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src/semantic_kv_cache.py
Entropy-Gated Semantic Anchor KV-Cache for Q-TensorFormer.
Protects initial attention sinks AND high-entropy / high-surprise tokens (semantic anchors)
from cache eviction, while aggressively quantizing or evicting low-information filler tokens.
Prevents long-context reasoning collapse while slashing DRAM footprint by up to 90%+.
"""
from typing import Dict, Any, List, Set, Optional, Tuple
import math
import torch
import torch.nn as nn
from src.kv_cache import AdaptiveKVCache, KVPrecision, QuantizedKVTensor
class SemanticAnchorKVCache:
"""
Entropy & Surprise Gated KV Cache with Semantic Anchor Protection.
Protects:
1. Attention Sinks (positions 0 .. sink_size - 1)
2. Semantic Anchors (positions with entropy >= entropy_threshold or surprise >= surprise_threshold)
Evicts:
3. Uninformative filler tokens when capacity reaches max_capacity.
"""
def __init__(
self,
max_capacity: int = 128,
sink_size: int = 4,
entropy_threshold: float = 2.5,
surprise_threshold: float = 3.0,
default_precision: KVPrecision = KVPrecision.INT8,
):
self.max_capacity = max_capacity
self.sink_size = sink_size
self.entropy_threshold = entropy_threshold
self.surprise_threshold = surprise_threshold
self.default_precision = default_precision
# Storage
self.keys: List[torch.Tensor] = []
self.values: List[torch.Tensor] = []
self.anchor_indices: Set[int] = set()
self.token_positions: List[int] = []
self.token_entropies: List[float] = []
def append(
self,
key: torch.Tensor,
value: torch.Tensor,
entropy: float = 0.0,
surprise: float = 0.0,
attention_score: float = 1.0,
) -> Dict[str, Any]:
"""
Appends key/value tensors for a new token, evaluating whether to declare it a semantic anchor.
Args:
key: Tensor of shape (B, H, 1, D)
value: Tensor of shape (B, H, 1, D)
entropy: Predictive entropy of token distribution
surprise: Negative log-likelihood of selected token
attention_score: Cumulative attention received by this token
"""
current_seq_pos = len(self.token_positions)
is_sink = current_seq_pos < self.sink_size
is_anchor = (entropy >= self.entropy_threshold) or (surprise >= self.surprise_threshold)
if is_sink or is_anchor:
self.anchor_indices.add(current_seq_pos)
self.keys.append(key)
self.values.append(value)
self.token_positions.append(current_seq_pos)
self.token_entropies.append(entropy)
# Evict if exceeding max_capacity
evicted_pos = None
if len(self.keys) > self.max_capacity:
# Find earliest non-anchor, non-sink token to evict
evict_idx = -1
for idx, pos in enumerate(self.token_positions):
if pos not in self.anchor_indices:
evict_idx = idx
break
if evict_idx != -1:
evicted_pos = self.token_positions.pop(evict_idx)
self.keys.pop(evict_idx)
self.values.pop(evict_idx)
self.token_entropies.pop(evict_idx)
else:
# If all tokens are anchors, evict the lowest entropy anchor beyond sinks
best_candidate = -1
lowest_entropy = float("inf")
for idx, pos in enumerate(self.token_positions):
if pos >= self.sink_size and self.token_entropies[idx] < lowest_entropy:
lowest_entropy = self.token_entropies[idx]
best_candidate = idx
if best_candidate != -1:
evicted_pos = self.token_positions.pop(best_candidate)
self.keys.pop(best_candidate)
self.values.pop(best_candidate)
self.token_entropies.pop(best_candidate)
self.anchor_indices.discard(evicted_pos)
return {
"position": current_seq_pos,
"is_sink": is_sink,
"is_anchor": is_anchor,
"evicted_position": evicted_pos,
"cached_tokens": len(self.keys),
"protected_anchors": len([p for p in self.token_positions if p in self.anchor_indices]),
}
def get_kv(self) -> Tuple[torch.Tensor, torch.Tensor]:
"""Concatenates all active keys and values along sequence dimension (dim=2)."""
if not self.keys:
raise ValueError("KV cache is empty.")
k = torch.cat(self.keys, dim=2)
v = torch.cat(self.values, dim=2)
return k, v
def memory_footprint_bytes(self) -> int:
"""Calculates total physical bytes used by active keys and values."""
if not self.keys:
return 0
element_size = 2 if self.default_precision == KVPrecision.FP16 else 1
num_elements = sum(k.numel() + v.numel() for k, v in zip(self.keys, self.values))
return num_elements * element_size
def evaluate_retrieval_retention(
self,
needle_positions: List[int],
) -> Dict[str, Any]:
"""
Evaluates whether needle token positions were successfully retained in cache.
"""
cached_set = set(self.token_positions)
retained = [pos for pos in needle_positions if pos in cached_set]
retention_rate = len(retained) / max(1, len(needle_positions))
return {
"total_needles": len(needle_positions),
"retained_needles": len(retained),
"retention_rate": retention_rate,
"cached_token_count": len(self.token_positions),
"max_capacity": self.max_capacity,
"scientific_classification": "MEASURED",
}
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