File size: 2,504 Bytes
24918f7 | 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 | #!/usr/bin/env python3
"""Head files: the weights one training run changes over the frozen backbone.
A head file is a safetensors file holding the head, scorer, type embedding,
and (optionally) action head state of a Laya `DecisionModel`, with header
metadata naming the backbone it was trained over. `pw-decide` refuses a head
whose `backbone` is not the loaded checkpoint's encoder.
headfile.py show head.safetensors
"""
import argparse
import json
import struct
import sys
from pathlib import Path
HEAD_MODULES = ("head", "scorer", "type_emb", "act_head")
FORMAT = "pw-decide-head/1"
def encoder_id(model_id):
"""The backbone id a checkpoint records, without loading its weights."""
import laya # noqa: PLC0415
model_dir = model_id
if not Path(model_dir).exists():
from huggingface_hub import snapshot_download # noqa: PLC0415
model_dir = snapshot_download(model_id, allow_patterns=["rl_agent_config.json"])
with open(Path(model_dir) / "rl_agent_config.json", encoding="utf-8") as fh:
return json.load(fh)["encoder"]
def save(path, model, label, model_id, extra=None):
"""Write the head modules of `model` as a head file."""
from safetensors.torch import save_file # noqa: PLC0415
tensors = {}
for name in HEAD_MODULES:
module = getattr(model, name, None)
if module is None:
continue
for key, value in module.state_dict().items():
tensors["%s.%s" % (name, key)] = value.detach().contiguous().cpu()
meta = {"format": FORMAT, "label": label, "model": model_id, "backbone": encoder_id(model_id)}
for key, value in (extra or {}).items():
meta[key] = value if isinstance(value, str) else json.dumps(value)
Path(path).parent.mkdir(parents=True, exist_ok=True)
save_file(tensors, str(path), metadata=meta)
def read_metadata(path):
with open(path, "rb") as fh:
n = struct.unpack("<Q", fh.read(8))[0]
header = json.loads(fh.read(n))
meta = header.pop("__metadata__", {})
return meta, header
def main():
ap = argparse.ArgumentParser()
sub = ap.add_subparsers(dest="cmd", required=True)
s = sub.add_parser("show")
s.add_argument("path")
args = ap.parse_args()
meta, tensors = read_metadata(args.path)
print(json.dumps(meta, indent=2))
print("%d tensors, %d bytes" % (len(tensors), sum(t["data_offsets"][1] - t["data_offsets"][0] for t in tensors.values())))
if __name__ == "__main__":
main()
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