diff --git "a/assets/index-D3DuJDE3.js" "b/assets/index-D3DuJDE3.js"
new file mode 100644--- /dev/null
+++ "b/assets/index-D3DuJDE3.js"
@@ -0,0 +1,2883 @@
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t=this.temporarySessionTensorIds.get(e);if(t){for(let n of t)Pe("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${n}}`),this.tensorManager.releaseTensorId(n);this.temporarySessionTensorIds.delete(e),this.activeSessionId=void 0}}async createMLContext(e){if(e instanceof GPUDevice){let n=this.mlContextCache.findIndex(r=>r.gpuDevice===e);if(n!==-1)return this.mlContextCache[n].mlContext;{let r=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:r}),r}}else if(e===void 0){let n=this.mlContextCache.findIndex(r=>r.options===void 0&&r.gpuDevice===void 0);if(n!==-1)return this.mlContextCache[n].mlContext;{let r=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:r}),r}}let t=this.mlContextCache.findIndex(n=>Op(n.options,e));if(t!==-1)return this.mlContextCache[t].mlContext;{let n=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:n}),n}}registerMLContext(e,t){this.mlContextBySessionId.set(e,t);let n=this.sessionIdsByMLContext.get(t);n||(n=new Set,this.sessionIdsByMLContext.set(t,n)),n.add(e),this.mlOpSupportLimitsBySessionId.has(e)||this.mlOpSupportLimitsBySessionId.set(e,t.opSupportLimits()),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(e,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e),this.sessionGraphOutputs.delete(e);let t=this.mlContextBySessionId.get(e);if(!t)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e),this.mlOpSupportLimitsBySessionId.delete(e);let n=this.sessionIdsByMLContext.get(t);if(n.delete(e),n.size===0){this.sessionIdsByMLContext.delete(t);let r=this.mlContextCache.findIndex(i=>i.mlContext===t);r!==-1&&this.mlContextCache.splice(r,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}getMLOpSupportLimits(e){return this.mlOpSupportLimitsBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){Pe("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,t,n,r,i){let s=ns.get(n);if(!s)throw new Error(`Unsupported ONNX data type: ${n}`);return this.tensorManager.ensureTensor(e??this.currentSessionId,t,s,r,i)}async createTemporaryTensor(e,t,n){Pe("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${t}, shape: ${n}}`);let r=ns.get(t);if(!r)throw new Error(`Unsupported ONNX data type: ${t}`);let i=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(e,i,r,n,!1);let s=this.temporarySessionTensorIds.get(e);return s?s.push(i):this.temporarySessionTensorIds.set(e,[i]),i}uploadTensor(e,t){if(!tt().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");Pe("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${t.byteLength}}`),this.tensorManager.upload(e,t)}async downloadTensor(e,t){return this.tensorManager.download(e,t)}createMLTensorDownloader(e,t){return async()=>{let n=await this.tensorManager.download(e);return Xu(n,t)}}registerMLTensor(e,t,n,r){let i=ns.get(n);if(!i)throw new Error(`Unsupported ONNX data type: ${n}`);let s=this.tensorManager.registerTensor(e,t,i,r);return Pe("verbose",()=>`[WebNN] registerMLTensor {tensor: ${t}, dataType: ${i}, dimensions: ${r}} -> {tensorId: ${s}}`),s}registerMLConstant(e,t,n,r,i,s,a=!1){if(!s)throw new Error("External mounted files are not available.");let o=e;e.startsWith("./")&&(o=e.substring(2));let l=s.get(o);if(!l)throw new Error(`File with name ${o} not found in preloaded files.`);if(t+n>l.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let d=l.slice(t,t+n).buffer,p;switch(i.dataType){case"float32":p=new Float32Array(d);break;case"float16":p=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(d):new Uint16Array(d);break;case"int32":p=new Int32Array(d);break;case"uint32":p=new Uint32Array(d);break;case"int64":if(a){let f=su(new Uint8Array(d),"int64");p=new Int32Array(f.buffer),i.dataType="int32"}else p=new BigInt64Array(d);break;case"uint64":p=new BigUint64Array(d);break;case"int8":p=new Int8Array(d);break;case"int4":case"uint4":case"uint8":p=new Uint8Array(d);break;default:throw new Error(`Unsupported data type: ${i.dataType} in creating WebNN Constant from external data.`)}return Pe("verbose",()=>`[WebNN] registerMLConstant {dataType: ${i.dataType}, shape: ${i.shape}}} ${a?"(Note: it was int64 data type and registered to int32 as workaround)":""}`),r.constant(i,p)}registerGraphInput(e){this.temporaryGraphInputs.push(e)}registerGraphOutput(e){this.temporaryGraphOutputs.push(e)}isGraphInput(e,t){let n=this.sessionGraphInputs.get(e);return n?n.includes(t):!1}isGraphOutput(e,t){let n=this.sessionGraphOutputs.get(e);return n?n.includes(t):!1}isGraphInputOutputTypeSupported(e,t,n=!0){let r=ns.get(Gr(t)),i=this.mlOpSupportLimitsBySessionId.get(e);return typeof r>"u"?!1:n?!!(i!=null&&i.input.dataTypes.includes(r)):!!(i!=null&&i.output.dataTypes.includes(r))}flush(){}}}),Yu=re(()=>{}),sl,ua,da,Np,Bp,al,au,Dp,Q0,w$=re(()=>{nr(),Yu(),sl=new Map([[64,250],[128,200],[256,200],[512,200],[2048,230],[4096,200],[8192,50],[16384,50],[32768,50],[65536,50],[131072,50],[262144,50],[524288,50],[1048576,50],[2097152,30],[4194304,20],[8388608,10],[12582912,10],[16777216,10],[26214400,15],[33554432,22],[44236800,2],[58982400,6],[67108864,6],[134217728,6],[167772160,6]]),ua=[],da=e=>Math.ceil(Number(e)/16)*16,Np=e=>{for(let t=0;t<ua.length;t++){let n=ua[t];if(e<=n)return n}return Math.ceil(e/16)*16},Bp=1,al=()=>Bp++,au=async(e,t,n,r)=>{let i=da(n),s=e.device.createBuffer({size:i,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ});try{let a=e.getCommandEncoder();e.endComputePass(),a.copyBufferToBuffer(t,0,s,0,i),e.flush(),await s.mapAsync(GPUMapMode.READ);let o=s.getMappedRange();if(r){let l=r();return l.set(new Uint8Array(o,0,n)),l}else return new Uint8Array(o.slice(0,n))}finally{s.destroy()}},Dp=class{constructor(e){this.backend=e,this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.buffersPending=[],this.capturedPendingBuffers=new Map;for(let[t]of sl)ua.push(t),this.freeBuffers.set(t,[]),this.freeUniformBuffers.set(t,[]);this.sessionCount=0}upload(e,t){let n=t.buffer,r=t.byteOffset,i=t.byteLength,s=da(i),a=this.storageCache.get(e);if(!a)throw new Error("gpu data for uploading does not exist");if(Number(a.originalSize)!==i)throw new Error(`inconsistent data size. gpu data size=${a.originalSize}, data size=${i}`);let o=this.backend.device.createBuffer({mappedAtCreation:!0,size:s,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC}),l=o.getMappedRange();new Uint8Array(l).set(new Uint8Array(n,r,i)),o.unmap();let d=this.backend.device.createCommandEncoder();d.copyBufferToBuffer(o,0,a.gpuData.buffer,0,s),this.backend.device.queue.submit([d.finish()]),o.destroy(),Pe("verbose",()=>`[WebGPU] GpuDataManager.upload(id=${e})`)}memcpy(e,t){let n=this.storageCache.get(e);if(!n)throw new Error("source gpu data for memcpy does not exist");let r=this.storageCache.get(t);if(!r)throw new Error("destination gpu data for memcpy does not exist");if(n.originalSize!==r.originalSize)throw new Error("inconsistent source and destination gpu data size");let i=da(n.originalSize),s=this.backend.getCommandEncoder();this.backend.endComputePass(),s.copyBufferToBuffer(n.gpuData.buffer,0,r.gpuData.buffer,0,i)}registerExternalBuffer(e,t,n){let r;if(n){if(r=n[0],e===n[1])return Pe("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${t}) => id=${r}, buffer is the same, skip.`),r;if(this.backend.capturedCommandList.has(this.backend.currentSessionId))throw new Error(`Registering a different external buffer under graph capture mode is not supported yet.
+             Please use the previous external buffer!`)}else r=al();return this.storageCache.set(r,{gpuData:{id:r,type:0,buffer:e},originalSize:t}),Pe("verbose",()=>`[WebGPU] GpuDataManager.registerExternalBuffer(size=${t}) => id=${r}, registered.`),r}unregisterExternalBuffer(e){e!==void 0&&(this.storageCache.delete(e),Pe("verbose",()=>`[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${e}`))}create(e,t=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST){let n=Np(e),r,i=(t&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE,s=(t&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM;if(i||s){let o=(i?this.freeBuffers:this.freeUniformBuffers).get(n);o?o.length>0?r=o.pop():r=this.backend.device.createBuffer({size:n,usage:t}):r=this.backend.device.createBuffer({size:n,usage:t})}else r=this.backend.device.createBuffer({size:n,usage:t});let a={id:al(),type:0,buffer:r};return this.storageCache.set(a.id,{gpuData:a,originalSize:Number(e)}),Pe("verbose",()=>`[WebGPU] GpuDataManager.create(size=${e}) => id=${a.id}`),a}get(e){var t;return(t=this.storageCache.get(e))==null?void 0:t.gpuData}release(e){let t=typeof e=="bigint"?Number(e):e,n=this.storageCache.get(t);if(!n){if(this.storageCache.size===0)return 0;throw new Error("releasing data does not exist")}return Pe("verbose",()=>`[WebGPU] GpuDataManager.release(id=${t}), gpuDataId=${n.gpuData.id}`),this.storageCache.delete(t),this.buffersPending.push(n.gpuData.buffer),n.originalSize}async download(e,t){let n=this.storageCache.get(Number(e));if(!n)throw new Error("data does not exist");await au(this.backend,n.gpuData.buffer,n.originalSize,t)}refreshPendingBuffers(){if(this.buffersPending.length!==0)if(this.backend.sessionStatus==="default"){for(let e of this.buffersPending){let t=sl.get(e.size);if((e.usage&GPUBufferUsage.STORAGE)===GPUBufferUsage.STORAGE){let n=this.freeBuffers.get(e.size)||[];t===void 0||n.length>=t?e.destroy():n.push(e)}else if((e.usage&GPUBufferUsage.UNIFORM)===GPUBufferUsage.UNIFORM){let n=this.freeUniformBuffers.get(e.size)||[];t===void 0||n.length>=t?e.destroy():n.push(e)}else e.destroy()}this.buffersPending=[]}else{let e=this.capturedPendingBuffers.get(this.backend.currentSessionId);e||(e=[],this.capturedPendingBuffers.set(this.backend.currentSessionId,e));for(let t of this.buffersPending)e.push(t);this.buffersPending=[]}}dispose(){this.freeBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.freeUniformBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache.forEach(e=>{e.gpuData.buffer.destroy()}),this.capturedPendingBuffers.forEach(e=>{e.forEach(t=>{t.destroy()})}),this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.capturedPendingBuffers=new Map}onCreateSession(){this.sessionCount+=1}onReleaseSession(e){let t=this.capturedPendingBuffers.get(e);t&&(t.forEach(n=>{n.destroy()}),this.capturedPendingBuffers.delete(e)),this.sessionCount-=1,this.sessionCount===0&&(Pe("warning",()=>"[WebGPU] Clearing webgpu buffer cache"),this.storageCache.forEach(n=>{n.gpuData.buffer.destroy()}),this.storageCache=new Map)}},Q0=(...e)=>new Dp(...e)}),Pp,Fe,pt=re(()=>{Pp=class{constructor(e){Object.assign(this,e)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(e=>`${this[e]}`).join(";")),this.key}},Fe=e=>new Pp(e)}),ki,ca,xt,Rt,_e,ct,ou,bi,wr,we,rs,V,be,J0,Zu,Up,ey,Ee=re(()=>{Te(),Ce(),ki=64,ca=(e,t)=>{if(t===3)throw new Error("vec3 has same alignment as vec4, use vec4 instead");switch(Number(e)){case 10:return t>1?`vec${t}<f16>`:"f16";case 1:return t>1?`vec${t}<f32>`:"f32";case 6:return t>1?`vec${t}<i32>`:"i32";case 12:return t>1?`vec${t}<u32>`:"u32";case 7:if(t>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","i32"];case 13:if(t>1)throw new Error("currently not supported vecX of uint64 yet");return["vec2<u32>","u32"];case 9:if(t!==4)throw new Error("bool must be vec4");return["u32","vec4<bool>"];case 22:return"i32";case 21:return"u32";default:throw new Error(`Unknown data type: ${e}`)}},xt=(e,t=1)=>{let n=ca(e,t);return typeof n=="string"?n:n[0]},Rt=(e,t=1)=>{let n=ca(e,t);return typeof n=="string"?n:n[1]},_e=(...e)=>{let t=[];return e.forEach(n=>{n.length!==0&&t.push({type:12,data:n},{type:12,data:W.computeStrides(n)})}),t},ct=e=>e%4===0?4:e%2===0?2:1,ou=(e="f32",t,n="0")=>!t||t===1?`${e}(${n})`:`vec${t}<${e}>(${n})`,bi=(e,t,n)=>e==="f32"?n:t===1?`f32(${n})`:`vec${t}<f32>(${n})`,wr=(e,t)=>t===4?`(${e}.x + ${e}.y + ${e}.z + ${e}.w)`:t===2?`(${e}.x + ${e}.y)`:t===3?`(${e}.x + ${e}.y + ${e}.z)`:e,we=(e,t,n,r)=>e.startsWith("uniforms.")&&n>4?typeof t=="string"?r==="f16"?`${e}[(${t}) / 8][(${t}) % 8 / 4][(${t}) % 8 % 4]`:`${e}[(${t}) / 4][(${t}) % 4]`:r==="f16"?`${e}[${Math.floor(t/8)}][${Math.floor(t%8/4)}][${t%8%4}]`:`${e}[${Math.floor(t/4)}][${t%4}]`:n>1?`${e}[${t}]`:e,rs=(e,t,n,r,i)=>{let s=typeof n=="number",a=s?n:n.length,o=[...new Array(a).keys()],l=a<2?"u32":a<=4?`vec${a}<u32>`:`array<u32, ${a}>`,d=ca(t,i),p=typeof d=="string"?d:d[1],f=typeof d=="string"?d:d[0],c={indices:l,value:p,storage:f,tensor:t},m=Y=>typeof Y=="string"?Y:`${Y}u`,y={offsetToIndices:!1,indicesToOffset:!1,broadcastedIndicesToOffset:!1,set:!1,setByIndices:!1,get:!1,getByIndices:!1},w=s?"uniforms.":"",$=`${w}${e}_shape`,_=`${w}${e}_strides`,v="";for(let Y=0;Y<a-1;Y++)v+=`
+    let dim${Y} = current / ${we(_,Y,a)};
+    let rest${Y} = current % ${we(_,Y,a)};
+    indices[${Y}] = dim${Y};
+    current = rest${Y};
+    `;v+=`indices[${a-1}] = current;`;let I=a<2?"":`
+  fn o2i_${e}(offset: u32) -> ${c.indices} {
+    var indices: ${c.indices};
+    var current = offset;
+    ${v}
+    return indices;
+  }`,S=Y=>(y.offsetToIndices=!0,a<2?Y:`o2i_${e}(${Y})`),E=[];if(a>=2)for(let Y=a-1;Y>=0;Y--)E.push(`${we(_,Y,a)} * (indices[${Y}])`);let R=a<2?"":`
+  fn i2o_${e}(indices: ${c.indices}) -> u32 {
+    return ${E.join("+")};
+  }`,O=Y=>(y.indicesToOffset=!0,a<2?Y:`i2o_${e}(${Y})`),x=(...Y)=>a===0?"0u":`${c.indices}(${Y.map(m).join(",")})`,U=(Y,P)=>a<2?`${Y}`:`${we(Y,P,a)}`,q=(Y,P,ee)=>a<2?`${Y}=${ee};`:`${we(Y,P,a)}=${ee};`,J={},se=(Y,P)=>{y.broadcastedIndicesToOffset=!0;let ee=`${P.name}broadcastedIndicesTo${e}Offset`;if(ee in J)return`${ee}(${Y})`;let ne=[];for(let Ue=a-1;Ue>=0;Ue--){let lt=P.indicesGet("outputIndices",Ue+P.rank-a);ne.push(`${U(_,Ue)} * (${lt} % ${U($,Ue)})`)}return J[ee]=`fn ${ee}(outputIndices: ${P.type.indices}) -> u32 {
+             return ${ne.length>0?ne.join("+"):"0u"};
+           }`,`${ee}(${Y})`},ce=(Y,P)=>(()=>{if(c.storage===c.value)return`${e}[${Y}]=${P};`;if(c.storage==="vec2<u32>"&&c.value==="i32")return`${e}[${Y}]=vec2<u32>(u32(${P}), select(0u, 0xFFFFFFFFu, ${P} < 0));`;if(c.storage==="vec2<u32>"&&c.value==="u32")return`${e}[${Y}]=vec2<u32>(u32(${P}), 0u);`;if(c.storage==="u32"&&c.value==="vec4<bool>")return`${e}[${Y}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${P}));`;throw new Error(`not supported combination of storage type ${c.storage} and value type ${c.value} yet`)})(),L=Y=>(()=>{if(c.storage===c.value)return`${e}[${Y}]`;if(c.storage==="vec2<u32>"&&c.value==="i32")return`i32(${e}[${Y}].x)`;if(c.storage==="vec2<u32>"&&c.value==="u32")return`u32(${e}[${Y}].x)`;if(c.storage==="u32"&&c.value==="vec4<bool>")return`vec4<bool>(bool(${e}[${Y}] & 0xFFu), bool(${e}[${Y}] & 0xFF00u), bool(${e}[${Y}] & 0xFF0000u), bool(${e}[${Y}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${c.storage} and value type ${c.value} yet`)})(),ue=a<2?"":`
+  fn get_${e}ByIndices(indices: ${c.indices}) -> ${p} {
+    return ${L(`i2o_${e}(indices)`)};
+  }`,ie=a<2?"":(()=>{let Y=o.map(ee=>`d${ee}: u32`).join(", "),P=o.map(ee=>`d${ee}`).join(", ");return`
+  fn get_${e}(${Y}) -> ${p} {
+    return get_${e}ByIndices(${x(P)});
+  }`})(),te=(...Y)=>{if(Y.length!==a)throw new Error(`indices length must be ${a}`);let P=Y.map(m).join(",");return a===0?L("0u"):a===1?L(P[0]):(y.get=!0,y.getByIndices=!0,y.indicesToOffset=!0,`get_${e}(${P})`)},he=Y=>a<2?L(Y):(y.getByIndices=!0,y.indicesToOffset=!0,`get_${e}ByIndices(${Y})`),Q=a<2?"":`
+  fn set_${e}ByIndices(indices: ${c.indices}, value: ${p}) {
+    ${ce(`i2o_${e}(indices)`,"value")}
+  }`,ve=a<2?"":(()=>{let Y=o.map(ee=>`d${ee}: u32`).join(", "),P=o.map(ee=>`d${ee}`).join(", ");return`
+  fn set_${e}(${Y}, value: ${p}) {
+    set_${e}ByIndices(${x(P)}, value);
+  }`})();return{impl:()=>{let Y=[],P=!1;return y.offsetToIndices&&(Y.push(I),P=!0),y.indicesToOffset&&(Y.push(R),P=!0),y.broadcastedIndicesToOffset&&(Object.values(J).forEach(ee=>Y.push(ee)),P=!0),y.set&&(Y.push(ve),P=!0),y.setByIndices&&(Y.push(Q),P=!0),y.get&&(Y.push(ie),P=!0),y.getByIndices&&(Y.push(ue),P=!0),!s&&P&&Y.unshift(`const ${$} = ${c.indices}(${n.join(",")});`,`const ${_} = ${c.indices}(${W.computeStrides(n).join(",")});`),Y.join(`
+`)},type:c,offsetToIndices:S,indicesToOffset:O,broadcastedIndicesToOffset:se,indices:x,indicesGet:U,indicesSet:q,set:(...Y)=>{if(Y.length!==a+1)throw new Error(`indices length must be ${a}`);let P=Y[a];if(typeof P!="string")throw new Error("value must be string");let ee=Y.slice(0,a).map(m).join(",");return a===0?ce("0u",P):a===1?ce(ee[0],P):(y.set=!0,y.setByIndices=!0,y.indicesToOffset=!0,`set_${e}(${ee}, ${P})`)},setByOffset:ce,setByIndices:(Y,P)=>a<2?ce(Y,P):(y.setByIndices=!0,y.indicesToOffset=!0,`set_${e}ByIndices(${Y}, ${P});`),get:te,getByOffset:L,getByIndices:he,usage:r,name:e,strides:_,shape:$,rank:a}},V=(e,t,n,r=1)=>rs(e,t,n,"input",r),be=(e,t,n,r=1)=>rs(e,t,n,"output",r),J0=(e,t,n)=>rs(e,t,n,"atomicOutput",1),Zu=(e,t,n,r=1)=>rs(e,t,n,"internal",r),Up=class{constructor(e,t){this.normalizedDispatchGroup=e,this.limits=t,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(e){return`if (global_idx >= ${typeof e=="number"?`${e}u`:e}) { return; }`}mainStart(e=ki){let t=typeof e=="number"?e:e[0],n=typeof e=="number"?1:e[1],r=typeof e=="number"?1:e[2];if(t>this.limits.maxComputeWorkgroupSizeX||n>this.limits.maxComputeWorkgroupSizeY||r>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${t}, ${n}, ${r}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(t*n*r>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${t}, ${n}, ${r}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let i=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,s=i?`@builtin(global_invocation_id) global_id : vec3<u32>,
+    @builtin(workgroup_id) workgroup_id : vec3<u32>,
+    @builtin(local_invocation_index) local_idx : u32,
+    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
+                                             @builtin(local_invocation_id) local_id : vec3<u32>,
+    @builtin(local_invocation_index) local_idx : u32,
+    @builtin(workgroup_id) workgroup_id : vec3<u32>,
+    @builtin(num_workgroups) num_workgroups : vec3<u32>`,a=i?`let global_idx = global_id.x;
+         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
+             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
+         let global_idx = workgroup_index * ${t*n*r}u + local_idx;`;return`@compute @workgroup_size(${t}, ${n}, ${r})
+  fn main(${s}) {
+    ${a}
+  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let n=e.usage==="input"?"read":"read_write",r=e.usage==="atomicOutput"?"atomic<i32>":e.type.storage;return`@group(0) @binding(${t}) var<storage, ${n}> ${e.name}: array<${r}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
+`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(e),this.appendVariableUniforms(e)}registerInternalVariables(...e){return e.forEach(t=>this.registerInternalVariable(t)),this}registerUniform(e,t,n=1){return this.uniforms.push({name:e,type:t,length:n}),this}registerUniforms(e){return this.uniforms=this.uniforms.concat(e),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let e=[];for(let{name:t,type:n,length:r}of this.uniforms)if(r&&r>4)n==="f16"?e.push(`@align(16) ${t}:array<mat2x4<${n}>, ${Math.ceil(r/8)}>`):e.push(`${t}:array<vec4<${n}>, ${Math.ceil(r/4)}>`);else{let i=r==null||r===1?n:`vec${r}<${n}>`;e.push(`${t}:${i}`)}return`
+      struct Uniforms { ${e.join(", ")} };
+      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(`
+`)+this.internalVariables.map(e=>e.impl()).join(`
+`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=t=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(t)];return this.uniforms.map(t=>[e(t.type),t.length??1])}},ey=(e,t)=>new Up(e,t)}),Lp,ol,Wp,qp,Gp,Vp,en,ty,ny,_r=re(()=>{Te(),Ce(),pt(),Ee(),Lp=(e,t)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(t.length!==0&&t.length!==e[0].dims.length)throw new Error(`perm size ${t.length} does not match input rank ${e[0].dims.length}`)},ol=(e,t)=>t.length!==0?t:[...new Array(e).keys()].reverse(),Wp=(e,t)=>W.sortBasedOnPerm(e,ol(e.length,t)),qp=(e,t,n,r)=>{let i=`fn perm(i: ${r.type.indices}) -> ${n.type.indices} {
+    var a: ${n.type.indices};`;for(let s=0;s<t;++s)i+=`a[${e[s]}]=i[${s}];`;return i+="return a;}"},Gp=(e,t)=>{let n=[],r=[];for(let i=0;i<e.length;++i)e[i]!==1&&n.push(e[i]),e[t[i]]!==1&&r.push(t[i]);return{newShape:n,newPerm:r}},Vp=(e,t)=>{let n=0;for(let r=0;r<e.length;++r)if(t[e[r]]!==1){if(e[r]<n)return!1;n=e[r]}return!0},en=(e,t)=>{let n=e.dataType,r=e.dims.length,i=ol(r,t),s=Wp(e.dims,i),a=e.dims,o=s,l=r<2||Vp(i,e.dims),d;if(l)return d=y=>{let w=V("input",n,a,4),$=be("output",n,o,4);return`
+  ${y.registerUniform("output_size","u32").declareVariables(w,$)}
+  ${y.mainStart()}
+    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    output[global_idx] = input[global_idx];
+  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let y=W.size(s);return{outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(y/64/4)},programUniforms:[{type:12,data:Math.ceil(y/4)}]}},getShaderSource:d};let{newShape:p,newPerm:f}=Gp(e.dims,i),c=W.areEqual(f,[2,3,1]),m=W.areEqual(f,[3,1,2]);if(p.length===2||c||m){a=c?[p[0],p[1]*p[2]]:m?[p[0]*p[1],p[2]]:p,o=[a[1],a[0]];let y=16;return d=w=>{let $=V("a",n,a.length),_=be("output",n,o.length);return`
+  ${w.registerUniform("output_size","u32").declareVariables($,_)}
+  var<workgroup> tile : array<array<${_.type.value}, ${y+1}>, ${y}>;
+  ${w.mainStart([y,y,1])}
+    let stride = (uniforms.output_shape[1] - 1) / ${y} + 1;
+    let workgroup_id_x = workgroup_index % stride;
+    let workgroup_id_y = workgroup_index / stride;
+    let input_col = workgroup_id_y * ${y}u + local_id.x;
+    let input_row = workgroup_id_x * ${y}u + local_id.y;
+    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
+      tile[local_id.y][local_id.x] = ${$.getByIndices(`${$.type.indices}(input_row, input_col)`)};
+    }
+    workgroupBarrier();
+
+    let output_col = workgroup_id_x * ${y}u + local_id.x;
+    let output_row = workgroup_id_y * ${y}u + local_id.y;
+    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
+      ${_.setByIndices(`${_.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
+    }
+  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let w=W.size(s);return{outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(o[1]/y),y:Math.ceil(o[0]/y)},programUniforms:[{type:12,data:w},..._e(a,o)]}},getShaderSource:d}}return d=y=>{let w=V("a",n,a.length),$=be("output",n,o.length);return`
+  ${y.registerUniform("output_size","u32").declareVariables(w,$)}
+
+  ${qp(i,r,w,$)}
+
+  ${y.mainStart()}
+    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+
+    let indices = ${$.offsetToIndices("global_idx")};
+    let aIndices = perm(indices);
+
+    ${$.setByOffset("global_idx",w.getByIndices("aIndices"))}
+  }`},{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let y=W.size(s);return{outputs:[{dims:s,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:12,data:y},..._e(a,o)]}},getShaderSource:d}},ty=(e,t)=>{Lp(e.inputs,t.perm),e.compute(en(e.inputs[0],t.perm))},ny=e=>Fe({perm:e.perm})}),Fp,Hp,jp,Kp,Xp,Yp,Zp,Qp,Jp,ef,dn,ry,iy,sy,ay,oy,ly,uy,dy,cy,py,_$=re(()=>{Te(),Ce(),Ee(),Qu(),_r(),Fp={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},Hp={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},jp={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Kp={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},Xp=(e,t)=>{let n=[];for(let r=t-e;r<t;++r)n.push(r);return n},Yp=(e,t)=>{let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let i=t.map(s=>e[s]);return[n,i]},Zp=(e,t)=>{let n=e.length+t.length,r=[],i=0;for(let s=0;s<n;s++)t.indexOf(s)===-1?r.push(e[i++]):r.push(1);return r},Qp=(e,t)=>{for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0},Jp=(e,t)=>{let n=[];if(!Qp(e,t)){for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);e.forEach(r=>n.push(r))}return n},ef=(e,t,n,r,i,s,a)=>{let o=n[0].dims,l=W.size(s),d=W.size(a),p=V("_A",n[0].dataType,o),f=be("output",i,s),c=64;l===1&&(c=256);let m=`
+          var<workgroup> aBestValues : array<f32, ${c}>;
+       `,y=w=>`
+        ${w.registerUniform("reduceSize","u32").declareVariables(p,f)}
+        ${m}
+        fn DIV_CEIL(a : u32, b : u32) -> u32 {
+          return ((a - 1u) / b + 1u);
+         }
+         ${w.mainStart(c)}
+
+          let outputIndex = global_idx / ${c};
+          let offset = outputIndex * uniforms.reduceSize;
+
+          var bestValue = f32(${jp[r]});
+          let Length = uniforms.reduceSize;
+          for (var k = local_idx; k < Length; k = k + ${c}) {
+           let candidate = f32(${p.getByOffset("offset + k")});
+           bestValue = ${Fp[r]};
+          }
+          aBestValues[local_idx] = bestValue;
+          workgroupBarrier();
+
+         var reduceSize = min(Length, ${c}u);
+         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+             currentSize = reduceSize / 2u) {
+           let interval = DIV_CEIL(reduceSize, 2u);
+           if (local_idx < currentSize) {
+            let candidate = aBestValues[local_idx + interval];
+            bestValue = ${Hp[r]};
+            aBestValues[local_idx] = bestValue;
+           }
+           reduceSize = interval;
+           workgroupBarrier();
+         }
+
+         if (local_idx == 0u) {
+          ${f.setByOffset("outputIndex",`${r==="mean"?`${f.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${f.type.storage}(${Kp[r]})`}`)};
+         }
+        }`;return{name:e,shaderCache:{hint:`${t};${c}`,inputDependencies:["type"]},getShaderSource:y,getRunData:()=>({outputs:[{dims:s,dataType:i}],dispatchGroup:{x:l},programUniforms:[{type:12,data:d}]})}},dn=(e,t,n,r)=>{let i=e.inputs.length===1?n:lu(e.inputs,n),s=i.axes;s.length===0&&!i.noopWithEmptyAxes&&(s=e.inputs[0].dims.map((m,y)=>y));let a=W.normalizeAxes(s,e.inputs[0].dims.length),o=a,l=e.inputs[0],d=Jp(o,e.inputs[0].dims.length);d.length>0&&(l=e.compute(en(e.inputs[0],d),{inputs:[0],outputs:[-1]})[0],o=Xp(o.length,l.dims.length));let[p,f]=Yp(l.dims,o),c=p;i.keepDims&&(c=Zp(p,a)),e.compute(ef(t,i.cacheKey,[l],r,e.inputs[0].dataType,c,f),{inputs:[l]})},ry=(e,t)=>{dn(e,"ReduceMeanShared",t,"mean")},iy=(e,t)=>{dn(e,"ReduceL1Shared",t,"l1")},sy=(e,t)=>{dn(e,"ReduceL2Shared",t,"l2")},ay=(e,t)=>{dn(e,"ReduceLogSumExpShared",t,"logSumExp")},oy=(e,t)=>{dn(e,"ReduceMaxShared",t,"max")},ly=(e,t)=>{dn(e,"ReduceMinShared",t,"min")},uy=(e,t)=>{dn(e,"ReduceProdShared",t,"prod")},dy=(e,t)=>{dn(e,"ReduceSumShared",t,"sum")},cy=(e,t)=>{dn(e,"ReduceSumSquareShared",t,"sumSquare")},py=(e,t)=>{dn(e,"ReduceLogSumShared",t,"logSum")}}),cn,tf,Wa,lu,pn,nf,rf,sf,af,of,lf,uf,df,cf,pf,fn,fy,hy,my,gy,yy,by,wy,_y,vy,xy,Qu=re(()=>{Te(),Ce(),pt(),Ee(),_$(),cn=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},tf=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Wa=(e,t,n,r,i,s,a=!1,o=!1)=>{let l=[],d=n[0].dims,p=d.length,f=W.normalizeAxes(i,p),c=!o&&f.length===0;d.forEach((w,$)=>{c||f.indexOf($)>=0?a&&l.push(1):l.push(w)});let m=l.length,y=W.size(l);return{name:e,shaderCache:t,getShaderSource:w=>{let $=[],_=V("_A",n[0].dataType,p),v=be("output",s,m),I=r(_,v,f),S=I[2];for(let E=0,R=0;E<p;E++)c||f.indexOf(E)>=0?(a&&R++,S=`for(var j${E}: u32 = 0; j${E} < ${d[E]}; j${E}++) {
+                  ${I[2].includes("last_index")?`let last_index = j${E};`:""}
+                  ${_.indicesSet("input_indices",E,`j${E}`)}
+                  ${S}
+                }`):($.push(`${_.indicesSet("input_indices",E,v.indicesGet("output_indices",R))};`),R++);return`
+
+        ${w.registerUniform("output_size","u32").declareVariables(_,v)}
+
+        ${w.mainStart()}
+          ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+          var input_indices: ${_.type.indices};
+          let output_indices = ${v.offsetToIndices("global_idx")};
+
+          ${$.join(`
+`)}
+          ${I[0]}       // init ops for reduce max/min
+          ${I[1]}
+          ${S}
+          ${I[3]}
+          ${I.length===4?v.setByOffset("global_idx","value"):I.slice(4).join(`
+`)}
+        }`},getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:12,data:y},..._e(d,l)]})}},lu=(e,t)=>{let n=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(r=>n.push(Number(r))),Fe({axes:n,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},pn=(e,t,n,r)=>{let i=e.inputs,s=i.length===1?n:lu(i,n);e.compute(Wa(t,{hint:s.cacheKey,inputDependencies:["rank"]},[i[0]],s.noopWithEmptyAxes&&s.axes.length===0?tf:r,s.axes,i[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},nf=(e,t)=>{cn(e.inputs),pn(e,"ReduceLogSum",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,"value = log(value);"])},rf=(e,t)=>{cn(e.inputs),pn(e,"ReduceL1",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += abs(${n.getByIndices("input_indices")});`,""])},sf=(e,t)=>{cn(e.inputs),pn(e,"ReduceL2",t,(n,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},af=(e,t)=>{cn(e.inputs),pn(e,"ReduceLogSumExp",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += exp(${n.getByIndices("input_indices")});`,"value = log(value);"])},of=(e,t)=>{cn(e.inputs),pn(e,"ReduceMax",t,(n,r,i)=>{let s=[];for(let a=0;a<n.rank;a++)(i.indexOf(a)>=0||i.length===0)&&s.push(n.indicesSet("input_indices",a,0));return[`${s.join(`
+`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = max(value, ${n.getByIndices("input_indices")});`,""]})},lf=(e,t)=>{cn(e.inputs),pn(e,"ReduceMean",t,(n,r,i)=>{let s=1;for(let a=0;a<n.rank;a++)(i.indexOf(a)>=0||i.length===0)&&(s*=e.inputs[0].dims[a]);return["var sum = f32(0);","",`sum += f32(${n.getByIndices("input_indices")});`,`let value = ${r.type.value}(sum / ${s});`]})},uf=(e,t)=>{cn(e.inputs),pn(e,"ReduceMin",t,(n,r,i)=>{let s=[];for(let a=0;a<n.rank;a++)(i.indexOf(a)>=0||i.length===0)&&s.push(`input_indices[${a}] = 0;`);return[`${s.join(`
+`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = min(value, ${n.getByIndices("input_indices")});`,""]})},df=(e,t)=>{cn(e.inputs),pn(e,"ReduceProd",t,(n,r)=>[`var value = ${r.type.storage}(1);`,"",`value *= ${n.getByIndices("input_indices")};`,""])},cf=(e,t)=>{cn(e.inputs),pn(e,"ReduceSum",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,""])},pf=(e,t)=>{cn(e.inputs),pn(e,"ReduceSumSquare",t,(n,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += t * t;`,""])},fn=(e,t,n)=>{if(t.length===0)return n;let r=1,i=1;for(let s=0;s<t.length;s++)t.indexOf(s)===-1?r*=e[s]:i*=e[s];return i<32&&r>1024},fy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?lf(e,t):ry(e,t)},hy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?rf(e,t):iy(e,t)},my=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?sf(e,t):sy(e,t)},gy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?af(e,t):ay(e,t)},yy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?of(e,t):oy(e,t)},by=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?uf(e,t):ly(e,t)},wy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?df(e,t):uy(e,t)},_y=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?cf(e,t):dy(e,t)},vy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?pf(e,t):cy(e,t)},xy=(e,t)=>{fn(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?nf(e,t):py(e,t)}}),ll,$y,Sy,uu,v$=re(()=>{Te(),pt(),Qu(),ll=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},$y=(e,t)=>{ll(e.inputs);let n=(r,i,s)=>{let a=[];for(let o=0;o<r.rank;o++)(s.indexOf(o)>=0||s.length===0)&&a.push(`input_indices[${o}] = 0;`);return[`${a.join(`
+`)}`,`var value = ${r.getByIndices("input_indices")};
+var best_index : i32 = 0;`,`if (${r.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
+         value = ${r.getByIndices("input_indices")};
+         best_index = i32(last_index);
+       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Wa("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],n,[t.axis],7,t.keepDims),{inputs:[0]})},Sy=(e,t)=>{ll(e.inputs);let n=(r,i,s)=>{let a=[];for(let o=0;o<r.rank;o++)(s.indexOf(o)>=0||s.length===0)&&a.push(`input_indices[${o}] = 0;`);return[`${a.join(`
+`)}`,`var value = ${r.getByIndices("input_indices")};
+var best_index : i32 = 0;`,`if (${r.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
+         value = ${r.getByIndices("input_indices")};
+         best_index = i32(last_index);
+       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Wa("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],n,[t.axis],7,t.keepDims),{inputs:[0]})},uu=e=>Fe(e)}),ff,pa,hf,mf,gf,Ms,yf,Ty,Ju=re(()=>{Te(),Ce(),Yu(),Ee(),ff=(e,t)=>{let n=e[0],r=e[1],i=e[2],s=e[3],a=e[4],o=e[5];if(a&&o)throw new Error("Attention cannot have both past and attention_bias");if(n.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=n.dims[0],d=n.dims[1],p=n.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(r.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(r.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==r.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let f=i.dims[0]/3,c=f,m=c;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let I of t.qkvHiddenSizes)if(I%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=t.qkvHiddenSizes[0],c=t.qkvHiddenSizes[1],m=t.qkvHiddenSizes[2]}let y=d;if(f!==c)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==f+c+m)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let w=0;if(a){if(c!==m)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==c/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(w=a.dims[3])}let $=y+w,_=-1,v=0;if(s)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(o){if(o.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(o.dims[0]!==l||o.dims[1]!==t.numHeads||o.dims[2]!==d||o.dims[3]!==$)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:l,sequenceLength:d,pastSequenceLength:w,kvSequenceLength:y,totalSequenceLength:$,maxSequenceLength:_,inputHiddenSize:p,hiddenSize:f,vHiddenSize:m,headSize:Math.floor(f/t.numHeads),vHeadSize:Math.floor(m/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:v,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},pa=(e,t,n)=>t&&e?`
+      let total_sequence_length_input = u32(${t.getByOffset("0")});
+      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
+      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
+      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
+      total_sequence_length = u32(${e==null?void 0:e.getByOffset("batchIdx")}) + 1;
+      var past_sequence_length: u32 = 0;
+      if (is_first_prompt == false) {
+        past_sequence_length = total_sequence_length - sequence_length;
+      }
+       `:`
+    ${n?"let past_sequence_length = uniforms.past_sequence_length":""};
+    let present_sequence_length = total_sequence_length;
+    `,hf=(e,t,n,r,i,s,a,o)=>{let l=ct(a?1:s),d=64,p=s/l;p<d&&(d=32);let f=Math.ceil(s/l/d),c=[{type:12,data:t},{type:12,data:n},{type:12,data:r},{type:12,data:i},{type:12,data:p},{type:12,data:f}],m=xt(e.dataType,l),y=Rt(1,l),w=["type"];a&&w.push("type"),o&&w.push("type");let $=_=>{let v=be("x",e.dataType,e.dims,l),I=[v],S=a?V("seq_lens",a.dataType,a.dims):void 0;S&&I.push(S);let E=o?V("total_sequence_length_input",o.dataType,o.dims):void 0;E&&I.push(E);let R=Rt(e.dataType),O=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
+  var<workgroup> thread_max: array<f32, ${d}>;
+  var<workgroup> thread_sum: array<f32, ${d}>;
+  ${_.registerUniforms(O).declareVariables(...I)}
+  ${_.mainStart([d,1,1])}
+    let batchIdx = workgroup_id.z / uniforms.num_heads;
+    let headIdx = workgroup_id.z % uniforms.num_heads;
+    let sequence_length = uniforms.sequence_length;
+    var total_sequence_length = uniforms.total_sequence_length;
+    ${pa(S,E,!1)}
+    let local_offset = local_idx * uniforms.elements_per_thread;
+    let offset = (global_idx / ${d}) * uniforms.total_sequence_length + local_offset;
+    let seq_causal_length = ${a?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
+    var thread_max_vector = ${y}(-3.4028234663852886e+38f);
+    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
+      thread_max_vector = max(${y}(x[offset + i]), thread_max_vector);
+    }
+    thread_max[local_idx] = ${(()=>{switch(l){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${l}`)}})()};
+    workgroupBarrier();
+
+    var max_value =  f32(-3.4028234663852886e+38f);
+    for (var i = 0u; i < ${d}; i++) {
+      max_value = max(thread_max[i], max_value);
+    }
+
+    var sum_vector = ${y}(0);
+    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
+      sum_vector += exp(${y}(x[offset + i]) - max_value);
+    }
+    thread_sum[local_idx] = ${(()=>{switch(l){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${l}`)}})()};
+    workgroupBarrier();
+
+    var sum: f32 = 0;
+    for (var i = 0u; i < ${d}; i++) {
+      sum += thread_sum[i];
+    }
+
+    if (sum == 0) {
+      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
+        x[offset + i] = ${v.type.value}(${R}(1.0) / ${R}(seq_causal_length));
+      }
+    } else {
+      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
+        var f32input = ${y}(x[offset + i]);
+        x[offset + i] = ${v.type.value}(exp(f32input - max_value) / sum);
+      }
+    }
+      ${a?`
+        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
+          x[offset + total_seq_id] = ${v.type.value}(${R}(0));
+        }`:""};
+  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${d};${m};${l}`,inputDependencies:w},getShaderSource:$,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:i,z:t*n},programUniforms:c})}},mf=(e,t,n,r,i,s,a,o,l)=>{let d=a+s.kvSequenceLength,p=[s.batchSize,s.numHeads,s.sequenceLength,d],f=e>1&&r,c=s.kvNumHeads?s.kvNumHeads:s.numHeads,m=f?[s.batchSize,c,d,s.headSize]:void 0,y=s.nReps?s.nReps:1,w=s.scale===0?1/Math.sqrt(s.headSize):s.scale,$=ct(s.headSize),_=s.headSize/$,v=12,I={x:Math.ceil(d/v),y:Math.ceil(s.sequenceLength/v),z:s.batchSize*s.numHeads},S=[{type:12,data:s.sequenceLength},{type:12,data:_},{type:12,data:d},{type:12,data:s.numHeads},{type:12,data:s.headSize},{type:1,data:w},{type:12,data:a},{type:12,data:s.kvSequenceLength},{type:12,data:y}],E=f&&r&&W.size(r.dims)>0,R=["type","type"];E&&R.push("type"),i&&R.push("type"),o&&R.push("type"),l&&R.push("type");let O=[{dims:p,dataType:t.dataType,gpuDataType:0}];f&&O.push({dims:m,dataType:t.dataType,gpuDataType:0});let x=U=>{let q=V("q",t.dataType,t.dims,$),J=V("key",n.dataType,n.dims,$),se=[q,J];if(E){let Q=V("past_key",r.dataType,r.dims,$);se.push(Q)}i&&se.push(V("attention_bias",i.dataType,i.dims));let ce=o?V("seq_lens",o.dataType,o.dims):void 0;ce&&se.push(ce);let L=l?V("total_sequence_length_input",l.dataType,l.dims):void 0;L&&se.push(L);let ue=be("output",t.dataType,p),ie=[ue];f&&ie.push(be("present_key",t.dataType,m,$));let te=Rt(1,$),he=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
+  const TILE_SIZE = ${v}u;
+
+  var<workgroup> tileQ: array<${q.type.storage}, ${v*v}>;
+  var<workgroup> tileK: array<${q.type.storage}, ${v*v}>;
+  ${U.registerUniforms(he).declareVariables(...se,...ie)}
+  ${U.mainStart([v,v,1])}
+    // x holds the N and y holds the M
+    let headIdx = workgroup_id.z % uniforms.num_heads;
+    let kvHeadIdx = ${y===1?"headIdx":"headIdx / uniforms.n_reps"};
+    let kv_num_heads = ${y===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
+    let batchIdx = workgroup_id.z / uniforms.num_heads;
+    let m = workgroup_id.y * TILE_SIZE;
+    let n = workgroup_id.x * TILE_SIZE;
+    let sequence_length = uniforms.M;
+    var total_sequence_length = uniforms.N;
+    ${pa(ce,L,!0)}
+    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
+    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
+    ${E&&f?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
+    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
+    ${f?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
+    var value = ${te}(0);
+    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
+      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
+        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
+      }
+      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
+        var idx = TILE_SIZE * local_id.y + local_id.x;
+      ${E&&f?`
+              if (n + local_id.y < past_sequence_length) {
+                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
+              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
+                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
+              }`:`
+          if (n + local_id.y < uniforms.kv_sequence_length) {
+            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
+          }`}
+      ${f?`if (n + local_id.y < present_sequence_length) {
+        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
+      }`:""}
+      }
+      workgroupBarrier();
+
+      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
+          value += ${te}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
+      }
+
+      workgroupBarrier();
+    }
+
+    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
+      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
+      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
+      var sum: f32 = ${(()=>{switch($){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${$}`)}})()};
+        output[outputIdx] = ${ue.type.value} (sum * uniforms.alpha) + ${i?"attention_bias[outputIdx]":"0.0"};
+    }
+  }`};return{name:"AttentionProbs",shaderCache:{hint:`${$};${i!==void 0};${r!==void 0};${e}`,inputDependencies:R},getRunData:()=>({outputs:O,dispatchGroup:I,programUniforms:S}),getShaderSource:x}},gf=(e,t,n,r,i,s,a=void 0,o=void 0)=>{let l=s+i.kvSequenceLength,d=i.nReps?i.nReps:1,p=i.vHiddenSize*d,f=e>1&&r,c=i.kvNumHeads?i.kvNumHeads:i.numHeads,m=f?[i.batchSize,c,l,i.headSize]:void 0,y=[i.batchSize,i.sequenceLength,p],w=12,$={x:Math.ceil(i.vHeadSize/w),y:Math.ceil(i.sequenceLength/w),z:i.batchSize*i.numHeads},_=[{type:12,data:i.sequenceLength},{type:12,data:l},{type:12,data:i.vHeadSize},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:12,data:p},{type:12,data:s},{type:12,data:i.kvSequenceLength},{type:12,data:d}],v=f&&r&&W.size(r.dims)>0,I=["type","type"];v&&I.push("type"),a&&I.push("type"),o&&I.push("type");let S=[{dims:y,dataType:t.dataType,gpuDataType:0}];f&&S.push({dims:m,dataType:t.dataType,gpuDataType:0});let E=R=>{let O=V("probs",t.dataType,t.dims),x=V("v",n.dataType,n.dims),U=[O,x];v&&U.push(V("past_value",r.dataType,r.dims));let q=a?V("seq_lens",a.dataType,a.dims):void 0;a&&U.push(q);let J=o?V("total_sequence_length_input",o.dataType,o.dims):void 0;o&&U.push(J);let se=[be("output",t.dataType,y)];f&&se.push(be("present_value",t.dataType,m));let ce=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
+  const TILE_SIZE = ${w}u;
+  var<workgroup> tileQ: array<${O.type.value}, ${w*w}>;
+  var<workgroup> tileV: array<${O.type.value}, ${w*w}>;
+  ${R.registerUniforms(ce).declareVariables(...U,...se)}
+  ${R.mainStart([w,w,1])}
+   let headIdx = workgroup_id.z % uniforms.num_heads;
+   let batchIdx = workgroup_id.z / uniforms.num_heads;
+   let kvHeadIdx = ${d===1?"headIdx":"headIdx / uniforms.n_reps"};
+   let kv_num_heads = ${d===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
+   let m = global_id.y;
+   let n = global_id.x;
+   let sequence_length = uniforms.M;
+   var total_sequence_length = uniforms.K;
+   ${pa(q,J,!0)}
+   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
+   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
+   ${v&&f?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
+   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
+   ${f?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
+   var value = ${O.type.storage}(0);
+   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
+      if (m < uniforms.M && w + local_id.x < uniforms.K) {
+        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
+      }
+      if (n < uniforms.N && w + local_id.y < uniforms.K) {
+        var idx = TILE_SIZE * local_id.y + local_id.x;
+        ${v&&f?`
+        if (w + local_id.y < past_sequence_length) {
+          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
+        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
+          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
+        }
+      `:`
+            if (w + local_id.y < uniforms.kv_sequence_length) {
+              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
+            }`}
+        ${f?`
+            if (w + local_id.y < present_sequence_length) {
+          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
+        }`:""}
+      }
+     workgroupBarrier();
+     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
+       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
+     }
+     workgroupBarrier();
+   }
+
+   // we need to transpose output from BNSH_v to BSND_v
+   if (m < uniforms.M && n < uniforms.N) {
+     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
+       + headIdx * uniforms.N + n;
+     output[outputIdx] = value;
+   }
+  }`};return{name:"AttentionScore",shaderCache:{hint:`${r!==void 0};${e}`,inputDependencies:I},getRunData:()=>({outputs:S,dispatchGroup:$,programUniforms:_}),getShaderSource:E}},Ms=(e,t,n,r,i,s,a,o,l,d,p=void 0,f=void 0)=>{let c=Math.min(e.outputCount,1+(a?1:0)+(o?1:0)),m=c>1?d.pastSequenceLength:0,y=m+d.kvSequenceLength,w=l&&W.size(l.dims)>0?l:void 0,$=[t,n];c>1&&a&&W.size(a.dims)>0&&$.push(a),w&&$.push(w),p&&$.push(p),f&&$.push(f);let _=e.compute(mf(c,t,n,a,w,d,m,p,f),{inputs:$,outputs:c>1?[-1,1]:[-1]})[0];e.compute(hf(_,d.batchSize,d.numHeads,m,d.sequenceLength,y,p,f),{inputs:p&&f?[_,p,f]:[_],outputs:[]});let v=[_,r];c>1&&o&&W.size(o.dims)>0&&v.push(o),p&&v.push(p),f&&v.push(f),e.compute(gf(c,_,r,o,d,m,p,f),{inputs:v,outputs:c>1?[0,2]:[0]})},yf=(e,t)=>{let n=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],r=t.sequenceLength,i=t.inputHiddenSize,s=t.headSize,a=12,o={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],d=[{type:12,data:r},{type:12,data:i},{type:12,data:s},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],p=f=>{let c=be("output_q",l[0].dataType,n),m=be("output_k",l[0].dataType,n),y=be("output_v",l[0].dataType,n),w=V("input",l[0].dataType,l[0].dims),$=V("weight",l[1].dataType,l[1].dims),_=V("bias",l[2].dataType,l[2].dims),v=w.type.storage,I=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
+  const TILE_SIZE = ${a}u;
+  var<workgroup> tileInput: array<${v}, ${a*a}>;
+  var<workgroup> tileWeightQ: array<${v}, ${a*a}>;
+  var<workgroup> tileWeightK: array<${v}, ${a*a}>;
+  var<workgroup> tileWeightV: array<${v}, ${a*a}>;
+  ${f.registerUniforms(I).declareVariables(w,$,_,c,m,y)}
+  ${f.mainStart([a,a,1])}
+    let batchIndex = workgroup_id.z / uniforms.num_heads;
+    let headNumber = workgroup_id.z % uniforms.num_heads;
+    let m = global_id.y;
+    let n = global_id.x;
+
+    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
+    let biasOffsetQ = headNumber * uniforms.head_size;
+    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
+    let biasOffsetV = uniforms.hidden_size + biasOffsetK;
+
+    var valueQ = ${v}(0);
+    var valueK = ${v}(0);
+    var valueV = ${v}(0);
+    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
+      if (m < uniforms.M && w + local_id.x < uniforms.K) {
+        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
+      }
+      if (n < uniforms.N && w + local_id.y < uniforms.K) {
+        let offset = n + (w + local_id.y) * uniforms.ldb;
+        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
+        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
+        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
+      }
+      workgroupBarrier();
+      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
+        let inputTileOffset = TILE_SIZE * local_id.y + k;
+        let weightTileOffset = TILE_SIZE * k + local_id.x;
+        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
+        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
+        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
+      }
+
+      workgroupBarrier();
+    }
+
+    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
+    valueQ += bias[headOffset + biasOffsetQ];
+    valueK += bias[headOffset + biasOffsetK];
+    valueV += bias[headOffset + biasOffsetV];
+
+    let offset = workgroup_id.z * uniforms.M * uniforms.N;
+    if (m < uniforms.M && n < uniforms.N) {
+      let outputIdx = offset + m * uniforms.N + n;
+      output_q[outputIdx] = valueQ;
+      output_k[outputIdx] = valueK;
+      output_v[outputIdx] = valueV;
+    }
+  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:d}),getShaderSource:p},{inputs:l,outputs:[-1,-1,-1]})},Ty=(e,t)=>{let n=ff(e.inputs,t),[r,i,s]=yf(e,n);return Ms(e,r,i,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n)}}),bf,wf,_f,Iy,x$=re(()=>{on(),Te(),Ce(),pt(),Ee(),bf=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let n=(r,i,s)=>{let a=i.length;if(a!==r.length)throw new Error(`${s}: num dimensions != ${a}`);i.forEach((o,l)=>{if(o!==r[l])throw new Error(`${s}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let r=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);n(e[1].dims,r,"Invalid input scale"),n(e[2].dims,r,"Invalid input B"),n(e[3].dims,r,"Invalid input mean"),n(e[4].dims,r,"Invalid input var")}else n(e[1].dims,[1],"Invalid input scale"),n(e[2].dims,[1],"Invalid input B"),n(e[3].dims,[1],"Invalid input mean"),n(e[4].dims,[1],"Invalid input var")},wf=(e,t)=>{let{epsilon:n,spatial:r,format:i}=t,s=e[0].dims,a=r?ct(s[s.length-1]):1,o=i==="NHWC"&&s.length>1?a:1,l=W.size(s)/a,d=r,p=d?s.length:s,f=V("x",e[0].dataType,e[0].dims,a),c=V("scale",e[1].dataType,e[1].dims,o),m=V("bias",e[2].dataType,e[2].dims,o),y=V("inputMean",e[3].dataType,e[3].dims,o),w=V("inputVar",e[4].dataType,e[4].dims,o),$=be("y",e[0].dataType,p,a),_=()=>{let I="";if(r)I=`let cOffset = ${s.length===1?"0u":i==="NHWC"?`outputIndices[${s.length-1}] / ${a}`:"outputIndices[1]"};`;else if(i==="NCHW")I=`
+            ${$.indicesSet("outputIndices","0","0")}
+            let cOffset = ${$.indicesToOffset("outputIndices")};`;else{I=`var cIndices = ${c.type.indices}(0);
+                       cIndices[0] = outputIndices[${s.length-1}];`;for(let S=1;S<c.rank;S++)I+=`cIndices[${S}] = outputIndices[${S}];`;I+=`let cOffset = ${c.indicesToOffset("cIndices")};`}return I},v=I=>`
+  const epsilon = ${n};
+  ${I.registerUniform("outputSize","u32").declareVariables(f,c,m,y,w,$)}
+  ${I.mainStart()}
+  ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+    var outputIndices = ${$.offsetToIndices(`global_idx * ${a}`)};
+    ${_()}
+    let scale = ${c.getByOffset("cOffset")};
+    let bias = ${m.getByOffset("cOffset")};
+    let inputMean = ${y.getByOffset("cOffset")};
+    let inputVar = ${w.getByOffset("cOffset")};
+    let x = ${f.getByOffset("global_idx")};
+    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
+    ${$.setByOffset("global_idx","value")}
+  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${r}_${a}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:v,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:12,data:l},..._e(s)]:[{type:12,data:l}]})}},_f=e=>Fe(e),Iy=(e,t)=>{let{inputs:n,outputCount:r}=e,i=_f({...t,outputCount:r});if(Be.webgpu.validateInputContent&&bf(n,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(wf(n,i))}}),vf,xf,Cy,$$=re(()=>{Ce(),Ee(),vf=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},xf=e=>{let t=e[0].dims,n=e[0].dims[2],r=W.size(t)/4,i=e[0].dataType,s=V("input",i,t,4),a=V("bias",i,[n],4),o=V("residual",i,t,4),l=be("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(r/64)}}),getShaderSource:d=>`
+  const channels = ${n}u / 4;
+  ${d.declareVariables(s,a,o,l)}
+
+  ${d.mainStart()}
+    ${d.guardAgainstOutOfBoundsWorkgroupSizes(r)}
+    let value = ${s.getByOffset("global_idx")}
+      + ${a.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
+    ${l.setByOffset("global_idx","value")}
+  }`}},Cy=e=>{vf(e.inputs),e.compute(xf(e.inputs))}}),$f,Ve,Ey,ky,zy,Ay,My,Ry,Oy,Ny,By,Sf,Dy,Py,Uy,Ly,fs,Wy,ka,qy,Gy,Vy,Fy,Hy,jy,Ky,Xy,Yy,Zy,Qy,Jy,eb,tb,nb,rb,ul,ib,du,cu,sb,ab,ob,Tf,If,lb,ed=re(()=>{Te(),Ce(),pt(),Ee(),$f=(e,t,n,r,i,s,a)=>{let o=Math.ceil(t/4),l="";typeof i=="string"?l=`${i}(a)`:l=i("a");let d=V("inputData",n,[o],4),p=be("outputData",r,[o],4),f=[{name:"vec_size",type:"u32"}];return a&&f.push(...a),`
+      ${e.registerUniforms(f).declareVariables(d,p)}
+
+  ${s??""}
+
+  ${e.mainStart()}
+    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
+
+    let a = ${d.getByOffset("global_idx")};
+    ${p.setByOffset("global_idx",l)}
+  }`},Ve=(e,t,n,r,i,s=e.dataType,a,o)=>{let l=[{type:12,data:Math.ceil(W.size(e.dims)/4)}];return a&&l.push(...a),{name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:d=>$f(d,W.size(e.dims),e.dataType,s,n,r,o),getRunData:d=>({outputs:[{dims:e.dims,dataType:s}],dispatchGroup:{x:Math.ceil(W.size(d[0].dims)/64/4)},programUniforms:l})}},Ey=e=>{e.compute(Ve(e.inputs[0],"Abs","abs"))},ky=e=>{e.compute(Ve(e.inputs[0],"Acos","acos"))},zy=e=>{e.compute(Ve(e.inputs[0],"Acosh","acosh"))},Ay=e=>{e.compute(Ve(e.inputs[0],"Asin","asin"))},My=e=>{e.compute(Ve(e.inputs[0],"Asinh","asinh"))},Ry=e=>{e.compute(Ve(e.inputs[0],"Atan","atan"))},Oy=e=>{e.compute(Ve(e.inputs[0],"Atanh","atanh"))},Ny=e=>Fe(e),By=(e,t)=>{let n;switch(t.to){case 10:n="vec4<f16>";break;case 1:n="vec4<f32>";break;case 12:n="vec4<u32>";break;case 6:n="vec4<i32>";break;case 9:n="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(Ve(e.inputs[0],"Cast",n,void 0,t.cacheKey,t.to))},Sf=e=>{let t,n,r=e.length>=2&&e[1].data!==0,i=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=r?e[1].getFloat32Array()[0]:-34028234663852886e22,n=i?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=r?e[1].getUint16Array()[0]:64511,n=i?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return Fe({min:t,max:n})},Dy=(e,t)=>{let n=t||Sf(e.inputs),r=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"Clip",i=>`clamp(${i}, vec4<${r}>(uniforms.min), vec4<${r}>(uniforms.max))`,void 0,n.cacheKey,void 0,[{type:e.inputs[0].dataType,data:n.min},{type:e.inputs[0].dataType,data:n.max}],[{name:"min",type:r},{name:"max",type:r}]),{inputs:[0]})},Py=e=>{e.compute(Ve(e.inputs[0],"Ceil","ceil"))},Uy=e=>{e.compute(Ve(e.inputs[0],"Cos","cos"))},Ly=e=>{e.compute(Ve(e.inputs[0],"Cosh","cosh"))},fs=e=>Fe(e),Wy=(e,t)=>{let n=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"Elu",r=>`elu_vf32(${r})`,`
+  const elu_alpha_ = ${n}(${t.alpha});
+
+  fn elu_f32(a: ${n}) -> ${n} {
+  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
+  }
+
+  fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
+  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
+  }`,t.cacheKey))},ka=(e="f32")=>`
+const r0: ${e} = 0.3275911;
+const r1: ${e} = 0.254829592;
+const r2: ${e} = -0.284496736;
+const r3: ${e} = 1.421413741;
+const r4: ${e} = -1.453152027;
+const r5: ${e} = 1.061405429;
+
+fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> {
+  let absv = abs(v);
+  let x = 1.0 / (1.0 + r0 * absv);
+  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
+}`,qy=e=>{let t=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"Erf",n=>`erf_vf32(${n})`,ka(t)))},Gy=e=>{e.compute(Ve(e.inputs[0],"Exp","exp"))},Vy=e=>{e.compute(Ve(e.inputs[0],"Floor","floor"))},Fy=e=>{let t=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"Gelu",n=>`0.5 * ${n} * (1.0 + erf_vf32(${n} * 0.7071067811865475))`,ka(t)))},Hy=(e,t)=>{let n=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"LeakyRelu",r=>`select(leaky_relu_alpha_ * ${r}, ${r}, ${r} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${t.alpha});`,t.cacheKey))},jy=e=>{e.compute(Ve(e.inputs[0],"Not",t=>`!${t}`))},Ky=e=>{e.compute(Ve(e.inputs[0],"Neg",t=>`-${t}`))},Xy=e=>{e.compute(Ve(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},Yy=e=>{let t=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"Relu",n=>`select(vec4<${t}>(0.0), ${n}, ${n} > vec4<${t}>(0.0))`))},Zy=e=>{e.compute(Ve(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},Qy=e=>Fe(e),Jy=(e,t)=>{let n=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"HardSigmoid",r=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${t.alpha} * ${r} + vec4<${n}>(${t.beta})))`,void 0,t.cacheKey))},eb=e=>{e.compute(Ve(e.inputs[0],"Sin","sin"))},tb=e=>{e.compute(Ve(e.inputs[0],"Sinh","sinh"))},nb=e=>{e.compute(Ve(e.inputs[0],"Sqrt","sqrt"))},rb=e=>{e.compute(Ve(e.inputs[0],"Tan","tan"))},ul=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,ib=e=>{e.compute(Ve(e.inputs[0],"Tanh",ul))},du=(e="f32")=>`
+const fast_gelu_a: ${e} = 0.5;
+const fast_gelu_b: ${e} = 0.7978845608028654;
+const fast_gelu_c: ${e} = 0.035677408136300125;
+
+fn tanh_v(v: vec4<${e}>) -> vec4<${e}> {
+  return ${ul("v")};
+}
+`,cu=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,sb=e=>{let t=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"FastGelu",cu,du(t),void 0,e.inputs[0].dataType))},ab=(e,t)=>{let n=Rt(e.inputs[0].dataType);return e.compute(Ve(e.inputs[0],"ThresholdedRelu",r=>`select(vec4<${n}>(0.0), ${r}, ${r} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${t.alpha});`,t.cacheKey)),0},ob=e=>{e.compute(Ve(e.inputs[0],"Log","log"))},Tf=(e,t)=>`
+const alpha = vec4<${e}>(${t});
+const one = ${e}(1.0);
+const zero = ${e}(0.0);
+
+fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> {
+  let v = x *alpha;
+  var x1 : vec4<${e}>;
+  for (var i = 0; i < 4; i = i + 1) {
+    if (v[i] >= zero) {
+      x1[i] = one / (one + exp(-v[i]));
+    } else {
+      x1[i] = one - one / (one + exp(v[i]));
+    }
+  }
+  return x * x1;
+}
+`,If=e=>`quick_gelu_impl(${e})`,lb=(e,t)=>{let n=Rt(e.inputs[0].dataType);e.compute(Ve(e.inputs[0],"QuickGelu",If,Tf(n,t.alpha),t.cacheKey,e.inputs[0].dataType))}}),Cf,Ef,ub,S$=re(()=>{Ce(),Ee(),ed(),Cf=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Ef=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let n=V("input",e[0].dataType,e[0].dims,4),r=V("bias",e[0].dataType,[e[0].dims[2]],4),i=be("output",e[0].dataType,t,4),s=W.size(t)/4,a=xt(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:o=>`
+  const M_SQRT2 = sqrt(2.0);
+  const halfChannels = ${e[0].dims[2]/4/2}u;
+
+  ${o.declareVariables(n,r,i)}
+
+  ${ka(a)}
+
+  ${o.mainStart()}
+    ${o.guardAgainstOutOfBoundsWorkgroupSizes(s)}
+    let biasIdx = global_idx % halfChannels;
+    let batchIndex = global_idx / halfChannels;
+    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
+    let valueLeft = input[inputOffset] + bias[biasIdx];
+    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
+    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);
+
+    ${i.setByOffset("global_idx","valueLeft * geluRight")}
+  }`}},ub=e=>{Cf(e.inputs),e.compute(Ef(e.inputs))}}),kf,zf,hn,db,cb,pb,fb,hb,mb,gb,yb,bb,wb,T$=re(()=>{Te(),Ce(),Ee(),kf=(e,t,n,r,i,s,a,o,l,d,p,f)=>{let c,m;typeof o=="string"?c=m=(v,I)=>`${o}((${v}),(${I}))`:typeof o=="function"?c=m=o:(c=o.scalar,m=o.vector);let y=be("outputData",p,r.length,4),w=V("aData",l,t.length,4),$=V("bData",d,n.length,4),_;if(i)if(s){let v=W.size(t)===1,I=W.size(n)===1,S=t.length>0&&t[t.length-1]%4===0,E=n.length>0&&n[n.length-1]%4===0;v||I?_=y.setByOffset("global_idx",m(v?`${w.type.value}(${w.getByOffset("0")}.x)`:w.getByOffset("global_idx"),I?`${$.type.value}(${$.getByOffset("0")}.x)`:$.getByOffset("global_idx"))):_=`
+            let outputIndices = ${y.offsetToIndices("global_idx * 4u")};
+            let offsetA = ${w.broadcastedIndicesToOffset("outputIndices",y)};
+            let offsetB = ${$.broadcastedIndicesToOffset("outputIndices",y)};
+            ${y.setByOffset("global_idx",m(a||S?w.getByOffset("offsetA / 4u"):`${w.type.value}(${w.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||E?$.getByOffset("offsetB / 4u"):`${$.type.value}(${$.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
+          `}else _=y.setByOffset("global_idx",m(w.getByOffset("global_idx"),$.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let v=(I,S,E="")=>{let R=`aData[indexA${S}][componentA${S}]`,O=`bData[indexB${S}][componentB${S}]`;return`
+            let outputIndices${S} = ${y.offsetToIndices(`global_idx * 4u + ${S}u`)};
+            let offsetA${S} = ${w.broadcastedIndicesToOffset(`outputIndices${S}`,y)};
+            let offsetB${S} = ${$.broadcastedIndicesToOffset(`outputIndices${S}`,y)};
+            let indexA${S} = offsetA${S} / 4u;
+            let indexB${S} = offsetB${S} / 4u;
+            let componentA${S} = offsetA${S} % 4u;
+            let componentB${S} = offsetB${S} % 4u;
+            ${I}[${S}] = ${E}(${c(R,O)});
+          `};p===9?_=`
+            var data = vec4<u32>(0);
+            ${v("data",0,"u32")}
+            ${v("data",1,"u32")}
+            ${v("data",2,"u32")}
+            ${v("data",3,"u32")}
+            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:_=`
+            ${v("outputData[global_idx]",0)}
+            ${v("outputData[global_idx]",1)}
+            ${v("outputData[global_idx]",2)}
+            ${v("outputData[global_idx]",3)}
+          `}return`
+        ${e.registerUniform("vec_size","u32").declareVariables(w,$,y)}
+
+        ${f??""}
+
+        ${e.mainStart()}
+        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
+        ${_}
+      }`},zf=(e,t,n,r,i,s,a=n.dataType)=>{let o=n.dims.map(Number),l=r.dims.map(Number),d=!W.areEqual(o,l),p=o,f=W.size(o),c=!1,m=!1,y=[d];if(d){let w=Ei.calcShape(o,l,!1);if(!w)throw new Error("Can't perform binary op on the given tensors");p=w.slice(),f=W.size(p);let $=W.size(o)===1,_=W.size(l)===1,v=o.length>0&&o[o.length-1]%4===0,I=l.length>0&&l[l.length-1]%4===0;y.push($),y.push(_),y.push(v),y.push(I);let S=1;for(let E=1;E<p.length;E++){let R=o[o.length-E],O=l[l.length-E];if(R===O)S*=R;else break}S%4===0?(m=!0,c=!0):($||_||v||I)&&(c=!0)}else c=!0;return y.push(c),{name:e,shaderCache:{hint:t+y.map(w=>w.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:w=>kf(w,o,l,p,c,d,m,i,n.dataType,r.dataType,a,s),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(f/64/4)},programUniforms:[{type:12,data:Math.ceil(W.size(p)/4)},..._e(o,l,p)]})}},hn=(e,t,n,r,i,s)=>{e.compute(zf(t,i??"",e.inputs[0],e.inputs[1],n,r,s))},db=e=>{hn(e,"Add",(t,n)=>`${t}+${n}`)},cb=e=>{hn(e,"Div",(t,n)=>`${t}/${n}`)},pb=e=>{hn(e,"Equal",{scalar:(t,n)=>`u32(${t}==${n})`,vector:(t,n)=>`vec4<u32>(${t}==${n})`},void 0,void 0,9)},fb=e=>{hn(e,"Mul",(t,n)=>`${t}*${n}`)},hb=e=>{let t=V("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;hn(e,"Pow",{scalar:(n,r)=>`pow_custom(${n},${r})`,vector:(n,r)=>`pow_vector_custom(${n},${r})`},`
+    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
+      if (b == ${t}(0.0)) {
+        return ${t}(1.0);
+      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
+        return ${t}(pow(f32(a), f32(b))); // NaN
+      }
+      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
+    }
+    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
+      // TODO: implement vectorized pow
+      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
+    }
+      `)},mb=e=>{hn(e,"Sub",(t,n)=>`${t}-${n}`)},gb=e=>{hn(e,"Greater",{scalar:(t,n)=>`u32(${t}>${n})`,vector:(t,n)=>`vec4<u32>(${t}>${n})`},void 0,void 0,9)},yb=e=>{hn(e,"Less",{scalar:(t,n)=>`u32(${t}<${n})`,vector:(t,n)=>`vec4<u32>(${t}<${n})`},void 0,void 0,9)},bb=e=>{hn(e,"GreaterOrEqual",{scalar:(t,n)=>`u32(${t}>=${n})`,vector:(t,n)=>`vec4<u32>(${t}>=${n})`},void 0,void 0,9)},wb=e=>{hn(e,"LessOrEqual",{scalar:(t,n)=>`u32(${t}<=${n})`,vector:(t,n)=>`vec4<u32>(${t}<=${n})`},void 0,void 0,9)}}),Af,Mf,Rf,Of,_b,vb,I$=re(()=>{Te(),Ce(),pt(),Ee(),Af=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let n=0,r=e[n],i=r.dataType,s=r.dims.length;e.forEach((a,o)=>{if(o!==n){if(a.dataType!==i)throw new Error("input tensors should be one type");if(a.dims.length!==s)throw new Error("input tensors should have the same shape");a.dims.forEach((l,d)=>{if(d!==t&&l!==r.dims[d])throw new Error("non concat dimensions must match")})}})},Mf=(e,t)=>`
+  fn calculateInputIndex(index: u32) -> u32 {
+    let sizeInConcatAxis = array<u32, ${e}u>(${t});
+    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
+      if (index < sizeInConcatAxis[i]) {
+        return i;
+      }
+    }
+    return ${e}u;
+  }`,Rf=(e,t)=>{let n=e.length,r=[];for(let i=0;i<n;++i){let s=t.setByOffset("global_idx",e[i].getByIndices("indices"));n===1?r.push(s):i===0?r.push(`if (inputIndex == ${i}u) { ${s} }`):i===n-1?r.push(`else { ${s} }`):r.push(`else if (inputIndex == ${i}) { ${s} }`)}return r.join(`
+`)},Of=(e,t,n,r)=>{let i=W.size(n),s=new Array(e.length),a=new Array(e.length),o=0,l=[],d=[],p=[{type:12,data:i}];for(let w=0;w<e.length;++w)o+=e[w].dims[t],s[w]=o,d.push(e[w].dims.length),a[w]=V(`input${w}`,r,d[w]),l.push("rank"),p.push({type:12,data:s[w]});for(let w=0;w<e.length;++w)p.push(..._e(e[w].dims));p.push(..._e(n));let f=be("output",r,n.length),c=f.indicesGet("indices",t),m=Array.from(Array(s.length).keys()).map(w=>`uniforms.sizeInConcatAxis${w}`).join(","),y=w=>`
+
+  ${(()=>{w.registerUniform("outputSize","u32");for(let $=0;$<e.length;$++)w.registerUniform(`sizeInConcatAxis${$}`,"u32");return w.declareVariables(...a,f)})()}
+
+  ${Mf(s.length,m)}
+
+  ${w.mainStart()}
+    ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+
+    var indices = ${f.offsetToIndices("global_idx")};
+
+    let inputIndex = calculateInputIndex(${c});
+    if (inputIndex != 0u) {
+      let sizeInConcatAxis = array<u32, ${s.length}u>(${m});
+      ${c} -= sizeInConcatAxis[inputIndex - 1u];
+    }
+
+    ${Rf(a,f)}
+  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:n,dataType:r}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:p}),getShaderSource:y}},_b=(e,t)=>{let n=e.inputs,r=n[0].dims,i=W.normalizeAxis(t.axis,r.length);Af(n,i);let s=r.slice();s[i]=n.reduce((o,l)=>o+(l.dims.length>i?l.dims[i]:0),0);let a=n.filter(o=>W.size(o.dims)>0);e.compute(Of(a,i,s,n[0].dataType),{inputs:a})},vb=e=>Fe({axis:e.axis})}),Yr,Zr,Qr,td,ti=re(()=>{Te(),Ce(),Yr=(e,t,n="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${n}(uniforms.clip_min)), ${t}(${n}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${n}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
+              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
+        `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},Zr=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},Qr=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},td=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="HardSigmoid"){let[n,r]=(e==null?void 0:e.activation_params)||[.2,.5];return{activation:t,alpha:n,beta:r}}else if(t==="Clip"){let[n,r]=(e==null?void 0:e.activation_params)||[j0,K0];return{activation:t,clipMax:r,clipMin:n}}else if(t==="LeakyRelu"){let[n]=(e==null?void 0:e.activation_params)||[.01];return{activation:t,alpha:n}}return{activation:t}}}),It,xb,nd=re(()=>{It=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},xb=e=>`
+      ${e?"value = value + getBiasByOutputCoords(coords);":""}
+      `}),$b,C$=re(()=>{$b=e=>`
+fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
+  return dot(coords, vec4<i32>(
+      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
+}
+fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
+  return dot(coords, vec4<i32>(
+    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
+}
+`}),xs,rd,id=re(()=>{Te(),Ce(),Ee(),ti(),xs=(e,t,n,r,i)=>{let s=r-n;return`
+      ${Array.from({length:n}).map((a,o)=>`
+      if (${we(t.shape,o,t.rank)} != 1) {
+        ${t.indicesSet(e,o,we(i,o+s,r))}
+      } else {
+        ${t.indicesSet(e,o,0)}
+      }`).join("")}
+`},rd=(e,t,n,r,i=!1,s)=>{let a=e[0].dims,o=e[1].dims,l=a[a.length-2],d=o[o.length-1],p=a[a.length-1],f=ct(d),c=ct(p),m=ct(l),y=W.size(n)/f/m,w=e.length>2,$=r?r.slice(0,-2):n.slice(0,-2),_=[W.size($),l,d],v=[{type:12,data:y},{type:12,data:l},{type:12,data:d},{type:12,data:p}];Zr(t,v),v.push(..._e($,a,o)),w&&v.push(..._e(e[2].dims)),v.push(..._e(_));let I=S=>{let E=Zu("batch_dims",e[0].dataType,$.length),R=V("a",e[0].dataType,a.length,c),O=V("b",e[1].dataType,o.length,f),x=be("output",e[0].dataType,_.length,f),U=xt(x.type.tensor),q=Yr(t,x.type.value,U),J=[R,O],se="";if(w){let ue=i?f:1;J.push(V("bias",e[2].dataType,e[2].dims.length,ue)),se=`${i?`value += bias[col / ${ue}];`:`value += ${x.type.value}(bias[row + i]);`}`}let ce=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Qr(t,ce);let L=()=>{let ue=`var a_data: ${R.type.value};`;for(let ie=0;ie<c;ie++)ue+=`
+              let b_data${ie} = b[(b_offset + (k + ${ie}) * uniforms.N + col) / ${f}];`;for(let ie=0;ie<m;ie++){ue+=`a_data = a[(a_offset + (row + ${ie}) * uniforms.K + k) / ${c}];`;for(let te=0;te<c;te++)ue+=`
+            values[${ie}] = fma(${O.type.value}(a_data${c===1?"":`[${te}]`}), b_data${te}, values[${ie}]);
+`}return ue};return`
+  ${S.registerUniforms(ce).registerInternalVariables(E).declareVariables(...J,x)}
+  ${S.mainStart()}
+    ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    let col = (global_idx % (uniforms.N / ${f})) * ${f};
+    var index1 = global_idx / (uniforms.N / ${f});
+    let stride1 = uniforms.M / ${m};
+    let row = (index1 % stride1) * ${m};
+    let batch = index1 / stride1;
+
+    ${n.length===2?"":`let batch_indices = ${E.offsetToIndices("batch")};`}
+
+    var a_indices: ${R.type.indices};
+    ${xs("a_indices",R,R.rank-2,E.rank,"batch_indices")}
+    ${R.indicesSet("a_indices",R.rank-2,0)}
+    ${R.indicesSet("a_indices",R.rank-1,0)}
+    let a_offset = ${R.indicesToOffset("a_indices")};
+
+    var b_indices: ${O.type.indices};
+    ${xs("b_indices",O,O.rank-2,E.rank,"batch_indices")}
+    ${O.indicesSet("b_indices",O.rank-2,0)}
+    ${O.indicesSet("b_indices",O.rank-1,0)}
+    let b_offset = ${O.indicesToOffset("b_indices")};
+    var values: array<${x.type.value}, ${m}>;
+    for (var k: u32 = 0u; k < uniforms.K; k = k + ${c}) {
+      ${L()}
+    }
+    for (var i = 0u; i < ${m}u; i++) {
+      var value = values[i];
+      ${se}
+      ${q}
+      let cur_indices = ${x.type.indices}(batch, row + i, col);
+      let offset = ${x.indicesToOffset("cur_indices")};
+      ${x.setByOffset(`offset / ${f}`,"value")};
+    }
+  }
+  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${f};${c};${m};${i}`,inputDependencies:w?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:s?s(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:v}),getShaderSource:I}}}),Nf,Bf,pu,dl,Df,fu,Pf,qa,sd=re(()=>{Te(),Ce(),Ee(),ti(),id(),nd(),Nf=(e,t)=>e?`
+        mm_Asub[inputRow][inputCol] = mm_readA(batch,
+          kStart + inputRow,
+          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
+        `:`
+        mm_Asub[inputRow][inputCol] = mm_readA(batch,
+          globalRow + innerRow,
+          kStart / innerElementSize + inputCol${t?", batchIndices":""});
+        `,Bf=(e,t)=>e?`
+        let ACached0 = mm_Asub[k * innerElementSize][localRow];
+        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
+        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
+        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
+        for (var i = 0; i < rowPerThread; i = i + 1) {
+          acc[i] = BCached0 * ACached0[i] + acc[i];
+          acc[i] = BCached1 * ACached1[i] + acc[i];
+          acc[i] = BCached2 * ACached2[i] + acc[i];
+          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
+        }`:`
+        for (var i = 0; i < rowPerThread; i = i + 1) {
+          let ACached = mm_Asub[tileRow + i][k];
+          acc[i] = BCached0 * ACached.x + acc[i];
+          acc[i] = BCached1 * ACached.y + acc[i];
+          acc[i] = BCached2 * ACached.z + acc[i];
+          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
+        }`,pu=(e,t,n="f32",r,i=!1,s=32,a=!1,o=32)=>{let l=t[1]*e[1],d=t[0]*e[0],p=i?l:s,f=i?s:l,c=p/t[0],m=s/t[1];if(!((i&&c===4&&e[1]===4||!i&&(c===3||c===4))&&p%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4.
+      Otherwise, innerElementSize ${c} must be 3 or 4.
+  tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
+var<workgroup> mm_Asub: array<array<vec${c}<${n}>, ${p/c}>, ${f}>;
+var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${d/e[0]}>, ${s}>;
+
+const rowPerThread = ${e[1]};
+const colPerThread = ${e[0]};
+const innerElementSize = ${c};
+const tileInner = ${s};
+
+@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
+fn main(@builtin(local_invocation_id) localId : vec3<u32>,
+        @builtin(global_invocation_id) globalId : vec3<u32>,
+        @builtin(workgroup_id) workgroupId : vec3<u32>) {
+  let localRow = i32(localId.y);
+  let tileRow = localRow * rowPerThread;
+  let tileCol = i32(localId.x);
+
+  let globalRow =i32(globalId.y) * rowPerThread;
+  let globalCol = i32(globalId.x);
+  let batch = ${a?"0":"i32(globalId.z)"};
+  ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}
+  let globalRowStart = i32(workgroupId.y) * ${l};
+
+  let num_tiles = ${a?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
+  var kStart = ${a?`i32(globalId.z) * ${o}`:"0"};
+
+  var acc: array<vec4<${n}>, rowPerThread>;
+
+  // Loop over shared dimension.
+  let tileRowB = localRow * ${m};
+  for (var t = 0; t < num_tiles; t = t + 1) {
+      // Load one tile of A into local memory.
+      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+          let inputRow = tileRow + innerRow;
+          let inputCol = tileCol;
+          ${Nf(i,r)}
+      }
+
+      // Load one tile of B into local memory.
+      for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {
+          let inputRow = tileRowB + innerRow;
+          let inputCol = tileCol;
+          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?", batchIndices":""});
+      }
+      kStart = kStart + tileInner;
+      workgroupBarrier();
+
+      // Compute acc values for a single thread.
+      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
+          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
+          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
+          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
+          ${c===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}
+
+          ${Bf(i,c)}
+      }
+
+      workgroupBarrier();
+  }
+
+  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
+  }
+}`},dl=(e,t)=>e?`
+            mm_Asub[inputRow][inputCol] = mm_readA(batch,
+              kStart + inputRow,
+              globalRowStart + inputCol${t?", batchIndices":""});
+            `:`
+            mm_Asub[inputRow][inputCol] = mm_readA(batch,
+              globalRowStart + inputRow,
+              kStart + inputCol${t?", batchIndices":""});
+            `,Df=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fu=(e,t,n="f32",r,i=!1,s=32,a=!1,o=32,l=!1)=>{let d=e[1]*t[1],p=e[0]*t[0],f=i?d:s,c=i?s:d;if(!(c%t[1]===0&&f%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${c} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${f} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let m=c/t[1],y=f/t[0],w=s/t[1],$=l?`
+    let localRow = i32(localId.y);
+    let localCol = i32(localId.x);
+    let globalRowStart = i32(workgroupId.y) * ${d};
+    let globalColStart = i32(workgroupId.x) * ${p};
+
+    // Loop over shared dimension.
+    for (var t = 0; t < num_tiles; t = t + 1) {
+      // Load one tile of A into local memory.
+      for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${t[1]}) {
+        for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${t[0]}) {
+          ${dl(i,r)}
+        }
+      }
+      // Load one tile of B into local memory.
+      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {
+            for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) {
+          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
+            kStart + inputRow,
+            globalColStart + inputCol${r?", batchIndices":""});
+        }
+      }
+      kStart = kStart + tileInner;
+      workgroupBarrier();
+
+      // Compute acc values for a single thread.
+      var BCached : array<${n}, colPerThread>;
+      for (var k = 0; k < tileInner; k = k + 1) {
+        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
+          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];
+        }
+        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}
+          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
+            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
+                ACached * BCached[innerCol];
+          }
+        }
+      }
+      workgroupBarrier();
+    }
+    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+      let gRow = globalRowStart + localRow + innerRow * ${t[1]};
+      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
+        let gCol = globalColStart + localCol + innerCol * ${t[0]};
+        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
+      }
+    }
+    `:`
+let tileRow = i32(localId.y) * rowPerThread;
+let tileCol = i32(localId.x) * colPerThread;
+
+let globalRow = i32(globalId.y) * rowPerThread;
+let globalCol = i32(globalId.x) * colPerThread;
+let globalRowStart = i32(workgroupId.y) * ${d};
+
+let tileRowA = i32(localId.y) * ${m};
+let tileColA = i32(localId.x) * ${y};
+let tileRowB = i32(localId.y) * ${w};
+// Loop over shared dimension.
+for (var t = 0; t < num_tiles; t = t + 1) {
+  // Load one tile of A into local memory.
+  for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {
+    for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) {
+      let inputRow = tileRowA + innerRow;
+      let inputCol = tileColA + innerCol;
+      ${dl(i,r)}
+    }
+  }
+
+  // Load one tile of B into local memory.
+  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {
+    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
+      let inputRow = tileRowB + innerRow;
+      let inputCol = tileCol + innerCol;
+      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
+        kStart + inputRow,
+        globalCol + innerCol${r?", batchIndices":""});
+    }
+  }
+  kStart = kStart + tileInner;
+  workgroupBarrier();
+
+  // Compute acc values for a single thread.
+  var BCached : array<${n}, colPerThread>;
+  for (var k = 0; k < tileInner; k = k + 1) {
+    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
+      BCached[inner] = mm_Bsub[k][tileCol + inner];
+    }
+
+    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+      ${Df(i)}
+      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
+        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
+      }
+    }
+  }
+
+  workgroupBarrier();
+}
+
+for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
+  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
+    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
+        acc[innerRow][innerCol]);
+  }
+}
+`;return`
+  var<workgroup> mm_Asub : array<array<${n}, ${f}>, ${c}>;
+  var<workgroup> mm_Bsub : array<array<${n}, ${p}>, ${s}>;
+  const rowPerThread = ${e[1]};
+  const colPerThread = ${e[0]};
+  const tileInner = ${s};
+
+@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
+fn main(@builtin(local_invocation_id) localId : vec3<u32>,
+        @builtin(global_invocation_id) globalId : vec3<u32>,
+        @builtin(workgroup_id) workgroupId : vec3<u32>) {
+    let batch = ${a?"0":"i32(globalId.z)"};
+    ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}
+    let num_tiles = ${a?`${Math.ceil(o/s)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
+    var kStart = ${a?`i32(globalId.z) * ${o}`:"0"};
+
+    var acc : array<array<${n}, colPerThread>, rowPerThread>;
+    ${$}
+  }
+`},Pf=(e,t,n,r,i=!1)=>{let[s,a,o,l]=r,d=xt(r[0].type.tensor);return`
+    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${s.type.indices}) -> ${It(e,d)} {
+      var value = ${It(e,d)}(0.0);
+      let col = colIn * ${e};
+      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
+      {
+        var aIndices: ${a.type.indices};
+        ${xs("aIndices",a,a.rank-2,s.rank,"batchIndices")}
+        ${a.indicesSet("aIndices",a.rank-2,"u32(row)")}
+        ${a.indicesSet("aIndices",a.rank-1,"u32(colIn)")}
+        value = ${a.getByIndices("aIndices")};
+      }
+      return value;
+    }
+
+    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${s.type.indices}) -> ${It(e,d)} {
+      var value = ${It(e,d)}(0.0);
+      let col = colIn * ${e};
+      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
+      {
+        var bIndices: ${o.type.indices};
+        ${xs("bIndices",o,o.rank-2,s.rank,"batchIndices")}
+        ${o.indicesSet("bIndices",o.rank-2,"u32(row)")}
+        ${o.indicesSet("bIndices",o.rank-1,"u32(colIn)")}
+        value = ${o.getByIndices("bIndices")};
+      }
+      return value;
+    }
+
+    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${It(e,d)}) {
+      let col = colIn * ${e};
+      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
+        var value = valueIn;
+        let coords = vec3<i32>(batch, row, colIn);
+        ${t?`value = value + ${i?"bias[colIn]":`${It(e,d)}(bias[row])`};`:""}
+        ${n}
+        ${l.setByIndices("vec3<u32>(coords)","value")}
+      }
+    }
+    `},qa=(e,t,n,r,i=!1,s)=>{let a=e[0].dims,o=e[1].dims,l=a.slice(0,-2),d=o.slice(0,-2),p=r?r.slice(0,-2):n.slice(0,-2),f=W.size(p),c=a[a.length-2],m=a[a.length-1],y=o[o.length-1],w=m%4===0&&y%4===0,$=c<=8?[4,1,1]:[4,4,1],_=[8,8,1],v=[Math.ceil(y/_[0]/$[0]),Math.ceil(c/_[1]/$[1]),Math.ceil(f/_[2]/$[2])],I=w?4:1,S=[...l,c,m/I],E=S.length,R=[...d,m,y/I],O=R.length,x=[f,c,y/I],U=[{type:6,data:c},{type:6,data:y},{type:6,data:m}];Zr(t,U),U.push(..._e(p,S,R));let q=["rank","rank"],J=e.length>2;J&&(U.push(..._e(e[2].dims)),q.push("rank")),U.push(..._e(x));let se=ce=>{let L=p.length,ue=Zu("batchDims",e[0].dataType,L,1),ie=xt(e[0].dataType),te=V("a",e[0].dataType,E,I),he=V("b",e[1].dataType,O,I),Q=be("result",e[0].dataType,x.length,I),ve=[te,he];if(J){let Ue=i?I:1;ve.push(V("bias",e[2].dataType,e[2].dims.length,Ue))}let Y=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Qr(t,Y);let P=xt(Q.type.tensor),ee=Yr(t,Q.type.value,P),ne=Pf(I,J,ee,[ue,te,he,Q],i);return`
+  ${ce.registerUniforms(Y).registerInternalVariables(ue).declareVariables(...ve,Q)}
+  ${ne}
+  ${w?pu($,_,ie,ue):fu($,_,ie,ue)}
+                   `};return{name:"MatMul",shaderCache:{hint:`${$};${t.activation};${w};${i}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:s?s(n):n,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:U}),getShaderSource:se}}}),Uf,Sb,E$=re(()=>{Te(),nr(),Ee(),ti(),nd(),C$(),sd(),Uf=(e,t,n,r,i=!1,s,a=4,o=4,l=4,d="f32")=>{let p=U=>{switch(U){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${U} is not supported.`)}},f=U=>{switch(U){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${U} is not supported.`)}},c=e?`
+    let coord = vec4<i32>(batch, xRow, xCol, xCh);
+    `:`
+    let coord = vec4<i32>(batch, xCh, xRow, xCol);
+    `,m=e?`
+    let coords = vec4<i32>(
+      batch,
+      row / outWidth,
+      row % outWidth,
+      col);
+    `:`
+    let coords = vec4<i32>(
+      batch,
+      row,
+      col / outWidth,
+      col % outWidth);
+    `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",w=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",$=e?"row":"col",_=e?"col":"row",v=`
+    let inChannels = i32(uniforms.w_shape[2]);
+    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
+    let outRow = ${$} / outWidth;
+    let outCol = ${$} % outWidth;
+
+    let WRow = ${_} / (i32(uniforms.w_shape[1]) * inChannels);
+    let WCol = ${_} / inChannels % i32(uniforms.w_shape[1]);
+    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    let xCh = ${_} % inChannels;
+    var resData = ${It(a,d)}(0.0);
+    // The bounds checking is always needed since we use it to pad zero for
+    // the 'same' padding type.
+    if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${w}) {
+      ${c}
+      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
+      ${p(a)}
+    }
+    return resData;`,I=e?t&&r?`
+    let col = colIn * ${a};
+    ${v}`:`
+    let col = colIn * ${a};
+    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
+      ${v}
+    }
+    return ${It(a,d)}(0.0);`:r&&n?`
+    let col = colIn * ${a};
+    ${v}`:`
+    let col = colIn * ${a};
+    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
+      ${v}
+    }
+    return ${It(a,d)}(0.0);`,S=e?r&&n?f(o):`
+    let col = colIn * ${o};
+    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
+      ${f(o)}
+    }
+    return ${It(o,d)}(0.0);`:`
+    let col = colIn * ${o};
+    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
+      ${f(o)}
+    }
+    return ${It(o,d)}(0.0);`,E=It(l,d),R=It(e?a:o,d),O=It(e?o:a,d),x=Yr(s,E,d);return`
+    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {
+      ${e?I:S}
+    }
+
+    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${O} {
+      ${e?S:I}
+    }
+
+    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${E}) {
+      let col = colIn * ${l};
+      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
+      {
+      var value = valueIn;
+      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
+      ${m}
+      ${xb(i)}
+      ${x}
+      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
+      }
+    }`},Sb=(e,t,n,r,i,s,a,o,l)=>{let d=t.format==="NHWC",p=d?e[0].dims[3]:e[0].dims[1],f=n[0],c=d?n[2]:n[3],m=d?n[1]:n[2],y=d?n[3]:n[1],w=d&&(p%4===0||p%3===0)&&y%4===0,$=d?y:c*m,_=d?c*m:y,v=[8,8,1],I=r<=8?[4,1,1]:[4,4,1],S=[Math.ceil($/v[0]/I[0]),Math.ceil(_/v[1]/I[1]),Math.ceil(f/v[2]/I[2])];Pe("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${S}`);let E=w?d&&p%4!==0?3:4:1,R=v[1]*I[1],O=v[0]*I[0],x=Math.max(v[0]*E,v[1]),U=r%R===0,q=i%O===0,J=s%x===0,se=w?[E,4,4]:[1,1,1],ce=[{type:6,data:r},{type:6,data:i},{type:6,data:s},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];Zr(t,ce),ce.push(..._e(e[0].dims,e[1].dims));let L=["rank","rank"];a&&(ce.push(..._e(e[2].dims)),L.push("rank")),ce.push(..._e(n));let ue=ie=>{let te=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Qr(t,te);let he=w?4:1,Q=xt(e[0].dataType),ve=`
+      fn setOutputAtIndex(flatIndex : i32, value : ${w?`vec4<${Q}>`:Q}) {
+        result[flatIndex] = ${w?`vec4<${Q}>`:Q}(value);
+      }
+      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${w?`vec4<${Q}>`:Q}) {
+        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
+        setOutputAtIndex(flatIndex ${w?"/ 4":""}, value);
+      }`,Y=V("x",e[0].dataType,e[0].dims.length,E===3?1:E),P=V("w",e[1].dataType,e[1].dims.length,he),ee=[Y,P],ne=be("result",e[0].dataType,n.length,he);if(a){let Ue=V("bias",e[2].dataType,e[2].dims.length,he);ee.push(Ue),ve+=`
+        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${w?`vec4<${Q}>`:Q} {
+          return bias[coords.${d?"w":"y"}${w?"/ 4":""}];
+        }`}return`
+        ${$b("uniforms.result_strides")}
+        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
+        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
+        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
+        ${ie.registerUniforms(te).declareVariables(...ee,ne)}
+        ${ve}
+        ${Uf(d,U,q,J,a,t,se[0],se[1],se[2],Q)}
+        ${w?pu(I,v,Q,void 0,!d,x):fu(I,v,Q,void 0,!d,x,!1,void 0,o)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${E};${w};${U};${q};${J};${R};${O};${x}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:l?l(n):n,dataType:e[0].dataType}],dispatchGroup:{x:S[0],y:S[1],z:S[2]},programUniforms:ce}),getShaderSource:ue}}}),Lf,cl,is,Wf,pl,qf,Tb,Ib,k$=re(()=>{Te(),nr(),Ce(),Ee(),ti(),nd(),Lf=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t},cl=e=>typeof e=="number"?[e,e,e]:e,is=(e,t)=>t<=1?e:e+(e-1)*(t-1),Wf=(e,t,n,r=1)=>{let i=is(t,r);return Math.floor((e[0]*(n-1)-n+i)/2)},pl=(e,t,n,r,i)=>{i==null&&(i=Wf(e,t[0],r[0]));let s=[0,0,0,n];for(let a=0;a<3;a++)e[a]+2*i>=t[a]&&(s[a]=Math.trunc((e[a]-t[a]+2*i)/r[a]+1));return s},qf=(e,t,n,r,i,s,a,o,l,d)=>{let p,f,c,m;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let y=pl([t,n,r,1],[o,l,d],1,[i,s,a],e);f=y[0],c=y[1],m=y[2]}else if(Array.isArray(e)){if(!e.every((w,$,_)=>w===_[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let y=pl([t,n,r,1],[o,l,d],1,[i,s,a],e[0]);f=y[0],c=y[1],m=y[2]}else if(e==="SAME_UPPER"){f=Math.ceil(t/i),c=Math.ceil(n/s),m=Math.ceil(r/a);let y=(f-1)*i+o-t,w=(c-1)*s+l-n,$=(m-1)*a+d-r,_=Math.floor(y/2),v=y-_,I=Math.floor(w/2),S=w-I,E=Math.floor($/2),R=$-E;p={top:I,bottom:S,left:E,right:R,front:_,back:v}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:f,outHeight:c,outWidth:m}},Tb=(e,t,n,r,i,s=!1,a="channelsLast")=>{let o,l,d,p,f;if(a==="channelsLast")[o,l,d,p,f]=e;else if(a==="channelsFirst")[o,f,l,d,p]=e;else throw new Error(`Unknown dataFormat ${a}`);let[c,,m,y,w]=t,[$,_,v]=cl(n),[I,S,E]=cl(r),R=is(m,I),O=is(y,S),x=is(w,E),{padInfo:U,outDepth:q,outHeight:J,outWidth:se}=qf(i,l,d,p,$,_,v,R,O,x),ce=s?c*f:c,L=[0,0,0,0,0];return a==="channelsFirst"?L=[o,ce,q,J,se]:a==="channelsLast"&&(L=[o,q,J,se,ce]),{batchSize:o,dataFormat:a,inDepth:l,inHeight:d,inWidth:p,inChannels:f,outDepth:q,outHeight:J,outWidth:se,outChannels:ce,padInfo:U,strideDepth:$,strideHeight:_,strideWidth:v,filterDepth:m,filterHeight:y,filterWidth:w,effectiveFilterDepth:R,effectiveFilterHeight:O,effectiveFilterWidth:x,dilationDepth:I,dilationHeight:S,dilationWidth:E,inShape:e,outShape:L,filterShape:t}},Ib=(e,t,n,r,i,s)=>{let a=s==="channelsLast";a?e[0].dims[3]:e[0].dims[1];let o=[64,1,1],l={x:n.map(($,_)=>_)},d=[Math.ceil(Lf(l.x.map($=>n[$]))/o[0]),1,1];Pe("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${d}`);let p=1,f=W.size(n),c=[{type:12,data:f},{type:12,data:r},{type:12,data:i},{type:12,data:t.strides},{type:12,data:t.dilations}];Zr(t,c),c.push(..._e(e[0].dims,e[1].dims));let m=["rank","rank"],y=e.length===3;y&&(c.push(..._e(e[2].dims)),m.push("rank")),c.push(..._e(n));let w=$=>{let _=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:r.length},{name:"pads",type:"u32",length:i.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];Qr(t,_);let v=1,I=xt(e[0].dataType),S=V("x",e[0].dataType,e[0].dims.length,p),E=V("W",e[1].dataType,e[1].dims.length,v),R=[S,E],O=be("result",e[0].dataType,n.length,v),x="";if(y){let J=V("bias",e[2].dataType,e[2].dims.length,v);R.push(J),x+=`
+        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${I} {
+          return bias[${a?we("coords",4,5):we("coords",1,5)}];
+        }`}let U=It(p,I),q=Yr(t,U,I);return`
+            ${x}
+            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
+              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
+              return ${S.getByIndices("aIndices")};
+            }
+            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
+              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
+              return ${E.getByIndices("aIndices")};
+            }
+          ${$.registerUniforms(_).declareVariables(...R,O)}
+          ${$.mainStart()}
+          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+              let coords = ${O.offsetToIndices("global_idx")};
+              let batch = ${we("coords",0,S.rank)};
+              let d2 = ${a?we("coords",S.rank-1,S.rank):we("coords",1,S.rank)};
+              let xFRCCorner = vec3<u32>(${a?we("coords",1,S.rank):we("coords",2,S.rank)},
+              ${a?we("coords",2,S.rank):we("coords",3,S.rank)},
+              ${a?we("coords",3,S.rank):we("coords",4,S.rank)}) * uniforms.strides - uniforms.pads;
+              let xFCorner = xFRCCorner.x;
+              let xRCorner = xFRCCorner.y;
+              let xCCorner = xFRCCorner.z;
+              let xShapeY = ${a?we("uniforms.x_shape",1,S.rank):we("uniforms.x_shape",2,S.rank)};
+              let xShapeZ = ${a?we("uniforms.x_shape",2,S.rank):we("uniforms.x_shape",3,S.rank)};
+              let xShapeW = ${a?we("uniforms.x_shape",3,S.rank):we("uniforms.x_shape",4,S.rank)};
+              let xShapeU = ${a?we("uniforms.x_shape",4,S.rank):we("uniforms.x_shape",1,S.rank)};
+              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
+              let inputDepthVec4Remainder = xShapeU % 4;
+
+              var value = 0.0;
+              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
+                let xF = xFCorner + wF * uniforms.dilations[0];
+                if (xF < 0 || xF >= xShapeY) {
+                  continue;
+                }
+
+                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
+                  let xR = xRCorner + wR * uniforms.dilations[1];
+                  if (xR < 0 || xR >= xShapeZ) {
+                    continue;
+                  }
+
+                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
+                    let xC = xCCorner + wC * uniforms.dilations[2];
+                    if (xC < 0 || xC >= xShapeW) {
+                      continue;
+                    }
+
+                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
+                      ${a?`let xValues = vec4<f32>(
+                               getX(batch, xF, xR, xC, d1),
+                               getX(batch, xF, xR, xC, d1 + 1),
+                               getX(batch, xF, xR, xC, d1 + 2),
+                               getX(batch, xF, xR, xC, d1 + 3));
+                            `:`let xValues = vec4<f32>(
+                               getX(batch, d1, xF, xR, xC),
+                               getX(batch, d1 + 1, xF, xR, xC),
+                               getX(batch, d1 + 2, xF, xR, xC),
+                               getX(batch, d1 + 3, xF, xR, xC));
+                            `}
+                            let wValues = vec4<f32>(
+                              getW(d2, d1, wF, wR, wC),
+                              getW(d2, d1 + 1, wF, wR, wC),
+                              getW(d2, d1 + 2, wF, wR, wC),
+                              getW(d2, d1 + 3, wF, wR, wC));
+                      value += dot(xValues, wValues);
+                    }
+                    if (inputDepthVec4Remainder == 1) {
+                        ${a?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
+                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
+                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
+                    } else if (inputDepthVec4Remainder == 2) {
+                      ${a?`let xValues = vec2<f32>(
+                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
+                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
+                      `:`let xValues = vec2<f32>(
+                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
+                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
+                    `}
+                    let wValues = vec2<f32>(
+                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
+                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
+                      value += dot(xValues, wValues);
+                    } else if (inputDepthVec4Remainder == 3) {
+                      ${a?`let xValues = vec3<f32>(
+                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
+                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
+                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
+                      `:`let xValues = vec3<f32>(
+                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
+                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
+                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
+                    `}
+                    let wValues = vec3<f32>(
+                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
+                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
+                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
+                      value += dot(xValues, wValues);
+                    }
+                  }
+                }
+              }
+              ${y?"value = value + getBiasByOutputCoords(coords)":""};
+              ${q}
+              result[global_idx] = f32(value);
+          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${a};${p};${y}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:d[0],y:d[1],z:d[2]},programUniforms:c}),getShaderSource:w}}}),Cb,Eb,z$=re(()=>{Te(),Ce(),Ee(),ti(),Cb=(e,t,n,r)=>{let i=e.length>2,s=i?"value += b[output_channel];":"",a=e[0].dims,o=e[1].dims,l=t.format==="NHWC",d=l?n[3]:n[1],p=d/t.group,f=l&&p>=4?ct(d):1,c=W.size(n)/f,m=[{type:12,data:c},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:p}];Zr(t,m),m.push(..._e(a,[o[0],o[1],o[2],o[3]/f]));let y=i?["rank","rank","rank"]:["rank","rank"];m.push(..._e([n[0],n[1],n[2],n[3]/f]));let w=$=>{let _=be("output",e[0].dataType,n.length,f),v=xt(_.type.tensor),I=Yr(t,_.type.value,v),S=V("x",e[0].dataType,a.length),E=V("w",e[1].dataType,o.length,f),R=[S,E];i&&R.push(V("b",e[2].dataType,e[2].dims,f));let O=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Qr(t,O);let x=l?`
+      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
+        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
+
+        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
+          continue;
+        }
+
+        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
+          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
+          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
+            continue;
+          }
+
+          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
+            let input_channel = in_channel_offset + wInChannel;
+            let xVal = ${S.get("batch","xHeight","xWidth","input_channel")};
+            let wVal = ${E.get("wHeight","wWidth","wInChannel","output_channel")};
+            value += xVal * wVal;
+          }
+        }
+      }
+      `:`
+      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
+        let input_channel = in_channel_offset + wInChannel;
+        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
+          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];
+
+          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
+            continue;
+          }
+
+          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
+            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
+            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
+              continue;
+            }
+
+            let xVal = ${S.get("batch","input_channel","xHeight","xWidth")};
+            let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")};
+            value += xVal * wVal;
+          }
+        }
+      }
+      `;return`
+  ${$.registerUniforms(O).declareVariables(...R,_)}
+
+  ${$.mainStart()}
+    ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+
+    let outputIndices = ${_.offsetToIndices("global_idx")};
+    let batch: u32 = outputIndices[0];
+    let output_channel: u32 = outputIndices[${l?3:1}];
+    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * uniforms.strides - uniforms.pads;
+    let group_id: u32 = output_channel * ${f} / uniforms.output_channels_per_group;
+    var in_channel_offset = group_id * uniforms.w_shape[${l?2:1}];
+
+    var value: ${_.type.value} = ${_.type.value}(0);
+    ${x}
+    ${s}
+    ${I}
+    ${_.setByOffset("global_idx","value")}
+  }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${f}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:r?r(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:m}),getShaderSource:w}},Eb=(e,t,n,r)=>{let i=e.length>2,s=ct(n[3]),a=ct(n[2]),o=W.size(n)/s/a,l=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/s],d=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/s],p=[n[0],n[1],n[2],n[3]/s],f=[{type:12,data:o},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];Zr(t,f),f.push(..._e(l,d,p));let c=(a-1)*t.strides[1]+d[1],m=y=>{let w=be("output",e[0].dataType,p.length,s),$=xt(w.type.tensor),_=Yr(t,w.type.value,$),v=V("x",e[0].dataType,l.length,s),I=V("w",e[1].dataType,d.length,s),S=[v,I];i&&S.push(V("b",e[2].dataType,e[2].dims,s));let E=i?"value += b[output_channel];":"",R=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Qr(t,R),`
+  ${y.registerUniforms(R).declareVariables(...S,w)}
+  ${y.mainStart()}
+    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    let width0 = uniforms.output_shape[3];
+    let output_channel = global_idx % width0;
+    var index1 = global_idx / width0;
+    let width1 = uniforms.output_shape[2] / ${a}u;
+    let col = (index1 % width1) * ${a}u;
+    index1 = index1 / width1;
+    let row = index1 % uniforms.output_shape[1];
+    let batch = index1 / uniforms.output_shape[1];
+
+    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;
+
+    var x_vals: array<${v.type.value}, ${c}>;
+    var values: array<${w.type.value}, ${a}>;
+    let input_channel = output_channel;
+    // Use constant instead of uniform can give better performance for w's height/width.
+    for (var w_height: u32 = 0u; w_height < ${d[0]}; w_height++) {
+      let x_height = x_corner.x + i32(w_height);
+      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
+        for (var i = 0; i < ${c}; i++) {
+          let x_width = x_corner.y + i;
+          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
+            x_vals[i] = ${v.get("batch","u32(x_height)","u32(x_width)","input_channel")};
+          } else {
+            x_vals[i] = ${v.type.value}(0);
+          }
+        }
+        for (var w_width: u32 = 0u; w_width < ${d[1]}; w_width++) {
+          let w_val = ${I.get("w_height","w_width","0","output_channel")};
+          for (var i = 0u; i < ${a}u; i++) {
+            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
+          }
+        }
+      }
+    }
+
+    for (var i = 0u; i < ${a}u; i++) {
+      var value = values[i];
+      ${E}
+      ${_}
+      ${w.set("batch","row","col + i","output_channel","value")};
+    }
+  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${s};${a};${c};${d[0]};${d[1]}`,inputDependencies:i?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r?r(n):n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:f}),getShaderSource:m}}}),Gf,fa,Vf,ha,hu,fl,Ff,Hf,mu,A$=re(()=>{Ce(),E$(),k$(),sd(),z$(),ti(),id(),_r(),Gf=(e,t,n,r,i,s)=>{let a=e[0],o=e.slice(s?1:2,s?3:4),l=o.length,d=t[0],p=t.slice(2).map((c,m)=>c+(c-1)*(n[m]-1)),f=o.map((c,m)=>c+r[m]+r[m+l]).map((c,m)=>Math.floor((c-p[m]+i[m])/i[m]));return f.splice(0,0,a),f.splice(s?3:1,0,d),f},fa=[2,3,1,0],Vf=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let n=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],r=e[1].dims[1]*t.group;if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ha=(e,t)=>{let n=e.kernelShape.slice();n.length<t[1].dims.length-2&&n.push(...Array(t[1].dims.length-2-n.length).fill(0));for(let s=2;s<t[1].dims.length;++s)n[s-2]===0&&(n[s-2]=t[1].dims[s]);let r=e.pads.slice();La.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,n,r,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:n,pads:r}),i},hu=e=>{let t=td(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,s=e.group,a=e.kernel_shape,o=e.pads,l=e.strides,d=e.w_is_const();return{autoPad:r,format:n,dilations:i,group:s,kernelShape:a,pads:o,strides:l,wIsConst:d,...t,cacheKey:`${e.format};${t.activation};`}},fl=(e,t,n,r)=>{let i=n.format==="NHWC",s=Gf(t[0].dims,t[1].dims,n.dilations,n.pads,n.strides,i);if(n.group!==1){let R=[t[0]];if(i){let O=e.kernelCustomData.wT??e.compute(en(t[1],fa),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=O),R.push(O)}else R.push(t[1]);t.length===3&&R.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&i&&t[1].dims[0]===n.group&&t[1].dims[1]===1&&n.dilations[0]===1&&n.dilations[1]===1?e.compute(Eb(R,n,s,r),{inputs:R}):e.compute(Cb(R,n,s,r),{inputs:R});return}let a=t.length===3,o=t[0].dims[i?1:2],l=t[0].dims[i?2:3],d=t[0].dims[i?3:1],p=t[1].dims[2],f=t[1].dims[3],c=s[i?1:2],m=s[i?2:3],y=s[i?3:1],w=i&&p===o&&f===l&&n.pads[0]===0&&n.pads[1]===0;if(w||p===1&&f===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let R=s[0],O,x,U,q=[];if(i){let ce=e.kernelCustomData.wT??e.compute(en(t[1],fa),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ce),w){let L=o*l*d;O=t[0].reshape([1,R,L]),x=ce.reshape([1,L,y]),U=[1,R,y]}else O=t[0].reshape([R,o*l,d]),x=ce.reshape([1,d,y]),U=[R,c*m,y];q.push(O),q.push(x)}else O=t[0].reshape([R,d,o*l]),x=t[1].reshape([1,y,d]),U=[R,y,c*m],q.push(x),q.push(O);a&&q.push(t[2]);let J=U[2],se=q[0].dims[q[0].dims.length-1];J<8&&se<8?e.compute(rd(q,n,s,U,i,r),{inputs:q}):e.compute(qa(q,n,s,U,i,r),{inputs:q});return}let $=!0,_=e.kernelCustomData.wT??e.compute(en(t[1],fa),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=_);let v=[t[0],_];a&&v.push(t[2]);let I=i?c*m:y,S=i?y:c*m,E=p*f*d;e.compute(Sb(v,n,s,I,S,E,a,$,r),{inputs:v})},Ff=(e,t)=>{let n=t.format==="NHWC",r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&r.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),a=[1].concat(t.dilations),o=[1].concat(t.kernelShape),l=ha({...t,pads:i,strides:s,dilations:a,kernelShape:o},r);fl(e,r,l,d=>n?[d[0],d[2],d[3]]:[d[0],d[1],d[3]])},Hf=(e,t,n)=>{let r=n.format==="NHWC"?"channelsLast":"channelsFirst",i=ha(n,t),s=n.autoPad==="NOTSET"?n.pads:n.autoPad,a=Tb(t[0].dims,t[1].dims,n.strides,n.dilations,s,!1,r);e.compute(Ib(t,i,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],r))},mu=(e,t)=>{if(Vf(e.inputs,t),e.inputs[0].dims.length===3)Ff(e,t);else if(e.inputs[0].dims.length===5)Hf(e,e.inputs,t);else{let n=ha(t,e.inputs);fl(e,e.inputs,n)}}}),kb,M$=re(()=>{Te(),nr(),Ce(),Ee(),kb=(e,t,n)=>{let r=e.length>2,i=t.outputShape,s=t.format==="NHWC",a=t.group,o=e[1].dims,l=o[2]/a,d=o[3],p=s?ct(l):1,f=s&&d===1&&l>=4,c=f?Math.floor(l/4)*4:Math.floor(l/p)*p,m=l-c,y=s?ct(d):1,w=s?d===1?p:y:1,$=W.size(i)/y,_=[Math.ceil($/64),1,1];Pe("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${_}`);let v=["rank","rank"],I=[t.strides[0],t.strides[1]],S=[t.kernelShape[s?1:2],t.kernelShape[s?2:3]],E=[t.dilations[0],t.dilations[1]],R=[S[0]+(t.dilations[0]<=1?0:(t.kernelShape[s?1:2]-1)*(t.dilations[0]-1)),S[1]+(t.dilations[1]<=1?0:(t.kernelShape[s?2:3]-1)*(t.dilations[1]-1))],O=[R[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),R[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],x=[{type:12,data:$},{type:12,data:I},{type:12,data:S},{type:12,data:E},{type:12,data:R},{type:6,data:O},{type:12,data:c},{type:12,data:l},{type:12,data:d},..._e(e[0].dims,e[1].dims)];r&&(x.push(..._e(e[2].dims)),v.push("rank")),x.push(..._e(i));let U=q=>{let J=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:I.length},{name:"filter_dims",type:"u32",length:S.length},{name:"dilations",type:"u32",length:S.length},{name:"effective_filter_dims",type:"u32",length:R.length},{name:"pads",type:"i32",length:O.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],se=xt(e[0].dataType),ce=s?1:2,L=s?2:3,ue=s?3:1,ie=V("W",e[1].dataType,e[1].dims.length,w),te=V("Dy",e[0].dataType,e[0].dims.length,p),he=[te,ie];r&&he.push(V("bias",e[2].dataType,[i[ue]].length,y));let Q=be("result",e[0].dataType,i.length,y),ve=()=>{let ee="";if(f)p===4?ee+=`
+        let xValue = ${te.getByOffset("x_offset")};
+        let wValue = ${ie.getByOffset("w_offset")};
+        dotProd = dotProd + dot(xValue, wValue);
+        x_offset += 1u;
+        w_offset += 1u;`:p===2?ee+=`
+          dotProd = dotProd + dot(vec4<${se}>(${te.getByOffset("x_offset")}, ${te.getByOffset("x_offset + 1u")}), vec4<${se}>(${ie.getByOffset("w_offset")}, ${ie.getByOffset("w_offset + 1u")}));
+          x_offset += 2u;
+          w_offset += 2u;`:p===1&&(ee+=`
+          dotProd = dotProd + dot(vec4<${se}>(${te.getByOffset("x_offset")}, ${te.getByOffset("x_offset + 1u")}, ${te.getByOffset("x_offset + 2u")}, ${te.getByOffset("x_offset + 3u")}), vec4<${se}>(${ie.getByOffset("w_offset")}, ${ie.getByOffset("w_offset + 1u")}, ${ie.getByOffset("w_offset + 2u")}, ${ie.getByOffset("w_offset + 3u")}));
+          x_offset += 4u;
+          w_offset += 4u;`);else if(ee+=`
+                  let xValue = ${s?te.getByOffset(`${te.indicesToOffset(`${te.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):te.get("batch","inputChannel","idyR","idyC")};
+        `,p===1)ee+=`
+          let w_offset = ${ie.indicesToOffset(`${ie.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
+          let wValue = ${ie.getByOffset(`w_offset / ${w}`)};
+          dotProd = dotProd + xValue * wValue;`;else for(let ne=0;ne<p;ne++)ee+=`
+            let wValue${ne} = ${ie.getByOffset(`${ie.indicesToOffset(`${ie.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${ne}, wOutChannel)`)} / ${w}`)};
+            dotProd = dotProd + xValue[${ne}] * wValue${ne};`;return ee},Y=()=>{if(m===0)return"";if(!f)throw new Error(`packInputAs4 ${f} is not true.`);let ee="";if(p===1){ee+="dotProd = dotProd";for(let ne=0;ne<m;ne++)ee+=`
+            + ${te.getByOffset(`x_offset + ${ne}`)} * ${ie.getByOffset(`w_offset + ${ne}`)}`;ee+=";"}else if(p===2){if(m!==2)throw new Error(`Invalid inputChannelsRemainder ${m}.`);ee+=`
+          let xValue = ${te.getByOffset("x_offset")};
+          let wValue = ${ie.getByOffset("w_offset")};
+          dotProd = dotProd + dot(xValue, wValue);`}return ee},P=`
+            let outputIndices = ${Q.offsetToIndices(`global_idx * ${y}`)};
+            let batch = ${Q.indicesGet("outputIndices",0)};
+            let d1 = ${Q.indicesGet("outputIndices",ue)};
+            let r = ${Q.indicesGet("outputIndices",ce)};
+            let c = ${Q.indicesGet("outputIndices",L)};
+            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
+            let dyRCorner = dyCorner.x;
+            let dyCCorner = dyCorner.y;
+            let groupId = d1 / uniforms.output_channels_per_group;
+            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
+            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+            // ? = to be determined. : = across all values in that axis.
+            var dotProd = ${Q.type.value}(0.0);
+            var wR: u32 = 0;
+            if (uniforms.dilations.x == 1) {
+              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
+              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
+            }
+            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
+              if (wR % uniforms.dilations.x != 0) {
+                continue;
+              }
+              let dyR = (${se}(dyRCorner) + ${se}(wR)) / ${se}(uniforms.strides[0]);
+              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
+              if (dyR < 0.0 || dyR >= ${se}(uniforms.Dy_shape[${ce}]) || fract(dyR) > 0.0 ||
+                  wRPerm < 0) {
+                continue;
+              }
+              let idyR: u32 = u32(dyR);
+              var wC: u32 = 0;
+              if (uniforms.dilations.y == 1) {
+                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
+                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
+              }
+              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
+                if (wC % uniforms.dilations.y != 0) {
+                  continue;
+                }
+                let dyC = (${se}(dyCCorner) + ${se}(wC)) / ${se}(uniforms.strides.y);
+                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
+                if (dyC < 0.0 || dyC >= ${se}(uniforms.Dy_shape[${L}]) ||
+                    fract(dyC) > 0.0 || wCPerm < 0) {
+                  continue;
+                }
+                let idyC: u32 = u32(dyC);
+                var inputChannel = groupId * uniforms.input_channels_per_group;
+                ${f?`
+                var x_offset = ${te.indicesToOffset(`${te.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p};
+                var w_offset = ${ie.indicesToOffset(`${ie.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${w};
+                  `:""}
+                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${f?4:p}) {
+                  ${ve()}
+                  inputChannel = inputChannel + ${f?4:p};
+                }
+                ${Y()}
+                wC = wC + uniforms.strides.y - 1;
+              }
+              wR = wR + uniforms.strides[0] - 1;
+            }
+            let value = dotProd${r?` + bias[d1 / ${y}]`:""};
+            ${Q.setByOffset("global_idx","value")};
+          `;return`
+    ${q.registerUniforms(J).declareVariables(...he,Q)}
+      ${q.mainStart()}
+      ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
+    ${P}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};${p}${w}${y}${f}${m}`,inputDependencies:v},getRunData:()=>({dispatchGroup:{x:_[0],y:_[1],z:_[2]},outputs:[{dims:n?n(i):i,dataType:e[0].dataType}],programUniforms:x}),getShaderSource:U}}}),jf,Kf,Xf,hl,zb,Yf,ml,Zf,Ab,R$=re(()=>{M$(),ti(),_r(),jf=(e,t,n,r,i,s)=>(e-1)*t+n+(r-1)*i+1-s,Kf=(e,t,n,r,i)=>{let s=Math.floor(e/2);t==="SAME_UPPER"?(n[r]=s,n[i]=e-s):t==="SAME_LOWER"&&(n[r]=e-s,n[i]=s)},Xf=(e,t,n,r,i,s,a,o,l,d)=>{let p=e.length-2,f=d.length===0;l.length<p&&l.push(...Array(p-l.length).fill(0));let c=e[0],m=t[o?3:1]*i;for(let y=0,w=e.length-p-(o?1:0);y<p;++y,++w){let $=e[w],_=f?$*a[y]:d[y],v=jf($,a[y],s[y],t[w],n[y],_);Kf(v,r,s,y,y+p),f&&d.push(a[y]*($-1)+l[y]+(t[w]-1)*n[y]+1-s[y]-s[y+p])}d.splice(0,0,c),d.splice(o?3:1,0,m)},hl=(e,t)=>{let n=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((f,c)=>f*c,1)===0){n.length=0;for(let f=2;f<t[1].dims.length;++f)n.push(t[1].dims[f])}let r=e.format==="NHWC";n.splice(0,0,t[1].dims[0]),n.splice(r?3:1,0,t[1].dims[1]);let i=e.pads.slice(),s=e.outputShape.slice(),a=e.outputPadding.slice(),o=t[0].dims,l=e.dilations.slice();if(l.reduce((f,c)=>f+c,0)===0){let f=t[0].dims.length-2;l=new Array(f).fill(1)}let d=e.strides.slice();if(d.reduce((f,c)=>f+c,0)===0){let f=t[0].dims.length-2;d=new Array(f).fill(1)}Xf(o,n,l,e.autoPad,e.group,i,d,r,a,s);let p=Object.assign({},e);return Object.assign(p,{kernelShape:n,pads:i,outputPadding:a,outputShape:s,dilations:l,strides:d}),p},zb=e=>{let t=td(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,s=e.group??1,a=e.kernelShape,o=e.pads,l=e.strides,d=e.wIsConst(),p=e.outputPadding,f=e.outputShape;return{autoPad:r,format:n,dilations:i,group:s,kernelShape:a,outputPadding:p,outputShape:f,pads:o,strides:l,wIsConst:d,...t,cacheKey:`${e.format};${t.activation};`}},Yf=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let n=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],r=e[1].dims[0];if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let s=e[0].dims.length-2;if(t.dilations.reduce((a,o)=>a+o,0)>0&&t.dilations.length!==s)throw new Error(`dilations should be ${s}D`);if(t.strides.reduce((a,o)=>a+o,0)>0&&t.strides.length!==s)throw new Error(`strides should be ${s}D`);if(t.pads.reduce((a,o)=>a+o,0)>0&&t.pads.length!==s*2)throw new Error(`pads should be ${s*2}D`);if(t.outputPadding.length!==s&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${s}D`);if(t.kernelShape.reduce((a,o)=>a+o,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ml=(e,t,n,r)=>{let i=e.kernelCustomData.wT??e.compute(en(t[1],[2,3,0,1]),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=i);let s=[t[0],i];t.length===3&&s.push(t[2]),e.compute(kb(s,n,r),{inputs:s})},Zf=(e,t)=>{let n=t.format==="NHWC",r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&r.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let s=t.dilations;(s.length===0||s[0]===0)&&(s=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let o=t.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],a=[1].concat(a),s=[1].concat(s),i=[1].concat(i);let l=t.outputPadding;l=[0].concat(l);let d=hl({...t,pads:o,strides:a,dilations:s,kernelShape:i,outputPadding:l},r);ml(e,r,d,p=>n?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},Ab=(e,t)=>{if(Yf(e.inputs,t),e.inputs[0].dims.length===3)Zf(e,t);else{let n=hl(t,e.inputs);ml(e,e.inputs,n)}}}),Qf,Mb,Rb,O$=re(()=>{Te(),Ce(),pt(),Ee(),Qf=(e,t,n,r)=>{let i=W.size(t),s=t.length,a=V("input",e,s),o=be("output",e,s),l=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),d=W.normalizeAxis(l,s),p=f=>{let c=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,m=we("uniforms.input_shape","uniforms.axis",s),y=r.reverse?c+(r.exclusive?" + 1":""):"0",w=r.reverse?m:c+(r.exclusive?"":" + 1");return`
+                ${f.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,o)}
+                ${f.mainStart()}
+                  ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+                  var inputIndices = ${o.offsetToIndices("global_idx")};
+                  var sum = ${o.type.value}(0);
+                  let first : i32 = ${y};
+                  let last : i32 = ${w};
+                  for (var i : i32 = first; i < last; i++) {
+                    ${a.indicesSet("inputIndices","uniforms.axis","u32(i)")};
+                    sum = sum + ${a.getByIndices("inputIndices")};
+                  }
+                  ${o.setByOffset("global_idx","sum")};
+                }`};return{name:"CumSum",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},{type:12,data:d},..._e(t,t)]}),getShaderSource:p}},Mb=(e,t)=>{let n=e.inputs[0].dims,r=e.inputs[0].dataType,i=e.inputs[1];e.compute(Qf(r,n,i,t),{inputs:[0]})},Rb=e=>{let t=e.exclusive===1,n=e.reverse===1;return Fe({exclusive:t,reverse:n})}}),Jf,eh,th,Ob,Nb,N$=re(()=>{Te(),Ce(),pt(),Ee(),Jf=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},eh=(e,t,n,r)=>{let i=[];i.push(`fn perm(i: ${r.type.indices}) -> ${n.type.indices} {
+    var a: ${n.type.indices};`);for(let s=0;s<t;++s)i.push(n.indicesSet("a",e[s],`i[${s}]`));return i.push("return a;}"),i.join(`
+`)},th=(e,t)=>{let n,r,i,s,a,o,l=t.format==="NHWC",d=t.blocksize,p=t.mode==="DCR";l?([n,r,i,s]=e.dims,a=p?[n,r,i,d,d,s/d**2]:[n,r,i,s/d**2,d,d],o=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([n,r,i,s]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],a=p?[n,d,d,s/d**2,r,i]:[n,s/d**2,d,d,r,i],o=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let f=e.reshape(a),c=f.dims.length,m=e.dataType,y=V("a",m,c),w=be("output",m,c),$=_=>`
+  ${_.registerUniform("output_size","u32").declareVariables(y,w)}
+
+  ${eh(o,c,y,w)}
+
+  ${_.mainStart()}
+    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+
+    let indices = ${w.offsetToIndices("global_idx")};
+    let aIndices = perm(indices);
+
+    ${w.setByOffset("global_idx",y.getByIndices("aIndices"))}
+  }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:_=>{let v=l?[n,r*d,i*d,s/d**2]:[n,s/d**2,r*d,i*d],I=W.size(v),S=f.dims,E=W.sortBasedOnPerm(S,o);return{outputs:[{dims:v,dataType:_[0].dataType}],dispatchGroup:{x:Math.ceil(I/64)},programUniforms:[{type:12,data:I},..._e(S,E)]}},getShaderSource:$}},Ob=(e,t)=>{Jf(e.inputs),e.compute(th(e.inputs[0],t))},Nb=e=>Fe({blocksize:e.blocksize,mode:e.mode,format:e.format})}),ma,ss,gl,nh,rh,ih,sh,yl,ah,Bb,Db,B$=re(()=>{Te(),Ce(),pt(),Ee(),ma="[a-zA-Z]|\\.\\.\\.",ss="("+ma+")+",gl="^"+ss+"$",nh="("+ss+",)*"+ss,rh="^"+nh+"$",ih=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let n=this.symbolToIndices.get(e);n===void 0?n=[t]:n.push(t),this.symbolToIndices.set(e,n)}},sh=class{constructor(e,t){var i;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[n,r]=t.includes("->")?t.split("->",2):[t,""];if(!n.match(RegExp(rh)))throw new Error("Invalid LHS term");if(n.split(",").forEach((s,a)=>{let o=e[a].dims.slice();if(!s.match(RegExp(gl)))throw new Error("Invalid LHS term");let l=this.processTerm(s,!0,o,a);this.lhs.push(l)}),r==="")r+=[...this.symbolToInfo.entries()].filter(([s,a])=>a.count===1||s==="...").map(([s])=>s).join("");else if(!r.match(RegExp(ss)))throw new Error("Invalid RHS");(i=r.match(RegExp(ma,"g")))==null||i.forEach(s=>{if(s==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let a=this.symbolToInfo.get(s);if(a===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(a.dimValue)}}),this.rhs=this.processTerm(r,!1,this.outputDims)}addSymbol(e,t,n){let r=this.symbolToInfo.get(e);if(r!==void 0){if(r.dimValue!==t&&r.count!==1)throw new Error("Dimension mismatch");r.count++,r.inputIndices.push(n)}else r={count:1,dimValue:t,inputIndices:[n]};this.symbolToInfo.set(e,r)}processTerm(e,t,n,r=-1){let i=n.length,s=!1,a=[],o=0;if(!e.match(RegExp(gl))&&!t&&e!=="")throw new Error("Invalid LHS term");let l=e.match(RegExp(ma,"g")),d=new ih(r);return l==null||l.forEach((p,f)=>{if(p==="..."){if(s)throw new Error("Only one ellipsis is allowed per input term");s=!0;let c=i-l.length+1;if(c<0)throw new Error("Ellipsis out of bounds");if(a=n.slice(o,o+c),this.hasEllipsis){if(this.ellipsisDims.length!==a.length||this.ellipsisDims.toString()!==a.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=a;else throw new Error("Ellipsis must be specified in the LHS");for(let m=0;m<a.length;m++){let y=String.fromCharCode(48+m);d.addSymbol(y,f+m),this.addSymbol(y,n[o++],r)}}else d.addSymbol(p,f+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(p,n[o++],r)}),d}},yl=e=>e+"_max",ah=(e,t,n,r)=>{let i=e.map(d=>d.length).map((d,p)=>V(`input${p}`,t,d)),s=W.size(r),a=be("output",t,r.length),o=[...n.symbolToInfo.keys()].filter(d=>!n.rhs.symbolToIndices.has(d)),l=d=>{let p=[],f="var prod = 1.0;",c="var sum = 0.0;",m="sum += prod;",y=[],w=[],$=[],_=[],v=n.symbolToInfo.size===n.rhs.symbolToIndices.size;n.symbolToInfo.forEach((S,E)=>{var R;if(n.rhs.symbolToIndices.has(E)){let O=(R=n.rhs.symbolToIndices.get(E))==null?void 0:R[0];O!==void 0&&n.lhs.forEach((x,U)=>{if(S.inputIndices.includes(U)){let q=x.symbolToIndices.get(E);if(q===void 0)throw new Error("Invalid symbol error");q.forEach(J=>{p.push(`${i[U].indicesSet(`input${U}Indices`,J,a.indicesGet("outputIndices",O))}`)})}})}else n.lhs.forEach((O,x)=>{if(S.inputIndices.includes(x)){let U=O.symbolToIndices.get(E);if(U===void 0)throw new Error("Invalid symbol error");U.forEach(q=>{y.push(`${i[x].indicesSet(`input${x}Indices`,q,`${E}`)}`)}),_.push(`prod *= ${i[x].getByIndices(`input${x}Indices`)};`)}}),w.push(`for(var ${E}: u32 = 0; ${E} < uniforms.${yl(E)}; ${E}++) {`),$.push("}")});let I=v?[...p,`let sum = ${i.map((S,E)=>S.getByIndices(`input${E}Indices`)).join(" * ")};`]:[...p,c,...w,...y,f,..._,m,...$];return`
+            ${d.registerUniforms(o.map(S=>({name:`${yl(S)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,a)}
+
+            ${d.mainStart()}
+            ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+            var outputIndices = ${a.offsetToIndices("global_idx")};
+            ${i.map((S,E)=>`var input${E}Indices: ${i[E].type.indices};`).join(`
+`)}
+            ${I.join(`
+`)};
+            ${a.setByOffset("global_idx","sum")};
+          }`};return{name:"Einsum",shaderCache:{hint:n.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let d=o.filter(f=>n.symbolToInfo.has(f)).map(f=>{var c;return{type:12,data:((c=n.symbolToInfo.get(f))==null?void 0:c.dimValue)||0}});d.push({type:12,data:s});let p=e.map((f,c)=>[..._e(f)]).reduce((f,c)=>f.concat(c),d);return p.push(..._e(r)),{outputs:[{dims:r,dataType:t}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:p}},getShaderSource:l}},Bb=(e,t)=>{let n=new sh(e.inputs,t.equation),r=n.outputDims,i=e.inputs.map((s,a)=>s.dims);e.compute(ah(i,e.inputs[0].dataType,n,r))},Db=e=>{let t=e.equation.replace(/\s+/g,"");return Fe({equation:t})}}),oh,bl,lh,uh,Pb,D$=re(()=>{Te(),Ce(),Ee(),oh=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,n=Array.from(e[1].getBigInt64Array(),Number),r=n.length<t.length?0:n.length-t.length,i=t.length<n.length?0:t.length-n.length;for(;r<n.length&&i<t.length;++r,++i)if(n[r]!==t[i]&&n[r]!==1&&t[i]!==1)throw new Error("Expand requires shape to be broadcastable to input")},bl=(e,t)=>{let n=e.length-t.length,r=[];for(let i=0;i<n;++i)r.push(e[i]);for(let i=0;i<t.length;++i)r.push(t[i]===1?e[i+n]:t[i]);return r},lh=(e,t)=>e.length>t.length?bl(e,t):bl(t,e),uh=e=>{let t=e[0].dims,n=Array.from(e[1].getBigInt64Array(),Number),r=lh(t,n),i=e[0].dataType,s=i===9||W.size(t)===1,a=i===9||t.length>0&&t[t.length-1]%4===0?4:1,o=s||r.length>0&&r[r.length-1]%4===0?4:1,l=Math.ceil(W.size(r)/o),d=f=>{let c=V("input",i,t.length,a),m=be("output",i,r.length,o),y;if(i===9){let w=($,_,v="")=>`
+          let outputIndices${_} = ${m.offsetToIndices(`outputOffset + ${_}u`)};
+          let offset${_} = ${c.broadcastedIndicesToOffset(`outputIndices${_}`,m)};
+          let index${_} = offset${_} / 4u;
+          let component${_} = offset${_} % 4u;
+          ${$}[${_}] = ${v}(${c.getByOffset(`index${_}`)}[component${_}]);
+        `;y=`
+        let outputOffset = global_idx * ${o};
+        var data = vec4<u32>(0);
+        ${w("data",0,"u32")}
+        ${w("data",1,"u32")}
+        ${w("data",2,"u32")}
+        ${w("data",3,"u32")}
+        ${m.setByOffset("global_idx","data")}
+      }`}else y=`
+        let outputIndices = ${m.offsetToIndices(`global_idx * ${o}`)};
+        let inputOffset = ${c.broadcastedIndicesToOffset("outputIndices",m)};
+        let data = ${m.type.value}(${c.getByOffset(`inputOffset / ${a}`)});
+        ${m.setByOffset("global_idx","data")}
+      }`;return`
+    ${f.registerUniform("vec_size","u32").declareVariables(c,m)}
+    ${f.mainStart()}
+    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
+    ${y}`},p=[{type:12,data:l},..._e(t,r)];return{name:"Expand",shaderCache:{hint:`${r.length};${a}${o}`,inputDependencies:["rank"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:p})}},Pb=e=>{oh(e.inputs),e.compute(uh(e.inputs),{inputs:[0]})}}),dh,Ub,P$=re(()=>{Te(),Ce(),Ee(),ed(),dh=e=>{let t=e[0].dataType,n=W.size(e[0].dims),r=W.size(e[1].dims),i=r%4===0,s=a=>{let o=V("x",t,[1],4),l=V("bias",t,[1],4),d=be("y",t,[1],4),p=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],f=m=>`
+      let bias${m}_offset: u32 = (global_idx * 4 + ${m}) % uniforms.bias_size;
+      let bias${m} = ${l.getByOffset(`bias${m}_offset / 4`)}[bias${m}_offset % 4];`,c=i?`
+      let bias = ${l.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${f(0)}${f(1)}${f(2)}${f(3)}
+      let bias = ${o.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(p).declareVariables(o,l,d)}
+
+    ${du(Rt(t))}
+
+    ${a.mainStart(ki)}
+      ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")}
+
+      let x = ${o.getByOffset("global_idx")};
+      ${c}
+      let x_in = x + bias;
+      ${d.setByOffset("global_idx",cu("x_in"))}
+    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${i}`,inputDependencies:["type","type"]},getShaderSource:s,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(n/4)},{type:12,data:r}],dispatchGroup:{x:Math.ceil(n/ki/4)}})}},Ub=e=>{e.inputs.length<2||W.size(e.inputs[1].dims)===0?sb(e):e.compute(dh(e.inputs))}}),ch,ph,Lb,Wb,U$=re(()=>{Te(),Ce(),pt(),Ee(),ch=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},ph=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n.length,s=W.normalizeAxis(t.axis,i),a=n.slice(0);a.splice(s,1,...r);let o=n[s],l=e[0].dataType===9?4:1,d=Math.ceil(W.size(a)/l),p=[{type:12,data:d},{type:6,data:o},{type:12,data:s},..._e(e[0].dims,e[1].dims,a)],f=c=>{let m=V("data",e[0].dataType,e[0].dims.length,l),y=V("inputIndices",e[1].dataType,e[1].dims.length),w=be("output",e[0].dataType,a.length,l),$=v=>{let I=r.length,S=`var indicesIndices${v}  = ${y.type.indices}(0);`;for(let E=0;E<I;E++)S+=`${I>1?`indicesIndices${v}[${E}]`:`indicesIndices${v}`} = ${a.length>1?`outputIndices${v}[uniforms.axis + ${E}]`:`outputIndices${v}`};`;S+=`
+          var idx${v} = ${y.getByIndices(`indicesIndices${v}`)};
+          if (idx${v} < 0) {
+            idx${v} = idx${v} + uniforms.axisDimLimit;
+          }
+          var dataIndices${v} : ${m.type.indices};
+        `;for(let E=0,R=0;E<i;E++)E===s?(S+=`${i>1?`dataIndices${v}[${E}]`:`dataIndices${v}`} = u32(idx${v});`,R+=I):(S+=`${i>1?`dataIndices${v}[${E}]`:`dataIndices${v}`} = ${a.length>1?`outputIndices${v}[${R}]`:`outputIndices${v}`};`,R++);return S},_;if(e[0].dataType===9){let v=(I,S,E="")=>`
+          let outputIndices${S} = ${w.offsetToIndices(`outputOffset + ${S}u`)};
+          ${$(S)};
+          let offset${S} = ${m.indicesToOffset(`dataIndices${S}`)};
+          let index${S} = offset${S} / 4u;
+          let component${S} = offset${S} % 4u;
+          ${I}[${S}] = ${E}(${m.getByOffset(`index${S}`)}[component${S}]);
+        `;_=`
+        let outputOffset = global_idx * ${l};
+        var value = vec4<u32>(0);
+        ${v("value",0,"u32")}
+        ${v("value",1,"u32")}
+        ${v("value",2,"u32")}
+        ${v("value",3,"u32")}
+        ${w.setByOffset("global_idx","value")}
+      `}else _=`
+      let outputIndices = ${w.offsetToIndices("global_idx")};
+      ${$("")};
+      let value = ${m.getByIndices("dataIndices")};
+      ${w.setByOffset("global_idx","value")};
+      `;return`
+      ${c.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,y,w)}
+      ${c.mainStart()}
+        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+        ${_}
+      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:f}},Lb=e=>Fe({axis:e.axis}),Wb=(e,t)=>{let n=e.inputs;ch(n),e.compute(ph(e.inputs,t))}}),fh,qb,Gb,L$=re(()=>{Te(),Ce(),Ee(),fh=(e,t,n,r,i,s,a,o,l)=>{let d=[{type:12,data:s},{type:12,data:r},{type:12,data:i},{type:12,data:n},{type:12,data:a},{type:12,data:o},{type:12,data:l}],p=[s];d.push(..._e(t.dims,p));let f=c=>{let m=V("indices_data",t.dataType,t.dims.length),y=be("input_slice_offsets_data",12,1,1),w=[m,y],$=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:i.length},{name:"sizes_from_slice_dims_data",type:"u32",length:n.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
+  ${c.registerUniforms($).declareVariables(...w)}
+  ${c.mainStart()}
+    ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    let batch_idx = global_idx / uniforms.num_slices_per_batch;
+    let base_offset = batch_idx * uniforms.input_batch_stride;
+
+    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
+    var relative_slice_offset = 0;
+    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
+      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
+      let input_dim_idx = uniforms.batch_dims + dim_idx;
+      if (index < 0) {
+        ${i.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
+      }
+      ${n.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
+    }
+
+    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
+  }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${i.length}_${n.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:d}),getShaderSource:f},{inputs:[t],outputs:[-1]})[0]},qb=(e,t)=>{let n=e.inputs,r=n[0].dims,i=n[0].dataType,s=n[1].dims,a=s[s.length-1],o=W.sizeToDimension(s,s.length-1),l=W.sizeFromDimension(r,t.batchDims+a),d=W.sizeToDimension(r,t.batchDims),p=W.sizeFromDimension(r,t.batchDims),f=o/d,c=new Array(a),m=l;for(let S=0;S<a;++S)c[a-1-S]=m,m*=r[t.batchDims+a-1-S];let y=fh(e,n[1],c,t.batchDims,r,o,f,p,a),w=t.batchDims+a;if(w>r.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let $=s.slice(0,-1).concat(r.slice(w)),_=W.size($),v=[{type:12,data:_},{type:12,data:l},..._e(n[0].dims,y.dims,$)],I=S=>{let E=V("data",n[0].dataType,n[0].dims.length),R=V("slice_offsets",12,y.dims.length),O=be("output",n[0].dataType,$.length);return`
+          ${S.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(E,R,O)}
+            ${S.mainStart()}
+            ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
+          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
+        }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:$,dataType:i}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:v}),getShaderSource:I},{inputs:[n[0],y]})},Gb=e=>({batchDims:e.batch_dims,cacheKey:""})}),hh,mh,Vb,Fb,W$=re(()=>{Te(),Ce(),pt(),Ee(),hh=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let n=W.normalizeAxis(t.quantizeAxis,e[0].dims.length),r=t.blockSize,i=e[0],s=e[2],a=e.length===4?e[3]:void 0;if(s.dims.length!==i.dims.length||!i.dims.map((o,l)=>l===n?Math.ceil(o/r)===s.dims[l]:o===s.dims[l]).reduce((o,l)=>o&&l,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==i.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==s.dims.length||!a.dims.map((o,l)=>o===s.dims[l]).reduce((o,l)=>o&&l,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},mh=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n.length,s=W.normalizeAxis(t.gatherAxis,i),a=W.normalizeAxis(t.quantizeAxis,i),o=n.slice(0);o.splice(s,1,...r);let l=W.size(o),d=e[2].dataType,p=e[0].dataType===22,f=[{type:12,data:l},{type:12,data:a},{type:12,data:s},{type:12,data:t.blockSize},..._e(...e.map((m,y)=>m.dims),o)],c=m=>{let y=V("data",e[0].dataType,e[0].dims.length),w=V("inputIndices",e[1].dataType,e[1].dims.length),$=V("scales",e[2].dataType,e[2].dims.length),_=e.length>3?V("zeroPoint",e[3].dataType,e[3].dims.length):void 0,v=be("output",d,o.length),I=[y,w,$];_&&I.push(_);let S=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
+        ${m.registerUniforms(S).declareVariables(...I,v)}
+        ${m.mainStart()}
+        let output_indices = ${v.offsetToIndices("global_idx")};
+        var indices_indices = ${w.type.indices}(0);
+        ${r.length>1?`
+          for (var i: u32 = 0; i < ${r.length}; i++) {
+            let index = ${v.indicesGet("output_indices","uniforms.gather_axis + i")};
+            ${w.indicesSet("indices_indices","i","index")};
+          }`:`indices_indices = ${v.indicesGet("output_indices","uniforms.gather_axis")};`};
+        var data_indices = ${y.type.indices}(0);
+        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
+          let index = ${v.indicesGet("output_indices","i")};
+          ${y.indicesSet("data_indices","i","index")};
+        }
+        var index_from_indices = ${w.getByIndices("indices_indices")};
+        if (index_from_indices < 0) {
+          index_from_indices += ${n[s]};
+        }
+        ${y.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
+        for (var i = uniforms.gather_axis + 1; i < ${o.length}; i++) {
+          let index = ${v.indicesGet("output_indices",`i + ${r.length} - 1`)};
+          ${y.indicesSet("data_indices","i","index")};
+        }
+        let data_offset = ${y.indicesToOffset("data_indices")};
+        let data_index = data_offset % 8;
+        // Convert 4-bit packed data to 8-bit packed data.
+        let packed_4bit_quantized_data = ${y.getByOffset("data_offset / 8")};
+        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
+        let quantized_data_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
+        let quantized_data = quantized_data_vec[data_index / 2];
+        var scale_indices = data_indices;
+        let quantize_axis_index = ${$.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
+        ${$.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
+        var scale = ${$.getByIndices("scale_indices")};
+        ${_?`
+              let zero_point_indices = scale_indices;
+              let zero_point_offset = ${_.indicesToOffset("zero_point_indices")};
+              let zero_point_index = zero_point_offset % 8;
+              let packed_4bit_zero_points = ${_.getByOffset("zero_point_offset / 8")};
+              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
+              let zero_point_vec = ${p?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
+              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
+        let dequantized_data = ${Rt(d)}(quantized_data - zero_point) * scale;
+        ${v.setByOffset("global_idx","dequantized_data")};
+    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((m,y)=>y!==1).map(m=>m.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(m,y)=>"rank")},getRunData:()=>({outputs:[{dims:o,dataType:d}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:f}),getShaderSource:c}},Vb=(e,t)=>{let n=e.inputs;hh(n,t),e.compute(mh(e.inputs,t))},Fb=e=>Fe({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})}),gh,yh,Hb,jb,q$=re(()=>{Te(),Ce(),pt(),Ee(),gh=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
+                     indices input tensors be of same rank.`)},yh=(e,t)=>{let n=e[0].dims,r=e[0].dataType,i=n.length,s=e[1].dims,a=e[1].dataType,o=W.normalizeAxis(t.axis,i),l=n[o],d=s.slice(0),p=W.size(d),f=V("input",r,i),c=V("indicesInput",a,s.length),m=be("output",r,d.length),y=[{type:12,data:p},{type:6,data:l},{type:12,data:o}];return y.push(..._e(n,s,d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:y}),getShaderSource:w=>`
+      ${w.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,c,m)}
+      ${w.mainStart()}
+      ${w.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+
+      let outputIndices = ${m.offsetToIndices("global_idx")};
+
+      var idx = ${c.getByOffset("global_idx")};
+      if (idx < 0) {
+        idx = idx + uniforms.axisDimLimit;
+      }
+      var inputIndices = ${f.type.indices}(outputIndices);
+      ${f.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
+      let value = ${f.getByIndices("inputIndices")};
+
+      ${m.setByOffset("global_idx","value")};
+  }`}},Hb=e=>Fe({axis:e.axis}),jb=(e,t)=>{let n=e.inputs;gh(n),e.compute(yh(e.inputs,t))}}),bh,wh,Kb,Xb,G$=re(()=>{Te(),Ce(),Ee(),bh=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},wh=(e,t)=>{let n=e[0].dims.slice(),r=e[1].dims.slice(),[i,s,a]=H0.getShapeOfGemmResult(n,t.transA,r,t.transB,e.length===3?e[2].dims:void 0),o=[i,s];if(!o)throw new Error("Can't use gemm on the given tensors");let l=16,d=Math.ceil(s/l),p=Math.ceil(i/l),f=!0,c=W.size(o),m=[{type:12,data:f?d:c},{type:12,data:i},{type:12,data:s},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],y=["type","type"];e.length===3&&(m.push(..._e(e[2].dims)),y.push("rank")),m.push(..._e(o));let w=_=>{let v="";t.transA&&t.transB?v="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?v="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?v="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(v="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let I=t.alpha===1?"":"value *= uniforms.alpha;",S=V("a",e[0].dataType,e[0].dims),E=V("b",e[1].dataType,e[1].dims),R=S.type.value,O=null,x=[S,E];e.length===3&&(O=V("c",e[2].dataType,e[2].dims.length),x.push(O));let U=be("output",e[0].dataType,o.length);x.push(U);let q=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
+  ${_.registerUniforms(q).declareVariables(...x)}
+
+  ${_.mainStart()}
+    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+
+    let m = global_idx / uniforms.N;
+    let n = global_idx % uniforms.N;
+
+    var value = ${R}(0);
+    for (var k: u32 = 0u; k < uniforms.K; k++) {
+      ${v}
+    }
+
+    ${I}
+    ${O!=null?`let cOffset = ${O.broadcastedIndicesToOffset("vec2(m, n)",U)}; value += ${R}(uniforms.beta) * ${O.getByOffset("cOffset")};`:""}
+    output[global_idx] = value;
+  }`},$=_=>{let v=V("a",e[0].dataType,e[0].dims),I=V("b",e[1].dataType,e[1].dims),S=null,E=[v,I];e.length===3&&(S=V("c",e[2].dataType,e[2].dims.length),E.push(S));let R=be("output",e[0].dataType,o.length);E.push(R);let O=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],x="",U="";t.transA&&t.transB?(U=`
+      var col = tile_row_start + local_id.x;
+      var row = k_start + local_id.y;
+      if (col < uniforms.M && row < uniforms.K) {
+        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
+      } else {
+        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
+      }
+
+      col = k_start + local_id.x;
+      row = tile_col_start + local_id.y;
+      if (col < uniforms.K && row < uniforms.N) {
+        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
+      } else {
+        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
+      }
+      `,x="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(U=`
+      var col = tile_row_start + local_id.x;
+      var row = k_start + local_id.y;
+      if (col < uniforms.M && row < uniforms.K) {
+        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
+      } else {
+        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
+      }
+
+      col = tile_col_start + local_id.x;
+      row = k_start + local_id.y;
+      if (col < uniforms.N && row < uniforms.K) {
+        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
+      } else {
+        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
+      }
+      `,x="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(U=`
+      var col = k_start + local_id.x;
+      var row = tile_row_start + local_id.y;
+      if (col < uniforms.K && row < uniforms.M) {
+        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
+      } else {
+        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
+      }
+
+      col = k_start + local_id.x;
+      row = tile_col_start + local_id.y;
+      if (col < uniforms.K && row < uniforms.N) {
+        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
+      } else {
+        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
+      }
+      `,x="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(U=`
+      var col = k_start + local_id.x;
+      var row = tile_row_start + local_id.y;
+      if (col < uniforms.K && row < uniforms.M) {
+        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
+      } else {
+        tile_a[local_id.y][local_id.x] = ${v.type.value}(0);
+      }
+
+      col = tile_col_start + local_id.x;
+      row = k_start + local_id.y;
+      if (col < uniforms.N && row < uniforms.K) {
+        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
+      } else {
+        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
+      }
+      `,x="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let q=t.alpha===1?"":"value *= uniforms.alpha;";return`
+  ${_.registerUniforms(O).declareVariables(...E)}
+  var<workgroup> tile_a: array<array<${v.type.storage}, ${l}>, ${l}>;
+  var<workgroup> tile_b: array<array<${I.type.storage}, ${l}>, ${l}>;
+  ${_.mainStart([l,l,1])}
+    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${l};
+    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${l};
+    let num_tiles = (uniforms.K - 1) / ${l} + 1;
+    var k_start = 0u;
+    var value = ${R.type.value}(0);
+    for (var t: u32 = 0u; t < num_tiles; t++) {
+      ${U}
+      k_start = k_start + ${l};
+      workgroupBarrier();
+
+      for (var k: u32 = 0u; k < ${l}; k++) {
+        ${x}
+      }
+      workgroupBarrier();
+    }
+
+    ${q}
+    let m = tile_row_start + local_id.y;
+    let n = tile_col_start + local_id.x;
+    ${S!=null?`let cOffset = ${S.broadcastedIndicesToOffset("vec2(m, n)",R)}; value += ${R.type.value}(uniforms.beta) * ${S.getByOffset("cOffset")};`:""}
+    if (m < uniforms.M && n < uniforms.N) {
+      output[m * uniforms.N + n] = value;
+    }
+  }`};return f?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:d*p},programUniforms:m}),getShaderSource:$}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:m}),getShaderSource:w}},Kb=e=>{let t=e.transA,n=e.transB,r=e.alpha,i=e.beta;return{transA:t,transB:n,alpha:r,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},Xb=(e,t)=>{bh(e.inputs),e.compute(wh(e.inputs,t))}}),In,Fn,Nr,Br,_h,vh,xh,$h,Sh,Th,Ih,Ch,Yb,Zb,V$=re(()=>{Te(),Ce(),pt(),Ee(),[In,Fn,Nr,Br]=[0,1,2,3],_h=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},vh=`
+  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
+    let cubic_alpha = -0.75f;
+    let x_abs = abs(x);
+    var coeffs: vec4<f32>;
+    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
+    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
+    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
+    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
+    return coeffs;
+  }
+`,xh=e=>`
+  fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} {
+    var v: vec4<f32>;
+    var coeffs = gs_get_cubic_coeffs(x);
+    for (var i = 0; i < 4; i++) {
+      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
+    }
+    coeffs = gs_get_cubic_coeffs(y);
+    let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
+    return pixel;
+  }
+`,$h=e=>`
+  fn gs_denormalize(n: f32, length: i32) -> f32 {
+    ${e.alignCorners===0?`
+    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
+    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
+    `:`
+    // alignCorners: true => [-1, 1] to [0, length - 1]
+    return (n + 1.0) / 2.0 * (f32(length - 1));
+    `}
+  }
+`,Sh=e=>`
+  ${e.paddingMode==="reflection"?`
+      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
+        var dx = 0.0;
+        var fx = f32(x);
+        let range = x_max - x_min;
+        if (fx < x_min) {
+          dx = x_min - fx;
+          let n = u32(dx / range);
+          let r = dx - f32(n) * range;
+          if (n % 2 == 0) {
+            fx = x_min + r;
+          } else {
+            fx = x_max - r;
+          }
+        } else if (fx > x_max) {
+          dx = fx - x_max;
+          let n = u32(dx / range);
+          let r = dx - f32(n) * range;
+          if (n % 2 == 0) {
+            fx = x_max - r;
+          } else {
+            fx = x_min + r;
+          }
+        }
+        return u32(fx);
+      }`:""}
+`,Th=(e,t,n)=>`
+  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${t} {
+     var pixel = ${t}(0);
+     var indices = vec4<u32>(0);
+     indices[${In}] = batch;
+     indices[${Fn}] = channel;`+(()=>{switch(n.paddingMode){case"zeros":return`
+          if (r >= 0 && r < H && c >=0 && c < W) {
+            indices[${Nr}] = u32(r);
+            indices[${Br}] = u32(c);
+          } else {
+            return ${t}(0);
+          }
+        `;case"border":return`
+          indices[${Nr}] = u32(clamp(r, 0, H - 1));
+          indices[${Br}] = u32(clamp(c, 0, W - 1));
+        `;case"reflection":return`
+          indices[${Nr}] = gs_reflect(r, border[1], border[3]);
+          indices[${Br}] = gs_reflect(c, border[0], border[2]);
+        `;default:throw new Error(`padding mode ${n.paddingMode} is not supported`)}})()+`
+    return ${e.getByIndices("indices")};
+  }
+`,Ih=(e,t,n)=>(()=>{switch(n.mode){case"nearest":return`
+          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${In}], indices[${Fn}], border);
+        `;case"bilinear":return`
+          let x1 = i32(floor(x));
+          let y1 = i32(floor(y));
+          let x2 = x1 + 1;
+          let y2 = y1 + 1;
+
+          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${In}], indices[${Fn}], border);
+          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${In}], indices[${Fn}], border);
+          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${In}], indices[${Fn}], border);
+          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${In}], indices[${Fn}], border);
+
+          let dx2 = ${t}(f32(x2) - x);
+          let dx1 = ${t}(x - f32(x1));
+          let dy2 = ${t}(f32(y2) - y);
+          let dy1 = ${t}(y - f32(y1));
+          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
+        `;case"bicubic":return`
+          let x0 = i32(floor(x)) - 1;
+          let y0 = i32(floor(y)) - 1;
+          var p: mat4x4<${t}>;
+          for (var h = 0; h < 4; h++) {
+            for (var w = 0; w < 4; w++) {
+              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${In}], indices[${Fn}], border);
+            }
+          }
+
+          let dx = x - f32(x0 + 1);
+          let dy = y - f32(y0 + 1);
+          let result = gs_bicubic_interpolate(p, dx, dy);
+        `;default:throw new Error(`mode ${n.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,Ch=(e,t)=>{let n=V("x",e[0].dataType,e[0].dims.length),r=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],i=V("grid",e[1].dataType,r.length,2),s=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(s=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[In,Fn,Nr,Br]=[0,3,1,2]);let a=be("output",e[0].dataType,s.length),o=n.type.value,l=W.size(s),d=[{type:12,data:l},..._e(e[0].dims,r,s)],p=f=>`
+  ${f.registerUniform("output_size","u32").declareVariables(n,i,a)}
+  ${vh}
+  ${xh(o)}
+  ${$h(t)}
+  ${Sh(t)}
+  ${Th(n,o,t)}
+
+  ${f.mainStart()}
+    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+      let H_in = i32(uniforms.x_shape[${Nr}]);
+      let W_in = i32(uniforms.x_shape[${Br}]);
+
+      ${t.alignCorners===0?`
+      let x_min = -0.5;
+      let x_max = f32(W_in) - 0.5;
+      let y_min = -0.5;
+      let y_max = f32(H_in) - 0.5;
+      `:`
+      let x_min = 0.0;
+      let x_max = f32(W_in) - 1.0;
+      let y_min = 0.0;
+      let y_max = f32(H_in) - 1.0;
+      `};
+      let border = vec4<f32>(x_min, y_min, x_max, y_max);
+
+      let indices = ${a.offsetToIndices("global_idx")};
+      var grid_indices = vec3<u32>(indices[${In}], indices[${Nr}], indices[${Br}]);
+      let nxy = ${i.getByIndices("grid_indices")};
+      var x = gs_denormalize(f32(nxy[0]), W_in);
+      var y = gs_denormalize(f32(nxy[1]), H_in);
+
+      ${Ih(a,o,t)}
+  }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:f=>{let c=W.size(s);return{outputs:[{dims:s,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:d}},getShaderSource:p}},Yb=(e,t)=>{_h(e.inputs),e.compute(Ch(e.inputs,t))},Zb=e=>Fe({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})}),Pt,Eh,Qb,wl,kh,hs,Jb,ew=re(()=>{Te(),Ce(),pt(),Yu(),Ju(),Ee(),_r(),Pt=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,Eh=(e,t)=>{let n=e[0],r=Pt(e,1),i=Pt(e,2),s=Pt(e,3),a=Pt(e,4),o=Pt(e,5),l=Pt(e,6),d=Pt(e,7);if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=n.dims[0],f=n.dims[1],c=n.dims.length===3?n.dims[2]:t.numHeads*n.dims[4],m=f,y=0,w=0,$=Math.floor(c/t.numHeads);if(l&&d&&W.size(l.dims)&&W.size(d.dims)){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(l.dims[0]!==p||l.dims[1]!==t.numHeads||l.dims[3]!==$)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[0]!==p||d.dims[1]!==t.numHeads||d.dims[3]!==$)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[2]!==d.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');y=l.dims[2],w=l.dims[2]}else if(l&&W.size(l.dims)||d&&W.size(d.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let _;if(r&&W.size(r.dims)>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(r.dims.length<3||r.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(r.dims.length===3){if(r.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');_=2,m=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==t.numHeads||r.dims[3]!==2||r.dims[4]!==$)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');_=5,m=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==$)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');_=0,m=r.dims[2]}}else{if(n.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(n.dims[2]!==t.numHeads||n.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');_=3}if(s&&W.size(s.dims)>0){if(s.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(r&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let v=y+m,I=0;if(a&&W.size(a.dims)>0){I=8;let O=a.dims;throw O.length===1?O[0]===p?I=1:O[0]===3*p+2&&(I=3):O.length===2&&O[0]===p&&O[1]===v&&(I=5),I===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let S=!1,E=c;if(i&&W.size(i.dims)>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(m!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=i.dims[2]}else{if(m!==i.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');E=i.dims[1]*i.dims[3],S=!0}}let R=!1;if(a&&W.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(o&&W.size(o.dims)>0){if(o.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(o.dims[0]!==p||o.dims[1]!==t.numHeads||o.dims[2]!==f||o.dims[3]!==v)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:f,pastSequenceLength:y,kvSequenceLength:m,totalSequenceLength:v,maxSequenceLength:w,inputHiddenSize:0,hiddenSize:c,vHiddenSize:E,headSize:$,vHeadSize:Math.floor(E/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:I,scale:t.scale,broadcastResPosBias:R,passPastInKv:S,qkvFormat:_}},Qb=e=>Fe({...e}),wl=Fe({perm:[0,2,1,3]}),kh=(e,t,n,r,i,s,a)=>{let o=[r,i,s],l=W.size(o),d=[{type:12,data:l},{type:12,data:a},{type:12,data:s}],p=f=>{let c=be("qkv_with_bias",t.dataType,o),m=V("qkv",t.dataType,o),y=V("bias",n.dataType,o),w=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
+  ${f.registerUniforms(w).declareVariables(m,y,c)}
+  ${f.mainStart()}
+    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;
+
+    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
+  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:p},{inputs:[t,n],outputs:[-1]})[0]},hs=(e,t,n,r,i,s,a,o)=>{let l=s;if(a&&W.size(a.dims)>0){if(r===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=kh(e,s,a,t,r,n*i,o),l=l.reshape([t,r,n,i]),n===1||r===1?l:e.compute(en(l,wl.perm),{inputs:[l],outputs:[-1]})[0]}else return s.dims.length===3&&(l=s.reshape([t,r,n,i])),n===1||r===1?l:e.compute(en(l,wl.perm),{inputs:[l],outputs:[-1]})[0]},Jb=(e,t)=>{let n=Eh(e.inputs,t),r=e.inputs[0],i=Pt(e.inputs,1),s=Pt(e.inputs,2),a=Pt(e.inputs,3),o=Pt(e.inputs,4),l=Pt(e.inputs,5),d=Pt(e.inputs,6),p=Pt(e.inputs,7);if(r.dims.length===5)throw new Error("Packed QKV is not implemented");if((i==null?void 0:i.dims.length)===5)throw new Error("Packed KV is not implemented");let f=i&&s&&i.dims.length===4&&s.dims.length===4,c=hs(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,r,a,0);if(f)return Ms(e,c,i,s,o,void 0,d,p,l,n);if(!i||!s)throw new Error("key and value must be provided");let m=hs(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,i,a,n.hiddenSize),y=hs(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,s,a,2*n.hiddenSize);Ms(e,c,m,y,o,void 0,d,p,l,n)}}),zh,Ah,Mh,Rh,gu,tw,nw,rw=re(()=>{Te(),Ce(),pt(),Ee(),zh=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Ah=(e,t)=>{let n=[],r=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>n.push(Number(i))),r=n.length),Fe({numOutputs:r,axis:t.axis,splitSizes:n})},Mh=e=>`
+fn calculateOutputIndex(index: u32) -> u32 {
+    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
+    if (index < ${we("uniforms.size_in_split_axis","i",e)}) {
+        return i;
+    }
+    }
+    return ${e}u;
+}`,Rh=e=>{let t=e.length,n=[];for(let r=0;r<t;++r){let i=e[r].setByIndices("indices","input[global_idx]");t===1?n.push(i):r===0?n.push(`if (output_number == ${r}u) { ${i} }`):r===t-1?n.push(`else { ${i} }`):n.push(`else if (output_number == ${r}) { ${i} }`)}return`
+      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
+        ${n.join(`
+`)}
+      }`},gu=(e,t)=>{let n=e[0].dims,r=W.size(n),i=e[0].dataType,s=W.normalizeAxis(t.axis,n.length),a=new Array(t.numOutputs),o=V("input",i,n.length),l=new Array(t.numOutputs),d=[],p=[],f=0,c=[{type:12,data:r}];for(let y=0;y<t.numOutputs;y++){f+=t.splitSizes[y],l[y]=f;let w=n.slice();w[s]=t.splitSizes[y],p.push(w),a[y]=be(`output${y}`,i,w.length),d.push({dims:p[y],dataType:e[0].dataType})}c.push({type:12,data:l},..._e(n,...p));let m=y=>`
+  ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...a)}
+  ${Mh(l.length)}
+  ${Rh(a)}
+
+  ${y.mainStart()}
+    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}
+
+    var indices = ${o.offsetToIndices("global_idx")};
+    var index = ${o.indicesGet("indices",s)};
+    let output_number = calculateOutputIndex(index);
+    if (output_number != 0) {
+      index -= ${we("uniforms.size_in_split_axis","output_number - 1u",l.length)};
+      ${o.indicesSet("indices",s,"index")};
+    }
+    writeBufferData(output_number, indices, global_idx);
+  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:m,getRunData:()=>({outputs:d,dispatchGroup:{x:Math.ceil(r/64)},programUniforms:c})}},tw=(e,t)=>{zh(e.inputs);let n=e.inputs.length===1?t:Ah(e.inputs,t);e.compute(gu(e.inputs,n),{inputs:[0]})},nw=e=>{let t=e.axis,n=e.splitSizes,r=e.numOutputs<0?n.length:e.numOutputs;if(r!==n.length)throw new Error("numOutputs and splitSizes length must be equal");return Fe({axis:t,numOutputs:r,splitSizes:n})}}),Oh,Ga,iw,sw=re(()=>{Te(),Ce(),pt(),Ee(),Oh=(e,t)=>{let[n,r,i,s]=e,{numHeads:a,rotaryEmbeddingDim:o}=t;if(n.dims.length!==3&&n.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${n.dims.length}`);if(!W.areEqual(r.dims,[])&&!W.areEqual(r.dims,[1])&&r.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${r.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(s.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${s.dims.length}`);if(!W.areEqual(i.dims,s.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(o>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let l=n.dims[0],d=n.dims[n.dims.length-2],p=i.dims[0],f=W.sizeFromDimension(n.dims,1)/d,c=o===0?i.dims[1]*2:f/a;if(o>c)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(r.dims.length===2){if(l!==r.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${r.dims[0]}`);if(d!==r.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${r.dims[1]}`)}if(c/2!==i.dims[1]&&o/2!==i.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${i.dims[1]}`);if(d>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Ga=(e,t)=>{let{interleaved:n,numHeads:r,rotaryEmbeddingDim:i,scale:s}=t,a=e[0].dims[0],o=W.sizeFromDimension(e[0].dims,1),l=e[0].dims[e[0].dims.length-2],d=o/l,p=e[2].dims[1],f=i===0?p*2:d/r,c=new Array(a,l,d/f,f-p),m=W.computeStrides(c),y=[{type:1,data:s},{type:12,data:c},{type:12,data:m},...e[0].dims.length===3?new Array({type:12,data:[o,d,f,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[o,f,l*f,1]}):[],..._e(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],w=$=>{let _=V("input",e[0].dataType,e[0].dims.length),v=V("position_ids",e[1].dataType,e[1].dims.length),I=V("cos_cache",e[2].dataType,e[2].dims.length),S=V("sin_cache",e[3].dataType,e[3].dims.length),E=be("output",e[0].dataType,e[0].dims.length);return $.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:c.length},{name:"global_strides",type:"u32",length:m.length},{name:"input_output_strides",type:"u32",length:m.length}]),`
+        ${$.declareVariables(_,v,I,S,E)}
+
+        ${$.mainStart(ki)}
+          let half_rotary_emb_dim = uniforms.${I.name}_shape[1];
+          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
+          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
+          ${$.guardAgainstOutOfBoundsWorkgroupSizes("size")}
+
+          if (bsnh[3] < half_rotary_emb_dim) {
+            let position_ids_idx =
+                ${v.broadcastedIndicesToOffset("bsnh.xy",be("",v.type.tensor,2))};
+            let position_id =
+                u32(${v.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
+            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${n});
+            let j = i + select(half_rotary_emb_dim, 1, ${n});
+            let re = ${_.getByOffset("i")} * ${I.get("position_id","bsnh[3]")} -
+                ${_.getByOffset("j")} * ${S.get("position_id","bsnh[3]")};
+            ${E.setByOffset("i","re")}
+            let im = ${_.getByOffset("i")} * ${S.get("position_id","bsnh[3]")} +
+                ${_.getByOffset("j")} * ${I.get("position_id","bsnh[3]")};
+            ${E.setByOffset("j","im")}
+          } else {
+            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
+            ${E.setByOffset("k",_.getByOffset("k"))}
+          }
+        }`};return{name:"RotaryEmbedding",shaderCache:{hint:Fe({interleaved:n}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:w,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(W.size(c)/ki)},programUniforms:y})}},iw=(e,t)=>{Oh(e.inputs,t),e.compute(Ga(e.inputs,t))}}),Nh,Bh,_l,Dh,aw,F$=re(()=>{pt(),Te(),Ju(),ew(),rw(),_r(),sw(),Ee(),Nh=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let n=e[0],r=e[1],i=e[2],s=e[3],a=e[4];if(t.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let o=!1,l=n.dims[0],d=n.dims[1],p=n.dims.length===3?o?n.dims[2]/3:n.dims[2]:t.numHeads*n.dims[4],f=d,c=0,m=!r||r.dims.length===0,y=Math.floor(m?p/(t.numHeads+2*t.kvNumHeads):p/t.numHeads);m&&(p=y*t.numHeads);let w=s&&s.dims.length!==0,$=a&&a.dims.length!==0;if(w&&s.dims.length===4&&s.dims[0]===l&&s.dims[1]!==t.kvNumHeads&&s.dims[2]===t.kvNumHeads&&s.dims[3]===y)throw new Error("BSNH pastKey/pastValue is not supported");if(w&&$){if(s.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');c=s.dims[2]}else if(w||$)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let _=1;if(r&&r.dims.length>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(r.dims.length<3||r.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(r.dims.length===3){if(n.dims[2]%r.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');f=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==t.numHeads||r.dims[3]!==2||r.dims[4]!==y)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');f=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==y)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');f=r.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==t.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');_=3}let v=0,I=!1,S=t.kvNumHeads?y*t.kvNumHeads:p;if(i&&i.dims.length>0){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(f!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');S=i.dims[2]}else{if(f!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');S=i.dims[1]*i.dims[3],I=!0}}let E=e.length>4?e[5]:void 0;if(E&&E.dims.length!==1&&E.dims[0]!==l)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:l,sequenceLength:d,pastSequenceLength:c,kvSequenceLength:f,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:S,headSize:y,vHeadSize:Math.floor(S/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:v,scale:t.scale,broadcastResPosBias:!1,passPastInKv:I,qkvFormat:_}},Bh=Fe({perm:[0,2,1,3]}),_l=(e,t,n)=>{let r=t,i=n.kvNumHeads;return t.dims.length===3&&n.kvSequenceLength!==0&&(r=t.reshape([n.batchSize,n.kvSequenceLength,i,n.headSize]),r=e.compute(en(r,Bh.perm),{inputs:[r],outputs:[-1]})[0]),r},Dh=(e,t,n,r)=>{let i=7,s=["type","type"],a=[e*t],o=e*t,l=[{type:12,data:o},{type:12,data:t},{type:12,data:e}],d=p=>{let f=V("seq_lens",n.dataType,n.dims),c=V("total_seq_lens",r.dataType,r.dims),m=be("pos_ids",i,a),y=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
+  ${p.registerUniforms(y).declareVariables(f,c,m)}
+  ${p.mainStart()}
+    ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+    let total_sequence_length = u32(${c.getByOffset("0")});
+    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
+    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
+    let batch_idx = global_idx / uniforms.sequence_length;
+    let sequence_idx = i32(global_idx % uniforms.sequence_length);
+    var pos_id: i32 = 0;
+    let seqlen = ${f.getByOffset("batch_idx")};
+    let total_seqlen = seqlen + 1;
+    if (is_first_prompt) {
+      if (sequence_idx < total_seqlen) {
+        pos_id = sequence_idx;
+      } else {
+        pos_id = 1;
+      }
+      ${m.setByOffset("global_idx","pos_id")}
+    } else if (is_subsequent_prompt) {
+      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
+      if (past_seqlen + sequence_idx < total_seqlen) {
+        pos_id = past_seqlen + sequence_idx;
+      } else {
+        pos_id = 1;
+      }
+      ${m.setByOffset("global_idx","pos_id")}
+    } else if (global_idx < uniforms.batch_size) {
+      ${m.setByOffset("global_idx","seqlen")}
+    };
+  }
+  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${t}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:l}),getShaderSource:d}},aw=(e,t)=>{var S;let n=Nh(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((S=e.inputs[1])==null?void 0:S.dims.length)===5)throw new Error("Packed KV is not implemented");let r=e.inputs[0],i=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,s=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,a=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,o=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,l=e.inputs.length>4?e.inputs[5]:void 0,d=e.inputs.length>5?e.inputs[6]:void 0,p=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=Fe({axis:2,numOutputs:3,splitSizes:[n.numHeads*n.headSize,p*n.headSize,p*n.headSize]}),[c,m,y]=!i&&!s?e.compute(gu([r],f),{inputs:[r],outputs:[-1,-1,-1]}):[r,i,s],w,$;if(t.doRotary){let E=e.compute(Dh(n.batchSize,n.sequenceLength,l,d),{inputs:[l,d],outputs:[-1]})[0],R=e.inputs[7],O=e.inputs[8],x=Fe({interleaved:t.rotaryInterleaved!==0,numHeads:n.numHeads,rotaryEmbeddingDim:0,scale:t.scale}),U=[c,E,R,O],q=[-1];w=e.compute(Ga(U,x),{inputs:U,outputs:q})[0],U.splice(0,1,m);let J=Fe({interleaved:t.rotaryInterleaved!==0,numHeads:n.kvNumHeads,rotaryEmbeddingDim:0,scale:t.scale});$=e.compute(Ga(U,J),{inputs:U,outputs:q})[0]}let _=hs(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,t.doRotary?w:c,void 0,0),v=_l(e,t.doRotary?$:m,n),I=_l(e,y,n);Ms(e,_,v,I,void 0,void 0,a,o,void 0,n,l,d)}}),vl,Ph,Uh,ow,H$=re(()=>{Te(),Ce(),_r(),Ee(),vl=(e,t,n,r,i,s,a,o)=>{let l=ct(s),d=l===1?"f32":`vec${l}f`,p=l===1?"vec2f":`mat2x${l}f`,f=i*a,c=64;f===1&&(c=256);let m=[i,a,s/l],y=[i,a,2],w=["rank","type","type"],$=[];$.push(..._e(m,y));let _=v=>{let I=V("x",t.dataType,3,l),S=V("scale",n.dataType,n.dims),E=V("bias",r.dataType,r.dims),R=be("output",1,3,2),O=[I,S,E,R];return`
+  var<workgroup> workgroup_shared : array<${p}, ${c}>;
+  const workgroup_size = ${c}u;
+  ${v.declareVariables(...O)}
+  ${v.mainStart(c)}
+    let batch = workgroup_index / uniforms.x_shape[1];
+    let channel = workgroup_index % uniforms.x_shape[1];
+    let hight = uniforms.x_shape[2];
+    // initialize workgroup memory
+    var sum = ${d}(0);
+    var squared_sum = ${d}(0);
+    for (var h = local_idx; h < hight; h += workgroup_size) {
+      let value = ${d}(${I.get("batch","channel","h")});
+      sum += value;
+      squared_sum += value * value;
+    }
+    workgroup_shared[local_idx] = ${p}(sum, squared_sum);
+    workgroupBarrier();
+
+    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
+      if (local_idx < currSize) {
+        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
+      }
+      workgroupBarrier();
+    }
+    if (local_idx == 0) {
+      let sum_final = ${wr("workgroup_shared[0][0]",l)} / f32(hight * ${l});
+      let squared_sum_final = ${wr("workgroup_shared[0][1]",l)} / f32(hight * ${l});
+
+      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${o}));
+      let channel_scale = inv_std_dev * f32(scale[channel]);
+      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
+      output[workgroup_index] = vec2f(channel_scale, channel_shift);
+    }
+  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o};${c}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:y,dataType:1}],dispatchGroup:{x:f},programUniforms:$}),getShaderSource:_},{inputs:[t,n,r],outputs:[-1]})[0]},Ph=(e,t,n)=>{let r=t[0].dims,i=r,s=2,a=r[0],o=r[1],l=W.sizeFromDimension(r,s),d=ct(l),p=W.size(i)/d,f=vl(e,t[0],t[1],t[2],a,l,o,n.epsilon),c=[a,o,l/d],m=[a,o],y=["type","none"],w=$=>{let _=V("x",t[0].dataType,c.length,d),v=V("scale_shift",1,m.length,2),I=be("output",t[0].dataType,c.length,d),S=[_,v,I];return`
+  ${$.registerUniform("output_size","u32").declareVariables(...S)}
+  ${$.mainStart()}
+  ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+      let outputIndices = ${I.offsetToIndices("global_idx")};
+      let batch = outputIndices[0];
+      let channel = outputIndices[1];
+      let scale_shift = ${v.getByIndices("vec2<u32>(batch, channel)")};
+      let value = ${_.getByOffset("global_idx")} * ${I.type.value}(scale_shift.x) + ${I.type.value}(scale_shift.y);
+      ${I.setByOffset("global_idx","value")};
+  }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${d}`,inputDependencies:y},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},..._e(c,m,c)]}),getShaderSource:w},{inputs:[t[0],f]})},Uh=(e,t,n)=>{let r=t[0].dims,i=r,s=r[0],a=r[r.length-1],o=W.sizeFromDimension(r,1)/a,l=ct(a),d=W.size(i)/l,p=[{type:12,data:o},{type:12,data:Math.floor(a/l)}],f=["type","type"],c=!1,m=[0,r.length-1];for(let _=0;_<r.length-2;_++)c=c||r[_+1]!==1,m.push(_+1);c=c&&r[r.length-1]!==1;let y=c?e.compute(en(e.inputs[0],m),{inputs:[e.inputs[0]],outputs:[-1]})[0]:e.inputs[0].reshape(Array.from({length:r.length},(_,v)=>r[m[v]])),w=vl(e,y,t[1],t[2],s,o,a,n.epsilon),$=_=>{let v=xt(t[0].dataType),I=l===1?"vec2f":`mat${l}x2f`,S=O=>{let x=O===0?"x":"y",U=l===1?"f32":`vec${l}f`;switch(l){case 1:return`${v}(${U}(scale.${x}))`;case 2:return`vec2<${v}>(${U}(scale[0].${x}, scale[1].${x}))`;case 4:return`vec4<${v}>(${U}(scale[0].${x}, scale[1].${x}, scale[2].${x}, scale[3].${x}))`;default:throw new Error(`Not supported compoents ${l}`)}},E=V("input",t[0].dataType,t[0].dims,l),R=be("output",t[0].dataType,i,l);return`
+  @group(0) @binding(0) var<storage, read> input : array<${E.type.storage}>;
+  @group(0) @binding(1) var<storage, read> scale_input : array<${I}>;
+  @group(0) @binding(2) var<storage, read_write> output : array<${R.type.storage}>;
+  struct Uniforms {H: u32, C : u32};
+  @group(0) @binding(3) var<uniform> uniforms: Uniforms;
+
+  ${_.mainStart()}
+    let current_image_number = global_idx / (uniforms.C * uniforms.H);
+    let current_channel_number = global_idx % uniforms.C;
+
+    let scale_offset = current_image_number * uniforms.C + current_channel_number;
+    let scale = scale_input[scale_offset];
+    output[global_idx] = fma(input[global_idx], ${S(0)}, ${S(1)});
+  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p}),getShaderSource:$},{inputs:[t[0],w]})},ow=(e,t)=>{t.format==="NHWC"?Uh(e,e.inputs,t):Ph(e,e.inputs,t)}}),Lh,Wh,lw,j$=re(()=>{Te(),Ce(),Ee(),Lh=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Wh=(e,t,n)=>{let r=t.simplified,i=e[0].dims,s=e[1],a=!r&&e[2],o=i,l=W.normalizeAxis(t.axis,i.length),d=W.sizeToDimension(i,l),p=W.sizeFromDimension(i,l),f=W.size(s.dims),c=a?W.size(a.dims):0;if(f!==p||a&&c!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.
+       Size of scale and bias (if provided) must match this.
+       Got scale size of ${f} and bias size of ${c}`);let m=[];for(let E=0;E<i.length;++E)E<l?m.push(i[E]):m.push(1);let y=ct(p),w=["type","type"],$=[{type:12,data:d},{type:1,data:p},{type:12,data:Math.floor(p/y)},{type:1,data:t.epsilon}];a&&w.push("type");let _=n>1,v=n>2,I=E=>{let R=xt(e[0].dataType),O=[V("x",e[0].dataType,e[0].dims,y),V("scale",s.dataType,s.dims,y)];a&&O.push(V("bias",a.dataType,a.dims,y)),O.push(be("output",e[0].dataType,o,y)),_&&O.push(be("mean_data_output",1,m)),v&&O.push(be("inv_std_output",1,m));let x=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
+  ${E.registerUniforms(x).declareVariables(...O)}
+  ${E.mainStart()}
+    ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
+    let offset = global_idx * uniforms.norm_size_vectorized;
+    var mean_vector = ${ou("f32",y)};
+    var mean_square_vector = ${ou("f32",y)};
+
+    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
+      let value = ${bi(R,y,"x[h + offset]")};
+      mean_vector += value;
+      mean_square_vector += value * value;
+    }
+    let mean = ${wr("mean_vector",y)} / uniforms.norm_size;
+    let inv_std_dev = inverseSqrt(${wr("mean_square_vector",y)} / uniforms.norm_size ${r?"":"- mean * mean"} + uniforms.epsilon);
+
+    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
+      let f32input = ${bi(R,y,"x[j + offset]")};
+      let f32scale = ${bi(R,y,"scale[j]")};
+      output[j + offset] = ${O[0].type.value}((f32input ${r?"":"- mean"}) * inv_std_dev * f32scale
+        ${a?`+ ${bi(R,y,"bias[j]")}`:""}
+      );
+    }
+
+    ${_?"mean_data_output[global_idx] = mean":""};
+    ${v?"inv_std_output[global_idx] = inv_std_dev":""};
+  }`},S=[{dims:o,dataType:e[0].dataType}];return _&&S.push({dims:m,dataType:1}),v&&S.push({dims:m,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${y};${n};${r}`,inputDependencies:w},getRunData:()=>({outputs:S,dispatchGroup:{x:Math.ceil(d/64)},programUniforms:$}),getShaderSource:I}},lw=(e,t)=>{Lh(e.inputs),e.compute(Wh(e.inputs,t,e.outputCount))}}),qh,uw,K$=re(()=>{Ce(),id(),sd(),qh=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},uw=e=>{qh(e.inputs);let t=Ei.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let n=t[t.length-1],r=e.inputs[0].dims[e.inputs[0].dims.length-1];if(n<8&&r<8)e.compute(rd(e.inputs,{activation:""},t));else{let i=t[t.length-2],s=W.size(e.inputs[0].dims.slice(0,-2)),a=W.size(e.inputs[1].dims.slice(0,-2));if(s!==1&&i===1&&a===1){let o=e.inputs[0].reshape([1,s,r]),l=e.inputs[1].reshape([1,r,n]),d=[1,s,n],p=[o,l];e.compute(qa(p,{activation:""},t,d),{inputs:p})}else e.compute(qa(e.inputs,{activation:""},t))}}}),Gh,Vh,Fh,dw,cw,X$=re(()=>{Te(),Ce(),pt(),Ee(),Gh=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let n=e[0],r=n.dims.length;if(n.dims[r-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let i=Math.floor((t.k+t.blockSize-1)/t.blockSize),s=t.blockSize/8*t.bits,a=e[1];if(!W.areEqual(a.dims,[t.n,i,s]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let o=e[2].dims;if(W.size(o)!==t.n*i)throw new Error("scales input size error.");if(e.length===4){let l=e[3].dims,d=t.n*(t.bits===8?i:Math.floor((i*t.bits+7)/8));if(W.size(l)!==d)throw new Error("zeroPoints input size error.")}},Vh=(e,t)=>{let n=e[0].dims,r=n.length,i=n[r-2],s=t.k,a=t.n,o=n.slice(0,r-2),l=W.size(o),d=e[1].dims[2]/4,p=e[0].dataType,f=ct(t.k),c=ct(d),m=ct(a),y=o.concat([i,a]),w=i>1&&a/m%2===0?2:1,$=W.size(y)/m/w,_=64,v=[],I=[l,i,s/f],S=W.convertShape(e[1].dims).slice();S.splice(-1,1,d/c),v.push(..._e(I)),v.push(..._e(S)),v.push(..._e(e[2].dims)),e.length===4&&v.push(..._e(W.convertShape(e[3].dims)));let E=[l,i,a/m];v.push(..._e(E));let R=O=>{let x=I.length,U=V("a",e[0].dataType,x,f),q=V("b",12,S.length,c),J=V("scales",e[2].dataType,e[2].dims.length),se=[U,q,J],ce=e.length===4?V("zero_points",12,e[3].dims.length):void 0;ce&&se.push(ce);let L=E.length,ue=be("output",e[0].dataType,L,m),ie=xt(e[0].dataType),te=(()=>{switch(f){case 1:return`array<${ie}, 8>`;case 2:return`mat4x2<${ie}>`;case 4:return`mat2x4<${ie}>`;default:throw new Error(`${f}-component is not supported.`)}})(),he=Math.floor(32/t.bits),Q=Math.floor(he/8),ve=()=>{let ee="";for(let ne=0;ne<Q;ne++){let Ue=ne*t.bits*4,lt=Ue+t.bits;ee+=`
+          // reuse a data (pass ${ne})
+            var input_offset${ne>0?ne:""} = ${ne===0?U.indicesToOffset(`${U.type.indices}(batch, row, word_offset)`):"input_offset"};
+            var a_data${ne>0?ne:""}: ${te};
+            for (var j${ne>0?ne:""}: u32 = 0; j${ne>0?ne:""} < ${8/f}; j${ne>0?ne:""}++) {
+              a_data${ne>0?ne:""}[j${ne>0?ne:""}] = ${U.getByOffset(`input_offset${ne>0?ne:""}`)};
+              input_offset${ne>0?ne:""}++;
+            }
+          `;for(let Xe=0;Xe<m*w;Xe++)ee+=`
+            b_value = ${c===1?`b${Xe}_data`:`b${Xe}_data[i]`};
+            ${t.bits===2?`{
+              let half_word = b_value >> ${ne*16}u;
+              let byte_lo = half_word & 0xFFu;
+              let byte_hi = (half_word >> 8u) & 0xFFu;
+              let spread_word = (byte_lo & 0xFu) | ((byte_lo >> 4u) << 8u) | ((byte_hi & 0xFu) << 16u) | ((byte_hi >> 4u) << 24u);
+              b_value_lower = unpack4xU8(spread_word & b_mask);
+              b_value_upper = unpack4xU8((spread_word >> 2u) & b_mask);
+            }`:`b_value_lower = unpack4xU8((b_value >> ${Ue}u) & b_mask);
+            b_value_upper = unpack4xU8((b_value >> ${lt}u) & b_mask);`}
+            b_quantized_values = ${te}(${Array.from({length:4},(Ke,ft)=>`${ie}(b_value_lower[${ft}]), ${ie}(b_value_upper[${ft}])`).join(", ")});
+            b_dequantized_values = ${f===1?`${te}(${Array.from({length:8},(Ke,ft)=>`(b_quantized_values[${ft}] - ${ce?`zero_point${Xe}`:"zero_point"}) * scale${Xe}`).join(", ")});`:`(b_quantized_values - ${te}(${Array(8).fill(`${ce?`zero_point${Xe}`:"zero_point"}`).join(",")})) * scale${Xe};`};
+            workgroup_shared[local_id.x * ${w} + ${Math.floor(Xe/m)}]${m>1?`[${Xe%m}]`:""} += ${Array.from({length:8/f},(Ke,ft)=>`${f===1?`a_data${ne>0?ne:""}[${ft}] * b_dequantized_values[${ft}]`:`dot(a_data${ne>0?ne:""}[${ft}], b_dequantized_values[${ft}])`}`).join(" + ")};
+          `}return ee},Y=()=>{let ee=`
+            var col_index = col * ${m};
+            ${ce?`
+            let zero_point_values_per_byte: u32 = ${Math.floor(8/t.bits)}u;
+            let zero_point_bytes_per_col = (nBlocksPerCol + zero_point_values_per_byte - 1u) / zero_point_values_per_byte;
+            var zero_point_byte_count: u32;
+            var zero_point_word_index: u32;
+            var zero_point_byte_offset: u32;
+            let zero_point_sub_offset: u32 = block % zero_point_values_per_byte;
+            var zero_point_bits_offset: u32;
+            var zero_point_word: u32;`:`
+            // The default zero point is ${Math.pow(2,t.bits-1)} for unsigned ${t.bits}-bit quantization.
+            let zero_point = ${ie}(${Math.pow(2,t.bits-1).toFixed(1)});`}
+            `;for(let ne=0;ne<m*w;ne++)ee+=`
+            let scale${ne} = ${J.getByOffset("col_index * nBlocksPerCol + block")};
+            ${ce?`
+            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block / zero_point_values_per_byte);
+            zero_point_word_index = zero_point_byte_count >> 0x2u;
+            zero_point_byte_offset = zero_point_byte_count & 0x3u;
+            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_sub_offset * ${t.bits}u);
+            zero_point_word = ${ce.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
+            let zero_point${ne} = ${ie}((zero_point_word) & ${t.bits===2?"0x3u":"0xFu"});`:""}
+            col_index += 1;`;return ee},P=()=>{let ee=`col_index = col * ${m};`;for(let ne=0;ne<m*w;ne++)ee+=`
+            let b${ne}_data = ${q.getByIndices(`${q.type.indices}(col_index, block, word)`)};
+            col_index += 1;`;return ee+=`
+            var b_value: u32;
+            let b_mask: u32 = ${t.bits===2?"0x03030303u":"0x0F0F0F0Fu"};
+            var b_value_lower: vec4<u32>;
+            var b_value_upper: vec4<u32>;
+            var b_quantized_values: ${te};
+            var b_dequantized_values: ${te};`,ee};return`
+        var<workgroup> workgroup_shared: array<${ue.type.value}, ${w*_}>;
+        ${O.declareVariables(...se,ue)}
+        ${O.mainStart([_,1,1])}
+          let output_indices = ${ue.offsetToIndices(`(global_idx / ${_}) * ${w}`)};
+          let col = output_indices[2];
+          let row = output_indices[1];
+          let batch = output_indices[0];
+          let nBlocksPerCol = uniforms.b_shape[1];
+
+          for (var block = local_id.x; block < nBlocksPerCol; block += ${_}) {
+            //process one block
+            var word_offset: u32 = block * ${t.blockSize/f};
+            ${Y()}
+            for (var word: u32 = 0; word < ${d}; word += ${c}) {
+              ${P()}
+              for (var i: u32 = 0; i < ${c}; i++) {
+                ${ve()}
+                word_offset += ${he/f};
+              }
+            }
+          }
+          workgroupBarrier();
+
+          if (local_id.x < ${w}) {
+            var output_value: ${ue.type.value} = ${ue.type.value}(0);
+            var workgroup_shared_offset: u32 = local_id.x;
+            for (var b: u32 = 0u; b < ${_}u; b++) {
+              output_value += workgroup_shared[workgroup_shared_offset];
+              workgroup_shared_offset += ${w};
+            }
+            ${ue.setByIndices(`${ue.type.indices}(batch, row, col + local_id.x)`,"output_value")};
+          }
+        }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${f};${c};${m};${w};${_}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:y,dataType:p}],dispatchGroup:{x:$},programUniforms:v}),getShaderSource:R}},Fh=(e,t)=>{let n=e[0].dims,r=n.length,i=n[r-2],s=t.k,a=t.n,o=n.slice(0,r-2),l=W.size(o),d=e[1].dims[2]/4,p=e[0].dataType,f=ct(t.k),c=ct(d),m=o.concat([i,a]),y=128,w=a%8===0?8:a%4===0?4:1,$=y/w,_=Math.floor(32/t.bits),v=$*c*_,I=v/f,S=v/t.blockSize,E=W.size(m)/w,R=[],O=[l,i,s/f],x=W.convertShape(e[1].dims).slice();x.splice(-1,1,d/c),R.push(..._e(O)),R.push(..._e(x)),R.push(..._e(e[2].dims)),e.length===4&&R.push(..._e(W.convertShape(e[3].dims)));let U=[l,i,a];R.push(..._e(U));let q=J=>{let se=O.length,ce=V("a",e[0].dataType,se,f),L=V("b",12,x.length,c),ue=V("scales",e[2].dataType,e[2].dims.length),ie=[ce,L,ue],te=e.length===4?V("zero_points",12,e[3].dims.length):void 0;te&&ie.push(te);let he=U.length,Q=be("output",e[0].dataType,he),ve=xt(e[0].dataType),Y=()=>{switch(f){case 1:return`
+          let a_data0 = vec4<${ve}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
+          let a_data1 = vec4<${ve}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
+          let a_data0 = vec4<${ve}>(sub_a[word_offset], sub_a[word_offset + 1]);
+          let a_data1 = vec4<${ve}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
+          let a_data0 = sub_a[word_offset];
+          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${f}-component is not supported.`)}};return`
+        var<workgroup> sub_a: array<${ce.type.value}, ${I}>;
+        var<workgroup> inter_results: array<array<${Q.type.value}, ${$}>, ${w}>;
+        ${J.declareVariables(...ie,Q)}
+        ${J.mainStart([$,w,1])}
+          let output_indices = ${Q.offsetToIndices(`workgroup_index * ${w}`)};
+          let col = output_indices[2];
+          let row = output_indices[1];
+          let batch = output_indices[0];
+          let n_blocks_per_col = uniforms.b_shape[1];
+          let num_tiles =  (n_blocks_per_col - 1) / ${S} + 1;
+
+          // Loop over shared dimension.
+          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
+            let a_col_start = tile * ${I};
+            // load one tile A data into shared memory.
+            for (var a_offset = local_idx; a_offset < ${I}; a_offset += ${y})
+            {
+              let a_col = a_col_start + a_offset;
+              if (a_col < uniforms.a_shape[2])
+              {
+                sub_a[a_offset] = ${ce.getByIndices(`${ce.type.indices}(batch, row, a_col)`)};
+              } else {
+                sub_a[a_offset] = ${ce.type.value}(0);
+              }
+            }
+            workgroupBarrier();
+
+            // each thread process one block
+            let b_row = col + local_id.y;
+            let block = tile * ${S} + local_id.x;
+            ${te?`
+            let zero_point_values_per_byte: u32 = ${Math.floor(8/t.bits)}u;
+            let zero_point_bytes_per_col = (n_blocks_per_col + zero_point_values_per_byte - 1u) / zero_point_values_per_byte;
+            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block / zero_point_values_per_byte);
+            let zero_point_word_index = zero_point_byte_count >> 0x2u;
+            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
+            let zero_point_sub_offset: u32 = block % zero_point_values_per_byte;
+            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_sub_offset * ${t.bits}u);
+            let zero_point_word = ${te.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
+            let zero_point = ${ve}((zero_point_word) & ${t.bits===2?"0x3u":"0xFu"});`:`
+            // The default zero point is ${Math.pow(2,t.bits-1)} for unsigned ${t.bits}-bit quantization.
+            let zero_point = ${ve}(${Math.pow(2,t.bits-1).toFixed(1)});`}
+            let scale = ${ue.getByOffset("b_row * n_blocks_per_col + block")};
+            let b_data = ${L.getByIndices(`${L.type.indices}(b_row, block, 0)`)};
+            var word_offset = local_id.x * ${t.blockSize/f};
+            for (var i: u32 = 0; i < ${c}; i++) {
+              let b_value = ${c===1?"b_data":"b_data[i]"};
+              ${(()=>{let P=Math.floor(_/8),ee="";for(let ne=0;ne<P;ne++){let Ue=ne*t.bits*4,lt=Ue+t.bits;ee+=`
+              ${Y()}
+              {${t.bits===2?`
+                let half_word = b_value >> ${ne*16}u;
+                let byte_lo = half_word & 0xFFu;
+                let byte_hi = (half_word >> 8u) & 0xFFu;
+                let spread_word = (byte_lo & 0xFu) | ((byte_lo >> 4u) << 8u) | ((byte_hi & 0xFu) << 16u) | ((byte_hi >> 4u) << 24u);
+                let b_value_lower = unpack4xU8(spread_word & 0x03030303u);
+                let b_value_upper = unpack4xU8((spread_word >> 2u) & 0x03030303u);`:`
+                let b_value_lower = unpack4xU8((b_value >> ${Ue}u) & 0x0F0F0F0Fu);
+                let b_value_upper = unpack4xU8((b_value >> ${lt}u) & 0x0F0F0F0Fu);`}
+                let b_quantized_values = mat2x4<${ve}>(${Array.from({length:4},(Xe,Ke)=>`${ve}(b_value_lower[${Ke}]), ${ve}(b_value_upper[${Ke}])`).join(", ")});
+                let b_dequantized_values = (b_quantized_values - mat2x4<${ve}>(${Array(8).fill("zero_point").join(",")})) * scale;
+                inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(Xe,Ke)=>`${`dot(a_data${Ke}, b_dequantized_values[${Ke}])`}`).join(" + ")};
+              }
+              word_offset += ${8/f};`}return ee})()}
+            }
+            workgroupBarrier();
+          }
+
+          if (local_idx < ${w}) {
+            var output_value: ${Q.type.value} = ${Q.type.value}(0);
+            for (var b = 0u; b < ${$}; b++) {
+              output_value += inter_results[local_idx][b];
+            }
+            if (col + local_idx < uniforms.output_shape[2])
+            {
+              ${Q.setByIndices(`${Q.type.indices}(batch, row, col + local_idx)`,"output_value")}
+            }
+          }
+        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${f};${c};${$};${w}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:m,dataType:p}],dispatchGroup:{x:E},programUniforms:R}),getShaderSource:q}},dw=(e,t)=>{Gh(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(Fh(e.inputs,t)):e.compute(Vh(e.inputs,t))},cw=e=>Fe(e)}),Hh,jh,Kh,Xh,Yh,Zh,Qh,Jh,pw,Y$=re(()=>{Te(),Ce(),Ee(),Hh=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},jh=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
+            k = i32(${e.indicesGet("indices",i)}) - ${we("uniforms.pads",i,n)};
+            if (k < 0) {
+              break;
+            }
+            if (k >= i32(${we("uniforms.x_shape",i,t)})) {
+              break;
+            }
+            offset += k * i32(${we("uniforms.x_strides",i,t)});
+        `;return`
+          value = ${e.type.value}(uniforms.constant_value);
+          for (var i = 0; i < 1; i++) {
+            var offset = 0;
+            var k = 0;
+            ${r}
+            value = x[offset];
+          }
+      `},Kh=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
+                k = i32(${e.indicesGet("indices",i)}) - ${we("uniforms.pads",i,n)};
+                if (k < 0) {
+                  k = -k;
+                }
+                {
+                  let _2n_1 = 2 * (i32(${we("uniforms.x_shape",i,t)}) - 1);
+                  k = k % _2n_1;
+                  if(k >= i32(${we("uniforms.x_shape",i,t)})) {
+                    k = _2n_1 - k;
+                  }
+                }
+                offset += k * i32(${we("uniforms.x_strides",i,t)});
+            `;return`
+              var offset = 0;
+              var k = 0;
+              ${r}
+              value = x[offset];
+          `},Xh=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
+                k = i32(${e.indicesGet("indices",i)}) - ${we("uniforms.pads",i,n)};
+                if (k < 0) {
+                  k = 0;
+                }
+                if (k >= i32(${we("uniforms.x_shape",i,t)})) {
+                  k = i32(${we("uniforms.x_shape",i,t)}) - 1;
+                }
+                offset += k * i32(${we("uniforms.x_strides",i,t)});
+            `;return`
+              var offset = 0;
+              var k = 0;
+              ${r}
+              value = x[offset];
+          `},Yh=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
+                k = i32(${e.indicesGet("indices",i)}) - ${we("uniforms.pads",i,n)};
+                if (k < 0)  {
+                  k += i32(${we("uniforms.x_shape",i,t)}]);
+                }
+                if (k >= i32(${we("uniforms.x_shape",i,t)})) {
+                  k -= i32(${we("uniforms.x_shape",i,t)});
+                }
+                offset += k * i32(${we("uniforms.x_strides",i,t)});
+            `;return`
+              var offset = 0;
+              var k = 0;
+              ${r}
+              value = x[offset];
+          `},Zh=(e,t,n)=>{switch(n.mode){case 0:return jh(e,t,n.pads.length);case 1:return Kh(e,t,n.pads.length);case 2:return Xh(e,t,n.pads.length);case 3:return Yh(e,t,n.pads.length);default:throw new Error("Invalid mode")}},Qh=(e,t)=>{let n=W.padShape(e[0].dims.slice(),t.pads),r=e[0].dims,i=W.size(n),s=[{type:12,data:i},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;t.mode===0&&s.push({type:a?e[2].dataType:1,data:t.value}),s.push(..._e(e[0].dims,n));let o=["rank"],l=d=>{let p=be("output",e[0].dataType,n.length),f=V("x",e[0].dataType,r.length),c=f.type.value,m=Zh(p,r.length,t),y=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&y.push({name:"constant_value",type:a?c:"f32"}),`
+            ${d.registerUniforms(y).declareVariables(f,p)}
+            ${d.mainStart()}
+            ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+
+            let indices = ${p.offsetToIndices("global_idx")};
+
+            var value = ${c}(0);
+            ${m}
+            output[global_idx] = value;
+        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${a}`,inputDependencies:o},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(W.size(n)/64)},programUniforms:s}),getShaderSource:l}},Jh=(e,t)=>{if(e.length>1){let n=e[1].getBigInt64Array(),r=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,i=e[0].dims.length,s=new Int32Array(2*i).fill(0);if(e.length>=4){let o=e[3].getBigInt64Array();for(let l=0;l<o.length;l++)s[Number(o[l])]=Number(n[l]),s[Number(o[l])+i]=Number(n[l+o.length])}else n.forEach((o,l)=>s[Number(l)]=Number(o));let a=[];return s.forEach(o=>a.push(o)),{mode:t.mode,value:r,pads:a}}else return t},pw=(e,t)=>{Hh(e.inputs);let n=Jh(e.inputs,t);e.compute(Qh(e.inputs,n),{inputs:[0]})}}),as,xl,$l,Sl,Tl,em,tm,Il,Cl,fw,hw,El,mw,gw,kl,yw,bw,ww,_w,Z$=re(()=>{on(),Te(),Ce(),Ee(),as=e=>{if(Be.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},xl=(e,t,n)=>{let r=t.format==="NHWC",i=e.dims.slice();r&&i.splice(1,0,i.pop());let s=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),o=t.strides.slice(),l=s?t.dilations.slice():[],d=t.pads.slice();La.adjustPoolAttributes(n,i,a,o,l,d);let p=La.computePoolOutputShape(n,i,o,l,a,d,t.autoPad),f=Object.assign({},t);s?Object.assign(f,{kernelShape:a,strides:o,pads:d,dilations:l,cacheKey:t.cacheKey}):Object.assign(f,{kernelShape:a,strides:o,pads:d,cacheKey:t.cacheKey});let c=p.slice();return c.push(c.splice(1,1)[0]),[f,r?c:p]},$l=(e,t)=>{let n=t.format==="NHWC",r=W.size(e),i=W.size(t.kernelShape),s=[{type:12,data:r},{type:12,data:i}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let o=t.kernelShape[t.kernelShape.length-1],l=t.strides[t.strides.length-1],d=t.pads[t.pads.length/2-1],p=t.pads[t.pads.length-1],f=!!(d+p);s.push({type:12,data:o},{type:12,data:l},{type:12,data:d},{type:12,data:p}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let c=!1;if(t.kernelShape.length===2){let m=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],w=t.pads[t.pads.length/2-2],$=t.pads[t.pads.length-2];c=!!(w+$),s.push({type:12,data:m},{type:12,data:y},{type:12,data:w},{type:12,data:$}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,a,!0,f,c]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=W.computeStrides(t.kernelShape);s.push({type:12,data:o},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let l=t.pads.reduce((d,p)=>d+p);return[s,a,!!l,!1,!1]}},Sl=(e,t,n,r,i,s,a,o,l,d,p,f)=>{let c=i.format==="NHWC",m=t.type.value,y=be("output",t.type.tensor,r);if(i.kernelShape.length<=2){let w="",$="",_="",v=n-(c?2:1);if(p?w=`
+                for (var i: u32 = 0u; i < uniforms.kw; i++) {
+                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;
+                  if (xIndices[${v}] < 0 || xIndices[${v}]
+                      >= uniforms.x_shape[${v}]) {
+                    pad++;
+                    continue;
+                  }
+                  let x_val = x[${t.indicesToOffset("xIndices")}];
+                  ${s}
+                }`:w=`
+                for (var i: u32 = 0u; i < uniforms.kw; i++) {
+                  xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i;
+                  let x_val = x[${t.indicesToOffset("xIndices")}];
+                  ${s}
+                }`,i.kernelShape.length===2){let I=n-(c?3:2);f?$=`
+                for (var j: u32 = 0u; j < uniforms.kh; j++) {
+                  xIndices[${I}] = indices[${I}] * uniforms.sh - uniforms.phStart + j;
+                  if (xIndices[${I}] < 0 || xIndices[${I}] >= uniforms.x_shape[${I}]) {
+                    pad += i32(uniforms.kw);
+                    continue;
+                  }
+              `:$=`
+                for (var j: u32 = 0u; j < uniforms.kh; j++) {
+                  xIndices[${I}] = indices[${I}] * uniforms.sh - uniforms.phStart + j;
+                `,_=`
+              }
+            `}return`
+            ${e.registerUniforms(l).declareVariables(t,y)}
+
+            ${e.mainStart()}
+              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+
+              let indices = ${y.offsetToIndices("global_idx")};
+              var xIndices = ${y.offsetToIndices("global_idx")};
+
+              var value = ${m}(${o});
+              var pad = 0;
+              ${$}
+              ${w}
+              ${_}
+              ${a}
+
+              output[global_idx] = value;
+            }`}else{if(c)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let w=i.kernelShape.length,$=i.pads.length,_="";return d?_=`
+                if (xIndices[j] >= uniforms.x_shape[j]) {
+                  pad++;
+                  isPad = true;
+                  break;
+                }
+              }
+              if (!isPad) {
+                let x_val = x[${t.indicesToOffset("xIndices")}];
+                ${s}
+              }`:_=`
+              }
+              let x_val = x[${t.indicesToOffset("xIndices")}];
+              ${s}
+            `,`
+            ${e.registerUniforms(l).declareVariables(t,y)}
+
+            ${e.mainStart()}
+              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+              let indices = ${y.offsetToIndices("global_idx")};
+              var xIndices = ${y.offsetToIndices("global_idx")};
+
+              var offsets: array<u32, ${w}>;
+
+              var value = ${m}(${o});
+              var pad = 0;
+              var isPad = false;
+
+              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
+                var offset = i;
+                for (var j = 0u; j < ${w-1}u; j++) {
+                  offsets[j] = offset / ${we("uniforms.kernelStrides","j",w)};
+                  offset -= offsets[j] * ${we("uniforms.kernelStrides","j",w)};
+                }
+                offsets[${w-1}] = offset;
+
+                isPad = false;
+                for (var j = ${n-w}u; j < ${n}u; j++) {
+                  xIndices[j] = indices[j] * ${we("uniforms.strides",`j - ${n-w}u`,w)}
+                    + offsets[j - ${n-w}u] - ${we("uniforms.pads","j - 2u",$)};
+                  ${_}
+              }
+              ${a}
+
+              output[global_idx] = value;
+            }`}},Tl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,em=e=>`${Tl(e)};${e.countIncludePad}`,tm=e=>`${Tl(e)};${e.storageOrder};${e.dilations}`,Il=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Cl=(e,t,n,r)=>{let[i,s]=xl(t,r,n),a=V("x",t.dataType,t.dims.length),o=a.type.value,l="value += x_val;",d="";i.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[p,f,c,m,y]=$l(s,i);p.push(..._e(t.dims,s));let w=["rank"];return{name:e,shaderCache:{hint:`${r.cacheKey};${c};${m};${y}`,inputDependencies:w},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(W.size(s)/64)},programUniforms:p}),getShaderSource:$=>Sl($,a,t.dims.length,s.length,i,l,d,0,f,c,m,y)}},fw=e=>{let t=e.count_include_pad!==0,n=Il(e);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let r={countIncludePad:t,...n,cacheKey:""};return{...r,cacheKey:em(r)}},hw=(e,t)=>{as(e.inputs),e.compute(Cl("AveragePool",e.inputs[0],!1,t))},El={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},mw=e=>{let t=e.format;return{format:t,...El,cacheKey:t}},gw=(e,t)=>{as(e.inputs),e.compute(Cl("GlobalAveragePool",e.inputs[0],!0,t))},kl=(e,t,n,r)=>{let[i,s]=xl(t,r,n),a=`
+      value = max(x_val, value);
+    `,o="",l=V("x",t.dataType,t.dims.length),d=["rank"],[p,f,c,m,y]=$l(s,i);return p.push(..._e(t.dims,s)),{name:e,shaderCache:{hint:`${r.cacheKey};${c};${m};${y}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(W.size(s)/64)},programUniforms:p}),getShaderSource:w=>Sl(w,l,t.dims.length,s.length,i,a,o,t.dataType===10?-65504:-1e5,f,c,m,y)}},yw=(e,t)=>{as(e.inputs),e.compute(kl("MaxPool",e.inputs[0],!1,t))},bw=e=>{let t=e.storage_order,n=e.dilations,r=Il(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:n,...r,cacheKey:""};return{...i,cacheKey:tm(i)}},ww=e=>{let t=e.format;return{format:t,...El,cacheKey:t}},_w=(e,t)=>{as(e.inputs),e.compute(kl("GlobalMaxPool",e.inputs[0],!0,t))}}),nm,rm,vw,xw,Q$=re(()=>{Te(),Ce(),pt(),Ee(),nm=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((n,r)=>n===e[2].dims[r]).reduce((n,r)=>n&&r,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((i,s)=>s===t.axis||i===e[0].dims[s]).reduce((i,s)=>i&&s,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let n=e[0].dims[t.axis],r=e[1].dims[t.axis];if(t.blockSize<Math.ceil(n/r)||t.blockSize>Math.ceil(n/(r-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},rm=(e,t)=>{let n=W.normalizeAxis(t.axis,e[0].dims.length),r=e[0].dataType,i=r===3,s=e[0].dims,a=e[1].dataType,o=W.size(s),l=r===3||r===2,d=l?[Math.ceil(W.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,f=e.length>2?e[2]:void 0,c=f?l?[Math.ceil(W.size(f.dims)/4)]:f.dims:void 0,m=p.length===0||p.length===1&&p[0]===1,y=m===!1&&p.length===1,w=ct(o),$=m&&(!l||w===4),_=$?w:1,v=$&&!l?w:1,I=V("input",l?12:r,d.length,v),S=V("scale",a,p.length),E=f?V("zero_point",l?12:r,c.length):void 0,R=be("output",a,s.length,_),O=[I,S];E&&O.push(E);let x=[d,p];f&&x.push(c);let U=[{type:12,data:o/_},{type:12,data:n},{type:12,data:t.blockSize},..._e(...x,s)],q=J=>{let se=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
+      ${J.registerUniforms(se).declareVariables(...O,R)}
+      ${J.mainStart()}
+          ${J.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+          let output_indices = ${R.offsetToIndices("global_idx")};
+
+          // Set input x
+          ${l?`
+            let input = ${I.getByOffset("global_idx / 4")};
+            let x_vec = ${i?"unpack4xI8(input)":"unpack4xU8(input)"};
+            let x_value = ${_===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${I.getByOffset("global_idx")};`};
+
+          // Set scale input
+          ${m?`let scale_value= ${S.getByOffset("0")}`:y?`
+            let scale_index = ${R.indicesGet("output_indices","uniforms.axis")};
+            let scale_value= ${S.getByOffset("scale_index")};`:`
+            var scale_indices: ${S.type.indices} = output_indices;
+            let index = ${S.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
+            ${S.indicesSet("scale_indices","uniforms.axis","index")};
+            let scale_value= ${S.getByIndices("scale_indices")};`};
+
+          // Set zero-point input
+          ${E?m?l?`
+                let zero_point_input = ${E.getByOffset("0")};
+                let zero_point_vec =  ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
+                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${E.getByOffset("0")}`:y?l?`
+                let zero_point_index = ${R.indicesGet("output_indices","uniforms.axis")};
+                let zero_point_input = ${E.getByOffset("zero_point_index / 4")};
+                let zero_point_vec =  ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
+                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
+                let zero_point_index = ${R.indicesGet("output_indices","uniforms.axis")};
+                let zero_point_value = ${E.getByOffset("zero_point_index")};`:l?`
+                let zero_point_offset = ${S.indicesToOffset("scale_indices")};
+                let zero_point_input = ${E.getByOffset("zero_point_offset / 4")};
+                let zero_point_vec = ${i?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
+                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${E.getByIndices("scale_indices")};`:`let zero_point_value = ${l?i?"i32":"u32":I.type.value}(0);`};
+      // Compute and write output
+      ${R.setByOffset("global_idx",`${R.type.value}(x_value - zero_point_value) * scale_value`)};
+      }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:E?["rank","rank","rank"]:["rank","rank"]},getShaderSource:q,getRunData:()=>({outputs:[{dims:s,dataType:a}],dispatchGroup:{x:Math.ceil(o/_/64),y:1,z:1},programUniforms:U})}},vw=(e,t)=>{nm(e.inputs,t),e.compute(rm(e.inputs,t))},xw=e=>Fe({axis:e.axis,blockSize:e.blockSize})}),im,sm,$w,J$=re(()=>{on(),Te(),Ee(),im=(e,t,n)=>{let r=e===t,i=e<t&&n<0,s=e>t&&n>0;if(r||i||s)throw new Error("Range these inputs' contents are invalid.")},sm=(e,t,n,r)=>{let i=Math.abs(Math.ceil((t-e)/n)),s=[i],a=i,o=[{type:12,data:a},{type:r,data:e},{type:r,data:n},..._e(s)],l=d=>{let p=be("output",r,s.length),f=p.type.value,c=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`
+        ${d.registerUniforms(c).declareVariables(p)}
+        ${d.mainStart()}
+        ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;
+      }`};return{name:"Range",shaderCache:{hint:`${r}`},getShaderSource:l,getRunData:()=>({outputs:[{dims:s,dataType:r}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:o})}},$w=e=>{let t=0,n=0,r=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],n=e.inputs[1].getInt32Array()[0],r=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],n=e.inputs[1].getFloat32Array()[0],r=e.inputs[2].getFloat32Array()[0]),Be.webgpu.validateInputContent&&im(t,n,r),e.compute(sm(t,n,r,e.inputs[0].dataType),{inputs:[]})}}),am,om,Sw,Tw,e2=re(()=>{Te(),Ce(),pt(),Ee(),am=(e,t,n,r)=>{if(e!=="none"&&r!=="i32"&&r!=="u32"&&r!=="f32")throw new Error(`Input ${r} is not supported with reduction ${e}.`);let i=`{
+                var oldValue = 0;
+                loop {
+                  let newValueF32 =`,s=`;
+                  let newValue = bitcast<i32>(newValueF32);
+                  let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue);
+                  if res.exchanged {
+                    break;
+                  }
+                  oldValue = res.old_value;
+                }
+              }`;switch(e){case"none":return`${t}=${n};`;case"add":return r==="i32"||r==="u32"?`atomicAdd(&${t}, bitcast<${r}>(${n}));`:`
+              ${i}bitcast<${r}>(oldValue) + (${n})${s}`;case"max":return r==="i32"||r==="u32"?`atomicMax(&${t}, bitcast<${r}>(${n}));`:`
+                ${i}max(bitcast<f32>(oldValue), (${n}))${s}`;case"min":return r==="i32"||r==="u32"?`atomicMin(&${t}, bitcast<${r}>(${n}));`:`${i}min(bitcast<${r}>(oldValue), (${n}))${s}`;case"mul":return`${i}(bitcast<${r}>(oldValue) * (${n}))${s}`;default:throw new Error(`Reduction ${e} is not supported.`)}},om=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n,s=1,a=Math.ceil(W.sizeToDimension(r,r.length-1)/s),o=r[r.length-1],l=W.sizeFromDimension(n,o),d=[{type:12,data:a},{type:12,data:o},{type:12,data:l},..._e(e[1].dims,e[2].dims,i)],p=f=>{let c=V("indices",e[1].dataType,e[1].dims.length),m=V("updates",e[2].dataType,e[2].dims.length,s),y=t.reduction!=="none"&&t.reduction!==""?J0("output",e[0].dataType,i.length):be("output",e[0].dataType,i.length,s);return`
+      ${f.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(c,m,y)}
+      ${f.mainStart()}
+        ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+  var data_offset = 0u;
+  let indices_start = uniforms.last_index_dimension * global_idx;
+  let indices_end = indices_start + uniforms.last_index_dimension;
+  for (var i = indices_start; i < indices_end; i++) {
+    var index = i32(indices[i].x);
+    ${e[0].dims.length===1?`
+    let element_count_dim = uniforms.output_strides;
+    let dim_value = uniforms.output_shape;`:`
+    let element_count_dim = uniforms.output_strides[i - indices_start];
+    let dim_value = uniforms.output_shape[i - indices_start];`}
+    if (index >= 0) {
+      if (index >= i32(dim_value)) {
+        index = i32(dim_value - 1);
+      }
+    } else {
+      if (index < -i32(dim_value)) {
+        index = 0;
+      } else {
+        index += i32(dim_value);
+      }
+    }
+    data_offset += u32((u32(index) * element_count_dim));
+  }
+
+  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
+    let value = updates[uniforms.num_updates_elements * global_idx + i];
+    ${am(t.reduction,"output[data_offset + i]","value",y.type.value)}
+  }
+
+      }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:d}),getShaderSource:p}},Sw=e=>Fe({reduction:e.reduction}),Tw=(e,t)=>{e.compute(om(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}}),lm,um,dm,zl,cm,pm,fm,hm,mm,gm,ym,bm,Al,wm,_m,vm,xm,$m,Iw,Cw,t2=re(()=>{Te(),Ce(),pt(),Ee(),lm=(e,t)=>{if(e.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
+            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},um=(e,t,n)=>{t.every(i=>i>=0&&i<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let r=new Array(n).fill(1);return t.forEach((i,s)=>r[i]=e[s]),r},dm=(e,t,n,r,i,s)=>{let[a,o,l]=n>10?[1,2,3]:[-1,e.length>1?1:-1,-1],d=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(p=>s.push(p));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&e.length>o&&e[o].dims.length===1&&e[o].dims[0]>0){if(e[o].getFloat32Array().forEach(p=>r.push(p)),r.length!==0&&r.length!==d&&n>=18&&r.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");lm(r,t),t.axes.length>0&&um(r,t.axes,d).forEach((p,f)=>r[f]=p)}if(l>0&&e.length>l&&e[l].dims.length===1&&e[l].dims[0]>0&&(e[l].getBigInt64Array().forEach(p=>i.push(Number(p))),i.length!==0&&i.length!==d&&n>=18&&i.length!==t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(r.length!==0&&r.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==0&&i.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof r<"u"&&typeof i<"u"&&r.length>0&&i.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},zl=(e,t,n,r)=>`
+  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
+  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
+  // offset-by-one error later in floor().
+  let big = (${e}) * (${t});
+  let whole = ${r}(big / (${n}));
+  let fract = ${r}(big % (${n})) / ${r}(${n});
+  return whole + fract;
+`,cm=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
+     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`
+          if (xScale < 1.0 || floor(xScale) != xScale) {
+            return ${t}(xResized) / ${t}(xScale);
+          } else {
+            ${zl("xResized","lengthOriginal","lengthResized",t)}
+          }
+        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
+                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
+                  } else {
+                    return 0.0;
+                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
+                    return 0.0;
+                  } else {
+                    ${zl("xResized","lengthOriginal - 1","lengthResized - 1",t)}
+                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
+                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
+                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
+                        ${t}(lengthResized - 1);
+                  } else {
+                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
+                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
+                  const adjustment = ${t}(lengthResized) / outputWidth;
+                  const center = ${t}(lengthOriginal) / 2;
+                  const offset = center * (1 - adjustment);
+                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",pm=(e,t,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",fm=(e,t,n)=>{let r=new Array(n).fill(0).concat(new Array(n).fill(1)),i=e.length===0?r:e.slice();return t.length>0?(t.forEach((s,a)=>{r[s]=i[a],r[a+n]=i[t.length+a]}),r):i},hm=(e,t,n,r)=>{let i=[];if(n.length>0)if(r.length>0){if(e.forEach(s=>i.push(s)),Math.max(...r)>e.length)throw new Error("axes is out of bound");r.forEach((s,a)=>i[s]=n[a])}else n.forEach(s=>i.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((s,a)=>Math.round(s*t[a]))}return i},mm=(e,t,n)=>{let r=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return n.axes.length>0?(n.axes.forEach(s=>t[s]=r),n.axes.forEach(s=>i[s]=Math.round(e[s]*t[s]))):(t.fill(r,0,t.length),i.forEach((s,a)=>i[a]=Math.round(s*t[a]))),i},gm=(e,t,n,r,i)=>`
+    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${n.length}> {
+      var original_indices: array<${e.type.value}, ${n.length}>;
+      for (var i:u32 = 0; i < ${n.length}; i++) {
+        var output_index = ${e.indicesGet("output_indices","i")};
+        var scale = ${we("uniforms.scales","i",r)};
+        var roi_low = ${we("uniforms.roi","i",i)};
+        var roi_hi = ${we("uniforms.roi",`i + ${t.length}`,i)};
+        if (scale == 1.0) {
+          original_indices[i] = ${e.type.value}(output_index);
+        } else {
+          var input_shape_i = ${we("uniforms.input_shape","i",t.length)};
+          var output_shape_i = ${we("uniforms.output_shape","i",n.length)};
+          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
+                                                                           input_shape_i, roi_low, roi_hi);
+        }
+      }
+      return original_indices;
+    }`,ym=(e,t,n,r,i,s,a)=>`
+    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
+      var input_indices: ${e.type.indices};
+      for (var i:u32 = 0; i < ${r.length}; i++) {
+        var output_index = ${t.indicesGet("output_indices","i")};
+        var input_index: u32;
+        var scale = ${we("uniforms.scales","i",i)};
+        if (scale == 1.0) {
+          input_index = output_index;
+        } else {
+          var roi_low = ${we("uniforms.roi","i",s)};
+          var roi_hi = ${we("uniforms.roi",`i + ${n.length}`,s)};
+          var input_shape_i = ${we("uniforms.input_shape","i",n.length)};
+          var output_shape_i = ${we("uniforms.output_shape","i",r.length)};
+          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
+                                                                        input_shape_i, roi_low, roi_hi);
+          if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
+            if (original_idx < 0) {
+              input_index = 0;
+            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
+              input_index = input_shape_i - 1;
+            } else {
+              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
+            }
+          } else {
+            input_index = u32(original_idx);
+          }
+        }
+        ${e.indicesSet("input_indices","i","input_index")}
+      }
+      return input_indices;
+    }`,bm=(e,t)=>`
+    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
+      for (var i:u32 = 0; i < ${t.length}; i++) {
+        var input_index = ${e.indicesGet("input_indices","i")};
+        if (input_index < 0 || input_index >= ${we("uniforms.input_shape","i",t.length)}) {
+          return false;
+        }
+      }
+      return true;
+    }`,Al=(e,t,n,r)=>e.rank>r?`
+    ${e.indicesSet("input_indices",t,"channel")};
+    ${e.indicesSet("input_indices",n,"batch")};
+`:"",wm=(e,t,n,r,i)=>{let[s,a,o,l]=n.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`
+    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
+      var input_indices: ${e.type.indices};
+      ${e.indicesSet("input_indices",a,`max(0, min(row, ${n[a]} - 1))`)};
+      ${e.indicesSet("input_indices",o,`max(0, min(col, ${n[o]} - 1))`)};
+      ${Al(e,l,s,2)}
+      return ${e.getByIndices("input_indices")};
+    }
+
+    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {
+      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
+      var row:${d} = originalIndices[${a}];
+      var col:${d} = originalIndices[${o}];
+      ${r?`if (row < 0 || row > (${n[a]} - 1) || col < 0 || col > (${n[o]} - 1)) {
+        return ${i};
+      }`:""};
+      row = max(0, min(row, ${n[a]} - 1));
+      col = max(0, min(col, ${n[o]} - 1));
+      var row1: u32 = u32(row);
+      var col1: u32 = u32(col);
+      var row2: u32 = u32(row + 1);
+      var col2: u32 = u32(col + 1);
+      var channel: u32 = ${n.length>2?`u32(originalIndices[${l}])`:"0"};
+      var batch: u32 =  ${n.length>2?`u32(originalIndices[${s}])`:"0"};
+      var x11: ${d} = getInputValue(batch, channel, row1, col1);
+      var x12: ${d} = getInputValue(batch, channel, row1, col2);
+      var x21: ${d} = getInputValue(batch, channel, row2, col1);
+      var x22: ${d} = getInputValue(batch, channel, row2, col2);
+      var dx1: ${d} = abs(row - ${d}(row1));
+      var dx2: ${d} = abs(${d}(row2) - row);
+      var dy1: ${d} = abs(col - ${d}(col1));
+      var dy2: ${d} = abs(${d}(col2) - col);
+      if (row1 == row2) {
+        dx1 = 0.5;
+        dx2 = 0.5;
+      }
+      if (col1 == col2) {
+        dy1 = 0.5;
+        dy2 = 0.5;
+      }
+      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
+    }`},_m=(e,t,n,r,i,s,a,o,l,d)=>{let p=n.length===2,[f,c]=p?[0,1]:[2,3],m=e.type.value,y=w=>{let $=w===f?"row":"col";return`
+      fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${m} {
+        var output_index = ${t.indicesGet("output_indices",w)};
+        var originalIdx: ${m} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[w]},
+        ${r[w]}, ${n[w]}, ${s[w]}, ${s[w]} + ${n.length});
+        var fractOriginalIdx: ${m} = originalIdx - floor(originalIdx);
+        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);
+
+        if (${o} && (originalIdx < 0 || originalIdx > (${n[w]} - 1))) {
+          return ${l};
+        }
+        var data: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0);
+        for (var i: i32 = -1; i < 3; i++) {
+          var ${$}: ${m} = originalIdx + ${m}(i);
+          if (${$} < 0 || ${$} >= ${n[w]}) {
+            ${d?`coefs[i + 1] = 0.0;
+                        continue;`:o?`return ${l};`:`${$} = max(0, min(${$}, ${n[w]} - 1));`};
+          }
+        var input_indices_copy: ${e.type.indices} = input_indices;
+          ${e.indicesSet("input_indices_copy",w,`u32(${$})`)};
+          data[i + 1] = ${w===f?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
+        }
+        return cubicInterpolation1D(data, coefs);
+      }`};return`
+    ${y(f)};
+    ${y(c)};
+  fn getCubicInterpolationCoefs(s: ${m}) -> array<${m}, 4> {
+    var absS = abs(s);
+    var coeffs: array<${m}, 4> = array<${m}, 4>(0.0, 0.0, 0.0, 0.0);
+    var oneMinusAbsS: ${m} = 1.0 - absS;
+    var twoMinusAbsS: ${m} = 2.0 - absS;
+    var onePlusAbsS: ${m} = 1.0 + absS;
+    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};
+    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;
+    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
+    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};
+    return coeffs;
+  }
+
+  fn cubicInterpolation1D(x: array<${m}, 4>, coefs: array<${m}, 4>) -> ${m} {
+    var coefsSum: ${m} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
+    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
+  }
+
+  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${m} {
+    var input_indices: ${e.type.indices} = output_indices;
+    return colCubicInterpolation(input_indices, output_indices);
+  }
+    `},vm=(e,t,n,r,i)=>{let[s,a,o,l,d]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],p=e.type.value;return`
+    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${p} {
+      var input_indices: ${e.type.indices};
+      ${e.indicesSet("input_indices",a,`max(0, min(depth, ${n[a]} - 1))`)};
+      ${e.indicesSet("input_indices",o,`max(0, min(height, ${n[o]} - 1))`)};
+      ${e.indicesSet("input_indices",l,`max(0, min(width, ${n[l]} - 1))`)};
+      ${Al(e,d,s,3)}
+      return ${e.getByIndices("input_indices")};
+    }
+
+    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${p} {
+      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
+      var depth:${p} = originalIndices[${a}];
+      var height:${p} = originalIndices[${o}];
+      var width:${p} = originalIndices[${l}];
+      ${r?`if (depth < 0 || depth > (${n[a]} - 1) || height < 0 || height > (${n[o]} - 1) || width < 0 || (width > ${n[l]} - 1)) {
+      return ${i};
+        }`:""};
+
+    depth = max(0, min(depth, ${n[a]} - 1));
+      height = max(0, min(height, ${n[o]} - 1));
+      width = max(0, min(width, ${n[l]} - 1));
+      var depth1: u32 = u32(depth);
+      var height1: u32 = u32(height);
+      var width1: u32 = u32(width);
+      var depth2: u32 = u32(depth + 1);
+      var height2: u32 = u32(height + 1);
+      var width2: u32 = u32(width + 1);
+      var channel: u32 = ${n.length>3?`u32(originalIndices[${d}])`:"0"};
+      var batch: u32 =  ${n.length>3?`u32(originalIndices[${s}])`:"0"};
+
+      var x111: ${p} = getInputValue(batch, channel, depth1, height1, width1);
+      var x112: ${p} = getInputValue(batch, channel, depth1, height1, width2);
+      var x121: ${p} = getInputValue(batch, channel, depth1, height2, width1);
+      var x122: ${p} = getInputValue(batch, channel, depth1, height2, width2);
+      var x211: ${p} = getInputValue(batch, channel, depth2, height1, width1);
+      var x212: ${p} = getInputValue(batch, channel, depth2, height1, width2);
+      var x221: ${p} = getInputValue(batch, channel, depth2, height2, width1);
+      var x222: ${p} = getInputValue(batch, channel, depth2, height2, width2);
+      var dx1: ${p} = abs(depth - ${p}(depth1));
+      var dx2: ${p} = abs(${p}(depth2) - depth);
+      var dy1: ${p} = abs(height - ${p}(height1));
+      var dy2: ${p} = abs(${p}(height2) - height);
+      var dz1: ${p} = abs(width - ${p}(width1));
+      var dz2: ${p} = abs(${p}(width2) - width);
+      if (depth1 == depth2) {
+        dx1 = 0.5;
+        dx2 = 0.5;
+      }
+      if (height1 == height2) {
+        dy1 = 0.5;
+        dy2 = 0.5;
+      }
+      if (width1 == width2) {
+        dz1 = 0.5;
+        dz2 = 0.5;
+      }
+      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
+              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
+    }`},xm=(e,t,n,r,i,s)=>{let a=e.dims,o=fm(s,t.axes,a.length),l=hm(a,r,i,t.axes),d=r.slice();r.length===0&&(d=a.map((v,I)=>v===0?1:l[I]/v),t.keepAspectRatioPolicy!=="stretch"&&(l=mm(a,d,t)));let p=be("output",e.dataType,l.length),f=V("input",e.dataType,a.length),c=W.size(l),m=a.length===l.length&&a.every((v,I)=>v===l[I]),y=t.coordinateTransformMode==="tf_crop_and_resize",w=t.extrapolationValue,$=f.type.value,_=v=>`
+      ${m?"":`
+      ${cm(t.coordinateTransformMode,$)};
+      ${(()=>{switch(t.mode){case"nearest":return`
+              ${bm(f,a)};
+              ${pm(t.nearestMode,n,$)};
+              ${ym(f,p,a,l,d.length,o.length,y)};
+              `;case"linear":return`
+              ${gm(p,a,l,d.length,o.length)};
+              ${(()=>{if(a.length===2||a.length===4)return`${wm(f,p,a,y,w)}`;if(a.length===3||a.length===5)return`${vm(f,p,a,y,w)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
+            `;case"cubic":return`
+            ${(()=>{if(a.length===2||a.length===4)return`${_m(f,p,a,l,d,o,t.cubicCoeffA,y,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
+            `;default:throw Error("Invalid resize mode")}})()};
+      `}
+      ${v.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(f,p)}
+      ${v.mainStart()}
+        ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+        ${m?"output[global_idx] = input[global_idx];":`
+        let output_indices = ${p.offsetToIndices("global_idx")};
+        var input_indices: ${f.type.indices};
+        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
+                if (checkInputIndices(input_indices)) {
+                  output[global_idx] = ${f.getByIndices("input_indices")};
+                } else {
+                  output[global_idx] = ${t.extrapolationValue};
+                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
+`}
+      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${n}|${d.length>0?t.mode==="cubic"?d:d.length:""}|${i.length>0?i:""}|${o.length>0?o:""}|${m}|${t.mode==="nearest"?a.length:a}`,inputDependencies:["rank"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:[{type:12,data:c},{type:1,data:d},{type:1,data:o},..._e(a,l)]})}},$m=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Iw=(e,t)=>{let n=[],r=[],i=[],s=$m(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");dm(e.inputs,t,s,n,r,i),e.compute(xm(e.inputs[0],t,s,n,r,i),{inputs:[0]})},Cw=e=>{let t=e.antialias,n=e.axes,r=e.coordinateTransformMode,i=e.cubicCoeffA,s=e.excludeOutside!==0,a=e.extrapolationValue,o=e.keepAspectRatioPolicy,l=e.mode,d=e.nearestMode===""?"simple":e.nearestMode;return Fe({antialias:t,axes:n,coordinateTransformMode:r,cubicCoeffA:i,excludeOutside:s,extrapolationValue:a,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),Sm,Tm,Ew,n2=re(()=>{Te(),Ce(),Ee(),Sm=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],n=e[1],r=e[2];if(t.dataType!==n.dataType||t.dataType!==r.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(n.dims[n.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(r.dims.length!==1)throw new Error("Gamma must be 1D");if(r.dims[r.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},Tm=(e,t,n,r)=>{let i=t.simplified,s=e[0].dims,a=W.size(s),o=s,l=a,d=s.slice(-1)[0],p=r?s.slice(0,-1).concat(1):[],f=!i&&e.length>3,c=e.length>4,m=r&&n>1,y=r&&n>2,w=n>3,$=64,_=ct(d),v=[{type:12,data:l},{type:12,data:_},{type:12,data:d},{type:1,data:t.epsilon}],I=E=>{let R=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],O=[V("x",e[0].dataType,e[0].dims,_),V("skip",e[1].dataType,e[1].dims,_),V("gamma",e[2].dataType,e[2].dims,_)];f&&O.push(V("beta",e[3].dataType,e[3].dims,_)),c&&O.push(V("bias",e[4].dataType,e[4].dims,_)),O.push(be("output",e[0].dataType,o,_)),m&&O.push(be("mean_output",1,p)),y&&O.push(be("inv_std_output",1,p)),w&&O.push(be("input_skip_bias_sum",e[0].dataType,o,_));let x=xt(e[0].dataType),U=xt(1,_);return`
+
+      ${E.registerUniforms(R).declareVariables(...O)}
+      var<workgroup> sum_shared : array<${U}, ${$}>;
+      var<workgroup> sum_squared_shared : array<${U}, ${$}>;
+
+      ${E.mainStart([$,1,1])}
+        let ix = local_id.x;
+        let iy = global_id.x / ${$};
+
+        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
+        var stride = hidden_size_vectorized / ${$};
+        let offset = ix * stride + iy * hidden_size_vectorized;
+        let offset1d = stride * ix;
+        if (ix == ${$-1}) {
+          stride = hidden_size_vectorized - stride * ix;
+        }
+        for (var i: u32 = 0; i < stride; i++) {
+          let skip_value = skip[offset + i];
+          let bias_value = ${c?"bias[offset1d + i]":x+"(0.0)"};
+          let input_value = x[offset + i];
+          let value = input_value + skip_value + bias_value;
+          ${w?"input_skip_bias_sum[offset + i] = value;":""}
+          output[offset + i] = value;
+          let f32_value = ${bi(x,_,"value")};
+          sum_shared[ix] += f32_value;
+          sum_squared_shared[ix] += f32_value * f32_value;
+        }
+        workgroupBarrier();
+
+        var reduce_size : u32 = ${$};
+        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
+          reduce_size = curr_size + (reduce_size & 1);
+          if (ix < curr_size) {
+            sum_shared[ix] += sum_shared[ix + reduce_size];
+            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
+          }
+          workgroupBarrier();
+        }
+
+        let sum = sum_shared[0];
+        let square_sum = sum_squared_shared[0];
+        let mean = ${wr("sum",_)} / f32(uniforms.hidden_size);
+        let inv_std_dev = inverseSqrt(${wr("square_sum",_)} / f32(uniforms.hidden_size) ${i?"":"- mean * mean"} + uniforms.epsilon);
+        ${m?"mean_output[global_idx] = mean;":""}
+        ${y?"inv_std_output[global_idx] = inv_std_dev;":""}
+
+        for (var i: u32 = 0; i < stride; i++) {
+          output[offset + i] = (output[offset + i] ${i?"":`- ${x}(mean)`}) *
+            ${x}(inv_std_dev) * gamma[offset1d + i]
+            ${f?"+ beta[offset1d + i]":""};
+        }
+      }`},S=[{dims:o,dataType:e[0].dataType}];return n>1&&S.push({dims:p,dataType:1}),n>2&&S.push({dims:p,dataType:1}),n>3&&S.push({dims:s,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${_};${m};${y};${w}`,inputDependencies:e.map((E,R)=>"type")},getShaderSource:I,getRunData:()=>({outputs:S,dispatchGroup:{x:Math.ceil(l/d)},programUniforms:v})}},Ew=(e,t)=>{Sm(e.inputs);let n=[0];e.outputCount>1&&n.push(-3),e.outputCount>2&&n.push(-3),e.outputCount>3&&n.push(3),e.compute(Tm(e.inputs,t,e.outputCount,!1),{outputs:n})}}),Im,os,Cm,Ml,Em,km,kw,zw,r2=re(()=>{Te(),Ce(),pt(),Ee(),Im=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((n,r)=>{if(e[r+1].dataType!==6&&e[r+1].dataType!==7)throw new Error(`Input ${r} must be an array of int32 or int64`)})},os=(e,t)=>{let n=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(r=>n.push(Number(r)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(r=>n.push(Number(r)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return n},Cm=(e,t)=>{if(e.length>1){let n=os(e,1),r=os(e,2),i=os(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),Fe({starts:n,ends:r,axes:i})}else return t},Ml=(e,t,n,r,i)=>{let s=e;return e<0&&(s+=n[r[t]]),i[t]<0?Math.max(0,Math.min(s,n[r[t]]-1)):Math.max(0,Math.min(s,n[r[t]]))},Em=(e,t,n)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
+          var input_indices: ${e.type.indices};
+          var carry = 0u;
+          for (var i = ${n.length-1}; i >= 0; i--) {
+            let input_shape_i = ${we("uniforms.input_shape","i",n.length)};
+            let steps_i = ${we("uniforms.steps","i",n.length)};
+            let signs_i = ${we("uniforms.signs","i",n.length)};
+            let starts_i = ${we("uniforms.starts","i",n.length)};
+            var output_index = ${t.indicesGet("output_indices","i")};
+            var input_index = output_index * steps_i + starts_i + carry;
+            carry = input_index / input_shape_i;
+            input_index = input_index % input_shape_i;
+            if (signs_i < 0) {
+              input_index = input_shape_i - input_index - 1u + starts_i;
+            }
+            ${e.indicesSet("input_indices","i","input_index")};
+          }
+          return input_indices;
+      }`,km=(e,t)=>{let n=e[0].dims,r=W.size(n),i=t.axes.length>0?W.normalizeAxes(t.axes,n.length):[...Array(n.length).keys()],s=os(e,4);s.forEach(_=>_!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(i.length).fill(1));let a=t.starts.map((_,v)=>Ml(_,v,n,i,s)),o=t.ends.map((_,v)=>Ml(_,v,n,i,s));if(i.length!==a.length||i.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==n.length)for(let _=0;_<n.length;++_)i.includes(_)||(a.splice(_,0,0),o.splice(_,0,n[_]),s.splice(_,0,1));let l=s.map(_=>Math.sign(_));s.forEach((_,v,I)=>{if(_<0){let S=(o[v]-a[v])/_,E=a[v],R=E+S*s[v];a[v]=R,o[v]=E,I[v]=-_}});let d=n.slice(0);i.forEach((_,v)=>{d[_]=Math.ceil((o[_]-a[_])/s[_])});let p={dims:d,dataType:e[0].dataType},f=be("output",e[0].dataType,d.length),c=V("input",e[0].dataType,e[0].dims.length),m=W.size(d),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:s.length}],w=[{type:12,data:m},{type:12,data:a},{type:6,data:l},{type:12,data:s},..._e(e[0].dims,d)],$=_=>`
+      ${_.registerUniforms(y).declareVariables(c,f)}
+        ${Em(c,f,n)}
+        ${_.mainStart()}
+          ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
+          let output_indices = ${f.offsetToIndices("global_idx")};
+          let input_indices = calculateInputIndices(output_indices);
+          ${f.setByOffset("global_idx",c.getByIndices("input_indices"))}
+      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${a.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:w})}},kw=(e,t)=>{Im(e.inputs,t);let n=Cm(e.inputs,t);e.compute(km(e.inputs,n),{inputs:[0]})},zw=e=>{let t=e.starts,n=e.ends,r=e.axes;return Fe({starts:t,ends:n,axes:r})}}),zm,Am,Aw,Mw,i2=re(()=>{Te(),Ce(),pt(),_r(),Ee(),zm=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Am=(e,t)=>{let n=e.inputs[0],r=n.dims,i=W.size(r),s=r.length,a=W.normalizeAxis(t.axis,s),o=a<r.length-1,l,d=[];o?(d=Array.from({length:s},(O,x)=>x),d[a]=s-1,d[s-1]=a,l=e.compute(en(n,d),{inputs:[n],outputs:[-1]})[0]):l=n;let p=l.dims,f=p[s-1],c=i/f,m=ct(f),y=f/m,w=64;c===1&&(w=256);let $=(O,x)=>x===4?`max(max(${O}.x, ${O}.y), max(${O}.z, ${O}.w))`:x===2?`max(${O}.x, ${O}.y)`:x===3?`max(max(${O}.x, ${O}.y), ${O}.z)`:O,_=V("x",l.dataType,l.dims,m),v=be("result",l.dataType,l.dims,m),I=_.type.value,S=xt(l.dataType)==="f32"?`var threadMax = ${I}(-3.4028234663852886e+38f);`:`var threadMax = ${I}(-65504.0h);`,E=O=>`
+      var<workgroup> rowMaxShared : ${I};
+      var<workgroup> rowSumShared : ${I};
+      var<workgroup> threadShared : array<${I}, ${w}>;
+
+      fn getValue(row: i32, col: i32, row_stride: i32) -> ${I} {
+        let index = row * row_stride + col;
+        return x[index];
+      }
+
+      fn setValue(row: i32, col: i32, row_stride: i32, value: ${I}) {
+        let index = row * row_stride + col;
+        result[index] = value;
+      }
+      ${O.registerUniform("packedCols","i32").declareVariables(_,v)}
+      ${O.mainStart(w)}
+        let gindex = i32(global_idx);
+        let lindex = i32(local_idx);
+        const wg = ${w};
+        let row = gindex / wg;
+        let cols = uniforms.packedCols;
+        let row_stride : i32 = uniforms.packedCols;
+
+        // find the rows max
+        ${S}
+        for (var col = lindex; col < cols; col += wg) {
+          let value = getValue(row, col, row_stride);
+          threadMax = max(threadMax, value);
+        }
+        if (lindex < cols) {
+          threadShared[lindex] = threadMax;
+        }
+        workgroupBarrier();
+
+        var reduceSize = min(cols, wg);
+        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
+          reduceSize = currSize + (reduceSize & 1);
+          if (lindex < currSize) {
+            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
+          }
+          workgroupBarrier();
+        }
+        if (lindex == 0) {
+          rowMaxShared = ${I}(${$("threadShared[0]",m)});
+        }
+        workgroupBarrier();
+
+        // find the rows sum
+        var threadSum = ${I}(0.0);
+        for (var col = lindex; col < cols; col += wg) {
+          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
+          threadSum += subExp;
+        }
+        threadShared[lindex] = threadSum;
+        workgroupBarrier();
+
+        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
+          if (lindex < currSize) {
+            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
+          }
+          workgroupBarrier();
+        }
+        if (lindex == 0) {
+          rowSumShared = ${I}(${wr("threadShared[0]",m)});
+        }
+        workgroupBarrier();
+
+        // calculate final value for each element in the row
+        for (var col = lindex; col < cols; col += wg) {
+          var value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
+          // max operation protects against NaN since all values should be >=0
+          value = max(value, ${I}(0.0));
+          setValue(row, col, row_stride, value);
+        }
+      }`,R=e.compute({name:"Softmax",shaderCache:{hint:`${m};${w}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:l.dataType}],dispatchGroup:{x:c},programUniforms:[{type:6,data:y}]}),getShaderSource:E},{inputs:[l],outputs:[o?-1:0]})[0];o&&e.compute(en(R,d),{inputs:[R]})},Aw=(e,t)=>{zm(e.inputs),Am(e,t)},Mw=e=>Fe({axis:e.axis})}),Rl,Mm,Rm,Om,Rw,s2=re(()=>{Te(),Ce(),Ee(),Rl=e=>Array.from(e.getBigInt64Array(),Number),Mm=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Rl(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Rm=(e,t)=>{let n=[];for(let r=0;r<e.length;++r)n.push(e[r]*t[r]);return n},Om=(e,t)=>{let n=e[0].dims,r=t??Rl(e[1]),i=Rm(n,r),s=W.size(i),a=e[0].dataType,o=V("input",a,n.length),l=be("output",a,i.length),d=p=>`
+      const inputShape = ${o.indices(...n)};
+      ${p.registerUniform("output_size","u32").declareVariables(o,l)}
+      ${p.mainStart()}
+      ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
+      let output_indices = ${l.offsetToIndices("global_idx")};
+      var input_indices: ${o.type.indices};
+      for (var i = 0; i < ${n.length}; i++) {
+        let input_dim_i = ${o.indicesGet("uniforms.input_shape","i")};
+        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;
+
+        ${o.indicesSet("input_indices","i","input_dim_value")}
+      }
+      ${l.setByOffset("global_idx",o.getByIndices("input_indices"))}
+    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:[{type:12,data:s},..._e(e[0].dims,i)]}),getShaderSource:d}},Rw=e=>{Mm(e.inputs),e.compute(Om(e.inputs),{inputs:[0]})}}),Nm,Bm,Ow,a2=re(()=>{Te(),Ce(),Ee(),Nm=(e,t,n,r,i)=>{let s=be("output_data",i,n.length,4),a=V("a_data",t[1].dataType,t[1].dims.length,4),o=V("b_data",t[2].dataType,t[2].dims.length,4),l=V("c_data",t[0].dataType,t[0].dims.length,4),d,p=(f,c,m)=>`select(${c}, ${f}, ${m})`;if(!r)d=s.setByOffset("global_idx",p(a.getByOffset("global_idx"),o.getByOffset("global_idx"),l.getByOffset("global_idx")));else{let f=(c,m,y="")=>{let w=`a_data[index_a${m}][component_a${m}]`,$=`b_data[index_b${m}][component_b${m}]`,_=`bool(c_data[index_c${m}] & (0xffu << (component_c${m} * 8)))`;return`
+            let output_indices${m} = ${s.offsetToIndices(`global_idx * 4u + ${m}u`)};
+            let offset_a${m} = ${a.broadcastedIndicesToOffset(`output_indices${m}`,s)};
+            let offset_b${m} = ${o.broadcastedIndicesToOffset(`output_indices${m}`,s)};
+            let offset_c${m} = ${l.broadcastedIndicesToOffset(`output_indices${m}`,s)};
+            let index_a${m} = offset_a${m} / 4u;
+            let index_b${m} = offset_b${m} / 4u;
+            let index_c${m} = offset_c${m} / 4u;
+            let component_a${m} = offset_a${m} % 4u;
+            let component_b${m} = offset_b${m} % 4u;
+            let component_c${m} = offset_c${m} % 4u;
+            ${c}[${m}] = ${y}(${p(w,$,_)});
+          `};i===9?d=`
+            var data = vec4<u32>(0);
+            ${f("data",0,"u32")}
+            ${f("data",1,"u32")}
+            ${f("data",2,"u32")}
+            ${f("data",3,"u32")}
+            output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:d=`
+            ${f("output_data[global_idx]",0)}
+            ${f("output_data[global_idx]",1)}
+            ${f("output_data[global_idx]",2)}
+            ${f("output_data[global_idx]",3)}
+          `}return`
+        ${e.registerUniform("vec_size","u32").declareVariables(l,a,o,s)}
+        ${e.mainStart()}
+        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
+        ${d}
+      }`},Bm=e=>{let t=e[1].dims,n=e[2].dims,r=e[0].dims,i=e[1].dataType,s=!(W.areEqual(t,n)&&W.areEqual(n,r)),a=t,o=W.size(t);if(s){let d=Ei.calcShape(Ei.calcShape(t,n,!1),r,!1);if(!d)throw new Error("Can't perform where op on the given tensors");a=d,o=W.size(a)}let l=Math.ceil(o/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:d=>Nm(d,e,a,s,i),getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:Math.ceil(o/64/4)},programUniforms:[{type:12,data:l},..._e(r,t,n,a)]})}},Ow=e=>{e.compute(Bm(e.inputs))}}),Nw,o2=re(()=>{v$(),Ju(),x$(),$$(),S$(),T$(),I$(),A$(),R$(),O$(),N$(),B$(),D$(),P$(),U$(),L$(),W$(),q$(),G$(),V$(),F$(),H$(),j$(),K$(),X$(),ew(),Y$(),Z$(),Q$(),J$(),e2(),Qu(),t2(),sw(),n2(),r2(),i2(),rw(),s2(),_r(),ed(),a2(),Nw=new Map([["Abs",[Ey]],["Acos",[ky]],["Acosh",[zy]],["Add",[db]],["ArgMax",[Sy,uu]],["ArgMin",[$y,uu]],["Asin",[Ay]],["Asinh",[My]],["Atan",[Ry]],["Atanh",[Oy]],["Attention",[Ty]],["AveragePool",[hw,fw]],["BatchNormalization",[Iy]],["BiasAdd",[Cy]],["BiasSplitGelu",[ub]],["Cast",[By,Ny]],["Ceil",[Py]],["Clip",[Dy]],["Concat",[_b,vb]],["Conv",[mu,hu]],["ConvTranspose",[Ab,zb]],["Cos",[Uy]],["Cosh",[Ly]],["CumSum",[Mb,Rb]],["DepthToSpace",[Ob,Nb]],["DequantizeLinear",[vw,xw]],["Div",[cb]],["Einsum",[Bb,Db]],["Elu",[Wy,fs]],["Equal",[pb]],["Erf",[qy]],["Exp",[Gy]],["Expand",[Pb]],["FastGelu",[Ub]],["Floor",[Vy]],["FusedConv",[mu,hu]],["Gather",[Wb,Lb]],["GatherElements",[jb,Hb]],["GatherBlockQuantized",[Vb,Fb]],["GatherND",[qb,Gb]],["Gelu",[Fy]],["Gemm",[Xb,Kb]],["GlobalAveragePool",[gw,mw]],["GlobalMaxPool",[_w,ww]],["Greater",[gb]],["GreaterOrEqual",[bb]],["GridSample",[Yb,Zb]],["GroupQueryAttention",[aw]],["HardSigmoid",[Jy,Qy]],["InstanceNormalization",[ow]],["LayerNormalization",[lw]],["LeakyRelu",[Hy,fs]],["Less",[yb]],["LessOrEqual",[wb]],["Log",[ob]],["MatMul",[uw]],["MatMulNBits",[dw,cw]],["MaxPool",[yw,bw]],["Mul",[fb]],["MultiHeadAttention",[Jb,Qb]],["Neg",[Ky]],["Not",[jy]],["Pad",[pw]],["Pow",[hb]],["QuickGelu",[lb,fs]],["Range",[$w]],["Reciprocal",[Xy]],["ReduceMin",[by]],["ReduceMean",[fy]],["ReduceMax",[yy]],["ReduceSum",[_y]],["ReduceProd",[wy]],["ReduceL1",[hy]],["ReduceL2",[my]],["ReduceLogSum",[xy]],["ReduceLogSumExp",[gy]],["ReduceSumSquare",[vy]],["Relu",[Yy]],["Resize",[Iw,Cw]],["RotaryEmbedding",[iw]],["ScatterND",[Tw,Sw]],["Sigmoid",[Zy]],["Sin",[eb]],["Sinh",[tb]],["Slice",[kw,zw]],["SkipLayerNormalization",[Ew]],["Split",[tw,nw]],["Sqrt",[nb]],["Softmax",[Aw,Mw]],["Sub",[mb]],["Tan",[rb]],["Tanh",[ib]],["ThresholdedRelu",[ab,fs]],["Tile",[Rw]],["Transpose",[ty,ny]],["Where",[Ow]]])}),Bw,l2=re(()=>{on(),nr(),Ee(),Bw=class{constructor(e){this.backend=e,this.repo=new Map,this.attributesBound=!1}getArtifact(e){return this.repo.get(e)}setArtifact(e,t){this.repo.set(e,t)}run(e,t,n,r,i){Bn(e.programInfo.name);let s=this.backend.device,a=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let o=[];for(let d of t)o.push({binding:o.length,resource:{buffer:d.buffer}});for(let d of n)o.push({binding:o.length,resource:{buffer:d.buffer}});i&&o.push({binding:o.length,resource:i});let l=s.createBindGroup({layout:e.computePipeline.getBindGroupLayout(0),entries:o,label:e.programInfo.name});if(this.backend.sessionStatus==="capturing"){let d={kernelId:this.backend.currentKernelId,computePipeline:e.computePipeline,bindGroup:l,dispatchGroup:r};this.backend.capturedCommandList.get(this.backend.currentSessionId).push(d)}a.setPipeline(e.computePipeline),a.setBindGroup(0,l),a.dispatchWorkgroups(...r),this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2+1),this.backend.pendingDispatchNumber++,(this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber||this.backend.queryType==="at-passes")&&this.backend.endComputePass(),this.backend.pendingDispatchNumber>=this.backend.maxDispatchNumber&&this.backend.flush(),wn(e.programInfo.name)}dispose(){}build(e,t){Bn(e.name);let n=this.backend.device,r=[];[{feature:"shader-f16",extension:"f16"},{feature:"subgroups",extension:"subgroups"}].forEach(d=>{n.features.has(d.feature)&&r.push(`enable ${d.extension};`)});let i=ey(t,this.backend.device.limits),s=e.getShaderSource(i),a=`${r.join(`
+`)}
+${i.additionalImplementations}
+${s}`,o=n.createShaderModule({code:a,label:e.name});Pe("verbose",()=>`[WebGPU] ${e.name} shader code: ${a}`);let l=n.createComputePipeline({compute:{module:o,entryPoint:"main"},layout:"auto",label:e.name});return wn(e.name),{programInfo:e,computePipeline:l,uniformVariablesInfo:i.variablesInfo}}normalizeDispatchGroupSize(e){let t=typeof e=="number"?e:e.x,n=typeof e=="number"?1:e.y||1,r=typeof e=="number"?1:e.z||1,i=this.backend.device.limits.maxComputeWorkgroupsPerDimension;if(t<=i&&n<=i&&r<=i)return[t,n,r];let s=t*n*r,a=Math.ceil(Math.sqrt(s));if(a>i){if(a=Math.ceil(Math.cbrt(s)),a>i)throw new Error("Total dispatch size exceeds WebGPU maximum.");return[a,a,a]}else return[a,a,1]}}}),Dw={};Mi(Dw,{WebGpuBackend:()=>Pw});var Dm,Pm,Um,Pw,u2=re(()=>{on(),Te(),nr(),X0(),w$(),o2(),l2(),Dm=(e,t)=>{if(t.length!==e.length)throw new Error(`inputDependencies length ${t.length} is not equal to inputTensors length ${e.length}.`);let n=[];for(let r=0;r<e.length;++r){let i=e[r].dataType;switch(t[r]){case"none":{n.push("");break}case"type":{n.push(`${i}`);break}case"rank":{let s=e[r].dims.length;n.push(`${i};${s}`);break}case"dims":{let s=e[r].dims.join(",");n.push(`${i};${s}`);break}default:throw new Error(`unsupported input dependency: ${t[r]}`)}}return n.join("|")},Pm=(e,t,n)=>{var i,s;let r=e.name;return(i=e.shaderCache)!=null&&i.hint&&(r+="["+e.shaderCache.hint+"]"),r+=":"+n+`:${Dm(t,((s=e.shaderCache)==null?void 0:s.inputDependencies)??new Array(t.length).fill("dims"))}`,r},Um=class{constructor(e){e&&(this.architecture=e.architecture,this.vendor=e.vendor)}isArchitecture(e){return this.architecture===e}isVendor(e){return this.vendor===e}},Pw=class{constructor(){this.currentSessionId=null,this.currentKernelId=null,this.commandEncoder=null,this.computePassEncoder=null,this.maxDispatchNumber=16,this.pendingDispatchNumber=0,this.pendingKernels=[],this.pendingQueries=new Map,this.sessionStatus="default",this.capturedCommandList=new Map,this.capturedPendingKernels=new Map,this.sessionExternalDataMapping=new Map}get currentKernelCustomData(){if(this.currentKernelId===null)throw new Error("currentKernelCustomData(): currentKernelId is null. (should not happen)");let e=this.kernelCustomData.get(this.currentKernelId);return e||(e={},this.kernelCustomData.set(this.currentKernelId,e)),e}async initialize(e,t){this.env=e;let n=[],r={requiredLimits:{maxComputeWorkgroupStorageSize:t.limits.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:t.limits.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:t.limits.maxStorageBufferBindingSize,maxBufferSize:t.limits.maxBufferSize,maxComputeInvocationsPerWorkgroup:t.limits.maxComputeInvocationsPerWorkgroup,maxComputeWorkgroupSizeX:t.limits.maxComputeWorkgroupSizeX,maxComputeWorkgroupSizeY:t.limits.maxComputeWorkgroupSizeY,maxComputeWorkgroupSizeZ:t.limits.maxComputeWorkgroupSizeZ},requiredFeatures:n},i=s=>t.features.has(s)&&n.push(s)&&!0;i("chromium-experimental-timestamp-query-inside-passes")||i("timestamp-query"),i("shader-f16"),i("subgroups"),this.device=await t.requestDevice(r),this.adapterInfo=new Um(t.info||await t.requestAdapterInfo()),this.gpuDataManager=Q0(this),this.programManager=new Bw(this),this.kernels=new Map,this.kernelPersistentData=new Map,this.kernelCustomData=new Map,Ku(e.logLevel,!!e.debug),this.device.onuncapturederror=s=>{s.error instanceof GPUValidationError&&console.error(`An uncaught WebGPU validation error was raised: ${s.error.message}`)},Object.defineProperty(this.env.webgpu,"device",{value:this.device,writable:!1,enumerable:!0,configurable:!1}),Object.defineProperty(this.env.webgpu,"adapter",{value:t,writable:!1,enumerable:!0,configurable:!1}),this.setQueryType()}dispose(){typeof this.querySet<"u"&&this.querySet.destroy(),this.gpuDataManager.dispose()}getCommandEncoder(){return this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder()),this.commandEncoder}getComputePassEncoder(){if(!this.computePassEncoder){let e=this.getCommandEncoder(),t={};this.queryType==="at-passes"&&(t.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:this.pendingDispatchNumber*2,endOfPassWriteIndex:this.pendingDispatchNumber*2+1}),this.computePassEncoder=e.beginComputePass(t)}return this.computePassEncoder}endComputePass(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}flush(){if(!this.commandEncoder)return;Bn(),this.endComputePass();let e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var r;let t=new BigUint64Array(e.getMappedRange()),n=this.pendingQueries.get(e);for(let i=0;i<t.length/2;i++){let s=n[i],a=s.kernelId,o=this.kernels.get(a),l=o.kernelType,d=o.kernelName,p=s.programName,f=s.inputTensorViews,c=s.outputTensorViews,m=t[i*2],y=t[i*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=m);let w=Number(m-this.queryTimeBase),$=Number(y-this.queryTimeBase);if(!Number.isSafeInteger(w)||!Number.isSafeInteger($))throw new RangeError("incorrect timestamp range");if((r=this.env.webgpu.profiling)!=null&&r.ondata)this.env.webgpu.profiling.ondata({version:1,inputsMetadata:f.map(_=>({dims:_.dims,dataType:Yn(_.dataType)})),outputsMetadata:c.map(_=>({dims:_.dims,dataType:Yn(_.dataType)})),kernelId:a,kernelType:l,kernelName:d,programName:p,startTime:w,endTime:$});else{let _="";f.forEach((I,S)=>{_+=`input[${S}]: [${I.dims}] | ${Yn(I.dataType)}, `});let v="";c.forEach((I,S)=>{v+=`output[${S}]: [${I.dims}] | ${Yn(I.dataType)}, `}),console.log(`[profiling] kernel "${a}|${l}|${d}|${p}" ${_}${v}start time: ${w} ns, execution time: ${$-w} ns`)}Da("GPU",`${p}::${m}::${y}`)}e.unmap(),this.pendingQueries.delete(e)}),wn()}run(e,t,n,r,i,s){Bn(e.name);let a=[];for(let v=0;v<t.length;++v){let I=t[v].data;if(I===0)continue;let S=this.gpuDataManager.get(I);if(!S)throw new Error(`no GPU data for input: ${I}`);a.push(S)}let{outputs:o,dispatchGroup:l,programUniforms:d}=e.getRunData(t),p=n.length===0?o.map((v,I)=>I):n;if(p.length!==o.length)throw new Error(`Output size ${p.length} must be equal to ${o.length}.`);let f=[],c=[];for(let v=0;v<o.length;++v){if(!Number.isInteger(p[v])||p[v]<-3||p[v]>=s)throw new Error(`Invalid output index: ${p[v]}`);if(p[v]===-3)continue;let I=p[v]===-1,S=p[v]===-2,E=I||S?i(o[v].dataType,o[v].dims):r(p[v],o[v].dataType,o[v].dims);if(f.push(E),E.data===0)continue;let R=this.gpuDataManager.get(E.data);if(!R)throw new Error(`no GPU data for output: ${E.data}`);if(I&&this.temporaryData.push(R),S){let O=this.kernelPersistentData.get(this.currentKernelId);O||(O=[],this.kernelPersistentData.set(this.currentKernelId,O)),O.push(R)}c.push(R)}if(a.length!==t.length||c.length!==f.length){if(c.length===0)return wn(e.name),f;throw new Error(`Program ${e.name} has zero-sized tensor(s) in inputs or outputs. This is not supported now.`)}let m;if(d){let v=0,I=[];d.forEach(O=>{let x=typeof O.data=="number"?[O.data]:O.data;if(x.length===0)return;let U=O.type===10?2:4,q,J;O.type===10?(J=x.length>4?16:x.length>2?8:x.length*U,q=x.length>4?16:U*x.length):(J=x.length<=2?x.length*U:16,q=16),v=Math.ceil(v/J)*J,I.push(v);let se=O.type===10?8:4;v+=x.length>4?Math.ceil(x.length/se)*q:x.length*U});let S=16;v=Math.ceil(v/S)*S;let E=new ArrayBuffer(v);d.forEach((O,x)=>{let U=I[x],q=typeof O.data=="number"?[O.data]:O.data;if(O.type===6)new Int32Array(E,U,q.length).set(q);else if(O.type===12)new Uint32Array(E,U,q.length).set(q);else if(O.type===10)new Uint16Array(E,U,q.length).set(q);else if(O.type===1)new Float32Array(E,U,q.length).set(q);else throw new Error(`Unsupported uniform type: ${Yn(O.type)}`)});let R=this.gpuDataManager.create(v,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(R.buffer,0,E,0,v),this.gpuDataManager.release(R.id),m={offset:0,size:v,buffer:R.buffer}}let y=this.programManager.normalizeDispatchGroupSize(l),w=y[1]===1&&y[2]===1,$=Pm(e,t,w),_=this.programManager.getArtifact($);if(_||(_=this.programManager.build(e,y),this.programManager.setArtifact($,_),Pe("info",()=>`[artifact] key: ${$}, programName: ${e.name}`)),d&&_.uniformVariablesInfo){if(d.length!==_.uniformVariablesInfo.length)throw new Error(`Uniform variables count mismatch: expect ${_.uniformVariablesInfo.length}, got ${d.length} in program "${_.programInfo.name}".`);for(let v=0;v<d.length;v++){let I=d[v],S=I.type,E=typeof I.data=="number"?1:I.data.length,[R,O]=_.uniformVariablesInfo[v];if(S!==R||E!==O)throw new Error(`Uniform variable ${v} mismatch: expect type ${R} with size ${O}, got type ${S} with size ${E} in program "${_.programInfo.name}".`)}}if(Pe("info",()=>`[ProgramManager] run "${e.name}" (key=${$}) with ${y[0]}x${y[1]}x${y[2]}`),this.queryType!=="none"||this.sessionStatus==="capturing"){let v={kernelId:this.currentKernelId,programName:_.programInfo.name,inputTensorViews:t,outputTensorViews:f};this.pendingKernels.push(v),this.sessionStatus==="capturing"&&this.capturedPendingKernels.get(this.currentSessionId).push(v)}return this.programManager.run(_,a,c,y,m),wn(e.name),f}upload(e,t){this.gpuDataManager.upload(e,t)}memcpy(e,t){this.gpuDataManager.memcpy(e,t)}async download(e,t){await this.gpuDataManager.download(e,t)}alloc(e){return this.gpuDataManager.create(e).id}free(e){return this.gpuDataManager.release(e)}createKernel(e,t,n,r){let i=Nw.get(e);if(!i)throw new Error(`kernel not implemented: ${e}`);let s={kernelType:e,kernelName:r,kernelEntry:i[0],attributes:[i[1],n]};this.kernels.set(t,s)}releaseKernel(e){let t=this.kernelPersistentData.get(e);if(t){for(let n of t)this.gpuDataManager.release(n.id);this.kernelPersistentData.delete(e)}this.kernelCustomData.delete(e),this.kernels.delete(e)}computeKernel(e,t,n){let r=this.kernels.get(e);if(!r)throw new Error(`kernel not created: ${e}`);let i=r.kernelType,s=r.kernelName,a=r.kernelEntry,o=r.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${i}] ${s}" is not allowed to be called recursively`);this.currentKernelId=e,o[0]&&(o[1]=o[0](o[1]),o[0]=void 0),Pe("info",()=>`[WebGPU] Start to run kernel "[${i}] ${s}"...`);let l=this.env.debug;this.temporaryData=[];try{return l&&this.device.pushErrorScope("validation"),a(t,o[1]),0}catch(d){return n.push(Promise.resolve(`[WebGPU] Kernel "[${i}] ${s}" failed. ${d}`)),1}finally{l&&n.push(this.device.popErrorScope().then(d=>d?`GPU validation error for kernel "[${i}] ${s}": ${d.message}`:null));for(let d of this.temporaryData)this.gpuDataManager.release(d.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(e,t,n,r){let i=this.sessionExternalDataMapping.get(e);i||(i=new Map,this.sessionExternalDataMapping.set(e,i));let s=i.get(t),a=this.gpuDataManager.registerExternalBuffer(n,r,s);return i.set(t,[a,n]),a}unregisterBuffers(e){let t=this.sessionExternalDataMapping.get(e);t&&(t.forEach(n=>this.gpuDataManager.unregisterExternalBuffer(n[0])),this.sessionExternalDataMapping.delete(e))}getBuffer(e){let t=this.gpuDataManager.get(e);if(!t)throw new Error(`no GPU data for buffer: ${e}`);return t.buffer}createDownloader(e,t,n){return async()=>{let r=await au(this,e,t);return Xu(r.buffer,n)}}writeTimestamp(e){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,e)}setQueryType(){var e;this.queryType="none",(((e=this.env.webgpu.profiling)==null?void 0:e.mode)==="default"||(typeof this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){Pe("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){Pe("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){Pe("info","replay"),this.sessionStatus="replaying";let e=this.capturedCommandList.get(this.currentSessionId),t=this.capturedPendingKernels.get(this.currentSessionId),n=e.length;this.pendingKernels=[];for(let r=0;r<n;r++){let i=this.getComputePassEncoder(),s=e[r];this.writeTimestamp(this.pendingDispatchNumber*2),i.setPipeline(s.computePipeline),i.setBindGroup(0,s.bindGroup),i.dispatchWorkgroups(...s.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(t[r]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}}),Uw={};Mi(Uw,{init:()=>Lw});var ga,Lm,Lw,d2=re(()=>{Te(),nr(),Ce(),b$(),ga=class Ww{constructor(t,n,r,i){this.module=t,this.dataType=n,this.data=r,this.dims=i}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=W.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=W.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=W.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let t=W.size(this.dims);return t===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(W.size(t)!==W.size(this.dims))throw new Error("Invalid new shape");return new Ww(this.module,this.dataType,this.data,t)}},Lm=class{constructor(e,t,n){this.module=e,this.backend=t,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=t.adapterInfo;let r=e.PTR_SIZE,i=n/e.PTR_SIZE,s=r===4?"i32":"i64";this.opKernelContext=Number(e.getValue(r*i++,s));let a=Number(e.getValue(r*i++,s));this.outputCount=Number(e.getValue(r*i++,s)),this.customDataOffset=Number(e.getValue(r*i++,"*")),this.customDataSize=Number(e.getValue(r*i++,s));let o=[];for(let l=0;l<a;l++){let d=Number(e.getValue(r*i++,s)),p=Number(e.getValue(r*i++,"*")),f=Number(e.getValue(r*i++,s)),c=[];for(let m=0;m<f;m++)c.push(Number(e.getValue(r*i++,s)));o.push(new ga(e,d,p,c))}this.inputs=o}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(e,t){var a;let n=((a=t==null?void 0:t.inputs)==null?void 0:a.map(o=>typeof o=="number"?this.inputs[o]:o))??this.inputs,r=(t==null?void 0:t.outputs)??[],i=(o,l,d)=>new ga(this.module,l,this.output(o,d),d),s=(o,l)=>{let d=Vr(o,l);if(!d)throw new Error(`Unsupported data type: ${o}`);let p=d>0?this.backend.gpuDataManager.create(d).id:0;return new ga(this.module,o,p,l)};return this.backend.run(e,n,r,i,s,this.outputCount)}output(e,t){let n=this.module.stackSave();try{let r=this.module.PTR_SIZE,i=r===4?"i32":"i64",s=this.module.stackAlloc((1+t.length)*r);this.module.setValue(s,t.length,i);for(let a=0;a<t.length;a++)this.module.setValue(s+r*(a+1),t[a],i);return this.module._JsepOutput(this.opKernelContext,e,s)}catch(r){throw new Error(`Failed to generate kernel's output[${e}] with dims [${t}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${r}`)}finally{this.module.stackRestore(n)}}},Lw=async(e,t,n,r)=>{let i=t.jsepInit;if(!i)throw new Error("Failed to initialize JSEP. 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+* @license
+* Copyright 2021 Google LLC. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+* =============================================================================
+*//**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ *//**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */const Ur=32;function Fa(e,t){if(!t)return;if(Array.isArray(t))return t[0];const n=e==null?void 0:e[0];if(n)return t[n]}function Fm(e,t,n){if(e)return Array.isArray(e)?e[n]:e[t]}function fi(e){if(!e)return"";const t=e/(1024*1024),n=Math.round(t*10)/10;return Number.isInteger(n)?`${n}MB`:`${n.toFixed(1)}MB`}function wa(e){return e<1e3?`${Math.round(e)}ms`:`${(e/1e3).toFixed(1)}s`}function Pl(e,t){return!e||!t?null:`${(e/1e3/t).toFixed(2)}s/tile`}function h2(e){return JSON.stringify(e,null,2)}function rn(e){return Number.isFinite(e??NaN)?Math.round(e*1e4)/1e4:e}function De(e,t,n){return Math.min(n,Math.max(t,e))}function Hm(e,t){if(t<=1)return 0;const n=(t-1)*2,r=(e%n+n)%n;return r<t?r:n-r}function Ul(e){const t=String(e??"").trim();if(!t)return null;const n=Number.parseInt(t,10);return!Number.isFinite(n)||n<1?null:n}function m2(e){const t=String(e??"").trim();if(!t)return null;const n=Number.parseFloat(t);return Number.isFinite(n)?De(Math.round(n*10)/10,1,4):null}function 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