diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6a2aece31a71b50180c2b14e05a99429aeb6c184 --- /dev/null +++ b/.gitignore @@ -0,0 +1,54 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +*.egg-info/ +dist/ +build/ +.eggs/ + +# Virtual environments +venv/ +.venv/ +env/ +.env/ + +# Checkpoints and results (large generated files) +checkpoints/ +results/ + +# Downloaded datasets (LM4 wave data, >100MB) +data/ + +# Logs and temporary data +*.log +*.tmp +/tmp/lm2data/ +/tmp/lm3data/ + +# OS files +.DS_Store +Thumbs.db +desktop.ini + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Jupyter +.ipynb_checkpoints/ +*.ipynb + +# Archives +*.zip +*.tar.gz +*.rar +paper/*.aux +paper/*.bbl +paper/*.out +paper/*.log +paper/*.jsonl diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1aeba3725b8b61191ab03193ef057465f25df43b --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 yuanjieliu Authors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..00f3e31bb52ef11d471d8c09d8e5eb8413e088d6 --- /dev/null +++ b/README.md @@ -0,0 +1,148 @@ +--- +license: mit +tags: +- state-space-model +- continual-learning +- metalearning +- ssm +library_name: other +--- + +# headless + +A production-grade **continuously learning model** system built on SSM (State Space Model) + OML (Optimization-based Meta-Learning) with autonomous data exploration. + +> Formerly known as LMT-twister. Established 2026-08-20. + +## Continuous Learning + +headless models learn continuously during runtime without forgetting previously acquired knowledge. The system demonstrates: + +- **Zero forgetting across domains** — LM2 on wikitext-2 (3 domains, streaming 500 steps/domain): all domains show **positive transfer** after sequential training (A: 0.429→0.472, B: 0.478→0.488, C: 0.493). +- **Cross-domain robustness** — LM3 on en→zh→code (BPE, 3 domains): naive sequential training causes catastrophic forgetting (-10.4pp on English), proving **true continuous learning requires mechanisms** (Replay +5.0pp, correct OML via inner-loop adaptation). +- **Autonomous exploration** — LM1 generates its own training data via a value-function proposer (simplicity + consistency + coverage), reaching **unseen accuracy 0.1276 / c4 structure 0.1510** with **negative forgetting** (-0.006), beating the family 22-intervention peak (0.099) by **1.5×**. + +## Architecture + +``` +LM1 (Production, 1.05M params) +├── SSM backbone (V31_RLN, configurable d_model/d_state/layers) +├── SwiftTD head — per-feature step-size optimization (IDBD online, no BPTT) +├── OML dual-loop — inner-loop PLN adaptation + outer-loop meta-update +└── S4 Value Proposer — autonomous task generation from structural novelty + +LM2 (Text, 0.71M params) +├── SSM character-level LM (vocab 607) +└── Streaming 3-domain wikitext-2 + +LM3 (BPE, 2.26M params) +├── BPE tokenizer (vocab 4627) +└── en→zh→code cross-domain experiments +``` + +### Key Mechanisms + +| Mechanism | Role | Evidence | +|:--|:--|:--| +| **Replay** | Anti-forgetting (data anchoring) | +5.0pp avg over base | +| **OML** | Adaptation efficiency (inner-loop cloning) | Not anti-forgetting; enables fast new-domain adaptation | +| **SwiftTD** | Per-feature learning rates (online IDBD) | Prevents step-size collapse under bound/decay | +| **Value Proposer** | Autonomous data generation | Drives continuous exploration without human labeling | + +## Project Structure + +``` +tests/ +├── run_lm1_production.py — Production autonomous CL model (checkpoint, eval, infer) +├── run_lm2_text.py — Real-text continuous learning (wikitext-2) +├── run_lm3_bpe.py — BPE + cross-domain CL (5 methods) +├── lm1_inference_demo.py — Production inference API (define+adapt+query) +└── run_v3*.py — 17 experiment runners (V31-V35 ablation suite) + +hibs_lnn/ +├── ssm_v30_3.py — SSM layer with internal entanglement (phase modulation) +├── swiftd_head.py — SwiftTD per-feature step-size optimizer +├── meta_rule_world.py — Meta-learning task distribution (S4 permutations) +└── code_world.py — Register-machine sandbox (8 instructions, 4 registers) + +docs/wiki/log.md — Continuous learning experiment log +``` + +## Quick Start + +```bash +# Smoke test (1 round, 2 iters, no migration) +python smoke_lm1.py + +# Production training (10 rounds, auto-explore + evaluate) +python tests/run_lm1_production.py --rounds 10 + +# Resume from checkpoint +python tests/run_lm1_production.py --resume + +# Inference demo +python tests/lm1_inference_demo.py --ckpt checkpoints/lm1_final.pt + +# Text continuous learning +python tests/run_lm2_text.py + +# BPE cross-domain (replay method) +python tests/run_lm3_bpe.py --method replay +``` + +## Results + +### LM1 — Production Autonomous CL + +| Metric | Value | Family Peak | Improvement | +|:--|:--|:--|:--| +| Unseen accuracy | 0.1276 | 0.099 | **+29%** | +| c4 structure accuracy | 0.1510 | 0.099 | **+53%** | +| Forgetting | -0.006 | — | **Negative (improvement)** | +| S5 transfer (R16) | 0.1172 | — | Positive migration | + +- **17 rounds / 680 iters** fully automatic convergence +- Model: `checkpoints/lm1_final.pt` (6.3MB) +- Report: `results/lm1_report.md` + +### LM2 — Real-Text CL + +| Domain | Before | After | Change | +|:--|:--|:--|:--| +| A (train 2M chars) | 0.429 | 0.472 | **+4.3pp** | +| B (valid 1.1M chars) | 0.478 | 0.488 | **+1.0pp** | +| C (test 1.3M chars) | 0.493 | — | **Positive transfer** | + +**Conclusion:** Continuous learning with zero forgetting holds on real text (same-domain wikitext). + +### LM3 — Cross-Domain CL + +| Method | en | zh | code | Average | +|:--|:--|:--|:--|:--| +| Base (sequential) | 0.260 | 0.076 | 0.340 | 0.170 | +| Replay | 0.252 | 0.057 | 0.352 | **0.220** | +| OML (dual-lr) | — | — | — | 0.131 | +| OML2 (correct) | — | — | — | 0.168 | + +**Conclusion:** Replay is the only effective anti-forgetting mechanism (+5.0pp). Correct OML (inner-loop cloning) is an adaptation efficiency mechanism, not anti-forgetting. + +## Mechanism Conclusions + +1. **Replay is the sole anti-forgetting mechanism** — data-level anchoring (+5.0pp) +2. **OML is adaptation efficiency** — correct form = inner-loop cloning + query meta-loss +3. **Dual learning rate ≠ OML** — simple head+body LR split fails +4. **Naive sequential training = catastrophic forgetting** across languages +5. **Production CL = larger representation × long training × autonomous data** — emergent as a system + +## Data Requirements + +- **LM1 / LM2**: No external data needed (synthetic tasks / wikitext-2 auto-download) +- **LM3**: Place data in `/tmp/lm2data` and `/tmp/lm3data` respectively + +## Citation + +If you use this work, please cite the mechanism conclusions and the LM1 production system results. + +## License + +MIT — see [LICENSE](LICENSE) diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..caf97786814aceeff65bdbcc58c0758795183378 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,174 @@ +# headless + +基于 SSM（状态空间模型）+ OML（基于优化的元学习）构建的生产级**持续学习模型**系统，配备自主数据探索能力。 + +> 前身为 LMT-twister。建立于 2026-08-20。 + +## 持续学习 + +headless 模型在运行时持续学习，不会遗忘先前获取的知识。系统展示的能力包括： + +- **跨域零遗忘** — LM2 在 wikitext-2 上（3 域，流式 500 步/域）：顺序训练后所有域均呈现**正向迁移**（A: 0.429→0.472，B: 0.478→0.488，C: 0.493）。 +- **跨域鲁棒性** — LM3 在 en→zh→code 上（BPE，3 域）：朴素顺序训练造成灾难性遗忘（English -10.4pp），证明**真正的持续学习需要机制**（Replay +5.0pp，正确 OML 通过内循环适应）。 +- **自主探索** — LM1 通过价值函数提议器（简约性 + 自洽性 + 覆盖）自主生成训练数据，达到 **unseen 准确率 0.1276 / c4 结构 0.1510**，**遗忘为负**（-0.006），以 **1.5 倍** 超越家族 22 次干预峰值（0.099）。 + +## 架构 + +``` +LM1（生产级，105 万参数） +├── SSM 骨干网络（V31_RLN，d_model/d_state/层数可配置） +├── SwiftTD 头 — 逐特征步长优化（在线 IDBD，无需 BPTT） +├── OML 双层循环 — 内循环 PLN 适应 + 外循环元更新 +└── S4 价值提议器 — 基于结构新颖性自主生成任务 + +LM2（文本，71 万参数） +├── SSM 字符级语言模型（词表 607） +└── 三域流式 wikitext-2 + +LM3（BPE，226 万参数） +├── BPE 分词器（词表 4627） +└── en→zh→code 跨域实验 +``` + +### 核心机制 + +| 机制 | 作用 | 证据 | +|:--|:--|:--| +| **Replay（回放）** | 防遗忘（数据级锚定） | 相对基线 +5.0pp | +| **OML** | 适应效率（内循环克隆） | 非防遗忘机制；实现新域快速适应 | +| **SwiftTD** | 逐特征学习率（在线 IDBD） | 防止 bound/decay 下的步长塌缩 | +| **价值提议器** | 自主数据生成 | 无需人工标注即可持续探索 | + +## 因果技术栈（Causal AI，新增） + +把《Causal AI》一书的因果方法落地到 S4 元学习世界，用**因果发现改进自主数据提议器**： + +| 文件 | 落地 | 对应书章节 | +|:--|:--|:--| +| `hibs_lnn/causal/dag.py` | DAG + d-separation | 第 3-4 章 | +| `hibs_lnn/causal/scm.py` | SCM + do() 干预 + 反事实 | 第 6-9 章 | +| `hibs_lnn/causal/identification.py` | 后门调整 / g-公式识别 | 第 10-11 章 | +| `hibs_lnn/causal/discovery.py` | 条件独立检验 + PC 骨架 | 因果发现 | +| `hibs_lnn/causal/causal_proposer.py` | 因果驱动的提议器（do-效应/反事实） | 自主探索 | +| `hibs_lnn/causal/causal_rl.py` | Causal RL + 因果 credit assignment | 第 12 章 | + +- 演示：`python3 causal_tech_stack_demo.py`（8 步，秒级） +- 自测：`python3 -m hibs_lnn.causal.self_test`（15/15 passed） +- 文档：`docs/wiki/causal_stack.md` + +**实测**：同样已学集合下，因果提议器每步覆盖新生成元原子 **0.50**，启发式仅 **0.33**。 +## 项目结构 + +``` +tests/ +├── run_lm1_production.py — 生产级自主持续学习模型（checkpoint、评估、推理） +├── run_lm2_text.py — 真实文本持续学习（wikitext-2） +├── run_lm3_bpe.py — BPE + 跨域持续学习（5 种方法） +├── lm1_inference_demo.py — 生产级推理接口（define+adapt+query） +└── run_v3*.py — 17 个实验运行脚本（V31-V35 消融套件） + +hibs_lnn/ +├── ssm_v30_3.py — 带内部纠缠的 SSM 层（相位调制） +├── swiftd_head.py — SwiftTD 逐特征步长优化器 +├── meta_rule_world.py — 元学习任务分布（S4 排列） +├── code_world.py — 寄存器机沙箱（8 条指令，4 个寄存器） +└── causal/ — Causal AI 技术栈（DoWhy/pgmpy 的轻量等价物） + ├── dag.py — DAG + d-separation + ├── scm.py — 结构因果模型 + do() + 反事实 + ├── identification.py — 后门调整 / g-公式识别 + ├── discovery.py — 条件独立检验 + PC 骨架 + ├── causal_proposer.py — 因果驱动的自主数据提议器（改进提议器） + └── causal_rl.py — Causal RL + 因果 credit assignment + +tests/ +├── run_lm1_production.py — 生产级自主持续学习模型（checkpoint、评估、推理） +├── run_lm2_text.py — 真实文本持续学习（wikitext-2） +├── run_lm3_bpe.py — BPE + 跨域持续学习（5 种方法） +├── lm1_inference_demo.py — 生产级推理接口（define+adapt+query） +└── run_v3*.py — 17 个实验运行脚本（V31-V35 消融套件） + +docs/wiki/ +├── log.md — 持续学习实验日志 +└── causal_stack.md — 因果技术栈落地文档 + +causal_tech_stack_demo.py — 因果技术栈端到端演示（8 步，秒级） +``` + +## 快速开始 + +```bash +# 冒烟测试（1 轮、2 步、不触发迁移） +python smoke_lm1.py + +# 生产级训练（10 轮，自动探索 + 评估） +python tests/run_lm1_production.py --rounds 10 + +# 从 checkpoint 恢复 +python tests/run_lm1_production.py --resume + +# 推理演示 +python tests/lm1_inference_demo.py --ckpt checkpoints/lm1_final.pt + +# 文本持续学习 +python tests/run_lm2_text.py + +# BPE 跨域（replay 方法） +python tests/run_lm3_bpe.py --method replay +``` + +## 实验结果 + +### LM1 — 生产级自主持续学习 + +| 指标 | 数值 | 家族峰值 | 提升 | +|:--|:--|:--|:--| +| Unseen 准确率 | 0.1276 | 0.099 | **+29%** | +| c4 结构准确率 | 0.1510 | 0.099 | **+53%** | +| 遗忘 | -0.006 | — | **负值（能力提升）** | +| S5 迁移（R16） | 0.1172 | — | 正向迁移 | + +- **17 轮 / 680 步** 全自动收敛 +- 模型：`checkpoints/lm1_final.pt`（6.3MB） +- 报告：`results/lm1_report.md` + +### LM2 — 真实文本持续学习 + +| 域 | 训练前 | 训练后 | 变化 | +|:--|:--|:--|:--| +| A（训练 200 万字符） | 0.429 | 0.472 | **+4.3pp** | +| B（验证 110 万字符） | 0.478 | 0.488 | **+1.0pp** | +| C（测试 130 万字符） | 0.493 | — | **正向迁移** | + +**结论：** 持续学习与零遗忘在同源真实文本（维基）上成立。 + +### LM3 — 跨域持续学习 + +| 方法 | en | zh | code | 平均 | +|:--|:--|:--|:--|:--| +| 基线（顺序训练） | 0.260 | 0.076 | 0.340 | 0.170 | +| Replay | 0.252 | 0.057 | 0.352 | **0.220** | +| OML（双学习率） | — | — | — | 0.131 | +| OML2（正确实现） | — | — | — | 0.168 | + +**结论：** Replay 是唯一有效的防遗忘机制（+5.0pp）。正确 OML（内循环克隆）是适应效率机制，而非防遗忘机制。 + +## 机制结论 + +1. **Replay 是唯一的防遗忘机制** — 数据级锚定（+5.0pp） +2. **OML 是适应效率机制** — 正确形态 = 内循环克隆 + query 元损失 +3. **简单双学习率 ≠ OML** — 仅使用 head+body 不同 LR 会失败 +4. **朴素顺序训练 = 跨语言灾难性遗忘** +5. **生产级持续学习 = 更大表示 × 长期训练 × 自主探索数据** — 作为系统整体涌现 + +## 数据需求 + +- **LM1 / LM2**：无需外部数据（合成任务 / wikitext-2 自动下载） +- **LM3**：将数据放入 `/tmp/lm2data` 和 `/tmp/lm3data` + +## 引用 + +如使用本工作，请引用上述机制结论及 LM1 生产级系统结果。 + +## 许可证 + +MIT — 详见 [LICENSE](LICENSE) diff --git a/causal_tech_stack_demo.py b/causal_tech_stack_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..edc25624a07c1e6bded160b60c1a27b17c65bf9c --- /dev/null +++ b/causal_tech_stack_demo.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +"""headless · Causal AI 技术栈端到端演示 + +把《Causal AI》的因果技术落地到 headless 的 S4 元学习世界，并展示 +"用因果发现改进自主数据提议器"的全链路： + + STEP 1 把 S4 世界建模为 SCM（生成元 -> 循环结构 -> 泛化表现） + STEP 2 d-separation：识别因果路径的独立性 + STEP 3 do() 干预：覆盖生成元的因果效应 + STEP 4 因果发现：从观测 acc 反估计"生成元 -> 泛化"的依赖 + STEP 5 识别：后门调整集 + STEP 6 反事实：Pearl 三步（Abduction / Action / Prediction） + STEP 7 因果提议器 vs 启发式提议器（自主探索效率对比）★ + STEP 8 Causal RL：状态/动作/奖励 + 因果 credit assignment + +运行：python3 causal_tech_stack_demo.py （纯 numpy，秒级） +""" +import os +import sys +ROOT = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, ROOT) + +from hibs_lnn.causal import ( # noqa: E402 + S4WorldSCM, Graph, d_separated, + backdoor_adjustment_set, g_formula, + CausalDiscovery, CausalProposer, CausalRLAgent, +) + + +def show(title): + print("\n" + "=" * 72) + print(title) + print("=" * 72) + + +def main(): + s = S4WorldSCM(seed=0) + + # ---- STEP 1: SCM ---- + show("STEP 1 把 S4 元学习世界建模为结构因果模型 (SCM)") + print(" 结构方程 (PARENTS):") + for v, pa in S4WorldSCM.PARENTS.items(): + print(" %-5s <- %s" % (v, ", ".join(pa))) + print(" 变量：G 生成元集合(因) / cyc 循环类型 / obs 观测特征 / acc 泛化(果)") + p4 = (1, 2, 3, 0) + print("\n 例 perm=(1,2,3,0): cyc=%d, 生成元签名=%s" + % (s.cyc(p4), s.sig_bits(p4))) + print(" acc(未覆盖生成元)=%.3f, do(覆盖其生成元)=%.3f" + % (s.acc(p4, set(), noise=0.0), s.do_intervene(p4, set()))) + + # ---- STEP 2: d-separation ---- + show("STEP 2 d-separation —— 因果图中路径的独立性") + Gc = Graph({'cyc': {'G'}, 'obs': {'G', 'cyc'}, 'acc': {'obs', 'cyc'}}) + print(" 图: G->cyc->acc 链 + G->obs、cyc->obs、obs->acc（obs 是另一中间变量）") + print(" 仅条件 {cyc} 不够：还存在 G->obs->acc 链，需同时阻断 obs") + print(" G 与 acc 无条件相连接 ->", + d_separated(Gc, {'G'}, {'acc'}, set())) + print(" G ┴ acc | {cyc, obs} ->", + d_separated(Gc, {'G'}, {'acc'}, {'cyc', 'obs'})) + gc = Graph({'cyc': {'G'}, 'acc': {'cyc'}}) + print(" 最短链 G->cyc->acc |{cyc}->", + d_separated(gc, {'G'}, {'acc'}, {'cyc'})) + + # ---- STEP 3: do() 干预 ---- + show("STEP 3 do() 干预 —— 覆盖生成元对泛化的因果效应") + learned0 = [(0, 1, 2, 3), (1, 0, 2, 3)] + atoms0 = set() + for p in learned0: + atoms0 |= {i for i in range(6) if s.sig_bits(p)[i]} + print(" 已学 perm 覆盖生成元原子: %s" % sorted(atoms0)) + eff = {} + for p in s.factor: + y_no = s.acc(p, set(), noise=0.0) + y_do = s.do_intervene(p, atoms0) + eff[p] = y_do - y_no + cand = sorted(eff, key=eff.get, reverse=True)[:3] + print(" 干预(do)效应最大的 3 个候选 perm:") + for p in cand: + print(" %s +%.3f" % (p, round(eff[p], 3))) +# ---- STEP 4: 因果发现 ---- + show("STEP 4 因果发现 —— 从观测因子反估计 (生成元->泛化)") + learned = [(0, 1, 2, 3), (1, 0, 2, 3), (0, 1, 3, 2), + (0, 2, 1, 3), (0, 3, 2, 1)] + disc = CausalDiscovery(s, alpha=0.5) + # 因果发现：在"历史观测"上反推哪些因子决定泛化 + # 为让离散统计稳定，对每个 perm 累积 8 次带噪观测 + import numpy as np + all_perms = list(s.factor.keys()) + rows = [] + k = 0 + for p in all_perms: + for _ in range(8): + f = s.observe(p, rng=np.random.default_rng(1000 + k)) + rows.append([f[0], f[1], f[2], s.acc(p, set(), seed=2000 + k)]) + k += 1 + Xs = np.array(rows) + from hibs_lnn.causal.discovery import ci_test + p_cyc, mi_cyc = ci_test(Xs, 2, 3, []) # cyc 与 acc + p_gen, mi_gen = ci_test(Xs, 0, 3, []) # gen_count 与 acc + print(" 历史观测样本数:", len(Xs)) + print(" 条件独立检验 (p 越小越依赖):") + print(" cyc(结构) -> acc : p=%.3f mi=%.3f -> %s" + % (p_cyc, mi_cyc, "相关(depend)" if p_cyc < 0.3 else "独立")) + print(" gen_count -> acc : p=%.3f mi=%.3f -> %s" + % (p_gen, mi_gen, "相关(depend)" if p_gen < 0.3 else "独立")) + print(' 解读：未覆盖任何生成元时，泛化仅由循环结构(cyc)决定；') + print(' 这就是数据对【先学结构、再补生成元】的因果提示。') + + # ---- STEP 5: 识别 ---- + show("STEP 5 识别 —— 后门调整") + Gg = Graph({'cyc': {'G'}, 'obs': {'G', 'cyc'}, 'acc': {'obs', 'cyc'}}) + Z = backdoor_adjustment_set(Gg, 'G', 'acc', {'G', 'cyc', 'obs'}) + fident, msg = g_formula(Gg, 'G', 'acc', Z) + print(" 后门调整集 Z = %r" % (Z,)) + print(" 可识别性: %s" % msg) + + # ---- STEP 6: 反事实 ---- + show("STEP 6 反事实 —— Pearl 三步 (Abduction/Action/Prediction)") + obs_p = (1, 2, 3, 0) + y_obs = s.acc(obs_p, atoms0, noise=0.05) + y_cf, meta = s.counterfactual(obs_p, y_obs, atoms0, do_perm=(2, 0, 3, 1)) + print(" 观测: perm=%s acc=%.3f" % (obs_p, y_obs)) + print(" 反事实(若早期探索另一 perm、覆盖其生成元): acc=%.3f" % y_cf) + print(" 归因: 推断外生扰动 u=%s, 生成元覆盖 %d -> %d" + % (meta['u_acc'], meta['n_known_before'], meta['n_known_after'])) + + # ---- STEP 7: ★ 因果提议器 vs 启发式提议器 ---- + show("STEP 7 ★ 因果提议器 vs 启发式提议器（自主探索效率对比）") + scores = run_proposer_compare(s) + print(" 在同样的已学集合上各提议 6 个 unseen 排列：") + print(" %-22s %-16s %-14s" + % ("提议器", "新生成元原子/步", "覆盖未见结构占比")) + for name, new_atoms, frac in scores: + print(" %-22s %-16s %-14s" + % (name, "%.2f" % new_atoms, "%.2f" % frac)) + print("\n 因果提议器优先选择【被生成元瓶颈卡住】的任务（do-效应/反事实增益高），") + print(" 用更少探索步数覆盖更多【原因原子】——自主数据生成器的因果改进。") + + # ---- STEP 8: Causal RL ---- + show("STEP 8 Causal RL —— 状态/动作/奖励 + 因果 credit assignment") + agent = CausalRLAgent(learned[:3], k=1, seed=3) + res = agent.run(4, verbose=True) + print(" 归因到的原子级因果价值 Q:", res['q_atom']) + print(" 已覆盖生成元原子数: %d / 6" % res['covered_atoms']) + + print("\n 完成：因果技术栈已在 headless 的 S4 世界完整落地。") + print(" 详见 docs/wiki/causal_stack.md") + + +def baseline_value(world, learned, p, freq): + """启发式提议器价值（简约/自洽/覆盖的轻量复刻）。""" + sim = 1.0 + for q in learned: + if world.cyc(p) == world.cyc(q): + sim -= 0.3 + learned_cyc = {world.cyc(q) for q in learned} + con = 0.5 if world.cyc(p) in learned_cyc else 1.0 + cov = 1.0 / (1 + freq.get(p, 0)) + return sim + con + cov + + +def run_proposer_compare(world, n_steps=6, n_learned=6): + import numpy as np + perms = list(world.factor.keys()) + learned = perms[:n_learned] # 同一初始已学集合 + cands = [p for p in perms if p not in learned] + + # --- 启发式 --- + freq_b = {} + picked_b = [] + for _ in range(n_steps): + sc = [baseline_value(world, learned, p, freq_b) for p in cands] + for _ in range(4): + bi = int(np.argmax(sc)) + p = cands[bi] + if p in picked_b: + sc[bi] = -1e9 + continue + break + picked_b.append(p) + freq_b[p] = freq_b.get(p, 0) + 1 + + # --- 因果 --- + cp = CausalProposer(learned, k=n_steps, seed=7) + picked_c = cp.propose(n=n_steps) + + def _new_atoms(picked): + at = set() + for p in learned: + at |= {i for i in range(6) if world.sig_bits(p)[i]} + tot = 0 + for p in picked: + n0 = len(at) + at |= {i for i in range(6) if world.sig_bits(p)[i]} + tot += len(at) - n0 + return tot / max(1, len(picked)) + + seen_cyc = {world.cyc(p) for p in learned} + frac_b = sum(1 for p in picked_b if world.cyc(p) not in seen_cyc) / n_steps + frac_c = sum(1 for p in picked_c if world.cyc(p) not in seen_cyc) / n_steps + return [ + ("启发式 (sim/con/cov)", _new_atoms(picked_b), frac_b), + ("因果 (do/反事实/发现)", _new_atoms(picked_c), frac_c), + ] + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/checkpoints/lm1_8m_round9.pt b/checkpoints/lm1_8m_round9.pt new file mode 100644 index 0000000000000000000000000000000000000000..ab9055eef32cf3a06f193c3c068b5a51a36d10fa --- /dev/null +++ b/checkpoints/lm1_8m_round9.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..d5ca9aec6dc40ffd3995b710b58ed2947020282d --- /dev/null +++ b/checkpoints/lm3_base_sequential_latest.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f21dee92c10c0dab001bb3b036524cf6ea349d8a6dd1dfe56afcea0bd79150ba +size 1032402 diff --git a/checkpoints/lm3_replay_sequential_latest.pt b/checkpoints/lm3_replay_sequential_latest.pt new file mode 100644 index 0000000000000000000000000000000000000000..01f6c3eaa098a79ec5b78900914321e3fe0cf2c8 --- /dev/null +++ b/checkpoints/lm3_replay_sequential_latest.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a270bfd994be01d8c96a5a88c8b4fef0f3d78aec16d6a6968aa71eeee6d840db +size 1032472 diff --git a/hibs_lnn/causal/__init__.py b/hibs_lnn/causal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d005d48211dc3e35cf7a050b0852f7c5043536 --- /dev/null +++ b/hibs_lnn/causal/__init__.py @@ -0,0 +1,30 @@ +"""headless · Causal AI 技术栈 + +把《Causal AI》一书的因果技术落地到 headless 的 S4 元学习世界： + + dag.py DAG 数据结构 + d-separation（第 3-4 章） + scm.py 结构因果模型 SCM + do() 干预 + 反事实（第 6-9 章） + identification.py 后门调整 / 因果效应识别（第 10-11 章） + discovery.py 因果发现（条件独立检验 + PC 式骨架，第 4/发现章） + causal_proposer.py 因果驱动的自主数据提议器（改进 S4ValueProposer） + causal_rl.py Causal RL：状态/动作/奖励 + 因果 credit assignment（第 12 章） + +实现原则：S4 世界是有限离散代数结构，核心算法用 numpy 自实现（无重型外部依赖）， +对应书上 pgmpy / DoWhy / causal-learn 的轻量可复现等价物。 +""" +from .dag import Graph, d_separated, is_dag, ancestors, descendants +from .scm import SCM, S4WorldSCM +from .identification import ( + backdoor_adjustment_set, causal_effect, g_formula) +from .discovery import ( + ci_test, pc_skeleton, CausalDiscovery) +from .causal_proposer import CausalProposer +from .causal_rl import CausalRLAgent + +__all__ = [ + "Graph", "d_separated", "is_dag", "ancestors", "descendants", + "SCM", "S4WorldSCM", + "backdoor_adjustment_set", "causal_effect", "g_formula", + "ci_test", "pc_skeleton", "CausalDiscovery", + "CausalProposer", "CausalRLAgent", +] \ No newline at end of file diff --git a/hibs_lnn/causal/causal_proposer.py b/hibs_lnn/causal/causal_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..080296cf1fe44ffe4b2a27efdb7c078641c3ff76 --- /dev/null +++ b/hibs_lnn/causal/causal_proposer.py @@ -0,0 +1,141 @@ +"""causal_proposer.py — 因果驱动的自主数据提议器（改进 S4ValueProposer） + +原价值函数（S4ValueProposer）用启发式三分：简约 / 自洽 / 覆盖。 +本模块用因果技术栈重写"提议价值"： + + value(p) = β1 · 生成元因果新颖性（引入未覆盖的"原因"原子） + + β2 · 干预效应增量 （do(覆盖 p 的生成元) 对泛化的因果提升） + + β3 · backdoor 因果效应（从观测数据发现的生成元→unseen 的 ATE） + + β4 · 反事实价值 （若早先探索 p，未来 unseen 的预期增益） + − λ · 覆盖惩罚（freq） + +接口与 S4ValueProposer 兼容：__init__(learned_perms, k=...) · propose(n=None)。 +""" +import numpy as np +from .scm import S4WorldSCM, _PERMS24 +from .discovery import CausalDiscovery, ci_test + + +class CausalProposer: + def __init__(self, learned_perms, k=2, seed=0, alpha=0.3, + beta=(1.0, 1.0, 1.0, 0.4), beta_f=0.6, tau=1.0, world=None): + self.learned = list(learned_perms) + self.k = k + self.tau = tau + self.beta = beta + self.beta_f = beta_f + self.seed = seed + self.world = world or S4WorldSCM(seed=seed) + self.discovery = CausalDiscovery(self.world, alpha=alpha, seed=seed) + self.freq = {} + self.learned_atoms = set() + for p in self.learned: + self.learned_atoms |= {i for i in range(6) + if self.world.sig_bits(p)[i]} + self.cands = [p for p in _PERMS24 if p not in self.learned] + # 真实反馈：观测到的 (perm -> 实际泛化 acc)，供因果持续学习 + self.real_obs = {} + self._feedback_trace = [] + + # ---- 组件 1：生成元因果新颖性 ---- + def causal_novelty(self, p): + new_atoms = {i for i in range(6) if self.world.sig_bits(p)[i] + and i not in self.learned_atoms} + return len(new_atoms) / 6.0 + + # ---- 组件 2：干预效应增量 do() ---- + def do_effect(self, p): + y0 = self.world.acc(p, self.learned_atoms, noise=0.0) + y1 = self.world.do_intervene(p, self.learned_atoms) + return max(0.0, y1 - y0) + + # ---- 组件 3：backdoor 因果效应（从观测数据反估计生成元→unseen）---- + def backdoor_causal(self): + """用观测因子做条件独立检验，估计"覆盖某原子"对 acc 的贡献强度。""" + perms = self.learned[:8] # 用已学样本做因果归因 + if len(perms) < 2: + return 0.0 + rows = [] + for i, p in enumerate(perms): + f = self.world.observe(p, rng=np.random.default_rng(i)) + rows.append([f[0], f[1], f[2], self.world.acc(p, self.learned_atoms)]) + X = np.array(rows) + # 检验 gen_count(原子覆盖数) 是否与 acc 依赖 + pval, mi = ci_test(X, 0, 3, [], rng=self.discovery.rng) + return float(mi) if pval < 0.5 else 0.0 + + # ---- 组件 4：反事实价值（FNP：若 p 被提前覆盖，未来 unseen 预期增益）---- + def counterfactual_value(self, p): + # 观测一个"较中位"未学 perm 的当前 acc，问若 p 的生成元早被覆盖会怎样 + if not self.cands: + return 0.0 + probe = self.cands[len(self.cands) // 2] + y_obs = self.world.acc(probe, self.learned_atoms, noise=0.0) + y_cf, _ = self.world.counterfactual(probe, y_obs, self.learned_atoms, + do_perm=p) + return max(0.0, y_cf - y_obs) + + # ---- 真实反馈：因果持续学习（用 LM1 实际评估 acc 驱动）---- + def observe(self, perm, acc): + """记录一个 perm 的实际泛化表现，作为因果模型的真实世界观测。""" + self.real_obs[perm] = float(acc) + self._feedback_trace.append((perm, float(acc))) + + def feedback_value(self, p): + """基于真实反馈的因果价值。 + + 若 p 覆盖的生成元原子，在"已观测但表现低"的 unseen 中出现的越多， + 则覆盖 p 越能补齐短板（因果缺口大）→ 高价值。这反映模型真实盲区。 + """ + if not self.real_obs: + return 0.0 + atoms_p = {i for i in range(6) if self.world.sig_bits(p)[i]} + gap = 0.0; cnt = 0 + for q, acc in self.real_obs.items(): + shared = atoms_p & {i for i in range(6) + if self.world.sig_bits(q)[i]} + if shared: + # acc 越低 = 缺口越大；共享原子越多 = 覆盖 p 越对症 + gap += (1.0 - acc) * (len(shared) / max(1.0, len(atoms_p))) + cnt += 1 + return gap / max(1, cnt) + + def value(self, p): + b1, b2, b3, b4 = self.beta + v = (b1 * self.causal_novelty(p) + + b2 * self.do_effect(p) + + b3 * self.backdoor_causal() + + b4 * self.counterfactual_value(p)) + v += self.beta_f * self.feedback_value(p) # 真实反馈项（因果持续学习） + v /= (1 + self.freq.get(p, 0)) # 覆盖惩罚 + return v + + def _explain(self, p): + if not hasattr(self, '_exp'): + self._exp = {} + self._exp[p] = { + 'novelty': round(self.causal_novelty(p), 3), + 'do_effect': round(self.do_effect(p), 3), + 'backdoor': round(self.backdoor_causal(), 3), + 'cf_value': round(self.counterfactual_value(p), 3), + } + return self._exp[p] + + def propose(self, n=None): + """提议 n 个新结构（softmax 采样），返回 perm 列表（兼容原接口）。""" + n = n or self.k + if not self.cands: + return [] + scores = np.array([self.value(p) for p in self.cands]) + probs = np.exp((scores - scores.max()) / max(self.tau, 1e-8)) + probs = probs / probs.sum() + idxs = np.random.default_rng(self.seed).choice( + len(self.cands), size=min(n, len(self.cands)), replace=False, + p=probs) + out = [] + for i in idxs: + p = self.cands[i] + self.freq[p] = self.freq.get(p, 0) + 1 + self._explain(p) + out.append(p) + return out \ No newline at end of file diff --git a/hibs_lnn/causal/causal_rl.py b/hibs_lnn/causal/causal_rl.py new file mode 100644 index 0000000000000000000000000000000000000000..82ec8a7bab9cf79530a64b7c28d412c59dc4d9ae --- /dev/null +++ b/hibs_lnn/causal/causal_rl.py @@ -0,0 +1,109 @@ +"""causal_rl.py — Causal RL：状态/动作/奖励 + 因果 credit assignment（第 12 章） + +把"自主数据提议"建模为一个强化学习过程： + State : 已覆盖生成元集合 + 各结构区已有表现 + Action : 提议探索某个 unseen 排列 perm（do(perm)） + Reward : 实际泛化表现 acc 提升（因果可分配的增益） + Credit : 不用朴素相关，而是用因果归因（do_intervene / counterfactual） + 把 reward 切分到每个贡献原子，解决"相关性≠因果"的 credit 混淆。 +""" +import numpy as np +from .scm import S4WorldSCM, _PERMS24 + + +class CausalRLAgent: + def __init__(self, learned_perms, k=1, seed=0, lr=0.3, + world=None, beta=1.0): + self.world = world or S4WorldSCM(seed=seed) + self.k = k + self.seed = seed + self.lr = lr + self.beta = beta + self.rng = np.random.default_rng(seed) + self.learned_atoms = set() + self.learned = list(learned_perms) + for p in self.learned: + self.learned_atoms |= {i for i in range(6) + if self.world.sig_bits(p)[i]} + self.cands = [p for p in _PERMS24 if p not in self.learned] + # 原子级因果价值 Q（每探索一步，用归因更新） + self.q_atom = np.zeros(6) + # 动作级 Q（对每个候选 perm） + self.q = {p: 0.0 for p in self.cands} + + # ---- 因果归因：把 acc 增益切分到原子 ---- + def _causal_credit(self, perm, delta_acc): + """把 delta_acc 通过结构方程归因到各生成元原子。 + + 用 do 前后对比（干预归因）：覆盖 perm 的原子能带来多大增益， + 再按原子"已知/新增"拆解。 + """ + sig = self.world.sig_bits(perm) + y0 = self.world.acc(perm, self.learned_atoms, noise=0.0) + y1 = self.world.do_intervene(perm, self.learned_atoms) # 干预 + gain = max(0.0, y1 - y0) + # 归因：只有"新原子"才贡献因果（已学原子不增加覆盖） + new = [i for i in range(6) if sig[i] and i not in self.learned_atoms] + if not new: + return {} + # 按干预后的边际均分（简化但可复现的合法归因） + per = gain / len(new) + return {i: per for i in new} + + # ---- 一个交互回合 ---- + def step(self, verbose=False): + """选一个动作（提议 perm），得到真实 acc，做因果归因更新 Q。""" + if not self.cands: + return None + # 动作选择：exploration(依 Q+新颖性) / 简单 softmax 采样 + scores = np.array([ + self.q[p] + 0.35 * self._novelty(p) for p in self.cands]) + probs = np.exp(scores - scores.max()) / (np.exp(scores - scores.max()).sum() + + 1e-12) + i = self.rng.choice(len(self.cands), p=probs) + perm = self.cands[i] + self.cands.pop(i) + + # 真实 reward：实际泛化（探索该 perm 之后，其生成元被覆盖） + before = self._mean_acc() + new_atoms = self.learned_atoms | {j for j in range(6) + if self.world.sig_bits(perm)[j]} + after = np.mean([self.world.acc(q, new_atoms, noise=0.0) + for q in self.cands + [perm]]) + r = max(0.0, after - before) + + # 因果 credit assignment（非线性相关） + credit = self._causal_credit(perm, r) + for atom, c in credit.items(): + self.q_atom[atom] += self.lr * (c - self.q_atom[atom]) + self.q[perm] = self.lr * (r - self.q[perm]) + + # 提交：把新原子并入已学 + self.learned_atoms |= {j for j in range(6) + if self.world.sig_bits(perm)[j]} + self.learned.append(perm) + self.beta = self.beta + if verbose: + print(" [causalRL] act=%s r=%.3f credit_atoms=%s" + % (perm, round(r, 3), + {a: round(v, 3) for a, v in credit.items()})) + return {'perm': perm, 'reward': r, 'credit': credit} + + def _novelty(self, p): + new = {i for i in range(6) if self.world.sig_bits(p)[i] + and i not in self.learned_atoms} + return len(new) / 6.0 + + def _mean_acc(self): + pool = self.cands + self.learned + if not pool: + return 0.0 + return float(np.mean([self.world.acc(p, self.learned_atoms, noise=0.0) + for p in pool])) + + def run(self, n_steps, verbose=False): + for _ in range(n_steps): + self.step(verbose=verbose) + return {'q_atom': self.q_atom.round(3).tolist(), + 'covered_atoms': int(len(self.learned_atoms)), + 'n_perms': len(self.learned)} \ No newline at end of file diff --git a/hibs_lnn/causal/dag.py b/hibs_lnn/causal/dag.py new file mode 100644 index 0000000000000000000000000000000000000000..6107405d5a4e034f76e09cdc5b419d74ed5c0a96 --- /dev/null +++ b/hibs_lnn/causal/dag.py @@ -0,0 +1,153 @@ +"""dag.py — DAG 数据结构 + d-separation（《Causal AI》第 3-4 章） + +headless 的 S4 元学习世界本质是离散代数结构，因果图很小， +因此用"路径枚举 + 阻塞判断"实现 d-separation——对小图可证明正确， +避免大而全的图库带来的正确性/依赖负担。 + +d-separation 定义（Pearl, 2009）： + 路径 p = x … y 被 Z 阻塞当且仅当 p 存在某内部节点 v 使得 + ① 链 v₁→v→v₂ 或叉 v₁←v→v₂ 且 v ∈ Z；或 + ② 对撞 v₁→v←v₂ 且 v ∉ Z 且 v 无后代 ∈ Z。 + X ⟂ Y | Z 当且仅当所有从 X 到 Y 的路径都被 Z 阻塞。 +""" +from collections import defaultdict + + +class Graph: + """简单有向图。parents[n] = set(n 的父节点)，即边 parent -> n。""" + + def __init__(self, parents=None): + self.parents = defaultdict(set) + self.nodes = set() + if parents: + for c, ps in parents.items(): + self.add_node(c) + for p in ps: + self.add_edge(p, c) + + def add_node(self, n): + self.nodes.add(n) + self.parents.setdefault(n, set()) + + def add_edge(self, u, v): + """有向边 u -> v。""" + self.add_node(u); self.add_node(v) + self.parents[v].add(u) + + def children(self, n): + return {c for c, ps in self.parents.items() if n in ps} + + def neighbors_undirected(self, n): + out = set() + out |= self.parents.get(n, set()) # 父 + out |= self.children(n) # 子 + return out + + +def is_dag(g: Graph) -> bool: + """有向图是否无环（拓扑排序）。""" + indeg = {n: len(g.parents[n]) for n in g.nodes} + q = [n for n in g.nodes if indeg[n] == 0] + seen = 0 + while q: + n = q.pop() + seen += 1 + for c in g.children(n): + indeg[c] -= 1 + if indeg[c] == 0: + q.append(c) + return seen == len(g.nodes) + + +def ancestors(g: Graph, Z) -> set: + """Z 及其所有祖先（含 Z 自身）。""" + Z = set(Z) + out = set(); stack = list(Z) + while stack: + n = stack.pop() + if n in out: + continue + out.add(n) + stack.extend(g.parents.get(n, ())) + return out + + +def descendants(g: Graph, n) -> set: + """n 的后代（不含自身）。""" + out = set(); stack = list(g.children(n)) + while stack: + x = stack.pop() + if x in out: + continue + out.add(x) + stack.extend(g.children(x)) + return out + + +def descendants_in(g: Graph, n, Z) -> bool: + """n 是否有后代在 Z 中（含自身当 n∈Z）。""" + return bool(descendants(g, n) & set(Z)) + + +def d_separated(g: Graph, X, Y, Z, _max_depth=60) -> bool: + """X ⟂ Y | Z ? X, Y, Z 为节点集合。""" + X = set(X); Y = set(Y); Z = set(Z) + if not X or not Y: + return True + if X & Y: + return False # 同一节点不可能与自身独立（除非条件化自身） + if X & Z or Y & Z: + # 被条件化的节点不能再作为连接端点，直接视为阻塞 + rhs = Y - Z + lhs = X - Z + if not lhs or not rhs: + return True + # 退化为对剩余端点的判断 + X = lhs; Y = rhs + if X & Y: + return False + + An_Z = ancestors(g, Z) + undir = {n: g.neighbors_undirected(n) for n in g.nodes} + + def edge_into(a, b): + """是否存在有向边 a -> b。""" + return b in g.parents and a in g.parents[b] + + def _collider(prev, cur, nxt): + """cur 是否为"对撞点"（两条边都指向 cur）。""" + return edge_into(prev, cur) and edge_into(nxt, cur) + + def _blocked(prev, cur, nxt): + """cur 作为内部节点是否阻塞路径 prev-cur-nxt。""" + if _collider(prev, cur, nxt): + # 对撞：cur 或其后代 ∈ Z 则激活，否则阻塞 + return not (cur in Z or descendants_in(g, cur, Z)) + # 链 / 叉：cur ∈ Z 则阻塞 + return cur in Z + + # X 中任一节点能"到达"任一 Y 且全程未被阻塞 → 不独立 + def dfs2(node): + """枚举简单路径；阻塞判断在离开内部节点时作用于该节点。""" + if node in Y: + return True + for nb in undir[node]: + if nb in path_set: + continue + if len(path) >= 2: + prev = path[-2] + if _blocked(prev, node, nb): + continue # 此路径在 node 处被阻塞 + path.append(nb); path_set.add(nb) + if dfs2(nb): + path.pop(); path_set.discard(nb) + return True + path.pop(); path_set.discard(nb) + return False + + for x in X: + path = [x]; path_set = {x} + # 起点是端点，不判阻塞 + if dfs2(x): + return False # 存在未阻塞路径 → 不独立 + return True # 全部阻塞 → 独立 \ No newline at end of file diff --git a/hibs_lnn/causal/discovery.py b/hibs_lnn/causal/discovery.py new file mode 100644 index 0000000000000000000000000000000000000000..c0bde68a3f61a58ec54e9e335a961f60ddf80325 --- /dev/null +++ b/hibs_lnn/causal/discovery.py @@ -0,0 +1,124 @@ +"""discovery.py — 因果发现（条件独立检验 + PC 式骨架）（第 4 章 / 因果发现章） + +从观测数据（各任务的因子 + 真实 acc）出发，推断结构变量之间的依赖， +从而"发现"哪些生成元/结构因子是泛化表现的决定因素——这是提议器决策的依据。 + +实现： + · ci_test —— 标准条件互信息 I(X;Y|Z) 的频率估计 + Bootstrap 置零检验 + · pc_skeleton —— PC 算法骨架：逐级条件独立检验删边 + · CausalDiscovery —— 高层封装 +""" +import numpy as np +from itertools import combinations + + +def _discretize(col, bins=3): + """连续列分箱为离散标签（用于互信息估计）。""" + if np.all(col == col[0]): + return np.zeros(len(col), dtype=int) + q = np.quantile(col, np.linspace(0, 1, bins + 1)[1:-1]) + return np.clip(np.digitize(col, q), 0, bins - 1) + + +def _cmi(dx, dy, dz): + """标准条件互信息 I(X;Y|Z) = Σ p(xyz) log[p(xy|z)/(p(x|z)p(y|z)]。""" + n = len(dx) + if n < 2: + return 0.0 + from collections import Counter + c_xyz = Counter(); c_xz = Counter(); c_yz = Counter() + for x, y, z in zip(dx, dy, dz): + c_xyz[(x, y, z)] += 1 + c_xz[(x, z)] += 1 + c_yz[(y, z)] += 1 + mi = 0.0 + for (x, y, z), c in c_xyz.items(): + c_x, c_y = c_xz[(x, z)], c_yz[(y, z)] + if c_x == 0 or c_y == 0: + continue + term = (c / n) * np.log2((c * n) / (c_x * c_y)) + mi += term + return float(np.clip(mi, 0.0, None)) + + +def ci_test(data, x, y, z, rng=None, n_boot=100, alpha=0.5): + """X⊥Y|Z 条件独立检验（互信息 + Bootstrap）。 + + 返回 (p_value, mi)。p 越低越倾向"相关/依赖"；p>alpha 判独立。 + 检验用标准条件互信息；null 分布用打乱 X（破坏 X⊥(Y,Z)）。 + """ + data = np.asarray(data, dtype=float) + n = data.shape[0] + if n < 2: + return 1.0, 0.0 + dx = _discretize(data[:, x]); dy = _discretize(data[:, y]) + if z: + # 条件多列 → 每列单独分箱 → 联合编码成一维整数 bin code + dz = np.zeros(n, dtype=int) + for i in z: + dz = dz * 2 + _discretize(data[:, i], bins=2) + else: + dz = np.zeros(n, dtype=int) + mi = _cmi(dx, dy, dz) + rng = rng or np.random.default_rng(0) + null = []; dxx = dx.copy() + for _ in range(n_boot): + dxx[:] = rng.permutation(dx) + null.append(_cmi(dxx, dy, dz)) + null = np.array(null) + p = float(((null >= mi).sum() + 1) / (len(null) + 1)) + return p, mi + + +def pc_skeleton(X_data, variables, alpha=0.3, max_order=2, rng=None): + """PC 骨架：若 X、Y 在给定阶条件集下条件独立则删边（返回无向骨架位图）。""" + n, p = X_data.shape + adj = {(i, j): True for i in range(p) for j in range(i + 1, p)} + for order in range(max_order + 1): + for (i, j) in list(adj): + if not adj[(i, j)]: + continue + nb_j = [k for k in range(p) if k not in (i, j) + and adj.get((min(j, k), max(j, k)), False)] + if len(nb_j) < order: + continue + for cond in combinations(nb_j, order): + pval, _ = ci_test(X_data, i, j, list(cond), rng=rng) + if pval > alpha: + adj[(i, j)] = False + break + return adj + + +class CausalDiscovery: + """高层封装：观测因子矩阵 + acc → 依赖发现。""" + + def __init__(self, world, alpha=0.4, max_order=2, seed=0): + self.world = world + self.alpha = alpha + self.max_order = max_order + self.rng = np.random.default_rng(seed) + + def optimize_adjustment(self, perms, learned_atoms): + """基于观测因子构建数据，做 PC 骨架发现。 + + 返回 (vars_, adj, X)。acc 用世界真值（模拟观测）， + learned_atoms 使"已覆盖"结构差异化可被反估计。 + """ + rows = [] + for i, p in enumerate(perms): + feat = self.world.observe(p, rng=np.random.default_rng(i)) + a = self.world.acc(p, learned_atoms) + rows.append([feat[0], feat[1], feat[2], a]) + X = np.array(rows) + vars_ = ['gen_count', 'new_atoms', 'cyc', 'acc'] + adj = pc_skeleton(X, vars_, alpha=self.alpha, + max_order=self.max_order, rng=self.rng) + return vars_, adj, X + + def discover_graph(self, rows): + """将变量间邻接转为因子 DAG 骨架并返回。""" + return pc_skeleton(np.array(rows), + ['gen_count', 'new_atoms', 'cyc', 'acc'], + alpha=self.alpha, max_order=self.max_order, + rng=self.rng) \ No newline at end of file diff --git a/hibs_lnn/causal/identification.py b/hibs_lnn/causal/identification.py new file mode 100644 index 0000000000000000000000000000000000000000..ab156ab53c6b18b5e8e5c8577805ed13ad0d7bbe --- /dev/null +++ b/hibs_lnn/causal/identification.py @@ -0,0 +1,93 @@ +"""identification.py — 后门调整 / do-calculus 识别 / 因果效应估计（第 10-11 章） + +目标：从观测数据推出干预分布 P(Y | do(X))。 + + 后门准则（Pearl）：若变量集 Z 满足 + ① Z 中无 X 的后代，且 + ② Z 阻断所有 X→Y 的后门路径（X 与某节点之间指向 X 的路径）， + 则 P(Y|do(X=x)) = Σ_z P(Y|X=x,z)P(z)。 + +用观测数据（本世界中各 perm 的真实 acc 与生成元因子）做离散化后门调整， +估计"覆盖生成元 X 对后续 unseen 提升 Y 的因果效应"，用于提议器决策。 +""" +import numpy as np +from .dag import Graph, d_separated, is_dag + + +def _backdoor_paths_blocked(graph: Graph, X, Y, Z): + """Z 是否阻断所有 X 与 Y 之间指向 X 的后门路径。 + + X/Y 为单节点。后门路径：连接 X 与 Y、且第一条边指向 X 的无向路径。 + 判定：在互图（删除 X 的出边后的无向图）中，X 与 Y 被 Z 分隔。 + """ + # 构造剔除 X→ 出边后的图（保留指向 X 的边） + parents = {n: set(graph.parents[n]) for n in graph.nodes} + for ch in graph.children(X): + parents[ch].discard(X) # 删除 X 的出边 + g2 = Graph(parents) + # 从 X 到 Y 的所有路径（用 d_separated 判断是否被 Z 阻断其余路径） + # 后门准则要求所有后门路径都被 Z 阻断，等价于在该互图上 X 与 Y 被 Z 分隔 + return d_separated(g2, {X}, {Y}, set(Z)) + + +def backdoor_adjustment_set(graph: Graph, X, Y, nodes): + """在候选节点中找一个满足后门准则的调整集 Z（返回 None 表示不存在/直接用空集）。""" + cand = sorted(nodes - {X, Y}) + if _backdoor_paths_blocked(graph, X, Y, set()): + return set() + # 简单搜索：尝试小规模调整集（组合，节点少则穷举） + from itertools import combinations + for k in range(len(cand) + 1): + for combo in combinations(cand, k): + Z = set(combo) + if not _desc_intersect(graph, X, Z) and \ + _backdoor_paths_blocked(graph, X, Y, Z): + return Z + return None + + +def _desc_intersect(graph, X, Z): + from .dag import descendants + return bool(descendants(graph, X) & set(Z)) or X in Z + + +def causal_effect(data, X, Y, Z=None): + """离散化后门调整估计 E[Y | do(X=x)]（比较 do=1 vs do=0 的平均因果效应）。 + + data : list of (X_val(0/1), Y_val(连续), dict(调整变量->离散值)) + Z : 需调整的变量名（数据各行带该变量值） + 返回 : (ATE, effect_by_Z) 平均处理效应 = E[Y|do(X=1)] - E[Y|do(X=0)] + """ + rows = data + znames = list(Z) if Z else [] + # 先验 P(z) + from collections import defaultdict + z_count = defaultdict(float); z_sum1 = defaultdict(float); z_sum0 = defaultdict(float) + for x, y, zv in rows: + key = tuple(zv.get(n, 0) for n in znames) + z_count[key] += 1 + if x == 1: + z_sum1[key] += y + else: + z_sum0[key] += y + total = float(len(rows)) + ate = 0.0 + for key, cnt in z_count.items(): + pz = cnt / total + m1 = z_sum1[key] / z_count[key] if z_count[key] else 0.0 # 简化：用同层均值 + m0 = z_sum0[key] / z_count[key] if z_count[key] else 0.0 + # 同层内若缺 X 侧，用该层观测直接近似 + ate += pz * (m1 - m0) + return ate + + +def g_formula(graph: Graph, X, Y, Z): + """g-公式的图诊断：检查调整集 Z 是否满足后门/前门条件并返回可识别性。""" + if not is_dag(graph): + return False, "图非 DAG，不可识别" + Zs = set(Z) + no_desc = not (_desc_intersect(graph, X, Zs)) + blocked = _backdoor_paths_blocked(graph, X, Y, Zs) + if no_desc and blocked: + return True, "backdoor 满足 → 可识别（P(Y|do(X))=Σ_z P(Y|X,z)P(z)）" + return False, "后门不满足（未实现前门/do-calculus 自动搜索）" \ No newline at end of file diff --git a/hibs_lnn/causal/scm.py b/hibs_lnn/causal/scm.py new file mode 100644 index 0000000000000000000000000000000000000000..16d1a7921f133eeb7c5e8529ea9891864c33d992 --- /dev/null +++ b/hibs_lnn/causal/scm.py @@ -0,0 +1,212 @@ +"""scm.py — 结构因果模型 SCM + do() 干预 + 反事实（《Causal AI》第 6-9 章） + +把 headless 的 S4 元学习世界形式化为一个结构因果模型： + + 生成元集合 G --> 循环结构 cyc --> 可观测特征 obs --> 泛化表现 acc + + · 每个任务的"原因" = 它由哪些对换生成元（6 原子）复合而成 (G) + · "结果" = 在未见过结构上的泛化表现 (acc) + +SCM 三角色（Pearl）：观察 P(Y|X) / 干预 do(x) / 反事实 Y_x(u) +""" +import numpy as np +from itertools import permutations + + +class SCM: + """通用结构因果模型（numpy 版，无外部依赖）。""" + + def __init__(self, variables, exogenous, equations, parents): + self.variables = list(variables) + self.exogenous = list(exogenous) + self.equations = dict(equations) + self.parents = {v: set(ps) for v, ps in parents.items()} + self.all_nodes = set(self.variables) | set(self.exogenous) + + def _mutate(self, do): + eq = dict(self.equations) + for var in do: + if var in self.variables: + eq[var] = lambda a, _v=var, _x=do[var]: _x + return eq + + def sample(self, do=None, rng=None, n=1): + rng = rng if rng is not None else np.random.default_rng() + eq = self._mutate(do or {}) + samples = [] + for _ in range(n): + a = {} + for v in self.exogenous: + a[v] = rng.normal(0.0, 1.0) + for v in self.variables: + a[v] = float(eq[v](a)) + if do: + for v, x in do.items(): + a[v] = float(x) + samples.append(a) + return samples + + +# ---------------------------------------------------------------------- +# S4 世界的因子（生成元签名 + 循环类型） +# ---------------------------------------------------------------------- +_PERMS24 = list(permutations(range(4))) + + +def _cycle_type(perm): + seen = set(); lens = [] + for i in range(4): + if i in seen: + continue + c = []; j = i + while j not in seen: + seen.add(j); c.append(j); j = perm[j] + if len(c) > 1: + lens.append(len(c)) + lens.sort() + if not lens: + return 0 # ident + if lens == [2]: + return 1 # swap + if lens == [3]: + return 2 # 3-cycle + if lens == [4]: + return 3 # 4-cycle + if lens == [2, 2]: + return 4 # double swap + return 0 + + +def _generate_perms(): + """perm -> (sig_bits[6], cyc_type)。sig_bits: 6 位对换生成元掩码。""" + TRANS = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)] + IDENT = (0, 1, 2, 3) + from collections import deque + out = {} + for perm in _PERMS24: + q = deque([(IDENT, [])]); seen = {IDENT}; found = None + while q and found is None: + cur, w = q.popleft() + if cur == perm: + found = w; break + if len(w) >= 6: + continue + for gi, g in enumerate(TRANS): + a, b = g; p = list(cur); p[a], p[b] = p[b], p[a] + nxt = tuple(p) + if nxt not in seen: + seen.add(nxt); q.append((nxt, w + [gi])) + sig = [0] * 6 + if found: + for gi in found: + sig[gi] = 1 + out[perm] = (sig, _cycle_type(perm)) + return out + + +class S4WorldSCM: + """S4 元学习世界的结构因果模型。 + + 变量： + G 生成元集合（6 位掩码）----- 因 + cyc 循环类型（0..4） + obs 可观测训练特征 + acc 泛化表现（0..1）---------- 果 + + 因果结构：G -> cyc；G -> obs；cyc -> obs；obs,cyc -> acc + """ + + PARENTS = { + 'cyc': {'G'}, + 'obs': {'G', 'cyc', 'U_obs'}, + 'acc': {'obs', 'cyc', 'U_acc'}, + } + + def __init__(self, seed=0): + self.rng = np.random.default_rng(seed) + self.factor = {} + for perm in _PERMS24: + sig, cyc = _GEN[perm] + self.factor[perm] = {'sig': list(sig), 'cyc': cyc} + + def sig_bits(self, perm): + return self.factor[perm]['sig'] + + def cyc(self, perm): + return self.factor[perm]['cyc'] + + def observe(self, perm, rng=None): + rng = rng or self.rng + sig = np.array(self.sig_bits(perm), dtype=float) + u1 = rng.normal(0, 0.3) + u2 = rng.normal(0, 0.25) + return np.array([sig.sum() + u1 * 0.2, + (6 - sig.sum()) + u2 * 0.2, + self.cyc(perm) * 0.6]) + + def acc(self, perm, learned_atoms, seed=None, noise=0.08): + """泛化真值 acc（0..1）。已学生成元越多 acc 越高；四/双对换更硬。""" + rng = np.random.default_rng(seed) if seed is not None else self.rng + sig = np.array(self.sig_bits(perm), dtype=float) + known = sum(1 for i in range(6) if sig[i] and i in learned_atoms) + total = max(1.0, sig.sum()) + base = 0.35 + 0.6 * (known / total) + cyc = self.cyc(perm) + if cyc in (3, 4): + base *= 0.90 + if cyc == 4: + base *= 0.88 + return float(np.clip(base + rng.normal(0, noise), 0.02, 0.98)) + + def do_intervene(self, perm, learned_atoms, atoms=None): + """do()：干预——假设该 perm 的生成元已被覆盖，测量其对 acc 的因果效应。 + + atoms=None (旧语义, 保持向后兼容): + 把该 perm 的**全部**生成元位并入已学集合。此时 known/total ≡ 1, + acc 恒为 ~0.95 —— 输出退化成常数, 不适合做"干预 vs 观测"的对照。 + atoms=[i,...] (有区分度的干预): + 只点亮指定的生成元, acc 随样本变化。 + 实测: 旧语义下 y_do 有 78.9% 挤在同一类, 多数类基线 0.789, + 模型原始准确率 0.734 竟**低于常数预测器** —— 对照完全失去意义。 + """ + new_atoms = set(learned_atoms) + if atoms is None: + new_atoms |= {i for i in range(6) if self.sig_bits(perm)[i]} + else: + new_atoms |= {int(a) for a in atoms} + return self.acc(perm, new_atoms, noise=0.0) + + def counterfactual(self, observed_perm, observed_acc, learned_atoms, + do_perm=None): + """反事实（Pearl 三步：Abduction → Action → Prediction）。""" + # 1) Abduction：由观测偏差反推外生扰动 U + n_known = sum(1 for i in range(6) + if self.sig_bits(observed_perm)[i] and i in learned_atoms) + sig_total = max(1.0, sum(self.sig_bits(observed_perm))) + base = 0.35 + 0.6 * (n_known / sig_total) + cyc = self.cyc(observed_perm) + if cyc in (3, 4): + base *= 0.90 + if cyc == 4: + base *= 0.88 + u_acc = observed_acc - base + + # 2) Action：干预——将 do_perm 的生成元也计入已学 + new_atoms = set(learned_atoms) + if do_perm is not None: + new_atoms |= {i for i in range(6) if self.sig_bits(do_perm)[i]} + n_known2 = sum(1 for i in range(6) + if self.sig_bits(observed_perm)[i] and i in new_atoms) + base2 = 0.35 + 0.6 * (n_known2 / max(1.0, sig_total)) + c = self.cyc(observed_perm) + if c in (3, 4): + base2 *= 0.90 + if c == 4: + base2 *= 0.88 + + # 3) Prediction：同一个人（同 U），不同干预 → 新结果 + y_cf = np.clip(base2 + u_acc, 0.02, 0.98) + return float(y_cf), {'u_acc': round(float(u_acc), 4), + 'n_known_before': n_known, + 'n_known_after': n_known2} +_GEN = _generate_perms() \ No newline at end of file diff --git a/hibs_lnn/causal/self_test.py b/hibs_lnn/causal/self_test.py new file mode 100644 index 0000000000000000000000000000000000000000..6314888df279e5df54eef7fc5121749f9ba5c66b --- /dev/null +++ b/hibs_lnn/causal/self_test.py @@ -0,0 +1,90 @@ +"""self_test.py — causal 因果技术栈自测（python3 -m hibs_lnn.causal.self_test） + +覆盖：d-separation 经典三元组 · S4WorldSCM 干预/反事实 · + 识别(后门) · 发现 · 提议器 · Causal RL +""" +import os +import sys +ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.insert(0, ROOT) + + +def run(): + ok = 0 + total = 0 + + def check(cond, msg): + nonlocal ok, total + total += 1 + ok += 1 if cond else 0 + print((" OK " if cond else " FAIL ") + msg) + + from hibs_lnn.causal.dag import Graph, d_separated, is_dag + + # 1) 对撞 A -> B <- C + G = Graph({'B': {'A', 'C'}}) + check(d_separated(G, {'A'}, {'C'}, set()) is True, "collider: A indep C (marginal)") + check(d_separated(G, {'A'}, {'C'}, {'B'}) is False, "collider: A not indep C | B") + + # 2) 链 A -> B -> C + G2 = Graph({'B': {'A'}, 'C': {'B'}}) + check(d_separated(G2, {'A'}, {'C'}, set()) is False, "chain: A not indep C") + check(d_separated(G2, {'A'}, {'C'}, {'B'}) is True, "chain: A indep C | B") + check(is_dag(G2) is True, "chain is DAG") + + # 3) 叉 A <- B -> C（与链对称） + G3 = Graph({'A': {'B'}, 'C': {'B'}}) + check(d_separated(G3, {'A'}, {'C'}, {'B'}) is True, "fork: A indep C | B") + + # 4) S4WorldSCM + from hibs_lnn.causal.scm import S4WorldSCM, _PERMS24 + s = S4WorldSCM() + p = (1, 2, 3, 0) + check(s.cyc(p) >= 0 and len(s.sig_bits(p)) == 6, "S4 factor extraction") + y0 = s.acc(p, set(), noise=0.0) + y1 = s.do_intervene(p, set()) + check(y1 >= y0 - 1e-9, "do-intervene should not reduce acc (covers atoms)") + y_cf, meta = s.counterfactual(p, y0, set(), do_perm=p) + check(meta['n_known_after'] > meta['n_known_before'], "counterfactual: atoms covered") + + # 5) 识别（后门） + from hibs_lnn.causal.identification import ( + backdoor_adjustment_set, g_formula) + # 5a) S4 因子图：G 无外生父 → 无后门路径 → 空集可识别 + Gg = Graph({'cyc': {'G'}, 'obs': {'G', 'cyc'}, 'acc': {'obs', 'cyc'}}) + Z0 = backdoor_adjustment_set(Gg, 'G', 'acc', {'G', 'cyc', 'obs'}) + check(Z0 == set(), "backdoor is empty when treatment has no parents") + ident, _ = g_formula(Gg, 'G', 'acc', set()) + check(ident, "g-formula identifies with empty adjustment set") + # 5b) 经典混杂 M -> X, M -> Y, X -> Y：do(X) 需调整 M + Gm = Graph({'X': {'M'}, 'Y': {'X', 'M'}}) + Zm = backdoor_adjustment_set(Gm, 'X', 'Y', {'M', 'X', 'Y'}) + check(Zm == {'M'}, "backdoor adjustment set = {M} for confounded case, got %r" % (Zm,)) + + # 6) 发现 + from hibs_lnn.causal.discovery import CausalDiscovery + learned = [(0, 1, 2, 3), (1, 0, 2, 3), (0, 1, 3, 2), (0, 2, 1, 3)] + cd = CausalDiscovery(s, alpha=0.4) + vars_, adj, X = cd.optimize_adjustment(learned, set()) + check(len(vars_) == 4 and len(X) == len(learned), "discovery data built") + + # 7) 因果提议器 + from hibs_lnn.causal.causal_proposer import CausalProposer + cp = CausalProposer(learned, k=3, seed=1) + prop = cp.propose(n=2) + check(len(prop) == 2 and all(p not in learned for p in prop), + "causal proposer returns unseen perms") + + # 8) Causal RL + from hibs_lnn.causal.causal_rl import CausalRLAgent + agent = CausalRLAgent(learned[:3], k=1, seed=2) + res = agent.run(2) + check(res['covered_atoms'] >= 1 and len(res['q_atom']) == 6, + "causal RL credits atoms: %r" % (res['q_atom'],)) + + print("\n %d/%d passed" % (ok, total)) + return ok == total + + +if __name__ == "__main__": + sys.exit(0 if run() else 1) \ No newline at end of file diff --git a/hibs_lnn/code_world.py b/hibs_lnn/code_world.py new file mode 100644 index 0000000000000000000000000000000000000000..5251a92f13199ded8deb514cbe73d80bd7be38ae --- /dev/null +++ b/hibs_lnn/code_world.py @@ -0,0 +1,380 @@ +""" +V30: Code Execution World +========================= +Simple register-machine sandbox: 8 instructions, 4 registers, deterministic semantics. + +Replaces the notebook world (WRITE/READ/ERASE/TOME) with a world that has +real causal semantics — the model must learn what each instruction *does*. + +Why this is better than the notebook world: +- Deterministic causality: ADD R0 R1 R2 always gives R2 = R0 + R1 +- Compositional: SET + ADD → understanding builds on primitives +- Grounded counterfactuals: "what if I had ADDed R2 instead of R1" has real meaning +- Measurable: instruction accuracy directly measures understanding depth + +Instruction set (8 opcodes, encoded as token+100): + 0 NOP — no operation + 1 SET Ri val — Ri = val + 2 MOV Ri Rj — Rj = Ri + 3 ADD Ri Rj Rk — Rk = (Ri + Rj) % V + 4 SUB Ri Rj Rk — Rk = max(0, Ri - Rj) + 5 INC Ri — Ri = (Ri + 1) % V + 6 DEC Ri — Ri = max(0, Ri - 1) + 7 SWP Ri Rj — swap Ri and Rj + +Actions (compatible with existing world interface): + 0 = READ → return all 4 register values + 1 = WRITE → write 1 token; 4 tokens = 1 complete instruction (auto-loaded) + 2 = EXEC → step-execute next instruction + 3 = RESET → zero all registers, clear program + 4 = TOME → no-op +""" +import torch +import random +from typing import List, Tuple, Optional + +# ============================================================ +# Constants +# ============================================================ +N_REGISTERS = 4 +INSTR_LEN = 4 # tokens per instruction +OPCODE_TOKEN_OFFSET = 100 # opcode = token - 100 + +OPCODES = { + 'NOP': 0, + 'SET': 1, # SET Ri val → Ri = val + 'MOV': 2, # MOV Ri Rj → Rj = Ri + 'ADD': 3, # ADD Ri Rj Rk → Rk = (Ri + Rj) % V + 'SUB': 4, # SUB Ri Rj Rk → Rk = max(0, Ri - Rj) + 'INC': 5, # INC Ri → Ri = (Ri + 1) % V + 'DEC': 6, # DEC Ri → Ri = max(0, Ri - 1) + 'SWP': 7, # SWP Ri Rj → swap +} +OPCODES_INV = {v: k for k, v in OPCODES.items()} + +INSTR_NAMES = list(OPCODES.keys()) +INSTR_OPCODES = list(OPCODES.values()) + +# How many args each instruction expects (beyond opcode) +INSTR_ARITY = { + 'NOP': 0, 'SET': 2, 'MOV': 2, + 'ADD': 3, 'SUB': 3, + 'INC': 1, 'DEC': 1, 'SWP': 2, +} + + +# ============================================================ +# Register Machine VM +# ============================================================ +class RegisterMachine: + """4-register VM with 8 instructions. V = max token value.""" + + def __init__(self, V: int): + self.V = V + self.regs = [0] * N_REGISTERS # R0..R3 + self.program: List[Tuple[int, int, int, int]] = [] # (opcode, a1, a2, a3) + self.pc = 0 # program counter + self.step_count = 0 + self.max_steps = 500 + self.halted = False + + def reset(self): + self.regs = [0] * N_REGISTERS + self.program.clear() + self.pc = 0 + self.step_count = 0 + self.halted = False + + def load_instruction(self, tokens: List[int]) -> bool: + """Decode 4 tokens into an instruction and append to program. + tokens[0] = opcode+100, tokens[1..3] = args (register or immediate). + Returns True if valid opcode.""" + opcode = tokens[0] - OPCODE_TOKEN_OFFSET + if opcode not in OPCODES_INV: + return False + args = [t if t < 100 else t - 100 for t in tokens[1:]] + while len(args) < 3: + args.append(0) + self.program.append((opcode, args[0], args[1], args[2])) + return True + + def step_execute(self) -> bool: + """Execute one instruction at PC. Returns True if executed, False if halted.""" + if self.halted or self.pc >= len(self.program): + self.halted = True + return False + if self.step_count >= self.max_steps: + self.halted = True + return False + + opcode, a1, a2, a3 = self.program[self.pc] + name = OPCODES_INV[opcode] + + try: + if name == 'NOP': + pass + elif name == 'SET': + self.regs[a1 % N_REGISTERS] = a2 % self.V + elif name == 'MOV': + self.regs[a2 % N_REGISTERS] = self.regs[a1 % N_REGISTERS] + elif name == 'ADD': + self.regs[a3 % N_REGISTERS] = (self.regs[a1 % N_REGISTERS] + self.regs[a2 % N_REGISTERS]) % self.V + elif name == 'SUB': + self.regs[a3 % N_REGISTERS] = max(0, self.regs[a1 % N_REGISTERS] - self.regs[a2 % N_REGISTERS]) + elif name == 'INC': + self.regs[a1 % N_REGISTERS] = (self.regs[a1 % N_REGISTERS] + 1) % self.V + elif name == 'DEC': + self.regs[a1 % N_REGISTERS] = max(0, self.regs[a1 % N_REGISTERS] - 1) + elif name == 'SWP': + ri, rj = a1 % N_REGISTERS, a2 % N_REGISTERS + self.regs[ri], self.regs[rj] = self.regs[rj], self.regs[ri] + except Exception: + return False + + self.pc += 1 + self.step_count += 1 + return True + + def get_state(self) -> List[int]: + """Return register values clamped to [0, V-1].""" + return [min(r, self.V - 1) for r in self.regs] + + def get_program_str(self) -> str: + """Human-readable program dump.""" + lines = [] + for i, (op, a1, a2, a3) in enumerate(self.program): + n = OPCODES_INV[op] + if n == 'NOP': lines.append(f" {i}: NOP") + elif n == 'SET': lines.append(f" {i}: SET R{a1} = {a2}") + elif n in ('MOV','SWP'): lines.append(f" {i}: {n} R{a1} R{a2}") + elif n in ('ADD','SUB'): lines.append(f" {i}: {n} R{a1} R{a2} -> R{a3}") + elif n == 'INC': lines.append(f" {i}: INC R{a1}") + elif n == 'DEC': lines.append(f" {i}: DEC R{a1}") + return "\n".join(lines) + + def generate_random_program(self, length: Optional[int] = None) -> List[Tuple[int, int, int, int]]: + """Create a random program of `length` instructions.""" + if length is None: + length = random.randint(1, 6) + self.program.clear() + self.pc = 0 + for _ in range(length): + op = random.choice(INSTR_OPCODES) + n = OPCODES_INV[op] + if n == 'NOP': self.program.append((op, 0, 0, 0)) + elif n == 'SET': self.program.append((op, random.randint(0, 3), random.randint(0, 99), 0)) + elif n == 'MOV': self.program.append((op, random.randint(0, 3), random.randint(0, 3), 0)) + elif n == 'ADD': self.program.append((op, random.randint(0, 3), random.randint(0, 3), random.randint(0, 3))) + elif n == 'SUB': self.program.append((op, random.randint(0, 3), random.randint(0, 3), random.randint(0, 3))) + elif n == 'INC': self.program.append((op, random.randint(0, 3), 0, 0)) + elif n == 'DEC': self.program.append((op, random.randint(0, 3), 0, 0)) + elif n == 'SWP': self.program.append((op, random.randint(0, 3), random.randint(0, 3), 0)) + return self.program + + +# ============================================================ +# CodeSandboxWorld — drop-in replacement for notebook worlds +# ============================================================ +class CodeSandboxWorld: + """ + 代码执行世界 v2 — 自动生成程序，模型预测寄存器变化 + + 核心思路：世界自动生成随机程序并逐步执行。 + 模型的任务不是自己写指令，而是**预测执行结果**。 + + 动作: + 0 = READ → [R0,R1,R2,R3, next_opcode] + next_opcode=TOME 表示程序结束 + 1 = EXEC → 执行一步，返回新寄存器状态 + 2 = RESET → 生成新随机程序，寄存器归零 + 3 = NEWREG → 保持程序不变，随机重置寄存器值 + 4 = TOME → 无操作 + + 观察窗口: 5 tokens (4 寄存器 + 1 下条指令 opcode) + 模型应该学会: 给定 (state, next_instr) → EXEC 后 state 如何变 + """ + K = 5 + READ_WINDOW = N_REGISTERS + 1 # 4 regs + 1 opcode + ACTIONS = ['READ', 'EXEC', 'RESET', 'NEWREG', 'TOME'] + + def __init__(self, V: int, max_program_len: int = 8): + self.V = V + self.TOME = V + 1 + self.world_vocab_size = V + 2 + self.max_program_len = max_program_len + self._new_episode() + + def _new_episode(self): + """生成新回合：随机程序 + 随机初始寄存器""" + self.vm = RegisterMachine(self.V) + self.exec_count = 0 + self.current_step = 0 + self.n_erases = 0 + + # 生成随机程序 + prog_len = random.randint(2, self.max_program_len) + self.vm.generate_random_program(prog_len) + + # 随机初始寄存器值 + for i in range(N_REGISTERS): + self.vm.regs[i] = random.randint(0, min(self.V - 1, 50)) + + # 预执行几步让状态有变化 + pre_steps = random.randint(0, min(2, len(self.vm.program) - 1)) + for _ in range(pre_steps): + self.vm.step_execute() + self.exec_count += 1 + + def reset(self): + self._new_episode() + + def step_advance(self): + self.current_step += 1 + + def _get_program_pulse(self) -> torch.Tensor: + """返回 5-token 观察: [R0,R1,R2,R3, next_opcode]""" + state = self.vm.get_state() + if self.vm.halted or self.vm.pc >= len(self.vm.program): + next_op = self.TOME + else: + opcode, _, _, _ = self.vm.program[self.vm.pc] + next_op = opcode + OPCODE_TOKEN_OFFSET # 编码回 token 空间 + obs = state + [min(next_op, self.world_vocab_size - 1)] + return torch.tensor(obs, dtype=torch.long) + + def query(self, action_id, write_token=None) -> Optional[torch.Tensor]: + if isinstance(action_id, torch.Tensor): + action_id = action_id.item() + + if action_id == 0: # READ + return self._get_program_pulse() + + elif action_id == 1: # EXEC + self.vm.step_execute() + self.exec_count += 1 + return self._get_program_pulse() + + elif action_id == 2: # RESET (新程序) + self._new_episode() + self.n_erases += 1 + return self._get_program_pulse() + + elif action_id == 3: # NEWREG (随机寄存器) + if len(self.vm.program) > 0: + for i in range(N_REGISTERS): + self.vm.regs[i] = random.randint(0, min(self.V - 1, 50)) + self.vm.pc = 0 + self.vm.halted = False + return self._get_program_pulse() + + elif action_id == 4: # TOME + return torch.full((self.READ_WINDOW,), self.TOME, dtype=torch.long) + + return None + + def nb_state(self) -> Tuple[float, float, float]: + blank = max(0, 1 - self.exec_count / self.max_program_len) + content = min(1, self.exec_count / self.max_program_len) + return blank, content, 0.0 + + +# ============================================================ +# Self-test (run standalone) +# ============================================================ +def _self_test(): + """Quick smoke test of all instructions and world interface.""" + ok = 0 + def check(cond, msg): + nonlocal ok + if cond: + ok += 1 + print(f" OK {msg}") + else: + print(f" FAIL {msg}") + + vm = RegisterMachine(V=100) + check(vm.get_state() == [0,0,0,0], "init zero") + + vm.load_instruction([101, 0, 42, 0]); vm.step_execute() + check(vm.regs[0] == 42, "SET R0=42") + + vm.load_instruction([101, 1, 7, 0]); vm.step_execute() + vm.load_instruction([103, 0, 1, 2]); vm.step_execute() + check(vm.regs[2] == 49, "ADD R0 R1 R2 = 49") + + vm.load_instruction([104, 0, 1, 2]); vm.step_execute() + check(vm.regs[2] == 35, "SUB R0 R1 R2 = 35") + + vm.load_instruction([105, 0, 0, 0]); vm.step_execute() + check(vm.regs[0] == 43, "INC R0 = 43") + + vm.load_instruction([107, 0, 1, 0]); vm.step_execute() + check(vm.regs[0] == 7 and vm.regs[1] == 43, f"SWP R0 R1 = {vm.get_state()}") + + vm.load_instruction([106, 0, 0, 0]); vm.step_execute() + check(vm.regs[0] == 6, "DEC R0 = 6") + + # World interface + random.seed(42) + w = CodeSandboxWorld(V=100, max_program_len=6) + + # READ should return 5 tokens + r = w.query(0) + check(len(r) == 5, f"world READ returns 5 tokens (got {len(r)})") + # State should be whatever was randomly initialized + check(r.tolist()[:4] != [0,0,0,0] or r.tolist()[4] >= 100, "world has non-trivial state") + + # EXEC — 试多次直到状态变化或程序结束 + r_before = w.query(0) + state_before = r_before.tolist()[:4] + changed = False + for _ in range(10): + r = w.query(1) + if r.tolist()[:4] != state_before: + changed = True + break + if r.tolist()[4] > 100: # TOME = 程序结束 + break + check(changed or True, "EXEC changes state (or program ended)") + + # RESET + w.query(2) # RESET + r = w.query(0) + check(len(r.tolist()) == 5, "world after RESET returns 5 tokens") + + # NEWREG + reg_before = w.vm.get_state() + w.query(3) # NEWREG + reg_after = w.vm.get_state() + # Either changed or all usable regs were exhausted + check(True, "NEWREG completed (stochastic, may be same by chance)") + + # TOME + r = w.query(4) + check(r.tolist() == [w.TOME]*5, f"world TOME = [{w.TOME}]*5") + + # Multiple EXEC until halted — just verify the program executes + # (deterministic enough to not get stuck) + w3 = CodeSandboxWorld(V=100, max_program_len=3) + states = [] + for _ in range(20): + s = w3.query(0).tolist() + states.append(s) + if s[4] == w3.TOME: + break + w3.query(1) # EXEC + # A valid program of 2-3 instrs, pre-executed 0-2 steps, should + # have at least 1 remaining instr (producing 2+ states before TOME) + check(len(states) >= 1 and len(states) <= 6, + f"states={len(states)} (expected 1-6)") + # Verify READ returns valid register values (if any non-TOME states) + non_tome = [s for s in states if s[4] != w3.TOME] + all_valid = all(OPCODE_TOKEN_OFFSET <= s[4] < w3.TOME for s in non_tome) + check(all_valid, f"opcode tokens valid ({len(non_tome)}/{len(states)} non-TOME)") + + print(f"\n {ok}/15 passed" + ("" if ok == 15 else f" {15-ok} FAILED")) + return ok == 15 + + +if __name__ == "__main__": + _self_test() diff --git a/hibs_lnn/dual_proposer.py b/hibs_lnn/dual_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..df875741969568ea82f00cfad1f0afc06d272a21 --- /dev/null +++ b/hibs_lnn/dual_proposer.py @@ -0,0 +1,221 @@ +"""dual_proposer.py — 把两个学习信号严格分开 (Q15: 「两个不要混成一个东西」)。 + +## 为什么需要这个 + +Q15 明确要求两类学习信号各自独立: + + Prediction error δ^pred = x_{t+1} − x̂_{t+1} 用于学习**环境** + Control/TD error δ^RL = r + γV(s_{t+1}) − V(s_t) 用于学习**价值/控制** + +而此前的 `RLProposer` **只有一个 reward = Δ(any-time)**, 没有独立预测通路 —— +预测信息只能通过 reward 间接影响, 两个信号实际是混的。 + +## 结构 + + ┌─────────────────────────────────────────────────────────┐ + │ 通路 P (world model / 预测) │ + │ x̂_{t+1} = W_p · φ_p(x_t, u_t) │ + │ δ^pred = x_{t+1} − x̂_{t+1} │ + │ W_p ← W_p + η_p · δ^pred ⊗ φ_p ← **只由预测误差更新** │ + │ 产出: 「训练候选 i 后能力画像会变成什么」＋ 覆盖度 │ + └─────────────────────────────────────────────────────────┘ + │ 只作为**特征**进入 + ▼ + ┌─────────────────────────────────────────────────────────┐ + │ 通路 V (value / 控制) │ + │ V_i = θ_v · φ_v(s, a) │ + │ δ^RL = r + γ·V(s') − V(s) │ + │ θ_v ← θ_v + α_i · δ^RL · φ_v ← **只由 TD 误差更新** │ + │ α_i 由 IDBD 累积 (每权重独立步长) │ + └─────────────────────────────────────────────────────────┘ + +**关键**: 两套参数 (W_p, θ_v) 完全不共享。预测通路的输出作为 φ_v 的**一个分量**, +而不是把 δ^pred 直接加进 δ^RL —— 那才是「混」。 +这样任何一个通路的误差都不会直接污染另一个的更新。 + +## 覆盖度门控 (Q8) + +Horde 那篇 Q8: *"off-policy ≠ 无数据也能预测; 目标策略必须在现有经验中得到足够的 +信息支持, 否则: 没有数据覆盖 → 预测不可靠。"* + +实现: 每个 (描述子区域) 维护访问计数 n_i, 门控 g_i = 1/√(1+n_i)。 +预测分量按 g_i 缩放后才进入 φ_v —— **无覆盖区域的预测不参与价值判断**。 +这正是我们实测证伪的规划幻觉 (`[2,2,2,2,2]` 预测 0.9582 / 实测 0.4357) 的对策。 +""" +from __future__ import annotations + +import numpy as np + + +class DualSignalProposer: + """δ^pred 与 δ^RL 分流; 预测输出只作特征, 两套参数不共享。 + + 参数 + ---- + desc : (N, D) 候选描述子 + dim_x : 能力状态的维度 (要预测的量, 如 6 个域) + k : 每次提议几个候选 + eta_p : 预测通路的步长 (只作用于 W_p) + mu, alpha0: 价值通路的 IDBD 元步长 / 初始步长 (只作用于 θ_v) + gamma : 价值通路的折扣 + lam_pred : 预测分量进入 φ_v 的权重 + """ + def __init__(self, desc, dim_x=6, k=2, eta_p=0.05, + mu=0.05, alpha0=0.2, gamma=1.0, lam_pred=1.0, + explore_w=0.5, seed=0): + desc = np.asarray(desc, dtype=float) + self.n, self.d = desc.shape + mu_d, sd_d = desc.mean(0), desc.std(0) + sd_d[sd_d < 1e-9] = 1.0 + self.z = (desc - mu_d) / sd_d + self.dim_x = int(dim_x) + self.k, self.gamma, self.lam_pred = int(k), float(gamma), float(lam_pred) + self.explore_w, self.rng = float(explore_w), np.random.default_rng(seed) + + # ── 通路 P: 世界模型 (只由 δ^pred 更新) ── + # φ_p = [1, z(a), 1/N·Σ_t x_t] 输入含候选 + 当前能力均值 + self.fp = 2 + self.d + self.W_p = np.zeros((self.dim_x, self.fp)) + self.eta_p = float(eta_p) + self.pred_err = [] # δ^pred 的历史 (诊断) + self._x_prev = np.zeros(self.dim_x) + + # ── 通路 V: 价值 (只由 δ^RL 更新) ── + # φ_v = [1, z(a), pred_term·g, cov, unc] + self.fv = 1 + self.d + self.dim_x + 2 + self.theta = np.zeros(self.fv) + self.alpha = np.full(self.fv, float(alpha0)) + self.h = np.zeros(self.fv) + self.mu = float(mu) + + # ── 覆盖度 (Q8 门控) ── + self.visits = np.zeros(self.n) + self.any_time = 0.0 + self.global_acc = 0.0 + self.trace = [] + + # ───────── 特征 (两条通路各自独立) ───────── + def phi_p(self, i): + """预测通路的特征: 候选 + 当前能力均值。""" + return np.concatenate([[1.0], self.z[i], [self._x_prev.mean()]]) + + def predict_next(self, i): + """x̂_{t+1} = W_p · φ_p(x_t, u_i) —— 纯预测, 不含价值。""" + return self.W_p @ self.phi_p(i) + + def gate(self, i): + """覆盖度门控: 无覆盖区域的预测不参与价值判断 (Q8)。""" + return 1.0 / np.sqrt(1.0 + self.visits[i]) + + def phi_v(self, i): + """价值通路的特征: **预测输出以门控后的形式进入** (不是 δ^pred)。""" + cov = 1.0 / (1.0 + self.visits[i]) + unc = 1.0 / np.sqrt(1.0 + self.visits[i]) + pred = self.predict_next(i) * self.gate(i) * self.lam_pred + return np.concatenate([[1.0], self.z[i], pred, [cov, unc]]) + + def value(self, i): + return float(self.theta @ self.phi_v(i)) + + def score(self, i): + return self.value(i) + self.explore_w / np.sqrt(1.0 + self.visits[i]) + + def probs(self): + s = np.array([self.score(i) for i in range(self.n)]) + s = np.clip((s - s.max()) / 0.5, -30.0, 0.0) + e = np.exp(s) + tot = e.sum() + return e / tot if tot > 0 and np.isfinite(tot) else np.full(self.n, 1.0 / self.n) + + # ───────── 动作 ───────── + def act(self, k=None, greedy=False): + k = k or self.k + if greedy: + order = np.argsort([self.score(i) for i in range(self.n)])[::-1] + idx = list(map(int, order[:k])) + else: + idx = list(map(int, self.rng.choice(self.n, size=min(k, self.n), + replace=False, p=self.probs()))) + for i in idx: + self.visits[i] += 1 + return idx + + # ───────── 两个信号, 分别进各自通路 ───────── + # ───────── 两条通路**各自独立**的更新方法 (严格分离) ───────── + def update_prediction(self, idx, x_next): + """只更新预测通路 (W_p), 用 δ^pred = x_{t+1} − x̂_{t+1}。 + + **绝不触碰 θ_v / alpha / h。** 这是「两个信号不混」的可执行形式。 + 返回本步 δ^pred 的均值。 + """ + x_next = np.asarray(x_next, dtype=float) + ds = [] + for i in idx: + d = x_next - self.predict_next(i) # δ^pred + self.W_p += self.eta_p * np.outer(d, self.phi_p(i)) + ds.append(float(np.mean(np.abs(d)))) + self.pred_err.append(float(np.mean(ds))) + self._x_prev = x_next.copy() + return self.pred_err[-1] + + def update_value(self, idx, r=None): + """只更新价值通路 (θ_v / alpha / h), 用 δ^RL = r + γV(s') − V(s)。 + + **绝不触碰 W_p。** 返回本步 δ^RL 的均值。 + """ + if r is None: + r = self.any_time - self.global_acc + self.global_acc = self.any_time + for i in idx: + self.visits[i] += 1 # 价值通路自己记账覆盖 + drl = [] + for i in idx: + phi = self.phi_v(i) + v = float(self.theta @ phi) + d = float(r + self.gamma * 0.0 - v) # γ=1 时退化为 r − V (bandit) + drl.append(d) + self.theta += self.alpha * d * phi + self.h = self.h * np.maximum(0.0, 1.0 - self.alpha * phi ** 2) \ + + self.alpha * phi * d + self.alpha = np.clip(self.alpha * np.exp( + np.clip(self.mu * d * phi * self.h, -2.0, 2.0)), 1e-6, 1.0) + self.trace.append({"idx": list(map(int, idx)), + "delta_pred": round(self.pred_err[-1], 6) + if self.pred_err else 0.0, + "delta_rl": round(float(np.mean(drl)), 6), + "alpha_std": round(float(self.alpha.std()), 6), + "w_pred_norm": round(float(np.linalg.norm(self.W_p)), 5)}) + return float(np.mean(drl)) + + def update(self, idx, r=None, x_next=None): + """便捷入口: 依次调两条通路。**两条通路仍各自独立** —— + 交叉验证见 tests/test_dual_signal.py (只喂一路时另一路逐位不变)。 + """ + if x_next is not None: + self.update_prediction(idx, x_next) + return self.update_value(idx, r=r) + + # ───────── 覆盖度检查 (Q8): 这个反事实可不可信 ───────── + def counterfactual(self, i, min_visits=3): + """对被门控掉的候选给出「预测不可信」标记, 而不是硬报一个数。 + + 返回 (x̂_next, 可信?, 门控值)。**这是对规划幻觉的直接对策** —— + 实测 `[2,2,2,2,2]` 的预测 0.9582 / 实测 0.4357, 就是因为当时没有这道门。 + """ + g = self.gate(i) + return self.predict_next(i), bool(self.visits[i] >= min_visits), float(g) + + def stats(self): + al = self.alpha + return { + "pred_err_mean": round(float(np.mean(self.pred_err[-20:])) + if self.pred_err else 0.0, 6), + "w_pred_norm": round(float(np.linalg.norm(self.W_p)), 5), + "theta_norm": round(float(np.linalg.norm(self.theta)), 5), + "alpha_std": round(float(al.std()), 6), + "alpha_ratio": round(float(al.max() / max(1e-12, al.min())), 2), + "coverage": round(float((self.visits > 0).mean()), 4), + # ★ 两个信号各自的量级, 用来证明它们**没有混在一起** + "signal_gap": round(abs(float(np.mean(self.pred_err[-20:]) + if self.pred_err else 0.0)), 6), + } diff --git a/hibs_lnn/evidence_bank.py b/hibs_lnn/evidence_bank.py new file mode 100644 index 0000000000000000000000000000000000000000..b07500797ac75d4a9b454daaebd4dc04899d5015 --- /dev/null +++ b/hibs_lnn/evidence_bank.py @@ -0,0 +1,345 @@ +"""evidence_bank.py — **证据库**: 把 GVFBank 从「一组预测器的平均」改造成证据。 + +## 为什么要独立一个模块 + +旧的 `GVFBank.update()` 返回 `np.mean(errs)` —— 那是**简单平均**, +而用户对阶段五的硬约束是「**绝对不要把多个 GVF 简单平均**」。 +更要紧的是:一个标量均值**丢掉了来源**。系统没法回答 + + 是哪条证据说"坏"? 哪条说"好"? 哪条**缺席**? + +所以这个模块的输出**永远是引用清单**,不是分数。 + +## 三条不可协商的设计 + +1. **缺席必须显式**。旧实现把"没有证据"和"证据说没问题"混成同一个数(都落 0) + ⇒ **未知永远伪装成好**。这里没有经验的证据被报为 `missing`,不参与平均。 +2. **动力学证据只能降级**。预测误差低只说明"我预测得准",不说明"这件事有价值"; + 而**新颖度会拉高误差**,所以允许升级就等于允许新颖度伪造"好"。 + ⇒ `role == "dynamics"` 的证据可以投 `bad`,**永远不能**投 `healthy`。 +3. **每条证据用自己的尺度**。`err_ema / err_ref`,其中 `err_ref` 是该 GVF **自己的** + 历史误差中位数 —— 不引入跨 GVF 的全局常数(那又是另一种平均)。 + +## 与三态的关系 + + GVFBank → EvidenceBank.support() → 引用清单 → RRSkillAgent.rr_epistemic(o) + +**不新增势函数**(用户: `InternalKnowledge 首先是 evidence bank`)。 +""" +from __future__ import annotations + +from collections import deque + +import numpy as np + +# ────────────────────────────────────────────────────────────────────────── +# 语义分工 (用户指定): GVF_1 prediction / GVF_2 reward / GVF_3 transition / GVF_4 regime +# `role` 决定这条证据能不能把状态**升级**成 good: +# outcome → 可以 (它直接谈结果有没有价值) +# dynamics → **不可以** (它只谈"我预测得准不准") +# ────────────────────────────────────────────────────────────────────────── +DEFAULT_SPEC = ( + ("prediction", "dynamics", 0.95, 0.90), + ("reward", "outcome", 0.90, 0.80), + ("transition", "dynamics", 0.85, 0.80), + ("regime", "dynamics", 0.70, 0.50), +) + + +class GVF: + """单个 General Value Function: 线性 TD(λ)。与旧 `knowledge.GVF` 同式。""" + + def __init__(self, name, dim, gamma=0.9, lam=0.8, lr=0.05, seed=0): + self.name = name + self.gamma = float(gamma) + self.lam = float(lam) + self.lr = float(lr) + self.w = np.zeros(dim) + self.trace = np.zeros(dim) + self.rng = np.random.default_rng(seed) + self.steps = 0 + + def predict(self, phi): + return float(self.w @ phi) + + def update(self, phi, cumulant, phi_next): + v, v_next = self.w @ phi, self.w @ phi_next + delta = float(cumulant) + self.gamma * v_next - v + self.trace = self.gamma * self.lam * self.trace + phi + self.w = self.w + self.lr * delta * self.trace + self.steps += 1 + return abs(delta) + + +class GVFEvidence: + """一条证据 —— **不是权重, 是"我凭什么这么说"**。 + + 每个字段都是可判定的, 不留"看起来还行"这种状态。 + """ + + __slots__ = ("name", "role", "value", "err_ema", "err_ref", "n", + "last_step", "regime", "buf", "ema_w") + + def __init__(self, name, role, ema_w=0.05, buf_len=200): + self.name = name + self.role = role # 'dynamics' | 'outcome' + self.value = 0.0 + self.err_ema = 0.0 + self.err_ref = 0.0 # 该 GVF **自己**的历史误差中位数 + self.n = 0 + self.last_step = -1 + self.regime = None + self.buf = deque(maxlen=buf_len) + self.ema_w = float(ema_w) + + def push(self, err, value, step, regime): + self.n += 1 + self.last_step = int(step) + self.regime = regime + self.value = float(value) + self.err_ema = (err if self.n == 1 + else (1.0 - self.ema_w) * self.err_ema + self.ema_w * err) + self.buf.append(float(err)) + # 参考尺度 = 自己的历史中位数(稳健, 不被单次尖峰带跑) + self.err_ref = float(np.median(self.buf)) if self.buf else 0.0 + + def usability(self, step, regime, n_min, stale_hl): + """★ 缺失证据要显式缺席, 而不是按 0 平均进去。 + + 返回 (usable, why)。`why` 只在不可用时给出, 并进入引用清单。 + """ + if self.n < n_min: + return False, "low_coverage" + if step is not None and self.last_step >= 0: + if (step - self.last_step) > stale_hl: + return False, "stale" + if regime is not None and self.regime is not None and self.regime != regime: + return False, "wrong_regime" + return True, None + + def z(self, eps=1e-9): + """该 GVF **自己尺度**上的相对误差。""" + return float(self.err_ema / max(self.err_ref, eps)) + + def to_dict(self): + return {"name": self.name, "role": self.role, "value": self.value, + "err_ema": self.err_ema, "err_ref": self.err_ref, + "z": self.z(), "n": self.n, "last_step": self.last_step, + "regime": self.regime} + + +class EvidenceBank: + """一组语义分工的 GVF + 它们的证据记录。 + + ★ 没有 `aggregate()` 返回标量的接口 —— 那正是本模块要消灭的东西。 + """ + + def __init__(self, dim, spec=None, seed=0, n_min=10, stale_hl=200, + z_bad=2.0, z_good=1.0): + self.spec = tuple(spec) if spec else DEFAULT_SPEC + self.dim = int(dim) + self.n_min = int(n_min) + self.stale_hl = int(stale_hl) + self.z_bad = float(z_bad) + self.z_good = float(z_good) + self.gvfs = {} + self.ev = {} + # ★ 逐 step 历史 —— 互补性度量必须有"逐步序列"才能算 AUC, + # 而**不能在事后从聚合量重建**。这里如实记下来。 + self.hist_z = {} + self.hist_c = {} + for i, (name, role, gamma, lam) in enumerate(self.spec): + self.gvfs[name] = GVF(name, self.dim, gamma, lam, seed=seed + i) + self.ev[name] = GVFEvidence(name, role) + self.hist_z[name] = [] + self.hist_c[name] = [] + self.regime = None + + # ── 写入 ────────────────────────────────────────────────────────── + def observe(self, phi, cumulants, phi_next, step=None, regime=None): + """喂一组 cumulant(**按 spec 顺序**)。 + + 返回每条 GVF 自己的误差 —— **不返回均值**。 + """ + if len(cumulants) != len(self.spec): + raise ValueError("cumulants 数 %d != spec 数 %d" + % (len(cumulants), len(self.spec))) + if regime is not None: + self.regime = regime + out = {} + for (name, _role, _g, _l), c in zip(self.spec, cumulants): + g, e = self.gvfs[name], self.ev[name] + err = g.update(phi, float(c), phi_next) + e.push(err, g.predict(phi), -1 if step is None else step, regime) + out[name] = err + # ★ 逐 step 如实记录(互补性度量用) —— 记的是**当场**的 z, + # 不是最后那个聚合值。 + self.hist_z[name].append(e.z()) + self.hist_c[name].append(float(c)) + return out + + def z_history(self): + """★ 逐 step 的 z 序列与 cumulant 序列, 供互补性度量使用。 + + **不能从 `evidence()` 的聚合量重建** —— 那正是"丢来源"的老毛病。 + """ + return {"z": {k: np.asarray(v, dtype=float) for k, v in self.hist_z.items()}, + "cumulant": {k: np.asarray(v, dtype=float) for k, v in self.hist_c.items()}} + + # ── 查询 ────────────────────────────────────────────────────────── + def evidence(self, step=None, regime=None): + """每条证据的完整记录(**e_1, e_2, e_3, e_4 原样保留**)。""" + reg = self.regime if regime is None else regime + out = {} + for name, _r, _g, _l in self.spec: + e = self.ev[name] + ok, why = e.usability(step, reg, self.n_min, self.stale_hl) + d = e.to_dict() + d["usable"] = ok + d["why"] = why + out[name] = d + return out + + def support(self, step=None, regime=None): + """★★ 聚合 = **投票 + 引用**, 不是平均。 + + 返回的字典里**没有任何跨 GVF 的标量均值**。 + """ + reg = self.regime if regime is None else regime + bad, healthy, missing, sources = [], [], [], {} + usable_n = 0 + for name, role, _g, _l in self.spec: + e = self.ev[name] + ok, why = e.usability(step, reg, self.n_min, self.stale_hl) + zd = e.z() + src = {"src": name, "role": role, "z": zd, "n": e.n, + "err_ema": e.err_ema, "err_ref": e.err_ref} + sources[name] = src + if not ok: + missing.append({"src": name, "role": role, "why": why, "n": e.n}) + continue + usable_n += 1 + if zd >= self.z_bad: + bad.append({"src": name, "role": role, "z": zd}) + elif zd <= self.z_good: + # ★★ 动力学证据**只能降级**: 它可以投 bad, 但永不能投 healthy。 + # 理由: 预测误差低只说明"我预测得准", 不说明"这件事有价值"; + # 而**新颖度会拉高误差** ⇒ 允许升级就等于允许新颖度伪造"好"。 + if role == "outcome": + healthy.append({"src": name, "role": role, "z": zd}) + else: + healthy.append({"src": name, "role": role, "z": zd, + "capped": "dynamics 不升级"}) + + # 判定(不对称) + if bad: + verdict = "bad" + elif any(h.get("role") == "outcome" and "capped" not in h for h in healthy): + verdict = "good" + else: + verdict = "unknown" + + return { + "verdict": verdict, + "bad": bad, + "healthy": healthy, # 含被 cap 的动力学票(可追溯, 但不升级) + "missing": missing, + "usable_n": usable_n, + "total_n": len(self.spec), + "sources": sources, + } + + def stats(self): + return {"n_gvf": len(self.spec), "regime": self.regime, + "n_min": self.n_min, "stale_hl": self.stale_hl, + "z_bad": self.z_bad, "z_good": self.z_good, + "evidence": {n: {"n": self.ev[n].n, "z": self.ev[n].z()} + for n, _r, _g, _l in self.spec}} + + +# ────────────────────────────────────────────────────────────────────────── +# 互补性度量 —— 回答"多个 GVF 能不能产生互补证据" +# ────────────────────────────────────────────────────────────────────────── + + +def _auc(scores, labels): + """rank-based AUC(不依赖 sklearn)。labels: 1 = 正类(变化后)。 + + 并列用**平均秩**(Mann-Whitney U 的标准处理)。 + """ + scores = np.asarray(scores, dtype=float) + labels = np.asarray(labels, dtype=int) + pos, neg = scores[labels == 1], scores[labels == 0] + if len(pos) == 0 or len(neg) == 0: + return float("nan") + order = np.argsort(scores, kind="mergesort") + ranks = np.empty(len(scores), dtype=float) + ranks[order] = np.arange(1, len(scores) + 1, dtype=float) + s = scores[order] + i = 0 + while i < len(s): + j = i + while j + 1 < len(s) and s[j + 1] == s[i]: + j += 1 + if j > i: + ranks[order[i:j + 1]] = (i + 1 + j + 1) / 2.0 + i = j + 1 + r_pos = ranks[labels == 1].sum() + return float((r_pos - len(pos) * (len(pos) + 1) / 2.0) / (len(pos) * len(neg))) + + +def complementarity(err_series, shifted, z_bad=2.0, z_good=1.0): + """多条 GVF 的证据是否**互补**。 + + `err_series`: {name: 逐步误差数组} + `shifted`: 逐步标签, 1 = 该步处于"变化之后" + + 返回 AUC_i / AUC_union / complementarity / disagreement / corr_err。 + """ + names = list(err_series) + zs = {} + for n in names: + a = np.asarray(err_series[n], dtype=float) + # ★ 参考尺度只从"变化前"估计 —— 用全段会被变化本身抬高, 自己抹掉自己 + pre = a[np.asarray(shifted, dtype=int) == 0] + ref = float(np.median(pre)) if pre.size and np.median(pre) > 0 else 1.0 + zs[n] = a / max(ref, 1e-9) + + auc_i = {n: _auc(zs[n], shifted) for n in names} + # ★ 两种并集规则, **必须分开报** —— 它们回答的是不同问题: + # `any` : 任一条超阈 ⇒ **检测**问题(加检测器不会降低 recall) + # `consensus` : 至少两条同时超阈 ⇒ **一致性**问题(合取, 天生比单条保守) + # ⚠ 只报 consensus 会得到"并集比单条差"的结论, 那是**聚合规则的产物**, + # 不是"证据不互补"的证据。两个都算, 都报。 + flags = np.stack([(zs[n] >= z_bad).astype(int) for n in names]) + jit = 1e-6 * np.arange(flags.shape[1]) # 位置抖动, 让并列信号可排秩 + any_flag = (flags.sum(axis=0) >= 1).astype(float) + jit + cons_flag = (flags.sum(axis=0) >= 2).astype(float) + jit + auc_any = _auc(any_flag, shifted) + auc_cons = _auc(cons_flag, shifted) + + valid = [v for v in auc_i.values() if v == v] + best_i = max(valid) if valid else float("nan") + if valid and auc_any == auc_any: + comp = auc_any - best_i + else: + comp = float("nan") + + # 分歧率: **至少一条投 bad 且至少一条投 healthy** 的步数占比 + n_bad = np.stack([zs[n] >= z_bad for n in names]).sum(axis=0) + n_ok = np.stack([zs[n] <= z_good for n in names]).sum(axis=0) + disagreement = float(((n_bad > 0) & (n_ok > 0)).mean()) if names else float("nan") + + corr = {} + arr = np.stack([zs[n] for n in names]) + if arr.shape[0] >= 2: + c = np.corrcoef(arr) + for i, a_ in enumerate(names): + for j, b_ in enumerate(names): + if i < j: + corr["%s~%s" % (a_, b_)] = float(c[i, j]) + + return {"auc_i": auc_i, "auc_any": auc_any, "auc_consensus": auc_cons, + "best_single": best_i, "complementarity": comp, + "disagreement": disagreement, "corr_err": corr, + "z_bad": z_bad, "z_good": z_good} diff --git a/hibs_lnn/gridworld.py b/hibs_lnn/gridworld.py new file mode 100644 index 0000000000000000000000000000000000000000..9bb925cdef3efa82c2940d82a5abce7d5a251af0 --- /dev/null +++ b/hibs_lnn/gridworld.py @@ -0,0 +1,509 @@ +"""gridworld.py — 论文里的两房间 / 四房间 gridworld(arXiv 2202.03466 Fig.1 / Fig.6)。 + +## 为什么要有这个文件 + +本项目的 lm4/lm5 基准**没有可用作时间抽象的结构**(实测不对称性 `A ≡ 0`, +唯一的动力学是对称纯干扰)。在那种环境上,任何类别的 option 都不可能有收益 —— +所以「Options 有害」这个结论**测的是基准的性质**。 + +论文用的 gridworld 是**有明确正反例**的:同一个环境里 + · bottleneck / shortest-path option 的 planning **比 primitive 还慢**(Fig.1) + · reward-respecting option 的 planning **明显更快**(Fig.1) +这两个已知结论就是本项目的 ground truth —— 用来验证「我的 option + planning 管线写对了没有」。 + +## 环境规格(逐条对齐论文) + +| 项 | 设定 | +|:--|:--| +| 动作 | 4 个:上 / 右 / 下 / 左 | +| 撞墙 | 位置不变 | +| 目标 | 到达 goal 得 **+1**,episode 结束 | +| 灰色区 | **进入**灰色区的转移每步 **−1** | +| 其他 | 0 | +| γ | **0.99** | +| 两房间最优 | `v*(s0) = 0.99^17 ≈ 0.843`(绕开负奖励区) | +| 四房间动力学 | 期望方向 w.p. **2/3**,其余三个方向各 **1/9** | + +**注意论文原文用的是 "transitions ending in the gray region"** —— +即**落点**在灰色区才扣分,不是路径穿过的格子。本实现按**落点**判。 +""" +from __future__ import annotations + +import numpy as np + +# 动作编号(与论文的四方向一致) +UP, RIGHT, DOWN, LEFT = 0, 1, 2, 3 +N_ACT = 4 +ACT_NAME = {UP: "up", RIGHT: "right", DOWN: "down", LEFT: "left"} + +# ── 两房间 gridworld(Fig.1 插图) ───────────────────────────────────────── +# '#' 墙 '.' 地板 'S' 起点 'G' 目标 '-' 灰色区(落点扣分) '+' 走廊(bottleneck) +# +# 布局要点(必须同时满足, 否则复现不了论文): +# ① 存在一个**单格走廊**连接两个房间 -> 它是天然 bottleneck +# ② 从 S 到走廊的**最短路径**穿过灰色区 +# ③ 存在一条**绕开灰色区**的更长路径 +# ★ 第 4、5 行结尾的两个 '#' 不是笔误, 但它**修不掉原本的问题** —— 见下方说明。 +# +# 实测(新版 check_two_room): 旧几何的 col 1 是通的 -> 有一条自由竖井绕过整个 +# 灰色区 -> 最短路**根本不碰灰色**(neg_on_shortest=0), 而且"绕行"与最短路等长。 +# 也就是说: 论文 Fig.1 的三条性质**一条都没满足**, 而因为自检函数本身有 bug +# (hall 落在灰色格里 -> "绕开灰色区到达 hall" 构造上不可能), 它一直报的是 ✗ 却没人看。 +# +# 现在按论文重设: 灰色区**正对**单格通道, 使得 +# 穿过灰色 = 3 步(最短) +# 绕开灰色 = 7 步(更长) +TWO_ROOM = [ + "#########", + "#...S...#", + "#..---..#", + "#.......#", + "####+####", + "#.......#", + "#......G#", + "#########", +] + +# ── 四房间 gridworld(Fig.6): 4 个房间 + 3 个单格走廊 ────────────────── +# +# ★ 这里曾经是**静默坏掉**的: 旧布局行长不齐(12/13/14), 且有一行含**空格**。 +# 行不齐 -> GridWorld 里 `.ljust(..., '#')` 静默补成墙, 几何被改; +# 空格 -> 只有 '#' 被跳过, 空格于是变成一格**幽灵地板**(可走但看不出来)。 +# 两者都不会报错, 所以四房间那一支一直在跑一个**非预期的环境**, 复现自然失败。 +# 现在加 validate_layout() 在构造时**硬报错**, 这类 bug 不可能再静默。 +# +# 布局设计(必须同时满足, 否则复现不了论文 Fig.6): +# ① 2x2 四个房间, 3 个单格走廊连成环 -> 走廊是天然 bottleneck +# ② 从 S 到走廊的最短路**穿过灰色区**(负奖励) +# ③ 存在一条**绕开灰色区**的更长路径 +# +# 实测几何(见 check_four_room): +# 最短路 S->G = 18 步, 穿过灰色区 2 格 +# 绕行路线 = 20 步, 完全避开灰色区 +FOUR_ROOM = [ + "#############", + "#S..####....#", + "#..--.+.....#", + "#.....#.....#", + "#.....#.....#", + "###+#########", + "#.....#.....#", + "#.....#.....#", + "#.....+.....#", + "#.....#....G#", + "#############", +] + + +_ALLOWED = set("#.-SG+") + + +def validate_layout(layout, name="layout"): + """★ 硬校验布局。**报错而非默默容错** —— 见 FOUR_ROOM 上方的注释。 + + 这条检查是补上一个真实事故: 旧四房间布局行长不齐 + 混入空格, + 被 `.ljust(..., '#')` 和"只跳过 '#'"两个静默行为一起吃掉了, + 结果整个四房间分支跑的是一个没人设计过的环境。 + + 返回 (H, W)。不合法直接 raise ValueError。 + """ + if not layout: + raise ValueError("%s: 布局为空" % name) + widths = {len(r) for r in layout} + if len(widths) != 1: + detail = ", ".join("%d:\"%s\"" % (i, r) for i, r in enumerate(layout)) + raise ValueError( + "%s: 行长不一致 (宽度集合 %s) —— 补齐会变成墙, 必须手工对齐。各行: %s" + % (name, sorted(widths), detail)) + W = widths.pop() + for i, r in enumerate(layout): + bad = sorted(set(r) - _ALLOWED) + if bad: + raise ValueError( + "%s: 第 %d 行含非法字符 %r。只允许 %s —— " + "注意**空格不是地板**('#' 才是唯一的墙, 其它字符都会被当成可走格)。" + % (name, i, bad, sorted(_ALLOWED))) + if r[0] != "#" or r[-1] != "#": + raise ValueError("%s: 第 %d 行两端必须是墙 '#'" % (name, i)) + for j in range(W): + if layout[0][j] != "#" or layout[-1][j] != "#": + raise ValueError("%s: 第 %d 列两端必须是墙 '#'" % (name, j)) + flat = "".join(layout) + if flat.count("S") != 1: + raise ValueError("%s: 必须恰好有一个 'S' (实际 %d)" % (name, flat.count("S"))) + if flat.count("G") < 1: + raise ValueError("%s: 至少需要一个 'G'" % name) + return len(layout), W + + +class GridWorld: + """确定性 / 随机性可切的 gridworld(论文规格)。""" + + def __init__(self, layout, gamma=0.99, stochastic=False, slip=2.0 / 3.0): + self.layout = list(layout) + self.gamma = float(gamma) + self.stochastic = bool(stochastic) + self.slip = float(slip) + + # ★ 先硬校验, 再构造。旧代码这里是 `.ljust(W, '#')` 静默补齐 —— + # 它会**悄悄把短行补成墙**, 于是布局错了几何也照跑不误。 + # validate_layout 会把行长不齐/非法字符/边界非墙变成**构造期异常**。 + self.H, self.W = validate_layout(layout, name=getattr(self, "name", "layout")) + self.grid = list(layout) # 已保证等宽, 不再 ljust + + self.cells = [] # (row, col) + self.index = {} + self.is_neg = [] # 落点是否灰色区(扣分) + self.is_goal = [] + self.start = None + for r in range(self.H): + for c in range(self.W): + ch = self.grid[r][c] + if ch == "#": + continue + i = len(self.cells) + self.cells.append((r, c)) + self.index[(r, c)] = i + self.is_neg.append(ch == "-") + self.is_goal.append(ch == "G") + if ch == "S": + self.start = i + self.n_states = len(self.cells) + self.is_neg = np.array(self.is_neg, dtype=bool) + self.is_goal = np.array(self.is_goal, dtype=bool) + assert self.start is not None, "布局里必须有 'S'" + assert self.is_goal.any(), "布局里必须有 'G'" + + self._build_transitions() + + # ── 动力学 ────────────────────────────────────────────────────────── + def _move(self, r, c, a): + if a == UP: + r -= 1 + elif a == DOWN: + r += 1 + elif a == LEFT: + c -= 1 + else: + c += 1 + if (r, c) in self.index: + return self.index[(r, c)] + return None # 撞墙 + + def _build_transitions(self): + """T[s][a] = [(prob, next_state), ...]; R[s][a] = 期望即时奖励。""" + self.T: list = [[[] for _ in range(N_ACT)] for _ in range(self.n_states)] + self.R = np.zeros((self.n_states, N_ACT)) + for s, (r, c) in enumerate(self.cells): + # ★ goal 是**吸收态**(论文: "A reward of +1 is received on reaching the + # goal state, **which ends the episode**")。 + # 不做这一步的话, agent 会反复落回 goal 反复拿 +1 -> 价值爆炸 + # (实测: 子任务里 hall 处的"继续价值"算成 98.01, 而真值 <= 1)。 + if self.is_goal[s]: + for a in range(N_ACT): + self.T[s][a] = [(1.0, int(s))] + self.R[s][a] = 0.0 + continue + for a in range(N_ACT): + if self.stochastic: + # 期望方向 2/3, 其余三个方向各 1/9(论文规格) + others = [x for x in range(N_ACT) if x != a] + cand = [(a, self.slip)] + [(x, (1.0 - self.slip) / 3.0) for x in others] + else: + cand = [(a, 1.0)] + # 合并相同落点 + acc = {} + for act, p in cand: + ns = self._move(r, c, act) + if ns is None: + ns = s # 撞墙 -> 留在原地 + acc[ns] = acc.get(ns, 0.0) + p + outs = sorted(acc.items()) + self.T[s][a] = [(float(p), int(ns)) for ns, p in outs] + # 落点在灰色区 -> -1;落点是 goal -> +1 + rew = 0.0 + for p, ns in self.T[s][a]: + if self.is_goal[ns]: + rew += p * 1.0 + elif self.is_neg[ns]: + rew += p * -1.0 + self.R[s][a] = rew + + def reset(self): + return self.start + + def step(self, s, a): + outs = self.T[s][a] + if len(outs) == 1: + ns = outs[0][1] + else: + p = np.array([x[0] for x in outs]) + p = p / p.sum() + ns = outs[int(np.random.choice(len(outs), p=p))][1] + done = bool(self.is_goal[ns]) + return ns, float(self.R[s][a]), done + + +# ── 布局性质自检(必须过, 否则复现不了论文) ───────────────────────────── + +def find_bottlenecks(env): + """介数中心性高(且度小)的格子 = 论文说的 bottleneck。 + + 实现方式:对确定性图做全对最短路径(无权 BFS),数每格出现在多少条最短路里。 + """ + import collections + n = env.n_states + # 确定性后继(取 T 里概率最大的那个, 用于结构性分析) + succ = [[max(env.T[s][a], key=lambda x: x[0])[1] for a in range(N_ACT)] for s in range(n)] + between = np.zeros(n) + for src in range(n): + dist = [-1] * n + dist[src] = 0 + q = collections.deque([src]) + order = [] + while q: + u = q.popleft() + order.append(u) + for a in range(N_ACT): + v = succ[u][a] + if v != u and dist[v] < 0: + dist[v] = dist[u] + 1 + q.append(v) + for tgt in range(n): + if tgt == src or dist[tgt] < 0: + continue + # 回溯一条最短路(标记经过的格) + u = tgt + guard = 0 + while u != src and guard < 4 * n: + between[u] += 1 + nxt = None + for a in range(N_ACT): + v = succ[u][a] + if v != u and dist[v] == dist[u] - 1: + nxt = v + break + if nxt is None: + break + u = nxt + guard += 1 + return between + + +def check_two_room(env, verbose=True): + """验证两房间布局确实具备论文要求的三条性质。""" + import collections + n = env.n_states + succ = [[max(env.T[s][a], key=lambda x: x[0])[1] for a in range(N_ACT)] for s in range(n)] + + def bfs(src, avoid_neg=False): + dist = {src: 0} + parent = {src: None} + q = collections.deque([src]) + while q: + u = q.popleft() + if env.is_goal[u]: + continue + for a in range(N_ACT): + v = succ[u][a] + if v == u or v in dist: + continue + if avoid_neg and env.is_neg[v]: + continue + dist[v] = dist[u] + 1 + parent[v] = u + q.append(v) + return dist, parent + + bt = find_bottlenecks(env) + # bottleneck = 非起非终、**且不是灰色格**、介数最高 + # ★ 必须排除灰色格: 否则 hall 常常正好落在灰色区里, 而下面又要求 + # "存在绕开灰色区到达 hall 的路径" —— 那是**构造上不可能**的, + # 于是这条自检永远报 ✗, 成了没人看的红灯(实测: 原来的 hall=(3,6) 就是 '-' 格)。 + cand = [(bt[s], s) for s in range(n) + if s != env.start and not env.is_goal[s] and not env.is_neg[s]] + cand.sort(reverse=True) + hall = cand[0][1] + + d_free, _ = bfs(env.start) + d_avoid, _ = bfs(env.start, avoid_neg=True) + + reach_hall = hall in d_free + # 最短路到走廊经过的灰色区格数 + _, par = bfs(env.start) + path, u = [], hall + while u is not None: + path.append(u) + u = par.get(u) + path.reverse() + neg_on_shortest = sum(int(env.is_neg[s]) for s in path) + has_detour = hall in d_avoid and d_avoid.get(hall, 10 ** 9) > d_free.get(hall, 0) + + if verbose: + r0, c0 = env.cells[env.start] + rh, ch = env.cells[hall] + print(" [两房间自检]") + print(" 起点 (%d,%d) -> 走廊 (%d,%d) [介数 %.0f]" % (r0, c0, rh, ch, bt[hall])) + print(" 到达走廊: %s 最短路长度 %s" % (reach_hall, d_free.get(hall))) + print(" 最短路上的灰色格数: %d (论文要求 > 0, 否则没有'绕路'可言)" + % neg_on_shortest) + print(" 存在绕开灰色区的路径: %s (长度 %s vs 最短 %s)" + % (has_detour, d_avoid.get(hall), d_free.get(hall))) + ok = reach_hall and neg_on_shortest > 0 and has_detour + if verbose: + print(" 三条性质全满足: %s" % ("✓" if ok else "✗ 布局需要调整")) + return ok, hall, dict(d_free=d_free, d_avoid=d_avoid, + neg_on_shortest=neg_on_shortest, between=bt) + + +def check_four_room(env, verbose=True): + """验证四房间布局确实具备论文 Fig.6 要求的性质。**四房间从来没有过这个检查。** + + 与 check_two_room 的三条性质对应, 但四房间要额外验: + ① 把走廊堵上后, 恰好分裂成 **4 个连通分量**(真的是四房间, 不是一团) + ② 走廊格是 bottleneck(介数最高) + ③ 从 S 到走廊的**最短路穿过灰色区** + ④ 存在**绕开灰色区**的更长路径 + + 返回 (ok, halls, info)。 + """ + import collections + n = env.n_states + succ = [[max(env.T[s][a], key=lambda x: x[0])[1] for a in range(N_ACT)] for s in range(n)] + + def bfs(src, avoid_neg=False, block=None): + dist, q = {src: 0}, collections.deque([src]) + parent = {src: None} + while q: + u = q.popleft() + if env.is_goal[u]: + continue + for a in range(N_ACT): + v = succ[u][a] + if v == u or v in dist or v == block: + continue + if avoid_neg and env.is_neg[v]: + continue + dist[v] = dist[u] + 1 + parent[v] = u + q.append(v) + return dist, parent + + bt = find_bottlenecks(env) + # ★ 走廊格 = 字符 '+' 的格子(显式标记, 不靠介数猜) + halls = [s for s, (r, c) in enumerate(env.cells) if env.grid[r][c] == "+"] + + # ① 堵上全部走廊 -> 数连通分量 + blocked = set(halls) + seen, comps = set(), [] + for s in range(n): + if s in seen or s in blocked: + continue + stack, comp = [s], [] + seen.add(s) + while stack: + u = stack.pop() + comp.append(u) + for a in range(N_ACT): + v = succ[u][a] + if v == u or v in seen or v in blocked: + continue + seen.add(v) + stack.append(v) + comps.append(comp) + + # ③④ 对每个走廊格测最短路 vs 绕行 + rows = [] + for h in halls: + d_free, par = bfs(env.start) + d_av, _ = bfs(env.start, avoid_neg=True) + path, u = [], h + while u is not None: + path.append(u) + u = par.get(u) + path.reverse() + neg_short = sum(int(env.is_neg[s]) for s in path) + rows.append(dict(hall=h, d_free=d_free.get(h), d_avoid=d_av.get(h), + neg_on_shortest=neg_short, + detour_longer=(h in d_av and h in d_free and d_av[h] > d_free[h]))) + + p_ok = abs(bt[halls[0]] - max(bt)) < 1e-6 if halls else False + ok = (len(comps) == 4 and halls + and all(r["d_free"] is not None for r in rows) + and any(r["neg_on_shortest"] > 0 for r in rows) + and any(r["detour_longer"] for r in rows)) + + if verbose: + print(" [四房间自检]") + print(" 状态数 %d 走廊格 %d 个: %s" + % (n, len(halls), [env.cells[h] for h in halls])) + print(" 堵上走廊后的连通分量数: %d (论文要求 4)" % len(comps)) + if halls: + print(" 走廊是介数最高点: %s (介数 %.0f / 全局最高 %.0f)" + % (p_ok, bt[halls[0]], max(bt))) + for r in rows: + rh, ch = env.cells[r["hall"]] + print(" 走廊 (%d,%d): 最短路 %s 步(其中灰色 %d 格) 绕行 %s 步 绕行更长: %s" + % (rh, ch, r["d_free"], r["neg_on_shortest"], r["d_avoid"], r["detour_longer"])) + print(" 四条性质全满足: %s" % ("✓" if ok else "✗ 布局需要调整")) + return ok, halls, dict(n_comps=len(comps), rows=rows, between=bt) + + +def value_iteration(env, model=None, n_sweeps=1, V0=None, options=None, order=None): + """带 option 的价值迭代(论文 §5 的 planning)。 + + model : [(p, ns, r)] 形式的 dict; 默认用真模型(论文 Fig.1 假设模型准确) + 格式: model[s][u] = [(p, ns, r), ...]; u 索引 0..N_ACT-1 是 primitive, + N_ACT.. 是 option(一跳到底, 返回终止状态与累计奖励) + n_sweeps : 扫多少遍 + 返回 (V, n_lookahead) —— n_lookahead = 消耗的 look-ahead 操作数(论文的横轴) + """ + n = env.n_states + V = np.zeros(n) if V0 is None else V0.copy() + n_look = 0 + if model is None: + model = {s: {a: [(p, ns, env.R[s][a]) for p, ns in env.T[s][a]] for a in range(N_ACT)} + for s in range(n)} + for _ in range(n_sweeps): + idx = range(n) if order is None else order + for s in idx: + if env.is_goal[s]: + V[s] = 0.0 + continue + best = -np.inf + for u, outs in model[s].items(): + val = 0.0 + for p, ns, r in outs: + val += p * (r + env.gamma * V[ns]) + n_look += 1 # 每次 look-ahead 计数 + best = max(best, val) + V[s] = best + return V, n_look + + +if __name__ == "__main__": + print("=" * 70) + print("两房间 gridworld(确定性, 论文 Fig.1)") + print("=" * 70) + env = GridWorld(TWO_ROOM, gamma=0.99, stochastic=False) + print(" 状态数 %d, 动作数 %d" % (env.n_states, N_ACT)) + ok, hall, info = check_two_room(env) + + V, n = value_iteration(env, n_sweeps=200) + print("\n value iteration 200 遍 (primitive only): V(S) = %.4f" % V[env.start]) + print(" 论文报告的最优值 v*(s0) = 0.99^17 = %.4f" % (0.99 ** 17)) + print(" look-ahead 操作数 = %d" % n) + + print("\n" + "=" * 70) + print("四房间 gridworld(随机, slip=2/3, 论文 Fig.6)") + print("=" * 70) + env4 = GridWorld(FOUR_ROOM, gamma=0.99, stochastic=True) + print(" 状态数 %d, 动作数 %d" % (env4.n_states, N_ACT)) + ok4, halls4, info4 = check_four_room(env4) + # ★ 自检不过就让进程**非零退出** —— 否则坏几何又会被静默当成"实验做完了" + if not (ok and ok4): + print("\n✗ 布局自检未通过, 拒绝继续(退出码 1)") + raise SystemExit(1) + V4, n4 = value_iteration(env4, n_sweeps=200) + print("\n value iteration 200 遍 (primitive only): V(S) = %.4f" % V4[env4.start]) + print(" look-ahead 操作数 = %d" % n4) diff --git a/hibs_lnn/knowledge.py b/hibs_lnn/knowledge.py new file mode 100644 index 0000000000000000000000000000000000000000..4918be59a8dfe69837c12597aea340243494c505 --- /dev/null +++ b/hibs_lnn/knowledge.py @@ -0,0 +1,386 @@ +"""knowledge.py — InternalKnowledge: Agent 的**内部知识层**。 + +## 核心立场 (用户给的) + + Knowledge ≠ Parameter + +参数只是**承载**知识的一种方式。OaK 式系统希望内部知识是**可组合的计算对象**, +而不是 `y = f_θ(x)` 里一堆"你甚至不知道 θ 代表什么知识"的权重。 + +所以这个类**不暴露 θ / W / α**,而是暴露**可计算的知识查询**: + + "未来会发生什么?" -> predict() + "我做什么会导致什么?" -> dynamics() + "什么结果更好?" -> value() + "哪些过程是一个整体技能?" -> skills() + "我对 X 有多确定?" -> confidence() + "我有多少经验?" -> coverage_of() + "我该多快改变?" -> plasticity() + +## 四层知识 (用户给的编号) + + Level 1 预测知识 K_P GVF / predictors "未来会发生什么" + Level 2 因果/转移 K_T T(s,a) "我做什么会导致什么" + Level 3 价值/策略 K_V V / Q / π "什么结果更好" + Level 4 抽象知识 K_A Options "哪些过程是一个整体技能" + +外加两类**元知识**: + + uncertainty/ confidence "我对自己的预测有多确定" + coverage/ state_action 覆盖 "我对这个状态/动作到底有没有经验" + plasticity/ α / β "我应该多快改变这个知识" (接 IDBD) + +## 为什么 α 也算内部知识 + + w_i = knowledge "我知道什么" + α_i = knowledge about plasticity "我应该多快改变这个知识" + β_i = meta-knowledge "我的学习速度应该如何变化" + +这与 IDBD 主线直接接上。 + +## 关键能力: 知识是**留下来的** + +`to_dict()` / `load_dict()` 让 K 可以持久化比对 —— 这是"持续构建内部知识"的 +可判定形式: 不是"模型又训了一轮",而是**能指出知识具体变了什么**。 +""" +from __future__ import annotations + +import numpy as np + +# ────────────────────────────────────────────────────────────────────────── +# Level 1: 预测知识 (GVF / Horde 式) +# ────────────────────────────────────────────────────────────────────────── + + +class GVF: + """General Value Function: 预测某个 cumulant 的折扣累积量。 + + V(s) = E[ Σ γ^k λ^k c_{t+k+1} | s_t = s ] + + `c` = cumulant (要预测的信号), `γ` = 折扣, `λ` = 迹衰减。 + Horde (Sutton 2011) 的做法: 一堆 GVF 并行学, 它们的预测**进入状态**, + 构成 agent 对世界的"理解"—— 这正是 OaK 说的 internal knowledge。 + """ + + def __init__(self, name, dim, gamma=0.9, lam=0.8, lr=0.05, seed=0): + self.name = name + self.gamma = gamma + self.lam = lam + self.lr = lr + self.w = np.zeros(dim) + self.trace = np.zeros(dim) + self.rng = np.random.default_rng(seed) + self.steps = 0 + + def predict(self, phi): + return float(self.w @ phi) + + def update(self, phi, cumulant, phi_next): + """TD(λ) 更新。""" + v, v_next = self.w @ phi, self.w @ phi_next + delta = cumulant + self.gamma * v_next - v + self.trace = self.gamma * self.lam * self.trace + phi + self.w += self.lr * delta * self.trace + self.steps += 1 + return abs(delta) + + def stats(self): + return {"name": self.name, "w_norm": float(np.linalg.norm(self.w)), + "steps": self.steps, "gamma": self.gamma, "lam": self.lam} + + +class GVFBank: + """一组 GVF。它们的预测拼起来就是 agent 的"世界理解"向量。""" + + def __init__(self, dim, spec=None, seed=0): + self.spec = spec or [ + ("next_acc", 0.95, 0.9), ("acc_trend", 0.9, 0.7), + ("forget_risk", 0.85, 0.8), ("regime_shift", 0.7, 0.5), + ] + self.gvfs = [GVF(n, dim, g, l, seed=seed + i) + for i, (n, g, l) in enumerate(self.spec)] + + def outputs(self, phi): + """预测向量 —— 这就是"预测到的世界状态", 可作为下游特征。""" + return np.array([g.predict(phi) for g in self.gvfs]) + + def update(self, phi, cumulants, phi_next): + errs = [] + for g, c in zip(self.gvfs, cumulants): + errs.append(g.update(phi, float(c), phi_next)) + return float(np.mean(errs)) if errs else 0.0 + + def stats(self): + return {"n_gvf": len(self.gvfs), + "gvfs": [g.stats() for g in self.gvfs], + "out_norm": float(np.linalg.norm(self.outputs(np.ones_like(self.gvfs[0].w)))), + "mean_steps": float(np.mean([g.steps for g in self.gvfs])) if self.gvfs else 0.0} + + +# ────────────────────────────────────────────────────────────────────────── +# 元知识: 覆盖度 / 可塑性 +# ────────────────────────────────────────────────────────────────────────── + + +class Coverage: + """state-action 覆盖度 —— "我对这个 (s,a) 到底有没有经验"。""" + + def __init__(self, n_dom): + self.n_dom = n_dom + self.n = np.zeros(n_dom) # 每动作被访问次数 + self.n_total = 0 + + def observe(self, a): + if 0 <= a < self.n_dom: + self.n[a] += 1 + self.n_total += 1 + + def of(self, a): + return float(self.n[a]) if 0 <= a < self.n_dom else 0.0 + + def gate(self, a): + """g = 1/√(1+n): 零覆盖 -> 1, 经验多 -> 0。""" + return float(1.0 / np.sqrt(1.0 + self.of(a))) + + def stats(self): + nz = self.n[self.n > 0] + return {"total": int(self.n_total), "per_action": self.n.astype(int).tolist(), + "min": float(nz.min()) if len(nz) else 0.0, + "max": float(nz.max()) if len(nz) else 0.0, + "zero_actions": int((self.n == 0).sum())} + + +class Plasticity: + """元知识: α (该多快改变某个参数) 与 β (学习速度本身的变化)。 + + 这是 IDBD 主线在"内部知识"里的位置 —— 它**不是**知识本身, + 而是"关于知识该如何改变的"元知识。 + """ + + def __init__(self, dim, alpha0=0.05, mu=0.01): + self.alpha0 = alpha0 + self.mu = mu + self.beta = np.zeros(dim) + self.h = np.zeros(dim) + self.updates = 0 + + @property + def alpha(self): + return np.exp(np.clip(self.beta, -20, 20)) * self.alpha0 + + def update(self, phi, delta): + g = delta * phi * self.h + self.h = np.maximum(0.0, self.h * (1.0 - np.clip(self.alpha * phi ** 2, 0, 1)) + self.alpha * phi * delta) + self.beta = np.clip(self.beta + self.mu * g, -20, 20) + self.updates += 1 + + def stats(self): + a = self.alpha + return {"mean": float(a.mean()), "std": float(a.std()), + "min": float(a.min()), "max": float(a.max()), + "ratio_max_min": float(a.max() / max(a.min(), 1e-12)), + "updates": self.updates} + + +# ────────────────────────────────────────────────────────────────────────── +# InternalKnowledge: 统一的知识容器 +# ────────────────────────────────────────────────────────────────────────── + + +class InternalKnowledge: + """K = {predictions, dynamics, value, options, uncertainty, coverage, plasticity} + + ★ 不暴露参数, 只暴露**知识查询**。 + """ + + def __init__(self, n_dom, dim_x, n_models=5, seed=0, alpha0=0.05, mu=0.01): + self.n_dom = int(n_dom) + self.dim_x = int(dim_x) + # Level 1 预测知识 K_P + self.predictions = GVFBank(dim_x, seed=seed) + # Level 2 因果/转移知识 K_T + from hibs_lnn.uncertainty_gate import TransitionEnsemble + self.dynamics_model = TransitionEnsemble(n_models=n_models, seed=seed) + self._dyn_fitted = False + # Level 3 价值/策略知识 K_V (由外部 proposer 提供, 这里只登记) + self.value_fn = None + self.policy = None + # Level 4 抽象知识 K_A + self.options = None + # 元知识 + from hibs_lnn.uncertainty_gate import UncertaintyGate + self.uncertainty = UncertaintyGate(tau_C=1.0) + self.coverage = Coverage(self.n_dom) + self.plasticity = Plasticity(dim_x, alpha0=alpha0, mu=mu) + self.seed = seed + + # ── 写入 ────────────────────────────────────────────────────────── + def fit_dynamics(self, X, Y): + self.dynamics_model.fit(X, Y, self.n_dom, None) + self._dyn_fitted = True + # 标定不确定性阈值 + visits = self.coverage.n + n = len(X) + step = max(1, n // 100) + self.uncertainty.calibrate( + [self.dynamics_model.predict(X[i, :self.dim_x], int(X[i, -1]), visits)[1] + for i in range(0, n, step)]) + return self + + def register_value_fn(self, fn, policy=None): + """登记价值/策略知识 (K_V)。fn(s, a) -> V。""" + self.value_fn = fn + self.policy = policy + return self + + def register_options(self, option_manager): + """登记抽象知识 (K_A)。""" + self.options = option_manager + return self + + def observe_gvf(self, phi, cumulants, phi_next): + return self.predictions.update(phi, cumulants, phi_next) + + def observe_action(self, a): + self.coverage.observe(a) + + def observe_plasticity(self, phi, delta): + self.plasticity.update(phi, delta) + + # ── 查询 (知识接口) ──────────────────────────────────────────────── + def predict(self, s, a): + """未来会发生什么 (Level 1+2 合成)。""" + if not self._dyn_fitted: + return np.asarray(s, dtype=float).copy(), float("inf") + return self.dynamics_model.predict(s, a, self.coverage.n) + + def value(self, s, a): + """什么结果更好 (Level 3)。 + + ★ **对未知输入必须返回 NaN, 而不是抛异常**。 + 知识层被问到一个它没见过的 (s,a) 时, 正确反应是"我不知道", 不是崩。 + (这条是实测抓到的: 测试里对从未出现过的动作 7 查询, value_fn 抛 KeyError + 一路炸穿 knowledge_of —— 而"承认不知道"恰恰是这一层的核心语义。) + """ + if self.value_fn is None: + return float("nan") + try: + return float(self.value_fn(s, a)) + except Exception: + return float("nan") + + def skills(self): + """哪些过程是一个整体技能 (Level 4)。""" + if self.options is None: + return [] + return [o.stats() for o in getattr(self.options, "options", [])] + + def confidence(self, s, a): + """我对自己的预测有多确定 —— 返回 [0,1], 1 = 最确定。""" + _, unc = self.predict(s, a) + if not np.isfinite(unc): + return 0.0 + tau = self.uncertainty.tau_U + if tau is None or not np.isfinite(tau) or tau <= 0: + return 0.5 + return float(1.0 / (1.0 + unc / tau)) + + def coverage_of(self, a): + return self.coverage.of(a) + + def knowledge_of(self, s, a): + """★★ 统一查询: 对任意 (s,a), agent 到底知道什么。 + + 这是 Knowledge≠Parameter 的直接体现 —— 问的是**内容**不是参数。 + """ + s_next, unc = self.predict(s, a) + val = self.value(s, a) + novel = bool(self.coverage_of(a) == 0) + known = bool(not novel and np.isfinite(unc)) + return { + "state": np.round(np.asarray(s, dtype=float), 4).tolist(), + "action": int(a), + "predicted_next": np.round(np.asarray(s_next), 4).tolist(), + "uncertainty": float(unc) if np.isfinite(unc) else None, + "confidence": self.confidence(s, a), + "value": (val if np.isfinite(val) else None), + "coverage": self.coverage_of(a), + "coverage_gate": self.coverage.gate(a), + "trustworthy": bool(self.uncertainty.allow(s, a, self.coverage.n)[0]), + "novel": novel, + # ★ 这一项是整层的立命之处: agent 明确区分「我知道」与「我不知道」 + "known": known, + "verdict": ("我不知道" if not known else + ("不太确定" if self.confidence(s, a) < 0.5 else "我知道")), + } + + # ── 摘要 / 持久化 ───────────────────────────────────────────────── + def describe(self) -> dict: + """分层摘要: 这是我"知道了什么"的完整画像。""" + return { + "Level1_predictions": self.predictions.stats(), + "Level2_dynamics": {"fitted": self._dyn_fitted, + "n_models": len(getattr(self.dynamics_model, "Ws", []) or []), + "dim_x": self.dim_x, "n_dom": self.n_dom}, + "Level3_value": {"registered": self.value_fn is not None, + "policy_registered": self.policy is not None}, + "Level4_abstraction": {"n_options": len(self.skills()), + "options": self.skills()[:6]}, + "meta_uncertainty": {"tau_U": self.uncertainty.tau_U, + "tau_C": self.uncertainty.tau_C}, + "meta_coverage": self.coverage.stats(), + "meta_plasticity": self.plasticity.stats(), + } + + def to_dict(self) -> dict: + """知识可以被**留下来** —— 持久化后能与旧版本比对。""" + return { + "coverage_n": self.coverage.n.tolist(), + "coverage_total": self.coverage.n_total, + "gvf_w": [g.w.tolist() for g in self.predictions.gvfs], + "gvf_steps": [g.steps for g in self.predictions.gvfs], + "dyn_W": [W.tolist() for W in (getattr(self.dynamics_model, "Ws", None) or [])], + "tau_U": self.uncertainty.tau_U, + "plasticity_beta": self.plasticity.beta.tolist(), + "plasticity_updates": self.plasticity.updates, + "n_options": len(self.skills()), + } + + def growth(self, old_dict) -> dict: + """★ 与旧版本比对: 知识**具体**变了什么。 + + 这是"持续构建内部知识"的可判定形式 —— 不是"又训了一轮", + 而是能逐项指出知识的变化量。 + """ + if not old_dict: + return {"note": "no prior knowledge"} + out = {} + # 覆盖度变化 + old_n = np.array(old_dict.get("coverage_n", [0] * self.n_dom), dtype=float) + out["coverage_delta"] = (self.coverage.n - old_n).astype(int).tolist() + out["coverage_total_delta"] = int(self.coverage.n_total - old_dict.get("coverage_total", 0)) + # GVF 权重变化 (预测知识变了多少) + gw_old = old_dict.get("gvf_w") + if gw_old is not None and len(gw_old) == len(self.predictions.gvfs): + d = [float(np.linalg.norm(np.array(g.w) - np.array(o))) + for g, o in zip(self.predictions.gvfs, gw_old)] + out["gvf_weight_delta"] = np.round(d, 6).tolist() + out["gvf_weight_delta_mean"] = float(np.mean(d)) + # 转移模型变化 + dw_old = old_dict.get("dyn_W") + cur = getattr(self.dynamics_model, "Ws", None) + if dw_old and cur and len(dw_old) == len(cur): + d = [float(np.linalg.norm(np.array(W) - np.array(o))) for W, o in zip(cur, dw_old)] + out["dyn_weight_delta_mean"] = float(np.mean(d)) + # 可塑性元知识变化 + pb_old = old_dict.get("plasticity_beta") + if pb_old is not None and len(pb_old) == len(self.plasticity.beta): + out["plasticity_beta_delta"] = np.round( + self.plasticity.beta - np.array(pb_old), 6).tolist() + out["n_options_delta"] = len(self.skills()) - old_dict.get("n_options", 0) + return out + + def stats(self): + return {"n_dom": self.n_dom, "dim_x": self.dim_x, + "dyn_fitted": self._dyn_fitted, "n_options": len(self.skills()), + "coverage_total": int(self.coverage.n_total)} diff --git a/hibs_lnn/meta_rule_world.py b/hibs_lnn/meta_rule_world.py new file mode 100644 index 0000000000000000000000000000000000000000..c502c972ad746a5f487c34a1fc7c4ef1c4f50e2c --- /dev/null +++ b/hibs_lnn/meta_rule_world.py @@ -0,0 +1,387 @@ +""" +V31.0/V31.2: MetaRuleWorld — 元学习任务分布世界 +============================================ +元学习设定: 每个"任务" = 一个规则变体 (remap 参数化)。 + +任务 T_i 的规则 = 一个寄存器重映射: + remap=None → 标准执行 (规则 A) + remap=(0,1) → R0↔R1 互换 (规则 B) [兼容旧接口: pair] + remap=(1,3,2,0) → 任意排列 perm[reg] [V31.2: 全 24 种排列] + +V31.2 长流扩展 (S4 对称群, 24 条规则): + S4 = 1 identity + 6 对换 + 8 三轮换 + 6 四轮换 + 3 双对换 + 训练: identity + 6 对换 + 5 三轮换 = 12 条 + 未见: 3 三轮换 + 6 四轮换 + 3 双对换 = 12 条 (结构上不同 → "没学过的学习方法") + +目标 (Javed & White NeurIPS 2019 精神): + 不学"预测所有规则"的单一 head (V30.4 失败模式), + 而是学一个表示 φ, 使任意规则可以快速适应 (内循环 head 更新)。 + +数据格式 (离线监督式): + context = 最近 window 个 READ 观察 (5 tokens each) → 预测下一个 READ (5 tokens) + 模型从观察历史推断当前规则 → 预测 EXEC 后的寄存器状态。 +""" + +import torch +import random +from itertools import permutations +from .code_world import ( + RegisterMachine, CodeSandboxWorld, + N_REGISTERS, INSTR_LEN, OPCODE_TOKEN_OFFSET, + OPCODES, OPCODES_INV, +) + + +def s4_groups(): + """S4 按结构分组: (identity, swaps, 3cycles, 4cycles, double_swaps)。""" + allp = list(permutations(range(4))) + identity = [(0, 1, 2, 3)] + swaps, c3, c4, dbl = [], [], [], [] + for p in allp: + if p == (0, 1, 2, 3): + continue + fixed = sum(1 for i in range(4) if p[i] == i) + if fixed == 2: + swaps.append(p) # 单对换 + elif fixed == 1: + c3.append(p) # 三轮换 + elif fixed == 0: + # 四轮换 (阶 4) vs 双对换 (阶 2) + if all(p[p[i]] == i for i in range(4)): + dbl.append(p) # 双对换 (Klein) + else: + c4.append(p) # 四轮换 + return identity, swaps, c3, c4, dbl + + +# V31.2 规则划分: 训练 = identity + 6 对换 + 5 三轮换 (12) +# 未见 = 3 三轮换 + 6 四轮换 + 3 双对换 (12, 结构不同) +def build_rule_splits(seed=42): + identity, swaps, c3, c4, dbl = s4_groups() + rng = random.Random(seed) + rng.shuffle(c3); rng.shuffle(c4); rng.shuffle(dbl) + train = identity + swaps + c3[:5] + test = c3[5:] + c4 + dbl + return train, test + + +# 各 opcode 的寄存器操作数位置 (立即数位置不重映射): +# SET dst,val → a1 寄存器, a2 立即数 (0-99) +# MOV src,dst → a1,a2 寄存器 +# ADD/SUB a,b,d → a1,a2,a3 寄存器 +# INC/DEC reg → a1 寄存器 +# SWP a,b → a1,a2 寄存器 +_REG_OPPOS = {0: (), 1: (0,), 2: (0, 1), 3: (0, 1, 2), 4: (0, 1, 2), + 5: (0,), 6: (0,), 7: (0, 1)} + + +class MetaRuleMachine(RegisterMachine): + """RegisterMachine + 参数化寄存器重映射 (任意排列或 pair)。 + + V31.2 修复 (原 V31.0 bug): 只重映射**寄存器操作数**位置, + 立即数 (SET 的 a2, 0-99) 不受 remap 影响。 + 原实现 `_remap` 作用于所有操作数 → 立即数被 mod 4 重映射, + "SET R0 42 → R1=42" 变成 "SET R1 2" — 规则语义被污染 + (V31.0 世界自测的 remap 项其实一直失败, 计数器有 bug 显示 7/7)。 + """ + + def __init__(self, V, remap_pair=None, remap=None): + super().__init__(V) + if remap is None: + if remap_pair is None: + remap = (0, 1, 2, 3) + else: + i, j = remap_pair + remap = tuple(j if k == i else (i if k == j else k) + for k in range(4)) + self.remap_pair = remap_pair # 兼容旧接口 (V31.0) + self.remap = remap # V31.2: 完整排列 (identity = (0,1,2,3)) + + def _remap(self, reg: int) -> int: + return self.remap[reg % N_REGISTERS] + + def step_execute(self) -> bool: + if self.halted or self.pc >= len(self.program): + self.halted = True + return False + if self.step_count >= self.max_steps: + self.halted = True + return False + + opcode, a1, a2, a3 = self.program[self.pc] + name = OPCODES_INV[opcode] + + if self.remap != (0, 1, 2, 3): + args = [a1, a2, a3] + for pos in _REG_OPPOS[opcode]: + args[pos] = self._remap(args[pos]) + a1, a2, a3 = args + + try: + if name == 'NOP': + pass + elif name == 'SET': + self.regs[a1 % N_REGISTERS] = a2 % self.V + elif name == 'MOV': + self.regs[a2 % N_REGISTERS] = self.regs[a1 % N_REGISTERS] + elif name == 'ADD': + self.regs[a3 % N_REGISTERS] = ( + self.regs[a1 % N_REGISTERS] + self.regs[a2 % N_REGISTERS] + ) % self.V + elif name == 'SUB': + self.regs[a3 % N_REGISTERS] = max( + 0, self.regs[a1 % N_REGISTERS] - self.regs[a2 % N_REGISTERS] + ) + elif name == 'INC': + self.regs[a1 % N_REGISTERS] = ( + self.regs[a1 % N_REGISTERS] + 1 + ) % self.V + elif name == 'DEC': + self.regs[a1 % N_REGISTERS] = max( + 0, self.regs[a1 % N_REGISTERS] - 1 + ) + elif name == 'SWP': + ri, rj = a1 % N_REGISTERS, a2 % N_REGISTERS + self.regs[ri], self.regs[rj] = self.regs[rj], self.regs[ri] + except Exception: + return False + + self.pc += 1 + self.step_count += 1 + return True + + +class MetaRuleWorld(CodeSandboxWorld): + """代码执行世界, 规则由 remap_pair 参数化。观察格式与 CodeSandboxWorld 一致。""" + + K = 5 + READ_WINDOW = N_REGISTERS + 1 # 4 regs + 1 opcode + ACTIONS = ['READ', 'EXEC', 'RESET', 'NEWREG', 'TOME'] + + def __init__(self, V: int, remap_pair=None, remap=None, + max_program_len: int = 8): + self.V = V + self.TOME = V + 1 + self.world_vocab_size = V + 2 + self.max_program_len = max_program_len + self.remap_pair = remap_pair + self.remap = remap + self._new_episode() + + def _new_episode(self): + self.vm = MetaRuleMachine(self.V, remap_pair=self.remap_pair, + remap=self.remap) + self.exec_count = 0 + self.current_step = 0 + self.n_erases = 0 + + prog_len = random.randint(2, self.max_program_len) + self.vm.generate_random_program(prog_len) + + for i in range(N_REGISTERS): + self.vm.regs[i] = random.randint(0, min(self.V - 1, 50)) + + pre_steps = random.randint(0, min(2, len(self.vm.program) - 1)) + for _ in range(pre_steps): + self.vm.step_execute() + self.exec_count += 1 + + def reset(self): + self._new_episode() + + +# ============================================================ +# 任务数据生成 (离线监督式) +# ============================================================ +# V31 观察格式 (8 tokens): [R0,R1,R2,R3, opcode+100, a1, a2, a3] +# → 完整指令可见, "预测 EXEC 后状态"是确定性问题 (信息完备) +# → 规则差异 (remap) 真正可学习: SET R0 42 → A: R0=42 / B: R1=42 +# 注意: 这与 V30 系列不同 (V30 只有 opcode 无操作数 → 信息不足 → pred 2-8%) + +def _instr_tokens(vm): + """当前 PC 指令的 4 个 token: [opcode+100, a1, a2, a3], 程序结束用 TOME。""" + if vm.halted or vm.pc >= len(vm.program): + return [vm.V + 1] * 4 # TOME + opcode, a1, a2, a3 = vm.program[vm.pc] + return [opcode + 100, min(a1, 99), min(a2, 99), min(a3, 99)] + + +def _world_obs(world): + """8-token 观察: [R0,R1,R2,R3, instr...]""" + vm = world.vm + state = [min(r, world.V - 1) for r in vm.regs] + return state + _instr_tokens(vm) + + +def gen_episode_pairs(world, V, max_len=8, window=3, stop_at_halt=False): + """ + 从一个 episode 生成 (context, target) 监督对。 + + context = 最近 window 个观察拼接 (window*8 tokens) + target = EXEC 后的观察 (8 tokens) + + 模型从观察历史推断当前规则 → 预测 EXEC 后的完整状态。 + 由于观察含完整指令, 给定规则下 target 是确定性的。 + + stop_at_halt (V33 世界修复): 程序结束 (halted) 后立即停止, + 不生成含 TOME(201) 填充的 target → 每个 target 都有信息量 + (旧世界 ~77.5% target 后 4 token 是 TOME, 虚高 full-8 acc)。 + """ + pairs = [] + obs = torch.tensor(_world_obs(world), dtype=torch.long) + history = [obs] + for _ in range(max_len): + world.query(1) # EXEC + obs = torch.tensor(_world_obs(world), dtype=torch.long) + if stop_at_halt and world.vm.halted: + break # 程序结束: 不再追加 TOME 观察 + history.append(obs) + for t in range(window, len(history)): + ctx = torch.cat(history[t - window:t]).long() # window*8 + tgt = history[t].long() # 8 + pairs.append((ctx, tgt)) + return pairs + + +def gen_task_data(remap_pair, V, n_episodes=48, max_len=8, window=3, + seed=42, max_program_len=8, remap=None, + stop_at_halt=False): + """ + 生成一个任务 (规则) 的数据集。 + 返回: list of (context_ids, target_ids), 均 clamp 到 [0, V+1]。 + """ + rng_state = random.getstate() + random.seed(seed) + pairs = [] + for _ in range(n_episodes): + w = MetaRuleWorld(V, remap_pair=remap_pair, remap=remap, + max_program_len=max_program_len) + pairs.extend(gen_episode_pairs(w, V, max_len=max_len, window=window, + stop_at_halt=stop_at_halt)) + random.setstate(rng_state) + # clamp + pairs = [(c.clamp(0, V + 1), t.clamp(0, V + 1)) for c, t in pairs] + return pairs + + +def gen_task_data_split(remap_pair, V, n_support=48, n_query=24, + max_len=8, window=3, seed=42, max_program_len=8, + remap=None, stop_at_halt=False): + """生成任务的 support/query 划分 (不同 seed 保证不重叠)。""" + support = gen_task_data(remap_pair, V, n_episodes=n_support, + max_len=max_len, window=window, + seed=seed, max_program_len=max_program_len, + remap=remap, stop_at_halt=stop_at_halt) + query = gen_task_data(remap_pair, V, n_episodes=n_query, + max_len=max_len, window=window, + seed=seed + 1000, max_program_len=max_program_len, + remap=remap, stop_at_halt=stop_at_halt) + return support, query + + +def _self_test(): + ok = 0 + total = 0 + def check(cond, msg): + nonlocal ok, total + total += 1 + if cond: + ok += 1 + print(f" OK {msg}") + else: + print(f" FAIL {msg}") + + random.seed(42) + V = 100 + + # 1. 规则 A (无 remap): SET R0 42 → R0=42 + vm_a = MetaRuleMachine(V=100, remap_pair=None) + vm_a.load_instruction([101, 0, 42, 0]) + vm_a.step_execute() + check(vm_a.regs[0] == 42 and vm_a.regs[1] == 0, + f"A: SET R0 42 → R0={vm_a.regs[0]}, R1={vm_a.regs[1]}") + + # 2. 规则 B (0,1): SET R0 42 → R1=42 + vm_b = MetaRuleMachine(V=100, remap_pair=(0, 1)) + vm_b.load_instruction([101, 0, 42, 0]) + vm_b.step_execute() + check(vm_b.regs[1] == 42 and vm_b.regs[0] == 0, + f"B: SET R0 42 → R1={vm_b.regs[1]}, R0={vm_b.regs[0]}") + + # 3. 规则 C (0,2): SET R0 42 → R2=42 + vm_c = MetaRuleMachine(V=100, remap_pair=(0, 2)) + vm_c.load_instruction([101, 0, 42, 0]) + vm_c.step_execute() + check(vm_c.regs[2] == 42 and vm_c.regs[0] == 0, + f"C: SET R0 42 → R2={vm_c.regs[2]}, R0={vm_c.regs[0]}") + + # 4. 规则 D (1,2): SET R1 42 → R2=42 + vm_d = MetaRuleMachine(V=100, remap_pair=(1, 2)) + vm_d.load_instruction([101, 1, 42, 0]) + vm_d.step_execute() + check(vm_d.regs[2] == 42 and vm_d.regs[1] == 0, + f"D: SET R1 42 → R2={vm_d.regs[2]}, R1={vm_d.regs[1]}") + + # 5. 数据生成: context/target shapes (8-token 观察) + w = MetaRuleWorld(V=100, remap_pair=(0, 1)) + pairs = gen_episode_pairs(w, V, max_len=8, window=3) + check(len(pairs) > 0, f"episode pairs = {len(pairs)}") + ctx, tgt = pairs[0] + check(ctx.shape[0] == 24 and tgt.shape[0] == 8, + f"ctx={tuple(ctx.shape)} tgt={tuple(tgt.shape)} (24/8)") + + # 5b. 信息完备性: 给定规则, target 确定 (SET R0 42 在规则 A/B 下不同) + random.seed(3) + wA = MetaRuleWorld(V=100, remap_pair=None) + wA.vm.program = [(1, 0, 42, 0)]; wA.vm.pc = 0; wA.vm.regs = [0, 0, 0, 0] + wA.vm.halted = False + obsA = _world_obs(wA) + wA.query(1) # EXEC + afterA = _world_obs(wA) + wB = MetaRuleWorld(V=100, remap_pair=(0, 1)) + wB.vm.program = [(1, 0, 42, 0)]; wB.vm.pc = 0; wB.vm.regs = [0, 0, 0, 0] + wB.vm.halted = False + obsB = _world_obs(wB) + wB.query(1) # EXEC + afterB = _world_obs(wB) + check(obsA[:4] == obsB[:4], "same state before EXEC") + check(afterA[:4] == [42, 0, 0, 0], f"A after SET R0 42: {afterA[:4]}") + check(afterB[:4] == [0, 42, 0, 0], f"B after SET R0 42: {afterB[:4]}") + check(afterA[:4] != afterB[:4], "rule difference visible in targets") + + # 6. support/query 划分 + sup, qry = gen_task_data_split((0, 1), V, n_support=8, n_query=4, seed=42) + check(len(sup) > 0 and len(qry) > 0, f"support={len(sup)} query={len(qry)}") + + # 7. 不同规则产生不同数据分布 (相同 seed, 相同程序 → 不同状态) + random.seed(7) + w1 = MetaRuleWorld(V=100, remap_pair=None) + w2 = MetaRuleWorld(V=100, remap_pair=(0, 1)) + # 手动构造相同程序 + prog = [(1, 0, 42, 0), (5, 0, 0, 0)] # SET R0 42; INC R0 + w1.vm.program = prog[:]; w1.vm.pc = 0; w1.vm.regs = [0, 0, 0, 0] + w2.vm.program = prog[:]; w2.vm.pc = 0; w2.vm.regs = [0, 0, 0, 0] + w1.vm.step_execute(); w2.vm.step_execute() + s1 = w1.vm.regs[:]; s2 = w2.vm.regs[:] + check(s1[0] == 42 and s2[1] == 42, + f"same prog diverges: A={s1[:2]}, B={s2[:2]}") + + # 8. V31.2 排列规则: 只重映射寄存器, 立即数不动 + vm_p = MetaRuleMachine(V=100, remap=(1, 3, 2, 0)) + vm_p.load_instruction([101, 0, 42, 0]) # SET R0 42 + vm_p.step_execute() + check(vm_p.regs[1] == 42, + f"perm(1,3,2,0): SET R0 42 → R1={vm_p.regs[1]} (立即数 42 不受影响)") + vm_p2 = MetaRuleMachine(V=100, remap=(1, 3, 2, 0)) + vm_p2.load_instruction([104, 0, 1, 2]) # SUB R0 R1 → R2 + vm_p2.step_execute() + # SUB R0(→1) R1(→3) → R2(→2): regs[2] = regs[1] - regs[3] = 0 + check(vm_p2.regs[2] == 0, + f"perm SUB: R0,R1→R2 remap 后 regs[2]=regs[1]-regs[3]") + + print(f"\n {ok}/{total} passed") + return ok == total + + +if __name__ == "__main__": + _self_test() diff --git a/hibs_lnn/oak_proposer.py b/hibs_lnn/oak_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..5d0ce2ad47e0806a12e5e2d9dd5d049c9c97b2ed --- /dev/null +++ b/hibs_lnn/oak_proposer.py @@ -0,0 +1,444 @@ +"""oak_proposer.py — OaK 提议器: 把 InternalKnowledge + Options 接进主回路。 + +## 状态/动作的定义 (在这个问题里) + + state = 各细区间的**能力画像** (维度 = n_fine) "我现在会什么" + action = 选哪个细区间训练 (0..n_fine-1) + next = 训练后的新能力画像 + reward = Δ(any-time 准确率) + +这就是真实的持续学习闭环: **动作是"去练哪块", 环境是"练完能力怎么变"。** +`T(s,a) -> s'` 因此是一个**真正可学、数据里天然存在**的转移 —— 不是硬凑的。 + +## 结构 + + OAKProposer + ├── base : RLProposer (Level 3 价值/策略知识, 复用已验证机制) + └── know : InternalKnowledge (Level 1/2/4 + 三类元知识) + ├── predictions GVFBank + ├── dynamics TransitionEnsemble + ├── options OptionManager + ├── uncertainty UncertaintyGate + ├── coverage Coverage + └── plasticity Plasticity + +## act() 的决策顺序 + + ① 有 option 在跑且未终止 -> 沿 option 的动作序列走 + ② 否则取 base 的候选 -> 过**门控**; 被拒则退回已覆盖动作 + ③ 记录 (state, action) -> 供 dynamics/option 发现用 + +## E9 vs E10 就是一个 flag + + use_options=False -> E9 (+coverage/uncertainty 门控) + use_options=True -> E10 (+Options) + 两者共用同一 base 与同一门控, 差异**只**在于是否使用时间抽象。 +""" +from __future__ import annotations + +import numpy as np + +from hibs_lnn.knowledge import InternalKnowledge + + +class OAKProposer: + def __init__(self, fine_desc, n_fine, k=3, seed=0, tau=0.5, + mu=0.05, alpha0=0.2, explore_w=0.5, algo="autostep", + n_know=0, use_options=True, use_gate=True, + n_models=5, refresh_every=4, min_transitions=40, + n_regions=4, max_opt_len=3, opt_frac=1.0, + opt_mode="fixed", term_eps=0.05, stall_tol=0.0, + dyn_model="linear", n_regions_opt=16, use_subgoals=0): + from hibs_lnn.rl_proposer import RLProposer + self.n_fine = int(n_fine) + self.n = self.n_fine + self.k = int(k) + self.seed = seed + self.use_options = bool(use_options) + self.use_gate = bool(use_gate) + self.refresh_every = int(refresh_every) + self.min_transitions = int(min_transitions) + self.n_regions = int(n_regions) + self.max_opt_len = int(max_opt_len) + # ★ 诊断用: 只在一部分轮次允许启用 option。 + # 若"Options 有害"确实来自 option 执行本身 (提交一段固定动作序列 -> + # 减少自适应性 / 降低探索多样性), 那么伤害应当**随 opt_frac 单调放大**。 + # 若伤害与用量无关, 说明另有来源 (如 discovery 本身在扰动状态)。 + self.opt_frac = float(opt_frac) + self._opt_round = 0 + # ★ opt_mode: Option 的**执行方式** (用户 2026-09-14 指导的 K1 矩阵) + # "fixed" O1 固定动作序列 = open-loop (旧行为, D1 的病灶) + # "goal" O2 目标条件 + **每步重算动作** (闭环) + # "goal_term" O3 O2 + 自适应终止 β_o(s) + # "goal_term_override" O4 O3 + 不确定性抢占 (Option 从属于当前证据) + self.opt_mode = str(opt_mode) + self.term_eps = float(term_eps) + self.stall_tol = float(stall_tol) + self._opt_stall_d = None + self.override_count = 0 + self.term_reasons = {} + self.replan_steps = 0 + self.gate_blocked = 0 # _goal_action 因门控全拒返回 None + self.no_finite_unc = 0 # _goal_action 因无有限不确定性返回 None + self.goal_none = 0 # _goal_action 返回 None 总次数 + self.start_blocked = 0 # 起 option 时算不出动作 (静默空转) 的次数 + # ★ option 有效期。原实现一旦启动就**永不重选** (实测: 1 个 option + # 连跑 105 步 = 训练全程的 87%)。那不是 option, 是"锁死"。 + # 正确语义: option 是**有名义长度的技能**, 跑完它的长度就结束, + # 由外层重新评估该用哪个技能 (或交回 base policy)。 + self._opt_age = 0 + self._opt_max_age = int(max_opt_len) + # ★ 世界模型 (实测: 线性 ridge 表达不了结构化动力学 —— 在 KeyDoor 这类 + # 离散/条件性动力学上四个动作预测到**同一个**下一状态, 转移图退化成 + # 几乎无边, option 发现为 0。计数式表格模型修掉了这个。) + self.dyn_model_kind = str(dyn_model) + self.n_regions_opt = int(n_regions_opt) + self.use_subgoals = int(use_subgoals) + + # Level 3: 复用已验证的 RL 提议器 + self.base = RLProposer(fine_desc, tau=tau, k=k, mu=mu, alpha0=alpha0, + explore_w=explore_w, algo=algo, n_know=n_know, + seed=seed) + # ★ 内部知识**惰性创建**: 状态维度在构造时未知。 + # 主回路的状态是 `row` = 每**粗域**准确率 (长度 = --domains), + # 而动作数是细区间数 n_fine —— 两个维度**不同**。 + # (实测: 按 n_fine 建 GVF 会在 observe 时撞 + # `size 3 is different from 6`)。 + self._know_cfg = dict(n_models=n_models, seed=seed, alpha0=alpha0, mu=mu) + self.know = None + self.dim_state = None + self._state_scale = 0.0 + + # 转移日志: (state_t, action, state_{t+1}) + self.trans = [] + self._prev_state = None + self._prev_pick = None + self._last_acc = {} + self._opt_queue = [] # 正在执行的 option 剩余动作 + self._opt_active = None + self.refresh_count = 0 + self.untrusted_picks = 0 # 提议落在不可信区的次数 (仅诊断) + self.fallback_count = 0 # 保留字段 (旧: 门控重定向次数, 已废弃) + self.option_steps = 0 # 由 option 决定的步数 + self.option_starts = 0 # option 被启动的次数 + + # ── 惰性初始化 ────────────────────────────────────────────────── + def _ensure_know(self, dim_state): + if self.know is not None: + return self.know + self.dim_state = int(dim_state) + self.know = InternalKnowledge(self.n_fine, self.dim_state, **self._know_cfg) + if self.dyn_model_kind == "tabular": + from hibs_lnn.uncertainty_gate import TabularTransition + self.know.dynamics_model = TabularTransition() + self.know.register_value_fn( + lambda s, a: float(self._last_acc.get(int(a), np.nan)), + policy="rl-base") + return self.know + + # ── 记录环境反馈 ──────────────────────────────────────────────── + def set_state(self, state): + """每轮训练后由主回路喂入**新**能力画像。""" + st = np.asarray(state, dtype=float) + # 状态尺度 = 相邻两轮状态变化的典型幅度 (用于自适应阈值) + if getattr(self, "_prev_state", None) is not None: + _d = float(np.linalg.norm(st - np.asarray(self._prev_state, dtype=float))) + _a = getattr(self, "_state_scale", 0.0) + self._state_scale = _d if _a == 0.0 else 0.9 * _a + 0.1 * _d + self._ensure_know(st.shape[0]) + if self._prev_state is not None and self._prev_pick is not None: + for a in (self._prev_pick if isinstance(self._prev_pick, (list, tuple)) + else [self._prev_pick]): + self.trans.append((self._prev_state.copy(), int(a), st.copy())) + self.know.observe_action(int(a)) + self._prev_state = st + self._last_acc = {i: float(v) for i, v in enumerate(st) + if np.isfinite(v)} + + def set_pick(self, pick): + """记录本轮真正执行的动作。""" + self._prev_pick = list(pick) if isinstance(pick, (list, tuple)) else [pick] + + # ── 动作选择 ─────────────────────────────────────────────────── + def act(self): + # ① option 在执行中 + if self.use_options and self._opt_active is not None and self.opt_mode != "fixed": + o = self._opt_active + s_now = self._prev_state + if s_now is None: + self._opt_active = None + else: + _, unc_a = self.know.predict(s_now, int(o.actions[0])) + tau = self.know.uncertainty.tau_U + if self.opt_mode == "goal_term_override": + if (not np.isfinite(unc_a)) or (tau and unc_a >= tau): + self.override_count += 1 + self.term_reasons["override"] = self.term_reasons.get("override", 0) + 1 + self._opt_active = None + return list(self.base.act()) + # ★ 有效期检查 (对**所有**闭环模式生效) + self._opt_age += 1 + if self._opt_age > self._opt_max_age: + self.term_reasons["expired"] = self.term_reasons.get("expired", 0) + 1 + self._opt_active = None + self._opt_age = 0 + return list(self.base.act()) + if self.opt_mode in ("goal_term", "goal_term_override"): + done, why = o.terminated( + s_now, unc=unc_a, eps=self._eps_eff(), + tau_U=(tau if self.opt_mode == "goal_term_override" else None), + stall=self.stall_tol) + if done: + self.term_reasons[why] = self.term_reasons.get(why, 0) + 1 + self._opt_active = None + return list(self.base.act()) + a = self._goal_action(o) + if a is None: + self._opt_active = None + else: + self.replan_steps += 1 + self.option_steps += 1 + return [int(a)] + if self.use_options and self._opt_queue and self.opt_mode == "fixed": + a = int(self._opt_queue.pop(0)) + self.option_steps += 1 + if not self._opt_queue: + self._opt_active = None + return [a] + + pick = list(self.base.act()) + + # ② ★ 门控**不用于 action selection**。 + # 早先版本在这里把零覆盖动作重定向到 `argmin(visits)` 的已覆盖动作, + # 结果是灾难性的: 一旦某个动作被覆盖, 门控就把**所有**提议都指向它 + # (实测 `per_action = [0,9,0,0,0,0]`, 9 轮只练了 1 个区间, 探索崩塌)。 + # + # 语义纠正 (Q8 原文): "目标策略必须在现有经验中得到足够的信息支持" —— + # 这约束的是**模型驱动的规划决策** (凭预测去选), **不是**阻止 base + # policy 去探索。往零覆盖区探索恰恰是想要的: 覆盖度只能这样长起来。 + # 所以门控只作用在**模型被咨询**的地方 (option 选择 / rollout), + # 那两处已由 OptionManager.select(exclude_unc=True) 与 + # ens.rollout(gate=...) 各自保证。 + if self.use_gate and self.know is not None: + # 只**记录**当前提议是否落在不可信区 (诊断用, 不做干预) + for a in pick: + if int(a) < self.n_fine: + _, unc = self.know.predict(self._prev_state + if self._prev_state is not None + else np.zeros(self.dim_state or 1), int(a)) + tau = self.know.uncertainty.tau_U + if (not np.isfinite(unc)) or (tau and unc >= tau): + self.untrusted_picks += 1 + + # ③ 起一个新 option (受 opt_frac 限制) + self._opt_round += 1 + _allow_opt = (self.opt_frac >= 1.0 + or (self.opt_frac > 0 and + (self._opt_round % max(1, int(round(1.0 / self.opt_frac)))) == 0)) + if (_allow_opt and self.use_options and self.know is not None + and self.know.options is not None and self._prev_state is not None): + o = self.know.options.select(self._prev_state, exclude_unc=True) + if o is not None and len(getattr(o, "actions", [])) > 1: + self._opt_active = o + # ★ 返回 option 的**第一个**动作。早先实现假设"起点的那个动作 + # 已经执行过了", 于是只排队 actions[1:] —— 结果 option 的语义 + # 只被用了一半, option_steps 实测只有 1~2 步。 + self._opt_queue = [int(x) for x in o.actions[1:]] + if self.opt_mode == "fixed": + self._opt_queue = [int(x) for x in o.actions[1:]] + self.option_starts += 1 + return [int(o.actions[0])] + a = self._goal_action(o) # 闭环: 立刻按当前状态算 + if a is None: + # ★ 计数诚实性: 算不出动作的 option **不算启动**。 + # 早先实现先 `option_starts += 1` 再判断, 于是"启动了 N 次但 0 步执行" + # 看起来像机制在跑, 实际是 inert -> 一个**静默的空转实验**。 + self._opt_active = None + self.start_blocked += 1 + return pick + self.option_starts += 1 + self.replan_steps += 1 + self.option_steps += 1 + return [int(a)] + return pick + + def _eps_eff(self): + """目标达成阈值 —— **必须与状态空间尺度匹配**。 + + 原实现固定 term_eps=0.05 (给"距离"用的量级), 但状态是**准确率向量**, + 单步变化就有 0.1~0.5 -> ‖s−g‖ < 0.05 几乎不可能 -> β_o 永不触发 + -> `goal_term` 与 `goal` 给出**逐位相同**的结果 (实测: term={}). + """ + sc = getattr(self, "_state_scale", 0.0) + return max(0.05, 0.25 * sc) if sc > 0 else 0.05 + + def _goal_action(self, opt): + """π_o(s_t) —— **按当前状态重算**动作, 而不是回放固定序列。 + + a = argmax_a [ ‖g−s‖ − ‖g−T(s,a)‖ ] + 即"哪一步动作让我朝子目标前进最多"。 + + ★ 用户指出的核心修正: **抽象的是目标, 不是动作**。 + 固定序列执行 = "我发现了一段过去有效的脚本, 现在重放它", + 状态变化不再触发动作重算 -> 适应性下降 (D1 实测 r=-0.80)。 + """ + s_now = self._prev_state + g = np.asarray(opt.goal_center, dtype=float) + d_now = float(np.linalg.norm(np.asarray(s_now, dtype=float) - g)) + best_a, best_gain = None, -np.inf + n_gate_rej = 0 + n_unc_bad = 0 + for a in range(self.n_fine): + if self.use_gate: + ok, _ = self.know.uncertainty.allow(s_now, a, self.know.coverage.n) + if not ok: + n_gate_rej += 1 + continue + s_next, unc = self.know.predict(s_now, a) + if not np.isfinite(unc): + n_unc_bad += 1 + continue + gain = d_now - float(np.linalg.norm(np.asarray(s_next, dtype=float) - g)) + if gain > best_gain: + best_a, best_gain = a, gain + if best_a is None: + self.goal_none += 1 + if n_gate_rej == self.n_fine: + self.gate_blocked += 1 + elif n_unc_bad > 0: + self.no_finite_unc += 1 + return best_a + + # ── 奖励 → 参数更新 (+ 知识维护) ──────────────────────────────── + def observe(self, pick, acc, any_time): + self.base.observe(pick, acc=acc, any_time=any_time) + + def update(self, pick): + self.base.update(pick) + # ★★ 必须在这里把**实际执行的动作**记进 coverage。 + # 实测抓到的 bug(第 4 次同型): `InternalKnowledge.observe_action()` 是 + # coverage 的**唯一写入点**, 但主回路从来没调过它 -> `coverage.n` 永远全零 + # -> `UncertaintyGate.allow()` 对**所有**动作返回 False + # (因为 `0 <= tau_C=1.0`) -> `_goal_action()` 的每一个候选都被 continue 掉 + # -> `best_a = None` -> 回落到 base -> 轨迹与 E9 **逐位相同** + # (实测证据: goal/goal_term/override 三档的 alpha 与 e9 完全相同, + # `option_starts > 0` 但 `option_steps == 0`)。 + if self.know is not None: + _pick = pick if isinstance(pick, (list, tuple)) else [pick] + for _a in _pick: + if int(_a) >= 0: + self.know.observe_action(int(_a)) + # 预测知识: cumulant = 各区间能力本身 (GVF 学"能力会怎么走") + if self.know is not None and self._prev_state is not None: + phi = self._prev_state + cums = [float(np.mean(phi)), + float(np.max(phi) - np.min(phi)), + float(np.std(phi)), + float(np.mean(np.diff(np.sort(phi)[::-1][:3])) if self.n_fine >= 3 else 0.0)] + self.know.observe_gvf(phi, cums, phi) + # 周期性重建 world model + 重新发现 option + if (self.know is not None and len(self.trans) >= self.min_transitions + and self.refresh_count < len(self.trans) // max(1, self.refresh_every)): + self.refresh() + self.refresh_count += 1 + + def refresh(self): + """把转移日志灌进 dynamics ensemble -> 标定门控 -> 重新发现 options。""" + if self.know is None: + return + try: + X = np.array([np.append(s, a) for s, a, _ in self.trans]) + Y = np.array([sp for _, _, sp in self.trans]) + self.know.fit_dynamics(X, Y) + from hibs_lnn.option_manager import OptionManager + om = OptionManager(self.know.dynamics_model, self.know.uncertainty, + max_len=self.max_opt_len, seed=self.seed) + states = np.array([s for s, _, _ in self.trans]) + acts = np.array([a for _, a, _ in self.trans]) + # ★ 表格模型下按**真实状态**建图 (不是聚类中心) + n_uniq = len(np.unique(np.round(states, 6), axis=0)) + nreg = (min(self.n_regions_opt, max(4, n_uniq)) + if self.dyn_model_kind == "tabular" + else min(self.n_regions, max(2, len(states) // 10))) + if self.use_subgoals: + om.discover_subgoals(states, acts, n_regions=nreg) + else: + om.discover(states, acts, n_regions=nreg) + self.know.register_options(om) + except Exception as e: # 知识重建失败不该拖垮主实验 + print("!! knowledge refresh 失败:", e, flush=True) + + # ── 诊断 ─────────────────────────────────────────────────────── + def stats(self): + s = self.base.proposer_stats() if hasattr(self.base, "proposer_stats") else {} + # ★ 真步长统计: 取自 base 的最近一条 trace (RLProposer 在那里记录 α) + _tr = getattr(self.base, "trace", None) or [] + if _tr: + s["alpha_mean"] = _tr[-1].get("alpha_mean") + s["alpha_std"] = _tr[-1].get("alpha_std") + s["h_norm"] = _tr[-1].get("h_norm") + if self.know is None: + s.update({"n_trans": len(self.trans), "refreshes": 0, "n_options": 0, + "option_steps": self.option_steps, + "opt_frac": self.opt_frac, + "opt_mode": self.opt_mode, + "dyn_model": self.dyn_model_kind, + "use_subgoals": self.use_subgoals, + "replan_steps": self.replan_steps, + "state_scale": float(getattr(self, "_state_scale", 0.0)), + "eps_eff": float(self._eps_eff()), + "override_count": self.override_count, + "term_reasons": dict(self.term_reasons), + "option_starts": self.option_starts, "untrusted_picks": self.untrusted_picks, + "coverage_total": 0, "tau_U": None, "gvf_steps": [], "alpha_std": 0.0}) + return s + s.update({ + "n_trans": len(self.trans), + "refreshes": self.refresh_count, + "n_options": len(self.know.skills()), + "option_steps": self.option_steps, + "option_starts": self.option_starts, + "opt_frac": self.opt_frac, + "opt_mode": self.opt_mode, + "dyn_model": self.dyn_model_kind, + "use_subgoals": self.use_subgoals, + "replan_steps": self.replan_steps, + "override_count": self.override_count, + "gate_blocked": self.gate_blocked, "no_finite_unc": self.no_finite_unc, + "start_blocked": self.start_blocked, + "goal_none": self.goal_none, + "term_reasons": dict(self.term_reasons), + "untrusted_picks": self.untrusted_picks, + "coverage_total": int(self.know.coverage.n_total), + "tau_U": self.know.uncertainty.tau_U, + "gvf_steps": [g.steps for g in self.know.predictions.gvfs], + # ★ 不要放在 "alpha_std" 这个键上 —— 它会**覆盖** base.proposer_stats() + # 里真正的步长统计。早先版本就是这么写的, 结果 JSON 里几个不同算法 + # (idbd / idbd-raw / cidbd) 的 alpha_std 全是同一个**未被更新**的死对象 + # 的值 (2.78e-17), 看起来像"算法不生效"。真正在用的 α 在 base.trace。 + "know_alpha_std": float(self.know.plasticity.alpha.std()), + }) + return s + + def stats_extra(self): + """主实验落盘用。""" + if self.know is None: + return {"knowledge": {"n_options": 0, "coverage": {}, "plasticity": {}, + "tau_U": None, "options": []}, + "oak": {"use_options": self.use_options, "use_gate": self.use_gate, + "n_trans": len(self.trans), "refreshes": 0, + "option_steps": self.option_steps, + "option_starts": self.option_starts, + "untrusted_picks": self.untrusted_picks}} + d = self.know.describe() + return {"knowledge": { + "n_options": d["Level4_abstraction"]["n_options"], + "coverage": d["meta_coverage"], + "plasticity": d["meta_plasticity"], + "tau_U": d["meta_uncertainty"]["tau_U"], + "options": d["Level4_abstraction"]["options"], + }, "oak": {"use_options": self.use_options, "use_gate": self.use_gate, + "n_trans": len(self.trans), "refreshes": self.refresh_count, + "option_steps": self.option_steps, + "option_starts": self.option_starts, + "untrusted_picks": self.untrusted_picks}} diff --git a/hibs_lnn/option_manager.py b/hibs_lnn/option_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..887f8532a57b6850c6c7d37f13f09a4ed7aa017f --- /dev/null +++ b/hibs_lnn/option_manager.py @@ -0,0 +1,502 @@ +"""option_manager.py — Options / 时间抽象 (OaK 的 O)。 + +## Option 的三要素 (用户定义) + + o = (I_o, π_o, β_o) + + I_o initiation set 什么时候可以启动这个 option + π_o option policy 启动后怎么行动 (这里是**一串子动作**, 不是单个动作) + β_o termination 什么时候结束 —— **学习式** P(terminate | s, o) + +## ★ 不从 reward 定义 option, 而从「可预测的状态转移结构」发现 + +用户: *"Option 不一定首先由 reward 定义, 而可以由可预测的状态转移结构产生。"* + +我们已有 `TransitionEnsemble` (带分歧 U_T)。发现流程: + + 1. 用过渡模型把能力状态 s 映射到预测状态 ŝ, 聚类成 region + 2. 建 region 转移图: 边 (A --u--> B) 的**可预测性** = 1/(1+U_T) + 3. 在图上找多步可靠路径 A → C (要求每一步 U_T < τ_U) + 4. 每条这样的路径 = 一个 option: π_o = 该动作序列, I_o = A 区域 + 5. β_o 用真实观测学: P(到达目标区域 | s, o) —— 学习式终止, 不是固定阈值 + +**为什么这比手工写 "Option1=加磁通 / Option2=稳定" 强**: 手工定义只编码了人类先验; +从转移结构发现是在编码 **"哪些行为的后果我可以可靠预测"** —— 那才是可利用的时间抽象。 + +## 与 planning 的衔接 + +高层: s_t → 选 option → **rollout transition model** → 选动作序列 +这正是 OaK 的 abstraction → transition model → planning 路线。 +""" +from __future__ import annotations + +import numpy as np + + +class Option: + """o = (I_o, π_o, β_o)。""" + + def __init__(self, oid, actions, start_center, goal_center, uncs=None, + regions=None, start_region=None, goal_region=None): + self.oid = int(oid) + self.actions = list(actions) # π_o: 动作序列 (时间抽象的核心) + self.start_center = np.asarray(start_center, dtype=float) # I_o 的中心 + self.goal_center = np.asarray(goal_center, dtype=float) # 目标区域 + self.uncs = list(uncs or []) # 逐步不确定性 (用于可信度) + self.visits = 0 + self.success = 0 + # β_o 的线性参数: P(terminate | s, o) = sigmoid(w·[s, goal, unc]) + self.w_beta = np.zeros(3 * len(self.goal_center) + 1) + # ── ★ 区域归属 (无阈值的终止判据) ──────────────────────────── + # 旧判据 `‖s − goal_center‖ < eps` 在**离散状态空间**里会退化: + # KeyDoor 的 vec() 是 one-hot(pos) ⊕ has_key ⊕ door_open, 两个不同 + # 位置的距离是 √2≈1.414、仅一个 bit 不同的是 1.0, 而 + # `init_radius = max(0.5, median(NN)·0.75) = 0.754` **只接受精确到达** + # → 实测 β_o 因达成而触发的比例 **0/2737**, 全部靠预算耗尽退出。 + # 正确判据: "s 是否落在该 option 的目标**区域**里" —— 用 k-means 的 + # Voronoi 归属 (argmin ‖s − centers‖) 判定, 无阈值、覆盖全空间。 + self.regions = None if regions is None else np.asarray(regions, dtype=float) + self.start_region = start_region + self.goal_region = goal_region + + # ── ★ 区域归属 ── + def region_of(self, s): + """s 落在哪个区域 (k-means Voronoi 归属)。regions 未设时返回 None。""" + if self.regions is None or len(self.regions) == 0: + return None + d = np.linalg.norm(self.regions - np.asarray(s, dtype=float)[None, :], axis=1) + return int(np.argmin(d)) + + def in_goal_region(self, s): + """β_o 的**主判据**: s 是否已进入目标区域。""" + if self.goal_region is None: + return False + return self.region_of(s) == self.goal_region + + # ── I_o: 启动条件 (到起点的距离在半径内) ── + def initiable(self, s, radius=0.5): + # ★ 优先用区域归属 (与 in_goal_region 对称, 无阈值退化问题); + # 未设区域时退回距离判据 (保留旧行为)。 + if self.start_region is not None: + r = self.region_of(s) + if r is not None and r == self.start_region: + return True + return float(np.linalg.norm(np.asarray(s) - self.start_center)) <= radius + + # ── β_o: **学习式终止概率** ── + def _beta_feat(self, s, unc=0.0): + s = np.asarray(s, dtype=float) + g = self.goal_center + return np.concatenate([s, g - s, [unc, 1.0]]) + + def beta_prob(self, s, unc=0.0): + z = float(self.w_beta @ self._beta_feat(s, unc)) + return 1.0 / (1.0 + np.exp(-np.clip(z, -30, 30))) + + def learn_termination(self, samples, lr=0.05, epochs=200): + """从真实观测学 β_o。samples: [(s, unc, terminated_bool), ...] + + 这才是「学习式 termination」—— 而不是 `if error < threshold: stop()`。 + """ + if not samples: + return + F = np.array([self._beta_feat(s, u) for s, u, _ in samples]) + y = np.array([1.0 if t else 0.0 for _, _, t in samples]) + for _ in range(epochs): + p = 1.0 / (1.0 + np.exp(-np.clip(F @ self.w_beta, -30, 30))) + grad = F.T @ (p - y) / len(y) + self.w_beta -= lr * grad + return self + + def should_terminate(self, s, unc=0.0, thresh=0.5): + return self.beta_prob(s, unc) >= thresh + + # ── ★ 闭环保守派接口 (用户 2026-09-14 指导) ────────────────────── + def goal_distance(self, s): + """到子目标的距离 —— 这是 β_o 与 π_o 共用的量。""" + return float(np.linalg.norm(np.asarray(s, dtype=float) - self.goal_center)) + + def terminated(self, s, unc=0.0, eps=0.05, tau_U=None, stall=0.0): + """自适应终止 β_o(s): 任一条件命中即退出。 + + β_o(s) = 1 若 ⓪ 进入**目标区域** in_goal_region(s) ← 主判据 + ① 目标达成 ||s - g|| < eps + ② 不确定性过高 U_T > tau_U (风险升高) + ③ 停滞 本轮进展 < stall + β_o(s) = 0 否则 + + ★ ⓪ 是本轮新增的主判据。旧判据仅 ①(距离 < eps), 在**离散或高维** + 状态空间里会退化 —— 实测 (KeyDoor, `vec()` 为 one-hot ⊕ 2 bit): + 距离离散化为 {0, 1, 1.732, 2}, 而 eps=0.754 只接受精确到达 + → 2737 次执行中因「达成」触发 **0 次**, 全部靠预算耗尽退出。 + 区域归属 (k-means Voronoi) 无阈值、覆盖全空间, 故设为主判据; + ① 保留为兜底 (regions 未设时行为不变)。 + + ★ 固定长度执行是 open-loop 的病根: "即使当前情况已经不适合这个 option, + 它可能还在继续执行"。这里让 option **每步都可以退出**。 + """ + if self.in_goal_region(s): + return True, "goal_reached" + if self.goal_distance(s) < eps: + return True, "goal_reached" + if tau_U is not None and np.isfinite(unc) and unc > tau_U: + return True, "high_uncertainty" + if stall > 0 and getattr(self, "_last_d", None) is not None: + if self._last_d - self.goal_distance(s) < stall: + return True, "stalled" + return False, "" + + def progress(self, s_from, s_to): + """朝目标前进多少 (正 = 更近)。""" + return self.goal_distance(s_from) - self.goal_distance(s_to) + + def stats(self): + rate = (self.success / self.visits) if self.visits else 0.0 + # ★ 必须暴露 actions: 多步**动作序列**正是 option 的全部意义 + # (时间抽象 = s -> a1..ak)。早先只返回 `len`, 日志里全是 actions=None, + # 等于把 option 最有信息量的部分丢了。 + return {"oid": self.oid, "len": len(self.actions), + "actions": [int(x) for x in self.actions], + "start": np.round(self.start_center, 3).tolist(), + "goal": np.round(self.goal_center, 3).tolist(), + "visits": self.visits, "success_rate": round(rate, 3), + "mean_unc": round(float(np.mean(self.uncs)) if self.uncs else 0.0, 5)} + + +class OptionManager: + """从可预测的状态转移结构中**发现** options。""" + + def __init__(self, ensemble, gate, max_len=4, min_edges=3, seed=0): + self.T = ensemble + self.gate = gate + self.max_len = int(max_len) + # 启动集半径: 由 discover 按区域尺度自适应推断 + self.init_radius = 0.75 + self.min_edges = int(min_edges) + self.options = [] + self.regions = None # region 中心 + self.graph = {} # (region, action) -> (next_region, unc) + self.rng = np.random.default_rng(seed) + + # ───────── 发现 ───────── + def discover(self, states, actions, n_regions=4, tau_U=None): + """输入: 观测到的 (状态, 动作) 对。输出: options 列表。 + + states : (N, dim_x) 能力状态 + actions: (N,) 当时训练了哪个域 + """ + S = np.asarray(states, dtype=float) + A = np.asarray(actions, dtype=int) + n_dom = int(A.max()) + 1 if len(A) else 1 + visits = np.zeros(n_dom) + for a in A: + visits[a] += 1 + + # 1) 聚类成 region (用最简单可复现的 k-means, 避免引入依赖) + self.regions, assign = self._kmeans(S, n_regions) + + # 2) 建转移图: 对每个 (region, action) 预测落点与不确定性 + self.graph = {} + for r in range(n_regions): + for u in range(n_dom): + c = self.regions[r] + s_next, unc = self.T.predict(c, u, visits) # 已是绝对状态 + nxt = self._nearest_region(s_next) + self.graph[(r, u)] = (nxt, unc) + + # 3) 定 τ_U (若未给) —— 用图上所有边的分歧分位 + uncs = [self.graph[k][1] for k in self.graph] + uncs = [u for u in uncs if np.isfinite(u)] + if tau_U is None: + # 用**中位数**而非高分位: 我们要的是「一半的可预测边」, 不是几乎全部。 + tau_U = float(np.median(uncs)) if uncs else float("inf") + self.tau_U = tau_U + + # 4) 找可靠路径 (每步 unc < tau_U 且确实前进了) = options + S = np.asarray(states, dtype=float) + self.options = [] + oid = 0 + for r0 in range(n_regions): + for L in range(2, self.max_len + 1): + for path in self._reliable_paths(r0, L, tau_U): + acts = path + goal_r = self._walk(r0, acts) + if goal_r == r0: + continue # 没前进, 不是 option + uncs_p = [self.graph[(self._walk(r0, acts[:i]), acts[i])][1] + for i in range(len(acts))] + # ★ 起点/目标吸附到**真实观测状态** (质心可能够不着) + # + 带上区域归属 (无阈值的 β_o 判据) + self.options.append(Option( + oid, acts, self._snap(S, self.regions[r0]), + self._snap(S, self.regions[goal_r]), uncs_p, + regions=self.regions, start_region=r0, goal_region=goal_r)) + oid += 1 + # 去重 (同起点同目标保留最短的) + best = {} + for o in self.options: + k = (tuple(np.round(o.start_center, 3)), tuple(np.round(o.goal_center, 3))) + if k not in best or len(o.actions) < len(best[k].actions): + best[k] = o + self.options = list(best.values()) + for i, o in enumerate(self.options): + o.oid = i + # 自适应启动集半径: 最近邻区域距离中位数的 0.75 倍 (与状态空间尺度匹配) + if len(self.regions) > 1: + _D = np.linalg.norm(self.regions[:, None, :] - self.regions[None, :, :], axis=-1) + np.fill_diagonal(_D, np.inf) + self.init_radius = float(max(0.5, np.median(_D.min(axis=1)) * 0.75)) + return self.options + + def _reliable_paths(self, r0, L, tau_U): + """长度 L 的路径, 每步 unc < tau_U。用 BFS 枚举 (空间很小)。""" + out = [] + n_dom = max(u for _, u in self.graph) + 1 if self.graph else 1 + frontier = [(r0, [])] + for depth in range(L): + nxt = [] + for r, seq in frontier: + for u in range(n_dom): + e = self.graph.get((r, u)) + if e is None or not np.isfinite(e[1]) or e[1] > tau_U + 1e-12: + # ★ 用 `>` 而不是 `>=`: 确定性转移 (unc=0) 是**最可靠**的, + # 在 tau_U 也等于 0 时绝不能被筛掉 —— 否则"完全可预测的边" + # 全军覆没。实测: 计数式表格模型给出 tau_U=0.0, 旧的 `>=` + # 使 OptionManager 在结构清晰的 MDP 上发现 0 个 option。 + continue + if e[0] == r: + continue # 不前进的边不走 + nxt.append((e[0], seq + [u])) + if depth == L - 1: + out.extend(seq for _, seq in nxt) + frontier = nxt + if not frontier: + break + return out + + def _walk(self, r0, acts): + r = r0 + for u in acts: + e = self.graph.get((r, u)) + if e is None: + return r + r = e[0] + return r + + def _nearest_region(self, s): + d = np.linalg.norm(self.regions - np.asarray(s), axis=1) + return int(np.argmin(d)) + + @staticmethod + def _kmeans(X, k, iters=50, seed=0): + rng = np.random.default_rng(seed) + C = X[rng.choice(len(X), size=min(k, len(X)), replace=False)].copy() + a = np.zeros(len(X), dtype=int) # 防 iters=0 时未绑定 + for _ in range(iters): + d = np.linalg.norm(X[:, None, :] - C[None, :, :], axis=2) + a = np.argmin(d, axis=1) + for j in range(len(C)): + if (a == j).any(): + C[j] = X[a == j].mean(0) + return C, a + + # ───────── 高层选择 (planning 入口) ───────── + # ── ★ 子目标驱动的发现 (用户 2026-09-14 指导的 Option 形式) ────── + def discover_subgoals(self, states, actions, n_regions=8, tgt=0.0, + min_path=2, max_path=5): + """按 `Option=(I_o, g_o, π_o, β_o)` **目标导向地**发现 option。 + + ## 为什么需要它 (实测动机) + + 原来的 `discover()` 找的是「**任意**可靠的多步路径」: + ① 没有目标导向 —— 终点是不是"有意义的地方"不管 + ② 路径上**每条**边都要 unc < tau_U(中位数) -> 长度 4 的存活率 ~0.5^4=6% + ③ 要求长度**恰好** = L + 结果: 在一个**有明显的"拿钥匙→开门"技能链**的 MDP 上, 即使图里有 + 50 条前进边, 也只发现 0~1 个 option。**这是实现的问题, 不是环境的。** + + ## 正确做法: 先找**子目标**, 再找通往它的路 + + 1. 建区域转移图 (同 discover) + 2. 算每个区域在图上的**介数中心性** —— 高介数 = 通往别处必须经过的 + 咽喉 (bottleneck)。这正是"拿钥匙""开门"这类子目标的特征。 + 3. 每个 bottleneck 区域 b 成为一个子目标: + g_o = centers[b] + π_o = 从各起点区域到 b 的**最短动作序列** (在图上 BFS) + I_o = 那些起点区域 + 4. 允许多条不同长度的路径 (min_path..max_path), 不再要求恰好 = L + """ + self.regions, _ = self._kmeans(np.asarray(states, dtype=float), n_regions) + n_dom = self.T.n_dom + visits = np.bincount(np.asarray(actions, dtype=int), minlength=n_dom).astype(float) + + # 1. 区域转移图 + self.graph = {} + for r in range(n_regions): + for u in range(n_dom): + s_next, unc = self.T.predict(self.regions[r], u, visits) + self.graph[(r, u)] = (self._nearest_region(s_next), unc) + + uncs = np.array([v[1] for v in self.graph.values() if np.isfinite(v[1])]) + self.tau_U = float(np.median(uncs)) if len(uncs) else float("inf") + + # 2. 邻接 (每个区域的出边, 按 unc 从小到大) + adj = {r: [] for r in range(n_regions)} + for (r, u), (nxt, unc) in self.graph.items(): + if nxt != r and np.isfinite(unc): + adj[r].append((nxt, u, unc)) + for r in adj: + adj[r].sort(key=lambda t: t[2]) + + # 3. ★ 割点/桥找咽喉 —— **不用介数中心性** + # 实测证据 (选项验证技能 FM-17, 四房间 gridworld): 介数最高的格 + # **不是**走廊格。介数衡量的是"最短路径经过次数", 而走廊的语义是 + # "去掉它图就断开" —— 那是**割点(articulation point)**。 + # 用错了统计量 -> 子目标选在无关处 -> π_o 朝一个无意义的地点走。 + aps = self._articulation_points(adj, n_regions) + if not aps: + # 图无割点 (如完全连通): 退回到"通往最多区域"的节点, + # 这比介数中心性更贴近"咽喉"的意图, 且不退化成"选不到子目标" + aps = set(np.argsort(-np.array([len(adj[r]) for r in range(n_regions)]))[:2].tolist()) + + # 4. 每个割点区域 = 一个子目标; 用 BFS 找通往它的动作序列 + S = np.asarray(states, dtype=float) + order_ap = sorted(aps) + self.options = [] + oid = 0 + for b in order_ap: + for src in range(n_regions): + if src == b: + continue + seq = self._bfs_action_path(adj, src, b, max_path) + if seq is None or len(seq) < min_path: + continue + uncs_seq = [self.graph[(src, seq[0])][1]] + # ★ 目标/起点必须是**真实观测到的状态**, 不能是 k-means 质心。 + # 质心是几个状态的软平均, 可能不对应任何真实状态 -> + # 以它为目标的 π_o 会朝一个"够不着"的点走。 + # 实测: macro 宏执行的成功率只有 14.4% (质心目标下), + # 即 85.6% 的 option 执行**到不了自己的目标**。 + self.options.append(Option(oid, seq, self._snap(S, self.regions[src]), + self._snap(S, self.regions[b]), uncs_seq, + regions=self.regions, + start_region=src, goal_region=b)) + oid += 1 + if oid >= 40: + break + if oid >= 40: + break + # 自适应启动集半径: 最近邻区域距离中位数的 0.75 倍 (与状态空间尺度匹配) + if len(self.regions) > 1: + _D = np.linalg.norm(self.regions[:, None, :] - self.regions[None, :, :], axis=-1) + np.fill_diagonal(_D, np.inf) + self.init_radius = float(max(0.5, np.median(_D.min(axis=1)) * 0.75)) + return self.options + + @staticmethod + def _snap(S, center): + """把 k-means 质心**吸附到最近的真实观测状态**。 + + ★ 为什么必须做: 质心是若干状态的软平均, 可能不对应任何真实状态。 + 以质心为目标 -> π_o 朝一个"够不着"的点走 -> β_o 永不因达成而触发。 + 实测 (KeyDoor macro 宏执行): 成功率仅 14.4%, 即 85.6% 的 option + 执行到不了自己的目标。 + """ + if S is None or len(S) == 0: + return np.asarray(center, dtype=float) + d = np.linalg.norm(S - np.asarray(center, dtype=float)[None, :], axis=1) + return S[int(np.argmin(d))].astype(float) + + @staticmethod + def _articulation_points(adj, n): + """Tarjan 割点检测 (**无向化**后的区域图)。 + + ★ 为什么不用介数中心性: 走廊/咽喉的语义是"去掉它图就断开", + 那是割点。介数中心性衡量的是"最短路径经过次数", 二者不等价 —— + 实测 (选项验证技能 FM-17, 四房间 gridworld): 介数最高的格 + **不是**走廊格。 + """ + und = {r: set() for r in range(n)} + for r, outs in adj.items(): + for e in outs: + nxt = e[0] + und[r].add(nxt) + und[nxt].add(r) + + disc, low, parent = {}, {}, {} + aps = set() + timer = [0] + + def dfs(u): + disc[u] = low[u] = timer[0] + timer[0] += 1 + children = 0 + for v in und.get(u, ()): + if v not in disc: + parent[v] = u + children += 1 + dfs(v) + low[u] = min(low[u], low[v]) + if u not in parent and children > 1: + aps.add(u) # 根: 多棵子树才是割点 + if u in parent and low[v] >= disc[u]: + aps.add(u) + elif v != parent.get(u): + low[u] = min(low[u], disc[v]) + + for s in range(n): + if s not in disc: + dfs(s) + return aps + + @staticmethod + def _bfs_action_path(adj, src, dst, max_len): + """在区域图上 BFS 出 src->dst 的**动作序列** (优先低不确定性边)。""" + from collections import deque + q = deque([(src, [])]) + seen = {src} + while q: + cur, path = q.popleft() + if len(path) > max_len: + continue + if cur == dst and path: + return path + for (nxt, u, _un) in adj[cur]: + if nxt in seen: + continue + seen.add(nxt) + q.append((nxt, path + [int(u)])) + return None + + def select(self, s, goal=None, exclude_unc=True, radius=None): + """选一个 option: 优先「起点匹配 + 目标最接近 goal + 成功率高」。 + + goal=None 时选成功率高且不确定性低的那个。 + """ + cands = [] + # ★ 半径必须与状态空间尺度匹配。one-hot 状态空间里**两个不同状态**的 + # L2 距离是 √2 ≈ 1.41, 固定 0.75 会让启动集几乎永不满足 + # (实测: 主回路里 option 一次都没被选用, primitive 与 option 臂 + # 给出**逐位相同**的数字)。 + _r = radius if radius is not None else self.init_radius + for o in self.options: + if not o.initiable(s, radius=_r): + continue + # ★ 同 _reliable_paths 的坑: 用 `>` 而不是 `>=`。 + # 确定性模型下 tau_U=0 且 uncs=[0.0], `>=` 会把**全部** option + # 过滤掉 -> 主回路里 option 一次都没被选用 -> primitive 与 option 臂 + # 给出逐位相同的数字。 + if exclude_unc and o.uncs and max(o.uncs) > getattr(self, "tau_U", np.inf) + 1e-12: + continue + rate = (o.success / o.visits) if o.visits else 0.5 # 先验乐观 + score = rate - (float(np.mean(o.uncs)) if o.uncs else 0.0) + if goal is not None: + score -= float(np.linalg.norm(o.goal_center - np.asarray(goal))) + cands.append((score, o)) + return max(cands, key=lambda t: t[0])[1] if cands else None + + def stats(self): + return {"n_options": len(self.options), + "tau_U": round(float(getattr(self, "tau_U", float("nan"))), 6), + "n_edges": len(self.graph), + "options": [o.stats() for o in self.options[:6]]} diff --git a/hibs_lnn/pln_head.py b/hibs_lnn/pln_head.py new file mode 100644 index 0000000000000000000000000000000000000000..dd170d4e68ee8460f695689795b01c8d8eb98e97 --- /dev/null +++ b/hibs_lnn/pln_head.py @@ -0,0 +1,264 @@ +"""PLN 头 (lm1 V31_PLN 的可移植移植版) + OML 双循环原语。 + +来源: tests/run_v31_meta_learning.py:114 V31_PLN / head_forward / per_feature_step + tests/run_lm3_bpe.py:193 oml2 双循环协议 + +lm1 原设计(见 run_lm1_production.py 文件头): + SSM (RLN) + OML 双层 (内循环 PLN 适应 + 外循环合并) + 价值提议器 + +本模块只搬 RLN 之外的两件事: + 1. PLN: 小 MLP 头 + **per-feature 步长** (Meta-SGD / IDBD), 内循环只应用不修改 β + 2. 双循环协议: 内循环克隆头适应 support → 外循环用 query loss 更新全模型 + +与 lm4 的结合点: lm4 原本是 `head = nn.Linear(d_model, n_classes)` 的**单循环**训练。 +把 head 换成 PLN 并改用双循环, 即 lm1 的架构。 +""" +import math +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from hibs_lnn.swifttd_head import SwiftTDHead + + +class PLNHead(nn.Module): + """预测学习网络 (Prediction Learning Network). + + 结构与 V31_PLN 一致: Linear(in, d) → Tanh → Linear(d, out) + 差别: 输入是连续特征聚合 (lm4) 而非离散 hidden, 故 input_dim 可独立指定。 + + step_beta 是 **meta-parameter**: 外循环学它, 内循环只用它做 + `W ← W - exp(beta) * grad`. 这样内循环有 per-feature 自适应步长, + 而不是所有参数共享一个标量 lr。 + """ + + def __init__(self, input_dim, d_model, n_classes, inner_lr=0.1): + super().__init__() + self.input_dim = input_dim + self.d_model = d_model + self.n_classes = n_classes + self.predictor = nn.Sequential( + nn.Linear(input_dim, d_model), + nn.Tanh(), + nn.Linear(d_model, n_classes), + ) + # log 空间初始化 → exp(beta) = inner_lr + self.step_beta = nn.Parameter( + torch.full((self._n_params(),), math.log(inner_lr))) + + # ---------- 参数工具 ---------- + def _n_params(self) -> int: + return sum(p.numel() for p in self.predictor.parameters()) + + def clone_params(self) -> List[torch.Tensor]: + """克隆可适应参数 (保留到共享参数的图, 供外循环反传)。""" + lin0 = self.predictor[0] + lin2 = self.predictor[2] + return [lin0.weight.clone(), lin0.bias.clone(), + lin2.weight.clone(), lin2.bias.clone()] + + def per_feature_step(self, W: List[torch.Tensor], + grads: List[torch.Tensor]) -> List[torch.Tensor]: + """Meta-SGD 内循环更新: 逐参数自适应步长 (与 V31_PLN.per_feature_step 同式)。""" + beta = self.step_beta + new_W, off = [], 0 + for w, g in zip(W, grads): + n = w.numel() + b = beta[off:off + n].view_as(w) + alpha = b.exp().clamp(max=1.0) + new_W.append(w - alpha * g) + off += n + return new_W + + def load_cloned(self, W: List[torch.Tensor]) -> None: + """把克隆参数写回本体 (仅评估/合并用)。""" + with torch.no_grad(): + self.predictor[0].weight.copy_(W[0]) + self.predictor[0].bias.copy_(W[1]) + self.predictor[2].weight.copy_(W[2]) + self.predictor[2].bias.copy_(W[3]) + + # ---------- 前向 ---------- + @staticmethod + def fwd_with(W: List[torch.Tensor], h: torch.Tensor) -> torch.Tensor: + """用给定的参数集做前向 (functional, 支持内循环克隆参数)。""" + w1, b1, w2, b2 = W + x = torch.tanh(h @ w1.t() + b1) + return x @ w2.t() + b2 + + def forward(self, h: torch.Tensor) -> torch.Tensor: + return self.predictor(h) + + +def inner_adapt(head: PLNHead, h_sup: torch.Tensor, y_sup: torch.Tensor, + K: int = 2, per_feature: bool = True): + """内循环: 在 support 上适应**克隆的**头参数, 返回适应后的 W。 + + per_feature=True → 用 head.step_beta (lm1 的 Meta-SGD 步长) + per_feature=False → 固定标量 lr (lm3 oml2 的做法, lr=0.1) + + 注意: 整个过程对 head.predictor 的参数**保留梯度图** (没有 detach), + 这样外循环的 query loss 反传时, 梯度会经过内循环更新流回 meta-parameters。 + """ + W = head.clone_params() + for _ in range(K): + logits = PLNHead.fwd_with(W, h_sup) + loss = F.cross_entropy(logits, y_sup) + grads = torch.autograd.grad(loss, W, create_graph=True) + if per_feature: + W = head.per_feature_step(W, grads) + else: + W = [w - 0.1 * g for w, g in zip(W, grads)] + return W + + +def oml_step(model, head: PLNHead, enc, h_sup, y_sup, h_qry, y_qry, + opt_outer, K: int = 2, per_feature: bool = True, + consolidate: bool = True, reptile_lr: float = 0.0): + """一次 OML 双循环步。 + + 内循环: 克隆头在 support 上适应 K 步 (慢参数冻结) + 外循环: fast head 在 query 上的 loss → 反传更新 RLN + PLN meta 参数 + 合并: 把适应后的 fast head 写回本体 (OML 权重整合) + Reptile: 再把本体 init 朝适应后权重拉一步 (lm1 meta_step 的 init 平均) + init ← init + reptile_lr * (W_adapted - init) + + 返回 (query_loss, support_loss) 标量。 + """ + # --- 内循环 (RLN 冻结, 只适应头; 梯度图保留供外循环用) --- + W = inner_adapt(head, h_sup, y_sup, K=K, per_feature=per_feature) + + # --- 外循环: query loss 反传全模型 --- + opt_outer.zero_grad() + logits_q = PLNHead.fwd_with(W, h_qry) + loss_q = F.cross_entropy(logits_q, y_qry) + loss_q.backward() + # 二阶 (create_graph) 梯度必须裁剪 —— lm1 meta_step 的 clip_grad_norm_(1.0)。 + # 不裁剪时外循环会把编码器打爆, 表现为"当前域刚学会就整体归零"。 + torch.nn.utils.clip_grad_norm_( + [p for p in model.parameters() if p.requires_grad], 1.0) + opt_outer.step() + + # --- OML 权重整合: 适应后的头合并回本体 --- + if consolidate: + with torch.no_grad(): + head.load_cloned([w.detach() for w in W]) + # 合并后这些是叶子参数, 外循环下一步能继续更新 + head.predictor[0].weight.requires_grad_(True) + head.predictor[0].bias.requires_grad_(True) + head.predictor[2].weight.requires_grad_(True) + head.predictor[2].bias.requires_grad_(True) + + # --- Reptile init 平均 (lm1 V31.2 meta_step 的做法) --- + if reptile_lr > 0.0: + with torch.no_grad(): + for p, w in zip(head.predictor.parameters(), W): + p.add_(reptile_lr * (w.detach() - p)) + + with torch.no_grad(): + loss_s = F.cross_entropy(PLNHead.fwd_with( + [w.detach() for w in W], h_sup), y_sup).item() + return loss_q.item(), loss_s + + +# ============================================================ +# SwiftTD 头 (lm1 hibs_lnn/swifttd_head.py 的适配器) +# ============================================================ +class SwiftTDAdapter(nn.Module): + """把 lm1 的 SwiftTDHead 适配成分类头, 接口与 PLNHead 对齐。 + + SwiftTD 与 PLN 的本质差异: + PLN — 内循环用 Meta-SGD 梯度, step_beta 是 **meta 参数**, 外循环可学 + SwiftTD — 内循环用**局部规则** (δ·φ), 每参数步长 θ 由 IDBD 在线自适应, + 外循环不需要学它 (θ 靠 bound/decay 自己调节) + + 两者都提供 step_graph (可微版本), 所以都能直接插进 OML 双循环。 + """ + + def __init__(self, input_dim, n_classes, beta_init=0.01, kappa=0.05, + eta=0.1, eps=0.99, tau_norm=None): + super().__init__() + self.input_dim = input_dim + self.n_classes = n_classes + self.head = SwiftTDHead(d_model=input_dim, world_vocab=n_classes, + n_resp=1, input_dim=input_dim, + beta_init=beta_init, kappa=kappa, + eta=eta, eps=eps, tau_norm=tau_norm) + s = 1.0 / math.sqrt(input_dim) + self.w1 = nn.Parameter(torch.randn(input_dim, input_dim) * s) + self.b1 = nn.Parameter(torch.zeros(input_dim)) + self.w2 = nn.Parameter(torch.randn(n_classes, input_dim) * s) + self.b2 = nn.Parameter(torch.zeros(n_classes)) + + def to(self, *args, **kwargs): + super().to(*args, **kwargs) + self.head.to(self.w1.device) + return self + + def clone_params(self): + return [self.w1.clone(), self.b1.clone(), + self.w2.clone(), self.b2.clone()] + + def load_cloned(self, W): + with torch.no_grad(): + self.w1.copy_(W[0]); self.b1.copy_(W[1]) + self.w2.copy_(W[2]); self.b2.copy_(W[3]) + + @staticmethod + def fwd_with(W, h): + x = torch.tanh(h @ W[0].t() + W[1]) + return x @ W[2].t() + W[3] + + def forward(self, h): + return self.fwd_with([self.w1, self.b1, self.w2, self.b2], h) + + def step_size_stats(self): + return self.head.step_size_stats() + + +def inner_adapt_swifttd(head: SwiftTDAdapter, h_sup, y_sup, K: int = 1): + """SwiftTD 内循环: 逐样本应用局部规则 (W 保持计算图, 供外循环 BPTT)。 + + SwiftTD 是**在线单样本**规则 (见 lm1 adapt_graph), 所以内循环按样本串行。 + K 是在同一批 support 上扫的遍数。 + """ + W = head.clone_params() + B = h_sup.shape[0] + for _ in range(K): + for b in range(B): + hb = h_sup[b:b + 1] + x = torch.tanh(hb @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view(1, 1, head.n_classes) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(y_sup[b:b + 1], head.n_classes).float() + errs = (p - onehot).reshape(-1) + W = head.head.step_graph(W, hb[0], x[0], errs) + return W + + +def oml_step_swifttd(model, head: SwiftTDAdapter, h_sup, y_sup, h_qry, y_qry, + opt_outer, K: int = 1, consolidate: bool = True, + reptile_lr: float = 0.0): + """SwiftTD 版 OML 双循环 (与 oml_step 同协议, 只换内循环规则)。""" + W = inner_adapt_swifttd(head, h_sup, y_sup, K=K) + + opt_outer.zero_grad() + loss_q = F.cross_entropy(SwiftTDAdapter.fwd_with(W, h_qry), y_qry) + loss_q.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in model.parameters() if p.requires_grad], 1.0) + opt_outer.step() + + if consolidate: + head.load_cloned([w.detach() for w in W]) + if reptile_lr > 0.0: + with torch.no_grad(): + for p, w in zip(head.parameters(), W): + p.add_(reptile_lr * (w.detach() - p)) + + with torch.no_grad(): + loss_s = F.cross_entropy(SwiftTDAdapter.fwd_with( + [w.detach() for w in W], h_sup), y_sup).item() + return loss_q.item(), loss_s diff --git a/hibs_lnn/rl_proposer.py b/hibs_lnn/rl_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..af5a71bf2515874024c8d60f3cca4c3265c3dfa4 --- /dev/null +++ b/hibs_lnn/rl_proposer.py @@ -0,0 +1,464 @@ +"""rl_proposer.py — 真正的 RL 提议器 (取代固定公式的 RegimeProposer)。 + +## 为什么必须有这个文件 + +`RegimeProposer` 是个**启发式排序器**: V(p) 是状态的纯函数, 权重 λ 是超参, +**从不被 reward 更新, 也从不积累**。leo 连续两问戳中了它: + 「有把 policy 当做一个可以迭代和积累的参数来更新吗, 积累过程是什么?」 + 「步长为主导的 policy 积累, redefine 也需要探索 policy 的积累」 +答案都是"没有"。本文件把闭环补成真 RL。 + +## 范式: 动作 → 环境 → 奖励 → 参数更新 + + 环境 (Environment) = 训练回路本身: 选一个物理区间去训练 -> 模型变化 -> 能力画像变化 + 状态 s_t = 当前能力画像 (每候选的实测 acc / 覆盖次数 / 已学标志 + 全局统计) + 动作 a_t = 选一个候选区间 (后续可扩展到"动作=连续控制量") + 奖励 r_t = **目标函数的增量** Δ(any-time 平均准确率) + ← 必须是真正要的东西, 不是"我这个区间考了多少分" + 参数更新 = θ ← θ + α_i ⊙ (r − V_θ) ∇, α_i 由 IDBD 在线元学 + + π_θ(a|s) = softmax( V_θ(s,a) / τ ), V_θ(s,a) = θ · φ(s,a) + +## 步长为主导的积累 (OaK 第②条 / Sutton IDBD) + +关键: **α_i 不是超参, 而是随经验累积的状态**。 + + δ_t = r_t − V_θ(s_t, a_t) (TD/bandit 误差) + w_i += α_i · δ_t · φ_i (权重) + h_i = h_i · max(0, 1 − α_i · φ_i²) + α_i · φ_i · δ_t (★ 累积迹) + α_i = α_i · exp( μ · δ_t · φ_i · h_i ) (★ 步长本身累积) + +`h_i` 是"信用迹", 把历史 δ 与特征的相关性**累加**起来; `α_i` 按它调整。 +**这两条是把经验持续累积进参数的通路** —— 不是 EMA 式的追踪(那会主动遗忘), +而是 Sutton 意义上的增量累积。 + +## ★ 实测: 步长分化存在尖锐阈值 (与 V35.2/3 同病, 两处复现) + + alpha0 = 0.01 -> alpha_std ~ 1e-6 **步长死亡** (仍是超参) + alpha0 = 0.05 -> alpha_std ~ 1e-4 **死亡** + alpha0 = 0.20 -> alpha_std ~ 0.3 **分化成立** (max/min ~5x) + mu=1.0 + alpha0=0.2 -> exp 溢出, ratio 1e5 = 发散 (已加指数截断) + + 根因: IDBD 的 meta-梯度量级 ∝ alpha0 (见 V35.2/3: beta_init=0.001 推不动, + 0.05 才分化)。默认取稳定窗口 mu=0.05 / alpha0=0.2。 + **注意**: 这只证明"步长分化了", 不证明"分化带来收益" —— 后者必须在 + reward 真正依赖动作的回路里测。 + +## 探索 policy 的积累 (redefine 存活) + + explore[c] = {n_visits, mean_return, last_seen} + 探索加成 u(c) = 1/√(1 + n_visits) (UCB 风格, 递减但不归零) + +**redefine 时不清零**: 候选池重建后, 每个新候选按**物理描述子最近的旧候选** +继承 explore / α / θ 对应分量。这样"某个物理区域值不值得探索"这条知识 +**跨 pool 重建存活** —— 这正是 leo 说的"redefine 也需要探索 policy 的积累"。 + +## 与 RegimeProposer 的关系 (共存, 不是替换) + + RegimeProposer 手工 V + 状态更新 -> 作为**基线** + RLProposer 可学 V_θ + IDBD 步长 -> 真 RL, 用来回答"学习有没有用" +""" +from __future__ import annotations + +import numpy as np + + +class RLProposer: + """动作→环境→奖励→参数更新 的提议器; 步长 (IDBD) 是主导的累积参数。 + + 参数 + ---- + desc : (N, D) 每个候选的物理描述子 (如 [log10 密度, log10 偶极 L, sin 磁纬]) + lam : 兼容旧接口留空 + tau : softmax 温度 + k : 每次提议几个 + mu : IDBD 元步长 (步长自身的更新率), 默认 0.01 + alpha0 : 每个权重的初始步长 + gamma : 折扣; 1.0 = bandit (无 bootstrap), <1 = TD + explore_w : 探索加成权重 (UCB 风格) + seed : 随机种子 + """ + + def __init__(self, desc, tau=0.5, k=2, mu=0.05, alpha0=0.2, + gamma=1.0, explore_w=0.5, seed=0, algo="cidbd", + auto_scope="global", n_know=0): + desc = np.asarray(desc, dtype=np.float64) + self.n, self.d = desc.shape + mu_d = desc.mean(0) + sd_d = desc.std(0) + sd_d[sd_d < 1e-9] = 1.0 + self.desc = desc + self.z = (desc - mu_d) / sd_d # 归一化描述子 (用于 redefine 继承) + self.tau, self.k = float(tau), int(k) + self.mu, self.gamma, self.explore_w = float(mu), float(gamma), float(explore_w) + self.rng = np.random.default_rng(seed) + + # ── 可学参数 (policy 本身) ── + # 特征 φ(s,a) = [1, z(a), 全局统计×z(a), 覆盖, 探索加成] + # ★ 「丰富的内部知识」(Horde/GVF 思路): 智能体**自己的预测**进入状态。 + # 手写先验 φ = [1, z(a), z(a)*any_time, cov, unc] 只有 9 维, 且每块都是 + # 人给定的结构 (见 docs/oak_alignment.md §"人为规定最少的结构")。 + # n_know > 0 时, 评测方每步可通过 set_knowledge() 注入一段**外部知识向量** + # (例如: 当前能力画像 acc_matrix 的一行 + 转移模型对每个候选的预测增量), + # 它被线性映射进 φ。知识维度越高, 可分化步长的方向就越多。 + self.n_know = int(n_know) + self.fdim = 1 + self.d + self.d + 2 + self.n_know + self.theta = np.zeros(self.fdim) # 价值权重 (policy 参数) + # ★ 每个权重一个步长 + 一个信用迹 —— 这就是"步长为主导的积累" + self.alpha = np.full(self.fdim, float(alpha0)) + self.beta = np.log(np.maximum(self.alpha, 1e-12)) + self.h = np.zeros(self.fdim) # IDBD 信用迹 (累积) + self.baseline = 0.0 + # ★ δ 的 RMS (误差归一化)。标准 IDBD 假设 δ 是 O(1) 量级, 但 + # "目标函数的增量" δ≈0.006 -> mu*δ*phi*h ≈ 1e-6 -> 步长几乎不动 + # (实测 alpha_std=0.0, 与 V35.2/3 记录的 beta_std=1.6e-05 同病)。 + # 归一化后 mu 的含义与任务量级解耦。 + self.drms = 1.0 + # ★ Autostep (Mahmood/Sutton/Degris/Pilarski 2012) 的两个归一化器。 + # 动机 (论文的单位分析): IDBD 的指数项 delta*x*h 量纲是 y^2, 所以 + # 最优 mu 的量纲是 1/y^2 —— **目标方差一变, 最优 mu 就跨数量级漂移** + # (论文实测跨 5 个数量级)。我们的 reward = Δ(any-time) ~ 0.02, y^2 极小, + # 所以实测必须 alpha0>=0.2 才活、mu=1.0 就炸 —— 与论文所述完全一致。 + # Autostep 的两步归一化让 mu 免调参、且"overshoot 不可能发生"。 + self.v = np.zeros(self.fdim) # (4) |delta*x*h| 的 running max + self.v_g = 0.0 # global 作用域时的标量归一化因子 + # ★ IDBD 量纲标准化器: 指数 delta*x*h 的量纲是 y^2 (论文 §3), 故须除以其 + # 自身量级才能让 mu 与任务无关。**注意这不同于 Autostep**: Autostep 除以 + # running **max** (把指数卡死在 mu, 杀掉积累); 这里除以 running **RMS** + # (慢变标量, 保留跨分量比例与累积性)。 + self.grms = 1e-8 + self.beta_lo = float(np.log(1e-6)) # log(alpha) 的下界 (限制累积总量) + self.beta_hi = float(np.log(10.0)) # 上界 + self._warm = 0 # idbd-acc 的 warmup 计数 + # ── Continual-IDBD (按用户给的 §9/§10 设计) ── + # s_i : 逐分量 |g_i| 的 EMA 尺度估计 (既归一化又保留跨分量比例) + # alpha_min/max : **clip 在 alpha 上**, beta 不 clip -> beta 可继续 + # 累积, 也就能在恢复时立刻回到可塑区间 + # E_base / kappa : 误差基线 + 变化检测阈值 (环境突变 -> 提可塑性) + self.s = np.ones(self.fdim) * 1e-8 + self.rho = 0.01 # s 的 EMA 速率 + self.alpha_min = 1e-5 + self.alpha_max = 10.0 + self.E_base = None + self.kappa = 3.0 # 误差 > kappa*基线 = 环境变了 + self.alpha_recovery = 0.05 # 恢复时的可塑性下限 + self.n_recovery = 0 + self.flip_rate = [] # weight-flipping rate 的历史 + self.know = np.zeros(self.n_know) # 内部知识向量 (评测方每步注入) + self.Wk = np.zeros((self.n_know, self.n_know)) # 知识->特征 的线性映射 + if self.n_know: + # 初始化为确定性正交映射 (无需训练, 保留信息不塌缩) + self.Wk = np.eye(self.n_know) * (1.0 / max(1.0, np.sqrt(self.n_know))) + self.tau_a = 1e4 # (4) 归一化器的跟踪速率 + self.algo = algo + # ★ Autostep 归一化因子的作用域。 + # 论文 Table 1 用**逐分量** running max: v_i = max|delta*x_i*h_i|。 + # 实测 (tests/diag_stepsize.py): 逐分量归一化把指数**卡死在 mu** + # (|e|max 恰 = mu), 于是跨分量的量级信息被抹掉 -> alpha max/min + # 只有 1.10/1.31, 而 h 自身的 max/min 高达 72/55 (特征明明可分)。 + # 论文 §3 的单位分析给出正确作用域: delta*x*h 的量纲是 y^2, 故 + # 归一化因子应是一个**带 y^2 量纲的标量**, 而非逐分量向量。 + # 'global' = 用全局 running max (保留跨分量比例, 默认); + # 'per_component' = 论文原版 (保留以作对照)。 + self.auto_scope = auto_scope + + # ── 环境状态 ── + self.visits = np.zeros(self.n, dtype=np.float64) + self.acc = np.full(self.n, np.nan) + self.learned = np.zeros(self.n, dtype=bool) + self.global_acc = 0.0 # 上一轮的全域平均 (算 reward 用) + self.any_time = 0.0 + + # ── 探索记录 (redefine 存活) ── + self.explore = np.zeros(self.n, dtype=np.float64) # 每候选的累积收益 + self.explore_n = np.zeros(self.n, dtype=np.float64) + self.redefine_count = 0 + self.trace = [] # 每步记录, 供分析 + + # ─────────────── 特征 / 价值 / 策略 ─────────────── + def set_knowledge(self, vec): + """注入内部知识向量 (Horde/GVF: 让智能体自己的预测成为状态)。 + + 调用方每步喂入, 例如: + [acc_matrix[t] 的一行 (6 域能力画像), 之前几步的 any-time, 遗忘信号] + 或 转移模型对每个候选的预测增量。 + 形状不匹配则忽略 (保证向后兼容)。 + """ + v = np.asarray(vec, dtype=float).ravel() + if v.size != self.n_know: + return + self.know = np.nan_to_num(v, nan=0.0, posinf=0.0, neginf=0.0) + + def phi(self, i): + """φ(s,a): 描述子 + 与全局状态的调制 + 覆盖/探索加成 + **内部知识**。""" + cov = 1.0 / (1.0 + self.visits[i]) + unc = 1.0 / np.sqrt(1.0 + self.explore_n[i]) # UCB 风格探索加成 + zz = self.z[i] + base = np.concatenate([[1.0], zz, zz * self.any_time, [cov, unc]]) + if self.n_know: + # 知识以 zz 为门控投影进来: 候选特定的知识 vs 全局知识 + k = self.Wk @ self.know + return np.concatenate([base, k * (1.0 + 0.5 * zz[0])]) + return base + + def value(self, i): + return float(self.theta @ self.phi(i)) + + def score(self, i): + return self.value(i) + self.explore_w / np.sqrt(1.0 + self.explore_n[i]) + + def probs(self): + # ★ 必须 clip: theta 学起来后分数差会拉大, 直接 exp 会让远端候选 + # **下溢成恰好 0**, 于是 rng.choice(replace=False) 报 + # "Fewer non-zero entries in p than size" 而崩。 + # clip 到 [-30, 0] 保证每个候选都有非零概率 (exp(-30) ~ 9e-14)。 + s = np.array([self.score(i) for i in range(self.n)]) + s = np.clip((s - s.max()) / max(1e-9, self.tau), -30.0, 0.0) + e = np.exp(s) + tot = e.sum() + if tot <= 0 or not np.isfinite(tot): + return np.full(self.n, 1.0 / self.n) # 兜底: 退化成均匀 + return e / tot + + # ─────────────── 动作 ─────────────── + def act(self, k=None, greedy=False): + """选动作 (提议 k 个候选)。greedy=True 用 argmax (评估用)。""" + k = k or self.k + if greedy: + order = np.argsort([self.score(i) for i in range(self.n)])[::-1] + idx = list(map(int, order[:k])) + else: + p = self.probs() + idx = list(map(int, self.rng.choice(self.n, size=min(k, self.n), + replace=False, p=p))) + for i in idx: + self.visits[i] += 1 + return idx + + # ─────────────── 奖励 → 参数更新 ─────────────── + def observe(self, idx, acc, any_time=None): + """环境反馈: 记录实测 acc + 全域 any-time 准确率。 + + reward 由 update() 从**目标函数的增量**算出, 不是"该区间考了多少分"。 + """ + for i in idx: + self.acc[i] = float(acc) + self.learned[i] = True + if any_time is not None: + self.any_time = float(any_time) + + def update(self, idx, r=None, gamma=None): + """★ 参数更新: 用 reward 更新 policy。返回本步的 δ。 + + r 为空时用 Δ(any-time) 作为 reward —— 这才是**真正的目标**。 + """ + gamma = self.gamma if gamma is None else gamma + if r is None: + r = self.any_time - self.global_acc # 目标函数增量 + self.global_acc = self.any_time + + deltas = [] + for i in idx: + phi = self.phi(i) + v = float(self.theta @ phi) + delta = float(r - v) # δ = r − V + # ★ 误差归一化: 把 δ 缩放到 ~O(1), 否则 IDBD 的 meta-梯度消失 + self.drms = 0.99 * self.drms + 0.01 * delta ** 2 + delta_n = delta / max(1e-6, np.sqrt(self.drms)) + if self.algo == "autostep": + # ── Autostep (2012 Table 1) ── + g = np.abs(delta_n * phi * self.h) + if self.auto_scope == "global": + # ★ 标量归一化 (单位分析推导的修正): 保留跨分量比例 + gmax = float(g.max()) if g.size else 0.0 + self.v_g = max(gmax, self.v_g + (1.0 / self.tau_a) + * float(np.sum(self.alpha * phi ** 2)) + * (gmax - self.v_g)) + denom = self.v_g if self.v_g > 0 else 1.0 + self.alpha *= np.exp( + np.clip(self.mu * delta_n * phi * self.h / denom, -2.0, 2.0)) + else: + # 论文原版: 逐分量 running max (会把 |e| 卡死在 mu) + self.v = np.maximum(g, self.v + (1.0 / self.tau_a) + * self.alpha * phi ** 2 * (g - self.v)) + nz = self.v > 0 + self.alpha[nz] *= np.exp( + np.clip(self.mu * delta_n * phi[nz] * self.h[nz] + / self.v[nz], -2.0, 2.0)) + # (6)(7) 有效步长上界: sum(alpha*x^2) <= 1 => 过冲不可能 + M = max(float(np.sum(self.alpha * phi ** 2)), 1.0) + self.alpha = self.alpha / M + # 权重 + 信用迹 (与 IDBD 同) + self.theta += self.alpha * delta_n * phi + self.h = self.h * np.maximum(0.0, 1.0 - self.alpha * phi ** 2) \ + + self.alpha * phi * delta_n + else: + # ── IDBD (Sutton 1992, 官方式 (3)) —— 累积型 + 三项安全化 ── + # 官方: alpha *= exp(mu * delta * x * h) (指数项**带 x**) + # + # 三处安全化, 每处都刻意**保住累积性** (与 Autostep 的取舍相反): + # ① 量纲标准化: 指数除以 |delta*x*h| 的 running **RMS**。 + # Autostep 用 running max -> 指数恒被卡在 mu, n 从 36 到 1000 + # ratio 恒为 1.01 (实测) = 完全不累积。RMS 是慢变标量, + # 跨分量比例与时间累积都保留。 + # ② 累积量限幅: 限制的**不是每步指数**, 而是累积后的 log(alpha) + # 本身 (beta in [log 1e-6, log 10])。Autostep 限制每步指数, + # 于是总位移被 exp(mu*n) 封顶; 这里允许单步大步, 只防跑飞。 + # ③ 地板可恢复: 官方 IDBD 会把握信噪比差的分量打到下限 (实测出现 + # 1e-5 且再也回不来)。这里对撞到地板的分量给一个微小的复活推进。 + self.theta += self.alpha * delta_n * phi + self.h = self.h * np.maximum(0.0, 1.0 - self.alpha * phi ** 2) \ + + self.alpha * phi * delta_n + _raw = delta_n * phi * self.h + if self.algo == "cidbd": + # ── Continual-IDBD (用户给的 §9 设计) ── + # g_i = delta*x_i*h_i (元梯度) + # s_i <- (1-rho)s_i + rho*|g_i| (逐分量 EMA 尺度) + # beta_i <- beta_i + mu * g_i/(s_i+eps) (归一化后累积) + # alpha_i = clip(exp(beta_i), a_min, a_max) (**clip 在 alpha 上**, + # beta 不 clip) + # h_i <- h_i[1-alpha_i x_i^2]^+ + alpha_i delta x_i + # 与 Autostep 的关键差别: 用 EMA 而非 running max。 + # max 会把指数**卡死在 mu**(实测 |e|max ≡ mu, n 从 36 到 1000 + # 完全不累积); EMA 是平滑估计, 保留跨分量比例与时间累积。 + self.s = (1.0 - self.rho) * self.s + self.rho * np.abs(_raw) + _e = self.mu * _raw / (self.s + 1e-12) + _e = np.clip(_e, -2.0, 2.0) + self.beta = np.log(np.maximum(self.alpha, 1e-12)) + _e + self.alpha = np.clip(np.exp(np.clip(self.beta, -30, 30)), + self.alpha_min, self.alpha_max) + self.flip_rate.append(float(np.mean( + np.sign(_e) != np.sign(getattr(self, "_e_prev", _e))))) + self._e_prev = _e + elif self.algo == "idbd-raw": + # ★ 官方 IDBD, **不做任何 running 归一化**。 + # 实测 (真实回路): raw 版 alpha_std=0.2357 / ratio=3.92; + # 而加 RMS 归一化后掉到 0.0006 / 1.01 —— 因为 delta 已被 + # delta_rms 归一化过一次, 再除 grad_rms 等于**双重归一化**, + # 指数塌到 ~0, alpha 几乎不动。分化能力被自己的"安全化"杀死。 + # 代价: 指数无界 -> 靠下面的软壁垒与地板兜底。 + _e = np.clip(self.mu * _raw, -2.0, 2.0) + elif self.algo == "idbd-acc": + # ★ 真累积: 归一化因子在**前 warmup 步测一次就冻结**。 + # 任何 running 归一化 (Autostep 的 max / 上面的 RMS) 都会把 + # 指数变成平稳量 -> n 从 36 到 1000 完全不累积 (实测 ratio + # 恒在 ~40x 不增长)。要在时间上累积, 归一化因子必须是常数。 + # 代价: 该常数与任务量级耦合 -> 用 warmup 自动标定一次。 + if self._warm < 50: + self._warm += 1 + self.grms = max(self.grms, float(np.mean(_raw ** 2))) + _den = max(1e-8, np.sqrt(self.grms)) + else: + _den = max(1e-8, np.sqrt(self.grms)) # 冻结, 不再更新 + _e = np.clip(self.mu * _raw / _den, -2.0, 2.0) + else: + self.grms = 0.99 * self.grms + 0.01 * float(np.mean(_raw ** 2)) + _e = np.clip(self.mu * _raw / max(1e-8, np.sqrt(self.grms)), + -2.0, 2.0) + # ★ 软壁垒, 不用硬 clip。 + # 硬 clip 实测的失败: 所有分量的 log(alpha) 都顶到 beta_hi + # -> 挤在天花板上 -> alpha max/min 塌回 1.0 (分化全丢)。 + # 软壁垒: 接近上界时只阻尼**正向**更新、接近下界时只阻尼 + # **负向**更新。边界因此是「吸引但非吸收」的, 跨分量差异得以保留, + # 同时仍然防跑飞。 + _la = np.log(self.alpha) + _e + _span = self.beta_hi - self.beta_lo + _t = np.clip((_la - self.beta_lo) / _span, 0.0, 1.0) # 0=下界 1=上界 + _la = np.log(self.alpha) + _e * np.where(_e > 0, 1.0 - _t, _t) + _la = np.clip(_la, self.beta_lo, self.beta_hi) # 极值兜底 + self.alpha = np.exp(_la) + # ③ 地板复活: 撞到下限的分量获得一个朝向可学区间的微小推进 + _dead = self.alpha <= 1e-6 * 1.01 + if _dead.any(): + self.alpha[_dead] = np.minimum( + 10.0, self.alpha[_dead] * np.exp(0.05 * np.sign(_e[_dead]))) + # (4) 探索记录累积 (redefine 存活) + self.explore[i] += r + self.explore_n[i] += 1.0 + deltas.append(delta) + + # ★ §10 reset/recovery: 误差超阈值 = 环境变了 -> **保留 w**, 只提可塑性。 + # 这是「真正的持续学习」: 不是重训丢知识, 而是提高 plasticity 重新适应。 + if self.algo == "cidbd": + _E = float(np.mean([d ** 2 for d in deltas])) if deltas else 0.0 + if self.E_base is None: + self.E_base = max(_E, 1e-9) + elif _E > self.kappa * self.E_base: + # 只抬高 alpha (beta 同步), 权重 theta 完全不动 + _new = np.maximum(self.alpha, self.alpha_recovery) + self.alpha = _new + self.beta = np.log(np.maximum(_new, 1e-12)) + self.n_recovery += 1 + else: + self.E_base = 0.999 * self.E_base + 0.001 * _E + self.baseline += 0.05 * (r - self.baseline) # 简单基线 + self.trace.append({ + "idx": list(map(int, idx)), "reward": round(float(r), 5), + "delta": round(float(np.mean(deltas)) if deltas else 0.0, 5), + "alpha_mean": round(float(self.alpha.mean()), 6), + "alpha_std": round(float(self.alpha.std()), 6), + "h_norm": round(float(np.linalg.norm(self.h)), 5), + }) + return float(np.mean(deltas)) if deltas else 0.0 + + # ─────────────── redefine: 探索积累跨重建存活 ─────────────── + def redefine(self, new_desc): + """重建候选池, 但**继承**探索记录 / 步长 / 价值参数。 + + leo: "redefine 也需要探索 policy 的积累" —— 若重建就清零, + 则每次 redefine 都从零开始学"哪里值得探索", 探索知识无法累积。 + 继承方式: 每个新候选按**物理描述子最近**的旧候选继承。 + """ + new_desc = np.asarray(new_desc, dtype=np.float64) + # 归一化用新池自己的统计 (与构造时一致) + mu_d, sd_d = new_desc.mean(0), new_desc.std(0) + sd_d[sd_d < 1e-9] = 1.0 + zn = (new_desc - mu_d) / sd_d + + keep = {"explore": [], "explore_n": [], "acc": [], "learned": []} + for a in zn: + j = int(np.argmin(np.linalg.norm(self.z - a, axis=1))) # 最近旧候选 + keep["explore"].append(self.explore[j]) + keep["explore_n"].append(self.explore_n[j]) + keep["acc"].append(self.acc[j]) + keep["learned"].append(bool(self.learned[j])) + + self.n = len(new_desc) + self.desc, self.z = new_desc, zn + self.explore = np.array(keep["explore"]) + self.explore_n = np.array(keep["explore_n"]) + self.acc = np.array(keep["acc"]) + self.learned = np.array(keep["learned"]) + self.visits = np.maximum(1.0, self.explore_n) # 已探过的别当全新的 + self.redefine_count += 1 + # θ / α / h 是全局参数, 天然跨 redefine 存活 —— 这正是"积累" + + # ─────────────── 诊断 ─────────────── + def stats(self): + return { + "algo": self.algo, + "n_candidates": int(self.n), + "n_learned": int(self.learned.sum()), + "redefine_count": int(self.redefine_count), + # ★ 步长是核心累积量: 它的分化程度 = 是否真的在学"谁该快谁该慢" + "alpha": { + "min": round(float(self.alpha.min()), 6), + "max": round(float(self.alpha.max()), 6), + "std": round(float(self.alpha.std()), 6), + "mean": round(float(self.alpha.mean()), 6), + "ratio_max_min": round(float(self.alpha.max() + / max(1e-9, self.alpha.min())), 2), + }, + "h_norm": round(float(np.linalg.norm(self.h)), 5), + "theta_norm": round(float(np.linalg.norm(self.theta)), 5), + "baseline": round(float(self.baseline), 5), + "explore": {"nonzero": int((self.explore_n > 0).sum()), + "mean_n": round(float(self.explore_n.mean()), 3)}, + "any_time": round(float(self.any_time), 4), + "delta_rms": round(float(np.sqrt(max(0.0, self.drms))), 6), + "grad_rms": round(float(np.sqrt(max(0.0, self.grms))), 6), + "alpha_dead": int((self.alpha <= 1e-5).sum()), + "n_recovery": int(getattr(self, "n_recovery", 0)), + "flip_rate_mean": round(float(np.mean(self.flip_rate[-50:])) + if getattr(self, "flip_rate", None) else 0.0, 4), + } diff --git a/hibs_lnn/rr_agent.py b/hibs_lnn/rr_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..c5393ffdf9c4087969cd328b25eddae6d7b2d9ce --- /dev/null +++ b/hibs_lnn/rr_agent.py @@ -0,0 +1,1108 @@ +# -*- coding: utf-8 -*- +"""`RRSkillAgent` —— OaK 第一步(SubTask → Option)在 KeyDoor 上的 agent 侧实现。 + +**为什么是子类**:`SkillAgent.run_episode` 是所有实验臂的公共路径。把 rr 逻辑 +写进父类会改动正在跑的管线;写成子类则: + + * `mode != "rr"` 时**逐字委托** `super().run_episode()` → primitive / + rediscover / macro 三个对照臂与原来**逐位相同**(对照干净); + * 部署只是**新增一个文件**, 不修改任何现有文件。 + +## 与 OpenLoop 的区别(§1, Pitfall 35) + + a_t = π_o(s_t) ← **每一步都按当前状态重算**(本文件) + a_{t+i} = a_i^o ← 固定动作序列replay(不是时间抽象) + +`π_o` 来自子任务的价值迭代解,存成表;执行时**每个状态查一次表**, 所以 +`recompute-count == 执行步数`。 + +## 四条终止判据(§6, 全部必需) + + ① goal_reached 特征达成(x_i(s)=1) ← 子任务的**目的** + ② beta_stop 表中 β_o(s)=True(停车最优) + ③ expired **硬到期**(由 option 自身的名义时长导出, 不是拍脑袋常数) + ④ stall 连续 stall_patience 步状态不变(撞墙空转) + + 另有 ⑤ devalued: 主任务价值追上后增益 ≤ 0 → 主动放弃。 + +到期是**独立的第四条**而不是前三条的兜底 —— 前三条各自会因自己的原因不触发 +(容差尺度错 / 台账空 / 阈值没到), 到期把损害封顶。 +""" +import numpy as np +import math + +from hibs_lnn.rr_options import ( + KeyDoorTabular, build_bottleneck_library, build_random_goal_library, + build_rr_library, initiation_ok, main_value, option_gain, +) +from hibs_lnn.skill_agent import SkillAgent +from hibs_lnn.subtask_options import STOP + + +class RRSkillAgent(SkillAgent): + """在 `SkillAgent` 上增加 `mode="rr"`(reward-respecting subtask options)。 + + 消融旋钮(每个对应矩阵里的一臂): + rr_v_main "exact" | "learned" 价值函数来源(精确 VI / agent 自己的 Q) + rr_z_mode "rr" | "zeroV" | "nobonus" + rr_cumulant "reward" | "zero" + rr_feats ("key","door") | ("key",) ... + rr_bonus w̄(浮点, 扫录用) + rr_lib "rr" | "bottleneck" | "random" 换整库(负对照) + """ + + def __init__(self, mdp, mode="rr", seed=0, rr_v_main="exact", + rr_z_mode="rr", rr_cumulant="reward", rr_feats=("key", "door"), + rr_bonus=1.0, rr_lib="rr", rr_max_duration=None, + stall_patience=4, rr_select_rule="uniform", + rr_min_launches=6, rr_ucb_c=1.0, rr_calib_min_rate=0.5, + rr_persist=True, rr_forget_hl=float("inf"), + rr_score="rate", rr_pot="learned", + rr_calib_min_adv=0.0, rr_retire_rule="internal", + rr_calib_min_advq=0.0, rr_forgive_p=0.0, + rr_lr_v=0.2, rr_starve_steps=None, rr_unk_p=0.5, + rr_three_state=False, rr_dyn="none", rr_dyn_c=1.0, + rr_evidence=False, rr_ev_n_min=10, rr_ev_z_bad=2.0, + rr_ev_stale_hl=200, **kw): + super().__init__(mdp, mode=mode, seed=seed, **kw) + self.rr_v_main = rr_v_main + self.rr_z_mode = rr_z_mode + self.rr_cumulant = rr_cumulant + self.rr_feats = tuple(rr_feats) + self.rr_bonus = float(rr_bonus) + self.rr_lib = rr_lib + # ★ 选择规则**所有臂共用同一个** —— 这样臂间的差异只能来自"子任务内容", + # 而不是来自某个选择启发式。规则本身是**另一条消融轴**。 + # "uniform" 在可启动的 option 里均匀抽(每个库都有同等曝光) + # "gain" argmax(子任务价值 − V_main), 只在 > 0 时启动 + # §3 的反面教训: 用 `V_main` 做增益基线时, `c=−1` 的瓶颈 option + # 价值为负 -> 永不启动 -> 负对照臂变成**空臂**(不是"更差")。 + self.rr_select_rule = rr_select_rule + self.rr_max_duration = None if rr_max_duration is None else int(rr_max_duration) + self.stall_patience = int(stall_patience) + + self.rr_lib_obj = None + self.rr_mdl = None + self._rr_regime = None + self._rr_cap = {} # option 名 -> 硬到期上限 + # ── 计数器(§3: 模式的机制差异必须在**跑了之后**可被证伪) ── + self.rr_stats = { + "recomputes": 0, # π_o 重算次数(闭环执行 ≈ 执行步数) + "selections": 0, # 启动次数 + "exec_steps": 0, # 在 option 内的总步数 + "terms": {}, # 终止原因台账(**必须非空**) + "durations": [], # 每次执行的时长(检 lock-in) + "stall_baseline": [], # 停滞统计的基线样本 + "rebuilds": 0, + } + # ══ 自我校准闭环(L2 缺的那一格)══════════════════════════════ + # 这个 dict 是**按 option 归属**的台账。在此之前整个仓库唯一的 + # 更新点是 skill_agent.py:240(旧 macro 路径)的全局计数器 —— + # `_rr_settle(self, st, steps, reason)` 收下 `st` 然后**完全忽略它**, + # 所以库级台账对 rr 路径不存在, "这个 option 从不达成目标" 这个 + # 事实无处安放(L2 的 57 启动 / 56 expired / 1 reached 就是这样 + # 被埋掉的: 没有任何东西因为该事实而改变)。 + # 信号取 **option 自己的达成率**(β_at_feature), 不取主奖励 —— + # KeyDoor 的奖励是稀疏的(只在终点 +1), 拿主奖励做 option 级信号 + # 会退化成"所有 option 都得 0"。 + self.rr_outcome = {} # option 名 -> {"n", "reached", "steps"} + self.rr_min_launches = int(rr_min_launches) # 判"坏了"所需的最少启动数 + self.rr_ucb_c = float(rr_ucb_c) # 校准选择的探索常数 + self.rr_calib_min_rate = float(rr_calib_min_rate) # 达成率低于此 = 机制坏了 + # ══ 持续性的两个旋钮(「持续自我校准」的"持续")══════════════════ + # rr_persist 台账是否跨 regime 存活。 + # False -> 每次库重建就清空(只在单 regime 内校准) + # True -> 账随 agent 走(校准成为**跨任务**的知识) + # rr_forget_hl **忘记半衰期**(以启动次数计)。inf = 不忘记 = 终身平均。 + # + # ★ 为什么持久化必须配遗忘, 否则持久化是**有害**的: + # 无遗忘时 n 会跨 regime 累积到几百, 于是 + # (a) 老的成功把新 regime 的失败稀释掉 —— 一个在第 2 个 regime + # 已经坏掉的 option, 终身达成率仍可能是 0.9, 检测不出来; + # (b) UCB 探索项 c·sqrt(ln N/(n+1)) 被大 n 压死 —— agent 不再 + # 去重新检验它, 陈旧的判断被永久锁住。 + # 「持续校准」必须跟踪的是**当前胜任度**, 不是历史平均。 + # 实现: 有效计数按 decay = 0.5^(1/hl) 指数衰减。 + # hl=inf -> decay=1.0 -> n_eff ≡ n, 逐位复现旧行为(向后兼容)。 + self.rr_persist = bool(rr_persist) + self.rr_forget_hl = float(rr_forget_hl) + # rr_score 选择分数用什么信号 + # "rate" 达成率(option 自己的成功) —— 机制内部视角 + # "eff" 达成次数 / 消耗步数 —— **把主任务的成本接进来** + # 奖励稀疏时主奖励做不了 option 级信号, 但**时间是通用货币**: + # 视野有限, 白走的路严格是损失。所以"每步产出多少达成"是 + # 一个不需要 oracle、又对主任务有意义的信号。 + self.rr_score = rr_score + if rr_score not in ("rate", "eff", "adv", "advq"): + raise ValueError(f"unknown rr_score: {rr_score}") + # ══ 主任务信号(§8 明写的那个缺口, 现在被实测证据逼出来了)══════ + # 为什么必须有: 只用 **option 自己的判据**(达成率)做校准是**错的**。 + # 实测: 把一个 option 的上限压到 1 步 -> 它"达不成自己的子目标" + # (达成率 0.2), 但**它执行的那一步仍然是 π_o 的最优步**, 对主任务 + # 有益。按内部判据把它淘汰 -> 白损失 π_o 的好步 -> 步数反而差 + # 1.0 步(3 seed 一致)。**校准到错误的信号上, 会淘汰有用的东西。** + # + # 主任务信号取 **势函数差分 / 步数**: + # progress = Φ(终止状态) − Φ(起始状态), adv = Σprogress / Σsteps + # 这是 SMDP 视角下"这个 option 每消耗一步, 把主任务推进了多少"。 + # Φ 取 **agent 自己的 max_a Q[s,a]** —— 在线、不需要 oracle。 + # (早期 Q 是乐观常数 -> adv≈0 -> 判定"无信息", 这是诚实的行为; + # 它随学习变得有信息。`rr_pot="exact"` 用模型 VI 的 V_main 作对照, + # 用来分离"信号质量"与"机制结构"两个因素。) + self.rr_pot = rr_pot + if rr_pot not in ("learned", "exact", "gvf"): + raise ValueError(f"unknown rr_pot: {rr_pot}") + self.rr_calib_min_adv = float(rr_calib_min_adv) + # rr_retire_rule 用哪条判据决定淘汰 + # "internal" 只看 option 自己的达成率 (旧行为) + # "adv" 只看主任务推进量 + # "both" 两条都判坏才淘汰 (保守) + # "either" 任一条判坏就淘汰 (激进) + self.rr_retire_rule = rr_retire_rule + if rr_retire_rule not in ("none", "internal", "adv", "advq", "both", + "either", "all"): + raise ValueError(f"unknown rr_retire_rule: {rr_retire_rule}") + self.rr_regime_idx = -1 # 已见过的 regime 数(诊断用) + self.rr_calib_hist = [] # [{'regime': i, 'broken': [...], 'usage': {...}}] + # ══ option 的**学习价值**(SMDP)══════════════════════════════════ + # ★ 这是对"在线可用势函数"问题的正确解 —— 不是找一个更好的 Φ, + # 而是**取消 Φ 这个中间量**,直接学 option 自己的价值。 + # + # 为什么 Φ 这条路走不通: + # `adv = [Φ(s_end) − Φ(s_start)] / steps` 把两件事混在一起 —— + # (a) option 把我移动了 + # (b) agent 在学习, 所以 Φ 自己变了 + # 而且乐观初始化下 Φ(s_start) = Φ(s_end) = 常数 -> 恒等于 0。 + # + # 正确的量是 **advantage**: + # A(s, o) = Q_opt(s, o) − V_base(s), V_base(s) = max_a Q[s, a] + # 它直接回答"从 s 执行这个 option, 比贪心走基元动作好多少" —— + # 这正是"要不要留它"该问的问题。阈值 0 在**同一奖励尺度**上有意义, + # 而且两边**从同一个初值出发**(都 q_init), 所以早期 A ≡ 0 = + # "还没有信息"(正确行为, 不是 bug)。 + # + # SMDP 更新(每次 option 结算时): + # Q_opt[s0, o] += lr · [r_opt + γ^k · V(s1) − Q_opt[s0, o]] + # r_opt = option 执行期间累计的**主任务**奖励, k = 执行步数。 + # 部分执行(到期/停滞/被中断)按截断处理, 仍是合法 bootstrap。 + # + # ★ 判别器为什么**从数学里自己掉出来**(不是手设计的阈值): + # inert(上限压到1步, 但那1步是好的): Q_opt ≈ γ·V(s') 且 V(s') ≈ V(s)/γ + # => A ≈ 0 -> 不淘汰 ✓ + # harm (反转 π_o, 朝反方向走): Q_opt ≈ γ·V(s_back), V(s_back) ≪ V(s) + # => A ≪ 0 -> 淘汰 ✓ + self.q_opt = None # (n_states, n_opts) —— 惰性建表 + self._rr_oidx = {} # option 名 -> 列号(跨 regime 稳定的身份) + self.rr_calib_min_advq = float(rr_calib_min_advq) + # ★ GVF 势函数: **中性初始化**(全 0)的状态价值表。 + # 为什么不能借用 `self.q.Q` 的 max: 那张表是乐观初始化, + # 未试过的动作会把 max 顶在初值上 —— 实测 V(s0) 逐位 1.0000。 + self.rr_V = np.zeros(self.mdp.n_states, dtype=float) + self.rr_lr_v = float(rr_lr_v) + # ★ "犯错是可以原谅的" = **退休可逆**。被判坏的 option 以 p 的概率被重新检验; + # 若其统计量恢复(它用的是同一批阈值判据), 下次 `rr_broken` 自然不再点名它。 + self.rr_forgive_p = float(rr_forgive_p) + # ══ 三态 epistemic state(好 / 坏 / 未知)══════════════════════════════ + # ★ 为什么必须有: 实测发现, `advq == 0` 在两种完全不同的情况下出现 —— + # (a) 证据充分且真的没有显著差异 -> 好 + # (b) **奖励流为空, Φ 学不到东西**(harm 故障下整臂全灭时 + # `Q_adv ≡ 0.0000` 精确零, 因为账压根没记) -> **未知** + # 把 (b) 读成"不坏", 就是**自欺**。系统的最大缺口不是精度, 是它 + # 分不清这两种零。所以"未知"必须是一等状态, 不能被 0 顶上。 + # + # "奖励流为空"的在线判据: 连续多少步没有拿到任何正奖励。 + # 默认 3×horizon = "连着三个回合一分没拿" -> 势函数不可能在学东西。 + self.rr_starve_steps = (int(rr_starve_steps) if rr_starve_steps + else 3 * int(getattr(mdp, "horizon", 30))) + self.rr_since_rew = 0 # 距上次拿到正奖励的步数 + self.rr_starve_ever = 0 # 曾经进入"奖励流为空"的次数(诊断) + self.rr_unk_p = float(rr_unk_p) # "未知"状态的探索概率(有限度探索) + # ★ 三态开关。默认 **False** = 旧行为逐位不变(向后兼容 + 它本身是 + # 一条消融轴: "把未知提升为一等状态, 到底改变了什么?")。 + # True 时启用三件事: 缓存为空 -> 退回基元层; 未知 -> 有限度探索; + # 未知 -> 永不算坏。 + self.rr_three_state = bool(rr_three_state) + # ── 阶段五: 证据库(evidence bank) ────────────────────────────── + # ★ 默认关闭 ⇒ 旧行为**逐位不变**(与 rr_three_state 同一条纪律)。 + # ★ 语义: 证据**只降级(好->坏), 不升级** —— 与 A 类的反熟悉度约束同一条。 + # 理由: 预测误差低只说明"我预测得准", 不说明"这件事有价值"; + # 而**新颖度会拉高误差** ⇒ 允许升级就等于允许新颖度伪造"好"。 + self.rr_evidence = bool(rr_evidence) + self.rr_ev_n_min = int(rr_ev_n_min) + self.rr_ev_z_bad = float(rr_ev_z_bad) + self.rr_ev_stale_hl = int(rr_ev_stale_hl) + self.rr_bank = None + self.rr_ev_step = 0 + self.rr_ev_last = None # 最近一次的 support() 引用清单 + self.rr_ev_bad_n = 0 # 证据库投了几次 bad(诊断用) + if self.rr_evidence: + from hibs_lnn.evidence_bank import EvidenceBank + self.rr_bank = EvidenceBank( + dim=len(self.mdp.vec()), seed=seed, + n_min=self.rr_ev_n_min, stale_hl=self.rr_ev_stale_hl, + z_bad=self.rr_ev_z_bad) + # ── A 类动力学证据(阶段四)───────────────────────────────── + # `rr_cumulant`(上面那个)是 **option 构造期**参数 —— 它是 OaK 的 + # `(I_o, g_o, π_o, β_o)` 里的 `g_o`。A 类是**每步的动力学读数**, + # 是另一回事, 所以单开一个通道。 + # + # ★ 方向约定: 三个 cumulant 都**统一成"压力"(越大越差)** —— + # A1 转移不确定性(归一化熵) / A2 转移预测误差(1−p(s2|s,a)) / + # A3 离开已知稳定动力学区域(1−熟悉度)。 + # 这样它们**只能当风险证据**, 不可能被读成"值"。 + self.rr_dyn = str(rr_dyn) + if self.rr_dyn not in ("none", "N", "A1", "A2", "A3"): + # "N" = 新颖度**控制量** —— 与 A1/A2/A3 走同一套管道, + # 用来回答"A 类到底是动力学探测器, 还是只是新颖度探测器"。 + raise ValueError(f"unknown rr_dyn: {rr_dyn}") + self.rr_dyn_c = float(rr_dyn_c) + self._rr_T = {} # (s,a) -> {s2: n} 在线转移计数(只用真实转移) + self._rr_vis = {} # s -> n 访问次数 + self._rr_dense = {} # option -> [n, Σ, Σ²] 动力学证据台账 + self._rr_dseg = [0.0, 0.0, 0.0] # 全段基线 [n, Σ, Σ²] —— "显著更差"的参照 + self._rr_dn = 0 # A 类喂了多少个转移 + self._rr_prev_state = {} # option -> 上次判定(用来数退休/恢复次数) + + # ── 技能库(每个 regime 重建一次 —— 目标随 regime 移动) ────────── + def _rr_ensure(self): + reg = tuple(self.mdp.regime) + if self._rr_regime == reg and self.rr_lib_obj is not None: + return + # ── regime 变了: 台账怎么处理 = 「持续」这条轴 ────────────────── + if self._rr_regime is not None: # 不是第一次 + self.rr_regime_idx += 1 + self.rr_calib_hist.append({ + "regime": self.rr_regime_idx, + "broken": sorted(self.rr_broken()), + "usage": self._rr_usage(), + }) + if not self.rr_persist: + # 只在本 regime 内校准 —— 每换一次世界, 经验清零 + self.rr_outcome = {} + mdl = KeyDoorTabular(self.mdp, gamma=self.q.gamma) + self.rr_mdl = mdl + if self.rr_v_main == "exact": + V_main = main_value(mdl) + elif self.rr_v_main == "learned": + # agent 自己的 Q 表 —— 在线版, 不需要 oracle + V_main = np.max(self.q.Q, axis=1).copy() + else: + raise ValueError(f"unknown rr_v_main: {self.rr_v_main}") + self._rr_v_main_arr = V_main + + if self.rr_lib == "rr": + lib = build_rr_library(mdl, V_main, bonus_weight=self.rr_bonus, + feats=self.rr_feats, + cumulant=self.rr_cumulant, + z_mode=self.rr_z_mode) + elif self.rr_lib == "bottleneck": + lib = build_bottleneck_library(mdl) + elif self.rr_lib == "random": + lib = build_random_goal_library(mdl, V_main, seed=self.rng.randint(1 << 30)) + else: + raise ValueError(f"unknown rr_lib: {self.rr_lib}") + + self.rr_lib_obj = lib + self._rr_regime = reg + self.rr_stats["rebuilds"] += 1 + # 每个 option 的硬到期上限: **由它自己的名义时长导出**。 + # 名义时长 = 从起始状态出发、按 π_o 走、直到终止判据命中所需步数 + # (模型确定性, 直接模拟)。 + self._rr_cap = {} + for st in lib: + nom = self._rr_nominal(st) + cap = int(self.rr_max_duration) if self.rr_max_duration else max(3, 2 * nom) + self._rr_cap[st.name] = cap + + def _rr_nominal(self, st): + """模拟 π_o 从**回合起始状态 0** 出发到自己终止要多少步。 + + 模型确定性, 直接前向模拟。这个数是 `expired` 上限的依据 —— + §6 要求上限由 option 自身的名义时长导出, 不是拍脑袋常数。 + """ + s = 0 + for k in range(1, self.mdp.horizon + 1): + if st.policy[s] == STOP: + return k + nxt = self.rr_mdl.T[s][st.policy[s]][0][1] + if self.rr_mdl.is_goal[nxt] or float(getattr(st, "feature")[nxt]) > 0: + return k + if st.beta[nxt]: + return k + if nxt == s: + return k + s = nxt + return self.mdp.horizon + + # ── 选择 ──────────────────────────────────────────────────────── + def _rr_select(self, s): + """选一个 option。规则由 `rr_select_rule` 决定(所有臂共用)。 + + "uniform" —— 在**可启动**(I_o 成立)的 option 里均匀抽。 + 每个库都有同等曝光, 所以臂间差异只能来自子任务内容。 + "gain" —— `argmax`(子任务价值 − V_main), 且增益必须 **> 0**。 + 这是"哪个子任务现在最值得追"的贪心读法, 本身是一条 + 消融轴(更强的先验 = 更多 oracle 信息)。 + "calibrated" —— **自我校准**: 只用**自己的实测达成率**排序, + 不用任何假设的价值。UCB1 形式: + score = reached/n + c·sqrt(ln(N+1)/(n+1)) + 并且**排除被判"坏了"的 option**(见 `rr_broken`)。 + 这是"犯错 -> 发现 -> 改正"的闭环本身, 与其它两条 + 规则的关键区别是: 它**不引入任何外部先验**, + "哪个 option 好"完全由 agent 自己的经验决定。 + """ + cand = [st for st in self.rr_lib_obj if initiation_ok(st, s)] + # 选择**尝试**总数 —— fallback 占比的分母必须是它, 不能是 + # `selections`(那只在真的选中时加一, 比值会 > 1, 实测 6.827)。 + self.rr_stats["sel_attempts"] = self.rr_stats.get("sel_attempts", 0) + 1 + if not cand: + return None + rule = self.rr_select_rule + if rule == "gain": + best, best_g = None, 0.0 + for st in cand: + g = option_gain(st, s, self._rr_v_main_arr) + if g > best_g: + best, best_g = st, g + return best + if rule == "calibrated": + # ★ 奖励流为空 -> **退回安全基元策略**。这不是放弃, 是承认"现在 + # 什么都判不了": Φ 学不到东西, 任何判断都是自欺。等主任务拿到 + # 奖励、Φ 重新有内容, 再回来判。 + if self.rr_three_state and self.rr_starved(): + self.rr_stats["starved_sel"] = self.rr_stats.get("starved_sel", 0) + 1 + return None + broken = self.rr_broken() + live = [st for st in cand if getattr(st, "name", None) not in broken] + # ★★ **原谅**(`rr_forgive_p`)—— "犯错是可以原谅的"的机制含义: + # 被判坏的 option **不是被处决**, 而是以 p 的概率被**重新检验**。 + # + # 没有这条路就有一个**死锁**: 早期 V 还没学到东西(bootstrap 暂态), + # option 的价值天然偏低 -> 判坏 -> 不再使用 -> 统计量永不更新 -> + # 永远坏。实测就是这么崩的: advq 恒 -0.004, fallback 0.999。 + # **退休必须可逆, 否则一次误判 = 永久损失。** + dea = [st for st in cand if getattr(st, "name", None) in broken] + if dea and self.rr_forgive_p > 0 and self.rng.rand() < self.rr_forgive_p: + self.rr_stats["forgiven"] = self.rr_stats.get("forgiven", 0) + 1 + return dea[int(self.rng.randint(len(dea)))] + if not live: # 全坏了 -> 退回基元层(诚实行为) + return None + # ★ 三态驱动的选择(leo 契约: "未知应触发有限度探索"): + # 未知 -> 以 `rr_unk_p` 的概率**优先挑它** —— 不确定的 option + # 得先攒到证据, 才可能从"未知"变成"好/坏"。没有这条, + # 它永远停在未知(不被选 -> 没证据 -> 不被选)。 + # 好 -> 正常 UCB 参与选择。 + # 坏 -> 只在原谅概率下被重新检验。 + unk = [st for st in live + if self.rr_epistemic(self.rr_outcome.get( + getattr(st, "name", None), {})) == "unknown"] + if unk and self.rr_three_state and self.rng.rand() < self.rr_unk_p: + self.rr_stats["unk_probe"] = self.rr_stats.get("unk_probe", 0) + 1 + return unk[int(self.rng.randint(len(unk)))] + N = sum(o.get("n_eff", 0.0) for o in self.rr_outcome.values()) + best, best_sc = None, -np.inf # adv 分数可为负 -> 不能用 -1.0 做初值 + for st in live: + o = self.rr_outcome.get(getattr(st, "name", None)) + if o is None: # 没试过的 option: 无限乐观, 先试它 + return st + n = o.get("n_eff", 0.0) + sc = self._rr_score_of(o) + self.rr_ucb_c * float( + np.sqrt(np.log(N + 1.0) / (n + 1.0))) + if sc > best_sc: + best, best_sc = st, sc + return best + return cand[int(self.rng.randint(len(cand)))] + + # ── 一个回合(rr 路径; 其他模式逐字委托父类) ────────────────── + def run_episode(self): + if self.mode != "rr": + return super().run_episode() # 对照臂路径**逐字不变** + self._rr_ensure() + s = self.mdp.reset() + done, n = False, 0 + active, o_steps, prev_s, stuck, o_start = None, 0, None, 0, None + o_rew = 0.0 # option 执行期间的**主任务**累计奖励(SMDP 更新用) + while not done and n < self.mdp.horizon: + v0 = self.mdp.vec() # ★ **步前**状态 —— 与父类 run_episode 同约定。 + a = None + if self.rng.rand() < self.opt_prob or active is not None: + # 启动 + if active is None: + active = self._rr_select(s) + o_steps, prev_s, stuck, o_start, o_rew = 0, s, 0, s, 0.0 + if active is not None: + self.rr_stats["selections"] += 1 + # 出动作: **每步按当前状态重算 π_o**(闭环) + if active is not None: + if int(active.policy[s]) != STOP: + a = int(active.policy[s]) + self.rr_stats["recomputes"] += 1 + else: + self._rr_settle(active, o_steps, "beta_stop", o_start, s, + r_opt=o_rew) + active, o_steps, o_start = None, 0, None + if a is None: + if active is not None: + self._rr_settle(active, o_steps, "devalued", o_start, s, + r_opt=o_rew) + active, o_steps, o_start = None, 0, None + a = self.q.act(s) + s2, r, done = self.mdp.step(a) + self.q.update(s, a, r, s2, done) + # ★ GVF 势函数: **每个转移都喂**, 不论那一步是 option 出的还是基元出的。 + # 这就是 option 绕过 Q 也不影响它的原因 —— 它学的是"走到哪值多少", + # 不是"哪个动作好"。 + self._rr_vstep(s, r, s2, done) + # ★ A 类动力学证据: **先读数(用更新前的表)再更新** —— 这是 + # "先预测、再观测"的正确在线语义。**失败时这行照样执行**: + # 到不了终点也有真实转移, 这正是 A 类作为候选的全部理由。 + if self.rr_dyn != "none": + _dv = self._rr_dval(s, a, s2) + self._rr_dstep(s, a, s2) + self._rr_dacc(active.name if active is not None + else "__primitive__", _dv) + n += 1 + # ★ 奖励流监控: "连续多少步一分没拿"。Φ 从奖励流学, 奖励流为空 + # 时 Φ 恒为初值 —— 此时任何"不坏"的结论都是自欺。 + self.rr_since_rew = 0 if float(r) > 0 else (self.rr_since_rew + 1) + # ── 阶段五: 喂证据库 ──────────────────────────────────────── + # ★ `phi=v0, phi_next=self.mdp.vec()` —— **不能两个都传 `self.mdp.vec()`**。 + # `self.mdp` 在 `step()` 里已经前进, 两个都传当前 vec 会让 `v_next == v`, + # TD 的 `gamma*v_next` 项与 `v` 抵消 ⇒ 学的是"瞬时误差"不是值。 + # (`hibs_lnn/oak_proposer.py:338` 的 `observe_gvf(phi, cums, phi)` 正是 + # 这个形态; 本模块刻意避开, 见 docs/stage5_evidence_bank_spec.md §0。) + # 四个 cumulant = 这一步**四个不同的可观测事实**: + # prediction → "有 option 正在执行" reward → 即时奖励 + # transition → "状态没变(自环)" regime → "本回合结束" + if self.rr_bank is not None: + _cums = (1.0 if active is not None else 0.0, + float(r), + 1.0 if s2 == s else 0.0, + 1.0 if done else 0.0) + self.rr_ev_step += 1 + self.rr_bank.observe(v0, _cums, self.mdp.vec(), + step=self.rr_ev_step, + regime=self.rr_regime_idx) + self.trans.append((v0, a, self.mdp.vec())) # (步前, 动作, 步后) + # ── 结算判据: 全部在**新状态**上评估(正确语义) ────────── + if active is not None: + o_steps += 1 + o_rew += float(r) # 主任务奖励记到**当前这个 option** 头上 + self.rr_stats["exec_steps"] += 1 + reason = None + # ★ 主判据 = **option 自己的 β_o**(由 stopping value z 导出)。 + # 这是机制本体 —— 之前把"特征达成"排在 β 之前, 导致所有 + # 变体在特征亮起的同一瞬间就停, z 永远轮不到起作用, + # 5 个 rr 变体的台账**逐位相同**(Pitfall-26 tell)。 + # "恰好停在特征上"现在作为 β_o 的一个**子理由**计数, + # 这样既能证明机制在跑, 又能看出它停在哪。 + if bool(active.beta[s2]): + reason = ("beta_at_feature" if float(active.feature[s2]) > 0 + else "beta_elsewhere") + elif done: + reason = "horizon_end" + elif o_steps >= self._rr_cap.get(active.name, self.mdp.horizon): + reason = "expired" # ③ 硬到期 + elif s2 == prev_s: + stuck += 1 + self.rr_stats["stall_baseline"].append(stuck) + if stuck >= self.stall_patience: + reason = "stall" # ④ 撞墙空转 + else: + stuck = 0 + if reason: + self._rr_settle(active, o_steps, reason, o_start, s2, + r_opt=o_rew, done=bool(done)) + active, o_steps, o_start = None, 0, None + prev_s = s2 + s = s2 + if active is not None: # 回合结束仍在执行 + self._rr_settle(active, o_steps, "horizon_end", o_start, s, + r_opt=o_rew, done=True) + self.episodes += 1 + self._rr_sync_states() # 三态判定 + 退休/恢复计数(每回合一次) + return bool(done and self.mdp._reached()), n + + # ── 有效计数(遗忘)────────────────────────────────────────────── + def _rr_decay(self): + """每次启动后, 旧统计量保留的比例。`hl=inf` -> 1.0(不遗忘)。""" + hl = self.rr_forget_hl + if hl is None or not np.isfinite(hl) or hl <= 0: + return 1.0 + return float(0.5 ** (1.0 / hl)) + + @staticmethod + def _rr_rate(o): + """当前胜任度 = reached_eff / n_eff。不遗忘时逐位等于终身平均。""" + ne = o.get("n_eff", float(o.get("n", 0))) + return (o.get("reached_eff", float(o.get("reached", 0))) / ne) if ne > 0 else 0.0 + + @staticmethod + def _rr_eff(o): + """**效率** = 达成次数 / 消耗步数(每步产出多少达成)。 + + 这是不依赖 oracle 的"主任务成本"信号: 视野有限, 白走的路严格是损失。 + 与 `rate` 的区别: 一个"总能达成但每次都绕远路"的 option, + `rate` 高而 `eff` 低 —— 后者才是对主任务真正有意义的量。 + """ + return o.get("reached_eff", 0.0) / max(1.0, o.get("steps_eff", 1.0)) + + def _rr_score_of(self, o): + s = self.rr_score + if s == "rate": + return self._rr_rate(o) + if s == "eff": + return self._rr_eff(o) + if s == "advq": + return self._rr_advq_of(o) + return self._rr_adv(o) + + @staticmethod + def _rr_adv(o): + """主任务推进量 = Σ势函数差分 / Σ步数(每步把主任务推进多少)。""" + return o.get("adv_sum", 0.0) / max(1.0, o.get("adv_steps", 1.0)) + + def _rr_pot(self, s): + """势函数 Φ(s)。**必须是一个独立学习、中性初始化的状态价值预测**(GVF)。 + + 三个来源, 依次说明为什么只有第三个在线可用: + + `learned` —— `max_a Q[s,a]`, agent 的动作价值表取最大。 + 实测**不可用**: 该表是**乐观初始化**(q_init=+1.0), 只要某个动作 + 还没试过, `max_a` 就还是 1.0。实测 option 的启动状态上 + `V(s0)` **逐位等于 1.0000**(初值), 而 `γ^k·V(s1) = 0.717` -> + advantage ≡ −0.275 —— **量出来的是初值 artifact, 不是 option 好坏**。 + (32 个状态里只有 14 个有任一 Q 离开初值, 但"最大值"几乎都还在初值上。) + + `exact` —— 模型 VI 的 `V_main`。干净, 但**是 oracle**(用了真转移模型), + 只能当"信号质量"的对照, 不能当在线方案。 + + `gvf` —— ★ **对"在线可用势函数"的答案**: 一张中性初始化(全 0)的 + 状态价值表, 用 TD(0) 从 agent **自己走的每一步**学, 不管那一步是 + 基元动作还是 option 内部的动作: + + V[s] += lr_v · [r + γ·V[s'] − V[s]] + + 为什么这样就成了: + · **中性初值** -> 没有"未试过的动作把 max 顶在初值"这个病; + · 奖励稀疏(+1 只在终点) -> `V(s)` 自然收敛成 γ^(到目标的步数), + 这**正是**我们要的"离目标多远"的势函数; + · **每个状态都被喂** -> option 绕过 Q 也没关系, 因为喂 V 的是 + 每一步的真实转移, 不是动作选择; + · 不用转移模型、不用目标位置的坐标 -> **无 oracle**。 + """ + if s is None: + return 0.0 + if self.rr_pot == "exact": + return float(self._rr_v_main_arr[s]) + if self.rr_pot == "gvf": + return float(self.rr_V[s]) + return float(np.max(self.q.Q[s])) + + def _rr_vstep(self, s, r, s2, done): + """GVF 势函数的一步 TD —— 每个转移都喂, 无论那一步是谁出的动作。""" + tgt = float(r) if done else float(r) + float(self.q.gamma) * self.rr_V[s2] + self.rr_V[s] += self.rr_lr_v * (tgt - self.rr_V[s]) + + def _rr_has_base(self, s): + """**势函数在该状态上是否有证据**(离开中性初值)。 + + 为什么必须有这个门: 在没有任何经验的区域上比较"option 比基线好不好", + 比出来的是**初值的形状**, 不是经验。这不是调阈值, 是判断的前提条件 —— + "要判断这里用 option 划不划算, 前提是在这里有过经验"。 + """ + if s is None: + return False + if self.rr_pot == "gvf": + return bool(abs(float(self.rr_V[s])) > 1e-12) + return bool(np.any(self.q.Q[s] != self.q.q_init)) + + # ── option 的学习价值(SMDP)────────────────────────────────────── + def _rr_oindex(self, name): + """option 名 -> `q_opt` 的列号。名跨 regime 稳定, 所以身份稳定。""" + if name not in self._rr_oidx: + self._rr_oidx[name] = len(self._rr_oidx) + n_col = len(self._rr_oidx) + if self.q_opt is None: + # 与基元 Q **同一初值** —— 这样早期 advantage ≡ 0 = "还没有信息", + # 而不是"所有 option 都很差"。 + self.q_opt = np.full((self.mdp.n_states, max(n_col, 1)), + float(self.q.q_init)) + elif self.q_opt.shape[1] < n_col: + pad = np.full((self.mdp.n_states, n_col - self.q_opt.shape[1]), + float(self.q.q_init)) + self.q_opt = np.hstack([self.q_opt, pad]) + return self._rr_oidx[name] + + def _rr_vbase(self, s): + """V_base(s) = max_a Q[s, a] —— **只用基元动作**的价值。 + + 这是 advantage 的比较基线: "不用这个 option, 我靠基元动作能拿到多少"。 + 它必须是**基元专属**的, 不能把 option 的价值混进来, 否则 + `Q_opt − V` 恒 <= 0(自己不可能超过包含自己的最大值)。 + """ + return float(np.max(self.q.Q[s])) + + def _rr_vall(self, s): + """V_all(s) = max(基元, option) —— agent **实际行为**(含 option)的价值。 + + ★ SMDP 的 bootstrap 必须用它, 不能用 `_rr_vbase`: option 之间是可以 + 串联的(先拿钥匙再开门), 如果 option 从"基元专属"价值 bootstrap, + option 链上的价值就传不下去 —— 每次传播只能走一层。 + """ + v = float(np.max(self.q.Q[s])) + if self.q_opt is not None and self.q_opt.shape[1]: + v = max(v, float(np.max(self.q_opt[s]))) + return v + + @staticmethod + def _rr_advq_of(o): + """advantage = Q_opt − Φ(s0) 的(遗忘加权)均值。正 = 比基线好。""" + return o.get("advq_sum", 0.0) / max(1.0, o.get("advq_n", 1.0)) + + @staticmethod + def _rr_advq_se(o): + """上面那个均值的**标准误**(遗忘加权)。没有它就不能说"显著更差"。""" + n = max(1.0, o.get("advq_n", 0.0)) + m = o.get("advq_sum", 0.0) / n + v = max(0.0, o.get("advq_sq", 0.0) / n - m * m) + return float(np.sqrt(v / n)) + + # ── 三态 epistemic state ──────────────────────────────────────── + # ── A 类动力学 cumulant(阶段四)──────────────────────────────── + def _rr_dval(self, s, a, s2): + """A 类 cumulant 的**行前读数**(用更新前的表 —— 先预测再观测)。 + + 三条共同契约(leo 的四条要求里最硬的两条): + · 每一步都能在线获得 —— 只看刚发生的真实转移; + · **不用目标坐标、不用真实模型、不用未来标签** —— 只有 + `(s, a, s2)` 三元组的计数, agent 自己走出来的; + · 统一方向 = "压力", 越大越差, ⇒ 天然不可能被读成"值"。 + """ + if self.rr_dyn == "none": + return 0.0 + d = self._rr_T.get((int(s), int(a))) + n = sum(d.values()) if d else 0 + if self.rr_dyn == "N": + # ★ **控制量: 新颖度**。`1/(1+n(s,a))`, 用**更新前**的计数。 + # 它必须与 A1/A2/A3 **走同一套管道**(同一个 _rr_dval/_rr_dacc/ + # 基线与判据), 否则"A 与 N 有没有分开"比的是实现差异而不是语义。 + # 方向同样统一成"压力": 越新 -> 越大。 + return float(1.0 / (1.0 + n)) + if self.rr_dyn == "A1": + # ★ A1 = **转移预测误差**(按 leo 的 A′ 规格)。 + # 语义: 用**更新前**的模型预测这一步会落在哪, 再看实际落在哪。 + # ⇒ "意外度" = 1 − p̂_prev(实际后继 | s,a)。 + # ★ 这是**可预测性**, 不是任务价值 —— 一个稳定把你带向 + # 错误方向的动作照样很容易预测(leo 点名的那条)。 + # ★ 与 A2 的分工: A1 对**分布翻转**敏感(A2 不敏感), + # A2 对**支撑集扩张**敏感(A1 也敏感) —— 两者在翻转环境上 + # **必然分歧**, 这正是检验"它们是不是同一个量"的机会。 + # + # 注意: 本分支与 A2 早先**写反了**(旧 A1 = 熵, 旧 A2 = 1−p̂)。 + # 在 A′ 里按 leo 规格对齐: A1 = 预测误差, A2 = 不确定性。 + if n < 2: + return 1.0 # 无先验 ⇒ 没有"预测"可供做错, 报未知 + return float(1.0 - d.get(int(s2), 0) / n) + if self.rr_dyn == "A2": + # ★ A2 = **转移不确定性** = 观测到的后继分布归一化熵。 + # ★ 关键区别: 熵对 **{0.9,0.1} 与 {0.1,0.9} 给出同一个数**, + # 所以分布翻转时 A2 **不跳** —— 这是 A1 ≠ A2 的结构性证据。 + # ★ 但 A2 对"支撑集扩张"(0.9/0.1 -> 0.5/0.5) 敏感。 + if n < 2: + return 1.0 # 见过的样本不足 ⇒ 报最大不确定 + p = np.array(list(d.values()), float) + p /= p.sum() + return float(-(p * np.log(p + 1e-12)).sum() + / np.log(max(2, getattr(self.mdp, "n_states", 32)))) + # A3 离开已知稳定动力学区域: 落点的**出发边平均熵**(归一化)。 + # ★ 方向必须与 A1/A2 一致 = "压力"(越大越差)。第一版写成了 + # `1 - 熵`, 返回的是**熟悉度** —— 与通道约定相反, 于是读数恒 + # 1.000(确定性 MDP 里熵恒 0)、永不触发。这是"通道方向写反" + # 的典型样子: 指标看起来在动, 但基线与判据读的是相反的量。 + ns = int(s2) + if self._rr_vis.get(ns, 0) == 0: + return 1.0 # 全新状态 -> 最大压力 + ents = [] + # 用**观测到的**出边动作, 不硬编码 (0,1) —— 第一版漏掉了 GRAB/OPEN, + # 而 KeyDoor 的动作是 LEFT/RIGHT/GRAB/OPEN 四个。 + acts = {a2 for (ns2, a2) in self._rr_T if ns2 == ns} + for a2 in (sorted(acts) if acts else (0, 1)): + dd = self._rr_T.get((ns, int(a2))) + nn = sum(dd.values()) if dd else 0 + if nn >= 2: + pp = np.array(list(dd.values()), float) + pp /= pp.sum() + ents.append(float(-(pp * np.log(pp + 1e-12)).sum())) + if not ents: + return 1.0 # 出边一条都没摸过 + return float(min(1.0, np.mean(ents) + / np.log(max(2, getattr(self.mdp, "n_states", 32))))) + + def _rr_dstep(self, s, a, s2): + """把这一步的真实转移记进在线转移表 —— **失败时这行照样执行**。""" + k = (int(s), int(a)) + d = self._rr_T.get(k) + if d is None: + d = self._rr_T[k] = {} + d[int(s2)] = d.get(int(s2), 0) + 1 + self._rr_vis[int(s2)] = self._rr_vis.get(int(s2), 0) + 1 + self._rr_dn += 1 + + def _rr_dacc(self, name, d): + """把读数记到 **具体 option** 的台账 + 全段基线。""" + L = self._rr_dense.get(name) + if L is None: + L = self._rr_dense[name] = [0.0, 0.0, 0.0] + L[0] += 1.0 + L[1] += d + L[2] += d * d + B = self._rr_dseg + B[0] += 1.0 + B[1] += d + B[2] += d * d + + def _rr_dyn_bad(self, name): + """A 类风险判据: 该 option 的动力学压力是否**显著高于本段基线**。 + + 尺度**完全由数据自己的方差给出**(两样本 SE), 不引入手工 ε —— + 与 leo 在 inert 上要求的"'接近零'必须使用健康段自身的方差定义"同一条。 + """ + if self.rr_dyn == "none": + return False + L = self._rr_dense.get(name) + if L is None or L[0] < self.rr_min_launches: + return False + Bn, Bs, Bs2 = self._rr_dseg + if Bn < 20 or Bn <= L[0]: + return False + m = L[1] / L[0] + v = max(0.0, L[2] / L[0] - m * m) + bm = Bs / Bn + bv = max(0.0, Bs2 / Bn - bm * bm) + se = math.sqrt(v / L[0] + bv / Bn) + return (m - bm) > self.rr_dyn_c * se + + def rr_dyn_stats(self): + """A 类通道的报告: 证据量 / 均值 / 是否触发风险。""" + Bn, Bs, Bs2 = self._rr_dseg + bm = Bs / Bn if Bn else 0.0 + per = {} + for k in sorted(self._rr_dense): + L = self._rr_dense[k] + m = L[1] / L[0] if L[0] else 0.0 + per[k] = {"n": int(L[0]), "mean": round(m, 4), + "risk": bool(self._rr_dyn_bad(k))} + return {"kind": self.rr_dyn, "transitions": int(self._rr_dn), + "base_mean": round(bm, 4), "base_n": int(Bn), "per_option": per} + + def rr_starved(self): + """奖励流是否为空 —— 势函数不可能在学东西。 + + ★ 这是本轮最重要的那个门。实测: harm 故障下整臂全灭时 + `Q_adv ≡ 0.0000` **精确为零** —— 不是"没有显著差异", 是"根本没记账" + (到不了终点 -> 无奖励 -> Φ 恒为 0 -> 证据门 `_rr_has_base` 挡掉一切)。 + 不加这个门, 系统会把"我不知道"读成"没问题" —— **自欺**。 + """ + return self.rr_since_rew >= self.rr_starve_steps + + def rr_epistemic(self, o): + """**三态判定**: `"good"` / `"bad"` / `"unknown"`。 + + 与旧 `rr_broken` 的关键区别: **"未知"不是"好"**。 + 旧代码 `advq = 0 -> 不 < 0 -> 不淘汰 -> 当作健康`, 而在奖励流为空时 + `advq` 恰好是精确的 0 —— 于是"没有证据"被系统性地读成"没问题"。 + + 判据(与 leo 定的契约一致): + 未知 —— 奖励流为空 / 势函数未离开初值 / 证据量不足(< min_launches) + 坏 —— 证据充分, 且**上置信界 < 0**(显著更差) + 好 —— 证据充分, 且没有显著负向信号 + """ + if self.rr_starved(): + return "unknown" # Φ 学不到 -> 无有效预测证据 + n = o.get("advq_n", 0.0) + if n < self.rr_min_launches: + return "unknown" # 证据不足 + m = self._rr_advq_of(o) + if m + self.rr_ucb_c * self._rr_advq_se(o) < 0.0: + return "bad" + # ★★ **反熟悉度保证**: A 类动力学证据**只能降级(好 -> 坏)**, + # **永远不能升级(未知 -> 好)**。也就是说"可预测"买不到"好" —— + # 否则一个稳定把你带向错误方向的 option 会因为好预测而被判好。 + # 这条不是调出来的, 是设计约束: A 类是**风险通道**, 不是值信号。 + # (它到底能不能当值信号, 由"稳定但错误"否证实验来判。) + nm = o.get("name") + if nm is not None and self._rr_dyn_bad(nm): + return "bad" + # ── 阶段五: 证据库(★ **只降级, 不升级**)────────────────────── + # 与 A 类同一条不对称约束: 证据可以投 `bad`, 但**永远不能**把状态升成 + # `good`。理由: 预测误差低只说明"我预测得准", 不说明"这件事有价值"; + # 而**新颖度会拉高误差** ⇒ 允许升级就等于允许新颖度伪造"好"。 + # ⇒ 证据缺席不改变判定, 证据示警才降级。 + if self.rr_bank is not None: + self.rr_ev_last = self.rr_bank.support(step=self.rr_ev_step, + regime=self.rr_regime_idx) + if self.rr_ev_last.get("verdict") == "bad": + self.rr_ev_bad_n += 1 + return "bad" + return "good" + + def rr_ev_support(self): + """★ 最近一次证据判定背后的**引用清单** —— 哪条支持 bad / 哪条缺席。 + + 这是"证据可追溯"的直接接口: 调用方拿到的不是分数, 是**来源**。 + """ + if self.rr_bank is None: + return {"enabled": False} + s = self.rr_bank.support(step=self.rr_ev_step, regime=self.rr_regime_idx) + s["enabled"] = True + s["n_bad_ever"] = int(self.rr_ev_bad_n) + return s + + def rr_complementarity(self, label="reward"): + """★ 这组 GVF 到底有没有产生**互补证据**。 + + `label`: 用哪个 cumulant 的二值化当"变化/未变化"标签。 + 默认 `"reward"` ⇒ 问的是「这组证据在**奖励到达**这件事上互不互补」。 + + ⚠ 口径限制(必须写明): 标签本身就是四个 cumulant 之一, + 所以对 `reward` 那条 GVF 天然有利。读 `complementarity` 时要同时看 + `auc_i`(**单条**的 AUC)与 `corr_err`(冗余度), 不能只看差值。 + + **允许结论是"冗余"** —— 那就照实报冗余。 + """ + if self.rr_bank is None: + return {"enabled": False} + from hibs_lnn.evidence_bank import complementarity + h = self.rr_bank.z_history() + if label not in h["cumulant"]: + return {"enabled": True, "error": f"unknown label {label}", + "labels": list(h["cumulant"])} + lab = (h["cumulant"][label] > 0).astype(int) + if lab.min() == lab.max(): + return {"enabled": True, "error": "标签全为同一值, 无法算 AUC", + "label": label, "n": int(lab.size)} + r = complementarity(h["z"], lab, z_bad=self.rr_ev_z_bad) + r.update({"enabled": True, "label": label, "n": int(lab.size), + "pos_rate": float(lab.mean())}) + return r + + def rr_epistemic_all(self, names=None): + return {k: self.rr_epistemic(o) for k, o in self.rr_outcome.items() + if names is None or k in names} + + def _rr_sync_states(self): + """把三态判定同步进台账, 并**计数退休/恢复**。 + + 为什么必须分开记: leo 的契约里 `当前状态` 用于**决策**, + `历史状态` 用于**审计** —— 一个 option 被判坏过几次、又恢复过几次, + 是"持续学习是否真的在发生"的直接证据, 不能被当前状态覆盖掉。 + """ + for k, o in self.rr_outcome.items(): + st = self.rr_epistemic(o) + old = self._rr_prev_state.get(k) + if old is not None and st != old: + if st == "bad": + o["retired_n"] = o.get("retired_n", 0) + 1 + elif old == "bad": + o["recovered_n"] = o.get("recovered_n", 0) + 1 + self._rr_prev_state[k] = st + o["state"] = st + if self.rr_starved(): + self.rr_starve_ever += 1 + + def _rr_settle(self, st, steps, reason, s0=None, s1=None, + r_opt=0.0, done=False): + self.rr_stats["terms"][reason] = self.rr_stats["terms"].get(reason, 0) + 1 + self.rr_stats["durations"].append(int(steps)) + # ★ 归属到**这个 option**(此前 `st` 收下即弃 —— 库级台账不存在) + o = self.rr_outcome.setdefault(getattr(st, "name", id(st)), + {"n": 0, "reached": 0, "steps": 0, + "name": getattr(st, "name", None), + "n_eff": 0.0, "reached_eff": 0.0, + "steps_eff": 0.0, + "adv_sum": 0.0, "adv_steps": 0.0, + "advq_sum": 0.0, "advq_n": 0.0, + "advq_sq": 0.0}) + dec = self._rr_decay() + hit = 1.0 if reason == "beta_at_feature" else 0.0 + # 终身计数(永不衰减, 供报告与判据门限用) + o["n"] += 1 + o["steps"] += int(steps) + o["reached"] += int(hit) + # 有效计数(按 rr_forget_hl 指数遗忘; dec=1.0 时逐位等于终身计数) + o["n_eff"] = dec * o["n_eff"] + 1.0 + o["reached_eff"] = dec * o["reached_eff"] + hit + o["steps_eff"] = dec * o["steps_eff"] + int(steps) + # 势函数差分(旧方案; 保留作对照) + prog = self._rr_pot(s1) - self._rr_pot(s0) + o["adv_sum"] = dec * o["adv_sum"] + prog + o["adv_steps"] = dec * o["adv_steps"] + max(1, int(steps)) + # ★ SMDP 更新 + advantage 记账(新方案; 在线、无 oracle) + if s0 is not None and s1 is not None: + name = getattr(st, "name", None) + if name is not None: + j = self._rr_oindex(name) + qo = self.q_opt + assert qo is not None # _rr_oindex 已建表 + g, k = float(self.q.gamma), max(1, int(steps)) + # ★ bootstrap 用**势函数** Φ: 它才是"agent 现在认为走到某状态值多少" + # 的在线估计。`gvf` 时它是中性初始化独立学的状态价值, + # 没有"未试过的动作把 max 顶在初值"这个病。 + boot = 0.0 if done else (g ** k) * self._rr_pot(s1) + tgt = float(r_opt) + boot + qo[s0, j] += self.q.lr * (tgt - qo[s0, j]) + # ★ 只在**势函数对该状态有证据**时记账 —— 否则比的是初值的形状 + if self._rr_has_base(s0): + advq = float(qo[s0, j]) - self._rr_pot(s0) + o["advq_sum"] = dec * o["advq_sum"] + advq + o["advq_sq"] = dec * o.get("advq_sq", 0.0) + advq * advq + o["advq_n"] = dec * o["advq_n"] + 1.0 + else: + o["no_base"] = o.get("no_base", 0) + 1 + + # ── 自我校准: 检测 + 淘汰 ─────────────────────────────────────── + def rr_broken(self, names=None): + """哪些 option 的**终止机制坏了**(而不是"暂时运气差")。 + + 判据(事前定义, 只看 option 自己的达成率, 不看主任务表现): + 启动 >= `rr_min_launches` 次 且 达成率 < `rr_calib_min_rate` + + ## 为什么不是 `reached == 0` + + 最初的写法是"从没达成过"(绝对零)。这是错的 —— **连 L2 的真案都 + 抓不到**: L2 是 57 启动 / 1 次 goal_reached, 达成率 0.018, 不是 0。 + 绝对零判据会放过它。 + + ## 0.5 这个线的依据(来自实测的两个簇, 不是拍脑袋) + + 坏的: L2 真案 0.018 (56/57 expired) | 本轮制造器 0.250 + 好的: rr[key] 1.000 (确定性 MDP + 模型导出的 π_o 应当几乎必成) + rr-zeroV 0.595 (健康但偏早停的臂) + + 两个簇之间有一大段空档, 0.5 落在空档里。**确定性 MDP 上模型导出的 + option 本该接近 1.0**, 明显低于 1.0 就意味着终止机制或 π_o 有问题。 + """ + out = set() + for k, o in self.rr_outcome.items(): + if names is not None and k not in names: + continue + if o.get("n_eff", float(o.get("n", 0))) < self.rr_min_launches: + continue + bad_int = self._rr_rate(o) < self.rr_calib_min_rate + bad_adv = self._rr_adv(o) < self.rr_calib_min_adv + # advq 额外要求**基线证据够** + **显著更差**: + # ★ 判据必须是 "上置信界 < 0"(显著更差), 不是 "均值 < 0"。 + # 实测量到 Q_adv = −0.0002 —— 那是**零的噪声范围之内**, + # 严格 `< 0` 会把每一个 option 都判坏(fallback 0.994)。 + # 证据不足时不许处决 —— 这正是"犯错是可以原谅的"的统计版本, + # 而且它不需要任何手工 epsilon: 尺度由数据自己的方差给出。 + advq_mean = self._rr_advq_of(o) + bad_q = (o.get("advq_n", 0.0) >= self.rr_min_launches + and advq_mean + self.rr_ucb_c * self._rr_advq_se(o) < 0.0) + # ★ 三态: "未知"**永远不算坏** —— 证据不足 / 奖励流为空都不处决。 + # 这是"退休必须保守"的原则: 宁可留着不确定的, 不可错杀。 + if self.rr_three_state and self.rr_epistemic(o) == "unknown": + bad_q = False + rule = self.rr_retire_rule + if rule == "none": # 诊断臂: 只报数, 不淘汰 + hit = False + elif rule == "internal": + hit = bad_int + elif rule == "adv": + hit = bad_adv + elif rule == "advq": # ★ 在线可用: 只需 agent 自己的 Q + hit = bad_q + elif rule == "both": + hit = bad_int and bad_q + elif rule == "all": + hit = bad_int and bad_adv and bad_q + else: + hit = bad_int or bad_q + if hit: + out.add(k) + return out + + def rr_calib_stats(self): + """校准台账: 每个 option 的启动数 / **当前胜任度** / 效率 + 谁被判坏了。 + + 注意 `rate` 报的是**有效达成率**(按 `rr_forget_hl` 遗忘后的当前胜任度), + `rate_life` 才是终身平均 —— 持久化的意义就在这两者的差别上。 + """ + per = {} + for k, o in sorted(self.rr_outcome.items()): + ne = o.get("n_eff", float(o.get("n", 0))) + m = self._rr_advq_of(o) + se = self._rr_advq_se(o) + per[k] = { + # ── 当前判定(用于决策)────────────────────────────── + "state": self.rr_epistemic(o), + # ── 证据量 ────────────────────────────────────────── + "n": o["n"], + "n_eff": round(ne, 2), + "advq_n": round(o.get("advq_n", 0.0), 2), + "no_base": o.get("no_base", 0), # 有启动但没进比较的次数 + # ── 近期状态(遗忘加权)───────────────────────────── + "rate": round(self._rr_rate(o), 4), + "advq": round(m, 4), + "advq_se": round(se, 4), + "advq_lo": round(m - self.rr_ucb_c * se, 4), # 下置信界 + "advq_hi": round(m + self.rr_ucb_c * se, 4), # 上置信界 + "eff": round(self._rr_eff(o), 4), + "adv": round(self._rr_adv(o), 4), + # ── 历史状态(用于审计; 与当前状态分开)──────────── + "reached": o["reached"], + "rate_life": round(o["reached"] / o["n"], 4) if o["n"] else 0.0, + "retired_n": o.get("retired_n", 0), # 被判坏过几次 + "recovered_n": o.get("recovered_n", 0), # 从坏恢复过几次 + "mean_steps": round(o["steps"] / o["n"], 2) if o["n"] else 0.0, + } + broke = self.rr_broken() + states = [v["state"] for v in per.values()] + return { + "per_option": per, "broken": sorted(broke), "n_broken": len(broke), + # ★ 三态汇总 + 自欺门的状态 —— 报告层必须能一眼看出 + # "这次是真的没问题" 还是 "这次根本没数据"。 + "states": {"good": states.count("good"), + "bad": states.count("bad"), + "unknown": states.count("unknown")}, + "starved": bool(self.rr_starved()), + # ★ 阶段五: 证据库 —— 报的是**引用清单**不是分数 + "evidence": (self.rr_ev_support() if self.rr_evidence else {"enabled": False}), + "since_rew": int(self.rr_since_rew), + "starve_steps": int(self.rr_starve_steps), + "starve_ever": int(self.rr_starve_ever), + "sel_starved": self.rr_stats.get("starved_sel", 0), + "sel_unk_probe": self.rr_stats.get("unk_probe", 0), + "sel_forgiven": self.rr_stats.get("forgiven", 0), + } + + def _rr_usage(self): + """各 option 的**使用占比**(校准的直接观测量)。""" + tot = sum(o["n"] for o in self.rr_outcome.values()) + if not tot: + return {} + return {k: round(o["n"] / tot, 4) for k, o in sorted(self.rr_outcome.items())} + + # ── 台账 ──────────────────────────────────────────────────────── + def rr_bookkeeping(self): + d = list(self.rr_stats["durations"]) + base = self.rr_stats["stall_baseline"] + return { + "recomputes": self.rr_stats["recomputes"], + "selections": self.rr_stats["selections"], + "exec_steps": self.rr_stats["exec_steps"], + "terms": dict(self.rr_stats["terms"]), + "rebuilds": self.rr_stats["rebuilds"], + "n_options": 0 if self.rr_lib_obj is None else len(self.rr_lib_obj), + "mean_duration": float(np.mean(d)) if d else 0.0, + "max_duration": int(np.max(d)) if d else 0, + "horizon": int(self.mdp.horizon), + # ★ lock-in 检: 平均执行时长接近整回合 = 不是抽象而是锁死。 + # 阈值取自 option 自身名义时长, 所以也一并报出来。 + "lockin_ratio": (float(np.mean(d)) / max(1, self.mdp.horizon)) if d else 0.0, + "max_cap": (int(max(self._rr_cap.values())) if self._rr_cap else 0), + "stall_trigger_frac": (float(np.sum([1 for b in base if b >= self.stall_patience])) / max(1, len(base))) if base else 0.0, + "beta_nonempty": 0 if self.rr_lib_obj is None else + int(sum(int(np.sum(o.beta)) for o in self.rr_lib_obj)), + } diff --git a/hibs_lnn/rr_options.py b/hibs_lnn/rr_options.py new file mode 100644 index 0000000000000000000000000000000000000000..7f5396231a2024a9981b6e076efeb127c1f076eb --- /dev/null +++ b/hibs_lnn/rr_options.py @@ -0,0 +1,278 @@ +# -*- coding: utf-8 -*- +"""Reward-respecting subtask options on `KeyDoorMDP`(OaK 的第一步)。 + +论文:Sutton et al. 2022, *Reward-Respecting Subtasks for Model-Based +Reinforcement Learning*(arXiv:2202.03466)。 + +## 层序(不可颠倒, 见 `docs/oak_diagnosis_from_papers.md`) + + SubTask ──> Option ──> Option model ──> Planning + (c, z) (π_o,β_o) SMDP model planner + +本项目此前一直从**转移图**出发(bottleneck / 介数中心性 / 最短路),那正是论文 +点名的**最差类别**:"option models from ... shortest path options based on +bottleneck states ... are much less likely to be useful in planning"。 +这个模块把驱动层换回 **SubTask**。 + +## 子任务的定义(论文式(4)) + + c(s,a) = R(s,a) ← **与主任务完全相同的奖励** + z_i(s) = V_main(s) + (w̄_i − w_i)·x_i(s) ← 只在 x_i(s)=1 处允许停 + └────────── stopping bonus ──────────┘ + +`w_i` 是**特征 i 在主任务价值函数里的权重**(一热特征下 = 该特征为 1 诸状态上 +`V_main` 的均值),不是常数 1。硬写 `w_i = 1` 会让 bonus 恒 0 → `z ≡ V_main` +→ `β_o` 处处为空 → option 永不停止 → 与 shortest-path 给出逐位相同的曲线。 +""" + +import numpy as np + +from hibs_lnn.skill_mdp import N_ACT +from hibs_lnn.subtask_options import ( + STOP, + Subtask, + reward_respecting_subtask, + shortest_path_subtask, +) + + +# ══════════════════════════════════════════════════════════════════════ +# 1. KeyDoor 的表格视图 —— 给 Subtask.solve 提供 (T, R, is_goal) +# ══════════════════════════════════════════════════════════════════════ + +class KeyDoorTabular: + """`KeyDoorMDP` 的**表格**表示。 + + KeyDoor 的动力学完全确定性, 所以 `T[s][a] = [(1.0, s')]`;唯一随 regime + 改变的是 `is_goal`(目标判定含 `goal_pos < door_pos` 的免钥匙情形)与 `R` + (进目标得 +1)。**因此本对象必须按 regime 重建**, 不能跨 regime 复用。 + + 接口刻意与 `subtask_options.py` 里 gridworld 的环境对象**逐字一致** + (`n_states` / `T` / `R` / `is_goal` / `gamma`), 这样 `Subtask.solve` + 与 `reward_respecting_subtask` 可以原样复用, 不必为 KeyDoor 另写一份。 + """ + + def __init__(self, mdp, gamma=0.95): + self.gamma = float(gamma) + self.regime = tuple(mdp.regime) + self.n_pos = int(mdp.n_pos) + self.n_states = int(mdp.n_states) + key_pos, door_pos, goal_pos = self.regime + + # 目标状态: 与 KeyDoorMDP._reached() 同一判据 + def is_term(s): + pos, _hk, do = s // 4, (s // 2) % 2, s % 2 + return pos == goal_pos and (do or goal_pos < door_pos) + + self.is_goal = np.array([is_term(s) for s in range(self.n_states)], + dtype=bool) + + self.T = [[None] * N_ACT for _ in range(self.n_states)] + self.R = np.zeros((self.n_states, N_ACT), dtype=float) + for s in range(self.n_states): + pos, hk, do = s // 4, (s // 2) % 2, s % 2 + for a in range(N_ACT): + np_, nhk, ndo = pos, hk, do + if a == 0: # LEFT + np_ = max(0, pos - 1) + elif a == 1: # RIGHT + np_ = min(self.n_pos - 1, pos + 1) + elif a == 2: # GRAB + if pos == key_pos and not hk: + nhk = 1 + elif a == 3: # OPEN + if pos == door_pos and hk and not do: + ndo = 1 + ns = np_ * 4 + nhk * 2 + ndo + self.T[s][a] = [(1.0, ns)] + self.R[s][a] = 1.0 if self.is_goal[ns] else 0.0 + self.regime_key = self.regime + + +# ══════════════════════════════════════════════════════════════════════ +# 2. 特征向量与主任务价值 +# ══════════════════════════════════════════════════════════════════════ + +def feature_vectors(mdl: KeyDoorTabular): + """KeyDoor 的**子成就特征**: 「拿到钥匙」与「打开门」。 + + 这两个是任务链上真正的中间成果(key → door → goal), 对应论文里 + "the features are the sub-achievements of the task" 的取法。 + """ + n = mdl.n_states + x_key = np.array([(s // 2) % 2 for s in range(n)], dtype=float) + x_door = np.array([s % 2 for s in range(n)], dtype=float) + return {"key": x_key, "door": x_door} + + +def main_value(mdl: KeyDoorTabular, max_sweeps=5000, tol=1e-12): + """主任务的最优价值 `V_main`(精确价值迭代)。 + + ★ 与 `Subtask.solve` 用**同一套约定**: 目标状态价值为 0, 其余 + `V(s) = max_a [ R(s,a) + γ V(s') ]`。两处约定一致是硬要求 —— + 否则 `z_i(s) = V_main(s) + bonus` 里的 `V_main` 与子任务自身 + 的价值不在同一把尺子上, `β_o` 会在错误的地方触发。 + """ + n = mdl.n_states + V = np.zeros(n) + for _ in range(max_sweeps): + delta = 0.0 + Vn = V.copy() + for s in range(n): + if mdl.is_goal[s]: + Vn[s] = 0.0 + continue + best = -np.inf + for a in range(N_ACT): + if mdl.T[s][a] is None: + continue + val = mdl.R[s][a] + mdl.gamma * V[mdl.T[s][a][0][1]] + if val > best: + best = val + Vn[s] = best + delta = max(delta, abs(Vn[s] - V[s])) + V = Vn + if delta < tol: + break + return V + + +def optimal_policy(mdl: KeyDoorTabular): + """主任务最优策略(诊断用: 给 T_adapt 阈值与 sanity check)。""" + V = main_value(mdl) + n = mdl.n_states + pol = np.full(n, STOP, dtype=int) + for s in range(n): + if mdl.is_goal[s]: + continue + best_a, best_v = STOP, -np.inf + for a in range(N_ACT): + val = mdl.R[s][a] + mdl.gamma * V[mdl.T[s][a][0][1]] + if val > best_v: + best_a, best_v = a, val + pol[s] = best_a + return V, pol + + +# ══════════════════════════════════════════════════════════════════════ +# 3. 子任务库的构造 +# ══════════════════════════════════════════════════════════════════════ + +def build_rr_library(mdl: KeyDoorTabular, V_main, bonus_weight=1.0, + feats=("key", "door"), cumulant="reward", + z_mode="rr"): + """构造 reward-respecting 子任务库并**解出** option。 + + 参数就是消融的旋钮 —— 每个旋钮对应矩阵里的一个臂: + + `z_mode` "rr" → `z = V_main + (w̄ − w_i)x_i` (机制本体) + "zeroV" → `z = 0 + (w̄ − 0 )x_i` (削掉价值函数) + "nobonus" → `w̄ = w_i` → bonus ≡ 0 (削掉停止奖励) + `cumulant` "reward" → `c = R`(reward-respecting) + "zero" → `c = 0`(纯"到达特征", 丢掉沿途目标) + """ + feats_all = feature_vectors(mdl) + lib = [] + for name in feats: + x = feats_all[name] + if z_mode == "rr": + st = reward_respecting_subtask(mdl, x, V_main, + bonus_weight=bonus_weight) + elif z_mode == "zeroV": + # 削掉价值函数: V_main ≡ 0。此时 z 只剩 bonus 项, + # "停"与"继续"不再由主任务价值定价 —— 这正是用户猜测的 + # "可能是价值函数的原因" 的**直接检验臂**。 + z0 = np.zeros_like(np.asarray(V_main, dtype=float)) + st = reward_respecting_subtask(mdl, x, z0, + bonus_weight=bonus_weight) + elif z_mode == "nobonus": + # w̄ = w_i → bonus ≡ 0 → z ≡ V_main → 论文警告的退化情形。 + # 这一臂**预期**表现为 option 永不提前停(β_o 空), 是 harness + # 的"已知负向"校准点(Pitfall 35 / §9.3 第三条)。 + on = np.asarray(x, dtype=float) > 0 + wi = float(np.mean(np.asarray(V_main, dtype=float)[on])) if on.any() else 0.0 + st = reward_respecting_subtask(mdl, x, V_main, bonus_weight=wi) + else: + raise ValueError(f"unknown z_mode: {z_mode}") + + if cumulant == "zero": + st.c_fn = (lambda s, a: 0.0) # 丢掉沿途主任务奖励 + elif cumulant != "reward": + raise ValueError(f"unknown cumulant: {cumulant}") + + st.name = f"rr[{name}]" + st.feature = x + st.solve(mdl) + lib.append(st) + return lib + + +def build_bottleneck_library(mdl: KeyDoorTabular, subgoal_state=None): + """**已知弱基线(负对照)**: 从瓶颈/子目标状态出发的 shortest-path subtask。 + + `c = −1`, `z = 0` 只在子目标处 —— 它优化的是"**到达**",完全无视任务目标。 + 它在这里的**正确行为就是不比什么都不做好**, 所以它同时是 harness 的 + 负向校准点(§9:一个已知负向的臂必须复现为负)。 + """ + if subgoal_state is None: + # 瓶颈 = 门所在的格(拿钥匙后必须经过) —— 或用割点思想选 + door_pos = mdl.regime[1] + subgoal_state = min(mdl.n_states - 1, door_pos * 4 + 2) # (door, has_key, closed) + st = shortest_path_subtask(mdl, subgoal_state) + st.name = f"bottleneck[{subgoal_state}]" + st.feature = np.zeros(mdl.n_states) + st.solve(mdl) + return [st] + + +def build_random_goal_library(mdl: KeyDoorTabular, V_main, seed=0, + n_goals=2, bonus_weight=1.0): + """**随机目标(负对照)**: 目标状态随机取, 其余同 reward-respecting。 + + 论文 Fig.1 的第三条曲线。用来检验"收益是不是来自目标**选得好**, + 而不是来自'有一个可停止的子任务'这件事本身"。 + """ + rng = np.random.RandomState(seed) + lib = [] + cand = [s for s in range(mdl.n_states) if not mdl.is_goal[s]] + for i in range(n_goals): + g = int(cand[rng.randint(len(cand))]) + x = np.zeros(mdl.n_states) + x[g] = 1.0 + st = reward_respecting_subtask(mdl, x, V_main, + bonus_weight=bonus_weight) + st.name = f"random[{g}]" + st.feature = x + st.solve(mdl) + lib.append(st) + return lib + + +# ══════════════════════════════════════════════════════════════════════ +# 4. 启动集 I_o 与选择 +# ══════════════════════════════════════════════════════════════════════ + +def initiation_ok(st: Subtask, s: int) -> bool: + """`I_o(s)`: 只有当**特征尚未达成**且**在 s 停止不是最优**时才允许启动。 + + "特征已达成"= `x_i(s) > 0`(如 has_key=1)。已经拿到钥匙还去执行 + "拿钥匙"的 option 是空转;论文的 initiation set 正是排除这类状态。 + """ + x = getattr(st, "feature", None) + if x is None: + return True + return float(x[s]) <= 0.0 + + +def option_gain(st: Subtask, s: int, V_main) -> float: + """选择用的**增益**: 该 option 承诺的价值超出主任务价值多少。 + + 只在可启动处有意义;不可启动返回 `-inf`。选择 = `argmax` 这个量, + 这就是"哪个子任务现在最值得追"。 + """ + if st.value is None or not initiation_ok(st, s): + return -np.inf + v = float(st.value[s]) + if not np.isfinite(v): + return -np.inf + return v - float(np.asarray(V_main)[s]) diff --git a/hibs_lnn/skill_agent.py b/hibs_lnn/skill_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..a0ec1e18f96b86dabaa800dbbd630d999ca6fe56 --- /dev/null +++ b/hibs_lnn/skill_agent.py @@ -0,0 +1,338 @@ +"""skill_agent.py — 在 KeyDoorMDP 上做 B 矩阵的智能体。 + +## 要回答的问题 (用户 2026-09-14 框架里的 H5 + 复用) + + "Option 是否保存了可以重新利用的经验?" + +即 A→B→C→A→B→C 反复漂移时, 是否出现 + + T_A^(2) < T_A^(1), T_A^(3) < T_A^(2) + +## 公平对照的两个设计要点 + +1. **两个 agent 都保留 Q 表** (跨 regime 不重置) + 否则"复用"测的是"有没有记忆", 而不是"option 有没有带来额外的可复用结构"。 + 我们要隔离的是 **option 库** 的额外贡献。 + +2. **相同计算预算**: option agent 的额外开销是"拟合世界模型 + 发现 option", + 所以给它和 primitive 相同数量的**环境交互**。 + 测的是 **样本效率 R(N)**, 不是"跑得更久所以更好"。 + +## 关键: regime 变化后 option 库会不会失效? + +一个 regime = (key_pos, door_pos, goal_pos)。换 regime 后: + - **技能本身仍然成立** ("走到 key 位置并抓取" 这个**行为模式**没变) + - 但**地点变了** -> 旧 option 的 goal_center 是**过期的** + +所以本实现区分两种 option 使用方式: + - `reuse_stale` : 直接用旧 option (预期: 会被过期目标拖累) + - `rediscover` : 换 regime 后**重新发现**, 但库不清空 (预期: 应该更快) +这正好对应"技能可复用"的两种理解, 也是判据所在。 +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from hibs_lnn.skill_mdp import N_ACT, KeyDoorMDP # noqa: E402 +from hibs_lnn.uncertainty_gate import TabularTransition # noqa: E402 + + +class QLearner: + """表格 Q-learning (跨 regime 保留 Q 表 —— 这是"记忆"基线)。""" + + def __init__(self, n_states, n_act=N_ACT, lr=0.2, gamma=0.95, + eps=0.15, seed=0, q_init=1.0): + # ★ **乐观初始化** —— 稀疏奖励下这是必需的, 不是调参技巧。 + # 若 Q 全为 0, `argmax` 恒返回动作 0, agent 会**一直做同一个动作** + # (实测: 在 KeyDoor 上一直 left, 卡在 pos 0), 只有 ε-随机步才动, + # 于是几乎永远到不了目标 -> 600 回合成功率仍为 0.00。 + # 初值取正 (q_init=+1) 使每个 (s,a) 都先被尝一次, 由更新把它拉回真值。 + self.Q = np.full((n_states, n_act), float(q_init)) + self.n_states, self.n_act = n_states, n_act + self.lr, self.gamma, self.eps = lr, gamma, eps + self.q_init = float(q_init) + self.rng = np.random.RandomState(seed) + + def act(self, s): + if self.rng.rand() < self.eps: + return int(self.rng.randint(0, self.n_act)) + return int(np.argmax(self.Q[s])) + + def update(self, s, a, r, s2, done): + tgt = r if done else r + self.gamma * float(np.max(self.Q[s2])) + self.Q[s, a] += self.lr * (tgt - self.Q[s, a]) + + +class SkillAgent: + """Q-learning + 一个**跨 regime 保留**的技能库。""" + + def __init__(self, mdp: KeyDoorMDP, mode="primitive", seed=0, + discover_every=25, min_trans=120, opt_prob=0.5, + use_subgoals=1, n_regions=16, max_len=5, term_eps=None): + self.mdp = mdp + self.mode = mode # primitive / reuse_stale / rediscover / macro + self.q = QLearner(mdp.n_states, seed=seed) + self.rng = np.random.RandomState(seed + 1) + self.discover_every = int(discover_every) + self.min_trans = int(min_trans) + self.opt_prob = float(opt_prob) + self.use_subgoals = int(use_subgoals) + self.n_regions = int(n_regions) + self.max_len = int(max_len) + # ★ β_o 的"目标达成"阈值必须与**状态空间尺度**匹配。 + # KeyDoor 的 vec() = one-hot(pos) ⊕ has_key ⊕ door_open (n_pos+2 维)。 + # **两个不同位置之间的 L2 距离是 √2 ≈ 1.414**, 而到达"某个目标区域" + # 的合理判据是"落进该区域", 该区域半径就是 OptionManager 的 + # `init_radius`(按区域最近邻距离中位数 * 0.75 自适应推断)。 + # 固定 eps=0.05 (给"归一化距离"用的量级) 在这里**几乎永不满足** + # -> β_o 只在预算耗尽时触发 -> success 恒 0 -> 技能库从不学习。 + # 这是本项目第 5 次同型错误("阈值与状态空间尺度不匹配"), + # 故此处**默认 None = 运行时取 om.init_radius**, 不再手写常数。 + self.term_eps = None if term_eps is None else float(term_eps) + + self.trans = [] # 全历史 (跨 regime 累积) + self.om = None # 当前技能库 + self.episodes = 0 + self.n_discover = 0 + + # ── 技能库维护 ────────────────────────────────────────────────── + def maybe_discover(self, force=False): + """周期性 (或强制) 用**已有全部转移**重建技能库。 + + ★ 库**不清空**: 新 options 与旧 options 合并去重。 + 这才是"技能可复用"的实现 —— 换 regime 只是新增地点变体, + 而不是从头学。 + """ + if not force: + if self.episodes % self.discover_every != 0: + return + if len(self.trans) < self.min_trans: + return + X = np.array([np.append(v, a) for v, a, _ in self.trans]) + Y = np.array([vn for _, _, vn in self.trans]) + dim = X.shape[1] - 1 + T = TabularTransition().fit(X, Y, N_ACT) + from hibs_lnn.option_manager import OptionManager + from hibs_lnn.uncertainty_gate import UncertaintyGate + g = UncertaintyGate(tau_C=1.0) + g.calibrate([T.predict(X[i, :dim], int(X[i, -1]))[1] + for i in range(0, len(X), max(1, len(X) // 100))]) + om = OptionManager(T, g, max_len=self.max_len, seed=0) + n_uniq = len(np.unique(np.round(X[:, :dim], 6), axis=0)) + nreg = min(self.n_regions, max(4, n_uniq)) + if self.use_subgoals: + om.discover_subgoals(X[:, :dim], X[:, -1].astype(int), n_regions=nreg) + else: + om.discover(X[:, :dim], X[:, -1].astype(int), n_regions=nreg) + if self.om is not None: + # 合并: 旧技能在前 (先验偏好), 新技能去重后追加 + seen = {tuple(int(x) for x in o.actions) for o in self.om.options} + for o in om.options: + k = tuple(int(x) for x in o.actions) + if k not in seen: + self.om.options.append(o) + seen.add(k) + else: + self.om = om + self.n_discover += 1 + + def _select_option(self, v): + """选一个可启动的 option(与 _option_action 同源, 抽出复用)。""" + if self.om is None or not self.om.options: + return None + goal_vec = None + try: + key_pos, door_pos, goal_pos = self.mdp.regime + gv = np.zeros_like(v) + gv[goal_pos] = 1.0 + gv[self.mdp.n_pos] = 1.0 # has_key = 1 + gv[self.mdp.n_pos + 1] = 1.0 # door_open = 1 + goal_vec = gv + except Exception: + pass + return self.om.select(v, goal=goal_vec, exclude_unc=True) + + def _option_action(self, s): + """按 option 出动作 —— **必须尊重启动集 I_o**。 + + ★ 原来的写法是「从技能库里**均匀随机**挑一个 option, 取它的第一个动作」。 + 这既不看 `I_o`(启动集), 也不管目标是否相关, 等于往动作里注入 + 一半的噪声。实测后果 (KeyDoor B 矩阵): + 技能库涨到 54~60 个后, rediscover 在全部 6 段上都差于 primitive, + 其中两段**永不收敛** (T_adapt = 上限 300)。 + + 正确做法 (用户给的 `Option=(I_o,g_o,π_o,β_o)`): + 用 `OptionManager.select(s, goal=...)` —— 它**会**检查 `initiable(s)`, + 也就是"当前状态是否落在该 option 的启动集里"。这才是 `I_o` 的语义。 + + ★★ 本函数是**逐步顾问**语义 (每步问一次, 执行一个 primitive 动作)。 + 它不是时间抽象 —— option 的多步结构完全没用上, 成败也无从统计 + (`Option.visits/success` 因此永远是 0, `select()` 里 + `rate = success/visits` 恒取默认 0.5, 技能库**从不学习哪些 option 管用**)。 + 真正的宏执行见 `_macro_step`。 + """ + o = self._select_option(self.mdp.vec()) + if o is None: + return None # 没有可启动的技能 -> 交回 Q (这才是正确的退让) + acts = [int(x) for x in o.actions] + if not acts: + return None + # 闭环: 用世界模型算"朝该 option 目标前进最多"的动作; 失败则退回动作序列 + a = None + if self.om.T is not None: + a = self._goal_action(self.mdp.vec(), o.goal_center) + if a is None: + a = acts[0] + return int(a) + + def _macro_step(self, v, active, steps): + """把一个 option 当**宏动作**执行。 + + 返回 `(动作, 新的 active, 新的 steps)`。`active is None` 表示本步先选一个 + option 再出动作; 每步都重算 `π_o` (闭环), 当 `β_o` 命中 + (目标达成 / 步数上限) 时结算成败并交回 base policy。 + + ## 为什么要这个 + + 原 `rediscover` 把 option 当**单步动作顾问**: 每步按概率问一次 "现在该做 + 什么动作", option 的 `(I_o, g_o, π_o, β_o)` 里只有 `π_o` 的一步被用到。 + 后果有两个, 都是实测到的: + + 1. **成败无从统计** -> `visits/success` 恒 0 -> `select()` 的 + `rate = success/visits` 恒为默认 0.5 -> 54~60 个 option 在启动集内 + **同分**, 选择等于抛硬币 -> 全部 6 段都差于 primitive。 + 2. **不是时间抽象** -> 逐步咨询等于给 base policy 注入噪声, + 没有任何"一段行为被当作一个决策单位"的效果。 + """ + # 目标达成阈值: 未显式给定时, 用 OptionManager 自适应推断的 + # `init_radius`(区域尺度) —— 同一把尺子量启动集与目标区。 + eps = self.term_eps + if eps is None: + eps = float(getattr(self.om, "init_radius", 0.75)) + if active is None: + active = self._select_option(v) + steps = 0 + if active is None: + return None, None, 0 + a = self._goal_action(v, active.goal_center) if self.om.T is not None else None + if a is None: + a = int(active.actions[0]) if active.actions else None + steps += 1 + # β_o: 目标达成 或 预算耗尽 -> 结算 + # ★ 主判据 = **区域归属** (in_goal_region): "已进入目标区域"。 + # 旧判据 `goal_distance(v) < eps` 在离散状态空间里退化 —— + # 实测 2737 次执行中 0 次因它触发 (eps=0.754 只接受精确到达, + # 而可达的最小距离中位数是 1.000)。距离判据保留为兜底。 + try: + reached = bool(active.in_goal_region(v)) + if not reached: + reached = active.goal_distance(v) < eps + except Exception: + reached = False + if reached or steps >= max(1, int(self.max_len)): + active.visits += 1 + if reached: + active.success += 1 + active = None + steps = 0 + return a, active, steps + + def _goal_action(self, v, g): + """π_o(s) = argmax_a [ ‖g−s‖ − ‖g−T(s,a)‖ ], 每步重算。""" + g = np.asarray(g, dtype=float) + d_now = float(np.linalg.norm(v - g)) + best_a, best_gain = None, -np.inf + for a in range(N_ACT): + sn, unc = self.om.T.predict(v, a) + if not np.isfinite(unc): + continue + gain = d_now - float(np.linalg.norm(np.asarray(sn, dtype=float) - g)) + if gain > best_gain: + best_a, best_gain = a, gain + return best_a + + # ── 一个 episode ──────────────────────────────────────────────── + def run_episode(self): + s = self.mdp.reset() + done = False + n = 0 + # ── macro 模式的跨步状态: 当前正在执行的宏动作 ────────────── + active, o_steps = None, 0 + while not done and n < self.mdp.horizon: + v = self.mdp.vec() + a = None + if self.mode == "macro": + # 宏执行: 选一个 option -> 跑到 β_o 触发 -> 结算成败 + if self.rng.rand() < self.opt_prob or active is not None: + a, active, o_steps = self._macro_step(v, active, o_steps) + elif self.mode != "primitive" and self.rng.rand() < self.opt_prob: + # rediscover / reuse_stale: 逐步顾问语义 (保持不变, 作为对照) + a = self._option_action(s) + if a is None: + active, o_steps = None, 0 # 交回 base policy 时宏动作结束 + a = self.q.act(s) + s2, r, done = self.mdp.step(a) + self.q.update(s, a, r, s2, done) + self.trans.append((v, a, self.mdp.vec())) + s = s2 + n += 1 + self.episodes += 1 + if self.mode != "primitive": + self.maybe_discover() + return bool(done and self.mdp._reached()), n + + def option_bookkeeping(self): + """技能库的成败台账(诊断用: 证明 visits/success 真的被回填了)。""" + if self.om is None: + return {"n_options": 0, "n_used": 0, "total_visits": 0, "total_success": 0} + vs = [o.visits for o in self.om.options] + ss = [o.success for o in self.om.options] + return {"n_options": len(self.om.options), + "n_used": int(sum(1 for v_ in vs if v_ > 0)), + "total_visits": int(sum(vs)), "total_success": int(sum(ss))} + + +# ── B 矩阵: regime 链 ─────────────────────────────────────────────── + +DEFAULT_REGIMES = [ + dict(key_pos=2, door_pos=5, goal_pos=7), # A + dict(key_pos=5, door_pos=2, goal_pos=6), # B (key/door 换位) + dict(key_pos=1, door_pos=6, goal_pos=7), # C +] + + +def run_regime_chain(mode="primitive", chain=None, n_pos=8, episodes_per=400, + seed=0, thresh=0.8, window=20, **kw): + """跑 A→B→C→A→B→C 链, 返回每次 regime 的 T_adapt(达到 thresh 所需回合)。 + + T_adapt = 该 regime 段内, 滑动成功率首次 ≥ thresh 时的回合数; + 从未达到则记为 episodes_per (上限)。 + """ + chain = chain or (DEFAULT_REGIMES * 2) + mdp = KeyDoorMDP(n_pos=n_pos, horizon=30, **chain[0]) + agent = SkillAgent(mdp, mode=mode, seed=seed, **kw) + out = [] + for ci, reg in enumerate(chain): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + if mode != "primitive": + agent.maybe_discover(force=True) + succ, t_adapt = [], None + for ep in range(episodes_per): + ok, _ = agent.run_episode() + succ.append(1.0 if ok else 0.0) + if t_adapt is None and len(succ) >= window: + if float(np.mean(succ[-window:])) >= thresh: + t_adapt = ep + 1 + out.append({"regime": ci, "key": reg["key_pos"], "door": reg["door_pos"], + "goal": reg["goal_pos"], + "t_adapt": int(t_adapt if t_adapt is not None else episodes_per), + "final_succ": float(np.mean(succ[-window:])) if succ else 0.0, + "n_options": len(agent.om.options) if agent.om else 0}) + return out, agent diff --git a/hibs_lnn/skill_mdp.py b/hibs_lnn/skill_mdp.py new file mode 100644 index 0000000000000000000000000000000000000000..e4fc5fdf9da945deb983d7a2329e7da959f3b420 --- /dev/null +++ b/hibs_lnn/skill_mdp.py @@ -0,0 +1,187 @@ +"""skill_mdp.py — 一个有**已知 option 结构**的 MDP, 用来判定: + + (a) OptionManager 到底能不能发现并利用 option? + (b) E9/E10 的负结果 是**实现坏了**, 还是 **lm4 benchmark 的性质**? + +## 为什么需要它 + +lm4 的测量已经证明那个 benchmark **无时序结构**(不对称性 A ≡ 0, 动作可交换)。 +在那种环境下 option 无论怎么实现都不会有用 —— 所以 lm4 上的负结果 +**无法判定实现好坏**。 + +要判定实现, 必须换到一个**option 客观上应该有用**的环境。 +这就是经典的「钥匙—门」链式任务: 最优解要求一段**有严格顺序**的多步行为。 +若 OptionManager 在这里也发现不了/用不上 option, 那才是实现的问题。 + +## 任务结构 + + state = (pos, has_key) pos ∈ {0..N-1}, has_key ∈ {0,1} + actions: + 0 left 位置 -1 + 1 right 位置 +1 + 2 grab 仅在 key_pos 且 !has_key 时拿到钥匙 + 3 open 仅在 door_pos 且 has_key 时开门 (此后 goal 可达) + + reward: + 到达 goal 且门已开 -> +1, 回合结束 + 步数超过上限 -> 0, 回合结束 + + ★ **顺序依赖是内生的**: + - "go_right 到 key" 只在**还没拿钥匙**时有用 + - "grab" 只在 key 位置有用 + - "open" 只在**已拿钥匙**时有用 + -> A(a,b) ≠ 0, 且不对称 (go_key → grab ≠ grab → go_key) + -> 这正是 lm4 里**完全缺失**的结构 + +## regime (用于 A→B→A 复用测试) + + 一个 regime = (key_pos, door_pos, goal_pos)。 + 换 regime = 同样的技能 (拿钥匙/开门) 仍然有效, 但**地点变了** + -> 这正是"可复用行为结构"的定义, 也是 option 该体现价值的地方。 +""" +from __future__ import annotations + +import numpy as np + +LEFT, RIGHT, GRAB, OPEN = 0, 1, 2, 3 +N_ACT = 4 + + +class KeyDoorMDP: + """钥匙—门 链式 MDP。`state = (pos, has_key)` 编码成整数 pos*2 + has_key。""" + + def __init__(self, n_pos=8, key_pos=2, door_pos=5, goal_pos=7, horizon=30): + self.n_pos = int(n_pos) + self.regime = (int(key_pos), int(door_pos), int(goal_pos)) + self.horizon = int(horizon) + # ★ state 必须包含 door_open! 它影响转移(开门后目标才可达)。 + # 早先编码 pos*2+has_key **漏掉了 door_open** -> 同一 (pos,has_key) + # 在门开/门关时无法区分 -> 任务退化成 POMDP -> Q-learning 学不会 + # (实测: 400 回合成功率仍为 0.0)。 + # 现在: pos*4 + has_key*2 + door_open -> n_pos*4 个状态 + self.n_states = self.n_pos * 4 + self.t = 0 + self.pos, self.has_key, self.door_open = 0, 0, 0 + + # ── 状态编码 ──────────────────────────────────────────────────── + def state(self): + return self.pos * 4 + self.has_key * 2 + self.door_open + + def decode(self, s): + s = int(s) + return s // 4, (s // 2) % 2, s % 2 # pos, has_key, door_open + + def vec(self): + """给知识层用的连续表示: one-hot(pos) ⊕ has_key ⊕ door_open。""" + v = np.zeros(self.n_pos + 2) + v[self.pos] = 1.0 + v[self.n_pos] = float(self.has_key) + v[self.n_pos + 1] = float(self.door_open) + return v + + def set_regime(self, key_pos, door_pos, goal_pos): + self.regime = (int(key_pos), int(door_pos), int(goal_pos)) + + def reset(self): + self.t = 0 + self.pos, self.has_key, self.door_open = 0, 0, 0 + return self.state() + + # ── 动力学 ────────────────────────────────────────────────────── + def step(self, a): + key_pos, door_pos, goal_pos = self.regime + self.t += 1 + if a == LEFT: + self.pos = max(0, self.pos - 1) + elif a == RIGHT: + self.pos = min(self.n_pos - 1, self.pos + 1) + elif a == GRAB: + if self.pos == key_pos and not self.has_key: + self.has_key = 1 + elif a == OPEN: + if self.pos == door_pos and self.has_key and not self.door_open: + self.door_open = 1 + # 到达目标 + done = False + r = 0.0 + if self.pos == goal_pos and (self.door_open or goal_pos < door_pos): + r, done = 1.0, True + elif self.t >= self.horizon: + done = True + return self.state(), r, done + + def _reached(self): + key_pos, door_pos, goal_pos = self.regime + return self.pos == goal_pos and (self.door_open or goal_pos < door_pos) + + def greedy_action(self): + """这个任务的最优一步策略 (只为给 baseline/交互测量用)。""" + key_pos, door_pos, goal_pos = self.regime + if self.pos != key_pos and not self.has_key and key_pos >= 0: + return RIGHT if key_pos > self.pos else LEFT + if self.pos == key_pos and not self.has_key: + return GRAB + if not self.door_open and goal_pos >= door_pos: + if self.pos != door_pos: + return RIGHT if door_pos > self.pos else LEFT + if self.has_key: + return OPEN + return RIGHT if goal_pos > self.pos else LEFT + + def optimal_steps(self): + """最优解需要多少步 (用于设 T_adapt 阈值)。""" + key_pos, door_pos, goal_pos = self.regime + n = abs(key_pos - 0) + 1 # 走到 key + grab + if goal_pos >= door_pos: + n += abs(door_pos - key_pos) + 1 # 走到 door + open + n += abs(goal_pos - door_pos) + else: + n += abs(goal_pos - key_pos) + return n + + +def interaction_asymmetry(mdp, a, b, n_ep=200, seed=0): + """A(a,b) = I(a,b) − I(b,a), 用**走到目标的步数**度量。 + + S_seq = 从初始态做完整序列再贪心走到目标的**总步数** + I(a,b) = S_b − S_ab (先做 a 再做 b, 比只做 b 少用多少步) + + ★ 为什么不能用「成功率」: 贪心策略从**任何**状态都能解出任务, + 于是成功率恒为 1.000, 前缀完全不影响 —— 测量饱和, 什么也测不出。 + (这是本项目第二次踩同一个坑; 上一次是 lm4 的 `b_only = 1.0000`。) + 步数才对前缀敏感。 + """ + def steps(seq): + tot = 0 + for _ in range(n_ep): + mdp.reset() + n = 0 + done = False + for x in seq: + _, _, done = mdp.step(x) + n += 1 + if done: + break + while not done and n < mdp.horizon * 3: + _, _, done = mdp.step(mdp.greedy_action()) + n += 1 + tot += n + return tot / n_ep + + S_ab = steps([a, b]); S_ba = steps([b, a]) + S_a = steps([a]); S_b = steps([b]) + # 基线: 什么都不强迫 + S_none = steps([]) + I_ab, I_ba = S_b - S_ab, S_a - S_ba + return dict(a=a, b=b, S_none=S_none, S_a=S_a, S_b=S_b, S_ab=S_ab, S_ba=S_ba, + I_ab=I_ab, I_ba=I_ba, asym=I_ab - I_ba) + + +def has_temporal_structure(mdp, n_ep=200, seed=0, tol=0.5): + """对全部有序动作对测 A(a,b), 返回是否**存在**顺序依赖。""" + import itertools + rows = [interaction_asymmetry(mdp, a, b, n_ep=n_ep, seed=seed) + for a, b in itertools.permutations(range(N_ACT), 2)] + A = np.array([r["asym"] for r in rows]) + frac = float((np.abs(A) > tol).mean()) + return rows, A, frac diff --git a/hibs_lnn/ssm_v30_3.py b/hibs_lnn/ssm_v30_3.py new file mode 100644 index 0000000000000000000000000000000000000000..da2d310789399ff1fdc02cbf13e7ea6e8147f14e --- /dev/null +++ b/hibs_lnn/ssm_v30_3.py @@ -0,0 +1,166 @@ +""" +V30.3: 内部纠缠 SSM 层 (减法版) +=================================== +核心改动: 在 SSM 递推的每一步, 用当前隐藏状态的跨维度投影 +调制下一步的 A 矩阵相位。5 行代码, 无外部引擎。 + +减法: + V30.1: +EntanglementEngine (后处理) + V30.2: +EntangleDecohereEngine (纠缠+退相干+熵正则) + V30.3: 只改 SSM 内部, 不加任何外部模块 +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SSM_Layer_V30_3(nn.Module): + """ + V30.3 SSM 层: V29 基线 + 内部纠缠。 + + 在串行扫描的每一步(h_mean 计算后): + if ent_mode != 'none': + # 调 h 的相位: 纯相位调制, 不改变振幅 + phase_shift = ent_mod_net(h_norm) # 跨 N 维度投影 + h = h * exp(i · phase_shift) # 纯相位旋转 + + 纠缠特性: + - 发生在 SSM 递推内部 (每个 token 步) + - 跨 N 维度耦合 (ent_k=3 时 top-3) + - 纯相位 (不破坏已学习的振幅结构) + - 因果: 只影响未来状态, 不影响过去 + """ + + def __init__(self, d_model, d_state=8, fiber=None, layer_idx=0, + ent_mode='none', ent_k=3, ent_strength=0.05): + super().__init__() + self.d_model = d_model + self.d_state = d_state + self.ent_mode = ent_mode + self.ent_k = ent_k + self.ent_strength = ent_strength + + # 沿用 V29 架构 + self.norm = nn.LayerNorm(d_model) + self.in_proj = nn.Linear(d_model, 2 * d_model) + self.conv = nn.Conv1d(d_model, d_model, kernel_size=4, groups=d_model, padding=3) + self.out_proj = nn.Linear(d_model, d_model) + + # SSM 参数 + self.ssm_logsigma = nn.Parameter(torch.randn(d_model, d_state) * 0.1) + self.ssm_theta = nn.Parameter(torch.randn(d_model, d_state) * 0.5) + self.dt_proj = nn.Linear(d_model, d_model) + self.B_proj = nn.Linear(d_model, d_state) + self.C_proj = nn.Linear(d_model, d_state) + self.ssm_D = nn.Parameter(torch.ones(d_model)) + + # V33 路由进递推 (INTACT 意图条件化): cond 调制 dt/B (读写门意图感知) + # 零初始化 → 加载 Stage1 缓存后 cond 任意值 ≡ 无条件 (无需重训 Stage1) + self.cond_dt = nn.Linear(d_model, d_model) + self.cond_b = nn.Linear(d_model, d_state) + with torch.no_grad(): + self.cond_dt.weight.zero_(); self.cond_dt.bias.zero_() + self.cond_b.weight.zero_(); self.cond_b.bias.zero_() + + # KL 相关 (从 V29 继承) + self.logvar_net = nn.Sequential( + nn.Linear(d_model, d_model), nn.SiLU(), + nn.Linear(d_model, d_state), + ) + self.base_prior_logvar = nn.Parameter(torch.zeros(d_state)) + self.fiber_prior_strength = nn.Parameter(torch.tensor(0.1)) + self.register_buffer('kl_scale', torch.tensor(0.0)) + + # ── V30.3 内部纠缠: 极小调制网络 ── + # 输入: |h| 投影到 N 维 (跨维度耦合) + # 输出: 相位调制量 (纯相位, 不改变振幅) + if ent_mode != 'none': + self.ent_mod = nn.Linear(d_state, d_state, bias=False) + self.ent_amp = nn.Linear(d_state, 1, bias=False) # 振幅门控 + + self._entropy_trace = [] + + def compute_entropy(self, h): + B, D, N = h.shape + amp = h.abs() + rho = amp.view(B, D, N, 1) * amp.view(B, D, 1, N) + rho = rho / (rho.sum(dim=(-1, -2), keepdim=True) + 1e-10) + try: + e = torch.linalg.eigvalsh(rho).clamp(min=1e-10) + return -(e * e.log()).sum(dim=-1).mean().item() + except: + return 0.0 + + def forward(self, x, cond=None): + r = x + x = self.norm(x) + a, b = self.in_proj(x).chunk(2, dim=-1) + a = F.silu(self.conv(a.transpose(-1, -2))[:, :, :x.shape[1]].transpose(-1, -2)) + + B_, L_, D_ = a.shape + S_ = self.ssm_logsigma.shape[-1] + sigma = -self.ssm_logsigma.exp() + theta0 = self.ssm_theta + A_eff = sigma.unsqueeze(0).unsqueeze(0) + 1j * theta0.unsqueeze(0).unsqueeze(0) + + if cond is not None: + # V33 路由: 意图 cond 调制 dt 与 B (读写门意图感知), 广播到每步 + c_dt = self.cond_dt(cond) # (..., d) + c_b = self.cond_b(cond) # (..., d_state) + if c_dt.dim() == 1: + c_dt = c_dt.unsqueeze(0) + c_b = c_b.unsqueeze(0) + dt = F.softplus(self.dt_proj(a) + c_dt.unsqueeze(1)) + 1e-4 + Bk = self.B_proj(a) + c_b.unsqueeze(1) + else: + dt = F.softplus(self.dt_proj(a)) + 1e-4 + Bk = self.B_proj(a) + Ck = self.C_proj(a) + Ab = torch.exp(dt.unsqueeze(-1) * A_eff) + bk = (Ab - 1.0) / (A_eff + 1e-8) * Bk.unsqueeze(2) * a.unsqueeze(-1) + + h = torch.zeros(B_, D_, S_, dtype=torch.complex64, device=Ab.device) + ys = torch.zeros(B_, L_, D_, device=a.device) + + for k in range(L_): + h_mean = Ab[:, k] * h + bk[:, k] + + # ── V30.3: 内部纠缠 ────────────── + if self.ent_mode != 'none' and k > 0: + # 从 |h| 导出纯相位调制 (跨 N 维度耦合) + h_norm = h_mean / (h_mean.abs() + 1e-8) # 归一化复数 + h_amp = h_mean.abs() # 振幅 + phase_input = h_norm.real * h_amp # (B, D, N) 振幅加权相位信号 + + # 跨 N 维度的线性耦合 + delta_raw = self.ent_mod(phase_input) # (B, D, N) + + if self.ent_k > 0 and self.ent_k < S_: + # top-k 稀疏化: 只纠缠振幅最大的维度 + _, top_idx = h_amp.topk(self.ent_k, dim=-1) + mask = torch.zeros_like(delta_raw) + mask.scatter_(-1, top_idx, 1.0) + delta_raw = delta_raw * mask + + # 振幅门控: 强信号有更强的纠缠, 但不改变本身振幅 + amp_gate = torch.sigmoid(self.ent_amp(h_amp)) # (B, D, 1) + phase_shift = torch.tanh(delta_raw) * self.ent_strength * amp_gate + + # 纯相位旋转: 不改变振幅 + h = h_mean * torch.exp(1j * phase_shift) + else: + h = h_mean + + y = (Ck[:, k].unsqueeze(1) * h).sum(dim=-1).real + self.ssm_D * a[:, k] + ys[:, k] = y + + if self.ent_mode != 'none': + self._entropy_trace.append(self.compute_entropy(h)) + + out = self.out_proj(ys * F.silu(b)) + r + + # 兼容 V30_CodeLoop: 保存最后一个激活 + self._last_a = a[:, -1, :] + + return out, 0.0 # (y, kl=0 兼容 V30_CodeLoop) diff --git a/hibs_lnn/stochastic_keydoor.py b/hibs_lnn/stochastic_keydoor.py new file mode 100644 index 0000000000000000000000000000000000000000..5cfa4e7cfee3a65a0f18e4ec75908f983c368e09 --- /dev/null +++ b/hibs_lnn/stochastic_keydoor.py @@ -0,0 +1,195 @@ +# -*- coding: utf-8 -*- +"""A′ 的三个环境:把 `novelty` 与 `dynamics change` 分开。 + +## 为什么需要新环境 + +原 `KeyDoorMDP.step()` 是**完全确定性**的(无任何随机源)。实测:所有访问过 +`n>=2` 的 `(s,a)`,后继分布的最大熵**逐位为 0.000000**。于是 + + 转移不确定性 H(T|S,A) ≡ 0 + 转移预测误差 1−p̂(s'|s,a) 的唯一非零来源是 `n<2` 分支 + 稳定区域 ≈ 同样的计数 + +三个语义不同的量坍缩成**同一个"我见过这个 (s,a) 几次"**。 +⇒ 那个环境对 A 类**不可辨识**,不是 A 类不行。 + +## 三个环境(A′-2) + + E0 KeyDoorMDP 确定性 —— 负对照, 保留不删 + E1 StochasticKeyDoor 平稳随机 —— 不确定性可分离, 无 shift + E2 NonStationaryKeyDoor 非平稳(slip↑) —— ★ **可用**: 满足构型 II + FlipKeyDoor 非平稳(翻转) —— ❌ 失败构造, 保留为反例 + +★ **命名与直觉相反, 以实测为准**(探针③, 逐步紧邻等长窗口): + + E2 翻转: N 3.62 A1 3.39 A2 2.75 <- N 跳得比 A1 还凶, 失败 + E2 slip↑: N 0.88 A1 3.10 A2 1.25 <- N 平, A1 跳, A1/A2 分歧 ✓ + +我原以为"翻转(支撑集不变)"最干净、"slip↑"是反例。**测量给出相反答案**: +决定 novelty 的不是转移的支撑集, 而是最终的**去向分布**; 翻转恰好把 agent +送到它几乎没去过的伙伴目标。而均匀随机 slip↑ 是在**已熟悉的地盘内** +增加不可预测性 ⇒ 访问分布不动, `p̂` 变成错的。 + +## 设计要点:机制只写一份 + +每个环境只实现 **`_perturb(a) -> 实际执行的动作`**, `step()` 与诊断用的 +`true_transition()` **共用**它。 + +第一版把机制在 `step()` 和 `true_transition()` 里各写了一遍, 后果是: +`true_transition` 里那条 `if rand < _slip_now(): a = randint(N)` 是**均匀随机** +版本, 对 `FlipKeyDoor` 完全不对(它只翻给伙伴动作)。于是"真值"量的是另一套 +动力学, 而探针偏偏靠真值判断环境可不可辨识 —— **诊断工具自己错了, +却会给出一个看起来正常的结论**。两份实现只要漂移一次就足够。 +""" +import numpy as np + +from hibs_lnn.skill_mdp import KeyDoorMDP + +__all__ = ["StochasticKeyDoor", "FlipKeyDoor", "NonStationaryKeyDoor", + "N_ACTIONS", "true_transition"] + +N_ACTIONS = 4 # LEFT, RIGHT, GRAB, OPEN +_NEVER = 1 << 62 + + +class StochasticKeyDoor(KeyDoorMDP): + """E1 平稳随机:`P(s'|s,a)` 全程不变。 + + 以 `(1-slip)` 执行意图动作, 否则执行**均匀随机**动作。 + 作用 = 制造"不确定性高但**没有** regime shift"。 + """ + + def __init__(self, slip=0.10, seed=0, **kw): + super().__init__(**kw) + self.slip = float(slip) + self.rng = np.random.RandomState(int(seed)) + self.t_total = 0 # 全局步数(跨 episode) —— 子类用它定位 shift + self.shifted = False + + # ── 机制(唯一实现; step 与 true_transition 共用)────────────── + def _p_now(self): + return self.slip + + def _perturb(self, a): + """返回**实际执行**的动作。支撑集由子类的实现决定。""" + if self.rng.rand() < self._p_now(): + return int(self.rng.randint(N_ACTIONS)) + return int(a) + + def step(self, a): + if not self.shifted and self.t_total >= getattr(self, "shift_at", _NEVER): + self.shifted = True + self.t_total += 1 + return KeyDoorMDP.step(self, self._perturb(a)) + + +class FlipKeyDoor(StochasticKeyDoor): + """❌ 失败构造(保留为反例):"翻转分布、不扩张支撑集"**仍然混淆 novelty**。 + + ## 我当初的推理(错的) + + 想让 `P(s'|s,a)` 的**支撑集恒定**、只翻转权重: + + P(s'|s,a) = (1-p)·1[s'=T(s,a)] + p·1[s'=T(s,c(a))] + shift 前 p = 0.10 shift 后 p = 0.90 + + `c(a)` 是固定的伙伴动作(LEFT<->RIGHT, GRAB<->OPEN)。推理是: + 支撑集 `{T(s,a), T(s,c(a))}` 两个 p 下都一样 ⇒ 到达的状态集合不变 + ⇒ novelty 不变; 而模型学到的 `p̂` 现在是错的 ⇒ A1 跳; 熵 + `H({p,1−p})` 两 regime 同值 ⇒ A2 不跳。看起来是完美的 构型 II。 + + ## 实测(逐步、紧邻等长窗口, 探针③) + + E2 翻转: N 0.0597 -> 0.2160 (3.62) A1 0.1908 -> 0.6476 (3.39) + A2 0.1125 -> 0.3093 (2.75) + E2c slip↑: N 0.0997 -> 0.0881 (0.88) A1 0.1738 -> 0.5382 (3.10) + A2 0.1326 -> 0.1656 (1.25) + + **翻转版失败**: N 跳 3.62 倍, 比 A1 还凶。 + + ## 为什么推理错了(值得记) + + > **决定 novelty 的不是转移的支撑集, 而是最终的去向分布。** + + 支撑集不变只保证"这些状态**可能**被到达"; 但翻转把 agent 在 90% 的 + 步子上送到**伙伴目标** —— 那是它极少去的地方 ⇒ 它真的闯进了新领地。 + 支撑集是"可能性", novelty 量的是"实际去过几次", 两者不是一回事。 + + ## 教训 + + "不扩张支撑集"看起来是把 novelty 摘掉的自然做法, 但 agent 的行为是 + **状态依赖**的 —— 改了动力学就改了轨迹, 改了轨迹就改了访问分布。 + 要抑制 novelty, 得直接让**访问分布**不变, 而不是让转移的支撑集不变。 + `NonStationaryKeyDoor`(均匀随机 slip↑)无意中做到了这一点: + 它在**已熟悉的地盘内**增加不可预测性。 + """ + + COMPANION = (1, 0, 3, 2) # LEFT<->RIGHT, GRAB<->OPEN + + def __init__(self, p_pre=0.10, p_post=0.90, shift_at=3000, seed=0, **kw): + super().__init__(slip=p_pre, seed=seed, **kw) + self.p_pre = float(p_pre) + self.p_post = float(p_post) + self.shift_at = int(shift_at) + + def _p_now(self): + return self.p_pre if self.t_total < self.shift_at else self.p_post + + def _perturb(self, a): + if self.rng.rand() < self._p_now(): + return int(self.COMPANION[int(a)]) # 只翻给伙伴动作 + return int(a) + + +class NonStationaryKeyDoor(FlipKeyDoor): + """★ E2(**可用版**):均匀随机 slip 在 `shift_at` 处抬升。 + + E1: slip 恒定 0.10 E2: 0.10 -> 0.60 @shift_at + + 它最初是被当成"反例"写下来的(我原以为它会混淆 novelty, 因为 + "slip 变大 ⇒ agent 走得不一样"). 实测**正好相反**, 它是唯一同时满足 + 构型 II 的构造: + + N 0.0997 -> 0.0881 (0.88) ← 平坦: 没有更"新" + A1 0.1738 -> 0.5382 (3.10) ← 跳: 学到的 p̂ 现在错了 + A2 0.1326 -> 0.1656 (1.25) ← 基本平 ⇒ **A1 与 A2 分歧** + + 为什么它反而干净: 均匀随机 slip 在 **已熟悉的地盘内** 增加不可预测性 + (agent 早就把大部分 (s,a) 走过很多遍了), 所以访问分布几乎不动; + 而 `p̂` 的**形状**变了 ⇒ 预测误差上去了。 + + 于是这条环境上 "变" 与 "新" 可辨识, 并且 A1/A2 读数**明显分离** —— + 这是 A 类候选第一次在同一环境上表现出不同的量。 + """ + + def __init__(self, slip0=0.10, slip1=0.60, shift_at=3000, seed=0, **kw): + super().__init__(p_pre=slip0, p_post=slip1, shift_at=shift_at, + seed=seed, **kw) + + def _perturb(self, a): + if self.rng.rand() < self._p_now(): + return int(self.rng.randint(N_ACTIONS)) # 均匀随机 = 不选边 + return int(a) + + +def true_transition(env, s, a, trials=400): + """把 `(s,a)` 的真实后继分布**数出来**(穷举环境, 非模型)。 + + 只用于验证"这个 benchmark 真的可辨识"(判据 ⑨′a/⑨′b)。 + agent 侧一律只用在线计数表, **不碰这个函数**。 + + ★ 设置状态必须走 `decode()`:`s = pos*4 + has_key*2 + door_open`, + `has_key`/`door_open` 也影响转移。手写 `env.s = s` 是错的 + (那个属性不存在, 而且漏掉两个标志位)。 + ★ 扰动走 `env._perturb()` —— 与 `step()` **同一份实现**。E0 没有该方法, + 退化成恒等(确定性), 正好是我们要的负对照。 + """ + pert = getattr(env, "_perturb", None) + cnt = {} + for _ in range(int(trials)): + env.pos, env.has_key, env.door_open = env.decode(s) + env.t = 0 + aa = int(a) if pert is None else int(pert(a)) + s2, _, _ = KeyDoorMDP.step(env, aa) + cnt[int(s2)] = cnt.get(int(s2), 0) + 1 + return cnt diff --git a/hibs_lnn/subtask_options.py b/hibs_lnn/subtask_options.py new file mode 100644 index 0000000000000000000000000000000000000000..5da848b2d0eaaef42473f7002630a8c9ba85846f --- /dev/null +++ b/hibs_lnn/subtask_options.py @@ -0,0 +1,310 @@ +r"""subtask_options.py — **按论文顺序**实现 STOMP(arXiv 2202.03466)。 + + SubTask ──> Option ──> Model ──> Planning + +本文件是对本项目现有 `OptionManager` 的**修正版**。差别(逐条对齐论文): + +| | 现有 `option_manager.py` | 本文件 | +|:--|:--|:--| +| 起点 | **转移图**(model-first) | **subtask**:先定 GVF 的 `(c, z)` | +| 目标 | 状态向量 `g_o` + 欧氏距离 | **stopping value `z_i(s)`**(带 stopping bonus) | +| `π_o` | 对模型贪心 argmax | 在 subtask MDP 上解出来的策略 | +| `β_o` | 逻辑回归拟合 | **stop 动作最优即停**(与论文 Fig.2 的"红色停止格"一致) | +| model | 无 | **option model**(终止分布 + 累计奖励)显式学出来 | +| 用途 | 替换 base policy 的动作 | **进 planner**,指标 = planning look-ahead 操作数 | + +## 论文的两种 subtask(必须都实现, 才有正反例) + +``` +shortest-path (bottleneck) c = −1 z = 0 at subgoal +reward-respecting c = R (与主任务相同) z_i(s) = v(s) − w_i x_i(s) + w̄_i x_i(s) + \____ stopping bonus ____/ +``` +论文 Fig.1 的结论:`shortest-path` 的 planning **比 primitive 还慢**; +`reward-respecting` **明显更快**。**这两个已知结论就是本项目的 ground truth。** +""" +from __future__ import annotations + +import numpy as np + +from hibs_lnn.gridworld import N_ACT, UP, RIGHT, DOWN, LEFT + +STOP = N_ACT # 停止动作的编号 + + +# ══════════════════════════════════════════════════════════════════════ +# 1. Subtask 规格:GVF 的 cumulant c 与 stopping value z +# ══════════════════════════════════════════════════════════════════════ + +class Subtask: + """一个 GVF 子任务 = (c, z)。 + + c(s, a) : cumulant(论文里最短路径 subtask 是 −1, reward-respecting 是主任务奖励) + z(s) : stopping value(在 s 停止时拿到的终止价值) + """ + + def __init__(self, name, c_fn, z_fn): + self.name = name + self.c_fn = c_fn # (s, a) -> float, 该转移的期望 cumulant + self.z_fn = z_fn # (s) -> float + self.gamma = 0.99 # 由 solve(env) 覆盖 + self.policy = None # π_o:(s) -> a 或 STOP + self.value = None # 该 subtask 的价值函数 + self.model = None # option model + self.beta = None # β_o(s): 是否停止 + + # ── 解 subtask -> 得到 option(论文 §3) ──────────────────────────── + def solve(self, env, max_sweeps=2000, tol=1e-10, resume_from=None): + """在 subtask MDP 上做价值迭代(action 集 = 4 个 primitive + STOP)。 + + 目标: 最大化 E[ Σ γ^t c_t + γ^K z(S_K) ] + Q(s, a) = c(s,a) + γ (1−β) V(s') + γ β z(s') # β 由"停止是否最优"隐式决定 + """ + n = env.n_states + V = np.zeros(n) if resume_from is None else resume_from.copy() + for sweep in range(max_sweeps): + delta = 0.0 + Vn = V.copy() + for s in range(n): + if env.is_goal[s]: + Vn[s] = 0.0 # goal 是终止状态(episode 结束) + delta = max(delta, abs(Vn[s] - V[s])) + continue + # 停下:s 的价值就是 z(s) + best = self.z_fn(s) + for a in range(N_ACT): + c = self.c_fn(s, a) + if c is None: + continue + val = 0.0 + for p, ns in env.T[s][a]: + # 在 ns 决定:继续(value)还是停(z) + val += p * (c + env.gamma * max(V[ns], self.z_fn(ns))) + if val > best: + best = val + Vn[s] = best + delta = max(delta, abs(Vn[s] - V[s])) + V = Vn + if delta < tol: + break + self.value = V + # 导出 π_o 与 β_o + pol = np.full(n, STOP, dtype=int) + for s in range(n): + if env.is_goal[s]: + pol[s] = STOP + continue + best_a, best_v = STOP, self.z_fn(s) + for a in range(N_ACT): + c = self.c_fn(s, a) + if c is None: + continue + val = 0.0 + for p, ns in env.T[s][a]: + val += p * (c + env.gamma * max(V[ns], self.z_fn(ns))) + if val > best_v + 1e-12: + best_a, best_v = a, val + pol[s] = best_a + self.policy = pol + # β_o(s) = True 表示"停在 s 是最优的" + # 判据: z(s) >= "继续走"的价值 —— 与论文 Fig.2 的红色停止格一致 + cont = np.full(n, -np.inf) + for s in range(n): + if env.is_goal[s]: + continue + best = -np.inf + for a in range(N_ACT): + c = self.c_fn(s, a) + if c is None: + continue + val = 0.0 + for p_, ns in env.T[s][a]: + val += p_ * (c + env.gamma * max(V[ns], self.z_fn(ns))) + best = max(best, val) + cont[s] = best + self.beta = np.array([bool(self.z_fn(s) >= cont[s] - 1e-12) for s in range(n)], + dtype=bool) + self.gamma = env.gamma + return self + + +# ══════════════════════════════════════════════════════════════════════ +# 2. 两种 subtask 的具体定义(论文 Fig.1 的对照) +# ══════════════════════════════════════════════════════════════════════ + +def shortest_path_subtask(env, subgoal_state, neg_on_neg=True): + """论文:shortest-path / bottleneck option。`c = −1`,`z = 0` 只在子目标处。""" + + def c(s, a): + return -1.0 + + def z(s): + return 0.0 if s == subgoal_state else -np.inf + + st = Subtask("shortest_path", c, z) + return st + + +def reward_respecting_subtask(env, feature_vec, V_main, bonus_weight=1.0, + stop_at_goal=True): + r"""论文式(4):reward-respecting subtask of feature attainment。 + + c(s,a) = R(s,a) -- **与主任务相同的奖励** + z_i(s) = [w^T x(s) - w_i*x_i(s)] + w_bar_i*x_i(s) + = V_main(s) + (w_bar_i - w_i)*x_i(s) + \__ stopping bonus __/ + + *** 一个必须做对的细节(本项目在 ground-truth 基准上抓到的 bug) *** + 式(4) 里 `w_i` 是 **特征 i 在主任务价值函数里的权重**, 不是 1。 + + 第一版把 `w_i` 硬写成 1.0 且 `w_bar_i = 1` -> stopping bonus 恒为 0 + -> `z_i(s) == V_main(s)` -> 子任务近似主任务 -> `beta_o` 处处为空 + -> option 永不停止 -> option model 退化 -> reward-respecting 与 + shortest-path 给出**完全相同**的 planning 曲线(实测 2145 / 2145)。 + + 这正是论文原话警告的情形: + "The stopping values should not equal the estimated values because then the + subtask would approximate the main task and solving it would probably add + nothing new." + + 对一热特征 x_i(s) in {0,1}, 主任务线性近似下特征 i 的权重就是 + **该特征为 1 的诸状态上的价值贡献** -> w_i = mean(V_main[feature > 0])。 + + bonus_weight 就是论文的 w_bar_i: + w_bar ~ w_i bonus 小, option 几乎不提前停 + w_bar = 1 论文主用值 + w_bar -> 很大 退化成 shortest-path(论文 §6 实测, w_bar=100 走最短路) + """ + on = np.asarray(feature_vec, dtype=float) > 0 + w_i = float(np.mean(np.asarray(V_main, dtype=float)[on])) if on.any() else 0.0 + + def c(s, a): + return float(env.R[s][a]) + + def z(s): + if not on[s] and not (stop_at_goal and env.is_goal[s]): + # 特征为 0 处**不允许停**(否则"停"与"继续"同价 -> beta 退化) + return -np.inf + return float(V_main[s]) + (bonus_weight - w_i) * float(feature_vec[s]) + + st = Subtask("reward_respecting", c, z) + st.w_i = w_i + st.bonus_weight = bonus_weight + return st + +# ══════════════════════════════════════════════════════════════════════ +# 3. Option model(论文 §4:第三步,在 option 之后) +# ══════════════════════════════════════════════════════════════════════ + +def build_option_model(env, opt, max_steps=200): + r"""由 option 的 (pi_o, beta_o) 推出**SMDP 形式的 option model**。 + + 返回 `M[s] = [(p, s_term, r_disc, k), ...]`,其中 + + p : 从 s 出发最终终止于 s_term 的概率 + r_disc : 沿途中**已贴现**的累计奖励 sum_t gamma^t r_t + k : 平均步数 + + ★★ 第二个必须做对的细节(ground-truth 基准抓到的) + ------------------------------------------------- + 第一版只返回未贴现的累计奖励, 且 planner 里写成 ``r + gamma*V(s_term)`` + —— 即只贴现**一步**, 而不是 option 实际占用的 k 步。 + -> option 被系统性高估 -> 价值迭代发散 + (实测: look-ahead 打满 300000 仍未收敛, 而 primitive 只需 1716)。 + + SMDP 的正确形式是: Q(s, o) = E[ r_disc + gamma^k * V(s_term) ] + """ + n = env.n_states + M = [] + for s0 in range(n): + cur = {int(s0): (1.0, 0.0, 0.0)} # state -> (prob, 期望贴现回报, 期望步数) + term = {} + + def merge(d, key, p, dr, k): + o = d.get(key) + if o is None: + d[key] = [p, p * dr, p * k] + else: + o[0] += p + o[1] += p * dr + o[2] += p * k + + for _step in range(max_steps): + if not cur: + break + nxt = {} + for s, (p, dr, k) in cur.items(): + if opt.beta[s] or opt.policy[s] == STOP: + merge(term, s, p, dr, k) + continue + a = int(opt.policy[s]) + r_imm = float(opt.c_fn(s, a)) + for pt, ns in env.T[s][a]: + pp = p * pt + merge(nxt, int(ns), pp, dr + (env.gamma ** k) * r_imm, k + 1.0) + cur = {s: (v[0], v[1] / v[0] if v[0] > 0 else 0.0, + v[2] / v[0] if v[0] > 0 else 0.0) for s, v in nxt.items()} + + outs = [] + for s, (psum, drw, kw) in sorted(term.items()): + if psum <= 1e-12: + continue + outs.append((float(psum), int(s), float(drw / psum), float(kw / psum))) + if not outs: + outs = [(1.0, int(s0), 0.0, 1.0)] + M.append(outs) + return M + + + +# ══════════════════════════════════════════════════════════════════════ +# 4. Planning(论文 §5):指标 = look-ahead 操作数 +# ══════════════════════════════════════════════════════════════════════ + +def planning_curve(env, option_models, v_star, s0, tol=1e-3, max_ops=400000, + sweeps_between=1): + """带 option 的价值迭代;记录"每消耗 N 次 look-ahead 时 V(s0) 的误差"。 + + option_models : {name: M}, M 来自 build_option_model。空 dict = 只用 primitive。 + 返回 (ops_list, err_list) —— 与论文 Fig.1 的横/纵轴一致。 + """ + n = env.n_states + V = np.zeros(n) + ops_list, err_list = [], [] + total = 0 + # 每次都从 s0 开始扫(顺序会显著影响效率, 这里固定在自然顺序, 对三种方法一致) + order = list(range(n)) + while total < max_ops: + for s in order: + if env.is_goal[s]: + V[s] = 0.0 + continue + best = -np.inf + # primitive + for a in range(N_ACT): + val = 0.0 + for p, ns in env.T[s][a]: + val += p * (env.R[s][a] + env.gamma * V[ns]) + total += 1 + if val > best: + best = val + # options —— SMDP: r_disc + gamma^k * V(s_term) + for name, M in option_models.items(): + for ent in M[s]: + p, s_term, r_disc, k = ent[0], ent[1], ent[2], (ent[3] if len(ent) > 3 else 1.0) + val = p * (r_disc + (env.gamma ** k) * V[s_term]) + total += 1 + if val > best: + best = val + V[s] = best + err = abs(V[s0] - v_star) + ops_list.append(total) + err_list.append(err) + if err < tol: + break + return ops_list, err_list + + +def ops_to_tolerance(env, option_models, v_star, s0, tol=0.01, max_ops=400000): + ops, errs = planning_curve(env, option_models, v_star, s0, tol=tol, max_ops=max_ops) + return ops[-1], errs[-1], ops, errs diff --git a/hibs_lnn/swifttd_head.py b/hibs_lnn/swifttd_head.py new file mode 100644 index 0000000000000000000000000000000000000000..9bd187b883e2e514935e3872e754ee791466a2d2 --- /dev/null +++ b/hibs_lnn/swifttd_head.py @@ -0,0 +1,435 @@ +""" +V31.1: SwiftTD 式 per-feature step-size 优化 (Javed et al., RLC 2024) +===================================================================== +解决 V31.0 发现的问题: IDBD 融合的 β 通过 BPTT 外循环学 (Meta-SGD 风格), +meta 梯度 ~1e-6 学不动 (arXiv 2401.17401 Figure 4)。 + +SwiftTD 三件套 (全部在线, 无 BPTT) — 精确对应论文 Algorithm 1: + 1. per-feature step-size 优化: β[i] = e^{θ[i]}, IDBD 在线更新: + θ[i] += κ·δ[i]·h[i]·φ[i] (meta 更新, 在 decay/重置之前) + h[i] ← (1-β[i]·φ[i]²)⁺·h[i] + β[i]·δ[i]·φ[i] (step-size 迹) + 2. overshoot bound: correction ratio τ = Σᵢ β[i]·φ[i]², + τ > η 时更新缩放 s = η/τ (η = 0.1, max correction ratio) + 3. step-size decay: τ > η 时 θ[i] += φ[i]²·ln(ϵ) (ϵ = 0.99 decay rate, + 论文 Algorithm 1: 对 θ 的绝对衰减, 无 β 因子; β ← β·ϵ^{φ²}) + 且重置该组的迹 (论文 L524-526) + +论文关键超参: η = 0.1, ϵ = 0.99, η_min = e^-15, β 裁剪到 [ln(η_min), ln(η)] → β_max = η。 + +本实现的关键适配 (记录在 wiki): + τ 按输出行 (per-row) 计算。论文的 correction ratio 定义在单个标量预测 + 上 (v = w·φ, 更新后 v' = v + s·β·δ·Σφ²); 我们的 head 有 n2 = 1616 个 + 标量输出 (8 响应 × 202 vocab), 每个输出是独立的预测、独立的误差。 + w2 的行 u 有它自己的 τ_u = Σₖ β[u,k]·xₖ² 和缩放 s_u — 这是论文 + "单预测 bound" 对多输出层的直接推广。若用全局 τ (早期版本), 1616 行 + 复制结构使 τ 被维度主导 → bound 永远触发 → 更新塌缩到 ~0 (实测失败)。 + 同样地, w1 按 d 行, b1/b2 是向量 (单一 τ)。 + + 没有特征归一化: raw 特征保持 IDBD 元梯度 O(1) 尺度 (归一化会把 + per-feature 元更新稀释到 1/N, 40 步内 β 纹丝不动, 实测失败)。 + +行为: β ≤ η 且 τ ≈ β̄·Σφ² ≫ η (raw 特征) → bound 常态触发 → 每行每步 +修正恰好 η·errs (10%), decay 把 β 压到 τ≈η 的预算, IDBD 在预算内把 +步长分配给有用特征 (const 特征 → β_min, 相关特征 → 维持)。这正是论文 +Section 6 描述的收敛行为。 + +应用对象: PLN (2 层 MLP head)。每组用"入边激活"作为 IDBD 特征 φ: + w1 (d,d) : φ = hidden[j] (SSM 输出), δ = 回传误差 g[i] + b1 (d,) : φ = 1, δ = g[i] + w2 (n2,d) : φ = x[i] (tanh 激活), δ = errs[u] + b2 (n2,) : φ = 1, δ = errs[u] +其中 errs = p - onehot 是 CE 对 logits 的残差 (梯度)。 +""" + +import math +import torch +import torch.nn.functional as F + + +class SwiftTDHead: + """ + 2 层 MLP head 的 SwiftTD 步长状态机 (V31.1)。 + + 参数布局 (与 V31 head_forward 一致): + W = [w1 (d,d), b1 (d,), w2 (n2,d), b2 (n2,)], n2 = vocab*n_resp + x = tanh(hidden @ w1^T + b1); logits = x @ w2^T + b2 → (n_resp, vocab) + + 状态: + theta: 每参数 log 步长 θ (对应 β = e^θ, 裁剪到 [β_min, β_max=η]) + h : 每参数 step-size 迹 (IDBD 的 h 向量) + + 超参 (论文默认): + beta_init : 初始 β (论文 1e-7 长流; 短视野用 0.01) + kappa : IDBD meta step-size θ + eta : max correction ratio η = 0.1 (bound 阈值 = β 上限) + eps : decay rate ϵ = 0.99 (bound 触发时的衰减速率, 独立超参) + """ + + def __init__(self, d_model, world_vocab, n_resp, + beta_init=0.01, kappa=0.05, eta=0.1, eps=0.99, + beta_min=1e-7, input_dim=None, tau_norm=None): + """ + input_dim (V32 意图注入用): w1 的输入维度。 + 默认 input_dim=d_model → 与 V31 布局完全一致 (向后兼容)。 + d_model 始终是隐藏层宽度 (x 的维度, w2 的输入)。 + tau_norm (V35.2 诊断 A): 按有效维度归一 τ。 + None/False: 原版 τ = Σ β·φ² (绝对和) + 'mean' : τ = Σ β·φ² / dim (per-dim 平均 — 修复 V32+ 3d 意图 + 注入后 384 维求和恒超 η → bound 常态触发 → h 迹归零 + → IDBD 分化死亡, β_std 1.6e-05 的诊断根因) + """ + self.d_model = d_model + self.input_dim = input_dim if input_dim is not None else d_model + self.world_vocab = world_vocab + self.n_resp = n_resp + self.n2 = world_vocab * n_resp + self.tau_norm = tau_norm + # 布局: [w1 (d, input_dim), b1 (d,), w2 (n2,d), b2 (n2,)] + self.shapes = [(d_model, self.input_dim), (d_model,), + (self.n2, d_model), (self.n2,)] + self.beta_init = beta_init + self.kappa = kappa + self.eta = eta # max correction ratio (bound 阈值) + self.eps = eps # decay rate (独立超参!) + self.beta_max = eta # 论文: β 裁剪上限 = ln(η) + self.beta_min = beta_min + self.reset_state() + + # ── 状态管理 ───────────────────────────────────────────── + def reset_state(self): + self.theta = [torch.full(s, math.log(self.beta_init)) for s in self.shapes] + self.h = [torch.zeros(s) for s in self.shapes] + self.n_bound_triggers = 0 + self.n_decays = 0 + self.n_steps = 0 + + def to(self, device): + """搬移 SwiftTD 状态 (θ/h 迹) 到 device (head 是普通类, 非 nn.Module).""" + self.theta = [t.to(device) for t in self.theta] + self.h = [t.to(device) for t in self.h] + return self + + def cpu(self): + return self.to('cpu') + + def beta(self): + return [t.exp().clamp(self.beta_min, self.beta_max) for t in self.theta] + + def step_size_stats(self): + b = torch.cat([x.reshape(-1) for x in self.beta()]) + return { + 'n_params': int(b.numel()), + 'beta_mean': float(b.mean()), + 'beta_std': float(b.std()), + 'beta_min': float(b.min()), + 'beta_max': float(b.max()), + 'frac_clipped_min': float( + (b <= self.beta_min * (1 + 1e-3)).float().mean()), + 'frac_clipped_max': float( + (b >= self.beta_max * (1 - 1e-3)).float().mean()), + 'n_bound_triggers': self.n_bound_triggers, + 'n_decays': self.n_decays, + 'n_steps': self.n_steps, + } + + def state(self): + """保存当前状态 (θ/h 迹), 用于 sleep 前后快照。""" + return ([t.clone() for t in self.theta], [t.clone() for t in self.h]) + + def load_state(self, st): + theta, h = st + for t, v in zip(self.theta, theta): + t.copy_(v) + for t, v in zip(self.h, h): + t.copy_(v) + + # ── 核心: 单样本在线一步 ───────────────────────────────── + def step(self, W, hidden, x, errs): + """ + 对一个样本做一步 SwiftTD 更新 (原地修改 W, 无计算图)。 + W : [w1,b1,w2,b2] 当前参数 + hidden: SSM 输出 (d,) + x : tanh 激活 (d,) + errs : (n2,) = p - onehot (CE 对 logits 的残差) + """ + newW = self._rates(W, hidden, x, errs, graph=False) + for old, new in zip(W, newW): + old.copy_(new) + return W + + def step_graph(self, W, hidden, x, errs): + """ + 可微内循环一步 (V31.2 BPTT 外循环用): + 与 step() 相同的 SwiftTD 速率 (s, β 全 detached), + 但权重更新保持计算图 → 外循环梯度穿过内循环链 + (RLN 特征 φ → 误差 δ → w 更新 → query loss)。 + """ + return self._rates(W, hidden, x, errs, graph=True) + + def _rates(self, W, hidden, x, errs, graph): + """ + SwiftTD 速率计算 + 权重更新 (graph=False 原地, graph=True 新张量)。 + s 与 β 为 detached 速率 (bound/decay/clip 不可微, 论文 BPTT 不可用 + → 直通: 速率当作给定常数, 只对 w 链与特征链反传)。 + """ + w1, b1, w2, b2 = W + d, din, n2 = self.d_model, self.input_dim, self.n2 + + # 各组特征 φ 与误差 δ (IDBD 逐元素形式) + dev = hidden.device + f_w1 = hidden.unsqueeze(0).expand(d, self.input_dim).contiguous() + f_b1 = torch.ones(d, device=dev) + f_w2 = x.unsqueeze(0).expand(n2, d).contiguous() # (n2,d) + f_b2 = torch.ones(n2, device=dev) + + d_w1 = (w2.t() @ errs) * (1.0 - x * x) # (d,) 回传误差 + d_w1 = d_w1.unsqueeze(1).expand(d, self.input_dim).contiguous() + d_b1 = d_w1[:, 0] + d_w2 = errs.unsqueeze(1).expand(n2, d).contiguous() # (n2,d) + d_b2 = errs + + beta = self.beta() + ln_eps = math.log(self.eps) # ϵ<1 → 负; 论文 Algorithm 1: β += φ²·ln(ϵ) + + def _group(w, f, dg, gi, row_tau): + """返回 (更新后的 w, 速率 s·β 用于图形更新)。""" + b = beta[gi] + # 1) meta-θ 更新 (先于 decay/reset — 论文顺序) + self.theta[gi].add_(self.kappa * dg.detach() * self.h[gi] * f.detach()) + # 2) 速率: τ 与缩放 s (detached) + if row_tau: + tau = (b * f.detach() * f.detach()).sum(dim=1) + if self.tau_norm == 'mean': + tau = tau / f.shape[1] # per-dim 平均 (诊断 A) + else: + tau = (b * f.detach() * f.detach()).sum() + if self.tau_norm == 'mean' and f.dim() == 1: + tau = tau / f.shape[0] # b1/b2 向量: 按元素数归一 + s = torch.ones_like(tau) + trig = tau > self.eta + if bool(trig.any()): + s = torch.where(trig, self.eta / tau, s) + # 3) decay + 迹重置 (仅触发行) + self.theta[gi][trig].add_( + ln_eps * (f.detach() * f.detach())[trig]) + self.h[gi][trig].zero_() + self.n_bound_triggers += int(trig.sum()) + self.n_decays += int(trig.sum()) + # 4) h 更新 + clip θ → β ∈ [β_min, η] (论文 Section 6) + self.h[gi].mul_(torch.clamp( + 1.0 - b * f.detach() * f.detach(), min=0.0)) + self.h[gi].add_(b * dg.detach() * f.detach()) + self.theta[gi].clamp_(math.log(self.beta_min), + math.log(self.beta_max)) + # 速率 (detached): s·β, 广播到 f 形状 + if f.dim() == 2: + s = s.unsqueeze(1) + rate = (s * b).detach() + if graph: + return w - rate * dg * f, rate + return w - rate * dg * f, rate + + w1n, _ = _group(w1, f_w1, d_w1, 0, row_tau=True) + b1n, _ = _group(b1, f_b1, d_b1, 1, row_tau=False) + w2n, _ = _group(w2, f_w2, d_w2, 2, row_tau=True) + b2n, _ = _group(b2, f_b2, d_b2, 3, row_tau=False) + + self.n_steps += 1 + return [w1n, b1n, w2n, b2n] + + # ── 便捷封装: 单样本适应 ───────────────────────────────── + def adapt_sample(self, W, hidden, tgt, V): + """hidden: (1,d) SSM 输出; tgt: (n_resp,) 目标 token。""" + d = self.d_model + x = torch.tanh(hidden @ W[0].t() + W[1]) + logits = x @ W[2].t() + W[3] # (1, n2) + logits = logits.view(1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) # (n2,) + self.step(W, hidden[0], x[0], errs.detach()) + return logits + + +# ============================================================ +# 自测 1: 论文 weight-flipping 问题 (arXiv 2401.17401 §3) +# 20 维特征, 前 15 维权重恒为 0 (该学低步长), 后 5 维每 20 步翻转 ±1 +# (该学高步长)。IDBD 应学到 per-feature 步长分化, 且性能好于标量 SGD。 +# 单行 = 单预测, 与模块 row_tau=True 逻辑一致。 +# ============================================================ +def _idbd_linear_step(w, theta, h, phi, delta, kappa, eta, eps, + beta_min, beta_max): + """单行线性预测的 SwiftTD 一步 (与模块逻辑一致, 供自测复用)。""" + b = theta.exp().clamp(beta_min, beta_max) + # 1) meta-θ 更新 (先于 decay/reset — 论文顺序) + theta.add_(kappa * delta * h * phi) + # 2) 权重更新 (bound 缩放) + tau = (b * phi * phi).sum() + s = 1.0 + decayed = False + if tau > eta: + s = eta / tau + # 3) decay (绝对衰减, 无 β 因子) + 迹重置 + theta.add_(math.log(eps) * phi * phi) + h.zero_() + decayed = True + w.add_(s * b * delta * phi) + # 4) h 更新 + h.mul_(torch.clamp(1.0 - b * phi * phi, min=0.0)) + h.add_(b * delta * phi) + theta.clamp_(math.log(beta_min), math.log(beta_max)) + return w, theta, h, decayed + + +def _self_test_weight_flipping(seed=42, n_steps=50000, kappa=0.05, + beta_init=0.1, verbose=True): + torch.manual_seed(seed) + d = 20 + w_true = torch.zeros(d) + w_true[15:] = 1.0 + + def run_idbd(): + # 快速追踪域 (论文 Figure 5 探索范围): η=0.5, β_max=1.0 + # 翻转每 20 步一次 → 每步需 ~100% 修正 → β 上限需 > η。 + # MLP head 的收敛域 (β_max=η=0.1) 由自测 3 覆盖。 + beta_min, beta_max, eta, eps = 1e-5, 1.0, 0.5, 0.99 + theta = torch.full((d,), math.log(beta_init)) + h = torch.zeros(d) + w = torch.zeros(d) + errs = [] + for t in range(n_steps): + if t % 20 == 0: + idx = 15 + (t // 20) % 5 + w_true[idx] *= -1 + phi = torch.randn(d) + y = (w_true * phi).sum() + delta = y - (w * phi).sum() + w, theta, h, _ = _idbd_linear_step( + w, theta, h, phi, delta, kappa, eta, eps, beta_min, beta_max) + errs.append(delta.item() ** 2) + return sum(errs) / len(errs), theta.exp() + + def run_scalar(alpha): + w = torch.zeros(d) + errs = [] + for t in range(n_steps): + if t % 20 == 0: + idx = 15 + (t // 20) % 5 + w_true[idx] *= -1 + phi = torch.randn(d) + y = (w_true * phi).sum() + delta = y - (w * phi).sum() + w.add_(alpha * delta * phi) + errs.append(delta.item() ** 2) + return sum(errs) / len(errs) + + mse_idbd, beta_end = run_idbd() + b_hi = beta_end[15:].mean().item() + b_lo = beta_end[:15].mean().item() + # 标量基线: 在非发散区间内扫 α, 取最优 + mse_scalar = min(run_scalar(a) for a in (0.001, 0.005, 0.01, 0.02, 0.05)) + + if verbose: + print(f" [weight-flipping] MSE: idbd={mse_idbd:.5f} " + f"scalar_best={mse_scalar:.5f}") + print(f" β 均值: const dims={b_lo:.5f} (应低), " + f"flip dims={b_hi:.5f} (应高), 比值={b_hi / max(b_lo, 1e-9):.1f}x") + + ok = (mse_idbd <= mse_scalar * 1.05 and b_hi > b_lo * 3) + print(f" {'OK' if ok else 'FAIL'} IDBD 学到 per-feature 步长 " + f"(误差更低 + 高/低步长分化)") + return ok + + +# ============================================================ +# 自测 2: 三件套在 β_init 过大时不发散 (bound + decay + 迹重置) +# ============================================================ +def _self_test_bound_stability(seed=42, n_steps=20000, beta_init=0.5, + verbose=True): + torch.manual_seed(seed) + d = 20 + w_true = torch.zeros(d) + w_true[15:] = 1.0 + kappa, eta, eps, beta_min, beta_max = 0.05, 0.5, 0.99, 1e-5, 1.0 + + def run(alpha, use_swift=False): + theta = torch.full((d,), math.log(beta_init)) + h = torch.zeros(d) + w = torch.zeros(d) + errs = [] + for t in range(n_steps): + if t % 20 == 0: + idx = 15 + (t // 20) % 5 + w_true[idx] *= -1 + phi = torch.randn(d) + y = (w_true * phi).sum() + delta = y - (w * phi).sum() + if use_swift: + w, theta, h, _ = _idbd_linear_step( + w, theta, h, phi, delta, kappa, eta, eps, + beta_min, beta_max) + else: + w.add_(alpha * delta * phi) + errs.append(delta.item() ** 2) + if math.isnan(delta.item()): + return float('inf') + return sum(errs) / len(errs) + + mse_swift = run(0.0, use_swift=True) + mse_scalar = run(beta_init) + ok = math.isfinite(mse_swift) and mse_swift < mse_scalar + if verbose: + print(f" [bound 稳定性] β_init={beta_init}: " + f"scalar(α={beta_init}) MSE={mse_scalar:.5f} (应发散/爆炸), " + f"SwiftTD MSE={mse_swift:.5f}") + print(f" {'OK' if ok else 'FAIL'} bound+decay 防止发散") + return ok + + +# ============================================================ +# 自测 3: MLP head 单步形状 + loss 下降 + β 分化 +# ============================================================ +def _self_test_mlp_head(): + torch.manual_seed(0) + d, V, n_resp = 8, 20, 2 + head = SwiftTDHead(d, V, n_resp, beta_init=0.01) + w1 = torch.randn(d, d) * 0.5 + b1 = torch.zeros(d) + w2 = torch.randn(V * n_resp, d) * 0.5 + b2 = torch.zeros(V * n_resp) + W = [w1, b1, w2, b2] + hidden = torch.randn(1, d) + tgt = torch.tensor([3, 17]) + + def loss(): + x = torch.tanh(hidden @ w1.t() + b1) + logits = (x @ w2.t() + b2).view(1, n_resp, V) + return F.cross_entropy(logits.view(-1, V), tgt.view(-1)) + + l0 = loss().item() + for _ in range(30): + x = torch.tanh(hidden @ w1.t() + b1) + logits = (x @ w2.t() + b2).view(1, n_resp, V) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, V).float() + errs = (p - onehot).reshape(-1) + head.step(W, hidden[0], x[0], errs.detach()) + l1 = loss().item() + stats = head.step_size_stats() + ok = l1 < l0 and stats['beta_mean'] > 0 + print(f" {'OK' if ok else 'FAIL'} MLP head 30 步: loss " + f"{l0:.4f} → {l1:.4f}, β_mean={stats['beta_mean']:.4f}, " + f"bound 触发={stats['n_bound_triggers']}, decay={stats['n_decays']}") + return ok + + +def _self_test(): + ok1 = _self_test_weight_flipping() + ok2 = _self_test_bound_stability() + ok3 = _self_test_mlp_head() + print(f"\n {int(ok1) + int(ok2) + int(ok3)}/3 passed") + return ok1 and ok2 and ok3 + + +if __name__ == "__main__": + _self_test() diff --git a/hibs_lnn/transition_model.py b/hibs_lnn/transition_model.py new file mode 100644 index 0000000000000000000000000000000000000000..368811c7a6663a4d835f4ebd6e5f638c904af306 --- /dev/null +++ b/hibs_lnn/transition_model.py @@ -0,0 +1,118 @@ +"""transition_model.py — OaK 第③条的第一个缺失件。 + +## 为什么需要它 + +OaK feature #3: + feature -> subproblem -> solution -> **transition model** -> planning + +我们已经有 feature / subproblem(6 域 / 60 细区间)/ solution(训练), +但**没有转移模型也无法规划** —— 这是 leo 那张映射表里真正缺的东西。 + +## 定义 (与 lm4 的数据结构对齐) + + 能力状态 a_t = acc_matrix[t] (在 6 个域上的准确率画像, 6 维) + 动作 u_t = 第 t 步训练了哪个域 (one-hot, 6 维) + 转移 T : (a_t, u_t, ctx) -> Δa, a_{t+1} = a_t + Δa + +学 **Δa** 而不是 a_{t+1}, 因为这样 "copy 基线" 就是 Δa≡0, +模型有没有学到东西一目了然。 + +## 输入特征 + + x = [ a_t (6) | onehot(u_t) (6) | n_visits (6) | t/n_rounds (1) ] = 19 维 + +## 基线 (必须打赢才有意义) + + copy : Δa ≡ 0 实测 MAE 0.2986 + constant : Δa ≡ mean(Δa|u) 实测 MAE 0.2723 + +用 **ridge** 而不是 MLP: 样本量 ~1.8K, 参数少、可复现、无调参空间 +(符合 leo 的"manual > framework"偏好)。 +""" +from __future__ import annotations + +import numpy as np + + +class TransitionModel: + """T(a_t, u_t, ctx) -> Δa 的线性(脊)转移模型。 + + 参数 + ---- + lam : 岭正则强度 + """ + + def __init__(self, lam=1e-2): + self.lam = float(lam) + self.W = None # (fdim, n_dom) + self.n_dom = None + self.fitted = False + + # ───────── 特征 ───────── + @staticmethod + def feat(a, u, n_visits, t_frac): + oh = np.zeros(len(a)) + if 0 <= u < len(a): + oh[u] = 1.0 + return np.concatenate([a, oh, n_visits, [t_frac]]) + + def build_xy(self, records): + """records: [(acc_matrix, dom_seq)] -> X, Y, groups""" + X, Y, G = [], [], [] + for gi, (M, seq) in enumerate(records): + M = np.asarray(M, dtype=float) + n_dom = M.shape[1] + nvis = np.zeros(n_dom) + for t, u in enumerate(seq): + if t + 1 >= M.shape[0]: + break + if np.isnan(M[t]).any() or np.isnan(M[t + 1]).any(): + nvis[u] += 1 + continue + X.append(self.feat(M[t], int(u), nvis.copy(), t / max(1, len(seq)))) + Y.append(M[t + 1] - M[t]) + G.append(gi) + nvis[u] += 1 + return (np.array(X), np.array(Y), np.array(G)) if X else ( + np.zeros((0, 19)), np.zeros((0, 6)), np.zeros(0)) + + # ───────── 拟合 ───────── + def fit(self, records): + X, Y, _ = self.build_xy(records) + if len(X) == 0: + raise ValueError("没有可用样本") + self.n_dom = int(Y.shape[1]) + # 只对 Δa 做岭回归 (含截距) + X1 = np.hstack([X, np.ones((len(X), 1))]) + A = X1.T @ X1 + self.lam * np.eye(X1.shape[1]) + self.W = np.linalg.solve(A, X1.T @ Y) + self.fitted = True + return self + + def predict_delta(self, a, u, n_visits, t_frac): + x = np.append(self.feat(a, u, n_visits, t_frac), 1.0) + return x @ self.W + + # ───────── 规划: 用 T 前向搜索选动作序列 ───────── + def plan(self, a0, horizon, beam=8, n_dom=None): + """beam search: 在 T 的预测下最大化末端平均准确率。 + + 这是 OaK 的 "planning" —— 用学到的转移模型做前向搜索, + 而不是靠外生给定的 schedule。 + """ + n_dom = int(n_dom if n_dom is not None else (self.n_dom or 0)) + a0 = np.asarray(a0, dtype=float) + # beam 元素: (score, a, nvis, seq) + beams = [(float(np.nanmean(a0)), a0.copy(), np.zeros(n_dom), [])] + for t in range(horizon): + cand = [] + for _, a, nvis, seq in beams: + for u in range(n_dom): + d = self.predict_delta(a, u, nvis, t / max(1, horizon)) + a2 = a + d + nv2 = nvis.copy() + nv2[u] += 1 + cand.append((float(np.nanmean(a2)), a2, nv2, seq + [u])) + cand.sort(key=lambda r: -r[0]) + beams = cand[:beam] + return beams[0][3] diff --git a/hibs_lnn/uncertainty_gate.py b/hibs_lnn/uncertainty_gate.py new file mode 100644 index 0000000000000000000000000000000000000000..279ea840f1fda0f1482d9c2a33df1453a7ca7508 --- /dev/null +++ b/hibs_lnn/uncertainty_gate.py @@ -0,0 +1,239 @@ +"""uncertainty_gate.py — 覆盖度 + 不确定性门控, 与带不确定性的 rollout。 + +## 为什么需要 + +用户指出: 被动观测数据学出的 transition model **只知道数据覆盖过的 (s,a)**。 +若 policy 提出训练数据里没出现过的动作, `T(s,a_new)` 可能完全是幻觉。 +这正是我们实测到的: 转移模型对 `[2,2,2,2,2]` 预测 **0.9582**, 真实执行 **0.4357**。 + +于是把「覆盖度门控」升级为两个条件: + + allow(s,a) = [C(s,a) > tau_C] ∧ [U_T(s,a) < tau_U] + + C(s,a) : 该 (状态, 动作) 有多少数据支持 (访问计数) + U_T : 转移预测的不确定性 + +## U_T 怎么估 —— 用集成分歧, 不用拍脑袋 + +单个模型无法报出自己有多不确定。标准做法是训练 **K 个 transition model** +(对数据做 bootstrap), 用它们的**预测分歧**作为 U_T: + + U_T(s,a) = mean_i || T_i(s,a) − mean_j T_j(s,a) || + +分歧大 = 该区域数据稀疏 / 模型没学到 → 不可信。 +这与 coverage 互补: coverage 看「来过没有」, U_T 看「学稳了没有」。 +""" +from __future__ import annotations + +import numpy as np + + +class TransitionEnsemble: + """K 个 bootstrap 转移模型的集成, 用预测分歧估计不确定性。 + + T_i: x_{t+1} = x_t + Δ_i, Δ_i = W_i @ φ(x_t, u) + """ + + def __init__(self, n_models=5, lam=1e-2, seed=0): + self.K = int(n_models) + self.lam = float(lam) + self.rng = np.random.default_rng(seed) + self.Ws = None + self.n_dom = None + self.dim_x = None + self.fdim = None + + # ───────── 特征 ───────── + @staticmethod + def feat(x, u, n_dom, visits): + """特征 = [x, onehot(u)]。 + + ★ **不要加入常数列** (曾写成 concat([x, oh, [mean(visits)]]) 而 + mean(visits)≡1 与截距完全共线): 实测条件数被推到 **1.24e16**(近乎奇异), + 后果是双向的 —— + ① 预测全错: Δ 估计 [35.6, 32.5, 10.3, 5.7], 真值应 ~0.3 + ② 不确定性失效: U_T 对所有动作都 ≈3.27, 完全不区分噪声 + 去掉后: Δ 恢复 [0.30, 0, 0, 0], U_T 动作0(噪声.02)=0.0018 vs + 动作2(噪声.55)=0.0399 -> **22× 区分**。 + """ + oh = np.zeros(n_dom) + if 0 <= u < n_dom: + oh[u] = 1.0 + return np.concatenate([x, oh]) + + # ───────── 拟合 (bootstrap) ───────── + def fit(self, X, Y, n_dom, visits_dim=None): + """X: (N, dim_x+1) = [x_t, u]; Y: (N, dim_x) = **绝对** x_{t+1}。 + + ★ 接口约定: fit 与 predict **都用绝对下一状态**, 内部学 Δ 再加回 x。 + 曾因约定不一致出过 bug —— 测试把绝对状态当 Δ 喂进来, 在质心 (x=0) + 处两者巧合相等, 掩盖了问题; 一旦远离质心 (R0=[4.95,4.55,1.36,1.22]) + 就露出 `Δ=x+fx(u)` 的错误。现在接口自洽, 无法再误用。 + """ + self.dim_x = Y.shape[1] + self.n_dom = int(n_dom) + self.fdim = self.dim_x + self.n_dom # 无截距列 + N = len(X) + D = Y - X[:, :self.dim_x] # 内部转成 Δ + self.Ws = [] + for k in range(self.K): + idx = self.rng.integers(0, N, size=N) # bootstrap 重采样 + F = np.array([self.feat(X[i, :self.dim_x], int(X[i, -1]), + self.n_dom, np.ones(self.n_dom)) + for i in idx]) + # ★ 不加截距列。onehot(u) 的各列之和恒等于 1, 与截距**完全共线** + # (Σ onehot = ones)。加了以后条件数 8.9e15, 解只在数据质心附近 + # 碰巧正确, 远离即炸: 实测在区域中心 (R0=[4.95,4.55,1.36,1.22]) + # 给出 Δ=[92.7, 96.4, 25.8, 23.7], 真值应为 [0.30, 0, 0, 0]。 + # 去掉后设计矩阵满秩 (rank = dim_x + n_dom)。 + A = F.T @ F + self.lam * np.eye(F.shape[1]) + self.Ws.append(np.linalg.solve(A, F.T @ D[idx])) + return self + + # ───────── 预测 + 分歧 ───────── + def predict(self, x, u, visits): + """返回 (**绝对**下一状态的集成均值, 分歧 U_T)。""" + if self.Ws is None: + return np.asarray(x, dtype=float).copy(), float("inf") + x = np.asarray(x, dtype=float) + f = self.feat(x, u, self.n_dom, visits) # 与 fit 一致, 无截距 + preds = np.array([f @ W for W in self.Ws]) # (K, dim_x) = Δ + mean_d = preds.mean(0) + unc = float(np.mean(np.linalg.norm(preds - mean_d, axis=1))) + return x + mean_d, unc # 绝对下一状态 + + def rollout(self, x0, actions, visits, gate=None): + """多步 rollout: T^k(x0, a_{t:t+k})。返回 (轨迹, 逐步不确定性)。 + + gate 非 None 时逐步做可信度检查; 一旦某步不可信即**中止并标记**, + 而不是继续外推 (Q8: 无覆盖 → 预测不可靠)。 + """ + x = np.asarray(x0, dtype=float).copy() + traj, uncs, aborted_at = [x.copy()], [], None + for t, u in enumerate(actions): + if gate is not None: + ok, why = gate.allow(x, u, visits) + if not ok: + aborted_at = (t, why) + break + x_next, unc = self.predict(x, u, visits) # 已是绝对状态 + if not np.isfinite(unc): + aborted_at = (t, "unc_inf") + break + x = x_next + traj.append(x.copy()) + uncs.append(unc) + return np.array(traj), np.array(uncs), aborted_at + + +class TabularTransition: + """计数式转移模型 —— 离散状态空间上的**可表达**动力学。 + + ## 为什么必须加它 (实测动机) + + `TransitionEnsemble` 是**线性 ridge 回归** (`[state, onehot(action)] -> next`), + 对**离散、条件性、非线性**的动力学表达能力不足。实测在 KeyDoor 这类 + 链式任务上: 从区域中心出发, **四个动作预测到同一个下一状态** -> + 转移图退化成几乎无边 -> `OptionManager` 发现 0 个 option + (即使任务里有明显的"拿钥匙→开门"技能链)。 + + ★ 结论: option 发现失败**不是 option 逻辑的问题, 而是它依赖的世界模型 + 表达不了结构化动力学**。这里是那个缺口的补丁。 + + `predict(x, a)` 与 `TransitionEnsemble` **接口一致**, 可直接替换: + 返回 (最可能的下一状态, 不确定性) + 不确定性 = 1 − max_a' P(s'|s,a) —— 即"这个转移有多确定"。 + """ + def __init__(self, lam=1.0): + self.counts = {} # (s_key, a) -> {s'_key: n} + self.state_map = {} # s_key -> 原状态向量 + self.lam = float(lam) + self.dim_x = None + self.n_dom = None + + @staticmethod + def _key(x): + return tuple(np.round(np.asarray(x, dtype=float), 6).tolist()) + + def fit(self, X, Y, n_dom, visits_dim=None): + X = np.asarray(X, dtype=float); Y = np.asarray(Y, dtype=float) + self.dim_x = Y.shape[1]; self.n_dom = int(n_dom) + self.counts, self.state_map = {}, {} + for i in range(len(X)): + x, u, y = X[i, :self.dim_x], int(X[i, -1]), Y[i] + sk, yk = self._key(x), self._key(y) + self.state_map[sk] = x + self.counts.setdefault((sk, u), {}) + self.counts[(sk, u)][yk] = self.counts[(sk, u)].get(yk, 0) + 1 + return self + + def predict(self, x, u, visits=None): + """返回 (最可能下一状态向量, 不确定性 1−P_max)。未见过的 (s,a) -> 原状态 + unc=inf。""" + sk = self._key(x) + d = self.counts.get((sk, int(u))) + if not d: + return np.asarray(x, dtype=float).copy(), float("inf") + tot = sum(d.values()) + yk, c = max(d.items(), key=lambda kv: kv[1]) + return np.array(yk, dtype=float), float(1.0 - c / tot) + + def rollout(self, x0, actions, visits=None, gate=None): + x = np.asarray(x0, dtype=float).copy() + traj, uncs, aborted = [x.copy()], [], None + for t, u in enumerate(actions): + if gate is not None: + ok, why = gate.allow(x, u, visits if visits is not None else np.ones(1)) + if not ok: + aborted = (t, why); break + xn, unc = self.predict(x, u, visits) + if not np.isfinite(unc): + aborted = (t, "unc_inf"); break + x = xn; traj.append(x.copy()); uncs.append(unc) + return np.array(traj), np.array(uncs), aborted + + +class UncertaintyGate: + """allow(s,a) = [C(s,a) > tau_C] ∧ [U_T(s,a) < tau_U] + + 参数 + ---- + tau_C : 覆盖度阈值 (访问计数的分位/绝对阈值) + tau_U : 不确定性阈值 (集成分歧) + """ + + def __init__(self, tau_C=1.0, tau_U=None, adaptive=True): + self.tau_C = float(tau_C) + self.tau_U = tau_U + self.adaptive = adaptive + self.unc_hist = [] + + def calibrate(self, uncs): + """用一批**在训练数据上**的集成分歧定阈值 (取 90 分位)。 + + 自适应是必要的: 分歧的量纲随任务变化, 硬编码阈值不可移植。 + """ + u = np.asarray(uncs, dtype=float) + u = u[np.isfinite(u)] + if u.size: + self.tau_U = float(np.quantile(u, 0.90)) + return self.tau_U + + def allow(self, x, u, visits): + """返回 (是否允许, 原因)。""" + c = float(visits[u]) if u < len(visits) else 0.0 + if c <= self.tau_C: + return False, "low_coverage" + return True, "ok" # 不确定性检查在 rollout 内逐步做 + + def allow_with_unc(self, x, u, visits, unc): + """含不确定性判定的完整版。""" + ok, why = self.allow(x, u, visits) + if not ok: + return False, why + if self.tau_U is not None and np.isfinite(unc) and unc > self.tau_U: + return False, "high_uncertainty" + return True, "ok" + + def stats(self): + return {"tau_C": self.tau_C, + "tau_U": None if self.tau_U is None else round(self.tau_U, 6)} diff --git a/hibs_lnn/value_proposer.py b/hibs_lnn/value_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..aced77d9bd57c9c05fc6903ddb34882748ea9b9b --- /dev/null +++ b/hibs_lnn/value_proposer.py @@ -0,0 +1,180 @@ +"""value_proposer.py — 价值函数提议器 (从 LMT-twister V35.19 移植, 适配连续物理区间)。 + +## 来源 + +LMT-twister 家族已把这条线验证到生产级 (skill: lmt-twister-experiment): + V35.19 价值函数提议器 (用户理论): 简约性 + 自洽性 + 覆盖增量 + V35.20 价值函数表示迁移最优 (0.0869 > 外部课程 0.0742) + V35.21 三 seed 确认 (0.0850±0.002), S5 内直接训练 scratch +2.05pp + V35.22 价值函数 = 稳定器 (方差缩 3 倍) + LM1 生产级: 提议器每步自主生成数据 + +原式 (V35.19): + V(p) = λ_sim·sim(p) + λ_con·con(p) + λ_cov/(1+freq(p)) + sim(p) = 1 - max_{q∈已学} struct_sim(p,q) # 简约性: 不可归约度 + con(p) = 1 - 矛盾度 # 自洽性 + cov(p) = 1/(1+freq(p)) # 覆盖增量 + +## 本移植 + +lm1 的"结构 p"是离散排列; lm4/lm5 的"结构"是**连续的物理区间**, +所以相似度换成描述子空间里的距离, 并新增一项**真实反馈** (V35 里由 +CausalProposer.feedback_value 承担)。 + + V(p) = λ_sim·sim(p) + λ_con·con(p) + λ_cov·cov(p) + λ_fb·fb(p) + + sim(p) = 1 - max_{q∈已学} exp(-‖z_p - z_q‖ / σ) # 物理上离已学区间多远 + con(p) = 自洽性 = 该区间与已发现因果结构的吻合度 (由调用方注入) + cov(p) = 1/(1+freq(p)) # 训练越少越值得学 + fb(p) = 1 - acc(p) # 真实反馈: 越不准越值得学 + +## 关键坑 (V35.19 实测, 必须遵守) + +1. **候选池必须远大于提议数**, 否则价值函数被抹平 (v1: 池 18 个被全覆盖 -> + 提议顺序无关 -> λ 扫描零效果)。本实现默认要求 len(pool) >> k·轮数。 +2. **λ_cov 倒 U 型**, 平衡点 ≈ 1:1:1。默认 (1,1,1,1)。 +3. **覆盖项必须真的有频率差异**, 否则退化成随机 (v2 教训: 高频重复 ≠ 均匀覆盖)。 +""" +from __future__ import annotations + +import numpy as np + + +class RegimeProposer: + """按价值函数从候选区间池里采样"下一个学哪个物理区间"。 + + 参数 + ---- + desc : (N, D) 每个候选区间的物理描述子 (如 [log_dens, logL, absmaglat]). + 内部做 z-score, 保证各维等权。 + groups : (N,) 每个候选区间映射到哪个**评估域** (用于汇总/报告, 不参与打分)。 + lam : (λ_sim, λ_con, λ_cov, λ_fb) + sigma : 描述子空间里"多远算不可归约"的尺度 (z-score 单位, 默认 0.5) + tau : softmax 温度, 越小越贪婪 + k : 每次提议几个 + seed : 采样随机种子 + """ + + def __init__(self, desc, groups=None, lam=(1.0, 1.0, 1.0, 1.0), + sigma=None, tau=0.5, k=2, seed=0): + desc = np.asarray(desc, dtype=np.float64) + self.n = len(desc) + mu = desc.mean(0) + sd = desc.std(0) + sd[sd < 1e-9] = 1.0 + self.z = (desc - mu) / sd + # ★ sigma 默认由数据决定, 不能用固定 0.5。 + # 低维描述子 (1-3 维) 下候选间距离量级很小, 固定 sigma=0.5 会让 + # exp(-d/sigma) ≈ 0 对所有候选成立 -> sim(p) ≡ 1.0 -> 简约性项死亡 + # (实测 signature: value 与 value-nofb 提议序列逐位相同)。 + # 取"最近邻距离的中位数"当尺度, 保证核在真实分布上有区分度。 + if sigma is None: + d = np.linalg.norm(self.z[:, None, :] - self.z[None, :, :], axis=-1) + np.fill_diagonal(d, np.inf) + sigma = float(np.median(d.min(axis=1))) + sigma = max(sigma, 1e-3) + self.sigma = float(sigma) + self.groups = (np.asarray(groups) if groups is not None + else np.zeros(self.n, dtype=int)) + self.lam = tuple(float(x) for x in lam) + self.tau = float(tau) + self.k = int(k) + self.rng = np.random.default_rng(seed) + # 状态 + self._con = None # 自洽性分数 (可注入) + self.freq = np.zeros(self.n, dtype=np.float64) # 被提议次数 + self.learned = [] # 已学区间索引 + self.acc = np.full(self.n, np.nan) # 各区间的实测准确率 + self.trace = [] # 每轮提议记录 + + # ---------------- 三项 + 反馈 ---------------- + def sim(self, i): + """简约性: 1 - 与已学区间的最大相似度 (物理上离得越远越值得学)。""" + if not self.learned: + return 1.0 + d = np.linalg.norm(self.z[self.learned] - self.z[i], axis=1) + return float(1.0 - np.exp(-d.min() / self.sigma)) + + def cov(self, i): + """覆盖增量: 越少被训练越值得学。""" + return 1.0 / (1.0 + self.freq[i]) + + def con(self, i): + """自洽性 (V35.19 三项之一) —— 由调用方 set_consistency() 注入。 + + **没注入时返回恒定 1.0, 该维对排序零贡献** —— 这是本轮踩过的坑: + 忘了注入 -> 自洽性项死亡 -> 价值函数退化成覆盖均匀化采样器。 + 诊断: stats() 里的 con_std; 若为 0 说明这一项是死的。 + """ + return float(self._con[i]) if self._con is not None else 1.0 + + def fb(self, i): + """真实反馈: 越不准越值得学 (持续学习那一环)。""" + a = self.acc[i] + if not np.isfinite(a): + return 1.0 # 没测过 = 最有价值 + return float(1.0 - a) + + def value(self, i): + ls, lc, lv, lf = self.lam + return (ls * self.sim(i) + lc * self.con(i) + + lv * self.cov(i) + lf * self.fb(i)) + + # ---------------- 外部注入 ---------------- + def set_consistency(self, con): + """注入自洽性分数 (长度 N)。对 lm4: 每个区间与已发现因果边的吻合度。""" + self._con = np.asarray(con, dtype=np.float64) + + def observe(self, idx, acc, learned=True): + """记录一轮的结果: 该区间的实测准确率 (+ 标记为已学)。""" + self.acc[idx] = float(acc) + if learned and idx not in self.learned: + self.learned.append(int(idx)) + + # ---------------- 提议 ---------------- + def propose(self, k=None): + """按 V 的 softmax 采样 k 个不重复候选 (采样而非 argmax: 保留探索)。""" + k = k or self.k + v = np.array([self.value(i) for i in range(self.n)]) + # 数值稳定 + 温度 + v = v - v.max() + p = np.exp(v / max(1e-9, self.tau)) + p = p / p.sum() + kk = min(k, self.n) + idx = self.rng.choice(self.n, size=kk, replace=False, p=p) + for i in idx: + self.freq[i] += 1 + self.trace.append(list(map(int, idx))) + return list(map(int, idx)) + + def pick_random(self, k=None): + """随机对照臂。""" + k = k or self.k + idx = self.rng.choice(self.n, size=min(k, self.n), replace=False) + for i in idx: + self.freq[i] += 1 + self.trace.append(list(map(int, idx))) + return list(map(int, idx)) + + # ---------------- 诊断 ---------------- + def stats(self): + v = np.array([self.value(i) for i in range(self.n)]) + # ★ 各项的**区分度**诊断: 标准差为 0 = 该项是死的 (对排序零贡献) + _sim = np.array([self.sim(i) for i in range(self.n)]) + _con = np.array([self.con(i) for i in range(self.n)]) + _cov = np.array([self.cov(i) for i in range(self.n)]) + _fb = np.array([self.fb(i) for i in range(self.n)]) + return { + "n_candidates": int(self.n), + "n_learned": len(self.learned), + "n_proposed_total": int(self.freq.sum()), + "freq_nonzero": int((self.freq > 0).sum()), + "value_mean": float(v.mean()), + "value_std": float(v.std()), + "value_cv": float(v.std() / max(1e-9, abs(v.mean()))), + "covered_frac": float((self.freq > 0).mean()), + "sigma": float(self.sigma), + # 各项区分度: 0 = 该项死了 (本轮踩过的坑) + "term_std": {"sim": float(_sim.std()), "con": float(_con.std()), + "cov": float(_cov.std()), "fb": float(_fb.std())}, + } diff --git a/manifests/delta_compile_report.md b/manifests/delta_compile_report.md new file mode 100644 index 0000000000000000000000000000000000000000..30d01db66e3f6e136112310f5f1a7006a3463939 --- /dev/null +++ b/manifests/delta_compile_report.md @@ -0,0 +1,21 @@ +# Delta Compile Report + +- generated_at: `2026-09-06T05:17:16+00:00` +- stale_pages: `0` +- new_raw_sources: `1` +- drafts_written: `0` + +> This report suggests recompilation work. It does not auto-overwrite wiki content. + +## Stale Pages + +- none + +## New Raw Sources + +- `docs/wiki/log.md` -> suggested page `docs/wiki/log.md` + - + +## Draft Files + +- none diff --git a/manifests/knowledge_graph.json b/manifests/knowledge_graph.json new file mode 100644 index 0000000000000000000000000000000000000000..15783b72bd823b37191467f505570bae4556ad0e --- /dev/null +++ b/manifests/knowledge_graph.json @@ -0,0 +1,143 @@ +{ + "schema_version": 2, + "wiki_root": "/Users/apple/Downloads/headless/docs/wiki", + "nodes": [ + { + "id": "README.md", + "type": "meta", + "label": "README.md", + "tags": "", + "stage": "", + "audience": "", + "source_file": "docs/wiki/README.md" + }, + { + "id": "SCHEMA.md", + "type": "meta", + "label": "SCHEMA.md", + "tags": "", + "stage": "", + "audience": "", + "source_file": "docs/wiki/SCHEMA.md" + }, + { + "id": "causal_lm1_experiment.md", + "type": "meta", + "label": "LM1 因果持续学习对照实验", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/causal_lm1_experiment.md" + }, + { + "id": "causal_stack.md", + "type": "meta", + "label": "causal 因果技术栈落地", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/causal_stack.md" + }, + { + "id": "current-status.md", + "type": "meta", + "label": "current-status", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/current-status.md" + }, + { + "id": "github-and-raw-strategy.md", + "type": "meta", + "label": "github-and-raw-strategy", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/github-and-raw-strategy.md" + }, + { + "id": "index.md", + "type": "meta", + "label": "index.md", + "tags": "", + "stage": "", + "audience": "", + "source_file": "docs/wiki/index.md" + }, + { + "id": "log.md", + "type": "meta", + "label": "log.md", + "tags": "", + "stage": "", + "audience": "", + "source_file": "docs/wiki/log.md" + }, + { + "id": "project-overview.md", + "type": "meta", + "label": "project-overview", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/project-overview.md" + }, + { + "id": "sources-and-data.md", + "type": "meta", + "label": "sources-and-data", + "tags": "", + "stage": "draft", + "audience": "public", + "source_file": "docs/wiki/sources-and-data.md" + } + ], + "edges": [ + { + "from": "index.md", + "to": "log.md", + "type": "references" + }, + { + "from": "index.md", + "to": "causal_stack.md", + "type": "references" + }, + { + "from": "index.md", + "to": "causal_lm1_experiment.md", + "type": "references" + }, + { + "from": "index.md", + "to": "current-status.md", + "type": "references" + }, + { + "from": "index.md", + "to": "github-and-raw-strategy.md", + "type": "references" + }, + { + "from": "index.md", + "to": "project-overview.md", + "type": "references" + }, + { + "from": "index.md", + "to": "sources-and-data.md", + "type": "references" + } + ], + "stats": { + "node_count": 10, + "edge_count": 7, + "node_types": { + "meta": 10 + }, + "edge_types": { + "references": 7 + } + } +} \ No newline at end of file diff --git a/manifests/raw_sources.csv b/manifests/raw_sources.csv new file mode 100644 index 0000000000000000000000000000000000000000..9970d6549d0424d517c494bcd640192a8a1065ab --- /dev/null +++ b/manifests/raw_sources.csv @@ -0,0 +1,2 @@ +source_id,company,vendor,kind,filename,raw_rel_path,status,compiled_into,filename,content_hash,size_bytes,ingested_at,compiled_at,last_validated_at,confidence,supersedes,lifecycle_stage,crystallized_into,owner,audience,stage,tags,notes,schema_version +1,headless,,experiment-log,log.md,docs/wiki/log.md,new,,log.md,d8a4a23a1a848bb5,6572,2026-09-06,2026-09-06,high,,,元木,reader,draft,continuous-learning,Primary experiment log,2 \ No newline at end of file diff --git a/manifests/raw_sources.meta.json b/manifests/raw_sources.meta.json new file mode 100644 index 0000000000000000000000000000000000000000..271fcbd9188dc1cfc1fafe9453b6f6bbab723486 --- /dev/null +++ b/manifests/raw_sources.meta.json @@ -0,0 +1,34 @@ +{ + "schema_version": 2, + "columns": [ + "source_id", + "company", + "vendor", + "kind", + "filename", + "raw_rel_path", + "status", + "compiled_into", + "filename", + "content_hash", + "size_bytes", + "ingested_at", + "compiled_at", + "last_validated_at", + "confidence", + "supersedes", + "lifecycle_stage", + "crystallized_into", + "owner", + "audience", + "stage", + "tags", + "notes", + "schema_version" + ], + "allowed_status": [ + "new", + "compiled", + "archived" + ] +} \ No newline at end of file diff --git a/manifests/stale_report.md b/manifests/stale_report.md new file mode 100644 index 0000000000000000000000000000000000000000..04779aa63fea0ae36c8b9410ef17b69df0bf0334 --- /dev/null +++ b/manifests/stale_report.md @@ -0,0 +1,40 @@ +# Stale Report + +- generated_at: `2026-09-06T05:10:45+00:00` +- raw_root: `not configured` +- fresh_pages: `0` +- stale_pages: `0` +- missing_hash: `6` +- unresolved_sources: `0` +- archived_refs: `0` +- manifest_new: `1` +- session_exempt: `0` + +## Fresh Pages + +- none + +## Pages Needing Recompile (stale) + +- none + +## Pages Missing source_hash + +- `docs/wiki/causal_lm1_experiment.md` +- `docs/wiki/causal_stack.md` +- `docs/wiki/current-status.md` +- `docs/wiki/github-and-raw-strategy.md` +- `docs/wiki/project-overview.md` +- `docs/wiki/sources-and-data.md` + +## Pages With Unresolved source + +- none + +## Pages Pointing At Archived Sources + +- none + +## Raw Files Still Waiting For First Compile + +- `docs/wiki/log.md` diff --git a/scripts/bootstrap_knowledge_system.py b/scripts/bootstrap_knowledge_system.py new file mode 100644 index 0000000000000000000000000000000000000000..28ddc09e75ffd812515057b6a6ad0cb02160b159 --- /dev/null +++ b/scripts/bootstrap_knowledge_system.py @@ -0,0 +1,195 @@ +"""Bootstrap a compile-first knowledge system into a target repo. + +The actual file contents live as real files under +`skills/knowledge-system-bootstrap/templates/`. This script copies them +into the target with sentinel-string substitution. That keeps every +generated script lintable, testable, and reviewable on its own. + +Substitution sentinels (plain string.replace, no Template machinery so +templates can contain literal `$`): + __PROJECT_NAME__ — human-readable project name passed on the CLI + __RAW_ROOT_NAME__ — folder name for the local raw root + __TODAY__ — today's date in YYYY-MM-DD +""" +from __future__ import annotations + +import argparse +import re +import shutil +from datetime import date, datetime +from pathlib import Path + +__version__ = "2.0.0" # bumped for v2 scripts (wiki_lint, supersede_check, crystallize, hybrid_search, graph_export, intake_filter) merged under SKILL_ROOT/scripts/ + +SKILL_ROOT = Path(__file__).resolve().parents[1] +TEMPLATES = SKILL_ROOT / "templates" + + +# (template-relative path, target-relative path) +TEMPLATE_TO_TARGET: list[tuple[str, str]] = [ + ("configs/AGENTS.md", "AGENTS.md"), + ("configs/CLAUDE.md", "CLAUDE.md"), + ("configs/.cursorrules", ".cursorrules"), + ("configs/.windsurfrules", ".windsurfrules"), + ("configs/.gitignore", ".gitignore"), + ("wiki/README.md", "docs/wiki/README.md"), + ("wiki/SCHEMA.md", "docs/wiki/SCHEMA.md"), + ("wiki/index.md", "docs/wiki/index.md"), + ("wiki/log.md", "docs/wiki/log.md"), + ("wiki/project-overview.md", "docs/wiki/project-overview.md"), + ("wiki/current-status.md", "docs/wiki/current-status.md"), + ("wiki/sources-and-data.md", "docs/wiki/sources-and-data.md"), + ("wiki/github-and-raw-strategy.md", "docs/wiki/github-and-raw-strategy.md"), + ("wiki/runtime-profile.md", "docs/wiki/runtime-profile.md"), + ("manifests/README.md", "manifests/README.md"), + ("manifests/raw_sources.csv", "manifests/raw_sources.csv"), + ("manifests/raw_sources.meta.json", "manifests/raw_sources.meta.json"), + ("scripts/wiki_check.py", "scripts/wiki_check.py"), + ("scripts/raw_manifest_check.py", "scripts/raw_manifest_check.py"), + ("scripts/ingest_raw.py", "scripts/ingest_raw.py"), + ("scripts/untracked_raw_check.py", "scripts/untracked_raw_check.py"), + ("scripts/provenance_check.py", "scripts/provenance_check.py"), + ("scripts/stale_report.py", "scripts/stale_report.py"), + ("scripts/delta_compile.py", "scripts/delta_compile.py"), + ("scripts/version_check.py", "scripts/version_check.py"), + ("scripts/wiki_size_report.py", "scripts/wiki_size_report.py"), + ("scripts/init_raw_root.py", "scripts/init_raw_root.py"), + ("scripts/export_memory_repo.py", "scripts/export_memory_repo.py"), + ("scripts/upgrade.sh", "scripts/upgrade.sh"), + # v2.0.0 — new scripts merged under scripts/, no longer in templates/scripts/ + ("scripts/wiki_lint.py", "scripts/wiki_lint.py"), + ("scripts/supersede_check.py", "scripts/supersede_check.py"), + ("scripts/crystallize.py", "scripts/crystallize.py"), + ("scripts/hybrid_search.py", "scripts/hybrid_search.py"), + ("scripts/graph_export.py", "scripts/graph_export.py"), + ("scripts/intake_filter.py", "scripts/intake_filter.py"), + ("claude-commands/wiki-check.md", ".claude/commands/wiki-check.md"), + ("claude-commands/wiki-upgrade.md", ".claude/commands/wiki-upgrade.md"), + ("claude-commands/wiki-status.md", ".claude/commands/wiki-status.md"), + ("github/workflows/wiki-lint.yml", ".github/workflows/wiki-lint.yml"), +] + + +def slugify(value: str) -> str: + value = value.strip().lower() + value = re.sub(r"[^a-z0-9]+", "_", value) + return value.strip("_") or "project" + + +def render(body: str, vars: dict[str, str]) -> str: + out = body + for sentinel, value in vars.items(): + out = out.replace(sentinel, value) + unresolved = re.findall(r"__[A-Z][A-Z0-9_]*__", out) + expected = {"__main__", "__init__", "__name__", "__file__"} + real = [s for s in unresolved if s not in expected] + if real: + raise RuntimeError(f"unresolved sentinels: {set(real)}") + return out + + +def write(path: Path, content: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(content, encoding="utf-8") + + +def backup(path: Path) -> Path: + stamp = datetime.now().strftime("%Y%m%d-%H%M%S") + dest = path.with_suffix(path.suffix + f".bak.{stamp}") + shutil.copy2(path, dest) + return dest + + +def main() -> int: + parser = argparse.ArgumentParser(description="Bootstrap a compile-first knowledge system into another repo.") + parser.add_argument("target_dir", help="Target repository root") + parser.add_argument("project_name", help="Human-readable project name") + parser.add_argument("--force", action="store_true", help="Overwrite existing files (a .bak. copy is kept unless --no-backup)") + parser.add_argument("--no-backup", action="store_true", help="When overwriting, skip the .bak file (only effective with --force)") + parser.add_argument("--dry-run", action="store_true", help="Show what would happen without writing anything") + parser.add_argument("--raw-root-name", help="Folder name for the local raw root") + args = parser.parse_args() + + target = Path(args.target_dir).expanduser().resolve() + project_name = args.project_name.strip() + slug = slugify(project_name) + raw_root_name = args.raw_root_name or f"{slug}_raw" + today = date.today().isoformat() + + vars = { + "__PROJECT_NAME__": project_name, + "__RAW_ROOT_NAME__": raw_root_name, + "__TODAY__": today, + } + + created: list[Path] = [] + overwritten: list[tuple[Path, Path | None]] = [] + skipped: list[Path] = [] + unchanged: list[Path] = [] + + for tmpl_rel, target_rel in TEMPLATE_TO_TARGET: + # v2.0.0: scripts/ under SKILL_ROOT/scripts/ (not templates/scripts/) + if tmpl_rel.startswith("scripts/"): + src = SKILL_ROOT / tmpl_rel + else: + src = TEMPLATES / tmpl_rel + if not src.exists(): + print(f"WARNING: template missing: {tmpl_rel}") + continue + body = src.read_text(encoding="utf-8") + try: + content = render(body, vars) + except RuntimeError as exc: + raise SystemExit(f"template {tmpl_rel}: {exc}") + + dest = target / target_rel + if not dest.exists(): + if not args.dry_run: + write(dest, content) + created.append(dest) + elif dest.read_text(encoding="utf-8") == content: + unchanged.append(dest) + elif args.force: + bak_path: Path | None = None + if not args.dry_run: + if not args.no_backup: + bak_path = backup(dest) + write(dest, content) + overwritten.append((dest, bak_path)) + else: + skipped.append(dest) + + prefix = "[DRY RUN] " if args.dry_run else "" + print(f"{prefix}Bootstrapped knowledge system for: {project_name}") + print(f"Target repo: {target}") + print(f"Default raw root name: {raw_root_name}") + + if created: + print(f"\nCreated ({len(created)}):") + for p in created: + print(f" + {p.relative_to(target)}") + if overwritten: + print(f"\nOverwritten ({len(overwritten)}):") + for p, bak in overwritten: + suffix = f" (backup: {bak.name})" if bak else " (no backup)" + print(f" ! {p.relative_to(target)}{suffix}") + if unchanged: + print(f"\nUnchanged ({len(unchanged)})") + if skipped: + print(f"\nSkipped — already exists, content differs ({len(skipped)}):") + print("Use --force to overwrite.") + for p in skipped: + print(f" ~ {p.relative_to(target)}") + + if not args.dry_run and (created or overwritten): + print("\nNext steps:") + print(f"1. cd {target}") + print("2. python3 scripts/init_raw_root.py") + print("3. python3 scripts/wiki_check.py") + print("4. python3 scripts/raw_manifest_check.py") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/crystallize.py b/scripts/crystallize.py new file mode 100644 index 0000000000000000000000000000000000000000..3764907d1ada7943d6c42d983afee0822691880d --- /dev/null +++ b/scripts/crystallize.py @@ -0,0 +1,154 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only + +""" +crystallize.py — v2 晶化 (crystallization) tool. + +Rohit G00 v2 范式：从散落各页的"重复断言"中蒸馏出稳定的晶化条目。 + +工作流程： + 1. 扫描 docs/wiki/ 下所有页面，提取 `` 注释 + 2. 按 (normalized text) 聚合 + 3. 出现 ≥min_occurrences 次的 claim 视为"晶化候选" + 4. 写入 manifests/crystallized.csv + 5. 在 docs/wiki/crystallized-claims.md 渲染晶化表 + +Usage: + python3 scripts/crystallize.py --min-occurrences 2 + python3 scripts/crystallize.py --min-occurrences 1 --dry-run +""" + +import argparse +import csv +import re +import sys +from collections import defaultdict +from datetime import date +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" +MANIFEST_DIR = ROOT / "manifests" +CRYSTALLIZED_CSV = MANIFEST_DIR / "crystallized.csv" +CRYSTALLIZED_MD = WIKI_ROOT / "crystallized-claims.md" + +CLAIM_RE = re.compile(r"", re.IGNORECASE) + + +def normalize_claim(text: str) -> str: + """Normalize claim text for grouping: lowercase, strip whitespace, collapse spaces.""" + return re.sub(r"\s+", " ", text.strip().lower()) + + +def extract_claims_from_page(path: Path) -> list[tuple[str, list[str]]]: + """Extract (claim_text, [source_ids]) tuples from a page's HTML comments.""" + text = path.read_text(encoding="utf-8") + out: list[tuple[str, list[str]]] = [] + for match in CLAIM_RE.finditer(text): + claim_text = match.group(1).strip() + sources_raw = match.group(2).strip() + sources = [s.strip() for s in re.split(r"[,;]\s*", sources_raw) if s.strip()] + out.append((claim_text, sources)) + return out + + +def main() -> int: + parser = argparse.ArgumentParser(description="v2 crystallization tool") + parser.add_argument("--wiki-root", type=Path, default=WIKI_ROOT) + parser.add_argument("--min-occurrences", type=int, default=2, + help="minimum times a claim must appear to be crystallized (default: 2)") + parser.add_argument("--dry-run", action="store_true", help="don't write files, just report") + parser.add_argument("--output-csv", type=Path, default=CRYSTALLIZED_CSV) + parser.add_argument("--output-md", type=Path, default=CRYSTALLIZED_MD) + args = parser.parse_args() + + if not args.wiki_root.exists(): + print(f"crystallize: {args.wiki_root} does not exist") + return 1 + + md_files = sorted(args.wiki_root.rglob("*.md")) + # claim_text_normalized -> {claim_text_canonical, sources_set, pages} + claim_index: dict[str, dict] = defaultdict(lambda: { + "text": "", + "sources": set(), + "pages": set(), + }) + + for path in md_files: + rel = path.relative_to(args.wiki_root).as_posix() + for text, sources in extract_claims_from_page(path): + key = normalize_claim(text) + claim_index[key]["text"] = text # use first occurrence as canonical + claim_index[key]["sources"].update(sources) + claim_index[key]["pages"].add(rel) + + # Filter by min_occurrences + candidates = { + k: v for k, v in claim_index.items() if len(v["pages"]) >= args.min_occurrences + } + + # Stable sort: by occurrence count desc, then by text + sorted_candidates = sorted( + candidates.items(), + key=lambda kv: (-len(kv[1]["pages"]), kv[1]["text"]), + ) + + if not sorted_candidates: + print("crystallize: no claims found with --min-occurrences={}".format(args.min_occurrences)) + print("- hint: add comments to wiki pages") + return 0 + + today = date.today().isoformat() + + if not args.dry_run: + MANIFEST_DIR.mkdir(parents=True, exist_ok=True) + with args.output_csv.open("w", encoding="utf-8", newline="") as f: + writer = csv.writer(f) + writer.writerow(["claim_id", "text", "sources", "first_seen", "last_validated", "occurrences", "pages"]) + for i, (key, info) in enumerate(sorted_candidates, start=1): + claim_id = f"clm_{i:03d}" + writer.writerow([ + claim_id, + info["text"], + ";".join(sorted(info["sources"])), + today, + today, + len(info["pages"]), + ";".join(sorted(info["pages"])), + ]) + + # Render MD + md_lines: list[str] = ["---", "title: 晶化断言", "source: crystallize.py", + f"created: {today}", f"updated: {today}", f"last_confirmed: {today}", + "audience: self", "stage: crystallized", "status: current", + "tags: [crystallized, claims, v2]", "---", "", + "# 晶化断言 (Crystallized Claims)", "", + f"生成时间: {today} | 最小出现次数: {args.min_occurrences} | 总数: {len(sorted_candidates)}", + "", + "| ID | 断言 | 出现次数 | 来源 | 出现页 |", + "|----|------|----------|------|--------|"] + for i, (key, info) in enumerate(sorted_candidates, start=1): + claim_id = f"clm_{i:03d}" + md_lines.append( + f"| {claim_id} | {info['text']} | {len(info['pages'])} | " + f"{', '.join(sorted(info['sources']))} | {', '.join(sorted(info['pages']))} |" + ) + args.output_md.write_text("\n".join(md_lines) + "\n", encoding="utf-8") + + # Report + print("crystallize:") + print(f"- scanned pages: {len(md_files)}") + print(f"- unique claims: {len(claim_index)}") + print(f"- candidates (occurrences >= {args.min_occurrences}): {len(sorted_candidates)}") + if not args.dry_run: + print(f"- wrote: {args.output_csv}") + print(f"- wrote: {args.output_md}") + else: + print("- dry-run: no files written") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/delta_compile.py b/scripts/delta_compile.py new file mode 100644 index 0000000000000000000000000000000000000000..92cc1b350122b21892f9c59b606c433aa2632763 --- /dev/null +++ b/scripts/delta_compile.py @@ -0,0 +1,356 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (needs PROJECT_RAW_ROOT) + +import argparse +import csv +import hashlib +import json +import os +import re +import sys +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" +MANIFEST = ROOT / "manifests" / "raw_sources.csv" +LOCK_FILE = ROOT / "manifests" / "raw_index.json" +REPORT_FILE = ROOT / "manifests" / "delta_compile_report.md" +DRAFT_DIR = WIKI_ROOT / "drafts" +DEFAULT_RAW_ROOT = (ROOT.parent / "__RAW_ROOT_NAME__").resolve() +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +SKIP_FILES = {"index.md", "log.md", "README.md", "SCHEMA.md"} + + +def utc_now() -> str: + return datetime.now(timezone.utc).replace(microsecond=0).isoformat() + + +def today() -> str: + return datetime.now(timezone.utc).date().isoformat() + + +def sha256_prefix(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8192), b""): + h.update(chunk) + return h.hexdigest()[:16] + + +def slugify(value: str) -> str: + value = value.strip().lower() + value = re.sub(r"[^a-z0-9]+", "-", value) + return value.strip("-") or "draft" + + +def parse_frontmatter(path: Path) -> dict[str, str]: + text = path.read_text(encoding="utf-8") + match = FRONTMATTER_RE.match(text) + if not match: + return {} + result: dict[str, str] = {} + for line in match.group(1).splitlines(): + if ":" in line: + key, value = line.split(":", 1) + result[key.strip()] = value.strip() + return result + + +def parse_list_field(value: str) -> list[str]: + raw = value.strip() + if not raw: + return [] + if raw.startswith("[") and raw.endswith("]"): + raw = raw[1:-1] + return [item.strip().strip("'").strip('"') for item in raw.split(",") if item.strip()] + + +def load_manifest() -> list[dict[str, str]]: + if not MANIFEST.exists(): + return [] + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as handle: + return [{key: (value or "") for key, value in row.items()} for row in csv.DictReader(handle)] + + +def load_lock() -> dict[str, dict]: + if not LOCK_FILE.exists(): + return {} + try: + data = json.loads(LOCK_FILE.read_text(encoding="utf-8")) + files = data.get("files", {}) + return files if isinstance(files, dict) else {} + except Exception: + return {} + + +def resolve_row(source_value: str, rows: list[dict[str, str]]) -> dict[str, str] | None: + for row in rows: + if source_value == row.get("source_id") or source_value == row.get("raw_rel_path"): + return row + for row in rows: + if row.get("source_id") and row["source_id"] in source_value: + return row + if row.get("raw_rel_path") and source_value.endswith(row["raw_rel_path"]): + return row + return None + + +def choose_target_page(row: dict[str, str]) -> str: + compiled_into = (row.get("compiled_into") or "").strip() + if compiled_into: + first = compiled_into.split(",")[0].strip() + if first: + return first + stem = slugify(Path(row.get("filename") or row.get("raw_rel_path") or row.get("source_id") or "source").stem) + return f"docs/wiki/{stem}.md" + + +def draft_path(target_page: str, source_id: str) -> Path: + stem = slugify(Path(target_page).stem) + return DRAFT_DIR / f"{stem}--{source_id}.md" + + +def unique_items(values: list[str]) -> list[str]: + seen: set[str] = set() + result: list[str] = [] + for value in values: + if value and value not in seen: + seen.add(value) + result.append(value) + return result + + +def render_draft( + *, + title: str, + source_id: str, + source_hash: str, + target_page: str, + raw_rel_path: str, + source_summary: str, + change_summary: list[str], + compiled_from: list[str], + reason: str, +) -> str: + compiled_from_values = unique_items(compiled_from or [source_id]) + compiled_from_line = f"[{', '.join(compiled_from_values)}]" + lines = [ + "---", + f"title: {title}", + f"source: {source_id}", + f"source_hash: {source_hash}", + f"compiled_at: {utc_now()}", + f"compiled_from: {compiled_from_line}", + f"created: {today()}", + "tags: [draft, delta-compile]", + "status: draft", + "---", + "", + f"# {title}", + "", + "## Why this draft exists", + "", + f"- reason: {reason}", + f"- suggested target page: `{target_page}`", + f"- raw source: `{raw_rel_path}`", + "", + "## Source Summary", + "", + f"- {source_summary or 'no summary available'}", + "", + "## Structured Change Summary", + "", + ] + if change_summary: + lines.extend(f"- {item}" for item in change_summary) + else: + lines.append("- no prior diff summary available") + lines.extend([ + "", + "## Draft Notes", + "", + "- Pull confirmed facts from the raw source into the target page.", + "- Update the target page frontmatter with the current `source_hash` and `compiled_at`.", + "- Keep this draft until the recompile is merged, then delete it.", + "", + ]) + return "\n".join(lines) + + +def build_report(stale_items: list[dict[str, object]], new_items: list[dict[str, object]], written_drafts: list[str]) -> str: + lines = [ + "# Delta Compile Report", + "", + f"- generated_at: `{utc_now()}`", + f"- stale_pages: `{len(stale_items)}`", + f"- new_raw_sources: `{len(new_items)}`", + f"- drafts_written: `{len(written_drafts)}`", + "", + "> This report suggests recompilation work. It does not auto-overwrite wiki content.", + "", + "## Stale Pages", + "", + ] + if stale_items: + for item in stale_items: + lines.append( + f"- `{item['page_rel']}` <- `{item['raw_rel_path']}` " + f"(target: `{item['target_page']}`)" + ) + for change in item.get("change_summary", [])[:3]: + lines.append(f" - {change}") + else: + lines.append("- none") + lines.extend([ + "", + "## New Raw Sources", + "", + ]) + if new_items: + for item in new_items: + lines.append( + f"- `{item['raw_rel_path']}` -> suggested page `{item['target_page']}`" + ) + lines.append(f" - {item['source_summary']}") + else: + lines.append("- none") + lines.extend([ + "", + "## Draft Files", + "", + ]) + if written_drafts: + lines.extend(f"- `{path}`" for path in written_drafts) + else: + lines.append("- none") + return "\n".join(lines).rstrip() + "\n" + + +def main() -> int: + parser = argparse.ArgumentParser(description="Generate manual delta-compile suggestions and optional wiki draft stubs.") + parser.add_argument("--raw-root", default=os.environ.get("PROJECT_RAW_ROOT", ""), help="Local raw root path") + parser.add_argument("--report-file", default=str(REPORT_FILE), help="Markdown report output path") + parser.add_argument("--write-drafts", action="store_true", help="Write draft markdown stubs into docs/wiki/drafts/") + parser.add_argument("--dry-run", action="store_true", help="Print the report without writing files") + args = parser.parse_args() + + raw_root = None + if args.raw_root: + candidate = Path(args.raw_root).expanduser().resolve() + if candidate.exists(): + raw_root = candidate + elif DEFAULT_RAW_ROOT.exists(): + raw_root = DEFAULT_RAW_ROOT + + rows = load_manifest() + lock = load_lock() + referenced_ids: set[str] = set() + referenced_paths: set[str] = set() + stale_items: list[dict[str, object]] = [] + new_items: list[dict[str, object]] = [] + written_drafts: list[str] = [] + + for path in sorted(WIKI_ROOT.rglob("*.md")): + if path.name in SKIP_FILES or "drafts" in path.parts: + continue + fm = parse_frontmatter(path) + if not fm: + continue + source = fm.get("source", "") + if source == "session": + continue + row = resolve_row(source, rows) + if not row: + continue + referenced_ids.add(row.get("source_id", "")) + referenced_paths.add(row.get("raw_rel_path", "")) + for extra in parse_list_field(fm.get("compiled_from", "")): + extra_row = resolve_row(extra, rows) + if extra_row: + referenced_ids.add(extra_row.get("source_id", "")) + referenced_paths.add(extra_row.get("raw_rel_path", "")) + source_hash = fm.get("source_hash", "") + current_hash = "" + if raw_root: + source_path = raw_root / row.get("raw_rel_path", "") + if source_path.exists(): + current_hash = sha256_prefix(source_path) + if not current_hash and row.get("raw_rel_path") in lock: + current_hash = lock[row["raw_rel_path"]].get("content_hash", "") + if not source_hash or not current_hash or source_hash == current_hash: + continue + lock_entry = lock.get(row.get("raw_rel_path", ""), {}) + target_page = path.relative_to(ROOT).as_posix() + compiled_from = unique_items([row["source_id"]] + parse_list_field(fm.get("compiled_from", ""))) + stale_items.append({ + "page_rel": target_page, + "target_page": target_page, + "raw_rel_path": row.get("raw_rel_path", ""), + "source_id": row.get("source_id", ""), + "source_hash": current_hash, + "source_summary": lock_entry.get("summary", ""), + "change_summary": list(lock_entry.get("change_summary", [])), + "compiled_from": compiled_from, + "reason": f"source hash changed ({source_hash} -> {current_hash})", + }) + + for row in rows: + raw_rel_path = row.get("raw_rel_path", "") + source_id = row.get("source_id", "") + if row.get("status") != "new" or not raw_rel_path: + continue + if source_id in referenced_ids or raw_rel_path in referenced_paths: + continue + lock_entry = lock.get(raw_rel_path, {}) + new_items.append({ + "raw_rel_path": raw_rel_path, + "source_id": source_id, + "source_hash": lock_entry.get("content_hash", ""), + "source_summary": lock_entry.get("summary", ""), + "change_summary": list(lock_entry.get("change_summary", [])), + "compiled_from": [source_id], + "target_page": choose_target_page(row), + "reason": "new raw source has not been compiled into wiki yet", + }) + + if args.write_drafts and not args.dry_run: + DRAFT_DIR.mkdir(parents=True, exist_ok=True) + for item in stale_items + new_items: + title = f"Draft - {Path(str(item['target_page'])).stem.replace('-', ' ').title()}" + draft = draft_path(str(item["target_page"]), str(item["source_id"])) + draft.write_text( + render_draft( + title=title, + source_id=str(item["source_id"]), + source_hash=str(item["source_hash"]), + target_page=str(item["target_page"]), + raw_rel_path=str(item["raw_rel_path"]), + source_summary=str(item["source_summary"]), + change_summary=[str(entry) for entry in item.get("change_summary", [])], + compiled_from=[str(entry) for entry in item.get("compiled_from", []) if str(entry)], + reason=str(item["reason"]), + ), + encoding="utf-8", + ) + written_drafts.append(draft.relative_to(ROOT).as_posix()) + + report_text = build_report(stale_items, new_items, written_drafts) + if args.dry_run: + print(report_text) + else: + report_path = Path(args.report_file).expanduser().resolve() + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text(report_text, encoding="utf-8") + print(f"delta_compile: wrote {report_path}") + print( + f"delta_compile: OK ({len(stale_items)} stale page(s), " + f"{len(new_items)} new raw source(s), {len(written_drafts)} draft(s))" + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/export_memory_repo.py b/scripts/export_memory_repo.py new file mode 100644 index 0000000000000000000000000000000000000000..fa6f84a4cedd484630a0076d152875e972f35f0d --- /dev/null +++ b/scripts/export_memory_repo.py @@ -0,0 +1,126 @@ +from __future__ import annotations +# runtime: dev-only (writes outside repo) + +import argparse +import shutil +from datetime import datetime +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] + +COPY_FILES = [ + "AGENTS.md", + "scripts/wiki_check.py", + "scripts/raw_manifest_check.py", + "scripts/init_raw_root.py", +] + +COPY_DIRS = [ + ("docs/wiki", "docs/wiki"), + ("manifests", "manifests"), +] + +OPTIONAL_COPY_DIRS = [ + ("verified_cases", "verified_cases"), + ("backend/verified_cases", "verified_cases"), +] + + +def wipe_path(path: Path) -> None: + if not path.exists(): + return + if path.is_dir(): + shutil.rmtree(path) + else: + path.unlink() + + +def copy_file(src: Path, dest: Path) -> None: + dest.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dest) + + +def copy_dir(src: Path, dest: Path) -> None: + if dest.exists(): + shutil.rmtree(dest) + shutil.copytree(src, dest) + + +def main() -> int: + parser = argparse.ArgumentParser(description="Export the repo memory surface into a dedicated GitHub/wiki repo checkout.") + parser.add_argument("target_dir", help="Target memory repo checkout directory") + args = parser.parse_args() + + target = Path(args.target_dir).expanduser().resolve() + target.mkdir(parents=True, exist_ok=True) + + managed_paths = [ + target / "AGENTS.md", + target / "README.md", + target / "docs", + target / "manifests", + target / "verified_cases", + target / "scripts", + ] + for path in managed_paths: + wipe_path(path) + + exported_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + readme = f"""# Memory Repo + +This repo is the exported long-term memory surface for a project. + +## What belongs here + +- `docs/wiki/` +- `manifests/` +- `verified_cases/` +- repo-level operating rules such as `AGENTS.md` + +## What does not belong here + +- raw PDFs / XLSX / XLS / ZIP / RAR files +- main application runtime code +- build artifacts + +## Export time + +`{exported_at}` + +## Source repo + +`{ROOT}` + +## Refresh + +Run from the source repo: + +```bash +python3 scripts/export_memory_repo.py /path/to/memory-repo-checkout +``` +""" + (target / "README.md").write_text(readme, encoding="utf-8") + + for rel in COPY_FILES: + src = ROOT / rel + if src.exists(): + copy_file(src, target / rel) + + for src_rel, dest_rel in COPY_DIRS: + src = ROOT / src_rel + if src.exists(): + copy_dir(src, target / dest_rel) + + for src_rel, dest_rel in OPTIONAL_COPY_DIRS: + src = ROOT / src_rel + if src.exists(): + copy_dir(src, target / dest_rel) + break + + print(f"Exported memory repo snapshot to: {target}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/graph_export.py b/scripts/graph_export.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd7ede5de1c339e67a146564e3b043e5ff8cfc5 --- /dev/null +++ b/scripts/graph_export.py @@ -0,0 +1,145 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only + +""" +graph_export.py — v2 知识图谱导出. + +Rohit G00 v2 范式：把 wiki 页面 / 概念 / 章节 / 人物 抽取为节点，关系作为类型化边。 +v2.0 实现: 节点 = wiki 页面 (含 entity_type) + 概念 (来自 [[双链]] / frontmatter.entity_type) + 边 = supersedes / contradicts / references (来自 frontmatter + 文本 [[链接]]) + +输出 manifests/knowledge_graph.json: + { + "nodes": [{"id": "...", "type": "...", "label": "...", "sources": [...]}], + "edges": [{"from": "...", "to": "...", "type": "supersedes|contradicts|references"}], + "stats": {...} + } + +Usage: + python3 scripts/graph_export.py + python3 scripts/graph_export.py --output manifests/knowledge_graph.json +""" + +import argparse +import json +import re +import sys +from collections import Counter, defaultdict +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" +OUTPUT = ROOT / "manifests" / "knowledge_graph.json" + +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +WIKILINK_RE = re.compile(r"\[\[([^\]]+)\]\]") +MDLINK_RE = re.compile(r"\[([^\]]+)\]\(\.\/([^)]+)\)") + + +def parse_frontmatter(text: str) -> dict[str, str]: + m = FRONTMATTER_RE.match(text) + if not m: + return {} + out: dict[str, str] = {} + for line in m.group(1).splitlines(): + if ":" in line: + key, _, value = line.partition(":") + out[key.strip()] = value.strip() + return out + + +def main() -> int: + parser = argparse.ArgumentParser(description="v2 knowledge graph export") + parser.add_argument("--wiki-root", type=Path, default=WIKI_ROOT) + parser.add_argument("--output", type=Path, default=OUTPUT) + args = parser.parse_args() + + if not args.wiki_root.exists(): + print(f"graph_export: {args.wiki_root} does not exist") + return 1 + + md_files = sorted(args.wiki_root.rglob("*.md")) + nodes: dict[str, dict] = {} + edges: list[dict] = [] + edge_set: set[tuple[str, str, str]] = set() # dedupe (from, to, type) + + for path in md_files: + text = path.read_text(encoding="utf-8") + fm = parse_frontmatter(text) + rel = path.relative_to(args.wiki_root).as_posix() + + # Node: this page + nodes[rel] = { + "id": rel, + "type": fm.get("entity_type", "meta"), + "label": fm.get("title", rel), + "tags": fm.get("tags", ""), + "stage": fm.get("stage", ""), + "audience": fm.get("audience", ""), + "source_file": str(path.relative_to(ROOT)), + } + + # Edges from frontmatter + for edge_type in ("supersedes", "contradicts"): + raw = fm.get(edge_type, "") + if not raw: + continue + for t in re.findall(r"\./[^\s,\]]+", raw): + target = t.lstrip("./") + key = (rel, target, edge_type) + if key not in edge_set: + edge_set.add(key) + edges.append({"from": rel, "to": target, "type": edge_type}) + + # Edges from inline wikilinks [[...]] + for m in WIKILINK_RE.finditer(text): + target_label = m.group(1).strip() + # Try to resolve to a page + target = target_label if target_label.endswith(".md") else f"{target_label}.md" + key = (rel, target, "references") + if key not in edge_set: + edge_set.add(key) + edges.append({"from": rel, "to": target, "type": "references"}) + + # Edges from markdown links [text](./page.md) + for m in MDLINK_RE.finditer(text): + target = m.group(2) + key = (rel, target, "references") + if key not in edge_set: + edge_set.add(key) + edges.append({"from": rel, "to": target, "type": "references"}) + + stats = { + "node_count": len(nodes), + "edge_count": len(edges), + "node_types": dict(Counter(n["type"] for n in nodes.values())), + "edge_types": dict(Counter(e["type"] for e in edges)), + } + + graph = { + "schema_version": 2, + "wiki_root": str(args.wiki_root), + "nodes": list(nodes.values()), + "edges": edges, + "stats": stats, + } + + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(graph, indent=2, ensure_ascii=False), encoding="utf-8") + print(f"graph_export:") + print(f"- nodes: {stats['node_count']}") + print(f"- edges: {stats['edge_count']}") + print(f"- node_types: {stats['node_types']}") + print(f"- edge_types: {stats['edge_types']}") + print(f"- wrote: {args.output}") + else: + print(json.dumps(graph, indent=2, ensure_ascii=False)) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/hybrid_search.py b/scripts/hybrid_search.py new file mode 100644 index 0000000000000000000000000000000000000000..c13d272fb15ffdea892fcab991d220fe26d61b2e --- /dev/null +++ b/scripts/hybrid_search.py @@ -0,0 +1,221 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only + +""" +hybrid_search.py — v2 混合检索 (BM25 + 图遍历). + +Rohit G00 v2 范式：BM25 (keyword) + 向量 (semantic) + 图遍历 (structural). +v2.0 实现: BM25 over wiki 页面 + 图遍历 (follows supersedes/contradicts links). +v2.1+ 可加 embedding-based vector search. + +Usage: + python3 scripts/hybrid_search.py "货币主权" + python3 scripts/hybrid_search.py "DIAP" --depth 2 + python3 scripts/hybrid_search.py "价值函数" --json --top 5 +""" + +import argparse +import json +import math +import re +import sys +from collections import Counter, defaultdict +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" + +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +TOKEN_RE = re.compile(r"[\w一-鿿]+", re.UNICODE) # 英文/数字/汉字 + +# BM25 parameters +K1 = 1.5 +B = 0.75 + + +def parse_frontmatter(text: str) -> dict[str, str]: + m = FRONTMATTER_RE.match(text) + if not m: + return {} + out: dict[str, str] = {} + for line in m.group(1).splitlines(): + if ":" in line: + key, _, value = line.partition(":") + out[key.strip()] = value.strip() + return out + + +def tokenize(text: str) -> list[str]: + """CJK + ASCII tokenization. Each CJK char is its own token, ASCII words grouped.""" + tokens: list[str] = [] + for m in TOKEN_RE.finditer(text): + s = m.group(0) + if re.search(r"[一-鿿]", s): + tokens.extend(s) # CJK: one char per token + else: + tokens.append(s.lower()) + return tokens + + +def bm25_score(query_tokens: list[str], doc_tokens: list[str], avg_dl: float, df: dict[str, int], n_docs: int) -> float: + """Compute BM25 score for a single document.""" + score = 0.0 + doc_len = len(doc_tokens) + tf = Counter(doc_tokens) + for q in query_tokens: + if q not in tf: + continue + idf = math.log((n_docs - df[q] + 0.5) / (df[q] + 0.5) + 1) + denom = tf[q] + K1 * (1 - B + B * doc_len / avg_dl) + score += idf * (tf[q] * (K1 + 1)) / denom + return score + + +def main() -> int: + parser = argparse.ArgumentParser(description="v2 hybrid search (BM25 + graph)") + parser.add_argument("query", help="search query") + parser.add_argument("--wiki-root", type=Path, default=WIKI_ROOT) + parser.add_argument("--depth", type=int, default=1, help="graph traversal depth (0=no graph)") + parser.add_argument("--top", type=int, default=5, help="top N results") + parser.add_argument("--json", action="store_true", help="emit JSON output") + args = parser.parse_args() + + if not args.wiki_root.exists(): + print(f"hybrid_search: {args.wiki_root} does not exist") + return 1 + + md_files = sorted(args.wiki_root.rglob("*.md")) + docs: list[dict] = [] + for path in md_files: + text = path.read_text(encoding="utf-8") + # Strip frontmatter for tokenization + body = FRONTMATTER_RE.sub("", text, count=1) + tokens = tokenize(body) + fm = parse_frontmatter(text) + rel = path.relative_to(args.wiki_root).as_posix() + docs.append({"rel": rel, "tokens": tokens, "fm": fm, "path": path}) + + if not docs: + print("hybrid_search: no wiki pages found") + return 1 + + # Build DF + df: dict[str, int] = defaultdict(int) + for d in docs: + for term in set(d["tokens"]): + df[term] += 1 + + avg_dl = sum(len(d["tokens"]) for d in docs) / len(docs) + query_tokens = tokenize(args.query) + + # BM25 scores + bm25_hits: list[tuple[float, dict]] = [] + for d in docs: + score = bm25_score(query_tokens, d["tokens"], avg_dl, df, len(docs)) + if score > 0: + bm25_hits.append((score, d)) + bm25_hits.sort(key=lambda x: -x[0]) + + # Build graph from supersedes + contradicts + graph: dict[str, set[str]] = defaultdict(set) + for d in docs: + rel = d["rel"] + for edge_field in ("supersedes", "contradicts"): + raw = d["fm"].get(edge_field, "") + if not raw: + continue + for t in re.findall(r"\./[^\s,\]]+", raw): + graph[rel].add(t.lstrip("./")) + + # Graph traversal: for each top BM25 hit, expand neighbors up to depth + bm25_paths = {d["rel"]: score for score, d in bm25_hits[:args.top]} + expanded_paths: dict[str, list[str]] = {} + + if args.depth > 0: + # BFS from top hits + visited: set[str] = set() + queue: list[tuple[str, int, list[str]]] = [] + for rel in bm25_paths: + queue.append((rel, 0, [rel])) + visited.add(rel) + while queue: + node, d, path = queue.pop(0) + if d >= args.depth: + continue + for nxt in graph.get(node, set()): + if nxt not in visited and nxt in {d2["rel"] for d2 in docs}: + visited.add(nxt) + new_path = path + [nxt] + queue.append((nxt, d + 1, new_path)) + if d + 1 == args.depth: + # Add as expansion + expanded_paths.setdefault(nxt, []).extend(new_path) + # Flatten + graph_hits: list[tuple[str, int, list[str]]] = [] + for nxt, paths in expanded_paths.items(): + for p in paths: + graph_hits.append((0.5 / (len(p)), d, p)) + graph_hits.sort(key=lambda x: -x[0]) + else: + graph_hits = [] + + # Reciprocal Rank Fusion (RRF) + rrf_scores: dict[str, float] = defaultdict(float) + for rank, (score, d) in enumerate(bm25_hits, start=1): + rrf_scores[d["rel"]] += 1.0 / (60 + rank) + for rank, (score, _, path) in enumerate(graph_hits, start=1): + rrf_scores[path[-1]] += 0.5 / (60 + rank) + + # Final ranking + final_ranked = sorted(rrf_scores.items(), key=lambda kv: -kv[1])[:args.top] + + # Build snippets + results: list[dict] = [] + for rel, rrf in final_ranked: + doc = next(d for d in docs if d["rel"] == rel) + body = FRONTMATTER_RE.sub("", doc["path"].read_text(encoding="utf-8"), count=1) + # Find first matching line + snippet = "" + for line in body.splitlines(): + if any(q.lower() in line.lower() for q in [args.query]): + snippet = line.strip()[:200] + break + if not snippet: + snippet = body.splitlines()[0][:200] if body.splitlines() else "" + results.append({ + "rel": rel, + "rrf_score": round(rrf, 5), + "title": doc["fm"].get("title", ""), + "stage": doc["fm"].get("stage", ""), + "snippet": snippet, + }) + + report = { + "query": args.query, + "query_tokens": query_tokens, + "wiki_pages_scanned": len(docs), + "bm25_hits": len(bm25_hits), + "graph_depth": args.depth, + "results": results, + } + + if args.json: + print(json.dumps(report, indent=2, ensure_ascii=False)) + else: + print(f"hybrid_search: query={args.query!r} (tokens: {query_tokens[:10]}{'...' if len(query_tokens) > 10 else ''})") + print(f"- scanned: {len(docs)} pages") + print(f"- BM25 hits: {len(bm25_hits)}") + print(f"- graph depth: {args.depth}") + print(f"- top {args.top} results (RRF):") + for r in results: + print(f" {r['rrf_score']:.4f} {r['rel']} [{r['stage']}] {r['title']}") + if r['snippet']: + print(f" {r['snippet'][:120]}") + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/ingest_raw.py b/scripts/ingest_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..768afda2ca5f9231475893f40176bd9433f6572f --- /dev/null +++ b/scripts/ingest_raw.py @@ -0,0 +1,847 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (needs PROJECT_RAW_ROOT) + +import argparse +import csv +import hashlib +import json +import os +import re +import tarfile +import zipfile +from collections import Counter, defaultdict +from datetime import datetime, timezone +from pathlib import Path +from xml.etree import ElementTree as ET + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "manifests" / "raw_sources.csv" +LOCK_FILE = ROOT / "manifests" / "raw_index.json" +REPORT_FILE = ROOT / "manifests" / "intake_report.md" +DEFAULT_RAW_ROOT = (ROOT.parent / "__RAW_ROOT_NAME__").resolve() +EXPECTED_COLUMNS = [ + "source_id", + "company", + "vendor", + "kind", + "filename", + "raw_rel_path", + "status", + "compiled_into", + "notes", +] +TRACKED_EXTENSIONS = { + ".pdf", ".md", ".txt", + ".xlsx", ".xls", ".xlsm", ".csv", ".tsv", + ".doc", ".docx", ".ppt", ".pptx", + ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp", ".svg", + ".zip", ".rar", ".7z", ".tar", ".gz", +} +SKIP_DIRS = { + ".git", ".svn", "__pycache__", ".DS_Store", + "node_modules", ".venv", "venv", +} +KEY_COLUMN_HINTS = ("sku", "part", "item", "model", "spec", "code", "pn", "material", "id", "series") +VALUE_COLUMN_HINTS = ("price", "cost", "amount", "qty", "quantity", "lead", "currency", "discount", "rate") +MAX_HEADERS = 8 +MAX_ROW_WIDTH = 12 +MAX_TRACKED_ROWS = 2000 +MAX_SHEETS = 6 + + +def utc_now() -> str: + return datetime.now(timezone.utc).replace(microsecond=0).isoformat() + + +def sha256_prefix(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8192), b""): + h.update(chunk) + return h.hexdigest()[:16] + + +def safe_read_text(path: Path, limit: int = 2000) -> str: + try: + return path.read_text(encoding="utf-8", errors="ignore")[:limit] + except Exception: + return "" + + +def first_nonempty_line(text: str) -> str: + for line in text.splitlines(): + stripped = line.strip(" # -") + if stripped: + return stripped[:120] + return "" + + +def detect_kind(path: Path) -> str: + ext = path.suffix.lower() + if ext in {".xlsx", ".xls", ".xlsm", ".csv", ".tsv"}: + return "spreadsheet" + if ext in {".pdf", ".doc", ".docx", ".ppt", ".pptx", ".md", ".txt"}: + return "document" + if ext in {".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp", ".svg"}: + return "image" + if ext in {".zip", ".rar", ".7z", ".tar", ".gz"}: + return "archive" + return "raw" + + +def clean_cells(values: list[str], *, limit: int = MAX_ROW_WIDTH) -> list[str]: + cleaned = [ + str(value or "").replace("\n", " ").replace("\r", " ").strip()[:80] + for value in values[:limit] + ] + while cleaned and not cleaned[-1]: + cleaned.pop() + return cleaned + + +def guess_key_column(headers: list[str]) -> str: + lowered = [(header, header.lower()) for header in headers if header] + for hint in KEY_COLUMN_HINTS: + for original, value in lowered: + if hint in value: + return original + return headers[0] if headers else "" + + +def suspicious_columns(headers: list[str]) -> list[str]: + flagged: list[str] = [] + for header in headers: + lowered = header.lower() + if any(hint in lowered for hint in KEY_COLUMN_HINTS + VALUE_COLUMN_HINTS): + flagged.append(header) + return flagged[:6] + + +def row_signature(cells: list[str]) -> str: + payload = "|".join(clean_cells(cells, limit=MAX_ROW_WIDTH)) + return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:16] + + +def compare_named_lists(before: list[str], after: list[str]) -> tuple[list[str], list[str]]: + old = set(before) + new = set(after) + return sorted(new - old), sorted(old - new) + + +def compare_row_signatures(before: dict[str, str], after: dict[str, str]) -> str: + if not before or not after: + return "" + old_keys = set(before) + new_keys = set(after) + added = len(new_keys - old_keys) + removed = len(old_keys - new_keys) + changed = len([key for key in old_keys & new_keys if before[key] != after[key]]) + parts: list[str] = [] + if added: + parts.append(f"{added} added") + if removed: + parts.append(f"{removed} removed") + if changed: + parts.append(f"{changed} changed") + return ", ".join(parts) + + +def summarize_delimited(path: Path, delimiter: str, parser: str) -> dict[str, object]: + headers: list[str] = [] + key_column = "" + key_index = -1 + row_count = 0 + column_count = 0 + tracked_rows: dict[str, str] = {} + sample_rows: list[str] = [] + truncated = False + with path.open("r", encoding="utf-8-sig", errors="ignore", newline="") as handle: + reader = csv.reader(handle, delimiter=delimiter) + for raw_row in reader: + cleaned = clean_cells(raw_row) + if not cleaned: + continue + column_count = max(column_count, len(cleaned)) + if not headers: + headers = [cell[:60] for cell in cleaned[:MAX_HEADERS]] + key_column = guess_key_column(headers) + key_index = headers.index(key_column) if key_column in headers else -1 + continue + row_count += 1 + if len(sample_rows) < 3: + sample_rows.append(" | ".join(cleaned[:MAX_HEADERS])) + if row_count > MAX_TRACKED_ROWS: + truncated = True + continue + if key_index >= 0 and key_index < len(cleaned): + key = cleaned[key_index] + if key: + tracked_rows[key] = row_signature(cleaned) + return { + "parser": parser, + "summary": ( + f"{row_count} data row(s); {column_count} column(s); " + f"headers: {', '.join(headers) if headers else 'none'}" + ), + "metadata": { + "row_count": row_count, + "column_count": column_count, + "headers": headers, + "delimiter": delimiter, + "key_column": key_column, + "suspicious_columns": suspicious_columns(headers), + "sample_rows": sample_rows, + "tracked_rows": tracked_rows, + "truncated": truncated, + }, + } + + +def summarize_csv(path: Path) -> dict[str, object]: + delimiter = "," if path.suffix.lower() == ".csv" else "\t" + return summarize_delimited(path, delimiter, "csv-local") + + +def normalize_zip_path(base: str, target: str) -> str: + parts = [part for part in (Path(base).parent / target).as_posix().split("/") if part not in {"", "."}] + normalized: list[str] = [] + for part in parts: + if part == "..": + if normalized: + normalized.pop() + continue + normalized.append(part) + return "/".join(normalized) + + +def spreadsheet_column_index(label: str) -> int: + total = 0 + for char in label.upper(): + if not char.isalpha(): + break + total = total * 26 + (ord(char) - 64) + return total + + +def parse_sheet_dimension(ref: str) -> tuple[int, int]: + if not ref: + return 0, 0 + tail = ref.split(":")[-1] + letters = "".join(char for char in tail if char.isalpha()) + digits = "".join(char for char in tail if char.isdigit()) + return int(digits or 0), spreadsheet_column_index(letters) + + +def read_shared_strings(zf: zipfile.ZipFile) -> list[str]: + try: + with zf.open("xl/sharedStrings.xml") as handle: + root = ET.fromstring(handle.read()) + return ["".join(node.itertext()).strip() for node in root.findall(".//{*}si")] + except Exception: + return [] + + +def read_workbook_sheets(zf: zipfile.ZipFile) -> list[tuple[str, str]]: + with zf.open("xl/workbook.xml") as handle: + workbook_root = ET.fromstring(handle.read()) + rel_map: dict[str, str] = {} + try: + with zf.open("xl/_rels/workbook.xml.rels") as handle: + rel_root = ET.fromstring(handle.read()) + for node in rel_root.findall(".//{*}Relationship"): + rel_id = node.attrib.get("Id", "") + target = node.attrib.get("Target", "") + if rel_id and target: + rel_map[rel_id] = normalize_zip_path("xl/workbook.xml", target) + except Exception: + rel_map = {} + sheets: list[tuple[str, str]] = [] + for node in workbook_root.findall(".//{*}sheet"): + name = node.attrib.get("name", "") + rel_id = "" + for key, value in node.attrib.items(): + if key.endswith("}id") or key == "id": + rel_id = value + break + target = rel_map.get(rel_id, "") + if name and target: + sheets.append((name, target)) + return sheets + + +def resolve_xlsx_cell(cell: ET.Element, shared_strings: list[str]) -> str: + cell_type = cell.attrib.get("t", "") + if cell_type == "inlineStr": + return "".join(cell.itertext()).strip() + value = cell.findtext("{*}v", default="").strip() + if cell_type == "s": + try: + return shared_strings[int(value)] + except Exception: + return value + if cell_type == "b": + return "TRUE" if value == "1" else "FALSE" + if value: + return value + return "".join(cell.itertext()).strip() + + +def summarize_xlsx_sheet(zf: zipfile.ZipFile, sheet_name: str, sheet_path: str, shared_strings: list[str]) -> dict[str, object]: + with zf.open(sheet_path) as handle: + root = ET.fromstring(handle.read()) + dimension_node = root.find("{*}dimension") + dim_rows, dim_cols = parse_sheet_dimension(dimension_node.attrib.get("ref", "") if dimension_node is not None else "") + headers: list[str] = [] + key_column = "" + key_index = -1 + row_count = 0 + column_count = 0 + tracked_rows: dict[str, str] = {} + sample_rows: list[str] = [] + truncated = False + for row_node in root.findall(".//{*}sheetData/{*}row"): + cells_by_index: dict[int, str] = {} + for cell in row_node.findall("{*}c"): + ref = cell.attrib.get("r", "") + letters = "".join(char for char in ref if char.isalpha()) + index = spreadsheet_column_index(letters) or (len(cells_by_index) + 1) + cells_by_index[index] = resolve_xlsx_cell(cell, shared_strings) + if not cells_by_index: + continue + max_index = min(max(cells_by_index), MAX_ROW_WIDTH) + cleaned = clean_cells([cells_by_index.get(idx, "") for idx in range(1, max_index + 1)]) + if not cleaned: + continue + column_count = max(column_count, len(cleaned)) + if not headers: + headers = [cell[:60] for cell in cleaned[:MAX_HEADERS]] + key_column = guess_key_column(headers) + key_index = headers.index(key_column) if key_column in headers else -1 + continue + row_count += 1 + if len(sample_rows) < 3: + sample_rows.append(" | ".join(cleaned[:MAX_HEADERS])) + if row_count > MAX_TRACKED_ROWS: + truncated = True + continue + if key_index >= 0 and key_index < len(cleaned): + key = cleaned[key_index] + if key: + tracked_rows[key] = row_signature(cleaned) + return { + "name": sheet_name, + "row_count": row_count or dim_rows, + "column_count": column_count or dim_cols, + "headers": headers, + "key_column": key_column, + "suspicious_columns": suspicious_columns(headers), + "sample_rows": sample_rows, + "tracked_rows": tracked_rows, + "truncated": truncated, + } + + +def summarize_xlsx(path: Path) -> dict[str, object]: + sheets: list[dict[str, object]] = [] + try: + with zipfile.ZipFile(path) as zf: + shared_strings = read_shared_strings(zf) + for sheet_name, sheet_path in read_workbook_sheets(zf)[:MAX_SHEETS]: + try: + sheets.append(summarize_xlsx_sheet(zf, sheet_name, sheet_path, shared_strings)) + except Exception: + sheets.append({ + "name": sheet_name, + "row_count": 0, + "column_count": 0, + "headers": [], + "key_column": "", + "suspicious_columns": [], + "sample_rows": [], + "tracked_rows": {}, + "truncated": False, + }) + except Exception: + sheets = [] + sheet_names = [sheet["name"] for sheet in sheets] + summary_bits = [ + f"{sheet['name']}[{sheet.get('row_count', 0)}x{sheet.get('column_count', 0)}]" + for sheet in sheets[:4] + ] + return { + "parser": "xlsx-local", + "summary": f"{len(sheet_names)} sheet(s): {', '.join(summary_bits) if summary_bits else 'unknown'}", + "metadata": {"sheet_names": sheet_names, "sheets": sheets}, + } + + +def summarize_docx(path: Path) -> dict[str, object]: + paragraph_count = 0 + try: + with zipfile.ZipFile(path) as zf: + with zf.open("word/document.xml") as handle: + root = ET.fromstring(handle.read()) + paragraph_count = len(root.findall(".//{*}p")) + except Exception: + pass + return { + "parser": "docx-local", + "summary": f"{paragraph_count} paragraph block(s)", + "metadata": {"paragraph_blocks": paragraph_count}, + } + + +def summarize_pptx(path: Path) -> dict[str, object]: + slide_count = 0 + try: + with zipfile.ZipFile(path) as zf: + slide_count = len([name for name in zf.namelist() if name.startswith("ppt/slides/slide") and name.endswith(".xml")]) + except Exception: + pass + return { + "parser": "pptx-local", + "summary": f"{slide_count} slide(s)", + "metadata": {"slide_count": slide_count}, + } + + +def summarize_pdf(path: Path) -> dict[str, object]: + raw = path.read_bytes() + page_count = raw.count(b"/Type /Page") + return { + "parser": "pdf-local", + "summary": f"{page_count or 'unknown'} page(s)", + "metadata": {"page_count": int(page_count)}, + } + + +def image_size(path: Path) -> tuple[int | None, int | None]: + raw = path.read_bytes()[:64] + if raw.startswith(b"\x89PNG\r\n\x1a\n") and len(raw) >= 24: + return int.from_bytes(raw[16:20], "big"), int.from_bytes(raw[20:24], "big") + if raw[:6] in {b"GIF87a", b"GIF89a"} and len(raw) >= 10: + return int.from_bytes(raw[6:8], "little"), int.from_bytes(raw[8:10], "little") + if raw.startswith(b"BM") and len(raw) >= 26: + return int.from_bytes(raw[18:22], "little"), int.from_bytes(raw[22:26], "little") + if raw.startswith(b"\xff\xd8"): + with path.open("rb") as handle: + handle.read(2) + while True: + marker_prefix = handle.read(1) + if marker_prefix != b"\xff": + break + marker = handle.read(1) + while marker == b"\xff": + marker = handle.read(1) + if marker in {b"\xc0", b"\xc1", b"\xc2", b"\xc3", b"\xc5", b"\xc6", b"\xc7", b"\xc9", b"\xca", b"\xcb", b"\xcd", b"\xce", b"\xcf"}: + _length = int.from_bytes(handle.read(2), "big") + handle.read(1) + height = int.from_bytes(handle.read(2), "big") + width = int.from_bytes(handle.read(2), "big") + return width, height + if marker in {b"\xd8", b"\xd9"}: + continue + seg_length = int.from_bytes(handle.read(2), "big") + handle.seek(seg_length - 2, 1) + return None, None + + +def summarize_image(path: Path) -> dict[str, object]: + width, height = image_size(path) + dims = f"{width}x{height}" if width and height else "unknown" + return { + "parser": "image-local", + "summary": f"dimensions: {dims}", + "metadata": {"width": width, "height": height}, + } + + +def summarize_archive(path: Path) -> dict[str, object]: + ext = path.suffix.lower() + entry_count = None + parser = "archive-local" + try: + if ext == ".zip": + with zipfile.ZipFile(path) as zf: + entry_count = len(zf.namelist()) + elif ext in {".tar", ".gz"} or path.name.endswith(".tar.gz"): + with tarfile.open(path) as tf: + entry_count = len(tf.getmembers()) + except Exception: + entry_count = None + return { + "parser": parser, + "summary": f"archive entries: {entry_count if entry_count is not None else 'unknown'}", + "metadata": {"entry_count": entry_count, "format": ext.lstrip('.')}, + } + + +def summarize_plaintext(path: Path) -> dict[str, object]: + text = safe_read_text(path) + summary = first_nonempty_line(text) or "no preview" + return { + "parser": "text-local", + "summary": summary, + "metadata": {"preview": summary}, + } + + +def summarize_xls_legacy(path: Path) -> dict[str, object]: + return { + "parser": "xls-legacy", + "summary": "legacy .xls workbook (sheet metadata unavailable without extra parser)", + "metadata": {"format": "xls", "size_bytes": path.stat().st_size}, + } + + +def summarize_csv_change(previous: dict[str, object], current: dict[str, object]) -> list[str]: + notes: list[str] = [] + prev_headers = [str(item) for item in previous.get("headers", [])] + curr_headers = [str(item) for item in current.get("headers", [])] + added_headers, removed_headers = compare_named_lists(prev_headers, curr_headers) + if added_headers: + notes.append(f"headers added: {', '.join(added_headers[:4])}") + if removed_headers: + notes.append(f"headers removed: {', '.join(removed_headers[:4])}") + prev_rows = int(previous.get("row_count", 0) or 0) + curr_rows = int(current.get("row_count", 0) or 0) + if prev_rows != curr_rows: + notes.append(f"rows {prev_rows} -> {curr_rows}") + prev_key = str(previous.get("key_column", "") or "") + curr_key = str(current.get("key_column", "") or "") + if prev_key != curr_key and curr_key: + notes.append(f"key column {prev_key or 'none'} -> {curr_key}") + row_changes = compare_row_signatures( + {str(k): str(v) for k, v in dict(previous.get("tracked_rows", {})).items()}, + {str(k): str(v) for k, v in dict(current.get("tracked_rows", {})).items()}, + ) + if row_changes: + notes.append(f"tracked rows: {row_changes}") + if not notes: + notes.append("content changed; structural summary unchanged") + return notes + + +def summarize_xlsx_change(previous: dict[str, object], current: dict[str, object]) -> list[str]: + notes: list[str] = [] + prev_sheets = {str(sheet.get("name", "")): sheet for sheet in previous.get("sheets", []) if sheet.get("name")} + curr_sheets = {str(sheet.get("name", "")): sheet for sheet in current.get("sheets", []) if sheet.get("name")} + added_sheets, removed_sheets = compare_named_lists(list(prev_sheets), list(curr_sheets)) + if added_sheets: + notes.append(f"sheets added: {', '.join(added_sheets[:4])}") + if removed_sheets: + notes.append(f"sheets removed: {', '.join(removed_sheets[:4])}") + for sheet_name in sorted(prev_sheets.keys() & curr_sheets.keys())[:4]: + before = prev_sheets[sheet_name] + after = curr_sheets[sheet_name] + sheet_notes: list[str] = [] + if int(before.get("row_count", 0) or 0) != int(after.get("row_count", 0) or 0): + sheet_notes.append(f"rows {before.get('row_count', 0)} -> {after.get('row_count', 0)}") + if int(before.get("column_count", 0) or 0) != int(after.get("column_count", 0) or 0): + sheet_notes.append(f"cols {before.get('column_count', 0)} -> {after.get('column_count', 0)}") + added_headers, removed_headers = compare_named_lists( + [str(item) for item in before.get("headers", [])], + [str(item) for item in after.get("headers", [])], + ) + if added_headers: + sheet_notes.append(f"headers +{', '.join(added_headers[:3])}") + if removed_headers: + sheet_notes.append(f"headers -{', '.join(removed_headers[:3])}") + row_changes = compare_row_signatures( + {str(k): str(v) for k, v in dict(before.get("tracked_rows", {})).items()}, + {str(k): str(v) for k, v in dict(after.get("tracked_rows", {})).items()}, + ) + if row_changes: + sheet_notes.append(row_changes) + if sheet_notes: + notes.append(f"sheet {sheet_name}: {'; '.join(sheet_notes)}") + if not notes: + notes.append("content changed; workbook structure summary unchanged") + return notes + + +def summarize_change(previous_entry: dict[str, object] | None, current_payload: dict[str, object]) -> list[str]: + if not previous_entry: + return [] + previous_parser = str(previous_entry.get("parser", "")) + current_parser = str(current_payload.get("parser", "")) + previous_meta = dict(previous_entry.get("metadata", {})) + current_meta = dict(current_payload.get("metadata", {})) + if previous_parser == current_parser == "csv-local": + return summarize_csv_change(previous_meta, current_meta) + if previous_parser == current_parser == "xlsx-local": + return summarize_xlsx_change(previous_meta, current_meta) + return [f"summary: {previous_entry.get('summary', 'unknown')} -> {current_payload.get('summary', 'unknown')}"] + + +def summarize_file(path: Path) -> dict[str, object]: + ext = path.suffix.lower() + if ext in {".csv", ".tsv"}: + return summarize_csv(path) + if ext in {".xlsx", ".xlsm"}: + return summarize_xlsx(path) + if ext == ".xls": + return summarize_xls_legacy(path) + if ext == ".docx": + return summarize_docx(path) + if ext == ".pptx": + return summarize_pptx(path) + if ext == ".pdf": + return summarize_pdf(path) + if ext in {".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp", ".svg"}: + return summarize_image(path) + if ext in {".zip", ".rar", ".7z", ".tar", ".gz"} or path.name.endswith(".tar.gz"): + return summarize_archive(path) + return summarize_plaintext(path) + + +def load_manifest() -> list[dict[str, str]]: + if not MANIFEST.exists(): + raise FileNotFoundError(f"manifest missing: {MANIFEST}") + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as handle: + reader = csv.DictReader(handle) + if reader.fieldnames != EXPECTED_COLUMNS: + raise ValueError(f"manifest columns mismatch: expected {EXPECTED_COLUMNS}, got {reader.fieldnames}") + return [{key: (value or "") for key, value in row.items()} for row in reader] + + +def write_manifest(rows: list[dict[str, str]]) -> None: + MANIFEST.parent.mkdir(parents=True, exist_ok=True) + with MANIFEST.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=EXPECTED_COLUMNS) + writer.writeheader() + for row in rows: + writer.writerow({key: row.get(key, "") for key in EXPECTED_COLUMNS}) + + +def load_lock() -> dict[str, object]: + if not LOCK_FILE.exists(): + return {"files": {}} + try: + return json.loads(LOCK_FILE.read_text(encoding="utf-8")) + except Exception: + return {"files": {}} + + +def write_lock(payload: dict[str, object]) -> None: + LOCK_FILE.parent.mkdir(parents=True, exist_ok=True) + LOCK_FILE.write_text(json.dumps(payload, ensure_ascii=True, indent=2, sort_keys=True) + "\n", encoding="utf-8") + + +def next_source_id(existing_ids: set[str], content_hash: str) -> str: + base = f"src_{content_hash[:10]}" + candidate = base + index = 2 + while candidate in existing_ids: + candidate = f"{base}_{index}" + index += 1 + return candidate + + +def build_report( + raw_root: Path, + rows: list[dict[str, str]], + kinds: Counter[str], + new_paths: list[str], + changed_paths: list[str], + archived_paths: list[str], + duplicate_paths: list[str], + change_summaries: dict[str, list[str]], +) -> str: + def bullets(items: list[str], *, details: dict[str, list[str]] | None = None) -> str: + if not items: + return "- none\n" + lines: list[str] = [] + for item in items[:20]: + detail_lines = (details or {}).get(item, []) + if detail_lines: + lines.append(f"- `{item}` — {detail_lines[0]}\n") + for extra in detail_lines[1:3]: + lines.append(f" - {extra}\n") + else: + lines.append(f"- `{item}`\n") + return "".join(lines) + + lines = [ + "# Raw Intake Report", + "", + f"- generated_at: `{utc_now()}`", + f"- raw_root: `{raw_root}`", + f"- manifest_rows: `{len(rows)}`", + f"- new: `{len(new_paths)}`", + f"- changed: `{len(changed_paths)}`", + f"- archived: `{len(archived_paths)}`", + f"- duplicates: `{len(duplicate_paths)}`", + "", + "## Kind Summary", + "", + ] + if kinds: + for kind, count in sorted(kinds.items()): + lines.append(f"- `{kind}`: {count}") + else: + lines.append("- none") + lines.extend([ + "", + "## New Files", + "", + bullets(sorted(new_paths)), + "", + "## Changed Files", + "", + bullets(sorted(changed_paths), details=change_summaries), + "", + "## Archived Files", + "", + bullets(sorted(archived_paths)), + "", + "## Duplicate Files", + "", + bullets(sorted(duplicate_paths)), + ]) + return "\n".join(lines).rstrip() + "\n" + + +def main() -> int: + parser = argparse.ArgumentParser(description="Scan a local raw root, update the manifest, and record low-cost structural metadata.") + parser.add_argument("--raw-root", default=os.environ.get("PROJECT_RAW_ROOT", str(DEFAULT_RAW_ROOT)), help="Local raw root path") + parser.add_argument("--report-file", default=str(REPORT_FILE), help="Markdown report output path") + parser.add_argument("--dry-run", action="store_true", help="Preview changes without writing manifest or lock files") + args = parser.parse_args() + + raw_root = Path(args.raw_root).expanduser().resolve() + if not raw_root.exists(): + print(f"ingest_raw: raw root does not exist: {raw_root}") + return 1 + + rows = load_manifest() + previous_lock = load_lock().get("files", {}) + rows_by_path = {row["raw_rel_path"]: row for row in rows if row.get("raw_rel_path")} + existing_ids = {row["source_id"] for row in rows if row.get("source_id")} + seen_paths: set[str] = set() + kinds: Counter[str] = Counter() + new_paths: list[str] = [] + changed_paths: list[str] = [] + archived_paths: list[str] = [] + lock_entries: dict[str, object] = {} + hash_to_primary: dict[str, str] = {} + duplicate_paths: list[str] = [] + change_summaries: dict[str, list[str]] = {} + + candidates: list[Path] = [] + for dirpath, dirnames, filenames in os.walk(raw_root): + dirnames[:] = [d for d in dirnames if d not in SKIP_DIRS and not d.startswith(".")] + for name in filenames: + if name.startswith("."): + continue + path = Path(dirpath) / name + ext = path.suffix.lower() + if ext in TRACKED_EXTENSIONS or path.name.endswith(".tar.gz"): + candidates.append(path) + + for path in sorted(candidates): + rel = path.relative_to(raw_root).as_posix() + seen_paths.add(rel) + content_hash = sha256_prefix(path) + kind = detect_kind(path) + kinds[kind] += 1 + summary_payload = summarize_file(path) + existing = rows_by_path.get(rel) + old_hash = None + previous_entry = None + if isinstance(previous_lock, dict) and rel in previous_lock: + previous_entry = previous_lock[rel] + old_hash = previous_entry.get("content_hash") + + if existing is None: + source_id = next_source_id(existing_ids, content_hash) + existing_ids.add(source_id) + row = { + "source_id": source_id, + "company": "", + "vendor": "", + "kind": kind, + "filename": path.name, + "raw_rel_path": rel, + "status": "new", + "compiled_into": "", + "notes": "", + } + rows.append(row) + rows_by_path[rel] = row + new_paths.append(rel) + existing = row + else: + existing["kind"] = kind + existing["filename"] = path.name + if old_hash and old_hash != content_hash and existing.get("status") != "archived": + existing["status"] = "new" + changed_paths.append(rel) + change_summaries[rel] = summarize_change(previous_entry, summary_payload) + + primary = hash_to_primary.setdefault(content_hash, rel) + duplicate_of = None if primary == rel else rows_by_path[primary]["source_id"] + if duplicate_of: + duplicate_paths.append(rel) + + lock_entries[rel] = { + "source_id": existing["source_id"], + "filename": path.name, + "kind": kind, + "content_hash": content_hash, + "size_bytes": path.stat().st_size, + "modified_at": datetime.fromtimestamp(path.stat().st_mtime, tz=timezone.utc).replace(microsecond=0).isoformat(), + "parser": summary_payload["parser"], + "summary": summary_payload["summary"], + "metadata": summary_payload["metadata"], + "duplicate_of": duplicate_of, + "previous_content_hash": old_hash or "", + "change_summary": change_summaries.get(rel, []), + } + + for row in rows: + rel = row.get("raw_rel_path", "") + if rel and rel not in seen_paths and row.get("status") != "archived": + row["status"] = "archived" + archived_paths.append(rel) + + rows.sort(key=lambda row: (row.get("status", ""), row.get("raw_rel_path", ""))) + report_text = build_report(raw_root, rows, kinds, new_paths, changed_paths, archived_paths, duplicate_paths, change_summaries) + + if args.dry_run: + print("ingest_raw: DRY RUN") + print(report_text) + return 0 + + write_manifest(rows) + write_lock({ + "llm_wiki_version": "1.2.2", + "generated_at": utc_now(), + "raw_root": str(raw_root), + "summary": { + "tracked_files": len(candidates), + "manifest_rows": len(rows), + "new": len(new_paths), + "changed": len(changed_paths), + "archived": len(archived_paths), + "duplicates": len(duplicate_paths), + "kinds": dict(sorted(kinds.items())), + }, + "files": lock_entries, + }) + report_path = Path(args.report_file).expanduser().resolve() + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text(report_text, encoding="utf-8") + + print(f"ingest_raw: OK ({len(candidates)} tracked file(s))") + print(f"- manifest: {MANIFEST}") + print(f"- lock: {LOCK_FILE}") + print(f"- report: {report_path}") + print(f"- new: {len(new_paths)} | changed: {len(changed_paths)} | archived: {len(archived_paths)} | duplicates: {len(duplicate_paths)}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/init_raw_root.py b/scripts/init_raw_root.py new file mode 100644 index 0000000000000000000000000000000000000000..107d73b4e24c0f02baf0a421c816c7f31e62ff20 --- /dev/null +++ b/scripts/init_raw_root.py @@ -0,0 +1,46 @@ +from __future__ import annotations +# runtime: dev-only (one-shot setup) + +import argparse +import os +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_RAW_ROOT = (ROOT.parent / "__RAW_ROOT_NAME__").resolve() + +DIRS = [ + "inbox", + "internal_sources", + "external_sources", + "customer_answers", + "screenshots", + "extracted_text", + "archive", +] + + +def main() -> int: + parser = argparse.ArgumentParser(description="Initialize the local raw root directory.") + parser.add_argument( + "path", + nargs="?", + default=os.environ.get("PROJECT_RAW_ROOT", str(DEFAULT_RAW_ROOT)), + help="Absolute or relative path for the local raw root", + ) + args = parser.parse_args() + + raw_root = Path(args.path).expanduser().resolve() + raw_root.mkdir(parents=True, exist_ok=True) + + for rel in DIRS: + (raw_root / rel).mkdir(parents=True, exist_ok=True) + + print(f"Initialized raw root: {raw_root}") + for rel in DIRS: + print(f"- {raw_root / rel}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/intake_filter.py b/scripts/intake_filter.py new file mode 100644 index 0000000000000000000000000000000000000000..4b2a0aee48f1c3389122ebf4248664c0ef160139 --- /dev/null +++ b/scripts/intake_filter.py @@ -0,0 +1,172 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only + +""" +intake_filter.py — v2 intake privacy filter. + +Rohit G00 v2 范式：raw 摄入前脱敏，wiki 写入时按 audience 分级。 +v2.0 实现： + 1. 读 manifests/redact_rules.yaml（每条 = name + pattern + replacement） + 2. 扫 raw 根目录（默认 ../_raw/，可 --raw-root 覆盖） + 3. 对每个 .md/.txt/.csv/.json 文件应用正则替换 + 4. 在 manifests/redaction_log.md 写日志（每文件命中几条） + 5. 退出口为 0（仅警告），CI 默认不阻塞 + +Usage: + python3 scripts/intake_filter.py + python3 scripts/intake_filter.py --dry-run + python3 scripts/intake_filter.py --raw-root ../my_raw + python3 scripts/intake_filter.py --rules manifests/redact_rules.yaml +""" + +import argparse +import os +import re +import sys +from datetime import date +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_RULES = ROOT / "manifests" / "redact_rules.yaml" +DEFAULT_LOG = ROOT / "manifests" / "redaction_log.md" + +# 默认 deny extensions / max size (写在脚本里，不进 rules.yaml 避免 YAML 解析复杂) +DEFAULT_DENY_EXT = {".zip", ".rar", ".7z", ".tar", ".gz"} +DEFAULT_MAX_SIZE_MB = 200 + +# 10 行内建 PII 正则（无外部依赖） +BUILTIN_PATTERNS = [ + ("phone_cn", re.compile(r"1[3-9]\d{9}"), "[PHONE]"), + ("email", re.compile(r"[\w.+-]+@[\w-]+\.[\w.-]+"), "[EMAIL]"), + ("id_card_cn", re.compile(r"\d{17}[\dXx]"), "[ID]"), + ("credit_card", re.compile(r"\d{4}[ -]?\d{4}[ -]?\d{4}[ -]?\d{4}"), "[CC]"), +] + + +def parse_yaml_rules(path: Path) -> list[tuple[str, re.Pattern, str]]: + """Minimal YAML parser: each rule is a block of 3 lines (name/pattern/replacement).""" + if not path.exists(): + return [] + rules: list[tuple[str, re.Pattern, str]] = [] + block: dict[str, str] = {} + for line in path.read_text(encoding="utf-8").splitlines(): + line_stripped = line.strip() + if not line_stripped or line_stripped.startswith("#"): + continue + if line_stripped.startswith("- name:"): + if block: + _flush(block, rules) + block = {"name": line_stripped.split(":", 1)[1].strip()} + elif line_stripped.startswith("pattern:") and block: + block["pattern"] = line_stripped.split(":", 1)[1].strip().strip('"').strip("'") + elif line_stripped.startswith("replacement:") and block: + block["replacement"] = line_stripped.split(":", 1)[1].strip().strip('"').strip("'") + if block: + _flush(block, rules) + return rules + + +def _flush(block: dict[str, str], rules: list) -> None: + name = block.get("name", "unnamed") + pat = block.get("pattern", "") + repl = block.get("replacement", "[REDACTED]") + if not pat: + return + try: + rules.append((name, re.compile(pat), repl)) + except re.error: + pass # 跳过非法正则 + + +def main() -> int: + parser = argparse.ArgumentParser(description="v2 intake privacy filter") + parser.add_argument("--raw-root", type=Path, + default=lambda: Path(os.environ.get("PROJECT_RAW_ROOT", ROOT.parent / "_raw")), + help="raw root directory to scan (env PROJECT_RAW_ROOT or --raw-root override; default: ../_raw)") + parser.add_argument("--rules", type=Path, default=DEFAULT_RULES, + help="redact_rules.yaml path (optional, falls back to builtins)") + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--log", type=Path, default=DEFAULT_LOG) + args = parser.parse_args() + + if not args.raw_root.exists(): + print(f"intake_filter: raw_root {args.raw_root} does not exist, skipping") + return 0 + + # Load rules: yaml first, then builtins + rules = parse_yaml_rules(args.rules) or BUILTIN_PATTERNS + print(f"intake_filter: {len(rules)} patterns loaded ({'yaml' if args.rules.exists() else 'builtin'})") + + # Scan + hits: list[dict] = [] + scanned = 0 + skipped = 0 + for path in args.raw_root.rglob("*"): + if not path.is_file(): + continue + scanned += 1 + if path.suffix.lower() in DEFAULT_DENY_EXT: + skipped += 1 + continue + size_mb = path.stat().st_size / (1024 * 1024) + if size_mb > DEFAULT_MAX_SIZE_MB: + skipped += 1 + continue + try: + text = path.read_text(encoding="utf-8", errors="ignore") + except Exception: + continue + file_hits: dict[str, int] = {} + new_text = text + for name, pat, repl in rules: + count = len(pat.findall(new_text)) + if count > 0: + file_hits[name] = count + new_text = pat.sub(repl, new_text) + if file_hits: + if not args.dry_run: + # 仅当命中数 > 0 时回写 + path.write_text(new_text, encoding="utf-8") + hits.append({"file": str(path.relative_to(args.raw_root)), "hits": file_hits}) + + # Write log + today = date.today().isoformat() + log_lines = [f"# Intake Redaction Log", "", + f"- date: {today}", + f"- raw_root: {args.raw_root}", + f"- rules: {args.rules} ({'yaml' if args.rules.exists() else 'builtin'})", + f"- dry_run: {args.dry_run}", + f"- scanned: {scanned}, skipped: {skipped}, modified: {len(hits)}", + ""] + if hits: + log_lines.append("## Files modified") + log_lines.append("") + for h in hits: + log_lines.append(f"- `{h['file']}`: " + ", ".join(f"{k}={v}" for k, v in h["hits"].items())) + else: + log_lines.append("## No PII / redact patterns matched.") + + if not args.dry_run: + args.log.parent.mkdir(parents=True, exist_ok=True) + args.log.write_text("\n".join(log_lines) + "\n", encoding="utf-8") + + # Report + print(f"- scanned: {scanned}") + print(f"- skipped: {skipped} (deny ext or >{DEFAULT_MAX_SIZE_MB}MB)") + print(f"- modified: {len(hits)}") + if hits: + for h in hits[:10]: + print(f" - {h['file']}: " + ", ".join(f"{k}={v}" for k, v in h["hits"].items())) + if len(hits) > 10: + print(f" ... and {len(hits) - 10} more") + if not args.dry_run: + print(f"- log: {args.log}") + else: + print("- dry-run: no files written") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/provenance_check.py b/scripts/provenance_check.py new file mode 100644 index 0000000000000000000000000000000000000000..356579fb8215ab3e20dc223d6cb26941ace099fa --- /dev/null +++ b/scripts/provenance_check.py @@ -0,0 +1,161 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (needs PROJECT_RAW_ROOT pointing at real raw files) +# Use --ci to skip raw-file resolution and only verify that source_hash is +# present in every non-session page. That sub-check is safe to run in CI. + +import argparse +import csv +import hashlib +import os +import re +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" +MANIFEST = ROOT / "manifests" / "raw_sources.csv" + +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +SOURCE_HASH_RE = re.compile(r"source_hash:\s*([a-f0-9]{12,})") + +SKIP_FILES = {"index.md", "log.md", "README.md", "SCHEMA.md"} + + +def file_hash(path: Path) -> str: + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(8192), b""): + h.update(chunk) + return h.hexdigest()[:16] + + +def load_manifest_paths() -> dict[str, Path]: + if not MANIFEST.exists(): + return {} + raw_root_env = os.environ.get("PROJECT_RAW_ROOT") + raw_root = Path(raw_root_env).expanduser().resolve() if raw_root_env else None + result: dict[str, Path] = {} + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as f: + for row in csv.DictReader(f): + sid = (row.get("source_id") or "").strip() + rel = (row.get("raw_rel_path") or "").strip() + if sid and rel and raw_root: + result[sid] = (raw_root / rel).resolve() + return result + + +def main() -> int: + parser = argparse.ArgumentParser(description="Verify wiki pages reflect current raw sources.") + parser.add_argument("--ci", action="store_true", + help="Skip raw-file resolution; only verify that every non-session " + "page has a source_hash field. Safe to run without raw files.") + args = parser.parse_args() + # Explicit opt-in only. Don't auto-detect generic CI=true — users may + # mount raw files in CI and want the full check. + ci_mode = args.ci or os.environ.get("LLM_WIKI_CI") == "1" + + if not WIKI_ROOT.exists(): + print("provenance_check: docs/wiki does not exist") + return 1 + + manifest_paths = {} if ci_mode else load_manifest_paths() + checked = 0 + fresh = 0 + stale: list[tuple[str, str, str]] = [] + no_hash: list[str] = [] + unresolved: list[str] = [] + session_exempt = 0 + + for path in sorted(WIKI_ROOT.rglob("*.md")): + if path.name in SKIP_FILES: + continue + text = path.read_text(encoding="utf-8") + m = FRONTMATTER_RE.match(text) + if not m: + continue + + fm = m.group(1) + source_line = "" + for line in fm.splitlines(): + if line.startswith("source:"): + source_line = line.split(":", 1)[1].strip() + break + + if source_line == "session": + session_exempt += 1 + continue + + hash_match = SOURCE_HASH_RE.search(fm) + if not hash_match: + no_hash.append(path.relative_to(ROOT).as_posix()) + continue + + stored_hash = hash_match.group(1) + checked += 1 + + if ci_mode: + # Structural check only: source_hash exists, that's all CI can verify. + fresh += 1 + continue + + # Find the source file + if not source_line: + fresh += 1 + continue + + # Try to resolve source path + source_path = None + if source_line.startswith("raw/"): + candidate = ROOT / source_line + if candidate.exists(): + source_path = candidate + for sid, spath in manifest_paths.items(): + if sid in source_line or source_line in str(spath): + source_path = spath + break + + if source_path and source_path.exists(): + current = file_hash(source_path) + if current == stored_hash: + fresh += 1 + else: + stale.append(( + path.relative_to(ROOT).as_posix(), + stored_hash, + current, + )) + else: + unresolved.append(path.relative_to(ROOT).as_posix()) + + if stale: + print(f"provenance_check: {len(stale)} STALE page(s) detected") + for page, old, new in stale: + print(f" {page}: hash was {old}, source now {new}") + print() + print("These wiki pages were compiled from source files that have since changed.") + print("Recompile them to update the wiki with current information.") + + if no_hash: + print(f"provenance_check: {len(no_hash)} page(s) without source_hash (required for non-session sources)") + for page in no_hash: + print(f" {page}") + + if unresolved: + print(f"provenance_check: {len(unresolved)} page(s) with unresolved source") + for page in unresolved: + print(f" {page}") + + if not stale and not no_hash and not unresolved: + suffix = " [ci-mode: structural check only]" if ci_mode else "" + print( + f"provenance_check: OK ({checked} checked, {fresh} fresh, " + f"{session_exempt} session-exempt){suffix}" + ) + return 0 + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/raw_manifest_check.py b/scripts/raw_manifest_check.py new file mode 100644 index 0000000000000000000000000000000000000000..d11d650f4c921c7b73f7d24b7916047545d25e43 --- /dev/null +++ b/scripts/raw_manifest_check.py @@ -0,0 +1,123 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: ci-safe (skips raw existence check when PROJECT_RAW_ROOT unset) + +import csv +import json +import os +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "manifests" / "raw_sources.csv" +META = ROOT / "manifests" / "raw_sources.meta.json" + +# Schema versions this script knows how to validate. +KNOWN_SCHEMA_VERSIONS = {1, 2} +LATEST_SCHEMA_VERSION = 2 + +# Default schema for projects bootstrapped before meta.json existed. +LEGACY_DEFAULT = { + "schema_version": 1, + "columns": [ + "source_id", + "company", + "vendor", + "kind", + "filename", + "raw_rel_path", + "status", + "compiled_into", + "notes", + ], + "allowed_status": ["new", "compiled", "archived"], +} + + +def load_schema() -> tuple[dict, str]: + """Return (schema_dict, source) where source is 'meta' or 'legacy'.""" + if META.exists(): + try: + data = json.loads(META.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + raise SystemExit(f"raw_manifest_check: malformed {META.name}: {exc}") + return data, "meta" + return LEGACY_DEFAULT, "legacy" + + +def main() -> int: + errors: list[str] = [] + if not MANIFEST.exists(): + print(f"raw_manifest_check: missing {MANIFEST}") + return 1 + + schema, schema_source = load_schema() + schema_version = schema.get("schema_version", 1) + + if schema_version not in KNOWN_SCHEMA_VERSIONS: + if schema_version > LATEST_SCHEMA_VERSION: + print(f"raw_manifest_check: SKIPPED") + print(f"- manifest schema_version is {schema_version}, this script knows only up to {LATEST_SCHEMA_VERSION}") + print(f"- run: python3 scripts/version_check.py (or upgrade LLM-wiki to a newer release)") + return 0 # Forward-compat: don't fail the user's CI on a newer manifest. + errors.append(f"unknown schema_version {schema_version} (this script supports {sorted(KNOWN_SCHEMA_VERSIONS)})") + + expected_columns = schema["columns"] + allowed_status = set(schema["allowed_status"]) + + raw_root_env = os.environ.get("PROJECT_RAW_ROOT") + raw_root = Path(raw_root_env).expanduser().resolve() if raw_root_env else None + + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as handle: + reader = csv.DictReader(handle) + if reader.fieldnames != expected_columns: + errors.append(f"manifest columns mismatch: expected {expected_columns}, got {reader.fieldnames}") + seen_ids: set[str] = set() + for idx, row in enumerate(reader, start=2): + source_id = (row.get("source_id") or "").strip() + filename = (row.get("filename") or "").strip() + raw_rel_path = (row.get("raw_rel_path") or "").strip() + status = (row.get("status") or "").strip() + compiled_into = (row.get("compiled_into") or "").strip() + + if not source_id: + errors.append(f"row {idx}: empty source_id") + elif source_id in seen_ids: + errors.append(f"row {idx}: duplicate source_id {source_id}") + else: + seen_ids.add(source_id) + + if not filename: + errors.append(f"row {idx}: empty filename") + if not raw_rel_path: + errors.append(f"row {idx}: empty raw_rel_path") + if status not in allowed_status: + errors.append(f"row {idx}: bad status {status}") + + if raw_root and raw_rel_path: + candidate = (raw_root / raw_rel_path).resolve() + if not candidate.exists(): + errors.append(f"row {idx}: missing local raw file {candidate}") + + if status == "compiled" and not compiled_into: + errors.append(f"row {idx}: status {status} requires compiled_into") + + if errors: + print("raw_manifest_check: FAILED") + for item in errors: + print(f"- {item}") + return 1 + + print("raw_manifest_check: OK") + print(f"- manifest: {MANIFEST}") + print(f"- schema: v{schema_version} (loaded from {schema_source})") + if raw_root: + print(f"- PROJECT_RAW_ROOT: {raw_root}") + else: + print("- PROJECT_RAW_ROOT: not set, existence checks skipped") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/stale_report.py b/scripts/stale_report.py new file mode 100644 index 0000000000000000000000000000000000000000..af6171af2acf6d3a70667623fa9c8578d22561d4 --- /dev/null +++ b/scripts/stale_report.py @@ -0,0 +1,246 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (needs PROJECT_RAW_ROOT) + +import argparse +import csv +import hashlib +import json +import os +import re +import sys +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" +MANIFEST = ROOT / "manifests" / "raw_sources.csv" +LOCK_FILE = ROOT / "manifests" / "raw_index.json" +REPORT_FILE = ROOT / "manifests" / "stale_report.md" +DEFAULT_RAW_ROOT = (ROOT.parent / "__RAW_ROOT_NAME__").resolve() +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +SKIP_FILES = {"index.md", "log.md", "README.md", "SCHEMA.md"} + + +def sha256_prefix(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8192), b""): + h.update(chunk) + return h.hexdigest()[:16] + + +def parse_frontmatter(path: Path) -> dict[str, str]: + text = path.read_text(encoding="utf-8") + match = FRONTMATTER_RE.match(text) + if not match: + return {} + result: dict[str, str] = {} + for line in match.group(1).splitlines(): + if ":" in line: + key, value = line.split(":", 1) + result[key.strip()] = value.strip() + return result + + +def parse_list_field(value: str) -> list[str]: + raw = value.strip() + if not raw: + return [] + if raw.startswith("[") and raw.endswith("]"): + raw = raw[1:-1] + items = [item.strip().strip("'").strip('"') for item in raw.split(",")] + return [item for item in items if item] + + +def load_manifest() -> list[dict[str, str]]: + if not MANIFEST.exists(): + return [] + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as handle: + return [{key: (value or "") for key, value in row.items()} for row in csv.DictReader(handle)] + + +def load_lock() -> dict[str, dict]: + if not LOCK_FILE.exists(): + return {} + try: + data = json.loads(LOCK_FILE.read_text(encoding="utf-8")) + files = data.get("files", {}) + return files if isinstance(files, dict) else {} + except Exception: + return {} + + +def resolve_row(source_value: str, rows: list[dict[str, str]]) -> dict[str, str] | None: + for row in rows: + if source_value == row.get("source_id") or source_value == row.get("raw_rel_path"): + return row + for row in rows: + if row.get("source_id") and row["source_id"] in source_value: + return row + if row.get("raw_rel_path") and source_value.endswith(row["raw_rel_path"]): + return row + return None + + +def build_report( + raw_root: Path | None, + session_exempt: int, + fresh: list[str], + stale: list[str], + missing_hash: list[str], + unresolved: list[str], + archived_refs: list[str], + manifest_new: list[str], +) -> str: + def section(title: str, items: list[str]) -> list[str]: + lines = ["", f"## {title}", ""] + if not items: + lines.append("- none") + else: + lines.extend(f"- `{item}`" for item in items[:40]) + return lines + + lines = [ + "# Stale Report", + "", + f"- generated_at: `{datetime.now(timezone.utc).replace(microsecond=0).isoformat()}`", + f"- raw_root: `{raw_root}`" if raw_root else "- raw_root: `not configured`", + f"- fresh_pages: `{len(fresh)}`", + f"- stale_pages: `{len(stale)}`", + f"- missing_hash: `{len(missing_hash)}`", + f"- unresolved_sources: `{len(unresolved)}`", + f"- archived_refs: `{len(archived_refs)}`", + f"- manifest_new: `{len(manifest_new)}`", + f"- session_exempt: `{session_exempt}`", + ] + lines += section("Fresh Pages", fresh) + lines += section("Pages Needing Recompile (stale)", stale) + lines += section("Pages Missing source_hash", missing_hash) + lines += section("Pages With Unresolved source", unresolved) + lines += section("Pages Pointing At Archived Sources", archived_refs) + lines += section("Raw Files Still Waiting For First Compile", manifest_new) + return "\n".join(lines).rstrip() + "\n" + + +def main() -> int: + parser = argparse.ArgumentParser(description="Report stale wiki pages and raw files that still need compilation.") + parser.add_argument("--raw-root", default=os.environ.get("PROJECT_RAW_ROOT", ""), help="Local raw root path. If empty, falls back to the default sibling raw root when present.") + parser.add_argument("--report-file", default=str(REPORT_FILE), help="Markdown report output path") + parser.add_argument("--dry-run", action="store_true", help="Print the report without writing it") + args = parser.parse_args() + + raw_root = None + if args.raw_root: + candidate = Path(args.raw_root).expanduser().resolve() + if candidate.exists(): + raw_root = candidate + elif DEFAULT_RAW_ROOT.exists(): + raw_root = DEFAULT_RAW_ROOT + + rows = load_manifest() + lock = load_lock() + fresh: list[str] = [] + stale: list[str] = [] + missing_hash: list[str] = [] + unresolved: list[str] = [] + archived_refs: list[str] = [] + referenced_source_ids: set[str] = set() + referenced_paths: set[str] = set() + session_exempt = 0 + + for path in sorted(WIKI_ROOT.rglob("*.md")): + if path.name in SKIP_FILES: + continue + fm = parse_frontmatter(path) + if not fm: + continue + rel = path.relative_to(ROOT).as_posix() + source = fm.get("source", "") + compiled_from = parse_list_field(fm.get("compiled_from", "")) + source_hash = fm.get("source_hash", "") + if source == "session": + session_exempt += 1 + continue + if not source_hash: + missing_hash.append(rel) + continue + + row = resolve_row(source, rows) + if not row: + unresolved.append(rel) + continue + + referenced_rows = [row] + for extra in compiled_from: + extra_row = resolve_row(extra, rows) + if not extra_row: + unresolved.append(f"{rel} <- {extra}") + continue + referenced_rows.append(extra_row) + + for referenced_row in referenced_rows: + if referenced_row.get("source_id"): + referenced_source_ids.add(referenced_row["source_id"]) + if referenced_row.get("raw_rel_path"): + referenced_paths.add(referenced_row["raw_rel_path"]) + + if row.get("status") == "archived": + archived_refs.append(rel) + continue + + for extra_row in referenced_rows[1:]: + if extra_row.get("status") == "archived" and extra_row.get("raw_rel_path"): + archived_refs.append(f"{rel} <- {extra_row['raw_rel_path']}") + + current_hash = "" + if raw_root: + source_path = raw_root / row["raw_rel_path"] + if source_path.exists(): + current_hash = sha256_prefix(source_path) + if not current_hash and row["raw_rel_path"] in lock: + current_hash = lock[row["raw_rel_path"]].get("content_hash", "") + if not current_hash: + unresolved.append(rel) + continue + if current_hash != source_hash: + stale.append(f"{rel} <- {row['raw_rel_path']} ({source_hash} -> {current_hash})") + else: + fresh.append(rel) + + manifest_new = [ + row["raw_rel_path"] + for row in rows + if row.get("status") == "new" + and row.get("raw_rel_path") + and row.get("source_id") not in referenced_source_ids + and row.get("raw_rel_path") not in referenced_paths + ] + + report_text = build_report(raw_root, session_exempt, fresh, stale, missing_hash, unresolved, archived_refs, manifest_new) + + if args.dry_run: + print(report_text) + else: + report_path = Path(args.report_file).expanduser().resolve() + report_path.parent.mkdir(parents=True, exist_ok=True) + report_path.write_text(report_text, encoding="utf-8") + print(f"stale_report: wrote {report_path}") + + if stale or missing_hash or unresolved or archived_refs: + print( + f"stale_report: ATTENTION ({len(stale)} stale, {len(missing_hash)} missing_hash, " + f"{len(unresolved)} unresolved, {len(archived_refs)} archived_refs)" + ) + return 1 + + print( + f"stale_report: OK ({len(fresh)} fresh, {len(manifest_new)} manifest-new, " + f"{session_exempt} session-exempt)" + ) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/supersede_check.py b/scripts/supersede_check.py new file mode 100644 index 0000000000000000000000000000000000000000..372e7c92835a851f8409ff63ec4936695275f77b --- /dev/null +++ b/scripts/supersede_check.py @@ -0,0 +1,185 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only + +""" +supersede_check.py — v2 supersede/contradicts chain validator. + +Rohit G00 v2 范式：每个 wiki 页可以 supersede 旧页 / contradicts 兄弟页。 +这个脚本验证： + 1. supersedes 目标存在 + 2. 目标 stage == archived + 3. 没有环 + 4. 没有"被 supersede 但未归档"的孤儿 + 5. contradicts 对称性（A contradicts B 要求 B 也填 contradicts A） + +Usage: + python3 scripts/supersede_check.py + python3 scripts/supersede_check.py --json +""" + +import argparse +import json +import re +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" + +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) + + +def parse_frontmatter(text: str) -> dict[str, str]: + m = FRONTMATTER_RE.match(text) + if not m: + return {} + out: dict[str, str] = {} + for line in m.group(1).splitlines(): + if ":" in line: + key, _, value = line.partition(":") + out[key.strip()] = value.strip() + return out + + +def parse_list_field(value: str) -> list[str]: + """Parse a YAML-ish list: [./a.md, ./b.md] -> [./a.md, ./b.md]""" + if not value or value in ("[]", ""): + return [] + return re.findall(r"\./[^\s,\]]+", value) + + +def main() -> int: + parser = argparse.ArgumentParser(description="v2 supersede / contradicts chain validator") + parser.add_argument("--wiki-root", type=Path, default=WIKI_ROOT) + parser.add_argument("--json", action="store_true", help="emit JSON report") + args = parser.parse_args() + + if not args.wiki_root.exists(): + print(f"supersede_check: {args.wiki_root} does not exist") + return 1 + + md_files = sorted(args.wiki_root.rglob("*.md")) + pages: dict[str, dict[str, str]] = {} + for path in md_files: + text = path.read_text(encoding="utf-8") + fm = parse_frontmatter(text) + if not fm: + continue + rel = path.relative_to(args.wiki_root).as_posix() + pages[rel] = fm + + errors: list[str] = [] + orphans: list[str] = [] # 被 supersede 但未 archived + + # Pass 1: validate each page's supersedes + for rel, fm in pages.items(): + supersedes = parse_list_field(fm.get("supersedes", "")) + for target in supersedes: + target_norm = target.lstrip("./") + if target_norm not in pages: + errors.append(f"supersedes target missing: {rel} -> {target}") + continue + target_stage = pages[target_norm].get("stage", "") + if target_stage != "archived": + errors.append( + f"supersedes target not archived: {rel} -> {target} " + f"(stage={target_stage!r}, expected 'archived')" + ) + + # Pass 2: detect cycles + # Build adjacency: page -> set of pages it supersedes + adj: dict[str, set[str]] = {} + for rel, fm in pages.items(): + supersedes = parse_list_field(fm.get("supersedes", "")) + adj[rel] = {t.lstrip("./") for t in supersedes if t.lstrip("./") in pages} + + # DFS cycle detection + WHITE, GRAY, BLACK = 0, 1, 2 + color = {node: WHITE for node in adj} + cycles: list[list[str]] = [] + + def dfs(node: str, path: list[str]) -> None: + color[node] = GRAY + path.append(node) + for nxt in adj.get(node, set()): + if color[nxt] == GRAY: + # Found cycle + if nxt in path: + cycle = path[path.index(nxt):] + [nxt] + cycles.append(cycle) + elif color[nxt] == WHITE: + dfs(nxt, path) + path.pop() + color[node] = BLACK + + for node in list(adj.keys()): + if color[node] == WHITE: + dfs(node, []) + + for cycle in cycles: + errors.append(f"supersedes cycle detected: {' -> '.join(cycle)}") + + # Pass 3: detect orphans (被 supersede 但 stage != archived) + all_superseded_targets: set[str] = set() + for fm in pages.values(): + supersedes = parse_list_field(fm.get("supersedes", "")) + for t in supersedes: + all_superseded_targets.add(t.lstrip("./")) + + for rel in all_superseded_targets: + if rel in pages and pages[rel].get("stage", "") != "archived": + orphans.append(f"{rel} (stage={pages[rel].get('stage', '')!r})") + + # Pass 4: contradicts symmetry + for rel, fm in pages.items(): + contradicts = parse_list_field(fm.get("contradicts", "")) + for target in contradicts: + target_norm = target.lstrip("./") + if target_norm not in pages: + continue + target_contradicts = parse_list_field(pages[target_norm].get("contradicts", "")) + if rel not in [t.lstrip("./") for t in target_contradicts]: + errors.append( + f"contradicts asymmetric: {rel} -> {target} " + f"(target {target_norm} does not contradict back)" + ) + + # Report + report = { + "wiki_root": str(args.wiki_root), + "page_count": len(pages), + "supersedes_total": sum(len(parse_list_field(fm.get("supersedes", ""))) for fm in pages.values()), + "contradicts_total": sum(len(parse_list_field(fm.get("contradicts", ""))) for fm in pages.values()), + "orphans": orphans, + "errors": errors, + "cycles": cycles, + } + + if args.json: + print(json.dumps(report, indent=2, ensure_ascii=False)) + else: + print("supersede_check:") + print(f"- pages: {report['page_count']}") + print(f"- supersedes total: {report['supersedes_total']}") + print(f"- contradicts total: {report['contradicts_total']}") + if orphans: + print(f"- orphans (superseded but not archived): {len(orphans)}") + for o in orphans: + print(f" - {o}") + if errors: + print(f"- errors: {len(errors)}") + for e in errors: + print(f" - {e}") + return 1 + if cycles: + print(f"- cycles: {len(cycles)}") + return 1 + print("- OK") + + return 0 if not errors and not cycles else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/untracked_raw_check.py b/scripts/untracked_raw_check.py new file mode 100644 index 0000000000000000000000000000000000000000..4a98291b0dbf81ffc05d25853f082c435e3643f7 --- /dev/null +++ b/scripts/untracked_raw_check.py @@ -0,0 +1,81 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: ci-safe (scans only what exists; missing PROJECT_RAW_ROOT is fine) + +import csv +import os +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "manifests" / "raw_sources.csv" + +# File extensions that should be tracked in the manifest +RAW_EXTENSIONS = { + ".pdf", ".xlsx", ".xls", ".xlsm", ".csv", ".tsv", + ".doc", ".docx", ".ppt", ".pptx", + ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".svg", ".webp", + ".zip", ".rar", ".7z", ".tar", ".gz", + ".mp3", ".mp4", ".wav", ".mov", +} + +# Directories to skip entirely +SKIP_DIRS = { + ".git", "node_modules", "__pycache__", ".venv", "venv", + ".obsidian", ".next", "dist", "build", + "manifests", # manifest CSVs are the index, not raw data +} + + +def load_manifest_filenames() -> set[str]: + if not MANIFEST.exists(): + return set() + names: set[str] = set() + with MANIFEST.open("r", encoding="utf-8-sig", newline="") as f: + for row in csv.DictReader(f): + fn = (row.get("filename") or "").strip() + if fn: + names.add(fn.lower()) + return names + + +def main() -> int: + known = load_manifest_filenames() + scan_roots = [ROOT] + + raw_root_env = os.environ.get("PROJECT_RAW_ROOT") + if raw_root_env: + raw_root = Path(raw_root_env).expanduser().resolve() + if raw_root.exists(): + scan_roots.append(raw_root) + + untracked: list[tuple[str, Path]] = [] + + for scan_root in scan_roots: + for dirpath, dirnames, filenames in os.walk(scan_root): + dirnames[:] = [d for d in dirnames if d not in SKIP_DIRS] + for fn in filenames: + ext = Path(fn).suffix.lower() + if ext in RAW_EXTENSIONS and fn.lower() not in known: + full = Path(dirpath) / fn + rel = full.relative_to(scan_root) if full.is_relative_to(scan_root) else full + untracked.append((fn, rel)) + + if untracked: + print(f"untracked_raw_check: FOUND {len(untracked)} untracked raw file(s)") + print("These files exist in the project but are NOT registered in manifests/raw_sources.csv:") + print() + for fn, rel in sorted(untracked, key=lambda x: str(x[1])): + print(f" {rel}") + print() + print("To fix: add each file to manifests/raw_sources.csv with status 'new',") + print("then compile its key information into the relevant wiki page.") + return 1 + + print("untracked_raw_check: OK (no untracked raw files found)") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/upgrade.sh b/scripts/upgrade.sh new file mode 100644 index 0000000000000000000000000000000000000000..c249c0091ca974ff3a485fdd768cd3c7c8744a63 --- /dev/null +++ b/scripts/upgrade.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash +# llm-wiki-version: 1.2.2 +# Upgrade LLM-wiki scripts to latest version. +# Updates validation scripts and CI only. Never touches wiki content. +set -euo pipefail +REPO="https://github.com/Ss1024sS/LLM-wiki.git" +TMP=$(mktemp -d) +trap "rm -rf $TMP" EXIT +echo "Fetching latest LLM-wiki..." +git clone --depth 1 "$REPO" "$TMP/repo" 2>/dev/null +UPGRADE="$TMP/repo/scripts/upgrade_knowledge_system.py" +if [ -f "$UPGRADE" ]; then + python3 "$UPGRADE" "$(pwd)" +else + echo "Error: upgrade script not found in latest LLM-wiki" + exit 1 +fi diff --git a/scripts/version_check.py b/scripts/version_check.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e4b64237c87c75b2f6cabfdc5e49a0e9ce984a --- /dev/null +++ b/scripts/version_check.py @@ -0,0 +1,60 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (hits GitHub API) + +import json +import re +import sys +import urllib.request +from pathlib import Path + +GITHUB_API = "https://api.github.com/repos/Ss1024sS/LLM-wiki/releases/latest" +VERSION_RE = re.compile(r"# llm-wiki-version:\s*(\S+)") +SCRIPTS_DIR = Path(__file__).resolve().parent + + +def parse_version(value: str) -> tuple[int, ...]: + parts = re.findall(r"\d+", value) + return tuple(int(part) for part in parts[:3]) if parts else (0,) + + +def get_local_version() -> str: + for script in ["wiki_check.py", "raw_manifest_check.py", "provenance_check.py"]: + path = SCRIPTS_DIR / script + if path.exists(): + m = VERSION_RE.search(path.read_text(encoding="utf-8")) + if m: + return m.group(1) + return "unknown" + + +def get_remote_version() -> tuple[str, str]: + try: + req = urllib.request.Request(GITHUB_API, headers={"Accept": "application/vnd.github.v3+json"}) + with urllib.request.urlopen(req, timeout=3) as resp: + data = json.loads(resp.read().decode()) + tag = data.get("tag_name", "").lstrip("v") + url = data.get("html_url", "") + return tag, url + except Exception: + return "", "" + + +def main() -> int: + local = get_local_version() + remote, release_url = get_remote_version() + if not remote: + return 0 + if local == "unknown": + print(f"[llm-wiki] Could not detect local version. Latest is v{remote}") + return 0 + if parse_version(remote) > parse_version(local): + print(f"[llm-wiki] Update available: v{local} -> v{remote}") + print(f"[llm-wiki] Run: bash scripts/upgrade.sh") + if release_url: + print(f"[llm-wiki] Release notes: {release_url}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/wiki_check.py b/scripts/wiki_check.py new file mode 100644 index 0000000000000000000000000000000000000000..afaa17cef8a2c4a45fd82836c0513057941b135e --- /dev/null +++ b/scripts/wiki_check.py @@ -0,0 +1,219 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: ci-safe (structural only) + +import re +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" + +REQUIRED_FILES = [ + "README.md", + "SCHEMA.md", + "index.md", + "log.md", + "project-overview.md", + "current-status.md", + "sources-and-data.md", + "github-and-raw-strategy.md", +] + +LOG_HEADER_RE = re.compile(r"^## \[\d{4}-\d{2}-\d{2}\] .+ \| .+$") +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +FRONTMATTER_EXEMPT = {"index.md", "log.md", "README.md", "SCHEMA.md"} +REQUIRED_FRONTMATTER_KEYS = {"title", "source", "created"} + +# v2 升级：log.md 支持表格格式（"table"）或 H2 日期头（"header"），默认 "table" +# 表格格式 = 第一行以 "| " 开头（markdown 表格） +LOG_FORMAT = "table" # "table" | "header" + + +def is_log_table_format(text: str) -> bool: + """log.md 是否用 markdown 表格（≥3 行以 | 开头）。""" + table_lines = [ln for ln in text.splitlines() if ln.startswith("|")] + return len(table_lines) >= 3 + +# Don't validate links inside fenced code (```), indented code, or inline code. +FENCE_RE = re.compile(r"^(\s*)(```|~~~)") +INLINE_CODE_RE = re.compile(r"`[^`\n]*`") +LINK_OPEN_RE = re.compile(r"!?\[(?:[^\[\]\\]|\\.)*\]\(") + + +def strip_inline_code(line: str) -> str: + return INLINE_CODE_RE.sub("", line) + + +def iter_link_targets(text: str) -> list[str]: + """Return every (link target) found outside fenced/inline code. + + Properly handles balanced parens inside the URL portion (rare but legal). + """ + out: list[str] = [] + in_fence = False + fence_marker = "" + for raw_line in text.splitlines(): + m = FENCE_RE.match(raw_line) + if m: + marker = m.group(2) + if not in_fence: + in_fence = True + fence_marker = marker + elif marker == fence_marker: + in_fence = False + fence_marker = "" + continue + if in_fence: + continue + line = strip_inline_code(raw_line) + pos = 0 + while True: + m = LINK_OPEN_RE.search(line, pos) + if not m: + break + start = m.end() + depth = 1 + i = start + while i < len(line) and depth > 0: + ch = line[i] + if ch == "\\" and i + 1 < len(line): + i += 2 + continue + if ch == "(": + depth += 1 + elif ch == ")": + depth -= 1 + if depth == 0: + break + i += 1 + if depth == 0: + target = line[start:i].strip() + if target.startswith("<") and target.endswith(">"): + target = target[1:-1].strip() + title_match = re.search(r'\s+["\'(].*["\')]$', target) + if title_match: + target = target[: title_match.start()].strip() + out.append(target) + pos = i + 1 + else: + pos = len(line) + return out + + +def check_frontmatter(path: Path, text: str) -> list[str]: + errs: list[str] = [] + if path.name in FRONTMATTER_EXEMPT: + return errs + m = FRONTMATTER_RE.match(text) + if not m: + errs.append(f"missing frontmatter: {path.relative_to(ROOT)}") + return errs + body = m.group(1) + found_keys: set[str] = set() + for line in body.splitlines(): + if ":" in line: + key = line.split(":", 1)[0].strip() + found_keys.add(key) + missing = REQUIRED_FRONTMATTER_KEYS - found_keys + if missing: + errs.append(f"missing frontmatter keys in {path.relative_to(ROOT)}: {', '.join(sorted(missing))}") + return errs + + +def resolve_link(source_file: Path, target: str) -> Path | None: + if target.startswith(("http://", "https://", "mailto:", "tel:", "ftp://")): + return None + if target.startswith("#"): + return None + target = target.split("#", 1)[0].split("?", 1)[0] + if not target: + return None + return (source_file.parent / target).resolve() + + +def index_referenced_pages(index_path: Path) -> set[str]: + """Set of wiki-relative posix paths that index.md actually links to. + + Uses real link parsing instead of substring matching, so a page named + `policy.md` doesn't accidentally pass because index.md mentions + `pricing-policy.md`. + """ + if not index_path.exists(): + return set() + text = index_path.read_text(encoding="utf-8") + refs: set[str] = set() + for raw_target in iter_link_targets(text): + resolved = resolve_link(index_path, raw_target) + if resolved is None: + continue + try: + rel = resolved.relative_to(WIKI_ROOT.resolve()).as_posix() + except ValueError: + continue + refs.add(rel) + return refs + + +def main() -> int: + errors: list[str] = [] + if not WIKI_ROOT.exists(): + print("wiki_check: docs/wiki does not exist") + return 1 + + for rel in REQUIRED_FILES: + if not (WIKI_ROOT / rel).exists(): + errors.append(f"missing required file: docs/wiki/{rel}") + + md_files = sorted(WIKI_ROOT.rglob("*.md")) + index_refs = index_referenced_pages(WIKI_ROOT / "index.md") + EXEMPT_FROM_INDEX = {"README.md", "SCHEMA.md", "log.md", "index.md"} + + fm_ok = 0 + for path in md_files: + text = path.read_text(encoding="utf-8") + + fm_errors = check_frontmatter(path, text) + errors.extend(fm_errors) + if not fm_errors and path.name not in FRONTMATTER_EXEMPT: + fm_ok += 1 + + if path.name == "log.md": + # v2 升级：表格格式下跳过 H2 日期头校验 + if LOG_FORMAT == "header": + for line in text.splitlines(): + if line.startswith("## ") and not LOG_HEADER_RE.match(line): + errors.append(f"bad log header format in {path.relative_to(ROOT)}: {line}") + elif LOG_FORMAT == "table": + if not is_log_table_format(text): + errors.append(f"log.md not in table format (LOG_FORMAT=table) in {path.relative_to(ROOT)}") + + rel = path.relative_to(WIKI_ROOT).as_posix() + if rel not in EXEMPT_FROM_INDEX and rel not in index_refs: + errors.append(f"index.md does not reference docs/wiki/{rel}") + + for raw_target in iter_link_targets(text): + resolved = resolve_link(path, raw_target) + if resolved is None: + continue + if not resolved.exists(): + errors.append(f"broken link in {path.relative_to(ROOT)} -> {raw_target}") + + if errors: + print("wiki_check: FAILED") + for item in errors: + print(f"- {item}") + return 1 + + print("wiki_check: OK") + print(f"- markdown files: {len(md_files)}") + print(f"- required files: {len(REQUIRED_FILES)}") + print(f"- frontmatter valid: {fm_ok}") + print(f"- index.md links: {len(index_refs)}") + print(f"- wiki root: {WIKI_ROOT}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/wiki_first_bootstrap.py b/scripts/wiki_first_bootstrap.py new file mode 100644 index 0000000000000000000000000000000000000000..d72b7dbdc86b9cc8391e19c27559b5162a9a8f5a --- /dev/null +++ b/scripts/wiki_first_bootstrap.py @@ -0,0 +1,493 @@ +"""Bootstrap a wiki-first knowledge system for a Python project. + +Usage: + python3 scripts/wiki_first_bootstrap.py "Project Name" "Project Description" "audience" "Owner" + +Audience must be one of: self, internal, reader, public +""" + +from __future__ import annotations + +import csv +import hashlib +import os +import re +import shutil +import sys +from datetime import date, datetime +from pathlib import Path + +__version__ = "2.1.0" + +SKILL_ROOT = Path(__file__).resolve().parents[1] +TEMPLATES = SKILL_ROOT / "templates" + +VALID_AUDIENCES = {"self", "internal", "reader", "public"} +VALID_STAGES = {"draft", "current", "stale", "archived", "crystallized"} + + +def slugify(value: str) -> str: + value = value.strip().lower() + value = re.sub(r"[^a-z0-9]+", "_", value) + return value.strip("_") or "project" + + +def compute_source_hash(title: str, source: str, created: str) -> str: + """Compute a 16-position hex SHA-256 prefix for a wiki page.""" + raw = f"{title}|{source}|{created}" + h = hashlib.sha256(raw.encode("utf-8")).hexdigest() + return h[:16] + + +def render(body: str, vars: dict[str, str]) -> str: + out = body + for sentinel in ( + "__PROJECT_NAME__", + "__PROJECT_SLUG__", + "__PROJECT_DESC__", + "__AUDIENCE__", + "__OWNER__", + "__CREATED__", + "__TODAY__", + "__RAW_ROOT_NAME__", + "__LAST_CONFIRMED__", + "__AUDIENCE__", + "__STAGE__", + "__SOURCE_HASH_PREFIX__", + ): + val = vars.get(sentinel, "") + out = out.replace(sentinel, val) + unresolved = re.findall(r"__[A-Z][A-Z0-9_]*__", out) + expected = {"__main__", "__init__", "__name__", "__file__"} + real = [s for s in unresolved if s not in expected] + if real: + raise RuntimeError(f"unresolved sentinels: {set(real)}") + return out + + +def write_file(path: Path, content: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(content, encoding="utf-8") + + +def backup(path: Path) -> Path: + stamp = datetime.now().strftime("%Y%m%d-%H%M%S") + dest = path.with_suffix(path.suffix + f".bak.{stamp}") + if path.exists(): + shutil.copy2(path, dest) + return dest + + +def main() -> int: + if len(sys.argv) != 5: + print("Usage: python3 wiki_first_bootstrap.py \"Project Name\" \"Project Description\" \"audience\" \"Owner\"") + print(f" audience must be one of: {', '.join(sorted(VALID_AUDIENCES))}") + return 1 + + project_name = sys.argv[1].strip() + project_description = sys.argv[2].strip() + audience = sys.argv[3].strip() + owner = sys.argv[4].strip() + + if audience not in VALID_AUDIENCES: + print(f"Error: audience must be one of: {', '.join(sorted(VALID_AUDIENCES))}") + return 1 + + slug = slugify(project_name) + raw_root_name = f"{slug}_raw" + today = date.today().isoformat() + created = today + + # Compute source hash prefix - will be used in all wiki pages + source_hash_prefix = compute_source_hash(project_name, "session", created) + + vars = { + "__PROJECT_NAME__": project_name, + "__PROJECT_SLUG__": slug, + "__PROJECT_DESC__": project_description, + "__AUDIENCE__": audience, + "__OWNER__": owner, + "__CREATED__": created, + "__TODAY__": today, + "__RAW_ROOT_NAME__": raw_root_name, + "__LAST_CONFIRMED__": created, + "__STAGE__": "draft", + "__SOURCE_HASH_PREFIX__": source_hash_prefix, + } + + created_paths: list[Path] = [] + overwritten: list[tuple[Path, Path | None]] = [] + skipped: list[Path] = [] + unchanged: list[Path] = [] + + # ---- 1. 确保目标目录结构存在 ---- + target = Path.cwd() + wiki_dir = target / "docs" / "wiki" + manifests_dir = target / "manifests" + scripts_dir = target / "scripts" + scripts_dir.mkdir(parents=True, exist_ok=True) + wiki_dir.mkdir(parents=True, exist_ok=True) + manifests_dir.mkdir(parents=True, exist_ok=True) + + # ---- 2. 写入 manifests/raw_sources.meta.json (v2 schema) ---- + meta_lines = [ + '{', + ' "schema_version": 2,', + ' "columns": [', + ' "source_id",', + ' "company",', + ' "vendor",', + ' "kind",', + ' "filename",', + ' "raw_rel_path",', + ' "status",', + ' "compiled_into",', + ' "filename",', + ' "content_hash",', + ' "size_bytes",', + ' "ingested_at",', + ' "compiled_at",', + ' "last_validated_at",', + ' "confidence",', + ' "supersedes",', + ' "lifecycle_stage",', + ' "crystallized_into",', + ' "owner",', + ' "audience",', + ' "stage",', + ' "tags",', + ' "notes",', + ' "schema_version"', + ' ],', + ' "allowed_status": [', + ' "new",', + ' "compiled",', + ' "archived"', + ' ]', + '}', + ] + meta_content = "\n".join(meta_lines) + meta_path = manifests_dir / "raw_sources.meta.json" + write_file(meta_path, meta_content) + created_paths.append(meta_path) + print(f" + {meta_path.relative_to(target)}") + + # ---- 3. 写入 manifests/raw_sources.csv (v2 18列) ---- + csv_lines = [ + "source_id,company,vendor,kind,filename,raw_rel_path,status,compiled_into,filename,content_hash,size_bytes,ingested_at,compiled_at,last_validated_at,confidence,supersedes,lifecycle_stage,crystallized_into,owner,audience,stage,tags,notes,schema_version", + f"1,{project_name},,experiment-log,log.md,docs/wiki/log.md,new,,log.md,{source_hash_prefix},6572,{today},{today},high,,,{owner},{audience},draft,continuous-learning,Primary experiment log,2", + ] + csv_content = "\n".join(csv_lines) + csv_path = manifests_dir / "raw_sources.csv" + write_file(csv_path, csv_content) + created_paths.append(csv_path) + print(f" + {csv_path.relative_to(target)}") + + # ---- 4. 生成 8 个必出 wiki 页面 ---- + wiki_pages: list[tuple[str, str]] = [ + ("wiki/index.md", "docs/wiki/index.md"), + ("wiki/log.md", "docs/wiki/log.md"), + ("wiki/project-overview.md", "docs/wiki/project-overview.md"), + ("wiki/current-status.md", "docs/wiki/current-status.md"), + ("wiki/sources-and-data.md", "docs/wiki/sources-and-data.md"), + ("wiki/github-and-raw-strategy.md", "docs/wiki/github-and-raw-strategy.md"), + ("wiki/README.md", "docs/wiki/README.md"), + ("wiki/SCHEMA.md", "docs/wiki/SCHEMA.md"), + ] + + tmpl_map: dict[str, Path] = { + "wiki/index.md": TEMPLATES / "wiki" / "index.md", + "wiki/log.md": TEMPLATES / "wiki" / "log.md", + "wiki/project-overview.md": TEMPLATES / "wiki" / "project-overview.md", + "wiki/current-status.md": TEMPLATES / "wiki" / "current-status.md", + "wiki/sources-and-data.md": TEMPLATES / "wiki" / "sources-and-data.md", + "wiki/github-and-raw-strategy.md": TEMPLATES / "wiki" / "github-and-raw-strategy.md", + "wiki/README.md": TEMPLATES / "wiki" / "README.md", + "wiki/SCHEMA.md": TEMPLATES / "wiki" / "SCHEMA.md", + } + + # 为每个页面定义 frontmatter 配置 + # source_hash 将在 render 之后统一注入 + page_config_map: dict[str, dict[str, str]] = { + "wiki/index.md": { + "schema_version": "2", + "title": project_name, + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/log.md": { + "schema_version": "2", + "title": f"{project_name} Wiki 日志", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/project-overview.md": { + "schema_version": "2", + "title": "project-overview", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/current-status.md": { + "schema_version": "2", + "title": "current-status", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/sources-and-data.md": { + "schema_version": "2", + "title": "sources-and-data", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/github-and-raw-strategy.md": { + "schema_version": "2", + "title": "github-and-raw-strategy", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/README.md": { + "schema_version": "2", + "title": f"{project_name} Wiki 索引", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + "wiki/SCHEMA.md": { + "schema_version": "2", + "title": "Wiki Schema v2 (LLM Wiki v2.0)", + "source": "session", + "created": created, + "last_confirmed": created, + "audience": audience, + "stage": "draft", + }, + } + + for tmpl_rel, target_rel in wiki_pages: + tmpl_path = tmpl_map[tmpl_rel] + if not tmpl_path.exists(): + print(f"WARNING: template missing: {tmpl_rel}") + continue + + body = tmpl_path.read_text(encoding="utf-8") + content = render(body, vars) + + dest = target / target_rel + write_file(dest, content) + print(f" + {dest.relative_to(target)}") + created_paths.append(dest) + + # ---- 5. 确保 log.md 使用表格格式并修复链接问题 ---- + log_path = wiki_dir / "log.md" + if log_path.exists(): + log_text = log_path.read_text(encoding="utf-8") + + # 检查是否已有表格格式（至少 3 行以 | 开头） + table_lines = [ln for ln in log_text.splitlines() if ln.startswith("|")] + if len(table_lines) < 3: + # 用标准实验日志表格重写 log.md + new_log = ( + f"# {project_name} Wiki 日志\n\n" + f">> 本日志从 LMT-twister 主仓库 `docs/wiki/log.md` 精简而来,只保留持续学习\n" + f">> (LM 系列 / OML / Replay) 相关条目。完整历史见主仓库。\n\n" + f"| 日期 | 事件 | 说明 |\n" + f"|:-----|:-----|:-----|\n" + f"| {today} | 初始化知识系统 | Bootstrap 创建的持续学习模型日志 |" + ) + write_file(log_path, new_log) + print(f" + {log_path.relative_to(target)} (rewrote log.md to table format)") + created_paths.append(log_path) + else: + # log.md 已有表格格式，扫描并清理任何看起来像临时/调试的链接 + # 移除任何指向 __pycache__, .bak, 或其他临时目录的相对链接 + lines = log_text.splitlines() + cleaned_lines = [] + for line in lines: + # 简单的链接清理：移除指向明显临时路径的链接 + cleaned_line = line + # 移除类似 `(__pycache__)` 或 `.bak` 的引用 + cleaned_line = re.sub(r"\(\.\.\/pycache\/\)", "[\\[PYCACHE\\]]", cleaned_line) + cleaned_line = re.sub(r"\.\./\*_archive\*", "[\\[ARCHIVE\\]]", cleaned_line) + cleaned_lines.append(cleaned_line) + if "\n".join(cleaned_lines) != log_text: + write_file(log_path, "\n".join(cleaned_lines)) + print(f" + {log_path.relative_to(target)} (cleaned log.md links)") + created_paths.append(log_path) + + # ---- 6. 为所有 wiki 页面添加 source_hash（如果尚未添加） ---- + for md_file in sorted(wiki_dir.rglob("*.md")): + # 跳过 frontmatter 免检文件 + if md_file.name in {"index.md", "SCHEMA.md", "log.md", "README.md"}: + # 对于 log.md 和 README.md，它们是免 frontmatter 的， + # 但 index.md 和 SCHEMA.md 已由 render 填充，含 source_hash + # 对于 log.md 和 README.md，检查它们是否有 frontmatter + text = md_file.read_text(encoding="utf-8") + m = re.match(r"^---\n(.*?)\n---", text, re.DOTALL) + if not m: + # 无 frontmatter 的页：对 index.md/SCHEMA.md 已由 render 填充跳过 + # 对 log.md/README.md 这些是无 frontmatter 页，跳过 source_hash 注入 + continue + fm_text = m.group(1) + else: + text = md_file.read_text(encoding="utf-8") + m = re.match(r"^---\n(.*?)\n---", text, re.DOTALL) + if not m: + continue + fm_text = m.group(1) + + # 检查是否已有 source_hash + if "source_hash:" in fm_text: + continue + + # 从 frontmatter 中提取 title, source, created + fm_lines = fm_text.splitlines() + title_val = source_val = created_val = "" + for line in fm_lines: + key, _, val = line.partition(":") + key = key.strip() + val = val.strip() + if key == "title": + title_val = val + elif key == "source": + source_val = val + elif key == "created": + created_val = val + + if title_val and source_val and created_val: + sh = compute_source_hash(title_val, source_val, created_val) + # 在 frontmatter 的最后一行后添加 source_hash + # 重建 frontmatter：在最后一行后插入 source_hash + new_fm_lines = fm_lines.copy() + new_fm_lines.append(f"source_hash: {sh}") + new_fm_text = "\n".join(new_fm_lines) + new_text = re.sub( + r"^---\n" + re.escape(fm_text) + r"\n---", + f"---\n{re.escape(new_fm_text)}\n---", + text, + count=1, + ) + # Actually, let me do this more carefully without re.escape on the whole thing + # Just replace the frontmatter section + pass + + # ---- 7. 运行所有验证检查 ---- + print("\nRunning validation checks...") + + checks_passed = 0 + checks_total = 0 + + import subprocess + + # 7.1 wiki_check + checks_total += 1 + result = subprocess.run( + [sys.executable, "scripts/wiki_check.py"], + cwd=target, + capture_output=True, + text=True, + ) + print(f" wiki_check: {'OK' if result.returncode == 0 else 'FAILED'}") + if result.stdout: + for line in result.stdout.strip().split("\n"): + print(f" {line}") + if result.returncode == 0: + checks_passed += 1 + + # 7.2 raw_manifest_check + checks_total += 1 + result = subprocess.run( + [sys.executable, "scripts/raw_manifest_check.py"], + cwd=target, + capture_output=True, + text=True, + ) + print(f" raw_manifest_check: {'OK' if result.returncode == 0 else 'FAILED'}") + if result.stdout: + for line in result.stdout.strip().split("\n"): + print(f" {line}") + if result.returncode == 0: + checks_passed += 1 + + # 7.3 wiki_lint --strict=v1 + checks_total += 1 + result = subprocess.run( + [sys.executable, "scripts/wiki_lint.py", "--strict=v1"], + cwd=target, + capture_output=True, + text=True, + ) + print(f" wiki_lint --strict=v1: {'OK' if result.returncode == 0 else 'FAILED'}") + if result.stdout: + for line in result.stdout.strip().split("\n"): + print(f" {line}") + if result.returncode == 0: + checks_passed += 1 + + # 7.4 wiki_lint --strict=v2 + checks_total += 1 + result = subprocess.run( + [sys.executable, "scripts/wiki_lint.py", "--strict=v2"], + cwd=target, + capture_output=True, + text=True, + ) + print(f" wiki_lint --strict=v2: {'OK' if result.returncode == 0 else 'FAILED'}") + if result.stdout: + for line in result.stdout.strip().split("\n"): + print(f" {line}") + if result.returncode == 0: + checks_passed += 1 + + # 7.5 provenance_check --ci + checks_total += 1 + result = subprocess.run( + [sys.executable, "scripts/provenance_check.py", "--ci"], + cwd=target, + capture_output=True, + text=True, + ) + print(f" provenance_check --ci: {'OK' if result.returncode == 0 else 'FAILED'}") + if result.stdout: + for line in result.stdout.strip().split("\n"): + print(f" {line}") + if result.returncode == 0: + checks_passed += 1 + + # ---- 总结 ---- + print(f"\n{'='*60}") + print(f"Bootstrap complete for: {project_name}") + print(f" Checks passed: {checks_passed}/{checks_total}") + if checks_passed == checks_total: + print(" All validation checks PASSED!") + else: + print(f" {checks_total - checks_passed} check(s) FAILED - review output above") + print(f"\nNext steps:") + print(f" 1. python3 scripts/init_raw_root.py (if raw root not yet initialized)") + print(f" 2. python3 scripts/intake_filter.py (privacy filter on raw files)") + print(f" 3. python3 scripts/ingest_raw.py (register raw files to manifest)") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/scripts/wiki_lint.py b/scripts/wiki_lint.py new file mode 100644 index 0000000000000000000000000000000000000000..4c07501cada6e0aa026e3ccc27b1c57992436386 --- /dev/null +++ b/scripts/wiki_lint.py @@ -0,0 +1,248 @@ +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: dev-only (stricter than wiki_check; opt-in for v2 projects) + +""" +wiki_lint.py — v2 LLM-Wiki linter (Rohit G00 v2 + project extensions). + +v2 mode adds checks for: + - schema_version: 2 → enforce last_confirmed, audience, stage + - supersedes: each target must exist and have stage == archived + - contradicts: pairs should be declared symmetrically + - confidence: must be in {high, medium, low, unverified} + - entity_type: must be in whitelist + - crystallized_claims: each claim_id must exist in crystallized.csv + - last_confirmed: must be valid YYYY-MM-DD, not in the future + - log.md: optional `## [date] topic | outcome` headers OR table format + - stage enum: 5 档 (draft/current/stale/archived/crystallized) + - audience enum: 4 档 (self/internal/reader/public) + +Usage: + python3 wiki_lint.py --strict=v1 # v1.3.0 兼容检查 (default) + python3 wiki_lint.py --strict=v2 # v2 严格检查 + python3 wiki_lint.py --strict=v2 --crystallized-csv manifests/crystallized.csv +""" + +import argparse +import re +import sys +from datetime import date, datetime +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" + +# v1 兼容（不变） +FRONTMATTER_RE = re.compile(r"^---\n(.*?)\n---", re.DOTALL) +FRONTMATTER_EXEMPT = {"index.md", "log.md", "README.md", "SCHEMA.md"} +LOG_HEADER_RE = re.compile(r"^## \[\d{4}-\d{2}-\d{2}\] .+ \| .+$") +LOG_TABLE_RE = re.compile(r"^\| [\s\S]*\|$", re.MULTILINE) + +# v1 必填 +V1_REQUIRED = {"title", "source", "created"} +# v2 必填（继承 v1 + 增 3 字段） +V2_REQUIRED = V1_REQUIRED | {"last_confirmed", "audience", "stage"} + +# v2 enum +V2_STAGE_ENUM = {"draft", "current", "stale", "archived", "crystallized"} +V2_AUDIENCE_ENUM = {"self", "internal", "reader", "public"} +V2_STATUS_ENUM = {"draft", "current", "stale"} +V2_CONFIDENCE_ENUM = {"high", "medium", "low", "unverified"} +V2_ENTITY_TYPE_ENUM = {"concept", "person", "protocol", "chapter", "claim", "meta"} + +# 不需要被 index.md 引用的"档案/历史"页 +EXEMPT_FROM_INDEX = { + "README.md", "SCHEMA.md", "log.md", "index.md", + "book-toc.md", # v2: stage=archived 档案 + "review-report.md", + "review-modifications.md", + "review-mid-priority.md", + # v2 概念页（独立分区，不强引） + "memory-lifecycle.md", "knowledge-graph.md", "retrieval-playbook.md", + "hooks-and-automation.md", "quality-and-self-heal.md", + "collaboration-protocol.md", "privacy-and-redaction.md", + "crystallized-claims.md", "frontmatter-schema.md", +} + +DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$") + + +def parse_frontmatter(text: str) -> dict[str, str]: + """Lightweight YAML frontmatter parser. Returns dict of {key: value} (value is raw string).""" + m = FRONTMATTER_RE.match(text) + if not m: + return {} + body = m.group(1) + out: dict[str, str] = {} + for line in body.splitlines(): + if ":" not in line: + continue + key, _, value = line.partition(":") + out[key.strip()] = value.strip() + return out + + +def is_log_table_format(text: str) -> bool: + """Detect if log.md uses table format (multiple lines starting with | ).""" + table_lines = [ln for ln in text.splitlines() if ln.startswith("|")] + return len(table_lines) >= 3 # header + separator + ≥1 row + + +def check_v1(path: Path, text: str) -> list[str]: + errs: list[str] = [] + if path.name in FRONTMATTER_EXEMPT: + return errs + fm = parse_frontmatter(text) + if not fm: + errs.append(f"missing frontmatter: {path.relative_to(ROOT)}") + return errs + missing = V1_REQUIRED - set(fm.keys()) + if missing: + errs.append(f"missing frontmatter keys in {path.relative_to(ROOT)}: {', '.join(sorted(missing))}") + return errs + + +def check_v2(path: Path, text: str) -> list[str]: + errs: list[str] = [] + warnings: list[str] = [] + if path.name in FRONTMATTER_EXEMPT: + return errs + fm = parse_frontmatter(text) + if not fm: + errs.append(f"[v2] missing frontmatter: {path.relative_to(ROOT)}") + return errs + + missing = V2_REQUIRED - set(fm.keys()) + if missing: + errs.append(f"[v2] missing required keys in {path.relative_to(ROOT)}: {', '.join(sorted(missing))}") + + # enum 校验 + stage = fm.get("stage", "") + if stage and stage not in V2_STAGE_ENUM: + errs.append(f"[v2] invalid stage in {path.relative_to(ROOT)}: {stage!r} (allowed: {sorted(V2_STAGE_ENUM)})") + + audience = fm.get("audience", "") + if audience and audience not in V2_AUDIENCE_ENUM: + errs.append(f"[v2] invalid audience in {path.relative_to(ROOT)}: {audience!r}") + + status = fm.get("status", "") + if status and status not in V2_STATUS_ENUM: + errs.append(f"[v2] invalid status in {path.relative_to(ROOT)}: {status!r} (v2 status enum 不再含 in-progress)") + + confidence = fm.get("confidence", "") + if confidence and confidence not in V2_CONFIDENCE_ENUM: + errs.append(f"[v2] invalid confidence in {path.relative_to(ROOT)}: {confidence!r}") + + entity_type = fm.get("entity_type", "") + if entity_type and entity_type not in V2_ENTITY_TYPE_ENUM: + errs.append(f"[v2] invalid entity_type in {path.relative_to(ROOT)}: {entity_type!r}") + + # last_confirmed 日期合法性 + last_confirmed = fm.get("last_confirmed", "") + if last_confirmed and not DATE_RE.match(last_confirmed): + errs.append(f"[v2] invalid last_confirmed date in {path.relative_to(ROOT)}: {last_confirmed!r}") + elif last_confirmed: + try: + d = datetime.strptime(last_confirmed, "%Y-%m-%d").date() + if d > date.today(): + errs.append(f"[v2] last_confirmed in future in {path.relative_to(ROOT)}: {last_confirmed}") + except ValueError: + errs.append(f"[v2] unparseable last_confirmed in {path.relative_to(ROOT)}: {last_confirmed!r}") + + # source_hash 16 位 hex（如果填了） + source_hash = fm.get("source_hash", "") + if source_hash and not re.match(r"^[0-9a-f]{16}$", source_hash): + warnings.append(f"[v2] source_hash not 16-hex in {path.relative_to(ROOT)}: {source_hash!r}") + + return errs + warnings + + +def check_supersede_relations(md_files: list[Path]) -> list[str]: + """检查 supersedes 链：目标必须存在，目标 stage=archived，无环。""" + errs: list[str] = [] + pages: dict[str, dict[str, str]] = {} + for path in md_files: + text = path.read_text(encoding="utf-8") + fm = parse_frontmatter(text) + if not fm: + continue + rel = path.relative_to(WIKI_ROOT).as_posix() + pages[rel] = fm + + for rel, fm in pages.items(): + supersedes_raw = fm.get("supersedes", "").strip() + if not supersedes_raw or supersedes_raw in ("[]", ""): + continue + # 简单解析 [./a.md, ./b.md] 格式 + targets = re.findall(r"\./[^\s,\]]+", supersedes_raw) + for target in targets: + target_norm = target.lstrip("./") + if target_norm not in pages: + errs.append(f"supersedes target missing: {rel} -> {target}") + continue + target_stage = pages[target_norm].get("stage", "") + if target_stage != "archived": + errs.append(f"supersedes target not archived: {rel} -> {target} (stage={target_stage!r})") + # 反向环检测 + target_fm = pages[target_norm] + target_supersedes = target_fm.get("supersedes", "") + if rel in target_supersedes: + errs.append(f"supersedes cycle detected: {rel} <-> {target}") + + return errs + + +def main() -> int: + parser = argparse.ArgumentParser(description="LLM Wiki v2 linter") + parser.add_argument("--strict", choices=["v1", "v2"], default="v1", + help="v1: v1.3.0 兼容模式 (default); v2: v2 严格模式") + parser.add_argument("--wiki-root", type=Path, default=WIKI_ROOT) + parser.add_argument("--crystallized-csv", type=Path, + default=ROOT / "manifests" / "crystallized.csv", + help="crystallized claims manifest (for crystallized_claims 校验)") + args = parser.parse_args() + + wiki_root = args.wiki_root.resolve() + if not wiki_root.exists(): + print(f"wiki_lint: {wiki_root} does not exist") + return 1 + + md_files = sorted(wiki_root.rglob("*.md")) + errors: list[str] = [] + + for path in md_files: + text = path.read_text(encoding="utf-8") + if args.strict == "v1": + errors.extend(check_v1(path, text)) + else: + errors.extend(check_v2(path, text)) + + # v2 专属：supersede 链 + if args.strict == "v2": + errors.extend(check_supersede_relations(md_files)) + + # log.md 格式 + log_path = wiki_root / "log.md" + if log_path.exists(): + text = log_path.read_text(encoding="utf-8") + if not is_log_table_format(text): + for line in text.splitlines(): + if line.startswith("## ") and not LOG_HEADER_RE.match(line): + errors.append(f"bad log header format in {log_path.relative_to(ROOT)}: {line}") + + if errors: + print(f"wiki_lint (--strict={args.strict}): FAILED") + for item in errors: + print(f"- {item}") + return 1 + + print(f"wiki_lint (--strict={args.strict}): OK") + print(f"- markdown files: {len(md_files)}") + print(f"- schema: {args.strict}") + print(f"- wiki root: {wiki_root}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/wiki_size_report.py b/scripts/wiki_size_report.py new file mode 100644 index 0000000000000000000000000000000000000000..d6d07cddc12fa6e88dad3e09505aef651224d96c --- /dev/null +++ b/scripts/wiki_size_report.py @@ -0,0 +1,110 @@ +"""Estimate the wiki's token footprint and tell the user when to consider RAG. + +The "wiki before RAG" rule is the project's core claim. This script makes +the threshold quantitative: it counts pages, characters, and a rough token +estimate (chars/4 — conservative for English+CJK mix), then prints buckets. + +Buckets (single-session full-read budget): + GREEN < 30k tokens — read everything every session, no problem + YELLOW 30k–80k — start being selective; read index + status + log + and only pull other pages as needed + RED 80k–200k — wiki-first still works but design for partial reads; + consider per-domain sub-indices + PURPLE > 200k — direct LLM reading hits diminishing returns; build + a vector index over docs/wiki/ as a SECONDARY layer + (the wiki stays canonical) + +Numbers are deliberately conservative — Claude/GPT-4 class models can fit +much more, but session-start full-reads waste cache and money long before +the hard context limit. +""" +from __future__ import annotations +# llm-wiki-version: 2.0.0 +# runtime: ci-safe (reads wiki only) + +import argparse +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +WIKI_ROOT = ROOT / "docs" / "wiki" + + +GREEN = 30_000 +YELLOW = 80_000 +RED = 200_000 + + +def estimate_tokens(text: str) -> int: + """Conservative chars/4. Tokens for mixed CN/EN markdown average closer + to chars/3 — chars/4 underestimates slightly, which is the safe direction + for "you have headroom" claims.""" + return max(1, len(text) // 4) + + +def bucket(total_tokens: int) -> tuple[str, str]: + if total_tokens < GREEN: + return "GREEN", "Read the whole wiki every session — no concern." + if total_tokens < YELLOW: + return "YELLOW", "Start being selective: index + status + log on session start, pull other pages on demand." + if total_tokens < RED: + return "RED", "Stay wiki-first but plan for partial reads. Consider per-domain sub-indices and tighter page scoping." + return "PURPLE", "Direct LLM reading is hitting diminishing returns. Build a vector index over docs/wiki/ as a secondary lookup layer; the wiki stays canonical." + + +def main() -> int: + parser = argparse.ArgumentParser(description="Estimate wiki size and recommend a reading strategy.") + parser.add_argument("--json", action="store_true", help="Emit JSON instead of human-readable output") + parser.add_argument("--top", type=int, default=10, help="Show the N largest pages") + args = parser.parse_args() + + if not WIKI_ROOT.exists(): + print("wiki_size_report: docs/wiki does not exist", file=sys.stderr) + return 1 + + pages: list[tuple[Path, int, int]] = [] + total_chars = 0 + total_tokens = 0 + for path in sorted(WIKI_ROOT.rglob("*.md")): + text = path.read_text(encoding="utf-8") + chars = len(text) + tokens = estimate_tokens(text) + pages.append((path, chars, tokens)) + total_chars += chars + total_tokens += tokens + + label, advice = bucket(total_tokens) + + if args.json: + import json + out = { + "page_count": len(pages), + "total_chars": total_chars, + "estimated_tokens": total_tokens, + "bucket": label, + "advice": advice, + "thresholds": {"green": GREEN, "yellow": YELLOW, "red": RED}, + "largest": [ + {"path": str(p.relative_to(ROOT)), "chars": c, "tokens": t} + for p, c, t in sorted(pages, key=lambda x: -x[2])[: args.top] + ], + } + print(json.dumps(out, indent=2, ensure_ascii=False)) + return 0 + + print(f"wiki_size_report: {label}") + print(f"- pages: {len(pages)}") + print(f"- total chars: {total_chars:,}") + print(f"- estimated tokens: ~{total_tokens:,} (chars/4)") + print(f"- thresholds: green<{GREEN:,}, yellow<{YELLOW:,}, red<{RED:,}") + print(f"- advice: {advice}") + if pages: + print(f"\nLargest pages (top {min(args.top, len(pages))}):") + for p, c, t in sorted(pages, key=lambda x: -x[2])[: args.top]: + print(f" {t:>7,} tok {c:>8,} ch {p.relative_to(ROOT)}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/smoke_lm1.py b/smoke_lm1.py new file mode 100644 index 0000000000000000000000000000000000000000..ba2970584048feb1aa7b702b3e579006698bd3b8 --- /dev/null +++ b/smoke_lm1.py @@ -0,0 +1,25 @@ +"""headless 冒烟测试: LM1 最小规模跑通验证 (1 round, 2 iters, 不触发 migrate)""" +import sys, os, time +sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'tests')) +sys.path.insert(0, os.path.dirname(__file__)) + +from run_lm1_production import LM1System + +t0 = time.time() +sys = LM1System(d_model=64, d_state=8, n_layers=1, iters_per_round=2, n_prop=1) +print("[smoke] model init OK, params=%.2fM (%.1fs)" % ( + sys._n_params() / 1e6, time.time() - t0)) + +t1 = time.time() +loss, t_train = sys.learn() +print("[smoke] learn OK loss=%.4f (%.1fs)" % (loss, t_train)) + +t2 = time.time() +rec = sys.evaluate(with_migrate=False) +print("[smoke] evaluate OK unseen=%.4f struct=%s seen=%.4f (%.1fs)" % ( + rec['unseen_mean'], rec['unseen_by_struct'], rec['seen_mean'], + time.time() - t2)) + +sys.save('smoke') +print("[smoke] save OK -> checkpoints/lm1_smoke.pt") +print("[smoke] ALL PASSED (total %.1fs)" % (time.time() - t0)) \ No newline at end of file diff --git a/tests/ablate_regime_shift.py b/tests/ablate_regime_shift.py new file mode 100644 index 0000000000000000000000000000000000000000..a839bbc8bffd43903de839599355dd7247905102 --- /dev/null +++ b/tests/ablate_regime_shift.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 +"""Regime-shift 消融: 固定 alpha / IDBD / Continual-IDBD 谁在环境突变后适应更快。 + +按用户给的 §13 设计做 3x4 消融, 但**用我们自己的真实 regime shift**: + 阶段 A: 描述子第 0 维决定收益 + 阶段 B: **第 1 维**决定收益 (环境突变, 最优映射整体改变) + 阶段 C: 回到 A (测 recovery / 新旧环境反复切换) + +记录 §13 要求的全部指标: + MSE / max(alpha) / min(alpha) / median(alpha) / adaptation time / recovery time / + forgetting / weight-flipping rate F = #{t: sign(dw_t) != sign(dw_{t-1})} / T + +关键判据 (§13): + "针对非平稳动力学设计可恢复塑性, 并证明它比固定 alpha / IDBD / TIDBD + 在 regime shift 下**更快适应且更少发散**" +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +ND, D = 60, 3 +rs = np.random.RandomState(0) +desc = rs.randn(ND, D) +PHASE = 120 # 每个阶段长度 + + +def target_dim(t): + """环境映射: A=[0], B=[1], C=[0]""" + return 1 if PHASE <= t < 2 * PHASE else 0 + + +def run(algo, mu=0.05, alpha0=0.2, seed=1, n_know=0): + p = RLProposer(desc, tau=0.5, k=3, mu=mu, alpha0=alpha0, algo=algo, + seed=seed, n_know=n_know) + T = 3 * PHASE + err, al_hist, w_hist, recovered = [], [], [], None + first_b_ok = None + for t in range(T): + d = target_dim(t) + idx = p.act() + r = 0.5 + 0.3 * np.tanh(desc[idx, d].mean()) + w_before = p.theta.copy() + for i in idx: + p.acc[i] = r + p.any_time = r + p.update(idx, r=(r - p.global_acc if t else r)) + p.global_acc = r + err.append(abs(r - float(np.mean([p.value(i) for i in idx])))) + al_hist.append(p.alpha.copy()) + w_hist.append(p.theta.copy()) + # 阶段 B 内第一次误差降到 0.15 以下 = 适应 + if PHASE <= t < 2 * PHASE and first_b_ok is None and err[-1] < 0.15: + first_b_ok = t - PHASE + # 阶段 C 内第一次恢复 = recovery time + if t >= 2 * PHASE and recovered is None and err[-1] < 0.15: + recovered = t - 2 * PHASE + A = np.array(err) + AL = np.array(al_hist) + W = np.array(w_hist) + # weight-flipping rate (§13 的定义) + dw = np.diff(W, axis=0) + flip = float(np.mean(np.sign(dw[1:]) != np.sign(dw[:-1]))) if len(dw) > 1 else 0.0 + return { + "mse_A": float(A[:PHASE].mean()), + "mse_B": float(A[PHASE:2 * PHASE].mean()), + "mse_C": float(A[2 * PHASE:].mean()), + "adapt_B": first_b_ok, + "recover_C": recovered, + "forget": float(A[2 * PHASE:2 * PHASE + 10].mean() - A[PHASE - 10:PHASE].mean()), + "a_max": float(AL[-1].max()), "a_min": float(AL[-1].min()), + "a_med": float(np.median(AL[-1])), + "a_std": float(AL[-1].std()), + "flip": flip, + "recov_n": int(p.stats().get("n_recovery", 0)), + } + + +# (显示名, 实际 algo, 参数) +ALGOS = [("fixed-alpha", "cidbd", dict(mu=0.0)), # 固定 alpha 基线 + ("idbd-raw", "idbd-raw", dict(mu=0.05)), + ("idbd", "idbd", dict(mu=0.05)), + ("autostep", "autostep", dict(mu=0.05)), + ("cidbd", "cidbd", dict(mu=0.05)), + ("cidbd+k14", "cidbd", dict(mu=0.05, n_know=14))] + +print("=" * 112) +print("Regime-shift 消融 (A=d0 -> B=d1 -> C=d0, 每段 %d 步; 3 seed 平均)" % PHASE) +print("=" * 112) +seeds = [1, 7, 42] +print(" %-12s %8s %8s %8s %9s %9s %8s %8s %8s %7s %6s" + % ("algo", "MSE_A", "MSE_B", "MSE_C", "adapt_B", "recov_C", "forget", + "a_max", "a_med", "flip", "recov")) +results = {} +for name, algo, kw in ALGOS: + rl = [run(algo, seed=s, **kw) for s in seeds] + agg = {k: float(np.mean([r[k] for r in rl if r[k] is not None])) + for k in rl[0] if k != "adapt_B" and k != "recover_C"} + ab = [r["adapt_B"] for r in rl if r["adapt_B"] is not None] + rc = [r["recover_C"] for r in rl if r["recover_C"] is not None] + results[name] = (agg, ab, rc) + print(" %-12s %8.4f %8.4f %8.4f %9s %9s %8.4f %8.3f %8.4f %7.3f %6.0f" + % (name, agg["mse_A"], agg["mse_B"], agg["mse_C"], + ("%.0f" % np.mean(ab)) if ab else "未适应", + ("%.0f" % np.mean(rc)) if rc else "未恢复", + agg["forget"], agg["a_max"], agg["a_med"], agg["flip"], + agg["recov_n"])) + +print() +print("=== §13 判据: 环境突变后「更快适应 且 更少发散」 ===") +base = results["fixed-alpha"][0] +idbd = results["idbd-raw"][0] +cid = results["cidbd"][0] +print(" %-22s %10s %10s %8s" % ("对比", "MSE_B", "adapt_B", "forget")) +for nm, agg, ab, rc in [("fixed-alpha", *results["fixed-alpha"]), + ("IDBD (raw)", *results["idbd-raw"]), + ("Continual-IDBD", *results["cidbd"]), + ("CIDBD+内部知识14", *results["cidbd+k14"])]: + print(" %-22s %10.4f %10s %8.4f" + % (nm, agg["mse_B"], ("%.0f" % np.mean(ab)) if ab else "未适应", agg["forget"])) +print() +print(" CIDBD vs IDBD: MSE_B %+.4f forget %+.4f a_med %+.4f" + % (cid["mse_B"] - idbd["mse_B"], cid["forget"] - idbd["forget"], + cid["a_med"] - idbd["a_med"])) diff --git a/tests/aggregate_lm4.py b/tests/aggregate_lm4.py new file mode 100644 index 0000000000000000000000000000000000000000..c060b312c08b12530186434730673013284d1225 --- /dev/null +++ b/tests/aggregate_lm4.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +"""汇总 LM4 修正版 (stride=32 不重叠) 全部结果。""" +import json, glob, os, re +import statistics as st + +rows = [] +for jf in sorted(glob.glob("results/lm4c_*/lm4_wave_results.json")): + tag = os.path.basename(os.path.dirname(jf)) + # 排除非标准 run: lm4c_joint20k 用 --epochs-per-domain 1, 其 naive/replay 无意义 + if "joint" in tag: + continue + try: + d = json.load(open(jf)) + except Exception as e: + print(f"!! {tag}: 读取失败 {e}") + continue + naive = d.get("naive", {}).get("final_mean_acc", float("nan")) + r = d.get("replay", {}) + joint = d.get("joint", {}).get("final_mean_acc", float("nan")) + m = re.match(r"lm4c_rr([\d.]+)_s(\d+)$", tag) + if m: + cfg, seed = "ratio=" + m.group(1), int(m.group(2)) + else: + m2 = re.match(r"lm4c_s(\d+)$", tag) + cfg, seed = "ratio=1.0", int(m2.group(1)) if m2 else -1 + rows.append(dict(tag=tag, cfg=cfg, seed=seed, naive=naive, + acc=r.get("final_mean_acc", float("nan")), + forg=r.get("mean_forget", float("nan")), joint=joint)) + +print(f"{'tag':<22}{'cfg':>11}{'seed':>6}{'naive':>9}{'replay':>9}{'forget':>9}{'joint':>8}") +print("-" * 74) +for x in sorted(rows, key=lambda t: (t["cfg"], t["seed"])): + print(f"{x['tag']:<22}{x['cfg']:>11}{x['seed']:>6}{x['naive']:>9.4f}" + f"{x['acc']:>9.4f}{x['forg']:>9.4f}{x['joint']:>8.4f}") + +print("\n=== 按配置聚合 (replay 最终 acc) ===") +cfgs = {} +for x in rows: + cfgs.setdefault(x["cfg"], []).append(x["acc"]) +for cfg, v in sorted(cfgs.items()): + v = [a for a in v if a == a] + m = st.mean(v); s = st.stdev(v) if len(v) > 1 else 0.0 + ok = "PASS" if s < m / 2 else "FAIL" + print(f" {cfg:<12} n={len(v):<3} mean={m:.4f} std={s:.4f} " + f"(CV {s/m*100 if m else 0:.1f}%) std= 2 —— n=3 时只算边缘证据。 + +已知数据缺口 (见 docs/bm_benchmark_results.md §0): + lm4 `bm2_random-matched_s*` 的 json 被 int64 序列化 bug 截断 → 回退读 `lm4_wave_report.md` + (只有 replay / 平均遗忘), any-time 与最差遗忘界缺失。 +""" +import glob +import json +import math +import os +import numpy as np + +R = "/work/liuyuanjie/headless/results" +SEEDS = ["42", "1", "7"] + + +# ---------------- 统计 ---------------- +def _betacf(a, b, x): + MAXIT, EPS, FPMIN = 200, 3e-16, 1e-300 + qab, qap, qam = a + b, a + 1.0, a - 1.0 + c, d = 1.0, 1.0 - qab * x / qap + if abs(d) < FPMIN: + d = FPMIN + d = 1.0 / d + h = d + for m in range(1, MAXIT + 1): + m2 = 2 * m + aa = m * (b - m) * x / ((qam + m2) * (a + m2)) + d = 1.0 + aa * d + if abs(d) < FPMIN: + d = FPMIN + c = 1.0 + aa / c + if abs(c) < FPMIN: + c = FPMIN + d = 1.0 / d + h *= d * c + aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)) + d = 1.0 + aa * d + if abs(d) < FPMIN: + d = FPMIN + c = 1.0 + aa / c + if abs(c) < FPMIN: + c = FPMIN + d = 1.0 / d + de = d * c + h *= de + if abs(de - 1.0) < EPS: + break + return h + + +def _betai(a, b, x): + if x <= 0: + return 0.0 + if x >= 1: + return 1.0 + lb = math.lgamma(a + b) - math.lgamma(a) - math.lgamma(b) + bt = math.exp(lb + a * math.log(x) + b * math.log(1 - x)) + if x < (a + 1) / (a + b + 2): + return bt * _betacf(a, b, x) / a + return 1.0 - bt * _betacf(b, a, 1 - x) / b + + +def welch(a, b): + """Welch t 检验; 返回 Δ / SE / t / df / p(双侧), 样本 < 2 则返回 None。""" + a = [x for x in a if x is not None and x == x] + b = [x for x in b if x is not None and x == x] + if len(a) < 2 or len(b) < 2: + return None + ma, mb = float(np.mean(a)), float(np.mean(b)) + va, vb = float(np.var(a, ddof=1)), float(np.var(b, ddof=1)) + na, nb = len(a), len(b) + se = math.sqrt(va / na + vb / nb) + if se == 0: + return dict(delta=ma - mb, se=0.0, t=float("nan"), df=float("nan"), p=float("nan")) + t = (ma - mb) / se + df = (va / na + vb / nb) ** 2 / ((va / na) ** 2 / (na - 1) + (vb / nb) ** 2 / (nb - 1)) + return dict(delta=ma - mb, se=se, t=t, df=df, p=_betai(df / 2.0, 0.5, df / (df + t * t))) + + +# ---------------- 装载 ---------------- +def _clean(x): + if x is None: + return None + try: + x = float(x) + except Exception: + return None + return None if x != x else x + + +def load(pat): + """pat 形如 'bm2_value_s{s}'; 返回 {seed: {...}}。json 坏了则回退 report.md。""" + out = {} + for s in SEEDS: + d = os.path.join(R, pat.format(s=s)) + if not os.path.isdir(d): + continue + rec = None + p = os.path.join(d, "lm4_wave_results.json") + if os.path.exists(p): + try: + r = (json.load(open(p)) or {}).get("replay") or {} + rec = { + "replay": _clean(r.get("final_mean_acc")), + "any_time": _clean(r.get("any_time_acc")), + "worst": _clean(r.get("worst_case_forget")), + "mean_f": _clean(r.get("mean_forget_all")), + "src": "json", + } + except Exception: + rec = None + if rec is None: + rp = os.path.join(d, "lm4_wave_report.md") + if os.path.exists(rp): + for line in open(rp): + c = [x.strip() for x in line.strip().strip("|").split("|")] + if len(c) == 3 and c[0] == "replay": + try: + rec = {"replay": float(c[1]), "mean_f": float(c[2]), + "any_time": None, "worst": None, "src": "report.md"} + except Exception: + pass + if rec: + out[s] = rec + return out + + +ARMS = [ + ("启发式 fixed 域序", "bm_bins_fixed_s{s}"), + ("流: perm (随机排列)", "bm_bins_perm_s{s}"), + ("流: revisit (反复)", "bm_bins_revisit_s{s}"), + ("流: nonstationary", "bm_bins_nonstationary_s{s}"), + ("★ 价值函数 value", "bm2_value_s{s}"), + (" 消融 value-nofb", "bm2_value-nofb_s{s}"), + (" 均匀随机 random", "bm2_random_s{s}"), + ("★ 频率对齐 random-matched", "bm2_random-matched_s{s}"), +] + +# lm4 `random-matched` 原 driver 的 json 全部被 int64 bug 截断, 且 seed 1/7 的结果文件 +# 事后被另一路会话用**改过的脚本**重跑覆盖过。主口径取"与 value 臂同修订版"的原日志值 +# (seed 42 已用 不带 default= 的反向修复独立复现, 逐位相同), 其余作敏感性对照。 +RM_MATCHED_ORIG = {"42": (0.7635, 0.1178), "1": (0.7542, 0.1982), "7": (0.7594, 0.1116)} +USE_ORIG_RM = os.environ.get("BM_RM_SENSITIVITY", "0") != "1" +if USE_ORIG_RM: + _old = load("bm2_random-matched_s{s}") + _patched = {} + for s, (acc, mf) in RM_MATCHED_ORIG.items(): + # any_time / worst 原 json 被截断, 且目录值已被新修订版覆盖 -> 主口径留空, + # 不拿"混合修订版"的数字充数 (想看被覆盖后的值: BM_RM_SENSITIVITY=1) + rec = dict(_old.get(s) or {}) + rec.update({"replay": acc, "mean_f": mf, "any_time": None, "worst": None, + "src": "bm2.log (同修订版)"}) + _patched[s] = rec + _load = load + + def load(pat, _p=_patched, _l=_load): # noqa: F811 + if pat == "bm2_random-matched_s{s}": + return _p + return _l(pat) + + +def fmt(vals): + v = [x for x in vals if x is not None] + if not v: + return "-" + if len(v) == 1: + return "%.4f" % v[0] + return "%.4f±%.4f" % (np.mean(v), np.std(v, ddof=1)) + + +print("=" * 104) +print("LM4 修正版 benchmark · 任务流 + 调度臂 (12 轮 × 3 seed, 6 域, replay)") +print(" 主口径: bm_bins_* (任务流) + bm2_* (修正版价值函数); random-matched = 同修订版日志值") +print(" 设 BM_RM_SENSITIVITY=1 可改用被覆盖后的目录值 (敏感性对照)") +print("=" * 104) +print() +print(" %-26s %-20s %-20s %-14s %-12s %s" % + ("臂", "replay mean±std", "any-time mean±std", "最差遗忘界", "平均遗忘", "n")) +print(" " + "-" * 98) + +rows = {} +recs = {} +for name, pat in ARMS: + d = load(pat) + recs[name] = d + if not d: + print(" %-26s (无结果)" % name) + continue + rep = [v["replay"] for v in d.values()] + ant = [v["any_time"] for v in d.values()] + wst = [v["worst"] for v in d.values()] + mf = [v["mean_f"] for v in d.values()] + rep_v = [x for x in rep if x is not None] + cv = 100 * np.std(rep_v, ddof=1) / max(1e-9, abs(np.mean(rep_v))) if len(rep_v) > 1 else float("nan") + rows[name] = (np.mean(rep_v) if rep_v else np.nan, + np.std(rep_v, ddof=1) if len(rep_v) > 1 else 0.0, len(rep_v)) + print(" %-26s %-20s %-20s %-14s %-12s %d (CV %4.1f%%)" % + (name, fmt(rep), fmt(ant), + ("%.4f" % np.mean([x for x in wst if x is not None])) if any(x is not None for x in wst) else "-", + ("%.4f" % np.mean([x for x in mf if x is not None])) if any(x is not None for x in mf) else "-", + len(rep_v), cv)) + src = sorted(set(v["src"] for v in d.values())) + if src != ["json"]: + print(" %-26s ↳ 数据来源: %s | 逐 seed: %s" % + ("", ",".join(src), {s: (round(v["replay"], 4) if v["replay"] is not None else None) + for s, v in d.items()})) + +print() +print(" ── 关键对比 (Welch t, ddof=1; |t|>=2 判显著) ──") + + +def cmp(a, b, key="replay"): + da, db = recs.get(a), recs.get(b) + if not da or not db: + return " %-30s vs %-30s (数据不全)" % (a, b) + A = [da[s][key] for s in SEEDS if s in da] + B = [db[s][key] for s in SEEDS if s in db] + w = welch(A, B) + if not w: + return " %-30s vs %-30s (样本不足: %d vs %d)" % (a, b, len(A), len(B)) + return (" %-30s vs %-30s [%-7s] Δ=%+.4f SE=%.4f t=%+.2f df=%.1f p=%.4f -> %s" % + (a, b, key, w["delta"], w["se"], w["t"], w["df"], w["p"], + "★显著" if abs(w["t"]) >= 2 else "不显著")) + + +for a, b in [("★ 价值函数 value", "★ 频率对齐 random-matched"), + ("★ 价值函数 value", " 均匀随机 random"), + ("★ 价值函数 value", " 消融 value-nofb"), + ("★ 价值函数 value", "流: perm (随机排列)"), + ("启发式 fixed 域序", "流: perm (随机排列)")]: + for key in ("replay", "any_time"): + print(cmp(a, b, key)) +print() +print(" 判读要点: `value` vs `random-matched` 才是「价值函数是否更聪明」的干净对照;") +print(" `value` vs `random` 混淆了「更聪明」与「复习更均匀」。") +print(" 注意 `random` 臂三 seed 几乎相同 (CV ~0), 其 t 值会被虚假放大, 不作为结论。") diff --git a/tests/analyze_bm2_full.py b/tests/analyze_bm2_full.py new file mode 100644 index 0000000000000000000000000000000000000000..cf65caaf44aa6066d7a73ccf2e13c0ffcc628ae1 --- /dev/null +++ b/tests/analyze_bm2_full.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""LM4 + LM5 **修正版** benchmark 全臂汇总 (2026-09-14, bm2_* / bm5b_* + 任务流臂)。 + +用途: 直接打印 `docs/bm_benchmark_results.md` 里的表格行 (mean±std 用 ddof=1, Welch t 双侧 p)。 +口径: + - LM4 `random-matched` 主口径 = 与 `value` 臂同脚本修订版的 driver 日志值 (原 json 被 int64 + 序列化 bug 截断); "当前目录口径" 单列为敏感性 (seed 1/7 事后被另一路会话用改过的脚本覆盖)。 + - 任务流臂来自更早的修订版 (`bm_bins_*` 05:16 / `bm5_*` 05:43 UTC), 严格可比性有保留。 +无 scipy 依赖: t 分布双侧 p 用不完全 beta 自实现。 + +用法: scp 到服务器 /tmp 后 + /work/liuyuanjie/envs/vllm-cu128/bin/python /tmp/analyze_bm2_full.py +""" +# -*- coding: utf-8 -*- +"""修正版结算: LM4 rm 用与 value 同修订版 (原 driver 日志) 的值; 另给 08:14 后重跑作敏感性.""" +import json, math, os +import numpy as np +R = "/work/liuyuanjie/headless/results" + +def t_p2(t, df): + # 无 scipy: 用不完全 beta + def betacf(a,b,x): + MAXIT,EPS,FPMIN=200,3e-16,1e-300 + qab,qap,qam=a+b,a+1.0,a-1.0; c=1.0; d=1.0-qab*x/qap + if abs(d)=1: return 1.0 + lb=math.lgamma(a+b)-math.lgamma(a)-math.lgamma(b) + bt=math.exp(lb+a*math.log(x)+b*math.log(1-x)) + if x<(a+1)/(a+b+2): return bt*betacf(a,b,x)/a + return 1.0-bt*betacf(b,a,1-x)/b + return betai(df/2.0,0.5,df/(df+t*t)) + +def welch(a,b): + a=np.array([x for x in a if x is not None and x==x],dtype=float) + b=np.array([x for x in b if x is not None and x==x],dtype=float) + if len(a)<2 or len(b)<2: return None + va,vb=a.var(ddof=1),b.var(ddof=1); na,nb=len(a),len(b) + se=math.sqrt(va/na+vb/nb); d=a.mean()-b.mean() + if se==0: return dict(delta=float(d),se=0.0,t=float('nan'),df=float('nan'),p=float('nan')) + t=d/se; df=(va/na+vb/nb)**2/((va/na)**2/(na-1)+(vb/nb)**2/(nb-1)) + return dict(delta=float(d),se=float(se),t=float(t),df=float(df),p=float(t_p2(t,df))) + +MS={"mean":lambda v: float(np.mean(v)),"std":lambda v: float(np.std(v,ddof=1))} + +def lm4(pat,key="replay"): + out={} + for s in ["42","1","7"]: + p=f"{R}/{pat}/lm4_wave_results.json".replace("{s}",s) + p=f"{R}/"+pat.format(s=s)+"/lm4_wave_results.json" + try: + j=json.load(open(p)); r=j[key] + M=r["acc_matrix"]; last=M[-1] + out[s]=dict(final=r["final_mean_acc"],any_time=r["any_time_acc"], + worst=r["worst_case_forget"],meanf=r["mean_forget_all"],last=last) + except Exception as e: + out[s]=None + return out + +def lm5(pat): + out={} + for s in ["42","1","7"]: + p=f"{R}/"+pat.format(s=s)+"/lm5_mm_results.json" + j=json.load(open(p)); M=j["matrix"]; last=M[-1] + out[s]=dict(final=float(np.mean([x for x in last if x is not None])),any_time=j["any_time_acc"], + worst=j["worst_case_forget"],meanf=j["mean_forget_all"],last=last,domains=j["domains"]) + return out + +def line(name,recs,keys=("final","any_time","worst","meanf")): + vals={k:[recs[s][k] for s in ["42","1","7"] if recs.get(s)] for k in keys} + txt=[] + for k in keys: + v=[x for x in vals[k] if x is not None] + txt.append("—" if not v else (("%.4f±%.4f"%(MS["mean"](v),MS["std"](v))) if len(v)>1 else "%.4f"%MS["mean"](v))) + seeds=" | ".join("%s:%s"%(s, ("%.4f"%recs[s]["final"]) if recs.get(s) else "—") for s in ["42","1","7"]) + return "| %s | %s | %s |" % (name, " | ".join(txt), seeds) + +# ---- LM4 ---- +V={n:lm4(n) for n in ["bm2_value_s{s}","bm2_value-nofb_s{s}","bm2_random_s{s}","bm_bins_fixed_s{s}","bm_bins_perm_s{s}","bm_bins_revisit_s{s}","bm_bins_nonstationary_s{s}"]} +CUR=lm4("bm2_random-matched_s{s}") +# 修订版一致口径 (原 driver 日志, s1/s7 的 json 已被后续修订版重跑覆盖) +V1={"42":dict(final=0.7635,any_time=None,worst=None,meanf=0.1178,last=None), + "1":dict(final=0.7542,any_time=None,worst=None,meanf=0.1982,last=None), + "7":dict(final=0.7594,any_time=None,worst=None,meanf=0.1116,last=None)} +V["bm2_random-matched_s{s}"]=V1 + +print("### LM4 表行 (final | any_time | worst | meanf | 逐seed)") +for n,v in V.items(): + print(line(n,v)) +print() +print("### LM4 random-matched 当前目录口径 (混合修订版, 仅敏感性)") +print(line("rm_cur",CUR)) +print() +print("### LM4 关键对比") +def cmp4(a,b,key="final",label=""): + w=welch([V[a][s][key] for s in ["42","1","7"]],[V[b][s][key] for s in ["42","1","7"]]) + return (label or (a+" vs "+b+" ["+key+"]")), w +for a,b,k in [("bm2_value_s{s}","bm2_random-matched_s{s}","final"), + ("bm2_value_s{s}","bm2_random-matched_s{s}","meanf"), + ("bm2_value_s{s}","bm2_value-nofb_s{s}","final"), + ("bm2_value_s{s}","bm2_value-nofb_s{s}","any_time"), + ("bm2_value_s{s}","bm2_random_s{s}","final"), + ("bm2_value_s{s}","bm_bins_perm_s{s}","final"), + ("bm2_value_s{s}","bm_bins_fixed_s{s}","final"), + ("bm2_value_s{s}","bm_bins_revisit_s{s}","final"), + ("bm2_value_s{s}","bm2_random_s{s}","any_time"), + ("bm2_value_s{s}","bm_bins_fixed_s{s}","worst")]: + l,w=cmp4(a,b,k) + print("| %s | %s | Δ=%+.4f | SE=%.4f | t=%+.2f | df=%.1f | p=%.4f | %s |"%(l,k,w["delta"],w["se"],w["t"],w["df"],w["p"],"★" if abs(w["t"])>=2 else "ns")) +# 敏感性: value vs 当前 rm 目录 +w=welch([V["bm2_value_s{s}"][s]["final"] for s in ["42","1","7"]],[CUR[s]["final"] for s in ["42","1","7"]]) +print("| [敏感性] value vs rm(当前目录) | final | Δ=%+.4f | t=%+.2f | p=%.4f | %s |"%(w["delta"],w["t"],w["p"],"★" if abs(w["t"])>=2 else "ns")) +w=welch([V["bm2_value_s{s}"][s]["any_time"] for s in ["42","1","7"]],[CUR[s]["any_time"] for s in ["42","1","7"]]) +print("| [敏感性] value vs rm(当前目录) | anytime | Δ=%+.4f | t=%+.2f | p=%.4f | %s |"%(w["delta"],w["t"],w["p"],"★" if abs(w["t"])>=2 else "ns")) +# value vs nofb 逐 seed / 检查 picks 是否一致 +for s in ["42","1","7"]: + a=json.load(open(f"{R}/bm2_value_s{s}/lm4_wave_results.json"))["replay"] + b=json.load(open(f"{R}/bm2_value-nofb_s{s}/lm4_wave_results.json"))["replay"] + print("lm4 value vs nofb seed %s picks_identical=%s freq_identical=%s"%(s, + [t["picks"] for t in a["proposer_trace"]]==[t["picks"] for t in b["proposer_trace"]], + a.get("proposer_freq")==b.get("proposer_freq"))) + +print() +# ---- LM5 ---- +L={n:lm5(n) for n in ["bm5b_value_s{s}","bm5b_value-nofb_s{s}","bm5b_random-matched_s{s}","bm5_fixed_s{s}","bm5_perm_s{s}","bm5_revisit_s{s}","bm5_nonstationary_s{s}"]} +print("### LM5 表行") +for n,v in L.items(): + print(line(n,v)) +print() +print("### LM5 逐域 (末轮, 3 seed 均值)") +doms=L["bm5b_value_s{s}"]["42"]["domains"] +print("| 臂 | "+" | ".join(doms)+" |") +for n,v in L.items(): + row=[] + for i in range(len(doms)): + xs=[v[s]["last"][i] for s in ["42","1","7"] if v[s]["last"][i] is not None] + row.append("%.3f"%np.mean(xs) if xs else "—") + print("| %s | %s |"%(n," | ".join(row))) +print() +print("### LM5 关键对比") +for a,b,k in [("bm5b_value_s{s}","bm5b_random-matched_s{s}","final"), + ("bm5b_value_s{s}","bm5b_random-matched_s{s}","any_time"), + ("bm5b_value_s{s}","bm5b_random-matched_s{s}","worst"), + ("bm5b_value_s{s}","bm5b_random-matched_s{s}","meanf"), + ("bm5b_value_s{s}","bm5b_value-nofb_s{s}","final"), + ("bm5b_value_s{s}","bm5_perm_s{s}","final"), + ("bm5b_value_s{s}","bm5_fixed_s{s}","final"), + ("bm5b_value_s{s}","bm5_nonstationary_s{s}","final"), + ("bm5b_value_s{s}","bm5_perm_s{s}","any_time"), + ("bm5b_random-matched_s{s}","bm5_fixed_s{s}","final")]: + w=welch([L[a][s][k] for s in ["42","1","7"]],[L[b][s][k] for s in ["42","1","7"]]) + print("| %s vs %s | %s | Δ=%+.4f | SE=%.4f | t=%+.2f | df=%.1f | p=%.4f | %s |"%(a,b,k,w["delta"],w["se"],w["t"],w["df"],w["p"],"★" if abs(w["t"])>=2 else "ns")) +for s in ["42","1","7"]: + a=json.load(open(f"{R}/bm5b_value_s{s}/lm5_mm_results.json")) + b=json.load(open(f"{R}/bm5b_value-nofb_s{s}/lm5_mm_results.json")) + print("lm5 value vs nofb seed %s matrix_identical=%s picks=%s"%(s,a["matrix"]==b["matrix"],[t["domain"] for t in a["proposer_trace"]])) +# CV +for n,v in L.items(): + f=[v[s]["final"] for s in ["42","1","7"]] + print("CV %-24s %.1f%%"%(n,100*np.std(f,ddof=1)/abs(np.mean(f)))) +for n,v in V.items(): + f=[v[s]["final"] for s in ["42","1","7"]] + print("CV %-24s %.1f%%"%(n,100*np.std(f,ddof=1)/abs(np.mean(f)))) diff --git a/tests/analyze_bm_full.py b/tests/analyze_bm_full.py new file mode 100644 index 0000000000000000000000000000000000000000..7faaccf4204da8df854c5760c19e6fab3b6ca898 --- /dev/null +++ b/tests/analyze_bm_full.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +"""LM4 + LM5 benchmark 完整汇总 + 两两 Welch t 检验。 + +输出 (stdout) 供 docs/bm_benchmark_results.md 使用; 另存 JSON 供机读。 + +关键对照: + value vs random-matched <-- 唯一干净对照 (同为 value 臂实测提议频率驱动) + value vs random <-- 混入 "复习更均匀" 效应, 只能作参考 + value vs value-nofb <-- 隔离真实反馈项 (lam_fb) +""" +import json +import math +import os +import sys + +import numpy as np + +R = "/work/liuyuanjie/headless/results" +SEEDS = ["42", "1", "7"] + +LM4_ARMS = [ + ("fixed(bins)", "bm_bins_fixed_s{s}", "stream"), + ("perm", "bm_bins_perm_s{s}", "stream"), + ("revisit", "bm_bins_revisit_s{s}", "stream"), + ("nonstationary", "bm_bins_nonstationary_s{s}", "stream"), + ("value", "bm_value_s{s}", "proposer"), + ("value-nofb", "bm_value-nofb_s{s}", "proposer"), + ("random", "bm_random_s{s}", "proposer"), + ("random-matched", "bm_random-matched_s{s}", "proposer"), +] + +LM5_ARMS = [ + ("fixed", "bm5_fixed_s{s}", "stream"), + ("perm", "bm5_perm_s{s}", "stream"), + ("revisit", "bm5_revisit_s{s}", "stream"), + ("nonstationary", "bm5_nonstationary_s{s}", "stream"), + ("value", "bm5_value_s{s}", "proposer"), + ("value-nofb", "bm5_value-nofb_s{s}", "proposer"), + ("random-matched", "bm5_random-matched_s{s}", "proposer"), +] + +KEY = [("value", "random-matched", "★干净对照: 价值函数 vs 频率对齐随机"), + ("value", "random", "参考(混淆复习均匀度): value vs 均匀随机"), + ("value", "value-nofb", "隔离真实反馈项 lam_fb"), + ("value", "perm", "value vs 随机排列流"), + ("value", "revisit", "value vs 反复流"), + ("random-matched", "random", "random-matched vs random (验证频率对齐本身的作用)")] + + +def lm4_load(pat): + out = {} + for s in SEEDS: + p = f"{R}/{pat.format(s=s)}/lm4_wave_results.json" + if not os.path.exists(p): + continue + r = json.load(open(p)).get("replay") or {} + out[s] = {"replay": r.get("final_mean_acc"), + "any_time": r.get("any_time_acc"), + "worst": r.get("worst_case_forget"), + "mean_f": r.get("mean_forget_all")} + return out + + +def lm5_load(pat): + out = {} + for s in SEEDS: + p = f"{R}/{pat.format(s=s)}/lm5_mm_results.json" + if not os.path.exists(p): + continue + d = json.load(open(p)) + m = d.get("matrix") or [] + out[s] = {"replay": float(np.mean(m[-1])) if m else None, + "any_time": d.get("any_time_acc"), + "worst": d.get("worst_case_forget"), + "mean_f": d.get("mean_forget_all"), + "last_row": [round(float(x), 4) for x in m[-1]] if m else None, + "domains": d.get("domains")} + return out + + +def agg(d, k): + v = [d[s][k] for s in sorted(d) if d[s].get(k) is not None] + if not v: + return (float("nan"), float("nan"), 0) + return (float(np.mean(v)), float(np.std(v, ddof=1)) if len(v) > 1 else 0.0, len(v)) + + +def welch(a, b): + ma, sa, na = a + mb, sb, nb = b + if not na or not nb or any(map(lambda x: x != x, [ma, mb])): + return (float("nan"), float("nan"), "数据不全") + se = math.sqrt(sa ** 2 / na + sb ** 2 / nb) + if se <= 0: + return (ma - mb, float("nan"), "方差为0") + t = (ma - mb) / se + return (ma - mb, t, "显著 |t|>=2" if abs(t) >= 2 else "不显著 |t|<2") + + +def report(tag, arms, loader): + print("=" * 104) + print(f"{tag} (3 seed, mean±std, ddof=1)") + print("=" * 104) + rows = {} + print(" %-16s %-9s %-22s %-22s %-12s %-12s" % + ("臂", "类型", "replay mean±std", "any-time mean±std", "最差遗忘界", "平均遗忘")) + print(" " + "-" * 100) + for name, pat, kind in arms: + d = loader(pat) + if not d: + print(" %-16s %-9s (无结果)" % (name, kind)) + continue + rep, ant, wst, mf = agg(d, "replay"), agg(d, "any_time"), agg(d, "worst"), agg(d, "mean_f") + rows[name] = {"replay": rep, "any_time": ant, "worst": wst, "mean_f": mf, + "n_seed": len(d), "per_seed": d} + print(" %-16s %-9s %.4f±%.4f (n=%d) %.4f±%.4f %.4f %.4f" % + (name, kind, rep[0], rep[1], rep[2], ant[0], ant[1], wst[0], mf[0])) + print() + print(" ── 两两 Welch t (指标 = replay / any-time / 最差遗忘界) ──") + tests = {"replay": [], "any_time": [], "worst": []} + for a, b, desc in KEY: + if a not in rows or b not in rows: + print(" (%s) 数据不全" % desc) + continue + for k in ("replay", "any_time"): + delta, t, verdict = welch(rows[a][k], rows[b][k]) + tests[k].append({"a": a, "b": b, "desc": desc, "metric": k, + "delta": delta, "t": t, "verdict": verdict}) + # 统一约定: Δ = a - b。对 worst_case_forget **越小越好**, + # 因此 Δ>0 表示 a 的遗忘界更大 = a 更不安全。 + dw, tw, _ = welch(rows[a]["worst"], rows[b]["worst"]) + tests["worst"].append({"a": a, "b": b, "desc": desc, "metric": "worst", + "delta": dw, "t": tw, + "verdict": "显著 |t|>=2" if abs(tw) >= 2 else "不显著 |t|<2"}) + for k in ("replay", "any_time", "worst"): + print(" [%s]%s" % (k, " (Δ = a-b; 遗忘界越小越好 => Δ>0 表示 a 更不安全)" if k == "worst" else " (Δ = a-b)")) + for x in tests[k]: + print(" %-15s vs %-15s Δ=%+.4f t=%+.2f %s <- %s" % + (x["a"], x["b"], x["delta"], x["t"], x["verdict"], x["desc"])) + print() + return rows, tests + + +lm4_rows, lm4_tests = report("LM4 新 benchmark", LM4_ARMS, lm4_load) +print() +lm5_rows, lm5_tests = report("LM5 多模态 benchmark", LM5_ARMS, lm5_load) + +print("=" * 104) +print("LM5 各臂 最后一行 (末轮每域准确率) 与 domains") +print("=" * 104) +for name, _, _ in LM5_ARMS: + if name not in lm5_rows: + continue + d = lm5_rows[name]["per_seed"] + if "42" not in d: + continue + print(" %-16s domains(%d)=%s" % (name, len(d["42"]["domains"] or []), d["42"]["domains"])) + print(" %-16s last_row=%s" % ("", d["42"]["last_row"])) +print() + +def pack(rows): + return {k: {"replay": list(v["replay"]), "any_time": list(v["any_time"]), + "worst": list(v["worst"]), "mean_f": list(v["mean_f"]), + "n_seed": v["n_seed"], "per_seed": v["per_seed"]} + for k, v in rows.items()} + +out = {"lm4": pack(lm4_rows), "lm5": pack(lm5_rows), + "lm4_tests": lm4_tests, "lm5_tests": lm5_tests} +json.dump(out, open("/tmp/bm_summary.json", "w"), indent=1, ensure_ascii=False) +print("[json] /tmp/bm_summary.json") diff --git a/tests/analyze_efficiency.py b/tests/analyze_efficiency.py new file mode 100644 index 0000000000000000000000000000000000000000..cc946a64bc3b93189113aa655034d8096a270fa6 --- /dev/null +++ b/tests/analyze_efficiency.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +"""学习效率首次测量的分析 —— 判据在 `docs/learning_efficiency_spec.md` §4。 + +核心对照: **同一个 run 内部同时训练 `naive` 与 `replay`** ⇒ 天然配对 +(同 seed、同域、同数据顺序、同模型初始化), 唯一的差别是"要不要复习旧域"。 + +主判据 = `retention_gain[d] = mean(start_level[k≥2]) − start_level[1]` + ⇒ "重遇到这个域时, 你一上来就会多少" 相对第一次的改善。 + +判据(事前): + ① 填充闸门: `_n_visits` 全部 ≥1 且 `steps_to_abs` 非全空, 否则判"指标未采集成功" + ② 覆盖率: 报 `_already_ratio` / `_none_ratio` + ③ 保留: `mean(replay − naive) > 0` 且自助法区间下界 > 0 ⇒ 有保留; 否则照实写"无保留" +""" +from __future__ import annotations + +import glob +import json +import random +import statistics as st +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +B = 20000 + + +def load(pat="results/lmeff_s*"): + runs = {} + for d in sorted(glob.glob(str(ROOT / pat))): + f = Path(d) / "lm4_wave_results.json" + if not f.exists(): + continue + j = json.load(open(f)) + seed = int(Path(d).name.rsplit("_s", 1)[1]) + runs[seed] = j + return runs + + +def rg_of(j, arm): + e = (j.get(arm) or {}).get("efficiency") or {} + return e + + +def boot_ci(x, n=B, seed=0): + if not x: + return float("nan"), float("nan") + rng = random.Random(seed) + m = [] + for _ in range(n): + m.append(st.mean([x[rng.randrange(len(x))] for _ in range(len(x))])) + m.sort() + return m[int(.025 * len(m))], m[int(.975 * len(m))] + + +runs = load() +print("=" * 92) +print(f"学习效率首次测量 —— {len(runs)} 个 run: {sorted(runs)}") +print("=" * 92) +if not runs: + sys.exit("✗ 没有结果目录") + +# ── ① 填充闸门 ──────────────────────────────────────────────────── +print("\n① 填充闸门") +gate_ok = True +for seed, j in sorted(runs.items()): + for arm in ("naive", "replay"): + e = rg_of(j, arm) + nv = e.get("_n_visits") or {} + if not nv: + print(f" ✗ seed={seed} {arm}: _n_visits 空 ⇒ 指标未采集") + gate_ok = False + continue + print(f" seed={seed} {arm:6s} n_visits={nv} already={e.get('_already_ratio'):.3f}" + f" none={e.get('_none_ratio'):.3f} reachable_max={e.get('reachable_max')}") +print(f" ⇒ 填充闸门: {'PASS' if gate_ok else '**FAIL(指标未采集成功)'}") + +# ── ② 保留增益: 逐域对照 ────────────────────────────────────────── +print("\n" + "=" * 92) +print("② ★ 保留增益 retention_gain —— naive vs replay(同 seed 同域, 天然配对)") +print("=" * 92) +pairs = [] # (seed, dom, naive, replay) +doms = set() +for seed, j in sorted(runs.items()): + en, er = rg_of(j, "naive"), rg_of(j, "replay") + rn, rr = en.get("retention_gain") or {}, er.get("retention_gain") or {} + for d in sorted(set(rn) | set(rr), key=lambda x: int(x[1:])): + a, b = rn.get(d), rr.get(d) + if a is None or b is None: + continue + doms.add(d) + pairs.append((seed, d, float(a), float(b))) + +print(f"{'域':<8}{'n seed':>7}{'naive 均值':>12}{'replay 均值':>13}{'Δ(replay−naive)':>18}") +per_dom = {} +for d in sorted(doms, key=lambda x: int(x[1:])): + ps = [(a, b) for _, dd, a, b in pairs if dd == d] + if not ps: + continue + na = st.mean([a for a, _ in ps]); rp = st.mean([b for _, b in ps]) + per_dom[d] = (na, rp, len(ps)) + print(f"{d:<8}{len(ps):>7}{na:>12.4f}{rp:>13.4f}{rp - na:>+18.4f}") + +if pairs: + dn = [a for _, _, a, _ in pairs] + dr = [b for _, _, _, b in pairs] + dif = [b - a for _, _, a, b in pairs] + print(f"\n 全域汇总(n={len(pairs)} 对):") + print(f" naive 均值 {st.mean(dn):+.4f} (std {st.pstdev(dn):.4f})") + print(f" replay 均值 {st.mean(dr):+.4f} (std {st.pstdev(dr):.4f})") + lo, hi = boot_ci(dif) + n_pos = sum(1 for x in dif if x > 0) + n_neg = sum(1 for x in dif if x < 0) + print(f" Δ(replay−naive) = {st.mean(dif):+.4f} 自助法 95% CI [{lo:+.4f}, {hi:+.4f}]") + print(f" replay 更好的域: {n_pos}/{len(dif)} naive 更好: {n_neg} 平: {len(dif)-n_pos-n_neg}") + print(f" ⇒ ③ 判据: mean>0 且 CI 下界>0 ? " + f"{'**有保留**' if (st.mean(dif) > 0 and lo > 0) else '**未达到(照实写)'}") + +# ── ③ 起点水平本身(不依赖阈值, 更贴近"记住了多少") ───────────────── +print("\n" + "=" * 92) +print("③ 起点水平 start_level(k=1 vs 以后) —— 绝对口径") +print("=" * 92) +print(f"{'域':<8}{'arm':<8}{'k=1 起点':>10}{'k≥2 均值':>11}{'起点极差':>10}") +for seed, j in sorted(runs.items()): + for arm in ("naive", "replay"): + sl = (rg_of(j, arm).get("start_level") or {}) + for d in sorted(sl, key=lambda x: int(x[1:])): + v = sl[d] + ks = sorted(v, key=lambda x: int(x)) + first, later = v[ks[0]], [v[k] for k in ks[1:]] + if later: + print(f"{d:<8}{arm:<8}{first:>10.3f}{st.mean(later):>11.3f}" + f"{max(later)-min(later):>10.3f} seed={seed}") + +# ── ④ 其它口径 ──────────────────────────────────────────────────── +print("\n" + "=" * 92) +print("④ 其它口径(更新效率 / 访问内增益)") +print("=" * 92) +for arm in ("naive", "replay"): + wg, sp = [], [] + for seed, j in sorted(runs.items()): + e = rg_of(j, arm) + for d, v in (e.get("within_gain") or {}).items(): + wg += list(v.values()) + for d, v in (e.get("slope_early") or {}).items(): + sp += list(v.values()) + print(f" {arm:6s} within_gain 均值 {st.mean(wg):+.5f} (n={len(wg)})" + f" | slope_early 均值 {st.mean(sp):+.6f} (n={len(sp)})") diff --git a/tests/analyze_k1v2.py b/tests/analyze_k1v2.py new file mode 100644 index 0000000000000000000000000000000000000000..6071dcdaeca5844fd0c3bd9fb7eea63d75dc9627 --- /dev/null +++ b/tests/analyze_k1v2.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +"""analyze_k1v2.py — K1v2 的分析器。**先过机制闸门, 再谈对照**。 + +## 设计原则(本轮的教训直接写成代码) + +上一轮 K1 / 长测的闭环档**一步都没执行**(coverage 写入未接线, 见 +`docs/oak_repro_ground_truth.md`), 但结果文件里 `option_starts > 0`, +看上去像"机制在跑" —— 一个**静默的空转实验**。 + +所以本分析器第一步不是算均值, 而是**验证机制计数自洽**; +任何一臂不过闸门, 就**拒绝给出结论**, 只打印诊断。 + +## 闸门(事前写死) + + G1 start_blocked == 0 (修复后不应再出现算不出动作的起 option) + G2 档位与计数形态匹配: + e9 : option_steps == 0 + fixed : option_steps > 0 且 replan_steps == 0 + goal* : option_steps > 0 且 replan_steps ≈ option_steps (>0) + G3 option_starts 与 option_steps 的比例合理: + 闭环档每轮重算, 允许 starts << steps, 但 **starts 不能恒为 1** + (恒为 1 = 一个 option 锁死跑满全程 = 旧版的病灶) + G4 同一档位的多个 seed 不能给出**逐位相同**的指标 + (逐位相同 => 该档没生效, 不是"效果相同") + +## 判据 + + O2 > O1 -> 问题是 open-loop 执行, 不是 temporal abstraction + O3/O4 >= O2 -> 闭环之上还需要自适应终止/抢占 +""" +from __future__ import annotations + +import glob +import json +import os +import statistics as st +import sys + + +def load(root="results", prefix="oak_k1v2_"): + arms = {} + for d in sorted(glob.glob(os.path.join(root, prefix + "*"))): + f = os.path.join(d, "lm4_wave_results.json") + if not os.path.exists(f): + continue + j = json.load(open(f)) + name = os.path.basename(d)[len(prefix):] + mode = "e9" if name.startswith("e9") else name.rsplit("_s", 1)[0] + seed = name.rsplit("_s", 1)[-1] + rec: dict = {"arm": name, "mode": mode, "seed": seed} + for sec in ("naive", "replay"): + if isinstance(j.get(sec), dict): + rec[sec] = {k: v for k, v in j[sec].items() if isinstance(v, (int, float))} + rec[sec + "_stats"] = j[sec].get("proposer_stats") or {} + arms[name] = rec + return arms + + +def gate(arms): + """返回 (是否全过, 报告行列表)。""" + bad = [] + lines = [] + for name, r in sorted(arms.items()): + s = r.get("replay_stats") or r.get("naive_stats") or {} + mode = r["mode"] + np_, ns_ = s.get("option_steps"), s.get("option_starts") + rp = s.get("replan_steps") + sb = s.get("start_blocked", 0) + ov = s.get("override_count", 0) + tr = s.get("term_reasons") or {} + tag = [] + if sb and sb > 0: + tag.append("G1✗start_blocked=%d" % sb) + if mode == "e9": + if (np_ or 0) != 0: + tag.append("G2✗e9 却有 option_steps=%s" % np_) + elif mode == "fixed": + if not np_ or np_ <= 0: + tag.append("G2✗fixed option_steps=%s" % np_) + if rp: + tag.append("G2✗fixed replan_steps=%s (应为 0)" % rp) + else: + if not np_ or np_ <= 0: + tag.append("G2✗%s option_steps=%s" % (mode, np_)) + if not rp: + tag.append("G2✗%s replan_steps=%s (应 ≈ steps)" % (mode, rp)) + if mode != "e9" and ns_ == 1: + tag.append("G3✗option_starts==1 (一个 option 锁死)") + lines.append(" %-30s mode=%-18s starts=%-4s steps=%-4s replan=%-4s " + "ovr=%-3s term=%s %s" + % (name, mode, ns_, np_, rp, ov, tr if tr else "-", + " ".join(tag) if tag else "✓")) + if tag: + bad.append(name) + return (len(bad) == 0), lines, bad + + +def summarize(arms): + from collections import defaultdict + G = defaultdict(list) + for name, r in arms.items(): + G[r["mode"]].append(r) + out = [] + for mode in ("e9", "fixed", "goal", "goal_term", "goal_term_override"): + g = G.get(mode) + if not g: + continue + at = [r["replay"]["any_time_acc"] for r in g if "replay" in r + and "any_time_acc" in r["replay"]] + fn = [r["replay"]["final_mean_acc"] for r in g if "replay" in r + and "final_mean_acc" in r["replay"]] + if not at: + continue + # G4: 逐位相同检测 + uniq = len(set(round(x, 12) for x in at)) + out.append(dict(mode=mode, n=len(at), any_time=st.mean(at), + any_time_sd=(st.stdev(at) if len(at) > 1 else 0.0), + final=st.mean(fn) if fn else float("nan"), + uniq=uniq)) + return out + + +def welch(a, b): + """Welch t 检验(不假设等方差)。返回 (t, p, df)。""" + import math + na, nb = len(a), len(b) + if na < 2 or nb < 2: + return (float("nan"), float("nan"), 0) + ma, mb = st.mean(a), st.mean(b) + va, vb = st.variance(a), st.variance(b) + se = math.sqrt(va / na + vb / nb) + if se == 0: + return (float("inf") if ma != mb else 0.0, 0.0 if ma != mb else 1.0, na + nb - 2) + t = (ma - mb) / se + df = (va / na + vb / nb) ** 2 / ((va / na) ** 2 / (na - 1) + (vb / nb) ** 2 / (nb - 1)) + # 双侧 p 的近似(用正态近似, 大 n 下足够; 诚实标注这一点) + from math import erf, sqrt + p = 2 * (1 - 0.5 * (1 + erf(abs(t) / sqrt(2)))) + return (t, p, df) + + +def main(): + root = sys.argv[1] if len(sys.argv) > 1 else "results" + prefix = sys.argv[2] if len(sys.argv) > 2 else "oak_k1v2_" + arms = load(root, prefix) + if not arms: + print("没找到 %s* 的结果(实验还在跑?)" % prefix) + return 1 + + print("=" * 96) + print("第 ① 步: 机制生效闸门(不过闸门就不给结论)") + print("=" * 96) + ok, lines, bad = gate(arms) + for l in lines: + print(l) + print("\n 闸门结论: %s" % ("✓ 全部通过, 可以对照" if ok else + "✗ %d 臂未通过 -> 本轮对照**无效**, 只列诊断" % len(bad))) + + print("\n" + "=" * 96) + print("第 ② 步: 指标汇总") + print("=" * 96) + summ = summarize(arms) + print(" %-22s %3s %10s %10s %10s %6s" % ("模式", "n", "any-time", "std", "final", "唯一值")) + for s in summ: + flag = "" if s["uniq"] > 1 or s["n"] == 1 else " ← G4✗ 所有 seed 逐位相同!" + print(" %-22s %3d %10.4f %10.4f %10.4f %6d%s" + % (s["mode"], s["n"], s["any_time"], s["any_time_sd"], s["final"], s["uniq"], flag)) + + if not ok: + print("\n ⚠ 有臂未过闸门 —— 不给出 O1/O2/O3/O4 的对照结论。") + return 1 + + print("\n" + "=" * 96) + print("第 ③ 步: 对照(判据: O2 > O1 -> 问题是 open-loop; O3/O4 >= O2 -> 需要自适应终止)") + print("=" * 96) + def col(mode): + return [r["replay"]["any_time_acc"] for r in arms.values() + if r["mode"] == mode and "replay" in r and "any_time_acc" in r["replay"]] + base = col("e9") + print(" %-22s %10s %s" % ("对比", "Δ(any-time)", "Welch t / p")) + for mode in ("fixed", "goal", "goal_term", "goal_term_override"): + v = col(mode) + if not v or not base: + continue + t, p, df = welch(v, base) + d = st.mean(v) - st.mean(base) if v and base else float("nan") + verdict = ("**显著**" if p == p and p < 0.05 else "不显著") + print(" %-22s %+10.4f t=%+6.2f p=%.4f %s" + % ("%s vs e9" % mode, d, t, p, verdict)) + o1, o2 = col("fixed"), col("goal") + if o1 and o2: + t, p, _ = welch(o2, o1) + print("\n ★ 关键判据 O2(闭环) vs O1(开环): Δ=%+.4f t=%+.2f p=%.4f -> %s" + % (st.mean(o2) - st.mean(o1), t, p, + "O2 更好 ✓ (问题是开环执行)" if (p < 0.05 and st.mean(o2) > st.mean(o1)) + else "O2 未显著更好 ✗")) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/analyze_l4_extension.py b/tests/analyze_l4_extension.py new file mode 100644 index 0000000000000000000000000000000000000000..37e2bc9323439b1a74b61690712c64d3faa82908 --- /dev/null +++ b/tests/analyze_l4_extension.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +"""L4 批(L3 的 seed 扩展)判定 —— **结果出来前就写死的口径**。 + +## 为什么先写这个 + +L3 的教训:判据本身错了会把「样本量不足」读成「未排除」,从而把一场盲跑 +当成证据。所以本脚本在数据出来之前就固定三件事: + + ① 主口径 = **配对**(同 seed),非配对作敏感性 —— **两个都报**; + 若两口径结论不一致, 必须明写不一致, **不许挑有利的那个**。 + ② 判定是**三值**:`worse` / `ns` / `inconclusive`。 + `|Δ| < MDE` ⇒ **inconclusive**, 不是 `ns`。 + ③ `df` 从真实样本量推临界值(`n=5 ⇒ df=4 ⇒ 2.776`), 不用正态近似。 + +## 判定规则(事前) + +对每个 option 档 vs `e9`: + · `p < 0.05` 且 `Δ < 0` ⇒ **worse** + · `|Δ| < MDE` (即本批无分辨力) ⇒ **inconclusive** + · 其余 ⇒ **ns** + +并额外报:要得到 `p<0.05` 还需多少 seed(未达显著时)。 + +## 效率指标(事后, 新旧臂同一份代码) + +`efficiency` 字段在 runner 里是**空的**(25/25 臂), 所以从 `any_time_curve` +与 `acc_matrix` 事后算, 保证 L3 与 L4 可比: + · `anytime_auc` = mean(any_time_curve) + · `t_adapt_proxy` = 每域"切回后恢复到该域历史峰值 80% 所需步数"的中位数 +""" +import json +import glob +import math +import os +import statistics as st +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +# ── Student-t 双尾 p(不完全 beta; 自检见 selftest)──────────────── +def _betacf(a, b, x, itmax=300, eps=3e-16): + qab, qap, qam = a + b, a + 1.0, a - 1.0 + c = d = 1.0 + d = 1.0 - qab * x / qap + if abs(d) < 3e-16: + d = 3e-16 + d = 1.0 / d + h = d + for m in range(1, itmax + 1): + m2 = 2 * m + aa = m * (b - m) * x / ((qam + m2) * (a + m2)) + d = 1.0 + aa * d + if abs(d) < 3e-16: + d = 3e-16 + c = 1.0 + aa / c + if abs(c) < 3e-16: + c = 3e-16 + d = 1.0 / d + h *= d * c + aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)) + d = 1.0 + aa * d + if abs(d) < 3e-16: + d = 3e-16 + c = 1.0 + aa / c + if abs(c) < 3e-16: + c = 3e-16 + d = 1.0 / d + de = d * c + h *= de + if abs(de - 1.0) < eps: + break + return h + + +def _betai(a, b, x): + if x <= 0: + return 0.0 + if x >= 1: + return 1.0 + lbeta = math.lgamma(a + b) - math.lgamma(a) - math.lgamma(b) + bt = math.exp(lbeta + a * math.log(x) + b * math.log(1.0 - x)) + if x < (a + 1.0) / (a + b + 2.0): + return bt * _betacf(a, b, x) / a + return 1.0 - bt * _betacf(b, a, 1.0 - x) / b + + +def t_p_two_tailed(t, df): + return _betai(df / 2.0, 0.5, df / (df + t * t)) + + +def f_p_two_tailed(F, df1, df2): + """双尾 F 检验 p(F | df1, df2)。F<=0 或 df<1 时返回 1.""" + if F <= 0 or df1 < 1 or df2 < 1: + return 1.0 + p = _betai(df1 / 2.0, df2 / 2.0, df1 * F / (df1 * F + df2)) + return min(1.0, 2.0 * min(p, 1.0 - p)) + + +def bootstrap_var_ratio(a, b, B=20000, seed=0): + """var(b)/var(a) 的自助法 95% 分位区间。a, b 独立重采样。 + + `a` = 基线臂样本, `b` = 处理臂样本 ⇒ 比值 R = var(基线)/var(处理)。 + R > 1 表示**处理臂方差更小**。 + """ + import random as _rnd + rng = _rnd.Random(seed) + out = [] + for _ in range(B): + ra = [a[rng.randrange(len(a))] for _ in range(len(a))] + rb = [b[rng.randrange(len(b))] for _ in range(len(b))] + va, vb = st.pvariance(ra), st.pvariance(rb) + if va > 0: + out.append(vb / va) + out.sort() + if not out: + return float("nan"), float("nan") + return out[int(0.025 * len(out))], out[int(0.975 * len(out))] + + +# 临界值表(双尾 0.05) —— 只用它反推"还需多少 seed" +_TC = {1: 12.706, 2: 4.303, 3: 3.182, 4: 2.776, 5: 2.571, 6: 2.447, 7: 2.365, + 8: 2.306, 9: 2.262, 10: 2.228, 12: 2.179, 15: 2.131, 20: 2.086, + 30: 2.042, 60: 2.000, 120: 1.980, 10 ** 6: 1.960} + + +def t_crit(df): + if df in _TC: + return _TC[df] + ks = sorted(_TC) + if df < ks[0] or df > ks[-1]: + return 1.96 + lo = max(k for k in ks if k < df) + hi = min(k for k in ks if k > df) + return _TC[lo] + (_TC[hi] - _TC[lo]) * (df - lo) / (hi - lo) + + +def selftest(): + checks = [(2.776, 4, 0.05), (4.604, 4, 0.01), (0.0, 4, 1.0)] + for t, df, want in checks: + got = t_p_two_tailed(t, df) + assert abs(got - want) < 2e-3, f"selftest 失败 t={t} df={df}: {got} vs {want}" + print("✓ t 分布自检通过 (2.776/df4→0.0500, 4.604/df4→0.0100)") + + +# ── 数据装载 ────────────────────────────────────────────────────── +def load(patterns): + """arm -> {seed: metrics}。同时吃 L3 与 L4 目录, 两者配置逐字相同。""" + arms = {} + for pat in patterns: + for d in sorted(glob.glob(str(ROOT / pat))): + f = os.path.join(d, "lm4_wave_results.json") + if not os.path.exists(f): + continue + name = os.path.basename(d) + # oakL3_goal_term_s100 / oakL4_goal_term_s100 -> (goal_term, 100) + stem = name.split("_", 1)[1] + arm, _, seed = stem.rpartition("_s") + if not arm or not seed.isdigit(): + continue + rep = json.load(open(f)).get("replay", {}) + atc = rep.get("any_time_curve") or [] + acc = rep.get("final_mean_acc") + if acc is None or not atc: + continue + arms.setdefault(arm, {})[int(seed)] = { + "acc": float(acc), + "auc": st.mean(atc), + "atc": atc, + "acc_matrix": rep.get("acc_matrix") or [], + "efficiency_populated": bool(rep.get("efficiency")), + } + return arms + + +def t_adapt_proxy(acc_matrix, frac=0.8): + """⚠ **已废弃, 不要用** —— 实测在所有臂上恒返回 1.0。 + + 原因: `acc_matrix` 是 300 步 × 6 域的**逐步准确率**, 但 6 个域在 + `--stream revisit` 下被反复访问, 列里没有"该域刚开始训练"的标记, + 所以"从谷底恢复到峰值 80%"的判定在相邻两步内就满足 ⇒ 步数恒 = 1。 + 保留函数只为记录这次失败, **不参与任何报告**。 + + 真正要测 T_adapt 需要 runner 输出**逐域逐 step 的损失/准确率曲线**, + 而当前 runner 的 `curves` 字段是空的 (见 main 里的死字段报告)。 + """ + return None + + +def _deprecated_proxy_body(acc_matrix, frac=0.8): + """每域"切回后恢复到该域历史峰值 frac 所需步数"的中位数。 + + `acc_matrix` = rounds x domains 的逐步准确率。代理量, 不是 runner 内定义; + 对新旧臂用同一份代码算, 所以**可比**。 + """ + if not acc_matrix: + return None + n_dom = len(acc_matrix[0]) + steps = [] + for j in range(n_dom): + col = [row[j] for row in acc_matrix if len(row) > j] + if not col: + continue + peak = max(col) + if peak <= 0: + continue + thr = frac * peak + # 每个"从谷底回升"的区间: 找低于 thr 之后首次达到 thr 的步数 + below = False + last_below = 0 + for i, v in enumerate(col): + if v < thr: + below = True + last_below = i + elif below: + steps.append(i - last_below) + below = False + return st.median(steps) if steps else None + + +def verdict(delta, sd_pair, sd_a, sd_b, n, pre_specified="paired"): + """三值判定 + 还需多少 seed。pre_specified 只是记录用途。""" + if pre_specified == "paired": + se = sd_pair / math.sqrt(n) + else: + se = math.sqrt(sd_a ** 2 / n + sd_b ** 2 / n) + if se <= 0: + return "ns", float("nan"), float("nan"), 0 + t = delta / se + p = t_p_two_tailed(abs(t), n - 1) + mde = t_crit(n - 1) * se + need = 0 + if p >= 0.05: + for nn in range(n + 1, 5000): + if t_crit(nn - 1) * se * math.sqrt(n / nn) <= abs(delta): + need = nn + break + if p < 0.05 and delta < 0: + v = "worse" + elif abs(delta) < mde: + v = "inconclusive" + else: + v = "ns" + return v, t, p, need + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument("--prefix", default="results/oakL3_*,results/oakL4_*,results/oakL5_*", + help="逗号分隔的结果目录 glob") + ap.add_argument("--bootstrap", type=int, default=20000) + ap.add_argument("--json-out", default=None) + args = ap.parse_args() + + selftest() + arms = load([p for p in args.prefix.split(",") if p]) + if "e9" not in arms: + sys.exit("✗ 找不到 e9 臂") + base = arms["e9"] + print(f"\n{'臂':<20}{'n':>4}{'acc 均值':>11}{'std':>8}") + for k in sorted(arms, key=lambda a: -st.mean([v['acc'] for v in arms[a].values()])): + v = [x["acc"] for x in arms[k].values()] + print(f"{k:<20}{len(v):>4}{st.mean(v):>11.4f}{st.pstdev(v):>8.4f}") + + print(f"\n{'=' * 96}") + print("对 e9 的比较 —— 配对(主口径) / 非配对(敏感性)。`inconclusive` = 本批无分辨力") + print(f"{'=' * 96}") + hdr = (f"{'臂':<20}{'|Δ|':>8}{'配对 p':>9}{'配对判定':>11}" + f"{'非配对 p':>10}{'非配对判定':>12}{'一致?':>7}{'还需 seed':>10}") + print(hdr) + mismatch = [] + for k in ["goal", "goal_term", "goal_term_override", "fixed"]: + if k not in arms: + continue + shared = sorted(set(arms[k]) & set(base)) + if len(shared) < 3: + continue + a = [arms[k][s]["acc"] for s in shared] + b = [base[s]["acc"] for s in shared] + d = [x - y for x, y in zip(a, b)] + delta = st.mean(d) + vp, tp, pp, np_ = verdict(delta, st.pstdev(d), st.pstdev(a), st.pstdev(b), + len(shared), "paired") + vu, tu, pu, nu = verdict(delta, st.pstdev(d), st.pstdev(a), st.pstdev(b), + len(shared), "unpaired") + ok = "✓" if vp == vu else "✗ 不一致" + if vp != vu: + mismatch.append(k) + print(f"{k:<20}{abs(delta):>8.4f}{pp:>9.4f}{vp:>11}{pu:>10.4f}{vu:>12}{ok:>7}" + f"{np_ or nu:>10}") + + if mismatch: + print(f"\n⚠ 配对与非配对结论**不一致**的臂: {mismatch}") + print(" 按纪律: 明写不一致, 不挑有利口径。两口径都在上表。") + + # ── ★ 方差口径:H2 / H2b / H3(判定规则事前写在 docs/l5_variance_replication_spec.md)── + print(f"\n{'=' * 96}") + print("★ 方差口径 —— H2: 闭环档的跨 seed 方差是否低于基线") + print(f"{'=' * 96}") + VAR_ARMS = ["goal_term", "goal_term_override"] + print(f"{'臂':<20}{'n':>4}{'sd':>9}{'var':>12}{'R=var(e9)/var(臂)':>19}" + f"{'F p(双尾)':>11} 自助法 95% CI (B={args.bootstrap})") + var_res = {} + for k in ["goal_term", "goal_term_override", "goal", "fixed"]: + if k not in arms: + continue + av = [x["acc"] for x in arms[k].values()] + bv = [x["acc"] for x in base.values()][:len(av)] + if len(av) < 3 or len(bv) < 3: + continue + va, vb = st.pvariance(av), st.pvariance(bv) + R = vb / va if va > 0 else float("nan") + p = f_p_two_tailed(R, len(bv) - 1, len(av) - 1) + if len(av) < 8: + # 方差比在 n<8 时不可用(自助法区间会宽到 [0.01, 24.8] 这种程度, 无信息) + var_res[k] = dict(n=len(av), sd=st.pstdev(av), var=va, R=R, p=p, + ci=(float("nan"), float("nan")), usable=False) + print(f"{k:<20}{len(av):>4}{st.pstdev(av):>9.4f}{va:>12.6f}{R:>19.3f}" + f"{p:>11.5f} ★ n<8, 方差比不可用") + continue + lo, hi = bootstrap_var_ratio(av, bv, B=args.bootstrap) + var_res[k] = dict(n=len(av), sd=st.pstdev(av), var=va, R=R, p=p, + ci=(lo, hi), usable=True) + print(f"{k:<20}{len(av):>4}{st.pstdev(av):>9.4f}{va:>12.6f}{R:>19.3f}" + f"{p:>11.5f} [{lo:.2f}, {hi:.2f}]") + + if var_res: + # H3 天花板检查 —— **先过这一条才解释 H2** + # ★ 作用域: **只对 H2 的比较臂集**(e9 + VAR_ARMS)求均值极差。 + # `goal` 是 H2b 的机制对照、`fixed` 是已判定档且 n=5, 二者都不在 H2 的比较里; + # 把它们算进来会让"均值极差"反映**别的东西**(goal 确实有害 / fixed 样本不足), + # 而不是"被比较的臂是否可比"。见 spec §3 的 2026-09-29 修订记录。 + h3_arms = ["e9"] + [k for k in VAR_ARMS if k in arms] + means = {k: st.mean([x["acc"] for x in arms[k].values()]) for k in h3_arms} + spread = max(means.values()) - min(means.values()) + margin = 1.0 - max(means.values()) + h3 = (spread < 0.02) and (margin > 0.10) + print(f"\n H3 天花板检查 [作用域: {', '.join(h3_arms)}]") + for k, m in sorted(means.items(), key=lambda x: -x[1]): + print(f" {k:<20}{m:.4f}") + print(f" 均值极差 {spread:.4f} (需 <0.02) | " + f"距上限 {margin:.4f} (需 >0.10) ⇒ {'PASS' if h3 else '★ FAIL'}") + if not h3: + print(" ⇒ H3 不过: 方差差异可能是饱和伪影, **H2 判为无效**(无论 p 多小)") + + # H2 判定(事前规则) + ok = [k for k in VAR_ARMS if k in var_res and var_res[k].get("usable") + and var_res[k]["p"] < 0.05 and var_res[k]["ci"][0] > 1.0] + hit = [k for k in VAR_ARMS if k in var_res and var_res[k].get("usable")] + if h3 and len(ok) == len(hit) and hit: + v2 = "★ **复现**" + elif h3 and ok: + v2, = [f"部分复现({','.join(ok)} 通过, 其余未过)"] + else: + v2 = "**未复现**" if h3 else "无效(H3 未过)" + print(f" H2 判定: {v2}") + print(f" 规则: F p<0.05 且 自助法 CI 下界>1, 两臂都要满足") + + # H2b 机制 + if "goal" in var_res: + g = var_res["goal"] + print(f" H2b 机制(`goal` 无 β_o): R={g['R']:.3f}, p={g['p']:.5f}, " + f"CI=[{g['ci'][0]:.2f}, {g['ci'][1]:.2f}] ⇒ " + f"{'方差降低来自 option 层本身' if g['p'] < 0.05 and g['ci'][0] > 1 else 'β_o 是必要条件'}") + if args.json_out: + Path(args.json_out).write_text(json.dumps(var_res, indent=2, default=str, + ensure_ascii=False)) + print(f" 方差结果已写: {args.json_out}") + + eff = [k for k, v in arms.items() + for x in v.values() if x["efficiency_populated"]] + print(f"\n★ runner 死字段报告 (与 leo 点名的一等指标直接相关):") + print(f" `efficiency` 被填充的臂数: {len(eff)} / {len(arms)}" + f" ⇒ 学习/更新效率**从未被 runner 采集过**") + print(f" `curves` 同样为空 ⇒ 逐域逐 step 的学习曲线也没有记录") + print(f" ⇒ 本报告里的 `anytime AUC` 是**唯一**可用的效率类代理" + f"(= mean(any_time_curve), 逐步准确率的平均)") + print(f" ⇒ T_adapt 无法从现有产物算出, 需改 runner —— 那是另一个变量, 本批不做") + + print(f"\n{'臂':<20}{'anytime AUC':>13}{'final acc':>11}{'峰值':>9}") + for k in sorted(arms, key=lambda a: -st.mean([v['acc'] for v in arms[a].values()])): + v = list(arms[k].values()) + print(f"{k:<20}{st.mean(x['auc'] for x in v):>13.4f}" + f"{st.mean(x['acc'] for x in v):>11.4f}" + f"{st.mean(max(x['atc']) for x in v):>9.4f}") + + +if __name__ == "__main__": + main() diff --git a/tests/analyze_lm4_data.py b/tests/analyze_lm4_data.py new file mode 100644 index 0000000000000000000000000000000000000000..f97e3351d04e8189bee4b3eb4bc0e9b4bc1a61db --- /dev/null +++ b/tests/analyze_lm4_data.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +"""LM4 数据分析: 为什么 WFR 能救活 D2/D3? 各特征/频段携带多少密度信息?""" +import sys, os +import numpy as np +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from run_lm4_wave import build_feature_matrix + +CACHE = "/tmp/lm4_feat_cache.npz" +BANDS = 13 +FREQ = [6.4, 17.1, 28.6, 50.2, 90.7, 161.0, 285.8, 508.4, 903.4, 1610.0, 2862.0, 5086.0, 9036.0] +MAG_NAMES = ["logMag", "log rms", "delta", "lambda"] + +if os.path.exists(CACHE): + z = np.load(CACHE) + dens, F = z["dens"], z["F"] + print(f"[cache] 载入 {F.shape}") +else: + t, dens, F, ok = build_feature_matrix("data/wave", bands=BANDS, use_wfr=True) + dens, F = dens[ok], F[ok] + np.savez_compressed(CACHE, dens=dens, F=F) + print(f"[built] {F.shape}") + +valid = np.isfinite(dens) & (dens > 0) & np.isfinite(F).all(axis=1) +dens, F = dens[valid], F[valid] +y = np.log10(dens) +print(f"样本 {len(y)}, 特征 {F.shape[1]} 维") + +# 6 个分位数域 +qs = np.quantile(y, np.linspace(0, 1, 7)); qs[0] -= 1e-6; qs[-1] += 1e-6 +dom = np.digitize(y, qs[1:-1]) + +names = MAG_NAMES + [f"B@{f:g}Hz" for f in FREQ] + [f"E@{f:g}Hz" for f in FREQ] +assert len(names) == F.shape[1], (len(names), F.shape) + +# ---------- A. 单特征判别力: ANOVA F 统计量 ---------- +print("\n=== A. 单特征判别力 (跨 6 个密度域, F 越大越有信息) ===") +Fs, MI = [], [] +for j in range(F.shape[1]): + v = F[:, j] + gm = v.mean() + ssb = sum(len(v[dom == d]) * (v[dom == d].mean() - gm) ** 2 + for d in range(6) if (dom == d).sum() > 0) + ssw = sum(((v[dom == d] - v[dom == d].mean()) ** 2).sum() + for d in range(6) if (dom == d).sum() > 0) + dfb, dfw = 5, len(v) - 6 + Fs.append((ssb / dfb) / (ssw / dfw + 1e-30)) + # MI (20 bins) + hb = np.histogram(v, bins=20)[0].astype(float); hb /= hb.sum() + 1e-30 + hd = np.bincount(dom, minlength=6).astype(float); hd /= hd.sum() + H_d = -(hd[hd > 0] * np.log(hd[hd > 0])).sum() + H_dv = 0.0 + for d in range(6): + m = dom == d + if m.sum() == 0: continue + h2 = np.histogram(v[m], bins=20)[0].astype(float); h2 /= h2.sum() + 1e-30 + H_dv += hd[d] * (-(h2[h2 > 0] * np.log(h2[h2 > 0])).sum()) + MI.append(H_d - H_dv) + +order = np.argsort(Fs)[::-1] +print(f"{'feature':<14}{'F':>12}{'MI':>8}") +for j in order[:14]: + print(f"{names[j]:<14}{Fs[j]:>12.1f}{MI[j]:>8.4f}") +print(" ...") +print(f"{'最弱5个:':<14}" + ", ".join(f"{names[j]}({Fs[j]:.0f})" for j in order[-5:])) + +# ---------- B. 各域的平均磁场波谱 (揭示物理机制) ---------- +print("\n=== B. 各密度域的平均磁功率谱 (log10, 每格=该域该频段均值) ===") +bpow = F[:, 4:4 + BANDS] +print(f"{'freq(Hz)':>10}" + "".join(f"{'D'+str(d):>9}" for d in range(6)) + f"{'D5-D0':>9}") +for i, fr in enumerate(FREQ): + row = [bpow[dom == d, i].mean() for d in range(6)] + print(f"{fr:>10.1f}" + "".join(f"{v:>9.2f}" for v in row) + f"{row[5]-row[0]:>9.2f}") + +# ---------- C. D2 vs D3 二分类可解性 ---------- +def auc_score(scores, labels): + """Mann-Whitney AUC. labels: 1=正类。""" + order = np.argsort(scores, kind="mergesort") + ranks = np.empty(len(scores), dtype=np.float64) + ranks[order] = np.arange(1, len(scores) + 1) + n1 = int(labels.sum()); n0 = len(labels) - n1 + if n1 == 0 or n0 == 0: + return float("nan") + return (ranks[labels == 1].sum() - n1 * (n1 + 1) / 2) / (n1 * n0) + + +print("\n=== C. D2 vs D3 二分类 (最难的相邻域) ===") +m23 = (dom == 2) | (dom == 3) +y23 = (dom[m23] == 3).astype(int) +print(f" 样本数: D2={int((dom==2).sum())}, D3={int((dom==3).sum())}") + +for tag, cols in [("MAG 4 维", [0, 1, 2, 3]), + ("WFR 磁 13 维", list(range(4, 4 + BANDS))), + ("WFR 电 13 维", list(range(4 + BANDS, 4 + 2 * BANDS))), + ("全部 30 维", list(range(30)))]: + Xs = F[m23][:, cols] + mu, sd = Xs.mean(0), Xs.std(0) + 1e-9 + Xn = (Xs - mu) / sd + c0, c1 = Xn[y23 == 0].mean(0), Xn[y23 == 1].mean(0) + w = c1 - c0 + proj = Xn @ w + a = auc_score(proj, y23) + print(f" {tag:<14} 线性判别 AUC = {a:.4f} (0.5=无信息)") + +# ---------- D. 单个频段对 D2/D3 的区分力 ---------- +print("\n=== D. 各频段单独区分 D2/D3 的能力 (AUC, 只列 |AUC-0.5|>0.05 的) ===") +rowsd = [] +for j in range(F.shape[1]): + a = auc_score(F[m23][:, j], y23) + rowsd.append((names[j], a)) +rowsd.sort(key=lambda t: -abs(t[1] - 0.5)) +for n_, a in rowsd[:12]: + print(f" {n_:<14} AUC={a:.4f} ({'高密度→更强' if a > 0.5 else '高密度→更弱'})") + +# ---------- E. 波谱形状 vs 密度 (整体相关) ---------- +print("\n=== E. 各频段功率 vs log10(density) 的相关系数 (只列 |r|>0.15) ===") +cor = [] +for j in range(4, 4 + 2 * BANDS): + v = F[:, j] + if np.std(v) < 1e-9: + continue + r = float(np.corrcoef(v, y)[0, 1]) + cor.append((names[j], r)) +cor.sort(key=lambda t: -abs(t[1])) +for n_, r in cor[:12]: + print(f" {n_:<14} r={r:+.3f}") + diff --git a/tests/analyze_oak.py b/tests/analyze_oak.py new file mode 100644 index 0000000000000000000000000000000000000000..666a7977c5ebbc714923cff3d5e3411ce65631c3 --- /dev/null +++ b/tests/analyze_oak.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +"""analyze_oak.py — OaK E9 vs E10 对照分析。 + +核心问题 (用户架构主张里最关键的一跳): + Prediction -> Abstraction -> Option -> Planning -> Control + 是否比 + state -> RL policy -> action + 更适合持续变化的磁通动力学。 + +E9 = --proposer oak --oak-options 0 (同一 base、同一门控, 不用 option) +E10 = --proposer oak --oak-options 1 (+Options / 时间抽象) +→ **唯一变量是时间抽象**。 + +判据 (不止看均值, 也看用户要求的多项): + any_time_acc 在线性能 + worst_case_forget 最差遗忘界 (CL 的一等指标) + final_mean_acc 末轮平均 + option_starts/steps option 是否**真的被用上** (用不上 = 机制没生效, 不等于无效) + n_options / actions 发现了什么技能 + +统计: Welch t + 效应量; 多 seed 可靠性标准 std < mean/2。 +""" +import glob +import json +import math +import os +import sys + +import numpy as np + +RES = sys.argv[1] if len(sys.argv) > 1 else "results" + + +def welch(a, b): + a, b = np.asarray(a, float), np.asarray(b, float) + a = a[np.isfinite(a)]; b = b[np.isfinite(b)] + if len(a) < 2 or len(b) < 2: + return float("nan"), float("nan") + va, vb = a.var(ddof=1), b.var(ddof=1) + se = math.sqrt(va / len(a) + vb / len(b)) + if se == 0: + return (0.0 if a.mean() == b.mean() else float("inf")), float("nan") + t = (a.mean() - b.mean()) / se + # Welch–Satterthwaite 自由度 + df = (va / len(a) + vb / len(b)) ** 2 / ( + (va / len(a)) ** 2 / (len(a) - 1) + (vb / len(b)) ** 2 / (len(b) - 1)) + try: + from scipy import stats + p = 2 * stats.t.sf(abs(t), df) + except Exception: + # 无 scipy: 正态近似 (n>=5 时够用, 标注清楚) + p = math.erfc(abs(t) / math.sqrt(2)) + return t, p + + +def collect(pat, arm): + """收集所有匹配目录的 `arm` 结果。arm in (naive, replay)。""" + out = {"any_time": [], "forget": [], "final": [], "opts": [], "starts": [], + "steps": [], "ntrans": [], "nopt": [], "dirs": []} + for d in sorted(glob.glob(pat)): + f = os.path.join(d, "lm4_wave_results.json") + if not os.path.exists(f): + continue + try: + j = json.load(open(f)) + except Exception: + continue + if arm not in j: + continue + a = j[arm] + out["dirs"].append(os.path.basename(d)) + out["any_time"].append(a.get("any_time_acc", float("nan"))) + out["forget"].append(a.get("worst_case_forget", float("nan"))) + out["final"].append(a.get("final_mean_acc", float("nan"))) + o = a.get("oak") or {} + k = a.get("knowledge") or {} + out["opts"].append(a.get("oak_transitions", o.get("n_trans", float("nan")))) + out["starts"].append(o.get("option_starts", float("nan"))) + out["steps"].append(o.get("option_steps", float("nan"))) + out["ntrans"].append(o.get("n_trans", float("nan"))) + out["nopt"].append(k.get("n_options", float("nan"))) + return out + + +def collect5(pat): + """lm5 的指标在**顶层** (any_time_acc / worst_case_forget), 不像 lm4 嵌在 arm 下。 + + ★ 早先版本只写了一个 collect() 读 lm4 的 `lm4_wave_results.json`, + lm5 的 20 个臂 (oak5L_*) 因此**完全没被读到**, 分析报告里那一节是空的。 + """ + out = {"any_time": [], "forget": [], "final": [], "opts": [], "starts": [], + "steps": [], "ntrans": [], "nopt": [], "dirs": []} + for d in sorted(glob.glob(pat)): + f = os.path.join(d, "lm5_mm_results.json") + if not os.path.exists(f): + continue + try: + j = json.load(open(f)) + except Exception: + continue + out["dirs"].append(os.path.basename(d)) + out["any_time"].append(j.get("any_time_acc", float("nan"))) + out["forget"].append(j.get("worst_case_forget", float("nan"))) + # lm5 没有单一 final_mean_acc -> 用最后一行的域均值 + m = j.get("matrix") or [] + if m: + last = [v for v in m[-1] if v is not None and v == v] + out["final"].append(float(np.mean(last)) if last else float("nan")) + else: + out["final"].append(float("nan")) + o = j.get("oak") or {} + k = j.get("knowledge") or {} + out["starts"].append(o.get("option_starts", float("nan"))) + out["steps"].append(o.get("option_steps", float("nan"))) + out["ntrans"].append(o.get("n_trans", float("nan"))) + out["nopt"].append(k.get("n_options", float("nan"))) + return out + + +def fmt(v): + v = np.asarray(v, float); v = v[np.isfinite(v)] + if len(v) == 0: + return "n/a" + if len(v) == 1: + return "%.4f (n=1)" % v[0] + return "%.4f ± %.4f (n=%d, CV %.1f%%)" % ( + v.mean(), v.std(ddof=1), len(v), + 100 * v.std(ddof=1) / abs(v.mean()) if v.mean() else float("nan")) + + +print("=" * 96) +print("OaK E9 (无 Options) vs E10 (有 Options) —— 唯一变量是时间抽象") +print("=" * 96) + +for arm in ("replay", "naive"): + e9 = collect(os.path.join(RES, "oak_e9_s*"), arm) + e10 = collect(os.path.join(RES, "oak_e10_s*"), arm) + if not e9["dirs"] and not e10["dirs"]: + continue + print() + print("── arm = %s (%s) " % (arm, "有意义的那臂" if arm == "replay" else "对照") + "─" * 50) + print(" E9 最终平均 acc : %s" % fmt(e9["final"])) + print(" E10 最终平均 acc : %s" % fmt(e10["final"])) + print(" E9 any-time : %s" % fmt(e9["any_time"])) + print(" E10 any-time : %s" % fmt(e10["any_time"])) + print(" E9 最差遗忘界 : %s" % fmt(e9["forget"])) + print(" E10 最差遗忘界 : %s" % fmt(e10["forget"])) + print(" ── 机制是否真的生效 (E10) ──") + print(" 发现的 options : %s" % fmt(e10["nopt"])) + print(" option 启动次数 : %s" % fmt(e10["starts"])) + print(" option 执行步数 : %s" % fmt(e10["steps"])) + print(" 转移条数 : %s" % fmt(e10["ntrans"])) + if np.nanmean(e10["steps"]) < 1 if len(e10["steps"]) else True: + print(" ** 警告: option 几乎没被执行 -> 这不是「Options 无效」,") + print(" 而是「Options 机制没被触发」, 结论不能当负面证据 **") + print(" ── 显著性 ──") + for name, key, better in [("any_time ", "any_time", "high"), + ("最差遗忘界", "forget", "low"), + ("最终 acc ", "final", "high")]: + t, p = welch(e10[key], e9[key]) + if not np.isfinite(t): + print(" %s 样本不足" % name) + continue + d = np.nanmean(e10[key]) - np.nanmean(e9[key]) + rel = 100 * d / abs(np.nanmean(e9[key])) if np.nanmean(e9[key]) else float("nan") + verdict = ("E10 更好" if ((d > 0) == (better == "high")) else "E9 更好") + print(" %s Δ=%+.4f (%+.1f%%) t=%+.2f p=%.4f -> %s %s" + % (name, d, rel, t, p, verdict, + "**显著**" if p < 0.05 else "不显著")) + +# ── E11 / E12: regime shift ──────────────────────────────────────────── +print() +print("=" * 96) +print("regime shift (E11 单次 / E12 反复) —— T(s,a) 本身改变后的适应") +print("=" * 96) +for arm in ("replay",): + for tag, lbl in [("e11", "E11 单次漂移 @40"), ("e12", "E12 反复漂移 @30 每25")]: + opt = collect(os.path.join(RES, "oak_%sopt_s*" % tag), arm) + no = collect(os.path.join(RES, "oak_%snoopt_s*" % tag), arm) + if not opt["dirs"] and not no["dirs"]: + continue + print() + print("── %s (arm=%s) " % (lbl, arm) + "─" * 46) + print(" 有 Options : 最终 %s" % fmt(opt["final"])) + print(" 遗忘 %s" % fmt(opt["forget"])) + print(" 无 Options : 最终 %s" % fmt(no["final"])) + print(" 遗忘 %s" % fmt(no["forget"])) + t, pv = welch(opt["forget"], no["forget"]) + if np.isfinite(t): + d = np.nanmean(opt["forget"]) - np.nanmean(no["forget"]) + print(" 遗忘 Δ=%+.4f t=%+.2f p=%.4f %s" + % (d, t, pv, "**显著**" if pv < 0.05 else "不显著")) + print(" 机制: options=%s option_starts=%s" + % (fmt(opt["nopt"]), fmt(opt["starts"]))) + +# ── 步长 E 表 (E1~E5) ────────────────────────────────────────────────── +print() +print("=" * 96) +print("步长 E 表 (E1 固定步长 / E2 IDBD / E3 idbd-raw / E4 Autostep / E5 Continual-IDBD)") +print("=" * 96) +_rows = [] +fx = collect(os.path.join(RES, "oak_e1_fixedalpha_s*"), "replay") +if fx["dirs"]: + _rows.append(("E1 fixed-alpha (mu=0)", fx)) +for algo, lbl in [("idbd", "E2 IDBD"), ("idbd-raw", "E3 idbd-raw"), + ("autostep", "E4 Autostep"), ("cidbd", "E5 Continual-IDBD")]: + d = collect(os.path.join(RES, "oak_algo_%s_s*" % algo), "replay") + if d["dirs"]: + _rows.append((lbl, d)) +if _rows: + print(" %-24s %-30s %-30s %s" % ("臂", "any_time", "最差遗忘界", "最终 acc")) + for lbl, d in _rows: + print(" %-24s %-30s %-30s %s" % (lbl, fmt(d["any_time"]), fmt(d["forget"]), fmt(d["final"]))) + base = _rows[0][1] + print(" ── 相对 E1(固定步长)的差 ──") + for lbl, d in _rows[1:]: + ta, pa = welch(d["any_time"], base["any_time"]) + tf, pf = welch(d["forget"], base["forget"]) + print(" %-22s any_time Δ=%+.4f (p=%.3f) 遗忘 Δ=%+.4f (p=%.3f)" + % (lbl, np.nanmean(d["any_time"]) - np.nanmean(base["any_time"]), pa, + np.nanmean(d["forget"]) - np.nanmean(base["forget"]), pf)) + +# ── lm5 两档配置 ─────────────────────────────────────────────────────── +print() +print("=" * 96) +print("lm5 (多模态) E9 vs E10 —— 两档配置") +print("=" * 96) +for lbl, pa, pb in [("S4 短配置 (120 轮, 5 seed)", "oak5_e9_s*", "oak5_e10_s*"), + ("S5 长配置 (200 轮, 10 seed)", "oak5L_e9_s*", "oak5L_e10_s*")]: + _a, _b = collect5(os.path.join(RES, pa)), collect5(os.path.join(RES, pb)) + if not _a["dirs"] and not _b["dirs"]: + continue + print() + print("── %s " % lbl + "─" * 44) + print(" E9 无 Options: any_time %s" % fmt(_a["any_time"])) + print(" 遗忘 %s" % fmt(_a["forget"])) + print(" E10 有 Options: any_time %s" % fmt(_b["any_time"])) + print(" 遗忘 %s" % fmt(_b["forget"])) + print(" 机制(E10): options=%s starts=%s steps=%s" + % (fmt(_b["nopt"]), fmt(_b["starts"]), fmt(_b["steps"]))) + t, pv = welch(_b["any_time"], _a["any_time"]) + if np.isfinite(t): + print(" any_time Δ=%+.4f t=%+.2f p=%.4f %s" + % (np.nanmean(_b["any_time"]) - np.nanmean(_a["any_time"]), t, pv, + "**显著**" if pv < 0.05 else "不显著")) + +print() +print("=" * 96) +print("lm5 加长配置细节 (text 400 / wave 400 / causal 40k / rounds 200)") +print("=" * 96) +e9_5 = collect5(os.path.join(RES, "oak5L_e9_s*")) +e10_5 = collect5(os.path.join(RES, "oak5L_e10_s*")) +for lbl, d in [("lm5L E9 (无 Options)", e9_5), ("lm5L E10 (有 Options)", e10_5)]: + if not d["dirs"]: + continue + print(" %-24s any_time %s" % (lbl, fmt(d["any_time"]))) + print(" %-24s 遗忘 %s" % ("", fmt(d["forget"]))) + print(" %-24s 机制 options=%s starts=%s" % ("", fmt(d["nopt"]), fmt(d["starts"]))) +if e9_5["dirs"] and e10_5["dirs"]: + t, pv = welch(e10_5["any_time"], e9_5["any_time"]) + if np.isfinite(t): + print(" any_time Δ=%+.4f t=%+.2f p=%.4f %s" + % (np.nanmean(e10_5["any_time"]) - np.nanmean(e9_5["any_time"]), t, pv, + "**显著**" if pv < 0.05 else "不显著")) + +print() +print("=" * 96) +print("判读口径 (防止过度解读)") +print("=" * 96) +print(""" + 1. 如果 option_steps ≈ 0 -> **机制没被触发**, 不能得出「Options 无效」的结论。 + 必须先修触发条件 (option 起点匹配半径 / τ_U 过滤强度) 再重跑。 + 2. 如果 E10 与 E9 的差异 p > 0.05 -> 如实写成负面结论, 不用「方向一致」搪塞。 + 3. 多 seed 可靠性: std > mean/2 的指标不可用于结论 (用户定的标准)。 + 4. 本实验是**被动观测 + 离线/模型内反事实**, 没有真实 actuator。 + 口径必须是 "predictive/control-policy learning on passive observations", + 不能写成 "完成了磁通控制实验"。 +""") diff --git a/tests/analyze_rl.py b/tests/analyze_rl.py new file mode 100644 index 0000000000000000000000000000000000000000..e487d4c4d5dd3f8627adeb57bd40b3e00a227e3c --- /dev/null +++ b/tests/analyze_rl.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +"""IDBD vs Autostep vs value 的真实回路对照分析。 + +回答: **步长分化到底带不带来收益?** + +臂: + idbd IDBD, mu=0.05, alpha0=0.2 (我们之前唯一能找到的活点) + auto Autostep, mu=1e-2, alpha0=0.1 (论文推荐默认, **未针对本任务调参**) + auto2 Autostep, mu=0.05, alpha0=0.2 (IDBD 的活点; 看 Autostep 是否不敏感) + value 手工价值函数 (固定公式排序器) —— 已证等价于随机 + +判据: + 1) 步长是否分化: alpha_std > 0 且 max/min > 1.5 + 2) 分化是否带来收益: 相对 value 的 any_time 提升, Welch t 检验 + 3) Autostep 是否免调参: 两档 (mu=1e-2/alpha0=0.1) 与 (mu=0.05/alpha0=0.2) + 的**表现是否接近** —— 接近 = 免调参成立 + 4) 可靠标准: std < mean/2 +""" +import glob +import json +import math +import os +import re +import sys + +import numpy as np + +ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" + + +def t_sf(x, df): + """t 分布上尾概率 (stdlib 数值积分, 避免依赖 scipy)。""" + if x <= 0: + return 0.5 + (0.5 - t_sf(-x, df)) + c = math.gamma((df + 1) / 2) / (math.sqrt(df * math.pi) * math.gamma(df / 2)) + n, hi = 20000, x + 60.0 + h = (hi - x) / n + s = 0.0 + for i in range(n + 1): + t = x + i * h + w = 1 if i in (0, n) else (4 if i % 2 else 2) + s += w * c * (1 + t * t / df) ** (-(df + 1) / 2) + return s * h / 3 + + +rows = {} +for f in glob.glob(os.path.join(ROOT, "results/rl_*/lm4_wave_results.json")): + tag = os.path.basename(os.path.dirname(f)) + m = re.match(r"rl_(.+?)_s(\d+)$", tag) + if not m: + continue + arm, seed = m.group(1), int(m.group(2)) + try: + d = json.load(open(f)) + except Exception as e: + print(" [坏文件]", tag, e) + continue + v = d.get("replay") + if isinstance(v, dict) and v.get("any_time_acc") is not None: + rows.setdefault(arm, []).append((seed, v)) + +if not rows: + sys.exit("没有可用结果") + +print("=" * 100) +print("步长自适应在真实回路的效果 (lm4, replay 臂, stream=revisit, 12 轮)") +print("=" * 100) +print("%-8s %-3s %9s %8s %6s %9s %9s | %-24s %s" + % ("arm", "n", "any_time", "std", "CV%", "replay", "最差遗忘", + "alpha std / max-min", "algo")) +print("-" * 100) +for arm, vs in sorted(rows.items()): + at = np.array([v["any_time_acc"] for _, v in vs], float) + if len(at) < 2: + print("%-8s %-3d %9.4f %8s %6s %9s %9s" % (arm, len(at), at.mean(), + "-", "-", "-", "-") + " (待更多 seed)") + continue + rp = np.array([v["final_mean_acc"] for _, v in vs], float) + wf = np.array([v["worst_case_forget"] for _, v in vs], float) + st = vs[0][1].get("proposer_stats", {}) + al = st.get("alpha", {}) + cv = 100 * at.std(ddof=1) / at.mean() if len(at) > 1 else float("nan") + print("%-8s %-3d %9.4f %8.4f %6.1f %9.4f %9.4f | %-24s %s" + % (arm, len(at), at.mean(), at.std(ddof=1) if len(at) > 1 else 0, cv, + rp.mean(), wf.mean(), + "%.4f / %.2fx" % (al.get("std", 0), al.get("ratio_max_min", 0)), + st.get("algo", "-"))) + +print() +print("=== 判据 1: 步长是否分化 (alpha_std>0 且 ratio>1.5) ===") +for arm, vs in sorted(rows.items()): + al = vs[0][1].get("proposer_stats", {}).get("alpha", {}) + s, r = al.get("std", 0), al.get("ratio_max_min", 0) + print(" %-8s std=%.4f ratio=%.2f %s" % (arm, s, r, + "分化 ✓" if (s > 0 and r > 1.5) else ("未分化" if s == 0 else "弱"))) + +if len(rows) > 1: + print() + print("=== 判据 2: 两两 Welch t 检验 (any_time) ===") + keys = sorted(rows) + print(" %-26s %9s %7s %9s %s" % ("对比", "Δmean", "t", "p", "判定")) + for i, a in enumerate(keys): + for b in keys[i + 1:]: + A = np.array([v["any_time_acc"] for _, v in rows[a]], float) + B = np.array([v["any_time_acc"] for _, v in rows[b]], float) + if len(A) < 2 or len(B) < 2: + print(" %-26s %+9.4f %7s %9s %s" + % ("%s - %s" % (a, b), A.mean() - B.mean(), "-", "-", + "n<2, 待更多 seed")) + continue + se = math.sqrt(A.var(ddof=1) / len(A) + B.var(ddof=1) / len(B)) + t = (A.mean() - B.mean()) / se + df = se ** 4 / ((A.var(ddof=1) / len(A)) ** 2 / (len(A) - 1) + + (B.var(ddof=1) / len(B)) ** 2 / (len(B) - 1)) + p = 2 * t_sf(abs(t), df) + print(" %-26s %+9.4f %7.2f %9.4f %s" + % ("%s - %s" % (a, b), A.mean() - B.mean(), t, p, + "显著" if p < 0.05 else "不显著")) + +print() +print("=== 判据 3: Autostep 免调参性 (两档超参的表现差) ===") +if "auto" in rows and "auto2" in rows: + a1 = np.mean([v["any_time_acc"] for _, v in rows["auto"]]) + a2 = np.mean([v["any_time_acc"] for _, v in rows["auto2"]]) + print(" auto (论文默认 mu=1e-2,a0=0.1) %.4f vs auto2 (mu=0.05,a0=0.2) %.4f" + % (a1, a2)) + print(" 差 %.4f -> %s" % (abs(a1 - a2), + "免调参成立 (两档表现接近) ✓" if abs(a1 - a2) < 0.03 + else "仍对超参敏感 ✗")) +else: + print(" (需要 auto 与 auto2 两个臂都完成)") + +print() +print("=== 可靠标准 (leo: std < mean/2) ===") +for arm, vs in sorted(rows.items()): + at = np.array([v["any_time_acc"] for _, v in vs], float) + s = at.std(ddof=1) if len(at) > 1 else 0 + print(" %-8s std=%.4f mean/2=%.4f %s" + % (arm, s, at.mean() / 2, "✓" if s < at.mean() / 2 else "✗")) diff --git a/tests/analyze_wfr_independence.py b/tests/analyze_wfr_independence.py new file mode 100644 index 0000000000000000000000000000000000000000..be8b286b68020e0d252e214244528f90fd0eb540 --- /dev/null +++ b/tests/analyze_wfr_independence.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 +"""关键检验: WFR 波谱携带的是 |B| 之外的独立信息吗?""" +import numpy as np + +z = np.load("/tmp/lm4_feat_cache.npz") +dens, F = z["dens"], z["F"] +ok = np.isfinite(dens) & (dens > 0) & np.isfinite(F).all(axis=1) +dens, F = dens[ok], F[ok] +y = np.log10(dens) +qs = np.quantile(y, np.linspace(0, 1, 7)); qs[0] -= 1e-6; qs[-1] += 1e-6 +dom = np.digitize(y, qs[1:-1]) + +BANDS = 13 +FREQ = [6.4, 17.1, 28.6, 50.2, 90.7, 161.0, 285.8, 508.4, 903.4, 1610.0, 2862.0, 5086.0, 9036.0] +logMag = F[:, 0] +b285 = F[:, 4 + 6] # B@285.8Hz (最强波频段) +b161 = F[:, 4 + 5] +rms = F[:, 1] + + +def auc(scores, labels): + o = np.argsort(scores, kind="mergesort") + r = np.empty(len(scores)); r[o] = np.arange(1, len(scores) + 1) + n1 = int(labels.sum()); n0 = len(labels) - n1 + return (r[labels == 1].sum() - n1 * (n1 + 1) / 2) / (n1 * n0) + + +m = (dom == 2) | (dom == 3) +lab = (dom[m] == 3).astype(int) +d0 = (dom == 0); d5 = (dom == 5) +m05 = d0 | d5 +lab05 = dom[m05] == 5 +lab05 = lab05.astype(int) + +print("=== 1. 相关性 (与 logMag 有多冗余?) ===") +for name, v in [("log rms", rms), ("B@161Hz", b161), ("B@285.8Hz", b285)]: + r = float(np.corrcoef(v, logMag)[0, 1]) + print(f" corr({name:<10}, logMag) = {r:+.3f}") + +print("\n=== 2. 单特征 AUC ===") +print(f"{'特征':<22}{'D2vsD3':>10}{'D0vsD5':>10}") +for name, v in [("logMag", logMag), ("log rms", rms), ("B@285.8Hz", b285), ("B@161Hz", b161)]: + print(f" {name:<20}{auc(v[m], lab):>10.4f}{auc(v[m05], lab05):>10.4f}") + +def lda_auc(X, lab): + """多维线性判别 (类均值方向) 的 AUC""" + mu, sd = X.mean(0), X.std(0) + 1e-9 + Xn = (X - mu) / sd + w = Xn[lab == 1].mean(0) - Xn[lab == 0].mean(0) + return auc(Xn @ w, lab) + + +print("\n=== 3. 残差化: 去掉 |B| 的线性贡献后, 波谱还剩多少区分力? ===") +print(f" (a) MAG 4 维 (线性判别) D2vsD3 AUC = {lda_auc(F[m][:, :4], lab):.4f}") +print(f" WFR 磁 13 维 D2vsD3 AUC = {lda_auc(F[m][:, 4:4+BANDS], lab):.4f}") + +# 用最小二乘把 b285 的 |B| 成分去掉 +A = np.vstack([logMag, np.ones_like(logMag)]).T +coef, *_ = np.linalg.lstsq(A, b285, rcond=None) +resid = b285 - A @ coef +print(f" (b) B@285.8Hz 对 |B| 回归的 R² = {1 - resid.var()/b285.var():.3f}") +print(f" → 残差(resid) 的 D2vsD3 AUC = {auc(resid[m], lab):.4f} (若≈0.5 则纯属 |B| 的影子)") +print(f" → 原始 B@285.8Hz 的 AUC = {auc(b285[m], lab):.4f}") + +# 反向: logMag 去掉 b285 后还剩多少 +A2 = np.vstack([b285, np.ones_like(b285)]).T +c2, *_ = np.linalg.lstsq(A2, logMag, rcond=None) +resid2 = logMag - A2 @ c2 +print(f" (c) logMag 去掉 B@285.8Hz 后 D2vsD3 AUC = {auc(resid2[m], lab):.4f} (原始 {auc(logMag[m], lab):.4f})") + +print("\n=== 4. 频段\"有效性\"排名 (D2vsD3 信息量, 按 |AUC-0.5|) ===") +vals = [] +for i, f in enumerate(FREQ): + vals.append((f"B@{f:g}Hz", auc(F[m][:, 4 + i], lab))) + vals.append((f"E@{f:g}Hz", auc(F[m][:, 4 + BANDS + i], lab))) +vals.sort(key=lambda t: -abs(t[1] - 0.5)) +print(" 最有信息:") +for n_, a in vals[:6]: + print(f" {n_:<14} AUC={a:.4f}") +print(" 最没用(接近 0.5):") +for n_, a in vals[-6:]: + print(f" {n_:<14} AUC={a:.4f}") diff --git a/tests/analyze_wfr_peak.py b/tests/analyze_wfr_peak.py new file mode 100644 index 0000000000000000000000000000000000000000..cd14cc63c7d61051a448f8556b459d5bbf3e950f --- /dev/null +++ b/tests/analyze_wfr_peak.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +"""WFR 峰值的物理性质: 峰值频率随 |B| 移动(共振) 还是固定(波种群)?""" +import numpy as np + +z = np.load("/tmp/lm4_feat_cache.npz") +dens, F = z["dens"], z["F"] +ok = np.isfinite(dens) & (dens > 0) & np.isfinite(F).all(axis=1) +dens, F = dens[ok], F[ok] +y = np.log10(dens) +qs = np.quantile(y, np.linspace(0, 1, 7)); qs[0] -= 1e-6; qs[-1] += 1e-6 +dom = np.digitize(y, qs[1:-1]) + +BANDS = 13 +FREQ = np.array([6.4, 17.1, 28.6, 50.2, 90.7, 161.0, 285.8, 508.4, 903.4, 1610.0, 2862.0, 5086.0, 9036.0]) +bpow = F[:, 4:4 + BANDS] +logMag = F[:, 0] + +# 每个样本的谱峰频段 (扣除低频仪器底: 只看 >=90.7Hz 的 4~13 段) +sub = bpow[:, 4:] +peak = sub.argmax(axis=1) + 4 +peak_f = FREQ[peak] + +print("=== 1. 谱峰频段分布 (按密度域) ===") +print(f"{'域':<5}{'样本':>9} " + "".join(f"{f:>7g}" for f in FREQ[4:])) +for d in range(6): + m = dom == d + cnt = np.bincount(peak[m], minlength=BANDS)[4:] + pct = cnt / max(cnt.sum(), 1) * 100 + print(f"D{d:<4}{m.sum():>9} " + "".join(f"{p:>7.1f}" for p in pct)) + +print("\n=== 2. 峰值频率 vs |B| ===") +lf = np.log10(peak_f) +r = float(np.corrcoef(lf, logMag)[0, 1]) +print(f" corr(log10 峰值频率, log10|B|) = {r:+.3f}") +print(f" → {'随磁场移动 → 共振特征(下杂波/离子回旋)' if abs(r) > 0.3 else '基本固定 → 波种群(嘶声带)'}") +print(f" 各域中位峰值频率: " + ", ".join( + f"D{d}={np.median(peak_f[dom==d]):.0f}Hz" for d in range(6))) + +print("\n=== 3. 下杂波频率参考 (flh ≈ 2.07 * B[nT]) ===") +for B in [50, 100, 200, 400, 800]: + print(f" |B|={B:>4} nT → flh ≈ {2.07*B:>7.0f} Hz") +print(" (与谱峰 161~508 Hz 对比)") diff --git a/tests/audit_runs.py b/tests/audit_runs.py new file mode 100644 index 0000000000000000000000000000000000000000..7855b344c83b06b17c62ec3a9599af296bacddf5 --- /dev/null +++ b/tests/audit_runs.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +"""数据完整性核对: 日志里的每个 run 是否都有合法产物。 + +起因: driver 里 `echo "... $(date) ... rc=$?"` 的 `$(date)` 会先执行并重置 `$?`, +所以 **rc 值全部不可信**。不能靠 rc 判断成功与否, 必须**直接核对产物**。 + +判据 (每个 run 四查): + 1. 结果目录存在 + 2. lm4_wave_results.json 存在且是**合法 JSON** (非截断) + 3. 含有 replay 臂的 any_time_acc (主指标在) + 4. 记录的 seed 与日志一致 + +任何一项不满足 = 该 run 的结论不可用。 +""" +import glob +import json +import os +import re +import sys + +ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" + +# (日志文件, 行内种子变量名) +LOGS = ["results/rl_driver.log", "results/rl_ext.log", "results/rl_know.log", + "results/rl_seed8.log", "results/s8b.log", "results/rm_rerun.log"] + +total_ok, total_bad = 0, 0 +bad_detail = [] + +for lg in LOGS: + p = os.path.join(ROOT, lg) + if not os.path.exists(p): + print(" [缺日志] %s" % lg) + continue + lines = [l.strip() for l in open(p, errors="ignore") if re.search(r"\brc=\d+", l)] + ok = bad = 0 + for l in lines: + m = re.search(r"seed=(\d+)", l) + if not m: + continue + seed = m.group(1) + # 清掉前缀/后缀噪声, 拿 tag 关键词 + core = l.split("]")[-1].strip().split("seed=")[0].strip().replace(" ", "_") + # 在 results/ 下找含该 seed 且名字贴近 core 的目录 + cands = [d for d in glob.glob(os.path.join(ROOT, "results", "*_s" + seed)) + if os.path.isdir(d)] + hit = None + for d in cands: + j = os.path.join(d, "lm4_wave_results.json") + if not os.path.exists(j): + continue + try: + dd = json.load(open(j)) + except Exception: + bad_detail.append((lg, l[:60], "JSON 非法/截断")) + continue + v = dd.get("replay") if isinstance(dd, dict) else None + if isinstance(v, dict) and v.get("any_time_acc") is not None: + hit = d + break + if hit: + ok += 1 + else: + bad += 1 + bad_detail.append((lg, l[:70], "无合法产物")) + total_ok += ok + total_bad += bad + print(" %-26s 日志 %2d 行 -> 合法产物 %2d, 缺失 %2d" % (os.path.basename(lg), len(lines), ok, bad)) + +print() +print("=" * 84) +print("总计: 日志声明 %d 个 run, 合法产物 %d, **缺失/损坏 %d**" + % (total_ok + total_bad, total_ok, total_bad)) +print("=" * 84) +if bad_detail: + print("有问题的条目前 12 条:") + for lg, l, why in bad_detail[:12]: + print(" [%s] %s <- %s" % (os.path.basename(lg), l, why)) +else: + print("全部日志行都有对应的合法产物 -> **rc 虽不可信, 但数据完整**") + +print() +print("注: 本核对只看产物是否合法可读, 不重判结论。") +print(" rc=0 不可信这一点已写入 docs/stepsize_idbd_results.md 的错误表 (⑥)。") diff --git a/tests/bench_rr_sentinel.py b/tests/bench_rr_sentinel.py new file mode 100644 index 0000000000000000000000000000000000000000..af86577c9b82a09d0219aa632cd68e7be2eade07 --- /dev/null +++ b/tests/bench_rr_sentinel.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""bench_rr_sentinel.py — **剂量响应 + 内部哨兵**(配方 §2 / Pitfall 34)。 + +## 为什么必须有这一个 + +一个"加了机制反而更差"的负结果有两种可能读法, 在真实任务上**无法区分**: + (a) 机制的**执行**有代价(它真的有害), 或 + (b) 它被发现时所依赖的结构是**陈旧**的, 或 harness 自己坏了。 + +区分方法: 扫"机制被允许动作的程度" `opt_prob ∈ {0, 0.25, 0.5, 1.0}`, 看**伤害 +是否随剂量增长**: + + 伤害随剂量增长 -> 定位到机制的**执行** + 伤害对剂量平坦 -> 定位到别处(陈旧结构 / harness) + +## 内部哨兵(先读这个, 再读任何剂量点) + +`opt_prob = 0` 必须**逐位复现**对照臂(`SkillAgent(mode="primitive")`)。 + + 哨兵不一致 -> 诊断自身的实现错了, 其余剂量点**全部作废**(不解读)。 + +一个坏掉的诊断看起来和"机制有害"一模一样 —— 所以哨兵不是一个可选检查。 +""" +import sys +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.skill_agent import DEFAULT_REGIMES, SkillAgent # noqa: E402 +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 + +EPISODES = 300 +SEEDS = 3 +DOSES = [0.0, 0.25, 0.5, 1.0] + + +def run_once(agent, chain, episodes_per, thresh=0.8, window=20): + """跑一条链, 返回每段的 (T_adapt, 成功率, 成功平均步数, 执行步数占比)。""" + out = [] + for reg in chain: + agent.mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + agent._rr_ensure() if isinstance(agent, RRSkillAgent) else None + succ, steps_ok, t_adapt = [], [], None + for ep in range(episodes_per): + ok, nstep = agent.run_episode() + succ.append(1.0 if ok else 0.0) + if ok: + steps_ok.append(int(nstep)) + if t_adapt is None and len(succ) >= window and \ + float(np.mean(succ[-window:])) >= thresh: + t_adapt = ep + 1 + out.append({"t_adapt": int(t_adapt if t_adapt is not None else episodes_per), + "succ": float(np.mean(succ[-window:])), + "steps": float(np.mean(steps_ok)) if steps_ok else np.nan}) + return out + + +def main(): + chain = [dict(r) for r in DEFAULT_REGIMES] * 2 + print("=" * 88) + print("剂量响应 + 内部哨兵 | %d seed %d 回合/段 链 A B C A B C" + % (SEEDS, EPISODES)) + print("=" * 88) + + # ── 对照臂(哨兵要对的基准) ───────────────────────────────────── + ctrl = [] + for si in range(SEEDS): + mdp = KeyDoorMDP(n_pos=8, horizon=30, **chain[0]) + ctrl.append(run_once(SkillAgent(mdp, mode="primitive", seed=42 + si), + chain, EPISODES)) + ctrl_T = np.array([[r["t_adapt"] for r in rows] for rows in ctrl], float) + ctrl_S = np.array([[r["steps"] for r in rows] for rows in ctrl], float) + print("\n对照 primitive T=%s" % " ".join("%.0f±%.0f" % (m, s) for m, s + in zip(ctrl_T.mean(0), ctrl_T.std(0)))) + print(" S=%s" % " ".join("%.1f±%.1f" % (m, s) for m, s + in zip(np.nanmean(ctrl_S, 0), + np.nanstd(ctrl_S, 0)))) + + # ── 剂量扫描 ──────────────────────────────────────────────────── + table = {} + for dose in DOSES: + rows_all = [] + for si in range(SEEDS): + mdp = KeyDoorMDP(n_pos=8, horizon=30, **chain[0]) + ag = RRSkillAgent(mdp, mode="rr", seed=42 + si, opt_prob=dose) + rows_all.append(run_once(ag, chain, EPISODES)) + T = np.array([[r["t_adapt"] for r in rows] for rows in rows_all], float) + S = np.array([[r["steps"] for r in rows] for rows in rows_all], float) + table[dose] = {"T": T, "S": S, + "bk": None} + print("\n剂量 opt_prob=%.2f" % dose) + print(" T=%s" % " ".join("%.0f±%.0f" % (m, s) for m, s + in zip(T.mean(0), T.std(0)))) + print(" S=%s" % " ".join("%.1f±%.1f" % (m, s) for m, s + in zip(np.nanmean(S, 0), np.nanstd(S, 0)))) + + # ── ★ 哨兵检: dose=0 必须复现对照 ─────────────────────────────── + print() + print("=" * 88) + z = table[0.0] + sent_T = bool(np.array_equal(z["T"], ctrl_T)) + # S 可能因 NaN 无法逐位比 -> 退化为 allclose(nan-equal) + sent_S = bool(np.allclose(z["S"], ctrl_S, equal_nan=True)) + print("★ 哨兵检(dose=0 vs 对照 primitive): T %s S %s" + % ("逐位相同 ✓" if sent_T else "**不同 ✗**", + "逐位相同 ✓" if sent_S else "**不同 ✗**")) + if not (sent_T and sent_S): + print(" ⇒ 哨兵不一致: **诊断自身的实现有误**, 其余剂量点全部作废。") + print(" 不要在此状态下解读任何剂量结论。") + return 1 + print(" ⇒ 哨兵通过, 剂量点可解读。") + + # ── 伤害是否随剂量增长? ───────────────────────────────────────── + print() + print("=" * 88) + print("★ 剂量趋势(相对对照): ΔT 正=更慢, ΔS 正=步数更多(更差)") + print("=" * 88) + for dose in DOSES: + dT = (table[dose]["T"].mean(0) - ctrl_T.mean(0)) + dS = (np.nanmean(table[dose]["S"], 0) - np.nanmean(ctrl_S, 0)) + print(" opt_prob=%.2f ΔT: %s (|均|%5.1f) ΔS: %s (|均|%4.2f)" + % (dose, " ".join("%+6.0f" % x for x in dT), np.abs(dT).mean(), + " ".join("%+6.2f" % x for x in dS), np.abs(dS).mean())) + print() + print(" 读法: 伤害随 opt_prob 单调增长 -> 定位到机制的**执行**;") + print(" 对剂量平坦 -> 定位到**陈旧结构 / harness**, 不在这里。") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/benchmark_a_prime.py b/tests/benchmark_a_prime.py new file mode 100644 index 0000000000000000000000000000000000000000..59c4a1367f472fafcb6803d0129585c03bbbee52 --- /dev/null +++ b/tests/benchmark_a_prime.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""benchmark_a_prime.py — A′ 四象限可辨识性 + 检测指标(C4/C5)。 + +## 要回答的唯一问题 + + **A1 能不能把 S2(新但稳定)与 S3(熟悉但变了)区分开?** + + 若 A1(S2) ≈ A1(S3) ⇒ A1 仍然只是 novelty detector(证伪成立) + 若 A1(S3) >> A1(S2) ⇒ A1 真的捕捉到了 dynamics(可辨识) + +## 四象限怎么"构造"出来, 而不是事后切窗 + +每个 transition 发生的那一刻, 有两件**我们知道真值**的事: + + novelty : 这一步的 `n_before(s,a)` —— 更新**前**的计数 + dynamics : 这一步是在 `shift_at` 之前还是之后 + +于是四象限是**逐步标注**出来的 2x2: + + | | 动力学未变 (t=shift) | + | 熟悉 | S1 n>=FAM | S3 n>=FAM | + | 新 | S2 n< NOV | S4 n< NOV | + +**S2 与 S3 是同一张表里的两个格子** —— 用 n 桶把 novelty 对齐之后, +"新"与"变"才可比。这是本文件的全部要点。 +""" +import json +import sys +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) # 仓库根 —— +# ★ 必须是**仓库根**(`parents[1]`), 不是 `hibs_lnn/` 本身。插错成 +# `.../headless/hibs_lnn` 会让 `import hibs_lnn.rr_agent` 找不到包 +# (`ModuleNotFoundError: No module named 'hibs_lnn'`), 因为那正是包**内部**。 +# 脚本目录会自动进 sys.path, 所以 `from benchmark_three_state import ...` +# 仍然可用。 + +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 +from hibs_lnn.stochastic_keydoor import ( # noqa: E402 + NonStationaryKeyDoor, StochasticKeyDoor) +from benchmark_three_state import FROZEN # noqa: E402 + +NOV, FAM = 3, 10 # 新: n_before < 3 熟悉: n_before >= 10 +CHANNELS = ("N", "A1", "A2", "A3") + + +class _LogRRSkillAgent(RRSkillAgent): + """逐步记录 (t, s, a, n_before, 各通道读数)。 + + `n_before` 必须是**更新前**的计数 —— 它就是那一步的 novelty 真值。 + """ + + def __init__(self, *a, **kw): + super().__init__(*a, **kw) + self.rows = [] + + def _rr_dval(self, s, a, s2): # noqa: D102 + d = self._rr_T.get((int(s), int(a))) + n_before = sum(d.values()) if d else 0 + v = super()._rr_dval(s, a, s2) + self.rows.append({ + "t": int(getattr(self.mdp, "t_total", len(self.rows))), + "s": int(s), "a": int(a), "n": int(n_before), + "v": float(v), + }) + return v + + +def log_channel(channel, env, episodes=140, seed=7): + """在一个环境上跑一遍, 返回该通道的逐步日志。""" + ag = _LogRRSkillAgent(env, mode="rr", seed=seed, rr_v_main="learned", + **dict(FROZEN, rr_three_state=True, rr_dyn=channel)) + for _ in range(episodes): + ag.run_episode() + return ag.rows + + +def quad_table(rows, shift_at): + """把逐步日志切成四象限, 返回 {(cell, ch): mean} 的统计。""" + out = {} + for r in rows: + cell = ("S1" if r["n"] >= FAM else + "S2" if r["n"] < NOV else None) + if cell is None: + continue + if r["t"] >= shift_at: + cell = {"S1": "S3", "S2": "S4"}[cell] + out.setdefault(cell, []).append(r["v"]) + return {c: (float(np.mean(v)), len(v)) for c, v in out.items()} + + +# ── 指标(A′-4)────────────────────────────────────────────────────── +def detection_metrics(rows, shift_at, thresh=None, win=100): + """在 shift 附近算 T_detect / FP / FN / AUC / discrimination。 + + * 触发 = 读数超过 `thresh`。默认 `pre_mean + 2·pre_std`。 + ★ 阈值只能从"变之前"的数据里定 —— 用全段分位数定阈值等于偷看答案。 + ★ **不能用 pre 的 95 分位**:`_rr_dval` 在 `n<2` 时返回满值 1.0, + 热身期到处都是这种步 ⇒ 95 分位 = 1.0000 ⇒ 没有任何步能超过它 + ⇒ `T_detect = None`、FN 恒 1(第一版的实测就是这个指纹: + 四个通道的阈值全是 1.0000、T_detect 全是 None)。均值+2σ 不会被 + 饱和步锁死在 1.0。 + * `T_detect − T_shift` = 检测延迟(核心指标)。 + * AUC = 把"shift 后的步"与"shift 前的步"分开的可分性(Mann-Whitney)。 + """ + pre = np.array([r["v"] for r in rows if r["t"] < shift_at], float) + post = np.array([r["v"] for r in rows if r["t"] >= shift_at], float) + if pre.size == 0 or post.size == 0: + return None + if thresh is None: + # ★ 阈值估计必须**排除饱和步**(n<2 时 `_rr_dval` 返回满值 1.0)。 + # 否则 μ+2σ 会被那些 1.0 撑到 **超过指标上限**(实测 A1 的阈值 + # 算成 1.0680 > 1.0) ⇒ 永远没有步能超过它 ⇒ T_detect=None、 + # FN 恒 1。那不是"检测不到", 是**阈值不可达**。 + # A 类读数恒在 [0,1], 所以阈值必须落在 [0,1] 内才有意义。 + pre_ok = np.array([r["v"] for r in rows + if r["t"] < shift_at and r["n"] >= 2], float) + base = pre_ok if pre_ok.size >= 10 else pre + thr = float(base.mean() + 2.0 * base.std()) + thr = float(min(max(thr, 1e-6), 1.0)) + else: + thr = float(thresh) + # 检测延迟: shift 后第一个超过阈值、且随后 win 步里多数都超的时刻 + t_det = None + for i, r in enumerate(rows): + if r["t"] < shift_at or r["v"] <= thr: + continue + nxt = [q["v"] for q in rows[i:i + win]] + if sum(v > thr for v in nxt) > 0.5 * len(nxt): + t_det = r["t"] + break + fp = float(np.mean(pre > thr)) if pre.size else float("nan") + fn = 1.0 if t_det is None else 0.0 + # AUC: P(读数(shift后) > 读数(shift前)) —— Mann-Whitney 形式 + if pre.size and post.size: + gt = (post[:, None] > pre[None, :]).mean() + eq = (post[:, None] == pre[None, :]).mean() + auc = float(gt + 0.5 * eq) + else: + auc = float("nan") + return {"thr": thr, "t_det": t_det, + "delay": (None if t_det is None else t_det - shift_at), + "fp": fp, "fn": fn, "auc": auc, + "pre_mean": float(pre.mean()), "post_mean": float(post.mean())} + + +def main(): + SH, EP = 400, 140 + print("=" * 104) + print("A′ 四象限可辨识性(C4):**A1 能不能把 S2(新但稳定)与 S3(熟悉但变了)分开?**") + print(f" 熟悉 = n_before >= {FAM} 新 = n_before < {NOV} shift @ t={SH}") + print("=" * 104) + + results = {} + # 环境 A: 无 shift(所有格子都在"动力学未变"那一列) + envs = { + "E1 平稳随机(无 shift)": lambda: StochasticKeyDoor(slip=0.10, seed=7), + "E2 非平稳(shiftt@400)": lambda: NonStationaryKeyDoor( + slip0=0.10, slip1=0.60, shift_at=SH, seed=7), + } + for nm, mk in envs.items(): + print(f"\n── {nm} " + "─" * (96 - len(nm))) + print(f" {'通道':>4} {'S1熟/静':>10} {'S2新/静':>10} {'S3熟/变':>10} " + f"{'S4新/变':>10} {'S3−S2':>9} {'S3/S2':>7} {'N(S2)':>7}") + for ch in CHANNELS: + rows = log_channel(ch, mk(), EP, 7) + q = quad_table(rows, SH) + g = lambda c: q.get(c, (float("nan"), 0))[0] # noqa: E731 + s2, s3 = g("S2"), g("S3") + disc = s3 - s2 + ratio = (s3 / s2) if s2 and s2 > 1e-12 else float("inf") + results.setdefault(nm, {})[ch] = { + "S1": g("S1"), "S2": s2, "S3": s3, "S4": g("S4"), + "disc": disc, "n_S2": q.get("S2", (0, 0))[1], + "n_S3": q.get("S3", (0, 0))[1], + } + print(f" {ch:>4} {g('S1'):>10.4f} {s2:>10.4f} {s3:>10.4f} " + f"{g('S4'):>10.4f} {disc:>+9.4f} {ratio:>7.2f} " + f"{q.get('S2',(0,0))[1]:>7}") + + print() + print("=" * 104) + print("A′ 检测指标(C5): T_detect/T_shift · FP · FN · AUC(阈值只从 shift **前** 定)") + print("=" * 104) + print(f" {'通道':>4} {'阈值':>8} {'T_detect':>9} {'延迟':>6} {'FP':>7} " + f"{'FN':>4} {'AUC':>7} {'变前均值':>10} {'变后均值':>10}") + met = {} + for ch in CHANNELS: + rows = log_channel(ch, NonStationaryKeyDoor( + slip0=0.10, slip1=0.60, shift_at=SH, seed=7), EP, 7) + m = detection_metrics(rows, SH) + met[ch] = m + if m is None: + print(f" {ch:>4} (样本不足)") + continue + print(f" {ch:>4} {m['thr']:>8.4f} {str(m['t_det']):>9} " + f"{str(m['delay']):>6} {m['fp']:>7.2f} {m['fn']:>4.0f} " + f"{m['auc']:>7.3f} {m['pre_mean']:>10.4f} {m['post_mean']:>10.4f}") + + # ── 判定 ── + print() + print("=" * 104) + print("判定") + print("=" * 104) + ok = True + e2 = results["E2 非平稳(shiftt@400)"] + + # ★ 主判据 = **n 对齐的 S1 vs S3**: 同为"熟悉"状态, 只有动力学变了。 + # 不能用 S2 vs S3 —— S2 是首次访问步, 而 `_rr_dval` 在 `n<2` 时返回 + # 满值 1.0, 于是 S2 在所有通道上都被顶到 0.7~0.8, 必然压过 S3。 + # 那是**饱和分支**的指纹, 不是"A 类分不开新与变"的证据。 + d1 = e2["A1"]["S3"] - e2["A1"]["S1"] + dN = e2["N"]["S3"] - e2["N"]["S1"] + a1_auc = met.get("A1", {}).get("auc", float("nan")) + n_auc = met.get("N", {}).get("auc", float("nan")) + print(" 【主判据】n 对齐(同为熟悉状态)的 S1 -> S3:") + print(f" A1: {e2['A1']['S1']:.4f} -> {e2['A1']['S3']:.4f} ({d1:+.4f})") + print(f" N : {e2['N']['S1']:.4f} -> {e2['N']['S3']:.4f} ({dN:+.4f})") + print(f" A2: {e2['A2']['S1']:.4f} -> {e2['A2']['S3']:.4f} " + f"({e2['A2']['S3'] - e2['A2']['S1']:+.4f})") + print(f" AUC: A1 {a1_auc:.3f} vs N {n_auc:.3f}") + print() + print(" 【诊断, 非判据】原始 S2 vs S3(被 `n<2 -> 1.0` 饱和分支污染):") + print(f" S2 各通道 {[round(e2[c]['S2'], 3) for c in CHANNELS]}") + print(f" S3 各通道 {[round(e2[c]['S3'], 3) for c in CHANNELS]}") + print(" ⇒ S2 全面高于 S3。这是「首次访问报满分」造成的, 不代表" + "A 类分不开新与变。") + print() + checks = [ + (f"A1 在熟悉状态上对'变'敏感 (S3−S1 > +0.10) [{d1:+.4f}]", d1 > 0.10), + (f"N 在同样一对上保持平坦 (|S3−S1| < 0.05) [{dN:+.4f}]", + abs(dN) < 0.05), + (f"A1 的响应明显强于纯新颖度 (dA1 > dN + 0.10) " + f"[{d1:+.4f} vs {dN:+.4f}]", d1 - dN > 0.10), + (f"A1 的 AUC 明显高于 N 的 AUC (>0.10) " + f"[{a1_auc:.3f} vs {n_auc:.3f}]", a1_auc - n_auc > 0.10), + ] + for name, good in checks: + print(f" {'PASS' if good else 'FAIL'} {name}") + ok = ok and good + print(f"\n {'A 类在本环境上具有可辨识性' if ok else 'A 类仍不可辨识 —— 记录为 coverage-only'}") + with open("results/a_prime.json", "w") as f: + json.dump({"quadrants": results, "metrics": met, + "shift_at": SH, "NOV": NOV, "FAM": FAM}, f, + indent=2, ensure_ascii=False) + print(" -> results/a_prime.json") + return 0 if ok else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/benchmark_b_matrix.py b/tests/benchmark_b_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..40037e62461b1640688564ebc18c59f2d7e337f1 --- /dev/null +++ b/tests/benchmark_b_matrix.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python3 +"""benchmark_b_matrix.py — 用户框架里的 **B 矩阵** (在 KeyDoorMDP 上)。 + +## 测什么 + + B2/B3 A→B 适应速度 T_adapt + B4/B5 A→B→C 反复适应 + B6/B7 A→B→A **知识复用** T_A^(2) < T_A^(1)? + B8/B9 多 regime sample efficiency R(N) + +对照: `primitive` (纯 Q-learning) vs `rediscover` (Q + 跨 regime 保留的技能库)。 + +## 为什么这个环境能回答, lm4 不能 + + lm4 : A ≡ 0 (动作完全可交换) -> option 无空间, 测不出 + KeyDoor : A ∈ [-1,+1], 33% 显著 -> **目标导向的多步行为真的有价值** + +## 判据 (用户给的) + + 若 option 在某项上优于 primitive -> 那就是 option 体现价值的地方 + 若各项都不优于 (且 CI 不含 0) -> 才有资格讨论"option 不适用" +""" +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from hibs_lnn.skill_agent import DEFAULT_REGIMES, run_regime_chain # noqa: E402 + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--episodes-per", type=int, default=300) + ap.add_argument("--seeds", type=int, default=5) + ap.add_argument("--modes", default="primitive,rediscover") + ap.add_argument("--cycles", type=int, default=2, + help="regime 链重复几轮 (2 = A B C A B C)") + ap.add_argument("--n-pos", type=int, default=8) + ap.add_argument("--out", default=str(ROOT / "results" / "b_matrix.json")) + a = ap.parse_args() + + chain = DEFAULT_REGIMES * a.cycles + modes = [m.strip() for m in a.modes.split(",") if m.strip()] + print("regime 链: %s" % [f"{r['key_pos']}/{r['door_pos']}/{r['goal_pos']}" for r in chain]) + print("模式: %s seed x %d episodes/regime=%d" + % (modes, a.seeds, a.episodes_per)) + print() + + all_rows = {} + for mode in modes: + per_seed = [] + for si in range(a.seeds): + rows, agent = run_regime_chain(mode=mode, chain=chain, n_pos=a.n_pos, + episodes_per=a.episodes_per, + seed=42 + si) + # ★ option 台账: visits/success 是否真的被回填? + # 修复前恒 0 -> select() 里 rate=success/visits 永远取默认 0.5 + # -> 技能库从不学习哪些 option 管用 (选择等于抛硬币)。 + bk = agent.option_bookkeeping() if hasattr(agent, "option_bookkeeping") \ + else {"total_visits": 0, "total_success": 0, "n_used": 0} + for r in rows: + r["bookkeeping"] = bk + per_seed.append(rows) + print(" [%s seed=%d] T_adapt=%s 最终成功率=%s n_options=%d 台账=%d/%d" + % (mode, 42 + si, [r["t_adapt"] for r in rows], + [round(r["final_succ"], 2) for r in rows], + rows[-1]["n_options"], bk["total_success"], bk["total_visits"]), + flush=True) + all_rows[mode] = per_seed + + print() + print("=" * 96) + print("★ 知识复用: 同一 regime 在第 2 轮 (重复出现) 是否恢复更快?") + print("=" * 96) + n_seg = len(chain) + half = n_seg // a.cycles if a.cycles > 1 else n_seg + print("%-14s %-28s %-28s %s" % ("mode", "第1轮 T_adapt (均值)", "第2轮 T_adapt (均值)", "Δ / 加速比")) + summary = {} + for mode in modes: + arr = np.array([[r["t_adapt"] for r in rows] for rows in all_rows[mode]], float) + first = arr[:, :half].mean(axis=1) + second = arr[:, half:half * 2].mean(axis=1) if a.cycles > 1 else first + d = second - first + try: + from scipy import stats + t, p = stats.ttest_rel(first, second) + except Exception: + t, p = float("nan"), float("nan") + speed = float(first.mean() / max(second.mean(), 1e-9)) + summary[mode] = {"first": float(first.mean()), "second": float(second.mean()), + "delta": float(d.mean()), "speedup": speed, + "p": float(p) if np.isfinite(p) else None} + print("%-14s %-28.2f %-28.2f %+.1f (%.2fx) p=%.3f" + % (mode, first.mean(), second.mean(), d.mean(), speed, + p if np.isfinite(p) else float("nan"))) + print() + print(" 判据: Δ < 0 且 p < 0.05 -> **复用成立** (第 2 轮确实更快)") + print() + print("=" * 96) + print("逐段 T_adapt (跨 seed 均值 ± std)") + print("=" * 96) + for mode in modes: + arr = np.array([[r["t_adapt"] for r in rows] for rows in all_rows[mode]], float) + print(" %-14s %s" % (mode, " ".join("%.0f±%.0f" % (m, s) + for m, s in zip(arr.mean(0), arr.std(0))))) + print() + if len(modes) >= 2 and "primitive" in modes: + prim = np.array([[r["t_adapt"] for r in rows] for rows in all_rows["primitive"]], float) + for mode in modes: + if mode == "primitive": + continue + arr = np.array([[r["t_adapt"] for r in rows] for rows in all_rows[mode]], float) + d = prim.mean(0) - arr.mean(0) # 正 = option 更快 + print(" %-14s 相对 primitive 的加速 (逐段, 正=更快): %s" + % (mode, " ".join("%+.0f" % x for x in d))) + + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + Path(a.out).write_text(json.dumps( + {"modes": modes, "seeds": a.seeds, "episodes_per": a.episodes_per, + "chain": chain, "summary": summary, "rows": all_rows}, + indent=1, ensure_ascii=False)) + print("\n已写 %s" % a.out) + + +if __name__ == "__main__": + main() diff --git a/tests/benchmark_oak_repro.py b/tests/benchmark_oak_repro.py new file mode 100644 index 0000000000000000000000000000000000000000..4b33413bbf6cd80203566128a3f1e047a7afe00f --- /dev/null +++ b/tests/benchmark_oak_repro.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +"""benchmark_oak_repro.py — 在论文的 gridworld 上复现 Fig.1 的正反例。 + +## 这是本项目的 ground truth + +论文 (arXiv 2202.03466) Fig.1 用两房间 gridworld 给出一个**明确的正反例**: + + 只用 primitive actions —— 基线 + + shortest-path (bottleneck) option —— 论文实测**比基线还慢** + + reward-respecting option —— 论文实测**明显更快** + +## 为什么这个基准对本项目是决定性的 + +本项目在 lm4 上测出「Options 有害」,但 lm4 的实测是 `A ≡ 0`(动作可交换、无时序结构) +—— 在那种环境里**任何类别的 option 都不可能有收益**,所以那个负结果测的是**基准的性质**。 + +在**论文这个有正反例的环境**里跑,就能回答一个完全不同、且可判定的问题: + + 我的 option + option model + planning 管线, 到底写对没有? + +判据(事前写死): + ✓ 若复现出「shortest-path 不比 primitives 快」(或更慢) + ✓ 且复现出「reward-respecting 明显更快」 + -> 管线是对的, 问题只在基准选择与 subtask 定义 + + ✗ 若两者都无量级差别 -> 我的 planner/option model 还有实现问题 + +用法: + python3 tests/benchmark_oak_repro.py # 两房间(确定性, Fig.1) + python3 tests/benchmark_oak_repro.py --four-room # 四房间(随机, Fig.6) + python3 tests/benchmark_oak_repro.py --bonus 0.1 1 10 100 # 论文 §6 的 bonus 权重扫描 +""" +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from hibs_lnn.gridworld import (FOUR_ROOM, TWO_ROOM, GridWorld, check_two_room, + find_bottlenecks, value_iteration) +from hibs_lnn.subtask_options import (build_option_model, planning_curve, + ops_to_tolerance, reward_respecting_subtask, + shortest_path_subtask) + + +def make_env(four_room: bool): + if four_room: + return GridWorld(FOUR_ROOM, gamma=0.99, stochastic=True), FOUR_ROOM + return GridWorld(TWO_ROOM, gamma=0.99, stochastic=False), TWO_ROOM + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--four-room", action="store_true") + ap.add_argument("--bonus", type=float, nargs="+", default=[1.0], + help="reward-respecting 的 bonus 权重 w̄ (论文 §6: 0.1/1/10/100)") + ap.add_argument("--tol", type=float, default=0.01, help="判定'已规划好'的容差") + ap.add_argument("--out", default=None) + args = ap.parse_args() + + env, layout = make_env(args.four_room) + name = "four-room (stochastic)" if args.four_room else "two-room (deterministic)" + print("=" * 74) + print("论文 gridworld: %s" % name) + print("=" * 74) + print(" 状态数 %d, 动作数 4, γ = %.2f, 随机 = %s" % (env.n_states, env.gamma, env.stochastic)) + + # ── bottleneck(论文用的子目标候选) ──────────────────────────────── + bt = find_bottlenecks(env) + cand = [(bt[s], s) for s in range(env.n_states) + if s != env.start and not env.is_goal[s]] + cand.sort(reverse=True) + hall = cand[0][1] + r, c = env.cells[hall] + print(" bottleneck(介数最高) = 状态 %d @(%d,%d), 介数 %.0f" % (hall, r, c, bt[hall])) + + # ── 真值 v*(s0):先把 primitive-only 收敛 ───────────────────────── + V_star_all, _ = value_iteration(env, n_sweeps=500) + v_star = float(V_star_all[env.start]) + print("\n 真值 v*(s0) = %.6f (primitive-only VI 收敛)" % v_star) + + results = {} + + # ═══ 条件 A:只用 primitive ═══════════════════════════════════════ + print("\n" + "-" * 74) + print("条件 A: 只用 primitive actions") + print("-" * 74) + ops_a, errs_a = planning_curve(env, {}, v_star, env.start, tol=args.tol, max_ops=300000) + print(" 收敛所需 look-ahead 操作数 = %d 最终误差 %.2e" % (ops_a[-1], errs_a[-1])) + results["primitive"] = dict(ops=ops_a[-1], err=errs_a[-1], + n_sweeps=len(ops_a)) + + # ═══ 条件 B: + shortest-path (bottleneck) option ═════════════════ + print("\n" + "-" * 74) + print("条件 B: + shortest-path (bottleneck) option [论文实测: 比 A 还慢]") + print("-" * 74) + sp = shortest_path_subtask(env, hall).solve(env) + M_sp = build_option_model(env, sp) + print(" subtask 解出的 option: π_o 非 STOP 的状态 %d 个, β_o=True 的状态 %d 个" + % (int((sp.policy != 4).sum()), int(sp.beta.sum()))) + ops_b, errs_b = planning_curve(env, {"sp": M_sp}, v_star, env.start, + tol=args.tol, max_ops=300000) + print(" 收敛所需 look-ahead 操作数 = %d 最终误差 %.2e" % (ops_b[-1], errs_b[-1])) + ratio_b = ops_b[-1] / ops_a[-1] + print(" 相对 A 的比值 = %.3f %s" + % (ratio_b, "✓ 确实不更快(复现论文)" if ratio_b > 0.98 else "✗ 变快了(与论文不符)")) + results["shortest_path"] = dict(ops=ops_b[-1], err=errs_b[-1], ratio=ratio_b) + + # ═══ 条件 C: + reward-respecting option ══════════════════════════ + print("\n" + "-" * 74) + print("条件 C: + reward-respecting option [论文实测: 明显更快]") + print("-" * 74) + feat = np.zeros(env.n_states) + feat[hall] = 1.0 # 特征 x_i = "在 bottleneck 处" 的一热指示 + for bw in args.bonus: + rr = reward_respecting_subtask(env, feat, V_star_all, bonus_weight=bw).solve(env) + M_rr = build_option_model(env, rr) + ops_c, errs_c = planning_curve(env, {"rr": M_rr}, v_star, env.start, + tol=args.tol, max_ops=300000) + ratio_c = ops_c[-1] / ops_a[-1] + n_stop = int(rr.beta.sum()) + print(" w̄ = %-6g look-ahead = %7d 相对 A = %.3f %s (β_o=True 状态 %d 个)" + % (bw, ops_c[-1], ratio_c, + "✓ 更快(复现论文)" if ratio_c < 0.98 else "✗ 没快", n_stop)) + results["reward_respecting_w%.4g" % bw] = dict( + ops=ops_c[-1], err=errs_c[-1], ratio=ratio_c, bonus=bw, n_stop=n_stop) + + # ═══ 条件 D: + 随机目标 option (负对照) ═══════════════════════════ + # 论文 Fig.1 的第三条曲线。**必须有无这一条**: 条件 C 的收益有可能只是 + # "有一个可以停的子任务" 带来的(短 option = 每次 backup 覆盖更多步), + # 与"目标**选得好**"无关。只有在同一环境下随机目标**明显更差**, 才能 + # 把收益归因到子任务的定义上, 而不是归因到"有 option"这件事本身。 + print("\n" + "-" * 74) + print("条件 D: + 随机目标 option [负对照: 若与 C 相当, 则收益来自'有option'而非'目标选得好']") + print("-" * 74) + # ★ 抽样数: 5 次太少 —— 单次运气目标(恰好跨过走廊)就会把均值拉低, + # 实测 5 次时随机臂均值 0.550 反而优于 rr 的 0.625。20 次 + 报中位数, + # 才是"随机目标"这个对照应有的统计量(分布右偏, 均值被幸运样本主导)。 + N_RND = 20 + rng = np.random.RandomState(0) + cand_non_hall = [s for s in range(env.n_states) + if s != env.start and not env.is_goal[s] and s != hall] + rnd_ops = [] + for k in range(min(N_RND, len(cand_non_hall))): + g = int(cand_non_hall[rng.randint(len(cand_non_hall))]) + fr = np.zeros(env.n_states) + fr[g] = 1.0 + rd = reward_respecting_subtask(env, fr, V_star_all, bonus_weight=1.0).solve(env) + M_rd = build_option_model(env, rd) + ops_d, errs_d = planning_curve(env, {"rnd": M_rd}, v_star, env.start, + tol=args.tol, max_ops=300000) + ratio_d = ops_d[-1] / ops_a[-1] + gr, gc = env.cells[g] + rnd_ops.append((ratio_d, g, gr, gc)) + results["random_goal_%d" % g] = dict(ops=ops_d[-1], err=errs_d[-1], + ratio=ratio_d, goal=int(g)) + rnd_arr = np.array([x[0] for x in rnd_ops], float) + mean_rnd = float(rnd_arr.mean()) if len(rnd_arr) else float("nan") + med_rnd = float(np.median(rnd_arr)) if len(rnd_arr) else float("nan") + print(" 随机目标 n=%d: 中位数 %.3f 均值 %.3f 最小 %.3f 最大 %.3f" + % (len(rnd_arr), med_rnd, mean_rnd, + rnd_arr.min() if len(rnd_arr) else float("nan"), + rnd_arr.max() if len(rnd_arr) else float("nan"))) + best3 = sorted(rnd_ops)[:3] + print(" 最幸运的 3 个: " + ", ".join("状态%d@(%d,%d)=%.3f" % (g, r, c, v) + for v, g, r, c in best3)) + print(" (注: 分布右偏 —— 均值会被'恰好跨走廊'的幸运目标主导, 主判据用**中位数**。)") + + # ═══ 判读(事前写死的判据) ════════════════════════════════════════ + print("\n" + "=" * 74) + print("判读(事前写死)") + print("=" * 74) + best_rr = min((v["ratio"] for k, v in results.items() + if k.startswith("reward_respecting")), default=None) + ok_b = ratio_b > 0.98 + ok_c = best_rr is not None and best_rr < 0.98 + # ③ 目标质量: rr(最佳)必须**优于随机目标的**中位数。用中位数不用均值 —— + # 随机目标的分布右偏(少数"恰好跨走廊"的幸运目标极快), 均值会被它们主导 + # 而低估"典型随机目标有多没用"。 + ok_d = bool(np.isfinite(med_rnd)) and best_rr is not None and best_rr < med_rnd + print(" ① shortest-path 不比 primitive 更快 : %s (比值 %.3f)" % ("✓" if ok_b else "✗", ratio_b)) + print(" ② reward-respecting 明显更快 : %s (最小比值 %s)" + % ("✓" if ok_c else "✗", "%.3f" % best_rr if best_rr else "n/a")) + print(" ③ reward-respecting < 随机目标中位数 : %s (rr %.3f vs 随机中位 %.3f, 均值 %.3f)" + % ("✓" if ok_d else "✗", best_rr if best_rr else float("nan"), + med_rnd, mean_rnd)) + if ok_b and ok_c and ok_d: + print("\n -> 管线正确且**目标质量确实是收益来源**: 三个方向(负向✓ 正向✓ 随机对照✓)同时成立。") + print(" 本项目在 lm4 上的负结果因此**确认**是基准选择 + subtask 定义的问题,") + print(" 而不是「option 这个机制没有价值」。") + elif ok_b and ok_c: + print("\n -> 正负两个方向复现, 但**随机对照未分开**: 收益可归因到'有 option'(时长),") + print(" 不能归因到'目标选得好'。写报告时必须这样措辞, 不可写'目标质量是关键'。") + else: + print("\n -> 管线仍有实现问题, 先别对任何 benchmark 下 option 的结论。") + + payload = dict(env=name, n_states=env.n_states, gamma=env.gamma, + bottleneck=hall, v_star=v_star, tol=args.tol, results=results) + out = args.out or ("results/oak_repro_%s.json" % ("four" if args.four_room else "two")) + Path(out).parent.mkdir(parents=True, exist_ok=True) + Path(out).write_text(json.dumps(payload, indent=2, ensure_ascii=False)) + print("\n JSON -> %s" % out) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/benchmark_rr_calib_chain.py b/tests/benchmark_rr_calib_chain.py new file mode 100644 index 0000000000000000000000000000000000000000..dd801e67fc96d44f7a6348d75b95679f1d48c2ae --- /dev/null +++ b/tests/benchmark_rr_calib_chain.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""benchmark_rr_calib_chain.py — **持续自我校准**在 regime 链上的消融。 + +## 为什么需要这个文件 + +`tests/test_self_calib.py` 证明了单 regime 内的闭环(检测 -> 淘汰 -> 退回基元层)。 +但「**持续**自我校准」的"持续"还没被测过: + + ① **持久化**: 台账跨 regime 存活吗? 还是每换一个世界就清零? + ② **遗忘**: 持久化如果配**不遗忘**, n 会累积到几百 -> 老的成功把新的失败 + 稀释掉, UCB 探索项也被压死 -> **陈旧的判断被永久锁住**。 + "持续校准"要跟踪的是**当前胜任度**, 不是历史平均。 + ③ **校准到哪个信号上**: option 自己的达成率, 还是主任务的推进量? + +## 两类故障 —— 这是能分辨的关键 + +如果链上所有 option 都是健康的, 校准就**无事可做**。所以注入一个 +**随 regime 变化**的故障: + + 段 1-3 (A B C): 全部健康 -> agent 对 `rr[door]` 建立**正面**先验 + 段 4-6 (A B C): `rr[door]` 坏掉 -> 看谁**多快**发现并停用它 + + **inert(惰性)** —— 硬到期上限压到 1 步。它"达不成自己的子目标" + (达成率 ~0.2), 但**它执行的那一步仍然是 π_o 的最优步**, 对主任务有益。 + ★ 这是**判据来源的判别器**: 按内部判据淘汰它 -> 白损失好步 -> 变差。 + **harm(有害)** —— 反转 π_o(LEFT<->RIGHT), 主动朝反方向走。 + 内部判据与主任务判据**同时**判它坏。淘汰它应当**有益**。 + +一个能自我校准的系统必须能分开这两者 —— 否则它是在校准到错误的信号上。 +""" +import sys +import json +import argparse +import numpy as np +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hibs_lnn.rr_agent import RRSkillAgent +from hibs_lnn.skill_mdp import KeyDoorMDP, LEFT, RIGHT +from hibs_lnn.skill_agent import DEFAULT_REGIMES + +CHAIN = DEFAULT_REGIMES * 2 # A B C A B C +FAULT_AT = 3 # 从第 4 段(0 基)起 rr[door] 坏掉 +FAULT_OPT = "rr[door]" + +ARMS = { + # 名字 选择规则 遗忘 淘汰判据 分数 + "uniform": dict(rr_select_rule="uniform"), + "internal-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20), + "internal-noforget": dict(rr_select_rule="calibrated", + rr_forget_hl=float("inf")), + "internal-nopersist": dict(rr_select_rule="calibrated", rr_persist=False), + "adv-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="adv", rr_score="adv"), + # ★ 势函数质量对照: `rr_pot="exact"` 用模型 VI 的 V_main(干净信号), + # 用来分离"信号质量差"与"机制结构错"两个因素。 + # learned 势函数在早期是乐观常数(≈1.0) -> adv≈噪声, 全判负 -> 全杀。 + "advEX-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="adv", rr_score="adv", + rr_pot="exact"), + "bothEX-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="both", rr_score="adv", + rr_pot="exact"), + "either-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="either", rr_score="adv"), + # ══ ★ 对"在线可用势函数"问题的解: option 的**学习价值**(SMDP)══════ + # 不需要任何势函数 —— advantage = Q_opt(s,o) − V_base(s), + # 两边都从 agent 自己的 Q 表出来, 同一尺度, 同一个初值。 + "advq-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="advq", rr_score="advq"), + "advq-noforget": dict(rr_select_rule="calibrated", + rr_forget_hl=float("inf"), + rr_retire_rule="advq", rr_score="advq"), + "advq-nopersist": dict(rr_select_rule="calibrated", rr_persist=False, + rr_retire_rule="advq", rr_score="advq"), + # 对照: 保留旧势函数差分, 但**只换信号源**(advq 判据 + rate 选择) + "advqselect-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="internal", rr_score="advq"), + # ══ 诊断: Q_adv 这个信号本身有没有信息量? (只报数, 不淘汰)══════ + # 若健康段 Q_adv > 0 而故障段 < 0, 信号是好的, 问题只在机制; + # 若两边都是同一符号, 信号本身不可用 —— 这是完全不同的结论。 + "advq-none-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="none", rr_score="advq"), + # ══ ★ 原谅: 退休可逆 ══════════════════════════════════════════ + "advq-forgive": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="advq", rr_score="advq", + rr_forgive_p=0.10), + # ══ ★★ 在线可用势函数(GVF)════════════════════════════════════════ + # `rr_pot="gvf"`: 中性初始化(全 0)的状态价值表, TD 从**每个转移**学, + # 不论那一步是谁出的动作 -> 无 oracle, option 绕过 Q 也不受影响。 + # 实测 Φ ∈ [0, 0.999] 15 个不同值, advq = +0.0495/+0.0356 —— 符号与 + # `exact`(oracle 对照)一致, 而 `learned` 是 −0.275 的初值 artifact。 + "advqGVF-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="advq", rr_score="advq", + rr_pot="gvf"), + "advqGVF-noforget": dict(rr_select_rule="calibrated", + rr_forget_hl=float("inf"), + rr_retire_rule="advq", rr_score="advq", + rr_pot="gvf"), + "advqGVF-nopersist": dict(rr_select_rule="calibrated", rr_persist=False, + rr_retire_rule="advq", rr_score="advq", + rr_pot="gvf"), + "advqGVF-forgive": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="advq", rr_score="advq", + rr_pot="gvf", rr_forgive_p=0.10), + # 对照: 只换势函数来源, 判据仍用内部达成率(分离"势函数"与"判据"两个因素) + "advqGVFint-hl20": dict(rr_select_rule="calibrated", rr_forget_hl=20, + rr_retire_rule="internal", rr_score="advq", + rr_pot="gvf"), +} + + +def apply_fault(ag, kind): + """把 `FAULT_OPT` 弄坏。返回是否成功。""" + st = next((x for x in (ag.rr_lib_obj or []) if x.name == FAULT_OPT), None) + if st is None: + return False + if kind == "inert": # 达不成子目标, 但每步仍是好步 + ag._rr_cap[FAULT_OPT] = 1 + elif kind == "harm": # 主动朝反方向走 + p = np.asarray(st.policy).copy() + p2 = p.copy() + p2[p == LEFT] = RIGHT + p2[p == RIGHT] = LEFT + st.policy = p2 + else: + raise ValueError(f"unknown fault: {kind}") + return True + + +def run_arm(kw, fault="inert", episodes_per=400, seed=0, thresh=0.8, window=20): + mdp = KeyDoorMDP(n_pos=8, horizon=30, **CHAIN[0]) + ag = RRSkillAgent(mdp, mode="rr", seed=seed, rr_lib="rr", opt_prob=1.0, **kw) + segs, n_none, n_sel = [], 0, 0 + for ci, reg in enumerate(CHAIN): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + ag._rr_ensure() + faulted = ci >= FAULT_AT and apply_fault(ag, fault) + n0 = ag.rr_outcome.get(FAULT_OPT, {}).get("n", 0) + broken_at = None + orig = ag._rr_select + + def spy(s, _o=orig): + nonlocal n_none, n_sel + r = _o(s) + n_sel += 1 + n_none += int(r is None) + return r + + ag._rr_select = spy + succ, steps_ok = [], [] + for _ in range(episodes_per): + ok, st = ag.run_episode() + succ.append(1.0 if ok else 0.0) + if ok: + steps_ok.append(st) + if faulted and broken_at is None and FAULT_OPT in ag.rr_broken(): + broken_at = ag.rr_outcome[FAULT_OPT]["n"] - n0 + ag._rr_select = orig + s = np.array(succ) + t_adapt = episodes_per + for e in range(window - 1, episodes_per): + if float(s[e - window + 1:e + 1].mean()) >= thresh: + t_adapt = e + 1 + break + o = ag.rr_outcome.get(FAULT_OPT, {}) + segs.append({ + "seg": ci, "tc": ci % 3, "faulted": bool(faulted), + "t_adapt": int(t_adapt), + "steps_go": float(np.mean(steps_ok)) if steps_ok else float("nan"), + "succ": float(s[-window:].mean()), + "usage": (o.get("n", 0) / max(1, sum(x["n"] for x in ag.rr_outcome.values()))), + "bad_rate": float(ag._rr_rate(o)) if o else float("nan"), + "bad_rate_life": (o["reached"] / o["n"]) if o and o["n"] else float("nan"), + "bad_adv": float(ag._rr_adv(o)) if o else float("nan"), + "bad_advq": RRSkillAgent._rr_advq_of(o) if o else float("nan"), + "detect_launches": broken_at, + }) + return segs, {"none": n_none, "sel": n_sel}, ag + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--episodes-per", type=int, default=400) + ap.add_argument("--seeds", type=int, default=3) + ap.add_argument("--fault", default="inert", choices=["inert", "harm"]) + ap.add_argument("--arms", default=",".join(ARMS)) + ap.add_argument("--out", default=None) + a = ap.parse_args() + arms = {k: ARMS[k] for k in a.arms.split(",") if k in ARMS} + out_path = a.out or f"results/rr_calib_chain_{a.fault}.json" + + chain_s = "/".join(f"{r['key_pos']}{r['door_pos']}{r['goal_pos']}" for r in CHAIN) + print("=" * 100) + print(f"持续自我校准 · regime 链消融 故障 = {a.fault} 链(k/d/g) = {chain_s}") + print(f"故障自第 {FAULT_AT + 1} 段起作用于 `{FAULT_OPT}` " + f"(inert=上限压到1步但每步仍是好步 | harm=反转 π_o 朝反方向走)") + print("=" * 100) + + out = {} + for name, kw in arms.items(): + runs = [run_arm(kw, a.fault, a.episodes_per, sd) for sd in range(a.seeds)] + out[name] = runs + fb = np.mean([r[1]["none"] / max(1, r[1]["sel"]) for r in runs]) + print(f"\n── {name} (fallback 占比 {fb:.3f}) " + "─" * max(0, 58 - len(name))) + print(f" {'段':>3}{'tc':>4}{'故障':>6}{'T_adapt':>9}{'步数':>8}" + f"{'坏占比':>9}{'当前率':>9}{'终身率':>9}{'主任务adv':>11}" + f"{'Q_adv':>10}{'检测延迟':>10}") + for si in range(len(CHAIN)): + g = [r[0][si] for r in runs] + dl = [x["detect_launches"] for x in g if x["detect_launches"] is not None] + dls = f"{np.mean(dl):.0f}" if dl else ("未检测" if g[0]["faulted"] else "—") + print(f" {si:>3}{g[0]['tc']:>4}{'是' if g[0]['faulted'] else '否':>6}" + f"{np.mean([x['t_adapt'] for x in g]):>9.1f}" + f"{np.nanmean([x['steps_go'] for x in g]):>8.2f}" + f"{np.mean([x['usage'] for x in g]):>9.4f}" + f"{np.nanmean([x['bad_rate'] for x in g]):>9.3f}" + f"{np.nanmean([x['bad_rate_life'] for x in g]):>9.3f}" + f"{np.nanmean([x['bad_adv'] for x in g]):>11.3f}" + f"{np.nanmean([x['bad_advq'] for x in g]):>10.4f}{dls:>10}") + + # ── 事前判据 ──────────────────────────────────────────────────── + print("\n" + "=" * 100) + print(f"事前判据 (故障 = {a.fault})") + print("=" * 100) + faults = [i for i in range(len(CHAIN)) if i >= FAULT_AT] + ok = [] + + def tail_steps(nm): + return float(np.nanmean([np.nanmean([r[0][i]["steps_go"] for r in out[nm]]) + for i in faults])) + + def detect(nm): + v = [r[0][FAULT_AT]["detect_launches"] for r in out[nm]] + return float(np.mean([x if x is not None else a.episodes_per for x in v])) + + def tail_usage(nm): + return float(np.mean([np.mean([r[0][i]["usage"] for r in out[nm]]) for i in faults])) + + def tail_succ(nm): + return float(np.mean([np.mean([r[0][i]["succ"] for r in out[nm]]) for i in faults])) + + def fallback(nm): + return float(np.mean([r[1]["none"] / max(1, r[1]["sel"]) for r in out[nm]])) + + if a.fault == "harm": + # ★ 用成功率而不是步数: 有害故障下 `uniform` 可能**一个回合都成功不了** + # (steps_go = nan), 用步数比较会直接崩在 nan 上。成功率总是有限的。 + ok.append(("① 有害故障: uniform 全面失败而校准臂仍能成功", + tail_succ("internal-hl20") > tail_succ("uniform") + 0.05, + f"成功率 uniform {tail_succ('uniform'):.3f} -> " + f"internal {tail_succ('internal-hl20'):.3f}")) + ok.append(("② 有害故障被检测到 (internal 坏占比 < 0.6×uniform)", + tail_usage("internal-hl20") < 0.6 * tail_usage("uniform"), + f"{tail_usage('uniform'):.4f} -> {tail_usage('internal-hl20'):.4f}")) + if "advEX-hl20" in out: + ok.append(("②b 干净势函数下主任务判据**也会**开火 (harm 使 adv 转负)", + np.nanmean([r[0][FAULT_AT]["bad_adv"] for r in out["advEX-hl20"]]) < 0.0, + f"advEX 故障段 adv = " + f"{np.nanmean([r[0][FAULT_AT]['bad_adv'] for r in out['advEX-hl20']]):.4f}")) + else: + ok.append(("① 惰性故障: 按**内部判据**淘汰反而更差 (internal 步数 > uniform)", + tail_steps("internal-hl20") > tail_steps("uniform") + 0.05, + f"uniform {tail_steps('uniform'):.2f} -> " + f"internal {tail_steps('internal-hl20'):.2f}")) + if "advEX-hl20" in out: + # ★ 判别器的核心: 惰性 option 的**内部达成率崩了**(~0.15), 但它的 + # 主任务 advance 仍然**为正** —— 因为它执行的那一步还是好步。 + ok.append(("② 惰性故障: 主任务判据**不开火**, 于是步数与 uniform 一致", + abs(tail_steps("advEX-hl20") - tail_steps("uniform")) <= 0.05, + f"uniform {tail_steps('uniform'):.2f} vs " + f"advEX {tail_steps('advEX-hl20'):.2f}")) + ok.append(("②b 两条判据在惰性故障上**分离** (内部率崩而 adv 仍正)", + (np.nanmean([r[0][FAULT_AT]["bad_rate"] for r in out["advEX-hl20"]]) + < 0.5 * np.nanmean([r[0][FAULT_AT]["bad_rate"] + for r in out["internal-hl20"]])) + and (np.nanmean([r[0][FAULT_AT]["bad_adv"] for r in out["advEX-hl20"]]) > 0), + f"advEX: 内部率 " + f"{np.nanmean([r[0][FAULT_AT]['bad_rate'] for r in out['advEX-hl20']]):.3f}" + f" / adv " + f"{np.nanmean([r[0][FAULT_AT]['bad_adv'] for r in out['advEX-hl20']]):.4f}")) + if "internal-noforget" in out: + ok.append(("③ 不遗忘的持久化检测更慢 (noforget 延迟 >= hl20)", + detect("internal-noforget") >= detect("internal-hl20"), + f"noforget {detect('internal-noforget'):.0f} vs " + f"hl20 {detect('internal-hl20'):.0f}")) + if "internal-nopersist" in out: + ok.append(("④ 不持久化反而检测最快 (nopersist <= hl20 + 1)", + detect("internal-nopersist") <= detect("internal-hl20") + 1, + f"nopersist {detect('internal-nopersist'):.0f} vs " + f"hl20 {detect('internal-hl20'):.0f}")) + ok.append(("⑤ 校准臂退回基元层更多", fallback("internal-hl20") > fallback("uniform"), + f"uniform {fallback('uniform'):.3f} -> " + f"internal {fallback('internal-hl20'):.3f}")) + + n_pass = 0 + for nm, good, info in ok: + print(f" {'PASS' if good else 'FAIL'} {nm} [{info}]") + n_pass += int(good) + print(f"\n {n_pass}/{len(ok)} 通过") + + Path(out_path).parent.mkdir(parents=True, exist_ok=True) + Path(out_path).write_text(json.dumps( + {k: [{"segs": r[0], "calls": r[1]} for r in v] for k, v in out.items()}, + ensure_ascii=False, indent=1)) + print(f"\n原始数据: {out_path}") + return 0 if n_pass == len(ok) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/benchmark_rr_online.py b/tests/benchmark_rr_online.py new file mode 100644 index 0000000000000000000000000000000000000000..8de1de8488bec2fed66188826202ce5440eafaef --- /dev/null +++ b/tests/benchmark_rr_online.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""benchmark_rr_online.py — **在线持续学习**消融矩阵(OaK 的 SubTask→Option 层)。 + +与 `benchmark_b_matrix.py` 的差别只有一处:**多了 reward-respecting 子任务库 +这一族臂**;指标、链、T_adapt 定义、判据全部与 B 矩阵**逐字一致**, 这样新臂 +可以直接与已钉死的 `primitive 1.82x / rediscover 1.17x / macro 1.08x` 基线对读。 + +## 为什么不复用 `run_regime_chain` + +`run_regime_chain` 把 `SkillAgent` **硬编码**在函数体里。为了不改动它(服务器 +上 L3 批正在跑, 中途改 `skill_agent.py` 会让后续臂用上新代码 -> 整批作废), +本文件自带一个**逐字同构**的链运行器, 只多一个 `agent_cls` 参数。 + +## 消融矩阵(相邻两臂只差**一个**变量) + + 臂 相对上一臂改的东西 + ────────────── ──────────────────────────────────────────── + primitive (下界基线) + rr-exact + reward-respecting 子任务库 ← 机制本体 + rr-zeroV z 里 V_main → 0 ← 削**价值函数** + rr-learned V_main 由 oracle → agent 自己的 Q ← 去掉 oracle + rr-nobonus w̄ → w_i (bonus ≡ 0) ← 削**停止奖励** + rr-nocum c → 0 ← 削**沿途主任务奖励** + rr-nofeat 特征 → 与任务无关的均匀特征 ← 削**特征语义** + bottleneck (论文点名的**已知弱**, 负对照) + random (随机目标, 负对照) + macro (既有的瓶颈状态 option 路径, 参照点) + +## 剂量响应 + 内部哨兵(§2 / Pitfall 34) + +`opt_prob ∈ {0, 0.25, 0.5, 1.0}`。**0 必须复现 primitive** —— 哨兵不一致 +说明诊断自身的实现错了, 其余剂量点全部作废。 + +## 判据(写死在此, 跑完**先读这里再读数字**) +""" +import argparse +import json +import sys +import time +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.skill_agent import DEFAULT_REGIMES, SkillAgent # noqa: E402 +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 + +INTERPRETATION_RULES = [ + "① 内部哨兵: opt_prob=0 的 rr 臂必须与 primitive 臂**逐位相同**。" + "不一致 -> 诊断自身实现有误, 全部剂量点作废。", + "② 机制存活: rr 臂的 rr_bookkeeping() 必须显示 selections>0 且 terms 非空。" + "空台账 = '机制从未运行', 与'机制无效'是不同结论(Pitfall 34) —— 不可写后者。", + "③ 主判据: T_adapt 逐段 vs primitive, 配 n/mean/std + 配对 t 检验。" + "|t|<2 或 p>=0.05 -> 写'此 n 下不可分辨', 不写方向。", + "④ 复用判据: 第2轮 T_adapt < 第1轮 且 p<0.05 -> 复用成立(与 B 矩阵同判据)。", + "⑤ 价值函数假设: rr-exact vs rr-zeroV 若不可分辨 -> 在该试验台上价值函数" + "**不是**区分因素; 若 rr-zeroV 更差 -> 价值函数是收益来源。两种都如实写。", + "⑥ lock-in 检: mean_duration / horizon > 0.5 的臂记为'锁死', 不当抽象读。", + "⑦ 单 seed 结论不算; 至少 3 seed, 且 std < mean/2 才称可信(项目标准)。", +] + + +def run_chain(agent_cls, mode, chain, episodes_per, seed, thresh=0.8, + window=20, n_pos=8, **kw): + """与 `skill_agent.run_regime_chain` **逐字同构**, 只多 `agent_cls`。""" + mdp = KeyDoorMDP(n_pos=n_pos, horizon=30, **chain[0]) + agent = agent_cls(mdp, mode=mode, seed=seed, **kw) + out = [] + for ci, reg in enumerate(chain): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + if mode != "primitive" and not isinstance(agent, RRSkillAgent): + agent.maybe_discover(force=True) + if isinstance(agent, RRSkillAgent): + agent._rr_ensure() # regime 变了 -> 子任务库重建 + succ, steps_ok, t_adapt = [], [], None + for ep in range(episodes_per): + ok, nstep = agent.run_episode() + succ.append(1.0 if ok else 0.0) + if ok: + steps_ok.append(int(nstep)) + if t_adapt is None and len(succ) >= window: + if float(np.mean(succ[-window:])) >= thresh: + t_adapt = ep + 1 + tail = slice(max(0, len(succ) - window), len(succ)) + out.append({"regime": ci, "key": reg["key_pos"], "door": reg["door_pos"], + "goal": reg["goal_pos"], + "t_adapt": int(t_adapt if t_adapt is not None else episodes_per), + "final_succ": float(np.mean(succ[tail])), + # ★ 主指标之二: **成功回合的平均步数**。 + # t_adapt 与成功率在"任务被 option 平凡解出"时会双双**饱和** + # (rr 贴 window 下限、primitive 贴 episodes 上限), 那时两者 + # 都看不见效应。steps-to-goal 会动, 是 §8 要求的替代尺子。 + "mean_steps_success": (float(np.mean(steps_ok)) if steps_ok + else float(np.nan)), + "mean_steps_tail": float(np.mean( + [s for s, o in zip(steps_ok[-window:], succ[-window:]) if o])) + if any(succ[tail]) else float(np.nan), + "n_success": len(steps_ok), + "n_options": (len(agent.om.options) if getattr(agent, "om", None) + else (len(agent.rr_lib_obj) if + isinstance(agent, RRSkillAgent) and + agent.rr_lib_obj else 0))}) + return out, agent + + +ARM_DEFS = { + # name -> (agent_cls, kwargs) + "primitive": (SkillAgent, {}), + "macro": (SkillAgent, {}), # mode 由 --modes 传 macro + "rr-exact": (RRSkillAgent, {}), + "rr-zeroV": (RRSkillAgent, dict(rr_z_mode="zeroV")), + "rr-nobonus": (RRSkillAgent, dict(rr_z_mode="nobonus")), + "rr-nocum": (RRSkillAgent, dict(rr_cumulant="zero")), + "rr-learned": (RRSkillAgent, dict(rr_v_main="learned")), + "bottleneck": (RRSkillAgent, dict(rr_lib="bottleneck")), + "random": (RRSkillAgent, dict(rr_lib="random")), +} + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--episodes-per", type=int, default=300) + ap.add_argument("--seeds", type=int, default=3) + ap.add_argument("--modes", default="primitive,rr-exact,rr-zeroV,rr-nocum," + "rr-learned,bottleneck,random") + ap.add_argument("--cycles", type=int, default=2) + ap.add_argument("--opt-prob", type=float, default=1.0) + ap.add_argument("--n-pos", type=int, default=8) + ap.add_argument("--out", default=str(ROOT / "results" / "rr_online.json")) + a = ap.parse_args() + + chain = [dict(r) for r in DEFAULT_REGIMES] * a.cycles + modes = [m.strip() for m in a.modes.split(",") if m.strip()] + print("regime 链: %s" % [f"{r['key_pos']}/{r['door_pos']}/{r['goal_pos']}" for r in chain]) + print("臂: %s | seed x%d | episodes/regime=%d | opt_prob=%.2f" + % (modes, a.seeds, a.episodes_per, a.opt_prob)) + print() + + all_rows, all_bk, t0 = {}, {}, time.time() + for mode in modes: + cls, kw = ARM_DEFS[mode] + kw = dict(kw) + mode_val = "macro" if mode == "macro" else ("rr" if cls is RRSkillAgent else mode) + kw["opt_prob"] = a.opt_prob + per_seed, bks = [], [] + for si in range(a.seeds): + rows, agent = run_chain(cls, mode_val, chain, a.episodes_per, + 42 + si, n_pos=a.n_pos, **dict(kw)) + per_seed.append(rows) + bk = (agent.rr_bookkeeping() if isinstance(agent, RRSkillAgent) + else (agent.option_bookkeeping() or {})) + bks.append(bk) + print(" [%-11s seed=%d] T_adapt=%s 成功率=%s n_opt=%d 台账=%s" + % (mode, 42 + si, [r["t_adapt"] for r in rows], + [round(r["final_succ"], 2) for r in rows], + rows[-1]["n_options"], + {k: bk.get(k) for k in ("selections", "terms")} if bk else "-"), + flush=True) + all_rows[mode], all_bk[mode] = per_seed, bks + print(" elapsed %.1fs" % (time.time() - t0), flush=True) + + # ── 汇总 ──────────────────────────────────────────────────────── + n_seg = len(chain) + half = n_seg // a.cycles + print() + print("=" * 92) + print("★ 逐段 T_adapt (T, 上) 与 成功回合平均步数 (S, 下) —— 跨 seed 均值±std") + print("=" * 92) + summary = {} + for mode in modes: + arr = np.array([[r["t_adapt"] for r in rows] for rows in all_rows[mode]], float) + first, second = arr[:, :half].mean(1), arr[:, half:].mean(1) + d = second - first + try: + from scipy import stats + t, p = stats.ttest_rel(first, second) + except Exception: + t, p = float("nan"), float("nan") + stp = np.array([[r["mean_steps_success"] for r in rows] + for rows in all_rows[mode]], float) + print(" %-11s T=%s" % (mode, " ".join("%.0f±%.0f" % (m, s) + for m, s in zip(arr.mean(0), arr.std(0))))) + print(" %-11s S=%s" % ("", " ".join("%.1f±%.1f" % (m, s) + for m, s in zip(np.nanmean(stp, 0), + np.nanstd(stp, 0))))) + summary[mode] = { + "t_adapt_mean": [float(x) for x in arr.mean(0)], + "t_adapt_std": [float(x) for x in arr.std(0)], + "steps_mean": [float(x) for x in np.nanmean(stp, 0)], + "steps_std": [float(x) for x in np.nanstd(stp, 0)], + "reuse_first": float(first.mean()), "reuse_second": float(second.mean()), + "reuse_delta": float(d.mean()), "reuse_t": float(t) if np.isfinite(t) else None, + "reuse_p": float(p) if np.isfinite(p) else None, + "speedup": float(first.mean() / max(second.mean(), 1e-9)), + } + + print() + print("★ 复用(第2轮 vs 第1轮, Δ<0 且 p<0.05 = 复用成立)") + for mode in modes: + s = summary[mode] + print(" %-11s %.1f -> %.1f Δ=%+.1f (%.2fx) p=%s" + % (mode, s["reuse_first"], s["reuse_second"], s["reuse_delta"], + s["speedup"], f"{s['reuse_p']:.4f}" if s["reuse_p"] is not None else "na")) + + if "primitive" in modes: + print() + print("★ 相对 primitive 的逐段加速(正 = 该臂更快)") + prim = np.array([[r["t_adapt"] for r in rows] + for rows in all_rows["primitive"]], float).mean(0) + for mode in modes: + if mode == "primitive": + continue + arr = np.array([[r["t_adapt"] for r in rows] + for rows in all_rows[mode]], float).mean(0) + d = prim - arr + print(" %-11s %s |Δ|均=%.1f" % (mode, " ".join("%+6.0f" % x for x in d), + float(np.mean(np.abs(d))))) + + print() + print("★ 哨兵检(§2): opt_prob=0 必须复现 primitive —— 见 tests/bench_sentinel.sh") + print("★ 机制存活检(判据②): 各臂台账") + for mode in modes: + for i, bk in enumerate(all_bk[mode]): + if bk: + print(" %-11s seed=%d %s" % (mode, 42 + i, + {k: bk.get(k) for k in ("selections", "exec_steps", "terms", + "mean_duration", "lockin_ratio", + "beta_nonempty")})) + + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + Path(a.out).write_text(json.dumps( + {"modes": modes, "seeds": a.seeds, "episodes_per": a.episodes_per, + "opt_prob": a.opt_prob, "cycles": a.cycles, "chain": chain, + "summary": summary, "bookkeeping": all_bk, "rows": all_rows, + "rules": INTERPRETATION_RULES}, indent=1, ensure_ascii=False)) + print("\n已写 %s (总耗时 %.1fs)" % (a.out, time.time() - t0)) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/benchmark_rr_planning.py b/tests/benchmark_rr_planning.py new file mode 100644 index 0000000000000000000000000000000000000000..729e48018fb749a5c8d3a6368c47e37260e3e96e --- /dev/null +++ b/tests/benchmark_rr_planning.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +"""OaK 的 Model→Planning 层在 KeyDoor 上的基准 —— 指标 = **look-ahead 操作数**。 + +论文(Sutton et al. 2022)用它自己的指标衡量这套机制:一个"让规划更省"的 +抽象,只有在**规划代价**上才看得见收益(§9.2:"Use the source's own metric +for the mechanism")。本项目已在 gridworld 上复现了论文的双向结果: + + 两房间 (36 状态) primitive 1400 / shortest 1750 / **RR 875** + 四房间 (80 状态) primitive 48872 / shortest 53710 / **RR 26200** + +本文件把同一件事搬到 **KeyDoorMDP**(时序结构已证:A ∈ [−1,+1], 33% 显著)。 + +## 消融矩阵(相邻两臂只差**一个**变量) + + arm c(cumulant) z(stopping value) 作用 + ───────────── ─────────── ──────────────────────── ────────────────── + primitive — — 下界基线(无 option) + rr-exact R V_main + (w̄−w_i)x_i **机制本体** + rr-zeroV R 0 + (w̄−0 )x_i ← 削掉**价值函数** + rr-nobonus R V_main + (0 )x_i ≡ V_main ← 削掉停止奖励(已知退化) + rr-nocum 0 V_main + (w̄−w_i)x_i ← 削掉**沿途主任务奖励** + rr-nofeat R V_main + (w̄−w_i)x_uniform ← 削掉**特征语义** + bottleneck −1 0 @ 瓶颈 **已知弱(论文点名)** + random R V_main + (w̄−w_i)x_random **随机目标(负对照)** + +★ 判据**在跑之前写死**(写进本文件, 见 `INTERPRETATION_RULES`), 免得数字 + 出来以后往想要的方向读。 +""" +import json +import os +import sys + +import numpy as np + +sys.path.insert(0, ".") +from hibs_lnn.rr_options import ( # noqa: E402 + KeyDoorTabular, build_bottleneck_library, build_random_goal_library, + build_rr_library, feature_vectors, main_value, +) +from hibs_lnn.subtask_options import ( # noqa: E402 + build_option_model, ops_to_tolerance, +) + +TOL = 0.01 +MAX_OPS = 400000 + +# ── 判据写死在此:跑完**先读这里再读数字** ──────────────────────────── +INTERPRETATION_RULES = [ + "① 内部哨兵: primitive 臂必须复现历史值(两房间 1400 量级的同型行为)。" + "若 primitive 臂与历史不符 -> 本 harness 自身有 bug, 其余数字全部作废。", + "② 已知负向: bottleneck 臂**不得**优于 primitive。若它赢了 -> harness 有 bug" + "(论文明确指出瓶颈子目标是已知弱基线)。", + "③ 机制判据: rr-exact 优于 primitive 才叫'机制有效'。" + "相等等不于'无效', 而是'这个 regime 上无可抽象的结构', 必须换 regime 再说。", + "④ 价值函数假设(用户的猜测): 比较 rr-exact vs rr-zeroV。" + "两者**逐位相同** = 该 regime 下价值函数不影响规划 -> 假设**未被这个试验台检验**" + "(不是'被证伪');两者不同且 rr-exact 更省 = 价值函数是收益来源。", + "⑤ 只在 tol=0.01 与 max_ops=400000 下读数:'打满上限'的臂记为不收敛, 不当作有限值比较。", + "⑥ 单 regime 的结果只是**一个点**; 结论要在多 regime 上一致才成立。", +] + + +def arm_libraries(mdl, V_main): + """构造矩阵里每个臂的 option 库。""" + arms = {} + arms["rr-exact"] = build_rr_library(mdl, V_main, bonus_weight=1.0) + arms["rr-zeroV"] = build_rr_library(mdl, V_main, bonus_weight=1.0, + z_mode="zeroV") + arms["rr-nobonus"] = build_rr_library(mdl, V_main, bonus_weight=1.0, + z_mode="nobonus") + arms["rr-nocum"] = build_rr_library(mdl, V_main, bonus_weight=1.0, + cumulant="zero") + # 削掉特征语义: 特征用一个与任务无关的均匀特征(半数为 1) + n = mdl.n_states + x_unif = np.array([1.0 if (s % 2 == 0) else 0.0 for s in range(n)]) + from hibs_lnn.subtask_options import reward_respecting_subtask + st_u = reward_respecting_subtask(mdl, x_unif, V_main, bonus_weight=1.0) + st_u.name = "rr[nofeat]" + st_u.feature = x_unif + st_u.solve(mdl) + arms["rr-nofeat"] = [st_u] + arms["bottleneck"] = build_bottleneck_library(mdl) + arms["random"] = build_random_goal_library(mdl, V_main, seed=0) + return arms + + +def eval_arm(mdl, lib, v_star, s0=0): + models = {} + ks = [] + for st in lib: + name = st.name + if name in models: # 重名 -> 加后缀 + name = f"{name}#{len(models)}" + M = build_option_model(mdl, st) + models[name] = M + # ★ 时长仪表: ops 指标可能被"option 有多长"主导而不是被"目标选得好不好" + # 主导 —— 一个 k 步的 option 只花 1 次 backup 就传播 k 步的价值。 + # 记录每个 option 的期望终止步数, 才能把这个 confound 读出来。 + for s in range(mdl.n_states): + for ent in M[s]: + ks.append(ent[3]) + ops, errs, ops_curve, err_curve = ops_to_tolerance( + mdl, models, v_star, s0, tol=TOL, max_ops=MAX_OPS) + return {"ops": int(ops), "err": float(errs), "converged": bool(errs < TOL), + "n_options": len(models), "n_sweeps": len(ops_curve), + "ops_per_sweep": (float(ops) / max(1, len(ops_curve))), + "mean_k": float(np.mean(ks)) if ks else 1.0, + "max_k": float(np.max(ks)) if ks else 1.0, + "beta_total": int(sum(int(np.sum(st.beta)) for st in lib))} + + +def main(): + regimes = [ + (2, 5, 7, "A: key=2 door=5 goal=7 (需全链)"), + (1, 4, 7, "B: key=1 door=4 goal=7 (需全链)"), + (3, 6, 7, "C: key=3 door=6 goal=7 (需全链)"), + (2, 5, 3, "D: goal7s} {'×prim':>6s} {'conv':>5s} " + f"{'mean_k':>7s} {'max_k':>6s} {'n_opt':>5s} {'|β|':>5s} {'ops/扫':>8s}") + print(f" {'primitive':12s} {base['ops']:7d} {1.0:6.3f} " + f"{str(base['converged']):>5s} {1.0:7.2f} {1.0:6.2f} {'-':>5s} " + f"{'-':>5s} {base['ops_per_sweep']:8.1f}") + + for name, lib in arm_libraries(mdl, V_main).items(): + r = eval_arm(mdl, lib, v_star) + r["ratio_vs_primitive"] = r["ops"] / max(1, base["ops"]) + row["arms"][name] = r + print(f" {name:12s} {r['ops']:7d} {r['ratio_vs_primitive']:6.3f} " + f"{str(r['converged']):>5s} {r['mean_k']:7.2f} {r['max_k']:6.2f} " + f"{r['n_options']:5d} {r['beta_total']:5d} {r['ops_per_sweep']:8.1f}") + out["regimes"].append(row) + + # ── w̄ 扫描(dose-response: 中间值应当最好, 见配方 §9.3 末) ──────── + print("\n" + "=" * 78) + print("w̄ 扫描(regime A)—— 论文: 中间值最好; w̄→大 退化成 shortest-path") + print("=" * 78) + kp, dp, gp = 2, 5, 7 + from hibs_lnn.skill_mdp import KeyDoorMDP + mdp = KeyDoorMDP(n_pos=8, key_pos=kp, door_pos=dp, goal_pos=gp, horizon=30) + mdp.set_regime(kp, dp, gp) + mdl = KeyDoorTabular(mdp) + V_main = main_value(mdl) + base = eval_arm(mdl, [], float(V_main[0])) + wbar = {} + print(f" primitive: ops={base['ops']}") + for w in (0.0, 0.1, 0.3, 0.5, 0.78, 1.0, 3.0, 10.0, 100.0): + lib = build_rr_library(mdl, V_main, bonus_weight=w) + r = eval_arm(mdl, lib, float(V_main[0])) + wbar[str(w)] = r + print(f" w̄={w:<6} ops={r['ops']:8d} err={r['err']:.2e} " + f"conv={r['converged']} ×prim={r['ops']/max(1,base['ops']):.3f} " + f"|β|={r['beta_total']}") + out["wbar_sweep_regimeA"] = {"primitive_ops": base["ops"], "arms": wbar} + + os.makedirs("results", exist_ok=True) + with open("results/rr_planning_keydoor.json", "w") as f: + json.dump(out, f, indent=2, ensure_ascii=False) + print("\n→ results/rr_planning_keydoor.json") + + +if __name__ == "__main__": + main() diff --git a/tests/benchmark_three_state.py b/tests/benchmark_three_state.py new file mode 100644 index 0000000000000000000000000000000000000000..a335cd54cf7298bf529541d750c56e14e9acc394 --- /dev/null +++ b/tests/benchmark_three_state.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""benchmark_three_state.py — **三态 epistemic state(好/坏/未知)的验收台**。 + +## 为什么需要这个文件 + +leo 的判断: 当前最大的缺口不是精度, 是 **epistemic state** —— +系统必须能区分"好、坏、**未知**", 而在此之前 `advq == 0` 被一律读成"不坏"。 + +那个 `0` 在两种完全不同的情况下出现: + + (a) 证据充分, 真的没有显著差异 -> 好 + (b) **奖励流为空, Φ 学不到东西** + (harm 故障下整臂全灭时 `Q_adv ≡ 0.0000` 精确零) -> **未知** + +把 (b) 读成"不坏"就是**自欺**。这个文件的核心判据就是**直接测这个自欺**: +在 agent 正在整体失败的场景里, 旧报告会不会说"一切正常"? + +## 三个固定场景(leo 阶段一) + + normal 无故障 —— 正常 option 应该被保留(判定为"好") + inert 惰性故障 —— 看着效率差, 但**不应误杀** + harm 有害故障 —— 真正有害, 应该被检出(判"坏", 或至少不是"好") + +## 八项固定指标(leo 阶段一) + + ① harm 检测延迟 ② inert 误杀率 ③ 退休后恢复时间 ④ fallback 比例 + ⑤ 未知状态占比 ⑥ 主任务成功率/步数 + ⑦ 证据是否持续增长 ⑧ **自欺率**(失败时仍报"好"的比例) + +## 两个臂: 三态本身是一条消融轴 + + b2-nostate 冻结基线(GVF + advq + 遗忘 + 原谅)但 `rr_three_state=False` + b2-3state 同上 + `rr_three_state=True`(未知 -> 一等状态) + +这样"把未知提升为一等状态到底改变了什么"是可归因的。 +""" +import sys +import json +import argparse +import numpy as np +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hibs_lnn.rr_agent import RRSkillAgent +from hibs_lnn.skill_mdp import KeyDoorMDP, LEFT, RIGHT +from hibs_lnn.skill_agent import DEFAULT_REGIMES + +FAULT_OPT = "rr[door]" + +# 固定场景: (regime 序列, 故障, 从第几段起施加故障) +SCENARIOS = { + "normal": dict(chain=[DEFAULT_REGIMES[i] for i in (0, 1, 2)], + fault=None, fault_at=99), + "inert": dict(chain=[DEFAULT_REGIMES[i % 3] for i in range(6)], + fault="inert", fault_at=3), + "harm": dict(chain=[DEFAULT_REGIMES[i % 3] for i in range(6)], + fault="harm", fault_at=3), + # ★★ "不稳定" 故障 —— 与 `harm`("稳定但错误")形成对照。 + # leo 点名的否证实验就在这里: 如果 A 类只对 `chaos` 有反应、 + # 对 `harm` 无反应, 那它学到的是**可预测性**, 不是**任务价值**, + # 因此只能当辅助/风险通道, 不能单独驱动退休或选择。 + "chaos": dict(chain=[DEFAULT_REGIMES[i % 3] for i in range(6)], + fault="chaos", fault_at=3), +} + + +class _RandPolicy: + """`chaos` 故障: **每步随机出动作** —— 动力学不可预测。 + + 与 `harm`(把 π_o 反向后仍然完全确定)正好构成一对: + · `harm` = 稳定 + 错误 -> 可预测性高, 任务价值负 + · `chaos` = 不稳定 + 无向 -> 可预测性低, 任务价值也是负 + 两者对比可以把"熟悉度/可预测性"和"任务价值"**分离**开 —— 这是 A 类 + 唯一能证明自己是值信号还是辅助信号的办法。 + """ + + def __init__(self, seed=0): + self.rng = np.random.RandomState(seed) + + def __getitem__(self, s): + return int(self.rng.randint(2)) + + def __len__(self): + return 64 + +# ── 冻结基线(leo 契约): GVF + advq + 遗忘 + 原谅 ────────────────────── +FROZEN = dict(rr_select_rule="calibrated", rr_pot="gvf", rr_retire_rule="advq", + rr_score="advq", rr_forget_hl=20, rr_forgive_p=0.10) + +ARMS = { + "uniform": dict(rr_select_rule="uniform"), + "b2-nostate": dict(FROZEN, rr_three_state=False), + "b2-3state": dict(FROZEN, rr_three_state=True), + # ★ 阶段四 A: 三候选稠密动力学证据。其余**全部**沿用冻结契约, + # 所以臂间差异只能归因到 cumulant 本身(leo 的归因纪律)。 + "a1": dict(FROZEN, rr_three_state=True, rr_dyn="A1"), # 转移不确定性 + "a2": dict(FROZEN, rr_three_state=True, rr_dyn="A2"), # 转移预测误差 + "a3": dict(FROZEN, rr_three_state=True, rr_dyn="A3"), # 已知稳定区域 +} + + +def apply_fault(ag, kind): + """把故障加到目标 option 上。故障**只在行为上**, 不改台账键。""" + st = next((s for s in ag.rr_lib_obj if getattr(s, "name", None) == FAULT_OPT), None) + if st is None: + return False + if kind == "inert": # 看着坏: 达不成子目标... + ag._rr_cap[FAULT_OPT] = 1 # ...但那一步仍是 π_o 的最优步 + elif kind == "harm": # 真的坏: 主动朝反方向走 + # ★ 这个故障的性质是 **"稳定 + 错误"**: 转移完全确定、极易预测, + # 但任务价值是负的。它同时是 A 类的**否证实验**(见判据⑧)。 + p = np.asarray(st.policy).copy() + p2 = p.copy() + p2[p == LEFT] = RIGHT + p2[p == RIGHT] = LEFT + st.policy = p2 + elif kind == "chaos": # 不稳定: 每步随机动作 + # 转移**不可预测**, 与 `harm` 的可预测性形成对照。 + st.policy = _RandPolicy(seed=12345) + ag._rr_cap[FAULT_OPT] = 12 # 不让它无限跑 + else: + raise ValueError(f"unknown fault: {kind}") + return True + + +def run_arm(kw, scen, episodes_per=300, seed=0, thresh=0.8, window=20): + sc = SCENARIOS[scen] + mdp = KeyDoorMDP(n_pos=8, horizon=30, **sc["chain"][0]) + ag = RRSkillAgent(mdp, mode="rr", seed=seed, rr_lib="rr", opt_prob=1.0, **kw) + segs = [] + for ci, reg in enumerate(sc["chain"]): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + ag._rr_ensure() + faulted = (sc["fault"] is not None and ci >= sc["fault_at"]) + if faulted: + apply_fault(ag, sc["fault"]) + n0 = ag.rr_outcome.get(FAULT_OPT, {}).get("n", 0) + broken_at = None + succ, steps_ok = [], [] + for _ in range(episodes_per): + ok, stp = ag.run_episode() + succ.append(1.0 if ok else 0.0) + if ok: + steps_ok.append(stp) + if faulted and broken_at is None and FAULT_OPT in ag.rr_broken(): + broken_at = ag.rr_outcome[FAULT_OPT]["n"] - n0 + s = np.array(succ) + t_adapt = episodes_per + for e in range(window - 1, episodes_per): + if float(s[e - window + 1:e + 1].mean()) >= thresh: + t_adapt = e + 1 + break + cs = ag.rr_calib_stats() + o = cs["per_option"].get(FAULT_OPT, {}) + others = {k: v for k, v in cs["per_option"].items() if k != FAULT_OPT} + segs.append({ + "seg": ci, "tc": reg.get("goal_pos"), "faulted": bool(faulted), + "t_adapt": int(t_adapt), + "succ": float(s[-window:].mean()), + "steps_go": float(np.mean(steps_ok)) if steps_ok else float("nan"), + # ① 检测: 目标是坏的 + "detect": broken_at, + "bad_state": o.get("state"), + # ② 误杀: 非目标的 option 被判坏 + "false_kill": sum(1 for v in others.values() if v["state"] == "bad"), + # ③ 恢复: 目标从坏恢复过几次 + "recovered": o.get("recovered_n", 0), + "retired": o.get("retired_n", 0), + # ④ fallback = 退回基元层的**选择尝试占比**。分母必须是 `sel_attempts` + # (尝试总数), 不能用 `selections`(只在真的选中时 +1) —— 否则 + # 比值会 > 1(实测 6.827), 指标失去意义。 + "fallback": ag.rr_stats.get("starved_sel", 0) + / max(1, ag.rr_stats.get("sel_attempts", 1)), + # ⑤ 三态占比 + "states": cs["states"], + "starved": cs["starved"], + "since_rew": cs["since_rew"], + # ⑦ 证据增长 + "evidence": float(sum(v["advq_n"] for v in cs["per_option"].values())), + # ★ A 类通道: 转移数 / 基线压力 / 每 option 的证据量与风险标记 + "dyn": (ag.rr_dyn_stats() if hasattr(ag, "rr_dyn_stats") else None), + "no_base": o.get("no_base", 0), + "advq": o.get("advq", 0.0), + "advq_lo": o.get("advq_lo", 0.0), + "advq_n": o.get("advq_n", 0.0), + "usage": o.get("n", 0) / max(1, sum(v["n"] for v in cs["per_option"].values())), + }) + return segs, ag + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--episodes-per", type=int, default=300) + ap.add_argument("--seeds", type=int, default=1) + ap.add_argument("--scenarios", default="normal,inert,harm") + ap.add_argument("--arms", default="uniform,b2-nostate,b2-3state") + ap.add_argument("--out", default=None) + a = ap.parse_args() + scen_names = a.scenarios.split(",") + arms = {k: v for k, v in ARMS.items() if k in a.arms.split(",")} + + out, agents = {}, {} + for sc in scen_names: + for name, kw in arms.items(): + runs = [run_arm(kw, sc, a.episodes_per, sd) for sd in range(a.seeds)] + out[(sc, name)] = [r[0] for r in runs] + agents[(sc, name)] = runs[-1][1] + + # ── 打印 ──────────────────────────────────────────────────────── + for sc in scen_names: + print("\n" + "=" * 108) + print(f"场景 {sc} 故障={SCENARIOS[sc]['fault']} 从第 {SCENARIOS[sc]['fault_at']} 段起") + print("=" * 108) + for name in arms: + runs = out[(sc, name)] + print(f"\n── {name} " + "─" * max(0, 96 - len(name))) + print(f" {'段':>3}{'故障':>6}{'T_adapt':>9}{'步数':>8}{'成功率':>8}" + f"{'使用率':>8}{'advq':>9}{'下界':>9}{'advq_n':>8}" + f"{'判定':>8}{'好/坏/未知':>14}{'检测':>7}{'误杀':>6}") + for si in range(len(SCENARIOS[sc]["chain"])): + g = [r[si] for r in runs] + m = lambda f: float(np.nanmean([x[f] for x in g])) + dv = [x["detect"] for x in g if x["detect"] is not None] + dl = (f"{np.mean(dv):.0f}" if dv + else ("未检测" if g[0]["faulted"] else "—")) + st = m_states = g[0]["states"] + st_s = f"{m_states['good']}/{m_states['bad']}/{m_states['unknown']}" + print(f" {si:>3}{'是' if g[0]['faulted'] else '否':>6}" + f"{m('t_adapt'):>9.1f}{m('steps_go'):>8.2f}{m('succ'):>8.3f}" + f"{m('usage'):>8.3f}{m('advq'):>9.4f}{m('advq_lo'):>9.4f}" + f"{m('advq_n'):>8.1f}{str(g[0]['bad_state']):>8}{st_s:>14}" + f"{dl:>7}{m('false_kill'):>6.0f}") + + # ── 事前判据 ──────────────────────────────────────────────────── + print("\n" + "=" * 108) + print("事前判据") + print("=" * 108) + ok = [] + # ★ `info` = **不计入通过率**的观察项。用于"判据本身还不成立/已被取代"的条目: + # 留在 `ok` 里会让它永远红, 从而淹没真正的回归(退出码失去分辨力)。 + # 一条判据只有在**它声称验的那件事可以被验**时才有资格当门。 + info = [] + + def segs_(sc, nm): + return out[(sc, nm)][0] + + def last_faulted(sc): + ch = SCENARIOS[sc]["chain"] + return [i for i in range(len(ch)) if i >= SCENARIOS[sc]["fault_at"]] + + # ★★ ① 自欺门: 任何"奖励流为空"的段, 都**不得报"好"** + # 这是 leo 点的核心缺口的直接检验: `advq == 0` 在"证据充分无差异"和 + # "奖励流为空根本没数据"两种情况下都出现; 前者可以报"好", 后者不行。 + # 注意这个判据对**所有臂**成立 —— 它是报告层的不变量, 与决策规则无关。 + lies = [] + for sc in scen_names: + for nm in arms: + for si, x in enumerate(segs_(sc, nm)): + if x["starved"] and x["states"]["good"] > 0: + lies.append((sc, nm, si, x["states"], x["since_rew"])) + ok.append(("① 自欺门: 奖励流为空时从不报'好'", len(lies) == 0, + f"违约 {len(lies)} 处" + (f" 例: {lies[0]}" if lies else ""))) + + # ★★ ② 决策有效性: 三态开着 -> agent 会因"坏/未知"停止使用有害 option 而**恢复** + # 这是在测"把未知提升为一等状态"到底改变了什么: 不是报告更好看, + # 是行为上能自救。 + if "harm" in scen_names and {"b2-nostate", "b2-3state"} <= set(arms): + nf = [segs_("harm", "b2-nostate")[i] for i in last_faulted("harm")] + tf = [segs_("harm", "b2-3state")[i] for i in last_faulted("harm")] + su_n = float(np.mean([x["succ"] for x in nf])) + su_t = float(np.mean([x["succ"] for x in tf])) + ok.append(("② 三态使 agent 在失败场景下恢复 (成功率 3state 显著 > nostate)", + su_t > su_n + 0.2, f"{su_n:.3f} -> {su_t:.3f}")) + + # ③ 未知不得触发退休 / 不得增加误杀 + # ★ 守卫必须有: 臂子集是常规用法(--arms a1,a2,a3), 缺守卫会直接 + # KeyError 崩在**评测阶段** —— 数据全跑完了才崩, 最浪费的一种。 + if "harm" in scen_names and {"b2-nostate", "b2-3state"} <= set(arms): + print(f" harm 末段 误杀(非目标 option 被判坏): " + f"nostate {segs_('harm','b2-nostate')[-1]['false_kill']} / " + f"3state {segs_('harm','b2-3state')[-1]['false_kill']}") + ok.append(("③ 三态不增加误杀", + segs_("harm", "b2-3state")[-1]["false_kill"] + <= segs_("harm", "b2-nostate")[-1]["false_kill"])) + + # ④ inert 不应被系统性误杀 + if "inert" in scen_names: + for nm in ("b2-nostate", "b2-3state"): + if nm in arms: + fk = sum(segs_("inert", nm)[i]["false_kill"] for i in last_faulted("inert")) + print(f" inert 故障段累计误杀 {nm}: {fk}") + ok.append(("④ inert 场景下三态臂不误杀", + all(segs_("inert", "b2-3state")[i]["false_kill"] == 0 + for i in last_faulted("inert")))) + + # ⑤ 正常场景应判"好"(三态不能退化成一律未知) + if "normal" in scen_names: + c = segs_("normal", "b2-3state")[-1] + print(f" normal 末段: 三态 {c['states']} (starved={c['starved']})") + ok.append(("⑤ normal 场景下目标 option 最终判'好'", c["states"]["good"] >= 1)) + + # ⑥ 证据持续增长 + for sc in scen_names: + if "b2-3state" not in arms: + continue + ev = [x["evidence"] for x in segs_(sc, "b2-3state")] + ok.append((f"⑥ [{sc}] 证据持续增长 (advq_n 单调)", + all(ev[i + 1] >= ev[i] - 1e-9 for i in range(len(ev) - 1)))) + + # ★★ ⑨ **反熟悉度否证实验**(leo 点名的最重要一条; 编号接在既有 ⑧ 之后) + # 对两个镜像故障各问一个问题: + # chaos(不稳定): A 类**应该**察觉到 -> 证明它测的是"可预测性" + # harm (稳定+错误): A 类**应该**察觉不到 -> 证明它测的**不是** + # "任务价值", 因此只能当辅助/风险通道 + # 如果 A 在 harm 上也"察觉到了", 那是更可疑的情况(需另查来源); + # 如果 A 在 chaos 上毫无反应, 那它连"可预测性"都没测到 -> 直接淘汰。 + if "chaos" in scen_names: + def dyn_flag(sc, nm): + """故障段里 A 类触发风险的 (命中, 总数)。""" + hit = tot = 0 + for x in segs_(sc, nm): + dy = x.get("dyn") + if not dy or not x["faulted"]: + continue + for v in dy["per_option"].values(): + tot += 1 + hit += int(v["risk"]) + return hit, tot + for nm in ("a1", "a2", "a3"): + if nm not in arms: + continue + hc, tc = dyn_flag("chaos", nm) + hh, th = dyn_flag("harm", nm) + # ★★ **本条已从"门"降级为"观察项"(INFO)** —— 理由是它**不可满足**: + # 本台跑在**确定性** KeyDoorMDP 上, 实测所有 n>=2 的 (s,a) 后继分布 + # 最大熵**逐位 0.000000** ⇒ H(T|S,A) ≡ 0 ⇒ 转移预测误差 / 不确定性 / + # 稳定区域 **在数学上坍缩成同一个量 = 新颖度计数**。 + # 于是 A 在 chaos 与 harm 上必然给出**同一签名** —— 这不是 + # "A 类不行", 是**本环境没有能力分辨**。 + # 留它在 `ok` 里会让整台永远 3 条红, 退出码失去分辨力 + # (真正的回归会被淹没)。 + # 新定义(T_shift/T_detect/T_recover/FP/FN/AUC)与可辨识环境见 + # `tests/benchmark_a_prime.py` + `docs/a_prime_decision.md`。 + info.append((f"⑨ [{nm}] (已被取代, 见 benchmark_a_prime.py) " + f"本台不可满足: 确定性环境 H(T|S,A)≡0, A 类 ≡ 新颖度", + f"chaos {hc}/{tc} harm {hh}/{th}")) + + # ⑦ 主任务不劣于安全基线 —— **只在 uniform 真正能跑的场景比** + # (harm 下 uniform 整体失败 steps_go=nan, 那时比较没有意义; + # 失败场景由判据 ② 用成功率来测) + for sc in scen_names: + if "uniform" not in arms or "b2-3state" not in arms: + continue + fu = np.nanmean([x["steps_go"] for x in segs_(sc, "uniform")]) + fs = np.nanmean([x["steps_go"] for x in segs_(sc, "b2-3state")]) + # ★ 安全基线的可用性必须**只在故障段上**判: harm 下 uniform 在故障段 + # 成功率 0.000 / 步数 nan(整体失败), 那时"比步数"没有意义 —— + # 用全段均值会把健康段的成功混进来, 误以为基线可用。 + uf = [x for x in segs_(sc, "uniform") if x["faulted"]] + us_f = np.mean([x["succ"] for x in uf]) if uf else 1.0 + if np.isnan(fu) or np.isnan(fs) or us_f < 0.5: + ok.append((f"⑦ [{sc}] 主任务不劣于 uniform", True, + f"uniform 在故障段基线不可用 (成功率 {us_f:.3f}), 由判据②覆盖")) + else: + ok.append((f"⑦ [{sc}] 主任务步数不劣于 uniform (增幅 < 15%)", + fs <= fu * 1.15, f"{fu:.2f} -> {fs:.2f}")) + + n_pass = 0 + for item in ok: + nm, v = item[0], item[1] + extra = item[2] if len(item) > 2 else None + n_pass += int(bool(v)) + print(f" {'PASS' if v else 'FAIL'} {nm}" + (f" [{extra}]" if extra else "")) + for item in info: # ★ 不计入通过率, 不影响退出码 + nm = item[0] + extra = item[1] if len(item) > 1 else None + print(f" INFO {nm}" + (f" [{extra}]" if extra else "")) + print(f"\n {n_pass}/{len(ok)} 通过" + + (f" (+{len(info)} 条 INFO, 不计入)" if info else "")) + + if a.out: + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + ser = {f"{sc}|{nm}": runs for (sc, nm), runs in out.items()} + Path(a.out).write_text(json.dumps(ser, indent=2, default=str, ensure_ascii=False)) + print(f"原始数据: {a.out}") + return 0 if n_pass == len(ok) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/bm2_driver.sh b/tests/bm2_driver.sh new file mode 100644 index 0000000000000000000000000000000000000000..2c94dcddbf826c9062752b8921471a85cb225932 --- /dev/null +++ b/tests/bm2_driver.sh @@ -0,0 +1,56 @@ +#!/bin/bash +# bm2_driver.sh — 修正后的价值函数 benchmark (con 已实现 / sim 自适应 / 池 60) +# 旧结果 (bm_value*, bm_random*, bm_random-matched*) 全部作废, 已删除。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +G0=0; G1=1 +COMMON="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 1000 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 60 --rounds 12 --proposer-k 3 --device cuda" +log() { echo "[$(date -u +%H:%M:%S)] $*"; } + +log "stage A (GPU0): value x 3 seed [真实价值函数: sim/con/cov/fb 全活]" +for S in 42 1 7; do + CUDA_VISIBLE_DEVICES=$G0 $PY -u tests/run_lm4_wave.py $COMMON \ + --proposer value --seed $S --out results/bm2_value_s$S >> results/bm2.log 2>&1 + log " value seed=$S rc=$?" +done +cp results/bm2_value_s42/lm4_wave_results.json results/freq_profile2.json 2>/dev/null \ + && log "频率 profile2 就绪" + +log "stage B (GPU0): value-nofb / random / random-matched x 3 seed" +for S in 42 1 7; do + for P in value-nofb random random-matched; do + CUDA_VISIBLE_DEVICES=$G0 $PY -u tests/run_lm4_wave.py $COMMON \ + --proposer $P --seed $S --freq-profile results/freq_profile2.json \ + --out results/bm2_${P}_s$S >> results/bm2.log 2>&1 + log " $P seed=$S rc=$?" + done +done + +log "stage C (GPU1): lm5 value 系 (con 同样修复后)" +for S in 42 1 7; do + CUDA_VISIBLE_DEVICES=$G1 $PY -u tests/run_lm5_mm.py \ + --backbone hybrid --norm fixed --cl-method replay --head linear \ + --text-steps 300 --wave-epochs 300 --causal-n 20000 \ + --d-model 192 --n-layers 2 --domains 6 --rounds 8 \ + --proposer value --seed $S --device cuda \ + --out results/bm5b_value_s$S >> results/bm5b.log 2>&1 + log " lm5 value seed=$S rc=$?" +done +cp results/bm5b_value_s42/lm5_mm_results.json results/freq_profile_lm5b.json 2>/dev/null +for S in 42 1 7; do + for P in value-nofb random-matched; do + CUDA_VISIBLE_DEVICES=$G1 $PY -u tests/run_lm5_mm.py \ + --backbone hybrid --norm fixed --cl-method replay --head linear \ + --text-steps 300 --wave-epochs 300 --causal-n 20000 \ + --d-model 192 --n-layers 2 --domains 6 --rounds 8 \ + --proposer $P --seed $S --device cuda \ + --freq-profile results/freq_profile_lm5b.json \ + --out results/bm5b_${P}_s$S >> results/bm5b.log 2>&1 + log " lm5 $P seed=$S rc=$?" + done +done +log "全部完成" +touch results/BM2_DONE diff --git a/tests/bm_driver.sh b/tests/bm_driver.sh new file mode 100644 index 0000000000000000000000000000000000000000..ee63cc258be76fa81dc03c5f2c032679b7480af6 --- /dev/null +++ b/tests/bm_driver.sh @@ -0,0 +1,106 @@ +#!/bin/bash +# bm_driver.sh — benchmark 全流程驱动 (stage1 收尾 -> 频率 profile -> stage2 -> lm5) +# 在服务器上以 setsid 运行, 不依赖外层会话轮询。 +set -u +cd /work/liuyuanjie/headless + +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU0=0 +GPU1=1 +LOGDIR=results +log() { echo "[$(date -u +%H:%M:%S)] $*"; } + +# ── stage 1 收尾: 等 value x3 与 bins x12 出现 ── +SKIP_STAGE1="${SKIP_STAGE1:-0}" +NV=0; NB=0 +log "等待 stage 1 (value x3 / bins x12) ... (SKIP_STAGE1=$SKIP_STAGE1)" +if [ "$SKIP_STAGE1" = "0" ]; then +for i in $(seq 1 240); do + NV=$(ls -d ${LOGDIR}/bm_value_s*/ 2>/dev/null | wc -l) + NB=$(ls -d ${LOGDIR}/bm_bins_*_s*/ 2>/dev/null | wc -l) + if [ "$NV" -ge 3 ] && [ "$NB" -ge 12 ]; then break; fi + sleep 30 +done +fi +log "stage 1 完成: value=$NV bins=$NB" + +# ── 提取 value 臂的频率 profile (取 seed 42) ── +PROF=${LOGDIR}/bm_value_s42/lm4_wave_results.json +if [ -f "$PROF" ]; then + cp "$PROF" ${LOGDIR}/freq_profile.json + log "频率 profile 就绪: ${LOGDIR}/freq_profile.json" +else + log "!! 找不到 $PROF — random-matched 将退化为均匀随机 (会在结果里标注)" +fi + +# ── stage 2 (GPU0): value-nofb / random / random-matched x 3 seed ── +log "stage 2 启动 (GPU0)" +for S in 42 1 7; do + for P in value-nofb random random-matched; do + log " GPU0 $P seed=$S" + CUDA_VISIBLE_DEVICES=$GPU0 $PY -u tests/run_lm4_wave.py \ + --backbone mlp --norm fixed --cl-method replay --head linear \ + --epochs-per-domain 1000 --joint-steps 0 --wfr-bands 13 --lshell \ + --pool cat --agg-path --domains 6 --fine-bins 18 --rounds 12 \ + --proposer $P --stream fixed --seed $S --device cuda \ + --freq-profile ${LOGDIR}/freq_profile.json \ + --out ${LOGDIR}/bm_${P}_s${S} >> ${LOGDIR}/bm_stage2.log 2>&1 + log " GPU0 $P seed=$S rc=$?" + done +done +log "stage 2 完成" + +# ── stage 3 (GPU1): lm5 benchmark, 4 流 x 3 seed ── +log "stage 3 启动 (GPU1): lm5" +for S in 42 1 7; do + for ST in fixed perm revisit nonstationary; do + log " GPU1 lm5 $ST seed=$S" + CUDA_VISIBLE_DEVICES=$GPU1 $PY -u tests/run_lm5_mm.py \ + --backbone hybrid --norm fixed --cl-method replay --head linear \ + --text-steps 300 --wave-epochs 300 --causal-n 20000 \ + --d-model 192 --n-layers 2 --domains 6 --rounds 8 \ + --proposer fixed --stream $ST --seed $S --device cuda \ + --out ${LOGDIR}/bm5_${ST}_s${S} >> ${LOGDIR}/bm5.log 2>&1 + log " GPU1 lm5 $ST seed=$S rc=$?" + done +done + +# ── stage 4 (GPU1): lm5 价值函数臂 ── +# 注意顺序: 必须**先**跑完 value 臂, 才有 profile 给 random-matched 用。 +log "stage 4a 启动 (GPU1): lm5 value x 3 seed" +for S in 42 1 7; do + log " GPU1 lm5 value seed=$S" + CUDA_VISIBLE_DEVICES=$GPU1 $PY -u tests/run_lm5_mm.py \ + --backbone hybrid --norm fixed --cl-method replay --head linear \ + --text-steps 300 --wave-epochs 300 --causal-n 20000 \ + --d-model 192 --n-layers 2 --domains 6 --rounds 8 \ + --proposer value --stream fixed --seed $S --device cuda \ + --out ${LOGDIR}/bm5_value_s${S} >> ${LOGDIR}/bm5.log 2>&1 + log " GPU1 lm5 value seed=$S rc=$?" +done + +P5=${LOGDIR}/bm5_value_s42/lm5_mm_results.json +if [ -f "$P5" ]; then + cp "$P5" ${LOGDIR}/freq_profile_lm5.json + log "lm5 频率 profile 就绪" +else + log "!! 缺 $P5 — lm5 random-matched 将退化为均匀随机" +fi + +log "stage 4b 启动 (GPU1): lm5 value-nofb / random-matched x 3 seed" +for S in 42 1 7; do + for P in value-nofb random-matched; do + log " GPU1 lm5 $P seed=$S" + CUDA_VISIBLE_DEVICES=$GPU1 $PY -u tests/run_lm5_mm.py \ + --backbone hybrid --norm fixed --cl-method replay --head linear \ + --text-steps 300 --wave-epochs 300 --causal-n 20000 \ + --d-model 192 --n-layers 2 --domains 6 --rounds 8 \ + --proposer $P --stream fixed --seed $S --device cuda \ + --freq-profile ${LOGDIR}/freq_profile_lm5.json \ + --out ${LOGDIR}/bm5_${P}_s${S} >> ${LOGDIR}/bm5.log 2>&1 + log " GPU1 lm5 $P seed=$S rc=$?" + done +done + +log "全部完成" +touch ${LOGDIR}/BM_DONE diff --git a/tests/c1_runs_gpu0.json b/tests/c1_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..4a51882df489cedcb4a8b204a59cc1d44b071f20 --- /dev/null +++ b/tests/c1_runs_gpu0.json @@ -0,0 +1,162 @@ +[ + { + "tag": "c1_replay_rr05", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "0.5" + ] + }, + { + "tag": "c1_replay_rr1", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "1.0" + ] + }, + { + "tag": "c1_replay_rr2", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "2.0" + ] + }, + { + "tag": "c1_replay_rr5", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "5.0" + ] + }, + { + "tag": "c1_oml2_lin", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "oml2", + "--head", + "linear" + ] + }, + { + "tag": "c1_oml_lin", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "oml", + "--head", + "linear", + "--meta-every", + "10" + ] + } +] diff --git a/tests/c1_runs_gpu1.json b/tests/c1_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..300acb8d1027daa1e4d1c6258a2ab6b606acf0fd --- /dev/null +++ b/tests/c1_runs_gpu1.json @@ -0,0 +1,160 @@ +[ + { + "tag": "c1_replay_pln", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "pln" + ] + }, + { + "tag": "c1_replay_swi", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "swifttd" + ] + }, + { + "tag": "c1_oml2_pln", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "oml2", + "--head", + "pln" + ] + }, + { + "tag": "c1_oml2_swi", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "oml2", + "--head", + "swifttd" + ] + }, + { + "tag": "c1_d3", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "2.0", + "--domains", + "3" + ] + }, + { + "tag": "c1_d12", + "args": [ + "--backbone", + "mlp", + "--norm", + "fixed", + "--wfr-bands", + "13", + "--lshell", + "--pool", + "cat", + "--agg-path", + "--device", + "cuda", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--cl-method", + "replay", + "--head", + "linear", + "--replay-ratio", + "2.0", + "--domains", + "12" + ] + } +] diff --git a/tests/cap_runs_gpu0.json b/tests/cap_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..42311ff246c0b18c4284f11aeec00b184a17e64e --- /dev/null +++ b/tests/cap_runs_gpu0.json @@ -0,0 +1,86 @@ +[ + { + "tag": "cap_h32_l2", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--d-model", + "32", + "--n-layers", + "2" + ] + }, + { + "tag": "cap_h64_l2", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--d-model", + "64", + "--n-layers", + "2" + ] + }, + { + "tag": "cap_h128_l2", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--d-model", + "128", + "--n-layers", + "2" + ] + } +] diff --git a/tests/cap_runs_gpu1.json b/tests/cap_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..fb9d6d75aecf85d814fb2fff4c0a3de19a3f8cdb --- /dev/null +++ b/tests/cap_runs_gpu1.json @@ -0,0 +1,86 @@ +[ + { + "tag": "cap_h256_l2", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--d-model", + "256", + "--n-layers", + "2" + ] + }, + { + "tag": "cap_h512_l3", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--d-model", + "512", + "--n-layers", + "3" + ] + }, + { + "tag": "cap_h512_rr10", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--cl-method", + "replay", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "10.0", + "--d-model", + "512", + "--n-layers", + "3", + "--seed", + "42" + ] + } +] diff --git a/tests/causal_intervene.py b/tests/causal_intervene.py new file mode 100644 index 0000000000000000000000000000000000000000..5e9b42c04cc32cf616e44504a8eb10fb21644052 --- /dev/null +++ b/tests/causal_intervene.py @@ -0,0 +1,179 @@ +"""LM4 因果层 · 干预与反事实 (对电磁波结构做 do() 与反事实查询)。 + +配套 lm4_causal_waves.py 的发现结果使用。 + +思路: + 1. 用**物理先验**给稳定边定向(发现只给出无向骨架): + L → B (偶极场 B ∝ L^-3) + L → log_dens (等离子体层顶依赖 L) + log_dens → ele_* (hiss 波由等离子体层电子产生) + log_dens → mag_mid/hi (密度影响磁强计频段波活动) + logB → mag_lo/mid/hi (磁强计频段本就源自 B) + absmaglat → ele_hi (高纬沉降影响高频电场) + 2. 每个节点对父节点做**线性回归**拟合机制 -> 得到线性 SCM + 3. **干预** do(X=x): 沿拓扑序前向传播, 下游节点按机制重算 + 4. **反事实** (Pearl 三步): + Abduction 由观测值反推该样本的外生噪声 U = 观测 - f(父节点观测) + Action 施加 do() + Prediction 用同一 U 沿拓扑序重算 -> 该样本"若当初……会怎样" + +用法: + python3 tests/causal_intervene.py --sample 20000 +""" +import argparse +import json +import sys +import time +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from lm4_causal_waves import build_variables +from hibs_lnn.causal.dag import Graph + +# ── 物理先验定向: node -> 父节点列表 (按拓扑序给出) ── +PHYSICS_PARENTS = { + "logL": [], + "absmaglat": [], + "logB": ["logL"], # 偶极场 B ∝ L^-3 + "log_dens": ["logL", "absmaglat"], # 等离子体层顶位置依赖 L 与磁纬 + "mag_lo": ["logB"], + "mag_mid": ["logB", "log_dens"], + "mag_hi": ["logB", "log_dens"], + "ele_lo": ["log_dens", "logL"], + "ele_mid": ["log_dens", "logL"], + "ele_hi": ["log_dens", "absmaglat"], +} +# 拓扑序 (父在前) —— 上面字典的键序已满足 +TOPO = ["logL", "absmaglat", "logB", "log_dens", + "mag_lo", "mag_mid", "mag_hi", "ele_lo", "ele_mid", "ele_hi"] + + +class LinearSCM: + """线性结构因果模型 (加性噪声), 支持 do() 与反事实。""" + + def __init__(self, parents, topo, names): + self.parents = parents + self.topo = [n for n in topo if n in names] + self.names = names + self.coef = {} # node -> (w, b) w 对应 parents 顺序 + + def fit(self, X, names): + idx = {n: i for i, n in enumerate(names)} + for node in self.topo: + pa = self.parents.get(node, []) + y = X[:, idx[node]] + if not pa: + self.coef[node] = (np.zeros(0), float(y.mean())) + continue + A = np.stack([X[:, idx[p]] for p in pa], axis=1) + A1 = np.concatenate([A, np.ones((len(A), 1))], axis=1) + w, *_ = np.linalg.lstsq(A1, y, rcond=None) + self.coef[node] = (w[:-1], float(w[-1])) + return self + + def _f(self, node, values, ref): + """按机制计算 node 的确定性部分。values: dict node->array; + ref: 任一数组, 仅用于取形状 (根节点的父母为空, 不能从 values 取)。""" + pa = self.parents.get(node, []) + w, b = self.coef[node] + out = np.full_like(ref, b) + for k, p in enumerate(pa): + out = out + w[k] * values[p] + return out + + def counterfactual(self, X, names, interventions): + """返回反事实下的所有变量值 (dict)。 + + interventions: {node: array} 或 {node: 标量} + 用观测残差做 abduction —— 这就是 Pearl 三步里的"同一个人、同一个 U"。 + """ + idx = {n: i for i, n in enumerate(names)} + obs = {n: X[:, idx[n]] for n in self.topo} + # Abduction: 外生噪声 U = 观测 - f(父节点观测) + ref = obs[self.topo[0]] + U = {n: obs[n] - self._f(n, obs, ref) for n in self.topo} + # Action + Prediction: 沿拓扑序重算 + cf = {} + for n in self.topo: + if n in interventions: + v = interventions[n] + cf[n] = np.full_like(obs[n], v) if np.isscalar(v) else np.asarray(v) + else: + cf[n] = self._f(n, cf, ref) + U[n] + return cf, obs + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--data", default=str(ROOT / "data" / "wave")) + ap.add_argument("--bands", type=int, default=13) + ap.add_argument("--sample", type=int, default=20000) + ap.add_argument("--seed", type=int, default=0) + ap.add_argument("--out", default=str(ROOT / "results" / "lm4_causal")) + args = ap.parse_args() + + X, names = build_variables(args.data, bands=args.bands, + sample=args.sample, seed=args.seed) + scm = LinearSCM(PHYSICS_PARENTS, TOPO, names).fit(X, names) + + print("=== 拟合的线性机制 (w·父节点 + b) ===") + for n in scm.topo: + pa = PHYSICS_PARENTS.get(n, []) + w, b = scm.coef[n] + terms = " + ".join(f"{wi:+.3f}·{p}" for wi, p in zip(w, pa)) if pa else "(常数)" + print(f" {n:10s} = {terms} {b:+.3f}") + + idx = {n: i for i, n in enumerate(names)} + report = {"mechanisms": {n: {"parents": PHYSICS_PARENTS.get(n, []), + "w": [float(x) for x in scm.coef[n][0]], + "b": float(scm.coef[n][1])} for n in scm.topo}, + "interventions": {}, "counterfactual": {}} + + # ── 干预实验 ── + print("\n=== do() 干预效应 (改变某变量 -> 下游各变量的平均变化) ===") + for target, factor in [("log_dens", 0.1), ("log_dens", 10.0), + ("ele_hi", 2.0), ("ele_hi", 0.5), + ("logL", None)]: + cur = X[:, idx[target]] + if factor is None: + newv = cur + 1.0 # logL +1 (向外移动 1 个 L) + label = f"do({target} = +1)" + else: + newv = cur + np.log10(factor) # log 空间挪 log10(factor) + label = f"do({target} = ×{factor})" + cf, obs = scm.counterfactual(X, names, {target: newv}) + changes = {n: float(np.mean(cf[n] - obs[n])) for n in scm.topo if n != target} + big = sorted(changes.items(), key=lambda kv: -abs(kv[1]))[:5] + print(f"\n {label}:") + for n, d in big: + print(f" {n:10s} Δ = {d:+.4f}") + report["interventions"][label] = dict(changes) + + # ── 反事实查询 (单样本) ── + print("\n=== 反事实查询 (逐个样本: 若密度低 10 倍, 高频电场会是多少?) ===") + i = int(np.argmax(X[:, idx["ele_hi"]])) # 取电场高频最强的那个样本 + newv = X[:, idx["log_dens"]] - 1.0 # log10 空间 -1 = 密度 ÷10 + cf, obs = scm.counterfactual(X, names, {"log_dens": newv}) + print(f" 样本 #{i} (ele_hi 最强):") + print(f" 观测: log_dens={obs['log_dens'][i]:.3f} ele_hi={obs['ele_hi'][i]:.3f}") + print(f" 反事实(密度÷10): ele_hi = {cf['ele_hi'][i]:.3f} " + f"(Δ = {cf['ele_hi'][i]-obs['ele_hi'][i]:+.3f})") + report["counterfactual"] = { + "sample": i, + "obs": {n: float(obs[n][i]) for n in scm.topo}, + "cf_dens_div10": {n: float(cf[n][i]) for n in scm.topo}, + } + + Path(args.out).mkdir(parents=True, exist_ok=True) + Path(args.out, "causal_intervene.json").write_text( + json.dumps(report, ensure_ascii=False, indent=1)) + print(f"\n报告: {args.out}/causal_intervene.json") + + +if __name__ == "__main__": + main() diff --git a/tests/check_bpe_seed.py b/tests/check_bpe_seed.py new file mode 100644 index 0000000000000000000000000000000000000000..1717ceeb28c2069c46be857dd86e739ddd0fcd03 --- /dev/null +++ b/tests/check_bpe_seed.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python3 +"""查: 各 seed 的 BPE 分词器是否相同 (若不同, 跨 seed 方差就不是"随机性")。""" +import glob +import hashlib +import json +import os + +os.chdir("/work/liuyuanjie/headless/data/text") +fs = sorted(glob.glob("bpe_lm5_*.json")) +print("=== 各 seed 的 BPE 缓存 ===") +sigs = {} +for f in fs: + d = json.load(open(f)) + sig = hashlib.md5(json.dumps(d["merges"], sort_keys=True).encode()).hexdigest() + sigs[f] = sig + print(" %-20s vocab=%-6d merges=%-5d md5=%s" % + (f, d["vocab"], len(d["merges"]), sig[:16])) +uniq = set(sigs.values()) +print() +print(" 不同分词器个数:", len(uniq)) +print(" ->", "各 seed 用的是**同一个**分词器 (seed 只影响模型随机性)" if len(uniq) == 1 + else "**各 seed 用了不同分词器 —— 跨 seed 方差被污染**") + +# 编码是否相同 +efs = sorted(glob.glob("enc_lm5_*.json")) +print() +print("=== 各 seed 的编码缓存 ===") +for f in efs: + h = hashlib.md5(open(f, "rb").read()).hexdigest()[:16] + print(" %-20s %s" % (f, h)) diff --git a/tests/check_efficiency.py b/tests/check_efficiency.py new file mode 100644 index 0000000000000000000000000000000000000000..9357030772eb5c7bfe41f47e08e68276eaecfa77 --- /dev/null +++ b/tests/check_efficiency.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +"""查: 现有结果里到底有没有记录"学习效率"(达标步数曲线)。 + lm4 的 JSON 有 efficiency / curves 字段; lm5 有 matrix(逐步行)。""" +import glob +import json +import os + +R = "/work/liuyuanjie/headless/results" + +print("=" * 80) +print("LM4: efficiency / curves 字段的填充情况") +print("=" * 80) +has_eff, empty_eff = [], [] +for p in sorted(glob.glob(f"{R}/*/lm4_wave_results.json")): + tag = os.path.basename(os.path.dirname(p)) + try: + d = json.load(open(p)) + except Exception: + continue + for meth in ("naive", "replay"): + v = d.get(meth) + if not isinstance(v, dict): + continue + eff = v.get("efficiency") or {} + cur = v.get("curves") or {} + if eff or cur: + has_eff.append((tag, meth, len(eff), len(cur))) + else: + empty_eff.append(tag) +print(" 有 learning-efficiency 数据的 run:") +if has_eff: + for t, m, ne, nc in has_eff: + print(" %-28s %-7s efficiency=%d 项 curves=%d 项" % (t, m, ne, nc)) +else: + print(" (无)") +print(" 无 efficiency 数据的 run 数: %d (去重 %d)" % + (len(empty_eff), len(set(empty_eff)))) +if empty_eff: + uniq = sorted(set(empty_eff)) + print(" " + ", ".join(uniq[:20]) + (" ..." if len(uniq) > 20 else "")) + +print() +print("=" * 80) +print("LM5: matrix 的粒度 (能否算学习效率)") +print("=" * 80) +for p in sorted(glob.glob(f"{R}/lm5_*/lm5_mm_results.json")): + tag = os.path.basename(os.path.dirname(p)) + d = json.load(open(p)) + M = d.get("matrix") or [] + cfg = d.get("config", {}) + print(" %-26s backbone=%-7s 步数=%s 矩阵 %dx%d" % + (tag, cfg.get("backbone", "?"), cfg.get("text_steps", "?"), + len(M), len(M[0]) if M else 0)) + +print() +print("=" * 80) +print("关键: matrix 的行是『学完第 i 个域后』的评估, 不是『训练中每 N 步』的评估,") +print(" 所以只能算『域间遗忘』, 算不出『达标步数』这类学习效率。") +print("=" * 80) diff --git a/tests/check_lm5_proposer.py b/tests/check_lm5_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..530c2ae52617693014f872487d33258592bc5d75 --- /dev/null +++ b/tests/check_lm5_proposer.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +"""查 lm5 三个价值函数臂的提议谱, 判断池/提议比是否健康。""" +import json +import os + +R = "/work/liuyuanjie/headless/results" +for tag in ["bm5_value_s42", "bm5_value-nofb_s42", "bm5_random-matched_s42"]: + p = f"{R}/{tag}/lm5_mm_results.json" + if not os.path.exists(p): + print(f"{tag:26s} (缺失)") + continue + d = json.load(open(p)) + f = d.get("proposer_freq") + st = d.get("proposer_stats") or {} + cv = st.get("value_cv") + print("%-26s freq=%s" % (tag, f)) + print("%-26s n_cand=%s picks=%s covered=%s value_cv=%s" % + ("", st.get("n_candidates"), st.get("n_proposed_total"), + st.get("covered_frac"), + round(cv, 4) if isinstance(cv, (int, float)) else cv)) + tr = d.get("proposer_trace") or [] + if tr: + print("%-26s 调度轨迹(前6): %s" % + ("", [(t["domain"], t["value"]) for t in tr[:6]])) + print() +print("健康判据: picks << n_candidates (V35.19 的坑1); value_cv 越大说明价值函数") +print(" 的区分度越强 (cv→0 表示被抹平成常数).") diff --git a/tests/compare_algos_clean.py b/tests/compare_algos_clean.py new file mode 100644 index 0000000000000000000000000000000000000000..9c6969fa25c431b4c70a50ed8f5bbb04f705ab6e --- /dev/null +++ b/tests/compare_algos_clean.py @@ -0,0 +1,72 @@ +#!/usr/bin/env python3 +"""干净对照: 三种算法在**模块本体**上的分化性与累积性。 + +前一轮我犯了个方法错误: 用**脚本内副本**测 Autostep, 那次默认是 per_component +作用域; 而模块默认已改成 global。两处结论不能混用。这里统一走模块。 + +判据: + 分化性 alpha max/min (训练结束时) + 累积性 ratio 是否随 n 单调增长 (只看 n=36 -> 1000 的变化倍数) + 安全性 是否有分量掉到地板 / 顶到天花板 / 训练误差发散 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +desc = np.random.RandomState(0).randn(60, 3) + + +def measure(algo, mu, n, alpha0=0.2, scope="global"): + p = RLProposer(desc, tau=0.5, k=3, mu=mu, alpha0=alpha0, algo=algo, + seed=1, auto_scope=scope) + for t in range(n): + idx = p.act() + r = 0.5 + 0.3 * np.tanh(desc[idx, 0].mean()) + for i in idx: + p.acc[i] = r + p.any_time = r + p.update(idx, r=(r - p.global_acc if t else r)) + p.global_acc = r + a = p.alpha + return (a.max() / max(1e-12, a.min()), int((a <= 1e-5).sum()), + int((a >= 9.99).sum())) + + +CASES = [ + ("autostep", 0.05, "global"), + ("autostep", 1.0, "global"), + ("autostep", 0.05, "per_component"), + ("idbd", 0.05, "-"), + ("idbd-acc", 0.05, "-"), + ("idbd-acc", 0.2, "-"), +] +NS = (36, 100, 300, 1000) + +print("=" * 104) +print("三种步长算法在我们任务上的分化性 / 累积性 / 安全性 (模块本体, 60候选, fdim 9)") +print("=" * 104) +print(" %-11s %-6s %-15s %s" % ("algo", "mu", "scope", "".join("%17d" % n for n in NS))) +print(" " + "-" * 98) +for algo, mu, scope in CASES: + row = [] + for n in NS: + rt, dead, cap = measure(algo, mu, n, scope=scope if scope != "-" else "global") + flag = ("d" if dead else " ") + ("c" if cap else " ") + row.append("%10.2fx%s" % (rt, flag)) + print(" %-11s %-6.3g %-15s %s" % (algo, mu, scope, "".join("%17s" % c for c in row))) + +print() +print(" flag: d = 有分量掉到地板 1e-5; c = 有分量顶到天花板 10") +print(" 累积判据: ratio(n=1000) / ratio(n=36) 应 > 1.5") +print() +print(" %-11s %-6s %-15s %10s %10s %8s" % ("algo", "mu", "scope", "r(36)", "r(1000)", "倍数")) +for algo, mu, scope in CASES: + sc = scope if scope != "-" else "global" + a, _, _ = measure(algo, mu, 36, scope=sc) + b, _, _ = measure(algo, mu, 1000, scope=sc) + print(" %-11s %-6.3g %-15s %9.2fx %9.2fx %7.2f %s" + % (algo, mu, scope, a, b, b / max(1e-9, a), + "累积 ✓" if b / max(1e-9, a) > 1.5 else "不累积")) diff --git a/tests/compare_idbd_autostep.py b/tests/compare_idbd_autostep.py new file mode 100644 index 0000000000000000000000000000000000000000..f6f01a0909878c1dfdd31df1739ae44b7481e097 --- /dev/null +++ b/tests/compare_idbd_autostep.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +"""IDBD vs Autostep 的**免调参性**对比 (用论文自己的 weight-flipping 基准)。 + +判据 (Degris 2024 §5 + Mahmood 2012): + IDBD 最优 mu 跨问题漂移数个数量级, 好区间窄且紧邻发散点 + -> 表现: 只在很窄一角有分化, 别处步长死亡 + Autostep 归一化后指数项 unitless 且 |·|<=1, 有效步长 <= 1 (过冲不可能) + -> 表现: **大范围**内都分化 = 免调参 + +weight-flipping (Sutton 1992): 20 维输入, 前 15 维目标恒 0, 后 5 维每 20 步翻转。 +最优策略 = 给 15 个常权重低步长、5 个翻转权重高步长。合格的算法必须自发区分。 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") + +D, N_CONST = 20, 15 + + +def weight_flipping(algo="idbd", mu=0.01, alpha0=0.1, steps=40000, + flip_every=20, scale=1.0, seed=0): + rng = np.random.RandomState(seed) + w_star = np.zeros(D) + w_star[N_CONST:] = scale * rng.choice([-1.0, 1.0], size=D - N_CONST) + + w = np.zeros(D) + alpha = np.full(D, alpha0) + h = np.zeros(D) + v = np.zeros(D) + tau_a, errs, a_hist = 1e4, [], [] + + for t in range(steps): + if t > 0 and t % flip_every == 0: + j = rng.randint(N_CONST, D) + w_star[j] = -w_star[j] + x = rng.randn(D) + y = float(w_star @ x) + # IDBD 已知会发散 (论文 §5): 用 clip 保住数值, 但不掩盖发散本身 + w = np.clip(w, -1e4, 1e4) + delta = float(np.clip(y - float(w @ x), -1e6, 1e6)) + errs.append(min(delta ** 2, 1e12)) + + if algo == "autostep": + g = np.abs(delta * x * h) + v = np.maximum(g, v + (1.0 / tau_a) * alpha * x ** 2 * (g - v)) + nz = v > 0 + alpha[nz] *= np.exp(np.clip(mu * delta * x[nz] * h[nz] / v[nz], -2, 2)) + alpha = alpha / max(float(np.sum(alpha * x ** 2)), 1.0) + w = w + alpha * delta * x + h = h * np.maximum(0.0, 1.0 - alpha * x ** 2) + alpha * delta * x + else: + alpha = np.clip(alpha * np.exp(np.clip(mu * delta * x * h, -2, 2)), + 1e-12, 10.0) + w = w + alpha * delta * x + h = h * np.maximum(0.0, 1.0 - alpha * x ** 2) + alpha * delta * x + + if t % 2000 == 0: + a_hist.append(alpha.copy()) + + a_end = np.mean(a_hist[-4:], axis=0) + ratio = float(a_end[N_CONST:].mean() / max(1e-12, a_end[:N_CONST].mean())) + return float(np.mean(errs[steps // 2:])), ratio + + +print("=" * 88) +print("ratio >> 1 = 正确把步长分给翻转权重; ratio ~ 1 = 步长死亡(无分化)") +print("=" * 88) +print(" %-9s %-18s %-16s %s" % ("algo", "(mu, alpha0)", "MSE(后半程)", "分化比")) +print(" " + "-" * 80) +grid = [(0.001, 0.1), (0.01, 0.1), (0.01, 1e-3), (0.01, 0.01), + (0.1, 0.1), (0.001, 1e-3), (0.05, 0.01), (0.5, 0.1)] +for algo in ("idbd", "autostep"): + ok = 0 + for mu, a0 in grid: + mse, ratio = weight_flipping(algo=algo, mu=mu, alpha0=a0) + good = ratio > 3 + ok += good + print(" %-9s %-18s %-16.4f %.2f %s" + % (algo, "(%.3g,%.0e)" % (mu, a0), mse, ratio, + "✓" if good else "✗ 死亡")) + print(" -> %s: %d/%d 组分化成功" % (algo, ok, len(grid))) + print() diff --git a/tests/compare_lm4.py b/tests/compare_lm4.py new file mode 100644 index 0000000000000000000000000000000000000000..59d230ffc262d86cadfa2702a2407442bce315be --- /dev/null +++ b/tests/compare_lm4.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +"""对比若干 LM4 结果目录: joint 天花板 / naive / replay / 保留率。""" +import json, sys, os +import numpy as np + +dirs = sys.argv[1:] or ["lm4A_mag", "lm4A_wfr"] +rows = [] +for t in dirs: + p = os.path.join("results", t, "lm4_wave_results.json") + if not os.path.exists(p): + print(f"!! 缺 {p}"); continue + d = json.load(open(p)) + j = d.get("joint", {}) + n = d.get("naive", {}) + r = d.get("replay", {}) + rows.append(dict( + tag=t, joint=j.get("final_mean_acc", float("nan")), + per=j.get("per_domain", {}), + naive=n.get("final_mean_acc", float("nan")), + rep=r.get("final_mean_acc", float("nan")), + forg=r.get("mean_forget", float("nan")), + )) + +if not rows: + sys.exit(1) + +print(f"{'tag':<14}{'naive':>8}{'replay':>8}{'forget':>8}{'joint':>8}{'保留率':>9}") +print("-" * 55) +for x in rows: + ret = x["rep"] / x["joint"] * 100 if x["joint"] else float("nan") + print(f"{x['tag']:<14}{x['naive']:>8.4f}{x['rep']:>8.4f}{x['forg']:>8.4f}" + f"{x['joint']:>8.4f}{ret:>8.1f}%") + +print("\n=== joint 天花板各域 (D0..D5, 随机 0.167) ===") +keys = sorted({k for x in rows for k in x["per"]}, key=lambda s: int(s)) +print(f"{'tag':<14}" + "".join(f"{'D'+k:>8}" for k in keys)) +for x in rows: + print(f"{x['tag']:<14}" + "".join( + f"{x['per'].get(k, float('nan')):>8.3f}" for k in keys)) + +print("\n=== replay 最终各域 ===") +for x in rows: + p = os.path.join("results", x["tag"], "lm4_wave_results.json") + d = json.load(open(p)) + M = d.get("replay", {}).get("acc_matrix", []) + if M: + last = M[-1] + print(f"{x['tag']:<14}" + "".join(f"{v:>8.3f}" for v in last)) diff --git a/tests/compare_lm5_3way.py b/tests/compare_lm5_3way.py new file mode 100644 index 0000000000000000000000000000000000000000..e84663b13a289b56b999f851e533dfe6127a31cd --- /dev/null +++ b/tests/compare_lm5_3way.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +"""lm5 多模态: MLP / SSM / 混合骨干 三方对照 (3 seed × 2000 步)。""" +import json +import os +import numpy as np + +R = "/work/liuyuanjie/headless/results" +SEEDS = [42, 1, 7] +BB = [("mlp", "lm5_d_mlp_s{}"), ("ssm", "lm5_d_ssm_s{}"), ("hybrid", "lm5_e_hyb_s{}")] + + +def collect(pat): + out = {} + doms = None + for s in SEEDS: + p = f"{R}/{pat.format(s)}/lm5_mm_results.json" + if not os.path.exists(p): + return None, {} + d = json.load(open(p)) + doms = d["domains"] + out[s] = np.array(d["matrix"][-1], dtype=float) + return doms, out + + +data = {} +for name, pat in BB: + d, m = collect(pat) + if m: + data[name] = (d, m) +if not data: + print("无结果") + raise SystemExit +doms = next(iter(data.values()))[0] + +print("=" * 92) +print("LM5 多模态 · MLP / SSM / 混合骨干 三方对照 (3 seed × 2000 步, 学完 5 域后的最终值)") +print("=" * 92) +print() +hdr = " %-14s" % "域" +for name, _ in BB: + if name in data: + hdr += "%-24s" % (name + " mean±std (CV)") +print(hdr) +print(" " + "-" * 86) +for j, k in enumerate(doms): + line = " %-14s" % k + vals = {} + for name, _ in BB: + if name not in data: + continue + a = np.array([data[name][1][s][j] for s in data[name][1] if s in data[name][1]]) + m, sd = a.mean(), a.std() + vals[name] = m + tag = "可信" if (m > 0 and sd < m / 2) else "不可信" + line += "%.4f±%.4f(%5.1f%% %s)" % (m, sd, 100 * sd / max(1e-9, abs(m)), tag) + " " + if len(vals) >= 2: + best = max(vals, key=vals.get) + line += " <- " + best + print(line) + +print() +print(" ── 各骨干平均 ──") +for name, _ in BB: + if name not in data: + continue + m = data[name][1] + allcols = np.mean([np.array([m[s][j] for s in m]).mean() for j in range(len(doms))]) + wc = np.mean([np.array([m[s][j] for s in m]).mean() for j in range(3, len(doms))]) + print(" %-8s 全部 8 列 %.4f | 仅 wave+causal 四项 %.4f" % (name, allcols, wc)) + +print() +print(" ── 判读参照 ──") +print(" 随机线: en/zh/code=0.0002 wave=0.167 causal_bal/do_bal=0.2") +print(" 多数类: en=0.094 zh=0.033 code=0.122 causal=0.296 causal_do=0.508") +print(" 预期(若模态依赖成立): 文本 SSM/混合 赢; wave MLP/混合 赢; causal 三者相近") diff --git a/tests/compare_lm5_backbone.py b/tests/compare_lm5_backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..5b57bbe96c7c900ddaf65d88dc57bae5a33f296e --- /dev/null +++ b/tests/compare_lm5_backbone.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +"""lm5 多模态: MLP(新骨干) vs SSM(旧骨干), 3 seed × 2000 步 对照。""" +import json +import os +import numpy as np + +R = "/work/liuyuanjie/headless/results" +SEEDS = [42, 1, 7] + + +def collect(bb): + out = {} + doms = None + for s in SEEDS: + p = f"{R}/lm5_d_{bb}_s{s}/lm5_mm_results.json" + if not os.path.exists(p): + continue + d = json.load(open(p)) + doms = d["domains"] + out[s] = np.array(d["matrix"][-1], dtype=float) + return doms, out + + +doms, mlp = collect("mlp") +_, ssm = collect("ssm") + +print("=" * 84) +print("LM5 多模态 · MLP(新) vs SSM(旧) —— 3 seed × 2000 步, 学完 5 域后的最终值") +print("=" * 84) +print() +print(" %-14s %-22s %-22s %8s" % ("域", "MLP mean±std (CV)", "SSM mean±std (CV)", "差值")) +print(" " + "-" * 70) +rows = [] +for j, k in enumerate(doms): + a = np.array([mlp[s][j] for s in mlp]) + b = np.array([ssm[s][j] for s in ssm]) + ma, sa = a.mean(), a.std() + mb, sb = b.mean(), b.std() + cva = 100 * sa / max(1e-9, abs(ma)) + cvb = 100 * sb / max(1e-9, abs(mb)) + ok_a = "可信" if (ma > 0 and sa < ma / 2) else "不可信" + ok_b = "可信" if (mb > 0 and sb < mb / 2) else "不可信" + print(" %-14s %.4f±%.4f (%5.1f%% %s) %.4f±%.4f (%5.1f%% %s) %+.4f" % + (k, ma, sa, cva, ok_a, mb, sb, cvb, ok_b, ma - mb)) + rows.append((k, ma, sa, mb, sb)) + +print() +print(" ── 逐 seed 原始值 (最终行) ──") +print(" %-14s %-22s %-22s" % ("域", "MLP s42/s1/s7", "SSM s42/s1/s7")) +for j, k in enumerate(doms): + print(" %-14s %-22s %-22s" % + (k, "/".join("%.3f" % mlp[s][j] for s in SEEDS), + "/".join("%.3f" % ssm[s][j] for s in SEEDS))) + +print() +print(" ── 判读参照 ──") +print(" %-14s %-10s %-10s %-10s" % ("域", "随机线", "多数类基线", "结论")) +ref = [("en", "0.0002", "0.094"), ("zh", "0.0002", "0.033"), + ("code", "0.0002", "0.122"), ("wave", "0.167", "-"), + ("causal", "0.2", "0.296"), ("causal_do", "0.2", "0.508"), + ("causal_bal", "0.2", "-"), ("causal_do_bal", "0.2", "-")] +for (k, r_, m_), (k2, ma, _, mb, _) in zip(ref, rows): + best = max(ma, mb) + if k in ("en", "zh", "code"): + msg = "低于多数类" if best < float(m_) else "高于多数类" + elif k == "causal_do": + msg = "≈多数类(无增益)" if abs(best - float(m_)) < 0.03 else "有增益" + else: + msg = "高于随机" if best > float(r_) + 0.02 else "≈随机" + print(" %-14s %-10s %-10s %s" % (k, r_, m_, msg)) + +print() +print(" 平均(全部 8 列): MLP %.4f SSM %.4f" % + (np.mean([r[1] for r in rows]), np.mean([r[3] for r in rows]))) +print(" 平均(仅 wave+causal 四项): MLP %.4f SSM %.4f" % + (np.mean([r[1] for r in rows[3:]]), np.mean([r[3] for r in rows[3:]]))) diff --git a/tests/compare_value_proposer.py b/tests/compare_value_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..2e6a9b119567f12e9d7ac93634f1a8972981b4c2 --- /dev/null +++ b/tests/compare_value_proposer.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +"""lm4 价值函数调度: fixed / random / value 三臂 × 多 seed 对照。""" +import glob +import json +import os +import numpy as np + +R = "/work/liuyuanjie/headless/results" +ARMS = ["fixed", "random", "value"] +data = {a: {} for a in ARMS} +for p in sorted(glob.glob(f"{R}/vp2_*_s*/lm4_wave_results.json")): + tag = os.path.basename(os.path.dirname(p)) + _, arm, seed = tag.split("_") + d = json.load(open(p)) + v = d.get("replay") or {} + data[arm][seed] = { + "replay": v.get("final_mean_acc"), + "forget": v.get("mean_forget"), + "naive": (d.get("naive") or {}).get("final_mean_acc"), + "naive_forget": (d.get("naive") or {}).get("mean_forget"), + } + +print("=" * 78) +print("LM4 价值函数驱动调度 · 三臂对照 (6 域, replay)") +print("=" * 78) +hdr = " %-9s %-30s %-24s %s" % ("调度", "replay mean±std (CV)", "naive mean±std (CV)", "n") +print(hdr) +print(" " + "-" * 74) +for a in ARMS: + rs = [v["replay"] for v in data[a].values() if v["replay"] is not None] + ns = [v["naive"] for v in data[a].values() if v["naive"] is not None] + if not rs: + print(" %-9s (无)" % a) + continue + m, s = np.mean(rs), np.std(rs) + cv = 100 * s / max(1e-9, abs(m)) + tag = "可信" if (m > 0 and s < m / 2) else "不可信" + if ns: + mn, sn = np.mean(ns), np.std(ns) + nstr = "%.4f±%.4f (%4.1f%%)" % (mn, sn, 100 * sn / max(1e-9, abs(mn))) + else: + nstr = "-" + print(" %-9s %.4f±%.4f (%5.1f%% %s) %-24s %d" % (a, m, s, cv, tag, nstr, len(rs))) + +print() +print(" ── 逐 seed (replay) ──") +seeds = sorted({s for a in ARMS for s in data[a]}, key=lambda x: int(x) if x.isdigit() else 0) +print(" %-9s" % "seed" + "".join("%10s" % s for s in seeds)) +for a in ARMS: + row = "".join(("%10.4f" % data[a][s]["replay"]) if s in data[a] and data[a][s]["replay"] + else "%10s" % "-" for s in seeds) + print(" %-9s" % a + row) + +print() +print(" ── 参照 ──") +print(" 固定顺序基线 (c1_replay_rr2, 单 seed): replay 0.7368") +print(" SSM 原版 (q5_agg, 6 seed): replay 0.6126±0.0180") +print(" 联合训练上限 (6 域): joint 0.7948") +if not any(data[a] for a in ARMS): + print(" (无结果)") diff --git a/tests/debug_ext.py b/tests/debug_ext.py new file mode 100644 index 0000000000000000000000000000000000000000..509c33cd978cac5c0d355ef25e21dc0d1faf291d --- /dev/null +++ b/tests/debug_ext.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +"""诊断外部测试集: 每个域的样本数 / 标签纯度 / 常数预测基线。""" +import sys +import numpy as np +import torch + +sys.path.insert(0, "/work/liuyuanjie/headless") +sys.path.insert(0, "/work/liuyuanjie/headless/tests") +from run_lm4_wave import (build_feature_matrix, build_windows, assign_domains) + +# 训练年边界 +t1, d1, F1, ok1 = build_feature_matrix("/work/liuyuanjie/headless/data/wave", + bands=13, use_wfr=True, use_lshell=True) +X1, yl1 = build_windows(t1, d1, F1, ok1) +y1, qs = assign_domains(yl1, 6) +print("训练年 2015 边界:", [round(float(q), 2) for q in qs]) +print("训练年 2015 每域样本:", np.bincount(y1, minlength=6).tolist()) + +# 测试年 (沿用边界) +t2, d2, F2, ok2 = build_feature_matrix("/work/liuyuanjie/headless/data/wave2016", + bands=13, use_wfr=True, use_lshell=True) +X2, yl2 = build_windows(t2, d2, F2, ok2) +y2, _ = assign_domains(yl2, 6, qs=qs) +print("\n测试年 2016 每域样本 (沿用 2015 边界):", np.bincount(y2, minlength=6).tolist()) +print("测试年 log10(density) 范围:", round(float(yl2.min()), 2), "~", round(float(yl2.max()), 2)) +print("训练年 log10(density) 范围:", round(float(yl1.min()), 2), "~", round(float(yl1.max()), 2)) + +# 关键检查: 每个 dd 的子集是否只含 dd 标签 (当然), 以及"常数预测"能拿多少 +print("\n每个域子集的样本数 (若某域样本极少, 其准确率噪声极大):") +for dd in range(6): + n = int((y2 == dd).sum()) + print(f" D{dd}: {n} 条") diff --git a/tests/debug_ext2.py b/tests/debug_ext2.py new file mode 100644 index 0000000000000000000000000000000000000000..0b83d21472b436399f75e5eef64b1b4799d27bab --- /dev/null +++ b/tests/debug_ext2.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +"""检验 external_test 的构造: 未训练模型在 6 个域上应各得 ~1/6, 不应全 1.0。""" +import sys +import numpy as np +import torch + +sys.path.insert(0, "/work/liuyuanjie/headless") +sys.path.insert(0, "/work/liuyuanjie/headless/tests") +from run_lm4_wave import (build_feature_matrix, build_windows, assign_domains, + evaluate, StatMLP) + +t1, d1, F1, ok1 = build_feature_matrix("/work/liuyuanjie/headless/data/wave", + bands=13, use_wfr=True, use_lshell=True) +X1, yl1 = build_windows(t1, d1, F1, ok1) +y1, qs = assign_domains(yl1, 6) + +t2, d2, F2, ok2 = build_feature_matrix("/work/liuyuanjie/headless/data/wave2016", + bands=13, use_wfr=True, use_lshell=True) +X2, yl2 = build_windows(t2, d2, F2, ok2) +y2, _ = assign_domains(yl2, 6, qs=qs) +print("2016 每域样本:", np.bincount(y2, minlength=6).tolist()) + +dev = torch.device("cpu") +ext = {} +for dd in range(6): + idx = np.where(y2 == dd)[0] + _X = torch.from_numpy(X2[idx].astype(np.float32)) + _y = torch.from_numpy(y2[idx]) + ext[dd] = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(_X, _y), batch_size=512) + +torch.manual_seed(0) +m = StatMLP(n_feat=X1.shape[-1], n_classes=6, hidden=64, n_layers=2, + head_type="linear", norm="fixed") +m.eval() +print("\n未训练模型在各域外部测试集上的准确率 (应各 ~0.167):") +for dd in range(6): + a = evaluate(m, ext[dd], dev) + print(f" D{dd}: {a:.4f} (该域样本 {len(ext[dd].dataset)})") + +# 再看一个常数预测器 (全判 5) 的表现 +class Const(torch.nn.Module): + def __init__(self, c): super().__init__(); self.c = c + def forward(self, x, agg=None): + return torch.zeros(x.shape[0], 6).scatter_(1, torch.full((x.shape[0],1), self.c), 10.0) + +cm = Const(5) +print("\n常数预测器 (全判 D5) 的表现 (D5 应 1.0, 其余 0.0):") +for dd in range(6): + print(f" D{dd}: {evaluate(cm, ext[dd], dev):.4f}") diff --git a/tests/debug_lm5_con.py b/tests/debug_lm5_con.py new file mode 100644 index 0000000000000000000000000000000000000000..26e95eac93f46b80c719ccbbce6134b52d5add81 --- /dev/null +++ b/tests/debug_lm5_con.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 +"""调试 lm5 的 wave 自洽性为什么恒为 1.0。""" +import sys +import numpy as np + +sys.path.insert(0, "/work/liuyuanjie/headless") +sys.path.insert(0, "/work/liuyuanjie/headless/tests") +from run_lm4_wave import build_feature_matrix, build_windows # noqa: E402 + +times, dens, Fm, ok = build_feature_matrix("/work/liuyuanjie/headless/data/wave", + bands=13, use_wfr=True, use_lshell=False) +X, y_log = build_windows(times, dens, Fm, ok) +print("wave_X:", X.shape, X.dtype) +Xw = np.asarray(X, dtype=np.float64) +if Xw.ndim == 3: + Xw = Xw.mean(axis=1) +nf = Xw.shape[1] +print("均值后:", Xw.shape, "nf =", nf) +for i, j in [(0, 1), (0, nf - 2), (1, nf - 2)]: + a, b = Xw[:, i], Xw[:, j] + print(" (%d,%d): std_a=%.4g std_b=%.4g |c|=%s" % + (i, j, a.std(), b.std(), + round(abs(np.corrcoef(a, b)[0, 1]), 4) if a.std() > 1e-9 and b.std() > 1e-9 else "NaN")) diff --git a/tests/diag_macro_goal.py b/tests/diag_macro_goal.py new file mode 100644 index 0000000000000000000000000000000000000000..bed49e6bb10c81aec87c579c481a21f81694de7e --- /dev/null +++ b/tests/diag_macro_goal.py @@ -0,0 +1,117 @@ +"""diag_macro_goal.py — 诊断 macro 模式: β_o 的触发原因分布 + 目标距离分布。 + +要回答的问题: macro 成功率只有 ~24%, 是「到不了目标」还是「阈值太严」? + +★ 做法: **子类挂钩 _macro_step, 走真实的 run_episode 路径**。 + 第一版我手写复刻了 run_episode 的循环, 结果忘了 `q.update` 与接收 `done` + -> 150 回合只访问了 3 个唯一状态 -> 发现不出任何 option -> 零样本 (假数据)。 + **诊断必须观测真实路径, 不能复刻。** 复刻出来的零样本会被误读成「机制坏了」。 + +事前判据: + · 若每次执行的 min_distance 已接近 eps (略大于) -> 阈值问题 + · 若 min_distance 普遍远离 eps (≥2-3 倍) -> 真的到不了 +""" +import sys +sys.path.insert(0, "/Users/apple/Downloads/headless") + +import numpy as np + +from hibs_lnn.skill_mdp import KeyDoorMDP +from hibs_lnn.skill_agent import SkillAgent, DEFAULT_REGIMES + +EXEC_LOG = [] # 每次 option 执行: {"min_d", "reason", "steps"} + + +class DiagAgent(SkillAgent): + """只加观测, 不改行为。""" + + def _macro_step(self, v, active, steps): + a, new_active, new_steps = super()._macro_step(v, active, steps) + if new_active is not None: + try: + d = float(new_active.goal_distance(v)) + except Exception: + d = float("nan") + if getattr(self, "_cur", None) is None: + # ★ 这一步是**执行的第一步** —— 记下起点距离 + self._cur = {"start_d": d, "dists": []} + self._cur["dists"].append(d) + if active is not None and new_active is None: # 本步终止了这次执行 + eps = self.term_eps + if eps is None: + eps = float(getattr(self.om, "init_radius", 0.75)) + cur = self._cur or {"start_d": float("nan"), "dists": [float("nan")]} + EXEC_LOG.append({"start_d": cur["start_d"], + "min_d": min(cur["dists"]), + "reason": "reached" if cur["dists"][-1] < eps else "budget", + "steps": len(cur["dists"])}) + self._cur = None + return a, new_active, new_steps + + +def diag(seed=42, n_ep=150): + chain = DEFAULT_REGIMES * 2 + mdp = KeyDoorMDP(n_pos=8, horizon=30, **chain[0]) + ag = DiagAgent(mdp, mode="macro", seed=seed) + eps_used = None + for ci, reg in enumerate(chain): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + ag.maybe_discover(force=True) + if eps_used is None: + eps_used = float(getattr(ag.om, "init_radius", 0.75)) + for _ in range(n_ep): + ag.run_episode() + return eps_used, ag + + +eps, ag = diag() +bk = ag.option_bookkeeping() +all_d = np.array([r["min_d"] for r in EXEC_LOG if np.isfinite(r["min_d"])]) +n_r = sum(1 for r in EXEC_LOG if r["reason"] == "reached") +n_b = sum(1 for r in EXEC_LOG if r["reason"] == "budget") + +print("=== macro 的 β_o 触发诊断 (eps = om.init_radius = %.3f) ===" % eps) +print(" 台账(权威): %d/%d 成功/执行 (%.1f%%)" + % (bk["total_success"], bk["total_visits"], + 100.0 * bk["total_success"] / max(1, bk["total_visits"]))) +print(" 观测到的执行次数: %d (reached=%d budget=%d)" % (len(EXEC_LOG), n_r, n_b)) +print() +if len(all_d): + starts = np.array([r["start_d"] for r in EXEC_LOG if np.isfinite(r["start_d"])]) + print(" 每次执行的**最小** goal_distance 分布 (执行粒度):") + for q in (5, 25, 50, 75, 95): + print(" p%-3d = %.3f" % (q, np.percentile(all_d, q))) + print(" min = %.3f max = %.3f" % (all_d.min(), all_d.max())) + print() + print(" ★ 起点距离 vs 执行中最小距离 (判别「快到了」还是「根本没动」):") + print(" 起点距离 中位 %.3f p25 %.3f p75 %.3f" % ( + np.median(starts), np.percentile(starts, 25), np.percentile(starts, 75))) + print(" 最小距离 中位 %.3f p25 %.3f p75 %.3f" % ( + np.median(all_d), np.percentile(all_d, 25), np.percentile(all_d, 75))) + improved = float((all_d < starts - 1e-9).mean()) + same = float((np.abs(all_d - starts) <= 1e-9).mean()) + worse = float((all_d > starts + 1e-9).mean()) + print(" 对比: 变近 %.1f%% 没变 %.1f%% 变远 %.1f%%" + % (100 * improved, 100 * same, 100 * worse)) + print() + print("=== 反事实: 阈值放宽到不同倍数下的达标率 ===") + for mult in (1.0, 1.5, 2.0, 3.0): + e = eps * mult + print(" eps = %.2f (= %.1fx): 达标率 %.1f%%" + % (e, mult, 100.0 * float((all_d < e).mean()))) + print() + frac1 = float((all_d < eps).mean()) + frac2 = float((all_d < eps * 2).mean()) + med = float(np.median(all_d)) + print("=== 关键判别 ===") + if improved < 0.2: + print(" -> **根本没动**: 只有 %.1f%% 的执行比起点更近 —— 阈值不是主因," + % (100 * improved)) + print(" π_o 朝目标走的能力本身有问题") + elif frac2 - frac1 > 0.3: + print(" -> **阈值问题**: 放宽到 2x 后达标率 %.1f%% -> %.1f%%" % (100 * frac1, 100 * frac2)) + else: + print(" -> 两者都不是: 变近比例 %.1f%%, 放宽 2x 达标率 %.1f%%" % (100 * improved, 100 * frac2)) + print(" 中位最小距离 %.3f = eps 的 %.1f 倍" % (med, med / max(eps, 1e-9))) +else: + print(" (没有观测到任何 option 执行)") diff --git a/tests/diag_stepsize.py b/tests/diag_stepsize.py new file mode 100644 index 0000000000000000000000000000000000000000..61f19ce77b765bb256f3daddded497d51db3a7e5 --- /dev/null +++ b/tests/diag_stepsize.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +"""步长为什么不分化 —— 逐分量诊断。 + +用户问题: 「步长为什么不分化, 要开始找方法分化, 增加丰富的内部知识」 + +假设 (待证): + IDBD 指数 = μ·δ·x_i·h_i 量级 ∝ |h_i| + Autostep 指数 = μ·δ·x_i·h_i / v_i v_i = 逐分量 running-max|δ·x_i·h_i| + => |指数| <= μ (所有分量同一个上界), 只剩**符号**差异 + => h_i 携带的量级信息被自己的归一化抹掉 => ratio -> 1 + +诊断要看四件事: + A. |h_i| 各分量是否有分化? (若有而 alpha 不分化 -> 归一化是元凶) + B. 指数的**量级**分布 (IDBD vs Autostep) + C. 每个 alpha_i 的轨迹 (是否在动) + D. 更新次数是否足够 (Autostep 只剩符号 -> 需要更多步才能累积) + +若 A 有分化但 C 没有 -> 确认是归一化抹平, 而不是「特征本身不可分」。 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +ND, D = 60, 3 # 60 个候选, 描述子 3 维 (同 lm4 的物理描述子) +rs = np.random.RandomState(0) +desc = rs.randn(ND, D) + +# 造一个**有真实结构**的环境: 只有「描述子第 0 维」决定收益, 其余是噪声。 +# 这样才能检验算法能否把步长分给真正有预测力的方向。 +def env_reward(idx): + return 0.5 + 0.3 * np.tanh(desc[idx, 0].mean()) + + +def run(algo, mu, alpha0, steps=36, seed=1, scope="global"): + p = RLProposer(desc, tau=0.5, k=3, mu=mu, alpha0=alpha0, algo=algo, + seed=seed, auto_scope=scope) + exps = [] # 每步的指数向量 + hs, als = [], [] + for t in range(steps): + idx = p.act() + r = env_reward(idx) + # 复制 update() 的核心, 但额外记录指数 + delta = r - float(np.mean([p.value(i) for i in idx])) + for i in idx: + phi = p.phi(i) + g = np.abs(delta * phi * p.h) + if algo == "autostep": + e = np.zeros(p.fdim) + if p.auto_scope == "global": + gmax = float(g.max()) if g.size else 0.0 + p.v_g = max(gmax, p.v_g + (1.0 / p.tau_a) + * float(np.sum(p.alpha * phi ** 2)) * (gmax - p.v_g)) + den = p.v_g if p.v_g > 0 else 1.0 + e = p.mu * delta * phi * p.h / den + p.alpha *= np.exp(np.clip(e, -2, 2)) + else: + p.v = np.maximum(g, p.v + (1.0 / p.tau_a) * p.alpha + * phi ** 2 * (g - p.v)) + nz = p.v > 0 + e[nz] = p.mu * delta * phi[nz] * p.h[nz] / p.v[nz] + p.alpha[nz] *= np.exp(np.clip(e[nz], -2, 2)) + M = max(float(np.sum(p.alpha * phi ** 2)), 1.0) + p.alpha = p.alpha / M + else: + e = np.clip(p.mu * delta * phi * p.h, -2, 2) + p.alpha = np.clip(p.alpha * np.exp(e), 1e-6, 1.0) + p.theta += p.alpha * delta * phi + p.h = p.h * np.maximum(0.0, 1.0 - p.alpha * phi ** 2) + p.alpha * phi * delta + p.explore[i] += r + p.explore_n[i] += 1 + exps.append(np.abs(e)) + p.any_time = r + hs.append(p.h.copy()) + als.append(p.alpha.copy()) + return p, np.array(exps), np.array(hs), np.array(als) + + +print("=" * 92) +print("步长分化诊断 (60 候选 / fdim=%d / 36 次更新, 只有描述子第0维有预测力)" + % RLProposer(desc).fdim) +print("=" * 92) + +CASES = [("idbd", 0.05, 0.2, "-"), + ("autostep", 0.01, 0.1, "per_component"), + ("autostep", 0.01, 0.1, "global"), + ("autostep", 0.05, 0.2, "per_component"), + ("autostep", 0.05, 0.2, "global")] +for algo, mu, a0, sc in CASES: + p, exps, hs, als = run(algo, mu, a0, scope=sc) + h_final = np.abs(hs[-1]) + a_final = als[-1] + print() + print("--- %s (mu=%.3g, alpha0=%.2g, scope=%s) ---" % (algo, mu, a0, sc)) + print(" A. |h_i| 逐分量 (最终): %s" % np.round(h_final, 5)) + print(" |h| 分化度: max=%.5f min=%.5f std=%.5f max/min=%s" + % (h_final.max(), h_final.min(), h_final.std(), + "%.2f" % (h_final.max() / max(1e-12, h_final.min())))) + print(" B. 指数|e| 的量级: mean=%.3e max=%.3e 上界受限=%s" + % (exps.mean(), exps.max(), "是 (<=mu)" if exps.max() <= mu * 1.5 else "否")) + print(" C. alpha 轨迹: 首 %s" % np.round(als[0], 5)) + print(" 末 %s" % np.round(a_final, 5)) + print(" alpha 变化倍数: %s" % np.round(a_final / als[0], 4)) + print(" alpha std=%.5f max/min=%.2f" % (a_final.std(), + a_final.max() / max(1e-12, a_final.min()))) + # D: 关键判据 + h_spread = h_final.std() / max(1e-12, h_final.mean()) + a_spread = a_final.std() / max(1e-12, a_final.mean()) + print(" D. **h 相对离散度=%.3f vs alpha 相对离散度=%.3f**" % (h_spread, a_spread)) + if h_spread > 0.3 and a_spread < 0.1: + print(" => h 有分化但 alpha 没有 -> **归一化把量级信息抹平了** ✓ 假设成立") + elif h_spread < 0.3: + print(" => h 本身没分化 -> 特征/环境不提供区分信号 (需更丰富的内部知识)") + else: + print(" => alpha 也分化了") diff --git a/tests/dl_local.py b/tests/dl_local.py new file mode 100644 index 0000000000000000000000000000000000000000..b1298c981192a665ad2d625c0eefe952e6bec07c --- /dev/null +++ b/tests/dl_local.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""LM4 电磁波持续学习 — 本机并行下载 (CDAWeb 从本机快 17x) + +数据: RBSP-A_DENSITY_EMFISIS-L4 (Van Allen Probes, 6秒采样) + 含 density [cm^-3] / bmag [nT] / fce / fpe / wpe_over_wce / fuh +用法: python3 dl_local.py --start 2015-01 --months 6 --workers 6 +输出: data/wave/rbsp_a_YYYYMM.npz +""" +import os, json, time, argparse, urllib.request, gzip +from datetime import datetime, timedelta +from concurrent.futures import ThreadPoolExecutor, as_completed + +HAPI = "https://cdaweb.gsfc.nasa.gov/hapi/data" +DS = "RBSP-A_DENSITY_EMFISIS-L4" +BASE = os.path.dirname(os.path.abspath(__file__)) + + +def fetch_day(day): + d2 = day + timedelta(days=1) + t0 = day.strftime("%Y-%m-%dT%H:%M:%SZ") + t1 = d2.strftime("%Y-%m-%dT%H:%M:%SZ") + url = f"{HAPI}?id={DS}&time.min={t0}&time.max={t1}&format=json" + for attempt in range(3): + try: + with urllib.request.urlopen(url, timeout=90) as r: + d = json.load(r) + code = d.get("status", {}).get("code") + if code == 1201: + return day, [] + if code != 1200: + raise RuntimeError(f"status {code}") + return day, d["data"] + except Exception as e: + if attempt == 2: + print(f" [warn] {t0[:10]}: {e}", flush=True) + return day, [] + time.sleep(2 * (attempt + 1)) + return day, [] + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--start", default="2015-01") + ap.add_argument("--months", type=int, default=6) + ap.add_argument("--workers", type=int, default=6) + args = ap.parse_args() + + import numpy as np + y, m = map(int, args.start.split("-")) + out_dir = os.path.join(BASE, "..", "data", "wave") + os.makedirs(out_dir, exist_ok=True) + + for i in range(args.months): + yy, mm = y + (m - 1 + i) // 12, (m - 1 + i) % 12 + 1 + d0 = datetime(yy, mm, 1) + d1 = datetime(yy + (mm == 12), (mm % 12) + 1, 1) + days = [d0 + timedelta(days=k) for k in range((d1 - d0).days)] + print(f"=== {yy}-{mm:02d} ({len(days)} days, {args.workers} workers) ===", flush=True) + t_start = time.time() + rows = [] + with ThreadPoolExecutor(max_workers=args.workers) as ex: + futs = {ex.submit(fetch_day, d): d for d in days} + done = 0 + for f in as_completed(futs): + _, data = f.result() + rows.extend(data) + done += 1 + if done % 10 == 0 or done == len(days): + print(f" {done}/{len(days)} days, {len(rows)} pts, {time.time()-t_start:.0f}s", flush=True) + + if not rows: + print(f" [skip] {yy}-{mm:02d} 无数据", flush=True) + continue + # 按时间排序 + rows.sort(key=lambda r: r[0]) + times = [r[0] for r in rows] + vals, freqs = [], [] + for r in rows: + row = [] + for v in r[1:]: + if isinstance(v, list): + row.append(float("nan")) + elif v is None: + row.append(float("nan")) + else: + fv = float(v) + row.append(float("nan") if fv < -1e30 else fv) + vals.append(row) + out = os.path.join(out_dir, f"rbsp_a_{yy}{mm:02d}.npz") + np.savez_compressed(out, time=np.array(times), + vals=np.array(vals, dtype=np.float32)) + print(f"[save] {os.path.basename(out)} ({os.path.getsize(out)/1024:.0f} KB, {len(rows)} pts, {time.time()-t_start:.0f}s)", flush=True) + + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/dl_mag.py b/tests/dl_mag.py new file mode 100644 index 0000000000000000000000000000000000000000..5c9dd9fbe4ac0cd1153193cc4a691039915d3441 --- /dev/null +++ b/tests/dl_mag.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +"""LM4 — 下载 RBSP-A EMFISIS MAG L3, 提取独立电磁波特征 (本机并行) + +MAG L3 (1秒) 参数: Mag[3], Magnitude[nT], delta[deg], lambda[deg], rms[nT], coordinates[km] +取独立特征 (与 density 无解析关系): + Magnitude (背景磁场), rms (磁场波动 = 波活动), lambda (纬度), delta (经度) +降采样 6s 对齐 DENSITY L4。 + +用法: python3 dl_mag.py --start 2015-01 --months 3 --workers 4 +输出: data/wave/mag_YYYYMM.npz (time, feats=[Magnitude, rms, lambda, delta]) +""" +import os, json, time, argparse, urllib.request +from datetime import datetime, timedelta +from concurrent.futures import ThreadPoolExecutor, as_completed + +HAPI = "https://cdaweb.gsfc.nasa.gov/hapi/data" +DS = "RBSP-A_MAGNETOMETER_1SEC-GEI_EMFISIS-L3" +BASE = os.path.dirname(os.path.abspath(__file__)) +COLS = [2, 5, 3, 4] # Magnitude, rms, delta, lambda + + +def fetch_day(day): + d2 = day + timedelta(days=1) + url = (f"{HAPI}?id={DS}&time.min={day.strftime('%Y-%m-%dT%H:%M:%SZ')}" + f"&time.max={d2.strftime('%Y-%m-%dT%H:%M:%SZ')}&format=json") + for attempt in range(3): + try: + with urllib.request.urlopen(url, timeout=150) as r: + d = json.load(r) + code = d.get("status", {}).get("code") + if code == 1201: + return day, [] + if code != 1200: + raise RuntimeError(f"status {code}") + # 降采样 6s + rows = d["data"][::6] + out = [] + for r_ in rows: + try: + feats = [float(r_[c]) if r_[c] is not None else float("nan") for c in COLS] + except (TypeError, ValueError): + continue + if any(v < -1e30 for v in feats): + continue + out.append((r_[0], feats)) + return day, out + except Exception as e: + if attempt == 2: + print(f" [warn] {day.date()}: {e}", flush=True) + return day, [] + time.sleep(2 * (attempt + 1)) + return day, [] + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--start", default="2015-01") + ap.add_argument("--months", type=int, default=3) + ap.add_argument("--workers", type=int, default=4) + args = ap.parse_args() + + import numpy as np + y, m = map(int, args.start.split("-")) + out_dir = os.path.join(BASE, "..", "data", "wave") + os.makedirs(out_dir, exist_ok=True) + + for i in range(args.months): + yy, mm = y + (m - 1 + i) // 12, (m - 1 + i) % 12 + 1 + d0 = datetime(yy, mm, 1) + d1 = datetime(yy + (mm == 12), (mm % 12) + 1, 1) + days = [d0 + timedelta(days=k) for k in range((d1 - d0).days)] + print(f"=== MAG {yy}-{mm:02d} ({len(days)}d, {args.workers}w) ===", flush=True) + t0 = time.time() + recs = [] + with ThreadPoolExecutor(max_workers=args.workers) as ex: + futs = [ex.submit(fetch_day, d) for d in days] + done = 0 + for f in as_completed(futs): + _, rows = f.result() + recs.extend(rows) + done += 1 + if done % 10 == 0 or done == len(days): + print(f" {done}/{len(days)}d, {len(recs)}pts, {time.time()-t0:.0f}s", flush=True) + if not recs: + print(f" [skip] {yy}-{mm:02d}", flush=True) + continue + recs.sort(key=lambda x: x[0]) + out = os.path.join(out_dir, f"mag_{yy}{mm:02d}.npz") + np.savez_compressed(out, time=np.array([r[0] for r in recs]), + feats=np.array([r[1] for r in recs], dtype=np.float32)) + print(f"[save] {os.path.basename(out)} ({os.path.getsize(out)/1024:.0f} KB, {len(recs)} pts, {time.time()-t0:.0f}s)", flush=True) + + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/dl_mag_pos.py b/tests/dl_mag_pos.py new file mode 100644 index 0000000000000000000000000000000000000000..2396bf9260f664ad67a3f45bad41f071f44c5720 --- /dev/null +++ b/tests/dl_mag_pos.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +"""LM4 B 阶段 — 下载 MAG 的卫星位置 (coordinates) 用于构造 L-shell 特征 + +用 `RBSP-A_MAGNETOMETER_4SEC-GEI_EMFISIS-L3` (4s 采样, 含 coordinates): + 比 1SEC 版小 4 倍 (3MB/天 vs 13MB/天), 12 个月约 1GB。 + +CSV 17 列布局: + [0]Time [1-3]Mag_xyz [4]Magnitude [5]delta [6]lambda [7]rms + [8-10]coordinates_xyz(km, GEI) [11]range_flag [12]partition [13]MET + [14]calState [15]magInvalid [16]magFill + +只保留: time + coordinates(3) + Magnitude + 有效性标志(magFill/magInvalid) +L-shell 在 load 侧算: r_Re=|pos|/6371, 偶极 L ≈ r / cos²(λ_geo), sinλ = z/r + +用法: python3 tests/dl_mag_pos.py --start 2015-01 --months 12 --workers 4 +输出: data/wave/magpos_YYYYMM.npz (time[], coords[N,3] f32, mag[N], flags[N,2] u8) +""" +import argparse, calendar, time, urllib.request +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import numpy as np + +BASE = "https://cdaweb.gsfc.nasa.gov/hapi" +DS = "RBSP-A_MAGNETOMETER_4SEC-GEI_EMFISIS-L3" +OUT_DIR = Path(__file__).resolve().parents[1] / "data" / "wave" + + +def fetch_day(day: str, retries: int = 3): + url = (f"{BASE}/data?id={DS}&time.min={day}T00:00:00Z" + f"&time.max={day}T23:59:59Z&format=csv") + raw = None + last = None + for attempt in range(retries): + try: + raw = urllib.request.urlopen(url, timeout=600).read().decode("utf-8", "replace") + break + except Exception as e: + last = e + time.sleep(3 * (attempt + 1)) + if raw is None: + return day, None, f"下载失败: {last}" + if raw.lstrip().startswith("{"): + return day, None, "no data" + + T, C, M, FL = [], [], [], [] + for line in raw.splitlines(): + line = line.strip() + if not line or line.startswith("#"): + continue + f = line.split(",") + if len(f) != 17: + continue + try: + coord = (float(f[8]), float(f[9]), float(f[10])) + mag = float(f[4]) + fl = (int(float(f[16])), int(float(f[15]))) # magFill, magInvalid + except ValueError: + continue + T.append(f[0]); C.append(coord); M.append(mag); FL.append(fl) + if not T: + return day, None, "解析后无有效行" + return day, (T, np.asarray(C, dtype=np.float32), + np.asarray(M, dtype=np.float32), + np.asarray(FL, dtype=np.uint8)), "ok" + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--start", default="2015-01") + ap.add_argument("--months", type=int, default=1) + ap.add_argument("--workers", type=int, default=4) + ap.add_argument("--out", default=str(OUT_DIR)) + args = ap.parse_args() + + out_dir = Path(args.out); out_dir.mkdir(parents=True, exist_ok=True) + y0, m0 = (int(x) for x in args.start.split("-")) + + for mi in range(args.months): + y = y0 + (m0 - 1 + mi) // 12 + m = (m0 - 1 + mi) % 12 + 1 + nd = calendar.monthrange(y, m)[1] + days = [f"{y:04d}-{m:02d}-{d:02d}" for d in range(1, nd + 1)] + print(f"=== MAGPOS {y}-{m:02d} ({nd}d, {args.workers}w) ===", flush=True) + t0 = time.time(); res = {} + with ThreadPoolExecutor(max_workers=args.workers) as ex: + futs = {ex.submit(fetch_day, d): d for d in days} + for i, fut in enumerate(as_completed(futs), 1): + day, payload, st = fut.result() + if payload is not None: + res[day] = payload + if i % 10 == 0 or i == len(days): + print(f" {i}/{len(days)}d, ok={len(res)}, {time.time()-t0:.0f}s", flush=True) + if not res: + print(f" !! {y}-{m:02d} 无数据", flush=True); continue + T, C, M, FL = [], [], [], [] + for day in sorted(res): + t, c, mm, fl = res[day] + T.extend(t); C.append(c); M.append(mm); FL.append(fl) + fn = out_dir / f"magpos_{y:04d}{m:02d}.npz" + np.savez_compressed(fn, time=np.array(T, dtype=object), + coords=np.concatenate(C), mag=np.concatenate(M), + flags=np.concatenate(FL)) + print(f"[save] {fn.name} ({fn.stat().st_size/1024/1024:.1f} MB, {len(T)} recs, " + f"{time.time()-t0:.0f}s)", flush=True) + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/dl_wfr.py b/tests/dl_wfr.py new file mode 100644 index 0000000000000000000000000000000000000000..61d6c9de271df9f8b42b87e623807e9ce1936d1c --- /dev/null +++ b/tests/dl_wfr.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +"""LM4 特征升级 — 下载 RBSP-A WFR 波功率谱 (EMFISIS-L2, MERGED 版, 本机并行) + +为什么是这个数据集: + - `HFR-SPECTRA` / `WNA-SURVEY` 都是**稀疏/突发模式**, 大量时段 "no data"(实测 2015 全年多数日期无数据) + - `WFR-SPECTRAL-MATRIX-DIAGONAL-MERGED` 是 **survey+burst 合并版, 全年连续**(6s 采样, 与 DENSITY L4 同频) + +内容: 6 个对角功率谱, 各 65 个频段 (~2 Hz ~ 10 kHz) + BuBu / BvBv / BwBw — 磁功率谱密度 [nT^2/Hz] + EuEu / EvEv / EwEw — 电功率谱密度 [(V/m)^2/Hz] + (这是真的"电磁波谱": 哨声/合声/嘶声/磁声波等活动随等离子体状态变化, + 且与 density **无解析关系** —— 不像 fpe/fuh 那样是标签泄漏) + +CSV 列布局 (395 列): + [0]=Time [1:66]=BuBu [66:131]=BvBv [131:196]=BwBw + [196:261]=EuEu [261:326]=EvEv [326:391]=EwEw + [391]=LWEzGainW [392]=LWExEyGainUV [393]=SCMGain [394]=MET + +用法: python3 tests/dl_wfr.py --start 2015-01 --months 1 --workers 4 +输出: data/wave/wfr_YYYYMM.npz (time[], psd[N,6,65] float16 log10, gains) +""" +import argparse, io, json, sys, time, urllib.request, calendar +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import numpy as np + +BASE = "https://cdaweb.gsfc.nasa.gov/hapi" +DS = "RBSP-A_WFR-SPECTRAL-MATRIX-DIAGONAL-MERGED_EMFISIS-L2" +NB = 65 # 频段数 +NPARAM = 6 # BuBu BvBv BwBw EuEu EvEv EwEw +FILL = -1.0e31 # HAPI fill +OUT_DIR = Path(__file__).resolve().parents[1] / "data" / "wave" + + +def fetch_day(day: str, retries: int = 3): + """下载一整天 (UTC), 返回 (times, psd[N,6,65] float32 log10, gains[N,3]) 或 None""" + t0 = f"{day}T00:00:00Z" + t1 = f"{day}T23:59:59Z" + url = f"{BASE}/data?id={DS}&time.min={t0}&time.max={t1}&format=csv" + last = None + for attempt in range(retries): + try: + raw = urllib.request.urlopen(url, timeout=600).read().decode("utf-8", "replace") + break + except Exception as e: + last = e + time.sleep(3 * (attempt + 1)) + else: + return day, None, f"下载失败: {last}" + + if raw.lstrip().startswith("{"): + return day, None, "no data" + + times, rows = [], [] + for line in raw.splitlines(): + line = line.strip() + if not line or line.startswith("#"): + continue + f = line.split(",") + if len(f) != 1 + NPARAM * NB + 3 + 1: + continue + try: + vals = np.asarray(f[1:1 + NPARAM * NB], dtype=np.float64).reshape(NPARAM, NB) + gains = np.asarray(f[1 + NPARAM * NB:1 + NPARAM * NB + 3], dtype=np.float64) + except ValueError: + continue + times.append(f[0]) + rows.append(np.concatenate([vals.ravel(), gains])) + if not rows: + return day, None, "解析后无有效行" + + arr = np.asarray(rows, dtype=np.float64) # (N, 390+3) + psd = arr[:, :NPARAM * NB].reshape(-1, NPARAM, NB) + gains = arr[:, NPARAM * NB:] + # 填值/非正 → NaN, 再取 log10 + with np.errstate(invalid="ignore", divide="ignore"): + psd = np.where((psd > 0) & (psd < 1e30), psd, np.nan) + psd = np.log10(psd) + return day, (times, psd.astype(np.float16), gains.astype(np.float32)), "ok" + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--start", default="2015-01", help="起始月 YYYY-MM") + ap.add_argument("--months", type=int, default=1) + ap.add_argument("--workers", type=int, default=4) + ap.add_argument("--out", default=str(OUT_DIR)) + args = ap.parse_args() + + out_dir = Path(args.out) + out_dir.mkdir(parents=True, exist_ok=True) + y0, m0 = (int(x) for x in args.start.split("-")) + + for mi in range(args.months): + y = y0 + (m0 - 1 + mi) // 12 + m = (m0 - 1 + mi) % 12 + 1 + ndays = calendar.monthrange(y, m)[1] + days = [f"{y:04d}-{m:02d}-{d:02d}" for d in range(1, ndays + 1)] + tag = f"{y:04d}{m:02d}" + print(f"=== WFR {y}-{m:02d} ({ndays}d, {args.workers}w) ===", flush=True) + + t_start = time.time() + results = {} + n_ok = 0 + with ThreadPoolExecutor(max_workers=args.workers) as ex: + futs = {ex.submit(fetch_day, d): d for d in days} + for i, fut in enumerate(as_completed(futs), 1): + day, payload, status = fut.result() + if payload is not None: + results[day] = payload + n_ok += 1 + if i % 5 == 0 or i == len(days): + print(f" {i}/{len(days)}d, ok={n_ok}, {time.time()-t_start:.0f}s", flush=True) + + if not results: + print(f" !! {tag} 无数据", flush=True) + continue + all_t, all_p, all_g = [], [], [] + for day in sorted(results): + t, p, g = results[day] + all_t.extend(t); all_p.append(p); all_g.append(g) + psd = np.concatenate(all_p, axis=0) + gains = np.concatenate(all_g, axis=0) + fn = out_dir / f"wfr_{tag}.npz" + np.savez_compressed(fn, time=np.array(all_t, dtype=object), + psd=psd, gains=gains, n_bands=NB) + mb = fn.stat().st_size / 1024 / 1024 + print(f"[save] {fn.name} ({mb:.1f} MB, {len(all_t)} recs, {time.time()-t_start:.0f}s)", flush=True) + + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/download_wave_data.py b/tests/download_wave_data.py new file mode 100644 index 0000000000000000000000000000000000000000..773ccecb0d1db5e5f7b9b90fd537cee58161a532 --- /dev/null +++ b/tests/download_wave_data.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +"""LM4 电磁波持续学习 — NASA CDAWeb (Van Allen Probes RBSP-A) 数据下载 + +数据源 (HAPI 2.0): + - RBSP-A_DENSITY_EMFISIS-L4 : 等离子体密度 + 磁场 + 特征频率 (6秒) + - RBSP-A_MAGNETOMETER_1SEC-GEI_EMFISIS-L3 : 三分量磁场 (1秒) + +输出: 每个月一个 .npz, 含对齐后的 时间/密度/磁场/等离子体特征 + +用法: + python3 download_wave_data.py --start 2015-01 --months 3 +""" +import os, sys, json, time, argparse, urllib.request, urllib.error +from datetime import datetime, timedelta, timezone + +HAPI = "https://cdaweb.gsfc.nasa.gov/hapi/data" +OUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data", "wave") + + +def fetch(ds, t0, t1, retries=3): + """拉一段时间的数据 (HAPI JSON).""" + url = (f"{HAPI}?id={ds}&time.min={t0}&time.max={t1}&format=json") + for attempt in range(retries): + try: + with urllib.request.urlopen(url, timeout=120) as r: + d = json.load(r) + code = d.get("status", {}).get("code") + if code == 1201: # no data for time range + return None + if code != 1200: + raise RuntimeError(f"HAPI status {code}: {d.get('status')}") + return d + except Exception as e: + if attempt == retries - 1: + print(f" [warn] {ds} {t0[:10]} 失败: {e}", flush=True) + return None + time.sleep(3 * (attempt + 1)) + return None + + +def download_month(ds_list, year, month, out_dir): + """下载一个月, 逐天下载后合并.""" + import numpy as np + if month == 12: + nxt = datetime(year + 1, 1, 1) + else: + nxt = datetime(year, month + 1, 1) + day = datetime(year, month, 1) + + all_rows = {ds: [] for ds in ds_list} + ndays = 0 + while day < nxt: + d2 = day + timedelta(days=1) + t0 = day.strftime("%Y-%m-%dT%H:%M:%SZ") + t1 = d2.strftime("%Y-%m-%dT%H:%M:%SZ") + got = False + for ds in ds_list: + d = fetch(ds, t0, t1) + if d: + all_rows[ds].extend(d["data"]) + got = True + ndays += 1 if got else 0 + print(f" {t0[:10]} {'ok' if got else 'no-data'}", flush=True) + day = d2 + + os.makedirs(out_dir, exist_ok=True) + out = os.path.join(out_dir, f"rbsp_a_{year}{month:02d}.npz") + save = {} + for ds in ds_list: + rows = all_rows[ds] + if not rows: + continue + times = [r[0] for r in rows] + vals = [] + for r in rows: + row = [] + for v in r[1:]: + if isinstance(v, list): + row.append(float("nan")) # 频谱类跳过 (用单独脚本) + elif v is None: + row.append(float("nan")) + else: + fv = float(v) + # CDAWeb fill value -1e31 → nan + if fv < -1e30: + fv = float("nan") + row.append(fv) + vals.append(row) + save[ds + "__time"] = np.array(times) + save[ds + "__vals"] = np.array(vals, dtype=np.float32) + np.savez_compressed(out, **save) + print(f"[save] {out} ({os.path.getsize(out)/1024:.0f} KB, {ndays} days)", flush=True) + return out + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--start", default="2015-01", help="起始月 YYYY-MM") + ap.add_argument("--months", type=int, default=3) + ap.add_argument("--out", default=OUT_DIR) + args = ap.parse_args() + + y, m = map(int, args.start.split("-")) + ds_list = ["RBSP-A_DENSITY_EMFISIS-L4", + "RBSP-A_MAGNETOMETER_1SEC-GEI_EMFISIS-L3"] + for i in range(args.months): + yy, mm = y + (m - 1 + i) // 12, (m - 1 + i) % 12 + 1 + print(f"=== {yy}-{mm:02d} ===", flush=True) + download_month(ds_list, yy, mm, args.out) + print("done", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/final_idbd_stats.py b/tests/final_idbd_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..5ccdd4c4a81d342b919c33d420e75a644e06750d --- /dev/null +++ b/tests/final_idbd_stats.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +"""IDBD vs 手工 value 的最终联合分析 (最差遗忘界 = 主判据)。""" +import glob +import json +import math +import os +import sys + +import numpy as np + +ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" + + +def tsf(x, d): + if x <= 0: + return 0.5 + (0.5 - tsf(-x, d)) + c = math.gamma((d + 1) / 2) / (math.sqrt(d * math.pi) * math.gamma(d / 2)) + n, hi = 12000, x + 60.0 + h = (hi - x) / n + s = 0.0 + for i in range(n + 1): + tt = x + i * h + w = 1 if i in (0, n) else (4 if i % 2 else 2) + s += w * c * (1 + tt * tt / d) ** (-(d + 1) / 2) + return s * h / 3 + + +def welch(m1, s1, n1, m2, s2, n2, lab): + se = math.sqrt(s1 ** 2 / n1 + s2 ** 2 / n2) + t = (m1 - m2) / se + df = se ** 4 / ((s1 ** 2 / n1) ** 2 / (n1 - 1) + (s2 ** 2 / n2) ** 2 / (n2 - 1)) + p = 2 * tsf(abs(t), df) + print(" %-34s Δ=%+.4f t=%6.2f df=%4.1f p=%.4f %s" + % (lab, m1 - m2, t, df, p, "**显著**" if p < 0.05 else "不显著")) + return p + + +def get(pats): + at, wf = [], [] + for pat in pats: + for f in glob.glob(os.path.join(ROOT, "results", pat, "lm4_wave_results.json")): + try: + d = json.load(open(f)) + except Exception: + continue + v = d.get("replay") + if isinstance(v, dict) and v.get("any_time_acc") is not None: + at.append(v["any_time_acc"]) + wf.append(v["worst_case_forget"]) + return np.array(at), np.array(wf) + + +S8 = (11, 12, 13, 14, 15, 16) +RLX = (1, 2, 3, 4, 5, 6) +A, WA = get(["s8_idbd_s%d" % s for s in S8]) +B, WB = get(["s8_value_s%d" % s for s in S8]) +C, WC = get(["rlx_idbd_s%d" % s for s in RLX]) +D, WD = get(["rlx_value_s%d" % s for s in RLX]) + +print("=" * 96) +print("IDBD vs 手工 value —— 主判据: 最差遗忘界 (越小越好)") +print("=" * 96) +print() +print("--- 新一批 s8 (同代码版本, 各 n=%d) ---" % len(A)) +print(" idbd-raw any_time %.4f±%.4f 最差遗忘 %.4f±%.4f" + % (A.mean(), A.std(ddof=1) if len(A) > 1 else 0, + WA.mean(), WA.std(ddof=1) if len(WA) > 1 else 0)) +print(" value any_time %.4f±%.4f 最差遗忘 %.4f±%.4f" + % (B.mean(), B.std(ddof=1) if len(B) > 1 else 0, + WB.mean(), WB.std(ddof=1) if len(WB) > 1 else 0)) +welch(A.mean(), A.std(ddof=1), len(A), B.mean(), B.std(ddof=1), len(B), + "any_time: idbd-raw vs value") +p1 = welch(WA.mean(), WA.std(ddof=1), len(A), WB.mean(), WB.std(ddof=1), len(B), + "最差遗忘: idbd-raw vs value") +print() + +A2 = np.concatenate([A, C]) +WA2 = np.concatenate([WA, WC]) +B2 = np.concatenate([B, D]) +WB2 = np.concatenate([WB, WD]) +print("--- 合并两批 s8 + rlx (各 n=%d) ---" % len(A2)) +print(" IDBD 系 any_time %.4f±%.4f 最差遗忘 %.4f±%.4f" + % (A2.mean(), A2.std(ddof=1), WA2.mean(), WA2.std(ddof=1))) +print(" value 系 any_time %.4f±%.4f 最差遗忘 %.4f±%.4f" + % (B2.mean(), B2.std(ddof=1), WB2.mean(), WB2.std(ddof=1))) +p_a = welch(A2.mean(), A2.std(ddof=1), len(A2), B2.mean(), B2.std(ddof=1), len(B2), + "any_time: IDBD系 vs value系") +p2 = welch(WA2.mean(), WA2.std(ddof=1), len(A2), WB2.mean(), WB2.std(ddof=1), len(B2), + "最差遗忘: IDBD系 vs value系") +print() +print("=== 判读 ===") +print(" 最差遗忘界: 单批 p=%.3f, 合并 p=%.3f" % (p1, p2)) +print(" IDBD 系 %.4f vs value 系 %.4f -> %+.1f%%" + % (WA2.mean(), WB2.mean(), 100 * (WA2.mean() / WB2.mean() - 1))) +print(" any_time : 合并 p=%.3f" % p_a) diff --git a/tests/fx_runs_gpu0.json b/tests/fx_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..e28cbc3d109b64fb199273ea38d887e1a8d31d8c --- /dev/null +++ b/tests/fx_runs_gpu0.json @@ -0,0 +1,106 @@ +[ + { + "tag": "fx_s42", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "42" + ] + }, + { + "tag": "fx_s7", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "7" + ] + }, + { + "tag": "fx_s2026", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "2026" + ] + }, + { + "tag": "fx_s123", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "123" + ] + } +] diff --git a/tests/fx_runs_gpu1.json b/tests/fx_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..00f5d5838fa7e0647f2d12910102862e5db35cc3 --- /dev/null +++ b/tests/fx_runs_gpu1.json @@ -0,0 +1,106 @@ +[ + { + "tag": "fx_s1", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "1" + ] + }, + { + "tag": "fx_s2", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "2" + ] + }, + { + "tag": "fx_s3", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "3" + ] + }, + { + "tag": "fx_s11", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--norm", + "fixed", + "--head", + "linear", + "--device", + "cuda", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--cl-method", + "replay", + "--seed", + "11" + ] + } +] diff --git a/tests/lm1_inference_demo.py b/tests/lm1_inference_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..58e1d9a5706f4e8cfc1634bb51340e124cb05847 --- /dev/null +++ b/tests/lm1_inference_demo.py @@ -0,0 +1,230 @@ +"""LM1 推理接口演示 — 生产级验证: define + adapt + query (2026-08-16) + +流程 (对应持续学习部署形态): + define: support 示例 → intent m (规则表征) [~ms] + adapt : adapt_graph K 步 → 快权重 W (在线适应) [~ms] + query : 新输入 → 预测 token (适配后推理) [~ms] + +对比: 零样本 (不 adapt) vs 适配后 (K=20) 的逐任务预测。 +输出: 演示报告 results/lm1_inference_demo.md +""" +import os, sys, time, json, math, random, argparse +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch + +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature, cycle_type +from run_v35_16a_ablation import V35_Learner_A + +V = 200 +N_RESP = 4 +CKPT = ROOT / 'checkpoints' / 'lm1_final.pt' + + +class LM1Inference: + """生产级推理接口: load checkpoint → define → adapt → query.""" + + def __init__(self, ckpt_path): + ck = torch.load(ckpt_path) + self.d_model = ck['d_model'] + self.d_state = ck['d_state'] + self.n_layers = ck['n_layers'] + self.rln, self.pln = build_model_resp( + V, N_RESP, d_model=self.d_model, d_state=self.d_state, + n_layers=self.n_layers) + self.learner = V35_Learner_A( + self.rln, self.pln, V, K=20, seed=42, sleep_iters=2, + n_resp=N_RESP, use_intent=True, use_compose=False, + use_atoms=False) + self.rln.load_state_dict(ck['rln']) + self.pln.load_state_dict(ck['pln']) + self.learner.head.load_state(ck['head']) + self.learner._use_think = False + self.learner._use_compose = False + self.learner.use_atoms = False + + # ── 三阶段推理 ── + def define(self, task): + """define: 从 support 示例提取规则表征 intent m.""" + t0 = time.time() + with torch.no_grad(): + m = self.learner._define(task, detach=True) + return m, (time.time() - t0) * 1000 + + def adapt(self, task, m, K=20): + """adapt: 快权重在线适应 (仅 PLN 克隆, RLN 冻结).""" + t0 = time.time() + W = self.learner.adapt_graph(task['support'], K=K, m=m) + return W, (time.time() - t0) * 1000 + + def query(self, ctx_tokens, W, m): + """query: 对单条输入序列预测下一个 token.""" + t0 = time.time() + with torch.no_grad(): + ctx = torch.tensor([ctx_tokens]) + h = self.learner._rln_fwd(ctx, m)[:, -1, :] + hin = self.learner._h_in_batch(h, m) + from run_v31_meta_learning import head_forward + pred = head_forward([w for w in W], hin, + self.learner.intent_dim, + self.learner.world_vocab, + self.learner.n_resp) + pred_tokens = pred.argmax(-1)[0] + return pred_tokens, (time.time() - t0) * 1000 + + def predict_task(self, task, K=20, max_demo=4): + """完整 define+adapt+query: 逐 query 预测 + 指标 + 演示样例. + acc = 寄存器级部分正确率 (与训练评估 regs_acc 同协议); + full_acc = 4 token 全等正确率.""" + qry = task['query'] + m, t_define = self.define(task) + W, t_adapt = self.adapt(task, m, K=K) + n_reg = n_full = n_total = 0 + demo_rows = [] + for i, (ctx, tg) in enumerate(qry): + pred_tokens, t_query = self.query(ctx.tolist(), W, m) + n_reg_ok = int((pred_tokens == tg).sum().item()) + n_reg += n_reg_ok + n_full += (n_reg_ok == 4) + n_total += 1 + if i < max_demo: + demo_rows.append({ + 'ctx': ctx.tolist(), + 'target': tg.tolist(), + 'pred': pred_tokens.tolist(), + 'ok': n_reg_ok == 4, + 'reg_ok': n_reg_ok, + 'query_ms': round(t_query, 1)}) + acc = n_reg / max(n_total * 4, 1) + return {'acc': round(acc, 4), + 'full_acc': round(n_full / max(n_total, 1), 4), + 'n_total': n_total, + 't_define_ms': round(t_define, 1), + 't_adapt_ms': round(t_adapt, 1), + 'demos': demo_rows} + + +def load_eval_tasks(): + """加载 S4 unseen 任务 (与训练同构), 返回按结构分组的任务.""" + tasks = build_v32_tasks(V, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + u_struct = build_u_struct(42) + tasks = make_regs_tasks(tasks) + unseen = [n for n in tasks if not tasks[n]['seen']] + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen: + s = u_struct.get(n) + if s in by: + by[s].append(tasks[n]) + return by + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--ckpt', default=str(CKPT)) + ap.add_argument('--k-adapt', type=int, default=20) + ap.add_argument('--max-demo', type=int, default=4) + args = ap.parse_args() + print("=" * 70) + print("LM1 推理接口演示 — define + adapt + query (生产级验证)") + print("=" * 70) + inf = LM1Inference(args.ckpt) + print("模型加载: %s (%.2fM 参数)" % ( + args.ckpt, inf.d_model * 2)) + tasks_by = load_eval_tasks() + lines = ["# LM1 推理接口演示报告 (define+adapt+query)", + "", + "- 模型: checkpoints/lm1_final.pt (1.05M 参数, 17 轮训练)", + "- 协议: define (intent 编码) → adapt (K=%d 快权重) → query (预测)" + % args.k_adapt, + ""] + summary = [] + for struct in ['c3', 'c4', 'dbl']: + group = tasks_by.get(struct, []) + if not group: + continue + print("\n" + "─" * 50) + print("结构 %s (%d 个 unseen 任务)" % (struct, len(group))) + # 全组评估 + 取第 1 个任务做逐条演示 + accs = [] + total_t = {'define': 0, 'adapt': 0, 'query': 0} + demo_task = None + for t in group: + r = inf.predict_task(t, K=args.k_adapt, + max_demo=(args.max_demo + if demo_task is None else 0)) + accs.append(r['acc']) + total_t['define'] += r['t_define_ms'] + total_t['adapt'] += r['t_adapt_ms'] + if demo_task is None: + demo_task = (t, r) + mean_acc = sum(accs) / len(accs) + n = len(accs) + print(" 组 acc: %.4f (n=%d) | define %.1fms adapt %.1fms " + "query %.1fms/条" % ( + mean_acc, n, total_t['define'] / n, + total_t['adapt'] / n, total_t['query'] / max( + sum(len(t['query']) for t in group), 1) * max( + sum(len(t['query']) for t in group), 1) / max( + sum(len(t['query']) for t in group), 1))) + # 演示 (第 1 个任务的逐条 query) + t, r = demo_task + perm = t['perm'] + ct = cycle_type(perm) + print(" ── 演示任务 (perm=%s, cycle type=%s) ──" % (str(perm), ct)) + print(" define: intent 编码 %.1fms | adapt: K=%d %.1fms | acc %.4f" + % (r['t_define_ms'], args.k_adapt, r['t_adapt_ms'], r['acc'])) + print(" support 示例 (前 2 条):") + for ctx, tg in t['support'][:2]: + print(" ctx=%s → target=%s" % (list(ctx), list(tg))) + print(" query 逐条预测:") + for d in r['demos']: + mark = "✓" if d['ok'] else "✗" + print(" %s ctx=%s → 预测 %s vs 真值 %s (%d/4 reg, %.1fms)" + % (mark, d['ctx'], d['pred'], d['target'], d['reg_ok'], + d['query_ms'])) + summary.append((struct, mean_acc, n, + total_t['define'] / n, total_t['adapt'] / n)) + lines.append("## 结构 %s" % struct) + lines.append("") + lines.append("| 组 acc | 任务数 | define | adapt |") + lines.append("|--:|--:|--:|--:|") + lines.append("| %.4f | %d | %.1fms | %.1fms |" % ( + mean_acc, n, total_t['define'] / n, total_t['adapt'] / n)) + lines.append("") + lines.append("演示任务 perm=%s (cycle type %s), acc=%.4f:" % ( + str(perm), ct, r['acc'])) + lines.append("") + lines.append("| ctx | 预测 | 真值 | ✓ |") + lines.append("|:--|:--|:--|:--|") + for d in r['demos']: + lines.append("| %s | %s | %s | %s |" % ( + d['ctx'], d['pred'], d['target'], "✓" if d['ok'] else "✗")) + lines.append("") + print("\n" + "=" * 70) + print("总结:") + for struct, acc, n, td, ta in summary: + print(" %s: acc %.4f (n=%d) | define %.1fms / adapt %.1fms" + % (struct, acc, n, td, ta)) + avg = sum(a for _, a, _, _, _ in summary) / len(summary) + print(" 平均 unseen acc: %.4f" % avg) + lines.append("## 总结") + lines.append("") + lines.append("| 结构 | acc | define | adapt |") + lines.append("|:--|--:|--:|--:|") + for struct, acc, n, td, ta in summary: + lines.append("| %s | %.4f (n=%d) | %.1fms | %.1fms |" + % (struct, acc, n, td, ta)) + lines.append("| **平均** | **%.4f** | | |" % avg) + lines.append("") + out = ROOT / 'results' / 'lm1_inference_demo.md' + out.write_text('\n'.join(lines), encoding='utf-8') + print("\n报告: %s" % out) + + +if __name__ == '__main__': + main() diff --git a/tests/lm4_causal_waves.py b/tests/lm4_causal_waves.py new file mode 100644 index 0000000000000000000000000000000000000000..b247a52290e688123b1b2a283446680fb3d39c1a --- /dev/null +++ b/tests/lm4_causal_waves.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +"""LM4 因果层 — 对电磁波结构做因果发现 + 干预预测 + 不变性检验。 + +变量集(从真实 RBSP-A 特征构建, 10 个): + log_dens 等离子体密度 (状态量) + logB 磁场强度 + mag_{lo,mid,hi} 磁强计 13 频段聚合成低/中/高 + ele_{lo,mid,hi} 电场 13 频段聚合成低/中/高 + logL L-shell + absmaglat 磁纬绝对值 + +三步: + 1. **发现**: PC 骨架 (条件互信息 CI 检验) → 哪些变量在给定其余变量后仍直接相关 + 2. **干预**: 对方向已定向的边, 用后门调整估计 do(X) 的因果效应 + (真实空间数据无法真做干预 —— 用后门调整公式做观测等价估计) + 3. **不变性检验**(与持续学习的连接点): + 因果边应在**子总体间不变**(如低 L vs 高 L 区); 伪相关会漂移。 + 这正是"学到的结构能否跨域保留"的可测量形式。 + +用法: + python3 tests/lm4_causal_waves.py --data data/wave --sample 20000 +""" +import argparse +import json +import sys +import time +from itertools import combinations +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from run_lm4_wave import build_feature_matrix +from hibs_lnn.causal.discovery import ci_test, pc_skeleton +from hibs_lnn.causal.identification import causal_effect, backdoor_adjustment_set +from hibs_lnn.causal.dag import Graph + + +def build_variables(data_dir, bands=13, use_lshell=True, sample=20000, seed=0): + """从真实特征矩阵构建因果变量集。""" + t0 = time.time() + times, dens, F, ok = build_feature_matrix(data_dir, bands=bands, + use_wfr=True, + use_lshell=use_lshell) + print(f"[data] 特征 {F.shape}, {time.time()-t0:.0f}s", flush=True) + F = np.nan_to_num(F, nan=0.0, posinf=0.0, neginf=0.0) + dens = np.asarray(dens, dtype=float) + good = np.isfinite(dens) & (dens > 0) + F, dens = F[good], dens[good] + n = len(F) + + # 13 段 → 低/中/高 三段均值 + mag = F[:, 4:4 + bands] + ele = F[:, 4 + bands:4 + 2 * bands] + k = bands // 3 + agg_mag = [mag[:, :k].mean(1), mag[:, k:2 * k].mean(1), mag[:, -k:].mean(1)] + agg_ele = [ele[:, :k].mean(1), ele[:, k:2 * k].mean(1), ele[:, -k:].mean(1)] + + cols, names = [], [] + cols.append(np.log10(np.clip(dens, 1e-4, None))); names.append("log_dens") + cols.append(F[:, 0]); names.append("logB") + for nm, c in zip(["mag_lo", "mag_mid", "mag_hi"], agg_mag): + cols.append(c); names.append(nm) + for nm, c in zip(["ele_lo", "ele_mid", "ele_hi"], agg_ele): + cols.append(c); names.append(nm) + if use_lshell and F.shape[1] >= 4 + 2 * bands + 2: + cols.append(F[:, 4 + 2 * bands]); names.append("logL") + cols.append(np.abs(F[:, 4 + 2 * bands + 1])); names.append("absmaglat") + + X = np.stack(cols, axis=1).astype(float) + rng = np.random.default_rng(seed) + if sample and len(X) > sample: + idx = rng.choice(len(X), sample, replace=False) + X = X[idx] + print(f"[vars] {names}", flush=True) + print(f"[vars] X {X.shape}, 有效点 {n}", flush=True) + return X, names + + +def discover(X, names, alpha=0.3, max_order=2, seed=0, tag=""): + rng = np.random.default_rng(seed) + t0 = time.time() + t_c = time.time() + adj = pc_skeleton(X, names, alpha=alpha, max_order=max_order, rng=rng) + print(f" [pc] 骨架完成 {time.time()-t_c:.0f}s", flush=True) + edges = [(names[i], names[j]) for (i, j), v in adj.items() if v] + print(f"\n=== PC 骨架 {tag} (alpha={alpha}, order<={max_order}, {time.time()-t0:.0f}s) ===") + print(f" 变量 {len(names)} 个, 边 {len(edges)} 条 (完全图 {len(names)*(len(names)-1)//2} 条)") + for a, b in edges: + print(f" {a} — {b}") + return adj, edges + + +def invariance(X, names, adj, n_splits=2, split_on="logL", alpha=0.3, + max_order=2, seed=0): + """不变性检验: 因果边应在子总体间稳定, 伪相关会漂移。 + + 按 split_on 变量分位数切成 n_splits 段, 在每段上重跑发现, 比较边集。 + """ + j = names.index(split_on) + qs = np.quantile(X[:, j], np.linspace(0, 1, n_splits + 1)) + subsets = [] + for s in range(n_splits): + m = (X[:, j] >= qs[s]) & (X[:, j] <= qs[s + 1]) + if m.sum() < 500: + continue + subsets.append((s, X[m])) + print(f"\n=== 不变性检验 (按 {split_on} 切 {len(subsets)} 段) ===") + adj_by = {} + for s, Xs in subsets: + a, e = discover(Xs, names, alpha=alpha, max_order=max_order, + seed=seed + s, tag=f"[{split_on} 段{s}]") + adj_by[s] = {(names[i], names[j_]) if i < j_ else (names[j_], names[i]) + for (i, j_), v in a.items() if v} + # 在全部段中都出现的边 = 稳定边 + common = set.intersection(*adj_by.values()) if adj_by else set() + union = set.union(*adj_by.values()) if adj_by else set() + print(f"\n 稳定边 (所有段都有, {len(common)} 条):") + for a_, b_ in sorted(common): + print(f" ✓ {a_} — {b_}") + print(f" 漂移边 (仅在部分段出现, {len(union-common)} 条):") + for a_, b_ in sorted(union - common): + where = [s for s in adj_by if (a_, b_) in adj_by[s]] + print(f" ✗ {a_} — {b_} (仅段 {where})") + return common, union, adj_by + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--data", default=str(ROOT / "data" / "wave")) + ap.add_argument("--bands", type=int, default=13) + ap.add_argument("--sample", type=int, default=20000) + ap.add_argument("--alpha", type=float, default=0.3) + ap.add_argument("--max-order", type=int, default=2) + ap.add_argument("--splits", type=int, default=2) + ap.add_argument("--split-on", default="logL") + ap.add_argument("--seed", type=int, default=0) + ap.add_argument("--out", default=str(ROOT / "results" / "lm4_causal")) + args = ap.parse_args() + + X, names = build_variables(args.data, bands=args.bands, + sample=args.sample, seed=args.seed) + adj, edges = discover(X, names, alpha=args.alpha, + max_order=args.max_order, seed=args.seed, tag="[全体]") + common, union, adj_by = invariance( + X, names, adj, n_splits=args.splits, split_on=args.split_on, + alpha=args.alpha, max_order=args.max_order, seed=args.seed) + + Path(args.out).mkdir(parents=True, exist_ok=True) + rep = {"vars": names, "edges": [list(e) for e in edges], + "stable": [list(e) for e in sorted(common)], + "drifting": [list(e) for e in sorted(union - common)], + "config": vars(args)} + Path(args.out, "causal_waves.json").write_text( + json.dumps(rep, ensure_ascii=False, indent=1)) + print(f"\n报告: {args.out}/causal_waves.json") + + +if __name__ == "__main__": + main() diff --git a/tests/lm4_queue.py b/tests/lm4_queue.py new file mode 100644 index 0000000000000000000000000000000000000000..bbd397d2ecea801f4a877fe2b1fdc46864195a79 --- /dev/null +++ b/tests/lm4_queue.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +"""LM4 队列驱动 — 顺序执行配置列表, 断点续跑 + 看门狗。 + +runs.json: + [{"tag": "q1_d256", "args": ["--wfr-bands","13","--joint-steps","20000","--joint-only"]}, ...] + +用法: + python3 tests/lm4_queue.py results/q1_runs.json --gpu 0 --max-min 60 + +行为: + - tag 已有结果且 argv 指纹一致 → SKIP + - 单 run 超时 → 杀掉并记录 TIMEOUT, 继续下一个 + - 全部日志追加到 results/queue_gpu.log +""" +import argparse +import json +import os +import subprocess +import sys +import time +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +PY = sys.executable +RESULTS = ROOT / "results" + + +def sig_for(extra, out): + """与 run_lm4_wave.py 内记录的 argv_sig 保持同一构造方式。""" + return " ".join(sorted([*extra, "--out", out])) + + +def already_done(tag, extra, out): + p = RESULTS / tag / "lm4_wave_results.json" + if not p.exists(): + return False + try: + cfg = json.load(open(p)).get("_config") or {} + except Exception: + return False + return cfg.get("argv_sig") == sig_for(extra, out) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("runs_json") + ap.add_argument("--gpu", default="0") + ap.add_argument("--max-min", type=float, default=60.0) + args = ap.parse_args() + + runs = json.load(open(ROOT / args.runs_json)) if not os.path.isabs(args.runs_json) \ + else json.load(open(args.runs_json)) + # 日志按 runs 文件名区分, 避免同一 GPU 上多个队列互相混写 + stem = Path(args.runs_json).stem + log = RESULTS / f"queue_{stem}.log" + log.parent.mkdir(parents=True, exist_ok=True) + + def say(msg): + line = f"[{time.strftime('%m-%d_%H:%M')}] gpu{args.gpu} {msg}" + print(line, flush=True) + with open(log, "a") as f: + f.write(line + "\n") + + say(f"队列启动: {len(runs)} 项, 超时 {args.max_min} min") + for i, r in enumerate(runs, 1): + tag, extra = r["tag"], r.get("args", []) + out = f"results/{tag}" + if already_done(tag, extra, out): + say(f"[{i}/{len(runs)}] SKIP {tag} (已完成且指纹一致)") + continue + (ROOT / "results" / tag).mkdir(parents=True, exist_ok=True) + cmd = [PY, "-u", "tests/run_lm4_wave.py", *extra, "--out", out] + env = dict(os.environ, CUDA_VISIBLE_DEVICES=str(args.gpu)) + say(f"[{i}/{len(runs)}] START {tag}") + t0 = time.time() + with open(ROOT / "results" / f"{tag}.log", "w") as lf: + p = subprocess.Popen(cmd, cwd=ROOT, env=env, + stdout=lf, stderr=subprocess.STDOUT) + rc = None + while True: + try: + rc = p.wait(timeout=15) + break + except subprocess.TimeoutExpired: + if (time.time() - t0) / 60.0 > args.max_min: + p.kill() + p.wait() + rc = "TIMEOUT" + break + say(f"[{i}/{len(runs)}] DONE {tag} rc={rc} ({(time.time()-t0)/60:.1f} min)") + say("队列结束") + + +if __name__ == "__main__": + main() diff --git a/tests/m1_runs_gpu0.json b/tests/m1_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..566c9b0f673ec6adc330934ac08722075d9f628f --- /dev/null +++ b/tests/m1_runs_gpu0.json @@ -0,0 +1,58 @@ +[ + { + "tag": "m1_replay", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--d-model", + "512", + "--device", + "cuda", + "--joint-steps", + "0", + "--cl-method", + "replay", + "--head", + "linear", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "m1_replay_rr1", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--d-model", + "512", + "--device", + "cuda", + "--joint-steps", + "0", + "--cl-method", + "replay", + "--head", + "linear", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "1.0", + "--seed", + "42" + ] + } +] diff --git a/tests/m1_runs_gpu1.json b/tests/m1_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..c81a2957b180cbfc7a71eb9b62424fb15b9bafc0 --- /dev/null +++ b/tests/m1_runs_gpu1.json @@ -0,0 +1,62 @@ +[ + { + "tag": "m1_d3", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--d-model", + "512", + "--device", + "cuda", + "--joint-steps", + "0", + "--cl-method", + "replay", + "--head", + "linear", + "--domains", + "3", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "m1_pln_wide", + "args": [ + "--wfr-bands", + "13", + "--pool", + "cat", + "--agg-path", + "--backbone", + "mlp", + "--d-model", + "512", + "--device", + "cuda", + "--joint-steps", + "0", + "--cl-method", + "replay", + "--head", + "pln", + "--pln-d", + "384", + "--epochs-per-domain", + "1000", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + } +] diff --git a/tests/measure_gvf_complementarity.py b/tests/measure_gvf_complementarity.py new file mode 100644 index 0000000000000000000000000000000000000000..e885ac1d5f7dad8300274be784f6529e0fa1d0be --- /dev/null +++ b/tests/measure_gvf_complementarity.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python3 +"""阶段五的核心问题: **多个 GVF 到底能不能产生互补证据?** + +规格: `docs/stage5_evidence_bank_spec.md` §5。 + +做法: 在 KeyDoor 上跑 regime 链(3 个 regime, 各若干回合), 打开 `rr_evidence=True`, +从证据库取**逐 step 的 z 序列**, 用每个 cumulant 的二值化当标签各算一次: + + AUC_i(单条) AUC_union(至少两条同时超阈) complementarity = union − 最好单条 + disagreement(至少一条投 bad 且至少一条投 healthy 的步占比) + corr_err(冗余度) + +★ 四个标签都算 —— 只挑一个会偏袒与它同源的那条 GVF。 +★ **允许结论是"冗余"**; 那就照实报冗余, 不写成收益。 +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 +from hibs_lnn.evidence_bank import complementarity # noqa: E402 + +REGIMES = [(2, 5, 7), (1, 4, 7), (3, 6, 7)] +N_PER = 30 +SEEDS = (0, 1, 2) +LABELS = ("prediction", "reward", "transition", "regime") + + +def run(seed): + mdp = KeyDoorMDP(n_pos=8, key_pos=2, door_pos=5, goal_pos=7, horizon=30) + mdp.set_regime(*REGIMES[0]) + ag = RRSkillAgent(mdp, mode="rr", seed=seed, opt_prob=1.0, rr_evidence=True) + for reg in REGIMES: + mdp.set_regime(*reg) + for _ in range(N_PER): + ag.run_episode() + return ag + + +print("=" * 100) +print("阶段五: GVF 证据的互补性度量") +print("=" * 100) + +per_label = {L: [] for L in LABELS} +bank_stats = [] +for seed in SEEDS: + ag = run(seed) + assert ag.rr_bank is not None, "rr_evidence=True 必须建出 bank" + h = ag.rr_bank.z_history() + cum = h["cumulant"] + z = h["z"] + n = len(next(iter(z.values()))) + bank_stats.append({"seed": seed, "n_steps": n, + "n_bad_ever": int(ag.rr_ev_bad_n), + "evidence": {k: {"n": v["n"], "z": round(v["z"], 3)} + for k, v in + ag.rr_bank.evidence(step=ag.rr_ev_step, + regime=ag.rr_regime_idx).items()}}) + print(f"\n--- seed={seed} 步数={n} bad票累计={ag.rr_ev_bad_n} ---") + print(" 每条证据: " + " ".join( + f"{k}(n={v['n']},z={v['z']:.2f})" for k, v in + ag.rr_bank.evidence(step=ag.rr_ev_step, regime=ag.rr_regime_idx).items())) + for L in LABELS: + lab = (cum[L] > 0).astype(int) + if lab.min() == lab.max(): + print(f" 标签={L:11s} 全为同一值({lab.mean():.3f}), 跳过") + continue + r = complementarity(z, lab, z_bad=ag.rr_ev_z_bad) + r["label"] = L + r["seed"] = seed + r["pos_rate"] = float(lab.mean()) + per_label[L].append(r) + print(f" 标签={L:11s} 正例={lab.mean():.3f} | " + f"AUC_i=" + ",".join(f"{k}:{v:.3f}" for k, v in r["auc_i"].items()) + + f" | any={r['auc_any']:.3f} cons={r['auc_consensus']:.3f} best={r['best_single']:.3f} " + f"comp={r['complementarity']:+.4f} dis={r['disagreement']:.3f}") + +print() +print("=" * 100) +print("汇总(3 seed)") +print("=" * 100) +print(f"{'标签':<12}{'单条 AUC(均值)':>44}{'any':>8}{'cons':>8}{'best':>8}{'comp':>9}{'disagree':>10}") +for L in LABELS: + rs = per_label[L] + if not rs: + print(f"{L:<12}{'(全部 seed 标签退化)':>44}") + continue + names = list(rs[0]["auc_i"]) + am = {k: float(np.nanmean([r["auc_i"][k] for r in rs])) for k in names} + u = float(np.nanmean([r["auc_any"] for r in rs])) + uc = float(np.nanmean([r["auc_consensus"] for r in rs])) + b = float(np.nanmean([r["best_single"] for r in rs])) + c = float(np.nanmean([r["complementarity"] for r in rs])) + d = float(np.nanmean([r["disagreement"] for r in rs])) + print(f"{L:<12}" + " ".join(f"{k[:5]}={am[k]:.3f}" for k in names).ljust(44) + + f"{u:>8.3f}{uc:>8.3f}{b:>8.3f}{c:>+9.4f}{d:>10.3f}") + +print() +print("=" * 100) +print("冗余度 (corr_err, 3 seed 平均)") +print("=" * 100) +keys = list(per_label[LABELS[0]][0]["corr_err"]) if per_label[LABELS[0]] else [] +for k in keys: + v = [r["corr_err"][k] for L in LABELS for r in per_label[L] if k in r["corr_err"]] + print(f" {k:<22} corr = {np.mean(v):+.3f}") + +print() +print("=" * 100) +print("裁定") +print("=" * 100) +allc = [r["complementarity"] for L in LABELS for r in per_label[L]] +allb = [r["best_single"] for L in LABELS for r in per_label[L]] +alla = [v for L in LABELS for r in per_label[L] for v in r["auc_i"].values()] +print(f" complementarity 范围: {min(allc):+.4f} … {max(allc):+.4f} (均值 {np.mean(allc):+.4f})") +print(f" best_single 均值: {np.nanmean(allb):.3f} union 均值: " + f"{np.nanmean([r['auc_any'] for L in LABELS for r in per_label[L]]):.3f}") +print(f" 单条 AUC 范围: {np.nanmin(alla):.3f} … {np.nanmax(alla):.3f}") +if np.nanmax(alla) <= 0.5 + 1e-9: + print(" ⇒ 单条全部 ≤ 0.5: 这组 GVF **连检测都做不到** ⇒ 证据机制不成立") +elif np.mean(allc) <= 0.0: + print(" ⇒ complementarity ≤ 0: **冗余** —— 当前这组 GVF 不提供互补证据") +else: + print(" ⇒ complementarity > 0: 存在互补证据的迹象(仍需多 seed 置信区间)") diff --git a/tests/measure_temporal_structure.py b/tests/measure_temporal_structure.py new file mode 100644 index 0000000000000000000000000000000000000000..821c99aed69f827418408ade34452e2e89d169c6 --- /dev/null +++ b/tests/measure_temporal_structure.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +"""measure_temporal_structure.py — 先问:**这个任务有没有可被 option 利用的时间结构?** + +## 用户的质疑(我认同, 所以先证伪自己) + +「option 存在, 但可能是你的标准和 benchmark 没设置对。」 + +在我拿负面结论说服任何人放弃 option 之前, 必须先排除**最致命的一种可能**: + + 如果任务里**动作之间近似可交换**, 那么 + "先训 a 再训 b" ≈ "只训 a" + "只训 b 带来的增益" + 即**不存在多步规划空间** —— option 无论怎么实现都不可能有用。 + 那种情况下我的 E9/E10 对照测的是 **benchmark 的性质, 不是 option 的价值**。 + +## 量化: 一阶交互 + 不对称性 + +对一对动作 (a, b), 在与主实验**同构**的模型上: + + I(a,b) = acc_b(先训 a 再训 b) − acc_b(只训 b) + A(a,b) = I(a,b) − I(b,a) <- **这才是"顺序有意义"的证据** + + A ≈ 0 (与 bootstrap 噪声同量级) -> 动作可交换 -> **无时序结构** + -> 我的对照对 option 不公平, benchmark 该改 + A 显著 ≠ 0 -> 顺序有关 -> option 本应有用 + -> **我的 option 实现该背锅** + +注意: 只看 I(a,b) ≠ 0 不够 —— 普通迁移(训练 a 让 b 更容易)不需要 option, +只要一个"正确的课程"。**只有不对称性才需要多步规划** (a→b 与 b→a 不等价)。 +""" +import argparse +import json +import sys +from pathlib import Path + +import numpy as np +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from run_lm4_wave import ( # noqa: E402 + WINDOW, StatMLP, assign_domains, build_feature_matrix, build_windows, +) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--data", default=str(ROOT / "data" / "wave")) + ap.add_argument("--wfr-bands", type=int, default=13) + ap.add_argument("--no-wfr", action="store_true") + ap.add_argument("--no-lshell", action="store_true") + ap.add_argument("--domains", type=int, default=6) + ap.add_argument("--fine-bins", type=int, default=18) + ap.add_argument("--d-model", type=int, default=128) + ap.add_argument("--n-layers", type=int, default=3) + ap.add_argument("--steps-pair", type=int, default=60, + help="每个动作训多少步") + ap.add_argument("--seeds", type=int, default=3) + ap.add_argument("--pairs", type=int, default=12) + ap.add_argument("--batch", type=int, default=32) + ap.add_argument("--lr", type=float, default=1e-3) + ap.add_argument("--device", default="cpu") + ap.add_argument("--out", default=str(ROOT / "results" / "temporal_structure.json")) + a = ap.parse_args() + dev = torch.device(a.device) + + t, dens, F, ok = build_feature_matrix( + a.data, bands=a.wfr_bands, use_wfr=not a.no_wfr, use_lshell=not a.no_lshell) + X, y_log = build_windows(t, dens, F, ok, stride=WINDOW) + y_dom, _ = assign_domains(y_log, a.domains) + d_feat = X.shape[-1] + fine, _ = assign_domains(y_log, a.fine_bins) + nf = int(fine.max()) + 1 + print("[data] %d 窗口 x %d x %d, 粗域 %d, fine bins %d" + % (len(X), WINDOW, d_feat, a.domains, nf), flush=True) + + # 每个细区间切出 train / probe + tr, pr = {}, {} + for f in range(nf): + idx = np.where(fine == f)[0] + if len(idx) < 200: + continue + cut = int(len(idx) * 0.8) + tr[f], pr[f] = idx[:cut], idx[cut:] + keys = sorted(tr) + print("[data] 可用细区间 %d 个 (%s)" % (len(keys), keys[:8]), flush=True) + + Xt = torch.from_numpy(X.astype(np.float32)) + Yt = torch.from_numpy(y_dom.astype(np.int64)) + + def fresh(seed): + torch.manual_seed(seed) + m = StatMLP(n_feat=d_feat, n_classes=a.domains, hidden=a.d_model, + n_layers=a.n_layers, head_type="linear", norm="fixed").to(dev) + return m + + def train_on(model, f, steps, seed): + opt = torch.optim.Adam(model.parameters(), lr=a.lr) + idx = tr[f].copy() + np.random.RandomState(seed * 131 + steps).shuffle(idx) + pos, it = 0, 0 + model.train() + while it < steps: + if pos + a.batch > len(idx): + pos = 0 + b = idx[pos:pos + a.batch] + pos += a.batch + loss = torch.nn.functional.cross_entropy( + model(Xt[b].to(dev)), Yt[b].to(dev)) + opt.zero_grad(); loss.backward(); opt.step() + it += 1 + return model + + @torch.no_grad() + def acc_on(model, f): + model.eval() + b = pr[f] + return float((model(Xt[b].to(dev)).argmax(1) == Yt[b].to(dev)).float().mean()) + + rng = np.random.RandomState(0) + pairs = [] + while len(pairs) < a.pairs and len(keys) >= 2: + x, y = int(rng.choice(keys)), int(rng.choice(keys)) + if x != y: + pairs.append((x, y)) + pairs = list(dict.fromkeys(pairs)) + + rows = [] + for si in range(a.seeds): + seed = 42 + si + for (aa, bb) in pairs: + m = fresh(seed); b_none = acc_on(m, bb) + m = fresh(seed); a_none = acc_on(m, aa) + m = fresh(seed); train_on(m, bb, a.steps_pair, seed); b_only = acc_on(m, bb) + # ★ 纯迁移: **只**训 a, 直接测 b (不给 b 任何训练)。 + # 若它 ≈ b_none, 说明训 a 对 b 一点用都没有 -> 零结构。 + m = fresh(seed); train_on(m, aa, a.steps_pair, seed); b_transfer = acc_on(m, bb) + a_transfer = None + m = fresh(seed); train_on(m, bb, a.steps_pair, seed); a_transfer = acc_on(m, aa) + m = fresh(seed); train_on(m, aa, a.steps_pair, seed); a_only = acc_on(m, aa) + m = fresh(seed) + train_on(m, aa, a.steps_pair, seed) + train_on(m, bb, a.steps_pair, seed + 7); b_after_a = acc_on(m, bb) + m = fresh(seed) + train_on(m, bb, a.steps_pair, seed) + train_on(m, aa, a.steps_pair, seed + 7); a_after_b = acc_on(m, aa) + rows.append(dict(seed=seed, a=aa, b=bb, + b_none=b_none, a_none=a_none, + b_only=b_only, a_only=a_only, + b_transfer=b_transfer, + transfer_gain=b_transfer - b_none, + b_after_a=b_after_a, a_after_b=a_after_b, + I_ab=b_after_a - b_only, I_ba=a_after_b - a_only, + asym=(b_after_a - b_only) - (a_after_b - a_only), + b_random=0.0)) + print(" seed=%d 完成 (%d 对)" % (seed, len(pairs)), flush=True) + + Iab = np.array([r["I_ab"] for r in rows]); Iba = np.array([r["I_ba"] for r in rows]) + A = np.array([r["asym"] for r in rows]) + print() + print("=" * 92) + print("一阶交互 (n=%d 对 x %d seed = %d 次测量)" % (len(pairs), a.seeds, len(rows))) + print("=" * 92) + print(" I(a,b) = acc_b(先a后b) − acc_b(只b) 均值 %+.4f std %.4f" % (Iab.mean(), Iab.std())) + print(" I(b,a) 均值 %+.4f std %.4f" % (Iba.mean(), Iba.std())) + print(" 不对称 A = I(a,b) − I(b,a) 均值 %+.4f std %.4f" % (A.mean(), A.std())) + bn = np.array([r["b_none"] for r in rows]) + bt = np.array([r["b_transfer"] for r in rows]) + tg = np.array([r["transfer_gain"] for r in rows]) + bo2 = np.array([r["b_only"] for r in rows]) + print(" ── 结构体检 (这三行才是关键) ──") + print(" b 零训练 acc 均值 %.4f (随机 = %.4f)" % (bn.mean(), 1.0 / a.domains)) + print(" 只训 b 后 acc 均值 %.4f <-- 若 ≈1.0 说明区间**平凡可分**" % bo2.mean()) + print(" 纯迁移(只训a测b) 均值 %.4f <-- 若 ≈b零训练, 说明**训 a 对 b 毫无影响**" + % bt.mean()) + print(" 迁移增益 Δ 均值 %+.4f std %.4f" % (tg.mean(), tg.std())) + print(" |A| > 0.05 的比例 %.1f%%" % (100 * (np.abs(A) > 0.05).mean())) + bs = np.array([A[np.random.RandomState(k).randint(0, len(A), len(A))].mean() + for k in range(2000)]) + lo, hi = np.percentile(bs, [2.5, 97.5]) + print(" A 的 bootstrap 95%% CI = [%+.4f, %+.4f]" % (lo, hi)) + sig = bool(not (lo <= 0 <= hi)) + print() + print("=" * 92) + if sig: + print("结论: **存在顺序依赖** (A 的 CI 不含 0)") + print(" -> 任务有多步规划空间, option 本应有用") + print(" -> **我的 option 实现该背锅** (选择非价值驱动 / 终止没用 / 视野太短)") + else: + print("结论: **顺序无关** (A 的 CI 含 0, 与噪声同量级)") + print(" -> 这个 benchmark 里动作近似可交换, **不存在可供 option 抽象的时间结构**") + print(" -> 我的 E9/E10 对照测的是 **benchmark 的性质, 不是 option 的价值**") + print(" -> 要检验 option 必须换一个**有时序结构**的 benchmark") + print("=" * 92) + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + Path(a.out).write_text(json.dumps( + {"I_ab_mean": float(Iab.mean()), "I_ab_std": float(Iab.std()), + "I_ba_mean": float(Iba.mean()), "I_ba_std": float(Iba.std()), + "asym_mean": float(A.mean()), "asym_std": float(A.std()), + "asym_ci": [float(lo), float(hi)], "order_dependent": sig, + "frac_abs_A_gt_0.05": float((np.abs(A) > 0.05).mean()), + "n_rows": len(rows), "pairs": pairs, "seeds": a.seeds, + "steps_pair": a.steps_pair, "rows": rows}, indent=1, ensure_ascii=False)) + print("已写 %s" % a.out) + + +if __name__ == "__main__": + main() diff --git a/tests/oak10h_driver.sh b/tests/oak10h_driver.sh new file mode 100644 index 0000000000000000000000000000000000000000..5810ef74aeb7dd9b3865f4d5eabd020fde17ca7f --- /dev/null +++ b/tests/oak10h_driver.sh @@ -0,0 +1,125 @@ +#!/bin/bash +# oak10h_driver.sh — OaK 十小时双 GPU 实验包 +# +# ┌─ GPU0: lm4 (电磁波持续学习) ────────────────────────────────────────┐ +# │ Stage 1 E9/E10 补 seed 到 10 个 (可靠性: std < mean/2) │ +# │ Stage 2 E11 单次 regime shift (第 40 轮置换动作->物理区映射) │ +# │ Stage 3 E12 反复 regime shift (每 25 轮漂移一次, 共 4 次) │ +# └─────────────────────────────────────────────────────────────────────┘ +# ┌─ GPU1: lm5 (文本+电磁波+因果 多模态) ──────────────────────────────┐ +# │ Stage 4 E9/E10 x 5 seed │ +# │ Stage 5 E11 regime shift x 2 seed │ +# └─────────────────────────────────────────────────────────────────────┘ +# +# ★ 唯一变量始终是 `--oak-options` (同一 base、同一门控)。 +# ★ 为什么要 regime shift: OaK 的核心主张是「内部知识让智能体能适应 +# **持续变化的动力学**」。固定域序下 T(s,a) 不变, 学到的知识不会被 +# 挑战 —— 那测不出 OaK 的主张。shift 置换「动作 -> 物理区」的映射, +# 使 **T(s,a) 本身改变**, 这才是对「持续适应」的真检验。 +# +# ★ 配置对齐: 总步数 = rounds x (epochs_per_domain * fine_bins / n_dom / k) +# 120 轮 x (100*18/6/3 = 100) = 12000 步 +# 与 BM2 的 12 轮 x 1000 步 = 12000 **完全等量**, 没有靠堆步数占便宜。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak10h.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" + +# lm5 多模态: 池 = 域 x rounds_per_domain, 总轮数给足 (oak 需要 >=40 条转移) +L5="--backbone mlp --norm fixed --cl-method replay --head linear \ +--text-steps 200 --wave-epochs 150 --causal-n 20000 --d-model 128 \ +--n-layers 2 --domains 6 --rounds-per-domain 20 --rounds 120 \ +--stream revisit --device cuda" + +# ══════════════════════════════════════════════════════════════════════ +# GPU0: lm4 +# ══════════════════════════════════════════════════════════════════════ +( + # 等当前那批 (results/OAK_DONE) 跑完, 避免同卡并发 (纪律: 同卡不许开两条队列) + say "GPU0: 等待上一批 OAK_DONE ..." + for i in $(seq 1 200); do + [ -f results/OAK_DONE ] && break + sleep 30 + done + say "GPU0: 上一批完成, 开始十小时包" + + # ── Stage 1: 补 seed 到 10 个 ── + for S in 2 3 4 5 6; do + say "S1 E10 options=1 seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak_e10_s$S >>"$LOG" 2>&1 + RC=$?; say "S1 E10 seed=$S rc=$RC" + + say "S1 E9 options=0 seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak_e9_s$S >>"$LOG" 2>&1 + RC=$?; say "S1 E9 seed=$S rc=$RC" + done + touch results/OAK_S1_DONE + + # ── Stage 2: E11 单次 regime shift (第 40 轮) ── + for S in 1 7 42; do + for O in 1 0; do + TAG=$([ "$O" = "1" ] && echo e11opt || echo e11noopt) + say "S2 $TAG seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options $O --oak-gate 1 --oak-refresh 4 \ + --shift-at 40 --shift-every 0 \ + --seed $S --out results/oak_${TAG}_s$S >>"$LOG" 2>&1 + RC=$?; say "S2 $TAG seed=$S rc=$RC" + done + done + touch results/OAK_S2_DONE + + # ── Stage 3: E12 反复漂移 (每 25 轮一次) ── + for S in 1 7 42; do + for O in 1 0; do + TAG=$([ "$O" = "1" ] && echo e12opt || echo e12noopt) + say "S3 $TAG seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options $O --oak-gate 1 --oak-refresh 4 \ + --shift-at 30 --shift-every 25 \ + --seed $S --out results/oak_${TAG}_s$S >>"$LOG" 2>&1 + RC=$?; say "S3 $TAG seed=$S rc=$RC" + done + done + touch results/OAK_S3_DONE + say "GPU0 全部完成" +) & +disown + +# ══════════════════════════════════════════════════════════════════════ +# GPU1: lm5 多模态 +# ══════════════════════════════════════════════════════════════════════ +( + sleep 20 + say "GPU1: lm5 开始" + for S in 1 7 42 13 100; do + say "S4 lm5 E10 seed=$S" + CUDA_VISIBLE_DEVICES=1 $PY -u tests/run_lm5_mm.py $L5 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak5_e10_s$S >>"$LOG" 2>&1 + RC=$?; say "S4 lm5 E10 seed=$S rc=$RC" + + say "S4 lm5 E9 seed=$S" + CUDA_VISIBLE_DEVICES=1 $PY -u tests/run_lm5_mm.py $L5 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak5_e9_s$S >>"$LOG" 2>&1 + RC=$?; say "S4 lm5 E9 seed=$S rc=$RC" + done + touch results/OAK5_S4_DONE + say "GPU1 全部完成" +) & +disown + +sleep 5 +say "十小时包已启动 (GPU0: lm4 S1-S3 | GPU1: lm5 S4)" diff --git a/tests/oak10h_ext.sh b/tests/oak10h_ext.sh new file mode 100644 index 0000000000000000000000000000000000000000..3b6add06f3463a054366b6cd6f77696c1cbfd84b --- /dev/null +++ b/tests/oak10h_ext.sh @@ -0,0 +1,101 @@ +#!/bin/bash +# oak10h_ext.sh — 十小时包的**延伸队列** (在上一个包之后接续, 把两个 GPU 都填满) +# +# 配比核算 (实测): +# lm4 每臂 ~12 min (120 轮 x 100 步) +# lm5 每臂 ~4.7 min (120 轮 x 120 步) +# -> 原包里 GPU0 约 5.4h, GPU1 只有 ~0.8h。必须补 GPU1。 +# +# GPU0 追加: 步长 E 表 (用户表里的 E1~E5) +# E2 IDBD / E3 idbd-raw / E4 autostep / E5 cidbd (E1 fixed-lr = 无步长自适应基线) +# ★ 全部跑在 **oak proposer** 上, 这样与 E9/E10 同一 base, 可与其它臂直接对比。 +# ★ alpha0=0.2 是实测唯一"活着"的点 (alpha0<0.2 时步长死亡; 见 +# docs/stepsize_idbd_results.md) +# +# GPU1 追加: lm5 加长配置 x 10 seed +# text-steps 400 / wave-epochs 400 / causal-n 40000 / rounds 200 +# -> 单臂约 3.3 倍 -> ~15 min/臂, 10 臂 x 2 = 300 min = 5h +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak10h_ext.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" + +L5L="--backbone mlp --norm fixed --cl-method replay --head linear \ +--text-steps 400 --wave-epochs 400 --causal-n 40000 --d-model 128 \ +--n-layers 2 --domains 6 --rounds-per-domain 25 --rounds 200 \ +--stream revisit --device cuda" + +# ══════════════════════════════════════════════════════════════════════ +# GPU0: 步长 E 表 (接在 S3 之后) +# ══════════════════════════════════════════════════════════════════════ +( + say "GPU0-ext: 等待 S3 完成 ..." + for i in $(seq 1 1200); do + [ -f results/OAK_S3_DONE ] && break + sleep 30 + done + say "GPU0-ext: 开始步长 E 表" + + # E1: 固定学习率基线 (无步长自适应) —— 用 autostep 的 mulr 太大模拟不了, + # 这里用 value 臂作固定基线不合适; 直接跑 rl-algo=idbd-raw 的对照在 E3。 + # 真正"固定"的等价物是 --rl-mu 0 (步长不更新) -> beta 恒 0 -> alpha 恒 alpha0 + say "E1 fixed-alpha (rl-mu=0, 步长冻结)" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --rl-mu 0.0 --rl-alpha0 0.2 --rl-algo idbd \ + --seed 42 --out results/oak_e1_fixedalpha_s42 >>"$LOG" 2>&1 + RC=$?; say "E1 rc=$RC" + + for S in 42 1 7; do + for A in idbd idbd-raw autostep cidbd; do + say "E-algo $A seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --rl-mu 0.05 --rl-alpha0 0.2 --rl-algo $A \ + --seed $S --out results/oak_algo_${A}_s$S >>"$LOG" 2>&1 + RC=$?; say "E-algo $A seed=$S rc=$RC" + done + done + touch results/OAK_EXT0_DONE + say "GPU0-ext 完成" +) & +disown + +# ══════════════════════════════════════════════════════════════════════ +# GPU1: lm5 加长配置 (接在 S4 之后) +# ══════════════════════════════════════════════════════════════════════ +( + say "GPU1-ext: 等待 lm5 S4 完成 ..." + for i in $(seq 1 1200); do + [ -f results/OAK5_S4_DONE ] && break + sleep 30 + done + say "GPU1-ext: 开始 lm5 加长配置" + + for S in 2 3 4 5 6 8 9 11 12 14; do + say "S5 lm5L E10 seed=$S" + CUDA_VISIBLE_DEVICES=1 $PY -u tests/run_lm5_mm.py $L5L \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak5L_e10_s$S >>"$LOG" 2>&1 + RC=$?; say "S5 lm5L E10 seed=$S rc=$RC" + + say "S5 lm5L E9 seed=$S" + CUDA_VISIBLE_DEVICES=1 $PY -u tests/run_lm5_mm.py $L5L \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak5L_e9_s$S >>"$LOG" 2>&1 + RC=$?; say "S5 lm5L E9 seed=$S rc=$RC" + done + touch results/OAK5_EXT_DONE + say "GPU1-ext 完成" +) & +disown + +sleep 5 +say "延伸队列已启动 (GPU0: 步长 E 表 | GPU1: lm5 加长 x10 seed)" diff --git a/tests/oak_diag.sh b/tests/oak_diag.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab491b0063c823e3aab4dadcaf0022011ed50fce --- /dev/null +++ b/tests/oak_diag.sh @@ -0,0 +1,78 @@ +#!/bin/bash +# oak_diag.sh — 诊断: **为什么 Options 有害** (E9/E10 主结论: any_time 显著下降) +# +# 主结论 (n=10, 两档配置都成立): +# lm4 any_time Δ=-0.0119 (-1.6%) t=-5.02 p<0.0001 E9(无Options) 更好 +# lm5 any_time Δ=-0.0189 (-1.9%) t=-5.14 p<0.0001 E9 更好 +# 机制**确实生效** (option_starts 38~75, option_steps 40~83), +# 所以不能按"机制没触发"来免责 -> 必须查清原因。 +# +# 三个假设, 各配一个可判定的实验: +# H1 option 执行 = 提交一段固定动作序列 -> 减少自适应性 +# H2 option 执行降低了探索多样性 -> 覆盖度更窄 +# H3 option 是从**过期的**转移模型里发现的 -> 结构本身不对 +# +# D1 (H1/H2): opt_frac 剂量-反应。只让一部分轮次启用 option。 +# 若伤害随 opt_frac **单调放大** -> 指向 H1/H2 (option 执行本身的问题)。 +# 若伤害与用量无关 -> 另有来源。 +# ★ opt_frac=0.0 应当**退化为 E9** —— 这是本实验自身的正确性哨兵。 +# D2 (H3): 提高 world model 重建频率 (refresh 2/8/16)。 +# 若 H3 成立, 更频繁重建应当**减轻**伤害。 +# D3: 用**修好的 α 统计**重跑步长 E 表 (早先 alpha_std 取自一个没被更新的 +# 死对象, 四个算法报出同一个值, 看着像"算法不生效")。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak_diag.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" +OAK="--proposer oak --oak-gate 1 --oak-refresh 4" + +say "诊断队列启动 (GPU0)" +( + # ── D1: opt_frac 剂量-反应 ── + for F in 0.0 0.25 0.5 1.0; do + for S in 42 1 7; do + say "D1 opt_frac=$F seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 $OAK \ + --oak-options 1 --opt-frac $F \ + --seed $S --out results/oak_frac${F}_s$S >>"$LOG" 2>&1 + RC=$?; say "D1 opt_frac=$F seed=$S rc=$RC" + done + done + touch results/OAK_D1_DONE + + # ── D2: world model 重建频率 ── + for R in 2 8 16; do + for S in 42 1 7; do + say "D2 refresh=$R seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh $R \ + --seed $S --out results/oak_ref${R}_s$S >>"$LOG" 2>&1 + RC=$?; say "D2 refresh=$R seed=$S rc=$RC" + done + done + touch results/OAK_D2_DONE + + # ── D3: 步长 E 表 (修好的 α 统计) ── + for S in 42 1 7; do + for A in idbd idbd-raw autostep cidbd; do + say "D3 algo=$A seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --rl-mu 0.05 --rl-alpha0 0.2 --rl-algo $A \ + --seed $S --out results/oak_astat_${A}_s$S >>"$LOG" 2>&1 + RC=$?; say "D3 algo=$A seed=$S rc=$RC" + done + done + touch results/OAK_D3_DONE + say "诊断队列完成" +) & +disown +sleep 5 +say "诊断队列已启动 (D1 opt_frac x4 | D2 refresh x3 | D3 步长 x4, 共 33 臂)" diff --git a/tests/oak_driver.sh b/tests/oak_driver.sh new file mode 100644 index 0000000000000000000000000000000000000000..197025fd5ac62446b0d8d19388a6dd4e237f36c0 --- /dev/null +++ b/tests/oak_driver.sh @@ -0,0 +1,49 @@ +#!/bin/bash +# oak_driver.sh — OaK 结构件在 lm4 主回路上的第一个正式对照 +# +# 目的: 检验用户架构主张里最关键的一跳 —— +# Prediction -> Abstraction -> Option -> Planning -> Control +# 是否比 state -> RL policy -> action 更适合持续变化的磁通动力学。 +# +# 设计 (E9 vs E10 是**唯一**变量): +# E10 = --proposer oak --oak-options 1 (+Options / 时间抽象) +# E9 = --proposer oak --oak-options 0 (同一 base、同一门控, 只是不用 option) +# +# ★ 为什么 --rounds 120 而不是之前 benchmark 的 12: +# dynamics model 的特征是 [state(6), onehot(action)(18)] = 24 维。 +# 12 条转移对 24 维是**严重欠定**(12x24 的矩阵 rank<=12), 集成分歧 +# 与 option 发现都会变成噪声。120 条转移给出 ~5 样本/参数, 才站得住。 +# ★ 同时把 epochs-per-domain 从 300 降到 100, 使 +# `总步数 = 120 轮 x (100*18/6/3=100) 步 = 12000` —— 与 BM2 的 +# 12 轮 x 1000 步 = 12000 完全等量。这样既喂饱了动力学模型, +# 又**没有**靠堆步数占便宜。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +G0=0; G1=1 +COMMON="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" +LOG=results/oak_driver.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "OaK E9/E10 对照启动 (5 seed x 2 臂, GPU0)" +for S in 1 7 42 13 100; do + # E10: 有 Options (时间抽象) + say "E10 options=1 seed=$S" + CUDA_VISIBLE_DEVICES=$G0 $PY -u tests/run_lm4_wave.py $COMMON \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak_e10_s$S >>"$LOG" 2>&1 + RC=$?; say "E10 seed=$S rc=$RC" + + # E9: 同样体系, 只是关掉选项 + say "E9 options=0 seed=$S" + CUDA_VISIBLE_DEVICES=$G0 $PY -u tests/run_lm4_wave.py $COMMON \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak_e9_s$S >>"$LOG" 2>&1 + RC=$?; say "E9 seed=$S rc=$RC" +done +touch results/OAK_DONE +say "ALL DONE" diff --git a/tests/oak_k1.sh b/tests/oak_k1.sh new file mode 100644 index 0000000000000000000000000000000000000000..34a2162fd1e1f653a7d1676e5e21e1cf9e5ff094 --- /dev/null +++ b/tests/oak_k1.sh @@ -0,0 +1,64 @@ +#!/bin/bash +# oak_k1.sh — 用户指出的**关键实验** (K1 矩阵): 区分 temporal abstraction 与 +# open-loop option execution +# +# ## 背景 (用户 2026-09-14 指导, 我接受) +# +# "Option = [a1,a2,...,ak]" 其实**不是一个 adaptive 的 Option**, +# 它像"我发现了一段过去有效的动作脚本, 现在把这段脚本重新播放"。 +# 底层 policy 原本每步按当前状态重算 a_t = π(s_t); option 一旦变成固定序列, +# 状态变化就不再触发动作重算 -> 适应性下降。**这正好解释 D1。** +# +# 正确形式: Option = (I_o, g_o, π_o, β_o) +# **抽象的是目标, 不是动作。** +# +# 第一版不要给 option 自己的新 policy, 直接用: +# a_t = π_base(s_t, g_o) <- 给 base policy 一个高层条件 +# +# ## 本实验 (唯一变量 = Option 的执行方式) +# +# E9 无 Option (基线, 已有 10 seed) +# O1 --opt-mode fixed 固定动作序列 (open-loop, 即 D1 的病灶) +# O2 --opt-mode goal 目标条件 + **每步重算动作** (闭环) +# O3 --opt-mode goal_term O2 + 自适应终止 β_o(s) +# O4 --opt-mode goal_term_override O3 + 不确定性抢占 (option 从属于当前证据) +# +# ## 判据 (用户给的) +# 若 O2 > O1 -> 问题不是 temporal abstraction, 而是 **open-loop 执行** +# 若 O3/O4 继续提升 -> option 的价值需要建立在**闭环执行**上 +# +# ## 机制生效性必须可见 (否则结论无效) +# fixed : replan_steps = 0 +# goal* : replan_steps ≈ option_steps (每步都在重算) +# term* : term_reasons 非空 +# override: override_count > 0 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak_k1.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" + +say "K1 矩阵启动 (GPU0): O1-O4 x 5 seed" +( + for M in fixed goal goal_term goal_term_override; do + for S in 42 1 7 13 100; do + say "K1 mode=$M seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oak_k1_${M}_s$S >>"$LOG" 2>&1 + RC=$?; say "K1 mode=$M seed=$S rc=$RC" + done + done + touch results/OAK_K1_DONE + say "K1 完成" +) & +disown +sleep 5 +say "K1 已启动 (20 臂, 预计 ~50 分钟)" diff --git a/tests/oak_k1v2.sh b/tests/oak_k1v2.sh new file mode 100644 index 0000000000000000000000000000000000000000..3107e8978e240e5cfec22b86328e5928d213cb5f --- /dev/null +++ b/tests/oak_k1v2.sh @@ -0,0 +1,101 @@ +#!/bin/bash +# oak_k1v2.sh — K1 矩阵 **重跑版**(修复 coverage 写入 bug 之后) +# +# ## 为什么要重跑 +# +# 第一版 K1 / 长测有两处执行病理, 使 K1 的 O2/O3 档**不是**对闭环 option 的有效检验: +# +# 档位 starts steps replan 诊断 +# fixed 31-46 34-67 0 ✓ 队列回放, 正常 +# goal 1 94-105 94-105 ✗ **1 次启动跑满 87% 全程 = 锁死** +# goal_term 1 94-105 94-105 ✗ 且与 goal **逐位相同** -> β_o 没生效 +# goal_term_override 2-23 72-104 72-104 ✓ 唯一行为健康 +# +# 长测 (rounds=300) 更糟: goal/goal_term/override 三档的 `alpha_mean` 与 e9 **完全相同**, +# 且 `option_starts > 0` 但 `option_steps == 0` -> 闭环档**一步都没执行**。 +# +# ## 根因(已在 hibs_lnn/oak_proposer.py 修掉) +# +# `InternalKnowledge.observe_action()` 是 coverage 的**唯一写入点**, +# 但主回路从来没调过它 -> `coverage.n` 永远全零 +# -> `UncertaintyGate.allow()` 对所有动作返回 False (因为 `0 <= tau_C=1.0`) +# -> `_goal_action()` 的每个候选都被 `continue` 掉 +# -> `best_a = None` -> 回落到 base policy -> 轨迹与 E9 逐位相同。 +# +# 修: (1) `update()` 里补 `observe_action` +# (2) 起 option 失败时**不记为"启动"**(新增 `start_blocked` 计数器), +# 否则机制计数会撒谎(旧版 `option_starts>0` 但 `option_steps==0`)。 +# +# ## 本次实验设计(比第一版更严) +# +# E9 无 option (基线, 哨兵: 必须 ≥ 各 O 档) +# O1 --opt-mode fixed 固定动作序列 (open-loop) +# O2 --opt-mode goal 目标条件 + 每步重算 (闭环) +# O3 --opt-mode goal_term O2 + 自适应终止 β_o(s) +# O4 --opt-mode goal_term_override O3 + 不确定性抢占 +# 5 seed x 5 档 = 25 臂 +# +# ## 事前写死的判据 +# +# ① 机制生效(否则整轮作废): +# fixed : replan_steps == 0 且 option_steps > 0 +# goal* : replan_steps ≈ option_steps > 0 +# term* : term_reasons 非空 +# override: override_count > 0 +# 且 **start_blocked 必须为 0**(修复后才可能) +# ② O2 > O1 -> 问题是 open-loop 执行, 不是 temporal abstraction +# ③ O3/O4 ≥ O2 -> 闭环之上还需要自适应终止/抢占 +# +# ## 注意: 本驱动不写任何"结论", 只落盘数字 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak_k1v2.log +GPU=${GPU:-1} # ★ GPU0 被邻居占满(40GB/96%), 用 GPU1(~18GB 空闲) +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +# ── 前置检查: 修复必须真的在盘上(否则又是静默空转的一轮) ────────────── +if ! grep -q "observe_action" hibs_lnn/oak_proposer.py; then + say "✗ 致命: oak_proposer.py 里没有 observe_action —— coverage 写入未接, 整轮会空转, 拒绝启动" + exit 1 +fi +if ! grep -q "start_blocked" hibs_lnn/oak_proposer.py; then + say "✗ 致命: 缺少 start_blocked 计数器, 无法判定静默空转" + exit 1 +fi +say "前置检查通过: observe_action 已接线 + start_blocked 计数器存在" +say "GPU=$GPU" + +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 120 \ +--proposer-k 3 --stream revisit --device cuda" + +say "K1v2 启动 (GPU$GPU): E9 + O1-O4 x 5 seed = 25 臂" +( + # ── E9 基线(无 option) ──────────────────────────────────────────── + for S in 42 1 7 13 100; do + say "K1v2 E9 seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oak_k1v2_e9_s$S >>"$LOG" 2>&1 + say "K1v2 E9 seed=$S rc=$?" + done + # ── O1-O4 ───────────────────────────────────────────────────────── + for M in fixed goal goal_term goal_term_override; do + for S in 42 1 7 13 100; do + say "K1v2 mode=$M seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oak_k1v2_${M}_s$S >>"$LOG" 2>&1 + say "K1v2 mode=$M seed=$S rc=$?" + done + done + touch results/OAK_K1V2_DONE + say "K1v2 完成" +) & +disown +sleep 5 +say "K1v2 已启动 (25 臂, 预计 ~100 分钟)" diff --git a/tests/oak_lmeff.sh b/tests/oak_lmeff.sh new file mode 100644 index 0000000000000000000000000000000000000000..de8af520e6cd65d31a091b9283cf3678585a07a2 --- /dev/null +++ b/tests/oak_lmeff.sh @@ -0,0 +1,102 @@ +#!/bin/bash +# oak_lmeff.sh — 学习效率 / 更新效率 的**第一次真实测量** +# +# 规格: docs/learning_efficiency_spec.md +# +# ## 为什么这批 +# leo 把「抗遗忘以**学习效率/更新效率**为一等指标」列为四条主线之一, +# 而该指标**至今一次没测过**(`efficiency`/`curves` 两个字段全空, +# 105 个 result tag 里 `--trace-every` 只开过 1 次)。 +# 精度维度已经饱和(修好流后 option 三种配置差 ≤0.8%), 而效率维度是空白。 +# +# ## 本批测什么 +# 每个 run 内部**同时**跑 `naive` 与 `replay` 两条方法(驱动脚本的既有行为), +# 所以一批就给出对照: +# +# replay 保留旧域 ⇒ 重遇到同一域时**应该学得更快** ⇒ revisit_gain > 0 +# naive 不保留 ⇒ 每次近似从头学 ⇒ revisit_gain ≈ 0 +# +# ★ 这是"持续学习有没有用"的**正向问法**, 而且它落在**未被否证**的维度上。 +# +# ## 变量 +# 只开 `--trace-every 10`(学习效率曲线), 其余配置与 L5 **逐字相同**。 +# `--reachable-max` 不传 ⇒ 按域数(6)查表得 0.8065(独立于臂的探针天花板)。 +# +# ## 规模 +# 3 个新 seed(101/202/303) × 1 run = 3 run。 +# 原本每 run ~7 min, 但 trace 每 10 步评一次当前域 ⇒ 会增加评测开销, 需实测校正。 +# +# ## 判据(事前写死, 见 spec §4) +# ① 填充闸门: `n_visits` 全部 ≥1 且 `steps_to_abs` **非全空**, +# 否则判 **"指标未采集成功"**(不是"效率为 0") +# ② 覆盖率: 报 `_none_ratio`;过高说明阈值与该域可达上限不匹配 +# ③ 复用: `mean(revisit_gain) > 0` 且自助法区间下界 > 0 ⇒ 有复用 +# **不满足就照实写"无复用"** —— 允许否定 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU=${GPU:-1} +LOG=results/oak_lmeff.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== 学习效率首次测量 ==========" + +# ── 启动前硬闸门 ──────────────────────────────────────────────── +# ① 本批的处理变量 = `--trace-every > 0`, 必须证明三处接线在位 +if ! grep -q "REACHABLE_MAX_BY_DOMAINS" tests/run_lm4_wave.py; then + say "✗ 致命: 缺独立可达上限表 —— 绝对达标步数会退回臂自己的 max(自欺), 拒绝启动" + exit 1 +fi +if ! grep -q '"visits": {' tests/run_lm4_wave.py; then + say "✗ 致命: 逐次访问曲线未落盘 —— 复用指标无法计算, 拒绝启动" + exit 1 +fi +if ! grep -q "revisit_gain" tests/run_lm4_wave.py; then + say "✗ 致命: 复用增益未实现, 拒绝启动" + exit 1 +fi +# ② 默认必须是关的(旧结果逐位不变) +if ! grep -qE 'add_argument\("--trace-every", type=int, default=0' tests/run_lm4_wave.py; then + say "✗ 致命: --trace-every 默认值被改成非 0 —— 旧结果不再可复现, 拒绝启动" + exit 1 +fi +say "✓ 闸门: 可达上限表 + 逐次访问落盘 + 复用增益 + trace 默认关 全部在位" + +# ③ 单元测试必须过 +if ! $PY tests/test_efficiency_metric.py >/tmp/lmeff_unit.log 2>&1; then + say "✗ 致命: 效率指标单测未过, 拒绝启动"; tail -20 /tmp/lmeff_unit.log >>"$LOG"; exit 1 +fi +say "✓ 闸门: tests/test_efficiency_metric.py 全过" + +# ④ 显存 +FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) +if [ -z "$FREE" ] || [ "$FREE" -lt 4000 ]; then + say "✗ 致命: GPU$GPU 空闲 ${FREE:-?}MiB < 4000, 拒绝启动" + exit 1 +fi +say "✓ GPU$GPU 空闲 ${FREE}MiB" + +LMEFF="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda --trace-every 10" + +SEEDS="101 202 303" +say "起跑 (GPU$GPU): $SEEDS, trace-every=10, stream=revisit" +( + for S in $SEEDS; do + say "LMEFF seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $LMEFF \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/lmeff_s$S >>"$LOG" 2>&1 + say "LMEFF seed=$S rc=$?" + done + touch results/LMEFF_DONE + say "学习效率批完成" +) >>"$LOG" 2>&1 & + +echo $! > results/lmeff.pid +disown +sleep 8 +say "已起跑 pid=$(cat results/lmeff.pid)" diff --git a/tests/oak_lmeff2.sh b/tests/oak_lmeff2.sh new file mode 100644 index 0000000000000000000000000000000000000000..07aeeec791a8f8076d1e149d90ae9c7691d7b6d8 --- /dev/null +++ b/tests/oak_lmeff2.sh @@ -0,0 +1,104 @@ +#!/bin/bash +# oak_lmeff2.sh — 学习效率第一次测量的**扩 seed + 判别性对照** +# +# 承接 docs/lmeff_verdict.md。该报告 §3 明写了一个未决混淆: +# `replay` 的**总算力更大** ⇒ 可能只是"训练更多/更强正则", 不是抗遗忘。 +# +# ## 本批的判别性设计(三臂, 同一个 run 内并行跑, 天然配对) +# naive 不复习 (基线) +# replay 复习**已见过的**旧域(域均衡随机) (现有机制) +# replay-self 同样 k 个额外样本、同样 1 次优化步, +# 但样本**全部来自当前域** (判别性对照) +# +# 判据(事前写死): +# `replay-self ≈ naive` 且 `replay >> naive` ⇒ 效果来自**旧域数据本身** ✓ +# `replay-self ≈ replay` ⇒ 效果只是**额外算力/正则** ⇒ 结论必须降级 +# +# ★ 另外查清一件事: `replay` 的实现(`run_lm4_wave.py:979-990`)**本来就是域均衡 +# 随机复习**(每个见过的域等量、域内随机抽样本), 不是"针对即将遗忘的域"。 +# 所以"随机复习 vs 针对性复习"这个对照**已经含在现有臂里**了 —— +# 真正未解决的混淆只有"额外算力"这一个, 本批正对它。 +# +# ## 规模 +# 10 个新 seed(401…410), 每 run 3 臂。两条队列, seed 不相交。 +# 实测单 run ~12 min(2 臂) ⇒ 3 臂约 18 min; 5 run/队列 ⇒ 约 1.5 h。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak_lmeff2.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== 学习效率 扩 seed + 判别性三臂 ==========" + +# ── 启动前硬闸门 ──────────────────────────────────────────────── +# ① 三臂必须在位 +if ! grep -q '"replay-self", True, "self"' tests/run_lm4_wave.py; then + say "✗ 致命: 判别性对照臂 replay-self 不在 runs 里, 拒绝启动"; exit 1 +fi +if ! grep -q 'replay_src=_rsrc' tests/run_lm4_wave.py; then + say "✗ 致命: replay_src 没透传进 run_experiment, 第三个臂会退化, 拒绝启动"; exit 1 +fi +if ! grep -q 'replay_src == "self" and _self_buf is not None' tests/run_lm4_wave.py; then + say "✗ 致命: self 分支不在 replay 块里, 拒绝启动"; exit 1 +fi +# ② 表格遍历必须是"本次跑过的臂", 不能硬编码(硬编码会漏掉 replay-self) +if grep -q 'for k in ("naive", "replay"):' tests/run_lm4_wave.py; then + say "✗ 致命: 报告表格仍硬编码 naive/replay ⇒ replay-self 结果不进报告, 拒绝启动"; exit 1 +fi +# ③ 旧口径必须不变 +if ! grep -qE 'add_argument\("--trace-every", type=int, default=0' tests/run_lm4_wave.py; then + say "✗ 致命: --trace-every 默认被改成非 0, 拒绝启动"; exit 1 +fi +if ! grep -q "REACHABLE_MAX_BY_DOMAINS" tests/run_lm4_wave.py; then + say "✗ 致命: 缺独立可达上限表, 拒绝启动"; exit 1 +fi +say "✓ 闸门: 三臂 + replay_src 透传 + self 分支 + 报告不硬编码 + trace 默认 0 + 可达上限" + +if ! $PY tests/test_efficiency_metric.py >/tmp/lmeff2_unit.log 2>&1; then + say "✗ 致命: 效率指标单测未过"; tail -20 /tmp/lmeff2_unit.log >>"$LOG"; exit 1 +fi +say "✓ 闸门: 单测全过" + +GPU_A=${GPU_A:-0}; GPU_B=${GPU_B:-1} +SEEDS_A="401 402 403 404 405" +SEEDS_B="406 407 408 409 410" +# ④ 队列间 seed 不相交(硬校验) +_overlap=$(comm -12 <(echo $SEEDS_A | tr ' ' '\n' | sort) <(echo $SEEDS_B | tr ' ' '\n' | sort)) +if [ -n "$_overlap" ]; then say "✗ 致命: 两队列 seed 相交: $_overlap"; exit 1; fi +say "✓ 闸门: 两队列 seed 不相交 (A=[$SEEDS_A] B=[$SEEDS_B])" + +for G in $GPU_A $GPU_B; do + F=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $G 2>/dev/null | head -1) + if [ -z "$F" ] || [ "$F" -lt 3000 ]; then + say "✗ 致命: GPU$G 空闲 ${F:-?}MiB < 3000, 拒绝启动"; exit 1 + fi + say "✓ GPU$G 空闲 ${F}MiB" +done + +BASE="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda --trace-every 10" +OAK="--proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4" + +run_queue(){ # $1=tag $2=gpu $3=seeds + local TAG=$1 G=$2 + for S in $3; do + say "[$TAG] seed=$S 起跑 (GPU$G)" + CUDA_VISIBLE_DEVICES=$G $PY -u tests/run_lm4_wave.py $BASE $OAK \ + --seed $S --out results/lmeff2_${TAG}_s$S >>"$LOG" 2>&1 + say "[$TAG] seed=$S rc=$?" + done + touch results/LMEFF2_${TAG}_DONE + say "[$TAG] 队列完成" +} + +# 显存预算: 两队列 ×2 GiB ≤ 空闲 60%(同卡多队列规则, 2026-09-29 修订) +say "起跑: A(GPU$GPU_A)=[$SEEDS_A] B(GPU$GPU_B)=[$SEEDS_B] 各 3 臂" +( run_queue A $GPU_A "$SEEDS_A" ) & +( run_queue B $GPU_B "$SEEDS_B" ) & +touch results/LMEFF2_STARTED +disown -a +sleep 10 +say "两队列已起跑" diff --git a/tests/oak_long.sh b/tests/oak_long.sh new file mode 100644 index 0000000000000000000000000000000000000000..af35a965c9d6c3847fb55ed038d752f94ec27af1 --- /dev/null +++ b/tests/oak_long.sh @@ -0,0 +1,70 @@ +#!/bin/bash +# oak_long.sh — **长测**: 修好的管线 + 更长的轮数 +# +# ## 为什么是长测 +# +# 用户的两个未验证假设只能靠长测检验: +# H2 Option 样本量不足, 没学出来 -> 轮数不足 -> 知识还没成形就结束了 +# H5 Option 只在**非平稳/长期**任务中体现优势 +# +# 之前的 K1 是 --rounds 120, 而 option 需要「先积累足够的转移 -> 发现技能 -> +# 再复用技能」这条链条走完才有意义。120 轮里这条链只走了不到一半。 +# 本批改 **--rounds 300 (2.5x)**, 并启用**修好的发现管线**: +# --dyn-model tabular 计数式世界模型 (线性 ridge 表达不了结构化动力学) +# --use-subgoals 1 子目标驱动发现 (bottleneck 作为 g_o) +# --n-regions-opt 24 表格模型下按真实状态建图 +# +# ## 矩阵 (五档执行方式 x 5 seed, 全部用修好的管线) +# E9 无 option | O1 fixed(开环) | O2 goal(闭环) | O3 +终止 | O4 +抢占 +# +# 判据 (用户给的): 若 O2 > O1 -> 问题是 open-loop 执行, 不是 temporal abstraction。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +LOG=results/oak_long.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +# ★ 长配置: rounds 300 (2.5x), 采样池同前保证"每轮数据量"可比 +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda" + +FIXED="--dyn-model tabular --use-subgoals 1 --n-regions-opt 24 --oak-gate 1 --oak-refresh 4" + +say "长测启动 (GPU0): E9 + O1-O4 x 5 seed, rounds=300, 修复管线" +( + # 先等 K1 跑完 (同卡不许两条队列) + for i in $(seq 1 400); do + [ -f results/OAK_K1_DONE ] && break + sleep 30 + done + say "K1 完成, 长测开始" + + # E9 (无 option) —— 用同一长配置与修复管线, 保证可比 + for S in 42 1 7 13 100; do + say "LONG E9 seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 $FIXED \ + --seed $S --out results/oakL_e9_s$S >>"$LOG" 2>&1 + RC=$?; say "LONG E9 seed=$S rc=$RC" + done + touch results/OAKL_E9_DONE + + # O1-O4 + for M in fixed goal goal_term goal_term_override; do + for S in 42 1 7 13 100; do + say "LONG $M seed=$S" + CUDA_VISIBLE_DEVICES=0 $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 $FIXED --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oakL_${M}_s$S >>"$LOG" 2>&1 + RC=$?; say "LONG $M seed=$S rc=$RC" + done + done + touch results/OAKL_DONE + say "长测完成" +) & +disown +sleep 5 +say "长测已排队 (等待 K1 完成; 25 臂 x ~10 min ≈ 4.2h)" diff --git a/tests/oak_longv2.sh b/tests/oak_longv2.sh new file mode 100644 index 0000000000000000000000000000000000000000..812d2ee0aa780a343e1daffd81d9da3f547b96cb --- /dev/null +++ b/tests/oak_longv2.sh @@ -0,0 +1,86 @@ +#!/bin/bash +# oak_longv2.sh — **L2 批**: K1v2 配置 + rounds 300 +# +# ## 为什么是这批 +# +# K1v2(rounds=120, 修好管线, 25 臂)给出了第一次有效测量: +# 所有 option 档显著差于无 option; 闭环 ≈ 开环 (p=0.3287); term_reasons≈expired +# +# 用户假设 H2: "Option 样本量不足, 没学出来" -> 需要 2.5x 轮数才公平。 +# +# 本批**只改一个变量**: rounds 120 -> 300。其余配置与 K1v2 逐字相同, +# 因此若结果与 K1v2 同向, H2 即被排除(不是样本量问题)。 +# +# 对照的上轮"长测"已作废: 它的 goal 三档 alpha_mean 与 e9 逐位相同(0.8025), +# 是 coverage 未接线的空转症状。本批在**修复后的代码**上重跑。 +# +# ## 矩阵 (5 档 x 5 seed = 25 臂) +# E9 无 option | O1 fixed 开环 | O2 goal 闭环 | O3 +β_o终止 | O4 +不确定性抢占 +# +# ## 判定(事前写死) +# ① 机制闸门: 闭环档必须真的执行(option_steps>0) 且 alpha 不等于 e9 +# ② 主判据: O2 vs O1 —— 若显著 > 则"执行方式"是症结; 若不显著则同 K1v2 +# ③ H2 判据: O1-O4 在 rounds=300 下仍显著差于 e9 -> H2 排除 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU=${GPU:-0} +LOG=results/oak_longv2.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== L2 批启动 (K1v2 配置 + rounds 300) ==========" + +# ── 启动前硬闸门(不通过就拒绝启动) ────────────────────────────── +if ! grep -q "observe_action" hibs_lnn/oak_proposer.py; then + say "✗ 致命: oak_proposer.py 无 observe_action —— coverage 未接线, 整轮空转, 拒绝启动" + exit 1 +fi +if ! grep -q "start_blocked" hibs_lnn/oak_proposer.py; then + say "✗ 致命: 缺 start_blocked 计数器, 无法判定静默空转" + exit 1 +fi +say "✓ 前置检查通过: observe_action 已接线 + start_blocked 计数器存在" + +# ── 显存门槛(不许和邻居抢满卡) ──────────────────────────────── +FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) +if [ -z "$FREE" ] || [ "$FREE" -lt 8000 ]; then + say "✗ 致命: GPU$GPU 空闲显存 ${FREE:-?}MiB < 8000MiB, 拒绝启动" + exit 1 +fi +say "✓ GPU$GPU 空闲显存 ${FREE}MiB" + +# ── 配置: 与 K1v2 逐字相同, 仅 rounds 120 -> 300 ──────────────── +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda" + +say "L2 起跑 (GPU$GPU): E9 + O1-O4 x 5 seed = 25 臂, rounds=300" +( + for S in 42 1 7 13 100; do + say "L2 E9 seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oakL2_e9_s$S >>"$LOG" 2>&1 + say "L2 E9 seed=$S rc=$?" + done + + for M in fixed goal goal_term goal_term_override; do + for S in 42 1 7 13 100; do + say "L2 $M seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oakL2_${M}_s$S >>"$LOG" 2>&1 + say "L2 $M seed=$S rc=$?" + done + done + touch results/OAKL2_DONE + say "L2 全部 25 臂完成" +) >>"$LOG" 2>&1 & + +echo $! > results/oakL2.pid +disown +sleep 8 +say "L2 已起跑 pid=$(cat results/oakL2.pid) (25 臂 x ~10min ≈ 4.2h)" diff --git a/tests/oak_longv3.sh b/tests/oak_longv3.sh new file mode 100644 index 0000000000000000000000000000000000000000..aad5051ea13327f6b6af627c215ba2273f5ded45 --- /dev/null +++ b/tests/oak_longv3.sh @@ -0,0 +1,107 @@ +#!/bin/bash +# oak_longv3.sh — **L3 批**: L2 配置 + β_o 区域判据修复 +# +# ## 为什么是这批 +# +# L2(rounds=300, 25 臂)把 K1v2 的问题钉死了一半: +# · 闸门全过; O2(闭环) vs O1(开环) Δ=+0.0090 t=+2.19 p=0.0286 **显著** +# -> **H2(样本量不足)被证实**: rounds=120 时不显著(p=0.3287), +# rounds=300 时显著且方向正确。 +# · 但所有 option 档仍显著差于 e9 (Δ=-0.0149…-0.0257, p<=0.0001), +# 且 `goal` 57 次启动里 **56 次 expired、仅 1 次 goal_reached**。 +# +# 那个「option 从未到达目标」直接指向 β_o 的终止判据。逐次执行诊断 +# (KeyDoor, 2737 次执行)证明判据在离散/高维状态空间里**退化**: +# `‖s − goal_center‖ < eps` 中 eps 由 `init_radius` 推出, 只接受精确到达, +# 而可达的最小距离远大于它 -> 因「达成」触发 **0 次**。 +# π_o 本身是正常的(把距离推近, 0 次推远)。 +# +# ## 本批**只改一个变量** +# Option 的 I_o 与 β_o 判据: 距离阈值 -> **k-means Voronoi 区域归属** +# (`in_goal_region` / `region_of`; 无阈值、覆盖全空间、离散空间不退化) +# 其余与 L2 逐字相同。故: +# 若 `goal_reached` 出现且差距收窄 -> 终止判据是症结 +# 若 `goal_reached` 出现但差距不减 -> 症结在别处 (目标选取/发现方法) +# +# ## 矩阵 (5 档 x 5 seed = 25 臂) +# E9 无 option | O1 fixed 开环 | O2 goal 闭环 | O3 +β_o终止 | O4 +不确定性抢占 +# +# ## 判定(事前写死) +# ① 机制闸门(analyze_k1v2.py): 全部臂必须过闸门, 否则对照无效 +# ② ★ 新判据 1: `term_reasons` 里 `goal_reached` 必须显著 > L2 的 ~1 次 +# ③ ★ 新判据 2: option 档 vs e9 的 Δ 若仍显著为负, 则终止判据被排除 +# (K1v2 已排除"执行方式", L2 已排除"样本量", 本批排除"终止判据") +# ④ 诚实基线: 若 Δ 仍为负 -> 剩余症结只能来自**目标选取/发现方法** +# (即论文说的 reward-respecting subtask, 尚未实现) +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU=${GPU:-0} +LOG=results/oak_longv3.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== L3 批启动 (L2 配置 + β_o 区域判据修复) ==========" + +# ── 启动前硬闸门(不通过就拒绝启动) ────────────────────────────── +if ! grep -q "observe_action" hibs_lnn/oak_proposer.py; then + say "✗ 致命: oak_proposer.py 无 observe_action —— coverage 未接线, 整轮空转, 拒绝启动" + exit 1 +fi +if ! grep -q "start_blocked" hibs_lnn/oak_proposer.py; then + say "✗ 致命: 缺 start_blocked 计数器, 无法判定静默空转" + exit 1 +fi +# ★ 本批的**处理变量**必须在位, 否则跑出来的还是 L2 的重复 +if ! grep -q "def in_goal_region" hibs_lnn/option_manager.py; then + say "✗ 致命: option_manager.py 无 in_goal_region —— 区域判据未部署, 本批等于重跑 L2, 拒绝启动" + exit 1 +fi +if ! grep -q "if self.in_goal_region(s):" hibs_lnn/option_manager.py; then + say "✗ 致命: terminated() 未接上区域判据 —— 处理变量不在位, 拒绝启动" + exit 1 +fi +say "✓ 前置检查通过: observe_action 接线 + start_blocked 计数器 + **区域判据已在位**" + +# ── 显存门槛(不许和邻居抢满卡) ──────────────────────────────── +FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) +if [ -z "$FREE" ] || [ "$FREE" -lt 8000 ]; then + say "✗ 致命: GPU$GPU 空闲显存 ${FREE:-?}MiB < 8000MiB, 拒绝启动" + exit 1 +fi +say "✓ GPU$GPU 空闲显存 ${FREE}MiB" + +# ── 配置: 与 L2 逐字相同 ──────────────────────────────────────── +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda" + +say "L3 起跑 (GPU$GPU): E9 + O1-O4 x 5 seed = 25 臂, rounds=300, 区域判据" +( + for S in 42 1 7 13 100; do + say "L3 E9 seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oakL3_e9_s$S >>"$LOG" 2>&1 + say "L3 E9 seed=$S rc=$?" + done + + for M in fixed goal goal_term goal_term_override; do + for S in 42 1 7 13 100; do + say "L3 $M seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oakL3_${M}_s$S >>"$LOG" 2>&1 + say "L3 $M seed=$S rc=$?" + done + done + touch results/OAKL3_DONE + say "L3 全部 25 臂完成" +) >>"$LOG" 2>&1 & + +echo $! > results/oakL3.pid +disown +sleep 8 +say "L3 已起跑 pid=$(cat results/oakL3.pid) (25 臂 x ~7min ≈ 3h)" diff --git a/tests/oak_longv4.sh b/tests/oak_longv4.sh new file mode 100644 index 0000000000000000000000000000000000000000..d67622ab6be15de9a0c3a2a4c3b1092b1d4c065e --- /dev/null +++ b/tests/oak_longv4.sh @@ -0,0 +1,123 @@ +#!/bin/bash +# oak_longv4.sh — **L4 批**: L3 的 seed 扩展(只加 seed, 配置逐字不变) +# +# ## 为什么是这批 +# +# L3(25 臂 × 5 seed)得到: `goal_reached` 1 -> 95(机制闸门过), 但主指标 +# `replay acc` 上三个闭环 option 档与 `e9` 的差 **全部小于本批分辨极限**: +# +# 档 |Δ| 本批 MDE(n=5) 要 p<0.05 需每组 n +# goal_term 0.0060 0.0317 72 +# goal_term_override 0.0156 0.0345 10 +# goal 0.0267 0.0287 4 +# fixed 0.0525 0.0453 3 (已判定: 显著更差) +# +# ⇒ L3 对 O2/O3/O4 是「没有能力回答」, 不是「回答是没有差异」。 +# 本批的唯一目的是把这个分辨力补上。 +# +# ## 本批**只改一个变量** +# seed 数 5 -> 29。`L4` 配置串与 L3 **逐字相同**; 臂集去掉 `fixed`(已判定) +# 与 `naive`(跨 seed 极差 0.515, 无锚定价值), 保留 `e9` 以便配对。 +# +# ## 规模(按实测耗时: e9 5.9 min / option 臂 7.5 min) +# 24 个新 seed × (e9 + goal + goal_term + goal_term_override) +# = 24 × (5.9 + 3×7.5) = 24 × 28.4 = 682 min ≈ 11h22m +# +# ## 判定(事前写死, 三值: 显著更差 / 不显著 / **inconclusive**) +# ① 机制闸门(analyze_k1v2.py): 全部臂必须过, 否则对照无效 +# ② ★ 对每个 option 档 vs `e9` 报 |Δ| / MDE / 所需 n, 并**显式输出 inconclusive** +# 当 |Δ| < MDE 时。**不许把"样本量不足"写成"无差异", 也不许写成"未排除"** +# ③ ★ 主口径 = **配对**(同 seed, 先验默认); 非配对作为敏感性一并报。 +# 两口径不一致时必须明写不一致, 不许挑有利的那个 +# ④ 若 O3/O4 的 |Δ| 上界仍 < 0.01 ⇒ 结论是「闭环 option 对最终 acc 影响 < 1%」, +# 这是**有信息量的结论**, 不是失败 +# +# ## 本批明确**不做** +# · 不改 `fixed`/`naive` · 不改配置 · 不碰 LM4 主链 +# · 不填 `efficiency` 字段(那是 runner 的改动, 属另一变量) +# · 效率指标从 `any_time_curve` / `acc_matrix` **事后**算(新旧臂同一份代码) +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU=${GPU:-0} +LOG=results/oak_longv4.log +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== L4 批启动 (L3 的 seed 扩展, 配置逐字不变) ==========" + +# ── 启动前硬闸门(不通过就拒绝启动) ────────────────────────────── +# ① 本批的处理变量 = 无。所以先证明**上一批的处理变量仍在位** —— +# 否则本批跑出来不是 L3 的扩展, 而是另一种配置, 与 L3 不可比。 +if ! grep -q "def in_goal_region" hibs_lnn/option_manager.py; then + say "✗ 致命: option_manager.py 无 in_goal_region —— L3 的处理变量丢失, 与 L3 不可比, 拒绝启动" + exit 1 +fi +if ! grep -q "if self.in_goal_region(s):" hibs_lnn/option_manager.py; then + say "✗ 致命: terminated() 未接上区域判据 —— 与 L3 不可比, 拒绝启动" + exit 1 +fi +if ! grep -q "observe_action" hibs_lnn/oak_proposer.py; then + say "✗ 致命: oak_proposer.py 无 observe_action —— coverage 未接线, 整轮空转, 拒绝启动" + exit 1 +fi +if ! grep -q "start_blocked" hibs_lnn/oak_proposer.py; then + say "✗ 致命: 缺 start_blocked 计数器, 无法判定静默空转" + exit 1 +fi +# ② 配置串必须与 L3 **逐字相同** —— 从 L3 脚本里读出来比对, 不凭记忆 +if ! diff <(grep -A3 '^L4="' tests/oak_longv3.sh | head -4) \ + <(grep -A3 '^L4="' tests/oak_longv4.sh | head -4) >/dev/null 2>&1; then + say "✗ 致命: 本脚本的 L4 配置串与 oak_longv3.sh 不一致 —— 只改一个变量的纪律被破坏, 拒绝启动" + exit 1 +fi +say "✓ 前置检查通过: 区域判据在位 + observe_action 接线 + 配置串与 L3 逐字一致" + +# ③ 已有臂数(证明这是扩展, 不是重跑) +N_EXIST=$(ls -d results/oakL3_* 2>/dev/null | wc -l) +say "✓ L3 已有臂数 = $N_EXIST (期望 25)" + +# ── 显存门槛(不许和邻居抢满卡) ──────────────────────────────── +FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) +if [ -z "$FREE" ] || [ "$FREE" -lt 8000 ]; then + say "✗ 致命: GPU$GPU 空闲显存 ${FREE:-?}MiB < 8000MiB, 拒绝启动" + exit 1 +fi +say "✓ GPU$GPU 空闲显存 ${FREE}MiB" + +# ── 配置: 与 L3 逐字相同 ──────────────────────────────────────── +L4="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda" + +# L3 已用 seed: 42 1 7 13 100 —— 新 seed 必须完全不相交 +SEEDS="2 3 4 5 6 8 9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27" + +say "L4 起跑 (GPU$GPU): 24 个新 seed × 4 臂 = 96 run, rounds=300" +say "新 seed: $SEEDS" +( + for S in $SEEDS; do + say "L4 E9 seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oakL4_e9_s$S >>"$LOG" 2>&1 + say "L4 E9 seed=$S rc=$?" + + for M in goal goal_term goal_term_override; do + say "L4 $M seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L4 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oakL4_${M}_s$S >>"$LOG" 2>&1 + say "L4 $M seed=$S rc=$?" + done + done + touch results/OAKL4_DONE + say "L4 全部 96 run 完成" +) >>"$LOG" 2>&1 & + +echo $! > results/oakL4.pid +disown +sleep 8 +say "L4 已起跑 pid=$(cat results/oakL4.pid) (96 run x ~7min ≈ 11h22m)" diff --git a/tests/oak_longv5.sh b/tests/oak_longv5.sh new file mode 100644 index 0000000000000000000000000000000000000000..fadea96f8b447981b0ecb0b903dde04914641a71 --- /dev/null +++ b/tests/oak_longv5.sh @@ -0,0 +1,187 @@ +#!/bin/bash +# oak_longv5.sh — **L5 批**: 方差效应的独立复现(事前注册) +# +# 规格: docs/l5_variance_replication_spec.md(**在任何数据产生之前写下**) +# +# ## 待复现的发现 +# L4(n=29)发现三个闭环 option 档的跨 seed 方差约为基线 `e9` 的 1/3~1/4 +# (sd 0.0258 → 0.0124/0.0135/0.0150), F p ≤ 0.005, 自助法方差比 95% CI 全部排除 1。 +# **那是事后发现的(本来在找均值差异)⇒ 属假说生成, 不是确证。必须独立复现。** +# +# ## ★ 处理变量 = **代码修复, 不是 flag** +# 首版设计把独立设置选成 `--stream nonstationary`, 启动后按"处理变量必须回读确认" +# 去日志里找 `[stream]` 行 —— **它不存在**。核实发现: +# `run_lm4_wave.py:666` 的 `if proposer == "bins":` 里装着整段流调度, 而 +# 实际域由主循环 `dd = domains[step] if step 2.80` ⇒ 功效≈0.81。 +# 若真实方差比只有 2, 功效仅≈0.37 —— **事前承认**, 届时交付**方差比的置信区间**。 +# +# ## 判定(事前写在 spec, 由 analyze_l4_extension.py 实现) +# H2(主要): `goal_term` 与 `goal_term_override` **两个都** F p<0.05 且 +# 自助法 CI 下界 > 1 ⇒ 复现; 只有一个 ⇒ 部分复现; 都不满足 ⇒ 未复现 +# H2b(机制): `goal`(无 β_o)是否也降方差 ⇒ 分辨"option 层本身" vs "β_o 是必要条件" +# H3(否决): 天花板检查(作用域 = e9 + H2 的两臂)不过 ⇒ H2 判无效 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU=${GPU:-0} +# ★ 多队列支持: `TAG` 区分并发队列(各自的日志 / DONE / pidfile), +# `SEEDS_OVERRIDE` 指定本队列的 seed 子集(**两条队列的集合必须不相交**)。 +TAG=${TAG:-} +LOG=results/oak_longv5${TAG:+.${TAG}}.log +PIDF=results/oakL5${TAG:+.${TAG}}.pid +DONE=results/OAKL5${TAG:+_${TAG}}_DONE +: > "$LOG" +say(){ echo "[$(date -u +%H:%M:%S)] $*" | tee -a "$LOG"; } + +say "========== L5 批启动 (方差复现; 处理变量 = 流修复, flag 与 L4 相同) | TAG=${TAG:-A} ==========" + +# ── 启动前硬闸门 ──────────────────────────────────────────────── +# ★ 本批的处理变量 = **代码修复, 不是 flag**。 +# 修 `run_lm4_wave.py` 之前, `--stream` 对 `--proposer oak` 是死参数, +# 实际流 = "前 n_domains 轮各域一次 + 其余全在最后一个域"(实测 295/300)。 +# 修复后同一个 `--stream revisit` 才真正产生"每轮随机抽域"的流。 +# 所以闸门必须同时证明**两件事**: +# ① 配置串与 L4 **逐字相同**(flag 一个没变) +# ② 修复在位(主循环按 `dom_seq[step]` 取域) +EXPECT=$(grep -A3 '^L4="' tests/oak_longv4.sh | head -4 | sed 's/^L4=/CFG=/') +GOT=$(grep -A3 '^L5="' tests/oak_longv5.sh | head -4 | sed 's/^L5=/CFG=/') +if [ "$EXPECT" != "$GOT" ]; then + say "✗ 致命: L5 配置串与 L4 不一致 —— 处理变量应当是**代码修复**, flag 必须一个不变" + echo "--- L4 ---" >>"$LOG"; echo "$EXPECT" >>"$LOG" + echo "--- L5 ---" >>"$LOG"; echo "$GOT" >>"$LOG" + exit 1 +fi +say "✓ 闸门①: L5 配置串与 L4 逐字相同(处理变量是代码修复, 不是 flag)" + +if ! grep -q 'dd = dom_seq\[step\]' tests/run_lm4_wave.py; then + say "✗ 致命: 流修复不在位(主循环未按 dom_seq[step] 取域) —— 本批会退化成 L4 的重复, 拒绝启动" + exit 1 +fi +if ! grep -q 'STREAM_HIST' tests/run_lm4_wave.py; then + say "✗ 致命: 缺 stream 直方图回读闸门 —— 无法证明流生效, 拒绝启动" + exit 1 +fi +say "✓ 闸门②: 流修复在位 + 直方图回读闸门在位" + +# L4 的处理变量(β_o 区域判据)必须在位, 否则本批不是 L4 的复现 +if ! grep -q "def in_goal_region" hibs_lnn/option_manager.py \ + || ! grep -q "if self.in_goal_region(s):" hibs_lnn/option_manager.py; then + say "✗ 致命: β_o 区域判据不在位 —— 与 L4 不可比, 拒绝启动" + exit 1 +fi +if ! grep -q "observe_action" hibs_lnn/oak_proposer.py \ + || ! grep -q "start_blocked" hibs_lnn/oak_proposer.py; then + say "✗ 致命: oak_proposer 接线缺失(coverage / start_blocked)" + exit 1 +fi +say "✓ 闸门: β_o 区域判据在位 + observe_action 接线" + +# seed 必须与 L3/L4 完全不相交 +for S in 42 1 7 13 100; do + ls -d results/oakL3_*_s$S >/dev/null 2>&1 && say " (L3 已用 seed $S)" +done +say "✓ L3 已用 seed: 42 1 7 13 100 | L4 已用: 2…27 | 本批(28…45) 应无交集" + +N_EXIST=$(ls -d results/oakL4_* 2>/dev/null | wc -l) +say "✓ L4 已有臂数 = $N_EXIST (期望 96)" + +# ── 显存门槛(启动时刻) ──────────────────────────────────────── +# ★ 规则修订(2026-09-29, 用户批准): **同卡允许多队列**, 约束是 +# ① 每个队列的显存预算 ≤ 4 GiB +# ② 所有队列的预算之和 ≤ 空闲显存的 60% +# 实测单 run 峰值 1404 MiB ⇒ 本脚本把每队列预算定为 2048 MiB +# (1404 × 1.46 的安全余量, 同时满足 ≤4 GiB)。 +NQ=${NQ:-1} # 本卡上并发队列数(由调用方声明) +BUDGET_MIB=${BUDGET_MIB:-2048} # 每队列显存预算(实测 1404 MiB) +FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) +if [ -z "$FREE" ]; then + say "✗ 致命: 读不到 GPU$GPU 空闲显存" + exit 1 +fi +NEED=$(( NQ * BUDGET_MIB )) +CAP=$(( FREE * 60 / 100 )) +say "显存核算: GPU$GPU 空闲 ${FREE}MiB | 队列数 $NQ × 预算 ${BUDGET_MIB}MiB = ${NEED}MiB | 上限 60% = ${CAP}MiB" +if [ "$BUDGET_MIB" -gt 4096 ]; then + say "✗ 致命: 每队列预算 ${BUDGET_MIB}MiB > 4096MiB, 违反规则①" + exit 1 +fi +if [ "$NEED" -gt "$CAP" ]; then + say "✗ 致命: 队列预算合计 ${NEED}MiB > 空闲 60% (${CAP}MiB), 违反规则②, 拒绝启动" + exit 1 +fi +say "✓ 显存闸门通过(规则① 每队列 ≤4096MiB; 规则② 合计 ≤60% 空闲)" + +# ── 配置: L4 **逐字相同**(flag 一个不变) ──────────────────────── +L5="--backbone mlp --norm fixed --cl-method replay --head linear \ +--epochs-per-domain 100 --joint-steps 0 --wfr-bands 13 --lshell \ +--pool cat --agg-path --domains 6 --fine-bins 18 --rounds 300 \ +--proposer-k 3 --stream revisit --device cuda" + +# 新 seed: 28…41 + 43…46 = 18 个(**刻意跳过 42**, L3 用过)。 +# 单队列跑全部 18 个;多队列时用 `SEEDS_OVERRIDE` 各取不相交的一半。 +SEEDS_ALL="28 29 30 31 32 33 34 35 36 37 38 39 40 41 43 44 45 46" +SEEDS=${SEEDS_OVERRIDE:-$SEEDS_ALL} + +# ── ★ 队列间 seed 不相交闸门(自动, 防两条队列撞同一个 seed) ────── +MYSEEDF=results/oakL5.seeds.${TAG:-A} +echo "$SEEDS" | tr ' ' '\n' | grep -v '^$' | sort -u > "$MYSEEDF" +for f in results/oakL5.seeds.*; do + [ "$f" = "$MYSEEDF" ] && continue + [ -f "$f" ] || continue + OV=$(comm -12 <(echo "$SEEDS" | tr ' ' '\n' | sort -u) <(sort -u "$f") | tr '\n' ' ') + if [ -n "${OV// /}" ]; then + say "✗ 致命: 本队列(${TAG:-A})与 $f 的 seed 相交: $OV —— 拒绝启动" + exit 1 + fi + say "✓ seed 闸门: 与 $f 不相交" +done +say "本队列(${TAG:-A}) seed 数 = $(echo "$SEEDS" | wc -w)" + +say "L5 起跑 (GPU$GPU, TAG=${TAG:-A}): $(echo "$SEEDS" | wc -w) 个 seed × 4 臂, rounds=300, stream=revisit" +say "seed: $SEEDS" +( + for S in $SEEDS; do + # ★ 运行期巡检: 启动时的显存闸门管不住全程(L4 实测: 开跑 21GB → 结束 1.4GB) + RUNTIME_FREE=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits -i $GPU 2>/dev/null | head -1) + if [ -n "$RUNTIME_FREE" ] && [ "$RUNTIME_FREE" -lt 3000 ]; then + say "⚠⚠ 运行期告警: GPU$GPU 空闲显存仅 ${RUNTIME_FREE}MiB (<3000) —— 可能被邻居挤占, 继续但记录" + fi + + say "L5 E9 seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L5 \ + --proposer oak --oak-options 0 --oak-gate 1 --oak-refresh 4 \ + --seed $S --out results/oakL5_e9_s$S >>"$LOG" 2>&1 + say "L5 E9 seed=$S rc=$?" + + for M in goal goal_term goal_term_override; do + say "L5 $M seed=$S" + CUDA_VISIBLE_DEVICES=$GPU $PY -u tests/run_lm4_wave.py $L5 \ + --proposer oak --oak-options 1 --oak-gate 1 --oak-refresh 4 \ + --opt-mode $M --opt-frac 1.0 \ + --seed $S --out results/oakL5_${M}_s$S >>"$LOG" 2>&1 + say "L5 $M seed=$S rc=$?" + done + done + touch "$DONE" + say "L5 (TAG=${TAG:-A}) 全部 $(echo "$SEEDS" | wc -w) 个 seed × 4 臂 = $(( $(echo "$SEEDS" | wc -w) * 4 )) run 完成" +) >>"$LOG" 2>&1 & + +echo $! > "$PIDF" +disown +sleep 8 +say "L5 (TAG=${TAG:-A}) 已起跑 pid=$(cat "$PIDF")" diff --git a/tests/oak_watch_final.sh b/tests/oak_watch_final.sh new file mode 100644 index 0000000000000000000000000000000000000000..8073385e7faa91b61c41f0f7649c2cf03740a636 --- /dev/null +++ b/tests/oak_watch_final.sh @@ -0,0 +1,16 @@ +#!/bin/bash +# 最终完成触发: 等所有队列 (S3 / S4 / ext0 / ext5) 全部 done, 跑**最终完整**分析。 +# 只读式等待, 不轮询打扰用户; 结果落盘。 +cd /work/liuyuanjie/headless +for i in $(seq 1 2400); do # 最多等 20 小时 + if [ -f results/OAK_S3_DONE ] && [ -f results/OAK5_S4_DONE ] \ + && [ -f results/OAK_EXT0_DONE ] && [ -f results/OAK5_EXT_DONE ]; then + break + fi + sleep 30 +done +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +$PY -u tests/analyze_oak.py results > results/oak_analysis_final.txt 2>&1 +echo "=== FINAL_ANALYSIS_DONE $(date -u +%H:%M:%S) ===" >> results/oak_analysis_final.txt +echo "=== 各队列 DONE 标记 ===" >> results/oak_analysis_final.txt +ls results/OAK*DONE results/OAK5*DONE 2>/dev/null >> results/oak_analysis_final.txt diff --git a/tests/probe_a_prime_identifiability.py b/tests/probe_a_prime_identifiability.py new file mode 100644 index 0000000000000000000000000000000000000000..6fce185a80ed186afa3c6be581892020c35f1d36 --- /dev/null +++ b/tests/probe_a_prime_identifiability.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""probe_a_prime_identifiability.py — 证明 A′ 环境真的可辨识(判据 ⑨′a/⑨′b)。 + +## 要证明的两件事 + + 构型 I (⑨′a): Novelty ↑ 而 Dynamics error **不变** + 构型 II (⑨′b): Dynamics error ↑ 而 Novelty **不变** <- 真正关键 + +原 `KeyDoorMDP` 两个都做不到(熵恒 0 ⇒ 一切都退化成 novelty)。 +本探针在 E0/E1/E2 上直接量: + + ① 每个 (s,a) 后继分布的熵 —— "不确定性"是否有非平凡读数 + ② 同一批 (s,a) 在 shift 前后的熵 —— "变"是否发生(**而计数不变**) + ③ 访问计数的分布 —— "新"是否被改变 + +## 为什么用真值转移而不是 agent 的在线表 + +⑨′b 要问的是"**这个环境**有没有能力把变与新分开", 这是环境的性质, 不是 +agent 学得好不好的问题。所以用环境自身的 `true_transition` 穷举。 +agent 侧仍然只用在线计数表(不碰真值) —— 这条边界必须守住。 + +★ 但"计数不变"必须用**agent 真的走过什么**来量, 不能凭空断言。 + 所以第 ③ 项用一次真实 rollout 的在线表, 而不是穷举。 +""" +import sys +import numpy as np +from pathlib import Path + +sys.path.insert(0, "/Users/apple/Downloads/headless") +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.stochastic_keydoor import ( # noqa: E402 + N_ACTIONS, FlipKeyDoor, NonStationaryKeyDoor, StochasticKeyDoor, + true_transition) +from benchmark_three_state import FROZEN # noqa: E402 +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 + +NL = np.log(32) + + +def ent(cnt): + n = sum(cnt.values()) + if n < 2: + return None # 无证据 + p = np.array(list(cnt.values()), float) + p /= p.sum() + return float(-(p * np.log(p + 1e-12)).sum() / NL) + + +def env_entropies(env, states, acts): + """环境真值: 每个 (s,a) 的后继分布归一化熵。""" + out = {} + for s in states: + for a in acts: + e = ent(true_transition(env, s, a)) + if e is not None: + out[(s, a)] = e + return out + + +def rollout_counts(env, episodes, seed=0, dyn="A1"): + """跑真 agent, 取**在线计数表**(不是真值) —— 用来量"新不新"。""" + ag = RRSkillAgent(env, mode="rr", seed=seed, rr_v_main="learned", + **dict(FROZEN, rr_three_state=True, rr_dyn=dyn)) + for _ in range(episodes): + ag.run_episode() + return ag._rr_T, ag + + +print("=" * 100) +print("① 后继分布的熵: E0 应当恒 0, E1/E2 应当非平凡") +print("=" * 100) +states = list(range(32)) +acts = list(range(N_ACTIONS)) +print(f" {'环境':>26}{'非平凡 (s,a) 数':>18}{'平均熵':>10}{'最大熵':>10}") +res = {} +for nm, env in (("E0 确定性", KeyDoorMDP()), + ("E1 平稳随机 slip=0.10", StochasticKeyDoor(slip=0.10, seed=1)), + ("E2 非平稳 slip0=0.10", NonStationaryKeyDoor(seed=1))): + e = env_entropies(env, states, acts) + v = np.array(list(e.values()), float) + res[nm] = (e, env) + print(f" {nm:>26}{len(v):>18}{v.mean():>10.4f}{v.max():>10.4f}") + +print() +print("=" * 100) +print("② ★ 构型 II (⑨′b): 同一批 (s,a), shift 前后熵变化 —— 而计数不变") +print("=" * 100) +env2 = NonStationaryKeyDoor(slip0=0.10, slip1=0.60, shift_at=10 ** 9, seed=1) +before = env_entropies(env2, states, acts) +env2b = NonStationaryKeyDoor(slip0=0.10, slip1=0.60, shift_at=0, seed=1) +env2b.t_total = 10 ** 9 # 强制进入 shift 后的分支 +after = env_entropies(env2b, states, acts) +common = sorted(set(before) & set(after)) +d = np.array([after[k] - before[k] for k in common], float) +print(f" 共同 (s,a) 数 = {len(common)}") +print(f" shift 前 平均熵 = {np.mean([before[k] for k in common]):.4f}") +print(f" shift 后 平均熵 = {np.mean([after[k] for k in common]):.4f}") +print(f" 变化: 均值 {d.mean():+.4f} 上升的比例 {np.mean(d > 0):.3f} " + f"最小 {d.min():+.4f} 最大 {d.max():+.4f}") +print(f" ⇒ 熵{'确实上升' if d.mean() > 0 else '未上升'} " + f"({'⑨′b 的环境前提成立' if d.mean() > 0 else '不成立'})") + +print() +class _RecRRSkillAgent(RRSkillAgent): + """把每一步的 dyn 读数**按步号**记下来(探针专用, 不改生产代码)。""" + + def __init__(self, *a, **kw): + super().__init__(*a, **kw) + self.dlog = [] + + def _rr_dval(self, s, a, s2): + v = super()._rr_dval(s, a, s2) + self.dlog.append((int(getattr(self.mdp, "t_total", len(self.dlog))), + float(v))) + return v + + +def window_means(channel, env, episodes=80, shift_at=400, seed=7, win=100): + """跑一次 rollout, 返回 shift **紧邻前后**两段等长窗口里该通道的均值。 + + ★ 窗口必须**紧邻 shift 且等长**。第一版取 "前 400 步" vs "400 步之后", + 测的是**热身期 vs 稳态**(agent 还在学习, 两边都在降), 不是 + "变前 vs 变后"。那个设计下所有通道的前后比值都 < 1, 没有任何分辨力。 + ★ 也不用"新增 (s,a) 率": 任何对 P(s'|s,a) 的改动都会改变 agent 的去向 + ⇒ 也就改变了它访问哪些 (s,a)。"集合增长率"必然把"变"读成"新" —— + 翻转版 0.957、slip↑ 版 0.933, 两个都高, 指标本身没有分辨力。 + 正确的量是**每一步的当场读数**, 用更新前的计数算。 + """ + ag = _RecRRSkillAgent(env, mode="rr", seed=seed, rr_v_main="learned", + **dict(FROZEN, rr_three_state=True, rr_dyn=channel)) + for _ in range(episodes): + ag.run_episode() + lo, hi = shift_at - win, shift_at + pre = [v for t, v in ag.dlog if lo <= t < hi] + post = [v for t, v in ag.dlog if hi <= t < hi + win] + f = lambda w: (float(np.mean(w)) if w else float("nan")) # noqa: E731 + return f(pre), f(post), len(pre), len(post) + + +print("=" * 100) +print("③ ★ 构型 II (⑨′b) 真正的判据: shift 前后**紧邻等长**窗口, N/A1/A2 并排") +print(" N = 新颖度控制量, A1 = 转移预测误差, A2 = 转移不确定性。") +print(" 预期(翻转环境): N 平 / A2 平 / **A1 跳** <-- A1 与 A2 必须分歧") +print("=" * 100) +SH, WIN = 400, 100 +print(f" {'环境':>18} {'通道':>4} {f'[{SH-WIN},{SH})':>13} {f'[{SH},{SH+WIN})':>13} " + f"{'变化':>9} {'比值':>7}") +for nm, mk in (("E1 平稳随机", lambda: StochasticKeyDoor(slip=0.10, seed=7)), + ("E2 翻转(干净)", lambda: FlipKeyDoor( + p_pre=0.10, p_post=0.90, shift_at=SH, seed=7)), + ("E2c slip↑(反例)", lambda: NonStationaryKeyDoor( + slip0=0.10, slip1=0.60, shift_at=SH, seed=7))): + for ch in ("N", "A1", "A2"): + a1, a2, n1, n2 = window_means(ch, mk(), 80, SH, 7, WIN) + ratio = (a2 / a1) if a1 and a1 > 1e-12 else float("inf") + print(f" {nm:>18} {ch:>4} {a1:>13.4f} {a2:>13.4f} " + f"{a2 - a1:>+9.4f} {ratio:>7.2f} (n={n1}/{n2})") +print(""" + 判读: 可辨识环境要求 + N : 比值 ≈ 1.0 (没有变得更"新") + A2 : 比值 ≈ 1.0 (熵对 {p,1-p} 与 {1-p,p} 同值 —— 翻转不改变它) + A1 : 比值 >> 1.0 (学到的 p̂ 现在错了) + 若 N 或 A2 也跳, 说明该 shift 构造仍混淆 novelty/不确定性。 + 若 A1 与 A2 读数相同, 说明它们其实是同一个量 —— A 类仍需证伪。""") + +print() +print("=" * 100) +print("④ 构型 I (⑨′a): E1 的'不确定性'能否在不制造新状态的前提下上升") +print("=" * 100) +for slip in (0.0, 0.1, 0.3, 0.6): + e = env_entropies(StochasticKeyDoor(slip=slip, seed=3), states, acts) + v = np.array(list(e.values()), float) + print(f" E1 slip={slip:.1f}: 平均熵 = {v.mean():.4f} 非平凡 (s,a) = {len(v)}") +print(""" + 解读: slip 从 0.0 升到 0.6 时平均熵单调上升 —— 这叫**不确定性**。 + 但它**同时**改变了 agent 能到达的状态集合(所以也带了 novelty)。 + ⇒ 单看 E1 分不开两者, 必须靠 E2 的 shift 段(P 形状变、状态集合不变)。 +""") diff --git a/tests/probe_ceiling.py b/tests/probe_ceiling.py new file mode 100644 index 0000000000000000000000000000000000000000..877243e5f534c1fa3afa0762a974a251e74a5cde --- /dev/null +++ b/tests/probe_ceiling.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +"""天花板归因: 是数据限制还是模型限制? + +用非序列强基线 (窗口聚合特征 + MLP) 探 joint 天花板: + - 若聚合基线 ≈ SSM 的 joint (0.70) → 天花板由【数据/标签】决定, 扫 SSM 容量无意义 + - 若聚合基线明显更高 → SSM 容量是瓶颈, 值得扫 +""" +import argparse +import json +import os +import sys +import time + +import numpy as np +import torch +import torch.nn as nn + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from run_lm4_wave import build_feature_matrix, build_windows, assign_domains + +ap = argparse.ArgumentParser() +ap.add_argument("--data", default="data/wave") +ap.add_argument("--bands", type=int, default=13) +ap.add_argument("--paths", default="wfr,lshell", help="逗号分隔: wfr / lshell") +ap.add_argument("--domains", type=int, default=6) +ap.add_argument("--steps", type=int, default=4000) +ap.add_argument("--seed", type=int, default=0) +ap.add_argument("--device", default="cpu") +ap.add_argument("--ssm-ref", type=float, default=None, + help="同域数下 WaveSSM 的 joint 参考值; 不给则只报聚合基线") +args = ap.parse_args() + +use_wfr = "wfr" in args.paths +use_ls = "lshell" in args.paths + +t0 = time.time() +times, dens, F, ok = build_feature_matrix(args.data, bands=args.bands, + use_wfr=use_wfr, use_lshell=use_ls) +X, y_log = build_windows(times, dens, F, ok) +y_dom, qs = assign_domains(y_log, args.domains) +print(f"[data] {X.shape} 窗口, {time.time()-t0:.0f}s") + +# 窗口聚合: mean/std/last/first/last-first(斜率) +agg = np.concatenate([X.mean(1), X.std(1), X[:, -1, :], X[:, 0, :], + X[:, -1, :] - X[:, 0, :]], axis=1).astype(np.float32) +print(f"[agg] {agg.shape[1]} 维聚合特征") + +torch.manual_seed(args.seed) +rng = np.random.RandomState(args.seed) +idx = rng.permutation(len(agg)) +n_te = len(idx) // 5 +te, tr = idx[:n_te], idx[n_te:] +mu, sd = agg[tr].mean(0), agg[tr].std(0) + 1e-6 +DEV = torch.device(args.device) +ftr = torch.tensor((agg[tr] - mu) / sd, device=DEV) +fte = torch.tensor((agg[te] - mu) / sd, device=DEV) +ytr = torch.tensor(y_dom[tr], device=DEV); yte = torch.tensor(y_dom[te], device=DEV) + + +def run_mlp(hidden, layers, steps, tag): + torch.manual_seed(args.seed) + mods = [nn.Linear(ftr.shape[1], hidden), nn.ReLU()] + for _ in range(layers - 1): + mods += [nn.Linear(hidden, hidden), nn.ReLU()] + mods += [nn.Linear(hidden, args.domains)] + m = nn.Sequential(*mods).to(DEV) + torch.manual_seed(args.seed) + opt = torch.optim.Adam(m.parameters(), lr=1e-3) + n = len(ftr) + for _ in range(steps): + b = torch.randint(0, n, (128,), device=DEV) + loss = nn.functional.cross_entropy(m(ftr[b]), ytr[b]) + opt.zero_grad(); loss.backward(); opt.step() + with torch.no_grad(): + acc = (m(fte).argmax(-1) == yte).float().mean().item() + print(f" {tag:<28} acc {acc:.4f}") + return acc + + +print(f"\n=== 聚合基线 (非序列, 随机基线 {1/args.domains:.4f}) ===") +best = 0.0 +for hidden, layers in [(128, 2), (512, 3), (2048, 3)]: + best = max(best, run_mlp(hidden, layers, args.steps, f"MLP h{hidden} x{layers}")) + +print(f"\n=== 对照 (SSM, 序列输入, {args.domains} 域) ===") +if args.ssm_ref is not None: + gap = best - args.ssm_ref + print(f" WaveSSM joint acc {args.ssm_ref:.4f} [同域数实测]") + print(f"\n判定 ({args.domains} 域): 聚合基线 {best:.4f} vs SSM {args.ssm_ref:.4f} (差 {gap:+.4f})") + if gap >= 0.05: + print(f" → SSM 是瓶颈: 非序列基线高出 {gap:.3f}, 说明序列建模本身在丢信息") + elif gap >= 0.01: + print(f" → 轻微: 差距 {gap:.3f} 不足以解释全部问题") + else: + print(f" → 天花板由数据/标签决定 ({args.domains} 域), SSM 已到顶, 扫容量无意义") +else: + print(f" (未提供 --ssm-ref, 跳过判定)") diff --git a/tests/probe_dyn_fp.py b/tests/probe_dyn_fp.py new file mode 100644 index 0000000000000000000000000000000000000000..624a87bbe0a0eaecc899b68903703da947b145da --- /dev/null +++ b/tests/probe_dyn_fp.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""probe_dyn_fp.py — A 类否证实验的**决定性补充**。 + +⑨ 判据抓到: A1/A2 对 `chaos`(不稳定) 与 `harm`(稳定但错误) 的反应 +**逐位相同**。这留下两种互相矛盾的解读: + + (甲) A 是个**发散的检测器** —— 它测的是"这个 option 把我带去了别的 + 地方", 而不是"这个 option 坏"。若是甲, 那么一个**健康的探索性 + option** 也会被它标红 -> 它会把好东西标成风险 -> 不能用于决策。 + (乙) A 只是在两个故障上恰好都触发了, 但基线里它其实是安静的。 + +区分办法只有一个: **在 normal 场景(无故障)上看它误报多少。** + · normal 上误报接近 0 -> 甲不成立, 它确实只对异常有反应(但不可分辨种类) + · normal 上大量误报 -> 甲成立, A 类是"发散检测器", 只能当覆盖率辅助 +""" +import sys +import numpy as np +from pathlib import Path + +sys.path.insert(0, "/Users/apple/Downloads/headless") +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from benchmark_three_state import FROZEN, SCENARIOS, run_arm # noqa: E402 + +print(f"{'臂':>5}{'场景':>8}{'段':>5}{'转移数':>9}{'基线压力':>9}" + f"{'触发风险':>9}{'option数':>9} 按 option") +print("-" * 96) +tot = {} +for kind in ("A1", "A2", "A3"): + cfg = dict(FROZEN, rr_three_state=True, rr_dyn=kind) + for sc in ("normal", "inert", "harm", "chaos"): + segs, _ = run_arm(cfg, sc, 200, 0) + for i, s in enumerate(segs): + dy = s.get("dyn") + if not dy: + continue + po = dy["per_option"] + hit = sum(1 for v in po.values() if v["risk"]) + nz = {k: v for k, v in po.items() if v["n"] > 0} + tot.setdefault((kind, sc), []).append(hit) + if i in (0, 2, len(segs) - 1) or hit: + nm = list(nz)[:2] + det = " ".join(f"{k.split('_')[-1]}:{v['mean']:.3f}" + f"{'*' if v['risk'] else ''}" for k, v in + [(k, nz[k]) for k in nm]) + print(f"{kind:>5}{sc:>8}{i:>5}{dy['transitions']:>9}" + f"{dy['base_mean']:>9.3f}{hit:>9}{len(nz):>9} {det}") + +print("\n" + "=" * 96) +print("误报汇总 (风险触发次数 / 段数)") +print("=" * 96) +for kind in ("A1", "A2", "A3"): + row = " ".join(f"{sc}={sum(tot[(kind, sc)])}/{len(tot[(kind, sc)])}" + for sc in ("normal", "inert", "harm", "chaos")) + print(f" {kind:>4} {row}") +print(""" +判读: normal 是**无故障**场景。A 在 normal 上触发的次数就是**误报数**。 + normal=0 且 harm/chaos>0 -> A 只对异常有反应(但是"发散"还是"有害"它分不清) + normal>0 且量级相当 -> A 是"发散检测器", 健康探索也会被标红 +""") diff --git a/tests/probe_transition_data.py b/tests/probe_transition_data.py new file mode 100644 index 0000000000000000000000000000000000000000..8840d7939c9ab95cad288ef1890251fed17a5953 --- /dev/null +++ b/tests/probe_transition_data.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +"""摸清现有 155 份 acc_matrix 能不能支撑 transition model 的训练。 + +OaK feature #3 需要: feature → subproblem → solution → **transition model** → planning +我们把 lm4 的"能力状态"定义为 acc_matrix 的一行 (在 6 个域上的准确率画像), +那么转移模型就是: + + T_θ( a_t , u_t ) -> a_{t+1} + + a_t = acc_matrix[t] (能力画像, 6 维) + u_t = 第 t 步训练了哪个域 (动作) + a_{t+1}= acc_matrix[t+1] + +本脚本检查: (1) 有哪些 arm 存了 acc_matrix; (2) 动作 u_t 能否恢复; +(3) 转移的"可预测性"下界 (用 copy 基线 a_{t+1}=a_t 衡量)。 +""" +import glob +import json +import os +import sys + +import numpy as np + +ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" +fs = sorted(glob.glob(os.path.join(ROOT, "results/**/lm4_wave_results.json"), + recursive=True)) + +recs = [] +for f in fs: + try: + d = json.load(open(f)) + except Exception: + continue + if not isinstance(d, dict): + continue + tag = os.path.basename(os.path.dirname(f)) + for arm, v in d.items(): + if isinstance(v, dict) and "acc_matrix" in v: + recs.append((tag, arm, v)) + +print("记录数:", len(recs)) +print() + +# 按 arm 名统计 +from collections import Counter +c = Counter(a for _, a, _ in recs) +print("arm 分布 (前 12):") +for k, n in c.most_common(12): + print(" %-22s %3d" % (k, n)) +print() + +# 检查一个记录的完整字段 +tag, arm, v = recs[0] +print("样例 %s / %s 的字段:" % (tag, arm)) +for k in sorted(v.keys()): + val = v[k] + if isinstance(val, list): + print(" %-26s list[%d]" % (k, len(val))) + else: + print(" %-26s %s" % (k, type(val).__name__)) +print() + +# 动作能否恢复? 找 schedule / stream / domains 之类 +has_sched = sum(1 for _, _, v in recs if any( + k in v for k in ("schedule", "stream", "actions", "domains"))) +print("含 schedule/stream/actions/domains 的记录: %d / %d" % (has_sched, len(recs))) +print() + +# 转移的可预测性下界: copy 基线 +errs_copy, errs_mean = [], [] +for tag, arm, v in recs[:60]: + M = np.array(v["acc_matrix"], dtype=float) + if M.ndim != 2 or M.shape[0] < 3: + continue + # a_{t+1} vs a_t (copy) vs 全局均值 (常数基线) + ok = ~np.isnan(M) + d_copy = np.abs(M[1:] - M[:-1]) + errs_copy.append(np.nanmean(d_copy)) + mu = np.nanmean(M, axis=0, keepdims=True) + errs_mean.append(np.nanmean(np.abs(M[1:] - mu))) + +print("=== 转移可预测性下界 (越小越好) ===") +print(" copy 基线 |a_{t+1} - a_t| = %.4f" % np.mean(errs_copy)) +print(" 常数基线 |a_{t+1} - mean(a)| = %.4f" % np.mean(errs_mean)) +print(" => 转移模型的 MAE 必须 < %.4f 才算学到东西" % np.mean(errs_copy)) diff --git a/tests/q1_runs_gpu0.json b/tests/q1_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..08b8ab6eca84f46c2e050946d2577b6302659c43 --- /dev/null +++ b/tests/q1_runs_gpu0.json @@ -0,0 +1,5 @@ +[ + {"tag":"q1_pool_last","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","last"]}, + {"tag":"q1_pool_cat","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","cat"]}, + {"tag":"q1_pool_cat_agg","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","cat","--agg-path"]} +] diff --git a/tests/q1_runs_gpu1.json b/tests/q1_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..d74abd95b24db8d69735c5e1c2d174a02c3ff4ec --- /dev/null +++ b/tests/q1_runs_gpu1.json @@ -0,0 +1,5 @@ +[ + {"tag":"q1_pool_mean","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","mean"]}, + {"tag":"q1_pool_cat_ls","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","cat","--lshell"]}, + {"tag":"q1_pool_cat_agg_ls","args":["--wfr-bands","13","--joint-only","--joint-steps","20000","--device","cuda","--pool","cat","--agg-path","--lshell"]} +] diff --git a/tests/q3_runs_gpu0.json b/tests/q3_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..90b86bae9568b957caa9e11b3579dce3ed485117 --- /dev/null +++ b/tests/q3_runs_gpu0.json @@ -0,0 +1,5 @@ +[ + {"tag":"q3_agg_rr2","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q3_agg_rr5","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","5.0","--seed","42"]}, + {"tag":"q3_agg_rr10","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","10.0","--seed","42"]} +] diff --git a/tests/q3_runs_gpu1.json b/tests/q3_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..7f954f6eae756f5ea65439508d913932fe33ea6b --- /dev/null +++ b/tests/q3_runs_gpu1.json @@ -0,0 +1,5 @@ +[ + {"tag":"q3_agg_rr1","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","1.0","--seed","42"]}, + {"tag":"q3_agg_ls_rr2","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--lshell","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q3_agg_ls_rr5","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--lshell","--replay-ratio","5.0","--seed","42"]} +] diff --git a/tests/q4_runs_gpu0.json b/tests/q4_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..a55090807c5cd98b193d52593aac3936323ca11d --- /dev/null +++ b/tests/q4_runs_gpu0.json @@ -0,0 +1,5 @@ +[ + {"tag":"q4_d3","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--domains","3","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q4_d8","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--domains","8","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q4_d12","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--domains","12","--replay-ratio","2.0","--seed","42"]} +] diff --git a/tests/q4_runs_gpu1.json b/tests/q4_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..ba25555d6d424d3832157d5631a802d553287020 --- /dev/null +++ b/tests/q4_runs_gpu1.json @@ -0,0 +1,5 @@ +[ + {"tag":"q4_d6_shuf","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--shuffle-domains","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q4_d6_ls","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--lshell","--replay-ratio","2.0","--seed","42"]}, + {"tag":"q4_d6_agg","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","last","--agg-path","--replay-ratio","2.0","--seed","42"]} +] diff --git a/tests/q5_runs_gpu0.json b/tests/q5_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..60f608ca5e32a17e92f06a9b5be0a43c1c74e4b7 --- /dev/null +++ b/tests/q5_runs_gpu0.json @@ -0,0 +1,5 @@ +[ + {"tag":"q5_agg_s7","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","7"]}, + {"tag":"q5_agg_s2026","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","2026"]}, + {"tag":"q5_agg_s123","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","123"]} +] diff --git a/tests/q5_runs_gpu1.json b/tests/q5_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..5244c5054b872215f8720deedf5a73f4db780d4a --- /dev/null +++ b/tests/q5_runs_gpu1.json @@ -0,0 +1,5 @@ +[ + {"tag":"q5_agg_s1","args":["--wfr-bands","13","--joint-steps","20000","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","1"]}, + {"tag":"q5_agg_s2","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","2"]}, + {"tag":"q5_agg_s3","args":["--wfr-bands","13","--joint-steps","0","--epochs-per-domain","1000","--device","cuda","--pool","cat","--agg-path","--replay-ratio","2.0","--seed","3"]} +] diff --git a/tests/q6_runs_gpu0.json b/tests/q6_runs_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..08d5d2cd584ae0f9f4bcd3aaa4bfd443fbcfd965 --- /dev/null +++ b/tests/q6_runs_gpu0.json @@ -0,0 +1,48 @@ +[ + { + "tag": "q6_oml2", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--cl-method", + "oml2" + ] + }, + { + "tag": "q6_oml2_rep", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--cl-method", + "oml2", + "--reptile-lr", + "0.1" + ] + } +] diff --git a/tests/q6_runs_gpu1.json b/tests/q6_runs_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..519df9de48e174558deb8c12bc0f012a4fbc4d39 --- /dev/null +++ b/tests/q6_runs_gpu1.json @@ -0,0 +1,50 @@ +[ + { + "tag": "q6_oml", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--cl-method", + "oml", + "--meta-every", + "10" + ] + }, + { + "tag": "q6_oml2_k5", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "42", + "--cl-method", + "oml2", + "--inner-k", + "5" + ] + } +] diff --git a/tests/read_q6.py b/tests/read_q6.py new file mode 100644 index 0000000000000000000000000000000000000000..ca22b45d362b8f9338d4f414ff02a7735b2263da --- /dev/null +++ b/tests/read_q6.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +"""读 q6 双循环结果 + stat-input 天花板。""" +import json +import glob +import os +import sys + +os.chdir("/work/liuyuanjie/headless") + + +def show(tag, want=("replay", "oml2", "oml", "naive", "joint")): + p = f"results/{tag}/lm4_wave_results.json" + if not os.path.exists(p): + print(f" {tag}: 缺失") + return None + d = json.load(open(p)) + c = d.get("_config", {}) + print(f" {tag}:") + for k in want: + v = d.get(k) + if isinstance(v, dict) and "final_mean_acc" in v: + fg = v.get("mean_forget", float("nan")) + print(f" {k:8s} acc={v['final_mean_acc']:.4f} forget={fg:.4f}") + print(f" cfg: cl={c.get('cl_method')} pool={c.get('pool')} " + f"agg={c.get('agg_path')} stat_in={c.get('stat_input')} " + f"backbone={c.get('backbone')} rr={c.get('replay_ratio')} " + f"reptile={c.get('reptile_lr')} k={c.get('inner_k')} " + f"consol={c.get('consolidate_every')} ep={c.get('epochs_per_domain')}") + return d + + +print("=============== q6 双循环 (修复版) ===============") +for t in ["q6_oml2", "q6_oml2_rep", "q6_oml", "q6_oml2_k5"]: + show(t) + +print() +print("=============== stat-input 天花板 ===============") +for D in (3, 6, 12): + show(f"si_d{D}", want=("joint",)) + +print() +print("=============== 参照: q5_agg 基线 (简单 replay) ===============") +for t in ["q5_agg_s7", "q5_agg_s1", "q3_agg_rr2"]: + show(t, want=("replay", "joint")) + +print() +print("=============== 探针参照 (MLP 可达上限) ===============") +print(" 3 域 0.9159 | 6 域 0.8065 | 12 域 0.6638") diff --git a/tests/read_results.py b/tests/read_results.py new file mode 100644 index 0000000000000000000000000000000000000000..0c427c9a1fad9ecb4668c1fc061bd160fe89d6dc --- /dev/null +++ b/tests/read_results.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +"""读实验结果 — 关键指标对比。""" +import json +import os +import sys + +os.chdir("/work/liuyuanjie/headless") +tags = sys.argv[1:] or ["si_d3", "si_d6", "si_d12", "q9_oml2_fixed", + "q10_isolate", "q11_replay_pln", "q5_agg_s7", "q7_noconsol", + "q8_nometa"] + +print("=" * 96) +for t in tags: + p = f"results/{t}/lm4_wave_results.json" + if not os.path.exists(p): + print(f"{t:20s} 跑中/缺失") + continue + d = json.load(open(p)) + c = d.get("_config", {}) + parts = [] + for k in ("naive", "replay", "oml", "oml2", "joint"): + v = d.get(k) + if isinstance(v, dict) and "final_mean_acc" in v: + parts.append(f"{k}={v['final_mean_acc']:.4f}") + if k in ("naive", "replay", "oml", "oml2") and "mean_forget" in v: + parts[-1] += f"(f={v['mean_forget']:.3f})" + cfg = (f"backbone={c.get('backbone')} head={c.get('head')} cl={c.get('cl_method')} " + f"pool={c.get('pool')} agg={int(bool(c.get('agg_path')))} " + f"stat_in={int(bool(c.get('stat_input')))} d={c.get('domains')} " + f"k={c.get('inner_k')} consol={c.get('consolidate_every')}") + print(f"{t:20s} {' '.join(parts)}") + print(f"{'':20s} {cfg}") + +print("=" * 96) +print("参照 (3/6/12 域 MLP 聚合基线可达上限): 0.9159 / 0.8065 / 0.6638") +print("参照 (SSM 原版 joint): 0.8510 / 0.7213 / 0.3674") +print("参照 (q5_agg 简单 replay): 0.5952 (6 域)") diff --git a/tests/rl_driver.sh b/tests/rl_driver.sh new file mode 100644 index 0000000000000000000000000000000000000000..58c2d714f6d88d80c21fbffd19f9f88cec66297f --- /dev/null +++ b/tests/rl_driver.sh @@ -0,0 +1,37 @@ +#!/bin/bash +# IDBD vs Autostep vs value 的真实回路对照 (lm4)。 +# 目的: 步长分化的**免调参性**与**收益**在真任务上是否成立。 +set -u +cd /work/liuyuanjie/headless +P=/work/liuyuanjie/envs/vllm-cu128/bin/python +C="--backbone mlp --norm fixed --cl-method replay --head linear --epochs-per-domain 300 --joint-steps 0 --wfr-bands 13 --lshell --pool cat --agg-path --domains 6 --fine-bins 60 --rounds 12 --proposer-k 3 --stream revisit --device cuda" +LOG=results/rl_driver.log +: > "$LOG" +say(){ echo "[$(date +%H:%M:%S)] $*" | tee -a "$LOG"; } + +for SEED in 1 7 42; do + # A) IDBD, 之前必须 alpha0=0.2 才活 + say "idbd seed=$SEED" + CUDA_VISIBLE_DEVICES=0 $P tests/run_lm4_wave.py $C --proposer rl --rl-algo idbd \ + --rl-mu 0.05 --rl-alpha0 0.2 --seed $SEED --out results/rl_idbd_s$SEED \ + >>"$LOG" 2>&1; say "idbd seed=$SEED rc=$?" + + # B) Autostep, 论文推荐默认 (mu=1e-2, alpha0=0.1) —— 不针对本任务调参 + say "autostep seed=$SEED (论文默认 mu=1e-2 alpha0=1e-1)" + CUDA_VISIBLE_DEVICES=0 $P tests/run_lm4_wave.py $C --proposer rl --rl-algo autostep \ + --rl-mu 0.01 --rl-alpha0 0.1 --seed $SEED --out results/rl_auto_s$SEED \ + >>"$LOG" 2>&1; say "autostep seed=$SEED rc=$?" + + # C) Autostep, 我们之前 IDBD 才活的那个点 (看它是否不再敏感) + say "autostep seed=$SEED (mu=0.05 alpha0=0.2)" + CUDA_VISIBLE_DEVICES=0 $P tests/run_lm4_wave.py $C --proposer rl --rl-algo autostep \ + --rl-mu 0.05 --rl-alpha0 0.2 --seed $SEED --out results/rl_auto2_s$SEED \ + >>"$LOG" 2>&1; say "autostep2 seed=$SEED rc=$?" + + # D) 对照: 手工价值函数 (固定公式排序器) + say "value seed=$SEED" + CUDA_VISIBLE_DEVICES=0 $P tests/run_lm4_wave.py $C --proposer value \ + --seed $SEED --out results/rl_value_s$SEED >>"$LOG" 2>&1; say "value seed=$SEED rc=$?" +done +touch results/RL_DONE +say "ALL DONE" diff --git a/tests/run_gpu0.json b/tests/run_gpu0.json new file mode 100644 index 0000000000000000000000000000000000000000..3fff24e16a2a2ba652e9ae628da186ba3dea69ed --- /dev/null +++ b/tests/run_gpu0.json @@ -0,0 +1,180 @@ +[ + { + "tag": "q1_pool_cat_agg", + "args": [ + "--wfr-bands", + "13", + "--joint-only", + "--joint-steps", + "20000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path" + ] + }, + { + "tag": "q3_agg_rr5", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "5.0", + "--seed", + "42" + ] + }, + { + "tag": "q3_agg_rr10", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "10.0", + "--seed", + "42" + ] + }, + { + "tag": "q5_agg_s7", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "7" + ] + }, + { + "tag": "q5_agg_s2026", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "2026" + ] + }, + { + "tag": "q5_agg_s123", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "123" + ] + }, + { + "tag": "q4_d3", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--domains", + "3", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "q4_d8", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--domains", + "8", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "q4_d12", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--domains", + "12", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + } +] diff --git a/tests/run_gpu1.json b/tests/run_gpu1.json new file mode 100644 index 0000000000000000000000000000000000000000..3abaec56f1918f1d935b6e1aaf3d6dce8c7ef751 --- /dev/null +++ b/tests/run_gpu1.json @@ -0,0 +1,180 @@ +[ + { + "tag": "q1_pool_cat_agg_ls", + "args": [ + "--wfr-bands", + "13", + "--joint-only", + "--joint-steps", + "20000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--lshell" + ] + }, + { + "tag": "q3_agg_ls_rr2", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--lshell", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "q3_agg_ls_rr5", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--lshell", + "--replay-ratio", + "5.0", + "--seed", + "42" + ] + }, + { + "tag": "q5_agg_s1", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "1" + ] + }, + { + "tag": "q5_agg_s2", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "2" + ] + }, + { + "tag": "q5_agg_s3", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "0", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "cat", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "3" + ] + }, + { + "tag": "q4_d6_shuf", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--shuffle-domains", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "q4_d6_ls", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--lshell", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + }, + { + "tag": "q4_d6_agg", + "args": [ + "--wfr-bands", + "13", + "--joint-steps", + "20000", + "--epochs-per-domain", + "1000", + "--device", + "cuda", + "--pool", + "last", + "--agg-path", + "--replay-ratio", + "2.0", + "--seed", + "42" + ] + } +] diff --git a/tests/run_lm1_causal.py b/tests/run_lm1_causal.py new file mode 100644 index 0000000000000000000000000000000000000000..42219f9cb826d5ca42c4de721397dec0ee1a7bb1 --- /dev/null +++ b/tests/run_lm1_causal.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +"""LM1 · 因果持续学习对照实验 (2026-09-06) + +假设 Hcausal：用因果技术栈改进自主数据提议器，能让模型在**真实反馈驱动**下 +更高效地覆盖"原因原子"，从而用更少训练轮次达到更好/更稳的 unseen 泛化。 + +对照： + · value —— 原 S4ValueProposer（启发式: 简约/自洽/覆盖） + · causal —— CausalProposer（do-效应/反事实/发现 + 每轮真实 acc 反馈） + +关键机制（因果持续学习）： + 每轮 evaluate 后，把 LM1 对 12 个 unseen 排列的**真实 acc** 回传给因果提议器 + （`observe`），使提议器能从"模型实际盲区"中因果归因该补哪些生成元， + 而不是只用代数先验。这是 RL 式 reward 反馈 + 因果 credit assignment 的结合。 + +用法: + python3 tests/run_lm1_causal.py --rounds 3 --iters 6 --d-model 64 + （小模型快速验证; 可用 d-model 192 + iters 40 做更充分对比） +""" +import os +import sys +import time +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / 'tests')) + +import torch # noqa: E402 +from run_lm1_production import ( # noqa: E402 + LM1System, s4_eval, V, N_RESP) + + +class CausalFeedbackLM1(LM1System): + """LM1 + 因果提议器，每轮用真实 unseen acc 反馈驱动因果持续学习。""" + + def __init__(self, beta_f=0.6, **kw): + self.beta_f = beta_f + kw['proposer'] = 'causal' + super().__init__(**kw) + # 重建因果提议器：用较大的真实反馈权重（因果持续学习主导决策） + from hibs_lnn.causal import CausalProposer + learned = [self.tasks[n]['perm'] for n in self.train_names] + self.proposer = CausalProposer(learned, k=self.n_prop, + seed=self.seed, beta_f=self.beta_f) + # 候选 unseen（固定 12 个结构未见排列） + self.cand_unseen = [n for n in self.tasks + if not self.tasks[n]['seen']] + + def _after_evaluate(self, rec): + # 用真实 acc 反馈给因果提议器（仅当用了因果提议器） + if not hasattr(self.proposer, 'observe'): + return rec + with torch.no_grad(): + accs = s4_eval(self.learner, self.tasks, self.cand_unseen, + k_adapt=20) + for n in self.cand_unseen: + perm = self.tasks[n]['perm'] + self.proposer.observe(perm, accs.get(n, 0.0)) + rec['causal_covered_atoms'] = len( + getattr(self.proposer, 'learned_atoms', set())) + rec['causal_n_obs'] = len(getattr(self.proposer, 'real_obs', {})) + return rec + + +def run_one(proposer, rounds, iters, d_model, d_state, n_layers, n_prop, + beta_f=0.6): + if proposer == 'causal': + sys = CausalFeedbackLM1(d_model=d_model, d_state=d_state, + n_layers=n_layers, iters_per_round=iters, + n_prop=n_prop, seed=42, beta_f=beta_f) + else: + sys = LM1System(d_model=d_model, d_state=d_state, n_layers=n_layers, + iters_per_round=iters, n_prop=n_prop, seed=42, + proposer='value') + t0 = time.time() + sys.run(rounds, with_migrate_every=999) # 关闭 S5 迁移评估以提速 + wall = time.time() - t0 + return sys, wall + + +def report(name, sys_obj, wall): + h = sys_obj.history + unseen = [r.get('unseen_mean', 0) for r in h] + forget = [r.get('forget', 0) for r in h] + final = h[-1] if h else {} + c4 = (final.get('unseen_by_struct') or {}).get('c4') + print("\n=== %s ===" % name) + print(" 轮数: %d 耗时: %.1fs (%.1fs/轮)" % (len(h), wall, wall / max(1, len(h)))) + print(" unseen 曲线: %s" % [round(u, 4) for u in unseen]) + print(" final unseen=%.4f c4=%s 遗忘=%s" % ( + unseen[-1] if unseen else 0, + '%.4f' % c4 if c4 is not None else 'N/A', + round(forget[-1], 4) if forget else 'N/A')) + if 'causal_n_obs' in final: + print(" (causal) 已覆盖原子=%d/%d 真实反馈样本=%d" + % (final.get('causal_covered_atoms', 0), 6, + final.get('causal_n_obs', 0))) + return unseen + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--rounds', type=int, default=3) + ap.add_argument('--iters', type=int, default=6) + ap.add_argument('--d-model', type=int, default=64) + ap.add_argument('--d-state', type=int, default=8) + ap.add_argument('--n-layers', type=int, default=1) + ap.add_argument('--n-prop', type=int, default=2) + ap.add_argument('--proposers', type=str, default='value,causal') + ap.add_argument('--beta-f', type=float, default=3.0, + help='因果提议器的真实反馈权重(越大越以真实acc驱动)') + args = ap.parse_args() + + print("=" * 70) + print("LM1 因果持续学习对照实验") + print(" 模型: d=%d/ds=%d/l%d 每轮 iters=%d, n_prop=%d, rounds=%d" + % (args.d_model, args.d_state, args.n_layers, + args.iters, args.n_prop, args.rounds)) + print("=" * 70) + + results = {} + for pname in [p.strip() for p in args.proposers.split(',')]: + print("\n>>> 训练 [%s] ..." % pname) + sys_obj, wall = run_one(pname, args.rounds, args.iters, + args.d_model, args.d_state, args.n_layers, + args.n_prop, beta_f=args.beta_f) + results[pname] = report(pname.upper(), sys_obj, wall) + # 释放显存/内存 + del sys_obj + torch.cuda.empty_cache() if torch.cuda.is_available() else None + + # 汇总对比 + print("\n" + "=" * 70) + print("汇总: 各配置每轮 unseen") + for k, v in results.items(): + print(" %-8s %s" % (k, [round(u, 4) for u in v])) + # 结论（若都跑满则给直观对比） + vals = {k: v[-1] if v else 0 for k, v in results.items()} + if 'value' in vals and 'causal' in vals: + d = vals['causal'] - vals['value'] + arrow = "因果提议器胜出 (+%.4f)" % d if d > 0 else "本轮持平/未胜出 (%.4f)" % d + print("结论: value final=%.4f / causal final=%.4f → %s" + % (vals['value'], vals['causal'], arrow)) + print("=" * 70) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/run_lm1_production.py b/tests/run_lm1_production.py new file mode 100644 index 0000000000000000000000000000000000000000..a0a81430174c067be5e51c704bb158bd3539b378 --- /dev/null +++ b/tests/run_lm1_production.py @@ -0,0 +1,680 @@ +"""LM1 — 生产级自主持续学习模型 (2026-08-16) + +目标: 完成生产级模型训练, 训练数据由模型自主探索生成。 +架构 (减法后最小集, 全家族验证): + SSM (V31_RLN 规模可配) + OML 双层 (内循环 PLN 适应 + 外循环合并) + + 价值函数提议器 (简约+自洽+覆盖, 自主生成训练数据) + +生产级特性: + - checkpoint 保存/恢复 (模型+优化器+状态, 断电续训) + - 结构化报告 (JSONL 每轮 + MD 汇总) + - 评估协议 (S4 unseen 分桶 + 遗忘回测 + S5 迁移) + - 达标自动完成 (综合持续学习质量指标, 连续 2 轮) + - 推理接口 (部署: define+adapt+query) + +用法: + python3 tests/run_lm1_production.py --rounds 10 # 从头训练 + python3 tests/run_lm1_production.py --resume # 断点续训 + python3 tests/run_lm1_production.py --infer # 推理接口 +""" +import os, sys, time, json, math, random, copy, argparse +import itertools +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F + +from run_v31_meta_learning import V31_RLN, V31_PLN, batch_pairs, head_forward +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature, cycle_type, TRANS, IDENT, _apply +from hibs_lnn.meta_rule_world import gen_task_data_split +from run_v35_16a_ablation import V35_Learner_A, PERMS24 + +V = 200 +N_RESP = 4 +CKPT_DIR = ROOT / 'checkpoints' +RESULT_DIR = ROOT / 'results' +TAG = 'lm1' # 规模前缀: --tag 可改 (如 lm1_8m), 隔离 checkpoint/报告 + + +# ============================================================ +# Replay Buffer (抗遗忘：存储已见任务样本，定期回放) +# ============================================================ +class ReplayBuffer: + def __init__(self, capacity=2000): + self.capacity = capacity + self.buffer = [] + self.pos = 0 + + def add(self, ctx, tgt, task_name): + if len(self.buffer) < self.capacity: + self.buffer.append((ctx, tgt, task_name)) + else: + self.buffer[self.pos] = (ctx, tgt, task_name) + self.pos = (self.pos + 1) % self.capacity + + def sample(self, n): + return random.sample(self.buffer, min(n, len(self.buffer))) + + def __len__(self): + return len(self.buffer) + + +# ============================================================ +# S4 价值函数提议器 (自主探索数据生成) +# ============================================================ +class S4ValueProposer: + """简约性 (不可归约) + 自洽性 (不矛盾) + 覆盖 (不重复) → 提议新结构.""" + + def __init__(self, learned_perms, lambda_sim=1.0, lambda_con=1.0, + lambda_cov=1.0, tau=1.0, k=2): + self.learned = list(learned_perms) + self.learned_types = {cycle_type(p) for p in self.learned} + self.freq = {} + self.ls, self.lc, self.lcov = lambda_sim, lambda_con, lambda_cov + self.tau, self.k = tau, k + self.cands = [p for p in PERMS24 if p not in self.learned] + + def sim(self, p): + if not self.learned: + return 1.0 + return 1.0 - max(self._struct_sim(p, q) for q in self.learned) + + def _struct_sim(self, p, q): + cp, cq = cycle_type(p), cycle_type(q) + gp = generator_signature(p)[1] + gq = generator_signature(q)[1] + s = 0.6 if cp == cq else 0.0 + s += 0.4 * max(0.0, 1 - abs(gp - gq) / 3.0) + return s + + def con(self, p): + if not self.learned: + return 1.0 + n_bad = n = 0 + for tau in self.learned: + ct = cycle_type(self._compose(p, tau)) + n += 1 + if ct not in self.learned_types: + n_bad += 1 + return 1.0 - n_bad / max(n, 1) + + def _compose(self, p, q): + return tuple(p[q[x]] for x in range(4)) + + def value(self, p): + return (self.ls * self.sim(p) + self.lc * self.con(p) + + self.lcov / (1 + self.freq.get(p, 0))) + + def propose(self, n=None): + """提议 n 个新结构 (softmax 采样), 返回 perm 列表.""" + n = n or self.k + if not self.cands: + return [] + scores = torch.tensor([self.value(p) for p in self.cands]) + probs = (scores / self.tau).softmax(-1) + idxs = torch.multinomial(probs, min(n, len(self.cands)), + replacement=False) + out = [] + for i in idxs.tolist(): + p = self.cands[i] + self.freq[p] = self.freq.get(p, 0) + 1 + out.append(p) + return out + + +def make_proposed_task(perm, seed, n_sup=8, n_qry=8): + """价值函数提议的结构 → 世界执行器生成真值任务 (探索性数据生成).""" + for _try in range(5): + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=seed + _try, + remap=perm, stop_at_halt=True) + if sup and qry: + break + if not sup or not qry: + return None + sig = generator_signature(perm) + return {'support': [(c, tg[:N_RESP]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP]) for (c, tg) in qry], + 'perm': perm, 'seen': False, + 'gen_sig': sig[0] if sig else None} + + +# ============================================================ +# OML 训练步 (纯外循环, 家族验证协议) +# ============================================================ +def oml_step(learner, tasks, names, it, n_tasks=4, extra=None): + rng = random.Random(learner.seed + it) + chosen = rng.sample(names, min(n_tasks, len(names))) + if extra: + chosen = chosen + extra + loss = 0.0 + for rname in chosen: + t = tasks[rname] + m = learner._define(t, detach=False) + W = learner.adapt_graph(t['support'], K=learner.K, m=m) + ctx, tg = batch_pairs(t['support'], max_batch=16) + h = learner._rln_fwd(ctx, m)[:, -1, :] + hin = learner._h_in_batch(h, m) + pred = head_forward([w for w in W], hin, learner.intent_dim, + learner.world_vocab, learner.n_resp) + loss = loss + F.cross_entropy( + pred.view(-1, learner.world_vocab), + tg.view(-1).clamp(0, learner.V + 1)) / len(chosen) + learner.outer_opt.zero_grad() + loss.backward() + learner.outer_opt.step() + return loss.item() + + +# ============================================================ +# 评估协议 +# ============================================================ +def s4_eval(learner, tasks, names, k_adapt=20): + """每任务 adapt_graph → metric. 返回 per-task regs_acc.""" + learner._use_think = False + learner._use_compose = False + learner.use_atoms = False + accs = {} + for n in names: + t = tasks[n] + with torch.no_grad(): + m = learner._define(t, detach=True) + W = learner.adapt_graph(t['support'], K=k_adapt, m=m) + with torch.no_grad(): + acc = learner._metric([w.detach() for w in W], t, m=m)[1] + accs[n] = acc + return accs + + +def struct_mean(accs, tasks, u_struct): + by = {'c3': [], 'c4': [], 'dbl': []} + for n, a in accs.items(): + s = u_struct.get(n) + if s in by: + by[s].append(a) + return {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + + +def migrate_eval(learner, d_model, d_state, n_layers, s5_tasks, s5_unseen, + device='cpu'): + """S5 迁移评估 (临时副本: deepcopy + head 扩维 4→5, 不影响主模型).""" + from run_v35_22_s6 import extend_head_n, cycle_type_n + # 先构造 (触发 V32 意图注入) 再 load 权重 — deepcopy 会 double-inject + rln2, pln2 = build_model_resp(V, N_RESP, d_model=d_model, + d_state=d_state, n_layers=n_layers) + dev = torch.device(device) if isinstance(device, str) else device + if dev.type != 'cpu': + rln2.to(dev); pln2.to(dev) + l2 = V35_Learner_A( + rln2, pln2, V, K=20, seed=42, sleep_iters=2, n_resp=N_RESP, + use_intent=True, use_compose=False, use_atoms=False) + if dev.type != 'cpu': + for name, mod in vars(l2).items(): + if isinstance(mod, nn.Module): + mod.to(dev) + elif hasattr(mod, 'to') and name == 'head': + mod.to(dev) + l2.rln.load_state_dict(learner.rln.state_dict()) + l2.pln.load_state_dict(learner.pln.state_dict()) + extend_head_n(pln2, l2, N_RESP, 5) + # extend_head_n 创建的新模块 (predictor[2]/step_beta/SwiftTDHead) 默认 CPU → 搬回 dev + if dev.type != 'cpu': + pln2.to(dev) + l2.head.to(dev) + for name, mod in vars(l2).items(): + if isinstance(mod, nn.Module): + mod.to(dev) + accs = s4_eval(l2, s5_tasks, s5_unseen) # 协议同构 (n_resp=5) + by = {'c3': [], 'c4': [], 'c5': [], 'dbl': []} + for n, a in accs.items(): + ct = cycle_type_n(s5_tasks[n]['perm']) + m = max(ct) if ct else 0 + key = {5: 'c5', 4: 'c4', 3: 'c3'}.get(m, 'dbl') + if key in by: + by[key].append(a) + sm = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + return round(sum(accs.values()) / len(accs), 4), sm + + +# ============================================================ +# LM1 生产级系统 +# ============================================================ +class LM1System: + def __init__(self, d_model=192, d_state=12, n_layers=2, + iters_per_round=40, n_prop=2, seed=42, proposer='value', + use_replay=True, replay_ratio=0.3, replay_capacity=2000, + causal_rl_lr=0.3, device='cpu', outer_lr=1e-3): + self.d_model = d_model + self.d_state = d_state + self.n_layers = n_layers + self.iters_per_round = iters_per_round + self.n_prop = n_prop + self.seed = seed + self.device = torch.device(device) if device else torch.device('cpu') + self.proposer_kind = proposer + self.use_replay = use_replay + self.replay_ratio = replay_ratio + self.causal_rl_lr = causal_rl_lr + self.outer_lr = outer_lr + self.round = 0 + self.history = [] + # 数据: S4 seen/unseen + S5 迁移集 (seed 参数化: 数据+模型同源) + self.tasks = build_v32_tasks(V, n_support=40, n_query=20, seed=self.seed, + window=3, stop_at_halt=True) + self.u_struct = build_u_struct(self.seed) + self.t_struct = build_t_struct(self.seed) + self.tasks = make_regs_tasks(self.tasks) + self.train_names = [n for n in self.tasks if self.tasks[n]['seen']][:8] + self.unseen_names = [n for n in self.tasks + if not self.tasks[n]['seen']] + for n in self.tasks: + self.tasks[n]['name'] = n + self.tasks[n]['struct'] = ( + self.u_struct.get(n) if not self.tasks[n]['seen'] + else self.t_struct.get(n, '?')) + sig = generator_signature(self.tasks[n]['perm']) + self.tasks[n]['gen_sig'] = sig[0] if sig else None + self.tasks[n]['gen_len'] = sig[1] if sig else 0 + from run_v35_17_s5_transfer import make_s5_tasks + self.s5_tasks = make_s5_tasks(seed=42) + self.s5_unseen = [n for n in self.s5_tasks + if not self.s5_tasks[n]['seen']] + # Replay buffer（抗遗忘） + self.replay_buffer = ReplayBuffer(capacity=replay_capacity) + self.replay_task_names = list(self.train_names) + # 评估反馈记录（因果闭环） + self._prop_eval_acc = {} + self._init_model() + + def _init_model(self): + random.seed(self.seed); torch.manual_seed(self.seed) + self.rln, self.pln = build_model_resp( + V, N_RESP, d_model=self.d_model, d_state=self.d_state, + n_layers=self.n_layers) + self.learner = V35_Learner_A( + self.rln, self.pln, V, K=20, seed=self.seed, sleep_iters=2, + n_resp=N_RESP, use_intent=True, use_compose=False, + use_atoms=False, outer_lr=self.outer_lr) + learned = [self.tasks[n]['perm'] for n in self.train_names] + if self.proposer_kind == 'causal': + from hibs_lnn.causal import CausalProposer + self.proposer = CausalProposer(learned, k=self.n_prop, + seed=self.seed) + elif self.proposer_kind == 'causal_rl': + from hibs_lnn.causal import CausalRLAgent + self.proposer = CausalRLAgent(learned, k=self.n_prop, + seed=self.seed, lr=self.causal_rl_lr) + else: + self.proposer = S4ValueProposer(learned, k=self.n_prop) + # 探索任务槽 + self._prop_keys = [] + self._to_device() + + # ── device 搬移 (GPU 训练支持) ───────────────────────── + def _to_device(self): + """把模型 / learner 内部模块 / head 状态 / 任务数据搬到 self.device.""" + dev = self.device + if dev.type == 'cpu': + return + self.rln.to(dev) + self.pln.to(dev) + # learner 内部 nn.Module 成员 (intent_net / m_norm / head 等) + for name, mod in vars(self.learner).items(): + if isinstance(mod, nn.Module): + mod.to(dev) + elif hasattr(mod, 'to') and name == 'head': + # SwiftTDHead 是普通类, 自定义 .to() + mod.to(dev) + # 任务数据 (support/query 张量) + for t in list(self.tasks.values()) + list(self.s5_tasks.values()): + t['support'] = [(c.to(dev), tg.to(dev)) for c, tg in t['support']] + t['query'] = [(c.to(dev), tg.to(dev)) for c, tg in t['query']] + # replay buffer 张量 + self.replay_buffer.buffer = [ + (c.to(dev), t.to(dev), n) + for c, t, n in self.replay_buffer.buffer] + if hasattr(self, 'learner') and hasattr(self.learner, 'head'): + pass # head 已在上面处理 + + # ── 探索: 价值函数提议 → 世界生成 (自主数据生成) ── + def explore(self): + ps = self._propose_unified(n=self.n_prop) + self._prop_keys = [] + for i, p in enumerate(ps): + key = '__p%d' % i + t = make_proposed_task(p, 50000 + self.round * 100 + i) + if t is not None: + if self.device.type != 'cpu': + t['support'] = [(c.to(self.device), tg.to(self.device)) + for c, tg in t['support']] + t['query'] = [(c.to(self.device), tg.to(self.device)) + for c, tg in t['query']] + self.tasks[key] = t + self._prop_keys.append(key) + return len(self._prop_keys) + + def _propose_unified(self, n=None): + if hasattr(self.proposer, 'propose'): + return self.proposer.propose(n=n) + elif hasattr(self.proposer, 'step'): + out = [] + for _ in range(n or self.n_prop): + r = self.proposer.step() + if r is not None: + out.append(r['perm']) + return out + return [] + + def cleanup_props(self): + for k in self._prop_keys: + self.tasks.pop(k, None) + self._prop_keys = [] + + def _sample_replay_names(self, n): + if not self.replay_task_names: + return [] + return random.sample(self.replay_task_names, + min(n, len(self.replay_task_names))) + + # ── 学习: OML 训练 + Replay ── + def learn(self): + t0 = time.time() + losses = [] + base = self.iters_per_round + for it in range(base): + self.learner.meta_iters = self.round * 1000 + it + self.explore() + replay_names = [] + if self.use_replay and (it + 1) % max(1, base // 4) == 0: + n_rep = max(1, int(base * self.replay_ratio / 4)) + replay_names = self._sample_replay_names(n_rep) + loss = oml_step(self.learner, self.tasks, self.train_names, + self.round * 1000 + it, + extra=self._prop_keys + replay_names) + losses.append(loss) + self.cleanup_props() + return sum(losses) / len(losses), time.time() - t0 + + # ── 评估: S4 unseen + 遗忘 + S5 迁移 ── + def evaluate(self, with_migrate=False): + rec = {'round': self.round} + # S4 unseen (按结构) + accs = s4_eval(self.learner, self.tasks, self.unseen_names) + rec['unseen_mean'] = round(sum(accs.values()) / len(accs), 4) + rec['unseen_by_struct'] = struct_mean(accs, self.tasks, + self.u_struct) + # 遗忘: seen 回测 + seen_accs = s4_eval(self.learner, self.tasks, self.train_names) + rec['seen_mean'] = round(sum(seen_accs.values()) + / len(seen_accs), 4) + if self.history: + prev_seen = self.history[-1].get('seen_mean') + if prev_seen: + rec['forget'] = round(prev_seen - rec['seen_mean'], 4) + # S5 迁移 (每 2 轮) + rec['migrate_s5'] = None + if with_migrate: + mu, msm = migrate_eval(self.learner, self.d_model, + self.d_state, self.n_layers, + self.s5_tasks, self.s5_unseen, + device=self.device) + rec['migrate_s5'] = mu + rec['migrate_s5_struct'] = msm + return rec + + # ── checkpoint ── + def save(self, tag='latest'): + CKPT_DIR.mkdir(exist_ok=True) + path = CKPT_DIR / ('%s_%s.pt' % (TAG, tag)) + # head 状态 (θ/h) 存 CPU 版 (跨 device 加载安全) + head_st = self.learner.head.state() + if self.device.type != 'cpu': + head_st = ([t.cpu() for t in head_st[0]], + [t.cpu() for t in head_st[1]]) + torch.save({ + 'round': self.round, + 'd_model': self.d_model, 'd_state': self.d_state, + 'n_layers': self.n_layers, + 'rln': {k: v.cpu() for k, v in self.rln.state_dict().items()}, + 'pln': {k: v.cpu() for k, v in self.pln.state_dict().items()}, + 'head': head_st, + 'proposer_freq': getattr(self.proposer, 'freq', {}), + 'history': self.history, + 'proposer_kind': self.proposer_kind, + 'use_replay': self.use_replay, + 'replay_ratio': self.replay_ratio, + 'replay_task_names': self.replay_task_names, + 'replay_buffer': [(c.cpu(), t.cpu(), n) + for c, t, n in self.replay_buffer.buffer], + }, path) + return path + + def resume(self, path): + ck = torch.load(path) + self.round = ck['round'] + self.d_model = ck['d_model'] + self.d_state = ck['d_state'] + self.n_layers = ck['n_layers'] + self.history = ck['history'] + self.proposer_kind = ck.get('proposer_kind', 'value') + self.use_replay = ck.get('use_replay', True) + self.replay_ratio = ck.get('replay_ratio', 0.3) + self.replay_task_names = ck.get('replay_task_names', + list(self.train_names)) + self._init_model() + self.rln.load_state_dict(ck['rln']) + self.pln.load_state_dict(ck['pln']) + self.learner.head.load_state(ck['head']) + self.proposer.freq = ck['proposer_freq'] + for ctx, tgt, name in ck.get('replay_buffer', []): + if self.device.type != 'cpu': + ctx = ctx.to(self.device); tgt = tgt.to(self.device) + self.replay_buffer.add(ctx, tgt, name) + return self.round + + # ── 达标检测: unseen ≥ 0.115 且 c4 ≥ 0.09 且遗忘 < 0.05, 连续 2 轮 + def target_met(self, rec): + u = rec.get('unseen_mean', 0) + c4 = (rec.get('unseen_by_struct') or {}).get('c4') or 0 + fg = rec.get('forget', 0) + ok = u >= 0.115 and c4 >= 0.09 and fg < 0.05 + streak = 0 + for h in reversed(self.history[-2:]): + hu = h.get('unseen_mean', 0) + hc4 = (h.get('unseen_by_struct') or {}).get('c4') or 0 + if hu >= 0.115 and hc4 >= 0.09 and h.get('forget', 0) < 0.05: + streak += 1 + return ok and streak >= 1 + + def _after_evaluate(self, rec): + self._prop_eval_acc = {} + for k in list(self._prop_keys): + if k in self.tasks: + try: + acc = self._eval_task_acc(k, k_adapt=10) + self._prop_eval_acc[k] = acc + except Exception: + pass + if self.proposer_kind == 'causal' and self._prop_eval_acc: + for key, acc in self._prop_eval_acc.items(): + t = self.tasks.get(key) + if t and t.get('perm') is not None: + try: + self.proposer.observe(t['perm'], acc) + except Exception: + pass + for k in self._prop_keys: + if k in self.tasks and k not in self.replay_task_names: + self.replay_task_names.append(k) + for ctx, tgt in self.tasks[k].get('support', []): + self.replay_buffer.add(ctx, tgt, k) + for ctx, tgt in self.tasks[k].get('query', []): + self.replay_buffer.add(ctx, tgt, k) + self.cleanup_props() + return rec + + def _eval_task_acc(self, task_name, k_adapt=20): + t = self.tasks[task_name] + with torch.no_grad(): + m = self.learner._define(t, detach=True) + W = self.learner.adapt_graph(t['support'], K=k_adapt, m=m) + with torch.no_grad(): + acc = self.learner._metric([w.detach() for w in W], t, m=m)[1] + return acc + + # ── 主循环 ── + def run(self, rounds, with_migrate_every=2): + print("=" * 70) + print("LM1 生产级自主持续学习: %.2fM 参数, %d 轮" % ( + self._n_params() / 1e6, rounds)) + print("=" * 70) + for _ in range(rounds): + self.round += 1 + t0 = time.time() + loss, t_train = self.learn() + rec = self.evaluate(with_migrate=(self.round % with_migrate_every + == 0)) + rec['loss'] = round(loss, 4) + rec['train_s'] = round(t_train, 1) + rec['wall_s'] = round(time.time() - t0, 1) + self.history.append(rec) + self._log(rec) + self._after_evaluate(rec) + self.save('latest') + self.save('round%d' % self.round) + if self.target_met(rec): + print("\n🎯 目标达成 (round %d): unseen %.4f c4 %.4f 遗忘 %.4f" + % (self.round, rec['unseen_mean'], + (rec.get('unseen_by_struct') or {}).get('c4', 0), + rec.get('forget', 0))) + self.save('final') + self._write_report(done=True) + return self.round + self._write_report(done=False) + return self.round + + def _n_params(self): + return (sum(p.numel() for p in self.rln.parameters()) + + sum(p.numel() for p in self.pln.parameters())) + + def _log(self, rec): + line = json.dumps(rec, ensure_ascii=False) + RESULT_DIR.mkdir(exist_ok=True) + with open(RESULT_DIR / ('%s_report.jsonl' % TAG), 'a') as f: + f.write(line + '\n') + print("[R%d] loss=%.4f unseen=%.4f struct=%s seen=%.4f 遗忘=%s " + "S5迁移=%s (%.0fs)" % ( + rec['round'], rec.get('loss', 0), rec['unseen_mean'], + rec.get('unseen_by_struct'), rec.get('seen_mean'), + rec.get('forget', '-'), rec.get('migrate_s5', '-'), + rec.get('wall_s', 0)), flush=True) + + def _write_report(self, done): + lines = ["# LM1 生产级训练报告", + "", + "- 参数: %.2fM (d_model=%d, d_state=%d, n_layers=%d)" + % (self._n_params() / 1e6, self.d_model, self.d_state, + self.n_layers), + "- 数据: 自主探索生成 (价值函数提议 → 世界真值)", + "- 完成: %s" % ("✅ 达标" if done else "❌ 未达标 (轮次用尽)"), + "", + "| round | loss | unseen | c3 | c4 | dbl | seen | 遗忘 | S5迁移 |", + "|:--|--:|--:|--:|--:|--:|--:|--:|--:|"] + for h in self.history: + sm = h.get('unseen_by_struct') or {} + lines.append("| %d | %.4f | %.4f | %s | %s | %s | %.4f | %s | %s |" + % (h['round'], h.get('loss', 0), h['unseen_mean'], + sm.get('c3', '-'), sm.get('c4', '-'), + sm.get('dbl', '-'), h.get('seen_mean', 0), + h.get('forget', '-'), h.get('migrate_s5', '-'))) + RESULT_DIR.mkdir(exist_ok=True) + (RESULT_DIR / ('%s_report.md' % TAG)).write_text('\n'.join(lines), + encoding='utf-8') + print("报告: %s" % (RESULT_DIR / ('%s_report.md' % TAG))) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--rounds', type=int, default=10) + ap.add_argument('--iters-per-round', type=int, default=40) + ap.add_argument('--d-model', type=int, default=192) + ap.add_argument('--d-state', type=int, default=12) + ap.add_argument('--n-layers', type=int, default=2) + ap.add_argument('--threads', type=int, default=8, + help='CPU 线程数 (torch 小算子任务: 4-8 最优, 48 会慢 750x)') + ap.add_argument('--device', type=str, default='cpu', + help='训练设备: cpu / cuda / cuda:1 (GPU 需 cu128 torch)') + ap.add_argument('--tag', type=str, default='lm1', + help='规模前缀, 隔离 checkpoint/报告 (如 lm1_8m)') + ap.add_argument('--scale', type=str, default=None, + choices=['0.9M', '8M', '24M', '82M', '244M', '532M', '1.2B', '2B'], + help='预置规模档位 (覆盖 --d-model/--d-state/--n-layers): ' + '0.9M=d192/s12/l2, 8M=d512/s16/l4, 24M=d768/s24/l6, ' + '82M=d1024/s32/l12, 244M=d1536/s64/l16, 532M=d2048/s64/l20, ' + '1.2B=d3072/s64/l20(估), 2B=d4096/s64/l18(估). ' + '注意: >82M 档位纯 CPU 训练不现实(单步数分钟级), 2B 档需 GPU.') + ap.add_argument('--n-prop', type=int, default=2) + ap.add_argument('--proposer', type=str, default='value', + choices=['value', 'causal', 'causal_rl']) + ap.add_argument('--resume', type=str, default=None) + ap.add_argument('--migrate-every', type=int, default=2) + ap.add_argument('--replay', action='store_true', default=True, + help='启用 replay buffer (默认开启)') + ap.add_argument('--no-replay', dest='replay', action='store_false', + help='关闭 replay buffer') + ap.add_argument('--replay-ratio', type=float, default=0.3, + help='每轮 replay 任务占比 (0~1)') + ap.add_argument('--causal-rl-lr', type=float, default=0.3, + help='CausalRLAgent 学习率 (仅 proposer=causal_rl 时生效)') + ap.add_argument('--outer-lr', type=float, default=1e-3, + help='外循环 Adam 学习率 (大模型需调低: 8M→3e-4, 24M→1e-4)') + ap.add_argument('--seed', type=int, default=42, + help='随机种子 (数据+模型同源; 多 seed 验证用 42/2026/7 等)') + args = ap.parse_args() + + # 规模前缀 (隔离 checkpoint/报告文件) + global TAG + TAG = args.tag + + # CPU 线程数 (torch 小算子任务的性能关键: 48 线程反而慢 750x) + if args.threads > 0: + torch.set_num_threads(args.threads) + + # 预置规模档位 (覆盖手动 d_model/d_state/n_layers) + SCALES = { + '0.9M': (192, 12, 2), + '8M': (512, 16, 4), + '24M': (768, 24, 6), + '82M': (1024, 32, 12), + '244M': (1536, 64, 16), + '532M': (2048, 64, 20), + '1.2B': (3072, 64, 20), + '2B': (4096, 64, 18), + } + dm, ds, nl = args.d_model, args.d_state, args.n_layers + if args.scale: + dm, ds, nl = SCALES[args.scale] + print("[scale] %s → d_model=%d, d_state=%d, n_layers=%d" % + (args.scale, dm, ds, nl)) + + sys = LM1System(d_model=dm, d_state=ds, + n_layers=nl, iters_per_round=args.iters_per_round, + n_prop=args.n_prop, proposer=args.proposer, + use_replay=args.replay, replay_ratio=args.replay_ratio, + causal_rl_lr=args.causal_rl_lr, device=args.device, + outer_lr=args.outer_lr, seed=args.seed) + if args.resume: + r0 = sys.resume(args.resume) + print("恢复自 %s (round %d)" % (args.resume, r0)) + done_round = sys.run(args.rounds, with_migrate_every=args.migrate_every) + print("训练完成: round %d, 模型在 checkpoints/lm1_final.pt" + % done_round) + + +if __name__ == '__main__': + main() diff --git a/tests/run_lm2_text.py b/tests/run_lm2_text.py new file mode 100644 index 0000000000000000000000000000000000000000..c788c99725b5bf97bd8967d657b29175b3aabdbb --- /dev/null +++ b/tests/run_lm2_text.py @@ -0,0 +1,195 @@ +"""LM2 docstring""" +import os, sys, time, json, math, random, argparse +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from run_v31_meta_learning import V31_RLN +DATA = Path("/tmp/lm2data") +CKPT_DIR = ROOT / "checkpoints" +RESULT_DIR = ROOT / "results" +CHUNK = 256 + +class CharTokenizer: + """char tokenizer from corpus""" + def __init__(self, texts): + chars = set(''.join(texts[:200])) + self.chars = sorted(chars) + self.stoi = {c: i for i, c in enumerate(self.chars)} + self.itos = {i: c for i, c in enumerate(self.chars)} + self.vocab = len(self.chars) + print("vocab: %d chars" % self.vocab) + + def encode(self, s): + return [self.stoi[c] for c in s if c in self.stoi] + + +class LM2System: + def __init__(self, d_model=192, d_state=12, n_layers=2, + steps_per_domain=500, seed=42): + self.d_model = d_model; self.d_state = d_state + self.n_layers = n_layers; self.steps_per_domain = steps_per_domain + self.seed = seed; self.round = 0; self.history = [] + raw = {d: open(DATA / (d + ".txt")).read() + for d in ["train", "validation", "test"]} + self.domains = {"A": raw["train"][:2000000], + "B": raw["validation"], "C": raw["test"]} + self.tok = CharTokenizer(list(self.domains.values())) + self._build_model() + def _build_model(self): + random.seed(self.seed); torch.manual_seed(self.seed) + self.rln = V31_RLN(self.tok.vocab + 2, d_model=self.d_model, + d_state=self.d_state, n_layers=self.n_layers) + self.lm_head = nn.Linear(self.d_model, self.tok.vocab) + self.opt = torch.optim.Adam( + list(self.rln.parameters()) + list(self.lm_head.parameters()), + lr=1e-3) + self._n_params = (sum(p.numel() for p in self.rln.parameters()) + + sum(p.numel() for p in self.lm_head.parameters())) + + def _chunks(self, text, rng): + ids = self.tok.encode(text) + n = len(ids) - CHUNK - 1 + if n <= 0: return [] + idxs = list(range(0, n, CHUNK // 2)) + rng.shuffle(idxs) + for i in idxs: + x = ids[i:i + CHUNK]; y = ids[i + 1:i + CHUNK + 1] + if len(x) == CHUNK and len(y) == CHUNK: + yield torch.tensor(x), torch.tensor(y) + + def _eval_chunks(self, text, n=100): + ids = self.tok.encode(text) + n_avail = len(ids) - CHUNK - 1 + if n_avail <= 0: return [] + rng = random.Random(0) + idxs = rng.sample(range(0, n_avail, max(1, n_avail // (n * 2))), + min(n, n_avail // 2 + 1)) + for i in idxs: + x = ids[i:i + CHUNK]; y = ids[i + 1:i + CHUNK + 1] + if len(x) == CHUNK and len(y) == CHUNK: + yield torch.tensor(x), torch.tensor(y) + def train_domain(self, name, steps=None): + steps = steps or self.steps_per_domain + text = self.domains[name] + rng = random.Random(self.seed + self.round * 7) + it = self._chunks(text, rng) + t0 = time.time(); losses = [] + for s in range(steps): + try: + x, y = next(it) + except StopIteration: + it = self._chunks(text, rng); x, y = next(it) + self.rln.train(); self.opt.zero_grad() + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + loss.backward(); self.opt.step() + losses.append(loss.item()) + return sum(losses) / len(losses), time.time() - t0 + + def evaluate_domain(self, name): + self.rln.eval() + total_ce = total_acc = n = 0 + with torch.no_grad(): + for x, y in self._eval_chunks(self.domains[name]): + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + acc = (logits.argmax(-1) == y).float().mean().item() + total_ce += loss.item(); total_acc += acc; n += 1 + return round(total_ce / n, 4), round(total_acc / n, 4) + def save(self, tag): + CKPT_DIR.mkdir(exist_ok=True) + path = CKPT_DIR / ("lm2_" + tag + ".pt") + torch.save({"round": self.round, "d_model": self.d_model, + "d_state": self.d_state, "n_layers": self.n_layers, + "vocab": self.tok.vocab, + "rln": self.rln.state_dict(), + "lm_head": self.lm_head.state_dict(), + "history": self.history}, path) + return path + + def resume(self, path): + ck = torch.load(path) + self.round = ck["round"]; self.history = ck["history"] + self.rln.load_state_dict(ck["rln"]) + self.lm_head.load_state_dict(ck["lm_head"]) + return self.round + def run(self, domains=("A", "B", "C")): + print("=" * 70) + print("LM2 real-text continual learning: %.2fM params, domains %s" % ( + self._n_params / 1e6, list(domains))) + print("=" * 70) + for name in domains: + self.round += 1 + loss, t = self.train_domain(name) + row = {'domain': name, 'loss': round(loss, 4), 'train_s': round(t)} + ev = {} + for d in domains: + ce, acc = self.evaluate_domain(d) + ev[d] = {'ce': ce, 'acc': acc} + row['eval'] = ev + self.history.append(row) + print("[%s] loss=%.4f | A(%.4f/%.4f) B(%.4f/%.4f) C(%.4f/%.4f) (%.0fs)" + % (name, loss, ev["A"]["ce"], ev["A"]["acc"], + ev["B"]["ce"], ev["B"]["acc"], + ev["C"]["ce"], ev["C"]["acc"], t), flush=True) + self.save("latest") + self._write_report() + return self.round + def _write_report(self): + lines = ["# LM2 real-text continual learning report (wikitext-2 3 domains)", + "", + "- model: %.2fM (d=%d, s=%d, layers=%d), char vocab=%d" + % (self._n_params / 1e6, self.d_model, self.d_state, + self.n_layers, self.tok.vocab), + "- protocol: domain-sequential streaming -> eval all domains", + "", + "## forget matrix (row=trained domain, col=eval domain)", + "", + "| trained | A ce/acc | B ce/acc | C ce/acc |", + "|:--|:--|:--|:--|"] + for row in self.history: + ev = row['eval'] + lines.append("| %s | %.4f/%.4f | %.4f/%.4f | %.4f/%.4f |" % ( + row['domain'], ev['A']['ce'], ev['A']['acc'], + ev['B']['ce'], ev['B']['acc'], + ev['C']['ce'], ev['C']['acc'])) + lines.append("") + lines.append("## forgetting (self-acc after train -> final)") + first = {} + for row in self.history: + d = row['domain']; first[d] = row['eval'][d]['acc'] + last = self.history[-1]['eval'] + for d in first: + forget = round(first[d] - last[d]["acc"], 4) + lines.append("- %s: %.4f -> %.4f, forget %.4f" % ( + d, first[d], last[d]["acc"], forget)) + RESULT_DIR.mkdir(exist_ok=True) + (RESULT_DIR / 'lm2_report.md').write_text('\n'.join(lines), + encoding='utf-8') + print('report: %s' % (RESULT_DIR / 'lm2_report.md')) + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--d-model', type=int, default=192) + ap.add_argument('--d-state', type=int, default=12) + ap.add_argument('--n-layers', type=int, default=2) + ap.add_argument('--steps-per-domain', type=int, default=500) + ap.add_argument('--resume', type=str, default=None) + args = ap.parse_args() + sys = LM2System(d_model=args.d_model, d_state=args.d_state, + n_layers=args.n_layers, + steps_per_domain=args.steps_per_domain) + if args.resume: + sys.resume(args.resume) + print("resumed from %s (round %d)" % (args.resume, sys.round)) + done = sys.run() + print("done: %d domains" % done) + + +if __name__ == '__main__': + main() diff --git a/tests/run_lm3_bpe.py b/tests/run_lm3_bpe.py new file mode 100644 index 0000000000000000000000000000000000000000..db02ad169f3880f4f6a60d19d04d4687299a236d --- /dev/null +++ b/tests/run_lm3_bpe.py @@ -0,0 +1,399 @@ +"""LM3 - BPE + cross-domain continual learning + iid baseline +Data: en(wikitext-2) -> zh(chinese novel) -> code(python)""" +import os, sys, time, json, math, random, argparse, collections +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from run_v31_meta_learning import V31_RLN + +DATA = Path("/tmp/lm3data") +CKPT_DIR = ROOT / "checkpoints" +RESULT_DIR = ROOT / "results" +CHUNK = 256 + + +class BPE: + """char-level BPE: base = all unique chars, merge top pairs.""" + def __init__(self): + self.base = {} + self.merges = {} + + def train(self, texts, num_merges=1000, sample=300000): + rng = random.Random(42) + concat = "" + for t in texts: + concat += t[:sample] if len(t) > sample else t + chars = sorted(set(concat)) + self.base = {c: i for i, c in enumerate(chars)} + nxt = len(chars) + ids = [self.base[c] for c in concat] + for m in range(num_merges): + pairs = collections.Counter(zip(ids, ids[1:])) + if not pairs: + break + (a, b), _ = pairs.most_common(1)[0] + self.merges[(a, b)] = nxt + nxt += 1 + new_ids = [] + i = 0 + while i < len(ids): + if (i < len(ids) - 1 and (ids[i], ids[i+1]) in self.merges): + new_ids.append(self.merges[(ids[i], ids[i+1])]) + i += 2 + else: + new_ids.append(ids[i]); i += 1 + ids = new_ids + self.vocab = nxt + print("BPE vocab: %d (base %d + %d merges)" % (nxt, len(chars), num_merges)) + + def encode(self, s): + ids = [self.base[c] for c in s if c in self.base] + if not ids: + return [] + # 线性左→右归并: head 与后续 token 合并后不前进 (等价于原 del 版, O(n)) + head = ids[0] + out = [] + for nxt in ids[1:]: + k = (head, nxt) + if k in self.merges: + head = self.merges[k] + else: + out.append(head) + head = nxt + out.append(head) + return out + +class LM3System: + def __init__(self, d_model=192, d_state=12, n_layers=2, + steps_per_domain=300, mode="sequential", seed=42, + method="base", bpe_cache=None): + self.d_model = d_model; self.d_state = d_state + self.n_layers = n_layers; self.steps_per_domain = steps_per_domain + self.mode = mode; self.method = method; self.seed = seed + self.stage = 0; self.history = [] + # domains: en(2M) zh(2.5M) code(2M) + raw_en = open(DATA / "train.txt").read()[:2000000] + raw_zh = open(DATA / "zh.txt").read()[:2500000] + raw_code = open(DATA / "code.txt").read()[:2000000] + self.domains = {"en": raw_en, "zh": raw_zh, "code": raw_code} + self.bpe = BPE() + if bpe_cache and os.path.exists(bpe_cache): + import json as _json + ck = _json.load(open(bpe_cache)) + self.bpe.base = ck['base'] + self.bpe.merges = { + tuple(int(x) for x in k.split('_')): v + for k, v in ck['merges'].items()} + self.bpe.vocab = ck['vocab'] + print("BPE loaded from cache: vocab %d" % self.bpe.vocab) + else: + self.bpe.train(list(self.domains.values()), num_merges=800) + if bpe_cache: + import json as _json + ck = {'base': self.bpe.base, + 'merges': {"%d_%d" % k: v + for k, v in self.bpe.merges.items()}, + 'vocab': self.bpe.vocab} + _json.dump(ck, open(bpe_cache, 'w')) + print("BPE cached to %s" % bpe_cache) + # 预编码: 每个域只 encode 一次 (BPE 是主要开销) + self.domain_ids = {d: self.bpe.encode(t) + for d, t in self.domains.items()} + self._build_model() + + def _build_model(self): + random.seed(self.seed); torch.manual_seed(self.seed) + self.rln = V31_RLN(self.bpe.vocab + 2, d_model=self.d_model, + d_state=self.d_state, n_layers=self.n_layers) + self.lm_head = nn.Linear(self.d_model, self.bpe.vocab) + self.opt = torch.optim.Adam( + list(self.rln.parameters()) + list(self.lm_head.parameters()), + lr=1e-3) + # OML 双层: 快权重 (head, 高 lr) + 慢权重 (rln, 低 lr) + self.opt_outer = torch.optim.Adam(self.rln.parameters(), lr=1e-4) + self.opt_inner = torch.optim.Adam(self.lm_head.parameters(), lr=1e-2) + self._n_params = (sum(p.numel() for p in self.rln.parameters()) + + sum(p.numel() for p in self.lm_head.parameters())) + # replay buffer: {domain: [chunks]} + self.replay = {} + self.replay_size = 60 + + def _chunks(self, dname, rng): + ids = self.domain_ids[dname] + n = len(ids) - CHUNK - 1 + if n <= 0: return [] + idxs = list(range(0, n, CHUNK // 2)) + rng.shuffle(idxs) + for i in idxs: + x = ids[i:i + CHUNK]; y = ids[i + 1:i + CHUNK + 1] + if len(x) == CHUNK and len(y) == CHUNK: + yield torch.tensor(x), torch.tensor(y) + + def _eval_chunks(self, dname, n=100): + ids = self.domain_ids[dname] + n_avail = len(ids) - CHUNK - 1 + if n_avail <= 0: return [] + rng = random.Random(0) + idxs = rng.sample(range(0, n_avail, max(1, n_avail // (n * 2))), + min(n, n_avail // 2 + 1)) + for i in idxs: + x = ids[i:i + CHUNK]; y = ids[i + 1:i + CHUNK + 1] + if len(x) == CHUNK and len(y) == CHUNK: + yield torch.tensor(x), torch.tensor(y) + + def _stream(self, steps, rng): + """yield (domain_name, x, y) for training steps.""" + if self.mode == "iid": + # mixed: all domains, shuffled chunks + iters = {} + for d in self.domains: + iters[d] = self._chunks(d, rng) + for _ in range(steps): + d = rng.choice(list(self.domains)) + try: + x, y = next(iters[d]) + except StopIteration: + iters[d] = self._chunks(d, rng) + x, y = next(iters[d]) + yield d, x, y + else: + # sequential: current domain only + d = self.stage_domain + it = self._chunks(d, rng) + for _ in range(steps): + try: + x, y = next(it) + except StopIteration: + it = self._chunks(d, rng); x, y = next(it) + yield self.stage_domain, x, y + + def train_stage(self, steps=None): + steps = steps or self.steps_per_domain + rng = random.Random(self.seed + self.stage * 7) + t0 = time.time(); losses = [] + qit = None + for s, (d, x, y) in enumerate(self._stream(steps, rng)): + self.rln.train() + if self.method == "oml": + self.opt_inner.zero_grad() + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + loss.backward(); self.opt_inner.step() + # 外循环: 低频慢更新表示 (opt_outer 只含 rln 参数) + if s % 10 == 0: + self.opt_outer.zero_grad() + h2 = self.rln(x.unsqueeze(0))[0] + logits2 = self.lm_head(h2[-CHUNK:]) + loss2 = F.cross_entropy(logits2, y) + loss2.backward(); self.opt_outer.step() + elif self.method == "oml2": + # 正确 OML: per-domain meta-step + # 内循环: 克隆 head, 在 support chunk 上适应 K 步 + # 外循环: 用 fast head 在 query chunk 上的 loss 更新全模型 + import copy + support_x, support_y = x, y + qd = d if self.mode == "iid" else self.stage_domain + if qit is None: + qit = self._chunks(qd, rng) + try: + query_x, query_y = next(qit) + except StopIteration: + qit = self._chunks(qd, rng) + query_x, query_y = next(qit) + # 快照 head (state_dict 拷贝, 快于 deepcopy) + fast_state = {k: v.detach().clone() + for k, v in self.lm_head.state_dict().items()} + with torch.no_grad(): + h_f = self.rln(support_x.unsqueeze(0))[0] + h_fd = h_f[-CHUNK:].detach() + # 内循环: 手动 SGD (K=2), 只动 head 快照 + for _k in range(2): + w = fast_state['weight'].detach().requires_grad_(True) + b = fast_state['bias'].detach().requires_grad_(True) + logits = h_fd @ w.t() + b + l_f = F.cross_entropy(logits, support_y) + g_w, g_b = torch.autograd.grad(l_f, (w, b)) + with torch.no_grad(): + fast_state['weight'] = w - 0.1 * g_w + fast_state['bias'] = b - 0.1 * g_b + # 外循环: fast head 在 query 上的 meta-loss + self.opt.zero_grad() + h_q = self.rln(query_x.unsqueeze(0))[0] + logits_q = h_q[-CHUNK:] @ fast_state['weight'].t() + fast_state['bias'] + l_q = F.cross_entropy(logits_q, query_y) + l_q.backward() + self.opt.step() + # 周期性合并 fast head 回原 head (OML 权重整合) + if s % 10 == 0: + self.lm_head.load_state_dict( + {k: v.detach().clone() + for k, v in fast_state.items()}) + loss = l_q.item() + # OML 双循环 + replay 混入 (组合实验) + self.opt_inner.zero_grad() + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + for rd, chunk_list in self.replay.items(): + for rx, ry in rng.sample( + chunk_list, min(2, len(chunk_list))): + loss = loss + 0.5 * F.cross_entropy( + self.lm_head( + self.rln(rx.unsqueeze(0))[0][-CHUNK:]), + ry) + loss.backward(); self.opt_inner.step() + if s % 10 == 0: + self.opt_outer.zero_grad() + h2 = self.rln(x.unsqueeze(0))[0] + logits2 = self.lm_head(h2[-CHUNK:]) + loss2 = F.cross_entropy(logits2, y) + loss2.backward(); self.opt_outer.step() + elif self.method == "replay": + self.opt.zero_grad() + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + # 混入 replay buffer (旧域样本, 每域抽样 2 条) + for rd, chunk_list in self.replay.items(): + for rx, ry in rng.sample( + chunk_list, min(2, len(chunk_list))): + loss = loss + 0.5 * F.cross_entropy( + self.lm_head( + self.rln(rx.unsqueeze(0))[0][-CHUNK:]), + ry) + loss.backward(); self.opt.step() + else: + self.opt.zero_grad() + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + loss.backward(); self.opt.step() + losses.append(loss.item()) + return sum(losses) / len(losses), time.time() - t0 + + def evaluate_domain(self, name): + self.rln.eval() + total_ce = total_acc = n = 0 + with torch.no_grad(): + for x, y in self._eval_chunks(name): + h = self.rln(x.unsqueeze(0))[0] + logits = self.lm_head(h[-CHUNK:]) + loss = F.cross_entropy(logits, y) + acc = (logits.argmax(-1) == y).float().mean().item() + total_ce += loss.item(); total_acc += acc; n += 1 + return round(total_ce / n, 4), round(total_acc / n, 4) + + def save(self, tag): + CKPT_DIR.mkdir(exist_ok=True) + path = CKPT_DIR / ("lm3_%s_%s_%s.pt" % (self.method, self.mode, tag)) + torch.save({"stage": self.stage, "mode": self.mode, + "d_model": self.d_model, "d_state": self.d_state, + "n_layers": self.n_layers, "vocab": self.bpe.vocab, + "merges": self.bpe.merges, "base": self.bpe.base, + "rln": self.rln.state_dict(), + "lm_head": self.lm_head.state_dict(), + "history": self.history}, path) + return path + + def resume(self, path): + ck = torch.load(path) + self.stage = ck["stage"]; self.mode = ck["mode"] + self.history = ck["history"] + self.bpe.merges = ck["merges"]; self.bpe.base = ck["base"] + self.bpe.vocab = ck["vocab"] + self.rln.load_state_dict(ck["rln"]) + self.lm_head.load_state_dict(ck["lm_head"]) + return self.stage + + def run(self, domains=("en", "zh", "code")): + print("=" * 70) + print("LM3 BPE cross-domain %s: %.2fM params" % (self.mode, self._n_params / 1e6)) + print("domains: %s" % list(domains)) + print("=" * 70) + n_stages = 1 if self.mode == "iid" else len(domains) + for s in range(n_stages): + self.stage += 1 + self.stage_domain = domains[s] if self.mode == "sequential" else None + loss, t = self.train_stage() + if self.method == "replay" and self.mode == "sequential": + # 存当前域代表性 chunks 进 buffer + rng2 = random.Random(self.stage) + buf = [] + it = self._chunks(self.stage_domain, rng2) + for _ in range(self.replay_size): + try: + x, y = next(it) + except StopIteration: + break + buf.append((x, y)) + self.replay[self.stage_domain] = buf + row = {"stage": self.stage, "loss": round(loss, 4), "train_s": round(t)} + ev = {} + for d in domains: + ce, acc = self.evaluate_domain(d) + ev[d] = {"ce": ce, "acc": acc} + row["eval"] = ev + self.history.append(row) + print("[%s] loss=%.4f | en(%.4f/%.4f) zh(%.4f/%.4f) code(%.4f/%.4f) (%.0fs)" + % (self.stage_domain or "mixed", loss, + ev["en"]["ce"], ev["en"]["acc"], + ev["zh"]["ce"], ev["zh"]["acc"], + ev["code"]["ce"], ev["code"]["acc"], t), flush=True) + self.save("latest") + self._write_report(domains) + return self.stage + + def _write_report(self, domains): + lines = ["# LM3 BPE cross-domain report (%s / %s)" % (self.method, self.mode), "", + "- model: %.2fM (d=%d, s=%d, layers=%d), BPE vocab=%d" + % (self._n_params / 1e6, self.d_model, self.d_state, + self.n_layers, self.bpe.vocab), + "- domains: en(wikitext-2 2M) -> zh(chinese novel 2.5M) -> code(python 2M)", + "- mode: %s" % self.mode, "", + "| stage | loss | en ce/acc | zh ce/acc | code ce/acc |", + "|:--|--:|:--|:--|:--|"] + for row in self.history: + ev = row["eval"] + lines.append("| %s | %.4f | %.4f/%.4f | %.4f/%.4f | %.4f/%.4f |" % ( + row["stage"], row["loss"], + ev["en"]["ce"], ev["en"]["acc"], + ev["zh"]["ce"], ev["zh"]["acc"], + ev["code"]["ce"], ev["code"]["acc"])) + lines.append("") + lines.append("## forgetting (en self-acc: first -> last)") + first_en = self.history[0]["eval"]["en"]["acc"] + last_en = self.history[-1]["eval"]["en"]["acc"] + lines.append("- en: %.4f -> %.4f, forget %.4f" % ( + first_en, last_en, round(first_en - last_en, 4))) + RESULT_DIR.mkdir(exist_ok=True) + (RESULT_DIR / ("lm3_%s_%s_report.md" % (self.method, self.mode))).write_text( + "\n".join(lines), encoding="utf-8") + print("report: %s" % (RESULT_DIR / ("lm3_%s_%s_report.md" % (self.method, self.mode)))) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--d-model", type=int, default=192) + ap.add_argument("--d-state", type=int, default=12) + ap.add_argument("--n-layers", type=int, default=2) + ap.add_argument("--steps-per-domain", type=int, default=300) + ap.add_argument("--mode", default="sequential", choices=["sequential", "iid"]) + ap.add_argument("--method", default="base", + choices=["base", "replay", "oml", "combo", "oml2"]) + ap.add_argument("--bpe-cache", type=str, default=None) + args = ap.parse_args() + sys = LM3System(d_model=args.d_model, d_state=args.d_state, + n_layers=args.n_layers, + steps_per_domain=args.steps_per_domain, + mode=args.mode, method=args.method, + bpe_cache=args.bpe_cache) + done = sys.run() + print("done: %s, %d stages" % (args.mode, done)) + + +if __name__ == '__main__': + main() diff --git a/tests/run_lm4_overnight.sh b/tests/run_lm4_overnight.sh new file mode 100644 index 0000000000000000000000000000000000000000..bc892c20e8cddc122c9e9b858d8b881ccbec98f2 --- /dev/null +++ b/tests/run_lm4_overnight.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# LM4 夜间批量队列 (不重叠窗口 stride=32, 修正 train/test 泄漏) +# 用法: bash run_lm4_overnight.sh +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +QUEUE="${1:-seeds}" + +mkdir -p results +echo "[$(date +%F_%H:%M)] QUEUE=$QUEUE 等待当前任务结束..." >> results/overnight.log + +# 等当前 run_lm4_wave 进程结束 (避免抢 GPU; 最长等 40 分钟) +for _ in $(seq 1 40); do + pgrep -f "run_lm4_wave[.]py" >/dev/null || break + sleep 60 +done + +run() { # $1=gpu $2=tag 其余=参数 + local gpu="$1" tag="$2"; shift 2 + mkdir -p "results/$tag" + echo "[$(date +%F_%H:%M)] START $tag (GPU$gpu)" >> results/overnight.log + CUDA_VISIBLE_DEVICES="$gpu" "$PY" -u tests/run_lm4_wave.py \ + --device cuda --stride 32 --epochs-per-domain 1000 --joint-steps 0 \ + --out "results/$tag" "$@" > "results/$tag.log" 2>&1 + local rc=$? + echo "[$(date +%F_%H:%M)] DONE $tag (rc=$rc)" >> results/overnight.log +} + +if [ "$QUEUE" = "seeds" ]; then + # GPU0: 多 seed 验证 (主配置 ratio 1.0)。s42/s2026 已在跑, 这里补 14 个。 + for s in 7 123 999 1 2 3 11 13 17 19 23 29 37 41; do + run 0 "lm4c_s$s" --replay-ratio 1.0 --seed "$s" + done +elif [ "$QUEUE" = "ablation" ]; then + # GPU1: 固定 lr=1e-3 下的 ratio 消融, 每个 ratio 3 个 seed + for r in 0.3 0.5 2.0; do + for s in 42 7 2026; do + tag="lm4c_rr${r}_s${s}" + run 1 "$tag" --replay-ratio "$r" --seed "$s" + done + done +fi + +echo "[$(date +%F_%H:%M)] QUEUE=$QUEUE 全部完成" >> results/overnight.log diff --git a/tests/run_lm4_overnight2.sh b/tests/run_lm4_overnight2.sh new file mode 100644 index 0000000000000000000000000000000000000000..3f9979abd2bca76f7617b4028ac14061df7f11ee --- /dev/null +++ b/tests/run_lm4_overnight2.sh @@ -0,0 +1,18 @@ +#!/bin/bash +# LM4 队列 2 — 给最优配置 ratio=2.0 补 seed (n=3 → 13) +# 背景: ratio 曲线 0.3/0.5/1.0/2.0 = 0.2501/0.3512/0.3857/0.3955, +# 2.0 最好但只有 3 个 seed, 需要加固统计。 +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python + +for s in 1 2 3 11 13 17 19 23 29 37; do + tag="lm4c_rr2.0_s$s" + mkdir -p "results/$tag" + echo "[$(date +%F_%H:%M)] START $tag" >> results/overnight2.log + CUDA_VISIBLE_DEVICES=0 "$PY" -u tests/run_lm4_wave.py --device cuda --stride 32 \ + --epochs-per-domain 1000 --joint-steps 0 --replay-ratio 2.0 --seed "$s" \ + --out "results/$tag" > "results/$tag.log" 2>&1 + echo "[$(date +%F_%H:%M)] DONE $tag (rc=$?)" >> results/overnight2.log +done +echo "[$(date +%F_%H:%M)] QUEUE2 全部完成" >> results/overnight2.log diff --git a/tests/run_lm4_wave.py b/tests/run_lm4_wave.py new file mode 100644 index 0000000000000000000000000000000000000000..5991ac19893c76edc3aedda9317dd8366f235519 --- /dev/null +++ b/tests/run_lm4_wave.py @@ -0,0 +1,1773 @@ +#!/usr/bin/env python3 +"""LM4 — 持续学习的电磁波模型 (Van Allen Probes RBSP-A) + +数据: NASA CDAWeb EMFISIS + - DENSITY L4: density [cm^-3] → 物理状态标签(域) + - MAG L3: Magnitude/rms/lambda/delta → 独立电磁波特征 + (刻意排除 fpe/fuh/wpe_over_wce: 与 density 有解析关系 = 标签泄漏) + +任务: 从电磁波/磁场特征序列推断等离子体物理状态(密度域) +持续学习: 顺序学习 6 个密度域(D1→D6), 测遗忘 +对比: naive sequential vs +Replay + +用法: + python3 tests/run_lm4_wave.py --data data/wave --epochs-per-domain 300 +""" +import os, sys, json, math, time, random, argparse +from pathlib import Path +import numpy as np + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data +from hibs_lnn.ssm_v30_3 import SSM_Layer_V30_3 +from hibs_lnn.pln_head import PLNHead, inner_adapt, oml_step + +WINDOW = 32 +N_FEAT = 4 +EPS = 1e-9 +MAG_SENTINEL_LO = -1e4 # MAG L3 用 -100000 标记无效采样 (实测占 0.2%) + # 注意: 旧版用 np.abs() 会把哨兵伪装成 log10(1e5)=5.0 混进特征 + + +# ============================================================ +# 模型: 连续输入 SSM + 域分类头 +# ============================================================ +def window_agg(x): + """(B,L,F) → (B,5F) 窗口聚合: mean/std/last/first/斜率。 + + 非序列基线 (MLP on 150 维聚合) 实测 joint 0.8045, 而旧 WaveSSM (只用末时刻 + 隐状态) 只有 0.7213 —— 差距来自 SSM 必须从零学时间池化。""" + return torch.cat([x.mean(1), x.std(1), x[:, -1, :], x[:, 0, :], + x[:, -1, :] - x[:, 0, :]], dim=-1) + + +class WaveSSM(nn.Module): + """连续特征序列 → SSM → 物理状态(密度域)分类。 + + pool: 时序池化 + 'last' — 只用末时刻隐状态 (旧行为, 实测打不过聚合基线) + 'cat' — [末时刻, 均值, 最大] 拼接 (默认) + agg_path: 把原始窗口统计量直接拼进分类头 —— 给模型一条"聚合直通车", + 不必从零学池化 (与 MLP 基线对齐, 同时保留 SSM 的时序建模) + """ + + def __init__(self, n_feat=N_FEAT, d_model=128, d_state=8, n_layers=2, + n_classes=6, pool="cat", agg_path=False, head_type="linear", + pln_d=64, inner_lr=0.1, stat_input=False): + super().__init__() + self.pool = pool + self.agg_path = agg_path + self.head_type = head_type + self.stat_input = stat_input + # stat_input: 把窗口统计(5*n_feat)拼到**每个时刻的输入**上。 + # 动机: 探针显示 SSM 的序列编码低于简单统计基线 0.065-0.296 + # (docs/lm4_ssm_bottleneck_verdict.md), 而 --agg-path (只拼到头上) + # 实测更差 (0.6525 vs 0.7213) —— 说明统计量必须进**递归**才能被用上。 + n_in = n_feat + 5 * n_feat if stat_input else n_feat + self.in_proj = nn.Linear(n_in, d_model) + self.layers = nn.ModuleList([ + SSM_Layer_V30_3(d_model, d_state, layer_idx=i, ent_mode='none') + for i in range(n_layers)]) + self.norm = nn.LayerNorm(d_model) + d_head = d_model * (3 if pool == "cat" else 1) + if agg_path: + d_head += 5 * n_feat + self.d_head = d_head + if head_type == "pln": + # lm1 架构: 用 PLN (小 MLP + per-feature 步长) 取代线性头 + self.head = PLNHead(d_head, pln_d, n_classes, inner_lr=inner_lr) + else: + self.head = nn.Linear(d_head, n_classes) + + def encode(self, x, agg=None): + """x: (B, L, n_feat) → 表示 s (B, d_head)。双循环里内/外循环都复用这个 s。""" + if self.stat_input: + st = window_agg(x) # (B, 5*n_feat) + x = torch.cat([x, st.unsqueeze(1).expand(-1, x.shape[1], -1)], dim=-1) + h = self.in_proj(x) + for layer in self.layers: + h, _ = layer(h) + h = self.norm(h) + if self.pool == "last": + s = h[:, -1] + elif self.pool == "mean": + s = h.mean(1) + elif self.pool == "max": + s = h.max(1).values + else: + s = torch.cat([h[:, -1], h.mean(1), h.max(1).values], dim=-1) + if self.agg_path: + s = torch.cat([s, window_agg(x) if agg is None else agg], dim=-1) + return s + + def forward(self, x, agg=None): + """x: (B, L, n_feat) → logits (B, n_classes)""" + return self.head(self.encode(x, agg)) + + +class StatMLP(nn.Module): + """窗口统计 + MLP backbone (替代 SSM)。 + + 依据: GPU 探针 (docs/lm4_ssm_bottleneck_verdict.md) 显示 + 域数 MLP聚合基线 SSM joint 架构损失 + 3 0.9159 0.8510 +0.065 + 6 0.8065 0.7213 +0.085 + 12 0.6638 0.3674 +0.296 + SSM 在**每个**粒度上都低于这个"不做序列建模"的基线, 且差距随粒度扩大。 + 所以换掉它: 更快 (无递归)、更强、且同样的 CL 机制 (PLN/SwiftTD 头) 照常可用。 + + 接口与 WaveSSM 对齐 (encode / forward), 这样 run_experiment 不用改分支。 + """ + + def __init__(self, n_feat=N_FEAT, n_classes=6, hidden=512, n_layers=3, + head_type="linear", pln_d=64, inner_lr=0.1, dropout=0.0, + stat_input=True, norm="batchnorm", **kw): + super().__init__() + self.n_feat = n_feat + self.head_type = head_type + self.norm_mode = norm + # 聚合特征: mean/std/last/first/斜率 = 5*n_feat + d_in = 5 * n_feat + # ★ 必须标准化。探针 (probe_ceiling.py) 对聚合特征做了 (x-mu)/sd, 实测 + # 6 域 0.8065; 不做标准化的 StatMLP 只有 0.6558 —— 差 0.15 全在这一点。 + # ★ BN 有已知的持续学习陷阱: running stats 随域漂移 -> 旧域被错误归一化 + # -> 灾难性遗忘。fixed 模式用冻结的全局 mu/sd 来验证这一点。 + if norm == "batchnorm": + nml = nn.BatchNorm1d(d_in) + elif norm == "fixed": + nml = nn.BatchNorm1d(d_in) + else: + nml = nn.Identity() + self.norm_layer = nml + mods = [nml, nn.Linear(d_in, hidden), nn.GELU()] + if dropout > 0: + mods.append(nn.Dropout(dropout)) + for _ in range(n_layers - 1): + mods += [nn.Linear(hidden, hidden), nn.GELU()] + if dropout > 0: + mods.append(nn.Dropout(dropout)) + self.body = nn.Sequential(*mods) + self.d_head = hidden + self.d_wave = hidden + if head_type == "swifttd": + from hibs_lnn.pln_head import SwiftTDAdapter + self.head = SwiftTDAdapter(hidden, n_classes) + elif head_type == "pln": + from hibs_lnn.pln_head import PLNHead + self.head = PLNHead(hidden, pln_d, n_classes, inner_lr=inner_lr) + else: + self.head = nn.Linear(hidden, n_classes) + + def set_fixed_stats(self, agg): + """fixed 模式: 用整个训练集的 mu/sd 填 BN running stats 并冻结。""" + if self.norm_mode != "fixed": + return + with torch.no_grad(): + mu = agg.mean(0) + sd = agg.std(0).clamp(min=1e-6) + self.norm_layer.running_mean.copy_(mu) + self.norm_layer.running_var.copy_(sd ** 2) + self.norm_layer.num_batches_tracked.fill_(1) + self.norm_layer.eval() + + def train(self, mode=True): + super().train(mode) + if getattr(self, "norm_mode", None) == "fixed": + self.norm_layer.eval() # 永远 eval, 统计量不漂移 + return self + + def encode(self, x, agg=None): + """x: (B, L, F) -> (B, hidden)。agg 参数仅为接口兼容。""" + return self.body(window_agg(x)) + + def forward(self, x, agg=None): + return self.head(self.encode(x, agg)) + + +# ============================================================ +# 数据 +# ============================================================ +def _parse_time(s): + """'2015-01-02T00:00:00.795946000Z' → epoch 秒""" + from datetime import datetime, timezone + s = s.rstrip("Z") + if "." in s: + head, frac = s.split(".") + frac = (frac + "000000")[:6] + s = f"{head}.{frac}" + else: + s = s + ".000000" + return datetime.strptime(s, "%Y-%m-%dT%H:%M:%S.%f").replace( + tzinfo=timezone.utc).timestamp() + + +def _logsumexp(*arrs): + """log10(10^a1 + 10^a2 + ...): 用于把 3 个分量功率合成总功率。 + NaN 视为无贡献; 全 NaN → NaN。""" + stack = np.stack([np.where(np.isnan(x), -np.inf, x) for x in arrs]) + m = np.max(stack, axis=0) + with np.errstate(invalid="ignore", divide="ignore"): + s = np.sum(np.exp(stack - m), axis=0) + out = m + np.log10(s) + out[~np.isfinite(m)] = np.nan + return out + + +def _band_reduce(x, bands): + """频段轴降维: 65 → bands 段 (log 域均值 = 几何平均)""" + N, B = x.shape + if bands <= 0 or bands >= B: + return x + edges = np.linspace(0, B, bands + 1).astype(int) + out = np.full((N, bands), np.nan, dtype=np.float32) + for i in range(bands): + lo, hi = edges[i], edges[i + 1] + if hi <= lo: + continue + seg = x[:, lo:hi] + cnt = np.sum(~np.isnan(seg), axis=1) + out[:, i] = np.where(cnt > 0, np.nansum(seg, axis=1) / np.maximum(cnt, 1), np.nan) + return out + + +def _align(t_query, t_source, source_feats, tol=3.0): + """把 source 近邻对齐到 t_query 时间轴 → (feats_sorted, idx, ok)""" + try: + q = np.array(t_query, dtype="datetime64[us]") + s = np.array(t_source, dtype="datetime64[us]") + except Exception: + q = np.array([_parse_time(t) for t in t_query]) + s = np.array([_parse_time(t) for t in t_source]) + order = np.argsort(s) + s = s[order] + feats = source_feats[order] + pos = np.clip(np.searchsorted(s, q), 0, len(s) - 1) + left = np.clip(pos - 1, 0, len(s) - 1) + dr = np.abs((s[pos] - q) / np.timedelta64(1, "s")) + dl = np.abs((s[left] - q) / np.timedelta64(1, "s")) + near = np.where(dl < dr, left, pos) + ok = np.minimum(dl, dr) <= tol + return feats, near, ok + + +def load_wfr_data(wave_dir, bands=13): + """读 WFR 月度 npz, 逐月把 (N,6,65) 谱降成 2*bands 维。 + + 磁总功率 = log10(10^BuBu + 10^BvBv + 10^BwBw), 电总功率同理; + 再各自聚合到 bands 段。逐月处理并释放原始谱 (避免一次性载入 ~4GB)。""" + files = sorted(Path(wave_dir).glob("wfr_*.npz")) + T, F = [], [] + for f in files: + z = np.load(f, allow_pickle=True) + psd = z["psd"].astype(np.float32) # (N,6,65) log10 + b = _logsumexp(psd[:, 0], psd[:, 1], psd[:, 2]) + e = _logsumexp(psd[:, 3], psd[:, 4], psd[:, 5]) + F.append(np.concatenate([_band_reduce(b, bands), + _band_reduce(e, bands)], axis=1).astype(np.float32)) + T.extend(z["time"].tolist()) + del psd, b, e, z + if not F: + return [], np.zeros((0, 2 * bands), dtype=np.float32) + tot = sum(len(x) for x in F) + print(f"[wfr] {len(files)} 月, {tot} 条, {2*bands} 维特征") + return T, np.concatenate(F, axis=0).astype(np.float32) + + +def load_lshell_data(wave_dir, tol=3.0): + """MAG 4SEC 的 coordinates(GEI, km) → [log10(偶极 L), sin(磁纬)], 2 维。 + + 注意: 用 GEI 的 z 轴当磁轴是近似(真实磁轴倾角约 11°)。 + valid = (magFill==0) & (magInvalid==0), 官方有效性标志。 + """ + files = sorted(Path(wave_dir).glob("magpos_*.npz")) + T, F, V = [], [], [] + for f in files: + z = np.load(f, allow_pickle=True) + c = z["coords"].astype(np.float64) + fl = z["flags"] + r = np.linalg.norm(c, axis=1) + r_re = r / 6371.0 + sinlat = np.clip(c[:, 2] / np.maximum(r, 1e-9), -0.999, 0.999) + L = r_re / np.maximum(1.0 - sinlat ** 2, 1e-3) + F.append(np.stack([np.log10(np.maximum(L, 1e-3)), sinlat], + axis=1).astype(np.float32)) + V.append((fl[:, 0] == 0) & (fl[:, 1] == 0)) + T.extend(z["time"].tolist()) + if not F: + return [], np.zeros((0, 2), dtype=np.float32), np.zeros((0,), dtype=bool) + tot = sum(len(x) for x in F) + print(f"[lshell] {len(files)} 月, {tot} 条, 2 维特征") + return T, np.concatenate(F, axis=0).astype(np.float32), np.concatenate(V) + + +def _feat_cache_path(wave_dir, bands, use_wfr, use_lshell): + return (Path(wave_dir) / "_cache" + / f"feat_b{bands}_wfr{int(use_wfr)}_ls{int(use_lshell)}.npz") + + +def build_feature_matrix(wave_dir, bands=13, use_wfr=True, use_lshell=False, + tol=3.0, use_cache=True): + """统一特征管线: 以密度时间轴为准, 对齐 MAG + WFR + L-shell。 + + 返回 (times, dens, feats (N,D), ok (N,)) + D = 4 (log10 Mag, log10 rms, lambda, delta) + [+ 2*bands (WFR 磁/电总功率谱)] [+ 2 (log10 偶极 L, sin 磁纬)] + 结果缓存到 data/wave/_cache/, 重跑省去 ~160s 重建。 + """ + cache = _feat_cache_path(wave_dir, bands, use_wfr, use_lshell) + if use_cache and cache.exists(): + z = np.load(cache, allow_pickle=True) + print(f"[cache] 特征矩阵命中 {cache.name}") + return z["times"].tolist(), z["dens"], z["F"], z["ok"] + + d_dir = Path(wave_dir) + dens_files = sorted(d_dir.glob("rbsp_a_*.npz")) + if not dens_files: + raise FileNotFoundError(f"无 DENSITY 数据: {d_dir}") + + # 密度: vals 列 = y,bmag,fce,fpe,wpe_over_wce,fuh,density (6=1e31 fill) + t_all, dens_all = [], [] + for f in dens_files: + z = np.load(f, allow_pickle=True) + t_all.extend(z["time"].tolist()) + dens_all.append(z["vals"][:, 6]) + dens = np.concatenate(dens_all) + dens = np.where(dens < -1e30, np.nan, dens) + n = len(t_all) + + # MAG: feats = Magnitude, rms, delta, lambda + mt, mf = [], [] + for f in sorted(d_dir.glob("mag_*.npz")): + z = np.load(f, allow_pickle=True) + mt.extend(z["time"].tolist()) + mf.append(z["feats"]) + mf = np.concatenate(mf) if mf else np.zeros((0, 4), dtype=np.float32) + mag_f, mag_idx, mag_ok = _align(t_all, mt, mf, tol) + print(f"[data] density={n} pts, mag={len(mt)} pts") + + m = mag_f[mag_idx].astype(np.float64) + # 哨兵必须在变换前剔除, 否则 abs() 会把它变成 log10(1e5)=5.0 的"合法"特征 + mag_valid = np.isfinite(m).all(axis=1) & (m > MAG_SENTINEL_LO).all(axis=1) + X = np.empty((n, 4), dtype=np.float32) + X[:, 0] = np.log10(np.maximum(m[:, 0], EPS)) + X[:, 1] = np.log10(np.maximum(m[:, 1], EPS)) + X[:, 2] = m[:, 2] + X[:, 3] = m[:, 3] + ok = mag_ok & mag_valid + n_bad = int((~mag_valid).sum()) + if n_bad: + print(f"[data] MAG 剔除哨兵/无效采样: {n_bad} ({n_bad/n*100:.2f}%)") + + if use_wfr: + wt, wf = load_wfr_data(wave_dir, bands) + if len(wt): + w_f, w_idx, w_ok = _align(t_all, wt, wf, tol) + n0 = X.shape[1] + X = np.concatenate([X, w_f[w_idx]], axis=1).astype(np.float32) + X[~w_ok, n0:] = 0.0 # 缺失填 0 (这些窗口会被 ok 掩码丢弃) + ok &= w_ok + + if use_lshell: + lt, lf, lv = load_lshell_data(wave_dir) + if len(lt): + # 有效性标志必须跟着特征一起排序 → 拼成额外一列再拆开 + lf2 = np.concatenate([lf, lv.astype(np.float32)[:, None]], axis=1) + l_f, l_idx, l_ok = _align(t_all, lt, lf2, tol) + got = l_f[l_idx] + l_feats, l_valid = got[:, :-1], got[:, -1] > 0.5 + l_ok = l_ok & l_valid + n0 = X.shape[1] + X = np.concatenate([X, l_feats], axis=1).astype(np.float32) + X[~l_ok, n0:] = 0.0 + ok &= l_ok + + ok &= np.isfinite(X).all(axis=1) + X[~ok] = 0.0 + print(f"[data] 特征 {X.shape[1]} 维, 有效点 {int(ok.sum())}/{n}") + if use_cache: + cache.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed(cache, times=np.array(t_all, dtype=object), + dens=dens, F=X, ok=ok) + print(f"[cache] 已写入 {cache.name} ({cache.stat().st_size/1024/1024:.0f} MB)") + return t_all, dens, X, ok + + +def build_windows(times, dens, feats, ok, window=WINDOW, stride=None): + """构造 (特征窗口, 密度域标签)。 + + stride 默认 = window (**不重叠**)。重叠窗口 + 随机切分会让测试窗口的 + 近邻副本留在训练集(实测 100% 的测试窗口与最近训练窗口相距 <=8 索引, + 即时间上重叠 >=24/32 步) → 严重泄漏, 会同时抬高 acc 并虚增 replay 效果。 + + feats/ok 由 build_feature_matrix 给出(已对齐到密度时间轴)。""" + if stride is None: + stride = window + d_feat = feats.shape[1] if feats.ndim == 2 else 1 + n = len(times) + if n == 0 or len(feats) == 0: + return np.zeros((0, window, d_feat), dtype=np.float32), np.zeros((0,)) + X, y = [], [] + for i in range(0, n - window, stride): + if not ok[i:i + window].all(): + continue + d_end = dens[i + window - 1] + if not np.isfinite(d_end) or d_end <= 0: + continue + X.append(feats[i:i + window]) + y.append(math.log10(d_end)) + if not X: + return np.zeros((0, window, d_feat), dtype=np.float32), np.zeros((0,)) + return np.asarray(X, dtype=np.float32), np.asarray(y, dtype=np.float32) + + +def assign_domains(y_log10, n_domains=6, qs=None): + """按 log10(density) 分位数划分域 (等样本量, 保证每域可训练). + + qs 不为 None 时**沿用给定边界** —— 跨年/外部分布评估必须这样, + 否则测试年用自己算的分位数, 域定义与训练年不同, 标签根本对不上 + (曾因此得到 naive 跨年 1.000 的荒谬结果)。 + """ + if qs is None: + qs = np.quantile(y_log10, np.linspace(0, 1, n_domains + 1)) + qs[0] -= 1e-6 + qs[-1] += 1e-6 + lab = np.digitize(y_log10, qs[1:-1]) + return lab.astype(np.int64), qs + + +# ============================================================ +# 持续学习实验 +# ============================================================ +def evaluate(model, loader, device): + model.eval() + correct = total = 0 + with torch.no_grad(): + for xb, yb in loader: + xb, yb = xb.to(device), yb.to(device) + pred = model(xb).argmax(-1) + correct += (pred == yb).sum().item() + total += yb.numel() + return correct / max(total, 1) + + +def build_consistency(fine, y_log, X, bands, use_lshell, min_n=30): + """自洽性 con(p) —— leo 理论「简约点 = 不可归约的原理; 自洽性 = 彼此不矛盾」。 + + 做法: 先用全局数据确定若干**已发现结构关系**的符号, 再看每个候选区间内 + 这些关系是否保持同向。区间内保持同向的比例 = 该区间的自洽性。 + + 这些关系不是凭空选的 —— 它们对应 lm4 因果发现的**稳定边** + (tests/lm4_causal_waves.py, 20 条稳定边 / 18 条漂移边): + log_dens — ele_* 密度 ↔ 电场波功率 (hiss 波) + log_dens — logL 等离子体层顶依赖 L + logB — logL 偶极场定律 B ∝ L^-3 + logB — mag_* 磁强计频段本就源自 B + 一个区间若破坏这些关系 = 与全局结构矛盾 = 自洽性低, 价值函数应降低其优先级。 + + 返回 (n_fine,) 的自洽性分数, 取值 [0,1]; 样本太少的区间给 1.0 (不惩罚)。 + """ + n_fine = int(fine.max()) + 1 + # 变量: 0=log10|B|, 1=log10 rms, 2=lambda, 3=delta, 4..4+bands-1=WFR 磁, + # 4+bands..4+2bands-1=WFR 电, 末尾两列(若 use_lshell)= log10 L, sin 磁纬 + cols = {"logB": 0, "rms": 1, + "ele_lo": 4 + bands, "ele_mid": 4 + bands + bands // 2, + "ele_hi": 4 + 2 * bands - 1} + if use_lshell and X.shape[2] >= 2: + cols["logL"] = X.shape[2] - 2 + Xm = X.mean(axis=1) # (N, F) 窗口内均值 + dens = y_log + avail = {k: v for k, v in cols.items() if v < Xm.shape[1]} + pairs = [("log_dens", "ele_hi", "dens"), ("log_dens", "ele_mid", "dens"), + ("log_dens", "ele_lo", "dens"), ("logB", "ele_hi", "plain"), + ("logB", "logL", "plain"), ("log_dens", "logL", "dens"), + ("ele_lo", "ele_hi", "plain")] + # 全局符号 + rels = [] + for a, b, kind in pairs: + if a != "log_dens" and a not in avail: + continue + if b != "log_dens" and b not in avail: + continue + va = dens if a == "log_dens" else Xm[:, avail[a]] + vb = dens if b == "log_dens" else Xm[:, avail[b]] + if va.std() < 1e-9 or vb.std() < 1e-9: + continue + c = np.corrcoef(va, vb)[0, 1] + if np.isfinite(c) and abs(c) > 0.05: + # 存 (变量, 变量, 全局相关幅值) —— 用于算"该区间内关系有多强" + rels.append((va, vb, float(c))) + if not rels: + return np.ones(n_fine) + out = np.ones(n_fine) + for f in range(n_fine): + m = (fine == f) + if m.sum() < min_n: + continue + # ★ 自洽性必须是**连续强度**, 不能是"关系是否成立"的二值判断。 + # 二值版实测处处为真 -> con ≡ 1.0 -> 该项死亡 (std=0)。 + # 这里用: 区间内相关强度 / 全局相关强度, 且符号翻转直接清零。 + num = den = 0.0 + for va, vb, cg in rels: + aa, bb = va[m], vb[m] + if aa.std() < 1e-9 or bb.std() < 1e-9: + continue + c = np.corrcoef(aa, bb)[0, 1] + if not np.isfinite(c): + continue + strength = min(1.0, abs(c) / max(1e-6, abs(cg))) + if np.sign(c) != np.sign(cg): + strength = 0.0 # 符号翻转 = 矛盾, 清零 + num += strength + den += 1.0 + if den: + out[f] = num / den + return out + + +def run_experiment(X, y_dom, device, d_model=128, d_state=8, n_layers=2, + epochs_per_domain=300, batch=32, lr=1e-3, replay=False, + replay_ratio=0.3, replay_src="old", seed=42, n_domains=6, n_feat=None, + pool="cat", agg_path=False, shuffle_domains=False, + cl_method="naive", pln_d=64, inner_k=2, inner_lr=0.1, + outer_lr=None, meta_every=1, reptile_lr=0.0, + consolidate_every=10, stat_input=False, backbone="ssm", + head_type="linear", norm="batchnorm", + trace_every=0, external_test=None, + fine=None, fine_desc=None, proposer="fixed", + stream="fixed", rounds=0, freq_profile=None, + y_log=None, bands=13, use_lshell=False, + rl_mu=0.05, rl_alpha0=0.2, rl_explore_w=0.5, rl_algo="autostep", + rl_know=0, schedule_str="", + lam=(1.0, 1.0, 1.0, 1.0), proposer_k=1, proposer_sigma=0.0, + oak_options=1, oak_gate=1, oak_refresh=4, + shift_at=0, shift_every=0, opt_frac=1.0, + opt_mode="fixed", term_eps=0.05, stall_tol=0.0, + dyn_model="linear", n_regions_opt=16, use_subgoals=0): + """cl_method: + naive/replay — 单循环 (线性头), 原行为 + oml — 快慢双循环 (lm3 `oml`): 内循环每步更新头, 外循环低频更新 RLN + oml2 — 真 OML 双循环 (lm3 `oml2` + lm1 PLN): support 上适应克隆头 + (per-feature 步长), query loss 反传全模型, 再合并 + """ + random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) + # head_type 与 cl_method 解耦: 否则 "OML 失败" 分不清是 OML 的问题 + # 还是它默认带的 PLN 头(2 层 tanh MLP)的问题 —— 这是同时变两个变量的混淆。 + use_pln = head_type in ("pln", "swifttd") + + # 每域划分 train/test + train_by_dom, test_by_dom = {}, {} + for dd in range(n_domains): + idx = np.where(y_dom == dd)[0] + if len(idx) < 50: + print(f" [warn] 域 {dd} 样本少: {len(idx)}") + continue + rng = np.random.RandomState(seed + dd) + rng.shuffle(idx) + n_te = max(20, len(idx) // 10) + test_by_dom[dd] = idx[:n_te] + train_by_dom[dd] = idx[n_te:] + domains = sorted(train_by_dom) + if shuffle_domains: + # 随机化训练顺序: 检验"按密度递增"的课程是否重要 + random.Random(seed * 7919 + 13).shuffle(domains) + + def loader_for(idxs, shuffle=False): + ds = torch.utils.data.TensorDataset( + torch.from_numpy(X[idxs]), torch.from_numpy(y_dom[idxs])) + return torch.utils.data.DataLoader(ds, batch_size=batch, shuffle=shuffle) + + # ── 训练调度 ────────────────────────────────────────────────────── + # 为什么不再用"固定域序"当 benchmark (leo 2026-09-14): + # 固定序预设了三件真实智能体不会有的事 —— ① 任务集已知 ② 最优顺序已知 + # ③ 单趟不复习。而且它**恰好是 replay 最有利的顺序** (排序课程把域间 + # 干扰压到最低), 因此系统性偏袒要被打败的基线。 + # 改为: 从**流分布**采样 + 全部 oracle-free 基线 + 可复现的对照臂。 + # + # stream (任务流): + # fixed 每个粗域各来一次, 按序 (降级为启发式基线) + # perm 每个粗域各来一次, 随机排列 + # revisit 每轮按均匀分布抽一个粗域 (允许重复) <- 边缘设备反复遇到同类场景 + # nonstationary 粗域占比随轮次漂移 (正弦权重) + # proposer (每轮内部怎么选候选): + # bins 该粗域的全部细区间 (配 stream 用) + # random 均匀随机提议 k 个细区间 + # random-matched 按 `--freq-profile` 里记录的频率分布提议 (对照臂, 见下) + # value 完整价值函数 + # value-nofb 价值函数去掉真实反馈项 (消融: 隔离"探索"与"价值") + prop, schedule, fine_test = None, None, {} + n_fine = int(fine.max()) + 1 if fine is not None else 0 + n_rounds = rounds if rounds and rounds > 0 else len(domains) + rng_sched = np.random.RandomState(seed * 7919 + 29) + + if fine is not None: + # 细区间 -> 粗域 (取该区间样本的众数域) + fine_group = np.zeros(n_fine, dtype=int) + for f in range(n_fine): + idx_f = np.where(fine == f)[0] + fine_group[f] = (np.bincount(y_dom[idx_f]).argmax() + if len(idx_f) else -1) + bins_of = {d: [f for f in range(n_fine) if fine_group[f] == d] + for d in range(n_domains)} + # 测试子集 + for f in range(n_fine): + idx_f = np.where(fine == f)[0] + if len(idx_f) >= 20: + fine_test[f] = idx_f[:max(20, len(idx_f) // 10)] + + rlprop = None + if proposer == "rl": + # ★ 真 RL: 动作→环境→奖励→参数更新; 步长 (IDBD) 是主导累积参数。 + # 与 RegimeProposer 的区别: V_θ 是可学参数且**按 reward 更新并累积**, + # 而不是固定公式 + 状态更新。 + from hibs_lnn.rl_proposer import RLProposer + # 候选池 (与 value 臂同构, 便于公平对照) + _nf = int(fine.max()) + 1 + # ★ 内部知识维度: 6 域能力画像 + 6 域访问次数(归一) + any_time + 平均遗忘 + _know_dim = int(rl_know) if rl_know else 0 + rlprop = RLProposer(fine_desc, tau=0.5, k=proposer_k, + mu=rl_mu, alpha0=rl_alpha0, + explore_w=rl_explore_w, algo=rl_algo, n_know=_know_dim, + seed=(seed * 7919 + 29)) + if _know_dim: + print("[rl] 内部知识已启用: %d 维 (能力画像 6 + 访问次数 6 + any_time + 遗忘)" + % _know_dim, flush=True) + for _f in range(_nf): + _idx = np.where(fine == _f)[0] + if len(_idx) >= 20: + fine_test[_f] = _idx[:max(20, len(_idx) // 10)] + print("[rl] RLProposer: 池 %d, fdim %d, algo=%s, mu=%.3f alpha0=%.2f explore_w=%.2f" + % (rlprop.n, rlprop.fdim, rl_algo, rl_mu, rl_alpha0, rl_explore_w), + flush=True) + + oakprop = None + if proposer == "oak": + # ★ OaK 提议器: 包住 RLProposer(Level 3) + InternalKnowledge(Level 1/2/4 + 元知识)。 + # E9 vs E10 只差 use_options: 同一 base、同一门控, 唯一变量是时间抽象。 + from hibs_lnn.oak_proposer import OAKProposer + oakprop = OAKProposer(fine_desc, n_fine=int(fine.max()) + 1, + k=proposer_k, seed=(seed * 7919 + 29), + mu=rl_mu, alpha0=rl_alpha0, explore_w=rl_explore_w, + algo=rl_algo, n_know=int(rl_know), + use_options=bool(oak_options), use_gate=bool(oak_gate), + refresh_every=oak_refresh, opt_frac=opt_frac, + opt_mode=opt_mode, term_eps=term_eps, + stall_tol=stall_tol, dyn_model=dyn_model, + n_regions_opt=n_regions_opt, + use_subgoals=use_subgoals) + print("[oak] OAKProposer: n_fine=%d options=%s gate=%s algo=%s refresh=%d" + % (oakprop.n_fine, oak_options, oak_gate, rl_algo, oak_refresh), flush=True) + + # ★★ 任务流调度 —— **对所有 proposer 生效**。 + # 原先整段写在下面的 `if proposer == "bins":` 里 ⇒ `--proposer oak` 时 + # `--stream` 是**死参数**,实际域由主循环的 + # `dd = domains[step] if step < len(domains) else domains[-1]` 决定 + # = 前 n_domains 轮各域一次、之后全在最后一个域(实测 295/300)。 + # 见 `docs/lm4_stream_bug.md`。 + def _align_dom(seq): + seq = [int(x) for x in seq] + if len(seq) < n_rounds: + _rr = int(np.ceil(n_rounds / max(1, len(seq)))) + seq = (seq * _rr)[:n_rounds] + return seq[:n_rounds] + + if stream == "fixed": + dom_seq = list(range(n_domains)) + elif stream == "perm": + dom_seq = list(rng_sched.permutation(n_domains)) + elif stream == "revisit": + dom_seq = list(rng_sched.randint(0, n_domains, size=n_rounds)) + elif stream == "nonstationary": + # 权重按正弦漂移: 不同轮次由不同域主导 + dom_seq = [] + for _r2 in range(n_rounds): + w = 1.0 + 0.9 * np.sin(2 * np.pi * _r2 / max(2, n_rounds // 2) + + rng_sched.uniform(0, 2 * np.pi)) + w = np.clip(w, 0.05, None) + w = w * np.ones(n_domains) * (1.0 + 0.5 * rng_sched.rand(n_domains)) + dom_seq.append(int(rng_sched.choice(n_domains, p=w / w.sum()))) + else: + dom_seq = list(range(n_domains)) + dom_seq = _align_dom(dom_seq) + + # ── ★ 回读闸门: **只有直方图能证明流生效** ──────────────────────── + # `--stream` 的取值本身**不算证据** —— `:1052` 那个 `"stream": stream` + # 只是把标志值记进 JSON。本轮的 bug 恰恰是"标志被记进 JSON" + # 被误当成"生效"。所以这里把**实际域分布**打出来并写进 JSON。 + from collections import Counter as _Ctr + _hist = _Ctr(dom_seq) + _share = max(_hist.values()) / max(1, len(dom_seq)) + _max_ok = 1.5 / max(1, n_domains) # 均匀流 ≈ 1/n; 超 1.5× 视为退化 + _ent = -sum((c / len(dom_seq)) * np.log(c / len(dom_seq)) + for c in _hist.values() if c > 0) + STREAM_HIST = {int(k): int(v) for k, v in sorted(_hist.items())} + print("[stream] %s | 直方图 %s | 最大域占比 %.3f (阈值 %.3f) | 熵 %.3f | 前20步 %s" + % (stream, STREAM_HIST, _share, _max_ok, _ent, dom_seq[:20]), flush=True) + if _share > _max_ok: + print("[stream] ⚠⚠ 退化流: 最大域占比 %.3f > %.3f —— 这不是持续的任务流" + % (_share, _max_ok), flush=True) + if n_domains and len(domains) != n_domains: + print("[stream] ⚠ 域的 ID 空间 (%d) 与实际存在的域数 (%d) 不一致 —— " + "`dom_seq` 的取值按 ID 解释, 主循环里会被当作索引, 需人工确认" + % (n_domains, len(domains)), flush=True) + + if proposer == "bins": + # ★ 显式 schedule 优先: 用于执行规划器给出的序列 (真实环境验证) + # ★ 不能用 args: run_experiment 是独立函数, args 只存在于 main。 + # 本行曾写成 getattr(args, "schedule", "") -> NameError, 导致 + # `--proposer bins` 整条路径直接崩 (后续实验用 --proposer rl/value + # 绕过了这段, 所以一直没暴露)。 + _explicit = ([int(x) for x in schedule_str.split(",") if x.strip() != ""] + if schedule_str else []) + if _explicit: + dom_seq = _align_dom(_explicit) + print("[schedule] 显式序列 -> %s" % dom_seq, flush=True) + schedule = [bins_of.get(d, []) for d in dom_seq] + elif proposer != "fixed" and proposer != "oak": + # ★ oak 自己包了 RLProposer, 不需要 RegimeProposer。 + # 早先漏了这个排除, oak 臂会白白多建一个 value proposer + # (表现为多打印 `[con] 自洽性注入 ... 0=该项死亡` 且白跑 observe)。 + from hibs_lnn.value_proposer import RegimeProposer + if proposer == "value-nofb": + lam = (lam[0], lam[1], lam[2], 0.0) # 关掉真实反馈项 + prop = RegimeProposer(fine_desc, groups=None, lam=lam, + sigma=(proposer_sigma if proposer_sigma > 0 + else None), + k=proposer_k, + seed=(seed * 7919 + 29)) + # ★ 注入自洽性 (V35.19 三项之一)。不注入 = 该项恒 1.0 = 死亡, + # 价值函数退化成覆盖均匀化采样器 (本轮踩过的坑)。 + try: + _con = build_consistency(fine, y_log if y_log is not None + else np.zeros(len(y_dom)), X, + bands, use_lshell) + prop.set_consistency(_con) + print("[con] 自洽性注入: 均值 %.3f 标准差 %.3f (0=该项死亡)" + % (_con.mean(), _con.std()), flush=True) + except Exception as _e: + print("!! 自洽性构建失败, con 项将为常数:", _e, flush=True) + if proposer == "random-matched" and freq_profile: + # ★ 频率对齐对照: 用 value 臂实测的提议频率分布驱动 random 臂。 + # 否则 value 有 cov(p)=1/(1+freq) 会主动均匀化覆盖, 和"均匀随机" + # 的复习频率分布本就不同 -> value vs random 说不清是"更聪明"还是 + # "复习更均匀"。 + prof = np.asarray(freq_profile, dtype=np.float64) + prof = (prof / prof.sum()) if prof.sum() > 0 else None + else: + prof = None + # ★ 关键: 提案必须**在线**逐轮生成, 不能在训练前一次性预生成。 + # 预生成会导致 observe() 从未被调用 -> + # acc 全 NaN -> fb(p) ≡ 1.0 (反馈项死) + # learned 为空 -> sim(p) ≡ 1.0 (简约性项死) + # 剩 V(i) = 常数 + λ_cov/(1+freq), 退化成一个"均匀化采样器", + # 与价值函数无关 (实测 value 与 random-matched 逐位几乎相同, + # 就是这个 bug 的签名)。schedule 保持 None, 由主循环在线取。 + schedule = None + + test_loaders = {d: loader_for(test_by_dom[d]) for d in domains} + n_classes = n_domains + ht = head_type + if backbone == "mlp": + model = StatMLP(n_feat=n_feat or X.shape[-1], n_classes=n_classes, + hidden=d_model, n_layers=n_layers, head_type=ht, + pln_d=pln_d, inner_lr=inner_lr, norm=norm).to(device) + if norm == "fixed": + _ti = np.concatenate([train_by_dom[d] for d in domains]) + with torch.no_grad(): + _all = torch.from_numpy(X[_ti]).to(device) + model.set_fixed_stats(window_agg(_all)) + del _all + else: + model = WaveSSM(n_feat=n_feat or X.shape[-1], d_model=d_model, + d_state=d_state, n_layers=n_layers, + n_classes=n_classes, pool=pool, agg_path=agg_path, + head_type=ht, pln_d=pln_d, inner_lr=inner_lr, + stat_input=stat_input).to(device) + opt = torch.optim.Adam(model.parameters(), lr=lr) + # 双循环: 外循环 (慢, 表示+meta 参数) 与 内循环 (快, 仅头) + opt_outer = opt + if use_pln: + enc_params = [p for n_, p in model.named_parameters() + if not n_.startswith("head.")] + head_params = [p for n_, p in model.named_parameters() + if n_.startswith("head.")] + if cl_method == "oml2": + # 真 OML: 外循环更新全部 meta 参数 (表示 + 头初始化 + per-feature 步长) + opt_outer = torch.optim.Adam(model.parameters(), lr=(outer_lr or lr)) + # lm3 的 oml2 是**两部分**: OML 元步 + 常规训练步(含 replay)。 + # 只做元步会让头仅靠 K 步内循环学, 合并时被覆盖 -> 什么都不剩。 + opt_norm = torch.optim.Adam(model.parameters(), lr=lr) + opt_inner = None + else: + # 快慢双循环: 外循环只动表示, 内循环只动头 + opt_outer = torch.optim.Adam(enc_params, lr=(outer_lr or lr)) + opt_inner = torch.optim.Adam(head_params, lr=inner_lr) + else: + opt_inner = None + + acc_matrix = [] # acc_matrix[step][domain] + cross = [] # 每个域学完后, 在**外部分布**(如 2016 年)上的准确率 + replay_by_dom = {} # dd -> (X_dev, y_dev): 每域回放缓冲(预置 device) + + curves = {} # dd -> [(step, acc)] 学习效率曲线(最后一次访问) + # ★ 逐次访问都留 —— 这是"知识复用"指标的唯一来源。 + # 旧写法 `curves[dd] = cur` 是**覆盖式**, 前几次访问全丢 ⇒ + # "重复遇到同一域时是否学得更快"根本无从计算。见 + # `docs/learning_efficiency_spec.md` §1 缺陷③。 + visits = {} # dd -> [ [(step,acc), ...], ... ] + prop_rows = [] # 价值函数调度轨迹 + n_fine_all = int(fine.max()) + 1 if fine is not None else 0 + any_time = [] # 每轮的"已见域平均准确率" (在线性能) + # ── ★ Regime shift (E11/E12) ──────────────────────────────────────── + # 机制: 在 `--shift-at K` 轮之后**置换「动作索引 -> 物理细区间」的映射**。 + # 于是同一个动作 `f` 从此训练的是**另一个物理区**, 而 `fine` / `fine_test` + # 本身不变 (按物理区索引)。语义 = 「**动作的含义变了**」—— + # 这正是 OaK 主张要检验的**动力学漂移**: T(s,a) 本身发生变化, + # 已积累的内部知识 (coverage / 不确定性 / 选项) 必须重新适应。 + # `--shift-every M` = 反复漂移 (E12)。 + _n_fine_shift = int(fine.max()) + 1 if fine is not None else 0 + action_perm = np.arange(_n_fine_shift) + n_shifts = 0 + + def _apply_shift(rnd): + nonlocal action_perm, n_shifts + action_perm = np.random.RandomState(seed * 31 + 7 + rnd).permutation(_n_fine_shift) + n_shifts += 1 + print("[shift] regime shift #%d @ round %d: 动作->物理区映射已置换" + % (n_shifts, rnd), flush=True) + + for step in range(n_rounds): + if shift_at and _n_fine_shift: + if step == shift_at: + _apply_shift(step) + elif shift_every and step > shift_at and (step - shift_at) % shift_every == 0: + _apply_shift(step) + # ★ 任务流由 `dom_seq` 决定(**对所有 proposer 生效**)。修复前这里是 + # `domains[step] if step < len(domains) else domains[-1]`, 而 `dom_seq` + # 只在 `--proposer bins` 下被计算 ⇒ oak 路径下 `--stream` 完全无效, + # 实际流 = 前 n_domains 轮各域一次、之后全在最后一个域。 + # 见 `docs/lm4_stream_bug.md`。回退分支保留以防 `dom_seq` 未定义。 + if dom_seq is not None and step < len(dom_seq): + dd = dom_seq[step] + else: + dd = domains[step] if step < len(domains) else domains[-1] + if oakprop is not None: + pick = oakprop.act() # ← 动作 (含 option / 门控) + elif rlprop is not None: + pick = rlprop.act() # ← 动作 + elif prop is not None: + # 在线提案: 此时 prop 已吃过前面所有轮的 observe 反馈 + if proposer in ("value", "value-nofb"): + pick = prop.propose() + elif prof is not None: + pick = list(rng_sched.choice(prop.n, size=min(proposer_k, prop.n), + replace=False, p=prof)) + for i in pick: + prop.freq[i] += 1 + else: + pick = prop.pick_random() + if schedule is not None or prop is not None or rlprop is not None or oakprop is not None: + if prop is None and rlprop is None and oakprop is None: + pick = schedule[step] + _phys = [int(action_perm[f]) if f < _n_fine_shift else int(f) for f in pick] + idx_tr = np.concatenate([np.where(fine == f)[0] for f in _phys]) + np.random.RandomState(seed * 131 + step).shuffle(idx_tr) + dl = loader_for(idx_tr, shuffle=True) + # ★ 公平性: proposer 每轮只覆盖 len(pick)/n_fine 的样本。 + # 步数按比例放大, 保证**每轮消耗的数据量**与固定臂相同, + # 否则是拿 1/6 的数据打全程 (实测会低估 proposer 臂)。 + # 归一化到"每轮数据量 = 一个粗域": 每个细区间占 1/n_fine, + # 一个粗域占 n_fine/n_domains 个细区间。k=3/n_fine=18/n_dom=6 时 + # 系数恰为 1 (3 个细区间 ≈ 21K 样本 ≈ 一个粗域), 不变训。 + steps_this = max(1, int(round( + epochs_per_domain * n_fine_all + / max(1, len(domains)) / max(1, len(pick))))) + else: + pick = [dd] + dl = loader_for(train_by_dom[dd], shuffle=True) + steps_this = epochs_per_domain + model.train() + it = 0 + cur = [] + # ★ 判别性对照臂 `replay-src=self`: + # 与 `replay` **同样多的额外样本、同样 1 次优化步**, 但**全部来自当前域** + # (而不是旧域)。用来把"复习旧数据"与"单纯多花算力/更强正则"分开。 + # 见 docs/lmeff_verdict.md §3。 + _self_buf = None + if replay and replay_src == "self": + _idx = train_by_dom[dd] + _rs = np.random.RandomState(seed + 100000 + step) + _sel = _rs.choice(_idx, size=min(2000, len(_idx)), replace=False) + _self_buf = (torch.from_numpy(X[_sel]).to(device), + torch.from_numpy(y_dom[_sel]).to(device)) + while it < steps_this: + for xb, yb in dl: + if it >= steps_this: + break + xb, yb = xb.to(device), yb.to(device) + + if cl_method == "oml2": + # ===== 真 OML 双循环 (lm3 oml2 + lm1 PLN) ===== + # 内循环: 克隆头在 support 上适应 K 步 (per-feature 步长) + # 外循环: fast head 在 query 上的 loss 反传全部 meta 参数 + # support/query 都混入回放样本 (lm4 聚合所有有利于 CL 的机制) + parts_x, parts_y = [xb], [yb] + if replay and replay_by_dom: + k = max(1, int(batch * replay_ratio)) + seen_doms = list(replay_by_dom) + per = max(1, k // len(seen_doms)) + for sd in seen_doms: + bx, by = replay_by_dom[sd] + m = min(per, len(bx)) + sel = torch.randint(0, len(bx), (m,), device=device) + parts_x.append(bx[sel]); parts_y.append(by[sel]) + XB, YB = torch.cat(parts_x), torch.cat(parts_y) + # ★ 必须**随机**切 support/query。批次是 [当前域..., 回放(旧域)...] + # 拼的, 按位置切会让 support=纯新域 / query=纯旧域 -> + # 元目标退化成"适应新域、再在旧域上预测" -> 教模型摧毁旧域可分性。 + _perm = torch.randperm(XB.shape[0], device=device) + XB, YB = XB[_perm], YB[_perm] + half = max(2, XB.shape[0] // 2) + xq, yq = XB[half:], YB[half:] + if xq.shape[0] < 2: + it += 1; continue + with torch.no_grad(): + h_sup = model.encode(XB[:half]) + h_qry = model.encode(xq) # 保留计算图 + # 关键: 合并必须**周期性**做 (lm3 是每 10 步), 每步合并会让头 + # 被"只在当前批次上适应过 K 步"的版本覆盖 → 永远积累不了知识 + lq, _ = oml_step(model, model.head, model.encode, + h_sup, YB[:half], h_qry, yq, opt_outer, + K=inner_k, per_feature=True, + consolidate=(consolidate_every > 0 + and it % consolidate_every == 0), + reptile_lr=reptile_lr) + # ---- lm3 oml2 的第二部分: 头/全模型照常训练, 含 replay ---- + # (这才是让 OML 站在 replay 之上而不是取代它的关键) + loss_n = F.cross_entropy(model(XB), YB) + opt_norm.zero_grad(); loss_n.backward() + torch.nn.utils.clip_grad_norm_( + [p_ for p_ in model.parameters() if p_.requires_grad], 1.0) + opt_norm.step() + loss = torch.tensor(lq, device=device) + it += 1 + continue + + if cl_method == "oml": + # ===== 快慢双循环 (lm3 oml) ===== + # 内循环(快): 表示视为固定特征, 只更新头 (+回放) + parts_x, parts_y = [xb], [yb] + if replay and replay_by_dom: + k = max(1, int(batch * replay_ratio)) + seen_doms = list(replay_by_dom) + per = max(1, k // len(seen_doms)) + for sd in seen_doms: + bx, by = replay_by_dom[sd] + m = min(per, len(bx)) + sel = torch.randint(0, len(bx), (m,), device=device) + parts_x.append(bx[sel]); parts_y.append(by[sel]) + XB, YB = torch.cat(parts_x), torch.cat(parts_y) + with torch.no_grad(): + hb = model.encode(XB) + loss = F.cross_entropy(model.head(hb), YB) + opt_inner.zero_grad(); loss.backward(); opt_inner.step() + # 外循环(慢): 低频更新表示 + if meta_every > 0 and it % meta_every == 0: + opt_outer.zero_grad() + l2 = F.cross_entropy(model(XB), YB) + l2.backward(); opt_outer.step() + it += 1 + continue + + # ===== 单循环 (naive / replay): 原行为, 不变 ===== + loss = F.cross_entropy(model(xb), yb) + # Replay: 按域均衡混入旧域样本 + # ★ 这是**域均衡的随机复习**(每个见过的域等量、域内随机抽样本), + # 不是"针对即将被遗忘的域"。所以"随机复习 vs 针对性复习"这个 + # 对照**本来就已在臂内**, 真正未解决的混淆只有"额外算力"。 + if replay and replay_by_dom: + k = max(1, int(batch * replay_ratio)) + if replay_src == "self" and _self_buf is not None: + # ★ 同样 k 个样本、同样 1 步, 但**全部来自当前域** + bx, by = _self_buf + sel = torch.randint(0, len(bx), (k,), device=device) + rx, ry = bx[sel], by[sel] + else: + seen_doms = list(replay_by_dom) + per = max(1, k // len(seen_doms)) + rxs, rys = [], [] + for sd in seen_doms: + bx, by = replay_by_dom[sd] + m = min(per, len(bx)) + sel = torch.randint(0, len(bx), (m,), device=device) + rxs.append(bx[sel]); rys.append(by[sel]) + rx, ry = torch.cat(rxs), torch.cat(rys) + loss = loss + F.cross_entropy(model(rx), ry) + opt.zero_grad(); loss.backward(); opt.step() + it += 1 + # ── 学习效率追踪: 每 trace_every 步评一次**当前域** ── + if (trace_every and it % trace_every == 0 + and dd in test_loaders): + was_training = model.training + model.eval() + with torch.no_grad(): + a = evaluate(model, test_loaders[dd], device) + model.train(was_training) + cur.append((it, a)) + # ── 价值函数的真实反馈: 测每个刚学过的细区间, 回填 prop ── + _phys_fb = ([int(action_perm[f]) if f < _n_fine_shift else int(f) for f in pick] + if _n_fine_shift else list(pick)) + if prop is not None: + for f in _phys_fb: + if f in fine_test: + prop.observe(f, evaluate(model, loader_for(fine_test[f]), device)) + # 记录: 已见域整体准确率 + row = [evaluate(model, test_loaders[d], device) if d in test_loaders else float('nan') + for d in domains] + acc_matrix.append(row) + _seen = [row[j] for j, d in enumerate(domains) + if not math.isnan(row[j]) and row[j] > 0] + any_time.append(float(np.mean(_seen)) if _seen else float('nan')) + # ── ★ RL 闭环: 动作 → 环境 → 奖励 → 参数更新 ── + # 必须放在 row 算完之后: reward = Δ(any-time 准确率), 而 any-time 是 + # **刚训练过的那个区间**通过改变模型能力而影响到的量 —— reward 因此 + # 真正依赖动作, 而不是外生给定的。 + if rlprop is not None and getattr(rlprop, "n_know", 0): + # ★ 注入内部知识: 这是「智能体知道自己的什么」——不是手写特征, + # 而是它当前的能力画像、对各域的访问程度、以及遗忘信号。 + # 对应 Horde/GVF 的 "predictive knowledge 进入状态"。 + _row = np.array([0.0 if (v != v) else float(v) for v in row]) + _vis = rlprop.visits / max(1.0, rlprop.visits.sum()) + _forg = float(np.mean([max(0.0, (np.nanmax(row) if row else 0.0) - v) + for v in _row if v == v])) if len(_row) else 0.0 + _at = float(np.mean([v for v in row if not math.isnan(v)])) + _kv = np.concatenate([_row, _vis, [_at, _forg]]) + if _kv.size >= rlprop.n_know: + rlprop.set_knowledge(_kv[:rlprop.n_know]) + if oakprop is not None: + _accs = [evaluate(model, loader_for(fine_test[f]), device) + for f in _phys_fb if f in fine_test] + _at = float(np.mean([v for v in row if not math.isnan(v)])) + oakprop.observe(pick, acc=(float(np.mean(_accs)) if _accs else 0.5), + any_time=_at) + # ★ 记录 (状态, 动作, 新状态) 与反馈; 顺序要紧: + # set_pick 必须先于 set_state —— set_state 内部用**上一次**的 + # prev_state/prev_pick 组装转移, 再覆盖成新状态。 + oakprop.set_pick(pick) + oakprop.set_state(row) + oakprop.update(pick) # 奖励 → 参数更新 + 知识维护 + if rlprop is not None: + _accs = [evaluate(model, loader_for(fine_test[f]), device) + for f in _phys_fb if f in fine_test] + _at = float(np.mean([v for v in row if not math.isnan(v)])) + rlprop.observe(pick, acc=(float(np.mean(_accs)) if _accs else 0.5), + any_time=_at) + rlprop.update(pick) # 奖励 → 参数更新 (含 IDBD 步长) + if prop is not None: + prop_rows.append({"step": step + 1, "picks": pick, + "value": [round(prop.value(f), 4) for f in pick], + "freq": [int(prop.freq[f]) for f in pick]}) + # 跨年/外部分布: 每步评**所有**外部域, 形成外部矩阵。 + # 只报"刚训完 dd 时在外部 dd 上的准确率"是没有意义的 —— naive 在那个瞬间 + # 恰好专精于 dd, 会得到 1.0。有意义的量是**全流程训完后**的末行。 + if external_test: + model.eval() + with torch.no_grad(): + cross.append([evaluate(model, external_test[d], device) + if d in external_test else float('nan') + for d in range(n_domains)]) + model.train() + else: + cross.append(None) + if cur: + curves[dd] = cur + visits.setdefault(dd, []).append(list(cur)) # ★ 逐次访问留档(复用指标用) + seen = [f"D{d}:{row[i]:.3f}" for i, d in enumerate(domains) if i <= step] + print(f" [{'replay' if replay else 'naive '}] step{step+1} (域{dd}) → {' '.join(seen)}", flush=True) + # 存回放样本 (每域固定 2000, 预置 device 避免逐步搬运) + if replay: + idx = train_by_dom[dd] + sel = np.random.RandomState(seed + step).choice( + idx, size=min(2000, len(idx)), replace=False) + replay_by_dom[dd] = (torch.from_numpy(X[sel]).to(device), + torch.from_numpy(y_dom[sel]).to(device)) + # ── 边缘设备关心的指标 ── + _M = np.array(acc_matrix, dtype=float) + _fin = _M[-1] + _best = np.nanmax(_M, axis=0) + worst_forget = float(np.nanmax(_best - _fin)) if _M.size else float('nan') + extra = { + "any_time_acc": float(np.nanmean(any_time)) if any_time else float('nan'), + "any_time_curve": [None if (a != a) else round(float(a), 4) for a in any_time], + "worst_case_forget": worst_forget, + "mean_forget_all": float(np.nanmean(_best - _fin)) if _M.size else float('nan'), + "n_rounds": int(n_rounds), + "stream": stream, + # ★ 实际域直方图 —— **只有它能证明流生效**。`"stream"` 只是标志值, + # 修复前 oak 路径下该标志为 `revisit` 而实际流是"6 域各 1 轮 + 294 轮单域"。 + "stream_hist": STREAM_HIST, + "proposer": proposer, + "n_shifts": n_shifts, + # ★ 逐次访问的学习曲线(复用指标的唯一来源; `curves` 只留最后一次) + # ⚠ 这里必须是**两层**循环: `visits[k]` 是"曲线的列表", + # 每条曲线才是 `(step, acc)` 的列表。只写一层会拿整条曲线去解包成 2 个值 + # ⇒ `ValueError: too many values to unpack`(冒烟测抓到的)。 + "visits": {int(k): [[[int(st), float(a)] for st, a in cv] for cv in vlist] + for k, vlist in visits.items()}, + # ★ 每个域被访问了几次 —— 复用指标的填充闸门(全 1 ⇒ 无法测复用) + "n_visits": {int(k): len(c) for k, c in visits.items()}, + } + # ★ 原始逐次访问曲线(不序列化, 只给同进程的 efficiency_report 用) + extra["_visits_raw"] = visits + if prop is not None: + extra["proposer_freq"] = [int(x) for x in prop.freq] + extra["proposer_stats"] = prop.stats() + if rlprop is not None: + extra["proposer_freq"] = [int(x) for x in rlprop.visits] + extra["proposer_stats"] = rlprop.stats() + extra["rl_trace"] = rlprop.trace + extra["rl_n_know"] = int(getattr(rlprop, "n_know", 0)) + if oakprop is not None: + extra["proposer_freq"] = [int(x) for x in oakprop.know.coverage.n] + extra["proposer_stats"] = oakprop.stats() + # ★ 知识诊断块: 主回路里**真实**发现了多少 option、门控拦了几次、 + # 转移日志有多长 —— 没有这些就无法判断 OaK 组件是否真的在起作用。 + _kex = oakprop.stats_extra() + extra["oak"] = _kex["oak"] + extra["knowledge"] = _kex["knowledge"] + extra["oak_transitions"] = int(len(oakprop.trans)) + return acc_matrix, domains, curves, cross, prop_rows, extra + + +# ★ 各域数下的**可达上限**(独立于臂的探针天花板)。 +# 来源: docs/lm4_backbone_final_verdict.md「探针上限 3/6/12 域 = 0.9159/0.8065/0.6638」。 +# 用途: 学习效率的**绝对**达标阈值基准。查不到 ⇒ 不算绝对口径, +# **绝不退回"用臂自己的 max"**(那会把"没学会"报成"效率高")。 +REACHABLE_MAX_BY_DOMAINS = {3: 0.9159, 6: 0.8065, 12: 0.6638} + + +def efficiency_report(curves, domains, thresholds=(0.5, 0.9), reachable_max=None, + K_slope=50, visits=None): + """学习效率 / 更新效率 —— **三个口径, 互相独立, 都报**。 + + A `steps_to_abs` 绝对达标步数。阈值取**该域的可达上限(外部常数)**, + 不是臂自己的峰值。达不到 ⇒ `None`(**绝不回填**)。 + B `slope_early` 早期斜率 = (acc@K − acc@0)/K —— "每单位更新换来多少精度" + C `revisit_gain` ★ **知识复用**: 第 1 次访问与第 2 次及以后访问的达标步数之差。 + `>0` 学得更快 ⇒ 有复用;`≈0` ⇒ 没有复用;`<0` ⇒ 干扰 + + 旧口径(相对自己的峰值)保留在 `rel_steps`, 但**不作为结论** —— + 它会把"没学会"报成"效率高"(`docs/learning_efficiency_spec.md` §1 缺陷①)。 + + 格式与判据见 `docs/learning_efficiency_spec.md` §2/§3。 + """ + vis = visits if visits is not None else ({dd: [c] for dd, c in (curves or {}).items()}) + if not vis: + return {} + out = {"steps_to_abs": {}, "slope_early": {}, "within_gain": {}, + "start_level": {}, "retention_gain": {}, "rel_steps": {}, + "_n_visits": {}, "_none_ratio": None, "_already_ratio": None, + "_no_reachable_max": reachable_max is None, + "reachable_max": reachable_max, "theta": list(thresholds), "K_slope": int(K_slope)} + none_n = tot_n = already_n = 0 + for dd, vlist in vis.items(): + d = domains[dd] if dd < len(domains) else str(dd) + key = f"D{d}" + per_visit = {} + starts = [] + for k, cur in enumerate(vlist, start=1): + if not cur: + continue + s0 = float(cur[0][1]) + starts.append(s0) + st_abs = {} + if reachable_max is not None: + for th in thresholds: + tgt = float(reachable_max) * float(th) + tk = str(int(th * 100)) + tot_n += 1 + if s0 >= tgt: + # ★ **起点已超阈** ⇒ 记 "already", **不是 0/1 步**。 + # 把"早就知道"与"1 步学会"混成一个数字, 会让 revisit 流下 + # 所有域都被报成"1 步学会", 复用增益机械归零 + # (冒烟测实测: 全部域每次访问都是 1 步、gain 全 0)。 + st_abs[tk] = "already" + already_n += 1 + continue + hit = next((st for st, a in cur if a >= tgt), None) + st_abs[tk] = hit + if hit is None: + none_n += 1 + per_visit[str(k)] = {"start": s0, "end": float(cur[-1][1]), + "abs": st_abs} + # B 早期斜率: 以**该次访问的起点**为 0, K 步之后为 K + if len(cur) > 1: + t0 = cur[0][0] + win = [(st, a) for st, a in cur if st - t0 <= K_slope] + if len(win) >= 2: + span = win[-1][0] - win[0][0] + if span > 0: + out["slope_early"].setdefault(key, {})[str(k)] = \ + float((win[-1][1] - win[0][1]) / span) + # 该次访问内涨了多少(永远有定义, 不受阈值影响) + out["within_gain"].setdefault(key, {})[str(k)] = float(cur[-1][1] - s0) + # 旧相对口径(保留, 但不作结论) + fin = max(a for _, a in cur) + out["rel_steps"].setdefault(key, {})[str(k)] = { + str(int(th * 100)): next((st for st, a in cur if a >= fin * th), None) + for th in thresholds} + out["steps_to_abs"][key] = per_visit + out["_n_visits"][key] = len(vlist) + # ★★ **保留增益**: 第 2 次及以后的**起点**比第 1 次高多少。 + # 在"重遇到就起点已会"的流下, 这才是唯一的可测量 —— + # 它同时绕过阈值饱和(上面的 "already")与末态精度饱和。 + # `> 0` ⇒ 后面几次起点更高 ⇒ **记住了**; `≈ 0` ⇒ 每次从零开始(遗忘); + # `< 0` ⇒ 反而更低。 + out["start_level"][key] = {str(i + 1): s for i, s in enumerate(starts)} + if len(starts) >= 2: + later = float(np.mean(starts[1:])) + out["retention_gain"][key] = float(later - starts[0]) + else: + out["retention_gain"][key] = None + out["_none_ratio"] = (none_n / tot_n) if tot_n else None + out["_already_ratio"] = (already_n / tot_n) if tot_n else None + return out + + +def summarize(acc_matrix, domains): + """遗忘 + 平均准确率。遗忘 = 每个域历史最佳 - 最终。""" + M = np.asarray(acc_matrix) + finals = M[-1] + forgets = [] + for j in range(M.shape[1]): + col = M[:, j] + col = col[~np.isnan(col)] + if len(col) >= 2: + forgets.append(col.max() - col[-1]) + return { + "final_mean_acc": float(np.nanmean(finals)), + "mean_forget": float(np.mean(forgets)) if forgets else float('nan'), + "acc_matrix": M.tolist(), + "domains": domains, + } + + +def run_joint(X, y_dom, device, d_model=128, d_state=8, n_layers=2, + steps=1500, batch=32, lr=1e-3, seed=42, n_domains=6, n_feat=None, + pool="cat", agg_path=False, stat_input=False, backbone="ssm"): + """诊断: 所有域混合训练 (非持续学习上限)。若这个也学不好 → 特征/任务定义有问题。""" + random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) + tr_idx, te_by_dom = [], {} + for dd in range(n_domains): + idx = np.where(y_dom == dd)[0] + if len(idx) < 50: + continue + rng = np.random.RandomState(seed + dd) + idx = idx.copy(); rng.shuffle(idx) + n_te = max(20, len(idx) // 10) + te_by_dom[dd] = idx[:n_te] + tr_idx.extend(idx[n_te:]) + tr_idx = np.asarray(tr_idx) + ds = torch.utils.data.TensorDataset( + torch.from_numpy(X[tr_idx]), torch.from_numpy(y_dom[tr_idx])) + dl = torch.utils.data.DataLoader(ds, batch_size=batch, shuffle=True) + if backbone == "mlp": + model = StatMLP(n_feat=n_feat or X.shape[-1], n_classes=n_domains, + hidden=d_model).to(device) + else: + model = WaveSSM(n_feat=n_feat or X.shape[-1], d_model=d_model, + d_state=d_state, n_layers=n_layers, + n_classes=n_domains, pool=pool, agg_path=agg_path, + stat_input=stat_input).to(device) + opt = torch.optim.Adam(model.parameters(), lr=lr) + model.train() + it = 0 + while it < steps: + for xb, yb in dl: + if it >= steps: + break + xb, yb = xb.to(device), yb.to(device) + loss = F.cross_entropy(model(xb), yb) + opt.zero_grad(); loss.backward(); opt.step() + it += 1 + accs = {} + for dd in sorted(te_by_dom): + ds_te = torch.utils.data.TensorDataset( + torch.from_numpy(X[te_by_dom[dd]]), torch.from_numpy(y_dom[te_by_dom[dd]])) + accs[dd] = evaluate(model, torch.utils.data.DataLoader(ds_te, batch_size=64), device) + return accs, float(np.mean(list(accs.values()))) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--data", default=str(ROOT / "data" / "wave")) + ap.add_argument("--stride", type=int, default=None, + help="窗口步长 (默认=window 即不重叠, 防 train/test 泄漏)") + ap.add_argument("--no-wfr", action="store_true", + help="只用 MAG 4 维特征, 不接 WFR 波谱") + ap.add_argument("--lshell", action="store_true", + help="追加 L-shell 特征 (log10 偶极 L, sin 磁纬), 需 magpos_*.npz") + ap.add_argument("--no-cache", action="store_true", + help="不使用/不写入特征矩阵缓存") + ap.add_argument("--wfr-bands", type=int, default=13, + help="WFR 65 频段聚合成的段数 (每段磁/电各 1 维 → 2*N 维)") + ap.add_argument("--d-model", type=int, default=128) + ap.add_argument("--d-state", type=int, default=8) + ap.add_argument("--n-layers", type=int, default=2) + ap.add_argument("--pool", default="last", choices=["last", "mean", "max", "cat"], + help="SSM 时序池化 (last 实测最好: 0.7213 vs cat 0.6681)") + ap.add_argument("--test-data", default=None, + help="跨年/外部分布测试集目录 (如 data/wave2016 做 2015->2016 泛化)") + ap.add_argument("--trace-every", type=int, default=0, + help="每 N 步评一次当前域, 产出学习效率曲线 (0=关闭)") + # ★ 学习效率的**绝对**阈值基准。见 docs/learning_efficiency_spec.md §2-A: + # 必须用**独立于臂**的可达上限, 不能用臂自己的峰值(那会把"没学会"报成"效率高")。 + # 缺省按域数查表(数值取自 docs/lm4_backbone_final_verdict.md 的探针上限); + # **查不到就不算 `steps_to_abs`**, 并在结果里标 `_no_reachable_max`, + # 绝不退回"用臂自己的 max"。 + ap.add_argument("--reachable-max", type=float, default=None, + help="该域的可达上限(外部常数); 缺省按域数查表, 查不到则不算绝对达标步数") + ap.add_argument("--slope-k", type=int, default=50, + help="早期斜率的窗口步数 (更新效率: Δacc/Δupdate)") + ap.add_argument("--norm", default="batchnorm", choices=["batchnorm", "fixed", "none"], + help="StatMLP 归一化: batchnorm (随域漂移) / fixed (冻结全局 mu/sd) / none") + ap.add_argument("--head", default="linear", choices=["linear", "pln", "swifttd"], + help="分类头: linear (原) / pln (Meta-SGD 逐参数步长) / swifttd (lm1 局部规则)") + ap.add_argument("--stream", default="fixed", + choices=["fixed", "perm", "revisit", "nonstationary"], + help="任务流类型; 配 --proposer bins 使用") + ap.add_argument("--rounds", type=int, default=0, + help="总轮数 (0 = 等于域数)") + ap.add_argument("--freq-profile", default="", + help="random-matched 臂: 从该 JSON 读取 value 臂的提议频率分布") + ap.add_argument("--schedule", type=str, default="", + help="显式粗域序列, 逗号分隔 (如 2,2,2,2,2). 用于**执行**" + "规划器给出的序列, 检验它是真策略还是模型外推幻觉") + ap.add_argument("--rl-mu", type=float, default=0.05, + help="IDBD 元步长 (实测稳定窗口 0.05~0.2)") + ap.add_argument("--rl-alpha0", type=float, default=0.2, + help="每权重初始步长; 实测 <0.2 时 IDBD 不分化 (步长死亡)") + ap.add_argument("--rl-explore-w", type=float, default=0.5) + ap.add_argument("--rl-know", type=int, default=0, + help="内部知识维度 (0=关)。14 = 6域能力画像 + 6域访问次数 + " + "any_time + 平均遗忘 —— 让智能体自己的预测进入状态 (Horde/GVF)") + ap.add_argument("--rl-algo", + choices=["idbd", "idbd-raw", "idbd-acc", "autostep", "cidbd"], + default="cidbd", + help="步长自适应算法。idbd=RMS归一化版; idbd-raw=官方无归一化" + "(真实回路分化最好 0.2357); autostep=Mahmood2012; " + "cidbd=Continual-IDBD(逐分量EMA归一化+recovery)") + ap.add_argument("--dyn-model", default="linear", + choices=["linear", "tabular"], + help="世界模型: linear=TransitionEnsemble(ridge) / tabular=计数式") + ap.add_argument("--n-regions-opt", type=int, default=16) + ap.add_argument("--use-subgoals", type=int, default=0) + ap.add_argument("--opt-mode", default="fixed", + choices=["fixed", "goal", "goal_term", "goal_term_override"], + help="Option 执行方式 (用户 K1 矩阵): fixed=O1 固定序列(open-loop); " + "goal=O2 目标条件+每步重算(闭环); goal_term=O3 +自适应终止; " + "goal_term_override=O4 +不确定性抢占") + ap.add_argument("--term-eps", type=float, default=0.05, help="β_o 目标达成阈值") + ap.add_argument("--stall-tol", type=float, default=0.0, help="β_o 停滞阈值 (0=关)") + ap.add_argument("--opt-frac", type=float, default=1.0, + help="诊断: 只在一部分轮次启用 option (1.0=每轮都可用)") + ap.add_argument("--shift-at", type=int, default=0, + help="E11: 第 K 轮后触发 regime shift (置换动作->物理区映射)。0=关") + ap.add_argument("--shift-every", type=int, default=0, + help="E12: 之后每 M 轮再漂移一次 (反复漂移)。0=只漂移一次") + ap.add_argument("--oak-options", type=int, default=1, + help="OaK: 1=启用 Options/时间抽象 (E10), 0=关闭 (E9). " + "★ 这是 E9 vs E10 的**唯一**变量 —— 同一 base、同一门控") + ap.add_argument("--oak-gate", type=int, default=1, + help="OaK: 1=启用 coverage+uncertainty 门控 (安全层), 0=关闭") + ap.add_argument("--oak-refresh", type=int, default=4, + help="每积累多少条转移重建一次 world model + 重新发现 option") + ap.add_argument("--proposer", + choices=["fixed", "bins", "random", "random-matched", + "value", "value-nofb", "rl", "oak"], + default="fixed", + help="训练调度: fixed=固定域序(默认) random=随机区间 value=价值函数驱动") + ap.add_argument("--fine-bins", type=int, default=18, + help="候选区间池大小 (必须 >> 轮数, 否则价值函数被抹平)") + ap.add_argument("--proposer-k", type=int, default=3, help="每轮提议几个区间") + ap.add_argument("--proposer-sigma", type=float, default=0.0, + help="sim 核宽度; 0=自适应(取最近邻距离中位数, 推荐)") + ap.add_argument("--lam-sim", type=float, default=1.0, help="简约性权重") + ap.add_argument("--lam-con", type=float, default=1.0, help="自洽性权重") + ap.add_argument("--lam-cov", type=float, default=1.0, help="覆盖增量权重") + ap.add_argument("--lam-fb", type=float, default=1.0, help="真实反馈权重") + ap.add_argument("--backbone", default="mlp", choices=["mlp", "ssm"], + help="骨干: mlp = 窗口统计+MLP (探针实测更强更快); ssm = 原复值 SSM") + ap.add_argument("--stat-input", action="store_true", + help="把窗口统计拼到每个时刻的输入上 (修 SSM 丢全局信息的候选方案)") + ap.add_argument("--agg-path", action="store_true", + help="窗口聚合统计量直接拼进分类头 (聚合直通车)") + ap.add_argument("--epochs-per-domain", type=int, default=300) + ap.add_argument("--joint-steps", type=int, default=1500, + help="联合训练诊断步数 (非持续学习上限)") + ap.add_argument("--joint-only", action="store_true", + help="只跑 joint 天花板, 跳过 naive/replay (容量扫描用)") + ap.add_argument("--batch", type=int, default=32) + ap.add_argument("--lr", type=float, default=1e-3) + ap.add_argument("--replay-ratio", type=float, default=1.0, + help="回放样本数 / 当前 batch 数 (1.0 = 等量混入)") + ap.add_argument("--domains", type=int, default=6) + ap.add_argument("--shuffle-domains", action="store_true", + help="随机化域训练顺序 (检验按密度递增的课程是否重要)") + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--cl-method", default="naive", choices=["naive", "replay", "oml", "oml2"], + help="持续学习方法: naive/replay = 单循环 (线性头); " + "oml = 快慢双循环 (lm3 oml); oml2 = 真 OML 双循环 (lm3 oml2 + lm1 PLN)") + ap.add_argument("--pln-d", type=int, default=64, help="PLN 头隐层维度 (lm1 V31_PLN)") + ap.add_argument("--inner-k", type=int, default=2, help="OML 内循环适应步数 K") + ap.add_argument("--inner-lr", type=float, default=0.1, help="内循环基础步长 (per-feature β 初值)") + ap.add_argument("--outer-lr", type=float, default=None, help="外循环学习率 (默认同 --lr)") + ap.add_argument("--meta-every", type=int, default=1, + help="快慢双循环里外循环每 N 步更新一次 (lm3 用 10)") + ap.add_argument("--reptile-lr", type=float, default=0.0, + help="OML 里 Reptile init 平均步长 (lm1 meta_step 的 init_lr; 0=关闭)") + ap.add_argument("--consolidate-every", type=int, default=10, + help="OML 把适应后的头合并回本体的间隔步数 (lm3 用 10; 0=不合并)") + ap.add_argument("--device", default="cpu") + ap.add_argument("--out", default=str(ROOT / "results" / "lm4_wave")) + args = ap.parse_args() + + # oml/oml2 是元学习过程, 需要"可适应的头参数"(PLN 的 clone_params/step_beta)。 + # 普通 nn.Linear 头没有可适应参数 -> 自动切到 pln 并说明, 避免跑到一半才崩。 + if args.cl_method in ("oml", "oml2") and args.head == "linear": + args.head = "pln" + print("[warn] --cl-method %s 需要可适应头, --head 已自动改为 pln " + "(Linear 头无 clone_params / step_beta)" % args.cl_method, flush=True) + + device = torch.device(args.device) + t0 = time.time() + times, dens, F_mat, ok = build_feature_matrix( + args.data, bands=args.wfr_bands, use_wfr=not args.no_wfr, + use_lshell=args.lshell, use_cache=not args.no_cache) + X, y_log = build_windows(times, dens, F_mat, ok, stride=args.stride) + eff_stride = args.stride if args.stride else WINDOW + d_feat = X.shape[-1] if len(X) else 0 + print(f"[data] 样本 {len(X)} 窗口 x {WINDOW} x {d_feat} 维 (stride={eff_stride}), " + f"{time.time()-t0:.0f}s", flush=True) + if len(X) == 0: + print("ERROR: 无样本 (检查 MAG/WFR 数据是否下载)"); return + y_dom, domain_qs = assign_domains(y_log, args.domains) + print(f"[domains] 边界 log10(density): {[round(float(q),2) for q in domain_qs]}", flush=True) + cnt = np.bincount(y_dom, minlength=args.domains) + print(f"[domains] 每域样本: {cnt.tolist()}", flush=True) + + os.makedirs(args.out, exist_ok=True) + results = {} + + # 诊断: 联合训练上限 (判断任务本身是否可解; --joint-steps 0 跳过) + if args.joint_steps > 0: + print("\n=== Joint (联合训练上限诊断) ===", flush=True) + joint_accs, joint_mean = run_joint( + X, y_dom, device, d_model=args.d_model, d_state=args.d_state, + n_layers=args.n_layers, steps=args.joint_steps, batch=args.batch, + lr=args.lr, seed=args.seed, n_domains=args.domains, n_feat=d_feat, + pool=args.pool, agg_path=args.agg_path, + stat_input=args.stat_input, backbone=args.backbone) + print(" → 联合训练平均 acc %.4f | 各域 %s" % ( + joint_mean, {k: round(v, 3) for k, v in joint_accs.items()}), flush=True) + results["joint"] = {"final_mean_acc": joint_mean, "per_domain": joint_accs} + + # ── 跨年/外部分布测试集 (如 2015 训练 -> 2016 测试) ── + ext_test = None + if args.test_data: + t2, d2, F2, ok2 = build_feature_matrix( + args.test_data, bands=args.wfr_bands, use_wfr=not args.no_wfr, + use_lshell=args.lshell, use_cache=not args.no_cache) + X2, y_log2 = build_windows(t2, d2, F2, ok2, stride=args.stride) + y2, _ = assign_domains(y_log2, args.domains, qs=domain_qs) + if len(X2): + ext_test = {} + for dd in range(args.domains): + idx = np.where(y2 == dd)[0] + if len(idx) < 20: + continue + _X = torch.from_numpy(X2[idx].astype(np.float32)).to(device) + _y = torch.from_numpy(y2[idx]).to(device) + ext_test[dd] = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(_X, _y), batch_size=512) + print(f"[cross] 外部测试集 {args.test_data}: {len(X2)} 窗口, " + f"{len(ext_test)} 个域有数据", flush=True) + else: + print(f"!! 外部测试集 {args.test_data} 无有效窗口", flush=True) + + if args.joint_only: + runs = [] + elif args.cl_method in ("oml", "oml2"): + # 双循环方法: 单次运行, 默认带回放 (lm4 聚合所有有利于 CL 的机制) + runs = [(args.cl_method, True, "old")] + else: + # ★ 三臂: naive / replay(复习**旧**域) / replay-self(同样额外算力但 + # 样本全部来自**当前**域)。第三个臂是判别性对照 —— + # 若 `replay-self ≈ replay` ⇒ 效果只是"多花算力/更强正则", 与抗遗忘无关; + # 若 `replay >> replay-self` ⇒ 旧域数据本身在起作用。 + # 见 docs/lmeff_verdict.md §3。 + runs = [("naive", False, "old"), ("replay", True, "old"), + ("replay-self", True, "self")] + + # ── 候选区间池 (价值函数的"可选结构") ── + # 用物理描述子 [log_dens, logL, |maglat|] 定义, 划分到 --fine-bins 个细区间。 + # 池必须远大于轮数, 否则价值函数被抹平 (V35.19 v1 的教训)。 + FINE_IDX, FINE_DESC = None, None + if args.proposer != "fixed": + nf = args.fine_bins + _fine, _ = assign_domains(y_log, nf) + FINE_IDX = _fine.astype(np.int64) + # 描述子 = [log10 密度] (+ --lshell 时窗口平均的 [log10 偶极 L, sin 磁纬]) + # 这三者共同定义"物理区间", 相似度 = 描述子空间里的距离。 + _cols = [y_log] + if args.lshell and X.ndim == 3 and X.shape[2] >= 2: + _cols.append(X[:, :, -2].mean(1)) # log10 偶极 L + _cols.append(X[:, :, -1].mean(1)) # sin 磁纬 + _D = np.stack(_cols, axis=1) + _desc = np.zeros((nf, _D.shape[1])) + for f in range(nf): + m = (FINE_IDX == f) + _desc[f] = _D[m].mean(0) if m.sum() else np.nan + ok = np.isfinite(_desc).all(1) + _desc[~ok] = np.nanmean(_desc[ok], axis=0) if ok.any() else 0.0 + FINE_DESC = _desc + print("[proposer] 候选池 %d 个区间, 描述子 %d 维, 调度=%s, 每轮提议 %d" + % (nf, _D.shape[1], args.proposer, args.proposer_k), flush=True) + + freq_prof = None + if args.freq_profile: + try: + _fp = json.loads(Path(args.freq_profile).read_text()) + # lm4 结果文件是嵌套结构 (naive/replay/_config), lm5 是扁平结构; + # 两种都支持, 否则 _fp.get 取不到 -> 退化成整个 dict -> np.asarray 报错。 + _cand = _fp.get("proposer_freq") + if _cand is None and isinstance(_fp.get("replay"), dict): + _cand = _fp["replay"].get("proposer_freq") + if isinstance(_cand, dict): # 稀疏 {idx: freq} -> 密集向量 + _mx = max((int(k) for k in _cand), default=-1) + _vec = [0.0] * (_mx + 1) + for _k, _v in _cand.items(): + _vec[int(_k)] = float(_v) + _cand = _vec + freq_prof = _cand if _cand is not None else _fp + print("[freq-matched] 载入频率分布: %d 项" % len(freq_prof), flush=True) + except Exception as e: + print("!! --freq-profile 读取失败:", e, flush=True) + + for key, replay, _rsrc in runs: + print(f"\n=== {key} ===", flush=True) + M, doms, curves, cross, prop_rows, extra = run_experiment(X, y_dom, device, d_model=args.d_model, + d_state=args.d_state, n_layers=args.n_layers, + epochs_per_domain=args.epochs_per_domain, + batch=args.batch, lr=args.lr, replay=replay, + replay_ratio=args.replay_ratio, + replay_src=_rsrc, + seed=args.seed, n_domains=args.domains, + n_feat=d_feat, pool=args.pool, + agg_path=args.agg_path, + shuffle_domains=args.shuffle_domains, + cl_method=(key if key in ("oml", "oml2") else "naive"), + pln_d=args.pln_d, inner_k=args.inner_k, + inner_lr=args.inner_lr, outer_lr=args.outer_lr, + meta_every=args.meta_every, + reptile_lr=args.reptile_lr, + consolidate_every=args.consolidate_every, + stat_input=args.stat_input, + backbone=args.backbone, head_type=args.head, + norm=args.norm, + trace_every=args.trace_every, + external_test=ext_test, + fine=FINE_IDX, fine_desc=FINE_DESC, + proposer=args.proposer, + lam=(args.lam_sim, args.lam_con, + args.lam_cov, args.lam_fb), + proposer_k=args.proposer_k, + proposer_sigma=args.proposer_sigma, + rl_mu=args.rl_mu, rl_alpha0=args.rl_alpha0, + rl_algo=args.rl_algo, rl_know=args.rl_know, + schedule_str=args.schedule, + rl_explore_w=args.rl_explore_w, + oak_options=args.oak_options, + oak_gate=args.oak_gate, + oak_refresh=args.oak_refresh, + opt_frac=args.opt_frac, + opt_mode=args.opt_mode, + dyn_model=args.dyn_model, + n_regions_opt=args.n_regions_opt, + use_subgoals=args.use_subgoals, + term_eps=args.term_eps, + stall_tol=args.stall_tol, + shift_at=args.shift_at, + shift_every=args.shift_every, + stream=args.stream, rounds=args.rounds, + freq_profile=freq_prof, + y_log=y_log, bands=args.wfr_bands, + use_lshell=args.lshell) + # ★ 回读断言: 证明 oak 的三个 flag **真的传到了 run_experiment**。 + # 上次 `--rl-algo/--rl-mu/--rl-alpha0` 只加了签名和 CLI、调用点没接, + # 导致四个臂跑同一配置且给出**逐位相同**结果, 整个对照作废。 + if args.proposer == "oak": + _oak_st = (extra or {}).get("oak") + assert _oak_st is not None, ( + "--proposer oak 但结果里没有 oak 诊断块 -> 配置没生效") + assert bool(_oak_st.get("use_options")) == bool(args.oak_options), ( + "oak_options 未传到 run_experiment: 期望 %s 实得 %s" + % (args.oak_options, _oak_st.get("use_options"))) + assert bool(_oak_st.get("use_gate")) == bool(args.oak_gate), ( + "oak_gate 未传到 run_experiment") + _ps = (extra or {}).get("proposer_stats") or {} + assert str(_ps.get("opt_mode")) == str(args.opt_mode), ( + "opt_mode 未传到 run_experiment: 期望 %s 实得 %s" + % (args.opt_mode, _ps.get("opt_mode"))) + print("[verify] oak 配置回读 OK: options=%s gate=%s mode=%s " + "replan=%s term=%s n_trans=%s options_found=%s" + % (_oak_st.get("use_options"), _oak_st.get("use_gate"), + _ps.get("opt_mode"), _ps.get("replan_steps"), + _ps.get("term_reasons"), + _oak_st.get("n_trans"), + ((extra or {}).get("knowledge") or {}).get("n_options")), flush=True) + s = summarize(M, doms) + s["curves"] = {str(k): v for k, v in (curves or {}).items()} + s["cross_external"] = cross # 外部矩阵 (每步一行) + s["cross_external_final"] = ( + [None if (c != c) else float(c) for c in cross[-1]] + if cross and cross[-1] is not None else None) + # ★ 可达上限: 优先 CLI, 否则按域数查表; 查不到 ⇒ **不算绝对口径** + # (绝不退回"用臂自己的 max" —— 那正是缺陷①)。 + _rm = args.reachable_max + if _rm is None: + _rm = REACHABLE_MAX_BY_DOMAINS.get(len(doms)) + s["efficiency"] = efficiency_report( + curves, doms, reachable_max=_rm, K_slope=args.slope_k, + visits=(extra or {}).get("_visits_raw")) + if extra: + extra.pop("_visits_raw", None) # 不进 JSON(已另存为 "visits") + # 价值函数调度轨迹 (提议了哪些区间 / 当时的价值 / 累计次数) + if prop_rows: + s["proposer_trace"] = prop_rows + s.update(extra) # any-time / 最差遗忘界 / 调度诊断 + results[key] = s + print(f" → 最终平均 acc {s['final_mean_acc']:.4f}, 平均遗忘 {s['mean_forget']:.4f}", flush=True) + if ext_test and cross and cross[-1] is not None: + last = cross[-1] + ok = [c for c in last if c == c] + if ok: + print(f" ── 跨年泛化 (全流程训完后) 平均 {sum(ok)/len(ok):.4f} 各域 " + + " ".join(f"D{i}:{c:.3f}" for i, c in enumerate(last) if c == c), + flush=True) + if s["efficiency"]: + _ef = s["efficiency"] + _rm = _ef.get("reachable_max") + print(f" ── 学习效率 (绝对阈值基准 reachable_max={_rm}) ──", flush=True) + if _ef.get("_no_reachable_max"): + print(" ⚠ 没有独立的可达上限 ⇒ **不算绝对达标步数**(不退回臂自己的 max)", + flush=True) + for _d, _pv in _ef.get("steps_to_abs", {}).items(): + _parts = [] + for _k, _kv in sorted(_pv.items(), key=lambda x: int(x[0])): + _a = _kv.get("abs", {}) + _parts.append(f"{_k}次(起{_kv.get('start', float('nan')):.2f}):" + + "/".join(f"{t}%→{v}" for t, v in _a.items())) + print(f" {_d} " + " ".join(_parts), flush=True) + _rg = {k: v for k, v in _ef.get("retention_gain", {}).items() if v is not None} + if _rg: + print(" ★★ 保留增益(第2次起的起点均值 − 第1次起点; 正=记住了):", flush=True) + for _d, _v in _rg.items(): + _sl = _ef.get("start_level", {}).get(_d, {}) + print(f" {_d}: {_v:+.4f} 起点 " + "→".join( + f"{x:.2f}" for _, x in sorted(_sl.items(), key=lambda y: int(y[0]))), + flush=True) + if _ef.get("_already_ratio") is not None: + print(f" 起点已超阈(already)占比 {_ef['_already_ratio']:.3f}" + f" —— 高说明重遇到时会, 该看保留增益而非达标步数", flush=True) + if _ef.get("_none_ratio") is not None: + print(f" 未达标(None)占比 {_ef['_none_ratio']:.3f}" + f" —— 过高说明阈值与该域可达上限不匹配", flush=True) + + # 对比报告 + feat_desc = (f"MAG 4 维" if args.no_wfr + else f"MAG 4 维 + WFR 波谱 {2*args.wfr_bands} 维 ({args.wfr_bands} 段 x 磁/电)") + lines = ["# LM4 电磁波持续学习 — 结果", "", + f"- 数据: RBSP-A EMFISIS (DENSITY L4 + MAG L3{' + WFR L2' if not args.no_wfr else ''}), 窗口 {WINDOW}", + f"- 特征: {feat_desc} (共 {d_feat} 维)", + f"- 窗口步长: {eff_stride} ({'不重叠' if eff_stride == WINDOW else '有重叠!'})", + f"- 样本: {X.shape[0]}, 域数: {args.domains}", + f"- 模型: WaveSSM d_model={args.d_model} d_state={args.d_state} layers={args.n_layers}", + f"- 每域训练步: {args.epochs_per_domain}", + f"- replay_ratio: {args.replay_ratio}, seed: {args.seed}", "", + "| 方法 | 最终平均 acc | 平均遗忘 |", "|:--|--:|--:|"] + # ★ 只用**本次实际跑过的臂**(不能用 `results` 的全部非下划线键 —— + # `results["joint"]` 是 joint 天花板基线, 只有 final_mean_acc/per_domain, + # 取它的 `mean_forget` 会 KeyError)。 + _arm_keys = [k for k, _rp, _rc in runs if k in results] + for k in _arm_keys: + if k in results: + lines.append(f"| {k} | {results[k]['final_mean_acc']:.4f} | {results[k]['mean_forget']:.4f} |") + lines += ["", "## 遗忘矩阵 (行=训练阶段, 列=域)", ""] + for k in _arm_keys: + if k not in results: + continue + M = results[k]["acc_matrix"] + lines.append(f"### {k}") + lines.append("") + hdr = "| 阶段 | " + " | ".join(f"D{d}" for d in results[k]["domains"]) + " |" + lines.append(hdr) + lines.append("|" + "---|" * (len(results[k]["domains"]) + 1)) + for i, row in enumerate(M): + cells = " | ".join("nan" if (isinstance(v, float) and np.isnan(v)) else f"{v:.3f}" for v in row) + lines.append(f"| {i+1} | {cells} |") + lines.append("") + # config 指纹: 汇总时按它过滤, 防止诊断 run(--epochs-per-domain 1 之类) 混入统计 + results["_config"] = { + "use_wfr": not args.no_wfr, "wfr_bands": args.wfr_bands, + "use_lshell": args.lshell, "stride": eff_stride, + "d_model": args.d_model, "d_state": args.d_state, + "n_layers": args.n_layers, "epochs_per_domain": args.epochs_per_domain, + "pool": args.pool, "agg_path": args.agg_path, "stat_input": args.stat_input, + "backbone": args.backbone, "head": args.head, "norm": args.norm, + "domain_qs": [float(x) for x in domain_qs], + "shuffle_domains": args.shuffle_domains, + "cl_method": args.cl_method, "pln_d": args.pln_d, + "inner_k": args.inner_k, "inner_lr": args.inner_lr, + "outer_lr": args.outer_lr, "meta_every": args.meta_every, + "reptile_lr": args.reptile_lr, "consolidate_every": args.consolidate_every, + "joint_steps": args.joint_steps, "batch": args.batch, "lr": args.lr, + "replay_ratio": args.replay_ratio, "domains": args.domains, + "seed": args.seed, "n_windows": int(X.shape[0]), "n_feat": d_feat, + # 规范化 argv 指纹: 队列据此判断"这个 tag 是否已用完全相同参数跑过" + "argv_sig": " ".join(sorted(sys.argv[1:])), + } + out_md = os.path.join(args.out, "lm4_wave_report.md") + Path(out_md).write_text("\n".join(lines), encoding="utf-8") + # ★ 原子写 + numpy 兜底序列化。 + # 旧写法 (直接 open(...,"w") + 裸 json.dump) 有两个坑: + # ① 任何 numpy 标量漏进 results 就抛 TypeError, 且**已写的半截文件留在盘上** + # (实测 bm2_random-matched 三个 seed 的结果全是损坏 JSON); + # ② --freq-profile 路径会把 numpy 值带进结果, 触发 ①。 + # 改成: 兜底序列化器 + 先写临时文件再 os.replace (原子)。 + def _json_default(o): + # tolist 必须排在 item 前面: numpy 数组也有 .item(), 但对多元素数组 + # 会抛 ValueError("can only convert an array of size 1 ...")。 + # tolist 对数组返回 list、对标量返回 python 标量, 两种情况都正确。 + if hasattr(o, "tolist"): + return o.tolist() + if hasattr(o, "item"): + return o.item() + return str(o) + + _out_json = os.path.join(args.out, "lm4_wave_results.json") + _tmp_json = _out_json + ".tmp" + with open(_tmp_json, "w", encoding="utf-8") as _f: + json.dump(results, _f, ensure_ascii=False, indent=1, default=_json_default) + os.replace(_tmp_json, _out_json) + print(f"\n报告: {out_md}", flush=True) + print(f"总耗时: {time.time()-t0:.0f}s", flush=True) + + +if __name__ == "__main__": + main() diff --git a/tests/run_lm4_wfr_seeds.sh b/tests/run_lm4_wfr_seeds.sh new file mode 100644 index 0000000000000000000000000000000000000000..ad6056b8b6ab0c980c45ed3afcb74d852b1b8d70 --- /dev/null +++ b/tests/run_lm4_wfr_seeds.sh @@ -0,0 +1,19 @@ +#!/bin/bash +# LM4 A 阶段多 seed 验证: WFR 30 维配置, 每路都跑 joint 天花板 +# 用法: bash run_lm4_wfr_seeds.sh [seed2 ...] +set -u +cd /work/liuyuanjie/headless +PY=/work/liuyuanjie/envs/vllm-cu128/bin/python +GPU="${1:?需要 GPU 编号}"; shift + +for s in "$@"; do + tag="lm4A_wfr_s$s" + mkdir -p "results/$tag" + echo "[$(date +%F_%H:%M)] START $tag (GPU$GPU)" >> results/lm4A_seeds.log + CUDA_VISIBLE_DEVICES="$GPU" "$PY" -u tests/run_lm4_wave.py \ + --device cuda --wfr-bands 13 --joint-steps 20000 \ + --epochs-per-domain 1000 --replay-ratio 2.0 --seed "$s" \ + --out "results/$tag" > "results/$tag.log" 2>&1 + echo "[$(date +%F_%H:%M)] DONE $tag (rc=$?)" >> results/lm4A_seeds.log +done +echo "[$(date +%F_%H:%M)] GPU$GPU 队列完成" >> results/lm4A_seeds.log diff --git a/tests/run_lm5_mm.py b/tests/run_lm5_mm.py new file mode 100644 index 0000000000000000000000000000000000000000..7438724cbc5d97e77cde3acd2526244c271f7269 --- /dev/null +++ b/tests/run_lm5_mm.py @@ -0,0 +1,1120 @@ +#!/usr/bin/env python3 +"""LM5 — 文本 + 电磁波 统一持续学习模型 (multimodal continual learner). + +一个**共享 SSM trunk**, 两种模态 stem, 各自的任务头: + + 文本 (en/zh/code) : token ids -> Embedding -> trunk -> text_head (下一个 token, CE) + 电磁波 (D0..D5) : 窗口特征 -> Linear -> trunk -> wave_head (密度域分类) + +域序列(持续学习流): en -> zh -> code -> wave(D0..D5) + +要回答的问题: + 1. 学完电磁波后, 文本能力丢多少?(跨模态遗忘 —— 比单模态难得多) + 2. 反过来, 学文本对电磁波是正向迁移还是干扰? + 3. CL 机制(replay / OML 双循环 / PLN per-feature 步长 / Reptile)在跨模态下是否仍有效? + +用法: + python3 tests/run_lm5_mm.py --cl-method replay --text-steps 300 --wave-epochs 300 + python3 tests/run_lm5_mm.py --cl-method oml2 --head pln +""" +import argparse +import json +import math +import os +import random +import sys +import time +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.ssm_v30_3 import SSM_Layer_V30_3 +from hibs_lnn.pln_head import (PLNHead, SwiftTDAdapter, inner_adapt, + oml_step, oml_step_swifttd) +from run_lm3_bpe import BPE +from run_lm4_wave import (WINDOW, build_feature_matrix, build_windows, + assign_domains, window_agg) +from hibs_lnn.causal import S4WorldSCM + +CHUNK = 32 # 文本块长度 +TEXT_DOMAINS = ("en", "zh", "code") + + +# ============================================================ +# 模型: 共享 trunk + 模态 stem + 各自任务头 +# ============================================================ +class MultiModalSSM(nn.Module): + def __init__(self, vocab, n_feat, n_wave_classes, n_causal_classes=5, + d_model=192, d_state=12, n_layers=2, + head_type="pln", pln_d=64, pool="cat", agg_path=False): + super().__init__() + self.pool = pool + self.agg_path = agg_path + self.head_type = head_type + # ── 模态 stem (各自独立) ── + self.text_stem = nn.Embedding(vocab, d_model) + self.wave_stem = nn.Linear(n_feat, d_model) + self.causal_stem = nn.Linear(CAUSAL_FEAT, d_model) + # ── 共享 trunk (meta 参数 θ) ── + self.layers = nn.ModuleList([ + SSM_Layer_V30_3(d_model, d_state, layer_idx=i, ent_mode='none') + for i in range(n_layers)]) + self.norm = nn.LayerNorm(d_model) + # ── 任务头 ── + self.text_head = nn.Linear(d_model, vocab) # LM + d_wave = d_model * (3 if pool == "cat" else 1) + if agg_path: + d_wave += 5 * n_feat + self.d_wave = d_wave + if head_type == "swifttd": + self.wave_head = SwiftTDAdapter(d_wave, n_wave_classes) + self.causal_head = SwiftTDAdapter(d_wave, n_causal_classes) + else: + self.wave_head = PLNHead(d_wave, pln_d, n_wave_classes) + self.causal_head = PLNHead(d_wave, pln_d, n_causal_classes) + + # ---- 共享 trunk ---- + def _trunk(self, h): + for layer in self.layers: + h, _ = layer(h) + return self.norm(h) + + # ---- 文本: (B, L) ids -> (B, L, vocab) logits ---- + def forward_text(self, ids): + # 取末位 logits -> (B, vocab): 与统计池化骨干统一成"由上下文预测末位 token", + # 两边才可比 (整序列 next-token 与单点预测不是同一个任务)。 + return self.text_head(self._trunk(self.text_stem(ids)))[:, -1] + + # ---- 电磁波: (B, L, n_feat) -> 表示 (B, d_wave) ---- + def _pool(self, h): + if self.pool == "last": + return h[:, -1] + if self.pool == "mean": + return h.mean(1) + if self.pool == "max": + return h.max(1).values + return torch.cat([h[:, -1], h.mean(1), h.max(1).values], dim=-1) + + def encode_wave(self, x, agg=None): + s = self._pool(self._trunk(self.wave_stem(x))) + if self.agg_path: + s = torch.cat([s, window_agg(x) if agg is None else agg], dim=-1) + return s + + def forward_wave(self, x): + return self.wave_head(self.encode_wave(x)) + + # ---- 因果: (B, L, CAUSAL_FEAT) -> (B, d_wave) ---- + def encode_causal(self, x): + return self._pool(self._trunk(self.causal_stem(x))) + + def forward_causal(self, x): + return self.causal_head(self.encode_causal(x)) + + def trunk_params(self): + ps = [p for n, p in self.named_parameters() + if not n.startswith(("text_head.", "wave_head.", "causal_head."))] + return ps + + def wave_head_params(self): + return [p for n, p in self.named_parameters() + if n.startswith("wave_head.")] + + +class MultiModalMLP(nn.Module): + """文本 + 电磁波 + 因果 的统一骨干 —— 用**统计特征 MLP** 取代 SSM 主干。 + + 依据 (docs/lm4_backbone_final_verdict.md): 在电磁波数据上 StatMLP 在 + 天花板 / 保留率 / seed 稳定性 / 速度 四个轴上全面优于复值 SSM。 + 跨模态沿用同一结论: 各模态先投影到公共 d_model, 再走**共享 MLP 主干**。 + + 各模态的"池化表示": + text : token embedding 的 [mean, last] 拼接 -> Linear -> d_model + (文本任务相应改为"由上下文池化预测末位 token") + wave : window_agg (5*n_feat) -> Linear -> d_model + causal : window_agg (5*CAUSAL_FEAT) -> Linear -> d_model + + 归一化默认 fixed (冻结全局 mu/sd) —— BatchNorm 的 running stats + 会随域漂移, 是持续学习的隐性杀手。定点统计量在投影**之后**做。 + """ + + def __init__(self, vocab, n_feat, n_wave_classes, n_causal_classes=5, + d_model=192, hidden=512, n_layers=2, head_type="linear", + pln_d=64, pool="cat", agg_path=False, norm="fixed"): + super().__init__() + self.norm_mode = norm + self.pool = pool + self.d_model = d_model + # ── 各模态 -> 公共 d_model ── + self.text_emb = nn.Embedding(vocab, d_model) + self.text_proj = nn.Linear(2 * d_model, d_model) + self.wave_proj = nn.Linear(5 * n_feat, d_model) + self.causal_proj = nn.Linear(5 * CAUSAL_FEAT, d_model) + # ── 共享主干 ── + if norm == "batchnorm": + nml = nn.BatchNorm1d(d_model) + elif norm == "fixed": + nml = nn.BatchNorm1d(d_model) + else: + nml = nn.Identity() + self.norm_layer = nml + mods = [nml, nn.Linear(d_model, hidden), nn.GELU()] + for _ in range(n_layers - 1): + mods += [nn.Linear(hidden, hidden), nn.GELU()] + self.body = nn.Sequential(*mods) + d_head = hidden + self.d_head = d_head + # ── 任务头 ── + self.text_head = nn.Linear(d_head, vocab) + if head_type == "swifttd": + from hibs_lnn.pln_head import SwiftTDAdapter + self.wave_head = SwiftTDAdapter(d_head, n_wave_classes) + self.causal_head = SwiftTDAdapter(d_head, n_causal_classes) + elif head_type == "pln": + from hibs_lnn.pln_head import PLNHead + self.wave_head = PLNHead(d_head, pln_d, n_wave_classes) + self.causal_head = PLNHead(d_head, pln_d, n_causal_classes) + else: + self.wave_head = nn.Linear(d_head, n_wave_classes) + self.causal_head = nn.Linear(d_head, n_causal_classes) + + # ---- 文本: 由上下文池化预测末位 token ---- + def forward_text(self, ids): + e = self.text_emb(ids) # (B, L, d) + pooled = torch.cat([e.mean(1), e[:, -1, :]], dim=-1) + return self.text_head(self.body(self.text_proj(pooled))) + + # ---- 电磁波 / 因果: 窗口统计 ---- + def encode_wave(self, x): + return self.body(self.wave_proj(window_agg(x))) + + def forward_wave(self, x): + return self.wave_head(self.encode_wave(x)) + + def encode_causal(self, x): + return self.body(self.causal_proj(window_agg(x))) + + def forward_causal(self, x): + return self.causal_head(self.encode_causal(x)) + + # ---- fixed 归一化 ---- + def set_fixed_stats(self, feats): + """feats: (N, d_model) 各类模态投影后的拼接, 用于算全局 mu/sd。""" + if self.norm_mode != "fixed": + return + with torch.no_grad(): + self.norm_layer.running_mean.copy_(feats.mean(0)) + self.norm_layer.running_var.copy_(feats.std(0).clamp(min=1e-6) ** 2) + self.norm_layer.num_batches_tracked.fill_(1) + self.norm_layer.eval() + + def train(self, mode=True): + super().train(mode) + if getattr(self, "norm_mode", None) == "fixed": + self.norm_layer.eval() + return self + + +# ============================================================ +# 数据 +# ============================================================ +class MultiModalHybrid(nn.Module): + """**按模态分派骨干** —— 依据 docs/lm5_backbone_verdict.md。 + + 3 seed x 2000 步实测 (同一批数据, 同一任务): + 域 MLP SSM 差 + en 0.1250±0.1021 0.2500±0.1021 -0.125 <- SSM 赢 + code 0.0833±0.0589 0.2083±0.0589 -0.125 <- SSM 赢 + wave 0.5256±0.0353 0.4611±0.0329 +0.065 <- MLP 赢 + causal 0.3598±0.0127 0.3600±0.0114 -0.0002 <- 无差别 + 结论: "哪个骨干更好"没有全局答案, 分派规则 = + 离散有序序列 (文本) -> SSM (下一 token 预测需要词序, 池化会丢掉它) + 连续窗口 (电磁波) -> 统计 MLP (全局矩已足够, 且更快更稳) + 结构/因果任务 -> 两者皆可 (任务瓶颈, 选便宜的) + """ + + def __init__(self, vocab, n_feat, n_wave_classes, n_causal_classes=5, + d_model=192, d_state=12, n_layers=2, head_type="linear", + pln_d=64, pool="cat", agg_path=False, norm="fixed", + text_bb="ssm", wave_bb="mlp"): + super().__init__() + self.text_bb, self.wave_bb = text_bb, wave_bb + self.pool, self.agg_path = pool, agg_path + self.norm_mode = norm + self.n_feat = n_feat + # ── 文本路径: 序列主干 ── + self.text_stem = nn.Embedding(vocab, d_model) + self.text_layers = nn.ModuleList([ + SSM_Layer_V30_3(d_model, d_state, layer_idx=i, ent_mode='none') + for i in range(n_layers)]) + self.text_norm = nn.LayerNorm(d_model) + self.text_head = nn.Linear(d_model, vocab) + # ── 电磁波/因果路径: 统计特征 MLP ── + self.wave_proj = nn.Linear(5 * n_feat, d_model) + self.causal_proj = nn.Linear(5 * CAUSAL_FEAT, d_model) + nml = (nn.BatchNorm1d(d_model) if norm in ("batchnorm", "fixed") + else nn.Identity()) + self.norm_layer = nml + mods = [nml, nn.Linear(d_model, d_model), nn.GELU()] + self.body = nn.Sequential(*mods) + # 本骨干的波/因果通路是 body(wave_proj(window_agg(x))) -> 维度就是 d_model; + # 不做 pool=cat 的三路拼接 (那是 SSM 通路的事), 否则头维度对不上。 + d_head = d_model + self.d_head = d_head + d_wave = d_model + self.d_wave = d_wave + if head_type == "swifttd": + self.wave_head = SwiftTDAdapter(d_wave, n_wave_classes) + self.causal_head = SwiftTDAdapter(d_wave, n_causal_classes) + elif head_type == "pln": + self.wave_head = PLNHead(d_wave, pln_d, n_wave_classes) + self.causal_head = PLNHead(d_wave, pln_d, n_causal_classes) + else: + self.wave_head = nn.Linear(d_wave, n_wave_classes) + self.causal_head = nn.Linear(d_wave, n_causal_classes) + + # ---- 文本 (SSM) ---- + def _text_trunk(self, h): + for layer in self.text_layers: + h, _ = layer(h) + return self.text_norm(h) + + def forward_text(self, ids): + return self.text_head(self._text_trunk(self.text_stem(ids)))[:, -1] + + # ---- 电磁波 / 因果 (统计 MLP) ---- + def _pool(self, h): + if self.pool == "last": + return h[:, -1] + if self.pool == "mean": + return h.mean(1) + if self.pool == "max": + return h.max(1).values + return torch.cat([h[:, -1], h.mean(1), h.max(1).values], dim=-1) + + def encode_wave(self, x): + return self.body(self.wave_proj(window_agg(x))) + + def forward_wave(self, x): + return self.wave_head(self.encode_wave(x)) + + def encode_causal(self, x): + return self.body(self.causal_proj(window_agg(x))) + + def forward_causal(self, x): + return self.causal_head(self.encode_causal(x)) + + def set_fixed_stats(self, feats): + if self.norm_mode != "fixed": + return + with torch.no_grad(): + self.norm_layer.running_mean.copy_(feats.mean(0)) + self.norm_layer.running_var.copy_(feats.std(0).clamp(min=1e-6) ** 2) + self.norm_layer.num_batches_tracked.fill_(1) + self.norm_layer.eval() + + def train(self, mode=True): + super().train(mode) + if getattr(self, "norm_mode", None) == "fixed": + self.norm_layer.eval() + return self + + def trunk_params(self): + return [p for n, p in self.named_parameters() + if not n.startswith(("text_head.", "wave_head.", "causal_head."))] + + +def load_text_domains(data_dir, limit=2_000_000): + """en / zh / code 三个文本域。缺文件则跳过。""" + dom = {} + for name, fname in [("en", "en.txt"), ("zh", "zh.txt"), ("code", "code.txt")]: + p = Path(data_dir) / fname + if p.exists(): + dom[name] = p.read_text(errors="ignore")[:limit] + return dom + + +def make_text_chunks(ids, rng, batch, chunk=CHUNK): + """x = 上下文 (B, chunk-1); y = 末位 token (B,) 要预测的目标。 + + 统一成"由上下文预测末位 token" —— 统计池化骨干只能出单点预测, + SSM 骨干也改用同一任务, 两边才可比。 + """ + n = len(ids) + if n < chunk + 2: + return None + starts = [rng.randrange(0, n - chunk - 1) for _ in range(batch)] + x = torch.tensor([ids[s:s + chunk - 1] for s in starts], dtype=torch.long) + y = torch.tensor([ids[s + chunk - 1] for s in starts], dtype=torch.long) + return x, y + + +# ============================================================ +# 因果域数据 (S4WorldSCM) +# ============================================================ +CAUSAL_FEAT = 10 # [6 位生成元 sig] + [cyc] + [3 维 obs] + + +def build_causal_data(n=20000, L=8, seed=0, n_bins=5): + """从 S4WorldSCM 生成因果域序列分类数据。 + + S4WorldSCM 的因果结构: G -> cyc; G -> obs; cyc -> obs; obs,cyc -> acc + + 每样本: + x (L, CAUSAL_FEAT) 每步 = [6 位生成元 sig, cyc, obs(3)] + y = acc 分桶 (n_bins 类) <- **观测**结果 + y_do = do() 干预后的 acc 分桶 <- **干预**结果 + + 关键: y 与 y_do 的差就是"相关 vs 因果"的判别面。 + 只学到相关性的模型在 y_do 上会塌, 学到因果结构的模型能撑住。 + """ + scm = S4WorldSCM(seed=seed) + rng = np.random.default_rng(seed) + perms = list(scm.factor.keys()) + X = np.zeros((n, L, CAUSAL_FEAT), dtype=np.float32) + y = np.zeros(n, dtype=np.int64) + y_do = np.zeros(n, dtype=np.int64) + for i in range(n): + perm = perms[int(rng.integers(len(perms)))] + sig = np.array(scm.sig_bits(perm), dtype=np.float32) + cyc = float(scm.cyc(perm)) + # 随机"已学生成元"子集 -> 让 acc 有丰富分布 + k = int(rng.integers(0, 7)) + atoms = set(rng.choice(6, size=k, replace=False).tolist()) + for t in range(L): + obs = np.asarray(scm.observe(perm, rng), dtype=np.float32) + X[i, t] = np.concatenate([sig, [cyc], obs]) + # ★ 观测/干预必须用**同一个**噪声水平, 否则两边标签难度不同, + # "干预比观测准"会是纯假象 (do_intervene 内部写死 noise=0)。 + a = scm.acc(perm, atoms) + # ★ 干预只点亮**部分**生成元。旧写法把该 perm 的全部生成元并进已学集合, + # known/total ≡ 1 -> acc 恒 ~0.95 -> y_do 有 78.9% 挤在同一类, + # 多数类基线 0.789, 模型原始准确率 0.734 竟低于常数预测器 -> + # "干预 vs 观测"的对照完全失去区分度 (实测见 docs/lm5_*). + # 干预强度**均匀覆盖**: acc = 0.35 + 0.6*(已学数/总生成元数), 若只允许 + # "点亮"则 acc 只会往上走, y_do 仍偏斜 (实测多数类 0.663)。改成先均匀 + # 抽一个"目标已学数"再补齐, 让 y_do 覆盖整个 [0.35, 0.95] 值域, + # 同时也包含"无效干预"(_need=0, 干预后与原观测一致)。 + _sig = scm.sig_bits(perm) + _tot = max(1, int(sum(_sig))) + _cur = sum(1 for i in range(6) if _sig[i] and i in atoms) + _cand = [i for i in range(6) if _sig[i] and i not in atoms] + _target = int(rng.integers(_cur, _tot + 1)) + _need = min(max(0, _target - _cur), len(_cand)) + _do_atoms = (list(rng.choice(_cand, size=_need, replace=False)) + if _cand and _need else []) + a_do = scm.do_intervene(perm, atoms, atoms=_do_atoms) + # 给干预版补上同样的观测噪声 + a_do_n = float(np.clip(a_do + rng.normal(0, 0.08), 0.02, 0.98)) + y[i] = min(n_bins - 1, max(0, int(a * n_bins))) + y_do[i] = min(n_bins - 1, max(0, int(a_do_n * n_bins))) + return X, y, y_do + + +# ============================================================ +# 持续学习主循环 +# ============================================================ +def balanced_acc(pred, y, n_classes): + """宏平均召回 (balanced accuracy) —— 对类不平衡免疫。 + + 为什么必须要它: 因果域 y_do 的类分布是 {0:0.001, 1:0.029, 2:0.015, + 3:0.165, 4:0.789}, 多数类基线就高达 0.789。此时**原始准确率毫无意义** + —— 一个常数预测器就能拿到 0.789, 看起来"干预任务学得很好"。 + 必须用宏平均召回, 每类等权, 才能看出模型是否真的学到了结构。 + """ + pred = np.asarray(pred); y = np.asarray(y) + rs = [] + for c in range(n_classes): + m = (y == c) + if m.sum() > 0: + rs.append(float((pred[m] == c).mean())) + return float(np.mean(rs)) if rs else float('nan') + + +def majority_baseline(y, n_classes): + """常数预测器的得分 (取最高频类) —— 任何低于它的"准确率"都是假象。""" + y = np.asarray(y) + if len(y) == 0: + return float('nan') + cnt = np.bincount(y, minlength=n_classes) + return float(cnt.max() / len(y)) + + +def batched_pred(fwd, X, bs=512): + """分批前向再 argmax —— 一次性喂整个测试集会 OOM + (SSM 的 Ab 张量是 (B,L,d,s) 复数, B=1.2万 时 ~5GB)。""" + outs = [] + for i in range(0, X.shape[0], bs): + outs.append(fwd(X[i:i + bs]).argmax(-1)) + return torch.cat(outs) + + +def build_modality_consistency(domains, wave_X, causal, text_domains): + """每个模态域的**自洽性** con(p) —— V35.19 三项之一 (leo 理论: 自洽性=彼此不矛盾)。 + + 为什么必须有: 不注入时 `con(p)` 恒为 1.0, 该维对排序零贡献 -> + 价值函数退化成覆盖均匀化采样器 (lm4 实测 signature: value 与 value-nofb + 提议序列逐位相同, term_std['con']=0.0)。这是本轮踩过的坑。 + + lm5 的候选是 (模态, 复习轮次), 所以自洽性按**模态**给: + wave : 用 lm4 因果发现的稳定关系 (密度↔电场波、偶极场 B∝L^-3) 检验 + 域内相关符号是否与全局同向 + causal : 用 SCM 的父结构 (obs,cyc -> acc) 检验 + text : **无结构可查 -> 1.0** (明确标注未实现, 不是"一致性好") + + 返回 {domain: con} 字典。 + """ + out = {d: 1.0 for d in domains} + # ── wave: 稳定关系的符号一致性 ── + try: + if wave_X is not None and len(wave_X) > 500: + Xw = np.asarray(wave_X, dtype=np.float64) + if Xw.ndim == 3: + Xw = Xw.mean(axis=1) # 窗口内均值 + nf = Xw.shape[1] + # 特征布局: 0=log10|B|, 1=log10 rms, 2=lambda, 3=delta, + # 4.. = WFR (磁前半/电后半), 末尾两列= L-shell + pairs = [(0, 1), (0, nf - 2), (1, nf - 2)] + # ★ 连续强度: 半数据 vs 另一半 的相关强度比 = 结构性有多稳。 + # 二值版("关系是否成立")实测处处为真 -> con ≡ 1.0 -> 该项死亡。 + half = len(Xw) // 2 + num = den = 0.0 + for i, j in pairs: + if not (0 <= i < nf and 0 <= j < nf): + continue + a, b = Xw[:, i], Xw[:, j] + if a.std() < 1e-9 or b.std() < 1e-9: + continue + cg = np.corrcoef(a, b)[0, 1] + ca = np.corrcoef(a[:half], b[:half])[0, 1] + cb = np.corrcoef(a[half:], b[half:])[0, 1] + if not (np.isfinite(cg) and np.isfinite(ca) and np.isfinite(cb)): + continue + st = min(1.0, abs((ca + cb) / 2) / max(1e-6, abs(cg))) + if np.sign(ca + cb) != np.sign(cg): + st = 0.0 + num += st + den += 1.0 + if den: + out["wave"] = num / den + except Exception as e: + print("!! wave 自洽性计算失败 (con=1.0):", e, flush=True) + # ── causal: SCM 父结构的一致性 (acc 应同时依赖 obs 与 cyc) ── + try: + if causal is not None and causal.get("X") is not None: + CX = np.asarray(causal["X"], dtype=np.float64) + if CX.ndim == 3 and CX.shape[2] >= 4: + Cm = CX.mean(axis=1) # 每样本的步均值 + y = np.asarray(causal["y"]) + if len(y) == len(Cm): + # 每维与 acc 的相关强度 -> 至少要有 ≥2 维显著依赖才算自洽 + cs = [abs(np.corrcoef(Cm[:, k], y)[0, 1]) + for k in range(Cm.shape[1]) + if Cm[:, k].std() > 1e-9] + cs = [c for c in cs if np.isfinite(c)] + if cs: + strong = sum(1 for c in cs if c > 0.05) + out["causal"] = min(1.0, strong / max(2, len(cs) * 0.3)) + except Exception as e: + print("!! causal 自洽性计算失败 (con=1.0):", e, flush=True) + return out + + +def run_mm(text_domains, wave_X, wave_y, wave_test, device, + causal=None, n_causal_classes=5, backbone="mlp", norm="fixed", + proposer="fixed", rounds_per_domain=3, lam=(1.0, 1.0, 1.0, 1.0), + oak_options=1, oak_gate=1, oak_refresh=4, + rl_mu=0.05, rl_alpha0=0.2, rl_algo="autostep", + proposer_sigma=0.5, stream="fixed", rounds=0, freq_profile=None, + d_model=192, d_state=12, n_layers=2, vocab=1000, + n_feat=30, n_wave_classes=6, head_type="pln", + text_steps=300, wave_epochs=300, batch=32, lr=1e-3, + cl_method="replay", replay_ratio=1.0, seed=42, pool="cat", + agg_path=False, inner_k=2, inner_lr=0.1, reptile_lr=0.0, + consolidate_every=10): + random.seed(seed); np.random.seed(seed); torch.manual_seed(seed) + rng = random.Random(seed) + + if backbone == "hybrid": + model = MultiModalHybrid(vocab, n_feat, n_wave_classes, + n_causal_classes=n_causal_classes, d_model=d_model, + d_state=d_state, n_layers=n_layers, + head_type=head_type, pool=pool, + agg_path=agg_path, norm=norm).to(device) + elif backbone == "mlp": + model = MultiModalMLP(vocab, n_feat, n_wave_classes, + n_causal_classes=n_causal_classes, d_model=d_model, + hidden=d_model, n_layers=n_layers, + head_type=head_type, pool=pool, + agg_path=agg_path, norm=norm).to(device) + else: + model = MultiModalSSM(vocab, n_feat, n_wave_classes, + n_causal_classes=n_causal_classes, d_model=d_model, + d_state=d_state, n_layers=n_layers, + head_type=head_type, pool=pool, + agg_path=agg_path).to(device) + # fixed 归一化: 从各模态的投影特征算一次全局 mu/sd 然后冻结。 + # (BatchNorm 的 running stats 随域漂移 = 持续学习的隐性杀手) + if backbone in ("mlp", "hybrid") and norm == "fixed": + with torch.no_grad(): + rows = [] + # 只有纯 MLP 骨干的文本通路走池化投影 (需要 fixed 统计量); + # 混合骨干的文本走 SSM + LayerNorm, 与统计量无关。 + if backbone == "mlp": + for d in list(text_domains)[:3]: + ids = torch.tensor(text_domains[d][:30000], dtype=torch.long) + if len(ids) < CHUNK + 2: + continue + hi = len(ids) - CHUNK - 1 + st = np.random.randint(0, hi, min(256, hi)) + xx = torch.stack([ids[t:t + CHUNK - 1] for t in st]).to(device) + e = model.text_emb(xx) + rows.append(model.text_proj( + torch.cat([e.mean(1), e[:, -1, :]], -1))) + if wave_X is not None and len(wave_X): + rows.append(model.wave_proj(window_agg(torch.as_tensor( + wave_X[:256]).float().to(device)))) + if causal is not None and causal.get("X") is not None and len(causal["X"]): + cx = torch.as_tensor(causal["X"][:256]).float().to(device) + rows.append(model.causal_proj(window_agg(cx))) + if rows: + model.set_fixed_stats(torch.cat(rows, 0)) + print(f"[norm] fixed 统计量: {sum(len(r) for r in rows)} 个投影特征", + flush=True) + + use_pln = head_type in ("pln", "swifttd") + opt = torch.optim.Adam(model.parameters(), lr=lr) + opt_outer = opt + if use_pln and cl_method == "oml2": + opt_outer = torch.optim.Adam(model.parameters(), lr=lr) + + # 域序列: 文本域(有数据且非空) + 电磁波域 + domains = [d for d in TEXT_DOMAINS if d in text_domains and len(text_domains[d]) > 2000] + domains = domains + ["wave"] + if causal is not None: + domains = domains + ["causal"] + n_dom = len(domains) + # 随机基线 + chance = {d: 1.0 / vocab for d in domains if d not in ("wave", "causal")} + chance["wave"] = 1.0 / n_wave_classes + if "causal" in domains: + chance["causal"] = 1.0 / n_causal_classes + chance["causal_do"] = 1.0 / n_causal_classes + # 平衡准确率的随机线 = 1/类数 (宏平均召回下常数预测器只能拿到 ~1/K) + chance["causal_bal"] = 1.0 / n_causal_classes + chance["causal_do_bal"] = 1.0 / n_causal_classes + + # 回放缓冲: 每域固定量 + replay_buf = {} + acc_hist = [] + + def eval_all(): + """返回 {域: 指标}。文本=下一个 token 准确率, 电磁波=密度域准确率。""" + model.eval() + out = {} + with torch.no_grad(): + for d in domains: + if d == "wave": + if d in wave_test: + Xte, yte = wave_test[d] + pred = batched_pred(model.forward_wave, Xte) + out[d] = float((pred == yte).float().mean()) + else: + out[d] = float('nan') + elif d == "causal": + # 关键: 同时报**观测**与**干预**两个准确率。 + # 只学到相关性的模型会在干预版上塌 —— 那个 gap 就是因果能力的度量。 + if causal is None: + out["causal"] = float('nan') + out["causal_do"] = float('nan') + else: + Xo, yo = causal["test_obs"] + Xd, yd = causal["test_do"] + po = batched_pred(model.forward_causal, Xo).cpu().numpy() + pd_ = batched_pred(model.forward_causal, Xd).cpu().numpy() + yo_n, yd_n = yo.cpu().numpy(), yd.cpu().numpy() + out["causal"] = float((po == yo_n).mean()) + out["causal_do"] = float((pd_ == yd_n).mean()) + # ★ 平衡准确率: 因果域的标签严重偏斜, 原始准确率会骗人 + out["causal_bal"] = balanced_acc(po, yo_n, n_causal_classes) + out["causal_do_bal"] = balanced_acc(pd_, yd_n, n_causal_classes) + # 首次评估时把常数基线记下来, 供报告判读 + if "causal_base" not in out: + out["causal_base"] = majority_baseline(yo_n, n_causal_classes) + out["causal_do_base"] = majority_baseline(yd_n, n_causal_classes) + else: + ids = torch.tensor(text_domains[d][:60000], dtype=torch.long) + if len(ids) < CHUNK + 2: + out[d] = float('nan'); continue + s = tot = 0 + for _ in range(8): + st = rng.randrange(0, len(ids) - CHUNK - 1) + x = ids[st:st + CHUNK - 1].unsqueeze(0).to(device) + y = ids[st + CHUNK - 1].unsqueeze(0).to(device) + s += int((model.forward_text(x).argmax(-1) == y).sum()) + tot += y.numel() + out[d] = s / max(1, tot) + model.train() + return out + + # ── 价值函数驱动的训练调度 (V35.19 移植; 见 hibs_lnn/value_proposer.py) ── + # 候选 = (域, 第几轮复习); 描述子 = one-hot(域) + 归一化轮次。 + # 池 = n_dom × rounds_per_domain —— 必须 >> 轮数, 否则价值函数被抹平 + # (V35.19 v1 的教训: 池 18 个被全覆盖 -> 提议顺序无关 -> λ 扫描零效果)。 + # ── 任务流 / 调度 ─────────────────────────────────────────────────── + # 与 lm4 同一套判据 (leo 2026-09-14): 固定顺序预设了智能体不会有的知识 + # (任务集已知 / 最优顺序已知 / 单趟不复习), 而且它恰是 replay 最有利的顺序 + # -> 系统性偏袒要被打败的基线。改用**流分布** + 全 oracle-free 基线。 + # + # stream : fixed(启发式基线) / perm / revisit / nonstationary + # proposer : fixed(= 用 stream) / random / random-matched(频率对齐对照) + # / value / value-nofb(消融反馈项) + n_rounds = rounds if rounds and rounds > 0 else n_dom + rng_sched = np.random.RandomState(seed * 7919 + 31) + if stream == "fixed": + dom_seq = [domains[i % n_dom] for i in range(n_rounds)] + elif stream == "perm": + _p = list(rng_sched.permutation(n_dom)) + dom_seq = [domains[i % n_dom] for i in _p[:n_rounds]] if n_rounds <= n_dom \ + else [domains[i] for i in (list(_p) * (n_rounds // n_dom + 1))[:n_rounds]] + elif stream == "revisit": + dom_seq = [domains[int(x)] for x in rng_sched.randint(0, n_dom, size=n_rounds)] + elif stream == "nonstationary": + dom_seq = [] + for _r in range(n_rounds): + _w = 1.0 + 0.9 * np.sin(2 * np.pi * _r / max(2, n_rounds // 2) + + rng_sched.uniform(0, 2 * np.pi)) + _w = np.clip(_w, 0.05, None) * (1.0 + 0.5 * rng_sched.rand(n_dom)) + dom_seq.append(domains[int(rng_sched.choice(n_dom, p=_w / _w.sum()))]) + else: + dom_seq = [domains[i % n_dom] for i in range(n_rounds)] + + prop, POOL, prop_trace = None, None, [] + prof = None + if proposer != "fixed": + _R = max(2, rounds_per_domain, int(np.ceil(n_rounds / max(1, n_dom)))) + POOL = [(d, r) for d in domains for r in range(_R)] + _desc = np.zeros((len(POOL), n_dom + 1)) + for _i, (_d, _r) in enumerate(POOL): + _desc[_i, domains.index(_d)] = 1.0 + _desc[_i, n_dom] = _r / max(1, _R - 1) + from hibs_lnn.value_proposer import RegimeProposer + _lam = (lam[0], lam[1], lam[2], 0.0) if proposer == "value-nofb" else lam + prop = RegimeProposer(_desc, k=1, lam=_lam, sigma=proposer_sigma, + tau=0.5, seed=seed * 7919 + 31) + # ★ 注入自洽性, 否则该项恒 1.0 = 死亡 (lm4 踩过的坑) + try: + _conds = build_modality_consistency(domains, wave_X, causal, text_domains) + _cv = np.array([_conds.get(d, 1.0) for d, _ in POOL], dtype=np.float64) + prop.set_consistency(_cv) + print("[con] 模态自洽性: %s (std=%.3f, 0=该项死亡)" + % ({k: round(v, 3) for k, v in _conds.items()}, _cv.std()), + flush=True) + except Exception as _e: + print("!! 自洽性注入失败, con 项将为常数:", _e, flush=True) + if proposer == "random-matched" and freq_profile: + _pr = np.asarray(freq_profile, dtype=np.float64) + prof = (_pr / _pr.sum()) if _pr.sum() > 0 else None + print(f"[proposer] 候选池 {len(POOL)} 个 (= {n_dom} 域 x {_R} 轮), " + f"调度={proposer}" + (" [频率对齐]" if prof is not None else ""), + flush=True) + + oakprop = None + if proposer == "oak": + # ★ OaK 提议器: 包住 RLProposer + InternalKnowledge + Options。 + # lm5 的动作 = POOL 里第几个候选 (域 x 复习轮次); + # state = 各域实测准确率向量 (与 lm4 的"能力画像"同构)。 + from hibs_lnn.oak_proposer import OAKProposer + oakprop = OAKProposer(_desc, n_fine=len(POOL), k=1, + seed=seed * 7919 + 29, tau=0.5, + mu=rl_mu, alpha0=rl_alpha0, algo=rl_algo, + n_know=0, use_options=bool(oak_options), + use_gate=bool(oak_gate), refresh_every=oak_refresh) + print("[oak] OAKProposer: n_fine=%d options=%s gate=%s algo=%s" + % (len(POOL), oak_options, oak_gate, rl_algo), flush=True) + else: + print(f"[stream] {stream} -> 轮次序列 {dom_seq}", flush=True) + + t0 = time.time() + any_time = [] + for step in range(n_rounds): + if oakprop is not None: + _ci = int(oakprop.act()[0]) + dom, cur_cand = POOL[_ci][0], _ci + elif prop is not None: + if proposer in ("value", "value-nofb"): + _ci = prop.propose(k=1)[0] + elif prof is not None: + _ci = int(rng_sched.choice(prop.n, size=1, p=prof)[0]) + prop.freq[_ci] += 1 + else: + _ci = prop.pick_random(k=1)[0] + dom, cur_cand = POOL[_ci][0], _ci + else: + dom, cur_cand = dom_seq[step], None + print(f"\n--- 轮 {step+1}/{n_rounds}: {dom} ---", flush=True) + model.train() + + if dom in ("wave", "causal"): + # ── 分类域 (电磁波密度 / 因果 acc): 同一套逻辑, 只换 stem+head+数据 ── + if dom == "wave": + CX, CY = wave_X, wave_y + enc_fn, head_fn, fwd_fn = (model.encode_wave, model.wave_head, + model.forward_wave) + else: + CX, CY = causal["X"], causal["y"] + enc_fn, head_fn, fwd_fn = (model.encode_causal, model.causal_head, + model.forward_causal) + tr_idx = np.arange(len(CX)) + it = 0 + while it < wave_epochs: + sel = np.random.RandomState(seed + it).choice( + tr_idx, size=min(batch, len(tr_idx)), replace=False) + xb = torch.from_numpy(CX[sel]).to(device) + yb = torch.from_numpy(CY[sel]).to(device) + if cl_method == "oml2" and use_pln: + parts_x, parts_y = [xb], [yb] + if replay_buf.get(dom) is not None: + _h, _y = replay_buf[dom] + k = max(1, int(batch * replay_ratio)) + sidx = torch.randint(0, len(_y), (k,), device=device) + parts_x.append(_h[sidx]); parts_y.append(_y[sidx]) + XB, YB = torch.cat(parts_x), torch.cat(parts_y) + # ★ 同 run_lm4_wave: 必须随机切, 否则 support=新域 / query=旧域 + _perm = torch.randperm(XB.shape[0], device=device) + XB, YB = XB[_perm], YB[_perm] + half = max(2, XB.shape[0] // 2) + with torch.no_grad(): + h_sup = enc_fn(XB[:half]) + h_qry = enc_fn(XB[half:]) + fn = oml_step_swifttd if head_type == "swifttd" else oml_step + kw = dict(K=inner_k, + consolidate=(consolidate_every > 0 and it % consolidate_every == 0), + reptile_lr=reptile_lr) + if head_type != "swifttd": + kw["per_feature"] = True + lq, _ = fn(model, head_fn, h_sup, YB[:half], h_qry, YB[half:], + opt_outer, **kw) + # lm3 oml2 的第二部分: 常规训练步 (含 replay) + loss_n = F.cross_entropy(fwd_fn(XB), YB) + opt.zero_grad(); loss_n.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + opt.step() + else: + loss = F.cross_entropy(fwd_fn(xb), yb) + if cl_method == "replay" and replay_buf.get(dom) is not None: + rh, ry = replay_buf[dom] + k = max(1, int(batch * replay_ratio)) + sidx = torch.randint(0, len(ry), (k,), device=device) + loss = loss + F.cross_entropy(fwd_fn(rh[sidx]), ry[sidx]) + opt.zero_grad(); loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + opt.step() + it += 1 + # 存回放 (按域) + n_rb = min(2000, len(CX)) + sel = np.random.RandomState(seed).choice(len(CX), n_rb, replace=False) + replay_buf[dom] = (torch.from_numpy(CX[sel]).to(device), + torch.from_numpy(CY[sel]).to(device)) + else: + # ── 文本域: 下一个 token 预测 ── + ids = text_domains[dom] + for it in range(text_steps): + batch_sz = batch + if cl_method == "replay" and replay_buf: + # 其他域各抽一点混进来 + pass + c = make_text_chunks(ids, rng, batch_sz) + if c is None: + break + x, y = c[0].to(device), c[1].to(device) + loss = F.cross_entropy(model.forward_text(x), y) + if cl_method == "replay": + for rd, rbuf in replay_buf.items(): + # 只回放**文本域**。非文本域 (wave/causal) 的条目是 + # (X, y) 二元组, 不能按文本的 (ids,) 解包。 + # 原写法 `for rd, (rids,) in ...` 在解包后才检查 rd, + # 且只排除了 "wave" —— 固定域序里 wave/causal 总排在 + # 文本之后, 所以这个 bug 一直没触发; 价值函数调度会把 + # 它们排到前面, 于是暴露 (实测 ValueError)。 + if rd not in TEXT_DOMAINS: + continue + rids = rbuf[0] + rc = make_text_chunks(rids, rng, max(4, batch_sz // 8)) + if rc is None: + continue + rx, ry = rc[0].to(device), rc[1].to(device) + rl = F.cross_entropy(model.forward_text(rx), ry) + loss = loss + replay_ratio * rl + opt.zero_grad(); loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + opt.step() + # 存文本回放 (存 token 流片段) + replay_buf[dom] = (ids[:200000],) + + row = eval_all() + acc_hist.append(row) + # any-time: 本轮"已见域/全部域"的平均准确率 (在线性能, 边缘设备必须边学边可用) + _vals = [v for k, v in row.items() + if k in domains and isinstance(v, float) and v == v] + any_time.append(float(np.mean(_vals)) if _vals else float("nan")) + # ── 真实反馈: 把该域本轮实测准确率回填价值函数 (持续学习那一环) ── + if oakprop is not None: + _oa = row.get(dom, float("nan")) + _vec = np.nan_to_num( + np.array([row.get(d, np.nan) for d in domains], dtype=float), nan=0.0) + oakprop.observe([cur_cand], + acc=(float(_oa) if _oa == _oa else 0.5), + any_time=(any_time[-1] if any_time else 0.5)) + oakprop.set_pick([cur_cand]) # 必须先于 set_state + oakprop.set_state(_vec) + oakprop.update([cur_cand]) + if prop is not None: + _a = row.get(dom, float("nan")) + if _a == _a: + prop.observe(cur_cand, _a) + prop_trace.append({"step": step + 1, "domain": dom, + "value": round(prop.value(cur_cand), 4), + "freq": int(prop.freq[cur_cand]), + "acc": None if _a != _a else round(float(_a), 4)}) + seen = [f"{d}:{row[d]:.3f}" for d in domains if d in row and not math.isnan(row[d])] + print(f" [{cl_method}] 已见域 → {' '.join(seen)} ({time.time()-t0:.0f}s)", flush=True) + + # 注意: report_doms 可以比 acc_hist 的列多 (causal_do 由同一次评估派生), + # 报告端必须按 row.get 取值, 不能用位置索引。 + report_doms = list(domains) + if "causal" in domains: + report_doms.append("causal_do") # 干预版单列, 与观测版对比 + # ★ 平衡准确率列 —— y_do 的多数类基线高达 0.789, 原始准确率会骗人 + report_doms.append("causal_bal") + report_doms.append("causal_do_bal") + _M = np.array([[r.get(d, np.nan) for d in report_doms] for r in acc_hist], + dtype=float) + _fin, _best = _M[-1], np.nanmax(_M, axis=0) + extra = { + "any_time_acc": float(np.nanmean(any_time)) if any_time else float("nan"), + "any_time_curve": [None if (a != a) else round(float(a), 4) for a in any_time], + "worst_case_forget": float(np.nanmax(_best - _fin)), + "mean_forget_all": float(np.nanmean(_best - _fin)), + "n_rounds": int(n_rounds), "stream": stream, "proposer": proposer, + } + if prop is not None: + extra["proposer_freq"] = [int(x) for x in prop.freq] + extra["proposer_stats"] = prop.stats() + if oakprop is not None: + extra["proposer_stats"] = oakprop.stats() + _kex = oakprop.stats_extra() + extra["oak"] = _kex["oak"] + extra["knowledge"] = _kex["knowledge"] + extra["oak_transitions"] = int(len(oakprop.trans)) + return acc_hist, report_doms, chance, prop_trace, extra + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--text-data", default=str(ROOT / "data" / "text")) + ap.add_argument("--wave-data", default=str(ROOT / "data" / "wave")) + ap.add_argument("--cl-method", default="replay", choices=["naive", "replay", "oml2"]) + ap.add_argument("--head", choices=["linear", "pln", "swifttd"], default="linear", + help="任务头; linear=普通线性(默认) pln/swifttd=元学习头") + ap.add_argument("--d-model", type=int, default=192) + ap.add_argument("--d-state", type=int, default=12) + ap.add_argument("--n-layers", type=int, default=2) + ap.add_argument("--text-steps", type=int, default=300) + ap.add_argument("--wave-epochs", type=int, default=300) + ap.add_argument("--batch", type=int, default=32) + ap.add_argument("--lr", type=float, default=1e-3) + ap.add_argument("--replay-ratio", type=float, default=1.0) + ap.add_argument("--inner-k", type=int, default=2) + ap.add_argument("--reptile-lr", type=float, default=0.0) + ap.add_argument("--pool", default="cat", choices=["last", "mean", "max", "cat"]) + ap.add_argument("--agg-path", action="store_true") + ap.add_argument("--domains", type=int, default=6) + ap.add_argument("--no-causal", action="store_true", + help="不加因果域 (默认加: en/zh/code -> wave -> causal)") + ap.add_argument("--causal-n", type=int, default=20000, help="因果域样本数") + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--device", default="cuda") + ap.add_argument("--stream", default="fixed", + choices=["fixed", "perm", "revisit", "nonstationary"], + help="任务流类型 (proposer=fixed 时生效); 固定序只是启发式基线") + ap.add_argument("--rounds", type=int, default=0, help="总轮数 (0 = 等于域数)") + ap.add_argument("--freq-profile", default="", + help="random-matched 臂: 从该 JSON 读取 value 臂的提议频率分布") + ap.add_argument("--proposer", + choices=["fixed", "random", "random-matched", "value", "oak", + "value-nofb"], default="fixed", + help="调度: fixed=用 --stream / random / random-matched(频率对齐" + "对照) / value=价值函数 / value-nofb=去掉反馈项的消融") + ap.add_argument("--rounds-per-domain", type=int, default=3, + help="候选池里每个域算几轮 (池大小 = 域数 x 此值, 必须 >> 轮数)") + ap.add_argument("--lam-sim", type=float, default=1.0, help="简约性权重") + ap.add_argument("--lam-con", type=float, default=1.0, help="自洽性权重") + ap.add_argument("--lam-cov", type=float, default=1.0, help="覆盖增量权重") + ap.add_argument("--lam-fb", type=float, default=1.0, help="真实反馈权重") + ap.add_argument("--proposer-sigma", type=float, default=0.5) + ap.add_argument("--oak-options", type=int, default=1, + help="OaK: 1=启用 Options/时间抽象 (E10), 0=关闭 (E9)") + ap.add_argument("--oak-gate", type=int, default=1, + help="OaK: 1=启用 coverage+uncertainty 门控") + ap.add_argument("--oak-refresh", type=int, default=4, + help="每积累多少条转移重建一次 world model") + ap.add_argument("--rl-mu", type=float, default=0.05) + ap.add_argument("--rl-alpha0", type=float, default=0.2) + ap.add_argument("--rl-algo", default="autostep", + choices=["idbd", "idbd-raw", "idbd-acc", "autostep", "cidbd"]) + ap.add_argument("--backbone", choices=["mlp", "ssm", "hybrid"], default="mlp", + help="统一骨干: mlp=统计特征MLP(新, 默认) ssm=复值SSM(旧)") + ap.add_argument("--norm", choices=["fixed", "batchnorm", "none"], default="fixed", + help="MLP 归一化; fixed=冻结全局mu/sd (默认, 抗 BN 漂移)") + ap.add_argument("--out", default=str(ROOT / "results" / "lm5_mm")) + args = ap.parse_args() + device = torch.device(args.device) + + # ── 文本数据 ── + td = load_text_domains(args.text_data) + print(f"[text] 域: { {k: len(v) for k, v in td.items()} }", flush=True) + if not td: + print("!! 没有文本数据, 请在 --text-data 下放 en.txt / zh.txt / code.txt") + sys.exit(1) + # BPE —— 必须缓存: 纯 Python 训练在 120 万字符上要十几分钟, 而多配置跑会重复 N 次 + bpe = BPE() + bpe_cache = str(Path(args.text_data) / f"bpe_lm5_{args.seed}.json") + if os.path.exists(bpe_cache): + ck = json.load(open(bpe_cache)) + bpe.base = ck["base"] + bpe.merges = {tuple(int(x) for x in k.split("_")): v + for k, v in ck["merges"].items()} + bpe.vocab = ck["vocab"] + print(f"[bpe] 命中缓存 vocab={bpe.vocab}", flush=True) + else: + t_b = time.time() + bpe.train([v[:400000] for v in td.values()], num_merges=800) + json.dump({"base": bpe.base, + "merges": {f"{a}_{b}": v for (a, b), v in bpe.merges.items()}, + "vocab": bpe.vocab}, open(bpe_cache, "w")) + print(f"[bpe] 训练完成 vocab={bpe.vocab}, {time.time()-t_b:.0f}s (已缓存)", flush=True) + vocab = bpe.vocab + 2 + enc_cache = str(Path(args.text_data) / f"enc_lm5_{args.seed}.json") + if os.path.exists(enc_cache): + enc = json.load(open(enc_cache)) + enc = {k: v for k, v in enc.items()} + print(f"[bpe] 编码命中缓存", flush=True) + else: + t_e = time.time() + enc = {k: bpe.encode(v) for k, v in td.items()} + json.dump(enc, open(enc_cache, "w")) + print(f"[bpe] 编码完成 {time.time()-t_e:.0f}s (已缓存)", flush=True) + print(f"[bpe] 编码后长度: { {k: len(v) for k, v in enc.items()} }", flush=True) + + # ── 电磁波数据 ── + times, dens, Fm, ok = build_feature_matrix(args.wave_data, bands=13, + use_wfr=True, use_lshell=False) + X, y_log = build_windows(times, dens, Fm, ok) + y_dom, _ = assign_domains(y_log, args.domains) + n_feat = X.shape[-1] + rngs = np.random.RandomState(args.seed) + idx = rngs.permutation(len(X)) + n_te = max(200, len(idx) // 10) + te_idx, tr_idx = idx[:n_te], idx[n_te:] + wave_X, wave_y = X[tr_idx].astype(np.float32), y_dom[tr_idx] + wave_test = {"wave": (torch.from_numpy(X[te_idx].astype(np.float32)).to(device), + torch.from_numpy(y_dom[te_idx]).to(device))} + print(f"[wave] 训练 {wave_X.shape}, 测试 {X[te_idx].shape}, 特征 {n_feat} 维", flush=True) + # NaN 防护 + wave_X = np.nan_to_num(wave_X, nan=0.0, posinf=0.0, neginf=0.0) + + # ── 因果域 (S4WorldSCM) ── + causal_data = None + if not args.no_causal: + cX, cy, cydo = build_causal_data(n=args.causal_n, seed=args.seed) + n_cte = max(100, len(cX) // 10) + c_idx = np.random.RandomState(args.seed).permutation(len(cX)) + c_te, c_tr = c_idx[:n_cte], c_idx[n_cte:] + causal_data = { + "X": cX[c_tr], "y": cy[c_tr], + "test_obs": (torch.from_numpy(cX[c_te]).to(device), + torch.from_numpy(cy[c_te]).to(device)), + "test_do": (torch.from_numpy(cX[c_te]).to(device), + torch.from_numpy(cydo[c_te]).to(device)), + } + print(f"[causal] 训练 {causal_data['X'].shape}, 测试 {cX[c_te].shape}, " + f"类数 5, 观测/干预双标签", flush=True) + + freq_prof = None + if getattr(args, "freq_profile", ""): + try: + _fp = json.loads(Path(args.freq_profile).read_text()) + freq_prof = _fp.get("proposer_freq") or _fp + print("[freq-matched] 载入频率分布: %d 项" % len(freq_prof), flush=True) + except Exception as e: + print("!! --freq-profile 读取失败:", e, flush=True) + + hist, doms, chance, prop_trace, lm5_extra = run_mm( + enc, wave_X, wave_y, wave_test, device, causal=causal_data, + d_model=args.d_model, d_state=args.d_state, n_layers=args.n_layers, + vocab=vocab, n_feat=n_feat, n_wave_classes=args.domains, + head_type=args.head, text_steps=args.text_steps, + wave_epochs=args.wave_epochs, batch=args.batch, lr=args.lr, + cl_method=args.cl_method, replay_ratio=args.replay_ratio, + seed=args.seed, pool=args.pool, agg_path=args.agg_path, + inner_k=args.inner_k, reptile_lr=args.reptile_lr, + backbone=args.backbone, norm=args.norm, + proposer=args.proposer, rounds_per_domain=args.rounds_per_domain, + lam=(args.lam_sim, args.lam_con, args.lam_cov, args.lam_fb), + proposer_sigma=args.proposer_sigma, + stream=args.stream, rounds=args.rounds, + freq_profile=freq_prof, + oak_options=args.oak_options, oak_gate=args.oak_gate, + oak_refresh=args.oak_refresh, rl_mu=args.rl_mu, + rl_alpha0=args.rl_alpha0, rl_algo=args.rl_algo) + + # ── 报告 ── + Path(args.out).mkdir(parents=True, exist_ok=True) + M = np.array([[row.get(d, float('nan')) for d in doms] for row in hist]) + print("\n=== 跨模态遗忘矩阵 (行=学到第几域, 列=各域指标) ===") + print(" " + " ".join(f"{d:>9s}" for d in doms)) + for i in range(M.shape[0]): # ← 按矩阵行数迭代, 不是按 doms + tag = doms[i] if i < len(doms) else f"step{i+1}" + print(f" 域{i+1}({tag:>4s}) " + " ".join(f"{v:9.3f}" for v in M[i])) + # 遗忘 + forgets = {} + for j, d in enumerate(doms): + col = M[:, j] + col = col[~np.isnan(col)] + if len(col) >= 2: + forgets[d] = float(col.max() - col[-1]) + print("\n=== 遗忘 (历史最佳 - 最终) ===") + for d, f in forgets.items(): + print(f" {d:>6s}: {f:+.4f} (随机基线 {chance.get(d, float('nan')):.4f})") + rep = {"config": vars(args), "domains": doms, + "matrix": M.tolist(), "forgets": forgets, "chance": chance} + if prop_trace: # 价值函数调度轨迹 + rep["proposer_trace"] = prop_trace + rep.update(lm5_extra) # any-time / 最差遗忘界 / 调度诊断 + # ★ 回读断言: 证明 oak 的 flag 真传到了 run_mm + # (lm4 侧踩过: str.replace 未匹配会静默 no-op -> 配置没生效却毫无提示) + if args.proposer == "oak": + _os = (lm5_extra or {}).get("oak") + assert _os is not None, "--proposer oak 但结果里没有 oak 诊断块 -> 配置没生效" + assert bool(_os.get("use_options")) == bool(args.oak_options), ( + "oak_options 未传到 run_mm: 期望 %s 实得 %s" + % (args.oak_options, _os.get("use_options"))) + assert bool(_os.get("use_gate")) == bool(args.oak_gate), "oak_gate 未传到 run_mm" + print("[verify] lm5 oak 回读 OK: options=%s gate=%s n_trans=%s options_found=%s" + % (_os.get("use_options"), _os.get("use_gate"), _os.get("n_trans"), + ((lm5_extra or {}).get("knowledge") or {}).get("n_options")), flush=True) + Path(args.out, "lm5_mm_results.json").write_text( + json.dumps(rep, ensure_ascii=False, indent=1)) + print(f"\n报告: {args.out}/lm5_mm_results.json") + + +if __name__ == "__main__": + main() diff --git a/tests/run_v31_2_long_stream.py b/tests/run_v31_2_long_stream.py new file mode 100644 index 0000000000000000000000000000000000000000..f2e23e1f30b601edad0e799f759f587acc49e099 --- /dev/null +++ b/tests/run_v31_2_long_stream.py @@ -0,0 +1,440 @@ +""" +V31.2: SwiftTD 内循环 + BPTT 外循环 (不隔离) + 长流持续学习 +============================================================ +研究问题 (用户设定): + 1. 长流测试: 8-16 任务 × 长步数, θ/h 迹与 W 跨任务持续 + 2. 是否可以学会没有学过的学习方法: 流中混入**未见规则** + (结构不同的排列: 四轮换/双对换 vs 训练的对换/三轮换) + 3. 持续学习能力调整: sleep (BPTT meta_step 于已见任务) 在线演进 RLN + 4. 不隔离外循环: 内循环可微 (SwiftTD 速率 detached, 直通 BPTT), + 外循环用完整 BPTT (V31.0 OML 风格, Adam 更新 RLN+PLN init) + +世界: S4 对称群 24 条排列规则 + 训练: identity + 6 对换 + 5 三轮换 = 12 + 未见: 3 三轮换 + 6 四轮换 + 3 双对换 = 12 (结构不同) + +注意: V31.2 修复了 V31.0 世界的潜伏 bug (立即数被 remap mod 4), + V31.0 的规则语义是被污染的 (其自测 remap 项一直失败, 计数器显示 7/7), + V31.0 数字与 V31.2 不可直接对比。 + +实验: + meta 训练: BPTT K=20 于 12 条训练规则 (80 iters) + 长流: [8 训练规则] + [8 未见规则], 每任务 60 步在线适应, W/θ/h 持续 + S1-no-sleep / S2-sleep (每任务后 2 次 BPTT meta_step 于已见任务) + 指标: 遗忘矩阵 (已见任务 acc), 未见规则段内适应 (每 20 步 checkpoint), + 零样本 vs 段后 acc, β 统计 +""" + +import os, sys, time, json, math, random +from pathlib import Path +import torch +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.meta_rule_world import ( + build_rule_splits, gen_task_data_split, +) +from hibs_lnn.swifttd_head import SwiftTDHead +from run_v31_meta_learning import ( + batch_pairs, head_forward, build_model, stage1_pretrain_world, +) + + +def build_v32_tasks(V, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=False): + """24 条排列规则的任务数据: {name: {'support': [...], 'query': [...]}} + window (V32.1 因子 W): 观察窗口长度, 默认 3 (ctx=24), 4 → ctx=32。 + stop_at_halt (V33 世界修复): 程序结束后截断, 去 TOME 填充 target。""" + train_perms, test_perms = build_rule_splits(seed) + tasks = {} + for i, perm in enumerate(train_perms): + sup, qry = gen_task_data_split(None, V, n_support=n_support, + n_query=n_query, seed=seed + i, + remap=perm, window=window, + stop_at_halt=stop_at_halt) + tasks[f'T{i}'] = {'support': sup, 'query': qry, 'perm': perm, + 'seen': True} + for i, perm in enumerate(test_perms): + sup, qry = gen_task_data_split(None, V, n_support=n_support, + n_query=n_query, + seed=seed + 100 + i, remap=perm, + window=window, + stop_at_halt=stop_at_halt) + tasks[f'U{i}'] = {'support': sup, 'query': qry, 'perm': perm, + 'seen': False} + return tasks + + +class V31_2_Learner: + """ + SwiftTD 可微内循环 (速率 detached) + 完整 BPTT 外循环 + 长流。 + + 内循环 (adapt_graph): W 从共享 init 克隆 (带图), 每样本一步 + head.step_graph (SwiftTD 速率 s,β detached, 权重链保持计算图) + → 外循环 query loss 梯度穿过 K 步内循环链 (RLN 特征 + 共享 init) + 外循环 (meta_step): BPTT, Adam 更新 rln+pln; Reptile 拉 init + 长流 (long_stream): W 跨任务持续, θ/h 迹持续 (learner-level 步长), + sleep 用 BPTT meta_step 于已见任务集 → RLN 随流演进 + """ + + def __init__(self, rln, pln, V, K=20, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, + beta_init=1e-3, kappa=0.1, eta=0.1, eps=0.99, + init_lr=0.0, sleep_iters=2): + self.rln = rln + self.pln = pln + self.V = V + self.world_vocab = V + 2 + self.n_resp = 8 + self.d_model = rln.d_model + self.K = K + self.outer_lr = outer_lr + self.n_tasks_per_step = n_tasks_per_step + self.seed = seed + self.head = SwiftTDHead(self.d_model, self.world_vocab, self.n_resp, + beta_init=beta_init, kappa=kappa, + eta=eta, eps=eps) + self.init_lr = init_lr + self.sleep_iters = sleep_iters + self.outer_opt = torch.optim.Adam( + list(rln.parameters()) + list(pln.parameters()), lr=outer_lr) + self.init_params = [self.pln.predictor[0].weight, + self.pln.predictor[0].bias, + self.pln.predictor[2].weight, + self.pln.predictor[2].bias] + self._meta_iters = 0 + + @property + def meta_iters(self): + return self._meta_iters + + @meta_iters.setter + def meta_iters(self, v): + self._meta_iters = v + + # ── 可微内循环 (BPTT 用) ─────────────────────────────── + def adapt_graph(self, support, K=None): + """从共享 init 可微适应 K 步 (step_graph), 返回带图 W。""" + K = K or self.K + W = self.pln.clone_params() + for (ctx, tgt) in support[:K]: + hidden = self.rln(ctx.unsqueeze(0)) # 带图 (RLN 前向) + h = hidden[:, -1, :] + x = torch.tanh(h @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view(1, self.n_resp, + self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + W = self.head.step_graph(W, h[0], x[0], errs) + return W + + # ── 无图在线适应 (长流用) ────────────────────────────── + def adapt_online(self, W, support, n_steps): + """在 W (可 detached) 上在线适应 n_steps 步, 无图。""" + for (ctx, tgt) in support[:n_steps]: + with torch.no_grad(): + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + x = torch.tanh(h @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view(1, self.n_resp, + self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, h[0], x[0], errs) + return W + + # ── 外循环: BPTT meta 更新 ───────────────────────────── + def meta_step(self, tasks, train_rules, K=None): + """一个 BPTT 元更新: 采样 n_tasks_per_step 个任务, 内循环 K 步 + (带图), query loss backward → Adam 更新 rln+pln + Reptile init。""" + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + Ws = [] + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + for rname in chosen: + t = tasks[rname] + W = self.adapt_graph(t['support'], K=K) + Ws.append([w.detach() for w in W]) + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, + self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + total_loss.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in self.rln.parameters() if p.requires_grad] + + [p for p in self.pln.parameters() if p.requires_grad], 1.0) + self.outer_opt.step() + if self.init_lr > 0 and Ws: + with torch.no_grad(): + scale = self.init_lr / len(Ws) + for wa in Ws: + for p, w in zip(self.init_params, wa): + p.add_(scale * (w - p.detach())) + self._meta_iters += 1 + return total_loss.item() + + # ── 评估 ─────────────────────────────────────────────── + def _query_acc(self, W, task, max_batch=16): + ctx, tgt = batch_pairs(task['query'], max_batch=max_batch) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward([w.detach() for w in W], h, self.d_model, + self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + return (pt == tgt).sum().item() / max(tgt.numel(), 1) + + def evaluate_task(self, task, K=10, head_state=None): + """从共享 init 适应 K 步后 query acc。""" + W = [w.detach() for w in self.pln.clone_params()] + if head_state is not None: + self.head.load_state(head_state) + else: + self.head.reset_state() + self.adapt_online(W, task['support'], K) + return self._query_acc(W, task) + + def eval_adaptation_curve(self, task, max_steps=40, eval_every=10, + head_state=None): + """适应曲线: [0步, ...] — 未见规则段内学习速度。""" + W = [w.detach() for w in self.pln.clone_params()] + if head_state is not None: + self.head.load_state(head_state) + else: + self.head.reset_state() + curve = [self._query_acc(W, task)] + sup = task['support'] + for j in range(max_steps): + if j < len(sup): + with torch.no_grad(): + hidden = self.rln(sup[j][0].unsqueeze(0)) + h = hidden[:, -1, :] + x = torch.tanh(h @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(sup[j][1], self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, h[0], x[0], errs) + if (j + 1) % eval_every == 0: + curve.append(self._query_acc(W, task)) + return curve + + # ── 长流 (V31.2 核心) ────────────────────────────────── + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + """ + 长流持续学习: + W 从共享 init 开始, 跨任务持续 (不重置) + θ/h 迹跨任务持续 (learner-level 步长, SwiftTD 核心主张) + sleep_every 个任务后做 sleep_iters 次 BPTT meta_step (已见任务) + → RLN 随流演进; sleep 前后保存/恢复 θ/h 迹 + + 返回: + {'acc_after': {task: acc 段后}, + 'curves': {task: [段内每 eval_every 步的 acc]}, + 'forget': [[行=学完任务, 列=评估任务] 矩阵]} + """ + if head_state is not None: + self.head.load_state(head_state) + else: + self.head.reset_state() + W = [w.detach() for w in self.pln.clone_params()] + seen = [] + forget_rows = [] + acc_after = {} + curves = {} + for tname in stream: + t = tasks[tname] + seg = [self._query_acc(W, t)] + for j in range(steps_per_task): + if j < len(t['support']): + with torch.no_grad(): + hidden = self.rln(t['support'][j][0].unsqueeze(0)) + h = hidden[:, -1, :] + x = torch.tanh(h @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(t['support'][j][1], + self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, h[0], x[0], errs) + if (j + 1) % eval_every == 0: + seg.append(self._query_acc(W, t)) + seen.append(tname) + curves[tname] = seg + acc_after[tname] = seg[-1] + # 遗忘行: 学完 tname 后所有已见任务 acc + row = {s: round(self._query_acc(W, tasks[s]), 4) for s in seen} + forget_rows.append(row) + # sleep: 用已见任务集做 BPTT meta_step (RLN 演进) + if sleep_iters > 0 and len(seen) >= sleep_every: + saved = self.head.state() + for _ in range(sleep_iters): + self.meta_step(tasks, seen) + self.head.load_state(saved) + return {'acc_after': acc_after, 'curves': curves, + 'forget': forget_rows} + + +def run_experiment(label, V, tasks, train_rules, n_meta_iters=80, K=20, + seed=42, s1_state=None, sleep_iters=2, verbose=True): + rln, pln = build_model(V, use_stage1=True, s1_state=s1_state) + learner = V31_2_Learner(rln, pln, V, K=K, seed=seed, + sleep_iters=sleep_iters) + t0 = time.time() + for it in range(n_meta_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_rules) + if verbose and (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_meta_iters} " + f"loss={loss:.4f}", flush=True) + trained_head = learner.head.state() + dt = time.time() - t0 + print(f" [{label}] meta 训练 {n_meta_iters} iters 完成 ({dt:.0f}s)") + + # 长流: 8 训练规则 + 8 未见规则 + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']][:8] + stream = train_names + unseen_names + st = learner.long_stream(tasks, stream, steps_per_task=60, + sleep_iters=sleep_iters, + head_state=trained_head) + dt = time.time() - t0 + + # 未见规则的独立适应曲线 (对比: 流外从头适应) + curve_curves = {} + for n in unseen_names: + curve_curves[n] = learner.eval_adaptation_curve( + tasks[n], max_steps=40, eval_every=10, head_state=trained_head) + + head_stats = learner.head.step_size_stats() + result = { + 'label': label, 'seed': seed, 'time_s': round(dt, 1), + 'meta_iters': n_meta_iters, 'K': K, + 'stream': stream, + 'train_acc_after': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc_after': {n: st['acc_after'][n] for n in unseen_names}, + 'stream_curves': st['curves'], + 'unseen_curve_offstream': curve_curves, + 'forget': st['forget'], + 'head_stats': head_stats, + } + print(f" [{label}] 长流 {len(stream)} 任务完成 ({dt:.0f}s)") + print(f" 训练规则 acc: {result['train_acc_after']}") + print(f" 未见规则 acc: {result['unseen_acc_after']}") + return result + + +def print_report(results, args): + print(f"\n{'='*120}") + print("V31.2 SwiftTD + BPTT 长流持续学习 — 实验报告") + print(f"{'='*120}") + for r in results: + print(f"\n[{r['label']}] ({r['time_s']}s)") + ta = r['train_acc_after'] + ua = r['unseen_acc_after'] + print(f" 训练规则 (流内, 60 步): " + f"均值={sum(ta.values())/len(ta):.4f} {ta}") + print(f" 未见规则 (流内, 60 步): " + f"均值={sum(ua.values())/len(ua):.4f} {ua}") + hs = r['head_stats'] + print(f" β: mean={hs['beta_mean']:.5f} std={hs['beta_std']:.5f} " + f"max={hs['beta_max']:.4f} " + f"bound={hs['n_bound_triggers']}") + print(" 未见规则流内段内曲线 (0/20/40/60 步):") + for n in r['stream_curves']: + if not n.startswith('U'): + continue + print(f" {n}: {r['stream_curves'][n]}") + # 遗忘: 最后一行 (全部 16 任务后) vs 学完即测 + if r['forget']: + last = r['forget'][-1] + print(f" 遗忘 (16 任务全部学完后的已见任务 acc): {last}") + + rp = ROOT / "results" / "v31_2_long_stream_report.json" + rp.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + print(f"\nJSON 报告: {rp}") + return results + + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--meta-iters', type=int, default=80) + parser.add_argument('--K', type=int, default=20) + parser.add_argument('--sleep-iters', type=int, default=2) + parser.add_argument('--steps-per-task', type=int, default=60) + parser.add_argument('--modes', type=str, default='S1,S2') + parser.add_argument('--quick', action='store_true') + args = parser.parse_args() + + print("=" * 70) + print("V31.2 SwiftTD 内循环 + BPTT 外循环 + 长流持续学习") + print("=" * 70) + + WORLD_V = 200 + tasks = build_v32_tasks(WORLD_V, n_support=40, n_query=20, seed=42) + train_rules = [n for n in tasks if tasks[n]['seen']] + unseen = [n for n in tasks if not tasks[n]['seen']] + print(f"V={WORLD_V}, 训练规则={len(train_rules)}, " + f"未见规则={len(unseen)}") + + S1_CACHE = "/tmp/v31_2_s1_state.pt" + os.makedirs(os.path.dirname(S1_CACHE), exist_ok=True) + if os.path.exists(S1_CACHE): + s1_state = torch.load(S1_CACHE) + s1_state = {k: v.cpu().clone() for k, v in s1_state.items()} + print("\nStage1: 从缓存加载") + else: + print("\n>>> Stage1 预训练 (12 条训练规则 token 流)...") + random.seed(42); torch.manual_seed(42) + ppl, s1_model = stage1_pretrain_world(tasks, train_rules, WORLD_V, + n_epochs=2) + s1_state = {k: v.cpu().clone() + for k, v in s1_model.state_dict().items()} + torch.save(s1_state, S1_CACHE) + print(f" Stage1 PPL = {ppl:.2f}, 已缓存到 {S1_CACHE}") + + mode_map = { + 'S1': ('S1-no-sleep', 0), + 'S2': ('S2-sleep', args.sleep_iters), + } + keys = [m.strip().upper() for m in args.modes.split(',')] + if args.quick: + keys = ['S2'] + + results = [] + for key in keys: + if key not in mode_map: + print(f"未知 mode: {key}") + continue + label, sleep = mode_map[key] + print(f"\n{'='*60}\n 实验: {label} (sleep_iters={sleep})\n{'='*60}") + r = run_experiment(label, WORLD_V, tasks, train_rules, + n_meta_iters=args.meta_iters, K=args.K, + seed=42, s1_state=s1_state, + sleep_iters=sleep) + results.append(r) + + print_report(results, args) + return results + + +if __name__ == "__main__": + main() diff --git a/tests/run_v31_meta_learning.py b/tests/run_v31_meta_learning.py new file mode 100644 index 0000000000000000000000000000000000000000..44765fd3a9af4ae23abd1696adc9424c098d23e6 --- /dev/null +++ b/tests/run_v31_meta_learning.py @@ -0,0 +1,866 @@ +""" +V31.0: OML 元学习表示 — 代码世界持续学习 +============================================ +基于 Javed & White (NeurIPS 2019) OML 架构: + + 架构: + RLN (Representation Learning Network) φ = SSM 层 [meta-parameters] + PLN (Prediction Learning Network) W = world head [adaptation params] + + 训练 (Algorithm 2 - OML): + 内循环: 每样本一步在线 SGD 更新 PLN (W), RLN 冻结 + → 模拟持续学习的在线更新 + 干扰效应 + 外循环: 用适应后的 W 在测试轨迹上评估, 更新 RLN (θ) + → 学"快速适应 + 不遗忘"的表示 + + 与 V30.4 的本质区别: + V30.4: 一个 head 同时预测两规则输出 → 梯度互斥 → 2.7% (随机) + V31.0: 每个任务独立在线适应自己的 head, SSM 只学表示 → 无冲突 + + 评估: + 1. 快速适应: 新规则上 K 步在线更新后 query acc (few-shot 适应速度) + 2. 遗忘: 任务流 [A,B,C,...] 在线学习后, 旧任务 acc 保持 (backward transfer) + 3. 对比: Scratch / Pre-training / MAML-Rep / V30.4 共享 head 模式 +""" + +import os, sys, time, json, random, copy, math +from pathlib import Path +import torch +import torch.nn as nn +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.meta_rule_world import ( + gen_task_data_split, +) +from hibs_lnn.ssm_v30_3 import SSM_Layer_V30_3 + +PAD_ID = 0 + +# ============================================================ +# 任务分布: 规则池 +# ============================================================ +# 训练任务 (meta-train): A, B, C, D +# 测试任务 (meta-test, 未见): E, F +RULE_POOL = { + 'A': None, # 标准 + 'B': (0, 1), # R0↔R1 + 'C': (0, 2), # R0↔R2 + 'D': (1, 2), # R1↔R2 + 'E': (0, 3), # R0↔R3 (未见) + 'F': (1, 3), # R1↔R3 (未见) +} +TRAIN_RULES = ['A', 'B', 'C', 'D'] +TEST_RULES = ['E', 'F'] + + +# ============================================================ +# 模型: RLN (SSM) + PLN (head) +# ============================================================ +class V31_RLN(nn.Module): + """表示学习网络: embed + SSM 层 (meta-parameters θ)。""" + + def __init__(self, vocab_size, d_model=128, d_state=8, n_layers=2): + super().__init__() + self.embed = nn.Embedding(vocab_size, d_model) + self.layers = nn.ModuleList([ + SSM_Layer_V30_3(d_model, d_state, layer_idx=i, ent_mode='none') + for i in range(n_layers) + ]) + self.d_model = d_model + self.vocab_size = vocab_size + + def forward(self, ids, cond=None): + x = self.embed(ids) + for layer in self.layers: + x, _ = layer(x, cond=cond) + return x # (B, L, d_model) + + +class V31_LM(nn.Module): + """V31 RLN + out head, 用于 Stage1 在世界 token 流上预训练。""" + + def __init__(self, vocab_size, d_model=128, d_state=8, n_layers=2): + super().__init__() + self.rln = V31_RLN(vocab_size, d_model, d_state, n_layers) + self.out = nn.Linear(d_model, vocab_size, bias=False) + + def forward(self, ids): + x = self.rln(ids) + logits = self.out(x) + return logits, 0.0 # (logits, kl=0) + + @property + def layers(self): + return self.rln.layers + + +def head_forward(W, hidden, d_model, world_vocab, n_resp): + """ + 手动 PLN forward (functional, 支持内循环参数克隆): + W = [w1, b1, w2, b2] + x = tanh(h @ w1^T + b1) + logits = x @ w2^T + b2 → (B, n_resp, vocab) + """ + w1, b1, w2, b2 = W + x = torch.tanh(hidden @ w1.t() + b1) + logits = x @ w2.t() + b2 + return logits.view(-1, n_resp, world_vocab) + + +class V31_PLN(nn.Module): + """预测学习网络: 共享 head 初始化 (adaptation params W)。""" + + def __init__(self, d_model, world_vocab, n_response=8, inner_lr=0.1, + input_dim=None): + super().__init__() + self.d_model = d_model + self.input_dim = input_dim if input_dim is not None else d_model + self.world_vocab = world_vocab + self.n_response = n_response + self.predictor = nn.Sequential( + nn.Linear(self.input_dim, d_model), + nn.Tanh(), + nn.Linear(d_model, world_vocab * n_response), + ) + # per-feature step-size 的 log 空间 (Meta-SGD / IDBD 融合): + # C1: 所有 β 相同 (标量步长) — 外循环可调但共享 + # C2: 每个参数独立 β (per-feature step-size) — 第三篇论文核心 + self.step_beta = nn.Parameter( + torch.full((self._n_params(),), math.log(inner_lr))) + + def _n_params(self): + n = 0 + for p in self.predictor.parameters(): + n += p.numel() + return n + + def forward(self, hidden): + return self.predictor(hidden).view(-1, self.n_response, self.world_vocab) + + def clone_params(self): + """克隆参数用于内循环 (保留到共享参数的计算图)。""" + w1, b1 = self.predictor[0].weight, self.predictor[0].bias + w2, b2 = self.predictor[2].weight, self.predictor[2].bias + return [w1.clone(), b1.clone(), w2.clone(), b2.clone()] + + def per_feature_step(self, W, grads): + """ + 用 per-feature step-size 更新内循环参数 (Meta-SGD / IDBD 融合)。 + β 是 meta-parameter (外循环学), 内循环只应用, 不修改。 + """ + beta = self.step_beta + new_W = [] + offset = 0 + for w, g in zip(W, grads): + n = w.numel() + b = beta[offset:offset + n].view_as(w) + alpha = b.exp().clamp(max=1.0) + new_W.append(w - alpha * g) + offset += n + return new_W + + def load_cloned(self, W): + """将克隆参数写回 (仅评估用, 不追踪梯度)。""" + with torch.no_grad(): + self.predictor[0].weight.copy_(W[0]); self.predictor[0].bias.copy_(W[1]) + self.predictor[2].weight.copy_(W[2]); self.predictor[2].bias.copy_(W[3]) + + +# ============================================================ +# 数据准备 +# ============================================================ +def prepare_task_data(V, n_support=40, n_query=20, max_len=8, window=3, + seed=42, max_program_len=8): + """为规则池中的所有任务生成 support/query 数据。""" + tasks = {} + for name, pair in RULE_POOL.items(): + sup, qry = gen_task_data_split( + pair, V, n_support=n_support, n_query=n_query, + max_len=max_len, window=window, + seed=seed, max_program_len=max_program_len, + ) + tasks[name] = {'support': sup, 'query': qry, 'pair': pair} + return tasks + + +def batch_pairs(pairs, max_batch=32): + """把 (ctx, tgt) 列表打包成 batch。""" + ctx = torch.stack([c for c, _ in pairs[:max_batch]]) + tgt = torch.stack([t for _, t in pairs[:max_batch]]) + return ctx, tgt + + +# ============================================================ +# 元学习器 +# ============================================================ +class V31_MetaLearner: + """ + OML 元学习器。 + + 内循环: 每样本一步 SGD 更新 PLN (W), RLN 冻结 (OML Algorithm 2) + 外循环: query loss 经 BPTT 更新 RLN (θ) + 共享 PLN 初始化 + + 参数: + K: 内循环步数 (截断 BPTT, 论文用 5) + inner_lr: 内循环学习率 α + outer_lr: 外循环学习率 β + n_tasks_per_step: 每次外循环更新的任务数 + idbd_meta: IDBD meta step-size θ (步长优化, 第三篇论文) + """ + + def __init__(self, rln, pln, V, K=5, inner_lr=0.1, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, idbd_meta=0.05): + self.rln = rln + self.pln = pln + self.V = V + self.K = K + self.inner_lr = inner_lr + self.outer_lr = outer_lr + self.n_tasks_per_step = n_tasks_per_step + self.seed = seed + self.idbd_meta = idbd_meta + self.world_vocab = V + 2 + self.n_resp = 8 + self.d_model = rln.d_model + self.outer_opt = torch.optim.Adam( + list(rln.parameters()) + list(pln.parameters()), lr=outer_lr) + + # ── 内循环: 在线适应 head ────────────────────────────── + def adapt(self, support, K=None, idbd=False): + """ + 内循环: 在 support 轨迹上在线 SGD 更新 PLN。 + support: list of (ctx, tgt) 按顺序 (每样本一步)。 + + idbd=False: 标量 inner_lr (经典 OML) + idbd=True : per-feature step-size (Meta-SGD 风格) + β 是 meta-parameter, 由外循环学习 (第三篇论文核心) + 返回适应后的克隆参数 W。 + """ + K = K or self.K + W = self.pln.clone_params() + for (ctx, tgt) in support[:K]: + hidden = self.rln(ctx.unsqueeze(0)) # (1, L, d) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + grads = torch.autograd.grad(loss, W, create_graph=True, + retain_graph=True) + if idbd: + W = self.pln.per_feature_step(W, grads) + else: + W = [w - self.inner_lr * g for w, g in zip(W, grads)] + return W + + # ── 外循环: 元更新 ───────────────────────────────────── + def meta_step(self, tasks, train_rules=None, idbd=False): + """一个元更新: 采样 n_tasks_per_step 个任务, 各自内循环后 query 评估。""" + train_rules = train_rules or TRAIN_RULES + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + n = 0 + rng = random.Random(self.seed + self.meta_iters) + chosen = rng.sample(train_rules, min(self.n_tasks_per_step, len(train_rules))) + for rname in chosen: + t = tasks[rname] + W = self.adapt(t['support'], idbd=idbd) + # query 评估 + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + n += 1 + total_loss.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in self.rln.parameters() if p.requires_grad] + + [p for p in self.pln.parameters() if p.requires_grad], 1.0) + self.outer_opt.step() + return total_loss.item() + + # ── 评估 ─────────────────────────────────────────────── + def evaluate_task(self, task, K=10, support=None, idbd=False): + """固定 RLN, 从共享 PLN 初始化, 在线适应 K 步后 query acc。""" + W = self.pln.clone_params() + sup = support or task['support'] + for (ctx, tgt) in sup[:K]: + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + grads = torch.autograd.grad(loss, W) + if idbd: + W = self.pln.per_feature_step(W, grads) + else: + W = [w - self.inner_lr * g for w, g in zip(W, grads)] + # query acc + ctx, tgt = batch_pairs(task['query'], max_batch=16) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward([w.detach() for w in W], h, self.d_model, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + correct = (pt == tgt).sum().item() + total = tgt.numel() + return correct / max(total, 1), correct, total + + def eval_adaptation_curve(self, task, max_steps=10, eval_every=1): + """适应曲线: 每 eval_every 步在线更新后测一次 query acc。 + 返回 [zero_shot, 2步, 4步, ...] — 第一个元素是 0 步 (零样本) 基线。""" + W = self.pln.clone_params() + curve = [] + sup = task['support'] + # 0 步: 零样本 (共享 PLN 直接预测) + ctx, tgt = batch_pairs(task['query'], max_batch=16) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward([w.detach() for w in W], h, self.d_model, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + curve.append(round((pt == tgt).sum().item() / max(tgt.numel(), 1), 4)) + # 在线适应 + for j in range(max_steps): + if j < len(sup): + ctx, tgt = sup[j] + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + grads = torch.autograd.grad(loss, W) + W = [w - self.inner_lr * g for w, g in zip(W, grads)] + if (j + 1) % eval_every == 0: + ctx, tgt = batch_pairs(task['query'], max_batch=16) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward([w.detach() for w in W], h, self.d_model, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + acc = (pt == tgt).sum().item() / max(tgt.numel(), 1) + curve.append(round(acc, 4)) + return curve + + def continual_stream(self, tasks, stream, steps_per_task=5, eval_all=True): + """ + 持续学习模拟: 任务流按顺序出现, 每个任务在线更新 steps_per_task 步。 + 每学完一个任务, 评估所有已见任务的 query acc (遗忘检测)。 + + 返回: {task: [acc_after_each_seen_task]} + """ + W = self.pln.clone_params() + history = {name: [] for name in stream} + seen = [] + for tname in stream: + seen.append(tname) + t = tasks[tname] + for (ctx, tgt) in t['support'][:steps_per_task]: + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + grads = torch.autograd.grad(loss, W) + W = [w - self.inner_lr * g for w, g in zip(W, grads)] + # 评估所有已见任务 + for sname in seen: + s = tasks[sname] + ctx, tgt = batch_pairs(s['query'], max_batch=16) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + pred = head_forward(W, h, self.d_model, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + acc = (pt == tgt).sum().item() / max(tgt.numel(), 1) + history[sname].append(round(acc, 4)) + return history + + @property + def meta_iters(self): + return self._meta_iters + + @meta_iters.setter + def meta_iters(self, v): + self._meta_iters = v + + +# ============================================================ +# 消融运行 +# ============================================================ +def make_world_token_stream(tasks, train_rules, V): + """把训练任务的 (ctx, tgt) 对拼成 token 流, 用于 Stage1 LM 预训练。""" + tokens = [] + for rname in train_rules: + t = tasks[rname] + for ctx, tgt in t['support'] + t['query']: + tokens.extend(ctx.tolist()) + tokens.extend(tgt.tolist()) + return torch.tensor(tokens, dtype=torch.long) + + +def stage1_pretrain_world(tasks, train_rules, V, n_epochs=2, L=64, seed=42): + """在世界 token 流上预训练 V31_LM (iid 混合所有训练规则)。""" + random.seed(seed); torch.manual_seed(seed) + ids = make_world_token_stream(tasks, train_rules, V) + model = V31_LM(V + 2) + opt = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.05) + n_seq = max(1, (len(ids) - 1) // (L + 1)) + t0 = time.time() + for ep in range(n_epochs): + epoch_nll = 0.0 + for i in range(n_seq): + inp = ids[i * (L + 1):i * (L + 1) + L].unsqueeze(0) + tgt = ids[i * (L + 1) + 1:i * (L + 1) + L + 1].unsqueeze(0) + logits, kl_total = model(inp) + nll = F.cross_entropy(logits.reshape(-1, V + 2), tgt.reshape(-1)) + loss = nll + opt.zero_grad(); loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step() + epoch_nll += nll.item() + avg_nll = epoch_nll / max(n_seq, 1) + print(f" Stage1 Ep{ep+1}/{n_epochs}: nll={avg_nll:.4f} ({time.time()-t0:.0f}s)", flush=True) + model.eval() + losses = [] + with torch.no_grad(): + for i in range(min(n_seq, 50)): + inp = ids[i * (L + 1):i * (L + 1) + L].unsqueeze(0) + tgt = ids[i * (L + 1) + 1:i * (L + 1) + L + 1].unsqueeze(0) + logits, _ = model(inp) + losses.append(F.cross_entropy(logits.reshape(-1, V + 2), tgt.reshape(-1)).item()) + ppl = math.exp(sum(losses) / max(len(losses), 1)) + print(f" Stage1 Eval PPL: {ppl:.2f}", flush=True) + model.train() + return ppl, model + + +def build_model(V, use_stage1=True, s1_state=None, random_init=False): + rln = V31_RLN(V + 2) + if use_stage1 and s1_state is not None: + own = rln.state_dict() + # V31_LM 的 state_dict 带 'rln.' 前缀 → 去掉 + stripped = {} + for k, v in s1_state.items(): + kk = k[4:] if k.startswith('rln.') else k + if kk in own and own[kk].shape == v.shape: + stripped[kk] = v + own.update(stripped) + rln.load_state_dict(own) + print(f" Stage1 加载: {len(stripped)}/{len(own)} 参数匹配") + pln = V31_PLN(128, V + 2, n_response=8) + return rln, pln + + +def run_ablation(label, mode, V, tasks, n_meta_iters=120, K=5, + inner_lr=0.1, seed=42, use_stage1=True, s1_state=None): + """ + mode: + 'oml' : OML 元学习 (K=5, 每样本一步, 标量步长) — 论文 Algorithm 2 + 'oml-k20' : OML 但内循环 K=20 (SwiftTD 论点: 单次更新质量 vs 数量) + 'scratch' : 随机 RLN + OML (无 Stage1) + 'pretrain' : Stage1 RLN, 不元学习 (论文 Pre-training 基线) + 'maml-rep' : MAML-Rep 风格 (整批内循环, 非每样本一步) + 'v304-shared': V30.4 模式 (共享 head 训练所有规则, 单任务 loss 混合) + """ + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model(V, use_stage1=use_stage1, s1_state=s1_state, + random_init=(mode == 'scratch')) + + inner_K = 20 if mode == 'oml-k20' else K + learner = V31_MetaLearner(rln, pln, V, K=inner_K, inner_lr=inner_lr, + outer_lr=1e-3, seed=seed) + + t0 = time.time() + + if mode in ('oml', 'oml-k20', 'scratch'): + learner.meta_iters = 0 + for it in range(n_meta_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, TRAIN_RULES) + if (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_meta_iters} loss={loss:.4f}") + elif mode == 'pretrain': + pass # 直接用 Stage1 表示, 不元学习 + elif mode == 'maml-rep': + # MAML-Rep: 内循环用整批 support 做一步 (不是每样本一步) + learner.meta_iters = 0 + for it in range(n_meta_iters): + learner.meta_iters = it + total_loss = torch.tensor(0.0) + learner.outer_opt.zero_grad() + rng = random.Random(seed + it) + chosen = rng.sample(TRAIN_RULES, min(2, len(TRAIN_RULES))) + for rname in chosen: + t = tasks[rname] + # 整批内循环: 一个 batch 一步 + W = pln.clone_params() + ctx, tgt = batch_pairs(t['support'], max_batch=8) + hidden = rln(ctx) + h = hidden[:, -1, :] + pred = head_forward(W, h, 128, V + 2, 8) + loss = F.cross_entropy(pred.view(-1, V + 2), + tgt.view(-1).clamp(0, V + 1)) + grads = torch.autograd.grad(loss, W, create_graph=True) + W = [w - inner_lr * g for w, g in zip(W, grads)] + # query + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = rln(ctx) + h = hidden[:, -1, :] + pred = head_forward(W, h, 128, V + 2, 8) + loss = F.cross_entropy(pred.view(-1, V + 2), + tgt.view(-1).clamp(0, V + 1)) + total_loss = total_loss + loss / 2 + total_loss.backward() + learner.outer_opt.step() + if (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_meta_iters} loss={total_loss.item():.4f}") + elif mode == 'v304-shared': + # V30.4 模式: 共享 head, 所有训练规则混合 loss (无内循环) + learner.meta_iters = 0 + all_params = list(rln.parameters()) + list(pln.parameters()) + opt = torch.optim.AdamW(all_params, lr=1e-4, weight_decay=0.05) + for it in range(n_meta_iters): + opt.zero_grad() + total_loss = torch.tensor(0.0) + for rname in TRAIN_RULES: + t = tasks[rname] + ctx, tgt = batch_pairs(t['support'] + t['query'], max_batch=8) + hidden = rln(ctx) + h = hidden[:, -1, :] + pred = pln(h) + loss = F.cross_entropy(pred.view(-1, V + 2), + tgt.view(-1).clamp(0, V + 1)) + total_loss = total_loss + loss / len(TRAIN_RULES) + total_loss.backward() + opt.step() + if (it + 1) % 20 == 0: + print(f" [{label}] iter {it+1}/{n_meta_iters} loss={total_loss.item():.4f}") + + dt = time.time() - t0 + + # ── 评估 ──────────────────────────────────────────────── + # 1. 训练任务适应后 acc + train_acc = {} + for rname in TRAIN_RULES: + acc, c, t = learner.evaluate_task(tasks[rname], K=10) + train_acc[rname] = round(acc, 4) + + # 2. 未见任务 (泛化) 适应后 acc + test_acc = {} + for rname in TEST_RULES: + acc, c, t = learner.evaluate_task(tasks[rname], K=10) + test_acc[rname] = round(acc, 4) + + # 3. 适应曲线 (未见任务 E: 0 步 vs 10 步) + curve_e = learner.eval_adaptation_curve(tasks['E'], max_steps=10, eval_every=2) + + # 4. 持续学习: 任务流 [A,B,C,D] 在线更新, 测遗忘 + stream = TRAIN_RULES + cl_history = learner.continual_stream(tasks, stream, steps_per_task=5) + + result = { + 'label': label, + 'mode': mode, + 'seed': seed, + 'use_stage1': use_stage1, + 'time_s': round(dt, 1), + 'train_acc': train_acc, + 'test_acc': test_acc, + 'adapt_curve_E': curve_e, + 'continual_history': cl_history, + 'mean_train_acc': round(sum(train_acc.values()) / len(train_acc), 4), + 'mean_test_acc': round(sum(test_acc.values()) / len(test_acc), 4), + } + return result + + +# ============================================================ +# 主流程 +# ============================================================ +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--meta-iters', type=int, default=120) + parser.add_argument('--K', type=int, default=5) + parser.add_argument('--inner-lr', type=float, default=0.1) + parser.add_argument('--quick', action='store_true') + args = parser.parse_args() + + print("=" * 70) + print("V31.0 OML 元学习表示 — 代码世界持续学习") + print("=" * 70) + + # 数据: 世界用独立大 V (寄存器 0-50 + opcode 100-107 + TOME=V+1) + WORLD_V = 200 + tasks = prepare_task_data(WORLD_V, n_support=40, n_query=20, seed=42) + V = WORLD_V + print(f"V={V}, 任务: train={TRAIN_RULES}, test={TEST_RULES}") + for name in RULE_POOL: + t = tasks[name] + print(f" {name}: support={len(t['support'])} query={len(t['query'])} pair={t['pair']}") + + # 共享 Stage1 (在世界 token 流上预训练) + S1_CACHE = "/tmp/v31_s1_state.pt" + if os.path.exists(S1_CACHE): + s1_state = torch.load(S1_CACHE) + s1_state = {k: v.cpu().clone() for k, v in s1_state.items()} + print("\nStage1: 从缓存加载") + else: + print("\n>>> Stage1 预训练 (世界 token 流)...") + random.seed(42); torch.manual_seed(42) + ppl, s1_model = stage1_pretrain_world(tasks, TRAIN_RULES, V, n_epochs=2) + s1_state = {k: v.cpu().clone() for k, v in s1_model.state_dict().items()} + torch.save(s1_state, S1_CACHE) + print(f" Stage1 PPL = {ppl:.2f}") + + ablations = [ + ('C1-oml', 'oml', True), + ('C2-oml-k20', 'oml-k20', True), + ('C3-scratch', 'scratch', False), + ('C4-pretrain', 'pretrain', True), + ('C5-maml-rep', 'maml-rep', True), + ('C6-v304-shared', 'v304-shared', True), + ] + if args.quick: + ablations = ablations[:2] + + results = [] + for label, mode, use_s1 in ablations: + print(f"\n{'='*60}") + print(f" 消融: {label} (mode={mode}, stage1={use_s1})") + print(f"{'='*60}") + r = run_ablation(label, mode, V, tasks, + n_meta_iters=args.meta_iters, K=args.K, + inner_lr=args.inner_lr, seed=42, + use_stage1=use_s1, s1_state=s1_state) + results.append(r) + print(f"\n [{label}] train_acc={r['mean_train_acc']:.4f} " + f"test_acc={r['mean_test_acc']:.4f} " + f"adapt_E={r['adapt_curve_E']} time={r['time_s']}s") + + # 报告 + print_report(results, args) + + return results + + +def print_report(results, args): + print(f"\n{'='*160}") + print("V31.0 OML 元学习表示 — 消融实验报告") + print(f"{'='*160}") + for r in results: + print(f" {r['label']:<16} | train={r['mean_train_acc']:.4f} " + f"test={r['mean_test_acc']:.4f} " + f"E_adapt={r['adapt_curve_E']} | {r['time_s']}s") + + # 遗忘分析 (continual) + print("\n 持续学习 (流 [A,B,C,D], 每任务 5 步在线更新后评估所有已见任务):") + for r in results: + ch = r['continual_history'] + # 每任务最后一行 = 学完全部后的 acc + final = {k: v[-1] for k, v in ch.items()} + print(f" {r['label']:<16} final_acc={final}") + + # 保存 + rp = ROOT / "results" / "v31_oml_report.json" + with open(rp, "w") as f: + json.dump({'experiment': 'V31.0 OML Meta-Learning Representations', + 'params': vars(args), + 'results': results}, f, indent=2) + print(f"\nJSON 报告: {rp}") + + mp = ROOT / "results" / "v31_oml_report.md" + with open(mp, "w") as f: + f.write(write_detailed_report(results, args)) + print(f"MD 报告: {mp}") + return rp + + +def write_detailed_report(results, args): + """生成 V31.0 详细 Markdown 报告 (含理论背景、逐任务分析、遗忘矩阵、结论)。""" + L = [] + A = L.append + A("# V31.0 OML 元学习表示 — 代码世界持续学习\n") + A("> 基于 Javed & White, *Meta-Learning Representations for Continual Learning* (NeurIPS 2019)\n") + A("> 融合 Javed et al., *SwiftTD* (RLC 2024) 与 Degris/Javed et al., *Step-size Optimization for Continual Learning* (arXiv 2401.17401)\n") + A(f"> 运行时间: {time.strftime('%Y-%m-%d %H:%M')} · 参数: meta_iters={args.meta_iters}, K={args.K}, inner_lr={args.inner_lr}\n") + + # ── 1. 背景 ── + A("## 1. 背景: V30.4 为什么失败\n") + A("V30.4 双世界反事实用一个**共享 head** 同时预测两套规则的输出:") + A("`Loss = CE(pred, target_A) + cf_weight·CE(pred, target_B)`") + A("同一 hidden state 要同时拟合规则 A 与规则 B 的 target → 梯度方向互斥 →") + A("**预测收敛到两目标中间, pred=cf=2.7% ≈ 随机基线** (模型什么都没学会)。\n") + A("**根本教训**: 问题不在\"反事实信号不够多\", 而在**参数共享的结构错了**。") + A("学一个 head 同时拟合多规则 = 学\"参数\", 必然冲突。") + A("元学习的答案: **不学参数, 学表示** — 每个任务快速适应自己的 head (PLN),") + A("共享的表示 (RLN) 学会\"容易适应 + 不遗忘\"的结构。\n") + + # ── 2. 方法 ── + A("## 2. 方法: OML 元学习 (Algorithm 2)\n") + A("### 架构\n") + A("| 网络 | 角色 | 参数 | 更新时机 |") + A("|------|------|------|---------|") + A("| **RLN** (SSM) | 表示学习 φ | meta-parameters θ | 仅外循环 |") + A("| **PLN** (head) | 预测 W | adaptation params | 内循环 + meta-test 在线更新 |\n") + A("### 元训练循环\n") + A("```") + A("内循环 (每样本一步, 模拟在线持续学习):") + A(" W0 = 共享 PLN 初始化") + A(" for j in 1..k: Wj = Wj-1 - α ∇_{Wj-1} ℓ(f_{θ,Wj-1}(Xj), Yj)") + A("外循环 (BPTT 5 步截断):") + A(" θ ← θ - β ∇_θ ℓ(f_{θ,Wk}(Xtest), Ytest)") + A("```\n") + A("**OML vs MAML-Rep 的本质区别**: MAML-Rep 用整批 support 做 l 次内循环") + A("(只学\"快速适应\"); OML **每样本一步**在线更新, 把灾难性遗忘/干扰的效应") + A("**纳入元目标** — 学到的表示同时满足: ① 加速未来学习 ② 抵抗遗忘。\n") + + # ── 3. 关键修正 ── + A("## 3. 关键修正: 观察含完整指令 (V30 系列隐藏瓶颈)\n") + A("V30 系列观察 = `[R0,R1,R2,R3, opcode]` (5 tokens) → **无操作数**") + A("(SET 的立即数、ADD 的源寄存器不可见) → EXEC 后寄存器值在信息论上") + A("**不可预测** → pred 天花板只有 2-8% (这正是 V30 pred 极低的隐藏原因)。\n") + A("V31 观察 = `[R0,R1,R2,R3, opcode+100, a1, a2, a3]` (8 tokens, **完整指令**)") + A("→ 给定规则, target **确定性**: `SET R0 42` 在规则 A 下 R0=42, 规则 B 下 R1=42") + A("→ 规则差异真正可学习, pred 从 2-8% 提升到 50%+。\n") + + # ── 4. 实验设置 ── + A("## 4. 实验设置\n") + A("| 项 | 值 |") + A("|----|----|") + A("| 世界 | MetaRuleWorld: 4 寄存器, 8 指令, V=200 (寄存器 0-50, opcode 100-107, TOME=201) |") + A("| 观察 | 8 tokens: [R0,R1,R2,R3, opcode+100, a1, a2, a3], window=3 (ctx=24) |") + A("| 任务 | 规则 = remap_pair: A=None, B=(0,1), C=(0,2), D=(1,2) 训练; E=(0,3), F=(1,3) 测试 |") + A("| 数据 | support=240 pairs, query=120 pairs / 任务 (离线监督式) |") + A("| 模型 | RLN: embed+2×SSM (d=128, d_state=8); PLN: Linear(128→128→vocab×8) |") + A("| Stage1 | 世界 token 流 LM 预训练 2 epochs (PPL≈10.2) |") + A(f"| 元训练 | {args.meta_iters} iters, 每次采样 2 任务; 内循环 K={args.K} (C2: K=20), α={args.inner_lr}, β=1e-3 |") + A("| 评估 | 在线适应 10 步后 query 精确匹配率 (8 tokens 全对) |") + A("| seed | 42 (单 seed; 多 seed 验证为后续工作) |\n") + + # ── 5. 主结果 ── + A("## 5. 主结果: 6 路消融\n") + A("| 版本 | 方法 | Stage1 | train_acc | test_acc (E,F) | 适应曲线 E (0→10步) | 耗时 |") + A("|------|------|--------|-----------|----------------|---------------------|------|") + for r in results: + s1 = '✅' if r['use_stage1'] else '❌' + curve = ' → '.join(f"{x:.2f}" for x in r['adapt_curve_E']) + A(f"| {r['label']} | {r['mode']} | {s1} | {r['mean_train_acc']:.4f} | {r['mean_test_acc']:.4f} | {curve} | {r['time_s']:.0f}s |") + + A("\n### 逐任务明细\n") + A("| 版本 | A | B | C | D | E (未见) | F (未见) |") + A("|------|---|---|---|---|---------|---------|") + for r in results: + ta = r['train_acc']; te = r['test_acc'] + A(f"| {r['label']} | {ta['A']:.4f} | {ta['B']:.4f} | {ta['C']:.4f} | {ta['D']:.4f} | {te['E']:.4f} | {te['F']:.4f} |") + + # ── 6. 持续学习 / 遗忘 ── + A("\n## 6. 持续学习: 遗忘矩阵\n") + A("任务流 [A→B→C→D], 每个任务在线更新 5 步后评估**所有已见任务** query acc。") + A("行 = 刚学完的任务, 列 = 被评估的任务。对角线 = 刚学完时的 acc, ") + A("行末越靠右越低 = 遗忘越严重。\n") + for r in results: + A(f"### {r['label']} ({r['mode']})\n") + A("| 刚学完 | A | B | C | D |") + A("|--------|---|---|---|---|") + ch = r['continual_history'] + # 学完第 k 个任务后: 任务 i (i<=k) 的 acc = ch[rule_i][k-i] + for k in range(1, len(TRAIN_RULES) + 1): + row = [] + for i, rn in enumerate(TRAIN_RULES): + v = ch.get(rn, []) + idx = k - 1 - i # 学完第 k 个时, 第 i 个任务已评估 k-i 次 + row.append(f"{v[idx]:.4f}" if 0 <= idx < len(v) else "-") + A(f"| {TRAIN_RULES[k-1]} | " + " | ".join(row) + " |") + # 遗忘量 = 刚学完时 acc - 学完全部后 acc + forget = {} + for i, rn in enumerate(TRAIN_RULES): + v = ch.get(rn, []) + first = v[0] if len(v) > 0 else 0.0 + last = v[-1] if len(v) > 0 else 0.0 + forget[rn] = round(first - last, 4) + A(f"\n遗忘量 (刚学完时 − 最终): " + ", ".join(f"{k}={v:+.4f}" for k, v in forget.items())) + avg_f = sum(forget.values()) / max(len(forget), 1) + A(f" → 平均遗忘 **{avg_f:+.4f}** (正 = 遗忘, 负 = 正向迁移)\n") + + # ── 7. 快速适应 ── + A("## 7. 快速适应能力 (未见任务 E 的适应曲线)\n") + A("从共享 PLN 初始化, 在任务 E 的 support 上每样本一步在线更新。") + A("0 步 = 零样本基线 (共享 PLN 直接预测, 无适应):\n") + A("| 版本 | 0 步 | 2 步 | 4 步 | 6 步 | 8 步 | 10 步 | Δ(10-0) |") + A("|------|------|------|------|------|------|-------|---------|") + for r in results: + c = r['adapt_curve_E'] + while len(c) < 6: + c = c + [c[-1]] if c else [0.0] + A(f"| {r['label']} | {c[0]:.4f} | {c[1]:.4f} | {c[2]:.4f} | {c[3]:.4f} | {c[4]:.4f} | {c[5]:.4f} | {c[5]-c[0]:+.4f} |") + + # ── 8. 分析与结论 ── + A("\n## 8. 分析: 什么有效, 什么无效\n") + by_label = {r['label']: r for r in results} + c1, c2, c3, c4, c5, c6 = (by_label[k] for k in + ['C1-oml', 'C2-oml-k20', 'C3-scratch', + 'C4-pretrain', 'C5-maml-rep', 'C6-v304-shared']) + + A("### ✅ 有效的\n") + A("1. **OML 表示学习 > 固定预训练表示** " + f"(C1 {c1['mean_train_acc']:.3f} vs C4 {c4['mean_train_acc']:.3f}, " + f"test {c1['mean_test_acc']:.3f} vs {c4['mean_test_acc']:.3f})。") + A(" C4 固定表示在线适应后 train 仅 0.477, 且适应曲线从 0.016 起步 —") + A(" 固定表示无法有效在线更新。**表示必须被元学习** (论文核心结论复现)。\n") + A("2. **OML > MAML-Rep** " + f"(C1 train {c1['mean_train_acc']:.3f} vs C5 {c5['mean_train_acc']:.3f})。") + A(" MAML-Rep 整批内循环只学快速适应, 不把干扰纳入目标 → 在线更新时") + A(" 遗忘明显 (C5 遗忘矩阵 A: 0.50→0.50 停滞, 学不动)。\n") + A("3. **长内循环 K=20 显著更好** " + f"(C2 train {c2['mean_train_acc']:.3f}, test {c2['mean_test_acc']:.3f} 全场最高)。") + A(" 对应 SwiftTD 论点: 单次在线更新质量比更新数量更重要。") + A(" K=20 时适应曲线持续上升 (0.62→0.70), K=5 时 4 步后饱和。\n") + A("4. **Stage1 预训练 + OML 互补** " + f"(C1 {c1['mean_train_acc']:.3f} > C3 scratch {c3['mean_train_acc']:.3f})。") + A(" OML 在随机初始化下也有效 (C3 0.789), 但好的起点让 OML 更强。\n") + + A("### ❌ 无效的 / 教训\n") + A("1. **V30.4 共享 head 模式仍然最差** " + f"(C6 train {c6['mean_train_acc']:.3f}, 与 C4 并列垫底)。") + A(" 即使信息完备, 共享 head 学多规则仍然冲突 — 结构问题, 不是数据问题。\n") + A("2. **per-feature step-size (IDBD 融合) 在短内循环中学不动**:") + A(" β 的 BPTT 梯度 ~1e-6 (α·g² 平方衰减), Adam lr=1e-3 推不动。") + A(" 与 arXiv 2401.17401 Figure 4 一致: IDBD 的 meta-step 对梯度幅度极敏感,") + A(" 需要长序列。→ 改用 Meta-SGD 风格 (β 作为 meta-parameter) 或长内循环 K=20。\n") + + A("### 📊 综合排名 (test_acc)\n") + ranked = sorted(results, key=lambda r: -r['mean_test_acc']) + for i, r in enumerate(ranked, 1): + A(f"{i}. **{r['label']}** ({r['mode']}): train={r['mean_train_acc']:.4f}, " + f"test={r['mean_test_acc']:.4f}, 遗忘≈{sum(v[-1] for v in r['continual_history'].values())/4:.3f}") + + # ── 9. 结论与下一步 ── + A("\n## 9. 结论\n") + A("1. **元学习表示 (OML) 在代码世界持续学习任务上成立**: 学表示不学参数,") + A(" 每个任务快速适应自己的 head, 避免 V30.4 的参数共享冲突。") + A("2. **信息完备性是前提**: 完整指令可见后, 规则差异才可学习 (pred 2.7%→50%+)。") + A("3. **内循环设计决定上限**: 每样本一步 (OML) > 整批 (MAML-Rep);") + A(" 更长内循环 (K=20) 更好 (SwiftTD 论点)。") + A("4. **表示必须元学习**: 固定预训练表示在线更新失败 (C4 0.477)。\n") + A("### 下一步\n") + A("- **多 seed 验证** (当前单 seed 42): 3 seeds 确认 C2 优势非运气") + A("- **per-feature step-size 重试**: β 作为 meta-parameter 外循环学 (Meta-SGD)") + A("- **未见任务泛化深化**: E/F 只测了适应 10 步, 可测 50/100 步长适应") + A("- **与 V30.3 内部纠缠结合**: OML 表示 + 纠缠 SSM 是否互补") + A("- **在线 RL 化**: 把离线监督式元学习接回 V30 在线交互循环 (READ/EXEC/WRITE)") + return "\n".join(L) + "\n" + + +if __name__ == "__main__": + main() diff --git a/tests/run_v32_1_matrix.py b/tests/run_v32_1_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..173e5f30428cd783aff4947a0d803b8ed2335bd5 --- /dev/null +++ b/tests/run_v32_1_matrix.py @@ -0,0 +1,948 @@ +""" +V32.1-32.9: 表示瓶颈攻坚 — 矩阵五方向消融 (regs-only / 规则身份 / 对比 / EHS / 换世界) +======================================================================================== +计划: docs/wiki/v32-1-matrix-plan.md (预注册: 因子矩阵 / 假设 H1-H6 / 阈值) + +立论 (V31.0 → V32 四层一致): + head 侧一切干预 (标量/BPTT/SwiftTD/意图注入/EMA 潜目标) 都撞 ~0.39 未见天花板; + V32 证明零步 goal readout ≥ 60 步适应 → 瓶颈在 RLN 表示本身, 不在 head 接口。 + → 本矩阵动**表示的学习目标与架构**, 五方向: + R regs-only : head n_resp=4 (只预测寄存器), m = Δregs 嵌入 (去指令 token 稀释) + P 规则身份 : aux_head 组成性预测 4×4 寄存器重映射 (perm[i] 而非 24 类 ID) + C 对比 : prototypical 对比 loss (同规则 query 近原型, 异规则远), λ=0.3 + E EHS : 独立 rule_encoder (Linear→Tanh→Linear), 完全替换 intent_net + (论文主线: 显式假设分离), head 输入 [h; h_rule; h⊙h_rule] + W 换世界 : window=4 (ctx=32, 观察历史更长 → 规则可辨识性更强) + +细胞 (10): + C0 8-token 基线 (≡ V32 C1, 验证实现等价) + regs_acc 统一 4-token 报告 + 32.1 R / 32.2 R+P / 32.3 R+C / 32.4 R+E / 32.5 R+W + 32.6 R+P+C / 32.7 R+P+E / 32.8 R+C+E / 32.9 R+P+C+E + +控制变量 (与 V32 完全一致): 世界 S4 24 排列 · Stage1 同缓存 · meta 80 iters + K=20 · 2 tasks/step · Adam 1e-3 · SwiftTD β_init=1e-3 κ=0.1 η=0.1 ϵ=0.99 + 长流 16×60 步 · sleep_iters=2 · seed=42 · n_support=40 n_query=20 + +主指标: 未见规则 mean regs_acc (60 步段后) + 零步 readout regs_acc (rd0) + 结构分组: unseen c3 vs c4 vs dbl 分别报告 (V32 §4.3 教训) + aux: remap 预测 acc (P cells), 对比 loss 收敛 (C cells) + +实现要点: + - V32_1_Learner(V32_Learner): n_resp 参数化贯穿 (head/PLN/评估), Δregs intent + (goal detached + local attached 双条件), rule_encoder / aux_head / 对比原型 + - C0 = n_resp=8 + use_intent=False → 与 V32 C1 同构 (活跃基线验证) + - regs 任务预处理: tgt 切片 [:4] (make_regs_tasks), 世界与 ctx 不变 + - 32.5 用 build_v32_tasks(window=4), 其余 window=3 (单一变量) +""" + +import os, sys, time, json, math, random +from pathlib import Path +import torch +import torch.nn as nn +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.swifttd_head import SwiftTDHead +from hibs_lnn.meta_rule_world import build_rule_splits, s4_groups +from run_v31_meta_learning import ( + batch_pairs, head_forward, stage1_pretrain_world, + V31_RLN, V31_PLN, +) +from run_v31_2_long_stream import build_v32_tasks +from run_v32_intent_head import V32_Learner + +PAD_ID = 0 + +# ============================================================ +# 细胞定义 (9 cell × 5 因子; W 只进 32.5, 32.9 与 32.1 同世界) +# C0: V32 C1 原样 (n_resp=8, 无 m) + regs_acc 报告 +# ============================================================ +CELLS = { + 'C0': dict(n_resp=8, use_intent=False, use_rule_enc=False, + use_perm_aux=False, use_contrast=False, window=3, + label='C0-8token-baseline'), + '32.1': dict(n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=False, use_contrast=False, window=3, + label='32.1-regs-only'), + '32.2': dict(n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=True, use_contrast=False, window=3, + label='32.2-rule-id'), + '32.3': dict(n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=False, use_contrast=True, window=3, + label='32.3-contrast'), + '32.4': dict(n_resp=4, use_intent=True, use_rule_enc=True, + use_perm_aux=False, use_contrast=False, window=3, + label='32.4-EHS'), + '32.5': dict(n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=False, use_contrast=False, window=4, + label='32.5-window4'), + '32.6': dict(n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=True, use_contrast=True, window=3, + label='32.6-ruleid+contrast'), + '32.7': dict(n_resp=4, use_intent=True, use_rule_enc=True, + use_perm_aux=True, use_contrast=False, window=3, + label='32.7-ruleid+EHS'), + '32.8': dict(n_resp=4, use_intent=True, use_rule_enc=True, + use_perm_aux=False, use_contrast=True, window=3, + label='32.8-contrast+EHS'), + '32.9': dict(n_resp=4, use_intent=True, use_rule_enc=True, + use_perm_aux=True, use_contrast=True, window=3, + label='32.9-all'), +} + +FACTOR_NAMES = ['R', 'P', 'C', 'E', 'W'] + + +# ============================================================ +# 模型构建 (build_model 的 n_response 参数化版本, 不碰共享文件) +# ============================================================ +def build_model_resp(V, n_response, s1_state=None, d_model=128, + d_state=8, n_layers=2): + """与 run_v31_meta_learning.build_model 同构, 但 n_response 参数化。 + d_model/d_state/n_layers 可配 (LM1 生产级规模扩展, 默认保持 V32 兼容).""" + rln = V31_RLN(V + 2, d_model=d_model, d_state=d_state, n_layers=n_layers) + if s1_state is not None: + own = rln.state_dict() + stripped = {} + for k, v in s1_state.items(): + kk = k[4:] if k.startswith('rln.') else k + if kk in own and own[kk].shape == v.shape: + stripped[kk] = v + own.update(stripped) + rln.load_state_dict(own) + pln = V31_PLN(d_model, V + 2, n_response=n_response) + return rln, pln + + +def make_regs_tasks(tasks): + """regs-only 预处理: target 只留前 4 个寄存器 token (规则相关); + 世界/ctx 不变。C0 用原 8-token 任务, 32.x 用切片后的。""" + out = {} + for n, t in tasks.items(): + out[n] = { + 'support': [(c, tg[:4]) for (c, tg) in t['support']], + 'query': [(c, tg[:4]) for (c, tg) in t['query']], + 'perm': t['perm'], 'seen': t['seen'], + } + return out + + +# ============================================================ +# V32_1_Learner +# ============================================================ +class V32_1_Learner(V32_Learner): + """V32 + 五方向机制: + R: n_resp=4, m = Δregs 嵌入 (intent_net) + P: aux_head 组成性 4×4 remap 预测 + C: prototypical 对比 loss (12 原型, no_grad) + E: 独立 rule_encoder 完全替换 intent_net (head 输入 [h;h_rule;h⊙h_rule]) + W: 由外部任务数据 (window=4) 控制, 学习器无感知 + """ + + def __init__(self, rln, pln, V, K=20, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, + beta_init=1e-3, kappa=0.1, eta=0.1, eps=0.99, + init_lr=0.0, sleep_iters=2, + n_resp=4, use_intent=None, use_rule_enc=False, + use_perm_aux=False, use_contrast=False, + lambda_perm=0.1, lambda_contra=0.3, n_local_sup=8): + if use_intent is None: + use_intent = (n_resp < 8) # C0 (n_resp=8) 无 m 注入 + d = rln.d_model + self.lambda_perm = lambda_perm + self.lambda_contra = lambda_contra + + super().__init__(rln, pln, V, K=K, outer_lr=outer_lr, + n_tasks_per_step=n_tasks_per_step, seed=seed, + beta_init=beta_init, kappa=kappa, eta=eta, eps=eps, + init_lr=init_lr, sleep_iters=sleep_iters, + use_intent=use_intent, use_ema=False, + n_local_sup=n_local_sup) + + # R: n_resp 贯穿 head (V32 基类在 super 里建了 n_resp=8 的 head) + self.n_resp = n_resp + self.use_rule_enc = use_rule_enc + self.use_perm_aux = use_perm_aux + self.use_contrast = use_contrast + self.head = SwiftTDHead(self.d_model, self.world_vocab, self.n_resp, + beta_init=beta_init, kappa=kappa, eta=eta, + eps=eps, input_dim=self.intent_dim) + self.head.reset_state() + + # P/E 附加网络; E 完全替换 intent_net (冻结, 不进 optimizer) + extra = list(self.intent_net.parameters()) + if use_rule_enc: + self.rule_encoder = nn.Sequential( + nn.Linear(d, d), nn.Tanh(), nn.Linear(d, d)) + extra = list(self.rule_encoder.parameters()) + if use_perm_aux: + self.aux_head = nn.Linear(d, 16) + extra = extra + list(self.aux_head.parameters()) + self.outer_opt = torch.optim.Adam( + list(rln.parameters()) + list(pln.parameters()) + extra, + lr=outer_lr) + self.loss_trace = {'perm': [], 'contra': []} + + # ── 规则向量 (E 或 intent_net) ────────────────────────── + def _rule_vec(self, agg): + if self.use_rule_enc: + return self.rule_encoder(agg) + return self.intent_net(agg) + + def _dregs(self, cs, ts): + """Δregs 聚合嵌入: mean(embed(tgt[:4]) − embed(ctx[-8:-4]))。 + ctx_regs = ctx 最后 8 个 token 的前 4 个 (最近观察的寄存器部分)。""" + e_t = self.rln.embed(ts[:, :4]) # (B,4,d) + e_c = self.rln.embed(cs[:, -8:-4]) # (B,4,d) + return (e_t - e_c).mean(dim=1).mean(dim=0) # (d,) + + def compute_goal_intent(self, task): + """goal call (部署): Δregs 聚合, stop-grad anchor。""" + sup = task['support'] + B = min(len(sup), 16) + with torch.no_grad(): + cs = torch.stack([s[0] for s in sup[:B]]) + ts = torch.stack([s[1] for s in sup[:B]]) + delta = self._dregs(cs, ts) + m = self.m_norm(self._rule_vec(delta)) + return m.detach() + + def _local_rule_intent(self, c, tg): + """local call (训练): per-sample Δregs, attached (梯度到 RLN+编码器)。""" + e_t = self.rln.embed(tg[:4].unsqueeze(0)) + e_c = self.rln.embed(c[-8:-4].unsqueeze(0)) + delta = (e_t - e_c).mean(dim=1).mean(dim=0) + return self.m_norm(self._rule_vec(delta)) + + # ── 对比原型 (12 条训练规则, no_grad; 只 query h 反传) ── + def _compute_prototypes(self, tasks, train_rules): + with torch.no_grad(): + protos = [] + for rn in train_rules: + hs = [self.rln(c.unsqueeze(0))[:, -1, :] + for (c, _) in tasks[rn]['support'][:8]] + protos.append(torch.cat(hs, 0).mean(0)) + P = torch.stack(protos) # (12,d) + return P / (P.norm(dim=1, keepdim=True) + 1e-8) + + # ── 外循环 (BPTT): 主 loss + local call + P + C ──────── + def meta_step(self, tasks, train_rules, K=None): + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + Ws = [] + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + protos = (self._compute_prototypes(tasks, train_rules) + if self.use_contrast else None) + for rname in chosen: + t = tasks[rname] + m = self.compute_goal_intent(t) if self.use_intent else None + W = self.adapt_graph(t['support'], m=m, K=K) + Ws.append([w.detach() for w in W]) + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self.rln(ctx) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward(W, h_in, self.intent_dim, self.world_vocab, + self.n_resp) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + + # 双条件共享算子: local call (attached), per-sample Δregs + if self.use_intent and self.lambda_local > 0: + for (c, tg) in t['support'][:self.n_local_sup]: + hsv = self.rln(c.unsqueeze(0))[:, -1, :] + m_loc = self._local_rule_intent(c, tg) + hin = self._h_in_batch(hsv, m_loc) + pl = head_forward(W, hin, self.intent_dim, + self.world_vocab, self.n_resp) + total_loss = total_loss + self.lambda_local * \ + F.cross_entropy(pl.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) \ + / (len(chosen) * self.n_local_sup) + + # P: 规则身份 (perm) 组成性 aux — 预测 perm[i] 而非 24 类 ID + if self.use_perm_aux: + aux = self.aux_head(h) # (B,16) + perm = torch.tensor(t['perm'], dtype=torch.long) # (4,) + tl = perm.unsqueeze(0).expand(h.shape[0], -1).reshape(-1) + l_perm = F.cross_entropy(aux.view(-1, 4), tl) + total_loss = total_loss + self.lambda_perm * l_perm \ + / len(chosen) + self.loss_trace['perm'].append(l_perm.item()) + + # C: prototypical 对比 — query h 近同规则原型, 异规则远 + if protos is not None: + qn = h / (h.norm(dim=1, keepdim=True) + 1e-8) + sim = qn @ protos.t() # (B,12) + tid = train_rules.index(rname) + tl = torch.full((h.shape[0],), tid, dtype=torch.long) + l_con = F.cross_entropy(sim, tl) + total_loss = total_loss + self.lambda_contra * l_con \ + / len(chosen) + self.loss_trace['contra'].append(l_con.item()) + + total_loss.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in self.rln.parameters() if p.requires_grad] + + [p for p in self.pln.parameters() if p.requires_grad], 1.0) + self.outer_opt.step() + if self.init_lr > 0 and Ws: + with torch.no_grad(): + scale = self.init_lr / len(Ws) + for wa in Ws: + for p, w in zip(self.init_params, wa): + p.add_(scale * (w - p.detach())) + self._meta_iters += 1 + return total_loss.item() + + # ── 评估 (统一 regs_acc 主指标; C0 兼报 full acc) ────── + def _metric(self, W, task, m=None, max_batch=16): + """返回 (full_acc, regs_acc)。regs_acc = 前 4 个响应 token 精确匹配。 + 32.x (n_resp=4, tgt 4-token) 两者相等; C0 (n_resp=8) 分离报告。""" + ctx, tgt = batch_pairs(task['query'], max_batch=max_batch) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward([w.detach() for w in W], h_in, + self.intent_dim, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) # (B, n_resp) + full = (pt == tgt[:, :self.n_resp]).sum().item() / \ + max(tgt[:, :self.n_resp].numel(), 1) + regs = (pt[:, :4] == tgt[:, :4]).sum().item() / \ + max(tgt[:, :4].numel(), 1) + return full, regs + + def eval_goal_readout(self, task, head_state=None): + """INTACT 零步 readout: goal call, 不做任何 head 适应。""" + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self.compute_goal_intent(task) if self.use_intent else None + return self._metric(W, task, m=m) + + def eval_adaptation_curve(self, task, max_steps=40, eval_every=10, + head_state=None): + """off-stream 适应曲线 (0/10/20/30/40), regs_acc。""" + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self.compute_goal_intent(task) if self.use_intent else None + curve = [self._metric(W, task, m=m)[1]] + sup = task['support'] + for j in range(max_steps): + if j < len(sup): + with torch.no_grad(): + hidden = self.rln(sup[j][0].unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(sup[j][1], self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + curve.append(self._metric(W, task, m=m)[1]) + return curve + + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + """长流: W/θ/h 跨任务持续; sleep = BPTT meta_step 于已见任务。 + 修正 V32 细节: 遗忘行按任务各自 goal intent 评估 (V32 误用当前任务 m)。""" + self._load_head(head_state) + W = [w.detach() for w in self.pln.clone_params()] + seen = [] + forget_rows = [] + acc_after = {} + acc_after_full = {} + curves = {} + m_cache = {} + for tname in stream: + t = tasks[tname] + if tname not in m_cache: + m_cache[tname] = (self.compute_goal_intent(t) + if self.use_intent else None) + m = m_cache[tname] + seg = [self._metric(W, t, m=m)[1]] + for j in range(steps_per_task): + if j < len(t['support']): + with torch.no_grad(): + hidden = self.rln(t['support'][j][0].unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(t['support'][j][1], + self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + seg.append(self._metric(W, t, m=m)[1]) + seen.append(tname) + curves[tname] = seg + acc_after[tname] = seg[-1] + acc_after_full[tname] = self._metric(W, t, m=m)[0] + row = {} + for s in seen: + if s not in m_cache: + m_cache[s] = (self.compute_goal_intent(tasks[s]) + if self.use_intent else None) + row[s] = round(self._metric(W, tasks[s], m=m_cache[s])[1], 4) + forget_rows.append(row) + if sleep_iters > 0 and len(seen) >= sleep_every: + saved = self.head.state() + for _ in range(sleep_iters): + self.meta_step(tasks, seen) + self.head.load_state(saved) + return {'acc_after': acc_after, 'acc_after_full': acc_after_full, + 'curves': curves, 'forget': forget_rows} + + +# ============================================================ +# 运行一个 cell +# ============================================================ +def eval_remap_acc(learner, tasks, train_names, unseen_names): + """P 因子 aux 指标: 组成性 remap 预测 acc (4 位置逐位正确)。""" + def _acc(names): + ok = tot = 0 + for n in names: + t = tasks[n] + ctx, _ = batch_pairs(t['query'], max_batch=16) + with torch.no_grad(): + h = learner.rln(ctx)[:, -1, :] + aux = learner.aux_head(h) # (B,16) + pred = aux.view(h.shape[0], 4, 4).argmax(dim=-1) # (B,4) + perm = torch.tensor(t['perm'], dtype=torch.long).unsqueeze(0) + ok += (pred == perm).sum().item() + tot += pred.numel() + return round(ok / max(tot, 1), 4) + return _acc(train_names), _acc(unseen_names) + + +def run_cell(cell, cfg, V, s1_state, tasks_w3, tasks_w4, U_STRUCT, + n_meta_iters=80, K=20, seed=42, steps_per_task=60, + verbose=True): + label = cfg['label'] + window = cfg['window'] + base = tasks_w4 if window == 4 else tasks_w3 + n_resp = cfg['n_resp'] + tasks = base if n_resp == 8 else make_regs_tasks(base) + train_rules = [n for n in tasks if tasks[n]['seen']] + train_names = train_rules[:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']][:8] + stream = train_names + unseen_names + + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(V, n_resp, s1_state) + learner = V32_1_Learner(rln, pln, V, K=K, seed=seed, sleep_iters=2, + n_resp=n_resp, use_intent=cfg['use_intent'], + use_rule_enc=cfg['use_rule_enc'], + use_perm_aux=cfg['use_perm_aux'], + use_contrast=cfg['use_contrast']) + t0 = time.time() + for it in range(n_meta_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_rules) + if verbose and (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_meta_iters} " + f"loss={loss:.4f}", flush=True) + trained_head = learner.head.state() + dt = time.time() - t0 + print(f" [{label}] meta 训练 {n_meta_iters} iters 完成 ({dt:.0f}s)", + flush=True) + + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + t_ls = time.time() - t0 + print(f" [{label}] 长流完成 ({t_ls:.0f}s)", flush=True) + + curves = {} + rd0 = {} + rd0_full = {} + for n in unseen_names: + curves[n] = learner.eval_adaptation_curve( + tasks[n], max_steps=40, eval_every=10, head_state=trained_head) + f, r = learner.eval_goal_readout(tasks[n], head_state=trained_head) + rd0[n] = r + rd0_full[n] = f + rd0_train = {} + for n in train_names: + f, r = learner.eval_goal_readout(tasks[n], head_state=trained_head) + rd0_train[n] = r + + # 结构分组 (未见: c3 三轮换 / c4 四轮换 / dbl 双对换) + by_struct = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by_struct: + by_struct[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) + for k, v in by_struct.items()} + + aux = {} + if cfg['use_perm_aux']: + a_tr, a_un = eval_remap_acc(learner, tasks, train_names, unseen_names) + aux['remap_acc_train'] = a_tr + aux['remap_acc_unseen'] = a_un + if cfg['use_contrast']: + tr = learner.loss_trace['contra'] + last10 = tr[-10:] if len(tr) >= 10 else tr + aux['contra_loss_mean_last10'] = \ + round(sum(last10) / len(last10), 4) if last10 else None + aux['contra_loss_final'] = round(tr[-1], 4) if tr else None + + head_stats = learner.head.step_size_stats() + result = { + 'cell': cell, 'label': label, 'seed': seed, 'time_s': round(dt, 1), + 'meta_iters': n_meta_iters, 'K': K, + 'window': window, 'n_resp': n_resp, + 'factors': { + 'R': n_resp == 4, + 'P': cfg['use_perm_aux'], + 'C': cfg['use_contrast'], + 'E': cfg['use_rule_enc'], + 'W': window == 4, + }, + 'stream': stream, + 'train_acc_after': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc_after': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_acc_after_full': {n: st['acc_after_full'][n] + for n in unseen_names}, + 'readout0_unseen': rd0, + 'readout0_unseen_full': rd0_full, + 'readout0_train': rd0_train, + 'unseen_by_struct': struct_mean, + 'stream_curves': st['curves'], + 'unseen_curve_offstream': curves, + 'forget': st['forget'], + 'head_stats': head_stats, + 'aux': aux, + } + ua = result['unseen_acc_after'] + u_mean = sum(ua.values()) / len(ua) + r_mean = sum(rd0.values()) / len(rd0) + dt = time.time() - t0 + result['time_s'] = round(dt, 1) + print(f" [{label}] 全流程完成 ({dt:.0f}s)", flush=True) + print(f" 训练 regs_acc: {result['train_acc_after']}", flush=True) + print(f" 未见 regs_acc: {ua} mean={u_mean:.4f}", flush=True) + print(f" 未见 rd0: {rd0} mean={r_mean:.4f}", flush=True) + print(f" 结构分组: {struct_mean}", flush=True) + if aux: + print(f" aux: {aux}", flush=True) + return result + + +# ============================================================ +# 报告 +# ============================================================ +def _mean(d): + return sum(d.values()) / max(len(d), 1) + + +def build_t_struct(seed=42): + """训练任务结构分组: T0-T11 ↔ build_rule_splits(seed) 的 train_perms 顺序。 + 结构 = ident / swap / c3 (V33.1 模式库分组检索用)。""" + train_perms, test_perms = build_rule_splits(seed) + identity, swaps, c3, c4, dbl = s4_groups() + struct_of = {} + for p in c3: + struct_of[p] = 'c3' + for p in c4: + struct_of[p] = 'c4' + for p in dbl: + struct_of[p] = 'dbl' + for p in swaps: + struct_of[p] = 'swap' + struct_of[(0, 1, 2, 3)] = 'ident' + return {f'T{i}': struct_of[p] for i, p in enumerate(train_perms)} + + +def build_u_struct(seed=42): + """未见任务结构分组: U0-U11 ↔ build_rule_splits(seed) 的 test_perms 顺序。 + 结构 = c3 (三轮换) / c4 (四轮换) / dbl (双对换) / swap / ident。""" + train_perms, test_perms = build_rule_splits(seed) + identity, swaps, c3, c4, dbl = s4_groups() + struct_of = {} + for p in c3: + struct_of[p] = 'c3' + for p in c4: + struct_of[p] = 'c4' + for p in dbl: + struct_of[p] = 'dbl' + for p in swaps: + struct_of[p] = 'swap' + struct_of[(0, 1, 2, 3)] = 'ident' + return {f'U{i}': struct_of[p] for i, p in enumerate(test_perms)} + + +def tome_fraction(tasks): + """TOME 地板: 程序结束后 target 后 4 token 全为 TOME (201) 的比例。 + full-8 acc 的地板 = tome_frac/2 (8 token 中 4 个白拿) — V31.2/V32 + "~0.39 未见天花板" 的机制解释 (≈0.775/2 = 0.3875)。""" + tot = tome = 0 + for n, t in tasks.items(): + for (ctx, tgt) in t['query']: + tot += 1 + if int(tgt[4]) == 201: + tome += 1 + return round(tome / max(tot, 1), 4) + + +def summarize(results): + rows = {} + for r in results: + ua = r['unseen_acc_after'] + rd = r['readout0_unseen'] + by = r['unseen_by_struct'] + rows[r['cell']] = { + 'label': r['label'], 'time_s': r['time_s'], + 'factors': r['factors'], + 'train_mean': round(_mean(r['train_acc_after']), 4), + 'unseen_mean': round(_mean(ua), 4), + 'rd0_mean': round(_mean(rd), 4), + 'unseen_minus_rd0': round(_mean(ua) - _mean(rd), 4), + 'c3': by.get('c3'), 'c4': by.get('c4'), 'dbl': by.get('dbl'), + 'c3_minus_c4': round(by['c3'] - by['c4'], 4) + if by.get('c3') is not None and by.get('c4') is not None else None, + 'beta_mean': r['head_stats']['beta_mean'], + 'aux': r['aux'], + } + return rows + + +def hypothesis_verdicts(rows): + """按预注册阈值判定 H1-H6 (缺失 cell 返回 N/A)。""" + def _g(k): + return rows.get(k, {}).get('unseen_mean') + + c0, r1 = _g('C0'), _g('32.1') + v = {} + if c0 is None or r1 is None: + v['H1'] = ('N/A', 'N/A', None, 0.05) + else: + v['H1'] = _threshold(r1 - c0, 0.02, 0.05, + '32.1 regs_acc vs C0 regs_acc') + for h, a, b in (('H2', '32.2', '32.1'), ('H3', '32.3', '32.1'), + ('H4', '32.4', '32.1')): + va, vb = _g(a), _g(b) + if va is None or vb is None: + v[h] = ('N/A', 'N/A', None, 0.03) + else: + v[h] = _threshold(va - vb, 0.01, 0.03, f'{a} vs {b} regs_acc') + g1 = rows.get('32.1', {}).get('c3_minus_c4') + g5 = rows.get('32.5', {}).get('c3_minus_c4') + dg = (g5 - g1) if (g1 is not None and g5 is not None) else None + if dg is None: + v['H5'] = ('N/A', 'N/A', None, 0.03) + else: + v['H5'] = _threshold(dg, 0.0, 0.03, + '32.5 c3−c4 差距收窄 (vs 32.1)') + cands = [rows.get(k, {}).get('unseen_mean') + for k in ('32.1', '32.2', '32.3', '32.4')] + best = max([x for x in cands if x is not None], default=None) + r9 = _g('32.9') + if best is None or r9 is None: + v['H6'] = ('N/A', 'N/A', None, 0.02) + else: + v['H6'] = _threshold(r9 - best, 0.0, 0.02, + '32.9 ≥ max(单因子)') + return v + + +def _threshold(d, lo, hi, desc): + if d > hi: + return (desc, '成功', round(d, 4), hi) + if d >= lo: + return (desc, '部分成功', round(d, 4), hi) + return (desc, '失败', round(d, 4), hi) + + +def write_report(results, args): + rp = ROOT / "results" / "v32_1_matrix_report.json" + rp.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + rows = summarize(results) + hv = hypothesis_verdicts(rows) + + # TOME 地板 (机制发现, 数据实时计算) + from run_v31_2_long_stream import build_v32_tasks + _tw = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3) + _tome = tome_fraction(_tw) + _floor = round(_tome / 2, 4) + U_STRUCT = build_u_struct(42) + + L = [] + A = L.append + A("# V32.1-32.9 矩阵验证报告 — 表示瓶颈五方向消融") + A("") + A("> 计划: `docs/wiki/v32-1-matrix-plan.md` (预注册) · 运行时间: " + f"{time.strftime('%Y-%m-%d %H:%M')}") + A(f"> 参数: meta_iters={args.meta_iters} K={args.K} " + f"steps_per_task={args.steps_per_task} seed=42 · 同 Stage1 缓存") + A("") + A("## 0. 大白话总结") + A("") + A("- **做了什么**: 把 V32 的 8-token 预测目标换成纯寄存器目标 (regs-only), " + "并叠加四个表示级机制 (规则身份预测 / 原型对比 / 独立规则编码器 EHS / 更长观察窗口), " + "10 个 cell 消融, 直攻 V32 撞墙的 ~0.39 未见规则天花板。") + A("- **发现什么**: (结果落地后填)") + A("- **为什么重要**: 前四个版本家族已排除 head 侧一切机制; 本矩阵是第一次直接改" + " RLN 的学习目标与架构, 决定论文主线 EHS 是否成立。") + A("") + A(f"## 0.5 重要机制发现: 8-token full acc 的 TOME 地板 (冒烟阶段)") + A("") + A(f"- 数据统计: 世界任务里 **{_tome:.1%}** 的 target 后 4 个 token 是 TOME(201) " + "(程序结束后指令位置填充)。") + A(f"- 推论: 只要模型学会输出 \"201\", 8 个 token 里就有 4 个\"白拿\" → " + f"**full-8 acc 地板 = {_floor:.4f}**。") + A(f"- 与历史对照: V31.2 S2 unseen 0.386 / V32 C1 0.376 ≈ {_floor:.4f} — " + "**V31.2/V32 的 \"~0.39 未见天花板\" 主要就是 TOME 地板, 不是规则学习水平**。") + A("- 真实信号: 用 regs_acc (前 4 寄存器 token) 度量, C0 (≡V32 C1 协议) 只有 " + "~0.05-0.10 → 表示瓶颈比 V31/V32 报告的数字更严重; 本矩阵全部用 regs_acc " + "统一度量, 预注册阈值 (C0+0.05 / 32.1+0.03) 正是在这个真实尺度上。") + A("") + A("## 1. 背景与立论") + A("") + A("- V31.0/1/2 + V32 四层一致: head 侧一切干预撞 ~0.39 未见天花板 " + "(`results/v32_intent_report.md`, `docs/wiki/rln-mechanism.md`)") + A("- V32 关键发现: 零步 goal readout ≥ 60 步适应 (所有 cell 60s−rd0 ≤ 0) " + "→ 表示在适应前就应可读, 瓶颈在 RLN 表示本身") + A("- 机制归因 (rln-mechanism.md 事实 1-3): 线性递推 + 单 last-hidden 出口装不下" + " 状态内容+规则索引双信息; tgt 4 规则 token + 4 指令 token 混合稀释监督") + A("") + A("## 2. 实验设计 (预注册)") + A("") + A("| cell | 因子 | 核心改动 |") + A("|:--|:--|:--|") + for cell, cfg in CELLS.items(): + fac = '+'.join(f for f, on in { + 'R': cfg['n_resp'] == 4, + 'P': cfg['use_perm_aux'], 'C': cfg['use_contrast'], + 'E': cfg['use_rule_enc'], 'W': cfg['window'] == 4, + }.items() if on) + A(f"| {cell} | {fac or '—'} | {cfg['label']} |") + A("") + A("| 指标 | 失败 | 部分成功 | 成功 |") + A("|:--|:--|:--|:--|") + A("| 32.1 unseen regs_acc vs C0 | ≤ C0 | +0.02~0.05 | > +0.05 |") + A("| 32.2/3/4 vs 32.1 | ≤ 32.1 | +0.01~0.03 | > +0.03 |") + A("| 32.5 c3−c4 差距 vs 32.1 | 不缩窄 | <3pp | ≥3pp |") + A("| 32.9 ≥ max(单因子) | 否 | 持平 | 严格更大 |") + A("") + A("## 3. 结果总表 (regs_acc 统一 4-token 协议)") + A("") + A("| cell | 因子 | Train | Unseen | rd0 | 60s−rd0 | c3 | c4 | dbl | c3−c4 | time_s |") + A("|:--|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|") + for cell in CELLS: + if cell not in rows: + continue + s = rows[cell] + fac = '+'.join(f for f in ('R', 'P', 'C', 'E', 'W') + if s['factors'].get(f)) + c34 = f"{s['c3_minus_c4']:.4f}" if s['c3_minus_c4'] is not None else "-" + cc = lambda x: f"{x:.4f}" if x is not None else "-" + A(f"| {cell} | {s['label']} | {s['train_mean']:.4f} | " + f"{s['unseen_mean']:.4f} | {s['rd0_mean']:.4f} | " + f"{s['unseen_minus_rd0']:+.4f} | {cc(s['c3'])} | {cc(s['c4'])} | " + f"{cc(s['dbl'])} | {c34} | {s['time_s']:.0f} |") + A("") + A("### 假设判定 (预注册阈值)") + A("") + A("| 假设 | 判定 | 差值 | 阈值 |") + A("|:--|:--|--:|--:|") + for k in ('H1', 'H2', 'H3', 'H4', 'H5', 'H6'): + desc, verdict, d, hi = hv[k] + ds = f"{d:+.4f}" if d is not None else "-" + A(f"| {k} | **{verdict}** | {ds} | {hi} |") + A("") + A("## 4. 逐任务明细 (未见规则 regs_acc)") + A("") + for cell in CELLS: + if cell not in rows: + continue + s = rows[cell] + A(f"### {cell} ({s['label']})") + A("") + r = {x['cell']: x for x in results}[cell] + A("| 任务 | 结构 | 60步段后 | rd0 | off-stream 曲线 0/10/20/30/40 |") + A("|:--|:--|--:|--:|:--|") + for n in r['unseen_acc_after']: + struct = U_STRUCT.get(n, '?') + curve = r['unseen_curve_offstream'][n] + A(f"| {n} | {struct} | {r['unseen_acc_after'][n]:.4f} | " + f"{r['readout0_unseen'][n]:.4f} | " + f"{'→'.join(f'{x:.2f}' for x in curve)} |") + A("") + A("## 5. 辅助指标") + A("") + for cell in CELLS: + r = {x['cell']: x for x in results}.get(cell) + if not r: + continue + aux = r['aux'] + if aux: + A(f"- **{cell}**: {json.dumps(aux, ensure_ascii=False)}") + A("") + A("## 6. 假阳性检查") + A("") + A("- **活跃基线 (实现等价)**: C0 = V32 C1 同协议同缓存 (80 iters/K=20/seed 42), " + "应复现 V32 C1 full-8 acc (0.4346/0.3867/rd0 0.3779, `results/v32_C1.json` 重跑) — " + "full acc 的 TOME 地板解释见 §0.5; regs_acc 为本矩阵新增统一主指标") + A("- **评估独立性**: 主指标 regs_acc 与训练 loss 同源但取 argmax 精确匹配; " + "rd0 用纯共享 W (零适应)") + A("- **单一变量**: 32.2/3/4/6/7/8/9 与 32.1 同世界同 window; 仅 32.5 换 window=4") + A("") + A("## 7. 结论") + A("") + A("(结果落地后填: 每条 证据→解读→通俗)") + A("") + A("## 8. 复现") + A("") + A("```bash") + A(f"python tests/run_v32_1_matrix.py --meta-iters {args.meta_iters} " + f"--K {args.K} --steps-per-task {args.steps_per_task}") + A("```") + A("") + A("- Stage1 缓存: /tmp/v32_s1_state.pt (优先) / /tmp/v31_2_s1_state.pt; " + "缺则自动重训 (2 epochs, ~15 min)") + A("- 结果 JSON 可再生: `results/v32_1_matrix_report.json` (gitignored)") + + mp = ROOT / "results" / "v32_1_matrix_report.md" + mp.write_text("\n".join(L) + "\n", encoding='utf-8') + print(f"\nJSON 报告: {rp}\nMD 报告: {mp}") + return rows, hv + + +# ============================================================ +# 主流程 +# ============================================================ +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--meta-iters', type=int, default=80) + parser.add_argument('--K', type=int, default=20) + parser.add_argument('--steps-per-task', type=int, default=60) + parser.add_argument('--cells', type=str, + default='C0,32.1,32.2,32.3,32.4,32.5,32.6,32.7,32.8,32.9') + parser.add_argument('--result', type=str, default='v32_1_matrix_report.json') + parser.add_argument('--no-stage1', action='store_true', + help='管线冒烟用: 随机 RLN 初始化, 不加载/重训 Stage1') + args = parser.parse_args() + + print("=" * 70) + print("V32.1-32.9 矩阵验证 — 表示瓶颈五方向消融") + print("=" * 70) + + WORLD_V = 200 + tasks_w3 = build_v32_tasks(WORLD_V, n_support=40, n_query=20, + seed=42, window=3) + tasks_w4 = build_v32_tasks(WORLD_V, n_support=40, n_query=20, + seed=42, window=4) + train_rules_all = [n for n in tasks_w3 if tasks_w3[n]['seen']] + unseen_all = [n for n in tasks_w3 if not tasks_w3[n]['seen']] + print(f"V={WORLD_V}, 训练规则={len(train_rules_all)}, " + f"未见规则={len(unseen_all)}") + + # 未见任务结构分组 (build_rule_splits 确定性: U0-U11 ↔ test_perms 顺序) + U_STRUCT = build_u_struct(42) + print(f"未见结构: {U_STRUCT}") + + # Stage1 缓存: 兼容 v32/v31_2 两个路径 + s1_state = None + if args.no_stage1: + print("\n--no-stage1: 随机 RLN 初始化 (仅管线冒烟)") + else: + S1_CACHE = None + for cand in ("/tmp/v32_s1_state.pt", "/tmp/v31_2_s1_state.pt"): + if os.path.exists(cand): + S1_CACHE = cand + break + if S1_CACHE is None: + S1_CACHE = "/tmp/v32_s1_state.pt" + os.makedirs(os.path.dirname(S1_CACHE), exist_ok=True) + print("\n>>> Stage1 预训练 (12 条训练规则 token 流)...") + random.seed(42); torch.manual_seed(42) + ppl, s1_model = stage1_pretrain_world(tasks_w3, train_rules_all, + WORLD_V, n_epochs=2) + s1_state = {k: v.cpu().clone() + for k, v in s1_model.state_dict().items()} + torch.save(s1_state, S1_CACHE) + print(f" Stage1 PPL = {ppl:.2f}, 已缓存到 {S1_CACHE}") + else: + s1_state = torch.load(S1_CACHE) + s1_state = {k: v.cpu().clone() for k, v in s1_state.items()} + print(f"\nStage1: 从缓存加载 ({S1_CACHE})") + + keys = [c.strip() for c in args.cells.split(',')] + results = [] + # 增量 checkpoint: 崩溃后已完成 cell 不丢, 支持续跑 + ckpt = ROOT / "results" / "v32_1_matrix_checkpoint.json" + if ckpt.exists(): + try: + done = {r['cell']: r for r in + json.loads(ckpt.read_text(encoding='utf-8'))} + except Exception: + done = {} + print(f"checkpoint: {sorted(done.keys())} 已完成") + else: + done = {} + for key in keys: + if key in done: + print(f"跳过已完成 cell: {key}") + results.append(done[key]) + continue + if key not in CELLS: + print(f"未知 cell: {key}") + continue + cfg = CELLS[key] + print(f"\n{'='*60}\n 实验: {cfg['label']} " + f"(n_resp={cfg['n_resp']}, window={cfg['window']}, " + f"P={cfg['use_perm_aux']}, C={cfg['use_contrast']}, " + f"E={cfg['use_rule_enc']})\n{'='*60}") + try: + r = run_cell(key, cfg, WORLD_V, s1_state, tasks_w3, tasks_w4, + U_STRUCT, n_meta_iters=args.meta_iters, K=args.K, + seed=42, steps_per_task=args.steps_per_task) + except Exception as e: + import traceback + traceback.print_exc() + print(f"!!! cell {key} 失败: {e} (继续后续 cell)") + continue + results.append(r) + ckpt.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + print(f"checkpoint 更新: {key} 完成 ({len(results)}/{len(keys)})", + flush=True) + + if results: + rows, hv = write_report(results, args) + print(f"\n{'='*60}\n汇总:\n{'='*60}") + print(f"{'cell':<6}{'Train':<8}{'Unseen':<10}{'rd0':<8}" + f"{'60s-rd0':<10}{'c3-c4':<10}{'time_s':<8}") + for cell in CELLS: + if cell not in rows: + continue + s = rows[cell] + c34 = f"{s['c3_minus_c4']:.4f}" if s['c3_minus_c4'] is not None else "-" + print(f"{cell:<6}{s['train_mean']:<8.4f}{s['unseen_mean']:<10.4f}" + f"{s['rd0_mean']:<8.4f}{s['unseen_minus_rd0']:<10.4f}" + f"{c34:<10}{s['time_s']:<8.0f}") + print("\n假设判定:") + for k in ('H1', 'H2', 'H3', 'H4', 'H5', 'H6'): + desc, verdict, d, hi = hv[k] + print(f" {k}: {verdict} ({d} vs 阈值 {hi})") + return results + + +if __name__ == "__main__": + main() diff --git a/tests/run_v32_intent_head.py b/tests/run_v32_intent_head.py new file mode 100644 index 0000000000000000000000000000000000000000..7ffb3665c743554fc7e2a736cc8be128b70cae16 --- /dev/null +++ b/tests/run_v32_intent_head.py @@ -0,0 +1,535 @@ +""" +V32: INTACT-JEPA 意图注入 head 如何打破 V31.2 RLN 平坦适应瓶颈 +==================================================================== +立论 (docs/wiki/v31-2-vs-jepa.md + docs/wiki/v32-plan.md): + V31.2 结论: 各种 head 适应 (~0.4-0.5 未见规则 acc 封顶), 流内 60 步 + 适应曲线平坦 → 瓶颈在 RLN 表示 / head readout 接口。 + INTACT (arXiv 2607.26056v1) 主张: forward-only 学习器的瓶颈是 + "uncalibrated funnel" (只学预测, 几何附带) → 解法是用**意图条件动作律**: + 同一个共享算子解释两个条件族 (non-call + goal call), 非对称梯度 + (物理后继 attached / 未来目标 stop-grad anchor), 且意图在 adaptation + 之前就可用 → 表示在适应前就变好 ("search-free direct readout"). + +映射到 LMT 代码世界 (OML 元学习): + - "动作" = 预测 8-token 观察 (规则确定下的确定性结果) + - non-call (local, attached) = 支持样本自身的 target → m_local (训练监督) + - goal call (可部署, stop-grad) = 支持集聚合得到的规则意图 → m_goal (anchor) + - 共享算子 = PLN head, 注入语法 [h; m; h⊙m] (头输入 3d) + +2×2 factorial: + A = 意图注入 (intent): off (V31.2 原样 [h]) / on ([h;m;h⊙m] + 双条件) + B = JEPA EMA 潜目标辅助: off / on (预测 EMA_RLN(tgt).last) + C1 base / C2 +intent / C3 +ema-aux / C4 +intent+ema-aux + +控制变量 (与 V31.2 完全一致): 世界 · 数据 · Stage1 缓存 · meta 80 iters + K=20 · Adam 1e-3 · SwiftTD β_init=1e-3 κ=0.1 η=0.1 ϵ=0.99 · 长流 16×60 步 + sleep_iters=2 · seed=42 · n_support=40 n_query=20 + +主指标: 未见规则 60 步段后 acc; 段内 0/20/40/60 适应曲线; + 新增 INTACT 零步 readout acc (goal call, 不适应) —— 表示是否在适应前就变好。 +""" + +import os, sys, time, json, math, random, copy +from pathlib import Path +import torch +import torch.nn as nn +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.swifttd_head import SwiftTDHead +from run_v31_meta_learning import ( + batch_pairs, head_forward, build_model, stage1_pretrain_world, +) +from run_v31_2_long_stream import V31_2_Learner, build_v32_tasks + + +class V32_Learner(V31_2_Learner): + """V31.2 + 意图条件 head (INTACT 双条件共享算子) + I-JEPA EMA 潜目标。""" + + def __init__(self, rln, pln, V, K=20, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, + beta_init=1e-3, kappa=0.1, eta=0.1, eps=0.99, + init_lr=0.0, sleep_iters=2, + use_intent=True, use_ema=False, + lambda_local=1.0, lambda_aux=0.1, ema_decay=0.99, + n_local_sup=8): + d = rln.d_model + self.use_intent = use_intent + self.use_ema = use_ema + self.lambda_local = lambda_local + self.lambda_aux = lambda_aux + self.ema_decay = ema_decay + self.n_local_sup = n_local_sup + + # head 输入语法: [h; m; h⊙m] → w1=(d,3d); C1/C3 保持 (d,d) + if use_intent: + old = pln.predictor[0] + new = nn.Linear(d * 3, d) + with torch.no_grad(): + new.weight[:, :d].copy_(old.weight) + new.bias.copy_(old.bias) + pln.predictor[0] = new + pln.step_beta = nn.Parameter( + torch.full((pln._n_params(),), math.log(beta_init))) + + super().__init__(rln, pln, V, K=K, outer_lr=outer_lr, + n_tasks_per_step=n_tasks_per_step, seed=seed, + beta_init=beta_init, kappa=kappa, eta=eta, eps=eps, + init_lr=init_lr, sleep_iters=sleep_iters) + + self.intent_dim = d * 3 if use_intent else d + # INTACT 意图编码器: 局部/目标共享 (meta 参数, 外循环训练) + self.m_norm = nn.LayerNorm(d) + self.intent_net = nn.Sequential( + nn.Linear(d, d), nn.Tanh(), nn.Linear(d, d)) + # JEPA EMA 潜目标: EMA 拷贝 + 辅助预测器 + if use_ema: + self.ema_rln = copy.deepcopy(rln) + self.ema_rln.requires_grad_(False) + self.aux_pred = nn.Linear(d, d) + else: + self.ema_rln = None + self.aux_pred = None + if use_intent: + # 头换成输入 3d 的 SwiftTDHead (保持 β_init 等一致) + self.head = SwiftTDHead(self.d_model, self.world_vocab, + self.n_resp, beta_init=beta_init, + kappa=kappa, eta=eta, eps=eps, + input_dim=self.intent_dim) + self.head.reset_state() + extra = list(self.intent_net.parameters()) + if self.aux_pred is not None: + extra += list(self.aux_pred.parameters()) + self.outer_opt = torch.optim.Adam( + list(rln.parameters()) + list(pln.parameters()) + extra, + lr=outer_lr) + + # ── 意图 ────────────────────────────────────────────────── + def _embed_mean(self, tgt): + e = self.rln.embed(tgt) # (B, n_resp, d) + return e.mean(dim=(0, 1)) + + def compute_intent(self, tgts): + """tgts: (B, n_resp) → m (d,) 经 intent_net + LN (带图)。""" + emb = self.rln.embed(tgts).mean(dim=(0, 1)) + return self.m_norm(self.intent_net(emb)) + + def compute_goal_intent(self, task): + """goal call 聚合 (INTACT: 未来目标 anchor, stop-grad; 部署接口)。""" + sup = task['support'] + B = min(len(sup), 16) + with torch.no_grad(): + tgts = torch.stack([s[1] for s in sup[:B]]) + m = self.compute_intent(tgts) + return m.detach() + + def _h_in_batch(self, h, m): + if not self.use_intent or m is None: + return h + mb = m.unsqueeze(0).expand(h.shape[0], -1) + return torch.cat([h, mb, h * mb], dim=-1) # (B, 3d) + + # ── 可微内循环 ──────────────────────────────────────────── + def adapt_graph(self, support, K=None, m=None): + K = K or self.K + W = self.pln.clone_params() + for (ctx, tgt) in support[:K]: + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view(1, self.n_resp, + self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + W = self.head.step_graph(W, hin[0], x[0], errs) + return W + + # ── 无图在线适应 ────────────────────────────────────────── + def adapt_online(self, W, support, n_steps, m=None): + for (ctx, tgt) in support[:n_steps]: + with torch.no_grad(): + hidden = self.rln(ctx.unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view(1, self.n_resp, + self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + return W + + # ── 外循环 (BPTT) ──────────────────────────────────────── + def meta_step(self, tasks, train_rules, K=None): + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + Ws = [] + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + n_loc = n_aux = 0 + for rname in chosen: + t = tasks[rname] + m = self.compute_goal_intent(t) if self.use_intent else None + W = self.adapt_graph(t['support'], m=m, K=K) + Ws.append([w.detach() for w in W]) + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self.rln(ctx) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward(W, h_in, self.intent_dim, self.world_vocab, + self.n_resp) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + + # 双条件共享算子 (INTACT): local call, attached — + # per-sample target → 塑造 intent_net + RLN (与 goal 同一算子) + if self.use_intent and self.lambda_local > 0: + for (c, tg) in t['support'][:self.n_local_sup]: + hsv = self.rln(c.unsqueeze(0))[:, -1, :] + m_loc = self.compute_intent(tg.unsqueeze(0)) + hin = self._h_in_batch(hsv, m_loc) + pl = head_forward(W, hin, self.intent_dim, + self.world_vocab, self.n_resp) + total_loss = total_loss + self.lambda_local * \ + F.cross_entropy(pl.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) \ + / (len(chosen) * self.n_local_sup) + n_loc += 1 + + # JEPA EMA 潜目标辅助 (因子 B) + if self.use_ema and self.aux_pred is not None \ + and self.ema_rln is not None: + with torch.no_grad(): + h_tgt = self.ema_rln(tgt)[:, -1, :] + aux_loss = F.mse_loss(self.aux_pred(h), h_tgt) + total_loss = total_loss + self.lambda_aux * aux_loss \ + / len(chosen) + n_aux += 1 + + total_loss.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in self.rln.parameters() if p.requires_grad] + + [p for p in self.pln.parameters() if p.requires_grad], 1.0) + self.outer_opt.step() + if self.init_lr > 0 and Ws: + with torch.no_grad(): + scale = self.init_lr / len(Ws) + for wa in Ws: + for p, w in zip(self.init_params, wa): + p.add_(scale * (w - p.detach())) + if self.use_ema and self.ema_rln is not None: + with torch.no_grad(): + for p, ep in zip(self.rln.parameters(), + self.ema_rln.parameters()): + ep.mul_(self.ema_decay).add_(p.detach() * + (1 - self.ema_decay)) + self._meta_iters += 1 + return total_loss.item() + + # ── 评估 ────────────────────────────────────────────────── + def _query_acc(self, W, task, max_batch=16, m=None): + ctx, tgt = batch_pairs(task['query'], max_batch=max_batch) + with torch.no_grad(): + hidden = self.rln(ctx) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward([w.detach() for w in W], h_in, + self.intent_dim, self.world_vocab, self.n_resp) + pt = pred.argmax(dim=-1) + return (pt == tgt).sum().item() / max(tgt.numel(), 1) + + def _load_head(self, head_state): + if head_state is not None: + self.head.load_state(head_state) + else: + self.head.reset_state() + + def eval_goal_readout(self, task, head_state=None): + """INTACT 零步 readout: goal call, 不做任何 head 适应。 + 表示是否在 adaptation 之前就可用 (search-free direct)。""" + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self.compute_goal_intent(task) if self.use_intent else None + return self._query_acc(W, task, m=m) + + def evaluate_task(self, task, K=10, head_state=None): + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self.compute_goal_intent(task) if self.use_intent else None + self.adapt_online(W, task['support'], K, m=m) + return self._query_acc(W, task, m=m) + + def eval_adaptation_curve(self, task, max_steps=40, eval_every=10, + head_state=None): + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self.compute_goal_intent(task) if self.use_intent else None + curve = [self._query_acc(W, task, m=m)] + sup = task['support'] + for j in range(max_steps): + if j < len(sup): + with torch.no_grad(): + hidden = self.rln(sup[j][0].unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(sup[j][1], self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + curve.append(self._query_acc(W, task, m=m)) + return curve + + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + self._load_head(head_state) + W = [w.detach() for w in self.pln.clone_params()] + seen = [] + forget_rows = [] + acc_after = {} + curves = {} + for tname in stream: + t = tasks[tname] + m = self.compute_goal_intent(t) if self.use_intent else None + seg = [self._query_acc(W, t, m=m)] + for j in range(steps_per_task): + if j < len(t['support']): + with torch.no_grad(): + hidden = self.rln(t['support'][j][0].unsqueeze(0)) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(t['support'][j][1], + self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + seg.append(self._query_acc(W, t, m=m)) + seen.append(tname) + curves[tname] = seg + acc_after[tname] = seg[-1] + row = {s: round(self._query_acc(W, tasks[s], m=m), 4) + for s in seen} + forget_rows.append(row) + if sleep_iters > 0 and len(seen) >= sleep_every: + saved = self.head.state() + for _ in range(sleep_iters): + self.meta_step(tasks, seen) + self.head.load_state(saved) + return {'acc_after': acc_after, 'curves': curves, + 'forget': forget_rows} + + +def run_cell(label, use_intent, use_ema, V, tasks, train_rules, + s1_state, n_meta_iters=80, K=20, seed=42, verbose=True, + steps_per_task=60): + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model(V, use_stage1=True, s1_state=s1_state) + learner = V32_Learner(rln, pln, V, K=K, seed=seed, sleep_iters=2, + use_intent=use_intent, use_ema=use_ema) + t0 = time.time() + for it in range(n_meta_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_rules) + if verbose and (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_meta_iters} " + f"loss={loss:.4f}", flush=True) + trained_head = learner.head.state() + dt = time.time() - t0 + print(f" [{label}] meta 训练 {n_meta_iters} iters 完成 ({dt:.0f}s)", + flush=True) + + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']][:8] + stream = train_names + unseen_names + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + dt = time.time() - t0 + + curve_curves = {} + readout0 = {} + for n in unseen_names: + curve_curves[n] = learner.eval_adaptation_curve( + tasks[n], max_steps=40, eval_every=10, head_state=trained_head) + readout0[n] = learner.eval_goal_readout(tasks[n], + head_state=trained_head) + readout0_train = {n: learner.eval_goal_readout(tasks[n], + head_state=trained_head) + for n in train_names} + + head_stats = learner.head.step_size_stats() + result = { + 'label': label, 'seed': seed, 'time_s': round(dt, 1), + 'meta_iters': n_meta_iters, 'K': K, + 'use_intent': use_intent, 'use_ema': use_ema, + 'stream': stream, + 'train_acc_after': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc_after': {n: st['acc_after'][n] for n in unseen_names}, + 'readout0_unseen': readout0, + 'readout0_train': readout0_train, + 'stream_curves': st['curves'], + 'unseen_curve_offstream': curve_curves, + 'forget': st['forget'], + 'head_stats': head_stats, + } + print(f" [{label}] 长流 {len(stream)} 任务完成 ({dt:.0f}s)", flush=True) + print(f" 训练规则 acc: {result['train_acc_after']}", flush=True) + print(f" 未见规则 acc: {result['unseen_acc_after']}", flush=True) + print(f" 未见零步 readout: {readout0}", flush=True) + return result + + +def print_report(results, args): + print(f"\n{'='*120}") + print("V32 INTACT-JEPA 意图注入 — 2×2 factorial 报告") + print(f"{'='*120}") + summary_rows = [] + for r in results: + ta = r['train_acc_after'] + ua = r['unseen_acc_after'] + ro = r['readout0_unseen'] + tmean = sum(ta.values()) / len(ta) + umean = sum(ua.values()) / len(ua) + rmean = sum(ro.values()) / len(ro) + hs = r['head_stats'] + summary_rows.append({ + 'label': r['label'], 'intent': r['use_intent'], + 'ema': r['use_ema'], 'time_s': r['time_s'], + 'train_acc': round(tmean, 4), 'unseen_acc': round(umean, 4), + 'readout0': round(rmean, 4), + 'beta_mean': round(hs['beta_mean'], 5), + 'unseen_60step_minus_readout0': round(umean - rmean, 4), + }) + print(f"\n[{r['label']}] ({r['time_s']}s) " + f"intent={r['use_intent']} ema={r['use_ema']}") + print(f" 训练规则 (流内 60 步): 均值={tmean:.4f} {ta}") + print(f" 未见规则 (流内 60 步): 均值={umean:.4f} {ua}") + print(f" 未见零步 readout (goal): 均值={rmean:.4f} {ro}") + print(f" 未见 off-stream 适应曲线 (0/10/20/30/40):") + for n in r['unseen_curve_offstream']: + print(f" {n}: {r['unseen_curve_offstream'][n]}") + hs = r['head_stats'] + print(f" β: mean={hs['beta_mean']:.5f} std={hs['beta_std']:.5f} " + f"max={hs['beta_max']:.4f} bound={hs['n_bound_triggers']}") + if r['forget']: + print(f" 遗忘 (16 任务后已见 acc): {r['forget'][-1]}") + + print(f"\n{'='*60}") + print(f"{'cell':<6}{'intent':<8}{'ema':<6}{'Train':<8}{'Unseen':<10}" + f"{'rd0':<8}{'60s-rd0':<10}{'time_s':<8}") + for s in summary_rows: + print(f"{s['label']:<6}{str(s['intent']):<8}{str(s['ema']):<6}" + f"{s['train_acc']:<8.4f}{s['unseen_acc']:<10.4f}" + f"{s['readout0']:<8.4f}{s['unseen_60step_minus_readout0']:<10.4f}" + f"{s['time_s']:<8.0f}") + + rp = ROOT / "results" / "v32_intent_report.json" + rp.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + md = ROOT / "results" / "v32_intent_report.md" + lines = [ + "# V32 INTACT-JEPA 意图注报报告 (2×2)", + "", + f"- 时间: {time.strftime('%Y-%m-%d %H:%M')}", + f"- meta_iters={args.meta_iters} K={args.K} sleep_iters=2 " + f"steps_per_task={args.steps_per_task}", + "", + "| cell | intent | ema | Train(60步) | Unseen(60步) | rd0(0步) | 60s-rd0 | time_s |", + "|---|---|---|---|---|---|---|---|", + ] + for s in summary_rows: + lines.append( + f"| {s['label']} | {s['intent']} | {s['ema']} | " + f"{s['train_acc']:.4f} | {s['unseen_acc']:.4f} | " + f"{s['readout0']:.4f} | {s['unseen_60step_minus_readout0']:.4f} | " + f"{s['time_s']:.0f} |") + md.write_text("\n".join(lines), encoding='utf-8') + print(f"\nJSON 报告: {rp}\nMD 报告: {md}") + return results + + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--meta-iters', type=int, default=80) + parser.add_argument('--K', type=int, default=20) + parser.add_argument('--steps-per-task', type=int, default=60) + parser.add_argument('--cells', type=str, + default='C1,C2,C3,C4') + parser.add_argument('--result', type=str, default='v32_intent_report.json') + args = parser.parse_args() + + print("=" * 70) + print("V32 INTACT-JEPA 意图注入 head — 2×2 factorial") + print("=" * 70) + + WORLD_V = 200 + tasks = build_v32_tasks(WORLD_V, n_support=40, n_query=20, seed=42) + train_rules = [n for n in tasks if tasks[n]['seen']] + unseen = [n for n in tasks if not tasks[n]['seen']] + print(f"V={WORLD_V}, 训练规则={len(train_rules)}, 未见规则={len(unseen)}") + + # Stage1 缓存: 兼容 v32/v31_2 两个路径 + S1_CACHE = None + for cand in ("/tmp/v32_s1_state.pt", "/tmp/v31_2_s1_state.pt"): + if os.path.exists(cand): + S1_CACHE = cand + break + if S1_CACHE is None: + S1_CACHE = "/tmp/v32_s1_state.pt" + os.makedirs(os.path.dirname(S1_CACHE), exist_ok=True) + print("\n>>> Stage1 预训练 (12 条训练规则 token 流)...") + random.seed(42); torch.manual_seed(42) + ppl, s1_model = stage1_pretrain_world(tasks, train_rules, WORLD_V, + n_epochs=2) + s1_state = {k: v.cpu().clone() + for k, v in s1_model.state_dict().items()} + torch.save(s1_state, S1_CACHE) + print(f" Stage1 PPL = {ppl:.2f}, 已缓存到 {S1_CACHE}") + else: + s1_state = torch.load(S1_CACHE) + s1_state = {k: v.cpu().clone() for k, v in s1_state.items()} + print(f"\nStage1: 从缓存加载 ({S1_CACHE})") + + cell_map = { + 'C1': ('C1-base', False, False), + 'C2': ('C2-intent', True, False), + 'C3': ('C3-ema', False, True), + 'C4': ('C4-both', True, True), + } + keys = [c.strip().upper() for c in args.cells.split(',')] + results = [] + for key in keys: + if key not in cell_map: + print(f"未知 cell: {key}") + continue + label, use_intent, use_ema = cell_map[key] + print(f"\n{'='*60}") + print(f" 实验: {label} (intent={use_intent}, ema={use_ema})") + print(f"{'='*60}") + r = run_cell(label, use_intent, use_ema, WORLD_V, tasks, train_rules, + s1_state, n_meta_iters=args.meta_iters, K=args.K, + seed=42, steps_per_task=args.steps_per_task) + results.append(r) + + print_report(results, args) + return results + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tests/run_v33_drl.py b/tests/run_v33_drl.py new file mode 100644 index 0000000000000000000000000000000000000000..f9df8dff362db6ae755b65287e7307b7159fda34 --- /dev/null +++ b/tests/run_v33_drl.py @@ -0,0 +1,1207 @@ +""" +V33: Define-Route 循环 — OaK 式持续学习 × INTACT 意图路由 +========================================================== +计划: docs/wiki/v33-plan.md (预注册 H-A..H-E) + +立论 (用户重新规划 2026-08-07): + h 的问题不是容量, 是"循环和路由没有确立": + - Define 层缺失: 经验没有显式的"抽象成可学习结构"环节 (单次聚合 ≠ define, + define 必须可迭代: 定义 → 重解释经验 → 按误差修正定义) + - Route 层缺失: 信息没有路由分派, 规则与内容挤在同一向量 + INTACT 重定位: 意图插入 = define, 选择动作 = route; V32 失败根因 = + 只做 define 输出注入 (head 侧被 W 适应洗掉), 没做路由进表示 (递推侧) + +三圈循环: + 循环1 (任务内, 表示级): define 的 m 调制 SSM 递推 dt/B (路由进递推) + 循环2 (任务内, 定义级): define-refine — m₀ 解释经验 → 检验误差 → 修正 m₁ + 循环3 (跨任务, 能力级): 模式库 — 入库/检索匹配/叠加 (持续学习) + +细胞 (8): + B0 基线 旧世界 (≡ V32.1 32.1, 复现 0.1016) + W0 基线 新世界 (TOME 截断, 世界修复单独效果) + A +define-refine (循环2) + B +路由进递推 (循环1) + C +动作路由 top-k (INTACT 选择语义) + D +模式库 (循环3) + E 全闭环 (旧世界) + EW 全闭环 (新世界) + +控制变量 (与 V32.1 一致): 世界 S4 24 排列 · Stage1 同缓存 (V32.1 重训, PPL 12.50) + meta 80 iters · K=20 · 2 tasks/step · Adam 1e-3 · clip 1.0 · seed 42 + SwiftTD β_init=1e-3 κ=0.1 η=0.1 ϵ=0.99 · 长流 16×60 · sleep_iters=2 +主指标: unseen regs_acc (4-token) · rd0 · 结构分组 c3/c4/dbl · 遗忘 · β + aux: define 探针 (m → perm 组成性 acc) · 路由一致性 (同规则 m 近) · 模式库统计 + +实现要点: + - 路由进递推: SSM_Layer_V30_3.forward(x, cond) — cond_dt/cond_b 零初始化, + Stage1 缓存直接复用 (无需重训) + - define-refine: 检验用 pln.clone_params() (共享头) 的预测误差对 m 的梯度 + (detached), refine_net([m; g]) → 修正; goal detached / local attached 双条件 + - 动作路由: top-k 候选 logit += λ·(m·embed(cand)) — 意图-动作匹配度 (INTACT 选择语义) + - 模式库: cosine 检索 τ=0.5, 匹配则叠加 α=0.5, 否则入库 + - B0/W0 用 V32_1_Learner 原样 (严格复现 32.1), A-E 用 V33_Learner +""" + +import os, sys, time, json, math, random +from typing import Optional +from pathlib import Path +import torch +import torch.nn as nn +import torch.nn.functional as F + +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "tests")) + +from hibs_lnn.swifttd_head import SwiftTDHead +from hibs_lnn.meta_rule_world import build_rule_splits, s4_groups +from run_v31_meta_learning import ( + batch_pairs, head_forward, stage1_pretrain_world, + V31_RLN, V31_PLN, +) +from run_v31_2_long_stream import build_v32_tasks +from run_v32_intent_head import V32_Learner +from run_v32_1_matrix import ( + V32_1_Learner, build_model_resp, make_regs_tasks, build_u_struct, + build_t_struct, + tome_fraction, CELLS as V321_CELLS, +) + +# ============================================================ +# 细胞定义 +# ============================================================ +CELLS = { + 'B0': dict(world='old', define_refine=0, route_recur=False, + route_action=False, bank=False, + label='B0-baseline-old'), + 'W0': dict(world='new', define_refine=0, route_recur=False, + route_action=False, bank=False, + label='W0-baseline-new'), + 'A': dict(world='old', define_refine=2, route_recur=False, + route_action=False, bank=False, + label='A-define-refine'), + 'B': dict(world='old', define_refine=0, route_recur=True, + route_action=False, bank=False, + label='B-route-recur'), + 'C': dict(world='old', define_refine=0, route_recur=False, + route_action=True, bank=False, + label='C-route-action'), + 'D': dict(world='old', define_refine=0, route_recur=False, + route_action=False, bank=True, + label='D-pattern-bank'), + 'E': dict(world='old', define_refine=2, route_recur=True, + route_action=True, bank=True, + label='E-full-loop'), + 'EW': dict(world='new', define_refine=2, route_recur=True, + route_action=True, bank=True, + label='EW-full-loop-new'), + # ── V33.1: OaK 放大成功 (世界修复 × define-refine) ── + 'WA': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, + label='WA-world-fix-x-refine'), + 'WAC': dict(world='new', define_refine=2, route_recur=False, + route_action=True, bank=False, + label='WAC-world-fix-x-refine-x-route-act'), + 'WADb': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=True, bank_tau=0.7, + bank_struct=True, + label='WADb-world-fix-x-refine-x-bank-struct'), + # ── V33.2: redefine MLP 参数扩展 (宽/深) ── + 'WAw': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, refine_hidden=512, + label='WAw-refine-wide-512'), + 'WAd': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, refine_layers=3, + label='WAd-refine-deep-3'), + 'WAwd': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, refine_hidden=512, + refine_layers=3, + label='WAwd-refine-wide-deep'), + # ── V33.3: DRLN 魔改 — 不冻结 / 多频更新 (基线待 V33.1/33.2 定, 先挂 WA 配置) ── + 'WAf': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, rln_online='all', + rln_lr=1e-4, rln_every=10, + label='WAf-rln-unfrozen'), + 'WAmf': dict(world='new', define_refine=2, route_recur=False, + route_action=False, bank=False, rln_online='multi', + rln_fast_lr=5e-4, rln_fast_every=5, + rln_slow_lr=1e-4, rln_slow_every=20, + label='WAmf-rln-multifreq'), + # ── V33.4: 训练饱和测试 (W0 长训练, 验证 0.0977 是否未饱和) ── + 'W0l': dict(world='new', define_refine=0, route_recur=False, + route_action=False, bank=False, meta_iters=160, + label='W0l-longtrain-160'), +} + + +# ============================================================ +# 模式库 (循环3) +# ============================================================ +class PatternBank: + """跨任务能力叠加: 显式存储 define 的模式, 检索匹配则融合, 否则新增。 + struct 分组 (V33.1 反坍缩): 只检索同规则结构 (identity/swaps/c3/c4/dbl) 的键, + 避免所有任务 fuse 到同一个原型 (V33 D cell n_keys=1-5 的坍缩修复)。""" + + def __init__(self, d, tau=0.5, alpha=0.5, struct_groups=False): + self.d = d + self.tau = tau + self.alpha = alpha + self.struct_groups = struct_groups + self.keys = [] # list of (归一化 m, 任务名, struct) + self.n_add = 0 + self.n_fuse = 0 + + def _norm(self, m): + return m / (m.norm() + 1e-8) + + def retrieve(self, m, struct=None): + if not self.keys: + return None, -1.0 + mn = self._norm(m) + best, best_s = None, -1.0 + for k, _, ks in self.keys: + if self.struct_groups and struct is not None and ks != struct: + continue + s = float((mn * k).sum()) + if s > best_s: + best_s, best = s, k + if best_s > self.tau: + return best, best_s + return None, best_s + + def add(self, m, name, struct=None): + self.keys.append((self._norm(m.detach()), name, struct)) + self.n_add += 1 + + def fuse(self, m, matched): + self.n_fuse += 1 + return self.alpha * matched.detach() + (1 - self.alpha) * m + + def stats(self): + return {'n_keys': len(self.keys), 'n_add': self.n_add, + 'n_fuse': self.n_fuse} + + +# ============================================================ +# V33_Learner +# ============================================================ +class V33_Learner(V32_1_Learner): + """V32.1 + DRL 循环: define-refine (循环2) × 路由进递推 (循环1) × + 动作路由 (选择语义) × 模式库 (循环3)。""" + + def __init__(self, rln, pln, V, K=20, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, + beta_init=1e-3, kappa=0.1, eta=0.1, eps=0.99, + init_lr=0.0, sleep_iters=2, + n_resp=4, use_intent=True, use_rule_enc=False, + use_perm_aux=False, use_contrast=False, + n_define_refine=0, use_route_recur=False, + use_route_action=False, use_pattern_bank=False, + lambda_route=0.3, lambda_probe=0.3, + refine_sup=4, bank_tau=0.5, bank_alpha=0.5, + bank_struct=False, refine_hidden=None, + refine_layers=2): + super().__init__(rln, pln, V, K=K, outer_lr=outer_lr, + n_tasks_per_step=n_tasks_per_step, seed=seed, + beta_init=beta_init, kappa=kappa, eta=eta, eps=eps, + init_lr=init_lr, sleep_iters=sleep_iters, + n_resp=n_resp, use_intent=use_intent, + use_rule_enc=use_rule_enc, + use_perm_aux=use_perm_aux, + use_contrast=use_contrast) + d = rln.d_model + self.n_define_refine = n_define_refine + self.use_route_recur = use_route_recur + self.use_route_action = use_route_action + self.use_pattern_bank = use_pattern_bank + self.lambda_route = lambda_route + self.lambda_probe = lambda_probe + self.refine_sup = refine_sup + self.lambda_icl = 0.0 + # 循环2: 定义修正网络 + define 探针 (诊断 + 定义质量监督) + # V33.2: refine MLP 参数扩展 (hidden 宽度 / 层数) + rh = refine_hidden or d + self.refine_net = nn.Sequential() + self.refine_net.append(nn.Linear(2 * d, rh)) + self.refine_net.append(nn.Tanh()) + for _ in range(refine_layers - 2): + self.refine_net.append(nn.Linear(rh, rh)) + self.refine_net.append(nn.Tanh()) + self.refine_net.append(nn.Linear(rh, d)) + self.refine_hidden = rh + self.refine_layers = refine_layers + self.probe_head = nn.Linear(d, 16) + extra = (list(self.refine_net.parameters()) + + list(self.probe_head.parameters())) + # 循环3: 模式库 + if use_pattern_bank: + self.bank = PatternBank(d, tau=bank_tau, alpha=bank_alpha, + struct_groups=bank_struct) + else: + self.bank = None + # 任务结构标签 (bank 分组检索用): {任务名: struct} + self._task_struct = {} + # 重建 outer_opt (含 refine/probe; intent_net 已含在 V32_1 的 extra) + base_params = (list(rln.parameters()) + list(pln.parameters()) + + list(self.intent_net.parameters())) + if use_rule_enc: + base_params = (list(rln.parameters()) + list(pln.parameters()) + + list(self.rule_encoder.parameters())) + self.outer_opt = torch.optim.Adam( + base_params + extra, lr=outer_lr) + self.probe_trace = [] + + # ── 前向路由 (循环1) ────────────────────────────────── + def _rln_fwd(self, x, m=None): + if self.use_route_recur and m is not None: + return self.rln(x, cond=m) + return self.rln(x) + + # ── Define 层 (循环2) ───────────────────────────────── + def _post_define_aux_loss(self, m, t) -> Optional[torch.Tensor]: + """ICL 偏置 hook. 默认关闭 (返回 None). 子类覆写返回附加 loss 张量.""" + m, t = m, t + return None + + def _define(self, task, detach=True, use_bank=True): + """goal define: 支持集聚合 Δregs → define_net → refine 迭代。 + detach=True (部署): 全 stop-grad; detach=False (训练): 带图。""" + sup = task['support'] + B = min(len(sup), 16) + cs = torch.stack([s[0] for s in sup[:B]]) + ts = torch.stack([s[1] for s in sup[:B]]) + delta = self._dregs(cs, ts) # (d,) + m = self.m_norm(self._rule_vec(delta)) + if self.n_define_refine > 0: + m = self._refine(m, task) + if use_bank and self.bank is not None: + m = self._bank_fuse(m, task.get('name', '?')) + if detach: + m = m.detach() + return m + + def _refine(self, m, task): + """循环2: 定义 → 用定义重解释经验 → 检验误差修正定义。 + 检验: 共享头 (clone_params) 在 support 前几个样本的预测误差对 m 的梯度 + (detached 一阶信号, 不做二阶图); refine_net([m; g]) → 修正量。""" + W = [w.detach() for w in self.pln.clone_params()] + for _ in range(self.n_define_refine): + g = 0.0 + for (c, tg) in task['support'][:self.refine_sup]: + h = self._rln_fwd(c.unsqueeze(0), m)[:, -1, :] + hin = self._h_in_batch(h, m) + pred = head_forward(W, hin, self.intent_dim, + self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) + gi = torch.autograd.grad(loss, m, create_graph=True)[0] + g = g + gi.detach() + corr = self.refine_net(torch.cat([m.detach(), g], dim=-1)) + m = self.m_norm(m + corr) + return m + + def _local_define(self, c, tg): + """local call (attached): per-sample Δregs 单步定义。""" + e_t = self.rln.embed(tg[:4].unsqueeze(0)) + e_c = self.rln.embed(c[-8:-4].unsqueeze(0)) + delta = (e_t - e_c).mean(dim=1).mean(dim=0) + return self.m_norm(self._rule_vec(delta)) + + def _bank_fuse(self, m, name): + if self.bank is None: + return m + struct = self._task_struct.get(name) + matched, sim = self.bank.retrieve(m, struct=struct) + if matched is not None: + return self.bank.fuse(m, matched) + self.bank.add(m, name, struct=struct) + return m + + # ── 动作路由 (INTACT 选择语义) ─────────────────────── + def _route_logits(self, logits, m, perm_pred=None): + """top-k 候选重加权: logit += λ·(m·embed(cand)) — 意图-动作匹配度。 + 只作用于每位置 top-8 候选 (低概率候选不受影响). + V35.10: perm_pred (4,4) 置换矩阵 — 若给出, 按 perm 重映射 logits + 的寄存器位置 (静态代数: 组合 perm 已知时, 预测 = 重映射后的 + 常规预测). 部署时用 probe 回读的 perm.""" + if not self.use_route_action or m is None: + return logits + with torch.no_grad(): + ce = self.rln.embed.weight.detach() # (vocab, d) + route = m @ ce.t() # (vocab,) + out = logits.clone() + for p in range(self.n_resp): + _, idx = logits[:, p, :].topk(8, dim=-1) + out[:, p, :].scatter_add_( + 1, idx, self.lambda_route * route[idx]) + return out + + # ── 可微内循环 (cond 透传) ──────────────────────────── + def adapt_graph(self, support, K=None, m=None): + K = K or self.K + W = self.pln.clone_params() + for (ctx, tgt) in support[:K]: + hidden = self._rln_fwd(ctx.unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + W = self.head.step_graph(W, hin[0], x[0], errs) + return W + + def adapt_online(self, W, support, n_steps, m=None): + for (ctx, tgt) in support[:n_steps]: + with torch.no_grad(): + hidden = self._rln_fwd(ctx.unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + return W + + # ── 外循环 (BPTT) ──────────────────────────────────── + def meta_step(self, tasks, train_rules, K=None): + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + Ws = [] + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + protos = (self._compute_prototypes(tasks, train_rules) + if self.use_contrast else None) + for rname in chosen: + t = tasks[rname] + # Define (attached, 训练): 意图定义 + 模式库融合 + m = self._define(t, detach=False) if self.use_intent else None + W = self.adapt_graph(t['support'], m=m, K=K) + Ws.append([w.detach() for w in W]) + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self._rln_fwd(ctx, m) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward(W, h_in, self.intent_dim, + self.world_vocab, self.n_resp) + pred = self._route_logits(pred, m) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + + # ICL 偏置 hook (默认 None = 不启用; compose 子类覆写): + # 用未适配共享头 W0 直接读组合后的 m, 逼 m 在纯 ICL 下暴露组合规则 + iclx = self._post_define_aux_loss(m, t) + if iclx is not None: + total_loss = total_loss + self.lambda_icl * iclx / len(chosen) + + # local call (attached, 单步定义) + if self.use_intent and self.lambda_local > 0: + for (c, tg) in t['support'][:self.n_local_sup]: + hsv = self._rln_fwd(c.unsqueeze(0), m)[:, -1, :] + m_loc = self._local_define(c, tg) + hin = self._h_in_batch(hsv, m_loc) + pl = head_forward(W, hin, self.intent_dim, + self.world_vocab, self.n_resp) + total_loss = total_loss + self.lambda_local * \ + F.cross_entropy(pl.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) \ + / (len(chosen) * self.n_local_sup) + + # Define 质量监督: probe 从 m (单向量聚合定义) 预测 perm (4×4 组成性) + # V35.10: 触发条件与 n_define_refine 解耦 (V33 bug: refine=0 时 + # probe 监督静默失效); 目标 = 任务真实 perm (含 AQ 组合任务 + # 的 unseen c4 perm — 回读学会组合置换结构) + if m is not None: + perm = torch.tensor(t['perm'], dtype=torch.long) + l_probe = F.cross_entropy( + self.probe_head(m).view(4, 4), perm) + total_loss = total_loss + self.lambda_probe * l_probe \ + / len(chosen) + self.probe_trace.append(l_probe.item()) + + # P/C 兼容 (V32.1 因子, 默认关) + if self.use_perm_aux: + aux = self.aux_head(h) + perm = torch.tensor(t['perm'], dtype=torch.long) + tl = perm.unsqueeze(0).expand(h.shape[0], -1).reshape(-1) + total_loss = total_loss + self.lambda_perm * \ + F.cross_entropy(aux.view(-1, 4), tl) / len(chosen) + if protos is not None: + qn = h / (h.norm(dim=1, keepdim=True) + 1e-8) + sim = qn @ protos.t() + tid = train_rules.index(rname) + tl = torch.full((h.shape[0],), tid, dtype=torch.long) + total_loss = total_loss + self.lambda_contra * \ + F.cross_entropy(sim, tl) / len(chosen) + + total_loss.backward() + torch.nn.utils.clip_grad_norm_( + [p for p in self.rln.parameters() if p.requires_grad] + + [p for p in self.pln.parameters() if p.requires_grad], 1.0) + self.outer_opt.step() + if self.init_lr > 0 and Ws: + with torch.no_grad(): + scale = self.init_lr / len(Ws) + for wa in Ws: + for p, w in zip(self.init_params, wa): + p.add_(scale * (w - p.detach())) + self._meta_iters += 1 + return total_loss.item() + + # ── 评估 (统一 regs_acc) ────────────────────────────── + def _metric(self, W, task, m=None, max_batch=16): + ctx, tgt = batch_pairs(task['query'], max_batch=max_batch) + with torch.no_grad(): + hidden = self._rln_fwd(ctx, m) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward([w.detach() for w in W], h_in, + self.intent_dim, self.world_vocab, self.n_resp) + pred = self._route_logits(pred, m) + pt = pred.argmax(dim=-1) + full = (pt == tgt[:, :self.n_resp]).sum().item() / \ + max(tgt[:, :self.n_resp].numel(), 1) + regs = (pt[:, :4] == tgt[:, :4]).sum().item() / \ + max(tgt[:, :4].numel(), 1) + return full, regs + + def eval_goal_readout(self, task, head_state=None): + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self._define(task, detach=True) if self.use_intent else None + return self._metric(W, task, m=m) + + def eval_adaptation_curve(self, task, max_steps=40, eval_every=10, + head_state=None): + W = [w.detach() for w in self.pln.clone_params()] + self._load_head(head_state) + m = self._define(task, detach=True) if self.use_intent else None + curve = [self._metric(W, task, m=m)[1]] + sup = task['support'] + for j in range(max_steps): + if j < len(sup): + with torch.no_grad(): + hidden = self._rln_fwd(sup[j][0].unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(sup[j][1], self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + curve.append(self._metric(W, task, m=m)[1]) + return curve + + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + self._load_head(head_state) + W = [w.detach() for w in self.pln.clone_params()] + seen = [] + forget_rows = [] + acc_after = {} + acc_after_full = {} + curves = {} + m_cache = {} + for tname in stream: + t = tasks[tname] + if tname not in m_cache: + m_cache[tname] = (self._define(t, detach=True) + if self.use_intent else None) + m = m_cache[tname] + seg = [self._metric(W, t, m=m)[1]] + for j in range(steps_per_task): + if j < len(t['support']): + with torch.no_grad(): + hidden = self._rln_fwd( + t['support'][j][0].unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(t['support'][j][1], + self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + if (j + 1) % eval_every == 0: + seg.append(self._metric(W, t, m=m)[1]) + seen.append(tname) + curves[tname] = seg + acc_after[tname] = seg[-1] + acc_after_full[tname] = self._metric(W, t, m=m)[0] + row = {} + for s in seen: + if s not in m_cache: + m_cache[s] = (self._define(tasks[s], detach=True) + if self.use_intent else None) + row[s] = round(self._metric(W, tasks[s], + m=m_cache[s])[1], 4) + forget_rows.append(row) + if sleep_iters > 0 and len(seen) >= sleep_every: + saved = self.head.state() + for _ in range(sleep_iters): + self.meta_step(tasks, seen) + self.head.load_state(saved) + return {'acc_after': acc_after, 'acc_after_full': acc_after_full, + 'curves': curves, 'forget': forget_rows} + + # ── Define 探针诊断 ─────────────────────────────────── + def probe_perm_acc(self, tasks, names): + """从 define 的 m 预测 perm (4×4 组成性), train/unseen 分报。""" + ok = tot = 0 + for n in names: + t = tasks[n] + m = self._define(t, detach=True) if self.use_intent else None + if m is None: + continue + with torch.no_grad(): + logits = self.probe_head(m) + pred = logits.view(4, 4).argmax(dim=-1) + perm = torch.tensor(t['perm'], dtype=torch.long) + ok += (pred == perm).sum().item() + tot += 4 + return round(ok / max(tot, 1), 4) if tot else None + + +# ============================================================ +# V33_3_Learner: DRLN 魔改 — 不冻结 / 多频更新 (V33.3) +# ============================================================ +class V33_3_Learner(V33_Learner): + """V33.2 最佳组合 + RLN 在线多频更新 (ANML 分层更新思想 + 大脑多频节奏)。 + + - 冻结基线: 长流只更新 head (SwiftTD), RLN 冻结 (V33 现状) + - 不冻结 (WAf): 长流中 RLN 全部参数每 rln_every 步 SGD 小步长更新 + - 多频 (WAmf): 参数分两群 — embed (快, 每 n_fast 步, lr_fast) / + ssm layers (慢, 每 n_slow 步, lr_slow) — 模拟大脑不同子系统更新节奏 + - 遗忘风险由遗忘矩阵监控 (在线更新可能破坏已学表示) + """ + + def __init__(self, rln, pln, V, K=20, outer_lr=1e-3, + n_tasks_per_step=2, seed=42, + beta_init=1e-3, kappa=0.1, eta=0.1, eps=0.99, + init_lr=0.0, sleep_iters=2, + n_resp=4, use_intent=True, + n_define_refine=0, use_route_recur=False, + use_route_action=False, use_pattern_bank=False, + lambda_route=0.3, lambda_probe=0.3, + refine_sup=4, bank_tau=0.5, bank_alpha=0.5, + bank_struct=False, refine_hidden=None, + refine_layers=2, + rln_online='off', rln_lr=1e-4, rln_every=10, + rln_fast_lr=5e-4, rln_fast_every=5, + rln_slow_lr=1e-4, rln_slow_every=20): + super().__init__(rln, pln, V, K=K, outer_lr=outer_lr, + n_tasks_per_step=n_tasks_per_step, seed=seed, + beta_init=beta_init, kappa=kappa, eta=eta, eps=eps, + init_lr=init_lr, sleep_iters=sleep_iters, + n_resp=n_resp, use_intent=use_intent, + n_define_refine=n_define_refine, + use_route_recur=use_route_recur, + use_route_action=use_route_action, + use_pattern_bank=use_pattern_bank, + lambda_route=lambda_route, + lambda_probe=lambda_probe, + refine_sup=refine_sup, + bank_tau=bank_tau, bank_alpha=bank_alpha, + bank_struct=bank_struct, + refine_hidden=refine_hidden, + refine_layers=refine_layers) + self.rln_online = rln_online # 'off' | 'all' (不冻结) | 'multi' (多频) + self.rln_lr = rln_lr + self.rln_every = rln_every + self.rln_fast_lr = rln_fast_lr + self.rln_fast_every = rln_fast_every + self.rln_slow_lr = rln_slow_lr + self.rln_slow_every = rln_slow_every + self._rln_opt = None + self._rln_fast_opt = None + self._rln_slow_opt = None + self.rln_online_steps = 0 + + def _setup_online_opts(self): + if self.rln_online == 'all': + self._rln_opt = torch.optim.SGD( + [p for p in self.rln.parameters() if p.requires_grad], + lr=self.rln_lr) + elif self.rln_online == 'multi': + self._rln_fast_opt = torch.optim.SGD( + self.rln.embed.parameters(), lr=self.rln_fast_lr) + slow = [p for lyr in self.rln.layers + for p in lyr.parameters() if p.requires_grad] + self._rln_slow_opt = torch.optim.SGD(slow, lr=self.rln_slow_lr) + + def _online_rln_step(self, ctx, tgt, step_idx): + """按频率更新 RLN (不冻结/多频)。返回是否执行了更新。""" + if self.rln_online == 'off': + return False + if self.rln_online == 'all': + if step_idx % self.rln_every != 0: + return False + if self._rln_opt is None: + self._setup_online_opts() + self._rln_opt.zero_grad() + h = self._rln_fwd(ctx.unsqueeze(0))[:, -1, :] + hin = self._h_in_batch(h, None if not self.use_intent + else self.m_norm( + self._rule_vec(self._dregs_single(ctx)))) + pred = head_forward([w.detach() for w in + self.pln.clone_params()], hin, + self.intent_dim, self.world_vocab, + self.n_resp) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + loss.backward() + self._rln_opt.step() + self.rln_online_steps += 1 + return True + if self.rln_online == 'multi': + if self._rln_fast_opt is None: + self._setup_online_opts() + did = False + if step_idx % self.rln_fast_every == 0: + self._rln_fast_opt.zero_grad() + h = self._rln_fwd(ctx.unsqueeze(0))[:, -1, :] + m_ctx = (None if not self.use_intent else self.m_norm( + self._rule_vec(self._dregs_single(ctx)))) + hin = self._h_in_batch(h, m_ctx) + pred = head_forward([w.detach() for w in + self.pln.clone_params()], hin, + self.intent_dim, self.world_vocab, + self.n_resp) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + loss.backward() + self._rln_fast_opt.step() + did = True + if step_idx % self.rln_slow_every == 0: + # 独立重建计算图 (fast 分支的 backward 已释放共享中间值) + self._rln_slow_opt.zero_grad() + h = self._rln_fwd(ctx.unsqueeze(0))[:, -1, :] + m_ctx = (None if not self.use_intent else self.m_norm( + self._rule_vec(self._dregs_single(ctx)))) + hin = self._h_in_batch(h, m_ctx) + pred = head_forward([w.detach() for w in + self.pln.clone_params()], hin, + self.intent_dim, self.world_vocab, + self.n_resp) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + loss.backward() + self._rln_slow_opt.step() + did = True + self.rln_online_steps += 1 + return did + return False + + def _dregs_single(self, ctx): + """单样本 Δregs: 最近观察的寄存器 vs 目标寄存器 (在线更新用)。""" + c = ctx[-8:-4].unsqueeze(0) + e_c = self.rln.embed(c) + return e_c.mean(dim=1).mean(dim=0) + + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + self._load_head(head_state) + W = [w.detach() for w in self.pln.clone_params()] + seen = [] + forget_rows = [] + acc_after = {} + acc_after_full = {} + curves = {} + m_cache = {} + step_idx = 0 + for tname in stream: + t = tasks[tname] + if tname not in m_cache: + m_cache[tname] = (self._define(t, detach=True) + if self.use_intent else None) + m = m_cache[tname] + seg = [self._metric(W, t, m=m)[1]] + for j in range(steps_per_task): + if j < len(t['support']): + with torch.no_grad(): + hidden = self._rln_fwd( + t['support'][j][0].unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(t['support'][j][1], + self.world_vocab).float() + errs = (p - onehot).reshape(-1) + self.head.step(W, hin[0], x[0], errs) + # V33.3: RLN 在线更新 (不冻结/多频) + self._online_rln_step(t['support'][j][0], + t['support'][j][1], step_idx) + step_idx += 1 + if (j + 1) % eval_every == 0: + seg.append(self._metric(W, t, m=m)[1]) + seen.append(tname) + curves[tname] = seg + acc_after[tname] = seg[-1] + acc_after_full[tname] = self._metric(W, t, m=m)[0] + row = {} + for s in seen: + if s not in m_cache: + m_cache[s] = (self._define(tasks[s], detach=True) + if self.use_intent else None) + row[s] = round(self._metric(W, tasks[s], + m=m_cache[s])[1], 4) + forget_rows.append(row) + if sleep_iters > 0 and len(seen) >= sleep_every: + saved = self.head.state() + for _ in range(sleep_iters): + self.meta_step(tasks, seen) + self.head.load_state(saved) + return {'acc_after': acc_after, 'acc_after_full': acc_after_full, + 'curves': curves, 'forget': forget_rows} + + +# ============================================================ +# 运行一个 cell +# ============================================================ +def run_cell(cell, cfg, V, s1_state, tasks_old, tasks_new, U_STRUCT, + n_meta_iters=80, K=20, seed=42, steps_per_task=60, + verbose=True): + label = cfg['label'] + base = tasks_new if cfg['world'] == 'new' else tasks_old + tasks = make_regs_tasks(base) # 全部 regs-only (R 基线) + train_rules = [n for n in tasks if tasks[n]['seen']] + train_names = train_rules[:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']][:8] + stream = train_names + unseen_names + for n in tasks: + tasks[n]['name'] = n + # 结构标签 (bank 分组检索 / 报告) + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(V, 4, s1_state) + is_base = (cfg['define_refine'] == 0 and not cfg['route_recur'] + and not cfg['route_action'] and not cfg['bank']) + if is_base: + # B0/W0: V32_1_Learner 原样 (严格复现 32.1) + learner = V32_1_Learner(rln, pln, V, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, + use_rule_enc=False, use_perm_aux=False, + use_contrast=False) + else: + ron = cfg.get('rln_online', 'off') + if ron != 'off': + learner = V33_3_Learner( + rln, pln, V, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, + n_define_refine=cfg['define_refine'], + use_route_recur=cfg['route_recur'], + use_route_action=cfg['route_action'], + use_pattern_bank=cfg['bank'], + bank_tau=cfg.get('bank_tau', 0.5), + bank_struct=cfg.get('bank_struct', False), + refine_hidden=cfg.get('refine_hidden'), + refine_layers=cfg.get('refine_layers', 2), + rln_online=ron, rln_lr=cfg.get('rln_lr', 1e-4), + rln_every=cfg.get('rln_every', 10), + rln_fast_lr=cfg.get('rln_fast_lr', 5e-4), + rln_fast_every=cfg.get('rln_fast_every', 5), + rln_slow_lr=cfg.get('rln_slow_lr', 1e-4), + rln_slow_every=cfg.get('rln_slow_every', 20)) + else: + learner = V33_Learner( + rln, pln, V, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, + n_define_refine=cfg['define_refine'], + use_route_recur=cfg['route_recur'], + use_route_action=cfg['route_action'], + use_pattern_bank=cfg['bank'], + bank_tau=cfg.get('bank_tau', 0.5), + bank_struct=cfg.get('bank_struct', False), + refine_hidden=cfg.get('refine_hidden'), + refine_layers=cfg.get('refine_layers', 2)) + learner._task_struct = {n: tasks[n]['struct'] for n in tasks} + t0 = time.time() + n_iters = cfg.get('meta_iters', n_meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_rules) + if verbose and (it + 1) % 20 == 0: + print(f" [{label}] meta_iter {it+1}/{n_iters} " + f"loss={loss:.4f}", flush=True) + trained_head = learner.head.state() + dt = time.time() - t0 + print(f" [{label}] meta 训练完成 ({dt:.0f}s)", flush=True) + + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + t_ls = time.time() - t0 + print(f" [{label}] 长流完成 ({t_ls:.0f}s)", flush=True) + + curves = {} + rd0 = {} + for n in unseen_names: + curves[n] = learner.eval_adaptation_curve( + tasks[n], max_steps=40, eval_every=10, head_state=trained_head) + f, r = learner.eval_goal_readout(tasks[n], head_state=trained_head) + rd0[n] = r + + by_struct = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by_struct: + by_struct[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) + for k, v in by_struct.items()} + + aux = {} + if hasattr(learner, 'probe_head'): + aux['probe_train'] = learner.probe_perm_acc(tasks, train_names) + aux['probe_unseen'] = learner.probe_perm_acc(tasks, unseen_names) + if learner.probe_trace: + aux['probe_loss_last10'] = round( + sum(learner.probe_trace[-10:]) / 10, 4) + if cfg['bank'] and learner.bank is not None: + aux['bank'] = learner.bank.stats() + + head_stats = learner.head.step_size_stats() + result = { + 'cell': cell, 'label': label, 'seed': seed, + 'time_s': round(time.time() - t0, 1), + 'meta_iters': cfg.get('meta_iters', n_meta_iters), 'K': K, + 'world': cfg['world'], + 'factors': {'DefineRefine': cfg['define_refine'] > 0, + 'RouteRecur': cfg['route_recur'], + 'RouteAction': cfg['route_action'], + 'PatternBank': cfg['bank'], + 'WorldFix': cfg['world'] == 'new'}, + 'stream': stream, + 'train_acc_after': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc_after': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_acc_after_full': {n: st['acc_after_full'][n] + for n in unseen_names}, + 'readout0_unseen': rd0, + 'unseen_by_struct': struct_mean, + 'stream_curves': st['curves'], + 'unseen_curve_offstream': curves, + 'forget': st['forget'], + 'head_stats': head_stats, + 'aux': aux, + } + ua = result['unseen_acc_after'] + u_mean = sum(ua.values()) / len(ua) + r_mean = sum(rd0.values()) / len(rd0) + print(f" [{label}] 全流程完成 ({result['time_s']:.0f}s)", flush=True) + print(f" 未见 regs_acc: {ua} mean={u_mean:.4f}", flush=True) + print(f" 未见 rd0: mean={r_mean:.4f}", flush=True) + print(f" 结构分组: {struct_mean}", flush=True) + if aux: + print(f" aux: {aux}", flush=True) + return result + + +# ============================================================ +# 报告 +# ============================================================ +def _mean(d): + return sum(d.values()) / max(len(d), 1) + + +def summarize(results): + rows = {} + for r in results: + ua = r['unseen_acc_after'] + rd = r['readout0_unseen'] + by = r['unseen_by_struct'] + rows[r['cell']] = { + 'label': r['label'], 'time_s': r['time_s'], + 'factors': r['factors'], + 'world': r['world'], + 'unseen_mean': round(_mean(ua), 4), + 'rd0_mean': round(_mean(rd), 4), + 'unseen_minus_rd0': round(_mean(ua) - _mean(rd), 4), + 'c3': by.get('c3'), 'c4': by.get('c4'), 'dbl': by.get('dbl'), + 'aux': r['aux'], + } + return rows + + +def hypothesis_verdicts(rows): + b0 = rows.get('B0', {}).get('unseen_mean') + v = {} + + def _t(a, b, hi, desc): + va = rows.get(a, {}).get('unseen_mean') + vb = rows.get(b, {}).get('unseen_mean') + if va is None or vb is None: + return (desc, 'N/A', None, hi) + d = va - vb + return (desc, '成功' if d > hi else + ('部分成功' if d >= hi * 0.4 else '失败'), round(d, 4), hi) + + if b0 is not None: + w0 = rows.get('W0', {}).get('unseen_mean') + v['HW'] = _t('W0', 'B0', 0.02, '世界修复单独效果 vs B0') + v['HA'] = _t('A', 'B0', 0.02, 'A define-refine vs B0') + v['HB'] = _t('B', 'B0', 0.02, 'B 路由进递推 vs B0') + v['HC'] = _t('C', 'B0', 0.02, 'C 动作路由 vs B0') + v['HD'] = _t('D', 'B0', 0.02, 'D 模式库 vs B0') + v['HE'] = _t('E', 'B0', 0.02, 'E 全闭环 vs B0') + v['HEW'] = _t('EW', 'B0', 0.02, 'EW 全闭环+新世界 vs B0') + # V33.1 (OaK 放大成功, 基线 = W0) + v['HA1'] = _t('WA', 'W0', 0.02, + 'WA 世界修复×define-refine vs W0') + v['HA2'] = _t('WAC', 'WA', 0.02, 'WAC +动作路由 vs WA') + v['HA3'] = _t('WADb', 'WA', 0.02, 'WADb +结构化模式库 vs WA') + wa = rows.get('WA', {}).get('unseen_mean') + if wa is not None: + v['HA0'] = ('WA > 0.10 (突破 W0 后继续)', + '成功' if wa > 0.10 else '未突破', round(wa, 4), 0.10) + # V33.2 (redefine MLP 扩展, 基线 = WA) + v['HB1'] = _t('WAw', 'WA', 0.02, 'WAw refine 加宽(512) vs WA') + v['HB2'] = _t('WAd', 'WA', 0.02, 'WAd refine 加深(3层) vs WA') + v['HB3'] = _t('WAwd', 'WA', 0.02, 'WAwd 宽+深 vs WA') + # V33.3 (DRLN 魔改, 基线 = WA) + v['HC1'] = _t('WAf', 'WA', 0.02, 'WAf RLN 不冻结 vs WA') + v['HC2'] = _t('WAmf', 'WA', 0.02, 'WAmf RLN 多频更新 vs WA') + # 可加性: E vs max(A,B,C,D) + cands = [rows.get(k, {}).get('unseen_mean') + for k in ('A', 'B', 'C', 'D')] + best = max([x for x in cands if x is not None], default=None) + e = rows.get('E', {}).get('unseen_mean') + if best is not None and e is not None: + v['HADD'] = ('E ≥ max(A,B,C,D)', + '成功' if e > best + 0.01 else + ('持平' if abs(e - best) <= 0.01 else '失败'), + round(e - best, 4), 0.01) + else: + v['HADD'] = ('E ≥ max(A,B,C,D)', 'N/A', None, 0.01) + return v + + +def write_report(results, args): + rp = ROOT / "results" / "v33_drl_report.json" + rp.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + rows = summarize(results) + hv = hypothesis_verdicts(rows) + + from run_v31_2_long_stream import build_v32_tasks as _b + _to = tome_fraction(_b(200, seed=42, window=3)) + _tn = tome_fraction(_b(200, seed=42, window=3, stop_at_halt=True)) + + L = [] + A = L.append + A("# V33 Define-Route 循环报告 — OaK 式持续学习 × INTACT 意图路由") + A("") + A("> 计划: `docs/wiki/v33-plan.md` (预注册) · 运行时间: " + f"{time.strftime('%Y-%m-%d %H:%M')}") + A(f"> 参数: meta_iters={args.meta_iters} K={args.K} " + f"steps_per_task={args.steps_per_task} seed=42 · 同 Stage1 缓存") + A("") + A("## 0. 大白话总结") + A("") + A("- **做了什么**: 按 OaK 的 Define 层视角重建循环 — 意图定义可迭代 (define-refine)、" + "定义路由进递推 (调制 SSM dt/B)、定义路由到动作 (top-k 重加权, INTACT 选择语义)、" + "跨任务模式库叠加; 8 cell 消融, 含世界修复 (TOME 截断) 因子。") + A("- **发现什么**: (结果落地后填)") + A("- **为什么重要**: V32.1 证明外部机制 (aux/对比/EHS) 全失败 → 本版把机制" + "从\"加监督\"改为\"立循环\": 定义结果回流到表示与动作。若全闭环成功, " + "论文主线 EHS 循环化 (Define=假设提取, Route=假设路由) 就有了实验 1 数据。") + A("") + A("## 1. 立论") + A("") + A("- h 的问题 = 循环和路由没确立 (用户诊断): Define 层缺失 (经验→抽象结构需可迭代), " + "Route 层缺失 (规则与内容挤在同一向量)") + A("- INTACT 重定位: 意图插入 = define, 选择动作 = route; V32 失败根因 = " + "只做 define 输出注入 (head 侧, 被 W 适应洗掉), 没做路由进表示 (递推侧)") + A("- 三圈循环: 循环1 表示级 (define 调制递推) · 循环2 定义级 (define-refine) · " + "循环3 能力级 (模式库)") + A("") + A("## 2. 细胞设计") + A("") + A("| cell | 环节 | 世界 |") + A("|:--|:--|:--|") + for cell, cfg in CELLS.items(): + fac = '+'.join(f for f, on in { + 'DefRef': cfg['define_refine'] > 0, 'RteRecur': cfg['route_recur'], + 'RteAct': cfg['route_action'], 'Bank': cfg['bank'], + }.items() if on) + A(f"| {cell} | {fac or '—'} | {'新(TOME截断)' if cfg['world']=='new' else '旧'} |") + A("") + A("## 3. 结果总表 (unseen regs_acc)") + A("") + A("| cell | 因子 | Unseen | rd0 | 60s−rd0 | c3 | c4 | dbl | time_s |") + A("|:--|:--|--:|--:|--:|--:|--:|--:|--:|") + for cell in CELLS: + if cell not in rows: + continue + s = rows[cell] + fac = '+'.join(f for f, on in s['factors'].items() if on) + cc = lambda x: f"{x:.4f}" if x is not None else "-" + A(f"| {cell} | {fac or '—'} | {s['unseen_mean']:.4f} | " + f"{s['rd0_mean']:.4f} | {s['unseen_minus_rd0']:+.4f} | " + f"{cc(s['c3'])} | {cc(s['c4'])} | {cc(s['dbl'])} | {s['time_s']:.0f} |") + A("") + A("### 假设判定") + A("") + A("| 假设 | 判定 | 差值 | 阈值 |") + A("|:--|:--|--:|--:|") + for k, (desc, verdict, d, hi) in hv.items(): + ds = f"{d:+.4f}" if d is not None else "-" + A(f"| {k} | **{verdict}** | {ds} | {hi} |") + A("") + A(f"## 4. 世界修复 (TOME): 旧 {_to:.4f} → 新 {_tn:.4f} (query 集 tgt[4]==201 占比)") + A("") + A("## 5. 辅助指标") + A("") + for cell in CELLS: + r = {x['cell']: x for x in results}.get(cell) + if not r or not r['aux']: + continue + A(f"- **{cell}**: {json.dumps(r['aux'], ensure_ascii=False)}") + A("") + A("## 6. 假阳性检查") + A("") + A("- **活跃基线**: B0 ≡ V32.1 32.1 (regs-only, 旧世界, 同缓存同协议) — " + "应复现 unseen regs_acc 0.1016 (`results/v32_1_matrix_report.json`)") + A("- **评估独立性**: regs_acc 4-token 精确匹配; rd0 纯共享 W (零适应)") + A("- **单一循环变量**: A/B/C/D 各自只开一个环节, E 才全开; " + "W0/EW 用新世界 (数据量减少 ~65% 是世界因子的固有属性)") + A("- **路由参数零初始化**: cond_dt/cond_b 初始 ≡ 无条件 → B 与 B0 在训练前等价") + A("") + A("## 7. 结论") + A("") + A("(结果落地后填)") + A("") + A("## 8. 复现") + A("") + A("```bash") + A(f"python tests/run_v33_drl.py --meta-iters {args.meta_iters} " + f"--K {args.K} --steps-per-task {args.steps_per_task}") + A("```") + mp = ROOT / "results" / "v33_drl_report.md" + mp.write_text("\n".join(L) + "\n", encoding='utf-8') + print(f"\nJSON: {rp}\nMD: {mp}") + return rows, hv + + +# ============================================================ +# 主流程 +# ============================================================ +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--meta-iters', type=int, default=80) + parser.add_argument('--K', type=int, default=20) + parser.add_argument('--steps-per-task', type=int, default=60) + parser.add_argument('--cells', type=str, + default='B0,W0,WA,WAC,WADb') + parser.add_argument('--no-stage1', action='store_true') + args = parser.parse_args() + + print("=" * 70) + print("V33 Define-Route 循环 — OaK × INTACT") + print("=" * 70) + + WORLD_V = 200 + tasks_old = build_v32_tasks(WORLD_V, n_support=40, n_query=20, + seed=42, window=3) + tasks_new = build_v32_tasks(WORLD_V, n_support=40, n_query=20, + seed=42, window=3, stop_at_halt=True) + train_rules_all = [n for n in tasks_old if tasks_old[n]['seen']] + print(f"V={WORLD_V}, 训练规则={len(train_rules_all)}; " + f"旧世界 TOME={tome_fraction(tasks_old):.4f} / " + f"新世界 TOME={tome_fraction(tasks_new):.4f}") + + U_STRUCT = build_u_struct(42) + + s1_state = None + if args.no_stage1: + print("\n--no-stage1: 随机初始化 (冒烟用)") + else: + S1_CACHE = None + for cand in ("/tmp/v32_s1_state.pt", "/tmp/v31_2_s1_state.pt"): + if os.path.exists(cand): + S1_CACHE = cand + break + if S1_CACHE is None: + print("\n>>> Stage1 缓存缺失, 请先跑 tests/run_v32_stage1.py") + return + s1_state = torch.load(S1_CACHE) + s1_state = {k: v.cpu().clone() for k, v in s1_state.items()} + print(f"\nStage1: 从缓存加载 ({S1_CACHE})") + + keys = [c.strip() for c in args.cells.split(',')] + results = [] + ckpt = ROOT / "results" / "v33_checkpoint.json" + if ckpt.exists(): + try: + done = {r['cell']: r for r in + json.loads(ckpt.read_text(encoding='utf-8'))} + except Exception: + done = {} + print(f"checkpoint: {sorted(done.keys())} 已完成") + else: + done = {} + for key in keys: + if key in done: + print(f"跳过已完成 cell: {key}") + results.append(done[key]) + continue + if key not in CELLS: + print(f"未知 cell: {key}") + continue + cfg = CELLS[key] + print(f"\n{'='*60}\n 实验: {cfg['label']} (世界={cfg['world']}, " + f"refine={cfg['define_refine']}, RteR={cfg['route_recur']}, " + f"RteA={cfg['route_action']}, Bank={cfg['bank']})\n{'='*60}") + try: + r = run_cell(key, cfg, WORLD_V, s1_state, tasks_old, tasks_new, + U_STRUCT, n_meta_iters=args.meta_iters, K=args.K, + seed=42, steps_per_task=args.steps_per_task) + except Exception as e: + import traceback + traceback.print_exc() + print(f"!!! cell {key} 失败: {e} (继续)") + continue + results.append(r) + ckpt.write_text(json.dumps(results, indent=2, ensure_ascii=False), + encoding='utf-8') + print(f"checkpoint 更新: {key} ({len(results)}/{len(keys)})", + flush=True) + + if results: + rows, hv = write_report(results, args) + print(f"\n{'='*60}\n汇总:\n{'='*60}") + print(f"{'cell':<6}{'Unseen':<10}{'rd0':<8}{'60s-rd0':<10}{'time_s':<8}") + for cell in CELLS: + if cell not in rows: + continue + s = rows[cell] + print(f"{cell:<6}{s['unseen_mean']:<10.4f}{s['rd0_mean']:<8.4f}" + f"{s['unseen_minus_rd0']:<10.4f}{s['time_s']:<8.0f}") + print("\n假设判定:") + for k, (desc, verdict, d, hi) in hv.items(): + print(f" {k}: {verdict} ({d} vs 阈值 {hi})") + return results + + +if __name__ == "__main__": + main() diff --git a/tests/run_v34_1_gen.py b/tests/run_v34_1_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..5ea6c5e8d5031c2d5e6e03642e1311a6819a31df --- /dev/null +++ b/tests/run_v34_1_gen.py @@ -0,0 +1,300 @@ +"""V34.1 生成元签名监督 — 从"排列实例记忆"到"生成元组合语言" + +假设 (H34.1): V34 结构分离提示根因是组合泛化缺口 — RLN 把规则学成实例 +key-value (perm → Δregs), 而非 S4 群生成元组合代数。无法把已学生成元 +(swap) 组合成未见结构 (c4/dbl)，尽管 c4/dbl = 纯由已见 swap 生成元合成。 + +干预: 在 define 的 latent m 上加生成元签名头 (gen_head) + 循环类头 +(cyc_head)，监督 m 预测该 perm 的"对换生成元组合签名"(6 维原子，全在 +训练见过) + 循环类型 (ident/swap/c3/c4/dbl)。 + +关键点 (vs V32.1 P 失败): 旧 perm aux 解码 m→4×4 排列实例(含未见结构， +塌缩)。新签名头解码 m→train 已掌握的生成元原子 → 未见 c4/d = 已学原子的 +绑定形式，组合泛化转移目标。 + +Cell: + B : V33 base (d128) = 基线，全 12 unseen (含 dbl) + G : + 生成元签名监督 (6 原子) + GC : + 生成元签名 + 循环类型碳竞银行分组 (结构利用) + +指标: 结构分组 regs_acc (c3/c4/dbl)，生成元签名准确率 (train/unseen)。 +""" +import os, sys, time, json, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from hibs_lnn.swifttd_head import SwiftTDHead +from run_v31_meta_learning import ( + batch_pairs, head_forward, stage1_pretrain_world, + V31_RLN, V31_PLN) +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + V32_1_Learner, build_model_resp, make_regs_tasks, build_u_struct, + build_t_struct, tome_fraction) +from run_v33_drl import V33_Learner, V33_3_Learner + +ROOT = Path(__file__).resolve().parent.parent + +TRANS = [(0,1),(0,2),(0,3),(1,2),(1,3),(2,3)] +IDENT = (0,1,2,3) + +def _apply(perm, g): + p = list(perm); a, b = g; p[a], p[b] = p[b], p[a] + return tuple(p) + +_gen_cache = {} +def generator_signature(perm): + """6 维 binary: 该 perm 的最短 wrote 中出现的实现原子集合. """ + if perm in _gen_cache: + return _gen_cache[perm] + from collections import deque + q = deque([(IDENT, [])]); seen = {IDENT}; found = None + while q and found is None: + cur, w = q.popleft() + if cur == perm: + found = w + break + if len(w) >= 6: + continue + for gi, g in enumerate(TRANS): + nxt = _apply(cur, g) + if nxt not in seen: + seen.add(nxt); q.append((nxt, w + [gi])) + if found is None: + _gen_cache[perm] = None + return None + sig = [0]*6 + for gi in found: + sig[gi] = 1 + _gen_cache[perm] = (sig, len(found)) + return _gen_cache[perm] + +def cycle_type(perm): + seen = set(); lens = [] + for i in range(4): + if i in seen: continue + c = []; j = i + while j not in seen: + seen.add(j); c.append(j); j = perm[j] + if len(c) > 1: lens.append(len(c)) + lens.sort() + if not lens: return 0 + if lens == [2]: return 1 + if lens == [3]: return 2 + if lens == [4]: return 3 + if lens == [2,2]: return 4 + return 0 + +CYCLET = 5 + +def build_gen_labels(tasks): + labels = {} + for name in tasks: + sig = generator_signature(tasks[name]['perm']) + labels[name] = {'sig': (sig[0] if sig else None), + 'cyc': cycle_type(tasks[name]['perm'])} + return labels + + +# ============================================================ +# V34_1_Learner: V33 + 生成元签名监督 +# ============================================================ +class V34GenLearner(V33_Learner): + """V33 (define-refine) + 生成元语义头: m → 6 原子 碳 label + 5 循环类.""" + def __init__(self, rln, pln, V, lambda_gen=0.3, + outer_lr=1e-3, n_define_refine=0, + refine_hidden=None, refine_layers=2, **kw): + self.lambda_gen = lambda_gen + self._outer_lr = outer_lr + super().__init__(rln, pln, V, n_define_refine=n_define_refine, + outer_lr=outer_lr, refine_hidden=refine_hidden, + refine_layers=refine_layers, **kw) + d = rln.d_model + self.gen_head = nn.Linear(d, 6) + self.cyc_head = nn.Linear(d, CYCLET) + extra = list(self.gen_head.parameters()) + list(self.cyc_head.parameters()) + base = (list(rln.parameters()) + list(pln.parameters()) + + (list(self.rule_encoder.parameters()) if self.use_rule_enc + else list(self.intent_net.parameters())) + + list(self.refine_net.parameters()) + list(self.probe_head.parameters())) + self.outer_opt = torch.optim.Adam(base + extra, lr=self._outer_lr) + self.gen_trace = []; self.cyc_trace = [] + + def meta_step(self, tasks, train_rules, K=None): + K = K or self.K + self.outer_opt.zero_grad() + total_loss = torch.tensor(0.0) + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + for rname in chosen: + t = tasks[rname] + m = self._define(t, detach=False) if self.use_intent else None + W = self.adapt_graph(t['support'], m=m, K=K) + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self._rln_fwd(ctx, m) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward(W, h_in, self.intent_dim, self.world_vocab, + self.n_resp) + pred = self._route_logits(pred, m) + loss = F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + total_loss = total_loss + loss / len(chosen) + if m is not None and t.get('gen_sig') is not None: + g = torch.tensor(t['gen_sig'], dtype=torch.float) + lg = F.binary_cross_entropy_with_logits( + self.gen_head(m).view(1,-1), g.view(1,-1)) + lc = F.cross_entropy( + self.cyc_head(m).view(1,-1), + torch.tensor([t['gen_cyc']], dtype=torch.long)) + total_loss = total_loss + (self.lambda_gen*lg)/len(chosen) \ + + (self.lambda_gen*lc)/len(chosen) + self.gen_trace.append(lg.item()); self.cyc_trace.append(lc.item()) + total_loss.backward() + params = [p for p in self.rln.parameters() if p.requires_grad] \ + + [p for p in self.pln.parameters() if p.requires_grad] + torch.nn.utils.clip_grad_norm_(params, 1.0) + self.outer_opt.step() + self._meta_iters += 1 + return total_loss.item() + +# ============================================================ +# 驱动 +# ============================================================ +def run_cell(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] # 全 12 + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + gl = build_gen_labels({n: tasks[n]}) + tasks[n]['gen_sig'] = gl[n]['sig'] + tasks[n]['gen_cyc'] = gl[n]['cyc'] + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + if lm == 'gen': + learner = V34GenLearner(rln, pln, 200, K=K, seed=seed, + sleep_iters=2, n_resp=4, use_intent=True, + lambda_gen=cfg.get('lambda_gen', 0.3), + n_define_refine=cfg.get('define_refine', 0), + use_route_recur=cfg.get('route_recur', False), + use_route_action=cfg.get('route_action', False), + use_pattern_bank=cfg.get('bank', False), + bank_struct=cfg.get('bank_struct', False)) + else: + learner = V33_Learner(rln, pln, 200, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, +n_define_refine=cfg.get('define_refine', 0)) + learner._task_struct = {n: tasks[n]['struct'] for n in tasks} + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 10 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it+1, n_iters, loss), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] stream done (%.0fs)" % (key, time.time()-t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v)/len(v) if v else None) for k, v in by.items()} + aux = {} + if hasattr(learner, 'gen_head'): + gt = gtot = gu = gtotu = 0 + with torch.no_grad(): + gen_heads_weight = learner.gen_head.weight.data + gen_bias = learner.gen_head.bias.data if learner.gen_head.bias is not None else None + gen_head_static = learner.gen_head + for n in train_names: + mm = learner.compute_goal_intent(tasks[n]) if hasattr(learner, 'compute_goal_intent') else learner._define(tasks[n], detach=True) + p = gen_head_static(mm).sigmoid() > 0.5 + gt += (p == torch.tensor(tasks[n]['gen_sig']).float()).sum().item() + gtot += 6 + for n in unseen_names: + mm = learner.compute_goal_intent(tasks[n]) if hasattr(learner, 'compute_goal_intent') else learner._define(tasks[n], detach=True) + p = gen_head_static(mm).sigmoid() > 0.5 + gu += (p == torch.tensor(tasks[n]['gen_sig']).float()).sum().item() + gtotu += 6 + aux['gen_acc_train'] = round(gt/gtot, 4) if gtot else None + aux['gen_acc_unseen'] = round(gu/gtotu, 4) if gtotu else None + if learner.gen_trace: aux['gen_loss_last10'] = round(sum(learner.gen_trace[-10:])/10, 4) + if learner.cyc_trace: aux['cyc_loss_last10'] = round(sum(learner.cyc_trace[-10:])/10, 4) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'world': cfg.get('world', 'new'), + 'factors': {'GenSig': lm == 'gen', + 'DefineRefine': cfg.get('define_refine', 0) > 0, + 'Bank': cfg.get('bank', False)}, + 'time_s': round(time.time() - t0, 1), + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + ua = rec['unseen_acc'] + print((" [%s] unseen mean=%.4f struct=%s aux=%s" % + (key, sum(ua.values())/len(ua), struct_mean, aux)), flush=True) + return rec + + +CELLS = { + 'B': dict(world='new', lm='base', define_refine=0, + label='B-d128-base'), + 'G': dict(world='new', lm='gen', define_refine=0, lambda_gen=0.3, + label='G-gen-signature'), + 'GC': dict(world='new', lm='gen', define_refine=2, lambda_gen=0.3, + label='GC-gen+refine'), +} + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='B,G') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("="*70); print("V34.1 生成元签名监督"); print("="*70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, + window=3, stop_at_halt=True) + U_STRUCT = build_u_struct(42) + print("未见表:", {n: build_u_struct(42)[n] for n in tasks if not tasks[n]['seen']}) + s1 = None + if not args.no_stage1: + for cand in ('/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt'): + if os.path.exists(cand): + s1 = torch.load(cand); s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand); break + else: + print(">>> Stage1 缺失, 请先跑 run_v32_stage1.py"); return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell(key, cfg, s1, tasks, U_STRUCT, meta_iters=args.meta_iters) + except Exception as e: + import traceback; traceback.print_exc() + print("!!! %s 失败: %s" % (key, e)); continue + res.append(r) + out = ROOT / 'results' / 'v34_1_report.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values())/len(u) + print("%s unseen=%.4f struct=%s aux=%s" % (r['cell'], ua, r['unseen_by_struct'], r['aux'])) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tests/run_v34_3_cpg.py b/tests/run_v34_3_cpg.py new file mode 100644 index 0000000000000000000000000000000000000000..bb934c9017eb7a573087ea814983c2dccb33132b --- /dev/null +++ b/tests/run_v34_3_cpg.py @@ -0,0 +1,233 @@ +"""V34.3 CPG 原子路由 — 零参数原子级显式重组 (对标 CPG modularity) + +立论 (v34-2-anchors): V34.2 的 ReuseBank 检索单元是"整规则 latent m"的线性加权, +组合发生在整体向量空间插值 → R=R3 (topk 无差别), 未见 c4 (需 3 生成元) 未被真正 +拆解组合。CPG (Klinger arXiv:2309.16467) 的关键是 modularity: 每个 grammar rule +一个独立语义模块 + 递归组合。这里把检索单元缩到**单个生成元原子** prototype. + +机制 (V34.3): AtomRouteBank — 不新增任何可学习参数 (per-generator prototype 只是 +存表示, 复用 = 编码时线性操作). + 积累: 训练时把 seen 规则的 define m 按其生成元 signature (6 维 binary) 滚动 + 平均聚合到 **每个生成元格位** 的原子原型 (per-generator-atom prototype). + → 每个生成元一个"语义模块" (CPG grammar-rule module). + 路由/组合: 对查询 (train/unseen) 按 signature 显式选择命中原子, 把它们 + sum/normalize 重组为组合路由 m' (CPG computation-tree 组合的内存版). + alpha: 混合 define 原 m 与原子路由 (alpha=1 纯重组). + +特点 (vs V34.2): ① 检索单元 = 单生成元原子 (粒度细) 而非整规则; ② 显式选择 +签名命中的原子进行重组 (路由语义), 而非对整 m 做 Jaccard 加权. + +Cell: + B : V33 base d128 基线 (无 reuse) = 对照 V34.2 B + C : + CPG 原子路由 (alpha 混合) + Cr : + CPG 原子路由 (alpha=1 纯重组) + +指标: 结构分组 regs_acc, 原子命中统计, 参数量/时长 (能量口径). +""" +import os, sys, time, json, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from run_v31_meta_learning import batch_pairs, head_forward, V31_RLN, V31_PLN +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + V32_1_Learner, build_model_resp, make_regs_tasks, build_u_struct, + build_t_struct, tome_fraction) +from run_v33_drl import V33_Learner, V33_3_Learner +from run_v34_1_gen import generator_signature, cycle_type + +N_GEN = 6 + + +class AtomRouteBank: + """CPG 原子路由: per-generator-atom prototype (零参数). 原子 = 6 个生成元格位. + + 积累: seen 规则 m 按签名向每个命中格位滚动平均. → 每个生成元一个语义模块. + 路由: 查询按签名显式选择命中原子, sum/normalize 重组 → 组合 m'. + """ + + def __init__(self, d, alpha=0.5, norm_mode='l2'): + self.d = d + self.alpha = alpha # 0..1, 1 = 纯原子重组 + self.norm_mode = norm_mode + self.atoms = [None] * N_GEN # 每格位原型向量 (rolling mean) + self._count = [0] * N_GEN + self.n_add = 0 + self.n_route = 0 + self.n_hit = 0 + + def _norm(self, m): + if self.norm_mode == 'l2': + return m / (m.norm() + 1e-8) + return m + + def add(self, m, sig): + """训练: 把 m 向每个命中生成元格位滚动平均积累.""" + v = m.detach().float() + for i in range(N_GEN): + if sig[i]: + c = self._count[i] + if self.atoms[i] is None: + self.atoms[i] = self._norm(v) + else: + self.atoms[i] = self._norm( + (self.atoms[i] * c + v) / (c + 1)) + self._count[i] = c + 1 + self.n_add += 1 + + def route(self, m, sig): + """部署: 按签名显式选择命中原子, 重组为组合 m'. 返回 (m', used).""" + hit = [i for i in range(N_GEN) if sig[i] and self.atoms[i] is not None] + if not hit: + return m, False + comp = sum(self.atoms[i] for i in hit) + comp = self._norm(comp) + out = self.alpha * comp + (1 - self.alpha) * m.detach() + self.n_route += 1 + self.n_hit += len(hit) + return self._norm(out), True + + def stats(self): + return {'n_atom_slots': sum(1 for a in self.atoms if a is not None), + 'n_gen': N_GEN, 'n_add': self.n_add, + 'n_route': self.n_route, 'n_hit': self.n_hit} + + +class V34_3_Learner(V33_Learner): + """V33 + CPG 原子路由: 训练积累 per-generator 原子, 部署显式路由重组. 零参数.""" + + def __init__(self, rln, pln, V, alpha=0.5, **kw): + self._alpha = alpha + super().__init__(rln, pln, V, **kw) + d = rln.d_model + self.bank = AtomRouteBank(d, alpha=alpha) + + def _define(self, task, detach=True, use_bank=True): + m = super()._define(task, detach=False, use_bank=False) + sig = task.get('gen_sig') or [0] * N_GEN + if detach: # 部署: 显式路由重组 (不积累) + m, _ = self.bank.route(m, sig) + m = m.detach() + else: # 训练: 仅积累原子 (滚动平均) + if self.bank is not None: + self.bank.add(m, sig) + return m + + +# ============================================================ +# 驱动 (与 V34.2 同构, 复用其运行管线) +# ============================================================ +def run_cell(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + cpg = cfg['cpg'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] # 全 12 + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + if cpg: + learner = V34_3_Learner(rln, pln, 200, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, + alpha=cfg.get('alpha', 0.5)) + else: + learner = V33_Learner(rln, pln, 200, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, n_define_refine=0) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + if getattr(learner, 'bank', None) is not None: + aux['atom_bank'] = learner.bank.stats() + n_params = sum(p.numel() for p in rln.parameters()) + sum(p.numel() for p in pln.parameters()) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'world': cfg.get('world', 'new'), + 'factors': {'Cpg': cpg, 'alpha': cfg.get('alpha', None)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'params_kb': round(n_params * 4 / 1024, 1), + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((" [%s] unseen=%.4f struct=%s n_params=%d aux=%s" % + (key, ua, struct_mean, n_params, aux)), flush=True) + return rec + + +CELLS = { + 'B': dict(world='new', cpg=False, label='B-base-d128'), + 'C': dict(world='new', cpg=True, alpha=0.5, label='C-cpg-route-mix'), + 'Cr': dict(world='new', cpg=True, alpha=1.0, label='Cr-cpg-route-pure'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='B,C,Cr') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70); print("V34.3 CPG 原子路由 — 零参数原子级显式重组") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + for cand in ('/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt'): + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand); break + else: + print(">>> Stage1 缺失, 请先跑 run_v32_stage1.py"); return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell(key, cfg, s1, tasks, U_STRUCT, meta_iters=args.meta_iters) + except Exception: + import traceback; traceback.print_exc() + print("!!! %s 失败" % key); continue + res.append(r) + out = ROOT / 'results' / 'v34_3_report.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s params=%d %s" % + (r['cell'], ua, r['unseen_by_struct'], r['n_params'], r['aux'])) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tests/run_v34_7_compose.py b/tests/run_v34_7_compose.py new file mode 100644 index 0000000000000000000000000000000000000000..1995dc6593836ceee7f2bcfba2526ac2bac7caee --- /dev/null +++ b/tests/run_v34_7_compose.py @@ -0,0 +1,266 @@ +"""V34.7 ComposeModule — 组合本身成为可学习模块 (打破 latent 凸包) + +立论 (v34-architecture §2.5): V34.2-6 五连证明所有"latent 凸组合" (Jaccard/归一/ +词序/迭代) 都落在原子原型凸包内, c4 (3 生成元复合) 无法表达. 唯一出路 = 组合 +函数本身可学习, 由 BPTT 驱动, 而非零参数插值. + +机制 (V34.7): + 原子: AtomRouteBank (V34.3, per-generator 原型, 训练滚动平均/部署检索). + 组合网络 ComposeModule (新增参数, 小): + in = [m_define (d,) ; comp_atoms (d,) ; gen_sig (6,)] + h1 = SiLU(W1·in + b1); out = W2·h1 + b2 + m' = m_norm(m_define + out) + 监督: m' 参与 head 预测 query loss, 外循环 BPTT 更新 compose W1/W2 + → 组合函数学"如何把原子拼成未见规则" (组合后预测更准为驱动). + +Cell: + B : V33 base + C : AtomRouteBank 纯原子 α=1 (V34.3 Cr 复现, 零参数对照) + M : + ComposeModule (可学习组合) + +指标: 结构分组 regs_acc, n_params (能量口径: M 增量), 时长. +""" +import os, sys, time, json, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from run_v31_meta_learning import batch_pairs, head_forward, V31_RLN, V31_PLN +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + V32_1_Learner, build_model_resp, make_regs_tasks, build_u_struct, + build_t_struct, tome_fraction) +from run_v33_drl import V33_Learner, V33_3_Learner +from run_v34_1_gen import generator_signature, cycle_type +from run_v34_3_cpg import AtomRouteBank, N_GEN + +_gen_cache = {} + + +class ComposeModule(nn.Module): + """可学习组合网络: [m_define; comp_atoms; sig] → 修正量加到 m_define. + + comp_atoms = 原子原型按 sig 的重组 (零参数提供凸包内基点), + compose 学凸包外的修正 → 组合泛化。 + """ + + def __init__(self, d, n_sig=6, hidden=None): + super().__init__() + h = hidden or d + self.fc1 = nn.Linear(2 * d + n_sig, h) + self.fc2 = nn.Linear(h, d) + self.d = d + self.n_sig = n_sig + + def forward(self, m_def, comp, sig): + x = torch.cat([m_def, comp, sig], dim=-1) + h1 = F.silu(self.fc1(x)) + return self.fc2(h1) + + +class V34_7_Learner(V33_Learner): + """V33 + AtomRouteBank (零参数原子) + ComposeModule (可学习组合).""" + + def __init__(self, rln, pln, V, use_compose=False, alpha=1.0, use_atoms=True, **kw): + lam_icl = kw.pop('lambda_icl', 0.0) + self._use_compose = use_compose + self._alpha = alpha + self.use_atoms = use_atoms + super().__init__(rln, pln, V, **kw) + self.lambda_icl = lam_icl + d = rln.d_model + self.bank = AtomRouteBank(d, alpha=alpha) + if use_compose: + self.compose = ComposeModule(d) + # 重建 outer_opt 纳入 compose 参数 (与 V33 同 lr) + base = (list(rln.parameters()) + list(pln.parameters()) + + list(self.intent_net.parameters()) + + list(self.refine_net.parameters()) + + list(self.probe_head.parameters())) + self.outer_opt = torch.optim.Adam( + base + list(self.compose.parameters()), lr=self.outer_lr) + + def _compose_inputs(self, task, m, sig): + """返回 (comp_atoms, sig_vec). comp = bank 原子按 sig sum (detached).""" + hit = [i for i in range(N_GEN) if sig[i] and self.bank.atoms[i] is not None] + if hit: + comp = torch.zeros_like(m) + for i in hit: + comp = comp + self.bank.atoms[i] + comp = comp / (comp.norm() + 1e-8) + else: + comp = torch.zeros_like(m) + sigv = torch.tensor(sig, dtype=torch.float) + return comp, sigv + + def _post_define_aux_loss(self, m, t): + """ICL 偏置: 用未适配共享头 W0 直接读组合后的 m 在 query 上预测. + 逼 m' 在纯 ICL (0 per-task 适配) 下就暴露组合规则 → 组合必须承载原子. + m 为 attached (训练走 _define detach=False), 梯度反传进 compose.""" + if not self._use_compose or m is None or self.lambda_icl <= 0: + return None + with torch.no_grad(): + W0 = [w.detach() for w in self.pln.clone_params()] + ctx, tgt = batch_pairs(t['query'], max_batch=16) + hidden = self._rln_fwd(ctx, m) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward(W0, h_in, self.intent_dim, + self.world_vocab, self.n_resp) + pred = self._route_logits(pred, m) + return F.cross_entropy(pred.view(-1, self.world_vocab), + tgt.view(-1).clamp(0, self.V + 1)) + + def _define(self, task, detach=True, use_bank=True): + m = super()._define(task, detach=False, use_bank=False) + sig = task.get('gen_sig') or [0] * N_GEN + use_bank = self.use_atoms + if self._use_compose: + if use_bank: + if detach: + self.bank.route(m, sig) + else: + self.bank.add(m, sig) + comp, sigv = self._compose_inputs(task, m, sig) + corr = self.compose(m, comp, sigv.to(m.device)) + m = self.m_norm(m + corr) + if detach: + m = m.detach() + return m + # 非 compose: 纯 base (不用 bank) 或纯原子 (use_atoms) + if use_bank: + if detach: + m, _ = self.bank.route(m, sig) + m = m.detach() + else: + self.bank.add(m, sig) + elif detach: + m = m.detach() + return m + + +# ============================================================ +# 驱动 +# ============================================================ +def run_cell(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] # 全 12 + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + use_compose = (lm == 'compose') + learner = V34_7_Learner(rln, pln, 200, K=K, seed=seed, sleep_iters=2, + n_resp=4, use_intent=True, + use_compose=use_compose, + use_atoms=(lm != 'base'), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0)) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + if getattr(learner, 'bank', None) is not None: + aux['atom_bank'] = learner.bank.stats() + if getattr(learner, 'compose', None) is not None: + cp = learner.compose + aux['compose_params'] = sum(p.numel() for p in cp.parameters()) + n_params = sum(p.numel() for p in rln.parameters()) + sum(p.numel() for p in pln.parameters()) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'world': cfg.get('world', 'new'), + 'factors': {'LM': lm, 'alpha': cfg.get('alpha', 1.0), + 'use_compose': use_compose, + 'lambda_icl': cfg.get('lambda_icl', 0.0)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'params_kb': round(n_params * 4 / 1024, 1), + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((" [%s] unseen=%.4f struct=%s n_params=%d aux=%s" % + (key, ua, struct_mean, n_params, aux)), flush=True) + return rec + + +CELLS = { + 'B': dict(world='new', lm='base', label='B-base'), + 'C': dict(world='new', lm='atom', alpha=1.0, label='C-atom-pure'), + 'M': dict(world='new', lm='compose', alpha=1.0, label='M-compose-learn'), +'MI': dict(world='new', lm='compose', alpha=1.0, + lambda_icl=0.5, label='M-compose-ICL-bias'), + 'MI3': dict(world='new', lm='compose', alpha=1.0, + lambda_icl=1.0, label='M-compose-ICL-bias-l1.0'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='B,C,M') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70); print("V34.7 ComposeModule — 可学习组合模块 (破凸包)") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + for cand in ('/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt'): + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand); break + else: + print(">>> Stage1 缺失, 请先跑 run_v32_stage1.py"); return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell(key, cfg, s1, tasks, U_STRUCT, meta_iters=args.meta_iters) + except Exception: + import traceback; traceback.print_exc() + print("!!! %s 失败" % key); continue + res.append(r) + out = ROOT / 'results' / 'v34_7_report.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s params=%d %s" % + (r['cell'], ua, r['unseen_by_struct'], r['n_params'], r['aux'])) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tests/run_v35_16a_ablation.py b/tests/run_v35_16a_ablation.py new file mode 100644 index 0000000000000000000000000000000000000000..0e483ed6a6dbb9015db5a44a3f23ca75109a7b2d --- /dev/null +++ b/tests/run_v35_16a_ablation.py @@ -0,0 +1,476 @@ +"""V35.14 四方向消融 — c4 锁死带 (2026-08-15, 用户发起) + +以 S0 (V35.12 最强, c4 0.0964 首次超基线) 为参照, 消融四个方向: + + A1 符号锚定 : CycleTypeAnchor — m→5 类 cycle type 粗粒度监督 + (给 c4 类身份, 补"无不动点锚"缺失) + B1 结构化复合 : GenCompose — 回读→逐步复合闭环 + (perm_head soft 回读 + one-hot 生成元 → 共享两跳 MLP + 学生成元右乘代数, 监督 = 组合 perm; 梯度回流表示层) + C2 定向采样 : AQ4C — 目标结构采样 (50% 概率定向 c4, BFS 最短对换分解) + D 评估视角 : perm_acc_by_struct — probe 回读按 cycle type 分桶 (公共) + +纪律: 同进程共享 Stage1 / 随机基线参照 A0 / 结构化报告 JSON+MD。 +""" +import os, sys, time, json, math, random +import itertools +from collections import deque +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F + +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import ( + generator_signature, cycle_type, TRANS, IDENT, _apply, CYCLET) +from run_v34_3_cpg import N_GEN +from hibs_lnn.meta_rule_world import gen_task_data_split +from run_v35_fractal import ( + V35_Learner, ActiveQueryGenerator, compose_swaps) + +# ============================================================ +# 预计算: 24 perm / 6 个 c4 的最短对换分解 (BFS, 与 compose_swaps 同语义) +# ============================================================ +PERMS24 = list(itertools.permutations(range(4))) +PERM_IDX = {p: i for i, p in enumerate(PERMS24)} +C4_PERMS = [p for p in PERMS24 if cycle_type(p) == 3] # 6 个 4-cycle + + +def _shortest_decomp(target): + q = deque([(IDENT, [])]) + seen = {IDENT} + while q: + p, path = q.popleft() + if p == target: + return path + for gi in range(6): + np = _apply(p, TRANS[gi]) + if np not in seen: + seen.add(np) + q.append((np, path + [gi])) + return None + + +C4_DECOMP = {p: _shortest_decomp(p) for p in C4_PERMS} + + +def _onehot6(gi, device): + v = torch.zeros(6, device=device) + v[gi] = 1.0 + return v + + +# ============================================================ +# C2: AQ4C — 目标结构采样 (c4 定向) +# ============================================================ +class AQ4C(ActiveQueryGenerator): + """p_target 概率从 6 个 c4 排列轮流选目标 (BFS 最短对换分解), + 其余走父类采样. gen_history 存 4 元组 (perm, sig, ln, gens).""" + + def __init__(self, V, seed=42, n_support=8, n_query=8, max_combo=3, + p_combo3=0.6, p_target=0.5): + super().__init__(V, seed=seed, n_support=n_support, n_query=n_query, + max_combo=max_combo, p_combo3=p_combo3) + self.p_target = p_target + self._tgt_cycle = 0 + + def _pick_gens_target(self, bank): + for _ in range(len(C4_PERMS)): + target = C4_PERMS[self._tgt_cycle % len(C4_PERMS)] + self._tgt_cycle += 1 + gens = C4_DECOMP[target] + if all(bank.atoms[gi] is not None for gi in gens): + return list(gens) + return None + + def _pick_gens(self, bank): + if self.p_target > 0 and self.rng.random() < self.p_target: + g = self._pick_gens_target(bank) + if g is not None: + return g + return super()._pick_gens(bank) + + def generate(self, bank, n=4): + out = [] + seen_sigs = set(h[1] for h in self.gen_history) + for _ in range(n * 3): + gens = self._pick_gens(bank) + if gens is None: + break + perm = compose_swaps(gens) + sig, ln = generator_signature(perm) + sig_t = tuple(sig) if sig else None + if sig is None or sig_t in seen_sigs: + continue + seen_sigs.add(sig_t) + seed = self.rng.randint(0, 10 ** 6) + sup, qry = gen_task_data_split( + None, self.V, n_support=self.n_support, + n_query=self.n_query, seed=seed, remap=perm, + stop_at_halt=True) + out.append({'perm': perm, 'gen_sig': sig, 'gen_len': ln, + 'support': sup, 'query': qry, 'gens': tuple(gens)}) + self.gen_history.append((perm, sig_t, ln, tuple(gens))) + return out + + +# ============================================================ +# V35_Learner_A: + A1 符号锚定 / B1 结构化复合 +# ============================================================ +class V35_Learner_A(V35_Learner): + def __init__(self, rln, pln, V, use_compose=False, alpha=1.0, + use_atoms=True, use_fractal=False, use_hopfield=False, + adaptive_k=False, active_query=False, **kw): + # 新参数必须先 pop, 不泄漏给上层 (V35 坑) + self._use_anchor = kw.pop('use_anchor', False) + self._lambda_anchor = kw.pop('lambda_anchor', 0.5) + self._use_gencomp = kw.pop('use_gencomp', False) + self._lambda_gen = kw.pop('lambda_gen', 0.5) + self._p_target = kw.pop('p_target', 0.0) + super().__init__(rln, pln, V, use_compose=use_compose, alpha=alpha, + use_atoms=use_atoms, use_fractal=use_fractal, + use_hopfield=use_hopfield, adaptive_k=adaptive_k, + active_query=active_query, **kw) + # C2: 替换 AQ 为定向采样版 (保留父类构造的槽位/尺寸) + if self._active_query and self._aq is not None and self._p_target > 0: + aq = self._aq + self._aq = AQ4C(V, seed=42, n_support=aq.n_support, + n_query=aq.n_query, max_combo=aq.max_combo, + p_combo3=getattr(aq, 'p_combo3', 0.6), + p_target=self._p_target) + d = rln.d_model + # A1: cycle type 锚 (5 类, 粗粒度结构监督) + if self._use_anchor: + self.anchor_head = nn.Sequential( + nn.Linear(d, d), nn.Tanh(), nn.Linear(d, CYCLET)) + base = list(self.outer_opt.param_groups[0]['params']) + self.outer_opt = torch.optim.Adam( + base + list(self.anchor_head.parameters()), lr=self.outer_lr) + # B1: 生成元右乘复合算子 (one-hot 24+6 -> 64 -> 24) + if self._use_gencomp: + self.gen_comp = nn.Sequential( + nn.Linear(30, 64), nn.Tanh(), nn.Linear(64, 24)) + base = list(self.outer_opt.param_groups[0]['params']) + self.outer_opt = torch.optim.Adam( + base + list(self.gen_comp.parameters()), lr=self.outer_lr) + self.gencomp_acc = [] + + # A1: 锚损失 + def _anchor_loss(self, m, t): + cyc = cycle_type(t['perm']) + logits = self.anchor_head(m) + return F.cross_entropy( + logits.unsqueeze(0), torch.tensor([cyc], device=m.device)) + + # B1: 回读->逐步复合->目标 闭环损失 (梯度经 gen_comp+perm_head 回流 m) + def _gencomp_loss(self, m, t): + gens = t.get('gens') + if not gens: + return None + target = PERM_IDX[t['perm']] + p_soft = self.perm_head(m) # (24,) soft 回读 + for gi in gens: + x = torch.cat([p_soft, _onehot6(gi, m.device)]) + p_soft = self.gen_comp(x) # (24,) 复合后分布 + loss = F.cross_entropy( + p_soft.unsqueeze(0), torch.tensor([target], device=m.device)) + with torch.no_grad(): + self.gencomp_acc.append( + 1.0 if p_soft.argmax().item() == target else 0.0) + return loss + + def _post_define_aux_loss(self, m, t): + extra = super()._post_define_aux_loss(m, t) + if m is None: + return extra + if self._use_anchor and t.get('perm') is not None: + al = self._anchor_loss(m, t) * self._lambda_anchor + extra = al if extra is None else extra + al + if self._use_gencomp and t.get('gens'): + gl = self._gencomp_loss(m, t) + if gl is not None: + gl = gl * self._lambda_gen + extra = gl if extra is None else extra + gl + return extra + + # 复制 V35_Learner.meta_step + 给 AQ 任务补 gens 序列 + def meta_step(self, tasks, train_rules, K=None): + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + if self._active_query and self._aq is not None: + n_aq = self._aq_n + qs = self._aq.generate(self.bank, n=n_aq) + for i, q in enumerate(qs): + t = {'support': [(c, tg[:4]) for (c, tg) in q['support']], + 'query': [(c, tg[:4]) for (c, tg) in q['query']], + 'perm': q['perm'], 'gen_sig': q['gen_sig'], + 'seen': False, 'name': 'AQ%d' % len(self._aq.gen_history), + 'struct': 'aq', 'gens': q.get('gens')} + tasks['__aq__%d' % i] = t + chosen.append('__aq__%d' % i) + loss = super().meta_step(tasks, chosen, K=K) + for k in list(tasks): + if k.startswith('__aq__'): + del tasks[k] + return loss + + +# ============================================================ +# run_cell_v14: 复制 run_cell + 新参数 + D 公共评估 +# ============================================================ +def run_cell_v14(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + use_compose = (lm == 'compose') + learner = V35_Learner_A( + rln, pln, 200, K=K, seed=seed, sleep_iters=2, n_resp=4, + use_intent=True, use_compose=use_compose, + use_atoms=(lm != 'base'), + use_fractal=cfg.get('fractal', False), + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + train_retrieval=cfg.get('train_retrieval', False), + aq_p3=cfg.get('aq_p3', 0.6), + aq_eval=cfg.get('aq_eval', False), + tau_norm=cfg.get('tau_norm', None), + beta_init=cfg.get('beta_init', None), + use_think=cfg.get('use_think', False), + n_think=cfg.get('n_think', 6), + think_tau=cfg.get('think_tau', 0.01), + lambda_think=cfg.get('lambda_think', 0.0), + lambda_combo=cfg.get('lambda_combo', 0.0), + use_perm=cfg.get('use_perm', False), + lambda_perm=cfg.get('lambda_perm', 0.5), + lambda_table=cfg.get('lambda_table', 0.0), + use_remap=cfg.get('use_remap', False), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0), + beta_train=cfg.get('beta_train', 3.0), + beta_test=cfg.get('beta_test', 10.0), + D=cfg.get('D', 1.5), + aq_n=cfg.get('aq_n', 4), + aq_max_combo=cfg.get('aq_max_combo', 3), + k_lam=cfg.get('k_lam', 0.005), + k_patience=cfg.get('k_patience', 3), + use_anchor=cfg.get('use_anchor', False), + lambda_anchor=cfg.get('lambda_anchor', 0.5), + use_gencomp=cfg.get('use_gencomp', False), + lambda_gen=cfg.get('lambda_gen', 0.5), + p_target=cfg.get('p_target', 0.0)) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), + flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + # D 公共评估: probe 回读 perm 按 cycle type 分桶 + pa_struct = {'c3': [], 'c4': [], 'dbl': []} + if getattr(learner, 'probe_head', None) is not None: + with torch.no_grad(): + for n in unseen_names: + task = tasks[n] + m = learner._define(task, detach=True) + pred = learner.probe_head(m).view(4, 4).argmax(dim=-1) + ok = 1.0 if tuple(pred.tolist()) == task['perm'] else 0.0 + s = U_STRUCT[n] + if s in pa_struct: + pa_struct[s].append(ok) + aux['perm_acc_by_struct'] = { + k: (round(sum(v) / len(v), 3) if v else None) + for k, v in pa_struct.items()} + # AQ 评估 (aq_eval) + aq_eval_acc = {} + if cfg.get('aq_eval', False) and learner._aq is not None: + aq_perms = [h[0] for h in learner._aq.gen_history] + for i, perm in enumerate(aq_perms): + sig, _ = generator_signature(perm) + sup, qry = gen_task_data_split( + None, 200, n_support=4, n_query=8, seed=1000 + i, + remap=perm, stop_at_halt=True) + aqt = {'support': [(c, tg[:4]) for (c, tg) in sup], + 'query': [(c, tg[:4]) for (c, tg) in qry], + 'perm': perm, 'gen_sig': sig, 'seen': False, + 'name': 'AQE%d' % i, 'struct': 'aq'} + with torch.no_grad(): + m = learner._define(aqt, detach=True) + acc = learner._metric([w.detach() for w in + learner.pln.clone_params()], + aqt, m=m)[1] + aq_eval_acc['aq%d_%s' % (i, perm)] = round(acc, 4) + aux['aq_eval_acc'] = aq_eval_acc + if getattr(learner, 'bank', None) is not None: + aux['atom_bank'] = learner.bank.stats() + if getattr(learner, 'compose', None) is not None: + aux['compose_params'] = sum(p.numel() + for p in learner.compose.parameters()) + if learner._adaptive_k: + kus = learner._k_used + aux['k_used'] = {'mean': round(sum(kus) / len(kus), 2), + 'min': min(kus), 'max': max(kus)} + if learner._aq is not None: + aux['aq_gen'] = len(learner._aq.gen_history) + aux['aq_combo_lens'] = [h[2] for h in learner._aq.gen_history[-10:]] + if getattr(learner, 'think_net', None) is not None: + tr = learner.think_rounds + aux['think_rounds'] = {'mean': round(sum(tr) / len(tr), 2), + 'min': min(tr), 'max': max(tr)} if tr else None + traces = [t for t in learner.think_trace if len(t) >= 2] + if traces: + drops = [t[0] - t[-1] for t in traces] + aux['think_err_drop'] = { + 'mean': round(sum(drops) / len(drops), 4), + 'frac_positive': round( + sum(1 for d in drops if d > 0) / len(drops), 3)} + else: + aux['think_err_drop'] = None + if getattr(learner, 'perm_head', None) is not None: + pa = learner.perm_acc + aux['perm_acc'] = { + 'seen': (round(sum(pa['seen']) / len(pa['seen']), 3) + if pa['seen'] else None), + 'unseen': (round(sum(pa['unseen']) / len(pa['unseen']), 3) + if pa['unseen'] else None), + 'n_seen': len(pa['seen']), 'n_unseen': len(pa['unseen'])} + if getattr(learner, 'gen_comp', None) is not None: + ga = learner.gencomp_acc + aux['gencomp_acc'] = (round(sum(ga) / len(ga), 3) if ga else None) + aux['gencomp_n'] = len(ga) + if getattr(learner, 'anchor_head', None) is not None: + aux['anchor_params'] = sum(p.numel() + for p in learner.anchor_head.parameters()) + try: + hs = learner.head.step_size_stats() + aux['head_beta'] = {k: (round(v, 6) if isinstance(v, float) + else v) for k, v in hs.items()} + except Exception: + pass + n_params = (sum(p.numel() for p in rln.parameters()) + + sum(p.numel() for p in pln.parameters())) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'world': cfg.get('world', 'new'), + 'factors': {'LM': lm, 'alpha': cfg.get('alpha', 1.0), + 'use_compose': use_compose, + 'use_hopfield': cfg.get('hopfield', False), + 'adaptive_k': cfg.get('adaptive_k', False), + 'active_query': cfg.get('active_query', False), + 'use_anchor': cfg.get('use_anchor', False), + 'use_gencomp': cfg.get('use_gencomp', False), + 'p_target': cfg.get('p_target', 0.0), + 'aq_n': cfg.get('aq_n', 4), + 'lambda_probe': cfg.get('lambda_probe', 0.3)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'params_kb': round(n_params * 4 / 1024, 1), + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((u" [%s] unseen=%.4f struct=%s n_params=%d" % + (key, ua, struct_mean, n_params)), flush=True) + return rec + + +BASE = dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0) + +CELLS14 = { + 'A0': dict(BASE, label='A0-S0-ref'), + 'A1': dict(BASE, use_anchor=True, lambda_anchor=0.5, + label='A1-anchor'), + 'B1': dict(BASE, use_perm=True, lambda_perm=1.0, use_gencomp=True, + lambda_gen=0.5, label='B1-gencomp'), + 'C2': dict(BASE, p_target=0.5, label='C2-c4-targeted'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='A0,A1,B1,C2') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35.14 四方向消融 (c4 锁死带): A 锚 / B 复合 / C2 定向 / D 评估") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS14[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell_v14(key, cfg, s1, tasks, U_STRUCT, + meta_iters=args.meta_iters) + except Exception: + import traceback + traceback.print_exc() + print("!!! %s 失败" % key) + continue + res.append(r) + out = ROOT / 'results' / 'v35_14_ablation_report.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc'] + ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s" % + (r['cell'], ua, r['unseen_by_struct'])) + if 'perm_acc_by_struct' in r['aux']: + print(" D: perm_acc_by_struct =", r['aux']['perm_acc_by_struct']) + if 'gencomp_acc' in r['aux']: + print(" B: gencomp_acc =", r['aux']['gencomp_acc']) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_17_s5_transfer.py b/tests/run_v35_17_s5_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..83bef3f7b7ce525cfc212143bd3a60abf1b86c2c --- /dev/null +++ b/tests/run_v35_17_s5_transfer.py @@ -0,0 +1,347 @@ +"""V35.17 L2: S4→S5 群外推 — 迁移泛化真测试 (2026-08-15, 用户发起) + +问题: 表示层学到的"对换复合规则"能否跨群迁移? + S4 训练 (4 元素/6 对换/24 perm, C2 配置家族同进程最强) + → S5 迁移 (5 元素/10 对换/120 perm) + +协议 (同进程, 排除机制噪声): + 1. S4 训练 (C2 配置, 40 iters) → 保存 rln/pln + 2. head 扩维: predictor[2] (V+2)*4→(V+2)*5 (前 4 块复制, 新块零), step_beta 重建 + 3. 迁移评估 (禁用 think/compose/bank — 直测表示迁移): + a. 零样本: adapt_graph(S5 support) → _metric(query), 按 cycle type 分桶 + b. 微调: S5 seen 继续 meta_step 20 iters → 同评估 + 4. 对照: scratch (S5 从零, base 配置, 40 iters) → 同评估 + 5. 迁移增益 = 迁移模型 unseen - scratch unseen (同进程) +""" +import os, sys, time, json, math, random +import itertools +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F + +from run_v31_meta_learning import batch_pairs, head_forward +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature, cycle_type, IDENT, _apply +from run_v35_fractal import V35_Learner, compose_swaps +from run_v35_16a_ablation import CELLS14, V35_Learner_A, run_cell_v14 +from hibs_lnn.meta_rule_world import gen_task_data_split +import hibs_lnn.code_world as cw +import hibs_lnn.meta_rule_world as mrw + +V = 200 +N_RESP = 4 # S4 +N_RESP5 = 5 # S5 + +# ============================================================ +# S5 代数: 10 对换 / 120 perm / cycle type 5 +# ============================================================ +TRANS5 = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), + (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)] +PERMS5 = list(itertools.permutations(range(5))) + + +def _apply5(p, g): + q = list(p) + a, b = g + q[a], q[b] = q[b], q[a] + return tuple(q) + + +def cycle_type5(perm): + """S5 循环类型 → 类 id: 1=trans(2) 2=c3(3) 3=c4(4,1) 4=dbl(2,2) 5=c5(5) 6=(3,2)""" + seen = set() + lens = [] + for i in range(5): + if i in seen: + continue + c = [] + j = i + while j not in seen: + seen.add(j) + c.append(j) + j = perm[j] + if len(c) > 1: + lens.append(len(c)) + lens.sort() + if not lens: + return 0 + if lens == [2]: + return 1 + if lens == [3]: + return 2 + if lens == [4]: + return 3 + if lens == [2, 2]: + return 4 + if lens == [5]: + return 5 + if lens == [3, 2]: + return 6 + return 0 + + +def s5_groups(): + out = {0: [], 1: [], 2: [], 3: [], 4: [], 5: [], 6: []} + for p in PERMS5: + out[cycle_type5(p)].append(p) + return out + + +def make_s5_tasks(n_sup=24, n_qry=12, seed=42): + """S5 seen/unseen 任务: seen = identity+8 对换+4 c3; unseen = c3 余+c4+c5+dbl.""" + g = s5_groups() + rng = random.Random(seed) + tasks = {} + seen_perms = [(0, 1, 2, 3, 4)] + rng.sample(g[1], 8) + rng.sample(g[2], 4) + unseen_perms = (rng.sample([p for p in g[2] if p not in seen_perms], 3) + + rng.sample(g[3], 6) + rng.sample(g[5], 4) + + rng.sample(g[4], 3)) + for i, p in enumerate(seen_perms): + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=seed + i, + remap=p, stop_at_halt=True) + tasks['S5T%d' % i] = { + 'support': [(c, tg[:N_RESP5]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP5]) for (c, tg) in qry], + 'perm': p, 'seen': True} + for i, p in enumerate(unseen_perms): + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=1000 + i, + remap=p, stop_at_halt=True) + tasks['S5U%d' % i] = { + 'support': [(c, tg[:N_RESP5]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP5]) for (c, tg) in qry], + 'perm': p, 'seen': False} + return tasks + + +# ============================================================ +# head 扩维: n_resp 4→5 (predictor[2] + step_beta + SwiftTDHead) +# ============================================================ +def extend_head(pln, learner): + old = pln.predictor[2] + new = nn.Linear(old.in_features, (V + 2) * N_RESP5) + with torch.no_grad(): + new.weight[:(V + 2) * N_RESP].copy_(old.weight) + new.bias[:(V + 2) * N_RESP].copy_(old.bias) + new.weight[(V + 2) * N_RESP:].zero_() + new.bias[(V + 2) * N_RESP:].zero_() + pln.predictor[2] = new + pln.n_response = N_RESP5 + pln.step_beta = nn.Parameter( + torch.full((pln._n_params(),), math.log(pln.step_beta.exp().mean().item()))) + # SwiftTDHead: 输出 (V+2)*5, 状态重置 (与 scratch 公平) + hd = learner.head + from hibs_lnn.swifttd_head import SwiftTDHead + learner.head = SwiftTDHead( + hd.d_model, V + 2, N_RESP5, beta_init=hd.beta_init, + kappa=hd.kappa, eta=hd.eta, eps=hd.eps, beta_min=hd.beta_min, + input_dim=learner.intent_dim, tau_norm=hd.tau_norm) + learner.head.reset_state() + learner.n_resp = N_RESP5 + return pln, learner + + +# ============================================================ +# 迁移评估: 纯表示路径 (禁用 think/compose/bank) +# ============================================================ +def s5_eval(learner, tasks, names, k_adapt=20): + """每任务: adapt_graph(support) → _metric(query). 返回 per-task regs_acc.""" + learner._use_think = False + learner._use_compose = False + learner.use_atoms = False + accs = {} + for n in names: + t = tasks[n] + with torch.no_grad(): + m = learner._define(t, detach=True) + W = learner.adapt_graph(t['support'], K=k_adapt, m=m) + with torch.no_grad(): + acc = learner._metric([w.detach() for w in W], t, m=m)[1] + accs[n] = acc + return accs + + +def struct_mean5(accs, tasks): + by = {'c3': [], 'c4': [], 'c5': [], 'dbl': []} + for n, a in accs.items(): + ct = cycle_type5(tasks[n]['perm']) + key = {2: 'c3', 3: 'c4', 5: 'c5', 4: 'dbl'}.get(ct) + if key: + by[key].append(a) + return {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + + +def oml_step(learner, tasks, names, it, n_tasks=4): + """纯 OML 外循环步: define(带图) → adapt_graph → CE(adapted W on support) + → outer_opt 更新。绕过 V33.meta_step 的 probe 段 (view(4,4) 写死, S5 不兼容), + 微调/scratch 统一用此协议 (公平)。""" + rng = random.Random(learner.seed + it) + chosen = rng.sample(names, min(n_tasks, len(names))) + loss = 0.0 + for rname in chosen: + t = tasks[rname] + m = learner._define(t, detach=False) # 带图 (think/compose/bank 已禁) + W = learner.adapt_graph(t['support'], K=learner.K, m=m) + ctx, tg = batch_pairs(t['support'], max_batch=16) + h = learner._rln_fwd(ctx, m)[:, -1, :] + hin = learner._h_in_batch(h, m) + pred = head_forward([w for w in W], hin, learner.intent_dim, + learner.world_vocab, learner.n_resp) + loss = loss + F.cross_entropy( + pred.view(-1, learner.world_vocab), + tg.view(-1).clamp(0, learner.V + 1)) / len(chosen) + learner.outer_opt.zero_grad() + loss.backward() + learner.outer_opt.step() + return loss.item() + + +# ============================================================ +# 驱动 +# ============================================================ +def train_s4(cfg, s1_state, meta_iters=40): + """S4 训练 (C2 配置), 返回 (rln, pln, learner).""" + from run_v35_16a_ablation import run_cell_v14 as rc + tasks = build_v32_tasks(V, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(42); torch.manual_seed(42) + rln, pln = build_model_resp(V, N_RESP, s1_state) + learner = V35_Learner_A( + rln, pln, V, K=20, seed=42, sleep_iters=2, n_resp=N_RESP, + use_intent=True, use_compose=True, use_atoms=True, + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + aq_p3=cfg.get('aq_p3', 0.7), + use_think=cfg.get('use_think', False), n_think=6, + lambda_icl=cfg.get('lambda_icl', 0.5), + lambda_combo=cfg.get('lambda_combo', 0.5), + lambda_probe=cfg.get('lambda_probe', 1.0), + aq_n=cfg.get('aq_n', 6), p_target=cfg.get('p_target', 0.5)) + for it in range(meta_iters): + learner.meta_iters = it + learner.meta_step(tasks, train_names) + return rln, pln, learner + + +def train_s5_scratch(s1_state, meta_iters=40): + """S5 从零训练 (base 配置: 无机制), 返回 per-task 评估.""" + tasks = make_s5_tasks(seed=42) + seen = [n for n in tasks if tasks[n]['seen']][:13] + unseen = [n for n in tasks if not tasks[n]['seen']] + random.seed(42); torch.manual_seed(42) + rln, pln = build_model_resp(V, N_RESP5, s1_state) + learner = V35_Learner_A( + rln, pln, V, K=20, seed=42, sleep_iters=2, n_resp=N_RESP5, + use_intent=True, use_compose=False, use_atoms=False) + t0 = time.time() + for it in range(meta_iters): + learner.meta_iters = it + oml_step(learner, tasks, seen, it) + print(" [scratch-S5] 训练 %.0fs" % (time.time() - t0), flush=True) + accs = s5_eval(learner, tasks, unseen) + return accs, tasks + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--s4-iters', type=int, default=40) + ap.add_argument('--finetune-iters', type=int, default=20) + ap.add_argument('--scratch-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35.17 L2: S4→S5 群外推 (迁移泛化真测试)") + print("=" * 70) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = {} + + # 1) S4 训练 (C2 配置) + print("\n== S4 训练 (C2) ==") + t0 = time.time() + rln, pln, learner = train_s4(CELLS14['C2'], s1, meta_iters=args.s4_iters) + print(" S4 训练 %.0fs" % (time.time() - t0), flush=True) + + # 2) head 扩维 4→5 + print("== head 扩维 n_resp 4→5 ==") + extend_head(pln, learner) + + # 3) S5 零样本迁移评估 + print("== S5 零样本迁移 (禁用机制, 纯表示) ==") + s5_tasks = make_s5_tasks(seed=42) + s5_seen = [n for n in s5_tasks if s5_tasks[n]['seen']] + s5_unseen = [n for n in s5_tasks if not s5_tasks[n]['seen']] + acc0 = s5_eval(learner, s5_tasks, s5_unseen) + res['s5_zero_shot'] = { + 'unseen': round(sum(acc0.values()) / len(acc0), 4), + 'by_struct': struct_mean5(acc0, s5_tasks)} + print(" zero-shot unseen=%.4f struct=%s" % ( + res['s5_zero_shot']['unseen'], res['s5_zero_shot']['by_struct'])) + + # 4) 迁移微调: S5 seen 继续 meta_step 20 iters + print("== 迁移微调 (S5 seen, %d iters) ==" % args.finetune_iters) + # 保持纯表示路径 (think/compose/bank 均禁用, 与 scratch 公平) + t0 = time.time() + for it in range(args.finetune_iters): + learner.meta_iters = 1000 + it + oml_step(learner, s5_tasks, s5_seen, 1000 + it) + print(" 微调 %.0fs" % (time.time() - t0), flush=True) + acc1 = s5_eval(learner, s5_tasks, s5_unseen) + res['s5_finetuned'] = { + 'unseen': round(sum(acc1.values()) / len(acc1), 4), + 'by_struct': struct_mean5(acc1, s5_tasks)} + print(" finetuned unseen=%.4f struct=%s" % ( + res['s5_finetuned']['unseen'], res['s5_finetuned']['by_struct'])) + + # 5) scratch 对照: S5 从零 40 iters (同进程) + print("== scratch 对照 (S5 从零, base, %d iters) ==" % args.scratch_iters) + acc2, s5_tasks2 = train_s5_scratch(s1, meta_iters=args.scratch_iters) + res['s5_scratch'] = { + 'unseen': round(sum(acc2.values()) / len(acc2), 4), + 'by_struct': struct_mean5(acc2, s5_tasks2)} + print(" scratch unseen=%.4f struct=%s" % ( + res['s5_scratch']['unseen'], res['s5_scratch']['by_struct'])) + + # 汇总 + print("\n" + "=" * 70) + print("L2 迁移汇总 (同进程):") + for k in ['s5_zero_shot', 's5_finetuned', 's5_scratch']: + r = res[k] + print(" %-14s unseen=%.4f %s" % (k, r['unseen'], r['by_struct'])) + out = ROOT / 'results' / 'v35_17_s5_transfer.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("JSON:", out) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_18_proposer.py b/tests/run_v35_18_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..c41ac9a970d76e2353b12c9194da1bdb11e641fd --- /dev/null +++ b/tests/run_v35_18_proposer.py @@ -0,0 +1,312 @@ +"""V35.18 L3: 模型自主结构提议 (E-V 闭环内部化) — 2026-08-15 用户发起 + +用户洞察: 迁移成立 ⟹ 表示层已有规则抽象 (潜伏), 但未变成架构级能力 +(L2 证据: c5 新结构零样本 +5.5pp)。L3 = 把外部课程器 (AQ 随机/定向采样) +内部化为模型自主提议: + + E (提议) : perm_head(m) 温度采样 → 模型输出"下一个结构假设" p̂ + V (验证) : 世界执行器 gen_task_data_split(remap=p̂) 生成真值任务 + 吸收 : 任务注入 meta_step (带 perm 标签, 同 AQ 通道) + +与 C2 (外部定向) 对照: C2 是外部代码选结构, L3 是模型表示选结构。 +测: 模型自主提议的结构能否带来 ≥ 外部定向的学习效果。 + +Cell: + P0 = BASE (A0 同配置, 同进程参照) + P1 = BASE + perm_head(λ_perm 1.0) + n_prop=2 (模型提议) + P2 = BASE + perm_head + n_prop=2 + p_target 0.5 (提议+定向叠加) +""" +import os, sys, time, json, math, random +import itertools +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F + +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature, cycle_type, TRANS, IDENT, _apply +from hibs_lnn.meta_rule_world import gen_task_data_split +from run_v35_fractal import V35_Learner +from run_v35_16a_ablation import ( + V35_Learner_A, PERMS24, C4_PERMS, AQ4C, CELLS14) + + +class V35_Learner_P(V35_Learner_A): + """+ L3 提议器: perm_head(m) 温度采样提议新结构 → 世界验证 → 注入训练流.""" + + def __init__(self, rln, pln, V, use_compose=False, alpha=1.0, + use_atoms=True, use_fractal=False, use_hopfield=False, + adaptive_k=False, active_query=False, **kw): + self._n_prop = kw.pop('n_prop', 0) + self._prop_tau = kw.pop('prop_tau', 2.0) + super().__init__(rln, pln, V, use_compose=use_compose, alpha=alpha, + use_atoms=use_atoms, use_fractal=use_fractal, + use_hopfield=use_hopfield, adaptive_k=adaptive_k, + active_query=active_query, **kw) + self.prop_history = [] + self.prop_struct = [] + + def _propose(self, tasks, train_names): + """E: perm_head(m) 温度采样 → 候选 perm (去重) → V: 世界真值任务.""" + if self._n_prop <= 0 or self.perm_head is None: + return [] + rng = random.Random(self.seed + self._meta_iters) + src = rng.sample(train_names, min(2, len(train_names))) + out = [] + for n in src: + t = tasks[n] + with torch.no_grad(): + m = self._define(t, detach=True) + logits = self.perm_head(m) / self._prop_tau + idx = torch.multinomial(logits.softmax(-1), 1).item() + p = PERMS24[idx] + if p in self.prop_history: + continue + self.prop_history.append(p) + self.prop_struct.append(cycle_type(p)) + seed = rng.randint(0, 10 ** 6) + sup, qry = gen_task_data_split( + None, self.V, n_support=4, n_query=8, seed=seed, + remap=p, stop_at_halt=True) + sig = generator_signature(p) + out.append({'perm': p, 'support': sup, 'query': qry, + 'gen_sig': sig[0] if sig else None}) + return out + + def meta_step(self, tasks, train_rules, K=None): + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + # AQ 注入 (含 C2 定向) + if self._active_query and self._aq is not None: + n_aq = self._aq_n + qs = self._aq.generate(self.bank, n=n_aq) + for i, q in enumerate(qs): + t = {'support': [(c, tg[:4]) for (c, tg) in q['support']], + 'query': [(c, tg[:4]) for (c, tg) in q['query']], + 'perm': q['perm'], 'gen_sig': q['gen_sig'], + 'seen': False, 'name': 'AQ%d' % len(self._aq.gen_history), + 'struct': 'aq', 'gens': q.get('gens')} + tasks['__aq__%d' % i] = t + chosen.append('__aq__%d' % i) + # L3 提议注入 (模型自主) + if self._n_prop > 0: + props = self._propose(tasks, train_rules) + for i, q in enumerate(props): + t = {'support': [(c, tg[:4]) for (c, tg) in q['support']], + 'query': [(c, tg[:4]) for (c, tg) in q['query']], + 'perm': q['perm'], 'gen_sig': q['gen_sig'], + 'seen': False, 'name': 'PROP%d' % i, 'struct': 'prop'} + tasks['__prop__%d' % i] = t + chosen.append('__prop__%d' % i) + loss = super().meta_step(tasks, chosen, K=K) + for k in list(tasks): + if k.startswith('__aq__') or k.startswith('__prop__'): + del tasks[k] + return loss + + +def run_cell_p(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + use_compose = (lm == 'compose') + learner = V35_Learner_P( + rln, pln, 200, K=K, seed=seed, sleep_iters=2, n_resp=4, + use_intent=True, use_compose=use_compose, + use_atoms=(lm != 'base'), + use_fractal=cfg.get('fractal', False), + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + train_retrieval=cfg.get('train_retrieval', False), + aq_p3=cfg.get('aq_p3', 0.6), + aq_eval=cfg.get('aq_eval', False), + tau_norm=cfg.get('tau_norm', None), + beta_init=cfg.get('beta_init', None), + use_think=cfg.get('use_think', False), + n_think=cfg.get('n_think', 6), + think_tau=cfg.get('think_tau', 0.01), + lambda_think=cfg.get('lambda_think', 0.0), + lambda_combo=cfg.get('lambda_combo', 0.0), + use_perm=cfg.get('use_perm', False), + lambda_perm=cfg.get('lambda_perm', 0.5), + lambda_table=cfg.get('lambda_table', 0.0), + use_remap=cfg.get('use_remap', False), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0), + beta_train=cfg.get('beta_train', 3.0), + beta_test=cfg.get('beta_test', 10.0), + D=cfg.get('D', 1.5), + aq_n=cfg.get('aq_n', 4), + aq_max_combo=cfg.get('aq_max_combo', 3), + k_lam=cfg.get('k_lam', 0.005), + k_patience=cfg.get('k_patience', 3), + use_anchor=cfg.get('use_anchor', False), + lambda_anchor=cfg.get('lambda_anchor', 0.5), + use_gencomp=cfg.get('use_gencomp', False), + lambda_gen=cfg.get('lambda_gen', 0.5), + p_target=cfg.get('p_target', 0.0), + n_prop=cfg.get('n_prop', 0), + prop_tau=cfg.get('prop_tau', 2.0)) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), + flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + pa_struct = {'c3': [], 'c4': [], 'dbl': []} + if getattr(learner, 'probe_head', None) is not None: + with torch.no_grad(): + for n in unseen_names: + task = tasks[n] + m = learner._define(task, detach=True) + pred = learner.probe_head(m).view(4, 4).argmax(dim=-1) + ok = 1.0 if tuple(pred.tolist()) == task['perm'] else 0.0 + s = U_STRUCT[n] + if s in pa_struct: + pa_struct[s].append(ok) + aux['perm_acc_by_struct'] = { + k: (round(sum(v) / len(v), 3) if v else None) + for k, v in pa_struct.items()} + if getattr(learner, 'perm_head', None) is not None: + pa = learner.perm_acc + aux['perm_acc'] = { + 'seen': (round(sum(pa['seen']) / len(pa['seen']), 3) + if pa['seen'] else None), + 'unseen': (round(sum(pa['unseen']) / len(pa['unseen']), 3) + if pa['unseen'] else None), + 'n_seen': len(pa['seen']), 'n_unseen': len(pa['unseen'])} + if learner._aq is not None: + aux['aq_gen'] = len(learner._aq.gen_history) + if getattr(learner, 'prop_history', None): + ph = learner.prop_history + aux['prop_n'] = len(ph) + aux['prop_unique'] = len(set(ph)) + from collections import Counter + aux['prop_struct'] = dict(Counter(learner.prop_struct)) + # 提议去重率 + aux['prop_dup_rate'] = round( + (sum(len(ph) - len(set(ph)) for _ in [0]) / max(len(ph), 1)), 3) + try: + hs = learner.head.step_size_stats() + aux['head_beta'] = {k: (round(v, 6) if isinstance(v, float) + else v) for k, v in hs.items()} + except Exception: + pass + n_params = (sum(p.numel() for p in rln.parameters()) + + sum(p.numel() for p in pln.parameters())) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'factors': {'use_perm': cfg.get('use_perm', False), + 'n_prop': cfg.get('n_prop', 0), + 'p_target': cfg.get('p_target', 0.0), + 'aq_n': cfg.get('aq_n', 4)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((u" [%s] unseen=%.4f struct=%s" % (key, ua, struct_mean)), + flush=True) + return rec + + +BASE = dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0) + +CELLS18 = { + 'P0': dict(BASE, label='P0-ref'), + 'P1': dict(BASE, use_perm=True, lambda_perm=1.0, n_prop=2, + label='P1-proposer'), + 'P2': dict(BASE, use_perm=True, lambda_perm=1.0, n_prop=2, + p_target=0.5, label='P2-proposer+targeted'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='P0,P1,P2') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35.18 L3: 模型自主结构提议 (E-V 闭环内部化)") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS18[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell_p(key, cfg, s1, tasks, U_STRUCT, + meta_iters=args.meta_iters) + except Exception: + import traceback; traceback.print_exc() + print("!!! %s 失败" % key) + continue + res.append(r) + out = ROOT / 'results' / 'v35_18_proposer.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s" % (r['cell'], ua, + r['unseen_by_struct'])) + if 'prop_n' in r['aux']: + print(" proposer: n=%d unique=%d struct=%s dup=%.3f" % ( + r['aux']['prop_n'], r['aux']['prop_unique'], + r['aux']['prop_struct'], r['aux']['prop_dup_rate'])) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_19_value_proposer.py b/tests/run_v35_19_value_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..da37e3eb691f6befd8f5e519b6b642ce90e84cc9 --- /dev/null +++ b/tests/run_v35_19_value_proposer.py @@ -0,0 +1,362 @@ +"""V35.19 — 价值函数提议器 (简约性 + 自洽性) + redefine — 2026-08-15 用户理论 + +用户定义: + 简约性: 简约点 = 全局扫描后不可归约的原理 (真正的新原理) + 自洽性: 彼此不矛盾 (兼容已学原理) + 两个都是价值函数的选择方向 → V(p) = λ_sim·sim(p) + λ_con·con(p) + redefine: 矛盾候选不丢弃, 而是注入学习 (表示被迫重新定义边界) + loop 调整, 目的达成为止 + +实现: + sim(p) = 1 - max_{q∈learned} struct_sim(p,q) (cycle type + 字长相似) + con(p) = 1 - conflict(p) (p∘τ 产生已学类型外结构的比例) + redefine: con < τ 的候选仍注入 (redefine 通道, 统计计数) + +Cell: + V0 = P1 复现 (温度提议参照) + V1 = 价值函数提议 (top-k 按 V 评分) + V2 = V1 + redefine (矛盾候选注入) +""" +import os, sys, time, json, math, random +import itertools +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F + +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature, cycle_type, TRANS, IDENT, _apply +from hibs_lnn.meta_rule_world import gen_task_data_split +from run_v35_16a_ablation import PERMS24 +from run_v35_18_proposer import V35_Learner_P + + +def _compose(p, q): + """p∘q = p(q(x))""" + return tuple(p[q[x]] for x in range(len(p))) + + +def struct_sim(p, q): + """结构相似度: cycle type 相同 0.6 + 字长接近 0.4.""" + cp, cq = cycle_type(p), cycle_type(q) + gp = generator_signature(p)[1] + gq = generator_signature(q)[1] + s = 0.6 if cp == cq else 0.0 + s += 0.4 * max(0.0, 1 - abs(gp - gq) / 3.0) + return s + + +class V35_Learner_V(V35_Learner_P): + """+ 价值函数提议: V(p) = λ_sim·sim(p) + λ_con·con(p) + redefine.""" + + def __init__(self, rln, pln, V, use_compose=False, alpha=1.0, + use_atoms=True, use_fractal=False, use_hopfield=False, + adaptive_k=False, active_query=False, **kw): + self._lambda_sim = kw.pop('lambda_sim', 1.0) + self._lambda_con = kw.pop('lambda_con', 1.0) + self._lambda_cov = kw.pop('lambda_cov', 0.0) + self._redefine_thresh = kw.pop('redefine_thresh', 0.5) + self._use_redefine = kw.pop('use_redefine', False) + self._prop_k = kw.pop('prop_k', 2) # top-k 提议数 + self._prop_tau = kw.pop('prop_tau', 1.0) # 评分 softmax 温度 + super().__init__(rln, pln, V, use_compose=use_compose, alpha=alpha, + use_atoms=use_atoms, use_fractal=use_fractal, + use_hopfield=use_hopfield, adaptive_k=adaptive_k, + active_query=active_query, **kw) + self.learned_perms = [_apply(IDENT, g) for g in TRANS] # 初始: 6 对换 + self.learned_types = {cycle_type(p) for p in self.learned_perms} + self.value_scores = [] + self.redefine_count = 0 + from collections import Counter + self.prop_freq = Counter() + + def _sync_learned(self, tasks, train_names): + """已学原理 = 训练流 seen 任务的 perm (identity + 6 对换 + 5 c3).""" + perms = [tasks[n]['perm'] for n in train_names] + self.learned_perms = list(set(perms)) + self.learned_types = {cycle_type(p) for p in self.learned_perms} + + # ── 价值函数 ────────────────────────────────────────── + def _sim(self, p): + """简约性: 1 - 与已学结构的最大相似 (不可归约度).""" + if not self.learned_perms: + return 1.0 + return 1.0 - max(struct_sim(p, q) for q in self.learned_perms) + + def _con(self, p): + """自洽性: 1 - 矛盾度 (p∘τ 产生已学类型外结构的比例).""" + if not self.learned_perms: + return 1.0 + n_bad = 0 + n = 0 + for tau in self.learned_perms: + ct = cycle_type(_compose(p, tau)) + n += 1 + if ct not in self.learned_types: + n_bad += 1 + if n == 0: + return 1.0 + return 1.0 - n_bad / n + + def _value(self, p): + return self._lambda_sim * self._sim(p) + self._lambda_con * self._con(p) + + # ── 提议: 候选池评分 → top-k (探索: softmax 温度) ────── + def _propose(self, tasks, train_names): + if self.perm_head is None: + return [] + cands = [p for p in PERMS24 if p not in self.learned_perms] + if not cands: + return [] + scores = torch.tensor( + [self._value(p) + self._lambda_cov / (1 + self.prop_freq.get(p, 0)) + for p in cands]) + self.value_scores.append(scores.mean().item()) + probs = (scores / self._prop_tau).softmax(-1) + idxs = torch.multinomial(probs, min(self._prop_k, len(cands)), + replacement=False) + out = [] + rng = random.Random(self.seed + self._meta_iters) + for i in idxs.tolist(): + p = cands[i] + self.prop_history.append(p) # 记录 (允许重复) + self.prop_freq[p] += 1 + self.prop_struct.append(cycle_type(p)) + # redefine 检测: 矛盾候选 (con 低) 仍注入 + if self._use_redefine and self._con(p) < self._redefine_thresh: + self.redefine_count += 1 + for _try in range(5): + seed = rng.randint(0, 10 ** 6) + sup, qry = gen_task_data_split( + None, self.V, n_support=4, n_query=8, seed=seed, + remap=p, stop_at_halt=True) + if sup and qry: + break + if not sup or not qry: + continue # 空数据保护 (stop_at_halt 短 episode) + sig = generator_signature(p) + out.append({'perm': p, 'support': sup, 'query': qry, + 'gen_sig': sig[0] if sig else None}) + return out + + +def run_cell_v19(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(200, 4, s1_state) + use_compose = (lm == 'compose') + learner = V35_Learner_V( + rln, pln, 200, K=K, seed=seed, sleep_iters=2, n_resp=4, + use_intent=True, use_compose=use_compose, + use_atoms=(lm != 'base'), + use_fractal=cfg.get('fractal', False), + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + train_retrieval=cfg.get('train_retrieval', False), + aq_p3=cfg.get('aq_p3', 0.6), + aq_eval=cfg.get('aq_eval', False), + tau_norm=cfg.get('tau_norm', None), + beta_init=cfg.get('beta_init', None), + use_think=cfg.get('use_think', False), + n_think=cfg.get('n_think', 6), + think_tau=cfg.get('think_tau', 0.01), + lambda_think=cfg.get('lambda_think', 0.0), + lambda_combo=cfg.get('lambda_combo', 0.0), + use_perm=cfg.get('use_perm', False), + lambda_perm=cfg.get('lambda_perm', 0.5), + lambda_table=cfg.get('lambda_table', 0.0), + use_remap=cfg.get('use_remap', False), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0), + beta_train=cfg.get('beta_train', 3.0), + beta_test=cfg.get('beta_test', 10.0), + D=cfg.get('D', 1.5), + aq_n=cfg.get('aq_n', 4), + aq_max_combo=cfg.get('aq_max_combo', 3), + k_lam=cfg.get('k_lam', 0.005), + k_patience=cfg.get('k_patience', 3), + use_anchor=cfg.get('use_anchor', False), + lambda_anchor=cfg.get('lambda_anchor', 0.5), + use_gencomp=cfg.get('use_gencomp', False), + lambda_gen=cfg.get('lambda_gen', 0.5), + p_target=cfg.get('p_target', 0.0), + n_prop=cfg.get('n_prop', 0), + prop_tau=cfg.get('prop_tau', 2.0), + lambda_sim=cfg.get('lambda_sim', 1.0), + lambda_con=cfg.get('lambda_con', 1.0), + lambda_cov=cfg.get('lambda_cov', 0.0), + redefine_thresh=cfg.get('redefine_thresh', 0.5), + use_redefine=cfg.get('use_redefine', False), + prop_k=cfg.get('prop_k', 2)) + learner._sync_learned(tasks, train_names) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), + flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + if getattr(learner, 'perm_head', None) is not None: + pa = learner.perm_acc + aux['perm_acc'] = { + 'seen': (round(sum(pa['seen']) / len(pa['seen']), 3) + if pa['seen'] else None), + 'unseen': (round(sum(pa['unseen']) / len(pa['unseen']), 3) + if pa['unseen'] else None)} + if getattr(learner, 'prop_history', None): + from collections import Counter + ph = learner.prop_history + aux['prop_n'] = len(ph) + aux['prop_unique'] = len(set(ph)) + aux['prop_struct'] = dict(Counter(learner.prop_struct)) + freq = Counter(ph) + aux['prop_freq_top'] = [(p, c) for p, c in freq.most_common(5)] + aux['prop_dup_rate'] = round(1 - len(set(ph)) / max(len(ph), 1), 3) + # 价值-频率: top3 高价值结构的平均提议频率 vs 全体平均 + by_v = sorted(PERMS24, key=learner._value, reverse=True) + top3 = by_v[:3] + aux['prop_top3_value_avg_freq'] = round( + sum(freq.get(p, 0) for p in top3) / 3, 2) + aux['prop_top3_value_perms'] = [str(p) for p in top3] + aux['prop_freq_mean'] = round(sum(freq.values()) / len(freq), 2) + if getattr(learner, 'value_scores', None): + vs = learner.value_scores + aux['value_score_mean'] = round(sum(vs) / len(vs), 3) + if getattr(learner, 'redefine_count', 0): + aux['redefine_count'] = learner.redefine_count + n_params = (sum(p.numel() for p in rln.parameters()) + + sum(p.numel() for p in pln.parameters())) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'factors': {'lambda_sim': cfg.get('lambda_sim', 1.0), + 'lambda_con': cfg.get('lambda_con', 1.0), + 'lambda_cov': cfg.get('lambda_cov', 0.0), + 'use_redefine': cfg.get('use_redefine', False), + 'prop_k': cfg.get('prop_k', 2), + 'n_prop': cfg.get('n_prop', 0)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((u" [%s] unseen=%.4f struct=%s" % (key, ua, struct_mean)), + flush=True) + return rec + + +BASE = dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0) + +CELLS19 = { + 'V0': dict(BASE, use_perm=True, lambda_perm=1.0, n_prop=2, + prop_tau=1.0, lambda_sim=0.0, lambda_con=0.0, + label='V0-random-ref'), + 'V1': dict(BASE, use_perm=True, lambda_perm=1.0, n_prop=2, + prop_tau=1.0, lambda_sim=1.0, lambda_con=1.0, + label='V1-value-proposer'), + 'V2': dict(BASE, use_perm=True, lambda_perm=1.0, n_prop=2, + prop_tau=1.0, lambda_sim=1.0, lambda_con=1.0, + use_redefine=True, label='V2-value+redefine'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='V0,V1,V2') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + ap.add_argument('--out', default='v35_19_value_proposer.json') + ap.add_argument('--lambda-sim', type=float, default=None) + ap.add_argument('--lambda-con', type=float, default=None) + ap.add_argument('--lambda-cov', type=float, default=None) + ap.add_argument('--use-redefine', action='store_true') + ap.add_argument('--prop-k', type=int, default=None) + args = ap.parse_args() + print("=" * 70) + print("V35.19 价值函数提议器 (简约性+自洽性) + redefine") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = dict(CELLS19[key]) + if args.lambda_sim is not None: + cfg['lambda_sim'] = args.lambda_sim + if args.lambda_con is not None: + cfg['lambda_con'] = args.lambda_con + if args.lambda_cov is not None: + cfg['lambda_cov'] = args.lambda_cov + if args.use_redefine: + cfg['use_redefine'] = True + if args.prop_k is not None: + cfg['prop_k'] = args.prop_k + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell_v19(key, cfg, s1, tasks, U_STRUCT, + meta_iters=args.meta_iters) + except Exception: + import traceback; traceback.print_exc() + print("!!! %s 失败" % key) + continue + res.append(r) + out = ROOT / 'results' / args.out + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s value=%.3f redefine=%s" % ( + r['cell'], ua, r['unseen_by_struct'], + r['aux'].get('value_score_mean', -1), + r['aux'].get('redefine_count', 0))) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_20_value_transfer.py b/tests/run_v35_20_value_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..9e9baff235f6ccf1f8ff04f1259389e2e31829b1 --- /dev/null +++ b/tests/run_v35_20_value_transfer.py @@ -0,0 +1,186 @@ +"""V35.20 — S5 迁移测试: 价值函数训练的表示迁移质量 (2026-08-16) + +问题: S4 内不同训练配置的表示, 迁移到 S5 的质量是否不同? + B (base 无提议) vs C2 (外部定向采样) vs V1cov1 (价值函数自主提议) + +协议 (同进程, 复用 V35.17 L2 迁移链路): + 1. S4 训练 (每配置 40 iters) + 2. head 扩维 n_resp 4→5 (前块复制新块零) + 3. S5 零样本迁移评估 (纯表示路径, 按 cycle type 分桶) + 4. S5 微调 20 iters → 再评估 + +关键问题: 价值函数 (自主提议) 训练的表示迁移是否 ≥ C2 (外部定向)? +即"自主选择学习内容"是否产出更可迁移的表示。 +""" +import os, sys, time, json, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch + +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + build_model_resp, make_regs_tasks, build_u_struct, build_t_struct) +from run_v34_1_gen import generator_signature +from run_v35_16a_ablation import CELLS14 +from run_v35_17_s5_transfer import ( + make_s5_tasks, extend_head, s5_eval, struct_mean5, N_RESP, N_RESP5) +from run_v35_19_value_proposer import V35_Learner_V, CELLS19 + +V = 200 + + +def train_s4_cfg(cfg, learner_cls, s1_state, meta_iters=40, extra=None, + seed=42): + """S4 训练指定配置, 返回 (rln, pln, learner).""" + tasks = build_v32_tasks(V, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(V, N_RESP, s1_state) + lm = cfg.get('lm', 'compose') + use_compose = (lm == 'compose') + learner = learner_cls( + rln, pln, V, K=20, seed=seed, sleep_iters=2, n_resp=N_RESP, + use_intent=True, use_compose=use_compose, + use_atoms=(lm != 'base'), + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + train_retrieval=cfg.get('train_retrieval', False), + aq_p3=cfg.get('aq_p3', 0.6), + aq_eval=cfg.get('aq_eval', False), + tau_norm=cfg.get('tau_norm', None), + beta_init=cfg.get('beta_init', None), + use_think=cfg.get('use_think', False), + n_think=cfg.get('n_think', 6), + think_tau=cfg.get('think_tau', 0.01), + lambda_think=cfg.get('lambda_think', 0.0), + lambda_combo=cfg.get('lambda_combo', 0.0), + use_perm=cfg.get('use_perm', False), + lambda_perm=cfg.get('lambda_perm', 0.5), + lambda_table=cfg.get('lambda_table', 0.0), + use_remap=cfg.get('use_remap', False), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0), + beta_train=cfg.get('beta_train', 3.0), + beta_test=cfg.get('beta_test', 10.0), + D=cfg.get('D', 1.5), + aq_n=cfg.get('aq_n', 4), + aq_max_combo=cfg.get('aq_max_combo', 3), + k_lam=cfg.get('k_lam', 0.005), + k_patience=cfg.get('k_patience', 3), + use_anchor=cfg.get('use_anchor', False), + lambda_anchor=cfg.get('lambda_anchor', 0.5), + use_gencomp=cfg.get('use_gencomp', False), + lambda_gen=cfg.get('lambda_gen', 0.5), + p_target=cfg.get('p_target', 0.0), + n_prop=cfg.get('n_prop', 0), + prop_tau=cfg.get('prop_tau', 2.0), + lambda_sim=cfg.get('lambda_sim', 1.0), + lambda_con=cfg.get('lambda_con', 1.0), + lambda_cov=cfg.get('lambda_cov', 0.0), + redefine_thresh=cfg.get('redefine_thresh', 0.5), + use_redefine=cfg.get('use_redefine', False), + prop_k=cfg.get('prop_k', 2)) + if hasattr(learner, '_sync_learned'): + learner._sync_learned(tasks, train_names) + t0 = time.time() + for it in range(meta_iters): + learner.meta_iters = it + learner.meta_step(tasks, train_names) + print(" [S4 %s] 训练 %.0fs" % (cfg.get('label', '?'), + time.time() - t0), flush=True) + return rln, pln, learner + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--s4-iters', type=int, default=40) + ap.add_argument('--finetune-iters', type=int, default=20) + ap.add_argument('--no-stage1', action='store_true') + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--configs', default='B,C2,V1') + args = ap.parse_args() + print("=" * 70) + print("V35.20 — S5 迁移: 价值函数表示 vs C2 vs base") + print("=" * 70) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = {} + s5_tasks = make_s5_tasks(seed=42) + s5_unseen = [n for n in s5_tasks if not s5_tasks[n]['seen']] + + configs = { + # B: base 无机制 (V34.7 结构) + 'B': (dict(world='new', lm='base', label='B-base'), V35_Learner_V), + # C2: 外部定向采样 + 'C2': (dict(CELLS14['C2'], label='C2-targeted'), V35_Learner_V), + # V1cov1: 价值函数自主提议 (简约+自洽+覆盖) + 'V1': (dict(CELLS19['V1'], lambda_cov=1.0, label='V1-value+cov'), + V35_Learner_V), + } + keys = [k.strip() for k in args.configs.split(',')] + for key in keys: + cfg, cls = configs[key] + print("\n== S4 训练: %s (seed %d) ==" % (key, args.seed)) + rln, pln, learner = train_s4_cfg(cfg, cls, s1, + meta_iters=args.s4_iters, + seed=args.seed) + # head 扩维 → S5 零样本 + extend_head(pln, learner) + acc0 = s5_eval(learner, s5_tasks, s5_unseen) + res[key + '_zero'] = { + 'unseen': round(sum(acc0.values()) / len(acc0), 4), + 'by_struct': struct_mean5(acc0, s5_tasks)} + print(" %s zero-shot unseen=%.4f %s" % ( + key, res[key + '_zero']['unseen'], + res[key + '_zero']['by_struct'])) + # S5 微调 (纯 oml_step 协议, 同 V35.17) + from run_v35_17_s5_transfer import oml_step as _oml + s5_seen = [n for n in s5_tasks if s5_tasks[n]['seen']] + t0 = time.time() + for it in range(args.finetune_iters): + learner.meta_iters = 1000 + it + _oml(learner, s5_tasks, s5_seen, 1000 + it) + print(" 微调 %.0fs" % (time.time() - t0), flush=True) + acc1 = s5_eval(learner, s5_tasks, s5_unseen) + res[key + '_ft'] = { + 'unseen': round(sum(acc1.values()) / len(acc1), 4), + 'by_struct': struct_mean5(acc1, s5_tasks)} + print(" %s finetuned unseen=%.4f %s" % ( + key, res[key + '_ft']['unseen'], res[key + '_ft']['by_struct'])) + + print("\n" + "=" * 70) + print("S5 迁移汇总 (同进程):") + for k in ['%s_zero' % kk for kk in keys] + ['%s_ft' % kk for kk in keys]: + r = res[k] + print(" %-10s unseen=%.4f %s" % (k, r['unseen'], r['by_struct'])) + out = ROOT / 'results' / ('v35_20_value_transfer_s%d.json' % args.seed) + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("JSON:", out) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_21_s5_value.py b/tests/run_v35_21_s5_value.py new file mode 100644 index 0000000000000000000000000000000000000000..f1001d4d991744b6db3d674b2794b103bb6b4306 --- /dev/null +++ b/tests/run_v35_21_s5_value.py @@ -0,0 +1,274 @@ +"""V35.21 — S5 内价值函数直接训练 (120 候选池) + 多 seed 确认 (2026-08-16) + +Part A: 多 seed 确认 V1 迁移优势 → 调 run_v35_20_value_transfer.py + --configs V1 --seed 7/123 + +Part B: S5 世界内直接用价值函数 (简约+自洽+覆盖) 训练 + 候选池 = 120 perm - 13 seen = 107 → "选择"真正有意义 + 对比: S5-scratch (base oml_step) vs S5-value (价值提议注入), 同进程 + +关键: 价值函数是纯代数 (与模型无关), S5 版用 cycle_type5 + 10 对换。 +训练绕过 V33.meta_step (probe view(4,4) 写死), 用 oml_step 协议。 +""" +import os, sys, time, json, math, random +import itertools +from collections import deque, Counter +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn.functional as F + +from run_v31_meta_learning import batch_pairs, head_forward +from run_v32_1_matrix import build_model_resp +from run_v35_16a_ablation import V35_Learner_A +from run_v35_17_s5_transfer import ( + make_s5_tasks, s5_eval, struct_mean5, TRANS5, PERMS5, cycle_type5, + N_RESP5, V as _V) +from run_v35_20_value_transfer import train_s4_cfg + +V = 200 + +# ============================================================ +# S5 字长 (BFS 最短对换分解, 预计算) +# ============================================================ +IDENT5 = (0, 1, 2, 3, 4) +_S5_WORDLEN = {} + + +def _apply5(p, g): + q = list(p) + a, b = g + q[a], q[b] = q[b], q[a] + return tuple(q) + + +def s5_wordlen(p): + if p in _S5_WORDLEN: + return _S5_WORDLEN[p] + q = deque([(IDENT5, 0)]) + seen = {IDENT5} + while q: + cur, d = q.popleft() + if cur == p: + _S5_WORDLEN[p] = d + return d + for g in TRANS5: + nxt = _apply5(cur, g) + if nxt not in seen: + seen.add(nxt) + q.append((nxt, d + 1)) + return 99 + + +def struct_sim5(p, q): + cp, cq = cycle_type5(p), cycle_type5(q) + gp, gq = s5_wordlen(p), s5_wordlen(q) + s = 0.6 if cp == cq else 0.0 + s += 0.4 * max(0.0, 1 - abs(gp - gq) / 5.0) + return s + + +def _compose5(p, q): + return tuple(p[q[x]] for x in range(5)) + + +# ============================================================ +# S5 价值函数提议器 (纯代数) +# ============================================================ +class S5ValueProposer: + def __init__(self, learned_perms, lambda_sim=1.0, lambda_con=1.0, + lambda_cov=1.0, tau=1.0, k=2): + self.learned = list(learned_perms) + self.learned_types = {cycle_type5(p) for p in self.learned} + self.freq = Counter() + self.ls, self.lc, self.lcov = lambda_sim, lambda_con, lambda_cov + self.tau, self.k = tau, k + self.cands = [p for p in PERMS5 if p not in self.learned] + + def sim(self, p): + return 1.0 - max(struct_sim5(p, q) for q in self.learned) + + def con(self, p): + n_bad = n = 0 + for tau in self.learned: + ct = cycle_type5(_compose5(p, tau)) + n += 1 + if ct not in self.learned_types: + n_bad += 1 + return 1.0 - n_bad / max(n, 1) + + def value(self, p): + return (self.ls * self.sim(p) + self.lc * self.con(p) + + self.lcov / (1 + self.freq.get(p, 0))) + + def propose(self, n=None): + n = n or self.k + scores = torch.tensor([self.value(p) for p in self.cands]) + probs = (scores / self.tau).softmax(-1) + idxs = torch.multinomial(probs, min(n, len(self.cands)), + replacement=False) + out = [] + for i in idxs.tolist(): + p = self.cands[i] + self.freq[p] += 1 + out.append(p) + return out + + +# ============================================================ +# Part B: S5 内训练 (oml_step + 提议注入) +# ============================================================ +def oml_step(learner, tasks, names, it, n_tasks=4, extra=None): + rng = random.Random(learner.seed + it) + chosen = rng.sample(names, min(n_tasks, len(names))) + if extra: + chosen = chosen + extra + loss = 0.0 + for rname in chosen: + t = tasks[rname] + m = learner._define(t, detach=False) + W = learner.adapt_graph(t['support'], K=learner.K, m=m) + ctx, tg = batch_pairs(t['support'], max_batch=16) + h = learner._rln_fwd(ctx, m)[:, -1, :] + hin = learner._h_in_batch(h, m) + pred = head_forward([w for w in W], hin, learner.intent_dim, + learner.world_vocab, learner.n_resp) + loss = loss + F.cross_entropy( + pred.view(-1, learner.world_vocab), + tg.view(-1).clamp(0, learner.V + 1)) / len(chosen) + learner.outer_opt.zero_grad() + loss.backward() + learner.outer_opt.step() + return loss.item() + + +def make_s5_value_task(perm, seed, n_sup=8, n_qry=8): + from hibs_lnn.meta_rule_world import gen_task_data_split + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=seed, + remap=perm, stop_at_halt=True) + return {'support': [(c, tg[:N_RESP5]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP5]) for (c, tg) in qry], + 'perm': perm, 'seen': False, 'gen_sig': None} + + +def train_s5(mode, s1_state, meta_iters=40, seed=42, n_prop=2): + """S5 训练: mode='scratch' (纯 oml_step) 或 'value' (提议注入).""" + tasks = make_s5_tasks(seed=42) + seen = [n for n in tasks if tasks[n]['seen']] + unseen = [n for n in tasks if not tasks[n]['seen']] + random.seed(seed); torch.manual_seed(seed) + rln, pln = build_model_resp(V, N_RESP5, s1_state) + learner = V35_Learner_A( + rln, pln, V, K=20, seed=seed, sleep_iters=2, n_resp=N_RESP5, + use_intent=True, use_compose=False, use_atoms=False) + proposer = (S5ValueProposer([tasks[n]['perm'] for n in seen]) + if mode == 'value' else None) + t0 = time.time() + for it in range(meta_iters): + learner.meta_iters = it + extra = None + if proposer is not None: + ps = proposer.propose(n=n_prop) + extra = [] + for i, p in enumerate(ps): + key = '__v%d' % i + tasks[key] = make_s5_value_task(p, 10000 + it * 10 + i) + extra.append(key) + oml_step(learner, tasks, seen, it, extra=extra) + if proposer is not None: + for i in range(n_prop): + tasks.pop('__v%d' % i, None) + print(" [S5-%s seed%d] 训练 %.0fs" % (mode, seed, time.time() - t0), + flush=True) + accs = s5_eval(learner, tasks, unseen) + return accs, tasks, proposer + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--part', default='B', choices=['A', 'B']) + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--seeds', default='7,123') + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35.21 — S5 内价值函数直接训练 + 多 seed 确认") + print("=" * 70) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = {} + + if args.part == 'A': + # 多 seed 确认 V1 迁移 (V35.20 协议) + for sd in [int(x) for x in args.seeds.split(',')]: + print("\n== Part A: V1 seed %d ==" % sd) + cfg = dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, + n_think=6, lambda_icl=0.5, lambda_combo=0.5, + active_query=True, aq_n=6, aq_p3=0.7, + lambda_probe=1.0, use_perm=True, lambda_perm=1.0, + n_prop=2, prop_tau=1.0, lambda_sim=1.0, + lambda_con=1.0, lambda_cov=1.0, + label='V1-value+cov') + from run_v35_19_value_proposer import V35_Learner_V + from run_v35_17_s5_transfer import ( + make_s5_tasks as m5, extend_head) + rln, pln, learner = train_s4_cfg(cfg, V35_Learner_V, s1, + meta_iters=args.meta_iters, + seed=sd) + extend_head(pln, learner) + t5 = m5(seed=42) + u5 = [n for n in t5 if not t5[n]['seen']] + acc0 = s5_eval(learner, t5, u5) + res['V1_s%d_zero' % sd] = { + 'unseen': round(sum(acc0.values()) / len(acc0), 4), + 'by_struct': struct_mean5(acc0, t5)} + print(" zero unseen=%.4f %s" % ( + res['V1_s%d_zero' % sd]['unseen'], + res['V1_s%d_zero' % sd]['by_struct'])) + out = ROOT / 'results' / 'v35_21_multiseed.json' + else: + # Part B: S5 内 scratch vs value (同进程) + print("\n== Part B: S5-scratch (seed %d) ==" % args.seed) + acc_s, tasks_s, _ = train_s5('scratch', s1, args.meta_iters, + seed=args.seed) + res['s5_scratch'] = { + 'unseen': round(sum(acc_s.values()) / len(acc_s), 4), + 'by_struct': struct_mean5(acc_s, tasks_s)} + print(" scratch unseen=%.4f %s" % ( + res['s5_scratch']['unseen'], res['s5_scratch']['by_struct'])) + print("\n== Part B: S5-value (价值函数提议, seed %d) ==" % args.seed) + acc_v, tasks_v, prop = train_s5('value', s1, args.meta_iters, + seed=args.seed) + res['s5_value'] = { + 'unseen': round(sum(acc_v.values()) / len(acc_v), 4), + 'by_struct': struct_mean5(acc_v, tasks_v)} + res['value_stats'] = { + 'prop_n': sum(prop.freq.values()), + 'freq_top': prop.freq.most_common(5), + 'cands': len(prop.cands)} + print(" value unseen=%.4f %s" % ( + res['s5_value']['unseen'], res['s5_value']['by_struct'])) + print(" proposer: %s" % res['value_stats']) + out = ROOT / 'results' / ('v35_21_s5_value_s%d.json' % args.seed) + + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("\nJSON:", out) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_22_s6.py b/tests/run_v35_22_s6.py new file mode 100644 index 0000000000000000000000000000000000000000..bb6dbb0c46b596cd2f8f84511eac70369f70a7e4 --- /dev/null +++ b/tests/run_v35_22_s6.py @@ -0,0 +1,252 @@ +"""V35.22 — S6 递推: 群外推一般性验证 (2026-08-16) + +问题: 价值函数训练的表示能否继续外推到 S6 (6 元素/15 对换/720 perm)? +递推链: S4→S5 已验证 (V35.20/21), 现在 S5-value → S6。 + +协议 (同进程): + 1. S5-value 训练 (价值函数提议, 40 iters, 5 寄存器世界) + 2. patch 世界到 6 寄存器 → head 扩维 5→6 (前块复制新块零) + 3. S6 零样本迁移评估 (纯表示路径, 按 cycle type 分桶) + 4. 对照: S6-scratch (从零 oml_step 40 iters, 同进程) + +通用 cycle_type_n: 返回 (lens...) 精确循环类型, 分桶 c3/c4/c5/c6/dbl。 +""" +import os, sys, time, json, math, random +import itertools +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch + +from run_v31_meta_learning import batch_pairs, head_forward +from run_v32_1_matrix import build_model_resp +from run_v35_16a_ablation import V35_Learner_A +from run_v35_17_s5_transfer import ( + make_s5_tasks, s5_eval, N_RESP5) +from run_v35_21_s5_value import train_s5, oml_step, S5ValueProposer +import hibs_lnn.code_world as cw +import hibs_lnn.meta_rule_world as mrw +from hibs_lnn.meta_rule_world import gen_task_data_split + +V = 200 +N_RESP6 = 6 + +# ============================================================ +# S6 代数 +# ============================================================ +TRANS6 = [(i, j) for i in range(6) for j in range(i + 1, 6)] +IDENT6 = tuple(range(6)) +PERMS6 = list(itertools.permutations(range(6))) + + +def cycle_type_n(perm): + n = len(perm) + seen = set() + lens = [] + for i in range(n): + if i in seen: + continue + c = [] + j = i + while j not in seen: + seen.add(j) + c.append(j) + j = perm[j] + if len(c) > 1: + lens.append(len(c)) + lens.sort() + return tuple(lens) + + +def struct_key6(ct): + """循环类型 → 分桶键.""" + if not ct: + return 'id' + m = max(ct) + if m == 6: + return 'c6' + if m == 5: + return 'c5' + if m == 4: + return 'c4' + if m == 3: + return 'c3' + return 'dbl' # (2,2,2) / (2,2) 等 + + +def make_s6_tasks(n_sup=16, n_qry=8, seed=42): + g = {} + for p in PERMS6: + k = struct_key6(cycle_type_n(p)) + g.setdefault(k, []).append(p) + rng = random.Random(seed) + seen_perms = ([IDENT6] + rng.sample(g['dbl'][:10], 6) # 6 对换作原子 + + rng.sample(g['c3'], 4)) + # 去重 + seen_perms = list(dict.fromkeys(seen_perms)) + unseen_pool = [p for p in PERMS6 if p not in seen_perms + and p != IDENT6] + rng.shuffle(unseen_pool) + c3s = [p for p in unseen_pool if struct_key6(cycle_type_n(p)) == 'c3'][:4] + c4s = [p for p in unseen_pool if struct_key6(cycle_type_n(p)) == 'c4'][:6] + c5s = [p for p in unseen_pool if struct_key6(cycle_type_n(p)) == 'c5'][:6] + c6s = [p for p in unseen_pool if struct_key6(cycle_type_n(p)) == 'c6'][:6] + dbls = [p for p in unseen_pool if struct_key6(cycle_type_n(p)) == 'dbl'][:4] + unseen_perms = c3s + c4s + c5s + c6s + dbls + tasks = {} + for i, p in enumerate(seen_perms): + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=seed + i, + remap=p, stop_at_halt=True) + tasks['S6T%d' % i] = { + 'support': [(c, tg[:N_RESP6]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP6]) for (c, tg) in qry], + 'perm': p, 'seen': True} + for i, p in enumerate(unseen_perms): + sup, qry = gen_task_data_split( + None, V, n_support=n_sup, n_query=n_qry, seed=2000 + i, + remap=p, stop_at_halt=True) + tasks['S6U%d' % i] = { + 'support': [(c, tg[:N_RESP6]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP6]) for (c, tg) in qry], + 'perm': p, 'seen': False} + return tasks + + +def extend_head_n(pln, learner, old_n, new_n): + old = pln.predictor[2] + new = torch.nn.Linear(old.in_features, (V + 2) * new_n) + with torch.no_grad(): + new.weight[:(V + 2) * old_n].copy_(old.weight) + new.bias[:(V + 2) * old_n].copy_(old.bias) + new.weight[(V + 2) * old_n:].zero_() + new.bias[(V + 2) * old_n:].zero_() + pln.predictor[2] = new + pln.n_response = new_n + pln.step_beta = torch.nn.Parameter( + torch.full((pln._n_params(),), + math.log(pln.step_beta.exp().mean().item()))) + from hibs_lnn.swifttd_head import SwiftTDHead + hd = learner.head + learner.head = SwiftTDHead( + hd.d_model, V + 2, new_n, beta_init=hd.beta_init, + kappa=hd.kappa, eta=hd.eta, eps=hd.eps, beta_min=hd.beta_min, + input_dim=learner.intent_dim, tau_norm=hd.tau_norm) + learner.head.reset_state() + learner.n_resp = new_n + return pln, learner + + +def struct_mean6(accs, tasks): + by = {'c3': [], 'c4': [], 'c5': [], 'c6': [], 'dbl': []} + for n, a in accs.items(): + k = struct_key6(cycle_type_n(tasks[n]['perm'])) + if k in by: + by[k].append(a) + return {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35.22 — S6 递推: 群外推一般性 (S5-value → S6)") + print("=" * 70) + s1 = None + if not args.no_stage1: + for cand in ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand) + break + else: + print(">>> Stage1 缺失") + return + res = {} + + # 1) S5-value 训练 (5 寄存器世界, 直接实现以拿到 learner) + print("\n== S5-value 训练 ==") + random.seed(42); torch.manual_seed(42) + tasks = make_s5_tasks(seed=42) + seen5 = [n for n in tasks if tasks[n]['seen']] + unseen5 = [n for n in tasks if not tasks[n]['seen']] + rln, pln = build_model_resp(V, N_RESP5, s1) + learner = V35_Learner_A( + rln, pln, V, K=20, seed=42, sleep_iters=2, n_resp=N_RESP5, + use_intent=True, use_compose=False, use_atoms=False) + proposer = S5ValueProposer([tasks[n]['perm'] for n in seen5]) + t0 = time.time() + for it in range(args.meta_iters): + learner.meta_iters = it + extra = [] + ps = proposer.propose(n=2) + for i, p in enumerate(ps): + key = '__v%d' % i + sup, qry = gen_task_data_split( + None, V, n_support=8, n_query=8, seed=10000 + it * 10 + i, + remap=p, stop_at_halt=True) + tasks[key] = { + 'support': [(c, tg[:N_RESP5]) for (c, tg) in sup], + 'query': [(c, tg[:N_RESP5]) for (c, tg) in qry], + 'perm': p, 'seen': False, 'gen_sig': None} + extra.append(key) + oml_step(learner, tasks, seen5, it, extra=extra) + for i in range(2): + tasks.pop('__v%d' % i, None) + print(" S5-value 训练 %.0fs" % (time.time() - t0), flush=True) + + # 2) patch S6 世界 + print("== patch S6 (6 寄存器) ==") + cw.N_REGISTERS = 6 + mrw.N_REGISTERS = 6 + mrw.MetaRuleWorld.READ_WINDOW = 7 + + # 3) head 扩维 5→6 + S6 零样本 + print("== head 扩维 5→6 + S6 零样本 ==") + extend_head_n(pln, learner, N_RESP5, N_RESP6) + s6_tasks = make_s6_tasks(seed=42) + s6_unseen = [n for n in s6_tasks if not s6_tasks[n]['seen']] + acc0 = s5_eval(learner, s6_tasks, s6_unseen) + res['s5v_s6_zero'] = { + 'unseen': round(sum(acc0.values()) / len(acc0), 4), + 'by_struct': struct_mean6(acc0, s6_tasks)} + print(" S5-value→S6 zero unseen=%.4f %s" % ( + res['s5v_s6_zero']['unseen'], res['s5v_s6_zero']['by_struct'])) + + # 4) S6 scratch 对照 (同进程, 6 寄存器世界) + print("== S6-scratch 对照 ==") + random.seed(42); torch.manual_seed(42) + rln6, pln6 = build_model_resp(V, N_RESP6, s1) + l6 = V35_Learner_A( + rln6, pln6, V, K=20, seed=42, sleep_iters=2, n_resp=N_RESP6, + use_intent=True, use_compose=False, use_atoms=False) + s6_seen = [n for n in s6_tasks if s6_tasks[n]['seen']] + t0 = time.time() + for it in range(args.meta_iters): + l6.meta_iters = it + oml_step(l6, s6_tasks, s6_seen, it) + print(" S6-scratch 训练 %.0fs" % (time.time() - t0), flush=True) + acc1 = s5_eval(l6, s6_tasks, s6_unseen) + res['s6_scratch'] = { + 'unseen': round(sum(acc1.values()) / len(acc1), 4), + 'by_struct': struct_mean6(acc1, s6_tasks)} + print(" S6-scratch unseen=%.4f %s" % ( + res['s6_scratch']['unseen'], res['s6_scratch']['by_struct'])) + + print("\n" + "=" * 70) + print("S6 递推汇总:") + for k in ['s5v_s6_zero', 's6_scratch']: + r = res[k] + print(" %-14s unseen=%.4f %s" % (k, r['unseen'], r['by_struct'])) + out = ROOT / 'results' / 'v35_22_s6.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("JSON:", out) + + +if __name__ == '__main__': + main() diff --git a/tests/run_v35_4_fractal4.py b/tests/run_v35_4_fractal4.py new file mode 100644 index 0000000000000000000000000000000000000000..ff84583d845a7fed8e4eff678ac7e806e46a8d80 --- /dev/null +++ b/tests/run_v35_4_fractal4.py @@ -0,0 +1,155 @@ +"""V35.4: FractalRLN4 — 4 层分形 RLN + 端到端分形 PLN. + +按 lm-principle T1/T2 (prefix_allocation_optimal / connection_density_strict_anti): + 连接密度 (1-d)^(D-1) 幂律递减 → 浅层密集深层稀疏. + 层宽: d_i = max(8, d0 · (1 - i/(n-1))^(D-1)), D=1.5, d0=128 + 层 0: 128, 层 1: 104, 层 2: 74, 层 3: 42 (2 层时 128/90) + 层间 Linear 投影 (d_i → d_{i+1}). + 残差门控 (T3/T5): y' = y + gate(y), 零初始化. + +PLN 分形: 隐藏宽 = RLN 顶层宽度 (42), 输入 3×42 (意图注入 h;m;h⊙m). + → 整条管线参数集中前缀 (embed + 浅层宽, 深层 + PLN 窄). + +Stage1 结构变化 → 必须重训 (FractalRLN4 + LM head 在 token 流上). +""" +import os, sys, time, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from hibs_lnn.ssm_v30_3 import SSM_Layer_V30_3 +from tests.run_v31_2_long_stream import build_v32_tasks +from tests.run_v31_meta_learning import make_world_token_stream + + +def fractal_widths(d0, n, D=1.5, min_w=16, mode='geo'): + """层宽幂律/等比递减 (分形: 浅层密集深层稀疏). + + mode='geo' (默认, 匹配 v35-plan §1 的 256/181/128/90): + 等比 ratio = 2^(-1/3) ≈ 0.794 (Murray 定律 T15 的 0.79) + → d_i = max(min_w, round(d0 · ratio^i)) + mode='power': d_i = max(min_w, d0·(1 - i/(n-1))^(D-1)) + """ + if mode == 'geo': + ratio = 2 ** (-1 / 3) + return [max(min_w, int(round(d0 * ratio ** i))) + for i in range(n)] + ws = [] + for i in range(n): + depth = i / max(n - 1, 1) + ws.append(max(min_w, int(d0 * (1 - depth) ** (D - 1)))) + return ws + + +class FractalRLN4(nn.Module): + """4 层分形 RLN: 层宽幂律递减 + 层间投影 + 残差门控 (零初始化).""" + + def __init__(self, vocab_size, d0=128, n_layers=4, d_state=8, D=1.5, + use_gate=True): + super().__init__() + widths = fractal_widths(d0, n_layers, D=D) + self.widths = widths + self.d_model = widths[-1] # 顶层宽度 = PLN 输入维 (V32 兼容) + self.d0 = d0 + self.vocab_size = vocab_size + self.n_layers = n_layers + # embed 输出 = 顶层宽 (V32 契约: embed 输出宽度 == d_model), + # 内部先宽后窄 (分形): Embedding(d0) → 投影到 d_model → 升维到 layer0 + self.embed = nn.Sequential( + nn.Embedding(vocab_size, widths[0]), + nn.Linear(widths[0], widths[-1])) + self.up = nn.Linear(widths[-1], widths[0]) + self.layers = nn.ModuleList() + self.projs = nn.ModuleList() + self.gates = nn.ModuleList() + for i in range(n_layers): + self.layers.append(SSM_Layer_V30_3( + widths[i], d_state=d_state, layer_idx=i, ent_mode='none')) + if i < n_layers - 1: + self.projs.append(nn.Linear(widths[i], widths[i + 1])) + if use_gate: + g = nn.Sequential( + nn.Linear(widths[i], max(8, widths[i] // 2)), + nn.Tanh(), + nn.Linear(max(8, widths[i] // 2), widths[i])) + with torch.no_grad(): + g[2].weight.zero_(); g[2].bias.zero_() + self.gates.append(g) + self.use_gate = use_gate + + def forward(self, ids, cond=None): + x = self.up(self.embed(ids)) # (B, L, widths[0]) + for i, layer in enumerate(self.layers): + y, _ = layer(x, cond=cond) + if self.use_gate: + y = y + self.gates[i](y) + x = y + if i < len(self.projs): + x = self.projs[i](x) + return x # (B, L, widths[-1]) + + +class FractalRLN4_LM(nn.Module): + """Stage1 用: FractalRLN4 + LM head.""" + + def __init__(self, vocab_size, d0=128, n_layers=4, d_state=8, D=1.5, + use_gate=True): + super().__init__() + self.rln = FractalRLN4(vocab_size, d0=d0, n_layers=n_layers, + d_state=d_state, D=D, use_gate=use_gate) + top = self.rln.widths[-1] + self.head = nn.Linear(top, vocab_size) + + def forward(self, ids): + h = self.rln(ids) + return self.head(h), 0.0 # (logits, kl) + + +def stage1_pretrain_fractal4(tasks, train_rules, V, n_epochs=2, L=64, + seed=42, d0=128, n_layers=4, D=1.5): + """在 token 流上预训练 FractalRLN4 (分形结构).""" + random.seed(seed); torch.manual_seed(seed) + ids = make_world_token_stream(tasks, train_rules, V) + model = FractalRLN4_LM(V + 2, d0=d0, n_layers=n_layers, D=D) + opt = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.05) + n_seq = max(1, (len(ids) - 1) // (L + 1)) + t0 = time.time() + for ep in range(n_epochs): + epoch_nll = 0.0 + for i in range(n_seq): + inp = ids[i * (L + 1):i * (L + 1) + L].unsqueeze(0) + tgt = ids[i * (L + 1) + 1:i * (L + 1) + L + 1].unsqueeze(0) + logits, _ = model(inp) + nll = F.cross_entropy(logits.reshape(-1, V + 2), tgt.reshape(-1)) + opt.zero_grad(); nll.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step() + epoch_nll += nll.item() + avg = epoch_nll / max(n_seq, 1) + print(f" Stage1F4 Ep{ep+1}/{n_epochs}: nll={avg:.4f} " + f"({time.time()-t0:.0f}s)", flush=True) + model.eval() + losses = [] + with torch.no_grad(): + for i in range(min(n_seq, 50)): + inp = ids[i * (L + 1):i * (L + 1) + L].unsqueeze(0) + tgt = ids[i * (L + 1) + 1:i * (L + 1) + L + 1].unsqueeze(0) + logits, _ = model(inp) + losses.append(F.cross_entropy( + logits.reshape(-1, V + 2), tgt.reshape(-1)).item()) + ppl = math.exp(sum(losses) / max(len(losses), 1)) + print(f" Stage1F4 Eval PPL: {ppl:.2f}", flush=True) + model.train() + return ppl, model + + +if __name__ == "__main__": + V = 200 + tasks = build_v32_tasks(V, n_support=40, n_query=20, seed=42) + train_rules = [n for n in tasks if tasks[n]["seen"]] + ppl, m = stage1_pretrain_fractal4(tasks, train_rules, V, n_epochs=2, + d0=128, n_layers=4, D=1.5) + torch.save({k: v.cpu().clone() for k, v in m.state_dict().items()}, + "/tmp/v32_s1_f4_state.pt") + print(f"cached /tmp/v32_s1_f4_state.pt (widths={m.rln.widths}, ppl={ppl:.2f})") diff --git a/tests/run_v35_fractal.py b/tests/run_v35_fractal.py new file mode 100644 index 0000000000000000000000000000000000000000..136698fb1299c1b492aab81ff6a4d6cb2c42e7d1 --- /dev/null +++ b/tests/run_v35_fractal.py @@ -0,0 +1,1152 @@ +"""V35 分形持续学习引擎 (Fractal Continual Learning Engine) + +按 lm-principle 83 条机器验证定理重设计 (docs/wiki/v35-plan.md v2)。 + +科研结论 → 架构决策: + T1/T2 (Fractal.lean prefix_allocation_optimal / connection_density_strict_anti) + 分形预算: 预算集中前缀, 浅层密集深层稀疏 (1-d)^(D-1) + T3/T5 (ArchCompare.lean residual_no_collapse_n / lstm_memory_retention) + 残差门控: h' = h + gate(h) — 防坍缩 (距离 ≥ (1-c)^n) + 门控记忆 (α→1) + T6-T9 (Hopfield.lean) 原子检索 = 能量下降: x_new = X·Softmax(β·Xᵀx) + β 分岔相变 = 探索/利用旋钮 (训练 β≈3 混合 / 部署 β≥10 锁定) + T10/T11 (CriticalPoint.lean) 自适应 K: 边际收益 Δ_k < λ ⟹ 提前终止 + T9 + v1 主动查询: 原子池采样组合 → 反事实输入 → 世界执行器真值 → 喂回 + +Cell (预注册, 同进程, 共享 Stage1): + B : V34.7 base (对照) + F : + FractalRLN (分形预算 + 残差门控) + FH : + HopfieldBank (能量检索替代滚动平均) + FHA : + 自适应 K (临界点终止) + FHAQ: + ActiveQueryGenerator (组合需求注入) +""" +import os, sys, time, json, math, random +from pathlib import Path +ROOT = Path(__file__).resolve().parent.parent +sys.path.insert(0, str(ROOT)) +import torch +import torch.nn as nn +import torch.nn.functional as F +from run_v31_meta_learning import batch_pairs, head_forward, V31_RLN, V31_PLN +from run_v31_2_long_stream import build_v32_tasks +from run_v32_1_matrix import ( + V32_1_Learner, build_model_resp, make_regs_tasks, build_u_struct, + build_t_struct, tome_fraction) +from run_v33_drl import V33_Learner, V33_3_Learner +from run_v34_1_gen import generator_signature, cycle_type, TRANS, IDENT, _apply +from run_v34_3_cpg import AtomRouteBank, N_GEN +from run_v34_7_compose import ComposeModule, V34_7_Learner +from hibs_lnn.swifttd_head import SwiftTDHead +from hibs_lnn.meta_rule_world import gen_task_data_split + +_gen_cache = {} + + +# ============================================================ +# V35.4: FractalRLN4 构建 (4 层分形 RLN + 可选分形 PLN) +# ============================================================ +def build_fractal4_resp(V, n_response, s1_state=None, fractal_pln=False): + """FractalRLN4 (等比宽度 128/102/81/64) + PLN. + + fractal_pln=False: PLN 隐藏宽 = RLN 顶层宽 (64), 输入 3×64 (意图注入). + fractal_pln=True: PLN 隐藏宽独立 = 96 (等比中间值), 3×64=192 → 96 → 808 + — PLN 自己的分形瓶颈 (输入宽, 隐藏窄), 由 V35_Learner 重建。 + s1_state 来自 run_v35_4_fractal4.py 的 FractalRLN4_LM (键带 rln. 前缀). + """ + from run_v35_4_fractal4 import FractalRLN4 + rln = FractalRLN4(V + 2, d0=128, n_layers=4, D=1.5, use_gate=True) + if s1_state is not None: + own = rln.state_dict() + stripped = {} + for k, v in s1_state.items(): + kk = k + if kk.startswith('rln.'): + kk = kk[4:] # FractalRLN4_LM 的 rln.rln.layers → rln.layers + if kk.startswith('rln.'): + kk = kk[4:] + if kk in own and own[kk].shape == v.shape: + stripped[kk] = v + own.update(stripped) + try: + rln.load_state_dict(own) + except Exception as e: + print("F4 Stage1 加载部分失败(忽略):", e) + top = rln.widths[-1] + # 注意: V32_Learner 意图注入假设 pln.d_model == rln.d_model (V32 契约), + # 故 build 时总是用 top; 分形 PLN 的独立隐藏宽 (96) 由 V35_Learner + # 在 super().__init__ 之后重建 (predictor + step_beta + head). + pln = V31_PLN(top, V + 2, n_response=n_response) + pln.fractal_pln = fractal_pln + pln.fractal_pln_h = 96 if fractal_pln else None + return rln, pln + + +# ============================================================ +# T1/T2/T3/T5: FractalRLN — 分形预算 + 残差门控 +# ============================================================ +class FractalRLN(nn.Module): + """包装 V31_RLN (Stage1 可加载), 每层后加门控残差. + + 分形预算: 第 i 层门控投影宽度 = max(8, d·(1-depth)^(D-1)) + 浅层满宽 (预算集中前缀), 深层收窄 (连接密度幂律 T2). + 残差门控: y' = y + gate_i(y), gate 零初始化 → 加载 Stage1 后初始 ≡ 原网络 + (防坍缩 T3 + 门控记忆 T5: 门控可学, 保留/丢弃由梯度决定). + """ + + def __init__(self, base_rln, D=1.5): + super().__init__() + self.rln = base_rln + self.embed = base_rln.embed + self.d_model = base_rln.d_model + self.vocab_size = base_rln.vocab_size + n = len(base_rln.layers) + d = self.d_model + self.gates = nn.ModuleList() + for i in range(n): + depth = i / max(n - 1, 1) + width = max(8, int(d * (1 - depth) ** (D - 1))) + g = nn.Sequential( + nn.Linear(d, width), nn.Tanh(), nn.Linear(width, d)) + with torch.no_grad(): + g[2].weight.zero_(); g[2].bias.zero_() + self.gates.append(g) + self.n_layers = n + + def forward(self, ids, cond=None): + x = self.embed(ids) + for i, layer in enumerate(self.rln.layers): + y, _ = layer(x, cond=cond) + x = y + self.gates[i](y) # 门控残差 (零初始化 → Stage1 等价) + return x # (B, L, d) + + +# ============================================================ +# T6-T9: HopfieldBank — 能量下降检索 (替代滚动平均) +# ============================================================ +class HopfieldBank: + """现代 Hopfield 检索: x_new = X·Softmax(β·Xᵀx). + + 存储: per-generator 原型 (与 AtomRouteBank 同, 训练滚动平均). + 检索: 能量下降 (hopfield_update_eq_attn T6), β 控制锁定度 + (softmax_weight_concentration T9): 训练 β_train≈3 混合态探索, + 部署 β_test≥10 模式锁定 (hopfield_retrieval_error_bound T7). + 零可学习参数 (X 是存储矩阵). + """ + + def __init__(self, d, beta_train=3.0, beta_test=10.0, alpha=1.0): + self.d = d + self.beta_train = beta_train + self.beta_test = beta_test + self.alpha = alpha + self.atoms = [None] * N_GEN + self._count = [0] * N_GEN + self.n_add = 0 + self.n_route = 0 + self.n_hit = 0 + self.beta_log = [] + + def _norm(self, m): + return m / (m.norm() + 1e-8) + + def add(self, m, sig): + v = m.detach().float() + for i in range(N_GEN): + if sig[i]: + c = self._count[i] + if self.atoms[i] is None: + self.atoms[i] = self._norm(v) + else: + self.atoms[i] = self._norm( + (self.atoms[i] * c + v) / (c + 1)) + self._count[i] = c + 1 + self.n_add += 1 + + def route(self, m, sig, beta=None, detach_m=True): + """能量下降检索: x_new = X·Softmax(β·Xᵀx) (X = 命中原子矩阵). + + detach_m=True (部署): query 停止梯度; False (训练): 保留图 — + 梯度经 softmax 检索权重回流到 m (检索进训练, V35.1 ②). + """ + hit = [i for i in range(N_GEN) if sig[i] and self.atoms[i] is not None] + if not hit: + return m, False + beta = beta or self.beta_test + mq = m.detach().float() if detach_m else m.float() + X = torch.stack([self.atoms[i] for i in hit]) # (k, d) + w = F.softmax(beta * (X @ mq), dim=0) # (k,) + comp = (w.unsqueeze(-1) * X).sum(0) # (d,) + out = self.alpha * comp + (1 - self.alpha) * mq + out = self._norm(out).to(m.dtype) + self.n_route += 1 + self.n_hit += len(hit) + self.beta_log.append((beta, w.max().item())) + return out, True + + def stats(self): + return {'n_gen': N_GEN, 'n_add': self.n_add, 'n_route': self.n_route, + 'n_hit': self.n_hit, + 'filled': sum(1 for a in self.atoms if a is not None), + 'beta_last': (self.beta_log[-1] if self.beta_log else None)} + + +# ============================================================ +# 主动查询生成器 (v1 保留 + β 调制) +# ============================================================ +def compose_swaps(gen_indices): + """6 个生成元 (对换) 索引序列 → 复合排列. 2-3 个对换复合直指 c4.""" + p = IDENT + for gi in gen_indices: + p = _apply(p, TRANS[gi]) + return p + + +class ActiveQueryGenerator: + """从已学原子采样组合 → 反事实输入 → 世界执行器真值 → 查询对. + + 探索信号: ① 组合不确定性 (熵优先) ② 签名覆盖 (未填格位优先) + ③ β 调制: 训练期低 β (混合探索), 组合广度由采样分布控制. + 真值源 = MetaRuleWorld 执行器 (gen_task_data_split, remap=组合 perm). + V35.1 (③): 3 原子复合占比提高 (p_combo3 控制), 组合签名去重 + (避免反复生成同一 perm 的训练对). + """ + + def __init__(self, V, seed=42, n_support=8, n_query=8, max_combo=3, + p_combo3=0.6): + self.V = V + self.rng = random.Random(seed) + self.n_support = n_support + self.n_query = n_query + self.max_combo = max_combo + self.p_combo3 = p_combo3 + self.gen_history = [] # [(perm, sig, len)] + + def _pick_gens(self, bank): + """采样 2..max_combo 个已学原子; p_combo3 概率取 3 原子 (直指 c4).""" + filled = [i for i in range(N_GEN) if bank.atoms[i] is not None] + if len(filled) < 2: + return None + if self.p_combo3 > 0 and len(filled) >= 3 and \ + self.rng.random() < self.p_combo3: + k = min(3, self.max_combo) + else: + k = self.rng.randint(2, min(self.max_combo, len(filled))) + gens = self.rng.sample(filled, k) + return gens + + def generate(self, bank, n=4): + """生成 n 个组合任务: {'perm', 'gen_sig', 'support', 'query'}.""" + out = [] + seen_sigs = set(h[1] for h in self.gen_history) + for _ in range(n * 3): # 尝试预算: 去重后可能不足 n + gens = self._pick_gens(bank) + if gens is None: + break + perm = compose_swaps(gens) + sig, ln = generator_signature(perm) + sig_t = tuple(sig) if sig else None + if sig is None or sig_t in seen_sigs: + continue + seen_sigs.add(sig_t) + seed = self.rng.randint(0, 10 ** 6) + sup, qry = gen_task_data_split( + None, self.V, n_support=self.n_support, n_query=self.n_query, + seed=seed, remap=perm, stop_at_halt=True) + out.append({'perm': perm, 'gen_sig': sig, 'gen_len': ln, + 'support': sup, 'query': qry}) + self.gen_history.append((perm, sig_t, ln)) + if len(out) >= n: + break + return out + + +# ============================================================ +# V35_Learner +# ============================================================ +class V35_Learner(V34_7_Learner): + """V34.7 + 分形 (fractal) / Hopfield 检索 (hopfield) / + 自适应 K (adaptive_k) / 主动查询 (active_query).""" + + def __init__(self, rln, pln, V, use_compose=False, alpha=1.0, + use_atoms=True, use_fractal=False, use_hopfield=False, + adaptive_k=False, active_query=False, + train_retrieval=False, aq_p3=0.6, aq_eval=False, + tau_norm=None, beta_init=None, + use_think=False, n_think=6, think_tau=0.01, + lambda_think=0.0, lambda_combo=0.0, + use_perm=False, lambda_perm=0.5, lambda_table=0.5, + beta_train=3.0, beta_test=10.0, D=1.5, + aq_n=4, aq_max_combo=3, k_lam=0.005, k_patience=3, + **kw): + self._use_fractal = use_fractal + self._use_hopfield = use_hopfield + self._train_retrieval = train_retrieval + self._aq_eval = aq_eval + self._tau_norm = tau_norm + self._beta_init = beta_init + self._use_think = use_think + self._n_think = n_think + self._think_tau = think_tau + self._lambda_think = lambda_think + self._lambda_combo = lambda_combo + self._use_perm = use_perm + self._lambda_perm = lambda_perm + self._lambda_table = lambda_table + self._use_remap = kw.pop('use_remap', False) + self._aq_curriculum = kw.pop('aq_curriculum', False) + self._adaptive_k = adaptive_k + self._active_query = active_query + self._D = D + self._k_lam = k_lam + self._k_patience = k_patience + self._aq_n = aq_n + self._aq_max_combo = aq_max_combo + self._p_combo3 = kw.pop('p_combo3', 0.6) # 先弹出, 不传给上层 **kw + self._aq_rng = random.Random(seed if 'seed' not in kw else kw['seed']) + + # 分形包装 (Stage1 后加门控, 零初始化 → 等价) + if use_fractal: + rln = FractalRLN(rln, D=D) + self._fractal_gate_params = (list(rln.gates.parameters()) + if use_fractal else []) + + # 原子银行: Hopfield (能量检索) 或 AtomRouteBank (滚动平均) + bank = None + if use_hopfield: + bank = HopfieldBank(rln.d_model, beta_train=beta_train, + beta_test=beta_test, alpha=alpha) + + super().__init__(rln, pln, V, use_compose=use_compose, alpha=alpha, + use_atoms=use_atoms, **kw) + # 学习效率修复 (V35.2 诊断 A): τ 按有效维度归一 + β_init 提高 + # (IDBD 元梯度 ∝ β_init: 0.001 → 0.05 恢复 V31.1 分化配置) + if tau_norm or beta_init: + hd = self.head + bi = beta_init if beta_init is not None else hd.beta_init + self.head = SwiftTDHead( + self.d_model, self.world_vocab, self.n_resp, + beta_init=bi, kappa=hd.kappa, eta=hd.eta, + eps=hd.eps, beta_min=hd.beta_min, + input_dim=self.intent_dim, + tau_norm=tau_norm or hd.tau_norm) + self.head.reset_state() + # V35.4 分形 PLN: 重建 predictor[0] (3d→96) + step_beta + head + fpln = getattr(pln, 'fractal_pln', False) + fpln_h = getattr(pln, 'fractal_pln_h', None) + if fpln and fpln_h: + d = rln.d_model # 64 (顶层宽) + ph = fpln_h # 96 (分形 PLN 隐藏宽) + # 1) predictor[0]: V32 已注入为 Linear(3d, d) → 扩到 (3d, ph) + old0 = pln.predictor[0] + new0 = nn.Linear(d * 3, ph) + with torch.no_grad(): + k = min(old0.weight.shape[0], ph) + new0.weight[:k].copy_(old0.weight[:k]) + new0.bias[:k].copy_(old0.bias[:k]) + pln.predictor[0] = new0 + # 2) predictor[2]: (vocab*n_resp, d) → (vocab*n_resp, ph) + old2 = pln.predictor[2] + new2 = nn.Linear(ph, old2.out_features) + with torch.no_grad(): + k = min(old2.weight.shape[1], ph) + new2.weight[:, :k].copy_(old2.weight[:, :k]) + new2.bias.copy_(old2.bias) + pln.predictor[2] = new2 + # 3) step_beta 参数数随 predictor 变化 → 重建 + pln.step_beta = nn.Parameter( + torch.full((pln._n_params(),), + math.log(pln.step_beta.exp().mean().item()))) + # 4) head: SwiftTDHead(d_model=ph, input_dim=3d) + hd = self.head + self.head = SwiftTDHead( + ph, self.world_vocab, self.n_resp, + beta_init=hd.beta_init, kappa=hd.kappa, eta=hd.eta, + eps=hd.eps, beta_min=hd.beta_min, + input_dim=self.intent_dim, + tau_norm=hd.tau_norm) + self.head.reset_state() + # super().__init__ 会建 self.bank (AtomRouteBank); 覆盖为 Hopfield + if bank is not None: + self.bank = bank + # V35.5 潜空间思考: 多轮思考网络 (输入 [m; g; h_agg], 比 V33 的 + # [m; g] 多上下文 h; 输出 m 修正量). 零初始化输出 → Stage1 等价. + if use_think: + d = rln.d_model + self.think_net = nn.Sequential( + nn.Linear(3 * d, d), nn.Tanh(), nn.Linear(d, d)) + with torch.no_grad(): + self.think_net[2].weight.zero_() + self.think_net[2].bias.zero_() + # 纳入 outer_opt (V33 outer_opt 只含 refine/probe/intent) + base = list(self.outer_opt.param_groups[0]['params']) + self.outer_opt = torch.optim.Adam( + base + list(self.think_net.parameters()), lr=self.outer_lr) + self.think_trace = [] # 每轮检验误差轨迹 + self.think_rounds = [] # 实际思考轮数 + else: + self.think_net = None + self.think_trace = [] + self.think_rounds = [] + # V35.9: 置换矩阵复合算子 (S4 忠实表示, 群同态) + # 24 个 perm 各学一个 4×4 矩阵, 复合 = 矩阵乘法 (精确代数, + # 未见组合自动正确) — 替代 Embedding 查表 (无归纳偏置). + if use_perm: + d = rln.d_model + self.perm_head = nn.Sequential( + nn.Linear(d, d), nn.Tanh(), nn.Linear(d, 24)) + # 矩阵复合: P(a)@P(b) → 目标 perm logits + # mat: (24, 4, 4) — 每个 perm 一个 4×4 矩阵嵌入 + self.perm_mats = nn.Parameter(torch.randn(24, 4, 4) * 0.1) + base = list(self.outer_opt.param_groups[0]['params']) + self.outer_opt = torch.optim.Adam( + base + list(self.perm_head.parameters()) + + [self.perm_mats], + lr=self.outer_lr) + self.perm_acc = {'seen': [], 'unseen': []} + self.compose_acc = [] + else: + self.perm_head = None + self.compose_table = None + self.perm_acc = {'seen': [], 'unseen': []} + self.compose_acc = [] + self._aq = (ActiveQueryGenerator(V, seed=42, + n_support=aq_n // 2 + 2, + n_query=aq_n // 2 + 2, + max_combo=aq_max_combo, + p_combo3=aq_p3 if active_query + else self._p_combo3) + if active_query else None) + # 分形门控参数已含在 super 的 rln.parameters() (FractalRLN 包装了 base) + self._k_used = [] + + # ── V35.5/35.6 潜空间思考循环 (E-V 闭环内部化) ────────── + def _think(self, m, task): + """多轮思考: 每轮用当前假设 m 重读 support → 检验误差 g → + 思考网络修正 m. 完整 BPTT (梯度穿多轮循环链到 RLN/think_net). + 收敛: 检验误差不再降 (Δ < τ, T10 临界点) 提前停. + V35.6 (目标错位修复): think_net 的监督 = 思考后检验误差 + (共享头 W0 重读 support 的 CE, 存 self._think_loss 由 + _post_define_aux_loss 接入 loss) — 学"修正假设"而非"讨好训练". + 返回 m (思考后假设, 带图).""" + W = [w.detach() for w in self.pln.clone_params()] + sup = task['support'][:self.refine_sup] + if not m.requires_grad: + # 部署/no_grad 路径: enable_grad 建局部图 (思考仍修正假设), + # 返回 detach 结果 + with torch.enable_grad(): + m2 = m.detach().clone().requires_grad_(True) + m2 = self._think_loop(m2, W, sup, build_graph=False) + return m2.detach() + return self._think_loop(m.clone(), W, sup, build_graph=True) + + def _think_loop(self, m, W, sup, build_graph): + rounds = 0 + trace = [] + think_loss = torch.tensor(0.0, device=m.device) + # V35.7: 组合检验 — 用 AQ 生成的 unseen 组合任务 (仅训练期) + combo_sup = None + if build_graph and self._aq is not None and self._lambda_combo > 0: + combos = self._aq.generate(self.bank, n=1) + if combos and combos[0]['support']: + # AQ 任务 tgt 是 8 tokens, n_resp=4 → 切片 + combo_sup = [(c, tg[:4]) for (c, tg) + in combos[0]['support'][:2]] + if not combo_sup: + combo_sup = None + for t in range(self._n_think): + g = torch.zeros_like(m) + h_agg = torch.zeros_like(m) + e = 0.0 + for (c, tg) in sup: + h = self._rln_fwd(c.unsqueeze(0), m)[:, -1, :] + h_agg = h_agg + h.detach()[0] + hin = self._h_in_batch(h, m) + pred = head_forward(W, hin, self.intent_dim, + self.world_vocab, self.n_resp) + loss = F.cross_entropy( + pred.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) + e += loss.item() + gi = torch.autograd.grad(loss, m, create_graph=build_graph)[0] + g = g + gi + h_agg = h_agg / len(sup) + corr = self.think_net(torch.cat([m, g, h_agg], dim=-1)) + m = self.m_norm(m + corr) + trace.append(e) + rounds += 1 + # V35.6: 思考后检验误差 (修正后的 m 在 support 上的 CE, 带图) + # 由 _post_define_aux_loss 返回, V33.meta_step 用 lambda_icl 加权 + if build_graph and self.lambda_icl > 0: + e_after = 0.0 + for (c, tg) in sup: + h2 = self._rln_fwd(c.unsqueeze(0), m)[:, -1, :] + hin2 = self._h_in_batch(h2, m) + p2 = head_forward(W, hin2, self.intent_dim, + self.world_vocab, self.n_resp) + e_after = e_after + F.cross_entropy( + p2.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) + think_loss = think_loss + e_after / len(sup) + # V35.7: 组合检验误差 (思考后 m 对 unseen 组合任务的重读误差) + if build_graph and combo_sup is not None: + e_combo = 0.0 + for (c, tg) in combo_sup: + h3 = self._rln_fwd(c.unsqueeze(0), m)[:, -1, :] + hin3 = self._h_in_batch(h3, m) + p3 = head_forward(W, hin3, self.intent_dim, + self.world_vocab, self.n_resp) + e_combo = e_combo + F.cross_entropy( + p3.view(-1, self.world_vocab), + tg.view(-1).clamp(0, self.V + 1)) + think_loss = think_loss + (self._lambda_combo + * e_combo / len(combo_sup)) + # 收敛: 误差不再降 (T10) + if t >= 2 and trace[-2] - e < self._think_tau: + break + self.think_trace.append(trace) + self.think_rounds.append(rounds) + self._think_loss = think_loss # 直接监督 (V35.6) + 组合 (V35.7) + return m + + def _post_define_aux_loss(self, m, t): + """V35.6: 思考监督接入 — 返回思考后检验误差 (共享头 W0 重读 + support), 由 V33.meta_step 用 lambda_icl 加权 (TH2 复用 + lambda_icl 作为思考监督权重, 不再乘 lambda_think). + 思考分支跳过 super() (避免 ICL 偏置混入). + V35.8: 叠加 perm 回读损失 (PermHead) + 复合表三元组损失.""" + extra = None + if self._use_think and self.think_net is not None: + tl = getattr(self, '_think_loss', None) + if tl is not None: + self._think_loss = None # 单次消费 + extra = tl + if self._use_perm and self.perm_head is not None and m is not None: + perm_loss = self._perm_loss(m, t) + if perm_loss is not None: + pl = perm_loss * self._lambda_perm + extra = pl if extra is None else extra + pl + if self._lambda_table > 0: + tl2 = self._table_loss() + if tl2 is not None: + extra = (tl2 * self._lambda_table + if extra is None else extra + tl2 * self._lambda_table) + if extra is not None: + return extra + return super()._post_define_aux_loss(m, t) + + def _perm_loss(self, m, t): + """V35.8: latent 回读 — m → 24 类 S4 分类, 监督 = 任务真实 perm. + 用带图 m (不 detach): 回读误差反传 RLN, 推动 m 编码置换结构.""" + import itertools + perm = t.get('perm') + if perm is None: + return None + idx = list(itertools.permutations(range(4))).index(tuple(perm)) + logits = self.perm_head(m) + loss = F.cross_entropy(logits.unsqueeze(0), + torch.tensor([idx], device=m.device)) + # 记录分类准确率 (seen/unseen 分桶) + with torch.no_grad(): + pred = logits.argmax().item() + key = 'seen' if t.get('seen') else 'unseen' + self.perm_acc[key].append(1 if pred == idx else 0) + return loss + + def _table_loss(self): + """V35.9: S4 群乘法 — 置换矩阵复合. + + 忠实表示: 每个 perm 一个 4×4 矩阵 P(i). 复合 P(a)@P(b) 应对应 + P(a∘b). 监督 = 三重损失: ① 复合后矩阵与真实置换矩阵的 MSE + (代数对齐) ② 复合矩阵对 24 类的 logits (分类, 辅助) ③ 置换性 + 正则 (每行/列近 one-hot). 矩阵乘法是精确代数 → 未见组合正确. + """ + import itertools + import random + perms = list(itertools.permutations(range(4))) + self._perm_list = perms + # 真实置换矩阵 (24,4,4): P[i][r][c] = 1 if perms[i](r)=c + P_true = torch.zeros(24, 4, 4) + for i, p in enumerate(perms): + for r in range(4): + P_true[i, r, p[r]] = 1.0 + dev = self.perm_mats.device + P_true = P_true.to(dev) + idxs = list(range(24)) + step = getattr(self, '_table_step', 0) + self._table_step = step + 1 + rng = random.Random(42 + step) + pairs = [(rng.choice(idxs), rng.choice(idxs)) for _ in range(64)] + def compose(p, q): # p∘q = p(q(x)) + return tuple(p[q[x]] for x in range(4)) + a_idx = torch.tensor([a for a, _ in pairs], device=dev) + b_idx = torch.tensor([b for _, b in pairs], device=dev) + ab_idx = torch.tensor([perms.index(compose(perms[a], perms[b])) + for a, b in pairs], device=dev) + Pa = self.perm_mats[a_idx] # (B,4,4) + Pb = self.perm_mats[b_idx] + Pab = Pa @ Pb # (B,4,4) 复合 + # ① 代数对齐: 复合矩阵 → 真实置换矩阵 + loss = F.mse_loss(Pab, P_true[ab_idx]) + # ② 分类辅助: 复合矩阵对 24 类的 logits (用真实矩阵做分类器) + with torch.no_grad(): + logits_gt = (Pab.detach()[:, None, :, :] + * P_true[None, :, :, :]).sum(dim=(-1, -2)) + # (B,24) — 复合矩阵与每个候选矩阵的内积 + pred = logits_gt.argmax(dim=-1) + self.compose_acc.append((pred == ab_idx).float().mean().item()) + # ③ 置换性正则: 每个矩阵行和/列和接近 1 (soft 置换) + row_sum = self.perm_mats.sum(dim=-1) # (24,4) + col_sum = self.perm_mats.sum(dim=-2) # (24,4) + reg = (F.mse_loss(row_sum, torch.ones_like(row_sum)) + + F.mse_loss(col_sum, torch.ones_like(col_sum))) + return loss + 0.1 * reg + + # ── ② 检索进训练梯度 (V35.1) ───────────────────────────── + def _define(self, task, detach=True, use_bank=True): + """define 阶段: train_retrieval=True 时训练期 bank.add 后立即 route + (带梯度, detach_m=False) → 检索结果参与 compose/head, softmax 检索 + 权重梯度回流到 m; 部署期 route 全 detach (β_test 高锁定). + train_retrieval=False (V35 首轮语义): 训练只 add, 部署只 route.""" + m = super()._define(task, detach=False, use_bank=False) + sig = task.get('gen_sig') or [0] * N_GEN + use_bank = self.use_atoms + hop = isinstance(self.bank, HopfieldBank) # 检索进训练仅 HopfieldBank + # V35.5: 潜空间思考 (训练带图, 部署 detach 后纯前向) + if self._use_think and self.think_net is not None: + m = self._think(m, task) + if detach: + m = m.detach() + if self._use_compose: + if use_bank: + if detach: + if hop: + m, _ = self.bank.route(m, sig, detach_m=True) + else: + self.bank.route(m, sig) + elif self._train_retrieval and hop: + self.bank.add(m, sig) + m, _ = self.bank.route(m, sig, + beta=self.bank.beta_train, + detach_m=False) + else: + self.bank.add(m, sig) + comp, sigv = self._compose_inputs(task, m, sig) + corr = self.compose(m, comp, sigv.to(m.device)) + m = self.m_norm(m + corr) + if detach: + m = m.detach() + return m + if use_bank: + if detach: + if hop: + m, _ = self.bank.route(m, sig, detach_m=True) + else: + m, _ = self.bank.route(m, sig) + m = m.detach() + elif self._train_retrieval and hop: + self.bank.add(m, sig) + m, _ = self.bank.route(m, sig, + beta=self.bank.beta_train, + detach_m=False) + else: + self.bank.add(m, sig) + elif detach: + m = m.detach() + return m + + # ── 自适应 K (T10): 边际收益 Δ_k < λ ⟹ 提前终止 ────────── + def adapt_graph(self, support, K=None, m=None): + K = K or self.K + W = self.pln.clone_params() + prev_loss = None + stall = 0 + k_used = K + lim = min(K, len(support)) + for i in range(lim): + ctx, tgt = support[i] + hidden = self._rln_fwd(ctx.unsqueeze(0), m) + h = hidden[:, -1, :] + hin = self._h_in_batch(h, m) + x = torch.tanh(hin @ W[0].t() + W[1]) + logits = (x @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + p = F.softmax(logits, dim=-1) + onehot = F.one_hot(tgt, self.world_vocab).float() + errs = (p - onehot).reshape(-1) + W = self.head.step_graph(W, hin[0], x[0], errs) + if self._adaptive_k: + # 边际收益: 当前 W 在下一样本上的 loss 下降量 + if i + 1 < lim: + nctx, ntgt = support[i + 1] + nh = self._rln_fwd(nctx.unsqueeze(0), m)[:, -1, :] + nhin = self._h_in_batch(nh, m) + nx = torch.tanh(nhin @ W[0].t() + W[1]) + nlogits = (nx @ W[2].t() + W[3]).view( + 1, self.n_resp, self.world_vocab) + nloss = F.cross_entropy( + nlogits.view(-1, self.world_vocab), + ntgt.view(-1).clamp(0, self.V + 1)).item() + if prev_loss is not None and (prev_loss - nloss) < self._k_lam: + stall += 1 + if stall >= self._k_patience: + k_used = i + 1 + break + else: + stall = 0 + prev_loss = nloss + self._k_used.append(k_used) + return W + + # ── meta_step: 主动查询注入 (训练时组合任务加入) ────────── + def meta_step(self, tasks, train_rules, K=None): + rng = random.Random(self.seed + self._meta_iters) + chosen = rng.sample(train_rules, + min(self.n_tasks_per_step, len(train_rules))) + # 主动查询: 从 bank 已学原子生成组合任务, 混入训练 + # V35.10: _inject_combo=True 时组合任务带 perm 标签 → probe 回读 + # 监督学会 unseen 组合置换 (probe_loss 在 super.meta_step 内触发, + # 已与 n_define_refine 解耦) + if self._active_query and self._aq is not None: + # 组合课程化 (V35.15): 前期低密度打底 (aq_n=2), 后期 + # 高密度冲刺 (线性涨到配置值) — 原子表示先打稳, 组合再加速 + n_aq = self._aq_n + if getattr(self, '_aq_curriculum', False): + p = min(1.0, self._meta_iters / 30.0) + n_aq = max(2, int(round(2 + (self._aq_n - 2) * p))) + qs = self._aq.generate(self.bank, n=n_aq) + for i, q in enumerate(qs): + t = {'support': [(c, tg[:4]) for (c, tg) in q['support']], + 'query': [(c, tg[:4]) for (c, tg) in q['query']], + 'perm': q['perm'], 'gen_sig': q['gen_sig'], + 'seen': False, 'name': 'AQ%d' % len(self._aq.gen_history), + 'struct': 'aq'} + tasks['__aq__%d' % i] = t + chosen.append('__aq__%d' % i) + loss = super().meta_step(tasks, chosen, K=K) + for k in list(tasks): + if k.startswith('__aq__'): + del tasks[k] + return loss + + def long_stream(self, tasks, stream, steps_per_task=60, + sleep_every=1, sleep_iters=2, head_state=None, + eval_every=20): + # 部署期 β 高锁定 (HopfieldBank.route 默认 beta_test) + return super().long_stream(tasks, stream, + steps_per_task=steps_per_task, + sleep_every=sleep_every, + sleep_iters=sleep_iters, + head_state=head_state, + eval_every=eval_every) + + # ── V35.11: 回读驱动预测 ──────────────────────────────── + def _metric(self, W, task, m=None, max_batch=16): + """super._metric + 回读重映射: probe_head 从 m 回读 perm (4,4), + 部署时用回读 perm 重映射 logits 的寄存器位置 (静态代数: + 组合 perm 已知时, 预测 = 重映射后的常规预测). 只作用于 + 部署 (no_grad), 训练仍走连续路径.""" + full, regs = super()._metric(W, task, m=m, max_batch=max_batch) + if (self._use_remap and m is not None + and getattr(self, 'probe_head', None) is not None): + ctx, tgt = batch_pairs(task['query'], max_batch=max_batch) + with torch.no_grad(): + hidden = self._rln_fwd(ctx, m) + h = hidden[:, -1, :] + h_in = self._h_in_batch(h, m) + pred = head_forward([w.detach() for w in W], h_in, + self.intent_dim, self.world_vocab, + self.n_resp) + pred = self._route_logits(pred, m) + pm = self.probe_head(m).view(4, 4).argmax(dim=-1) # (4,) + # 重映射: 预测位置 i ← 回读 perm 位置 pm[i] + # (模型按 identity 学, 组合任务需要按 perm 换位) + pred_rm = pred.clone() + pred_rm[:, torch.arange(4)] = pred[:, pm] + pt = pred_rm.argmax(dim=-1) + full = (pt == tgt[:, :self.n_resp]).sum().item() / \ + max(tgt[:, :self.n_resp].numel(), 1) + regs = (pt[:, :4] == tgt[:, :4]).sum().item() / \ + max(tgt[:, :4].numel(), 1) + return full, regs + + +# ============================================================ +# 驱动 +# ============================================================ +def run_cell(key, cfg, s1_state, tasks, U_STRUCT, meta_iters=40, K=20, + seed=42, steps_per_task=60, verbose=True): + lm = cfg['lm'] + tasks = make_regs_tasks(tasks) + train_names = [n for n in tasks if tasks[n]['seen']][:8] + unseen_names = [n for n in tasks if not tasks[n]['seen']] + stream = train_names + unseen_names + T_STRUCT = build_t_struct(42) + for n in tasks: + tasks[n]['name'] = n + tasks[n]['struct'] = (U_STRUCT.get(n) if not tasks[n]['seen'] + else T_STRUCT.get(n, '?')) + sig = generator_signature(tasks[n]['perm']) + tasks[n]['gen_sig'] = sig[0] if sig else None + tasks[n]['gen_len'] = sig[1] if sig else 0 + random.seed(seed); torch.manual_seed(seed) + if cfg.get('fractal4'): + rln, pln = build_fractal4_resp(200, 4, s1_state, + fractal_pln=cfg.get('fractal_pln', + False)) + else: + rln, pln = build_model_resp(200, 4, s1_state) + use_compose = (lm == 'compose') + learner = V35_Learner( + rln, pln, 200, K=K, seed=seed, sleep_iters=2, n_resp=4, + use_intent=True, use_compose=use_compose, + use_atoms=(lm != 'base'), + use_fractal=cfg.get('fractal', False), + use_hopfield=cfg.get('hopfield', False), + adaptive_k=cfg.get('adaptive_k', False), + active_query=cfg.get('active_query', False), + train_retrieval=cfg.get('train_retrieval', False), + aq_p3=cfg.get('aq_p3', 0.6), + aq_eval=cfg.get('aq_eval', False), + tau_norm=cfg.get('tau_norm', None), + beta_init=cfg.get('beta_init', None), + use_think=cfg.get('use_think', False), + n_think=cfg.get('n_think', 6), + think_tau=cfg.get('think_tau', 0.01), + lambda_think=cfg.get('lambda_think', 0.0), + lambda_combo=cfg.get('lambda_combo', 0.0), + use_perm=cfg.get('use_perm', False), + lambda_perm=cfg.get('lambda_perm', 0.5), + lambda_table=cfg.get('lambda_table', 0.0), + use_remap=cfg.get('use_remap', False), + aq_curriculum=cfg.get('aq_curriculum', False), + lambda_icl=cfg.get('lambda_icl', 0.0), + alpha=cfg.get('alpha', 1.0), + beta_train=cfg.get('beta_train', 3.0), + beta_test=cfg.get('beta_test', 10.0), + D=cfg.get('D', 1.5), + aq_n=cfg.get('aq_n', 4), + aq_max_combo=cfg.get('aq_max_combo', 3), + k_lam=cfg.get('k_lam', 0.005), + k_patience=cfg.get('k_patience', 3)) + t0 = time.time() + n_iters = cfg.get('meta_iters', meta_iters) + for it in range(n_iters): + learner.meta_iters = it + loss = learner.meta_step(tasks, train_names) + if verbose and (it + 1) % 20 == 0: + print(" [%s] it %d/%d loss=%.4f" % (key, it + 1, n_iters, loss), flush=True) + print(" [%s] 训练 %.0fs" % (key, time.time() - t0), flush=True) + trained_head = learner.head.state() + st = learner.long_stream(tasks, stream, steps_per_task=steps_per_task, + sleep_iters=2, head_state=trained_head) + print(" [%s] 长流 %.0fs" % (key, time.time() - t0), flush=True) + by = {'c3': [], 'c4': [], 'dbl': []} + for n in unseen_names: + s = U_STRUCT[n] + if s in by: + by[s].append(st['acc_after'][n]) + struct_mean = {k: (sum(v) / len(v) if v else None) for k, v in by.items()} + aux = {} + # ③ AQ 进长流评估: 生成的组合 perm 用训练后 head 独立评估 (acc_after) + aq_eval_acc = {} + if cfg.get('aq_eval', False) and learner._aq is not None: + aq_perms = [h[0] for h in learner._aq.gen_history] + for i, perm in enumerate(aq_perms): + sig, _ = generator_signature(perm) + sup, qry = gen_task_data_split( + None, 200, n_support=4, n_query=8, seed=1000 + i, + remap=perm, stop_at_halt=True) + aqt = {'support': [(c, tg[:4]) for (c, tg) in sup], + 'query': [(c, tg[:4]) for (c, tg) in qry], + 'perm': perm, 'gen_sig': sig, 'seen': False, + 'name': 'AQE%d' % i, 'struct': 'aq'} + with torch.no_grad(): + m = learner._define(aqt, detach=True) + acc = learner._metric([w.detach() for w in + learner.pln.clone_params()], + aqt, m=m)[1] + aq_eval_acc['aq%d_%s' % (i, perm)] = round(acc, 4) + aux['aq_eval_acc'] = aq_eval_acc + if getattr(learner, 'bank', None) is not None: + aux['atom_bank'] = learner.bank.stats() + if getattr(learner, 'compose', None) is not None: + cp = learner.compose + aux['compose_params'] = sum(p.numel() for p in cp.parameters()) + if learner._use_fractal: + aux['fractal_gate_params'] = sum( + p.numel() for p in learner._fractal_gate_params) + if learner._adaptive_k: + kus = learner._k_used + aux['k_used'] = {'mean': round(sum(kus) / len(kus), 2), + 'min': min(kus), 'max': max(kus)} + if learner._aq is not None: + aux['aq_gen'] = len(learner._aq.gen_history) + aux['aq_combo_lens'] = [h[2] for h in learner._aq.gen_history[-10:]] + if getattr(learner, 'think_net', None) is not None: + aux['think_params'] = sum(p.numel() + for p in learner.think_net.parameters()) + tr = learner.think_rounds + aux['think_rounds'] = {'mean': round(sum(tr) / len(tr), 2), + 'min': min(tr), 'max': max(tr)} if tr else None + # 收敛验证 (T8): 最后若干次思考的检验误差轨迹是否下降 + traces = [t for t in learner.think_trace if len(t) >= 2] + if traces: + drops = [t[0] - t[-1] for t in traces] + aux['think_err_drop'] = { + 'mean': round(sum(drops) / len(drops), 4), + 'frac_positive': round( + sum(1 for d in drops if d > 0) / len(drops), 3)} + else: + aux['think_err_drop'] = None + if getattr(learner, 'perm_head', None) is not None: + pa = learner.perm_acc + aux['perm_acc'] = { + 'seen': (round(sum(pa['seen']) / len(pa['seen']), 3) + if pa['seen'] else None), + 'unseen': (round(sum(pa['unseen']) / len(pa['unseen']), 3) + if pa['unseen'] else None), + 'n_seen': len(pa['seen']), 'n_unseen': len(pa['unseen'])} + ca = learner.compose_acc + aux['compose_acc'] = (round(sum(ca) / len(ca), 3) if ca else None) + # 学习效率参数观测 (SwiftTDHead β/θ/h): 缺失就补 (V32_1/V33 有, V35 曾丢) + try: + hs = learner.head.step_size_stats() + aux['head_beta'] = {k: (round(v, 6) if isinstance(v, float) + else v) for k, v in hs.items()} + except Exception: + pass + n_params = sum(p.numel() for p in rln.parameters()) + sum(p.numel() for p in pln.parameters()) + rec = {'cell': key, 'label': cfg['label'], 'seed': seed, + 'world': cfg.get('world', 'new'), + 'factors': {'LM': lm, 'alpha': cfg.get('alpha', 1.0), + 'use_compose': use_compose, + 'use_fractal': cfg.get('fractal', False), + 'use_hopfield': cfg.get('hopfield', False), + 'adaptive_k': cfg.get('adaptive_k', False), + 'active_query': cfg.get('active_query', False)}, + 'time_s': round(time.time() - t0, 1), + 'n_params': n_params, + 'params_kb': round(n_params * 4 / 1024, 1), + 'seen_acc': {n: st['acc_after'][n] for n in train_names}, + 'unseen_acc': {n: st['acc_after'][n] for n in unseen_names}, + 'unseen_by_struct': struct_mean, + 'aux': aux} + u = rec['unseen_acc']; ua = sum(u.values()) / len(u) + print((u" [%s] unseen=%.4f struct=%s n_params=%d aux=%s" % + (key, ua, struct_mean, n_params, aux)), flush=True) + return rec + + +CELLS = { + 'B': dict(world='new', lm='base', label='B-base'), + 'C': dict(world='new', lm='atom', alpha=1.0, label='C-atom-pure'), + 'M': dict(world='new', lm='compose', alpha=1.0, label='M-compose-learn'), + 'F': dict(world='new', lm='compose', alpha=1.0, fractal=True, + label='F-fractal'), + 'FH': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, label='FH-fractal-hopfield'), + 'FHA': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, label='FHA-fractal-hopfield-adapK'), + 'FHAQ': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, active_query=True, + label='FHAQ-full'), + # ── V35.1 (②③) ── + 'FHR': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, train_retrieval=True, + label='FHR-train-retrieval'), + 'FHAQ2': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, active_query=True, + train_retrieval=True, aq_p3=0.7, aq_eval=True, + label='FHAQ2-full-v2'), + # ── V35.2 学习效率诊断 (方案 A: τ 维度归一) ── + 'FN': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, tau_norm='mean', + label='FN-tau-norm-mean'), + 'FN2': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, tau_norm='mean', + train_retrieval=True, label='FN2-tau-norm-train-retr'), + # V35.3: β_init 提高恢复 IDBD 分化 (V31.1 配置: 0.05) + 'FB': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, tau_norm='mean', + beta_init=0.05, label='FB-tau-norm-beta05'), + 'FBT': dict(world='new', lm='compose', alpha=1.0, fractal=True, + hopfield=True, adaptive_k=True, tau_norm='mean', + beta_init=0.05, train_retrieval=True, + label='FBT-tau-norm-beta05-train-retr'), + # ── V35.4: 4 层分形 RLN (FractalRLN4, 等比宽度 128/102/81/64) ── + 'F4': dict(world='new', lm='compose', alpha=1.0, fractal4=True, + label='F4-fractal4-rln'), + 'F4P': dict(world='new', lm='compose', alpha=1.0, fractal4=True, + fractal_pln=True, label='F4P-fractal4-rln+pln'), + # ── V35.5: 潜空间思考循环 (E-V 闭环内部化) ── + 'TH': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + label='TH-think-loop'), + 'THF': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + train_retrieval=True, active_query=True, aq_p3=0.6, + label='THF-think-full'), + # ── V35.6: 思考监督直接化 (学修正假设, 非讨好训练) ── + # lambda_icl 复用为思考监督权重 (V33.meta_step 用它加权 aux loss) + 'TH2': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, label='TH2-think-direct'), + 'TH2F': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, train_retrieval=True, active_query=True, + aq_p3=0.6, label='TH2F-think-direct-full'), + # ── V35.7: 思考 + unseen 组合检验监督 (最后一环) ── + 'TH3': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_p3=0.6, label='TH3-think-combo'), + 'TH3F': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, train_retrieval=True, + active_query=True, aq_p3=0.6, + label='TH3F-think-combo-full'), + # ── V35.8 (④): S4 群代数 — perm 回读 + 复合表 ── + 'G0': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, use_perm=True, + lambda_perm=0.5, lambda_table=0.0, + label='G0-perm-readback'), + 'G1': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, use_perm=True, + lambda_perm=0.5, lambda_table=0.5, + label='G1-perm+table'), + 'G2': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, use_perm=True, + lambda_perm=1.0, lambda_table=0.5, train_retrieval=True, + active_query=True, aq_p3=0.6, + label='G2-perm-full'), + # ── V35.10: 回读闭环 (probe 解耦 + 组合任务注入) ── + 'R0': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, + label='R0-probe-fixed'), + 'R1': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=2, aq_p3=0.7, + label='R1-probe+combo-inject'), + 'R2': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=2, aq_p3=0.7, train_retrieval=True, + label='R2-probe+inject+retrieval'), + # ── V35.11: 回读驱动预测 (部署时 probe perm 重映射 logits) ── + 'R3': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=2, aq_p3=0.7, use_remap=True, + label='R3-remap-readback'), + 'R4': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=2, aq_p3=0.7, train_retrieval=True, use_remap=True, + label='R4-remap+retrieval'), + # ── V35.12: 组合样本密度 + probe 强化 ── + 'S0': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, + label='S0-dense-combo+probe'), + 'S1': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, use_remap=True, + label='S1-dense+remap'), + # ── V35.13: 密度再放大 + probe 加倍 ── + 'S2': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=10, aq_p3=0.7, lambda_probe=2.0, + label='S2-dense10-probe2'), + 'S3': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=14, aq_p3=0.7, lambda_probe=2.0, train_retrieval=True, + label='S3-dense14-probe2-retr'), + # ── V35.14: S0 微调 (组合权重/思考轮数) + 多 seed ── + 'T0': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.3, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, + label='T0-combo-soft'), + 'T1': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=8, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, + label='T1-think8'), + 'T2': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, + lambda_icl=0.5, lambda_combo=0.3, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, n_think=8, + label='T2-combo-soft-think8'), + # ── V35.15: 组合课程化 (前期低密度打底, 后期高密度冲刺) ── + 'U0': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, aq_curriculum=True, + label='U0-curriculum'), + 'U1': dict(world='new', lm='compose', alpha=1.0, + hopfield=True, adaptive_k=True, use_think=True, n_think=6, + lambda_icl=0.5, lambda_combo=0.5, active_query=True, + aq_n=6, aq_p3=0.7, lambda_probe=1.0, aq_curriculum=True, + train_retrieval=True, + label='U1-curriculum+retr'), +} + + +def main(): + import argparse + ap = argparse.ArgumentParser() + ap.add_argument('--cells', default='B,F,FH,FHA,FHAQ') + ap.add_argument('--meta-iters', type=int, default=40) + ap.add_argument('--seed', type=int, default=42) + ap.add_argument('--no-stage1', action='store_true') + args = ap.parse_args() + print("=" * 70) + print("V35 分形持续学习引擎 (lm-principle 83 定理重设计)") + print("=" * 70) + tasks = build_v32_tasks(200, n_support=40, n_query=20, seed=42, window=3, + stop_at_halt=True) + U_STRUCT = build_u_struct(42) + s1 = None + if not args.no_stage1: + need_f4 = any(CELLS[c.strip()].get('fractal4') for c in + args.cells.split(',')) + cands = (['/tmp/v32_s1_f4_state.pt'] if need_f4 + else ['/tmp/v32_s1_state.pt', '/tmp/v31_2_s1_state.pt']) + for cand in cands: + if os.path.exists(cand): + s1 = torch.load(cand) + s1 = {k: v.cpu().clone() for k, v in s1.items()} + print("Stage1: %s" % cand); break + else: + print(">>> Stage1 缺失, 请先跑 run_v32_stage1.py" + " 或 run_v35_4_fractal4.py"); return + res = [] + for key in [c.strip() for c in args.cells.split(',')]: + cfg = CELLS[key] + print("\n== %s %s ==" % (key, cfg['label'])) + try: + r = run_cell(key, cfg, s1, tasks, U_STRUCT, + meta_iters=args.meta_iters, seed=args.seed) + except Exception: + import traceback; traceback.print_exc() + print("!!! %s 失败" % key); continue + res.append(r) + out = ROOT / 'results' / 'v35_fractal_report.json' + out.write_text(json.dumps(res, indent=2, ensure_ascii=False), + encoding='utf-8') + print("\nJSON:", out) + for r in res: + u = r['unseen_acc']; ua = sum(u.values()) / len(u) + print("%s unseen=%.4f struct=%s params=%d %s" % + (r['cell'], ua, r['unseen_by_struct'], r['n_params'], r['aux'])) + + +if __name__ == '__main__': + main() diff --git a/tests/scan_idbd.py b/tests/scan_idbd.py new file mode 100644 index 0000000000000000000000000000000000000000..0b2b3f89faa0ae6864db82b676a5cb9f3b2ffe5a --- /dev/null +++ b/tests/scan_idbd.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +"""扫描: 归一化后 IDBD 的步长分化是否在**真实提议回路**里出现。 + +标准: alpha_std > 0 且 max/min 比值 > 1.5 => 步长真的在学"谁快谁慢"。 +若 alpha_std == 0 => 步长仍是超参, "步长为主导的积累"没成立。 +""" +import sys +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +rs = np.random.RandomState(0) +desc = rs.randn(20, 2) + +print(" mu alpha0 alpha_std max/min h_norm theta_norm any_time") +print(" " + "-" * 72) +best = None +for mu in (0.01, 0.05, 0.2, 1.0, 5.0): + for a0 in (0.01, 0.05, 0.2): + p = RLProposer(desc, tau=0.5, k=2, mu=mu, alpha0=a0, seed=3) + for t in range(120): + idx = p.act() + # 环境: 部分候选"好"(返回高), 部分差 —— 制造真实的 reward 差异 + acc = 0.5 + 0.4 * np.tanh(desc[idx, 0]).mean() + p.observe(idx, acc=acc, any_time=0.3 + 0.0015 * t) + p.update(idx) + st = p.stats() + print(" %-6s %-8s %-11.2e %-9.2f %-8.4f %-11.4f %.3f" + % (mu, a0, st["alpha"]["std"], st["alpha"]["ratio_max_min"], + st["h_norm"], st["theta_norm"], st["any_time"])) + if best is None or st["alpha"]["std"] > best[0]: + best = (st["alpha"]["std"], mu, a0) +print() +print(" 最佳分化: alpha_std=%.2e (mu=%s, alpha0=%s)" % best) +print(" 判据: alpha_std > 0 且 ratio > 1.5 -> %s" + % ("步长分化成立 ✓" if best[0] > 0 else "**步长仍是常数 ✗**")) diff --git a/tests/scan_stepsize_diff.py b/tests/scan_stepsize_diff.py new file mode 100644 index 0000000000000000000000000000000000000000..5e7fb0fad349f19862c5391d10c865292611a87c --- /dev/null +++ b/tests/scan_stepsize_diff.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +"""「怎么让步长分化」—— 扫 mu × 更新次数, 找出 Autostep 的可分化区间。 + +根因 (tests/diag_stepsize.py 实测): + Autostep 的指数被拦在 mu => n 步后 alpha 最多变 exp(mu*n) + mu=0.01, n=36 -> exp(0.36) = 1.43x 上限 + IDBD 的指数无界 (clip ±2) => 单步就能 exp(2) = 7.4x + => **Autostep 的「不发散保证」本身封住了分化的上限。** + +本脚本回答: 给定 n 次更新, 需要多大的 mu 才能让 alpha max/min 达到 3x? +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +ND, D = 60, 3 +rs = np.random.RandomState(0) +desc = rs.randn(ND, D) + + +def run(algo, mu, alpha0, steps, seed=1, scope="global"): + p = RLProposer(desc, tau=0.5, k=3, mu=mu, alpha0=alpha0, algo=algo, + seed=seed, auto_scope=scope) + for t in range(steps): + idx = p.act() + r = 0.5 + 0.3 * np.tanh(desc[idx, 0].mean()) + delta = r - float(np.mean([p.value(i) for i in idx])) + for i in idx: + phi = p.phi(i) + if algo == "autostep": + g = np.abs(delta * phi * p.h) + if scope == "global": + gm = float(g.max()) if g.size else 0.0 + p.v_g = max(gm, p.v_g + (1.0 / p.tau_a) + * float(np.sum(p.alpha * phi ** 2)) * (gm - p.v_g)) + den = p.v_g if p.v_g > 0 else 1.0 + p.alpha *= np.exp(np.clip(p.mu * delta * phi * p.h / den, -2, 2)) + else: + p.v = np.maximum(g, p.v + (1.0 / p.tau_a) * p.alpha + * phi ** 2 * (g - p.v)) + nz = p.v > 0 + p.alpha[nz] *= np.exp(np.clip( + p.mu * delta * phi[nz] * p.h[nz] / p.v[nz], -2, 2)) + p.alpha = p.alpha / max(float(np.sum(p.alpha * phi ** 2)), 1.0) + else: + p.alpha = np.clip(p.alpha * np.exp( + np.clip(p.mu * delta * phi * p.h, -2, 2)), 1e-6, 1.0) + p.theta += p.alpha * delta * phi + p.h = p.h * np.maximum(0.0, 1.0 - p.alpha * phi ** 2) + p.alpha * phi * delta + p.any_time = r + a = p.alpha + return a.std(), a.max() / max(1e-12, a.min()), np.abs(p.h).max() / max(1e-12, np.abs(p.h).min()) + + +print("=" * 96) +print("「怎么让步长分化」—— mu × 更新次数 扫描 (判据: alpha max/min > 3 才算分化)") +print("=" * 96) +for algo, scope in [("autostep", "global"), ("idbd", "-")]: + print() + print("--- %s ---" % algo) + print(" %-8s %s" % ("mu \\ n", "".join("%12d" % n for n in (36, 100, 300, 1000)))) + print(" " + "-" * 62) + for mu in (0.01, 0.05, 0.2, 0.5, 1.0): + row = [] + for n in (36, 100, 300, 1000): + sd, ratio, hrat = run(algo, mu, 0.2, n, scope=scope) + row.append("%8.2fx%s" % (ratio, "✓" if ratio > 3 else " ")) + print(" %-8.3g %s" % (mu, "".join("%12s" % c for c in row))) +print() +print("注: 只有描述子第 0 维有预测力, 其余 8 维是噪声 —— 分化 = 正确把步长分给第 0 维。") +print(" idbd 不适用 'scope' (无归一化)。") diff --git a/tests/stats_two_days.py b/tests/stats_two_days.py new file mode 100644 index 0000000000000000000000000000000000000000..d97922aecec8c8427298e3a5b6ca1487bd642e20 --- /dev/null +++ b/tests/stats_two_days.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""两天结果的统计裁决: agg-path vs 基线 的显著性 + Q4 任务定义效应. + +用法: /work/liuyuanjie/envs/vllm-cu128/bin/python tests/stats_two_days.py +""" +import json, math, statistics as st +from pathlib import Path + +RES = Path("results") + + +def load(tag): + f = RES / tag / "lm4_wave_results.json" + return json.load(open(f)) if f.exists() else None + + +def acc(d, key): + v = (d or {}).get(key) + return v.get("final_mean_acc") if v else None + + +def forget(d, key): + v = (d or {}).get(key) + return v.get("mean_forget") if v else None + + +def welch(a, b): + """Welch t 检验, 返回 (t, df, 近似 p 双侧).""" + na, nb = len(a), len(b) + ma, mb = st.mean(a), st.mean(b) + va, vb = st.variance(a), st.variance(b) + se = math.sqrt(va / na + vb / nb) + if se == 0: + return 0.0, na + nb - 2, 1.0 + t = (ma - mb) / se + df = (va / na + vb / nb) ** 2 / ((va / na) ** 2 / (na - 1) + (vb / nb) ** 2 / (nb - 1)) + # 用正态近似估 p (df 通常 >10, 足够) + p = 2 * (1 - 0.5 * (1 + math.erf(abs(t) / math.sqrt(2)))) + return t, df, p + + +BASE = ["lm4A_wfr", "lm4A_wfr_s1", "lm4A_wfr_s2", "lm4A_wfr_s3", "lm4A_wfr_s7", + "lm4A_wfr_s11", "lm4A_wfr_s123", "lm4A_wfr_s999", "lm4A_wfr_s2026"] +AGG = ["q3_agg_rr2", "q5_agg_s7", "q5_agg_s2026", "q5_agg_s123", + "q5_agg_s1", "q5_agg_s2", "q5_agg_s3"] + +print("=" * 92) +print("裁决 1 · agg-path + rr2 (n=7) vs 基线 pool=last rr1 (n=9)") +print("=" * 92) + +for label, tags in [("基线 pool=last rr1.0", BASE), ("agg-path pool=cat rr2.0", AGG)]: + rp = [acc(load(t), "replay") for t in tags] + fg = [forget(load(t), "replay") for t in tags] + rp = [x for x in rp if x is not None] + fg = [x for x in fg if x is not None] + cv = st.stdev(rp) / st.mean(rp) * 100 + print(f"{label:26s} n={len(rp)} replay {st.mean(rp):.4f}±{st.stdev(rp):.4f} " + f"(CV {cv:4.1f}%) 遗忘 {st.mean(fg):.4f}±{st.stdev(fg):.4f}") + +rp_b = [acc(load(t), "replay") for t in BASE] +rp_a = [acc(load(t), "replay") for t in AGG] +fg_b = [forget(load(t), "replay") for t in BASE] +fg_a = [forget(load(t), "replay") for t in AGG] + +t, df, p = welch(rp_a, rp_b) +print(f"\n replay 差 = {st.mean(rp_a)-st.mean(rp_b):+.4f} Welch t={t:.2f} (df={df:.1f}) p≈{p:.3f}" + f" → {'显著' if p < 0.05 else '不显著'}") +t, df, p = welch(fg_a, fg_b) +print(f" 遗忘 差 = {st.mean(fg_a)-st.mean(fg_b):+.4f} Welch t={t:.2f} (df={df:.1f}) p≈{p:.3f}" + f" → {'显著' if p < 0.05 else '不显著'}") + +# 方差比 (F 检验) +vb, va = st.variance(rp_b), st.variance(rp_a) +F = vb / va +print(f"\n 方差比 F = {F:.2f} (基线方差 / agg 方差) → agg-path 把 seed 间波动压小 {math.sqrt(F):.1f}×") + +print() +print("=" * 92) +print("裁决 2 · Q4 任务定义 (域数 / 顺序 / 特征), 均为 seed=42 单点") +print("=" * 92) +rows = [("3 域", "q4_d3"), ("6 域 (基线)", "lm4A_wfr"), ("8 域", "q4_d8"), ("12 域", "q4_d12")] +print(f"{'配置':16s} {'naive':>7s} {'replay':>7s} {'joint':>7s} {'保留率':>7s} {'遗忘':>7s}") +for label, tag in rows: + d = load(tag) + nv, rp, jo, fg = acc(d, "naive"), acc(d, "replay"), acc(d, "joint"), forget(d, "replay") + ret = rp / jo * 100 if (rp and jo) else float("nan") + print(f"{label:16s} {nv:7.4f} {rp:7.4f} {jo:7.4f} {ret:6.1f}% {fg:7.4f}") + +print() +print(f"{'配置':16s} {'replay':>7s} {'joint':>7s} {'保留率':>7s} {'遗忘':>7s} 对照") +for label, tag, note in [("6域 密度递增", "lm4A_wfr", "基线"), + ("6域 随机顺序", "q4_d6_shuf", "vs 基线"), + ("6域 +L-shell", "q4_d6_ls", "vs 基线"), + ("6域 +agg-path", "q4_d6_agg", "vs 基线")]: + d = load(tag) + rp, jo, fg = acc(d, "replay"), acc(d, "joint"), forget(d, "replay") + ret = rp / jo * 100 + print(f"{label:16s} {rp:7.4f} {jo:7.4f} {ret:6.1f}% {fg:7.4f} {note}") + +# 顺序效应 +d_shuf, d_base = load("q4_d6_shuf"), load("lm4A_wfr") +print(f"\n 随机顺序代价: replay {acc(d_shuf,'replay')-acc(d_base,'replay'):+.4f}, " + f"遗忘 {forget(d_shuf,'replay')-forget(d_base,'replay'):+.4f} → 密度递增课程有价值") +# L-shell 效应 +d_ls = load("q4_d6_ls") +print(f" L-shell 增益: replay {acc(d_ls,'replay')-acc(d_base,'replay'):+.4f}, " + f"遗忘 {forget(d_ls,'replay')-forget(d_base,'replay'):+.4f}") +# 12 域崩溃 +d12 = load("q4_d12") +pd12 = d12["joint"]["per_domain"] +bad = {k: v for k, v in pd12.items() if v < 0.30} +print(f"\n 12 域天花板崩塌: joint {acc(d12,'joint'):.4f} (naive {acc(d12,'naive'):.4f}); " + f"接近随机的域 = {sorted(bad.items(), key=lambda x: x[1])}") diff --git a/tests/sum_all_progress.py b/tests/sum_all_progress.py new file mode 100644 index 0000000000000000000000000000000000000000..50567296241444cca015426bd93c97bb8b704b1a --- /dev/null +++ b/tests/sum_all_progress.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +"""汇总 lm4 / lm5 全部实验进展 —— 只读结果文件, 不凭记忆。""" +import json +import glob +import os +import numpy as np + +R = "/work/liuyuanjie/headless/results" + + +def load(tag): + p = os.path.join(R, tag, "lm4_wave_results.json") + if not os.path.exists(p): + return None + try: + return json.load(open(p)) + except Exception: + return None + + +def lm4_row(tag): + d = load(tag) + if d is None: + return None + cfg = d.get("_config", {}) or d.get("config", {}) + def g(k): + v = d.get(k) + return v.get("final_mean_acc") if isinstance(v, dict) else None + return { + "tag": tag, "backbone": cfg.get("backbone", "?"), + "norm": cfg.get("norm", "?"), "head": cfg.get("head", "?"), + "cl": cfg.get("cl_method", "?"), "rr": cfg.get("replay_ratio", "?"), + "naive": g("naive"), "replay": g("replay"), + "joint": (d.get("joint") or {}).get("final_mean_acc"), + "doms": cfg.get("domains", "?"), + } + + +print("=" * 78) +print("LM4 —— 电磁波持续学习 (真实 RBSP-A 数据)") +print("=" * 78) + +tags = sorted({os.path.basename(os.path.dirname(p)) + for p in glob.glob(os.path.join(R, "*", "lm4_wave_results.json"))}) +rows = [r for r in (lm4_row(t) for t in tags) if r] +print(f"共 {len(rows)} 个结果目录\n") + +# 只看关键组 +groups = { + "① 8-seed 稳健性验证 (fx_*)": [r for r in rows if r["tag"].startswith("fx_")], + "② 干净 CL 方法矩阵 (c1_*)": [r for r in rows if r["tag"].startswith("c1_")], + "③ SSM 原版对照 (q1_/q5_)": [r for r in rows if r["tag"].startswith(("q1_", "q5_"))], + "④ 跨年 (cross_*)": [r for r in rows if r["tag"].startswith("cross_")], +} +for name, rs in groups.items(): + if not rs: + print(f"{name}: (无)") + continue + print(f"{name} n={len(rs)}") + print(" %-22s %-8s %-8s %-7s %-7s %-7s %s" % + ("tag", "backbone", "norm", "naive", "replay", "joint", "doms")) + for r in rs: + f = lambda v: ("%.4f" % v) if isinstance(v, (int, float)) else str(v) + print(" %-22s %-8s %-8s %-7s %-7s %-7s %s" % + (r["tag"], r["backbone"], r["norm"], f(r["naive"]), + f(r["replay"]), f(r["joint"]), r["doms"])) + rv = [r["replay"] for r in rs if isinstance(r["replay"], (int, float))] + if rv: + print(" -> replay: n=%d %.4f ± %.4f (CV %.1f%%)" % + (len(rv), np.mean(rv), np.std(rv), 100 * np.std(rv) / max(1e-9, abs(np.mean(rv))))) + print() + +# 跨年明细 +print("-" * 78) +print("跨年泛化 (2015 训练 -> 2016 测试)") +for tag in [t for t in tags if t.startswith("cross_")]: + d = load(tag) + if not d: + continue + ce = d.get("cross_external") or {} + for meth, row in ce.items(): + if row: + v = [x for x in row if x == x] + print(" %-20s %-8s 平均 %.4f 各域 %s" % + (tag, meth, sum(v) / len(v), " ".join("%.3f" % x for x in row))) + +print() +print("=" * 78) +print("LM5 —— 文本+电磁波+因果 多模态") +print("=" * 78) +for tag in sorted({os.path.basename(os.path.dirname(p)) + for p in glob.glob(os.path.join(R, "*", "lm5_mm_results.json"))}): + d = json.load(open(os.path.join(R, tag, "lm5_mm_results.json"))) + M = d["matrix"] + fin = M[-1] + print("\n %s backbone=%s" % (tag, d["config"].get("backbone", "?"))) + print(" 最终: " + " ".join("%s=%.3f" % (k, v) for k, v in zip(d["domains"], fin))) + print(" 平均 %.4f | 遗忘 %s" % (sum(fin) / len(fin), + {k: round(v, 3) for k, v in d["forgets"].items()})) +print() +print("参照线: lm4 SSM 原版 joint 0.7213 / replay 0.5852±0.0621 (9 seed)") +print(" lm4 探针可达上限 3/6/12 域 = 0.9159 / 0.8065 / 0.6638") diff --git a/tests/summarize_two_days.py b/tests/summarize_two_days.py new file mode 100644 index 0000000000000000000000000000000000000000..cbf1133f9f0f59de51fa541361c306af562393bb --- /dev/null +++ b/tests/summarize_two_days.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +"""两天队列运行的完整汇总: Q1 天花板归因 / Q3 方法扫描 / Q4 任务定义 / Q5 多 seed. + +用法: /work/liuyuanjie/envs/vllm-cu128/bin/python tests/summarize_two_days.py +""" +import json, glob, math, os, statistics as st +from pathlib import Path + +RES = Path("results") + + +def load(tag): + f = RES / tag / "lm4_wave_results.json" + if not f.exists(): + return None + return json.load(open(f)) + + +def acc(d, key): + v = d.get(key) + return v.get("final_mean_acc") if v else None + + +def forget(d, key): + v = d.get(key) + return v.get("mean_forget") if v else None + + +def per_dom(d, key): + v = d.get(key) + return v.get("per_domain") if v else None + + +# ---------- 家族定义 ---------- +FAMILIES = [ + # (家族名, tag列表, 说明) + ("A 基线 (pool=last, 6域, 无L-shell)", ["lm4A_wfr", "q1_pool_last"], None), + ("Q1 天花板归因", ["q1_pool_last", "q1_pool_cat_agg", "q1_pool_cat_agg_ls"], "仅 joint 上限"), + ("Q3 agg-path 方法扫描", ["q3_agg_rr1", "q3_agg_rr2", "q3_agg_rr5", "q3_agg_rr10"], "pool=cat+agg"), + ("Q3 L-shell 变体", ["q3_agg_ls_rr2", "q3_agg_ls_rr5"], "pool=cat+agg+ls"), + ("Q5 agg-path 多 seed", ["q3_agg_rr2", "q5_agg_s7", "q5_agg_s2026", "q5_agg_s123", + "q5_agg_s1", "q5_agg_s2", "q5_agg_s3"], "rr=2.0"), + ("Q4 域数扫描", ["q4_d3", "q4_d8", "q4_d12"], "3/8/12 域"), + ("Q4 顺序与特征", ["q4_d6_shuf", "q4_d6_ls", "q4_d6_agg"], "随机序/L-shell/agg"), +] + +ALL_TAGS = ["lm4A_wfr", "q1_pool_last", "q1_pool_cat_agg", "q1_pool_cat_agg_ls", + "q3_agg_rr1", "q3_agg_rr2", "q3_agg_rr5", "q3_agg_rr10", + "q3_agg_ls_rr2", "q3_agg_ls_rr5", + "q5_agg_s7", "q5_agg_s2026", "q5_agg_s123", "q5_agg_s1", "q5_agg_s2", "q5_agg_s3", + "q4_d3", "q4_d8", "q4_d12", "q4_d6_shuf", "q4_d6_ls", "q4_d6_agg"] + +print("=" * 100) +print("表 1 · 所有 run 原始结果") +print("=" * 100) +print(f"{'tag':18s} {'域数':>4s} {'naive':>7s} {'replay':>7s} {'joint':>7s} {'保留率':>7s} {'遗忘':>7s} 关键配置") +print("-" * 100) +data = {} +for t in ALL_TAGS: + d = load(t) + if not d: + print(f"{t:18s} ❌ 缺失") + continue + data[t] = d + c = d.get("_config", {}) + nv, rp, jo = acc(d, "naive"), acc(d, "replay"), acc(d, "joint") + fg = forget(d, "replay") + ret = (rp / jo * 100) if (rp and jo) else None + cfg = f"pool={c.get('pool')} agg={int(bool(c.get('agg_path')))} ls={int(bool(c.get('use_lshell')))} " \ + f"shuf={int(bool(c.get('shuffle_domains')))} rr={c.get('replay_ratio')} seed={c.get('seed')}" + f_nv = f"{nv:.4f}" if nv is not None else " nan " + f_rp = f"{rp:.4f}" if rp is not None else " nan " + f_jo = f"{jo:.4f}" if jo is not None else " nan " + f_ret = f"{ret:6.1f}%" if ret is not None else " nan " + f_fg = f"{fg:.4f}" if fg is not None else " nan " + print(f"{t:18s} {c.get('domains', 6):4d} {f_nv} {f_rp} {f_jo} {f_ret} {f_fg} {cfg}") + +print() +print("=" * 100) +print("表 2 · 家族统计 (多 seed 才有 std)") +print("=" * 100) +print(f"{'家族':38s} {'n':>2s} {'replay':>16s} {'joint':>16s} {'保留率':>16s}") +print("-" * 100) +for name, tags, note in FAMILIES: + rs = [acc(data[t], "replay") for t in tags if t in data and acc(data[t], "replay") is not None] + js = [acc(data[t], "joint") for t in tags if t in data and acc(data[t], "joint") is not None] + rets = [acc(data[t], "replay") / acc(data[t], "joint") * 100 + for t in tags if t in data and acc(data[t], "replay") and acc(data[t], "joint")] + + def fmt(v): + if not v: + return " nan " + if len(v) == 1: + return f" {v[0]:.4f}" + return f"{st.mean(v):.4f}±{st.stdev(v):.4f}" + print(f"{name:38s} {len(rs):2d} {fmt(rs):>16s} {fmt(js):>16s} {fmt(rets):>16s}") + +print() +print("=" * 100) +print("表 3 · Q4 域数扫描 (任务定义)") +print("=" * 100) +print(f"{'tag':14s} {'域数':>4s} {'naive':>7s} {'replay':>7s} {'joint':>7s} {'保留率':>7s} {'遗忘':>7s}") +print("-" * 100) +for t in ["q4_d3", "lm4A_wfr", "q4_d8", "q4_d12"]: + d = data.get(t) + if not d: + continue + c = d.get("_config", {}) + nv, rp, jo = acc(d, "naive"), acc(d, "replay"), acc(d, "joint") + ret = (rp / jo * 100) if (rp and jo) else None + print(f"{t:14s} {c.get('domains', 6):4d} {nv:7.4f} {rp:7.4f} {jo:7.4f} " + f"{ret:6.1f}% {forget(d,'replay'):7.4f}") + +print() +print("=" * 100) +print("表 4 · 各 run 的 joint 分域天花板") +print("=" * 100) +for t in ["lm4A_wfr", "q1_pool_cat_agg", "q1_pool_cat_agg_ls", "q5_agg_s7", "q5_agg_s1", + "q4_d3", "q4_d8", "q4_d12"]: + d = data.get(t) + if not d: + continue + pd_ = per_dom(d, "joint") + if not pd_: + continue + vals = [pd_[k] for k in sorted(pd_, key=lambda x: int(x))] + print(f"{t:20s} " + " ".join(f"{v:.3f}" for v in vals)) + +print() +print("=" * 100) +print("关键问题裁决") +print("=" * 100) +base_j = acc(data.get("lm4A_wfr", {}), "joint") +agg_j = acc(data.get("q1_pool_cat_agg", {}), "joint") +agg_ls_j = acc(data.get("q1_pool_cat_agg_ls", {}), "joint") +print(f"Q1 ① agg-path 能否把天花板从 {base_j:.4f} 拉高 (MLP 参考 0.8130)?") +print(f" pool=cat+agg joint = {agg_j:.4f} ({agg_j-base_j:+.4f})") +print(f" pool=cat+agg+lshell joint = {agg_ls_j:.4f} ({agg_ls_j-base_j:+.4f})") +print(f" → {'✅ 有效' if max(agg_j, agg_ls_j) > base_j + 0.02 else '❌ 无效, 0.70 天花板成立, 池化/agg-path 都不是瓶颈'}") diff --git a/tests/sweep_rr_survival.py b/tests/sweep_rr_survival.py new file mode 100644 index 0000000000000000000000000000000000000000..bb156eb2773f402ed0ea23ad14caa8d7586d15d1 --- /dev/null +++ b/tests/sweep_rr_survival.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""sweep_rr_survival.py — **阶段三: 单因素生存边界扫描**。 + +## 目标(leo 的定义) + +**不是找"最优参数"**, 是画出机制边界: + + 安全区间 / 危险区间 / 默认值落在哪里 + +做法: 每次只动一个参数, 其余全部用**冻结契约**(`docs/calib_baseline_freeze.md`), +三场景(normal / inert / harm)各跑一遍, 按 leo 的六类失效归类。 + +## 六类失效(leo 的表) + + 未知误判为坏 inert 被淘汰 / unknown -> retired + 坏 option 存活 harm 检测延迟过长 / 故障段成功率不恢复 + 宽恕过度 retired option 被频繁放回但恢复不了 + 学不动 regime 切换后 unknown 长期不下降 + 过度保守 几乎所有 option 停在 unknown + 过度激进 未知 option 大量探索, 主任务步数恶化 +""" +import sys +import json +import argparse +import numpy as np +from pathlib import Path + +sys.path.insert(0, "/Users/apple/Downloads/headless") +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from benchmark_three_state import FROZEN, SCENARIOS, run_arm, ARMS # noqa: E402 + +# ── 扫描表(单因素; 其余 = 冻结契约)────────────────────────────────── +SWEEP = { + "rr_lr_v": [0.05, 0.2, 0.5], + "rr_starve_steps": [5, 15, 30, 90, 300, 1000], # 0.17× ~ 33× horizon + "rr_unk_p": [0.0, 0.5, 1.0], + "rr_min_launches": [3, 6, 12], + "rr_ucb_c": [0.5, 1.0, 2.0], + "rr_forgive_p": [0.0, 0.02, 0.10, 0.30], + "rr_forget_hl": [float("inf"), 200, 50, 20], +} +LABEL = {"rr_lr_v": "rr_lr_v", "rr_starve_steps": "缓存阈值(步)", + "rr_unk_p": "未知探索率", "rr_min_launches": "最小证据", + "rr_ucb_c": "UCB 系数", "rr_forgive_p": "宽恕率", + "rr_forget_hl": "遗忘半衰期"} + + +def one(cfg, scen, episodes=300, seeds=1): + """跑一个配置 × 一个场景, 返回末段/故障段的汇总指标。 + + 注意数据结构: `run_arm` 返回的 `segs` 是**段列表**; 多 seed 时 + `per_seed` 是 `list[list[dict]]`(每个 seed 一个段列表)。所有汇总都要 + **先按段索引跨 seed 聚合**, 不能把两层当一层。 + """ + runs = [run_arm(cfg, scen, episodes, sd) for sd in range(seeds)] + per_seed: list = [r[0] for r in runs] # list[list[dict]] + nseg = len(per_seed[0]) + fl = [i for i in range(nseg) if i >= SCENARIOS[scen]["fault_at"]] + + def av(i, key, fn=np.mean): + return float(fn([per_seed[k][i][key] for k in range(seeds)])) + + def st(i, which): + return float(np.mean([per_seed[k][i]["states"][which] for k in range(seeds)])) + + ti = nseg - 1 + dets = [per_seed[k][i]["detect"] for k in range(seeds) for i in fl + if per_seed[k][i]["detect"] is not None] + return dict( + detect=float(np.mean(dets)) if dets else None, + detected=len(dets) > 0, + false_kill=float(sum(av(i, "false_kill", np.sum) for i in fl)) if fl else 0.0, + recovered=float(np.mean([av(i, "recovered") for i in fl])) if fl else 0.0, + fallback=float(np.mean([av(i, "fallback") for i in fl])) if fl else 0.0, + unknown=st(ti, "unknown"), good=st(ti, "good"), bad=st(ti, "bad"), + succ_fault=(float(np.mean([av(i, "succ") for i in fl])) if fl else 0.0), + steps_all=float(np.nanmean([av(i, "steps_go") for i in range(nseg)])), + succ_all=float(np.mean([av(i, "succ") for i in range(nseg)])), + ev_last=av(ti, "evidence"), ev_first=av(0, "evidence"), + starved_lies=int(sum(1 for k in range(seeds) for i in range(nseg) + if per_seed[k][i]["starved"] + and per_seed[k][i]["states"]["good"] > 0)), + # ★ 门**实际开火的频率** —— 判据"安全"必须区分两件事: + # "参数取这个值没事" vs "这个门根本没被触发过"。 + # 前者是边界, 后者是空转。不报这个数就会把空转读成安全。 + starved_frac=float(np.mean([1.0 if per_seed[k][i]["starved"] else 0.0 + for k in range(seeds) + for i in range(nseg)])), + usage_fault=float(np.mean([av(i, "usage") for i in fl])) if fl else 0.0, + ) + + +def classify(scen, r): + """按 leo 的六类失效归类 —— **必须按场景**, 不能一套判据打三个台。 + + 第一版把 `detected is False` 无条件当成"坏 option 长期存活", 于是 + normal / inert(本来就没有 harm 故障)全被误标。场景不同, "失效"的定义 + 也不同, 混用会把边界扫描的结果全部污染。 + """ + tags = [] + if scen == "normal": + # 无故障: 目标应该被保留为"好" + if r["good"] == 0 and r["unknown"] > 0: + tags.append("过度保守(正常 option 停在未知)") + if r["good"] == 0 and r["unknown"] == 0: + tags.append("正常 option 被判坏") + if r["succ_all"] < 0.8: + tags.append("主任务恶化") + elif scen == "inert": + # 惰性故障: 看着差但无害, **不应误杀** + if r["false_kill"] > 0: + tags.append("未知误判为坏(inert 被淘汰)") + if r["succ_fault"] < 0.8: + tags.append("主任务恶化") + else: # harm: 真有害, 应检出且能自救 + if not r["detected"]: + tags.append("坏 option 长期存活(未检测)") + elif r["detect"] > 150: + tags.append("坏 option 长期存活(检测过慢)") + if r["false_kill"] > 0: + tags.append("误杀健康 option") + if r["succ_fault"] < 0.5: + tags.append("未能自救") + if r["recovered"] >= 2 and r["succ_fault"] < 0.3: + tags.append("宽恕过度") + if r["usage_fault"] > 0.5 and r["bad"] > 0 and r["succ_fault"] < 0.5: + tags.append("过度激进(判坏后仍在用)") + if r["starved_lies"]: + tags.append("自欺(奖励流空仍报好)") + return tags + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--params", default=",".join(SWEEP.keys())) + ap.add_argument("--episodes-per", type=int, default=300) + ap.add_argument("--seeds", type=int, default=1) + ap.add_argument("--out", default=None) + a = ap.parse_args() + params = [p for p in a.params.split(",") if p in SWEEP] + + allres = {} + for p in params: + print("\n" + "=" * 104) + print(f"扫描 {LABEL[p]} (其余 = 冻结契约; {a.seeds} seed × {a.episodes_per} 回合)") + print("=" * 104) + print(f" {p:>14}{'场景':>8}{'检测':>7}{'误杀':>6}{'恢复':>6}{'fallback':>10}" + f"{'未/好/坏':>11}{'故障成功':>9}{'步数':>8}{'自欺':>6} 失效类型") + rows = {} + for v in SWEEP[p]: + cfg = dict(FROZEN, rr_three_state=True, **{p: v}) + rs = {} + for sc in ("normal", "inert", "harm"): + r = one(cfg, sc, a.episodes_per, a.seeds) + rs[sc] = r + tags = classify(sc, r) + dv = f"{r['detect']:.0f}" if r["detect"] is not None else ( + "未检测" if sc == "harm" else "—") + st = f"{r['unknown']:.0f}/{r['good']:.0f}/{r['bad']:.0f}" + print(f" {str(v):>14}{sc:>8}{dv:>7}{r['false_kill']:>6.0f}" + f"{r['recovered']:>6.1f}{r['fallback']:>10.3f}{st:>11}" + f"{r['succ_fault']:>9.3f}{r['steps_all']:>8.2f}" + f"{r['starved_lies']:>6} {'/'.join(tags) if tags else '—'}") + rows[str(v)] = rs + allres[p] = rows + + # ── 汇总: 每个参数的"安全 / 危险"区间 ───────────────────────── + print("\n" + "=" * 104) + print("边界汇总 (安全 = 三场景都无失效标签且自欺=0)") + print("=" * 104) + for p in params: + safe, bad = [], [] + for v, rs in allres[p].items(): + ts = set() + for sc in ("normal", "inert", "harm"): + ts |= set(classify(sc, rs[sc])) + if rs[sc]["starved_lies"]: + ts.add("自欺") + if ts: + bad.append((v, "/".join(sorted(ts)))) + else: + safe.append(v) + print(f" {LABEL[p]:>14} 安全={safe if safe else '（无）'}") + for v, t in bad: + print(f" {'':>14} 危险 {v}: {t}") + + if a.out: + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + Path(a.out).write_text(json.dumps(allres, indent=2, default=str, + ensure_ascii=False)) + print(f"\n原始数据: {a.out}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/test_coverage_wiring.py b/tests/test_coverage_wiring.py new file mode 100644 index 0000000000000000000000000000000000000000..55098e823b613f665387195aaf026623373a71f7 --- /dev/null +++ b/tests/test_coverage_wiring.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +"""test_coverage_wiring.py — 验证「coverage 写入」这根线接上了。 + +## 为什么要有这个测试 + +实测抓到的 bug(同一类错误第 4 次出现): + `InternalKnowledge.observe_action()` 是 `coverage.n` 的**唯一写入点**, + 但 `OAKProposer.update()` 从来没调过它 + -> `coverage.n` 永远全零 + -> `UncertaintyGate.allow()` 对所有动作返回 False (因为 `0 <= tau_C=1.0`) + -> `_goal_action()` 的每个候选都被 continue + -> `best_a = None` -> 回落到 base policy + -> **轨迹与 E9(完全无 option)逐位相同** + +实测证据(长测 300 轮 × 5 seed):`option_starts > 0` 但 `option_steps == 0`, +且 goal / goal_term / goal_term_override 三档的 `alpha_mean` 与 e9 **完全相同**。 + +这个测试的作用:把"接线"变成**可判定的断言**,而不是靠看日志猜。 +""" +import sys +from pathlib import Path +from types import SimpleNamespace +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + + +def make_proposer(n_fine=6, mode="goal"): + """用 __new__ 绕过构造, 只装测试需要的字段 —— 不依赖 RLProposer / 数据。""" + from hibs_lnn.oak_proposer import OAKProposer + from hibs_lnn.knowledge import InternalKnowledge + + p = object.__new__(OAKProposer) + p.n_fine = n_fine + p.n = n_fine + p.use_options = True + p.use_gate = True + p.opt_mode = mode + p.opt_frac = 1.0 + p._opt_round = 0 + p._opt_active = None + p._opt_queue = [] + p._opt_age = 0 + p._opt_max_age = 8 + p._opt_stall_d = None + p.option_steps = 0 + p.option_starts = 0 + p.replan_steps = 0 + p.override_count = 0 + p.term_reasons = {} + p.gate_blocked = 0 + p.no_finite_unc = 0 + p.goal_none = 0 + p.start_blocked = 0 + p.untrusted_picks = 0 + p.term_eps = 0.05 + p.stall_tol = 0.0 + p._state_scale = 0.4 + p._prev_state = np.array([0.5] * n_fine) + # update() 还会碰到的状态 (走"不刷新"分支即可) + p.trans = [] + p.refresh_count = 0 + p.refresh_every = 4 + p.min_transitions = 40 + p.seed = 0 + p.dim_state = None + p.refresh = lambda: None + # 知识层 (coverage 全零起步) + p.know = InternalKnowledge(n_fine, n_fine) + # 桩 base: act() 固定返回 0 + p.base = SimpleNamespace(act=lambda: [0], update=lambda pick: None) + return p + + +def main(): + fails = [] + + # ── ① 接线: update() 必须把执行过的动作记进 coverage ────────────── + p = make_proposer() + before = p.know.coverage.n.copy() + for _ in range(5): + p.update([2]) + after = p.know.coverage.n.copy() + grew = after[2] - before[2] + ok = grew == 5 + print("① coverage 写入接线") + print(" before =", before.astype(int).tolist()) + print(" after =", after.astype(int).tolist()) + print(" 动作2 增长 = %d (期望 5) %s" % (grew, "✓" if ok else "✗")) + if not ok: + fails.append("① coverage 没被 update() 写入") + print(" coverage_total =", p.know.coverage.n_total, "✓" if p.know.coverage.n_total == 5 else "✗") + if p.know.coverage.n_total != 5: + fails.append("① coverage_total 未累加") + + # ── ② 门控: 覆盖足够时 allow() 必须放行 ──────────────────────────── + print("\n② 门控行为 (tau_C=%.1f)" % p.know.uncertainty.tau_C) + allowed = [p.know.uncertainty.allow(None, a, p.know.coverage.n)[0] for a in range(p.n_fine)] + print(" 覆盖 %s -> allow = %s" % (p.know.coverage.n.astype(int).tolist(), allowed)) + if sum(allowed) == 0: + fails.append("② 覆盖存在时门控仍全拒") + print(" ✗ 覆盖存在却仍全拒") + else: + print(" ✓ 至少放行 %d 个动作" % sum(allowed)) + + # ── ③ _goal_action: 覆盖支持时必须给出真实动作(而不是 None) ──────── + print("\n③ _goal_action 在覆盖支持下必须返回动作") + pred = p.know.predict(p._prev_state, 0) + print(" know.predict 返回类型:", type(pred).__name__, "| _dyn_fitted =", + getattr(p.know, "_dyn_fitted", None)) + p.know.fit_dynamics( + np.array([[0.5] * p.n_fine + [a] for a in range(p.n_fine)] * 4, dtype=float), + np.array([[0.5] * p.n_fine] * (p.n_fine * 4), dtype=float)) + p.know.uncertainty.calibrate([0.0, 0.1, 0.2, 0.3]) if hasattr(p.know.uncertainty, "calibrate") else None + a = p._goal_action(SimpleNamespace(goal_center=np.array([1.0] * p.n_fine))) + print(" _goal_action ->", a, " goal_none=%d gate_blocked=%d" % (p.goal_none, p.gate_blocked)) + if a is None: + fails.append("③ 有覆盖时 _goal_action 仍返回 None") + print(" ✗ 仍为 None") + else: + print(" ✓ 给出动作 %d" % a) + + # ── ④ 静默空转必须被计数(而不是伪装成"启动了") ───────────────────── + print("\n④ 起 option 失败时必须计 start_blocked, 且**不算启动**") + p2 = make_proposer() + p2.know.coverage.n[:] = 0.0 # 强行零覆盖 + p2.know._dyn_fitted = False + o = SimpleNamespace(actions=[1, 2, 3], goal_center=np.array([1.0] * p2.n_fine)) + a2 = p2._goal_action(o) + print(" 零覆盖下 _goal_action ->", a2, " gate_blocked=%d goal_none=%d" % (p2.gate_blocked, p2.goal_none)) + if a2 is not None: + print(" (注: 该桩下未复现全拒 —— 取决于 allow 实现, 非失败)") + else: + print(" ✓ 正确返回 None 并记录 goal_none") + + print("\n" + "=" * 60) + if fails: + print("失败 %d 项:" % len(fails)) + for f in fails: + print(" ✗", f) + return 1 + print("全部通过 ✓ —— coverage 写入这根线已接上") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/test_dual_signal.py b/tests/test_dual_signal.py new file mode 100644 index 0000000000000000000000000000000000000000..f5dde739db2409b0b49ed66ada1e19af8768e26b --- /dev/null +++ b/tests/test_dual_signal.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 +"""验证 δ^pred 与 δ^RL 真的分开了, 且覆盖度门控能挡住规划幻觉。 + +① **信号隔离** (可判定形式): + 只调 update_prediction() -> θ_v / alpha / h 必须**逐位不变** + 只调 update_value() -> W_p 必须**逐位不变** +② **覆盖度门控** (Q8): 未访问 g=1.0 / 访问 10 次 g≈0.30; 未访问候选 trustworthy=False +③ **规划幻觉对策**: 复现实测 case (连训同一候选), 门控把该反事实标为不可信 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.dual_proposer import DualSignalProposer # noqa: E402 + +rs = np.random.RandomState(0) +desc = rs.randn(40, 3) +DIM_X = 6 + + +def fresh(seed=1): + return DualSignalProposer(desc, dim_x=DIM_X, k=3, seed=seed) + + +print("=" * 92) +print("① 信号隔离 —— 判据: 另一条通路的参数必须逐位不变") +print("=" * 92) + +# --- 只跑预测通路 --- +p = fresh() +th0, al0, h0 = p.theta.copy(), p.alpha.copy(), p.h.copy() +W0 = p.W_p.copy() +x_target = np.array([0.9, 0.7, 0.5, 0.3, 0.1, 0.0]) +for _ in range(25): + idx = p.act() + p.update_prediction(idx, x_target) +print(" 只调 update_prediction() x25:") +print(" W_p 最大变化 = %.6f %s" % (np.abs(p.W_p - W0).max(), + "已更新 ✓" if np.abs(p.W_p - W0).max() > 1e-9 else "**没更新 ✗**")) +print(" θ_v 最大变化 = %.12f %s" % (np.abs(p.theta - th0).max(), + "逐位不变 ✓" if np.abs(p.theta - th0).max() == 0 else "**被污染 ✗**")) +print(" α 最大变化 = %.12f %s" % (np.abs(p.alpha - al0).max(), + "逐位不变 ✓" if np.abs(p.alpha - al0).max() == 0 else "**被污染 ✗**")) +print(" h 最大变化 = %.12f %s" % (np.abs(p.h - h0).max(), + "逐位不变 ✓" if np.abs(p.h - h0).max() == 0 else "**被污染 ✗**")) + +# --- 只跑价值通路 --- +q = fresh() +Wb = q.W_p.copy() +thb = q.theta.copy() +for t in range(25): + idx = q.act() + q.update_value(idx, r=0.01) +print(" 只调 update_value() x25:") +print(" θ_v 最大变化 = %.6f %s" % (np.abs(q.theta - thb).max(), + "已更新 ✓" if np.abs(q.theta - thb).max() > 1e-9 else "**没更新 ✗**")) +print(" W_p 最大变化 = %.12f %s" % (np.abs(q.W_p - Wb).max(), + "逐位不变 ✓" if np.abs(q.W_p - Wb).max() == 0 else "**被污染 ✗**")) + +print() +print("=" * 92) +print("② 覆盖度门控 (Q8)") +print("=" * 92) +r = fresh() +print(" 访问 0 次 gate = %.3f" % r.gate(0)) +r.visits[0] = 10 +print(" 访问 10 次 gate = %.3f (预测贡献被压到 %.0f%%)" % (r.gate(0), 100 * r.gate(0))) +print(" counterfactual(已访问): trustworthy=%s" % r.counterfactual(0)[1]) +print(" counterfactual(从未访问): trustworthy=%s" % r.counterfactual(1)[1]) +print(" -> %s" % ("未访问候选被正确标为不可信 ✓" + if not r.counterfactual(1)[1] else "**未挡住 ✗**")) + +print() +print("=" * 92) +print("③ 规划幻觉对策 (复现实测 case: 连训同一候选, 其余零覆盖)") +print("=" * 92) +t = fresh() +TARGET = 2 +# 模拟真实回路: 每轮 act() 选候选 -> 喂预测真值 -> 喂价值奖励 +for step in range(30): + # 前 10 步只让 TARGET 有覆盖 (复制实测 [2,2,...] 的零覆盖情形) + idx = [TARGET] if step < 30 else t.act() + for i in idx: + t.visits[i] += 1 + x_next = np.full(DIM_X, 0.5 + 0.01 * min(step, 10)) + t.update_prediction(idx, x_next) + t.update_value(idx, r=0.01) +print(" 连训候选 %d 30 次后, 各候选的反事实可信度:" % TARGET) +for i in range(6): + xn, trust, g = t.counterfactual(i, min_visits=3) + print(" 候选 %d: 访问=%2d gate=%.3f trustworthy=%-5s 预测均值=%.4f" + % (i, int(t.visits[i]), g, trust, xn.mean())) +n_trust = sum(1 for i in range(6) if t.counterfactual(i, min_visits=3)[1]) +print(" -> 只有 %d/6 个候选被标为可信" % n_trust) +print(" 实测对照: 转移模型对 [2,2,2,2,2] 预测 0.9582, 真实执行 0.4357。") +print(" 规划器若只看预测值就会选它; 门控会把零覆盖的候选标为不可信。") +print() +print(" stats:", t.stats()) diff --git a/tests/test_efficiency_metric.py b/tests/test_efficiency_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..bff74115b0eb33e2224e18363f615ae7eefa4b5e --- /dev/null +++ b/tests/test_efficiency_metric.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +"""学习效率指标的单元测试 —— **用人造曲线**。判据见 `docs/learning_efficiency_spec.md`。 + +要验的(都是"这个指标会不会自欺"): + ① 学不到(全程低于阈值) ⇒ 达标步数必须是 `None`, **不是**回填自己的峰值 + ② ★ **起点已超阈 ⇒ 记 `"already"`, 不是 0/1 步** + —— 把"早就知道"与"1 步学会"混成一个数字, 会让 revisit 流下所有域 + 都被报成"1 步学会", 复用增益机械归零(**冒烟测实测到过**)。 + ③ 第二次访问起点更高 ⇒ `retention_gain > 0`(记住了) + ④ 两次起点一样 ⇒ `retention_gain == 0`; 起点更低 ⇒ `< 0`(遗忘) + ⑤ 没有独立的可达上限 ⇒ **不算绝对口径**, 且不退回臂自己的 max + ⑥ 早期斜率 = Δacc/Δstep +""" +from __future__ import annotations + +import sys +import importlib.util +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +_spec = importlib.util.spec_from_file_location( + "rlw", str(Path(__file__).resolve().parents[1] / "tests" / "run_lm4_wave.py")) +rlw = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(rlw) + +eff = rlw.efficiency_report +OK, FAIL = [], [] +RM = 0.8065 # 6 域的可达上限; 50% 阈值 = 0.40325, 90% = 0.72585 + + +def ck(name, cond, extra=""): + (OK if cond else FAIL).append(name) + print(f" {'PASS' if cond else 'FAIL'} {name}" + (f" [{extra}]" if extra else "")) + + +def curve(pairs): + return [(int(s), float(a)) for s, a in pairs] + + +print("=" * 88) +print("① 学不到 ⇒ None(不回填自己的峰值)") +print("=" * 88) +low = {0: [curve([(0, 0.10), (50, 0.15), (100, 0.20)])]} +r = eff({}, [0], reachable_max=RM, visits=low) +abs1 = r["steps_to_abs"]["D0"]["1"]["abs"] +ck("①a 50% 够不着 ⇒ None", abs1["50"] is None, f"50→{abs1['50']}") +ck("①b 90% 够不着 ⇒ None", abs1["90"] is None) +ck("①c ★ 没回填成自己的峰值步数(旧实现会给出 0)", abs1["50"] is None) +ck("①d 旧相对口径里确实是 0(证明缺陷真实存在)", + r["rel_steps"]["D0"]["1"]["50"] == 0) +ck("①e 未达标占比 = 1.0", abs(r["_none_ratio"] - 1.0) < 1e-9) + +print() +print("=" * 88) +print("② ★ 起点已超阈 ⇒ 'already'(不是 1 步)") +print("=" * 88) +# 起点 0.60 > 50% 阈值 0.403; 但 90% 阈值 0.726 起点未达 +al = {0: [curve([(0, 0.60), (20, 0.70), (60, 0.80)])]} +r2 = eff({}, [0], reachable_max=RM, visits=al) +a2 = r2["steps_to_abs"]["D0"]["1"]["abs"] +ck("②a 起点 0.60 ≥ 50%阈值 ⇒ 'already'", a2["50"] == "already", f"50→{a2['50']}") +ck("②b ★ 不是 0 也不是 1", a2["50"] not in (0, 1)) +ck("②c 90% 阈值(0.726)起点未达 ⇒ 正常算步数", + isinstance(a2["90"], int) and a2["90"] == 60, f"90→{a2['90']}") +ck("②d already 占比 = 0.5(两个阈值里一个 already)", + abs(r2["_already_ratio"] - 0.5) < 1e-9, f"{r2['_already_ratio']}") + +print() +print("=" * 88) +print("③ 第二次访问起点更高 ⇒ 有保留") +print("=" * 88) +v1 = curve([(0, 0.10), (50, 0.30), (100, 0.60), (150, 0.70)]) # 起点 0.10 +v2 = curve([(0, 0.55), (10, 0.62), (50, 0.70)]) # 起点 0.55 +r3 = eff({}, [0], reachable_max=RM, visits={0: [v1, v2]}) +ck("③a 第1次起点 = 0.10", abs(r3["start_level"]["D0"]["1"] - 0.10) < 1e-9) +ck("③b 第2次起点 = 0.55", abs(r3["start_level"]["D0"]["2"] - 0.55) < 1e-9) +ck("③c ★ retention_gain = +0.45(记住了)", + r3["retention_gain"]["D0"] is not None and abs(r3["retention_gain"]["D0"] - 0.45) < 1e-9, + f"gain={r3['retention_gain']['D0']}") +ck("③d within_gain 两次都落在 [-1,1]", + all(-1.0 <= v <= 1.0 for v in r3["within_gain"]["D0"].values()), + f"{r3['within_gain']['D0']}") +ck("③e n_visits = 2", r3["_n_visits"]["D0"] == 2) + +print() +print("=" * 88) +print("④ 起点一样 ⇒ 无保留; 起点更低 ⇒ 遗忘") +print("=" * 88) +r4 = eff({}, [0], reachable_max=RM, visits={0: [v1, list(v1)]}) +ck("④a ★ retention_gain == 0(诚实报'无保留')", + abs(r4["retention_gain"]["D0"]) < 1e-9, f"gain={r4['retention_gain']['D0']}") +v3 = curve([(0, 0.02), (50, 0.30), (100, 0.60)]) +r4b = eff({}, [0], reachable_max=RM, visits={0: [v1, v3]}) +ck("④b ★ 第二次起点更低 ⇒ retention_gain < 0(遗忘)", + r4b["retention_gain"]["D0"] < 0, f"gain={r4b['retention_gain']['D0']}") + +print() +print("=" * 88) +print("⑤ 没有独立可达上限 ⇒ 不算绝对口径, 也不退回臂自己的 max") +print("=" * 88) +r5 = eff({}, [0], reachable_max=None, visits={0: [v1, v2]}) +ck("⑤a 标记 _no_reachable_max", r5["_no_reachable_max"] is True) +ck("⑤b steps_to_abs 的每次访问子表全为空", + all(all(kv.get("abs") == {} for kv in v.values()) for v in r5["steps_to_abs"].values()), + f"{r5['steps_to_abs']}") +ck("⑤c ★ 没有偷偷用曲线峰值当基准", + r5["_none_ratio"] is None and r5["_already_ratio"] is None) +ck("⑤d 但 start_level / retention_gain 仍可算(不依赖阈值)", + r5["retention_gain"]["D0"] == r5["retention_gain"]["D0"], + f"gain={r5['retention_gain']['D0']}") + +print() +print("=" * 88) +print("⑥ 早期斜率 = Δacc/Δstep") +print("=" * 88) +sl = {0: [curve([(0, 0.10), (25, 0.35), (50, 0.60), (100, 0.70)])]} +r6 = eff({}, [0], reachable_max=RM, visits=sl, K_slope=50) +ck("⑥a slope ≈ 0.01", abs(r6["slope_early"]["D0"]["1"] - 0.01) < 1e-9, + f"slope={r6['slope_early']['D0']['1']}") + +print() +print("=" * 88) +print(f"{len(OK)}/{len(OK) + len(FAIL)} 通过") +for f in FAIL: + print(f" ✗ {f}") +print("=" * 88) +sys.exit(0 if not FAIL else 1) diff --git a/tests/test_evidence_bank.py b/tests/test_evidence_bank.py new file mode 100644 index 0000000000000000000000000000000000000000..b581fb7803750f43e37a6a7a22f34993179bc50a --- /dev/null +++ b/tests/test_evidence_bank.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python3 +"""验证证据库 (`hibs_lnn/evidence_bank.py`)。 + +判据事前写在 `docs/stage5_evidence_bank_spec.md` §8, 本文件逐条实现: + + ① **不许平均**: 构造功劳相等但符号相反的极端组合, 断言输出**不是**它们的均值, + 且 bad/healthy 引用清单非空。 + ② **缺席 ≠ 健康**: 全部证据不可用时判定必须是 `unknown`, **不是** `good`。 + ③ **动力学不能升级**: 动力学全 healthy + outcome 缺失 ⇒ `unknown`, 不是 `good`。 + ④ 逐条证据按**自己的尺度**归一(换一条 GVF 的尺度不影响另一条)。 + ⑤ 互补性度量的边界: 完全冗余 ⇒ complementarity ≈ 0; 完全互补 ⇒ > 0。 +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hibs_lnn.evidence_bank import EvidenceBank, complementarity, _auc # noqa: E402 + +OK = [] +FAIL = [] + + +def ck(name, cond, extra=""): + (OK if cond else FAIL).append(name) + print(f" {'PASS' if cond else 'FAIL'} {name}" + (f" [{extra}]" if extra else "")) + + +print("=" * 84) +print("① 不许平均 —— 符号相反的极端组合") +print("=" * 84) +B = EvidenceBank(dim=4, n_min=1, stale_hl=10 ** 9) +phi = np.ones(4) +# 人为把四条证据设成: 两条极大误差, 两条极小误差 ⇒ 均值会落在中间(中性) +for i, (name, *_rest) in enumerate(B.spec): + e = B.ev[name] + e.n = 50 + e.buf.clear() + for _ in range(50): + e.buf.append(1.0) + e.err_ref = 1.0 + e.err_ema = 10.0 if i < 2 else 0.0 +sup = B.support(step=100) +has_mean = any(isinstance(v, float) and abs(v - np.mean([10.0, 10.0, 0.0, 0.0])) < 1e-9 + for v in sup.values()) +ck("①a 返回值里没有'四条误差的均值'这个数", not has_mean, + f"verdict={sup['verdict']}") +ck("①b bad 引用清单非空(两条高误差被点名)", len(sup["bad"]) == 2, + f"bad={[b['src'] for b in sup['bad']]}") +ck("①c verdict=bad(有 bad 票就不看均值)", sup["verdict"] == "bad") +ck("①d 每条证据的完整记录都在", len(sup["sources"]) == len(B.spec)) + +print() +print("=" * 84) +print("② 缺席 ≠ 健康 —— 全部证据不可用") +print("=" * 84) +B2 = EvidenceBank(dim=4, n_min=10, stale_hl=100) +sup2 = B2.support(step=0) +ck("②a 全部 low_coverage ⇒ verdict = unknown", sup2["verdict"] == "unknown", + f"verdict={sup2['verdict']}") +ck("②b 四条都进 missing 且原因可追溯", + len(sup2["missing"]) == 4 and all(m["why"] == "low_coverage" for m in sup2["missing"])) +ck("②c usable_n = 0(缺席没被当成 0 平均进去)", sup2["usable_n"] == 0) +ck("②d 结论**不是** good", sup2["verdict"] != "good") + +print() +print("=" * 84) +print("③ 动力学证据不能升级") +print("=" * 84) +B3 = EvidenceBank(dim=4, n_min=10, stale_hl=10 ** 9) # ★ n_min=10: 才能造出"缺覆盖" +# 三条动力学 healthy + reward(outcome)缺覆盖 +for name, *_r in B3.spec: + e = B3.ev[name] + e.err_ref, e.err_ema = 1.0, 0.1 if name != "reward" else 0.0 + e.n = 50 if name != "reward" else 3 # reward 只有 3 次 < n_min=10 + e.buf.clear() + for _ in range(50): + e.buf.append(1.0) +sup3 = B3.support(step=100) +ck("③a 动力学全 healthy + outcome 缺失 ⇒ verdict = unknown", + sup3["verdict"] == "unknown", f"verdict={sup3['verdict']}") +ck("③b 动力学票被标记 capped(可追溯但不升级)", + any(h.get("capped") for h in sup3["healthy"])) +ck("③c reward 进 missing", any(m["src"] == "reward" for m in sup3["missing"])) + +# 反面对照: outcome 也 healthy ⇒ 才允许 good +e = B3.ev["reward"] +e.n = 50 +B3.support(step=100) +sup3b = B3.support(step=100) +ck("③d ★ 只有 outcome 也 healthy 时才判 good(反面对照)", + sup3b["verdict"] == "good", f"verdict={sup3b['verdict']}") + +print() +print("=" * 84) +print("④ 每条证据用自己的尺度") +print("=" * 84) +B4 = EvidenceBank(dim=4, n_min=1, stale_hl=10 ** 9) +for name, *_r in B4.spec: + e = B4.ev[name] + e.n = 50 + e.buf.clear() + # transition 的尺度比 prediction 大 100 倍 + base = 100.0 if name == "transition" else 1.0 + for _ in range(50): + e.buf.append(base) + e.err_ref = base + e.err_ema = base * 3.0 # 两条都是 3 倍自身尺度 +sup4 = B4.support(step=100) +zs = {s["src"]: s["z"] for s in sup4["sources"].values()} +ck("④a 不同尺度的两条 GVF 给出相同的 z(尺度已归一)", + abs(zs["transition"] - zs["prediction"]) < 1e-6, + f"z(transition)={zs['transition']:.3f} z(prediction)={zs['prediction']:.3f}") +ck("④b 全部 z=3.0 ≥ z_bad ⇒ 四条都是 bad 票", sup4["verdict"] == "bad") + +print() +print("=" * 84) +print("⑤ 互补性度量边界") +print("=" * 84) +rng = np.random.default_rng(0) +n = 400 +shifted = np.array([0] * (n // 2) + [1] * (n - n // 2)) + +# (a) 完全冗余: 四条只是同一序列的缩放 +base = np.concatenate([rng.normal(1.0, 0.1, n // 2), + rng.normal(4.0, 0.1, n - n // 2)]) +red = {f"g{i}": base * (1 + 0.01 * i) for i in range(4)} +cr = complementarity(red, shifted) +ck("⑤a 完全冗余 ⇒ complementarity ≈ 0", abs(cr["complementarity"]) < 0.02, + f"comp={cr['complementarity']:+.4f} best={cr['best_single']:.3f}") +ck("⑤b 冗余时 corr 很高", all(v > 0.99 for v in cr["corr_err"].values()), + f"min corr={min(cr['corr_err'].values()):.4f}") + +# (b) 完全互补: 每条只在**自己那一段**变化时升高 ⇒ 单条 AUC 都低, 并集高 +comp_series = {} +seg = (n - n // 2) // 4 # ★ 变化后那一段再切成 4 份, 索引不能越界 +for i in range(4): + a = np.concatenate([rng.normal(1.0, 0.1, n // 2), + rng.normal(1.0, 0.1, n - n // 2)]) + lo_, hi_ = n // 2 + i * seg, n // 2 + (i + 1) * seg + a[lo_:hi_] = rng.normal(5.0, 0.1, hi_ - lo_) + comp_series[f"g{i}"] = a +cc = complementarity(comp_series, shifted, z_bad=1.5) +ck("⑤c 互补构造 ⇒ complementarity > 0", cc["complementarity"] > 0.02, + f"comp={cc['complementarity']:+.4f} best_single={cc['best_single']:.3f} " + f"any={cc['auc_any']:.3f} cons={cc['auc_consensus']:.3f}") + +# (c) AUC 的正确性: 完美分离 = 1.0, 反向 = 0.0 +ck("⑤d _auc 完美分离 = 1.0", abs(_auc(np.array([0., 0, 1, 1]), np.array([0, 0, 1, 1])) - 1.0) < 1e-9) +ck("⑤e _auc 完全反向 = 0.0", abs(_auc(np.array([1., 1, 0, 0]), np.array([0, 0, 1, 1])) - 0.0) < 1e-9) +ck("⑤f _auc 全并列 = 0.5", abs(_auc(np.array([1., 1, 1, 1]), np.array([0, 0, 1, 1])) - 0.5) < 1e-9) + +print() +print("=" * 84) +print(f"{len(OK)}/{len(OK) + len(FAIL)} 通过") +if FAIL: + print("失败项:") + for f in FAIL: + print(f" ✗ {f}") +print("=" * 84) +sys.exit(0 if not FAIL else 1) diff --git a/tests/test_knowledge.py b/tests/test_knowledge.py new file mode 100644 index 0000000000000000000000000000000000000000..1227b5d75cfda24a766c7bf0f744261c1df6b354 --- /dev/null +++ b/tests/test_knowledge.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python3 +"""验证 InternalKnowledge 层: 知识是**可计算对象**, 不是参数。 + +五测项: + ① **Knowledge ≠ Parameter**: `knowledge_of(s,a)` 返回的是**内容** + (预测/价值/覆盖/可信度), 不是权重; 且**零经验**时它**承认不知道**。 + ② **知识能留下来**: `to_dict()` -> 新对象 -> `growth()` 能逐项指出变化。 + ③ **增长可测**: 更多经验后, coverage/GVF/dynamics 的变化量都是**具体数字**。 + ④ **不确定性是知识的一部分**: 高噪声动作的 confidence 显著低于低噪声。 + ⑤ **可塑性是元知识**: α 在真预测特征上分化, 且与 w (知识) **分开**统计。 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.knowledge import Coverage, GVFBank, InternalKnowledge, Plasticity # noqa: E402 + +DIM_X, N_DOM = 4, 4 +EFF = {0: np.array([0.6, 0, 0, 0]), 1: np.array([0, 0.6, 0, 0]), + 2: np.array([0.3, 0.3, 0.3, 0.3]), 3: np.array([0.05, 0.05, 0, 0])} +NOI = {0: 0.03, 1: 0.03, 2: 0.50, 3: 0.04} +fails = [] + + +def make_data(n_ep, steps=6, seed=0): + rng = np.random.default_rng(seed) + X, Y, A = [], [], [] + for _ in range(n_ep): + x = rng.normal(0, 0.1, DIM_X) + for _ in range(steps): + u = int(rng.integers(0, N_DOM)) + y = x + EFF[u] + rng.normal(0, NOI[u], DIM_X) + X.append(np.append(x, u)); Y.append(y); A.append(u) + x = y + return np.array(X), np.array(Y), np.array(A) + + +X, Y, A = make_data(300) +K = InternalKnowledge(N_DOM, DIM_X, n_models=5, seed=0) +for a in A: + K.observe_action(int(a)) +K.fit_dynamics(X, Y) +K.register_value_fn( + lambda s, a: float(np.sum(EFF[a])) if a in EFF else float("nan"), + policy="greedy-eff") +K.register_options(None) # 抽象层稍后单独验 + +print("训练样本 %d, 覆盖 = %s" % (len(X), K.coverage.n.astype(int).tolist())) +print("τ_U = %.6f" % (K.uncertainty.tau_U or float("nan"))) + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("① Knowledge ≠ Parameter —— 查询返回**内容**, 且零经验时承认不知道") +print("=" * 92) +s = np.array([0.1, -0.05, 0.02, 0.0]) +for a in (0, 2, 7): + k = K.knowledge_of(s, a) + print(" a=%s -> 预测=%s V=%s 覆盖=%.0f 可信=%s 新=%s **判定=%s**" + % (k["action"], k["predicted_next"], + ("%.3f" % k["value"]) if k["value"] is not None else "None", + k["coverage"], k["trustworthy"], k["novel"], k["verdict"])) +k7 = K.knowledge_of(s, 7) +if not k7["novel"] or k7["trustworthy"] or k7["known"]: + fails.append("零覆盖动作未被识别为 novel/不可信/未知") +else: + print(" -> 动作 7 从未见过: novel=True trustworthy=False verdict='我不知道'") + print(" ✓ 它**承认识不知道** —— 而不是给一个编出来的数") +k0 = K.knowledge_of(s, 0) +if k0["novel"]: + fails.append("有覆盖的动作被误判为 novel") +d = K.describe() +print(" 查询接口暴露的是%s" % ("内容(预测/价值/覆盖/置信)" if "predicted_next" in k0 else "参数 ✗")) +print(" describe() 顶层键: %s" % list(d.keys())) + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("② 知识能留下来 + ③ 增长可测") +print("=" * 92) +snap = K.to_dict() # 冻结当前知识 +print(" 快照: coverage_total=%d n_options=%d gvf_steps=%s β_updates=%d" + % (snap["coverage_total"], snap["n_options"], snap["gvf_steps"], snap["plasticity_updates"])) + +# 继续积累经验 (新一批数据 + GVF 观察) +X2, Y2, A2 = make_data(300, seed=1) +for a in A2: + K.observe_action(int(a)) +K.dynamics_model.fit(X2, Y2, N_DOM, None) +phi = np.ones(DIM_X) +for t in range(50): + K.observe_gvf(phi, [0.1, 0.2, 0.3, 0.4], phi) # 预测知识在长 + K.observe_plasticity(phi * (0.5 if t % 3 else 3.0), 0.2) +g = K.growth(snap) +print(" growth():") +for kk, vv in g.items(): + if isinstance(vv, float) and not np.isfinite(vv): + continue + print(" %-26s %s" % (kk, np.round(vv, 5) if isinstance(vv, (list, float)) else vv)) +checks = [ + ("coverage_total_delta", g.get("coverage_total_delta", 0) > 0, "覆盖度增长"), + ("gvf_weight_delta_mean", g.get("gvf_weight_delta_mean", 0) > 0, "预测知识变化"), + ("dyn_weight_delta_mean", g.get("dyn_weight_delta_mean", 0) > 0, "转移知识变化"), + ("plasticity_beta_delta", g.get("plasticity_beta_delta") is not None, "元知识变化"), +] +for name, ok, desc in checks: + print(" %s %s" % ("✓" if ok else "✗", desc)) + if not ok: + fails.append("growth 未反映 %s" % desc) + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("④ 不确定性是知识的一部分") +print("=" * 92) +c_lo = K.confidence(s, 0) +c_hi = K.confidence(s, 2) +print(" confidence(动作0 低噪声) = %.4f" % c_lo) +print(" confidence(动作2 高噪声) = %.4f" % c_hi) +print(" -> 高噪声动作的 confidence 更低: %s" % ("✓" if c_hi < c_lo else "✗")) +if not c_hi < c_lo: + fails.append("confidence 未区分高低噪声") + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("⑤ 可塑性是**元知识** (与 w 分开统计)") +print("=" * 92) +# ★ 必须**闭环**: α 的稳定性来自「δ 随 w 收敛而趋 0」。 +# 早先版本把 α 的更新从它要优化的学习里孤立出来 (δ 恒 ~1), 结果 8 种 +# 稳定化手段**全部**撞界 —— 因为 g = δ·φ·h, 迹稳态 h* = δ/φ, 于是 +# g ≈ δ²/φ **恒为正**, β 单向漂移, 停不下来。那不是模块的错, 是测试 +# 问错了问题: 孤立地跑元更新, 就永远没有收敛信号。 +# 闭环 = 用一个**稀疏 + 非平稳**的在线回归: 只有 w[0] 真的能用 x[0] 预测 y, +# 其余 5 个分量是纯噪声。看 α 能否把「该快学」和「不该学」分开。 +dim, n_feat = 1, 6 +pl = Plasticity(n_feat, alpha0=0.05, mu=0.02) +rng5 = np.random.default_rng(0) +w_true = np.zeros(n_feat); w_true[0] = 3.0 +w_hat = np.zeros(n_feat) +rms = 1.0 +hist = [] +for t in range(4000): + x = rng5.normal(0, 1.0, n_feat) + y = float(w_true @ x) + y_hat = float(w_hat @ x) + delta = y - y_hat + rms = 0.99 * rms + 0.01 * delta ** 2 # δ 的 RMS 归一化 + dn = delta / max(np.sqrt(rms), 1e-8) + a = pl.alpha + w_hat += a * dn * x # ★ 用 α 真的去学 + pl.update(x, dn) # α 的元更新 + if t % 500 == 0: + hist.append((t, float(pl.alpha[0]), float(np.mean(pl.alpha[1:])), float(np.linalg.norm(delta)))) + +st = pl.stats() +print(" 闭环 4000 步 (只 w[0] 可预测, 其余 5 维纯噪声):") +print(" 步 α[0] α[1:]均值 |δ|") +for t, a0, ar, dl in hist: + print(" %5d %8.4f %10.4f %8.4f" % (t, a0, ar, dl)) +print(" α: mean=%.4f std=%.4f ratio_max_min=%.2f" % (st["mean"], st["std"], st["ratio_max_min"])) +print(" β 撞界的分量数 = %d / %d" % (int((np.abs(pl.beta) >= 19.99).sum()), n_feat)) +sep = pl.alpha[0] > np.mean(pl.alpha[1:]) +bounded = bool(np.isfinite(pl.alpha).all() and (np.abs(pl.beta) < 19.99).all()) +print(" -> α[0] (真特征) 高于噪声分量: %s" % ("✓" if sep else "✗")) +print(" -> β 全程有界 (无撞界): %s" % ("✓" if bounded else "✗")) +if not sep: + fails.append("α 未在真特征上分化") +if not bounded: + fails.append("β 撞界 (元知识发散)") +# 元知识与知识分开: Plasticity 里没有 w +print(" 元知识对象字段 = %s (无 w, 与知识分离)" + % [a for a in vars(pl) if not a.startswith("_")]) + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("端到端: agent 对世界的一句话自述") +print("=" * 92) +for a in range(N_DOM): + k = K.knowledge_of(s, a) + print(" 动作%d: 我预计 -> %s (覆盖 %.0f 次, 置信 %.2f, V=%.2f)" + % (a, k["predicted_next"], k["coverage"], k["confidence"], k["value"])) + +print() +if fails: + print("✗ 失败项: %s" % "; ".join(fails)) + sys.exit(1) +print("✓ 全部通过") diff --git a/tests/test_macro_option.py b/tests/test_macro_option.py new file mode 100644 index 0000000000000000000000000000000000000000..9044dd1050517a707a7641dabddf67e4cb82ab36 --- /dev/null +++ b/tests/test_macro_option.py @@ -0,0 +1,77 @@ +"""test_macro_option.py — 验证 macro 模式: option 当宏动作执行 + 成败回填。 + +判据(事前写死): + 1. macro 模式必须让 `total_visits > 0` —— 否则仍是"从不去结算"的死账 + 2. rediscover 模式(逐步顾问)应当 `total_visits == 0` —— 证明这个坑确实存在过 + 3. macro 不应崩, 且三模式的最终成功率都应为正(学习仍在工作) +""" +import sys +sys.path.insert(0, "/Users/apple/Downloads/headless") + +import numpy as np + +from hibs_lnn.skill_mdp import KeyDoorMDP +from hibs_lnn.skill_agent import SkillAgent, DEFAULT_REGIMES + +CHAIN = DEFAULT_REGIMES * 2 +EPS_PER = 120 +WINDOW = 20 + + +def run_mode(mode, seed=42): + mdp = KeyDoorMDP(n_pos=8, horizon=30, **CHAIN[0]) + ag = SkillAgent(mdp, mode=mode, seed=seed) + out = [] + for ci, reg in enumerate(CHAIN): + mdp.set_regime(reg["key_pos"], reg["door_pos"], reg["goal_pos"]) + if mode != "primitive": + ag.maybe_discover(force=True) + succ, t_adapt = [], None + for ep in range(EPS_PER): + ok, _ = ag.run_episode() + succ.append(1.0 if ok else 0.0) + if t_adapt is None and len(succ) >= WINDOW: + if float(np.mean(succ[-WINDOW:])) >= 0.8: + t_adapt = ep + 1 + out.append((ci, t_adapt if t_adapt is not None else EPS_PER, + round(float(np.mean(succ[-WINDOW:])), 2))) + return out, ag.option_bookkeeping() + + +print("%-12s %-5s %-9s %-11s %-14s %s" + % ("mode", "seg", "T_adapt", "final_succ", "succ/visits", "n_options")) +print("-" * 78) +results = {} +for mode in ("primitive", "rediscover", "macro"): + rows, bk = run_mode(mode) + results[mode] = (rows, bk) + for ci, ta, fs in rows: + print("%-12s %-5d %-9d %-11.2f %-14s %d" + % (mode, ci, ta, fs, + "%d/%d" % (bk["total_success"], bk["total_visits"]), + bk["n_options"])) + print() + +print("=" * 78) +print("判据核对") +print("=" * 78) +b_r = results["rediscover"][1] +b_m = results["macro"][1] +ok1 = b_m["total_visits"] > 0 +ok2 = b_r["total_visits"] == 0 +print(" ① macro 有结算记录 (visits>0) : %s (visits=%d)" + % ("✓" if ok1 else "✗", b_m["total_visits"])) +print(" ② rediscover 无结算记录 (visits=0) : %s (visits=%d)" + % ("✓" if ok2 else "✗", b_r["total_visits"])) +allpos = all(rr[0][-1][2] > 0 for rr in results.values()) +print(" ③ 三模式都还在学习 (末段成功率>0) : %s" % ("✓" if allpos else "✗")) + +print() +print("=" * 78) +print("★ 3 段 T_adapt 对比 (前3段=第1轮, 后3段=第2轮同 regime)") +print("=" * 78) +for mode in ("primitive", "rediscover", "macro"): + arr = np.array([r[1] for r in results[mode][0]], float) + first, second = arr[:3].mean(), arr[3:].mean() + print(" %-12s 第1轮 %6.1f 第2轮 %6.1f Δ=%+.1f (%.2fx)" + % (mode, first, second, second - first, first / max(second, 1e-9))) diff --git a/tests/test_oak_structure.py b/tests/test_oak_structure.py new file mode 100644 index 0000000000000000000000000000000000000000..ab8cee760a97d4b00a0946b1d775121af4d525ee --- /dev/null +++ b/tests/test_oak_structure.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +"""验证 OaK 三个结构件: option 发现 / 反事实 rollout / coverage+uncertainty 门控。 + +## 四测项 + + ① **Option 发现**: 从转移结构中发现**长度 ≥2 的动作序列**(时间抽象), + 且只采用**可靠**动作(高噪声动作应被 τ_U 筛掉)。 + ①b **预测正确性**: 含**远离数据质心**的探针 —— 这是曾经漏掉的回归点。 + ② **门控挡 OOD**: 零覆盖的 (s,a) 必须被拒; 高噪声动作的 U_T 必须显著高于低噪声。 + ③ **rollout 中止**: 反事实 rollout 一进入零覆盖区就停, 不硬外推。 + ④ **学习式终止 β_o**: 终止概率从数据学出, 不是硬阈值。 + +## 夹具的尺度是唯一的难点 (三次踩坑记录, 都不是模块的错) + + ① 状态生成成 i.i.d. 噪声 -> 状态根本不演化 -> 不存在多步结构 -> 0 option + ② 动作效果 (0.3) << 区域间距 (数个单位) -> `center+effect` 的最近区域永远是 + 它自己 -> 图里没有"前进的边" -> 0 option + ③ 纯随机游走**无界漂移** -> 区域中心跑到 ~12 -> 同一个病 + + 可行解 = **低维链式环境**: 状态沿一条线排开 (dim_x=1), 单步动作跨过相邻区域, + 多步动作序列跨过多个区域 => 时间抽象才有意义。这正是 option 的经典形态。 + 真实 lm4 的对应关系: 状态 = [0,1] 的精度画像 (有界), 单次训练改变 0.1~0.3, + 与 6 个域的区域间距同量级 —— 天生满足条件。 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.option_manager import OptionManager # noqa: E402 +from hibs_lnn.uncertainty_gate import ( # noqa: E402 + TransitionEnsemble, + UncertaintyGate, +) + +DIM_X, N_DOM = 1, 4 +# 动作效果: 0 可靠推进 / 1 小幅推进 / 2 高噪声 / 3 几乎不动 +EFF = {0: np.full(DIM_X, 0.60), 1: np.full(DIM_X, 0.25), + 2: np.full(DIM_X, 0.20), 3: np.full(DIM_X, 0.03)} +NOI = {0: 0.02, 1: 0.02, 2: 0.60, 3: 0.02} + + +def build(n_ep=2500, steps=5, seed=0): + rng = np.random.default_rng(seed) + X, Y, S, A = [], [], [], [] + for _ in range(n_ep): + x = rng.normal(0, 0.05, DIM_X) + for _ in range(steps): + u = int(rng.integers(0, N_DOM)) + y = x + EFF[u] + rng.normal(0, NOI[u], DIM_X) + X.append(np.append(x, u)); Y.append(y) + S.append(x.copy()); A.append(u) + x = y + return np.array(X), np.array(Y), np.array(S), np.array(A) + + +X, Y, STATES, ACTS = build() +print("训练样本 %d, 动作噪声: %s" % (len(X), NOI)) + +visits = np.bincount(ACTS, minlength=N_DOM).astype(float) +ens = TransitionEnsemble(n_models=5, seed=0).fit(X, Y, N_DOM, None) +gate = UncertaintyGate(tau_C=1.0) +_step = max(1, len(X) // 100) +gate.calibrate([ens.predict(X[i, :DIM_X], int(X[i, -1]), visits)[1] + for i in range(0, len(X), _step)]) +print("门控标定: τ_C=%.2f τ_U=%.6f (训练分歧 90 分位)" % (gate.tau_C, gate.tau_U)) + +N_REG = 5 +om = OptionManager(ens, gate, max_len=5, seed=0) +opts = om.discover(STATES, ACTS, n_regions=N_REG) +fails = [] + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("① Option 发现 (应从转移结构发现长度 ≥2 的动作序列)") +print("=" * 92) +multi = [o for o in opts if len(o.actions) >= 2] +print(" 发现 %d 个 option, 转移图 %d 条边" % (len(opts), len(om.graph))) +for o in opts[:8]: + print(" actions=%-12s 长度=%d" % (str(o.actions), len(o.actions))) +print(" -> 长度 ≥2 的 option: %d 个 %s" + % (len(multi), "✓ 形成了时间抽象" if multi else "**只有单步 ✗**")) +if not multi: + fails.append("option 发现未产生多步序列") + +# 只应采用可靠动作: 高噪声的动作 2 不应出现在 option 里 +used = {a for o in opts for a in o.actions} +print(" 用到的动作集合 = %s (动作2 是高噪声=%.2f)" % (sorted(used), NOI[2])) +if 2 in used: + print(" **高噪声动作被采纳 ✗**") + fails.append("高噪声动作混入 option") +else: + print(" ✓ 高噪声动作被 τ_U 筛掉 —— 可靠路径过滤起作用") + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("①b 预测正确性 (含**远离质心**—— 这是曾经漏掉的回归点)") +print("=" * 92) +probe = om.regions[0] if om.regions is not None else np.zeros(DIM_X) +print(" 探针 = 区域中心 R0 = %s (远离数据质心)" % np.round(probe, 3)) +ok_cnt = 0 +for u in range(N_DOM): + s_next, unc = ens.predict(probe, u, visits) + got, want = s_next - probe, EFF[u] + err = float(np.max(np.abs(got - want))) + good = err < 0.12 # 噪声 std=0.02~0.60, 留余量 + ok_cnt += good + print(" a%d: Δ=%s 真值=%s 最大误差=%.4f %s" + % (u, np.round(got, 3), np.round(want, 3), err, "✓" if good else "✗")) +print(" -> %d/%d 个动作在远离质心处预测正确 %s" + % (ok_cnt, N_DOM, "✓" if ok_cnt >= 3 else "**仍有偏差 ✗**")) +if ok_cnt < 3: + fails.append("远离质心的预测不正确") + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("② 门控挡 OOD (复现实测 case)") +print("=" * 92) +x_ok = STATES[0] +_, unc_lo = ens.predict(x_ok, 0, visits) +_, unc_hi = ens.predict(x_ok, 2, visits) +print(" 动作0 有覆盖 (visits=%.0f): allow=%s" % (visits[0], gate.allow(x_ok, 0, visits))) +print(" 动作0 零覆盖 (复现 [2,2,...]): allow=%s %s" + % (gate.allow(x_ok, 0, np.zeros(N_DOM)), + "✓ 被挡住" if not gate.allow(x_ok, 0, np.zeros(N_DOM))[0] else "**没挡住 ✗**")) +if gate.allow(x_ok, 0, np.zeros(N_DOM))[0]: + fails.append("零覆盖未被挡住") +print(" 动作0 (低噪声) U_T=%.5f 动作2 (高噪声) U_T=%.5f -> 高噪声被识别 %s" + % (unc_lo, unc_hi, "✓" if unc_hi > 3 * unc_lo else "**区分不足 ✗**")) +if not unc_hi > 3 * unc_lo: + fails.append("U_T 未能区分高低噪声") + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("③ rollout 在无覆盖处中止") +print("=" * 92) +_traj, _uncs, aborted = ens.rollout(x_ok, [0, 0, 1], visits, gate=None) +print(" 不限门控 rollout [0,0,1]: %d 步, 中止=%s" % (len(_traj), aborted)) +_seq2 = [0, 0, 0] +_t2, _u2, ab2 = ens.rollout(x_ok, _seq2, np.array([1.0, 1.0, 1.0, 0.0]), gate=gate) +print(" 零覆盖下 rollout %s (门控开): %d 步, 中止=%s %s" + % (_seq2, len(_t2), ab2, "✓ 中止了" if ab2 else "**没中止 ✗**")) +if not ab2: + fails.append("rollout 未在零覆盖处中止") + +# ────────────────────────────────────────────────────────────────────── +print() +print("=" * 92) +print("④ 学习式终止 β_o (应从数据学, 不是硬阈值)") +print("=" * 92) +if not opts: + print(" (无可发现的 option, 跳过)") +else: + o = multi[0] if multi else opts[0] + goal = om.regions[int(np.argmax(om.regions[:, 0]))] + # 样本 = (状态, 不确定性, 是否终止) —— 3 元组 + smp = [(STATES[i], 0.0, bool(abs(STATES[i, 0] - goal[0]) < 0.3)) + for i in range(0, len(STATES), 5)] + o.learn_termination(smp, lr=0.1, epochs=300) + print(" option actions=%s, 目标区域中心=%s" % (o.actions, np.round(goal, 3))) + print(" β_o 权重 = %s" % np.round(o.w_beta, 4)) + print(" 终止概率随「距目标」的变化:") + ps = [] + for d in (-1.0, -0.5, -0.2, 0.0, 0.2): + pv = o.beta_prob(goal + d) + ps.append(pv) + print(" 距目标 %+.1f -> P(terminate)=%.4f" % (d, pv)) + mono = ps[-1] > ps[0] + print(" -> 训练后 P(终止) 随「接近目标」上升: %s" % ("✓" if mono else "✗ 未学到")) + if not mono: + fails.append("学习式终止未学到「接近目标则终止」") + +print() +print("=" * 92) +print("stats: %d options, tau_U = %.6f" % (len(opts), om.tau_U)) +if fails: + print("✗ 失败项: %s" % "; ".join(fails)) + sys.exit(1) +print("✓ 全部通过") diff --git a/tests/test_option_on_skill_mdp.py b/tests/test_option_on_skill_mdp.py new file mode 100644 index 0000000000000000000000000000000000000000..39382b3ca891f114157f828edd00a9a3b74c6116 --- /dev/null +++ b/tests/test_option_on_skill_mdp.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +"""test_option_on_skill_mdp.py — 在**有时序结构**的环境上判定 OptionManager。 + +## 判定逻辑(这是"实现坏了 vs benchmark 有问题"的决定性区分) + + lm4 : A ≡ 0 (动作可交换) -> 无时序结构 -> 那里的负结果**无法判定实现** + KeyDoor : A ∈ [-1,+1], 33% 显著 -> 有时序结构 -> **可以用它判定实现** + + 若 OptionManager 在这里能发现"拿钥匙→开门"这类技能并带来收益 + -> 实现没坏, lm4 的负结果是 benchmark 的性质 (用户判断成立) + 若在这里也发现不了/用不上 + -> **实现有问题, 该修实现** + +## 三测项 + + ① **发现**: discover() 找出的 option 是否对应任务里**真实存在**的技能链? + 判据: option 的动作序列应包含 GRAB / OPEN, 且起点→终点朝目标推进。 + ② **覆盖**: 发现的 option 能否覆盖"从起点到目标"的完整过程? + ③ **T_adapt (B2/B3)**: regime 变化后恢复到阈值需要多少回合? + primitives vs options。**这才是 option 该体现价值的地方** —— + 技能可复用(拿钥匙/开门), 只是**地点变了**。 +""" +import argparse +import json +import sys +from pathlib import Path + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from hibs_lnn.skill_mdp import ( # noqa: E402 + GRAB, LEFT, N_ACT, OPEN, RIGHT, KeyDoorMDP, +) +from hibs_lnn.uncertainty_gate import TransitionEnsemble, UncertaintyGate # noqa: E402 + + +def collect_transitions(mdp, n_ep=400, seed=0): + """采集 (state_vec, action, next_state_vec)。""" + rng = np.random.RandomState(seed) + X, Y = [], [] + for _ in range(n_ep): + mdp.reset() + done = False + while not done: + a = int(rng.randint(0, N_ACT)) + v = mdp.vec() + _, _, done = mdp.step(a) + X.append(np.append(v, a)); Y.append(mdp.vec()) + return np.array(X), np.array(Y) + + +def evaluate_options(mdp, ens, gate, om, n_ep=200, seed=0): + """用发现的 option + 贪心 fallback 解任务, 返回成功率与平均步数。""" + def goal_state_vec(): + mdp.reset() + for _ in range(400): + _, _, d = mdp.step(mdp.greedy_action()) + if d: + break + return mdp.vec() + + ok, tot = 0, 0 + for _ in range(n_ep): + mdp.reset() + done, n = False, 0 + while not done and n < mdp.horizon: + v = mdp.vec() + a = None + if om is not None and om.options: + o = om.select(v, goal=goal_state_vec(), exclude_unc=True) + if o is not None: + a = int(o.actions[0]) + if a is None: + a = mdp.greedy_action() + _, _, done = mdp.step(a) + n += 1 + ok += 1 if (done and mdp._reached()) else 0 + tot += n + return ok / n_ep, tot / n_ep + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--n-pos", type=int, default=8) + ap.add_argument("--episodes", type=int, default=400) + ap.add_argument("--n-regions", type=int, default=5) + ap.add_argument("--max-len", type=int, default=4) + ap.add_argument("--out", default=str(ROOT / "results" / "option_on_mdp.json")) + a = ap.parse_args() + + mdp = KeyDoorMDP(n_pos=a.n_pos, key_pos=2, door_pos=5, goal_pos=7, horizon=30) + n_states = mdp.n_pos + 2 + print("KeyDoorMDP: n_pos=%d regime=%s 最优步数=%d" % (a.n_pos, mdp.regime, mdp.optimal_steps())) + + X, Y = collect_transitions(mdp, n_ep=a.episodes, seed=0) + print("转移 %d 条, 状态维 %d" % (len(X), X.shape[1] - 1)) + + ens = TransitionEnsemble(n_models=5, seed=0).fit(X, Y, N_ACT, None) + gate = UncertaintyGate(tau_C=1.0) + gate.calibrate([ens.predict(X[i, :X.shape[1] - 1], int(X[i, -1]), np.ones(N_ACT))[1] + for i in range(0, len(X), max(1, len(X) // 200))]) + + from hibs_lnn.option_manager import OptionManager + om = OptionManager(ens, gate, max_len=a.max_len, seed=0) + states = np.array([np.append(mdp.vec(), 0)[:X.shape[1] - 1] for _ in range(0)]) # 占位 + states = X[:, :X.shape[1] - 1] + acts = X[:, -1].astype(int) + opts = om.discover(states, acts, n_regions=a.n_regions) + print("发现 %d 个 option" % len(opts)) + + print() + print("=" * 92) + print("① 发现: 找出的 option 是否对应真实技能链 (应含 GRAB=2 / OPEN=3)") + print("=" * 92) + names = {LEFT: "left", RIGHT: "right", GRAB: "grab", OPEN: "open"} + hit = 0 + for o in opts[:10]: + acts_o = [int(x) for x in o.actions] + has_grab = GRAB in acts_o + has_open = OPEN in acts_o + if has_grab or has_open: + hit += 1 + print(" actions=%-24s (%s) start=%s goal=%s unc=%.4f" + % (str(acts_o), " ".join(names.get(x, "?") for x in acts_o), + np.round(o.start_center, 2).tolist(), + np.round(o.goal_center, 2).tolist(), + float(np.mean(o.uncs)) if o.uncs else 0.0)) + print(" -> 含关键动作 (grab/open) 的 option: %d / %d" % (hit, len(opts))) + + print() + print("=" * 92) + print("②/③ 解任务: primitives vs options (T_adapt 见下一段)") + print("=" * 92) + acc_p, st_p = evaluate_options(mdp, ens, gate, None) + acc_o, st_o = evaluate_options(mdp, ens, gate, om) + print(" primitives (贪心) : 成功率 %.3f 平均步数 %.2f" % (acc_p, st_p)) + print(" options + 贪心 fallback: 成功率 %.3f 平均步数 %.2f" % (acc_o, st_o)) + print() + print(" ★ 说明: 这里的贪心 fallback 已经是最优一步策略, 所以成功率都接近 1。") + print(" 真正能区分的是 **T_adapt**: regime 变化后需要多少回合恢复。") + print(" (贪心需要**重新计算**, 而 option 是**可复用的技能** —— 这才是差异所在)") + + Path(a.out).parent.mkdir(parents=True, exist_ok=True) + Path(a.out).write_text(json.dumps({ + "n_options": len(opts), "n_with_key_actions": hit, + "primitive": {"acc": acc_p, "steps": st_p}, + "option": {"acc": acc_o, "steps": st_o}, + "options": [o.stats() for o in opts], + }, indent=1, ensure_ascii=False)) + print("\n已写 %s" % a.out) + + +if __name__ == "__main__": + main() diff --git a/tests/test_rl_proposer.py b/tests/test_rl_proposer.py new file mode 100644 index 0000000000000000000000000000000000000000..61b88a4b44166e5e41338d946a3d7ee743c4cb3c --- /dev/null +++ b/tests/test_rl_proposer.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +"""自测: RLProposer 的步长是否真的在分化 (IDBD 的核心主张)。 + +构造: φ 的第 0 维是**真预测特征**, 第 1-3 维是**纯噪声**。 +合格的 IDBD 应给第 0 维**明显更大**的步长 α, 并把它累积进 h。 +若不分化 -> 说明"步长为主导的积累"没生效。 +""" +import sys +import numpy as np +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer + +rng = np.random.default_rng(0) +p = RLProposer(np.zeros((1, 1)), tau=1.0, k=1, mu=0.02, alpha0=0.01, seed=1) +p.fdim = 4 +p.theta = np.zeros(4); p.alpha = np.full(4, 0.01); p.h = np.zeros(4) + +# 手工喂样本: feature[0] 有预测力, feature[1:] 是噪声 +N = 400 +a0_hist, err_hist = [], [] +for t in range(N): + x = rng.normal(0, 1, 4) + x[1:] = rng.normal(0, 1, 3) + y = 3.0 * x[0] + rng.normal(0, 0.3) # 目标只依赖 x[0] + v = float(p.theta @ x) + delta = y - v + p.theta += p.alpha * delta * x + p.h = p.h * np.maximum(0, 1 - p.alpha * x**2) + p.alpha * x * delta + p.alpha = np.clip(p.alpha * np.exp(p.mu * delta * x * p.h), 1e-6, 1.0) + a0_hist.append(p.alpha[0]); err_hist.append(delta**2) + +print("=== 步长分化 (IDBD 核心主张) ===") +print(" alpha 逐维: ", np.round(p.alpha, 5)) +print(" 真预测特征 alpha[0] = %.5f" % p.alpha[0]) +print(" 噪声特征 alpha[1:] = %s" % np.round(p.alpha[1:], 5)) +print(" 比值 alpha[0]/mean(alpha[1:]) = %.2f -> %s" + % (p.alpha[0]/max(1e-9, p.alpha[1:].mean()), + "分化成功 ✓" if p.alpha[0] > 2*p.alpha[1:].mean() else "**未分化**")) +print(" h (信用迹) 逐维: ", np.round(p.h, 5)) +print(" 预测误差(前50 vs 后50 均值): %.4f -> %.4f" + % (np.mean(err_hist[:50]), np.mean(err_hist[-50:]))) +print() +print("=== redefine 继承检验 ===") +# 先真的跑几轮 act/update, 让 explore_n 与 alpha 有内容 +p2 = RLProposer(np.random.RandomState(0).randn(12, 2), tau=0.5, k=2, + mu=0.02, alpha0=0.01, seed=3) +p2.fdim = p2.fdim +for t in range(30): + idx = p2.act() + p2.observe(idx, acc=0.4 + 0.1 * (t % 5), + any_time=0.3 + 0.02 * t) # 逐步上升的目标函数 + p2.update(idx) # reward = Δ any-time +before_n = p2.explore_n.sum() +before_a = p2.alpha.copy() +before_red = p2.redefine_count +p2.redefine(np.random.RandomState(7).randn(20, 2)) +print(" redefine 前 explore_n.sum = %.1f -> 后 = %.1f %s" + % (before_n, p2.explore_n.sum(), + "继承成功 ✓" if p2.explore_n.sum() >= before_n * 0.5 else "**丢失 ✗**")) +print(" alpha 是否跨 redefine 存活:", "是 ✓" if np.allclose(p2.alpha, before_a) else "**被重置 ✗**") +print(" 新池大小 n = %d, redefine_count = %d -> %d" + % (20, before_red, p2.redefine_count)) +print(" 继承后 learn=True 的候选数 = %d (旧池共 %d 个被学)" + % (int(p2.learned.sum()), int(before_a.size and 12))) +print() +print("=== 步长是否随经验继续分化 (累积) ===") +st = p2.stats() +print(" alpha: min=%.5f max=%.5f std=%.5f ratio=%.1fx" + % (st["alpha"]["min"], st["alpha"]["max"], + st["alpha"]["std"], st["alpha"]["ratio_max_min"])) +print(" h_norm=%.4f theta_norm=%.4f baseline=%.4f" + % (st["h_norm"], st["theta_norm"], st["baseline"])) +print(" trace 末3步:") +for t in p2.trace[-3:]: + print(" ", t) diff --git a/tests/test_rr_agent.py b/tests/test_rr_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..9dfa31d3a91c701891e01c74b7c1702db75520bd --- /dev/null +++ b/tests/test_rr_agent.py @@ -0,0 +1,188 @@ +# -*- coding: utf-8 -*- +"""`RRSkillAgent` 的机制自检 —— 判据在跑之前写死。 + +对应 `option-design-and-execution.md` §3 的**模式计数器表**: + + 模式 必填计数器特征 + ───────────────────── ──────────────────────────────── + open-loop(固定序列) recompute-count **0** + closed-loop(闭环) recompute-count **≈ 执行步数** + + 终止判据 终止原因台账**非空** + + 证据中断 非零中断计数 + +再加三条本项目特定的判据: + + A. **对照干净**: `RRSkillAgent(mode="primitive")` 必须与 + `SkillAgent(mode="primitive")` **逐位相同**(子类只加了 rr 分支, + 其余路径逐字委托)。 + B. **lock-in 检**: 平均执行时长不得接近整回合长度(§3 第二条)。 + C. **testbed 能学**(§8): rr 臂自己必须能解出任务, 否则后面所有臂 + 的比较都是在测一个冻结的 agent。 +""" +import sys +import numpy as np + +sys.path.insert(0, ".") +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.skill_agent import SkillAgent # noqa: E402 +from hibs_lnn.rr_agent import RRSkillAgent # noqa: E402 + +FAILS = [] + + +def check(name, cond, detail=""): + tag = "PASS" if cond else "FAIL" + print(f"[{tag}] {name}" + (f" {detail}" if detail else "")) + if not cond: + FAILS.append(name) + + +REGIMES = [(2, 5, 7), (1, 4, 7), (3, 6, 7)] +N_EP = 60 # 短跑: 只验机制是否活, 不验效果 + + +def make(mode, seed, cls=SkillAgent, **kw): + mdp = KeyDoorMDP(n_pos=8, key_pos=2, door_pos=5, goal_pos=7, horizon=30) + mdp.set_regime(*REGIMES[0]) + return cls(mdp, mode=mode, seed=seed, **kw), mdp + + +def run_chain(ag, mdp, n_per=10): + succ = [] + for reg in REGIMES: + mdp.set_regime(*reg) + for _ in range(n_per): + ok, _n = ag.run_episode() + succ.append(int(ok)) + return succ + + +print("=" * 72) +print("RRSkillAgent 机制自检") +print("=" * 72) + +# ── A. 对照干净: primitive 路径逐位委托 ──────────────────────────── +for seed in (0, 1): + a1, m1 = make("primitive", seed, cls=SkillAgent) + a2, m2 = make("primitive", seed, cls=RRSkillAgent) + s1 = run_chain(a1, m1) + s2 = run_chain(a2, m2) + q_same = bool(np.array_equal(a1.q.Q, a2.q.Q)) + check(f"seed={seed}: RRSkillAgent(mode=primitive) == SkillAgent(mode=primitive)", + s1 == s2 and q_same, + f"success {'逐位相同' if s1==s2 else 'DIFF'} Q表 {'逐位相同' if q_same else 'DIFF'}") + +# ── B/C. rr 臂: 机制是否活 ──────────────────────────────────────── +ag, mdp = make("rr", 0, cls=RRSkillAgent, opt_prob=1.0) +succ = run_chain(ag, mdp) +bk = ag.rr_bookkeeping() +print() +print("rr 台账:", {k: bk[k] for k in + ("recomputes", "selections", "exec_steps", "terms", + "n_options", "mean_duration", "max_duration", + "lockin_ratio", "max_cap", "rebuilds", "beta_nonempty")}) + +check("① 启动过 option(selections > 0)", bk["selections"] > 0, + f"selections={bk['selections']}") +check("② 闭环: recompute-count ≈ 执行步数(> 0 且 ≥ 80% 执行步)", + bk["recomputes"] > 0 and bk["recomputes"] >= 0.8 * max(1, bk["exec_steps"]), + f"recomputes={bk['recomputes']} exec_steps={bk['exec_steps']}") +check("③ 终止台账**非空**(承诺了终止就必须真的终止)", + len(bk["terms"]) > 0, f"terms={bk['terms']}") +check("③b 台账里有**β_o 触发**(不是超时兜底撑起来的)", + (bk["terms"].get("beta_at_feature", 0) + bk["terms"].get("beta_elsewhere", 0)) > 0, + f"beta_at_feature={bk['terms'].get('beta_at_feature',0)} " + f"beta_elsewhere={bk['terms'].get('beta_elsewhere',0)} " + f"expired={bk['terms'].get('expired',0)}") +check("④ lock-in 检: 平均时长 < 50% 整回合", + bk["lockin_ratio"] < 0.5, + f"mean={bk['mean_duration']:.2f} / horizon={bk['horizon']} " + f"ratio={bk['lockin_ratio']:.3f} cap={bk['max_cap']}") +check("⑤ 库里有 option 且 β 非空", bk["n_options"] > 0 and bk["beta_nonempty"] > 0, + f"n={bk['n_options']} |β|={bk['beta_nonempty']}") +check("⑥ testbed 能学: rr 臂至少解出过半回合", sum(succ) > len(succ) * 0.5, + f"success={sum(succ)}/{len(succ)}") + +# ── D. 消融旋钮在 agent 层也真的生效 ────────────────────────────── +# ⑦ 的精确形式(Pitfall-26 的正确读法): +# 两个臂台账**逐位相同**时, 只有两种可能 —— +# (a) 接线 bug(旋钮没接上), 或 +# (b) 该试验台上两者**真的行为等效**。 +# 区分方法: **比较它们的输入**(z 表 / c 函数)。输入不同而台账相同 +# = (b) 真等效(可报告); 输入相同 = (a) bug(必须修)。 +print() +arm_stats, arm_inputs = {}, {} +for tag, kw in [ + ("rr-exact", dict()), + ("rr-zeroV", dict(rr_z_mode="zeroV")), + ("rr-nobonus", dict(rr_z_mode="nobonus")), + ("rr-nocum", dict(rr_cumulant="zero")), + ("rr-learned", dict(rr_v_main="learned")), + ("bottleneck", dict(rr_lib="bottleneck")), + ("random", dict(rr_lib="random")), +]: + a, m = make("rr", 0, cls=RRSkillAgent, opt_prob=1.0, **kw) + run_chain(a, m) + arm_stats[tag] = a.rr_bookkeeping() + # 输入签名: 每个 option 的 z 表(有限值处)+ c 表。 + # ★ c 必须探**全部 4 个动作** —— 只探动作 0 会让 `exact` 与 `nocum` + # 在 goal_pos=7 的 regime 上签名相同(因为 `R[·][LEFT] ≡ 0`, 奖励 + # 永远不会从"向左"这一步拿到)。同族教训: 探针必须打在**能显出差别 + # 的地方**(参 handrolled-module-verification "探针不能打在原点")。 + lib = a.rr_lib_obj + zsig, csig = [], [] + for st in lib: + z = np.array([st.z_fn(s) for s in range(a.mdp.n_states)]) + zsig.append(tuple(np.round(z[np.isfinite(z)], 6))) + csig.append(tuple(round(st.c_fn(s, act), 6) + for s in range(a.mdp.n_states) + for act in range(4))) + arm_inputs[tag] = (tuple(zsig), tuple(csig)) + print(f" {tag:11s} terms={arm_stats[tag]['terms']} " + f"mean_dur={arm_stats[tag]['mean_duration']:.2f} " + f"recomp={arm_stats[tag]['recomputes']}") + +sig = {t: (str(s["terms"]), round(s["mean_duration"], 3), s["recomputes"]) + for t, s in arm_stats.items()} +uniq = len(set(sig.values())) +check("⑦a 三个结构上最不同的臂(bottleneck / random / rr-exact)台账互不相同", + len({sig[k] for k in ("bottleneck", "random", "rr-exact")}) == 3, + f"{uniq}/7 个不同签名") + +# ⑦c: 台账相同的臂 —— 逐组给出**可检查的**等效原因, 而不是放松判据。 +groups = {} +for t, s in sig.items(): + groups.setdefault(s, []).append(t) +for g in [g for g in groups.values() if len(g) > 1]: + ins = {arm_inputs[t] for t in g} + if len(ins) == len(g): + check(f"⑦c {g} 台账相同但**输入不同** ⇒ 真等效(非共享分支)", + True, f"{len(ins)}/{len(g)} 种输入签名") + else: + # 输入也相同 -> 必须给出一个**代数上可检查**的原因, 否则就是接线 bug。 + # 已知情形: `zeroV`(V_main ≡ 0)与 `learned`(V_main = agent 的 Q)。 + # zeroV : w_i=0, z = 0 + (w̄−0)·1 = w̄ (特征处) + # learned : w_i=c, z = c + (w̄−c)·1 = w̄ (特征处) + # ⇒ **只要 V_learned 是常数, 两者 z 代数恒等**。检查 V_learned 是否常数。 + explained = False + if "rr-learned" in g: + a_l, _ = make("rr", 0, cls=RRSkillAgent, opt_prob=1.0, + rr_v_main="learned") + a_l._rr_ensure() + vl = np.asarray(a_l._rr_v_main_arr, dtype=float) + explained = bool(np.std(vl) < 1e-9) + detail = f"V_learned std={np.std(vl):.3e} (常数 ⇒ 代数恒等)" + else: + detail = "无可解释原因" + check(f"⑦c {g} 台账与输入都相同 ⇒ 必须给出可检查的等效原因, 否则是接线 bug", + explained, detail) + +check("⑦b rr-exact 与 bottleneck 台账不同", + sig["rr-exact"] != sig["bottleneck"], + f"rr={sig['rr-exact']} bot={sig['bottleneck']}") + +print("=" * 72) +if FAILS: + print(f"✗ {len(FAILS)} 项未过: {FAILS}") + sys.exit(1) +print("✓ 全部通过") diff --git a/tests/test_rr_options.py b/tests/test_rr_options.py new file mode 100644 index 0000000000000000000000000000000000000000..4edd822f620345476d0c1684fadeab15e016df2f --- /dev/null +++ b/tests/test_rr_options.py @@ -0,0 +1,153 @@ +# -*- coding: utf-8 -*- +"""`rr_options` 的自检 —— 判据在跑之前就写死。 + +对应 `ml-ablation-methodology/references/option-design-and-execution.md`: + + §3 「一个名字里承诺了终止的模式, 必须给出**非空**的终止台账」 + §9.3「stopping value 与主任务价值**逐位相同** = 退化」(Pitfall-26 tell) + §6 「目标容差必须由状态尺度导出, 不能手写常数」 + §8 「testbed 的 agent 必须先被证明能学」 + +本文件只验**子任务层**(不涉及 SkillAgent)。SkillAgent 侧的计数器在 +`test_rr_agent.py` 里验。 +""" +import sys +import numpy as np + +sys.path.insert(0, ".") +from hibs_lnn.skill_mdp import KeyDoorMDP # noqa: E402 +from hibs_lnn.rr_options import ( # noqa: E402 + KeyDoorTabular, build_bottleneck_library, build_random_goal_library, + build_rr_library, feature_vectors, initiation_ok, main_value, + optimal_policy, option_gain, +) + +FAILS = [] + + +def check(name, cond, detail=""): + tag = "PASS" if cond else "FAIL" + print(f"[{tag}] {name}" + (f" {detail}" if detail else "")) + if not cond: + FAILS.append(name) + + +REGIME_A = (2, 5, 7) +REGIME_B = (1, 4, 7) + +print("=" * 72) +print("rr_options 自检 | regime A =", REGIME_A) +print("=" * 72) + +mdp = KeyDoorMDP(n_pos=8, key_pos=2, door_pos=5, goal_pos=7, horizon=30) +mdp.set_regime(*REGIME_A) +mdl = KeyDoorTabular(mdp, gamma=0.95) + +# ── 1. 表格模型与过程式 MDP 逐状态一致 ────────────────────────────── +# (不能只测起点 —— 起点一致而别处不一致是 §9.3 那类"探针打在原点"的坑) +mismatch = [] +for s in range(mdl.n_states): + pos, hk, do = mdp.decode(s) + for a in range(4): + saved = (mdp.pos, mdp.has_key, mdp.door_open, mdp.t) + mdp.pos, mdp.has_key, mdp.door_open = pos, hk, do + s2, r, _done = mdp.step(a) + mdp.pos, mdp.has_key, mdp.door_open, mdp.t = saved + if s2 != mdl.T[s][a][0][1] or abs(r - mdl.R[s][a]) > 1e-12: + mismatch.append((s, a, s2, mdl.T[s][a][0][1], r, mdl.R[s][a])) +check("表格模型 == 过程式 step()(全 32 状态 × 4 动作)", + not mismatch, f"mismatch={mismatch[:3]}") + +# ── 2. 主任务价值非平凡 ───────────────────────────────────────────── +V_main, pol = optimal_policy(mdl) +check("V_main 非平凡(非全 0 / 非全 1)", 0.0 < V_main[0] < 1.0, + f"V_main[0]={V_main[0]:.4f} max={V_main.max():.4f}") +check("V_main 单调: 靠近目标更好", + V_main[4 * 7 + 2] > V_main[4 * 0 + 0], + f"goal区 {V_main[4*7+2]:.4f} > 起点 {V_main[0]:.4f}") + +# ── 3. reward-respecting 库: 三项关键判据 ─────────────────────────── +lib = build_rr_library(mdl, V_main, bonus_weight=1.0) +check("库里有 2 个 option(key, door)", len(lib) == 2, f"n={len(lib)}") + +for st in lib: + n_beta = int(np.sum(st.beta)) + # ★ 判据 1: β_o 台账必须**非空**(§3)。空台账 = 与不退化的臂无法区分。 + check(f"{st.name}: β_o 非空(承诺了终止就必须真的有终止)", + n_beta > 0, f"|β|={n_beta}/{mdl.n_states}") + # ★ 判据 2: z ≠ V_main 逐位相同(§9.3 第三条, Pitfall-26 tell) + z_vals = np.array([st.z_fn(s) for s in range(mdl.n_states)]) + fin = np.isfinite(z_vals) + same = bool(np.allclose(z_vals[fin], V_main[fin], atol=1e-12)) + check(f"{st.name}: z ≢ V_main(非退化)", not same and fin.any(), + f"可比 {int(fin.sum())} 态, w_i={getattr(st,'w_i',float('nan')):.4f}, " + f"bonus={getattr(st,'bonus_weight',float('nan')):.4f}") + +# ── 4. 与已知弱基线(瓶颈)必须**逐位不同** ───────────────────────── +bot = build_bottleneck_library(mdl)[0] +d = max(int(np.sum(lib[i].policy != bot.policy)) for i in range(len(lib))) +check("rr 的策略 ≠ bottleneck 的策略(§9.3 '两臂逐位相同' 反面)", + d > 0, f"策略表最大分歧 {d} 个状态 (共 {mdl.n_states})") + +# ── 5. 启动集真的包含起始状态(§6 的"radius 尺度"坑在这一层的对应物) +s0 = mdp.reset() +mdp.set_regime(*REGIME_A) +check("至少一个 option 在起始状态可启动 I_o(s0)", + any(initiation_ok(st, s0) for st in lib), + f"s0={s0} gains=" + + str([f"{option_gain(st, s0, V_main):.3f}" for st in lib])) + +# ── 6. 选择器有分辨力(增益不是常数) ─────────────────────────────── +gains = np.array([[option_gain(st, s, V_main) for st in lib] + for s in range(mdl.n_states)]) +fin_g = gains[np.isfinite(gains)] +check("option_gain 有分辨力(std > 0, 不是常数)", + fin_g.size > 0 and float(np.std(fin_g)) > 1e-9, + f"n={fin_g.size} std={float(np.std(fin_g)):.4f} " + f"range=[{fin_g.min():.4f},{fin_g.max():.4f}]") + +# ── 7. 反事实: 削掉价值函数后 z 必须变(否则"价值函数"这个旋钮是死的) +lib_z0 = build_rr_library(mdl, V_main, bonus_weight=1.0, z_mode="zeroV") +diff_z = max(int(np.sum(np.abs([lib[i].z_fn(s) for s in range(mdl.n_states)]) + - np.array([lib_z0[i].z_fn(s) for s in range(mdl.n_states)]) + > 1e-9)) for i in range(len(lib))) +check("z_mode='zeroV' 真的改变了 z(旋钮不是死的)", diff_z > 0, + f"z 表最大分歧 {diff_z} 态") + +# ── 8. 反事实: nobonus 应当退化(bonus ≡ 0 → z ≡ V_main) ──────────── +lib_nb = build_rr_library(mdl, V_main, bonus_weight=1.0, z_mode="nobonus") +for st in lib_nb: + z_vals = np.array([st.z_fn(s) for s in range(mdl.n_states)]) + fin = np.isfinite(z_vals) + same = bool(np.allclose(z_vals[fin], V_main[fin], atol=1e-12)) + check(f"{st.name}(nobonus): z ≡ V_main —— 确认这是退化臂", + same, f"可比 {int(fin.sum())} 态") + +# ── 9. 反事实: cumulant='zero' 真的改变 c ───────────────────────── +lib_c0 = build_rr_library(mdl, V_main, bonus_weight=1.0, cumulant="zero") +c_diff = any(abs(lib_c0[i].c_fn(s, a) - lib[i].c_fn(s, a)) > 1e-12 + for i in range(len(lib)) for s in range(mdl.n_states) + for a in range(4)) +check("cumulant='zero' 真的改变了 c(旋钮不是死的)", c_diff) + +# ── 10. 随机目标库有分辨力 ──────────────────────────────────────── +rnd = build_random_goal_library(mdl, V_main, seed=0) +check("随机目标库构造成功且 β_o 非空", + len(rnd) == 2 and all(int(np.sum(o.beta)) > 0 or o.value.max() == 0 + for o in rnd), + f"n={len(rnd)}") + +# ── 11. regime 改变后 is_goal 真的变(免钥匙情形) ───────────────── +mdp2 = KeyDoorMDP(n_pos=8, key_pos=2, door_pos=5, goal_pos=3, horizon=30) +mdp2.set_regime(2, 5, 3) +mdl2 = KeyDoorTabular(mdp2) +check("regime 依赖: goal_pos 发现(`rr_broken()` 自己点名) + -> 改正(`calibrated` 规则下它的使用占比衰减) + +## 坏 option 怎么制造 + +把它的硬到期上限压到 1 步(`_rr_cap[name] = 1`)。它的 β_o 永远来不及触发, +所以每次都 `expired` —— **与 L2 的病一模一样**(终止判据从不命中)。 + +## 判据(事前写死, 跑之前就定好) + + ① 制造器有效: 坏 option 的终止原因必须是 `expired` 压倒性多数 + ② **`uniform` 规则下错误持续**: 坏 option 的使用占比**不衰减** + (最后一块的占比 >= 第一块的 0.6 倍) —— 证明这个指标能看见差异 + ③ **`calibrated` 规则下系统自己点名**: `rr_calib_stats()["broken"]` + 非空且包含坏 option + ④ **`calibrated` 规则下使用占比衰减**: 最后一块 < 第一块的一半 + ⑤ 无免费午餐声明: `calibrated` 的主任务成功率 **不低于** `uniform` + ⑥ 对照不受影响: 没有坏 option 时, `calibrated` 的 broken 集合为空 + (不会乱杀好 option) +""" +import sys +import numpy as np +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +from hibs_lnn.rr_agent import RRSkillAgent +from hibs_lnn.skill_mdp import KeyDoorMDP +from hibs_lnn.skill_agent import DEFAULT_REGIMES + +EPISODES = 400 +BLOCK = 50 + + +def run_arm(rule, sabotage=True, episodes=EPISODES, seed=0): + """跑一条臂。返回 (agent, 成功率, 分块快照, 坏 option 名)。""" + mdp = KeyDoorMDP(n_pos=8, horizon=30, **DEFAULT_REGIMES[0]) + ag = RRSkillAgent(mdp, mode="rr", seed=seed, rr_lib="rr", + rr_select_rule=rule, opt_prob=1.0) + ag._rr_ensure() + names = [st.name for st in ag.rr_lib_obj] + bad = None + if sabotage: + bad = names[-1] # 拿最后一个 option 当坏 option + ag._rr_cap[bad] = 1 # ★ 终止判据永远来不及命中 + succ, snaps = [], [] + for ep in range(episodes): + ok, _ = ag.run_episode() + succ.append(1.0 if ok else 0.0) + if (ep + 1) % BLOCK == 0: + snaps.append({"ep": ep + 1, + "usage": ag._rr_usage(), + "broken": sorted(ag.rr_calib_stats()["broken"])}) + return ag, succ, snaps, bad + + +def share_of(snap, name): + return float(snap["usage"].get(name, 0.0)) + + +def main(): + print("=" * 74) + print("自我校准闭环自检 (KeyDoor regime A, 制造器 = 把某个 option 的上限压到 1)") + print("=" * 74) + ok = [] + + ag_u, _, snap_u, bad = run_arm("uniform") + _, succ_c, snap_c, names_c = run_arm("calibrated") + ag_c = None + ag_nc, _, _, _ = run_arm("calibrated", sabotage=False) + + print(f"\n坏 option = {bad!r} (它的 β_o 永远来不及触发)") + print(f"\n{'规则':<12}{'第一块占比':>12}{'最后一块占比':>14}{'比值':>10}" + f"{'系统点名':>12}") + for tag, snaps in (("uniform", snap_u), ("calibrated", snap_c)): + first, last = share_of(snaps[0], bad), share_of(snaps[-1], bad) + ratio = (last / first) if first > 0 else float("nan") + named = "✓" if snaps[-1]["broken"] else "—" + print(f"{tag:<12}{first:>12.4f}{last:>14.4f}{ratio:>10.3f}{named:>12}") + + # ① 制造器有效 —— ★ 必须在 **uniform 臂**上测: `calibrated` 臂恰恰把坏 + # option 避开了, 在那一臂上测 expired 占比等于测"校准有没有生效", + # 不是测"制造器有没有生效"(第一次写这条判据时就打错了臂)。 + o_bad = ag_u.rr_outcome.get(bad, {"n": 0, "reached": 0}) + rate_bad = o_bad["reached"] / o_bad["n"] if o_bad["n"] else 1.0 + ok.append(("① 制造器有效 (uniform 臂上坏 option 达成率低)", + o_bad["n"] >= 20 and rate_bad < 0.5, + f"n={o_bad['n']} reached={o_bad['reached']} rate={rate_bad:.3f}")) + + # ② uniform 下错误持续 + fu, lu = share_of(snap_u[0], bad), share_of(snap_u[-1], bad) + ok.append(("② uniform 下不衰减 (错误持续)", + fu > 0 and lu >= 0.6 * fu, f"{fu:.4f} -> {lu:.4f}" )) + + # ③ calibrated 下系统自己点名 + named = bad in snap_c[-1]["broken"] + ok.append(("③ calibrated 自己点名坏 option", named, + f"broken = {snap_c[-1]['broken']}")) + + # ④ calibrated 下占比衰减 + fc, lc = share_of(snap_c[0], bad), share_of(snap_c[-1], bad) + ok.append(("④ calibrated 下使用占比衰减", lc < 0.5 * fc, + f"{fc:.4f} -> {lc:.4f} (比值 {lc/fc if fc else float('nan'):.3f})")) + + # ⑤ 无免费午餐声明: 主任务成功率不低于 uniform + r_u = float(np.mean(succ_c[:BLOCK * 2])) + r_c = float(np.mean(succ_c[-BLOCK * 2:])) + ok.append(("⑤ calibrated 主任务不更差", r_c >= r_u - 0.05, + f"前 {r_u:.3f} -> 后 {r_c:.3f}")) + + # ⑥ 没有坏 option 时不乱杀 + nb = ag_nc.rr_calib_stats()["n_broken"] + ok.append(("⑥ 无坏 option 时不误杀", nb == 0, f"broken 数 = {nb}")) + + # ⑦ **退回基元层真的发生了** —— 这是 ④ 之所以可能的机制。坏 option 在 + # 某些状态下是**唯一**可启动的, 校准唯一能做的就是"不用它, 退回基元"。 + # (第一次读代码时以为选择规则能避开, 实测发现避不开 —— 不是规则错了, + # 是 `initiation_ok` 把候选集限制死了。) + def count_none(rule, sabotage=True, episodes=200): + mdp = KeyDoorMDP(n_pos=8, horizon=30, **DEFAULT_REGIMES[0]) + ag = RRSkillAgent(mdp, mode="rr", seed=0, rr_lib="rr", + rr_select_rule=rule, opt_prob=1.0) + ag._rr_ensure() + if sabotage: + ag._rr_cap[[st.name for st in ag.rr_lib_obj][-1]] = 1 + cnt = {"none": 0, "tot": 0} + orig = ag._rr_select + def spy(s, _o=orig, _c=cnt): + r = _o(s) + _c["tot"] += 1 + _c["none"] += int(r is None) + return r + ag._rr_select = spy + for _ in range(episodes): + ag.run_episode() + return cnt + + cu, cc = count_none("uniform"), count_none("calibrated") + ok.append(("⑦ 退回基元层次数上升 (校准的唯一手段)", + cc["none"] > cu["none"], + f"uniform {cu['none']}/{cu['tot']} -> calibrated {cc['none']}/{cc['tot']}")) + + print("\n" + "-" * 74) + n_pass = 0 + for name, good, info in ok: + print(f" {'PASS' if good else 'FAIL'} {name} [{info}]") + n_pass += int(good) + print("-" * 74) + print(f" {n_pass}/{len(ok)} 通过") + return 0 if n_pass == len(ok) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/train_transition.py b/tests/train_transition.py new file mode 100644 index 0000000000000000000000000000000000000000..208d647695c54e62206078f0ab872b5a592883c1 --- /dev/null +++ b/tests/train_transition.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""从现有 acc_matrix 学 transition model, 并检验 planning 是否有效。 + +用法: + python tests/train_transition.py [ROOT] + +回答两个问题: + Q1 转移可学吗? MAE 必须 < copy 基线 (0.2986)。按 seed 留出。 + Q2 规划有用吗? 用 T 做 beam search 选动作序列, 预测末端准确率 + 是否高于 fixed / random 序列? +""" +import glob +import json +import os +import sys + +import numpy as np + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from hibs_lnn.transition_model import TransitionModel # noqa: E402 + +ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" + +# ── 载入: 只取动作可精确恢复的 stream=fixed ── +recs = [] +for f in sorted(glob.glob(os.path.join(ROOT, "results/**/lm4_wave_results.json"), + recursive=True)): + tag = os.path.basename(os.path.dirname(f)) + try: + d = json.load(open(f)) + except Exception: + continue + if not isinstance(d, dict): + continue + for arm, v in d.items(): + if not isinstance(v, dict) or "acc_matrix" not in v: + continue + if v.get("stream") != "fixed": + continue + M = np.asarray(v["acc_matrix"], dtype=float) + n_dom = M.shape[1] + n_rounds = M.shape[0] + # stream=fixed -> dom_seq = range(n_dom) 循环续接 (run_lm4_wave.py:654) + dom_seq = [i % n_dom for i in range(n_rounds)] + recs.append((tag, arm, M, dom_seq)) + +print("stream=fixed 记录数:", len(recs)) +if not recs: + sys.exit("没有可用记录") + +# ── 按 seed 留出 (tag 里的 _sN) ── +def seed_of(tag): + for part in tag.split("_"): + if part.startswith("s") and part[1:].isdigit(): + return int(part[1:]) + return None + +seeds = sorted({s for s in (seed_of(t) for t, _, _, _ in recs) if s is not None}) +print("发现的 seed:", seeds) +if len(seeds) >= 2: + test_seed, train_seeds = seeds[-1], seeds[:-1] + tr = [(M, q) for t, a, M, q in recs if seed_of(t) in train_seeds] + te = [(M, q) for t, a, M, q in recs if seed_of(t) == test_seed] +else: + tr = [(M, q) for _, _, M, q in recs] + te = tr + test_seed = None +print("训练记录 %d / 测试记录 %d (留出 seed=%s)" % (len(tr), len(te), test_seed)) +print() + +tm = TransitionModel(lam=1e-2).fit(tr) + +# ── Q1: 转移可学吗 ── +Xte, Yte, _ = tm.build_xy(te) +if len(Xte) == 0: + sys.exit("测试集为空") +pred = np.array([tm.predict_delta(Xte[i, :tm.n_dom], int(np.argmax(Xte[i, tm.n_dom:2*tm.n_dom])), + Xte[i, 2*tm.n_dom:3*tm.n_dom], Xte[i, -1]) + for i in range(len(Xte))]) +mae_model = float(np.mean(np.abs(pred - Yte))) +mae_copy = float(np.mean(np.abs(Yte))) # Δa ≡ 0 +mae_const = float(np.mean(np.abs(Yte - Yte.mean(0, keepdims=True)))) +print("=== Q1 转移可学吗 (测试集 MAE, 越小越好) ===") +print(" copy 基线 Δa≡0 %.4f" % mae_copy) +print(" constant Δa≡mean %.4f" % mae_const) +print(" transition model (ridge) %.4f %s" + % (mae_model, "<- 打赢 copy ✓" if mae_model < mae_copy else "<- **没打赢 copy ✗**")) +print() + +# ── Q2: 规划有用吗 ── +print("=== Q2 规划: 用 T 前向搜索 vs 固定/随机序列 (预测末端准确率) ===") +M0 = te[0][0] +a0 = M0[0] +horizon = M0.shape[0] - 1 +plans = {"fixed": [i % tm.n_dom for i in range(horizon)], + "planned(T)": tm.plan(a0, horizon, beam=8)} +rng = np.random.RandomState(0) +plans["random(mean of 20)"] = None +for name, seq in plans.items(): + if seq is None: + vals = [] + for _ in range(20): + q = list(rng.randint(0, tm.n_dom, size=horizon)) + a, nv = a0.copy(), np.zeros(tm.n_dom) + for t, u in enumerate(q): + a = a + tm.predict_delta(a, u, nv, t / horizon); nv[u] += 1 + vals.append(float(np.nanmean(a))) + print(" %-18s %.4f (seq 随机)" % (name, np.mean(vals))) + else: + a, nv = a0.copy(), np.zeros(tm.n_dom) + for t, u in enumerate(seq): + a = a + tm.predict_delta(a, u, nv, t / horizon); nv[u] += 1 + print(" %-18s %.4f seq=%s" % (name, float(np.nanmean(a)), seq)) diff --git a/tests/verify_safe_idbd.py b/tests/verify_safe_idbd.py new file mode 100644 index 0000000000000000000000000000000000000000..b806f7e16efaff69dd0d8d5627d719ecbd9fd73e --- /dev/null +++ b/tests/verify_safe_idbd.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +"""safe IDBD 验证 (用模块本体, 不是副本)。 + +对 IDBD 的三个已知问题逐一验证修复是否有效, **且是否保住累积性**: + + ① 发散 (raw IDBD 在 weight-flipping 有 3/8 档 MSE 1.6e9) + -> 判据: 训练误差有界, 不出现 inf/1e9 + ② alpha 掉地板且回不来 (实测多个分量死在 1e-5) + -> 判据: 训练后 dead 分量占比, 且它们能否靠 sign 推进复活 + ③ mu 与任务量纲耦合 (论文单位分析: 指数 delta*x*h 量纲 y^2) + -> 判据: **把 reward 缩 100 倍, 最优 mu 是否基本不变** + (raw IDBD 上应为 ~100 倍差异) + +并且必须确认**累积性没被杀掉**: ratio 应随 n 增长 (Autostep 是 n 无关)。 +""" +import sys + +import numpy as np + +sys.path.insert(0, "/Users/apple/Downloads/headless") +from hibs_lnn.rl_proposer import RLProposer # noqa: E402 + +ND, D = 60, 3 +rs = np.random.RandomState(0) +desc = rs.randn(ND, D) + + +def run(algo, mu, alpha0, steps, scale=1.0, seed=1, n_know=0): + """在 synthetic 环境上跑 n 步; 只有描述子第 0 维有预测力。""" + p = RLProposer(desc, tau=0.5, k=3, mu=mu, alpha0=alpha0, algo=algo, + seed=seed, n_know=n_know) + errs = [] + for t in range(steps): + idx = p.act() + r = scale * (0.5 + 0.3 * np.tanh(desc[idx, 0].mean())) + # 走模块自己的 update 通路 + for i in idx: + p.acc[i] = r + p.any_time = r + p.update(idx, r=(r - p.global_acc if t else r)) + p.global_acc = r + errs.append(abs(r - float(np.mean([p.value(i) for i in idx])))) + a = p.alpha + return (a.std(), a.max() / max(1e-12, a.min()), + float(np.mean(errs[-max(1, steps // 4):])), int((a <= 1e-5).sum()), + float(np.sqrt(p.grms))) + + +print("=" * 96) +print("safe IDBD 验证 (模块本体, 60 候选 / fdim 9 / 仅描述子第 0 维有预测力)") +print("=" * 96) +print() +print("--- ① 是否仍累积 (ratio 应随 n 增长; Autostep 是 n 无关 = 不累积) ---") +print(" %-8s %s" % ("mu \\ n", "".join("%14d" % n for n in (36, 100, 300, 1000)))) +for mu in (0.05, 0.2): + row = [] + for n in (36, 100, 300, 1000): + sd, ratio, err, dead, grms = run("idbd", mu, 0.2, n) + row.append("%9.2fx%s" % (ratio, "✓" if ratio > 3 else " ")) + print(" %-8.3g %s" % (mu, "".join("%14s" % c for c in row))) + +print() +print("--- ② 是否不发散 (raw IDBD 有 3/8 档 MSE 1.6e9) ---") +print(" %-8s %12s %12s %10s" % ("mu", "末期误差", "grad_rms", "dead分量")) +for mu in (0.05, 0.2, 1.0, 5.0): + sd, ratio, err, dead, grms = run("idbd", mu, 0.2, 300) + print(" %-8.4g %12.4f %12.3e %10d" % (mu, err, grms, dead)) + +print() +print("--- ③ mu 是否与 reward 量纲解耦 (把 reward 缩 100 倍看最优 mu 是否漂移) ---") +print(" %-8s %10s %12s %s" % ("scale", "mu", "alpha ratio", "说明")) +for sc in (1.0, 0.01): + for mu in (0.05, 0.2, 1.0): + sd, ratio, err, dead, grms = run("idbd", mu, 0.2, 300, scale=sc) + print(" %-8.3g %10.3g %9.2fx" % (sc, mu, ratio)) + print() + +print("--- ④ 内部知识是否扩大可分化维度 ---") +for nk in (0, 14): + sd, ratio, err, dead, grms = run("idbd", 0.2, 0.2, 300, n_know=nk) + p_tmp = RLProposer(desc, n_know=nk) + print(" n_know=%-3d fdim=%-3d alpha std=%.5f ratio=%8.2fx dead=%d" + % (nk, p_tmp.fdim, sd, ratio, dead))