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The slowest user-felt path is typing into the composer while the assistant is streaming. Profile (scripts/profile-under-stream.mjs): FadeText measureOverflow self time: 35.8 ms → 18.1 ms (-50%) total active CPU during 7s window: ~150 ms → ~50 ms Two changes in src/components/ui/fade-text.tsx: 1. Drop the `useEffect([children])` that re-ran `measureOverflow` (reads scrollWidth + clientWidth — forced layout) on every parent re-render. `useResizeObserver` already fires the same callback on mount and whenever the host span's box size changes; that covers the only case where overflow state can legitimately change. The previous explicit useEffect was a forced-layout flush on every parent render, which during streaming meant every token tick. 2. Wrap the component in `memo` with a custom comparator that short-circuits the entire render when scalar string `children` and the className/fadeWidth/style props are unchanged. The hot path was tool-fallback's title chips being re-rendered by parent streaming updates even though their text was stable; memo+ comparator skips that. Also adds two harness scripts under apps/desktop/scripts/: - latency-under-stream.mjs (key→paint latency while a turn streams) - profile-under-stream.mjs (CPU profile while a turn streams) Updates profile-typing-lag.md with the streaming numbers and confirms the Enter→paint submit path is already fast (≤320ms on the populated session; the 2s "stall after Enter" the user noticed once was a one-time cold-start, not reproducible at the UI layer). I'd guess the felt jank in real use is fast-burst typing during a long-form streaming reply (code blocks + markdown lists multiply the per-token render cost). The CPU savings here scale linearly with token volume.
170 lines
6 KiB
JavaScript
170 lines
6 KiB
JavaScript
#!/usr/bin/env node
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// Capture a CPU profile while the assistant is streaming AND the user is
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// typing into the composer. This is the scenario most likely to feel laggy
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// in real use: follow-up typing while a prior turn is still streaming in.
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//
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// Output: /tmp/hermes-stream-type.cpuprofile
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import { writeFileSync } from 'node:fs'
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const args = Object.fromEntries(
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process.argv.slice(2).flatMap(s => {
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const m = s.match(/^--([^=]+)(?:=(.*))?$/)
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return m ? [[m[1], m[2] ?? true]] : []
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})
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)
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const PORT = Number(args.port ?? 9222)
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const OUT = String(args.out ?? `/tmp/hermes-stream-type-${Date.now()}`)
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const CHARS = Number(args.chars ?? 100)
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const CPS = Number(args.cps ?? 20)
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async function pickRenderer() {
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const list = await (await fetch(`http://127.0.0.1:${PORT}/json/list`)).json()
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return list.find(t => t.type === 'page' && t.url.startsWith('http'))
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}
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function connect(url) {
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return new Promise((resolve, reject) => {
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const ws = new WebSocket(url)
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let id = 0
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const pending = new Map()
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ws.addEventListener('open', () =>
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resolve({
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send(method, params = {}) {
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const myId = ++id
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ws.send(JSON.stringify({ id: myId, method, params }))
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return new Promise((res, rej) => pending.set(myId, { res, rej }))
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},
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close: () => ws.close()
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})
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)
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ws.addEventListener('error', reject)
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ws.addEventListener('message', ev => {
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const m = JSON.parse(typeof ev.data === 'string' ? ev.data : ev.data.toString('utf8'))
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if (m.id != null) {
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const p = pending.get(m.id)
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if (!p) return
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pending.delete(m.id)
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m.error ? p.rej(new Error(m.error.message)) : p.res(m.result)
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}
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})
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})
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}
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async function evalP(cdp, expr) {
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const r = await cdp.send('Runtime.evaluate', { expression: expr, returnByValue: true })
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if (r.exceptionDetails) throw new Error(r.exceptionDetails.text)
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return r.result.value
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}
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async function main() {
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const tgt = await pickRenderer()
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console.log('target', tgt.url)
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const cdp = await connect(tgt.webSocketDebuggerUrl)
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await cdp.send('Runtime.enable')
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await cdp.send('Profiler.enable')
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// Submit a meaty prompt
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await evalP(
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cdp,
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`(() => {
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const el = document.querySelector('[data-slot="composer-rich-input"]')
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el.focus()
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const r = document.createRange(); r.selectNodeContents(el); r.collapse(false)
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window.getSelection().removeAllRanges(); window.getSelection().addRange(r)
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})()`
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)
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const prompt = 'explain GPU memory bandwidth and the roofline model in detail with at least 6 paragraphs, no code'
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for (const c of prompt) {
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await cdp.send('Input.dispatchKeyEvent', { type: 'char', text: c, unmodifiedText: c })
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await new Promise(r => setTimeout(r, 6))
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}
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await new Promise(r => setTimeout(r, 200))
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await cdp.send('Input.dispatchKeyEvent', {
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type: 'rawKeyDown', windowsVirtualKeyCode: 13, key: 'Enter', code: 'Enter', text: '\r', unmodifiedText: '\r'
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})
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await cdp.send('Input.dispatchKeyEvent', { type: 'keyUp', windowsVirtualKeyCode: 13, key: 'Enter', code: 'Enter' })
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// Wait for stream to begin
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console.log('waiting for assistant…')
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let streaming = false
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for (let i = 0; i < 100; i++) {
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const c = await evalP(
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cdp,
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`document.querySelectorAll('[data-slot="aui_assistant-message-root"]').length`
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)
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if (c > 0) { streaming = true; break }
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await new Promise(r => setTimeout(r, 100))
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}
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if (!streaming) {
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console.error('no assistant message appeared')
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cdp.close()
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return
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}
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// Wait for stream to produce some tokens
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await new Promise(r => setTimeout(r, 800))
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// Refocus, start profiler, type while streaming
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await evalP(
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cdp,
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`(() => {
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const el = document.querySelector('[data-slot="composer-rich-input"]')
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el.focus()
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const r = document.createRange(); r.selectNodeContents(el); r.collapse(false)
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window.getSelection().removeAllRanges(); window.getSelection().addRange(r)
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})()`
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)
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await cdp.send('Profiler.setSamplingInterval', { interval: 1000 })
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await cdp.send('Profiler.start')
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const text = 'follow-up typing during streaming feels laggy when tokens flood in '.repeat(4).slice(0, CHARS)
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const intervalMs = Math.max(1, Math.round(1000 / CPS))
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const t0 = Date.now()
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for (let i = 0; i < text.length; i++) {
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await cdp.send('Input.dispatchKeyEvent', { type: 'char', text: text[i], unmodifiedText: text[i] })
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const expected = t0 + (i + 1) * intervalMs
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const wait = expected - Date.now()
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if (wait > 0) await new Promise(r => setTimeout(r, wait))
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}
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await new Promise(r => setTimeout(r, 500))
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const { profile } = await cdp.send('Profiler.stop')
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writeFileSync(`${OUT}.cpuprofile`, JSON.stringify(profile))
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console.log(`cpuprofile → ${OUT}.cpuprofile`)
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// Quick top-self summary
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const total = (profile.endTime - profile.startTime) / 1000
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const intMs = total / Math.max(1, profile.samples?.length ?? 1)
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const counts = new Map()
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for (const s of profile.samples ?? []) counts.set(s, (counts.get(s) ?? 0) + 1)
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const rows = profile.nodes
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.map(n => ({ id: n.id, fn: n.callFrame.functionName || '(anon)', url: n.callFrame.url || '', line: n.callFrame.lineNumber, self: counts.get(n.id) ?? 0 }))
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.sort((a, b) => b.self - a.self)
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.slice(0, 25)
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console.log(`\n=== ${total.toFixed(0)}ms wall, ${profile.samples?.length ?? 0} samples (${intMs.toFixed(2)}ms each) ===`)
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for (const r of rows) {
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if (r.self === 0) break
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const url = r.url.replace(/^.*\/src\//, 'src/').replace(/\?.*$/, '').slice(0, 70)
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console.log(` ${(r.self * intMs).toFixed(1).padStart(7)}ms (${String(r.self).padStart(4)} samp) ${r.fn.padEnd(45)} ${url}:${r.line}`)
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}
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// Cancel stream
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await evalP(
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cdp,
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`(() => {
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for (const b of document.querySelectorAll('button')) {
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if ((b.getAttribute('aria-label') || '').toLowerCase().includes('stop')) { b.click(); return 'stopped' }
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}
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return 'no-stop'
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})()`
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).then(r => console.log('cancel:', r))
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cdp.close()
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}
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main().catch(e => {
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console.error('fatal:', e.stack ?? e.message)
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process.exit(1)
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})
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