Struct Tensor 

pub struct Tensor<T>
where
    T: Scalar,
{ /* private fields */ }

An N-dimensional array of numeric values.

Tensors are the core data structure for all computations in Axonml. They support arbitrary dimensions, automatic broadcasting, and efficient memory sharing between views.

Implementations

impl<T> Tensor<T>

where T: Scalar,

pub fn from_storage( storage: Storage<T>, shape: &[usize], ) -> Result<Tensor<T>, Error>

Creates a new tensor from storage with the given shape.

Arguments

• storage - The underlying data storage
• shape - Shape of the tensor

Returns

New tensor, or error if shape doesn’t match storage size.

pub fn from_vec(data: Vec<T>, shape: &[usize]) -> Result<Tensor<T>, Error>

Creates a new tensor from a vector with the given shape.

Arguments

• data - Vector of data
• shape - Shape of the tensor

Returns

New tensor, or error if shape doesn’t match data length.

pub fn from_slice(data: &[T], shape: &[usize]) -> Result<Tensor<T>, Error>

Creates a new tensor from a slice with the given shape.

Arguments

• data - Slice of data to copy
• shape - Shape of the tensor

Returns

New tensor, or error if shape doesn’t match data length.

pub fn scalar(value: T) -> Tensor<T>

Creates a scalar tensor (0-dimensional).

Arguments

• value - The scalar value

Returns

New 0-dimensional tensor.

pub fn zeros(shape: &[usize]) -> Tensor<T>

Creates a tensor filled with zeros.

pub fn ones(shape: &[usize]) -> Tensor<T>

where T: Numeric,

Creates a tensor filled with ones.

pub fn full(shape: &[usize], value: T) -> Tensor<T>

Creates a tensor filled with a constant value.

pub fn randn(shape: &[usize]) -> Tensor<T>

where T: Float, StandardNormal: Distribution<T>,

Creates a tensor with random values from standard normal distribution.

pub fn rand(shape: &[usize]) -> Tensor<T>

where T: Float, Standard: Distribution<T>,

Creates a tensor with random values from uniform distribution [0, 1).

pub fn shape(&self) -> &[usize]

Returns the shape of the tensor.

pub fn strides(&self) -> &[isize]

Returns the strides of the tensor.

pub fn ndim(&self) -> usize

Returns the number of dimensions.

pub fn numel(&self) -> usize

Returns the total number of elements.

pub fn is_empty(&self) -> bool

Returns true if the tensor is empty (has zero elements).

pub fn size(&self, dim: i64) -> Result<usize, Error>

Returns the size of a specific dimension.

Arguments

• dim - Dimension index (supports negative indexing)

pub fn device(&self) -> Device

Returns the device this tensor is on.

pub fn is_contiguous(&self) -> bool

Returns true if the tensor is contiguous in memory.

pub fn is_scalar(&self) -> bool

Returns true if this tensor is a scalar (0-dimensional).

pub fn get(&self, indices: &[usize]) -> Result<T, Error>

Returns the element at the given indices.

Arguments

• indices - Multi-dimensional indices

pub fn set(&self, indices: &[usize], value: T) -> Result<(), Error>

Sets the element at the given indices.

Arguments

• indices - Multi-dimensional indices
• value - Value to set

pub fn item(&self) -> Result<T, Error>

Returns the scalar value for a 0-dimensional tensor.

pub fn to_vec(&self) -> Vec<T>

Returns the data as a contiguous vector.

If the tensor is already contiguous, this returns a reference. Otherwise, it copies the data into a new contiguous vector.

pub fn reshape(&self, new_shape: &[isize]) -> Result<Tensor<T>, Error>

Returns a new tensor with the specified shape.

The total number of elements must remain the same. Supports -1 in one dimension to infer the size.

Arguments

• new_shape - Target shape

pub fn flatten(&self) -> Tensor<T>

Returns a new tensor with a flattened shape.

pub fn squeeze(&self, dim: Option<i64>) -> Result<Tensor<T>, Error>

Returns a new tensor with dimensions of size 1 removed.

Arguments

• dim - Optional specific dimension to squeeze

pub fn unsqueeze(&self, dim: i64) -> Result<Tensor<T>, Error>

Returns a new tensor with a dimension of size 1 inserted.

Arguments

• dim - Position to insert the new dimension

pub fn transpose(&self, dim0: i64, dim1: i64) -> Result<Tensor<T>, Error>

Transposes two dimensions.

Arguments

• dim0 - First dimension
• dim1 - Second dimension

pub fn t(&self) -> Result<Tensor<T>, Error>

Returns the transpose of a 2D tensor.

pub fn permute(&self, dims: &[usize]) -> Result<Tensor<T>, Error>

Returns a permuted tensor with dimensions reordered.

Arguments

• dims - New order of dimensions

pub fn contiguous(&self) -> Tensor<T>

Returns a contiguous copy of the tensor.

pub fn to_device(&self, device: Device) -> Result<Tensor<T>, Error>

Transfers the tensor to a different device.

Arguments

• device - Target device

pub fn cpu(&self) -> Result<Tensor<T>, Error>

Transfers to CPU.

pub fn clone_deep(&self) -> Tensor<T>

Creates a deep copy of this tensor with its own storage.

impl<T> Tensor<T>

where T: Numeric,

pub fn fill_(&self, value: T)

Fills the tensor with a value.

pub fn zero_(&self)

Fills the tensor with zeros.

pub fn sum(&self) -> Tensor<T>

Returns the sum of all elements.

pub fn prod(&self) -> Tensor<T>

Returns the product of all elements.

pub fn max(&self) -> Result<Tensor<T>, Error>

Returns the maximum element.

pub fn min(&self) -> Result<Tensor<T>, Error>

Returns the minimum element.

pub fn argmax(&self) -> Result<usize, Error>

Returns the index of the maximum element.

pub fn argmin(&self) -> Result<usize, Error>

Returns the index of the minimum element.

impl<T> Tensor<T>

where T: Float,

pub fn mean(&self) -> Result<Tensor<T>, Error>

Returns the mean of all elements.

pub fn relu(&self) -> Tensor<T>

Applies ReLU activation: max(0, x).

pub fn sigmoid(&self) -> Tensor<T>

Applies sigmoid activation: 1 / (1 + exp(-x)).

pub fn tanh(&self) -> Tensor<T>

Applies tanh activation.

pub fn exp(&self) -> Tensor<T>

Applies exponential function.

pub fn ln(&self) -> Tensor<T>

Applies natural logarithm.

pub fn sqrt(&self) -> Tensor<T>

Applies square root.

pub fn pow(&self, exp: T) -> Tensor<T>

Computes element-wise power.

pub fn gelu(&self) -> Tensor<T>

GELU activation function (Gaussian Error Linear Unit).

pub fn silu(&self) -> Tensor<T>

SiLU/Swish activation function.

pub fn softmax(&self, dim: i32) -> Tensor<T>

Softmax along specified dimension.

pub fn log_softmax(&self, dim: i32) -> Tensor<T>

Log softmax along specified dimension.

pub fn mean_dim(&self, dim: i32, keepdim: bool) -> Tensor<T>

Mean along a dimension.

pub fn var_dim(&self, dim: i32, keepdim: bool) -> Tensor<T>

Variance along a dimension.

pub fn broadcast_to(&self, shape: &[usize]) -> Tensor<T>

Broadcasts tensor to a new shape.

pub fn slice(&self, ranges: &[Range<usize>]) -> Tensor<T>

Slices the tensor using ranges for each dimension.

impl<T> Tensor<T>

where T: Numeric,

pub fn add(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Element-wise addition with broadcasting.

pub fn sub(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Element-wise subtraction with broadcasting.

pub fn mul(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Element-wise multiplication with broadcasting.

pub fn div(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Element-wise division with broadcasting.

pub fn add_scalar(&self, scalar: T) -> Tensor<T>

Scalar addition.

pub fn mul_scalar(&self, scalar: T) -> Tensor<T>

Scalar multiplication.

pub fn neg(&self) -> Tensor<T>

Element-wise negation.

pub fn matmul(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Matrix multiplication with batching support.

Supports:

• 2D @ 2D: [m, k] @ [k, n] -> [m, n]
• 3D @ 3D: [batch, m, k] @ [batch, k, n] -> [batch, m, n]
• 4D @ 4D: [b1, b2, m, k] @ [b1, b2, k, n] -> [b1, b2, m, n]

pub fn dot(&self, other: &Tensor<T>) -> Result<Tensor<T>, Error>

Dot product for 1D tensors.

impl<T> Tensor<T>

where T: Scalar,

pub fn slice_dim0(&self, start: usize, end: usize) -> Result<Tensor<T>, Error>

Returns a slice of the tensor along the first dimension.

Arguments

• start - Start index (inclusive)
• end - End index (exclusive)

pub fn select(&self, dim: usize, index: usize) -> Result<Tensor<T>, Error>

Returns a view selecting a single index along a dimension.

This reduces the dimensionality by 1.

Arguments

• dim - Dimension to select from
• index - Index to select

pub fn narrow( &self, dim: usize, start: usize, length: usize, ) -> Result<Tensor<T>, Error>

Returns a narrow view along a dimension.

Arguments

• dim - Dimension to narrow
• start - Start index
• length - Length of the narrow view

pub fn chunk(&self, chunks: usize, dim: usize) -> Result<Vec<Tensor<T>>, Error>

Splits the tensor into chunks along a dimension.

Arguments

• chunks - Number of chunks
• dim - Dimension to split along

pub fn split( &self, sizes: &[usize], dim: usize, ) -> Result<Vec<Tensor<T>>, Error>

Splits the tensor into parts of specified sizes along a dimension.

Arguments

• sizes - Size of each part
• dim - Dimension to split along

impl<T> Tensor<T>

where T: Numeric,

pub fn gather( &self, dim: usize, indices: &Tensor<i64>, ) -> Result<Tensor<T>, Error>

Gathers values along a dimension according to indices.

Arguments

• dim - Dimension to gather along
• indices - Indices tensor

pub fn masked_select(&self, mask: &[bool]) -> Result<Tensor<T>, Error>

Returns elements selected by a boolean mask.

Arguments

• mask - Boolean mask tensor

pub fn masked_fill_(&self, mask: &[bool], value: T) -> Result<(), Error>

Sets elements according to a boolean mask.

Arguments

• mask - Boolean mask tensor
• value - Value to set where mask is true

Trait Implementations

impl<T> Add<T> for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the + operator.

fn add(self, scalar: T) -> <&Tensor<T> as Add<T>>::Output

Performs the + operation.

impl<T> Add for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the + operator.

fn add(self, other: &Tensor<T>) -> <&Tensor<T> as Add>::Output

Performs the + operation.

impl<T> Clone for Tensor<T>

where T: Clone + Scalar,

fn clone(&self) -> Tensor<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Collate<Tensor<f32>> for DefaultCollate

type Output = Tensor<f32>

The output batch type.

fn collate(&self, batch: Vec<Tensor<f32>>) -> Self::Output

Collates a vector of samples into a batch.

impl Collate<Tensor<f32>> for StackCollate

type Output = Tensor<f32>

The output batch type.

fn collate(&self, batch: Vec<Tensor<f32>>) -> Self::Output

Collates a vector of samples into a batch.

impl<T> Debug for Tensor<T>

where T: Scalar + Display,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<T> Display for Tensor<T>

where T: Scalar + Display,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<T> Div for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the / operator.

fn div(self, other: &Tensor<T>) -> <&Tensor<T> as Div>::Output

Performs the / operation.

impl<T> Mul<T> for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the * operator.

fn mul(self, scalar: T) -> <&Tensor<T> as Mul<T>>::Output

Performs the * operation.

impl<T> Mul for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the * operator.

fn mul(self, other: &Tensor<T>) -> <&Tensor<T> as Mul>::Output

Performs the * operation.

impl<T> Neg for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the - operator.

fn neg(self) -> <&Tensor<T> as Neg>::Output

Performs the unary - operation.

impl<T> Sub for &Tensor<T>

where T: Numeric,

type Output = Tensor<T>

The resulting type after applying the - operator.

fn sub(self, other: &Tensor<T>) -> <&Tensor<T> as Sub>::Output

Performs the - operation.