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@@ -47,5 +47,5 @@ webapp/models/*.meta.json
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# 从头重跑时旧产物挪到这里（不入库）
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ml/archive/
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# onnxruntime-web 本地副本（约 33 MB）：**入库**，离线/无 npm 时直接可用，不再依赖 CDN（2026-10-18 用户要求）
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# 来源 onnxruntime-web@1.20.1 dist，六个文件 + VERSION；ml/prep/vendor_ort.sh 在缺失时仍可补齐
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# onnxruntime-web 本地副本（bash ml/prep/vendor_ort.sh 生成，约 50 MB，不入库）
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webapp/vendor/ort/
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@@ -1,71 +1,181 @@
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# ML 运行手册审查（ml/README.md）· 精简版
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八轮审查（`0218d7f` → 本次）的已解决条目全部删去，原文在 git 历史里。本文件只留：实测数字 / 已定判据 / 仍待真数据回答的问题 / 正式训练步骤。
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六轮历史（`0218d7f` → `a5e779c`）：第一轮 13 条、第二轮可行性、第三~五轮处置、第六轮本机实跑。**已解决的条目按用户要求删去，完整原文在 git 历史里**。本文件只剩三部分：仍有效的实测数字 / 仍未解决的问题 / 已复核正常的项，供实现方下一轮处置。
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环境：本机 CBB-003（RTX 3090，训练可用 19–20 GB；28 核；Python 3.12 + torch 2.10；Edge + onnxruntime-web 1.20.1，已入库 `webapp/vendor/ort/`）。
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当前结论：第 1–6 步本机实测可跑；R4 通过；自检 7/8 通过（唯一失败项是 Windows 限制，已接受）；**审查条目已全部处置，下一步直接正式训练。**
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环境：本机 CBB-003（RTX 3090、28 核、Python 3.12 + torch 2.10 + onnxruntime 1.22、Edge + onnxruntime-web 1.20.1）。
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当前结论：第 1–6 步已实测可跑；**第 7–13 步零证据**；R4 部署口径（纯推理）已判通过，`bash ml/pipeline.sh r4` 端到端 rc=0（2026-10-18 本机修复后复跑）。A 组原三处中两处已修，只剩离线部署 A-3。
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---
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## 一、实测数字（对照用）
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## 一、仍有效的实测数字（后续对照用）
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| 项 | 数字 | 口径 |
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|---|---|---|
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| 对拍 300 条 | PASS（fk 3.4e-13 mm、特征 6e-8、间隙梯度 1.4e-6） | 第 2 步 |
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| 生成 | 1.56 s/条 | 第 4 步，80 条 |
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| 统计 | keepRate 0.977、samplesPerTask 36.5、cert.minGap p50 4.4 / p90 5.9 mm | 88 条，未加 3 mm 过滤 |
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| 预训练 | 4.92 M 参数、6208 样本/s、60k 步 ≈ 2.8 h、显存峰值 2.76 GiB | batch 1024 |
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| APG 显存 | batch 48 / plans 150 → 13.08 GiB；batch 128 必 OOM | d=192，1 步 |
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| R4 部署口径 | wasm·16 线程·proxy P99 **12.67 ms**（< 15 通过）；WebGPU P99 27.09，不用 | `feat_dyn` 新模型 |
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| 自检 | 7/8，3.5 分钟；`train_term=false`（Windows 下 TERM 送不到 python，超时靠 STOP 文件 + 每 10 分钟存档） | `selftest.json` |
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| 对拍 300 条 | PASS（fk 3.4e-13 mm、特征 6e-8、间隙梯度 1.4e-6），7.6 s | 第 2 步 |
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| 生成 | 1.56 s/条（80 条 122 s） | 第 4 步 |
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| 统计 | keepRate 0.977、samplesPerTask 36.5、projected 178.4 万、exec_s p50 2.3 / p90 3.0 / max 3.7 s、cert.minGap p50 4.4 / p90 5.9 mm | 88 条，**未加 3 mm 过滤** |
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| 预训练 | 4.92 M 参数、6208 样本/s（batch=1024）→ 60k 步 ≈ 2.8 h | 250 步冒烟 |
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## 二、已定的判据与做法
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R4（随机初始化模型；延迟只取决于结构，与是否训练无关）：
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| 接口 | 线程 | proxy | P50 | P95 | P99 |
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|---|---|---|---|---|---|
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| **enc_once（部署口径）** | 16 | 开 | 7.36 | 9.48 | **11.07** |
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| enc_once | 4 | 开 | 10.22 | 11.69 | 12.92 |
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| enc_once | 16 | 关 | 7.43 | 9.21 | 10.79 |
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| 旧接口（对照） | 16 | 开 | 9.53 | 11.29 | 16.47 |
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| 旧接口 | 4 | 开 | 13.88 | 16.35 | 17.19 |
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tickGapMax 全部 ≈21.5 ms（20 ms 节拍没被卡住）。背景：第五轮前默认（4 线程、无 proxy、旧接口、真查看器）P99 37.0；线程扫描 8 线程 24.9 / 16 线程 19.5 / 24 线程 25.8 → 16 最优，超线程反而挤。**部署口径纯推理 P99 11.07 < 15，R4 判通过**；`enc_once` 相对旧接口省约 30–33%。
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修复后由 `pipeline.sh r4` 自身复跑（两次）：部署口径 P99 **10.22 / 11.25**（P50 7.34 / 7.10），4 线程 13.58 / 13.64，无 proxy 9.28 / 10.92；webgpu 仍报 `[webgpu] backend not found`。
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真查看器同一会话（随机权重、`?policy=1&probe=1`）：inferMs P50/P99/max = **30.36 / 61.64 / 68**，safetyMs = **63.52 / 248.76 / 320.6**；622 次 plan、brakes 4、locks 48、projected 283、lookahead 219。单变量对照：`__POLICY.stop()` 后纯 infer 7.5 / 9.7 ms → 差额是主线程排队，不是模型慢。
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---
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## 二、仍未解决的问题
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### A. 会直接中断本机一键流程（原三处，两处已在本机修复）
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**已修复（本机 2026-10-18，`bash ml/pipeline.sh r4` 连跑两次均 rc=0、`pass=true`）**
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- 探针页复用 detached tensor：`feat` 的分配移进 `run()`（`webapp/r4_probe.html`）→ proxy=1 的两种配置均正常出数。
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- `pipeline.sh` 7 处 `json.load(open(...))` 全部补 `encoding='utf-8'`（`:90 :110 :118 :127 :136 :138 :144`）。
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- 附带发现并修掉：r4 分支末行 `[ "$r" = null ] && echo` 在 `r=pass` 时整体返回 1，`set -e` + EXIT trap 把**通过的 r4 记成 rc=1**（`all` 会因此停）。已改为 `if ... then ... fi`。
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**A-3 仍未修：`&ortLocal=1` + 默认 proxy 必然失败。**
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`ik-policy-glue.js` 把 `ort.env.wasm.wasmPaths` 设为相对前缀 `vendor/ort/`，ORT 对 `.mjs` 走 `import()` 需要绝对说明符 → `no available backend found. ERR: [wasm] TypeError: Failed to resolve module specifier 'vendor/ort/ort-wasm-simd-threaded.mjs'`。README 明确支持的离线部署路径在默认配置下不可用。（`ortLocal=1` + `proxy=0` 未测；`wasmPaths` 与 proxy 无关，大概率同样失败。）
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### B. 待实现方判断（数据已摆在上面；分歧按第五轮第〇节规则，以实现方为准）
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**B-1 R4 判据用哪个口径。** 纯推理 P99 11.07 判通过；但同一台机、同一会话，整周期 plan = infer 61.6 + safety 248.8（P99），随机权重下 1/3 的规划 >100 ms。如果 R4 的意义是"浏览器能不能按 60 ms 周期推一次"，纯推理口径会放过实际丢步的配置。供下一轮的建议（不要求现在改）：`r4.json` 记两个数 `infer_p99` 与 `plan_p99 = infer + safety`，后者另设一条线（如 <45 ms，给 60 ms 周期留余量）。
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**B-2 `safetyMs` 两把尺。** bench 在 Node 里跑同一份 `ik-policy.js`（CPU EP），浏览器里同一段代码 p99 249 ms——互不可替代，与"仿真安全层 ≠ 部署运行时"是同一类问题换了个位置。判据：同一批贴柱题两边各测一次，差 >3× 就要解释。建议核对 `certBudget=40` 在贴柱位形下的实际查询次数与 `makeCert` 的 memo 命中率——249 ms 的量级不是"随机策略质量"能解释的；真模型是否不同未知。**R9 至今无有效数据**（探针只测纯推理；闭环丢步率因探针一运行 CDP 就拿不到执行槽而没采到——这本身是主线程拥挤的证据）。
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**B-3 WebGPU 是判定顺序里的备用通过路径，但至今零数据。** 第五轮判定顺序写了"wasm 不过而 WebGPU 过，也判通过"，本机 headless 报 `[webgpu] backend not found`，且 CDN 间歇不可达（jsdelivr 多次 000、unpkg 200）→ 需要一条真能测到 WebGPU 的路（非 headless，或 A-3 修好后本地放 ort）。
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### C. 尚未实测（跑之前必须知道这些是空的）
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1. **第 6–13 步全部没跑过**：train 只有 250 步吞吐冒烟；m5 / apg / mine / export / bench / accept 零实测；`all` 端到端从未完整跑通。一次完整流程机时下界约 15–28 h，其中 **apg 2–4 h/轮、mine 6 轮都是估的**。
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2. **3 mm 间隙过滤的真实剔除比例（`gapDrop`）没测过**：只测过沙箱 30 条（剔 9/29，不具代表性）；上表 p50 4.4 mm 是未过滤口径。生成后看 `stats.json` 的 `gapDrop`。
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3. **R2 新判据（`label_std_norm`）的池无关性**：需在 1.7 M 样本上 `--pool 50000` 与 `--pool 200000` 两跑、差 <0.02 才成立；没做。默认 `R2_STRICT=0` 只警告，开着就是一道未校准的闸。
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4. **R8 出题耗时**：沙箱 2 题 9 分钟没出完；本机未测（`policy_bench.json` 会记 `requested` / `genS`，不足额时看警告）。
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5. **Windows 侧生命周期全部未实测**：关控制台黑窗口训练是否继续（`CREATE_NO_WINDOW`）、`/proc/$$/winpid` + `taskkill /T` 能否结束整树、Git Bash 自带 `timeout` 能否把 TERM 送到 python / node。
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6. **断点续跑的沙箱自测未在本机复核**：gen 截断续写（「续写：已有 N 条」）、train STOP 存档（rc 143、`last.pt` 第 N 步）、apg `--resume`、prep 二次运行「待打包 0 个」。
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### D. 小遗留
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`webapp/models/README.md` 过时：还写"由 `--fp16` 生成"、指向"第 8 步"（现为第 10 步、默认 fp32）。
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---
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## 三、第六轮已复核正常（不在问题清单里）
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- `bash ml/pipeline.sh r4` 端到端：修完 A 组两处后连跑两次 rc=0、`pass=true`。
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- 锁：`r4` 阶段拿到 `ml/runs/pipeline.lock`，结束已删。
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- N-5 三处防护在位：查看器拒绝 `random_init`（提示 `&probe=1`）、`policy_bench.cjs` 拒绝、`acceptance` 有"部署模型是训练产物"项。
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- `enc_once` 运行时对接一致：只送 1 行特征（`ik-policy-glue.js:52` / `ik-policy.js:344`），旧模型回退 `[B,F]`。
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- `r4` 导出：19.76 MB、`enc_once=true`、与 torch 对拍 2.2e-7。
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## 四、建议顺序
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1. ~~A 组修复并重跑 `r4`~~：已完成（本机 2026-10-18，rc=0、P99 11.25）。
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2. 接着 `FROM=gen TO=prep` 跑一次（数小时），拿 `gapDrop` 与 R2 两个口径的数——C-2 / C-3 随之消掉，B-2 的 Node 侧安全层数字可顺手从 m5 前的 bench 拿。
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3. B 组等上面的数字齐了再定；C-1 / C-5 / C-6 随第一次真跑逐项核对；A-3（离线 ort 加载）交给实现方。
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---
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## 五、实现方处置（第六轮，2026-10-18）
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逐条对应第二部分。分歧按第五轮第〇节规则以实现方为准，下一轮用实测数据定。
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**A-3 离线 ort 加载**：已修。`ik-policy-glue.js` 与 `r4_probe.html` 的本地前缀改为绝对 URL（`new URL('vendor/ort/', location.href)`），ORT 的 `import()` 不再把它当裸模块名。新增 `ml/prep/vendor_ort.sh`（`setup` 与 `r4` 自动调用）用 `npm pack onnxruntime-web@1.20.1` 把 `ort.min.js`、`ort.webgpu.min.js` 与两套 wasm/mjs 放进 `webapp/vendor/ort/`（33 MB，`.gitignore`）；探针检测到本地副本就默认用它，不再依赖间歇不可达的 jsdelivr（`ORT_LOCAL=0` 强制 CDN）。
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**GBK 兜底**：除了本机已补的 7 处，`pipeline.sh`、`console.bat`、`console.sh`、监控起的子进程统一 `PYTHONUTF8=1`，所有 Python 读写默认 UTF-8，不再逐处补 `encoding`。
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**B-1 R4 口径**：R4 保持纯推理判据（随机权重只代表推理开销，这点不变）。整周期另立 **R9**，用**训练好的模型**在**真查看器**里测：`python -m ml.eval.r4_probe --viewer`（`bench` 阶段自动跑，写 `ml/runs/r9_browser.json`），查看器 `&autotest=30` 跑与 Node bench **同种子同题**的 30 道贴柱题，判据 **整次规划 `planP99` < 45 ms 且饥饿节拍 < 1%**（饥饿 = 执行时钟到点时队列为空 = 实际丢步）。`r4.json` 另带 `viewer_ref`（随机模型的同一测试），只作参考不判定——随机策略几乎每次都要认证多条候选，安全层路径不代表真模型。验收汇总加 R9 一项。方案 R9 行同步改写（原"安全层 P99 < 5 ms"不可测也不对口径）。
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**B-2 安全层两把尺**：找到了原因，而且 Node 侧也一样慢，不是浏览器独有。沙箱 Node 单次调用：`gapMatrix` cap20 **3.6 ms** / cap40 **5.5 ms**、`collideFree` 1.5 ms、`criticalPairs` 3.1 ms，而推理只有 **1.5 ms**——**耗时在几何查询，不在模型**。具体三处：
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1. 截止只在候选之间检查，单条候选内部最多 40 次查询（≈150 ms）不受限 → 墙钟截止下沉到认证递归内部：第 1 条候选截止 45 ms，其余共享到 30 ms，到点判不过（`certFailWhy.time`），沿已认证刹停段停，安全性不变。
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2. `safetyMs` 从推理结束开始计，**把前瞻（第二次推理 + 8 份特征 = 8 次 gapMatrix cap40）算进去了** → 拆成 `lookaheadMs` / `safetyMs` / `planMs` 三项分别记。
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3. 前瞻在"卡死"时也触发，随机或差的策略几乎一直卡死 → 改为只在冷启动（新目标 / 目标跳变）前瞻，与仿真 `closed_loop.py`（只在首块前瞻）对齐；卡死由换向锁处理。
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沙箱 Node、同 2 道贴柱题、小模型实测（同一机器前后对比）：安全层 P99 **200.9 → 51.9 ms**，前瞻 P99 126 → 83 ms（只剩冷启动那几次），整次规划 P99 **236.7 → 63.3 ms**（P50 57.5）。代价是沙箱上 `time` 截止判不过占多数、刹停多（2 vCPU 上每次 gapMatrix 约 4 ms，45 ms 只够约 10 次查询；未训练小模型本身也差）——你机器单核快 2× 左右，真模型候选第 1 条通过率高，R9 以本机实测为准；若本机 R9 仍不过，下一轮把几何查询（特征、投影、认证）整体搬进 Worker，主线程只执行。
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另外 bench 与查看器都记 `certQueriesP99`、`certMemoHitRate`（沙箱 0.69–0.72）、`certFailWhy`；验收加"同题安全层 浏览器 / Node ≤ 3×"一项。
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**顺带发现并修掉一个真 bug**：第五轮 `enc_once` 后，前瞻的 8 行特征**各不相同**，但 `ortInfer` 只送了第 1 行 → 8 个候选的前瞻价值全是同一个观测算的，冷启动时的候选排序等于随机。现在 ONNX 的 `feat` 轴改为动态（1 行 = 主推理编码一次；K 行 = 前瞻逐行编码，`meta.feat_dyn=true`），导出时对拍 8 行不同特征与 `sample()` 一致（2e-7 量级）；第五轮导出的旧模型运行时自动退化为逐行推（正确但慢）。**你本机的 r4 模型需重新导出**（`bash ml/pipeline.sh r4` 会重导）。
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**B-3 WebGPU**：`backend not found` 的直接原因是 `ort.min.js` 里**没有 WebGPU 后端**（只在 `ort.webgpu.min.js`）。查看器与探针在 `ep=webgpu` 时改载 `ort.webgpu.min.js`；探针先记 `navigator.gpu` 与适配器信息（区分"没有 GPU"与"包不对"）；WebGPU 配置改用有界面窗口打开（无头模式拿不到真适配器），跑完自动关。
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||||
**D**：`webapp/models/README.md` 已改为 fp32、第 10 步、新输入签名。
|
||||
|
||||
**C 组**：第七轮已处置，下一步见第六节。
|
||||
|
||||
## 六、实现方处置与待本机验证（第七轮，2026-10-18）
|
||||
|
||||
本节是遗留问题的**唯一清单**：不需要计算的已在代码里修完（「本轮已修完」）；需要计算的只有一条技术验证命令（「本机技术验证」），由你的 PC 跑，沙箱不跑；做法已全部定下（「已定的做法」）；需要你确认的写在「还需要你确认的」。
|
||||
|
||||
---
|
||||
|
||||
### 6.1 本轮已修完（不需要计算）
|
||||
|
||||
| 遗留 | 处置 | 位置 |
|
||||
|---|---|---|
|
||||
| A-3 离线 ort 加载失败 | 本地前缀改绝对 URL；`ml/prep/vendor_ort.sh` 放好 ort 本地副本，探针默认用本地、不依赖 CDN | `ik-policy-glue.js`、`r4_probe.html`、`vendor_ort.sh` |
|
||||
| GBK 读中文 json | 全流程 `PYTHONUTF8=1`（pipeline / console.bat / console.sh / 监控子进程） | `pipeline.sh` 等 |
|
||||
| B-1 R4 口径 | R4 仍判纯推理；整周期另立 R9（真查看器、训练好的模型、与 bench 同题：`planP99` < 45 ms、饥饿 < 1%），验收加 R9 | `r4_probe.py --viewer`、`acceptance.py` |
|
||||
| B-2 安全层两把尺 | 原因定位：几何查询慢（gapMatrix 3.6–5.5 ms/次），不是模型。截止下沉到认证内部；耗时拆 4 项记录；前瞻只在冷启动；验收加"同题浏览器 / Node ≤ 3×" | `ik-policy.js`、`policy_bench.cjs` |
|
||||
| 前瞻 bug（新发现） | enc_once 后前瞻只送 1 行特征 → feat 轴改动态（`meta.feat_dyn`），导出对拍 K 行 | `policy.py`、`export_onnx.py` |
|
||||
| B-3 WebGPU 零数据 | 根因：`ort.min.js` 不含 WebGPU → 改载 `ort.webgpu.min.js`；有界面窗口测；记录适配器信息 | 同上 |
|
||||
| C-3 R2 池无关性 | 每次 R2 自动以 4 倍池再测一次，记 `pool_check.independent`（差 < 0.02） | `check_r2.py` |
|
||||
| C-4 R8 出题耗时 | 出过的题按碰撞指纹缓存，`policy_bench.json` 记 `genS`、`casesCached` | `policy_bench.cjs` |
|
||||
| C-5 / C-6 本机生命周期与续跑 | 新增一条命令自检（见「本机技术验证」 V1） | `ml/selftest.sh`、`ml/selftest_proc.py` |
|
||||
| C-1 第 7–13 步零实测 | 靠断点续跑兜底（V1 验证），正式训练分三段跑、出错从该阶段续；另备 `ml/smoke.sh` 全流程冒烟，不必跑 | `ml/smoke.sh`、`configs/smoke.json` |
|
||||
| 续跑后数值不一致 | `last.pt` 存 torch / CUDA / random 随机数状态，续跑 batch 顺序与一口气跑完一致 | `ml/common/ckpt.py` |
|
||||
| D 模型目录说明过时 | 已改（fp32、第 10 步、新签名） | `webapp/models/README.md` |
|
||||
|
||||
沙箱只做了语法检查与几秒级的对拍，**以上全部以你本机结果为准**。
|
||||
|
||||
---
|
||||
|
||||
### 6.2 本机技术验证（只验可行性与意外，不做正式训练）
|
||||
|
||||
一条命令，5–10 分钟，在仓库旁的副本目录里跑，不碰本仓库的数据与产物，跑完副本可删。
|
||||
|
||||
| # | 命令（Git Bash，仓库根目录） | 耗时 | 回传 | 验证什么 |
|
||||
|---|---|---|---|---|
|
||||
| V1 | `bash ml/pipeline.sh selftest` | 5–10 分钟 | `ml/runs/selftest.json` | 意外中断能否恢复：gen 截断续写、prep 二次运行、训练 STOP / 续训、APG 续跑；Windows 进程：控制台停止能否杀净整棵树、关掉黑窗口后流程是否继续、TERM 能否送到 python |
|
||||
|
||||
判读：`all_pass = true`。没过就把文件发我，不用排查。
|
||||
|
||||
不单独跑全流程冒烟：V1 通过即证明断点续跑可靠，正式训练中途某一步出错时，已完成的阶段与训练进度都保留，我修完后从出错那一步接着跑（`FROM=<阶段>`），损失只是出错到发现之间的空闲时间。为缩短这段时间，正式训练按"数据 → 训练 → 后训练与评测"三段跑，每段结束看一眼（见「正式训练」）。`ml/smoke.sh` 保留在仓库里，不作为必跑项。
|
||||
|
||||
---
|
||||
|
||||
### 6.3 已定的做法
|
||||
|
||||
| 事项 | 定为 |
|
||||
|---|---|
|
||||
| R9（真查看器、训练好的模型、30 道贴柱题） | 稳态 `planWarmP99` < 45 ms、运动中饥饿 < 1%、认证超时占比 < 5%；冷启动 P99、刹停率、`tickGapMax` 只报 |
|
||||
| R9 不过 | 几何查询（特征、投影、认证）搬进 Worker，主线程只执行 |
|
||||
| APG | batch 48、steps 8000（总任务量同原 128×3000），本进程显存上限浮动（整卡 − 已用 − 1 GB 余量，见 `ml/common/vram.py`） |
|
||||
| 离线 ort | 只用入库本地副本，缺失即报错，不退回 CDN |
|
||||
| 训练余量 3 mm | `stats.json` 间隙 p10 ≥ 3 mm 保持，否则降到 p10 |
|
||||
| `R2_STRICT` | `pool_check.independent=true` 且截距 < 0.10 时打开 |
|
||||
| 共识过滤 | 门 1 < 0.90 自动开 |
|
||||
| 分歧 | 实现方为准，下一轮数据定 |
|
||||
| R9 通过线 | 整次规划 P99 < 45 ms、饥饿节拍 < 1%（60 ms 周期留 15 ms 给渲染） |
|
||||
| 浏览器测试 | 有界面窗口（无头拿不到真 GPU） |
|
||||
| R9 本机不过 | 几何查询（特征、投影、认证）整体搬进 Worker，主线程只执行 |
|
||||
| 训练余量 3 mm、`R2_STRICT`、共识过滤 | 按正式数据段的 `stats.json` / `r2.json` 定：间隙 p10 ≥ 3 mm 保持 3 mm，否则降到 p10；R2 `pool_check.independent = true` 且截距 < 0.10 时打开 `R2_STRICT`；门 1 < 0.90 自动开共识 |
|
||||
|
||||
## 三、仍待真数据回答（正式训练会自动产出，不单独验证）
|
||||
---
|
||||
|
||||
1. `gapDrop`：3 mm 过滤的真实剔除比例 → `stats.json`。
|
||||
2. R2 池无关性 → `r2.json` 的 `pool_check`。
|
||||
3. R8 出题耗时 → `policy_bench.json` 的 `genS`。
|
||||
4. APG 收敛与机时（batch 48 下首测）→ APG 日志。
|
||||
5. 真模型 R9 → `r9_browser.json`。
|
||||
### 6.4 还需要你确认的
|
||||
|
||||
## 三′、首次正式预训练失败（2026-10-18，21850 步）与修正
|
||||
无。V1 结果回来后，我只按结果改代码；若结果暴露出需要你决定的事，再写进本节。
|
||||
|
||||
现象：闭环到达 0–0.2%、穿柱 77–97%；MF 残差 2 万步停在 7–8、17k–19k 尖峰到 96；梯度 100% 被裁剪。用 last.pt 实测定位：
|
||||
1. **多峰头塌成单峰（主因）**：同一状态 8 个候选间标准差 0.030、动作幅度 0.151。FK / 平滑损失加在 `one_step(随机噪声)` 上去对齐这一条老师 Y，最优解是条件均值 E[Y|X]，左绕 / 右绕取平均正好穿柱。→ 改加在 r = t、t ≤ 0.3 的配对去噪估计 x̂ = z − t·u 上。
|
||||
2. **MeanFlow 目标失控**：∂u/∂t 均方根 6–12，而 v 约 1.1，目标被自激的 JVP 项主导。时间嵌入 sin(1000·t) 太陡，JVP 又在 bf16 下算。→ 最高角频率 50；MF 分支 fp32；前 5000 步 p_neq = 0。
|
||||
3. **闭环分布偏移被低估（推断）**：增广只有 1°。→ 增广 40% / 3° / 12 步收回；新增 DAgger 阶段（策略走到的状态请老师重规划，2 轮）；开训前加老师轨迹回放自检；每次评估加开环误差与候选多样性。
|
||||
---
|
||||
|
||||
## 四、正式训练（三段，每段跑完回传）
|
||||
### 6.5 正式训练（以后再跑，不在本轮）
|
||||
|
||||
V1 通过后再跑，分三段，每段跑完把回传文件发我：
|
||||
|
||||
| 段 | 命令 | 耗时 | 回传 |
|
||||
|---|---|---|---|
|
||||
| 数据 | `FROM=kin TO=prep bash ml/pipeline.sh all` | 数小时 | `ml/data/stats.json`、`ml/data/r2.json`、`ml/runs/r4.json` |
|
||||
| 训练（含 DAgger） | `FRESH_FROM=prep FROM=prep TO=m5 bash ml/pipeline.sh all`（增广改了，样本要重打包；gen 不重跑） | 约 9–11 小时（未实测：prep 0.5 + 预训练约 5（fp32 变慢）+ DAgger 约 3.5 + m5 0.3） | `ml/runs/replay_teacher.json`、`ml/runs/base/log.jsonl`、`ml/runs/m5_gate_*.json` |
|
||||
| 训练 | `FROM=train TO=m5 bash ml/pipeline.sh all` | 约 3–6 小时 | `ml/runs/m5_gate_*.json` |
|
||||
| 后训练与评测 | `FROM=apg TO=accept bash ml/pipeline.sh all` | 约 10–20 小时 | `ml/runs/acceptance.json`、`policy_bench.json`、`r9_browser.json` |
|
||||
|
||||
中途出错：把出错信息发我，修完用 `FROM=<出错阶段>` 接着跑，前面的不重做。
|
||||
|
||||
---
|
||||
|
||||
## 七、第一次真跑（2026-10-18，网页「开始训练」）暴露的一处阻塞 + 门 1 实测
|
||||
|
||||
**阻塞（已修，一行）**：`pipeline.sh` 的 `run() { echo "== $*"; "$@"; }` 把命令回显写进 **stdout**，而 `gate1` 是 `run node ml/gen/consistency.cjs … | tee ml/data/consistency.json.tmp` → 回显被一起存进 `consistency.json`，文件首行变成 `== node ml/gen/consistency.cjs 300 4242`。`gen` 阶段读它判 CONSENSUS 时 `json.load` 当场 `JSONDecodeError: Expecting value: line 1 column 1 (char 0)`，`set -e` 下整条 `all` 停在 gen（阶段 rc：kin 0、r4 0、gate1 0、gen 1）。
|
||||
- 与平台无关，沙箱 Linux 同样会中，只是没跑到这一步。
|
||||
- 修法：回显改走 stderr（`echo "== $*" >&2`）。全仓只有 `gate1` 这一处把 `run` 的输出重定向进文件（`check_r2` 等用 `--out` 自写，不受影响）。
|
||||
- 已写坏的 `ml/data/consistency.json` 就地剥掉首行复用，**没有重跑 25–30 分钟的门禁**。
|
||||
- 建议实现方顺手加一条自检：任何被 `tee`/`>` 落盘成 JSON 的产物，读之前先确认首字符是 `{`。
|
||||
|
||||
**门 1 实测（第一次真数据）**：`tasks 300 / pairs 1133 / consistentRate 0.838 / consistentByGateRule 0.838 / gate FAIL / deterministic 300/300`，分类看 `C 0.824`、`B 0.844`，`devP50 3.6`、`devP90 145.1`。
|
||||
→ 0.838 < 0.90，**自动走共识分支**（`--consensus 4`），与方案 R1 首测 78% 同一侧；`worstAxisOfInconsistent [43,4,42,40,16,39]` 说明跳变在各轴上分布均匀，不是某一根轴的系统性问题。gen 因此按 4–5 h 那一档估，不是 1 h。
|
||||
任何一段中途出错：把出错信息发我，我修完后用 `FROM=<出错阶段>` 接着跑，前面的不重做。
|
||||
|
||||
@@ -361,8 +361,7 @@ id="toc-101-只能靠实测消除的风险按里程碑顺序">10.1
|
||||
<blockquote>
|
||||
<p>基于
|
||||
<code>main@3c7c814</code>（贴立柱修复后的规划器）。本文是方案，代码尚未编写。
|
||||
训练机：RTX 3090 24 GB，训练可用 19–20 GB（桌面常驻 4–5 GB，进程硬帽
|
||||
0.78）。部署：浏览器（后续真机）。</p>
|
||||
训练机：RTX 3090 24 GB。部署：浏览器（后续真机）。</p>
|
||||
</blockquote>
|
||||
<hr />
|
||||
<h2 id="0-定位与边界">0. 定位与边界</h2>
|
||||
@@ -880,9 +879,8 @@ WTA 谁更好</strong>；因此 WTA 只作为 M5
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>预训练 + 后训练</td>
|
||||
<td>用户 RTX 3090（可用 19–20 GB）</td>
|
||||
<td>预训练 batch 1024（峰值 2.76 GiB）；APG batch 48 × 8000 步（峰值
|
||||
13.08 GiB）</td>
|
||||
<td>用户 RTX 3090 24 GB</td>
|
||||
<td>batch 1024–2048</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>部署推理</td>
|
||||
@@ -1568,10 +1566,9 @@ d=128（<code>configs/small.json</code>）。K=8 不降</td>
|
||||
<td>整周期超标（推理 + 前瞻 + 安全层 > 60 ms
|
||||
周期，执行时钟饥饿丢步）</td>
|
||||
<td>训练好的模型在<strong>真查看器</strong>里跑与 bench 同一批 30
|
||||
道贴柱题：稳态规划 P99 < 45 ms、运动中饥饿节拍 < 1%、认证超时占比
|
||||
< 5%（<code>r4_probe --viewer</code> →
|
||||
<code>r9_browser.json</code>；冷启动 P99、刹停率、tickGapMax
|
||||
只报）；同题安全层浏览器 / Node 差 ≤ 3×</td>
|
||||
道贴柱题：整次规划 P99 < 45 ms、饥饿节拍 <
|
||||
1%（<code>r4_probe --viewer</code> →
|
||||
<code>r9_browser.json</code>）；同题安全层浏览器 / Node 差 ≤ 3×</td>
|
||||
<td>几何查询（features、投影、认证）移到
|
||||
Worker，主线程只执行；前瞻只在冷启动时做</td>
|
||||
<td>M7</td>
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
# Transformer 避障运动策略：最终方案
|
||||
|
||||
> 基于 `main@3c7c814`（贴立柱修复后的规划器）。本文是方案，代码尚未编写。
|
||||
> 训练机：RTX 3090 24 GB，训练可用 19–20 GB（桌面常驻 4–5 GB，进程硬帽 0.78）。部署：浏览器（后续真机）。
|
||||
> 训练机：RTX 3090 24 GB。部署：浏览器（后续真机）。
|
||||
|
||||
---
|
||||
|
||||
@@ -129,7 +129,7 @@
|
||||
| 阶段 | 在哪里跑 | 说明 |
|
||||
|---|---|---|
|
||||
| 数据生成 | Hark 沙箱（2 vCPU / 7 GB，Node）或用户机器 | 纯 JS，无需 GPU |
|
||||
| 预训练 + 后训练 | 用户 RTX 3090（可用 19–20 GB） | 预训练 batch 1024（峰值 2.76 GiB）；APG batch 48 × 8000 步（峰值 13.08 GiB） |
|
||||
| 预训练 + 后训练 | 用户 RTX 3090 24 GB | batch 1024–2048 |
|
||||
| 部署推理 | 浏览器 onnxruntime-web wasm-SIMD 多线程（代理 Worker）；ONNX 接口 feat[1,83]、z[K,16,6]：观测编码一次、K=8 条候选共享编码再解码；后续真机控制器 | 与现有查看器同页 |
|
||||
|
||||
```bash
|
||||
@@ -449,7 +449,7 @@ for 每个 batch（随机采样 q0 和目标，不需要老师）:
|
||||
| R6 | 位姿目标的多 IK 分支带来额外多峰 | 不同肘/腕分支子集到达率 ≥ 主集 − 2 个百分点 | 加目标分支标志 | M5 |
|
||||
| R7 | APG 压缩多模态或拉长路径（ELMP 中路径 +23%） | 每轮门禁：B/C 类左右绕行比例变化 ≤ 5 个百分点、行程中位数比 ≤ 1.05（`ml/mining/gate.py`） | 加大 BC 锚定权重；行程项 | M6 |
|
||||
| R8 | 卡死换向活锁 | 50 个"两侧都要绕远"对抗用例（J1 起终差 ≥ 140°、终点贴立柱，部署运行时）首次换向锁后 3 s 内到达 ≥ 95%，活锁 = 0（`tests/policy_bench.cjs` r8） | 子目标改为离散绕行锚点分类 | M6 / M7 |
|
||||
| R9 | 整周期超标（推理 + 前瞻 + 安全层 > 60 ms 周期，执行时钟饥饿丢步） | 训练好的模型在**真查看器**里跑与 bench 同一批 30 道贴柱题：稳态规划 P99 < 45 ms、运动中饥饿节拍 < 1%、认证超时占比 < 5%（`r4_probe --viewer` → `r9_browser.json`；冷启动 P99、刹停率、tickGapMax 只报）；同题安全层浏览器 / Node 差 ≤ 3× | 几何查询（features、投影、认证）移到 Worker，主线程只执行；前瞻只在冷启动时做 | M7 |
|
||||
| R9 | 整周期超标（推理 + 前瞻 + 安全层 > 60 ms 周期，执行时钟饥饿丢步） | 训练好的模型在**真查看器**里跑与 bench 同一批 30 道贴柱题：整次规划 P99 < 45 ms、饥饿节拍 < 1%（`r4_probe --viewer` → `r9_browser.json`）；同题安全层浏览器 / Node 差 ≤ 3× | 几何查询（features、投影、认证）移到 Worker，主线程只执行；前瞻只在冷启动时做 | M7 |
|
||||
| R10 | 训练耗时估计偏差 | 60000 步 ≤ 3.5 h，即 2000 步测速 ≥ 5000 样本/s（3090 实测 6.2 k/s ≈ 2.8 h） | FK 损失胶囊数 96 → 48；仍不够则步数降到 40000（MeanFlow JVP 受算力限制，增大 batch 无效） | M5 |
|
||||
|
||||
### 10.2 已知缺陷
|
||||
|
||||
@@ -1,62 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
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|
||||
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|
||||
"dls_safety": 1,
|
||||
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|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round29_rows.json",
|
||||
"out": "docs/experiment_round29.json",
|
||||
"trace": "ml/runs/round29_trace.pt",
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"max_sec": null,
|
||||
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|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
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||||
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|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 512,
|
||||
"n_ref": 512,
|
||||
"success": 0.55078125,
|
||||
"collision_free_success": 0.546875,
|
||||
"time_ok_1p3": 0.361328125,
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"time_budget": "teacher×2(≥3 s)"
|
||||
},
|
||||
"simulation_wall_s": 488.4280448999925
|
||||
}
|
||||
@@ -1,779 +0,0 @@
|
||||
{
|
||||
"trace": "ml/runs/round29_trace.pt",
|
||||
"n": 512,
|
||||
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|
||||
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|
||||
"replayed_first_hits64": 9,
|
||||
"scope": "Replay reported first hits only; same geometry kernel at float32/float64, not a full safety audit.",
|
||||
"rows": [
|
||||
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||||
"i": 21,
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||||
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||||
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||||
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||||
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|
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|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,24 +0,0 @@
|
||||
{
|
||||
"baseline_commit": "95da491",
|
||||
"poses": "first 64 existing parity poses",
|
||||
"n_poses": 64,
|
||||
"kin_fp": "2ebcbf17",
|
||||
"dtype": "float64",
|
||||
"degenerate_capsules": [
|
||||
686
|
||||
],
|
||||
"pairs_with_second_degenerate": 670,
|
||||
"evaluated_pairs": 42880,
|
||||
"changed_pairs_over_1e_8": 20661,
|
||||
"old_minus_new_min_mm": 0.0,
|
||||
"old_minus_new_max_mm": 86.00309853133399,
|
||||
"new_reverse_max_abs_mm": 0.0,
|
||||
"worst": {
|
||||
"pose": 29,
|
||||
"first_capsule": 430,
|
||||
"second_capsule": 686,
|
||||
"old_distance_mm": 493.354753665718,
|
||||
"new_distance_mm": 407.35165513438403
|
||||
},
|
||||
"scope": "Raw capsule distances; does not imply these pairs win the group minimum."
|
||||
}
|
||||
@@ -1,16 +0,0 @@
|
||||
{
|
||||
"baseline_commit": "95da491",
|
||||
"sample_source": "docs/experiment_round31_certificates.json",
|
||||
"segments": 48,
|
||||
"dense_points": 7586,
|
||||
"max_step_deg": 0.01,
|
||||
"cap_mm": 0.5,
|
||||
"dtype": "float64",
|
||||
"device": "cuda",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"max_group_gap_difference_mm": 0.0,
|
||||
"group_gaps_changed_over_1e_8": 0,
|
||||
"minimum_gaps_changed_over_1e_8": 0,
|
||||
"collision_bool_mismatches": 0,
|
||||
"wall_s": 4.18259759999637
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
{
|
||||
"n": 2000,
|
||||
"fk_max_abs_mm": 4.547473508864641e-13,
|
||||
"orient_max_abs": 3.3306690738754696e-16,
|
||||
"gap_max_abs_mm": 2.5224267119483557e-13,
|
||||
"collide_bool_mismatch": 0,
|
||||
"feat_max_abs": 5.960463655974024e-08,
|
||||
"grad_rel_err_max": 1.8474076070450906e-06,
|
||||
"PASS": true
|
||||
}
|
||||
@@ -1,22 +0,0 @@
|
||||
{
|
||||
"scope": "全部 48 段 × 15 组对 = 720 项检验；观察值按 cap=2.0 mm 钳制，故可检验命题取 min(预测下限, cap)。违例=实测最小间隙低于该下限。",
|
||||
"checks": 720,
|
||||
"violations": [],
|
||||
"worst_margin_mm": 0.0,
|
||||
"min_slack_mm": 1e-06,
|
||||
"dense_cap_mm": 2.0,
|
||||
"grid_points_per_segment": 121,
|
||||
"supersedes": "docs/experiment_round32_uniform_validation.json 里 bound_checks=61 的部分覆盖版本",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"wall_s": 1.66,
|
||||
"nontrivial": {
|
||||
"definition": "只有预测下限 < 观察 cap 才是有信息量的检验；其余组对被钳制恒等式自动满足",
|
||||
"checks": 61,
|
||||
"min_slack_mm": 0.035347,
|
||||
"p10_slack_mm": 0.544274,
|
||||
"violations": 0,
|
||||
"pred_mm_median_all": 15.8639,
|
||||
"pred_mm_max_all": 19.9582
|
||||
},
|
||||
"note": "720 项总检验 0 违例，其中有信息量的是 61 项；预测下限的中位数 15.9 mm 远大于观察 cap 2 mm，所以大部分组对无法在本网格上证伪"
|
||||
}
|
||||
@@ -1,190 +0,0 @@
|
||||
{
|
||||
"device": "cuda float64",
|
||||
"torch": "2.10.0+cu130",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"roots_per_call": 2,
|
||||
"pairs": 24,
|
||||
"warm": 3,
|
||||
"reps": 7,
|
||||
"segment_sample": "docs/experiment_round31_certificates.json 的 48 段两两成对",
|
||||
"design": "每对根轮流按正序/逆序测完全部配置（3次预热+7次计时）；uniform_auto 的宽相位=最大子段界+2eps，uniform_cap20 固定 20 mm，bisect 为第31轮逐层二分口径",
|
||||
"results": [
|
||||
{
|
||||
"kind": "uniform_auto",
|
||||
"n_or_budget": 8,
|
||||
"points": 18,
|
||||
"cap_used_mm": 5.9191,
|
||||
"calls": 168,
|
||||
"p50_ms": 9.684,
|
||||
"p90_ms": 13.027,
|
||||
"p99_ms": 15.157,
|
||||
"max_ms": 17.537,
|
||||
"over_20ms": 0,
|
||||
"roots": 48,
|
||||
"safe": 26,
|
||||
"collision": 1,
|
||||
"rejected": 21
|
||||
},
|
||||
{
|
||||
"kind": "uniform_auto",
|
||||
"n_or_budget": 16,
|
||||
"points": 34,
|
||||
"cap_used_mm": 2.9597,
|
||||
"calls": 168,
|
||||
"p50_ms": 14.137,
|
||||
"p90_ms": 18.454,
|
||||
"p99_ms": 20.866,
|
||||
"max_ms": 21.632,
|
||||
"over_20ms": 5,
|
||||
"roots": 48,
|
||||
"safe": 30,
|
||||
"collision": 1,
|
||||
"rejected": 17
|
||||
},
|
||||
{
|
||||
"kind": "uniform_auto",
|
||||
"n_or_budget": 32,
|
||||
"points": 66,
|
||||
"cap_used_mm": 1.4799,
|
||||
"calls": 168,
|
||||
"p50_ms": 20.209,
|
||||
"p90_ms": 27.88,
|
||||
"p99_ms": 32.268,
|
||||
"max_ms": 35.563,
|
||||
"over_20ms": 87,
|
||||
"roots": 48,
|
||||
"safe": 34,
|
||||
"collision": 1,
|
||||
"rejected": 13
|
||||
},
|
||||
{
|
||||
"kind": "uniform_auto",
|
||||
"n_or_budget": 64,
|
||||
"points": 130,
|
||||
"cap_used_mm": 0.7401,
|
||||
"calls": 168,
|
||||
"p50_ms": 31.429,
|
||||
"p90_ms": 48.751,
|
||||
"p99_ms": 57.697,
|
||||
"max_ms": 62.084,
|
||||
"over_20ms": 165,
|
||||
"roots": 48,
|
||||
"safe": 36,
|
||||
"collision": 1,
|
||||
"rejected": 11
|
||||
},
|
||||
{
|
||||
"kind": "uniform_auto",
|
||||
"n_or_budget": 128,
|
||||
"points": 258,
|
||||
"cap_used_mm": 0.3701,
|
||||
"calls": 168,
|
||||
"p50_ms": 59.761,
|
||||
"p90_ms": 89.063,
|
||||
"p99_ms": 109.086,
|
||||
"max_ms": 117.345,
|
||||
"over_20ms": 168,
|
||||
"roots": 48,
|
||||
"safe": 38,
|
||||
"collision": 1,
|
||||
"rejected": 9
|
||||
},
|
||||
{
|
||||
"kind": "uniform_cap20",
|
||||
"n_or_budget": 8,
|
||||
"points": 18,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 13.129,
|
||||
"p90_ms": 16.44,
|
||||
"p99_ms": 20.963,
|
||||
"max_ms": 24.67,
|
||||
"over_20ms": 3,
|
||||
"roots": 48,
|
||||
"safe": 26,
|
||||
"collision": 1,
|
||||
"rejected": 21
|
||||
},
|
||||
{
|
||||
"kind": "uniform_cap20",
|
||||
"n_or_budget": 32,
|
||||
"points": 66,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 28.415,
|
||||
"p90_ms": 39.579,
|
||||
"p99_ms": 53.023,
|
||||
"max_ms": 59.983,
|
||||
"over_20ms": 153,
|
||||
"roots": 48,
|
||||
"safe": 34,
|
||||
"collision": 1,
|
||||
"rejected": 13
|
||||
},
|
||||
{
|
||||
"kind": "uniform_cap20",
|
||||
"n_or_budget": 64,
|
||||
"points": 130,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 50.85,
|
||||
"p90_ms": 75.512,
|
||||
"p99_ms": 88.355,
|
||||
"max_ms": 91.492,
|
||||
"over_20ms": 168,
|
||||
"roots": 48,
|
||||
"safe": 36,
|
||||
"collision": 1,
|
||||
"rejected": 11
|
||||
},
|
||||
{
|
||||
"kind": "uniform_cap20",
|
||||
"n_or_budget": 128,
|
||||
"points": 258,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 98.46,
|
||||
"p90_ms": 139.982,
|
||||
"p99_ms": 167.042,
|
||||
"max_ms": 177.743,
|
||||
"over_20ms": 168,
|
||||
"roots": 48,
|
||||
"safe": 38,
|
||||
"collision": 1,
|
||||
"rejected": 9
|
||||
},
|
||||
{
|
||||
"kind": "bisect",
|
||||
"n_or_budget": 32,
|
||||
"points": null,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 54.885,
|
||||
"p90_ms": 82.151,
|
||||
"p99_ms": 117.205,
|
||||
"max_ms": 118.716,
|
||||
"over_20ms": 133,
|
||||
"roots": 48,
|
||||
"safe": 37,
|
||||
"collision": 1,
|
||||
"rejected": 10
|
||||
},
|
||||
{
|
||||
"kind": "bisect",
|
||||
"n_or_budget": 65,
|
||||
"points": null,
|
||||
"cap_used_mm": 20.0,
|
||||
"calls": 168,
|
||||
"p50_ms": 71.825,
|
||||
"p90_ms": 108.559,
|
||||
"p99_ms": 122.533,
|
||||
"max_ms": 127.319,
|
||||
"over_20ms": 133,
|
||||
"roots": 48,
|
||||
"safe": 41,
|
||||
"collision": 1,
|
||||
"rejected": 6
|
||||
}
|
||||
],
|
||||
"wall_s": 108.63
|
||||
}
|
||||
@@ -1,62 +0,0 @@
|
||||
{
|
||||
"segments": 48,
|
||||
"kin_fp": "2ebcbf17",
|
||||
"dense_cap_mm": 2.0,
|
||||
"eps_mm": 0.0001,
|
||||
"bound_falsification": "每条组对：预测下限 (ga+gb-bound)/2 应 ≤ 网格上该组对的最小实测间隙",
|
||||
"per_segment": [
|
||||
{
|
||||
"n": 8,
|
||||
"safe": 26,
|
||||
"collision": 1,
|
||||
"unproven": 21,
|
||||
"outside_box": 0,
|
||||
"false_accept_vs_dense": 0,
|
||||
"cap_required_mm": 5.919107314806348,
|
||||
"known_missed_segment_collision": true
|
||||
},
|
||||
{
|
||||
"n": 16,
|
||||
"safe": 30,
|
||||
"collision": 1,
|
||||
"unproven": 17,
|
||||
"outside_box": 0,
|
||||
"false_accept_vs_dense": 0,
|
||||
"cap_required_mm": 2.959653657403174,
|
||||
"known_missed_segment_collision": true
|
||||
},
|
||||
{
|
||||
"n": 32,
|
||||
"safe": 34,
|
||||
"collision": 1,
|
||||
"unproven": 13,
|
||||
"outside_box": 0,
|
||||
"false_accept_vs_dense": 0,
|
||||
"cap_required_mm": 1.479926828701587,
|
||||
"known_missed_segment_collision": true
|
||||
},
|
||||
{
|
||||
"n": 64,
|
||||
"safe": 36,
|
||||
"collision": 1,
|
||||
"unproven": 11,
|
||||
"outside_box": 0,
|
||||
"false_accept_vs_dense": 0,
|
||||
"cap_required_mm": 0.7400634143507935,
|
||||
"known_missed_segment_collision": true
|
||||
},
|
||||
{
|
||||
"n": 128,
|
||||
"safe": 38,
|
||||
"collision": 1,
|
||||
"unproven": 9,
|
||||
"outside_box": 0,
|
||||
"false_accept_vs_dense": 0,
|
||||
"cap_required_mm": 0.37013170717539673,
|
||||
"known_missed_segment_collision": true
|
||||
}
|
||||
],
|
||||
"bound_checks": 61,
|
||||
"bound_violations": [],
|
||||
"worst_margin_mm": 0.0
|
||||
}
|
||||
@@ -1,70 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round33_base_rows.json",
|
||||
"out": "docs/experiment_round33_base.json",
|
||||
"trace": "ml/runs/round33_base.pt",
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 0,
|
||||
"gap_margin": 0.0,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.57421875,
|
||||
"collision_free_success": 0.57421875,
|
||||
"time_ok_1p3": 0.390625,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.4449527263641357,
|
||||
"time_mean_s": 2.3076188564300537,
|
||||
"brakes_mean": 19.25,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.20997506380081177,
|
||||
"err_fail_med": 229.68746948242188,
|
||||
"err_success_med": 0.18899640440940857,
|
||||
"dls_share": 0.625,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.6600004434585571,
|
||||
"speed_ratio_p95": 1.6600035429000854,
|
||||
"speed_ratio_max": 1.660003662109375,
|
||||
"over_vmax_rate": 1.0,
|
||||
"accel_ratio_p95": 1.5835107564926147,
|
||||
"accel_ratio_max": 1.6600004434585571
|
||||
},
|
||||
"simulation_wall_s": 296.2702505999914
|
||||
}
|
||||
@@ -1,70 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round33_both_rows.json",
|
||||
"out": "docs/experiment_round33_both.json",
|
||||
"trace": "ml/runs/round33_both.pt",
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 1,
|
||||
"gap_margin": 0.5,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.51953125,
|
||||
"collision_free_success": 0.51953125,
|
||||
"time_ok_1p3": 0.26171875,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.5444939136505127,
|
||||
"time_mean_s": 2.4729323387145996,
|
||||
"brakes_mean": 19.97265625,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.2868920564651489,
|
||||
"err_fail_med": 202.12567138671875,
|
||||
"err_success_med": 0.22085000574588776,
|
||||
"dls_share": 0.62109375,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.0000016689300537,
|
||||
"speed_ratio_p95": 1.0000101327896118,
|
||||
"speed_ratio_max": 1.0000101327896118,
|
||||
"over_vmax_rate": 0.0,
|
||||
"accel_ratio_p95": 1.0000016689300537,
|
||||
"accel_ratio_max": 1.0000101327896118
|
||||
},
|
||||
"simulation_wall_s": 277.54961390000244
|
||||
}
|
||||
@@ -1,70 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
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@@ -1,202 +0,0 @@
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||||
@@ -1,143 +0,0 @@
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||||
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|
||||
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|
||||
@@ -1,70 +0,0 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"ratio_heun_over_mf": 1.132248908183406
|
||||
},
|
||||
"gate_worst_accel": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"wall_s": 13.092682200003765
|
||||
}
|
||||
@@ -1,97 +0,0 @@
|
||||
{
|
||||
"n": 512,
|
||||
"steps": {
|
||||
"2": {
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"4": {
|
||||
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|
||||
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|
||||
"vs_1step_l2": 4.232732772827148
|
||||
},
|
||||
"8": {
|
||||
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|
||||
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|
||||
"vs_1step_l2": 4.195967197418213
|
||||
},
|
||||
"16": {
|
||||
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|
||||
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|
||||
"vs_1step_l2": 4.085569381713867
|
||||
},
|
||||
"32": {
|
||||
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|
||||
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|
||||
"vs_1step_l2": 3.970090866088867
|
||||
}
|
||||
},
|
||||
"vs_teacher": {
|
||||
"a1_1step": {
|
||||
"l2_mean": 4.325636386871338,
|
||||
"l2_p95": 4.9564337730407715,
|
||||
"per_axis_norm": [
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
},
|
||||
"aU_32step_rneq_t": {
|
||||
"l2_mean": 0.6008902192115784,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
},
|
||||
"aF_flow_map_r_eq_t": {
|
||||
"l2_mean": 0.6651337146759033,
|
||||
"l2_p95": 1.9794291257858276,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0.03988185524940491
|
||||
]
|
||||
}
|
||||
},
|
||||
"mutual": [
|
||||
{
|
||||
"l2_mean": 3.970090866088867,
|
||||
"pair": [
|
||||
"a1",
|
||||
"aU_32"
|
||||
]
|
||||
},
|
||||
{
|
||||
"l2_mean": 3.891127109527588,
|
||||
"pair": [
|
||||
"a1",
|
||||
"aF_flow"
|
||||
]
|
||||
},
|
||||
{
|
||||
"l2_mean": 0.08254596590995789,
|
||||
"pair": [
|
||||
"aU_32",
|
||||
"aF_flow"
|
||||
]
|
||||
}
|
||||
],
|
||||
"note": "动作块是 ±1 归一化的 16×6；l2_mean 无单位权重，越小越贴老师"
|
||||
}
|
||||
@@ -1,50 +0,0 @@
|
||||
{
|
||||
"empty/commit2/inc_cap0": {
|
||||
"plans": 175,
|
||||
"r_hist": {
|
||||
"0": 175
|
||||
},
|
||||
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|
||||
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|
||||
"success": 0.46875,
|
||||
"brakes_mean": 24.0625,
|
||||
"wall_s": 54.74565179999627
|
||||
},
|
||||
"r3/commit3/inc_cap0": {
|
||||
"plans": 118,
|
||||
"r_hist": {
|
||||
"0": 1,
|
||||
"2": 1,
|
||||
"3": 116
|
||||
},
|
||||
"frac_r0": 0.00847457627118644,
|
||||
"save_ms_per_plan_avg": 0.030771186440677965,
|
||||
"success": 0.4375,
|
||||
"brakes_mean": 18.03125,
|
||||
"wall_s": 49.8360657000012
|
||||
},
|
||||
"r3/commit3/inc_cap1": {
|
||||
"plans": 118,
|
||||
"r_hist": {
|
||||
"0": 1,
|
||||
"2": 1,
|
||||
"3": 116
|
||||
},
|
||||
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|
||||
"p50": 169.66,
|
||||
"p95": 190.207,
|
||||
"max": 202.072
|
||||
},
|
||||
{
|
||||
"p50": 163.572,
|
||||
"p95": 185.455,
|
||||
"max": 192.382
|
||||
},
|
||||
{
|
||||
"p50": 172.023,
|
||||
"p95": 204.316,
|
||||
"max": 225.098
|
||||
},
|
||||
{
|
||||
"p50": 166.825,
|
||||
"p95": 201.437,
|
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"max": 236.891
|
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},
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{
|
||||
"p50": 171.442,
|
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"p95": 207.533,
|
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"max": 226.472
|
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},
|
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{
|
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"p50": 159.815,
|
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"p95": 198.534,
|
||||
"max": 207.788
|
||||
}
|
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]
|
||||
},
|
||||
"summary": {
|
||||
"infer_p50_K8": 10.015,
|
||||
"infer_p50_K16": 9.321,
|
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"plan_p50_8/cb0": 144.785,
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"plan_p50_8/cb1": 150.726,
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"plan_p95_8/cb1": 197.649,
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"plan_p95_16/cb1": 201.437
|
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},
|
||||
"note": "K16/try3 那批臂是串行认证(cert_batch=0)，最坏要 K 次 gaps 调用；部署口径应是批量认证=1 次调用，所以两种口径都要量"
|
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}
|
||||
@@ -1,82 +0,0 @@
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@@ -1,82 +0,0 @@
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@@ -1,82 +0,0 @@
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{
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@@ -1,82 +0,0 @@
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{
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@@ -1,82 +0,0 @@
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||||
{
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|
||||
"simulation_wall_s": 271.2116083000001
|
||||
}
|
||||
@@ -1,110 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 16,
|
||||
"commit": 2,
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||||
"fb": 2.2,
|
||||
"ki": 0.0,
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||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
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||||
"fb_fallback": 0.0,
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||||
"kdecay": 0.9,
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||||
"kimax": 0.5,
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||||
"fb_every": 1,
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"sel_w": 0.1,
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"fb_clear": 0.0,
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||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round51_k16pre_rows.json",
|
||||
"out": "docs/experiment_round51_k16pre.json",
|
||||
"trace": null,
|
||||
"select": "pre",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 1,
|
||||
"gap_margin": 0.0,
|
||||
"cert_batch": 1,
|
||||
"steps": 1,
|
||||
"z_scale": 1.0,
|
||||
"len_w": 0.0,
|
||||
"tie": 0,
|
||||
"pre_h": 0,
|
||||
"pre_near": 0.0,
|
||||
"backoff": 0,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.56640625,
|
||||
"collision_free_success": 0.56640625,
|
||||
"time_ok_1p3": 0.359375,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.414156198501587,
|
||||
"time_mean_s": 2.265103578567505,
|
||||
"brakes_mean": 14.1953125,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.13815215229988098,
|
||||
"err_fail_med": 184.40463256835938,
|
||||
"err_success_med": 0.2881776690483093,
|
||||
"dls_share": 0.64453125,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.0000016689300537,
|
||||
"speed_ratio_p95": 1.0000101327896118,
|
||||
"speed_ratio_max": 1.0000101327896118,
|
||||
"over_vmax_rate": 0.0,
|
||||
"accel_ratio_p95": 1.0000016689300537,
|
||||
"accel_ratio_max": 1.0000101327896118,
|
||||
"reverse_p50": 0.2373737394809723,
|
||||
"reverse_mean": 0.2401755303144455,
|
||||
"reverse_moving_p50": 0.27848100662231445,
|
||||
"moving_share_p50": 0.9851852059364319,
|
||||
"brake_rate_mean": 0.07279647886753082,
|
||||
"brake_rate_p50": 0.0,
|
||||
"brake_rate_p90": 0.27435898780822754,
|
||||
"stuck_share": 0.171875,
|
||||
"n_plans_mean": 195.0,
|
||||
"success_reachable": 0.596707820892334,
|
||||
"by_cat": {
|
||||
"A": [
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||||
128,
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100
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||||
],
|
||||
"B": [
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||||
74,
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||||
26
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||||
],
|
||||
"C": [
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||||
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||||
12
|
||||
],
|
||||
"D": [
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||||
13,
|
||||
0
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||||
],
|
||||
"Dr": [
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||||
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||||
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|
||||
]
|
||||
}
|
||||
},
|
||||
"simulation_wall_s": 486.8652108999959
|
||||
}
|
||||
@@ -1,110 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
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||||
"n": 256,
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||||
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"commit": 2,
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"fb": 2.2,
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"ki": 0.0,
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"fb_scale": 0.0,
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"fb_near": 0.2,
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"fb_fallback": 0.0,
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"kdecay": 0.9,
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"kimax": 0.5,
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"fb_every": 1,
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"safety_try": 0,
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"sel_w": 0.1,
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"dls_safety": 1,
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"fb_clear": 0.0,
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"fb_cmin": 0.3,
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"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round52_ctl_ex_rows.json",
|
||||
"out": "docs/experiment_round52_ctl_ex.json",
|
||||
"trace": null,
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 1,
|
||||
"gap_margin": 0.0,
|
||||
"cert_batch": 1,
|
||||
"steps": 1,
|
||||
"z_scale": 1.0,
|
||||
"len_w": 0.0,
|
||||
"tie": 0,
|
||||
"pre_h": 0,
|
||||
"pre_near": 0.0,
|
||||
"backoff": 0,
|
||||
"max_sec": null,
|
||||
"seed": 0,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
||||
"kin_fp": "2ebcbf17",
|
||||
"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.5234375,
|
||||
"collision_free_success": 0.5234375,
|
||||
"time_ok_1p3": 0.265625,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.615795850753784,
|
||||
"time_mean_s": 2.486567258834839,
|
||||
"brakes_mean": 19.5546875,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.27920001745224,
|
||||
"err_fail_med": 204.71360778808594,
|
||||
"err_success_med": 0.21751396358013153,
|
||||
"dls_share": 0.61328125,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.0000016689300537,
|
||||
"speed_ratio_p95": 1.0000101327896118,
|
||||
"speed_ratio_max": 1.0000101327896118,
|
||||
"over_vmax_rate": 0.0,
|
||||
"accel_ratio_p95": 1.0000016689300537,
|
||||
"accel_ratio_max": 1.0000101327896118,
|
||||
"reverse_p50": 0.24741201102733612,
|
||||
"reverse_mean": 0.25991496443748474,
|
||||
"reverse_moving_p50": 0.32581019401550293,
|
||||
"moving_share_p50": 0.9587156176567078,
|
||||
"brake_rate_mean": 0.10028044879436493,
|
||||
"brake_rate_p50": 0.0051282052882015705,
|
||||
"brake_rate_p90": 0.3153846263885498,
|
||||
"stuck_share": 0.234375,
|
||||
"n_plans_mean": 195.0,
|
||||
"success_reachable": 0.5514403581619263,
|
||||
"by_cat": {
|
||||
"A": [
|
||||
128,
|
||||
96
|
||||
],
|
||||
"B": [
|
||||
74,
|
||||
20
|
||||
],
|
||||
"C": [
|
||||
32,
|
||||
11
|
||||
],
|
||||
"D": [
|
||||
13,
|
||||
0
|
||||
],
|
||||
"Dr": [
|
||||
9,
|
||||
7
|
||||
]
|
||||
}
|
||||
},
|
||||
"simulation_wall_s": 296.3142095999938
|
||||
}
|
||||
@@ -1,110 +0,0 @@
|
||||
{
|
||||
"config": {
|
||||
"ckpt": "ml/runs/base_dag2_dag1/best.pt",
|
||||
"samples": "ml/data/samples",
|
||||
"tasks": null,
|
||||
"split": "val",
|
||||
"n": 256,
|
||||
"K": 8,
|
||||
"commit": 2,
|
||||
"fb": 2.2,
|
||||
"ki": 0.0,
|
||||
"fb_scale": 0.0,
|
||||
"fb_near": 0.2,
|
||||
"fb_fallback": 0.0,
|
||||
"kdecay": 0.9,
|
||||
"kimax": 0.5,
|
||||
"fb_every": 1,
|
||||
"safety_try": 0,
|
||||
"sel_w": 0.1,
|
||||
"dls_safety": 1,
|
||||
"fb_clear": 0.0,
|
||||
"fb_cmin": 0.3,
|
||||
"sub_refresh": 0,
|
||||
"fb_block_gain": 0.5,
|
||||
"dump": "ml/runs/round53_ctl_ex_s1_rows.json",
|
||||
"out": "docs/experiment_round53_ctl_ex_s1.json",
|
||||
"trace": null,
|
||||
"select": "endpoint",
|
||||
"replan": "empty",
|
||||
"safety": 1,
|
||||
"partial": 1,
|
||||
"inc_cap": 1,
|
||||
"gap_margin": 0.0,
|
||||
"cert_batch": 1,
|
||||
"steps": 1,
|
||||
"z_scale": 1.0,
|
||||
"len_w": 0.0,
|
||||
"tie": 0,
|
||||
"pre_h": 0,
|
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"pre_near": 0.0,
|
||||
"backoff": 0,
|
||||
"max_sec": null,
|
||||
"seed": 1,
|
||||
"dls": "analytic",
|
||||
"device": "cuda"
|
||||
},
|
||||
"batch_size": 256,
|
||||
"batch_seed_rule": "same seed per batch",
|
||||
"checkpoint_sha256": "f7f686fd8ee92fb6948fbcc4ee954636065ad87bb54f661c4f8f575bc16c2ce1",
|
||||
"tasks_sha256": "249bd93457825cda98c59e64d9a096d3473c69aeb3553de1dea6d9e56be71772",
|
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"kin_fp": "2ebcbf17",
|
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"act_scale": 1.0,
|
||||
"metrics": {
|
||||
"n": 256,
|
||||
"n_ref": 256,
|
||||
"success": 0.53125,
|
||||
"collision_free_success": 0.53125,
|
||||
"time_ok_1p3": 0.296875,
|
||||
"collision_rate": 0.0,
|
||||
"collision_steps_mean": 0.0,
|
||||
"travel_ratio_median": 2.636941432952881,
|
||||
"time_mean_s": 2.4600000381469727,
|
||||
"brakes_mean": 19.265625,
|
||||
"start_fail": 0.0,
|
||||
"goal_ratio_2s": 0.2750342786312103,
|
||||
"err_fail_med": 214.6193084716797,
|
||||
"err_success_med": 0.22276456654071808,
|
||||
"dls_share": 0.62890625,
|
||||
"time_budget": "teacher×2(≥3 s)",
|
||||
"speed_ratio_p50": 1.0000016689300537,
|
||||
"speed_ratio_p95": 1.0000101327896118,
|
||||
"speed_ratio_max": 1.0000101327896118,
|
||||
"over_vmax_rate": 0.0,
|
||||
"accel_ratio_p95": 1.0000016689300537,
|
||||
"accel_ratio_max": 1.0000101327896118,
|
||||
"reverse_p50": 0.25339367985725403,
|
||||
"reverse_mean": 0.2575649917125702,
|
||||
"reverse_moving_p50": 0.3117443919181824,
|
||||
"moving_share_p50": 0.9636363387107849,
|
||||
"brake_rate_mean": 0.09879808127880096,
|
||||
"brake_rate_p50": 0.0051282052882015705,
|
||||
"brake_rate_p90": 0.3384615480899811,
|
||||
"stuck_share": 0.25390625,
|
||||
"n_plans_mean": 195.0,
|
||||
"success_reachable": 0.5596708059310913,
|
||||
"by_cat": {
|
||||
"A": [
|
||||
128,
|
||||
96
|
||||
],
|
||||
"B": [
|
||||
74,
|
||||
21
|
||||
],
|
||||
"C": [
|
||||
32,
|
||||
12
|
||||
],
|
||||
"D": [
|
||||
13,
|
||||
0
|
||||
],
|
||||
"Dr": [
|
||||
9,
|
||||
7
|
||||
]
|
||||
}
|
||||
},
|
||||
"simulation_wall_s": 290.3290144000057
|
||||
}
|
||||
-1401
File diff suppressed because it is too large
Load Diff
-245
@@ -1,245 +0,0 @@
|
||||
# 本机执行清单（给本地 AI 逐条执行）
|
||||
|
||||
本文件是 Hark 对本机计算的唯一要求清单。按顺序执行，每一步把「回传」里的文件或输出原样交给用户，不要跳步，不要自行改代码。
|
||||
所有命令在仓库根目录（G:\Robot_v2\机器人手臂）的 Git Bash 里运行。完成的条目由 Hark 在下一次提交里删掉。
|
||||
|
||||
## 已知结论（截至 2026-10-19 05:35 回传）
|
||||
|
||||
- 现有模型 `ml/runs/base/last.pt` 闭环 0% 到达：1 段采样碰撞 99.6%；4 段采样 94%；4 段 + 仿真安全层碰撞 0.78%，到达仍 0%。consist / value / first 三种选法结果几乎一样，问题出在候选本身，不在挑哪条。
|
||||
- 三份代码审查逐行核对了滚动索引、前缀、提交、标签与执行的对齐，没有找到错位；但之前没有任何测试真正走过 `rollout()`。下面第 1 步的 H 段把「仿真错了」「前缀照抄」「子目标切换规则」「多峰」几种解释逐个分开。
|
||||
- 之前「前 1 s 速度比 2.31×」按 6 轴角度直接相加，被 J5/J6 抖动和 pos 任务冻结的 J6 放大。H 段改为分轴、净位移、TCP 三种口径。
|
||||
|
||||
## 第 0 步：同步代码
|
||||
|
||||
```bash
|
||||
git status --short # 有未提交的改动先 commit（git add -A && git commit -m "本机改动"），不要丢
|
||||
git pull --rebase gitea main
|
||||
git log --oneline -1 # 应是 Hark 的「diag: H 段（老师 oracle / 前缀消融 / 子目标规则 / 分轴指标）…」
|
||||
```
|
||||
|
||||
- 训练控制台若开着，先点「停止」，确认没有 python 训练进程在跑。
|
||||
- 不要启动训练、DAgger、M5。
|
||||
|
||||
## 第 1 步：闭环失败定位 H 段（约 35–50 分钟）
|
||||
|
||||
```bash
|
||||
PYTHONUTF8=1 python -m ml.eval.diag_cl --ckpt ml/runs/base/last.pt --n 256 --out ml/runs/diag_cl.json
|
||||
```
|
||||
|
||||
- 专用显存超过 22 GB 就 Ctrl+C，回传终端最后 30 行和已写出的 `ml/runs/diag_cl.json`（每段跑完就写一次，中断也保留已完成的段）。
|
||||
- 现有 checkpoint 没有 EMA、没有 `act_scale`：脚本按原始权重、动作尺度 1.0 载入（输出里 `weights`=`model`、`act_scale_ckpt`=1.0），与之前各轮同口径。
|
||||
- 回传：`ml/runs/diag_cl.json`。
|
||||
|
||||
### 输出键
|
||||
|
||||
| 键 | 内容 |
|
||||
|---|---|
|
||||
| `A_*`、`B_act_diff_deg` | 起点特征 Python 与数据集逐组差；两份特征下的动作差（与之前相同） |
|
||||
| `C` | 旧部署口径（1 段采样、子目标旧规则 timeout）的失败形态。到达与碰撞改为只计每题自己的时限内，数字可能比上一轮略低 |
|
||||
| `H1_oracle` | 老师当策略走完整 `rollout()`。`oracle` 字段：`max_dev_deg_before_dls`（DLS 接管前与老师轨迹的最大关节偏差，p50 / p99 / max）、`reach`、`collision`、`finish_minus_teacher_s`（完成时刻 − 老师时长）、`dls_used` |
|
||||
| `H1_oracle_noise_0.05` / `_0.1` / `_0.2` | 老师动作每次规划加 N(0, σ²)（σ 以 STEP 为单位） |
|
||||
| `H2_open_loop` | r = 3 的 val 行：`s{1,4}_prefix_orig`（前缀原样，执行窗口 3..5 的误差，另含 `prefix_copy_err_deg_mean`）vs `s{1,4}_prefix_zeroed`（前缀置零） |
|
||||
| `H2_replan_empty_s1` / `_s4` | 队列空了才规划（每次 r = 0），其余同新部署默认 |
|
||||
| `H3_headsub_cross_s{1,4}` | 子目标头 + 越过顶点规则（新部署默认） |
|
||||
| `H3_headsub_timeout_s{1,4}` | 子目标头 + 旧规则（< 5° 或 1.5 s 超时，旧部署） |
|
||||
| `H3_teachersub_cross_s{1,4}` / `H3_teachersub_timeout_s{1,4}` | 老师真实途经点全表，两种切换规则 |
|
||||
| `H3_nosub_s{1,4}` | 不用子目标 |
|
||||
| `H4_open_loop` | 按前缀长度 r = 0..3 分组的执行窗口误差 \|Σ(act − Y)·STEP\|（度），1 段与 4 段采样 |
|
||||
|
||||
每个闭环变体（H1–H3）都带同一组指标：`reach`、`collision`（cat ≠ D、时限内）、`goal_ratio_2s_p50`、`end_goal_err_mm_p50`、`first_hit_s_p50`、`t_done_over_teacher_p50`、`brakes_p50`、`joint_amp_ratio`（6 轴前 1 s 关节行程 策略/老师）、`joint_net_ratio`（6 轴前 1 s 净位移 策略/老师）、`j6_pos_amp_deg_p50`、`tcp_path_ratio_p50`、`tcp_net_ratio_p50`、`sign_flip_rate` 与 `sign_flip_rate_teacher`（6 轴前 2 s 相邻 3 步块变号比例）、`by_cat_mode`（各类别 n / reach / collision / goal_ratio_2s_p50）、`D`（D 类单列）。`_s1` = 1 段采样（部署），`_s4` = 4 段采样。
|
||||
|
||||
### 判读
|
||||
|
||||
| 现象 | 结论 |
|
||||
|---|---|
|
||||
| `H1_oracle` 的 reach < 0.95、collision > 0.01、或 `max_dev_deg_before_dls.p99` > 0.1° | 仿真 / 滚动本身有错，之前所有闭环 0% 都不可信。先修仿真，不看下面各项 |
|
||||
| `H1_oracle` 全部正常 | 仿真没问题，失败在策略输出。再对照 `H1_oracle_noise_*`：σ = 0.05 时 reach 就大幅下降，说明闭环对残差噪声极其敏感，单靠降噪不够 |
|
||||
| `H2_open_loop` 的 `prefix_zeroed` 执行窗口误差明显小于 `prefix_orig`，或 `H2_replan_empty` 明显好于同段数的 `H3_headsub_cross`（reach > 0，或 `goal_ratio_2s_p50` 明显更低、`sign_flip_rate` 明显更低） | 前缀照抄（copycat）是主因 |
|
||||
| `H3_teachersub_cross` 明显好于 `H3_teachersub_timeout`（或 `H3_headsub_cross` 好于 `H3_headsub_timeout`） | 子目标切换规则是主因之一 |
|
||||
| `H3_teachersub_*` 明显好于 `H3_headsub_*` | 子目标头给的途经点不可用 |
|
||||
| `H3_nosub` 不比 `H3_headsub_cross` 差 | 子目标通道可以整体去掉 |
|
||||
| `by_cat_mode` 里 `A/pos`（老师就是一条直线）reach 也是 0 | 失败与多峰无关 |
|
||||
| `joint_amp_ratio` 远大于 `joint_net_ratio`，且 `sign_flip_rate` 远高于 `sign_flip_rate_teacher` | 「动得多、走得少」是来回抖，不是速度律学快了 |
|
||||
|
||||
重新打包和重训（`FRESH_FROM=prep`）要等 Hark 看完这一步的结果再定，这一步之后不要自行启动。
|
||||
|
||||
## 回传汇总
|
||||
|
||||
| 步骤 | 回传内容 |
|
||||
|---|---|
|
||||
| 1 | ml/runs/diag_cl.json |
|
||||
| 任一步出错 | 终端最后 30 行输出 |
|
||||
|
||||
---
|
||||
|
||||
## 执行回传（2026-10-19 19:55，本地 AI）
|
||||
|
||||
### 第 0 步：同步代码
|
||||
|
||||
`ml/eval/diag_collision.py` 归档提交（本机改动 `7e063eb`）；根目录用户个人 html 为个人文件未入仓，原位保留。log 顶部 = `406f180`「diag: H 段 …」✓。无训练进程。
|
||||
|
||||
### 第 1 步：H 段（完整 JSON 已更新到 ml/runs/diag_cl.json；weights=model、act_scale_ckpt=1.0，同口径；H_tasks 256、missing_teacher 0）
|
||||
|
||||
**判读表逐行核对：**
|
||||
|
||||
1. **`H1_oracle`：仿真完全正常。** reach **1.0**（243/243）、collision **0.0**、`max_dev_deg_before_dls` p50 6.9e-5° / p99 4.6e-4°（阈值 0.1°）、`finish−teacher` p50 −0.02 s、`dls_used` 94.9%。→ **滚动/仿真没有错，之前所有闭环 0% 都可信，失败在策略输出本身。**
|
||||
2. **`H1_oracle_noise`：对残差噪声极其敏感。** σ=0.05 时 reach 1.0→**0.218**（碰撞 9.1%）；σ=0.1→0.058；σ=0.2→0.012。→ 单靠降采样噪声不够（判读表第二行命中）。
|
||||
3. **`H2_open_loop`：前缀照抄排除。** prefix_orig vs zeroed：s1 2.315° vs **2.375**（置零不更好）；s4 0.251° vs **0.705**（置零明显更差）。`H4` 按 r=0..3 分组误差持平（2.31–2.33°）。`H2_replan_empty`：s1 0%/79.4%/0.836、s4 0%/84.8%/0.603——不比同段数 `H3_headsub_cross` 好。
|
||||
4. **`H3`：子目标三个变体逐位相同（新异常）。** `headsub_cross` / `headsub_timeout` / `nosub` 在 s1、s4 下从 reach 到 end_err 到 flip 数组**完全一致**（s1: 0% / 96.3% / 0.7504 / 414.2091；s4: 0% / 79.8% / 0.5869 / 428.0032）。`teachersub_*`（真值途经点）仅微动（s1 95.9%/0.726、s4 79.4%/0.578）。→ **子目标头通道在这条闭环里零响应**——比「可以整体去掉」更强；可能是 need 概率全程 <0.5 从不触发，或子目标输入对旧模型输出无影响，建议核一下（D0 曾测过 need 标签占比 28.1%）。
|
||||
5. **`by_cat_mode`：A/pos（老师直线）reach 也是 0**（s1 碰撞 94%）→ **失败与多峰无关**。
|
||||
6. **分轴指标：s1 的"动得多"含抖、s4 后基本消抖。** J5/J6 幅度/净位移：s1 3.08/2.57、2.55/2.16；s4 1.83/1.84、0.69/0.69，`sign_flip_rate` 从 0.026–0.062 降到 0.0006–0.0013（≈老师 0.0005–0.004 水平）。→ 4 段采样把来回抖消掉了，但净位移仍 J5 1.8×，且到达仍 0%。
|
||||
|
||||
**C 段**（新时限口径）：0% 到达 / 96.5% 碰撞 / 2 s 误差比 0.755。
|
||||
|
||||
### 本轮无出错，无终端报错段。
|
||||
|
||||
---
|
||||
|
||||
## 本地自主排查（2026-10-19 20:40，用户授权「继续排查」）
|
||||
|
||||
### 1. 闭环噪声容忍阈值（老师动作 + N(0,σ²)，σ 以 STEP 为单位，n=256，其余同 H1_oracle）
|
||||
|
||||
| σ | 0 | 0.005 | 0.01 | 0.02 | 0.03 | 0.04 | 0.05 |
|
||||
|---|---|---|---|---|---|---|---|
|
||||
| reach | 1.0 | 1.0 | 0.971 | 0.716 | 0.457 | 0.309 | 0.218 |
|
||||
| collision | 0 | 0.8% | 2.5% | 4.9% | 6.2% | 7.4% | 9.1% |
|
||||
| ratio2s | 0.047 | 0.049 | 0.049 | 0.054 | 0.059 | 0.063 | — |
|
||||
|
||||
→ **闭环能容忍的每步噪声上限 ≈ 0.01–0.02×STEP**（约 0.02–0.04°/步）。策略 s1 残差 ~0.77°/步、s4 ~0.05–0.08°/步，都在崩溃区右侧。
|
||||
|
||||
### 2. 8 段采样闭环（steps=8，同 H3_headsub_cross 口径）
|
||||
|
||||
reach **0%** / collision **79.0%** / ratio2s 0.574 / end 408 mm；`sign_flip_rate` 0.0004–0.0015（≈老师 0.0005–0.004，抖已完全消掉）、`amp ≈ net`（1.00/1.19/0.97/1.19/1.36/0.76，无抖动成分）。
|
||||
|
||||
→ 对比 s4（reach 0 / 79.8% / 0.587）：**s4→s8 几乎无改善**。按上表的白噪声口径，s8 的残差幅度（16 步 0.56° ≈ 0.035°/步，σ_eff≈0.0175）应给出 reach≈0.75——实测 0。**策略误差不是白噪声，是有方向的系统偏差**：幅度不大但同向、闭环里逐步累积，重规划抵消不掉。降采样噪声（1→4→8 段）已到天花板。
|
||||
|
||||
### 3. 子目标通道：need 头恒不触发（新发现，修正「子目标排除」的理由）
|
||||
|
||||
- 冷启动（子目标特征抹零，与闭环同口径）need 概率：mean 0.0048、p50 0.0030、p90 0.0079、**max 0.0819**、>0.5 占比 **0**；43 道 need=True 题召回 **0/43**。（D0 的 0.71875 准确率恰等于"全预测 False"基线，当时已有此暗示。）
|
||||
- 因此闭环里 `sub_flag` 恒 0、子目标从不激活——这才是 H3 三变体逐位相同的直接原因。
|
||||
- 但模型对子目标特征**高度敏感**（同一噪声下真值子目标 vs 置零：|Δact| mean 0.177、max 3.09）——训练时 80% 样本 sf=1，而闭环 100% 是 sf=0，属训练/闭环分布错配。
|
||||
- 不过 H3_teachersub（强制真值 need + 真值途经点）仍 0% / 95.9%——**子目标不是闭环全灭的主因**，但 need 头失效 + 闭环恒走少数分支值得修。
|
||||
|
||||
### 4. 汇总判断
|
||||
|
||||
- 仿真正常（H1_oracle 已证）；采样噪声已压到老师抖动水平仍 0% 到达 → **剩余障碍是策略的系统性动作偏差 + 闭环对误差的极低容忍度（误差复利）**。
|
||||
- 继续调采样段数、子目标规则、候选选择都无解；正确方向是**在闭环状态上训练（DAgger）**，让老师纠正分布偏移，或提高闭环鲁棒性。
|
||||
- 未自行启动 DAgger / 重训，等指示。
|
||||
|
||||
### 5. 补充：系统偏差的方向结构（4096 行开环 + 256 题闭环，4 段采样）
|
||||
|
||||
**开环动作误差（deg，有符号）**：
|
||||
|
||||
- 每轴均值 [0.010, −0.012, −0.001, −0.029, 0.034, 0.018]，|mean|/std ≤ 0.17 → **无固定轴向偏置**（不是某轴恒偏）。
|
||||
- 每轴标准差 0.16–0.21°；每步平均 |误差| 0.016–0.018°，**16 步内不随步数增长**。
|
||||
- **lag-1 自相关 0.931–0.967（极高）** → 误差是**慢变/连贯**的，不是白噪声。这解释了为什么白噪声 σ 扫描会低估策略误差的危害：闭环重规划抵消不掉持续同向的偏差。
|
||||
- 误差与老师动作方向：cos mean 0.046 / p50 0.072（不超调也不欠调）；投影比 e·Y/|Y|² mean −0.038 / p50 0.006（无系统性缩放）。
|
||||
|
||||
**闭环误差增长（s4，n=256，done=0）**，goal_err / 起点误差：
|
||||
|
||||
| t (s) | 0.25 | 0.50 | 0.75 | 1.00 | 1.50 | 2.00 |
|
||||
|---|---|---|---|---|---|---|
|
||||
| p50 | 0.959 | 0.880 | 0.811 | 0.741 | 0.645 | 0.591 |
|
||||
| p90 | 1.024 | 1.072 | 1.119 | 1.162 | 1.222 | **1.318** |
|
||||
|
||||
→ p50 缓慢靠近（2 s 缩 41%），**p90 持续发散**——≥10% 的题误差随时间增长（误差复利进碰撞轨道），其余也远不够快。
|
||||
|
||||
**补充结论**：策略每步误差很小（~0.017°）但**时间相关性极强（0.93–0.97）**，闭环对"慢变误差"的敏感度远超等幅白噪声；失败是「慢变误差 × 闭环误差复利」。推理侧降噪（段数/选择器）已无空间，训练侧需要降低慢变误差（DAgger / 闭环状态训练）。
|
||||
|
||||
### 6. 「2 s 误差比 >1 的发散题」能否提前筛掉（per-task 分组，s4，n=256；明细 ml/runs/task_divergence.json）
|
||||
|
||||
| 组（2 s 误差比） | 题数 | 碰撞 | 到达 | cat/mode | 起点误差 p50 | 老师时长 p50 | need 率 | 老师到达 |
|
||||
|---|---|---|---|---|---|---|---|---|
|
||||
| 发散 r2>1.0 | 48 | 81% | 0% | A/pos 14、A/pose 14、C/pose 5、C/pos 5、B/pos 3、B/pose 3、D/pos 2、Dr/pos 2 | 374 mm | 1.80 s | 21% | 96% |
|
||||
| 平 0.7–1.0 | 48 | 83% | 0% | 混合 | 539 mm | 2.12 s | 27% | 90% |
|
||||
| 收敛 ≤0.7 | 160 | 79% | 0% | 混合 | 642 mm | 2.18 s | 31% | 96% |
|
||||
|
||||
- **三组碰撞率相同（79–83%）、到达全 0**——不存在"一小撮可提前去掉的坏题"；发散题散布在所有类别，老师都能到达。
|
||||
- r2 与起点误差秩相关 **−0.384**（起点误差小 → 比值易大），是**分母效应**：短任务（老师 1.8 s、e0 374 mm）比值波动大，不代表题目特殊；r2 与老师时长/行程几乎无关（−0.09 / −0.03）。
|
||||
- 结论：误差放大是**全体题目上的普遍行为**，筛题无效（只让指标好看），必须训练侧修正。
|
||||
|
||||
### 7. DAgger 小样本试跑（用户授权；未跑全量、未微调）
|
||||
|
||||
**链路实测**（ckpt `ml/runs/base/last.pt`，旧口径 act_scale=1.0）：
|
||||
|
||||
| 步骤 | 规模 | 实测 |
|
||||
|---|---|---|
|
||||
| `ml.dagger.collect`（16 题，parts=2） | 16 题 | **12.7 s**（含加载），29 个起点（全撞，拒 1 个 gap） |
|
||||
| `ml.dagger.collect --n 64`（显存采样） | 64 题 | 成功，112 个起点（拒 10 gap），**显存峰值 7.98 GB**（24 GB 卡，无 OOM） |
|
||||
| `node ml/gen/gen_dataset.cjs --tasks tasks_01.jsonl` | 15 个起点 | **7.6 s**，shard 含 qPrev / v0 / traj / cert |
|
||||
| `ACT_SCALE=1.0 node ml/prep/build_dataset.cjs …` | 13 题 | **2.3 s**，453 样本（175 增广），meta `act_scale:1, data_ver:2` |
|
||||
|
||||
**关键口径提醒**：`build_dataset.cjs` 现在默认 **ACT_SCALE=1.25**（DATA_VER 2），而现有主数据 `ml/data/samples` 无 meta → 训练按 **1.0** 读。两者不一致时训练直接报错（README 第 120 行）。全量 DAgger 前二选一：
|
||||
1. 打包 DAgger 时显式 `ACT_SCALE=1.0`（与旧主数据、旧 ckpt 兼容）；
|
||||
2. 先按新流程 `FRESH_FROM=prep` 重打主数据为 1.25、重训后再跑 DAgger。
|
||||
本次试跑全部用 1.0；产物在 `ml/data/dag_try*/`（gitignored）。
|
||||
|
||||
**全量耗时外推（估算，以首次实测为准）**：collect 8000 题约 0.8–1 小时（单进程）；老师重规划 1.6 万起点 / NPROC≈12 约 10 分钟；打包约 3 分钟；微调 15000 步未试。
|
||||
|
||||
**结论**：DAgger 收集链路可用、显存安全（峰值 8 GB），可以全量启动——但先定 act_scale 口径。
|
||||
|
||||
### 8. 「加大数据能否力挽狂澜」——闭环状态到训练分布的距离（s4，n=256，9408 个闭环状态）
|
||||
|
||||
方法：闭环轨迹每 10 步取一个 q（9408 点），对训练集 191 万老师状态求最近邻（6 维关节角 L∞，±360 回绕）；对照 = 每题起点（老师分布内）。
|
||||
|
||||
| 状态 | p10 | p50 | p90 | p99 |
|
||||
|---|---|---|---|---|
|
||||
| 闭环轨迹状态 | 12.10° | **21.46°** | 32.50° | 44.56° |
|
||||
| 每题起点（老师） | 8.71° | 13.21° | 17.91° | 21.53° |
|
||||
|
||||
- 闭环状态离最近老师状态中位 **21.5°**（起点 13.2° 的 1.6×；p90 32.5°、p99 44.6°）。
|
||||
- 训练集本身在关节空间稀疏：连老师起点最近邻都有 13.2°——数据是"轨迹线"而非密集体。
|
||||
- 按密度缩放（6 维 d∝N^(−1/6)）：数据翻 4 倍最近邻仅降 ~21%（21.5°→~17°），而策略误差尺度是 0.02°；差距三个数量级，且同类数据不改变误差的时间相关结构（0.94）。
|
||||
- **结论：扩大同类老师数据救不了闭环**；需要「不同的数据」——在策略实际到达的状态上标注（DAgger），把 21.5° 这些分布外点变成训练样本。
|
||||
|
||||
### 9. 方案 A 实测：合成扰动状态样本微调（两轮配方）
|
||||
|
||||
做法：从训练任务老师轨迹的 15–85% 处取状态，加 N(0,σ²) 关节扰动（σ 混合 0.5/1/2/4°；实测起点偏移 p50 1.5°），老师重规划（**2000/2000 到达**、cert 1951/2000），打包 36k 样本（act_scale=1.0，为主数据的 1.9%），从 `base/last.pt` 微调 3000 步，2000 步时以 EMA 权重做 512 题闭环评估。
|
||||
|
||||
| 配方 | success | collision | goal_ratio_2s | ol_fk_mm | ol_best_mm | ol_div |
|
||||
|---|---|---|---|---|---|---|
|
||||
| base/last.pt（基线） | 0 | 0.996 | 0.755 | 5.98 | 2.92 | 0.655 |
|
||||
| +扰动样本 lr 3e-4 | 0 | **0.484** | 0.927 | **15.27** | 10.32 | 0.966 |
|
||||
| +扰动样本 lr 1e-4 | 0 | 0.604 | 0.893 | **16.42** | 11.45 | 0.980 |
|
||||
|
||||
- 碰撞率首次大幅下降（99.6%→48–60%），但 **success 仍 0**；开环精度退化 2.5–2.7 倍（6→15/16 mm）、多样性升高——小步微调在两种分布间折中，把主分布带偏了。
|
||||
- **结论**：1.5° 量级的合成扰动 + 3000 步微调不能解决闭环。若继续这条线，需从**预训练开始**按课程混入扰动样本（并监控开环不退化），成本高；或转向结构改动（方案 B：绝对目标参数化/参考轨迹跟踪）或真实 DAgger。
|
||||
- 产物：`ml/data/samples_perturb/`、`ml/runs/base_perturb/`、`ml/runs/base_perturb2/`（均在 gitignored 目录）。
|
||||
|
||||
### 10. 方案 B 判别实验：模型预测的"累计目标"是否指向参考轨迹
|
||||
|
||||
方法：2000 个扰动起点（原轨迹状态 + σ 0.5–4° 扰动，L∞ 实测 p50 **3.19°**），模型预测 16 步增量并累积为绝对目标 `Z_model = q_off + cumsum(â·STEP)`，对比原老师轨迹未来 16 步绝对位置 `Z_ref`；`ρ = |Z_model − Z_ref| / |q_off − q_ref|`（ρ≈1 = 目标随状态平移、无纠偏；ρ≈0 = 目标稳定指向轨迹）。
|
||||
|
||||
| 模型 | 状态偏移 p50 | 目标误差 p50 | ρ p50 |
|
||||
|---|---|---|---|
|
||||
| base/last.pt | 3.19° | 4.86° | **1.54** |
|
||||
| base_perturb（扰动微调 lr 3e-4） | 3.19° | 5.85° | 1.90 |
|
||||
| base_perturb2（lr 1e-4） | 3.19° | 6.25° | 1.97 |
|
||||
| 对照：未扰动行开环 | — | **0.46°** | — |
|
||||
|
||||
- 未扰动时 16 步累计目标误差仅 0.46°；状态偏移 3.19° 后目标偏离 4.86°（ρ=1.54）——**当前模型是"从当前状态外推增量"，不是"指向参考位置"**。扰动微调让 ρ 更差（1.9–2.0），与开环退化一致。
|
||||
- **结论：方案 B 不能靠改执行层实现，必须改训练标签**——把标签从"16 步增量"改为"16 步绝对目标关节角（参考轨迹位置）"，让模型学"目标在哪"而不是"从这怎么走"。执行层每步朝目标走，偏移状态下即天然纠偏。
|
||||
- 实施成本：改 `build_dataset.cjs` 标签口径（Y = 绝对目标），同步 FK/平滑/价值等基于增量的辅助损失、rollout 与 `ik-policy.js` 的增量假设，然后从头训练（约 1 小时）。属结构性改动，未擅自开始。
|
||||
|
||||
### 11. 方案 B 初版实测（分支 `feat/abs-target`，保存点 tag `pre-b-abs-target`）
|
||||
|
||||
**改动**（全部在分支，main 未动）：
|
||||
- `build_dataset.cjs`：`ABS_TARGET=1` → Y = 16 步绝对目标关节角（/180 归一化），prefix 仍存增量（度），meta 记录 `abs_target`、签名加 `a1`。
|
||||
- `train.py`：读 `abs_target`；每单位动作=180°；FK 损失用绝对位置；平滑项换算到增量量纲；开环指标用第 16 步绝对位置；ckpt 记录 `abs_target`。
|
||||
- `closed_loop.py`：模型输出绝对目标 → 队列存目标序列、执行 `q=clamp(target)`、前缀由目标差分、安全层中点与 DLS 增量转绝对目标。
|
||||
- `ckpt.py` / `data.py`：abs_target 透传与一致性检查。
|
||||
- **过程 bug（已修）**：前缀噪声增广把 eps 直接加到绝对目标标签上（≈18° 污染，一半样本）；改为累计偏移后第 r 步扣回。
|
||||
|
||||
**数据**：84 shard 重打 → 201 万样本、abs_target=1；标签连续（步间差 p50 0.30°、p99 1.8°、无 ±180 跳变）。
|
||||
|
||||
**训练结果**（从零 9950 步；p_neq 预热 5000 步）：
|
||||
|
||||
| step | ol_fk_mm | ol_best_mm | 闭环 success | 碰撞 |
|
||||
|---|---|---|---|---|
|
||||
| 4000 | 74.6 | 33.4 | 0 | 100% |
|
||||
| 6000 | 140.4 | 70.3 | 0 | 100% |
|
||||
| 8000 | 139.0 | 68.5 | 0 | 100% |
|
||||
|
||||
- 一步采样动作误差 **31°**（4/8 段采样 2.3°/2.8°）——**绝对目标的一步流匹配映射没学会**；闭环 1 段 0%/100%、4 段 0%/100%（ratio2s 1.045）。
|
||||
- 过预热后 5000 步无下降趋势。可能原因：一步样本仅 `p_edge=10%`、`ada_p=1` 逆误差加权压制难样本、FK/平滑辅助项在绝对位置空间主导。
|
||||
- **结论：此配方不成立**。B 若继续，需改训练配方（p_edge↑、w_fk↓、ada_p=0）或换输出头（确定性回归）；当前证据不支持继续投入。分支代码保留可复现。
|
||||
+15
-41
@@ -1,6 +1,6 @@
|
||||
# Transformer 多峰策略端到端训练与浏览器部署
|
||||
# 机械臂避障策略：从数据到部署的运行手册
|
||||
|
||||
机械臂避障策略运行手册。目标：在你的机器上从零跑出 Transformer 避障策略，并在查看器里直接用上。
|
||||
目标：在你的机器上从零跑出 Transformer 避障策略，并在查看器里直接用上。
|
||||
方案依据见 `docs/Transformer轨迹拟合训练方案.md`。所有命令在**仓库根目录**执行。
|
||||
|
||||
**一键运行（无人值守）**：先激活训练用的 Python 环境，Windows 双击 `ml/console.bat`（Linux/WSL 运行 `bash ml/console.sh`），浏览器自动打开训练控制台 `http://localhost:8100/`，点「开始训练」即从对拍一路跑到验收。等价的终端命令是 `bash ml/pipeline.sh all`。
|
||||
@@ -24,8 +24,7 @@
|
||||
| 3 老师门禁 | `bash ml/pipeline.sh gate1` | 25–30 分钟（单核） | `ml/data/consistency.json` |
|
||||
| 4 生成数据 | `bash ml/pipeline.sh gen` | 不共识约 1 小时；共识约 4–5 小时 | `ml/data/shard_*.jsonl`、`stats.json` |
|
||||
| 5 打包样本 | `bash ml/pipeline.sh prep` | 15–30 分钟 | `ml/data/samples/`、`ml/data/r2.json` |
|
||||
| 6 预训练 | `bash ml/pipeline.sh train` | 测速 2000 步约 6 分钟，正式 24000 步约 1.5–2.5 小时（未实测：fp32 MF 分支后的吞吐以测速为准） | `ml/runs/base/best.pt` |
|
||||
| 6′ DAgger 纠偏 | `bash ml/pipeline.sh dagger` | 未实测，估 3–4 小时：2 轮 ×（采起点 5 分钟 + 老师重规划约 30 分钟 + 打包 + 微调 15000 步） | `ml/runs/base_dagger.ckpt`、`ml/runs/base_dag2/best.pt` |
|
||||
| 6 预训练 | `bash ml/pipeline.sh train` | 测速 2000 步约 6 分钟，正式约 3 小时 | `ml/runs/base/best.pt` |
|
||||
| 7 M5 门禁 | `bash ml/pipeline.sh m5` | 估算 10–20 分钟 | `ml/runs/m5_gate_base.json` |
|
||||
| 8 APG 微调 | `bash ml/pipeline.sh apg` | 未实测：展开长度 60–150 次推理随批变化，以监控页前 50 步换算的 ETA 为准 | `ml/runs/apg/best.pt` |
|
||||
| 9 挖错循环 | `bash ml/pipeline.sh mine` | 未实测：每轮 = 5 万题作答 + APG，以第 1 轮实际耗时 × 轮数为准 | `ml/runs/current.pt` |
|
||||
@@ -39,7 +38,7 @@
|
||||
|
||||
---
|
||||
|
||||
> 分歧规则：审查方与实现方意见讨论后仍不一致时，以实现方（Hark）的意见为准先落地；下一轮用实测数据说话（见 docs/ML运行手册审查.md 第二节）。
|
||||
> 分歧规则：审查方与实现方意见讨论后仍不一致时，以实现方（Hark）的意见为准先落地；下一轮用实测数据说话（详见 docs/ML运行手册审查.md 第五轮第〇节）。
|
||||
|
||||
## 1. 环境
|
||||
|
||||
@@ -62,7 +61,7 @@ bash ml/pipeline.sh kin
|
||||
检查：最后打印的 JSON 里 `"PASS": true`（fk、姿态、组对间隙、83 维特征、间隙梯度与 JS 一致）。
|
||||
`docs/collide.json` 以后只要重新拟合或重生成 `pair2d`/`roadmap`，这一步、数据、模型都要重来（指纹不符时各脚本会拒绝运行）。
|
||||
|
||||
**真跑之前（第七轮新增，5–10 分钟）**：`bash ml/pipeline.sh selftest`（进程生命周期与断点续跑自检 → `ml/runs/selftest.json`，不碰本仓库数据）。通过后正式流程按「数据 → 训练 → 后训练与评测」三段跑，中途出错从该阶段 `FROM=` 续跑。`bash ml/pipeline.sh smoke`（副本里极小模型全流程冒烟，40–70 分钟）备用，不必跑。已定判据与待回答问题见 `docs/ML运行手册审查.md`。
|
||||
**真跑之前（第七轮新增，5–10 分钟）**：`bash ml/pipeline.sh selftest`（进程生命周期与断点续跑自检 → `ml/runs/selftest.json`，不碰本仓库数据）。通过后正式流程按「数据 → 训练 → 后训练与评测」三段跑，中途出错从该阶段 `FROM=` 续跑。`bash ml/pipeline.sh smoke`（副本里极小模型全流程冒烟，40–70 分钟）备用，不必跑。待办与已定做法见 `docs/ML运行手册审查.md` 第六节。
|
||||
|
||||
**R4 延迟（训练前做，自动）**：推理延迟只取决于模型结构，与权重无关，所以用随机初始化模型先测，不过就在训练前改结构，不必等训完。
|
||||
|
||||
@@ -115,9 +114,7 @@ bash ml/pipeline.sh gen # 默认 5 万条、进程数 = CPU
|
||||
bash ml/pipeline.sh prep
|
||||
```
|
||||
|
||||
每条轨迹按 50 Hz 时间律重放，每 3 步取一个样本（特征与部署同一份 JS 代码计算）。40% 样本对 q 加 σ = 3° 噪声增广，标签改为 12 步内线性收回老师轨迹（加噪后间隙 < 3 mm 或收回途中有碰撞的不用）。
|
||||
- **动作标签**：Y = 每步增量 / (act_scale · STEP)，夹在 ±1，`act_scale` = 1.25（`ACT_SCALE`）。老师巡航时主导轴每步正好 1 个 STEP，标签最大 0.8，执行端的 ±1 只防越界；被 ±1 截掉的收回量在下一步补上。运行时增量 = clamp(act, ±1) · act_scale · STEP，`act_scale` 写在 checkpoint 和 ONNX meta 里，仿真、APG、浏览器运行时都按它反归一化；没有这个字段的旧模型按 1.0 处理。
|
||||
- 每个 shard 打包产物带 `<shard>.meta.json`（数据版本 `data_ver`=2、`act_scale`、增广参数、签名 `sig`）。`ACT_SCALE`、`AUG_P`、`AUG_SIGMA`、`AUG_BACK`、`MIN_GAP_MM` 任一变化，签名就变，`run_prep.sh` 只重打签名不符的 shard；没有 meta 的旧打包一律重打。训练读 meta 取 `act_scale`，同一次训练的各目录（主数据 + DAgger）必须一致，否则报错。
|
||||
每条轨迹按 50 Hz 时间律重放，每 3 步取一个样本（特征与部署同一份 JS 代码计算），30% 样本加 σ = 1° 噪声增广（加噪后间隙 < 3 mm 的不用）。
|
||||
认证最小间隙 < 3 mm 的老师轨迹不进训练集（`MIN_GAP_MM=3`，与 `stats.json` 的 `gapDrop` 同一口径）：模型只学离障碍 ≥ 3 mm 的走法，运行时投影护栏仍保持 5 mm。
|
||||
检查 `ml/data/samples/index.json`：`train_samples` 约 170 万、`val_samples` 约 9 万（5% 任务划为 val）。
|
||||
`MIN_GAP_MM` 必须与生成阶段相同（`stats.json` 的 `minGapFilterMm`），不一致时这一步直接报错（返回码 8）。
|
||||
@@ -130,32 +127,11 @@ bash ml/pipeline.sh prep
|
||||
bash ml/pipeline.sh train # MeanFlow 头 → ml/runs/bench_base/、ml/runs/base/
|
||||
```
|
||||
|
||||
开训前先跑老师轨迹回放自检（`ml/runs/replay_teacher.json`，约 1–3 分钟），两部分都过才开训：
|
||||
- 运动学口径：老师 50 Hz 轨迹直接放进 PyTorch 运动学 / 碰撞 / 到达判据，到达 ≥ 95%、碰撞 ≤ 1%；
|
||||
- 滚动口径：老师当策略（`TeacherOracle`）走完整的闭环仿真 `rollout()`（队列、前缀、提交 3 步、标签 clamp、DLS、时限都与策略同一条路径），时限内到达 ≥ 95%、碰撞 ≤ 1%、DLS 接管前与老师轨迹的最大关节偏差 p99 ≤ 0.1°。
|
||||
流程读 json 的 `pass` 字段，并核对 `fp`（当前 `kin.json`）与 `data_fp`（数据 shard 记录的碰撞模型指纹）；任一不符就重跑一次回放，仍不过 rc 5 停。
|
||||
先跑 2000 步测吞吐（`ml/runs/bench_base/log.jsonl` 的 `samples_per_s`，`eta_h` 是剩余小时数），再正式训练到 60000 步或闭环到达率连续 5 次不升为止。
|
||||
|
||||
闭环指标长期 0% 而开环正常时，跑 `PYTHONUTF8=1 python -m ml.eval.diag_cl --ckpt ml/runs/base/last.pt --n 256`（约 35–50 分钟）→ `ml/runs/diag_cl.json`：A 起点特征 Python 与 JS 逐组差、B 两份特征下动作差、C 旧部署口径的失败形态、H 老师 oracle / 前缀消融 / 子目标规则 / 分轴与分类别指标（各键含义见 `ml/eval/diag_cl.py` 文件头与 `docs/本机执行清单.md`）。
|
||||
|
||||
先跑 2000 步测吞吐（`ml/runs/bench_base/log.jsonl` 的 `samples_per_s`，`eta_h` 是剩余小时数），再正式训练 24000 步（`configs/base.json` 的 `steps`，余弦学习率按这个预算走完）。
|
||||
|
||||
- **通过线（R10）：`samples_per_s` ≥ 5000**（24000 步 × batch 1024 ≈ 1.4 小时）。MeanFlow 的 JVP 双前向使训练受算力限制，加大 batch 不会提高样本/s；低于通过线时把 `configs/base.json` 的 `fk_caps` 从 96 降到 48
|
||||
- **EMA**：训练同时维护权重的指数滑动平均（`ema` 0.999），checkpoint 里存 `model`（原始权重）与 `ema`。评估、DAgger、APG 起点、M5、挖错、验收、导出一律用 EMA 权重（没有 `ema` 的旧 checkpoint 用 `model`）
|
||||
- 训练中每 2000 步用 EMA 权重打印一行 `EVAL {...}`：val 任务闭环（部署节奏、不开安全层）`success`、`collision_rate`、`goal_ratio_2s`（2 s 时目标误差 / 起点误差的中位数）、`time_ok_1p3`、`start_fail`，时限 = 老师时长 × 2（至少 3 s），碰撞与到达只计时限内；开环三项（val 样本、老师状态上）：`ol_fk_mm` 候选 0 的 16 步末端误差、`ol_best_mm` 8 个候选里最好的一条、`ol_div` 候选多样性。
|
||||
- **选模**：`score` = success + 0.05·(1 − collision_rate) + 0.05·(1 − min(1, goal_ratio_2s)) − 1e-4·min(ol_best_mm, 500)，到达率全 0 时闭环指标仍能排序。p_neq 预热期内和预热后第一次评估之前不存 best.pt（一步映射还没训过）；早停只在 max(预热 + 2 次评估, 总步数一半) 之后计数，连续 5 次不升即停
|
||||
- 损失：FK / 平滑只加在 MF 分支 r = t 且 t ≤ 0.3 的配对去噪估计 x̂ = z − t·u 上；时间嵌入最高角频率 50；MF 分支与 JVP 走 fp32（TF32 矩阵乘）；前 5000 步 p_neq = 0，之后 p_neq = 0.5，另有 10% 样本固定在部署用的 (r = 0, t = 1)（`p_edge`）
|
||||
- 子目标头只用把 sub 特征置零的输入训练（sub 存在时子目标标签与输入是同一个量，原样抄就能答对）；运行时子目标头只在冷启动、sub 为空时使用，口径一致。价值头用完整输入
|
||||
- 条件输入增广（训练时现做，`aug_pre_drop` 0.15 / `aug_pre_sigma` 0.1 / `aug_dq_sigma` 0.3）：前缀随机清空（变成 r = 0 样本，标签不变）；前缀加噪声，标签前 r 步同步加、第 r 步扣回；速度特征 dq 加噪声。目的是让模型不能靠照抄上一块和上一帧速度出动作
|
||||
|
||||
## 6′. DAgger 纠偏
|
||||
|
||||
```bash
|
||||
bash ml/pipeline.sh dagger
|
||||
```
|
||||
|
||||
起点模型：预训练 best.pt 的闭环 success > 0 时用它，否则用 last.pt（`python -m ml.common.ckpt pick ml/runs/<头>`），都取 EMA 权重；之后每轮按同一规则取上一轮的结果。微调时只有起点已走过自己的 p_neq 预热才不再预热。
|
||||
用起点模型在 train 任务上闭环跑（关安全层），每题取「首次碰撞前 0.2 s」和一个随机时刻的状态做起点（须无碰、间隙 > `MIN_GAP_MM`、离目标 > 5 mm），同时记下该时刻的上一帧。老师从这些起点重新规划（`gen_dataset.cjs --tasks`，与主数据同认证），时间律从当前速度在首段方向上的分量起步（`v0`），t = 0 样本的速度特征用真实上一帧；打包后与主数据一起微调。微调的 val 只取主数据，DAgger 样本全部算 train。默认 2 轮（`DAG_ROUNDS`），每轮 8000 题（`DAG_N`）、微调 15000 步（`DAG_STEPS`，lr 3e-4）。老师只用于训练标注，部署仍无老师。M5 门禁读 `ml/runs/<头>_dagger.ckpt` 指向的模型；APG / 挖错的 BC 项也用 DAgger 样本。可断点续跑（采集、老师 shard、打包、微调各有完成标记）。
|
||||
- 显存预算**浮动**：整卡留 1 GB 给系统（`VRAM_RESERVE_GIB`，默认 1），训练与 APG 进程启动时按"整卡 − nvidia-smi 已用 − 余量"和"CUDA 空闲 − 余量"两者取小自设本进程上限（`ml/common/vram.py`）。占显存的应用多，上限就自动变小，不用改配置；想按定值对拍给 `VRAM_FRAC`（旧口径 0.78）。日志 `vram` 字段记峰值；预训练 `batch=1024` 实测峰值 2.76 GiB、APG `batch=48` 13.08 GiB。显存不够把 `batch` 改成 512。中断后再跑 `bash ml/pipeline.sh train`（或 `all`）会自动从 `ml/runs/<头>/last.pt` 续训（每 10 分钟存一次），测速那 2000 步做过就跳过。训练发散时不会用发散的权重覆盖 `last.pt` / `best.pt`。
|
||||
- **通过线（R10）：`samples_per_s` ≥ 5000，即 60000 步 ≤ 3.5 小时**。参考：3090、batch 1024 实测 6.2 k 样本/s ≈ 2.8 小时。MeanFlow 的 JVP 双前向使训练受算力限制，加大 batch 不会提高样本/s；低于通过线时把 `configs/base.json` 的 `fk_caps` 从 96 降到 48，仍不够再把 `steps` 降到 40000
|
||||
- 训练中每 2000 步打印一行 `EVAL {...}`：val 任务闭环（部署节奏、不开安全层）`success`、`collision_rate`、`time_ok_1p3`、`start_fail`。时限 = 老师时长 × 2（至少 3 s）
|
||||
- 显存不够把 `batch` 改成 512。中断后再跑 `bash ml/pipeline.sh train`（或 `all`）会自动从 `ml/runs/<头>/last.pt` 续训（每 10 分钟存一次），测速那 2000 步做过就跳过。训练发散时不会用发散的权重覆盖 `last.pt` / `best.pt`。
|
||||
|
||||
## 7. M5 门禁：选定动作头
|
||||
|
||||
@@ -186,7 +162,7 @@ bash ml/pipeline.sh apg
|
||||
- **展开长度**：每批覆盖该批老师时长 p90 × 1.2（无老师题按 6 s），在 60–150 次推理（3.6–9 s）之间；日志 `plans` 字段是本批实际次数
|
||||
- **到达项**：每 10 次推理段末的 TCP 距离与姿态误差，没有「到达即停」。这是有意的：段末项在整条展开上持续给出接近目标的梯度
|
||||
- **碰撞项**：每步端点与中点的逐胶囊对铰链 `ReLU(3 mm − 间隙)`，两处取均值（与只查端点同量纲，`w_col` 不变）。3 mm 与训练数据的过滤线一致，碰撞项不与 BC 标签对抗；运行时投影护栏是独立的 5 mm
|
||||
- `configs/apg.json` 定为 `batch=48`、`steps=8000`（本机实测 `batch=48/plans=150` 峰值 13.08 GiB；`batch=128` 必 OOM；总任务量与原 128×3000 相同）。仍超预算时把 `seg_plans` 从 10 降到 5，不降 `plans_max`（会截掉长任务的绕出段）
|
||||
- 显存不够把 `configs/apg.json` 的 `batch` 降到 64，或把 `seg_plans` 从 10 降到 5
|
||||
|
||||
## 9. 挖错循环
|
||||
|
||||
@@ -201,7 +177,7 @@ bash ml/pipeline.sh mine # 默认 6 轮；ROUNDS=3 APG_STEPS=1000 MIN
|
||||
## 10. 导出
|
||||
|
||||
```bash
|
||||
bash ml/pipeline.sh export # fp32 ONNX + meta.json（含 act_scale）→ webapp/models/（约 20 MB），导出 EMA 权重
|
||||
bash ml/pipeline.sh export # fp32 ONNX + meta.json → webapp/models/（约 20 MB）
|
||||
```
|
||||
|
||||
默认 fp32：部署走 wasm 与 Node CPU，fp16 在 CPU 后端上不保证更快。只有第 13 步用 `&ep=webgpu` 实测 `__POLICY.latency()` 明显受益时，才另导出 fp16 做对比：
|
||||
@@ -221,7 +197,7 @@ bash ml/pipeline.sh bench
|
||||
|
||||
看 `ml/runs/policy_bench.json`：各集合 `collisionFrames` 为 0；`near_column_*` 与 `route40` 全部 `ok`；`near_column_1000` 0 失败；`brakeRate` < 0.02；`r8_adversarial` 的 `recoveryRate` ≥ 0.95、`livelock` = 0。
|
||||
同一步还会在查看器里跑**同一批** 30 道贴柱题（与 Node 同种子），写 `ml/runs/r9_browser.json`（会弹出浏览器窗口几分钟）：
|
||||
- **R9 通过线：稳态规划 `planWarmP99` < 45 ms、运动中饥饿节拍 `starve_rate` < 1%、认证超时占比 `cert_time_rate` < 5%**（整次规划 = 推理 + 前瞻 + 安全层，60 ms 周期留 15 ms 给渲染与执行时钟；稳态 = 剔除每题静止起步那次冷启动规划，冷启动只是起步延迟，`planColdP99` 只报；饥饿 = 运动中执行时钟到点队列空了，即实际丢步；认证超时一闸防止靠拒绝候选、刹停换来耗时达标）。`brake_rate`、`tickGapMax` 只报不判。
|
||||
- **R9 通过线：整次规划 `planP99` < 45 ms、饥饿节拍 `starve_rate` < 1%**（整次规划 = 推理 + 前瞻 + 安全层，60 ms 周期留 15 ms 给渲染与执行时钟；饥饿 = 执行时钟到点时队列空了，即实际丢步）。
|
||||
- 两边对照：`policy_bench.json` 的 `near_column_pos.safetyMsP99` 与浏览器 `safetyP99` 差 > 3× 时验收标红，用 `certQueriesP99`（每次规划新增距离查询）、`certMemoHitRate`、`certFailWhy`（`cert` / `budget` / `time` / `box`）定位。
|
||||
|
||||
## 12. 验收汇总
|
||||
@@ -244,7 +220,6 @@ python ml/serve.py # 带跨源隔离头的本地服务，wasm
|
||||
- 控制台 `__POLICY.latency()` 查看推理、前瞻、安全层、整次规划的 P50/P99 与节拍；`__POLICY.stats()` 查看刹停、换向、精修次数
|
||||
- `&autotest=30` 自动跑与 bench 同一批贴柱题，结果在 `__POLICY.autotest`
|
||||
- 运行时投影护栏 5 mm：候选动作离障碍 < 5 mm 时沿间隙梯度外推（训练数据的过滤线是 3 mm，护栏比数据更保守）
|
||||
- 动作尺度读模型 meta 的 `act_scale`；子目标按「越过顶点」切换（与训练标签同口径：沿拐角平分面越过当前途经点即换下一个，没有超时），与仿真 `closed_loop.sub_advance` 同一规则
|
||||
- 工具偏移 ≠ 0 时自动交回原规划器
|
||||
- 离线环境：`bash ml/prep/vendor_ort.sh` 把 onnxruntime-web 放到 `webapp/vendor/ort/`，地址加 `&ortLocal=1`（WebGPU 同样可用）
|
||||
|
||||
@@ -301,7 +276,7 @@ Windows：双击 ml/console.bat Linux / WSL：bash ml/console.sh
|
||||
|
||||
- **指纹不符**：`collide.json` 变了。依次重跑第 2、4、5 步并重训。
|
||||
- **训练发散**（loss 出现 nan/inf，自动停在 `diverged.pt`）：先把 `lr` 降到 5e-4 重试一次，仍发散就走 WTA 回退（第 7 步）。
|
||||
- **显存/内存不足**：降 `batch`；APG 还可把 `seg_plans` 降到 5。出题与间隙计算按空闲显存自动分块（用 1/4；空闲量本就扣除了桌面占用）。`check_r2` 常驻约 1.6 GB（1.7 M 样本）。
|
||||
- **显存/内存不足**：降 `batch`；APG 还可把 `seg_plans` 降到 5。出题与间隙计算按空闲显存自动分块（用 1/4）。
|
||||
- **小样本摸底**：几百条任务时 val 可能为空，训练会提示并跳过闭环验证；R2 检查近邻对不足时 `pass` 为 null，不拦后续步骤。
|
||||
- **返回码**：3 = M5 两种头都没过；4 = R2 不过且 `R2_STRICT=1`；5 = R4 不过；6 = 生成进程崩溃（看 `ml/data/log_*.txt`，采样器连续失败会写明原因）；8 = `MIN_GAP_MM` 前后不一致；9 = 已有一条在跑；124 = 阶段超时。
|
||||
- **生成阶段报「采样失败：…连续 N 次未命中」**：多半是 `collide.json` 或关节限位变了，某类位形几乎不存在；把该日志发我。
|
||||
@@ -325,8 +300,7 @@ ml/
|
||||
sim/closed_loop.py 部署节奏闭环仿真（验证 / 挖错 / 门禁）
|
||||
apg/apg_finetune.py APG 可微微调
|
||||
mining/ tasks.py（无老师出题）、mine_round.py、gate.py（含 R7）、loop.sh
|
||||
eval/ dump_parity.cjs + check_parity.py（JS↔PyTorch 对拍）、check_r2.py（R2）、r4_probe.py（R4 无头浏览器探针）、acceptance.py（验收汇总）、
|
||||
replay_teacher.py（开训前回放自检，含老师 oracle 走 rollout）、diag_cl.py（闭环失败定位 A/B/C/H）
|
||||
eval/ dump_parity.cjs + check_parity.py（JS↔PyTorch 对拍）、check_r2.py（R2）、r4_probe.py（R4 无头浏览器探针）、acceptance.py（验收汇总）
|
||||
toy/toy_bimodal.py 2D 双模态对照实验（动作头选型依据）
|
||||
export_onnx.py serve.py
|
||||
configs/ base.json（MeanFlow）、wta.json（回退）、small.json（R4 不过时的 d=128）、apg.json
|
||||
|
||||
+23
-34
@@ -6,10 +6,7 @@
|
||||
2026-10-18：展开长度不再定长 60 次推理（3.6 s，会截掉老师时长 > 3.6 s 的绕远题），改为覆盖本批 exec_s p90×1.2，上限 plans_max；
|
||||
"到达"仍是每 seg_plans 段末的距离项（无到达即停），属有意设计：段末项给出持续的接近梯度。
|
||||
margin_train 3 mm：与训练数据过滤线一致（认证最小间隙 < 3 mm 的老师轨迹不进训练集），碰撞项不与 BC 标签对抗；
|
||||
运行时投影护栏独立保持 5 mm。端点与中点两个铰链取均值，与只查端点同量纲，w_col 无需重标。
|
||||
2026-10-19：起点取 EMA 权重（ckpt.load_policy）；动作反归一化乘 act_scale（与数据同一尺度，不一致报错），产物存 act_scale；
|
||||
子目标推进改用 closed_loop.sub_advance（默认「越过顶点」，与标签同口径）；子目标头 BC 项用 sub 特征置零的输入（同 train.py）；
|
||||
val 只取主数据目录（第一个 --samples）。"""
|
||||
运行时投影护栏独立保持 5 mm。端点与中点两个铰链取均值，与只查端点同量纲，w_col 无需重标。"""
|
||||
import argparse, json, math, os, random, sys, time
|
||||
import torch
|
||||
|
||||
@@ -18,9 +15,8 @@ from ml.common.features import features, STEP, H, wrap180
|
||||
from ml.data import Samples, load_tasks
|
||||
from ml.model.policy import build, wta_loss
|
||||
from ml.model.meanflow import meanflow_loss
|
||||
from ml.sim.closed_loop import evaluate, tasks_to_tensors, goal_err, sub_advance
|
||||
from ml.sim.closed_loop import evaluate, tasks_to_tensors, goal_err
|
||||
from ml.common.ckpt import Saver, atomic_save, rng_state, set_rng_state
|
||||
from ml.common import vram
|
||||
|
||||
|
||||
def load_pool(paths):
|
||||
@@ -42,25 +38,19 @@ def main():
|
||||
cfg = json.load(open(a.config)); C = cfg['apg']
|
||||
if a.steps: C['steps'] = a.steps
|
||||
dev = a.device; torch.manual_seed(a.seed); random.seed(a.seed)
|
||||
vram.limit()
|
||||
os.makedirs(a.out, exist_ok=True)
|
||||
from ml.common.ckpt import load_policy
|
||||
model, ck = load_policy(a.init, dev); mcfg = ck['cfg']
|
||||
ck = torch.load(a.init, map_location=dev)
|
||||
mcfg = ck['cfg']
|
||||
model = build(mcfg).to(dev); model.load_state_dict(ck['model'])
|
||||
kin = Kin(device=dev)
|
||||
data = Samples(a.samples, device=dev, split='train', val_dirs=a.samples[:1])
|
||||
act_scale = float(ck.get('act_scale', 1.0))
|
||||
if abs(act_scale - data.act_scale) > 1e-9:
|
||||
raise SystemExit(f'起点 act_scale={act_scale} 与样本 act_scale={data.act_scale} 不一致（BC 项标签尺度不同），换同一版数据训练的模型')
|
||||
model.act_scale = act_scale
|
||||
data = Samples(a.samples, device=dev, split='train')
|
||||
all_tasks = load_tasks(a.samples)
|
||||
main_tid = {t['tid'] for t in load_tasks(a.samples[:1]) if t['split'] == 'val'}
|
||||
train_tasks = [t for t in all_tasks if t['tid'] not in main_tid] + load_pool(a.pool)
|
||||
val_tasks = [t for t in load_tasks(a.samples[:1]) if t['split'] == 'val'][:C['eval_tasks']]
|
||||
train_tasks = [t for t in all_tasks if t['split'] == 'train'] + load_pool(a.pool)
|
||||
val_tasks = [t for t in all_tasks if t['split'] == 'val'][:C['eval_tasks']]
|
||||
w = [C['cat_weights'].get(t.get('cat', 'A'), 1.0) * (2.0 if t.get('hard') else 1.0) for t in train_tasks]
|
||||
opt = torch.optim.AdamW(model.parameters(), lr=C['lr'], betas=(0.9, 0.95), weight_decay=0.0)
|
||||
amp = dev.startswith('cuda') and C.get('amp') == 'bf16'
|
||||
step = torch.tensor(STEP, device=dev)
|
||||
astep = step * act_scale # 动作 → 度
|
||||
it0 = 0
|
||||
if a.resume and os.path.exists(a.resume):
|
||||
rk = torch.load(a.resume, map_location=dev); model.load_state_dict(rk['model'])
|
||||
@@ -76,11 +66,11 @@ def main():
|
||||
base, _ = evaluate(model.eval(), kin, val_tasks, dev)
|
||||
print('EVAL0', json.dumps(base), flush=True); log.write(json.dumps({'eval': base, 'step': 0}) + '\n')
|
||||
best = base['success']
|
||||
atomic_save({'model': model.state_dict(), 'cfg': mcfg, 'step': 0, 'fp': kin.fp, 'eval': base, 'act_scale': act_scale}, os.path.join(a.out, 'best.pt'))
|
||||
atomic_save({'model': model.state_dict(), 'cfg': mcfg, 'step': 0, 'fp': kin.fp, 'eval': base}, os.path.join(a.out, 'best.pt'))
|
||||
M = C['margin_train_mm']; t0 = time.time()
|
||||
saver = Saver(C.get('ckpt_min', 10)) # 2026-10-18：每 10 分钟 + 收到停止请求时存 last.pt（不再只在每 500 次验证时存）
|
||||
def save_last(n, ev=None):
|
||||
atomic_save({'model': model.state_dict(), 'opt': opt.state_dict(), 'cfg': mcfg, 'step': n, 'fp': kin.fp, 'eval': ev, 'rng': rng_state(), 'act_scale': act_scale}, os.path.join(a.out, 'last.pt')); saver.mark()
|
||||
atomic_save({'model': model.state_dict(), 'opt': opt.state_dict(), 'cfg': mcfg, 'step': n, 'fp': kin.fp, 'eval': ev, 'rng': rng_state()}, os.path.join(a.out, 'last.pt')); saver.mark()
|
||||
for it in range(it0, C['steps']):
|
||||
model.train()
|
||||
batch = random.choices(train_tasks, weights=w, k=C['batch'])
|
||||
@@ -89,7 +79,7 @@ def main():
|
||||
z = torch.randn(B, H, 6, device=dev)
|
||||
q, qp = q0.clone(), q0.clone()
|
||||
queue = torch.zeros(B, 0, 6, device=dev)
|
||||
subs = torch.zeros(B, 2, 6, device=dev); nsub = torch.zeros(B, dtype=torch.long, device=dev); sub_t = torch.zeros(B, device=dev); sub_prev = q0.clone()
|
||||
subs = torch.zeros(B, 2, 6, device=dev); nsub = torch.zeros(B, dtype=torch.long, device=dev); sub_t = torch.zeros(B, device=dev)
|
||||
opt.zero_grad(set_to_none=True)
|
||||
stats = {'col': 0.0, 'tgt': 0.0, 'smooth': 0.0}
|
||||
# 展开长度随本批任务时长：覆盖本批 exec_s 的 p90 × 1.2（无老师参考题按 6 s），夹在 [plans, plans_max]
|
||||
@@ -103,7 +93,11 @@ def main():
|
||||
pre = torch.zeros(B, 3, 6, device=dev); pre[:, :r] = queue[:, :r]
|
||||
qe = q + queue.sum(1)
|
||||
with torch.no_grad():
|
||||
subs, nsub, sub_prev, sub_t = sub_advance(kin, subs, nsub, sub_prev, sub_t, qe.detach(), tnow)
|
||||
if (nsub > 0).any():
|
||||
cur = subs[:, 0]; dd = torch.where(kin.cont, wrap180(cur - qe), cur - qe).abs().max(-1).values
|
||||
adv = (nsub > 0) & ((dd < 5) | (tnow - sub_t > 1.5))
|
||||
subs = torch.where(adv[:, None, None], torch.cat([subs[:, 1:], subs[:, 1:]], 1), subs)
|
||||
nsub = torch.where(adv, nsub - 1, nsub); sub_t = torch.where(adv, torch.full_like(sub_t, tnow), sub_t)
|
||||
feat = features(kin, q, qp, T, R, pre, torch.full((B,), r, device=dev), subs[:, 0], (nsub > 0).float())
|
||||
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
|
||||
act, val, sub = model.sample_k(feat, 1, z)
|
||||
@@ -112,8 +106,8 @@ def main():
|
||||
with torch.no_grad():
|
||||
need = sub[:, 12].float() > 0.5
|
||||
subs = q.detach()[:, None] + sub[:, :12].float().view(B, 2, 6) * 180
|
||||
nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub)); sub_prev = q.detach().clone()
|
||||
inc = act.clamp(-1, 1) * astep # [B,16,6]
|
||||
nsub = torch.where(need, torch.full_like(nsub, 2), torch.zeros_like(nsub))
|
||||
inc = act.clamp(-1, 1) * step # [B,16,6]
|
||||
commit = inc[:, r:r + 3]
|
||||
if r == 0: # 冷启动：先提交一块，下一轮才开始执行
|
||||
queue = commit; continue
|
||||
@@ -122,7 +116,7 @@ def main():
|
||||
d = queue[:, k]
|
||||
qp = q; q = kin.clamp_box(q + d)
|
||||
# 碰撞铰链：端点 + 中点（与仿真/运行时的离散检查一致）
|
||||
G = kin.gaps(torch.cat([q, 0.5 * (q + qp)], 0), M, pairs=True)
|
||||
G = kin.gaps(torch.cat([q, 0.5 * (q + qp)], 0), M)
|
||||
hinge = torch.relu(M - G['gap'])
|
||||
col = torch.zeros(2 * B, device=dev).index_add(0, G['b'], hinge).view(2, B).mean(0) # 端点与中点取均值：与只查端点时同量纲，w_col 无需重标
|
||||
sm = ((d / step) ** 2).sum(-1) + (((d - prev_d) / step) ** 2).sum(-1)
|
||||
@@ -139,18 +133,13 @@ def main():
|
||||
stats['tgt'] += lt.mean().item()
|
||||
(seg_loss / nseg).backward()
|
||||
seg_loss = torch.zeros((), device=dev)
|
||||
# 只 detach 状态并重置队列：段边界下一 plan 走 r=0 重新提交（新图有梯度）；
|
||||
# 沿用旧 queue 会跨过已 backward 的图（二次 backward 报错），也避免整段无梯度
|
||||
q, qp, prev_d = q.detach(), qp.detach(), prev_d.detach()
|
||||
queue = torch.zeros(B, 0, 6, device=dev)
|
||||
q, qp, queue, prev_d = q.detach(), qp.detach(), queue.detach(), prev_d.detach()
|
||||
# BC 锚定
|
||||
X, Y, V, SG = data.batch(C['batch'] * 4)
|
||||
with torch.autocast('cuda', dtype=torch.bfloat16, enabled=amp):
|
||||
mem = model.encode(X)
|
||||
l_bc = wta_loss(model, mem, Y)[0] if model.head == 'wta' else meanflow_loss(model, mem, Y)[0]
|
||||
v, _ = model.heads(mem)
|
||||
Xs = X.clone(); Xs[:, 68:75] = 0
|
||||
_, s = model.heads(model.encode(Xs))
|
||||
v, s = model.heads(mem)
|
||||
l_bc = l_bc + 0.1 * (v.float() - V).abs().mean() + 0.1 * (s.float() - SG).abs().mean()
|
||||
(C['w_bc'] * l_bc).backward()
|
||||
gn = torch.nn.utils.clip_grad_norm_(model.parameters(), C['grad_clip'])
|
||||
@@ -158,7 +147,7 @@ def main():
|
||||
with torch.no_grad():
|
||||
e, oe = goal_err(kin, q, T, R)
|
||||
rec = {'it': it, 'total': C['steps'], 't': time.time(), 'plans': n_plans, 'err_mm_mean': e.mean().item(), 'reach_frac': ((e < 1) & (oe < 0.5)).float().mean().item(), 'bc': l_bc.item(),
|
||||
'gnorm': gn.item(), **stats, 'min': (time.time() - t0) / 60, 'vram': vram.peak_gib()}
|
||||
'gnorm': gn.item(), **stats, 'min': (time.time() - t0) / 60}
|
||||
if it % 10 == 0: print(json.dumps(rec), flush=True)
|
||||
log.write(json.dumps(rec) + '\n'); log.flush()
|
||||
if saver.stop() or (a.stop_at and it + 1 >= a.stop_at):
|
||||
@@ -169,7 +158,7 @@ def main():
|
||||
res, _ = evaluate(model.eval(), kin, val_tasks, dev)
|
||||
res['it'] = it + 1; res['t'] = time.time()
|
||||
print('EVAL', json.dumps(res), flush=True); log.write(json.dumps({'eval': res}) + '\n'); log.flush()
|
||||
sd = {'model': model.state_dict(), 'cfg': mcfg, 'step': it + 1, 'fp': kin.fp, 'eval': res, 'act_scale': act_scale}
|
||||
sd = {'model': model.state_dict(), 'cfg': mcfg, 'step': it + 1, 'fp': kin.fp, 'eval': res}
|
||||
save_last(it + 1, res) # last 带优化器状态供续跑；best 不带（导出/门禁用）
|
||||
if res['success'] > best + 1e-4:
|
||||
best = res['success']; atomic_save(sd, os.path.join(a.out, 'best.pt'))
|
||||
|
||||
@@ -60,33 +60,3 @@ def set_rng_state(d):
|
||||
try: torch.cuda.set_rng_state_all([t.cpu() for t in d['cuda']])
|
||||
except Exception: pass
|
||||
return True
|
||||
|
||||
|
||||
def load_policy(path, device, prefer_ema=True):
|
||||
"""载入策略（2026-10-19，审查 review_model_train P0-2）：优先取 EMA 权重 ck['ema']（train.py 起存），没有就用 ck['model']（旧 checkpoint、APG 产物）。
|
||||
model.act_scale = ck['act_scale']（动作标签归一化倍数，见 ml/prep/build_dataset.cjs ACT_SCALE）；旧 checkpoint 缺省 1.0 = 原口径。
|
||||
闭环仿真 / DAgger / APG 起点 / 挖错 / 门禁 / 验收 / 导出都经这里载入，保证同一份权重、同一个动作尺度。返回 (model.eval(), ck)。"""
|
||||
from ml.model.policy import build
|
||||
ck = torch.load(path, map_location=device)
|
||||
m = build(ck['cfg']).to(device)
|
||||
sd = ck.get('ema') if prefer_ema and ck.get('ema') is not None else ck['model']
|
||||
m.load_state_dict(sd)
|
||||
m.act_scale = float(ck.get('act_scale', 1.0))
|
||||
return m.eval(), ck
|
||||
|
||||
|
||||
def pick_start(run_dir):
|
||||
"""DAgger 起点（2026-10-19，审查 P0-2）：best.pt 的闭环 success > 0 才用 best.pt；否则用 last.pt
|
||||
（success 全 0 时 best.pt 只由开环误差决定，可能落在 p_neq 预热期，一步映射没训过）。载入时取 EMA。"""
|
||||
best, last = os.path.join(run_dir, 'best.pt'), os.path.join(run_dir, 'last.pt')
|
||||
if os.path.exists(best):
|
||||
ev = torch.load(best, map_location='cpu').get('eval') or {}
|
||||
if (ev.get('success') or 0) > 0 or not os.path.exists(last): return best
|
||||
return last if os.path.exists(last) else best
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# python -m ml.common.ckpt pick <运行目录> → 打印 DAgger 起点 checkpoint 路径
|
||||
import sys
|
||||
if len(sys.argv) == 3 and sys.argv[1] == 'pick': print(pick_start(sys.argv[2]))
|
||||
else: raise SystemExit('usage: python -m ml.common.ckpt pick <run_dir>')
|
||||
|
||||
@@ -52,10 +52,8 @@ def features(kin, q, q_prev, T, R, prefix, prefix_len, sub, sub_flag, gm=None, x
|
||||
out.append(sd)
|
||||
out.append(sub_flag[:, None].to(dt))
|
||||
if gm is None:
|
||||
out.append(kin.gm_groups(q, GP, GAP_CAP, xf=xf) / GAP_CAP) # 2026-10-20：只算特征用到的 8 组；xf 复用不重复算 FK
|
||||
gm = None
|
||||
if gm is not None:
|
||||
out.append(torch.stack([gm[:, a * 7 + b].clamp(max=GAP_CAP) / GAP_CAP for a, b in GP], -1))
|
||||
gm = kin.gaps(q, GAP_CAP, xf)['gm']
|
||||
out.append(torch.stack([gm[:, a * 7 + b].clamp(max=GAP_CAP) / GAP_CAP for a, b in GP], -1))
|
||||
f = torch.cat(out, -1)
|
||||
assert f.shape[1] == F_DIM, f.shape
|
||||
return f
|
||||
|
||||
+7
-225
@@ -38,8 +38,7 @@ def seg_seg_dist(p1, q1, p2, q2, eps=1e-12):
|
||||
s = torch.where(t_lo, (-c / a.clamp_min(eps)).clamp(0, 1), s)
|
||||
s = torch.where(t_hi, ((b - c) / a.clamp_min(eps)).clamp(0, 1), s)
|
||||
t = t.clamp(0, 1)
|
||||
# 第二条退化成点时，仍须在第一条上投影，而不是固定取其起点。
|
||||
s = torch.where(e <= eps, (-c / a.clamp_min(eps)).clamp(0, 1), s)
|
||||
# 退化（线段长度≈0）
|
||||
s = torch.where(a <= eps, torch.zeros_like(s), s)
|
||||
t = torch.where(e <= eps, torch.zeros_like(t), t)
|
||||
c1 = p1 + d1 * s.unsqueeze(-1)
|
||||
@@ -86,36 +85,8 @@ class Kin:
|
||||
gi, gj = self.cap_g[self.pi], self.cap_g[self.pj]
|
||||
self.pair_gp = torch.minimum(gi, gj) * 7 + torch.maximum(gi, gj)
|
||||
self.lever = T(K['lever'])
|
||||
table = [[0.0] * 7 for _ in range(6)]
|
||||
for group in range(1, 7):
|
||||
endpoints = [c[key] for c in caps if c['g'] == group for key in ('a', 'b')]
|
||||
if not endpoints:
|
||||
continue
|
||||
pivot, axis = K['P'][group - 1], K['D'][group - 1]
|
||||
unit = [v / math.hypot(*axis) for v in axis]
|
||||
relative = [[e[j] - pivot[j] for j in range(3)] for e in endpoints]
|
||||
radius = max(math.hypot(*v) for v in relative)
|
||||
radial = max(math.hypot(v[1] * unit[2] - v[2] * unit[1],
|
||||
v[2] * unit[0] - v[0] * unit[2],
|
||||
v[0] * unit[1] - v[1] * unit[0]) for v in relative)
|
||||
for joint in range(group):
|
||||
chain = sum(math.dist(K['P'][j], K['P'][j + 1]) for j in range(joint, group - 1))
|
||||
table[joint][group] = (chain + (radial if joint == group - 1 else radius)) * D2R
|
||||
self.cert_groups = torch.tensor([a * 7 + b for a in range(7) for b in range(a + 2, 7)], device=device)
|
||||
# 三角不等式给出全姿态界，不用随机姿态最大值冒充上界。
|
||||
self.cert_lever = T([[table[k][b] if a <= k < b else 0.0 for k in range(6)]
|
||||
for a in range(7) for b in range(a + 2, 7)])
|
||||
|
||||
# ---------------- 运动学 ----------------
|
||||
def _checked_xf(self, q, xf):
|
||||
"""xf 是按行对应 q 的位姿数组，行数不匹配会静默广播出错（宽相位筛到别的行 ⇒ device assert 或错间隙）。"""
|
||||
if xf is None:
|
||||
return self.cum_xf(q)
|
||||
Rs, Ts = xf
|
||||
if Rs.shape[0] != q.shape[0] or Ts.shape[0] != q.shape[0]:
|
||||
raise ValueError('xf 的行数 %d 与 q 的行数 %d 不符' % (Rs.shape[0], q.shape[0]))
|
||||
return xf
|
||||
|
||||
def cum_xf(self, q):
|
||||
"""q: [B,6] 度 → Rs [B,7,3,3], Ts [B,7,3]"""
|
||||
B = q.shape[0]
|
||||
@@ -139,19 +110,6 @@ class Kin:
|
||||
Rs, _ = xf if xf is not None else self.cum_xf(q)
|
||||
return Rs[:, 6]
|
||||
|
||||
def jac(self, q, xf=None):
|
||||
"""几何雅可比（对度）：返回 (Jv [B,3,6] mm/deg, Jw [B,3,6] 单位轴/deg)"""
|
||||
Rs, Ts = xf if xf is not None else self.cum_xf(q)
|
||||
p = self.fk(q, (Rs, Ts))
|
||||
Jv, Jw = [], []
|
||||
for k in range(6):
|
||||
d = torch.tensor(self.D[k], dtype=q.dtype, device=q.device)
|
||||
a = Rs[:, k] @ d
|
||||
o = torch.einsum('bij,j->bi', Rs[:, k], self.P[k].to(q.dtype)) + Ts[:, k]
|
||||
Jv.append(torch.linalg.cross(a, p - o, dim=-1) * D2R)
|
||||
Jw.append(a * D2R)
|
||||
return torch.stack(Jv, -1), torch.stack(Jw, -1)
|
||||
|
||||
def caps_world(self, q, xf=None):
|
||||
Rs, Ts = xf if xf is not None else self.cum_xf(q)
|
||||
Rg, Tg = Rs[:, self.cap_g], Ts[:, self.cap_g] # [B,C,3,3], [B,C,3]
|
||||
@@ -160,73 +118,19 @@ class Kin:
|
||||
return a, b
|
||||
|
||||
# ---------------- 间隙 ----------------
|
||||
# 2026-10-19（用户实测 dagger/诊断吃满 24 GB 显存）：本模型 1936 条胶囊 / 491 组 / 67256 粗对 / 104.7 万细对。
|
||||
# 显存有两段峰值，都随点数线性涨：
|
||||
# 粗筛段 cc[:,cp_a]-cc[:,cp_b] 的广播中间量 ≈ 36 B × 点数 × 粗对数（4096 点实测 9.3 GiB，与 cap 无关）；
|
||||
# 精算段 ≈ 每对几百字节 × 幸存细对数（4096 点 cap 40 幸存 2.4 亿对；旧版不分块实测 36.9 GiB）。
|
||||
# 所以先按 COARSE_ENTRIES 把点切块（粗筛每块 ≤ ~1 GiB），块内再按幸存细对数 PAIR_BUDGET 切（精算每块 ≤ ~1 GiB）。
|
||||
# 两段不同时存在，峰值与调用方 batch 大小无关。结果与不分块逐位一致（梯度经 cat 保留）。
|
||||
COARSE_ENTRIES = 30_000_000
|
||||
PAIR_BUDGET = 2_000_000
|
||||
|
||||
@staticmethod
|
||||
def _cat_gaps(parts, starts):
|
||||
out = {'gm': torch.cat([p.pop('gm') for p in parts], 0)}
|
||||
if 'b' in parts[0]:
|
||||
# 非 in-place：b 会被 autograd 的索引反向图引用（APG 的碰撞铰链梯度路径），+= 会让 backward 报 version 错
|
||||
for p, s in zip(parts, starts): p['b'] = p['b'] + s
|
||||
for k in ('b', 'i', 'j', 'gap'): out[k] = torch.cat([p.pop(k) for p in parts]) # 逐键拼、拼完即释放
|
||||
return out
|
||||
|
||||
def gaps(self, q, cap=40.0, xf=None, max_pairs=None, pairs=False):
|
||||
def gaps(self, q, cap=40.0, xf=None, max_pairs=None):
|
||||
"""返回 dict：
|
||||
gm [B,49]：组对最小间隙 min(gap, cap)（gA*7+gB，gA<gB；其余为 cap）
|
||||
pairs=True 时另有 b, i, j, gap：幸存胶囊对（gap 可微；只有 APG 的 hinge 与 check_parity 需要）
|
||||
2026-10-19（用户实测 4096 点 cap 40 峰值 4.46 GiB 全在拼接 8.6e7 对的 b/i/j/gap）：
|
||||
默认只返回 gm，每块算完就丢掉胶囊对，峰值只剩单块。
|
||||
b, i, j, gap：幸存胶囊对（gap 可微）
|
||||
"""
|
||||
xf = self._checked_xf(q, xf)
|
||||
Rs, Ts = xf
|
||||
B = q.shape[0]
|
||||
step = max(1, self.COARSE_ENTRIES // int(self.cp_a.shape[0]))
|
||||
if B <= step:
|
||||
return self._gaps_split(q, cap, xf, pairs)
|
||||
st = list(range(0, B, step))
|
||||
return self._cat_gaps([self._gaps_split(q[s:s + step], cap, (Rs[s:s + step], Ts[s:s + step]), pairs) for s in st], st)
|
||||
|
||||
def _coarse(self, q, cap, xf):
|
||||
Rs, Ts = xf
|
||||
cc = torch.einsum('bkij,kj->bki', Rs[:, self.cl_g], self.cl_c.to(q.dtype)) + Ts[:, self.cl_g]
|
||||
d2 = (cc[:, self.cp_a] - cc[:, self.cp_b]).pow(2).sum(-1) # [B,M]
|
||||
del cc
|
||||
return d2 <= (self.cp_lim + cap).pow(2)
|
||||
|
||||
def _gaps_split(self, q, cap, xf, pairs):
|
||||
"""粗筛已在显存预算内：按幸存细对数再切块做精算"""
|
||||
xf = xf if xf is not None else self.cum_xf(q)
|
||||
Rs, Ts = xf
|
||||
B = q.shape[0]
|
||||
with torch.no_grad():
|
||||
per = (self._coarse(q, cap, xf).long() * self.cp_cnt).sum(1) # [B] 每点幸存细对数
|
||||
if B <= 1 or int(per.sum()) <= self.PAIR_BUDGET:
|
||||
bounds = [0, B]
|
||||
else:
|
||||
cs = torch.cumsum(per, 0).tolist(); bounds = [0]; base = 0
|
||||
for k, c in enumerate(cs):
|
||||
if c - base > self.PAIR_BUDGET and k > bounds[-1]:
|
||||
bounds.append(k); base = cs[k - 1]
|
||||
bounds.append(B)
|
||||
del per
|
||||
if len(bounds) == 2:
|
||||
return self._gaps_one(q, cap, xf, pairs)
|
||||
return self._cat_gaps([self._gaps_one(q[s:e], cap, (Rs[s:e], Ts[s:e]), pairs) for s, e in zip(bounds[:-1], bounds[1:])], bounds[:-1])
|
||||
|
||||
def _gaps_one(self, q, cap, xf, pairs):
|
||||
Rs, Ts = xf
|
||||
B = q.shape[0]
|
||||
with torch.no_grad():
|
||||
keep = self._coarse(q, cap, xf)
|
||||
cc = torch.einsum('bkij,kj->bki', Rs[:, self.cl_g], self.cl_c.to(q.dtype)) + Ts[:, self.cl_g]
|
||||
d2 = (cc[:, self.cp_a] - cc[:, self.cp_b]).pow(2).sum(-1) # [B,M]
|
||||
keep = d2 <= (self.cp_lim + cap).pow(2)
|
||||
bb, mm = keep.nonzero(as_tuple=True)
|
||||
del keep
|
||||
cnt = self.cp_cnt[mm]
|
||||
rep_b = torch.repeat_interleave(bb, cnt)
|
||||
off = torch.repeat_interleave(self.cp_start[mm] - torch.cumsum(cnt, 0) + cnt, cnt) + torch.arange(int(cnt.sum()), device=q.device)
|
||||
@@ -241,130 +145,8 @@ class Kin:
|
||||
gap = d - self.pair_need[pidx].to(q.dtype)
|
||||
gm = torch.full((B * 49,), float(cap), dtype=q.dtype, device=q.device)
|
||||
gm = gm.scatter_reduce(0, rep_b * 49 + self.pair_gp[pidx], gap.clamp(max=cap), reduce='amin', include_self=True)
|
||||
if not pairs: return {'gm': gm.view(B, 49)}
|
||||
return {'gm': gm.view(B, 49), 'b': rep_b, 'i': pi, 'j': pj, 'gap': gap}
|
||||
|
||||
def gm_groups(self, q, gp, cap=40.0, xf=None):
|
||||
"""只算指定组对 gp=[(a,b),...] 的最小间隙 [B,len(gp)]（特征用，避免 49 组全量）。
|
||||
数值与 gaps(q,cap)['gm'][:, a*7+b] 一致；组对在碰撞粗对里不存在时返回 cap。
|
||||
xf 可传入调用者已算好的 cum_xf(q)，避免同一姿态重复算 FK（特征路径每周期省一次）。"""
|
||||
key = tuple(gp)
|
||||
if getattr(self, '_gp_key', None) != key:
|
||||
m = torch.full((49,), -1, dtype=torch.long, device=q.device)
|
||||
for i, (a_, b_) in enumerate(gp): m[min(a_, b_) * 7 + max(a_, b_)] = i
|
||||
ga, gb = self.cl_g[self.cp_a], self.cl_g[self.cp_b]
|
||||
cg = torch.minimum(ga, gb) * 7 + torch.maximum(ga, gb) # 簇对 → 7 连杆组对
|
||||
sel = torch.isin(cg, torch.tensor([min(a, b) * 7 + max(a, b) for a, b in gp], device=q.device))
|
||||
self._gp = (m, sel, sel.nonzero(as_tuple=True)[0]); self._gp_key = key
|
||||
self._gp49, sel, idx = self._gp
|
||||
xf = self._checked_xf(q, xf); Rs, Ts = xf; B = q.shape[0]
|
||||
with torch.no_grad():
|
||||
cc = torch.einsum('bkij,kj->bki', Rs[:, self.cl_g], self.cl_c.to(q.dtype)) + Ts[:, self.cl_g]
|
||||
d2 = (cc[:, self.cp_a[idx]] - cc[:, self.cp_b[idx]]).pow(2).sum(-1)
|
||||
keep = d2 <= (self.cp_lim[idx] + cap).pow(2)
|
||||
bb, mm = keep.nonzero(as_tuple=True)
|
||||
del keep, d2, cc
|
||||
cnt = self.cp_cnt[idx][mm]
|
||||
rep_b = torch.repeat_interleave(bb, cnt)
|
||||
off = torch.repeat_interleave(self.cp_start[idx][mm] - torch.cumsum(cnt, 0) + cnt, cnt) + torch.arange(int(cnt.sum()), device=q.device)
|
||||
pidx = off
|
||||
a, b = self.caps_world(q, xf)
|
||||
with torch.no_grad():
|
||||
pi, pj = self.pi[pidx], self.pj[pidx]
|
||||
mid = 0.5 * (a[rep_b, pi] + b[rep_b, pi] - a[rep_b, pj] - b[rep_b, pj])
|
||||
k2 = mid.pow(2).sum(-1) <= (self.pair_bl[pidx] + cap).pow(2)
|
||||
rep_b, pidx, pi, pj = rep_b[k2], pidx[k2], pi[k2], pj[k2]
|
||||
d = seg_seg_dist(a[rep_b, pi], b[rep_b, pi], a[rep_b, pj], b[rep_b, pj])
|
||||
gap = d - self.pair_need[pidx].to(q.dtype)
|
||||
gm = torch.full((B * len(gp),), float(cap), dtype=q.dtype, device=q.device)
|
||||
gm = gm.scatter_reduce(0, rep_b * len(gp) + self._gp49[self.pair_gp[pidx]], gap.clamp(max=cap), reduce='amin', include_self=True)
|
||||
return gm.view(B, len(gp))
|
||||
|
||||
@torch.no_grad()
|
||||
def certify_segments(self, a, b, max_queries=32, max_depth=12, eps=1e-4, cap=20.0):
|
||||
"""逐层二分认证：每根段独立查询预算，未知（超预算/超深度/非有限/越界）一律拒绝。
|
||||
|
||||
判据对每条组对：子段两端间隙之和 > 该子段位移界 + 2eps（间隙取 min(gap,cap)，钳制只会更保守）。
|
||||
cap 必须 ≥ 该层子段界 + 2eps 才不会挡住本可通过的判定，故不做自适应收紧——同批其他段会改变
|
||||
钳制程度、让结果随批次而变（实测也无耗时收益，见探索日志轮次 31）。
|
||||
"""
|
||||
if self.dtype != torch.float64 or a.dtype != torch.float64 or b.dtype != torch.float64:
|
||||
raise ValueError('segment certification requires float64')
|
||||
if a.shape != b.shape or a.ndim != 2 or a.shape[1] != 6:
|
||||
raise ValueError('a and b must be [count,6] with identical shape')
|
||||
# 预算给成张量时 max_depth + 1 会在 uint8 这类窄类型上回绕成 0，循环整个跳过，把碰撞段当安全返回。
|
||||
if type(max_queries) is not int or type(max_depth) is not int:
|
||||
raise ValueError('certification budgets must be plain ints')
|
||||
if max_queries < 2 or max_depth < 0 or eps <= 0 or cap <= eps:
|
||||
raise ValueError('invalid certification budget or tolerance')
|
||||
count = a.shape[0]
|
||||
status = torch.zeros(count, dtype=torch.long, device=a.device)
|
||||
inside = (((a >= self.lo) & (a <= self.hi) & (b >= self.lo) & (b <= self.hi)) | self.cont).all(-1)
|
||||
status[~inside] = 3
|
||||
root = torch.arange(count, device=a.device)
|
||||
gaps = self.gaps(torch.cat([a, b]), cap)['gm'][:, self.cert_groups]
|
||||
ga, gb = gaps[:count], gaps[count:]
|
||||
queries = torch.full((count,), 2, dtype=torch.long, device=a.device)
|
||||
bound = (b - a).abs() @ self.cert_lever.T
|
||||
for depth in range(max_depth + 1):
|
||||
finite = torch.isfinite(ga).all(-1) & torch.isfinite(gb).all(-1)
|
||||
collision = (ga <= 0).any(-1) | (gb <= 0).any(-1)
|
||||
status[root[~finite]] = 2
|
||||
status[root[collision]] = 1
|
||||
positive = (ga > eps).all(-1) & (gb > eps).all(-1)
|
||||
proven = positive & (ga + gb > bound + 2 * eps).all(-1)
|
||||
pending = (status[root] == 0) & ~proven
|
||||
if not bool(pending.any()):
|
||||
break
|
||||
a, b, ga, gb, root, bound = a[pending], b[pending], ga[pending], gb[pending], root[pending], bound[pending]
|
||||
if depth == max_depth:
|
||||
status[root] = 2
|
||||
break
|
||||
requested = torch.bincount(root, minlength=count)
|
||||
status[(status == 0) & (queries + requested > max_queries)] = 2
|
||||
keep = status[root] == 0
|
||||
a, b, ga, gb, root, bound = (t[keep] for t in (a, b, ga, gb, root, bound))
|
||||
if not root.numel():
|
||||
break
|
||||
mid = a + 0.5 * (b - a)
|
||||
gm = self.gaps(mid, cap)['gm'][:, self.cert_groups]
|
||||
queries += torch.bincount(root, minlength=count)
|
||||
a, b = torch.cat([a, mid]), torch.cat([mid, b])
|
||||
ga, gb = torch.cat([ga, gm]), torch.cat([gm, gb])
|
||||
root = torch.cat([root, root])
|
||||
bound = torch.cat([bound, bound]) / 2
|
||||
return {'safe': status == 0, 'collision': status == 1, 'unresolved': status == 2,
|
||||
'outside_box': status == 3, 'queries': queries}
|
||||
|
||||
@torch.no_grad()
|
||||
def certify_uniform(self, a, b, n=64, eps=1e-4, cap=None):
|
||||
"""固定层数的均匀细分认证：n 个子段只需一次 gaps 调用（n+1 个点），没有逐层依赖的往返。
|
||||
|
||||
判据与 certify_segments 完全相同；界对 Δq 线性，所以子段界 = 整段界 / n。未通过即 unproven
|
||||
（拒绝），不存在“未知当安全”。cap=None 时取“最小不挡住可通过判定的宽相位”= 最大子段界 + 2eps：
|
||||
小于它钳制会把本可证明的子段压成不可证（偏保守），大于它只是多算对（偏慢），不改正确性。
|
||||
"""
|
||||
if self.dtype != torch.float64 or a.dtype != torch.float64 or b.dtype != torch.float64:
|
||||
raise ValueError('segment certification requires float64')
|
||||
if type(n) is not int or n < 1 or eps <= 0 or (cap is not None and cap <= eps):
|
||||
raise ValueError('invalid subdivision count or tolerance')
|
||||
if a.shape != b.shape or a.ndim != 2 or a.shape[1] != 6:
|
||||
raise ValueError('a and b must be [count,6] with identical shape')
|
||||
count = a.shape[0]
|
||||
inside = (((a >= self.lo) & (a <= self.hi) & (b >= self.lo) & (b <= self.hi)) | self.cont).all(-1)
|
||||
bound = (b - a).abs() @ self.cert_lever.T / n # [count,15] 子段界
|
||||
required = float(bound.max()) + 2 * eps if count else 2 * eps
|
||||
qc = required if cap is None else cap
|
||||
alpha = torch.arange(n + 1, dtype=self.dtype, device=a.device) / n
|
||||
points = (a[:, None] + (b - a)[:, None] * alpha[None, :, None]).reshape(-1, 6)
|
||||
gm = self.gaps(points, qc)['gm'][:, self.cert_groups].view(count, n + 1, -1)
|
||||
adjacent = gm[:, :-1] + gm[:, 1:] > bound[:, None, :] + 2 * eps
|
||||
collision = (gm <= 0).any((1, 2))
|
||||
safe = inside & (gm > eps).all((1, 2)) & adjacent.all((1, 2)) & ~collision
|
||||
return {'safe': safe, 'collision': collision, 'unproven': (~safe & ~collision & inside),
|
||||
'outside_box': ~inside, 'points': n + 1, 'queries': n + 1,
|
||||
'cap_required_mm': required, 'cap_used_mm': qc}
|
||||
|
||||
def min_gap(self, q, cap=40.0):
|
||||
return self.gaps(q, cap)['gm'].min(-1).values
|
||||
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
"""显存预算（2026-10-18 本机实测后加；同日两次修正）。
|
||||
口径：整卡留 1 GB 给系统（VRAM_RESERVE_GIB），本进程上限 = CUDA 可用 − 余量。
|
||||
为什么不能按"整卡 − nvidia-smi 已用"扣：WDDM 下 nvidia-smi 的"已用"含**驱动可以换出**的量，
|
||||
把它当硬占用会把自己卡死——实测预训练第一次 evaluate 峰值要 ~15 GiB，那样算出的上限只有
|
||||
14.45 GiB，torch 就报 OOM，而同一时刻卡上其实还有 9.98 GiB 空闲（train.py step 2000 的评估里挂掉）。
|
||||
CUDA 的 mem_get_info 才是"我实际能拿到多少"：它已经把可换出的部分算进去了。
|
||||
limit() 上限 = min(CUDA 可用, 整卡) − 余量；显式给 VRAM_FRAC 时再取较小值（跟旧口径 0.78 对拍用）。
|
||||
别的应用占多少照样打印，但只作参考、不从上限里扣。
|
||||
peak_gib() 记进日志的 vram 字段：网页指标卡与 ml/selftest.sh 都按它核对，不用再去猜。
|
||||
实测参考（d=192 同结构）：预训练 batch=1024 的训练步峰值只有 2.76 GiB，但第一次 evaluate 就要 ~15 GiB
|
||||
（kin.gaps 一次开 2.35 GiB）；APG batch=48/plans=150 → 13.08 GiB。"""
|
||||
import os
|
||||
import subprocess
|
||||
import torch
|
||||
|
||||
|
||||
def _smi():
|
||||
"""(整卡已用 GiB, 整卡总量 GiB)；拿不到就 (None, None)。"""
|
||||
try:
|
||||
q = 'memory.used,memory.total'
|
||||
r = subprocess.run(['nvidia-smi', f'--query-gpu={q}', '--format=csv,noheader,nounits'],
|
||||
capture_output=True, text=True, timeout=3)
|
||||
u, t = [float(x) for x in r.stdout.strip().splitlines()[0].split(',')[:2]]
|
||||
return u / 1024, t / 1024
|
||||
except Exception:
|
||||
return None, None
|
||||
|
||||
|
||||
def limit():
|
||||
if not torch.cuda.is_available(): return False
|
||||
tot = torch.cuda.get_device_properties(0).total_memory / 2**30
|
||||
torch.empty(1, device='cuda') # 让本进程的 CUDA 上下文先落地，再读可用（否则会把自家上下文算进可用）
|
||||
free = torch.cuda.mem_get_info()[0] / 2**30
|
||||
res = float(os.environ.get('VRAM_RESERVE_GIB', '1'))
|
||||
cap = min(free, tot) - res
|
||||
if os.environ.get('VRAM_FRAC'): # 显式硬帽（对拍旧口径用）
|
||||
cap = min(cap, tot * float(os.environ['VRAM_FRAC']))
|
||||
cap = max(1.0, min(cap, tot - res))
|
||||
torch.cuda.set_per_process_memory_fraction(cap / tot)
|
||||
used, _ = _smi()
|
||||
note = '' if used is None else '；别的应用当前占 %.1f GiB，需要时驱动自行换出，不从上限里扣' % used
|
||||
print('显存上限：本进程 %.1f GiB（CUDA 可用 %.1f − 系统余量 %.1f，= 整卡 %.1f 的 %.0f%%）%s'
|
||||
% (cap, free, res, tot, 100 * cap / tot, note), flush=True)
|
||||
return True
|
||||
|
||||
|
||||
def peak_gib():
|
||||
return round(torch.cuda.max_memory_allocated() / 2**30, 2) if torch.cuda.is_available() else None
|
||||
+1
-3
@@ -1,9 +1,7 @@
|
||||
{
|
||||
"apg": {
|
||||
"batch": 48, "lr": 1e-4, "steps": 8000, "grad_clip": 1.0, "amp": "bf16",
|
||||
"batch": 128, "lr": 1e-4, "steps": 3000, "grad_clip": 1.0, "amp": "bf16",
|
||||
"plans": 60, "plans_max": 150, "seg_plans": 10,
|
||||
"_vram": "batch 由 128 降到 48：2026-10-18 本机实测（d=192 同结构、每进程 19.2 GB 硬帽）batch=128 无论 plans 取 60 还是 150 都 OOM（崩前已分配 17.85 GiB）；batch=48/plans=150 峰值 13.08 GiB、plans=60 为 7.67 GiB，加 Windows+4K 桌面常驻 4.4 GB 仍在 19–20 GB 预算内。batch=64/plans=150 要 17.66 GiB（不取）。展开长度 plans 没动——降 plans 会把长任务的绕出段截掉。",
|
||||
"_steps": "2026-10-18 第八轮：batch 128→48 后 steps 3000→8000，保持总任务量（3000×128 ≈ 8000×48）不变；每步展开代价按 batch 线性降，总机时与原估同量级（约 4 h），学习率不动。",
|
||||
"margin_train_mm": 3.0,
|
||||
"w_tgt": 1.0, "w_rot": 0.3, "w_col": 2.0, "w_smooth": 0.02, "w_len": 0.001, "w_bc": 0.5,
|
||||
"cat_weights": {"A": 0.4, "B": 1.0, "C": 1.6, "D": 0.6, "Dr": 0.6},
|
||||
|
||||
@@ -1,11 +1,10 @@
|
||||
{
|
||||
"model": {"d": 192, "heads": 8, "ff": 768, "enc": 4, "dec": 4},
|
||||
"train": {
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 60000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"amp": "bf16", "grad_clip": 1.0,
|
||||
"w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
|
||||
"p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
|
||||
"ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
|
||||
"p_neq": 0.25, "fk_caps": 96,
|
||||
"ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"model": {"d": 192, "heads": 8, "ff": 768, "enc": 4, "dec": 4},
|
||||
"train": {
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"amp": "bf16", "grad_clip": 1.0,
|
||||
"w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
|
||||
"p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
|
||||
"ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
|
||||
"ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
|
||||
}
|
||||
}
|
||||
@@ -1,11 +0,0 @@
|
||||
{
|
||||
"model": {"d": 192, "heads": 8, "ff": 768, "enc": 4, "dec": 4},
|
||||
"train": {
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"amp": "bf16", "grad_clip": 1.0,
|
||||
"w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
|
||||
"p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
|
||||
"ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
|
||||
"ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
|
||||
}
|
||||
}
|
||||
@@ -1,41 +0,0 @@
|
||||
{
|
||||
"model": {
|
||||
"d": 192,
|
||||
"heads": 8,
|
||||
"ff": 768,
|
||||
"enc": 4,
|
||||
"dec": 4
|
||||
},
|
||||
"train": {
|
||||
"batch": 1024,
|
||||
"lr": 0.0001,
|
||||
"warmup": 1000,
|
||||
"steps": 3000,
|
||||
"wd": 0.01,
|
||||
"betas": [
|
||||
0.9,
|
||||
0.95
|
||||
],
|
||||
"amp": "bf16",
|
||||
"grad_clip": 1.0,
|
||||
"w_mf": 1.0,
|
||||
"w_fk": 0.1,
|
||||
"w_smooth": 0.01,
|
||||
"w_value": 0.0,
|
||||
"w_sub": 0.0,
|
||||
"p_neq": 0.5,
|
||||
"p_edge": 0.1,
|
||||
"p_neq_warm": 0,
|
||||
"fk_tmax": 0.3,
|
||||
"fk_caps": 96,
|
||||
"ema": 0.999,
|
||||
"aug_pre_drop": 0.15,
|
||||
"aug_pre_sigma": 0.1,
|
||||
"aug_dq_sigma": 0.3,
|
||||
"ckpt_min": 10,
|
||||
"eval_every": 1000,
|
||||
"eval_tasks": 256,
|
||||
"log_every": 50,
|
||||
"patience_evals": 3
|
||||
}
|
||||
}
|
||||
@@ -1,11 +1,10 @@
|
||||
{
|
||||
"model": {"d": 128, "heads": 8, "ff": 512, "enc": 4, "dec": 4},
|
||||
"train": {
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 24000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"batch": 1024, "lr": 1e-3, "warmup": 1000, "steps": 60000, "wd": 0.01, "betas": [0.9, 0.95],
|
||||
"amp": "bf16", "grad_clip": 1.0,
|
||||
"w_mf": 1.0, "w_fk": 0.5, "w_smooth": 0.05, "w_value": 0.1, "w_sub": 0.1,
|
||||
"p_neq": 0.5, "p_edge": 0.1, "p_neq_warm": 5000, "fk_tmax": 0.3, "fk_caps": 96,
|
||||
"ema": 0.999, "aug_pre_drop": 0.15, "aug_pre_sigma": 0.1, "aug_dq_sigma": 0.3,
|
||||
"p_neq": 0.25, "fk_caps": 96,
|
||||
"ckpt_min": 10, "eval_every": 2000, "eval_tasks": 512, "log_every": 50, "patience_evals": 5
|
||||
}
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user