FHElium

fhelium.backend.rns.moddown ​

Auxiliary-basis removal in coefficient and NTT representations.

divide_trailing_rows ​

function View source

python
def divide_trailing_rows(source: torch.Tensor, inverses: torch.Tensor, parameters: torch.Tensor, drop_count: int, *, half: torch.Tensor | None=None, row_order: torch.Tensor | None=None, rounding: str='nearest') -> torch.Tensor: ...

Return floor((x+h)/D) after arranging the divisor rows last.

D is the product of the trailing drop_count moduli. half supplies (D-1)/2 in the ordered basis for nearest rounding. Input storage is read-only.

moddown_qp_to_q ​

function View source

python
def moddown_qp_to_q(source, tensors, attributes): ...

Return floor((x + h)/P) mod Q, with h=0 or (P-1)/2.

rounding selects floor or nearest. Depth and CKKS scale are preserved.

moddown_ntt_qp_to_q ​

function View source

python
def moddown_ntt_qp_to_q(source: torch.Tensor, tensors: Mapping[str, torch.Tensor], attributes: Mapping[str, object], *, coefficient_c0: torch.Tensor | None=None) -> torch.Tensor: ...

Return x^Q/P+NTTQ(−r/P).

source holds NTT/Montgomery residues over active QP. Inverting only the P rows determines the coefficient remainder r used by coefficient-domain ModDown. Floor uses r∈[0,P); nearest uses r∈[−(P−1)/2,(P−1)/2]. Applying that same ModDown to zero Q rows produces −r/Pmodqi. Its forward NTT is added to the retained Q evaluations multiplied by P−1. The result is a non-aliasing active-Q NTT/Montgomery tensor; source storage is unchanged. When coefficient_c0 is supplied, it is added to component zero's coefficient correction before the forward NTT, adding NTT(c0) without another transform. The input c0 remains unchanged.

NativeModDownImplementation ​

class View source

python
NativeModDownImplementation(name: str = 'native-key-switch-moddown', supports_in_place: bool = False, operation_types: tuple[type[Operation], ...] = (rns.ModDownQpToQOp, rns.ModDownNttQpToQOp, rns.ModDownAddRescaleOp, rns.RoundedTrailingQuotientOp))

Divide RNS accumulators by auxiliary and depth-group products.

Attributes ​

NameTypeDefault/value
namestr'native-key-switch-moddown'
supports_in_placeboolFalse
operation_typestuple[type[Operation], ...](rns.ModDownQpToQOp, rns.ModDownNttQpToQOp, rns.ModDownAddRescaleOp, rns.RoundedTrailingQuotientOp)

tensor_requirements ​

method

python
def tensor_requirements(operation, config): ...

prepare_operation ​

method

python
def prepare_operation(operation): ...

resource_requirements ​

method

python
def resource_requirements(invocation: OperationInvocation) -> tuple[ResourceRequirement, ...]: ...

execute ​

method

python
def execute(invocation: OperationInvocation, values: tuple[torch.Tensor, ...], resources: tuple[BoundResource, ...], *, in_place: bool) -> tuple[torch.Tensor, ...]: ...

moddown_add_rescale ​

function View source

python
def moddown_add_rescale(products: torch.Tensor, base: torch.Tensor, tensors: Mapping[str, torch.Tensor], attributes: Mapping[str, object]) -> torch.Tensor: ...

Compute ⌊(v+Pa+HP+PHD)/(PD)⌋ on surviving Q rows.

Inputs are NTT/Montgomery bundles. The products' leading component extent selects the matching prefix of base components. Source storage and numerical operands are read-only. Output is coefficient/standard or NTT/Montgomery according to output_domain.