feat: parallel evolve + continuous gate wiring in runner (algo #6)
This commit is contained in:
+270
-113
@@ -12,6 +12,7 @@
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from __future__ import annotations
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import asyncio
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import json
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import math
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import random
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@@ -34,6 +35,7 @@ from app.harness.checkpoint import (
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)
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from app.harness.config import RunConfig # noqa: TC001 — 运行时 _compute_total_steps 使用
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from app.harness.gate_ladder import BaselineCache, GatePools, build_or_load_gate_pools
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from app.harness.log import HarnessLog
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from app.harness.observation import (
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write_dual_metric,
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write_epoch_report,
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@@ -45,7 +47,13 @@ from app.harness.observation import (
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)
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from app.harness.question_units import build_units, unit_correctness_view
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from app.harness.store import advance_version
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from app.harness.validate import Probation, ValidationOutcome
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from app.harness.validate import (
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GateSpec,
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Probation,
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ValidationOutcome,
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_ladder_units,
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validate_skills_concurrent,
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)
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from app.harness.workspace import (
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ResolvedPaths,
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archive_workspace,
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@@ -602,6 +610,32 @@ def _write_skip_report(
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)
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def _assert_disjoint_target_files(targets_by_type: dict[str, str]) -> None:
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"""断言本 step 各题型进化目标文件互不相同(设计 v3 §1 fail-fast)。
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题型并行进化 + 并行 gate 的前提是 skill 文件不相交;两题型 fallback 到
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同一 default-strategy.md 时并行会互相覆盖候选与 accept,必须显式中止
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而非静默串行(当前 12 题型均有专属文件,此断言防未来配置漂移)。
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参数:
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targets_by_type: {题型: 解析后 skill 文件名}。
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返回:
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无。
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异常:
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RuntimeError: 存在两个题型映射同一文件。
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"""
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seen: dict[str, str] = {}
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for task_type, target in targets_by_type.items():
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if target in seen:
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raise RuntimeError(
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f"题型 {seen[target]!r} 与 {task_type!r} 映射同一 skill 文件 {target!r},"
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"并行进化/gate 不支持共享目标文件(设计 v3 §1)"
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)
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seen[target] = task_type
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def _escape_sql_like(text: str) -> str:
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"""转义 SQL LIKE 模式中的全部特殊字符(`\\`、`%`、`_`)为字面匹配。
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@@ -1199,7 +1233,7 @@ class Runner:
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_guard_infra_failures(result, context="rollout")
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# -----------------------------------------------------------------------
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# _gate_batch_skills：per task_type gate
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# _gate_batch_skills：并行进化 + 连续并发 gate（四阶段）
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# -----------------------------------------------------------------------
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async def _gate_batch_skills(
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@@ -1211,18 +1245,95 @@ class Runner:
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pools: Pools,
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state: _TrainState,
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) -> None:
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"""按 task_type 独立 evolve → 局部验证 → accept/reject。"""
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from app.harness.workspace import VersionedSkillStore
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from core.evolution import evolve_single_skill
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"""按 task_type 并行 evolve → 连续并发 gate → 字母序统一落账。
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四阶段(设计 v3 §2.1):Phase A 并行进化(cooldown/无改动照旧跳过);
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Phase B 装配 GateSpec(阶梯出题 + 案例单元排除 + n_max 截断);
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Phase C validate_skills_concurrent(共享题槽,统计按阶梯序前缀推进,
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只读 state);Phase D 唯一写 state 阶段——按字母序 accept/reject 落账,
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与原串行语义等价(题型 skill 文件不相交,合并顺序仅为确定性)。
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参数:
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epoch / step / total_steps: 训练坐标。
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diagnosis: 本 step 诊断结果(skill_case_packs 按题型分组)。
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pools: 冻结三池。
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state: 训练状态(Phase D 唯一写入点)。
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返回:
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无。
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"""
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budget = edit_budget_at(
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global_step=state.global_step,
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total_steps=total_steps,
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start=self._config.edit_budget_start,
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end=self._config.edit_budget_end,
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)
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# ---- Phase A: 并行进化(冷却/无真实改动照旧写 skip 后出清) ----
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records = await self._evolve_types_parallel(epoch, step, diagnosis, budget, pools, state)
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if not records:
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return
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_assert_disjoint_target_files({t: r.target_file for t, r in records.items()})
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# ---- Phase B: 装配 GateSpec(阶梯出题,收编原 _run_gate_validation 前半) ----
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specs = self._assemble_gate_specs(epoch, step, diagnosis, records, pools, state)
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# ---- Phase C: 连续并发 gate(只读 state) ----
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with HarnessLog(str(self._paths.db_path), f"gate_e{epoch}_s{step}") as gate_log:
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outcomes = await validate_skills_concurrent(
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workspace_dir=self._config.workspace_dir,
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base_skills_version=self._current_version("skills"),
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specs=specs,
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gate_params=GateParams(
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e_confirm=self._config.gate_e_confirm,
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e_provisional=self._config.gate_e_provisional,
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w_net_min=self._config.gate_w_net_min,
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delta_min=self._config.gate_delta_min,
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lambda_dir=self._config.gate_lambda_dir,
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e_rollback=self._config.gate_e_rollback,
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),
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gate_guard_err=self._config.gate_guard_err,
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baseline_cache=state.baseline_cache,
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prompts_version=self._current_version("prompts"),
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run_inference=self._make_validate_run_inference_fn(gate_log),
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log=gate_log,
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concurrency=self._config.concurrency,
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)
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# ---- Phase D: 唯一写 state 阶段(字母序确定性落账) ----
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self._settle_gate_outcomes(epoch, step, records, outcomes, budget, pools, state)
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async def _evolve_types_parallel(
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self,
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epoch: int,
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step: int,
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diagnosis: DiagnosisResult,
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budget: int,
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pools: Pools,
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state: _TrainState,
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) -> dict[str, EvolutionRecord]:
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"""Phase A:各题型进化 asyncio.gather 并行,冷却/无改动路径写 skip 出队。
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cooldown 与"进化未产出真实改动"(rejected/skipped/内容未变)两类路径
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与原串行实现语义一致:写 skip_report 后不进 gate。进化互相独立
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(各题型 skill 文件不相交,VersionedSkillStore 只读基线版本),
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gather 并行不改变单题型结果。
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参数:
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epoch / step: 训练坐标。
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diagnosis: 本 step 诊断结果。
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budget: 当步编辑预算。
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pools: 冻结三池。
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state: 训练状态(只读)。
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返回:
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{题型: EvolutionRecord},仅含产出真实改动、待 gate 的题型。
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"""
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from app.harness.workspace import VersionedSkillStore
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from core.evolution import evolve_single_skill
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active_types: list[str] = []
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for task_type in sorted(diagnosis.skill_case_packs):
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# 冷却 admission control
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if state.gate_cooldown.get(task_type, 0) > 0:
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_write_skip_report(
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self._config.workspace_dir,
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@@ -1237,22 +1348,40 @@ class Runner:
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budget=budget,
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)
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continue
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active_types.append(task_type)
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if not active_types:
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return {}
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evolve_prompts = self._load_evolve_prompts()
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skills_version = self._current_version("skills")
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async def _evolve_one(task_type: str) -> EvolutionRecord:
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pack = diagnosis.skill_case_packs[task_type]
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skill_store = VersionedSkillStore(self._paths.skills_dir)
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evolve_prompts = self._load_evolve_prompts()
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record = await evolve_single_skill(
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return await evolve_single_skill(
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self._evolve_llm,
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pack,
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skill_store,
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evolve_prompts,
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self._current_version("skills"),
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skills_version,
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budget,
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self._config.appendix_consolidate_threshold,
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skill_update_mode=self._config.skill_update_mode,
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rejected=state.rejected_buffer.get(task_type, []),
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)
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# 进化未产出真实改动
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records = dict(
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zip(
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active_types,
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await asyncio.gather(*[_evolve_one(t) for t in active_types]),
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strict=True,
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)
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)
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# 无真实改动的题型照旧写 skipped 后出队
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gated: dict[str, EvolutionRecord] = {}
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for task_type in active_types:
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record = records[task_type]
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if record.status in ("rejected", "skipped") or (
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record.evolved_content == record.original_content
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):
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@@ -1270,11 +1399,107 @@ class Runner:
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rank_clip_triggered=bool(record.clip_info.get("triggered", False)),
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)
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continue
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gated[task_type] = record
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return gated
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outcome = await self._run_gate_validation(
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epoch, step, task_type, pack, record, pools, state
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def _assemble_gate_specs(
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self,
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epoch: int,
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step: int,
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diagnosis: DiagnosisResult,
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records: dict[str, EvolutionRecord],
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pools: Pools,
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state: _TrainState,
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) -> list[GateSpec]:
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"""Phase B:为每个待 gate 题型装配 GateSpec(阶梯出题 + 截断)。
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案例包按 unit 排除:把每个 case 的 question_id 映射到其所属 unit_id,
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命中单元整体排除,防止只排 AR pair 半个成员而给 gate 池灌半个 pair
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(下游 _ladder_units 会 fail-fast)。base_skill_content 读 step 起点
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版本(self._paths 在 Phase D accept 前不变),保证所有题型对同一
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基线版本验证。核心算法保真 #5。
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参数:
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epoch / step: 训练坐标(拼 gate_run_prefix)。
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diagnosis: 本 step 诊断结果(案例排除来源)。
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records: Phase A 产出的待 gate 进化记录。
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pools: 冻结三池(baseline_run_id)。
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state: 训练状态(只读 gate_pools / gate_epoch_observed)。
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返回:
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与 records 键序一致的 GateSpec 列表。
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异常:
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RuntimeError: 阶梯引用了题库中不存在的 unit_id。
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"""
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specs: list[GateSpec] = []
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for task_type, record in records.items():
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pack = diagnosis.skill_case_packs[task_type]
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exclude_units = {
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self._gate_questions_by_id[c.question_id].unit_id
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for c in pack.failure_cases + pack.success_cases
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if c.question_id in self._gate_questions_by_id
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}
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ladder_unit_ids = state.gate_pools.ladder_for(
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task_type,
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exclude_units,
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p_low=self._config.gate_p_low,
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p_high=self._config.gate_p_high,
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cold=not state.gate_epoch_observed,
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)
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# 观测落库
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missing = [uid for uid in ladder_unit_ids if uid not in self._gate_units_by_id]
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if missing:
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raise RuntimeError(
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f"gate 阶梯引用未知 unit: {missing[:5]}(gate_pools.json 与题库失配)"
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)
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ladder_items = [
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q for uid in ladder_unit_ids for q in self._gate_units_by_id[uid].questions
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]
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slug = task_type.lower().replace(" ", "-")
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specs.append(
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GateSpec(
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task_type=task_type,
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target_file=record.target_file,
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candidate_content=record.evolved_content,
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base_skill_content=(self._paths.skills_dir / record.target_file).read_text(
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encoding="utf-8"
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),
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units=tuple(_ladder_units(ladder_items)[: self._config.gate_n_max]),
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gate_run_prefix=f"{pools.baseline_run_id}_e{epoch}_s{step}_gate_{slug}",
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)
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)
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return specs
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def _settle_gate_outcomes(
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self,
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epoch: int,
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step: int,
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records: dict[str, EvolutionRecord],
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outcomes: dict[str, ValidationOutcome],
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budget: int,
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pools: Pools,
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state: _TrainState,
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) -> None:
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"""Phase D:按字母序统一落账(观测落库 + accept/reject 写 state)。
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本阶段是 _gate_batch_skills 唯一写 state 的阶段。字母序仅为确定性
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(题型 skill 文件不相交,accept 串行叠加时 _accept_skill 基于最新
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manifest 版本追加各自 target_file,互不覆盖),与原串行语义等价。
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参数:
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epoch / step: 训练坐标。
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records: Phase A 产出的进化记录。
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outcomes: Phase C 产出的 gate 判定。
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budget: 当步编辑预算(step_report 落账)。
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pools: 冻结三池。
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state: 训练状态(唯一写入点)。
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返回:
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无。
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"""
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for task_type in sorted(outcomes):
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record = records[task_type]
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outcome = outcomes[task_type]
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write_gate_evidence(
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str(self._paths.db_path),
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run_id=pools.baseline_run_id,
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@@ -1313,88 +1538,6 @@ class Runner:
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state.rejected_buffer, task_type, record, outcome, state.global_step
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)
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async def _run_gate_validation(
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self,
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epoch: int,
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step: int,
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task_type: str,
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pack: Any,
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record: EvolutionRecord,
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pools: Pools,
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state: _TrainState,
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) -> ValidationOutcome:
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"""CE-Gate 块序贯配对验证：阶梯出题 → 基线/候选逐块配对 → e-process 四出口。
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参数:
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epoch: 轮次。
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step: epoch 内 step。
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task_type: 待验证题型。
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pack: SkillCasePack。
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record: 进化产物。
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pools: 冻结三池。
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state: 训练状态。
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返回:
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ValidationOutcome。
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"""
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from app.harness.log import HarnessLog
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from app.harness.validate import validate_skill_local
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# 案例包按 unit 排除：把每个 case 的 question_id 映射到其所属 unit_id，
|
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# 命中单元整体排除，防止只排 AR pair 半个成员而给 gate 池灌半个 pair
|
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# （下游 _ladder_units 会 fail-fast）。核心算法保真 #5。
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exclude_units = {
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self._gate_questions_by_id[c.question_id].unit_id
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for c in pack.failure_cases + pack.success_cases
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if c.question_id in self._gate_questions_by_id
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}
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ladder_unit_ids = state.gate_pools.ladder_for(
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task_type,
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exclude_units,
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p_low=self._config.gate_p_low,
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p_high=self._config.gate_p_high,
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cold=not state.gate_epoch_observed,
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)
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missing = [uid for uid in ladder_unit_ids if uid not in self._gate_units_by_id]
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if missing:
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raise ValueError(
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f"gate 阶梯[{task_type}] 含 benchmark 中不存在的单元: "
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f"{missing[:5]}（gate_pools.json 与题库失配）"
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)
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# 单元展开为逐题（unit 内成员顺序保持），下游 validate 再按阶梯序聚合回单元。
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ladder_items = [q for uid in ladder_unit_ids for q in self._gate_units_by_id[uid].questions]
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base_skill_content = (self._paths.skills_dir / record.target_file).read_text(
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encoding="utf-8"
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)
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slug = task_type.lower().replace(" ", "-")
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run_inference_fn = self._make_validate_run_inference_fn()
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with HarnessLog(str(self._paths.db_path), f"gate_{slug}") as gate_log:
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return await validate_skill_local(
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workspace_dir=self._config.workspace_dir,
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base_skills_version=self._current_version("skills"),
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task_type=task_type,
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target_file=record.target_file,
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candidate_content=record.evolved_content,
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base_skill_content=base_skill_content,
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ladder_items=ladder_items,
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gate_params=GateParams(
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e_confirm=self._config.gate_e_confirm,
|
||||
e_provisional=self._config.gate_e_provisional,
|
||||
w_net_min=self._config.gate_w_net_min,
|
||||
delta_min=self._config.gate_delta_min,
|
||||
lambda_dir=self._config.gate_lambda_dir,
|
||||
e_rollback=self._config.gate_e_rollback,
|
||||
),
|
||||
gate_block=self._config.gate_block,
|
||||
gate_n_max=self._config.gate_n_max,
|
||||
gate_guard_err=self._config.gate_guard_err,
|
||||
baseline_cache=state.baseline_cache,
|
||||
prompts_version=self._current_version("prompts"),
|
||||
run_inference=run_inference_fn,
|
||||
log=gate_log,
|
||||
gate_run_prefix=(f"{pools.baseline_run_id}_e{epoch}_s{step}_gate_{slug}"),
|
||||
)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# accept / reject / probation
|
||||
# -----------------------------------------------------------------------
|
||||
@@ -2475,10 +2618,23 @@ class Runner:
|
||||
|
||||
return _noop_builder
|
||||
|
||||
def _make_validate_run_inference_fn(self):
|
||||
"""构造 validate 用的 RunInferenceFn（绑定共享依赖）。"""
|
||||
def _make_validate_run_inference_fn(self, gate_log: HarnessLog):
|
||||
"""构造 validate 用的 RunInferenceFn(绑定共享依赖与共享 HarnessLog)。
|
||||
|
||||
连续并发 gate 下本函数被逐单元高频并发调用:每次调用新建 HarnessLog
|
||||
连接会重现多连接争 SQLite 写锁(遥测同款教训),故复用调用方传入的
|
||||
单一 gate_log(单连接 + threading.Lock 串行化)。_record_run 按 run_id
|
||||
去重,避免逐单元重复 upsert。
|
||||
|
||||
参数:
|
||||
gate_log: 本 step gate 阶段共享的 HarnessLog 实例。
|
||||
|
||||
返回:
|
||||
符合 RunInferenceFn 协议的异步推理函数。
|
||||
"""
|
||||
from app.harness.inference import run_inference
|
||||
from app.harness.log import HarnessLog
|
||||
|
||||
recorded: set[str] = set()
|
||||
|
||||
async def _run(
|
||||
questions: list[GeneratedQuestion],
|
||||
@@ -2486,21 +2642,22 @@ class Runner:
|
||||
run_id: str,
|
||||
skills_dir: Path,
|
||||
) -> InferenceResult:
|
||||
self._record_run(run_id)
|
||||
with HarnessLog(str(self._paths.db_path), run_id) as log:
|
||||
return await run_inference(
|
||||
questions=questions,
|
||||
llm=self._llm,
|
||||
tool_dispatch_fn=self._make_tool_dispatch_fn(skills_dir=skills_dir),
|
||||
prompt_builder=self._make_prompt_builder(
|
||||
skills_dir=skills_dir, prompts_dir=self._paths.prompts_dir
|
||||
),
|
||||
log=log,
|
||||
run_id=run_id,
|
||||
concurrency=self._config.concurrency,
|
||||
max_steps=self._config.max_steps,
|
||||
skill_mode=self._config.skill_mode,
|
||||
)
|
||||
if run_id not in recorded:
|
||||
recorded.add(run_id)
|
||||
self._record_run(run_id)
|
||||
return await run_inference(
|
||||
questions=questions,
|
||||
llm=self._llm,
|
||||
tool_dispatch_fn=self._make_tool_dispatch_fn(skills_dir=skills_dir),
|
||||
prompt_builder=self._make_prompt_builder(
|
||||
skills_dir=skills_dir, prompts_dir=self._paths.prompts_dir
|
||||
),
|
||||
log=gate_log,
|
||||
run_id=run_id,
|
||||
concurrency=self._config.concurrency,
|
||||
max_steps=self._config.max_steps,
|
||||
skill_mode=self._config.skill_mode,
|
||||
)
|
||||
|
||||
return _run
|
||||
|
||||
|
||||
Reference in New Issue
Block a user