Struct DifferentiableTensor 

pub struct DifferentiableTensor { /* private fields */ }

A differentiable tensor that tracks gradients and compute graph information

Implementations

impl DifferentiableTensor

pub fn new(data: DenseTensor, requires_grad: bool) -> Self

Create a new differentiable tensor

pub fn with_graph( data: DenseTensor, requires_grad: bool, graph: &mut ComputeGraph, ) -> Self

Create a differentiable tensor with compute graph tracking

pub fn data(&self) -> &DenseTensor

Get the underlying data

pub fn data_mut(&mut self) -> &mut DenseTensor

Get mutable reference to data

pub fn grad(&self) -> Option<&DenseTensor>

Get the gradient (if computed)

pub fn grad_mut(&mut self) -> Option<&mut DenseTensor>

Get mutable reference to gradient

pub fn set_grad(&mut self, grad: DenseTensor)

Set the gradient

pub fn zero_grad(&mut self)

Clear the gradient

pub fn requires_grad(&self) -> bool

Check if this tensor requires gradient

pub fn tensor_id(&self) -> TensorId

Get the tensor ID

pub fn op_id(&self) -> Option<OpId>

Get the operation ID that produced this tensor

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

Get shape of the tensor

pub fn matmul( &self, other: &DifferentiableTensor, graph: &mut ComputeGraph, ) -> DifferentiableTensor

Matrix multiplication

pub fn add( &self, other: &DifferentiableTensor, graph: &mut ComputeGraph, ) -> DifferentiableTensor

Element-wise addition

pub fn sub( &self, other: &DifferentiableTensor, graph: &mut ComputeGraph, ) -> DifferentiableTensor

Element-wise subtraction

pub fn mul( &self, other: &DifferentiableTensor, graph: &mut ComputeGraph, ) -> DifferentiableTensor

Element-wise multiplication

pub fn relu(&self, graph: &mut ComputeGraph) -> DifferentiableTensor

ReLU activation

pub fn gelu(&self, graph: &mut ComputeGraph) -> DifferentiableTensor

GELU activation

pub fn softmax( &self, dim: isize, graph: &mut ComputeGraph, ) -> DifferentiableTensor

Softmax activation

pub fn transpose(&self, graph: &mut ComputeGraph) -> DifferentiableTensor

Transpose

pub fn detach(&self) -> DifferentiableTensor

Detach from compute graph (for inference)

pub fn backward(&mut self, graph: &mut ComputeGraph)

Perform backward pass

Trait Implementations

impl Clone for DifferentiableTensor

fn clone(&self) -> DifferentiableTensor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DifferentiableTensor

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

Formats the value using the given formatter.

impl From<DenseTensor> for DifferentiableTensor

Convert from DenseTensor

fn from(tensor: DenseTensor) -> Self

Converts to this type from the input type.