File size: 3,421 Bytes
58785a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | """Standard-library-only metadata reader for LoopVL weight snapshots.
No code is imported from the snapshot. Version 2 stores all data files at its
root; version 1 retains its original nested image-processor JSON for backwards
compatibility. Both versions use the same code distributed in this runtime.
"""
from __future__ import annotations
import json
from pathlib import Path
COMPONENT_DTYPES = {
"vision": "bfloat16",
"language": "bfloat16",
"projector": "float32",
"visual_gate": "float32",
"visual_anchor": "float32",
}
FLAT_DATA_FILES = (
"model.safetensors",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"tokenizer.json",
"tokenizer_config.json",
)
PROCESSOR_FIELDS = (
"do_convert_rgb", "do_normalize", "do_rescale", "do_resize",
"image_mean", "image_std", "max_tokens", "min_tokens", "patch_size",
"resample", "rescale_factor",
)
def read_checkpoint_metadata(model_dir: str | Path) -> dict:
"""Read and validate format metadata without torch, transformers or imports."""
root = Path(model_dir).expanduser().resolve()
raw = json.loads((root / "config.json").read_text(encoding="utf-8"))
if raw.get("model_type") != "loopvl" or raw.get("format_version") not in (1, 2):
raise ValueError("Expected LoopVL single-file checkpoint format_version 1 or 2")
if raw.get("component_dtypes") != COMPONENT_DTYPES:
raise ValueError("This loader requires the original BF16 cores / FP32 adapters")
for field in ("loopvl_config", "text_config", "vision_config"):
if not isinstance(raw.get(field), dict):
raise ValueError(f"Checkpoint lacks embedded {field}")
if raw["text_config"].get("model_type") != "hrm_text":
raise ValueError("Expected the embedded hrm_text language configuration")
if raw["vision_config"].get("model_type") != "penguinvl_vision_encoder":
raise ValueError("Expected the embedded penguinvl_vision_encoder configuration")
return raw
def read_processor_config(model_dir: str | Path, raw: dict | None = None) -> dict:
"""Return the original processor parameters, never a remote-code reference.
The old root preprocessor_config.json was only a bundle description and is
deliberately not used for format 1. Omitting the true image mean/std would
silently select CLIP defaults and change pixel values.
"""
root = Path(model_dir).expanduser().resolve()
if raw is None:
raw = read_checkpoint_metadata(root)
version = raw.get("format_version")
if version == 1:
path = root / "penguin_encoder" / "preprocessor_config.json"
elif version == 2:
path = root / "preprocessor_config.json"
else:
raise ValueError("Unsupported processor metadata format")
values = json.loads(path.read_text(encoding="utf-8"))
missing = sorted(set(PROCESSOR_FIELDS) - set(values))
if missing:
raise ValueError(f"Missing actual Penguin image-processor parameters: {missing}")
kind = values.get("image_processor_type", "PenguinVLImageProcessor")
if kind != "PenguinVLImageProcessor":
raise ValueError(f"Unexpected image_processor_type: {kind!r}")
# Loading uses the pinned GitHub class directly. Old auto_map metadata is
# not executable and must never redirect imports into a checkpoint folder.
values.pop("auto_map", None)
return values
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