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from __future__ import annotations
import hashlib
import json
import os
from pathlib import Path
import tempfile
from typing import Any, Mapping
from safetensors import safe_open
from safetensors.torch import save_file
import torch
from utils.matched_video_manifest import MatchedVideoManifestEntry
CACHE_FORMAT = "sf_matched_manifest_video_vjepa2_teacher_feature"
CACHE_FORMAT_VERSION = 1
CACHE_METADATA_KEY = "sf_matched_video_teacher_feature"
CACHE_TENSOR_KEY = "features"
# Independent from the fixed-reference central-64 protocol. Both new student
# and live teacher use the last 64 frames, i.e. 17..80 for the 81-frame source.
MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT: dict[str, Any] = {
"adapter_contract_version": 1,
"contract_version": 1,
"crop_size": 256,
"expected_source_frame_count": 81,
"feature_dim": 1024,
"feature_type": "vjepa2_dense_latent",
"frame_indices": [17, 80],
"frame_sampling": "last_contiguous",
"interpolation": "bilinear_antialias",
"layer_index": 15,
"model_family": "vjepa2-vitl-fpc64-256",
"model_config_sha256": (
"3dec96fe962e94e569182d3a7b9ef0dd74b6b8c89c337a428e43e10d593e70c9"
),
"normalization": "imagenet_default",
"num_frames": 64,
"num_tokens": 1024,
"output_dtype": "float32",
"output_shape": [1024, 1024],
"processor_config_sha256": (
"d2fab4418fc0390b62c4cd72ade56908a7929f80c62288adbe10dd8d23421227"
),
"resize_short_side": 292,
"sample_axis": "video",
"token_alignment": "same_deterministic_indices",
"token_indices_sha256": (
"a77ee1f9d20cba6b2f2a9a971c3d36610ca7f8aa2a55c616192582665be69d10"
),
"token_sampling": "uniform_linspace_round",
"transformers_version": "4.53.3",
}
MATCHED_PYAV_LAST64_DECODER_CONTRACT: dict[str, Any] = {
"backend": "pyav",
"backend_version": "13.1.0",
"contract_version": 1,
"expected_frame_indices": [17, 80],
"expected_source_frame_count": 81,
"frame_count_source": "container_decode_order_with_stream_frames_hint",
"num_frames": 64,
"output_dtype": "float32",
"output_layout": "TCHW",
"output_range": [0.0, 1.0],
"pixel_format": "rgb24",
"selection": "last_contiguous",
}
class MatchedVideoFeatureCacheValidationError(ValueError):
"""An existing cache file is stale, corrupt, or identity-incompatible."""
def canonical_json(value: Any) -> str:
return json.dumps(
value,
ensure_ascii=True,
sort_keys=True,
separators=(",", ":"),
allow_nan=False,
)
def canonical_sha256(value: Any) -> str:
return hashlib.sha256(canonical_json(value).encode("utf-8")).hexdigest()
MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT_SHA256 = canonical_sha256(
MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT
)
MATCHED_PYAV_LAST64_DECODER_CONTRACT_SHA256 = canonical_sha256(
MATCHED_PYAV_LAST64_DECODER_CONTRACT
)
def _is_sha256(value: str) -> bool:
return len(value) == 64 and all(c in "0123456789abcdef" for c in value)
def _entry_context(
entry: MatchedVideoManifestEntry | Mapping[str, Any],
) -> dict[str, Any]:
if isinstance(entry, MatchedVideoManifestEntry):
result = {
"sample_id": entry.sample_id,
"video_mtime_ns": int(entry.video_mtime_ns),
"video_name": entry.video_name,
"video_size_bytes": int(entry.video_size_bytes),
}
elif isinstance(entry, Mapping):
required = {
"sample_id", "video_mtime_ns", "video_name", "video_size_bytes",
}
missing = required - set(entry)
if missing:
raise ValueError(f"cache sample context is missing {sorted(missing)}")
result = {
"sample_id": str(entry["sample_id"]),
"video_mtime_ns": int(entry["video_mtime_ns"]),
"video_name": str(entry["video_name"]),
"video_size_bytes": int(entry["video_size_bytes"]),
}
else:
raise TypeError("entry must be MatchedVideoManifestEntry or mapping")
if not result["sample_id"] or result["sample_id"] != result["video_name"]:
raise ValueError("cache sample_id must equal video_name")
if result["video_size_bytes"] <= 0 or result["video_mtime_ns"] <= 0:
raise ValueError("cache video stat must be positive")
return result
def _tensor_sha256(value: torch.Tensor) -> str:
value = value.detach().cpu().contiguous()
return hashlib.sha256(value.numpy().tobytes(order="C")).hexdigest()
class MatchedVideoFeatureCache:
"""Lazy one-file-per-video FP32 cache with immutable identity checks."""
def __init__(
self,
cache_root: str | os.PathLike,
*,
expected_model_sha256: str,
expected_feature_contract_sha256: str,
feature_contract: Mapping[str, Any] | None = None,
decoder_contract: Mapping[str, Any] | None = None,
expected_shape: tuple[int, int] | None = None,
) -> None:
self.cache_root = Path(cache_root).expanduser().resolve()
self.model_sha256 = str(expected_model_sha256).lower()
if not _is_sha256(self.model_sha256):
raise ValueError("expected_model_sha256 must be lowercase SHA-256")
self.feature_contract = json.loads(canonical_json(
feature_contract or MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT
))
self.feature_contract_sha256 = canonical_sha256(self.feature_contract)
expected_hash = str(expected_feature_contract_sha256).lower()
if not _is_sha256(expected_hash):
raise ValueError(
"expected_feature_contract_sha256 must be lowercase SHA-256"
)
if self.feature_contract_sha256 != expected_hash:
raise ValueError(
"feature contract SHA-256 mismatch: "
f"expected={expected_hash}, actual={self.feature_contract_sha256}"
)
self.decoder_contract = json.loads(canonical_json(
decoder_contract or MATCHED_PYAV_LAST64_DECODER_CONTRACT
))
self.decoder_contract_sha256 = canonical_sha256(self.decoder_contract)
contract_shape = tuple(
int(value) for value in self.feature_contract.get("output_shape", ())
)
self.expected_shape = (
tuple(int(value) for value in expected_shape)
if expected_shape is not None else contract_shape
)
if len(self.expected_shape) != 2 or min(self.expected_shape) <= 0:
raise ValueError("expected feature shape must be positive [N,D]")
if contract_shape and self.expected_shape != contract_shape:
raise ValueError(
"expected_shape must match feature_contract.output_shape"
)
def _key_payload(
self, entry: MatchedVideoManifestEntry | Mapping[str, Any],
) -> dict[str, Any]:
return {
"decoder_contract": self.decoder_contract,
"decoder_contract_sha256": self.decoder_contract_sha256,
"feature_contract": self.feature_contract,
"feature_contract_sha256": self.feature_contract_sha256,
"format": CACHE_FORMAT,
"format_version": CACHE_FORMAT_VERSION,
"model_sha256": self.model_sha256,
"sample": _entry_context(entry),
}
def key_for(
self, entry: MatchedVideoManifestEntry | Mapping[str, Any],
) -> str:
digest = hashlib.sha256()
digest.update(b"sf-matched-video-teacher-feature-cache\0")
digest.update(canonical_json(self._key_payload(entry)).encode("utf-8"))
return digest.hexdigest()
def path_for_key(self, cache_key: str) -> Path:
key = str(cache_key).lower()
if not _is_sha256(key):
raise ValueError("cache_key must be lowercase SHA-256")
return self.cache_root / key[:2] / key[2:4] / f"{key}.safetensors"
def _expected_metadata(
self,
entry: MatchedVideoManifestEntry | Mapping[str, Any],
cache_key: str,
tensor_sha256: str,
) -> dict[str, Any]:
result = self._key_payload(entry)
result.update({
"cache_key": cache_key,
"tensor": {
"dtype": "float32", "key": CACHE_TENSOR_KEY,
"sha256": tensor_sha256, "shape": list(self.expected_shape),
},
})
return result
def _validate_tensor(self, feature: torch.Tensor) -> torch.Tensor:
if not isinstance(feature, torch.Tensor):
raise TypeError("teacher feature must be a torch.Tensor")
if feature.ndim == 3 and int(feature.shape[0]) == 1:
feature = feature[0]
if feature.ndim != 2 or tuple(feature.shape) != self.expected_shape:
raise ValueError(
f"teacher feature shape mismatch: {tuple(feature.shape)} "
f"!= {self.expected_shape}"
)
if not feature.is_floating_point():
raise TypeError("teacher feature must be floating point")
result = feature.detach().to(device="cpu", dtype=torch.float32).contiguous()
if not bool(torch.isfinite(result).all().item()):
raise FloatingPointError("teacher feature contains non-finite values")
return result
def load(
self,
entry: MatchedVideoManifestEntry | Mapping[str, Any],
*,
cache_key: str | None = None,
) -> torch.Tensor | None:
key = self.key_for(entry)
if cache_key is not None and str(cache_key) != key:
raise MatchedVideoFeatureCacheValidationError(
"caller cache_key does not match sample/model/contracts"
)
path = self.path_for_key(key)
if not path.exists():
return None
if not path.is_file():
raise MatchedVideoFeatureCacheValidationError(
f"cache path is not a regular file: {path}"
)
try:
with safe_open(path, framework="pt", device="cpu") as handle:
if list(handle.keys()) != [CACHE_TENSOR_KEY]:
raise MatchedVideoFeatureCacheValidationError(
f"cache tensor keys mismatch in {path}"
)
serialized = (handle.metadata() or {}).get(CACHE_METADATA_KEY)
if serialized is None:
raise MatchedVideoFeatureCacheValidationError(
f"cache metadata is missing in {path}"
)
metadata = json.loads(serialized)
feature = handle.get_tensor(CACHE_TENSOR_KEY).clone()
except MatchedVideoFeatureCacheValidationError:
raise
except Exception as exc:
raise MatchedVideoFeatureCacheValidationError(
f"failed to read safetensors cache {path}: {exc}"
) from exc
feature = self._validate_tensor(feature)
expected = self._expected_metadata(entry, key, _tensor_sha256(feature))
if metadata != expected:
raise MatchedVideoFeatureCacheValidationError(
f"cache metadata/identity mismatch (stale or corrupt) in {path}"
)
return feature
def write(
self,
entry: MatchedVideoManifestEntry | Mapping[str, Any],
feature: torch.Tensor,
*,
cache_key: str | None = None,
overwrite: bool = False,
) -> Path:
key = self.key_for(entry)
if cache_key is not None and str(cache_key) != key:
raise MatchedVideoFeatureCacheValidationError(
"caller cache_key does not match sample/model/contracts"
)
feature = self._validate_tensor(feature)
tensor_hash = _tensor_sha256(feature)
destination = self.path_for_key(key)
if destination.exists() and not overwrite:
existing = self.load(entry, cache_key=key)
if existing is None or _tensor_sha256(existing) != tensor_hash:
raise MatchedVideoFeatureCacheValidationError(
"refusing to replace a valid cache entry with different features: "
f"{destination}"
)
return destination
destination.parent.mkdir(parents=True, exist_ok=True)
metadata = self._expected_metadata(entry, key, tensor_hash)
descriptor, temporary_name = tempfile.mkstemp(
dir=destination.parent,
prefix=f".{destination.name}.", suffix=".tmp",
)
os.close(descriptor)
temporary = Path(temporary_name)
try:
temporary.unlink()
save_file(
{CACHE_TENSOR_KEY: feature}, str(temporary),
metadata={CACHE_METADATA_KEY: canonical_json(metadata)},
)
with temporary.open("rb") as handle:
os.fsync(handle.fileno())
if overwrite:
os.replace(temporary, destination)
else:
try:
os.link(temporary, destination)
except FileExistsError:
existing = self.load(entry, cache_key=key)
if existing is None or _tensor_sha256(existing) != tensor_hash:
raise MatchedVideoFeatureCacheValidationError(
"concurrent cache writer produced different features "
f"for {key}"
)
temporary.unlink(missing_ok=True)
finally:
temporary.unlink(missing_ok=True)
self.load(entry, cache_key=key)
return destination
__all__ = [
"CACHE_FORMAT", "CACHE_FORMAT_VERSION", "CACHE_METADATA_KEY",
"CACHE_TENSOR_KEY", "MATCHED_PYAV_LAST64_DECODER_CONTRACT",
"MATCHED_PYAV_LAST64_DECODER_CONTRACT_SHA256",
"MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT",
"MATCHED_VJEPA2_L15_LAST64_FEATURE_CONTRACT_SHA256",
"MatchedVideoFeatureCache", "MatchedVideoFeatureCacheValidationError",
"canonical_json", "canonical_sha256",
]
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