Struct OpenCL

pub struct OpenCL(/* private fields */);

OpenCL backend

Implementations

impl OpenCL

pub fn tensor<'a>(&'a self, data: impl IntoTensor<&'a Self>) -> Tensor<&'a Self>

Create new tensor

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

Create new tensor using values from standard normal distribution

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

Create new tensor using values from uniform distribution

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

Create new tensor by repeating single value

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

Create new tensor by repeating zeroes

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

Create new tensor by repeating ones

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

Create eye tensor

pub fn load( &self, path: impl AsRef<Path>, ) -> Result<Vec<Tensor<&OpenCL>>, ZyxError>

Load tensors from disk.

pub 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

Trait Implementations

impl Backend for &OpenCL

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, x: 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

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