| 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 "<missing>")) |
| 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() |
|
|
