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1bfc3f0 | 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 | """Fitted ActionCodec2Sparse token layer: spatial Set-BPE per sparse frame plus temporal rules."""
from __future__ import annotations
import heapq
import json
from collections.abc import Sequence
from numbers import Integral
from pathlib import Path
import numpy as np
from ..native_backend import native_kernel, resolve_backend
from ..configuration import _integer
from ..setbpe.spatial import SpatialVocabulary
from .layout import SparseLayout
SPARSE_BPE_NAME = "sparse_bpe.json"
FORMAT = "actioncodec2_sparse_bpe"
FORMAT_VERSION = 1
MAX_LOOKAHEAD = 32
def _ctz(mask: int) -> int:
return (mask & -mask).bit_length() - 1
class TokenTables:
"""Per-token geometry shared by the encoder, decoder, trainer and grammar."""
def __init__(self, support: Sequence[int], e_dim: int, lookahead: int) -> None:
if not 1 <= lookahead <= MAX_LOOKAHEAD:
raise ValueError(f"lookahead must be in [1, {MAX_LOOKAHEAD}]")
self.e_dim, self.lookahead, self.base = e_dim, lookahead, len(support)
self.masks: list[tuple[int, ...]] = []
self.patterns: list[tuple[tuple[int, int], ...]] = []
self.span: list[int] = []
self.rdim: list[int] = []
self.emask: list[int] = []
self.first_from: list[tuple[int, ...]] = []
for token, mask in enumerate(support):
if not mask:
raise ValueError("spatial token without support")
self._push(((0, token),), (int(mask),))
def _push(self, pattern, masks) -> None:
span = max(o for o, m in enumerate(masks) if m) + 1
masks = tuple(masks[:span])
self.masks.append(masks)
self.patterns.append(tuple(pattern))
self.span.append(span)
self.rdim.append(_ctz(masks[0]))
self.emask.append(sum(1 << o for o, m in enumerate(masks) if m >> self.e_dim & 1))
first, following = [], -1
for o in range(span - 1, -1, -1):
if masks[o]:
following = o
first.append(following)
self.first_from.append(tuple(reversed(first)))
def __len__(self) -> int:
return len(self.span)
def mask(self, token: int, offset: int) -> int:
masks = self.masks[token]
return masks[offset] if offset < len(masks) else 0
def compose(self, left: int, right: int, offset: int) -> tuple[int, ...] | None:
"""Union support of ``right`` rooted ``offset`` frames after ``left``, or ``None``."""
if not 0 <= offset < self.lookahead:
return None
span = max(self.span[left], offset + self.span[right])
if span > self.lookahead:
return None
out = [self.mask(left, o) for o in range(span)]
for o in range(self.span[right]):
if out[o + offset] & self.mask(right, o):
return None
out[o + offset] |= self.mask(right, o)
return tuple(out)
def add_merge(self, left: int, right: int, offset: int) -> int:
masks = self.compose(left, right, offset)
if masks is None:
raise ValueError("illegal temporal merge")
pattern = sorted(
self.patterns[left] + tuple((o + offset, t) for o, t in self.patterns[right])
)
self._push(pattern, masks)
return len(self) - 1
def rule_key(left: int, right: int, offset: int) -> int:
return (left << 40) | (right << 16) | offset
class Instances:
"""Mutable rooted token instances over one frame axis (reference implementation)."""
def __init__(self, tables: TokenTables, frames: int, dims: int) -> None:
self.tb, self.frames, self.dims = tables, frames, dims
self.tok: list[int] = []
self.root: list[int] = []
self.alive: list[bool] = []
self.member = np.full((frames, dims), -1, dtype=np.int64)
def key(self, i: int) -> tuple[int, int, int, int]:
e = self.tb.emask[self.tok[i]]
root = self.root[i]
return (root, 1 if e else 0, root + _ctz(e) if e else 0, self.tb.rdim[self.tok[i]])
def add(self, token: int, root: int) -> int:
self.tok.append(token)
self.root.append(root)
self.alive.append(True)
index = len(self.tok) - 1
self._paint(index, index)
return index
def _paint(self, i: int, value: int) -> None:
token, root = self.tok[i], self.root[i]
for o, mask in enumerate(self.tb.masks[token]):
while mask:
d = _ctz(mask)
mask &= mask - 1
if value >= 0 and self.member[root + o, d] >= 0:
raise RuntimeError("instance overlap")
self.member[root + o, d] = value
def kill(self, i: int) -> None:
self.alive[i] = False
self._paint(i, -1)
def _first(self, a: int, b: int) -> bool:
return (self.root[a], self.tb.rdim[self.tok[a]]) < (self.root[b], self.tb.rdim[self.tok[b]])
def partners(self, i: int, allow_zero: bool):
"""Ordered candidate pairs ``(a, b, offset)`` involving ``i`` in the lookahead window."""
tb, L, root = self.tb, self.tb.lookahead, self.root[i]
lo, hi = max(0, root - L + 1), min(self.frames - 1, root + L - 1)
for f in range(lo, hi + 1):
for d in range(self.dims):
j = int(self.member[f, d])
if j < 0 or j == i:
continue
tj, o = self.tok[j], f - self.root[j]
k = max(0, lo - self.root[j])
if tb.first_from[tj][k] != o or _ctz(tb.mask(tj, o)) != d:
continue
a, b = (i, j) if self._first(i, j) else (j, i)
off = self.root[b] - self.root[a]
if off >= L or (off == 0 and not allow_zero):
continue
if max(tb.span[self.tok[a]], off + tb.span[self.tok[b]]) > L:
continue
yield a, b, off
def valid(self, a: int, b: int, off: int) -> bool:
"""Whether merging ``a`` (rooted first) with ``b`` keeps the stream decodable."""
tb, ta, tbb = self.tb, self.tok[a], self.tok[b]
span = max(tb.span[ta], off + tb.span[tbb])
emask = tb.emask[ta] | (tb.emask[tbb] << off)
root = self.root[a]
merged = (root, 1 if emask else 0, root + _ctz(emask) if emask else 0, tb.rdim[ta])
for o in range(span):
mask = tb.mask(ta, o) | (tb.mask(tbb, o - off) if o >= off else 0)
if not mask:
continue
u = root + o
h = int(self.member[u, tb.e_dim])
if h >= 0 and h not in (a, b) and not self.key(h) > merged:
return False
if emask >> o & 1:
for d in range(self.dims):
j = int(self.member[u, d])
if j >= 0 and j not in (a, b) and not self.key(j) < merged:
return False
return True
def serialize(self) -> list[int]:
live = sorted((i for i, alive in enumerate(self.alive) if alive), key=self.key)
return [self.tok[i] for i in live]
class SparseVocabulary:
"""Fitted sparse token layer over one physical profile's dense cells."""
def __init__(self, layout: SparseLayout, spatial_merges, temporal_merges, lookahead: int):
self.layout = layout
self.lookahead = _integer(lookahead, "lookahead", positive=True)
self.spatial = SpatialVocabulary(layout.sparse_bins, tuple(map(tuple, spatial_merges)))
self.temporal_merges = tuple(
tuple(_integer(v, "temporal merge value") for v in m) for m in temporal_merges
)
self.tables = TokenTables([int(s) for s in self.spatial.support], layout.e_dim, lookahead)
self._rank = {}
for rank, (left, right, offset) in enumerate(self.temporal_merges):
if max(left, right) >= len(self.tables):
raise ValueError("a temporal merge must reference already-created tokens")
self.tables.add_merge(left, right, offset)
if self._rank.setdefault(rule_key(left, right, offset), rank) != rank:
raise ValueError("duplicate temporal rule")
self._native = None
@property
def vocab_size(self) -> int:
return len(self.tables)
def __getstate__(self):
return {**vars(self), "_native": None}
def _codec(self):
constructor = native_kernel("SparseCodec")
if constructor is not None and self._native is None:
self._native = constructor(
list(self.layout.bins),
self.layout.native_zero,
[tuple(m) for m in self.spatial.merges],
self.lookahead,
list(self.temporal_merges),
)
return self._native if constructor is not None else None
def _cells(self, cells) -> np.ndarray:
values = np.asarray(cells)
if values.ndim != 3 or values.shape[0] < 1 or values.shape[1] < 1:
raise ValueError("cells must have shape (B, T, D) with B, T >= 1")
if values.shape[2] != self.layout.dims or values.dtype.kind not in "iu":
raise ValueError(f"cells must be integers with {self.layout.dims} slots")
if np.any(values < -1) or np.any(values >= np.asarray(self.layout.bins)):
raise ValueError("cell outside the primitive range")
return values
def encode_batch(self, cells, *, backend: str = "auto", threads: int = 0) -> list[list[int]]:
"""Encode independent dense chunks ``(B, T, D)`` (``-1`` = inactive) into token rows."""
values = self._cells(cells)
if resolve_backend(backend) == "native" and self._codec() is not None:
return self._codec().encode_batch(np.asarray(values, dtype=np.int32), threads)
return [self._encode_reference(chunk) for chunk in values]
def _encode_reference(self, cells: np.ndarray) -> list[int]:
sparse = self.layout.sparsify(cells)
state = Instances(self.tables, len(sparse) + self.lookahead, self.layout.dims + 1)
for t, row in enumerate(sparse):
if np.any(row >= 0):
for token in self.spatial.encode(row.tolist()):
state.add(int(token), t)
heap = []
def offer(i):
for a, b, off in state.partners(i, True):
rank = self._rank.get(rule_key(state.tok[a], state.tok[b], off))
if rank is not None:
item = (rank, state.root[a], self.tables.rdim[state.tok[a]], state.root[b])
heapq.heappush(heap, (*item, a, b, off))
for i in range(len(state.tok)):
offer(i)
while heap:
rank, *_, a, b, off = heapq.heappop(heap)
if not state.alive[a] or not state.alive[b] or not state.valid(a, b, off):
continue
root = state.root[a]
state.kill(a)
state.kill(b)
offer(state.add(self.tables.base + rank, root))
return state.serialize()
def decode_batch(
self, rows: Sequence[Sequence[int]], horizon: int, active: Sequence[bool]
) -> np.ndarray:
"""Token rows to dense cells ``(B, horizon, D)``; inactive slots are ``-1``."""
if isinstance(horizon, bool) or not isinstance(horizon, int) or horizon < 1:
raise ValueError("horizon must be a positive integer")
flags = [bool(a) for a in active]
if len(flags) != self.layout.dims:
raise ValueError(f"active must have {self.layout.dims} flags")
checked = []
for row in rows:
if any(
isinstance(t, (bool, np.bool_))
or not isinstance(t, Integral)
or not 0 <= t < self.vocab_size
for t in row
):
raise ValueError("token ids must be integers within the sparse vocabulary")
checked.append([int(t) for t in row])
codec = self._codec()
if codec is not None:
return np.asarray(codec.decode_batch(checked, horizon, flags), dtype=np.int64)
out = np.empty((len(checked), horizon, self.layout.dims), dtype=np.int64)
for index, row in enumerate(checked):
out[index] = self.layout.densify(self._frames_reference(row, horizon, flags), flags)
return out
def _frames_reference(self, row: Sequence[int], horizon: int, active) -> np.ndarray:
tb, e_bit = self.tables, 1 << self.layout.e_dim
allowed = e_bit | sum(1 << d for d, flag in enumerate(active) if flag)
cover = [0] * (horizon + self.lookahead)
sparse = np.full((horizon, self.layout.dims + 1), -1, dtype=np.int64)
front = 0
for token in row:
if not 0 <= token < len(tb):
raise ValueError("token outside vocab")
if front >= horizon:
raise ValueError("token after the last open frame")
own: dict[int, int] = {}
for offset, spatial in tb.patterns[token]:
u, mask = front + offset, int(self.spatial.support[spatial])
if u >= horizon:
raise ValueError("token exceeds horizon")
if mask & ~allowed:
raise ValueError("token touches an inactive slot")
if own.get(u, 0) & mask:
raise ValueError("overlapping token supports")
if mask & e_bit and u == horizon - 1:
raise ValueError("end-of-frame atom on the last frame")
own[u] = own.get(u, 0) | mask
expansion = self.spatial.expansion[spatial]
sparse[u] = np.where(expansion >= 0, expansion, sparse[u])
for u, mask in own.items():
if cover[u] & mask:
raise ValueError("overlapping token supports")
if cover[u] & e_bit:
raise ValueError("token touches a closed frame")
cover[u] |= mask
while front < horizon and cover[front] & e_bit:
front += 1
return sparse
def to_dict(self) -> dict:
return {
"format": FORMAT,
"format_version": FORMAT_VERSION,
"layout": {"bins": list(self.layout.bins), "zero": list(self.layout.zero)},
"lookahead": self.lookahead,
"spatial_merges": [list(m) for m in self.spatial.merges],
"temporal_merges": [list(m) for m in self.temporal_merges],
}
@classmethod
def from_dict(cls, payload: dict) -> SparseVocabulary:
if payload.get("format") != FORMAT or payload.get("format_version") != FORMAT_VERSION:
raise ValueError(f"not an {FORMAT} v{FORMAT_VERSION} vocabulary")
layout = SparseLayout(tuple(payload["layout"]["bins"]), tuple(payload["layout"]["zero"]))
return cls(
layout, payload["spatial_merges"], payload["temporal_merges"], payload["lookahead"]
)
def save_pretrained(self, directory: str | Path) -> Path:
path = Path(directory) / SPARSE_BPE_NAME
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(self.to_dict(), separators=(",", ":")) + "\n", encoding="utf-8")
return path
@classmethod
def from_pretrained(cls, directory: str | Path) -> SparseVocabulary:
path = Path(directory) / SPARSE_BPE_NAME
return cls.from_dict(json.loads(path.read_text(encoding="utf-8")))
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