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fee0e43 | 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 | """Spatial Set-BPE fitting implementations.
Runtime vocabulary and serialization live in ``spatial``; this module is
loaded only by training entry points.
"""
from __future__ import annotations
import heapq
from collections.abc import Sequence
import numpy as np
from ..backend.native_backend import require_native, resolve_backend
from .setbpe_spatial import SpatialVocabulary
__all__ = ["train_spatial"]
class _SparseSpatialTrainer:
"""Columnar Set-BPE state with counters for observed pairs only."""
def __init__(
self,
codes: np.ndarray,
weights: np.ndarray,
bins: Sequence[int],
) -> None:
self.bins = tuple(int(value) for value in bins)
self.dimension = len(self.bins)
self.records = np.asarray(codes, dtype=np.int64)
self.weights = np.asarray(weights, dtype=np.int64)
if self.records.ndim != 2 or self.records.shape[1] != self.dimension:
raise ValueError("codes must have shape [U, D] matching the bin counts")
if self.weights.shape != (self.records.shape[0],):
raise ValueError("weights must have one entry per record")
counts = np.asarray(self.bins, dtype=np.int64)
if np.any(self.records < -1) or np.any(self.records >= counts[None, :]):
raise ValueError("codes must contain -1 or an in-range cell index")
if np.any(np.all(self.records < 0, axis=1)):
raise ValueError("every record must contain at least one active coordinate")
self.count = int(self.records.shape[0])
self.offsets = np.concatenate(
([0], np.cumsum(np.asarray(self.bins, dtype=np.int64)))
)
self.atom_count = int(self.offsets[-1])
self.occurrence: list[np.ndarray] = []
self.support: list[int] = []
self.anchor: list[int] = []
self.frequency: list[int] = []
for index in range(self.dimension):
column = self.records[:, index]
for value in range(self.bins[index]):
members = np.flatnonzero(column == value).astype(np.int64)
self._append_token(members, 1 << index, index)
self._scratch = np.zeros(self.count, dtype=bool)
self.merges: list[tuple[int, int]] = []
self.statistics = {
"sparse_updates": 0,
"sparse_heap_rebuilds": 0,
"sparse_pair_types_peak": 0,
}
# Row-wise mirror used to initialize and incrementally update pair counts.
self.member = np.where(
self.records >= 0,
self.offsets[:-1][None, :] + self.records,
-1,
)
self._slot_pairs = tuple(
(first, second)
for first in range(self.dimension)
for second in range(first + 1, self.dimension)
)
# The observed-pair backend is built on demand. Keys use two fixed
# 32-bit token fields, so appending vocabulary entries never invalidates
# the counter map as a width-based packed index would.
self._sparse_counts: dict[int, int] | None = None
self._sparse_heap: list[tuple[int, int, int]] = []
def _append_token(self, members: np.ndarray, support: int, anchor: int) -> int:
token = len(self.occurrence)
self.occurrence.append(members)
self.support.append(support)
self.anchor.append(anchor)
self.frequency.append(int(self.weights[members].sum()) if members.size else 0)
return token
def _intersect(self, left: np.ndarray, right: np.ndarray) -> np.ndarray:
if left.size > right.size:
left, right = right, left
if left.size == 0:
return left
self._scratch[left] = True
result = right[self._scratch[right]]
self._scratch[left] = False
return result
@staticmethod
def _sparse_key(left: int, right: int) -> int:
low, high = (left, right) if left < right else (right, left)
return (int(low) << 32) | int(high)
@staticmethod
def _sparse_pair(key: int) -> tuple[int, int]:
return key >> 32, key & 0xFFFFFFFF
def build_sparse_index(self) -> None:
"""Materialize exact counters only for currently observed pairs once."""
if self._sparse_counts is not None:
return
width = len(self.occurrence)
counts = np.zeros(width * width, dtype=np.int64)
for first, second in self._slot_pairs:
left = self.member[:, first]
right = self.member[:, second]
valid = (left >= 0) & (right >= 0)
if not valid.any():
continue
low = np.minimum(left[valid], right[valid])
high = np.maximum(left[valid], right[valid])
np.add.at(counts, low * width + high, self.weights[valid])
observed = np.flatnonzero(counts)
sparse: dict[int, int] = {}
heap: list[tuple[int, int, int]] = []
for packed in observed:
left, right = divmod(int(packed), width)
key = self._sparse_key(left, right)
value = int(counts[packed])
sparse[key] = value
heap.append((-value, left, right))
heapq.heapify(heap)
self._sparse_counts = sparse
self._sparse_heap = heap
self.statistics["sparse_pair_types_peak"] = len(sparse)
def sparse_best_pair(self) -> tuple[int, int, int] | None:
"""Return the exact winner from the observed-pair max heap."""
if self._sparse_counts is None:
self.build_sparse_index()
assert self._sparse_counts is not None
while self._sparse_heap:
negative, left, right = heapq.heappop(self._sparse_heap)
value = self._sparse_counts.get(self._sparse_key(left, right), 0)
if value == -negative:
return left, right, value
return None
def _update_sparse_index(
self,
left: int,
right: int,
child: int,
rows: np.ndarray,
members: np.ndarray,
mass: int,
) -> None:
if self._sparse_counts is None:
return
counts = self._sparse_counts
heap = self._sparse_heap
updates = 0
def change(key: int, delta: int) -> None:
nonlocal updates
new = counts.get(key, 0) + delta
if new < 0:
pair = self._sparse_pair(key)
raise RuntimeError(
f"sparse spatial pair counter became negative for {pair}"
)
if new:
counts[key] = new
pair = self._sparse_pair(key)
heapq.heappush(heap, (-new, pair[0], pair[1]))
else:
counts.pop(key, None)
updates += 1
change(self._sparse_key(left, right), -mass)
other_mass = np.zeros(len(self.occurrence), dtype=np.int64)
weights = self.weights[members]
for slot in range(rows.shape[1]):
values = rows[:, slot]
valid = (values >= 0) & (values != left) & (values != right)
if valid.any():
np.add.at(other_mass, values[valid], weights[valid])
for raw_other in np.flatnonzero(other_mass):
other = int(raw_other)
value = int(other_mass[other])
change(self._sparse_key(left, other), -value)
change(self._sparse_key(right, other), -value)
change(self._sparse_key(child, other), value)
self.statistics["sparse_updates"] += updates
self.statistics["sparse_pair_types_peak"] = max(
self.statistics["sparse_pair_types_peak"], len(counts)
)
if len(heap) > 2 * len(counts) + 65_536:
self._sparse_heap = [
(-value, *self._sparse_pair(key)) for key, value in counts.items()
]
heapq.heapify(self._sparse_heap)
self.statistics["sparse_heap_rebuilds"] += 1
def merge(self, left: int, right: int) -> int:
"""Apply one exact incremental merge update."""
members = self._intersect(self.occurrence[left], self.occurrence[right])
mass = int(self.weights[members].sum()) if members.size else 0
rows = self.member[members] if members.size else self.member[:0].copy()
self.occurrence[left] = np.setdiff1d(
self.occurrence[left], members, assume_unique=True
)
self.occurrence[right] = np.setdiff1d(
self.occurrence[right], members, assume_unique=True
)
self.frequency[left] -= mass
self.frequency[right] -= mass
token = self._append_token(
members,
self.support[left] | self.support[right],
min(self.anchor[left], self.anchor[right]),
)
self._update_sparse_index(left, right, token, rows, members, mass)
# Keep the row mirror in step. Slot order inside a row carries no
# meaning, so the child simply takes the left parent's slot.
if members.size:
rows[rows == right] = -1
rows[rows == left] = token
self.member[members] = rows
self.merges.append((left, right))
return token
def _train_spatial_python(
codes: np.ndarray,
weights: np.ndarray,
bins: Sequence[int],
*,
vocab_size: int,
progress: bool = False,
) -> tuple[SpatialVocabulary, dict[str, object]]:
"""Train Set-BPE with exact counters for observed row-local pairs.
``vocab_size`` is the total budget including the ``sum(bins)`` primitive
atoms. Training stops early when no positive-frequency compatible pair
remains, which happens once every record has collapsed to a single token.
The one-time counter build costs one dense pass; later work is limited to
records changed by the committed merge.
"""
trainer = _SparseSpatialTrainer(codes, weights, bins)
if vocab_size < trainer.atom_count:
raise ValueError(
f"vocab_size {vocab_size} is below the {trainer.atom_count} primitive atoms"
)
total_weight = int(trainer.weights.sum())
active_width = np.sum(trainer.records >= 0, axis=1, dtype=np.int64)
initial = int(np.dot(trainer.weights, active_width))
length = initial
trainer.build_sparse_index()
for step in range(vocab_size - trainer.atom_count):
winner = trainer.sparse_best_pair()
if winner is None:
break
left, right, frequency = winner
trainer.merge(left, right)
length -= frequency
if progress and step % 256 == 0:
print(
f" merge {step:>5} tokens/action {length / total_weight:.4f}",
flush=True,
)
vocabulary = SpatialVocabulary(
tuple(int(value) for value in bins), tuple(trainer.merges)
)
report = {
"records": int(trainer.count),
"weight": total_weight,
"atoms": trainer.atom_count,
"merges": len(trainer.merges),
"vocab_size": vocabulary.vocab_size,
"tokens_per_action_initial": initial / total_weight,
"tokens_per_action": length / total_weight,
"compression": initial / length,
"sparse_updates": trainer.statistics["sparse_updates"],
"sparse_heap_rebuilds": trainer.statistics["sparse_heap_rebuilds"],
"sparse_pair_types_peak": trainer.statistics["sparse_pair_types_peak"],
}
return vocabulary, report
def train_spatial(
codes: np.ndarray,
weights: np.ndarray,
bins: Sequence[int],
*,
vocab_size: int,
backend: str = "auto",
progress: bool = False,
**kwargs: object,
) -> tuple[SpatialVocabulary, dict[str, object]]:
"""Fit spatial Set-BPE, preferring the native kernel when it is built.
``codes`` is one row per distinct quantized action and ``weights`` its
multiplicity (see :func:`aggregate_records`); collapsing duplicates first is
lossless because identical actions follow identical merges forever.
``vocab_size`` is the *total* budget including the ``sum(bins)`` primitive
atoms. Training stops early once no positive-frequency compatible pair
remains. Both backends return the identical greedy merge sequence -- the
test suite proves it -- so ``backend`` only trades speed for portability.
"""
if resolve_backend(backend) == "python":
# ``threads`` controls only the native OpenMP kernel. The profile fit API carries
# it for either backend, but the Python reference has no such argument.
kwargs.pop("threads", None)
return _train_spatial_python(
codes, weights, bins, vocab_size=vocab_size, progress=progress, **kwargs
)
values = np.ascontiguousarray(codes, dtype=np.int32)
masses = np.ascontiguousarray(weights, dtype=np.int64)
merges, report = require_native().train_spatial(
values,
masses,
[int(count) for count in bins],
int(vocab_size),
int(kwargs.get("threads", 0)),
)
vocabulary = SpatialVocabulary(
tuple(int(count) for count in bins),
tuple((int(left), int(right)) for left, right in merges),
)
return vocabulary, dict(report)
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