commit b9b33c8ba1a3957862d1a8190330c29b5e999f5d Author: wuyang6 Date: Fri Nov 6 21:36:16 2026 +0800 Build model latency reference and duplex dialogue flow estimator diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..490b1e0 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,4 @@ +node_modules +.git +test +*.log diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9451024 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +dist/ +.DS_Store +*.log diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..89e7f72 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,14 @@ +FROM node:24-alpine AS build +WORKDIR /app +COPY package*.json ./ +RUN npm ci --no-audit --no-fund +COPY index.html ./ +COPY src ./src +COPY public ./public +RUN npm run build +FROM nginx:1.28-alpine +COPY nginx.conf /etc/nginx/conf.d/default.conf +COPY --from=build --chmod=0755 /app/dist/ /usr/share/nginx/html/ +RUN find /usr/share/nginx/html -type f -exec chmod 644 {} \; +EXPOSE 8080 +HEALTHCHECK --interval=30s --timeout=3s --start-period=5s CMD wget -q -O /dev/null http://127.0.0.1:8080/healthz || exit 1 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..436a541 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 K1412 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..ebe95c4 --- /dev/null +++ b/README.md @@ -0,0 +1,42 @@ +# 模型时延实验室 / Latency Lab + +公开模型时延对照与对话系统流程估算器。资料截止 **2026-11-06**。 + +- 58条带出处的性能记录，区分公开报告、理论时延、条件化结果和未知数据。 +- 文本、原生语音、TTS组件对照；参数规模、激活参数、硬件、精度、输入长度和并发条件。 +- 可编辑的有向流程：串行、多层级联、并行分支、全部/任一汇总、首包/文本块/完整结果触发。 +- 模型节点loop最大步数（1–100）、预计步数、每步思考与输出、上下文增长、步骤间工具等待。 +- 首结果关键路径、全部目标完成、自然语音播放、持续负载RTF、共享资源调度与敏感性区间。 +- 本地自动保存、JSON导入导出、CSV数据导出、专注画布和可缩放时间线。 + +## 本地运行 + +需要Node.js 24或满足Vite 8要求的Node版本。 + +```sh +npm ci +npm test +npm run dev +``` + +生产构建：`npm run build`。Docker镜像使用多阶段构建，Nginx监听8080，健康检查为`/healthz`。 + +## 计算边界 + +`src/catalog.js`保存公开来源和性能记录。`src/engine.js`保存工程估算与图调度，两者明确分开。 + +时间原点是用户停止说话。流式输入可以在此前持续预填充；只有尾包处理与判停等待计入后续延迟。图上禁止环路，重复调用在模型节点内部展开。并行组默认拥有独立资源，显式共享资源组按拓扑顺序串行调度。 + +流式连接允许提前启动下游，并保留上游最终数据对完成时间的约束。它采用平均供给率近似，不能替代逐帧背压和音素对齐仿真。RTF按预设每轮音频预算计算；模型Agent的循环工作也占用该预算，因此只用于设计负载检查。 + +最大步数表示预算情景，不代表任务一定成功或实际一定跑满。敏感性区间为模型与程序阶段的0.75–1.5倍变化，不是统计置信区间、p95或SLA。跨硬件倍率、首token模型、MoE与大尺寸外推均为可校准工程假设；未在本项目中执行模型硬件基准。 + +公开记录不能直接组成统一排行榜。未知参数量保持未知；首文本token、首音频、打断停止和RTF不混用。历史Cerebras结果不代表当前可用性。受控MTP接受数、放宽验证和纯理论数字有独立标记。 + +## 验证 + +Node测试覆盖串行/并行关键路径、任一汇总、流式完成约束、token门槛、loop上界、隐藏思考、共享资源、判停/网络边界、循环拒绝、导入验证和显存不足提示。浏览器检查桌面与移动布局及主要交互。 + +## 数据与许可 + +代码采用MIT许可。性能数字和说明由公开一手资料整理；每条记录保留来源链接，原始论文、商标及第三方资料权利属于各自作者。本仓库不分发第三方模型权重。 diff --git a/index.html b/index.html new file mode 100644 index 0000000..a5b1a93 --- /dev/null +++ b/index.html @@ -0,0 +1,16 @@ + + + + + + + + + + + + + 模型时延实验室 · Latency Lab + +
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"use-sync-external-store": "^1.2.2" + }, + "engines": { + "node": ">=12.7.0" + }, + "peerDependencies": { + "@types/react": ">=16.8", + "immer": ">=9.0.6", + "react": ">=16.8" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "immer": { + "optional": true + }, + "react": { + "optional": true + } + } + } + } +} diff --git a/package.json b/package.json new file mode 100644 index 0000000..1e31db7 --- /dev/null +++ b/package.json @@ -0,0 +1,19 @@ +{ + "name": "latency-lab", + "version": "1.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite --host 0.0.0.0 --port 4178", + "build": "vite build", + "preview": "vite preview --host 0.0.0.0 --port 4178", + "test": "node --test test/*.test.js" + }, + "dependencies": { + "@xyflow/react": "12.12.1", + "lucide-react": "1.70.0", + "react": "19.3.1", + "react-dom": "19.3.1" + }, + "devDependencies": { "vite": "8.3.6" } +} diff --git a/public/favicon.svg b/public/favicon.svg new file mode 100644 index 0000000..b586329 --- /dev/null +++ b/public/favicon.svg @@ -0,0 +1 @@ + diff --git a/public/preview.svg b/public/preview.svg new file mode 100644 index 0000000..abd8773 --- /dev/null +++ b/public/preview.svg @@ -0,0 +1,6 @@ + +K1412 RESEARCH / LATENCY LAB +模型时延实验室文本与实时语音模型对照 · 全双工流程估算 + +INPUT / STREAM持续监听AGENT / LOOP模型 × N步PARALLEL工具与检索FIRST AUDIO / CRITICAL PATH汇总、生成与播放58 SOURCE-ATTRIBUTED RECORDS / NOV 2026 + diff --git a/src/App.jsx b/src/App.jsx new file mode 100644 index 0000000..1cc0e1e --- /dev/null +++ b/src/App.jsx @@ -0,0 +1,140 @@ +import React, { useState,useEffect,useMemo,useRef,memo } from 'react'; +import { ReactFlow,Background,Controls,Handle,Position,applyNodeChanges,applyEdgeChanges,MarkerType } from '@xyflow/react'; +import { ArrowUpRight,ArrowRight,Activity,AudioLines,GitBranch,Clock3,Layers3,Plus,ChevronDown,ChevronRight,Download,Upload,RotateCcw,LayoutGrid,Trash2,Copy,Check,Search,BookOpen,SlidersHorizontal,Play,Pause,X,Info,Link2,Braces,Mic,Volume2,BrainCircuit,Workflow,ExternalLink,Menu } from 'lucide-react'; +import { RECORDS,SOURCES,CUTOFF,RELEASE,EVIDENCE,METRICS,recordValue,HARDWARE,SIZE_OPTIONS,CALIBRATION } from './catalog.js'; +import { DEFAULT_GLOBALS,KINDS,MODEL_KINDS,simulateGraph,estimateNode,validateGraph,validateDocument,autoLayout,getRecord,ms } from './engine.js'; +import { PRESETS,makeDocument,nodeData,toDocument } from './presets.js'; + +const STORE='latency-lab-v1'; +const icons={input:Mic,asr:AudioLines,llm:BrainCircuit,speech:AudioLines,tts:Volume2,program:Braces,merge:GitBranch,output:Volume2}; +function loadSaved(){try{const d=JSON.parse(localStorage.getItem(STORE)||'null');if(d&&!validateDocument(d).length)return d;}catch{}return makeDocument();} +function download(name,value,type='application/json'){const blob=new Blob([value],{type}),url=URL.createObjectURL(blob),a=document.createElement('a');a.href=url;a.download=name;a.click();setTimeout(()=>URL.revokeObjectURL(url),1000);} +const options = entries => entries.map(([value,label])=>); +function Field({label,children,hint}) {return ;} +function Num({value,onChange,min=0,max=1e6,step=1,label}){return onChange(Math.min(max,Math.max(min,Number(e.target.value)||0)))}/>;} +function Toggle({checked,onChange,children}){return ;} +function Tag({type='neutral',children}){return {children};} +function SourceLink({id,children}){const s=SOURCES[id];return s?{children||s.publisher}:null;} + +const StageNode=memo(function StageNode({data,selected}){ + const Icon=icons[data.kind]||Braces,r=data.result; + return
+ {data.kind!=='input'&&} +
{KINDS[data.kind]}{data.maxSteps>1&&↻ {data.maxSteps}}
+ {data.label} +
{MODEL_KINDS.includes(data.kind)?data.mode==='reference'?(getRecord(data.profileId)?.name||'来源参考'):`${data.arch==='moe'?`${data.size}B / A${data.active}B`:`${data.size}B`} · ${HARDWARE.find(h=>h.id===data.hardware)?.name||'自定义'}`:data.kind==='output'?'声音 / 最终结果的测量终点':data.kind==='input'?'t = 0 · 用户停说':`${data.durationMs} ms · 可修改`}
+
首出 {r?ms(r.first-r.start):'—'}完成 {r?ms(r.end-r.start):'—'}
+ {data.kind!=='output'&&} +
; +}); +const nodeTypes={stage:StageNode}; + +function Hero({onStart,onCompare}){ + return
+
INFERENCE / VOICE / AGENTS 研究快照 2026.11

从模型的毫秒，
到对话的节奏。

看清不同尺寸模型的输出速度，
再把节点连起来，计算一场对话真正的等待。

公开来源逐条标注 理论与报告分开 估算参数可校准
+
一场对话的时间切片ILLUSTRATION / ms
{Array.from({length:52},(_,i)=>)}
用户停说模型持续处理首段声音
输入流持续监听
推理流并行工作
输出流流式合成
↗
开始得早，结束也要算清。

首token ≠ 首音频 ≠ 完整任务

+
{RECORDS.length} 条

带来源的性能记录

0.5–2,400 B

覆盖主干 / 总参数规模

170 ms

7B语音模型公开接续报告 来源

≤ 100 步

每个模型节点的loop预算

+
; +} + +function Comparison({addProfile}){ + const [modality,setModality]=useState('all'),[evidence,setEvidence]=useState('all'),[metric,setMetric]=useState('all'),[query,setQuery]=useState(''),[expanded,setExpanded]=useState(null),[view,setView]=useState('records'),[sort,setSort]=useState('default'); + const [hw,setHw]=useState('h200'),[cards,setCards]=useState(1),[prompt,setPrompt]=useState(1024),[tokens,setTokens]=useState(128),[spec,setSpec]=useState(1); + const filtered=useMemo(()=>{const rows=RECORDS.filter(r=>(modality==='all'||r.modality===modality)&&(evidence==='all'||r.evidence===evidence)&&(metric==='all'||r.metric===metric)&&`${r.name} ${r.hardware} ${r.params}`.toLowerCase().includes(query.toLowerCase()));if(sort==='size')rows.sort((a,b)=>(a.size??Infinity)-(b.size??Infinity));return rows;},[modality,evidence,metric,query,sort]); + const exportCsv=()=>{const rows=[['模型','模态','参数口径','指标','数值','单位','硬件','量化','输入tokens','并发','证据','时间','来源','限制'],...filtered.map(r=>[r.name,r.modality,r.params,METRICS[r.metric],r.value??'',r.metric==='tps'?'tok/s':r.metric==='rtf'?'RTF':'ms',r.hardware,r.precision||'',r.input??'',r.concurrency??'',EVIDENCE[r.evidence],r.date,SOURCES[r.source].url,r.note||SOURCES[r.source].note])];download('model-latency-20261106.csv','\uFEFF'+rows.map(r=>r.map(v=>'"'+String(v).replaceAll('"','""')+'"').join(',')).join('\n'),'text/csv;charset=utf-8');}; + return
01 / THE EVIDENCE

模型时延对照

先比较同口径，再讨论极限。尺寸相同，部署条件仍可能相差很大。

+
+ {view==='records'?<>
{[['all','全部'],['text','纯文本'],['speech','端到端语音'],['tts','TTS组件']].map(([v,l])=>)}
+
{filtered.length} 条记录 · 截止 {CUTOFF}点击一行展开条件与限制
{filtered.map(r=>setExpanded(expanded===r.id?null:r.id)} tabIndex={0} onKeyDown={e=>{if(e.key==='Enter')setExpanded(expanded===r.id?null:r.id);}}>{expanded===r.id&&})}
模型 / 规模指标与结果部署条件证据与来源
{r.name}{r.params}{r.active?` · 激活${r.active}B`:''} {r.modality==='text'?'文本':r.modality==='tts'?'TTS':'语音'}{recordValue(r)}{r.scope}{r.metric==='tps'&&≈ {(1000/r.value).toFixed(2)} ms/token}{r.hardware}{r.precision||'精度未披露'}{r.concurrency?` · 并发${r.concurrency}`:''}{EVIDENCE[r.evidence]}{r.date}
这条数字说明什么

{r.note||SOURCES[r.source].note}

输入：{r.input?`${r.input.toLocaleString()} token`:r.inputSeconds?`${r.inputSeconds}秒音频/视频`:'未充分披露'}　输出：{r.output?`${r.output} token`:'未充分披露'}　{r.ttft?`另报告首token ${r.ttft}ms。`:''}{r.tps?` 另报告文本速度${r.tps} tok/s。`:''}{r.rtf?` RTF ${r.rtf}。`:''}

{SOURCES[r.source].title}
{r.value!=null&&r.metric!=='interrupt'&&}
{!filtered.length&&
没有匹配记录，试试其他筛选条件。
}
+

“最佳”必须带条件。历史晶圆级结果、单卡GPU、受控接受率、生产API观测不构成统一排行榜。公开报告也不等于本网站实测。

AMoE看两种尺寸。总参数影响存储，激活参数影响每步计算；不能把30B-A3B当作30B稠密模型。

:<>

FP8、模型已热身、低负载、不计网络与排队。以Qwen3/H20为速率锚点，硬件倍率和首token都是可校准的工程假设；不是硬件实测或物理下界。

{[.5,1.5,3,7,8,14,27,32,70,120,235,405].map(size=>{const r=estimateNode({...nodeData('llm','尺寸扫描'),hardware:hw,cards,size,inputTokens:prompt,outputTokens:tokens,speculative:spec});return ;})}
稠密模型规模估计首token估计输出速度完整输出估计可部署性 / 外推
{size}B稠密 · FP8{ms(r.first)}{r.rate.toFixed(1)} tok/s{(1000/r.rate).toFixed(1)} ms/token{ms(r.end)}{r.warnings.find(w=>w.includes('显存'))?显存不足:估算}{r.warnings.find(w=>w.includes('显存'))||'参数变化仅代表此工程模型下的趋势。'}
} +
; +} + +function NumberField({data,patch,keyName,label,hint,min=0,max=1e6,step=1}){return patch({[keyName]:v})} min={min} max={max} step={step}/>;} + +function NodeInspector({node,result,patch,remove,duplicate}){ + if(!node)return

选择一个节点

点击流程中的节点，编辑模型、参数和loop步数。也可以先从左侧添加节点。

; + const d=node.data,isModel=MODEL_KINDS.includes(d.kind),isText=d.kind==='llm'; + const profiles=d.kind==='asr'?[]:RECORDS.filter(r=>r.value!=null&&r.metric!=='interrupt'&&(isText?r.modality==='text':d.kind==='tts'?r.modality==='tts':r.modality==='speech')); + const chooseProfile=id=>{const r=getRecord(id);patch({profileId:id,mode:'reference',...(r?.size?{size:r.size}:{}),...(r?.active?{active:r.active,arch:'moe'}:{arch:'dense'})});}; + return <>
NODE CONFIG

节点设置

patch({label:e.target.value})}/>
{KINDS[d.kind]}{result&&{result.label}}
+ {isModel&&<> + {d.mode==='reference'?<>{getRecord(d.profileId)&&
{SOURCES[getRecord(d.profileId).source].title}

{getRecord(d.profileId).note||SOURCES[getRecord(d.profileId).source].note}

}:<>
patch({size:Math.max(.1,Number(e.target.value)||.1)})}/>
{d.arch==='moe'&&}
} + {d.mode==='custom'&&

手动校准

{isText?:}
} + {isText&&<>

一次推理的工作量

{d.mode==='estimate'&&}
} + {d.kind==='asr'&&<>patch({streamingInput:v})}>用户说话时持续识别{d.mode!=='custom'&&}} +

Agent / 模型 loop

逐步累计
{d.maxSteps>1&&<>{isText&&}patch({finalOnly:v})}>仅最后一步向下游输出

关闭后首包可能是中间反馈。loop指节点内部重复调用，不代表真实对话往返轮数。

}
patch({resource:e.target.value})}/>} + {(d.kind==='program'||d.kind==='merge'||d.kind==='output')&&} + {d.kind==='output'&&<>patch({target:v})}>作为主结果测量终点} + {d.kind==='input'&&

该节点代表用户停止说话。判停等待在“全局参数”中调整。流式识别可以提前处理用户说话期间的输入。

} + + {result&&

本节点的时间

开始
{ms(result.start)}
首出
{ms(result.first)}
结束
{ms(result.end)}
{result.resourceWait>0&&
资源等待
{ms(result.resourceWait)}
}
{result.assumptions.map((s,i)=>

{s}

)}{result.warnings.map((s,i)=>

{s}

)}
} + ; +} + +function Timeline({sim,nodes,selected,setSelected}){ + const [playing,setPlaying]=useState(false),[cursor,setCursor]=useState(0),[stepsOpen,setStepsOpen]=useState(false),timer=useRef(); + const total=sim.complete||1; + useEffect(()=>{if(!playing)return;let start=performance.now(),from=cursor;const tick=now=>{const next=from+(now-start)*(total/6000);if(next>=total){setCursor(total);setPlaying(false);}else{setCursor(next);timer.current=requestAnimationFrame(tick);}};timer.current=requestAnimationFrame(tick);return()=>cancelAnimationFrame(timer.current);},[playing,total]); + useEffect(()=>{setCursor(0);setPlaying(false);},[total]); + const result=selected?sim.results?.[selected]:null; + return

流程时间线 用户停说 = 0

绿色标出首结果关键路径。浅色区域是准备/等待，实色区域是输出。

0{ms(total*.25)}{ms(total*.5)}{ms(total*.75)}{ms(total)}
{sim.order.map(id=>{const r=sim.results[id],width=Math.max(.7,(r.end-r.start)/total*100),left=r.start/total*100,first=(r.first-r.start)/Math.max(1,r.end-r.start)*100;return ;})}
{result?.steps.length>1&&
{stepsOpen&&
{result.steps.map(s=>)}
步输入 / 输出 / 思考开始首出结束步前工具等待
{s.step}{s.input} / {s.output} / {s.reasoning}{ms(s.start)}{ms(s.first)}{ms(s.end)}{ms(s.toolWait)}
}
}
; +} + +function Studio({doc,setDoc,selected,setSelected,toast}){ + const nodes=doc.nodes,edges=doc.edges,g=doc.globals; + const [flow,setFlow]=useState(null),[panel,setPanel]=useState('node'),[edgeId,setEdgeId]=useState(null),[from,setFrom]=useState(''),[to,setTo]=useState(''),[release,setRelease]=useState('complete'),[focus,setFocus]=useState(false),[focusSettings,setFocusSettings]=useState(false); + const inputRef=useRef(),canvasRef=useRef(),history=useRef([]); + const sim=useMemo(()=>simulateGraph(nodes,edges,g),[nodes,edges,g]); + useEffect(()=>{if(!focus)return;const old=document.body.style.overflow;document.body.style.overflow='hidden';const escape=e=>{if(e.key==='Escape')setFocus(false);};window.addEventListener('keydown',escape);return()=>{document.body.style.overflow=old;window.removeEventListener('keydown',escape);};},[focus]); + const saveHistory=()=>{history.current.push(structuredClone(doc));if(history.current.length>35)history.current.shift();}; + const update=next=>{saveHistory();setDoc(typeof next==='function'?next(doc):next);}; + const patchNode=patch=>update({...doc,nodes:nodes.map(n=>n.id===selected?{...n,data:{...n.data,...patch}}:n)}); + const patchGlobals=patch=>update({...doc,globals:{...g,...patch}}); + const current=nodes.find(n=>n.id===selected),edge=edges.find(e=>e.id===edgeId); + const fit=()=>setTimeout(()=>flow?.fitView({padding:.18,duration:300}),80); + const choosePreset=key=>{update(makeDocument(key));setSelected(null);setEdgeId(null);fit();}; + const addNode=kind=>{if(nodes.length>=100){toast('最多支持100个节点。');return;}const id=`n-${Date.now().toString(36)}-${Math.random().toString(36).slice(2,5)}`;const rect=canvasRef.current?.getBoundingClientRect();const pos=flow&&rect?flow.screenToFlowPosition({x:rect.x+rect.width/2,y:rect.y+rect.height/2}):{x:100,y:100};update({...doc,nodes:[...nodes,{id,type:'stage',position:pos,data:nodeData(kind,KINDS[kind])}]});setSelected(id);setPanel('node');}; + const connect=c=>{if(!c.source||!c.target){toast('请选择起点和终点。');return;}if(edges.some(e=>e.source===c.source&&e.target===c.target)){toast('这两个节点已经相连。');return;}const next={id:`edge-${Date.now()}-${Math.random().toString(36).slice(2,5)}`,type:'smoothstep',source:c.source,target:c.target,data:{release:c.release||'complete',tokens:8}};const err=validateGraph(nodes,[...edges,next]);if(err.length){toast(err[0]);return;}update({...doc,edges:[...edges,next]});setEdgeId(next.id);setPanel('edge');}; + const removeNode=()=>{update({...doc,nodes:nodes.filter(n=>n.id!==selected),edges:edges.filter(e=>e.source!==selected&&e.target!==selected)});setSelected(null);}; + const duplicate=()=>{if(!current)return;const id=`n-${Date.now().toString(36)}`;update({...doc,nodes:[...nodes,{...structuredClone(current),id,position:{x:current.position.x+35,y:current.position.y+165},data:{...current.data,label:current.data.label+' 副本'}}]});setSelected(id);}; + const renderNodes=useMemo(()=>nodes.map(n=>({...n,selected:n.id===selected,data:{...n.data,result:sim.results?.[n.id],critical:sim.criticalNodes?.has(n.id)}})),[nodes,selected,sim]); + const renderEdges=useMemo(()=>edges.map(e=>({...e,selected:e.id===edgeId,animated:e.data?.release!=='complete'&&!e.data?.control,label:e.data?.control?'控制路径':e.data?.release==='chunk'?`${e.data.tokens||8} token后`:e.data?.release==='first'?'首包后':'完成后',markerEnd:{type:MarkerType.ArrowClosed,width:16,height:16,color:sim.criticalEdges?.has(e.id)?'#2d7854':'#91a39a'},style:{stroke:sim.criticalEdges?.has(e.id)?'#2d7854':'#91a39a',strokeWidth:sim.criticalEdges?.has(e.id)?2.2:1.5,...(e.data?.control?{strokeDasharray:'5 5'}:{})},labelStyle:{fill:'#50645b',fontSize:10},labelBgStyle:{fill:'#f7f8f4'},labelBgPadding:[5,3]})),[edges,edgeId,sim]); + const exportDoc=()=>download('dialogue-flow.json',JSON.stringify(toDocument(nodes,edges,g,doc.name),null,2)); + const importDoc=async e=>{const file=e.target.files?.[0];if(!file)return;try{if(file.size>2e6)throw new Error('文件超过2MB。');const parsed=JSON.parse(await file.text());const err=validateDocument(parsed);if(err.length)throw new Error(err[0]);update(parsed);setSelected(null);fit();toast('已导入流程。');}catch(error){toast(`导入失败：${error.message}`);}e.target.value='';}; + const onChanges=changes=>setDoc(prev=>({...prev,nodes:applyNodeChanges(changes,prev.nodes)})); + return
02 / BUILD THE CONVERSATION

把一场对话，搭出来。

拖拽、连线、并行、汇总。改变一个节点，看等待如何沿流程传递。

自动保存在本机
从一个结构开始{Object.entries(PRESETS).filter(([k])=>k!=='blank').map(([key,p])=>)}
+
+
{doc.name||'自定义流程'}{!sim.errors.length&&首出 {ms(sim.first)} 完成 {ms(sim.complete)}}
{focus&&}
+ setDoc(prev=>({...prev,edges:applyEdgeChanges(changes,prev.edges)}))} onConnect={connect} onInit={setFlow} onNodeClick={(_,n)=>{setSelected(n.id);setPanel('node');if(focus)setFocusSettings(true);}} onEdgeClick={(_,e)=>{setEdgeId(e.id);setPanel('edge');if(focus)setFocusSettings(true);}} onNodeDragStart={saveHistory} onPaneClick={()=>{setSelected(null);setEdgeId(null);}} onNodesDelete={ns=>{setDoc(prev=>({...prev,edges:prev.edges.filter(e=>!ns.some(n=>n.id===e.source||n.id===e.target))}));}} isValidConnection={c=>!validateGraph(nodes,[...edges,{id:'candidate',...c}]).length} fitView fitViewOptions={{padding:.16}} minZoom={.16} maxZoom={1.6} deleteKeyCode={['Backspace','Delete']} connectOnClick selectionOnDrag={false} ariaLabelConfig={{'controls.zoomIn.ariaLabel':'放大流程','controls.zoomOut.ariaLabel':'缩小流程','controls.fitView.ariaLabel':'显示完整流程','controls.interactive.ariaLabel':'切换交互'}}> + {!nodes.length&&

从第一个节点开始

点击左侧添加节点，或者加载一个示例结构。

} +
首结果关键路径 流式连接
+ +
+ {sim.errors.length?
{sim.errors.join(' ')}
:<>
{sim.hasAudio?'首段可播放音频':'首个目标结果'}方案估计{ms(sim.first)}敏感性范围 {ms(sim.range[0])} – {ms(sim.range[1])}
全部主目标完成{ms(sim.complete)}音频目标包含 {g.speechSeconds}s 自然播放
跑满loop预算{ms(sim.maxFirst)}首结果 / 完成 {ms(sim.maxComplete)}
持续处理负载1?'warn-text':''}>{sim.rtf.toFixed(2)} RTF{sim.rtf>1?'当前假设可能跟不上实时':'理想资源下具备实时余量'}

范围只把模型和程序阶段乘0.75–1.5，固定网络/判停不变；不是p95或统计置信区间。改变模型、隐藏思考、共享资源或loop后会立即重算。打断控制能力仍需真实音频系统验证。

{sim.warnings.length>0&&
估算条件与待校准项 {sim.warnings.length}
    {sim.warnings.map((w,i)=>
  • {w}
    • )}
}{setSelected(id);setPanel('node');}}/>} + {SIZE_OPTIONS.map(s=> +
; +} + +function Method(){ + return
03 / THE MODEL BEHIND THE MODEL

这些时间，怎么算。

结果的可信度，取决于计时边界和输入假设是否说清。

先定义你在等什么。

文本首token是模型开始生成的时间。隐藏思考可能发生在可见答案之前。语音首包是第一段可播放音频就绪的时间；终端播放还需要网络与缓冲。完整回复则要考虑所有输出和自然说话时长。

TTFT

首次模型token；对推理模型应另计首个可见答案。

TPOT

持续输出阶段每token平均耗时，近似1000÷token/s。

TTFA

首段可播放音频的时间，不能用文本TTFT代替。

RTF

处理/生成音频所需时间÷音频时长。小于1才有持续实时余量。

01

串行与并行

start(v) = max(各前驱满足条件的时刻)

默认等全部分支。选择“任一就绪”取min；前提是最早分支的结果可用。独立硬件分支可以重叠，共享资源组会增加排队。

02

流式开始，持续供给

首包 + (k − 1) / 输出速率

文本积累k个token后启动下游。完成时刻还受上游最终数据就绪约束，避免把提前启动误当作全部工作已完成。这里采用平均供给率，未模拟音素对齐、每帧抖动与流控背压。

03

Agent loop

Σ[首token + (思考 + 输出 − 1) / 速率] + (N − 1) × 工具等待

每步可增加上下文；默认仅最后一步对下游放行。最大步数定义预算情景，不保证真实任务能在预算内成功。选择预计步数后，同时显示跑满预算的结果。

04

参数规模外推

速率 = H20锚点 × 硬件系数 × 量化系数 × 并行收益

稠密锚点采用Qwen3官方FP8数据做对数插值；32B以上按反比外推。MoE用激活参数并乘额外开销。卡数收益上限4倍，不能靠无限加卡把时延降到零。

查看全部默认系数与计算限制

可复查的默认值

{HARDWARE.map(h=>)}
硬件相对H20速率系数
{h.name}{h.factor}×（假设）

量化速率系数：BF16 0.62，FP8 1，INT4 1.06。它们是近似值，不代表所有模型的固定规律。

首token与显存

Prefill默认8B/H20为18,000 token/s，固定开销14ms；随激活规模、缓存和硬件调整。缺少首token来源时使用该模型。公开记录中的硬件条件固定；更换条件应切回工程估算。

权重显存粗算：BF16 2字节/参数，FP8 1.05，INT4 0.55；另加max(2GB,8%)运行余量。这不是完整KV-cache容量规划。

未建模的因素

任务正确性、语音自然度、错误重试、冷启动、长上下文attention非线性、真实调度、网络抖动、设备音频延迟与多用户拥塞。模型间tokenizer也不同，不把token直接等同汉字。

怎样理解“理想时延”

统一条件估算采用已热身、低并发、稳定网络。端到端语音通过边听边处理缩短停说后的等待，不能因此声称复杂任务已完成。一个170ms的接续可以很自然，却不能代表检索、工具和多轮Agent也在170ms内结束。网站中的数据为公开资料调研，本次没有运行模型效果或硬件性能实验。

覆盖公开可核验的代表性尺寸与部署条件；不穷尽全部模型。未公开的参数规模保持未知，缺少首音频数据时也保留空缺。

; +} + +function Sources(){return
04 / PRIMARY SOURCES

让每个数字，都有来处。

截止 {CUTOFF} 查阅。只引用官方文档、官方模型卡或原始研究论文。

{Object.keys(SOURCES).length} 个一手来源
{Object.entries(SOURCES).map(([id,s],i)=>
{String(i+1).padStart(2,'0')}
{s.title}{s.publisher} / {s.date}

{s.note}

)}
;} + +export default function App(){ + const [tab,setTab]=useState(()=>location.hash==='#studio'?'studio':location.hash==='#method'?'method':location.hash==='#sources'?'sources':'overview'),[doc,setDoc]=useState(loadSaved),[selected,setSelected]=useState(null),[message,setMessage]=useState(''),[mobileMenu,setMobileMenu]=useState(false); + const toastTimer=useRef(); + const toast=text=>{setMessage(text);clearTimeout(toastTimer.current);toastTimer.current=setTimeout(()=>setMessage(''),4500);}; + useEffect(()=>{const timer=setTimeout(()=>{try{localStorage.setItem(STORE,JSON.stringify(toDocument(doc.nodes,doc.edges,doc.globals,doc.name)));}catch{toast('本机存储不可用，请导出JSON保存流程。');}},500);return()=>clearTimeout(timer);},[doc]); + const navigate=t=>{setTab(t);setMobileMenu(false);history.replaceState(null,'',t==='overview'?'#overview':`#${t}`);window.scrollTo({top:0,behavior:'instant'});}; + const addProfile=r=>{const id=`n-${Date.now().toString(36)}`,kind=r.modality==='text'?'llm':r.modality==='tts'?'tts':'speech',data={...nodeData(kind,r.name),mode:'reference',profileId:r.id,...(r.size?{size:r.size}:{}),...(r.active?{active:r.active,arch:'moe'}:{})};setDoc(prev=>({...prev,name:'自定义对话流程',nodes:[...prev.nodes,{id,type:'stage',position:{x:120,y:120+prev.nodes.length*30},data}]}));setSelected(id);navigate('studio');toast('已添加参考节点；首包或持续速度缺失时会明确使用工程假设。');}; + return <>
{e.preventDefault();navigate('overview');}}>模型时延实验室LATENCY LAB
K1412 RESEARCH
+
{tab==='overview'&&<>navigate('studio')} onCompare={()=>document.getElementById('compare').scrollIntoView({behavior:'smooth'})}/>
FROM A NUMBER TO A SYSTEM

一个模型快，
整条链路也快吗？

把多个模型、工具和语音模块连起来，找出真正决定体验的那条路径。

}{tab==='studio'&&} {tab==='method'&&}{tab==='sources'&&}
+
LATENCY LAB

公开证据 × 可校准的系统估算

资料截止 {CUTOFF}源代码与数据
{RELEASE} · 本机计算与存储，无需模型API密钥
{message&&
{message}
} + ; +} diff --git a/src/catalog.js b/src/catalog.js new file mode 100644 index 0000000..7f503be --- /dev/null +++ b/src/catalog.js @@ -0,0 +1,88 @@ +export const CUTOFF = '2026-11-06'; +export const RELEASE = 'K1412-LATENCY-LAB-20261106-v1'; +export const SOURCES = { + qwen3: { title: 'Qwen3 官方速度基准', publisher: 'Qwen', date: '2025 · 在线文档', url: 'https://github.com/QwenLM/Qwen3/blob/main/docs/source/getting_started/speed_benchmark.md', note: 'H20 96GB、SGLang 0.4.6、batch=1。原始 speed=(输入+输出)/总时间；本表只取输入1、输出2048的行，近似生成速度。不是跨硬件最佳成绩。' }, + glm: { title: 'GLM-4 官方推理数据', publisher: 'Z.ai', date: '2025 · 在线文档', url: 'https://github.com/zai-org/GLM-4/blob/main/inference/README.md', note: 'H100 80GB，具体卡数和输入长度逐行列出。短输入与长输入的 TTFT 不可混用。' }, + groq8: { title: 'Groq · Llama 3.1 8B', publisher: 'Groq', date: '在线文档 · 查询于截止日', url: 'https://console.groq.com/docs/model/llama-3.1-8b-instant', note: '服务商标称 token speed，未提供本地可复现的完整负载条件；不是 SLA。' }, + groq70: { title: 'Groq · Llama 3.3 70B', publisher: 'Groq', date: '在线文档 · 查询于截止日', url: 'https://console.groq.com/docs/model/llama-3.3-70b-versatile', note: 'LPU 专用推理平台，不能套用到单张 GPU。' }, + swift: { title: 'SwiftSpec 论文', publisher: 'ByteDance Seed 等', date: '2025-06-12', url: 'https://arxiv.org/html/2506.11309v1', note: '单请求低时延优化，8张 Hopper GPU、异步推测解码。速度随任务和接受率变化。' }, + cb70: { title: 'Cerebras · 70B 推理结果', publisher: 'Cerebras', date: '2024-10-24 · 历史结果', url: 'https://www.cerebras.ai/blog/cerebras-inference-3x-faster', note: '晶圆级硬件、16位权重与推测解码。厂商报告每用户2100 token/s，文中提示速度可上下波动约20%。不是当前 API 可用性承诺。' }, + cb405: { title: 'Cerebras · 405B 推理结果', publisher: 'Cerebras', date: '2024-11-18 · 历史结果', url: 'https://www.cerebras.ai/blog/llama-405b-inference', note: '1000-token 输入时报告969输出token/s、240ms TTFT；支持128K不等于该TTFT来自128K输入。' }, + qwen38: { title: 'TensorRT LLM · Qwen3.8 MoE 部署基准', publisher: 'NVIDIA', date: '1.4.0rc0 · 截止日可见版本', url: 'https://nvidia.github.io/TensorRT-LLM/1.4.0rc0/deployment-guide/deployment-guide-for-qwen3.8-qwen3.5-on-trtllm.html', note: 'GB300、TP16/EP1、并发1、8192输入/1024输出。MTP3的383.051 token/s使用受控接受数2.3，属于条件化性能点。' }, + r1: { title: 'TensorRT LLM · DeepSeek-R1 低时延优化', publisher: 'NVIDIA', date: '2025 · 技术文档', url: 'https://nvidia.github.io/TensorRT-LLM/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.html', note: '8×B200，1K输入/2K输出。峰值配置含NVFP4、MTP与放宽接受验证；不能视为输出分布不变的免费加速。' }, + omni2: { title: 'LLaMA-Omni 2 · 表6', publisher: 'ICT/CAS', date: '2025-05-05', url: 'https://arxiv.org/html/2505.02625v1#A2', note: '单张L40、从收到语音指令到第一段合成语音。0.5B至14B指语言主干；未计公网和终端播放。R=3/W=10为主实验；R=1/W=5更快，但音质有变化。' }, + moshi: { title: 'Moshi 技术论文', publisher: 'Kyutai', date: '2024 · 论文v2', url: 'https://arxiv.org/html/2410.00037v2', note: '160ms为架构理论时延，论文称实际200ms。连续流延迟与一问一答的完整任务耗时不同。' }, + persona: { title: 'PersonaPlex 官方模型卡', publisher: 'NVIDIA', date: '2026-01 · v1', url: 'https://huggingface.co/nvidia/personaplex-7b-v1', note: 'A100 80GB、PyTorch、FullDuplexBench。接续170ms；打断停止240ms是不同指标。主要为英语。' }, + voicechat: { title: 'Nemotron 3 VoiceChat 官方模型卡', publisher: 'NVIDIA', date: '2026-03-16 · v1', url: 'https://build.nvidia.com/nvidia/nemotron-voicechat/modelcard', note: '12B总参数、9B主干、H100、vLLM，报告260ms接续。早期评估版本；不与后续11B NemotronLabs检查点混用。' }, + minicpm: { title: 'MiniCPM-o 4.5 · 表11/12', publisher: 'OpenBMB', date: '2026-04-30', url: 'https://arxiv.org/html/2604.27393v1', note: '9B总参数。RTX4090/llama.cpp-omni/INT4的RTF=0.21；0.58s的首token来自64帧视觉输入，不是语音首包。' }, + qomni3: { title: 'Qwen3-Omni · 表1/2', publisher: 'Qwen', date: '2025-09-22', url: 'https://arxiv.org/html/2509.17765v1#S2.T2', note: 'Thinker 30B-A3B，另有3B-A0.3B Talker和编解码模块。234ms是无历史上下文的理论音频首包；表2并发1/4/6分别为234/728/1172ms。未给出可复现的具体GPU型号。' }, + qomni35: { title: 'Qwen3.5-Omni · 表1/2', publisher: 'Qwen', date: '2026-04-17', url: 'https://arxiv.org/html/2604.15804v1#S2.T2', note: '理论首包；Flash235ms/Plus435ms。论文只说扩展到数千亿参数，未公开两变体精确尺寸；部署资源不同，不能据此比较单位算力效率。' }, + qomni38: { title: 'Qwen3.8-Omni-Flash · 实际API性能', publisher: 'Qwen', date: '2026-09-18', url: 'https://qwen.ai/blog?id=qwen3.8-omni-flash#throughput--latency', note: '官方生产环境观测，纯音频6/12/20秒输入时首音频约978/983/1026ms。未公布精确参数、GPU、样本数及百分位。网络和端点判停边界未充分说明，不能视作端到端SLA。' }, + lfm3: { title: 'LFM3-Audio-2B 官方模型卡', publisher: 'Liquid AI', date: '2026-10-27', url: 'https://huggingface.co/LiquidAI/LFM3-Audio-2B', note: '流式编码可在用户说话时prefill，官方称首token低至50ms；没有声明50ms是首段可播放音频，也未给出该数值的完整硬件条件。' }, + qtts: { title: 'Qwen3-TTS · 表2', publisher: 'Qwen', date: '2026-01-22', url: 'https://arxiv.org/html/2601.15621v1#S3.T2', note: '12Hz版本0.6B/1.7B在并发1时报告97/101ms首包，RTF=0.288/0.313。内部vLLM V0、编解码compile与CUDA Graph；未公开具体GPU。这是TTS组件，不含语音识别和LLM响应。' }, + live: { title: 'GPT-Live 官方文档', publisher: 'OpenAI', date: '在线文档 · 截止日查阅', url: 'https://developers.openai.com/api/docs/guides/live', note: '原生全双工语音与后台任务委托；没有可用于按参数规模比较的公开精确尺寸和统一时延基准。后台任务完成时延需要另算。' }, + vad: { title: 'Realtime VAD 官方文档', publisher: 'OpenAI', date: '在线文档 · 截止日查阅', url: 'https://developers.openai.com/api/docs/guides/realtime-vad', note: 'silence_duration_ms可调，官方示例为500ms。判停等待是额外时延；更短等待可能增加误抢话。' }, + gemini: { title: 'Gemini Live API 官方文档', publisher: 'Google', date: '在线文档 · 截止日查阅', url: 'https://ai.google.dev/gemini-api/docs/live', note: '原生实时音频API。模型尺寸和统一的用户停说至首音频时延缺少公开可比数据。' }, +}; + +const row = (id, name, modality, size, metric, value, source, extra = {}) => ({ id, name, modality, size, metric, value, source, evidence: 'reported', hardware: '未披露', params: size == null ? '未公开' : `${size}B`, ...extra }); +export const RECORDS = [ + ...[[0.6,458.03],[1.7,333.90],[4,200.61],[8,150.25],[14,97.11],[32,46.17]].map(([s,v])=>row(`q3-${s}`,`Qwen3-${s}B`,'text',s,'tps',v,'qwen3',{hardware:'1×H20 96GB',precision:'FP8',input:1,output:2048,concurrency:1,scope:'纯文本生成近似',date:'2025',note:'极短输入，原指标含1个输入token。未报告首token；不等同长上下文速度。'})), + row('q3-32-q4','Qwen3-32B','text',32,'tps',47.67,'qwen3',{hardware:'1×H20 96GB',precision:'AWQ INT4',input:1,output:2048,concurrency:1,scope:'纯文本生成近似',date:'2025'}), + row('q3-30','Qwen3-30B-A3B','text',30,'tps',155.55,'qwen3',{active:3,hardware:'1×H20 96GB',precision:'FP8',input:1,output:2048,concurrency:1,scope:'MoE生成近似',date:'2025'}), + row('q3-235','Qwen3-235B-A22B','text',235,'tps',74.5,'qwen3',{active:22,hardware:'8×H20 96GB',precision:'BF16',input:1,output:2048,concurrency:1,scope:'MoE生成近似',date:'2025'}), + row('q3-235-fp8','Qwen3-235B-A22B','text',235,'tps',71.65,'qwen3',{active:22,hardware:'4×H20 96GB',precision:'FP8',input:1,output:2048,concurrency:1,scope:'MoE生成近似',date:'2025'}), + ...[[9,1000,50,44.4,1],[9,8000,390,39,1],[9,32000,2290,18.7,1],[9,100000,6800,14.1,1],[32,1000,160,24.4,1],[32,8000,1370,16.9,1],[32,32000,6750,8.1,2],[32,100000,37830,3,4]].map(([s,input,value,tps,cards])=>row(`glm-${s}-${input}`,`GLM-4-${s}B-0414`,'text',s,'ttft',value,'glm',{tps,input,cards,hardware:`${cards}×H100 80GB`,precision:'BF16',scope:'首个文本token',date:'2025-04'})), + row('groq8','Llama 3.1 8B · Groq','text',8,'tps',560,'groq8',{hardware:'Groq LPU 服务',precision:'服务商数值格式',scope:'服务商标称生成速度',date:'查询于2026-11-06',note:'无统一TTFT和完整并发条件。'}), + row('groq70','Llama 3.3 70B · Groq','text',70,'tps',280,'groq70',{hardware:'Groq LPU 服务',scope:'服务商标称生成速度',date:'查询于2026-11-06'}), + row('swift70','Llama 3 70B · SwiftSpec','text',70,'tps',348,'swift',{hardware:'8×Hopper GPU',precision:'论文优化配置',scope:'单请求低时延解码',concurrency:1,date:'2025-06-12',note:'任务相关的推测解码接受率；不是所有任务统一速度。'}), + row('cb70','Llama 3.1 70B · Cerebras','text',70,'tps',2100,'cb70',{hardware:'Cerebras 晶圆级系统',precision:'16位 + 推测解码',scope:'历史每用户生成结果',date:'2024-10-24',historical:true}), + row('cb405','Llama 3.1 405B · Cerebras','text',405,'tps',969,'cb405',{ttft:240,input:1000,hardware:'Cerebras 晶圆级系统',precision:'16位',scope:'历史每用户生成结果',date:'2024-11-18',historical:true}), + row('q38','Qwen3.8-2.4T-A95B','text',2400,'tps',133.502,'qwen38',{active:95,input:8192,output:1024,concurrency:1,hardware:'16×GB300 · TP16/EP1',precision:'FP8 · 无MTP',scope:'每用户解码速度',date:'2026 · 文档1.4.0rc0'}), + row('q38-mtp','Qwen3.8-2.4T-A95B · MTP3','text',2400,'tps',383.051,'qwen38',{active:95,input:8192,output:1024,concurrency:1,hardware:'16×GB300 · TP16/EP1',precision:'FP8 · MTP3',evidence:'conditional',scope:'受控接受数性能点',date:'2026 · 文档1.4.0rc0',note:'接受草稿数固定为2.3；不能当成自然负载的无条件实测速率。'}), + row('r1','DeepSeek-R1 · 峰值探索','text',671,'tps',368,'r1',{active:37,hardware:'8×B200',input:1000,output:2000,concurrency:1,precision:'混合精度 · NVFP4/MTP',evidence:'conditional',scope:'条件化峰值解码',date:'2025',note:'配置包含放宽接受验证，输出分布保持性不能由该速度证明。'}), + ...[[0.5,542.71],[1.5,552.76],[3,567.84],[7,582.91],[14,663.32]].map(([s,v])=>row(`omni2-${s}`,`LLaMA-Omni2-${s}B`,'speech',s,'audio',v,'omni2',{params:`${s}B 主干`,hardware:'1×L40',scope:'收到指令→首段语音',date:'2025-05-05',note:'R=3/W=10，未计公网与播放缓冲；尺寸仅语言主干。'})), + row('omni2-7-fast','LLaMA-Omni2-7B · 小块输出','speech',7,'audio',457.29,'omni2',{params:'7B 主干',hardware:'1×L40',scope:'收到指令→首段语音',date:'2025-05-05',note:'R=1/W=5；与较大块的语音质量不同，不能只按速度排名。'}), + row('llama-omni','LLaMA-Omni','speech',8,'audio',346.73,'omni2',{params:'8B 主干',hardware:'1×L40',scope:'论文复测首段语音',date:'2025-05-05'}), + row('glmvoice','GLM-4-Voice','speech',9,'audio',1562.81,'omni2',{params:'9B 主干',hardware:'1×L40',scope:'论文复测首段语音',date:'2025-05-05',note:'同一论文中的基线复测，不能代表该模型所有优化部署。'}), + row('moshi-theory','Moshi · 理论','speech',7,'audio',160,'moshi',{params:'7B 主干',evidence:'theoretical',scope:'连续流架构延迟',date:'2024',note:'算法时延，非公网用户停说至首音频SLA。'}), + row('moshi','Moshi · 实际','speech',7,'audio',200,'moshi',{params:'7B 主干',scope:'论文称实际流延迟',date:'2024'}), + row('persona','PersonaPlex-7B-v1','speech',7,'audio',170,'persona',{params:'7B 主干',hardware:'A100 80GB',precision:'PyTorch',scope:'用户停说→开始回复',date:'2026-01-15',duplex:true,note:'FullDuplexBench接续指标；不代表回答正确率。'}), + row('persona-interrupt','PersonaPlex · 打断','speech',7,'interrupt',240,'persona',{params:'7B 主干',hardware:'A100 80GB',scope:'用户打断→停止说话',date:'2026-01-15',duplex:true}), + row('minicpm','MiniCPM-o 4.5','speech',9,'rtf',0.21,'minicpm',{params:'9B 总参数',hardware:'RTX4090',precision:'INT4 · llama.cpp-omni',scope:'生成实时因子',date:'2026-04-30',duplex:true,note:'不是210ms首音频；未给出本条件下的可比首音频数。'}), + row('voicechat','Nemotron 3 VoiceChat v1','speech',12,'audio',260,'voicechat',{params:'12B总量 / 9B主干',hardware:'H100',precision:'vLLM',scope:'FullDuplexBench接续',date:'2026-03-16',duplex:true,note:'早期评估版本，不与后续NemotronLabs不同检查点合并。'}), + ...[[1,234,0.47],[4,728,0.56],[6,1172,0.66]].map(([c,v,rtf])=>row(`qomni3-${c}`,'Qwen3-Omni-30B-A3B','speech',30,'audio',v,'qomni3',{active:3,params:'Thinker 30B-A3B + Talker 3B-A0.3B',concurrency:c,rtf,evidence:'theoretical',scope:'理论音频首包',hardware:'未披露具体GPU',precision:'vLLM + compile / CUDA Graph',date:'2025-09-22'})), + row('qomni35-flash','Qwen3.5-Omni-Flash','speech',null,'audio',235,'qomni35',{params:'精确尺寸未公开',concurrency:1,rtf:0.178,evidence:'theoretical',scope:'理论音频首包',date:'2026-04-17'}), + row('qomni35-plus','Qwen3.5-Omni-Plus','speech',null,'audio',435,'qomni35',{params:'数千亿级 / 精确尺寸未公开',concurrency:1,rtf:0.187,evidence:'theoretical',scope:'理论音频首包',date:'2026-04-17'}), + ...[[6,978.36,591.26,84.87],[12,982.74,604.80,83.18],[20,1026.39,617.98,81.06]].map(([sec,v,ttft,tps])=>row(`qomni38-${sec}`,'Qwen3.8-Omni-Flash-Realtime','speech',null,'audio',v,'qomni38',{ttft,tps,rtf:0.1538,params:'未公开',inputSeconds:sec,scope:'生产API首音频',date:'2026-09-18',note:'官方生产观测；未披露p50/p95、硬件与完整计时边界。'})), + row('qomni38-video','Qwen3.8-Omni-Flash-Realtime · 音视频','speech',null,'audio',1214.73,'qomni38',{ttft:837.96,tps:84.89,rtf:0.1524,inputSeconds:6,scope:'6秒音视频首音频',date:'2026-09-18'}), + row('lfm3','LFM3-Audio-2B','speech',2,'ttft',50,'lfm3',{scope:'流式输入后的首token下限',date:'2026-10-27',note:'官方“低至”数值，硬件条件未完整说明；没有可比的首音频值。'}), + ...[[.6,1,97,.288],[1.7,1,101,.313],[.6,3,179,.338],[1.7,3,195,.363],[.6,6,299,.434],[1.7,6,333,.463]].map(([s,c,v,rtf])=>row(`qtts-${s}-${c}`,`Qwen3-TTS-12Hz-${s}B`,'tts',s,'audio',v,'qtts',{rtf,concurrency:c,scope:'TTS组件首音频包',hardware:'未披露具体GPU',precision:'内部vLLM V0 + compile',date:'2026-01-22'})), + row('gpt-live','GPT-Live','speech',null,'unknown',null,'live',{scope:'全双工API · 统一时延未知',date:'查询于2026-11-06',duplex:true,evidence:'unknown'}), + row('gemini-live','Gemini Live','speech',null,'unknown',null,'gemini',{scope:'原生音频API · 统一时延未知',date:'查询于2026-11-06',evidence:'unknown'}), +]; + +export const EVIDENCE = { reported:'公开报告', theoretical:'理论口径', conditional:'条件化结果', unknown:'数据未公开' }; +export const METRICS = { tps:'输出速度', ttft:'首token', audio:'首音频 / 接续', interrupt:'打断停止', rtf:'实时因子', unknown:'未报告' }; +export const recordValue = r => r.value == null ? '—' : r.metric==='tps' ? `${r.value.toLocaleString('en-US',{maximumFractionDigits:2})} tok/s` : r.metric==='rtf' ? `${r.value} RTF` : `${r.value.toLocaleString('en-US',{maximumFractionDigits:1})} ms`; +export const HARDWARE = [ + {id:'h20',name:'H20 96GB',factor:1,bandwidth:4, memory:96}, + {id:'h100',name:'H100 80GB',factor:1.3,bandwidth:3.35,memory:80}, + {id:'h200',name:'H200 141GB',factor:1.6,bandwidth:4.8,memory:141}, + {id:'b200',name:'B200 180GB',factor:2.4,bandwidth:8,memory:180}, + {id:'gb300',name:'GB300 288GB',factor:3.1,bandwidth:8,memory:288}, + {id:'4090',name:'RTX 4090 24GB',factor:0.48,bandwidth:1,memory:24}, + {id:'5090',name:'RTX 5090 32GB',factor:0.78,bandwidth:1.79,memory:32}, + {id:'cpu',name:'CPU / 统一内存（泛化）',factor:0.07,bandwidth:0.12,memory:64}, +]; +export const SIZE_OPTIONS = [0.5,0.6,1.5,1.7,2,3,4,7,8,9,12,14,27,32,70,120,235,405,671,2400]; + +// These are engineering defaults, deliberately separate from the source records above. +export const CALIBRATION = { + denseAnchors: [[0.6,458.03],[1.7,333.90],[4,200.61],[8,150.25],[14,97.11],[32,46.17]], + prefillTokensPerSecondAt8B: 18000, + prefillFloorMs: 14, + quantRateFactors: {bf16:0.62,fp8:1,int4:1.06}, + speculativeDefault: 1, + sensitivity: [0.75,1.5], +}; diff --git a/src/engine.js b/src/engine.js new file mode 100644 index 0000000..cf19d05 --- /dev/null +++ b/src/engine.js @@ -0,0 +1,238 @@ +import { RECORDS, HARDWARE, CALIBRATION } from './catalog.js'; + +export const DEFAULT_GLOBALS = { computeScale:1, programScale:1, networkMs:20, queueMs:0, vadMs:120, playbackBufferMs:60, speechSeconds:4, inputSeconds:3, sharedGPU:false, loopPlan:'max' }; +export const KINDS = { input:'语音输入', asr:'流式识别 / 编码', llm:'文本模型 / Agent', speech:'原生语音模型', tts:'流式语音合成', program:'程序 / 工具', merge:'分支汇总', output:'音频播放 / 目标输出' }; +export const MODEL_KINDS = ['llm','speech','tts','asr']; +const finite = (v,f=0) => Number.isFinite(Number(v)) ? Number(v) : f; +const num = (v,f=0,max=1e7) => Math.max(0,Math.min(max,finite(v,f))); +const recordMap = new Map(RECORDS.map(r=>[r.id,r])); +export const ms = v => !Number.isFinite(v) ? '—' : Math.abs(v)>=1000 ? `${(v/1000).toFixed(v>=10000?1:2)} s` : `${Math.round(v)} ms`; +export const getRecord = id => recordMap.get(id); + +function interpolate(size) { + const a=CALIBRATION.denseAnchors, s=Math.max(.1,size); + if(s<=a[0][0]) return a[0][1]*(a[0][0]/s)**.2; + for(let i=1;ih.id===d.hardware)||HARDWARE[0], cards=Math.max(1,Math.round(num(d.cards,1,256))); + const size=Math.max(.1,num(d.size,8,1e5)), active=d.arch==='moe'?Math.max(.1,Math.min(size,num(d.active,3,1e5))):size; + const quant=d.quant||'fp8', quantFactor=CALIBRATION.quantRateFactors[quant]||1; + const tpGain= Math.min(4,cards**.6)/(1+Math.log2(cards)*.08); + const audioHardware=hw.factor*tpGain*quantFactor; + const speculative=Math.max(1,num(d.speculative,1,8)); + let rate=Math.max(1,interpolate(active)*hw.factor*quantFactor*tpGain*speculative/(d.arch==='moe'?1.25:1)); + let assumptions=[], warnings=[], sourceId=null; + if(ref) { + sourceId=ref.source; + const rr=ref.metric==='tps'?ref.value:ref.tps; + if(rr) rate=rr; + assumptions.push('采用选中来源的部署配置；硬件/量化外推参数不参与该参考速率。'); + if(ref.metric==='tps'&&!ref.ttft) assumptions.push('来源未给首token，首token另用工程模型估算。'); + if(ref.evidence==='theoretical') warnings.push('参考值是理论口径，计算结果仍是方案估计。'); + if(ref.evidence==='conditional') warnings.push('参考速率依赖特定接受率或验证条件。'); + if(ref.historical) warnings.push('参考是历史性能点，不能保证当前API仍提供同样服务。'); + if(kind==='llm'&&ref.input&&num(d.inputTokens,1024)!==ref.input) warnings.push(`来源输入${ref.input} token，当前输入${num(d.inputTokens,1024)} token；首token或生成速度的迁移包含额外假设。`); + if(ref.inputSeconds&&ref.inputSeconds!==num(g.inputSeconds,3)) warnings.push(`来源使用${ref.inputSeconds}秒输入，当前为${num(g.inputSeconds,3)}秒；首包参考未由音频长度重新标定。`); + } else if(mode==='custom') { + rate=Math.max(.1,num(d.customTps,100,1e6)); + assumptions.push('使用手动填写的时延和速度。'); + } else { + assumptions.push('速度由H20/Qwen3锚点按参数规模、硬件系数、量化和卡数外推。'); + if(active>32||cards>1||hw.id!=='h20'||d.arch==='moe') warnings.push('该硬件/规模组合包含外推，请用实测校准。'); + if(speculative>1) warnings.push(`推测解码${speculative}倍为手动假设，接受率不足时不成立。`); + } + rate/=scale; + const maxSteps=Math.max(1,Math.round(num(d.maxSteps,1,100))); + const plannedSteps=forceMax||g.loopPlan==='max'?maxSteps:Math.max(1,Math.min(maxSteps,Math.round(num(d.expectedSteps,Math.min(2,maxSteps),100)))); + const stepsCount=model?plannedSteps:1; + const outputTokens=Math.max(1,Math.round(num(d.outputTokens,128,1e6))); + const hiddenTokens=Math.round(num(d.reasoningTokens,0,1e6)); + const inputTokens=Math.round(num(d.inputTokens,1024,1e6)); + const growth=Math.round(num(d.contextGrowth,256,1e6)); + const cached=Math.min(.99,num(d.cacheRatio,0,1)); + const toolMs=num(d.toolMs,0)*progScale; + const q=num(g.queueMs,0); + let first=0,end=0,rtf=0,tail=0,steps=[],busy=0; + if(kind==='input') { first=end=num(g.vadMs,120); assumptions=['时间原点为用户停止说话；此前音频采集时间不计入等待。']; } + else if(kind==='program'||kind==='merge') { first=end=num(d.durationMs,40)*progScale; assumptions=['程序节点耗时为可编辑的单次预算。']; } + else if(kind==='output') { first=num(d.durationMs,g.playbackBufferMs); end=first+(d.outputType==='text'?0:num(g.speechSeconds,4)*1000); assumptions=['播放完成包含自然语音时长；上游供给过慢会推迟结束。']; } + else if(model) { + const audioMs=num(g.speechSeconds,4)*1000; + const refFirst=ref ? (ref.metric==='audio'||ref.metric==='ttft'?ref.value:ref.ttft) : null; + const refRtf=ref?.metric==='rtf'?ref.value:ref?.rtf; + for(let i=0;i1) prefill*=Math.max(.15,uncached/ref.input); + } + if(mode==='custom') prefill=num(d.customFirstMs,100)*scale; + let sf=0,se=0,srtf=0; + if(kind==='llm') { + sf=prefill+hiddenTokens/rate*1000; + se=sf+(outputTokens-1)/rate*1000; + tail=1000/rate; + } else if(kind==='speech') { + // A text TTFT (e.g. LFM3's 50ms) is deliberately not reused as first audio. + sf=(mode==='custom'?num(d.customFirstMs,300)*scale:ref?.metric==='audio'?ref.value*scale:Math.max(80,(160+active*9)/Math.sqrt(audioHardware))*scale); + if(ref?.metric==='ttft') assumptions.push('来源只给首token；本节点首音频使用工程假设，未把二者等同。'); + if(ref?.metric==='rtf') assumptions.push('来源只给RTF，首音频另按工程假设；0.21 RTF不等于210ms首包。'); + srtf=(mode==='custom'?num(d.customRtf,.45,100):refRtf??(0.24+active*.025)/audioHardware)*scale; + if(ref&&refRtf==null) assumptions.push('来源未报告RTF，持续生成速度采用工程假设。'); + se=sf+audioMs*srtf; + tail=80*srtf; + } else if(kind==='tts') { + sf=mode==='custom'?num(d.customFirstMs,100)*scale:ref?.metric==='audio'?ref.value*scale:(70+Math.sqrt(size)*35)/audioHardware*scale; + srtf=(mode==='custom'?num(d.customRtf,.18,100):refRtf??(.12+size*.035)/audioHardware)*scale; + se=sf+audioMs*srtf; tail=80*srtf; + } else if(kind==='asr') { + const seconds=num(g.inputSeconds,3); + sf=(mode==='custom'?num(d.customFirstMs,100):num(d.durationMs,100)*(size/.6)**.5/audioHardware)*scale; + srtf=(mode==='custom'?num(d.customRtf,.15,100):.15*(size/.6)**.6/audioHardware)*scale; + se=sf+(d.streamingInput===false?seconds*1000*srtf:0); + sf=se; tail=se; + assumptions.push(d.streamingInput===false?'整段识别，计入全段处理时间。':'用户说话时持续处理，仅将尾包处理计入停说后的等待。'); + if(mode!=='custom')assumptions.push('尾包基线按0.6B/H20定义，并随规模、硬件与量化外推。'); + } + sf+=q; se+=q; + steps.push({step:i+1,start,first:start+sf,end:start+se,input:tokens,output:outputTokens,reasoning:hiddenTokens,toolWait:i?toolMs:0}); + if(i===0) first=start+sf; + end=start+se; + busy+=se; + rtf=Math.max(rtf,srtf); + } + if(d.finalOnly!==false&&steps.length) first=steps.at(-1).first; + if(kind==='llm') rtf=busy/Math.max(1000,audioMs); + } + if(!ref&&mode!=='custom'&&model) { + const bytesPerWeight=quant==='bf16'?2:quant==='int4'?.55:1.05; + const weightsGB=size*bytesPerWeight; + if(weightsGB+Math.max(2,weightsGB*.08)>hw.memory*cards) warnings.push(`估计权重与最小运行余量约${Math.round(weightsGB+Math.max(2,weightsGB*.08))}GB，超出所选显存；数值不能视为可部署结果。`); + } + return { first,end,tail,rate,rtf,steps,stepsCount,maxSteps,outputTokens,model,sourceId,assumptions:[...new Set(assumptions)],warnings:[...new Set(warnings)],label:ref?'来源锚点 + 方案估计':mode==='custom'?'手动校准':'工程外推' }; +} + +export function validateGraph(nodes,edges) { + const errors=[]; + if(!Array.isArray(nodes)||!Array.isArray(edges)) return ['流程必须包含节点和连线数组。']; + if(nodes.length>100||edges.length>300) return ['最多支持100个节点和300条连线。']; + const ids=new Set(); + for(const n of nodes) { + if(!n||typeof n.id!=='string'||!n.id||ids.has(n.id)) { errors.push('节点ID缺失或重复。'); continue; } + ids.add(n.id); + if(!n.data||!KINDS[n.data.kind]) errors.push(`节点${n.id}的类型无效。`); + if(!Number.isFinite(n.position?.x)||!Number.isFinite(n.position?.y)) errors.push('节点坐标必须是有限数值。'); + for(const [k,v] of Object.entries(n.data||{})) if(typeof v==='number'&&(!Number.isFinite(v)||v<0||v>1e7)) errors.push(`节点${n.id}的${k}超出范围。`); + } + const indeg=new Map(nodes.map(n=>[n.id,0])), adj=new Map(nodes.map(n=>[n.id,[]])); + for(const e of edges) { + if(!ids.has(e.source)||!ids.has(e.target)) { errors.push('连线指向了不存在的节点。'); continue; } + if(e.source===e.target) errors.push('请用节点内的loop步数表示循环。'); + indeg.set(e.target,indeg.get(e.target)+1); adj.get(e.source).push(e.target); + if(e.data?.networkMs!=null&&(!Number.isFinite(e.data.networkMs)||e.data.networkMs<0)) errors.push('连线网络时延必须为非负数。'); + } + const ready=nodes.filter(n=>indeg.get(n.id)===0).map(n=>n.id), order=[]; + while(ready.length) { const id=ready.shift(); order.push(id); for(const t of adj.get(id)) { indeg.set(t,indeg.get(t)-1); if(!indeg.get(t))ready.push(t); } } + if(order.length!==nodes.length) errors.push('流程存在环。图上使用有向无环结构，Agent循环放在模型节点内。'); + return [...new Set(errors)]; +} + +function schedule(nodes,edges,g,forceMax=false) { + const results={}, indeg=new Map(nodes.map(n=>[n.id,0])), outgoing=new Map(nodes.map(n=>[n.id,[]])); + const byId=new Map(nodes.map(n=>[n.id,n])); + edges.forEach(e=>{indeg.set(e.target,indeg.get(e.target)+1);outgoing.get(e.source).push(e);}); + const ready=nodes.filter(n=>!indeg.get(n.id)).map(n=>n.id), resources=new Map(), order=[]; + while(ready.length) { + const id=ready.shift(), n=byId.get(id), d=n.data, cost=estimateNode(d,g,forceMax), incoming=edges.filter(e=>e.target===id); + const arrivals=incoming.map(e=>{ + const p=results[e.source], mode=e.data?.release||'complete', network=num(e.data?.networkMs,g.networkMs); + let at=p.end,event='end'; + if(mode==='first'||mode==='chunk') { + const tokens=mode==='chunk'?Math.min(p.outputTokens,Math.max(1,num(e.data?.tokens,8))):1; + at=p.first+(p.kind==='llm'?(tokens-1)/p.rate*1000:0); at=Math.min(p.end,at);event='first'; + } + return {at:at+network,id:e.source,event,edge:e.id,network,stream:mode!=='complete'}; + }); + const selected=arrivals.length ? arrivals.reduce((a,b)=>d.join==='any'?(a.at<=b.at?a:b):(a.at>=b.at?a:b)) : null; + let start=selected?.at||0, parent=selected, resourceWait=0; + const resource=cost.model?(d.resource?.trim()||(g.sharedGPU?'shared-gpu':'')):''; + const prev=resource?resources.get(resource):null; + if(prev&&prev.end>start) { resourceWait=prev.end-start;start=prev.end;parent={id:prev.id,event:'end',resource:true}; } + let first=start+cost.first,end=start+cost.end,endParent=parent; + // Streaming means an early start, never that the producer's remaining output disappears. + for(const a of arrivals) if(a.stream&&(d.join!=='any'||a===selected)) { + const floor=results[a.id].end+a.network+cost.tail; + if(floor>end) {end=floor;endParent={id:a.id,event:'end',edge:a.edge};} + } + if(d.kind==='output'&&d.outputType!=='text') end=Math.max(end,first+num(g.speechSeconds,4)*1000); + first=Math.min(first,end); + results[id]={...cost,id,kind:d.kind,title:d.label,start,first,end,parent,endParent,resourceWait,resource,steps:cost.steps.map(s=>({...s,start:s.start+start,first:s.first+start,end:s.end+start}))}; + if(resource)resources.set(resource,{end,id}); order.push(id); + for(const e of outgoing.get(id)) {indeg.set(e.target,indeg.get(e.target)-1);if(!indeg.get(e.target))ready.push(e.target);} + } + const targets=nodes.filter(n=>n.data.kind==='output'&&n.data.target!==false); + const terminals=targets.length?targets:nodes.filter(n=>!outgoing.get(n.id).length&&n.data.target!==false); + const audioTargets=targets.filter(n=>n.data.outputType!=='text'); + const firstTargets=audioTargets.length?audioTargets:terminals; + const firstTarget=firstTargets.reduce((a,n)=>!a||results[n.id].first!a||results[n.id].end>results[a.id].end?n:a,null); + const criticalNodes=new Set(),criticalEdges=new Set(),completionNodes=new Set(); + function trace(id,event,set,seen=new Set(),captureEdges=true) { if(!id||seen.has(`${id}:${event}`))return;seen.add(`${id}:${event}`);set.add(id);const r=results[id],p=event==='end'?r.endParent:r.parent;if(p) {if(p.edge&&captureEdges)criticalEdges.add(p.edge);trace(p.id,p.event,set,seen,captureEdges);} } + if(firstTarget)trace(firstTarget.id,'first',criticalNodes); + if(completeTarget)trace(completeTarget.id,'end',completionNodes,new Set(),false); + const loads=new Map(); + for(const r of Object.values(results)) if(r.model) { const key=r.resource||r.id;loads.set(key,(loads.get(key)||0)+r.rtf); } + const rtf=Math.max(0,...loads.values()); + const warnings=Object.values(results).flatMap(r=>r.warnings.map(w=>`${r.title}：${w}`)); + if(targets.length) { + const connected=new Set(targets.map(n=>n.id)),queue=[...connected]; + while(queue.length){const id=queue.shift();for(const e of edges.filter(x=>x.target===id))if(!connected.has(e.source)){connected.add(e.source);queue.push(e.source);}} + for(const n of nodes)if(!connected.has(n.id)&&n.data.target!==false)warnings.push(`${n.data.label}未连到主输出，当前不计入主结果耗时。`); + } + if(!targets.length)warnings.push('没有主输出节点，当前结果取叶子节点；添加播放/目标输出可明确测量终点。'); + if(nodes.some(n=>n.data.join==='any'&&edges.filter(e=>e.target===n.id).length>1))warnings.push('“任一就绪”假设最早分支结果可用；未建模失败重试与答案正确性。'); + if(rtf>1)warnings.push('持续负载RTF超过1，所选资源/输出预算可能跟不上实时音频。'); + return {results,order,first:firstTarget?results[firstTarget.id].first:0,complete:completeTarget?results[completeTarget.id].end:0,firstTarget:firstTarget?.id,completeTarget:completeTarget?.id,criticalNodes,criticalEdges,completionNodes,rtf,warnings:[...new Set(warnings)],hasAudio:audioTargets.length>0}; +} + +export function simulateGraph(nodes,edges,globals=DEFAULT_GLOBALS) { + const errors=validateGraph(nodes,edges); + if(errors.length)return {errors}; + const g={...DEFAULT_GLOBALS,...globals}; + const base=schedule(nodes,edges,g), max=schedule(nodes,edges,g,true); + const low=schedule(nodes,edges,{...g,computeScale:g.computeScale*.75,programScale:g.programScale*.75}),high=schedule(nodes,edges,{...g,computeScale:g.computeScale*1.5,programScale:g.programScale*1.5}); + return {...base,errors:[],maxFirst:max.first,maxComplete:max.complete,range:[low.first,high.first],completeRange:[low.complete,high.complete]}; +} + +export function validateDocument(doc) { + if(!doc||typeof doc!=='object'||doc.version!==1)return ['不是受支持的流程文件（version=1）。']; + const errors=validateGraph(doc.nodes,doc.edges); + if(!doc.globals||typeof doc.globals!=='object')errors.push('全局参数缺失。'); + for(const [k,v] of Object.entries(doc.globals||{}))if(typeof v==='number'&&(!Number.isFinite(v)||v<0||v>1e7))errors.push(`全局参数${k}无效。`); + return errors; +} + +export function autoLayout(nodes,edges) { + if(validateGraph(nodes,edges).length)return nodes; + const levels={}, pending=new Set(nodes.map(n=>n.id)); + for(let i=0;ie.target===id).map(e=>e.source); + if(parents.every(p=>levels[p]!=null)){levels[id]=parents.length?Math.max(...parents.map(p=>levels[p]))+1:0;pending.delete(id);} + } + const grouped={};nodes.forEach(n=>(grouped[levels[n.id]]??=[]).push(n.id)); + return nodes.map(n=>({...n,position:{x:levels[n.id]*290,y:100+grouped[levels[n.id]].indexOf(n.id)*170}})); +} diff --git a/src/main.jsx b/src/main.jsx new file mode 100644 index 0000000..8ceb647 --- /dev/null +++ b/src/main.jsx @@ -0,0 +1,6 @@ +import React from 'react'; +import { createRoot } from 'react-dom/client'; +import App from './App.jsx'; +import '@xyflow/react/dist/style.css'; +import './styles.css'; +createRoot(document.getElementById('root')).render(); diff --git a/src/presets.js b/src/presets.js new file mode 100644 index 0000000..21de4ad --- /dev/null +++ b/src/presets.js @@ -0,0 +1,29 @@ +import { DEFAULT_GLOBALS, autoLayout } from './engine.js'; + +export function nodeData(kind,label) { + return { kind,label,mode:'estimate',size:kind==='tts'?.6:kind==='asr'?.6:8,active:3,arch:'dense',hardware:'h200',cards:1,quant:'fp8',inputTokens:1024,outputTokens:128,reasoningTokens:0,contextGrowth:256,cacheRatio:0,speculative:1,maxSteps:1,expectedSteps:1,toolMs:0,finalOnly:true,streamingInput:true,durationMs:kind==='program'?80:kind==='merge'?20:kind==='asr'?100:kind==='output'?60:0,customTps:150,customFirstMs:100,customRtf:.25,resource:'',join:'all',target:true,outputType:'audio' }; +} +const n=(id,kind,label,more={})=>({id,type:'stage',position:{x:0,y:0},data:{...nodeData(kind,label),...more}}); +const e=(source,target,release='complete',more={})=>({id:`${source}-${target}`,source,target,type:'smoothstep',data:{release,tokens:8,...more}}); +const preset=(name,description,nodes,edges,globals={})=>({name,description,version:1,nodes:autoLayout(nodes,edges),edges,globals:{...DEFAULT_GLOBALS,...globals}}); + +export const PRESETS = { + native:preset('原生全双工','输入与输出在同一模型内持续运行；另设独立的打断控制路径。',[ + n('in','input','用户停止说话'),n('voice','speech','原生语音对话',{mode:'reference',profileId:'persona',size:7,customRtf:.45}),n('play','output','主回复播放'),n('cancel','program','打断检测与取消',{durationMs:80,target:false}) + ],[e('in','voice','first'),e('voice','play','first'),e('in','cancel','first',{networkMs:0,control:true})],{vadMs:0}), + cascade:preset('流式级联','边听边识别；文本积累8个token后启动TTS，输入与输出可重叠。',[ + n('in','input','用户停止说话'),n('asr','asr','流式语音识别'),n('llm','llm','对话模型 · 8B'),n('tts','tts','流式TTS · 0.6B'),n('play','output','主回复播放'),n('cancel','program','并行打断控制',{durationMs:80,target:false}) + ],[e('in','asr','first'),e('asr','llm','complete'),e('llm','tts','chunk'),e('tts','play','first'),e('in','cancel','first',{networkMs:0,control:true})]), + parallel:preset('并行检索与汇总','检索和工具分支并行，汇总模型等到全部分支完成后组织回复。',[ + n('in','input','用户停止说话'),n('asr','asr','流式语音识别'),n('plan','llm','意图与任务规划',{size:3,outputTokens:32}),n('search','program','知识检索',{durationMs:180}),n('tool','program','业务工具',{durationMs:350}),n('merge','llm','汇总模型 · 14B',{size:14,outputTokens:160}),n('tts','tts','流式TTS'),n('play','output','主回复播放') + ],[e('in','asr','first'),e('asr','plan'),e('plan','search'),e('plan','tool'),e('search','merge'),e('tool','merge'),e('merge','tts','chunk'),e('tts','play','first')]), + agent:preset('Agent 多步循环','模型最多运行4步，每步输出96个token，步骤之间预留250ms工具等待。',[ + n('in','input','用户停止说话'),n('asr','asr','流式语音识别'),n('agent','llm','工具Agent · 14B',{size:14,maxSteps:4,expectedSteps:2,outputTokens:96,reasoningTokens:64,contextGrowth:384,toolMs:250,finalOnly:true}),n('tts','tts','流式TTS'),n('play','output','最终结果播放') + ],[e('in','asr','first'),e('asr','agent'),e('agent','tts','chunk'),e('tts','play','first')]), + blank:preset('空白流程','从左侧添加节点，也可用下方的连线表单连接。',[],[]), +}; + +export function makeDocument(key='cascade') { return structuredClone(PRESETS[key]); } +export function toDocument(nodes,edges,globals,name='自定义对话流程') { + return {version:1,name,createdAt:new Date().toISOString(),nodes:nodes.map(({id,type,position,data})=>({id,type,position,data})),edges:edges.map(({id,source,target,type,data})=>({id,source,target,type,data})),globals}; +} diff --git a/src/styles.css b/src/styles.css new file mode 100644 index 0000000..62338a5 --- /dev/null +++ b/src/styles.css @@ -0,0 +1,9 @@ +:root{font-family:'DM Sans',-apple-system,BlinkMacSystemFont,'PingFang SC','Microsoft YaHei',sans-serif;color:#1d332a;background:#f7f8f4;font-synthesis:none;--ink:#173a2b;--green:#286b4d;--lime:#d4ef9a;--muted:#7a897f;--line:#dce3d9;--paper:#fff;--mono:'IBM Plex Mono',ui-monospace,SFMono-Regular,monospace} 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{simulateGraph,estimateNode,validateGraph,validateDocument,DEFAULT_GLOBALS,autoLayout} from '../src/engine.js'; +import {makeDocument,nodeData,PRESETS} from '../src/presets.js'; +import {RECORDS,SOURCES} from '../src/catalog.js'; + +const g={...DEFAULT_GLOBALS,networkMs:0,vadMs:0,queueMs:0,speechSeconds:1}; +const node=(id,kind,extra={})=>({id,type:'stage',position:{x:0,y:0},data:{...nodeData(kind,id),...extra}}); +const edge=(source,target,release='complete',extra={})=>({id:`${source}-${target}`,source,target,data:{release,...extra}}); +const near=(a,b)=>assert.ok(Math.abs(a-b)<.00001,`${a} != ${b}`); + +test('serial stages add their service times',()=>{ + const n=[node('a','program',{durationMs:100}),node('b','program',{durationMs:200}),node('out','output',{durationMs:0,outputType:'text'})]; + const s=simulateGraph(n,[edge('a','b'),edge('b','out')],g); + near(s.first,300);near(s.complete,300); +}); +test('parallel all-join waits for longest branch rather than summing branches',()=>{ + const n=[node('in','input'),node('a','program',{durationMs:100}),node('b','program',{durationMs:300}),node('join','merge',{durationMs:20}),node('out','output',{durationMs:40,outputType:'text'})]; + const e=[edge('in','a'),edge('in','b'),edge('a','join'),edge('b','join'),edge('join','out')]; + const s=simulateGraph(n,e,g);near(s.first,360);assert.ok(s.criticalNodes.has('b'));assert.ok(!s.criticalNodes.has('a')); + const any=n.map(x=>x.id==='join'?{...x,data:{...x.data,join:'any'}}:x); + near(simulateGraph(any,e,g).first,160); +}); +test('streaming starts early, completion remains bounded by final upstream data',()=>{ + const n=[node('llm','llm',{mode:'custom',customFirstMs:100,customTps:100,outputTokens:101}),node('tts','tts',{mode:'custom',customFirstMs:50,customRtf:.1}),node('out','output',{durationMs:0})]; + const e=[edge('llm','tts','first'),edge('tts','out','first')],s=simulateGraph(n,e,g); + near(s.first,150);near(s.results.llm.end,1100);assert.ok(s.results.tts.end>=s.results.llm.end);assert.ok(s.complete>=s.first+1000); + const full=simulateGraph(n,[edge('llm','tts'),edge('tts','out','first')],g); + assert.ok(full.first>s.first); +}); +test('token gate adds k-1 decoding intervals',()=>{ + const n=[node('llm','llm',{mode:'custom',customFirstMs:100,customTps:100,outputTokens:101}),node('out','output',{durationMs:0,outputType:'text'})]; + near(simulateGraph(n,[edge('llm','out','chunk',{tokens:8})],g).first,170); +}); +test('loop maximum, inter-step tools, final-only output and context growth are explicit',()=>{ + const d={...nodeData('llm','agent'),mode:'custom',customFirstMs:100,customTps:100,outputTokens:11,maxSteps:3,expectedSteps:2,toolMs:50,contextGrowth:20,inputTokens:100,finalOnly:true}; + const r=estimateNode(d,g);near(r.end,700);near(r.first,600);assert.deepEqual(r.steps.map(x=>x.input),[100,120,140]); + const partial=estimateNode({...d,finalOnly:false},g);near(partial.first,100);near(partial.end,700); + const s=simulateGraph([node('agent','llm',d),node('out','output',{outputType:'text',durationMs:0})],[edge('agent','out','first')],{...g,loopPlan:'expected'}); + near(s.first,350);near(s.maxFirst,600);near(s.maxComplete,700);near(s.results.agent.end,450); +}); +test('hidden reasoning delays the first visible token',()=>{ + const d={...nodeData('llm','reasoner'),mode:'custom',customFirstMs:100,customTps:100,outputTokens:11,reasoningTokens:50}; + const r=estimateNode(d,g);near(r.first,600);near(r.end,700); +}); +test('shared model resources serialize parallel requests and change critical dependency',()=>{ + const n=[node('in','input'),node('a','llm',{mode:'custom',customFirstMs:100,customTps:100,outputTokens:41}),node('b','llm',{mode:'custom',customFirstMs:100,customTps:100,outputTokens:41}),node('out','output',{outputType:'text',durationMs:0})]; + const e=[edge('in','a'),edge('in','b'),edge('a','out'),edge('b','out')]; + near(simulateGraph(n,e,g).complete,500); + const s=simulateGraph(n,e,{...g,sharedGPU:true});near(s.complete,1000);near(s.results.b.resourceWait,500); +}); +test('source text TTFT is not interpreted as first playable audio',()=>{ + const r=estimateNode({...nodeData('speech','LFM3'),mode:'reference',profileId:'lfm3',size:2},g); + assert.notEqual(r.first,50);assert.ok(r.assumptions.some(x=>x.includes('只给首token'))); +}); +test('RTF-only reference uses its real factor without claiming first audio',()=>{ + const r=estimateNode({...nodeData('speech','MiniCPM'),mode:'reference',profileId:'minicpm',size:9},g); + near(r.rtf,.21);assert.ok(r.assumptions.some(x=>x.includes('来源只给RTF')));assert.notEqual(r.first,210); +}); +test('program scaling is global, and disconnected work is called out',()=>{ + const n=[node('a','program',{durationMs:100}),node('orphan','program',{durationMs:20}),node('out','output',{durationMs:0,outputType:'text'})]; + const s=simulateGraph(n,[edge('a','out')],{...g,programScale:2});near(s.first,200);assert.ok(s.warnings.some(x=>x.includes('orphan未连到主输出'))); +}); +test('audio component size and hardware settings affect engineering estimate',()=>{ + const a=estimateNode({...nodeData('asr','ASR'),hardware:'h20',size:.6},g); + const b=estimateNode({...nodeData('asr','ASR'),hardware:'h20',size:2},g); + assert.ok(b.end>a.end);const t1=estimateNode({...nodeData('tts','TTS'),cards:1},g),t2=estimateNode({...nodeData('tts','TTS'),cards:2},g);assert.ok(t2.end{ + const n=[node('in','input'),node('out','output',{durationMs:60,outputType:'text'})]; + near(simulateGraph(n,[edge('in','out','first')],{...g,vadMs:120,networkMs:20}).first,200); +}); +test('invalid cycles and dangling edges are rejected including imports',()=>{ + const n=[node('a','program'),node('b','program')]; + assert.ok(validateGraph(n,[edge('a','b'),edge('b','a')]).some(x=>x.includes('环'))); + assert.ok(validateGraph(n,[edge('a','missing')]).length); + assert.ok(validateDocument({version:1,nodes:n,edges:[],globals:{computeScale:-1}}).length); +}); +test('memory over-capacity is surfaced rather than silently claiming deployability',()=>{ + const r=estimateNode({...nodeData('llm','405B'),size:405,hardware:'4090',cards:1},g); + assert.ok(r.warnings.some(x=>x.includes('显存'))); +}); +test('source records have provenance and do not confuse the units of metrics',()=>{ + const ids=new Set(); + for(const r of RECORDS){assert.ok(!ids.has(r.id));ids.add(r.id);assert.ok(SOURCES[r.source]?.url.startsWith('https://'));assert.ok(r.scope);if(r.evidence==='unknown')assert.equal(r.value,null);else assert.ok(r.value>0);} + const q=RECORDS.find(r=>r.id==='q3-8');near(q.value,150.25);assert.equal(q.metric,'tps'); +}); +test('all presets compute finite targets and survive JSON round trip',()=>{ + for(const key of Object.keys(PRESETS)){const d=makeDocument(key);assert.deepEqual(validateDocument(d),[]);const s=simulateGraph(d.nodes,d.edges,d.globals);assert.deepEqual(s.errors,[]);assert.ok(Number.isFinite(s.first));assert.ok(Number.isFinite(s.complete));assert.deepEqual(validateDocument(JSON.parse(JSON.stringify(d))),[]);assert.equal(autoLayout(d.nodes,d.edges).length,d.nodes.length);} +});