File size: 12,873 Bytes
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 | """ActionCodec2Sparse vocabulary training: optional spatial stage, then certified temporal merges."""
from __future__ import annotations
import heapq
import time
from collections import defaultdict
from collections.abc import Sequence
import numpy as np
from ..native_backend import native_kernel, resolve_backend
from ..setbpe.spatial import aggregate_records
from ..setbpe.spatial_training import train_spatial
from .layout import SparseLayout, sparsify_chunks
from .vocabulary import Instances, SparseVocabulary, TokenTables, rule_key
MIN_PAIR_COUNT = 2
CERTIFY_BATCH = 32
def _unpack(key: int) -> tuple[int, int, int]:
return key >> 40, (key >> 16) & 0xFFFFFF, key & 0xFFFF
class _Stat:
__slots__ = (
"retired",
"raw",
"version",
"exact",
"exact_version",
"added",
"added_at_exact",
"dirty_epoch",
"live",
"queued_version",
"queued_count",
)
def __init__(self) -> None:
self.retired = self.live = False
self.raw = self.version = self.exact_version = self.added = self.added_at_exact = 0
self.dirty_epoch = self.queued_version = self.queued_count = 0
self.exact = -1
@property
def certified(self) -> bool:
return self.exact >= 0 and self.exact_version == self.version
@property
def bound(self) -> int:
"""Upper bound on the exact count: certified counts grow only by inserted pairs."""
if self.certified:
return self.exact
if self.exact >= 0:
return min(self.raw, self.exact + self.added - self.added_at_exact)
return self.raw
class ReferenceTrainer:
"""Python reference of the native temporal trainer (same interface and merges)."""
def __init__(
self, spatial_support, dims, lookahead, vocab_size, allow_zero_offset, min_count=2
):
self.tables = TokenTables([int(s) for s in spatial_support], dims - 1, lookahead)
self.dims, self.vocab_size = dims, vocab_size
self.allow_zero, self.min_count = allow_zero_offset, min_count
self.stats: dict[int, _Stat] = defaultdict(_Stat)
self.heap: list[tuple[int, int, int]] = []
self._merges: list[tuple[int, int, int]] = []
self.epoch = 0
self.counters = dict(dry_runs=0, rejected_by_validity=0, applied=0)
def set_instances(self, anchors, tokens, frames) -> None:
self.state = Instances(self.tables, int(frames), self.dims)
self.occurrence: dict[int, list[int]] = defaultdict(list)
for anchor, token in zip(
np.asarray(anchors).tolist(), np.asarray(tokens).tolist(), strict=True
):
if not 0 <= token < self.tables.base:
raise ValueError("token outside spatial vocab")
self.occurrence[token].append(self.state.add(token, anchor))
for values in self.occurrence.values():
values.sort(key=lambda i: self.state.root[i])
self.initial_instances = len(self.state.tok)
def merges(self) -> list[tuple[int, int, int]]:
return list(self._merges)
def _queue(self, key: int, stat: _Stat, count: int) -> None:
heapq.heappush(self.heap, (-count, key, stat.version))
stat.live, stat.queued_version, stat.queued_count = True, stat.version, count
def _pairs(self, left: int, right: int, offset: int):
st = self.state
for a in self.occurrence[left]:
if not st.alive[a]:
continue
f = st.root[a] + offset
if f >= st.frames:
continue
b = int(st.member[f, self.tables.rdim[right]])
if b >= 0 and b != a and st.alive[b] and st.tok[b] == right and st.root[b] == f:
yield a, b
def _count_exact(self, key: int) -> int:
left, right, offset = _unpack(key)
consumed, count = set(), 0
for a, b in self._pairs(left, right, offset):
if left == right and a in consumed:
continue
if not self.state.valid(a, b, offset):
continue
if left == right:
consumed.add(b)
count += 1
return count
def _take(self):
count, key, version = heapq.heappop(self.heap)
stat = self.stats.get(key)
if stat is None or stat.retired or not stat.live or version != stat.queued_version:
return None
stat.live = False
if version == stat.version:
return -count, key, stat
if stat.bound > 0:
self._queue(key, stat, stat.bound)
return None
def _current_certified(self, entry) -> bool:
_, key, version = entry
stat = self.stats.get(key)
return (
stat is not None
and not stat.retired
and stat.live
and version == stat.queued_version == stat.version
and stat.certified
)
def _certify(self, keys: list[int]) -> None:
self.counters["dry_runs"] += len(keys)
exact = [self._count_exact(key) for key in keys]
for key, count in zip(keys, exact, strict=True):
stat = self.stats[key]
stat.exact, stat.exact_version, stat.added_at_exact = count, stat.version, stat.added
if count >= self.min_count:
self._queue(key, stat, count)
def _apply(self, left: int, right: int, offset: int, child: int) -> list[int]:
st, delta = self.state, defaultdict(int)
pairs = list(self._pairs(left, right, offset))
tok = st.tok
for a, b in pairs:
if not st.alive[a] or not st.alive[b]:
continue
if not st.valid(a, b, offset):
self.counters["rejected_by_validity"] += 1
continue
for x, y, o in st.partners(a, self.allow_zero):
delta[rule_key(tok[x], tok[y], o)] -= 1
for x, y, o in st.partners(b, self.allow_zero):
if a not in (x, y):
delta[rule_key(tok[x], tok[y], o)] -= 1
root = st.root[a]
st.kill(a)
st.kill(b)
merged = st.add(child, root)
self.occurrence[child].append(merged)
for x, y, o in st.partners(merged, self.allow_zero):
delta[rule_key(tok[x], tok[y], o)] += 1
self.counters["applied"] += 1
dirty = []
for key, change in delta.items():
stat = self.stats[key]
stat.raw += change
stat.added += max(change, 0)
stat.version += 1
if stat.dirty_epoch != self.epoch:
stat.dirty_epoch = self.epoch
dirty.append(key)
return dirty
def train(self, progress: bool = False) -> dict:
del progress
st = self.state
for i in range(len(st.tok)):
for a, b, o in st.partners(i, self.allow_zero):
if a == i:
self.stats[rule_key(st.tok[a], st.tok[b], o)].raw += 1
initial_types = len(self.stats)
for key, stat in list(self.stats.items()):
self._queue(key, stat, stat.raw)
while len(self.tables) < self.vocab_size and self.heap:
taken = self._take()
if taken is None:
continue
count, key, stat = taken
if not stat.certified:
batch = [key]
while self.heap and len(batch) < CERTIFY_BATCH:
if self._current_certified(self.heap[0]):
break
following = self._take()
if following is not None:
batch.append(following[1])
self._certify(batch)
continue
if count != stat.exact or stat.exact < self.min_count:
continue
left, right, offset = _unpack(key)
if self.tables.compose(left, right, offset) is None:
stat.exact = 0
continue
child = self.tables.add_merge(left, right, offset)
self._merges.append((left, right, offset))
stat.retired = True
self.epoch += 1
for dirty in self._apply(left, right, offset, child):
other = self.stats[dirty]
if not other.retired and other.bound > 0:
if not other.live or other.bound > other.queued_count:
self._queue(dirty, other, other.bound)
return dict(
initial_instances=self.initial_instances,
final_instances=sum(st.alive),
initial_rule_types=initial_types,
merges=len(self._merges),
**self.counters,
)
def _spatial_instances(vocabulary: SparseVocabulary, frames: np.ndarray):
bins = np.asarray(vocabulary.layout.sparse_bins, dtype=np.int64) + 1
if float(np.prod(bins.astype(float))) < 2.0**62:
keys = np.ravel_multi_index((frames.astype(np.int64) + 1).T, bins)
unique, inverse = np.unique(keys, return_inverse=True)
rows = np.stack(np.unravel_index(unique, bins), axis=1) - 1
else:
rows, inverse = np.unique(frames.astype(np.int64), axis=0, return_inverse=True)
inverse = np.asarray(inverse).reshape(-1)
nonempty = np.flatnonzero(np.any(rows >= 0, axis=1))
counts = np.zeros(len(rows), dtype=np.int64)
codec = vocabulary._codec()
if codec is not None:
encoded_counts, flat = codec.spatial_encode(np.asarray(rows[nonempty], dtype=np.int32))
counts[nonempty] = encoded_counts
flat = np.asarray(flat, dtype=np.int32)
else:
parts = [vocabulary.spatial.encode(rows[i].tolist()) for i in nonempty]
counts[nonempty] = [len(p) for p in parts]
flat = np.asarray([t for p in parts for t in sorted(p)], dtype=np.int32)
offsets = np.concatenate(([0], np.cumsum(counts)))
per_frame = counts[inverse]
total = int(per_frame.sum())
position = np.arange(total) - np.repeat(np.cumsum(per_frame) - per_frame, per_frame)
return per_frame, flat[np.repeat(offsets[:-1][inverse], per_frame) + position]
def fit_sparse_vocabulary(
layout: SparseLayout,
chunks: Sequence[np.ndarray],
*,
spatial_vocab_size: int,
vocab_size: int,
lookahead: int,
backend: str = "auto",
threads: int = 0,
progress: bool = False,
) -> tuple[SparseVocabulary, dict]:
"""Fit spatial then temporal sparse Set-BPE on independent dense chunks ``[(T_i, D)]``."""
backend = resolve_backend(backend)
started = time.perf_counter()
frames, starts = sparsify_chunks(layout, chunks)
atoms = sum(layout.sparse_bins)
if spatial_vocab_size < atoms:
raise ValueError(f"spatial_vocab_size must be at least the {atoms} sparse atoms")
spatial_merges, spatial_report = (), {}
if spatial_vocab_size > atoms:
records = frames[np.any(frames >= 0, axis=1)]
codes, weights = aggregate_records(records, layout.sparse_bins)
spatial, spatial_report = train_spatial(
codes,
weights,
layout.sparse_bins,
vocab_size=spatial_vocab_size,
backend=backend,
threads=threads,
progress=progress,
)
spatial_merges = spatial.merges
spatial_done = time.perf_counter()
vocabulary = SparseVocabulary(layout, spatial_merges, (), lookahead)
per_frame, tokens = _spatial_instances(vocabulary, frames)
chunk = np.repeat(np.arange(len(starts) - 1), np.diff(starts))
anchors = np.repeat(np.arange(len(frames), dtype=np.int64) + chunk * lookahead, per_frame)
support = [int(s) for s in vocabulary.spatial.support]
arguments = (support, layout.dims + 1, lookahead, vocab_size, True, MIN_PAIR_COUNT)
if backend == "native":
trainer = native_kernel("SparseTrainer")(
*arguments, threads, bool(getattr(layout, "dense", False))
)
else:
trainer = ReferenceTrainer(*arguments)
trainer.set_instances(anchors, tokens.astype(np.int32), len(frames) + len(chunks) * lookahead)
del anchors
temporal_report = dict(trainer.train(progress))
vocabulary = SparseVocabulary(layout, spatial_merges, trainer.merges(), lookahead)
report = dict(
chunks=len(chunks),
frames=len(frames),
sparse_atoms=atoms,
atoms_per_frame=float(np.count_nonzero(frames >= 0) / len(frames)),
spatial_vocab_size=vocabulary.spatial.vocab_size,
vocab_size=vocabulary.vocab_size,
spatial_instances=len(tokens),
spatial=dict(spatial_report),
temporal=temporal_report,
seconds=dict(spatial=spatial_done - started, total=time.perf_counter() - started),
)
return vocabulary, report
|