Struct tract_core::ops::matmul::lir_unary::LirMatMulUnary

pub struct LirMatMulUnary {
    pub c_fact: TypedFact,
    pub c_m_axis: usize,
    pub c_n_axis: usize,
    pub micro_ops: ArrayD<(Arc<Tensor>, Vec<ProtoFusedSpec>)>,
    pub c_final_shape: ShapeFact,
    pub geometry: MatMulGeometry,
    pub mmm: Box<dyn MatMatMul>,
    pub reshape_post: Vec<AxisOp>,
}

Fields

c_fact: TypedFact

c_m_axis: usize

c_n_axis: usize

micro_ops: ArrayD<(Arc<Tensor>, Vec<ProtoFusedSpec>)>

c_final_shape: ShapeFact

geometry: MatMulGeometry

mmm: Box<dyn MatMatMul>

reshape_post: Vec<AxisOp>

Trait Implementations

impl Clone for LirMatMulUnary

fn clone(&self) -> LirMatMulUnary

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for LirMatMulUnary

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

Formats the value using the given formatter.

impl DynHash for LirMatMulUnary

fn dyn_hash(&self, hasher: &mut dyn Hasher)

impl EvalOp for LirMatMulUnary

fn is_stateless(&self) -> bool

fn state(
    &self,
    _session: &mut SessionState,
    _node_id: usize
) -> TractResult<Option<Box<dyn OpState>>>

fn eval(&self, inputs: TVec<Arc<Tensor>>) -> TractResult<TVec<Arc<Tensor>>>

impl Hash for LirMatMulUnary

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, 

Feeds a slice of this type into the given Hasher.

impl Op for LirMatMulUnary

fn name(&self) -> Cow<'_, str>

fn info(&self) -> TractResult<Vec<String>>

Short (one-line) strings giving hints on internal implementation or important configuration details to be displayed in dumps.

fn op_families(&self) -> &'static [&'static str]

Vector of short strings defining what families the op belongs too. tract-core defines “core”, “mir”, “lir”.

fn as_typed(&self) -> Option<&dyn TypedOp>

fn validation(&self) -> Validation

The kind of accuracy check that should be performed on operation when testing them.

fn same_as(&self, _other: &dyn Op) -> bool

Compare two ops.

impl TypedOp for LirMatMulUnary

fn output_facts(&self, _inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>>

Deduce output facts from input facts.

fn cost(&self, _inputs: &[&TypedFact]) -> TractResult<TVec<(Cost, TDim)>>

Computes a cost hint of the operation.

fn fuse(
    &self,
    model: &TypedModel,
    node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Fuse op after codegen to deal with local optimisations.

fn as_op(&self) -> &dyn Op

Reinterpret the TypedOp as an Op.

fn as_op_mut(&mut self) -> &mut dyn Op

Reinterpret the TypedOp as an Op, mutably.

fn invariants(
    &self,
    inputs: &[&TypedFact],
    outputs: &[&TypedFact]
) -> TractResult<Invariants>

fn declutter(
    &self,
    model: &TypedModel,
    node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Declutter the op to the tract_core operator set as much as possible.

fn suggested_axis_changes(&self) -> TractResult<TVec<(InOut, AxisOp)>>

fn change_axes(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    io: InOut,
    change: &AxisOp
) -> TractResult<Option<AxisChangeConsequence>>

fn slice_output(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    patch: &mut TypedModelPatch,
    output_slot: usize,
    axis: usize,
    start: usize,
    end: usize
) -> TractResult<Option<OutletId>>

fn quantize(
    &self,
    model: &TypedModel,
    node: &TypedNode,
    dt: DatumType,
    scale: f32,
    zero_point: i32
) -> TractResult<Option<Box<dyn TypedOp>>>

Transforms the op in an equivalent one, operating on dt (i8 or u8).

fn concretize_dims(
    &self,
    source: &TypedModel,
    node: &TypedNode,
    target: &mut TypedModel,
    mapping: &HashMap<OutletId, OutletId>,
    values: &SymbolValues
) -> TractResult<TVec<OutletId>>

Transform the op into by providing a value to one or more symbols.

fn codegen(
    &self,
    model: &TypedModel,
    node: &TypedNode
) -> TractResult<Option<TypedModelPatch>>

Translate the op into the most efficient form possible for execution.

fn nested_model_multipliers(
    &self,
    inputs: &[&TypedFact]
) -> Vec<(Cow<'_, str>, f64)>

Nested model multipliers, with label (for profiling).