43 lines
3.3 KiB
HTML
43 lines
3.3 KiB
HTML
<!doctype html>
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<meta charset="utf-8">
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<title>R4 推理延迟探针</title>
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<!-- 2026-10-18 第五轮：由 ml/eval/r4_probe.py 在无头浏览器里打开，测部署形态的单次推理（1 行特征 + K=8 条候选）。
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参数：model= 模型 URL；ep=wasm|webgpu；threads=auto|N；proxy=0|1；n= 次数；ortLocal=1 用 vendor/ort/。
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结果 POST 到 /r4/result；同时记录 20 ms 节拍的最大间隔（推理是否卡主线程）。也可手动打开看页面上的结果。 -->
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<pre id="o">运行中…</pre>
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<script>
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(async () => {
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const qs = new URLSearchParams(location.search), o = document.getElementById('o');
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const res = { ep: qs.get('ep') || 'wasm', threadsArg: qs.get('threads') || 'auto', hc: navigator.hardwareConcurrency, coi: self.crossOriginIsolated, ua: navigator.userAgent };
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const post = async () => { o.textContent = JSON.stringify(res, null, 1); try { await fetch('/r4/result', { method: 'POST', body: JSON.stringify(res) }); } catch (e) {} };
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try {
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const MODEL = qs.get('model'), N = +qs.get('n') || 150, K = 8, F = 83, H = 16;
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const BASE = qs.get('ortLocal') === '1' ? '/webapp/vendor/ort/' : 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.20.1/dist/';
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await new Promise((ok, no) => { const s = document.createElement('script'); s.src = BASE + 'ort.min.js'; s.onload = ok; s.onerror = () => no(new Error('加载 ort 失败：' + s.src)); document.head.appendChild(s); });
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const meta = await (await fetch(MODEL.replace('.onnx', '.meta.json'))).json();
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ort.env.wasm.wasmPaths = BASE;
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// 与 ik-policy-glue.js 同一套默认值
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ort.env.wasm.numThreads = !self.crossOriginIsolated ? 1 : (res.threadsArg === 'auto' ? Math.max(1, Math.min(16, Math.floor(res.hc * 0.6))) : +res.threadsArg);
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res.threads = ort.env.wasm.numThreads;
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res.proxy = res.ep === 'wasm' && qs.get('proxy') !== '0';
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const opts = { executionProviders: res.ep === 'webgpu' ? ['webgpu'] : ['wasm'], graphOptimizationLevel: 'all' };
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let sess = null;
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if (res.proxy) { ort.env.wasm.proxy = true; try { sess = await ort.InferenceSession.create(MODEL, opts); } catch (e) { res.proxyError = String(e); res.proxy = false; ort.env.wasm.proxy = false; } }
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if (!sess) sess = await ort.InferenceSession.create(MODEL, opts);
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res.encOnce = !!meta.enc_once;
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const feat = new Float32Array((res.encOnce ? 1 : K) * F).map(() => Math.random() - 0.5);
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const z = () => new Float32Array(K * H * 6).map(() => Math.random() * 2 - 1);
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const run = () => sess.run({ feat: new ort.Tensor('float32', feat, [res.encOnce ? 1 : K, F]), z: new ort.Tensor('float32', z(), [K, H, 6]) });
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for (let i = 0; i < 10; i++) await run();
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let last = performance.now(); const gaps = [];
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const tk = setInterval(() => { const t = performance.now(); gaps.push(t - last); last = t; }, 20);
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const ts = [];
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for (let i = 0; i < N; i++) { const t = performance.now(); await run(); ts.push(performance.now() - t); await new Promise(r => setTimeout(r, 40)); }
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clearInterval(tk);
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const p = (a, x) => { const s = a.slice().sort((u, v) => u - v); return +s[Math.min(s.length - 1, Math.floor(s.length * x))].toFixed(2); };
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Object.assign(res, { n: N, K, p50: p(ts, .5), p95: p(ts, .95), p99: p(ts, .99), tickGapP99: p(gaps, .99), tickGapMax: +Math.max(...gaps).toFixed(1) });
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} catch (e) { res.error = String(e && e.message || e); }
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await post();
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})();
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</script>
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