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from __future__ import annotations
from ..presets import canonical_preset_name
from ..routing import _schedule
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from pathlib import Path
from types import MappingProxyType
import numpy as np
import yaml
from .._validation import optional_finite_array
from ..batch_semantics import externalize_batch
from ..cache import BoundedCache, cached_prefix_state
from ..configuration import ConfigError, _integer, _mapping, _reject_unknown, _UniqueKeyLoader
from ..hf import save_huggingface_metadata
from ..presets import ComponentSpec, PresetConfig, format_action_spaces
from ..routing import (
_PROFILE_WIDTHS,
_PROFILES,
ProfileSpec,
_ProfileRuntime,
_second_order_boundaries,
_second_order_dims,
)
from ..semantic import resampled_horizon
from ..trajectory import ActiveTrajectory
from .processor import ActionCodec2SparseProcessor
SPARSE_ROUTER_MODEL_TYPE = "actioncodec2_sparse_router"
SPARSE_ROUTER_CONFIG_NAME = "sparse_router_config.yaml"
SPARSE_ROUTER_CONFIG_VERSION = 1
RoutedState = tuple[tuple[bool, ...], tuple]
@dataclass(frozen=True)
class _Segment:
component: ComponentSpec
spec: ProfileSpec
processor: ActionCodec2SparseProcessor
class _FrontTracker:
def __init__(self, segment: _Segment, horizon: int) -> None:
tables = segment.processor.vocabulary.tables
self.emask, self.offset = tables.emask, segment.spec.token_offset
self.vocab, self.horizon = len(tables), horizon
self.front, self.closed = 0, 0
def push(self, token: int) -> None:
self.closed |= self.emask[token] << self.front
while self.front < self.horizon and self.closed >> self.front & 1:
self.front += 1
class RoutedSparseGrammar:
"""Constrained-generation grammar of one preset and codec-frame horizon."""
def __init__(self, router: ActionCodec2SparseRouter, *, preset: str, horizon: int) -> None:
self.horizon = _integer(horizon, "horizon", positive=True)
self.segments = router._segments(router.preset(preset))
self.time_major = router.preset(preset).token_order == "time_major"
self.vocab_size, self.end_token = router.vocab_size, router.end_token
self._grammars = tuple(
s.processor.grammar(self.horizon, active_dims=s.component.active_dims)
for s in self.segments
)
self._initial: RoutedState = (
(False,) * len(self.segments),
tuple(g.initial() for g in self._grammars),
)
self._cache = BoundedCache(256)
def _scheduled(self, state: RoutedState) -> int | None:
fronts = [g.front(s) for g, s in zip(self._grammars, state[1], strict=True)]
return _schedule(fronts, state[0], self.time_major)
def _advance(self, state: RoutedState, token: int) -> RoutedState | None:
finished, states = state
index = self._scheduled(state)
if index is None:
return None
if token == self.end_token:
if finished.count(False) < 2 or not self._grammars[index].is_complete(states[index]):
return None
return (*finished[:index], True, *finished[index + 1 :]), states
local = token - self.segments[index].spec.token_offset
updated = self._grammars[index].advance(states[index], local)
if updated is None:
return None
return finished, (*states[:index], updated, *states[index + 1 :])
def _consume(self, tokens: Sequence[int]) -> RoutedState | None:
checked = []
for token in tokens:
if isinstance(token, bool) or not isinstance(token, (int, np.integer)):
return None
checked.append(int(token))
return cached_prefix_state(tuple(checked), self._cache, self._initial, self._advance)
def reserve_states(self, rows: int) -> None:
"""Size the prefix cache for ``rows`` streams advanced in lockstep."""
self._cache.reserve(4 * _integer(rows, "rows", positive=True))
def _mask_from_state(self, state: RoutedState) -> np.ndarray:
output = np.zeros(self.vocab_size, dtype=bool)
index = self._scheduled(state)
if index is None:
return output
finished, states = state
mask = self._grammars[index].mask(states[index])
start = self.segments[index].spec.token_offset
output[start : start + len(mask)] = mask
if finished.count(False) >= 2 and self._grammars[index].is_complete(states[index]):
output[self.end_token] = True
return output
def _state_complete(self, state: RoutedState) -> bool:
finished, states = state
open_ = [i for i, done in enumerate(finished) if not done]
return len(open_) == 1 and self._grammars[open_[0]].is_complete(states[open_[0]])
def next_token_mask(self, tokens: Sequence[int]) -> np.ndarray:
"""Boolean ``(vocab_size,)`` mask of legal next routed tokens."""
state = self._consume(tokens)
if state is None:
raise ValueError("token prefix is not legal for this sparse preset")
return self._mask_from_state(state)
def is_complete(self, tokens: Sequence[int]) -> bool:
"""Whether the stream may end here: one component left and its chunk complete."""
state = self._consume(tokens)
return state is not None and self._state_complete(state)
def covered_steps(self, tokens: Sequence[int]) -> int:
"""Leading codec frames that no further token can change."""
state = self._consume(tokens)
if state is None:
raise ValueError("token prefix is not legal for this sparse preset")
return min(
self.horizon if done else grammar.front(local)
for grammar, local, done in zip(self._grammars, state[1], state[0], strict=True)
)
def accepts(self, tokens: Sequence[int]) -> bool:
return self.is_complete(tokens)
class ActionCodec2SparseRouter:
"""Route action-space tensors through ActionCodec2Sparse profile namespaces."""
model_type = SPARSE_ROUTER_MODEL_TYPE
def __init__(
self,
profiles: Sequence[tuple[ProfileSpec, ActionCodec2SparseProcessor]],
presets: Sequence[PresetConfig],
) -> None:
runtimes: dict[str, _ProfileRuntime] = {}
offset = 0
for spec, processor in profiles:
if spec.name in runtimes or spec.name not in _PROFILES:
raise ConfigError(f"invalid or duplicate sparse profile {spec.name!r}")
if spec.token_offset != offset:
raise ConfigError("profile token namespaces must be contiguous and ordered")
if processor.vocab_size > spec.token_budget:
raise ConfigError(
f"profile {spec.name!r} uses {processor.vocab_size} tokens, "
f"exceeding budget {spec.token_budget}"
)
if processor.artifact_fingerprint != spec.artifact_fingerprint:
raise ConfigError(f"profile {spec.name!r} fingerprint does not match its artifact")
if processor.action_dim != _PROFILE_WIDTHS[spec.name]:
raise ConfigError(
f"profile {spec.name!r} must have width {_PROFILE_WIDTHS[spec.name]}"
)
runtimes[spec.name] = _ProfileRuntime(spec, processor)
offset += spec.token_budget
if not runtimes:
raise ConfigError("a sparse router needs at least one profile")
self.profiles = MappingProxyType(runtimes)
self._second_order_dims = {
name: _second_order_dims(runtime.processor.config) for name, runtime in runtimes.items()
}
self.end_token = offset
self.vocab_size = offset + 1
self._grammars = BoundedCache(8)
self._action_spaces: dict[str, PresetConfig] = {}
for preset in presets:
self.register_action_space(preset)
@classmethod
def from_processors(
cls,
processors: Mapping[str, ActionCodec2SparseProcessor],
*,
token_budgets: Mapping[str, int],
presets: Sequence[PresetConfig],
) -> ActionCodec2SparseRouter:
"""Assign stable contiguous namespaces in canonical joint/EEF order."""
if set(processors) != set(token_budgets) or set(processors) - _PROFILES:
raise ConfigError("processors and token_budgets must share joint/eef keys")
entries, offset = [], 0
for name in ("joint", "eef"):
if name in processors:
budget = _integer(token_budgets[name], f"token_budgets.{name}", positive=True)
spec = ProfileSpec(name, offset, budget, processors[name].artifact_fingerprint)
entries.append((spec, processors[name]))
offset += budget
return cls(entries, presets)
@property
def action_spaces(self) -> Mapping[str, PresetConfig]:
return MappingProxyType(self._action_spaces)
def preset(self, name: str) -> PresetConfig:
try:
return self._action_spaces[canonical_preset_name(name)]
except KeyError as error:
raise KeyError(
f"unknown preset {name!r}; available: {sorted(self._action_spaces)}"
) from error
def register_action_space(self, preset) -> PresetConfig:
"""Register a validated preset, mapping, or YAML path without changing token IDs."""
if isinstance(preset, Mapping):
preset = PresetConfig.from_dict(preset)
elif isinstance(preset, (str, Path)):
preset = PresetConfig.from_yaml(preset)
if not isinstance(preset, PresetConfig):
raise TypeError("register_action_space expects PresetConfig, a mapping, or a YAML path")
missing = {c.space for c in preset.components} - self.profiles.keys()
if missing:
raise ConfigError(
f"action space {preset.name!r} requires missing profiles: {sorted(missing)}"
)
existing = self._action_spaces.get(preset.name)
if existing is not None and existing != preset:
raise ConfigError(
f"action-space name {preset.name!r} is already registered differently"
)
self._action_spaces[preset.name] = preset
return preset
def second_order_boundaries(self, preset) -> dict[str, int]:
"""Map each component needing ``previous_first_order`` to its active width."""
if not isinstance(preset, PresetConfig):
preset = self.preset(preset)
return _second_order_boundaries(preset, self._second_order_dims)
def _validate_boundary_keys(
self, previous_first_order: Mapping[str, np.ndarray] | None, preset: PresetConfig
) -> None:
if previous_first_order is None:
previous_first_order = {}
if not isinstance(previous_first_order, Mapping):
raise ValueError(
"previous_first_order must map component names to canonical [B,D_active] boundaries"
)
unknown = set(previous_first_order) - {c.name for c in preset.components}
if unknown:
raise ValueError(f"unknown previous_first_order component(s): {sorted(unknown)}")
required = {
name: f"(B, {width})" for name, width in self.second_order_boundaries(preset).items()
}
missing = set(required) - previous_first_order.keys()
if missing:
raise ValueError(
"second-order tokenization requires previous_first_order with arrays "
f"{required}; missing {sorted(missing)}. Use canonical {preset.target_hz:g} Hz "
"reconstructed increments. codec.first_order_template(batch_size) explicitly "
"creates zeros; use zeros only if the preceding increment is known to be zero."
)
@staticmethod
def _boundary(previous_first_order, segment: _Segment):
return (
None
if previous_first_order is None
else previous_first_order.get(segment.component.name)
)
def describe_action_spaces(self, *, selected=None, action_dim=None) -> str:
return format_action_spaces(
self.action_spaces,
selected=selected,
action_dim=action_dim,
codec_name="ActionCodec2Sparse",
chunk_horizon_decode=True,
)
def __str__(self) -> str:
return self.describe_action_spaces()
def to(self, device: object) -> ActionCodec2SparseRouter:
"""Return ``self`` because routed processors use CPU NumPy arrays."""
del device
return self
def _segments(self, preset: PresetConfig) -> tuple[_Segment, ...]:
return tuple(
_Segment(c, self.profiles[c.space].spec, self.profiles[c.space].processor)
for c in preset.components
)
def grammar(self, *, preset: str, horizon: int) -> RoutedSparseGrammar:
key = (preset, int(horizon))
grammar = self._grammars.get(key)
if grammar is None:
grammar = RoutedSparseGrammar(self, preset=preset, horizon=horizon)
self._grammars[key] = grammar
return grammar
def encode_batch(
self,
inputs,
*,
preset: str,
fps=None,
current_state=None,
previous_first_order=None,
threads: int = 0,
) -> list[list[int]]:
"""Encode ``(B,T,D)`` source chunks at ``fps`` into routed token rows."""
config = self.preset(preset)
self._validate_boundary_keys(previous_first_order, config)
canonical = config._canonicalize_batch(inputs, source_hz=fps, current_state=current_state)
segments = self._segments(config)
streams = [
s.processor.encode_batch(
canonical[s.component.name],
active_dims=s.component.active_dims,
previous_first_order=self._boundary(previous_first_order, s),
threads=threads,
)
for s in segments
]
horizon = next(iter(canonical.values())).shape[1]
time_major = config.token_order == "time_major"
return [
self._interleave([rows[i] for rows in streams], segments, horizon, time_major)
for i in range(len(streams[0]))
]
def _interleave(self, streams, segments, horizon, time_major) -> list[int]:
trackers = [_FrontTracker(s, horizon) for s in segments]
cursors, finished, output = [0] * len(streams), [False] * len(streams), []
while True:
index = _schedule([t.front for t in trackers], finished, time_major)
if index is None:
return output
if cursors[index] == len(streams[index]):
if finished.count(False) == 1:
return output
finished[index] = True
output.append(self.end_token)
continue
token = int(streams[index][cursors[index]])
cursors[index] += 1
trackers[index].push(token)
output.append(token + segments[index].spec.token_offset)
def _split(self, row, segments, horizon, time_major) -> list[list[int]]:
trackers = [_FrontTracker(s, horizon) for s in segments]
finished, output = [False] * len(segments), [[] for _ in segments]
for position, raw in enumerate(row):
if isinstance(raw, bool) or not isinstance(raw, (int, np.integer)):
raise ValueError(f"tokens[{position}] must be an integer")
index = _schedule([t.front for t in trackers], finished, time_major)
if index is None:
raise ValueError("token after every component is finished")
token = int(raw)
if token == self.end_token:
if finished.count(False) < 2:
raise ValueError("the end token cannot close the last open component")
finished[index] = True
continue
local = token - segments[index].spec.token_offset
if not 0 <= local < trackers[index].vocab:
raise ValueError(
f"tokens[{position}] is outside component {segments[index].component.name!r}"
)
if trackers[index].front >= horizon:
raise ValueError("token after the last open frame")
trackers[index].push(local)
output[index].append(local)
return output
def _split_rows(self, rows, config, horizon):
horizon = _integer(horizon, "horizon", positive=True)
rows = list(rows)
if not rows:
raise ValueError("rows must not be empty")
segments = self._segments(config)
time_major = config.token_order == "time_major"
return segments, [self._split(row, segments, horizon, time_major) for row in rows]
def decode_batch(
self,
rows,
*,
preset: str,
horizon: int,
fps=None,
current_state=None,
previous_first_order=None,
) -> dict[str, np.ndarray]:
"""Decode rows of one codec-frame ``horizon`` to ``{component: (B,T_out,D_c)}``."""
config = self.preset(preset)
self._validate_boundary_keys(previous_first_order, config)
segments, splits = self._split_rows(rows, config, horizon)
state = optional_finite_array(current_state, shape=(len(splits), config.input_dim))
output_fps = config.source_hz if fps is None else fps
steps = resampled_horizon(horizon, source_hz=config.target_hz, target_hz=output_fps)
output = {}
for index, segment in enumerate(segments):
decoded = segment.processor.decode_batch(
[split[index] for split in splits],
horizon=horizon,
active_dims=segment.component.active_dims,
previous_first_order=self._boundary(previous_first_order, segment),
)
output[segment.component.name] = externalize_batch(
decoded,
segment.component,
source_hz=config.target_hz,
target_hz=output_fps,
target_length=steps,
method=config.interpolation,
current_state=state,
)
return output
def final_first_order(
self,
rows,
*,
preset: str,
horizon: int,
previous_first_order,
executed_steps: int | None = None,
) -> dict[str, np.ndarray]:
"""Component ``(B,D_active)`` boundaries after ``executed_steps`` codec frames."""
config = self.preset(preset)
if previous_first_order is None:
raise ValueError("final_first_order requires previous_first_order")
self._validate_boundary_keys(previous_first_order, config)
segments, splits = self._split_rows(rows, config, horizon)
output = {}
for index, segment in enumerate(segments):
if not segment.processor.requires_previous_first_order:
continue
output[segment.component.name] = segment.processor.final_first_order(
[split[index] for split in splits],
horizon=horizon,
previous_first_order=self._boundary(previous_first_order, segment),
active_dims=segment.component.active_dims,
executed_steps=executed_steps,
)
return output
def route_trajectories(
self,
inputs: np.ndarray,
*,
preset: str,
current_state: np.ndarray | None = None,
fps: float | None = None,
previous_first_order: Mapping[str, np.ndarray] | None = None,
) -> dict[str, list[ActiveTrajectory]]:
"""Expose canonical complete trajectories grouped by physical profile."""
preset_config = self.preset(preset)
self._validate_boundary_keys(previous_first_order, preset_config)
canonical = preset_config.canonicalize(inputs, current_state=current_state, source_hz=fps)
output: dict[str, list[ActiveTrajectory]] = {}
for component in preset_config.components:
trajectories = canonical[component.name]
processor = self.profiles[component.space].processor
previous = (
None if previous_first_order is None else previous_first_order.get(component.name)
)
dense = processor._dense_previous_first_order(
previous, component.active_dims, len(trajectories)
)
rows = (
[None] * len(trajectories)
if dense is None
else np.take(dense, component.active_dims, axis=1)
)
output.setdefault(component.space, []).extend(
ActiveTrajectory(t.values, t.active_dims, previous_first_order=p)
for t, p in zip(trajectories, rows, strict=True)
)
return output
@property
def new_action_tokens(self) -> list[str]:
"""Stable token names: profile namespaces (budget slots included), then the end token."""
names = [
f"<action_{name}_{index:04d}>"
for name, runtime in self.profiles.items()
for index in range(runtime.spec.token_budget)
]
return [*names, "<action_end>"]
def save_pretrained(self, directory) -> list[str]:
root = Path(directory)
files = []
for name, runtime in self.profiles.items():
files.extend(runtime.processor.save_pretrained(root / "profiles" / name))
manifest = {
"model_type": self.model_type,
"config_version": SPARSE_ROUTER_CONFIG_VERSION,
"profiles": [runtime.spec.to_dict() for runtime in self.profiles.values()],
"presets": [preset.to_dict() for preset in self.action_spaces.values()],
}
path = root / SPARSE_ROUTER_CONFIG_NAME
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(yaml.safe_dump(manifest, sort_keys=False), encoding="utf-8")
return [str(path), *files]
@staticmethod
def read_manifest(directory) -> tuple[tuple[ProfileSpec, ...], tuple[PresetConfig, ...]]:
"""Read profile specs and action spaces without loading numerical arrays."""
path = Path(directory) / SPARSE_ROUTER_CONFIG_NAME
payload = _mapping(
yaml.load(path.read_text(encoding="utf-8"), Loader=_UniqueKeyLoader), "router"
)
_reject_unknown(payload, {"model_type", "config_version", "profiles", "presets"}, "router")
if payload.get("model_type") != SPARSE_ROUTER_MODEL_TYPE:
raise ConfigError(f"router.model_type must be {SPARSE_ROUTER_MODEL_TYPE!r}")
if payload.get("config_version") != SPARSE_ROUTER_CONFIG_VERSION:
raise ConfigError(
f"unsupported sparse router config_version {payload.get('config_version')!r}"
)
profiles, presets = payload.get("profiles"), payload.get("presets")
if not isinstance(profiles, list) or not isinstance(presets, list):
raise ConfigError("router.profiles and router.presets must be lists")
return (
tuple(
ProfileSpec.from_dict(p, path=f"router.profiles[{i}]")
for i, p in enumerate(profiles)
),
tuple(
PresetConfig.from_dict(p, path=f"router.presets[{i}]")
for i, p in enumerate(presets)
),
)
@classmethod
def from_pretrained(cls, directory) -> ActionCodec2SparseRouter:
root = Path(directory)
specs, presets = cls.read_manifest(root)
profiles = [
(spec, ActionCodec2SparseProcessor.from_pretrained(root / "profiles" / spec.name))
for spec in specs
]
return cls(profiles, presets)
def save_router_metadata(directory, **processor_config) -> tuple[Path, Path]:
"""Hugging Face discovery files for a saved sparse router or bound frontend."""
return save_huggingface_metadata(
directory,
model_type="actioncodec2_sparse",
processor_class="ActionCodec2Sparse",
**processor_config,
)
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