EinopsBackend: Base Backend Class for Einops Tensor Operations
In einops: Flexible, Powerful, and Readable Tensor Operations
EinopsBackend R Documentation
Base Backend Class for Einops Tensor Operations
Description
Abstract base class that defines the interface for tensor operations
across different frameworks. All backend implementations must inherit
from this class and implement the required methods.
Methods
Public methods
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Method new()
Initialize the backend and check for required packages.
It is assumed that the constructor will fully load and
setup all dependencies and error otherwise.
Usage
EinopsBackend$new()
Returns
A new EinopsBackend instance.
Method print()
The print method for EinopsBackend instances
Usage
EinopsBackend$print(...)
Arguments
...arguments passed to pprint()
Returns
This object, invisibly
Method repr()
Get a string representation of this backend.
Usage
EinopsBackend$repr()
Returns
A character string describing the backend.
Method tensor_type()
Get the type of tensor this backend supports.
This method should be overridden in subclasses to return the specific
tensor type (e.g., "torch_tensor", "array").
Usage
EinopsBackend$tensor_type()
Returns
A string representing the tensor type.
Method preprocess()
Do any relevant preprocessing of a tensor before any operations are
done on it. This should always be called before running any backend
operations on a tensor
Usage
EinopsBackend$preprocess(x)
Arguments
xThe input raw tensor-like object
Returns
A preprocessed version of the input, may or may not have changed
classes
Method create_tensor()
Create a tensor of the specified type with given values and dimensions.
Usage
EinopsBackend$create_tensor(values, dims, ...)
Arguments
valuesA vector of values to initialize the tensor.
dimsA numeric vector specifying the dimensions of the tensor.
...Additional arguments for specific backend implementations.
Returns
A tensor of the specified type.
Method as_array()
Convert a tensor to a standard base::array()
Usage
EinopsBackend$as_array(x)
Arguments
xThe input tensor/array.
Returns
A standard array representation of the tensor.
Method flatten()
Return a flattened version of the tensor. Note that the
order of calling as_array and flatten does matter because
different frameworks may store data differently.
Usage
EinopsBackend$flatten(x)
Arguments
xThe input tensor/array
Returns
A 1 dimensional tensor
Method arange()
Usage
EinopsBackend$arange(start, stop)
Arguments
startinteger, inclusive
stopinteger, inclusive
Returns
a sequence from start to stop
Method shape()
Get the shape of a tensor.
Shape should return a tuple with integers or "shape symbols"
(which will evaluate to actual size).
Usage
EinopsBackend$shape(x)
Arguments
xThe input tensor/array.
Returns
A numeric vector representing the tensor shape.
Method reshape()
Reshape a tensor to the specified dimensions.
Usage
EinopsBackend$reshape(x, shape)
Arguments
xThe input tensor/array.
shapeA numeric vector specifying the new shape.
Returns
The reshaped tensor/array.
Method transpose()
Transpose a tensor along the specified axes.
Usage
EinopsBackend$transpose(x, axes)
Arguments
xThe input tensor/array.
axesA numeric vector specifying the new axis order.
Returns
The transposed tensor/array.
Method reduce()
Reduce a tensor along specified axes using the given operation.
Usage
EinopsBackend$reduce(x, operation, axes)
Arguments
xThe input tensor/array.
operationA character string specifying the reduction operation
(e.g., "sum", "mean", "max", "min", "prod", "all", "any"), OR
a two argument function, with the first argument being the tensor
to modify, and the second argument being an integer list of axes
to perform the reduction over.
axesA numeric vector specifying which axes to reduce over.
Returns
The reduced tensor/array.
Method stack_on_zeroth_dimension()
Stack multiple tensors along a new zeroth dimension.
Usage
EinopsBackend$stack_on_zeroth_dimension(tensors)
Arguments
tensorsA list of tensors/arrays to stack.
Returns
A tensor/array with the input tensors stacked along dimension 1.
Method add_axis()
Add a new axis to a tensor at the specified position.
Usage
EinopsBackend$add_axis(x, new_position)
Arguments
xThe input tensor/array.
new_positionThe position (1-based) where to insert the new axis.
Returns
The tensor/array with a new axis added.
Method add_axes()
Add multiple axes to a tensor and tile along specified axes.
This function adds new axes to the input tensor at the specified
positions and tiles the tensor along those axes according to the
provided lengths.
Usage
EinopsBackend$add_axes(x, n_axes, pos2len)
Arguments
xThe input tensor/array.
n_axesThe total number of axes after addition.
pos2lenint->int r2r::hashmap() mapping positions to lengths.
Returns
The tensor/array with new axes added and tiled as specified.
Method tile()
Tile (repeat) a tensor along each axis according to the repeat counts.
The repeats vector should have the same length as the tensor's shape.
Usage
EinopsBackend$tile(x, repeats)
Arguments
xThe input tensor/array.
repeatsA numeric vector specifying how many times to repeat
along each axis. Must have same length as x.shape.
Returns
The tiled tensor/array.
Method concat()
Concatenate tensors along the specified axis.
Assumes identical devices, dtypes and shapes except for the selected axis.
Usage
EinopsBackend$concat(tensors, axis)
Arguments
tensorsA list of tensors/arrays to concatenate.
axisThe axis along which to concatenate (1-based).
Returns
The concatenated tensor/array.
Method is_float_type()
Check if the tensor has a floating point data type.
Usage
EinopsBackend$is_float_type(x)
Arguments
xThe input tensor/array.
Returns
A logical value indicating if the tensor is of float type.
Method layers()
Get neural network layers specific to this backend.
Usage
EinopsBackend$layers()
Returns
Backend-specific layer implementations.
Method einsum()
Perform Einstein summation on tensors.
Usage
EinopsBackend$einsum(pattern, ...)
Arguments
patternA character string specifying the einsum pattern.
...Additional tensors to operate on.
Returns
The result of the einsum operation.
einops documentation built on Sept. 9, 2025, 5:29 p.m.
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| EinopsBackend | R Documentation |
Base Backend Class for Einops Tensor Operations
Description
Abstract base class that defines the interface for tensor operations across different frameworks. All backend implementations must inherit from this class and implement the required methods.
Methods
Public methods
Method new()
Initialize the backend and check for required packages. It is assumed that the constructor will fully load and setup all dependencies and error otherwise.
Usage
EinopsBackend$new()
Returns
A new EinopsBackend instance.
Method print()
The print method for EinopsBackend instances
Usage
EinopsBackend$print(...)
Arguments
...arguments passed to
pprint()
Returns
This object, invisibly
Method repr()
Get a string representation of this backend.
Usage
EinopsBackend$repr()
Returns
A character string describing the backend.
Method tensor_type()
Get the type of tensor this backend supports. This method should be overridden in subclasses to return the specific tensor type (e.g., "torch_tensor", "array").
Usage
EinopsBackend$tensor_type()
Returns
A string representing the tensor type.
Method preprocess()
Do any relevant preprocessing of a tensor before any operations are done on it. This should always be called before running any backend operations on a tensor
Usage
EinopsBackend$preprocess(x)
Arguments
xThe input raw tensor-like object
Returns
A preprocessed version of the input, may or may not have changed classes
Method create_tensor()
Create a tensor of the specified type with given values and dimensions.
Usage
EinopsBackend$create_tensor(values, dims, ...)
Arguments
valuesA vector of values to initialize the tensor.
dimsA numeric vector specifying the dimensions of the tensor.
...Additional arguments for specific backend implementations.
Returns
A tensor of the specified type.
Method as_array()
Convert a tensor to a standard base::array()
Usage
EinopsBackend$as_array(x)
Arguments
xThe input tensor/array.
Returns
A standard array representation of the tensor.
Method flatten()
Return a flattened version of the tensor. Note that the order of calling as_array and flatten does matter because different frameworks may store data differently.
Usage
EinopsBackend$flatten(x)
Arguments
xThe input tensor/array
Returns
A 1 dimensional tensor
Method arange()
Usage
EinopsBackend$arange(start, stop)
Arguments
startinteger, inclusive
stopinteger, inclusive
Returns
a sequence from start to stop
Method shape()
Get the shape of a tensor. Shape should return a tuple with integers or "shape symbols" (which will evaluate to actual size).
Usage
EinopsBackend$shape(x)
Arguments
xThe input tensor/array.
Returns
A numeric vector representing the tensor shape.
Method reshape()
Reshape a tensor to the specified dimensions.
Usage
EinopsBackend$reshape(x, shape)
Arguments
xThe input tensor/array.
shapeA numeric vector specifying the new shape.
Returns
The reshaped tensor/array.
Method transpose()
Transpose a tensor along the specified axes.
Usage
EinopsBackend$transpose(x, axes)
Arguments
xThe input tensor/array.
axesA numeric vector specifying the new axis order.
Returns
The transposed tensor/array.
Method reduce()
Reduce a tensor along specified axes using the given operation.
Usage
EinopsBackend$reduce(x, operation, axes)
Arguments
xThe input tensor/array.
operationA character string specifying the reduction operation (e.g., "sum", "mean", "max", "min", "prod", "all", "any"), OR a two argument function, with the first argument being the tensor to modify, and the second argument being an integer list of axes to perform the reduction over.
axesA numeric vector specifying which axes to reduce over.
Returns
The reduced tensor/array.
Method stack_on_zeroth_dimension()
Stack multiple tensors along a new zeroth dimension.
Usage
EinopsBackend$stack_on_zeroth_dimension(tensors)
Arguments
tensorsA list of tensors/arrays to stack.
Returns
A tensor/array with the input tensors stacked along dimension 1.
Method add_axis()
Add a new axis to a tensor at the specified position.
Usage
EinopsBackend$add_axis(x, new_position)
Arguments
xThe input tensor/array.
new_positionThe position (1-based) where to insert the new axis.
Returns
The tensor/array with a new axis added.
Method add_axes()
Add multiple axes to a tensor and tile along specified axes.
This function adds new axes to the input tensor at the specified positions and tiles the tensor along those axes according to the provided lengths.
Usage
EinopsBackend$add_axes(x, n_axes, pos2len)
Arguments
xThe input tensor/array.
n_axesThe total number of axes after addition.
pos2lenint->int
r2r::hashmap()mapping positions to lengths.
Returns
The tensor/array with new axes added and tiled as specified.
Method tile()
Tile (repeat) a tensor along each axis according to the repeat counts. The repeats vector should have the same length as the tensor's shape.
Usage
EinopsBackend$tile(x, repeats)
Arguments
xThe input tensor/array.
repeatsA numeric vector specifying how many times to repeat along each axis. Must have same length as x.shape.
Returns
The tiled tensor/array.
Method concat()
Concatenate tensors along the specified axis. Assumes identical devices, dtypes and shapes except for the selected axis.
Usage
EinopsBackend$concat(tensors, axis)
Arguments
tensorsA list of tensors/arrays to concatenate.
axisThe axis along which to concatenate (1-based).
Returns
The concatenated tensor/array.
Method is_float_type()
Check if the tensor has a floating point data type.
Usage
EinopsBackend$is_float_type(x)
Arguments
xThe input tensor/array.
Returns
A logical value indicating if the tensor is of float type.
Method layers()
Get neural network layers specific to this backend.
Usage
EinopsBackend$layers()
Returns
Backend-specific layer implementations.
Method einsum()
Perform Einstein summation on tensors.
Usage
EinopsBackend$einsum(pattern, ...)
Arguments
patternA character string specifying the einsum pattern.
...Additional tensors to operate on.
Returns
The result of the einsum operation.
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