, const std::unordered_map< std::string, TShape > &arg_shape_map, const std::unordered_map< std::string, int > &arg_dtype_map, const std::unordered_map< std::string, int > &arg_stype_map, const std::vector< OpReqType > &grad_req_types, const std::unordered_set< std::string > &param_names, std::vector< NDArray > in_args, std::vector< NDArray > arg_grads, std::vector< NDArray > aux_states, std::unordered_map< std::string, NDArray > shared_data_arrays=nullptr, Executor *shared_exec=nullptr)

Detailed Description

Executor of a computation graph. Executor can be created by Binding a symbol.

Member Typedef Documentation

typedef std::function<void(const char*, void*)> mxnet::Executor::MonitorCallback

the prototype of user-defined monitor callback

Constructor & Destructor Documentation

virtual mxnet::Executor::~Executor ( )

inlinevirtual

destructor

Member Function Documentation

virtual const std::unordered_map<std::string, NDArray>& mxnet::Executor::arg_grad_map ( ) const

pure virtual

get input argument graident map, key is arg name, value is gradient's NDArray.

Returns: input argument gradient map in the executor.

virtual const std::unordered_map<std::string, NDArray>& mxnet::Executor::aux_state_map ( ) const

pure virtual

get aux state map, key is arg name, value is aux state's NDArray.

Returns: aux state map in the executor.

virtual void mxnet::Executor::Backward	(	const std::vector< NDArray > &	head_grads,
		bool	is_train = `true`
	)

pure virtual

Perform a Backward operation of the Operator. This must be called after Forward. After this operation, NDArrays specified by grad_in_args_store will be updated accordingly. User is allowed to pass in an empty Array if the head node is loss function and head gradeitn is not needed.

Parameters

head_grads the gradient of head nodes to be backproped.

static Executor* mxnet::Executor::Bind	(	nnvm::Symbol	symbol,
		const Context &	default_ctx,
		const std::map< std::string, Context > &	group2ctx,
		const std::vector< NDArray > &	in_args,
		const std::vector< NDArray > &	arg_grad_store,
		const std::vector< OpReqType > &	grad_req_type,
		const std::vector< NDArray > &	aux_states,
		Executor *	shared_exec = `NULL`
	)

static

Create an operator by bind symbol with context and arguments. If user do not want to compute the gradients of i-th argument, grad_req_type[i] can be kNullOp.

Parameters

default_ctx	the default context of binding.
group2ctx	Context mapping group to context.
symbol	the symbol that specifies the output of Forward pass.
in_args	the NDArray that stores the input arguments to the symbol.
arg_grad_store	NDArray that is used to store the gradient output of the input arguments.
grad_req_type	requirment type of gradient saving. Can only be in {kNullOp, kAddTo, kWriteTo}.
aux_states	NDArray that is used as internal state in op
shared_exec	input executor to share memory with.

Returns: a new executor.

virtual void mxnet::Executor::Forward ( bool is_train )

pure virtual

Perform a Forward operation of Operator After this operation, user can get the result by using function head.

virtual const std::unordered_map<std::string, NDArray>& mxnet::Executor::in_arg_map ( ) const

pure virtual

get input argument map, key is arg name, value is arg's NDArray.

Returns: input argument map in the executor.

virtual const std::vector<NDArray>& mxnet::Executor::outputs ( ) const

pure virtual

get array of outputs in the executor.

Returns: array of outputs in the executor.

virtual void mxnet::Executor::PartialForward	(	bool	is_train,
		int	step,
		int *	step_left
	)

pure virtual

Perform a Partial Forward operation of Operator. Only issue operation specified by step. The caller must keep calling PartialForward with increasing steps, until step_left=0.

Parameters

is_train	Whether this is training phase.
step	current step, user can always start from 0
step_left	Number of steps left to finish the forward.

virtual void mxnet::Executor::Print ( std::ostream & os ) const

inlinevirtual

print the execution plan info to output stream.

Parameters

os	the output stream we like to print to.

virtual void mxnet::Executor::SetMonitorCallback ( const MonitorCallback & callback )

inlinevirtual

Install a callback to notify the completion of operation.

static Executor* mxnet::Executor::SimpleBind	(	nnvm::Symbol	symbol,
		const Context &	default_ctx,
		const std::map< std::string, Context > &	group2ctx,
		const std::vector< Context > &	in_arg_ctxes,
		const std::vector< Context > &	arg_grad_ctxes,
		const std::vector< Context > &	aux_state_ctxes,
		const std::unordered_map< std::string, TShape > &	arg_shape_map,
		const std::unordered_map< std::string, int > &	arg_dtype_map,
		const std::unordered_map< std::string, int > &	arg_stype_map,
		const std::vector< OpReqType > &	grad_req_types,
		const std::unordered_set< std::string > &	param_names,
		std::vector< NDArray > *	in_args,
		std::vector< NDArray > *	arg_grads,
		std::vector< NDArray > *	aux_states,
		std::unordered_map< std::string, NDArray > *	shared_data_arrays = `nullptr`,
		Executor *	shared_exec = `nullptr`
	)

static

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

include/mxnet/executor.h

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation