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"""Resolve experiment group configs into concrete diffulex_bench YAML files."""
from __future__ import annotations
import argparse
import copy
import json
import os
import re
from pathlib import Path
from typing import Any
import yaml
def merge_dict(base: dict[str, Any], override: dict[str, Any] | None) -> dict[str, Any]:
merged = copy.deepcopy(base)
if not override:
return merged
for key, value in override.items():
if isinstance(value, dict) and isinstance(merged.get(key), dict):
merged[key] = merge_dict(merged[key], value)
else:
merged[key] = copy.deepcopy(value)
return merged
def sanitize(value: str) -> str:
value = re.sub(r"[^A-Za-z0-9_.-]+", "_", value.strip())
return value.strip("_") or "run"
def split_list(raw: str) -> list[str]:
return [item for item in re.split(r"[,\s]+", raw.strip()) if item]
def match_filter(group_name: str, exp: dict[str, Any], patterns: list[str]) -> bool:
if not patterns:
return True
haystack = " ".join(
[group_name]
+ [
str(exp.get(key, ""))
for key in ("name", "variant", "task", "model", "decoding_strategy", "sampling_mode")
]
).lower()
return any(pattern.lower() in haystack for pattern in patterns)
def resolve_model_path(model: dict[str, Any]) -> str:
env_name = model.get("env")
raw = os.environ.get(env_name, "") if env_name else ""
if not raw:
raw = str(model["path"])
return str(Path(os.path.expandvars(raw)).expanduser())
def path_is_dir(path: str) -> bool:
try:
return Path(path).is_dir()
except OSError:
return False
def resolve_file(path: str, base_dir: Path) -> Path:
p = Path(path).expanduser()
if p.is_absolute():
return p
candidate = base_dir / p
if candidate.exists():
return candidate.resolve()
return (Path.cwd() / p).resolve()
def load_yaml(path: Path) -> dict[str, Any]:
with path.open("r", encoding="utf-8") as f:
return yaml.safe_load(f) or {}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config-dir", required=True)
parser.add_argument("--defaults-config", required=True)
parser.add_argument("--config-pattern", default="*.yml")
parser.add_argument("--config-files", default="")
parser.add_argument("--output-root", required=True)
parser.add_argument("--log-dir", required=True)
parser.add_argument("--plan-tsv", required=True)
parser.add_argument("--filter", default="")
parser.add_argument("--dataset-limit", default="")
parser.add_argument("--max-num-reqs", default="")
return parser.parse_args()
def main() -> None:
args = parse_args()
config_dir = Path(args.config_dir).expanduser()
if not config_dir.is_absolute():
config_dir = (Path.cwd() / config_dir).resolve()
defaults_path = resolve_file(args.defaults_config, config_dir)
base_config = load_yaml(defaults_path)
if args.config_files.strip():
group_files = [resolve_file(item, config_dir) for item in split_list(args.config_files)]
else:
group_files = sorted(config_dir.glob(args.config_pattern))
group_files = [p.resolve() for p in group_files if not p.name.startswith("_")]
output_root = Path(args.output_root).expanduser()
log_root = Path(args.log_dir).expanduser()
plan_tsv = Path(args.plan_tsv).expanduser()
resolved_root = output_root / "resolved_configs"
resolved_root.mkdir(parents=True, exist_ok=True)
log_root.mkdir(parents=True, exist_ok=True)
default_block = base_config.get("defaults", {})
base_engine = default_block.get("engine", {})
base_eval = default_block.get("eval", {})
base_thresholds = default_block.get("thresholds", {})
base_tasks = base_config.get("tasks", {})
base_models = base_config.get("models", {})
patterns = split_list(args.filter)
dataset_limit = args.dataset_limit.strip()
max_num_reqs = args.max_num_reqs.strip()
rows: list[dict[str, Any]] = []
for group_file in group_files:
group = load_yaml(group_file)
group_name = sanitize(str(group.get("name") or group_file.stem))
group_defaults = group.get("defaults", {})
default_engine = merge_dict(base_engine, group_defaults.get("engine"))
default_eval = merge_dict(base_eval, group_defaults.get("eval"))
default_thresholds = merge_dict(base_thresholds, group_defaults.get("thresholds"))
tasks = merge_dict(base_tasks, group.get("tasks"))
models = merge_dict(base_models, group.get("models"))
for source_index, exp in enumerate(group.get("experiments", []), start=1):
if not match_filter(group_name, exp, patterns):
continue
exp_name = sanitize(str(exp["name"]))
model = models[exp["model"]]
task_name = tasks[exp["task"]]
model_path = resolve_model_path(model)
row_index = len(rows) + 1
run_name = f"{row_index:02d}_{group_name}__{exp_name}"
run_dir = output_root / "runs" / run_name
config_path = resolved_root / f"{run_name}.yml"
log_path = log_root / f"{run_name}.log"
thresholds = merge_dict(default_thresholds, exp.get("thresholds"))
engine = merge_dict(default_engine, exp.get("engine"))
engine.update(
{
"model_path": model_path,
"model_name": model["model_name"],
"mask_token_id": model["mask_token_id"],
"decoding_strategy": exp["decoding_strategy"],
"sampling_mode": exp["sampling_mode"],
"block_size": int(exp["block_size"]),
"buffer_size": int(exp["buffer_size"]),
"page_size": int(exp.get("page_size", exp["block_size"])),
"decoding_thresholds": thresholds,
}
)
if max_num_reqs:
engine["max_num_reqs"] = int(max_num_reqs)
eval_config = merge_dict(default_eval, exp.get("eval"))
eval_config.update(
{
"dataset_name": task_name,
"output_dir": str(run_dir),
"use_run_subdirectory": False,
}
)
if dataset_limit:
eval_config["dataset_limit"] = int(dataset_limit)
concrete = {"engine": engine, "eval": eval_config}
with config_path.open("w", encoding="utf-8") as f:
yaml.safe_dump(concrete, f, sort_keys=False)
metadata = {
"group_file": str(group_file),
"group": group_name,
"source_index": source_index,
"name": exp["name"],
"variant": exp.get("variant"),
"model_key": exp["model"],
"model_path": model_path,
"task_key": exp["task"],
"dataset_name": task_name,
"config_path": str(config_path),
"output_dir": str(run_dir),
"log_file": str(log_path),
}
run_dir.mkdir(parents=True, exist_ok=True)
with (run_dir / "experiment_config.json").open("w", encoding="utf-8") as f:
json.dump(metadata, f, indent=2, ensure_ascii=False)
rows.append(
{
"index": row_index,
"group": group_name,
"name": exp["name"],
"variant": exp.get("variant", ""),
"task": exp["task"],
"model": exp["model"],
"model_path": model_path,
"model_exists": "1" if path_is_dir(model_path) else "0",
"config": str(config_path),
"output_dir": str(run_dir),
"log_file": str(log_path),
}
)
headers = [
"index",
"group",
"name",
"variant",
"task",
"model",
"model_path",
"model_exists",
"config",
"output_dir",
"log_file",
]
plan_tsv.parent.mkdir(parents=True, exist_ok=True)
with plan_tsv.open("w", encoding="utf-8") as f:
f.write("\t".join(headers) + "\n")
for row in rows:
f.write("\t".join(str(row[h]) for h in headers) + "\n")
print(f"Loaded defaults: {defaults_path}")
print("Loaded group configs:")
for group_file in group_files:
print(f" - {group_file}")
print(f"Resolved {len(rows)} experiment(s)")
print(f"Plan: {plan_tsv}")
print(f"Configs: {resolved_root}")
if __name__ == "__main__":
main()
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