TensorBase< Derived > Class Template Reference

NEML2's enhanced tensor type. More...

Detailed Description

template<class Derived>
class neml2::TensorBase< Derived >

NEML2's enhanced tensor type.

neml2::TensorBase derives from torch::Tensor and clearly distinguishes between "batched" dimensions from other dimensions. The shape of the "batched" dimensions is called the batch size, and the shape of the rest dimensions is called the base size.

#include <TensorBase.h>

Inheritance diagram for TensorBase< Derived >:

Public Member Functions
	TensorBase ()=default
	Default constructor.
	TensorBase (const torch::Tensor &tensor, Size batch_dim)
	Construct from another torch::Tensor.
	TensorBase (const Derived &tensor)
	Copy constructor.
	TensorBase (Real)=delete
Meta operations
Derived	clone (torch::MemoryFormat memory_format=torch::MemoryFormat::Contiguous) const
Derived	detach () const
	Discard function graph.
Derived	to (const torch::TensorOptions &options) const
	Change tensor options.
Derived	operator- () const
	Negation.
Tensor information
bool	batched () const
	Whether the tensor is batched.
Size	batch_dim () const
	Return the number of batch dimensions.
Size	base_dim () const
	Return the number of base dimensions.
TensorShapeRef	batch_sizes () const
	Return the batch size.
Size	batch_size (Size index) const
	Return the size of a batch axis.
TensorShapeRef	base_sizes () const
	Return the base size.
Size	base_size (Size index) const
	Return the size of a base axis.
Size	base_storage () const
	Return the flattened storage needed just for the base indices.
Getter and setter
Derived	batch_index (indexing::TensorIndicesRef indices) const
	Get a tensor by slicing on the batch dimensions.
neml2::Tensor	base_index (indexing::TensorIndicesRef indices) const
	Get a tensor by slicing on the base dimensions.
void	batch_index_put_ (indexing::TensorIndicesRef indices, const torch::Tensor &other)
	Set values by slicing on the batch dimensions.
void	base_index_put_ (indexing::TensorIndicesRef indices, const torch::Tensor &other)
	Set values by slicing on the base dimensions.
Modifiers
Return a new view of the tensor with values broadcast along the batch dimensions.
Derived	batch_expand (TensorShapeRef batch_size) const
neml2::Tensor	base_expand (TensorShapeRef base_size) const
	Return a new view of the tensor with values broadcast along the base dimensions.
template<class Derived2 >
Derived	batch_expand_as (const Derived2 &other) const
	Expand the batch to have the same shape as another tensor.
template<class Derived2 >
Derived2	base_expand_as (const Derived2 &other) const
	Expand the base to have the same shape as another tensor.
Derived	batch_expand_copy (TensorShapeRef batch_size) const
	Return a new tensor with values broadcast along the batch dimensions.
neml2::Tensor	base_expand_copy (TensorShapeRef base_size) const
	Return a new tensor with values broadcast along the base dimensions.
Derived	batch_reshape (TensorShapeRef batch_shape) const
	Reshape batch dimensions.
neml2::Tensor	base_reshape (TensorShapeRef base_shape) const
	Reshape base dimensions.
Derived	batch_unsqueeze (Size d) const
	Unsqueeze a batch dimension.
neml2::Tensor	base_unsqueeze (Size d) const
	Unsqueeze a base dimension.
Derived	batch_transpose (Size d1, Size d2) const
	Transpose two batch dimensions.
neml2::Tensor	base_transpose (Size d1, Size d2) const
	Transpose two base dimensions.

Static Public Member Functions
static Derived	empty_like (const Derived &other)
	Empty tensor like another, i.e. same batch and base shapes, same tensor options, etc.
static Derived	zeros_like (const Derived &other)
	Zero tensor like another, i.e. same batch and base shapes, same tensor options, etc.
static Derived	ones_like (const Derived &other)
	Unit tensor like another, i.e. same batch and base shapes, same tensor options, etc.
static Derived	full_like (const Derived &other, Real init)
static Derived	linspace (const Derived &start, const Derived &end, Size nstep, Size dim=0, Size batch_dim=-1)
	Create a new tensor by adding a new batch dimension with linear spacing between start and end.
static Derived	logspace (const Derived &start, const Derived &end, Size nstep, Size dim=0, Size batch_dim=-1, Real base=10)
	log-space equivalent of the linspace named constructor

Constructor & Destructor Documentation

TensorBase() [1/4]

template<class Derived >

TensorBase ( )

default

Default constructor.

TensorBase() [2/4]

template<class Derived >

TensorBase	(	const torch::Tensor &	tensor,
		Size	batch_dim )

Construct from another torch::Tensor.

TensorBase() [3/4]

template<class Derived >

TensorBase

(

const Derived &

tensor

)

Copy constructor.

TensorBase() [4/4]

template<class Derived >

TensorBase ( Real )

delete

Member Function Documentation

base_dim()

template<class Derived >

Size base_dim

(

)

const

Return the number of base dimensions.

base_expand()

template<class Derived >

neml2::Tensor base_expand

(

TensorShapeRef

base_size

)

const

Return a new view of the tensor with values broadcast along the base dimensions.

base_expand_as()

template<class Derived >

template<class Derived2 >

Derived2 base_expand_as

(

const Derived2 &

other

)

const

Expand the base to have the same shape as another tensor.

base_expand_copy()

template<class Derived >

neml2::Tensor base_expand_copy

(

TensorShapeRef

base_size

)

const

Return a new tensor with values broadcast along the base dimensions.

base_index()

template<class Derived >

neml2::Tensor base_index

(

indexing::TensorIndicesRef

indices

)

const

Get a tensor by slicing on the base dimensions.

base_index_put_()

template<class Derived >

void base_index_put_	(	indexing::TensorIndicesRef	indices,
		const torch::Tensor &	other )

Set values by slicing on the base dimensions.

base_reshape()

template<class Derived >

neml2::Tensor base_reshape

(

TensorShapeRef

base_shape

)

const

Reshape base dimensions.

base_size()

template<class Derived >

Size base_size

(

Size

index

)

const

Return the size of a base axis.

base_sizes()

template<class Derived >

TensorShapeRef base_sizes

(

)

const

Return the base size.

base_storage()

template<class Derived >

Size base_storage

(

)

const

Return the flattened storage needed just for the base indices.

base_transpose()

template<class Derived >

neml2::Tensor base_transpose	(	Size	d1,
		Size	d2 ) const

Transpose two base dimensions.

base_unsqueeze()

template<class Derived >

neml2::Tensor base_unsqueeze

(

Size

d

)

const

Unsqueeze a base dimension.

batch_dim()

template<class Derived >

Size batch_dim

(

)

const

Return the number of batch dimensions.

batch_expand()

template<class Derived >

Derived batch_expand

(

TensorShapeRef

batch_size

)

const

batch_expand_as()

template<class Derived >

template<class Derived2 >

Derived batch_expand_as

(

const Derived2 &

other

)

const

Expand the batch to have the same shape as another tensor.

batch_expand_copy()

template<class Derived >

Derived batch_expand_copy

(

TensorShapeRef

batch_size

)

const

Return a new tensor with values broadcast along the batch dimensions.

batch_index()

template<class Derived >

Derived batch_index

(

indexing::TensorIndicesRef

indices

)

const

Get a tensor by slicing on the batch dimensions.

batch_index_put_()

template<class Derived >

void batch_index_put_	(	indexing::TensorIndicesRef	indices,
		const torch::Tensor &	other )

Set values by slicing on the batch dimensions.

batch_reshape()

template<class Derived >

Derived batch_reshape

(

TensorShapeRef

batch_shape

)

const

Reshape batch dimensions.

batch_size()

template<class Derived >

Size batch_size

(

Size

index

)

const

Return the size of a batch axis.

batch_sizes()

template<class Derived >

TensorShapeRef batch_sizes

(

)

const

Return the batch size.

batch_transpose()

template<class Derived >

Derived batch_transpose	(	Size	d1,
		Size	d2 ) const

Transpose two batch dimensions.

batch_unsqueeze()

template<class Derived >

Derived batch_unsqueeze

(

Size

d

)

const

Unsqueeze a batch dimension.

batched()

template<class Derived >

bool batched

(

)

const

Whether the tensor is batched.

clone()

template<class Derived >

Derived clone

(

torch::MemoryFormat

memory_format = torch::MemoryFormat::Contiguous

)

const

Clone (take ownership)

detach()

template<class Derived >

Derived detach

(

)

const

Discard function graph.

empty_like()

template<class Derived >

Derived empty_like ( const Derived & other )

static

Empty tensor like another, i.e. same batch and base shapes, same tensor options, etc.

full_like()

template<class Derived >

Derived full_like ( const Derived & other, Real init )

static

Full tensor like another, i.e. same batch and base shapes, same tensor options, etc., but filled with a different value

linspace()

template<class Derived >

Derived linspace ( const Derived & start, const Derived & end, Size nstep, Size dim = 0, Size batch_dim = -1 )

static

Create a new tensor by adding a new batch dimension with linear spacing between start and end.

start and end must be broadcastable. The new batch dimension will be added at the user-specified dimension dim which defaults to 0.

For example, if start has shape (3, 2; 5, 5) and end has shape (3, 1; 5, 5), then

linspace(start, end, 100, 1);

will have shape (3, 100, 2; 5, 5), note the location of the new dimension and the broadcasting.

Parameters

start	The starting tensor
end	The ending tensor
nstep	The number of steps with even spacing along the new dimension
dim	Where to insert the new dimension
batch_dim	Batch dimension of the output

Returns

Tensor Linearly spaced tensor

logspace()

template<class Derived >

Derived logspace ( const Derived & start, const Derived & end, Size nstep, Size dim = 0, Size batch_dim = -1, Real base = 10 )

static

log-space equivalent of the linspace named constructor

ones_like()

template<class Derived >

Derived ones_like ( const Derived & other )

static

Unit tensor like another, i.e. same batch and base shapes, same tensor options, etc.

operator-()

template<class Derived >

Derived operator-

(

)

const

Negation.

to()

template<class Derived >

Derived to

(

const torch::TensorOptions &

options

)

const

Change tensor options.

zeros_like()

template<class Derived >

Derived zeros_like ( const Derived & other )

static

Zero tensor like another, i.e. same batch and base shapes, same tensor options, etc.