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from __future__ import annotations
from dataclasses import asdict, dataclass
import hashlib
import json
import os
from pathlib import Path
from typing import Any, Iterator, Sequence
import numpy as np
from torch.utils.data import Sampler
def _identity_files(dataset_root: Path) -> list[Path]:
files = list((dataset_root / "data").rglob("*.parquet"))
files.extend((dataset_root / "videos").rglob("*.mp4"))
meta_root = dataset_root / "meta"
if meta_root.exists():
for pattern in ("*.json", "*.jsonl", "*.parquet"):
files.extend(meta_root.rglob(pattern))
return sorted(set(files), key=lambda path: path.relative_to(dataset_root).as_posix())
def compute_dataset_content_fingerprint(dataset_root: str | Path) -> dict[str, Any]:
"""Record the immutable LeRobot data and metadata file inventory."""
root = Path(dataset_root)
entries = []
total_bytes = 0
for file_path in _identity_files(root):
relative_path = file_path.relative_to(root).as_posix()
file_size = file_path.stat().st_size
entries.append({"path": relative_path, "bytes": file_size})
total_bytes += file_size
return {
"file_count": len(entries),
"total_bytes": total_bytes,
"files": entries,
}
def validate_dataset_content_inventory(
dataset_root: str | Path, fingerprint: dict[str, Any]
) -> None:
"""Check that the current file inventory matches the recorded dataset inventory."""
root = Path(dataset_root)
current_entries = [
{
"path": path.relative_to(root).as_posix(),
"bytes": path.stat().st_size,
}
for path in _identity_files(root)
]
if current_entries != fingerprint.get("files"):
raise RuntimeError(
f"Dataset file inventory differs from its content fingerprint: {root}"
)
if len(current_entries) != fingerprint.get("file_count"):
raise RuntimeError("Dataset content fingerprint has an inconsistent file count")
if sum(entry["bytes"] for entry in current_entries) != fingerprint.get("total_bytes"):
raise RuntimeError("Dataset content fingerprint has an inconsistent byte count")
def _normalize_probabilities(values: Sequence[float], name: str) -> tuple[float, ...]:
probabilities = np.asarray(values, dtype=np.float64)
if probabilities.ndim != 1 or probabilities.size == 0:
raise ValueError(f"{name} must be a non-empty one-dimensional sequence")
if not np.all(np.isfinite(probabilities)) or np.any(probabilities <= 0):
raise ValueError(f"{name} must contain finite, strictly positive values")
probabilities /= probabilities.sum(dtype=np.float64)
return tuple(float(value) for value in probabilities)
def _stateless_seed(seed: int, epoch: int, global_draw_id: int) -> int:
payload = f"{int(seed)}:{int(epoch)}:{int(global_draw_id)}".encode("utf-8")
return int.from_bytes(hashlib.sha256(payload).digest()[:8], "little", signed=False)
@dataclass(frozen=True)
class EpisodeSupport:
trajectory_index: int
trajectory_id: int | str
anchors: tuple[int, ...]
def __post_init__(self) -> None:
if not self.anchors:
raise ValueError("episode support must contain at least one anchor")
if len(set(self.anchors)) != len(self.anchors):
raise ValueError("episode anchors must be unique")
@dataclass(frozen=True)
class DatasetSupport:
name: str
probability: float
episodes: tuple[EpisodeSupport, ...]
episode_probabilities: tuple[float, ...]
def __post_init__(self) -> None:
if not self.episodes:
raise ValueError(f"dataset {self.name!r} has no valid episodes")
if len(self.episodes) != len(self.episode_probabilities):
raise ValueError("episode support and probability lengths differ")
normalized = _normalize_probabilities(self.episode_probabilities, "episode_probabilities")
if not np.allclose(normalized, self.episode_probabilities, rtol=0, atol=1e-12):
raise ValueError("episode probabilities must already sum to one")
@dataclass(frozen=True)
class LogicalDraw:
epoch: int
global_draw_id: int
def __post_init__(self) -> None:
if self.epoch < 0 or self.global_draw_id < 0:
raise ValueError("epoch and global_draw_id must be non-negative")
@dataclass(frozen=True)
class SampleTrace:
manifest_sha256: str
epoch: int
global_draw_id: int
dataset_index: int
dataset_name: str
trajectory_index: int
trajectory_id: int | str
anchor_index: int
base_index: int
baseline_probability: float
proposal_sha256: str | None = None
proposal_probability: float | None = None
importance_weight: float = 1.0
def to_dict(self) -> dict[str, Any]:
return asdict(self)
class ValidAnchorManifest:
schema_version = 1
def __init__(self, datasets: Sequence[DatasetSupport], identity: dict[str, Any] | None = None):
if not datasets:
raise ValueError("manifest must contain at least one dataset")
dataset_probabilities = _normalize_probabilities(
[dataset.probability for dataset in datasets], "dataset_probabilities"
)
if not np.allclose(
dataset_probabilities,
[dataset.probability for dataset in datasets],
rtol=0,
atol=1e-12,
):
raise ValueError("dataset probabilities must already sum to one")
self.datasets = tuple(datasets)
self.identity = identity or {}
self._dataset_probabilities = np.asarray(
[dataset.probability for dataset in self.datasets], dtype=np.float64
)
self._episode_probabilities = tuple(
np.asarray(dataset.episode_probabilities, dtype=np.float64)
for dataset in self.datasets
)
canonical = json.dumps(self.to_dict(include_hash=False), sort_keys=True, separators=(",", ":"))
self.sha256 = hashlib.sha256(canonical.encode("utf-8")).hexdigest()
@classmethod
def from_hierarchy(
cls,
dataset_names: Sequence[str],
dataset_weights: Sequence[float],
episodes: Sequence[Sequence[tuple[int | str, Sequence[int], float]]],
identity: dict[str, Any] | None = None,
) -> "ValidAnchorManifest":
if len(dataset_names) != len(dataset_weights) or len(dataset_names) != len(episodes):
raise ValueError("dataset names, weights, and episode supports must have equal lengths")
dataset_probabilities = _normalize_probabilities(dataset_weights, "dataset_weights")
supports = []
for dataset_index, (name, dataset_episodes) in enumerate(zip(dataset_names, episodes)):
if not dataset_episodes:
raise ValueError(f"dataset {name!r} has no valid episodes")
episode_probabilities = _normalize_probabilities(
[entry[2] for entry in dataset_episodes], f"episode_weights[{dataset_index}]"
)
episode_supports = tuple(
EpisodeSupport(
trajectory_index=trajectory_index,
trajectory_id=trajectory_id,
anchors=tuple(int(anchor) for anchor in anchors),
)
for trajectory_index, (trajectory_id, anchors, _) in enumerate(dataset_episodes)
)
supports.append(
DatasetSupport(
name=str(name),
probability=dataset_probabilities[dataset_index],
episodes=episode_supports,
episode_probabilities=episode_probabilities,
)
)
return cls(supports, identity=identity)
@classmethod
def from_dict(cls, payload: dict[str, Any]) -> "ValidAnchorManifest":
datasets = []
for dataset_payload in payload["datasets"]:
episodes = tuple(
EpisodeSupport(
trajectory_index=int(episode["trajectory_index"]),
trajectory_id=episode["trajectory_id"],
anchors=tuple(int(anchor) for anchor in episode["anchors"]),
)
for episode in dataset_payload["episodes"]
)
datasets.append(
DatasetSupport(
name=str(dataset_payload["name"]),
probability=float(dataset_payload["probability"]),
episodes=episodes,
episode_probabilities=tuple(
float(value) for value in dataset_payload["episode_probabilities"]
),
)
)
return cls(datasets, identity=payload.get("identity", {}))
@classmethod
def load(cls, path: str | Path) -> "ValidAnchorManifest":
with open(path, "r", encoding="utf-8") as manifest_file:
return cls.from_dict(json.load(manifest_file))
def save(self, path: str | Path) -> None:
destination = Path(path)
destination.parent.mkdir(parents=True, exist_ok=True)
temporary = destination.with_suffix(destination.suffix + ".tmp")
with open(temporary, "w", encoding="utf-8") as manifest_file:
json.dump(self.to_dict(), manifest_file, indent=2)
os.replace(temporary, destination)
def sample(self, seed: int, draw: LogicalDraw) -> SampleTrace:
rng = np.random.default_rng(_stateless_seed(seed, draw.epoch, draw.global_draw_id))
dataset_index = int(rng.choice(len(self.datasets), p=self._dataset_probabilities))
dataset = self.datasets[dataset_index]
trajectory_index = int(
rng.choice(len(dataset.episodes), p=self._episode_probabilities[dataset_index])
)
episode = dataset.episodes[trajectory_index]
anchor_index = int(rng.integers(len(episode.anchors)))
baseline_probability = (
dataset.probability
* dataset.episode_probabilities[trajectory_index]
/ len(episode.anchors)
)
return SampleTrace(
manifest_sha256=self.sha256,
epoch=int(draw.epoch),
global_draw_id=int(draw.global_draw_id),
dataset_index=dataset_index,
dataset_name=dataset.name,
trajectory_index=episode.trajectory_index,
trajectory_id=episode.trajectory_id,
anchor_index=anchor_index,
base_index=episode.anchors[anchor_index],
baseline_probability=float(baseline_probability),
)
def to_dict(self, include_hash: bool = True) -> dict[str, Any]:
payload = {
"schema_version": self.schema_version,
"identity": self.identity,
"datasets": [asdict(dataset) for dataset in self.datasets],
}
if include_hash:
payload["sha256"] = self.sha256
return payload
class StatelessDrawSampler(Sampler[LogicalDraw]):
"""Yield rank-disjoint logical draw ids; sampling itself stays inside the dataset."""
def __init__(
self,
num_local_draws: int,
rank: int = 0,
world_size: int = 1,
start_global_draw_id: int = 0,
) -> None:
if num_local_draws < 0:
raise ValueError("num_local_draws must be non-negative")
if world_size <= 0 or rank < 0 or rank >= world_size:
raise ValueError("rank must satisfy 0 <= rank < world_size")
self.num_local_draws = int(num_local_draws)
self.rank = int(rank)
self.world_size = int(world_size)
self.start_global_draw_id = int(start_global_draw_id)
if self.start_global_draw_id < 0:
raise ValueError("start_global_draw_id must be non-negative")
self.epoch = 0
self._resume_epoch = 0
def set_resume_position(self, epoch: int, start_global_draw_id: int) -> None:
"""Resume at an inclusive global draw cursor within one epoch."""
if epoch < 0 or start_global_draw_id < 0:
raise ValueError("resume epoch and draw id must be non-negative")
self.epoch = int(epoch)
self._resume_epoch = int(epoch)
self.start_global_draw_id = int(start_global_draw_id)
def set_epoch(self, epoch: int) -> None:
if epoch < 0:
raise ValueError("epoch must be non-negative")
self.epoch = int(epoch)
if self.epoch != self._resume_epoch:
self.start_global_draw_id = 0
def _first_local_draw_id(self) -> int:
return max(
0,
(self.start_global_draw_id - self.rank + self.world_size - 1)
// self.world_size,
)
def __iter__(self) -> Iterator[LogicalDraw]:
for local_draw_id in range(self._first_local_draw_id(), self.num_local_draws):
yield LogicalDraw(
epoch=self.epoch,
global_draw_id=local_draw_id * self.world_size + self.rank,
)
def __len__(self) -> int:
return max(0, self.num_local_draws - self._first_local_draw_id())
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