VariableStore Class Reference

#include <VariableStore.h>

Inheritance diagram for VariableStore:

Public Member Functions
	VariableStore (const OptionSet &options, NEML2Object *object)
LabeledAxis &	declare_axis (const std::string &name)
virtual void	setup_layout ()
	Setup the layouts of all the registered axes.
VariableBase *	input_view (const VariableName &)
	Get the view of an input variable.
VariableBase *	output_view (const VariableName &)
	Get the view of an output variable.
template<typename T = BatchTensor>
Variable< T > &	get_input_variable (const VariableName &name)
template<typename T = BatchTensor>
const Variable< T > &	get_input_variable (const VariableName &name) const
template<typename T = BatchTensor>
const Variable< T > &	get_output_variable (const VariableName &name)
template<typename T = BatchTensor>
const Variable< T > &	get_output_variable (const VariableName &name) const
LabeledAxis &	input_axis ()
const LabeledAxis &	input_axis () const
LabeledAxis &	output_axis ()
const LabeledAxis &	output_axis () const
Storage< VariableName, VariableBase > &	input_views ()
const Storage< VariableName, VariableBase > &	input_views () const
Storage< VariableName, VariableBase > &	output_views ()
const Storage< VariableName, VariableBase > &	output_views () const
LabeledVector &	input_storage ()
const LabeledVector &	input_storage () const
LabeledVector &	output_storage ()
const LabeledVector &	output_storage () const
LabeledMatrix &	derivative_storage ()
const LabeledMatrix &	derivative_storage () const
LabeledTensor3D &	second_derivative_storage ()
const LabeledTensor3D &	second_derivative_storage () const

Protected Member Functions
virtual void	cache (TorchShapeRef batch_shape)
	Cache the variable's batch shape.
virtual void	allocate_variables (TorchShapeRef batch_shape, const torch::TensorOptions &options, bool in, bool out, bool dout_din, bool d2out_din2)
	Allocate variable storages given the batch shape and tensor options.
virtual void	setup_input_views ()
	Tell each input variable view which tensor storage(s) to view into.
virtual void	setup_output_views ()
	Tell each output variable view which tensor storage(s) to view into.
virtual void	reinit_input_views ()
	Create the views for input variables.
virtual void	reinit_output_views (bool out, bool dout_din=true, bool d2out_din2=true)
	Create the views for output variables, and optionally for the derivative and second derivatives.
virtual void	detach_and_zero (bool out, bool dout_din=true, bool d2out_din2=true)
	Detach the tensor storages and set each element in the tensor to 0.
template<typename T , typename... S>
const Variable< T > &	declare_input_variable (S &&... name)
	Declare an input variable.
template<typename... S>
const Variable< BatchTensor > &	declare_input_variable (TorchSize sz, S &&... name)
	Declare an input variable (with unknown base shape at compile time)
template<typename T , typename... S>
const Variable< BatchTensor > &	declare_input_variable_list (TorchSize list_size, S &&... name)
	Declare an input variable that is a list of tensors of fixed size.
template<typename T , typename... S>
Variable< T > &	declare_output_variable (S &&... name)
	Declare an output variable.
template<typename... S>
Variable< BatchTensor > &	declare_output_variable (TorchSize sz, S &&... name)
	Declare an input variable (with unknown base shape at compile time)
template<typename T , typename... S>
Variable< BatchTensor > &	declare_output_variable_list (TorchSize list_size, S &&... name)
	Declare an output variable that is a list of tensors of fixed size.
template<typename T >
VariableName	declare_variable (LabeledAxis &axis, const VariableName &var) const
	Declare an item recursively on an axis.
VariableName	declare_variable (LabeledAxis &axis, const VariableName &var, TorchSize sz) const
	Declare an item (with known storage size) recursively on an axis.
VariableName	declare_subaxis (LabeledAxis &axis, const VariableName &subaxis) const
	Declare a subaxis recursively on an axis.

Constructor & Destructor Documentation

VariableStore()

VariableStore	(	const OptionSet &	options,
		NEML2Object *	object )

Member Function Documentation

allocate_variables()

void allocate_variables ( TorchShapeRef batch_shape, const torch::TensorOptions & options, bool in, bool out, bool dout_din, bool d2out_din2 )

protectedvirtual

Allocate variable storages given the batch shape and tensor options.

Parameters

batch_shape	Batch shape of the allocated tensors
options	Tensor options of the allocated tensors
in	Whether to allocate tensor storage for input
out	Whether to allocate tensor storage for output
dout_din	Whether to allocate tensor storage for the first derivatives
d2out_din2	Whether to allocate tensor storage for the second derivatives

Reimplemented in Model.

cache()

void cache ( TorchShapeRef batch_shape )

protectedvirtual

Cache the variable's batch shape.

Reimplemented in Model, and Model.

declare_axis()

LabeledAxis & declare_axis

(

const std::string &

name

)

declare_input_variable() [1/2]

template<typename T , typename... S>

const Variable< T > & declare_input_variable ( S &&... name )

inlineprotected

Declare an input variable.

declare_input_variable() [2/2]

template<typename... S>

const Variable< BatchTensor > & declare_input_variable ( TorchSize sz, S &&... name )

inlineprotected

Declare an input variable (with unknown base shape at compile time)

declare_input_variable_list()

template<typename T , typename... S>

const Variable< BatchTensor > & declare_input_variable_list ( TorchSize list_size, S &&... name )

inlineprotected

Declare an input variable that is a list of tensors of fixed size.

declare_output_variable() [1/2]

template<typename T , typename... S>

Variable< T > & declare_output_variable ( S &&... name )

inlineprotected

Declare an output variable.

declare_output_variable() [2/2]

template<typename... S>

Variable< BatchTensor > & declare_output_variable ( TorchSize sz, S &&... name )

inlineprotected

Declare an input variable (with unknown base shape at compile time)

declare_output_variable_list()

template<typename T , typename... S>

Variable< BatchTensor > & declare_output_variable_list ( TorchSize list_size, S &&... name )

inlineprotected

Declare an output variable that is a list of tensors of fixed size.

declare_subaxis()

VariableName declare_subaxis ( LabeledAxis & axis, const VariableName & subaxis ) const

inlineprotected

Declare a subaxis recursively on an axis.

declare_variable() [1/2]

template<typename T >

VariableName declare_variable ( LabeledAxis & axis, const VariableName & var ) const

inlineprotected

Declare an item recursively on an axis.

declare_variable() [2/2]

VariableName declare_variable ( LabeledAxis & axis, const VariableName & var, TorchSize sz ) const

inlineprotected

Declare an item (with known storage size) recursively on an axis.

derivative_storage() [1/2]

LabeledMatrix & derivative_storage ( )

inline

Derivative storage

derivative_storage() [2/2]

const LabeledMatrix & derivative_storage ( ) const

inline

detach_and_zero()

void detach_and_zero ( bool out, bool dout_din = true, bool d2out_din2 = true )

protectedvirtual

Detach the tensor storages and set each element in the tensor to 0.

Reimplemented in Model.

get_input_variable() [1/2]

template<typename T = BatchTensor>

Variable< T > & get_input_variable ( const VariableName & name )

inline

Get an input variable

get_input_variable() [2/2]

template<typename T = BatchTensor>

const Variable< T > & get_input_variable ( const VariableName & name ) const

inline

get_output_variable() [1/2]

template<typename T = BatchTensor>

const Variable< T > & get_output_variable ( const VariableName & name )

inline

Get an output variable

get_output_variable() [2/2]

template<typename T = BatchTensor>

const Variable< T > & get_output_variable ( const VariableName & name ) const

inline

input_axis() [1/2]

LabeledAxis & input_axis ( )

inline

Definition of the input variables

input_axis() [2/2]

const LabeledAxis & input_axis ( ) const

inline

input_storage() [1/2]

LabeledVector & input_storage ( )

inline

Input storage

input_storage() [2/2]

const LabeledVector & input_storage ( ) const

inline

input_view()

VariableBase * input_view

(

const VariableName &

name

)

Get the view of an input variable.

input_views() [1/2]

Storage< VariableName, VariableBase > & input_views ( )

inline

Input variable views

input_views() [2/2]

const Storage< VariableName, VariableBase > & input_views ( ) const

inline

output_axis() [1/2]

LabeledAxis & output_axis ( )

inline

Which variables this object defines as output

output_axis() [2/2]

const LabeledAxis & output_axis ( ) const

inline

output_storage() [1/2]

LabeledVector & output_storage ( )

inline

Output storage

output_storage() [2/2]

const LabeledVector & output_storage ( ) const

inline

output_view()

VariableBase * output_view

(

const VariableName &

name

)

Get the view of an output variable.

output_views() [1/2]

Storage< VariableName, VariableBase > & output_views ( )

inline

Output variable views

output_views() [2/2]

const Storage< VariableName, VariableBase > & output_views ( ) const

inline

reinit_input_views()

void reinit_input_views ( )

protectedvirtual

Create the views for input variables.

Reimplemented in Model.

reinit_output_views()

void reinit_output_views ( bool out, bool dout_din = true, bool d2out_din2 = true )

protectedvirtual

Create the views for output variables, and optionally for the derivative and second derivatives.

Reimplemented in Model.

second_derivative_storage() [1/2]

LabeledTensor3D & second_derivative_storage ( )

inline

Second derivative storage

second_derivative_storage() [2/2]

const LabeledTensor3D & second_derivative_storage ( ) const

inline

setup_input_views()

void setup_input_views ( )

protectedvirtual

Tell each input variable view which tensor storage(s) to view into.

Reimplemented in Model.

setup_layout()

void setup_layout ( )

virtual

Setup the layouts of all the registered axes.

setup_output_views()

void setup_output_views ( )

protectedvirtual

Tell each output variable view which tensor storage(s) to view into.

Reimplemented in Model.