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2.86 kB
| """Verified loaders for the CausalCellJEPA Hugging Face component bundle.""" | |
| from __future__ import annotations | |
| import copy | |
| import hashlib | |
| import json | |
| import sys | |
| from pathlib import Path | |
| from safetensors.torch import load_file | |
| def _sha256(path: Path) -> str: | |
| digest = hashlib.sha256() | |
| with path.open("rb") as handle: | |
| for block in iter(lambda: handle.read(1024 * 1024), b""): | |
| digest.update(block) | |
| return digest.hexdigest() | |
| def _release(root) -> tuple[Path, dict]: | |
| root = Path(root) | |
| manifest = json.loads((root / "MODEL_MANIFEST.json").read_text()) | |
| return root, manifest | |
| def verify_component(root, name: str) -> dict: | |
| root, release = _release(root) | |
| component = release["components"][name] | |
| for section in ("weights", "metadata"): | |
| record = component[section] | |
| path = root / record["path"] | |
| if path.stat().st_size != record["bytes"] or _sha256(path) != record["sha256"]: | |
| raise RuntimeError(f"integrity check failed for {path}") | |
| return component | |
| def load_tensor_component(root, name: str) -> tuple[dict, dict]: | |
| """Load a flat tensor payload plus its JSON-safe non-tensor metadata.""" | |
| root = Path(root) | |
| component = verify_component(root, name) | |
| tensors = load_file(root / component["weights"]["path"], device="cpu") | |
| metadata = json.loads((root / component["metadata"]["path"]).read_text()) | |
| return tensors, metadata | |
| def load_primary_dynamics(root): | |
| """Instantiate and load the proposal-locked primary population-dynamics model.""" | |
| root = Path(root).resolve() | |
| sys.path.insert(0, str(root / "code")) | |
| from causalcelljepa.dynamics import build_dynamics_model | |
| state, metadata = load_tensor_component(root, "stage2_primary") | |
| model = build_dynamics_model(metadata["configuration"]) | |
| model.load_state_dict(state, strict=True) | |
| return model, metadata | |
| def load_multiteacher_dynamics(root): | |
| """Instantiate and load the validation-selected exploratory multiteacher model.""" | |
| root = Path(root).resolve() | |
| sys.path.insert(0, str(root / "code")) | |
| from causalcelljepa.dynamics import build_dynamics_model | |
| state, metadata = load_tensor_component(root, "stage2_multiteacher_v4") | |
| config = copy.deepcopy(metadata["configuration"]) | |
| anchor = config["effect_anchor"] | |
| anchor["output_path"] = str(root / "weights/contextual_multiteacher_effect_anchor_v1.pt") | |
| anchor["manifest_path"] = str( | |
| root / "provenance/manifests/contextual_multiteacher_effect_anchor_v1.json" | |
| ) | |
| model = build_dynamics_model(config) | |
| model.load_state_dict(state, strict=True) | |
| return model, metadata | |
| def load_stage1_teacher_state(root) -> tuple[dict, dict]: | |
| """Return the frozen EMA teacher state dict and exact encoder metadata.""" | |
| return load_tensor_component(root, "stage1_teacher") | |