Enum NumPyOp 

pub enum NumPyOp {
    Array,
    Add,
    Subtract,
    Multiply,
    Divide,
    Dot,
    Sum,
    Mean,
    Max,
    Min,
    Reshape,
    Transpose,
}

NumPy operation types

Variants

Array

Array creation: np.array, np.zeros, np.ones

Add

Element-wise addition: np.add, a + b

Subtract

Element-wise subtraction: np.subtract, a - b

Multiply

Element-wise multiplication: np.multiply, a * b

Divide

Element-wise division: np.divide, a / b

Dot

Dot product / matrix multiply: np.dot, np.matmul, a @ b

Sum

Sum reduction: np.sum

Mean

Mean reduction: np.mean

Max

Max reduction: np.max

Min

Min reduction: np.min

Reshape

Reshape: np.reshape

Transpose

Transpose: np.transpose, a.T

Implementations

impl NumPyOp

pub fn complexity(&self) -> OpComplexity

Get the operation complexity for MoE routing

Trait Implementations

impl Clone for NumPyOp

fn clone(&self) -> NumPyOp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for NumPyOp

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

Formats the value using the given formatter.

impl Hash for NumPyOp

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for NumPyOp

fn eq(&self, other: &NumPyOp) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Eq for NumPyOp

impl StructuralPartialEq for NumPyOp