FHElium

fhelium.backend.ckks.materialization ​

Prepare one device's CKKS numerical tables and sampling state.

CkksDeviceResources ​

class View source

python
CkksDeviceResources(*, config: CkksConfig, device: str | torch.device, rns_layout: RnsLayout | None=None, ntt_backend: str | None=None, rng_seed: int | None=None, rng_nonce: int | None=None, rns_dtype: torch.dtype | None=None)

Own lazily constructed CKKS resources for one concrete device.

The default RNS layout follows the configuration's Q depth groups and hybrid decomposition. Callers can supply an existing layout for reuse.

rns_context ​

property

python
rns_context: RnsContext

Return the device's RNS arithmetic context.

ntt_context ​

property

python
ntt_context: NttContext

Return the NTT context composed with this device's RNS context.

rng ​

property

python
rng: Csprng

Return the stateful random stream assigned to this device.

key_generation ​

property

python
key_generation: KeyGenerationResource

Return the resources used to generate keys on this device.

periodic_encode_operands ​

method

python
def periodic_encode_operands(period: int, depth: int, basis: str): ...

Return compact tables and the existing live rounding stream.

encode_operands ​

method

python
def encode_operands() -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ...

Return the inverse-embedding permutation, twister, and live rounding state.

decode_operands ​

method

python
def decode_operands() -> tuple[torch.Tensor, torch.Tensor]: ...

Return the coefficient-embedding permutation and skewer.

key_switch_operands ​

method

python
def key_switch_operands(depth: int, *, moddown_rounding: str='nearest', modup_method: str='exact', rescale: bool=False, rescale_rounding: str='nearest', output_domain: str='coefficient', key_prime_ids: tuple[int, ...] | None=None, digit_prime_ids: tuple[tuple[int, ...], ...] | None=None) -> tuple[dict[str, torch.Tensor], dict[str, object]]: ...

Prepare Tensor views and digit ranges for a hybrid key switch.

rotation_sum_tables ​

method

python
def rotation_sum_tables(depth: int, *, rescale: bool=False, rounding: str='nearest', modup_method: str='exact', key_prime_ids: tuple[int, ...] | None=None, digit_prime_ids: tuple[tuple[int, ...], ...] | None=None) -> RotationSumTables: ...

Prepare and retain deterministic rotation-sum quotient tables.

modulus_raise_operands ​

method

python
def modulus_raise_operands(source_depth: int, target_depth: int) -> tuple[dict[str, torch.Tensor], dict[str, object]]: ...

Prepare centered-extension operands on this configuration's Q chain.

encapsulated_modulus_raise_operands ​

method

python
def encapsulated_modulus_raise_operands(source_depth: int, target_depth: int, low_p_moduli: tuple[int, ...], return_prime: int, return_p_moduli: tuple[int, ...], *, rounding: str='nearest') -> tuple[dict[str, torch.Tensor], dict[str, object]]: ...

Prepare low-switch, centered extension and return-switch operands.

rescale_operands ​

method

python
def rescale_operands(row_count: int, drop_count: int, *, include_p: bool=False, input_domain: str='coefficient') -> tuple[dict[str, torch.Tensor], dict[str, object]]: ...

Prepare quotient and transform tables for one rescale group.

reconstruction_operands ​

method

python
def reconstruction_operands(depth: int) -> tuple[torch.Tensor, ...]: ...

Prepare Q parameters and centered mixed-radix reconstruction tables.

bindings ​

property

python
bindings: ResourceBindings

Return this device's bindable random-stream execution resource.

materialize ​

method

python
def materialize(requirements: tuple[ResourceRequirement, ...], /) -> ResourceBindings: ...

Return all constructible missing resources in one batch.