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#!/usr/bin/env python3
from __future__ import annotations
import argparse
import contextlib
import io
import os
import re
import sys
from pathlib import Path
from typing import Dict, List, Optional, Tuple
THIS_DIR = Path(__file__).resolve().parent
PARENT_DIR = THIS_DIR.parent
if str(PARENT_DIR) not in sys.path:
sys.path.insert(0, str(PARENT_DIR))
from hf_resolver import (
resolve_inner_adapter,
resolve_medqa_dataset_arg,
resolve_outer_paths,
snapshot_repo,
task_for_inner_repo,
)
from load_from_repo import DATASET_DEFAULT_SPLIT, STYLE_SPECS
from inference_utils import (
inference_mas,
inference_mas_deliberation,
inference_mas_distill,
inference_mas_mixture,
)
GPQA_DEFAULT_CHOICE_OLD_PROMPT = 2
MBPPPLUS_TEMPERATURE = 0.2
class RunCapture:
def __init__(self) -> None:
self.stdout = io.StringIO()
def get_text(self) -> str:
return self.stdout.getvalue()
def build_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(description="Release inference runner for RecursiveMAS HF checkpoints.")
p.add_argument("--style", required=True, choices=list(STYLE_SPECS.keys()))
p.add_argument("--dataset", required=True, default="math500", choices=["math500", "medqa", "gpqa", "mbppplus", "aime25", "aime26", "livecodebench", "bamboogle", "hotpotqa"])
p.add_argument("--dataset_split", default="")
p.add_argument("--seed", type=int, default=42)
p.add_argument("--sample_seed", type=int, default=-1)
p.add_argument("--num_recursive_rounds", type=int, default=3)
p.add_argument("--num_samples", type=int, default=-1, help="Limit number of eval questions (-1 = all). Useful for quick canaries.")
p.add_argument("--batch_size", type=int, default=8)
p.add_argument("--latent_length", type=int, default=32)
p.add_argument("--temperature", type=float, default=0.6)
p.add_argument("--top_p", type=float, default=0.95)
p.add_argument("--top_k", type=int, default=-1)
p.add_argument("--num_rollouts", type=int, default=1, help="Stochastic rollouts for pass@k. AIME defaults to 10 (pass@10) if not set.")
p.add_argument("--lcb_use_private_tests", type=int, default=1, choices=[0, 1], help="LCB: 1 = public + private (hidden) tests, 0 = public only.")
# search-QA (deliberation + Tavily): keys file + per-sample output for later LLM-judge
p.add_argument("--tavily_keys_file", default="", help="File of Tavily keys (deliberation search datasets).")
p.add_argument("--tavily_sentinel_file", default="", help="Sentinel path written when all Tavily keys exhausted.")
p.add_argument("--result_jsonl", default="", help="Per-sample output (question/gold/pred/raw_output) for LLM-judge.")
p.add_argument("--trust_remote_code", type=int, default=1, choices=[0, 1])
p.add_argument("--device", default=None)
p.add_argument(
"--ckpt_override",
action="append",
default=[],
metavar="KEY=PATH",
help="Override a role/outer repo with a locally-trained checkpoint dir (no HF download), "
"e.g. --ckpt_override solver=/path/trained_solver --ckpt_override outer=/path/trained_outer. "
"Repeatable. Keys are the style's repo keys: sequential {planner,critic,solver,outer}; "
"mixture {math,code,science,summarizer,outer}; distillation {expert,learner,outer}; "
"deliberation {reflector,toolcaller,outer}.",
)
return p
def is_search_dataset(dataset: str) -> bool:
return dataset.strip().lower() in {"bamboogle", "hotpotqa"}
def validate_style_dataset(args: argparse.Namespace) -> None:
"""Fail fast on (style, dataset) pairs that would silently produce a meaningless metric.
The search-QA datasets (``bamboogle``/``hotpotqa``) are answered with the Deliberation
pipeline only: the Tool-Caller issues real web searches and the open-ended answers are
graded by an LLM judge. Running them under any other style would fall through to
string-match scoring and report a number that does not reflect the task, so we reject
the combination up front instead of returning a wrong result.
"""
if is_search_dataset(args.dataset):
if args.style != "deliberation":
raise SystemExit(
f"[error] dataset '{args.dataset}' is a search-QA task supported only by "
f"--style deliberation (got --style {args.style}). "
f"Re-run with --style deliberation, or choose a non-search dataset."
)
if not args.tavily_keys_file:
raise SystemExit(
f"[error] dataset '{args.dataset}' needs web search: pass "
f"--tavily_keys_file <path> (a file of Tavily keys) and set the "
f"API_KEY / API_BASE_URL / API_MODEL judge env vars. See inference/README.md."
)
def infer_dataset_split(dataset: str, explicit: str) -> str:
if explicit:
return explicit
return DATASET_DEFAULT_SPLIT.get(dataset.lower(), "test")
def infer_max_new_tokens(style: str, dataset: str) -> int:
ds = dataset.lower()
if ds == "math500":
if style == "sequential_light":
return 1000
return 2000
if ds in {"aime25", "aime26"}:
# AIME pass@10: light family uses 8k, all larger families use 16k.
return 8192 if style == "sequential_light" else 16000
if ds == "lcb":
# LiveCodeBench: 4096 generation tokens.
return 4096
if ds in {"bamboogle", "hotpotqa"}:
# Search-QA (deliberation + Tavily): 4000 generation tokens.
return 4000
return 4000
def infer_temperature(dataset: str, explicit: float) -> float:
if dataset.lower() == "mbppplus":
return MBPPPLUS_TEMPERATURE
# lcb uses the recommended temperature, or --temperature if explicitly provided.
return explicit
def _has_cli_flag(flag: str) -> bool:
# Matches both "--flag value" and "--flag=value" forms.
prefix = flag + "="
return any(arg == flag or arg.startswith(prefix) for arg in sys.argv[1:])
def apply_recommended_settings(args: argparse.Namespace) -> None:
recommended = inference_mas.get_release_recommended_settings(args.style, args.dataset)
if recommended is None:
return
field_to_flag = {
"seed": "--seed",
"batch_size": "--batch_size",
"latent_length": "--latent_length",
"num_recursive_rounds": "--num_recursive_rounds",
"temperature": "--temperature",
}
int_fields = {"seed", "batch_size", "latent_length", "num_recursive_rounds"}
mismatches: List[str] = []
for field_name, recommended_value in recommended.items():
flag = field_to_flag.get(field_name)
if flag is None:
continue
recommended_value = int(recommended_value) if field_name in int_fields else float(recommended_value)
explicit = _has_cli_flag(flag)
if not explicit:
setattr(args, field_name, recommended_value)
continue
if getattr(args, field_name) != recommended_value:
mismatches.append(f"{field_name}={recommended_value}")
if mismatches:
joined = ", ".join(mismatches)
print(
f"[note] We recommend to use provided settings to run "
f"{args.style} on {args.dataset}: {joined}"
)
def resolve_style_paths(style: str, dataset: str, repo_overrides: Optional[Dict[str, str]] = None) -> Dict[str, Path]:
spec = STYLE_SPECS[style]
repos = dict(spec["repos"])
if repo_overrides:
repos.update(repo_overrides)
task = task_for_inner_repo(dataset)
out: Dict[str, Path] = {}
def materialize(key: str) -> Path:
return snapshot_repo(str(repos[key]))
family = str(spec["family"])
if family == "sequential":
for key in ["planner", "critic", "solver", "outer"]:
out[key] = materialize(key)
out["planner_adapter"] = resolve_inner_adapter(out["planner"], task)
out["critic_adapter"] = resolve_inner_adapter(out["critic"], task)
out["solver_adapter"] = resolve_inner_adapter(out["solver"], task)
outer_paths = resolve_outer_paths(out["outer"], task=task)
out["outer_12"] = outer_paths["outer_12"]
out["outer_23"] = outer_paths["outer_23"]
out["outer_31"] = outer_paths["outer_31"]
return out
if family == "mixture":
for key in ["math", "code", "science", "summarizer", "outer"]:
out[key] = materialize(key)
out["math_adapter"] = resolve_inner_adapter(out["math"], None)
out["code_adapter"] = resolve_inner_adapter(out["code"], None)
out["science_adapter"] = resolve_inner_adapter(out["science"], None)
out["summarizer_adapter"] = resolve_inner_adapter(out["summarizer"], None)
outer_paths = resolve_outer_paths(out["outer"], task=None)
for key in ["outer_1s", "outer_2s", "outer_3s", "outer_s1", "outer_s2", "outer_s3"]:
out[key] = outer_paths[key]
return out
if family == "distillation":
for key in ["expert", "learner", "outer"]:
out[key] = materialize(key)
out["expert_adapter"] = resolve_inner_adapter(out["expert"], task)
out["learner_adapter"] = resolve_inner_adapter(out["learner"], task)
outer_paths = resolve_outer_paths(out["outer"], task=task)
out["outer_el"] = outer_paths["outer_el"]
out["outer_le"] = outer_paths["outer_le"]
return out
if family == "deliberation":
for key in ["reflector", "toolcaller", "outer"]:
out[key] = materialize(key)
out["reflector_adapter"] = resolve_inner_adapter(out["reflector"], None)
out["toolcaller_adapter"] = resolve_inner_adapter(out["toolcaller"], None)
outer_paths = resolve_outer_paths(out["outer"], task=None)
out["outer_rt"] = outer_paths["outer_rt"]
out["outer_tr"] = outer_paths["outer_tr"]
return out
raise ValueError(f"Unsupported style family: {family}")
def build_common_cli(args: argparse.Namespace, dataset_arg: str, dataset_split: str, latent_steps: int, max_new_tokens: int) -> List[str]:
temperature = infer_temperature(args.dataset, args.temperature)
out = [
"--dataset", dataset_arg,
"--dataset_split", dataset_split,
"--num_samples", str(args.num_samples),
"--seed", str(args.seed),
"--sample_seed", str(args.sample_seed),
"--num_rollouts", str(args.num_rollouts),
"--num_recursive_rounds", str(args.num_recursive_rounds),
"--batch_size", str(args.batch_size),
"--latent_steps", str(latent_steps),
"--max_new_tokens", str(max_new_tokens),
"--temperature", str(temperature),
"--top_p", str(args.top_p),
"--top_k", str(args.top_k),
"--ans_max_new_tokens", "-1",
"--mbppplus_timeout_s", "10",
"--mbppplus_num_prompt_tests", "3",
"--dtype", "auto",
"--outer_dtype", "auto",
"--trust_remote_code", str(args.trust_remote_code),
"--enable_thinking", "0",
]
if args.dataset.lower() == "lcb":
# private=1 => public + private (hidden) tests; 0 => public only. 6s/test.
out.extend(["--lcb_use_private_tests", str(args.lcb_use_private_tests), "--lcb_timeout_s", "6"])
if args.device is not None:
out.extend(["--device", str(args.device)])
out.append("--do_sample")
out.append("--ans")
return out
def extract_metric(output_text: str) -> Tuple[str, float]:
passk = re.findall(r"pass@(\d+)=([0-9]+(?:\.[0-9]+)?)%", output_text)
if passk:
k, val = passk[-1]
return f"pass@{k}", float(val)
matches = re.findall(r"accuracy=([0-9]+(?:\.[0-9]+)?)%", output_text)
if matches:
return "accuracy", float(matches[-1])
raise RuntimeError("Failed to parse final metric from inference output.")
def run_module(module, cli_args: List[str]) -> Tuple[str, float, str]:
old_argv = sys.argv[:]
capture = RunCapture()
try:
sys.argv = [module.__file__ or module.__name__] + cli_args
with contextlib.redirect_stdout(capture.stdout):
module.main()
except Exception:
captured = capture.get_text()
if captured.strip():
print(captured, file=sys.stderr, end="" if captured.endswith("\n") else "\n")
raise
finally:
sys.argv = old_argv
text = capture.get_text()
metric_name, metric_value = extract_metric(text)
return metric_name, metric_value, text
def build_cli_for_style(
args: argparse.Namespace,
family: str,
dataset_arg: str,
dataset_split: str,
paths: Dict[str, Path],
latent_steps: int,
max_new_tokens: int,
) -> Tuple[object, List[str]]:
common = build_common_cli(args, dataset_arg=dataset_arg, dataset_split=dataset_split, latent_steps=latent_steps, max_new_tokens=max_new_tokens)
if family == "sequential":
choice_old_prompt = GPQA_DEFAULT_CHOICE_OLD_PROMPT if args.dataset.lower() == "gpqa" else 0
cli = [
"--mas_shape", "chain",
"--agent1_model_name_or_path", str(paths["planner"]),
"--agent2_model_name_or_path", str(paths["critic"]),
"--agent3_model_name_or_path", str(paths["solver"]),
"--agent1_inner_aligner_path", str(paths["planner_adapter"]),
"--agent2_inner_aligner_path", str(paths["critic_adapter"]),
"--agent3_inner_aligner_path", str(paths["solver_adapter"]),
"--outer_12_path", str(paths["outer_12"]),
"--outer_23_path", str(paths["outer_23"]),
"--outer_31_path", str(paths["outer_31"]),
"--choice_old_prompt", str(choice_old_prompt),
"--solver_pre_question", "0",
"--inner_adapter_type_fallback", "ln_res_adapter",
"--outer_adapter_type_fallback", "outer_ln_res_adapter",
] + common
return inference_mas, cli
if family == "mixture":
cli = [
"--mas_shape", "hie",
"--agent1_model_name_or_path", str(paths["math"]),
"--agent2_model_name_or_path", str(paths["code"]),
"--agent3_model_name_or_path", str(paths["science"]),
"--agent4_model_name_or_path", str(paths["summarizer"]),
"--agent1_inner_aligner_path", str(paths["math_adapter"]),
"--agent2_inner_aligner_path", str(paths["code_adapter"]),
"--agent3_inner_aligner_path", str(paths["science_adapter"]),
"--agent4_inner_aligner_path", str(paths["summarizer_adapter"]),
"--outer_1s_path", str(paths["outer_1s"]),
"--outer_2s_path", str(paths["outer_2s"]),
"--outer_3s_path", str(paths["outer_3s"]),
"--outer_s1_path", str(paths["outer_s1"]),
"--outer_s2_path", str(paths["outer_s2"]),
"--outer_s3_path", str(paths["outer_s3"]),
"--inner_adapter_type_fallback", "ln_res_adapter",
"--outer_adapter_type_fallback", "outer_ln_res_adapter",
] + common
return inference_mas_mixture, cli
if family == "distillation":
cli = [
"--mas_shape", "distill",
"--expert_model_name_or_path", str(paths["expert"]),
"--learner_model_name_or_path", str(paths["learner"]),
"--expert_inner_aligner_path", str(paths["expert_adapter"]),
"--learner_inner_aligner_path", str(paths["learner_adapter"]),
"--outer_el_path", str(paths["outer_el"]),
"--outer_le_path", str(paths["outer_le"]),
"--inner_adapter_type_fallback", "ln_res_adapter",
"--outer_adapter_type_fallback", "outer_ln_res_adapter",
] + common
return inference_mas_distill, cli
if family == "deliberation":
cli = [
"--mas_shape", "deliberation",
"--reflector_model_name_or_path", str(paths["reflector"]),
"--toolcaller_model_name_or_path", str(paths["toolcaller"]),
"--reflector_inner_aligner_path", str(paths["reflector_adapter"]),
"--toolcaller_inner_aligner_path", str(paths["toolcaller_adapter"]),
"--outer_rt_path", str(paths["outer_rt"]),
"--outer_tr_path", str(paths["outer_tr"]),
"--inner_adapter_type_fallback", "ln_res_adapter",
"--outer_adapter_type_fallback", "outer_ln_res_adapter",
"--max_tool_rounds", "5",
"--python_timeout", "10.0",
"--python_cwd", ".",
"--result_max_chars", "6000",
] + common
cli.append("--quiet_tools")
# search-QA datasets: enable real Tavily (multi-key rotation) + per-sample jsonl for LLM-judge
if is_search_dataset(args.dataset) and args.tavily_keys_file:
cli += [
"--search_provider", "tavily",
"--tavily_keys_file", str(args.tavily_keys_file),
"--tavily_exhausted_file", "/tmp/tavily_exhausted.txt",
"--tavily_search_depth", "advanced",
"--tavily_max_results", "4",
]
if args.tavily_sentinel_file:
cli += ["--tavily_sentinel_file", str(args.tavily_sentinel_file)]
if args.result_jsonl:
cli += ["--result_jsonl", str(args.result_jsonl)]
return inference_mas_deliberation, cli
raise ValueError(f"Unsupported style family: {family}")
def main() -> int:
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("MAS_FORCE_DISABLE_TORCHVISION", "1")
args = build_parser().parse_args()
if args.dataset.lower() == "livecodebench":
args.dataset = "lcb" # internal dataset key
validate_style_dataset(args)
apply_recommended_settings(args)
if args.dataset.lower() in {"aime25", "aime26"} and not _has_cli_flag("--num_rollouts"):
args.num_rollouts = 10 # AIME defaults to pass@10
if args.dataset.lower() == "lcb" and not _has_cli_flag("--temperature"):
# LCB default temperature; only when the recommended table didn't already set one.
recommended = inference_mas.get_release_recommended_settings(args.style, args.dataset)
if not (recommended and "temperature" in recommended):
args.temperature = 0.2
repo_root = Path(__file__).resolve().parent
dataset_arg = resolve_medqa_dataset_arg(args.dataset, repo_root)
dataset_split = infer_dataset_split(args.dataset, args.dataset_split)
repo_overrides: Dict[str, str] = {}
for item in args.ckpt_override:
if "=" not in item:
raise ValueError(f"--ckpt_override must be KEY=PATH, got: {item!r}")
key, path = item.split("=", 1)
repo_overrides[key.strip()] = path.strip()
paths = resolve_style_paths(args.style, args.dataset, repo_overrides=repo_overrides)
family = str(STYLE_SPECS[args.style]["family"])
max_new_tokens = infer_max_new_tokens(args.style, args.dataset)
print(f"[run] style={args.style} dataset={args.dataset} rounds={args.num_recursive_rounds} batch_size={args.batch_size} latent_length={args.latent_length} max_new_tokens={max_new_tokens}")
module, cli = build_cli_for_style(
args=args,
family=family,
dataset_arg=dataset_arg,
dataset_split=dataset_split,
paths=paths,
latent_steps=args.latent_length,
max_new_tokens=max_new_tokens,
)
metric_name, metric_value, _ = run_module(module, cli)
print(f"[result] {metric_name}={metric_value:.2f}%")
return 0
if __name__ == "__main__":
raise SystemExit(main())