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import functools
import os
import shutil
import torch
from pathlib import Path
from urllib.request import Request, urlopen
from typing import Optional
def variant_cache_dir():
hf_hub_cache = os.environ.get("HF_HUB_CACHE")
if hf_hub_cache is not None:
return Path(hf_hub_cache) / "md_variants"
hf_home = os.environ.get("HF_HOME")
if hf_home is not None:
return Path(hf_home) / "hub" / "md_variants"
return Path("~/.cache/huggingface/hub").expanduser() / "md_variants"
def cached_variant_path(variant_id: str):
variant, *rest = variant_id.split("/", 1)
step = rest[0] if rest else "final"
cache_dir = variant_cache_dir() / variant
os.makedirs(cache_dir, exist_ok=True)
dest = cache_dir / f"{step}.pt"
if dest.exists():
return dest
md_endpoint = os.getenv("MOONDREAM_ENDPOINT", "https://api.moondream.ai")
headers = {"User-Agent": "moondream-torch"}
api_key = os.getenv("MOONDREAM_API_KEY")
if api_key is not None:
headers["X-Moondream-Auth"] = api_key
req = Request(f"{md_endpoint}/v1/variants/{variant_id}/download", headers=headers)
with urlopen(req) as r, open(dest, "wb") as f:
shutil.copyfileobj(r, f)
return dest
def nest(flat):
tree = {}
for k, v in flat.items():
parts = k.split(".")
d = tree
for p in parts[:-1]:
d = d.setdefault(p, {})
d[parts[-1]] = v
return tree
@functools.lru_cache(maxsize=5)
def variant_state_dict(variant_id: Optional[str] = None, device: str = "cpu"):
if variant_id is None:
return None
state_dict = torch.load(
cached_variant_path(variant_id), map_location=device, weights_only=True
)
# TODO: Move these into the training code that saves checkpoints...
rename_rules = [
("text_model.transformer.h", "text.blocks"),
(".mixer", ".attn"),
(".out_proj", ".proj"),
(".Wqkv", ".qkv"),
(".parametrizations.weight.0", ""),
]
new_state_dict = {}
for key, tensor in state_dict.items():
new_key = key
for old, new in rename_rules:
if old in new_key:
new_key = new_key.replace(old, new)
new_state_dict[new_key] = tensor
return nest(new_state_dict)
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