Trait Backend

pub trait Backend: Copy {
    // Required methods
    fn plot_graph<'a, B: Backend + 'a>(
        self,
        tensors: impl IntoIterator<Item = &'a Tensor<B>>,
    ) -> String;
    fn randn(
        self,
        shape: impl Into<Shape>,
        dtype: DType,
    ) -> Result<Tensor<Self>, ZyxError>;
    fn uniform(
        self,
        shape: impl Into<Shape>,
        range: Range<impl Scalar>,
    ) -> Result<Tensor<Self>, ZyxError>;
    fn shape(self, x: Id) -> Shape;
    fn dtype(self, x: Id) -> DType;
    fn backward(
        self,
        x: Id,
        sources: &BTreeSet<Id>,
    ) -> Result<BTreeMap<Id, Id>, ZyxError>;
    fn load<T: Scalar>(self, id: Id) -> Result<Vec<T>, ZyxError>;
    fn store<T: Scalar, IT>(self, iter: IT) -> Result<Id, ZyxError>
       where IT: IntoIterator<Item = T>,
             IT::IntoIter: ExactSizeIterator;
    fn push(self, node: Node) -> Result<Id, ZyxError>;
    fn release(self, x: Id) -> Result<(), ZyxError>;
    fn retain(self, x: Id);

    // Provided methods
    fn tensor(
        self,
        data: impl IntoTensor<Self>,
    ) -> Result<Tensor<Self>, ZyxError> { ... }
    fn full(
        self,
        shape: impl Into<Shape>,
        value: impl Scalar,
    ) -> Result<Tensor<Self>, ZyxError> { ... }
    fn zeros(
        self,
        shape: impl Into<Shape>,
        dtype: DType,
    ) -> Result<Tensor<Self>, ZyxError> { ... }
    fn ones(
        self,
        shape: impl Into<Shape>,
        dtype: DType,
    ) -> Result<Tensor<Self>, ZyxError> { ... }
    fn eye(self, n: usize, dtype: DType) -> Result<Tensor<Self>, ZyxError> { ... }
}

Backend for tensors. Tensor requires that all backends implement this trait and only this trait.

Required Methods

fn plot_graph<'a, B: Backend + 'a>( self, tensors: impl IntoIterator<Item = &'a Tensor<B>>, ) -> String

Create graph of operations between tensors in dot format for visualization

fn randn( self, shape: impl Into<Shape>, dtype: DType, ) -> Result<Tensor<Self>, ZyxError>

Create new tensor using values from standard normal distribution

fn uniform( self, shape: impl Into<Shape>, range: Range<impl Scalar>, ) -> Result<Tensor<Self>, ZyxError>

Create new tensor using values from uniform distribution

fn shape(self, x: Id) -> Shape

Get shape if tensor x

fn dtype(self, x: Id) -> DType

Get dtype of tensor x

fn backward( self, x: Id, sources: &BTreeSet<Id>, ) -> Result<BTreeMap<Id, Id>, ZyxError>

Calculate derivatives of x w.r.t. sources. Returns map source id -> gradient id

fn load<T: Scalar>(self, id: Id) -> Result<Vec<T>, ZyxError>

Returns iterator over data stored in backend

fn store<T: Scalar, IT>(self, iter: IT) -> Result<Id, ZyxError>

where IT: IntoIterator<Item = T>, IT::IntoIter: ExactSizeIterator,

Store iterator into backend as tensor

fn push(self, node: Node) -> Result<Id, ZyxError>

Create new tensor from given operation

fn release(self, x: Id) -> Result<(), ZyxError>

Decrease reference count of tensor

fn retain(self, x: Id)

Increase reference count of tensor

Provided Methods

fn tensor(self, data: impl IntoTensor<Self>) -> Result<Tensor<Self>, ZyxError>

Create new tensor

fn full( self, shape: impl Into<Shape>, value: impl Scalar, ) -> Result<Tensor<Self>, ZyxError>

Create new tensor by repeating single value

fn zeros( self, shape: impl Into<Shape>, dtype: DType, ) -> Result<Tensor<Self>, ZyxError>

Create new tensor by repeating zeroes

fn ones( self, shape: impl Into<Shape>, dtype: DType, ) -> Result<Tensor<Self>, ZyxError>

Create new tensor by repeating ones

fn eye(self, n: usize, dtype: DType) -> Result<Tensor<Self>, ZyxError>

Create eye tensor

Dyn Compatibility

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors