TensorLinalgBackend

tenferro_linalg::backend

Trait TensorLinalgBackend 

Source 
pub trait TensorLinalgBackend<T>
where
    T: KernelLinalgScalar,
{
    type Context;


Show 15 methods    // Required methods
    fn has_linalg_support(op: LinalgCapabilityOp) -> bool;
    fn solve_ex(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
        b: &Tensor<T>,
    ) -> Result<SolveTensorExResult<T>, Error>;
    fn solve(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
        b: &Tensor<T>,
    ) -> Result<Tensor<T>, Error>;
    fn lu_solve(
        ctx: &mut Self::Context,
        factors: &Tensor<T>,
        pivots: &Tensor<i32>,
        b: &Tensor<T>,
    ) -> Result<Tensor<T>, Error>;
    fn solve_triangular(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
        b: &Tensor<T>,
        upper: bool,
    ) -> Result<Tensor<T>, Error>;
    fn qr(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<QrTensorResult<T>, Error>;
    fn thin_svd(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<SvdTensorResult<T>, Error>;
    fn svdvals(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<Tensor<<T as LinalgScalar>::Real>, Error>;
    fn lu_factor_ex(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<LuTensorExResult<T>, Error>;
    fn lu_factor(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<LuTensorResult<T>, Error>;
    fn lu_factor_no_pivot(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<LuTensorResult<T>, Error>;
    fn cholesky_ex(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<CholeskyTensorExResult<T>, Error>;
    fn cholesky(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<Tensor<T>, Error>;
    fn eigen_sym(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<EigenTensorResult<T>, Error>;
    fn eig(
        ctx: &mut Self::Context,
        a: &Tensor<T>,
    ) -> Result<EigTensorResult<T>, Error>;

}

Required Associated Types§

Source

type Context

Required Methods§

Source

fn has_linalg_support(op: LinalgCapabilityOp) -> bool

Source

fn solve_ex( ctx: &mut Self::Context, a: &Tensor<T>, b: &Tensor<T>, ) -> Result<SolveTensorExResult<T>, Error>

Solve a square linear system while returning per-batch numerical status.

§Examples
ⓘ
use tenferro_linalg_prims::TensorLinalgPrims;

fn accepts_backend<B: TensorLinalgPrims<f64>>() {}
let _ = accepts_backend::<todo!()>;
Source

fn solve( ctx: &mut Self::Context, a: &Tensor<T>, b: &Tensor<T>, ) -> Result<Tensor<T>, Error>

Source

fn lu_solve( ctx: &mut Self::Context, factors: &Tensor<T>, pivots: &Tensor<i32>, b: &Tensor<T>, ) -> Result<Tensor<T>, Error>

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fn solve_triangular( ctx: &mut Self::Context, a: &Tensor<T>, b: &Tensor<T>, upper: bool, ) -> Result<Tensor<T>, Error>

Source

fn qr( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<QrTensorResult<T>, Error>

Source

fn thin_svd( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<SvdTensorResult<T>, Error>

Source

fn svdvals( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<Tensor<<T as LinalgScalar>::Real>, Error>

Source

fn lu_factor_ex( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<LuTensorExResult<T>, Error>

Compute an LU factorization while returning per-batch numerical status.

§Examples
ⓘ
use tenferro_linalg_prims::TensorLinalgPrims;

fn accepts_backend<B: TensorLinalgPrims<f64>>() {}
let _ = accepts_backend::<todo!()>;
Source

fn lu_factor( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<LuTensorResult<T>, Error>

Source

fn lu_factor_no_pivot( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<LuTensorResult<T>, Error>

Compute an LU factorization without pivoting.

Backends may support this only on a subset of devices or dtypes.

§Examples
ⓘ
use tenferro_linalg_prims::TensorLinalgPrims;

fn accepts_backend<B: TensorLinalgPrims<f64>>() {}
let _ = accepts_backend::<todo!()>;
Source

fn cholesky_ex( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<CholeskyTensorExResult<T>, Error>

Compute a Cholesky factorization while returning per-batch numerical status.

§Examples
ⓘ
use tenferro_linalg_prims::TensorLinalgPrims;

fn accepts_backend<B: TensorLinalgPrims<f64>>() {}
let _ = accepts_backend::<todo!()>;
Source

fn cholesky(ctx: &mut Self::Context, a: &Tensor<T>) -> Result<Tensor<T>, Error>

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fn eigen_sym( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<EigenTensorResult<T>, Error>

Source

fn eig( ctx: &mut Self::Context, a: &Tensor<T>, ) -> Result<EigTensorResult<T>, Error>

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§