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from __future__ import annotations
import heapq
from collections.abc import Sequence
import numpy as np
from ..native_backend import require_native, resolve_backend
from .spatial import SpatialVocabulary
__all__ = ["train_spatial"]
class _SparseSpatialTrainer:
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,
}
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)
)
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)
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]]:
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.'
if resolve_backend(backend) == "python":
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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