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CKNNACallback — Centered Kernel Nearest-Neighbor Alignment between
SAE activations and input embeddings.
Logs CKNNA scores for four cross-space pairs:
- text activations <-> text embeddings
- audio activations <-> audio embeddings
- text activations <-> audio embeddings
- audio activations <-> text embeddings
Reads from pl_module.{val,test}_{activations,embeddings} populated by
LightningSAE._eval_step.
"""
from __future__ import annotations
import logging
from typing import Dict, List, Optional
import torch
from lightning.pytorch import Trainer
from lightning.pytorch.core import LightningModule
from steerable_retrieval.callbacks.utils import BaseCallback
from steerable_retrieval.callbacks.energy import _get_dataset_name
log = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# HSIC helpers (self-contained, no external deps)
# ---------------------------------------------------------------------------
def _hsic_unbiased(K: torch.Tensor, L: torch.Tensor) -> torch.Tensor:
"""Unbiased HSIC estimator (Song et al., JMLR 2012, Eq. 5)."""
m = K.shape[0]
K_tilde = K.clone().fill_diagonal_(0)
L_tilde = L.clone().fill_diagonal_(0)
hsic = (
(K_tilde * L_tilde.T).sum()
+ K_tilde.sum() * L_tilde.sum() / ((m - 1) * (m - 2))
- 2 * (K_tilde @ L_tilde).sum() / (m - 2)
)
return hsic / (m * (m - 3))
def _hsic_biased(K: torch.Tensor, L: torch.Tensor) -> torch.Tensor:
"""Biased HSIC (original CKA)."""
H = torch.eye(K.shape[0], dtype=K.dtype, device=K.device) - 1.0 / K.shape[0]
return torch.trace(K @ H @ L @ H)
# ---------------------------------------------------------------------------
# CKNNA metric
# ---------------------------------------------------------------------------
def cknna(
feats_A: torch.Tensor,
feats_B: torch.Tensor,
topk: int,
distance_agnostic: bool = False,
unbiased: bool = True,
) -> float:
"""
Centered Kernel Nearest-Neighbor Alignment.
Args:
feats_A: [N, D_A] feature matrix.
feats_B: [N, D_B] feature matrix.
topk: number of nearest neighbors (>= 2).
distance_agnostic: if True, use binary neighbor overlap only.
unbiased: if True, use unbiased HSIC and exclude self-similarities.
Returns:
CKNNA score (float).
"""
n = feats_A.shape[0]
if topk < 2:
raise ValueError("CKNNA requires topk >= 2")
topk = min(topk, n - 1) if unbiased else min(topk, n)
K = feats_A @ feats_A.T
L = feats_B @ feats_B.T
device = feats_A.device
hsic_fn = _hsic_unbiased if unbiased else _hsic_biased
def similarity(K_: torch.Tensor, L_: torch.Tensor, k: int) -> torch.Tensor:
if unbiased:
K_hat = K_.clone().fill_diagonal_(float("-inf"))
L_hat = L_.clone().fill_diagonal_(float("-inf"))
else:
K_hat, L_hat = K_, L_
_, topk_K_idx = torch.topk(K_hat, k, dim=1)
_, topk_L_idx = torch.topk(L_hat, k, dim=1)
mask_K = torch.zeros(n, n, device=device).scatter_(1, topk_K_idx, 1.0)
mask_L = torch.zeros(n, n, device=device).scatter_(1, topk_L_idx, 1.0)
mask = mask_K * mask_L
if distance_agnostic:
return (mask.sum()).float()
return hsic_fn(mask * K_, mask * L_)
sim_kl = similarity(K, L, topk)
sim_kk = similarity(K, K, topk)
sim_ll = similarity(L, L, topk)
denom = (torch.sqrt(sim_kk * sim_ll) + 1e-6).item()
return sim_kl.item() / denom
# ---------------------------------------------------------------------------
# Callback
# ---------------------------------------------------------------------------
class CKNNACallback(BaseCallback):
"""
Computes CKNNA between SAE activations and input embeddings at the end of
each validation / test epoch.
Pairs logged:
- text_act <-> text_emb
- audio_act <-> audio_emb
- text_act <-> audio_emb
- audio_act <-> text_emb
"""
def __init__(
self,
enable_on_validation: bool = True,
enable_on_test: bool = True,
every_n_steps: int = None,
every_n_epochs: int = 1,
topk: int = 10,
max_samples: int = 2048,
distance_agnostic: bool = False,
unbiased: bool = True,
prefix: str = "CKNNA",
):
super().__init__(every_n_steps=every_n_steps, every_n_epochs=every_n_epochs)
self.enable_on_validation = enable_on_validation
self.enable_on_test = enable_on_test
self.topk = topk
self.max_samples = max_samples
self.distance_agnostic = distance_agnostic
self.unbiased = unbiased
self.prefix = prefix
def on_validation_epoch_end(self, trainer: Trainer, pl_module: LightningModule):
if not self.enable_on_validation:
return
if not (self._check_step(trainer, pl_module) or self._check_epoch(trainer, pl_module)):
return
self._compute_and_log(trainer, pl_module, mode="val")
def on_test_epoch_end(self, trainer: Trainer, pl_module: LightningModule):
if not self.enable_on_test:
return
if not (self._check_step(trainer, pl_module) or self._check_epoch(trainer, pl_module)):
return
self._compute_and_log(trainer, pl_module, mode="test")
@staticmethod
@torch.no_grad()
def _compute(
Za: Optional[torch.Tensor],
Zt: Optional[torch.Tensor],
Ea: Optional[torch.Tensor],
Et: Optional[torch.Tensor],
topk: int = 10,
max_samples: int = 2048,
distance_agnostic: bool = False,
unbiased: bool = True,
) -> Dict[str, float]:
"""
Pure computation — no trainer / pl_module / logging.
Args:
Za: audio activations [N_a, C] or None
Zt: text activations [N_t, C] or None
Ea: audio embeddings [N_a, D] or None
Et: text embeddings [N_t, D] or None
topk: number of nearest neighbors for CKNNA
max_samples: subsample limit
distance_agnostic: binary neighbor overlap only
unbiased: use unbiased HSIC
Returns:
dict mapping pair label -> CKNNA score (float).
"""
pairs = [
(Za, Ea, "audio_act__audio_emb"),
(Zt, Et, "text_act__text_emb"),
(Zt, Ea, "text_act__audio_emb"),
(Za, Et, "audio_act__text_emb"),
]
scores = {}
for feats_A, feats_B, label in pairs:
if feats_A is None or feats_B is None:
continue
n = min(feats_A.size(0), feats_B.size(0))
fA, fB = feats_A[:n], feats_B[:n]
if n > max_samples:
idx = torch.randperm(n, device=fA.device)[:max_samples]
fA, fB = fA[idx], fB[idx]
try:
scores[label] = cknna(
fA.float(), fB.float(),
topk=topk,
distance_agnostic=distance_agnostic,
unbiased=unbiased,
)
except Exception as e:
log.warning(f"[CKNNACallback] Failed for {label}: {e}")
return scores
@torch.no_grad()
def _compute_and_log(self, trainer: Trainer, pl_module: LightningModule, mode: str):
"""Gather data from module, call _compute, then log."""
from steerable_retrieval.callbacks.energy import _resolve_modality_tensors
all_acts = getattr(pl_module, f"{mode}_activations", {})
all_embs = getattr(pl_module, f"{mode}_embeddings", {})
device = pl_module.device
for dataloader_idx in all_acts:
acts_dl = all_acts.get(dataloader_idx, {})
embs_dl = all_embs.get(dataloader_idx, {})
Za = _resolve_modality_tensors(acts_dl, "audio", device, trainer)
Zt = _resolve_modality_tensors(acts_dl, "text", device, trainer)
Ea = _resolve_modality_tensors(embs_dl, "audio", device, trainer)
Et = _resolve_modality_tensors(embs_dl, "text", device, trainer)
scores = self._compute(
Za, Zt, Ea, Et,
topk=self.topk,
max_samples=self.max_samples,
distance_agnostic=self.distance_agnostic,
unbiased=self.unbiased,
)
dataset_name = _get_dataset_name(trainer, dataloader_idx, mode)
log_prefix = f"{self.prefix}/{dataset_name}"
for label, score in scores.items():
pl_module.log(f"{log_prefix}/{label}", score, prog_bar=False, sync_dist=True)
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