91 lines
7.0 KiB
JavaScript
91 lines
7.0 KiB
JavaScript
/* ============================================================================
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* ik-policy-glue.js —— 查看器接入 Transformer 策略（URL 加 ?policy=1 启用）
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* ?policy=1 用 models/traj_policy.onnx
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* ?policy=models/xxx.onnx 指定模型
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* &ep=webgpu 改用 WebGPU 后端（默认 wasm-SIMD 多线程）
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* &threads=N wasm 线程数（默认 min(16, ⌊0.6×逻辑核⌋)；2026-10-18 R4 实测 28 核机 16 最优、24 反而变慢、4 线程 P99 37 ms）
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* &proxy=0 关闭推理代理 Worker（默认开：wasm 推理不占主线程，见下）
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* &probe=1 允许加载 --random 导出的延迟探针模型（默认拒绝，防把随机策略当真模型跑）
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* 2026-10-18 第五轮：ort.env.wasm.proxy=true 把 wasm 推理挪到 ort 自带的 Worker。此前推理在主线程同步算，
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* R4 实测时 CDP 15 s 拿不到执行槽，50 Hz tick 也被它拖住——丢步不只来自推理时长本身。
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* 目标变化（松手求解 / 实时追踪）都交给 ArmPolicy.Runner：50 Hz 执行时钟放在 Web Worker（不受 rAF 60 Hz 与后台节流影响），
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* 每 3 步推理一次，安全层认证后才执行；ik-glue 的逐帧碰撞守卫不参与（策略执行不走 animTo），由 Runner 安全层保证。
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* onnxruntime-web 默认从 jsDelivr 加载；离线部署把 ort.min.js 与 *.wasm 放到 vendor/ort/ 并加 &ortLocal=1。
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* 多线程 wasm 需要页面跨源隔离（COOP/COEP 头，见 ml/README.md）；没有时自动退回单线程。
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* ==========================================================================*/
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(function () {
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'use strict';
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const qs = new URLSearchParams(location.search);
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if (!qs.has('policy')) return;
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const MODEL = qs.get('policy') && qs.get('policy') !== '1' ? qs.get('policy') : 'models/traj_policy.onnx';
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const ORT_BASE = qs.get('ortLocal') === '1' ? 'vendor/ort/' : 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.20.1/dist/';
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const badge = () => document.getElementById('ikBadge');
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const say = (t, cls) => { const b = badge(); if (b) { b.textContent = t; b.className = 'badge ' + (cls || 'wait'); } console.log('[policy]', t); };
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let ready = false, runner = null, running = false, worker = null;
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window.__POLICY = { active: () => ready, setGoal: () => false, stats: () => runner && runner.stats };
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function loadScript(src) { return new Promise((res, rej) => { const s = document.createElement('script'); s.src = src; s.onload = res; s.onerror = () => rej(new Error('load ' + src)); document.head.appendChild(s); }); }
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async function waitIK() { for (let i = 0; i < 200 && !(window.IK && window.IK.solver && window.ArmPolicy); i++) await new Promise(r => setTimeout(r, 50)); if (!window.IK) throw new Error('IK 未就绪'); }
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async function boot() {
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try {
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await waitIK();
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const S = window.IK.solver;
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const meta = await (await fetch(MODEL.replace('.onnx', '.meta.json'))).json();
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if (meta.fp !== S.modelFingerprint()) throw new Error('模型碰撞指纹 ' + meta.fp + ' ≠ 当前 ' + S.modelFingerprint() + '：collide.json 已变，须重训');
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if (meta.feat_ver !== ArmPolicy.FEAT_VER) throw new Error('特征版本不符：模型 ' + meta.feat_ver + ' / 运行时 ' + ArmPolicy.FEAT_VER);
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if (meta.tcp0 && meta.tcp0.some((v, i) => Math.abs(v - S.TCP0[i]) > 0.01)) throw new Error('TCP 与训练时不同：' + S.TCP0 + ' vs ' + meta.tcp0);
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const probe = !!meta.random_init;
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if (probe && qs.get('probe') !== '1') throw new Error('这是随机初始化的延迟探针模型（meta.random_init），不是训练产物；仅测延迟时加 &probe=1');
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if (!window.ort) await loadScript(ORT_BASE + 'ort.min.js');
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ort.env.wasm.wasmPaths = ORT_BASE;
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const hc = navigator.hardwareConcurrency || 1;
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ort.env.wasm.numThreads = !self.crossOriginIsolated ? 1 : (+qs.get('threads') || Math.max(1, Math.min(16, Math.floor(hc * 0.6))));
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const ep = qs.get('ep') === 'webgpu' ? ['webgpu', 'wasm'] : ['wasm'];
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let sess = null, proxy = ep[0] === 'wasm' && qs.get('proxy') !== '0'; // proxy 只支持 wasm EP
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if (proxy) {
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ort.env.wasm.proxy = true;
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try { sess = await ort.InferenceSession.create(MODEL, { executionProviders: ep, graphOptimizationLevel: 'all' }); }
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catch (e) { console.warn('[policy] 代理 Worker 不可用，退回主线程推理：', e); proxy = false; ort.env.wasm.proxy = false; sess = null; }
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}
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if (!sess) sess = await ort.InferenceSession.create(MODEL, { executionProviders: ep, graphOptimizationLevel: 'all' });
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const infer = ArmPolicy.ortInfer(ort, sess, { encOnce: !!meta.enc_once });
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runner = new ArmPolicy.Runner(S, infer, {});
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/* 预热：首次推理有编译开销 */
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await infer(new Float32Array(8 * ArmPolicy.F_DIM), new Float32Array(8 * ArmPolicy.H * 6), 8);
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const tick = URL.createObjectURL(new Blob(['let h=null;onmessage=e=>{clearInterval(h);if(e.data>0)h=setInterval(()=>postMessage(0),e.data)}'], { type: 'text/javascript' }));
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worker = new Worker(tick);
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worker.onmessage = onTick;
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window.__POLICY = {
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active: () => ready,
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setGoal: (T, R, tool) => {
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if (tool) { say('策略模式暂不支持工具偏移 ≠ 0，已交回规划器', 'badb'); return false; }
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if (!running || !runner.q) runner.reset(window.IK.curQ());
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runner.setGoal(T, R); start(); return true;
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},
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stop, stats: () => runner.stats, runner: () => runner,
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latency: () => { const p = (a, x) => { const s = a.slice().sort((u, v) => u - v); return s.length ? s[Math.min(s.length - 1, Math.floor(s.length * x))] : null; };
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return { inferP50: p(runner.stats.inferMs, .5), inferP99: p(runner.stats.inferMs, .99), safetyP50: p(runner.stats.safetyMs, .5), safetyP99: p(runner.stats.safetyMs, .99), threads: ort.env.wasm.numThreads, ep: ep[0], proxy, encOnce: !!meta.enc_once, probe }; }
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};
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ready = true;
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const how = ep[0] + '，' + ort.env.wasm.numThreads + ' 线程' + (proxy ? '，推理在 Worker' : '，推理在主线程');
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if (probe) say('随机探针模型（仅测延迟，动作无意义）：' + how, 'badb');
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else say('策略模式就绪（' + how + '）：拖动目标即由策略执行', 'ok');
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} catch (e) { say('策略模式不可用：' + e.message, 'badb'); console.error(e); }
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}
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function start() { if (running) return; running = true; worker.postMessage(ArmPolicy.DT * 1000); }
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function stop() { running = false; if (worker) worker.postMessage(0); }
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function onTick() {
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if (!running) return;
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if (runner.needPlan()) runner.plan().catch(e => { console.error(e); stop(); say('策略推理出错：' + e.message, 'badb'); });
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const q = runner.tick();
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if (q) window.IK.setJoints(q, false);
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if (runner.done && !runner.queue.length) {
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stop();
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const { e, oe } = runner.err(runner.q);
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say('策略到达 · 误差 ' + e.toFixed(2) + ' mm' + (runner.goal.R ? ' / ' + oe.toFixed(2) + '°' : '') + ' · 刹停 ' + runner.stats.brakes + ' 次', 'ok');
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} else if (runner.brakeRun >= 2) say('策略：安全层连续拦截，减速重规划中…', 'wait');
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}
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boot();
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})();
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