Enum AttentionNormalizationOp 

#[repr(i32)]
pub enum AttentionNormalizationOp {
    kNONE = 0,
    kSOFTMAX = 1,
}

AttentionNormalizationOp

Enumerates the operations that may be performed by the normalization in the attention subgraph.

Variants

kNONE = 0

kSOFTMAX = 1

< Apply softmax normalization on the attention scores on the s_kv dimension.

Trait Implementations

impl Clone for AttentionNormalizationOp

fn clone(&self) -> AttentionNormalizationOp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl ExternType for AttentionNormalizationOp

type Id = (n, v, i, n, f, e, r, _1, (), A, t, t, e, n, t, i, o, n, N, o, r, m, a, l, i, z, a, t, i, o, n, O, p)

A type-level representation of the type’s C++ namespace and type name.

type Kind = Trivial

Either cxx::kind::Opaque or cxx::kind::Trivial.

impl From<AttentionNormalizationOp> for AttentionNormalizationOp

fn from(value: AttentionNormalizationOp) -> Self

Converts to this type from the input type.

impl Hash for AttentionNormalizationOp

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 Into<AttentionNormalizationOp> for AttentionNormalizationOp

fn into(self) -> AttentionNormalizationOp

Converts this type into the (usually inferred) input type.

impl PartialEq for AttentionNormalizationOp

fn eq(&self, other: &AttentionNormalizationOp) -> 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 AttentionNormalizationOp

impl SharedPtrTarget for AttentionNormalizationOp

impl StructuralPartialEq for AttentionNormalizationOp

impl UniquePtrTarget for AttentionNormalizationOp

impl VectorElement for AttentionNormalizationOp

impl WeakPtrTarget for AttentionNormalizationOp