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fhelium.backend.rns.tables ​

RNS quotient tables and depth-indexed rescaling data.

rescale_inverse_matrix ​

function View source

python
def rescale_inverse_matrix(rns_context: RnsContext) -> torch.Tensor: ...

moddown_inverse_tables ​

function View source

python
def moddown_inverse_tables(rns_context: RnsContext) -> tuple[torch.Tensor, ...]: ...

RescaleTables ​

class View source

python
RescaleTables(rns_context: RnsContext, dropped_prime_inverses_montgomery: torch.Tensor)

Own pairwise and prefix-product inverses used by RNS rescaling.

Attributes ​

NameTypeDefault/value
rns_contextRnsContext
dropped_prime_inverses_montgomerytorch.Tensor

device ​

property

python
device: torch.device

active_views ​

method

python
def active_views(row_count: int, *, include_p: bool) -> tuple[torch.Tensor, torch.Tensor, int]: ...

Return preselected parameters, inverses, and dropped modulus.

prefix_inverse ​

method

python
def prefix_inverse(row_count: int, drop_count: int, *, include_p: bool) -> torch.Tensor: ...

Return M−1Rmodqi for each surviving prime.

M is the product of the leading drop_count active primes. The resource retains the vector for reuse with the same prime interval.

tensor_operands ​

method

python
def tensor_operands(row_count: int, drop_count: int, *, include_p: bool, ntt_context: NttContext | None=None) -> tuple[dict[str, torch.Tensor], dict[str, object]]: ...

Expose quotient tables and scalar rounding offsets for one group.