from __future__ import annotations import ast import json from collections import Counter from pathlib import Path ROOT = Path(__file__).resolve().parents[1] DATA = ROOT / "data" / "ClawBenchPro_base100_hard100_quality" VERL = ROOT / "verl" def require_file(path: Path) -> None: if not path.is_file(): raise FileNotFoundError(path) def main() -> None: required_bundle_files = [ ROOT / "v14" / "0708_new" / "inference_clawbenchpro.sh", ROOT / "v14" / "0708_new" / "score_clawbenchpro.sh", ROOT / "v14" / "0710" / "inference.sh", ROOT / "scripts" / "setup_full_npu_environment.sh", VERL / "recipe" / "nanoclaw" / "inference.py", VERL / "recipe" / "nanoclaw" / "nanoclaw.py", VERL / "recipe" / "nanoclaw" / "nanoclaw_tool_config.yaml", VERL / "recipe" / "nanoclaw" / "score_clawbenchpro.py", VERL / "verl" / "experimental" / "agent_loop" / "agent_loop.py", VERL / "verl" / "experimental" / "agent_loop" / "tool_agent_loop.py", DATA / "benchmark_manifest.json", DATA / "quality_selection_report.json", DATA / "quality_selection_manifest.jsonl", DATA / "_SUCCESS", ROOT / "BUNDLE_VALIDATION.json", ] for path in required_bundle_files: require_file(path) inference_shell = (ROOT / "v14" / "0708_new" / "inference_clawbenchpro.sh").read_text(encoding="utf-8") inference_core_shell = (ROOT / "v14" / "0710" / "inference.sh").read_text(encoding="utf-8") scoring_shell = (ROOT / "v14" / "0708_new" / "score_clawbenchpro.sh").read_text(encoding="utf-8") scoring_python = (VERL / "recipe" / "nanoclaw" / "score_clawbenchpro.py").read_text(encoding="utf-8") setup_shell = (ROOT / "scripts" / "setup_full_npu_environment.sh").read_text(encoding="utf-8") if "SETUP_ENVIRONMENT=${SETUP_ENVIRONMENT:-1}" not in inference_shell: raise ValueError("inference entrypoint must default to full environment installation") required_multi_model_markers = ( "MODEL_CHECKPOINTS", "MODEL_PATH_LIST", "MODEL_NAME_LIST", "MODEL_INPUT_VALIDATE_ONLY", "INPUT_MODEL_PATHS", "CONTINUE_ON_MODEL_ERROR", "MODEL_SWITCH_COOLDOWN", "MODEL_RESOURCE_RELEASE_TIMEOUT", "model checkpoint path must be absolute", ) missing_multi_model_markers = [marker for marker in required_multi_model_markers if marker not in inference_shell] if missing_multi_model_markers: raise ValueError(f"multi-checkpoint inference entrypoint is incomplete: {missing_multi_model_markers}") required_core_multi_model_markers = ( 'for model_index in "${!MODEL_PATHS[@]}"', 'model_output_root=${OUTPUT_ROOT}/${model_name}', "wait_for_ray_available_npu_resources", "MODEL_RESOLVED_NAMES", "duplicate model output name", ) missing_core_markers = [marker for marker in required_core_multi_model_markers if marker not in inference_core_shell] if missing_core_markers: raise ValueError(f"core multi-checkpoint loop is incomplete: {missing_core_markers}") required_scoring_multi_model_markers = ( "discover_models", "for model_dir in models", 'output_root / model_dir.name', 'output_root / "leaderboard.csv"', ) missing_scoring_markers = [marker for marker in required_scoring_multi_model_markers if marker not in scoring_python] if missing_scoring_markers: raise ValueError(f"multi-model scoring is incomplete: {missing_scoring_markers}") if "SETUP_ENVIRONMENT=${SETUP_ENVIRONMENT:-1}" not in scoring_shell: raise ValueError("scoring entrypoint must default to full environment installation") if 'source "${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh"' not in scoring_shell: raise ValueError("scoring entrypoint does not source the full NPU environment installer") required_install_markers = ( "torch==2.9.0", "torch-npu==2.9.0", "VLLM_TARGET_DEVICE=empty pip install .", "triton==3.5.0", "pip install -r requirements-npu.txt", "transformers==5.3.0", "openai httpx", ) missing_install_markers = [marker for marker in required_install_markers if marker not in setup_shell] if missing_install_markers: raise ValueError(f"full NPU environment installer is incomplete: {missing_install_markers}") manifest = json.loads((DATA / "benchmark_manifest.json").read_text(encoding="utf-8")) tasks = manifest.get("tasks") if not isinstance(tasks, list) or manifest.get("task_count") != 200 or len(tasks) != 200: raise ValueError("benchmark_manifest.json must declare exactly 200 tasks") categories = Counter(str(item.get("quality_category")) for item in tasks) if categories != {"base": 100, "hard": 100}: raise ValueError(f"unexpected category counts: {dict(categories)}") task_dirs = sorted(path for path in DATA.glob("data_*") if path.is_dir()) if len(task_dirs) != 200: raise ValueError(f"expected 200 task directories, found {len(task_dirs)}") if {path.name for path in task_dirs} != {str(item.get("task_id")) for item in tasks}: raise ValueError("task directories do not match benchmark manifest") llm_judge = 0 required_task_keys = ("env_builder", "prompt", "verifier", "yaml") for task_dir in task_dirs: task_manifest = json.loads((task_dir / "manifest.json").read_text(encoding="utf-8")) files = task_manifest.get("files") if not isinstance(files, dict): raise ValueError(f"invalid task manifest: {task_dir}") for key in required_task_keys: require_file(task_dir / str(files.get(key) or "")) require_file(task_dir / "_env_builder_impl.py") verifier = task_dir / str(files["verifier"]) verifier_text = verifier.read_text(encoding="utf-8", errors="replace") if any(marker in verifier_text for marker in ("OpenAI(", "client.chat.completions", "httpx.Client")): llm_judge += 1 for python_file in (task_dir / str(files["env_builder"]), task_dir / "_env_builder_impl.py", verifier): ast.parse(python_file.read_text(encoding="utf-8"), filename=str(python_file)) if llm_judge != 134: raise ValueError(f"expected 134 LLM Judge verifiers, found {llm_judge}") symlinks = [path for path in ROOT.rglob("*") if path.is_symlink()] if symlinks: raise ValueError(f"bundle contains symlinks and is not self-contained: {symlinks[:10]}") for python_file in ( VERL / "recipe" / "nanoclaw" / "inference.py", VERL / "recipe" / "nanoclaw" / "score_clawbenchpro.py", ): ast.parse(python_file.read_text(encoding="utf-8"), filename=str(python_file)) print( json.dumps( { "bundle_root": str(ROOT), "tasks": 200, "categories": dict(categories), "deterministic_verifiers": 66, "llm_judge_verifiers": llm_judge, "inference_full_environment_install_default": True, "inference_multi_checkpoint_input": True, "script_top_model_array_smoke_count": 3, "absolute_checkpoint_paths_required": True, "script_top_model_strings": True, "tabular_model_config_removed": True, "per_model_output_directories": True, "scoring_full_environment_install_default": True, "scoring_multi_model_discovery": True, "symlinks": 0, "status": "ok", }, ensure_ascii=False, indent=2, ) ) if __name__ == "__main__": main()