Struct Df64EinsumJvp 

pub struct Df64EinsumJvp { /* private fields */ }

The forward tangent of a two-input contraction.

The tangent of out = einsum(lhs, rhs) is einsum(lhs_dot, rhs) + einsum(lhs, rhs_dot), so the helper contracts each tangent with the other operand and adds the two results in the extended scalar. Its tangent availability is a payload field, because a linearization need not have a tangent for both operands, and the rule must not materialise a zero tangent for one that is absent.

Examples

use tenferro_ad::extension::ExtensionOp;
use tenferro_df64_proof::extension::Df64EinsumJvp;

let tangent = Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[true, false]).expect("a contraction");
assert_eq!(<Df64EinsumJvp as ExtensionOp>::input_count(&tangent), 3);
assert_eq!(<Df64EinsumJvp as ExtensionOp>::output_count(&tangent), 1);

Implementations

impl Df64EinsumJvp

pub fn of(inputs: &[&[u32]], out: &[u32], tangents: &[bool]) -> Result<Self>

Build the tangent for a pattern and one tangent availability.

Errors

Returns an error when the operand list is not a valid pattern (an operand carries no label, or an output label appears in no operand), when the tangent mask does not have one entry per operand, or when no operand carries a tangent, because then there is nothing to differentiate.

Examples

use tenferro_df64_proof::extension::Df64EinsumJvp;

assert!(Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[true, true]).is_ok());
assert!(Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[false, false]).is_err());

pub fn input_labels(&self) -> &[Vec<u32>]

The tangent’s pattern and the availability of each operand’s tangent.

Examples

use tenferro_df64_proof::extension::Df64EinsumJvp;

let tangent = Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[true, true]).expect("a tangent");
assert_eq!(tangent.tangents(), &[true, true]);

pub fn out_labels(&self) -> &[u32]

The output’s labels, in the output’s axis order.

Examples

use tenferro_df64_proof::extension::Df64EinsumJvp;

let tangent = Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[true, false])
    .expect("a tangent");
assert_eq!(tangent.out_labels(), &[0, 2]);

pub fn tangents(&self) -> &[bool]

Which operands carry a tangent, in operand order.

Examples

use tenferro_df64_proof::extension::Df64EinsumJvp;

let tangent = Df64EinsumJvp::of(&[&[0, 1], &[1, 2]], &[0, 2], &[true, false])
    .expect("a tangent");
assert_eq!(tangent.tangents(), &[true, false]);

Trait Implementations

impl Clone for Df64EinsumJvp

fn clone(&self) -> Df64EinsumJvp

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Df64EinsumJvp

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

Formats the value using the given formatter.

impl Eq for Df64EinsumJvp

impl ExtensionOp for Df64EinsumJvp

fn family_id(&self) -> &'static str

Stable, process-independent family identifier.

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

Hash the payload (everything except family_id).

fn payload_eq(&self, other: &dyn ExtensionOp) -> bool

Structural equality against another extension value.

fn clone_arc(&self) -> Arc<dyn ExtensionOp>

Deep-clone the payload behind an Arc.

fn as_any(&self) -> &dyn Any

Upcast this extension to &dyn Any for downcasting in payload_eq.

fn input_count(&self) -> usize

Number of primal inputs. MUST be constant for any given Arc<dyn ExtensionOp> value.

fn output_count(&self) -> usize

Number of outputs. MUST match the length of the vector returned by a successful Self::infer_output_meta call.

fn semantic_effects(&self) -> ExtensionEffectDeclaration<'_>

Declare observable semantic effects for this extension payload.

fn semantic_aliases(&self) -> ExtensionAliasDeclaration<'_>

Declare semantic output aliasing for this extension payload.

fn scalar_identity(&self) -> Option<&'static str>

Canonical identity of an externally defined scalar this operation carries.

fn infer_output_meta( &self, ctx: &mut ExtensionShapeContext<'_>, ) -> Result<Vec<(DType, Vec<SymDim>)>>

Infer output dtypes and shapes for each output slot.

fn lower_to_standard_ops( &self, _builder: &mut GraphBuilder<StdTensorOp>, _inputs: &[ValueRef<StdTensorOp>], _input_dtypes: &[DType], _input_shapes: &[&[SymDim]], ) -> Result<ExtensionStandardLowering, ExtensionLoweringError>

Try to expand this extension into standard tensor graph operations.

fn prune_outputs(&self, _live_outputs: &[bool]) -> Option<Arc<dyn ExtensionOp>>

Optionally return an equivalent op that produces only live outputs.

impl PartialEq for Df64EinsumJvp

fn eq(&self, other: &Df64EinsumJvp) -> bool

Equality operator ==.

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

Inequality operator !=.

impl StructuralPartialEq for Df64EinsumJvp