File size: 47,285 Bytes
309d3a9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 | """Recursive criterion-grown token vocabulary primitives.
This module is intentionally small and CPU-testable. It establishes the Q32
mechanism contract before the recursive tokenizer is connected to the streaming
trainer: learned tokens are emitted as discrete ids, may parent later tokens,
receive gradients, and resume with exact optimizer state.
"""
from __future__ import annotations
import copy
from dataclasses import dataclass
import hashlib
import json
import math
from typing import Any, Iterable, Sequence
import torch
from torch import nn
from .collapse import _weighted_index_add_
@dataclass(frozen=True)
class CascadeToken:
token_id: int
span: tuple[int, ...]
generation: int
left_parent: int | None
right_parent: int | None
class RecursiveCascadeVocabulary(nn.Module):
"""A monotone DAG vocabulary over canonical base-token spans."""
def __init__(
self,
base_embeddings: torch.Tensor,
*,
max_span_length: int = 32,
) -> None:
super().__init__()
if base_embeddings.ndim != 2 or not base_embeddings.shape[0]:
raise ValueError("base_embeddings must be a nonempty matrix")
if not base_embeddings.is_floating_point() or not bool(
torch.isfinite(base_embeddings).all()
):
raise ValueError("base_embeddings must be finite floating point")
if max_span_length < 2:
raise ValueError("max_span_length must be at least two")
self.dim = int(base_embeddings.shape[1])
self.max_span_length = int(max_span_length)
self.base_size = int(base_embeddings.shape[0])
self.embeddings = nn.ParameterList(
nn.Parameter(row.detach().clone()) for row in base_embeddings
)
self._tokens = tuple(
CascadeToken(index, (index,), 0, None, None)
for index in range(self.base_size)
)
@property
def tokens(self) -> tuple[CascadeToken, ...]:
return self._tokens
def _span_index(self) -> dict[tuple[int, ...], int]:
return {token.span: token.token_id for token in self._tokens}
def tokenize(self, base_ids: Sequence[int]) -> tuple[int, ...]:
"""Greedily emit longest canonical spans with stable id tie-breaking."""
sequence = tuple(int(value) for value in base_ids)
if any(value < 0 or value >= self.base_size for value in sequence):
raise ValueError("tokenize input must contain only base token ids")
spans = self._span_index()
by_first: dict[int, list[tuple[tuple[int, ...], int]]] = {}
for span, token_id in spans.items():
by_first.setdefault(span[0], []).append((span, token_id))
for candidates in by_first.values():
candidates.sort(key=lambda item: (-len(item[0]), item[1]))
emitted: list[int] = []
position = 0
while position < len(sequence):
chosen_span = (sequence[position],)
chosen_id = sequence[position]
for span, token_id in by_first.get(sequence[position], ()):
if sequence[position : position + len(span)] == span:
chosen_span, chosen_id = span, token_id
break
emitted.append(chosen_id)
position += len(chosen_span)
return tuple(emitted)
@staticmethod
def adjacent_pairs(token_ids: Sequence[int]) -> tuple[tuple[int, int], ...]:
emitted = tuple(int(value) for value in token_ids)
return tuple(zip(emitted, emitted[1:]))
def lookup(self, token_ids: Sequence[int]) -> torch.Tensor:
ids = tuple(int(value) for value in token_ids)
if not ids:
raise ValueError("lookup requires at least one token")
if any(value < 0 or value >= len(self.embeddings) for value in ids):
raise ValueError("lookup token id is outside the vocabulary")
return torch.stack([self.embeddings[value] for value in ids])
def encode_mean(self, base_ids: Sequence[int]) -> tuple[torch.Tensor, tuple[int, ...]]:
emitted = self.tokenize(base_ids)
return self.lookup(emitted).mean(dim=0), emitted
@staticmethod
def _average_parent_state(
optimizer: torch.optim.Optimizer,
left: nn.Parameter,
right: nn.Parameter,
) -> dict[Any, Any]:
left_state = optimizer.state.get(left, {})
right_state = optimizer.state.get(right, {})
if not left_state or set(left_state) != set(right_state):
raise ValueError("both parents require aligned initialized optimizer state")
result: dict[Any, Any] = {}
for name in left_state:
left_value, right_value = left_state[name], right_state[name]
if torch.is_tensor(left_value) != torch.is_tensor(right_value):
raise ValueError(f"optimizer parent state type differs: {name}")
if not torch.is_tensor(left_value):
if left_value != right_value:
raise ValueError(f"optimizer parent scalar differs: {name}")
result[name] = left_value
elif left_value.ndim == 0:
if not torch.equal(left_value, right_value):
raise ValueError(f"optimizer parent step differs: {name}")
result[name] = left_value.detach().clone()
else:
if left_value.shape != left.shape or right_value.shape != right.shape:
raise ValueError(f"optimizer parent row shape differs: {name}")
result[name] = (0.5 * (left_value + right_value)).detach().clone()
return result
def promote_pairs(
self,
pairs: Iterable[tuple[int, int]],
*,
optimizer: torch.optim.Optimizer,
) -> tuple[CascadeToken, ...]:
"""Materialize every new canonical span and attach it to Adam in place."""
requested = tuple((int(left), int(right)) for left, right in pairs)
if not requested:
return ()
if len(set(requested)) != len(requested):
raise ValueError("promotion pairs must be unique")
existing = self._span_index()
additions: list[CascadeToken] = []
for left_id, right_id in requested:
if not (0 <= left_id < len(self._tokens) and 0 <= right_id < len(self._tokens)):
raise ValueError("promotion parent id is outside the vocabulary")
left_token, right_token = self._tokens[left_id], self._tokens[right_id]
span = left_token.span + right_token.span
if len(span) > self.max_span_length:
raise ValueError("promotion exceeds max_span_length")
if span in existing:
raise ValueError("promotion span already exists")
generation = max(left_token.generation, right_token.generation) + 1
token_id = len(self._tokens) + len(additions)
parameter = nn.Parameter(
0.5
* (
self.embeddings[left_id].detach()
+ self.embeddings[right_id].detach()
)
)
state = self._average_parent_state(
optimizer, self.embeddings[left_id], self.embeddings[right_id]
)
self.embeddings.append(parameter)
optimizer.param_groups[0]["params"].append(parameter)
optimizer.state[parameter] = state
token = CascadeToken(token_id, span, generation, left_id, right_id)
additions.append(token)
existing[span] = token_id
self._tokens = (*self._tokens, *additions)
return tuple(additions)
def snapshot(self, optimizer: torch.optim.Optimizer) -> dict[str, Any]:
return {
"version": 1,
"protocol": "recursive-cascade-micro-state-v1",
"base_size": self.base_size,
"dim": self.dim,
"max_span_length": self.max_span_length,
"tokens": [
{
"token_id": token.token_id,
"span": list(token.span),
"generation": token.generation,
"left_parent": token.left_parent,
"right_parent": token.right_parent,
}
for token in self._tokens
],
# PyTorch state_dict values alias live parameter/optimizer storage.
# A resume snapshot must be immutable while the source run keeps
# training, so clone the complete trees at the snapshot boundary.
"model": copy.deepcopy(self.state_dict()),
"optimizer": copy.deepcopy(optimizer.state_dict()),
}
@classmethod
def from_snapshot(
cls,
payload: dict[str, Any],
*,
optimizer_kwargs: dict[str, Any],
) -> tuple["RecursiveCascadeVocabulary", torch.optim.AdamW]:
if (
not isinstance(payload, dict)
or payload.get("version") != 1
or payload.get("protocol") != "recursive-cascade-micro-state-v1"
):
raise ValueError("recursive cascade snapshot identity differs")
tokens = payload.get("tokens")
if not isinstance(tokens, list) or len(tokens) < int(payload["base_size"]):
raise ValueError("recursive cascade snapshot tokens are invalid")
model_state = payload.get("model")
if not isinstance(model_state, dict):
raise ValueError("recursive cascade snapshot model is invalid")
base_rows = torch.stack(
[model_state[f"embeddings.{index}"] for index in range(int(payload["base_size"]))]
)
restored = cls(base_rows, max_span_length=int(payload["max_span_length"]))
restored_tokens: list[CascadeToken] = []
for index, row in enumerate(tokens):
token = CascadeToken(
int(row["token_id"]),
tuple(int(value) for value in row["span"]),
int(row["generation"]),
None if row["left_parent"] is None else int(row["left_parent"]),
None if row["right_parent"] is None else int(row["right_parent"]),
)
if token.token_id != index:
raise ValueError("recursive cascade token ids are not contiguous")
restored_tokens.append(token)
for index in range(restored.base_size, len(restored_tokens)):
restored.embeddings.append(
nn.Parameter(model_state[f"embeddings.{index}"].detach().clone())
)
restored._tokens = tuple(restored_tokens)
restored.load_state_dict(model_state, strict=True)
optimizer = torch.optim.AdamW(restored.parameters(), **optimizer_kwargs)
optimizer.load_state_dict(payload["optimizer"])
return restored, optimizer
def recursive_pair_key(left: int, right: int) -> int:
"""Return a collision-free Cantor identity for two non-negative token ids."""
left, right = int(left), int(right)
if left < 0 or right < 0:
raise ValueError("recursive pair parents must be non-negative")
total = left + right
key = total * (total + 1) // 2 + right
if key > torch.iinfo(torch.int64).max:
raise OverflowError("recursive pair identity exceeds int64")
return key
def decode_recursive_pair_key(key: int) -> tuple[int, int]:
"""Invert :func:`recursive_pair_key` exactly using integer arithmetic."""
key = int(key)
if key < 0:
raise ValueError("recursive pair key must be non-negative")
diagonal = (math.isqrt(8 * key + 1) - 1) // 2
diagonal_start = diagonal * (diagonal + 1) // 2
right = key - diagonal_start
left = diagonal - right
if recursive_pair_key(left, right) != key:
raise ValueError("recursive pair key is not canonical")
return left, right
def _recursive_pair_keys(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor:
if left.dtype != torch.long or right.dtype != torch.long:
raise ValueError("recursive token ids must use torch.long")
if bool((left < 0).any()) or bool((right < 0).any()):
raise ValueError("recursive token ids must be non-negative")
total = left + right
# Cantor pairing is exact while the triangular term fits signed int64.
if total.numel() and int(total.max()) > 3_037_000_498:
raise OverflowError("recursive pair identity exceeds int64")
return total * (total + 1) // 2 + right
def _proposal_hash(
left: torch.Tensor, right: torch.Tensor, buckets: int
) -> torch.Tensor:
"""Preserve the Q15 proposal-row mapping at the exact Q32 launch."""
return ((left * 2_654_435_761 + right * 40_503).abs()) % int(buckets)
class ExactRecursiveDiscovery:
"""Unbounded exact pair/language statistics for one finite half-window."""
def __init__(self, *, n_languages: int) -> None:
if n_languages <= 0:
raise ValueError("recursive discovery needs a positive language count")
self.n_languages = int(n_languages)
self.records: dict[int, list[float | int]] = {}
self.observed_occurrences = 0
self.transferred_records = 0
@torch.no_grad()
def observe(
self,
pair_key: torch.Tensor,
language: torch.Tensor,
probability: torch.Tensor,
gradient: torch.Tensor,
) -> None:
if not pair_key.numel():
return
utility = -probability.detach().float() * gradient.detach().float()
finite = (
torch.isfinite(utility)
& torch.isfinite(probability.detach().float())
& (language >= 0)
& (language < self.n_languages)
)
if not bool(finite.any()):
return
pair_key = pair_key.detach()[finite].to(torch.long)
language = language.detach()[finite].to(torch.long)
probability = probability.detach()[finite].float()
utility = utility[finite]
self.observed_occurrences += int(pair_key.numel())
composite = pair_key * self.n_languages + language
unique, inverse = torch.unique(composite, return_inverse=True)
utility_sum = torch.zeros(unique.numel(), device=utility.device)
probability_sum = torch.zeros(unique.numel(), device=utility.device)
support = torch.zeros(unique.numel(), dtype=torch.long, device=utility.device)
positive_utility_support = torch.zeros(
unique.numel(), dtype=torch.long, device=utility.device
)
utility_sum.index_add_(0, inverse, utility)
probability_sum.index_add_(0, inverse, probability)
support.index_add_(0, inverse, torch.ones_like(inverse))
positive_utility_support.index_add_(
0, inverse, (utility > 0).to(torch.long)
)
rows = zip(
unique.cpu().tolist(),
utility_sum.cpu().tolist(),
probability_sum.cpu().tolist(),
support.cpu().tolist(),
positive_utility_support.cpu().tolist(),
strict=True,
)
for composite_key, value, probability_value, count, positive_count in rows:
key = int(composite_key)
row = self.records.setdefault(key, [0.0, 0.0, 0, 0])
row[0] = float(row[0]) + float(value)
row[1] = float(row[1]) + float(probability_value)
row[2] = int(row[2]) + int(count)
row[3] = int(row[3]) + int(positive_count)
self.transferred_records += 1
def snapshot_records(self) -> tuple[dict[str, float | int], ...]:
result = []
for composite, row in sorted(self.records.items()):
pair_key, language = divmod(composite, self.n_languages)
utility, probability_sum, support, positive_utility_support = (
float(row[0]), float(row[1]), int(row[2]), int(row[3])
)
result.append(
{
"composite_key": composite,
"pair_key": pair_key,
"language_index": language,
"utility": utility,
"probability_sum": probability_sum,
"captured_support": support,
"positive_utility_support": positive_utility_support,
"mean_probability": probability_sum / support,
}
)
return tuple(result)
class DenseRecursiveCascade(nn.Module):
"""Batched recursive tokenizer plus one dense trainable learned-token table.
Accepted rules are applied once per structural generation. Consequently a
token born at boundary ``g`` can be emitted and used by discovery during the
following interval, but a transition cannot recursively consume rows it is
creating itself. This makes every structural update an atomic DAG layer.
"""
def __init__(
self,
*,
base_size: int,
dim: int,
n_languages: int,
residual_buckets: int,
max_span_length: int = 32,
restored_state: dict[str, torch.Tensor] | None = None,
) -> None:
super().__init__()
if base_size <= 0 or dim <= 0 or n_languages <= 0:
raise ValueError("recursive cascade dimensions must be positive")
if residual_buckets <= 0 or max_span_length < 2:
raise ValueError("recursive residual capacity/span limit is invalid")
self.base_size = int(base_size)
self.dim = int(dim)
self.n_languages = int(n_languages)
self.residual_buckets = int(residual_buckets)
self.max_span_length = int(max_span_length)
restored = dict(restored_state or {})
metadata_names = {
"rule_left", "rule_right", "rule_generation", "span_offsets",
"span_values", "rule_active", "learned",
}
legacy_metadata_names = metadata_names - {"rule_active"}
if restored and set(restored) not in (metadata_names, legacy_metadata_names):
raise ValueError("recursive cascade restored state has an invalid schema")
learned = restored.get("learned", torch.empty((0, self.dim)))
if learned.ndim != 2 or learned.shape[1] != self.dim:
raise ValueError("recursive learned table has an invalid shape")
self.learned = nn.Parameter(learned.detach().clone())
count = int(learned.shape[0])
defaults = {
"rule_left": torch.empty(0, dtype=torch.long),
"rule_right": torch.empty(0, dtype=torch.long),
"rule_generation": torch.empty(0, dtype=torch.long),
"span_offsets": torch.zeros(1, dtype=torch.long),
"span_values": torch.empty(0, dtype=torch.long),
}
for name, default in defaults.items():
value = restored.get(name, default).detach().clone().to(torch.long)
self.register_buffer(name, value, persistent=True)
active = restored.get(
"rule_active", torch.ones(count, dtype=torch.bool)
).detach().clone().to(torch.bool)
self.register_buffer("rule_active", active, persistent=True)
if not (
self.rule_left.numel() == count
and self.rule_right.numel() == count
and self.rule_generation.numel() == count
and self.rule_active.numel() == count
and self.span_offsets.numel() == count + 1
and int(self.span_offsets[0]) == 0
and int(self.span_offsets[-1]) == self.span_values.numel()
):
raise ValueError("recursive rule metadata does not align with learned rows")
if count and (
bool((self.rule_generation <= 0).any())
or bool((self.rule_generation[1:] < self.rule_generation[:-1]).any())
or int(self.rule_left.max()) >= self.base_size + count
or int(self.rule_right.max()) >= self.base_size + count
):
raise ValueError("recursive rule DAG metadata is invalid")
self._validate_metadata()
self.proposal_merged = nn.Parameter(torch.zeros(self.residual_buckets, self.dim))
self.proposal_score = nn.Parameter(torch.zeros(self.residual_buckets))
self.language_bias = nn.Parameter(torch.full((self.n_languages,), -2.0))
self.discovery: ExactRecursiveDiscovery | None = None
self.usage_counts: torch.Tensor | None = None
@property
def learned_count(self) -> int:
return int(self.learned.shape[0])
@property
def generation(self) -> int:
return int(self.rule_generation[-1]) if self.rule_generation.numel() else 0
@property
def active_count(self) -> int:
return int(self.rule_active.sum())
def configure_discovery(self) -> None:
self.discovery = ExactRecursiveDiscovery(n_languages=self.n_languages)
self.usage_counts = torch.zeros(
self.learned_count, dtype=torch.long, device=self.learned.device
)
def snapshot_usage_token_ids(self) -> tuple[int, ...]:
if self.usage_counts is None:
raise RuntimeError("recursive usage collection is not configured")
rows = torch.nonzero(self.usage_counts > 0, as_tuple=False).squeeze(1)
return tuple(
self.base_size + int(row) for row in rows.detach().cpu().tolist()
)
def active_pair_keys(self) -> tuple[int, ...]:
left = self.rule_left.detach().cpu().tolist()
right = self.rule_right.detach().cpu().tolist()
active = self.rule_active.detach().cpu().tolist()
return tuple(
recursive_pair_key(int(left[row]), int(right[row]))
for row, enabled in enumerate(active)
if enabled
)
def token_span(self, token_id: int) -> tuple[int, ...]:
token_id = int(token_id)
if 0 <= token_id < self.base_size:
return (token_id,)
row = token_id - self.base_size
if row < 0 or row >= self.learned_count:
raise ValueError("recursive token id is outside the vocabulary")
start, stop = int(self.span_offsets[row]), int(self.span_offsets[row + 1])
return tuple(int(value) for value in self.span_values[start:stop].tolist())
def _validate_metadata(self) -> None:
seen_pairs: set[tuple[int, int]] = set()
seen_spans: set[tuple[int, ...]] = set()
left_values = self.rule_left.detach().cpu().tolist()
right_values = self.rule_right.detach().cpu().tolist()
generation_values = self.rule_generation.detach().cpu().tolist()
active_values = self.rule_active.detach().cpu().tolist()
offsets = self.span_offsets.detach().cpu().tolist()
span_values = self.span_values.detach().cpu().tolist()
row_spans = [
tuple(int(value) for value in span_values[offsets[row] : offsets[row + 1]])
for row in range(self.learned_count)
]
generations = set(int(value) for value in generation_values)
if generations and generations != set(range(1, max(generations) + 1)):
raise ValueError("recursive structural generations are not contiguous")
for row in range(self.learned_count):
token_id = self.base_size + row
left = int(left_values[row])
right = int(right_values[row])
generation = int(generation_values[row])
if left >= token_id or right >= token_id:
raise ValueError("recursive rule references a non-earlier token")
left_generation = (
0 if left < self.base_size else int(generation_values[left - self.base_size])
)
right_generation = (
0 if right < self.base_size else int(generation_values[right - self.base_size])
)
if left_generation >= generation or right_generation >= generation:
raise ValueError("recursive rule parent is not from an earlier generation")
pair = (left, right)
span = row_spans[row]
expected = (
((left,) if left < self.base_size else row_spans[left - self.base_size])
+ ((right,) if right < self.base_size else row_spans[right - self.base_size])
)
if pair in seen_pairs or span in seen_spans:
raise ValueError("recursive rule or canonical span is duplicated")
if span != expected or not span or len(span) > self.max_span_length:
raise ValueError("recursive canonical span metadata is invalid")
if min(span) < 0 or max(span) >= self.base_size:
raise ValueError("recursive canonical span contains a non-base id")
seen_pairs.add(pair)
seen_spans.add(span)
for row in range(self.learned_count):
if not bool(active_values[row]):
continue
for parent in (int(left_values[row]), int(right_values[row])):
if parent >= self.base_size and not bool(
active_values[parent - self.base_size]
):
raise ValueError("active recursive rule has an inactive parent")
def metadata_sha256(self) -> str:
payload = {
name: getattr(self, name).detach().cpu().tolist()
for name in (
"rule_left", "rule_right", "rule_generation", "span_offsets",
"span_values", "rule_active",
)
}
return hashlib.sha256(
json.dumps(payload, separators=(",", ":"), sort_keys=True).encode("utf-8")
).hexdigest()
def _lookup(
self, token_ids: torch.Tensor, base_embedding: torch.Tensor
) -> torch.Tensor:
if base_embedding.ndim != 2 or tuple(base_embedding.shape) != (
self.base_size, self.dim
):
raise ValueError("base embedding does not match recursive cascade")
if token_ids.numel() and (
int(token_ids.min()) < 0
or int(token_ids.max()) >= self.base_size + self.learned_count
):
raise ValueError("recursive token id is outside the vocabulary")
result = torch.empty(
(token_ids.numel(), self.dim),
dtype=base_embedding.dtype,
device=base_embedding.device,
)
base = token_ids < self.base_size
if bool(base.any()):
result[base] = base_embedding[token_ids[base]]
if bool((~base).any()):
result[~base] = self.learned[token_ids[~base] - self.base_size].to(
dtype=base_embedding.dtype
)
return result
@staticmethod
def _nonoverlapping_leftmost(candidate: torch.Tensor) -> torch.Tensor:
if candidate.dtype != torch.bool or candidate.ndim != 1:
raise ValueError("recursive merge candidates must be a boolean vector")
if not candidate.numel():
return candidate
positions = torch.arange(candidate.numel(), device=candidate.device)
previous_false = torch.cat(
(torch.ones(1, dtype=torch.bool, device=candidate.device), ~candidate[:-1])
)
starts = candidate & previous_false
start_positions = torch.where(starts, positions, torch.full_like(positions, -1))
last_start = torch.cummax(start_positions, dim=0).values
return candidate & ((positions - last_start).remainder(2) == 0)
def retokenize(
self, content: torch.Tensor, segment: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
"""Apply the complete rule DAG to one packed batch on its current device."""
if content.dtype != torch.long or segment.dtype != torch.long:
raise ValueError("recursive packed tokens and segments must use torch.long")
if content.ndim != 1 or segment.shape != content.shape:
raise ValueError("recursive packed tokens and segments must align")
if content.numel() and (
int(content.min()) < 0 or int(content.max()) >= self.base_size
):
raise ValueError("recursive tokenizer input must contain base ids only")
emitted, emitted_segment = content, segment
for generation in range(1, self.generation + 1):
if emitted.numel() < 2:
break
rows = torch.nonzero(
(self.rule_generation == generation) & self.rule_active,
as_tuple=False,
).squeeze(1)
if not rows.numel():
continue
rule_keys = _recursive_pair_keys(
self.rule_left[rows], self.rule_right[rows]
)
order = torch.argsort(rule_keys, stable=True)
rule_keys = rule_keys[order]
rule_ids = rows[order] + self.base_size
inside = emitted_segment[:-1] == emitted_segment[1:]
adjacent = _recursive_pair_keys(emitted[:-1], emitted[1:])
positions = torch.searchsorted(rule_keys, adjacent)
safe = positions.clamp(max=rule_keys.numel() - 1)
found = inside & (positions < rule_keys.numel()) & (
rule_keys[safe] == adjacent
)
chosen = self._nonoverlapping_leftmost(found)
if not bool(chosen.any()):
continue
chosen_positions = torch.nonzero(chosen, as_tuple=False).squeeze(1)
replacement = emitted.clone()
replacement[chosen_positions] = rule_ids[safe[chosen_positions]]
keep = torch.ones_like(emitted, dtype=torch.bool)
keep[chosen_positions + 1] = False
emitted = replacement[keep]
emitted_segment = emitted_segment[keep]
return emitted, emitted_segment
def pool(
self,
base_embedding: torch.Tensor,
content: torch.Tensor,
segment: torch.Tensor,
language: torch.Tensor,
n_sentences: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Retokenize, train emitted rows, and observe next-generation pairs."""
emitted, emitted_segment = self.retokenize(content, segment)
if self.training and self.usage_counts is not None and emitted.numel():
learned_rows = emitted[emitted >= self.base_size] - self.base_size
if learned_rows.numel():
self.usage_counts.index_add_(
0,
learned_rows,
torch.ones_like(learned_rows, dtype=self.usage_counts.dtype),
)
vectors = self._lookup(emitted, base_embedding)
weight = torch.ones(emitted.numel(), dtype=vectors.dtype, device=vectors.device)
numerator = torch.zeros(
n_sentences, self.dim, dtype=vectors.dtype, device=vectors.device
)
denominator = torch.zeros(n_sentences, dtype=vectors.dtype, device=vectors.device)
denominator.index_add_(0, emitted_segment, weight)
if emitted.numel() > 1:
inside = emitted_segment[:-1] == emitted_segment[1:]
if bool(inside.any()):
left, right = emitted[:-1][inside], emitted[1:][inside]
pair_segment = emitted_segment[:-1][inside]
pair_language = language[pair_segment]
pair_key = _recursive_pair_keys(left, right)
bucket = _proposal_hash(left, right, self.residual_buckets)
probability = torch.sigmoid(
self.proposal_score[bucket] + self.language_bias[pair_language]
)
if (
self.discovery is not None
and self.training
and probability.requires_grad
):
saved_key = pair_key.detach()
saved_language = pair_language.detach()
saved_probability = probability.detach()
def observe(gradient: torch.Tensor) -> None:
if self.discovery is not None:
self.discovery.observe(
saved_key, saved_language, saved_probability, gradient
)
probability.register_hook(observe)
positions = torch.nonzero(inside, as_tuple=False).squeeze(1)
half = 0.5 * probability
weight = weight.index_add(0, positions, -half)
weight = weight.index_add(0, positions + 1, -half)
_weighted_index_add_(
numerator,
pair_segment,
self.proposal_merged[bucket],
probability,
)
denominator.index_add_(0, pair_segment, -probability)
_weighted_index_add_(numerator, emitted_segment, vectors, weight)
return (
numerator / denominator.clamp_min(1e-3).unsqueeze(1),
emitted,
emitted_segment,
)
@staticmethod
def _optimizer_row(
optimizer: torch.optim.Optimizer,
parameter: nn.Parameter,
row: int,
) -> dict[Any, Any]:
state = optimizer.state.get(parameter, {})
if not state:
raise ValueError("recursive promotion requires initialized parent Adam state")
result: dict[Any, Any] = {}
for name, value in state.items():
if not torch.is_tensor(value):
result[name] = value
elif value.ndim == 0:
result[name] = value.detach().clone()
elif tuple(value.shape) == tuple(parameter.shape):
result[name] = value[row].detach().clone()
else:
raise ValueError(f"unknown recursive optimizer state shape: {name}")
return result
@staticmethod
def _average_states(left: dict[Any, Any], right: dict[Any, Any]) -> dict[Any, Any]:
if set(left) != set(right):
raise ValueError("recursive parent Adam states differ")
result: dict[Any, Any] = {}
for name in left:
a, b = left[name], right[name]
if torch.is_tensor(a) != torch.is_tensor(b):
raise ValueError(f"recursive parent Adam state type differs: {name}")
if not torch.is_tensor(a):
if a != b:
raise ValueError(f"recursive parent Adam scalar differs: {name}")
result[name] = a
elif a.ndim == 0:
if not torch.equal(a, b):
raise ValueError(f"recursive parent Adam step differs: {name}")
result[name] = a.detach().clone()
else:
if a.shape != b.shape:
raise ValueError(f"recursive parent Adam row differs: {name}")
result[name] = (0.5 * (a + b)).detach().clone()
return result
def _parent_state(
self,
optimizer: torch.optim.Optimizer,
base_embedding: nn.Parameter,
token_id: int,
) -> dict[Any, Any]:
if token_id < self.base_size:
return self._optimizer_row(optimizer, base_embedding, token_id)
return self._optimizer_row(
optimizer, self.learned, token_id - self.base_size
)
def promotable_pair_keys(
self, pair_keys: Iterable[int]
) -> tuple[tuple[int, ...], dict[str, int]]:
existing_pairs = {
recursive_pair_key(int(left), int(right))
for left, right in zip(
self.rule_left.detach().cpu().tolist(),
self.rule_right.detach().cpu().tolist(),
strict=True,
)
}
existing_spans = {
self.token_span(token_id)
for token_id in range(self.base_size, self.base_size + self.learned_count)
}
accepted: list[int] = []
rejections = {
"already_rule": 0,
"unknown_parent": 0,
"duplicate_span": 0,
"span_too_long": 0,
}
total = self.base_size + self.learned_count
for key in sorted({int(value) for value in pair_keys}):
left, right = decode_recursive_pair_key(key)
if key in existing_pairs:
rejections["already_rule"] += 1
continue
if left >= total or right >= total:
rejections["unknown_parent"] += 1
continue
span = self.token_span(left) + self.token_span(right)
if len(span) > self.max_span_length:
rejections["span_too_long"] += 1
continue
if span in existing_spans:
rejections["duplicate_span"] += 1
continue
accepted.append(key)
existing_spans.add(span)
return tuple(accepted), rejections
def deactivate_unobserved(
self, observed_token_ids: Iterable[int]
) -> dict[str, Any]:
"""Deactivate the exact active sub-DAG absent from both audit windows.
Every active ancestor of an observed learned token is retained. All
remaining active rules can be disabled without changing either observed
token stream: they were neither emitted nor needed to emit a descendant.
Physical rows and Adam state remain stable for cheap future reactivation.
"""
observed = sorted({int(value) for value in observed_token_ids})
if any(
token_id < self.base_size
or token_id >= self.base_size + self.learned_count
for token_id in observed
):
raise ValueError("recursive usage contains an unknown learned token")
active = self.rule_active.detach().cpu().tolist()
left = self.rule_left.detach().cpu().tolist()
right = self.rule_right.detach().cpu().tolist()
keep = [False] * self.learned_count
if observed:
rows = [token_id - self.base_size for token_id in observed]
if not all(active[row] for row in rows):
raise ValueError("recursive usage contains an inactive token")
for row in rows:
keep[row] = True
# Child ids are always larger than learned parent ids, so one reverse
# pass closes the retained set over every active learned ancestor.
for row in range(self.learned_count - 1, -1, -1):
if not keep[row]:
continue
for parent in (int(left[row]), int(right[row])):
if parent >= self.base_size:
keep[parent - self.base_size] = True
rows = [
row for row, enabled in enumerate(active) if enabled and not keep[row]
]
if rows:
device_rows = torch.tensor(
rows, dtype=torch.long, device=self.rule_active.device
)
self.rule_active[device_rows] = False
token_ids = [self.base_size + row for row in rows]
return {
"deactivated_rows": len(token_ids),
"active_rows": self.active_count,
"token_ids_sha256": hashlib.sha256(
json.dumps(token_ids, separators=(",", ":")).encode("utf-8")
).hexdigest(),
}
def reactivate_pair_keys(self, pair_keys: Iterable[int]) -> dict[str, Any]:
"""Reactivate inactive exact rules whose complete parent path is active."""
left_values = self.rule_left.detach().cpu().tolist()
right_values = self.rule_right.detach().cpu().tolist()
by_key = {
recursive_pair_key(int(left), int(right)): row
for row, (left, right) in enumerate(
zip(left_values, right_values, strict=True)
)
}
reactivated_keys: list[int] = []
reactivated_ids: list[int] = []
rejected_inactive_parent = 0
active = self.rule_active.detach().cpu().tolist()
for key in sorted({int(value) for value in pair_keys}):
row = by_key.get(key)
if row is None or bool(active[row]):
continue
parents = (int(left_values[row]), int(right_values[row]))
if any(
parent >= self.base_size
and not bool(active[parent - self.base_size])
for parent in parents
):
rejected_inactive_parent += 1
continue
active[row] = True
reactivated_keys.append(key)
reactivated_ids.append(self.base_size + row)
if reactivated_ids:
rows = torch.tensor(
[token_id - self.base_size for token_id in reactivated_ids],
dtype=torch.long,
device=self.rule_active.device,
)
self.rule_active[rows] = True
return {
"reactivated_rows": len(reactivated_ids),
"active_rows": self.active_count,
"pair_keys": reactivated_keys,
"token_ids": reactivated_ids,
"rejected_inactive_parent": rejected_inactive_parent,
}
def grow(
self,
pair_keys: Iterable[int],
*,
base_embedding: nn.Parameter,
optimizer: torch.optim.Optimizer,
initialization: str = "parent_mean",
) -> dict[str, Any]:
"""Atomically append one dense generation and migrate live Adam state."""
if initialization not in {"parent_mean", "parent_sum"}:
raise ValueError("unknown recursive row initialization")
accepted, rejections = self.promotable_pair_keys(pair_keys)
if not accepted:
return {
"generation": self.generation,
"added_rows": 0,
"learned_rows": self.learned_count,
"active_rows": self.active_count,
"rejections": rejections,
}
pairs = tuple(decode_recursive_pair_key(key) for key in accepted)
old_parameter = self.learned
old_count = self.learned_count
new_values = []
new_states = []
spans = []
for left, right in pairs:
parent_ids = torch.tensor(
[left, right], dtype=torch.long, device=base_embedding.device
)
parent_values = self._lookup(parent_ids, base_embedding)
new_values.append(
parent_values.sum(dim=0)
if initialization == "parent_sum"
else parent_values.mean(dim=0)
)
new_states.append(
self._average_states(
self._parent_state(optimizer, base_embedding, left),
self._parent_state(optimizer, base_embedding, right),
)
)
spans.append(self.token_span(left) + self.token_span(right))
new_parameter = nn.Parameter(
torch.cat((old_parameter.detach(), torch.stack(new_values)), dim=0),
requires_grad=True if not old_count else old_parameter.requires_grad,
)
occurrences = sum(
candidate is old_parameter
for group in optimizer.param_groups
for candidate in group["params"]
)
if occurrences not in ({0, 1} if not old_count else {1}):
raise ValueError("recursive learned table must occur once in the optimizer")
old_state = optimizer.state.get(old_parameter, {})
migrated: dict[Any, Any] = {}
for name in new_states[0]:
additions = [state[name] for state in new_states]
first = additions[0]
if not torch.is_tensor(first):
if any(value != first for value in additions[1:]):
raise ValueError(f"recursive added Adam scalar differs: {name}")
migrated[name] = first
elif first.ndim == 0:
if any(not torch.equal(value, first) for value in additions[1:]):
raise ValueError(f"recursive added Adam step differs: {name}")
if old_state and not torch.equal(old_state[name], first):
raise ValueError(f"recursive existing/added Adam step differs: {name}")
migrated[name] = first.detach().clone()
else:
added = torch.stack(additions)
if old_count:
old_value = old_state.get(name)
if (
not torch.is_tensor(old_value)
or tuple(old_value.shape) != tuple(old_parameter.shape)
):
raise ValueError(f"recursive existing Adam rows differ: {name}")
added = added.to(device=old_value.device, dtype=old_value.dtype)
migrated[name] = torch.cat((old_value.detach(), added), dim=0)
else:
migrated[name] = added
for group in optimizer.param_groups:
replaced: list[nn.Parameter] = []
for parameter in group["params"]:
replaced.append(
new_parameter if parameter is old_parameter else parameter
)
if occurrences == 0 and parameter is base_embedding:
replaced.append(new_parameter)
group["params"] = replaced
optimizer.state.pop(old_parameter, None)
optimizer.state[new_parameter] = migrated
self.learned = new_parameter
device = self.rule_left.device
self.rule_left = torch.cat(
(self.rule_left, torch.tensor([left for left, _ in pairs], device=device))
)
self.rule_right = torch.cat(
(self.rule_right, torch.tensor([right for _, right in pairs], device=device))
)
new_generation = self.generation + 1
self.rule_generation = torch.cat(
(
self.rule_generation,
torch.full((len(pairs),), new_generation, dtype=torch.long, device=device),
)
)
self.rule_active = torch.cat(
(
self.rule_active,
torch.ones(len(pairs), dtype=torch.bool, device=device),
)
)
span_values = [value for span in spans for value in span]
lengths = torch.tensor([len(span) for span in spans], dtype=torch.long, device=device)
appended_offsets = self.span_offsets[-1] + lengths.cumsum(0)
self.span_offsets = torch.cat((self.span_offsets, appended_offsets))
self.span_values = torch.cat(
(self.span_values, torch.tensor(span_values, dtype=torch.long, device=device))
)
self.discovery = None
return {
"generation": new_generation,
"added_rows": len(pairs),
"learned_rows": self.learned_count,
"active_rows": self.active_count,
"token_ids": list(range(self.base_size + old_count, self.base_size + self.learned_count)),
"pairs": [[left, right] for left, right in pairs],
"rejections": rejections,
"initialization": initialization,
"metadata_sha256": self.metadata_sha256(),
}
|