iomgaa
02b8145b7f
Codex CHANGES_REQUESTED 复审： - Critical：diag_fingerprint 用 config.diag.model，但 Phase 1 诊断 LLM 从 .env SEARCH_LLM_MODEL 构建，两者不一致会让信号以错误模型指纹落库，破坏可复现/ resume/隔离。build_diagnosis_deps 新增 expected_model 参数，Phase 1 执行前 fail loud 校验 config.model == settings.search_llm_model（附两值）。 - Minor：config/video_split.yaml diag.model 注释由 JUDGE_LLM_MODEL 更正为 SEARCH_LLM_MODEL，与实现对齐。 - 补两个单测：模型不一致 fail loud + 缺凭证 fail loud。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
698 lines
27 KiB
Python
698 lines
27 KiB
Python
"""结果驱动视频级切分的自包含两阶段 CLI 入口。
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把整条离线管线的编排从 shell 搬进 Python：一次调用内联串起
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Phase 1 离线诊断（run_baseline_diagnosis，LLM 重活，断点续跑幂等）→
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Phase 2 冻结切分（build_split，纯 code-controlled，产出 pools.json + manifest）→
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McNemar 功效护栏（validation 池错题数达阈校验）。
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设计要点：
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- 诊断口径指纹 = (诊断 prompt 版本, 模型名, git 短 SHA) 三分量合成，隔离不同
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诊断配置的信号；换 prompt / 模型 / 代码实现即换指纹，旧信号不被覆盖。
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- 真实依赖组装参考 app/harness/runner.py::_run_diagnosis：GovernedLLMClient
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(search llm, thinking=True) + RunLogImpl(harness.db) + VersionedSkillStore +
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DiagnosePrompts(项目根 prompts/) + tree_data={}（由诊断管线内部按需加载）。
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- 缺 .env / config 关键项一律 fail loud（P5），绝不静默兜底。
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- `--dry-run` 用假 deps 跑通两阶段 wiring 不真调 LLM，打印将执行的步骤 + 指纹，
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用于校验装配正确性（对齐 CLAUDE.md §2.5 smoke test）。
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编排函数（run_pipeline）通过依赖注入接收 DiagnosisDeps / signal_store / wrong_ids /
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questions，便于单测用假实现替换、不触真实 LLM 与 harness.db。
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import datetime
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import os
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import sqlite3
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import subprocess
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from dataclasses import dataclass
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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import yaml
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from loguru import logger
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from app.harness.baseline_diagnosis import DiagnosisDeps, run_baseline_diagnosis
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from app.harness.build_split import SplitBuildConfig, SplitBuildResult, build_split
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from app.harness.split_selection import diag_fingerprint
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from app.question_gen.loader import load_benchmark
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if TYPE_CHECKING:
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from app.harness.pools import Pools
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from core.evolution.protocols import DiagnosisSignalStore
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from core.types import GeneratedQuestion
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# 与 core.evolution.diagnose._INFRA_STOP_REASONS 对齐：执行/解析层失败排除出可诊断错题。
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_INFRA_STOP_REASONS: frozenset[str] = frozenset({"error", "parse_error"})
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# 工程路径默认值（少变；可经 CLI 单次覆盖）。诊断信号表建在 harness.db。
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_DEFAULT_HARNESS_DB = Path("workspaces/default/harness.db")
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_DEFAULT_QUESTIONS_DIR = Path("store/questions/benchmarks/Video-MME")
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_DEFAULT_OUT_DIR = Path("workspaces/video-split")
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# ---------------------------------------------------------------------------
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# 配置解析（fail loud）
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True)
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class VideoSplitConfig:
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"""结果驱动视频级切分的科研旋钮快照（从 config/video_split.yaml 解析）。
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字段:
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baseline_run_id: 基线 run 标识（错题诊断与切分依据）。
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n_trainval: trainval 目标视频数（多样性阶段填充上限）。
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epsilon: test 相对全局最大允许分布偏差（题型 / 难度两维）。
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report_floor: per-type 报告门限，题数 ≥ 此值的 task_type 才入 ε 约束。
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val_wrong_min: validation 池最少错题数（McNemar 功效阈；0=不检查）。
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val_ratio: validation 占 trainval 视频组总数的比例。
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seed: 贪心选择器预洗牌 + 视频组题级切分种子。
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floor_k: 各高信号 task_type 的 T2 defect 下限（硬约束）。
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prompt_version: 诊断 prompt 版本标识（指纹分量）。
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model: 执行诊断的模型名（指纹分量）。
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"""
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baseline_run_id: str
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n_trainval: int
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epsilon: float
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report_floor: int
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val_wrong_min: int
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val_ratio: float
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seed: int
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floor_k: dict[str, int]
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prompt_version: str
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model: str
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def _require(section: dict[str, Any], keys: tuple[str, ...], where: str) -> None:
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"""校验 section 含全部必填键，缺任一即 fail loud（P5，不静默兜底）。
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参数:
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section: 待校验的配置子字典。
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keys: 必填键元组。
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where: 出错信息中标注的段名（如 "video_split"）。
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异常:
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SystemExit: 存在缺失键。
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"""
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missing = [k for k in keys if k not in section]
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if missing:
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raise SystemExit(f"config {where} 段缺关键项 {missing}，无法运行（P5 fail loud）")
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def parse_config(raw: dict[str, Any]) -> VideoSplitConfig:
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"""把 yaml 原始字典解析为 VideoSplitConfig，缺关键项 fail loud。
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参数:
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raw: yaml.safe_load 的顶层字典，需含 video_split / diag 两段。
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返回:
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VideoSplitConfig 冻结快照。
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异常:
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SystemExit: 缺 video_split / diag 段或段内关键项。
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"""
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if "video_split" not in raw or "diag" not in raw:
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raise SystemExit("config 缺 video_split / diag 段，无法运行（P5 fail loud）")
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vs = raw["video_split"]
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dg = raw["diag"]
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_require(
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vs,
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(
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"baseline_run_id",
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"n_trainval",
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"epsilon",
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"report_floor",
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"val_wrong_min",
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"val_ratio",
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"seed",
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"floor_k",
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),
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"video_split",
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)
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_require(dg, ("prompt_version", "model"), "diag")
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return VideoSplitConfig(
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baseline_run_id=vs["baseline_run_id"],
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n_trainval=vs["n_trainval"],
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epsilon=vs["epsilon"],
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report_floor=vs["report_floor"],
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val_wrong_min=vs["val_wrong_min"],
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val_ratio=vs["val_ratio"],
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seed=vs["seed"],
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floor_k=dict(vs["floor_k"]),
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prompt_version=dg["prompt_version"],
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model=dg["model"],
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)
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def load_config(config_path: Path) -> VideoSplitConfig:
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"""读取并解析 video_split yaml 配置文件（缺文件 / 关键项 fail loud）。
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参数:
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config_path: yaml 配置路径。
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返回:
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VideoSplitConfig。
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异常:
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SystemExit: 文件不存在或缺关键项。
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"""
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if not config_path.exists():
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raise SystemExit(f"config 文件不存在: {config_path}（P5 fail loud）")
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raw = yaml.safe_load(config_path.read_text(encoding="utf-8"))
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return parse_config(raw)
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def git_short_sha() -> str:
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"""取当前 git 短 SHA 作为诊断口径指纹的代码分量（诊断代码变则指纹变）。
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返回:
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git rev-parse --short HEAD 输出（去空白）。
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异常:
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SystemExit: 非 git 仓库或 git 不可用（fail loud，指纹不可缺分量）。
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"""
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try:
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out = subprocess.run(
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["git", "rev-parse", "--short", "HEAD"],
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capture_output=True,
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text=True,
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check=True,
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)
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except (subprocess.CalledProcessError, FileNotFoundError) as exc:
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raise SystemExit(f"无法获取 git 短 SHA 作为诊断代码版本: {exc}（P5 fail loud）") from exc
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sha = out.stdout.strip()
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if not sha:
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raise SystemExit("git rev-parse --short HEAD 返回空，诊断指纹缺代码分量（P5 fail loud）")
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return sha
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# ---------------------------------------------------------------------------
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# 真实依赖组装（参考 runner.py::_run_diagnosis）
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# ---------------------------------------------------------------------------
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class _DiagLLMSettings:
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"""诊断 LLM 的工程配置（从 .env 读取 search llm 凭证 + 韧性旋钮）。
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仅承载诊断所需字段（搜索 LLM = 诊断 judge），不复用 main.InfraSettings 以免
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构造整套适配器（embed / vlm）的重活；缺关键凭证 fail loud。
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"""
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def __init__(self) -> None:
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class _Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", extra="ignore")
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search_llm_model: str = ""
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search_llm_base_url: str = ""
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search_llm_api_key: str = ""
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redis_url: str = ""
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redis_cache_ttl: int = 86400
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llm_timeout: float = 300.0
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llm_max_retries: int = 3
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llm_retry_base_delay: float = 20.0
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llm_retry_max_delay: float = 120.0
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llm_circuit_breaker_threshold: int = 48
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llm_circuit_breaker_cooldown: float = 60.0
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llm_ttft_timeout: float = 30.0
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llm_inter_token_timeout: float = 15.0
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self._s = _Settings()
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def __getattr__(self, name: str) -> Any:
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return getattr(self._s, name)
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def _build_redis_cache(settings: Any) -> Any | None:
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"""按 .env redis_url 构建响应缓存（不可用则降级 None，与 main 一致）。"""
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if not settings.redis_url:
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return None
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try:
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import redis.asyncio as aioredis
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from adapters.redis_cache import RedisResponseCache
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redis_client = aioredis.from_url(settings.redis_url, decode_responses=True)
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ttl_s = settings.redis_cache_ttl if settings.redis_cache_ttl > 0 else None
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return RedisResponseCache(redis=redis_client, ttl_s=ttl_s)
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except Exception:
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logger.warning("Redis 缓存不可用，诊断降级为无缓存模式")
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return None
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def build_diagnosis_deps(
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*, harness_db: Path, concurrency: int, expected_model: str
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) -> DiagnosisDeps:
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"""组装 Phase 1 诊断的真实依赖束（GovernedLLMClient + RunLogImpl + prompts）。
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与 runner.py::_run_diagnosis 对齐：search LLM（thinking=True）作诊断 judge，
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RunLogImpl 只读读取 harness.db 的 predictions/traces，VersionedSkillStore 读技能，
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DiagnosePrompts 从项目根 prompts/ 加载，tree_data={} 由诊断管线内部按需加载。
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参数:
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harness_db: harness.db 路径（诊断读预测 + 信号落库同库）。
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concurrency: 诊断并发上限。
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expected_model: config.diag.model（诊断口径指纹的模型分量）。必须与 .env
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SEARCH_LLM_MODEL 一致——指纹里的 model 与实际诊断所用 model 不一致会让
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信号以错误模型指纹落库，破坏可复现 / resume / 口径隔离，故此处 fail loud。
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返回:
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DiagnosisDeps 冻结依赖束。
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异常:
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SystemExit: .env 缺 search LLM 凭证（model / base_url / api_key 任一为空），
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或 config.diag.model 与 .env SEARCH_LLM_MODEL 不一致（指纹漂移防护）。
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"""
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from adapters.breaker import CircuitBreaker
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from adapters.llm import GovernedLLMClient
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from adapters.telemetry import SQLiteTelemetryRecorder
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from app.harness.log import RunLogImpl
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from app.harness.workspace import VersionedSkillStore
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settings = _DiagLLMSettings()
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if not (
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settings.search_llm_model and settings.search_llm_base_url and settings.search_llm_api_key
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):
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raise SystemExit(
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"诊断 LLM 凭证缺失：.env 需配置 SEARCH_LLM_MODEL / SEARCH_LLM_BASE_URL / "
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"SEARCH_LLM_API_KEY（P5 fail loud，不静默兜底）"
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)
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if expected_model != settings.search_llm_model:
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raise SystemExit(
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"诊断模型指纹漂移：config.diag.model="
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f"{expected_model!r} 与 .env SEARCH_LLM_MODEL={settings.search_llm_model!r} "
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"不一致；指纹里的 model 必须等于实际诊断所用 model（P5 fail loud，"
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"请对齐 config/video_split.yaml diag.model 与 .env SEARCH_LLM_MODEL）"
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)
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telemetry_db = Path("logs/telemetry.db")
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telemetry_db.parent.mkdir(parents=True, exist_ok=True)
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telemetry = SQLiteTelemetryRecorder(telemetry_db)
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breaker = CircuitBreaker(
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fail_threshold=max(settings.llm_circuit_breaker_threshold, 1),
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cooldown_s=settings.llm_circuit_breaker_cooldown,
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)
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llm = GovernedLLMClient(
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model=settings.search_llm_model,
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base_url=settings.search_llm_base_url,
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api_key=settings.search_llm_api_key,
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provider=settings.search_llm_model.split("-")[0],
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thinking=True,
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breaker=breaker,
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cache=_build_redis_cache(settings),
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telemetry=telemetry,
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timeout_s=settings.llm_timeout,
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ttft_timeout_s=settings.llm_ttft_timeout,
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inter_token_timeout_s=settings.llm_inter_token_timeout,
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max_retries=settings.llm_max_retries,
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retry_base_delay_s=settings.llm_retry_base_delay,
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retry_max_delay_s=settings.llm_retry_max_delay,
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)
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return DiagnosisDeps(
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run_log=RunLogImpl(str(harness_db)),
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llm=llm,
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skill_store=VersionedSkillStore(_diagnosis_skills_dir()),
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prompts=_load_diagnose_prompts(),
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tree_data={},
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concurrency=concurrency,
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)
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def _diagnosis_skills_dir() -> Path:
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"""诊断用技能目录：种子 store 的当前技能版本（诊断读技能遵从判定）。
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诊断只读技能内容判断"是否遵从技能"，用 store 种子 v1 即可（与基线 run 一致）。
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"""
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return Path("store/skills/v1")
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def _load_diagnose_prompts() -> Any:
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"""加载诊断模板束（从项目根 prompts/ 读取；与 runner._load_diagnose_prompts 一致）。"""
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from core.evolution.types import DiagnosePrompts
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def _read(name: str) -> str:
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p = Path("prompts") / name
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return p.read_text(encoding="utf-8") if p.exists() else ""
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return DiagnosePrompts(
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defect_vs_lapse=_read("defect_vs_lapse.md"),
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reasoning_sub=_read("reasoning_sub.md"),
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span_eval_system=_read("span_eval_system.md"),
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span_eval_user=_read("span_eval_user.md"),
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missed_nodes=_read("missed_nodes.md"),
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skill_adherence=_read("skill_adherence.md"),
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confirmation_bias=_read("confirmation_bias.md"),
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evidence_sufficiency=_read("evidence_sufficiency.md"),
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)
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def _normalize_choice(choice: str | None) -> str:
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"""选项归一：strip → 大写 → 取首字母（None 归一为空串）。"""
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return (choice or "").strip().upper()[:1]
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def load_diagnosable_wrong_ids(harness_db: Path, baseline_run_id: str) -> list[str]:
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"""从 harness.db 读 baseline run 的可诊断错题 question_id（保序、canonical 首行）。
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可诊断错题判据：canonical 首行（rowid 最小）预测非空 且 stop_reason 非 INFRA
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（error / parse_error）且 归一后预测 != 答案。INFRA / 空预测题不进 wrong_ids
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（run_diagnosis 内部也会二次排除，此处前置过滤减少无谓 LLM 调用）。
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参数:
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harness_db: harness.db 路径（只读打开）。
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baseline_run_id: 基线 run 标识。
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返回:
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可诊断错题 question_id 列表（按 rowid 升序 canonical 顺序，去重）。
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异常:
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SystemExit: 该 run 无任何预测行（fail loud）。
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"""
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conn = sqlite3.connect(f"file:{harness_db}?mode=ro", uri=True)
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conn.row_factory = sqlite3.Row
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try:
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rows = conn.execute(
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"SELECT question_id, prediction, answer, stop_reason "
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"FROM predictions WHERE run_id = ? ORDER BY rowid",
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(baseline_run_id,),
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).fetchall()
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finally:
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conn.close()
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if not rows:
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raise SystemExit(
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f"run_id={baseline_run_id} 在 {harness_db} 无任何预测行，无法诊断（P5 fail loud）"
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)
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seen: set[str] = set()
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wrong_ids: list[str] = []
|
||
for row in rows:
|
||
qid = row["question_id"]
|
||
if qid in seen:
|
||
continue
|
||
seen.add(qid)
|
||
prediction = (row["prediction"] or "").strip()
|
||
if not prediction or row["stop_reason"] in _INFRA_STOP_REASONS:
|
||
continue
|
||
if _normalize_choice(row["prediction"]) != _normalize_choice(row["answer"]):
|
||
wrong_ids.append(qid)
|
||
return wrong_ids
|
||
|
||
|
||
def load_questions_by_id(questions_dir: Path) -> dict[str, GeneratedQuestion]:
|
||
"""加载 benchmark 全部题并建 question_id → GeneratedQuestion 映射。
|
||
|
||
覆盖 wrong_ids 与 run_diagnosis 返回的全部 infra/degraded 题（取 video_id/task_type）。
|
||
|
||
参数:
|
||
questions_dir: benchmark 题库目录。
|
||
|
||
返回:
|
||
question_id → GeneratedQuestion 映射。
|
||
"""
|
||
return {q.question_id: q for q in load_benchmark(questions_dir)}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# McNemar 功效护栏
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def check_mcnemar_power(pools: Pools, val_wrong_min: int) -> int:
|
||
"""校验 validation 池错题数达 McNemar 功效阈，不足即 fail loud。
|
||
|
||
build_split 按契约用 split_by_video_assignment(val_wrong_min=0)（不破契约），
|
||
故功效护栏在 capstone 层单独核验：val 错题数 < 阈 → 验证信号不足以支撑可靠比较。
|
||
|
||
参数:
|
||
pools: 冻结三池（含 validation 与 correctness）。
|
||
val_wrong_min: 最少错题数阈（0 = 不检查）。
|
||
|
||
返回:
|
||
validation 池实际错题数（供日志）。
|
||
|
||
异常:
|
||
SystemExit: val_wrong_min > 0 且 val 错题数 < 阈（P5 fail loud，不静默放行）。
|
||
"""
|
||
val_wrong = sum(1 for q in pools.validation if not pools.correctness[q.question_id])
|
||
if val_wrong_min > 0 and val_wrong < val_wrong_min:
|
||
raise SystemExit(
|
||
f"validation 池错题数 {val_wrong} < val_wrong_min={val_wrong_min}，"
|
||
"验证信号不足以支撑可靠比较（McNemar 检验功效不够）。"
|
||
"请放大 val_ratio / 调整旋钮后重跑，勿静默放行。"
|
||
)
|
||
return val_wrong
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 两阶段编排（依赖注入，便于单测）
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
async def run_pipeline(
|
||
*,
|
||
config: VideoSplitConfig,
|
||
fingerprint: str,
|
||
diagnosis_deps: DiagnosisDeps,
|
||
signal_store: DiagnosisSignalStore,
|
||
wrong_ids: list[str],
|
||
questions: dict[str, GeneratedQuestion],
|
||
harness_db: Path,
|
||
questions_dir: Path,
|
||
out_dir: Path,
|
||
generated_at: str,
|
||
) -> SplitBuildResult:
|
||
"""内联两阶段：Phase 1 诊断 → Phase 2 冻结切分 → McNemar 护栏。
|
||
|
||
参数:
|
||
config: 科研旋钮快照。
|
||
fingerprint: 诊断口径指纹（已合成，作诊断信号主键之一）。
|
||
diagnosis_deps: Phase 1 诊断依赖束（真实或假实现）。
|
||
signal_store: 诊断信号存储端口（Phase 1 写、Phase 2 读）。
|
||
wrong_ids: 待诊断的可诊断错题 question_id 列表。
|
||
questions: question_id → GeneratedQuestion 映射。
|
||
harness_db: harness.db 路径（Phase 2 读 canonical 预测）。
|
||
questions_dir: benchmark 题库目录（Phase 2 加载题库切池）。
|
||
out_dir: 冻结产物目录（pools.json + split_manifest.json）。
|
||
generated_at: 生成时间戳（ISO 字符串，由调用方传入保证可复现）。
|
||
|
||
返回:
|
||
SplitBuildResult（冻结三池 + manifest + assignment）。
|
||
"""
|
||
# Phase 1: 离线诊断（断点续跑幂等：done_question_ids 已完成题跳过）。
|
||
logger.info(
|
||
"Phase 1 离线诊断：baseline={} 待诊断错题 {} 题", config.baseline_run_id, len(wrong_ids)
|
||
)
|
||
await run_baseline_diagnosis(
|
||
baseline_run_id=config.baseline_run_id,
|
||
diag_fingerprint=fingerprint,
|
||
wrong_ids=wrong_ids,
|
||
questions=questions,
|
||
store=signal_store,
|
||
deps=diagnosis_deps,
|
||
)
|
||
|
||
# Phase 2: 冻结切分（读诊断信号 → 贪心选择 → 视频组原子切三池 → 冻结 + 六条断言）。
|
||
out_dir.mkdir(parents=True, exist_ok=True)
|
||
logger.info("Phase 2 冻结切分：out={}", out_dir)
|
||
result = build_split(
|
||
db_path=harness_db,
|
||
baseline_run_id=config.baseline_run_id,
|
||
signal_store=signal_store,
|
||
diag_fingerprint=fingerprint,
|
||
questions_dir=questions_dir,
|
||
config=SplitBuildConfig(
|
||
n_trainval=config.n_trainval,
|
||
floor_k=config.floor_k,
|
||
epsilon=config.epsilon,
|
||
report_floor=config.report_floor,
|
||
select_seed=config.seed,
|
||
val_ratio=config.val_ratio,
|
||
split_seed=config.seed,
|
||
),
|
||
out_path=out_dir / "pools.json",
|
||
manifest_path=out_dir / "split_manifest.json",
|
||
generated_at=generated_at,
|
||
)
|
||
|
||
# McNemar 功效护栏（build_split 契约外的 capstone 层校验）。
|
||
val_wrong = check_mcnemar_power(result.pools, config.val_wrong_min)
|
||
logger.info(
|
||
"切分冻结完成：pools={} manifest={} val错题={}/{}(阈)",
|
||
out_dir / "pools.json",
|
||
out_dir / "split_manifest.json",
|
||
val_wrong,
|
||
config.val_wrong_min,
|
||
)
|
||
return result
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 真实执行 / dry-run 入口
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def _resolve_paths(args: argparse.Namespace) -> tuple[Path, Path, Path]:
|
||
"""解析 harness_db / questions_dir / out_dir（CLI 覆盖默认工程路径）。"""
|
||
harness_db = args.harness_db or _DEFAULT_HARNESS_DB
|
||
questions_dir = args.questions_dir or _DEFAULT_QUESTIONS_DIR
|
||
out_dir = args.out_dir or _DEFAULT_OUT_DIR
|
||
return harness_db, questions_dir, out_dir
|
||
|
||
|
||
def _execute_real(config: VideoSplitConfig, fingerprint: str, args: argparse.Namespace) -> None:
|
||
"""真实执行两阶段管线：组装真实 deps、读错题、跑诊断 + 冻结切分。"""
|
||
harness_db, questions_dir, out_dir = _resolve_paths(args)
|
||
if not harness_db.exists():
|
||
raise SystemExit(f"harness.db 不存在: {harness_db}（P5 fail loud）")
|
||
wrong_ids = load_diagnosable_wrong_ids(harness_db, config.baseline_run_id)
|
||
questions = load_questions_by_id(questions_dir)
|
||
deps = build_diagnosis_deps(
|
||
harness_db=harness_db, concurrency=args.concurrency, expected_model=config.model
|
||
)
|
||
|
||
from adapters.baseline_diagnosis_store import SqliteDiagnosisSignalStore
|
||
|
||
store = SqliteDiagnosisSignalStore(str(harness_db))
|
||
try:
|
||
asyncio.run(
|
||
run_pipeline(
|
||
config=config,
|
||
fingerprint=fingerprint,
|
||
diagnosis_deps=deps,
|
||
signal_store=store,
|
||
wrong_ids=wrong_ids,
|
||
questions=questions,
|
||
harness_db=harness_db,
|
||
questions_dir=questions_dir,
|
||
out_dir=out_dir,
|
||
generated_at=datetime.datetime.now(datetime.UTC).isoformat(),
|
||
)
|
||
)
|
||
finally:
|
||
store.close()
|
||
|
||
|
||
class _DryRunLLM:
|
||
"""dry-run 假 LLM：被真实调用即报错，保证不真调 LLM。"""
|
||
|
||
async def complete(self, *args: Any, **kwargs: Any) -> Any:
|
||
raise AssertionError("dry-run 不应真调 LLM.complete")
|
||
|
||
|
||
class _DryRunLog:
|
||
"""dry-run 假 RunLog：predictions/traces 均返回空，诊断不真正执行。"""
|
||
|
||
async def get_predictions(self, run_id: str, *, question_ids: list[str] | None = None) -> list:
|
||
return []
|
||
|
||
async def get_traces(self, run_id: str, *, question_ids: list[str] | None = None) -> list:
|
||
return []
|
||
|
||
|
||
def _execute_dry_run(config: VideoSplitConfig, fingerprint: str, args: argparse.Namespace) -> None:
|
||
"""dry-run：用假 deps 跑通 Phase 1 wiring（空错题 → 诊断早返回），打印步骤 + 指纹。
|
||
|
||
Phase 2 build_split 需真实诊断信号方能冻结，dry-run 不真实冻结，仅打印其计划；
|
||
Phase 1 用空 wrong_ids 走 run_baseline_diagnosis 早返回路径，验证装配可调用而不触 LLM。
|
||
"""
|
||
harness_db, questions_dir, out_dir = _resolve_paths(args)
|
||
logger.info("=== dry-run：校验两阶段装配（不真调 LLM / 不冻结产物）===")
|
||
logger.info(
|
||
"诊断口径指纹 diag_fingerprint={} (prompt={} model={})",
|
||
fingerprint,
|
||
config.prompt_version,
|
||
config.model,
|
||
)
|
||
logger.info(
|
||
"解析路径：harness_db={} questions_dir={} out_dir={}", harness_db, questions_dir, out_dir
|
||
)
|
||
logger.info(
|
||
"旋钮：n_trainval={} epsilon={} report_floor={} val_ratio={} seed={} "
|
||
"val_wrong_min={} floor_k={}",
|
||
config.n_trainval,
|
||
config.epsilon,
|
||
config.report_floor,
|
||
config.val_ratio,
|
||
config.seed,
|
||
config.val_wrong_min,
|
||
config.floor_k,
|
||
)
|
||
|
||
fake_deps = DiagnosisDeps(
|
||
run_log=_DryRunLog(),
|
||
llm=_DryRunLLM(),
|
||
skill_store=object(),
|
||
prompts=object(),
|
||
tree_data={},
|
||
concurrency=args.concurrency,
|
||
)
|
||
|
||
from adapters.baseline_diagnosis_store import SqliteDiagnosisSignalStore
|
||
|
||
dry_db = out_dir / "_dry_run_signals.db"
|
||
dry_db.parent.mkdir(parents=True, exist_ok=True)
|
||
store = SqliteDiagnosisSignalStore(str(dry_db))
|
||
try:
|
||
logger.info("Phase 1 装配 OK：run_baseline_diagnosis 以空错题走早返回路径（不触 LLM）")
|
||
asyncio.run(
|
||
run_baseline_diagnosis(
|
||
baseline_run_id=config.baseline_run_id,
|
||
diag_fingerprint=fingerprint,
|
||
wrong_ids=[],
|
||
questions={},
|
||
store=store,
|
||
deps=fake_deps,
|
||
)
|
||
)
|
||
finally:
|
||
store.close()
|
||
dry_db.unlink(missing_ok=True)
|
||
logger.info(
|
||
"Phase 2 装配 OK：真实执行将调 build_split 冻结 pools.json + manifest（dry-run 跳过）"
|
||
)
|
||
logger.info("=== dry-run 通过：两阶段装配可调用，指纹已算出 ===")
|
||
|
||
|
||
def build_arg_parser() -> argparse.ArgumentParser:
|
||
"""构建 CLI 参数解析器。"""
|
||
parser = argparse.ArgumentParser(description="结果驱动视频级切分两阶段 CLI（诊断 → 冻结切分）")
|
||
parser.add_argument("--config", type=Path, default=Path("config/video_split.yaml"))
|
||
parser.add_argument("--dry-run", action="store_true", dest="dry_run")
|
||
parser.add_argument("--gpu", type=str, default=None, help="可选：设置 CUDA_VISIBLE_DEVICES")
|
||
parser.add_argument("--concurrency", type=int, default=8, help="诊断并发上限")
|
||
parser.add_argument("--harness-db", type=Path, default=None, dest="harness_db")
|
||
parser.add_argument("--questions-dir", type=Path, default=None, dest="questions_dir")
|
||
parser.add_argument("--out-dir", type=Path, default=None, dest="out_dir")
|
||
return parser
|
||
|
||
|
||
def main(argv: list[str] | None = None) -> None:
|
||
"""CLI 入口：解析参数 → 载配置 → 算指纹 → dry-run 或真实两阶段执行。
|
||
|
||
参数:
|
||
argv: 可选参数列表（默认 sys.argv[1:]），便于测试注入。
|
||
"""
|
||
from dotenv import load_dotenv
|
||
|
||
load_dotenv()
|
||
args = build_arg_parser().parse_args(argv)
|
||
if args.gpu is not None:
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
|
||
logger.info("CUDA_VISIBLE_DEVICES={}", args.gpu)
|
||
|
||
config = load_config(args.config)
|
||
fingerprint = diag_fingerprint(config.prompt_version, config.model, git_short_sha())
|
||
|
||
if args.dry_run:
|
||
_execute_dry_run(config, fingerprint, args)
|
||
return
|
||
_execute_real(config, fingerprint, args)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|