diff --git "a/index.html" "b/index.html"
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+++ "b/index.html"
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Bonsai 27B · Prism ML</title>
+<meta name="description" content="Bonsai 27B by Prism ML — a 27-billion-parameter 1-bit model running entirely in your browser on WebGPU. No server. 3.8 GB.">
+<link rel="icon" 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+<body class="stage-landing">
+<canvas id="sceneBG" aria-hidden="true"></canvas>
+<canvas id="sceneA" aria-hidden="true"></canvas>
+<canvas id="sceneB" aria-hidden="true"></canvas>
+<div class="vignette"></div>
+<div class="grain"></div>
+<div class="ui">
+<header>
+<div class="brand"><span class="logo-mark"><svg viewBox="0 0 575 412" fill="currentColor" aria-hidden="true"><path transform="translate(53 -1)" d="m217 375-42-72 84-144h49L217 1 1 375z"/><path transform="translate(297 181)" d="M29 1 1 49h28z"/><path d="M438 182H326v48h112z"/><path transform="translate(255 253)" d="M29 1 1 49h28z"/><path d="M510 254H284v48h226z"/><path transform="translate(269 325)" d="M29 1H1l28 48z"/><path d="M462 326H298v48h164z"/></svg></span>BONSAI 27B</div>
+</header>
+<div class="foot">
+<div id="uiLand">
+<h1>
+<span class="line"><span>27B parameters.</span></span>
+<span class="line"><span>1-bit weights.</span></span>
+<span class="line"><span>In your <span class="strong">browser.</span></span></span>
+</h1>
+<p class="sub" id="modelInfo">
+<b>Bonsai&nbsp;27B</b> by <b>Prism&nbsp;ML</b> is a 27&nbsp;billion parameter dense language
+model quantized to 1-bit precision (3.8&nbsp;GB vs. original 54&nbsp;GB).
+Everything runs entirely locally in your browser on WebGPU &mdash; no data leaves your device.
+Agentic WebGPU kernel optimization by <b>Fable&nbsp;5</b> and <b>GPT&nbsp;5.6&nbsp;Sol</b>.
+</p>
+<button class="cta" id="loadCta" type="button">LOAD MODEL &rarr;</button>
+<div class="cta-note" id="ctaNote" hidden></div>
+</div>
+<div id="uiLoad" class="readout" role="progressbar" aria-valuemin="0" aria-valuemax="100"
+aria-valuenow="0" aria-label="Model download progress">
+<span class="rise"><span class="pct"><span id="pct">0</span><span class="sym">%</span></span></span>
+<span class="rise"><span class="status" id="status" aria-live="polite">REQUESTING WEBGPU DEVICE</span></span>
+<span class="rise"><span>
+<span class="stats">
+<span id="statA">0.00 / 3.80 GB</span>
+</span>
+<span class="bar"><i id="barFill"></i></span><br>
+<span class="load-error" id="loadError" hidden>
+<span class="load-error-msg" id="loadErrorMsg"></span><br>
+<button class="begin retry" id="retryBtn" type="button">RETRY &rarr;</button>
+<button class="begin retry" id="changeTokenBtn" type="button" hidden>CHANGE TOKEN &rarr;</button>
+</span>
+</span></span>
+</div>
+</div>
+</div>
+<div id="gate" hidden>
+<div class="gate-inner">
+<div class="gate-brand"><span class="logo-mark"><svg viewBox="0 0 575 412" fill="currentColor" aria-hidden="true"><path transform="translate(53 -1)" d="m217 375-42-72 84-144h49L217 1 1 375z"/><path transform="translate(297 181)" d="M29 1 1 49h28z"/><path d="M438 182H326v48h112z"/><path transform="translate(255 253)" d="M29 1 1 49h28z"/><path d="M510 254H284v48h226z"/><path transform="translate(269 325)" d="M29 1H1l28 48z"/><path d="M462 326H298v48h164z"/></svg></span>BONSAI 27B</div>
+<div class="gate-step">PRIVATE ACCESS</div>
+<h2 class="gate-title">This bonsai is<br><span class="strong">still private.</span></h2>
+<p class="gate-lead">Enter a Hugging&nbsp;Face access token with read access to
+<a class="gate-repo" href="https://huggingface.co/prism-ml/Bonsai-27B-gguf" target="_blank" rel="noopener">prism-ml/Bonsai-27B-gguf</a>.
+It stays in this browser &mdash; requests go only to huggingface.co.</p>
+<div class="gate-field" id="gateField">
+<input id="gateInput" class="gate-input" type="password" spellcheck="false" autocomplete="off"
+placeholder="hf_&hellip;" aria-label="Hugging Face access token">
+<button class="gate-show" id="gateShow" type="button">SHOW</button>
+</div>
+<p class="gate-error" id="gateError" role="alert" hidden></p>
+<button class="cta gate-continue" id="gateContinue" type="button">CONTINUE &rarr;</button>
+<div class="gate-links">
+<a href="https://huggingface.co/settings/tokens" target="_blank" rel="noopener">GET A TOKEN &nearr;</a>
+<span class="gate-dot">&middot;</span>
+<a href="https://huggingface.co/prism-ml/Bonsai-27B-gguf" target="_blank" rel="noopener">MODEL PAGE &nearr;</a>
+</div>
+</div>
+</div>
+<div id="veil" hidden><span class="veil-text">VALIDATING ACCESS</span></div>
+<section id="chatx" aria-label="Chat with Bonsai 27B">
+<header class="c-head">
+<div class="c-head-inner">
+<div class="c-brand"><span class="logo-mark"><svg viewBox="0 0 575 412" fill="currentColor" aria-hidden="true"><path transform="translate(53 -1)" d="m217 375-42-72 84-144h49L217 1 1 375z"/><path transform="translate(297 181)" d="M29 1 1 49h28z"/><path d="M438 182H326v48h112z"/><path transform="translate(255 253)" d="M29 1 1 49h28z"/><path d="M510 254H284v48h226z"/><path transform="translate(269 325)" d="M29 1H1l28 48z"/><path d="M462 326H298v48h164z"/></svg></span>BONSAI 27B</div>
+<div class="c-status" id="cStatus">
+<span class="c-dot" id="cDot"></span>
+<span class="c-status-text" id="cStatusText">READY</span>
+</div>
+<div class="c-actions">
+<button class="c-btn" id="kernelsBtn" type="button">VIEW KERNELS</button>
+<button class="c-btn" id="clearBtn" type="button" disabled>CLEAR</button>
+</div>
+</div>
+</header>
+<div class="c-scroll" id="cScroll">
+<div class="c-thread" id="cThread">
+<div class="c-welcome" id="cWelcome">
+<div class="c-welcome-glyph" aria-hidden="true"><span class="logo-mark"><svg viewBox="0 0 575 412" fill="currentColor"><path transform="translate(53 -1)" d="m217 375-42-72 84-144h49L217 1 1 375z"/><path transform="translate(297 181)" d="M29 1 1 49h28z"/><path d="M438 182H326v48h112z"/><path transform="translate(255 253)" d="M29 1 1 49h28z"/><path d="M510 254H284v48h226z"/><path transform="translate(269 325)" d="M29 1H1l28 48z"/><path d="M462 326H298v48h164z"/></svg></span></div>
+<h2>What is on <span class="thin">your mind?</span></h2>
+<p>The entire 27&nbsp;billion parameter model runs entirely on your device.
+No data ever leaves your machine.</p>
+<div class="c-seeds" id="cSeeds"></div>
+</div>
+</div>
+</div>
+<footer class="c-composer-wrap">
+<div class="c-composer">
+<div class="c-field">
+<textarea id="cInput" rows="1" placeholder="Ask anything&hellip;" disabled></textarea>
+<span class="c-bulb-wrap">
+<button class="c-bulb" id="thinkToggle" type="button" aria-pressed="false"
+aria-label="Toggle thinking mode" disabled>
+<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true">
+<path class="bulb-glass" d="M12 2.5a6.5 6.5 0 0 1 4 11.62c-.72.55-1 1.4-1 2.38h-6c0-.98-.28-1.83-1-2.38A6.5 6.5 0 0 1 12 2.5z"/>
+<path d="M9.5 19.5h5"/><path d="M10.5 22h3"/>
+</svg>
+</button>
+<span class="c-tip" id="thinkTip" role="tooltip">THINKING OFF</span>
+</span>
+<button class="c-send" id="cSend" type="button" disabled aria-label="Send">
+<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M5 12h14"/><path d="m13 6 6 6-6 6"/></svg>
+</button>
+<button class="c-stop" id="cStop" type="button" aria-label="Stop generation">
+<svg viewBox="0 0 24 24" fill="currentColor"><rect x="7" y="7" width="10" height="10" rx="1.5"/></svg>
+</button>
+</div>
+<div class="c-meta-row">
+<span id="cLive"></span>
+</div>
+</div>
+</footer>
+</section>
+<div class="kx" id="kernelsOverlay" hidden>
+<div class="kx-backdrop" data-close></div>
+<div class="kx-panel" role="dialog" aria-modal="true" aria-label="Rendered kernels">
+<div class="kx-head">
+<div class="kx-title">
+<h3>Kernels</h3>
+<span class="kx-sub" id="kxSub"></span>
+</div>
+<button class="kx-close" type="button" aria-label="Close" data-close>
+<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round"><path d="M6 6l12 12M18 6 6 18"/></svg>
+</button>
+</div>
+<div class="kx-body">
+<div class="kx-side"><div class="kx-list" id="kxList"></div></div>
+<div class="kx-view">
+<div class="kx-intro" id="kxIntro">
+<div class="kx-intro-inner">
+<div class="kx-spark" aria-hidden="true"><span class="kx-spark-ring"></span><span class="kx-spark-core"></span><span class="kx-spark-glyph"><span class="logo-mark"><svg viewBox="0 0 575 412" fill="currentColor"><path transform="translate(53 -1)" d="m217 375-42-72 84-144h49L217 1 1 375z"/><path transform="translate(297 181)" d="M29 1 1 49h28z"/><path d="M438 182H326v48h112z"/><path transform="translate(255 253)" d="M29 1 1 49h28z"/><path d="M510 254H284v48h226z"/><path transform="translate(269 325)" d="M29 1H1l28 48z"/><path d="M462 326H298v48h164z"/></svg></span></span></div>
+<h2 class="kx-intro-title">What are kernels?</h2>
+<p class="kx-intro-lead">Kernels are the low-level GPU programs that do the model's actual
+math &mdash; the 1-bit matrix multiplies, linear attention and normalizations behind every
+token. Each one is a WGSL compute shader, compiled by your browser for this GPU.</p>
+<ul class="kx-points">
+<li><b>Written by AI agents.</b> Every kernel in this list was authored by
+Fable&nbsp;5 and GPT&nbsp;5.6&nbsp;Sol &mdash; then tested for correctness and
+benchmarked on the shapes it serves before earning its place.</li>
+<li><b>1-bit weights, full-speed math.</b> Packed binary weights are decoded inside the
+kernel itself &mdash; what you read below is the real inference path.</li>
+<li><b>Fully local.</b> These shaders &mdash; and your conversation &mdash; never leave
+this machine.</li>
+</ul>
+<div class="kx-intro-hint">SELECT A KERNEL TO READ ITS REAL SOURCE</div>
+</div>
+</div>
+<div class="kx-source" id="kxSource" hidden>
+<div class="kx-view-head">
+<span class="kx-name" id="kxName"></span>
+<div class="kx-view-actions">
+<span class="kx-lines" id="kxLines"></span>
+<button class="kx-copy" id="kxCopy" type="button">COPY</button>
+</div>
+</div>
+<pre class="kx-code"><code id="kxCode"></code></pre>
+</div>
+</div>
+</div>
+</div>
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+      }`,fragmentShader:`
+      varying vec2 vP;
+      void main(){
+        vec2 g = abs(fract(vP / 2.2) - 0.5);
+        float lx = smoothstep(0.487, 0.5, g.x);
+        float ly = smoothstep(0.487, 0.5, g.y);
+        float line = max(lx, ly);
+        float r = length(vP * vec2(1.0, 1.6));
+        float vig = 1.0 - smoothstep(5.0, 20.0, r);
+        float glow = exp(-r * r * 0.020) * 0.16;
+        vec3 col = vec3(0.30, 0.36, 0.52) * line * 0.09 * vig
+                 + vec3(0.10, 0.12, 0.20) * glow;
+        gl_FragColor = vec4(col, 1.0);
+      }`});const grid=new THREE.Mesh(new THREE.PlaneGeometry(60,32),gridMat);grid.position.set(0,.2,-8);grid.renderOrder=0;scene.add(grid);const backlight=new THREE.Sprite(new THREE.SpriteMaterial({map:glowTex,color:8359867,transparent:true,opacity:.07,blending:THREE.AdditiveBlending,depthTest:false,depthWrite:false}));backlight.scale.setScalar(11);backlight.position.set(.4,.3,-4);backlight.renderOrder=2;scene.add(backlight);const DUST_N=160;const dustGeo=new THREE.BufferGeometry;{const pos=new Float32Array(DUST_N*3);const seed=new Float32Array(DUST_N);for(let i=0;i<DUST_N;i++){pos[i*3]=(Math.random()*2-1)*11;pos[i*3+1]=(Math.random()*2-1)*6;pos[i*3+2]=-6+Math.random()*8;seed[i]=Math.random()}dustGeo.setAttribute("position",new THREE.BufferAttribute(pos,3));dustGeo.setAttribute("aSeed",new THREE.BufferAttribute(seed,1))}const dustMat=new THREE.ShaderMaterial({transparent:true,depthWrite:false,depthTest:false,blending:THREE.AdditiveBlending,uniforms:{uTime:{value:0},uDpr:{value:DPR}},vertexShader:`
+      attribute float aSeed;
+      uniform float uTime; uniform float uDpr;
+      varying float vA;
+      void main(){
+        vec3 p = position;
+        p.x += sin(uTime * 0.12 + aSeed * 7.0) * 0.6;
+        p.y += cos(uTime * 0.10 + aSeed * 13.0) * 0.4;
+        vec4 mv = modelViewMatrix * vec4(p, 1.0);
+        gl_PointSize = (1.5 + aSeed * 2.5) * (14.0 / -mv.z) * uDpr;
+        vA = 0.5 + 0.5 * sin(uTime * (0.4 + aSeed * 0.7) + aSeed * 20.0);
+        gl_Position = projectionMatrix * mv;
+      }`,fragmentShader:`
+      varying float vA;
+      void main(){
+        float d = length(gl_PointCoord - 0.5);
+        float a = smoothstep(0.5, 0.1, d) * vA * 0.35;
+        gl_FragColor = vec4(0.75, 0.80, 0.95, a);
+      }`});const dust=new THREE.Points(dustGeo,dustMat);dust.frustumCulled=false;dust.renderOrder=2;scene.add(dust);function onResize(){const w=window.innerWidth,h=window.innerHeight;renderer.setSize(w,h);const aspect=w/h;camera.aspect=aspect;camZ=Math.min(9.6/Math.min(1,aspect/1.15),15.5);camera.updateProjectionMatrix()}window.addEventListener("resize",onResize);onResize();const clock=new THREE.Clock;let t=0;(function animate(){if(document.body.classList.contains("stage-chat"))return;requestAnimationFrame(animate);t+=Math.min(clock.getDelta(),.05);const tA=t*SPD;dustMat.uniforms.uTime.value=tA;textMat.opacity=.05+.02*(.5+.5*Math.sin(tA*.35));backlight.material.opacity=.06+.02*(.5+.5*Math.sin(tA*.6));camera.position.set(Math.sin(tA*.13)*.15,.35+Math.cos(tA*.1)*.08,camZ);camera.lookAt(0,.05,0);renderer.render(scene,camera)})()})();if(!window.THREE){App.flatMode()}else if(START_STAGE==="landing"){App.bootLanding=function(){if(App._landingBooted)return;App._landingBooted=true;(function(){const SPD=matchMedia("(prefers-reduced-motion: reduce)").matches?.35:1;const clamp=(v,a,b)=>v<a?a:v>b?b:v;const clamp012=v=>v<0?0:v>1?1:v;const hermite=x=>x*x*(3-2*x);const sstep=(a,b,x)=>hermite(clamp012((x-a)/(b-a)));const wrapPI=a=>{while(a>Math.PI)a-=Math.PI*2;while(a<-Math.PI)a+=Math.PI*2;return a};function boot(){if(!window.THREE)throw new Error("three.js failed to load");const R=1.85;const DEPTH=2.1;const N_CENTER=1.17;const SPREAD=3;const L_RED=650,L_VIOLET=410;const CAU_B=(1.228-1.115)/(1/(L_VIOLET*L_VIOLET)-1/(L_RED*L_RED));const CAU_A=1.115-CAU_B/(L_RED*L_RED);const lambdaOf=w=>L_RED+(L_VIOLET-L_RED)*w;const nOf=w=>{const l=lambdaOf(w);return CAU_A+CAU_B/(l*l)};function waveColor(l){let r,g,b;if(l<440){r=-(l-440)/60;g=0;b=1}else if(l<490){r=0;g=(l-440)/50;b=1}else if(l<510){r=0;g=1;b=-(l-510)/20}else if(l<580){r=(l-510)/70;g=1;b=0}else if(l<645){r=1;g=-(l-645)/65;b=0}else{r=1;g=0;b=0}const f=l<420?.45+.55*(l-395)/25:l>645?.5+.5*(700-l)/55:1;return[r*f,g*f,b*f]}const specColor=w=>waveColor(lambdaOf(w));const NC=24;const NK=40;const NKI=8;const N_COL=Array.from({length:NC},(_,c)=>nOf(c/(NC-1)));const PULSE_W=[0,.2,.4,.6,.8,1];const TILT=.12;const RAY={px:0,py:.12,dx:Math.cos(TILT),dy:Math.sin(TILT)};const SLOPE=RAY.dy/RAY.dx;const LIGHT_SPEED=4;const CV=LIGHT_SPEED/SPD;const T0=.25;const EXIT_LEN=13.5;const canvas=document.getElementById("sceneA");const renderer=new THREE.WebGLRenderer({canvas,antialias:true,alpha:true,powerPreference:"high-performance"});renderer.setClearColor(329225,0);const DPR=Math.min(window.devicePixelRatio||1,2);renderer.setPixelRatio(DPR);const scene=new THREE.Scene;const camera=new THREE.PerspectiveCamera(42,1,.1,100);camera.position.set(0,.35,9.6);camera.lookAt(0,.05,0);let camZ=9.6;const viewX={left:-9,right:9};function makeGlowTexture(){const c=document.createElement("canvas");c.width=c.height=128;const g=c.getContext("2d");const grd=g.createRadialGradient(64,64,0,64,64,64);grd.addColorStop(0,"rgba(255,255,255,1)");grd.addColorStop(.22,"rgba(255,255,255,.85)");grd.addColorStop(.55,"rgba(255,255,255,.18)");grd.addColorStop(1,"rgba(255,255,255,0)");g.fillStyle=grd;g.fillRect(0,0,128,128);const t=new THREE.CanvasTexture(c);t.minFilter=THREE.LinearFilter;return t}function makeWordTexture(word2){const W=2048,H=400;const c=document.createElement("canvas");c.width=W;c.height=H;const g=c.getContext("2d");g.fillStyle="#ffffff";g.textBaseline="middle";const font=px2=>`700 ${px2}px 'Inter','SF Pro Display',-apple-system,'Segoe UI',Roboto,'Helvetica Neue',Arial,sans-serif`;const measure=(px2,sp2)=>{g.font=font(px2);let t2=-sp2;for(const ch of word2)t2+=g.measureText(ch).width+sp2;return t2};let px=250,sp=70;const total0=measure(px,sp);const fit=Math.min(1,(W-120)/total0);px*=fit;sp*=fit;let x=(W-measure(px,sp))/2;for(const ch of word2){g.fillText(ch,x,H/2+10*fit);x+=g.measureText(ch).width+sp}const t=new THREE.CanvasTexture(c);t.minFilter=THREE.LinearFilter;return{texture:t,aspect:H/W}}const glowTex=makeGlowTexture();const word=makeWordTexture("BONSAI 27B");const TP={z:-5,cx:0,cy:.15,hw:8.6,hh:8.6*word.aspect};const LOCAL_V=Array.from({length:3},(_,i)=>{const a=Math.PI/2+i*(Math.PI*2/3);return{x:R*Math.cos(a),y:R*Math.sin(a)}});const shape=new THREE.Shape;shape.moveTo(LOCAL_V[0].x,LOCAL_V[0].y);shape.lineTo(LOCAL_V[1].x,LOCAL_V[1].y);shape.lineTo(LOCAL_V[2].x,LOCAL_V[2].y);shape.closePath();const prismGeo=new THREE.ExtrudeGeometry(shape,{depth:DEPTH,bevelEnabled:false});prismGeo.translate(0,0,-DEPTH/2);const prism=new THREE.Group;scene.add(prism);const backMesh=new THREE.Mesh(prismGeo,new THREE.MeshBasicMaterial({color:658708,transparent:true,opacity:.5,side:THREE.BackSide,depthWrite:false}));backMesh.renderOrder=4;prism.add(backMesh);const glassMat=new THREE.ShaderMaterial({transparent:true,depthWrite:false,side:THREE.FrontSide,uniforms:{uCam:{value:new THREE.Vector3},uTex:{value:word.texture},uTime:{value:0},uPlane:{value:new THREE.Vector4(TP.cx,TP.cy,TP.hw,TP.hh)},uPlaneZ:{value:TP.z}},vertexShader:`
+      varying vec3 vN; varying vec3 vW;
+      void main(){
+        vN = normalize(mat3(modelMatrix) * normal);
+        vec4 w = modelMatrix * vec4(position, 1.0);
+        vW = w.xyz;
+        gl_Position = projectionMatrix * viewMatrix * w;
+      }`,fragmentShader:`
+      uniform vec3 uCam; uniform sampler2D uTex; uniform float uTime;
+      uniform vec4 uPlane; uniform float uPlaneZ;
+      varying vec3 vN; varying vec3 vW;
+
+      // A dark-room gradient the glass can reflect and refract, so every
+      // face carries a soft vertical sheen even off the word plane.
+      vec3 room(vec3 d){
+        float h = clamp(d.y * 0.6 + 0.5, 0.0, 1.0);
+        return mix(vec3(0.010, 0.012, 0.020), vec3(0.075, 0.095, 0.150), h);
+      }
+
+      void main(){
+        vec3 N = normalize(vN);
+        vec3 V = normalize(vW - uCam);
+        float ndv  = abs(dot(N, -V));
+        float fres = pow(1.0 - ndv, 2.6);
+        vec3 col = vec3(0.016, 0.020, 0.034);
+
+        // Bend the view ray into the glass and sample the backdrop word
+        // where it lands, with a slight RGB split — chromatic aberration.
+        vec3 Rr = refract(V, N, 1.0 / 1.45);
+        col += room(Rr) * 0.55;
+        if (Rr.z < -0.001){
+          float tt = (uPlaneZ - vW.z) / Rr.z;
+          vec2 hit = vW.xy + Rr.xy * tt;
+          vec2 uv = vec2((hit.x - uPlane.x) / (2.0 * uPlane.z) + 0.5,
+                         (hit.y - uPlane.y) / (2.0 * uPlane.w) + 0.5);
+          if (uv.x > 0.0 && uv.x < 1.0 && uv.y > 0.0 && uv.y < 1.0){
+            vec2 ca = Rr.xy * 0.05;
+            float tr = texture2D(uTex, uv + ca).r;
+            float tg = texture2D(uTex, uv).g;
+            float tb = texture2D(uTex, uv - ca).b;
+            col += vec3(tr, tg, tb) * 0.5;
+          }
+        }
+
+        // Mirror sheen at grazing angles + a tight bright lip on the rim.
+        col += room(reflect(V, N)) * fres * 1.7;
+        col += vec3(0.60, 0.66, 0.80) * pow(1.0 - ndv, 6.0) * 0.38;
+
+        float sheen = 0.5 + 0.5 * sin(vW.x * 1.7 + vW.y * 2.3 + uTime * 0.6);
+        col += vec3(0.020, 0.025, 0.035) * sheen;
+        col += vec3(0.50, 0.56, 0.68) * fres * 0.35;
+        gl_FragColor = vec4(col, 0.74 + fres * 0.20);
+      }`});const glassMesh=new THREE.Mesh(prismGeo,glassMat);glassMesh.renderOrder=5;prism.add(glassMesh);const edgeMat=new THREE.LineBasicMaterial({color:16777215,transparent:true,opacity:.5,blending:THREE.AdditiveBlending,depthTest:false,depthWrite:false});const edges=new THREE.LineSegments(new THREE.EdgesGeometry(prismGeo),edgeMat);edges.renderOrder=8;prism.add(edges);const BEAM_VERT=`
+    attribute vec3 aTangent;
+    attribute float aSide;
+    attribute float aT;
+    uniform float uWidth;
+    varying float vT; varying float vSide;
+    void main(){
+      vT = aT; vSide = aSide;
+      vec4 mv = modelViewMatrix * vec4(position, 1.0);
+      vec3 tv = (modelViewMatrix * vec4(position + aTangent, 1.0)).xyz - mv.xyz;
+      vec3 toCam = normalize(-mv.xyz);
+      vec3 sideDir = cross(normalize(tv), toCam);
+      float L = length(sideDir);
+      sideDir = (L > 0.0001) ? sideDir / L : vec3(0.0, 1.0, 0.0);
+      mv.xyz += sideDir * aSide * uWidth;
+      gl_Position = projectionMatrix * mv;
+    }`;const BEAM_FRAG=`
+    uniform vec3 uColor;
+    uniform float uOpacity; uniform float uTime; uniform float uReveal;
+    uniform float uTailFade; uniform float uSeed;
+    varying float vT; varying float vSide;
+    void main(){
+      float s = vSide;
+      float core = exp(-s * s * 20.0);
+      float halo = exp(-s * s * 4.5) * 0.5;
+      float prof = core + halo;
+      float tail = mix(1.0, 0.16 + 0.84 * pow(1.0 - vT, 1.5), uTailFade);
+      float rev = clamp((uReveal - vT) / 0.12, 0.0, 1.0);
+      float shimmer = 0.9 + 0.1 * sin(vT * 30.0 - uTime * 4.5 + uSeed * 17.0);
+      float a = prof * tail * rev * shimmer * uOpacity;
+      gl_FragColor = vec4(uColor * (0.72 + 0.85 * core), a);
+    }`;function makeBeam(n,hex,{width=.05,opacity=1,tailFade=0,order=6}={}){const geo=new THREE.BufferGeometry;const pos=new Float32Array(n*2*3);const tan=new Float32Array(n*2*3);const side=new Float32Array(n*2);const tArr=new Float32Array(n*2);for(let i=0;i<n;i++){side[2*i]=1;side[2*i+1]=-1;tArr[2*i]=tArr[2*i+1]=i/(n-1)}const idx=new Uint16Array((n-1)*6);for(let i=0;i<n-1;i++){const o=i*6,v=i*2;idx[o]=v;idx[o+1]=v+1;idx[o+2]=v+2;idx[o+3]=v+1;idx[o+4]=v+3;idx[o+5]=v+2}geo.setIndex(new THREE.BufferAttribute(idx,1));const posAttr=new THREE.BufferAttribute(pos,3).setUsage(THREE.DynamicDrawUsage);const tanAttr=new THREE.BufferAttribute(tan,3).setUsage(THREE.DynamicDrawUsage);geo.setAttribute("position",posAttr);geo.setAttribute("aTangent",tanAttr);geo.setAttribute("aSide",new THREE.BufferAttribute(side,1));geo.setAttribute("aT",new THREE.BufferAttribute(tArr,1));const mat=new THREE.ShaderMaterial({transparent:true,depthWrite:false,depthTest:false,blending:THREE.AdditiveBlending,side:THREE.DoubleSide,uniforms:{uColor:{value:new THREE.Color(hex)},uWidth:{value:width},uOpacity:{value:opacity},uTime:{value:0},uReveal:{value:0},uTailFade:{value:tailFade},uSeed:{value:Math.random()*10}},vertexShader:BEAM_VERT,fragmentShader:BEAM_FRAG});const mesh=new THREE.Mesh(geo,mat);mesh.frustumCulled=false;mesh.renderOrder=order;scene.add(mesh);const update=pts=>{for(let k=0;k<n;k++){const p=pts[k];const a=pts[k>0?k-1:0];const b=pts[k<n-1?k+1:n-1];let tx=b.x-a.x,ty=b.y-a.y,tz=b.z-a.z;const L=Math.sqrt(tx*tx+ty*ty+tz*tz)||1;tx/=L;ty/=L;tz/=L;const o=k*6;pos[o]=p.x;pos[o+1]=p.y;pos[o+2]=p.z;pos[o+3]=p.x;pos[o+4]=p.y;pos[o+5]=p.z;tan[o]=tx;tan[o+1]=ty;tan[o+2]=tz;tan[o+3]=tx;tan[o+4]=ty;tan[o+5]=tz}posAttr.needsUpdate=true;tanAttr.needsUpdate=true};return{mat,update}}const vecArray=n=>Array.from({length:n},()=>new THREE.Vector3);const INC_N=56,REF_N=16,RES_N=24;const INC_PTS=vecArray(INC_N);const REF_PTS=vecArray(REF_N);const RES_PTS=vecArray(RES_N);const incoming=makeBeam(INC_N,16777215,{width:.06,opacity:.95});const reflectBeam=makeBeam(REF_N,16777215,{width:.04,opacity:.09,tailFade:1});const residualBeam=makeBeam(RES_N,16777215,{width:.045,opacity:.1,tailFade:1});const allBeams=[incoming,reflectBeam,residualBeam];const SHEET_VERT=`
+    attribute float aW;      // spectral coordinate across the fan
+    attribute float aT;      // parameter along the path
+    attribute float aAlpha;  // per-column validity (eases out on TIR)
+    attribute float aRev;    // per-column propagation front
+    attribute vec3  aColor;  // spectral RGB
+    varying float vW; varying float vT; varying float vA; varying float vRev;
+    varying vec3 vCol;
+    void main(){
+      vW = aW; vT = aT; vA = aAlpha; vRev = aRev; vCol = aColor;
+      gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0);
+    }`;const SHEET_FRAG=`
+    uniform float uTime; uniform float uOpacity;
+    uniform float uHeadWhite; uniform float uHeadK;
+    uniform float uAlongBase; uniform float uAlongK;
+    varying float vW; varying float vT; varying float vA; varying float vRev;
+    varying vec3 vCol;
+    float hash(vec2 p){ return fract(sin(dot(p, vec2(127.1, 311.7))) * 43758.5453); }
+    void main(){
+      float edge  = smoothstep(0.0, 0.05, vW) * smoothstep(1.0, 0.95, vW);
+      float along = uAlongBase + (1.0 - uAlongBase) * exp(-vT * uAlongK);
+      along *= 1.0 - smoothstep(0.90, 1.0, vT);
+      float rev = clamp((vRev - vT) / 0.10, 0.0, 1.0);
+      float grain = 0.88 + 0.24 * hash(vec2(vT * 211.0 + vW * 97.0, floor(uTime * 24.0)));
+      vec3 col = mix(vCol, vec3(1.0), uHeadWhite * exp(-vT * uHeadK));
+      gl_FragColor = vec4(col, edge * along * rev * vA * grain * uOpacity);
+    }`;function makeSheet(cols,rows,{opacity,headWhite,headK,alongBase,alongK,order=6}){const count=cols*rows;const geo=new THREE.BufferGeometry;const pos=new Float32Array(count*3);const aW=new Float32Array(count);const aT=new Float32Array(count);const aA=new Float32Array(count);const aRev=new Float32Array(count);const aCol=new Float32Array(count*3);for(let k=0;k<rows;k++){for(let c=0;c<cols;c++){const i=k*cols+c,w=c/(cols-1);aW[i]=w;aT[i]=k/(rows-1);const rgb=specColor(w);aCol[i*3]=rgb[0];aCol[i*3+1]=rgb[1];aCol[i*3+2]=rgb[2]}}const idx=new Uint16Array((cols-1)*(rows-1)*6);let o=0;for(let k=0;k<rows-1;k++){for(let c=0;c<cols-1;c++){const v=k*cols+c;idx[o++]=v;idx[o++]=v+1;idx[o++]=v+cols;idx[o++]=v+1;idx[o++]=v+cols+1;idx[o++]=v+cols}}geo.setIndex(new THREE.BufferAttribute(idx,1));const posAttr=new THREE.BufferAttribute(pos,3).setUsage(THREE.DynamicDrawUsage);const aAttr=new THREE.BufferAttribute(aA,1).setUsage(THREE.DynamicDrawUsage);const revAttr=new THREE.BufferAttribute(aRev,1).setUsage(THREE.DynamicDrawUsage);geo.setAttribute("position",posAttr);geo.setAttribute("aAlpha",aAttr);geo.setAttribute("aRev",revAttr);geo.setAttribute("aW",new THREE.BufferAttribute(aW,1));geo.setAttribute("aT",new THREE.BufferAttribute(aT,1));geo.setAttribute("aColor",new THREE.BufferAttribute(aCol,3));const mat=new THREE.ShaderMaterial({transparent:true,depthWrite:false,depthTest:false,blending:THREE.AdditiveBlending,side:THREE.DoubleSide,uniforms:{uTime:{value:0},uOpacity:{value:opacity},uHeadWhite:{value:headWhite},uHeadK:{value:headK},uAlongBase:{value:alongBase},uAlongK:{value:alongK}},vertexShader:SHEET_VERT,fragmentShader:SHEET_FRAG});const mesh=new THREE.Mesh(geo,mat);mesh.frustumCulled=false;mesh.renderOrder=order;scene.add(mesh);const setPoint=(k,c,x,y,z)=>{const i=(k*cols+c)*3;pos[i]=x;pos[i+1]=y;pos[i+2]=z};const setColumnScalar=(arr,c,v)=>{for(let k=0;k<rows;k++)arr[k*cols+c]=v};return{mat,cols,rows,pos,setPoint,setAlpha:(c,v)=>setColumnScalar(aA,c,v),setRev:(c,v)=>setColumnScalar(aRev,c,v),commit(){posAttr.needsUpdate=aAttr.needsUpdate=revAttr.needsUpdate=true}}}function sampleSheet(sheet,c,u,out){const f=clamp012(u)*(sheet.rows-1);const k=Math.min(sheet.rows-2,Math.floor(f));const m=f-k;const i0=(k*sheet.cols+c)*3,i1=((k+1)*sheet.cols+c)*3;const p=sheet.pos;out.set(p[i0]+(p[i1]-p[i0])*m,p[i0+1]+(p[i1+1]-p[i0+1])*m,p[i0+2]+(p[i1+2]-p[i0+2])*m)}const exitSheet=makeSheet(NC,NK,{opacity:.92,headWhite:.55,headK:5.5,alongBase:.34,alongK:1.5});const innerSheet=makeSheet(NC,NKI,{opacity:.3,headWhite:.65,headK:4,alongBase:.55,alongK:.9});const TRI=[{x:0,y:0},{x:0,y:0},{x:0,y:0}];const TRI_C={x:0,y:0};function updateTri(rotZ,bob){const c=Math.cos(rotZ),s=Math.sin(rotZ);for(let i=0;i<3;i++){const v=LOCAL_V[i];TRI[i].x=v.x*c-v.y*s;TRI[i].y=v.x*s+v.y*c+bob}TRI_C.x=(TRI[0].x+TRI[1].x+TRI[2].x)/3;TRI_C.y=(TRI[0].y+TRI[1].y+TRI[2].y)/3}const cross2=(ax,ay,bx,by)=>ax*by-ay*bx;function castRay(px,py,dx,dy,skip,out){let best=Infinity,be=-1,bx=0,by=0,bnx=0,bny=0;for(let i=0;i<3;i++){if(i===skip)continue;const a=TRI[i],b=TRI[(i+1)%3];const ex=b.x-a.x,ey=b.y-a.y;const den=cross2(dx,dy,ex,ey);if(Math.abs(den)<1e-9)continue;const wx=a.x-px,wy=a.y-py;const t=cross2(wx,wy,ex,ey)/den;const s=cross2(wx,wy,dx,dy)/den;if(t>1e-4&&s>=-1e-4&&s<=1.0001&&t<best){best=t;be=i;bx=px+dx*t;by=py+dy*t;let nx=ey,ny=-ex;const L=Math.sqrt(nx*nx+ny*ny)||1;nx/=L;ny/=L;const mx=(a.x+b.x)/2,my=(a.y+b.y)/2;if(nx*(mx-TRI_C.x)+ny*(my-TRI_C.y)<0){nx=-nx;ny=-ny}bnx=nx;bny=ny}}if(be<0)return false;out.t=best;out.x=bx;out.y=by;out.nx=bnx;out.ny=bny;out.edge=be;return true}function refract2(ix,iy,nx,ny,eta,out){let d=ix*nx+iy*ny;if(d>0){nx=-nx;ny=-ny;d=-d}const cosi=-d;const k=1-eta*eta*(1-cosi*cosi);if(k<0)return false;const f=eta*cosi-Math.sqrt(k);out.x=eta*ix+f*nx;out.y=eta*iy+f*ny;return true}function reflect2(ix,iy,nx,ny,out){const d=ix*nx+iy*ny;out.x=ix-2*d*nx;out.y=iy-2*d*ny}const MAX_TRACE_PTS=5;const makeTraceRec=()=>({pts:Array.from({length:MAX_TRACE_PTS},()=>({x:0,y:0})),count:0,ex:0,ey:0,dx:0,dy:0,len:0,valid:false});const TRACES=Array.from({length:NC},makeTraceRec);const CTRACE=makeTraceRec();const ENTRY={valid:false,x:0,y:0,nx:0,ny:0,edge:-1};const HIT_E={t:0,x:0,y:0,nx:0,ny:0,edge:-1};const HIT_I={t:0,x:0,y:0,nx:0,ny:0,edge:-1};const TDIR={x:0,y:0};function trace(n,rec){rec.count=0;rec.valid=false;if(!ENTRY.valid)return;if(!refract2(RAY.dx,RAY.dy,ENTRY.nx,ENTRY.ny,1/n,TDIR))return;rec.pts[0].x=ENTRY.x;rec.pts[0].y=ENTRY.y;rec.count=1;let cx=ENTRY.x,cy=ENTRY.y,dx=TDIR.x,dy=TDIR.y,skip=ENTRY.edge;let len=0;for(let b=0;b<3;b++){if(!castRay(cx,cy,dx,dy,skip,HIT_I))return;len+=HIT_I.t;rec.pts[rec.count].x=HIT_I.x;rec.pts[rec.count].y=HIT_I.y;rec.count++;if(refract2(dx,dy,HIT_I.nx,HIT_I.ny,n,TDIR)){rec.ex=HIT_I.x;rec.ey=HIT_I.y;rec.dx=TDIR.x;rec.dy=TDIR.y;rec.len=len;rec.valid=true;return}reflect2(dx,dy,HIT_I.nx,HIT_I.ny,TDIR);dx=TDIR.x;dy=TDIR.y;cx=HIT_I.x;cy=HIT_I.y;skip=HIT_I.edge}}const SEGL=new Float64Array(MAX_TRACE_PTS);function writeInnerColumn(rec,c,zOff){const rows=innerSheet.rows,cnt=rec.count;if(cnt<2)return;let total=0;for(let i=1;i<cnt;i++){const dx=rec.pts[i].x-rec.pts[i-1].x;const dy=rec.pts[i].y-rec.pts[i-1].y;SEGL[i]=Math.sqrt(dx*dx+dy*dy);total+=SEGL[i]}if(total<1e-6)return;let seg=1,acc=0;for(let k=0;k<rows;k++){const target=total*k/(rows-1);while(seg<cnt-1&&acc+SEGL[seg]<target){acc+=SEGL[seg];seg++}const u=SEGL[seg]>1e-9?(target-acc)/SEGL[seg]:0;const a=rec.pts[seg-1],b=rec.pts[seg];innerSheet.setPoint(k,c,a.x+(b.x-a.x)*u,a.y+(b.y-a.y)*u,zOff)}}function writeExitColumn(c,w,ex,ey,ang0,tA,zOff){const rows=exitSheet.rows,step=EXIT_LEN/(rows-1);const angT=ang0*(1-.55*clamp012(Math.cos(ang0)));let x=ex,y=ey;for(let k=0;k<rows;k++){const u=k/(rows-1);const e=hermite(u)*.9;const ang=ang0+(angT-ang0)*e;if(k>0){x+=Math.cos(ang)*step;y+=Math.sin(ang)*step}const sway=Math.sin(tA*.8+w*5.4+u*2.4)*.14*u;exitSheet.setPoint(k,c,x-Math.sin(ang)*sway,y+Math.cos(ang)*sway,zOff)}}function buildIncoming(tA,hasEntry){const x0=viewX.left-.5;const y0=RAY.py+(x0-RAY.px)*SLOPE;let x1,y1;if(hasEntry){x1=ENTRY.x;y1=ENTRY.y}else{x1=viewX.right+1;y1=RAY.py+(x1-RAY.px)*SLOPE}for(let k=0;k<INC_N;k++){const u=k/(INC_N-1);const x=x0+(x1-x0)*u;let y=y0+(y1-y0)*u;const envL=sstep(.02,.18,u);const envR=hasEntry?hermite(clamp012((x1-x)/3)):sstep(.02,.18,1-u);y+=Math.sin((x-CV*tA)*.65)*.05*envL*envR;INC_PTS[k].set(x,y,0)}if(hasEntry)INC_PTS[INC_N-1].set(ENTRY.x,ENTRY.y,0)}function buildReflect(){reflect2(RAY.dx,RAY.dy,ENTRY.nx,ENTRY.ny,TDIR);const L=6;for(let k=0;k<REF_N;k++){const u=k/(REF_N-1);REF_PTS[k].set(ENTRY.x+TDIR.x*L*u,ENTRY.y+TDIR.y*L*u,0)}}function buildResidual(){const L=12;for(let k=0;k<RES_N;k++){const u=k/(RES_N-1);RES_PTS[k].set(ENTRY.x+RAY.dx*L*u,ENTRY.y+RAY.dy*L*u,.01)}}function makeSprite(hex,scale,opacity,order){const s=new THREE.Sprite(new THREE.SpriteMaterial({map:glowTex,color:hex,transparent:true,opacity,blending:THREE.AdditiveBlending,depthTest:false,depthWrite:false}));s.scale.setScalar(scale);s.renderOrder=order;scene.add(s);return s}const apexDot=makeSprite(16777215,.12,.9,9);const sourceDot=makeSprite(16777215,.17,.95,9);const entryGlow=makeSprite(14542591,.3,0,9);const exitGlow=makeSprite(16777215,.5,0,9);const cornerDots=[1,2].map(()=>makeSprite(16777215,.085,0,9));const pulseHex=w=>new THREE.Color(...specColor(w)).getHex();const NP=5,T_EMIT=2.2,CYCLE=NP*T_EMIT;const whitePulses=Array.from({length:NP},()=>makeSprite(16777215,.085,0,9));const colorPulses=PULSE_W.map(w=>{const hex=pulseHex(w);return Array.from({length:NP},()=>makeSprite(hex,.075,0,9))});const PULSE_COL=PULSE_W.map(w=>Math.round(w*(NC-1)));const washes=PULSE_W.map(w=>makeSprite(pulseHex(w),5.5,0,2));const SAMP=new THREE.Vector3;function samplePts(pts,u,out){const f=clamp012(u)*(pts.length-1);const i=Math.min(pts.length-2,Math.floor(f));out.copy(pts[i]).lerp(pts[i+1],f-i)}const APEX_LOCAL=new THREE.Vector3(0,R,DEPTH/2+.02);const CORNER_LOCAL=LOCAL_V.slice(1).map(v=>new THREE.Vector3(v.x,v.y,DEPTH/2+.02));const APEX_W=new THREE.Vector3;let dragging=false,lastPX=0,lastPY=0;let userX=0,userY=0,userZ=0;let velX=0,velY=0,velZ=0;let velTrail=[];let autoAmp=1,lastInteract=-10;let mouseNX=0,mouseNY=0;let parX=0,parY=0,parTX=0,parTY=0;let tGlobal=0;canvas.addEventListener("pointerdown",e=>{dragging=true;velX=velY=velZ=0;velTrail=[{t:performance.now(),x:userX,y:userY,z:userZ}];lastPX=e.clientX;lastPY=e.clientY;lastInteract=tGlobal;document.body.classList.add("grabbing");try{canvas.setPointerCapture(e.pointerId)}catch(err){}});window.addEventListener("pointermove",e=>{if(!App.landingActive)return;mouseNX=e.clientX/window.innerWidth*2-1;mouseNY=e.clientY/window.innerHeight*2-1;if(!dragging)return;const dx=e.clientX-lastPX,dy=e.clientY-lastPY;lastPX=e.clientX;lastPY=e.clientY;userZ+=dx*.006;userY=clamp(userY+dx*.0028,-.5,.5);userX=clamp(userX+dy*.0035,-.3,.3);const nowMs=performance.now();velTrail.push({t:nowMs,x:userX,y:userY,z:userZ});while(velTrail.length>2&&nowMs-velTrail[0].t>120)velTrail.shift();lastInteract=tGlobal});function endDrag(){if(!dragging)return;dragging=false;if(velTrail.length>1){const a=velTrail[0],b=velTrail[velTrail.length-1];const wdt=Math.max((b.t-a.t)/1e3,1/240);velZ=clamp((b.z-a.z)/wdt,-6,6);velY=clamp((b.y-a.y)/wdt,-2,2);velX=clamp((b.x-a.x)/wdt,-2,2)}velTrail=[];lastInteract=tGlobal;document.body.classList.remove("grabbing")}window.addEventListener("pointerup",endDrag);window.addEventListener("pointercancel",endDrag);function onResize(){const w=window.innerWidth,h=window.innerHeight;renderer.setSize(w,h);const aspect=w/h;camera.aspect=aspect;camZ=Math.min(9.6/Math.min(1,aspect/1.15),15.5);camera.updateProjectionMatrix();const halfH=Math.tan(THREE.MathUtils.degToRad(camera.fov/2))*camZ;viewX.right=halfH*aspect+1.2;viewX.left=-viewX.right}window.addEventListener("resize",onResize);onResize();const colAlpha=new Float32Array(NC);let entryAlpha=0;let trapGlow=0;let lastAC=-.06;const T_OUT=new Float32Array(NC);function castEntry(){const sx=viewX.left-2;const sy=RAY.py+(sx-RAY.px)*SLOPE;const hit=castRay(sx,sy,RAY.dx,RAY.dy,-1,HIT_E)&&HIT_E.nx*RAY.dx+HIT_E.ny*RAY.dy<-.001;ENTRY.valid=hit;if(hit){ENTRY.x=HIT_E.x;ENTRY.y=HIT_E.y;ENTRY.nx=HIT_E.nx;ENTRY.ny=HIT_E.ny;ENTRY.edge=HIT_E.edge}return hit}function centerAngle(){let aC;if(CTRACE.valid){aC=Math.atan2(CTRACE.dy,CTRACE.dx)}else{let sum=0,cnt=0;for(const rec of TRACES){if(rec.valid){sum+=Math.atan2(rec.dy,rec.dx);cnt++}}aC=cnt>0?sum/cnt:lastAC}lastAC=aC;return aC}function updatePulses(tP,dIn,airT,lamp,hasEntry){for(let s=0;s<NP;s++){const emit=s*T_EMIT;const live=tP>=emit;const age=live?(tP-emit)%CYCLE:0;const dAir=age*CV;const wSpr=whitePulses[s];if(live&&dAir<dIn){samplePts(INC_PTS,dAir/dIn,SAMP);wSpr.position.copy(SAMP);wSpr.material.opacity=.85*lamp}else wSpr.material.opacity=0;for(let i=0;i<6;i++){const spr=colorPulses[i][s];const c=PULSE_COL[i];if(!live||!hasEntry||colAlpha[c]<.05||TRACES[c].len<1e-6||age<=airT){spr.material.opacity=0;continue}const tOut=T_OUT[c];if(age<tOut){const u=(age-airT)*(CV/N_COL[c])/TRACES[c].len;sampleSheet(innerSheet,c,u,SAMP);spr.position.copy(SAMP);spr.material.opacity=.9*colAlpha[c]}else if(age<tOut+EXIT_LEN/CV){sampleSheet(exitSheet,c,(age-tOut)*CV/EXIT_LEN,SAMP);spr.position.copy(SAMP);spr.material.opacity=.85*colAlpha[c]}else spr.material.opacity=0}}}function updateOptics(tA,dt,rotZ,bob){updateTri(rotZ,bob);const hasEntry=castEntry();const ease=1-Math.exp(-6*dt);entryAlpha+=((hasEntry?1:0)-entryAlpha)*ease;const tP=Math.max(0,tA-T0);const lamp=clamp012(tP/.3);buildIncoming(tA,hasEntry);incoming.update(INC_PTS);const x0=viewX.left-.5;const dIn=hasEntry?Math.hypot(ENTRY.x-x0,ENTRY.y-(RAY.py+(x0-RAY.px)*SLOPE)):(viewX.right+1-x0)/RAY.dx;const airT=dIn/CV;const sinceEntry=Math.max(0,tP-airT);incoming.mat.uniforms.uReveal.value=clamp012(CV*tP/dIn);if(hasEntry){buildReflect();reflectBeam.update(REF_PTS);buildResidual();residualBeam.update(RES_PTS)}const pastEntry=sinceEntry*CV;reflectBeam.mat.uniforms.uOpacity.value=.09*entryAlpha;residualBeam.mat.uniforms.uOpacity.value=.1*entryAlpha;reflectBeam.mat.uniforms.uReveal.value=clamp012(pastEntry/6);residualBeam.mat.uniforms.uReveal.value=clamp012(pastEntry/12);trace(N_CENTER,CTRACE);for(let c=0;c<NC;c++)trace(N_COL[c],TRACES[c]);const aC=centerAngle();let gx=0,gy=0,ga=0,alive=0,tFirstOut=Infinity;for(let c=0;c<NC;c++){const rec=TRACES[c];const w=c/(NC-1);colAlpha[c]+=((hasEntry&&rec.valid?1:0)-colAlpha[c])*ease;const zOff=(w-.5)*.3;if(rec.valid){alive++;const ai=Math.atan2(rec.dy,rec.dx);writeExitColumn(c,w,rec.ex,rec.ey,aC+wrapPI(ai-aC)*SPREAD,tA,zOff);writeInnerColumn(rec,c,zOff);T_OUT[c]=airT+rec.len*N_COL[c]/CV}const glassRev=rec.len>1e-6?clamp012(sinceEntry*(CV/N_COL[c])/rec.len):0;innerSheet.setAlpha(c,colAlpha[c]);innerSheet.setRev(c,glassRev);exitSheet.setAlpha(c,colAlpha[c]);exitSheet.setRev(c,clamp012(Math.max(0,tP-T_OUT[c])*CV/EXIT_LEN));if(rec.valid||colAlpha[c]>.05){gx+=rec.ex*colAlpha[c];gy+=rec.ey*colAlpha[c];ga+=colAlpha[c];if(rec.valid&&T_OUT[c]<tFirstOut)tFirstOut=T_OUT[c]}}exitSheet.commit();innerSheet.commit();const trapT=hasEntry?(1-alive/NC)*.85:0;trapGlow+=(trapT-trapGlow)*(1-Math.exp(-3*dt));innerSheet.mat.uniforms.uOpacity.value=.3*(1+trapGlow*1.6);for(let i=0;i<6;i++){const c=PULSE_COL[i];sampleSheet(exitSheet,c,.38,SAMP);washes[i].position.set(SAMP.x,SAMP.y,-2);washes[i].material.opacity=.05*colAlpha[c]*clamp012(Math.max(0,tP-T_OUT[c])*CV/5)}updatePulses(tP,dIn,airT,lamp,hasEntry);const exitFront=clamp012(Math.max(0,tP-tFirstOut)*CV/1.5);if(ga>.05)exitGlow.position.set(gx/ga,gy/ga,.05);exitGlow.scale.setScalar(.5*(1+.12*Math.sin(tA*3)));exitGlow.material.opacity=.9*clamp012(ga/(NC*.5))*exitFront;if(hasEntry)entryGlow.position.set(ENTRY.x,ENTRY.y,.05);entryGlow.material.opacity=.7*entryAlpha*clamp012(pastEntry/.7);samplePts(INC_PTS,.03,SAMP);sourceDot.position.copy(SAMP);sourceDot.scale.setScalar(.17+.02*Math.sin(tA*2.1));sourceDot.material.opacity=.95*lamp;incoming.mat.uniforms.uOpacity.value=.95*lamp*(.97+.02*Math.sin(tA*9.1)+.015*Math.sin(tA*3.7));for(const b of allBeams)b.mat.uniforms.uTime.value=tA;exitSheet.mat.uniforms.uTime.value=tA;innerSheet.mat.uniforms.uTime.value=tA;return lamp}const clock=new THREE.Clock;const THIRD=Math.PI*2/3;let ready=false;let spectrumSeen=false;function animate(){if(!App.landingActive)return;requestAnimationFrame(animate);const dt=Math.min(clock.getDelta(),.05);tGlobal+=dt;const tA=tGlobal*SPD;if(!dragging&&(velZ||velY||velX)){userZ+=velZ*dt;userY=clamp(userY+velY*dt,-.5,.5);userX=clamp(userX+velX*dt,-.3,.3);velZ*=Math.exp(-dt*1.5);velY*=Math.exp(-dt*3.5);velX*=Math.exp(-dt*3.5);if(Math.abs(velZ)>.05)lastInteract=tGlobal;else{if(Math.abs(velZ)<.03)velZ=0;if(Math.abs(velY)<.02)velY=0;if(Math.abs(velX)<.02)velX=0}}const idle=tGlobal-lastInteract;const ampTarget=dragging?0:idle>1.6?1:0;autoAmp+=(ampTarget-autoAmp)*(1-Math.exp(-dt*1.4));if(!dragging&&idle>1.6){const dec=Math.exp(-dt*.22);userX*=dec;userY*=dec;const home=Math.round(userZ/THIRD)*THIRD;userZ=home+(userZ-home)*dec}const amp=autoAmp;const rotZ=(Math.sin(tA*.31)*.15+Math.sin(tA*.127)*.06)*amp+userZ;const rotY=Math.sin(tA*.21)*.32*amp+userY;const rotX=(Math.sin(tA*.165)*.09+.02)*amp+userX;const bob=Math.sin(tA*.5)*.055*amp;prism.rotation.set(rotX,rotY,rotZ);prism.position.y=bob;prism.updateMatrixWorld();const lamp=updateOptics(tA,dt,rotZ,bob);if(!spectrumSeen&&exitGlow.material.opacity>.03){spectrumSeen=true;document.body.classList.add("spectrum")}APEX_W.copy(APEX_LOCAL).applyMatrix4(prism.matrixWorld);apexDot.position.copy(APEX_W);apexDot.material.opacity=.9*lamp;for(let i=0;i<2;i++){APEX_W.copy(CORNER_LOCAL[i]).applyMatrix4(prism.matrixWorld);cornerDots[i].position.copy(APEX_W);cornerDots[i].material.opacity=lamp*(.35+.25*Math.sin(tA*2+i*2.1))}glassMat.uniforms.uTime.value=tA;glassMat.uniforms.uCam.value.copy(camera.position);edgeMat.opacity=.45+.08*Math.sin(tA*1.3)+trapGlow*.3;if(!dragging){parTX=mouseNX*.38;parTY=-mouseNY*.22}const pe=1-Math.exp(-dt*3);parX+=(parTX-parX)*pe;parY+=(parTY-parY)*pe;camera.position.set(parX+Math.sin(tA*.13)*.15,.35+parY+Math.cos(tA*.1)*.08,camZ);camera.lookAt(0,.05,0);renderer.render(scene,camera);if(!ready){ready=true;document.body.classList.add("ready");setTimeout(()=>document.body.classList.add("spectrum"),7e3)}}animate();App._disposeLanding=function(){try{renderer.dispose()}catch(err){}}}try{boot()}catch(err){console.error(err)}})()};if(!window.__BONSAI_HOLD_LANDING)App.bootLanding()}if(window.THREE)(function(){var TAU=Math.PI*2;var UP=new THREE.Vector3(0,1,0);var MOT=REDUCED?.35:1;var clamp=THREE.MathUtils.clamp;var lerp=THREE.MathUtils.lerp;function mulberry32(a){return function(){a|=0;a=a+1831565813|0;var t=Math.imul(a^a>>>15,1|a);t=t+Math.imul(t^t>>>7,61|t)^t;return((t^t>>>14)>>>0)/4294967296}}function easeOutCubic(t){return 1-Math.pow(1-t,3)}function easeOutBack(t){var c=1.35;return 1+(c+1)*Math.pow(t-1,3)+c*Math.pow(t-1,2)}function SC(hex){return new THREE.Color(hex).convertSRGBToLinear()}function canvasTex(size,draw){var cv=document.createElement("canvas");cv.width=cv.height=size;draw(cv.getContext("2d"),size);return new THREE.CanvasTexture(cv)}var glowTex=canvasTex(256,function(g,s){var r=g.createRadialGradient(s/2,s/2,0,s/2,s/2,s/2);r.addColorStop(0,"rgba(255,255,255,1)");r.addColorStop(.25,"rgba(255,255,255,0.5)");r.addColorStop(.6,"rgba(255,255,255,0.11)");r.addColorStop(1,"rgba(255,255,255,0)");g.fillStyle=r;g.fillRect(0,0,s,s)});var petalTex=canvasTex(64,function(g,s){g.translate(s/2,s/2);g.scale(1,1.45);var r=g.createRadialGradient(0,0,1,0,0,s*.32);r.addColorStop(0,"rgba(255,240,246,1)");r.addColorStop(.55,"rgba(244,206,221,0.95)");r.addColorStop(1,"rgba(238,190,208,0)");g.fillStyle=r;g.beginPath();g.arc(0,0,s*.32,0,TAU);g.fill()});petalTex.encoding=THREE.sRGBEncoding;var barkTex=canvasTex(256,function(g,s){g.fillStyle="#463327";g.fillRect(0,0,s,s);for(var i=0;i<170;i++){var x=Math.random()*s;g.strokeStyle=Math.random()<.5?"rgba(26,17,11,"+(.1+Math.random()*.26)+")":"rgba(99,77,58,"+(.08+Math.random()*.2)+")";g.lineWidth=1+Math.random()*2.2;g.beginPath();g.moveTo(x,0);var y=0;while(y<s){y+=8+Math.random()*14;g.lineTo(x+(Math.random()-.5)*7,y)}g.stroke()}for(var j=0;j<700;j++){g.fillStyle="rgba(0,0,0,"+Math.random()*.12+")";g.fillRect(Math.random()*s,Math.random()*s,1.5,1.5)}});barkTex.wrapS=barkTex.wrapT=THREE.RepeatWrapping;barkTex.encoding=THREE.sRGBEncoding;var groundTex=canvasTex(512,function(g,s){var r=g.createRadialGradient(s/2,s/2,0,s/2,s/2,s/2);r.addColorStop(0,"#10141c");r.addColorStop(.45,"#0a0d13");r.addColorStop(1,"#050609");g.fillStyle=r;g.fillRect(0,0,s,s)});groundTex.encoding=THREE.sRGBEncoding;var shadowTex=canvasTex(256,function(g,s){var r=g.createRadialGradient(s/2,s/2,0,s/2,s/2,s/2);r.addColorStop(0,"rgba(0,0,0,0.62)");r.addColorStop(.6,"rgba(0,0,0,0.25)");r.addColorStop(1,"rgba(0,0,0,0)");g.fillStyle=r;g.fillRect(0,0,s,s)});var canvas=byId("sceneB");var renderer;try{renderer=new THREE.WebGLRenderer({canvas,antialias:true,alpha:true})}catch(e){App.flatMode();return}renderer.setPixelRatio(Math.min(window.devicePixelRatio||1,2));renderer.setSize(window.innerWidth,window.innerHeight);renderer.outputEncoding=THREE.sRGBEncoding;renderer.toneMapping=THREE.ACESFilmicToneMapping;renderer.toneMappingExposure=1.28;renderer.shadowMap.enabled=true;renderer.shadowMap.type=THREE.PCFSoftShadowMap;renderer.setClearColor(329225,0);var scene=new THREE.Scene;scene.fog=new THREE.FogExp2(329225,.045);var camera=new THREE.PerspectiveCamera(39,window.innerWidth/window.innerHeight,.1,100);var hemi=new THREE.HemisphereLight(3556700,1053723,.95);scene.add(hemi);var moonLight=new THREE.DirectionalLight(14083327,1);moonLight.position.set(-5.5,8.5,-4);moonLight.castShadow=true;moonLight.shadow.mapSize.set(2048,2048);moonLight.shadow.camera.left=-6;moonLight.shadow.camera.right=6;moonLight.shadow.camera.top=6;moonLight.shadow.camera.bottom=-6;moonLight.shadow.camera.near=2;moonLight.shadow.camera.far=24;moonLight.shadow.bias=-4e-4;moonLight.shadow.normalBias=.025;moonLight.target.position.set(0,1.4,0);scene.add(moonLight,moonLight.target);var fillLight=new THREE.DirectionalLight(7045022,.4);fillLight.position.set(4,3,6);scene.add(fillLight);var ember=new THREE.PointLight(16763296,.1,9,2);ember.position.set(-2.6,.9,2.4);scene.add(ember);var canopyLight=new THREE.PointLight(16752568,0,5.5,2);canopyLight.position.set(0,2.4,0);scene.add(canopyLight);var ground=new THREE.Mesh(new THREE.CircleGeometry(24,64),new THREE.MeshStandardMaterial({map:groundTex,roughness:.95,metalness:0}));ground.rotation.x=-Math.PI/2;ground.receiveShadow=true;scene.add(ground);function contactShadow(radius,opacity,x,z){var m=new THREE.Mesh(new THREE.PlaneGeometry(radius*2,radius*2),new THREE.MeshBasicMaterial({map:shadowTex,transparent:true,opacity,depthWrite:false}));m.rotation.x=-Math.PI/2;m.position.set(x,.004,z);scene.add(m)}contactShadow(1.9,.55,0,0);contactShadow(.55,.4,2.15,.7);var pot=new THREE.Mesh(new THREE.LatheGeometry([new THREE.Vector2(.62,0),new THREE.Vector2(1.02,.02),new THREE.Vector2(1.13,.1),new THREE.Vector2(1.19,.26),new THREE.Vector2(1.21,.42),new THREE.Vector2(1.28,.47),new THREE.Vector2(1.3,.55),new THREE.Vector2(1.22,.57),new THREE.Vector2(1.15,.55),new THREE.Vector2(1.08,.5)],56),new THREE.MeshPhysicalMaterial({color:SC(2305602),roughness:.34,metalness:.06,clearcoat:.75,clearcoatRoughness:.35,side:THREE.DoubleSide}));pot.castShadow=true;pot.receiveShadow=true;scene.add(pot);var soil=new THREE.Mesh(new THREE.SphereGeometry(1,24,12),new THREE.MeshStandardMaterial({color:SC(1182983),roughness:1}));soil.scale.set(1.04,.13,1.04);soil.position.y=.5;soil.receiveShadow=true;scene.add(soil);var stone=new THREE.Mesh(new THREE.IcosahedronGeometry(.3,1),new THREE.MeshPhysicalMaterial({color:SC(2304564),roughness:.45,clearcoat:.25,clearcoatRoughness:.5}));stone.scale.set(1,.55,.82);stone.position.set(2.15,.16,.7);stone.rotation.y=.7;stone.castShadow=true;stone.receiveShadow=true;scene.add(stone);function makeSprite(color,opacity,sx,sy,x,y,z){var m=new THREE.SpriteMaterial({map:glowTex,color,transparent:true,opacity,depthWrite:false,fog:false,blending:THREE.AdditiveBlending});var s=new THREE.Sprite(m);s.scale.set(sx,sy,1);s.position.set(x,y,z);scene.add(s);return s}var barkMat=new THREE.MeshStandardMaterial({map:barkTex,bumpMap:barkTex,bumpScale:.012,roughness:.92,metalness:0});var blossomMat=new THREE.MeshStandardMaterial({vertexColors:true,roughness:.62,metalness:0,emissive:SC(2101274),emissiveIntensity:.55});var mossMatA=new THREE.MeshStandardMaterial({color:SC(2240541),roughness:1});var mossMatB=new THREE.MeshStandardMaterial({color:SC(3096103),roughness:1});var MOSS_GEO=new THREE.SphereGeometry(1,8,6);var padPalette=[SC(16245738),SC(15780825),SC(15119302),SC(16512243),SC(15914974)];var WHITE=new THREE.Color(16777215);var blossomTpl=null;function makeBlossoms(rx,ry,count,rng){if(!blossomTpl){var tpl=new THREE.IcosahedronGeometry(1,0);blossomTpl=tpl.index?tpl.toNonIndexed():tpl}var sp=blossomTpl.attributes.position.array;var sn=blossomTpl.attributes.normal.array;var vc=sp.length/3;var P=new Float32Array(count*vc*3);var N=new Float32Array(count*vc*3);var C=new Float32Array(count*vc*3);var q=new THREE.Quaternion,e=new THREE.Euler;var v=new THREE.Vector3,nv=new THREE.Vector3,col=new THREE.Color;var o=0;for(var b=0;b<count;b++){var th=rng()*TAU,u=rng()*2-1,r2=Math.sqrt(1-u*u);var rr=Math.pow(rng(),.34);var px=r2*Math.cos(th)*rx*rr;var py=u*ry*rr;var pz=r2*Math.sin(th)*rx*rr;var s=(.085+rng()*.085+rx*.05)*.92;e.set(rng()*Math.PI,rng()*Math.PI,rng()*Math.PI);q.setFromEuler(e);col.copy(padPalette[rng()*padPalette.length|0]);col.lerp(WHITE,Math.max(0,py/ry)*.45+rng()*.12);for(var i=0;i<vc;i++){v.set(sp[i*3],sp[i*3+1]*.82,sp[i*3+2]).multiplyScalar(s).applyQuaternion(q);P[o]=v.x+px;P[o+1]=v.y+py;P[o+2]=v.z+pz;nv.set(sn[i*3],sn[i*3+1],sn[i*3+2]).applyQuaternion(q);N[o]=nv.x;N[o+1]=nv.y;N[o+2]=nv.z;C[o]=col.r;C[o+1]=col.g;C[o+2]=col.b;o+=3}}var g=new THREE.BufferGeometry;g.setAttribute("position",new THREE.BufferAttribute(P,3));g.setAttribute("normal",new THREE.BufferAttribute(N,3));g.setAttribute("color",new THREE.BufferAttribute(C,3));return g}var DUR=[.3,.13,.09,.07];var NSEG=[9,6,5,4];function buildTree(seed){var rng=mulberry32(seed);var R=function(a,b){return a+(b-a)*rng()};var group=new THREE.Group;var segs=[],pads=[],bloomers=[],joints=[];var leanA=R(0,TAU);var leanV=new THREE.Vector3(Math.cos(leanA),0,Math.sin(leanA)*.6).normalize();function addSeg(p0,p1,r0,r1,t0,t1){var dir=new THREE.Vector3().subVectors(p1,p0);var len=dir.length();if(len<1e-4)return;dir.normalize();var geo=new THREE.CylinderGeometry(Math.max(r1,.004),Math.max(r0,.005),len,8,1);geo.translate(0,len/2,0);var m=new THREE.Mesh(geo,barkMat);m.position.copy(p0);m.quaternion.setFromUnitVectors(UP,dir);m.castShadow=true;m.visible=false;group.add(m);segs.push({mesh:m,t0,t1})}function addJoint(p,r,t){var m=new THREE.Mesh(MOSS_GEO,barkMat);m.position.copy(p);m.castShadow=true;m.visible=false;group.add(m);joints.push({mesh:m,r,t})}function grow(pos,dir,len,rad,depth,t0){var n=NSEG[depth];var dur=DUR[depth]*R(.9,1.12);var endRad=depth===0?rad*.4:Math.max(rad*.28,.011);var ph=R(0,TAU);var bendM=R(.95,1.35);var p=pos.clone();var d=dir.clone().normalize();var pts=[p.clone()];var outward=d.clone();outward.y=0;if(outward.lengthSq()<.001)outward.set(Math.cos(leanA+Math.PI),0,Math.sin(leanA+Math.PI));outward.normalize();for(var i=0;i<n;i++){var f=(i+1)/n;var steer=new THREE.Vector3;if(depth===0){var sway=(Math.sin(f*Math.PI*1.9+ph)*.8+Math.sin(f*Math.PI*.9)*.5)*bendM;steer.addScaledVector(leanV,sway*.75);steer.y=.85}else{steer.y=f<.55?-.42:-.42+(f-.55)/.45*1.35;steer.addScaledVector(outward,.6)}d.addScaledVector(steer,1.7/n);d.x+=R(-1,1)*.09;d.y+=R(-1,1)*.05;d.z+=R(-1,1)*.09;d.normalize();var sl=len/n*R(.88,1.12);var np=p.clone().addScaledVector(d,sl);if(np.y<.75){np.y=.75+(.75-np.y)*.25;d.copy(np).sub(p).normalize()}pts.push(np.clone());p=np}var rAt=function(f2){return rad+(endRad-rad)*Math.pow(f2,.85)};for(var jI=1;jI<n;jI++){var jr=rAt(jI/n);if(jr>=.026)addJoint(pts[jI],jr,t0+dur*(jI/n))}for(var k=0;k<n;k++){addSeg(pts[k],pts[k+1],rAt(k/n),rAt((k+1)/n),t0+dur*(k/n),t0+dur*((k+1)/n))}return{pts,rAt,n,dEnd:d.clone(),tEnd:t0+dur,tAt:function(f2){return t0+dur*f2}}}function addPad(pos,rx,tStart){var ry=rx*R(.42,.55);var count=Math.floor(26+rx*62);var geo=makeBlossoms(rx,ry,count,rng);var mesh=new THREE.Mesh(geo,blossomMat);mesh.castShadow=true;var g=new THREE.Group;g.position.copy(pos);g.add(mesh);var sm=new THREE.SpriteMaterial({map:glowTex,color:SC(16767462),transparent:true,opacity:0,depthWrite:false,blending:THREE.AdditiveBlending,fog:false});var spr=new THREE.Sprite(sm);spr.scale.set(rx*4.6,rx*3.4,1);g.add(spr);g.scale.setScalar(1e-4);g.visible=false;group.add(g);pads.push({g,spr:sm,base:pos.clone(),rx,ready:Math.min(tStart,.86),t0:.9,t1:.99,phase:R(0,TAU),k:0,done:false,popT:0,sx:0,sz:0})}function padPos(info){return info.pts[info.n].clone().add(new THREE.Vector3(R(-1,1)*.05,.1+R(0,.06),R(-1,1)*.05))}function limb(pos,dir,len,rad,depth,t0){var info=grow(pos,dir,len,Math.max(rad,.02),depth,t0);if(depth===1){var fm=R(.45,.7);var idx=Math.max(1,Math.round(fm*info.n));var md=info.pts[idx].clone().sub(info.pts[idx-1]).normalize().applyAxisAngle(UP,R(.5,.95)*(rng()<.5?-1:1));md.y=R(-.05,.25);limb(info.pts[idx],md.normalize(),len*R(.5,.65),info.rAt(idx/info.n)*.75,2,info.tAt(idx/info.n));for(var k=0;k<2;k++){var fd=info.dEnd.clone().applyAxisAngle(UP,(k?-1:1)*R(.35,.75));fd.y+=R(.05,.35);fd.normalize();limb(info.pts[info.n],fd,len*R(.45,.6),info.rAt(1)*.9,2,info.tEnd)}}else if(depth===2){if(rng()<.62){var td=info.dEnd.clone().applyAxisAngle(UP,R(-.6,.6));td.y+=R(.1,.4);td.normalize();limb(info.pts[info.n],td,len*R(.5,.65),info.rAt(1)*.9,3,info.tEnd);if(rng()<.5)addPad(padPos(info),R(.42,.6),info.tEnd)}else{addPad(padPos(info),R(.55,.78),info.tEnd)}}else if(depth===3){addPad(padPos(info),R(.36,.5),info.tEnd)}return info}var base=new THREE.Vector3(0,.55,0);for(var ri=0;ri<6;ri++){var ra=ri/6*TAU+R(-.3,.3);var od=new THREE.Vector3(Math.cos(ra),0,Math.sin(ra));var rp0=base.clone().addScaledVector(od,.04);rp0.y=.6;var rp1=base.clone().addScaledVector(od,R(.24,.34));rp1.y=.53;addSeg(rp0,rp1,.16*R(.5,.7),.012,.02+ri*.008,.1+ri*.008)}for(var mj=0;mj<9;mj++){var mrr=R(.1,.62),maa=R(0,TAU);var mm=new THREE.Mesh(MOSS_GEO,mj%3?mossMatA:mossMatB);mm.position.set(Math.cos(maa)*mrr,.615-mrr*.09,Math.sin(maa)*mrr);var msx=R(.08,.17);mm.castShadow=true;mm.visible=false;group.add(mm);bloomers.push({node:mm,s:new THREE.Vector3(msx,msx*.36,msx*R(.8,1.2)),t0:.02+mj*.01,t1:.1+mj*.012})}var trunk=grow(base,leanV.clone().multiplyScalar(.45).add(UP).normalize(),R(2.45,2.85),R(.15,.185),0,.02);var attach=[.34,.52,.7,.86];for(var ai=0;ai<attach.length;ai++){var af=clamp(attach[ai]+R(-.05,.05),.3,.9);var aidx=Math.round(af*trunk.n);var yaw=leanA+Math.PI+ai*2.399+R(-.3,.3);var adir=new THREE.Vector3(Math.cos(yaw),R(-.12,.12),Math.sin(yaw)).normalize();var alen=lerp(1.85,.85,af)*R(.85,1.15);limb(trunk.pts[aidx],adir,alen,trunk.rAt(af)*.58,1,trunk.tAt(Math.min(1,aidx/trunk.n)))}for(var ci=0;ci<2;ci++){var cy=R(0,TAU);var cdir=new THREE.Vector3(Math.cos(cy)*.7,1,Math.sin(cy)*.7).normalize();limb(trunk.pts[trunk.n],cdir,R(.7,.95),trunk.rAt(1)*.85,2,trunk.tEnd)}addPad(trunk.pts[trunk.n].clone().add(new THREE.Vector3(0,.28,0)),R(.6,.8),trunk.tEnd+.02);pads.sort(function(a,b){return a.base.y-b.base.y});var bs0=.55,bs1=.985;var slot=(bs1-bs0)/Math.max(pads.length,1);for(var si=0;si<pads.length;si++){var pdd=pads[si];pdd.t0=Math.max(bs0+si*slot,pdd.ready+.01);pdd.t1=Math.min(pdd.t0+Math.max(slot*2.2,.045),.995)}var cen=new THREE.Vector3;if(pads.length){for(var pi=0;pi<pads.length;pi++)cen.add(pads[pi].base);cen.multiplyScalar(1/pads.length)}else{cen.set(0,2.3,0)}return{group,segs,pads,bloomers,joints,canopy:cen}}var PET_N=110;var petals=new THREE.InstancedMesh(new THREE.PlaneGeometry(.085,.12),new THREE.MeshBasicMaterial({map:petalTex,transparent:true,opacity:.9,depthWrite:false,side:THREE.DoubleSide}),PET_N);petals.frustumCulled=false;petals.instanceMatrix.setUsage(THREE.DynamicDrawUsage);scene.add(petals);var pet=[];for(var pj=0;pj<PET_N;pj++){pet.push({on:false,p:new THREE.Vector3,r:new THREE.Euler,vr:{x:0,y:0,z:0},vy:0,ph:0,life:0,size:1})}var DUMMY=new THREE.Object3D;function spawnPetal(){var cands=[];for(var i=0;i<T.pads.length;i++)if(T.pads[i].k>.55)cands.push(T.pads[i]);if(!cands.length)return;for(var j=0;j<PET_N;j++){var q=pet[j];if(q.on)continue;var pd=cands[Math.random()*cands.length|0];var th=Math.random()*TAU,u=Math.random()*2-1,r2=Math.sqrt(1-u*u);q.p.set(pd.base.x+r2*Math.cos(th)*pd.rx*.9,pd.base.y+u*pd.rx*.45,pd.base.z+r2*Math.sin(th)*pd.rx*.9);q.r.set(Math.random()*TAU,Math.random()*TAU,Math.random()*TAU);q.vr.x=(Math.random()-.5)*2.4;q.vr.y=(Math.random()-.5)*2.4;q.vr.z=(Math.random()-.5)*2.4;q.vy=.05+Math.random()*.1;q.ph=Math.random()*TAU;q.life=0;q.size=.75+Math.random()*.5;q.on=true;return}}var spawnAcc=0;function updatePetals(dt,t){var rate=bloom>.35?lerp(0,3.6,(bloom-.35)/.65):0;if(state.doneAt&&t-doneAtLocal<4)rate*=1.8;spawnAcc+=rate*dt*MOT;while(spawnAcc>1){spawnAcc-=1;spawnPetal()}for(var i=0;i<PET_N;i++){var q=pet[i];if(!q.on){DUMMY.position.set(0,-10,0);DUMMY.scale.setScalar(1e-4);DUMMY.rotation.set(0,0,0)}else{q.vy=Math.min(q.vy+dt*.15,.5);q.p.y-=q.vy*dt;q.p.x+=(Math.sin(t*1.2+q.ph)*.35+wind*.45+gustX*1.1)*dt;q.p.z+=(Math.cos(t*.9+q.ph)*.18+gustZ*1.1)*dt;q.r.x+=q.vr.x*dt;q.r.y+=q.vr.y*dt;q.r.z+=q.vr.z*dt;q.life+=dt;if(q.p.y<.03)q.on=false;var fade=Math.min(1,(q.p.y-.02)*4)*Math.min(1,q.life*3);DUMMY.position.copy(q.p);DUMMY.rotation.copy(q.r);DUMMY.scale.setScalar(Math.max(q.size*fade,.001))}DUMMY.updateMatrix();petals.setMatrixAt(i,DUMMY.matrix)}petals.instanceMatrix.needsUpdate=true}var bloom=0;function updateGrowth(p){var i,k;for(i=0;i<T.segs.length;i++){var s=T.segs[i];k=(p-s.t0)/(s.t1-s.t0);if(k<=0){s.mesh.visible=false;continue}s.mesh.visible=true;var kk=Math.min(k,1);var tk=clamp((p-s.t0)/(.985-s.t0),0,1);var th=.34+.66*easeOutCubic(tk);s.mesh.scale.set(th,Math.max(kk,.001),th)}for(i=0;i<T.joints.length;i++){var jn=T.joints[i];if(p<=jn.t){jn.mesh.visible=false;continue}jn.mesh.visible=true;var jk=clamp((p-jn.t)/(.985-jn.t),0,1);jn.mesh.scale.setScalar(jn.r*(.34+.66*easeOutCubic(jk)))}for(i=0;i<T.bloomers.length;i++){var b=T.bloomers[i];k=clamp((p-b.t0)/(b.t1-b.t0),0,1);if(k<=0){b.node.visible=false;continue}b.node.visible=true;b.node.scale.copy(b.s).multiplyScalar(Math.max(easeOutBack(k),1e-4))}var sum=0;for(i=0;i<T.pads.length;i++){var pd=T.pads[i];k=clamp((p-pd.t0)/(pd.t1-pd.t0),0,1);pd.k=k;sum+=k;if(k<=0){pd.g.visible=false;continue}pd.g.visible=true;pd.g.scale.setScalar(Math.max(easeOutBack(k),1e-4))}bloom=T.pads.length?sum/T.pads.length:0}var T=buildTree(SEED);scene.add(T.group);canopyLight.position.copy(T.canopy);updateGrowth(FREEZE!==null?FREEZE:0);var shakeAmp=0,shakeSeed=0;window.addEventListener("pointerdown",function(){if(App.stage!=="loading")return;shakeAmp=Math.min(shakeAmp+.85,1.15);shakeSeed=Math.random()*TAU;if(bloom>.2)for(var i=0;i<9;i++)spawnPetal()});var gustX=0,gustZ=0,leanX=0,leanZ=0;var lastPX=null;var camAngCur=0;window.addEventListener("pointermove",function(e){if(App.stage!=="loading"){lastPX=null;return}if(lastPX!==null){var dx=clamp((e.clientX-lastPX)/window.innerWidth*3,-.35,.35);gustX+=dx*Math.cos(camAngCur);gustZ+=dx*-Math.sin(camAngCur);var m=Math.sqrt(gustX*gustX+gustZ*gustZ);if(m>1.3){gustX*=1.3/m;gustZ*=1.3/m}}lastPX=e.clientX});window.addEventListener("pointerleave",function(){lastPX=null});function onResize(){var a=window.innerWidth/window.innerHeight;camera.aspect=a;camera.fov=a<1?clamp(39/a*.92,39,60):39;camera.updateProjectionMatrix();renderer.setSize(window.innerWidth,window.innerHeight)}window.addEventListener("resize",onResize);onResize();var clock=new THREE.Clock;var elapsed=0,wind=0,doneAtLocal=-1;function animate(){if(document.body.classList.contains("stage-chat"))return;requestAnimationFrame(animate);var dt=Math.min(clock.getDelta(),.05);elapsed+=dt;var t=elapsed;var 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te(e2.value?1:0);case"float":return new _e(e2.value?1:0);case"string":return new U(e2.value?"true":"false");default:throw new Error(`Unknown BooleanValue filter: ${r.value}`)}throw new Error(`Cannot apply filter "${r.value}" to type: ${e2.type}`)}else if(t.type==="CallExpression"){let r=t;if(r.callee.type!=="Identifier")throw new Error(`Unknown filter: ${r.callee.type}`);let s=r.callee.value;if(s==="tojson"){let[,a]=this.evaluateArguments(r.args,n),o=a.get("indent")??new ge;if(!(o instanceof te||o instanceof ge))throw new Error("If set, indent must be a number");let i=a.get("ensure_ascii")??new J(false);if(!(i instanceof J))throw new Error("If set, ensure_ascii must be a boolean");let u=a.get("sort_keys")??new J(false);if(!(u instanceof J))throw new Error("If set, sort_keys must be a boolean");let l=a.get("separators")??new ge,c=null;if(l instanceof ie||l instanceof gi){if(l.value.length!==2)throw new Error("separators must be a tuple of two strings");let[d,f]=l.value;if(!(d instanceof U)||!(f instanceof U))throw new Error("separators must be a tuple of two strings");c=[d.value,f.value]}else if(!(l instanceof ge))throw new Error("If set, separators must be a tuple of two strings");return new U(qt(e2,{indent:o.value,ensureAscii:i.value,sortKeys:u.value,separators:c}))}else if(s==="join"){let a;if(e2 instanceof U)a=Array.from(e2.value);else if(e2 instanceof ie)a=e2.value.map(l=>l.value);else throw new Error(`Cannot apply filter "${s}" to type: ${e2.type}`);let[o,i]=this.evaluateArguments(r.args,n),u=o.at(0)??i.get("separator")??new U("");if(!(u instanceof U))throw new Error("separator must be a string");return new U(a.join(u.value))}else if(s==="int"||s==="float"){let[a,o]=this.evaluateArguments(r.args,n),i=a.at(0)??o.get("default")??(s==="int"?new te(0):new _e(0));if(e2 instanceof U){let u=s==="int"?parseInt(e2.value,10):parseFloat(e2.value);return isNaN(u)?i:s==="int"?new te(u):new _e(u)}else{if(e2 instanceof te||e2 instanceof _e)return e2;if(e2 instanceof J)return 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ie(e2.value.slice().sort((d,f)=>{let p=c(d),g=c(f),m=Ls(p,g,u.value);return i.value?-m:m}))}case"selectattr":case"rejectattr":{let a=s==="selectattr";if(e2.value.some(d=>!(d instanceof De)))throw new Error(`\`${s}\` can only be applied to array of objects`);if(r.args.some(d=>d.type!=="StringLiteral"))throw new Error(`arguments of \`${s}\` must be strings`);let[o,i,u]=r.args.map(d=>this.evaluate(d,n)),l;if(i){let d=n.tests.get(i.value);if(!d)throw new Error(`Unknown test: ${i.value}`);l=d}else l=(...d)=>d[0].__bool__().value;let c=e2.value.filter(d=>{let f=d.value.get(o.value),p=f?l(f,u):false;return a?p:!p});return new ie(c)}case"map":{let[,a]=this.evaluateArguments(r.args,n);if(a.has("attribute")){let o=a.get("attribute");if(!(o instanceof U))throw new Error("attribute must be a string");let i=a.get("default"),u=e2.value.map(l=>{if(!(l instanceof De))throw new Error("items in map must be an object");let c=Ai(l,o.value);return c instanceof fe?i??new fe:c});return new ie(u)}else throw new Error("`map` expressions without `attribute` set are not currently supported.")}}throw new Error(`Unknown ArrayValue filter: ${s}`)}else if(e2 instanceof U){switch(s){case"indent":{let[a,o]=this.evaluateArguments(r.args,n),i=a.at(0)??o.get("width")??new te(4);if(!(i instanceof te))throw new Error("width must be a number");let u=a.at(1)??o.get("first")??new J(false),l=a.at(2)??o.get("blank")??new J(false),c=e2.value.split(`
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fe}evaluateUnaryExpression(e2,t){let n=this.evaluate(e2.argument,t);if(e2.operator.value==="not")return new J(!n.value);throw new SyntaxError(`Unknown operator: ${e2.operator.value}`)}evaluateTernaryExpression(e2,t){return this.evaluate(e2.condition,t).__bool__().value?this.evaluate(e2.trueExpr,t):this.evaluate(e2.falseExpr,t)}evalProgram(e2,t){return this.evaluateBlock(e2.body,t)}evaluateBlock(e2,t){let n="";for(let r of e2){let s=this.evaluate(r,t);s.type!=="NullValue"&&s.type!=="UndefinedValue"&&(n+=s.toString())}return new U(n)}evaluateIdentifier(e2,t){return t.lookupVariable(e2.value)}evaluateCallExpression(e2,t){let[n,r]=this.evaluateArguments(e2.args,t);r.size>0&&n.push(new Cn(r));let s=this.evaluate(e2.callee,t);if(s.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${s.type}`);return s.value(n,t)}evaluateSliceExpression(e2,t,n){if(!(e2 instanceof ie||e2 instanceof U))throw new Error("Slice object must be an array or string");let r=this.evaluate(t.start,n),s=this.evaluate(t.stop,n),a=this.evaluate(t.step,n);if(!(r instanceof te||r instanceof fe))throw new Error("Slice start must be numeric or undefined");if(!(s instanceof te||s instanceof fe))throw new Error("Slice stop must be numeric or undefined");if(!(a instanceof te||a instanceof fe))throw new Error("Slice step must be numeric or undefined");return e2 instanceof ie?new ie(di(e2.value,r.value,s.value,a.value)):new U(di(Array.from(e2.value),r.value,s.value,a.value).join(""))}evaluateMemberExpression(e2,t){let n=this.evaluate(e2.object,t),r;if(e2.computed){if(e2.property.type==="SliceExpression")return this.evaluateSliceExpression(n,e2.property,t);r=this.evaluate(e2.property,t)}else e2.property.type==="IntegerLiteral"?r=new te(e2.property.value):r=new U(e2.property.value);let s;if(n instanceof De){if(!(r instanceof U))throw new Error(`Cannot access property with non-string: got ${r.type}`);s=n.value.get(r.value)??n.builtins.get(r.value)}else if(n instanceof ie||n instanceof U)if(r instanceof te)s=n.value.at(r.value),n instanceof U&&(s=new U(n.value.at(r.value)));else if(r instanceof U)s=n.builtins.get(r.value);else throw new Error(`Cannot access property with non-string/non-number: got ${r.type}`);else{if(!(r instanceof U))throw new Error(`Cannot access property with non-string: got ${r.type}`);s=n.builtins.get(r.value)}return s instanceof it?s:new fe}evaluateSet(e2,t){let n=e2.value?this.evaluate(e2.value,t):this.evaluateBlock(e2.body,t);if(e2.assignee.type==="Identifier"){let r=e2.assignee.value;t.setVariable(r,n)}else if(e2.assignee.type==="TupleLiteral"){let r=e2.assignee;if(!(n instanceof ie))throw new Error(`Cannot unpack non-iterable type in set: ${n.type}`);let s=n.value;if(s.length!==r.value.length)throw new Error(`Too ${r.value.length>s.length?"few":"many"} items to unpack in set`);for(let a=0;a<r.value.length;++a){let o=r.value[a];if(o.type!=="Identifier")throw new Error(`Cannot unpack to non-identifier in set: ${o.type}`);t.setVariable(o.value,s[a])}}else if(e2.assignee.type==="MemberExpression"){let r=e2.assignee,s=this.evaluate(r.object,t);if(!(s instanceof De))throw new Error("Cannot assign to member of non-object");if(r.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");s.value.set(r.property.value,n)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(e2.assignee)}`);return new ge}evaluateIf(e2,t){let n=this.evaluate(e2.test,t);return this.evaluateBlock(n.__bool__().value?e2.body:e2.alternate,t)}evaluateFor(e2,t){let n=new ot(t),r,s;if(e2.iterable.type==="SelectExpression"){let l=e2.iterable;s=this.evaluate(l.lhs,n),r=l.test}else s=this.evaluate(e2.iterable,n);if(!(s instanceof ie||s instanceof De))throw new Error(`Expected iterable or object type in for loop: got ${s.type}`);s instanceof De&&(s=s.keys());let a=[],o=[];for(let l=0;l<s.value.length;++l){let c=new ot(n),d=s.value[l],f;if(e2.loopvar.type==="Identifier")f=p=>p.setVariable(e2.loopvar.value,d);else if(e2.loopvar.type==="TupleLiteral"){let p=e2.loopvar;if(d.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${d.type}`);let g=d;if(p.value.length!==g.value.length)throw new Error(`Too ${p.value.length>g.value.length?"few":"many"} items to unpack`);f=m=>{for(let A=0;A<p.value.length;++A){if(p.value[A].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${p.value[A].type}`);m.setVariable(p.value[A].value,g.value[A])}}}else throw new Error(`Invalid loop variable(s): ${e2.loopvar.type}`);r&&(f(c),!this.evaluate(r,c).__bool__().value)||(a.push(d),o.push(f))}let i="",u=true;for(let l=0;l<a.length;++l){let c=new Map([["index",new te(l+1)],["index0",new te(l)],["revindex",new te(a.length-l)],["revindex0",new te(a.length-l-1)],["first",new J(l===0)],["last",new J(l===a.length-1)],["length",new te(a.length)],["previtem",l>0?a[l-1]:new 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c=0;c<e2.callerArgs.length;++c){let d=e2.callerArgs[c];if(d.type!=="Identifier")throw new Error(`Caller parameter must be an identifier, got ${d.type}`);l.setVariable(d.value,i[c]??new fe)}return this.evaluateBlock(e2.body,l)}),[r,s]=this.evaluateArguments(e2.call.args,t);r.push(new Cn(s));let a=this.evaluate(e2.call.callee,t);if(a.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${a.type}`);let o=new ot(t);return o.setVariable("caller",n),a.value(r,o)}evaluateFilterStatement(e2,t){let n=this.evaluateBlock(e2.body,t);return this.applyFilter(n,e2.filter,t)}evaluate(e2,t){if(!e2)return new fe;switch(e2.type){case"Program":return this.evalProgram(e2,t);case"Set":return this.evaluateSet(e2,t);case"If":return this.evaluateIf(e2,t);case"For":return this.evaluateFor(e2,t);case"Macro":return this.evaluateMacro(e2,t);case"CallStatement":return this.evaluateCallStatement(e2,t);case"Break":throw new pi;case"Continue":throw new fi;case"IntegerLiteral":return new te(e2.value);case"FloatLiteral":return new _e(e2.value);case"StringLiteral":return new U(e2.value);case"ArrayLiteral":return new ie(e2.value.map(n=>this.evaluate(n,t)));case"TupleLiteral":return new gi(e2.value.map(n=>this.evaluate(n,t)));case"ObjectLiteral":{let n=new Map;for(let[r,s]of e2.value){let a=this.evaluate(r,t);if(!(a instanceof U))throw new Error(`Object keys must be strings: got ${a.type}`);n.set(a.value,this.evaluate(s,t))}return new De(n)}case"Identifier":return this.evaluateIdentifier(e2,t);case"CallExpression":return this.evaluateCallExpression(e2,t);case"MemberExpression":return this.evaluateMemberExpression(e2,t);case"UnaryExpression":return this.evaluateUnaryExpression(e2,t);case"BinaryExpression":return this.evaluateBinaryExpression(e2,t);case"FilterExpression":return this.evaluateFilterExpression(e2,t);case"FilterStatement":return this.evaluateFilterStatement(e2,t);case"TestExpression":return this.evaluateTestExpression(e2,t);case"SelectExpression":return this.evaluateSelectExpression(e2,t);case"Ternary":return this.evaluateTernaryExpression(e2,t);case"Comment":return new ge;default:throw new SyntaxError(`Unknown node type: ${e2.type}`)}}};xe=`
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t=$e(e2);return{adapterInfo:{...t.adapterInfo},features:Array.from(t.features.values()).sort(),wgslLanguageFeatures:Array.from(t.wgslLanguageFeatures.values()).sort(),limits:{...t.limits}}}function Hs(e2,t){if(!e2)return null;for(let n of e2.requiredFeatures??[])if(!t.features.has(n))return`requires device.features.has("${n}")`;for(let n of e2.requiredWGSLLanguageFeatures??[])if(!t.wgslLanguageFeatures.has(n))return`requires device.wgslLanguageFeatures.has("${n}")`;for(let[n,r]of Object.entries(e2.requiredLimits??{})){if(r===void 0)continue;let s=t.limits[n];if(typeof s!="number"||s<r)return`requires device.limits.${n} >= ${r}`}if(e2.requiredSubgroupMinSize!==void 0){let n=t.adapterInfo.subgroupMinSize;if(typeof n!="number")return`requires adapterInfo.subgroupMinSize >= ${e2.requiredSubgroupMinSize} (adapter does not report subgroup sizes)`;if(n<e2.requiredSubgroupMinSize)return`requires adapterInfo.subgroupMinSize >= ${e2.requiredSubgroupMinSize}`}return null}function qi(e2){let 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o=t.get(a);o?o.push(n):t.set(a,[n])}}for(let[n,r]of t)r.length<2&&t.delete(n);return t}function tu(e2){let t=lg(e2),n=new Map;for(let[r,s]of Object.entries(e2)){if(s.kind!=="tensor")continue;let a=new Set([r,s.semantic,s.role].filter(o=>typeof o=="string"&&o.length>0));n.set(r,[...a].filter(o=>!t.has(o)))}return n}function nu(e2){return e2.map(t=>{if(t.buffer.type==="uniform"){if(!t.struct)throw new Error(`WebGPU uniform binding ${t.name} requires struct`);return Vs(t.name,yr(t.struct.name,t.struct.fields.map(Cg)),{semantic:t.semantic,binding:t.binding,group:t.group})}return js(t.name,{arg:t.arg,access:t.buffer.type==="storage"?"read_write":"read",elementType:t.elementType,semantic:t.semantic??t.role,role:t.role,binding:t.binding,group:t.group})})}function dg(e2,t){let n=new Map;for(let[r,s]of Object.entries(e2.bindingSets??{})){if(!Array.isArray(s)||s.length===0)throw new Error(`WebGPU manifest ${t} bindingSet "${r}" must be a non-empty binding array`);n.set(r,s)}return n}function 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t={...e2.requiredFeatures?{requiredFeatures:e2.requiredFeatures}:{},...e2.requiredLimits?{requiredLimits:e2.requiredLimits}:{},...e2.requiredWGSLLanguageFeatures?{requiredWGSLLanguageFeatures:e2.requiredWGSLLanguageFeatures}:{},...e2.requiredSubgroupMinSize!==void 0?{requiredSubgroupMinSize:e2.requiredSubgroupMinSize}:{}};if(t.requiredSubgroupMinSize!==void 0&&(!Number.isInteger(t.requiredSubgroupMinSize)||t.requiredSubgroupMinSize<1))throw new Error(`WebGPU variant ${e2.id??e2.name??"?"} requiredSubgroupMinSize must be a positive integer`);return Object.keys(t).length===0?null:t}function Cg(e2){if(e2.type==="u32")return Us(e2.name,e2);if(e2.type==="i32")return Fs(e2.name,e2);if(e2.type==="f32")return Zs(e2.name,e2);throw new Error(`Unsupported WebGPU uniform field type: ${e2.type}`)}function Sg(e2,t){for(let[n,r]of Object.entries(e2)){if(!/^[A-Za-z_][A-Za-z0-9_]*$/.test(n))throw new Error(`${t}.${n} is not a valid arg 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+`)){let s=Qg.exec(r);if(s)for(let a of s[1].split(",")){let o=a.trim();if(o.length===0)continue;let i=xg[o];i&&!t.has(i)&&n.push(`enable ${o}; (requires device feature "${i}")`)}}return n}var xg,Qg,_u=q(()=>{"use strict";xg=Object.freeze({f16:"shader-f16",subgroups:"subgroups",chromium_experimental_subgroup_matrix:"chromium-experimental-subgroup-matrix"}),Qg=/^\s*enable\s+([^;]+);/});var Su={};bn(Su,{executeWebGpuPlan:()=>Dg,materializeComputePassDescriptorTemplate:()=>ku,materializeWebGpuExecutionPlan:()=>Ln});function yu(e2){let t=0;for(let n of e2){if(n.type!=="storage"&&n.type!=="read-only-storage")continue;let r=n,s=typeof r.size=="number"?r.size:typeof r.tensor?.byteLength=="number"?r.tensor.byteLength:void 0;typeof s=="number"&&Number.isFinite(s)&&s>0&&(t+=s)}return t}function bu(e2,t){return t<=0?e2?{profile:e2}:{}:{profile:{...e2??{},bytesMoved:t}}}async function Dg(e2,t,n={}){let r=Ln(e2,t);try{if(r.programs.length===0)return;if(typeof e2.runProgramSequence=="function")await e2.runProgramSequence(r.programs,n);else for(let s of r.programs)await e2.runProgram(s,n)}finally{r.cleanup()}}function Ln(e2,t,n={}){let r=n.scratchResources,s=r===void 0,a=r===void 0?Lg(e2,t):qg(t,r),o=[],i=[],u=false,l=()=>{if(u)return;u=true;let c;for(let d of o)try{d()}catch(f){c??=f}if(s)try{vu(a)}catch(d){c??=d}if(c!==void 0)throw c};try{for(let c=0;c<t.program.passes.length;++c){let d=t.program.passes[c],f=t.plan.passes[c],p=`${t.program.op} (variant ${t.program.variant}, pass ${d.id})`,g=Mg(t,d.bindingSpecs,f.uniforms,a);if(Og(d,g))continue;Cu(p,d.bindings,A=>g[A.name]);let m=Zi(e2,d.bindings,g,{labelPrefix:d.name});o.push(m.cleanup),i.push({name:d.name,source:d.source,entryPoint:d.entryPoint,cacheKey:`webgpu:${t.program.key}:pass=${d.pipelineCacheId}`,bindings:m.bindings,dispatchWorkgroups:f.dispatchWorkgroups,...d.submitAfter?{submitAfter:true}:{},...bu(d.profile,yu(m.bindings))})}return{programs:i,cleanup:l}}catch(c){try{l()}catch(d){throw new AggregateError([c,d],"WebGPU plan materialization and cleanup both failed")}throw c}}function ku(e2,t){Cu(`${e2.name} (program ${e2.programKey}, pass ${e2.id})`,e2.bindingSpecs,r=>ia(r,t));let n=e2.bindingSpecs.map((r,s)=>Pg(e2,r,t,s));return{name:e2.name,source:e2.source,entryPoint:e2.entryPoint,cacheKey:e2.cacheKey,bindings:n,dispatchWorkgroups:e2.dispatchWorkgroups,...e2.submitAfter?{submitAfter:true}:{},...bu(e2.profile,yu(n))}}function Pg(e2,t,n,r){let s=ia(t,n);if(s==null)throw new Error(`Missing resource for compute pass descriptor template binding ${t.name}`);let a=t.binding??r;if(t.buffer.type==="uniform"){if(!Xs(s))throw new Error(`Compute pass descriptor template ${e2.name} requires caller-owned uniform buffer for ${t.name}`);return{buffer:s,type:"uniform",binding:a,...t.group?{group:t.group}:{}}}let o=s,i=typeof o.byteOffset=="number"?o.byteOffset:0,u=typeof o.byteLength=="number"?o.byteLength:void 0;return{tensor:s,type:t.buffer.type,binding:a,...t.group?{group:t.group}:{},...i?{offset:i}:{},...u!==void 0?{size:u}:{}}}function ia(e2,t){return t[e2.arg??""]??t[e2.name]}function Lg(e2,t){let n={};if(t.plan.scratches.length===0)return n;if(typeof e2.empty!="function")throw new Error("WebGPU multi-pass plan requires a runtime with empty(dtype, shape)");try{for(let r of t.plan.scratches)n[r.id]=e2.empty(r.dtype,r.shape,`webgpu-scratch-${r.id}`)}catch(r){try{vu(n)}catch(s){throw new AggregateError([r,s],"WebGPU scratch allocation and cleanup both failed")}throw r}return n}function qg(e2,t){let n={};for(let r of e2.plan.scratches){let s=t[r.id];if(s==null)throw new Error(`Missing caller-owned WebGPU scratch resource ${r.id}`);let a=s;if(a.dtype!==r.dtype||!Rg(a.shape,r.shape)){let o=Array.isArray(a.shape)?`[${a.shape.join(",")}]`:String(a.shape);throw new Error(`Caller-owned WebGPU scratch ${r.id} must be ${r.dtype}[${r.shape.join(",")}], got ${String(a.dtype)}${o}`)}n[r.id]=s}return n}function Rg(e2,t){return Array.isArray(e2)&&e2.length===t.length&&e2.every((n,r)=>n===t[r])}function vu(e2){let t;for(let n of Object.values(e2))if(typeof n.destroy=="function")try{n.destroy()}catch(r){t??=r}if(t!==void 0)throw t}function Mg(e2,t,n,r){let s={};for(let a of t){if(a.buffer.type==="uniform"){let o=wu(e2,a,r);s[a.name]=o??n[a.name];continue}s[a.name]=wu(e2,a,r)}return s}function wu(e2,t,n){return n[t.name]??n[t.semantic??""]??ia(t,e2.request.resources)}function Cu(e2,t,n){let r=null;for(let s of t){let a=Kg(s);if(a===null)continue;let o=oa(n(s));if(o===null)continue;r===null&&(r=new Map);let i=r.get(o.buffer);if(i===void 0){r.set(o.buffer,{name:s.name,access:a,view:o});continue}if(i.access==="read"&&a==="read")continue;if(i.access==="read_write"&&a==="read_write"){if(!Wg(i.view.offset,i.view.end,o.offset,o.end))continue;throw new Error(`${e2}: storage bindings "${i.name}" and "${s.name}" alias overlapping writable ranges of the same GPU buffer; 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u=this.applyManifestDerive(a),{variant:l,scope:c}=this.selectVariantAndScope(u,n,r.variant),d=fu(l,c),f=ou(this.manifest,l,c,this.programSpecialization(l,c,d)),p=this.programCache.getOrCreate(f,()=>this.buildProgram(f,l,c,d)),g=iu(c),m=uu(f,g),A=this.planCache.getOrCreate(m,()=>this.buildPlan(m,f,g,l,c,d));return this.prepareCache.set(o,{program:p,plan:A}),{program:p,plan:A,request:n}}clearPreparedState(){this.prepareCache.clear(),this.programCache.clear(),this.planCache.clear()}inferOutputs(t,n={}){let r=Ug(t,n),s=this.inferCache.get(r);if(s)return s;this.rejectUnknownKeys(this.manifest.id,"binding",t,n.args);let a=new Map;for(let[c,d]of Object.entries(this.manifest.args))d.kind==="tensor"&&(d.role==="output"||d.role==="inout")&&d.semantic&&a.set(d.semantic,c);for(let c of this.manifest.outputs){if(c.shape!==void 0||c.optional)continue;let d=a.get(c.role)??c.role,f=t[d];if(f===void 0||!Gr(f))throw new Error(`${this.manifest.id}: output "${c.role}" has no shape expression — add 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${a}`);return Fg(o,i,`webgpu:${s.program.key}:pass=${o.pipelineCacheId}`,s.program.key,s.plan.key,a)}buildProgram(t,n,r,s){let a=new Map,o=s.map(i=>this.buildProgramPass(n,i,r,a));return Object.freeze({key:t,op:this.manifest.id,variant:n.id,variantVersion:n.version,passes:Object.freeze(o)})}buildProgramPass(t,n,r,s){let a=n.pass,o={...r,repeat:n.repeat},i=this.passConstants(t,a,o),u={...o,variant:t,pass:a,constants:i},l=Yt(nu(a.bindings)),c=[Fi(l),Ys(l,i)].filter(Boolean).join(`
+`),d=this.passSourceInputs(a.source,u),f={...u,sourceContext:d},p=this.buildTemplateSource(t,a,f,c),g=n.pipelineBaseId;if(n.repeated){let A=s.get(n.pipelineBaseId);A===void 0&&(A=new Map,s.set(n.pipelineBaseId,A));let h=A.get(p);h!==void 0?(p=h.source,g=h.pipelineCacheId):(g=`${n.pipelineBaseId}|source=${A.size}`,A.set(p,{source:p,pipelineCacheId:g}))}let m=ht(a.profile,f);return Object.freeze({id:n.id,pipelineCacheId:g,name:a.name??`${this.manifest.id}.${t.id}.${a.id}`,source:p,entryPoint:a.entryPoint,bindings:l,bindingSpecs:a.bindings,...Object.keys(m).length>0?{profile:m}:{},...a.submitAfter?{submitAfter:true}:{},...a.viewAlias?{viewAlias:a.viewAlias}:{},reads:a.reads,writes:a.writes})}buildTemplateSource(t,n,r,s){let a=ca(n.source.shader,this.assets.readText(n.source.shader),u=>this.assets.readText(u),new Set),o=ta(a,{...r,env:{device:ea(r.device),wgsl:{resourceDeclarations:s}}}),i=hu(o,r.device.features);if(i.length>0)throw new Error(`WebGPU op ${this.manifest.id} variant ${t.id} pass ${n.id} rendered WGSL enable directives the device does not support: ${i.join(", ")}. Gate the directive in the template (env.device.features) or declare the feature in the variant's requires.requiredFeatures.`);return o}programSpecialization(t,n,r){return aa(t.passSequence)?{variant:ht(t.constants,{...n,variant:t}),passes:r.map(s=>{let a=s.pass,o={...n,repeat:s.repeat};return{id:s.id,pipelineBaseId:s.pipelineBaseId,constants:ht(a.constants,{...o,variant:t,pass:a}),source:this.passSourceSpecialization(t,a,o),...a.submitAfter?{submitAfter:true}:{}}})}:{variant:ht(t.constants,{...n,variant:t}),passes:t.passes.map(s=>({id:s.id,constants:ht(s.constants,{...n,variant:t,pass:s}),source:this.passSourceSpecialization(t,s,n),...s.submitAfter?{submitAfter:true}:{}}))}}passSourceSpecialization(t,n,r){let s=this.passConstants(t,n,r),a={...r,variant:t,pass:n,constants:s};return{kind:"template",shader:n.source.shader,sourceVersion:n.source.version??1,inputs:this.passSourceInputs(n.source,a)}}buildPlan(t,n,r,s,a,o){let i=this.allOutputsEmpty(a),u=o.map(d=>d.pass),l=cu(s.intermediates,u,a,i),c=o.map(d=>this.buildPlanPass(s,d,a,i));return Object.freeze({key:t,programKey:n,shapeKey:r,scratches:l,passes:Object.freeze(c)})}allOutputsEmpty(t){let n=0;for(let[r,s]of Object.entries(this.manifest.args)){if(s.role!=="output"&&s.role!=="inout")continue;let a=t.resources[r];if(!nt(a)||(n+=1,a.shape.reduce((o,i)=>o*i,1)!==0))return false}return n>0}buildPlanPass(t,n,r,s){let a=n.pass,o={...r,repeat:n.repeat},i=this.passConstants(t,a,o);if(s)return Object.freeze({id:n.id,dispatchWorkgroups:[0,0,0],uniforms:this.zeroFilledUniforms(a)});let u={...o,variant:t,pass:a,constants:i},l={...u,sourceContext:this.passSourceInputs(a.source,u)},c=a.bindings.filter(d=>d.buffer.type==="uniform").reduce((d,f)=>(d[f.name]=this.uniformValues(f,a.uniforms[f.name],l),d),{});return Object.freeze({id:n.id,dispatchWorkgroups:this.passDispatchWorkgroups(a,l),uniforms:c})}zeroFilledUniforms(t){let n={};for(let r of t.bindings)r.buffer.type!=="uniform"||!r.struct||(n[r.name]=Object.fromEntries(r.struct.fields.map(s=>[s.name,0])));return n}passDispatchWorkgroups(t,n){let r=n.device.limits.maxComputeWorkgroupsPerDimension;return[Ne(t.dispatch.x,n),Ne(t.dispatch.y??1,n),Ne(t.dispatch.z??1,n)].map((s,a)=>{if(!Number.isInteger(s)||Number(s)<0)throw new Error(`WebGPU pass ${t.id} dispatch axis ${a} must resolve to a nonnegative integer, got ${String(s)}`);if(Number(s)>r)throw new Error(`WebGPU pass ${t.id} dispatch axis ${a} = ${s} exceeds maxComputeWorkgroupsPerDimension (${r}); clamp the dispatch (min(..., ${r})) and grid-stride over the remainder, or fold into 2D/3D.`);return Number(s)})}uniformValues(t,n,r){let s=t.struct;if(!s)throw new Error(`WebGPU uniform binding ${t.name} requires struct`);let a={};for(let o of s.fields){let i=n?.[o.name]??o.value??r.args[o.name]??o.default;if(i===void 0)continue;let u=Ne(i,r),l=o.type==="f32";if(typeof u!="number"||Number.isNaN(u)||!Number.isFinite(u)&&!l)throw new Error(`WebGPU uniform ${t.name}.${o.name} must resolve to a finite number`);a[o.name]=u}return a}passConstants(t,n,r){return{...r.dtypes,...ht(t.constants,{...r,variant:t,pass:n}),...ht(n.constants,{...r,variant:t,pass:n})}}passSourceInputs(t,n){return Xg(ht(t.inputs,n),`WebGPU template source ${t.shader} inputs`)}selectVariantAndScope(t,n,r){let s=this.selectVariant(t,r),a=this.applyVariantDerive(t,n,s);return{variant:s,scope:a}}selectVariant(t,n){let r=[];for(let s of this.variantCandidates(n)){let a=this.checkVariant(s,t);if(a.ok)return s;r.push(`${s.id}: ${a.reason}`)}throw new Error(`No supported WebGPU variant for ${this.manifest.id}; rejected ${r.join("; ")}`)}variantCandidates(t){if(t!==void 0){let r=this.manifest.variants.find(s=>s.id===t);if(!r)throw new Error(`WebGPU op ${this.manifest.id} has no variant ${t}`);return[r]}return[...this.manifest.variants].sort((r,s)=>r.default!==s.default?r.default?-1:1:s.priority-r.priority)}checkVariant(t,n){let r=Hs(t.requires,n.device);if(r)return{ok:false,reason:r};let s=Hg(t,n.device,n.present);if(s)return{ok:false,reason:s};let a={...n,variant:t},o=Array.isArray(t.when)?t.when:[t.when],i=o.length>1;for(let u of o){let l;try{l=Ne(u,a)}catch(c){return{ok:false,reason:`when eval error in \`${typeof u=="string"?u:String(u)}\`: ${c.message}`}}if(l!==true){let c=typeof u=="string"?u:String(u);return{ok:false,reason:i?`when clause failed: \`${c}\` → ${String(l)}`:`when guard resolved to ${String(l)}`}}}if(t.selectAbove!==null){let u;try{u=Ne(t.selectAbove,a)}catch(l){return{ok:false,reason:`selectAbove eval error in \`${t.selectAbove}\`: ${l.message}`}}if(u!==true)return{ok:false,reason:`below perf floor: \`${t.selectAbove}\` → ${String(u)}`}}return{ok:true}}buildBaseScope(t,n){let r={...n.resources},s={...this.manifest.attributes,...n.attrs??{}},a={...n.args??{}},o={...n.sourceContext??{}};for(let[p,g]of Object.entries(r))!nt(g)&&typeof g!="object"&&(a[p]=g);let i=Object.fromEntries(Object.keys({...this.manifest.args,...r}).map(p=>[p,r[p]!==void 0&&r[p]!==null])),u={},l={},c={},d={};for(let p of this.scopeNamesByArg.keys()){let g=r[p];if(nt(g))for(let m of this.scopeNamesByArg.get(p))u[m]=g.shape,l[m]=g.shape.length,c[m]=g.dtype}for(let p of[...this.manifest.inputs,...this.manifest.outputs]){if(!p.dtype)continue;let g=c[p.role];g!==void 0&&(d[p.dtype]=Js(g))}return this.validateInputDtypes(c),{op:this.manifest,device:t,attrs:s,args:a,sourceContext:o,resources:r,present:i,shapes:u,ranks:l,tensorDtypes:c,dtypes:d,derived:{},tunables:{...this.manifest.tunables,...n.tunables??{}},constants:{},repeat:{}}}validateInputDtypes(t){for(let n of this.manifest.inputs){let r=n.dtype;if(!r)continue;let s=t[n.role];if(s===void 0)continue;let a=this.manifest.typeConstraints[r];if(Array.isArray(a)&&a.length>0&&!a.includes(s))throw new Error(`${this.manifest.id}: input "${n.role}" has dtype ${s}, not allowed by type constraint ${r} = [${a.join(", ")}]`)}}applyManifestDerive(t){let n={};for(let[r,s]of Object.entries(this.manifest.derive))n[r]=At(s,{...t,derived:n});return{...t,derived:n}}applyVariantDerive(t,n,r){let s={...t,variant:r,tunables:{...this.manifest.tunables,...r.tunables,...n.tunables??{}}},a={...t.derived};for(let[o,i]of Object.entries(r.derive))a[o]=At(i,{...s,derived:a});return{...s,derived:a}}rejectUnknownKeys(t,n,r,s){for(let a of Object.keys(r))if(!this.acceptedResourceKeys.has(a))throw new Error(`${t}: unknown ${n} "${a}" — not a declared arg or binding name (known: ${[...this.acceptedResourceKeys].sort().join(", ")})`);for(let a of Object.keys(s??{}))if(!this.acceptedArgKeys.has(a))throw new Error(`${t}: unknown arg "${a}" — not declared in manifest.args (known: ${[...this.acceptedArgKeys].sort().join(", ")})`)}validateRequest(t){this.rejectUnknownKeys(`WebGPU op ${this.manifest.id}`,"resource",t.resources,t.args);for(let[n,r]of Object.entries(this.manifest.args)){let s=t.resources[n]??t.args?.[n];if(s==null){if(r.required!==false)throw new Error(`WebGPU op ${this.manifest.id} missing required arg ${n}`);continue}Ng(n,r,s)}}};Zg=/{%\s*include\s+["']([^"']+)["']\s*%}/g;Bu=new WeakMap;tA=new Set(Object.keys(tt))});var Tu,Eu,Gu=q(()=>{Tu=new Map([["ops/_shared/llama-decode-q1-proj.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const OUTPUT_SIZE: u32 = {{ outputSize }}u;
+const INPUT_SIZE_Q1_WORDS: u32 = {{ inputSizeQ1Words }}u;
+const BLOCKS_PER_ROW: u32 = {{ blocksPerRow }}u;
+const WEIGHT_OFFSET: u32 = {{ weightOffset }}u;
+const WG: u32 = {{ q1WorkgroupSize }}u;
+const TILE_ROWS: u32 = 4u;
+
+var<workgroup> partials0: array<f32, WG>;
+var<workgroup> partials1: array<f32, WG>;
+var<workgroup> partials2: array<f32, WG>;
+var<workgroup> partials3: array<f32, WG>;
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) subgroup_lane: u32,
+  @builtin(subgroup_size) subgroup_size: u32
+{%- endif %}
+) {
+  let output_base = wid.x * TILE_ROWS;
+  let lid = lid3.x;
+  if (output_base >= OUTPUT_SIZE) {
+    return;
+  }
+
+  let tile = output_base / TILE_ROWS;
+  var acc0 = 0.0;
+  var acc1 = 0.0;
+  var acc2 = 0.0;
+  var acc3 = 0.0;
+  for (var word_index = lid; word_index < INPUT_SIZE_Q1_WORDS; word_index = word_index + WG) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let activation_base = word_index * 8u;
+{%- if activationVec4 == "vec4<f32>" %}
+    let v0 = activation[activation_base];
+    let v1 = activation[activation_base + 1u];
+    let v2 = activation[activation_base + 2u];
+    let v3 = activation[activation_base + 3u];
+    let v4 = activation[activation_base + 4u];
+    let v5 = activation[activation_base + 5u];
+    let v6 = activation[activation_base + 6u];
+    let v7 = activation[activation_base + 7u];
+{%- else %}
+    let v0 = vec4<f32>(activation[activation_base]);
+    let v1 = vec4<f32>(activation[activation_base + 1u]);
+    let v2 = vec4<f32>(activation[activation_base + 2u]);
+    let v3 = vec4<f32>(activation[activation_base + 3u]);
+    let v4 = vec4<f32>(activation[activation_base + 4u]);
+    let v5 = vec4<f32>(activation[activation_base + 5u]);
+    let v6 = vec4<f32>(activation[activation_base + 6u]);
+    let v7 = vec4<f32>(activation[activation_base + 7u]);
+{%- endif %}
+    let dot4 = q1_dot_word_t4(
+      WEIGHT_OFFSET, tile, BLOCKS_PER_ROW, col_block, word_id,
+      v0, v1, v2, v3, v4, v5, v6, v7
+    );
+    acc0 = acc0 + dot4.x;
+    acc1 = acc1 + dot4.y;
+    acc2 = acc2 + dot4.z;
+    acc3 = acc3 + dot4.w;
+  }
+{%- if useSubgroups %}
+  let subgroup_sum0 = subgroupAdd(acc0);
+  let subgroup_sum1 = subgroupAdd(acc1);
+  let subgroup_sum2 = subgroupAdd(acc2);
+  let subgroup_sum3 = subgroupAdd(acc3);
+  if (WG == subgroup_size) {
+    if (lid == 0u) {
+      partials0[0] = subgroup_sum0;
+      partials1[0] = subgroup_sum1;
+      partials2[0] = subgroup_sum2;
+      partials3[0] = subgroup_sum3;
+    }
+    workgroupBarrier();
+  } else {
+    if (subgroup_lane == 0u) {
+      let subgroup_index = lid / subgroup_size;
+      partials0[subgroup_index] = subgroup_sum0;
+      partials1[subgroup_index] = subgroup_sum1;
+      partials2[subgroup_index] = subgroup_sum2;
+      partials3[subgroup_index] = subgroup_sum3;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total0 = 0.0;
+      var total1 = 0.0;
+      var total2 = 0.0;
+      var total3 = 0.0;
+      let subgroup_count = WG / subgroup_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total0 = total0 + partials0[i];
+        total1 = total1 + partials1[i];
+        total2 = total2 + partials2[i];
+        total3 = total3 + partials3[i];
+      }
+      partials0[0] = total0;
+      partials1[0] = total1;
+      partials2[0] = total2;
+      partials3[0] = total3;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials0[lid] = acc0;
+  partials1[lid] = acc1;
+  partials2[lid] = acc2;
+  partials3[lid] = acc3;
+  workgroupBarrier();
+
+  var stride = WG / 2u;
+  loop {
+    if (lid < stride) {
+      partials0[lid] = partials0[lid] + partials0[lid + stride];
+      partials1[lid] = partials1[lid] + partials1[lid + stride];
+      partials2[lid] = partials2[lid] + partials2[lid + stride];
+      partials3[lid] = partials3[lid] + partials3[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+{%- endif %}
+
+  if (lid == 0u) {
+    hidden[output_base] = hidden[output_base] + partials0[0];
+    hidden[output_base + 1u] = hidden[output_base + 1u] + partials1[0];
+    hidden[output_base + 2u] = hidden[output_base + 2u] + partials2[0];
+    hidden[output_base + 3u] = hidden[output_base + 3u] + partials3[0];
+  }
+}
+`],["ops/_shared/q1-dot-helpers.wgsl.jinja",`{% include "ops/_shared/q1-signs.wgsl.jinja" %}
+
+fn q1_dot_word_t4(
+  block_offset: u32,
+  tile: u32,
+  blocks_per_row: u32,
+  col_block: u32,
+  word_id: u32,
+  a0: vec4<f32>,
+  a1: vec4<f32>,
+  a2: vec4<f32>,
+  a3: vec4<f32>,
+  a4: vec4<f32>,
+  a5: vec4<f32>,
+  a6: vec4<f32>,
+  a7: vec4<f32>
+) -> vec4<f32> {
+  let group_idx = tile * blocks_per_row + col_block;
+  let bit_vec = q1_bits[block_offset + group_idx * 4u + word_id];
+{%- if scaleScalar == "f32" %}
+  let s0 = q1_scales[block_offset + group_idx * 4u + 0u];
+  let s1 = q1_scales[block_offset + group_idx * 4u + 1u];
+  let s2 = q1_scales[block_offset + group_idx * 4u + 2u];
+  let s3 = q1_scales[block_offset + group_idx * 4u + 3u];
+{%- else %}
+  let s0 = f32(q1_scales[block_offset + group_idx * 4u + 0u]);
+  let s1 = f32(q1_scales[block_offset + group_idx * 4u + 1u]);
+  let s2 = f32(q1_scales[block_offset + group_idx * 4u + 2u]);
+  let s3 = f32(q1_scales[block_offset + group_idx * 4u + 3u]);
+{%- endif %}
+  let unscaled0 = dot(a0, q1_signs4_unit(bit_vec.x, 0u)) +
+    dot(a1, q1_signs4_unit(bit_vec.x, 4u)) +
+    dot(a2, q1_signs4_unit(bit_vec.x, 8u)) +
+    dot(a3, q1_signs4_unit(bit_vec.x, 12u)) +
+    dot(a4, q1_signs4_unit(bit_vec.x, 16u)) +
+    dot(a5, q1_signs4_unit(bit_vec.x, 20u)) +
+    dot(a6, q1_signs4_unit(bit_vec.x, 24u)) +
+    dot(a7, q1_signs4_unit(bit_vec.x, 28u));
+  let unscaled1 = dot(a0, q1_signs4_unit(bit_vec.y, 0u)) +
+    dot(a1, q1_signs4_unit(bit_vec.y, 4u)) +
+    dot(a2, q1_signs4_unit(bit_vec.y, 8u)) +
+    dot(a3, q1_signs4_unit(bit_vec.y, 12u)) +
+    dot(a4, q1_signs4_unit(bit_vec.y, 16u)) +
+    dot(a5, q1_signs4_unit(bit_vec.y, 20u)) +
+    dot(a6, q1_signs4_unit(bit_vec.y, 24u)) +
+    dot(a7, q1_signs4_unit(bit_vec.y, 28u));
+  let unscaled2 = dot(a0, q1_signs4_unit(bit_vec.z, 0u)) +
+    dot(a1, q1_signs4_unit(bit_vec.z, 4u)) +
+    dot(a2, q1_signs4_unit(bit_vec.z, 8u)) +
+    dot(a3, q1_signs4_unit(bit_vec.z, 12u)) +
+    dot(a4, q1_signs4_unit(bit_vec.z, 16u)) +
+    dot(a5, q1_signs4_unit(bit_vec.z, 20u)) +
+    dot(a6, q1_signs4_unit(bit_vec.z, 24u)) +
+    dot(a7, q1_signs4_unit(bit_vec.z, 28u));
+  let unscaled3 = dot(a0, q1_signs4_unit(bit_vec.w, 0u)) +
+    dot(a1, q1_signs4_unit(bit_vec.w, 4u)) +
+    dot(a2, q1_signs4_unit(bit_vec.w, 8u)) +
+    dot(a3, q1_signs4_unit(bit_vec.w, 12u)) +
+    dot(a4, q1_signs4_unit(bit_vec.w, 16u)) +
+    dot(a5, q1_signs4_unit(bit_vec.w, 20u)) +
+    dot(a6, q1_signs4_unit(bit_vec.w, 24u)) +
+    dot(a7, q1_signs4_unit(bit_vec.w, 28u));
+  return vec4<f32>(s0 * unscaled0, s1 * unscaled1, s2 * unscaled2, s3 * unscaled3);
+}
+`],["ops/_shared/q1-signs.wgsl.jinja",`fn q1_signs4_unit(word: u32, shift: u32) -> vec4<f32> {
+  let bits = (vec4<u32>(word) >> vec4<u32>(shift, shift + 1u, shift + 2u, shift + 3u)) & vec4<u32>(1u);
+  return select(vec4<f32>(-1.0), vec4<f32>(1.0), bits == vec4<u32>(1u));
+}
+`],["ops/_shared/argmax-wg-tree.wgsl.jinja",`{% if argmaxTieLowId %}
+
+{% endif %}
+  best_values[lid] = best_value;
+  best_indices[lid] = best_token;
+  workgroupBarrier();
+
+  var am_stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < am_stride) {
+      let other_value = best_values[lid + am_stride];
+      let other_index = best_indices[lid + am_stride];
+{% if argmaxTieLowId %}
+      if (other_value > best_values[lid] || (other_value == best_values[lid] && other_index < best_indices[lid])) {
+{% else %}
+      if (other_value > best_values[lid]) {
+{% endif %}
+        best_values[lid] = other_value;
+        best_indices[lid] = other_index;
+      }
+    }
+    workgroupBarrier();
+    if (am_stride == 1u) {
+      break;
+    }
+    am_stride = am_stride / 2u;
+  }
+
+  if (lid == 0u) {
+    best_values_out[block] = best_values[0];
+    best_ids_out[block] = best_indices[0];
+  }
+`],["ops/_shared/kv-cache-index.wgsl.jinja",`fn cache_index(pos: u32, head: u32, dim: u32) -> u32 {
+  return (((LAYER * CACHE_LEN + pos) * NUM_KV_HEADS + head) * HEAD_DIM + dim);
+}
+`],["ops/_shared/reduce-sum-wg.wgsl.jinja",`fn reduce_sum(value: f32, tid: u32) -> f32 {
+  partial[tid] = value;
+  workgroupBarrier();
+  var stride = WG / 2u;
+  loop {
+    if (stride == 0u) {
+      break;
+    }
+    if (tid < stride) {
+      partial[tid] = partial[tid] + partial[tid + stride];
+    }
+    stride = stride / 2u;
+    workgroupBarrier();
+  }
+  return partial[0];
+}`],["ops/_shared/binary-lut4-matmul.wgsl.jinja",`{%- if lutScalar == "f16" %}
+enable f16;
+{% endif -%}
+
+{% set M_VECTOR_WIDTH = source.mVectorWidth if source.mVectorWidth is defined else 1 %}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const IN_FEATURES: u32 = {{ inFeatures }}u;
+const OUT_FEATURES: u32 = {{ outFeatures }}u;
+const M_TILE: u32 = {{ tileM }}u;
+const N_TILE: u32 = {{ tileN }}u;
+const N_PER_THREAD: u32 = {{ nPerThread }}u;
+const IN_FEATURES_V4: u32 = IN_FEATURES / 4u;
+const NUM_GROUPS: u32 = IN_FEATURES / 128u;
+const WG_SIZE: u32 = N_TILE / N_PER_THREAD;
+const LUT_M_ROWS: u32 = M_TILE / {{ M_VECTOR_WIDTH }}u;
+const LUT_ENTRIES: u32 = LUT_M_ROWS * 32u * 16u;
+const LUT_NIBBLES: u32 = LUT_M_ROWS * 32u;
+{%- if source.layout == "q1_row4" %}
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+{%- endif %}
+
+{%- if M_VECTOR_WIDTH == 4 %}
+
+alias LutValue = vec4<{{ lutScalar }}>;
+alias AccValue = vec4<f32>;
+{%- else %}
+alias LutValue = {{ lutScalar }};
+alias AccValue = f32;
+{%- endif %}
+var<workgroup> lut: array<LutValue, LUT_ENTRIES>;
+{% if not assumeBiasNegHalfScale %}
+var<workgroup> sumA: array<AccValue, LUT_M_ROWS>;
+{% endif %}
+
+fn lutIndex(m_row: u32, nibble: u32, mask: u32) -> u32 {
+  return (m_row * 32u + nibble) * 16u + mask;
+}
+
+{%- if source.layout == "q1_row4" %}
+
+fn q1RowWord(vec_index: u32, row_lane: u32) -> u32 {
+  return q1_bits[vec_index * 4u + row_lane];
+}
+
+fn q1Scale(index: u32) -> f32 {
+{%- if scaleScalar == "f32" %}
+  return q1_scales[index];
+{%- else %}
+  return f32(q1_scales[index]);
+{%- endif %}
+}
+{%- endif %}
+
+@compute @workgroup_size({{ workgroupSize }}, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(num_workgroups) nwg: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>
+) {
+  let tid = lid.x;
+  let n_block = wg.x * N_TILE;
+
+  let m_block = (wg.y + wg.z * nwg.y) * M_TILE;
+{%- if source.layout == "q1_row4" and source.dynamicM is defined and source.dynamicM %}
+
+  let active_m = min(real_len[0], params.m);
+  if (m_block >= active_m) { return; }
+{%- elif source.layout == "q1_row4" %}
+  let active_m = params.m;
+{%- else %}
+  let active_m = params.M;
+{%- endif %}
+  let n_base = n_block + tid;
+
+{% for nn in range(nPerThread) %}
+  let n{{ nn }} = n_base + {{ nn }}u * WG_SIZE;
+  let n{{ nn }}_valid = n{{ nn }} < OUT_FEATURES;
+{% endfor %}
+
+{% for nn in range(nPerThread) %}
+{% for m in range(0, tileM, M_VECTOR_WIDTH) %}
+  var acc{{ nn }}_{{ m }} = AccValue(0.0);
+{% endfor %}
+{% endfor %}
+
+  for (var g = 0u; g < NUM_GROUPS; g = g + 1u) {
+    for (var i = tid; i < LUT_NIBBLES; i = i + WG_SIZE) {
+      let nibble = i & 31u;
+      let m_local = i >> 5u;
+{%- if M_VECTOR_WIDTH == 4 %}
+      let m = m_block + m_local * 4u;
+      var v0 = {{ aVec4 }}(0.0);
+      var v1 = {{ aVec4 }}(0.0);
+      var v2 = {{ aVec4 }}(0.0);
+      var v3 = {{ aVec4 }}(0.0);
+{%- if source.layout == "q1_row4" %}
+      if (m < active_m) { v0 = a[m * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 1u < active_m) { v1 = a[(m + 1u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 2u < active_m) { v2 = a[(m + 2u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 3u < active_m) { v3 = a[(m + 3u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+{%- else %}
+      if (m < params.M) { v0 = a[m * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 1u < params.M) { v1 = a[(m + 1u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 2u < params.M) { v2 = a[(m + 2u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 3u < params.M) { v3 = a[(m + 3u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+{%- endif %}
+      let x = vec4<f32>(f32(v0.x), f32(v1.x), f32(v2.x), f32(v3.x));
+      let yv = vec4<f32>(f32(v0.y), f32(v1.y), f32(v2.y), f32(v3.y));
+      let z = vec4<f32>(f32(v0.z), f32(v1.z), f32(v2.z), f32(v3.z));
+      let wv = vec4<f32>(f32(v0.w), f32(v1.w), f32(v2.w), f32(v3.w));
+{%- else %}
+      let m = m_block + m_local;
+      var v = {{ aVec4 }}(0.0);
+{%- if source.layout == "q1_row4" %}
+      if (m < active_m) {
+{%- else %}
+      if (m < params.M) {
+{%- endif %}
+        v = a[m * IN_FEATURES_V4 + g * 32u + nibble];
+      }
+      let x = f32(v.x);
+      let yv = f32(v.y);
+      let z = f32(v.z);
+      let wv = f32(v.w);
+{%- endif %}
+      let xy = x + yv;
+      let xz = x + z;
+      let yz = yv + z;
+      let xyz = xy + z;
+      let base = (m_local * 32u + nibble) * 16u;
+
+{% if assumeBiasNegHalfScale %}
+
+      let c = 0.5 * (x + yv + z + wv);
+      lut[base] = LutValue(-c);
+      lut[base + 1u] = LutValue(x - c);
+      lut[base + 2u] = LutValue(yv - c);
+      lut[base + 3u] = LutValue(xy - c);
+      lut[base + 4u] = LutValue(z - c);
+      lut[base + 5u] = LutValue(xz - c);
+      lut[base + 6u] = LutValue(yz - c);
+      lut[base + 7u] = LutValue(xyz - c);
+      lut[base + 8u] = LutValue(wv - c);
+      lut[base + 9u] = LutValue(x + wv - c);
+      lut[base + 10u] = LutValue(yv + wv - c);
+      lut[base + 11u] = LutValue(xy + wv - c);
+      lut[base + 12u] = LutValue(z + wv - c);
+      lut[base + 13u] = LutValue(xz + wv - c);
+      lut[base + 14u] = LutValue(yz + wv - c);
+      lut[base + 15u] = LutValue(xyz + wv - c);
+{% else %}
+      lut[base] = LutValue(0.0);
+      lut[base + 1u] = LutValue(x);
+      lut[base + 2u] = LutValue(yv);
+      lut[base + 3u] = LutValue(xy);
+      lut[base + 4u] = LutValue(z);
+      lut[base + 5u] = LutValue(xz);
+      lut[base + 6u] = LutValue(yz);
+      lut[base + 7u] = LutValue(xyz);
+      lut[base + 8u] = LutValue(wv);
+      lut[base + 9u] = LutValue(x + wv);
+      lut[base + 10u] = LutValue(yv + wv);
+      lut[base + 11u] = LutValue(xy + wv);
+      lut[base + 12u] = LutValue(z + wv);
+      lut[base + 13u] = LutValue(xz + wv);
+      lut[base + 14u] = LutValue(yz + wv);
+      lut[base + 15u] = LutValue(xyz + wv);
+{% endif %}
+    }
+    workgroupBarrier();
+
+{% if not assumeBiasNegHalfScale %}
+    for (var mi = tid; mi < LUT_M_ROWS; mi = mi + WG_SIZE) {
+      var s = AccValue(0.0);
+      for (var nibble = 0u; nibble < 32u; nibble = nibble + 1u) {
+        s = s + AccValue(lut[lutIndex(mi, nibble, 15u)]);
+      }
+      sumA[mi] = s;
+    }
+    workgroupBarrier();
+{% endif %}
+
+{% for nn in range(nPerThread) %}
+    if (n{{ nn }}_valid) {
+{%- if source.layout == "q1_row4" %}
+      let row_tile{{ nn }} = n{{ nn }} / 4u;
+      let row_lane{{ nn }} = n{{ nn }} & 3u;
+      let group_base{{ nn }} = params.block_offset + (row_tile{{ nn }} * NUM_GROUPS + g) * 4u;
+      let p0{{ nn }} = q1RowWord(group_base{{ nn }}, row_lane{{ nn }});
+      let p1{{ nn }} = q1RowWord(group_base{{ nn }} + 1u, row_lane{{ nn }});
+      let p2{{ nn }} = q1RowWord(group_base{{ nn }} + 2u, row_lane{{ nn }});
+      let p3{{ nn }} = q1RowWord(group_base{{ nn }} + 3u, row_lane{{ nn }});
+      let scale{{ nn }} = 2.0 * q1Scale(group_base{{ nn }} + row_lane{{ nn }});
+{%- else %}
+      let packed{{ nn }} = bits_buf[n{{ nn }} * NUM_GROUPS + g];
+      let p0{{ nn }} = packed{{ nn }}.x;
+      let p1{{ nn }} = packed{{ nn }}.y;
+      let p2{{ nn }} = packed{{ nn }}.z;
+      let p3{{ nn }} = packed{{ nn }}.w;
+      let sb{{ nn }} = (n{{ nn }} * NUM_GROUPS + g) * 2u;
+{% if assumeBiasNegHalfScale %}
+{% if scaleOnly %}
+      let scale{{ nn }} = f32(scaleBias[n{{ nn }} * NUM_GROUPS + g]);
+{% else %}
+      let scale{{ nn }} = f32(scaleBias[sb{{ nn }}]);
+{% endif %}
+{% else %}
+      let scale{{ nn }} = f32(scaleBias[sb{{ nn }}]);
+      let bias{{ nn }} = f32(scaleBias[sb{{ nn }} + 1u]);
+{% endif %}
+{%- endif %}
+{% for m in range(0, tileM, M_VECTOR_WIDTH) %}
+      var qa{{ nn }}_{{ m }} = AccValue(0.0);
+{% endfor %}
+{% for w in range(4) %}
+{% for b in range(4) %}
+      {
+        let byte{{ nn }}_{{ w }}_{{ b }} = (p{{ w }}{{ nn }} >> {{ b * 8 }}u) & 0xffu;
+        let lo{{ nn }}_{{ w }}_{{ b }} = byte{{ nn }}_{{ w }}_{{ b }} & 0x0fu;
+        let hi{{ nn }}_{{ w }}_{{ b }} = byte{{ nn }}_{{ w }}_{{ b }} >> 4u;
+{% for m in range(0, tileM, M_VECTOR_WIDTH) %}
+        qa{{ nn }}_{{ m }} = qa{{ nn }}_{{ m }}
+          + AccValue(lut[lutIndex({{ m }}u / {{ M_VECTOR_WIDTH }}u, {{ w * 8 + b * 2 }}u, lo{{ nn }}_{{ w }}_{{ b }})])
+          + AccValue(lut[lutIndex({{ m }}u / {{ M_VECTOR_WIDTH }}u, {{ w * 8 + b * 2 + 1 }}u, hi{{ nn }}_{{ w }}_{{ b }})]);
+{% endfor %}
+      }
+{% endfor %}
+{% endfor %}
+{% for m in range(0, tileM, M_VECTOR_WIDTH) %}
+{% if assumeBiasNegHalfScale %}
+      acc{{ nn }}_{{ m }} = acc{{ nn }}_{{ m }} + scale{{ nn }} * qa{{ nn }}_{{ m }};
+{% else %}
+      acc{{ nn }}_{{ m }} = acc{{ nn }}_{{ m }} + scale{{ nn }} * qa{{ nn }}_{{ m }} + bias{{ nn }} * sumA[{{ m }}u / {{ M_VECTOR_WIDTH }}u];
+{% endif %}
+{% endfor %}
+    }
+{% endfor %}
+    workgroupBarrier();
+  }
+
+{% for nn in range(nPerThread) %}
+  if (n{{ nn }}_valid) {
+{% if M_VECTOR_WIDTH == 4 %}
+{% for m in range(0, tileM, 4) %}
+{% for lane in range(4) %}
+    {
+      let mr = m_block + {{ m + lane }}u;
+{%- if source.layout == "q1_row4" %}
+      if (mr < active_m) {
+        y[mr * OUT_STRIDE + DST_COL + n{{ nn }}] = {{ yScalar }}(acc{{ nn }}_{{ m }}[{{ lane }}u]);
+      }
+{%- else %}
+      if (mr < params.M) {
+        y[mr * OUT_FEATURES + n{{ nn }}] = {{ yScalar }}(acc{{ nn }}_{{ m }}[{{ lane }}u]);
+      }
+{%- endif %}
+    }
+{% endfor %}
+{% endfor %}
+{% else %}
+{% for m in range(tileM) %}
+    {
+      let mr = m_block + {{ m }}u;
+{%- if source.layout == "q1_row4" %}
+      if (mr < active_m) {
+        y[mr * OUT_STRIDE + DST_COL + n{{ nn }}] = {{ yScalar }}(acc{{ nn }}_{{ m }});
+      }
+{%- else %}
+      if (mr < params.M) {
+        y[mr * OUT_FEATURES + n{{ nn }}] = {{ yScalar }}(acc{{ nn }}_{{ m }});
+      }
+{%- endif %}
+    }
+{% endfor %}
+{% endif %}
+  }
+{% endfor %}
+}
+`],["ops/_shared/prefill-matmul-q1-tiled.wgsl.jinja",`enable f16;
+
+{{ env.wgsl.resourceDeclarations }}
+
+const K: u32 = {{ inFeatures }}u;
+const N: u32 = {{ outFeatures }}u;
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+const BLOCK_OFFSET: u32 = {{ blockOffset }}u;
+const BPR: u32 = K / 128u;
+const K_V4: u32 = K / 4u;
+const BM: u32 = {{ bm }}u;
+const BN: u32 = 32u;
+const BK_V4: u32 = 32u;
+const N_TILE: u32 = {{ nTile }}u;
+const WG: u32 = 128u;
+
+var<workgroup> a_tile: array<vec4<f16>, {{ bm * 32 }}>;
+var<workgroup> b_tile: array<vec4<f16>, 1024>;
+
+{% include "ops/_shared/q1-signs.wgsl.jinja" %}
+
+fn q1_scale(index: u32) -> f32 {
+{%- if scaleScalar == "f32" %}
+  return q1_scales[index];
+{%- else %}
+  return f32(q1_scales[index]);
+{%- endif %}
+}
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>,
+  @builtin(num_workgroups) nwg: vec3<u32>
+) {
+  let tid = lid3.x;
+  let m_local = tid / N_TILE;
+  let n_local = tid - m_local * N_TILE;
+  let n_tiles = (N + BN - 1u) / BN;
+  let m_tiles = (params.m + BM - 1u) / BM;
+
+  for (var tile_n = wid.x; tile_n < n_tiles; tile_n = tile_n + nwg.x) {
+    let n_base = tile_n * BN;
+    for (var tile_m = wid.y; tile_m < m_tiles; tile_m = tile_m + nwg.y) {
+      let m_base = tile_m * BM;
+      let m = m_base + m_local;
+{% for nn in range(accs) %}
+      var acc{{ nn }} = 0.0;
+{% endfor %}
+
+      for (var kb = 0u; kb < BPR; kb = kb + 1u) {
+        let kv_base = kb * BK_V4;
+        for (var i = tid; i < BM * BK_V4; i = i + WG) {
+          let tm = i / BK_V4;
+          let kv = i - tm * BK_V4;
+          let gm = m_base + tm;
+          if (gm < params.m) {
+            a_tile[i] = vec4<f16>(a[gm * K_V4 + kv_base + kv]);
+          } else {
+            a_tile[i] = vec4<f16>(0.0h);
+          }
+        }
+
+        for (var i = tid; i < BN * BK_V4; i = i + WG) {
+          let tn = i / BK_V4;
+          let kv = i - tn * BK_V4;
+          let row = n_base + tn;
+          var decoded = vec4<f32>(0.0);
+          if (row < N) {
+            let row_tile = row / 4u;
+            let row_lane = row & 3u;
+            let word_id = kv / 8u;
+            let shift = (kv & 7u) * 4u;
+            let group = row_tile * BPR + kb;
+            let sign_word = q1_bits[(BLOCK_OFFSET + group * 4u + word_id) * 4u + row_lane];
+            let scale_index = BLOCK_OFFSET + group * 4u + row_lane;
+            decoded = q1_signs4_unit(sign_word, shift) * q1_scale(scale_index);
+          }
+          b_tile[i] = vec4<f16>(decoded);
+        }
+        workgroupBarrier();
+
+        if (m < params.m) {
+          for (var kv = 0u; kv < BK_V4; kv = kv + 1u) {
+            let av = vec4<f32>(a_tile[m_local * BK_V4 + kv]);
+{% for nn in range(accs) %}
+            acc{{ nn }} += dot(av, vec4<f32>(b_tile[(n_local + {{ nn * nTile }}u) * BK_V4 + kv]));
+{% endfor %}
+          }
+        }
+        workgroupBarrier();
+      }
+
+      if (m < params.m) {
+        let y_base = m * OUT_STRIDE + DST_COL + n_base + n_local;
+{% for nn in range(accs) %}
+        if (n_base + n_local + {{ nn * nTile }}u < N) { y[y_base + {{ nn * nTile }}u] = {{ yScalar }}(acc{{ nn }}); }
+{% endfor %}
+      }
+    }
+  }
+}
+`],["ops/_shared/attn-flash-decode-splitk-merge.wgsl.jinja",`{% if usesF16 %}
+enable f16;
+{% endif %}
+{% if splitQueries is not defined %}{% set splitQueries = false %}{% endif %}
+{% if hasGate is not defined %}{% set hasGate = false %}{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+
+{% if source.layout == "bhsd" %}
+
+{% elif source.layout == "layer_cache" %}
+
+{% else %}
+
+{% endif %}
+const HEAD_DIM_V4: u32 = {{ headDimV4 }}u;
+const Q_HEADS: u32 = {{ qNumHeads }}u;
+const NUM_SPLITS: u32 = {{ numSplits }}u;
+{% if splitQueries %}
+const Q_SEQ: u32 = {{ qSeq }}u;
+{% endif %}
+{% if source.layout == "bsh" %}
+const Q_HIDDEN_V4: u32 = {{ qHiddenV4 }}u;
+{% endif %}
+{% if hasBias %}
+
+const HEAD_DIM: u32 = {{ headDim }}u;
+const Q_HIDDEN: u32 = {{ qHidden }}u;
+{% endif %}
+{% include "ops/_shared/softmax-stable-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size(HEAD_DIM_V4, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>
+) {
+{% if splitQueries %}
+  let queryToken = wg.x;
+{% endif %}
+  let h = wg.y;
+  let b = wg.z;
+  let d4 = lid.x;
+  if (h >= Q_HEADS || d4 >= HEAD_DIM_V4{% if splitQueries %} || queryToken >= Q_SEQ{% endif %}) {
+    return;
+  }
+
+{% if splitQueries %}
+  let mdBase = ((b * Q_SEQ + queryToken) * Q_HEADS + h) * NUM_SPLITS;
+{% else %}
+  let mdBase = (b * Q_HEADS + h) * NUM_SPLITS;
+{% endif %}
+  var globalMax = -FLT_MAX;
+  for (var s: u32 = 0u; s < NUM_SPLITS; s = s + 1u) {
+    globalMax = max(globalMax, partial_max[mdBase + s]);
+  }
+  var globalDenom = 0.0;
+  var outV = vec4<f32>(0.0);
+  for (var s: u32 = 0u; s < NUM_SPLITS; s = s + 1u) {
+    let w = exp_shift(partial_max[mdBase + s], globalMax);
+    globalDenom = globalDenom + partial_denom[mdBase + s] * w;
+{% if splitQueries %}
+    let pBase = (((b * Q_SEQ + queryToken) * Q_HEADS + h) * NUM_SPLITS + s) * HEAD_DIM_V4;
+{% else %}
+    let pBase = ((b * Q_HEADS + h) * NUM_SPLITS + s) * HEAD_DIM_V4;
+{% endif %}
+    outV = outV + partial_out[pBase + d4] * w;
+  }
+
+{% if source.layout == "bsh" %}
+{% if splitQueries %}
+  let qBaseV4 = (b * Q_SEQ + queryToken) * Q_HIDDEN_V4 + h * HEAD_DIM_V4;
+{% else %}
+  let qBaseV4 = b * Q_HIDDEN_V4 + h * HEAD_DIM_V4;
+{% endif %}
+{% elif source.layout == "layer_cache" %}
+  let qBaseV4 = h * HEAD_DIM_V4;
+{% else %}
+  let qBaseV4 = (b * Q_HEADS + h) * HEAD_DIM_V4;
+{% endif %}
+  var outValue = outV / globalDenom;
+{% if hasBias %}
+
+  let vBiasBase = 2u * Q_HIDDEN + h * HEAD_DIM + d4 * 4u;
+  outValue = outValue + vec4<f32>(bias[vBiasBase], bias[vBiasBase + 1u], bias[vBiasBase + 2u], bias[vBiasBase + 3u]);
+{% endif %}
+{% if hasGate %}
+
+  let gateV = vec4<f32>(gate[qBaseV4 + d4]);
+  outValue = outValue * (vec4<f32>(1.0) / (vec4<f32>(1.0) + exp(-gateV)));
+{% endif %}
+  output[qBaseV4 + d4] = vec4<{{ scalar }}>(outValue);
+}
+`],["ops/_shared/attn-flash-decode-splitk.wgsl.jinja",`{% if useSubgroups is not defined %}{% set useSubgroups = true %}{% endif %}
+{% if splitQueries is not defined %}{% set splitQueries = false %}{% endif %}
+{% if useSubgroups %}
+enable subgroups;
+{% endif %}
+{% if usesF16 %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+
+{% if source.layout == "bhsd" %}
+
+{% elif source.layout == "layer_cache" %}
+
+{% else %}
+
+{% endif %}
+const HEAD_DIM: u32 = {{ headDim }}u;
+const HEAD_DIM_V4: u32 = {{ headDimV4 }}u;
+const Q_HEADS: u32 = {{ qNumHeads }}u;
+const KV_HEADS: u32 = {{ kvNumHeads }}u;
+{% if source.layout == "bsh" %}
+const Q_HIDDEN_V4: u32 = {{ qHiddenV4 }}u;
+const KV_HIDDEN_V4: u32 = {{ kvHiddenV4 }}u;
+{% elif source.layout == "layer_cache" %}
+const LAYER: u32 = {{ layer }}u;
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const ATTN_SCALE: f32 = {{ scale }};
+{% endif %}
+const WG: u32 = {{ tunables.WORKGROUP_SIZE }}u;
+const NUM_SPLITS: u32 = {{ numSplits }}u;
+{% if splitQueries %}
+const Q_SEQ: u32 = {{ qSeq }}u;
+{% endif %}
+
+{% set coopQk = useSubgroups and headDimV4 >= 8 and not (usesF16 and headDimV4 <= 32) %}
+{% set jGroups = (tunables.WORKGROUP_SIZE / headDimV4)|int %}
+{% set jSplitV = (tunables.WORKGROUP_SIZE % headDimV4 == 0) and (jGroups >= 2) %}
+{% include "ops/_shared/softmax-stable-helpers.wgsl.jinja" %}
+
+var<workgroup> q_shared: array<vec4<f32>, HEAD_DIM_V4>;
+var<workgroup> running_out: array<vec4<f32>, HEAD_DIM_V4>;
+var<workgroup> probs: array<f32, WG>;
+{% if coopQk %}
+var<workgroup> sval_sh: array<f32, WG>;
+{% endif %}
+{% if jSplitV %}
+var<workgroup> vacc_sh: array<vec4<f32>, WG>;
+{% endif %}
+{% set combineSubgroups = useSubgroups %}
+{% include "ops/_shared/softmax-md-combine.wgsl.jinja" %}
+
+fn scale_value() -> f32 {
+{% if source.layout == "layer_cache" %}
+  return ATTN_SCALE;
+{% else %}
+  if (params.scale != 0.0) { return params.scale; }
+  return inverseSqrt(f32(HEAD_DIM));
+{% endif %}
+}
+
+{% if hasBias %}
+
+fn load_bias4(base: u32, d4: u32) -> vec4<f32> {
+  let offset = base + d4 * 4u;
+  return vec4<f32>(bias[offset], bias[offset + 1u], bias[offset + 2u], bias[offset + 3u]);
+}
+{% endif %}
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>{% if useSubgroups %},
+  @builtin(subgroup_size) sgSize: u32{% endif %}
+) {
+{% if useSubgroups %}
+
+  if (sgSize == 0u || sgSize > WG || WG % sgSize != 0u) { return; }
+{% endif %}
+{% if splitQueries %}
+  let queryToken = wg.x / NUM_SPLITS;
+  let split = wg.x % NUM_SPLITS;
+{% else %}
+  let split = wg.x;
+{% endif %}
+  let h = wg.y;
+  let b = wg.z;
+  if (h >= Q_HEADS || split >= NUM_SPLITS{% if splitQueries %} || queryToken >= Q_SEQ{% endif %}{% if source.layout == "layer_cache" %} || params.past_len >= CACHE_LEN{% endif %}) {
+    return;
+  }
+  let tid = lid.x;
+  let hKv = h / (Q_HEADS / KV_HEADS);
+{% if source.layout == "layer_cache" %}
+  let kvSeq = params.past_len + 1u;
+{% else %}
+  let kvSeq = params.kvSeq;
+{% endif %}
+
+{% if source.layout == "bsh" %}
+{% if splitQueries %}
+  let qBaseV4 = (b * Q_SEQ + queryToken) * Q_HIDDEN_V4 + h * HEAD_DIM_V4;
+{% else %}
+  let qBaseV4 = b * Q_HIDDEN_V4 + h * HEAD_DIM_V4;
+{% endif %}
+  let kvBaseV4 = b * kvSeq * KV_HIDDEN_V4 + hKv * HEAD_DIM_V4;
+  let kvTokenStrideV4 = KV_HIDDEN_V4;
+{% elif source.layout == "layer_cache" %}
+  let qBaseV4 = h * HEAD_DIM_V4;
+  let kvBaseV4 = (LAYER * CACHE_LEN * KV_HEADS + hKv) * HEAD_DIM_V4;
+  let kvTokenStrideV4 = KV_HEADS * HEAD_DIM_V4;
+{% else %}
+  let qBaseV4 = (b * Q_HEADS + h) * HEAD_DIM_V4;
+  let kvBaseV4 = (b * KV_HEADS + hKv) * kvSeq * HEAD_DIM_V4;
+  let kvTokenStrideV4 = HEAD_DIM_V4;
+{% endif %}
+
+{% if hasWindow %}
+
+  var windowStart: u32 = 0u;
+  if (kvSeq > params.windowSize) {
+    windowStart = kvSeq - params.windowSize;
+  }
+  let activeKeys = kvSeq - windowStart;
+  let keysPerSplit = (activeKeys + NUM_SPLITS - 1u) / NUM_SPLITS;
+  let splitStart = windowStart + split * keysPerSplit;
+{% else %}
+  let keysPerSplit = (kvSeq + NUM_SPLITS - 1u) / NUM_SPLITS;
+  let splitStart = split * keysPerSplit;
+{% endif %}
+  var splitEnd = splitStart + keysPerSplit;
+  if (splitEnd > kvSeq) {
+    splitEnd = kvSeq;
+  }
+
+  for (var d4 = tid; d4 < HEAD_DIM_V4; d4 = d4 + WG) {
+    var qv = vec4<f32>(query[qBaseV4 + d4]);
+{% if hasBias %}
+    qv = qv + load_bias4(h * HEAD_DIM, d4);
+{% endif %}
+    q_shared[d4] = qv;
+    running_out[d4] = vec4<f32>(0.0);
+  }
+  workgroupBarrier();
+
+  let scale = scale_value();
+  var runningMax = -FLT_MAX;
+  var runningDenom = 0.0;
+
+  var kjBase = splitStart;
+  loop {
+    if (kjBase >= splitEnd) {
+      break;
+    }
+    let kj = kjBase + tid;
+    let keyAllowed = kj < splitEnd;
+    let tileCount = min(WG, splitEnd - kjBase);
+
+    var score = -FLT_MAX;
+    var m = -FLT_MAX;
+    var dPart = 0.0;
+{% if coopQk %}
+
+    let sgPerWg = WG / sgSize;
+    let qkRounds = (tileCount + sgPerWg - 1u) / sgPerWg;
+    let lane = tid % sgSize;
+    let sgInWg = tid / sgSize;
+    for (var rr: u32 = 0u; rr < qkRounds; rr = rr + 1u) {
+      let j = rr * sgPerWg + sgInWg;
+      var accS: f32 = 0.0;
+      if (j < tileCount) {
+        let kRowV4 = kvBaseV4 + (kjBase + j) * kvTokenStrideV4;
+        for (var d4: u32 = lane; d4 < HEAD_DIM_V4; d4 = d4 + sgSize) {
+          accS = accS + dot(q_shared[d4], vec4<f32>(key[kRowV4 + d4]));
+        }
+      }
+      let sj = subgroupAdd(accS);
+      if (lane == 0u && j < tileCount) {
+        sval_sh[j] = sj;
+      }
+    }
+    workgroupBarrier();
+    if (keyAllowed) {
+      score = sval_sh[tid] * scale;
+      m = score;
+      dPart = 1.0;
+    }
+{% else %}
+    if (keyAllowed) {
+      let kRowV4 = kvBaseV4 + kj * kvTokenStrideV4;
+      var acc: f32 = 0.0;
+      for (var d4: u32 = 0u; d4 < HEAD_DIM_V4; d4 = d4 + 1u) {
+        acc = acc + dot(q_shared[d4], vec4<f32>(key[kRowV4 + d4]));
+      }
+      score = acc * scale;
+      m = score;
+      dPart = 1.0;
+    }
+{% endif %}
+    let tile = combine_partials(m, dPart, tid{% if useSubgroups %}, sgSize{% endif %});
+
+    let newMax = max(runningMax, tile.x);
+    let correction = exp_shift(runningMax, newMax);
+    runningDenom = runningDenom * correction + tile.y * exp_shift(tile.x, newMax);
+    runningMax = newMax;
+
+    var prob = 0.0;
+    if (keyAllowed) {
+      prob = exp_shift(score, newMax);
+    }
+    probs[tid] = prob;
+    workgroupBarrier();
+
+{% if jSplitV %}
+
+    const J_GROUPS: u32 = {{ jGroups }}u;
+    let jg = tid / HEAD_DIM_V4;
+    let d4v = tid % HEAD_DIM_V4;
+    var vacc = vec4<f32>(0.0);
+    var jj = jg;
+    loop {
+      if (jj >= tileCount) { break; }
+      vacc = vacc + probs[jj] * vec4<f32>(value[kvBaseV4 + (kjBase + jj) * kvTokenStrideV4 + d4v]);
+      jj = jj + J_GROUPS;
+    }
+    vacc_sh[tid] = vacc;
+    workgroupBarrier();
+    for (var d4: u32 = tid; d4 < HEAD_DIM_V4; d4 = d4 + WG) {
+      var a4 = running_out[d4] * correction;
+      for (var g: u32 = 0u; g < J_GROUPS; g = g + 1u) {
+        a4 = a4 + vacc_sh[g * HEAD_DIM_V4 + d4];
+      }
+      running_out[d4] = a4;
+    }
+    workgroupBarrier();
+{% else %}
+    for (var d4: u32 = tid; d4 < HEAD_DIM_V4; d4 = d4 + WG) {
+      var vSum = vec4<f32>(0.0);
+      for (var i: u32 = 0u; i < tileCount; i = i + 1u) {
+        vSum = vSum + probs[i] * vec4<f32>(value[kvBaseV4 + (kjBase + i) * kvTokenStrideV4 + d4]);
+      }
+      running_out[d4] = running_out[d4] * correction + vSum;
+    }
+    workgroupBarrier();
+{% endif %}
+
+    kjBase = kjBase + WG;
+  }
+
+{% if splitQueries %}
+  let partialBase = (((b * Q_SEQ + queryToken) * Q_HEADS + h) * NUM_SPLITS + split) * HEAD_DIM_V4;
+{% else %}
+  let partialBase = ((b * Q_HEADS + h) * NUM_SPLITS + split) * HEAD_DIM_V4;
+{% endif %}
+  for (var d4: u32 = tid; d4 < HEAD_DIM_V4; d4 = d4 + WG) {
+    partial_out[partialBase + d4] = running_out[d4];
+  }
+  if (tid == 0u) {
+{% if splitQueries %}
+    let mdBase = ((b * Q_SEQ + queryToken) * Q_HEADS + h) * NUM_SPLITS + split;
+{% else %}
+    let mdBase = (b * Q_HEADS + h) * NUM_SPLITS + split;
+{% endif %}
+    partial_max[mdBase] = runningMax;
+    partial_denom[mdBase] = runningDenom;
+  }
+}
+`],["ops/_shared/softmax-md-combine.wgsl.jinja",`{% if combineSubgroups %}
+
+var<workgroup> partialM: array<f32, WG>;
+var<workgroup> partialD: array<f32, WG>;
+var<workgroup> combinedMD: vec2<f32>;
+
+fn combine_partials(m: f32, d: f32, lidx: u32, sgSize: u32) -> vec2<f32> {
+  let sgM = subgroupMax(m);
+
+  let sgD = subgroupAdd(d * exp_shift(m, sgM));
+  if (sgSize == WG) {
+    return vec2<f32>(sgM, sgD);
+  }
+  let subgroupCount = (WG + sgSize - 1u) / sgSize;
+
+  if (lidx < subgroupCount) {
+    partialM[lidx] = -FLT_MAX;
+    partialD[lidx] = 0.0;
+  }
+  workgroupBarrier();
+  if (subgroupElect()) {
+    let slot = lidx / sgSize;
+    partialM[slot] = sgM;
+    partialD[slot] = sgD;
+  }
+  workgroupBarrier();
+  if (lidx == 0u) {
+    var accM = -FLT_MAX;
+    var accD = 0.0;
+    for (var i = 0u; i < subgroupCount; i = i + 1u) {
+      let mNew = max(accM, partialM[i]);
+      accD = accD * exp_shift(accM, mNew) + partialD[i] * exp_shift(partialM[i], mNew);
+      accM = mNew;
+    }
+    combinedMD = vec2<f32>(accM, accD);
+  }
+  workgroupBarrier();
+  return combinedMD;
+}
+{% else %}
+var<workgroup> partialM: array<f32, WG>;
+var<workgroup> partialD: array<f32, WG>;
+
+fn combine_partials(m: f32, d: f32, lidx: u32) -> vec2<f32> {
+  partialM[lidx] = m;
+  partialD[lidx] = d;
+  workgroupBarrier();
+  var stride = WG / 2u;
+  loop {
+    if (stride == 0u) {
+      break;
+    }
+    if (lidx < stride) {
+      let m1 = partialM[lidx];
+      let d1 = partialD[lidx];
+      let m2 = partialM[lidx + stride];
+      let d2 = partialD[lidx + stride];
+      let mNew = max(m1, m2);
+      partialD[lidx] = d1 * exp_shift(m1, mNew) + d2 * exp_shift(m2, mNew);
+      partialM[lidx] = mNew;
+    }
+    workgroupBarrier();
+    stride = stride / 2u;
+  }
+  let merged = vec2<f32>(partialM[0], partialD[0]);
+
+  workgroupBarrier();
+  return merged;
+}
+{% endif %}
+`],["ops/_shared/softmax-stable-helpers.wgsl.jinja",`const FLT_MAX: f32 = 3.4028234663852886e38;
+
+fn is_finite_f32(value: f32) -> bool {
+  return select(false, value <= FLT_MAX, value >= -FLT_MAX);
+}
+
+fn shifted_value(value: f32, maxValue: f32) -> f32 {
+  let equalFiniteMax = select(false, value == maxValue, is_finite_f32(maxValue));
+  return select(value - maxValue, 0.0, equalFiniteMax);
+}
+
+fn exp_shift(value: f32, maxValue: f32) -> f32 {
+  return exp(shifted_value(value, maxValue));
+}
+
+fn is_nan_f32(value: f32) -> bool {
+  let bits = bitcast<u32>(value);
+  return (bits & 0x7f800000u) == 0x7f800000u && (bits & 0x007fffffu) != 0u;
+}
+`],["ops/_shared/linear-attention-common.wgsl.jinja",`{%- macro read_scalar(name, index, dtype) -%}
+{%- if dtype == "float16" -%}
+f32({{ name }}[{{ index }}]){%- else -%}{{ name }}[{{ index }}]{%- endif -%}
+{%- endmacro -%}
+{%- macro write_scalar(expr, dtype) -%}
+{%- if dtype == "float16" -%}
+f16({{ expr }}){%- else -%}{{ expr }}{%- endif -%}
+{%- endmacro -%}
+
+{%- macro emit_qwen35_stable_decay() %}
+{% if stateF16 %}
+fn qwen35_stable_decay(gate_value: f32) -> f32 {
+
+  if (gate_value != gate_value) { return gate_value; }
+
+  if (gate_value > 89.0) { return exp(gate_value); }
+  if (gate_value < -104.0) { return 0.0; }
+
+  let exponent_f = floor(fma(gate_value, 1.4426950408889634, 0.5));
+  var remainder = fma(-exponent_f, 0.693145751953125, gate_value);
+  remainder = fma(-exponent_f, 1.428606765330187e-6, remainder);
+
+  var polynomial = 0.0000248015873015873;
+  polynomial = fma(polynomial, remainder, 0.0001984126984126984);
+  polynomial = fma(polynomial, remainder, 0.001388888888888889);
+  polynomial = fma(polynomial, remainder, 0.008333333333333333);
+  polynomial = fma(polynomial, remainder, 0.041666666666666664);
+  polynomial = fma(polynomial, remainder, 0.16666666666666666);
+  polynomial = fma(polynomial, remainder, 0.5);
+  polynomial = fma(polynomial, remainder, 1.0);
+  polynomial = fma(polynomial, remainder, 1.0);
+
+  let exponent = i32(exponent_f);
+  if (exponent > 127) {
+
+    return (polynomial * 2.0) * bitcast<f32>(0x7f000000u);
+  }
+  if (exponent >= -126) {
+    let scale = bitcast<f32>(u32(exponent + 127) << 23u);
+    return polynomial * scale;
+  }
+  if (exponent >= -149) {
+
+    let scale = bitcast<f32>(1u << u32(exponent + 149));
+    return polynomial * scale;
+  }
+  return 0.0;
+}
+{% endif %}
+{%- endmacro -%}
+
+{%- macro qwen35_decay(expr) -%}
+{%- if stateF16 -%}qwen35_stable_decay({{ expr }}){%- else -%}exp({{ expr }}){%- endif -%}
+{%- endmacro -%}
+
+{%- macro emit_tiled_setup() %}
+  let head_dim_k = params.qPackedDim / params.qNumHeads;
+  let head_dim_v = params.vPackedDim / params.kvNumHeads;
+  let n_key_heads = params.kPackedDim / head_dim_k;
+  let heads_per_group = params.qNumHeads / params.kvNumHeads;
+  let kv_per_key_head = params.kvNumHeads / n_key_heads;
+  let dv_tiles = (head_dim_v + TILE_V - 1u) / TILE_V;
+  let scale = select(inverseSqrt(f32(head_dim_k)), params.scale, params.scale != 0.0);
+
+  let workgroup_idx = wg.x + wg.y * nwg.x;
+  let dv_tile_idx = workgroup_idx % dv_tiles;
+  let bh = workgroup_idx / dv_tiles;
+  let head_idx = bh % params.kvNumHeads;
+  let batch_idx = bh / params.kvNumHeads;
+  if (batch_idx >= params.batchSize) {
+    return;
+  }
+
+  let dv_start = dv_tile_idx * TILE_V;
+  let packed_out = params.qNumHeads * head_dim_v;
+  let key_head_idx = head_idx / kv_per_key_head;
+{%- endmacro -%}
+
+{%- macro emit_serial_query_groups(first_group) %}
+    for (var qg = {{ first_group }}u; qg < heads_per_group; qg = qg + 1u) {
+      let q_head = head_idx * heads_per_group + qg;
+      let q_base = bt * params.qPackedDim + q_head * HEAD_DIM_K;
+      var q_out = 0.0;
+      for (var d = 0u; d < HEAD_DIM_K; d = d + 1u) {
+        q_out = q_out + state[d] * {{ read_scalar("query", "q_base + d", queryDtype) }};
+      }
+      let out_idx = bt * packed_out + q_head * head_dim_v + dv_idx;
+      output[out_idx] = {{ write_scalar("q_out * scale", outputDtype) }};
+    }
+{%- endmacro -%}
+
+{%- macro emit_qwen35_decode_header() %}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+{%- if stateF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KEY_HEADS: u32 = {{ numKeyHeads }}u;
+
+const HEAD_DIM_K: u32 = {{ headDimK }}u;
+const HEAD_DIM_V: u32 = {{ headDimV }}u;
+const TILE_V: u32 = {{ tileV }}u;
+const WG: u32 = {{ workgroupSize }}u;
+{% endmacro -%}
+
+{%- macro emit_decode_tile_setup() %}
+  let head = wid.x / ((HEAD_DIM_V + TILE_V - 1u) / TILE_V);
+  let tile = wid.x % ((HEAD_DIM_V + TILE_V - 1u) / TILE_V);
+  let dv_start = tile * TILE_V;
+  if (head >= NUM_HEADS) { return; }
+{%- endmacro -%}
+
+{%- macro emit_gather_function(name, value_type, buffer, has_base, zero) %}
+fn {{ name }}({% if has_base %}base: u32{% endif %}) -> {{ value_type }} {
+  var s0 = {{ zero }};
+  var s1 = {{ zero }};
+  var s2 = {{ zero }};
+  var s3 = {{ zero }};
+  var i = 0u;
+  for (; i + 3u < WG; i = i + 4u) {
+    s0 = s0 + {{ buffer }}[{% if has_base %}base + {% endif %}i];
+    s1 = s1 + {{ buffer }}[{% if has_base %}base + {% endif %}i + 1u];
+    s2 = s2 + {{ buffer }}[{% if has_base %}base + {% endif %}i + 2u];
+    s3 = s3 + {{ buffer }}[{% if has_base %}base + {% endif %}i + 3u];
+  }
+  var total = (s0 + s1) + (s2 + s3);
+  for (; i < WG; i = i + 1u) { total = total + {{ buffer }}[{% if has_base %}base + {% endif %}i]; }
+  return total;
+}
+{%- endmacro -%}
+
+{%- macro emit_pair_gather(explain) %}
+    for (var j = tid; j < TILE_V; j = j + WG) {
+{% if explain %}
+
+{% endif %}
+      var rp0 = vec2<f32>(0.0);
+      var rp1 = vec2<f32>(0.0);
+      var rp2 = vec2<f32>(0.0);
+      var rp3 = vec2<f32>(0.0);
+      var rp4 = vec2<f32>(0.0);
+      var rp5 = vec2<f32>(0.0);
+      var rp6 = vec2<f32>(0.0);
+      var rp7 = vec2<f32>(0.0);
+      var i = 0u;
+      for (; i + 7u < WG; i = i + 8u) {
+        let base = j * WG + i;
+        rp0 = rp0 + red_rp[base + 0u];
+        rp1 = rp1 + red_rp[base + 1u];
+        rp2 = rp2 + red_rp[base + 2u];
+        rp3 = rp3 + red_rp[base + 3u];
+        rp4 = rp4 + red_rp[base + 4u];
+        rp5 = rp5 + red_rp[base + 5u];
+        rp6 = rp6 + red_rp[base + 6u];
+        rp7 = rp7 + red_rp[base + 7u];
+      }
+      var rp_total = rp0 + rp1 + rp2 + rp3 + rp4 + rp5 + rp6 + rp7;
+      for (; i < WG; i = i + 1u) {
+        rp_total = rp_total + red_rp[j * WG + i];
+      }
+      red_rp[j * WG] = rp_total;
+    }
+{%- endmacro -%}
+
+{%- macro emit_kq_gather() %}
+      var kq0 = vec4<f32>(0.0);
+      var kq1 = vec4<f32>(0.0);
+      var kq2 = vec4<f32>(0.0);
+      var kq3 = vec4<f32>(0.0);
+      var i = 0u;
+      for (; i + 15u < WG; i = i + 16u) {
+        kq0 = kq0 + vec4<f32>(red_kq[i + 0u], red_kq[i + 1u], red_kq[i + 2u], red_kq[i + 3u]);
+        kq1 = kq1 + vec4<f32>(red_kq[i + 4u], red_kq[i + 5u], red_kq[i + 6u], red_kq[i + 7u]);
+        kq2 = kq2 + vec4<f32>(red_kq[i + 8u], red_kq[i + 9u], red_kq[i + 10u], red_kq[i + 11u]);
+        kq3 = kq3 + vec4<f32>(red_kq[i + 12u], red_kq[i + 13u], red_kq[i + 14u], red_kq[i + 15u]);
+      }
+      var kq4 = kq0 + kq1 + kq2 + kq3;
+      for (; i + 3u < WG; i = i + 4u) {
+        kq4 = kq4 + vec4<f32>(red_kq[i + 0u], red_kq[i + 1u], red_kq[i + 2u], red_kq[i + 3u]);
+      }
+      var kq_total = dot(kq4, vec4<f32>(1.0));
+      for (; i < WG; i = i + 1u) {
+        kq_total = kq_total + red_kq[i];
+      }
+      red_kq[0] = kq_total;
+{%- endmacro -%}
+`],["ops/_shared/reduce-tree.wgsl.jinja",`{% macro wgsl_tree_reduce_f32(name, mode, buffer="partial", wg="WG", trailingBarrier=true) %}
+fn {{ name }}(value: f32, tid: u32) -> f32 {
+  {{ buffer }}[tid] = value;
+  workgroupBarrier();
+
+  var n: u32 = {{ wg }};
+  loop {
+    let half = (n + 1u) / 2u;
+    if (tid < n - half) {
+{% if mode == "max" %}
+      {{ buffer }}[tid] = max({{ buffer }}[tid], {{ buffer }}[tid + half]);
+{% else %}
+      {{ buffer }}[tid] = {{ buffer }}[tid] + {{ buffer }}[tid + half];
+{% endif %}
+    }
+    workgroupBarrier();
+    n = half;
+    if (n == 1u) {
+      break;
+    }
+  }
+
+  let reduced = {{ buffer }}[0];
+{% if trailingBarrier %}
+  workgroupBarrier();
+{% endif %}
+  return reduced;
+}
+{% endmacro %}
+`]]),Eu=new Map([["com.xenova.AddInPlace",{manifest:{schemaVersion:1,domain:"com.xenova",name:"AddInPlace",sinceVersion:1,inputs:[{role:"Y",dtype:"Y"},{role:"X",dtype:"X"}],outputs:[{role:"Y",dtype:"Y",shape:"shapes.yT"}],typeConstraints:{Y:["float32","float16"],X:["float32","float16"]},args:{yT:{kind:"tensor",semantic:"Y",role:"inout"},xT:{kind:"tensor",semantic:"X",role:"input"},count:{kind:"u32",semantic:"kernel.count"}},tunables:{WORKGROUP_SIZE:64},variants:[{id:"scalar",when:["args.count >= 0","numel(shapes.yT) >= args.count","numel(shapes.xT) >= args.count","f16Ok(dtypes.Y)","f16Ok(dtypes.X)"],passes:[{id:"main",name:"AddInPlace",shader:"add-in-place.wgsl.jinja",bindings:[{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$Y"},{name:"x",arg:"xT",semantic:"X",role:"input",buffer:{type:"read-only-storage"},elementType:"$X"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"count",type:"u32",value:"args.count"},{name:"wgY",type:"u32",value:"min(ceil(args.count / tunables.WORKGROUP_SIZE), device.limits.maxComputeWorkgroupsPerDimension)"}]}}],dispatch:{threads:"args.count",workgroupSize:"tunables.WORKGROUP_SIZE"},reads:["Y","X"],writes:["Y"]}]}]},assets:[["add-in-place.wgsl.jinja",`{% if Y == "f16" or X == "f16" %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+
+const WG: u32 = {{ tunables.WORKGROUP_SIZE }}u;
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(@builtin(workgroup_id) wg: vec3<u32>, @builtin(local_invocation_id) lid: vec3<u32>) {
+  let wg_idx = wg.x + wg.y * params.wgY;
+  let i = wg_idx * WG + lid.x;
+  if (i >= params.count) {
+    return;
+  }
+
+  let yv = f32(y[i]);
+  let xv = f32(x[i]);
+  y[i] = {{ "f16(yv + xv)" if dtypes.Y == "f16" else "yv + xv" }};
+}
+`]]}],["com.xenova.LlamaDecodeBestArgmax",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeBestArgmax",sinceVersion:1,inputs:[{role:"BestValues",dtype:"float32",rank:1},{role:"BestIds",dtype:"uint32",rank:1}],outputs:[{role:"Output",dtype:"uint32",rank:1,shape:[1]}],args:{bestValuesT:{kind:"tensor",semantic:"BestValues",role:"input"},bestIdsT:{kind:"tensor",semantic:"BestIds",role:"input"},outputT:{kind:"tensor",semantic:"Output",role:"output"},outputOffset:{kind:"u32",semantic:"output_offset",required:false},workgroupSize:{kind:"u32",semantic:"workgroup_size"},inputCount:{kind:"u32",semantic:"input_count",required:false}},derive:{effInputCount:"args.inputCount if args.inputCount else args.workgroupSize"},variants:[{id:"reduce",priority:0,when:["ranks.bestValuesT == 1","ranks.bestIdsT == 1","ranks.outputT == 1",'tensorDtypes.bestValuesT == "float32"','tensorDtypes.bestIdsT == "uint32"','tensorDtypes.outputT == "uint32"',"args.workgroupSize > 0","args.workgroupSize <= 256","(args.workgroupSize == 1 or args.workgroupSize == 2 or args.workgroupSize == 4 or args.workgroupSize == 8 or args.workgroupSize == 16 or args.workgroupSize == 32 or args.workgroupSize == 64 or args.workgroupSize == 128 or args.workgroupSize == 256)","effInputCount > 0","effInputCount <= dim(shapes.bestValuesT, 0)","dim(shapes.bestIdsT, 0) >= effInputCount","dim(shapes.outputT, 0) > (args.outputOffset if args.outputOffset else 0)"],constants:{workgroupSize:"args.workgroupSize",inputCount:"effInputCount",outputOffset:"args.outputOffset if args.outputOffset else 0",f32Min:'"-3.4028234663852886e38"'},passes:[{id:"main",name:"LlamaDecodeBestArgmax",shader:"decode-best-argmax.wgsl.jinja",bindings:[{name:"best_values_in",arg:"bestValuesT",semantic:"BestValues",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"best_ids_in",arg:"bestIdsT",semantic:"BestIds",role:"input",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"output",arg:"outputT",semantic:"Output",role:"output",buffer:{type:"storage"},elementType:"u32"}],dispatch:{x:1},reads:["BestValues","BestIds"],writes:["Output"]}]}]},assets:[["decode-best-argmax.wgsl.jinja",`{{ env.wgsl.resourceDeclarations }}
+
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+const INPUT_COUNT: u32 = {{ inputCount }}u;
+const OUTPUT_OFFSET: u32 = {{ outputOffset }}u;
+
+var<workgroup> best_values: array<f32, {{ workgroupSize }}>;
+var<workgroup> best_indices: array<u32, {{ workgroupSize }}>;
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(@builtin(local_invocation_id) lid3: vec3<u32>) {
+  let lid = lid3.x;
+  var value = {{ f32Min }};
+  var index = 0u;
+  for (var input = lid; input < INPUT_COUNT; input = input + WORKGROUP_SIZE) {
+    let candidate = best_values_in[input];
+    if (candidate > value) {
+      value = candidate;
+      index = best_ids_in[input];
+    }
+  }
+  best_values[lid] = value;
+  best_indices[lid] = index;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      let other_value = best_values[lid + stride];
+      let other_index = best_indices[lid + stride];
+      if (other_value > best_values[lid]) {
+        best_values[lid] = other_value;
+        best_indices[lid] = other_index;
+      }
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+
+  if (lid == 0u) {
+    output[OUTPUT_OFFSET] = best_indices[0];
+  }
+}
+`]]}],["com.xenova.LlamaDecodeDownProj",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeDownProj",sinceVersion:1,inputs:[{role:"Hidden",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"Intermediate",dtype:"I",rank:1},{role:"Q4Bits",dtype:"uint32",rank:1,optional:true},{role:"Q4Scales",dtype:"S",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Hidden",dtype:"float32",rank:1,shape:["args.hiddenSize"]}],tunables:{OUTPUTS_PER_WG:2,DENSE_WORKGROUP_SIZE:64,Q1_WORKGROUP_SIZE:64},typeConstraints:{W:["float32","float16"],I:["float32","float16"],S:["float32","float16"]},args:{hiddenT:{kind:"tensor",semantic:"Hidden",role:"inout"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},intermediateT:{kind:"tensor",semantic:"Intermediate",role:"input"},hiddenSize:{kind:"u32",semantic:"hidden_size"},intermediateSize:{kind:"u32",semantic:"intermediate_size"},downOffset:{kind:"u32",semantic:"down_proj_offset"},q4BitsT:{kind:"tensor",semantic:"Q4Bits",role:"weights",required:false},q4ScalesT:{kind:"tensor",semantic:"Q4Scales",role:"weights",required:false},format:{kind:"string",semantic:"packed_quant_format",required:false,oneOf:["q4_0","q4_k","q8_0","iq","lut32","lut16","q2k","q6k"]},actRelu2:{kind:"u32",semantic:"activation_relu_squared",required:false},lut:{kind:"u32",semantic:"dequant_lut_id",required:false},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false}},bindingSets:{dense:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightVec4"},{name:"activation",arg:"intermediateT",semantic:"Intermediate",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationVec4"}],q1:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"activation",arg:"intermediateT",semantic:"Intermediate",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationVec4"}],q4:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q4_bits",arg:"q4BitsT",semantic:"Q4Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q4_scales",arg:"q4ScalesT",semantic:"Q4Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"intermediate",arg:"intermediateT",semantic:"Intermediate",role:"input",buffer:{type:"read-only-storage"},elementType:"$intermediateVec4"}]},derive:{fmt:'args.format if args.format else "q4_0"',quantBits:'8 if (fmt == "q8_0" or fmt == "iq") else 4',hasMin:'1 if fmt == "q4_k" else 0',iq:'1 if fmt == "iq" else 0',lut16:'1 if fmt == "lut16" else 0',q2k:'1 if fmt == "q2k" else 0',q6k:'1 if fmt == "q6k" else 0',deviceWorkgroupCap:"min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",denseWorkgroupLimit:"min(tunables.DENSE_WORKGROUP_SIZE, deviceWorkgroupCap)",denseWorkgroupSize:"max(1, pow2ceil(denseWorkgroupLimit + 1) / 2)",denseWorkgroupBytes:"denseWorkgroupSize * tunables.OUTPUTS_PER_WG * 4",denseDeviceOk:"tunables.OUTPUTS_PER_WG >= 1 and tunables.OUTPUTS_PER_WG <= denseWorkgroupSize and denseWorkgroupBytes <= device.limits.maxComputeWorkgroupStorageSize",q1WorkgroupLimit:"min(tunables.Q1_WORKGROUP_SIZE, deviceWorkgroupCap)",q1WorkgroupSize:"max(1, pow2ceil(q1WorkgroupLimit + 1) / 2)",q1WorkgroupBytes:"q1WorkgroupSize * 4 * 4",q1DeviceOk:"q1WorkgroupBytes <= device.limits.maxComputeWorkgroupStorageSize"},variants:[{id:"q1",priority:0,when:["present.q1BitsT","q1DeviceOk","ranks.hiddenT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.intermediateT == 1",'tensorDtypes.hiddenT == "float32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','(tensorDtypes.intermediateT == "float32" or tensorDtypes.intermediateT == "float16")','((tensorDtypes.q1ScalesT != "float16" and tensorDtypes.intermediateT != "float16") or device.features.has("shader-f16"))',"args.hiddenSize > 0","args.hiddenSize % 4 == 0","args.intermediateSize > 0","args.intermediateSize % 128 == 0","dim(shapes.hiddenT, 0) == args.hiddenSize","dim(shapes.intermediateT, 0) == args.intermediateSize","dim(shapes.q1BitsT, 0) >= (args.downOffset + args.hiddenSize * (args.intermediateSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= args.downOffset + args.hiddenSize * (args.intermediateSize / 128)"],constants:{usesF16:'tensorDtypes.q1ScalesT == "float16" or tensorDtypes.intermediateT == "float16"',useSubgroups:'device.features.has("subgroups")',activationVec4:'"vec4<f16>" if tensorDtypes.intermediateT == "float16" else "vec4<f32>"',scaleScalar:"dtypes.S",outputSize:"args.hiddenSize",inputSizeQ1Words:"args.intermediateSize / 32",blocksPerRow:"args.intermediateSize / 128",weightOffset:"args.downOffset",q1WorkgroupSize:"q1WorkgroupSize"},passes:[{id:"main",name:"LlamaDecodeDownProjQ1",shader:"ops/_shared/llama-decode-q1-proj.wgsl.jinja",bindings:"q1",dispatch:{x:"ceil(args.hiddenSize / 4)"},reads:["Hidden","Q1Bits","Q1Scales","Intermediate"],writes:["Hidden"]}]}]},assets:[]}],["com.xenova.LlamaDecodeGateUp",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeGateUp",sinceVersion:1,inputs:[{role:"Normed",dtype:"N",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"Q4Bits",dtype:"uint32",rank:1,optional:true},{role:"Q4Scales",dtype:"S",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Intermediate",dtype:"I",rank:1,shape:["args.intermediateSize"]}],typeConstraints:{N:["float32","float16"],W:["float32","float16"],I:["float32","float16"],S:["float32","float16"]},args:{normedT:{kind:"tensor",semantic:"Normed",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},intermediateT:{kind:"tensor",semantic:"Intermediate",role:"output"},hiddenSize:{kind:"u32",semantic:"hidden_size"},intermediateSize:{kind:"u32",semantic:"intermediate_size"},gateOffset:{kind:"u32",semantic:"gate_proj_offset"},upOffset:{kind:"u32",semantic:"up_proj_offset"},activation:{kind:"string",semantic:"activation",required:false,oneOf:["silu","fast_silu"]},q4BitsT:{kind:"tensor",semantic:"Q4Bits",role:"weights",required:false},q4ScalesT:{kind:"tensor",semantic:"Q4Scales",role:"weights",required:false},format:{kind:"string",semantic:"packed_quant_format",required:false,oneOf:["q4_0","q4_k","q8_0","iq","lut32","lut16"]},lut:{kind:"u32",semantic:"dequant_lut_id",required:false},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false}},derive:{fmt:'args.format if args.format else "q4_0"',quantBits:'8 if (fmt == "q8_0" or fmt == "iq") else 4',hasMin:'1 if fmt == "q4_k" else 0',iq:'1 if fmt == "iq" else 0',lut16:'1 if fmt == "lut16" else 0'},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.normedT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.intermediateT == 1",'(tensorDtypes.normedT == "float32" or tensorDtypes.normedT == "float16")','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','(tensorDtypes.intermediateT == "float32" or tensorDtypes.intermediateT == "float16")','((tensorDtypes.normedT != "float16" and tensorDtypes.q1ScalesT != "float16" and tensorDtypes.intermediateT != "float16") or device.features.has("shader-f16"))',"args.hiddenSize > 0","args.hiddenSize % 128 == 0","args.intermediateSize > 0","args.intermediateSize % 4 == 0","dim(shapes.normedT, 0) == args.hiddenSize","dim(shapes.intermediateT, 0) == args.intermediateSize","dim(shapes.q1BitsT, 0) >= max(args.gateOffset + args.intermediateSize * (args.hiddenSize / 128), args.upOffset + args.intermediateSize * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= max(args.gateOffset + args.intermediateSize * (args.hiddenSize / 128), args.upOffset + args.intermediateSize * (args.hiddenSize / 128))"],constants:{usesF16:'tensorDtypes.normedT == "float16" or tensorDtypes.q1ScalesT == "float16" or tensorDtypes.intermediateT == "float16"',useSubgroups:'device.features.has("subgroups")',fastSilu:'args.activation == "fast_silu"',normedVec4:'"vec4<f16>" if tensorDtypes.normedT == "float16" else "vec4<f32>"',scaleScalar:"dtypes.S",intermediateScalar:"dtypes.I",hiddenSizeQ1Words:"args.hiddenSize / 32",blocksPerRow:"args.hiddenSize / 128",intermediateSize:"args.intermediateSize",gateOffset:"args.gateOffset",upOffset:"args.upOffset"},passes:[{id:"main",name:"LlamaDecodeGateUpQ1",source:{kind:"template",shader:"decode-gate-up-q1.wgsl.jinja",version:2,inputs:{optimized:true}},bindings:[{name:"normed",arg:"normedT",semantic:"Normed",role:"input",buffer:{type:"read-only-storage"},elementType:"$normedVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"intermediate",arg:"intermediateT",semantic:"Intermediate",role:"output",buffer:{type:"storage"},elementType:"$intermediateScalar"}],dispatch:{x:"ceil(args.intermediateSize / 4)"},reads:["Normed","Q1Bits","Q1Scales"],writes:["Intermediate"]}]}]},assets:[["decode-gate-up-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE_Q1_WORDS: u32 = {{ hiddenSizeQ1Words }}u;
+const BPR: u32 = {{ blocksPerRow }}u;
+const INTERMEDIATE_SIZE: u32 = {{ intermediateSize }}u;
+const W_GATE_Q1: u32 = {{ gateOffset }}u;
+const W_UP_Q1: u32 = {{ upOffset }}u;
+const WORKGROUP_SIZE: u32 = 64u;
+const TILE_ROWS: u32 = 4u;
+
+var<workgroup> partials_gate: array<vec4<f32>, 64>;
+var<workgroup> partials_up: array<vec4<f32>, 64>;
+
+fn silu(x: f32) -> f32 {
+{%- if fastSilu %}
+  let sigmoid = 0.5 + 0.5 * x / (1.0 + abs(x));
+  return x * sigmoid;
+{%- else %}
+  return x / (1.0 + exp(-x));
+{%- endif %}
+}
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size(64)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let j0 = wid.x * TILE_ROWS;
+  let lid = lid3.x;
+  if (j0 >= INTERMEDIATE_SIZE) {
+    return;
+  }
+
+  let tile = j0 / TILE_ROWS;
+  var acc0 = 0.0;
+  var acc1 = 0.0;
+  var acc2 = 0.0;
+  var acc3 = 0.0;
+  var acc_u0 = 0.0;
+  var acc_u1 = 0.0;
+  var acc_u2 = 0.0;
+  var acc_u3 = 0.0;
+  for (var word_index = lid; word_index < HIDDEN_SIZE_Q1_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let base4 = word_index * 8u;
+{%- if normedVec4 == "vec4<f32>" %}
+    let n0 = normed[base4];
+    let n1 = normed[base4 + 1u];
+    let n2 = normed[base4 + 2u];
+    let n3 = normed[base4 + 3u];
+    let n4 = normed[base4 + 4u];
+    let n5 = normed[base4 + 5u];
+    let n6 = normed[base4 + 6u];
+    let n7 = normed[base4 + 7u];
+{%- else %}
+    let n0 = vec4<f32>(normed[base4]);
+    let n1 = vec4<f32>(normed[base4 + 1u]);
+    let n2 = vec4<f32>(normed[base4 + 2u]);
+    let n3 = vec4<f32>(normed[base4 + 3u]);
+    let n4 = vec4<f32>(normed[base4 + 4u]);
+    let n5 = vec4<f32>(normed[base4 + 5u]);
+    let n6 = vec4<f32>(normed[base4 + 6u]);
+    let n7 = vec4<f32>(normed[base4 + 7u]);
+{%- endif %}
+    let gate4 = q1_dot_word_t4(W_GATE_Q1, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    let up4 = q1_dot_word_t4(W_UP_Q1, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    acc0 += gate4.x;
+    acc1 += gate4.y;
+    acc2 += gate4.z;
+    acc3 += gate4.w;
+    acc_u0 += up4.x;
+    acc_u1 += up4.y;
+    acc_u2 += up4.z;
+    acc_u3 += up4.w;
+  }
+{%- if useSubgroups %}
+
+  let subgroup_gate = subgroupAdd(vec4<f32>(acc0, acc1, acc2, acc3));
+  let subgroup_up = subgroupAdd(vec4<f32>(acc_u0, acc_u1, acc_u2, acc_u3));
+  if (WORKGROUP_SIZE == sg_size) {
+    if (lid == 0u) {
+      partials_gate[0] = subgroup_gate;
+      partials_up[0] = subgroup_up;
+    }
+    workgroupBarrier();
+  } else {
+    if (sg_lid == 0u) {
+      let subgroup_index = lid / sg_size;
+      partials_gate[subgroup_index] = subgroup_gate;
+      partials_up[subgroup_index] = subgroup_up;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total_gate = vec4<f32>(0.0);
+      var total_up = vec4<f32>(0.0);
+      let subgroup_count = WORKGROUP_SIZE / sg_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total_gate += partials_gate[i];
+        total_up += partials_up[i];
+      }
+      partials_gate[0] = total_gate;
+      partials_up[0] = total_up;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials_gate[lid] = vec4<f32>(acc0, acc1, acc2, acc3);
+  partials_up[lid] = vec4<f32>(acc_u0, acc_u1, acc_u2, acc_u3);
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials_gate[lid] += partials_gate[lid + stride];
+      partials_up[lid] += partials_up[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+{%- endif %}
+
+  if (lid == 0u) {
+    let gate = partials_gate[0];
+    let up = partials_up[0];
+{%- if intermediateScalar == "f32" %}
+    intermediate[j0 + 0u] = silu(gate.x) * up.x;
+    intermediate[j0 + 1u] = silu(gate.y) * up.y;
+    intermediate[j0 + 2u] = silu(gate.z) * up.z;
+    intermediate[j0 + 3u] = silu(gate.w) * up.w;
+{%- else %}
+    intermediate[j0 + 0u] = {{ intermediateScalar }}(silu(gate.x) * up.x);
+    intermediate[j0 + 1u] = {{ intermediateScalar }}(silu(gate.y) * up.y);
+    intermediate[j0 + 2u] = {{ intermediateScalar }}(silu(gate.z) * up.z);
+    intermediate[j0 + 3u] = {{ intermediateScalar }}(silu(gate.w) * up.w);
+{%- endif %}
+  }
+}
+`]]}],["com.xenova.LlamaDecodeLmHeadQ1ArgmaxTiled",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeLmHeadQ1ArgmaxTiled",sinceVersion:1,inputs:[{role:"Normed",dtype:"N",rank:1},{role:"Q1Bits",dtype:"uint32",rank:1},{role:"Q1Scales",dtype:"S",rank:1}],outputs:[{role:"BestValues",dtype:"float32",rank:1,shape:["args.numBlocks"]},{role:"BestIds",dtype:"uint32",rank:1,shape:["args.numBlocks"]}],typeConstraints:{N:["float32","float16"],S:["float32","float16"]},args:{normedT:{kind:"tensor",semantic:"Normed",role:"input"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights"},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights"},bestValuesT:{kind:"tensor",semantic:"BestValues",role:"output"},bestIdsT:{kind:"tensor",semantic:"BestIds",role:"output"},hiddenSize:{kind:"u32",semantic:"hidden_size"},vocabSize:{kind:"u32",semantic:"vocab_size"},lmHeadOffset:{kind:"u32",semantic:"lm_head_q1_block_offset"},numBlocks:{kind:"u32",semantic:"num_blocks",required:false},colMajor:{kind:"u32",semantic:"block_col_major_pack",required:false}},tunables:{COL_MAJOR:-1},variants:[{id:"q1",priority:0,when:["ranks.normedT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.bestValuesT == 1","ranks.bestIdsT == 1",'(tensorDtypes.normedT == "float32" or tensorDtypes.normedT == "float16")','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','tensorDtypes.bestValuesT == "float32"','tensorDtypes.bestIdsT == "uint32"',"f16Ok(dtypes.N) and f16Ok(dtypes.S)","args.hiddenSize > 0","args.hiddenSize % 128 == 0","args.vocabSize > 0","(not args.numBlocks or args.numBlocks > 0)","dim(shapes.normedT, 0) == args.hiddenSize","dim(shapes.bestValuesT, 0) >= (args.numBlocks if args.numBlocks else 256)","dim(shapes.bestIdsT, 0) >= (args.numBlocks if args.numBlocks else 256)","dim(shapes.q1BitsT, 0) >= (args.lmHeadOffset + args.vocabSize * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= args.lmHeadOffset + args.vocabSize * (args.hiddenSize / 128)"],constants:{usesF16:'tensorDtypes.normedT == "float16" or tensorDtypes.q1ScalesT == "float16"',normedVec4:'"vec4<f16>" if tensorDtypes.normedT == "float16" else "vec4<f32>"',scaleScalar:"dtypes.S",hiddenSize:"args.hiddenSize",hiddenSizeVec4:"args.hiddenSize / 4",hiddenSizeBlocks:"args.hiddenSize / 128",vocabSize:"args.vocabSize",lmHeadOffset:"args.lmHeadOffset",numBlocks:"args.numBlocks if args.numBlocks else 256",colMajor:"1 if (tunables.COL_MAJOR if tunables.COL_MAJOR >= 0 else args.colMajor) else 0",f32Min:'"-3.4028234663852886e38"'},passes:[{id:"main",name:"LlamaDecodeLmHeadQ1ArgmaxTiled",source:{kind:"template",shader:"decode-lm-head-q1-argmax-tiled.wgsl.jinja",version:2,inputs:{optimized:true}},bindings:[{name:"normed",arg:"normedT",semantic:"Normed",role:"input",buffer:{type:"read-only-storage"},elementType:"$normedVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"best_values_out",arg:"bestValuesT",semantic:"BestValues",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"best_ids_out",arg:"bestIdsT",semantic:"BestIds",role:"output",buffer:{type:"storage"},elementType:"u32"}],dispatch:{x:"args.numBlocks if args.numBlocks else 256"},reads:["Normed","Q1Bits","Q1Scales"],writes:["BestValues","BestIds"]}]}]},assets:[["decode-lm-head-q1-argmax-tiled.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const VOCAB_SIZE: u32 = {{ vocabSize }}u;
+const HIDDEN_SIZE_VEC4: u32 = {{ hiddenSizeVec4 }}u;
+const HIDDEN_SIZE_BLOCKS: u32 = {{ hiddenSizeBlocks }}u;
+const W_LM_HEAD_Q1: u32 = {{ lmHeadOffset }}u;
+const WORKGROUP_SIZE: u32 = 256u;
+const NUM_BLOCKS: u32 = {{ numBlocks }}u;
+const F32_MIN: f32 = {{ f32Min }};
+
+var<workgroup> best_values: array<f32, 256>;
+var<workgroup> best_indices: array<u32, 256>;
+var<workgroup> shared_acts: array<vec4<f32>, {{ hiddenSizeVec4 }}>;
+
+fn q1_scale(block: u32) -> f32 {
+  return f32(q1_scales[block]);
+}
+
+{% include "ops/_shared/q1-signs.wgsl.jinja" %}
+
+fn load_normed(value: {{ normedVec4 }}) -> vec4<f32> {
+  return vec4<f32>(value);
+}
+
+@compute @workgroup_size(256)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let block = wid.x;
+  let lid = lid3.x;
+
+  for (var i = lid; i < HIDDEN_SIZE_VEC4; i = i + WORKGROUP_SIZE) {
+    shared_acts[i] = load_normed(normed[i]);
+  }
+  workgroupBarrier();
+
+  var best_token = 0u;
+  var best_value = F32_MIN;
+  for (var vocab = block * WORKGROUP_SIZE + lid; vocab < VOCAB_SIZE; vocab = vocab + WORKGROUP_SIZE * NUM_BLOCKS) {
+    var acc = 0.0;
+    let row_block_base = W_LM_HEAD_Q1 + vocab * HIDDEN_SIZE_BLOCKS;
+    for (var cb = 0u; cb < HIDDEN_SIZE_BLOCKS; cb = cb + 1u) {
+      let block_idx = {% if colMajor %}W_LM_HEAD_Q1 + cb * VOCAB_SIZE + vocab{% else %}row_block_base + cb{% endif %};
+      let bw = q1_bits[block_idx];
+      let s = q1_scale(block_idx);
+      let act_base = cb * 32u;
+      let unscaled =
+        dot(shared_acts[act_base + 0u],  q1_signs4_unit(bw.x, 0u))  +
+        dot(shared_acts[act_base + 1u],  q1_signs4_unit(bw.x, 4u))  +
+        dot(shared_acts[act_base + 2u],  q1_signs4_unit(bw.x, 8u))  +
+        dot(shared_acts[act_base + 3u],  q1_signs4_unit(bw.x, 12u)) +
+        dot(shared_acts[act_base + 4u],  q1_signs4_unit(bw.x, 16u)) +
+        dot(shared_acts[act_base + 5u],  q1_signs4_unit(bw.x, 20u)) +
+        dot(shared_acts[act_base + 6u],  q1_signs4_unit(bw.x, 24u)) +
+        dot(shared_acts[act_base + 7u],  q1_signs4_unit(bw.x, 28u)) +
+        dot(shared_acts[act_base + 8u],  q1_signs4_unit(bw.y, 0u))  +
+        dot(shared_acts[act_base + 9u],  q1_signs4_unit(bw.y, 4u))  +
+        dot(shared_acts[act_base + 10u], q1_signs4_unit(bw.y, 8u))  +
+        dot(shared_acts[act_base + 11u], q1_signs4_unit(bw.y, 12u)) +
+        dot(shared_acts[act_base + 12u], q1_signs4_unit(bw.y, 16u)) +
+        dot(shared_acts[act_base + 13u], q1_signs4_unit(bw.y, 20u)) +
+        dot(shared_acts[act_base + 14u], q1_signs4_unit(bw.y, 24u)) +
+        dot(shared_acts[act_base + 15u], q1_signs4_unit(bw.y, 28u)) +
+        dot(shared_acts[act_base + 16u], q1_signs4_unit(bw.z, 0u))  +
+        dot(shared_acts[act_base + 17u], q1_signs4_unit(bw.z, 4u))  +
+        dot(shared_acts[act_base + 18u], q1_signs4_unit(bw.z, 8u))  +
+        dot(shared_acts[act_base + 19u], q1_signs4_unit(bw.z, 12u)) +
+        dot(shared_acts[act_base + 20u], q1_signs4_unit(bw.z, 16u)) +
+        dot(shared_acts[act_base + 21u], q1_signs4_unit(bw.z, 20u)) +
+        dot(shared_acts[act_base + 22u], q1_signs4_unit(bw.z, 24u)) +
+        dot(shared_acts[act_base + 23u], q1_signs4_unit(bw.z, 28u)) +
+        dot(shared_acts[act_base + 24u], q1_signs4_unit(bw.w, 0u))  +
+        dot(shared_acts[act_base + 25u], q1_signs4_unit(bw.w, 4u))  +
+        dot(shared_acts[act_base + 26u], q1_signs4_unit(bw.w, 8u))  +
+        dot(shared_acts[act_base + 27u], q1_signs4_unit(bw.w, 12u)) +
+        dot(shared_acts[act_base + 28u], q1_signs4_unit(bw.w, 16u)) +
+        dot(shared_acts[act_base + 29u], q1_signs4_unit(bw.w, 20u)) +
+        dot(shared_acts[act_base + 30u], q1_signs4_unit(bw.w, 24u)) +
+        dot(shared_acts[act_base + 31u], q1_signs4_unit(bw.w, 28u));
+      acc = acc + s * unscaled;
+    }
+    if (acc > best_value) {
+      best_value = acc;
+      best_token = vocab;
+    }
+  }
+
+{% include "ops/_shared/argmax-wg-tree.wgsl.jinja" %}
+}
+`]]}],["com.xenova.LlamaDecodeQkNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeQkNorm",sinceVersion:1,inputs:[{role:"Q",dtype:"A",rank:1},{role:"K",dtype:"A",rank:1},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Q",dtype:"A",rank:1,shape:["args.numHeads * args.headDim"]},{role:"K",dtype:"A",rank:1,shape:["args.numKvHeads * args.headDim"]}],typeConstraints:{A:["float32","float16"],W:["float32","float16"]},args:{qT:{kind:"tensor",semantic:"Q",role:"inout"},kT:{kind:"tensor",semantic:"K",role:"inout"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},rmsEps:{kind:"f32",semantic:"rms_eps"},qNormOffset:{kind:"u32",semantic:"q_norm_offset"},kNormOffset:{kind:"u32",semantic:"k_norm_offset"}},variants:[{id:"scalar",priority:0,when:["ranks.qT == 1","ranks.kT == 1","ranks.weightsT == 1","tensorDtypes.qT == tensorDtypes.kT",'(tensorDtypes.qT == "float32" or tensorDtypes.qT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")',"f16Ok(dtypes.A) and f16Ok(dtypes.W)","args.numHeads > 0","args.numKvHeads > 0","args.headDim > 0","dim(shapes.qT, 0) >= args.numHeads * args.headDim","dim(shapes.kT, 0) >= args.numKvHeads * args.headDim","dim(shapes.weightsT, 0) >= max(args.qNormOffset + args.headDim, args.kNormOffset + args.headDim)"],constants:{usesF16:'tensorDtypes.qT == "float16" or tensorDtypes.weightsT == "float16"',activationScalar:"dtypes.A",weightScalar:"dtypes.W",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",headDim:"args.headDim",rmsEps:"args.rmsEps",qNormOffset:"args.qNormOffset",kNormOffset:"args.kNormOffset"},passes:[{id:"main",name:"LlamaDecodeQkNorm",shader:"decode-qk-norm.wgsl.jinja",bindings:[{name:"q",arg:"qT",semantic:"Q",role:"inout",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"k",arg:"kT",semantic:"K",role:"inout",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"}],dispatch:{x:"args.numHeads + args.numKvHeads"},reads:["Q","K","Weights"],writes:["Q","K"]}]}]},assets:[["decode-qk-norm.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const RMS_EPS: f32 = {{ rmsEps }};
+const W_Q_NORM: u32 = {{ qNormOffset }}u;
+const W_K_NORM: u32 = {{ kNormOffset }}u;
+const WORKGROUP_SIZE: u32 = 64u;
+
+var<workgroup> partials: array<f32, 64>;
+
+fn load_activation(value: {{ activationScalar }}) -> f32 {
+  return f32(value);
+}
+
+fn load_weight(value: {{ weightScalar }}) -> f32 {
+  return f32(value);
+}
+
+fn store_activation(value: f32) -> {{ activationScalar }} {
+  return {{ activationScalar }}(value);
+}
+
+@compute @workgroup_size(64)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let index = wid.x;
+  let lid = lid3.x;
+  let is_k = index >= NUM_HEADS;
+  let head = select(index, index - NUM_HEADS, is_k);
+  if ((!is_k && head >= NUM_HEADS) || (is_k && head >= NUM_KV_HEADS)) {
+    return;
+  }
+
+  var sum = 0.0;
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let offset = head * HEAD_DIM + d;
+    var value = 0.0;
+    if (is_k) {
+      value = load_activation(k[offset]);
+    } else {
+      value = load_activation(q[offset]);
+    }
+    sum = fma(value, value, sum);
+  }
+  partials[lid] = sum;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials[lid] = partials[lid] + partials[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+
+  let scale = inverseSqrt(partials[0] / f32(HEAD_DIM) + RMS_EPS);
+
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let offset = head * HEAD_DIM + d;
+    let weight_offset = select(W_Q_NORM, W_K_NORM, is_k) + d;
+    let weight = load_weight(weights[weight_offset]);
+    if (is_k) {
+      k[offset] = store_activation(load_activation(k[offset]) * scale * weight);
+    } else {
+      q[offset] = store_activation(load_activation(q[offset]) * scale * weight);
+    }
+  }
+}
+`]]}],["com.xenova.LlamaDecodeRmsNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaDecodeRmsNorm",sinceVersion:1,inputs:[{role:"Hidden",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Normed",dtype:"Y",rank:1,shape:["args.hiddenSize"]}],typeConstraints:{W:["float32","float16"],Y:["float32","float16"]},args:{hiddenT:{kind:"tensor",semantic:"Hidden",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},normedT:{kind:"tensor",semantic:"Normed",role:"output"},hiddenSize:{kind:"u32",semantic:"hidden_size"},rmsEps:{kind:"f32",semantic:"rms_eps"},normOffset:{kind:"u32",semantic:"norm_offset"}},variants:[{id:"scalar",priority:0,when:["ranks.hiddenT == 1","ranks.weightsT == 1","ranks.normedT == 1",'tensorDtypes.hiddenT == "float32"','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','(tensorDtypes.normedT == "float32" or tensorDtypes.normedT == "float16")',"f16Ok(dtypes.W) and f16Ok(dtypes.Y)","args.hiddenSize > 0","args.hiddenSize % 4 == 0","args.normOffset % 4 == 0","dim(shapes.hiddenT, 0) == args.hiddenSize","dim(shapes.normedT, 0) == args.hiddenSize","dim(shapes.weightsT, 0) >= args.normOffset + args.hiddenSize"],constants:{usesF16:'tensorDtypes.weightsT == "float16" or tensorDtypes.normedT == "float16"',weightVec4:'"vec4<f16>" if tensorDtypes.weightsT == "float16" else "vec4<f32>"',normedVec4:'"vec4<f16>" if tensorDtypes.normedT == "float16" else "vec4<f32>"',hiddenSize:"args.hiddenSize",hiddenSizeVec4:"args.hiddenSize / 4",rmsEps:"args.rmsEps",normOffsetVec4:"args.normOffset / 4"},passes:[{id:"main",name:"LlamaDecodeRmsNorm",shader:"decode-rms-norm.wgsl.jinja",bindings:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightVec4"},{name:"normed",arg:"normedT",semantic:"Normed",role:"output",buffer:{type:"storage"},elementType:"$normedVec4"}],dispatch:{x:1},reads:["Hidden","Weights"],writes:["Normed"]}]}]},assets:[["decode-rms-norm.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE: u32 = {{ hiddenSize }}u;
+const HIDDEN_SIZE_VEC4: u32 = {{ hiddenSizeVec4 }}u;
+const RMS_EPS: f32 = {{ rmsEps }};
+const W_NORM_VEC4: u32 = {{ normOffsetVec4 }}u;
+const WORKGROUP_SIZE: u32 = 256u;
+
+var<workgroup> partials: array<f32, 256>;
+
+fn sum4(value: vec4<f32>) -> f32 {
+  return value.x + value.y + value.z + value.w;
+}
+
+fn load_weight(value: {{ weightVec4 }}) -> vec4<f32> {
+  return vec4<f32>(value);
+}
+
+fn store_normed(value: vec4<f32>) -> {{ normedVec4 }} {
+  return {{ normedVec4 }}(value);
+}
+
+@compute @workgroup_size(256)
+fn main(@builtin(local_invocation_id) lid3: vec3<u32>) {
+  let lid = lid3.x;
+  var sum = 0.0;
+  for (var dim = lid; dim < HIDDEN_SIZE_VEC4; dim = dim + WORKGROUP_SIZE) {
+    let value = hidden[dim];
+    sum = sum + sum4(value * value);
+  }
+  partials[lid] = sum;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials[lid] = partials[lid] + partials[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+
+  let variance = partials[0];
+  let scale = inverseSqrt(variance / f32(HIDDEN_SIZE) + RMS_EPS);
+  for (var dim = lid; dim < HIDDEN_SIZE_VEC4; dim = dim + WORKGROUP_SIZE) {
+    normed[dim] = store_normed(hidden[dim] * vec4<f32>(scale) * load_weight(weights[W_NORM_VEC4 + dim]));
+  }
+}
+`]]}],["com.xenova.LlamaEmbed",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaEmbed",sinceVersion:1,inputs:[{role:"InputToken",dtype:"uint32",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true},{role:"EmbedQ8",dtype:"uint32",rank:1,optional:true},{role:"EmbedQ8Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Hidden",dtype:"float32",rank:1,shape:["args.hiddenSize"]}],typeConstraints:{W:["float32","float16"],S:["float32","float16"]},args:{inputT:{kind:"tensor",semantic:"InputToken",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},hiddenT:{kind:"tensor",semantic:"Hidden",role:"output"},hiddenSize:{kind:"u32",semantic:"hidden_size"},vocabSize:{kind:"u32",semantic:"vocab_size"},embedOffset:{kind:"u32",semantic:"embed_offset",required:false},tokenOffset:{kind:"u32",semantic:"token_offset",required:false},pastLen:{kind:"u32",semantic:"past_len",required:false},cacheLen:{kind:"u32",semantic:"cache_len",required:false},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false},embedQ8T:{kind:"tensor",semantic:"EmbedQ8",role:"weights",required:false},embedQ8ScalesT:{kind:"tensor",semantic:"EmbedQ8Scales",role:"weights",required:false}},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.inputT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.hiddenT == 1",'tensorDtypes.inputT == "uint32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','tensorDtypes.hiddenT == "float32"',"f16Ok(dtypes.S)","args.hiddenSize > 0","args.hiddenSize % 128 == 0","args.vocabSize > 0","dim(shapes.hiddenT, 0) == args.hiddenSize","dim(shapes.inputT, 0) > (args.tokenOffset if args.tokenOffset else 0)","dim(shapes.q1BitsT, 0) >= (args.embedOffset + args.vocabSize * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= args.embedOffset + args.vocabSize * (args.hiddenSize / 128)"],constants:{usesF16:'tensorDtypes.q1ScalesT == "float16"',scaleScalar:"dtypes.S",hiddenSize:"args.hiddenSize",embedOffset:"args.embedOffset"},passes:[{id:"main",name:"LlamaEmbedQ1",source:{kind:"template",shader:"embed-q1.wgsl.jinja",version:1,inputs:{optimized:true}},bindings:[{name:"input_token",arg:"inputT",semantic:"InputToken",role:"input",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"params",arg:"paramsT",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen if args.cacheLen else 0"},{name:"token_offset",type:"u32",value:"args.tokenOffset if args.tokenOffset else 0"}]}}],dispatch:{x:"ceil(args.hiddenSize / 64)"},reads:["InputToken","Q1Bits","Q1Scales"],writes:["Hidden"]}]}]},assets:[["embed-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE: u32 = {{ hiddenSize }}u;
+const W_EMBED_Q1: u32 = {{ embedOffset }}u;
+
+fn q1_scale(block: u32) -> f32 {
+  return f32(q1_scales[block]);
+}
+
+fn q1_weight(block_offset: u32, row: u32, cols: u32, col: u32) -> f32 {
+  let blocks_per_row = cols / 128u;
+  let block = block_offset + row * blocks_per_row + col / 128u;
+  let bit_index = col % 128u;
+  let block_words = q1_bits[block];
+  let word = block_words[bit_index / 32u];
+  let bit = (word >> (bit_index % 32u)) & 1u;
+  let scale = q1_scale(block);
+  return select(-scale, scale, bit == 1u);
+}
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let dim = gid.x;
+  if (dim >= HIDDEN_SIZE) {
+    return;
+  }
+  let token = input_token[params.token_offset];
+  hidden[dim] = q1_weight(W_EMBED_Q1, token, HIDDEN_SIZE, dim);
+}
+`]]}],["com.xenova.LlamaPrefillAttention",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillAttention",sinceVersion:1,inputs:[{role:"Qkv",dtype:"Q",rank:2},{role:"CacheKeys",dtype:"C",rank:1},{role:"CacheValues",dtype:"C",rank:1}],outputs:[{role:"Attn",dtype:"O",rank:2,shape:["args.seqLen","args.numHeads * args.headDim"]}],typeConstraints:{Q:["float32","float16"],C:["float32","float16"],O:["float32","float16"]},args:{qkvT:{kind:"tensor",semantic:"Qkv",role:"input"},cacheKeysT:{kind:"tensor",semantic:"CacheKeys",role:"input"},cacheValuesT:{kind:"tensor",semantic:"CacheValues",role:"input"},attnT:{kind:"tensor",semantic:"Attn",role:"output"},layer:{kind:"u32",semantic:"layer"},cacheLen:{kind:"u32",semantic:"cache_len"},pastLen:{kind:"u32",semantic:"past_len",required:false},seqLen:{kind:"u32",semantic:"seq_len"},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},scale:{kind:"f32",semantic:"scale"},workgroupSize:{kind:"u32",semantic:"workgroup_size"}},derive:{qkvStride:"(args.numHeads + 2 * args.numKvHeads) * args.headDim",qHidden:"args.numHeads * args.headDim",cacheElements:"(args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim",baseContract:'ranks.qkvT == 2 and (ranks.cacheKeysT == 1 or ranks.cacheKeysT == 5) and (ranks.cacheValuesT == 1 or ranks.cacheValuesT == 5) and ranks.attnT == 2 and tensorDtypes.cacheKeysT == tensorDtypes.cacheValuesT and (tensorDtypes.qkvT == "float32" or tensorDtypes.qkvT == "float16") and (tensorDtypes.cacheKeysT == "float32" or tensorDtypes.cacheKeysT == "float16") and (tensorDtypes.attnT == "float32" or tensorDtypes.attnT == "float16") and ((tensorDtypes.qkvT != "float16" and tensorDtypes.cacheKeysT != "float16" and tensorDtypes.attnT != "float16") or device.features.has("shader-f16")) and args.cacheLen > 0 and args.seqLen > 0 and args.numHeads > 0 and args.numKvHeads > 0 and args.numHeads % args.numKvHeads == 0 and args.headDim > 0 and dim(shapes.qkvT, 1) == qkvStride and numel(shapes.qkvT) >= args.seqLen * qkvStride and dim(shapes.attnT, 1) == qHidden and numel(shapes.attnT) >= args.seqLen * qHidden and numel(shapes.cacheKeysT) >= cacheElements and numel(shapes.cacheValuesT) >= cacheElements',flashContract:"baseContract and args.headDim % 32 == 0",flashDispatchFits:"ceilDiv(args.seqLen, 16) <= device.limits.maxComputeWorkgroupsPerDimension and args.numHeads <= device.limits.maxComputeWorkgroupsPerDimension"},bindingSets:{scalarAttention:[{name:"qkv",arg:"qkvT",semantic:"Qkv",role:"input",buffer:{type:"read-only-storage"},elementType:"$qScalar"},{name:"cache_keys",arg:"cacheKeysT",semantic:"CacheKeys",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheScalar"},{name:"cache_values",arg:"cacheValuesT",semantic:"CacheValues",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheScalar"},{name:"attn_out",arg:"attnT",semantic:"Attn",role:"output",buffer:{type:"storage"},elementType:"$outScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"},{name:"seq_len",type:"u32",value:"args.seqLen"}]}}],flashAttention:[{name:"qkv",arg:"qkvT",semantic:"Qkv",role:"input",buffer:{type:"read-only-storage"},elementType:"$qVec4"},{name:"cache_keys",arg:"cacheKeysT",semantic:"CacheKeys",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheVec4"},{name:"cache_values",arg:"cacheValuesT",semantic:"CacheValues",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheVec4"},{name:"attn_out",arg:"attnT",semantic:"Attn",role:"output",buffer:{type:"storage"},elementType:"$outVec4"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"},{name:"seq_len",type:"u32",value:"args.seqLen"}]}}]},variants:[{id:"scalar",priority:0,when:["baseContract","args.workgroupSize == 8 or args.workgroupSize == 16 or args.workgroupSize == 32 or args.workgroupSize == 64 or args.workgroupSize == 128 or args.workgroupSize == 256"],constants:{usesF16:'tensorDtypes.qkvT == "float16" or tensorDtypes.cacheKeysT == "float16" or tensorDtypes.attnT == "float16"',qScalar:"dtypes.Q",cacheScalar:"dtypes.C",outScalar:"dtypes.O",layer:"args.layer",cacheLen:"args.cacheLen",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",numKvGroups:"args.numHeads / args.numKvHeads",headDim:"args.headDim",qHidden:"qHidden",qkvStride:"qkvStride",scaling:"args.scale",workgroupSize:"args.workgroupSize"},passes:[{id:"main",name:"LlamaPrefillAttention",shader:"prefill-attention.wgsl.jinja",bindings:"scalarAttention",dispatch:{x:"args.numHeads",y:"min(args.seqLen, device.limits.maxComputeWorkgroupsPerDimension)"},reads:["Qkv","CacheKeys","CacheValues"],writes:["Attn"]}]},{id:"flash",priority:2,when:["flashContract",'device.features.has("subgroups")','has(device.adapterInfo, "subgroupMinSize")',"device.adapterInfo.subgroupMinSize >= 32","flashDispatchFits"],constants:{usesF16:'tensorDtypes.qkvT == "float16" or tensorDtypes.cacheKeysT == "float16" or tensorDtypes.attnT == "float16"',outScalar:"dtypes.O",cacheLen:"args.cacheLen",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",numKvGroups:"args.numHeads / args.numKvHeads",headDim:"args.headDim",qHidden:"qHidden",qkvStride:"qkvStride",scaling:"args.scale",tileQ:"16",tileK:"16",lpq:"8",qVec4:'"vec4<f16>" if tensorDtypes.qkvT == "float16" else "vec4<f32>"',cacheVec4:'"vec4<f16>" if tensorDtypes.cacheKeysT == "float16" else "vec4<f32>"',outVec4:'"vec4<f16>" if tensorDtypes.attnT == "float16" else "vec4<f32>"',useSubgroups:"1"},passes:[{id:"main",name:"LlamaPrefillAttention",shader:"prefill-attention-flash.wgsl.jinja",bindings:"flashAttention",dispatch:{x:"ceilDiv(args.seqLen, 16)",y:"args.numHeads"},reads:["Qkv","CacheKeys","CacheValues"],writes:["Attn"]}],requiredFeatures:["subgroups"]},{id:"flash_nosg",priority:1,when:["flashContract","flashDispatchFits"],constants:{usesF16:'tensorDtypes.qkvT == "float16" or tensorDtypes.cacheKeysT == "float16" or tensorDtypes.attnT == "float16"',outScalar:"dtypes.O",cacheLen:"args.cacheLen",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",numKvGroups:"args.numHeads / args.numKvHeads",headDim:"args.headDim",qHidden:"qHidden",qkvStride:"qkvStride",scaling:"args.scale",tileQ:"16",tileK:'15 if tensorDtypes.qkvT == "float32" and tensorDtypes.cacheKeysT == "float32" and tensorDtypes.attnT == "float32" else 16',lpq:"8",qVec4:'"vec4<f16>" if tensorDtypes.qkvT == "float16" else "vec4<f32>"',cacheVec4:'"vec4<f16>" if tensorDtypes.cacheKeysT == "float16" else "vec4<f32>"',outVec4:'"vec4<f16>" if tensorDtypes.attnT == "float16" else "vec4<f32>"',useSubgroups:"0"},passes:[{id:"main",name:"LlamaPrefillAttention",shader:"prefill-attention-flash.wgsl.jinja",bindings:"flashAttention",dispatch:{x:"ceilDiv(args.seqLen, 16)",y:"args.numHeads"},reads:["Qkv","CacheKeys","CacheValues"],writes:["Attn"]}]}]},assets:[["prefill-attention-flash.wgsl.jinja",`{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const NUM_KV_GROUPS: u32 = {{ numKvGroups }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const HEAD_DIM_V4: u32 = HEAD_DIM / 4u;
+const Q_HIDDEN_V4: u32 = {{ qHidden }}u / 4u;
+const QKV_STRIDE_V4: u32 = {{ qkvStride }}u / 4u;
+const KV_HIDDEN_V4: u32 = NUM_KV_HEADS * HEAD_DIM / 4u;
+const ATTN_SCALING: f32 = {{ scaling }};
+const TILE_Q: u32 = {{ tileQ }}u;
+const TILE_K: u32 = {{ tileK }}u;
+const LPQ: u32 = {{ lpq }}u;
+const SLICE: u32 = HEAD_DIM / (4u * LPQ);
+const WG: u32 = TILE_Q * LPQ;
+const NEG_INF: f32 = -3.4028234663852886e38;
+
+{%- if usesF16 %}
+var<workgroup> k_tile: array<vec4<f16>, TILE_K * HEAD_DIM_V4>;
+var<workgroup> v_tile: array<vec4<f16>, TILE_K * HEAD_DIM_V4>;
+{%- else %}
+var<workgroup> k_tile: array<vec4<f32>, TILE_K * HEAD_DIM_V4>;
+var<workgroup> v_tile: array<vec4<f32>, TILE_K * HEAD_DIM_V4>;
+{%- endif %}
+{%- if not useSubgroups %}
+
+var<workgroup> red: array<f32, WG>;
+{%- endif %}
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>{%- if useSubgroups %},
+  @builtin(subgroup_size) sgSize: u32
+{%- endif %}
+) {
+
+  if (wg.x * TILE_Q >= params.seq_len) {
+    return;
+  }
+{%- if useSubgroups %}
+
+  if (sgSize < LPQ) {
+    return;
+  }
+{%- endif %}
+  let h = wg.y;
+  let tid = lid.x;
+  let qSub = tid / LPQ;
+  let lane = tid % LPQ;
+  let qIdx = wg.x * TILE_Q + qSub;
+  let absPos = params.past_len + qIdx;
+  let qValid = qIdx < params.seq_len && absPos < CACHE_LEN;
+  let hKv = h / NUM_KV_GROUPS;
+
+  let qClamped = min(qIdx, params.seq_len - 1u);
+  let qBase4 = qClamped * QKV_STRIDE_V4 + h * HEAD_DIM_V4 + lane * SLICE;
+  var qr: array<vec4<f32>, SLICE>;
+  var o: array<vec4<f32>, SLICE>;
+  for (var c = 0u; c < SLICE; c = c + 1u) {
+    qr[c] = vec4<f32>(qkv[qBase4 + c]);
+    o[c] = vec4<f32>(0.0);
+  }
+  var m = NEG_INF;
+  var l = 0.0;
+
+  let maxKj = min(params.past_len + qClamped + 1u, CACHE_LEN);
+  let lastQ = min(wg.x * TILE_Q + TILE_Q - 1u, params.seq_len - 1u);
+  let wgEnd = min(params.past_len + lastQ + 1u, CACHE_LEN);
+  let kvHeadBase4 = hKv * HEAD_DIM_V4;
+
+  var kStart = 0u;
+  loop {
+    if (kStart >= wgEnd) {
+      break;
+    }
+
+    workgroupBarrier();
+    for (var i = tid; i < TILE_K * HEAD_DIM_V4; i = i + WG) {
+      let slot = i / HEAD_DIM_V4;
+      let d4 = i % HEAD_DIM_V4;
+      let kj = kStart + slot;
+      if (kj < wgEnd) {
+        let base4 = kj * KV_HIDDEN_V4 + kvHeadBase4 + d4;
+{%- if usesF16 %}
+        k_tile[i] = vec4<f16>(cache_keys[base4]);
+        v_tile[i] = vec4<f16>(cache_values[base4]);
+      } else {
+        k_tile[i] = vec4<f16>(0.0);
+        v_tile[i] = vec4<f16>(0.0);
+      }
+{%- else %}
+        k_tile[i] = vec4<f32>(cache_keys[base4]);
+        v_tile[i] = vec4<f32>(cache_values[base4]);
+      } else {
+        k_tile[i] = vec4<f32>(0.0);
+        v_tile[i] = vec4<f32>(0.0);
+      }
+{%- endif %}
+    }
+    workgroupBarrier();
+
+{%- for kk in range(tileK) %}
+
+    var s{{ kk }} = NEG_INF;
+    {
+      let kj = kStart + {{ kk }}u;
+      var part = 0.0;
+      let kb = {{ kk }}u * HEAD_DIM_V4 + lane * SLICE;
+      for (var c = 0u; c < SLICE; c = c + 1u) {
+        part = part + dot(qr[c], vec4<f32>(k_tile[kb + c]));
+      }
+{%- if useSubgroups %}
+      part = part + subgroupShuffleXor(part, 1u);
+{%- if lpq >= 4 %}
+      part = part + subgroupShuffleXor(part, 2u);
+{%- endif %}
+{%- if lpq >= 8 %}
+      part = part + subgroupShuffleXor(part, 4u);
+{%- endif %}
+{%- else %}
+
+      red[tid] = part;
+      workgroupBarrier();
+      let cbase = tid - lane;
+      part = 0.0;
+      for (var j = 0u; j < LPQ; j = j + 1u) {
+        part = part + red[cbase + j];
+      }
+      workgroupBarrier();
+{%- endif %}
+      if (kj < maxKj) {
+        s{{ kk }} = part * ATTN_SCALING;
+      }
+    }
+{%- endfor %}
+
+    var tileMax = s0;
+{%- for kk in range(1, tileK) %}
+    tileMax = max(tileMax, s{{ kk }});
+{%- endfor %}
+    let newMax = max(m, tileMax);
+    let corr = select(exp(m - newMax), 0.0, m == NEG_INF);
+{%- for kk in range(tileK) %}
+    let p{{ kk }} = select(0.0, exp(s{{ kk }} - newMax), s{{ kk }} != NEG_INF);
+{%- endfor %}
+    l = l * corr + ({% for kk in range(tileK) %}{% if kk > 0 %} + {% endif %}p{{ kk }}{% endfor %});
+    for (var c = 0u; c < SLICE; c = c + 1u) {
+      var acc = o[c] * corr;
+{%- for kk in range(tileK) %}
+      acc = acc + p{{ kk }} * vec4<f32>(v_tile[{{ kk }}u * HEAD_DIM_V4 + lane * SLICE + c]);
+{%- endfor %}
+      o[c] = acc;
+    }
+    m = newMax;
+
+    kStart = kStart + TILE_K;
+  }
+
+  if (qValid) {
+    let outBase4 = qIdx * Q_HIDDEN_V4 + h * HEAD_DIM_V4 + lane * SLICE;
+    let inv = 1.0 / l;
+    for (var c = 0u; c < SLICE; c = c + 1u) {
+      attn_out[outBase4 + c] = vec4<{{ outScalar }}>(o[c] * inv);
+    }
+  }
+}
+`],["prefill-attention.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const LAYER: u32 = {{ layer }}u;
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const NUM_KV_GROUPS: u32 = {{ numKvGroups }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const Q_HIDDEN: u32 = {{ qHidden }}u;
+const QKV_STRIDE: u32 = {{ qkvStride }}u;
+const ATTN_SCALING: f32 = {{ scaling }};
+const WG: u32 = {{ workgroupSize }}u;
+
+var<workgroup> partial: array<f32, WG>;
+var<workgroup> running_max: f32;
+var<workgroup> running_denom: f32;
+var<workgroup> running_out: array<f32, HEAD_DIM>;
+var<workgroup> previous_scale: f32;
+var<workgroup> current_score: f32;
+
+{% include "ops/_shared/kv-cache-index.wgsl.jinja" %}
+
+{% include "ops/_shared/reduce-sum-wg.wgsl.jinja" %}
+
+@compute @workgroup_size(WG, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(num_workgroups) ngroups: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>
+) {
+
+  let head = wg.x;
+  if (head >= NUM_HEADS) {
+    return;
+  }
+  let kv_head = head / NUM_KV_GROUPS;
+  let tid = lid.x;
+
+  for (var query_token: u32 = wg.y; query_token < params.seq_len; query_token = query_token + ngroups.y) {
+    let abs_pos = params.past_len + query_token;
+    if (abs_pos >= CACHE_LEN) {
+      continue;
+    }
+
+    if (tid == 0u) {
+      running_max = -3.4028234663852886e38;
+      running_denom = 0.0;
+    }
+    for (var d: u32 = tid; d < HEAD_DIM; d = d + WG) {
+      running_out[d] = 0.0;
+    }
+    workgroupBarrier();
+
+    let q_row = query_token * QKV_STRIDE + head * HEAD_DIM;
+    for (var key_token: u32 = 0u; key_token <= abs_pos; key_token = key_token + 1u) {
+      var partial_dot = 0.0;
+      for (var d: u32 = tid; d < HEAD_DIM; d = d + WG) {
+        partial_dot = partial_dot + f32(qkv[q_row + d]) * f32(cache_keys[cache_index(key_token, kv_head, d)]);
+      }
+      let score = reduce_sum(partial_dot, tid) * ATTN_SCALING;
+      if (tid == 0u) {
+        current_score = score;
+        let next_max = max(running_max, score);
+        let old_factor = exp(running_max - next_max);
+        let new_factor = exp(score - next_max);
+        previous_scale = old_factor;
+        running_max = next_max;
+        running_denom = running_denom * old_factor + new_factor;
+      }
+      workgroupBarrier();
+
+      let probability_numerator = exp(current_score - running_max);
+      for (var d: u32 = tid; d < HEAD_DIM; d = d + WG) {
+        running_out[d] = running_out[d] * previous_scale + probability_numerator * f32(cache_values[cache_index(key_token, kv_head, d)]);
+      }
+      workgroupBarrier();
+    }
+
+    let inv_denom = 1.0 / running_denom;
+    for (var d: u32 = tid; d < HEAD_DIM; d = d + WG) {
+      let out_index = query_token * Q_HIDDEN + head * HEAD_DIM + d;
+      attn_out[out_index] = {{ outScalar }}(running_out[d] * inv_denom);
+    }
+
+    workgroupBarrier();
+  }
+}
+`]]}],["com.xenova.LlamaPrefillEmbed",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillEmbed",sinceVersion:1,inputs:[{role:"Tokens",dtype:"uint32",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"EmbedQ8",dtype:"uint32",rank:1,optional:true},{role:"EmbedQ8Scales",dtype:"S",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Hidden",dtype:"Y",rank:2,shape:["args.seqLen","args.hiddenSize"]}],typeConstraints:{W:["float32","float16"],Y:["float32","float16"],S:["float32","float16"]},args:{tokensT:{kind:"tensor",semantic:"Tokens",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},hiddenT:{kind:"tensor",semantic:"Hidden",role:"output"},embedOffset:{kind:"u32",semantic:"embed_offset",required:false},hiddenSize:{kind:"u32",semantic:"hidden_size"},seqLen:{kind:"u32",semantic:"seq_len"},embedQ8T:{kind:"tensor",semantic:"EmbedQ8",role:"weights",required:false},embedQ8ScalesT:{kind:"tensor",semantic:"EmbedQ8Scales",role:"weights",required:false},vocabSize:{kind:"u32",semantic:"vocab_size",required:false},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false}},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.tokensT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.hiddenT == 2",'tensorDtypes.tokensT == "uint32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','(tensorDtypes.hiddenT == "float32" or tensorDtypes.hiddenT == "float16")','((tensorDtypes.q1ScalesT != "float16" and tensorDtypes.hiddenT != "float16") or device.features.has("shader-f16"))',"args.hiddenSize > 0","args.vocabSize > 0","args.seqLen > 0","args.hiddenSize % 128 == 0","dim(shapes.hiddenT, 1) == args.hiddenSize","numel(shapes.hiddenT) >= args.seqLen * args.hiddenSize","numel(shapes.tokensT) >= args.seqLen","dim(shapes.q1BitsT, 0) >= (args.embedOffset + args.vocabSize * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= args.embedOffset + args.vocabSize * (args.hiddenSize / 128)"],constants:{usesF16:'tensorDtypes.q1ScalesT == "float16" or tensorDtypes.hiddenT == "float16"',scaleScalar:"dtypes.S",outScalar:"dtypes.Y",hiddenSize:"args.hiddenSize",embedOffset:"args.embedOffset if args.embedOffset else 0",blocksPerRow:"args.hiddenSize / 128"},passes:[{id:"main",name:"LlamaPrefillEmbedQ1",shader:"prefill-embed-q1.wgsl.jinja",bindings:[{name:"tokens",arg:"tokensT",semantic:"Tokens",role:"input",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"output",buffer:{type:"storage"},elementType:"$outScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"seq_len",type:"u32",value:"args.seqLen"}]}}],dispatch:{threads:"args.seqLen * args.hiddenSize",workgroupSize:"64"},reads:["Tokens","Q1Bits","Q1Scales"],writes:["Hidden"]}]}]},assets:[["prefill-embed-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE: u32 = {{ hiddenSize }}u;
+const W_EMBED_Q1: u32 = {{ embedOffset }}u;
+const BPR: u32 = {{ blocksPerRow }}u;
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let gi = gid.x + gid.y * nwg.x * 64u;
+  let total = params.seq_len * HIDDEN_SIZE;
+  if (gi >= total) {
+    return;
+  }
+  let t = gi / HIDDEN_SIZE;
+  let d = gi % HIDDEN_SIZE;
+  let token = tokens[t];
+  let block = W_EMBED_Q1 + token * BPR + d / 128u;
+  let bit_index = d % 128u;
+  let block_words = q1_bits[block];
+  let word = block_words[bit_index / 32u];
+  let bit = (word >> (bit_index % 32u)) & 1u;
+  let scale = f32(q1_scales[block]);
+  hidden[gi] = {{ outScalar }}(select(-scale, scale, bit == 1u));
+}
+`]]}],["com.xenova.LlamaPrefillGateUpQ1",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillGateUpQ1",sinceVersion:1,inputs:[{role:"A",dtype:"A",rank:2},{role:"Q1Bits",dtype:"uint32",rank:1},{role:"Q1Scales",dtype:"S",rank:1},{role:"RealLen",dtype:"uint32",rank:1,optional:true},{role:"Y",dtype:"Y",rank:2}],outputs:[{role:"Y",dtype:"Y",rank:2,shape:["args.M","args.intermediateSize"]}],typeConstraints:{A:["float32","float16"],S:["float32","float16"],Y:["float32","float16"]},tunables:{PORTABLE_LUT4_MVEC:4},derive:{q1Blocks:"max(args.gateOffset, args.upOffset) + args.intermediateSize * (args.inFeatures / 128)",baseContract:'ranks.aT == 2 and ranks.q1BitsT == 1 and ranks.q1ScalesT == 1 and ranks.yT == 2 and tensorDtypes.q1BitsT == "uint32" and (tensorDtypes.aT == "float32" or tensorDtypes.aT == "float16") and (tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16") and (tensorDtypes.yT == "float32" or tensorDtypes.yT == "float16") and args.M > 0 and args.inFeatures > 0 and args.inFeatures % 128 == 0 and args.intermediateSize > 0 and args.intermediateSize % 64 == 0 and numel(shapes.aT) >= args.M * args.inFeatures and numel(shapes.yT) >= args.M * args.intermediateSize and numel(shapes.q1BitsT) >= q1Blocks * 4 and numel(shapes.q1ScalesT) >= q1Blocks'},args:{aT:{kind:"tensor",semantic:"A",role:"input"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights"},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights"},realLenT:{kind:"tensor",semantic:"RealLen",role:"input",required:false},yT:{kind:"tensor",semantic:"Y",role:"inout"},M:{kind:"u32",semantic:"M"},inFeatures:{kind:"u32",semantic:"in_features"},intermediateSize:{kind:"u32",semantic:"intermediate_size"},gateOffset:{kind:"u32",semantic:"gate_block_offset"},upOffset:{kind:"u32",semantic:"up_block_offset"},activation:{kind:"string",semantic:"activation",required:false,oneOf:["silu","fast_silu"]}},bindingSets:{static:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"gate_offset",type:"u32",value:"args.gateOffset"},{name:"up_offset",type:"u32",value:"args.upOffset"},{name:"m",type:"u32",value:"args.M"}]}}],dynamic:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"real_len",arg:"realLenT",semantic:"RealLen",role:"input",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"gate_offset",type:"u32",value:"args.gateOffset"},{name:"up_offset",type:"u32",value:"args.upOffset"},{name:"m",type:"u32",value:"args.M"}]}}]},variants:[{id:"portable_lut4_f16_dynamic_m",priority:110,requiredFeatures:["shader-f16"],when:["present.realLenT",'ranks.realLenT == 1 and numel(shapes.realLenT) >= 1 and tensorDtypes.realLenT == "uint32"','device.features.has("shader-f16")',"baseContract"],derive:{lutM:"16 if (args.M >= 128 or (args.M >= 16 and args.inFeatures <= 5120 and args.intermediateSize > 1024)) else (1 if args.intermediateSize <= 1024 else (4 if args.M >= 4 else args.M))",lutN:"256"},constants:{usesF16:true,dynamicM:true,fastSilu:'args.activation == "fast_silu"',roundProjectionF16:'tensorDtypes.yT == "float16"',scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",intermediateSize:"args.intermediateSize",tileM:"lutM",tileN:"lutN",workgroupSize:"lutN",mVectorWidth:"4 if tunables.PORTABLE_LUT4_MVEC == 4 and lutM % 4 == 0 and args.intermediateSize >= args.inFeatures else 1",lutScalar:'"f16"',yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillGateUpQ1PortableLut4F16DynamicM",shader:"prefill-gate-up-q1-lut4.wgsl.jinja",bindings:"dynamic",dispatch:{workgroups:"ceilDiv(args.M, lutM)",x:"ceilDiv(args.intermediateSize, lutN)"},reads:["A","Q1Bits","Q1Scales","RealLen"],writes:["Y"]}]},{id:"portable_lut4_f16",priority:100,requiredFeatures:["shader-f16"],when:["not present.realLenT",'device.features.has("shader-f16")',"baseContract"],derive:{lutM:"16 if (args.M >= 128 or (args.M >= 16 and args.inFeatures <= 5120 and args.intermediateSize > 1024)) else (1 if args.intermediateSize <= 1024 else (4 if args.M >= 4 else args.M))",lutN:"256"},constants:{usesF16:true,dynamicM:false,fastSilu:'args.activation == "fast_silu"',roundProjectionF16:'tensorDtypes.yT == "float16"',scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",intermediateSize:"args.intermediateSize",tileM:"lutM",tileN:"lutN",workgroupSize:"lutN",mVectorWidth:"4 if tunables.PORTABLE_LUT4_MVEC == 4 and lutM % 4 == 0 and args.intermediateSize >= args.inFeatures else 1",lutScalar:'"f16"',yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillGateUpQ1PortableLut4F16",shader:"prefill-gate-up-q1-lut4.wgsl.jinja",bindings:"static",dispatch:{workgroups:"ceilDiv(args.M, lutM)",x:"ceilDiv(args.intermediateSize, lutN)"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}]}]},assets:[["prefill-gate-up-q1-lut4.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const IN_FEATURES: u32 = {{ inFeatures }}u;
+const INTERMEDIATE_SIZE: u32 = {{ intermediateSize }}u;
+const M_TILE: u32 = {{ tileM }}u;
+const N_TILE: u32 = {{ tileN }}u;
+const WG_SIZE: u32 = {{ workgroupSize }}u;
+const M_VECTOR_WIDTH: u32 = {{ mVectorWidth }}u;
+const IN_FEATURES_V4: u32 = IN_FEATURES / 4u;
+const NUM_GROUPS: u32 = IN_FEATURES / 128u;
+const LUT_M_ROWS: u32 = M_TILE / M_VECTOR_WIDTH;
+const LUT_ENTRIES: u32 = LUT_M_ROWS * 32u * 16u;
+const LUT_NIBBLES: u32 = LUT_M_ROWS * 32u;
+
+{%- if mVectorWidth == 4 %}
+alias LutValue = vec4<{{ lutScalar }}>;
+alias AccValue = vec4<f32>;
+{%- else %}
+alias LutValue = {{ lutScalar }};
+alias AccValue = f32;
+{%- endif %}
+
+var<workgroup> lut: array<LutValue, LUT_ENTRIES>;
+
+fn lutIndex(m_row: u32, nibble: u32, mask: u32) -> u32 {
+  return (m_row * 32u + nibble) * 16u + mask;
+}
+
+fn q1RowWord(vec_index: u32, row_lane: u32) -> u32 {
+  return q1_bits[vec_index * 4u + row_lane];
+}
+
+fn q1Scale(index: u32) -> f32 {
+{%- if scaleScalar == "f32" %}
+  return q1_scales[index];
+{%- else %}
+  return f32(q1_scales[index]);
+{%- endif %}
+}
+
+@compute @workgroup_size({{ workgroupSize }}, 1, 1)
+fn main(
+  @builtin(workgroup_id) wg: vec3<u32>,
+  @builtin(num_workgroups) nwg: vec3<u32>,
+  @builtin(local_invocation_id) lid: vec3<u32>
+) {
+  let tid = lid.x;
+  let n = wg.x * N_TILE + tid;
+  let n_valid = n < INTERMEDIATE_SIZE;
+
+  let m_block = (wg.y + wg.z * nwg.y) * M_TILE;
+{%- if dynamicM %}
+  let active_m = min(real_len[0], params.m);
+  if (m_block >= active_m) { return; }
+{%- else %}
+  let active_m = params.m;
+{%- endif %}
+
+{% for m in range(0, tileM, mVectorWidth) %}
+  var gate_acc_{{ m }} = AccValue(0.0);
+  var up_acc_{{ m }} = AccValue(0.0);
+{% endfor %}
+
+  for (var g = 0u; g < NUM_GROUPS; g = g + 1u) {
+
+    for (var i = tid; i < LUT_NIBBLES; i = i + WG_SIZE) {
+      let nibble = i & 31u;
+      let m_local = i >> 5u;
+{%- if mVectorWidth == 4 %}
+      let m = m_block + m_local * 4u;
+      var v0 = {{ aVec4 }}(0.0);
+      var v1 = {{ aVec4 }}(0.0);
+      var v2 = {{ aVec4 }}(0.0);
+      var v3 = {{ aVec4 }}(0.0);
+      if (m < active_m) { v0 = a[m * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 1u < active_m) { v1 = a[(m + 1u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 2u < active_m) { v2 = a[(m + 2u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      if (m + 3u < active_m) { v3 = a[(m + 3u) * IN_FEATURES_V4 + g * 32u + nibble]; }
+      let x = vec4<f32>(f32(v0.x), f32(v1.x), f32(v2.x), f32(v3.x));
+      let yv = vec4<f32>(f32(v0.y), f32(v1.y), f32(v2.y), f32(v3.y));
+      let z = vec4<f32>(f32(v0.z), f32(v1.z), f32(v2.z), f32(v3.z));
+      let wv = vec4<f32>(f32(v0.w), f32(v1.w), f32(v2.w), f32(v3.w));
+{%- else %}
+      let m = m_block + m_local;
+      var v = {{ aVec4 }}(0.0);
+      if (m < active_m) {
+        v = a[m * IN_FEATURES_V4 + g * 32u + nibble];
+      }
+      let x = f32(v.x);
+      let yv = f32(v.y);
+      let z = f32(v.z);
+      let wv = f32(v.w);
+{%- endif %}
+      let xy = x + yv;
+      let xz = x + z;
+      let yz = yv + z;
+      let xyz = xy + z;
+      let c = 0.5 * (x + yv + z + wv);
+      let base = (m_local * 32u + nibble) * 16u;
+
+      lut[base] = LutValue(-c);
+      lut[base + 1u] = LutValue(x - c);
+      lut[base + 2u] = LutValue(yv - c);
+      lut[base + 3u] = LutValue(xy - c);
+      lut[base + 4u] = LutValue(z - c);
+      lut[base + 5u] = LutValue(xz - c);
+      lut[base + 6u] = LutValue(yz - c);
+      lut[base + 7u] = LutValue(xyz - c);
+      lut[base + 8u] = LutValue(wv - c);
+      lut[base + 9u] = LutValue(x + wv - c);
+      lut[base + 10u] = LutValue(yv + wv - c);
+      lut[base + 11u] = LutValue(xy + wv - c);
+      lut[base + 12u] = LutValue(z + wv - c);
+      lut[base + 13u] = LutValue(xz + wv - c);
+      lut[base + 14u] = LutValue(yz + wv - c);
+      lut[base + 15u] = LutValue(xyz + wv - c);
+    }
+    workgroupBarrier();
+
+    if (n_valid) {
+      let row_tile = n / 4u;
+      let row_lane = n & 3u;
+{% for stream in ["gate", "up"] %}
+      let {{ stream }}_group_base = params.{{ stream }}_offset + (row_tile * NUM_GROUPS + g) * 4u;
+      let {{ stream }}_p0 = q1RowWord({{ stream }}_group_base, row_lane);
+      let {{ stream }}_p1 = q1RowWord({{ stream }}_group_base + 1u, row_lane);
+      let {{ stream }}_p2 = q1RowWord({{ stream }}_group_base + 2u, row_lane);
+      let {{ stream }}_p3 = q1RowWord({{ stream }}_group_base + 3u, row_lane);
+      let {{ stream }}_scale = 2.0 * q1Scale({{ stream }}_group_base + row_lane);
+{% for m in range(0, tileM, mVectorWidth) %}
+      var {{ stream }}_qa_{{ m }} = AccValue(0.0);
+{% endfor %}
+{% for word in range(4) %}
+{% for byte in range(4) %}
+      {
+        let packed_byte = ({{ stream }}_p{{ word }} >> {{ byte * 8 }}u) & 0xffu;
+        let lo = packed_byte & 0x0fu;
+        let hi = packed_byte >> 4u;
+{% for m in range(0, tileM, mVectorWidth) %}
+        {{ stream }}_qa_{{ m }} = {{ stream }}_qa_{{ m }}
+          + AccValue(lut[lutIndex({{ m }}u / M_VECTOR_WIDTH, {{ word * 8 + byte * 2 }}u, lo)])
+          + AccValue(lut[lutIndex({{ m }}u / M_VECTOR_WIDTH, {{ word * 8 + byte * 2 + 1 }}u, hi)]);
+{% endfor %}
+      }
+{% endfor %}
+{% endfor %}
+{% for m in range(0, tileM, mVectorWidth) %}
+      {{ stream }}_acc_{{ m }} = {{ stream }}_acc_{{ m }} + {{ stream }}_scale * {{ stream }}_qa_{{ m }};
+{% endfor %}
+{% endfor %}
+    }
+
+    workgroupBarrier();
+  }
+
+  if (n_valid) {
+{% for m in range(0, tileM, mVectorWidth) %}
+{%- if mVectorWidth == 4 %}
+{%- if roundProjectionF16 %}
+    let gate_{{ m }} = vec4<f32>(vec4<f16>(gate_acc_{{ m }}));
+    let up_{{ m }} = vec4<f32>(vec4<f16>(up_acc_{{ m }}));
+{%- else %}
+    let gate_{{ m }} = gate_acc_{{ m }};
+    let up_{{ m }} = up_acc_{{ m }};
+{%- endif %}
+{%- if fastSilu %}
+    let sigmoid_{{ m }} = vec4<f32>(0.5) + vec4<f32>(0.5) * gate_{{ m }} / (vec4<f32>(1.0) + abs(gate_{{ m }}));
+    let activated_{{ m }} = (gate_{{ m }} * sigmoid_{{ m }}) * up_{{ m }};
+{%- else %}
+    let activated_{{ m }} = (gate_{{ m }} / (vec4<f32>(1.0) + exp(-gate_{{ m }}))) * up_{{ m }};
+{%- endif %}
+{% for lane in range(4) %}
+    {
+      let mr = m_block + {{ m + lane }}u;
+      if (mr < active_m) {
+        y[mr * INTERMEDIATE_SIZE + n] = {{ yScalar }}(activated_{{ m }}[{{ lane }}u]);
+      }
+    }
+{% endfor %}
+{%- else %}
+{%- if roundProjectionF16 %}
+    let gate_{{ m }} = f32(f16(gate_acc_{{ m }}));
+    let up_{{ m }} = f32(f16(up_acc_{{ m }}));
+{%- else %}
+    let gate_{{ m }} = gate_acc_{{ m }};
+    let up_{{ m }} = up_acc_{{ m }};
+{%- endif %}
+{%- if fastSilu %}
+    let sigmoid_{{ m }} = 0.5 + 0.5 * gate_{{ m }} / (1.0 + abs(gate_{{ m }}));
+    let activated_{{ m }} = gate_{{ m }} * sigmoid_{{ m }};
+{%- else %}
+    let activated_{{ m }} = gate_{{ m }} / (1.0 + exp(-gate_{{ m }}));
+{%- endif %}
+    let mr_{{ m }} = m_block + {{ m }}u;
+    if (mr_{{ m }} < active_m) {
+      y[mr_{{ m }} * INTERMEDIATE_SIZE + n] = {{ yScalar }}(activated_{{ m }} * up_{{ m }});
+    }
+{%- endif %}
+{% endfor %}
+  }
+}
+`]]}],["com.xenova.LlamaPrefillMatmulQ1",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillMatmulQ1",sinceVersion:1,inputs:[{role:"A",dtype:"A",rank:2},{role:"Q1Bits",dtype:"uint32",rank:1},{role:"Q1Scales",dtype:"S",rank:1},{role:"RealLen",dtype:"uint32",rank:1,optional:true},{role:"Y",dtype:"Y",rank:2}],outputs:[{role:"Y",dtype:"Y",rank:2,shape:["args.M","args.outStride"]}],tunables:{PORTABLE_LUT4_MVEC:4,SMALL_M_SPLIT_K:1,SMALL_M_N128_SPLIT_K:1,SMALL_M_SPLIT_K_VALUE:4,SMALL_M_DEEP_SPLIT_K:16,SMALL_M_N128_EXPAND_SPLIT_K:4,SMALL_M_N128_NARROW_SPLIT_K:16},derive:{sgmatSubgroup32:'has(device.adapterInfo, "subgroupMinSize") and has(device.adapterInfo, "subgroupMaxSize") and device.adapterInfo.subgroupMinSize == 32 and device.adapterInfo.subgroupMaxSize == 32',q1Blocks:"args.blockOffset + args.outFeatures * (args.inFeatures / 128)",baseContract:'ranks.aT == 2 and ranks.q1BitsT == 1 and ranks.q1ScalesT == 1 and ranks.yT == 2 and tensorDtypes.q1BitsT == "uint32" and (tensorDtypes.aT == "float32" or tensorDtypes.aT == "float16") and (tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16") and (tensorDtypes.yT == "float32" or tensorDtypes.yT == "float16") and args.M > 0 and args.inFeatures > 0 and args.inFeatures % 128 == 0 and args.outFeatures > 0 and args.dstColStart + args.outFeatures <= args.outStride and numel(shapes.aT) >= args.M * args.inFeatures and numel(shapes.yT) >= args.M * args.outStride and numel(shapes.q1BitsT) >= q1Blocks * 4 and numel(shapes.q1ScalesT) >= q1Blocks'},typeConstraints:{A:["float32","float16"],S:["float32","float16"],Y:["float32","float16"]},args:{aT:{kind:"tensor",semantic:"A",role:"input"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights"},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights"},realLenT:{kind:"tensor",semantic:"RealLen",role:"input",required:false},yT:{kind:"tensor",semantic:"Y",role:"inout"},M:{kind:"u32",semantic:"M"},inFeatures:{kind:"u32",semantic:"in_features"},outFeatures:{kind:"u32",semantic:"out_features"},blockOffset:{kind:"u32",semantic:"block_offset"},outStride:{kind:"u32",semantic:"out_stride"},dstColStart:{kind:"u32",semantic:"dst_col_start"}},bindingSets:{portableQ1:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"block_offset",type:"u32",value:"args.blockOffset"},{name:"m",type:"u32",value:"args.M"}]}}],rowTile4Q1:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"block_offset",type:"u32",value:"args.blockOffset"},{name:"m",type:"u32",value:"args.M"}]}}],portableQ1Dynamic:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"real_len",arg:"realLenT",semantic:"RealLen",role:"input",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"block_offset",type:"u32",value:"args.blockOffset"},{name:"m",type:"u32",value:"args.M"}]}}],splitKPartials:[{name:"a",arg:"aT",semantic:"A",role:"input",buffer:{type:"read-only-storage"},elementType:"$aVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"u32"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"partials",semantic:"partials",role:"scratch",buffer:{type:"storage"},elementType:"f32"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"block_offset",type:"u32",value:"args.blockOffset"},{name:"m",type:"u32",value:"args.M"}]}}],splitKReduce:[{name:"partials",semantic:"partials",role:"scratch",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"y",arg:"yT",semantic:"Y",role:"inout",buffer:{type:"storage"},elementType:"$yScalar"}]},variants:[{id:"narrow",priority:50,when:["baseContract","f16Ok(dtypes.A) and f16Ok(dtypes.S) and f16Ok(dtypes.Y)","args.M <= device.limits.maxComputeWorkgroupsPerDimension","args.outFeatures % 4 == 0","args.outFeatures % 64 != 0"],constants:{usesF16:'tensorDtypes.aT == "float16" or tensorDtypes.q1ScalesT == "float16" or tensorDtypes.yT == "float16"',useSubgroups:'device.features.has("subgroups")',scaleScalar:"dtypes.S",inWords:"args.inFeatures / 32",blocksPerRow:"args.inFeatures / 128",outTiles:"args.outFeatures / 4",inVec4:"args.inFeatures / 4",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1Narrow",shader:"prefill-matmul-q1-narrow.wgsl.jinja",bindings:"rowTile4Q1",dispatch:{x:"args.M"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}]},{id:"portable_lut4_f16_dynamic_m",priority:116,requiredFeatures:["shader-f16"],when:["present.realLenT",'ranks.realLenT == 1 and numel(shapes.realLenT) >= 1 and tensorDtypes.realLenT == "uint32"','device.features.has("shader-f16")',"args.M >= 64","baseContract","args.outFeatures % 64 == 0"],derive:{lutM:"16 if (args.M >= 128 or (args.M >= 16 and args.inFeatures <= 5120 and args.outFeatures > 1024)) else (1 if args.outFeatures <= 1024 else (4 if args.M >= 4 else args.M))",lutN:"256"},constants:{usesF16:true,scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",tileM:"lutM",tileN:"lutN",nPerThread:1,workgroupSize:"lutN",assumeBiasNegHalfScale:true,scaleOnly:true,lutScalar:'"f16"',yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1PortableLut4F16DynamicM",source:{kind:"template",shader:"ops/_shared/binary-lut4-matmul.wgsl.jinja",version:3,inputs:{layout:'"q1_row4"',mVectorWidth:"4 if tunables.PORTABLE_LUT4_MVEC == 4 and lutM % 4 == 0 and args.outFeatures >= args.inFeatures else 1",dynamicM:true}},bindings:"portableQ1Dynamic",dispatch:{workgroups:"ceilDiv(args.M, lutM)",x:"ceilDiv(args.outFeatures, lutN)"},reads:["A","Q1Bits","Q1Scales","RealLen"],writes:["Y"]}]},{id:"portable_lut4_f16",priority:115,requiredFeatures:["shader-f16"],when:["not present.realLenT",'device.features.has("shader-f16")','args.M >= 64 or not (device.features.has("subgroups") and device.features.has("chromium-experimental-subgroup-matrix")) or (args.M == 16 and (2 * args.inFeatures <= args.outFeatures or (3 * args.inFeatures <= 2 * args.outFeatures and args.outFeatures <= 2 * args.inFeatures) or (args.outFeatures < args.inFeatures and args.inFeatures < 4 * args.outFeatures)))',"baseContract","args.outFeatures % 64 == 0"],derive:{lutM:"16 if (args.M >= 128 or (args.M >= 16 and args.inFeatures <= 5120 and args.outFeatures > 1024)) else (1 if args.outFeatures <= 1024 else (4 if args.M >= 4 else args.M))",lutN:"256"},constants:{usesF16:true,scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",tileM:"lutM",tileN:"lutN",nPerThread:1,workgroupSize:"lutN",assumeBiasNegHalfScale:true,scaleOnly:true,lutScalar:'"f16"',yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1PortableLut4F16",source:{kind:"template",shader:"ops/_shared/binary-lut4-matmul.wgsl.jinja",version:3,inputs:{layout:'"q1_row4"',mVectorWidth:"4 if tunables.PORTABLE_LUT4_MVEC == 4 and lutM % 4 == 0 and args.outFeatures >= args.inFeatures else 1"}},bindings:"portableQ1",dispatch:{workgroups:"ceilDiv(args.M, lutM)",x:"ceilDiv(args.outFeatures, lutN)"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}]},{id:"portable_lut4_f32",priority:70,when:["baseContract",'tensorDtypes.aT == "float32"','tensorDtypes.q1ScalesT == "float32"','tensorDtypes.yT == "float32"',"args.outFeatures % 64 == 0"],derive:{lutM:"16 if (args.M >= 16 and device.limits.maxComputeWorkgroupStorageSize >= 32768) else (8 if args.M >= 8 else args.M)",lutN:"256"},constants:{usesF16:false,scaleScalar:'"f32"',inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",tileM:"lutM",tileN:"lutN",nPerThread:1,workgroupSize:"lutN",assumeBiasNegHalfScale:true,scaleOnly:true,lutScalar:'"f32"',yScalar:'"f32"',aVec4:'"vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1PortableLut4F32",source:{kind:"template",shader:"ops/_shared/binary-lut4-matmul.wgsl.jinja",version:3,inputs:{layout:'"q1_row4"',mVectorWidth:1}},bindings:"portableQ1",dispatch:{workgroups:"ceilDiv(args.M, lutM)",x:"ceilDiv(args.outFeatures, lutN)"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}]},{id:"portable_tiled",priority:75,requiredFeatures:["shader-f16"],when:["not present.realLenT",'device.features.has("shader-f16")',"baseContract","args.outFeatures % 64 == 0"],derive:{portableBM:"16 if args.M <= 16 else 32"},constants:{scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",blockOffset:"args.blockOffset",bm:"portableBM",nTile:"128 / portableBM",accs:"portableBM / 4",yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1PortableTiled",source:{kind:"template",shader:"ops/_shared/prefill-matmul-q1-tiled.wgsl.jinja",version:2,inputs:{portable:true}},bindings:"portableQ1",dispatch:{x:"min(ceilDiv(args.outFeatures, 32), device.limits.maxComputeWorkgroupsPerDimension)",y:"min(ceilDiv(args.M, portableBM), device.limits.maxComputeWorkgroupsPerDimension)"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}]},{id:"subgroup_matrix_small_m_splitk_wg64_n64",priority:113,when:["not present.realLenT","tunables.SMALL_M_SPLIT_K == 1",'device.features.has("shader-f16")','device.features.has("subgroups")','device.features.has("chromium-experimental-subgroup-matrix")',"sgmatSubgroup32","baseContract","args.M <= 16","args.inFeatures % 512 == 0","args.outFeatures % 64 == 0","ceilDiv(args.outFeatures, 64) <= device.limits.maxComputeWorkgroupsPerDimension","ceilDiv(args.M * args.outFeatures, 256) <= device.limits.maxComputeWorkgroupsPerDimension","ceilDiv(args.M, 16) <= device.limits.maxComputeWorkgroupsPerDimension"],derive:{effectiveSmallMSplitK:"tunables.SMALL_M_DEEP_SPLIT_K if args.inFeatures >= 8192 and 2 * args.outFeatures <= args.inFeatures else tunables.SMALL_M_SPLIT_K_VALUE"},constants:{usesF16:true,scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"',mSize:"args.M",splitK:"effectiveSmallMSplitK",writePartials:true},intermediates:[{id:"partials",dtype:"float32",shape:"[effectiveSmallMSplitK * args.M * args.outFeatures]"}],passes:[{id:"partials",name:"LlamaPrefillMatmulQ1SmallMSplitKPartials",shader:"prefill-matmul-q1-sg-small-m-wg64-n64.wgsl.jinja",bindings:"splitKPartials",dispatch:{x:"ceilDiv(args.outFeatures, 64)",y:"ceilDiv(args.M, 16)",z:"effectiveSmallMSplitK"},reads:["A","Q1Bits","Q1Scales"],writes:["partials"]},{id:"reduce",name:"LlamaPrefillMatmulQ1SmallMSplitKReduce",shader:"prefill-matmul-q1-small-m-splitk-reduce.wgsl.jinja",bindings:"splitKReduce",dispatch:{x:"ceilDiv(args.M * args.outFeatures, 256)"},reads:["partials"],writes:["Y"]}],requiredFeatures:["shader-f16","subgroups","chromium-experimental-subgroup-matrix"]},{id:"subgroup_matrix_small_m_n128_splitk_wg128",priority:114,when:["not present.realLenT","tunables.SMALL_M_N128_SPLIT_K == 1 and (args.outFeatures >= args.inFeatures or 4 * args.outFeatures <= args.inFeatures)",'device.features.has("shader-f16")','device.features.has("subgroups")','device.features.has("chromium-experimental-subgroup-matrix")',"sgmatSubgroup32","baseContract","args.M <= 16","args.inFeatures % (512 if args.outFeatures >= args.inFeatures else 1024) == 0","args.outFeatures % 128 == 0","ceilDiv(args.outFeatures, 128) <= device.limits.maxComputeWorkgroupsPerDimension","ceilDiv(args.M * args.outFeatures, 256) <= device.limits.maxComputeWorkgroupsPerDimension","ceilDiv(args.M, 16) <= device.limits.maxComputeWorkgroupsPerDimension"],derive:{effectiveN128SplitK:"tunables.SMALL_M_N128_EXPAND_SPLIT_K if args.outFeatures >= args.inFeatures else (tunables.SMALL_M_N128_NARROW_SPLIT_K if args.inFeatures >= 5120 else 8)"},constants:{usesF16:true,scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"',mSize:"args.M",splitK:"effectiveN128SplitK"},intermediates:[{id:"partials",dtype:"float32",shape:"[effectiveN128SplitK * args.M * args.outFeatures]"}],passes:[{id:"partials",name:"LlamaPrefillMatmulQ1SmallMN128SplitKPartials",shader:"prefill-matmul-q1-sg-m16-n128-wg128.wgsl.jinja",bindings:"splitKPartials",dispatch:{x:"ceilDiv(args.outFeatures, 128)",y:"ceilDiv(args.M, 16)",z:"effectiveN128SplitK"},reads:["A","Q1Bits","Q1Scales"],writes:["partials"]},{id:"reduce",name:"LlamaPrefillMatmulQ1SmallMN128SplitKReduce",shader:"prefill-matmul-q1-small-m-splitk-reduce.wgsl.jinja",bindings:"splitKReduce",dispatch:{x:"ceilDiv(args.M * args.outFeatures, 256)"},reads:["partials"],writes:["Y"]}],requiredFeatures:["shader-f16","subgroups","chromium-experimental-subgroup-matrix"]},{id:"subgroup_matrix",priority:100,when:["not present.realLenT",'device.features.has("shader-f16")','device.features.has("subgroups")','device.features.has("chromium-experimental-subgroup-matrix")',"sgmatSubgroup32","baseContract","args.M > 8","args.outFeatures % 64 == 0","ceilDiv(args.outFeatures, 64) <= device.limits.maxComputeWorkgroupsPerDimension","ceilDiv(args.M, 32) <= device.limits.maxComputeWorkgroupsPerDimension"],constants:{scaleScalar:"dtypes.S",inFeatures:"args.inFeatures",outFeatures:"args.outFeatures",outStride:"args.outStride",dstColStart:"args.dstColStart if args.dstColStart else 0",yScalar:'"f16" if tensorDtypes.yT == "float16" else "f32"',aVec4:'"vec4<f16>" if tensorDtypes.aT == "float16" else "vec4<f32>"'},passes:[{id:"main",name:"LlamaPrefillMatmulQ1",shader:"prefill-matmul-q1-sg.wgsl.jinja",bindings:"portableQ1",dispatch:{x:"ceilDiv(args.outFeatures, 64)",y:"ceilDiv(args.M, 32)"},reads:["A","Q1Bits","Q1Scales"],writes:["Y"]}],requiredFeatures:["shader-f16","subgroups","chromium-experimental-subgroup-matrix"]}]},assets:[["prefill-matmul-q1-narrow.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const IN_WORDS: u32 = {{ inWords }}u;
+const BPR: u32 = {{ blocksPerRow }}u;
+const OUT_TILES: u32 = {{ outTiles }}u;
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+const IN_VEC4: u32 = {{ inVec4 }}u;
+const WORKGROUP_SIZE: u32 = 64u;
+
+var<workgroup> partials0: array<f32, 64>;
+var<workgroup> partials1: array<f32, 64>;
+var<workgroup> partials2: array<f32, 64>;
+var<workgroup> partials3: array<f32, 64>;
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size(64)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let t = wid.x;
+  let lid = lid3.x;
+  if (t >= params.m) {
+    return;
+  }
+  let aBase = t * IN_VEC4;
+
+  for (var tile = 0u; tile < OUT_TILES; tile = tile + 1u) {
+    var acc0 = 0.0;
+    var acc1 = 0.0;
+    var acc2 = 0.0;
+    var acc3 = 0.0;
+    for (var word_index = lid; word_index < IN_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+      let col_block = word_index / 4u;
+      let word_id = word_index % 4u;
+      let base4 = aBase + word_index * 8u;
+      let n0 = vec4<f32>(a[base4]);
+      let n1 = vec4<f32>(a[base4 + 1u]);
+      let n2 = vec4<f32>(a[base4 + 2u]);
+      let n3 = vec4<f32>(a[base4 + 3u]);
+      let n4 = vec4<f32>(a[base4 + 4u]);
+      let n5 = vec4<f32>(a[base4 + 5u]);
+      let n6 = vec4<f32>(a[base4 + 6u]);
+      let n7 = vec4<f32>(a[base4 + 7u]);
+      let dot4 = q1_dot_word_t4(params.block_offset, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+      acc0 += dot4.x;
+      acc1 += dot4.y;
+      acc2 += dot4.z;
+      acc3 += dot4.w;
+    }
+{%- if useSubgroups %}
+    let ss = subgroupAdd(vec4<f32>(acc0, acc1, acc2, acc3));
+    if (WORKGROUP_SIZE == sg_size) {
+      if (lid == 0u) {
+        partials0[0] = ss.x;
+        partials1[0] = ss.y;
+        partials2[0] = ss.z;
+        partials3[0] = ss.w;
+      }
+      workgroupBarrier();
+    } else {
+      if (sg_lid == 0u) {
+        let subgroup_index = lid / sg_size;
+        partials0[subgroup_index] = ss.x;
+        partials1[subgroup_index] = ss.y;
+        partials2[subgroup_index] = ss.z;
+        partials3[subgroup_index] = ss.w;
+      }
+      workgroupBarrier();
+      if (lid == 0u) {
+        var total0 = 0.0;
+        var total1 = 0.0;
+        var total2 = 0.0;
+        var total3 = 0.0;
+        let subgroup_count = WORKGROUP_SIZE / sg_size;
+        for (var i = 0u; i < subgroup_count; i = i + 1u) {
+          total0 = total0 + partials0[i];
+          total1 = total1 + partials1[i];
+          total2 = total2 + partials2[i];
+          total3 = total3 + partials3[i];
+        }
+        partials0[0] = total0;
+        partials1[0] = total1;
+        partials2[0] = total2;
+        partials3[0] = total3;
+      }
+      workgroupBarrier();
+    }
+{%- else %}
+    partials0[lid] = acc0;
+    partials1[lid] = acc1;
+    partials2[lid] = acc2;
+    partials3[lid] = acc3;
+    workgroupBarrier();
+
+    var stride = WORKGROUP_SIZE / 2u;
+    loop {
+      if (lid < stride) {
+        partials0[lid] = partials0[lid] + partials0[lid + stride];
+        partials1[lid] = partials1[lid] + partials1[lid + stride];
+        partials2[lid] = partials2[lid] + partials2[lid + stride];
+        partials3[lid] = partials3[lid] + partials3[lid + stride];
+      }
+      workgroupBarrier();
+      if (stride == 1u) {
+        break;
+      }
+      stride = stride / 2u;
+    }
+{%- endif %}
+
+    if (lid == 0u) {
+      let yBase = t * OUT_STRIDE + DST_COL + tile * 4u;
+      y[yBase + 0u] = {{ yScalar }}(partials0[0]);
+      y[yBase + 1u] = {{ yScalar }}(partials1[0]);
+      y[yBase + 2u] = {{ yScalar }}(partials2[0]);
+      y[yBase + 3u] = {{ yScalar }}(partials3[0]);
+    }
+
+    workgroupBarrier();
+  }
+}
+`],["prefill-matmul-q1-sg-m16-n128-wg128.wgsl.jinja",`enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+
+{{ env.wgsl.resourceDeclarations }}
+
+const K: u32 = {{ inFeatures }}u;
+const K_V4: u32 = K / 4u;
+const BPR_W: u32 = K / 32u;
+const BPR128: u32 = K / 128u;
+const SPLIT_K: u32 = {{ splitK }}u;
+const WORDS_PER_SPLIT: u32 = BPR_W / SPLIT_K;
+const TILE_ROWS: u32 = 16u;
+const TILE_COLS: u32 = 128u;
+const TILE_K: u32 = 32u;
+const SG_SIZE: u32 = 32u;
+
+var<workgroup> tile_A: array<f16, 16 * 32>;
+var<workgroup> tile_B: array<f16, 128 * 32>;
+
+var<workgroup> scratch: array<array<f32, 64>, 16>;
+
+@compute @workgroup_size(128, 1, 1)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_index) local_idx: u32
+) {
+  let n0 = wid.x * TILE_COLS;
+  let m0 = wid.y * TILE_ROWS;
+  let subgroup_id = local_idx / SG_SIZE;
+  let subgroup_col = subgroup_id * 32u;
+  let split_id = wid.z;
+  let first_word = split_id * WORDS_PER_SPLIT;
+
+  var matC00: subgroup_matrix_result<f32, 8, 8>;
+  var matC01: subgroup_matrix_result<f32, 8, 8>;
+  var matC02: subgroup_matrix_result<f32, 8, 8>;
+  var matC03: subgroup_matrix_result<f32, 8, 8>;
+  var matC10: subgroup_matrix_result<f32, 8, 8>;
+  var matC11: subgroup_matrix_result<f32, 8, 8>;
+  var matC12: subgroup_matrix_result<f32, 8, 8>;
+  var matC13: subgroup_matrix_result<f32, 8, 8>;
+
+  let a_row = local_idx / 8u;
+  let a_quad = local_idx % 8u;
+
+  let row = n0 + local_idx;
+  let tile = row / 4u;
+  let rit = row % 4u;
+  let group_base = params.block_offset + tile * BPR128 * 4u;
+
+  for (var slice_word = 0u; slice_word < WORDS_PER_SPLIT; slice_word = slice_word + 1u) {
+    let kb = first_word + slice_word;
+    var av = vec4<f32>(0.0);
+    if (m0 + a_row < params.m) {
+      av = vec4<f32>(a[(m0 + a_row) * K_V4 + kb * 8u + a_quad]);
+    }
+    let abase = a_row * TILE_K + a_quad * 4u;
+    tile_A[abase] = f16(av.x);
+    tile_A[abase + 1u] = f16(av.y);
+    tile_A[abase + 2u] = f16(av.z);
+    tile_A[abase + 3u] = f16(av.w);
+
+    let col_block = kb / 4u;
+    let word_id = kb % 4u;
+    let idx = group_base + col_block * 4u + word_id;
+    let sign_word = q1_bits[idx * 4u + rit];
+    let scale = f16(f32(q1_scales[group_base + col_block * 4u + rit]));
+    let neg = -scale;
+    let bbase = local_idx * TILE_K;
+    for (var j = 0u; j < TILE_K; j = j + 1u) {
+      tile_B[bbase + j] = select(neg, scale, ((sign_word >> j) & 1u) == 1u);
+    }
+    workgroupBarrier();
+
+    for (var step = 0u; step < TILE_K; step = step + 8u) {
+      let matA0 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, step, false, TILE_K);
+      let matA1 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, 8u * TILE_K + step, false, TILE_K);
+      let boff = subgroup_col * TILE_K + step;
+      let matB0 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff, true, TILE_K);
+      let matB1 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 8u * TILE_K, true, TILE_K);
+      let matB2 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 16u * TILE_K, true, TILE_K);
+      let matB3 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 24u * TILE_K, true, TILE_K);
+      matC00 = subgroupMatrixMultiplyAccumulate(matA0, matB0, matC00);
+      matC01 = subgroupMatrixMultiplyAccumulate(matA0, matB1, matC01);
+      matC02 = subgroupMatrixMultiplyAccumulate(matA0, matB2, matC02);
+      matC03 = subgroupMatrixMultiplyAccumulate(matA0, matB3, matC03);
+      matC10 = subgroupMatrixMultiplyAccumulate(matA1, matB0, matC10);
+      matC11 = subgroupMatrixMultiplyAccumulate(matA1, matB1, matC11);
+      matC12 = subgroupMatrixMultiplyAccumulate(matA1, matB2, matC12);
+      matC13 = subgroupMatrixMultiplyAccumulate(matA1, matB3, matC13);
+    }
+    workgroupBarrier();
+  }
+
+  if (subgroup_id < 2u) {
+    let bank = subgroup_id * 8u;
+    subgroupMatrixStore(&scratch[bank], 0u, matC00, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 1u], 0u, matC01, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 2u], 0u, matC02, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 3u], 0u, matC03, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 4u], 0u, matC10, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 5u], 0u, matC11, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 6u], 0u, matC12, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 7u], 0u, matC13, false, 8u);
+  }
+  workgroupBarrier();
+
+  for (var i = local_idx; i < TILE_ROWS * 64u; i = i + 128u) {
+    let r = i / 64u;
+    let c = i % 64u;
+    if (m0 + r < params.m) {
+      let b = (c / 32u) * 8u + (r / 8u) * 4u + ((c % 32u) / 8u);
+      let e = (r % 8u) * 8u + (c % 8u);
+      partials[(split_id * params.m + m0 + r) * {{ outFeatures }}u + n0 + c] = scratch[b][e];
+    }
+  }
+  workgroupBarrier();
+
+  if (subgroup_id >= 2u) {
+    let bank = (subgroup_id - 2u) * 8u;
+    subgroupMatrixStore(&scratch[bank], 0u, matC00, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 1u], 0u, matC01, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 2u], 0u, matC02, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 3u], 0u, matC03, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 4u], 0u, matC10, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 5u], 0u, matC11, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 6u], 0u, matC12, false, 8u);
+    subgroupMatrixStore(&scratch[bank + 7u], 0u, matC13, false, 8u);
+  }
+  workgroupBarrier();
+
+  for (var i = local_idx; i < TILE_ROWS * 64u; i = i + 128u) {
+    let r = i / 64u;
+    let c = i % 64u;
+    let out_c = 64u + c;
+    if (m0 + r < params.m) {
+      let b = (c / 32u) * 8u + (r / 8u) * 4u + ((c % 32u) / 8u);
+      let e = (r % 8u) * 8u + (c % 8u);
+      partials[(split_id * params.m + m0 + r) * {{ outFeatures }}u + n0 + out_c] = scratch[b][e];
+    }
+  }
+}
+`],["prefill-matmul-q1-sg-small-m-wg64-n64.wgsl.jinja",`enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+
+{{ env.wgsl.resourceDeclarations }}
+
+const K: u32 = {{ inFeatures }}u;
+const K_V4: u32 = K / 4u;
+const BPR_W: u32 = K / 32u;
+const BPR128: u32 = K / 128u;
+const SPLIT_K: u32 = {{ splitK }}u;
+const WORDS_PER_SPLIT: u32 = BPR_W / SPLIT_K;
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+const TILE_ROWS: u32 = 16u;
+const TILE_COLS: u32 = 64u;
+const TILE_K: u32 = 32u;
+const SG_SIZE: u32 = 32u;
+
+var<workgroup> tile_A: array<f16, 16 * 32>;
+var<workgroup> tile_B: array<f16, 64 * 32>;
+
+var<workgroup> scratch: array<array<f32, 64>, 16>;
+
+@compute @workgroup_size(64, 1, 1)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_index) local_idx: u32
+) {
+  let n0 = wid.x * TILE_COLS;
+  let m0 = wid.y * TILE_ROWS;
+  let subgroup_id = local_idx / SG_SIZE;
+  let subgroup_col = subgroup_id * 32u;
+  let split_id = wid.z;
+  let first_word = split_id * WORDS_PER_SPLIT;
+
+  var matC00: subgroup_matrix_result<f32, 8, 8>;
+  var matC01: subgroup_matrix_result<f32, 8, 8>;
+  var matC02: subgroup_matrix_result<f32, 8, 8>;
+  var matC03: subgroup_matrix_result<f32, 8, 8>;
+  var matC10: subgroup_matrix_result<f32, 8, 8>;
+  var matC11: subgroup_matrix_result<f32, 8, 8>;
+  var matC12: subgroup_matrix_result<f32, 8, 8>;
+  var matC13: subgroup_matrix_result<f32, 8, 8>;
+
+  let a_row = local_idx / 4u;
+  let a_quad = (local_idx % 4u) * 2u;
+
+  let row = n0 + local_idx;
+  let tile = row / 4u;
+  let rit = row % 4u;
+  let group_base = params.block_offset + tile * BPR128 * 4u;
+
+  for (var slice_word = 0u; slice_word < WORDS_PER_SPLIT; slice_word = slice_word + 1u) {
+    let kb = first_word + slice_word;
+    var av0 = vec4<f32>(0.0);
+    var av1 = vec4<f32>(0.0);
+    if (m0 + a_row < params.m) {
+      av0 = vec4<f32>(a[(m0 + a_row) * K_V4 + kb * 8u + a_quad]);
+      av1 = vec4<f32>(a[(m0 + a_row) * K_V4 + kb * 8u + a_quad + 1u]);
+    }
+    let abase = a_row * TILE_K + a_quad * 4u;
+    tile_A[abase] = f16(av0.x);
+    tile_A[abase + 1u] = f16(av0.y);
+    tile_A[abase + 2u] = f16(av0.z);
+    tile_A[abase + 3u] = f16(av0.w);
+    tile_A[abase + 4u] = f16(av1.x);
+    tile_A[abase + 5u] = f16(av1.y);
+    tile_A[abase + 6u] = f16(av1.z);
+    tile_A[abase + 7u] = f16(av1.w);
+
+    let col_block = kb / 4u;
+    let word_id = kb % 4u;
+    let idx = group_base + col_block * 4u + word_id;
+    let sign_word = q1_bits[idx * 4u + rit];
+    let scale = f16(f32(q1_scales[group_base + col_block * 4u + rit]));
+    let neg = -scale;
+    let bbase = local_idx * TILE_K;
+    for (var j = 0u; j < TILE_K; j = j + 1u) {
+      tile_B[bbase + j] = select(neg, scale, ((sign_word >> j) & 1u) == 1u);
+    }
+    workgroupBarrier();
+
+    for (var step = 0u; step < TILE_K; step = step + 8u) {
+      let matA0 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, step, false, TILE_K);
+      let matA1 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, 8u * TILE_K + step, false, TILE_K);
+      let boff = subgroup_col * TILE_K + step;
+      let matB0 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff, true, TILE_K);
+      let matB1 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 8u * TILE_K, true, TILE_K);
+      let matB2 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 16u * TILE_K, true, TILE_K);
+      let matB3 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 24u * TILE_K, true, TILE_K);
+      matC00 = subgroupMatrixMultiplyAccumulate(matA0, matB0, matC00);
+      matC01 = subgroupMatrixMultiplyAccumulate(matA0, matB1, matC01);
+      matC02 = subgroupMatrixMultiplyAccumulate(matA0, matB2, matC02);
+      matC03 = subgroupMatrixMultiplyAccumulate(matA0, matB3, matC03);
+      matC10 = subgroupMatrixMultiplyAccumulate(matA1, matB0, matC10);
+      matC11 = subgroupMatrixMultiplyAccumulate(matA1, matB1, matC11);
+      matC12 = subgroupMatrixMultiplyAccumulate(matA1, matB2, matC12);
+      matC13 = subgroupMatrixMultiplyAccumulate(matA1, matB3, matC13);
+    }
+    workgroupBarrier();
+  }
+
+  let bank = subgroup_id * 8u;
+  subgroupMatrixStore(&scratch[bank], 0u, matC00, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 1u], 0u, matC01, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 2u], 0u, matC02, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 3u], 0u, matC03, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 4u], 0u, matC10, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 5u], 0u, matC11, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 6u], 0u, matC12, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 7u], 0u, matC13, false, 8u);
+  workgroupBarrier();
+
+  for (var i = local_idx; i < TILE_ROWS * TILE_COLS; i = i + 64u) {
+    let r = i / TILE_COLS;
+    let c = i % TILE_COLS;
+    if (m0 + r < params.m) {
+      let b = (c / 32u) * 8u + (r / 8u) * 4u + ((c % 32u) / 8u);
+      let e = (r % 8u) * 8u + (c % 8u);
+{%- if writePartials %}
+      partials[(split_id * params.m + m0 + r) * {{ outFeatures }}u + n0 + c] = scratch[b][e];
+{%- else %}
+      y[(m0 + r) * OUT_STRIDE + DST_COL + n0 + c] = {{ yScalar }}(scratch[b][e]);
+{%- endif %}
+    }
+  }
+}
+`],["prefill-matmul-q1-sg.wgsl.jinja",`enable f16;
+enable subgroups;
+enable chromium_experimental_subgroup_matrix;
+diagnostic(off, chromium.subgroup_matrix_uniformity);
+
+{{ env.wgsl.resourceDeclarations }}
+
+const K: u32 = {{ inFeatures }}u;
+const K_V4: u32 = K / 4u;
+const N: u32 = {{ outFeatures }}u;
+const BPR_W: u32 = K / 32u;
+const BPR128: u32 = K / 128u;
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+const TILE_ROWS: u32 = 32u;
+const TILE_COLS: u32 = 64u;
+const TILE_K: u32 = 32u;
+const SUB_ROWS: u32 = 16u;
+const SUB_COLS: u32 = 32u;
+
+var<workgroup> tile_A: array<f16, 32 * 32>;
+var<workgroup> tile_B: array<f16, 64 * 32>;
+
+var<workgroup> scratch: array<array<f32, 64>, 32>;
+
+@compute @workgroup_size(128, 1, 1)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_index) local_idx: u32,
+  @builtin(subgroup_size) sg_size: u32
+) {
+  let m0 = wid.y * TILE_ROWS;
+  let n0 = wid.x * TILE_COLS;
+
+  let subtile_id = local_idx / sg_size;
+  let subtile_idx = subtile_id / 2u;
+  let subtile_idy = subtile_id % 2u;
+  let base_A = subtile_idy * SUB_ROWS;
+  let base_B = subtile_idx * SUB_COLS;
+
+  var matC00: subgroup_matrix_result<f32, 8, 8>;
+  var matC01: subgroup_matrix_result<f32, 8, 8>;
+  var matC02: subgroup_matrix_result<f32, 8, 8>;
+  var matC03: subgroup_matrix_result<f32, 8, 8>;
+  var matC10: subgroup_matrix_result<f32, 8, 8>;
+  var matC11: subgroup_matrix_result<f32, 8, 8>;
+  var matC12: subgroup_matrix_result<f32, 8, 8>;
+  var matC13: subgroup_matrix_result<f32, 8, 8>;
+
+  let am = local_idx / 4u;
+  let aq = (local_idx % 4u) * 2u;
+
+  let l = local_idx & 63u;
+  let row = n0 + l;
+  let tile = row / 4u;
+  let rit = row % 4u;
+  let group_base = params.block_offset + tile * BPR128 * 4u;
+
+  for (var kb = 0u; kb < BPR_W; kb = kb + 1u) {
+
+    var av0 = vec4<f32>(0.0);
+    var av1 = vec4<f32>(0.0);
+    if (m0 + am < params.m) {
+      av0 = vec4<f32>(a[(m0 + am) * K_V4 + kb * 8u + aq]);
+      av1 = vec4<f32>(a[(m0 + am) * K_V4 + kb * 8u + aq + 1u]);
+    }
+    let abase = am * TILE_K + aq * 4u;
+    tile_A[abase] = f16(av0.x);
+    tile_A[abase + 1u] = f16(av0.y);
+    tile_A[abase + 2u] = f16(av0.z);
+    tile_A[abase + 3u] = f16(av0.w);
+    tile_A[abase + 4u] = f16(av1.x);
+    tile_A[abase + 5u] = f16(av1.y);
+    tile_A[abase + 6u] = f16(av1.z);
+    tile_A[abase + 7u] = f16(av1.w);
+
+    if (local_idx < 64u) {
+      let col_block = kb / 4u;
+      let word_id = kb % 4u;
+      let idx = group_base + col_block * 4u + word_id;
+      let sign_word = q1_bits[idx * 4u + rit];
+      let scale = f16(f32(q1_scales[group_base + col_block * 4u + rit]));
+      let neg = -scale;
+      let bbase = l * TILE_K;
+
+      for (var j = 0u; j < 32u; j = j + 1u) {
+        tile_B[bbase + j] = select(neg, scale, ((sign_word >> j) & 1u) == 1u);
+      }
+    }
+    workgroupBarrier();
+
+    for (var step = 0u; step < TILE_K; step = step + 8u) {
+      let matA0 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, (base_A + 0u) * TILE_K + step, false, TILE_K);
+      let matA1 = subgroupMatrixLoad<subgroup_matrix_left<f16, 8, 8>>(&tile_A, (base_A + 8u) * TILE_K + step, false, TILE_K);
+      let boff = base_B * TILE_K + step;
+      let matB0 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff, true, TILE_K);
+      let matB1 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 8u * TILE_K, true, TILE_K);
+      let matB2 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 16u * TILE_K, true, TILE_K);
+      let matB3 = subgroupMatrixLoad<subgroup_matrix_right<f16, 8, 8>>(&tile_B, boff + 24u * TILE_K, true, TILE_K);
+      matC00 = subgroupMatrixMultiplyAccumulate(matA0, matB0, matC00);
+      matC01 = subgroupMatrixMultiplyAccumulate(matA0, matB1, matC01);
+      matC02 = subgroupMatrixMultiplyAccumulate(matA0, matB2, matC02);
+      matC03 = subgroupMatrixMultiplyAccumulate(matA0, matB3, matC03);
+      matC10 = subgroupMatrixMultiplyAccumulate(matA1, matB0, matC10);
+      matC11 = subgroupMatrixMultiplyAccumulate(matA1, matB1, matC11);
+      matC12 = subgroupMatrixMultiplyAccumulate(matA1, matB2, matC12);
+      matC13 = subgroupMatrixMultiplyAccumulate(matA1, matB3, matC13);
+    }
+    workgroupBarrier();
+  }
+
+  let bank = subtile_id * 8u;
+  subgroupMatrixStore(&scratch[bank + 0u], 0u, matC00, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 1u], 0u, matC01, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 2u], 0u, matC02, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 3u], 0u, matC03, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 4u], 0u, matC10, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 5u], 0u, matC11, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 6u], 0u, matC12, false, 8u);
+  subgroupMatrixStore(&scratch[bank + 7u], 0u, matC13, false, 8u);
+  workgroupBarrier();
+
+  for (var i = local_idx; i < TILE_ROWS * TILE_COLS; i = i + 128u) {
+    let r = i / TILE_COLS;
+    let c = i % TILE_COLS;
+    if (m0 + r < params.m) {
+
+      let b = (c / 32u) * 16u + (r / 16u) * 8u + ((r % 16u) / 8u) * 4u + ((c % 32u) / 8u);
+      let e = (r % 8u) * 8u + (c % 8u);
+      y[(m0 + r) * OUT_STRIDE + DST_COL + n0 + c] = {{ yScalar }}(scratch[b][e]);
+    }
+  }
+}
+`],["prefill-matmul-q1-small-m-splitk-reduce.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const M: u32 = {{ mSize }}u;
+const N: u32 = {{ outFeatures }}u;
+const SPLIT_K: u32 = {{ splitK }}u;
+const OUT_STRIDE: u32 = {{ outStride }}u;
+const DST_COL: u32 = {{ dstColStart }}u;
+
+@compute @workgroup_size(256, 1, 1)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let output_index = gid.x;
+  if (output_index >= M * N) {
+    return;
+  }
+
+  var sum = 0.0;
+  for (var split = 0u; split < SPLIT_K; split = split + 1u) {
+    sum += partials[split * M * N + output_index];
+  }
+
+  let row = output_index / N;
+  let col = output_index % N;
+  y[row * OUT_STRIDE + DST_COL + col] = {{ yScalar }}(sum);
+}
+`]]}],["com.xenova.LlamaPrefillQkNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillQkNorm",sinceVersion:1,inputs:[{role:"Qkv",dtype:"A",rank:2},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Qkv",dtype:"A",rank:2,shape:["args.seqLen","args.qkvStride"]}],typeConstraints:{A:["float32","float16"],W:["float32","float16"]},args:{qkvT:{kind:"tensor",semantic:"Qkv",role:"inout"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},qNormOffset:{kind:"u32",semantic:"q_norm_offset"},kNormOffset:{kind:"u32",semantic:"k_norm_offset"},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},seqLen:{kind:"u32",semantic:"seq_len"},qkvStride:{kind:"u32",semantic:"qkv_stride"},rmsEps:{kind:"f32",semantic:"rms_eps"}},variants:[{id:"scalar",priority:0,when:["ranks.qkvT == 2","ranks.weightsT == 1",'(tensorDtypes.qkvT == "float32" or tensorDtypes.qkvT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','((tensorDtypes.qkvT != "float16" and tensorDtypes.weightsT != "float16") or device.features.has("shader-f16"))',"args.seqLen > 0","args.numHeads > 0","args.numKvHeads > 0","args.headDim > 0","args.qkvStride == (args.numHeads + 2 * args.numKvHeads) * args.headDim","dim(shapes.qkvT, 1) == args.qkvStride","numel(shapes.qkvT) >= args.seqLen * args.qkvStride","numel(shapes.weightsT) >= args.qNormOffset + args.headDim","numel(shapes.weightsT) >= args.kNormOffset + args.headDim"],constants:{usesF16:'tensorDtypes.qkvT == "float16" or tensorDtypes.weightsT == "float16"',activationScalar:"dtypes.A",weightScalar:"dtypes.W",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",headDim:"args.headDim",qHidden:"args.numHeads * args.headDim",qkvStride:"args.qkvStride",seqLen:"args.seqLen",rmsEps:"args.rmsEps",qNormOffset:"args.qNormOffset",kNormOffset:"args.kNormOffset"},passes:[{id:"main",name:"LlamaPrefillQkNorm",shader:"prefill-qk-norm.wgsl.jinja",bindings:[{name:"qkv",arg:"qkvT",semantic:"Qkv",role:"inout",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"}],dispatch:{x:"args.numHeads + args.numKvHeads",y:"args.seqLen"},reads:["Qkv","Weights"],writes:["Qkv"]}]}]},assets:[["prefill-qk-norm.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const Q_HIDDEN: u32 = {{ qHidden }}u;
+const QKV_STRIDE: u32 = {{ qkvStride }}u;
+const SEQ_LEN: u32 = {{ seqLen }}u;
+const RMS_EPS: f32 = {{ rmsEps }};
+const W_Q_NORM: u32 = {{ qNormOffset }}u;
+const W_K_NORM: u32 = {{ kNormOffset }}u;
+const WORKGROUP_SIZE: u32 = 64u;
+
+var<workgroup> partials: array<f32, 64>;
+
+@compute @workgroup_size(64)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let t = wid.y;
+  if (t >= SEQ_LEN) {
+    return;
+  }
+  let head_index = wid.x;
+  let lid = lid3.x;
+  let is_k = head_index >= NUM_HEADS;
+  let head = select(head_index, head_index - NUM_HEADS, is_k);
+  if (is_k && head >= NUM_KV_HEADS) {
+    return;
+  }
+  let region = select(0u, Q_HIDDEN, is_k);
+  let base = t * QKV_STRIDE + region + head * HEAD_DIM;
+  let norm_off = select(W_Q_NORM, W_K_NORM, is_k);
+
+  var sum = 0.0;
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let x = f32(qkv[base + d]);
+    sum = fma(x, x, sum);
+  }
+  partials[lid] = sum;
+  workgroupBarrier();
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials[lid] = partials[lid] + partials[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+  let scale = inverseSqrt(partials[0] / f32(HEAD_DIM) + RMS_EPS);
+
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let x = f32(qkv[base + d]);
+    qkv[base + d] = {{ activationScalar }}(x * scale * f32(weights[norm_off + d]));
+  }
+}
+`]]}],["com.xenova.LlamaPrefillRmsNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"LlamaPrefillRmsNorm",sinceVersion:1,inputs:[{role:"X",dtype:"X",rank:2},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Y",dtype:"Y",rank:2,shape:["args.rows","args.dim"]}],typeConstraints:{X:["float32","float16"],W:["float32","float16"],Y:["float32","float16"]},args:{xT:{kind:"tensor",semantic:"X",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},yT:{kind:"tensor",semantic:"Y",role:"output"},normOffset:{kind:"u32",semantic:"norm_offset"},rows:{kind:"u32",semantic:"rows"},dim:{kind:"u32",semantic:"dim"},eps:{kind:"f32",semantic:"eps"}},variants:[{id:"scalar",priority:0,when:["ranks.xT == 2","ranks.weightsT == 1","ranks.yT == 2",'(tensorDtypes.xT == "float32" or tensorDtypes.xT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','(tensorDtypes.yT == "float32" or tensorDtypes.yT == "float16")','((tensorDtypes.xT != "float16" and tensorDtypes.weightsT != "float16" and tensorDtypes.yT != "float16") or device.features.has("shader-f16"))',"args.rows > 0","args.dim > 0","numel(shapes.xT) >= args.rows * args.dim","numel(shapes.yT) >= args.rows * args.dim"],constants:{usesF16:'tensorDtypes.xT == "float16" or tensorDtypes.weightsT == "float16" or tensorDtypes.yT == "float16"',xScalar:"dtypes.X",wScalar:"dtypes.W",yScalar:"dtypes.Y",dim:"args.dim"},passes:[{id:"main",name:"LlamaPrefillRmsNorm",shader:"prefill-rms-norm.wgsl.jinja",bindings:[{name:"x",arg:"xT",semantic:"X",role:"input",buffer:{type:"read-only-storage"},elementType:"$xScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$wScalar"},{name:"y",arg:"yT",semantic:"Y",role:"output",buffer:{type:"storage"},elementType:"$yScalar"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"norm_offset",type:"u32",value:"args.normOffset"},{name:"rows",type:"u32",value:"args.rows"},{name:"eps",type:"f32",value:"args.eps"}]}}],dispatch:{workgroups:"args.rows"},reads:["X","Weights"],writes:["Y"]}]}]},assets:[["prefill-rms-norm.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const DIM: u32 = {{ dim }}u;
+const WG: u32 = 256u;
+
+var<workgroup> partials: array<f32, 256>;
+
+@compute @workgroup_size(256)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+
+  let row = wid.x + wid.y * nwg.x;
+  if (row >= params.rows) {
+    return;
+  }
+  let lid = lid3.x;
+  let base = row * DIM;
+
+  var sum = 0.0;
+  for (var d = lid; d < DIM; d = d + WG) {
+    let v = f32(x[base + d]);
+    sum = sum + v * v;
+  }
+  partials[lid] = sum;
+  workgroupBarrier();
+
+  var stride = WG / 2u;
+  loop {
+    if (lid < stride) {
+      partials[lid] = partials[lid] + partials[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+
+  let scale = inverseSqrt(partials[0] / f32(DIM) + params.eps);
+  for (var d = lid; d < DIM; d = d + WG) {
+    y[base + d] = {{ yScalar }}(f32(x[base + d]) * scale * f32(weights[params.norm_offset + d]));
+  }
+}
+`]]}],["com.xenova.Qwen35DecodeFullAttention",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeFullAttention",sinceVersion:1,inputs:[{role:"Q",dtype:"float32",rank:1},{role:"Gate",dtype:"float32",rank:1},{role:"CacheKeys",dtype:"C",rank:1},{role:"CacheValues",dtype:"C",rank:1}],outputs:[{role:"Attn",dtype:"float32",rank:1,shape:["args.numHeads * args.headDim"]}],typeConstraints:{C:["float32","float16"]},args:{qT:{kind:"tensor",semantic:"Q",role:"input"},gateT:{kind:"tensor",semantic:"Gate",role:"input"},cacheKeysT:{kind:"tensor",semantic:"CacheKeys",role:"input"},cacheValuesT:{kind:"tensor",semantic:"CacheValues",role:"input"},attnT:{kind:"tensor",semantic:"Attn",role:"output"},layer:{kind:"u32",semantic:"layer"},cacheLen:{kind:"u32",semantic:"cache_len"},pastLen:{kind:"u32",semantic:"past_len",required:false},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},scale:{kind:"f32",semantic:"scale"}},tunables:{WORKGROUP_SIZE:256},variants:[{id:"splitk",priority:20,when:["ranks.qT == 1","ranks.gateT == 1","(ranks.cacheKeysT == 1 or ranks.cacheKeysT == 5)","(ranks.cacheValuesT == 1 or ranks.cacheValuesT == 5)","ranks.attnT == 1",'tensorDtypes.qT == "float32"','tensorDtypes.gateT == "float32"','tensorDtypes.attnT == "float32"',"tensorDtypes.cacheKeysT == tensorDtypes.cacheValuesT",'(tensorDtypes.cacheKeysT == "float32" or tensorDtypes.cacheKeysT == "float16")',"f16Ok(tensorDtypes.cacheKeysT)","args.cacheLen > 0","(args.pastLen if args.pastLen else 0) < args.cacheLen","args.numHeads > 0","args.numKvHeads > 0","args.numHeads % args.numKvHeads == 0","args.headDim > 0","args.headDim % 4 == 0","args.headDim <= 1024","dim(shapes.qT, 0) == args.numHeads * args.headDim","dim(shapes.gateT, 0) == args.numHeads * args.headDim","dim(shapes.attnT, 0) == args.numHeads * args.headDim","numel(shapes.cacheKeysT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim","numel(shapes.cacheValuesT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim"],derive:{numSplits:"8"},constants:{usesF16:'tensorDtypes.cacheKeysT == "float16"',cacheVec4:'"vec4<f16>" if tensorDtypes.cacheKeysT == "float16" else "vec4<f32>"',scalar:'"f32"',qNumHeads:"args.numHeads",kvNumHeads:"args.numKvHeads",headDim:"args.headDim",headDimV4:"args.headDim / 4",numSplits:"numSplits",layer:"args.layer",cacheLen:"args.cacheLen",scale:"args.scale",hasBias:false,hasGate:true,hasWindow:false,useSubgroups:false,combineSubgroups:false},intermediates:[{id:"partialOut",dtype:"float32",shape:"[args.numHeads * numSplits * args.headDim]"},{id:"partialMax",dtype:"float32",shape:"[args.numHeads * numSplits]"},{id:"partialDenom",dtype:"float32",shape:"[args.numHeads * numSplits]"}],passes:[{id:"partial",name:"Qwen35DecodeFullAttention.SplitK",source:{kind:"template",shader:"ops/_shared/attn-flash-decode-splitk.wgsl.jinja",inputs:{layout:'"layer_cache"'}},bindings:[{name:"query",arg:"qT",semantic:"Q",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"key",arg:"cacheKeysT",semantic:"CacheKeys",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheVec4"},{name:"value",arg:"cacheValuesT",semantic:"CacheValues",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheVec4"},{name:"partial_out",semantic:"partialOut",role:"scratch",buffer:{type:"storage"},elementType:"vec4<f32>"},{name:"partial_max",semantic:"partialMax",role:"scratch",buffer:{type:"storage"},elementType:"f32"},{name:"partial_denom",semantic:"partialDenom",role:"scratch",buffer:{type:"storage"},elementType:"f32"},{name:"params",arg:"paramsT",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"}]}}],dispatch:{x:"numSplits",y:"args.numHeads",z:1},reads:["Q","CacheKeys","CacheValues"],writes:["partialOut","partialMax","partialDenom"]},{id:"merge",name:"Qwen35DecodeFullAttention.SplitKMerge",source:{kind:"template",shader:"ops/_shared/attn-flash-decode-splitk-merge.wgsl.jinja",inputs:{layout:'"layer_cache"'}},bindings:[{name:"partial_out",semantic:"partialOut",role:"scratch",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"partial_max",semantic:"partialMax",role:"scratch",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"partial_denom",semantic:"partialDenom",role:"scratch",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"gate",arg:"gateT",semantic:"Gate",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"output",arg:"attnT",semantic:"Attn",role:"output",buffer:{type:"storage"},elementType:"vec4<f32>"}],dispatch:{x:1,y:"args.numHeads",z:1},reads:["partialOut","partialMax","partialDenom","Gate"],writes:["Attn"]}]},{id:"scalar",priority:0,when:["ranks.qT == 1","(ranks.cacheKeysT == 1 or ranks.cacheKeysT == 5)","(ranks.cacheValuesT == 1 or ranks.cacheValuesT == 5)",'tensorDtypes.qT == "float32"',"tensorDtypes.cacheKeysT == tensorDtypes.cacheValuesT",'(tensorDtypes.cacheKeysT == "float32" or tensorDtypes.cacheKeysT == "float16")',"f16Ok(tensorDtypes.cacheKeysT)","args.cacheLen > 0","(args.pastLen if args.pastLen else 0) < args.cacheLen","args.numHeads > 0","args.numKvHeads > 0","args.numHeads % args.numKvHeads == 0","args.headDim > 0","dim(shapes.qT, 0) == args.numHeads * args.headDim","dim(shapes.gateT, 0) == args.numHeads * args.headDim","dim(shapes.attnT, 0) == args.numHeads * args.headDim","numel(shapes.cacheKeysT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim","numel(shapes.cacheValuesT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim"],constants:{usesF16:'tensorDtypes.cacheKeysT == "float16"',cacheScalar:"dtypes.C",layer:"args.layer",cacheLen:"args.cacheLen",numHeads:"args.numHeads",numKvHeads:"args.numKvHeads",numKvGroups:"args.numHeads / args.numKvHeads",headDim:"args.headDim",scale:"args.scale"},passes:[{id:"main",name:"Qwen35DecodeFullAttention",shader:"decode-full-attention.wgsl.jinja",bindings:[{name:"q",arg:"qT",semantic:"Q",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"gate",arg:"gateT",semantic:"Gate",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"cache_keys",arg:"cacheKeysT",semantic:"CacheKeys",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheScalar"},{name:"cache_values",arg:"cacheValuesT",semantic:"CacheValues",role:"input",buffer:{type:"read-only-storage"},elementType:"$cacheScalar"},{name:"attn_out",arg:"attnT",semantic:"Attn",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"params",arg:"paramsT",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"}]}}],dispatch:{x:"args.numHeads"},reads:["Q","Gate","CacheKeys","CacheValues"],writes:["Attn"]}]}]},assets:[["decode-full-attention.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const LAYER: u32 = {{ layer }}u;
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const NUM_KV_GROUPS: u32 = {{ numKvGroups }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const ATTN_SCALING: f32 = {{ scale }};
+const WORKGROUP_SIZE: u32 = 128u;
+
+const SCORE_TILE: u32 = {{ cacheLen if cacheLen < 4096 else 4096 }}u;
+var<workgroup> scores: array<f32, {{ cacheLen if cacheLen < 4096 else 4096 }}>;
+var<workgroup> partials: array<f32, 128>;
+var<workgroup> run_m: f32;
+var<workgroup> run_l: f32;
+
+{% include "ops/_shared/kv-cache-index.wgsl.jinja" %}
+
+@compute @workgroup_size(128)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let head = wid.x;
+  let lid = lid3.x;
+  if (head >= NUM_HEADS || params.past_len >= CACHE_LEN) { return; }
+  let kv_head = head / NUM_KV_GROUPS;
+  let q_base = head * HEAD_DIM;
+
+  if (lid == 0u) { run_m = -3.4028234663852886e+38; run_l = 0.0; }
+  var acc: array<f32, {{ ((headDim + 127) / 128) | int }}>;
+  for (var a = 0u; a < {{ ((headDim + 127) / 128) | int }}u; a = a + 1u) { acc[a] = 0.0; }
+  workgroupBarrier();
+
+  let total = params.past_len + 1u;
+  var tile_start = 0u;
+  loop {
+    if (tile_start >= total) { break; }
+    let tile_len = min(SCORE_TILE, total - tile_start);
+
+    var local_max = -3.4028234663852886e+38;
+    for (var i = lid; i < tile_len; i = i + WORKGROUP_SIZE) {
+      let src = tile_start + i;
+      var score = 0.0;
+      for (var d = 0u; d < HEAD_DIM; d = d + 1u) {
+        score = fma(q[q_base + d], f32(cache_keys[cache_index(src, kv_head, d)]), score);
+      }
+      score = score * ATTN_SCALING;
+      scores[i] = score;
+      local_max = max(local_max, score);
+    }
+    partials[lid] = local_max;
+    workgroupBarrier();
+    var stride = WORKGROUP_SIZE / 2u;
+    loop {
+      if (lid < stride) { partials[lid] = max(partials[lid], partials[lid + stride]); }
+      workgroupBarrier();
+      if (stride == 1u) { break; }
+      stride = stride / 2u;
+    }
+    let tile_max = partials[0];
+    workgroupBarrier();
+
+    let m_old = run_m;
+    let new_m = max(m_old, tile_max);
+    let corr = exp(m_old - new_m);
+
+    var local_denom = 0.0;
+    for (var i = lid; i < tile_len; i = i + WORKGROUP_SIZE) {
+      let e = exp(scores[i] - new_m);
+      scores[i] = e;
+      local_denom = local_denom + e;
+    }
+    partials[lid] = local_denom;
+    workgroupBarrier();
+    stride = WORKGROUP_SIZE / 2u;
+    loop {
+      if (lid < stride) { partials[lid] = partials[lid] + partials[lid + stride]; }
+      workgroupBarrier();
+      if (stride == 1u) { break; }
+      stride = stride / 2u;
+    }
+    let tile_denom = partials[0];
+    workgroupBarrier();
+
+{% for a in range(((headDim + 127) / 128) | int) %}
+    {
+      let d = lid + {{ a * 128 }}u;
+      if (d < HEAD_DIM) {
+        var s = 0.0;
+        for (var i = 0u; i < tile_len; i = i + 1u) {
+          s = fma(scores[i], f32(cache_values[cache_index(tile_start + i, kv_head, d)]), s);
+        }
+        acc[{{ a }}] = acc[{{ a }}] * corr + s;
+      }
+    }
+{% endfor %}
+    if (lid == 0u) {
+      run_l = run_l * corr + tile_denom;
+      run_m = new_m;
+    }
+    workgroupBarrier();
+    tile_start = tile_start + SCORE_TILE;
+  }
+
+  let denom = run_l;
+{% for a in range(((headDim + 127) / 128) | int) %}
+  {
+    let d = lid + {{ a * 128 }}u;
+    if (d < HEAD_DIM) {
+
+      attn_out[q_base + d] = (acc[{{ a }}] / denom) * (1.0 / (1.0 + exp(-gate[q_base + d])));
+    }
+  }
+{% endfor %}
+}
+`]]}],["com.xenova.Qwen35DecodeFullQkv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeFullQkv",sinceVersion:1,inputs:[{role:"Normed",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true},{role:"Q4Bits",dtype:"uint32",rank:1,optional:true},{role:"Q4Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Q",dtype:"float32",rank:1,shape:["args.qSize"]},{role:"QGate",dtype:"float32",rank:1,shape:["args.qSize"]},{role:"K",dtype:"float32",rank:1,shape:["args.kvSize"]},{role:"V",dtype:"float32",rank:1,shape:["args.kvSize"]}],tunables:{DENSE_WORKGROUP_SIZE:64,Q4_WORKGROUP_SIZE:32,Q1_WORKGROUP_SIZE:64},typeConstraints:{W:["float32","float16"],S:["float32","float16"]},args:{normedT:{kind:"tensor",semantic:"Normed",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},qT:{kind:"tensor",semantic:"Q",role:"output"},qGateT:{kind:"tensor",semantic:"QGate",role:"output"},kT:{kind:"tensor",semantic:"K",role:"output"},vT:{kind:"tensor",semantic:"V",role:"output"},qSize:{kind:"u32",semantic:"attention_size"},kvSize:{kind:"u32",semantic:"kv_size"},headDim:{kind:"u32",semantic:"head_dim"},hiddenSize:{kind:"u32",semantic:"hidden_size"},qOffset:{kind:"u32",semantic:"q_proj_offset"},kOffset:{kind:"u32",semantic:"k_proj_offset"},vOffset:{kind:"u32",semantic:"v_proj_offset"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false},q4BitsT:{kind:"tensor",semantic:"Q4Bits",role:"weights",required:false},q4ScalesT:{kind:"tensor",semantic:"Q4Scales",role:"weights",required:false},format:{kind:"string",semantic:"packed_quant_format",required:false,oneOf:["q4_0","q4_k","q8_0","iq","lut32","lut16"]},lut:{kind:"u32",semantic:"dequant_lut_id",required:false}},derive:{fmt:'args.format if args.format else "q4_0"',quantBits:'8 if (fmt == "q8_0" or fmt == "iq") else 4',hasMin:'1 if fmt == "q4_k" else 0',iq:'1 if fmt == "iq" else 0',lut16:'1 if fmt == "lut16" else 0',deviceWorkgroupCap:"min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",denseWorkgroupLimit:"min(tunables.DENSE_WORKGROUP_SIZE, deviceWorkgroupCap)",denseWorkgroupSize:"max(1, pow2ceil(denseWorkgroupLimit + 1) / 2)",q4WorkgroupLimit:"min(tunables.Q4_WORKGROUP_SIZE, deviceWorkgroupCap)",q4WorkgroupSize:"max(1, pow2ceil(q4WorkgroupLimit + 1) / 2)",q1WorkgroupLimit:"min(tunables.Q1_WORKGROUP_SIZE, deviceWorkgroupCap)",q1WorkgroupSize:"max(1, pow2ceil(q1WorkgroupLimit + 1) / 2)",denseResourcesFit:"denseWorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize and 2 * args.qSize + 2 * args.kvSize <= device.limits.maxComputeWorkgroupsPerDimension",q4ResourcesFit:"q4WorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize and 2 * args.qSize + 2 * args.kvSize <= device.limits.maxComputeWorkgroupsPerDimension",q1ResourcesFit:"q1WorkgroupSize * 16 <= device.limits.maxComputeWorkgroupStorageSize and ceilDiv(2 * args.qSize + 2 * args.kvSize, 4) <= device.limits.maxComputeWorkgroupsPerDimension"},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.normedT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1",'tensorDtypes.normedT == "float32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")',"f16Ok(tensorDtypes.q1ScalesT)","q1ResourcesFit","args.qSize > 0","args.kvSize > 0","args.headDim > 0","args.qSize % args.headDim == 0","args.headDim % 4 == 0","args.kvSize % 4 == 0","args.hiddenSize > 0","args.hiddenSize % 128 == 0","dim(shapes.normedT, 0) == args.hiddenSize","dim(shapes.qT, 0) == args.qSize","dim(shapes.qGateT, 0) == args.qSize","dim(shapes.kT, 0) == args.kvSize","dim(shapes.vT, 0) == args.kvSize","dim(shapes.q1BitsT, 0) >= max(args.qOffset + 2 * args.qSize * (args.hiddenSize / 128), args.kOffset + args.kvSize * (args.hiddenSize / 128), args.vOffset + args.kvSize * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= max(args.qOffset + 2 * args.qSize * (args.hiddenSize / 128), args.kOffset + args.kvSize * (args.hiddenSize / 128), args.vOffset + args.kvSize * (args.hiddenSize / 128))"],constants:{usesF16:'tensorDtypes.q1ScalesT == "float16"',useSubgroups:'device.features.has("subgroups")',scaleScalar:"dtypes.S",workgroupSize:"q1WorkgroupSize",blocksPerRow:"args.hiddenSize / 128",inWords:"args.hiddenSize / 32",qSize:"args.qSize",kvSize:"args.kvSize",headDim:"args.headDim",total:"2 * args.qSize + 2 * args.kvSize",qOffset:"args.qOffset",kOffset:"args.kOffset",vOffset:"args.vOffset"},passes:[{id:"main",name:"Qwen35DecodeFullQkvQ1",shader:"decode-full-qkv-q1.wgsl.jinja",bindings:[{name:"normed",arg:"normedT",semantic:"Normed",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"q",arg:"qT",semantic:"Q",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"q_gate",arg:"qGateT",semantic:"QGate",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"output",buffer:{type:"storage"},elementType:"f32"}],dispatch:{x:"ceil((2 * args.qSize + 2 * args.kvSize) / 4)"},reads:["Normed","Q1Bits","Q1Scales"],writes:["Q","QGate","K","V"]}]}]},assets:[["decode-full-qkv-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const BPR: u32 = {{ blocksPerRow }}u;
+const IN_WORDS: u32 = {{ inWords }}u;
+const Q_SIZE: u32 = {{ qSize }}u;
+const KV_SIZE: u32 = {{ kvSize }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const TOTAL: u32 = {{ total }}u;
+const W_Q_Q1: u32 = {{ qOffset }}u;
+const W_K_Q1: u32 = {{ kOffset }}u;
+const W_V_Q1: u32 = {{ vOffset }}u;
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+const TILE_ROWS: u32 = 4u;
+
+var<workgroup> partials0: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials1: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials2: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials3: array<f32, {{ workgroupSize }}>;
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let index0 = wid.x * TILE_ROWS;
+  let lid = lid3.x;
+  if (index0 >= TOTAL) {
+    return;
+  }
+
+  var block_offset = W_Q_Q1;
+  var row0 = index0;
+  if (index0 < Q_SIZE) {
+    let head = index0 / HEAD_DIM;
+    let d = index0 % HEAD_DIM;
+    row0 = head * (2u * HEAD_DIM) + d;
+  } else if (index0 < 2u * Q_SIZE) {
+    let qg = index0 - Q_SIZE;
+    let head = qg / HEAD_DIM;
+    let d = qg % HEAD_DIM;
+    row0 = head * (2u * HEAD_DIM) + HEAD_DIM + d;
+  } else if (index0 < 2u * Q_SIZE + KV_SIZE) {
+    block_offset = W_K_Q1;
+    row0 = index0 - 2u * Q_SIZE;
+  } else {
+    block_offset = W_V_Q1;
+    row0 = index0 - 2u * Q_SIZE - KV_SIZE;
+  }
+  let tile = row0 / 4u;
+
+  var acc0 = 0.0;
+  var acc1 = 0.0;
+  var acc2 = 0.0;
+  var acc3 = 0.0;
+  for (var word_index = lid; word_index < IN_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let base4 = word_index * 8u;
+    let n0 = normed[base4];
+    let n1 = normed[base4 + 1u];
+    let n2 = normed[base4 + 2u];
+    let n3 = normed[base4 + 3u];
+    let n4 = normed[base4 + 4u];
+    let n5 = normed[base4 + 5u];
+    let n6 = normed[base4 + 6u];
+    let n7 = normed[base4 + 7u];
+    let dot4 = q1_dot_word_t4(block_offset, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    acc0 += dot4.x;
+    acc1 += dot4.y;
+    acc2 += dot4.z;
+    acc3 += dot4.w;
+  }
+{%- if useSubgroups %}
+
+  let ss = subgroupAdd(vec4<f32>(acc0, acc1, acc2, acc3));
+  if (WORKGROUP_SIZE == sg_size) {
+    if (lid == 0u) {
+      partials0[0] = ss.x;
+      partials1[0] = ss.y;
+      partials2[0] = ss.z;
+      partials3[0] = ss.w;
+    }
+    workgroupBarrier();
+  } else {
+    if (sg_lid == 0u) {
+      let subgroup_index = lid / sg_size;
+      partials0[subgroup_index] = ss.x;
+      partials1[subgroup_index] = ss.y;
+      partials2[subgroup_index] = ss.z;
+      partials3[subgroup_index] = ss.w;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total0 = 0.0;
+      var total1 = 0.0;
+      var total2 = 0.0;
+      var total3 = 0.0;
+      let subgroup_count = WORKGROUP_SIZE / sg_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total0 = total0 + partials0[i];
+        total1 = total1 + partials1[i];
+        total2 = total2 + partials2[i];
+        total3 = total3 + partials3[i];
+      }
+      partials0[0] = total0;
+      partials1[0] = total1;
+      partials2[0] = total2;
+      partials3[0] = total3;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials0[lid] = acc0;
+  partials1[lid] = acc1;
+  partials2[lid] = acc2;
+  partials3[lid] = acc3;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials0[lid] = partials0[lid] + partials0[lid + stride];
+      partials1[lid] = partials1[lid] + partials1[lid + stride];
+      partials2[lid] = partials2[lid] + partials2[lid + stride];
+      partials3[lid] = partials3[lid] + partials3[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+{%- endif %}
+
+  if (lid == 0u) {
+    let v0 = partials0[0];
+    let v1 = partials1[0];
+    let v2 = partials2[0];
+    let v3 = partials3[0];
+    if (index0 < Q_SIZE) {
+      q[index0 + 0u] = v0;
+      q[index0 + 1u] = v1;
+      q[index0 + 2u] = v2;
+      q[index0 + 3u] = v3;
+    } else if (index0 < 2u * Q_SIZE) {
+      let qg = index0 - Q_SIZE;
+      q_gate[qg + 0u] = v0;
+      q_gate[qg + 1u] = v1;
+      q_gate[qg + 2u] = v2;
+      q_gate[qg + 3u] = v3;
+    } else if (index0 < 2u * Q_SIZE + KV_SIZE) {
+      let kIdx = index0 - 2u * Q_SIZE;
+      k[kIdx + 0u] = v0;
+      k[kIdx + 1u] = v1;
+      k[kIdx + 2u] = v2;
+      k[kIdx + 3u] = v3;
+    } else {
+      let vIdx = index0 - 2u * Q_SIZE - KV_SIZE;
+      v[vIdx + 0u] = v0;
+      v[vIdx + 1u] = v1;
+      v[vIdx + 2u] = v2;
+      v[vIdx + 3u] = v3;
+    }
+  }
+}
+`]]}],["com.xenova.Qwen35DecodeGatedRmsNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeGatedRmsNorm",sinceVersion:1,inputs:[{role:"X",dtype:"float32",rank:1},{role:"Z",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Y",dtype:"float32",rank:1,shape:["args.numValueHeads * args.valueHeadDim"]}],typeConstraints:{W:["float32","float16"]},args:{xT:{kind:"tensor",semantic:"X",role:"input"},zT:{kind:"tensor",semantic:"Z",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},yT:{kind:"tensor",semantic:"Y",role:"output"},numValueHeads:{kind:"u32",semantic:"num_value_heads"},valueHeadDim:{kind:"u32",semantic:"value_head_dim"},normOffset:{kind:"u32",semantic:"norm_offset"},rmsEps:{kind:"f32",semantic:"rms_eps"},workgroupSize:{kind:"u32",semantic:"workgroup_size"}},variants:[{id:"dense",priority:0,when:["ranks.xT == 1","ranks.zT == 1","ranks.weightsT == 1","ranks.yT == 1",'tensorDtypes.xT == "float32"','tensorDtypes.zT == "float32"','tensorDtypes.yT == "float32"','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")',"f16Ok(tensorDtypes.weightsT)","args.numValueHeads > 0","args.valueHeadDim > 0","args.workgroupSize >= args.valueHeadDim","dim(shapes.xT, 0) >= args.numValueHeads * args.valueHeadDim","dim(shapes.zT, 0) >= args.numValueHeads * args.valueHeadDim","dim(shapes.yT, 0) == args.numValueHeads * args.valueHeadDim","dim(shapes.weightsT, 0) >= args.normOffset + args.valueHeadDim"],constants:{usesF16:'tensorDtypes.weightsT == "float16"',useSubgroups:'device.features.has("subgroups")',weightScalar:"dtypes.W",numHeads:"args.numValueHeads",headDim:"args.valueHeadDim",normOffset:"args.normOffset",rmsEps:"args.rmsEps",workgroupSize:"args.workgroupSize"},passes:[{id:"main",name:"Qwen35DecodeGatedRmsNorm",shader:"decode-gated-rmsnorm.wgsl.jinja",bindings:[{name:"x",arg:"xT",semantic:"X",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"z",arg:"zT",semantic:"Z",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"},{name:"y",arg:"yT",semantic:"Y",role:"output",buffer:{type:"storage"},elementType:"f32"}],dispatch:{x:"args.numValueHeads"},reads:["X","Z","Weights"],writes:["Y"]}]}]},assets:[["decode-gated-rmsnorm.wgsl.jinja",`{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const W_NORM: u32 = {{ normOffset }}u;
+const EPS: f32 = {{ rmsEps }};
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+var<workgroup> partials: array<f32, {{ workgroupSize }}>;
+
+fn silu(x: f32) -> f32 {
+  return x / (1.0 + exp(-x));
+}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let head = wid.x;
+  let lid = lid3.x;
+  if (head >= NUM_HEADS) { return; }
+  let base = head * HEAD_DIM;
+  var acc = 0.0;
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let value = x[base + d];
+    acc = fma(value, value, acc);
+  }
+{%- if useSubgroups %}
+  let subgroup_sum = subgroupAdd(acc);
+  if (WORKGROUP_SIZE == sg_size) {
+    if (lid == 0u) {
+      partials[0] = subgroup_sum;
+    }
+    workgroupBarrier();
+  } else {
+    if (sg_lid == 0u) {
+      partials[lid / sg_size] = subgroup_sum;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total = 0.0;
+      let subgroup_count = WORKGROUP_SIZE / sg_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total = total + partials[i];
+      }
+      partials[0] = total;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials[lid] = acc;
+  workgroupBarrier();
+
+  var n = WORKGROUP_SIZE;
+  loop {
+    let half = (n + 1u) / 2u;
+    if (lid < n - half) {
+      partials[lid] = partials[lid] + partials[lid + half];
+    }
+    workgroupBarrier();
+    n = half;
+    if (n == 1u) {
+      break;
+    }
+  }
+{%- endif %}
+  let scale = inverseSqrt(partials[0] / f32(HEAD_DIM) + EPS);
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let idx = base + d;
+{%- if weightScalar == "f16" %}
+    let weight = f32(weights[W_NORM + d]);
+{%- else %}
+    let weight = weights[W_NORM + d];
+{%- endif %}
+    y[idx] = x[idx] * scale * weight * silu(z[idx]);
+  }
+}
+`]]}],["com.xenova.Qwen35DecodeLinearAttention",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeLinearAttention",sinceVersion:1,inputs:[{role:"State",dtype:"float32",rank:1},{role:"Q",dtype:"float32",rank:1},{role:"K",dtype:"float32",rank:1},{role:"V",dtype:"float32",rank:1},{role:"Gate",dtype:"float32",rank:1}],outputs:[{role:"State",dtype:"float32",rank:1,shape:["args.numHeads * args.headDimK * args.headDimV"]},{role:"Out",dtype:"float32",rank:1,shape:["args.numHeads * args.headDimV"]}],typeConstraints:{},args:{stateT:{kind:"tensor",semantic:"State",role:"inout"},qT:{kind:"tensor",semantic:"Q",role:"input"},kT:{kind:"tensor",semantic:"K",role:"input"},vT:{kind:"tensor",semantic:"V",role:"input"},gateT:{kind:"tensor",semantic:"Gate",role:"input"},outT:{kind:"tensor",semantic:"Out",role:"output"},numHeads:{kind:"u32",semantic:"linear_num_value_heads"},numKeyHeads:{kind:"u32",semantic:"linear_num_key_heads"},headDimK:{kind:"u32",semantic:"linear_key_head_dim"},headDimV:{kind:"u32",semantic:"linear_value_head_dim"},scale:{kind:"f32",semantic:"scale"},workgroupSizeVec4:{kind:"u32",semantic:"workgroup_size_vec4"},workgroupSizeScalar:{kind:"u32",semantic:"workgroup_size_scalar"}},bindingSets:{laState:[{name:"state_buf",arg:"stateT",semantic:"State",role:"inout",buffer:{type:"storage"},elementType:"$stateElem"},{name:"q",arg:"qT",semantic:"Q",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"gate",arg:"gateT",semantic:"Gate",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"out",arg:"outT",semantic:"Out",role:"output",buffer:{type:"storage"},elementType:"f32"}]},variants:[{id:"vec4",priority:2,when:["ranks.stateT == 1","ranks.qT == 1","ranks.kT == 1","ranks.vT == 1","ranks.gateT == 1","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"','device.features.has("subgroups")','has(device.adapterInfo, "subgroupMinSize")',"args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK > 0","args.headDimK % 4 == 0","args.headDimV > 0","args.workgroupSizeVec4 >= ceilDiv(args.headDimK, 4)","args.workgroupSizeVec4 <= device.adapterInfo.subgroupMinSize","args.workgroupSizeVec4 <= device.limits.maxComputeInvocationsPerWorkgroup","args.workgroupSizeVec4 <= device.limits.maxComputeWorkgroupSizeX","dim(shapes.qT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.kT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.vT, 0) >= args.numHeads * args.headDimV","dim(shapes.gateT, 0) >= 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","dim(shapes.outT, 0) == args.numHeads * args.headDimV"],constants:{numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",tileV:4,workgroupSize:"args.workgroupSizeVec4",scale:"args.scale",useSubgroups:true,stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"'},passes:[{id:"main",name:"Qwen35DecodeLinearAttention",shader:"decode-linear-attention-vec4.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 4)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]},{id:"vec4_gather",priority:3,when:["ranks.stateT == 1","ranks.qT == 1","ranks.kT == 1","ranks.vT == 1","ranks.gateT == 1","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"','not device.features.has("subgroups") or not has(device.adapterInfo, "subgroupMinSize") or args.workgroupSizeVec4 > device.adapterInfo.subgroupMinSize or (tensorDtypes.stateT == "float32" and args.numHeads == 16 and args.numKeyHeads == 16 and args.headDimK == 128 and args.headDimV == 128 and args.workgroupSizeVec4 == 32)',"args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK > 0","args.headDimK % 4 == 0","args.headDimV > 0","args.workgroupSizeVec4 >= ceilDiv(args.headDimK, 4)","args.workgroupSizeVec4 <= device.limits.maxComputeInvocationsPerWorkgroup","args.workgroupSizeVec4 <= device.limits.maxComputeWorkgroupSizeX","args.workgroupSizeVec4 * 36 + 8 <= device.limits.maxComputeWorkgroupStorageSize","dim(shapes.qT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.kT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.vT, 0) >= args.numHeads * args.headDimV","dim(shapes.gateT, 0) >= 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","dim(shapes.outT, 0) == args.numHeads * args.headDimV"],constants:{numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",tileV:4,workgroupSize:"args.workgroupSizeVec4",scale:"args.scale",useSubgroups:false,stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"'},passes:[{id:"main",name:"Qwen35DecodeLinearAttention.Gather",shader:"decode-linear-attention-vec4.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 4)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]},{id:"scalar",priority:0,when:["ranks.stateT == 1","ranks.qT == 1","ranks.kT == 1","ranks.vT == 1","ranks.gateT == 1","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"',"args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK > 0","args.headDimV > 0","args.workgroupSizeScalar >= args.headDimK","dim(shapes.qT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.kT, 0) >= args.numKeyHeads * args.headDimK","dim(shapes.vT, 0) >= args.numHeads * args.headDimV","dim(shapes.gateT, 0) >= 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","dim(shapes.outT, 0) == args.numHeads * args.headDimV"],constants:{useSubgroups:'device.features.has("subgroups")',numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",tileV:8,workgroupSize:"args.workgroupSizeScalar",scale:"args.scale",stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"'},passes:[{id:"main",name:"Qwen35DecodeLinearAttention",shader:"decode-linear-attention.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 8)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]}]},assets:[["decode-linear-attention-vec4.wgsl.jinja",`{% include "ops/_shared/linear-attention-common.wgsl.jinja" %}{{ emit_qwen35_decode_header() }}{{ emit_qwen35_stable_decay() }}const VEC4_LANES: u32 = 4u;
+const SCALE: f32 = {{ scale }};
+{%- if not useSubgroups %}
+
+var<workgroup> red_pair: array<vec2<f32>, WG * TILE_V>;
+var<workgroup> red_kq: array<f32, WG>;
+var<workgroup> red_l2: vec2<f32>;
+
+{{ emit_gather_function("gather_pair", "vec2<f32>", "red_pair", true, "vec2<f32>(0.0)") }}
+
+{{ emit_gather_function("gather_scalar", "f32", "red_kq", false, "0.0") }}
+{% endif %}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(@builtin(workgroup_id) wid: vec3<u32>,
+        @builtin(local_invocation_id) lid3: vec3<u32>{% if useSubgroups %},
+        @builtin(subgroup_invocation_id) sg_lid: u32,
+        @builtin(subgroup_size) sg_size: u32
+{% endif %}) {
+  let tid = lid3.x;
+  let dk_base = tid * VEC4_LANES;
+{{ emit_decode_tile_setup() }}
+  let lane_active = dk_base < HEAD_DIM_K;
+  let exp_g = {{ qwen35_decay("gate[NUM_HEADS + head]") }};
+  let beta_val = gate[head];
+
+  var k_vec = vec4<f32>(0.0);
+  var q_vec = vec4<f32>(0.0);
+  if (lane_active) {
+    let kq_base = (head % NUM_KEY_HEADS) * HEAD_DIM_K + dk_base;
+    k_vec = vec4<f32>(k[kq_base + 0u], k[kq_base + 1u], k[kq_base + 2u], k[kq_base + 3u]);
+    q_vec = vec4<f32>(q[kq_base + 0u], q[kq_base + 1u], q[kq_base + 2u], q[kq_base + 3u]);
+  }
+
+{% if useSubgroups %}
+  let l2 = subgroupAdd(vec2<f32>(dot(k_vec, k_vec), dot(q_vec, q_vec)));
+{% else %}
+  red_pair[tid] = vec2<f32>(dot(k_vec, k_vec), dot(q_vec, q_vec));
+  workgroupBarrier();
+  if (tid == 0u) {
+    red_l2 = gather_pair(0u);
+  }
+  workgroupBarrier();
+
+  let l2 = red_l2;
+{% endif %}
+  k_vec = k_vec * inverseSqrt(l2.x + 1e-6);
+  q_vec = q_vec * inverseSqrt(l2.y + 1e-6);
+
+  var state: array<vec4<f32>, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) { state[j] = vec4<f32>(0.0); }
+  if (lane_active) {
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+
+        let base = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + dk_base;
+
+        state[j] = vec4<f32>(
+          f32(state_buf[base + 0u]) * exp_g,
+          f32(state_buf[base + 1u]) * exp_g,
+          f32(state_buf[base + 2u]) * exp_g,
+          f32(state_buf[base + 3u]) * exp_g,
+        );
+      }
+    }
+  }
+
+{% if useSubgroups %}
+  let kq_dot = subgroupAdd(dot(k_vec, q_vec));
+  var ret_vals: array<f32, TILE_V>;
+  var pre_vals: array<f32, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+
+    let rp = subgroupAdd(vec2<f32>(dot(state[j], k_vec), dot(state[j], q_vec)));
+    ret_vals[j] = rp.x;
+    pre_vals[j] = rp.y;
+  }
+{% else %}
+
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    red_pair[j * WG + tid] = vec2<f32>(dot(state[j], k_vec), dot(state[j], q_vec));
+  }
+  red_kq[tid] = dot(k_vec, q_vec);
+  workgroupBarrier();
+  for (var j = tid; j < TILE_V; j = j + WG) {
+    red_pair[j * WG] = gather_pair(j * WG);
+  }
+
+  let kq_gather_lane = min(TILE_V, WG - 1u);
+  if (tid == kq_gather_lane) {
+    red_kq[0] = gather_scalar();
+  }
+  workgroupBarrier();
+  let kq_dot = red_kq[0];
+  var ret_vals: array<f32, TILE_V>;
+  var pre_vals: array<f32, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let rp = red_pair[j * WG];
+    ret_vals[j] = rp.x;
+    pre_vals[j] = rp.y;
+  }
+{% endif %}
+
+  var deltas: array<f32, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let dv = dv_start + j;
+    if (dv < HEAD_DIM_V) {
+      let v_val = v[head * HEAD_DIM_V + dv];
+      let delta = beta_val * (v_val - ret_vals[j]);
+      deltas[j] = delta;
+      if (tid == 0u) {
+        out[head * HEAD_DIM_V + dv] = (pre_vals[j] + delta * kq_dot) * SCALE;
+      }
+    } else {
+      deltas[j] = 0.0;
+    }
+  }
+
+  if (lane_active) {
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+        let updated = state[j] + k_vec * deltas[j];
+
+        let base = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + dk_base;
+        state_buf[base + 0u] = {% if stateF16 %}f16(updated.x){% else %}updated.x{% endif %};
+        state_buf[base + 1u] = {% if stateF16 %}f16(updated.y){% else %}updated.y{% endif %};
+        state_buf[base + 2u] = {% if stateF16 %}f16(updated.z){% else %}updated.z{% endif %};
+        state_buf[base + 3u] = {% if stateF16 %}f16(updated.w){% else %}updated.w{% endif %};
+      }
+    }
+  }
+}
+`],["decode-linear-attention.wgsl.jinja",`{% include "ops/_shared/linear-attention-common.wgsl.jinja" %}{{ emit_qwen35_decode_header() }}{{ emit_qwen35_stable_decay() }}const SCALE: f32 = {{ scale }};
+var<workgroup> red_retrieved: array<f32, WG * TILE_V>;
+var<workgroup> red_preout: array<f32, WG * TILE_V>;
+var<workgroup> red_kq: array<f32, WG>;
+var<workgroup> red_kk: array<f32, WG>;
+var<workgroup> red_qq: array<f32, WG>;
+var<workgroup> delta_buf: array<f32, TILE_V>;
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let tid = lid3.x;
+{{ emit_decode_tile_setup() }}
+  var state: array<f32, TILE_V>;
+  let exp_g = {{ qwen35_decay("gate[NUM_HEADS + head]") }};
+  let beta_val = gate[head];
+  var k_val = 0.0;
+  var q_val = 0.0;
+  if (tid < HEAD_DIM_K) {
+    k_val = k[(head % NUM_KEY_HEADS) * HEAD_DIM_K + tid];
+    q_val = q[(head % NUM_KEY_HEADS) * HEAD_DIM_K + tid];
+  }
+
+  red_kk[tid] = k_val * k_val;
+  red_qq[tid] = q_val * q_val;
+  workgroupBarrier();
+
+  var n_kk = WG;
+  loop {
+    let half_kk = (n_kk + 1u) >> 1u;
+    if (tid < n_kk - half_kk) {
+      red_kk[tid] = red_kk[tid] + red_kk[tid + half_kk];
+      red_qq[tid] = red_qq[tid] + red_qq[tid + half_kk];
+    }
+    workgroupBarrier();
+    n_kk = half_kk;
+    if (n_kk == 1u) { break; }
+  }
+  k_val = k_val * inverseSqrt(red_kk[0] + 1e-6);
+  q_val = q_val * inverseSqrt(red_qq[0] + 1e-6);
+  workgroupBarrier();
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let dv = dv_start + j;
+    if (tid < HEAD_DIM_K && dv < HEAD_DIM_V) {
+
+      let idx = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + tid;
+      state[j] = f32(state_buf[idx]) * exp_g;
+    } else {
+      state[j] = 0.0;
+    }
+  }
+{%- if useSubgroups %}
+  let subgroup_index = tid / sg_size;
+  let subgroup_count = WG / sg_size;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let sg_ret = subgroupAdd(state[j] * k_val);
+    let sg_pre = subgroupAdd(state[j] * q_val);
+    if (sg_lid == 0u) {
+      red_retrieved[j * WG + subgroup_index] = sg_ret;
+      red_preout[j * WG + subgroup_index] = sg_pre;
+    }
+  }
+  let sg_kq = subgroupAdd(k_val * q_val);
+  if (sg_lid == 0u) {
+    red_kq[subgroup_index] = sg_kq;
+  }
+  workgroupBarrier();
+  if (WG != sg_size) {
+    if (tid < TILE_V) {
+      var ret_total = 0.0;
+      var pre_total = 0.0;
+      for (var i = 1u; i < subgroup_count; i = i + 1u) {
+        ret_total = ret_total + red_retrieved[tid * WG + i];
+        pre_total = pre_total + red_preout[tid * WG + i];
+      }
+      red_retrieved[tid * WG] = red_retrieved[tid * WG] + ret_total;
+      red_preout[tid * WG] = red_preout[tid * WG] + pre_total;
+    }
+    if (tid == TILE_V) {
+      var kq_total = 0.0;
+      for (var i = 1u; i < subgroup_count; i = i + 1u) {
+        kq_total = kq_total + red_kq[i];
+      }
+      red_kq[0] = red_kq[0] + kq_total;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    red_retrieved[j * WG + tid] = state[j] * k_val;
+    red_preout[j * WG + tid] = state[j] * q_val;
+  }
+  red_kq[tid] = k_val * q_val;
+  workgroupBarrier();
+  var n_out = WG;
+  loop {
+    let half_out = (n_out + 1u) >> 1u;
+    if (tid < n_out - half_out) {
+      for (var j = 0u; j < TILE_V; j = j + 1u) {
+        red_retrieved[j * WG + tid] = red_retrieved[j * WG + tid] + red_retrieved[j * WG + tid + half_out];
+        red_preout[j * WG + tid] = red_preout[j * WG + tid] + red_preout[j * WG + tid + half_out];
+      }
+      red_kq[tid] = red_kq[tid] + red_kq[tid + half_out];
+    }
+    workgroupBarrier();
+    n_out = half_out;
+    if (n_out == 1u) { break; }
+  }
+{%- endif %}
+  if (tid == 0u) {
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+        let v_val = v[head * HEAD_DIM_V + dv];
+        let delta = beta_val * (v_val - red_retrieved[j * WG]);
+        delta_buf[j] = delta;
+        out[head * HEAD_DIM_V + dv] = (red_preout[j * WG] + delta * red_kq[0]) * SCALE;
+      } else {
+        delta_buf[j] = 0.0;
+      }
+    }
+  }
+  workgroupBarrier();
+  if (tid < HEAD_DIM_K) {
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+        let idx = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + tid;
+        state_buf[idx] = {% if stateF16 %}f16(state[j] + k_val * delta_buf[j]){% else %}state[j] + k_val * delta_buf[j]{% endif %};
+      }
+    }
+  }
+}
+`]]}],["com.xenova.Qwen35DecodeLinearInProjConv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeLinearInProjConv",sinceVersion:1,inputs:[{role:"Normed",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true},{role:"ConvState",dtype:"float32",rank:1},{role:"Q4Bits",dtype:"uint32",rank:1,optional:true},{role:"Q4Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"ConvState",dtype:"float32",rank:1,shape:["args.convDim * (args.convKernel - 1)"]},{role:"Q",dtype:"float32",rank:1,shape:["args.keyDim"]},{role:"K",dtype:"float32",rank:1,shape:["args.keyDim"]},{role:"V",dtype:"float32",rank:1,shape:["args.valueDim"]},{role:"Z",dtype:"float32",rank:1,shape:["args.valueDim"]},{role:"Gate",dtype:"float32",rank:1,shape:["2 * args.numHeads"]}],typeConstraints:{W:["float32","float16"],S:["float32","float16"]},args:{normedT:{kind:"tensor",semantic:"Normed",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},convStateT:{kind:"tensor",semantic:"ConvState",role:"inout"},qT:{kind:"tensor",semantic:"Q",role:"output"},kT:{kind:"tensor",semantic:"K",role:"output"},vT:{kind:"tensor",semantic:"V",role:"output"},zT:{kind:"tensor",semantic:"Z",role:"output"},gateT:{kind:"tensor",semantic:"Gate",role:"output"},keyDim:{kind:"u32",semantic:"linear_key_dim"},valueDim:{kind:"u32",semantic:"linear_value_dim"},convDim:{kind:"u32",semantic:"linear_conv_dim"},numHeads:{kind:"u32",semantic:"linear_num_value_heads"},convKernel:{kind:"u32",semantic:"linear_conv_kernel_dim"},hiddenSize:{kind:"u32",semantic:"hidden_size"},qkvOffset:{kind:"u32",semantic:"linear_in_proj_qkv_offset"},zOffset:{kind:"u32",semantic:"linear_in_proj_z_offset"},bOffset:{kind:"u32",semantic:"linear_in_proj_b_offset"},aOffset:{kind:"u32",semantic:"linear_in_proj_a_offset"},convWeightOffset:{kind:"u32",semantic:"linear_conv_weight_offset"},aNegExpOffset:{kind:"u32",semantic:"linear_a_neg_exp_offset"},dtBiasOffset:{kind:"u32",semantic:"linear_dt_bias_offset"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false},q4BitsT:{kind:"tensor",semantic:"Q4Bits",role:"weights",required:false},q4ScalesT:{kind:"tensor",semantic:"Q4Scales",role:"weights",required:false},format:{kind:"string",semantic:"packed_quant_format",required:false,oneOf:["q4_0","q4_k","q8_0","iq","lut32","lut16"]},lut:{kind:"u32",semantic:"dequant_lut_id",required:false}},derive:{fmt:'args.format if args.format else "q4_0"',quantBits:'8 if (fmt == "q8_0" or fmt == "iq") else 4',hasMin:'1 if fmt == "q4_k" else 0',iq:'1 if fmt == "iq" else 0',lut16:'1 if fmt == "lut16" else 0',deviceWorkgroupCap:"min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",denseWorkgroupLimit:"min(tunables.DENSE_WORKGROUP_SIZE, deviceWorkgroupCap)",denseWorkgroupSize:"max(1, pow2ceil(denseWorkgroupLimit + 1) / 2)",q4WorkgroupLimit:"min(tunables.Q4_WORKGROUP_SIZE, deviceWorkgroupCap)",q4WorkgroupSize:"max(1, pow2ceil(q4WorkgroupLimit + 1) / 2)",q1WorkgroupCandidate:'(min(tunables.Q1_LARGE_WORKGROUP_SIZE, tunables.Q1_PORTABLE_WORKGROUP_CAP) if not device.features.has("subgroups") else tunables.Q1_LARGE_WORKGROUP_SIZE) if args.hiddenSize >= 4096 else 32',q1WorkgroupLimit:"min(q1WorkgroupCandidate, deviceWorkgroupCap)",q1WorkgroupSize:"max(1, pow2ceil(q1WorkgroupLimit + 1) / 2)",outputRows:"args.convDim + args.valueDim + 2 * args.numHeads",denseResourcesFit:"denseWorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize and outputRows <= device.limits.maxComputeWorkgroupsPerDimension",q4ResourcesFit:"q4WorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize and outputRows <= device.limits.maxComputeWorkgroupsPerDimension",q1ResourcesFit:"q1WorkgroupSize * 16 <= device.limits.maxComputeWorkgroupStorageSize and ceilDiv(outputRows, 4) <= device.limits.maxComputeWorkgroupsPerDimension"},tunables:{DENSE_WORKGROUP_SIZE:64,Q4_WORKGROUP_SIZE:32,Q1_LARGE_WORKGROUP_SIZE:128,Q1_PORTABLE_WORKGROUP_CAP:64},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.normedT == 1","ranks.weightsT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.convStateT == 1",'tensorDtypes.normedT == "float32"','tensorDtypes.convStateT == "float32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")',"f16Ok(tensorDtypes.weightsT) and f16Ok(tensorDtypes.q1ScalesT)","q1ResourcesFit","args.keyDim > 0","args.valueDim > 0","args.valueDim % 4 == 0","args.numHeads > 0","args.numHeads % 4 == 0","args.convKernel > 1","args.convDim == 2 * args.keyDim + args.valueDim","args.convDim % 4 == 0","args.hiddenSize > 0","args.hiddenSize % 128 == 0","tunables.Q1_LARGE_WORKGROUP_SIZE == 32 or tunables.Q1_LARGE_WORKGROUP_SIZE == 64 or tunables.Q1_LARGE_WORKGROUP_SIZE == 128","dim(shapes.normedT, 0) == args.hiddenSize","dim(shapes.convStateT, 0) == args.convDim * (args.convKernel - 1)","dim(shapes.qT, 0) == args.keyDim","dim(shapes.kT, 0) == args.keyDim","dim(shapes.vT, 0) == args.valueDim","dim(shapes.zT, 0) == args.valueDim","dim(shapes.gateT, 0) == 2 * args.numHeads","dim(shapes.q1BitsT, 0) >= max(args.qkvOffset + args.convDim * (args.hiddenSize / 128), args.zOffset + args.valueDim * (args.hiddenSize / 128), args.bOffset + args.numHeads * (args.hiddenSize / 128), args.aOffset + args.numHeads * (args.hiddenSize / 128)) * 4","dim(shapes.q1ScalesT, 0) >= max(args.qkvOffset + args.convDim * (args.hiddenSize / 128), args.zOffset + args.valueDim * (args.hiddenSize / 128), args.bOffset + args.numHeads * (args.hiddenSize / 128), args.aOffset + args.numHeads * (args.hiddenSize / 128))","dim(shapes.weightsT, 0) >= args.convWeightOffset + args.convDim * args.convKernel","dim(shapes.weightsT, 0) >= args.aNegExpOffset + args.numHeads","dim(shapes.weightsT, 0) >= args.dtBiasOffset + args.numHeads"],constants:{usesF16:'tensorDtypes.weightsT == "float16" or tensorDtypes.q1ScalesT == "float16"',useSubgroups:'device.features.has("subgroups")',weightVec4:'"vec4<f16>" if tensorDtypes.weightsT == "float16" else "vec4<f32>"',weightScalar:"dtypes.W",scaleScalar:"dtypes.S",workgroupSize:"q1WorkgroupSize",blocksPerRow:"args.hiddenSize / 128",inWords:"args.hiddenSize / 32",keyDim:"args.keyDim",valueDim:"args.valueDim",convDim:"args.convDim",numHeads:"args.numHeads",convKernel:"args.convKernel",convState:"args.convKernel - 1",total:"args.convDim + args.valueDim + 2 * args.numHeads",qkvOffset:"args.qkvOffset",zOffset:"args.zOffset",bOffset:"args.bOffset",aOffset:"args.aOffset",convWeightOffset:"args.convWeightOffset",aNegExpOffset:"args.aNegExpOffset",dtBiasOffset:"args.dtBiasOffset"},passes:[{id:"main",name:"Qwen35DecodeLinearInProjConvQ1",shader:"decode-linear-in-proj-conv-q1.wgsl.jinja",bindings:[{name:"normed",arg:"normedT",semantic:"Normed",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightVec4"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"conv_state",arg:"convStateT",semantic:"ConvState",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q",arg:"qT",semantic:"Q",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"z",arg:"zT",semantic:"Z",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"gate",arg:"gateT",semantic:"Gate",role:"output",buffer:{type:"storage"},elementType:"f32"}],dispatch:{x:"ceil((args.convDim + args.valueDim + 2 * args.numHeads) / 4)"},reads:["Normed","Weights","Q1Bits","Q1Scales","ConvState"],writes:["ConvState","Q","K","V","Z","Gate"]}]}]},assets:[["decode-linear-in-proj-conv-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const BPR: u32 = {{ blocksPerRow }}u;
+const IN_WORDS: u32 = {{ inWords }}u;
+const KEY_DIM: u32 = {{ keyDim }}u;
+const VALUE_DIM: u32 = {{ valueDim }}u;
+const CONV_DIM: u32 = {{ convDim }}u;
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const CONV_KERNEL: u32 = {{ convKernel }}u;
+const CONV_STATE: u32 = {{ convState }}u;
+const TOTAL: u32 = {{ total }}u;
+const W_QKV_Q1: u32 = {{ qkvOffset }}u;
+const W_Z_Q1: u32 = {{ zOffset }}u;
+const W_B_Q1: u32 = {{ bOffset }}u;
+const W_A_Q1: u32 = {{ aOffset }}u;
+const W_CONV: u32 = {{ convWeightOffset }}u;
+const W_A_NEG_EXP: u32 = {{ aNegExpOffset }}u;
+const W_DT_BIAS: u32 = {{ dtBiasOffset }}u;
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+const TILE_ROWS: u32 = 4u;
+
+var<workgroup> partials0: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials1: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials2: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials3: array<f32, {{ workgroupSize }}>;
+
+fn silu(x: f32) -> f32 { return x / (1.0 + exp(-x)); }
+fn softplus(x: f32) -> f32 { return log(1.0 + exp(-abs(x))) + max(x, 0.0); }
+
+fn weight_at(element: u32) -> f32 {
+  let packed = weights[element / 4u];
+  let lane = element % 4u;
+{%- if weightScalar == "f16" %}
+  if (lane == 0u) { return f32(packed.x); }
+  if (lane == 1u) { return f32(packed.y); }
+  if (lane == 2u) { return f32(packed.z); }
+  return f32(packed.w);
+{%- else %}
+  if (lane == 0u) { return packed.x; }
+  if (lane == 1u) { return packed.y; }
+  if (lane == 2u) { return packed.z; }
+  return packed.w;
+{%- endif %}
+}
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let index0 = wid.x * TILE_ROWS;
+  let lid = lid3.x;
+  if (index0 >= TOTAL) {
+    return;
+  }
+
+  var block_offset = W_QKV_Q1;
+  var row0 = index0;
+  if (index0 >= CONV_DIM && index0 < CONV_DIM + VALUE_DIM) {
+    row0 = index0 - CONV_DIM;
+    block_offset = W_Z_Q1;
+  } else if (index0 >= CONV_DIM + VALUE_DIM && index0 < CONV_DIM + VALUE_DIM + NUM_HEADS) {
+    row0 = index0 - CONV_DIM - VALUE_DIM;
+    block_offset = W_B_Q1;
+  } else if (index0 >= CONV_DIM + VALUE_DIM + NUM_HEADS) {
+    row0 = index0 - CONV_DIM - VALUE_DIM - NUM_HEADS;
+    block_offset = W_A_Q1;
+  }
+  let tile = row0 / 4u;
+
+  var acc0 = 0.0;
+  var acc1 = 0.0;
+  var acc2 = 0.0;
+  var acc3 = 0.0;
+  for (var word_index = lid; word_index < IN_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let base4 = word_index * 8u;
+    let n0 = normed[base4];
+    let n1 = normed[base4 + 1u];
+    let n2 = normed[base4 + 2u];
+    let n3 = normed[base4 + 3u];
+    let n4 = normed[base4 + 4u];
+    let n5 = normed[base4 + 5u];
+    let n6 = normed[base4 + 6u];
+    let n7 = normed[base4 + 7u];
+    let dot4 = q1_dot_word_t4(block_offset, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    acc0 += dot4.x;
+    acc1 += dot4.y;
+    acc2 += dot4.z;
+    acc3 += dot4.w;
+  }
+{%- if useSubgroups %}
+  let subgroup_sum0 = subgroupAdd(acc0);
+  let subgroup_sum1 = subgroupAdd(acc1);
+  let subgroup_sum2 = subgroupAdd(acc2);
+  let subgroup_sum3 = subgroupAdd(acc3);
+  if (WORKGROUP_SIZE == sg_size) {
+    if (lid == 0u) {
+      partials0[0] = subgroup_sum0;
+      partials1[0] = subgroup_sum1;
+      partials2[0] = subgroup_sum2;
+      partials3[0] = subgroup_sum3;
+    }
+    workgroupBarrier();
+  } else {
+    if (sg_lid == 0u) {
+      let subgroup_index = lid / sg_size;
+      partials0[subgroup_index] = subgroup_sum0;
+      partials1[subgroup_index] = subgroup_sum1;
+      partials2[subgroup_index] = subgroup_sum2;
+      partials3[subgroup_index] = subgroup_sum3;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total0 = 0.0;
+      var total1 = 0.0;
+      var total2 = 0.0;
+      var total3 = 0.0;
+      let subgroup_count = WORKGROUP_SIZE / sg_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total0 = total0 + partials0[i];
+        total1 = total1 + partials1[i];
+        total2 = total2 + partials2[i];
+        total3 = total3 + partials3[i];
+      }
+      partials0[0] = total0;
+      partials1[0] = total1;
+      partials2[0] = total2;
+      partials3[0] = total3;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials0[lid] = acc0;
+  partials1[lid] = acc1;
+  partials2[lid] = acc2;
+  partials3[lid] = acc3;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials0[lid] = partials0[lid] + partials0[lid + stride];
+      partials1[lid] = partials1[lid] + partials1[lid + stride];
+      partials2[lid] = partials2[lid] + partials2[lid + stride];
+      partials3[lid] = partials3[lid] + partials3[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+{%- endif %}
+
+  if (lid < TILE_ROWS) {
+    var value = partials0[0];
+    if (lid == 1u) { value = partials1[0]; }
+    if (lid == 2u) { value = partials2[0]; }
+    if (lid == 3u) { value = partials3[0]; }
+    let index = index0 + lid;
+    let row = row0 + lid;
+    if (index < CONV_DIM) {
+      var conv_out = value * weight_at(W_CONV + row * CONV_KERNEL + CONV_STATE);
+      for (var tap = 0u; tap < CONV_STATE; tap = tap + 1u) {
+        let state_index = row * CONV_STATE + tap;
+        let prev = conv_state[state_index];
+        conv_out = fma(prev, weight_at(W_CONV + row * CONV_KERNEL + tap), conv_out);
+        if (tap + 1u < CONV_STATE) {
+          conv_state[state_index] = conv_state[state_index + 1u];
+        }
+      }
+      conv_state[row * CONV_STATE + CONV_STATE - 1u] = value;
+      let y = silu(conv_out);
+      if (row < KEY_DIM) { q[row] = y; }
+      else if (row < 2u * KEY_DIM) { k[row - KEY_DIM] = y; }
+      else { v[row - 2u * KEY_DIM] = y; }
+    } else if (index < CONV_DIM + VALUE_DIM) {
+      z[row] = value;
+    } else if (index < CONV_DIM + VALUE_DIM + NUM_HEADS) {
+      gate[row] = 1.0 / (1.0 + exp(-value));
+    } else {
+      gate[NUM_HEADS + row] = weight_at(W_A_NEG_EXP + row) * softplus(value + weight_at(W_DT_BIAS + row));
+    }
+  }
+}
+`]]}],["com.xenova.Qwen35DecodeOProj",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodeOProj",sinceVersion:1,inputs:[{role:"Hidden",dtype:"float32",rank:1},{role:"Weights",dtype:"W",rank:1,optional:true},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true},{role:"Attn",dtype:"float32",rank:1},{role:"Q4Bits",dtype:"uint32",rank:1,optional:true},{role:"Q4Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Hidden",dtype:"float32",rank:1,shape:["args.hiddenSize"]}],tunables:{DENSE_WORKGROUP_SIZE:64,Q1_WORKGROUP_SIZE:64},typeConstraints:{W:["float32","float16"],S:["float32","float16"]},args:{hiddenT:{kind:"tensor",semantic:"Hidden",role:"inout"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights",required:false},attnT:{kind:"tensor",semantic:"Attn",role:"input"},hiddenSize:{kind:"u32",semantic:"hidden_size"},inFeatures:{kind:"u32",semantic:"in_features"},oOffset:{kind:"u32",semantic:"o_proj_offset"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false},q4BitsT:{kind:"tensor",semantic:"Q4Bits",role:"weights",required:false},q4ScalesT:{kind:"tensor",semantic:"Q4Scales",role:"weights",required:false},format:{kind:"string",semantic:"packed_quant_format",required:false,oneOf:["q4_0","q4_k","q8_0","iq","lut32","lut16"]},lut:{kind:"u32",semantic:"dequant_lut_id",required:false}},bindingSets:{q4io:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q4_bits",arg:"q4BitsT",semantic:"Q4Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q4_scales",arg:"q4ScalesT",semantic:"Q4Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"attn_out",arg:"attnT",semantic:"Attn",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"}]},derive:{fmt:'args.format if args.format else "q4_0"',quantBits:'8 if (fmt == "q8_0" or fmt == "iq") else 4',hasMin:'1 if fmt == "q4_k" else 0',iq:'1 if fmt == "iq" else 0',lut16:'1 if fmt == "lut16" else 0',deviceWorkgroupCap:"min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",denseWorkgroupLimit:"min(tunables.DENSE_WORKGROUP_SIZE, deviceWorkgroupCap)",denseWorkgroupSize:"max(1, pow2ceil(denseWorkgroupLimit + 1) / 2)",q1WorkgroupLimit:"min(tunables.Q1_WORKGROUP_SIZE, deviceWorkgroupCap)",q1WorkgroupSize:"max(1, pow2ceil(q1WorkgroupLimit + 1) / 2)",q4WorkgroupSize:"32",q4MultiRowWorkgroupSize:"q4WorkgroupSize * 4",denseResourcesFit:"denseWorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize and args.hiddenSize <= device.limits.maxComputeWorkgroupsPerDimension",q1ResourcesFit:"q1WorkgroupSize * 16 <= device.limits.maxComputeWorkgroupStorageSize and ceilDiv(args.hiddenSize, 4) <= device.limits.maxComputeWorkgroupsPerDimension",q4WorkgroupFits:"q4WorkgroupSize == 32 and q4WorkgroupSize <= deviceWorkgroupCap and q4WorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize",q4ResourcesFit:"q4WorkgroupFits and args.hiddenSize <= device.limits.maxComputeWorkgroupsPerDimension",q4MultiRowResourcesFit:"q4WorkgroupFits and q4MultiRowWorkgroupSize <= deviceWorkgroupCap and q4MultiRowWorkgroupSize * 4 <= device.limits.maxComputeWorkgroupStorageSize"},variants:[{id:"q1",priority:0,when:["present.q1BitsT","ranks.hiddenT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.attnT == 1",'tensorDtypes.hiddenT == "float32"','tensorDtypes.attnT == "float32"','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")',"f16Ok(tensorDtypes.q1ScalesT)","q1ResourcesFit","args.hiddenSize > 0","args.inFeatures > 0","args.inFeatures % 128 == 0","dim(shapes.hiddenT, 0) == args.hiddenSize","dim(shapes.attnT, 0) == args.inFeatures","dim(shapes.q1BitsT, 0) >= (args.oOffset + args.hiddenSize * (args.inFeatures / 128)) * 4","dim(shapes.q1ScalesT, 0) >= args.oOffset + args.hiddenSize * (args.inFeatures / 128)","args.hiddenSize % 4 == 0"],constants:{usesF16:'tensorDtypes.q1ScalesT == "float16"',useSubgroups:'device.features.has("subgroups")',scaleScalar:"dtypes.S",workgroupSize:"q1WorkgroupSize",hiddenSize:"args.hiddenSize",blocksPerRow:"args.inFeatures / 128",inWords:"args.inFeatures / 32",oOffset:"args.oOffset"},passes:[{id:"main",name:"Qwen35DecodeOProjQ1",shader:"decode-o-proj-q1.wgsl.jinja",bindings:[{name:"hidden",arg:"hiddenT",semantic:"Hidden",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"attn_out",arg:"attnT",semantic:"Attn",role:"input",buffer:{type:"read-only-storage"},elementType:"vec4<f32>"}],dispatch:{x:"ceil(args.hiddenSize / 4)"},reads:["Hidden","Q1Bits","Q1Scales","Attn"],writes:["Hidden"]}]}]},assets:[["decode-o-proj-q1.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE: u32 = {{ hiddenSize }}u;
+const BPR: u32 = {{ blocksPerRow }}u;
+const IN_WORDS: u32 = {{ inWords }}u;
+const W_O_Q1: u32 = {{ oOffset }}u;
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+const TILE_ROWS: u32 = 4u;
+
+var<workgroup> partials0: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials1: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials2: array<f32, {{ workgroupSize }}>;
+var<workgroup> partials3: array<f32, {{ workgroupSize }}>;
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let index0 = wid.x * TILE_ROWS;
+  let lid = lid3.x;
+  if (index0 >= HIDDEN_SIZE) {
+    return;
+  }
+  let tile = index0 / 4u;
+
+  var acc0 = 0.0;
+  var acc1 = 0.0;
+  var acc2 = 0.0;
+  var acc3 = 0.0;
+  for (var word_index = lid; word_index < IN_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let base4 = word_index * 8u;
+    let n0 = attn_out[base4];
+    let n1 = attn_out[base4 + 1u];
+    let n2 = attn_out[base4 + 2u];
+    let n3 = attn_out[base4 + 3u];
+    let n4 = attn_out[base4 + 4u];
+    let n5 = attn_out[base4 + 5u];
+    let n6 = attn_out[base4 + 6u];
+    let n7 = attn_out[base4 + 7u];
+    let dot4 = q1_dot_word_t4(W_O_Q1, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    acc0 += dot4.x;
+    acc1 += dot4.y;
+    acc2 += dot4.z;
+    acc3 += dot4.w;
+  }
+{%- if useSubgroups %}
+
+  let ss = subgroupAdd(vec4<f32>(acc0, acc1, acc2, acc3));
+  if (WORKGROUP_SIZE == sg_size) {
+    if (lid == 0u) {
+      partials0[0] = ss.x;
+      partials1[0] = ss.y;
+      partials2[0] = ss.z;
+      partials3[0] = ss.w;
+    }
+    workgroupBarrier();
+  } else {
+    if (sg_lid == 0u) {
+      let subgroup_index = lid / sg_size;
+      partials0[subgroup_index] = ss.x;
+      partials1[subgroup_index] = ss.y;
+      partials2[subgroup_index] = ss.z;
+      partials3[subgroup_index] = ss.w;
+    }
+    workgroupBarrier();
+    if (lid == 0u) {
+      var total0 = 0.0;
+      var total1 = 0.0;
+      var total2 = 0.0;
+      var total3 = 0.0;
+      let subgroup_count = WORKGROUP_SIZE / sg_size;
+      for (var i = 0u; i < subgroup_count; i = i + 1u) {
+        total0 = total0 + partials0[i];
+        total1 = total1 + partials1[i];
+        total2 = total2 + partials2[i];
+        total3 = total3 + partials3[i];
+      }
+      partials0[0] = total0;
+      partials1[0] = total1;
+      partials2[0] = total2;
+      partials3[0] = total3;
+    }
+    workgroupBarrier();
+  }
+{%- else %}
+  partials0[lid] = acc0;
+  partials1[lid] = acc1;
+  partials2[lid] = acc2;
+  partials3[lid] = acc3;
+  workgroupBarrier();
+
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partials0[lid] = partials0[lid] + partials0[lid + stride];
+      partials1[lid] = partials1[lid] + partials1[lid + stride];
+      partials2[lid] = partials2[lid] + partials2[lid + stride];
+      partials3[lid] = partials3[lid] + partials3[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) {
+      break;
+    }
+    stride = stride / 2u;
+  }
+{%- endif %}
+
+  if (lid == 0u) {
+    hidden[index0 + 0u] = hidden[index0 + 0u] + partials0[0];
+    hidden[index0 + 1u] = hidden[index0 + 1u] + partials1[0];
+    hidden[index0 + 2u] = hidden[index0 + 2u] + partials2[0];
+    hidden[index0 + 3u] = hidden[index0 + 3u] + partials3[0];
+  }
+}
+`]]}],["com.xenova.Qwen35DecodePartialRopeCacheKv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35DecodePartialRopeCacheKv",sinceVersion:1,inputs:[{role:"Q",dtype:"float32",rank:1},{role:"K",dtype:"float32",rank:1},{role:"V",dtype:"float32",rank:1},{role:"CacheKeys",dtype:"C",rank:1},{role:"CacheValues",dtype:"C",rank:1},{role:"RopeCos",dtype:"float32",rank:1},{role:"RopeSin",dtype:"float32",rank:1}],outputs:[{role:"Q",dtype:"float32",rank:1,shape:["args.numHeads * args.headDim"]},{role:"CacheKeys",dtype:"C",rank:1,shape:["args.cacheLen * args.numKvHeads * args.headDim"]},{role:"CacheValues",dtype:"C",rank:1,shape:["args.cacheLen * args.numKvHeads * args.headDim"]}],typeConstraints:{C:["float32","float16"]},args:{qT:{kind:"tensor",semantic:"Q",role:"inout"},kT:{kind:"tensor",semantic:"K",role:"input"},vT:{kind:"tensor",semantic:"V",role:"input"},cacheKeysT:{kind:"tensor",semantic:"CacheKeys",role:"inout"},cacheValuesT:{kind:"tensor",semantic:"CacheValues",role:"inout"},cosT:{kind:"tensor",semantic:"RopeCos",role:"input"},sinT:{kind:"tensor",semantic:"RopeSin",role:"input"},layer:{kind:"u32",semantic:"layer"},cacheLen:{kind:"u32",semantic:"cache_len"},pastLen:{kind:"u32",semantic:"past_len",required:false},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},rotaryDim:{kind:"u32",semantic:"rotary_dim"}},variants:[{id:"scalar",priority:0,when:["ranks.qT == 1","ranks.kT == 1","ranks.vT == 1","(ranks.cacheKeysT == 1 or ranks.cacheKeysT == 5)","(ranks.cacheValuesT == 1 or ranks.cacheValuesT == 5)","(ranks.cosT == 1 or ranks.cosT == 2)","(ranks.sinT == 1 or ranks.sinT == 2)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"',"tensorDtypes.cacheKeysT == tensorDtypes.cacheValuesT",'(tensorDtypes.cacheKeysT == "float32" or tensorDtypes.cacheKeysT == "float16")',"f16Ok(tensorDtypes.cacheKeysT)","args.cacheLen > 0","args.numHeads > 0","args.numKvHeads > 0","args.headDim > 0","args.headDim % 2 == 0","args.rotaryDim % 2 == 0","args.rotaryDim <= args.headDim","dim(shapes.qT, 0) == args.numHeads * args.headDim","dim(shapes.kT, 0) == args.numKvHeads * args.headDim","dim(shapes.vT, 0) == args.numKvHeads * args.headDim","numel(shapes.cacheKeysT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim","numel(shapes.cacheValuesT) >= (args.layer + 1) * args.cacheLen * args.numKvHeads * args.headDim","((args.pastLen if args.pastLen else 0) >= args.cacheLen or numel(shapes.cosT) >= ((args.pastLen if args.pastLen else 0) + 1) * (args.rotaryDim / 2))","((args.pastLen if args.pastLen else 0) >= args.cacheLen or numel(shapes.sinT) >= ((args.pastLen if args.pastLen else 0) + 1) * (args.rotaryDim / 2))"],constants:{usesF16:'tensorDtypes.cacheKeysT == "float16"',cacheScalar:"dtypes.C",layer:"args.layer",cacheLen:"args.cacheLen",numKvHeads:"args.numKvHeads",headDim:"args.headDim",rotaryDim:"args.rotaryDim",rotaryHalf:"args.rotaryDim / 2",qSize:"args.numHeads * args.headDim",kvSize:"args.numKvHeads * args.headDim"},passes:[{id:"main",name:"Qwen35DecodePartialRopeCacheKv",shader:"decode-partial-rope-cache-kv.wgsl.jinja",bindings:[{name:"q",arg:"qT",semantic:"Q",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"cache_keys",arg:"cacheKeysT",semantic:"CacheKeys",role:"inout",buffer:{type:"storage"},elementType:"$cacheScalar"},{name:"cache_values",arg:"cacheValuesT",semantic:"CacheValues",role:"inout",buffer:{type:"storage"},elementType:"$cacheScalar"},{name:"rope_cos",arg:"cosT",semantic:"RopeCos",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"rope_sin",arg:"sinT",semantic:"RopeSin",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"params",arg:"paramsT",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"}]}}],dispatch:{x:"ceilDiv(max(args.numHeads * args.headDim, args.numKvHeads * args.headDim), 64)"},reads:["Q","K","V","CacheKeys","CacheValues","RopeCos","RopeSin"],writes:["Q","CacheKeys","CacheValues"]}]}]},assets:[["decode-partial-rope-cache-kv.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const LAYER: u32 = {{ layer }}u;
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const ROTARY_DIM: u32 = {{ rotaryDim }}u;
+const ROTARY_HALF: u32 = {{ rotaryHalf }}u;
+const Q_SIZE: u32 = {{ qSize }}u;
+const KV_SIZE: u32 = {{ kvSize }}u;
+
+fn rope_index(pos: u32, d: u32) -> u32 {
+  return pos * ROTARY_HALF + d;
+}
+
+{% include "ops/_shared/kv-cache-index.wgsl.jinja" %}
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let index = gid.x;
+  if (params.past_len >= CACHE_LEN) { return; }
+  let pos = params.past_len;
+  if (index < Q_SIZE) {
+    let head = index / HEAD_DIM;
+    let d = index % HEAD_DIM;
+    if (d < ROTARY_HALF) {
+      let base = head * HEAD_DIM;
+      let c = rope_cos[rope_index(pos, d)];
+      let s = rope_sin[rope_index(pos, d)];
+      let first = q[base + d];
+      let second = q[base + d + ROTARY_HALF];
+      q[base + d] = first * c - second * s;
+      q[base + d + ROTARY_HALF] = second * c + first * s;
+    }
+  }
+  if (index < KV_SIZE) {
+    let head = index / HEAD_DIM;
+    let d = index % HEAD_DIM;
+    if (d < ROTARY_HALF) {
+      let base = head * HEAD_DIM;
+      let c = rope_cos[rope_index(pos, d)];
+      let s = rope_sin[rope_index(pos, d)];
+      let first = k[base + d];
+      let second = k[base + d + ROTARY_HALF];
+      cache_keys[cache_index(pos, head, d)] = {{ cacheScalar }}(first * c - second * s);
+      cache_keys[cache_index(pos, head, d + ROTARY_HALF)] = {{ cacheScalar }}(second * c + first * s);
+    } else if (d >= ROTARY_DIM) {
+      cache_keys[cache_index(pos, head, d)] = {{ cacheScalar }}(k[index]);
+    }
+    cache_values[cache_index(pos, head, d)] = {{ cacheScalar }}(v[index]);
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillAssembleQkv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillAssembleQkv",sinceVersion:1,inputs:[{role:"Qg",dtype:"A",rank:2},{role:"K",dtype:"A",rank:2},{role:"V",dtype:"A",rank:2}],outputs:[{role:"Qkv",dtype:"A",rank:2,shape:["args.seqLen","args.qSize + 2 * args.kvSize"]},{role:"Gate",dtype:"A",rank:2,shape:["args.seqLen","args.qSize"]}],typeConstraints:{A:["float32","float16"]},args:{qgT:{kind:"tensor",semantic:"Qg",role:"input"},kT:{kind:"tensor",semantic:"K",role:"input"},vT:{kind:"tensor",semantic:"V",role:"input"},qkvT:{kind:"tensor",semantic:"Qkv",role:"output"},gateT:{kind:"tensor",semantic:"Gate",role:"output"},qSize:{kind:"u32",semantic:"attention_size"},kvSize:{kind:"u32",semantic:"kv_size"},headDim:{kind:"u32",semantic:"head_dim"},seqLen:{kind:"u32",semantic:"seq_len"}},variants:[{id:"scalar",priority:0,when:["ranks.qgT == 2","ranks.kT == 2","ranks.vT == 2","ranks.qkvT == 2","ranks.gateT == 2",'(tensorDtypes.qgT == "float32" or tensorDtypes.qgT == "float16")',"tensorDtypes.kT == tensorDtypes.qgT","tensorDtypes.vT == tensorDtypes.qgT","tensorDtypes.qkvT == tensorDtypes.qgT","tensorDtypes.gateT == tensorDtypes.qgT","f16Ok(tensorDtypes.qgT)","args.qSize > 0","args.kvSize > 0","args.headDim > 0","args.seqLen > 0","args.qSize % args.headDim == 0","numel(shapes.qgT) >= args.seqLen * 2 * args.qSize","numel(shapes.kT) >= args.seqLen * args.kvSize","numel(shapes.vT) >= args.seqLen * args.kvSize","numel(shapes.qkvT) >= args.seqLen * (args.qSize + 2 * args.kvSize)","numel(shapes.gateT) >= args.seqLen * args.qSize"],constants:{usesF16:'tensorDtypes.qgT == "float16"',activationScalar:"dtypes.A",qSize:"args.qSize",kvSize:"args.kvSize",headDim:"args.headDim",qkvStride:"args.qSize + 2 * args.kvSize",seqLen:"args.seqLen"},passes:[{id:"main",name:"Qwen35PrefillAssembleQkv",shader:"prefill-assemble-qkv.wgsl.jinja",bindings:[{name:"qg",arg:"qgT",semantic:"Qg",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"k_in",arg:"kT",semantic:"K",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"v_in",arg:"vT",semantic:"V",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"qkv",arg:"qkvT",semantic:"Qkv",role:"output",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"gate",arg:"gateT",semantic:"Gate",role:"output",buffer:{type:"storage"},elementType:"$activationScalar"}],dispatch:{threads:"args.seqLen * (args.qSize + 2 * args.kvSize)",workgroupSize:"64"},reads:["Qg","K","V"],writes:["Qkv","Gate"]}]}]},assets:[["prefill-assemble-qkv.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const Q_SIZE: u32 = {{ qSize }}u;
+const KV_SIZE: u32 = {{ kvSize }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const QKV_STRIDE: u32 = {{ qkvStride }}u;
+const SEQ_LEN: u32 = {{ seqLen }}u;
+
+fn store_act(value: f32) -> {{ activationScalar }} { return {{ activationScalar }}(value); }
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let g = gid.x + gid.y * nwg.x * 64u;
+  if (g >= SEQ_LEN * QKV_STRIDE) { return; }
+  let t = g / QKV_STRIDE;
+  let c = g % QKV_STRIDE;
+  if (c < Q_SIZE) {
+    let head = c / HEAD_DIM;
+    let d = c % HEAD_DIM;
+    let qg_base = t * 2u * Q_SIZE + head * 2u * HEAD_DIM + d;
+    qkv[g] = store_act(f32(qg[qg_base]));
+    gate[t * Q_SIZE + c] = store_act(f32(qg[qg_base + HEAD_DIM]));
+  } else if (c < Q_SIZE + KV_SIZE) {
+    qkv[g] = store_act(f32(k_in[t * KV_SIZE + (c - Q_SIZE)]));
+  } else {
+    qkv[g] = store_act(f32(v_in[t * KV_SIZE + (c - Q_SIZE - KV_SIZE)]));
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillAttentionGate",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillAttentionGate",sinceVersion:1,inputs:[{role:"Attn",dtype:"A",rank:2},{role:"Gate",dtype:"A",rank:2}],outputs:[{role:"Attn",dtype:"A",rank:2,shape:["args.seqLen","args.qSize"]}],typeConstraints:{A:["float32","float16"]},args:{attnT:{kind:"tensor",semantic:"Attn",role:"inout"},gateT:{kind:"tensor",semantic:"Gate",role:"input"},seqLen:{kind:"u32",semantic:"seq_len"},qSize:{kind:"u32",semantic:"attention_size"}},variants:[{id:"scalar",priority:0,when:["ranks.attnT == 2","ranks.gateT == 2",'(tensorDtypes.attnT == "float32" or tensorDtypes.attnT == "float16")',"tensorDtypes.gateT == tensorDtypes.attnT","f16Ok(tensorDtypes.attnT)","args.seqLen > 0","args.qSize > 0","numel(shapes.attnT) >= args.seqLen * args.qSize","numel(shapes.gateT) >= args.seqLen * args.qSize"],constants:{usesF16:'tensorDtypes.attnT == "float16"',activationScalar:"dtypes.A",count:"args.seqLen * args.qSize"},passes:[{id:"main",name:"Qwen35PrefillAttentionGate",shader:"prefill-attention-gate.wgsl.jinja",bindings:[{name:"attn",arg:"attnT",semantic:"Attn",role:"inout",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"gate",arg:"gateT",semantic:"Gate",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"}],dispatch:{threads:"args.seqLen * args.qSize",workgroupSize:"64"},reads:["Attn","Gate"],writes:["Attn"]}]}]},assets:[["prefill-attention-gate.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const COUNT: u32 = {{ count }}u;
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let i = gid.x + gid.y * nwg.x * 64u;
+  if (i >= COUNT) { return; }
+  attn[i] = {{ activationScalar }}(f32(attn[i]) * (1.0 / (1.0 + exp(-f32(gate[i])))));
+}
+`]]}],["com.xenova.Qwen35PrefillConv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillConv",sinceVersion:1,inputs:[{role:"Bcx",dtype:"A",rank:2},{role:"Weights",dtype:"W",rank:1},{role:"ConvStates",dtype:"float32",rank:1}],outputs:[{role:"Q",dtype:"float32",rank:2,shape:["args.seqLen","args.keyDim"]},{role:"K",dtype:"float32",rank:2,shape:["args.seqLen","args.keyDim"]},{role:"V",dtype:"float32",rank:2,shape:["args.seqLen","args.valueDim"]},{role:"ConvStates",dtype:"float32",rank:1,shape:["args.convDim * (args.convKernel - 1)"]}],typeConstraints:{A:["float32","float16"],W:["float32","float16"]},args:{bcxT:{kind:"tensor",semantic:"Bcx",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},convStatesT:{kind:"tensor",semantic:"ConvStates",role:"inout"},qT:{kind:"tensor",semantic:"Q",role:"output"},kT:{kind:"tensor",semantic:"K",role:"output"},vT:{kind:"tensor",semantic:"V",role:"output"},keyDim:{kind:"u32",semantic:"linear_key_dim"},valueDim:{kind:"u32",semantic:"linear_value_dim"},convDim:{kind:"u32",semantic:"linear_conv_dim"},convKernel:{kind:"u32",semantic:"linear_conv_kernel_dim"},convWeightOffset:{kind:"u32",semantic:"linear_conv_weight_offset"},seqLen:{kind:"u32",semantic:"seq_len"},realLen:{kind:"u32",semantic:"real_len",required:false}},variants:[{id:"scalar",priority:0,when:["ranks.bcxT == 2","ranks.weightsT == 1","ranks.convStatesT == 1","ranks.qT == 2","ranks.kT == 2","ranks.vT == 2",'(tensorDtypes.bcxT == "float32" or tensorDtypes.bcxT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','tensorDtypes.convStatesT == "float32"','((tensorDtypes.bcxT != "float16" and tensorDtypes.weightsT != "float16") or device.features.has("shader-f16"))',"args.keyDim > 0","args.valueDim > 0","args.seqLen > 0","args.convKernel > 1","args.convKernel <= 64","args.convDim == 2 * args.keyDim + args.valueDim","dim(shapes.bcxT, 1) == args.convDim","numel(shapes.bcxT) >= args.seqLen * args.convDim","numel(shapes.qT) >= args.seqLen * args.keyDim","numel(shapes.kT) >= args.seqLen * args.keyDim","numel(shapes.vT) >= args.seqLen * args.valueDim","numel(shapes.convStatesT) >= args.convDim * (args.convKernel - 1)","numel(shapes.weightsT) >= args.convWeightOffset + args.convDim * args.convKernel"],constants:{usesF16:'tensorDtypes.bcxT == "float16" or tensorDtypes.weightsT == "float16"',activationScalar:"dtypes.A",weightScalar:"dtypes.W",keyDim:"args.keyDim",valueDim:"args.valueDim",convDim:"args.convDim",convKernel:"args.convKernel",convState:"args.convKernel - 1",seqLen:"args.seqLen",convWeightOffset:"args.convWeightOffset"},passes:[{id:"main",name:"Qwen35PrefillConv",shader:"prefill-conv.wgsl.jinja",bindings:[{name:"bcx",arg:"bcxT",semantic:"Bcx",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"},{name:"conv_states",arg:"convStatesT",semantic:"ConvStates",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"q",arg:"qT",semantic:"Q",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"0"},{name:"cache_len",type:"u32",value:"0"},{name:"seq_len",type:"u32",value:"args.seqLen"},{name:"real_len",type:"u32",value:"args.realLen if args.realLen else args.seqLen"}]}}],dispatch:{x:"args.convDim"},reads:["Bcx","Weights","ConvStates"],writes:["Q","K","V","ConvStates"]}]}]},assets:[["prefill-conv.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const KEY_DIM: u32 = {{ keyDim }}u;
+const VALUE_DIM: u32 = {{ valueDim }}u;
+const CONV_DIM: u32 = {{ convDim }}u;
+const CONV_KERNEL: u32 = {{ convKernel }}u;
+const CONV_STATE: u32 = {{ convState }}u;
+const SEQ_LEN: u32 = {{ seqLen }}u;
+const W_CONV: u32 = {{ convWeightOffset }}u;
+const WORKGROUP_SIZE: u32 = 64u;
+
+var<workgroup> in_buf: array<f32, {{ seqLen }}>;
+var<workgroup> old_state: array<f32, {{ convState }}>;
+
+fn conv_w(c: u32, tap: u32) -> f32 { return f32(weights[W_CONV + c * CONV_KERNEL + tap]); }
+
+fn silu(x: f32) -> f32 { return x / (1.0 + exp(-x)); }
+
+@compute @workgroup_size(64)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let c = wid.x;
+  if (c >= CONV_DIM) { return; }
+  let lid = lid3.x;
+
+  if (lid < CONV_STATE) {
+    old_state[lid] = conv_states[c * CONV_STATE + lid];
+  }
+  workgroupBarrier();
+
+  for (var t = lid; t < SEQ_LEN; t = t + WORKGROUP_SIZE) {
+    in_buf[t] = f32(bcx[t * CONV_DIM + c]);
+  }
+  workgroupBarrier();
+
+  for (var t = lid; t < SEQ_LEN; t = t + WORKGROUP_SIZE) {
+    var acc = 0.0;
+    for (var k = 0u; k < CONV_KERNEL; k = k + 1u) {
+      let j = i32(t) - i32(CONV_KERNEL - 1u) + i32(k);
+      var inp = 0.0;
+      if (j >= 0) {
+        inp = in_buf[u32(j)];
+      } else {
+        inp = old_state[u32(i32(CONV_STATE) + j)];
+      }
+      acc = fma(conv_w(c, k), inp, acc);
+    }
+    let y = silu(acc);
+    if (c < KEY_DIM) {
+      q[t * KEY_DIM + c] = y;
+    } else if (c < 2u * KEY_DIM) {
+      k[t * KEY_DIM + (c - KEY_DIM)] = y;
+    } else {
+      v[t * VALUE_DIM + (c - 2u * KEY_DIM)] = y;
+    }
+  }
+
+  if (lid < CONV_STATE) {
+    let j = i32(params.real_len) - i32(CONV_STATE) + i32(lid);
+    var inp = 0.0;
+    if (j >= 0) {
+      inp = in_buf[u32(j)];
+    } else {
+      inp = old_state[u32(i32(CONV_STATE) + j)];
+    }
+    conv_states[c * CONV_STATE + lid] = inp;
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillGatedRmsNorm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillGatedRmsNorm",sinceVersion:1,inputs:[{role:"X",dtype:"float32",rank:2},{role:"Z",dtype:"A",rank:2},{role:"Weights",dtype:"W",rank:1}],outputs:[{role:"Y",dtype:"A",rank:2,shape:["args.seqLen","args.numValueHeads * args.valueHeadDim"]}],typeConstraints:{A:["float32","float16"],W:["float32","float16"]},args:{xT:{kind:"tensor",semantic:"X",role:"input"},zT:{kind:"tensor",semantic:"Z",role:"input"},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},yT:{kind:"tensor",semantic:"Y",role:"output"},numValueHeads:{kind:"u32",semantic:"linear_num_value_heads"},valueHeadDim:{kind:"u32",semantic:"linear_value_head_dim"},normOffset:{kind:"u32",semantic:"linear_norm_offset"},rmsEps:{kind:"f32",semantic:"rms_eps"},seqLen:{kind:"u32",semantic:"seq_len"},workgroupSize:{kind:"u32",semantic:"workgroup_size"}},variants:[{id:"scalar",priority:0,when:["ranks.xT == 2","ranks.zT == 2","ranks.weightsT == 1","ranks.yT == 2",'tensorDtypes.xT == "float32"','(tensorDtypes.zT == "float32" or tensorDtypes.zT == "float16")',"tensorDtypes.yT == tensorDtypes.zT",'(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','((tensorDtypes.zT != "float16" and tensorDtypes.weightsT != "float16") or device.features.has("shader-f16"))',"args.numValueHeads > 0","args.valueHeadDim > 0","args.seqLen > 0","args.workgroupSize >= args.valueHeadDim","numel(shapes.xT) >= args.seqLen * args.numValueHeads * args.valueHeadDim","numel(shapes.zT) >= args.seqLen * args.numValueHeads * args.valueHeadDim","numel(shapes.yT) >= args.seqLen * args.numValueHeads * args.valueHeadDim","numel(shapes.weightsT) >= args.normOffset + args.valueHeadDim"],constants:{usesF16:'tensorDtypes.zT == "float16" or tensorDtypes.weightsT == "float16"',activationScalar:"dtypes.A",weightScalar:"dtypes.W",numHeads:"args.numValueHeads",headDim:"args.valueHeadDim",valueDim:"args.numValueHeads * args.valueHeadDim",normOffset:"args.normOffset",rmsEps:"args.rmsEps",workgroupSize:"args.workgroupSize"},passes:[{id:"main",name:"Qwen35PrefillGatedRmsNorm",shader:"prefill-gated-rmsnorm.wgsl.jinja",bindings:[{name:"x",arg:"xT",semantic:"X",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"z",arg:"zT",semantic:"Z",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"},{name:"y",arg:"yT",semantic:"Y",role:"output",buffer:{type:"storage"},elementType:"$activationScalar"}],dispatch:{x:"args.numValueHeads",y:"args.seqLen"},reads:["X","Z","Weights"],writes:["Y"]}]}]},assets:[["prefill-gated-rmsnorm.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HEAD_DIM: u32 = {{ headDim }}u;
+const VALUE_DIM: u32 = {{ valueDim }}u;
+const W_NORM: u32 = {{ normOffset }}u;
+const EPS: f32 = {{ rmsEps }};
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+var<workgroup> partials: array<f32, {{ workgroupSize }}>;
+{% include "ops/_shared/reduce-tree.wgsl.jinja" %}
+{{ wgsl_tree_reduce_f32("reduce_sum", "sum", "partials", "WORKGROUP_SIZE") }}
+
+fn silu(x: f32) -> f32 { return x / (1.0 + exp(-x)); }
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let head = wid.x;
+  let token = wid.y;
+  let lid = lid3.x;
+  let base = token * VALUE_DIM + head * HEAD_DIM;
+  var acc = 0.0;
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let value = x[base + d];
+    acc = fma(value, value, acc);
+  }
+  let sum_sq = reduce_sum(acc, lid);
+  let scale = inverseSqrt(sum_sq / f32(HEAD_DIM) + EPS);
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let idx = base + d;
+    let weight = f32(weights[W_NORM + d]);
+    y[idx] = {{ activationScalar }}(x[idx] * scale * weight * silu(f32(z[idx])));
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillGates",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillGates",sinceVersion:1,inputs:[{role:"Braw",dtype:"A",rank:2,optional:true},{role:"Araw",dtype:"A",rank:2,optional:true},{role:"Normed",dtype:"A",rank:2,optional:true},{role:"Weights",dtype:"W",rank:1},{role:"Q1Bits",dtype:"uint32",rank:1,optional:true},{role:"Q1Scales",dtype:"S",rank:1,optional:true}],outputs:[{role:"Gate",dtype:"float32",rank:2,shape:["args.seqLen","2 * args.numHeads"]}],tunables:{SCALAR_WORKGROUP_SIZE:64,Q1_WORKGROUP_SIZE:64},typeConstraints:{A:["float32","float16"],W:["float32","float16"],S:["float32","float16"]},args:{brawT:{kind:"tensor",semantic:"Braw",role:"input",required:false},arawT:{kind:"tensor",semantic:"Araw",role:"input",required:false},normedT:{kind:"tensor",semantic:"Normed",role:"input",required:false},weightsT:{kind:"tensor",semantic:"Weights",role:"weights"},q1BitsT:{kind:"tensor",semantic:"Q1Bits",role:"weights",required:false},q1ScalesT:{kind:"tensor",semantic:"Q1Scales",role:"weights",required:false},gateT:{kind:"tensor",semantic:"Gate",role:"output"},hiddenSize:{kind:"u32",semantic:"hidden_size",required:false},bOffset:{kind:"u32",semantic:"linear_in_proj_b_q1_block_offset",required:false},aOffset:{kind:"u32",semantic:"linear_in_proj_a_q1_block_offset",required:false},numHeads:{kind:"u32",semantic:"linear_num_value_heads"},aNegExpOffset:{kind:"u32",semantic:"linear_a_neg_exp_offset"},dtBiasOffset:{kind:"u32",semantic:"linear_dt_bias_offset"},seqLen:{kind:"u32",semantic:"seq_len"}},derive:{deviceWorkgroupCap:"min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)",scalarWorkgroupLimit:"min(tunables.SCALAR_WORKGROUP_SIZE, deviceWorkgroupCap)",scalarWorkgroupSize:"max(1, pow2ceil(scalarWorkgroupLimit + 1) / 2)",q1WorkgroupLimit:"min(tunables.Q1_WORKGROUP_SIZE, deviceWorkgroupCap)",q1WorkgroupSize:"max(1, pow2ceil(q1WorkgroupLimit + 1) / 2)",foldedDispatchCapacity:"device.limits.maxComputeWorkgroupsPerDimension * device.limits.maxComputeWorkgroupsPerDimension",scalarDispatchFits:"ceilDiv(args.seqLen * args.numHeads, scalarWorkgroupSize) <= foldedDispatchCapacity",q1ResourcesFit:"q1WorkgroupSize * 32 <= device.limits.maxComputeWorkgroupStorageSize and args.seqLen * ceilDiv(args.numHeads, 4) <= device.limits.maxComputeWorkgroupsPerDimension"},variants:[{id:"scalar",priority:0,when:["present.brawT","present.arawT","not present.q1BitsT","ranks.brawT == 2","ranks.arawT == 2","ranks.weightsT == 1","ranks.gateT == 2",'(tensorDtypes.brawT == "float32" or tensorDtypes.brawT == "float16")','(tensorDtypes.arawT == "float32" or tensorDtypes.arawT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','((tensorDtypes.brawT != "float16" and tensorDtypes.weightsT != "float16") or device.features.has("shader-f16"))',"scalarDispatchFits","args.numHeads > 0","args.seqLen > 0","numel(shapes.brawT) >= args.seqLen * args.numHeads","numel(shapes.arawT) >= args.seqLen * args.numHeads","numel(shapes.gateT) >= args.seqLen * 2 * args.numHeads","numel(shapes.weightsT) >= args.aNegExpOffset + args.numHeads","numel(shapes.weightsT) >= args.dtBiasOffset + args.numHeads"],constants:{usesF16:'tensorDtypes.brawT == "float16" or tensorDtypes.weightsT == "float16"',activationScalar:"dtypes.A",weightScalar:"dtypes.W",workgroupSize:"scalarWorkgroupSize",numHeads:"args.numHeads",seqLen:"args.seqLen",aNegExpOffset:"args.aNegExpOffset",dtBiasOffset:"args.dtBiasOffset"},passes:[{id:"main",name:"Qwen35PrefillGates",shader:"prefill-gates.wgsl.jinja",bindings:[{name:"braw",arg:"brawT",semantic:"Braw",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"araw",arg:"arawT",semantic:"Araw",role:"input",buffer:{type:"read-only-storage"},elementType:"$activationScalar"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"},{name:"gate",arg:"gateT",semantic:"Gate",role:"output",buffer:{type:"storage"},elementType:"f32"}],dispatch:{threads:"args.seqLen * args.numHeads",workgroupSize:"scalarWorkgroupSize"},reads:["Braw","Araw","Weights"],writes:["Gate"]}]},{id:"q1_fused_short",priority:10,when:["not present.brawT","not present.arawT","present.normedT","present.q1BitsT","present.q1ScalesT","ranks.normedT == 2","ranks.weightsT == 1","ranks.q1BitsT == 1","ranks.q1ScalesT == 1","ranks.gateT == 2",'(tensorDtypes.normedT == "float32" or tensorDtypes.normedT == "float16")','(tensorDtypes.weightsT == "float32" or tensorDtypes.weightsT == "float16")','tensorDtypes.q1BitsT == "uint32"','(tensorDtypes.q1ScalesT == "float32" or tensorDtypes.q1ScalesT == "float16")',"f16Ok(dtypes.A) and f16Ok(dtypes.W) and f16Ok(dtypes.S)","q1ResourcesFit","args.hiddenSize > 0","args.hiddenSize % 128 == 0","args.numHeads > 0","args.numHeads % 4 == 0","args.seqLen > 0","args.seqLen <= 16","numel(shapes.normedT) >= args.seqLen * args.hiddenSize","numel(shapes.q1BitsT) >= (args.bOffset + args.numHeads * (args.hiddenSize / 128)) * 4","numel(shapes.q1BitsT) >= (args.aOffset + args.numHeads * (args.hiddenSize / 128)) * 4","numel(shapes.q1ScalesT) >= args.bOffset + args.numHeads * (args.hiddenSize / 128)","numel(shapes.q1ScalesT) >= args.aOffset + args.numHeads * (args.hiddenSize / 128)","numel(shapes.gateT) >= args.seqLen * 2 * args.numHeads","numel(shapes.weightsT) >= args.aNegExpOffset + args.numHeads","numel(shapes.weightsT) >= args.dtBiasOffset + args.numHeads"],constants:{usesF16:'tensorDtypes.normedT == "float16" or tensorDtypes.weightsT == "float16" or tensorDtypes.q1ScalesT == "float16"',roundProjectionF16:'tensorDtypes.normedT == "float16"',normedVec4:'"vec4<f16>" if tensorDtypes.normedT == "float16" else "vec4<f32>"',weightScalar:"dtypes.W",scaleScalar:"dtypes.S",workgroupSize:"q1WorkgroupSize",hiddenSize:"args.hiddenSize",inWords:"args.hiddenSize / 32",blocksPerRow:"args.hiddenSize / 128",numHeads:"args.numHeads",seqLen:"args.seqLen",bOffset:"args.bOffset",aOffset:"args.aOffset",aNegExpOffset:"args.aNegExpOffset",dtBiasOffset:"args.dtBiasOffset"},passes:[{id:"main",name:"Qwen35PrefillGatesQ1FusedShort",shader:"prefill-gates-q1-fused-short.wgsl.jinja",bindings:[{name:"normed",arg:"normedT",semantic:"Normed",role:"input",buffer:{type:"read-only-storage"},elementType:"$normedVec4"},{name:"weights",arg:"weightsT",semantic:"Weights",role:"weights",buffer:{type:"read-only-storage"},elementType:"$weightScalar"},{name:"q1_bits",arg:"q1BitsT",semantic:"Q1Bits",role:"weights",buffer:{type:"read-only-storage"},elementType:"vec4<u32>"},{name:"q1_scales",arg:"q1ScalesT",semantic:"Q1Scales",role:"weights",buffer:{type:"read-only-storage"},elementType:"$scaleScalar"},{name:"gate",arg:"gateT",semantic:"Gate",role:"output",buffer:{type:"storage"},elementType:"f32"}],dispatch:{x:"args.seqLen * ceilDiv(args.numHeads, 4)"},reads:["Normed","Weights","Q1Bits","Q1Scales"],writes:["Gate"]}]}]},assets:[["prefill-gates-q1-fused-short.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const HIDDEN_SIZE: u32 = {{ hiddenSize }}u;
+const IN_WORDS: u32 = {{ inWords }}u;
+const BPR: u32 = {{ blocksPerRow }}u;
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const HEAD_TILES: u32 = NUM_HEADS / 4u;
+const SEQ_LEN: u32 = {{ seqLen }}u;
+const B_OFFSET: u32 = {{ bOffset }}u;
+const A_OFFSET: u32 = {{ aOffset }}u;
+const W_A_NEG_EXP: u32 = {{ aNegExpOffset }}u;
+const W_DT_BIAS: u32 = {{ dtBiasOffset }}u;
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+
+var<workgroup> partial_b: array<vec4<f32>, {{ workgroupSize }}>;
+var<workgroup> partial_a: array<vec4<f32>, {{ workgroupSize }}>;
+
+fn softplus(x: f32) -> f32 { return log(1.0 + exp(-abs(x))) + max(x, 0.0); }
+
+{% include "ops/_shared/q1-dot-helpers.wgsl.jinja" %}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+) {
+  let t = wid.x / HEAD_TILES;
+  let tile = wid.x % HEAD_TILES;
+  let lid = lid3.x;
+  if (t >= SEQ_LEN) { return; }
+  let input_base = t * (HIDDEN_SIZE / 4u);
+
+  var b_acc = vec4<f32>(0.0);
+  var a_acc = vec4<f32>(0.0);
+  for (var word_index = lid; word_index < IN_WORDS; word_index = word_index + WORKGROUP_SIZE) {
+    let col_block = word_index / 4u;
+    let word_id = word_index % 4u;
+    let base4 = input_base + word_index * 8u;
+    let n0 = vec4<f32>(normed[base4 + 0u]);
+    let n1 = vec4<f32>(normed[base4 + 1u]);
+    let n2 = vec4<f32>(normed[base4 + 2u]);
+    let n3 = vec4<f32>(normed[base4 + 3u]);
+    let n4 = vec4<f32>(normed[base4 + 4u]);
+    let n5 = vec4<f32>(normed[base4 + 5u]);
+    let n6 = vec4<f32>(normed[base4 + 6u]);
+    let n7 = vec4<f32>(normed[base4 + 7u]);
+    b_acc += q1_dot_word_t4(B_OFFSET, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+    a_acc += q1_dot_word_t4(A_OFFSET, tile, BPR, col_block, word_id, n0, n1, n2, n3, n4, n5, n6, n7);
+  }
+
+  partial_b[lid] = b_acc;
+  partial_a[lid] = a_acc;
+  workgroupBarrier();
+  var stride = WORKGROUP_SIZE / 2u;
+  loop {
+    if (lid < stride) {
+      partial_b[lid] += partial_b[lid + stride];
+      partial_a[lid] += partial_a[lid + stride];
+    }
+    workgroupBarrier();
+    if (stride == 1u) { break; }
+    stride = stride / 2u;
+  }
+
+  if (lid < 4u) {
+    let h = tile * 4u + lid;
+    let b_raw = {% if roundProjectionF16 %}f32(f16(partial_b[0][lid])){% else %}partial_b[0][lid]{% endif %};
+    let a_raw = {% if roundProjectionF16 %}f32(f16(partial_a[0][lid])){% else %}partial_a[0][lid]{% endif %};
+    let row = t * 2u * NUM_HEADS;
+    gate[row + h] = 1.0 / (1.0 + exp(-b_raw));
+    gate[row + NUM_HEADS + h] = f32(weights[W_A_NEG_EXP + h]) *
+      softplus(a_raw + f32(weights[W_DT_BIAS + h]));
+  }
+}
+`],["prefill-gates.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const SEQ_LEN: u32 = {{ seqLen }}u;
+const W_A_NEG_EXP: u32 = {{ aNegExpOffset }}u;
+const W_DT_BIAS: u32 = {{ dtBiasOffset }}u;
+
+fn softplus(x: f32) -> f32 { return log(1.0 + exp(-abs(x))) + max(x, 0.0); }
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let g = gid.x + gid.y * nwg.x * {{ workgroupSize }}u;
+  if (g >= SEQ_LEN * NUM_HEADS) { return; }
+  let t = g / NUM_HEADS;
+  let h = g % NUM_HEADS;
+  let row = t * 2u * NUM_HEADS;
+  gate[row + h] = 1.0 / (1.0 + exp(-f32(braw[t * NUM_HEADS + h])));
+  gate[row + NUM_HEADS + h] = f32(weights[W_A_NEG_EXP + h]) * softplus(f32(araw[t * NUM_HEADS + h]) + f32(weights[W_DT_BIAS + h]));
+}
+`]]}],["com.xenova.Qwen35PrefillL2Norm",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillL2Norm",sinceVersion:1,inputs:[{role:"Q",dtype:"float32",rank:2},{role:"K",dtype:"float32",rank:2}],outputs:[{role:"Q",dtype:"float32",rank:2,shape:["args.seqLen","args.numKeyHeads * args.keyHeadDim"]},{role:"K",dtype:"float32",rank:2,shape:["args.seqLen","args.numKeyHeads * args.keyHeadDim"]}],typeConstraints:{},args:{qT:{kind:"tensor",semantic:"Q",role:"inout"},kT:{kind:"tensor",semantic:"K",role:"inout"},numKeyHeads:{kind:"u32",semantic:"linear_num_key_heads"},keyHeadDim:{kind:"u32",semantic:"linear_key_head_dim"},seqLen:{kind:"u32",semantic:"seq_len"},workgroupSize:{kind:"u32",semantic:"workgroup_size"}},variants:[{id:"scalar",priority:0,when:["ranks.qT == 2","ranks.kT == 2",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"',"args.numKeyHeads > 0","args.keyHeadDim > 0","args.seqLen > 0","args.workgroupSize >= args.keyHeadDim","numel(shapes.qT) >= args.seqLen * args.numKeyHeads * args.keyHeadDim","numel(shapes.kT) >= args.seqLen * args.numKeyHeads * args.keyHeadDim"],constants:{numHeads:"args.numKeyHeads",headDim:"args.keyHeadDim",workgroupSize:"args.workgroupSize"},passes:[{id:"main",name:"Qwen35PrefillL2Norm",shader:"prefill-l2norm.wgsl.jinja",bindings:[{name:"q",arg:"qT",semantic:"Q",role:"inout",buffer:{type:"storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"inout",buffer:{type:"storage"},elementType:"f32"}],dispatch:{x:"2 * args.numKeyHeads",y:"args.seqLen"},reads:["Q","K"],writes:["Q","K"]}]}]},assets:[["prefill-l2norm.wgsl.jinja",`{{ env.wgsl.resourceDeclarations }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const KEY_DIM: u32 = {{ numHeads }}u * {{ headDim }}u;
+const EPS: f32 = 1e-6;
+const WORKGROUP_SIZE: u32 = {{ workgroupSize }}u;
+var<workgroup> partials: array<f32, {{ workgroupSize }}>;
+{% include "ops/_shared/reduce-tree.wgsl.jinja" %}
+{{ wgsl_tree_reduce_f32("reduce_sum", "sum", "partials", "WORKGROUP_SIZE") }}
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>) {
+  let group = wid.x;
+  let token = wid.y;
+  let lid = lid3.x;
+  let is_k = group >= NUM_HEADS;
+  let head = select(group, group - NUM_HEADS, is_k);
+  let base = token * KEY_DIM + head * HEAD_DIM;
+  var acc = 0.0;
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    let value = select(q[base + d], k[base + d], is_k);
+    acc = fma(value, value, acc);
+  }
+  let sum_sq = reduce_sum(acc, lid);
+  let scale = inverseSqrt(sum_sq + EPS);
+  for (var d = lid; d < HEAD_DIM; d = d + WORKGROUP_SIZE) {
+    if (is_k) { k[base + d] = k[base + d] * scale; }
+    else { q[base + d] = q[base + d] * scale; }
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillLinearAttention",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillLinearAttention",sinceVersion:1,inputs:[{role:"State",dtype:"float32",rank:1},{role:"Q",dtype:"float32",rank:2},{role:"K",dtype:"float32",rank:2},{role:"V",dtype:"float32",rank:2},{role:"Gate",dtype:"float32",rank:2}],outputs:[{role:"State",dtype:"float32",rank:1,shape:["args.numHeads * args.headDimK * args.headDimV"]},{role:"Out",dtype:"float32",rank:2,shape:["args.seqLen","args.numHeads * args.headDimV"]}],typeConstraints:{},args:{stateT:{kind:"tensor",semantic:"State",role:"inout"},qT:{kind:"tensor",semantic:"Q",role:"input"},kT:{kind:"tensor",semantic:"K",role:"input"},vT:{kind:"tensor",semantic:"V",role:"input"},gateT:{kind:"tensor",semantic:"Gate",role:"input"},outT:{kind:"tensor",semantic:"Out",role:"output"},numHeads:{kind:"u32",semantic:"linear_num_value_heads"},numKeyHeads:{kind:"u32",semantic:"linear_num_key_heads"},headDimK:{kind:"u32",semantic:"linear_key_head_dim"},headDimV:{kind:"u32",semantic:"linear_value_head_dim"},scale:{kind:"f32",semantic:"scale"},seqLen:{kind:"u32",semantic:"seq_len"},workgroupSize:{kind:"u32",semantic:"workgroup_size"},realLen:{kind:"u32",semantic:"real_len",required:false}},bindingSets:{laState:[{name:"state_buf",arg:"stateT",semantic:"State",role:"inout",buffer:{type:"storage"},elementType:"$stateElem"},{name:"q",arg:"qT",semantic:"Q",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"k",arg:"kT",semantic:"K",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"v",arg:"vT",semantic:"V",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"gate",arg:"gateT",semantic:"Gate",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"out",arg:"outT",semantic:"Out",role:"output",buffer:{type:"storage"},elementType:"f32"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"0"},{name:"cache_len",type:"u32",value:"0"},{name:"seq_len",type:"u32",value:"args.seqLen"},{name:"real_len",type:"u32",value:"args.realLen if args.realLen else args.seqLen"}]}}]},variants:[{id:"vec4_subgroup",priority:2,when:["ranks.stateT == 1","ranks.qT == 2","ranks.kT == 2","ranks.vT == 2","ranks.gateT == 2","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"','device.features.has("subgroups")','has(device.adapterInfo, "subgroupMinSize")',"args.seqLen >= 16","args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK == 128","args.headDimV > 0","args.seqLen > 0","args.realLen <= args.seqLen if args.realLen else true","ceilDiv(args.headDimK, 4) <= device.adapterInfo.subgroupMinSize","ceilDiv(args.headDimK, 4) <= device.limits.maxComputeInvocationsPerWorkgroup","ceilDiv(args.headDimK, 4) <= device.limits.maxComputeWorkgroupSizeX","numel(shapes.qT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.kT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.vT) >= args.seqLen * args.numHeads * args.headDimV","numel(shapes.gateT) >= args.seqLen * 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","numel(shapes.outT) >= args.seqLen * args.numHeads * args.headDimV"],requiredFeatures:["subgroups"],requiredSubgroupMinSize:32,constants:{numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",seqLen:"args.seqLen",tileV:4,workgroupSize:"ceilDiv(args.headDimK, 4)",scale:"args.scale",stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"',useSubgroups:true},passes:[{id:"main",name:"Qwen35PrefillLinearAttention.Vec4Subgroup",shader:"prefill-linear-attention-vec4-gather.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 4)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]},{id:"vec4_gather",priority:1,when:["ranks.stateT == 1","ranks.qT == 2","ranks.kT == 2","ranks.vT == 2","ranks.gateT == 2","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"','not device.features.has("subgroups") or (tensorDtypes.stateT == "float16" and args.seqLen == 16 and (not args.realLen or args.realLen == 16) and args.numHeads == 48 and args.numKeyHeads == 16 and args.headDimK == 128 and args.headDimV == 128)',"args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK > 0","args.headDimK % 4 == 0","args.headDimV > 0","args.seqLen > 0","args.realLen <= args.seqLen if args.realLen else true","ceilDiv(args.headDimK, 4) <= device.limits.maxComputeInvocationsPerWorkgroup","ceilDiv(args.headDimK, 4) <= device.limits.maxComputeWorkgroupSizeX","ceilDiv(args.headDimK, 4) * 68 + 32 <= device.limits.maxComputeWorkgroupStorageSize","numel(shapes.qT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.kT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.vT) >= args.seqLen * args.numHeads * args.headDimV","numel(shapes.gateT) >= args.seqLen * 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","numel(shapes.outT) >= args.seqLen * args.numHeads * args.headDimV"],constants:{numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",seqLen:"args.seqLen",tileV:8,workgroupSize:"ceilDiv(args.headDimK, 4)",scale:"args.scale",stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"',useSubgroups:false},passes:[{id:"main",name:"Qwen35PrefillLinearAttention.Vec4Gather",shader:"prefill-linear-attention-vec4-gather.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 8)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]},{id:"scalar",priority:0,when:["ranks.stateT == 1","ranks.qT == 2","ranks.kT == 2","ranks.vT == 2","ranks.gateT == 2","f16Allowed(tensorDtypes.stateT)",'tensorDtypes.qT == "float32"','tensorDtypes.kT == "float32"','tensorDtypes.vT == "float32"','tensorDtypes.gateT == "float32"',"args.numHeads > 0","args.numKeyHeads > 0","args.numHeads % args.numKeyHeads == 0","args.headDimK > 0","args.headDimV > 0","args.seqLen > 0","args.realLen <= args.seqLen if args.realLen else true","args.workgroupSize >= args.headDimK","numel(shapes.qT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.kT) >= args.seqLen * args.numKeyHeads * args.headDimK","numel(shapes.vT) >= args.seqLen * args.numHeads * args.headDimV","numel(shapes.gateT) >= args.seqLen * 2 * args.numHeads","dim(shapes.stateT, 0) == args.numHeads * args.headDimK * args.headDimV","numel(shapes.outT) >= args.seqLen * args.numHeads * args.headDimV"],constants:{numHeads:"args.numHeads",numKeyHeads:"args.numKeyHeads",headDimK:"args.headDimK",headDimV:"args.headDimV",seqLen:"args.seqLen",workgroupSize:"args.workgroupSize",scale:"args.scale",stateF16:'tensorDtypes.stateT == "float16"',stateElem:'"f16" if tensorDtypes.stateT == "float16" else "f32"',useSubgroups:'device.features.has("subgroups")'},passes:[{id:"main",name:"Qwen35PrefillLinearAttention",shader:"prefill-linear-attention.wgsl.jinja",bindings:"laState",dispatch:{x:"args.numHeads * ceilDiv(args.headDimV, 8)"},reads:["State","Q","K","V","Gate"],writes:["State","Out"]}]}]},assets:[["prefill-linear-attention-vec4-gather.wgsl.jinja",`{% include "ops/_shared/linear-attention-common.wgsl.jinja" %}{% if useSubgroups %}
+enable subgroups;
+{% endif %}{% if stateF16 %}
+enable f16;
+{% endif -%}
+{{ env.wgsl.resourceDeclarations }}
+{{ emit_qwen35_stable_decay() }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+const NUM_KEY_HEADS: u32 = {{ numKeyHeads }}u;
+const HEAD_DIM_K: u32 = {{ headDimK }}u;
+const HEAD_DIM_V: u32 = {{ headDimV }}u;
+const KEY_DIM: u32 = {{ numKeyHeads }}u * {{ headDimK }}u;
+const VALUE_DIM: u32 = {{ numHeads }}u * {{ headDimV }}u;
+const TILE_V: u32 = {{ tileV }}u;
+const WG: u32 = {{ workgroupSize }}u;
+const SCALE: f32 = {{ scale }};
+
+{% if not useSubgroups %}
+var<workgroup> red_rp: array<vec2<f32>, WG * TILE_V>;
+var<workgroup> red_kq: array<f32, WG>;
+{% endif %}
+
+@compute @workgroup_size(WG)
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>{% if useSubgroups %},
+  @builtin(subgroup_size) sg_size: u32{% endif %}
+) {
+  let tid = lid3.x;
+  let dk_base = tid * 4u;
+  let tiles_per_head = (HEAD_DIM_V + TILE_V - 1u) / TILE_V;
+  let head = wid.x / tiles_per_head;
+  let tile = wid.x % tiles_per_head;
+  let dv_start = tile * TILE_V;
+  if (head >= NUM_HEADS) { return; }
+{% if useSubgroups %}
+  if (sg_size < WG) { return; }
+{% endif %}
+
+  var state: array<vec4<f32>, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let dv = dv_start + j;
+    if (dv < HEAD_DIM_V) {
+      let base = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + dk_base;
+      state[j] = vec4<f32>(
+        f32(state_buf[base + 0u]), f32(state_buf[base + 1u]),
+        f32(state_buf[base + 2u]), f32(state_buf[base + 3u]),
+      );
+    } else {
+      state[j] = vec4<f32>(0.0);
+    }
+  }
+
+  let real_len = params.real_len;
+  for (var t = 0u; t < real_len; t = t + 1u) {
+    let exp_g = {{ qwen35_decay("gate[t * 2u * NUM_HEADS + NUM_HEADS + head]") }};
+    let beta_val = gate[t * 2u * NUM_HEADS + head];
+    let kq_base = t * KEY_DIM + (head % NUM_KEY_HEADS) * HEAD_DIM_K + dk_base;
+    let k_vec = vec4<f32>(k[kq_base + 0u], k[kq_base + 1u], k[kq_base + 2u], k[kq_base + 3u]);
+    let q_vec = vec4<f32>(q[kq_base + 0u], q[kq_base + 1u], q[kq_base + 2u], q[kq_base + 3u]);
+
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      state[j] = state[j] * exp_g;
+    }
+    var deltas: array<f32, TILE_V>;
+{% if useSubgroups %}
+    let kq_dot = subgroupAdd(dot(k_vec, q_vec));
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      let rp = subgroupAdd(vec2<f32>(dot(state[j], k_vec), dot(state[j], q_vec)));
+      if (dv < HEAD_DIM_V) {
+        let v_val = v[t * VALUE_DIM + head * HEAD_DIM_V + dv];
+        let delta = beta_val * (v_val - rp.x);
+        deltas[j] = delta;
+        if (tid == 0u) {
+          out[t * VALUE_DIM + head * HEAD_DIM_V + dv] = (rp.y + delta * kq_dot) * SCALE;
+        }
+      } else {
+        deltas[j] = 0.0;
+      }
+    }
+{% else %}
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      red_rp[j * WG + tid] = vec2<f32>(dot(state[j], k_vec), dot(state[j], q_vec));
+    }
+    red_kq[tid] = dot(k_vec, q_vec);
+    workgroupBarrier();
+
+    if (WG == 32u && TILE_V == 8u) {
+      let gather_j = tid / 4u;
+      let gather_lane = tid % 4u;
+      var rp0 = vec2<f32>(0.0);
+      var rp1 = vec2<f32>(0.0);
+      var i = gather_lane;
+      for (; i + 4u < WG; i = i + 8u) {
+        rp0 += red_rp[gather_j * WG + i];
+        rp1 += red_rp[gather_j * WG + i + 4u];
+      }
+      for (; i < WG; i = i + 4u) {
+        rp0 += red_rp[gather_j * WG + i];
+      }
+      red_rp[gather_j * WG + gather_lane] = rp0 + rp1;
+      if (tid == 0u) {
+{{ emit_kq_gather() }}
+      }
+      workgroupBarrier();
+      if (tid < TILE_V) {
+        let base = tid * WG;
+        red_rp[base] = (red_rp[base] + red_rp[base + 1u]) +
+          (red_rp[base + 2u] + red_rp[base + 3u]);
+      }
+    } else {
+
+{{ emit_pair_gather(false) }}
+
+      let kq_gather_lane = min(TILE_V, WG - 1u);
+      if (tid == kq_gather_lane) {
+{{ emit_kq_gather() }}
+      }
+    }
+    workgroupBarrier();
+
+    let kq_dot = red_kq[0];
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+        let rp = red_rp[j * WG];
+        let v_val = v[t * VALUE_DIM + head * HEAD_DIM_V + dv];
+        let delta = beta_val * (v_val - rp.x);
+        deltas[j] = delta;
+        if (tid == 0u) {
+          out[t * VALUE_DIM + head * HEAD_DIM_V + dv] = (rp.y + delta * kq_dot) * SCALE;
+        }
+      } else {
+        deltas[j] = 0.0;
+      }
+    }
+{% endif %}
+
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      let dv = dv_start + j;
+      if (dv < HEAD_DIM_V) {
+        let updated = state[j] + k_vec * deltas[j];
+{%- if stateF16 %}
+        state[j] = updated;
+        if (t + 1u < real_len) {
+          state[j] = vec4<f32>(
+            f32(f16(updated.x)), f32(f16(updated.y)),
+            f32(f16(updated.z)), f32(f16(updated.w)),
+          );
+        }
+{% else %}
+        state[j] = updated;
+{% endif %}
+        if (t + 1u == real_len) {
+          let base = (head * HEAD_DIM_V + dv) * HEAD_DIM_K + dk_base;
+          state_buf[base + 0u] = {% if stateF16 %}f16(state[j].x){% else %}state[j].x{% endif %};
+          state_buf[base + 1u] = {% if stateF16 %}f16(state[j].y){% else %}state[j].y{% endif %};
+          state_buf[base + 2u] = {% if stateF16 %}f16(state[j].z){% else %}state[j].z{% endif %};
+          state_buf[base + 3u] = {% if stateF16 %}f16(state[j].w){% else %}state[j].w{% endif %};
+        }
+      }
+    }
+
+  }
+}
+`],["prefill-linear-attention.wgsl.jinja",`{% include "ops/_shared/linear-attention-common.wgsl.jinja" %}{%- if useSubgroups %}
+enable subgroups;
+{% endif -%}
+{% if stateF16 %}
+enable f16;
+{% endif -%}
+{{ env.wgsl.resourceDeclarations }}
+{{ emit_qwen35_stable_decay() }}
+
+const NUM_HEADS: u32 = {{ numHeads }}u;
+
+const NUM_KEY_HEADS: u32 = {{ numKeyHeads }}u;
+const HEAD_DIM_K: u32 = {{ headDimK }}u;
+const HEAD_DIM_V: u32 = {{ headDimV }}u;
+const KEY_DIM: u32 = {{ numKeyHeads }}u * {{ headDimK }}u;
+const VALUE_DIM: u32 = {{ numHeads }}u * {{ headDimV }}u;
+const TILE_V: u32 = 8u;
+const WG: u32 = {{ workgroupSize }}u;
+const SCALE: f32 = {{ scale }};
+var<workgroup> red_rp: array<vec2<f32>, WG * TILE_V>;
+var<workgroup> red_kq: array<f32, WG>;
+var<workgroup> delta_buf: array<f32, TILE_V>;
+
+@compute @workgroup_size({{ workgroupSize }})
+fn main(
+  @builtin(workgroup_id) wid: vec3<u32>,
+  @builtin(local_invocation_id) lid3: vec3<u32>
+{%- if useSubgroups %},
+  @builtin(subgroup_invocation_id) sg_lid: u32,
+  @builtin(subgroup_size) sg_size: u32
+{%- endif %}
+) {
+  let tid = lid3.x;
+  let tiles_per_head = (HEAD_DIM_V + TILE_V - 1u) / TILE_V;
+  let head = wid.x / tiles_per_head;
+  let tile = wid.x % tiles_per_head;
+  let dv_start = tile * TILE_V;
+  if (head >= NUM_HEADS) { return; }
+
+  var state: array<f32, TILE_V>;
+  for (var j = 0u; j < TILE_V; j = j + 1u) {
+    let dv = dv_start + j;
+    if (tid < HEAD_DIM_K && dv < HEAD_DIM_V) {
+
+      state[j] = f32(state_buf[(head * HEAD_DIM_V + dv) * HEAD_DIM_K + tid]);
+    } else {
+      state[j] = 0.0;
+    }
+  }
+
+  let real_len = params.real_len;
+
+  for (var t = 0u; t < real_len; t = t + 1u) {
+    let exp_g = {{ qwen35_decay("gate[t * 2u * NUM_HEADS + NUM_HEADS + head]") }};
+    let beta_val = gate[t * 2u * NUM_HEADS + head];
+    var k_val = 0.0;
+    var q_val = 0.0;
+    if (tid < HEAD_DIM_K) {
+      let kq_base = t * KEY_DIM + (head % NUM_KEY_HEADS) * HEAD_DIM_K + tid;
+      k_val = k[kq_base];
+      q_val = q[kq_base];
+    }
+
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      state[j] = state[j] * exp_g;
+    }
+{%- if useSubgroups %}
+
+    let subgroup_index = tid / sg_size;
+    let subgroup_count = (WG + sg_size - 1u) / sg_size;
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+
+      let sg_rp = subgroupAdd(vec2<f32>(state[j] * k_val, state[j] * q_val));
+      if (sg_lid == 0u) {
+        red_rp[j * WG + subgroup_index] = sg_rp;
+      }
+    }
+    let sg_kq = subgroupAdd(k_val * q_val);
+    if (sg_lid == 0u) {
+      red_kq[subgroup_index] = sg_kq;
+    }
+    workgroupBarrier();
+    if (WG != sg_size) {
+      for (var j = tid; j < TILE_V; j = j + WG) {
+        var rp_total = vec2<f32>(0.0);
+        for (var i = 1u; i < subgroup_count; i = i + 1u) {
+          rp_total = rp_total + red_rp[j * WG + i];
+        }
+        red_rp[j * WG] = red_rp[j * WG] + rp_total;
+      }
+      if (tid == 0u) {
+        var kq_total = 0.0;
+        for (var i = 1u; i < subgroup_count; i = i + 1u) {
+          kq_total = kq_total + red_kq[i];
+        }
+        red_kq[0] = red_kq[0] + kq_total;
+      }
+      workgroupBarrier();
+    }
+{%- else %}
+
+    for (var j = 0u; j < TILE_V; j = j + 1u) {
+      red_rp[j * WG + tid] = vec2<f32>(state[j] * k_val, state[j] * q_val);
+    }
+    red_kq[tid] = k_val * q_val;
+    workgroupBarrier();
+
+{{ emit_pair_gather(true) }}
+    if (tid == 0u) {
+{{ emit_kq_gather() }}
+    }
+    workgroupBarrier();
+{%- endif %}
+    if (tid == 0u) {
+      let kq = red_kq[0];
+      for (var j = 0u; j < TILE_V; j = j + 1u) {
+        let dv = dv_start + j;
+        if (dv < HEAD_DIM_V) {
+          let v_val = v[t * VALUE_DIM + head * HEAD_DIM_V + dv];
+          let rp = red_rp[j * WG];
+          let delta = beta_val * (v_val - rp.x);
+          delta_buf[j] = delta;
+          out[t * VALUE_DIM + head * HEAD_DIM_V + dv] = (rp.y + delta * kq) * SCALE;
+        } else {
+          delta_buf[j] = 0.0;
+        }
+      }
+    }
+    workgroupBarrier();
+
+    if (tid < HEAD_DIM_K) {
+      for (var j = 0u; j < TILE_V; j = j + 1u) {
+        let dv = dv_start + j;
+        if (dv < HEAD_DIM_V) {
+          let updated = state[j] + k_val * delta_buf[j];
+{%- if stateF16 %}
+
+          state[j] = updated;
+          if (t + 1u < real_len) {
+            state[j] = f32(f16(updated));
+          }
+{% else %}
+          state[j] = updated;
+{% endif %}
+        }
+      }
+
+      if (t + 1u == real_len) {
+        for (var j = 0u; j < TILE_V; j = j + 1u) {
+          let dv = dv_start + j;
+          if (dv < HEAD_DIM_V) {
+            state_buf[(head * HEAD_DIM_V + dv) * HEAD_DIM_K + tid] = {% if stateF16 %}f16(state[j]){% else %}state[j]{% endif %};
+          }
+        }
+      }
+    }
+
+  }
+}
+`]]}],["com.xenova.Qwen35PrefillPartialRopeCacheKv",{manifest:{schemaVersion:1,domain:"com.xenova",name:"Qwen35PrefillPartialRopeCacheKv",sinceVersion:1,inputs:[{role:"Qkv",dtype:"A",rank:2},{role:"CacheKeys",dtype:"C",rank:1},{role:"CacheValues",dtype:"C",rank:1},{role:"RopeCos",dtype:"float32"},{role:"RopeSin",dtype:"float32"}],outputs:[{role:"Qkv",dtype:"A",rank:2,shape:"shapes.qkvT"},{role:"CacheKeys",dtype:"C",rank:1,shape:["args.cacheLen * args.numKvHeads * args.headDim"]},{role:"CacheValues",dtype:"C",rank:1,shape:["args.cacheLen * args.numKvHeads * args.headDim"]}],typeConstraints:{A:["float32","float16"],C:["float32","float16"]},args:{qkvT:{kind:"tensor",semantic:"Qkv",role:"inout"},cacheKeysT:{kind:"tensor",semantic:"CacheKeys",role:"inout"},cacheValuesT:{kind:"tensor",semantic:"CacheValues",role:"inout"},cosT:{kind:"tensor",semantic:"RopeCos",role:"input"},sinT:{kind:"tensor",semantic:"RopeSin",role:"input"},layer:{kind:"u32",semantic:"layer"},cacheLen:{kind:"u32",semantic:"cache_len"},pastLen:{kind:"u32",semantic:"past_len",required:false},seqLen:{kind:"u32",semantic:"seq_len"},numHeads:{kind:"u32",semantic:"num_heads"},numKvHeads:{kind:"u32",semantic:"num_kv_heads"},headDim:{kind:"u32",semantic:"head_dim"},rotaryDim:{kind:"u32",semantic:"rotary_dim"}},variants:[{id:"scalar",priority:0,when:["ranks.qkvT == 2","ranks.cacheKeysT == 1","ranks.cacheValuesT == 1",'(tensorDtypes.qkvT == "float32" or tensorDtypes.qkvT == "float16")','(tensorDtypes.cacheKeysT == "float32" or tensorDtypes.cacheKeysT == "float16")','((tensorDtypes.qkvT != "float16" and tensorDtypes.cacheKeysT != "float16") or device.features.has("shader-f16"))',"args.numHeads > 0","args.numKvHeads > 0","args.headDim > 0","args.seqLen > 0","args.rotaryDim > 0","args.rotaryDim % 2 == 0","args.rotaryDim <= args.headDim","dim(shapes.qkvT, 1) == (args.numHeads + 2 * args.numKvHeads) * args.headDim","numel(shapes.cosT) >= (args.pastLen + args.seqLen) * (args.rotaryDim / 2)","numel(shapes.sinT) >= (args.pastLen + args.seqLen) * (args.rotaryDim / 2)","numel(shapes.cacheKeysT) >= args.cacheLen * args.numKvHeads * args.headDim","numel(shapes.cacheValuesT) >= args.cacheLen * args.numKvHeads * args.headDim"],constants:{usesF16:'tensorDtypes.qkvT == "float16" or tensorDtypes.cacheKeysT == "float16"',activationScalar:"dtypes.A",cacheScalar:"dtypes.C",layer:"args.layer",cacheLen:"args.cacheLen",numKvHeads:"args.numKvHeads",headDim:"args.headDim",rotaryDim:"args.rotaryDim",rotaryHalf:"args.rotaryDim / 2",qHidden:"args.numHeads * args.headDim",kvHidden:"args.numKvHeads * args.headDim",qkvStride:"(args.numHeads + 2 * args.numKvHeads) * args.headDim",qPairs:"args.numHeads * (args.rotaryDim / 2)",kvPairs:"args.numKvHeads * (args.rotaryDim / 2)",kvPass:"args.numKvHeads * (args.headDim - args.rotaryDim)",workPerToken:"args.numHeads * (args.rotaryDim / 2) + args.numKvHeads * (args.rotaryDim / 2) + args.numKvHeads * (args.headDim - args.rotaryDim) + args.numKvHeads * args.headDim"},passes:[{id:"main",name:"Qwen35PrefillPartialRopeCacheKv",shader:"prefill-partial-rope-cache-kv.wgsl.jinja",bindings:[{name:"qkv",arg:"qkvT",semantic:"Qkv",role:"inout",buffer:{type:"storage"},elementType:"$activationScalar"},{name:"cache_keys",arg:"cacheKeysT",semantic:"CacheKeys",role:"inout",buffer:{type:"storage"},elementType:"$cacheScalar"},{name:"cache_values",arg:"cacheValuesT",semantic:"CacheValues",role:"inout",buffer:{type:"storage"},elementType:"$cacheScalar"},{name:"rope_cos",arg:"cosT",semantic:"RopeCos",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"rope_sin",arg:"sinT",semantic:"RopeSin",role:"input",buffer:{type:"read-only-storage"},elementType:"f32"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"past_len",type:"u32",value:"args.pastLen if args.pastLen else 0"},{name:"cache_len",type:"u32",value:"args.cacheLen"},{name:"seq_len",type:"u32",value:"args.seqLen"}]}}],dispatch:{threads:"args.seqLen * constants.workPerToken",workgroupSize:"64"},reads:["Qkv","CacheKeys","CacheValues","RopeCos","RopeSin"],writes:["Qkv","CacheKeys","CacheValues"]}]}]},assets:[["prefill-partial-rope-cache-kv.wgsl.jinja",`{%- if usesF16 %}
+enable f16;
+{% endif -%}
+
+{{ env.wgsl.resourceDeclarations }}
+
+const LAYER: u32 = {{ layer }}u;
+const CACHE_LEN: u32 = {{ cacheLen }}u;
+const NUM_KV_HEADS: u32 = {{ numKvHeads }}u;
+const HEAD_DIM: u32 = {{ headDim }}u;
+const ROTARY_DIM: u32 = {{ rotaryDim }}u;
+const ROTARY_HALF: u32 = {{ rotaryHalf }}u;
+const Q_HIDDEN: u32 = {{ qHidden }}u;
+const KV_HIDDEN: u32 = {{ kvHidden }}u;
+const QKV_STRIDE: u32 = {{ qkvStride }}u;
+const Q_PAIRS: u32 = {{ qPairs }}u;
+const KV_PAIRS: u32 = {{ kvPairs }}u;
+const KV_PASS: u32 = {{ kvPass }}u;
+const PASS_PER_HEAD: u32 = {{ headDim }}u - {{ rotaryDim }}u;
+const WORK_PER_TOKEN: u32 = {{ workPerToken }}u;
+
+fn rope_index(pos: u32, d: u32) -> u32 { return pos * ROTARY_HALF + d; }
+
+{% include "ops/_shared/kv-cache-index.wgsl.jinja" %}
+
+fn store_cache(value: f32) -> {{ cacheScalar }} { return {{ cacheScalar }}(value); }
+fn store_act(value: f32) -> {{ activationScalar }} { return {{ activationScalar }}(value); }
+
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let g = gid.x + gid.y * nwg.x * 64u;
+  let token = g / WORK_PER_TOKEN;
+  if (token >= params.seq_len) { return; }
+  let pos = params.past_len + token;
+  if (pos >= CACHE_LEN) { return; }
+  let local = g % WORK_PER_TOKEN;
+  let row = token * QKV_STRIDE;
+
+  if (local < Q_PAIRS) {
+    let head = local / ROTARY_HALF;
+    let d = local % ROTARY_HALF;
+    let base = row + head * HEAD_DIM;
+    let rope = rope_index(pos, d);
+    let c = rope_cos[rope];
+    let s = rope_sin[rope];
+    let first = f32(qkv[base + d]);
+    let second = f32(qkv[base + d + ROTARY_HALF]);
+    qkv[base + d] = store_act(first * c - second * s);
+    qkv[base + d + ROTARY_HALF] = store_act(second * c + first * s);
+    return;
+  }
+
+  var after = local - Q_PAIRS;
+  if (after < KV_PAIRS) {
+    let head = after / ROTARY_HALF;
+    let d = after % ROTARY_HALF;
+    let base = row + Q_HIDDEN + head * HEAD_DIM;
+    let rope = rope_index(pos, d);
+    let c = rope_cos[rope];
+    let s = rope_sin[rope];
+    let first = f32(qkv[base + d]);
+    let second = f32(qkv[base + d + ROTARY_HALF]);
+    cache_keys[cache_index(pos, head, d)] = store_cache(first * c - second * s);
+    cache_keys[cache_index(pos, head, d + ROTARY_HALF)] = store_cache(second * c + first * s);
+    return;
+  }
+
+  after = after - KV_PAIRS;
+  if (after < KV_PASS) {
+    let head = after / PASS_PER_HEAD;
+    let d = ROTARY_DIM + (after % PASS_PER_HEAD);
+    let base = row + Q_HIDDEN + head * HEAD_DIM;
+    cache_keys[cache_index(pos, head, d)] = store_cache(f32(qkv[base + d]));
+    return;
+  }
+
+  let v_local = after - KV_PASS;
+  let head = v_local / HEAD_DIM;
+  let dim = v_local % HEAD_DIM;
+  let vbase = row + Q_HIDDEN + KV_HIDDEN + head * HEAD_DIM;
+  cache_values[cache_index(pos, head, dim)] = store_cache(f32(qkv[vbase + dim]));
+}
+`]]}],["com.xenova.StridedCopy",{manifest:{schemaVersion:1,domain:"com.xenova",name:"StridedCopy",sinceVersion:1,inputs:[{role:"Src",dtype:"S"}],outputs:[{role:"Dst",dtype:"D",shape:"shapes.dstT"}],typeConstraints:{S:["float32","float16","uint32","int32"],D:["float32","float16","uint32","int32"]},args:{srcT:{kind:"tensor",semantic:"Src",role:"input"},dstT:{kind:"tensor",semantic:"Dst",role:"output"},rows:{kind:"u32",semantic:"rows"},srcStride:{kind:"u32",semantic:"srcStride"},srcStart:{kind:"u32",semantic:"srcStart",required:false},dstStride:{kind:"u32",semantic:"dstStride"},dstStart:{kind:"u32",semantic:"dstStart",required:false},copyCols:{kind:"u32",semantic:"copyCols"}},variants:[{id:"scalar",priority:0,when:"f16Ok(dtypes.S) and f16Ok(dtypes.D)",constants:{vectorized:false,usesF16:'dtypes.S == "f16" or dtypes.D == "f16"',sourceElement:"dtypes.S",destElement:"dtypes.D",destScalar:"dtypes.D",colChunk:4096},passes:[{id:"main",name:"StridedCopy",shader:"strided-copy.wgsl.jinja",bindings:[{name:"s",arg:"srcT",semantic:"Src",role:"input",buffer:{type:"read-only-storage"},elementType:"$sourceElement"},{name:"d",arg:"dstT",semantic:"Dst",role:"output",buffer:{type:"storage"},elementType:"$destElement"},{name:"p",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"rows",type:"u32",value:"args.rows"},{name:"copyCols",type:"u32",value:"args.copyCols"},{name:"srcStride",type:"u32",value:"args.srcStride"},{name:"srcStart",type:"u32",value:"args.srcStart if args.srcStart else 0"},{name:"dstStride",type:"u32",value:"args.dstStride"},{name:"dstStart",type:"u32",value:"args.dstStart if args.dstStart else 0"}]}}],dispatch:{workgroups:"args.rows * ceil(args.copyCols / 4096)"},reads:["Src"],writes:["Dst"]}]}]},assets:[["strided-copy.wgsl.jinja",`{% if usesF16 %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+
+const COL_CHUNK: u32 = {{ colChunk }}u;
+@compute @workgroup_size(64, 1, 1)
+fn main(@builtin(workgroup_id) wg: vec3<u32>, @builtin(local_invocation_id) lid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let colTiles = max((p.copyCols + COL_CHUNK - 1u) / COL_CHUNK, 1u);
+  let flat = wg.x + wg.y * nwg.x;
+  if (flat >= p.rows * colTiles) {
+    return;
+  }
+  let r = flat / colTiles;
+  let col_base = (flat - r * colTiles) * COL_CHUNK;
+  let col_end = min(col_base + COL_CHUNK, p.copyCols);
+  var i: u32 = col_base + lid.x;
+  loop {
+    if (i >= col_end) {
+      break;
+    }
+{% if vectorized %}
+    d[r * p.dstStride + p.dstStart + i] = s[r * p.srcStride + p.srcStart + i];
+{% else %}
+    d[r * p.dstStride + p.dstStart + i] = {{ destScalar }}(f32(s[r * p.srcStride + p.srcStart + i]));
+{% endif %}
+    i = i + 64u;
+  }
+}
+`]]}],["com.xenova.SwiGlu",{manifest:{schemaVersion:1,domain:"com.xenova",name:"SwiGlu",sinceVersion:1,inputs:[{role:"X",dtype:"X",rank:2}],outputs:[{role:"Y",dtype:"Y",rank:2,shape:["args.rows","args.mlpInner"]}],typeConstraints:{X:["float32","float16"],Y:["float32","float16"]},args:{xT:{kind:"tensor",semantic:"X",role:"input"},yT:{kind:"tensor",semantic:"Y",role:"output"},rows:{kind:"u32",semantic:"rows"},mlpInner:{kind:"u32",semantic:"mlpInner"},activation:{kind:"string",semantic:"activation",required:false,oneOf:["silu","fast_silu"]}},bindingSets:{activation:[{name:"x",arg:"xT",semantic:"X",role:"input",buffer:{type:"read-only-storage"},elementType:"$xElement"},{name:"y",arg:"yT",semantic:"Y",role:"output",buffer:{type:"storage"},elementType:"$yElement"},{name:"params",semantic:"kernel.params",buffer:{type:"uniform"},struct:{name:"Params",fields:[{name:"rows",type:"u32",value:"args.rows"}]}}]},variants:[{id:"vec4_f16",priority:10,when:['dtypes.X == "f16"','dtypes.Y == "f16"','device.features.has("shader-f16")',"args.mlpInner % 4 == 0","ranks.xT == 2","ranks.yT == 2","args.rows == dim(shapes.xT, 0)","dim(shapes.xT, 1) == args.mlpInner * 2","dim(shapes.yT, 0) == args.rows","dim(shapes.yT, 1) == args.mlpInner"],constants:{vectorized:true,usesF16:true,fastSilu:'args.activation == "fast_silu"',xElement:'"vec4<f16>"',yElement:'"vec4<f16>"',yScalar:'"f16"',mlpInnerV4:"args.mlpInner / 4"},passes:[{id:"main",name:"SwiGlu",shader:"swiglu.wgsl.jinja",bindings:"activation",dispatch:{workgroups:"args.rows",y:"ceil((args.mlpInner / 4) / 64)"},reads:["X"],writes:["Y"]}]},{id:"vec4_f32",priority:10,when:['dtypes.X == "f32"','dtypes.Y == "f32"',"args.mlpInner % 4 == 0","ranks.xT == 2","ranks.yT == 2","args.rows == dim(shapes.xT, 0)","dim(shapes.xT, 1) == args.mlpInner * 2","dim(shapes.yT, 0) == args.rows","dim(shapes.yT, 1) == args.mlpInner"],constants:{vectorized:true,usesF16:false,fastSilu:'args.activation == "fast_silu"',xElement:'"vec4<f32>"',yElement:'"vec4<f32>"',yScalar:'"f32"',mlpInnerV4:"args.mlpInner / 4"},passes:[{id:"main",name:"SwiGlu",shader:"swiglu.wgsl.jinja",bindings:"activation",dispatch:{workgroups:"args.rows",y:"ceil((args.mlpInner / 4) / 64)"},reads:["X"],writes:["Y"]}]},{id:"scalar",priority:0,when:["ranks.xT == 2","ranks.yT == 2","args.rows == dim(shapes.xT, 0)","dim(shapes.xT, 1) == args.mlpInner * 2","dim(shapes.yT, 0) == args.rows","dim(shapes.yT, 1) == args.mlpInner","f16Ok(dtypes.X) and f16Ok(dtypes.Y)"],constants:{vectorized:false,usesF16:'dtypes.X == "f16" or dtypes.Y == "f16"',fastSilu:'args.activation == "fast_silu"',xElement:"dtypes.X",yElement:"dtypes.Y",yScalar:"dtypes.Y",mlpInner:"args.mlpInner"},passes:[{id:"main",name:"SwiGlu",shader:"swiglu.wgsl.jinja",bindings:"activation",dispatch:{workgroups:"args.rows",y:"ceil(args.mlpInner / 64)"},reads:["X"],writes:["Y"]}]}]},assets:[["swiglu.wgsl.jinja",`{% if usesF16 %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+{% if vectorized %}
+const MLP_V4: u32 = {{ mlpInnerV4 }}u;
+{% else %}
+const MLP: u32 = {{ mlpInner }}u;
+{% endif %}
+const WG: u32 = 64u;
+@compute @workgroup_size(WG, 1, 1)
+fn main(@builtin(workgroup_id) wg: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>, @builtin(local_invocation_id) lid: vec3<u32>) {
+
+  let r = wg.x + wg.z * nwg.x;
+  if (r >= params.rows) {
+    return;
+  }
+  let i0 = wg.y * WG + lid.x;
+{% if vectorized %}
+  if (i0 >= MLP_V4) {
+    return;
+  }
+  let row_base = r * 2u * MLP_V4;
+  let x1 = vec4<f32>(x[row_base + i0]);
+  let x2 = vec4<f32>(x[row_base + MLP_V4 + i0]);
+{% if fastSilu %}
+  let sigmoid = vec4<f32>(0.5) + vec4<f32>(0.5) * x1 / (vec4<f32>(1.0) + abs(x1));
+  y[r * MLP_V4 + i0] = {{ yElement }}((x1 * sigmoid) * x2);
+{% else %}
+  y[r * MLP_V4 + i0] = {{ yElement }}((x1 / (vec4<f32>(1.0) + exp(-x1))) * x2);
+{% endif %}
+{% else %}
+  if (i0 >= MLP) {
+    return;
+  }
+  let x1 = f32(x[r * 2u * MLP + i0]);
+  let x2 = f32(x[r * 2u * MLP + MLP + i0]);
+{% if fastSilu %}
+  let sigmoid = 0.5 + 0.5 * x1 / (1.0 + abs(x1));
+  y[r * MLP + i0] = {{ yScalar }}((x1 * sigmoid) * x2);
+{% else %}
+  y[r * MLP + i0] = {{ yScalar }}((x1 / (1.0 + exp(-x1))) * x2);
+{% endif %}
+{% endif %}
+}
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a=this.#t.dataStart+this.#t.info(t).offset;return this.#n(a+n,a+r,s.signal)}async#n(t,n,r){let s=await this.#e.readTensor(t,n);if(s)return this.onByteProgress?.(s.byteLength),s;let a=await this.#e.readRange(t,n,{signal:r,onByteProgress:this.onByteProgress??void 0});return await this.#e.writeTensor(t,n,a),a}async streamTensors(t,n,r={}){let s=t.map(a=>{let o=this.#t.info(a),i=this.#t.dataStart+o.offset;return{name:a,begin:i,end:i+o.byteLength}});await El(this.#e,s,async a=>{for(let o of a.tensors)await n(o.name,a.bytes.subarray(o.begin-a.begin,o.end-a.begin))},{concurrency:r.concurrency,chunkMaxBytes:r.chunkMaxBytes,chunkMaxGap:r.chunkMaxGap,signal:r.signal,onByteProgress:this.onByteProgress??void 0})}async close(){await this.#e.close()}}});function Be(e2,t,n="Config"){let r=e2[t];if(typeof r!="number"||!Number.isInteger(r)||r<=0)throw new Error(`${n}.${t} must be a positive integer`);return r}function jr(e2,t,n="Config"){for(let r of t)Be(e2,r,n)}var za=q(()=>{"use strict"});function Qh(e2){if(e2.rope_theta===void 0&&e2.rope_scaling==null)return;let t=e2.rope_scaling;return{rope_type:t?.rope_type??t?.type??"default",rope_theta:e2.rope_theta}}function Fn(e2){if(!e2||typeof e2!="object")throw new Error("Llama-like config must be an object");if(e2 instanceof wt)return e2;let t=e2;if(t.model_type==="llama")return new wt(t);if(t.model_type){if(typeof t.validate!="function")throw new Error("Llama-like configs with model_type must be constructed by their Config class");return e2}return new wt(t)}function dt(e2){return Array.isArray(e2.layer_types)&&e2.layer_types.some(t=>t==="conv")}function Ue(e2){return Array.isArray(e2.layer_types)&&e2.layer_types.some(t=>t==="linear_attention")}function Vr(e2){if(!Array.isArray(e2.layer_types))throw new Error("linear attention config requires layer_types");return e2.layer_types}function he(e2,t){if(typeof e2!="number")throw new Error(`${t} must be a number`);return e2}function Yr(e2){let t=e2.num_experts;return typeof t!="number"||t<=0?null:{numExperts:t,topK:he(e2.num_experts_per_tok,"config.num_experts_per_tok"),moeIntermediateSize:he(e2.moe_intermediate_size,"config.moe_intermediate_size"),numDenseLayers:he(e2.num_dense_layers,"config.num_dense_layers"),normTopkProb:e2.norm_topk_prob!==false,routedScale:typeof e2.routed_scaling_factor=="number"?e2.routed_scaling_factor:1,useExpertBias:!!e2.use_expert_bias}}function un(e2,t){return e2!==null&&t>=e2.numDenseLayers}function Tt(e2){let t=1;for(;t<e2;)t*=2;return t}function Dh(e2,t,n){if(jr(e2,n,t),e2.hidden_size!==e2.num_attention_heads*e2.head_dim)throw new Error(`${t} hidden_size must equal num_attention_heads * head_dim for V1`);if(e2.num_attention_heads%e2.num_key_value_heads!==0)throw new Error(`${t} num_attention_heads must be divisible by num_key_value_heads`);if(e2.head_dim%2!==0)throw new Error(`${t} head_dim must be even for RoPE`)}var wt,pt=q(()=>{"use strict";za();wt=class{vocab_size;hidden_size;intermediate_size;num_hidden_layers;num_attention_heads;num_key_value_heads;hidden_act;max_position_embeddings;initializer_range;rms_norm_eps;use_cache;pad_token_id;bos_token_id;eos_token_id;pretraining_tp;tie_word_embeddings;rope_parameters;attention_bias;attention_dropout;mlp_bias;head_dim;model_type;constructor(t={}){this.vocab_size=Be(t,"vocab_size","LlamaConfig"),this.hidden_size=Be(t,"hidden_size","LlamaConfig"),this.intermediate_size=Be(t,"intermediate_size","LlamaConfig"),this.num_hidden_layers=Be(t,"num_hidden_layers","LlamaConfig"),this.num_attention_heads=Be(t,"num_attention_heads","LlamaConfig"),this.num_key_value_heads=t.num_key_value_heads??this.num_attention_heads,this.hidden_act=t.hidden_act,this.max_position_embeddings=Be(t,"max_position_embeddings","LlamaConfig"),this.initializer_range=t.initializer_range,this.rms_norm_eps=t.rms_norm_eps,this.use_cache=t.use_cache,this.pad_token_id=t.pad_token_id,this.bos_token_id=t.bos_token_id,this.eos_token_id=t.eos_token_id,this.pretraining_tp=t.pretraining_tp,this.tie_word_embeddings=t.tie_word_embeddings,this.rope_parameters=t.rope_parameters??Qh(t),this.attention_bias=t.attention_bias,this.attention_dropout=t.attention_dropout,this.mlp_bias=t.mlp_bias,this.head_dim=t.head_dim??Math.floor(this.hidden_size/this.num_attention_heads),this.model_type="llama",this.validate()}validate(){if(Dh(this,"LlamaConfig",["vocab_size","hidden_size","intermediate_size","num_hidden_layers","num_attention_heads","num_key_value_heads","max_position_embeddings","head_dim"]),this.hidden_act!=="silu")throw new Error(`Unsupported Llama hidden_act for V1: ${this.hidden_act}`);if(this.attention_bias||this.mlp_bias)throw new Error("Llama megakernel V1 does not support attention_bias or mlp_bias");if(this.attention_dropout!==0)throw new Error("Llama megakernel V1 only supports attention_dropout=0");if(this.rope_parameters?.rope_type!=="default")throw new Error("Llama megakernel V1 only supports default RoPE")}toJSON(){return{...this,rope_parameters:{...this.rope_parameters}}}}});function Ha(e2){let t=e2.hidden_size,n=e2.intermediate_size,r=e2.num_attention_heads*e2.head_dim,s=e2.num_key_value_heads*e2.head_dim,a=[{id:"embed_tokens",name:"model.embed_tokens.weight",length:e2.vocab_size*t}];for(let o=0;o<e2.num_hidden_layers;++o)a.push({id:`layers.${o}.q_proj`,name:`model.layers.${o}.self_attn.q_proj.weight`,length:r*t},{id:`layers.${o}.k_proj`,name:`model.layers.${o}.self_attn.k_proj.weight`,length:s*t},{id:`layers.${o}.v_proj`,name:`model.layers.${o}.self_attn.v_proj.weight`,length:s*t},{id:`layers.${o}.o_proj`,name:`model.layers.${o}.self_attn.o_proj.weight`,length:t*r},{id:`layers.${o}.gate_proj`,name:`model.layers.${o}.mlp.gate_proj.weight`,length:n*t},{id:`layers.${o}.up_proj`,name:`model.layers.${o}.mlp.up_proj.weight`,length:n*t},{id:`layers.${o}.down_proj`,name:`model.layers.${o}.mlp.down_proj.weight`,length:t*n},{id:`layers.${o}.input_layernorm`,name:`model.layers.${o}.input_layernorm.weight`,length:t},{id:`layers.${o}.post_attention_layernorm`,name:`model.layers.${o}.post_attention_layernorm.weight`,length:t});return a.push({id:"model_norm",name:"model.norm.weight",length:t},{id:"lm_head",name:"lm_head.weight",length:e2.vocab_size*t}),a}var Fl=q(()=>{"use strict"});var Ph,ln,Ua=q(()=>{"use strict";en();Ph=64*1024,ln=class{runtime;tensor;constructor({runtime:t,tensor:n}){this.runtime=t,this.tensor=n}writeTensor({sourceDtype:t,targetDtype:n,sourceBytes:r,targetElementOffset:s}){el(t,n);let a=tl(t),o=nl(n);if(r.byteLength%a!==0)throw new Error(`source byte length ${r.byteLength} not divisible by ${a}`);let i=r.byteLength/a,u=s*o;if(t==="F32"&&n==="float32")return this.runtime.writeWeightsRange(this.tensor,u,r),s+i;if(t==="F16"&&n==="float16")return this.runtime.writeWeightsRange(this.tensor,u,r),s+i;let l=Math.max(1,Math.floor(Ph/o)),c=sl(t,n);for(let d=0;d<i;d+=l){let f=Math.min(l,i-d),p=r.subarray(d*a,(d+f)*a),g=c(p),m=u+d*o;this.runtime.writeWeightsRange(this.tensor,m,rl(g))}return s+i}}});var Fa=q(()=>{"use strict";qe()});function Vl(e2){return Xl({type:"tensor",required:true,...e2})}async function Yl(e2,{source:t=null,reader:n,context:r={},concurrency:s,chunkMaxBytes:a,onProgress:o=null,signal:i}={}){let u=e2 instanceof Xr?e2:new Xr(e2);if(!n)throw 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if(r.type==="group")this.#e(r,t,n);else throw new Error(`Unsupported weight plan spec type: ${r.type}`);return this}names(){return[...this.handlers.keys()]}async onChunk({bytes:t,tensors:n},r={}){let s=[];for(let a of n){let o=this.handlers.get(a.name);if(!o)continue;let i=t.subarray(a.offset,a.offset+a.length);s.push(Zl(o,i,r))}s.length&&await Promise.all(s)}async readSequential(t,n={}){for(let r of this.names()){let s=await t.read(r),a=this.handlers.get(r);a&&await Zl(a,s,n)}}assertComplete(){if(this.pending.size>0){let t=[...this.pending].slice(0,5).join(", ");throw new Error(`WeightPlan incomplete - ${this.pending.size} tensor(s) never arrived (first: ${t})`)}}#t(t,n,r){let s=$l(n,t.name);if(!s){jl(t,r);return}Nl(t,s),this.#n(t.name,async(a,o)=>{this.pending.delete(t.name),await Rh(t,a,{...r,...o,description:s})},t.progressLabel??t.name)}#e(t,n,r){let s={},a=t.names.filter(u=>{let l=$l(n,u);return l&&(s[u]=l),!l});if(a.length>0){jl(t,r,a);return}for(let u of t.names)Nl(t,s[u],u);let o={},i=t.names.length;for(let u of t.names)this.#n(u,async(l,c)=>{if(this.pending.delete(u),o[u]=l,--i===0)try{await Mh(t,o,{...r,...c,descriptions:s})}finally{for(let d of t.names)delete o[d]}},t.progressLabel??u)}#n(t,n,r=t){if(this.handlers.has(t))throw new Error(`Duplicate WeightPlan handler for tensor: ${t}`);this.handlers.set(t,{name:t,progressLabel:r,receive:n}),this.pending.add(t)}}});function Za(e2){return e2.replace(/[ \t]*\/\/.*$/gm,"").replace(/\n{2,}/g,`
+`).trim()}function Oh(e2,t,n,r,s,a,o,i,u,l=[0,0]){let c=Math.ceil(o/t.workgroupSize),d=e2.device.limits.maxComputeWorkgroupsPerDimension,f=[{tensor:n,type:"read-only-storage",binding:0},{tensor:r,type:"storage",binding:1},{tensor:s,type:"storage",binding:2},{buffer:e2.createUniformU32([o,a,l[0],l[1]],`${t.id}-params`),type:"uniform",binding:3}];u&&f.push({tensor:u,type:"read-only-storage",binding:4});let p={name:`com.xenova.transcode.${t.id}`,source:t.buildWgsl(i),entryPoint:"main",cacheKey:`com.xenova.transcode:${t.id}:${i?"f16":"f32"}`,bindings:f,dispatchWorkgroups:[Math.min(c,d),Math.ceil(c/d),1]};return e2.runProgram(p)}function Wh(e2,t,n){let r=n.get(t.name);if(!r){let s=new Uint8Array(Math.ceil(t.data.byteLength/4)*4);s.set(t.data),r=e2.tensorFromTypedArray("uint32",[s.byteLength/4],new Uint32Array(s.buffer)),n.set(t.name,r)}return r}function Jr(e2,t,n,r,s,a,o,i,u,l,c=[0,0]){if(!t)throw new Error("transcode kernel for this weight format is not in this bundle (built for the shipped model's weight formats — load that model's own GGUF, or rebuild)");let d=n.byteLength%4===0&&n.byteOffset%4===0,f;if(d)f=new Uint32Array(n.buffer,n.byteOffset,n.byteLength/4);else{let m=new Uint8Array(Math.ceil(n.byteLength/4)*4);m.set(n),f=new Uint32Array(m.buffer)}let p=e2.tensorFromTypedArray("uint32",[f.length],f);l?.track(p);let g=t.grid?Wh(e2,t.grid,u):void 0;return Oh(e2,t,p,r,s,a,o,i,g,c)}var cn,$a=q(()=>{"use strict";cn=class{#t=[];track(t){this.#t.push(t)}destroyAll(){for(let t of this.#t)t.destroy?.();this.#t.length=0}}});var Oe,rt,Na,Fe,Zn,tc=q(()=>{Oe={Q1_0:{id:"q1_0_to_q1",workgroupSize:64,buildWgsl:e2=>e2?`enable f16;
+@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f16>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = f16(v); }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  ss(ob, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[ob * 4u + w] = q[w]; }
+}`:`@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f32>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = v; }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  ss(ob, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[ob * 4u + w] = q[w]; }
+}`},Q1_0CM:{id:"q1_0_to_q1_colmajor",workgroupSize:64,buildWgsl:e2=>e2?`enable f16;
+@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f16>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = f16(v); }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  let bpr = params.aux0;
+  let row = bi / bpr;
+  let cb = bi - row * bpr;
+  let dst = params.blockOffset + cb * params.aux1 + row;
+  ss(dst, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[dst * 4u + w] = q[w]; }
+}`:`@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f32>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = v; }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  let bpr = params.aux0;
+  let row = bi / bpr;
+  let cb = bi - row * bpr;
+  let dst = params.blockOffset + cb * params.aux1 + row;
+  ss(dst, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[dst * 4u + w] = q[w]; }
+}`},Q1_0RT:{id:"q1_0_to_q1_rowtile",workgroupSize:64,buildWgsl:e2=>e2?`enable f16;
+@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f16>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = f16(v); }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  let bpr = params.aux0;
+  let row = bi / bpr;
+  let cb = bi - row * bpr;
+  let grp = (row >> 2u) * bpr + cb;
+  let r = row & 3u;
+  ss(params.blockOffset + grp * 4u + r, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  let dstBase = params.blockOffset * 4u + grp * 16u + r;
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[dstBase + w * 4u] = q[w]; }
+}`:`@group(0) @binding(0) var<storage, read> src: array<u32>;
+@group(0) @binding(1) var<storage, read_write> out_bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> out_scales: array<f32>;
+struct Params { numBlocks: u32, blockOffset: u32, aux0: u32, aux1: u32 }
+@group(0) @binding(3) var<uniform> params: Params;
+fn ss(i: u32, v: f32) { out_scales[i] = v; }
+fn sbyte(wordBase: u32, j: u32) -> u32 { return (src[wordBase + (j >> 2u)] >> (8u * (j & 3u))) & 0xFFu; }
+fn gbyte(j: u32) -> u32 { return (src[j >> 2u] >> (8u * (j & 3u))) & 0xFFu; }
+fn f16at(j: u32) -> f32 { return unpack2x16float(gbyte(j) | (gbyte(j + 1u) << 8u)).x; }
+fn word32at(j: u32) -> u32 { return gbyte(j) | (gbyte(j + 1u) << 8u) | (gbyte(j + 2u) << 16u) | (gbyte(j + 3u) << 24u); }
+fn load16(B: u32, out: ptr<function, array<u32, 4>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 4u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 4u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn load32(B: u32, out: ptr<function, array<u32, 8>>) {
+  let wb = B >> 2u; let sh = (B & 3u) * 8u;
+  if (sh == 0u) { for (var w = 0u; w < 8u; w = w + 1u) { (*out)[w] = src[wb + w]; } }
+  else { let inv = 32u - sh; var prev = src[wb]; for (var w = 0u; w < 8u; w = w + 1u) { let cur = src[wb + 1u + w]; (*out)[w] = (prev >> sh) | (cur << inv); prev = cur; } }
+}
+fn b4(w: ptr<function, array<u32, 4>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+fn b8(w: ptr<function, array<u32, 8>>, k: u32) -> u32 { return ((*w)[k >> 2u] >> ((k & 3u) * 8u)) & 0xFFu; }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let bi = gid.x + gid.y * nwg.x * 64u;
+  if (bi >= params.numBlocks) { return; }
+  let ob = params.blockOffset + bi;
+  let base = bi * 18u;
+  let bpr = params.aux0;
+  let row = bi / bpr;
+  let cb = bi - row * bpr;
+  let grp = (row >> 2u) * bpr + cb;
+  let r = row & 3u;
+  ss(params.blockOffset + grp * 4u + r, f16at(base));
+  var q: array<u32, 4>; load16(base + 2u, &q);
+  let dstBase = params.blockOffset * 4u + grp * 16u + r;
+  for (var w = 0u; w < 4u; w = w + 1u) { out_bits[dstBase + w * 4u] = q[w]; }
+}`}},rt={},Na={},Fe={},Zn={}});var nc,rc=q(()=>{"use strict";nc=`
+fn div_exact(x: f32, s: f32, t: f32) -> f32 { let q0 = x * t; let r = fma(-s, q0, x); return fma(r, t, q0); }
+fn recip_exact(s: f32) -> f32 { let t0 = 1.0 / s; let t1 = fma(fma(-s, t0, 1.0), t0, t0); return fma(fma(-s, t1, 1.0), t1, t1); }`});function zh(e2){return Za(`${e2?`enable f16;
+`:""}@group(0) @binding(0) var<storage, read> emb: array<f32>;
+@group(0) @binding(1) var<storage, read_write> packed: array<u32>;
+@group(0) @binding(2) var<storage, read_write> scales: array<${e2?"f16":"f32"}>;
+struct P { chunkRows: u32, cols: u32, rowOffset: u32 }
+@group(0) @binding(3) var<uniform> params: P;
+${sc}
+fn qi8(x: f32) -> i32 { return clamp(i32(r_half_up(x)), -127, 127); }
+var<workgroup> wmax: array<f32, 256>;
+@compute @workgroup_size(256)
+fn main(@builtin(workgroup_id) wid: vec3<u32>, @builtin(local_invocation_id) lid3: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let localRow = wid.x + wid.y * nwg.x;
+  if (localRow >= params.chunkRows) { return; }
+  let row = params.rowOffset + localRow;
+  let lid = lid3.x; let cols = params.cols; let rowOff = localRow * cols;
+  var m = 0.0;
+  for (var c = lid; c < cols; c = c + 256u) { m = max(m, abs(emb[rowOff + c])); }
+  wmax[lid] = m; workgroupBarrier();
+  for (var s = 128u; s > 0u; s = s >> 1u) { if (lid < s) { wmax[lid] = max(wmax[lid], wmax[lid + s]); } workgroupBarrier(); }
+  let maxAbs = wmax[0];
+  let scale = select(1.0, div_exact(maxAbs, 127.0, recip_exact(127.0)), maxAbs > 0.0);
+  let inv = recip_exact(scale);
+  if (lid == 0u) { scales[row] = ${e2?"f16(scale)":"scale"}; }
+  let cols4 = cols >> 2u;
+  for (var c4 = lid; c4 < cols4; c4 = c4 + 256u) {
+    let base = rowOff + c4 * 4u;
+    let q0 = qi8(emb[base] * inv); let q1 = qi8(emb[base + 1u] * inv);
+    let q2 = qi8(emb[base + 2u] * inv); let q3 = qi8(emb[base + 3u] * inv);
+    packed[row * cols4 + c4] = (u32(q0) & 0xFFu) | ((u32(q1) & 0xFFu) << 8u) | ((u32(q2) & 0xFFu) << 16u) | ((u32(q3) & 0xFFu) << 24u);
+  }
+}`)}function Hh(e2){return Za(`${e2?`enable f16;
+`:""}@group(0) @binding(0) var<storage, read> emb: array<f32>;
+@group(0) @binding(1) var<storage, read_write> bits: array<u32>;
+@group(0) @binding(2) var<storage, read_write> scales: array<${e2?"f16":"f32"}>;
+struct P { chunkBlocks: u32, cols: u32, blkOffset: u32 }
+@group(0) @binding(3) var<uniform> params: P;
+${sc}
+fn nib(q: i32) -> u32 { return u32(clamp(q, 0, 15)); }
+@compute @workgroup_size(64)
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  let localBlk = gid.x + gid.y * nwg.x * 64u;
+  if (localBlk >= params.chunkBlocks) { return; }
+  let blk = params.blkOffset + localBlk;
+  let blocksPerRow = params.cols >> 5u;
+  let base = (localBlk / blocksPerRow) * params.cols + (localBlk % blocksPerRow) * 32u;
+  var amax = 0.0; var vmax = 0.0;
+  for (var j = 0u; j < 32u; j = j + 1u) { let v = emb[base + j]; let a = abs(v); if (a > amax) { amax = a; vmax = v; } }
+  let d = vmax * -0.125;
+  let id = select(0.0, recip_exact(d), d != 0.0);
+  scales[blk] = ${e2?"f16(d)":"d"};
+  for (var w = 0u; w < 4u; w = w + 1u) {
+    var word = 0u;
+    for (var bb = 0u; bb < 4u; bb = bb + 1u) {
+      let k = w * 4u + bb;
+      let xlo = emb[base + k] * id; let xhi = emb[base + k + 16u] * id;
+      let qlo = nib(i32(r_half_up(xlo)) + 8);
+      let qhi = nib(i32(r_half_up(xhi)) + 8);
+      word = word | (((qlo | (qhi << 4u)) & 0xFFu) << (bb * 8u));
+    }
+    bits[blk * 4u + w] = word;
+  }
+}`)}async function ja(e2,t,n,r,s,a){let o=e2.device.limits.maxComputeWorkgroupsPerDimension,i=r/4,u=r/32,l=e2.empty("uint32",[n*i]),c=e2.empty("float32",[n]),d=e2.empty("uint32",[n*u*4]),f=e2.empty(s?"float16":"float32",[n*u]),p=Math.max(1,Math.min(n,a??(Math.floor(Uh/(r*4))||1)));for(let g=0;g<n;g+=p){let m=Math.min(p,n-g),A=await t(g,m),h=A instanceof Float32Array?e2.tensorFromTypedArray("float32",[m*r],A):A;await e2.runProgram({name:"com.xenova.transcode.embedQ8Rows",source:zh(false),entryPoint:"main",cacheKey:"com.xenova.transcode:embedQ8Rows:f32",bindings:[{tensor:h,type:"read-only-storage",binding:0},{tensor:l,type:"storage",binding:1},{tensor:c,type:"storage",binding:2},{buffer:e2.createUniformU32([m,r,g,0],"embedQ8-params"),type:"uniform",binding:3}],dispatchWorkgroups:[Math.min(m,o),Math.ceil(m/o),1]});let C=m*u,E=Math.ceil(C/64);await e2.runProgram({name:"com.xenova.transcode.embedQ4Rows",source:Hh(s),entryPoint:"main",cacheKey:`com.xenova.transcode:embedQ4Rows:${s?"f16":"f32"}`,bindings:[{tensor:h,type:"read-only-storage",binding:0},{tensor:d,type:"storage",binding:1},{tensor:f,type:"storage",binding:2},{buffer:e2.createUniformU32([C,r,g*u,0],"embedQ4-params"),type:"uniform",binding:3}],dispatchWorkgroups:[Math.min(E,o),Math.ceil(E/o),1]}),await e2.queueIdle(),h.destroy?.()}return{embedQ8:l,embedQ8Scales:c,lmHeadQ4:d,lmHeadQ4Scales:f}}var sc,Uh,ac=q(()=>{"use strict";$a();rc();sc=`${nc}
+fn r_half_up(x: f32) -> f32 { return floor(x + 0.5); }`;Uh=32*1024*1024});var Va=q(()=>{"use strict";$a();tc();ac()});function dn(e2,t){let n=e2.find(r=>r.id==="lm_head");return n?!t(n.name):false}function ic({runtime:e2,entries:t,stateDict:n,tieWordEmbeddings:r=false,modelName:s="model"}){let a=We(),o=[],i=0;for(let l of t){let c=n[l.name];if(c===void 0&&l.id==="lm_head"&&r){if(a.top.embed_tokens===void 0)throw new Error(`${s}: tied lm_head requires embed_tokens to come first in entries`);be(a,l.id,a.top.embed_tokens);continue}if(c===void 0)throw new Error(`Missing ${s} state dict weight: ${l.name}`);let d=Fh(c,l.name);if(l.transform&&(d=l.transform(d,l.shape)),d.length!==l.length)throw new Error(`Weight ${l.name} length ${d.length} does not match expected ${l.length}`);be(a,l.id,i),o.push(d),i+=d.length}let u=Nh(o,i,Float32Array);return{weights:e2.tensorFromTypedArray("float32",[u.length],u),offsets:a}}function ts(e2=8*1024*1024){let t=0;return n=>(t+=n,t<e2?Promise.resolve():(t=0,new Promise(r=>setTimeout(r,0))))}async function Ya({runtime:e2,config:t,entries:n,reader:r,source:s=null,canUseFloat16:a=pn,canUseQ8LmHead:o=Xa,canUseQ4LmHead:i=Ja,modelName:u="model",onProgress:l=null,signal:c,skipDenseIds:d}){let f=!!e2.device.features.has("shader-f16")&&a(t),p=f?"float16":"float32",g=f?2:4,m=dn(n,w=>r.has(w)),A=We(),h=[],C=0;for(let w of n){if(d?.has(w.id))continue;if(w.id==="lm_head"&&m){if(A.top.embed_tokens===void 0)throw new Error(`${u}: tied lm_head requires embed_tokens to come first in entries`);be(A,w.id,A.top.embed_tokens),h.push({entry:w,tied:true});continue}let _=r.describe(w.name);if(Mr(_.dtype),_.elementCount!==w.length)throw new Error(`${u} tensor ${w.name} length ${_.elementCount} does not match expected ${w.length}`);be(A,w.id,C),h.push({entry:w,tied:false,sourceDtype:_.dtype,sourceShape:_.shape,elementOffset:C}),C+=w.length}let 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P=F&&y?Zh(y,t.vocab_size,t.hidden_size):null,T=M&&y?$h(y,t.vocab_size,t.hidden_size):null,D=e2.device.features.has("shader-f16");return{weights:E,offsets:A,lmHeadQ4:P?e2.tensorFromTypedArray("uint32",[P.bits.length],P.bits):null,lmHeadQ4Scales:P?D?e2.tensorFromTypedArray("float16",[P.scales.length],Kt(P.scales)):e2.tensorFromTypedArray("float32",[P.scales.length],P.scales):null,lmHeadQ8:T?e2.tensorFromTypedArray("uint32",[T.packed.length],T.packed):null,lmHeadQ8Scales:T?e2.tensorFromTypedArray("float32",[T.scales.length],T.scales):null,packs:{},cacheDtype:p}}function Fh(e2,t){if(e2 instanceof Float32Array)return e2;if(jh(e2))return Float32Array.from(e2);if(Array.isArray(e2))return Float32Array.from(e2.flat(1/0));throw new Error(`Weight ${t} must be a Float32Array or array`)}function We(){return{top:{},layers:[]}}function be(e2,t,n){let r=/^layers\.(\d+)\.(.+)$/.exec(t);if(!r){e2.top[t]=n;return}let s=Number(r[1]);(e2.layers[s]??={})[r[2]]=n}function ft(e2,t){let 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a=t.reduce((m,A)=>m+(A==="linear_attention"?1:0),0);if(a===0)return{states:Array(t.length).fill(null),backing:null};let o=r.reduce((m,A)=>m*A,1),i=Ae(n),u=o*i,l=_c(u,to),c=a*l,d=e2.device.limits.maxBufferSize;if(Number.isFinite(d)&&d>0&&c>d)return{states:t.map((A,h)=>A==="linear_attention"?e2.empty(n,r,`${s}-${h}`):null),backing:null};let f=e2.empty(n,[c/i],s),p=0;return{states:t.map(m=>{if(m!=="linear_attention")return null;let A=wc(f,r,p*l);return p+=1,A}),backing:f}}function _c(e2,t){return Math.ceil(e2/t)*t}function wc(e2,t,n){let r=t.reduce((a,o)=>a*o,1),s=r*Ae(e2.dtype);if(n<0||n+s>e2.byteLength)throw new Error("mutable-state tensor view exceeds its backing buffer");return{dtype:e2.dtype,shape:[...t],byteOffset:(e2.byteOffset??0)+n,size:r,byteLength:s,buffer:e2.buffer,runtime:e2.runtime}}function t_(e2){let t=[];return no(t,e2.convStates,e2.standaloneStateBuffersPadded),dc(t,e2.linearConvStates,e2.linearConvStatesBacking,e2.standaloneStateBuffersPadded),dc(t,e2.linearRecurrentStates,e2.linearRecurrentStatesBacking,e2.standaloneStateBuffersPadded),t}function dc(e2,t,n,r){if(n){no(e2,n,true);return}if(Array.isArray(t))for(let s of t)no(e2,s,r)}function no(e2,t,n){t&&e2.push({dst:t,dstOffset:t.byteOffset??0,byteLength:yc(t,n,"clear")})}function yc(e2,t,n){if(e2.byteLength%4===0)return e2.byteLength;if(!t)throw new Error(`cannot ${n} ${e2.byteLength}-byte mutable-state tensor safely: its backing allocation is not known to include 4-byte padding`);return _c(e2.byteLength,4)}function pc(e2){return e2.convStates!==null||fc(e2.linearConvStates)||fc(e2.linearRecurrentStates)}function fc(e2){return Array.isArray(e2)&&e2.some(t=>t!==null)}function n_(e2,t){let n=mc(e2,t.linearConvStates,t.linearConvStatesBacking,"llama-cache-checkpoint-linear-conv"),r=mc(e2,t.linearRecurrentStates,t.linearRecurrentStatesBacking,"llama-cache-checkpoint-linear-recurrent");return{convStates:bc(e2,t.convStates,"llama-cache-checkpoint-conv"),linearConvStates:n.states,linearRecurrentStates:r.states,linearConvStatesBacking:n.backing,linearRecurrentStatesBacking:r.backing,standaloneStateBuffersPadded:true}}function bc(e2,t,n){return t?e2.empty(t.dtype,t.shape,n):null}function mc(e2,t,n,r){if(!Array.isArray(t)){if(n!==null)throw new Error("mutable-state backing has no tensor views");return{states:null,backing:null}}if(!n)return{states:t.map((i,u)=>bc(e2,i,`${r}-${u}`)),backing:null};let s=e2.empty(n.dtype,n.shape,r),a=n.byteOffset??0;return{states:t.map(i=>{if(!i)return null;if(i.buffer!==n.buffer||i.dtype!==n.dtype)throw new Error("mutable-state tensor views do not match their backing buffer");return wc(s,i.shape,(i.byteOffset??0)-a)}),backing:s}}function gc(e2,t,n){let r=[],s=e2.standaloneStateBuffersPadded&&t.standaloneStateBuffersPadded;return ro(r,e2.convStates,t.convStates,`llama-cache-${n}-conv`,s),Ac(r,e2.linearConvStates,t.linearConvStates,e2.linearConvStatesBacking,t.linearConvStatesBacking,`llama-cache-${n}-linear-conv`,s),Ac(r,e2.linearRecurrentStates,t.linearRecurrentStates,e2.linearRecurrentStatesBacking,t.linearRecurrentStatesBacking,`llama-cache-${n}-linear-recurrent`,s),r}function Ac(e2,t,n,r,s,a,o){if(r||s){if(!r||!s)throw new Error("mutable-state checkpoint packed backings do not match");ro(e2,r,s,a,true);return}if(!Array.isArray(t)||!Array.isArray(n)||t.length!==n.length){if(t===null&&n===null)return;throw new Error("mutable-state checkpoint tensor arrays do not match")}for(let i=0;i<t.length;++i)ro(e2,t[i],n[i],`${a}-${i}`,o)}function ro(e2,t,n,r,s){if(!t||!n){if(t===null&&n===null)return;throw new Error("mutable-state checkpoint tensors do not match")}if(t.dtype!==n.dtype||t.byteLength!==n.byteLength)throw new Error("mutable-state checkpoint tensor shape or dtype changed");let a=yc(t,s,"copy");e2.push({kind:"copy",name:r,src:t,dst:n,srcOffset:t.byteOffset??0,dstOffset:n.byteOffset??0,byteLength:a})}function hc(e2){e2&&(Ie(e2.convStates),rs(e2.linearConvStates,e2.linearConvStatesBacking),rs(e2.linearRecurrentStates,e2.linearRecurrentStatesBacking))}function rs(e2,t){if(t){Ie(t);return}Ie(e2)}var to,st,mn=q(()=>{"use strict";pt();eo();_t();qe();to=256;st=class e2{runtime;config;batchSize;maxLength;seqLength;keyStates;valueStates;convStates;linearConvStates;linearRecurrentStates;mutableStateCheckpoint;linearConvStatesBacking=null;linearRecurrentStatesBacking=null;standaloneStateBuffersPadded=false;mutableStateCheckpointStorage=null;mutableStateCheckpointPromise=null;constructor({runtime:t,config:n,batchSize:r,maxLength:s,keyStates:a,valueStates:o,convStates:i=null,linearConvStates:u=null,linearRecurrentStates:l=null,seqLength:c=0}){this.runtime=t,this.config=Fn(n),this.batchSize=r,this.maxLength=s,this.seqLength=c,this.keyStates=a,this.valueStates=o,this.convStates=i,this.linearConvStates=u,this.linearRecurrentStates=l,pc(this)&&(this.mutableStateCheckpoint=()=>this.mutableStateCheckpointPromise??=this.buildMutableStateCheckpoint())}static allocate(t,n,r,s,a="float32"){let o=Fn(n),i=[o.num_hidden_layers,r,Jh(s??o.max_position_embeddings,o.num_key_value_heads,o.head_dim,Ae(a)),o.num_key_value_heads,o.head_dim],u=e_(t,o,r),l=o.num_hidden_layers*r*o.hidden_size*(dt(o)?he(o.conv_L_cache,"config.conv_L_cache"):0),c=new e2({runtime:t,config:o,batchSize:r,maxLength:i[2],keyStates:t.empty(a,i,"llama-cache-keys"),valueStates:t.empty(a,i,"llama-cache-values"),convStates:dt(o)?t.empty("float32",[l],"llama-cache-conv"):null,linearConvStates:u.linearConvStates,linearRecurrentStates:u.linearRecurrentStates});return c.linearConvStatesBacking=u.linearConvStatesBacking,c.linearRecurrentStatesBacking=u.linearRecurrentStatesBacking,c.standaloneStateBuffersPadded=true,c.clearMutableState(),c}get_seq_length(){return this.seqLength}advance(t){if(!Number.isInteger(t)||t<0)throw new Error(`cache advance expects a non-negative integer, got ${t}`);let n=this.seqLength+t;if(n>this.maxLength)throw new Error(`cache length ${n} exceeds maxLength ${this.maxLength}`);this.seqLength=n}async buildMutableStateCheckpoint(){let t=this.mutableStateSource(),n=n_(this.runtime,t);this.mutableStateCheckpointStorage=n;try{let r=await this.runtime.prepareProgramSequence(gc(t,n,"capture")),s=await this.runtime.prepareProgramSequence(gc(n,t,"restore"));return{capture:()=>this.runtime.enqueuePreparedProgramSequence(r),restore:()=>this.runtime.enqueuePreparedProgramSequence(s)}}catch(r){throw hc(n),this.mutableStateCheckpointStorage===n&&(this.mutableStateCheckpointStorage=null),this.mutableStateCheckpointPromise=null,r}}mutableStateSource(){return{convStates:this.convStates,linearConvStates:this.linearConvStates,linearRecurrentStates:this.linearRecurrentStates,linearConvStatesBacking:this.linearConvStatesBacking,linearRecurrentStatesBacking:this.linearRecurrentStatesBacking,standaloneStateBuffersPadded:this.standaloneStateBuffersPadded}}clearMutableState(){let t=t_(this.mutableStateSource());t.length>0&&this.runtime.clearBuffers(t)}truncate(t){if(!Number.isInteger(t)||t<0||t>this.maxLength)throw new Error(`cache truncate expects an integer in [0, ${this.maxLength}], got ${t}`);if(t>this.seqLength)throw new Error(`cache truncate cannot extend length from ${this.seqLength} to ${t}; use advance()`);if(t!==0&&t!==this.seqLength&&pc(this))throw new Error(`cache with mutable conv/recurrent state cannot rewind from ${this.seqLength} to ${t}; reset to 0 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r.streamTensors([...Q.keys(),...W.keys()],async(T,D)=>{let w=Q.get(T);if(w){C.writeTensor({sourceDtype:w.sourceDtype,targetDtype:u,sourceBytes:D,targetElementOffset:w.elementOffset}),E+=1,o?.({processed:E,total:R,name:T});return}let _=W.get(T),v=r.info(T),k=v.shape[1],V=v.shape[0],j=k/128,$2=b_.has(Et(_.entry.id))&&V%k_===0&&k%128===0,ne=S.has(_.entry.id);await Jr(e2,ne?Oe.Q1_0CM:$2?Oe.Q1_0RT:Oe.Q1_0,D,M,b,_.blockOffset,_.entry.length/128,F,y,I,[j,ne?V:0]),P+=D.byteLength,P>=768*1024*1024&&(await e2.queueIdle(),I.destroyAll(),P=0),E+=1,o?.({processed:E,total:R,name:T}),await H(D.byteLength)}),await e2.queueIdle(),I.destroyAll(),{weights:h,offsets:f,packs:{q1:{bits:M,scales:b,offsets:p,blockCounts:g,packedIds:new Set(d.map(T=>T.entry.id)),colMajorIds:S}},lmHeadQ8:null,lmHeadQ8Scales:null,cacheDtype:u}}function Ao(e2){return e2===G.Q4_0?4:v_.has(e2)?null:Or(e2)?8:null}function ps(e2,t,n){let r=new Set;for(let s of e2){let a=Et(s.id);if(!t.has(a))continue;let 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o?i?4:8:null}function S_(e2,t,n,r){if(e2.byteLength%On!==0)throw new Error(`Q8_0 byte length ${e2.byteLength} is not a multiple of ${On}`);let s=e2.byteLength/On,a=new DataView(e2.buffer,e2.byteOffset,e2.byteLength);for(let o=0;o<s;++o){let i=o*On;n[r+o]=je(a.getUint16(i,true));let u=i+2;for(let l=0;l<8;++l){let c=0;for(let d=0;d<4;++d)c|=(e2[u+l*4+d]&255)<<d*8;t[(r+o)*8+l]=c>>>0}}}function B_(e2){let t=e2.length/Kr,n=new Uint32Array(t*8),r=new Float32Array(t);for(let s=0;s<t;++s){let a=s*Kr,o=0;for(let l=0;l<Kr;++l){let c=Math.abs(e2[a+l]);c>o&&(o=c)}let i=o/127,u=i>0?1/i:0;r[s]=i;for(let l=0;l<8;++l){let c=0;for(let d=0;d<4;++d){let f=Math.round(e2[a+l*4+d]*u);f>127?f=127:f<-127&&(f=-127),c|=(f&255)<<d*8}n[s*8+l]=c>>>0}}return{bits:n,scales:r}}function I_(e2){switch(e2){case G.Q4_K:return rt.Q4_K;case G.Q6_K:return rt.Q6_K;case G.Q5_K:return rt.Q5_K;case G.Q3_K:return rt.Q3_K;case G.Q2_K:return rt.Q2_K;case G.IQ4_XS:return rt.IQ4_XS;case G.IQ4_NL:return rt.IQ4_NL;case G.Q5_0:return rt.Q5_0;case G.Q5_1:return rt.Q5_1;default:return null}}function T_(e2,t){return e2===4?Oe.Q4_0:t===G.Q8_0?Oe.Q8_0:I_(t)}async function $n({runtime:e2,entries:t,gguf:n,nameToGGUF:r,signal:s},{routeName:a,include:o,guardElements:i=Ca,bitsLength:u,scalesLength:l,scalesF16:c,specForSlot:d,cpuFallback:f}){let p=We(),g=We(),m=new Set,A=[],h=0;for(let b of t){if(!o(b))continue;let S=r(b.name),y=n.info(S);if(y.elementCount%i!==0)throw new Error(`${a} tensor ${S} element count ${y.elementCount} not divisible by ${i}`);let I=y.elementCount/Ca;be(p,b.id,h),be(g,b.id,I),m.add(b.id),A.push({gname:S,baseBlock:h,blocks:I,type:y.type}),h+=I}if(A.length===0)return null;let C=c(A),E=new Map(A.map(b=>[b.gname,b])),R=ts();if(A.every(b=>d(b)!==null)){let b=e2.empty("uint32",[u(h)]),S=e2.empty(C?"float16":"float32",[l(h)]),y=new Map,I=new cn;return await n.streamTensors([...E.keys()],async(H,W)=>{let Q=E.get(H);await Jr(e2,d(Q),W,b,S,Q.baseBlock,Q.blocks,C,y,I),await R(W.byteLength)},{signal:s}),await e2.queueIdle(),I.destroyAll(),{bits:b,scales:S,offsets:p,blockCounts:g,packedIds:m,layout:A}}if(!f){let b=A.find(S=>d(S)===null);throw new Error(`${a}: no transcode kernel for ${b.gname} (type ${b.type})`)}let F=new Uint32Array(u(h)),M=new Float32Array(l(h));return await n.streamTensors([...E.keys()],async(b,S)=>{f(E.get(b),S,F,M),await R(S.byteLength)},{signal:s}),{bits:e2.tensorFromTypedArray("uint32",[F.length],F),scales:C?e2.tensorFromTypedArray("float16",[M.length],Kt(M)):e2.tensorFromTypedArray("float32",[M.length],M),offsets:p,blockCounts:g,packedIds:m,layout:A}}async function E_(e2,{packSuffixes:t,excludeKq4Ids:n,forceDenseIds:r}){let{runtime:s,entries:a,gguf:o,nameToGGUF:i}=e2,u=wo(a,o,i,t,n,r);if(u===null)return null;let l=u,c=await $n(e2,{routeName:"packed",include:d=>fs(d,o,i,t,n,r),bitsLength:d=>d*l,scalesLength:d=>d,scalesF16:()=>s.device.features.has("shader-f16"),specForSlot:d=>T_(u,d.type),cpuFallback:(d,f,p,g)=>{if(d.type===G.Q8_0)S_(f,p,g,d.baseBlock);else{let m=nn(d.type,f);if(!m)throw new Error(`packQ4GGUFWeights: ${d.gname} type ${d.type} is not dequantizable`);let A=B_(m);p.set(A.bits,d.baseBlock*8),g.set(A.scales,d.baseBlock)}}});return c&&{...c,packedQuantBits:u}}async function G_(e2,{kq4Ids:t}){return await $n(e2,{routeName:"kq4",include:r=>t.has(r.id),bitsLength:r=>r*4,scalesLength:r=>r*2,scalesF16:()=>e2.runtime.device.features.has("shader-f16"),specForSlot:r=>r.type===G.Q4_1?Oe.Q4_1:r.type===G.Q5_K?Na.Q5_K_TO_KQ4:Oe.Q4_K})}function yo(e2,t,n,r){return ps(e2,r,jc(e2,t,n,Ba,r))}async function x_(e2,{iqIds:t}){return await $n(e2,{routeName:"IQ",include:r=>t.has(r.id),bitsLength:r=>r*8,scalesLength:r=>r*2,scalesF16:()=>e2.runtime.device.features.has("shader-f16"),specForSlot:r=>Q_(r.type),cpuFallback:(r,s,a,o)=>{let i=gl(r.type,s);a.set(i.bits,r.baseBlock*8),o.set(i.scales,r.baseBlock*2)}})}function Q_(e2){return e2===G.IQ2_XXS?Oe.IQ2_XXS:e2===G.IQ2_XS?Oe.IQ2_XS:e2===G.IQ2_S?Oe.IQ2_S:e2===G.IQ3_S?Oe.IQ3_S:e2===G.IQ3_XXS?Oe.IQ3_XXS:null}function bo(e2,t,n,r){if(globalThis.process?.env?.LUT_PACKS==="0")return{lut32Ids:new Set,lut16Ids:new Set};let s=new Map(e2.map(l=>[l.id,l])),a=l=>{let c=s.get(l);if(!c||!r.has(Et(l)))return null;let d=n(c.name);return t.has(d)?ds.get(t.info(d).type)??null:null},o=ps(e2,r,l=>{let c=a(l);return c?`${c.lutId}|${c.per16?16:32}`:null}),i=new Set,u=new Set;for(let l of o)(a(l).per16?u:i).add(l);return{lut32Ids:i,lut16Ids:u}}async function zc(e2,{lutIds:t,per16:n}){let{runtime:r,entries:s,gguf:a,nameToGGUF:o}=e2,i=await $n(e2,{routeName:"LUT",include:l=>{if(!t.has(l.id))return false;let c=o(l.name),d=a.info(c),f=ds.get(d.type);if(!f||f.per16!==n)throw new Error(`packLutGGUFWeights: ${c} type ${d.type} is not a per${n?16:32} LUT route`);return true},bitsLength:l=>l*4,scalesLength:l=>l*(n?2:1),scalesF16:l=>r.device.features.has("shader-f16")&&l.every(c=>c.type===G.IQ4_NL),specForSlot:l=>ds.get(l.type).spec});if(i===null)return null;let u=new Map;for(let l of s)i.packedIds.has(l.id)&&u.set(l.id,ds.get(a.info(o(l.name)).type).lutId);return{...i,lutIds:u,lut16:n?1:0}}function ko(e2,t,n,r,s){let a=globalThis.process?.env,o=new Map(e2.map(l=>[l.id,l])),i=new Map;for(let l of go)i.set(l.key,a?.[l.env]==="0"?new Set:ps(e2,r,c=>{let d=Et(c);if(!D_.has(d)||!r.has(d))return null;let f=o.get(c);if(!f)return null;let p=n(f.name);return t.has(p)&&t.info(p).type===l.ggmlType?`${l.key}raw`:null}));let u=go.filter(l=>l.key!=="iq1"&&a?.[l.env]!=="1"&&a?.[l.env]!=="0");if(u.length>0){let l=u.flatMap(c=>[...i.get(c.key)]).reduce((c,d)=>{let f=o.get(d);if(!f)return c;let p=n(f.name);return t.has(p)?c+t.info(p).elementCount/32*16:c},0);if(!(s?.maxBufferSize!==void 0&&l>s.maxBufferSize))for(let c of u)i.set(c.key,new Set)}return i}async function P_(e2,t,n){let{runtime:r}=e2,s=t.pack;if(!s)throw new Error(`raw-IQ pack for ${t.key} is not in this bundle (built for the shipped model's weight formats — load that model's own GGUF, or rebuild)`);let a=await $n(e2,{routeName:`${t.key}-raw`,include:l=>n.has(l.id),guardElements:256,bitsLength:l=>l*t.wordsPerBlock,scalesLength:l=>l/8,scalesF16:()=>r.device.features.has("shader-f16"),specForSlot:()=>s.spec});if(!a)return null;let o=s.gemvGrid.data,i=new Uint32Array(o.buffer,o.byteOffset,o.length/4),u=r.tensorFromTypedArray("uint32",[i.length],i.slice());return{...a,grid:u}}async function Vc(e2,{packSuffixes:t}){let{entries:n,gguf:r,nameToGGUF:s}=e2,a=ho(n,r,s,t),o=ko(n,r,s,t,{maxBufferSize:e2.runtime.device.limits.maxBufferSize}),i=new Set([...o.values()].flatMap(m=>[...m])),{lut32Ids:u,lut16Ids:l}=bo(n,r,s,t);for(let m of i)u.delete(m),l.delete(m);let c=new Set([...u,...l]),d=_o(n,r,s,t),f=new Set([...yo(n,r,s,t)].filter(m=>!c.has(m)&&!i.has(m))),p={q4:await 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Error(`stepUniformPacker(${t}): ${p.source} declares field ${m} "${g.name}: ${g.kind}" but ${r.source} declares "${A.name}: ${A.kind}" — ops sharing one override buffer must agree on a common field prefix`)});let s=Math.max(4,Math.ceil(r.fields.length/4)*4);if(n!==void 0&&n!==s)throw new Error(`stepUniformPacker(${t}): g.uniform declares ${n*4} bytes but the struct (${r.fields.map(p=>p.name).join(", ")}) packs to ${s*4}`);let a=new ArrayBuffer(s*4),o=new Uint32Array(a),i=new Int32Array(a),u=new Float32Array(a),l=new Map(r.fields.map((p,g)=>[p.name,{...p,index:g}])),c=r.fields.filter(p=>!p.name.startsWith("_")).length,d=()=>r.fields.map(p=>p.name).join(", ");return Object.assign(p=>{let g=0;for(let m of Object.keys(p)){let A=l.get(m);if(!A)throw new Error(`stepUniformPacker(${t}): unknown field "${m}" (struct: ${d()})`);A.kind==="f32"?u[A.index]=p[m]:A.kind==="i32"?i[A.index]=p[m]:o[A.index]=p[m],g+=1}if(g!==c){let m=r.fields.filter(A=>!A.name.startsWith("_")&&!(A.name in p)).map(A=>A.name);throw new Error(`stepUniformPacker(${t}): missing field(s) ${m.join(", ")} (struct: ${d()})`)}return o},{fields:new Set(r.fields.map(p=>p.name))})}var ld,cd=q(()=>{"use strict";Ct();ld=new Map});var xo,Qo,ws,ys,Do=q(()=>{"use strict";po();cd();xo=class{steps=[];prefillSteps=null;get stepCount(){return this.steps.length}get prefillStepCount(){return(this.prefillSteps??this.steps).length}async step(t,n){return this.writeStepInputs(t,n),this.collector.enqueue(this.steps),(await this.runtime.readTensor(this.idsTensor))[0]}submitStep(t,n){return this.writeStepInputs(t,n),this.collector.enqueue(this.steps),this.runtime.readTensor(this.idsTensor).then(r=>r[0])}enqueueStep(t,n,{full:r=false}={}){this.writeStepInputs(t,n),this.collector.enqueue(r?this.steps:this.prefillSteps??this.steps)}async replayKnownInputs(t,n){for(let r=0;r<t.length;++r)this.enqueueStep(t[r],n+r);await this.runtime.queueIdle()}finish(){}measureGpuMs(){return this.runtime.measurePreparedSequenceGpuMs?this.runtime.measurePreparedSequenceGpuMs(this.steps):Promise.resolve(null)}},Qo=class extends xo{compiled;get collector(){return this.compiled.collector}get idsTensor(){return this.compiled.tensor("input_ids")}dispose(){this.compiled?.dispose()}},ws=class extends Qo{model;cache;emission;#t;#e=new Map;constructor(t,n){super(),this.model=t,this.cache=n}stepUniformPack(t){let n=this.#e.get(t);return n||(n=Go(this.#t,t,this.compiled.nodeVariants),this.#e.set(t,n)),n}get runtime(){return this.model.runtime}async prepareBuild(){}onCompiled(){}async build(t,{fuse:n=false}={}){await this.prepareBuild();let{emission:r,graph:s}=this.buildGraph(t,n);this.emission=r,this.#t=s,this.#e.clear();let a=r.nextTokenTailNodeCount,o=a===void 0?null:s.nodes.length-a;if(a!==void 0&&(!Number.isInteger(a)||a<=0||o===null||o<=0))throw new Error(`graph "${s.name}": next-token tail must be a positive proper suffix (got ${a} of ${s.nodes.length} 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this.prepareBuild(),this.emission=this.buildEmission(),this.#t.clear(),this.compiled=gn(this.emission.graph,this.runtime,{weights:this.emission.weights,states:this.emission.states,...this.emission.inputs?{inputs:this.emission.inputs}:{}});let t=this.emission.nextTokenTailNodeCount,n=t===void 0?null:this.emission.graph.nodes.length-t;if(t!==void 0&&(!Number.isInteger(t)||t<=0||n===null||n<=0))throw new Error(`graph "${this.emission.graph.name}": next-token tail must be a positive proper suffix (got ${t} of ${this.emission.graph.nodes.length} nodes)`);if(n===null)this.bodySteps=null,this.steps=await this.compiled.buildSteps();else{let r=await this.compiled.buildSteps({endNode:n}),s=await this.compiled.buildSteps({startNode:n});if(r.length===0||s.length===0)throw new Error(`graph "${this.emission.graph.name}": next-token tail split produced an empty prepared sequence`);this.bodySteps=r,this.steps=[...r,...s]}this.onCompiled()}writeInputs(t,n){if(t.length===0||t.length>this.blockLen)throw new Error(`prefill block ${this.blockLen} cannot run ${t.length} tokens`);let r=new Uint32Array(this.blockLen);r.set(t),this.runtime.host.writeBuffer(this.compiled.tensor("input_ids").buffer,0,r),this.writeRunInputs(t.length,n)}enqueueBody(t,n=0){if(!this.bodySteps)throw new Error(`prefill graph "${this.emission.graph.name}" has no removable next-token tail`);this.writeInputs(t,n),this.compiled.collector.enqueue(this.bodySteps)}async run(t,n=0){return this.writeInputs(t,n),this.compiled.collector.enqueue(this.steps),(await this.runtime.readTensor(this.compiled.tensor("next_token")))[0]}dispose(){this.compiled?.dispose()}}});function Dt(e2,t,n){let r=t.maxLength*e2.config.num_key_value_heads*e2.config.head_dim,s=n*r;return{cacheKeysT:Te(t.keyStates,s,r),cacheValuesT:Te(t.valueStates,s,r)}}function Ut(e2,t){return dd(e2,t)}function ew(e2,t,n){return dd(e2,n.map(r=>`layers.${t}.${r}`))}function dd(e2,t){let n=e2.packs.q1;if(!n)throw new Error("Q1 resource views requested but no q1 pack is loaded");let r=Po(n,t);return{q1BitsT:Te(n.bits,r.baseBlock*4,r.blockCount*4),q1ScalesT:Te(n.scales,r.baseBlock,r.blockCount),offset:s=>Qt(n.offsets,s,"offset")-r.baseBlock,colMajor:s=>n.colMajorIds.has(s)?1:0}}function Pt(e2,t){return(n,r,s)=>{let a=ew(t,n,r);return{bitsT:e2(`L${n}.q1bits.${s}`,a.q1BitsT),scalesT:e2(`L${n}.q1scales.${s}`,a.q1ScalesT),offset:o=>a.offset(o)}}}function tw(e2,t,n){return nw(e2,n.map(r=>`layers.${t}.${r}`))}function Lt(e2,t){return(n,r,s)=>{let a=tw(t,n,r);return{bitsT:e2(`L${n}.q4bits.${s}`,a.q4BitsT),scalesT:e2(`L${n}.q4scales.${s}`,a.q4ScalesT),offset:o=>a.offset(o),format:a.format,quantBits:a.quantBits,hasMin:a.hasMin,iq:a.iq,lut:a.lut,lut16:a.lut16,iqRaw:a.iqRaw,gridT:a.gridT?e2(`iqgrid${a.iqRaw}.${s}`,a.gridT):null}}}function nw(e2,t){let n=[{pack:e2.packs.iq1,iqRaw:1,wordsPerBlock:2},{pack:e2.packs.iq2xxs,iqRaw:2,wordsPerBlock:2},{pack:e2.packs.iq3xxs,iqRaw:3,wordsPerBlock:3}];for(let f of n){let p=f.pack;if(!p||ft(p.offsets,t[0])===void 0)continue;let g=Number.POSITIVE_INFINITY,m=0;for(let C of t){let E=Qt(p.offsets,C,"offset"),R=Qt(p.blockCounts,C,"block count");g=Math.min(g,E),m=Math.max(m,E+R)}let A=Math.floor(g/1024)*1024,h=m-A;return{q4BitsT:Te(p.bits,A*f.wordsPerBlock,h*f.wordsPerBlock),q4ScalesT:Te(p.scales,A/8,Math.ceil(h/8)),offset:C=>Qt(p.offsets,C,"offset")-A,format:"q4_0",hasMin:0,iq:0,lut:0,lut16:0,quantBits:4,iqRaw:f.iqRaw,gridT:p.grid}}let r=pd(e2,t[0]);if(r){let f=r.lutIds.get(t[0])??0;for(let m of t)if(r.lutIds.get(m)!==f)throw new Error(`co-bound LUT weights disagree on lutId: ${t.join(", ")}`);let p=r.lut16?2:1,g=Po(r,t);return{q4BitsT:Te(r.bits,g.baseBlock*4,g.blockCount*4),q4ScalesT:Te(r.scales,g.baseBlock*p,g.blockCount*p),offset:m=>Qt(r.offsets,m,"offset")-g.baseBlock,format:r.lut16?"lut16":"lut32",hasMin:0,iq:0,lut:f,lut16:r.lut16,quantBits:4,iqRaw:0,gridT:null}}let{iq:s,kq4:a,q4:o}=e2.packs,i=s&&ft(s.offsets,t[0])!==void 0?{pack:s,wordsPerBlock:8,scalesPerBlock:2,hasMin:0,iqFlag:1,format:"iq"}:a&&ft(a.offsets,t[0])!==void 0?{pack:a,wordsPerBlock:4,scalesPerBlock:2,hasMin:1,iqFlag:0,format:"q4_k"}:o?{pack:o,wordsPerBlock:o.packedQuantBits,scalesPerBlock:1,hasMin:0,iqFlag:0,format:o.packedQuantBits===8?"q8_0":"q4_0"}:null;if(!i)throw new Error(`No packed-quant route holds ${t[0]}`);let{pack:u,wordsPerBlock:l,scalesPerBlock:c}=i,d=Po(u,t);return{q4BitsT:Te(u.bits,d.baseBlock*l,d.blockCount*l),q4ScalesT:Te(u.scales,d.baseBlock*c,d.blockCount*c),offset:f=>Qt(u.offsets,f,"offset")-d.baseBlock,format:i.format,hasMin:i.hasMin,iq:i.iqFlag,lut:0,lut16:0,quantBits:l,iqRaw:0,gridT:null}}function Po(e2,t){let n=Number.POSITIVE_INFINITY,r=0;for(let i of t){let u=Qt(e2.offsets,i,"offset"),l=Qt(e2.blockCounts,i,"block count");n=Math.min(n,u),r=Math.max(r,u+l)}let a=Ae(e2.scales.dtype)===2?128:64,o=Math.floor(n/a)*a;return{baseBlock:o,blockCount:r-o}}function Ee(e2,t){let{q4:n,iq1:r,iq2xxs:s,iq3xxs:a}=e2.packs;return n!=null&&ft(n.offsets,t)!==void 0||rw(e2,t)||sw(e2,t)||pd(e2,t)!==null||[r,s,a].some(o=>o!=null&&ft(o.offsets,t)!==void 0)}function rw(e2,t){let{kq4:n}=e2.packs;return n!=null&&ft(n.offsets,t)!==void 0}function sw(e2,t){let{iq:n}=e2.packs;return n!=null&&ft(n.offsets,t)!==void 0}function pd(e2,t){for(let n of[e2.packs.lut32,e2.packs.lut16])if(n&&ft(n.offsets,t)!==void 0)return n;return null}function Qt(e2,t,n){let r=ft(e2,t);if(r===void 0||!Number.isInteger(r)||r<0)throw new Error(`Missing packed weight ${n} for ${t}`);return r}function Te(e2,t,n){if(!Number.isInteger(t)||t<0)throw new Error(`tensor view elementOffset must be a non-negative integer, got ${t}`);if(!Number.isInteger(n)||n<=0)throw new Error(`tensor view elementCount must be a positive integer, got ${n}`);let r=Ae(e2.dtype),s=e2.byteOffset??0,a=s+t*r,o=n*r;if(a+o>s+e2.byteLength)throw new Error(`tensor view range exceeds tensor byteLength for dtype ${e2.dtype}`);return{...e2,shape:[n],size:n,byteOffset:a,byteLength:o}}function Lo(e2,t,n){let r=t/2,s=new Float32Array(e2*r),a=new Float32Array(e2*r);for(let o=0;o<e2;++o)for(let i=0;i<r;++i){let u=1/Math.pow(n,i*2/t),l=o*u,c=o*r+i;s[c]=Math.cos(l),a[c]=Math.sin(l)}return{cos:s,sin:a}}function hn(e2){if(e2.ropeCache)return e2.ropeCache;let t=e2.config.rope_parameters?.rope_theta;if(typeof t!="number")throw new Error("RoPE cache requires config.rope_parameters.rope_theta");let n=e2.config.max_position_embeddings,r=e2.config.head_dim/2,{cos:s,sin:a}=Lo(n,e2.config.head_dim,t);return e2.ropeCache={cos:e2.runtime.tensorFromTypedArray("float32",[n,r],s),sin:e2.runtime.tensorFromTypedArray("float32",[n,r],a)},e2.ropeCache}var _n=q(()=>{"use strict";Wt();qe()});function iw(e2){let t=e2.config;if(Ue(t))throw new Error("emitLlamaDecode: linear-attention layers not yet supported (Qwen3.5 stage)");if(Re(e2)&&dt(t))throw new Error("emitLlamaDecode: Q1 conv layers not yet supported");if(Re(e2)&&!Ht(t))throw new Error("emitLlamaDecode: Q1 without qk-norm not yet supported (the packed path needs qk-norm)")}function Xn(e2,t){let n=e2.nodeCount,r=!!(t.model.lmHeadQ4&&t.model.lmHeadQ4Scales),s=!!(t.model.lmHeadQ8&&t.model.lmHeadQ8Scales),a=t.q1||r?512:aw,o=e2.scratch("bestValues","float32",[a]),i=e2.scratch("bestIds","uint32",[a]);if(t.q1){let u=e2.scratch("final.normed",t.normedDtype,[t.hiddenSize]);e2.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:t.hidden,weightsT:t.weights,normedT:u},{args:{hiddenSize:t.hiddenSize,rmsEps:t.rmsEps,normOffset:t.finalNormOffset}});let l=Ut(t.model,["lm_head"]);e2.op("com.xenova.LlamaDecodeLmHeadQ1ArgmaxTiled",{normedT:u,q1BitsT:t.w("lmhead.q1bits",l.q1BitsT),q1ScalesT:t.w("lmhead.q1scales",l.q1ScalesT),bestValuesT:o,bestIdsT:i},{args:{hiddenSize:t.hiddenSize,vocabSize:t.vocabSize,lmHeadOffset:l.offset("lm_head"),numBlocks:a,colMajor:l.colMajor("lm_head")}})}else if(r)e2.op("com.xenova.LlamaDecodeLmHeadQ4ArgmaxTiled",{hiddenT:t.hidden,q4BitsT:t.w("lmhead.q4bits",t.model.lmHeadQ4),q4ScalesT:t.w("lmhead.q4scales",t.model.lmHeadQ4Scales),weightsT:t.weights,bestValuesT:o,bestIdsT:i},{args:{hiddenSize:t.hiddenSize,vocabSize:t.vocabSize,lmHeadOffset:0,finalNormOffset:t.finalNormOffset,numBlocks:a}});else if(s){let u=e2.scratch("final.normQ8","uint32",[t.hiddenSize/4]),l=e2.scratch("final.normQ8Scale","float32",[1]);e2.op("com.xenova.LlamaDecodeFinalNormQuantizeQ8",{hiddenT:t.hidden,weightsT:t.weights,normQ8T:u,normScaleT:l},{args:{hiddenSize:t.hiddenSize,rmsEps:t.rmsEps,finalNormOffset:t.finalNormOffset}}),e2.op("com.xenova.LlamaDecodeLmHeadQ8ArgmaxTiled",{normQ8T:u,normScaleT:l,lmHeadQ8T:t.w("lmhead.q8bits",t.model.lmHeadQ8),lmHeadScalesT:t.w("lmhead.q8scales",t.model.lmHeadQ8Scales),bestValuesT:o,bestIdsT:i},{args:{vocabSize:t.vocabSize,hiddenSize:t.hiddenSize,numBlocks:a}})}else e2.op("com.xenova.LlamaDecodeFinalNormLmHeadArgmaxTiled",{hiddenT:t.hidden,weightsT:t.weights,bestValuesT:o,bestIdsT:i},{args:{hiddenSize:t.hiddenSize,vocabSize:t.vocabSize,rmsEps:t.rmsEps,finalNormOffset:t.finalNormOffset,lmHeadOffset:t.lmHeadOffset,numBlocks:a}});return e2.op("com.xenova.LlamaDecodeBestArgmax",{bestValuesT:o,bestIdsT:i,outputT:t.ids},{args:{outputOffset:0,workgroupSize:ow,inputCount:a}}),e2.nodeCount-n}function qo(e2,t,n){iw(e2);let r=e2.config,s=Ht(r),a=Re(e2),o=r.hidden_size,i=r.num_hidden_layers,u=r.num_attention_heads,l=r.num_key_value_heads,c=r.head_dim,d=l*c,f=dt(r)?he(r.conv_L_cache,"config.conv_L_cache"):0,p=r.intermediate_size,g=r.vocab_size,m=Number(r.rms_norm_eps),A=t.maxLength,h=yt(c),C=c===128?64:od(r),E=hn(e2),R=e2.offsets.top,F=e2.runtime.device.features.has("shader-f16"),M=F&&(e2.weights.dtype==="float16"||a)?"float16":"float32",b=a&&F?"float16":"float32",S=new mt,{w:y,state:I,boundWeights:H,states:W}=xt(S),Q=y("weights",e2.weights),P=y("rope.cos",E.cos),T=y("rope.sin",E.sin),D=S.uniform("params",16),w=Lt(y,e2),_=Pt(y,e2),v=(K,z,ee,ae)=>{let X=`layers.${z}.${ee}`;if(a){let Y=_(z,[ee],`o.${ee}`);return S.op("com.xenova.LlamaDecodeOProj",{hiddenT:K,q1BitsT:Y.bitsT,q1ScalesT:Y.scalesT,attnT:ae},{args:{hiddenSize:o,oOffset:Y.offset(X)}}).hiddenT}if(Ee(e2,X)){let Y=w(z,[ee],`o.${ee}`);return S.op("com.xenova.LlamaDecodeOProj",{hiddenT:K,q4BitsT:Y.bitsT,q4ScalesT:Y.scalesT,attnT:ae},{args:{hiddenSize:o,oOffset:Y.offset(X),format:Y.format,lut:Y.lut}}).hiddenT}return S.op("com.xenova.LlamaDecodeOProj",{hiddenT:K,weightsT:Q,attnT:ae},{args:{hiddenSize:o,oOffset:e2.offsets.layers[z][ee]}}).hiddenT},k=Yr(r),V=(K,z)=>{if(!un(k,z))throw new Error(`moeMlpInto: layer ${z} is not a MoE layer`);let ee=e2.offsets.layers[z],ae=S.scratch(`L${z}.moeProbs`,"float32",[k.numExperts]),X=S.scratch(`L${z}.moeRouteIdx`,"uint32",[k.topK]),Y=S.scratch(`L${z}.moeRouteW`,"float32",[k.topK]);S.op("com.xenova.LlamaDecodeMoeRoute",{hiddenT:K,weightsT:Q,probsT:ae},{args:{hiddenSize:o,numExperts:k.numExperts,rmsEps:m,normOffset:ee.post_attention_layernorm,routerOffset:ee.moe_router}});let re={hasBias:k.useExpertBias?1:0,biasOffset:k.useExpertBias?ee.moe_bias:0,normTopk:k.normTopkProb?1:0,routedScale:k.routedScale},L=S.scratch(`L${z}.moeInter`,M,[k.topK*k.moeIntermediateSize]);if(Ee(e2,`layers.${z}.gate_exps`)){let N=w(z,["gate_exps","up_exps"],"moegateup");S.op("com.xenova.LlamaDecodeMoeGateUpNorm",{hiddenT:K,q4BitsT:N.bitsT,q4ScalesT:N.scalesT,probsT:ae,routeIdxT:X,routeWT:Y,intermediateT:L,weightsT:Q,...N.gridT?{gridT:N.gridT}:{}},{args:{hiddenSize:o,moeIntermediateSize:k.moeIntermediateSize,numExperts:k.numExperts,topK:k.topK,rmsEps:m,normOffset:ee.post_attention_layernorm,...re,gateOffset:N.offset(`layers.${z}.gate_exps`),upOffset:N.offset(`layers.${z}.up_exps`),format:N.format,lut:N.lut,iqRaw:N.iqRaw}})}else S.op("com.xenova.LlamaDecodeMoeGateUpNorm",{hiddenT:K,weightsT:Q,probsT:ae,routeIdxT:X,routeWT:Y,intermediateT:L},{args:{hiddenSize:o,moeIntermediateSize:k.moeIntermediateSize,numExperts:k.numExperts,topK:k.topK,rmsEps:m,normOffset:ee.post_attention_layernorm,...re,gateOffset:ee.gate_exps,upOffset:ee.up_exps}});if(Ee(e2,`layers.${z}.down_exps`)){let N=w(z,["down_exps"],"moedown");return S.op("com.xenova.LlamaDecodeMoeDownProj",{hiddenT:K,q4BitsT:N.bitsT,q4ScalesT:N.scalesT,intermediateT:L,routeIdxT:X,routeWT:Y,...N.gridT?{gridT:N.gridT}:{}},{args:{hiddenSize:o,moeIntermediateSize:k.moeIntermediateSize,numExperts:k.numExperts,topK:k.topK,downOffset:N.offset(`layers.${z}.down_exps`),format:N.format,lut:N.lut,iqRaw:N.iqRaw}}).hiddenT}return S.op("com.xenova.LlamaDecodeMoeDownProj",{hiddenT:K,weightsT:Q,intermediateT:L,routeIdxT:X,routeWT:Y},{args:{hiddenSize:o,moeIntermediateSize:k.moeIntermediateSize,numExperts:k.numExperts,topK:k.topK,downOffset:ee.down_exps}}).hiddenT},j=(K,z)=>{if(un(k,z))return V(K,z);let ee=e2.offsets.layers[z],ae=S.scratch(`L${z}.intermediate`,M,[p]);if(a){let X=_(z,["gate_proj","up_proj"],"gateup");S.op("com.xenova.LlamaDecodeGateUpNorm",{hiddenT:K,q1BitsT:X.bitsT,q1ScalesT:X.scalesT,intermediateT:ae,weightsT:Q},{args:{hiddenSize:o,intermediateSize:p,rmsEps:m,normOffset:ee.post_attention_layernorm,gateOffset:X.offset(`layers.${z}.gate_proj`),upOffset:X.offset(`layers.${z}.up_proj`)}});let Y=_(z,["down_proj"],"down");return S.op("com.xenova.LlamaDecodeDownProj",{hiddenT:K,q1BitsT:Y.bitsT,q1ScalesT:Y.scalesT,intermediateT:ae},{args:{hiddenSize:o,intermediateSize:p,downOffset:Y.offset(`layers.${z}.down_proj`)}}).hiddenT}if(Ee(e2,`layers.${z}.gate_proj`)){let X=w(z,["gate_proj","up_proj"],"gateup");S.op("com.xenova.LlamaDecodeGateUpNorm",{hiddenT:K,q4BitsT:X.bitsT,q4ScalesT:X.scalesT,intermediateT:ae,weightsT:Q},{args:{hiddenSize:o,intermediateSize:p,rmsEps:m,normOffset:ee.post_attention_layernorm,gateOffset:X.offset(`layers.${z}.gate_proj`),upOffset:X.offset(`layers.${z}.up_proj`),format:X.format,lut:X.lut}})}else S.op("com.xenova.LlamaDecodeGateUpNorm",{hiddenT:K,weightsT:Q,intermediateT:ae},{args:{hiddenSize:o,intermediateSize:p,rmsEps:m,normOffset:ee.post_attention_layernorm,gateOffset:ee.gate_proj,upOffset:ee.up_proj}});if(Ee(e2,`layers.${z}.down_proj`)){let X=w(z,["down_proj"],"down");return S.op("com.xenova.LlamaDecodeDownProj",{hiddenT:K,q4BitsT:X.bitsT,q4ScalesT:X.scalesT,intermediateT:ae},{args:{hiddenSize:o,intermediateSize:p,downOffset:X.offset(`layers.${z}.down_proj`),format:X.format,lut:X.lut}}).hiddenT}return S.op("com.xenova.LlamaDecodeDownProj",{hiddenT:K,weightsT:Q,intermediateT:ae},{args:{hiddenSize:o,intermediateSize:p,downOffset:ee.down_proj}}).hiddenT},$2=S.stepInput("input_ids","uint32",[1]),ne;if(a){let K=Ut(e2,["embed_tokens"]);ne=S.op("com.xenova.LlamaEmbed",{inputT:$2,q1BitsT:y("embed.q1bits",K.q1BitsT),q1ScalesT:y("embed.q1scales",K.q1ScalesT),paramsT:D},{args:{hiddenSize:o,vocabSize:g,embedOffset:K.offset("embed_tokens"),tokenOffset:0}}).hiddenT}else e2.embedQ8&&e2.embedQ8Scales?ne=S.op("com.xenova.LlamaEmbed",{inputT:$2,embedQ8T:y("embed.q8bits",e2.embedQ8),embedQ8ScalesT:y("embed.q8scales",e2.embedQ8Scales),paramsT:D},{args:{hiddenSize:o,vocabSize:g,tokenOffset:0}}).hiddenT:ne=S.op("com.xenova.LlamaEmbed",{inputT:$2,weightsT:Q,paramsT:D},{args:{hiddenSize:o,vocabSize:g,embedOffset:R.embed_tokens,tokenOffset:0}}).hiddenT;for(let K=0;K<i;++K){let z=e2.offsets.layers[K];if(An(r,K)==="conv"){if(!t.convStates)throw new Error("emitLlamaDecode: conv layer requires cache.convStates");let ee=Te(t.convStates,K*o*f,o*f),ae=I(`conv.state.${K}`,ee),X=S.scratch(`L${K}.convY`,"float32",[o]);if(Ee(e2,`layers.${K}.conv_in_proj`)){let Y=S.scratch(`L${K}.convProj`,"float32",[3*o]),re=w(K,["conv_in_proj"],"convin");S.op("com.xenova.LlamaDecodeConvInProjNormQ4",{hiddenT:ne,q4BitsT:re.bitsT,q4ScalesT:re.scalesT,weightsT:Q,convProjT:Y},{args:{hiddenSize:o,rmsEps:m,normOffset:z.input_layernorm,convInOffset:re.offset(`layers.${K}.conv_in_proj`),format:re.format,lut:re.lut}}),S.op("com.xenova.LlamaDecodeConvDepthwise",{convProjT:Y,weightsT:Q,convStatesT:ae,convYT:X},{args:{layer:0,hiddenSize:o,convLCache:f,convWeightOffset:z.conv_weight}})}else{let Y=S.scratch(`L${K}.normed`,"float32",[o]);S.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:ne,weightsT:Q,normedT:Y},{args:{hiddenSize:o,rmsEps:m,normOffset:z.input_layernorm}}),S.op("com.xenova.LlamaDecodeConvInUpdate",{normedT:Y,weightsT:Q,convStatesT:ae,convYT:X},{args:{layer:0,hiddenSize:o,convLCache:f,convInOffset:z.conv_in_proj,convWeightOffset:z.conv_weight}})}ne=v(ne,K,"conv_out_proj",X)}else{let ee=Dt(e2,t,K),ae=I(`cache.key.${K}`,ee.cacheKeysT),X=I(`cache.value.${K}`,ee.cacheValuesT),Y;if(a){let L=S.scratch(`L${K}.qkv`,"float32",[o+2*d]),N=_(K,["q_proj","k_proj","v_proj"],"qkv");S.op("com.xenova.LlamaDecodeQkvNormQ1",{hiddenT:ne,q1BitsT:N.bitsT,q1ScalesT:N.scalesT,qkvT:L,weightsT:Q},{args:{hiddenSize:o,numKvHeads:l,headDim:c,rmsEps:m,normOffset:z.input_layernorm,qOffset:N.offset(`layers.${K}.q_proj`),kOffset:N.offset(`layers.${K}.k_proj`),vOffset:N.offset(`layers.${K}.v_proj`)}}),Y=S.op("com.xenova.LlamaDecodeQkNormRopeCacheKvPacked",{qkvT:L,weightsT:Q,cacheKeysT:ae,cacheValuesT:X,cosT:P,sinT:T,paramsT:D},{args:{layer:0,cacheLen:A,pastLen:n,hiddenSize:o,numHeads:u,numKvHeads:l,headDim:c,rmsEps:m,qNormOffset:z.q_layernorm,kNormOffset:z.k_layernorm}}).qkvT}else if(s){let L=S.scratch(`L${K}.q`,"float32",[o]),N=S.scratch(`L${K}.k`,"float32",[d]),x=S.scratch(`L${K}.v`,"float32",[d]);if(Ee(e2,`layers.${K}.q_proj`)){let O=w(K,["q_proj","k_proj","v_proj"],"qkv");S.op("com.xenova.LlamaDecodeQkvNormQ4",{hiddenT:ne,q4BitsT:O.bitsT,q4ScalesT:O.scalesT,weightsT:Q,qT:L,kT:N,vT:x},{args:{hiddenSize:o,numKvHeads:l,headDim:c,rmsEps:m,normOffset:z.input_layernorm,qOffset:O.offset(`layers.${K}.q_proj`),kOffset:O.offset(`layers.${K}.k_proj`),vOffset:O.offset(`layers.${K}.v_proj`),format:O.format,lut:O.lut}})}else{let O=S.scratch(`L${K}.normed`,"float32",[o]);S.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:ne,weightsT:Q,normedT:O},{args:{hiddenSize:o,rmsEps:m,normOffset:z.input_layernorm}}),S.op("com.xenova.LlamaDecodeQkv",{normedT:O,weightsT:Q,qT:L,kT:N,vT:x},{args:{hiddenSize:o,numKvHeads:l,headDim:c,qOffset:z.q_proj,kOffset:z.k_proj,vOffset:z.v_proj}})}Y=S.op("com.xenova.LlamaDecodeQkNormRopeCacheKv",{qT:L,kT:N,vT:x,weightsT:Q,cacheKeysT:ae,cacheValuesT:X,cosT:P,sinT:T,paramsT:D},{args:{layer:0,cacheLen:A,pastLen:n,numHeads:u,numKvHeads:l,headDim:c,rmsEps:m,qNormOffset:z.q_layernorm,kNormOffset:z.k_layernorm}}).qT}else{let L=S.scratch(`L${K}.q`,"float32",[o]);Y=S.op("com.xenova.LlamaDecodeQkvRopeCacheNorm",{hiddenT:ne,weightsT:Q,qT:L,cacheKeysT:ae,cacheValuesT:X,cosT:P,sinT:T,paramsT:D},{args:{layer:0,cacheLen:A,pastLen:n,hiddenSize:o,rmsEps:m,normOffset:z.input_layernorm,numHeads:u,numKvHeads:l,headDim:c,qOffset:z.q_proj,kOffset:z.k_proj,vOffset:z.v_proj}}).qT}let re;if(ad(r)){let L=S.op("com.xenova.LlamaDecodeAttentionSplitK",{qT:Y,cacheKeysT:ae,cacheValuesT:X,paramsT:D},{args:{layer:0,cacheLen:A,pastLen:n,numHeads:u,numKvHeads:l,headDim:c,scale:h,workgroupSize:C,tileSize:16,numKvChunks:To,minKeysPerChunk:rd}});re=S.op("com.xenova.LlamaDecodeAttentionSplitKMerge",{partialOutT:L.partialOutT,partialMetaT:L.partialMetaT},{args:{numHeads:u,headDim:c,numKvChunks:To}}).attnT}else{let L=S.scratch(`L${K}.attn`,"float32",[u*c]);re=S.op("com.xenova.LlamaDecodeAttention",{qT:Y,cacheKeysT:ae,cacheValuesT:X,attnT:L,paramsT:D},{args:{layer:0,cacheLen:A,pastLen:n,numHeads:u,numKvHeads:l,headDim:c,scale:h,workgroupSize:jn(r)}}).attnT}ne=v(ne,K,"o_proj",re)}ne=j(ne,K)}let Ge=Xn(S,{w:y,model:e2,hidden:ne,weights:Q,ids:$2,hiddenSize:o,vocabSize:g,rmsEps:m,finalNormOffset:R.model_norm,lmHeadOffset:R.lm_head,q1:a,normedDtype:b});return{graph:S.finish({name:"llama-decode",params:{positionOffset:n}}),weights:H,states:W,paramsName:"params",nextTokenTailNodeCount:Ge}}var aw,ow,yt,uw,lw,Yn,bs,Jn=q(()=>{"use strict";Vn();ud();Do();_n();Gt();pt();aw=256,ow=256;yt=e2=>Number(Math.pow(e2,-.5).toPrecision(9));uw={id:"rms-qkv",producer:"com.xenova.LlamaDecodeRmsNorm",producerOutput:"normedT",consumer:"com.xenova.LlamaDecodeQkv",consumerInput:"normedT",fuse:(e2,t)=>{let n=e2.args??{},r=t.args??{};return{op:"com.xenova.LlamaDecodeQkvNorm",inputs:{hiddenT:e2.inputs.hiddenT,weightsT:e2.inputs.weightsT},outputs:{...t.outputs},args:{hiddenSize:n.hiddenSize,numKvHeads:r.numKvHeads,headDim:r.headDim,rmsEps:n.rmsEps,normOffset:n.normOffset,qOffset:r.qOffset,kOffset:r.kOffset,vOffset:r.vOffset}}}},lw=[uw],Yn=class extends ws{writeStepUniforms(t){let n=this.stepUniformPack(this.emission.paramsName)({past_len:t,cache_len:this.cache.maxLength,token_offset:0});this.runtime.host.writeBuffer(this.compiled.uniformBuffer(this.emission.paramsName),0,n)}},bs=class extends Yn{buildGraph(t,n){let r=qo(this.model,this.cache,t);return{emission:r,graph:n?id(r.graph,lw):r.graph}}}});function Ro(e2){let t=e2.config;return!(Re(e2)||Ue(t))}function cw({packedQ1:e2,activationDtype:t,rows:n,inFeatures:r,intermediateSize:s}){return e2&&t==="float16"&&r%128===0&&s%64===0&&s>=r&&(n>=64||n===16&&2*s>=3*r)}function Mo({g:e2,model:t,weights:n,q4w:r,q1w:s,q1RealLenT:a,T:o,H:i,inter:u,eps:l,actGemm:c,fusedQ4:d,q1Activation:f}){let p=Re(t),g=(y,I,H,W,Q,P,T)=>e2.op("com.xenova.LlamaPrefillDequant",{q1BitsT:H.bitsT,q1ScalesT:H.scalesT,denseT:y},{args:{denseElems:I,blockOffset:W,outRows:Q,inFeatures:P,dstElemOffset:T}}).denseT,m=(y,I,H,W,Q,P,T,D)=>e2.op("com.xenova.LlamaPrefillMatmulQ1",{aT:y,q1BitsT:I.bitsT,q1ScalesT:I.scalesT,yT:P,...a&&W%64===0?{realLenT:a}:{}},{args:{M:o,inFeatures:Q,outFeatures:W,blockOffset:H,outStride:T,dstColStart:D}}).yT,A=(y,I)=>y%4===0&&I%128===0&&o<=65535,h=(y,I,H,W,Q,P,T,D)=>e2.op("com.xenova.LlamaPrefillMatmulQ4",{aT:y,q4BitsT:I.bitsT,q4ScalesT:I.scalesT,yT:P},{args:{M:o,inFeatures:Q,outFeatures:W,blockOffset:H,outStride:T,dstColStart:D,format:I.format,lut:I.lut}}).yT,C=(y,I,H)=>d&&!y.iqRaw&&I%64===0&&H%32===0,E=(y,I,H,W,Q,P,T)=>e2.op("com.xenova.LlamaPrefillDequant",{q4BitsT:H.bitsT,q4ScalesT:H.scalesT,denseT:y},{args:{denseElems:I,blockOffset:W,outRows:Q,inFeatures:P,dstElemOffset:T,format:H.format,lut:H.lut}}).denseT,R=(y,I,H,W,Q,P,T)=>{let D=e2.scratch(T,c,[o,Q]);if(p){let w=s(y,[H],H),_=w.offset(`layers.${y}.${H}`);if(A(Q,P))return m(I,w,_,Q,P,D,Q,0);let v=Q*P,k=g(e2.scratch(`${T}.w`,c,[v]),v,w,_,Q,P,0);return e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:k,yT:D},{args:{weightOffset:0,M:o,inFeatures:P,outFeatures:Q}}).yT}if(Ee(t,`layers.${y}.${H}`)){let w=r(y,[H],H);if(C(w,Q,P))return h(I,w,w.offset(`layers.${y}.${H}`),Q,P,D,Q,0);let _=Q*P,v=E(e2.scratch(`${T}.w`,c,[_]),_,w,w.offset(`layers.${y}.${H}`),Q,P,0);return e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:v,yT:D},{args:{weightOffset:0,M:o,inFeatures:P,outFeatures:Q}}).yT}return e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:n,yT:D},{args:{weightOffset:W,M:o,inFeatures:P,outFeatures:Q}}).yT},F=(y,I,H,W)=>{let Q=e2.scratch(W,c,[o,2*u]);if(p){let P=s(y,["gate_proj","up_proj"],"gateup");if(A(u,i)){let w=m(I,P,P.offset(`layers.${y}.gate_proj`),u,i,Q,2*u,0);return m(I,P,P.offset(`layers.${y}.up_proj`),u,i,w,2*u,u)}let T=2*u*i,D=e2.scratch(`${W}.w`,c,[T]);return D=g(D,T,P,P.offset(`layers.${y}.gate_proj`),u,i,0),D=g(D,T,P,P.offset(`layers.${y}.up_proj`),u,i,u*i),e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:D,yT:Q},{args:{weightOffset:0,M:o,inFeatures:i,outFeatures:2*u}}).yT}if(Ee(t,`layers.${y}.gate_proj`)){let P=r(y,["gate_proj","up_proj"],"gateup");if(C(P,u,i)){let w=h(I,P,P.offset(`layers.${y}.gate_proj`),u,i,Q,2*u,0);return h(I,P,P.offset(`layers.${y}.up_proj`),u,i,w,2*u,u)}let T=2*u*i,D=e2.scratch(`${W}.w`,c,[T]);return D=E(D,T,P,P.offset(`layers.${y}.gate_proj`),u,i,0),D=E(D,T,P,P.offset(`layers.${y}.up_proj`),u,i,u*i),e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:D,yT:Q},{args:{weightOffset:0,M:o,inFeatures:i,outFeatures:2*u}}).yT}return e2.op("com.xenova.LlamaPrefillMatmul",{aT:I,weightsT:n,yT:Q},{args:{weightOffset:H,M:o,inFeatures:i,outFeatures:2*u}}).yT},M=Yr(t.config),b=(y,I,H)=>{if(!un(M,y))throw new Error(`moeMlpInto: layer ${y} is not a MoE layer`);let W=e2.op("com.xenova.LlamaPrefillRmsNorm",{xT:H,weightsT:n,yT:e2.scratch(`L${y}.ffnorm`,c,[o,i])},{args:{normOffset:I.post_attention_layernorm,rows:o,dim:i,eps:l}}).yT,Q=e2.scratch(`L${y}.moeRouteIdx`,"uint32",[o*M.topK]),P=e2.scratch(`L${y}.moeRouteW`,"float32",[o*M.topK]);e2.op("com.xenova.LlamaPrefillMoeRoute",{normedT:W,weightsT:n,routeIdxT:Q,routeWT:P},{args:{hiddenSize:i,seqLen:o,numExperts:M.numExperts,topK:M.topK,routerOffset:I.moe_router,hasBias:M.useExpertBias?1:0,biasOffset:M.useExpertBias?I.moe_bias:0,normTopk:M.normTopkProb?1:0,routedScale:M.routedScale}});let T=M.moeIntermediateSize,D=e2.scratch(`L${y}.moeInter`,c,[o,M.topK*T]),w=Ee(t,`layers.${y}.gate_exps`),_=Ee(t,`layers.${y}.down_exps`),v=o*M.topK,k=null;if(w||_){let j=Math.ceil(v/16)+M.numExperts;k={list:e2.scratch(`L${y}.moeSlotList`,"uint32",[v]),tiles:e2.scratch(`L${y}.moeTiles`,"uint32",[j*3]),tileCount:e2.scratch(`L${y}.moeTileCount`,"uint32",[1])},e2.op("com.xenova.LlamaPrefillMoeGroup",{routeIdxT:Q,listT:k.list,tilesT:k.tiles,tileCountT:k.tileCount},{args:{seqLen:o,numExperts:M.numExperts,topK:M.topK}})}if(w&&k){let j=r(y,["gate_exps","up_exps"],"moegateup");e2.op("com.xenova.LlamaPrefillMoeGateUpGrouped",{normedT:W,q4BitsT:j.bitsT,q4ScalesT:j.scalesT,listT:k.list,tilesT:k.tiles,tileCountT:k.tileCount,intermediateT:D,...j.gridT?{gridT:j.gridT}:{}},{args:{hiddenSize:i,moeIntermediateSize:T,seqLen:o,numExperts:M.numExperts,topK:M.topK,gateOffset:j.offset(`layers.${y}.gate_exps`),upOffset:j.offset(`layers.${y}.up_exps`),format:j.format,lut:j.lut,iqRaw:j.iqRaw}})}else e2.op("com.xenova.LlamaPrefillMoeGateUp",{normedT:W,weightsT:n,routeIdxT:Q,intermediateT:D},{args:{hiddenSize:i,moeIntermediateSize:T,seqLen:o,numExperts:M.numExperts,topK:M.topK,gateOffset:I.gate_exps,upOffset:I.up_exps}});let V=e2.scratch(`L${y}.moedown`,c,[o,i]);if(_&&k){let j=r(y,["down_exps"],"moedown"),$2=e2.scratch(`L${y}.moeSlotY`,"float32",[v*i]);e2.op("com.xenova.LlamaPrefillMoeDownGrouped",{intermediateT:D,q4BitsT:j.bitsT,q4ScalesT:j.scalesT,listT:k.list,tilesT:k.tiles,tileCountT:k.tileCount,routeWT:P,slotYT:$2,...j.gridT?{gridT:j.gridT}:{}},{args:{hiddenSize:i,moeIntermediateSize:T,seqLen:o,numExperts:M.numExperts,topK:M.topK,downOffset:j.offset(`layers.${y}.down_exps`),format:j.format,lut:j.lut,iqRaw:j.iqRaw}}),e2.op("com.xenova.LlamaPrefillMoeDownReduce",{slotYT:$2,yT:V},{args:{hiddenSize:i,seqLen:o,topK:M.topK}})}else e2.op("com.xenova.LlamaPrefillMoeDown",{intermediateT:D,weightsT:n,routeIdxT:Q,routeWT:P,yT:V},{args:{hiddenSize:i,moeIntermediateSize:T,seqLen:o,numExperts:M.numExperts,topK:M.topK,downOffset:I.down_exps}});e2.op("com.xenova.AddInPlace",{yT:H,xT:V},{args:{count:o*i}})};return{dequant:E,fused:h,canFuse:C,proj:R,gateUp:F,mlpInto:(y,I,H)=>{if(un(M,y)){b(y,I,H);return}let W=e2.op("com.xenova.LlamaPrefillRmsNorm",{xT:H,weightsT:n,yT:e2.scratch(`L${y}.ffnorm`,c,[o,i])},{args:{normOffset:I.post_attention_layernorm,rows:o,dim:i,eps:l}}).yT,Q=e2.scratch(`L${y}.mlpact`,c,[o,u]),P;if(cw({packedQ1:p,activationDtype:c,rows:o,inFeatures:i,intermediateSize:u})){let D=s(y,["gate_proj","up_proj"],"gateup");P=e2.op("com.xenova.LlamaPrefillGateUpQ1",{aT:W,q1BitsT:D.bitsT,q1ScalesT:D.scalesT,yT:Q,...a?{realLenT:a}:{}},{args:{M:o,inFeatures:i,intermediateSize:u,gateOffset:D.offset(`layers.${y}.gate_proj`),upOffset:D.offset(`layers.${y}.up_proj`),...f?{activation:f}:{}}}).yT}else{let 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wt({vocab_size:_w(e2),hidden_size:s,intermediate_size:t["llama.feed_forward_length"],num_hidden_layers:a,num_attention_heads:r,num_key_value_heads:t["llama.attention.head_count_kv"]??r,head_dim:t["llama.attention.key_length"]??Math.floor(s/r),max_position_embeddings:t["llama.context_length"],rms_norm_eps:t["llama.attention.layer_norm_rms_epsilon"],rope_parameters:{rope_theta:t["llama.rope.freq_base"]??1e4,rope_type:"default"},hidden_act:"silu",use_cache:true,attention_bias:false,attention_dropout:0,mlp_bias:false,layer_types:Array.from({length:a},()=>"full_attention"),eos_token_id:t["tokenizer.ggml.eos_token_id"],bos_token_id:t["tokenizer.ggml.bos_token_id"],pad_token_id:t["tokenizer.ggml.padding_token_id"]??null,tie_word_embeddings:!e2.has("output.weight")})}var Aw,wd,Zt,hw,yw,yd=q(()=>{"use strict";pt();Fl();Wc();vo();Yc();Co();gs();Eo();Jn();Wo();ss();hd();No();_t();jo();Yo();Uo();eo();pt();mn();Aw=4,wd=So(()=>yw),Zt=class{runtime;config;weights;offsets;lmHeadQ8;lmHeadQ8Scales;lmHeadQ4;lmHeadQ4Scales;embedQ8;embedQ8Scales;packs;cacheDtype;programCache;defaultPositionIdsCache;ropeCache;qwen35RopeCache;#t=new Nn;#e=new Nn(t=>{for(let n of t.values())n.dispose()});#n;constructor({runtime:t,config:n,weights:r,offsets:s,lmHeadQ8:a=null,lmHeadQ8Scales:o=null,lmHeadQ4:i=null,lmHeadQ4Scales:u=null,embedQ8:l=null,embedQ8Scales:c=null,cacheDtype:d="float32",packs:f={}}){this.runtime=t,this.config=Fn(n),this.weights=r,this.offsets=s,this.lmHeadQ8=a,this.lmHeadQ8Scales=o,this.lmHeadQ4=i,this.lmHeadQ4Scales=u,this.embedQ8=l,this.embedQ8Scales=c,this.packs=f,this.cacheDtype=d,this.programCache=new Map,this.defaultPositionIdsCache=new Map,this.ropeCache=null}static ConfigClass=wt;static MODEL_NAME="Llama";static expectedWeightEntries=Ha;static DECODE_GRAPH_EMIT=(t,n)=>qo(t,n,0);static _coerceConfig(t){return mo(this,t)}static loadStateDict(t,n,r){return Oc(this,t,n,r)}static loadGGUFFile=wd.loadGGUFFile;static loadGGUF=wd.loadGGUF;dispose(){this.#t.disposeAll(),this.#e.disposeAll(),Ie(this.weights),Ie(this.lmHeadQ8),Ie(this.lmHeadQ8Scales),Ie(this.lmHeadQ4),Ie(this.lmHeadQ4Scales),Ie(this.embedQ8),Ie(this.embedQ8Scales);for(let t of Object.values(this.packs))Ie(t?.bits??null),Ie(t?.scales??null);Ie(this.ropeCache),Ie(this.qwen35RopeCache),Ie(this.defaultPositionIdsCache),this.programCache.clear(),this.ropeCache=null,this.qwen35RopeCache=null,this.lmHeadQ8=null,this.lmHeadQ8Scales=null,this.lmHeadQ4=null,this.lmHeadQ4Scales=null,this.embedQ8=null,this.embedQ8Scales=null,this.packs={}}async forward(t={}){return Ad(this,t)}async generate(t){let{input_ids:n,max_new_tokens:r,do_sample:s=false,force_token_path:a=false}=t;Fo(r,"generate"),Zo(s,"LlamaForCausalLM.generate");let{shape:o,data:i}=vs(n,"input_ids");if(o.length!==2)throw new Error("generate expects input_ids shape [batch, sequence]");let[u,l]=o,c=l+r;if(c>this.config.max_position_embeddings)throw new Error(`generated length ${c} exceeds max_position_embeddings ${this.config.max_position_embeddings}`);let d=new Uint32Array(u*c);for(let g=0;g<u;++g)for(let m=0;m<l;++m)d[g*c+m]=i[g*l+m];if(r===0)return this.runtime.tensorFromTypedArray("uint32",[u,c],d);if(a||hs(this.config))return this.#a({data:i,batchSize:u,promptLength:l,totalLength:c,maxNewTokens:r});let f=null,p=this.runtime.tensorFromTypedArray("uint32",[u,l],i);for(let g=0;g<r;++g){let m=await this.forward({input_ids:p,past_key_values:f,use_cache:true,logits_to_keep:1});f=m.past_key_values;let A=await this.runtime.readTensor(m.logits),h=m.logits.shape[1],C=new Uint32Array(u);for(let E=0;E<u;++E){let R=(E*h+h-1)*this.config.vocab_size,F=$o(A,R,this.config.vocab_size);C[E]=F,d[E*c+l+g]=F}p=this.runtime.tensorFromTypedArray("uint32",[u,1],C)}return this.runtime.tensorFromTypedArray("uint32",[u,c],d)}async*streamTokenIds(t){yield*this.#r(t,{fromCache:false})}createGenerationState(t={}){let n=t.batchSize??t.batch_size??1,r=_s(this),s=t.maxLength??t.max_length??r;if(n!==1)throw new Error("createGenerationState V1 supports batch size 1");if(!Number.isInteger(s)||s<1||s>r)throw new Error(`maxLength must be an integer in [1, ${r}]`);return As(this,s)}async*streamTokenIdsFromCache(t){yield*this.#r(t,{fromCache:true})}async*#r(t,{fromCache:n}){let r=n?"streamTokenIdsFromCache":"streamTokenIds",{input_ids:s,max_new_tokens:a,generation_state:o=null,past_key_values:i=null,do_sample:u=false,force_token_path:l=false}=t,c=n?o?.cache??i:null;Fo(a,r);let{eosTokenId:d,stopOnEos:f}=Ss(t,{eosTokenId:this.config.eos_token_id});if(Zo(u,`LlamaForCausalLM.${r}`),n&&c==null)throw new Error("streamTokenIdsFromCache requires generation_state, cache, or past_key_values");let{shape:p,data:g}=vs(s,"input_ids");if(p.length!==2)throw new Error(`${r} expects input_ids shape [batch, sequence]`);let[m,A]=p;if(m!==1)throw new Error(`${r} V1 supports batch size 1`);if(n){if(A<1)throw new Error("streamTokenIdsFromCache requires at least one input token");if(Cs(c,this.runtime,this.config,m),c.get_seq_length()+A+a>c.maxLength)throw new Error(`cache length ${c.get_seq_length()} + input length ${A} + max_new_tokens ${a} exceeds maxLength ${c.maxLength}`)}else{let C=A+a;if(C>this.config.max_position_embeddings)throw new Error(`generated length ${C} exceeds max_position_embeddings ${this.config.max_position_embeddings}`)}if(a===0)return;if(!l&&!hs(this.config)){yield*this.#s({tokenIds:g,inputLength:A,cache:c,maxNewTokens:a,eosTokenId:d,stopOnEos:f});return}let h=!n;if(n||(c=As(this,A+a).cache),!c)throw new Error(`${r} requires a generation cache`);try{yield*this.#l({tokenIds:g,inputLength:A,cache:c,eosTokenId:d,generationArgs:t})}finally{h&&c.dispose()}}async#a({data:t,batchSize:n,promptLength:r,totalLength:s,maxNewTokens:a}){if(n!==1)throw new Error("large-model generation V1 supports batch size 1");let o=new Uint32Array(s);if(o.set(t),a===0)return this.runtime.tensorFromTypedArray("uint32",[1,s],o);let i=As(this,s),u=0;try{for await(let l of this.#l({tokenIds:o,inputLength:r,cache:i.cache,generationArgs:{max_new_tokens:a,stop_on_eos:false}}))o[r+u]=l,u+=1}finally{i.cache.dispose()}return this.runtime.tensorFromTypedArray("uint32",[1,s],o)}async*#s({tokenIds:t,inputLength:n,cache:r=null,maxNewTokens:s,eosTokenId:a,stopOnEos:o}){let i=this.runtime.tensorFromTypedArray("uint32",[1,n],t);for(let u=0;u<s;++u){let l=await this.forward({input_ids:i,past_key_values:r,use_cache:true,logits_to_keep:1});r=l.past_key_values;let c=await this.runtime.readTensor(l.logits),d=l.logits.shape[1],f=$o(c,(d-1)*this.config.vocab_size,this.config.vocab_size);if(o&&Bs(f,a))return;yield f,i=this.runtime.tensorFromTypedArray("uint32",[1,1],new Uint32Array([f]))}}async*#l({tokenIds:t,inputLength:n,cache:r,eosTokenId:s,generationArgs:a}){let o=this.#f(r,{eosTokenId:s,pipelineDepth:nr(a,Aw,Ft)});yield*_d(o,t.subarray(0,n),a)}makeGraphDecodeSession(t){return new bs(this,t)}#o(t){return this.#t.getOrBuild(t,async()=>{let n=this.makeGraphDecodeSession(t);return await n.build(t.get_seq_length(),{fuse:true}),n})}prefillSupported(){return Ro(this)}makeGraphPrefillSession(t,n){return new ks(this,t,n)}#u(){return false?false:this.#n??=this.prefillSupported()}async#i(t,n){let r=await this.#e.getOrBuild(t,()=>new Map),s=r.get(n);return s||(s=this.makeGraphPrefillSession(t,n),await s.build(),r.set(n,s)),s}async warmup(t,n){if(await t.mutableStateCheckpoint?.(),!this.#u()){await Vo(this,t,[1]);return}let r=n?.promptTokens!==void 0?this.#d(n.promptTokens,t):Ic((a,o,i)=>this.planPrefillChunks(a,o,i),t.maxLength),s=r.find(a=>a+2<=t.maxLength);await Vo(this,t,[s??1]);for(let a of r)a!==s&&await this.#i(t,a)}#d(t,n){return io(this.planPrefillChunks(t,0,n.maxLength))}async#c(t,n,r){let s=await this.#o(t),a=n.length-1,o=false;for(let i=0;i<a;++i)s.enqueueStep(n[i],r+i,{full:o});return s.submitStep(n[a],r+a)}prefillMinTokens(){return vc/2}planPrefillChunks(t,n,r){return Cc({tokenCount:t,position:n,maxLength:r,minGraphTokens:this.prefillMinTokens()})}async#p(t,n,r){if(!this.#u())return this.#c(t,n,r);let s=this.planPrefillChunks(n.length,r,t.maxLength),a=0,o=r,i=0;for(let u=0;u<s.length;++u){let l=s[u],c=n.subarray(a,a+l.tokenCount);if(l.kind==="graph"){let d=await this.#i(t,l.blockLen);!(u===s.length-1)&&true?d.enqueueBody(c,o):i=await d.run(c,o)}else i=await this.#c(t,c,o);a+=l.tokenCount,o+=l.tokenCount}return i}#f(t,{eosTokenId:n,pipelineDepth:r}){return{cache:t,eosTokenId:n,pipelineDepth:r,prefill:(s,a)=>this.#p(t,s,a),beginDecode:()=>this.#o(t)}}},hw=new Set(["o_proj","gate_proj","up_proj","down_proj"]);yw=ms({configFromGGUF:ww,entriesFor:Ha,nameToGGUF:Hc,packSuffixes:hw,emitGraph:Zt.DECODE_GRAPH_EMIT,modelName:Zt.MODEL_NAME,construct:e2=>new Zt(e2)})});function Ts(e2){if(!Is(e2).qwen35_partial_rotary)return hn(e2);if(e2.qwen35RopeCache)return e2.qwen35RopeCache;let t=Is(e2).rotary_dim,n=t/2,r=Is(e2).max_position_embeddings,s=Number(Is(e2).rope_parameters.rope_theta),{cos:a,sin:o}=Lo(r,t,s);return e2.qwen35RopeCache={cos:e2.runtime.tensorFromTypedArray("float32",[r,n],a),sin:e2.runtime.tensorFromTypedArray("float32",[r,n],o)},e2.qwen35RopeCache}var Is,Xo=q(()=>{"use strict";_n();Is=e2=>e2.config});function rr(e2,t,n){let r=e2.config,s=r.hidden_size,a=r.num_hidden_layers,o=r.num_attention_heads,i=r.num_key_value_heads,u=r.head_dim,l=r.attention_size,c=r.kv_size,d=r.rotary_dim,f=r.intermediate_size,p=r.vocab_size,g=Number(r.rms_norm_eps),m=t.maxLength,A=r.layer_types,h=r.linear_key_dim,C=r.linear_value_dim,E=r.linear_conv_dim,R=r.linear_conv_kernel_dim,F=R-1,M=r.linear_num_key_heads,b=r.linear_num_value_heads,S=r.linear_key_head_dim,y=r.linear_value_head_dim,I=yt(u),H=yt(S),W=Math.max(64,Tt(y)),Q=Tt(Math.ceil(S/4)),P=Tt(S),T=Ts(e2),D=e2.offsets.top,w=new mt,{w:_,state:v,boundWeights:k,states:V}=xt(w),j=_("weights",e2.weights),$2=_("rope.cos",T.cos),ne=_("rope.sin",T.sin),Ge=w.uniform("params",16),K=Lt(_,e2),z=Re(e2),ee=!!e2.runtime.device.features.has("shader-f16"),ae=Pt(_,e2),X=(x,O,oe,pe,le,se)=>{if(z){let de=ae(x,[pe],`o.${pe}`);return w.op("com.xenova.Qwen35DecodeOProj",{hiddenT:O,q1BitsT:de.bitsT,q1ScalesT:de.scalesT,attnT:oe},{args:{hiddenSize:s,inFeatures:se,oOffset:de.offset(`layers.${x}.${pe}`)}}).hiddenT}if(Ee(e2,`layers.${x}.${pe}`)){let de=K(x,[pe],`o.${pe}`);return w.op("com.xenova.Qwen35DecodeOProj",{hiddenT:O,q4BitsT:de.bitsT,q4ScalesT:de.scalesT,attnT:oe},{args:{hiddenSize:s,inFeatures:se,oOffset:de.offset(`layers.${x}.${pe}`),format:de.format,lut:de.lut}}).hiddenT}return w.op("com.xenova.Qwen35DecodeOProj",{hiddenT:O,weightsT:j,attnT:oe},{args:{hiddenSize:s,inFeatures:se,oOffset:le}}).hiddenT},Y=(x,O,oe)=>{if(z){let se=w.scratch(`L${O}.intermediate`,ee?"float16":"float32",[f]),de=w.scratch(`L${O}.postNormed`,"float32",[s]);w.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:x,weightsT:j,normedT:de},{args:{hiddenSize:s,rmsEps:g,normOffset:oe.post_attention_layernorm}});let Z=ae(O,["gate_proj","up_proj"],"gateup");w.op("com.xenova.LlamaDecodeGateUp",{normedT:de,q1BitsT:Z.bitsT,q1ScalesT:Z.scalesT,intermediateT:se},{args:{hiddenSize:s,intermediateSize:f,gateOffset:Z.offset(`layers.${O}.gate_proj`),upOffset:Z.offset(`layers.${O}.up_proj`),...false?{activation:"fast_silu"}:{}}});let ce=ae(O,["down_proj"],"down");return w.op("com.xenova.LlamaDecodeDownProj",{hiddenT:x,q1BitsT:ce.bitsT,q1ScalesT:ce.scalesT,intermediateT:se},{args:{hiddenSize:s,intermediateSize:f,downOffset:ce.offset(`layers.${O}.down_proj`)}}).hiddenT}let pe=w.scratch(`L${O}.postNormed`,"float32",[s]);w.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:x,weightsT:j,normedT:pe},{args:{hiddenSize:s,rmsEps:g,normOffset:oe.post_attention_layernorm}});let le=w.scratch(`L${O}.intermediate`,"float32",[f]);if(Ee(e2,`layers.${O}.gate_proj`)){let se=K(O,["gate_proj","up_proj"],"gateup");w.op("com.xenova.LlamaDecodeGateUp",{normedT:pe,q4BitsT:se.bitsT,q4ScalesT:se.scalesT,intermediateT:le},{args:{hiddenSize:s,intermediateSize:f,gateOffset:se.offset(`layers.${O}.gate_proj`),upOffset:se.offset(`layers.${O}.up_proj`),format:se.format,lut:se.lut}})}else w.op("com.xenova.LlamaDecodeGateUp",{normedT:pe,weightsT:j,intermediateT:le},{args:{hiddenSize:s,intermediateSize:f,gateOffset:oe.gate_proj,upOffset:oe.up_proj}});if(Ee(e2,`layers.${O}.down_proj`)){let se=K(O,["down_proj"],"down");return w.op("com.xenova.LlamaDecodeDownProj",{hiddenT:x,q4BitsT:se.bitsT,q4ScalesT:se.scalesT,intermediateT:le},{args:{hiddenSize:s,intermediateSize:f,downOffset:se.offset(`layers.${O}.down_proj`),format:se.format,lut:se.lut}}).hiddenT}return w.op("com.xenova.LlamaDecodeDownProj",{hiddenT:x,weightsT:j,intermediateT:le},{args:{hiddenSize:s,intermediateSize:f,downOffset:oe.down_proj}}).hiddenT},re=w.stepInput("input_ids","uint32",[1]),L;if(z){let x=Ut(e2,["embed_tokens"]);L=w.op("com.xenova.LlamaEmbed",{inputT:re,q1BitsT:_("embed.q1bits",x.q1BitsT),q1ScalesT:_("embed.q1scales",x.q1ScalesT),paramsT:Ge},{args:{hiddenSize:s,vocabSize:p,embedOffset:x.offset("embed_tokens"),tokenOffset:0}}).hiddenT}else e2.embedQ8&&e2.embedQ8Scales?L=w.op("com.xenova.LlamaEmbed",{inputT:re,embedQ8T:_("embed.q8bits",e2.embedQ8),embedQ8ScalesT:_("embed.q8scales",e2.embedQ8Scales),paramsT:Ge},{args:{hiddenSize:s,vocabSize:p,tokenOffset:0}}).hiddenT:L=w.op("com.xenova.LlamaEmbed",{inputT:re,weightsT:j,paramsT:Ge},{args:{hiddenSize:s,vocabSize:p,embedOffset:D.embed_tokens,tokenOffset:0}}).hiddenT;for(let x=0;x<a;++x){let O=e2.offsets.layers[x];if(A[x]==="full_attention"){let oe=Dt(e2,t,x),pe=v(`cache.key.${x}`,oe.cacheKeysT),le=v(`cache.value.${x}`,oe.cacheValuesT),se=w.scratch(`L${x}.normed`,"float32",[s]);w.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:L,weightsT:j,normedT:se},{args:{hiddenSize:s,rmsEps:g,normOffset:O.input_layernorm}});let de=w.scratch(`L${x}.q`,"float32",[l]),Z=w.scratch(`L${x}.qGate`,"float32",[l]),ce=w.scratch(`L${x}.k`,"float32",[c]),Qe=w.scratch(`L${x}.v`,"float32",[c]);if(z){let ke=ae(x,["q_proj","k_proj","v_proj"],"qkv");w.op("com.xenova.Qwen35DecodeFullQkv",{normedT:se,q1BitsT:ke.bitsT,q1ScalesT:ke.scalesT,qT:de,qGateT:Z,kT:ce,vT:Qe},{args:{qSize:l,kvSize:c,headDim:u,hiddenSize:s,qOffset:ke.offset(`layers.${x}.q_proj`),kOffset:ke.offset(`layers.${x}.k_proj`),vOffset:ke.offset(`layers.${x}.v_proj`)}})}else if(Ee(e2,`layers.${x}.q_proj`)){let ke=K(x,["q_proj","k_proj","v_proj"],"qkv");w.op("com.xenova.Qwen35DecodeFullQkv",{normedT:se,q4BitsT:ke.bitsT,q4ScalesT:ke.scalesT,qT:de,qGateT:Z,kT:ce,vT:Qe},{args:{qSize:l,kvSize:c,headDim:u,hiddenSize:s,qOffset:ke.offset(`layers.${x}.q_proj`),kOffset:ke.offset(`layers.${x}.k_proj`),vOffset:ke.offset(`layers.${x}.v_proj`),format:ke.format,lut:ke.lut}})}else w.op("com.xenova.Qwen35DecodeFullQkv",{normedT:se,weightsT:j,qT:de,qGateT:Z,kT:ce,vT:Qe},{args:{qSize:l,kvSize:c,headDim:u,hiddenSize:s,qOffset:O.q_proj,kOffset:O.k_proj,vOffset:O.v_proj}});w.op("com.xenova.LlamaDecodeQkNorm",{qT:de,kT:ce,weightsT:j},{args:{numHeads:o,numKvHeads:i,headDim:u,rmsEps:g,qNormOffset:O.q_layernorm,kNormOffset:O.k_layernorm}}),w.op("com.xenova.Qwen35DecodePartialRopeCacheKv",{qT:de,kT:ce,vT:Qe,cacheKeysT:pe,cacheValuesT:le,cosT:$2,sinT:ne,paramsT:Ge},{args:{layer:0,cacheLen:m,pastLen:n,numHeads:o,numKvHeads:i,headDim:u,rotaryDim:d}});let at=w.scratch(`L${x}.attn`,"float32",[l]);w.op("com.xenova.Qwen35DecodeFullAttention",{qT:de,gateT:Z,cacheKeysT:pe,cacheValuesT:le,attnT:at,paramsT:Ge},{args:{layer:0,cacheLen:m,pastLen:n,numHeads:o,numKvHeads:i,headDim:u,scale:I}}),L=X(x,L,at,"o_proj",O.o_proj,l)}else{if(!t.linearConvStates?.[x]||!t.linearRecurrentStates?.[x])throw new Error(`emitQwen35Decode: linear layer ${x} is missing conv/recurrent state`);let oe=v(`linear.conv.${x}`,Te(t.linearConvStates[x],0,E*F)),pe=v(`linear.rec.${x}`,Te(t.linearRecurrentStates[x],0,b*S*y)),le=w.scratch(`L${x}.normed`,"float32",[s]);w.op("com.xenova.LlamaDecodeRmsNorm",{hiddenT:L,weightsT:j,normedT:le},{args:{hiddenSize:s,rmsEps:g,normOffset:O.input_layernorm}});let se=w.scratch(`L${x}.lq`,"float32",[h]),de=w.scratch(`L${x}.lk`,"float32",[h]),Z=w.scratch(`L${x}.lv`,"float32",[C]),ce=w.scratch(`L${x}.lz`,"float32",[C]),Qe=w.scratch(`L${x}.lgate`,"float32",[2*b]);if(z){let Se=ae(x,["linear_in_proj_qkv","linear_in_proj_z","linear_in_proj_b","linear_in_proj_a"],"inproj");w.op("com.xenova.Qwen35DecodeLinearInProjConv",{normedT:le,weightsT:j,q1BitsT:Se.bitsT,q1ScalesT:Se.scalesT,convStateT:oe,qT:se,kT:de,vT:Z,zT:ce,gateT:Qe},{args:{keyDim:h,valueDim:C,convDim:E,numHeads:b,convKernel:R,hiddenSize:s,qkvOffset:Se.offset(`layers.${x}.linear_in_proj_qkv`),zOffset:Se.offset(`layers.${x}.linear_in_proj_z`),bOffset:Se.offset(`layers.${x}.linear_in_proj_b`),aOffset:Se.offset(`layers.${x}.linear_in_proj_a`),convWeightOffset:O.linear_conv_weight,aNegExpOffset:O.linear_a_neg_exp,dtBiasOffset:O.linear_dt_bias}})}else if(Ee(e2,`layers.${x}.linear_in_proj_qkv`)){let Se=K(x,["linear_in_proj_qkv","linear_in_proj_z","linear_in_proj_b","linear_in_proj_a"],"inproj");w.op("com.xenova.Qwen35DecodeLinearInProjConv",{normedT:le,weightsT:j,q4BitsT:Se.bitsT,q4ScalesT:Se.scalesT,convStateT:oe,qT:se,kT:de,vT:Z,zT:ce,gateT:Qe},{args:{keyDim:h,valueDim:C,convDim:E,numHeads:b,convKernel:R,hiddenSize:s,qkvOffset:Se.offset(`layers.${x}.linear_in_proj_qkv`),zOffset:Se.offset(`layers.${x}.linear_in_proj_z`),bOffset:Se.offset(`layers.${x}.linear_in_proj_b`),aOffset:Se.offset(`layers.${x}.linear_in_proj_a`),convWeightOffset:O.linear_conv_weight,aNegExpOffset:O.linear_a_neg_exp,dtBiasOffset:O.linear_dt_bias,format:Se.format,lut:Se.lut}})}else w.op("com.xenova.Qwen35DecodeLinearInProjConv",{normedT:le,weightsT:j,convStateT:oe,qT:se,kT:de,vT:Z,zT:ce,gateT:Qe},{args:{keyDim:h,valueDim:C,convDim:E,numHeads:b,convKernel:R,hiddenSize:s,qkvOffset:O.linear_in_proj_qkv,zOffset:O.linear_in_proj_z,bOffset:O.linear_in_proj_b,aOffset:O.linear_in_proj_a,convWeightOffset:O.linear_conv_weight,aNegExpOffset:O.linear_a_neg_exp,dtBiasOffset:O.linear_dt_bias}});let at=w.scratch(`L${x}.lattn`,"float32",[C]);w.op("com.xenova.Qwen35DecodeLinearAttention",{stateT:pe,qT:se,kT:de,vT:Z,gateT:Qe,outT:at},{args:{numHeads:b,numKeyHeads:M,headDimK:S,headDimV:y,scale:H,workgroupSizeVec4:Q,workgroupSizeScalar:P}});let ke=w.scratch(`L${x}.lnormed`,"float32",[C]);w.op("com.xenova.Qwen35DecodeGatedRmsNorm",{xT:at,zT:ce,weightsT:j,yT:ke},{args:{numValueHeads:b,valueHeadDim:y,normOffset:O.linear_norm,rmsEps:g,workgroupSize:W}}),L=X(x,L,ke,"linear_out_proj",O.linear_out_proj,C)}L=Y(L,x,O)}let N=Xn(w,{w:_,model:e2,hidden:L,weights:j,ids:re,hiddenSize:s,vocabSize:p,rmsEps:g,finalNormOffset:D.model_norm,lmHeadOffset:D.lm_head,q1:z,normedDtype:z&&ee?"float16":"float32"});return{graph:w.finish({name:"qwen35-decode",params:{positionOffset:n}}),weights:k,states:V,paramsName:"params",nextTokenTailNodeCount:N}}var Es,Jo=q(()=>{"use strict";Vn();Jn();_n();Gt();Xo();pt();_t();Es=class extends Yn{buildGraph(t){let n=rr(this.model,this.cache,t);return{emission:n,graph:n.graph}}}});function sr(e2){return Ue(e2.config)}function ei(e2,t,n){if(!sr(e2))throw new Error("emitQwen35Prefill: only Qwen3.5 (mixed full/linear-attention) models are supported");let r=e2.config,s=n,a=r.hidden_size,o=r.num_hidden_layers,i=r.intermediate_size,u=r.vocab_size,l=Number(r.rms_norm_eps),c=t.maxLength,d=r.num_attention_heads,f=r.num_key_value_heads,p=r.head_dim,g=r.attention_size,m=r.kv_size,A=r.rotary_dim,h=g+2*m,C=yt(p),E=r.linear_key_dim,R=r.linear_value_dim,F=r.linear_conv_dim,M=r.linear_conv_kernel_dim,b=M-1,S=r.linear_num_key_heads,y=r.linear_num_value_heads,I=r.linear_key_head_dim,H=r.linear_value_head_dim,W=yt(I),Q=Math.max(64,Tt(I)),P=Math.max(64,Tt(I)),T=Math.max(64,Tt(H)),D="float32",w=e2.runtime.device.features.has("shader-f16"),_=Re(e2),v=w&&(_||e2.weights.dtype==="float16")?"float16":"float32",k=w&&e2.runtime.device.features.has("subgroups")&&e2.runtime.device.features.has("chromium-experimental-subgroup-matrix"),V=Ts(e2),j=e2.offsets.top,$2=new mt,{w:ne,state:Ge,boundWeights:K,states:z}=xt($2),ee=ne("weights",e2.weights),ae=ne("rope.cos",V.cos),X=ne("rope.sin",V.sin),Y=$2.uniform("params",16),re=_&&w&&s>=64?$2.stepInput("q1_real_len","uint32",[1]):void 0,L=Lt(ne,e2),N=Pt(ne,e2),{proj:x,mlpInto:O}=Mo({g:$2,model:e2,weights:ee,q4w:L,q1w:N,q1RealLenT:re,T:s,H:a,inter:i,eps:l,actGemm:v,fusedQ4:k,..._&&false?{q1Activation:"fast_silu"}:{}}),oe=_&&s<=16&&a%128===0&&y%4===0,pe=$2.stepInput("input_ids","uint32",[s]),le=Ko({g:$2,w:ne,weights:ee,model:e2,ids:pe,act:D,H:a,vocab:u,T:s,embedOffset:j.embed_tokens});for(let Z=0;Z<o;++Z){let ce=e2.offsets.layers[Z],Qe=$2.op("com.xenova.LlamaPrefillRmsNorm",{xT:le,weightsT:ee,yT:$2.scratch(`L${Z}.normed`,v,[s,a])},{args:{normOffset:ce.input_layernorm,rows:s,dim:a,eps:l}}).yT;if(An(r,Z)==="full_attention"){let at=Dt(e2,t,Z),ke=Ge(`cache.key.${Z}`,at.cacheKeysT),Se=Ge(`cache.value.${Z}`,at.cacheValuesT),Qs=x(Z,Qe,"q_proj",ce.q_proj,2*g,a,`L${Z}.qg`),ir=x(Z,Qe,"k_proj",ce.k_proj,m,a,`L${Z}.k`),Ds=x(Z,Qe,"v_proj",ce.v_proj,m,a,`L${Z}.v`),bt=$2.scratch(`L${Z}.qkv`,v,[s,h]),Nt=$2.scratch(`L${Z}.gate`,v,[s,g]);$2.op("com.xenova.Qwen35PrefillAssembleQkv",{qgT:Qs,kT:ir,vT:Ds,qkvT:bt,gateT:Nt},{args:{qSize:g,kvSize:m,headDim:p,seqLen:s}}),$2.op("com.xenova.LlamaPrefillQkNorm",{qkvT:bt,weightsT:ee},{args:{qNormOffset:ce.q_layernorm,kNormOffset:ce.k_layernorm,numHeads:d,numKvHeads:f,headDim:p,seqLen:s,qkvStride:h,rmsEps:l}}),$2.op("com.xenova.Qwen35PrefillPartialRopeCacheKv",{qkvT:bt,cacheKeysT:ke,cacheValuesT:Se,cosT:ae,sinT:X,params:Y},{args:{layer:0,cacheLen:c,pastLen:0,seqLen:s,numHeads:d,numKvHeads:f,headDim:p,rotaryDim:A}});let yn=$2.op("com.xenova.LlamaPrefillAttention",{qkvT:bt,cacheKeysT:ke,cacheValuesT:Se,attnT:$2.scratch(`L${Z}.attn`,v,[s,g]),params:Y},{args:{layer:0,cacheLen:c,pastLen:0,seqLen:s,numHeads:d,numKvHeads:f,headDim:p,scale:C,workgroupSize:64}}).attnT;$2.op("com.xenova.Qwen35PrefillAttentionGate",{attnT:yn,gateT:Nt},{args:{seqLen:s,qSize:g}});let Ps=x(Z,yn,"o_proj",ce.o_proj,a,g,`L${Z}.oproj`);$2.op("com.xenova.AddInPlace",{yT:le,xT:Ps},{args:{count:s*a}})}else{if(!t.linearConvStates?.[Z]||!t.linearRecurrentStates?.[Z])throw new Error(`emitQwen35Prefill: linear layer ${Z} is missing conv/recurrent state`);let at=Ge(`linear.conv.${Z}`,Te(t.linearConvStates[Z],0,F*b)),ke=Ge(`linear.rec.${Z}`,Te(t.linearRecurrentStates[Z],0,y*I*H)),Se=x(Z,Qe,"linear_in_proj_qkv",ce.linear_in_proj_qkv,F,a,`L${Z}.bcx`),Qs=x(Z,Qe,"linear_in_proj_z",ce.linear_in_proj_z,R,a,`L${Z}.z`),ir=$2.scratch(`L${Z}.lgate`,D,[s,2*y]),Ds=(()=>{if(oe){let ur=N(Z,["linear_in_proj_b","linear_in_proj_a"],"gates");return 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Ps=$2.op("com.xenova.Qwen35PrefillLinearAttention",{stateT:ke,qT:bt,kT:Nt,vT:yn,gateT:Ds,outT:$2.scratch(`L${Z}.lattn`,D,[s,R]),params:Y},{args:{numHeads:y,numKeyHeads:S,headDimK:I,headDimV:H,scale:W,seqLen:s,workgroupSize:P}}).outT,Td=$2.op("com.xenova.Qwen35PrefillGatedRmsNorm",{xT:Ps,zT:Qs,weightsT:ee,yT:$2.scratch(`L${Z}.lnormed`,v,[s,R])},{args:{numValueHeads:y,valueHeadDim:H,normOffset:ce.linear_norm,rmsEps:l,seqLen:s,workgroupSize:T}}).yT,Ed=x(Z,Td,"linear_out_proj",ce.linear_out_proj,a,R,`L${Z}.loproj`);$2.op("com.xenova.AddInPlace",{yT:le,xT:Ed},{args:{count:s*a}})}O(Z,ce,le)}let se=$2.nodeCount,de=Oo({g:$2,w:ne,weights:ee,model:e2,hidden:le,act:D,H:a,vocab:u,T:s,eps:l,finalNormOffset:j.model_norm,lmHeadOffset:j.lm_head});return{graph:$2.finish({name:"qwen35-prefill",params:{T:s}}),weights:K,states:z,paramsName:"params",lastRowUniform:de,...re?{q1RealLenInput:re.name}:{},nextTokenTailNodeCount:$2.nodeCount-se}}var Gs,ti=q(()=>{"use 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i.push({id:"model_norm",name:`model.layers.${e2.num_hidden_layers}.final_norm_layernorm.weight`,length:t},{id:"lm_head",name:"lm_head.weight",length:e2.vocab_size*t}),i}function Ve(e2,t,n,r){return{id:e2,name:t,length:n*r,shape:[n,r],transform:lc}}function Cw(e2){if(!Object.hasOwn(e2,"text_config"))return e2;if(!e2.text_config||typeof e2.text_config!="object")throw new Error("Qwen3_5Config.text_config must be an object");let t=e2.text_config;return{...t,architectures:e2.architectures??t.architectures,eos_token_id:e2.eos_token_id??t.eos_token_id,bos_token_id:e2.bos_token_id??t.bos_token_id,pad_token_id:e2.pad_token_id??t.pad_token_id}}function Sw(e2){let t=e2.rope_parameters;return{rope_theta:t.rope_theta,rope_type:t.rope_type,partial_rotary_factor:t.partial_rotary_factor,mrope_interleaved:t.mrope_interleaved,mrope_section:t.mrope_section}}var or,$t,ri=q(()=>{"use strict";yd();za();Wt();_t();us();Jo();ti();Gt();ss();or=class{vocab_size;hidden_size;intermediate_size;num_hidden_layers;num_attention_heads;num_key_value_heads;head_dim;linear_conv_kernel_dim;linear_key_head_dim;linear_value_head_dim;linear_num_key_heads;linear_num_value_heads;hidden_act;max_position_embeddings;initializer_range;rms_norm_eps;use_cache;tie_word_embeddings;attention_bias;attention_dropout;mlp_bias;rope_parameters;full_attention_interval;layer_types;pad_token_id;bos_token_id;eos_token_id;attention_qk_norm;attn_output_gate;model_type;attention_size;kv_size;linear_key_dim;linear_value_dim;linear_conv_dim;rotary_dim;qwen35_partial_rotary;constructor(t={}){let n=Cw(t);this.vocab_size=Be(n,"vocab_size","Qwen3_5Config"),this.hidden_size=Be(n,"hidden_size","Qwen3_5Config"),this.intermediate_size=Be(n,"intermediate_size","Qwen3_5Config"),this.num_hidden_layers=Be(n,"num_hidden_layers","Qwen3_5Config"),this.num_attention_heads=Be(n,"num_attention_heads","Qwen3_5Config"),this.num_key_value_heads=n.num_key_value_heads,this.head_dim=n.head_dim,this.linear_conv_kernel_dim=Be(n,"linear_conv_kernel_dim","Qwen3_5Config"),this.linear_key_head_dim=Be(n,"linear_key_head_dim","Qwen3_5Config"),this.linear_value_head_dim=Be(n,"linear_value_head_dim","Qwen3_5Config"),this.linear_num_key_heads=Be(n,"linear_num_key_heads","Qwen3_5Config"),this.linear_num_value_heads=Be(n,"linear_num_value_heads","Qwen3_5Config"),this.hidden_act=n.hidden_act,this.max_position_embeddings=Be(n,"max_position_embeddings","Qwen3_5Config"),this.initializer_range=n.initializer_range,this.rms_norm_eps=n.rms_norm_eps,this.use_cache=n.use_cache,this.tie_word_embeddings=n.tie_word_embeddings,this.attention_bias=n.attention_bias,this.attention_dropout=n.attention_dropout,this.mlp_bias=n.mlp_bias,this.rope_parameters=Sw(n),this.full_attention_interval=n.full_attention_interval,this.layer_types=n.layer_types,this.pad_token_id=n.pad_token_id,this.bos_token_id=n.bos_token_id,this.eos_token_id=n.eos_token_id,this.attention_qk_norm=true,this.attn_output_gate=true,this.model_type="qwen3_5_text",this.attention_size=this.num_attention_heads*this.head_dim,this.kv_size=this.num_key_value_heads*this.head_dim,this.linear_key_dim=this.linear_num_key_heads*this.linear_key_head_dim,this.linear_value_dim=this.linear_num_value_heads*this.linear_value_head_dim,this.linear_conv_dim=2*this.linear_key_dim+this.linear_value_dim,this.rotary_dim=n.rotary_dim,this.qwen35_partial_rotary=true,this.validate()}validate(){if(jr(this,["vocab_size","hidden_size","intermediate_size","num_hidden_layers","num_attention_heads","num_key_value_heads","head_dim","max_position_embeddings","linear_conv_kernel_dim","linear_key_head_dim","linear_value_head_dim","linear_num_key_heads","linear_num_value_heads","attention_size","kv_size","linear_key_dim","linear_value_dim","linear_conv_dim","rotary_dim"],"Qwen3_5Config"),this.hidden_act!=="silu")throw new Error(`Unsupported Qwen3_5 hidden_act for V1: ${this.hidden_act}`);if(this.attention_bias||this.mlp_bias)throw new Error("Qwen3_5 V1 does not support attention_bias or mlp_bias");if(this.attention_dropout!==0)throw new Error("Qwen3_5 V1 only supports attention_dropout=0");if(this.rope_parameters.rope_type!=="default")throw new Error("Qwen3_5 V1 only supports default RoPE");if(this.num_attention_heads%this.num_key_value_heads!==0)throw new Error("Qwen3_5Config num_attention_heads must be divisible by num_key_value_heads");if(this.head_dim%2!==0)throw new Error("Qwen3_5Config head_dim must be even for RoPE");if(this.rotary_dim%2!==0||this.rotary_dim>this.head_dim)throw new Error("Qwen3_5Config rotary_dim must be even and <= head_dim");if(this.linear_num_value_heads%this.linear_num_key_heads!==0)throw new Error("Qwen3_5Config linear_num_value_heads must be a multiple of linear_num_key_heads");if(this.linear_conv_kernel_dim<2)throw new Error("Qwen3_5Config linear_conv_kernel_dim must be >= 2");if(!Array.isArray(this.layer_types)||this.layer_types.length!==this.num_hidden_layers)throw new Error("Qwen3_5Config.layer_types length must match num_hidden_layers");if(this.layer_types.some(t=>t!=="linear_attention"&&t!=="full_attention"))throw new Error("Qwen3_5Config.layer_types entries must be linear_attention or full_attention");for(let[t,n]of[["hidden_size",this.hidden_size],["intermediate_size",this.intermediate_size],["attention_size",this.attention_size],["linear_value_dim",this.linear_value_dim]])if(n%4!==0)throw new Error(`Qwen3_5Config.${t} must be divisible by 4`)}toJSON(){return{...this,rope_parameters:{...this.rope_parameters}}}},$t=class extends Zt{static ConfigClass=or;static MODEL_NAME="Qwen3_5";static expectedWeightEntries=vw;static DECODE_GRAPH_EMIT=(t,n)=>rr(t,n,0);static async loadGGUFFile(t,n,r={}){let{readGGUFFile:s}=await Promise.resolve().then(()=>(gs(),Bo));return this.loadGGUF(t,{gguf:await s(n),onProgress:r.onProgress})}static async loadGGUF(t,{gguf:n,onProgress:r}){let{eagerGGUFModel:s}=await Promise.resolve().then(()=>(gs(),Bo)),{QWEN35_GGUF_LIFECYCLE:a}=await Promise.resolve().then(()=>(ni(),Cd));return s(n,{onProgress:r},a).load(t)}async forward(){throw new Error("Qwen3_5ForCausalLM.forward is not supported yet; use generate/streamTokenIds")}async generate(t){return super.generate({...t,force_token_path:true})}async*streamTokenIds(t){yield*super.streamTokenIds({...t,force_token_path:true})}async*streamTokenIdsFromCache(t){yield*super.streamTokenIdsFromCache({...t,force_token_path:true})}makeGraphDecodeSession(t){return new Es(this,t)}prefillSupported(){return false?false:sr(this)}makeGraphPrefillSession(t,n){return new Gs(this,t,n)}prefillMinTokens(){let t=this.runtime.device.features;return Re(this)?super.prefillMinTokens():t.has("shader-f16")&&this.weights.dtype==="float16"?0:257}planPrefillChunks(t,n,r){let s=this.runtime.device,a=s.adapterInfo,o=s.features.has("shader-f16")&&s.features.has("subgroups")&&s.features.has("chromium-experimental-subgroup-matrix")&&a.subgroupMinSize===32&&a.subgroupMaxSize===32,i=!o,u=void 0;return u!=="0"&&Re(this)&&(i||o)?u===void 0||u==="m32"?Bc({tokenCount:t,position:n,maxLength:r,shortBlock:16,normalMinTokens:super.prefillMinTokens(),bucketMinTokens:27,bucketMaxTokens:32,bucketBlock:32}):Sc({tokenCount:t,position:n,maxLength:r,shortBlock:16,normalMinTokens:super.prefillMinTokens()}):super.planPrefillChunks(t,n,r)}}});Rs();var Tp=class{constructor(e2){this.trie=this._build_trie(e2)}_build_trie(e2){let t=Object.create(null);for(let n of e2){let r=t;for(let s=0;s<n.length;++s){let a=n[s];r=r[a]??=Object.create(null)}r.end=n}return t}split(e2){let t=[],n=e2.length,r=0,s=0;for(;s<n;){let a=this.trie,o=null,i=s;for(;i<n&&(a=a[e2[i]]);)a.end&&(o=a.end),++i;o?(s>r&&t.push(e2.slice(r,s)),t.push(o),s+=o.length,r=s):++s}return r<n&&t.push(e2.slice(r)),t}},_i=Tp,Ep=class{constructor(e2){this.content=e2.content,this.id=e2.id,this.single_word=e2.single_word??false,this.lstrip=e2.lstrip??false,this.rstrip=e2.rstrip??false,this.special=e2.special??false,this.normalized=e2.normalized??!this.special}},Gp=Ep,Si=(()=>{let e2=[...Array.from({length:94},(s,a)=>a+33),...Array.from({length:12},(s,a)=>a+161),...Array.from({length:82},(s,a)=>a+174)],t=e2.slice(),n=0;for(let s=0;s<256;++s)e2.includes(s)||(e2.push(s),t.push(256+n),n+=1);let r=t.map(s=>String.fromCharCode(s));return Object.fromEntries(e2.map((s,a)=>[s,r[a]]))})(),xp=e2=>Object.fromEntries(Object.entries(e2).map(([t,n])=>[n,t])),Qp=xp(Si),wi=".,!?…。，、।۔،",Dp=new 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t},Kp=e2=>Bi(e2.toLowerCase()),Je=(...e2)=>Array.prototype.concat.apply([],e2),Os=e2=>new Map(Object.entries(e2)),Op=(e2,t)=>{let n=[],r=0;for(let s of e2.matchAll(t)){let a=s[0];r<s.index&&n.push(e2.slice(r,s.index)),a.length>0&&n.push(a),r=s.index+a.length}return r<e2.length&&n.push(e2.slice(r)),n},Bi=e2=>e2.replace(/\p{M}/gu,""),yi=(e2,t,n=[])=>{if(!e2||Array.isArray(e2)||typeof e2!="object")return`${t} must be a valid object`;for(let r of n)if(!(r in e2))return`${t} must contain a "${r}" property`;return null},Wp=e2=>e2.match(/\S+/g)||[],zp=class{constructor(){let e2=function(...t){return e2._call(...t)};return Object.setPrototypeOf(e2,new.target.prototype)}},Sn=zp,Hp=class extends Sn{constructor(e2){super(),this.config=e2}_call(e2){return this.normalize(e2)}},gt=Hp,Up=class extends gt{tokenize_chinese_chars(e2){let t=[];for(let n=0;n<e2.length;++n){let r=e2[n],s=r.charCodeAt(0);qp(s)?(t.push(" "),t.push(r),t.push(" ")):t.push(r)}return t.join("")}strip_accents(e2){return e2.normalize("NFD").replace(/\p{Mn}/gu,"")}is_control(e2){switch(e2){case"	":case`
+`:case"\r":return false;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e2)}}clean_text(e2){let t=[];for(let n of e2){let r=n.charCodeAt(0);r===0||r===65533||this.is_control(n)||(/^\s$/.test(n)?t.push(" "):t.push(n))}return t.join("")}normalize(e2){return this.config.clean_text&&(e2=this.clean_text(e2)),this.config.handle_chinese_chars&&(e2=this.tokenize_chinese_chars(e2)),this.config.lowercase?(e2=e2.toLowerCase(),this.config.strip_accents!==false&&(e2=this.strip_accents(e2))):this.config.strip_accents&&(e2=this.strip_accents(e2)),e2}},Fp=Up,Zp=class extends gt{constructor(e2){super(e2),this.charsmap=e2.precompiled_charsmap??null}normalize(e2){return e2=e2.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),e2=e2.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," "),e2.includes("～")?e2=e2.split("～").map(n=>n.normalize("NFKC")).join("～"):e2=e2.normalize("NFKC"),e2}},$p=Zp,Np=class extends gt{constructor(e2){super(e2),this.normalizers=(e2.normalizers??[]).map(t=>Ii(t))}normalize(e2){return this.normalizers.reduce((t,n)=>n?n.normalize(t):t,e2)}},jp=Np,Vp=class extends gt{normalize(e2){let t=mr(this.config.pattern??{});return t===null?e2:e2.replaceAll(t,this.config.content??"")}},Yp=Vp,Xp=class extends gt{constructor(){super(...arguments),this.form="NFC"}normalize(e2){return e2=e2.normalize(this.form),e2}},gr=Xp,Jp=class extends gr{constructor(){super(...arguments),this.form="NFC"}},ef=Jp,tf=class extends gr{constructor(){super(...arguments),this.form="NFD"}},nf=tf,rf=class extends gr{constructor(){super(...arguments),this.form="NFKC"}},sf=rf,af=class extends gr{constructor(){super(...arguments),this.form="NFKD"}},of=af,uf=class extends gt{normalize(e2){return this.config.strip_left&&this.config.strip_right?e2=e2.trim():(this.config.strip_left&&(e2=e2.trimStart()),this.config.strip_right&&(e2=e2.trimEnd())),e2}},lf=uf,cf=class extends gt{normalize(e2){return Bi(e2)}},df=cf,pf=class extends gt{normalize(e2){return e2.toLowerCase()}},ff=pf,mf=class extends gt{normalize(e2){return e2=this.config.prepend+e2,e2}},gf=mf;function Af(e2){if(e2===null)return null;switch(e2.type){case"BertNormalizer":return new Fp(e2);case"Precompiled":return new $p(e2);case"Sequence":return new jp(e2);case"Replace":return new Yp(e2);case"NFC":return new ef(e2);case"NFD":return new nf(e2);case"NFKC":return new sf(e2);case"NFKD":return new of(e2);case"Strip":return new lf(e2);case"StripAccents":return new df(e2);case"Lowercase":return new ff(e2);case"Prepend":return new gf(e2);default:throw new Error(`Unknown Normalizer type: ${e2.type}`)}}var Ii=Af,hf=class extends Sn{pre_tokenize(e2,t){return(Array.isArray(e2)?e2.map(n=>this.pre_tokenize_text(n,t)):this.pre_tokenize_text(e2,t)).flat()}_call(e2,t){return this.pre_tokenize(e2,t)}},et=hf,_f=class extends et{constructor(e2){super(),this.config=e2,this.add_prefix_space=this.config.add_prefix_space??false,this.trim_offsets=this.config.trim_offsets??false,this.use_regex=this.config.use_regex??true,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=Si,this.text_encoder=new TextEncoder}pre_tokenize_text(e2,t){return this.add_prefix_space&&!e2.startsWith(" ")&&(e2=" "+e2),(this.use_regex?e2.match(this.pattern)||[]:[e2]).map(r=>Array.from(this.text_encoder.encode(r),s=>this.byte_encoder[s]).join(""))}},wf=_f,yf=class extends et{pre_tokenize_text(e2,t){return e2.match(/\w+|[^\w\s]+/g)||[]}},bf=yf,kf=class extends et{constructor(e2){super(),this.replacement=e2.replacement??"▁",this.str_rep=e2.str_rep||this.replacement,this.prepend_scheme=e2.prepend_scheme??"always"}pre_tokenize_text(e2,t){let{section_index:n=void 0}=t??{},r=e2.replaceAll(" 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bf;case"WhitespaceSplit":return new Rf;case"Metaspace":return new vf(e2);case"ByteLevel":return new wf(e2);case"Split":return new Sf(e2);case"Punctuation":return new If(e2);case"Digits":return new Ef(e2);case"Replace":return new Df(e2);case"FixedLength":return new Kf(e2);default:throw new Error(`Unknown PreTokenizer type: ${e2.type}`)}}var Ti=Of,Wf=class extends Sn{constructor(e2){super(),this.config=e2,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??false}_call(e2){let t=this.encode(e2);return this.fuse_unk&&(t=Lp(t,this.tokens_to_ids,this.unk_token_id)),t}},Ar=Wf,zf=class extends Ar{constructor(e2){super(e2),this.max_input_chars_per_word=100,this.tokens_to_ids=Os(e2.vocab),this.unk_token_id=this.tokens_to_ids.get(e2.unk_token),this.unk_token=e2.unk_token,this.max_input_chars_per_word=e2.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(let[t,n]of this.tokens_to_ids)this.vocab[n]=t}encode(e2){let t=[];for(let n of e2){let r=[...n];if(r.length>this.max_input_chars_per_word){t.push(this.unk_token);continue}let s=false,a=0,o=[];for(;a<r.length;){let i=r.length,u=null;for(;a<i;){let l=r.slice(a,i).join("");if(a>0&&(l=this.config.continuing_subword_prefix+l),this.tokens_to_ids.has(l)){u=l;break}--i}if(u===null){s=true;break}o.push(u),a=i}s?t.push(this.unk_token):t.push(...o)}return t}},bi=zf,ki=class Ei{constructor(t,n){this.is_leaf=t,this.children=n}static default(){return new Ei(false,new Map)}},Hf=class{constructor(){this.root=ki.default()}extend(e2){for(let t of e2)this.push(t)}push(e2){let t=this.root;for(let n of e2){let r=t.children.get(n);r===void 0&&(r=ki.default(),t.children.set(n,r)),t=r}t.is_leaf=true}*common_prefix_search(e2){let t=this.root;if(t===void 0)return;let n="";for(let r of e2){if(n+=r,t=t.children.get(r),t===void 0)return;t.is_leaf&&(yield n)}}},Uf=Hf,Ms=class Gi{constructor(t,n,r,s,a){this.token_id=t,this.node_id=n,this.pos=r,this.length=s,this.score=a,this.prev=null,this.backtrace_score=0}clone(){let t=new Gi(this.token_id,this.node_id,this.pos,this.length,this.score);return t.prev=this.prev,t.backtrace_score=this.backtrace_score,t}},Ff=class{constructor(e2,t,n){this.chars=Array.from(e2),this.len=this.chars.length,this.bos_token_id=t,this.eos_token_id=n,this.nodes=[],this.begin_nodes=Array.from({length:this.len+1},()=>[]),this.end_nodes=Array.from({length:this.len+1},()=>[]);let r=new Ms(this.bos_token_id??0,0,0,0,0),s=new Ms(this.eos_token_id??0,1,this.len,0,0);this.nodes.push(r.clone()),this.nodes.push(s.clone()),this.begin_nodes[this.len].push(s),this.end_nodes[0].push(r)}insert(e2,t,n,r){let s=this.nodes.length,a=new Ms(r,s,e2,t,n);this.begin_nodes[e2].push(a),this.end_nodes[e2+t].push(a),this.nodes.push(a)}viterbi(){let e2=this.len,t=0;for(;t<=e2;){if(this.begin_nodes[t].length==0)return[];for(let o of this.begin_nodes[t]){o.prev=null;let i=0,u=null;for(let l of this.end_nodes[t]){let c=l.backtrace_score+o.score;(u===null||c>i)&&(u=l.clone(),i=c)}if(u!==null)o.prev=u,o.backtrace_score=i;else return[]}++t}let n=[],s=this.begin_nodes[e2][0].prev;if(s===null)return[];let a=s.clone();for(;a.prev!==null;)n.push(a.clone()),a=a.clone().prev.clone();return n.reverse(),n}piece(e2){return this.chars.slice(e2.pos,e2.pos+e2.length).join("")}tokens(){return this.viterbi().map(t=>this.piece(t))}token_ids(){return this.viterbi().map(t=>t.token_id)}},Zf=Ff;function $f(e2){if(e2.length===0)throw new Error("Array must not be empty");let t=e2[0],n=0;for(let r=1;r<e2.length;++r)e2[r]<t&&(t=e2[r],n=r);return[t,n]}var Nf=class extends Ar{constructor(e2,t){super(e2);let n=e2.vocab.length;this.vocab=new Array(n),this.scores=new Array(n);for(let r=0;r<n;++r)[this.vocab[r],this.scores[r]]=e2.vocab[r];this.unk_token_id=e2.unk_id,this.unk_token=this.vocab[e2.unk_id],this.tokens_to_ids=new Map(this.vocab.map((r,s)=>[r,s])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.min_score=$f(this.scores)[0],this.unk_score=this.min_score-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new Uf,this.trie.extend(this.vocab),this.fuse_unk=true}populate_nodes(e2){let t=e2.chars,n=1,r=0;for(;r<t.length;){let s=false,a=[],o=t.slice(r).join(""),i=this.trie.common_prefix_search(o);for(let u of i){a.push(u);let l=this.tokens_to_ids.get(u),c=this.scores[l],d=Mp(u);e2.insert(r,d,c,l),!s&&d===n&&(s=true)}s||e2.insert(r,n,this.unk_score,this.unk_token_id),r+=n}}tokenize(e2){let t=new Zf(e2,this.bos_token_id,this.eos_token_id);return this.populate_nodes(t),t.tokens()}encode(e2){let t=[];for(let n of e2){let r=this.tokenize(n);t.push(...r)}return t}},vi=Nf,jf=class{constructor(e2=(n,r)=>n>r,t=1/0){this._heap=[],this._comparator=e2,this._max_size=t}get size(){return this._heap.length}is_empty(){return this.size===0}peek(){return this._heap[0]}push(...e2){return this.extend(e2)}extend(e2){for(let t of e2)if(this.size<this._max_size)this._heap.push(t),this._sift_up();else{let n=this._smallest();this._comparator(t,this._heap[n])&&(this._heap[n]=t,this._sift_up_from(n))}return this.size}pop(){let e2=this.peek(),t=this.size-1;return t>0&&this._swap(0,t),this._heap.pop(),this._sift_down(),e2}replace(e2){let t=this.peek();return this._heap[0]=e2,this._sift_down(),t}_parent(e2){return(e2+1>>>1)-1}_left(e2){return(e2<<1)+1}_right(e2){return e2+1<<1}_greater(e2,t){return this._comparator(this._heap[e2],this._heap[t])}_swap(e2,t){let n=this._heap[e2];this._heap[e2]=this._heap[t],this._heap[t]=n}_sift_up(){this._sift_up_from(this.size-1)}_sift_up_from(e2){for(;e2>0&&this._greater(e2,this._parent(e2));)this._swap(e2,this._parent(e2)),e2=this._parent(e2)}_sift_down(){let e2=0;for(;this._left(e2)<this.size&&this._greater(this._left(e2),e2)||this._right(e2)<this.size&&this._greater(this._right(e2),e2);){let t=this._right(e2)<this.size&&this._greater(this._right(e2),this._left(e2))?this._right(e2):this._left(e2);this._swap(e2,t),e2=t}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}},Vf=jf,Yf=class{constructor(e2){this.capacity=e2,this.cache=new Map}get(e2){if(!this.cache.has(e2))return;let t=this.cache.get(e2);return this.cache.delete(e2),this.cache.set(e2,t),t}put(e2,t){this.cache.has(e2)&&this.cache.delete(e2),this.cache.set(e2,t),this.cache.size>this.capacity&&this.cache.delete(this.cache.keys().next().value)}clear(){this.cache.clear()}},Xf=Yf,Jf=class extends Ar{constructor(e2){super(e2),this.tokens_to_ids=Os(e2.vocab),this.unk_token_id=this.tokens_to_ids.get(e2.unk_token),this.unk_token=e2.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(let[n,r]of this.tokens_to_ids)this.vocab[r]=n;let t=Array.isArray(e2.merges[0]);this.merges=t?e2.merges:e2.merges.map(n=>n.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((n,r)=>[JSON.stringify(n),r])),this.end_of_word_suffix=e2.end_of_word_suffix,this.continuing_subword_suffix=e2.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??false,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??false,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new Xf(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(e2){if(e2.length===0)return[];let t=this.cache.get(e2);if(t!==void 0)return t;let n=Array.from(e2);this.end_of_word_suffix&&(n[n.length-1]+=this.end_of_word_suffix);let r=[];if(n.length>1){let s=new Vf((i,u)=>i.score<u.score),a={token:n[0],bias:0,prev:null,next:null},o=a;for(let i=1;i<n.length;++i){let u={bias:i/n.length,token:n[i],prev:o,next:null};o.next=u,this.add_node(s,o),o=u}for(;!s.is_empty();){let i=s.pop();if(i.deleted||!i.next||i.next.deleted)continue;if(i.deleted=true,i.next.deleted=true,i.prev){let l={...i.prev};i.prev.deleted=true,i.prev=l,l.prev?l.prev.next=l:a=l}let u={token:i.token+i.next.token,bias:i.bias,prev:i.prev,next:i.next.next};u.prev?(u.prev.next=u,this.add_node(s,u.prev)):a=u,u.next&&(u.next.prev=u,this.add_node(s,u))}for(let i=a;i!==null;i=i.next)r.push(i.token)}else r=n;if(this.continuing_subword_suffix)for(let s=0;s<r.length-1;++s)r[s]+=this.continuing_subword_suffix;return e2.length<this.max_length_to_cache&&this.cache.put(e2,r),r}add_node(e2,t){let n=this.bpe_ranks.get(JSON.stringify([t.token,t.next.token]));n!==void 0&&(t.score=n+t.bias,e2.push(t))}encode(e2){let t=[];for(let n of e2){if(this.ignore_merges&&this.tokens_to_ids.has(n)){t.push(n);continue}let r=this.bpe(n);for(let s of r)if(this.tokens_to_ids.has(s))t.push(s);else if(this.byte_fallback){let a=Array.from(this.text_encoder.encode(s)).map(o=>`<0x${o.toString(16).toUpperCase().padStart(2,"0")}>`);a.every(o=>this.tokens_to_ids.has(o))?t.push(...a):this.unk_token!=null&&t.push(this.unk_token)}else this.unk_token!=null&&t.push(this.unk_token)}return t}},Ci=Jf,em=class extends Ar{constructor(e2,t){super(e2);let n=e2.vocab;this.tokens_to_ids=Os(t.target_lang?n[t.target_lang]:n),this.bos_token=t.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=t.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=t.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(let[r,s]of this.tokens_to_ids)this.vocab[s]=r}encode(e2){return e2}},tm=em;function nm(e2,t){switch(e2.type){case"WordPiece":return new bi(e2);case"Unigram":return new vi(e2,t.eos_token);case"BPE":return new Ci(e2);default:if(e2.vocab)return Array.isArray(e2.vocab)?new vi(e2,t.eos_token):Object.hasOwn(e2,"continuing_subword_prefix")&&Object.hasOwn(e2,"unk_token")?Object.hasOwn(e2,"merges")?new Ci(e2):new bi(e2):new tm(e2,{target_lang:t.target_lang,bos_token:t.bos_token,eos_token:t.eos_token,pad_token:t.pad_token,unk_token:t.unk_token});throw new Error(`Unknown TokenizerModel type: ${e2?.type}`)}}var rm=nm,sm=class extends Sn{constructor(e2){super(),this.config=e2}_call(e2,...t){return this.post_process(e2,...t)}},Bn=sm,am=class extends Bn{post_process(e2,t=null,n=true){let r=t===null?this.config.single:this.config.pair,s=[],a=[];for(let o of r)"SpecialToken"in o?n&&(s.push(o.SpecialToken.id),a.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(s=Je(s,e2),a=Je(a,new Array(e2.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(s=Je(s,t),a=Je(a,new Array(t.length).fill(o.Sequence.type_id))));return{tokens:s,token_type_ids:a}}},om=am,im=class extends Bn{post_process(e2,t=null){return{tokens:e2,tokens_pair:t}}},um=im,lm=class extends Bn{constructor(e2){super(e2),this.sep=e2.sep,this.cls=e2.cls}post_process(e2,t=null,n=true){n&&(e2=Je([this.cls[0]],e2,[this.sep[0]]));let r=new Array(e2.length).fill(0);if(t){let s=[],a=n?[this.sep[0]]:[];e2=Je(e2,s,t,a),r=Je(r,new Array(t.length+s.length+a.length).fill(1))}return{tokens:e2,token_type_ids:r}}},cm=lm,dm=class extends Bn{constructor(e2){super(e2),this.sep=e2.sep,this.cls=e2.cls}post_process(e2,t,n=true){n&&(e2=Je([this.cls[0]],e2,[this.sep[0]]));let r=new Array(e2.length).fill(0);if(t){let s=n?[this.sep[0]]:[],a=n?[this.sep[0]]:[];e2=Je(e2,s,t,a),r=Je(r,new Array(t.length+s.length+a.length).fill(1))}return{tokens:e2,token_type_ids:r}}},pm=dm,fm=class extends Bn{constructor(e2){super(e2),this.processors=(e2.processors??[]).map(t=>xi(t))}post_process(e2,t=null,n=true){let r={tokens:e2,tokens_pair:t};for(let s of this.processors)r=s.post_process(r.tokens,r.tokens_pair,n);return r}},mm=fm;function gm(e2){if(e2===null)return null;switch(e2.type){case"TemplateProcessing":return new om(e2);case"ByteLevel":return new um(e2);case"BertProcessing":return new cm(e2);case"RobertaProcessing":return new pm(e2);case"Sequence":return new mm(e2);default:throw new Error(`Unknown PostProcessor type: ${e2.type}`)}}var xi=gm,Am=class extends Sn{constructor(e2){super(),this.config=e2,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets="trim_offsets"in e2?e2.trim_offsets:false}_call(e2){return this.decode(e2)}decode(e2){return this.decode_chain(e2).join("")}},ut=Am,hm=class extends ut{constructor(e2){super(e2),this.byte_decoder=Qp,this.text_decoder=new TextDecoder("utf-8",{fatal:false,ignoreBOM:true}),this.end_of_word_suffix=null}convert_tokens_to_string(e2){let t=e2.join(""),n=new Uint8Array([...t].map(r=>this.byte_decoder[r]));return this.text_decoder.decode(n)}decode_chain(e2){let t=[],n=[];for(let r of e2)this.added_tokens.find(s=>s.content===r)!==void 0?(n.length>0&&(t.push(this.convert_tokens_to_string(n)),n=[]),t.push(r)):n.push(r);return n.length>0&&t.push(this.convert_tokens_to_string(n)),t}},_m=hm,wm=class extends ut{constructor(e2){super(e2),this.cleanup=e2.cleanup}decode_chain(e2){return e2.map((t,n)=>{if(n!==0){let r=this.config.prefix;r&&t.startsWith(r)?t=t.replace(r,""):t=" "+t}return this.cleanup&&(t=Ks(t)),t})}},ym=wm,bm=class extends ut{constructor(e2){super(e2),this.replacement=e2.replacement??"▁"}decode_chain(e2){let t=[];for(let n=0;n<e2.length;++n){let r=e2[n].replaceAll(this.replacement," ");n==0&&r.startsWith(" ")&&(r=r.substring(1)),t.push(r)}return t}},km=bm,vm=class extends ut{constructor(e2){super(e2),this.suffix=e2.suffix??""}decode_chain(e2){return e2.map((t,n)=>t.replaceAll(this.suffix,n===e2.length-1?"":" "))}},Cm=vm,Sm=class extends ut{constructor(e2){super(e2),this.pad_token=e2.pad_token??"",this.word_delimiter_token=e2.word_delimiter_token??"",this.cleanup=e2.cleanup}convert_tokens_to_string(e2){if(e2.length===0)return"";let t=[e2[0]];for(let s=1;s<e2.length;++s)e2[s]!==t.at(-1)&&t.push(e2[s]);let r=t.filter(s=>s!==this.pad_token).join("");return this.cleanup&&(r=Ks(r).replaceAll(this.word_delimiter_token," ").trim()),r}decode_chain(e2){return[this.convert_tokens_to_string(e2)]}},Bm=Sm,Im=class extends ut{constructor(e2){super(e2),this.decoders=(e2.decoders??[]).map(t=>Qi(t))}decode_chain(e2){return this.decoders.reduce((t,n)=>n.decode_chain(t),e2)}},Tm=Im,Em=class extends ut{decode_chain(e2){let t=mr(this.config.pattern),n=this.config.content??"";return t===null?e2:e2.map(r=>r.replaceAll(t,n))}},Gm=Em,xm=class extends ut{decode_chain(e2){return[e2.join("")]}},Qm=xm,Dm=class extends ut{constructor(e2){super(e2),this.content=e2.content??"",this.start=e2.start??0,this.stop=e2.stop??0}decode_chain(e2){return e2.map(t=>{let n=0;for(let s=0;s<this.start&&t[s]===this.content;++s){n=s+1;continue}let r=t.length;for(let s=0;s<this.stop;++s){let a=t.length-s-1;if(t[a]===this.content){r=a;continue}else break}return t.slice(n,r)})}},Pm=Dm,Lm=class extends ut{constructor(e2){super(e2),this.text_decoder=new TextDecoder}decode_chain(e2){let t=[],n=[];for(let r of e2){let s=null;if(r.length===6&&r.startsWith("<0x")&&r.endsWith(">")){let a=parseInt(r.slice(3,5),16);isNaN(a)||(s=a)}if(s!==null)n.push(s);else{if(n.length>0){let a=this.text_decoder.decode(Uint8Array.from(n));t.push(a),n=[]}t.push(r)}}if(n.length>0){let r=this.text_decoder.decode(Uint8Array.from(n));t.push(r),n=[]}return t}},qm=Lm;function Rm(e2){if(e2===null)return null;switch(e2.type){case"ByteLevel":return new _m(e2);case"WordPiece":return new ym(e2);case"Metaspace":return new km(e2);case"BPEDecoder":return new Cm(e2);case"CTC":return new Bm(e2);case"Sequence":return new Tm(e2);case"Replace":return new Gm(e2);case"Fuse":return new Qm(e2);case"Strip":return new Pm(e2);case"ByteFallback":return new qm(e2);default:throw new Error(`Unknown Decoder type: ${e2.type}`)}}var Qi=Rm,Mm=class{constructor(e2,t){let n=yi(e2,"Tokenizer",["model","decoder","post_processor","pre_tokenizer","normalizer"]);if(n)throw new Error(n);let r=yi(t,"Config");if(r)throw new Error(r);this.tokenizer=e2,this.config=t,this.normalizer=Ii(this.tokenizer.normalizer),this.pre_tokenizer=Ti(this.tokenizer.pre_tokenizer),this.model=rm(this.tokenizer.model,this.config),this.post_processor=xi(this.tokenizer.post_processor),this.decoder=Qi(this.tokenizer.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];let s=[],a=[];this.added_tokens_map=new Map;for(let o of this.tokenizer.added_tokens){let i=new Gp(o);if(this.added_tokens.push(i),this.model.tokens_to_ids.set(i.content,i.id),this.model.vocab[i.id]=i.content,i.special&&(this.special_tokens.push(i.content),this.all_special_ids.push(i.id)),this.added_tokens_map.set(i.content,i),i.normalized&&this.normalizer!==null){let u=this.normalizer(i.content);a.push(u),this.added_tokens_map.set(u,i)}else s.push(i.content)}(this.config.additional_special_tokens??[]).forEach(o=>{this.special_tokens.includes(o)||this.special_tokens.push(o)}),this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.splitter_unnormalized=new _i(s),this.splitter_normalized=new _i(a),this.remove_space=this.config.remove_space,this.clean_up_tokenization_spaces=this.config.clean_up_tokenization_spaces??true,this.do_lowercase_and_remove_accent=this.config.do_lowercase_and_remove_accent??false}encode(e2,{text_pair:t=null,add_special_tokens:n=true,return_token_type_ids:r=null}={}){let{tokens:s,token_type_ids:a}=this.tokenize_helper(e2,{text_pair:t,add_special_tokens:n}),o=s.map(u=>this.added_tokens_map.get(u)?.id??this.model.tokens_to_ids.get(u)??this.model.unk_token_id),i={ids:o,tokens:s,attention_mask:new Array(o.length).fill(1)};return r&&a&&(i.token_type_ids=a),i}decode(e2,t={}){if(!Array.isArray(e2)||e2.length===0||!Rp(e2[0]))throw Error("token_ids must be a non-empty array of integers.");let n=e2.map(s=>this.model.vocab[Number(s)]??this.model.unk_token);t.skip_special_tokens&&(n=n.filter(s=>!this.special_tokens.includes(s)));let r=this.decoder?this.decoder(n):n.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(r=r.replaceAll(this.decoder.end_of_word_suffix," "),t.skip_special_tokens&&(r=r.trim())),(t.clean_up_tokenization_spaces??this.clean_up_tokenization_spaces)&&(r=Ks(r)),r}tokenize(e2,{text_pair:t=null,add_special_tokens:n=false}={}){return this.tokenize_helper(e2,{text_pair:t,add_special_tokens:n}).tokens}encode_text(e2){if(e2===null)return null;let t=this.splitter_unnormalized.split(e2);return t.forEach((n,r)=>{let s=this.added_tokens_map.get(n);s&&(s.lstrip&&r>0&&(t[r-1]=t[r-1].trimEnd()),s.rstrip&&r<t.length-1&&(t[r+1]=t[r+1].trimStart()))}),t.flatMap((n,r)=>{if(n.length===0)return[];if(this.added_tokens_map.has(n))return[n];if(this.remove_space===true&&(n=n.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(n=Kp(n)),this.normalizer!==null&&(n=this.normalizer(n)),n.length===0)return[];let s=this.splitter_normalized.split(n);return s.forEach((a,o)=>{let i=this.added_tokens_map.get(a);i&&(i.lstrip&&o>0&&(s[o-1]=s[o-1].trimEnd()),i.rstrip&&o<s.length-1&&(s[o+1]=s[o+1].trimStart()))}),s.flatMap(a=>{if(a.length===0)return[];if(this.added_tokens_map.has(a))return[a];let o=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:r}):[a];return this.model(o)})})}tokenize_helper(e2,{text_pair:t=null,add_special_tokens:n=true}){let r=this.encode_text(e2),s=this.encode_text(t||null);return this.post_processor?this.post_processor(r,s,n):{tokens:Je(r??[],s??[])}}token_to_id(e2){return this.model.tokens_to_ids.get(e2)}id_to_token(e2){return this.model.vocab[e2]}get_added_tokens_decoder(){let e2=new Map;for(let t of this.added_tokens)e2.set(t.id,t);return e2}get_vocab(e2=true){let t=new Map;for(let n=0;n<this.model.vocab.length;++n){let r=this.model.vocab[n];(e2||!this.added_tokens_map.has(r))&&t.set(r,n)}return t}},Di=Mm;zu();async function ga(e2={}){let t=globalThis.navigator?.gpu;if(!t)throw new Error("WebGPU is not available in this browser context");let n=await t.requestAdapter({powerPreference:e2.powerPreference??"high-performance"});if(!n)throw new Error("No WebGPU adapter was returned");let r=await n.requestDevice({requiredFeatures:Mu(n,e2.disabledFeatures),requiredLimits:e2.requiredLimits??Ou(n),label:e2.label??"webgpu-ml-runtime"});return r.addEventListener("uncapturederror",s=>{console.error("WebGPU uncaptured error:",s.error)}),Wu({gpu:t,adapter:n,device:r,deviceInfo:Ku(r,n,t),destroy:()=>r.destroy()})}_t();qe();function cA(e2){return e2===null||typeof e2!="object"?false:"destroy"in e2&&"mapAsync"in e2&&"getMappedRange"in e2&&!("buffer"in e2)}function St(e2){return cA(e2)?e2:e2.buffer}function Hu(e2){if(e2.tensor!==void 0&&e2.tensor!==null)return St(e2.tensor);if(e2.buffer!==void 0&&e2.buffer!==null)return St(e2.buffer);throw new Error(`binding ${e2.binding??"?"} is missing tensor or buffer`)}var Qr=class{pipelines=new Map;physicalPipelines=new Map;pipelineLayouts=new Map;bindGroupLayouts=new Map;bindGroups=new Map;maxBindGroupEntries=4096;bufferIds=new WeakMap;#t=1;captureShaders=false;capturedShaders=new Map;compileCount=0;compileTotalMs=0;#e;constructor(t){this.#e=t}async buildPipeline({cacheKey:t,name:n,entryPoint:r,layoutSignature:s,layoutFactory:a,sourceFactory:o}){let i=this.pipelines.get(t);if(i)return i;let u=await o();this.captureShaders&&!this.capturedShaders.has(n)&&this.capturedShaders.set(n,u);let l=s?this.physicalPipeline(u,r,s):void 0;if(l)return this.pipelines.set(t,l),l;let c=performance.now(),d=this.#e.createShaderModule(u,n),f=a(),p=await this.#e.createComputePipeline({label:n,layout:f,compute:{module:d,entryPoint:r}});return this.compileCount+=1,this.compileTotalMs+=performance.now()-c,this.pipelines.set(t,p),s&&this.setPhysicalPipeline(u,r,s,p),p}cachedBindGroups({name:t,cacheKey:n,pipeline:r,bindings:s}){let a=new Map;for(let l of s){let c=l.group??0,d=a.get(c);d?d.push(l):a.set(c,[l])}let o=[...a.keys()].sort((l,c)=>l-c),i=this.buildGroupBindGroup(t,n,r,0,a.get(0)??[]),u=o.filter(l=>l!==0).map(l=>({group:l,bindGroup:this.buildGroupBindGroup(t,n,r,l,a.get(l))}));return u.length>0?{bindGroup:i,extraBindGroups:u}:{bindGroup:i}}buildGroupBindGroup(t,n,r,s,a){let o=a.map((l,c)=>{let d=Hu(l),f={buffer:d,offset:l.offset??0};return l.size!==void 0&&l.size>0&&(f.size=l.size),{binding:l.binding??c,resource:f,cachePart:`${l.binding??c}:${this.bufferId(d)}:${f.offset}:${f.size??""}`}}),i=`${n}|g${s}|${o.map(l=>l.cachePart).join("|")}`,u=this.bindGroups.get(i);if(u===void 0&&(u=this.#e.device.createBindGroup({label:`${t}-bind-group-${s}`,layout:r.getBindGroupLayout(s),entries:o.map(({binding:l,resource:c})=>({binding:l,resource:c}))}),this.bindGroups.set(i,u),this.bindGroups.size>this.maxBindGroupEntries)){let l=this.bindGroups.keys().next().value;l!==void 0&&this.bindGroups.delete(l)}return u}bufferId(t){let n=this.bufferIds.get(t);return n===void 0&&(n=this.#t++,this.bufferIds.set(t,n)),n}pipelineLayout(t,n){let r=Uu(t,n),s=this.pipelineLayouts.get(r.signature);if(s)return s;let a=[];for(let i=0;i<=r.maxGroup;++i)a.push(this.bindGroupLayout(t,i,r.entries));let o=this.#e.device.createPipelineLayout({label:`${t}-layout`,bindGroupLayouts:a});return this.pipelineLayouts.set(r.signature,o),o}pipelineLayoutSignature(t,n){return Uu(t,n).signature}bindGroupLayout(t,n,r){let s=r.filter(u=>u.group===n).map(u=>({binding:u.binding,visibility:GPUShaderStage.COMPUTE,buffer:{type:u.type}})),a=s.map(u=>`${u.binding}:${u.buffer.type}`).sort().join("|"),o=this.bindGroupLayouts.get(a);if(o)return o;let i=this.#e.device.createBindGroupLayout({label:`${t}-bgl${n}`,entries:s});return this.bindGroupLayouts.set(a,i),i}clearBindGroups(){let t=this.bindGroups.size;return this.bindGroups.clear(),t}clearPrograms(){let t=this.pipelines.size;return this.bindGroups.clear(),this.pipelines.clear(),this.physicalPipelines.clear(),this.pipelineLayouts.clear(),this.bindGroupLayouts.clear(),this.captureShaders||this.capturedShaders.clear(),t}getRenderedShaders(){return[...this.capturedShaders].map(([t,n])=>({name:t,source:n}))}physicalPipeline(t,n,r){return this.physicalPipelines.get(Fu(t,n,r))?.find(a=>a.entryPoint===n&&a.layoutSignature===r&&a.source===t)?.pipeline}setPhysicalPipeline(t,n,r,s){let a=Fu(t,n,r),o=this.physicalPipelines.get(a),i={source:t,entryPoint:n,layoutSignature:r,pipeline:s};o?o.push(i):this.physicalPipelines.set(a,[i])}};function Uu(e2,t){let n=t.map((o,i)=>typeof o=="string"?{group:0,binding:i,type:o}:{group:o.group??0,binding:o.binding??i,type:o.type}),r=new Set,s=0;for(let o of n){if(!Number.isInteger(o.binding)||o.binding<0)throw new Error(`pipeline layout ${e2} has invalid binding index ${o.binding}`);if(!Number.isInteger(o.group)||o.group<0)throw new Error(`pipeline layout ${e2} has invalid bind group index ${o.group}`);let i=`${o.group}:${o.binding}`;if(r.has(i))throw new Error(`pipeline layout ${e2} has duplicate binding index ${o.binding} in @group(${o.group})`);if(typeof o.type!="string"||o.type.length===0)throw new Error(`pipeline layout ${e2} binding ${o.binding} is missing a buffer type`);r.add(i),s=Math.max(s,o.group)}let a=n.map(o=>`${o.group}:${o.binding}:${o.type}`).sort();return{signature:`maxGroup=${s}|${a.join("|")}`,maxGroup:s,entries:n}}function Fu(e2,t,n){return`${dA(e2)}:${e2.length}|entry=${t}|layout=${n}`}function dA(e2){let t=2166136261;for(let n=0;n<e2.length;++n)t^=e2.charCodeAt(n),t=Math.imul(t,16777619);return(t>>>0).toString(16).padStart(8,"0")}var Dr=class{pool=new Map;bytes=0;maxBytes;constructor(t=64*1024*1024){this.maxBytes=t}acquire(t,n){let r=this.pool.get(n);return r&&r.length>0?(this.bytes-=n,r.pop()):t.createBuffer({label:"tensor-readback",size:n,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ})}release(t,n){if(this.bytes+t>this.maxBytes){n.destroy();return}let r=this.pool.get(t);r||(r=[],this.pool.set(t,r)),r.push(n),this.bytes+=t}discard(t){try{t.destroy()}catch{}}clear(){let t=0;for(let n of this.pool.values())for(let r of n)r.destroy(),t++;return this.pool.clear(),this.bytes=0,t}};var pA=64*1024,Bt=class{runtime;dtype;shape;byteOffset;buffer;size;byteLength;destroyed;constructor({runtime:t,dtype:n,shape:r,buffer:s,byteOffset:a=0}){this.runtime=t,this.dtype=n,this.shape=r,this.byteOffset=a,this.buffer=s,this.size=we(r),this.byteLength=this.size*Ae(n),this.destroyed=false}destroy(){this.destroyed||(this.buffer.destroy(),this.destroyed=true)}},Lr=class{host;pipelines;profileSession;pendingProfileRuns;readback;destroyed;mappedTensorUploadsAvailable;#t=null;#e=Promise.resolve();#n=false;constructor({host:t}){this.host=t,this.pipelines=new Qr(t),this.profileSession=null,this.pendingProfileRuns=[],this.readback=new Dr,this.destroyed=false,this.mappedTensorUploadsAvailable=true}get device(){return this.host.deviceInfo}get captureShaders(){return this.pipelines.captureShaders}set captureShaders(t){this.pipelines.captureShaders=t}getRenderedShaders(){return this.pipelines.getRenderedShaders()}async destroy(){this.destroyed||(this.destroyed=true,this.clearBindGroupCache(),this.clearReadbackPool(),await this.#e,this.#i(),await this.host.destroy())}clearReadbackPool(){return this.readback.clear()}clearBindGroupCache(){return this.pipelines.clearBindGroups()}clearKernelCaches(){return this.pipelines.clearPrograms()}tensorFromTypedArray(t,n,r){if(!fA(t,r))throw new Error("Only float16/Uint16Array, float32/Float32Array and uint32/Uint32Array tensors are supported");let s=we(n);if(r.length!==s)throw new Error(`tensor data length ${r.length} does not match shape element count ${s}`);let a={label:"tensor",size:Pr(r.byteLength),usage:GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST|GPUBufferUsage.COPY_SRC};if(this.mappedTensorUploadsAvailable&&r.byteLength>=pA){let u=null;try{u=this.host.createBuffer({...a,mappedAtCreation:true});let l=u.getMappedRange();return r instanceof Float32Array?new Float32Array(l).set(r):r instanceof Uint16Array?new Uint16Array(l).set(r):r instanceof Int32Array?new Int32Array(l).set(r):new Uint32Array(l).set(r),u.unmap(),new Bt({runtime:this,dtype:t,shape:n,buffer:u})}catch{try{u?.destroy()}catch{}this.mappedTensorUploadsAvailable=false}}let o=this.host.createBuffer(a),i=r;if(r.byteLength%4!==0){let u=new Uint8Array(Pr(r.byteLength));u.set(new Uint8Array(r.buffer,r.byteOffset,r.byteLength)),i=u}return this.host.writeBuffer(o,0,i),new Bt({runtime:this,dtype:t,shape:n,buffer:o})}allocateWeightsBuffer({byteLength:t,dtype:n,shape:r,label:s="weights"}){if(!Zu(n))throw new Error(`Unsupported dtype: ${n}`);if(!Number.isInteger(t)||t<0)throw new Error(`byteLength must be a nonnegative integer, got ${t}`);let a=this.host.createBuffer({label:s,size:t,usage:GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_DST|GPUBufferUsage.COPY_SRC});return new Bt({runtime:this,dtype:n,shape:r,buffer:a})}writeWeightsRange(t,n,r){if(!(t instanceof Bt))throw new Error("writeWeightsRange expects a WebGpuTensor");if(!Number.isInteger(n)||n<0)throw new Error(`byteOffset must be a nonnegative integer, got ${n}`);if(n+r.byteLength>t.byteLength)throw new Error(`write range [${n}, ${n+r.byteLength}] exceeds tensor byteLength ${t.byteLength}`);this.host.writeBuffer(t.buffer,n,r)}async copyBufferToBuffer({src:t,dst:n,srcOffset:r=0,dstOffset:s=0,byteLength:a,wait:o=false}){if(a===0)return;let i=St(t),u=St(n),l=this.host.device.createCommandEncoder({label:"copyBufferToBuffer"});l.copyBufferToBuffer(i,r,u,s,a),await this.host.submit([l.finish()],{wait:o})}clearBuffers(t){let n=null;for(let{dst:r,dstOffset:s=0,byteLength:a}of t){if(!Number.isInteger(s)||s<0||s%4!==0)throw new Error(`clear buffer offset must be a non-negative multiple of 4, got ${s}`);if(!Number.isInteger(a)||a<0||a%4!==0)throw new Error(`clear buffer byteLength must be a non-negative multiple of 4, got ${a}`);a!==0&&(n??=this.host.device.createCommandEncoder({label:"clear-buffers"}),n.clearBuffer(St(r),s,a))}n&&this.host.device.queue.submit([n.finish()])}async queueIdle(){await this.host.device.queue.onSubmittedWorkDone()}empty(t,n,r="tensor-output"){if(!Zu(t))throw new Error(`Unsupported dtype: ${t}`);let s=we(n)*Ae(t),a=this.host.createBuffer({label:r,size:Pr(s),usage:GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST});return new Bt({runtime:this,dtype:t,shape:n,buffer:a})}readTensor(t){if(t.byteLength===0){let i=tt[t.dtype]?.arrayCtor;return i?Promise.resolve(new i(0)):Promise.reject(new Error(`Unsupported dtype: ${t.dtype}`))}let n=Pr(t.byteLength),r=this.readback.acquire(this.host,n),s=this.host.device.createCommandEncoder({label:"readTensor"});s.copyBufferToBuffer(t.buffer,t.byteOffset,r,0,n),this.host.device.queue.submit([s.finish()]);let{dtype:a,byteLength:o}=t;return(async()=>{let i;try{i=await this.host.mapRead(r,0,n)}catch(l){throw this.readback.discard(r),l}this.readback.release(n,r),n!==o&&(i=i.slice(0,o));let u=tt[a]?.arrayCtor;if(!u)throw new Error(`Unsupported dtype: ${a}`);return new u(i)})()}async runProgram(t,n={}){await this.runProgramSequence([t],n)}async runProgramSequence(t,n={}){let r=await this.prepareProgramSequence(t);await this.executePreparedProgramSequence(r,n)}async prepareProgramSequence(t){if(!Array.isArray(t)||t.length===0)throw new Error("prepareProgramSequence requires at least one program");let n=[];for(let r of t){if(mA(r)){n.push(gA(r));continue}let{name:s,source:a,entryPoint:o="main",cacheKey:i=s,bindings:u,dispatchWorkgroups:l,profile:c,submitAfter:d}=r;if(typeof a!="string"||a.length===0)throw new Error("program requires WGSL source");if(!Array.isArray(u)||u.length===0)throw new Error("program requires bindings");if(!Array.isArray(l)||l.length!==3)throw new Error("program requires a 3D dispatchWorkgroups array");let f=this.pipelines.pipelineLayoutSignature(s,u),p=await this.pipelines.buildPipeline({name:s,entryPoint:o,cacheKey:i,layoutSignature:f,layoutFactory:()=>this.pipelines.pipelineLayout(s,u),sourceFactory:()=>a}),{bindGroup:g,extraBindGroups:m}=this.pipelines.cachedBindGroups({name:s,cacheKey:i,pipeline:p,bindings:u});n.push({pipeline:p,bindGroup:g,...m?{extraBindGroups:m}:{},dispatchWorkgroups:l,name:s,cacheKey:i,profile:c,...d?{submitAfter:true}:{}})}return n}async executePreparedProgramSequence(t,n={}){if(!Array.isArray(t)||t.length===0)throw new Error("executePreparedProgramSequence requires at least one prepared step");let r=n.wait??false;if(this.profileSession){await this.#s();try{await this._runSteps(t,{wait:r,mergePass:false})}finally{this.#o()}return}await this._runSteps(t,{wait:r,mergePass:true})}preparedProgramSequenceRequiresCompletionFences(t){return t.some(n=>n.kind!=="copy"&&n.submitAfter===true&&!ct(n.dispatchWorkgroups))}#r=8;#a=[];async#s(){for(;this.#r<=0;)await new Promise(t=>this.#a.push(t));this.#r-=1}#l(){return this.#r<=0?false:(this.#r-=1,true)}#o(){this.#r+=1,this.#a.shift()?.()}enqueuePreparedProgramSequence(t){if(!Array.isArray(t)||t.length===0)throw new Error("enqueuePreparedProgramSequence requires at least one prepared step");let n=Aa(t);if(this.profileSession&&n.length===1&&this.#l()){let r=this._runSteps(t,{wait:false,mergePass:false});this.pendingProfileRuns.push(r.finally(()=>this.#o()));return}for(let r of n){let s=this.host.device.createCommandEncoder({label:"compute-dispatch"});ha(s,r),this.host.device.queue.submit([s.finish()])}}async flushDeferredProfile(){let t=this.pendingProfileRuns;this.pendingProfileRuns=[],await Promise.all(t)}async measurePreparedSequenceGpuMs(t,n=1){if(this.destroyed||!this.host.device.features.has("timestamp-query")||this.#n)return null;let r=this.#e,s;this.#e=new Promise(a=>{s=a}),await r;try{if(this.destroyed||this.#n)return null;let a=this.#t??=this.createTimestampResources(2),o=Aa(t);if(o.length>1)return await this.#u(o,n,a);let i=this.host.device.createCommandEncoder({label:"gpu-time-measure"});for(let p of t)p.kind==="copy"&&p.byteLength>0&&i.copyBufferToBuffer(p.src,p.srcOffset,p.dst,p.dstOffset,p.byteLength);let u=i.beginComputePass({label:"gpu-time-pass",timestampWrites:{querySet:a.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1}}),l=false,c=null,d=null;for(let p=0;p<n;++p)for(let g of t)if(!(g.kind==="copy"||ct(g.dispatchWorkgroups))){if(g.pipeline!==c&&(u.setPipeline(g.pipeline),c=g.pipeline),g.bindGroup!==d&&(u.setBindGroup(0,g.bindGroup),d=g.bindGroup),g.extraBindGroups)for(let m of g.extraBindGroups)u.setBindGroup(m.group,m.bindGroup),m.group===0&&(d=m.bindGroup);u.dispatchWorkgroups(g.dispatchWorkgroups[0],g.dispatchWorkgroups[1],g.dispatchWorkgroups[2]),l=true}if(u.end(),!l)return null;i.resolveQuerySet(a.querySet,0,2,a.resolveBuffer,0),i.copyBufferToBuffer(a.resolveBuffer,0,a.readbackBuffer,0,16),await this.host.submit([i.finish()],{wait:true});let f;try{f=await this.readTimestampBuffer(a.readbackBuffer,2)}catch{return this.#n=true,this.#i(),null}return Number(f[1]-f[0])/1e6}finally{s()}}async#u(t,n,r){if(!Number.isInteger(n)||n<=0)return null;let s=t.findIndex($u),a=-1;for(let u=t.length-1;u>=0;--u)if($u(t[u])){a=u;break}if(s<0||a<0)return null;let o=[];for(let u=0;u<n;++u)for(let l=0;l<t.length;++l){let c=this.host.device.createCommandEncoder({label:"gpu-time-measure-boundary"}),d=u===0&&l===s,f=u===n-1&&l===a;ha(c,t[l],{querySet:r.querySet,...d?{beginningOfSequenceWriteIndex:0}:{},...f?{endOfSequenceWriteIndex:1}:{}}),u===n-1&&l===t.length-1&&(c.resolveQuerySet(r.querySet,0,2,r.resolveBuffer,0),c.copyBufferToBuffer(r.resolveBuffer,0,r.readbackBuffer,0,16)),o.push(c.finish())}for(let u of o)await this.host.submit([u],{wait:true});let i;try{i=await this.readTimestampBuffer(r.readbackBuffer,2)}catch{return this.#n=true,this.#i(),null}return Number(i[1]-i[0])/1e6}#i(){let t=this.#t;this.#t=null,t&&(t.querySet.destroy(),t.resolveBuffer.destroy(),t.readbackBuffer.destroy())}pipelineCompileStats(){return{count:this.pipelines.compileCount,totalMs:this.pipelines.compileTotalMs}}memoryStats(){let t=this.host.memory;return t?{liveBytes:t.liveBytes,peakBytes:t.peakBytes}:null}markMemoryPeak(){this.host.memory?.markPeak()}createUniformU32(t,n){let r=new Uint32Array(t),s=this.host.createBuffer({label:n,size:r.byteLength,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});return this.host.writeBuffer(s,0,r),s}async _runSteps(t,{wait:n=false,mergePass:r}){let s=!!this.profileSession,a=Aa(t);if(!s&&r){let d=a.map(f=>{let p=this.host.device.createCommandEncoder({label:"compute-dispatch"});return ha(p,f),p.finish()});for(let f=0;f+1<d.length;++f)await this.host.submit([d[f]],{wait:true});await this.host.submit([d[d.length-1]],{wait:n||Nu(a[a.length-1])});return}let o=t.map((d,f)=>d.kind==="copy"||ct(d.dispatchWorkgroups)?-1:f).filter(d=>d>=0),i=s?o.length*2:0,u=s&&i>0?this.createTimestampResources(i):void 0,l=0,c=[];for(let d=0;d<a.length;++d){let f=this.host.device.createCommandEncoder({label:s?"profiled-compute-dispatch":"compute-dispatch"});for(let p of a[d]){if(p.kind==="copy"){p.byteLength>0&&f.copyBufferToBuffer(p.src,p.srcOffset,p.dst,p.dstOffset,p.byteLength);continue}if(ct(p.dispatchWorkgroups))continue;let 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u=e2.encode(t.slice(i.length),{add_special_tokens:false}).ids,l=[...o,...u];if(Ur(e2,l,{skip_special_tokens:false})===t)return{fullyEncodedIds:l,inputIds:l,prefixLength:n.length}}let s=e2.encode(t,{add_special_tokens:false}).ids;return{fullyEncodedIds:s,inputIds:s,prefixLength:wh(n,s)}}var Fr=class{#t;#e=[];#n=[];constructor(t){this.#t=t}get cacheLength(){return this.#e.length}prepare(t){return yh(this.#t,t,this.#e,this.#n)}commit(t,n,r){let s=[...t,...n];if(!Number.isInteger(r)||r<t.length||r>s.length)throw new Error(`chat token ledger cannot commit cache length ${r} for ${t.length} input + ${n.length} generated tokens`);this.#e=s.slice(0,r),this.#n=s.slice(r)}clear(){this.#e=[],this.#n=[]}};function Ql(e2,t){return e2?.assistantContent?.(t)??t.visibleText}function Zr(e2){return typeof e2=="number"&&Number.isFinite(e2)}function Dl(e2){let t=[],n="",r=0,s=0,a=o=>{if(!o.startsWith(n))return"";let i=o.slice(n.length);return n=o,i};return{push(o){t.push(o);let i=e2(t),u=i.length;for(;u>0&&i[u-1]==="�";)u--;let l=u!==i.length;return r=!l||u>s?0:r+1,s=u,a(l?r>3?i:i.slice(0,u):i)},flush(){if(t.length===0)return"";let o=e2(t);if(r=0,o.startsWith(n))return a(o);let i=0;for(;i<n.length&&i<o.length&&n[i]===o[i];)i+=1;let u=o.slice(i);return n=o,u},get text(){return n}}}var Pl=`<think>
+`,Ll=`<think>
+
+</think>
+
+`,bh="webgpu-template-policy-probe";function $r({rawText:e2,templateArgs:t}){let n=t.enable_thinking===false?Ll:Pl;return n.includes("</think>")||e2.includes("</think>")?n+e2:n+e2+(e2.endsWith(`
+`)?"":`
+`)+`</think>
+
+`}var kh=Object.freeze({templateArgs:Object.freeze({preserve_thinking:true}),assistantContent:$r});function Un(e2,t,n,r,s){return e2.render({...s,messages:n,tools:null,bos_token:t.bos_token,eos_token:t.eos_token,add_generation_prompt:r})}function ql(e2,t){let n={role:"user",content:bh},r=Object.freeze({preserve_thinking:true,enable_thinking:true}),s=Object.freeze({preserve_thinking:true,enable_thinking:false});try{let a=Un(e2,t,[n],true,r),o=Un(e2,t,[n],true,s);if(!a.endsWith(Pl)||!o.endsWith(Ll))return;let i=`reasoning
+</think>
+
+answer`;if(!Un(e2,t,[n,{role:"assistant",content:$r({visibleText:`reasoning
+answer`,rawText:i,templateArgs:r})}],false,r).startsWith(a+i))return;let l="unfinished reasoning";if(!Un(e2,t,[n,{role:"assistant",content:$r({visibleText:l,rawText:l,templateArgs:r})}],false,r).startsWith(a+l))return;let d="answer";return Un(e2,t,[n,{role:"assistant",content:$r({visibleText:d,rawText:d,templateArgs:s})}],false,s).startsWith(o+d)?kh:void 0}catch{return}}function Rl(e2,t){return ql(e2,t)}async function Kl(e2,t){let n=Math.max(1,Math.floor(t.baseDepth)),r=Math.max(n,Math.floor(t.maxDepth)),s=Ml(t.minSampleTokens,24,2),a=Number.isFinite(t.saturationRatio)?Math.max(.5,Math.min(1,t.saturationRatio)):.97,o=Ml(t.maxProbes,5,1),i=[n];for(;i.length<o&&i.at(-1)<r;){let d=i.at(-1);i.push(Math.min(r,d*2))}let u=[],l=null,c=0;for(let d of i){let f=Math.max(s,d+1),p=await e2(d,f);if(!Number.isFinite(p.tokensPerSecond)||p.tokensPerSecond<=0)throw new Error(`decode calibration at depth ${d} returned invalid throughput`);if(p.tokenIds.length!==f)throw new Error(`decode calibration at depth ${d} produced ${p.tokenIds.length}/${f} tokens`);let g=l===null||vh(l,p.tokenIds);if(u.push({depth:d,sampleTokens:f,tokensPerSecond:p.tokensPerSecond,outputMatchesReference:g}),!g)return Ka(u,a,"output-mismatch");if((l===null||p.tokenIds.length>l.length)&&(l=p.tokenIds),c>0&&p.tokensPerSecond<=c/a)return Ka(u,a,"saturated");c=p.tokensPerSecond}return Ka(u,a,"max-depth")}function Ka(e2,t,n){let r=e2.filter(o=>o.outputMatchesReference),s=Math.max(...r.map(o=>o.tokensPerSecond));return{depth:r.find(o=>o.tokensPerSecond>=s*t)?.depth??r[0]?.depth??1,samples:e2,totalSampleTokens:e2.reduce((o,i)=>o+i.sampleTokens,0),stoppedBecause:n}}function vh(e2,t){let n=Math.min(e2.length,t.length);for(let r=0;r<n;++r)if(e2[r]!==t[r])return false;return true}function Ml(e2,t,n){return Number.isFinite(e2)?Math.max(n,Math.floor(e2)):t}async function Ol(e2,t=6){let n=Math.max(1,Math.floor(t)),r=e2.tensorFromTypedArray("uint32",[1,1],new Uint32Array([0]));try{await e2.readTensor(r);let s=[];for(let a=0;a<n;++a){let o=performance.now();await e2.readTensor(r),s.push(performance.now()-o)}return s.sort((a,o)=>a-o),s[s.length>>1]}finally{r.destroy?.()}}var Nr=class{#t;#e;model;tokenizer;#n;#r;#a;#s;eosTokenIds;#l;generationState;#o=null;#u=false;destroyed=false;chatTemplateArgs={};constructor(t){this.#t=t.runtime,this.#e=t.ownsRuntime,this.model=t.model,this.tokenizer=t.tokenizer,this.#n=t.chatTemplate,this.#r=t.tokenizerConfig,this.#a=t.chatTemplatePolicy===null?void 0:t.chatTemplatePolicy??Rl(t.chatTemplate,t.tokenizerConfig),this.#s=new Fr(t.tokenizer),this.eosTokenIds=t.eosTokenIds,this.generationState=t.generationState,this.#l=t.defaultMaxNewTokens}get runtime(){return this.#t}acquireGenerationLease(){if(this.destroyed)throw new Error(`${this.constructor.name} has been disposed`);if(this.#u)throw new Error(`${this.constructor.name} is already generating`);this.#u=true;let t=true;return()=>{t&&(t=false,this.#u=false)}}get lastAssistantContent(){return this.#o}get contextLength(){return this.generationState.cache.maxLength}get contextFull(){return this.generationState.cache.get_seq_length()>=this.generationState.cache.maxLength}#i(){return Object.freeze({...this.#a?.templateArgs??{},...this.chatTemplateArgs})}#d(t,n,r){return this.#n.render({...r,messages:t,tools:null,bos_token:this.#r.bos_token,eos_token:this.#r.eos_token,add_generation_prompt:n})}renderPrompt(t,n=true){return this.#d(t,n,this.#i())}encodePrompt(t){return this.tokenizer.encode(this.renderPrompt(t),{add_special_tokens:false}).ids}async*generate(t,n={}){let r=this.acquireGenerationLease();this.#o=null;let s=this.#i(),a=[],o="",i=false;try{this.generationState.cache.get_seq_length()!==this.#s.cacheLength&&this.resetCache();let u=n.maxNewTokens??this.#l,l=n.eosTokenId??this.eosTokenIds,c=this.#d(t,true,s),d=this.#s.prepare(c),{inputIds:f,prefixLength:p}=d;p!==this.#s.cacheLength&&(this.resetCache(),p=0,f=d.fullyEncodedIds);let g=f.slice(p);g.length===0&&(this.resetCache(),f=d.fullyEncodedIds,g=f.slice());let m=this.generationState.cache.maxLength-this.generationState.cache.get_seq_length()-g.length;if(m<1)throw new Error(g.length>=this.generationState.cache.maxLength?`message is too long for the context window (${g.length} tokens; 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+{{ message['content'] }}<|im_end|>
+{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}`;function zl(e2){let t=Ul(e2,"tokenizer.ggml.tokens"),n=e2["tokenizer.ggml.merges"]??[],r={};return t.forEach((s,a)=>{r[s]=a}),Bh(e2)?{version:"1.0",truncation:null,padding:null,added_tokens:Oa(e2,t),normalizer:{type:"Replace",pattern:{String:" "},content:"▁"},pre_tokenizer:{type:"Split",pattern:{String:" "},behavior:"MergedWithPrevious",invert:false},post_processor:null,decoder:{type:"Sequence",decoders:[{type:"Replace",pattern:{String:"▁"},content:" 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t=Ul(e2,"tokenizer.ggml.tokens"),n=e2["tokenizer.ggml.bos_token_id"],r=e2["tokenizer.ggml.eos_token_id"],s=e2["tokenizer.ggml.unknown_token_id"],a=e2["tokenizer.ggml.padding_token_id"],o=Object.fromEntries(Oa(e2,t).map(i=>[i.id,{content:i.content,single_word:i.single_word,lstrip:i.lstrip,rstrip:i.rstrip,normalized:i.normalized,special:i.special}]));return{tokenizer_class:"Qwen2Tokenizer",model_max_length:e2["qwen3.context_length"]??e2["general.context_length"]??32768,bos_token:on(t,n),eos_token:on(t,r),unk_token:on(t,s),pad_token:on(t,a),bos_token_id:Number.isInteger(n)?n:void 0,eos_token_id:Number.isInteger(r)?r:void 0,unk_token_id:Number.isInteger(s)?s:void 0,pad_token_id:Number.isInteger(a)?a:void 0,chat_template:Wa(e2,t),added_tokens_decoder:o}}function Wa(e2,t=xh(e2)){let n=e2["tokenizer.chat_template"];if(typeof n=="string")return n;let r=e2["tokenizer.ggml.chat_template"];return typeof r=="string"?r:t.includes("<|im_start|>")&&t.includes("<|im_end|>")?Sh:null}function Ih(e2){let t=e2["tokenizer.ggml.pre"];return t==="qwen2"||t==="qwen3"||e2["general.architecture"]==="qwen3"?{type:"Sequence",pretokenizers:[{type:"Split",pattern:{Regex:Ch},behavior:"Isolated",invert:false},{type:"ByteLevel",add_prefix_space:false,trim_offsets:false,use_regex:false}]}:{type:"ByteLevel",add_prefix_space:false,trim_offsets:true,use_regex:true}}function Th(){return{type:"ByteLevel",add_prefix_space:false,trim_offsets:false,use_regex:false}}function Eh(){return{type:"ByteLevel",add_prefix_space:false,trim_offsets:false,use_regex:false}}function Oa(e2,t){let n=Array.isArray(e2["tokenizer.ggml.token_type"])?e2["tokenizer.ggml.token_type"]:[],r=new Set;for(let s of[e2["tokenizer.ggml.bos_token_id"],e2["tokenizer.ggml.eos_token_id"],e2["tokenizer.ggml.unknown_token_id"],e2["tokenizer.ggml.padding_token_id"]])Number.isInteger(s)&&r.add(s);for(let s=0;s<n.length;++s)Gh(n[s])&&r.add(s);return[...r].filter(s=>s>=0&&s<t.length).sort((s,a)=>s-a).map(s=>({id:s,content:t[s],single_word:false,lstrip:false,rstrip:false,normalized:false,special:true}))}function Gh(e2){return Number.isInteger(e2)&&e2!==1&&e2!==6}function Wl(e2){return Array.isArray(e2)?e2.map(t=>Array.isArray(t)?t.join(" "):String(t)):[]}function on(e2,t){return typeof t=="number"&&Number.isInteger(t)&&t>=0&&t<e2.length?e2[t]:void 0}function Ul(e2,t){let n=e2[t];if(!Array.isArray(n)||n.some(r=>typeof r!="string"))throw new Error(`GGUF metadata ${t} must be a string array`);return n}function xh(e2){let t=e2["tokenizer.ggml.tokens"];return Array.isArray(t)&&t.every(n=>typeof n=="string")?t:[]}ri();ni();Eo();Yo();var Id="prism-ml/Bonsai-27B-gguf",Bw="Bonsai-27B-Q1_0.gguf",wn=4,Sd=4096;function Bd(e2,t,n="main"){let r=e2??Id;return r.toLowerCase().endsWith(".gguf")?!/^https?:/i.test(r)&&typeof location<"u"?new URL(r,location.href).href:r:xl(r,{revision:n,file:t??Bw})}var ai=class e extends Nr{static DEFAULT_MODEL_ID=Id;#t;#e;#n;#r=wn;#a=0;constructor(t){let n=_s(t.model),r=Math.max(1,Math.min(t.maxLength??Math.min(n,Sd),n)),s=t.model.createGenerationState({maxLength:r});super({...t,generationState:s,defaultMaxNewTokens:Sd}),this.#t=s,this.#e=t.tokenizer.token_to_id("<think>")??null,this.#n=t.tokenizer.token_to_id("</think>")??null}get thinkOpenTokenId(){return this.#e}get thinkCloseTokenId(){return this.#n}streamTokens(t,n){return this.model.streamTokenIdsFromCache({input_ids:[t.suffixIds],generation_state:this.#t,max_new_tokens:t.maxNewTokens,eos_token_id:t.eosTokenId,stop_on_eos:t.stopOnEos,decode_pipeline_depth:n.decodePipelineDepth??this.#r})}get contextLength(){return this.#t.cache.maxLength}static async checkAvailability(t=null,n={}){let r=globalThis.navigator?.gpu;if(!r)return{ok:false,reason:"WebGPU isn't available in this browser. Try a recent Chrome or Edge."};let s=null;try{s=await r.requestAdapter({powerPreference:"high-performance"})}catch{}if(!s)return{ok:false,reason:"No WebGPU adapter is available on this device."};let a=Bd(t,n.file,n.revision??"main"),o=n.fetch??(n.accessToken?Ma(n.accessToken):void 0);try{let i=await kl(a,{fetch:o,signal:n.signal}),u=xs(ar(i),s,i);return u?{ok:false,reason:u}:{ok:true}}catch{return{ok:true}}}static async load(t=null,n={}){let r=n.onProgress??(()=>{}),s=Bd(t,n.file,n.revision??"main"),a=n.fetch??(n.accessToken?Ma(n.accessToken):void 0);r({status:"init",message:"Requesting WebGPU device"});let o=n.runtime??await ju(n.runtimeOptions??{}),i=!n.runtime;o.captureShaders=true;let u=async()=>{};try{r({status:"init",message:"Checking device support"});let l=await an.open(s,{fetch:a,signal:n.signal,cache:n.cache,force:n.force,cacheName:n.cacheName});u=()=>l.close();let c=xs(ar(l),o.device,l);if(c)throw new Error(c);r({status:"tokenizer",message:"Loading tokenizer"}),await si();let{tokenizer:d,chatTemplate:f,tokenizerConfig:p,eosTokenIds:g}=Iw(l);await si();let m=l.names().reduce((E,R)=>E+l.info(R).byteLength,0),A=0;l.onByteProgress=E=>{A+=E,r({status:"weights",kind:"bytes",loaded:A,total:m||null,fraction:m?Math.min(1,A/m):void 0,message:"Streaming weights"})};let h=await $t.loadGGUF(o,{gguf:l,onProgress:E=>{!Zr(E.total)||E.total<=0||r({status:"weights",kind:"tensors",loaded:E.processed,total:E.total,message:"Uploading to GPU"})}});await u();let C=new e({runtime:o,ownsRuntime:i,model:h,tokenizer:d,chatTemplate:f,tokenizerConfig:p,eosTokenIds:g,maxLength:n.maxLength});return r({status:"weights",kind:"tensors",message:"Compiling kernels (warmup)"}),await si(),await h.warmup(C.#t.cache),C.#a=await Ol(o),n.decodePipelineDepth!==void 0?C.#r=nr({decode_pipeline_depth:n.decodePipelineDepth},wn,Ft):(r({status:"weights",kind:"tensors",message:"Tuning decode pipeline"}),C.#r=await C.#s()),r({status:"ready",message:"Ready",fraction:1}),C}catch(l){throw await u().catch(()=>{}),i&&await o.destroy(),l}}get decodePipelineDepth(){return this.#r}get readbackLatencyMs(){return this.#a}async#s(){let t=Math.min(Ft,this.contextLength-1);return t<=wn?wn:(await this.calibrateDecodePipeline({baseDepth:wn,maxDepth:t,minSampleTokens:Math.min(24,this.contextLength)},r=>({decodePipelineDepth:r}))).depth}async benchmark(t,n={}){let r=n.maxNewTokens??64,s=nr({decode_pipeline_depth:n.decodePipelineDepth??this.#r},wn,Ft);return{...await this.benchmarkFixedTokenIds(this.encodePrompt(t),r,{decodePipelineDepth:s}),depth:s}}};function si(){return typeof requestAnimationFrame=="function"?new Promise(e2=>requestAnimationFrame(()=>e2())):new Promise(e2=>setTimeout(e2,0))}function Iw(e2){let t=e2.metadata,n=Hl(t),r=zl(t),s=new Di(r,n),a=typeof n.chat_template=="string"?n.chat_template:Wa(t);if(!a)throw new Error("Bonsai GGUF metadata is missing a chat_template");let o=Tw(n,s);return{tokenizer:s,chatTemplate:new pr(a),tokenizerConfig:n,eosTokenIds:o}}function Tw(e2,t){let n=new Set,r=e2.eos_token_id;if(Zr(r)&&n.add(r),typeof e2.eos_token=="string"){let s=t.token_to_id(e2.eos_token);Zr(s)&&n.add(s)}if(n.size===0)throw new Error("Bonsai GGUF metadata has no eos token id");return[...n]}var $I=ai;export{ai as Bonsai27B,Bw as DEFAULT_GGUF_FILE,Id as DEFAULT_MODEL_ID,$I as default,Bd as resolveGGUFUrl};const Bonsai27B=ai;const DEFAULT_GGUF_FILE=Bw;const $=id2=>document.getElementById(id2);const QSA=new URLSearchParams(location.search);const REQUIRE_HF_TOKEN=window.BONSAI_REQUIRE_HF_TOKEN===true;const MODEL_ID="prism-ml/Bonsai-27B-gguf";const MODEL_SRC=QSA.get("src")||MODEL_ID;const TOKEN_KEY="bonsai27b_hf_token_v1";const TOTAL_BYTES_FALLBACK=38e8;let chat=null;let loadState="idle";let loadBlocked=false;let accessToken=null;let messages=[];let isGenerating=false;let contextExhausted=false;let abortController=null;let reauthAfterGate=false;const SEED_EXAMPLES=[{label:"LOGIC PUZZLE",prompt:"You have three boxes labeled Apples, Oranges, and Mixed. Every label is wrong.\n\nYou may take one fruit from one box without looking inside. How can you correctly relabel all three boxes?"},{label:"GENERATE CODE",prompt:"Write a "},{label:"WRITE A HAIKU",prompt:"Write a haiku about a bonsai tree."}];const gate=$("gate"),gateInput=$("gateInput"),gateError=$("gateError");const gateContinue=$("gateContinue"),gateField=$("gateField"),veil=$("veil");async function validateToken(token){const trimmed=(token||"").trim();if(!trimmed)return{valid:false,error:"A token is required."};try{const res=await fetch(`https://huggingface.co/api/models/${MODEL_ID}`,{headers:{Authorization:`Bearer ${trimmed}`}});if(res.ok)return{valid:true};if(res.status===401)return{valid:false,error:"Invalid token."};let body=null;try{body=await res.json()}catch{}const errText=body?.error?String(body.error):"";if(res.status===404||errText.toLowerCase().includes("repository not found")){return{valid:false,error:"This token can't access the model. Request access on the model page, then try again."}}if(res.status===403)return{valid:false,error:"Access forbidden — the token needs read permission."};return{valid:false,error:errText||`Validation failed (HTTP ${res.status}).`}}catch{return{valid:null,error:"Couldn't reach huggingface.co to verify the token."}}}function showGate(prefill=""){veil.hidden=true;gate.hidden=false;gate.classList.remove("leave");if(prefill)gateInput.value=prefill;requestAnimationFrame(()=>requestAnimationFrame(()=>{gate.classList.add("show");gateInput.focus()}))}function showGateError(message){gateError.textContent=message;gateError.hidden=false;gateField.classList.add("error")}function clearGateError(){gateError.hidden=true;gateField.classList.remove("error")}function grant(token){accessToken=token;try{localStorage.setItem(TOKEN_KEY,token)}catch{}gate.classList.add("leave");setTimeout(()=>{gate.hidden=true;gate.classList.remove("show","leave")},550);if(veil.hidden===false){veil.classList.add("leave");setTimeout(()=>{veil.hidden=true;veil.classList.remove("leave")},850)}window.App?.bootLanding?.();runAvailabilityCheck();if(reauthAfterGate){reauthAfterGate=false;hideLoadError();startLoad()}}async function submitGate(){if(gateContinue.classList.contains("busy"))return;const token=gateInput.value.trim();if(!token){showGateError("A token is required.");return}clearGateError();gateContinue.classList.add("busy");gateContinue.textContent="VALIDATING …";const result=await validateToken(token);gateContinue.classList.remove("busy");gateContinue.innerHTML="CONTINUE &rarr;";if(result.valid===false){showGateError(result.error);return}grant(token)}gateContinue.addEventListener("click",e2=>{e2.preventDefault();submitGate()});gateInput.addEventListener("keydown",e2=>{if(e2.key==="Enter"){e2.preventDefault();submitGate()}});gateInput.addEventListener("input",clearGateError);$("gateShow").addEventListener("click",()=>{const hidden=gateInput.type==="password";gateInput.type=hidden?"text":"password";$("gateShow").textContent=hidden?"HIDE":"SHOW"});async function initAccess(){if(!REQUIRE_HF_TOKEN){runAvailabilityCheck();return}let stored=null;try{stored=localStorage.getItem(TOKEN_KEY)}catch{}if(!stored){showGate();return}veil.hidden=false;const result=await validateToken(stored);if(result.valid===false){try{localStorage.removeItem(TOKEN_KEY)}catch{}showGate(stored);showGateError(result.error+" Enter a current token to continue.");return}grant(stored)}async function runAvailabilityCheck(){if(!navigator.gpu){blockLoad("WebGPU isn't available in this browser. Try a recent Chrome or Edge.");return}try{const res=await Bonsai27B.checkAvailability(MODEL_SRC,{file:MODEL_SRC===MODEL_ID?DEFAULT_GGUF_FILE:void 0,accessToken:accessToken??void 0});if(res&&!res.ok&&res.reason&&loadState==="idle")blockLoad(res.reason)}catch{}}function blockLoad(reason){loadBlocked=true;const cta=$("loadCta");cta.textContent="UNAVAILABLE ON THIS DEVICE";cta.style.opacity="0.45";cta.style.pointerEvents="none";$("ctaNote").textContent=reason;$("ctaNote").hidden=false}$("loadCta").addEventListener("click",e2=>{if(loadBlocked){e2.preventDefault();e2.stopImmediatePropagation()}},true);window.BonsaiApp={startLoad,enterChat};async function startLoad(){if(loadState==="loading"||loadState==="ready"||loadBlocked)return;loadState="loading";hideLoadError();BonsaiLoader.set(0,TOTAL_BYTES_FALLBACK);BonsaiLoader.phase("REQUESTING WEBGPU DEVICE");if(document.body.classList.contains("stage-loading")){await new Promise(resolve=>setTimeout(resolve,1150))}try{chat=await Bonsai27B.load(MODEL_SRC,{file:MODEL_SRC===MODEL_ID?DEFAULT_GGUF_FILE:void 0,accessToken:accessToken??void 0,cache:QSA.has("nocache")?false:void 0,maxLength:Number.parseInt(QSA.get("ctx")??"",10)||void 0,onProgress:onLoadProgress});loadState="ready";window.__bonsaiChat=chat;prepChatUi();BonsaiLoader.done()}catch(error){console.error(error);loadState="failed";showLoadError(error)}}function onLoadProgress(event){if(event.status==="init"){BonsaiLoader.phase((event.message||"INITIALIZING").toUpperCase())}else if(event.status==="tokenizer"){BonsaiLoader.phase("PARSING TOKENIZER — 248K VOCAB")}else if(event.status==="weights"){if(event.kind==="bytes"&&Number.isFinite(event.loaded)){BonsaiLoader.phase(null);BonsaiLoader.set(event.loaded,Number.isFinite(event.total)&&event.total>0?event.total:TOTAL_BYTES_FALLBACK)}else if(event.kind==="tensors"){if(/warmup/i.test(event.message||"")){BonsaiLoader.phase("COMPILING WEBGPU KERNELS · WARMUP")}else if(Number.isFinite(event.total)&&event.total>0){BonsaiLoader.info({tensors:event.loaded,tensorsTotal:event.total})}}}}function showLoadError(error){const message=String(error?.message??error);document.body.classList.add("load-failed");BonsaiLoader.phase("LOAD FAILED");$("loadErrorMsg").textContent=message;$("loadError").hidden=false;const authish=REQUIRE_HF_TOKEN&&/\b40[134]\b|unauthorized|forbidden|invalid token|\btoken\b|repository not found|access (denied|restricted|to model)/i.test(message);$("changeTokenBtn").hidden=!authish}function hideLoadError(){document.body.classList.remove("load-failed");$("loadError").hidden=true;BonsaiLoader.phase(null)}$("retryBtn").addEventListener("click",e2=>{e2.preventDefault();if(loadState==="failed"){hideLoadError();startLoad()}});$("changeTokenBtn").addEventListener("click",e2=>{e2.preventDefault();try{localStorage.removeItem(TOKEN_KEY)}catch{}reauthAfterGate=true;showGate(accessToken??"")});if(document.body.classList.contains("stage-loading")&&!QSA.has("demo")&&!QSA.has("p"))startLoad();const chatx=$("chatx"),cScroll=$("cScroll"),cThread=$("cThread");const cInput=$("cInput"),cSend=$("cSend"),cStop=$("cStop");const cStatus=$("cStatus"),cStatusText=$("cStatusText"),cLive=$("cLive");BonsaiLoader.onReady(()=>setTimeout(enterChat,1800));function enterChat(){if(loadState!=="ready"||document.body.classList.contains("stage-chat"))return;document.body.classList.add("stage-chat");chatx.classList.add("show");setStatus("","READY");setTimeout(()=>cInput.focus(),450)}function prepChatUi(){cInput.disabled=false;$("clearBtn").disabled=false;$("thinkToggle").disabled=false;renderSeeds();refreshSend()}function setStatus(mode,text){cStatus.className="c-status"+(mode?" "+mode:"");if(text!==void 0)cStatusText.textContent=text}function renderSeeds(){const wrap=$("cSeeds");if(!wrap)return;wrap.replaceChildren(...SEED_EXAMPLES.map(seed=>{const b=document.createElement("button");b.className="c-seed";b.type="button";b.dataset.prompt=seed.prompt;b.textContent=seed.label;return b}))}document.addEventListener("click",e2=>{const seed=e2.target.closest(".c-seed");if(!seed||seed.disabled||!chat||isGenerating)return;cInput.value=seed.dataset.prompt||"";send()});cSend.addEventListener("click",send);cStop.addEventListener("click",()=>abortController?.abort());$("clearBtn").addEventListener("click",clearChat);cInput.addEventListener("input",()=>{autoGrow();refreshSend()});cInput.addEventListener("keydown",e2=>{if(e2.key==="Enter"&&!e2.shiftKey){e2.preventDefault();if(!cSend.disabled)send()}});function refreshSend(){cSend.disabled=isGenerating||contextExhausted||!chat||cInput.value.trim()===""}function autoGrow(){cInput.style.height="auto";cInput.style.height=`${Math.min(cInput.scrollHeight,180)}px`}function scrollDown(force=false){const nearBottom=cScroll.scrollHeight-cScroll.scrollTop-cScroll.clientHeight<90;if(force||nearBottom)cScroll.scrollTop=cScroll.scrollHeight}function setGenerating(on2){isGenerating=on2;cInput.disabled=on2;$("clearBtn").disabled=on2;$("thinkToggle").disabled=on2;cSend.style.display=on2?"none":"";cStop.style.display=on2?"grid":"none";document.querySelectorAll(".c-seed").forEach(s=>{s.disabled=on2});setStatus(on2?"busy":"",on2?thinkingEnabled?"REASONING …":"WRITING …":"READY");refreshSend()}let thinkingEnabled=false;const thinkToggle=$("thinkToggle");thinkToggle.addEventListener("click",()=>{thinkingEnabled=!thinkingEnabled;thinkToggle.classList.toggle("on",thinkingEnabled);thinkToggle.setAttribute("aria-pressed",String(thinkingEnabled));$("thinkTip").textContent=`THINKING ${thinkingEnabled?"ON":"OFF"}`});const welcomeTemplate=$("cWelcome").cloneNode(true);function removeWelcome(){$("cWelcome")?.remove()}function clearChat(){if(isGenerating)return;messages=[];chat?.reset();contextExhausted=false;cInput.disabled=false;cInput.placeholder="Ask anything…";setStatus("","READY");cThread.replaceChildren(welcomeTemplate.cloneNode(true));renderSeeds();cInput.focus()}function appendUser(text){const msg=document.createElement("div");msg.className="c-msg user";const role=document.createElement("div");role.className="c-role";role.textContent="YOU";const bubble=document.createElement("div");bubble.className="u-bubble";bubble.textContent=text;msg.append(role,bubble);cThread.appendChild(msg);scrollDown(true)}function appendAssistant(withThinking){const msg=document.createElement("div");msg.className="c-msg bot";msg.innerHTML=`
+    <div class="c-role">BONSAI</div>
+    ${withThinking?`
+    <div class="t-block live open">
+      <button class="t-head" type="button">
+        <span class="t-chev">&#9654;</span>
+        <span class="t-label t-shimmer">THINKING</span>
+      </button>
+      <div class="t-body"></div>
+    </div>`:""}
+    <div class="a-body"></div>`;const tBlock=msg.querySelector(".t-block");tBlock?.querySelector(".t-head").addEventListener("click",()=>{if(tBlock.classList.contains("live"))return;const open=tBlock.classList.toggle("open");if(open)tBlock.querySelector(".t-body").scrollTop=0});cThread.appendChild(msg);scrollDown(true);return msg}async function send(){const text=cInput.value.trim();if(!text||!chat||isGenerating||contextExhausted)return;removeWelcome();cInput.value="";autoGrow();appendUser(text);messages.push({role:"user",content:text});const thinkTurn=thinkingEnabled&&chat.thinkCloseTokenId!=null;chat.chatTemplateArgs={enable_thinking:thinkingEnabled,preserve_thinking:true};const msg=appendAssistant(thinkTurn);const tBlock=msg.querySelector(".t-block");const tBody=msg.querySelector(".t-body");const tLabel=msg.querySelector(".t-label");const aBody=msg.querySelector(".a-body");setGenerating(true);abortController=new AbortController;const closeId=chat.thinkCloseTokenId;let phase=thinkTurn?"think":"answer";let thinking="",answer="",closed=false;let startedAt=performance.now(),firstTokenAt=0,thinkEndedAt=0,tokens=0;const finishThinking=()=>{closed=true;thinkEndedAt=performance.now();tBlock.classList.remove("live","open");const seconds=((thinkEndedAt-(firstTokenAt||startedAt))/1e3).toFixed(1);tLabel.classList.remove("t-shimmer");tLabel.textContent=`THOUGHT FOR ${seconds}S`;if(!thinking.trim())tBlock.remove();setStatus("busy","WRITING …")};try{for await(const tok of chat.generate(messages,{signal:abortController.signal})){const now=performance.now();if(!firstTokenAt)firstTokenAt=now;if(tok.token!==null)tokens++;if(phase==="think"){if(tok.token===closeId){phase="answer";finishThinking()}else{thinking+=tok.delta;scheduleStream(()=>{tBody.textContent=thinking;tBody.scrollTop=tBody.scrollHeight})}}else{answer+=answer===""?tok.delta.replace(/^\s+/,""):tok.delta;scheduleStream(()=>renderAnswer(aBody,answer,true))}updateLiveStat({startedAt,firstTokenAt,now,tokens})}}catch(error){console.error(error);if(!answer){aBody.innerHTML="";const err=document.createElement("div");err.className="a-error";err.textContent=`Generation stopped: ${String(error?.message??error)}`;aBody.appendChild(err)}setStatus("error","ERROR · SEE CONSOLE")}finally{if(phase==="think"&&!closed){tBlock.classList.remove("live");tLabel.classList.remove("t-shimmer");tLabel.textContent="THINKING (INTERRUPTED)"}cancelStream();if(tBody?.isConnected){tBody.textContent=thinking;tBody.scrollTop=tBody.scrollHeight}if(answer||!aBody.firstChild)renderAnswer(aBody,answer,false);appendMeta(msg,{startedAt,firstTokenAt,thinkEndedAt,endedAt:performance.now(),tokens});scrollDown();const content=chat.lastAssistantContent;if(content!==null)messages.push({role:"assistant",content});setGenerating(false);cLive.textContent="";abortController=null;if(chat.contextFull)lockContextFull(msg);else cInput.focus()}}function lockContextFull(msg){contextExhausted=true;const note=document.createElement("div");note.className="a-ctxfull";note.textContent=`CONTEXT WINDOW FULL · ${chat.contextLength} TOKENS — PRESS CLEAR TO START FRESH`;msg.appendChild(note);cInput.disabled=true;cInput.placeholder="Context window full — press CLEAR to start fresh";refreshSend();setStatus("error","CONTEXT FULL");scrollDown()}function appendMeta(msg,{startedAt,firstTokenAt,thinkEndedAt,endedAt,tokens}){if(tokens<=0)return;const parts=[`${tokens} TOK`];if(thinkEndedAt)parts.push(`THOUGHT ${((thinkEndedAt-(firstTokenAt||startedAt))/1e3).toFixed(1)}S`);if(firstTokenAt)parts.push(`TTFT ${(firstTokenAt-startedAt).toFixed(0)} MS`);if(tokens>5&&firstTokenAt){parts.push(`${((tokens-1)/Math.max((endedAt-firstTokenAt)/1e3,1e-9)).toFixed(1)} TOK/S`)}const meta=document.createElement("div");meta.className="c-msg-meta";meta.textContent=parts.join("  ·  ");msg.appendChild(meta)}const LIVE_STAT_MS=150;let lastLiveStatAt=0;function updateLiveStat({startedAt,firstTokenAt,now,tokens}){if(tokens<=1){cLive.textContent=`TTFT ${(firstTokenAt-startedAt).toFixed(0)} MS`;lastLiveStatAt=now;return}if(now-lastLiveStatAt<LIVE_STAT_MS)return;lastLiveStatAt=now;cLive.textContent=`${((tokens-1)/Math.max((now-firstTokenAt)/1e3,1e-9)).toFixed(0)} TOK/S`}const STREAM_RENDER_MS=33;let streamPaint=null,renderQueued=false,lastRenderAt=0;function scheduleStream(paint){streamPaint=paint;if(renderQueued)return;renderQueued=true;const tick=()=>{if(!streamPaint){renderQueued=false;return}if(performance.now()-lastRenderAt<STREAM_RENDER_MS){requestAnimationFrame(tick);return}renderQueued=false;lastRenderAt=performance.now();const paintNow=streamPaint;streamPaint=null;paintNow();scrollDown()};requestAnimationFrame(tick)}function cancelStream(){streamPaint=null}let marked=null;let katexLib=null;const katexCache=new Map;let katexFragments=null;import("https://esm.sh/marked@17").then(m=>{marked=m.marked;marked.use({gfm:true,breaks:true});return import("https://esm.sh/katex@0.16").then(k=>{katexLib=k.default??k;marked.use(makeKatexExtension());ensureKatexCss()}).catch(()=>{})}).catch(()=>{marked=null});function ensureKatexCss(){if(document.querySelector("link[data-katex]"))return;const link=document.createElement("link");link.rel="stylesheet";link.href="https://cdn.jsdelivr.net/npm/katex@0.16/dist/katex.min.css";link.dataset.katex="1";document.head.appendChild(link)}function makeKatexExtension(){const inline={name:"katexInline",level:"inline",start(src){return src.match(/\\\(/)?.index},tokenizer(src){const m=/^\\\(([\s\S]+?)\\\)/.exec(src);if(m)return{type:"katexInline",raw:m[0],text:m[1]}},renderer(token){return stashKatex(token.text,false)}};const block={name:"katexBlock",level:"inline",start(src){return src.match(/\\\[/)?.index},tokenizer(src){const m=/^\\\[([\s\S]+?)\\\]/.exec(src);if(m)return{type:"katexBlock",raw:m[0],text:m[1]}},renderer(token){return stashKatex(token.text,true)}};const dollarBlock={name:"katexDollarBlock",level:"inline",start(src){return src.match(/\$\$/)?.index},tokenizer(src){const m=/^\$\$([\s\S]+?)\$\$/.exec(src);if(m)return{type:"katexDollarBlock",raw:m[0],text:m[1]}},renderer(token){return stashKatex(token.text,true)}};const dollarInline={name:"katexDollarInline",level:"inline",start(src){return src.match(/\$/)?.index},tokenizer(src){const m=/^\$(?!\s|\$)((?:\\.|[^\\$\n])+?)(?<!\s)\$(?!\d)/.exec(src);if(m)return{type:"katexDollarInline",raw:m[0],text:m[1]}},renderer(token){return stashKatex(token.text,false)}};return{extensions:[dollarBlock,block,inline,dollarInline]}}function renderKatex(text,display){const key=(display?"d:":"i:")+text;let html=katexCache.get(key);if(html===void 0){try{html=katexLib.renderToString(text.trim(),{throwOnError:false,displayMode:display})}catch{html=escapeHtml(text)}katexCache.set(key,html)}return html}function stashKatex(text,display){const html=renderKatex(text,display);if(!katexFragments)return html;return`<\!--katex:${katexFragments.push(html)-1}-->`}function trimIncompleteMath(text){let cut=-1;for(const[open,close]of[["\\[","\\]"],["\\(","\\)"]]){const lastOpen=text.lastIndexOf(open);if(lastOpen!==-1&&text.indexOf(close,lastOpen+open.length)===-1){if(cut===-1||lastOpen<cut)cut=lastOpen}}const dd2=text.split("$$").length-1;if(dd2%2===1){const lastOpen=text.lastIndexOf("$$");if(cut===-1||lastOpen<cut)cut=lastOpen}const tail=cut===-1?text:text.slice(0,cut);const m=/\$(?![\s\d$])[^$\n]*$/.exec(tail);if(m&&tail.split("$").length%2===0){if(cut===-1||m.index<cut)cut=m.index}return cut===-1?text:text.slice(0,cut)}function renderAnswer(el2,raw,withCaret){const text=withCaret&&katexLib?trimIncompleteMath(raw||""):raw||"";if(marked){try{katexFragments=[];let html=sanitizeHtml(marked.parse(text));if(katexFragments.length){html=html.replace(/<\!--katex:(\d+)-->/g,(_,i)=>katexFragments[+i]??"")}el2.innerHTML=html;if(withCaret)appendCaret(el2);return}catch{}finally{katexFragments=null}}const safe=escapeHtml(text);const paragraphs=safe.split(/\n{2,}/).map(p=>p.trim()).filter(Boolean);el2.innerHTML=paragraphs.map(p=>`<p>${formatInline(p).replace(/\n/g,"<br>")}</p>`).join("");if(withCaret)appendCaret(el2)}function appendCaret(el2){const caret=document.createElement("span");caret.className="a-caret";const 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kxCopySource="";$("kernelsBtn").addEventListener("click",openKernels);kernelsOverlay.addEventListener("click",e2=>{if(e2.target.closest("[data-close]"))closeKernels()});$("kxList").addEventListener("scroll",updateListFade,{passive:true});$("kxCopy").addEventListener("click",copyKernel);document.addEventListener("keydown",e2=>{if(e2.key==="Escape"&&!kernelsOverlay.hidden)closeKernels()});function openKernels(){kernels=(chat?chat.runtime.getRenderedShaders?.()??[]:[]).filter(k=>!/\btranscode\b|\.transcode\./i.test(k.name));const list=$("kxList");list.replaceChildren();$("kxSub").textContent=kernels.length?`${kernels.length} WGSL COMPUTE SHADERS · COMPILED FOR YOUR GPU`:"LOAD THE MODEL TO INSPECT ITS COMPILED KERNELS";kernels.forEach((k,i)=>{const item=document.createElement("button");item.className="kx-item";item.type="button";item.textContent=k.name;item.addEventListener("click",()=>selectKernel(i));list.appendChild(item)});$("kxSource").hidden=true;$("kxIntro").hidden=false;kxCopySource="";kernelsOverlay.hidden=false;document.body.classList.add("kx-locked");list.scrollTop=0;requestAnimationFrame(updateListFade)}function updateListFade(){const list=$("kxList");const atEnd=list.scrollHeight<=list.clientHeight+4||list.scrollTop>=list.scrollHeight-list.clientHeight-4;list.parentElement.classList.toggle("at-end",atEnd)}function selectKernel(i){const k=kernels[i];if(!k)return;$("kxIntro").hidden=true;$("kxSource").hidden=false;[...$("kxList").children].forEach((el2,j)=>el2.classList.toggle("active",j===i));$("kxName").textContent=k.name;$("kxLines").textContent=`${k.source.split("\n").length} LINES`;$("kxCode").innerHTML=highlightWgsl(k.source);$("kxCode").parentElement.scrollTop=0;kxCopySource=k.source}function closeKernels(){kernelsOverlay.hidden=true;document.body.classList.remove("kx-locked")}async function copyKernel(){if(!kxCopySource)return;try{await navigator.clipboard.writeText(kxCopySource);$("kxCopy").textContent="COPIED";setTimeout(()=>{$("kxCopy").textContent="COPY"},1200)}catch{}}const WGSL_KEYWORDS=new Set(["fn","let","var","const","const_assert","struct","if","else","for","loop","return","break","continue","switch","case","default","while","override","enable","requires","discard","alias","true","false","workgroup","storage","uniform","function","private","read","write","read_write","bitcast"]);const WGSL_TYPES=new Set(["u32","i32","f32","f16","bool","vec2","vec3","vec4","mat2x2","mat3x3","mat4x4","mat2x3","mat3x2","mat2x4","mat4x2","mat3x4","mat4x3","array","atomic","ptr","sampler"]);const WGSL_TOKEN=/(\/\/[^\n]*|\/\*[\s\S]*?\*\/)|(@[A-Za-z_]\w*)|([A-Za-z_]\w*)|(\d[\w.]*)|(\s+)|([\s\S])/g;function highlightWgsl(src){let out="";WGSL_TOKEN.lastIndex=0;let m;while(m=WGSL_TOKEN.exec(src)){const[tok,comment,attr,ident,num,ws2]=m;if(comment)out+=`<span class="k-cm">${escapeHtml(comment)}</span>`;else if(attr)out+=`<span class="k-at">${escapeHtml(attr)}</span>`;else if(ident){const cls=WGSL_KEYWORDS.has(ident)?"k-kw":WGSL_TYPES.has(ident)?"k-ty":null;out+=cls?`<span class="${cls}">${ident}</span>`:escapeHtml(ident)}else if(num)out+=`<span class="k-nu">${escapeHtml(num)}</span>`;else if(ws2)out+=ws2;else out+=escapeHtml(tok)}return out}const HTML_ESCAPES={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"};function escapeHtml(v){return String(v).replace(/[&<>"']/g,c=>HTML_ESCAPES[c])}initAccess();</script></body>
+</html>
\ No newline at end of file
