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from __future__ import annotations
import math
import time
from bisect import bisect_right
from collections import defaultdict
from collections.abc import Iterable, Sequence
import numpy as np
from ._validation import broadcast_vector
from .bpe import BPESerializer, SetBPESerializer
from .bpe_training import train_bpe
from .configuration import ActionCodec2Config, ConfigError
from .physical_codec import PhysicalCodec
from .primitives import PrimitiveStore
from .processor import ActionCodec2Processor
from .setbpe.spatial import aggregate_records
from .setbpe.spatial_training import train_spatial
from .setbpe.temporal_training import train_temporal
from .trajectory import TrajectoryLike, prepare_trajectory, trajectory_boundary
def _zero_anchored_grid(lower: np.float32, upper: np.float32, spacing: np.float32) -> np.ndarray:
below = int(np.ceil(-lower / spacing))
above = int(np.ceil(upper / spacing))
negative = np.linspace(lower, np.float32(0.0), below + 1, dtype=np.float32)[:-1]
positive = np.linspace(np.float32(0.0), upper, above + 1, dtype=np.float32)
return np.concatenate((negative, positive))
def _additive_axis(
lower: np.float32,
upper: np.float32,
distortion_limit: np.float32,
*,
zero_primitive: bool,
) -> np.ndarray:
width = np.float32(upper - lower)
if width == 0.0:
return np.asarray([lower], dtype=np.float32)
spacing = np.float32(2.0) * distortion_limit
if zero_primitive and lower < 0.0 < upper:
return _zero_anchored_grid(lower, upper, spacing)
count = 1 + int(np.ceil(width / spacing))
return np.linspace(lower, upper, count, dtype=np.float32)
def build_primitives(config: ActionCodec2Config) -> PrimitiveStore:
'Derive primitive arrays and force an exact zero on every axis.'
if config.train is None:
raise ConfigError("config.train is required to build primitives")
output: list[np.ndarray | None] = [None] * config.physical.input_dim
for component in config.physical.components.values():
width = len(component.dims)
if component.space == "additive":
domain = component.constraints[f"order_{component.primitive_order}"]
lower = broadcast_vector(
domain.minimum, width, dtype=np.dtype(np.float32), name="minimum"
)
upper = broadcast_vector(
domain.maximum, width, dtype=np.dtype(np.float32), name="maximum"
)
epsilon = broadcast_vector(
component.distortion_limit,
width,
dtype=np.dtype(np.float32),
name="distortion_limit",
)
if component.primitive_order == 2:
lower = lower - 2 * epsilon
upper = upper + 2 * epsilon
axes = [
_additive_axis(
lower[index],
upper[index],
epsilon[index],
zero_primitive=True,
)
for index in range(width)
]
else:
domain = component.constraints[f"order_{component.primitive_order}"]
radius_dtype = (
np.float64 if component.representation == "rotation_vector" else np.float32
)
lower = broadcast_vector(
domain.minimum, width, dtype=np.dtype(radius_dtype), name="minimum"
)
upper = broadcast_vector(
domain.maximum, width, dtype=np.dtype(radius_dtype), name="maximum"
)
corner = np.maximum(np.abs(lower), np.abs(upper))
source_radius = (
float(np.linalg.norm(corner))
if component.representation == "rotation_vector"
else float(np.sum(corner, dtype=np.float32))
)
source_radius = min(math.pi, source_radius)
epsilon = float(component.distortion_limit)
allowance = (3 if component.primitive_order == 2 else 1) * epsilon
cover_radius = float(min(math.pi, source_radius + allowance))
maximum_spacing = 2.0 * epsilon / math.sqrt(3.0)
count = int(math.ceil(2.0 * cover_radius / maximum_spacing)) + 1
if count % 2 == 0:
count += 1
axis = np.linspace(-cover_radius, cover_radius, count, dtype=np.float64)
if count % 2 == 1:
axis[count // 2] = 0.0
axes = [axis.copy() for _ in range(width)]
for dimension, axis in zip(component.dims, axes, strict=True):
output[dimension] = axis
if any(axis is None for axis in output):
raise RuntimeError("internal error: primitive construction left an unassigned dimension")
store = PrimitiveStore([axis for axis in output if axis is not None])
missing_zero = [
dimension
for dimension, axis in enumerate(store.values)
if not np.any(np.asarray(axis) == 0)
]
if missing_zero:
raise ConfigError(
"every fitted primitive axis must contain exact zero; missing dimension(s) "
f"{missing_zero}. Ensure each configured domain contains zero."
)
return store
def _validate_component_support(
active_dims: tuple[int, ...], config: ActionCodec2Config, *, index: int
) -> None:
active = set(active_dims)
for name, component in config.physical.components.items():
present = active.intersection(component.dims)
if component.space == "so3" and present and present != set(component.dims):
raise ValueError(f"trajectory {index} activates only part of SO(3) component {name!r}")
def _length_buckets(
pending: Sequence[tuple[np.ndarray, tuple[int, ...], np.ndarray | None]],
*,
maximum_ratio: float = 1.25,
) -> list[list[int]]:
ordered = sorted(range(len(pending)), key=lambda index: len(pending[index][0]))
buckets: list[list[int]] = []
current: list[int] = []
shortest = 0
for index in ordered:
length = len(pending[index][0])
if current and length > shortest * maximum_ratio:
buckets.append(current)
current = []
if not current:
shortest = length
current.append(index)
if current:
buckets.append(current)
return buckets
class _CellCorpus(Sequence[np.ndarray]):
_actioncodec2_validated = True
def __init__(self, chunks: list[np.ndarray], offsets: list[np.ndarray]) -> None:
self.chunks: list[np.ndarray | None] = list(chunks)
self.offsets = tuple(offsets)
counts = np.asarray([len(value) - 1 for value in offsets], dtype=np.int64)
self._episode_ends = np.cumsum(counts)
def __len__(self) -> int:
return int(self._episode_ends[-1]) if len(self._episode_ends) else 0
def __getitem__(self, index: int) -> np.ndarray:
if not isinstance(index, int):
raise TypeError("cell corpus indices must be integers")
if index < 0:
index += len(self)
if not 0 <= index < len(self):
raise IndexError(index)
chunk = bisect_right(self._episode_ends, index)
previous = 0 if chunk == 0 else int(self._episode_ends[chunk - 1])
local = index - previous
start, stop = self.offsets[chunk][local : local + 2]
values = self.chunks[chunk]
if values is None:
raise RuntimeError("cell corpus chunk has already been consumed")
return values[int(start) : int(stop)]
def __iter__(self):
for chunk, offsets in zip(self.chunks, self.offsets, strict=True):
if chunk is None:
raise RuntimeError("cell corpus chunk has already been consumed")
for start, stop in zip(offsets[:-1], offsets[1:], strict=True):
yield chunk[int(start) : int(stop)]
def release_chunk(self, index: int) -> None:
"""Release a replayed chunk while retaining compact episode metadata."""
self.chunks[index] = None
def _quantize_pending_batch(
pending: Sequence[tuple[np.ndarray, tuple[int, ...], np.ndarray | None]],
physical: PhysicalCodec,
cell_dtype: type[np.signedinteger],
) -> list[np.ndarray]:
result: list[np.ndarray | None] = [None] * len(pending)
dimension = physical.config.physical.input_dim
for indices in _length_buckets(pending):
length = max(len(pending[index][0]) for index in indices)
batch = np.zeros((len(indices), length, dimension), dtype=np.float32)
active_by_item: list[tuple[int, ...]] = []
for local, index in enumerate(indices):
trajectory, active_dims, _ = pending[index]
item = batch[local, : len(trajectory)]
item[:, active_dims] = trajectory
active_by_item.append(active_dims)
boundaries = [pending[index][2] for index in indices]
previous = (
None
if all(value is None for value in boundaries)
else np.stack(
[
np.zeros(dimension, dtype=np.float32) if value is None else value
for value in boundaries
]
)
)
encoded = physical.encode_batch(batch, previous_first_order=previous).astype(
cell_dtype, copy=False
)
for local, index in enumerate(indices):
item_length = len(pending[index][0])
dense = np.full((item_length, dimension), -1, dtype=cell_dtype)
dense[:, active_by_item[local]] = encoded[local][:item_length, active_by_item[local]]
result[index] = dense
if any(value is None for value in result):
raise RuntimeError("internal error: trajectory batch was not fully quantized")
return [value for value in result if value is not None]
def _quantize_trajectories(
trajectories: Iterable[TrajectoryLike],
physical: PhysicalCodec,
*,
batch_size: int,
max_cell_bytes: int | None,
) -> tuple[_CellCorpus, int, int, dict[int, int]]:
if batch_size < 1:
raise ValueError("batch_size must be positive")
cell_chunks: list[np.ndarray] = []
offset_chunks: list[np.ndarray] = []
cell_parts: list[np.ndarray] = []
cell_lengths: list[int] = []
chunk_actions = 0
action_count = 0
active_value_count = 0
arity_counts: dict[int, int] = defaultdict(int)
source_index = 0
retained_cell_bytes = 0
cell_dtype = np.int16 if max(physical.bins) <= np.iinfo(np.int16).max else np.int32
iterator = iter(trajectories)
def flush_cells() -> None:
nonlocal chunk_actions, retained_cell_bytes
if not cell_parts:
return
chunk = np.concatenate(cell_parts, axis=0)
projected = retained_cell_bytes + chunk.nbytes
if max_cell_bytes is not None and projected > max_cell_bytes:
raise MemoryError(
"quantized ActionCodec2 corpus would retain at least "
f"{projected / 2**30:.2f} GiB, exceeding the configured "
f"{max_cell_bytes / 2**30:.2f} GiB limit; use stratified fitting, "
"lower coverage, or explicitly raise --max-cell-memory-gib"
)
cell_chunks.append(chunk)
retained_cell_bytes = projected
offset_chunks.append(np.concatenate(([0], np.cumsum(cell_lengths, dtype=np.int64))))
cell_parts.clear()
cell_lengths.clear()
chunk_actions = 0
while True:
pending: list[tuple[np.ndarray, tuple[int, ...], np.ndarray | None]] = []
for _ in range(batch_size):
try:
raw = next(iterator)
except StopIteration:
break
trajectory, active_dims = prepare_trajectory(
raw,
index=source_index,
dimension=physical.config.physical.input_dim,
dtype=np.float32,
)
_validate_component_support(active_dims, physical.config, index=source_index)
source_index += 1
action_count += len(trajectory)
active_value_count += len(trajectory) * len(active_dims)
arity_counts[len(active_dims)] += 1
pending.append(
(trajectory, active_dims, trajectory_boundary(raw, physical.config, active_dims))
)
if not pending:
break
for value in _quantize_pending_batch(pending, physical, cell_dtype):
cell_parts.append(value)
cell_lengths.append(len(value))
chunk_actions += len(value)
if chunk_actions >= 1_000_000:
flush_cells()
flush_cells()
cells = _CellCorpus(cell_chunks, offset_chunks)
if not cells:
raise ValueError("training requires at least one complete trajectory")
return cells, action_count, active_value_count, dict(sorted(arity_counts.items()))
def _aggregate_trajectory_records(
cells: Sequence[np.ndarray], bins: Sequence[int], action_count: int
) -> tuple[np.ndarray, np.ndarray]:
del action_count
dimension = len(tuple(bins))
widths = tuple(int(count).bit_length() for count in bins)
if sum(widths) <= 64:
return _aggregate_packed_records(cells, bins, widths)
counts_by_row: dict[bytes, int] = defaultdict(int)
pending: list[np.ndarray] = []
pending_rows = 0
chunk_rows = 1_000_000
def flush() -> None:
nonlocal pending_rows
if not pending:
return
block = (
np.asarray(pending[0], dtype=np.int32)
if len(pending) == 1
else np.concatenate(pending, axis=0).astype(np.int32, copy=False)
)
unique, counts = aggregate_records(block, bins)
for row, count in zip(unique.astype(np.int32, copy=False), counts, strict=True):
counts_by_row[row.tobytes()] += int(count)
pending.clear()
pending_rows = 0
for trajectory in cells:
pending.append(np.asarray(trajectory, dtype=np.int32))
pending_rows += len(trajectory)
if pending_rows >= chunk_rows:
flush()
flush()
keys = sorted(counts_by_row)
records = np.frombuffer(b"".join(keys), dtype=np.int32).reshape(-1, dimension)
weights = np.fromiter((counts_by_row[key] for key in keys), dtype=np.int64, count=len(keys))
return records, weights
def _aggregate_packed_records(
cells: Sequence[np.ndarray], bins: Sequence[int], widths: Sequence[int]
) -> tuple[np.ndarray, np.ndarray]:
counts_by_key: dict[int, int] = defaultdict(int)
pending: list[np.ndarray] = []
pending_rows = 0
chunk_rows = 1_000_000
def flush() -> None:
nonlocal pending_rows
if not pending:
return
block = pending[0] if len(pending) == 1 else np.concatenate(pending, axis=0)
keys = np.zeros(len(block), dtype=np.uint64)
shift = 0
for dimension, width in enumerate(widths):
encoded = np.asarray(block[:, dimension], dtype=np.int64) + 1
keys |= encoded.astype(np.uint64) << np.uint64(shift)
shift += width
unique, counts = np.unique(keys, return_counts=True)
for key, count in zip(unique, counts, strict=True):
counts_by_key[int(key)] += int(count)
pending.clear()
pending_rows = 0
for trajectory in cells:
pending.append(trajectory)
pending_rows += len(trajectory)
if pending_rows >= chunk_rows:
flush()
flush()
keys = np.fromiter(sorted(counts_by_key), dtype=np.uint64, count=len(counts_by_key))
records = np.empty((len(keys), len(tuple(bins))), dtype=np.int32)
shift = 0
for dimension, width in enumerate(widths):
mask = np.uint64((1 << width) - 1)
records[:, dimension] = ((keys >> np.uint64(shift)) & mask).astype(np.int32) - 1
shift += width
weights = np.fromiter(
(counts_by_key[int(key)] for key in keys), dtype=np.int64, count=len(keys)
)
return records, weights
def _fit_report(
config: ActionCodec2Config,
primitives: PrimitiveStore,
codec: ActionCodec2Processor,
*,
trajectory_count: int,
actions: int,
active_values: int,
arity_counts: dict[int, int],
cell_dtype: str,
unique_records: int,
spatial_report: dict[str, object],
temporal_report: dict[str, object],
timestamps: tuple[float, float, float, float, float, float],
) -> dict[str, object]:
started, primitive_done, quantized, aggregated, spatial_done, finished = timestamps
scheme = config.set_bpe.scheme
stages: dict[str, object] = (
{"bpe": temporal_report}
if scheme == "bpe"
else {"spatial": spatial_report, "temporal": temporal_report}
)
return {
"trajectories": trajectory_count,
"actions": actions,
"active_values": active_values,
"trajectory_arities": {str(key): value for key, value in arity_counts.items()},
"bpe_boundary": "complete_component_trajectory",
"bpe_training_domain": "quantized_primitive_cells",
"physical_boundary": (
"explicit_previous_first_order"
if codec.requires_previous_first_order
else "zero_position_identity_orientation"
),
"component_primitive_orders": {
name: component.primitive_order
for name, component in config.physical.components.items()
},
"allow_cross_component_merge": config.set_bpe.allow_cross_component_merge,
"lookahead": config.set_bpe.lookahead,
"physical_bins": list(primitives.bins),
"cell_dtype": cell_dtype,
"unique_spatial_records": unique_records,
"primitive_atoms": sum(primitives.bins),
"tokenization_scheme": scheme,
**stages,
"vocab_size": codec.vocab_size,
"tokens_per_action": float(temporal_report["tokens_per_action"]),
"seconds": {
"primitives": primitive_done - started,
"quantize": quantized - primitive_done,
"aggregate": aggregated - quantized,
"spatial_train": spatial_done - aggregated,
"spatial": spatial_done - quantized,
"temporal": finished - spatial_done,
"total": finished - started,
},
}
def _fit_set_bpe(
cells: Sequence[np.ndarray],
config: ActionCodec2Config,
primitives: PrimitiveStore,
actions: int,
*,
backend: str,
threads: int,
progress: bool,
) -> tuple[SetBPESerializer, dict[str, object], dict[str, object], int, float, float]:
records, weights = _aggregate_trajectory_records(cells, primitives.bins, actions)
aggregated = time.perf_counter()
bpe = config.train.bpe if config.train is not None else None
assert bpe is not None
spatial, spatial_report = train_spatial(
records,
weights,
primitives.bins,
vocab_size=bpe.spatial_vocab_size,
backend=backend,
threads=threads,
progress=progress,
)
spatial_done = time.perf_counter()
vocabulary, temporal_report = train_temporal(
cells,
spatial,
vocab_size=bpe.vocab_size,
lookahead=config.set_bpe.lookahead,
backend=backend,
threads=threads,
progress=progress,
)
serializer = SetBPESerializer(
bins=primitives.bins,
lookahead=config.set_bpe.lookahead,
spatial_merges=spatial.merges,
temporal_merges=vocabulary.merges,
)
return serializer, spatial_report, temporal_report, len(records), aggregated, spatial_done
def _fit_bpe(
cells: Sequence[np.ndarray],
config: ActionCodec2Config,
primitives: PrimitiveStore,
*,
backend: str,
threads: int,
progress: bool,
) -> tuple[BPESerializer, dict[str, object], dict[str, object], int, float, float]:
started = time.perf_counter()
bpe = config.train.bpe if config.train is not None else None
assert bpe is not None
vocabulary, report = train_bpe(
cells,
primitives.bins,
vocab_size=bpe.vocab_size,
max_token_cells=config.set_bpe.lookahead * config.physical.input_dim,
backend=backend,
threads=threads,
progress=progress,
)
serializer = BPESerializer(
bins=primitives.bins,
lookahead=config.set_bpe.lookahead,
merges=vocabulary.merges,
)
return serializer, {}, report, 0, started, started
def _validate_fit_options(config: ActionCodec2Config, max_cell_bytes: int | None) -> None:
if config.train is None:
raise ConfigError("config.train is required for fitting")
if config.set_bpe.scheme == "sparse_set_bpe":
raise ConfigError("sparse_set_bpe profiles are fitted by ActionCodec2Sparse")
if max_cell_bytes is not None and (
isinstance(max_cell_bytes, bool)
or not isinstance(max_cell_bytes, int)
or max_cell_bytes < 1
):
raise ValueError("max_cell_bytes must be a positive integer or None")
if config.set_bpe.allow_cross_component_merge:
raise ConfigError(
"allow_cross_component_merge=true requires the future multi-component "
"corpus representation; v1 fitting supports the default isolated boundary only"
)
if config.set_bpe.scheme == "bpe" and config.train.bpe.spatial_vocab_size != (
config.train.bpe.vocab_size
):
raise ConfigError(
"set_bpe.scheme=bpe has a single merge stage; set "
"train.bpe.spatial_vocab_size equal to train.bpe.vocab_size to make the "
"unused split explicit"
)
class ProfileTrainer:
'Internal whole-trajectory fitter for one physical profile.'
def __init__(self, config: ActionCodec2Config) -> None:
self.config = config
def fit(
self,
trajectories: Iterable[TrajectoryLike],
*,
backend: str = "auto",
threads: int = 0,
batch_size: int = 256,
progress: bool = False,
max_cell_bytes: int | None = None,
) -> tuple[ActionCodec2Processor, dict[str, object]]:
'Fit primitives and the configured tokenization scheme on whole trajectories.'
config = self.config
_validate_fit_options(config, max_cell_bytes)
started = time.perf_counter()
primitives = build_primitives(config)
primitive_done = time.perf_counter()
atoms = sum(primitives.bins)
bpe = config.train.bpe
if bpe.spatial_vocab_size < atoms:
raise ConfigError(
f"train.bpe.spatial_vocab_size must be at least the {atoms} primitive atoms"
)
physical = PhysicalCodec(config, primitives)
cells, actions, active_values, arity_counts = _quantize_trajectories(
trajectories,
physical,
batch_size=batch_size,
max_cell_bytes=max_cell_bytes,
)
trajectory_count = len(cells)
first_cell_dtype = str(cells[0].dtype)
quantized = time.perf_counter()
if config.set_bpe.scheme == "unified_set_bpe":
from .unified_set_bpe import fit_unified_set_bpe
serializer, unified_report = fit_unified_set_bpe(
config, cells, threads=threads, backend=backend, progress=progress
)
temporal_report = {
**unified_report["temporal"],
"tokens_per_action": unified_report["temporal"]["final_instances"] / actions,
}
fitted = serializer, {}, temporal_report, 0, quantized, quantized
elif config.set_bpe.scheme == "bpe":
fitted = _fit_bpe(
cells, config, primitives, backend=backend, threads=threads, progress=progress
)
else:
fitted = _fit_set_bpe(
cells,
config,
primitives,
actions,
backend=backend,
threads=threads,
progress=progress,
)
serializer, spatial_report, temporal_report, unique_records, aggregated, spatial_done = (
fitted
)
codec = ActionCodec2Processor(config, primitives, serializer)
finished = time.perf_counter()
report = _fit_report(
config,
primitives,
codec,
trajectory_count=trajectory_count,
actions=actions,
active_values=active_values,
arity_counts=arity_counts,
cell_dtype=first_cell_dtype,
unique_records=unique_records,
spatial_report=spatial_report,
temporal_report=temporal_report,
timestamps=(
started,
primitive_done,
quantized,
aggregated,
spatial_done,
finished,
),
)
return codec, report
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