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from __future__ import annotations
import torch
from .scoring import fuse_modality_scores
DEFAULT_NUM_BINS = 5
DEFAULT_N_MIN = 32
DEFAULT_TEMPORAL_BETA = 0.3
DEFAULT_TEMPORAL_TOPK = 2
TEMPORAL_MODES = ("none", "mean", "ta", "st")
T_MAX_DEFAULT = 1000.0
def raw_signed_projection_score(table: torch.Tensor) -> torch.Tensor:
"""Sum routed signed projections without z-score changing their sign.
Accepts either ``[3, d_int]`` or ``[K, 3, d_int]`` and sums only the
modality axis. This score is deliberately separate from the normalized
TENP ranking score.
"""
if table.ndim == 2 and table.shape[0] == 3:
return table.detach().float().sum(dim=0)
if table.ndim == 3 and table.shape[1] == 3:
return table.detach().float().sum(dim=1)
raise ValueError(f"projection table must be [3,d] or [K,3,d], got {tuple(table.shape)}")
def _positive_percentile_rank(
importance_bin: torch.Tensor,
signed_bin: torch.Tensor,
) -> torch.Tensor:
"""Rank neurons within each bin, while raw non-positive neurons score zero."""
if importance_bin.ndim != 2 or signed_bin.shape != importance_bin.shape:
raise ValueError(
f"importance/signed bins must share [K,d], got "
f"{tuple(importance_bin.shape)} and {tuple(signed_bin.shape)}"
)
ranks = torch.zeros_like(importance_bin, dtype=torch.float32)
for bin_id in range(int(importance_bin.shape[0])):
positive = torch.nonzero(signed_bin[bin_id] > 0, as_tuple=False).reshape(-1)
n_pos = int(positive.numel())
if n_pos == 0:
continue
order = torch.argsort(importance_bin[bin_id, positive], stable=True)
percentile = torch.empty(n_pos, device=importance_bin.device, dtype=torch.float32)
percentile[order] = (
torch.arange(1, n_pos + 1, device=importance_bin.device, dtype=torch.float32)
/ float(n_pos)
)
ranks[bin_id, positive] = percentile
return ranks
def temporal_criticality(
importance_bin: torch.Tensor,
signed_bin: torch.Tensor,
topq: int = DEFAULT_TEMPORAL_TOPK,
adjacent: bool = False,
) -> torch.Tensor:
"""Top-Q mean of per-bin positive percentile ranks for each neuron."""
ranks = _positive_percentile_rank(importance_bin, signed_bin)
k = int(ranks.shape[0])
q = min(max(int(topq), 1), k)
if not adjacent or q == 1:
return torch.topk(ranks, q, dim=0).values.mean(dim=0)
windows = torch.stack(
[ranks[start : start + q].mean(dim=0) for start in range(k - q + 1)],
dim=0,
)
return windows.max(dim=0).values
def temporal_coverage(
importance_bin: torch.Tensor,
keep_idx: torch.Tensor,
eps: float = 1e-8,
) -> torch.Tensor:
"""Fraction of positive per-bin importance retained by ``keep_idx``."""
if importance_bin.ndim != 2:
raise ValueError(f"importance_bin must be [K,d], got {tuple(importance_bin.shape)}")
keep = keep_idx.detach().long().reshape(-1)
positive = importance_bin.detach().float().clamp_min(0)
retained = positive[:, keep].sum(dim=1)
return retained / positive.sum(dim=1).clamp_min(eps)
def timestep_to_bin(
timestep: float | None,
num_bins: int,
step_index: int | None = None,
num_steps: int | None = None,
t_max: float = T_MAX_DEFAULT,
) -> int:
"""Map a diffusion step onto `num_bins` equal bins.
Prefer inference `step_index` so each bin gets a similar number of solver
steps. Fall back to the scheduler value in `[0, t_max)`. Missing timestep
lands in bin 0, which keeps legacy dumps identical to plain TENP.
"""
k = max(int(num_bins), 1)
if k == 1:
return 0
if step_index is not None and num_steps:
n = max(int(num_steps), 1)
return min(k - 1, max(0, int(step_index) * k // n))
if timestep is None:
return 0
t = float(timestep)
if t_max <= 0:
return 0
return min(k - 1, max(0, int(t / float(t_max) * k)))
def merge_sparse_bins(
n_tokens_bin: torch.Tensor,
n_min: int = DEFAULT_N_MIN,
) -> torch.Tensor:
"""Keep-mask over bins. Sparse bins are folded into the nearest kept neighbor."""
counts = n_tokens_bin.detach().long().reshape(-1)
k = int(counts.numel())
keep = counts >= int(n_min)
if bool(keep.any()) is False:
keep = counts > 0
if bool(keep.any()) is False:
keep = torch.ones(k, dtype=torch.bool, device=counts.device)
keep[1:] = False
return keep
def fold_sparse_bin_mass(
n_tokens_bin: torch.Tensor,
values: torch.Tensor | None = None,
n_min: int = DEFAULT_N_MIN,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Move sparse-bin token mass (and optional values) onto the nearest kept bin.
Returns (folded_counts, folded_values). `values` is typically D_{e,k};
when omitted, only counts are folded.
"""
n = n_tokens_bin.detach().float().reshape(-1).clone()
keep = merge_sparse_bins(n, n_min=n_min)
if values is None:
v = torch.zeros_like(n)
has_v = False
else:
v = values.detach().float().reshape(-1).clone()
if v.numel() != n.numel():
raise ValueError(f"values {tuple(v.shape)} != n_tokens_bin {tuple(n.shape)}")
has_v = True
kept_idx = torch.nonzero(keep, as_tuple=False).reshape(-1)
for i in range(int(n.numel())):
if bool(keep[i]):
continue
if float(n[i]) <= 0:
continue
nearest = int(kept_idx[torch.argmin((kept_idx - i).abs())].item())
if has_v:
mass = float(n[nearest]) + float(n[i])
if mass > 0:
v[nearest] = (v[nearest] * n[nearest] + v[i] * n[i]) / mass
v[i] = 0
n[nearest] = n[nearest] + n[i]
n[i] = 0
return n, v
def routing_sensitivity_alpha(
n_tokens_bin: torch.Tensor,
sensitivity: torch.Tensor,
n_min: int = DEFAULT_N_MIN,
eps: float = 1e-8,
layer_sensitivity: torch.Tensor | None = None,
) -> torch.Tensor:
"""α_{e,k} = π_{e,k} D_{e,k} / sum_j(π D). Sparse bins fold into the nearest kept bin."""
n = n_tokens_bin.detach().float().reshape(-1)
d = sensitivity.detach().float().reshape(-1)
if d.numel() != n.numel():
raise ValueError(f"sensitivity {tuple(d.shape)} != n_tokens_bin {tuple(n.shape)}")
if layer_sensitivity is not None:
d_layer = layer_sensitivity.detach().float().reshape(-1)
if d_layer.numel() == d.numel():
keep = merge_sparse_bins(n, n_min=n_min)
d = torch.where(~keep, d_layer, d)
n, d = fold_sparse_bin_mass(n, d, n_min=n_min)
pi = n / n.sum().clamp_min(1.0)
raw = pi * d.clamp_min(0)
if float(raw.sum()) <= 0:
raw = pi
return raw / raw.sum().clamp_min(eps)
def mean_alpha(n_tokens_bin: torch.Tensor, n_min: int = DEFAULT_N_MIN) -> torch.Tensor:
keep = merge_sparse_bins(n_tokens_bin, n_min=n_min)
n_fold, _ = fold_sparse_bin_mass(n_tokens_bin, n_min=n_min)
w = torch.where(n_fold > 0, torch.ones_like(n_fold), torch.zeros_like(n_fold))
if float(w.sum()) <= 0:
w = keep.float()
return w / w.sum().clamp_min(1.0)
def fuse_bins_to_importance(
table_bin: torch.Tensor,
n_tokens_bin: torch.Tensor,
lambdas: tuple[float, float, float],
n_min: int = DEFAULT_N_MIN,
zscore: bool = True,
) -> torch.Tensor:
"""Per-bin modality fusion. `table_bin`: [K, 3, d_int] -> [K, d_int]."""
if table_bin.ndim != 3 or table_bin.shape[1] != 3:
raise ValueError(f"table_bin must be [K, 3, d_int], got {tuple(table_bin.shape)}")
k, _, d_int = table_bin.shape
folded = table_bin.detach().float().clone()
keep = merge_sparse_bins(n_tokens_bin, n_min=n_min)
kept_idx = torch.nonzero(keep, as_tuple=False).reshape(-1)
for bin_id in range(k):
if bool(keep[bin_id]):
continue
if float(folded[bin_id].abs().sum()) == 0:
continue
nearest = int(kept_idx[torch.argmin((kept_idx - bin_id).abs())].item())
folded[nearest] = folded[nearest] + folded[bin_id]
folded[bin_id] = 0
out = torch.zeros(k, d_int, device=table_bin.device, dtype=torch.float32)
for bin_id in range(k):
if not bool(keep[bin_id]):
continue
row = folded[bin_id]
if float(row.abs().sum()) == 0:
continue
out[bin_id] = fuse_modality_scores(row, lambdas=lambdas, zscore=zscore)
return out
def aggregate_temporal(
importance_bin: torch.Tensor,
alpha: torch.Tensor | None = None,
mode: str = "st",
beta: float = DEFAULT_TEMPORAL_BETA,
temporal_topk: int = DEFAULT_TEMPORAL_TOPK,
) -> torch.Tensor:
"""Reduce [K, d_int] bin scores to one neuron vector."""
if mode not in TEMPORAL_MODES:
raise ValueError(f"unknown temporal mode {mode!r}; expected one of {TEMPORAL_MODES}")
if importance_bin.ndim != 2:
raise ValueError(f"importance_bin must be [K, d_int], got {tuple(importance_bin.shape)}")
k = int(importance_bin.shape[0])
if mode == "none":
raise ValueError("mode 'none' is handled by the summed projection table, not here")
live = importance_bin.abs().sum(dim=1) > 0
if not bool(live.any()):
return importance_bin.sum(dim=0)
stacked = importance_bin[live]
if mode == "mean":
if alpha is None:
return stacked.mean(dim=0)
w = alpha.detach().float().reshape(-1)[live]
w = w / w.sum().clamp_min(1e-8)
return (w.unsqueeze(-1) * stacked).sum(dim=0)
if mode == "ta":
mean = stacked.mean(dim=0)
topk = min(max(int(temporal_topk), 1), int(stacked.shape[0]))
peak = stacked.topk(topk, dim=0).values.mean(dim=0)
return mean + float(beta) * peak
if alpha is None:
raise ValueError("ST-TENP needs alpha")
w = alpha.detach().float().reshape(-1)
if w.numel() != k:
raise ValueError(f"alpha {tuple(w.shape)} != K={k}")
w = torch.where(live, w, torch.zeros_like(w))
w = w / w.sum().clamp_min(1e-8)
return (w.unsqueeze(-1) * importance_bin).sum(dim=0)
def bin_coefficient_of_variation(importance_bin: torch.Tensor, eps: float = 1e-8) -> torch.Tensor:
"""Per-neuron CV across timestep bins. Empty bins are ignored."""
live = importance_bin.abs().sum(dim=1) > 0
stacked = importance_bin[live] if bool(live.any()) else importance_bin
mu = stacked.mean(dim=0)
sigma = stacked.std(dim=0, unbiased=False)
return sigma / (mu.abs() + eps)
def topk_jaccard(importance_bin: torch.Tensor, frac: float = 0.20) -> torch.Tensor:
"""Pairwise Jaccard of per-bin top-(frac) neurons. Diagonal is 1."""
k, d_int = importance_bin.shape
n_keep = max(1, int(round(float(frac) * d_int)))
n_keep = min(n_keep, d_int)
sets: list[set[int]] = []
live = importance_bin.abs().sum(dim=1) > 0
for bin_id in range(k):
if not bool(live[bin_id]):
sets.append(set())
continue
_, idx = torch.topk(importance_bin[bin_id], n_keep)
sets.append(set(int(x) for x in idx.tolist()))
jac = torch.zeros(k, k, dtype=torch.float32)
for i in range(k):
for j in range(k):
if not sets[i] or not sets[j]:
jac[i, j] = 0.0 if i != j else 1.0
continue
inter = len(sets[i] & sets[j])
union = len(sets[i] | sets[j])
jac[i, j] = float(inter) / float(union) if union else 0.0
return jac
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