feat(harness): validate.py — async 块序贯验证编排 + Probation 统一定义
This commit is contained in:
@@ -30,31 +30,7 @@ if TYPE_CHECKING:
|
||||
CHECKPOINT_SCHEMA_VERSION = 1
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Probation 类型（Task 10 validate.py 尚未就绪，暂定义在此供 checkpoint 使用）
|
||||
# Task 10 完成后迁移至 app/harness/validate.py 并改为 re-export。
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class Probation:
|
||||
"""一个题型的在途试用账本（每题型至多一个）。
|
||||
|
||||
属性:
|
||||
task_type: 题型。
|
||||
anchor_skills_version: 锚版本名（最近一个 CONFIRMED 的 skills 版本）。
|
||||
target_file: 该题型解析后的 skill 文件名。
|
||||
correctness_snapshot: 开账时该题型 val 题的对错快照（回滚时恢复）。
|
||||
opened_step: 开账时的 global_step（观测用）。
|
||||
pending_edits: 试用链上全部候选 edit 的黑名单素材（回滚时整链入黑名单）。
|
||||
"""
|
||||
|
||||
task_type: str
|
||||
anchor_skills_version: str
|
||||
target_file: str
|
||||
correctness_snapshot: dict[str, bool]
|
||||
opened_step: int
|
||||
pending_edits: list[RejectedEdit] = field(default_factory=list)
|
||||
from app.harness.validate import Probation # noqa: E402
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@@ -0,0 +1,665 @@
|
||||
"""async 块序贯验证编排 — CE-Gate 局部验证的唯一独立子编排器。
|
||||
|
||||
从 TRM4 core/harness/validate.py (626 行) 迁移，重大重构：
|
||||
- 同步 → async（run_inference 注入为 async callable）
|
||||
- _classify_quadrants → core.evolution.classify_quadrants 纯函数
|
||||
- 配对逻辑 → 复用 core.evolution.pair_block + 本地证据行组装
|
||||
- _load_run_rows / _candidate_correctness_from_db → 共享 log.query()
|
||||
- materialize_candidate_skill 保持同步（纯文件操作）
|
||||
|
||||
基线与候选在同一阶梯前缀上逐块配对，只数翻转（基线错→候选对 = W，
|
||||
基线对→候选错 = L），每块结束调 gate_decision 做四出口判定。
|
||||
基线侧逐题对错走 BaselineCache 内容寻址缓存，miss 才新鲜跑。
|
||||
判定逻辑全部在 core/evolution/gate，本模块只负责推理编排与证据收集。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import shutil
|
||||
import tempfile
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from app.harness.gate_ladder import BaselineCache, skill_hash
|
||||
from core.evolution import (
|
||||
GateParams,
|
||||
GateVerdict,
|
||||
RejectedEdit,
|
||||
classify_quadrants,
|
||||
gate_decision,
|
||||
pair_block,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from app.harness.inference import InferenceResult
|
||||
from app.harness.log import HarnessLog
|
||||
from core.types import GeneratedQuestion
|
||||
|
||||
|
||||
# gate_decision 的 decision → ValidationOutcome.stop_reason 映射
|
||||
_STOP_REASON_BY_DECISION: dict[str, str] = {
|
||||
"accept_confirmed": "confirmed",
|
||||
"reject_directional": "directional",
|
||||
"reject_futility": "futility",
|
||||
"accept_provisional": "provisional",
|
||||
"reject_inertia": "inertia",
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 注入协议
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class RunInferenceFn(Protocol):
|
||||
"""注入的推理函数协议。
|
||||
|
||||
调用方（runner）负责绑定 llm、tool_dispatch_fn、prompt_builder、
|
||||
log、concurrency、max_steps、skill_mode 等共享依赖。
|
||||
validate 侧只传 questions、run_id、skills_dir 三个逐块变化的参数。
|
||||
"""
|
||||
|
||||
async def __call__(
|
||||
self,
|
||||
questions: list[GeneratedQuestion],
|
||||
*,
|
||||
run_id: str,
|
||||
skills_dir: Path,
|
||||
) -> InferenceResult: ...
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 数据类型
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class InferenceRunConfig:
|
||||
"""一次推理运行的配置三元组，把"如何跑推理"内聚成一组。
|
||||
|
||||
字段:
|
||||
concurrency: 推理并发度。
|
||||
max_steps: 单题最大推理步数。
|
||||
skill_mode: 推理 skill 模式（"auto" / "manual" / "none"）。
|
||||
"""
|
||||
|
||||
concurrency: int
|
||||
max_steps: int
|
||||
skill_mode: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationOutcome:
|
||||
"""CE-Gate 局部验证结果：三态动作 + e-process 证据 + 已观测题逐题对错。
|
||||
|
||||
correctness 二轨语义：candidate_correctness 只含已观测题（早停后是
|
||||
阶梯前缀子集）；accept 时由 runner 按题粒度增量合并进 state.correctness。
|
||||
"""
|
||||
|
||||
action: str # accept_confirmed | accept_provisional | reject
|
||||
accepted: bool
|
||||
stop_reason: str # confirmed | directional | futility | provisional | inertia
|
||||
e_value: float
|
||||
w: int
|
||||
l: int # noqa: E741
|
||||
n_used: int
|
||||
delta_hat: float
|
||||
delta_shrunk: float
|
||||
baseline_acc: float # 已观测题上的基线准确率（观测口径）
|
||||
candidate_acc: float # 已观测题上的候选准确率（观测口径）
|
||||
improvements: list[str] = field(default_factory=list)
|
||||
regressions: list[str] = field(default_factory=list)
|
||||
persistent_fails: list[str] = field(default_factory=list)
|
||||
stable_successes: list[str] = field(default_factory=list)
|
||||
candidate_correctness: dict[str, bool] = field(default_factory=dict)
|
||||
evidence_rows: list[dict] = field(default_factory=list) # gate_evidence 逐题行，runner 落库
|
||||
|
||||
|
||||
@dataclass
|
||||
class Probation:
|
||||
"""一个题型的在途试用账本（每题型至多一个）。
|
||||
|
||||
字段:
|
||||
task_type: 题型。
|
||||
anchor_skills_version: 锚版本名（最近一个 CONFIRMED 的 skills 版本）——
|
||||
回滚时恢复该版本中本题型 skill 文件的内容。
|
||||
target_file: 该题型解析后的 skill 文件名。
|
||||
correctness_snapshot: 开账时该题型 val 题的对错快照（回滚时恢复）。
|
||||
opened_step: 开账时的 global_step（观测用）。
|
||||
pending_edits: 试用链上全部候选 edit 的黑名单素材（回滚时整链入黑名单）。
|
||||
"""
|
||||
|
||||
task_type: str
|
||||
anchor_skills_version: str
|
||||
target_file: str
|
||||
correctness_snapshot: dict[str, bool]
|
||||
opened_step: int
|
||||
pending_edits: list[RejectedEdit] = field(default_factory=list)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 同步辅助函数
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def materialize_candidate_skill(
|
||||
workspace_dir: Path,
|
||||
base_skills_version: str,
|
||||
target_file: str,
|
||||
content: str,
|
||||
) -> Path:
|
||||
"""将候选 skill 正文物化为 workspace 专用临时目录下唯一命名的候选 skills 目录。
|
||||
|
||||
复制基线 skills 目录到 .cand_tmp/ 下的唯一命名临时目录，然后覆写 target_file。
|
||||
构建失败时尽力清理已建临时目录再重抛原始异常。
|
||||
|
||||
参数:
|
||||
workspace_dir: Workspace 根目录。基线 skills 从 workspace_dir/skills/<base>
|
||||
复制，临时候选落 workspace_dir/.cand_tmp/。
|
||||
base_skills_version: 基线 skills 版本名。
|
||||
target_file: 被替换的 skill 文件名。
|
||||
content: 候选 skill 文件全文。
|
||||
|
||||
返回:
|
||||
新建的临时候选目录绝对路径。
|
||||
|
||||
契约:
|
||||
构建失败（OSError）时尽力清理已建临时目录再重抛原始异常；
|
||||
清理本身失败记 warning。
|
||||
"""
|
||||
cand_tmp_root = workspace_dir / ".cand_tmp"
|
||||
cand_tmp_root.mkdir(parents=True, exist_ok=True)
|
||||
cand_dir = Path(tempfile.mkdtemp(prefix=f"{base_skills_version}_cand_", dir=cand_tmp_root))
|
||||
try:
|
||||
base_dir = workspace_dir / "skills" / base_skills_version
|
||||
shutil.copytree(base_dir, cand_dir, dirs_exist_ok=True)
|
||||
(cand_dir / target_file).write_text(content, encoding="utf-8")
|
||||
except OSError:
|
||||
try:
|
||||
shutil.rmtree(cand_dir)
|
||||
except OSError as cleanup_err:
|
||||
logger.warning("候选物化失败后清理临时目录也失败 {}: {}", cand_dir, cleanup_err)
|
||||
raise
|
||||
return cand_dir
|
||||
|
||||
|
||||
def _load_run_rows(
|
||||
log: HarnessLog,
|
||||
run_id: str,
|
||||
) -> dict[str, dict[str, Any]]:
|
||||
"""读取单个 run 的逐题预测行并规范化轨迹字段。
|
||||
|
||||
从 predictions 表读取指定 run 的题目级记录，补充 _correct
|
||||
与规范化后的 steps 字段。保持同步（log.query）——仅在推理完成后调用。
|
||||
|
||||
参数:
|
||||
log: HarnessLog 共享实例（用 query 方法做只读 SELECT）。
|
||||
run_id: 待读取的预测 run_id。
|
||||
|
||||
返回:
|
||||
以 question_id 为键的行字典。每行至少包含 prediction、answer、
|
||||
_correct、steps 等字段。
|
||||
"""
|
||||
rows = log.query(
|
||||
"SELECT question_id, prediction, answer, steps_json FROM predictions WHERE run_id=?",
|
||||
(run_id,),
|
||||
)
|
||||
normalized: dict[str, dict[str, Any]] = {}
|
||||
for row in rows:
|
||||
raw_steps = row.get("steps_json")
|
||||
parsed_steps: Any = raw_steps
|
||||
if isinstance(raw_steps, str):
|
||||
try:
|
||||
parsed_steps = json.loads(raw_steps)
|
||||
except json.JSONDecodeError:
|
||||
parsed_steps = []
|
||||
steps = parsed_steps if isinstance(parsed_steps, list) else []
|
||||
normalized[row["question_id"]] = {
|
||||
**row,
|
||||
"_correct": row.get("prediction") == row.get("answer"),
|
||||
"steps": steps,
|
||||
}
|
||||
return normalized
|
||||
|
||||
|
||||
def _candidate_correctness_from_db(
|
||||
log: HarnessLog,
|
||||
run_id: str,
|
||||
chunk: list[GeneratedQuestion],
|
||||
) -> dict[str, bool]:
|
||||
"""从 db 读取候选/基线 run 在指定题目上的逐题对错。
|
||||
|
||||
参数:
|
||||
log: HarnessLog 共享实例。
|
||||
run_id: 推理 run_id。
|
||||
chunk: 题目列表。
|
||||
|
||||
返回:
|
||||
question_id -> 是否答对的映射。缺行的题目记为 False。
|
||||
"""
|
||||
rows = _load_run_rows(log, run_id)
|
||||
return {q.question_id: rows.get(q.question_id, {}).get("_correct", False) for q in chunk}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 块级 async 函数
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _resolve_baseline_block(
|
||||
chunk: list[GeneratedQuestion],
|
||||
task_type: str,
|
||||
s_hash: str,
|
||||
prompts_version: str,
|
||||
baseline_cache: BaselineCache,
|
||||
base_skills_dir: Path,
|
||||
run_inference: RunInferenceFn,
|
||||
log: HarnessLog,
|
||||
run_id: str,
|
||||
) -> tuple[dict[str, bool], int, int]:
|
||||
"""基线侧处理一个块：缓存优先，miss 的题新鲜跑基线版本并回写缓存。
|
||||
|
||||
参数:
|
||||
chunk: 当前块的题目列表。
|
||||
task_type: 当前验证题型（缓存键成分）。
|
||||
s_hash: 基线侧生效 skill 的内容哈希（缓存键成分）。
|
||||
prompts_version: 当前 prompts 版本（缓存键成分）。
|
||||
baseline_cache: 基线侧逐题对错缓存。
|
||||
base_skills_dir: 基线 skills 版本目录。
|
||||
run_inference: 注入的 async 推理函数。
|
||||
log: HarnessLog 共享实例（推理后读预测）。
|
||||
run_id: 本块基线 run_id。
|
||||
|
||||
返回:
|
||||
(b_map, errors_inc, denom_inc)：块内 question_id -> 基线对错、
|
||||
本块新增的 INFRA error 计数与推理题次分母增量（全命中时为 0, 0）。
|
||||
"""
|
||||
misses = [
|
||||
q
|
||||
for q in chunk
|
||||
if baseline_cache.get(task_type, s_hash, prompts_version, q.question_id) is None
|
||||
]
|
||||
errors_inc = 0
|
||||
denom_inc = 0
|
||||
if misses:
|
||||
r_b = await run_inference(misses, run_id=run_id, skills_dir=base_skills_dir)
|
||||
errors_inc = r_b.stop_reason_counts.get("error", 0)
|
||||
denom_inc = r_b.total
|
||||
fresh = _candidate_correctness_from_db(log, r_b.run_id, misses)
|
||||
for qid, correct in fresh.items():
|
||||
baseline_cache.put(task_type, s_hash, prompts_version, qid, correct)
|
||||
|
||||
b_map: dict[str, bool] = {}
|
||||
for q in chunk:
|
||||
val = baseline_cache.get(task_type, s_hash, prompts_version, q.question_id)
|
||||
assert val is not None, f"基线缓存补齐后仍有 miss: {q.question_id} run_id={run_id}"
|
||||
b_map[q.question_id] = val
|
||||
return b_map, errors_inc, denom_inc
|
||||
|
||||
|
||||
async def _run_candidate_block(
|
||||
chunk: list[GeneratedQuestion],
|
||||
cand_dir: Path,
|
||||
run_inference: RunInferenceFn,
|
||||
log: HarnessLog,
|
||||
run_id: str,
|
||||
) -> tuple[dict[str, bool], int, int]:
|
||||
"""候选侧处理一个块：全块新鲜跑候选版本并从 db 读逐题对错。
|
||||
|
||||
参数:
|
||||
chunk: 当前块的题目列表。
|
||||
cand_dir: 已物化的候选 skills 目录。
|
||||
run_inference: 注入的 async 推理函数。
|
||||
log: HarnessLog 共享实例（推理后读预测）。
|
||||
run_id: 本块候选 run_id。
|
||||
|
||||
返回:
|
||||
(c_map, errors_inc, denom_inc)。
|
||||
"""
|
||||
r_c = await run_inference(chunk, run_id=run_id, skills_dir=cand_dir)
|
||||
c_map = _candidate_correctness_from_db(log, r_c.run_id, chunk)
|
||||
return c_map, r_c.stop_reason_counts.get("error", 0), r_c.total
|
||||
|
||||
|
||||
def _build_evidence_rows(
|
||||
chunk: list[GeneratedQuestion],
|
||||
b_map: dict[str, bool],
|
||||
c_map: dict[str, bool],
|
||||
task_type: str,
|
||||
block_idx: int,
|
||||
) -> list[dict]:
|
||||
"""组装一个块的 gate_evidence 逐题证据行。
|
||||
|
||||
e_value 留 None 待块判定后回填，stop_reason 留空串待终态回填。
|
||||
|
||||
参数:
|
||||
chunk: 当前块的题目列表。
|
||||
b_map: 块内 question_id -> 基线对错。
|
||||
c_map: 块内 question_id -> 候选对错。
|
||||
task_type: 当前验证题型。
|
||||
block_idx: 当前块序号。
|
||||
|
||||
返回:
|
||||
逐题证据行列表。
|
||||
"""
|
||||
return [
|
||||
{
|
||||
"question_id": q.question_id,
|
||||
"task_type": task_type,
|
||||
"block_idx": block_idx,
|
||||
"baseline_correct": b_map[q.question_id],
|
||||
"candidate_correct": c_map[q.question_id],
|
||||
"e_value": None,
|
||||
"stop_reason": "",
|
||||
}
|
||||
for q in chunk
|
||||
]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# INFRA 护栏
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _check_infra_guard(errors: int, infra_denom: int, gate_guard_err: float) -> None:
|
||||
"""跨块累计 INFRA 错误率护栏：分母 >=10 且超阈值时 raise。
|
||||
|
||||
参数:
|
||||
errors: 两侧累计 error 计数。
|
||||
infra_denom: 两侧累计推理题次分母。
|
||||
gate_guard_err: 错误率阈值。
|
||||
|
||||
异常:
|
||||
RuntimeError: 错误率超阈值。
|
||||
"""
|
||||
if infra_denom >= 10 and errors / infra_denom > gate_guard_err:
|
||||
raise RuntimeError(f"gate 推理累计错误率过高 {errors / infra_denom:.0%}，中止本轮")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 终态组装
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _finalize_outcome(
|
||||
verdict: GateVerdict,
|
||||
w: int,
|
||||
l: int, # noqa: E741
|
||||
n_used: int,
|
||||
n_plan: int,
|
||||
base_obs: dict[str, bool],
|
||||
cand_obs: dict[str, bool],
|
||||
evidence_rows: list[dict],
|
||||
task_type: str,
|
||||
) -> ValidationOutcome:
|
||||
"""将块循环终态判定组装为 ValidationOutcome。
|
||||
|
||||
参数:
|
||||
verdict: 最后一块的 gate 判定结果。
|
||||
w: 累计 W（基线错→候选对翻转）。
|
||||
l: 累计 L（基线对→候选错翻转）。
|
||||
n_used: 已消费的阶梯题数。
|
||||
n_plan: 阶梯总题数。
|
||||
base_obs: 累计基线已观测对错。
|
||||
cand_obs: 累计候选已观测对错。
|
||||
evidence_rows: 逐题证据行。
|
||||
task_type: 验证题型（日志用）。
|
||||
|
||||
返回:
|
||||
ValidationOutcome。
|
||||
"""
|
||||
action = {
|
||||
"accept_confirmed": "accept_confirmed",
|
||||
"accept_provisional": "accept_provisional",
|
||||
}.get(verdict.decision, "reject")
|
||||
stop_reason = _STOP_REASON_BY_DECISION[verdict.decision]
|
||||
# 只有终态题的证据行才携带 stop_reason
|
||||
evidence_rows[-1]["stop_reason"] = stop_reason
|
||||
|
||||
quadrants = classify_quadrants({qid: (base_obs[qid], cand_obs[qid]) for qid in base_obs})
|
||||
baseline_acc = sum(base_obs.values()) / len(base_obs)
|
||||
candidate_acc = sum(cand_obs.values()) / len(cand_obs)
|
||||
accepted = action != "reject"
|
||||
|
||||
logger.info(
|
||||
"gate 局部验证[{}]: 基线{:.1%} → 候选{:.1%} (W={} L={} E={:.2f} n={}/{}) {}",
|
||||
task_type,
|
||||
baseline_acc,
|
||||
candidate_acc,
|
||||
w,
|
||||
l,
|
||||
verdict.e_value,
|
||||
n_used,
|
||||
n_plan,
|
||||
"接受" if accepted else "回滚",
|
||||
)
|
||||
|
||||
return ValidationOutcome(
|
||||
action=action,
|
||||
accepted=accepted,
|
||||
stop_reason=stop_reason,
|
||||
e_value=verdict.e_value,
|
||||
w=w,
|
||||
l=l,
|
||||
n_used=n_used,
|
||||
delta_hat=verdict.delta_hat,
|
||||
delta_shrunk=verdict.delta_shrunk,
|
||||
baseline_acc=baseline_acc,
|
||||
candidate_acc=candidate_acc,
|
||||
improvements=quadrants.improvements,
|
||||
regressions=quadrants.regressions,
|
||||
persistent_fails=quadrants.persistent_fails,
|
||||
stable_successes=quadrants.stable_successes,
|
||||
candidate_correctness=cand_obs,
|
||||
evidence_rows=evidence_rows,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 主编排
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _run_local_validation(
|
||||
workspace_dir: Path,
|
||||
cand_dir: Path,
|
||||
base_skills_version: str,
|
||||
task_type: str,
|
||||
base_skill_content: str,
|
||||
plan: list[GeneratedQuestion],
|
||||
gate_params: GateParams,
|
||||
gate_block: int,
|
||||
gate_guard_err: float,
|
||||
baseline_cache: BaselineCache,
|
||||
prompts_version: str,
|
||||
run_inference: RunInferenceFn,
|
||||
log: HarnessLog,
|
||||
gate_run_prefix: str,
|
||||
) -> ValidationOutcome:
|
||||
"""块序贯循环主体：逐块基线（缓存优先）/候选配对推理，块间 e-process 判定。
|
||||
|
||||
按 gate_block 切阶梯前缀，每块先补齐基线侧缓存 miss（新鲜跑基线版本
|
||||
并逐题写 BaselineCache），再全块跑候选，配对累计 W/L 后调 gate_decision；
|
||||
非 continue 即早停。题尽时最后一块的判定即终态（n_remaining=0 走
|
||||
provisional/inertia 分支），无循环外补判。
|
||||
|
||||
参数:
|
||||
workspace_dir: Workspace 根目录。
|
||||
cand_dir: 已物化的候选 skills 目录。
|
||||
base_skills_version: 基线 skills 版本名。
|
||||
task_type: 当前验证题型。
|
||||
base_skill_content: 基线侧生效 skill 全文（skill_hash 作缓存键成分）。
|
||||
plan: 已截断到 gate_n_max 的阶梯出题序。
|
||||
gate_params: e-process 判据阈值组。
|
||||
gate_block: 块大小。
|
||||
gate_guard_err: 跨块累计 INFRA 错误率护栏（分母 >=10 才触发）。
|
||||
baseline_cache: 基线侧逐题对错缓存。
|
||||
prompts_version: 当前 prompts 版本（缓存键成分）。
|
||||
run_inference: 注入的 async 推理函数。
|
||||
log: HarnessLog 共享实例。
|
||||
gate_run_prefix: 块 run_id 前缀（含 "_gate_" 标记）。
|
||||
|
||||
返回:
|
||||
ValidationOutcome。
|
||||
|
||||
关键实现:
|
||||
INFRA 护栏跨块累计基线+候选两侧的 error 计数，分母（总推理题次）>=10
|
||||
且错误率超 gate_guard_err 时直接 raise，避免坏批次污染判定。
|
||||
"""
|
||||
w = 0
|
||||
l = 0 # noqa: E741
|
||||
n_used = 0
|
||||
errors = 0
|
||||
infra_denom = 0
|
||||
evidence_rows: list[dict] = []
|
||||
base_obs: dict[str, bool] = {}
|
||||
cand_obs: dict[str, bool] = {}
|
||||
s_hash = skill_hash(base_skill_content)
|
||||
base_skills_dir = workspace_dir / "skills" / base_skills_version
|
||||
chunks = [plan[i : i + gate_block] for i in range(0, len(plan), gate_block)]
|
||||
verdict: GateVerdict | None = None
|
||||
|
||||
for block_idx, chunk in enumerate(chunks):
|
||||
# Phase 1: 基线侧（缓存优先，miss 新鲜跑）+ 候选侧（全块新鲜跑）
|
||||
b_map, err_b, den_b = await _resolve_baseline_block(
|
||||
chunk=chunk,
|
||||
task_type=task_type,
|
||||
s_hash=s_hash,
|
||||
prompts_version=prompts_version,
|
||||
baseline_cache=baseline_cache,
|
||||
base_skills_dir=base_skills_dir,
|
||||
run_inference=run_inference,
|
||||
log=log,
|
||||
run_id=f"{gate_run_prefix}_b{block_idx}_base",
|
||||
)
|
||||
c_map, err_c, den_c = await _run_candidate_block(
|
||||
chunk=chunk,
|
||||
cand_dir=cand_dir,
|
||||
run_inference=run_inference,
|
||||
log=log,
|
||||
run_id=f"{gate_run_prefix}_b{block_idx}_cand",
|
||||
)
|
||||
|
||||
# Phase 2: INFRA 护栏（跨块累计，分母 >=10 才触发）
|
||||
errors += err_b + err_c
|
||||
infra_denom += den_b + den_c
|
||||
_check_infra_guard(errors, infra_denom, gate_guard_err)
|
||||
|
||||
# Phase 3: 配对 + 证据行 + 块间判定
|
||||
qids = [q.question_id for q in chunk]
|
||||
pair_result = pair_block(b_map, c_map, qids)
|
||||
for qid, (b, c) in pair_result.observed.items():
|
||||
base_obs[qid] = b
|
||||
cand_obs[qid] = c
|
||||
|
||||
block_rows = _build_evidence_rows(chunk, b_map, c_map, task_type, block_idx)
|
||||
|
||||
w += pair_result.w
|
||||
l += pair_result.l # noqa: E741
|
||||
n_used += len(chunk)
|
||||
verdict = gate_decision(w, l, n_used, len(plan) - n_used, params=gate_params)
|
||||
|
||||
for row in block_rows:
|
||||
row["e_value"] = verdict.e_value
|
||||
evidence_rows.extend(block_rows)
|
||||
|
||||
if verdict.decision != "continue":
|
||||
break
|
||||
|
||||
# 最后一块判定即终态（n_remaining=0 → provisional/inertia）
|
||||
assert verdict is not None, "空阶梯应已在 validate_skill_local 入口拒绝"
|
||||
return _finalize_outcome(
|
||||
verdict=verdict,
|
||||
w=w,
|
||||
l=l,
|
||||
n_used=n_used,
|
||||
n_plan=len(plan),
|
||||
base_obs=base_obs,
|
||||
cand_obs=cand_obs,
|
||||
evidence_rows=evidence_rows,
|
||||
task_type=task_type,
|
||||
)
|
||||
|
||||
|
||||
async def validate_skill_local(
|
||||
workspace_dir: Path,
|
||||
base_skills_version: str,
|
||||
task_type: str,
|
||||
target_file: str,
|
||||
candidate_content: str,
|
||||
base_skill_content: str,
|
||||
ladder_items: list[GeneratedQuestion],
|
||||
gate_params: GateParams,
|
||||
gate_block: int,
|
||||
gate_n_max: int,
|
||||
gate_guard_err: float,
|
||||
baseline_cache: BaselineCache,
|
||||
prompts_version: str,
|
||||
run_inference: RunInferenceFn,
|
||||
log: HarnessLog,
|
||||
gate_run_prefix: str,
|
||||
) -> ValidationOutcome:
|
||||
"""块序贯配对验证：阶梯出题，基线/候选逐块配对，e-process 四出口早停。
|
||||
|
||||
参数:
|
||||
workspace_dir: workspace 根目录。
|
||||
base_skills_version: 基线 skills 版本名（候选物化复制源）。
|
||||
task_type: 待验证题型。
|
||||
target_file: fallback 解析后该题型的真实生效 skill 文件名
|
||||
（record.target_file，可能是共享 default-strategy.md）；
|
||||
候选物化写此文件，与 accept 路径同源。
|
||||
candidate_content: 候选 skill 全文。
|
||||
base_skill_content: 基线侧该题型解析后生效 skill 文件全文
|
||||
（skill_hash(base_skill_content) 作 BaselineCache 键成分）。
|
||||
ladder_items: 阶梯序题目列表（已排除本 step 案例包题）。
|
||||
gate_params: e-process 判据阈值组。
|
||||
gate_block: 块大小。
|
||||
gate_n_max: 单 gate 题数上限。
|
||||
gate_guard_err: 跨块累计 INFRA 错误率护栏（分母 >=10 才触发）。
|
||||
baseline_cache: 基线侧逐题对错缓存。
|
||||
prompts_version: 当前 prompts 版本（缓存键成分）。
|
||||
run_inference: 注入的 async 推理函数（RunInferenceFn 协议）。
|
||||
log: HarnessLog 共享实例（供 DB 回读逐题对错）。
|
||||
gate_run_prefix: gate 内推理 run_id 前缀，必须含 "_gate_"
|
||||
（防泄露过滤靠它识别）。块 run_id = f"{prefix}_b{block_idx}_{arm}"。
|
||||
|
||||
返回:
|
||||
ValidationOutcome。逐题证据记入 outcome.evidence_rows 随结果返回，
|
||||
gate_evidence 落库由调用方（runner）负责。
|
||||
"""
|
||||
if "_gate_" not in gate_run_prefix:
|
||||
raise ValueError(f"gate_run_prefix 必须含 '_gate_'（防泄露过滤依赖）: {gate_run_prefix!r}")
|
||||
if not ladder_items:
|
||||
raise ValueError(f"task_type={task_type} 阶梯为空，无法验证")
|
||||
|
||||
plan = ladder_items[:gate_n_max]
|
||||
cand_dir = materialize_candidate_skill(
|
||||
workspace_dir, base_skills_version, target_file, candidate_content
|
||||
)
|
||||
try:
|
||||
return await _run_local_validation(
|
||||
workspace_dir=workspace_dir,
|
||||
cand_dir=cand_dir,
|
||||
base_skills_version=base_skills_version,
|
||||
task_type=task_type,
|
||||
base_skill_content=base_skill_content,
|
||||
plan=plan,
|
||||
gate_params=gate_params,
|
||||
gate_block=gate_block,
|
||||
gate_guard_err=gate_guard_err,
|
||||
baseline_cache=baseline_cache,
|
||||
prompts_version=prompts_version,
|
||||
run_inference=run_inference,
|
||||
log=log,
|
||||
gate_run_prefix=gate_run_prefix,
|
||||
)
|
||||
finally:
|
||||
try:
|
||||
shutil.rmtree(cand_dir)
|
||||
except OSError as e:
|
||||
logger.warning("候选临时目录清理失败 {}: {}", cand_dir, e)
|
||||
Reference in New Issue
Block a user