refactor: remove block-sequential gate path and gate_block knob (algo #6)
config/train_videomme.yaml 同时收录待入库的实验配置变更(run_id v2 / concurrency 32 / batch_size 40)。tests/integration/test_v3_contract_e2e.py 的 run_id 断言按 Task 5 显式契约同步修正(原断言依赖旧隐式实例注入)。
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"""tests/unit/test_gate_unit_scope.py — gate 真实执行路径按 unit 口径跑。
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迁移自块序贯版 test_gate_block_unit.py（载体 validate_skill_local，Task 6 删除）：
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针对 app/harness/validate.py::validate_skills_concurrent（连续并发 gate 真实路径），
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断言混格阶梯下 gate 按 unit 口径运行：baseline_cache 键含 unit_id、n_used 按
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unit 累加、pair_block 折叠 AR pair、逐题 predictions 仍溯源。
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核心算法保真 #5（信息阶梯 e-process 口径从 question_id 迁至 unit_id）。
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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import pytest
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from app.harness.gate_ladder import BaselineCache, skill_hash
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from app.harness.inference import PREDICTIONS_SCHEMA, InferenceResult
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from app.harness.log import HarnessLog
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from app.harness.validate import GateSpec, _ladder_units, validate_skills_concurrent
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from core.evolution import GateParams
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from core.types import GeneratedQuestion
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if TYPE_CHECKING:
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from pathlib import Path
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_DEFAULT_GATE_PARAMS = GateParams(
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e_confirm=20.0,
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e_provisional=3.0,
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w_net_min=2,
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delta_min=0.05,
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lambda_dir=-2.0,
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e_rollback=10.0,
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)
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def _single(qid: str) -> GeneratedQuestion:
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"""构造非 AR single 题。"""
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return GeneratedQuestion(
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question_id=qid,
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video_id=f"v_{qid}",
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task_type="temporal",
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question="Q?",
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options=("A", "B", "C", "D"),
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answer="A",
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source_nodes=(),
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difficulty="easy",
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)
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def _pair(pair_id: str) -> list[GeneratedQuestion]:
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"""构造 AR 孪生对（original + mirror，共享 pair_id）。"""
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common = {
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"video_id": f"v_{pair_id}",
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"task_type": "temporal",
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"question": "Q?",
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"options": ("A", "B", "C", "D"),
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"answer": "A",
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"source_nodes": (),
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"difficulty": "easy",
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"pair_id": pair_id,
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"flip_axis": "before_after",
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}
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return [
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GeneratedQuestion(question_id=f"{pair_id}_o", question_role="pair_original", **common),
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GeneratedQuestion(question_id=f"{pair_id}_m", question_role="pair_mirror", **common),
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]
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def _setup_workspace(tmp_path: Path) -> Path:
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"""构建最小 workspace（skills/v1/temporal.md）。"""
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skills_dir = tmp_path / "skills" / "v1"
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skills_dir.mkdir(parents=True)
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(skills_dir / "temporal.md").write_text("baseline skill content", encoding="utf-8")
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return tmp_path
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def _make_log(workspace: Path, run_id: str = "test_master") -> HarnessLog:
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"""创建 HarnessLog 并建 predictions 表。"""
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log = HarnessLog(str(workspace / "harness.db"), run_id)
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log.create_table("predictions", PREDICTIONS_SCHEMA)
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return log
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def _insert_predictions(log: HarnessLog, run_id: str, per_q: dict[str, bool]) -> None:
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"""逐题写 predictions（溯源仍逐题）。"""
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for qid, correct in per_q.items():
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log.insert(
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"predictions",
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{
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"run_id": run_id,
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"video_id": "v0",
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"question_id": qid,
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"task_type": "temporal",
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"prediction": "A" if correct else "Z",
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"answer": "A",
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"evidence": "",
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"reasoning": "",
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"steps_used": 1,
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"prompt_tokens": 10,
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"completion_tokens": 10,
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"stop_reason": "completed",
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"steps_json": "[]",
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},
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)
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def _make_mock_run_inference(
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log: HarnessLog,
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baseline_correct: dict[str, bool],
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candidate_correct: dict[str, bool],
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):
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"""构建 mock RunInferenceFn，按 run_id 的 arm 后缀选基线/候选逐题对错。"""
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call_log: list[dict[str, Any]] = []
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async def mock_fn(
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questions: list[GeneratedQuestion],
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*,
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run_id: str,
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skills_dir: Path,
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) -> InferenceResult:
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src = baseline_correct if run_id.endswith("_base") else candidate_correct
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per_q = {q.question_id: src.get(q.question_id, False) for q in questions}
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_insert_predictions(log, run_id, per_q)
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call_log.append({"run_id": run_id, "qids": [q.question_id for q in questions]})
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total = len(questions)
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return InferenceResult(
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run_id=run_id,
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accuracy=sum(per_q.values()) / total if total else 0.0,
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total=total,
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correct=sum(per_q.values()),
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per_task_type={},
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steps_mean=1.0,
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token_usage={"prompt_tokens": 10, "completion_tokens": 10},
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stop_reason_counts={"completed": total},
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)
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return mock_fn, call_log
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def _mk_spec(ladder: list[GeneratedQuestion]) -> GateSpec:
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"""由混格阶梯题序构造单题型 GateSpec（units 经 _ladder_units 聚合）。"""
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return GateSpec(
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task_type="temporal",
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target_file="temporal.md",
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candidate_content="improved skill",
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base_skill_content="baseline skill content",
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units=tuple(_ladder_units(ladder)),
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gate_run_prefix="step1_gate_test",
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)
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async def _run_gate(workspace: Path, spec: GateSpec, mock_fn, log: HarnessLog, cache, params):
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"""跑单 spec 的 validate_skills_concurrent 并返回该题型的 outcome。"""
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outcomes = await validate_skills_concurrent(
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workspace_dir=workspace,
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base_skills_version="v1",
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specs=[spec],
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gate_params=params,
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gate_guard_err=0.5,
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baseline_cache=cache,
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prompts_version="p1",
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run_inference=mock_fn,
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log=log,
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concurrency=8,
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)
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return outcomes[spec.task_type]
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class TestLadderUnits:
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"""_ladder_units：阶梯题序聚合为单元并保持信息阶梯序。"""
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def test_pure_non_ar_is_identity(self) -> None:
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"""纯非 AR 输入 → 单元序恒等（unit 序==原题序、unit_id==question_id）。"""
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ladder = [_single("s0"), _single("s1"), _single("s2")]
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units = _ladder_units(ladder)
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assert [u.kind for u in units] == ["single", "single", "single"]
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# unit_id 逐一等于原 question_id，且顺序与输入完全一致
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assert [u.unit_id for u in units] == ["s0", "s1", "s2"]
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assert [q.question_id for u in units for q in u.questions] == ["s0", "s1", "s2"]
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def test_mixed_preserves_ladder_order(self) -> None:
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"""混格交错输入 → 按单元最早出现下标重排，pair 整锁、不被 single-first 污染。"""
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p1o, p1m = _pair("p1")
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# 交错布置：pair 两成员分居 idx 1、3；single 分居 idx 0、2、4
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ladder = [_single("s0"), p1o, _single("s1"), p1m, _single("s2")]
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units = _ladder_units(ladder)
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# 最早出现下标：s0=0, p1=min(1,3)=1, s1=2, s2=4 → 阶梯序 [s0,p1,s1,s2]
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assert [u.unit_id for u in units] == ["s0", "p1", "s1", "s2"]
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# pair 整锁为一个单元（含两成员），不被拆
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p1_unit = next(u for u in units if u.unit_id == "p1")
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assert p1_unit.kind == "pair"
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assert {q.question_id for q in p1_unit.questions} == {"p1_o", "p1_m"}
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def test_mixed_differs_from_build_units_default(self) -> None:
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"""混格重排必须纠正 build_units 的 single-first 顺序（否则阶梯序被污染）。"""
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from app.harness.question_units import build_units
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p1o, p1m = _pair("p1")
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ladder = [_single("s0"), p1o, _single("s1"), p1m, _single("s2")]
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default_order = [u.unit_id for u in build_units(ladder)]
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ladder_order = [u.unit_id for u in _ladder_units(ladder)]
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# build_units 把 pair 排到 single 之后；_ladder_units 恢复信息阶梯序
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assert default_order == ["s0", "s1", "s2", "p1"]
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assert ladder_order == ["s0", "p1", "s1", "s2"]
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assert ladder_order != default_order
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@pytest.mark.asyncio
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async def test_gate_n_used_counts_units_not_questions(tmp_path: Path) -> None:
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"""混格阶梯（1 pair + 2 single）→ n_used=3 单元，非 4 题（迁移自块序贯版）。"""
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workspace = _setup_workspace(tmp_path)
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log = _make_log(workspace)
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cache = BaselineCache(workspace / "baseline_cache.json")
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ladder = [*_pair("p1"), _single("s0"), _single("s1")]
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# 基线全错、候选全对 → 3 单元齐翻 W=3
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baseline = {"p1_o": False, "p1_m": False, "s0": False, "s1": False}
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candidate = {"p1_o": True, "p1_m": True, "s0": True, "s1": True}
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mock_fn, _ = _make_mock_run_inference(log, baseline, candidate)
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accept_params = GateParams(
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e_confirm=15.0,
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e_provisional=3.0,
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w_net_min=2,
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delta_min=0.05,
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lambda_dir=-2.0,
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e_rollback=10.0,
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)
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try:
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outcome = await _run_gate(workspace, _mk_spec(ladder), mock_fn, log, cache, accept_params)
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# n_used 按 unit 计（3），W 按 unit 计（3）
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assert outcome.n_used == 3
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assert outcome.w == 3
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assert outcome.l == 0
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# 证据行按 unit 口径（3 行），ladder_rank 沿阶梯序连续
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assert len(outcome.evidence_rows) == 3
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assert [r["ladder_rank"] for r in outcome.evidence_rows] == [0, 1, 2]
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# baseline_cache 键含 unit_id：pair 用 pair_id、single 用 question_id
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s_hash = skill_hash("baseline skill content")
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assert cache.get("temporal", s_hash, "p1", "p1") is False
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assert cache.get("temporal", s_hash, "p1", "s0") is False
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# 逐题 question_id 不作为 baseline_cache 键（pair 成员未单独缓存）
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assert cache.get("temporal", s_hash, "p1", "p1_o") is None
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# 逐题 predictions 仍溯源：候选 per-q 含两 pair 成员
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assert set(outcome.candidate_correctness) >= {"p1_o", "p1_m", "s0", "s1"}
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finally:
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log.close()
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@pytest.mark.asyncio
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async def test_gate_pair_partial_flip_not_counted(tmp_path: Path) -> None:
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"""AR pair 候选仅单向翻(T,F)→单元仍错，W 不被单题污染（迁移自块序贯版）。
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前缀逐单元判定下 2 单元小阶梯会在首单元 futility 早停，观测不到 pair 语义；
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补 2 个 single 拉长阶梯：4 单元中 3 个 single 翻转 → W=3（pair 不计入），
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candidate_acc = 3/4。
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"""
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workspace = _setup_workspace(tmp_path)
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log = _make_log(workspace)
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cache = BaselineCache(workspace / "baseline_cache.json")
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ladder = [*_pair("p1"), _single("s0"), _single("s1"), _single("s2")]
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baseline = {"p1_o": False, "p1_m": False, "s0": False, "s1": False, "s2": False}
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# pair 只翻一半（p1_o 对、p1_m 错）→ 单元 AND 仍错；singles 全翻对
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candidate = {"p1_o": True, "p1_m": False, "s0": True, "s1": True, "s2": True}
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mock_fn, _ = _make_mock_run_inference(log, baseline, candidate)
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try:
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outcome = await _run_gate(
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workspace, _mk_spec(ladder), mock_fn, log, cache, _DEFAULT_GATE_PARAMS
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)
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# 只有 single 单元翻转，pair 单元不计 W（保真 #5：不被 P/Q 单题污染）
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assert outcome.w == 3
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assert outcome.l == 0
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assert outcome.n_used == 4
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# candidate_acc 分母按 unit（4 单元，1 对）→ 3/4
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assert outcome.candidate_acc == 0.75
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finally:
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log.close()
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@pytest.mark.asyncio
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async def test_gate_baseline_cache_hit_by_unit(tmp_path: Path) -> None:
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"""基线缓存按 unit_id 预填充 → 基线侧全命中不发起推理（迁移自块序贯版）。
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阶梯补长到 4 单元避免首单元 futility 早停，覆盖 pair 与 single 两种 unit 键。
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"""
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workspace = _setup_workspace(tmp_path)
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log = _make_log(workspace)
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cache = BaselineCache(workspace / "baseline_cache.json")
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ladder = [*_pair("p1"), _single("s0"), _single("s1"), _single("s2")]
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s_hash = skill_hash("baseline skill content")
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# 按 unit_id 预填充（pair→pair_id，single→question_id），全错
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for unit_id in ("p1", "s0", "s1", "s2"):
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cache.put("temporal", s_hash, "p1", unit_id, False)
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baseline = {"p1_o": False, "p1_m": False, "s0": False, "s1": False, "s2": False}
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candidate = {"p1_o": True, "p1_m": True, "s0": True, "s1": True, "s2": True}
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mock_fn, call_log = _make_mock_run_inference(log, baseline, candidate)
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try:
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outcome = await _run_gate(
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workspace, _mk_spec(ladder), mock_fn, log, cache, _DEFAULT_GATE_PARAMS
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)
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base_calls = [c for c in call_log if c["run_id"].endswith("_base")]
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assert base_calls == [], "unit 键全命中不应发起基线推理"
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assert outcome.n_used == 4
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finally:
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log.close()
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