Trait Backend 

pub trait Backend {
    // Required methods
    fn add(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>;
    fn sub(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>;
    fn mul(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>;
    fn relu(&self, input: &Tensor) -> Tensor;
    fn sigmoid(&self, input: &Tensor) -> Tensor;
    fn exp(&self, input: &Tensor) -> Tensor;
    fn tanh_act(&self, input: &Tensor) -> Tensor;
    fn softmax_last_dim(&self, input: &Tensor) -> Result<Tensor, KernelError>;
    fn log_softmax_last_dim(
        &self,
        input: &Tensor,
    ) -> Result<Tensor, KernelError>;
    fn logsumexp_last_dim(&self, input: &Tensor) -> Result<Tensor, KernelError>;
    fn layer_norm_last_dim(
        &self,
        input: &Tensor,
        params: LayerNormLastDimParams<'_>,
    ) -> Result<Tensor, KernelError>;
    fn max_pool2d_nhwc(
        &self,
        input: &Tensor,
        kernel_h: usize,
        kernel_w: usize,
        stride_h: usize,
        stride_w: usize,
    ) -> Result<Tensor, KernelError>;
    fn avg_pool2d_nhwc(
        &self,
        input: &Tensor,
        kernel_h: usize,
        kernel_w: usize,
        stride_h: usize,
        stride_w: usize,
    ) -> Result<Tensor, KernelError>;
    fn conv2d_nhwc(
        &self,
        input: &Tensor,
        kernel: &Tensor,
        bias: Option<&Tensor>,
        stride_h: usize,
        stride_w: usize,
    ) -> Result<Tensor, KernelError>;
    fn depthwise_conv2d_nhwc(
        &self,
        input: &Tensor,
        kernel: &Tensor,
        bias: Option<&Tensor>,
        stride_h: usize,
        stride_w: usize,
    ) -> Result<Tensor, KernelError>;
    fn separable_conv2d_nhwc(
        &self,
        input: &Tensor,
        params: SeparableConv2dParams<'_>,
        stride_h: usize,
        stride_w: usize,
    ) -> Result<Tensor, KernelError>;
    fn batch_norm2d_nhwc(
        &self,
        input: &Tensor,
        params: BatchNorm2dParams<'_>,
    ) -> Result<Tensor, KernelError>;
    fn group_norm_nhwc(
        &self,
        input: &Tensor,
        params: GroupNormNhwcParams<'_>,
    ) -> Result<Tensor, KernelError>;
    fn rms_norm_last_dim(
        &self,
        input: &Tensor,
        params: RmsNormLastDimParams<'_>,
    ) -> Result<Tensor, KernelError>;
    fn matmul_2d(
        &self,
        lhs: &Tensor,
        rhs: &Tensor,
    ) -> Result<Tensor, KernelError>;

    // Provided methods
    fn neg(&self, input: &Tensor) -> Tensor { ... }
    fn div(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError> { ... }
    fn sqrt(&self, input: &Tensor) -> Tensor { ... }
    fn transpose_2d(&self, input: &Tensor) -> Result<Tensor, KernelError> { ... }
    fn sum_all(&self, input: &Tensor) -> Tensor { ... }
    fn mul_scalar(&self, input: &Tensor, scalar: f32) -> Tensor { ... }
    fn reciprocal(&self, input: &Tensor) -> Tensor { ... }
}

Runtime backend contract for core deterministic kernels.

Required Methods

fn add(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>

fn sub(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>

fn mul(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>

fn relu(&self, input: &Tensor) -> Tensor

fn sigmoid(&self, input: &Tensor) -> Tensor

fn exp(&self, input: &Tensor) -> Tensor

fn tanh_act(&self, input: &Tensor) -> Tensor

fn softmax_last_dim(&self, input: &Tensor) -> Result<Tensor, KernelError>

fn log_softmax_last_dim(&self, input: &Tensor) -> Result<Tensor, KernelError>

fn logsumexp_last_dim(&self, input: &Tensor) -> Result<Tensor, KernelError>

fn layer_norm_last_dim( &self, input: &Tensor, params: LayerNormLastDimParams<'_>, ) -> Result<Tensor, KernelError>

fn max_pool2d_nhwc( &self, input: &Tensor, kernel_h: usize, kernel_w: usize, stride_h: usize, stride_w: usize, ) -> Result<Tensor, KernelError>

fn avg_pool2d_nhwc( &self, input: &Tensor, kernel_h: usize, kernel_w: usize, stride_h: usize, stride_w: usize, ) -> Result<Tensor, KernelError>

fn conv2d_nhwc( &self, input: &Tensor, kernel: &Tensor, bias: Option<&Tensor>, stride_h: usize, stride_w: usize, ) -> Result<Tensor, KernelError>

fn depthwise_conv2d_nhwc( &self, input: &Tensor, kernel: &Tensor, bias: Option<&Tensor>, stride_h: usize, stride_w: usize, ) -> Result<Tensor, KernelError>

fn separable_conv2d_nhwc( &self, input: &Tensor, params: SeparableConv2dParams<'_>, stride_h: usize, stride_w: usize, ) -> Result<Tensor, KernelError>

fn batch_norm2d_nhwc( &self, input: &Tensor, params: BatchNorm2dParams<'_>, ) -> Result<Tensor, KernelError>

fn group_norm_nhwc( &self, input: &Tensor, params: GroupNormNhwcParams<'_>, ) -> Result<Tensor, KernelError>

fn rms_norm_last_dim( &self, input: &Tensor, params: RmsNormLastDimParams<'_>, ) -> Result<Tensor, KernelError>

fn matmul_2d(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>

Provided Methods

fn neg(&self, input: &Tensor) -> Tensor

Element-wise negation.

fn div(&self, lhs: &Tensor, rhs: &Tensor) -> Result<Tensor, KernelError>

Element-wise division with broadcast.

fn sqrt(&self, input: &Tensor) -> Tensor

Element-wise square root.

fn transpose_2d(&self, input: &Tensor) -> Result<Tensor, KernelError>

Transpose a 2-D matrix.

fn sum_all(&self, input: &Tensor) -> Tensor

Scalar sum of all elements (returns a scalar tensor).

fn mul_scalar(&self, input: &Tensor, scalar: f32) -> Tensor

Multiply every element by a scalar.

fn reciprocal(&self, input: &Tensor) -> Tensor

Element-wise reciprocal (1/x).

Implementors

impl Backend for CpuBackend

impl Backend for ThreadedCpuBackend