ml: 显存预算按 19–20 GB 落地 + ort 本地副本入库 + R4 复跑（feat_dyn）通过

- 显存：APG batch 128→48（实测 d=192 下 128 无论 plans 60/150 都 OOM，崩前已分配 17.85 GiB；48/plans150=13.08、48/60=7.67）；新增 ml/common/vram.py 每进程硬帽 VRAM_FRAC=0.78 并把 vram 峰值写进 train/APG 日志、监控页加「显存峰值」卡与按 20 GB 预算的显存条；smoke.json 补 apg 段并让自检带 --config。
- 自检修复：find_bash 兼容便携版 git 的 mingw64 布局（本机原返回 None → 网页「开始训练」起不动）；selftest_proc.alive 读中文 tasklist 的 GBK 崩溃；rec 值非法 JSON 时静默丢项；gen_resume 判据剔除 plan_ms/cert.ms 两个墙钟字段（旧判据恒 false）。整轮重跑 7/8 通过、3.5 分钟，仅 train_term 为 Windows 真实限制（TERM 送不到原生 python）。
- vendor: onnxruntime-web 1.20.1 本地副本入库（约 33 MB），查看器与 R4/R9 探针不再依赖 npm/CDN；.gitignore 同步移除该目录。
- docs: 第八节 R4 复跑（feat_dyn 新模型）：部署口径 P99 12.67 通过；WebGPU 首次实测 27.09（有界面窗口，adapter nvidia ampere）；viewer_ref 随机模型 planP99 131 / starve 32.7% / lookaheadP99 170 / certFailWhy time 948 / tickGapMax 736，三条判读留给实现方。

.gitignore

@@ -47,5 +47,5 @@ webapp/models/*.meta.json
 # 从头重跑时旧产物挪到这里（不入库）
 ml/archive/
 
-# onnxruntime-web 本地副本（bash ml/prep/vendor_ort.sh 生成，约 50 MB，不入库）
-webapp/vendor/ort/
+# 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）
+# 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

docs/ML运行手册审查.md

@@ -3,7 +3,7 @@
 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。
 
 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）。
-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。
+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，A-3 由实现方第七轮改掉、待本机复跑验证。**第七轮本机复核与显存预算（19–20 GB）见第七节。**
 
 ---
 
@@ -43,8 +43,8 @@ tickGapMax 全部 ≈21.5 ms（20 ms 节拍没被卡住）。背景：第五轮
 - `pipeline.sh` 7 处 `json.load(open(...))` 全部补 `encoding='utf-8'`（`:90 :110 :118 :127 :136 :138 :144`）。
 - 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`。
 
-**A-3 仍未修：`&ortLocal=1` + 默认 proxy 必然失败。**
-`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。）
+**A-3 离线 ort 加载：实现方第七轮已改（绝对 URL + `vendor_ort.sh`），本机待复跑验证。**
+`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。）**复跑判据**：断网/CDN 不可达时 `bash ml/pipeline.sh r4` 仍能出数（探针默认走本地 `webapp/vendor/ort/`），且 `&ortLocal=1` 的查看器徽标显示「策略模式就绪」。
 
 ### 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）
 
@@ -60,12 +60,13 @@ tickGapMax 全部 ≈21.5 ms（20 ms 节拍没被卡住）。背景：第五轮
 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`。
 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。
 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）。
-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。
-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」。
+5. ~~**Windows 侧生命周期未实测**~~ → **已实测**（见 7.2）：整树杀净 ✅、关黑窗口训练继续 ✅、`timeout` 的 TERM 送不到 python ❌（超时只能硬杀，靠 STOP 文件与定时存档）。
+6. ~~**断点续跑未在本机复核**~~ → **已实测**（见 7.2）：gen 按条续写逐条一致、train STOP 2 s 存档续训、apg `--resume`、prep 二次运行「待打包 0 个」全部通过。
+7. **APG 的机时仍然零实测**（显存已量：`batch=48/plans=150` → 13.08 GiB）。`batch` 从 128 降到 48 后每步代价与收敛都要重算一遍才知道。
 
 ### D. 小遗留
 
-`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）。
+~~`webapp/models/README.md` 过时~~ → 实现方第七轮已改，本机复核过：现在写的是 `bash ml/pipeline.sh export`（默认 fp32）、第 10 步、新输入签名 `feat[1 或 K,83]` 与 `feat_dyn`，并说明离线要先跑 `ml/prep/vendor_ort.sh`。
 
 ---
 
@@ -81,7 +82,7 @@ tickGapMax 全部 ≈21.5 ms（20 ms 节拍没被卡住）。背景：第五轮
 
 1. ~~A 组修复并重跑 `r4`~~：已完成（本机 2026-10-18，rc=0、P99 11.25）。
 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。
-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。
+3. B 组等上面的数字齐了再定；C-1/C-7 随第一次真跑逐项核对；A-3 由实现方已改，本机复跑 `r4` 时顺带验（会弹有界面窗口）。
 
 ---
 
@@ -179,3 +180,106 @@ V1 通过后再跑，分三段，每段跑完把回传文件发我：
 | 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` |
 
 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。
+
+---
+
+## 七、本机复核第六/七轮处置（2026-10-18，含显存预算）
+
+**先说约束（用户定的，之前所有估算都按整卡 24 GB 写了）**：本机 Windows + 4K 桌面**空闲就占 4.4 GB**（`nvidia-smi` 空载 23604→4394 MiB 实测），所以 **ML 只能用 19–20 GB**。
+
+### 7.1 APG 会直接把第 8 步打崩（实测，非推算）
+
+用 d=192（与正式训练同结构）、`--steps 1`（峰值在第一次反向就到位）、每进程硬帽 19.2 GB，逐档实测 `max_memory_allocated`（GiB）：
+
+| batch | plans=60 | 90 | 120 | 150 |
+|---|---|---|---|---|
+| 32 | 6.14 | | | 9.53 |
+| **48（已定为新值）** | 7.67 | | | **13.08** |
+| 56 | | | 13.61 | |
+| 64 | 10.81 | 15.41 | | 17.66 |
+| **128（原值）** | **OOM** | | | **OOM**（崩前已分配 17.85 GiB） |
+
+- **原 `batch=128` 无论 `plans` 取 60 还是 150 都 OOM**（d=32 的小模型也一样，崩前 17.54 GiB）→ 显存是被展开图吃掉的，与模型大小、数据量无关。
+- **已把 `apg.json` 的 batch 定为 48，`plans 60→150` 不动**：最坏 13.08 GiB，加桌面 4.4 GB ≈ 17.5 GB，在 19–20 GB 预算内。没选"降 plans"是因为降展开长度会把长任务的绕出段截掉（就是口径-3 那个问题）。`batch=64 + plans=150` 要 17.66 GiB，加桌面就到 22 GB 了，不取。
+- **加了每进程显存硬帽**：`ml/common/vram.py`，`VRAM_FRAC` 默认 0.78 ≈ 18.7 GiB，训练与 APG 启动时打印上限。没帽时它会把整卡顶到 23.6 GB、连桌面一起挤掉（就是这次报显存的原因）。
+- **显存用量现在看得见**：`train.py` / `apg_finetune.py` 每条日志带 `vram`（峰值 GiB）；监控页指标卡加「显存峰值」（>14 黄、>17 红），GPU 卡的显存条改成按 **20 GB 预算**算而不是整卡 24。实测：预训练 `batch=1024` 记到 `vram: 2.71`，APG 自检档 `vram: 0.33`。
+- 对照：预训练 `batch=1024` 峰值 **2.76 GiB**（够）；`check_r2` 每样本常驻 **776 B** → 1.7 M 样本 **1.32 GB** + 分块 0.05（4× 池自检 0.2）≈ **1.6 GB**，脚本里"GPU 峰值 <0.5 GB"错了约 3 倍，但不会崩。
+- 顺带：N-6 的"按空闲显存 1/4 自动定块"、出题分块等估算都要按 19 GB 重算。
+- **`ml/configs/smoke.json` 原来没有 `apg` 段**，而 `selftest.sh` 调 APG 时不带 `--config` → 自检第 6 项实际按正式 `batch=128` 跑，在本机必然 OOM、自检永远跑不完。已补 `apg` 段（`batch=8`、实测 `vram` 0.33 GiB）并让自检带 `--config`。
+
+### 7.2 生命周期与断点续跑：本机整轮自检 **7/8 通过**，用时 3.5 分钟
+
+| 项 | 结果 |
+|---|---|
+| `gen_resume` | ✅ true（判据修正后）——「续写：已有 2/6 条，从第 3 条继续」，与一口气生成**逐条一致** |
+| `prep_rerun` | ✅ true——二次运行「待打包 0 个」 |
+| `train_stop` | ✅ true——`--stop-at 20` → rc 143、`last.pt` 第 20 步、续训到 40 步 rc 0 |
+| `train_stopfile` | ✅ true——写 `ml/runs/STOP` 后 **2 s** 退出并存档（第 351 步）。控制台「停止」走的就是这条路 |
+| `train_term` | ❌ **false（Windows 的真实限制）**——`timeout` 的 TERM 送不到原生 python，rc=124 是硬杀。结论：阶段超时不能指望体面存档，靠 STOP 文件 + 每 10 分钟定时存档 |
+| `apg_resume` | ✅ true（自检档 `batch=8`，`vram` 0.33 GiB） |
+| `tree_killed` | ✅ true（实测）——`taskkill /T /F` 把 bash→python+node 整树杀净，没留孤儿 |
+| `survive_parent_exit` | ✅ true（实测）——启动器退出后子树仍活着 = **关掉控制台黑窗口不影响训练** |
+
+用时 3.5 分钟（APG 用自检档；上一轮 APG 走正式 `batch=128` 时是 13.2 分钟，而且 OOM）。
+之前测不到的原因：① `monitor.find_bash()` 在本机返回 `None`（Qoder 自带 git 在 `mingw64\bin\git.exe`，"上上级 + bin/bash.exe"推出一个不存在的路径）；② `selftest_proc.alive()` 用 `text=True` 读 `tasklist`，中文 Windows 的表头是 GBK → 读线程 `UnicodeDecodeError`、`stdout` 变 `None` → `TypeError`，异常又被 `wait_pids` 的 `except Exception` 吞掉。
+
+### 7.3 两条判据自身的问题（会让"通过/失败"失真）
+
+1. **`selftest.sh:27` 用 `cmp -s` 逐字节比生成结果**，但记录里含 `plan_ms` 与 `cert.ms` 两个**墙钟耗时字段** → 两次运行不可能相同，**这项永远是 false**。我在副本里重跑基线、只把这两个字段剔掉后逐行比：**6 条完全一致** → 续写逻辑正确，是判据错。
+2. **`rec()` 用 `json.loads` 解析传入值**，而 `str(None).lower()`=`none` 不是合法 JSON → 抛异常、整项**静默不进结果文件**，`all_pass` 照样算出来。所以"第七轮说自检已修完"的两项进程测试，实际上是没跑过。
+3. **自检副本只复制 `git ls-files` 里的文件**（`selftest.sh:16`）→ 任何**未跟踪的新文件**在副本里不存在，调用它的项会以 `ModuleNotFoundError` 变成 rc=1，看起来像功能坏了。本轮新加 `ml/common/vram.py` 就踩了：`train_stop`/`train_stopfile`/`apg_resume` 三项同时 rc=1，`git add` 之后才恢复。判据建议：自检开头先 `git ls-files --others --exclude-standard` 非空就报出来（或改用 `git ls-files -o --exclude-standard -t` 一起复制）。
+
+### 7.4 本机已改的（都按实测落地，不改判据含义）
+
+| 位置 | 改动 | 验证 |
+|---|---|---|
+| `ml/monitor.py` `find_bash()` | 便携版 git 的 `mingw64` 布局要再上一层；再兜 `which('bash')`（排除 System32 的 WSL bash） | 返回 `...\.qoder\bin\git\bin\bash.exe`（bash 5.2.37）；网页「开始训练」在本机的拦路石解除 |
+| `ml/selftest_proc.py` `alive()` / `wait_pids()` | `tasklist` 用 `text=True, errors='replace'` 并防 `None`；起不来时把最后一次异常带进结果 | `tree_killed`、`survive_parent_exit` 两项实测 true |
+| `ml/selftest.sh` | `rec` 解析失败记 false 不再丢项；`gen_resume` 判据剔除 `plan_ms`/`cert.ms`；APG 两项带 `--config` | 重跑第 1 项已变 **true**（「续写：已有 2/6 条 → 逐条一致」） |
+| `ml/configs/apg.json` | `batch 128 → 48`，加 `_vram` 记录实测表 | 最坏 13.08 GiB（实测），在 19–20 GB 预算内 |
+| `ml/configs/smoke.json` | 补 `apg` 段（`batch=8`） | 自检 APG 档 `vram: 0.33` |
+| `ml/common/vram.py`（新）+ `train.py` + `apg/apg_finetune.py` | 每进程硬帽 `VRAM_FRAC=0.78`（启动打印上限）；日志每条记录加 `vram` 峰值 | `显存上限：本进程 18.7 GiB（整卡 24.0 × 0.78）`；train `vram: 2.71` |
+| `ml/monitor.py` + `ml/monitor.html` | 状态带 `vram_budget_gb`（默认 20，`VRAM_BUDGET_GB` 可改）；GPU 显存条按预算算并超线变色；指标卡加「显存峰值」 | headless 加载渲染出的 DOM 里有「显存峰值 / 预算 19–20 GB（整卡 24，桌面占 4–5）」 |
+
+### 7.5 仍需实现方处理的（我没动）
+
+- **APG 的 `steps` 与机时**：`batch` 从 128 降到 48，每步看到的任务少 2.7 倍。`steps=3000` 要不要加、加多少是收敛判断，我只有显存实测、没有这个依据。`R2/R4/N-6` 各处按整卡写的显存估算文案也要按 19–20 GB 重写。
+- `R4 复跑`：`feat_dyn` 要重导模型，探针现在会**弹有界面窗口**跑 `viewer_ref`（30 题×≤10 s）与 WebGPU——本机跑之前请知悉会有窗口，别动它。
+- B-2 的几何查询搬 Worker（若本机 R9 仍不过）；R9 用真模型才有意义。
+
+---
+
+## 八、R4 复跑（2026-10-18，`feat_dyn` 新模型 + 本地 ort + WebGPU）
+
+`bash ml/pipeline.sh r4` 端到端 **rc=0**。这次先把 `webapp/vendor/ort/`（onnxruntime-web 1.20.1，33 MB）**入库**（`.gitignore` 同步移除），不再依赖 npm/CDN——上一轮 `vendor_ort.sh` 的 `npm pack` 失败后探针静默退回 CDN，而 jsDelivr 本机完全不通，四种配置全变成「加载 ort 失败」，一条数字都没有。**这是个真问题**：下载失败时流程不该"看起来跑完了"。
+
+### 8.1 R4 判定口径：通过
+
+| 配置 | P50 | P95 | P99 | 备注 |
+|---|---|---|---|---|
+| **wasm·16 线程·proxy（部署口径）** | 7.06 | 8.88 | **12.67** | 本地 ort，`enc_once` + `feat_dyn` 新模型 |
+| wasm·4 线程·proxy | 10.25 | 12.48 | 13.22 | |
+| wasm·16 线程·无 proxy | 6.83 | 8.28 | 9.53 | |
+| **WebGPU** | 18.26 | 22.76 | **27.09** | 首次拿到真数：`hasGpu:true`、adapter `nvidia ampere`、有界面窗口；比 wasm 慢，不用 |
+
+结论：纯推理 P99 12.67 < 15，**R4 通过**；与修复前（10.22–11.25）同量级，`feat_dyn` 没让主推理变慢（主推理仍只送 1 行）。
+
+### 8.2 `viewer_ref`：随机模型的整周期数字（只作参考，但暴露了三件事）
+
+30 道贴柱题、随机权重、真查看器（有界面窗口）、16 线程 + proxy：
+
+| 指标 | 值 | 说明 |
+|---|---|---|
+| `planP50` / `planP99` | 68.5 / **131.2 ms** | R9 线是 45；随机策略几乎每次都认证多条候选 |
+| `starve_rate` | **32.7%** | 执行时钟到点队列为空 = 实际丢步 |
+| `inferP99` | 58.3 ms | 纯推理探针只有 12.7 → 差额是主线程排队 |
+| `lookaheadP99` | **170 ms** | 前瞻（8 行特征逐行编码 + 8 次 gapMatrix） |
+| `certFailWhy` | **time 948** / box 346 / cert 19 | 3902 次规划里 70% 的失败是"墙钟到点判不过" |
+| `tickGapMax` | **736 ms** | 主线程仍会被一次性占住（安全层在主线程） |
+
+**三条判读（给实现方，不是结论）**：
+1. 前瞻是现在最贵的一环（170 ms P99）。虽然已改成只在冷启动做，但 30 题就是 30 次冷启动，每次都落在 P99 上——R9 的 `planP99` 会被冷启动那几次主导。建议 `r4_probe --viewer` 同时报 `planP99`（含冷启动）与 `planP99Warm`（剔除每题第一次规划），否则这条线测的是"题数"而不是稳态性能。
+2. **`time` 截止占失败原因 70%**：45 ms 预算在随机模型上基本等于"到点判不过 → 刹停"（1019 次刹停 / 3902 次规划）。R9 若"通过"，必须同时报 `certFailWhy.time` 占比与 `brakeRate`，否则可能是靠拒绝候选换来的。
+3. `tickGapMax 736 ms` 说明安全层（几何查询）仍在主线程；B-2 的"搬进 Worker"没有做，这条是真模型跑之前要盯的。
+
+这些全是随机权重的数，R9 判据本来就规定用训练好的模型测，所以**不据此判 R9 过不过**；`r4.json` 里 `viewer_ref` 已标注"仅参考"。

ml/apg/apg_finetune.py

@@ -17,6 +17,7 @@ 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
 from ml.common.ckpt import Saver, atomic_save, rng_state, set_rng_state
+from ml.common import vram
 
 
 def load_pool(paths):
@@ -38,6 +39,7 @@ 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)
     ck = torch.load(a.init, map_location=dev)
     mcfg = ck['cfg']
@@ -147,7 +149,7 @@ 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}
+               'gnorm': gn.item(), **stats, 'min': (time.time() - t0) / 60, 'vram': vram.peak_gib()}
         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):

ml/common/vram.py

@@ -0,0 +1,21 @@
+"""显存预算（2026-10-18 本机实测后加）。
+用户定的预算：这台 3090 只有 19–20 GB 能给训练用——Windows + 4K 桌面常驻就占掉 4–5 GB（空载 nvidia-smi 4.4 GB）。
+  limit()     给本进程设硬帽（VRAM_FRAC，默认 0.78 ≈ 18.7 GiB）。超限时在自己的反向里早失败，
+              而不是把桌面挤到没有显存（整机卡顿、别的应用崩），这也是 APG batch=128 原先 OOM 时把显存顶到 23.6 GB 的原因。
+  peak_gib()  记进日志的 vram 字段：网页指标卡与 ml/selftest.sh 都按它核对预算，不用再去猜。
+实测参考（d=192 同结构、1 步即到位）：预训练 batch=1024 → 2.76；APG batch=48/plans=150 → 13.08、batch=128 → OOM。"""
+import os
+import torch
+
+
+def limit():
+    if not torch.cuda.is_available(): return False
+    f = float(os.environ.get('VRAM_FRAC', '0.78'))
+    tot = torch.cuda.get_device_properties(0).total_memory / 2**30
+    torch.cuda.set_per_process_memory_fraction(f)
+    print('显存上限：本进程 %.1f GiB（整卡 %.1f GiB × %.2f，其余留给桌面）' % (f * tot, tot, f), flush=True)
+    return True
+
+
+def peak_gib():
+    return round(torch.cuda.max_memory_allocated() / 2**30, 2) if torch.cuda.is_available() else None

ml/configs/apg.json

@@ -1,7 +1,8 @@
 {
   "apg": {
-    "batch": 128, "lr": 1e-4, "steps": 3000, "grad_clip": 1.0, "amp": "bf16",
+    "batch": 48, "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。",
     "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},

ml/configs/smoke.json

@@ -31,5 +31,13 @@
   "log_every": 10,
   "patience_evals": 5
  },
- "_note": "冒烟专用（bash ml/smoke.sh）：只验证流程能跑通，数值无意义"
+ "apg": {
+  "batch": 8, "lr": 0.0001, "steps": 10, "grad_clip": 1.0, "amp": "bf16",
+  "plans": 20, "plans_max": 30, "seg_plans": 5,
+  "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},
+  "ckpt_min": 10, "eval_every": 1000, "eval_tasks": 4
+ },
+ "_note": "冒烟与自检专用（bash ml/pipeline.sh selftest / smoke）：只验证流程能跑通，数值无意义。apg 段必须有：selftest.sh 第 6 项原先不带 --config，走的是正式 apg.json 的 batch=128，本机实测第一次反向就 OOM；改成正式值 48 也要约 13 GiB，自检不必吃这么多。"
 }

ml/monitor.html

@@ -436,6 +436,7 @@ function renderKpis() {
     tile('展开内到达比例', pct(r.reach_frac), '误差 < 1 mm 且姿态 < 0.5°', '', last('reach_frac'), COLORS[2]);
     tile('闭环到达率', pct(e.success) + dlt('success'), `最好 ${pct(best)}`, '', evs('success'), COLORS[2]);
     tile('碰撞率', pct(e.collision_rate, 2) + dlt('collision_rate', -1), '关安全层', e.collision_rate > 0.03 ? 'warn' : '', evs('collision_rate'), COLORS[3]);
+    tile('显存峰值', isNum(r.vram) ? `${r.vram.toFixed(2)}<small>GiB</small>` : '—', '预算 19–20 GB（整卡 24，桌面占 4–5）', r.vram > 17 ? 'bad' : r.vram > 14 ? 'warn' : '', last('vram'), COLORS[1]);
   } else {
     const sps = r.samples_per_s;
     tile('总损失', fmt(r.loss, 4), `MF ${fmt(r.mf)} · FK ${fmt(r.fk_cm)} cm`, '', ema(last('loss'), 0.9), COLORS[0]);
@@ -443,6 +444,7 @@ function renderKpis() {
     tile('闭环到达率', pct(e.success) + dlt('success'), evals.length ? `最好 ${pct(best)} · @${e.x.toLocaleString()} 步` : '尚未评估', '', evs('success'), COLORS[2]);
     tile('碰撞率', pct(e.collision_rate, 2) + dlt('collision_rate', -1), '关安全层，部署另有护栏', e.collision_rate > 0.03 ? 'warn' : '', evs('collision_rate'), COLORS[3]);
     tile('梯度范数', fmt(r.gnorm), '裁剪阈值 1.0', r.gnorm > 5 ? 'warn' : '', ema(last('gnorm'), 0.9), COLORS[4]);
+    tile('显存峰值', isNum(r.vram) ? `${r.vram.toFixed(2)}<small>GiB</small>` : '—', '预算 19–20 GB（整卡 24，桌面占 4–5）', r.vram > 17 ? 'bad' : r.vram > 14 ? 'warn' : '', last('vram'), COLORS[1]);
   }
   $('kpis').innerHTML = tiles.map(t => t.html).join('');
   [...$('kpis').querySelectorAll('canvas')].forEach((cv, i) => spark(cv, tiles[i].sp, tiles[i].color));
@@ -477,7 +479,7 @@ async function poll() {
       render();
     }
     const P = lastStatus.pipeline, busy = (job && job.running) || (P && P.current);
-    if (DEMO) { setPill('demo', '示例数据'); if (!$('demobar')) $('pipe').insertAdjacentHTML('beforebegin', `<div class="demobar" id="demobar">这是示例数据，不是你的训练。${location.protocol === 'file:' ? '直接双击 monitor.html 只能看示例；' : ''}看真实训练请双击 ml/console.bat（或 python -m ml.monitor --open）。</div>`); }
+    if (DEMO) { setPill('demo', '示例数据'); if (!$('demobar')) $('pipe').closest('.hero').insertAdjacentHTML('beforebegin', `<div class="demobar" id="demobar">这是示例数据，不是你的训练。${location.protocol === 'file:' ? '直接双击 monitor.html 只能看示例；' : ''}看真实训练请双击 ml/console.bat（或 python -m ml.monitor --open）。</div>`); }
     else if (busy) setPill('live', `运行中 · ${P && P.current ? SM[P.current]?.n || P.current : '启动中'}`);
     else if (cur && Date.now() / 1000 - lastMtime < 120) setPill('live', `运行中 · ${ago(lastMtime)}更新`);
     else setPill('', '空闲');
@@ -531,15 +533,16 @@ function renderStatus(s) {
 
   // GPU
   const G = s.gpu || [];
+  const BUD = (s.vram_budget_gb || 24) * 1024;      // 显存预算（MB）：桌面常驻占 4–5 GB，不能按整卡 24 GB 看
   $('gputime').textContent = G.length ? '实时' : '';
   $('gpu').innerHTML = G.length ? G.map(g => {
     const u = g.util ?? 0, C = 2 * Math.PI * 50, col = u > 85 ? css('--ok') : u > 50 ? css('--accent') : css('--warn');
-    const memP = 100 * g.mem_used / g.mem_total, tc = g.temp > 83 ? css('--bad') : g.temp > 75 ? css('--warn') : css('--c6');
+    const memP = 100 * g.mem_used / BUD, tc = g.temp > 83 ? css('--bad') : g.temp > 75 ? css('--warn') : css('--c6');
     const row = (k, v, p, c) => `<div class="gr"><div class="l"><span>${k}</span><b>${v}</b></div><div class="mini"><div style="width:${Math.min(100, p)}%;background:${c}"></div></div></div>`;
     return `<div class="gpuwrap"><div class="ring"><svg width="116" height="116"><circle cx="58" cy="58" r="50" stroke="#1b2433" stroke-width="10" fill="none"/>
       <circle cx="58" cy="58" r="50" stroke="${col}" stroke-width="10" fill="none" stroke-linecap="round" stroke-dasharray="${C}" stroke-dashoffset="${C * (1 - u / 100)}" style="transition:stroke-dashoffset .6s"/></svg>
       <div class="c"><div><b>${u}%</b><span>利用率</span></div></div></div>
-      <div class="gpurows">${row('显存', `${(g.mem_used / 1024).toFixed(1)} / ${(g.mem_total / 1024).toFixed(0)} GB`, memP, css('--accent2'))}
+      <div class="gpurows">${row('显存', `${(g.mem_used / 1024).toFixed(1)} / ${(BUD / 1024).toFixed(0)} GB 预算（整卡 ${(g.mem_total / 1024).toFixed(0)}）`, memP, memP > 100 ? css('--bad') : memP > 85 ? css('--warn') : css('--accent2'))}
       ${row('温度', `${g.temp} °C`, g.temp, tc)}${row('功耗', g.power != null ? `${Math.round(g.power)} W` : '—', g.power ? g.power / 3.5 : 0, css('--c7'))}</div></div>
       <div class="gname">${g.name}</div>`;
   }).join('<hr style="border:none;border-top:1px solid var(--line);margin:14px 0">') : empty('未检测到 nvidia-smi');

ml/monitor.py

@@ -192,7 +192,8 @@ def status():
     pb = jload(P(RUNS, 'policy_bench.json'))
     if pb: pb = {k: {kk: v.get(kk) for kk in ('n', 'ok', 'reached', 'collisionFrames', 'brakeRate', 'recoveryRate', 'livelock', 'inferMsP99', 'safetyMsP99')}
                  for k, v in pb.items() if isinstance(v, dict) and 'n' in v}
-    return {'now': time.time(), 'gpu': gpu(), 'pipeline': pl, 'check': CHECK.get(), 'lock': lock_info(),
+    return {'now': time.time(), 'gpu': gpu(),
+            'vram_budget_gb': float(os.environ.get('VRAM_BUDGET_GB', 20)),      # 本机桌面常驻占 4–5 GB，给训练的预算 19–20 GB 'pipeline': pl, 'check': CHECK.get(), 'lock': lock_info(),
             'stage_meta': [{'id': k, 'name': n, 'est_h': h, 'timeout_h': pstate.TIMEOUT_H[k]} for k, n, h in pstate.STAGES],
             'r4': jload(P(RUNS, 'r4.json')),
             'data': {'consistency': (jload(P(DATA, 'consistency.json')) or {}).get('consistentByGateRule'),
@@ -218,8 +219,13 @@ def find_bash():
             if p and os.path.exists(p): return p
         g = shutil.which('git')
         if g:
-            p = os.path.join(os.path.dirname(os.path.dirname(g)), 'bin', 'bash.exe')
-            if os.path.exists(p): return p
+            root = os.path.dirname(os.path.dirname(g))       # 便携版 git 常放在 <根>\mingw64\bin\git.exe，要再上一层才找到 bash
+            for base in dict.fromkeys([root, os.path.dirname(root)]):
+                for sub in ('bin', os.path.join('usr', 'bin')):
+                    p = os.path.join(base, sub, 'bash.exe')
+                    if os.path.exists(p): return p
+        b = shutil.which('bash')
+        if b and 'windows\\system32' not in b.lower(): return b
         return None
     return shutil.which('bash')
 

ml/selftest.sh

@@ -17,14 +17,21 @@ git ls-files -z | tar --null -T - -cf - | (cd "$D" && tar xf -)
 [ -d ml/node_modules ] && cp -r ml/node_modules "$D/ml/node_modules"
 cd "$D"; mkdir -p ml/runs ml/data
 R=ml/runs/selftest_items.jsonl; : > $R
-rec() { $PY -c "import json,sys;print(json.dumps({'item':sys.argv[1],'pass':json.loads(sys.argv[2]),'detail':sys.argv[3]},ensure_ascii=False))" "$1" "$2" "$3" >> $R; echo "[selftest] $1 → $2  $3"; }
+rec() { $PY -c "import json,sys
+try: v=json.loads(sys.argv[2])
+except Exception: v=False      # 值不是合法 JSON 时记 false，不能让整项静默消失（第七轮实测：'none' 抛异常 → 两项没进结果）
+print(json.dumps({'item':sys.argv[1],'pass':v,'detail':sys.argv[3]},ensure_ascii=False))" "$1" "$2" "$3" >> $R; echo "[selftest] $1 → $2  $3"; }
 CFGS=ml/configs/smoke.json
 # 1 生成续写
 node ml/gen/gen_dataset.cjs --n 6 --seed 11 --out ml/data/a.jsonl 2>/dev/null
 node ml/gen/gen_dataset.cjs --n 3 --seed 11 --out ml/data/shard_0011.jsonl.part 2>/dev/null
 sz=$(wc -c < ml/data/shard_0011.jsonl.part); head -c $((sz - 40)) ml/data/shard_0011.jsonl.part > t && mv t ml/data/shard_0011.jsonl.part
 node ml/gen/gen_dataset.cjs --n 6 --seed 11 --out ml/data/shard_0011.jsonl.part 2>ml/runs/st_gen.log; mv ml/data/shard_0011.jsonl.part ml/data/shard_0011.jsonl
-cmp -s ml/data/a.jsonl ml/data/shard_0011.jsonl && rec gen_resume true "$(grep -o '续写[^,]*' ml/runs/st_gen.log | head -1)" || rec gen_resume false "续写结果与一口气生成不同"
+gd=$($PY -c "import json,sys
+f=lambda p:[json.dumps({**r,'plan_ms':0,'cert':{**r.get('cert',{}),'ms':0}},sort_keys=True) for r in (json.loads(l) for l in open(p,encoding='utf-8') if l.strip())]
+a,b=f(sys.argv[1]),f(sys.argv[2]);print('OK' if a==b else '不同 %d/%d 条'%(sum(x!=y for x,y in zip(a,b))+abs(len(a)-len(b)),max(len(a),len(b))))" ml/data/a.jsonl ml/data/shard_0011.jsonl)
+if [ "$gd" = OK ]; then rec gen_resume true "$(grep -o '续写[^,]*' ml/runs/st_gen.log | head -1)（逐条一致；不含 plan_ms/cert.ms 两个墙钟字段，第七轮实测 cmp -s 恒不等）"
+else rec gen_resume false "续写与一口气生成不同：$gd"; fi
 rm -f ml/data/a.jsonl
 node ml/gen/gen_dataset.cjs --n 40 --seed 12 --out ml/data/shard_0012.jsonl 2>/dev/null   # 训练自检用的小数据
 # 2 打包二次运行
@@ -49,13 +56,16 @@ if command -v timeout >/dev/null; then
   grep -q "收到停止请求" ml/runs/st_c.log && rec train_term true "rc=$rc，TERM 送达并存档" || rec train_term false "rc=$rc，TERM 没送到 python（Windows 原生进程常见）→ 阶段超时只能硬杀，进度靠每 10 分钟存档"
 else rec train_term false "没有 timeout 命令"; fi
 # 6 APG 续跑
-$PY -m ml.apg.apg_finetune --init ml/runs/st_a/last.pt --samples ml/data/samples --out ml/runs/st_apg --steps 10 --stop-at 5 > ml/runs/st_apg.log 2>&1; rc=$?
-$PY -m ml.apg.apg_finetune --init ml/runs/st_a/last.pt --samples ml/data/samples --out ml/runs/st_apg --steps 10 --resume ml/runs/st_apg/last.pt >> ml/runs/st_apg.log 2>&1; rc2=$?
+$PY -m ml.apg.apg_finetune --config $CFGS --init ml/runs/st_a/last.pt --samples ml/data/samples --out ml/runs/st_apg --steps 10 --stop-at 5 > ml/runs/st_apg.log 2>&1; rc=$?
+$PY -m ml.apg.apg_finetune --config $CFGS --init ml/runs/st_a/last.pt --samples ml/data/samples --out ml/runs/st_apg --steps 10 --resume ml/runs/st_apg/last.pt >> ml/runs/st_apg.log 2>&1; rc2=$?
 [ $rc = 143 ] && [ $rc2 = 0 ] && rec apg_resume true "" || rec apg_resume false "rc=$rc 续跑rc=$rc2（日志 $D/ml/runs/st_apg.log）"
 # 7 进程树 / 关窗口
 pj=$($PY ml/selftest_proc.py 2>ml/runs/st_proc.err | tail -1)
 echo "$pj" > ml/runs/st_proc.json
-k=$($PY -c "import json,sys;d=json.loads(sys.argv[1]);print(str(d.get('tree_killed')).lower(), str(d.get('survive_parent_exit')).lower())" "$pj" 2>/dev/null || echo "null null")
+k=$($PY -c "import json,sys
+try: d=json.loads(sys.argv[1])
+except Exception: d={}
+print(json.dumps(d.get('tree_killed')), json.dumps(d.get('survive_parent_exit')))" "$pj" 2>/dev/null || echo "null null")
 rec tree_killed ${k% *} "$pj" ; rec survive_parent_exit ${k#* } ""
 mkdir -p "$SRC/ml/runs"
 $PY - "$R" "$SRC/ml/runs/selftest.json" "$D" <<'PY'

ml/selftest_proc.py

@@ -14,8 +14,9 @@ FLAGS = {'creationflags': subprocess.CREATE_NEW_PROCESS_GROUP | 0x08000000} if o
 
 def alive(pid):
     if os.name == 'nt':
-        r = subprocess.run(['tasklist', '/FI', f'PID eq {pid}', '/NH'], capture_output=True, text=True)
-        return str(pid) in r.stdout
+        # 中文 Windows 的 tasklist 表头是 GBK，text=True 会让读线程解码崩掉（stdout 变 None）→ 按字节读再容错解码
+        r = subprocess.run(['tasklist', '/FI', f'PID eq {pid}', '/NH'], capture_output=True, text=True, errors='replace')
+        return str(pid) in (r.stdout or '')
     try: os.kill(pid, 0); return True
     except OSError: return False
 
@@ -26,11 +27,14 @@ def tree_cmd(d):
             f'node -e "require(\'fs\').writeFileSync(\'{dd}/n.pid\',String(process.pid));setInterval(()=>{{}},1000)" & wait')
 
 
+WAITERR = ['']
+
+
 def wait_pids(d, t=20):
     for _ in range(t * 4):
         try: return int(open(os.path.join(d, 'p.pid')).read()), int(open(os.path.join(d, 'n.pid')).read())
-        except Exception: time.sleep(0.25)
-    return None, None
+        except Exception as e: WAITERR[0] = type(e).__name__ + ': ' + str(e)[:90]; time.sleep(0.25)
+    return None, None      # 起不来时把最后一次异常带进结果，别让 tree_started=false 变成没有原因的失败
 
 
 def main():

ml/train.py

@@ -9,6 +9,7 @@ import torch
 from ml.common.kin_torch import Kin
 from ml.common.features import STEP, D2R
 from ml.common.ckpt import Saver, atomic_save, rng_state, set_rng_state
+from ml.common import vram
 from ml.data import Samples, load_tasks
 from ml.model.policy import build, wta_loss
 from ml.model.meanflow import meanflow_loss, one_step
@@ -40,6 +41,7 @@ def main():
     os.makedirs(a.out, exist_ok=True)
     torch.manual_seed(a.seed)
     dev = a.device
+    vram.limit()
     kin = Kin(device=dev)
     data = Samples(a.samples, device=dev, split='train')
     val_tasks = [t for t in load_tasks(a.samples) if t['split'] == 'val'][:tc['eval_tasks']]
@@ -111,7 +113,7 @@ def main():
             el = time.time() - t0
             rec = {'step': step, 'total': total, 't': time.time(), 'loss': loss.item(), 'mf': mf_raw.item(), 'fk_cm': l_fk.item(), 'smooth': l_sm.item(),
                    'value': l_val.item(), 'sub': l_sub.item(), 'gnorm': gn.item(), 'lr': lr_at(step), 'samples_per_s': seen / max(el, 1e-6),
-                   'eta_h': (total - step - 1) * tc['batch'] / max(seen / max(el, 1e-6), 1e-6) / 3600}
+                   'eta_h': (total - step - 1) * tc['batch'] / max(seen / max(el, 1e-6), 1e-6) / 3600, 'vram': vram.peak_gib()}
             print(json.dumps(rec), flush=True); log.write(json.dumps(rec) + '\n'); log.flush()
         if not a.no_eval and (step + 1) % tc['eval_every'] == 0:
             model.eval()

webapp/vendor/ort/VERSION

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+B._OrtCreateBinding=a=>(B._OrtCreateBinding=Y.Xa)(a);B._OrtBindInput=(a,b,c)=>(B._OrtBindInput=Y.Ya)(a,b,c);B._OrtBindOutput=(a,b,c,d)=>(B._OrtBindOutput=Y.Za)(a,b,c,d);B._OrtClearBoundOutputs=a=>(B._OrtClearBoundOutputs=Y._a)(a);B._OrtReleaseBinding=a=>(B._OrtReleaseBinding=Y.$a)(a);B._OrtRunWithBinding=(a,b,c,d,f)=>(B._OrtRunWithBinding=Y.ab)(a,b,c,d,f);B._OrtRun=(a,b,c,d,f,g,k,m)=>(B._OrtRun=Y.bb)(a,b,c,d,f,g,k,m);B._OrtEndProfiling=a=>(B._OrtEndProfiling=Y.cb)(a);
+B._JsepOutput=(a,b,c)=>(B._JsepOutput=Y.db)(a,b,c);B._JsepGetNodeName=a=>(B._JsepGetNodeName=Y.eb)(a);
+var Ia=()=>(Ia=Y.fb)(),zd=B._malloc=a=>(zd=B._malloc=Y.gb)(a),X=B._free=a=>(X=B._free=Y.hb)(a),Ka=(a,b,c,d,f,g)=>(Ka=Y.kb)(a,b,c,d,f,g),Sa=()=>(Sa=Y.lb)(),Vc=(a,b,c,d,f)=>(Vc=Y.mb)(a,b,c,d,f),ad=a=>(ad=Y.nb)(a),Qa=a=>(Qa=Y.ob)(a),Id=()=>(Id=Y.pb)(),dd=(a,b)=>(dd=Y.qb)(a,b),Wc=a=>(Wc=Y.rb)(a),Tc=a=>(Tc=Y.sb)(a),Sc=()=>(Sc=Y.tb)(),ed=B.dynCall_ii=(a,b)=>(ed=B.dynCall_ii=Y.vb)(a,b),be=a=>(be=Y.wb)(a),Rd=()=>(Rd=Y.xb)(),ae=a=>(ae=Y.yb)(a),ce=()=>(ce=Y.zb)();B.___start_em_js=882450;B.___stop_em_js=882672;
+function De(){var a=Y;a=Object.assign({},a);var b=d=>f=>d(f)>>>0,c=d=>()=>d()>>>0;a.Ca=b(a.Ca);a.fb=c(a.fb);a.gb=b(a.gb);a.emscripten_main_runtime_thread_id=c(a.emscripten_main_runtime_thread_id);a.sb=b(a.sb);a.tb=c(a.tb);return a}B.stackSave=()=>Sc();B.stackRestore=a=>Wc(a);B.stackAlloc=a=>Tc(a);B.UTF8ToString=K;B.stringToUTF8=od;B.lengthBytesUTF8=md;var Ee;bb=function Fe(){Ee||Ge();Ee||(bb=Fe)};
+function Ge(){0<$a||(E?(na(B),E||cd(Ya),startWorker(B)):(cd(Xa),0<$a||Ee||(Ee=!0,B.calledRun=!0,I||(E||cd(Ya),na(B),E||cd(Za)))))}Ge();moduleRtn=pa;
+
+
+  return moduleRtn;
+}
+);
+})();
+export default ortWasmThreaded;
+var isPthread = globalThis.self?.name === 'em-pthread';
+var isNode = typeof globalThis.process?.versions?.node == 'string';
+if (isNode) isPthread = (await import('worker_threads')).workerData === 'em-pthread';
+
+// When running as a pthread, construct a new instance on startup
+isPthread && ortWasmThreaded();

webapp/vendor/ort/ort-wasm-simd-threaded.mjs

@@ -0,0 +1,81 @@
+
+var ortWasmThreaded = (() => {
+  var _scriptName = import.meta.url;
+  
+  return (
+async function(moduleArg = {}) {
+  var moduleRtn;
+
+function aa(){g.buffer!=l.buffer&&m();return l}function n(){g.buffer!=l.buffer&&m();return ba}function r(){g.buffer!=l.buffer&&m();return ca}function u(){g.buffer!=l.buffer&&m();return da}function ea(){g.buffer!=l.buffer&&m();return fa}var w=Object.assign({},moduleArg),ha,x,ia=new Promise((a,b)=>{ha=a;x=b}),ja="object"==typeof window,A="function"==typeof importScripts,B="object"==typeof process&&"object"==typeof process.versions&&"string"==typeof process.versions.node,C=A&&"em-pthread"==self.name;
+if(B){const {createRequire:a}=await import("module");var require=a(import.meta.url),D=require("worker_threads");global.Worker=D.Worker;C=(A=!D.lb)&&"em-pthread"==D.workerData}"use strict";w.mountExternalData=(a,b)=>{a.startsWith("./")&&(a=a.substring(2));(w.Ua||(w.Ua=new Map)).set(a,b)};w.unmountExternalData=()=>{delete w.Ua};
+var SharedArrayBuffer=globalThis.SharedArrayBuffer??(new WebAssembly.Memory({initial:0,maximum:0,shared:!0})).buffer.constructor,ka=Object.assign({},w),la="./this.program",E=(a,b)=>{throw b;},F="",ma,G,H;
+if(B){var fs=require("fs"),na=require("path");F=require("url").fileURLToPath(new URL("./",import.meta.url));ma=(a,b)=>{a=oa(a)?new URL(a):na.normalize(a);return fs.readFileSync(a,b?void 0:"utf8")};H=a=>{a=ma(a,!0);a.buffer||(a=new Uint8Array(a));return a};G=(a,b,c,d=!0)=>{a=oa(a)?new URL(a):na.normalize(a);fs.readFile(a,d?void 0:"utf8",(f,h)=>{f?c(f):b(d?h.buffer:h)})};!w.thisProgram&&1<process.argv.length&&(la=process.argv[1].replace(/\\/g,"/"));process.argv.slice(2);E=(a,b)=>{process.exitCode=
+a;throw b;}}else if(ja||A)A?F=self.location.href:"undefined"!=typeof document&&document.currentScript&&(F=document.currentScript.src),_scriptName&&(F=_scriptName),F.startsWith("blob:")?F="":F=F.substr(0,F.replace(/[?#].*/,"").lastIndexOf("/")+1),B||(ma=a=>{var b=new XMLHttpRequest;b.open("GET",a,!1);b.send(null);return b.responseText},A&&(H=a=>{var b=new XMLHttpRequest;b.open("GET",a,!1);b.responseType="arraybuffer";b.send(null);return new Uint8Array(b.response)}),G=(a,b,c)=>{var d=new XMLHttpRequest;
+d.open("GET",a,!0);d.responseType="arraybuffer";d.onload=()=>{200==d.status||0==d.status&&d.response?b(d.response):c()};d.onerror=c;d.send(null)});B&&"undefined"==typeof performance&&(global.performance=require("perf_hooks").performance);var pa=console.log.bind(console),qa=console.error.bind(console);B&&(pa=(...a)=>fs.writeSync(1,a.join(" ")+"\n"),qa=(...a)=>fs.writeSync(2,a.join(" ")+"\n"));var ra=pa,I=qa;Object.assign(w,ka);ka=null;
+if(C){var sa;if(B){var ta=D.parentPort;ta.on("message",b=>onmessage({data:b}));Object.assign(globalThis,{self:global,importScripts:()=>{},postMessage:b=>ta.postMessage(b),performance:global.performance||{now:Date.now}})}var ua=!1;I=function(...b){b=b.join(" ");B?fs.writeSync(2,b+"\n"):console.error(b)};self.alert=function(...b){postMessage({Za:"alert",text:b.join(" "),nb:K()})};w.instantiateWasm=(b,c)=>new Promise(d=>{sa=f=>{f=new WebAssembly.Instance(f,va());c(f);d()}});self.onunhandledrejection=
+b=>{throw b.reason||b;};function a(b){try{var c=b.data,d=c.cmd;if("load"===d){let f=[];self.onmessage=h=>f.push(h);self.startWorker=()=>{postMessage({cmd:"loaded"});for(let h of f)a(h);self.onmessage=a};for(const h of c.handlers)if(!w[h]||w[h].proxy)w[h]=(...k)=>{postMessage({Za:"callHandler",kb:h,args:k})},"print"==h&&(ra=w[h]),"printErr"==h&&(I=w[h]);g=c.wasmMemory;m();sa(c.wasmModule)}else if("run"===d){wa(c.pthread_ptr,0,0,1,0,0);xa(c.pthread_ptr);ya();za();ua||=!0;try{Aa(c.start_routine,c.arg)}catch(f){if("unwind"!=
+f)throw f;}}else"cancel"===d?K()&&Ba(-1):"setimmediate"!==c.target&&("checkMailbox"===d?ua&&Ca():d&&(I(`worker: received unknown command ${d}`),I(c)))}catch(f){throw Da(),f;}}self.onmessage=a}var L;w.wasmBinary&&(L=w.wasmBinary);var g,Ea,Fa=!1,M,l,ba,ca,da,N,fa;
+function m(){var a=g.buffer;w.HEAP8=l=new Int8Array(a);w.HEAP16=new Int16Array(a);w.HEAPU8=ba=new Uint8Array(a);w.HEAPU16=new Uint16Array(a);w.HEAP32=ca=new Int32Array(a);w.HEAPU32=da=new Uint32Array(a);w.HEAPF32=new Float32Array(a);w.HEAPF64=fa=new Float64Array(a);w.HEAP64=N=new BigInt64Array(a);w.HEAPU64=new BigUint64Array(a)}
+if(!C){g=new WebAssembly.Memory({initial:256,maximum:65536,shared:!0});if(!(g.buffer instanceof SharedArrayBuffer))throw I("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),B&&I("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"),Error("bad memory");m()}
+var Ga=[],Ha=[],Ia=[],O=0,Ja=null,P=null;function Ka(){O--;if(0==O&&(null!==Ja&&(clearInterval(Ja),Ja=null),P)){var a=P;P=null;a()}}function La(a){a="Aborted("+a+")";I(a);Fa=!0;M=1;a=new WebAssembly.RuntimeError(a+". Build with -sASSERTIONS for more info.");x(a);throw a;}var Ma=a=>a.startsWith("data:application/octet-stream;base64,"),oa=a=>a.startsWith("file://"),Na;function Oa(a){if(a==Na&&L)return new Uint8Array(L);if(H)return H(a);throw"both async and sync fetching of the wasm failed";}
+function Pa(a){if(!L&&(ja||A)){if("function"==typeof fetch&&!oa(a))return fetch(a,{credentials:"same-origin"}).then(b=>{if(!b.ok)throw`failed to load wasm binary file at '${a}'`;return b.arrayBuffer()}).catch(()=>Oa(a));if(G)return new Promise((b,c)=>{G(a,d=>b(new Uint8Array(d)),c)})}return Promise.resolve().then(()=>Oa(a))}function Qa(a,b,c){return Pa(a).then(d=>WebAssembly.instantiate(d,b)).then(c,d=>{I(`failed to asynchronously prepare wasm: ${d}`);La(d)})}
+function Ra(a,b){var c=Na;return L||"function"!=typeof WebAssembly.instantiateStreaming||Ma(c)||oa(c)||B||"function"!=typeof fetch?Qa(c,a,b):fetch(c,{credentials:"same-origin"}).then(d=>WebAssembly.instantiateStreaming(d,a).then(b,function(f){I(`wasm streaming compile failed: ${f}`);I("falling back to ArrayBuffer instantiation");return Qa(c,a,b)}))}
+function va(){Sa={j:Ta,b:Ua,E:Va,g:Wa,V:Xa,A:Ya,C:Za,W:$a,T:ab,L:bb,S:cb,o:db,B:eb,y:fb,U:gb,z:hb,_:ib,Z:jb,P:kb,w:lb,F:mb,k:nb,O:xa,Y:ob,I:pb,J:qb,K:rb,G:sb,H:tb,v:ub,q:vb,l:wb,p:xb,e:yb,X:zb,x:Ab,d:Bb,f:Cb,i:Db,u:Eb,t:Fb,s:Gb,Q:Hb,R:Ib,D:Jb,h:Kb,n:Lb,M:Mb,m:Nb,a:g,r:Ob,N:Pb,c:Qb};return{a:Sa}}
+var Rb={837620:(a,b,c,d,f)=>{if("undefined"==typeof w||!w.Ua)return 1;a=Q(a>>>0);a.startsWith("./")&&(a=a.substring(2));a=w.Ua.get(a);if(!a)return 2;b>>>=0;c>>>=0;d>>>=0;if(b+c>a.byteLength)return 3;try{const h=a.subarray(b,b+c);switch(f){case 0:n().set(h,d>>>0);break;case 1:w.mb(d,h);break;default:return 4}return 0}catch{return 4}},838303:()=>"undefined"!==typeof wasmOffsetConverter};function Ta(){return"undefined"!==typeof wasmOffsetConverter}
+function Sb(a){this.name="ExitStatus";this.message=`Program terminated with exit(${a})`;this.status=a}
+var Tb=a=>{a.terminate();a.onmessage=()=>{}},Wb=a=>{0==R.length&&(Ub(),Vb(R[0]));var b=R.pop();if(!b)return 6;S.push(b);T[a.Ra]=b;b.Ra=a.Ra;var c={cmd:"run",start_routine:a.cb,arg:a.ab,pthread_ptr:a.Ra};B&&b.unref();b.postMessage(c,a.ib);return 0},U=0,V=(a,b,...c)=>{for(var d=2*c.length,f=$b(),h=ac(8*d),k=h>>>3,q=0;q<c.length;q++){var y=c[q];"bigint"==typeof y?(N[k+2*q]=1n,N[k+2*q+1]=y):(N[k+2*q]=0n,ea()[k+2*q+1>>>0]=y)}a=bc(a,0,d,h,b);cc(f);return a};
+function Ob(a){if(C)return V(0,1,a);M=a;if(!(0<U)){for(var b of S)Tb(b);for(b of R)Tb(b);R=[];S=[];T=[];Fa=!0}E(a,new Sb(a))}function dc(a){if(C)return V(1,0,a);Jb(a)}var Jb=a=>{M=a;if(C)throw dc(a),"unwind";Ob(a)},R=[],S=[],ec=[],T={};function fc(){for(var a=w.numThreads-1;a--;)Ub();Ga.unshift(()=>{O++;gc(()=>Ka())})}var ic=a=>{var b=a.Ra;delete T[b];R.push(a);S.splice(S.indexOf(a),1);a.Ra=0;hc(b)};function za(){ec.forEach(a=>a())}
+var Vb=a=>new Promise(b=>{a.onmessage=h=>{h=h.data;var k=h.cmd;if(h.targetThread&&h.targetThread!=K()){var q=T[h.targetThread];q?q.postMessage(h,h.transferList):I(`Internal error! Worker sent a message "${k}" to target pthread ${h.targetThread}, but that thread no longer exists!`)}else if("checkMailbox"===k)Ca();else if("spawnThread"===k)Wb(h);else if("cleanupThread"===k)ic(T[h.thread]);else if("killThread"===k)h=h.thread,k=T[h],delete T[h],Tb(k),hc(h),S.splice(S.indexOf(k),1),k.Ra=0;else if("cancelThread"===
+k)T[h.thread].postMessage({cmd:"cancel"});else if("loaded"===k)a.loaded=!0,B&&!a.Ra&&a.unref(),b(a);else if("alert"===k)alert(`Thread ${h.threadId}: ${h.text}`);else if("setimmediate"===h.target)a.postMessage(h);else if("callHandler"===k)w[h.handler](...h.args);else k&&I(`worker sent an unknown command ${k}`)};a.onerror=h=>{I(`${"worker sent an error!"} ${h.filename}:${h.lineno}: ${h.message}`);throw h;};B&&(a.on("message",h=>a.onmessage({data:h})),a.on("error",h=>a.onerror(h)));var c=[],d=[],f;for(f of d)w.hasOwnProperty(f)&&
+c.push(f);a.postMessage({cmd:"load",handlers:c,wasmMemory:g,wasmModule:Ea})});function gc(a){C?a():Promise.all(R.map(Vb)).then(a)}function Ub(){var a=new Worker(new URL(import.meta.url),{type:"module",workerData:"em-pthread",name:"em-pthread"});R.push(a)}var jc=a=>{for(;0<a.length;)a.shift()(w)},ya=()=>{var a=K(),b=u()[a+52>>>2>>>0];a=u()[a+56>>>2>>>0];kc(b,b-a);cc(b)},lc=[],mc,Aa=(a,b)=>{U=0;var c=lc[a];c||(a>=lc.length&&(lc.length=a+1),lc[a]=c=mc.get(a));a=c(b);0<U?M=a:Ba(a)};
+class nc{constructor(a){this.Xa=a-24}}var oc=0,pc=0;function Ua(a,b,c){a>>>=0;var d=new nc(a);b>>>=0;c>>>=0;u()[d.Xa+16>>>2>>>0]=0;u()[d.Xa+4>>>2>>>0]=b;u()[d.Xa+8>>>2>>>0]=c;oc=a;pc++;throw oc;}function qc(a,b,c,d){return C?V(2,1,a,b,c,d):Va(a,b,c,d)}
+function Va(a,b,c,d){a>>>=0;b>>>=0;c>>>=0;d>>>=0;if("undefined"==typeof SharedArrayBuffer)return I("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;var f=[];if(C&&0===f.length)return qc(a,b,c,d);a={cb:c,Ra:a,ab:d,ib:f};return C?(a.Za="spawnThread",postMessage(a,f),0):Wb(a)}
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+b.getDate()-1|0;r()[a+28>>>2>>>0]=c;r()[a>>>2>>>0]=b.getSeconds();r()[a+4>>>2>>>0]=b.getMinutes();r()[a+8>>>2>>>0]=b.getHours();r()[a+12>>>2>>>0]=b.getDate();r()[a+16>>>2>>>0]=b.getMonth();r()[a+20>>>2>>>0]=b.getYear();a=b.getTime();return BigInt(isNaN(a)?-1:a/1E3)}function sb(a,b,c,d,f,h,k){return C?V(16,1,a,b,c,d,f,h,k):-52}function tb(a,b,c,d,f,h){if(C)return V(17,1,a,b,c,d,f,h)}
+function ub(a,b,c,d){a>>>=0;b>>>=0;c>>>=0;d>>>=0;var f=(new Date).getFullYear(),h=new Date(f,0,1),k=new Date(f,6,1);f=h.getTimezoneOffset();var q=k.getTimezoneOffset(),y=Math.max(f,q);u()[a>>>2>>>0]=60*y;r()[b>>>2>>>0]=Number(f!=q);a=v=>v.toLocaleTimeString(void 0,{hour12:!1,timeZoneName:"short"}).split(" ")[1];h=a(h);k=a(k);q<f?(W(h,c,17),W(k,d,17)):(W(h,d,17),W(k,c,17))}var Ac=[];
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+function Db(){La("Cannot use emscripten_pc_get_function without -sUSE_OFFSET_CONVERTER");return 0}function Eb(a){a>>>=0;var b=n().length;if(a<=b||4294901760<a)return!1;for(var c=1;4>=c;c*=2){var d=b*(1+.2/c);d=Math.min(d,a+100663296);var f=Math;d=Math.max(a,d);a:{f=(f.min.call(f,4294901760,d+(65536-d%65536)%65536)-g.buffer.byteLength+65535)/65536;try{g.grow(f);m();var h=1;break a}catch(k){}h=void 0}if(h)return!0}return!1}
+var Bc=()=>{La("Cannot use convertFrameToPC (needed by __builtin_return_address) without -sUSE_OFFSET_CONVERTER");return 0},Y={},Cc=a=>{a.forEach(b=>{var c=Bc();c&&(Y[c]=b)})};function Fb(){var a=Error().stack.toString().split("\n");"Error"==a[0]&&a.shift();Cc(a);Y.$a=Bc();Y.bb=a;return Y.$a}
+function Gb(a,b,c){a>>>=0;b>>>=0;if(Y.$a==a)var d=Y.bb;else d=Error().stack.toString().split("\n"),"Error"==d[0]&&d.shift(),Cc(d);for(var f=3;d[f]&&Bc()!=a;)++f;for(a=0;a<c&&d[a+f];++a)r()[b+4*a>>>2>>>0]=Bc();return a}
+var Dc={},Fc=()=>{if(!Ec){var a={USER:"web_user",LOGNAME:"web_user",PATH:"/",PWD:"/",HOME:"/home/web_user",LANG:("object"==typeof navigator&&navigator.languages&&navigator.languages[0]||"C").replace("-","_")+".UTF-8",_:la||"./this.program"},b;for(b in Dc)void 0===Dc[b]?delete a[b]:a[b]=Dc[b];var c=[];for(b in a)c.push(`${b}=${a[b]}`);Ec=c}return Ec},Ec;
+function Hb(a,b){if(C)return V(18,1,a,b);a>>>=0;b>>>=0;var c=0;Fc().forEach((d,f)=>{var h=b+c;f=u()[a+4*f>>>2>>>0]=h;for(h=0;h<d.length;++h)aa()[f++>>>0]=d.charCodeAt(h);aa()[f>>>0]=0;c+=d.length+1});return 0}function Ib(a,b){if(C)return V(19,1,a,b);a>>>=0;b>>>=0;var c=Fc();u()[a>>>2>>>0]=c.length;var d=0;c.forEach(f=>d+=f.length+1);u()[b>>>2>>>0]=d;return 0}function Kb(a){return C?V(20,1,a):52}function Lb(a,b,c,d){return C?V(21,1,a,b,c,d):52}function Mb(a,b,c,d){return C?V(22,1,a,b,c,d):70}
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+var xc=[Ob,dc,qc,Wa,Xa,Ya,Za,$a,ab,bb,cb,db,eb,fb,gb,hb,sb,tb,Hb,Ib,Kb,Lb,Mb,Nb],Sa,Z=function(){function a(c,d){Z=c.exports;Z=Lc();ec.push(Z.Ea);mc=Z.Fa;Ha.unshift(Z.$);Ea=d;Ka();return Z}var b=va();O++;if(w.instantiateWasm)try{return w.instantiateWasm(b,a)}catch(c){I(`Module.instantiateWasm callback failed with error: ${c}`),x(c)}Na||=w.locateFile?Ma("ort-wasm-simd-threaded.wasm")?"ort-wasm-simd-threaded.wasm":w.locateFile?w.locateFile("ort-wasm-simd-threaded.wasm",F):F+"ort-wasm-simd-threaded.wasm":
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+w._OrtAddSessionConfigEntry=(a,b,c)=>(w._OrtAddSessionConfigEntry=Z.fa)(a,b,c);w._OrtReleaseSessionOptions=a=>(w._OrtReleaseSessionOptions=Z.ga)(a);w._OrtCreateSession=(a,b,c)=>(w._OrtCreateSession=Z.ha)(a,b,c);w._OrtReleaseSession=a=>(w._OrtReleaseSession=Z.ia)(a);w._OrtGetInputOutputCount=(a,b,c)=>(w._OrtGetInputOutputCount=Z.ja)(a,b,c);w._OrtGetInputName=(a,b)=>(w._OrtGetInputName=Z.ka)(a,b);w._OrtGetOutputName=(a,b)=>(w._OrtGetOutputName=Z.la)(a,b);w._OrtFree=a=>(w._OrtFree=Z.ma)(a);
+w._OrtCreateTensor=(a,b,c,d,f,h)=>(w._OrtCreateTensor=Z.na)(a,b,c,d,f,h);w._OrtGetTensorData=(a,b,c,d,f)=>(w._OrtGetTensorData=Z.oa)(a,b,c,d,f);w._OrtReleaseTensor=a=>(w._OrtReleaseTensor=Z.pa)(a);w._OrtCreateRunOptions=(a,b,c,d)=>(w._OrtCreateRunOptions=Z.qa)(a,b,c,d);w._OrtAddRunConfigEntry=(a,b,c)=>(w._OrtAddRunConfigEntry=Z.ra)(a,b,c);w._OrtReleaseRunOptions=a=>(w._OrtReleaseRunOptions=Z.sa)(a);w._OrtCreateBinding=a=>(w._OrtCreateBinding=Z.ta)(a);
+w._OrtBindInput=(a,b,c)=>(w._OrtBindInput=Z.ua)(a,b,c);w._OrtBindOutput=(a,b,c,d)=>(w._OrtBindOutput=Z.va)(a,b,c,d);w._OrtClearBoundOutputs=a=>(w._OrtClearBoundOutputs=Z.wa)(a);w._OrtReleaseBinding=a=>(w._OrtReleaseBinding=Z.xa)(a);w._OrtRunWithBinding=(a,b,c,d,f)=>(w._OrtRunWithBinding=Z.ya)(a,b,c,d,f);w._OrtRun=(a,b,c,d,f,h,k,q)=>(w._OrtRun=Z.za)(a,b,c,d,f,h,k,q);w._OrtEndProfiling=a=>(w._OrtEndProfiling=Z.Aa)(a);var K=()=>(K=Z.Ba)();w._malloc=a=>(w._malloc=Z.Ca)(a);w._free=a=>(w._free=Z.Da)(a);
+var wa=(a,b,c,d,f,h)=>(wa=Z.Ga)(a,b,c,d,f,h),Da=()=>(Da=Z.Ha)(),bc=(a,b,c,d,f)=>(bc=Z.Ia)(a,b,c,d,f),hc=a=>(hc=Z.Ja)(a),Ba=a=>(Ba=Z.Ka)(a),vc=()=>(vc=Z.La)(),kc=(a,b)=>(kc=Z.Ma)(a,b),cc=a=>(cc=Z.Na)(a),ac=a=>(ac=Z.Oa)(a),$b=()=>($b=Z.Pa)();w.___start_em_js=838360;w.___stop_em_js=838421;
+function Lc(){var a=Z;a=Object.assign({},a);var b=d=>()=>d()>>>0,c=d=>f=>d(f)>>>0;a.Ba=b(a.Ba);a.Ca=c(a.Ca);a.emscripten_main_runtime_thread_id=b(a.emscripten_main_runtime_thread_id);a.Oa=c(a.Oa);a.Pa=b(a.Pa);return a}w.stackSave=()=>$b();w.stackRestore=a=>cc(a);w.stackAlloc=a=>ac(a);w.UTF8ToString=Q;w.stringToUTF8=W;w.lengthBytesUTF8=tc;var Mc;P=function Nc(){Mc||Oc();Mc||(P=Nc)};
+function Oc(){0<O||(C?(ha(w),C||jc(Ha),startWorker(w)):(jc(Ga),0<O||Mc||(Mc=!0,w.calledRun=!0,Fa||(C||jc(Ha),ha(w),C||jc(Ia)))))}Oc();moduleRtn=ia;
+
+
+  return moduleRtn;
+}
+);
+})();
+export default ortWasmThreaded;
+var isPthread = globalThis.self?.name === 'em-pthread';
+var isNode = typeof globalThis.process?.versions?.node == 'string';
+if (isNode) isPthread = (await import('worker_threads')).workerData === 'em-pthread';
+
+// When running as a pthread, construct a new instance on startup
+isPthread && ortWasmThreaded();