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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 | """Dense single-stage spatial/temporal Set-BPE using AC2Sparse's merge trainer."""
from __future__ import annotations
import json
from collections.abc import Sequence
from dataclasses import dataclass
from numbers import Integral
from pathlib import Path
import numpy as np
from ._validation import active_dimensions
from .bpe import BPE_NAME, _dense_cells, _read_payload, _rules
from .cache import BoundedCache
from .grammar import _PrefixGrammar
from .sparse.training import fit_sparse_vocabulary
from .sparse.vocabulary import SparseVocabulary
@dataclass(frozen=True)
class DenseSetLayout:
'Keep every active physical cell and append an end-of-frame atom.'
bins: tuple[int, ...]
dense = True
@property
def dims(self):
return len(self.bins)
@property
def e_dim(self):
return self.dims
@property
def zero(self):
return (-2,) * self.dims
@property
def native_zero(self):
return [-2] * self.dims
@property
def sparse_bins(self):
return self.bins + (1,)
def sparsify(self, cells):
cells = np.asarray(cells, dtype=np.int64)
if cells.ndim != 2 or cells.shape[1] != self.dims or not len(cells):
raise ValueError("cells must be a nonempty dense trajectory")
if np.any(cells < -1) or np.any(cells >= np.asarray(self.bins)):
raise ValueError("cell outside the primitive range")
if np.any((cells >= 0) != (cells[0] >= 0)) or not np.any(cells[0] >= 0):
raise ValueError("active slots must remain fixed and nonempty")
out = np.full((len(cells), self.dims + 1), -1, dtype=np.int64)
out[:, : self.dims] = cells
out[:-1, self.dims] = 0
return out
def densify(self, frames, active):
cells = np.asarray(frames, dtype=np.int64)[:, : self.dims].copy()
flags = np.asarray(active, dtype=bool)
if np.any(cells[:, flags] < 0) or np.any(cells[:, ~flags] >= 0):
raise ValueError("dense stream is missing an active cell")
return cells
class UnifiedSetBPESerializer:
'Single-stage Set-BPE over complete physical cells and frame markers.'
scheme = "unified_set_bpe"
def __init__(self, *, bins: Sequence[int], lookahead: int, merges):
self.bins = tuple(int(v) for v in bins)
self.lookahead = int(lookahead)
self.merges = _rules(merges, 3, "merges")
self.layout = DenseSetLayout(self.bins)
self.vocabulary = SparseVocabulary(self.layout, (), self.merges, self.lookahead)
self.vocab_size = self.vocabulary.vocab_size
physical_mask = (1 << len(self.bins)) - 1
self.token_cell_counts = np.asarray(
[
sum((int(mask) & physical_mask).bit_count() for mask in masks)
for masks in self.vocabulary.tables.masks
],
dtype=np.int64,
)
def configure_encoding(self, *, spatial_search=False, temporal_search=False, **kwargs):
if spatial_search or temporal_search:
raise ValueError("packing search is not available for unified Set-BPE")
return self
def encode_batch(self, cells, *, active_dims=None):
dense, _ = _dense_cells(np.asarray(cells), self.bins, active_dims, ndim=3)
return self.vocabulary.encode_batch(dense)
def encode(self, cells, *, active_dims=None):
dense, _ = _dense_cells(np.asarray(cells), self.bins, active_dims)
return self.vocabulary.encode_batch(dense[None])[0]
def decode_batch(self, streams, *, active_dims=None):
dims = active_dimensions(active_dims, len(self.bins))
rows, horizons = [], []
for row in streams:
checked = []
for token in row:
if (
isinstance(token, bool)
or not isinstance(token, Integral)
or not 0 <= int(token) < self.vocab_size
):
raise ValueError("token outside vocabulary")
checked.append(int(token))
count = sum(int(self.token_cell_counts[token]) for token in checked)
horizon, remainder = divmod(count, len(dims))
if not checked or not horizon or remainder:
raise ValueError("incomplete unified Set-BPE stream")
rows.append(checked)
horizons.append(horizon)
if not rows or len(set(horizons)) != 1:
raise ValueError("streams must have the same nonempty horizon")
flags = [d in dims for d in range(len(self.bins))]
return self.vocabulary.decode_batch(rows, horizons[0], flags)
def decode(self, tokens, *, active_dims=None):
return self.decode_batch([tokens], active_dims=active_dims)[0]
def to_dict(self):
return {"merges": [list(rule) for rule in self.merges]}
@classmethod
def from_pretrained(cls, directory: str | Path, *, bins, lookahead):
payload = _read_payload(directory)
if set(payload) != {"merges"}:
raise ValueError("unified bpe.json requires exactly merges")
return cls(bins=bins, lookahead=lookahead, merges=payload["merges"])
def save_pretrained(self, directory: str | Path):
path = Path(directory) / BPE_NAME
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(self.to_dict(), indent=2) + "\n", encoding="utf-8")
return path
class UnifiedSetBPEGrammar(_PrefixGrammar):
'Dense frame-coverage grammar for unified Set-BPE.'
def __init__(self, serializer, *, horizon, active_dims=None):
if isinstance(horizon, bool) or not isinstance(horizon, int) or horizon < 1:
raise ValueError("horizon must be positive")
self.horizon, self.vocab_size = horizon, serializer.vocab_size
dims = active_dimensions(active_dims, len(serializer.bins))
self._full = sum(1 << d for d in dims)
self._e_bit = 1 << len(serializer.bins)
self._e_atom = sum(serializer.bins)
self._patterns = serializer.vocabulary.tables.masks
self._lookahead = serializer.lookahead
self._state_cache, self._mask_cache = BoundedCache(256), BoundedCache(64)
self._empty_state = self._initial_state()
self._native = None
def _compiled(self):
return None
def _initial_state(self):
return (0,) * self.horizon
def _covered_steps(self, state):
for index, mask in enumerate(state):
needed = self._full | (self._e_bit if index < self.horizon - 1 else 0)
if mask != needed:
return index
return self.horizon
def _state_is_complete(self, state):
return self._covered_steps(state) == self.horizon
def _advance(self, state, token):
if not 0 <= token < self.vocab_size:
return None
front = self._covered_steps(state)
if front == self.horizon:
return None
masks = self._patterns[token]
if front + len(masks) > self.horizon:
return None
updated = list(state)
allowed = self._full | self._e_bit
for offset, mask in enumerate(masks):
mask = int(mask)
if not mask:
continue
index = front + offset
if mask & ~allowed or mask & updated[index] or updated[index] & self._e_bit:
return None
if index == self.horizon - 1 and mask & self._e_bit:
return None
updated[index] |= mask
if mask & self._e_bit and updated[index] & self._full != self._full:
return None
return tuple(updated)
def _python_mask(self, state):
return np.asarray(
[self._advance(state, token) is not None for token in range(self.vocab_size)]
)
def completion_token(self, state, zero_cells, bins):
front = self._covered_steps(state)
missing = self._full & ~state[front]
if not missing:
return self._e_atom
dim = (missing & -missing).bit_length() - 1
return sum(bins[:dim]) + int(zero_cells[dim])
def fit_unified_set_bpe(config, cells, *, threads, backend="native", progress=True):
"""Fit AC2Sparse's one-stage Set-BPE on complete dense-cell chunks."""
from .training import build_primitives
primitives = build_primitives(config)
layout = DenseSetLayout(tuple(primitives.bins))
vocab, report = fit_sparse_vocabulary(
layout,
cells,
spatial_vocab_size=sum(layout.sparse_bins),
vocab_size=config.train.bpe.vocab_size,
lookahead=config.set_bpe.lookahead,
backend=backend,
threads=threads,
progress=progress,
)
serializer = UnifiedSetBPESerializer(
bins=primitives.bins, lookahead=config.set_bpe.lookahead, merges=vocab.temporal_merges
)
return serializer, report
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