fhelium.compile.frontend._specs

Semantic input-role declarations for source capture.

BatchMode

type alias View source

BatchMode = Literal['none', 'any']

InputSpec

class View source

InputSpec(role: ValueRole, depth: int = 0, scale: float | None = None, slots: SlotExtent = 'full', batch_mode: BatchMode = 'none', polynomial_domain: PolynomialDomainSpec | None = None, residue_representation: ResidueRepresentationSpec | None = None, static_value: StaticValue = None)

Declare one function input's role in the PyTorch-to-FHE interface.

encrypted declares a logical slot extent, batch policy, depth, and scale for a runtime Tensor or core Ciphertext. message declares public Python/PyTorch data passed through directly until an plaintext-preparation operation consumes it. plaintext declares a caller-owned core Plaintext whose state is validated at its encrypted consumer. static declares an immutable scalar specialized during capture and removed from the Program's runtime argument list.

Attributes

Name	Type	Default/value
role	ValueRole
depth	int	0
scale	float | None	None
slots	SlotExtent	'full'
batch_mode	BatchMode	'none'
polynomial_domain	PolynomialDomainSpec | None	None
residue_representation	ResidueRepresentationSpec | None	None
static_value	StaticValue	None

SlotExtent

data View source

SlotExtent = int | Literal['full']

StaticValue

data View source

StaticValue = bool | int | float | complex | str | None

ValueRole

type alias View source

ValueRole = Literal['encrypted', 'message', 'plaintext', 'static']

encrypted

function View source

def encrypted(*, depth: int=0, scale: float | None=None, slots: SlotExtent='full', batch_mode: BatchMode='none', polynomial_domain: PolynomialDomainSpec | None=None, residue_representation: ResidueRepresentationSpec | None=None) -> InputSpec: ...

Declare a secret slot input accepted as Tensor or Ciphertext.

depth and a non-None scale define the runtime CKKS input state; scale=None selects the execution Engine's default scale. slots specifies either the engine's full capacity or a final-axis extent. batch_mode='none' requires a one-dimensional Tensor and an unbatched Ciphertext; 'any' permits leading batch axes.

polynomial_domain and residue_representation may jointly declare a coefficient/standard or NTT/Montgomery input contract; omitting both leaves representation assignment to later passes.

These fields are frontend metadata. Later transforms and runtimes may use, refine, ignore, or diagnose them according to caller-selected policy. The callable retained by CapturedCallable.reference consumes its ordinary public Tensor argument.

message

function View source

def message() -> InputSpec: ...

Declare public Tensor/scalar data processed by ordinary PyTorch.

Message-only subgraphs execute as public calls. A mixed encrypted operation introduces operation-specific encoding and plaintext preparation; that preparation derives the required CKKS representation from its ciphertext consumer.

plaintext

function View source

def plaintext() -> InputSpec: ...

Declare a caller-owned FHElium Plaintext with runtime state.

Capture records this role without requiring a live core Plaintext. Runtime binding and state analysis are separate consumers. The callable used by CapturedCallable.reference consumes the public Tensor or scalar shadow supplied by the caller.

static

function View source

def static(value: StaticValue) -> InputSpec: ...

Specialize an immutable scalar Python input during capture.

A finite bool, int, float, complex, str, or None value participates in Python control and graph construction. Capture stores its serialized value in Program metadata and removes the parameter from CapturedCallable.runtime_signature and Program execution inputs.