File size: 9,690 Bytes
032a961 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """Hippocampal one-shot associative memory.
Biological motivation (complementary learning systems): a fast, episodic,
one-shot memory next to a slow statistical cortex (the spiking core).
Key design — hybrid addressing (dentate-gyrus separation + CA3 completion):
* The key of a memory is the last ``m`` tokens (patches), one 32-bit lane
per context slot.
* PRIMARY address: a hash of the FULL key (pattern separation — every
distinct context gets its own slot, so capacity is real).
* SECONDARY addresses: one per lane (auto-associative backup probes — a
query with a corrupted context still reaches the entry through its
intact lanes).
* Retrieval is two-tier: a full-key match at the primary address
dominates; otherwise candidates matching >= min_lanes lanes vote by
lane-match count x log evidence count.
This yields both capacity AND graceful degradation: corrupting 1 of 4
context tokens still retrieves the original continuation through the
intact lanes.
"""
from __future__ import annotations
import numpy as np
from .hashing import LANE_SALTS, splitmix64
class Hippocampus:
def __init__(
self,
m: int = 4,
size: int = 1 << 23,
min_lanes: int = 3,
) -> None:
self.m = m
self.size = size
self.min_lanes = min_lanes
# slot storage (vals are stable uint64 PATCH IDS, never indices)
self.keys = np.zeros((size, m), dtype=np.uint32)
self.vals = np.zeros(size, dtype=np.uint64)
self.counts = np.zeros(size, dtype=np.uint8)
self.used = np.zeros(size, dtype=np.bool_)
self.n_writes = 0
# ---------------------------------------------------------------- utils
def primary_addr(self, lane_keys: np.ndarray) -> int:
"""Pattern-separation hash of the FULL key -> primary slot
(dentate-gyrus style: every distinct context gets its own address)."""
z = (int(lane_keys[0])
^ ((int(lane_keys[1]) << 8 | int(lane_keys[1]) >> 24) & 0xFFFFFFFF)
^ ((int(lane_keys[2]) << 16 | int(lane_keys[2]) >> 16) & 0xFFFFFFFF)
^ ((int(lane_keys[3]) << 24 | int(lane_keys[3]) >> 8) & 0xFFFFFFFF))
return splitmix64(z) % self.size
def loo_addr(self, lane_keys: np.ndarray, l: int) -> int:
"""Leave-one-out hash of the key minus lane l -> CA3 completion slot.
A query whose lane l is corrupted can still compute this address
from its intact lanes and reach the stored entry."""
a = [int(lane_keys[i]) for i in range(self.m) if i != l]
z = a[0] ^ (((a[1] << 13) | (a[1] >> 19)) & 0xFFFFFFFF) \
^ (((a[2] << 26) | (a[2] >> 6)) & 0xFFFFFFFF)
return splitmix64(z ^ (0x9E3779B9 + l * 0x85EBCA6B)) % self.size
@staticmethod
def lane_keys(patch_ids) -> np.ndarray:
"""(m,) uint32 lane keys for a context of m patch ids."""
out = np.empty(len(patch_ids), dtype=np.uint32)
for lane, pid in enumerate(patch_ids):
out[lane] = splitmix64(int(pid) ^ LANE_SALTS[lane]) >> 32
return out
def lane_keys_stream(self, patch_ids: np.ndarray) -> np.ndarray:
"""Vectorized lane keys for a whole token stream: (T, m) uint32."""
T = len(patch_ids)
out = np.empty((T, self.m), dtype=np.uint32)
for lane in range(self.m):
# splitmix64 on an array of uint64 ids, take high 32 bits
z = patch_ids.astype(np.uint64) ^ np.uint64(LANE_SALTS[lane])
z = z + np.uint64(0x9E3779B97F4A7C15)
z = (z ^ (z >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)
z = (z ^ (z >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)
z = z ^ (z >> np.uint64(31))
out[:, lane] = (z >> np.uint64(32)).astype(np.uint32)
return out
# ---------------------------------------------------------------- write
def write(self, lane_keys: np.ndarray, val: int) -> None:
"""One-shot write of (context -> continuation patch id).
Stores the entry at its PRIMARY (full-key hash) slot — pattern
separation gives every distinct context its own address — plus
best-effort copies at the m lane slots for corrupted-cue retrieval.
"""
# primary slot: exact-context capacity
paddr = self.primary_addr(lane_keys)
if not self.used[paddr]:
self.keys[paddr] = lane_keys
self.vals[paddr] = val
self.counts[paddr] = 1
self.used[paddr] = True
elif np.array_equal(self.keys[paddr], lane_keys):
if self.vals[paddr] == val:
if self.counts[paddr] < 255:
self.counts[paddr] += 1
else:
c = int(self.counts[paddr])
if c > 1:
self.counts[paddr] = c - 1
else:
self.vals[paddr] = val
self.counts[paddr] = 1
elif int(self.counts[paddr]) <= 1:
# evict unconsolidated memories only (keep consolidated ones)
self.keys[paddr] = lane_keys
self.vals[paddr] = val
self.counts[paddr] = 1
# leave-one-out completion slots: robust to one corrupted lane
for l in range(self.m):
addr = self.loo_addr(lane_keys, l)
if not self.used[addr]:
self.keys[addr] = lane_keys
self.vals[addr] = val
self.counts[addr] = 1
self.used[addr] = True
elif np.array_equal(self.keys[addr], lane_keys):
if self.vals[addr] == val:
if self.counts[addr] < 255:
self.counts[addr] += 1
# else: a different key occupies this completion slot — keep it
self.n_writes += 1
def write_stream(self, lane_stream: np.ndarray, vals: np.ndarray) -> None:
"""Bulk one-shot write over a stream (val[t] follows context of
lane_stream[t])."""
for t in range(len(vals)):
self.write(lane_stream[t], int(vals[t]))
# ---------------------------------------------------------------- query
def query(self, lane_keys: np.ndarray):
"""Query with the current context. Returns (patch_id, weight) or
(None, 0.0).
Two-tier voting: a candidate matching ALL m lanes dominates any
partial match (a full-key entry must never lose to a partial match
with a higher count); within the winning tier, votes scale with
lane-match count x log evidence count.
"""
votes: dict[int, float] = {}
max_match: dict[int, int] = {}
# primary probe: exact full-key retrieval
paddr = self.primary_addr(lane_keys)
if self.used[paddr] and np.array_equal(self.keys[paddr], lane_keys):
v = int(self.vals[paddr])
c = int(self.counts[paddr])
votes[v] = votes.get(v, 0.0) + self.m * (1.0 + np.log1p(c))
max_match[v] = self.m
# leave-one-out probes: corrupted-cue completion (CA3)
for l in range(self.m):
addr = self.loo_addr(lane_keys, l)
if not self.used[addr]:
continue
match = int(np.count_nonzero(self.keys[addr] == lane_keys))
if match >= self.min_lanes:
v = int(self.vals[addr])
c = int(self.counts[addr])
w = match * (1.0 + np.log1p(c))
votes[v] = votes.get(v, 0.0) + w
if match > max_match.get(v, 0):
max_match[v] = match
if not votes:
return None, 0.0
if max(max_match.values()) == self.m:
votes = {v: w for v, w in votes.items() if max_match[v] == self.m}
best_v, best_w = max(votes.items(), key=lambda kv: kv[1])
return best_v, best_w
# ---------------------------------------------------------------- stats
def stats(self) -> dict:
n_used = int(self.used.sum())
return {
"slots": self.size,
"used": n_used,
"load_factor": n_used / self.size,
"n_writes": self.n_writes,
}
def _slots_of(self, lane_keys: np.ndarray) -> list[int]:
"""All derivable slot addresses of a key: primary + leave-one-out."""
out = [self.primary_addr(lane_keys)]
for l in range(self.m):
out.append(self.loo_addr(lane_keys, l))
return out
def top_entries(self, registry=None, min_count: int = 2, cap: int | None = None):
"""Deduplicated (lane_keys, val, count) list for export/eval, sorted by
count descending.
Dedup is EXACT: every slot an entry occupies is derivable from its
key (the primary full-key hash plus the m leave-one-out hashes), so
we mark all slots that actually hold this key. (Collisions may have
blocked some redundant writes — those slots hold a different key and
are not marked.)
"""
visited = np.zeros(self.size, dtype=np.bool_)
items = []
idx = np.nonzero(self.used)[0]
for addr in idx:
if visited[addr]:
continue
k = self.keys[addr]
c = int(self.counts[addr])
for a in self._slots_of(k):
if self.used[a] and np.array_equal(self.keys[a], k):
visited[a] = True
if c >= min_count:
items.append((k.copy(), int(self.vals[addr]), c))
items.sort(key=lambda it: it[2], reverse=True)
if cap is not None:
items = items[:cap]
return items
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