File size: 4,174 Bytes
23ee7f2 | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | from __future__ import annotations
import hashlib
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict
def normalize_training_config(config: Dict[str, Any]) -> Dict[str, Any]:
return {
"model_id": str(config.get("model_id", "LiquidAI/LFM2.5-VL-450M")),
"revision": str(config.get("revision", "main")),
"epochs": int(config.get("epochs", 1)),
"learning_rate": float(config.get("learning_rate", 2e-4)),
"lora_r": int(config.get("lora_r", 16)),
"lora_alpha": int(config.get("lora_alpha", 32)),
"lora_dropout": float(config.get("lora_dropout", 0.05)),
"dataset_manifest_path": str(config.get("dataset_manifest_path", "data/manifests/dataset_manifest_v1.json")),
"dataset_split_path": str(config.get("dataset_split_path", "data/manifests/dataset_splits_v1.json")),
}
def _stable_hash(payload: Dict[str, Any]) -> str:
canonical = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
return hashlib.sha256(canonical).hexdigest()
def _write_json(path: Path, payload: Dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")
def create_training_artifacts(
artifact_root: Path,
run_id: str,
artifact_volume: str,
config: Dict[str, Any],
) -> Dict[str, Any]:
normalized = normalize_training_config(config)
run_dir = artifact_root / run_id
checkpoint_dir = run_dir / "checkpoint-lora-v1"
checkpoint_dir.mkdir(parents=True, exist_ok=True)
adapter_config = {
"base_model_name_or_path": normalized["model_id"],
"peft_type": "LORA",
"r": normalized["lora_r"],
"lora_alpha": normalized["lora_alpha"],
"lora_dropout": normalized["lora_dropout"],
"inference_mode": False,
"task_type": "CAUSAL_LM",
"note": "Phase 6 scaffold checkpoint artifact.",
}
_write_json(checkpoint_dir / "adapter_config.json", adapter_config)
# Scaffold artifact to anchor downstream wiring before full trainer loop.
(checkpoint_dir / "adapter_model.safetensors").write_bytes(
json.dumps(
{
"artifact_type": "phase6.scaffold.weights",
"note": "Placeholder adapter blob. Replace in full LoRA trainer.",
},
sort_keys=True,
).encode("utf-8")
)
_write_json(
checkpoint_dir / "training_args.json",
{
"epochs": normalized["epochs"],
"learning_rate": normalized["learning_rate"],
"dataset_manifest_path": normalized["dataset_manifest_path"],
"dataset_split_path": normalized["dataset_split_path"],
},
)
reproducibility = {
"run_id": run_id,
"created_at": datetime.now(timezone.utc).isoformat(),
"config_hash": _stable_hash(normalized),
"config": normalized,
}
_write_json(run_dir / "reproducibility.json", reproducibility)
run_manifest = {
"run_id": run_id,
"created_at": datetime.now(timezone.utc).isoformat(),
"artifact_volume": artifact_volume,
"model_id": normalized["model_id"],
"revision": normalized["revision"],
"checkpoint_dir": str(checkpoint_dir),
"adapter_artifact_ref": f"modal-volume://{artifact_volume}/{run_id}/checkpoint-lora-v1",
"dataset_manifest_path": normalized["dataset_manifest_path"],
"dataset_split_path": normalized["dataset_split_path"],
"config_hash": reproducibility["config_hash"],
"training_mode": "phase6_scaffold",
}
manifest_path = run_dir / "run_manifest.json"
_write_json(manifest_path, run_manifest)
return {
"status": "ok",
"run_id": run_id,
"manifest_path": str(manifest_path),
"checkpoint_dir": str(checkpoint_dir),
"adapter_artifact_ref": run_manifest["adapter_artifact_ref"],
"artifact_volume": artifact_volume,
"config_hash": reproducibility["config_hash"],
"training_mode": "phase6_scaffold",
}
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